Include references and avoid plagiarism when answering this, it is a graduate research NON-EXPERIMENTAL STUDY paper: Review the following attached articles that have been provided as example studies

Include references and avoid plagiarism when answering this, it is a graduate research NON-EXPERIMENTAL STUDY paper: Review the following attached articles that have been provided as example studies
Downloaded from<004B0057005700530056001D00120012004D00520058005500510044004F00560011004F005A005A00110046005200500012 0046005200100046004400550047004C0052004F0052004A005C> by<0025004B002700300049001800480033002B002E004400590014005D00280052005800500014005700340049003100170044 000E004E002D002F004B0028003D004A00450056002C002B00520017003B0030004C0013004B0026005C005A0026003B 00140024003A0051003C003400530012002C004F00340055002B00270016005C0035004F003B004A00180039003D0024 001B0057004A004F004900250027004E00360012002B00130047003C005600170028004B004C00290013002F0044005D 003D00240033000E002D0050001B00350058003C004600440031004C002F00520034002B0029001A004A00200020> on01/24/2020 Downloaded from<004B0057005700530056001D00120012004D00520058005500510044004F00560011004F005A005A00110046005200500012 0046005200100046004400550047004C0052004F0052004A005C> by<0025004B002700300049001800480033002B002E004400590014005D00280052005800500014005700340049003100170044 000E004E002D002F004B0028003D004A00450056002C002B00520017003B0030004C0013004B0026005C005A0026003B 00140024003A0051003C003400530012002C004F00340055002B00270016005C0035004F003B004A00180039003D0024 001B0057004A004F004900250027004E00360012002B00130047003C005600170028004B004C00290013002F0044005D 003D00240033000E002D0050001B00350058003C004600440031004C002F00520034002B0029001A004A00200020> on01/24/2020 Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved. C URRENT O PINION Epidemiology of comorbidities in patients with hypertension Prajib L. Shrestha, Patricia A. Shrestha, and Rey P. Vivo Purpose of review Hypertension affects an enormous number of people and uncommonly presents in isolation. This review aims to summarize contemporary data that is relevant to the epidemiology of specific comorbidities occurring frequently in individuals with hypertension. Recent findings Hypertension is invariably diagnosed along with multiple comorbidities, particularly diabetes mellitus, obesity, chronic kidney disease, coronary artery disease, and heart failure. Evidence pertinent to prevalence, incidence, and temporal trends is reported, with an emphasis on differences according to age, gender, and race/ethnicity. Summary The clustering of these conditions requires a thorough —and often multidisciplinary —approach in the evaluation and management of individuals with hypertension. Populations with higher risk include the elderly, women, and racial/ethnic groups. Keywords comorbidity, epidemiology, hypertension INTRODUCTION Following the definition of hypertension (HTN) as a systolic blood pressure (SBP) of 140 mmHg or a diastolic blood pressure (DBP) of 90 mmHg, or the current use of antihypertensive medication, or being told by a healthcare professional at least twice that one has high blood pressure (BP), the American Heart Association’s (AHA) Heart Disease and Stroke Statistics 2016 Update reported that approximately 80 million US adults more than 20 years old have HTN (i.e., 32.6% prevalence) [1 &&]. With reference to age, 2.9 and 3.7% of male and female adolescents aged 12 – 19 years, respectively, were found to have poor BP [2]. Based on National Health and Nutrition Examination Survey (NHANES) data, the prevalence of HTN rises in direct proportion with the age of the population: 7.3, 32.4, and 65% among individuals aged 18 – 39 years, 40 – 59 years, and 60 years, respectively [3]. For US adults 80 years or older, 76.5% had HTN [4]. With respect to gender, more men than women have HTN until 45 years of age. The prevalence becomes similar from 45 to 64 years of age, whereas more women than men aged 65 or older have HTN according to NHANES data. Classified by race/ ethnicity, the prevalence of HTN among US blacks (42.1%) surpassed that of whites (28%), Hispanics (24.7%), and Asians (24.7%) [3]. Age-adjusted aware- ness of HTN, however, was similar among blacks, whites, and Hispanics, and lowest in Asians. This review will present contemporary evidence relevant to the epidemiology of comorbidities among individuals with HTN, specifically diabetes mellitus, obesity, chronic kidney disease (CKD), coronary artery disease (CAD), and heart failure. Differences pertinent to age, gender, and race/ethnicity will be discussed based upon available data. DIABETES MELLITUS HTN and diabetes mellitus are often co-existent. By itself, diabetes mellitus is a major risk factor for CAD; as a comorbidity in patients with HTN, the risk is amplified. HTN and diabetes mellitus may be Community Heart and Vascular Hospital, Indianapolis, Indiana, USA Correspondence to Rey P. Vivo, MD, FACC, Advanced Heart Failure Program, Community Heart and Vascular Hospital, 8075 N. Shadeland Avenue, Suite 310, Indianapolis, IN 46250, USA. Tel: +1 317 6218500;fax: +1 317 6218501; e-mail: [email protected] Curr Opin Cardiol 2016, 31:376 – 380 DOI:10.1097/HCO.0000000000000298 www.co-cardiology.com Volume 31 Number 4 July 2016 REVIEW Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved. diagnosed at the same time, but frequent cases of HTN preceding the onset of diabetes mellitus may suggest that it occurs independently or develops as part of the metabolic syndrome [5]. In type 1 dia- betes mellitus, the lifetime risk of HTN is estimated at 60%, and the presence of microalbuminuria accel- erates the risk [6,7]. Furthermore, approximately 75% of individuals with Type 2 diabetes mellitus are hypertensive. Hypertensive individuals have a 2.5 times higher risk of developing diabetes mellitus than matched patients with normal BP [8]. The relationship between these two conditions has been found to be mediated by interrelated physio- logic abnormalities, including impaired circadian BP cycle, dysregulation of the renin – angiotensin – aldosterone system, and micro-vascular and macro- vascular damage, among others [5,9]. An estimated 21.1 million adults in the United States have diagnosed diabetes mellitus, of whom 90 – 95% have type 2 diabetes mellitus [1 &&,10 &&]. An additional 8.1 million adults have undiagnosed diabetes mellitus, and 80.8 million adults have pre- diabetes. The prevalence of type 2 diabetes mellitus is generally similar across genders. However, there are several observed differences in the cardiovascu- lar consequences of diabetes mellitus between men and women [11 &]. On incidence, 1.7 million new cases of diabetes mellitus (type 1 or type 2) were diagnosed in US adults of at least 20 years of age in 2012 [10 &&]. It is projected that the total prevalence of diabetes mellitus in the United States will more than double from 2005 to 2050 (i.e., from 5.6 to 12.0%) across all populations, whereas more marked increases are expected among the oldest age groups (e.g., 220% increase among ages 65 – 74 years; 449% among ages 75 years), non-Hispanic blacks older than 75 years (606%), and Hispanics (127%) [12]. An inordinate burden of diabetes mellitus exists among racial/ethnic groups. Compared with non-Hispanic whites (7.6%), the age-adjusted pre- valence of its diagnosis is higher for American Indians/Alaska Natives (15.9%), non-Hispanic blacks (13.2%), Hispanics (12.8%), and Asian Americans (9.0%) [10 &&]. Data from the Multi-Ethnic Study of Atherosclerosis (MESA) [13] showed that over 5 years of follow-up, the incidence of diabetes mellitus was highest among Hispanics (11.3%), non-Hispanic blacks (9.5%), and Chinese Americans (7.7%), and lowest in non-Hispanic whites (6.3%). Findings from one study [14] demonstrated that among treated type 2 diabetes mellitus patients, non-Hispanic blacks and Mexican Americans were less likely to achieve glycemic control (hemoglobin A1C <7%) as compared with non-Hispanic whites. OBESITY It is established that excessive weight increases the risk of having elevated BP. Obesity (i.e., BMI 30 kg/ m2) confers a 3.5-fold higher likelihood of developing HTN [15]. Moreover, approximately 60 – 70% of HTN in adults is ascribable to adiposity, particularly centrally located body fat [16]. The following physio- logic abnormalities have been implicated in obesity- related HTN: sympathetic overdrive, renin – angio- tensin – aldosterone system activation, brain melano- cortinergic system abnormalities, adipocyte-derived hormonal dysregulation, insulin resistance, renal impairment, and vascular alterations [16,17 &]. Overweight and obesity have reached epidemic proportions globally. In the United States, the 2016 AHA Statistics revealed that 69 and 35% of adults are overweight and obese, respectively [1 &&]. Although the temporal trend in prevalence of obesity has been reported as stable among adults between 2003 – 2004 and 2011 – 2012, there has been an increase in preva- lence among women aged 60 years from 31.5 to 38.1% during the aforementioned time periods [18 &,19]. Among older people, rates of obesity have increased as a result of both the rise in the elderly population and the proportion of that population that is obese [20]. Between 1991 and 2000, the prevalence of obesity in the age group 60 – 69 years and greater than 70 years increased by 56 and 36%, respectively [21,22]. Hispanic men are more likely to be overweight (80%) than non-Hispanic white (73%) or black (69%) men. Non-Hispanic black (82%) and Hispanic (76%) women are more likely to be overweight than non- Hispanic white (61%) women. Hispanic and non- Hispanic black men and women are more likely to be obese than non-Hispanic white counterparts [1 &&]. CHRONIC KIDNEY DISEASE HTN is reported to occur in as many as greater than 85% of patients with CKD [23]. Elevated BP KEY POINTS HTN is often diagnosed with diabetes mellitus and obesity, and is a major risk factor for the development of CKD, CAD, and heart failure. The prevalence of obesity is higher among older people, whereas both diabetes mellitus and obesity disproportionately affect more Hispanic and non- Hispanic black than non-Hispanic white Americans. Similarly, the elderly and racial/ethnic minorities are more at risk for CKD, CAD, and heart failure. Epidemiology of comorbidities in hypertensive patients Shrestha et al. 0268-4705 Copyright 2016 Wolters Kluwer Health, Inc. All rights reserved. www.co-cardiology.com 377 Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved. is considered a leading cause for CKD; indeed, CKD is more prevalent (31.0%) in persons with HTN compared with those without it [1 &&]. HTN can result in elevated intraglomerular pressure, leading to impaired glomerular filtration [24]. Conversely, worsening CKD can make BP difficult to control because of increased intravascular volume and systemic vascular resistance [25 &]. In addition, resist- ant HTN (i.e., uncontrolled BP despite adherence to three optimally dosed antihypertensive medi- cations including a diuretic) is twice as high in patients with CKD as in those without [26 &]. Results from one study [27] demonstrated that 42% of patients with CKD had resistant HTN. CKD is defined as either kidney damage for 3 months, as confirmed by kidney biopsy or biomarkers of kidney damage, with or without a decrease in glomerular filtration rate (GFR), or GFR <60 for 3 months, with or without kidney damage [28]. According to the US Renal Data System 2014 annual report, the prevalence of CKD from 2007 to 2012 was estimated to be 13.6%. Stratified by stage, the prevalence of stages 1, 2, and 3 is 4.2, 3.0, and 5.9%, respectively. The combined preva- lence of stages 4 and 5 is 0.6%. End-stage renal disease (ESRD) is diagnosed when patients receive chronic renal replacement treatment such as hemodialysis, peritoneal dialysis, or kidney trans- plantation. Current estimates showed that ESRD is prevalent in more than 600 000 individuals, reflect- ing a four-fold increase from the 1970s through 2006 [29 &&]. The prevalence of CKD increases in direct proportion to aging: 5.7% in those 20 – 39 years, 8.9% in those 40 – 59 years, and 33.2% for those of at least 60 years of age [29 &&]. In the elderly ( 80 years old), the prevalence of CKD increased through time from 40.5% from 1988 – 1994 to 51.2% in 2005 – 2010 [30]. Women have been reported to be less aware of having impaired kidney function, despite a higher prevalence of CKD among them than in men. Although the incidence of ESRD in non-Hispanic blacks is four-fold higher than in non- Hispanic whites, both groups have paradoxically the same prevalence of CKD, with rates higher than those of Mexican Americans [31]. CORONARY ARTERY DISEASE HTN is an established independent risk factor for CAD in all populations and accounts for approxi- mately 47% of ischemic events [32]. The pathophy- siologic associations between HTN and CAD are impacted by neurohumoral activation (i.e., sympathetic nervous and renin – angiotensin – aldosterone systems); increased myocardial oxygen demand; endothelial dysfunction and vascular stiff- ening; and upregulation of oxidative stress, inflam- mation, and atherogenesis [33 &&]. The clustering of other risk factors including aging, diabetes mellitus, dyslipidemia, cigarette smoking, and left ventricular hypertrophy amplifies the likelihood of developing CAD [34]. Of note, antihypertensive management can reduce CAD risk. Recent data from the Standard Blood Pressure Intervention Trial (SPRINT) study [35 &] provided evidence that intensive (targeting SBP <120 mmHg) rather than standard (targeting SBP <140 mmHg) treatment resulted in lower rates of fatal and nonfatal major cardiovascular events (including acute coronary syndrome and decom- pensated heart failure) and death from any cause. According to data from the AHA, an estimated 15.5 million Americans 20 years old have CAD [1&&]. The total prevalence is projected to rise from approximately 6% to approximately 18% by 2030. The annual incidence of myocardial infarction (MI) is estimated at 550 000 new coronary events and 200 000 recurrent events. CAD comprises more than half of all cardiovascular events among individuals less than 75 years of age [1 &&]. Incremental rises in BP throughout middle and old age are significantly and directly related to CAD mortality rates. A meta- analysis of 1 million adults in 61 prospective studies found that between the ages 40 – 69 years, each 20 mmHg increase in SBP or 10 mmHg increase in DBP is associated with a two-fold higher risk for a fatal coronary event [36]. The annual absolute differences in risk are even greater in older age. Data from the Framingham Heart Study [37] revealed that whereas DBP is a more important risk predictor for CAD before 50 years of age, SBP and pulse pressure are more relevant for people older than 60 years. In the United States, CAD prevalence is 7.6% for men and 5.0% for women. Comparing temporal trends among middle-aged persons between the 1988 – 1994 and 1999 – 2004 periods, the prevalence of MI was significantly greater in men than in women in both periods, but showed a downtrend in men and an uptrend in women [38]. For incidence, the rates of total CAD for women lag behind men by 10 years, and by 20 years for MI and sudden death [1 &&]. However, the incidence of CAD rises sharply among women after menopause, with rates triple those of age-matched women who are still premenopausal [39]. After HTN onset, the most common first major cardiovascular event was CAD among men and stroke in women [40]. Broken down by race/ethnicity, CAD prevalence is as follows: non-Hispanic white men: 7.8% and women: 4.6%; non-Hispanic black men: 7.2% and women: 7.0%; Hispanic men: 6.7% and women: 5.9%; Asians: 3.3% [1 &&]. Based on a cohort of diverse Hypertension 378 www.co-cardiology.com Volume 31 Number 4 July 2016 Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved. patients (i.e., white, black, Hispanic, Native American, and Asian) with stable angina and acute coronary syndromes in the American College of Cardiology – National Cardiovascular Data Registry, non-Hispanic black women had the lowest risk- adjusted odds for significant CAD [41]. Non- Hispanic white women presenting with stable angina had a 1.34-fold higher in-hospital mortality compared with other groups. An analysis of MESA examined the distribution of coronary artery calcium on the basis of race/ethnicity [42]. Among women, non-Hispanic whites had the highest percentiles whereas Hispanics had the lowest. Among men, non-Hispanic whites had the highest calcium levels, non-Hispanic blacks had the lowest at younger ages, and Chinese the lowest at the older ages. HEART FAILURE Like HTN, heart failure is a major public health burden. HTN plays a key role in the pathogenesis of asymptomatic left ventricular hypertrophy transitioning into symptomatic heart failure with either preserved or reduced ejection fraction [43 – 45]. In response to high BP, left ventricular geometry undergoes remodeling, and this process is in turn impacted by demographic, genetic, and comorbid factors; pressure and volume load; and neurohormonal alterations [43]. Among hyperten- sive patients, the progression from hypertrophy to clinical heart failure may or may not be mediated by ischemic heart disease. Nearly 6 million people in the United States older than 20 years have heart failure, and the prevalence is projected to increase to over 8 million in 2030. Although CAD is the most common clinical cardiac disease that occurs in individuals with HTN, HTN is the strongest major risk factor among patients with heart failure [46]. About 75% of patients with heart failure have pre-existing HTN. The lifetime risk of heart failure for individuals with BP greater than 160/90 mmHg is double that of those with BP greater than 140/90 mmHg [1 &&]. A study [47] of the Framingham cohort reported that men and women at 40 years old have a 20% lifetime risk for developing new heart failure. The remaining lifetime risk remains approximately the same at age 80, even in the context of shorter life expectancy. Evidence also indicates that the annual rates of new heart failure events double with each 10-year age increase from 65 to 85 years and triple for women between ages 65 to 74 and 75 to 84 years. Based on data from MESA, non-Hispanic blacks have the highest risk of developing heart failure, followed by Hispanics, non-Hispanic whites, and Chinese Americans [48]. Findings from the Atherosclerosis Risk in Communities study [49] demonstrated that the 5-year case fatality rate after heart failure hospitalization was greater in non- Hispanic blacks than non-Hispanic whites. Differ- ences in heart failure clinical risk factors exist across racial/ethnic groups. The Health, Aging, and Body Composition Study [50] documented that HTN and CAD had the highest population attributable risk among non-Hispanic white and black persons; non-Hispanic blacks had a higher proportion of HTN, left ventricular hypertrophy and other mod- ifiable risk features. In contrast, Hispanic Americans have an inordinate cardiometabolic risk burden accounted for by higher rates of diabetes mellitus, obesity, and metabolic syndrome [51]. CONCLUSION To conclude, HTN frequently occurs with multiple comorbidities that may amplify the cardiovascular risk of an affected individual or a specific popu- lation. Therefore, a diagnosis of HTN should spur a thorough assessment and multidisciplinary man- agement of co-existing risk factors and/or clinical sequelae. Special attention should be given to more at-risk groups such as the elderly, women, and racial/ethnic populations. Acknowledgements None. Financial support and sponsorship None. Conflicts of interest There are no conflicts of interest. REFERENCES AND RECOMMENDED READINGPapers of particular interest, published within the annual period of review, have been highlighted as:& of special interest && of outstanding interest 1.&& Mozaffarian D, Benjamin EJ, Go AS, et al. Heart Disease and Stroke Statistics- 2016 Update: a report from the American Heart Association. Circulation 2016; 133:e38 – e360. A de nitive summary of the most up-to-date statistics relevant to cardiovascular disease and risk factors. 2. Shay CM, Ning H, Daniels SR, et al. Status of cardiovascular health in US adolescents: prevalence estimates from the National Health and Nutrition Examination Surveys (NHANES) 2005 – 2010. Circulation 2013; 127:1369 – 1376. 3. Nwankwo T, Yoon SS, Burt V, Gu Q. Hypertension among adults in the United States: National Health and Nutrition Examination Survey, 2011 – 2012. NCHS Data Brief 2013; 133:1 – 8. 4. Brom eld SG, Bowling CB, Tanner RM, et al. Trends in hypertension prevalence, awareness, treatment, and control among US adults 80 years and older, 1988 – 2010. J Clin Hypertens (Greenwich) 2014; 16:270 – 276. Epidemiology of comorbidities in hypertensive patients Shrestha et al. 0268-4705 Copyright 2016 Wolters Kluwer Health, Inc. All rights reserved. www.co-cardiology.com 379 Copyright © 2016 Wolters Kluwer Health, Inc. All rights reserved. 5. Schutta MH. Diabetes and hypertension: epidemiology of the relationship and pathophysiology of factors associated with these comorbid conditions. J Cardiometab Syndr 2007; 2:124 – 130. 6. Warram JH, Gearin G, Laffel L, et al. Effect of duration of type 1 diabetes on the prevalence of stages of diabetic nephropathy de ned by urinary albumin/ creatinine ratio. J Am Soc Nephrol 1996; 7:930 – 937. 7. Bakris GL, Williams M, Dworkin L, et al. Preserving renal function in adults with hypertension and diabetes: a consensus approach. Am J Kidney Dis 2000; 36:646 – 661. 8. Gress TW, Nieto FJ, Shahar E, et al. Hypertension and antihypertensive therapy as risk factors for type 2 diabetes mellitus. Atherosclerosis Risk in Communities Study. N Engl J Med 2000; 342:905 – 912. 9. Sowers JR. Insulin resistance and hypertension. Am J Physiol Heart Circ Physiol 2004; 286:H1597 – H1602. 10.&& Centers for Disease Control and Prevention. National Diabetes Statistics Report: estimates of diabetes and its burden in the United States, 2014. Atlanta, GA: US Department of Health and Human Services; 2014. A thorough summary of the state of diabetes statistics in the United States. 11.& Regensteiner JG, Golden S, Huebschmann AG, et al. Sex differences in the cardiovascular consequences of diabetes mellitus: a scienti c statement from the American Heart Association. Circulation 2015; 132:2424 – 2447. A special paper on how diabetes impacts men and women differently. 12. Narayan KM, Boyle JP, Geiss LS, et al. Impact of recent increase in incidence on future diabetes burden: U.S., 2005 – 2050. Diabetes Care 2006; 29:2114 – 2116. 13. Nettleton JA, Steffen LM, Ni H, et al. Dietary patterns and risk of incident type 2 diabetes in the Multi-Ethnic Study of Atherosclerosis (MESA). Diabetes Care 2008; 31:1777 – 1782. 14. Suh DC, Choi IS, Plauschinat C, et al. Impact of comorbid conditions and race/ethnicity on glycemic control among the US population with type 2diabetes, 1988 – 1994 to 1999 – 2004. J Diabetes Complications 2010; 24:382 – 391. 15. Mokdad AH, Ford ES, Bowman BA, et al. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 2003; 289:76 – 79. 16. Kotchen TA. Obesity-related hypertension: epidemiology, pathophysiology, and clinical management. Am J Hypertens 2010; 23:1170 – 1178. 17.& Rahmouni K. Obesity-associated hypertension: recent progress in decipher-ing the pathogenesis. Hypertension 2014; 64:215 – 221. A state-of-the-art summary of the pathogenetic relationship between hypertensionand obesity. 18.& Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adultobesity in the United States, 2011 – 2012. JAMA 2014; 311:806 – 814. Another excellent summary of diabetes statistics in the United States. 19. Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999 – 2010. JAMA 2012; 307:491 – 497. 20. Villareal DT, Apovian CM, Kushner RF, Klein S. Obesity in older adults: technical review and position statement of the American Society for Nutritionand NAASO, The Obesity Society. Obes Res 2005; 13:1849 – 1863. 21. Mokdad AH, Serdula MK, Dietz WH, et al. The spread of the obesity epidemic in the United States, 1991 – 1998. JAMA 1999; 282:1519 – 1522. 22. Mokdad AH, Bowman BA, Ford ES, et al. The continuing epidemics of obesity and diabetes in the United States. JAMA 2001; 286:1195 – 1200. 23. Rao MV, Qiu Y, Wang C, Bakris G. Hypertension and CKD: Kidney Early Evaluation Program (KEEP) and National Health and Nutrition ExaminationSurvey (NHANES), 1999 – 2004. Am J Kidney Dis 2008; 51:S30 – S37. 24. Keane WF, Eknoyan G. Proteinuria, albuminuria, risk, assessment, detection, elimination (PARADE): a position paper for the National Kidney Foundation. Am J Kidney Dis 1999; 33:1004 – 1010. 25.& Horowitz B, Miskulin D, Zager P. Epidemiology of hypertension in CKD. AdvChronic Kidney Dis 2015; 22:88 – 95. A noteworthy paper on the stated topic. 26.& Rossignol P, Massy ZA, Azizi M, et al. The double challenge of resistant hypertension and chronic kidney disease. Lancet 2015; 386:1588 – 1598. A similarly important paper on the stated topic. 27. Muntner P, Anderson A, Charleston J, et al. Hypertension awareness, treat- ment, and control in adults with CKD: results from the Chronic Renal Insuf ciency Cohort (CRIC) Study. Am J Kidney Dis 2010; 55:441 – 451. 28. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classi cation and strati cation. Am J Kidney Dis 2002; 39:S1 – S266. 29.&& Saran R, Li Y, Robinson B, et al. US Renal Data System 2014 annual data report: epidemiology of kidney disease in the United States. Am J Kidney Dis 2015; 66:S1 – S305. An updated report on CKD statistics. 30. Bowling CB, Sharma P, Fox CS, et al. Prevalence of reduced estimated glomerular ltration rate among the oldest old from 1988 – 1994 through2005 – 2010. JAMA 2013; 310:1284 – 1286. 31. Coresh J, Byrd-Holt D, Astor BC, et al. Chronic kidney disease awareness, prevalence, and trends among U.S. adults, 1999 to 2000. J Am Soc Nephrol 2005; 16:180 – 188. 32. Lawes CM, Vander Hoorn S, Rodgers A. Global burden of blood-pressure- related disease, 2001. Lancet 2008; 371:1513 – 1518. 33.&& Rosendorff C, Lackland DT, Allison M, et al. Treatment of hypertension in patients with coronary artery disease: a scienti c statement from the American Heart Association, American College of Cardiology, and American Society of Hypertension. J Am Coll Cardiol 2015; 65:1998 – 2038. An important statement guiding the management of persons with co-existing HTN and CAD. 34. Wilson PW. Established risk factors and coronary artery disease: the Fra- mingham Study. Am J Hypertens 1994; 7:7S – 12S. 35.& Wright JT Jr, Williamson JD, Whelton PK, et al. A randomized trial of intensive versus standard blood-pressure control. N Engl J Med 2015; 373:2103 – 2116. A very recent trial supporting the bene t of intensive vs. standard BP control. 36. LewingtonS,ClarkeR,QizilbashN, et al. Age-speci c relevance of usual blood pressure to vascular mortality: a m eta-analysis of individual data for one million adults in 61 prospective studies. Lancet 2002; 360:1903 – 1913. 37. Franklin SS, Larson MG, Khan SA, et al. Does the relation of blood pressure to coronary heart disease risk change with aging? The Framingham Heart Study. Circulation 2001; 103:1245 – 1249. 38. Tow ghi A, Zheng L, Ovbiagele B. Sex-speci c trends in midlife coronary heart disease risk and prevalence. Arch Intern Med 2009; 169:1762 – 1766. 39. Gordon T, Kannel WB, Hjortland MC, McNamara PM. Menopause and coronary heart disease. The Framingham Study. Ann Intern Med 1978; 89:157 – 161. 40. Lloyd-Jones DM, Leip EP, Larson MG, et al. Novel approach to examining rst cardiovascular events after hypertension onset. Hypertension 2005; 45:39 – 45. 41. Shaw LJ, Shaw RE, Merz CN, et al. Impact of ethnicity and gender differences on angiographic coronary artery disease prevalence and in-hospital mortalityin the American College of Cardiology-National Cardiovascular Data Registry. Circulation 2008; 117:1787 – 1801. 42. McClelland RL, Chung H, Detrano R, et al. Distribution of coronary artery calcium by race, gender, and age: results from the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation 2006; 113:30 – 37. 43. Drazner MH. The progression of hypertensive heart disease. Circulation 2011; 123:327 – 334. 44. Ho JE, Gona P, Pencina MJ, et al. Discriminating clinical features of heart failure with preserved vs. reduced ejection fraction in the community. Eur Heart J 2012; 33:1734 – 1741. 45. Lee DS, Gona P, Vasan RS, et al. Relation of disease pathogenesis and risk factors to heart failure with preserved or reduced ejection fraction: insightsfrom the Framingham Heart Study of the National Heart, Lung, and Blood Institute. Circulation 2009; 119:3070 – 3077. 46. Levy D, Larson MG, Vasan RS, et al. The progression from hypertension to congestive heart failure. JAMA 1996; 275:1557 – 1562. 47. Lloyd-Jones DM, Larson MG, Leip EP, et al. Lifetime risk for developing congestive heart failure: the Framingham Heart Study. Circulation 2002; 106:3068 – 3072. 48. Bahrami H, Kronmal R, Bluemke DA, et al. Differences in the incidence of congestive heart failure by ethnicity: the Multi-Ethnic Study of Atherosclerosis. Arch Intern Med 2008; 168:2138 – 2145. 49. Loehr LR, Rosamond WD, Chang PP, et al. Heart failure incidence and survival (from the Atherosclerosis Risk in Communities study). Am J Cardiol2008; 101:1016 – 1022. 50. Kalogeropoulos A, Georgiopoulou V, Kritchevsky SB, et al. Epidemiology of incident heart failure in a contemporary elderly cohort: the Health, Aging, and Body Composition Study. Arch Intern Med 2009; 169:708 – 715. 51. Vivo RP, Krim SR, Cevik C, Witteles RM. Heart failure in Hispanics. J Am Coll Cardiol 2009; 53:1167 – 1175. Hypertension 380 www.co-cardiology.com Volume 31 Number 4 July 2016
Include references and avoid plagiarism when answering this, it is a graduate research NON-EXPERIMENTAL STUDY paper: Review the following attached articles that have been provided as example studies
65 Obesity and kidney disease Obesidade e doença renal Obesidade tem sido apontada como uma importante causa de doenças renais, incluindo a doença renal crônica (DRC). A obesidade influencia o desenvolvimento de DRC, entre outros fatores, porque predispõe à nefropatia diabética, nefroesclerose hipertensiva e glomeruloesclerose segmentar e focal. O excesso de peso e a obesidade estão associados a alterações renais hemodinâmicas, estruturais e histológicas, além de alterações metabólicas e bioquímicas que levam à doença renal. O tecido adiposo é dinâmico e está envolvido na produção de “adipocinas”, como a leptina, adipnectina, fator de necrose tumoral- α, proteína quimiotática de monócitos-1, fator de transformação do crescimento- β e angiotensina-II. Uma série de eventos é desencadeada pela obesidade, incluindo resistência à insulina, intolerância à glicose, dislipidemia, aterosclerose e hipertensão. Há evidência de que a obesidade por si só pode levar ao desenvolvimento de doença renal. Mais estudos são necessários para melhor entender a associação entre obesidade e doença renal. Resumo Palavras-chave: obesidade; sobrepeso; falência renal crônica. Obesity has been pointed out as an important cause of kidney diseases. Due to its close association with diabetes and hypertension, excess weight and obesity are important risk factors for chronic kidney disease (CKD). Obesity influences CKD development, among other factors, because it predisposes to diabetic nephropathy, hypertensive nephrosclerosis and focal and segmental glomerulosclerosis. Excess weight and obesity are associated with hemodynamic, structural and histological renal changes, in addition to metabolic and biochemical alterations that lead to kidney disease. Adipose tissue is dynamic and it is involved in the production of “adipokines”, such as leptin, adiponectin, tumor necrosis factor- α, monocyte chemoattractant protein-1, transforming growth factor- β and angiotensin-II. A series of events is triggered by obesity, including insulin resistance, glucose intolerance, dyslipidemia, atherosclerosis and hypertension. There is evidence that obesity itself can lead to kidney disease development. Further studies are required to better understand the association between obesity and kidney disease. AbstRAct Keywords: obesity; overweight; kidney failure, chronic. AuthorsGeraldo Bezerra da Silva Junior 1 Ana Carla Sobral Novaes Bentes 1 Elizabeth De Francesco Daher 2 Sheila Maria Alvim de Matos 3 1 Universidade de Fortaleza.2 Universidade Federal do Ceará.3 Universidade Federal da Bahia. Submitted on: 6/13/2016. Approved on: 7/27/2016. Correspondence to:Geraldo Bezerra da Silva Junior.Universidade de Fortaleza – UNIFOR.Av Washington Soares, 1321, Bloco S, sala 1, Fortaleza, Ceará, Brazil. CEP: 60811-905E-mail: [email protected] DOI: 10.5935/0101-2800.20170011 IntRoduct Ion Chronic kidney disease (CKD) is currently a worldwide public health problem that increasingly affects individuals early in their productive years and brings negative consequences for quality of life. There is evidence that CKD is an independent risk factor for angina, acute myocardial infarction, heart failure, stroke, peripheral vascular disease and arrhythmias. 1,2 The incidence and prevalence of CKD are growing and are associated to the increase in the population’s life expectancy and the number of cases of systemic arterial hypertension (SAH) and diabetes mellitus (DM), the main causes of CKD. 3,4 Approximately 13% of the adult US population has CKD stages 1 to 4.5 In Brazil, the incidence and prevalence of CKD are not known, but it is estimated R EVIEW A RTICLE | A RTIGO DE R EVIS O R EVIEW A RTICLE | A RTIGO DE R EVIS O 66 The association between increased waist circumference, high blood pressure, high fasting glucose and dyslipidemia constitutes the condition known as metabolic syndrome, which is associated with high cardiovascular risk. 19-21 The role of MS as a cause of CKD has been little discussed, although it is a major cause of hypertension and diabetes mellitus , conditions that account for over 70% of cases of CKD. 7 Individuals with MS have a 2-to-3 fold higher risk of developing microalbuminuria than those without MS. 22 There is evidence that all MS components show a significant association with CKD. 11,12 In a longitudinal study of 3,437 individuals from South Korea, CKD was associated with MS, irrespective of body weight and obesity was associated with CKD, regardless of the presence of MS. 12 Waist circumference and other components of MS have shown an independent association with CKD. 3 There is a positive association between the number of MS components and the risk of CKD. 22 Central obesity seems to be more important than body mass index (BMI) as a risk factor for cardiovascular diseases and CKD 8. Several renal disorders are associated with overweight, obesity and metabolic syndrome, as summarized in Table 1. PAtho Phys Iology of obes Ity le AdIng to k Idney d Ise Ase overweight and obesity are associated with hemodynamic, structural and histopathological alterations in the kidney, as well as metabolic and biochemical alterations that predispose to kidney disease, even when renal function is normal in the conventional tests. 8,23 It is currently known that adipose tissue is not only a fat reservoir, but a dynamic tissue involved in the production of adipokine , such as leptin, adiponectin, tumor necrosis factor- α, monocyte chemoattractant protein-1, transforming growth factor- β and angiotensin-II. 23 A series of events is triggered by obesity, including insulin resistance, glucose intolerance, hyperlipidemia, atherosclerosis and hypertension, all of which are associated with increased cardiovascular risk. The association between CKD and dyslipidemia has also been described, but its causes are still unknown. Insulin action resistance, present in CKD, reduces the activity of lipoprotein lipase, which may be implicated in the pathophysiology of dyslipidemia in CKD. 24 that currently more than 100,000 Brazilian individuals are undergoing dialysis. 6 Obesity has been identified as a major cause of kidney disease, including CKD 7-9 with evidence of causality in several studies. 3,7,10-12 Due to the close association with DM and hypertension, overweight and obesity, which show epidemic proportions worldwide, 13 are important risk factors for CKD onset, especially in adults. Dietary aspects, including some dietary patterns have also been identified as possible risk factors for CKD. 14 Obesity influences the development of CKD, among other factors, by predisposing to diabetic nephropathy, hypertensive nephrosclerosis and focal and segmental glomerulosclerosis. 3,8 Considering the growing trend of excess weight, which already affected half of the Brazilian population in 2013, according to the results of the latest telephone survey, VIGITEL, 15 an increase in the number of CKD cases is expected. In a multicentric cohort study (ELSA-Brasil), which included 15,105 Brazilian adults, the observed rate of excess weight was high, affecting 65.9% of men and 60.8% of women. 16 This article discusses the pathophysiology aspects of the association between obesity and kidney disease, discussing the most important aspects and the most current evidence of this association. ePI dem Iolog Ic A l AsPects of obes Ity , met Abol Ic synd Rome A nd kIdney d Ise Ase overweight and obesity have increased incidence and prevalence worldwide. In the united states, it is estimated that one third of adults are overweight and one third are obese. 13 several studies have shown a significant association between obesity and kidney disease. 7,3,10-12 currently, an epidemic of both overweight and obesity, as well as of chronic kidney disease can be observed and these conditions may be associated. 13 In the last 15 years, a steady increase in the number of obese patients undergoing dialysis has been observed. 17 there is also evidence that high levels of serum creatinine are associated with an increased risk of metabolic syndrome (ms). 18 moreover, there is evidence that obesity, especially when present in the early years of adulthood, is a risk factor for the development of renal carcinoma. 8 An association between obesity and nephrolithiasis has also been described, particularly uric acid and calcium oxalate calculi. 8,9 J Bras Nefrol 2017;39(1):65-69 Obesity and kidney disease 67 contributes to obesity-related hypertension. There is evidence that renal denervation reduces sodium retention and hypertension in obesity, suggesting that SNS activation induced by obesity increases blood pressure mainly due to the sodium retention stimulus, rather than vasoconstriction. The mechanisms that lead to SNS activation in obesity are not yet fully understood, but several factors have been proposed as triggers for this stimulus, including hyperinsulinemia, hyperleptinemia, increased levels of fatty acids, angiotensin II levels and baroreceptor reflex alterations. 3,7,8 The increase in leptin levels is associated with SNS activation and its effect on blood pressure level increase also includes nitric oxide synthesis inhibition (potent vasodilator). An increased production of endothelin-1 has also been described in obese subjects, which further contributes to the elevation of blood pressure levels and consequently to renal dysfunction. 3 Recent studies have shown that endothelin-1 is increased in patients with intradialytic hypertension, 25 suggesting Obesity leads to increased renal tubular sodium reabsorption, impairing pressure natriuresis and causing volume expansion due to the activation of the sympathetic nervous system (SNS) and the renin- angiotensin-aldosterone system (RAAS). Compression also occurs in the kidneys, especially when visceral obesity is presente. 3,7,8 The increase in sodium reabsorption and consequent extracellular volume expansion is a central event in the development of SAH in obesity. Some evidence suggests that an increase in sodium reabsorption occurs in some segments in addition to the proximal tubule, possibly in the loop of Henle. 7 Also, there is an increase in renal blood flow, glomerular filtration rate (GFR) and filtration fraction. Glomerular hyperfiltration, associated with increased blood pressure and other metabolic alterations such as insulin resistance and DM, finally result in kidney damage and reduced GFR 3,7,8 . The sympathetic nervous system (SNS) activation also Hemodynamic/Physiologic changes Effective plasmatic flow increase GFR increase Filtration fraction increase Magnitude increase of albuminuria/proteinuria Anatomic changes Kidneys’ weight increase Glomerular surface increase Glomerulomegaly Glomerular basement membrane increase Mesangial matrix expansion Mesangial cell proliferation Mesangial cell proliferation Decrease in the number of podocytes per glomeruli Increase in the lenght of podocyte processes Pathology Increase in the proportional number of glomeruli with segmental and global sclerosis Obesity-associated glomerulopathy/FSGS Chronic kidney disease/ Glomerulopathies Diabetic nephropathy Hypertensive neprhosclerosis FSGS IgA nephropathy Other renal/urologic complications Higher incidence of renal carcinoma Higher incidence of nephrolithiasis (uric acid and calcium oxalate) Higher incidence of surgical complications and graft loss in the context of kidney transplantation End-stage renal disease (ESRD) Higher incidence of ESRD tAble 1 R enal abnoRmalities associated with oveR weight , obesity and metabolic syndRome * gFR: glomerular Filtration Rate; Fsgs = Focal and segmental glomerulos clerosos. adapted from Kopple & Feroze, 2011. J Bras Nefrol 2017;39(1):65-69 Obesity and kidney disease 68 that this substance plays a key role in the genesis of hypertension in patients with CKD and is possibly associated with hypertension in obese patients. Adipose tissue accumulation, especially in visceral adiposity, causes kidney compression, and a consequent increase in intrarenal pressure. The excess of retroperitoneal adipose tissue involves the kidneys and penetrates the renal hilum up to the medulla, causing compression of the renal medulla and increased hydrostatic pressure of the renal interstitial fluid. Excess visceral fat also increases the intra-abdominal pressure, causing further renal compression. 3,8 The physical compression of the kidney causes increased extracellular matrix formation in the renal medulla. As the kidneys are surrounded by a capsule, which has low compliance, extracellular matrix accumulation can further exacerbate intrarenal compression and increase the hydrostatic pressure of the interstitial fluid. This increase in intrarenal pressure, in turn, compresses the loop of Henle and peritubular capillaries (vasa recta), which reduces the flow of fluids through the renal tubules leading to sodium reabsorption by them. 3,7,8 Another important factor in the pathophysiology of obesity renal complications is the so-called “lipotoxicity”, which refers to the disorders caused by the exacerbated metabolism of fatty acids in non- adipose tissue, such as skeletal muscle, pancreatic islets, myocardium and possibly the kidneys. In “overnutrition” states, the supply of fatty acids to the tissues exceeds the metabolic needs, leading to a compensatory increase in their oxidation. The increase in fatty acid metabolism leads to the production and release of several substances harmful to the cells, such as the products of lipid peroxidation and triglycerides, which can induce apoptosis and fibrosis in non-adipose tissues. 3,7,8 Obesity is also associated with inflammation, as an increase in the production of inflammatory cytokines such as tumor necrosis factor- α, interleukin-6 and C-reactive protein can be observed, being called “adipokines” by some authors because of their production by adipocytes. Inflammation itself is a risk factor for renal function loss. 26,27 Renal fibrosis (interstitial and glomerular), in addition to the irreversible accumulation of extracellular matrix in kidney tissue, is associated with inflammation, processes that may be associated with the “adipokines”. 3,8 There is also evidence that obesity itself increases albumin excretion, which progressively increases with obesity severity and, in rare cases, can lead to nephrotic syndrome. 7 Obesity has also shown to be an important risk factor for renal disease progression in patients with glomerulopathies, such as in IgA nephropathy. 28 Focal and segmental glomerulosclerosis (FSGS) is the type of glomerulonephritis most often associated with obesity. 29 FSGS associated with obesity typically presents with nephrotic syndrome and progressive renal function loss. The morphological findings include glomerulomegaly, predominance of perihilar variant and mild podocyte fusion. 29 Weight loss, either by dieting or through bariatric surgery, leads to proteinuria reduction. 8 The association between obesity, MS and nephrolithiasis has been observed in some studies, mainly due to increased serum uric acid levels in obese individuals. 30 Although obesity represents a major risk factor for the development of cardiovascular disease, some studies have shown that obesity is a protective factor in individuals with end-stage CKD (undergoing dialysis), 8,31 perhaps because malnutrition is associated with higher mortality when compared to obesity. However, even in patients undergoing dialysis, visceral obesity is associated with increased risk of coronary calcification and adverse cardiovascular events. 17 The pathophysiology of obesity- related kidney disease is shown in Figure 1. Figure 1. Pathophysiology of the association between obesity and kidney disease. Adapted from Silva Junior & Matos, 2016. conclusIon The incidence of obesity has been increasing worldwide and is an important risk factor for kidney disease. There is evidence that obesity alone can lead to the development of kidney diseases, including chronic kidney disease, glomerulopathy and nephrolithiasis. The pathophysiology of obesity-related kidney disease includes anatomic and hemodynamic alterations in the renal system. More studies are required to better understand the association between obesity and kidney disease. J Bras Nefrol 2017;39(1):65-69 Obesity and kidney disease 69 RefeRences 1. Chronic Kidney Disease Prognosis Consortium, Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, de Jong PE, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet 2010;375:2073-81. PMID: 20483451 DOI: http://dx.doi.org/10.1016/S0140-6736(10)60674-5 2. Gansevoort RT, Correa-Rotter R, Hemmelgarn BR, Jafar TH, Heerspink HJ, Mann JF, et al. Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention. Lancet 2013;382:339-52. PMID: 23727170 DOI: http://dx.doi.org/10.1016/S0140-6736(13)60595-4 3. Kopple JD. Obesity and chronic kidney disease. J Ren Nutr 2010;20:S29-S30. DOI: http://dx.doi.org/10.1053/j.jrn.2010.05.008 4. Ecder T. Early diagnosis saves life: focus on patients with chronic kidney disease. Kidney Int Suppl 2013;3:335-6. DOI: http://dx.doi.org/10.1038/kisup.2013.70 5. Centers for Disease Control and Prevention (CDC). Prevalence of chronic kidney disease and associated risk factors–United States, 1999-2004. MMWR Morb Mortal Wkly Rep 2007;56:161-5. PMID: 17332726 6. Sesso RC, Lopes AA, Thomé FS, Lugon JR, Watanabe Y, Santos DR. Relatório do Censo Brasileiro de Diálise Crônica 2012. J Bras Nefrol 2014;36:48-53. DOI: http://dx.doi.org/10.5935/0101-2800.20140009 7. Hall JE, Henegar JR, Dwyer TM, Liu J, Silva AA, Kuo JJ, et al. Is obesity a major cause of chronic kidney disease? Adv Ren Replace Ther 2004;11:41-54. 8. Kopple JD, Feroze U. The effect of obesity on chronic kidney disease. J Ren Nutr 2011;21:66-71. DOI: http://dx.doi.org/10.1053/j.jrn.2010.10.009 9. Taylor EN, Stampfer MJ, Curhan GC. Obesity, weight gain, and the risk of kidney stones. JAMA 2005;293:455-62. PMID: 15671430 DOI: http://dx.doi.org/10.1001/jama.293.4.455 10. Chang A, van Horn L, Jacobs DR Jr, Liu K, Muntner P, Newsome B, et al. Lifestyle-related factors, obesity, and incident microalbuminuria: The CARDIA (Coronary Artery Risk Development in Young Adults) study. Am J Kidney Dis 2013;62:267-75. DOI: http://dx.doi.org/10.1053/j.ajkd.2013.02.363 11. Lee KB, Hyun YY, Kim H. DASH dietary pattern and chronic kidney disease in elderly Korean population. Nephrol Dial Transplant 2015;30:iii504-iii505. 12. Song YM, Sung J, Lee K. Longitudinal relationships of metabolic syndrome and obesity with kidney function: Healthy Twin Study. Clin Exp Nephrol 2015;19:887-94. DOI: http://dx.doi.org/10.1007/s10157-015-1083-5 13. Hariharan D, Vellanki K, Kramer H. The Western Diet and Chronic Kidney Disease. Curr Hypertens Rep 2015;17:16. DOI: http://dx.doi.org/10.1007/s11906-014-0529-6 14. Silva Junior, Matos SMA. Padrões alimentares e doença renal crônica. In Cruz J, Cruz HMM, Kirsztajn GM, Oliveira RB, Barros RT, eds. Atualidades em Nefrologia 14. São Paulo: Sarvier; 2016. 15. Brasil. Ministério da Saúde. Secretaria de Atenção à Saúde. VIGITEL Brasil 2013. Vigilância de fatores de risco para doenças crônicas por inquérito telefônico. Brasília: Ministério da Saúde; 2014. 16. Schmidt MI, Duncan BB, Mill JG, Lotufo PA, Chor D, Barreto SM, et al. Cohort Profile: Longitudinal Study of Adult Health (ELSA-Brasil). Int J Epidemiol 2014;44:68-75. PMID: 24585730 DOI: http://dx.doi.org/10.1093/ije/dyu027 17. Johansen KL, Lee C. Body composition in chronic kidney disease. Curr Opin Nephrol Hypertens 2015;24:268-75. 18. Wang J, Li X, Han X, Yang K, Liu B, Li Y, et al. Serum creatinine levels and risk of metabolic syndrome in a middle-aged and older Chinese population. Clin Chim Acta 2015;440:177-82. DOI: http://dx.doi.org/10.1016/j.cca.2014.11.025 19. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al.; International Diabetes Federation Task Force on Epidemiology and Prevention; Hational Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; International Association for the Study of Obesity. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120:1640-5. PMID: 19805654 DOI: http://dx.doi.org/10.1161/CIRCULATIONAHA.109.192644 20. Abd Elaziz KM, Gabal MS, Aldafrawy OA, Abou Seif HA, Allam MF. Prevalence of metabolic syndrome and cardiovascular risk factors among voluntary screened middle-aged and elderly Egyptians. J Public Health (Oxf) 2015;37:612-7. 21. Kul S, Uyarel H, Gul M, Kucukdagli OT, Bacaksiz A, Erdogan E, et al. Metabolic syndrome and long-term cardiovascular outcomes in NSTEMI with unstable angina. Nutr Metab Cardiovasc Dis 2014;24:176-82. DOI: http://dx.doi.org/10.1016/j.numecd.2013.07.003 22. Bi H, Wu Y, Zhao C, Long G. Association between the dietary factors and metabolic syndrome with chronic kidney disease in Chinese adults. Int J Clin Exp Med 2014;7:4448-54. 23. Declèves AE, Sharma K. Obesity and kidney disease: differential effects of obesity on adipose tissue and kidney inflammation and fibrosis. Curr Opin Nephrol Hypertens 2015;24:28-36. DOI: http://dx.doi.org/10.1097/MNH.0000000000000087 24. Avesani CM, Pereira AML, Cuppari L. Doença renal crônica. In: Cuppari L, ed. Nutrição nas doenças crônicas não-transmissíveis. Barueri: Manole; 2009. p. 267-330. 25. Gutiérrez-Adrianzén OA, Moraes ME, Almeida AP, Lima JW, Marinho MF, Marques AL, et al. Pathophysiological, cardiovascular and neuroendocrine changes in hypertensive patients during the hemodialysis session. J Hum Hypertens 2015;29:366-72. DOI: http://dx.doi.org/10.1038/jhh.2014.93 26. Bash LD, Erlinger TP, Coresh J, Marsh-Manzi J, Folsom AR, Astor BC. Inflammation, hemostasis, and the risk of kidney function decline in the Atherosclerosis Risk in Communities (ARIC) Study. Am J Kidney Dis 2009;53:596-605. DOI: http://dx.doi.org/10.1053/j.ajkd.2008.10.044 27. Lin J, Hu FB, Mantzoros C, Curhan GC. Lipid and inflammatory biomarkers and kidney function decline in type 2 diabetes. Diabetologia 2010;53:263-7. PMID: 19921505 DOI: http://dx.doi.org/10.1007/s00125-009-1597-z 28. Praga M, Hernández E, Herrero JC, Morales E, Revilla Y, Díaz-González R, et al. Influence of obesity on the appearance of proteinuria and renal insufficiency after unilateral nephrectomy. Kidney Int 2000;58:2111-8. PMID: 11044232 DOI: http://dx.doi.org/10.1111/j.1523-1755.2000.00384.x 29. Darouich S, Goucha R, Jaafoura MH, Zekri S, Ben Maiz H, Kheder A. Clinicopathological characteristics of obesity-associated focal segmental glomerulosclerosis. Ultrastruct Pathol 2011;35:176-82. DOI: http://dx.doi.org/10.3109/01913123.2011.584657 30. Wong YV, Cook P, Somani BK. The association of metabolic syndrome and urolithiasis. Int J Endocrinol 2015;2015:570674. PMID: 25873954 DOI: http://dx.doi.org/10.1155/2015/570674 31. Park J, Ahmadi SF, Streja E, Molnar MZ, Flegal KM, Gillen D, et al. Obesity paradox in end-stage kidney disease patients. Prog Cardiovasc Dis 2014;56:415-25. DOI: http://dx.doi.org/10.1016/j.pcad.2013.10.005 J Bras Nefrol 2017;39(1):65-69 Obesity and kidney disease
Include references and avoid plagiarism when answering this, it is a graduate research NON-EXPERIMENTAL STUDY paper: Review the following attached articles that have been provided as example studies
Incidence of type 2 diabetes, hypertension, and dyslipidemia in metabolically healthy obese and non-obese M. Fingeret a, P. Marques-Vidal b,* , P. Vollenweider b aNYU School of Medicine, New York, NY, USAbDepartment of Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland Received 5 January 2018; received in revised form 8 June 2018; accepted 13 June 2018 Available online 20 June 2018 KEYWORDS Obesity; Metabolically healthy obese; Prospective; Metabolic syndrome; Dyslipidemia; Hypertension; Type 2 diabetes mellitus; Epidemiology AbstractBackground and aims:Metabolically healthy obese (MHO) individuals are devoid of many metabolic abnormalities, but how this condition is maintained over time remains debated. We assessed the prevalence of MHO over time and the incidence of hypertension (HTN), dysli- pidemia, and type 2 diabetes mellitus (T2DM) in MHO as compared with metabolically healthy non obese (MHNO). Methods and results:Prospective, population-based study including 3038 participants (49.9 9.9 years; 1753 women) free from metabolic syndrome and cardiovascular disease at baseline and examined after a follow-up of 5.6 years and 10.9 years on average. At each follow-up, prevalence of MHO, MHNO, metabolically unhealthy not obese (MUNO), and metabolically unhealthy obese (MUO), as well as of HTN, dyslipidemia, and T2DM, was calculated and stratified by sex, age group, and education. At baseline, 179 (5.7%) MHO participants were identified, of which 62 (34.6%) and 79 (44.1%) remained MHO at 5.6 and 10.9 years follow-up, respectively. At 5.6 years follow-up, MHO partic- ipants were more likely to develop low HDL or be on hypolipidemic medication [multivariable- adjusted OR (95% CI): 1.56 (1.02e2.38)], to have dyslipidemia [1.94 (1.33e2.82)], and high triglyc- erides [2.07 (1.36e3.14)] than MHNO. At 10.9 years follow-up, MHO participants were signifi- cantly more likely to develop T2DM [3.44 (1.84e6.43)], dyslipidemia [1.64 (1.14e2.38)], and low HDL or be prescribed hypolipidemic medication [1.57 (1.08e2.27)] than MHNO. Conversely, no differences were found regarding hypertension. Conclusion:A considerable fraction of MHO individuals lose their status over time, and in meta- bolically healthy adults, obesity confers a higher risk of developing cardiovascular risk factors. ª2018 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Feder- ico II University. Published by Elsevier B.V. All rights reserved. Introduction Despite efforts to combat obesity, its prevalence, along with the prevalence of its associated cardiovascular risk factors (CVRFs), such as dyslipidemia, type II diabetesmellitus (T2DM), and hypertension (HTN), remains high [1]. In fact, such CVRFs continue to be responsible for an overwhelming number of deaths worldwide[2,3]. Recent studies have identified various obesity phenotypes, notably metabolically healthy obese (MHO) individuals, * Corresponding author. Office BH10-642, Department of Medicine, internal medicine, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland. Fax:þ41 21 314 73 73. E-mail addresses:[email protected](M. Fingeret),[email protected](P. Marques-Vidal),[email protected] (P. Vollenweider). https://doi.org/10.1016/j.numecd.2018.06.011 0939-4753/ª2018 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved. Nutrition, Metabolism & Cardiovascular Diseases (2018)28, 1036e10 4 4 Available online atwww.sciencedirect.com Nutrition, Metabolism & Cardiovascular Diseases journal homepage:www.elsevier.com/locate/nmcd who are devoid of multiple CVRFs, and metabolically un- healthy obese (MUHO), who present with many CVRFs [4e6]. However, the prevalence of MHO, as well as the comparative incidence of CVRFs and mortality, depends on the Definition of metabolic syndrome applied, as there is little consensus on gold standard criteria for categorizing individuals as metabolically healthy or unhealthy obese [7,8]. Indeed, in a previous study on a Caucasian cohort, we showed that the prevalence of the MHO phenotype ranged between 3.3 and 32.1% in men and between 11.4 and 43.3% in women[7]. Further work has also found a substantial prevalence of MHO in other ethnic groups[9,10]. Several studies have suggested that MHO is an unstable condition, commonly leading to the development of metabolic abnormalities, but results have been inconsis- tent[4,5,11e13 ]. In fact, few prospective studies have focused on the natural course of MHO. The bulk of these studies focused primarily on endpoints of cardiovascular disease (CVD), T2DM, and all-cause mortality but reported contradictory results[12,14e17 ]. Even fewer have exam- ined the evolution of MHO as it concerns the incidence of dyslipidemia, T2DM, and HTN for MHO in comparison to that for metabolically healthy non-obese (MHNO) in- dividuals. Clearly, the MHO state, along with its implica- tions, remains poorly understood. Thus, the aims of this study were to assess the preva- lence of metabolically healthy obesity and the incidence of HTN, dyslipidemia, and T2DM in the MHO as compared with that in the MHNO after a 10-year follow-up in an adult Swiss population-based sample. Participants and methods Participants Participants were from the CoLaus study, a prospective study intended to evaluate the prevalence of CVRFs and to identify genetic determinants of these risk factors in a Swiss population aged between 35 and 75 years at baseline[18]. Sampling was performed as follows: the source population was defined as all subjects within the age range of interest registered in the population register of the city of Lausanne, Switzerland. The register includes all subjects living in this city for more than 90 days. A simple, non-stratified random sample of 19 0830 subjects (corresponding to 35% of the source population) was drawn and the selected subjects were invited to participate by letter. If no answer was ob- tained, a second letter was sent, and if no answer was ob- tained, the subjects were contacted by phone. Inclusion criteria were: (a) written informed consent; (b) willingness to take part in the examination and to provide blood samples; (c) Caucasian origin; (d) French language ability. For this study, we added the following inclusion criteria: (a) participants who completed the baseline,first, and second follow-up examinations and (b) availability of all variables analysed. For eligibility, we excluded (a) metabol- ically unhealthy participants (obese and non-obese) at baseline, as defined by Joint Interim Statement (JIS) criteria (see below for more details) and (b) type 1 diabetics atbaseline. We further excluded (a) participants with cardio- vascular disease at baseline; (b) participants with missing data at baseline; (c) participants without follow-up and (d) participants with missing data at follow-up. Recruitment began in June 2003 and ended in May 2006, enrolling 6733 participants who underwent an interview, a physical exam, and a blood analysis. Thefirst follow-up was performed between April 2009 and September 2012, 5.6 years on average after the collection of baseline data. The second follow-up was performed between May 2014 and July 2016, 10.9 years on average after the collection of baseline data. The information collected was similar to that collected in the baseline ex- amination but contained questions regarding food con- sumption and detailed physical activity information. Methods All participants were examined in the morning after a fast of at least 8 h. They were probed about their personal and family history of CVD, CVRFs, and cardiovascular treat- ment. CVD status at baseline was confirmed by further checking the available information provided by the par- ticipants (i.e. doctors, hospital registers, etc.). Smoking status was defined as never, former (no matter how long before the interview) and current. Educational level was categorized as low (primary), middle (apprenticeship), upper middle (high school), and high (university) for highest completed level of education. Physical activity was defined by answering positively to exercising 2 or more times per week for at least 20 min per session. Body weight and height were measured while partici- pants stood without shoes in light indoor attire. Body weight was measured in kilogrammes to the nearest 100 g using a Seca scale (Hamburg, Germany) that was frequently calibrated. Height was measured to the nearest 5 mm using a Seca (Hamburg, Germany) height gauge. Waist circumference was measured mid-way between the lowest rib and the iliac crest using a non-stretchable tape. The average of two measurements was taken and rounded to the nearest 0.5 cm. Blood pressure (BP) was measured thrice using an Omron HEM-907 automated oscillo- metric sphygmomanometer after at least a 10 min rest in a seated position, and the average of the last two measure- ments was used. Venous blood samples (50 mL) were drawn in the fasting state. Most biological assays were performed at the clinical laboratory of the Lausanne university hospital (CHUV) within 2 h of blood collection on fresh samples. Glucose was assessed by glucose dehydrogenase with a maximum inter- and intra-assay CV of 2.1% and 1.0%, respectively; total cholesterol by CHOD-PAP (1.6%e1.7%); HDL-cholesterol by CHOD-PAPþPEGþcyclodextrin (3.6%e0.9%) and triglycerides by GPO-PAP (2.9%e1.5%). Definition of variables and outcomes Obesity was defined per World Health Organization (WHO) guidelines and convention as having a body mass Metabolically healthy obesity10 37 index (BMI), calculated as weight in kilogrammes divided by height in metres squared, as 30 kg/m 2[19]. Non- obesity was defined as BMI<30 kg/m 2. To classify in- dividuals as metabolically healthy or not healthy, we applied criteria outlined in the Joint Interim Statement (JIS) for Caucasian individuals in 2009. The criteria are as follows: (a) fasting plasma glucose 5.6 mmol/L or drug treatment; (b) fasting TG 1.7 mmol/L or drug treatment; (c) fasting HDL-C<1.30 mmol/L in women and <1.00 mmol/L in men or drug treatment; (d) Systolic blood pressure (SBP) 130 mm Hg, diastolic blood pressure (DBP) 85 mm Hg, or drug treatment; (e) waist circum- ference of 102 cm for men and 88 cm for women[20]. Per JIS recommendations, subjects with 2 or fewer of the JIS criteria were defined as metabolically healthy while subjects meeting 3 or more of the criteria were defined as metabolically unhealthy and thus suffering from metabolic syndrome[20]. Antihypertensive drug treatment was defined if the participant took any medication for high blood pressure. Lipid-lowering medication was defined if the participant took any medication listed under the Anatomical Therapeutic Chemical (ATC) classification as a C10 agent. Anti-diabetic drug treatment was defined if the participants took any medication to lower blood glucose. When calculating the incidence of T2DM, dyslipidemia, and HTN, we did not take self-reported physician di- agnoses into account given that many participants in our sample were diagnosed at the time of study. T2DM was defined as fasting plasma glucose 7.0 mmol/L and/or anti-diabetic drug treatment, per WHO guidelines[21]. HTN was defined two ways: (a) 140/90 mm Hg and (b) 130/85 mm Hg to consider both a less strict criterion for HTN and the JIS criterion. Dyslipidemia was broken down into: (a) fasting HDL-C<1.30 mmol/L in women and <1.00 mmol/L in men or drug treatment; (b) fasting TG 1.7 mmol/L and (c) any of the above criteria[20]. Ethical approval The institutional Ethics Committee of the University of Lausanne, which afterwards became the Ethics Commis- sion of Canton Vaud (www.cer-vd.ch) approved the baseline CoLaus study (reference 16/03). The approval was renewed for thefirst (reference 33/09) and the second (reference 26/14) follow-ups. The study was per- formed in agreement with the Helsinki declaration and its former amendments, and all participants gave their signed informed consent before entering the study. Statistical analysis Statistical analyses were performed using Stata version 14.2 for windows (Stata Corp, College Station, Texas, USA). Bivariate analyses were performed using chi-square or Fisher’s exact test for qualitative variables and Stu- dent’s t-test. In the bivariate comparisons, results were expressed as number of participants (percentage) or as average standard deviation. Changes in metabolic status during follow-up were expressed as percentage and (95%confidence interval). Multivariable analysis was performed using logistic regression and the results were expressed as Odds ratio (OR) and 95% confi dence interval (CI). When computing the ORs for CVRFs (HTN, T2DM, and dyslipi- demia), results were adjusted for baseline age, sex, smoking status, education, and physical activity. For each CVRF, analyses were performed on participants devoid of the condition at baseline. As subjects who did not participate the follow-up differed from those who participated by a number of characteristics, a sensitivity analysis was performed. First, a propensity score was computed using nonparticipation in follow-up (coded as yes/no) as the dependent variable and gender, age, BMI, smoking, education, and physical activity as the independent variables. For each participant, the probability of nonparticipation was estimated and used in an inverse probability weighting model[22]. Sta- tistical significance was assessed for a two-sided test with p<0.05. Results Characteristics of participants Of the initial 6733 participants, 1645 (24.43%) were excluded because of metabolic disease, and 8 (0.12%) because of type 1 diabetes at baseline, leaving 5080 par- ticipants (75.45%) eligible, of whom 203 (3.0% of the initial sample) presented with history of cardiovascular disease. After excluding participants devoid of follow-up data, the final analytical sample consisted of 3038 participants (45.2% of the initial sample). The reasons for exclusion are summarized inFig. 1. Excluded participants were more likely to be older, male, current smokers, less educated, and not physically active and had a higher average BMI (Supplemental table 1). The characteristics of the participants according to obesity status at baseline are summarized inTable 1. MHO participants were significantly less educated and less likely to complete physical activity and had a higher mean BMI and higher blood pressure levels than MNHO, while no differences were found for total cholesterol and fasting glucose. Metabolic health The results regarding metabolic health at 5.6 and 10.9 years are summarized inTable 2. Of the initial 170 (5.6%) participants with MHO at baseline, 59 (34.7%) and 78 (45.9%) kept their status at 5.6 and 10.9 years follow-up, respectively. At 5.6 years follow-up, more than half of MHO participants became MUO, while at 10.9 years follow-up roughly 40% were classified as MUO. A small percentage of MHO participants at baseline became MHNO atfirst follow-up and second follow-up. A very small per- centage of MHO participants at baseline became MUNO at eitherfirst or second follow-up. Females and males tended to follow similar trends, but more females remained MHO or became MHNO and more 1038M. Fingeret et al. males became MUO at bothfirst and second follow-ups. Similar trends across age groups were also observed, with the highest proportion of participants becoming MUO at first follow-up for all age groups except for ages 45e54 in which the percentage of those remaining MHO was the same as those who became MUO. At second follow-up, ages 35e54 were most likely to retain their MHO status and second most likely to be MUO while ages 55e75 were most likely to be MUO and secondarily MHO. At baseline, 2868 (94.4%) MHNO participants were identified, of which 2297 (80.1%) and 2340 (81.6%) remained MHNO at 5.6 and 10.9 years follow-up, respec- tively. At 5.6 years follow-up, 16.0% of MHNO participants at baseline became MUNO while at 10.9 years follow-up only 11.4% were MUNO. A small percentage of MHNO participants at baseline became MHO or MUO atfirst and second follow-ups. Females and males tended to follow similar patterns, but more females remained MHNO and more males became MUNO at bothfirst and second follow-ups. Similar trends across age groups were also observed as most MHNO retained their MHNO status, but as age increased, the percentage of those remaining MHNO decreased at both follow-ups. Correspondingly, as age increased, the likelihood of being MUNO increased at both follow-ups with the exception of age group 65e75.Incidence of cardiovascular risk factors The results regarding the incidence of cardiovascular risk factors are summarized inTable 3. In bivariate analysis, MHO participants had a higher incidence of T2DM, dysli- pidemia and high triglycerides than MHNO participants at both follow-ups, while no association was found with HTN. MHO participants also had a higher incidence of low HDL or hypolipidemic drug treatment at 10.9 years follow-up. The associations between MHO and incidence of T2DM or dyslipidemia were further confirmed by multivariable analysis at both follow-ups, while the association with high triglycerides was only found at 5.6 years follow-up. Sensitivity analyses using inverse probability weighting for non-participation due to lack of follow-up confirmed the results, the effect of MHO on low HDL becoming significant also at 5.6 years follow-up (Table 3). Discussion Our results show that a considerable fraction of MHO in- dividuals lose their status over time and that obesity significantly increases the risk of developing CVRFs in in- dividuals who are initially metabolically healthy, afinding in agreement with two large studies conducted in the USA [23,24]. Our results confirm that MHO should not be considered as a benign status, at least in the US and in Europe. Metabolic health Almost half of participants who were MHO at baseline, versus approximately 20% of MHNO participants, lost their metabolically healthy status after 10.9 years follow-up. Despite using slightly modified criteria to define metabolic health and a follow-up period of 6 years, we nearly replicated Soriguer et al.’s results, which showed that roughly half of MHO participants at baseline remained MHO and roughly 20% of MHNO became metabolically unhealthy 6 years later[25]. Furthermore, despite creating their own criteria to define metabolic health, Achilike et al. also found that almost half of those with MHO at baseline transformed into MUHO over 7.8 years of follow-up[13]. This stark difference between MHO and MHNO in main- taining their metabolic health over time illustrates the negative effect of obesity. Similarfindings were obtained when the analyses were stratified by age and sex with two notable exceptions: more women than men retained their good metabolic health and the percentage of participants within each age group who retained good metabolic health decreased with age. These results support previous findings that the prevalence of metabolic abnormalities has a positive association with age[10]and that men have a higher prevalence of metabolic abnormalities[18]. In this way, we showed that the MHO phenotype is relatively unstable over 10 years. Yet, due to the lack of consensus on a gold standard defi nition of metabolic syndrome, it is difficult to compare our results across the literature. We used the JIS criteria including abdominal Figure 1Selection procedure, Colaus study, Lausanne, Switzerland. Metabolically healthy obesity1039 obesity, as was applied in several other studies [10,12,15,26]. Despite using different definitions for meta- bolic health, other studies similarly revealed that the MHO condition is unstable[4,5,25,26]. In fact, MHO status may be akin to a“grace period”that will inevitably end after a given time. We saw that MHO participants who remained metabolically healthy after 10.9 years follow-up were significantly younger and had lower CVRF levels than MHO participants who became metabolically unhealthy (Supplemental table 2). As a result, it appears likely that MHO status is only transient before obese individuals develop additional metabolic abnormalities that put them at higher risk for CVD, death, and other comorbidities. Incidence of cardiovascular risk factors MHO participants had a higher likelihood of developing T2DM, low HDL or being on hypolipidemic medication, dyslipidemia, and high triglycerides depending on follow- up. When computing the relative incidence of CVRFs, we compared the MHO with individuals whose BMI was <30 kg/m 2. However, most other studies compared the incidences of CVRFs between MHO and metabolically healthy individuals of normal weight (BMI<25 kg/m 2). Thus, the differences between MHO and MHNO partici- pants should be smaller in our study, as the effect of overweight status (25 No difference was found between MHO and MHNO participants regarding the incidence of HTN, defined as either 130/85 or 140/90 mm Hg. A likely explanation resides in the small sample size, which led to a reduced statistical power. Indeed, the RR were compatible with an increased risk of developing HTN, although the association was not statistically significant. Interestingly, other studies also suggested that the impact of MHO on diabetes might be stronger (or occur sooner) than for other cardiovascular risk factors[24], the lowest RR being found for HTN. Chang et al., though using the same criteria to define metabolic syndrome, witnessed a significantly increased risk for pre- hypertension (defined as 130/85 mm Hg) in MHO in- dividuals compared with metabolically healthy normal weight individuals[30]. Similarly, Lee et al., though using different criteria to define metabolic health, found a higher incidence of HTN (defined as 140/90 mm Hg) in healthy obese compared with healthy normal weight individuals [31]. Our results may not indicate this association by virtue of our inclusion of overweight individuals into our refer- ence group and/or our small number of MHO participants. Strengths and limitations Strengths of our study include a 10-year follow-up period. Additionally, given our fairly homogenous study popula- tion, we were able to apply strict criteria to define meta- bolic health and thus metabolic syndrome, contrary to previous studies that did not take waist circumference into account[14,25,27,28]. Moreover, our study outlined in- cidences for multiple specific CVRFs, as opposed to exam- ining endpoints of all-cause mortality or CVD[6,12,29]. Our study also not only assessed the natural course of the MHO phenotype for CVRFs but additionally compared its natural course to that of the MHNO phenotype taken from the same source population, contrary to the many other studies that compared MHO individuals to metabolically healthy normal weight individuals[14,15,27e29]. Finally, to our knowledge, our study is one of few that looked specifically at the incidence of HTN in MHO individuals as compared with that in MHNO individuals. Our study also has several limitations. First, our rela- tively small number of MHO (NZ179) limited our sta- tistical power. This is a commonfinding in most studies on MHO, as this condition is relatively infrequent: 2.9% in one study of subjects aged 45 to 64[24]and 10% in a study of subjects aged 18 to 65[23]). This small number was partly due to the large number of participants without follow-up data; further, participants without follow-up data pre- sented more frequently with MHO than included Table 1Characteristics of metabolically healthy non-obese andmetabolically healthy obese participants at baseline (2003e2006),Colaus study, Lausanne, Switzerland. MHNO (NZ2868)MHO (NZ170)p-value Age (years) 49.9 9.9 49.4 9.7 0.486 Female sex 1658 (57.8) 95 (55.9) 0.621 Smoking status Never 1241 (43.3) 73 (42.9) 0.158 Former 895 (31.2) 63 (37.1) Current 732 (25.5) 34 (20.0) Education University 741 (25.8) 22 (12.9)<0.001 High school 806 (28.1) 37 (21.8) Apprenticeship 945 (33.0) 64 (37.7) Mandatory 376 (13.1) 47 (27.7) Physically active 1732 (60.4) 76 (44.7)<0.001 Body mass index (kg/m 2)23.7 2.8 32.8 3.0<0.001 SBP (mm Hg) 123 16 127 16 0.002 DBP (mm Hg) 77 10 82 10<0.001 Total cholesterol (mmol/L)5.5 1.0 5.6 1.1 0.455 Fasting glucose (mmol/L)5.2 0.6 5.3 0.5 0.084 MHO, metabolically healthy obese; MHNO, metabolically healthynon-obese; SBP, systolic blood pressure; DBP, diastolic blood pres-sure. Results are expressed as number of participants (percentage)or as mean standard deviation. Between-group comparisonsperformed using student’s t-test or chi square analysis. 10 4 0M. Fingeret et al. Table 2Prevalence of metabolically healthy obesity and metabolically healthy non-obesity, overall and stratified by sex or age group, 2006e2017, Colaus study, Lausanne, Switzerland. Baseline Status N First follow-up Second follow-up MHNO MHO MUNO MUO MHNO MHO MUNO MUO MHO Total 170 9.4 (5.5e14.8) 34.7 (27.6e42.4) 2.4 (0.6e5.9) 53.5 (45.7e61.2) 13.5 (8.8e19.6) 45.9 (38.2e53.7) 1.8 (0.4e5.1) 38.8 (31.5e46.6) By sex Female 95 12.6 (6.7e21.0) 38.9 (29.1e49.5) 3.2 (0.7e9.0) 45.3 (35.0e55.8) 15.8 (9.1e24.7) 49.5 (39.1e59.9) 2.1 (0.3e7.4) 32.6 (23.4e43.0) Male 75 5.3 (1.5e13.1) 29.3 (19.4e41.0) 1.3 (0e7.2) 64.0 (52.1e74.8) 10.7 (4.7e19.9) 41.3 (30.1e53.3) 1.3 (0e7.2) 46.7 (35.1e58.6) Age group (years) 35-44 67 9.0 (3.4e18.5) 41.8 (29.8e54.5) 1.5 (0e8.0) 47.8 (35.4e60.3) 11.9 (5.3e22.2) 50.7 (38.2e63.2) 3.0 (0.4e10.4) 34.3 (23.2e46.9) 45-54 57 12.3 (5.1e23.7) 42.1 (29.1e55.9) 0 (0e6.3) 45.6 (32.4e59.3) 7.0 (1.9e17) 52.6 (39.0e66.0) 1.8 (0e9.4) 38.6 (26e52.4) 55-64 31 6.5 (0.8e21.4) 16.1 (5.5e33.7) 6.5 (0.8e21.4) 71.0 (52.0e85.8) 25.8 (11.9e44.6) 29.0 (14.2 e48) 0 (0e11.2) 45.2 (27.3e64) 65-75 15 6.7 (0.2e31.9) 13.3 (1.7e40.5) 6.7 (0.2e31.9) 73.3 (44.9e92.2) 20.0 (4.3e48.1) 33.3 (11.8e61.6) 0 (0e21.8) 46.7 (21.3e73.4) MHNO Total 2868 80.1 (78.6e81.5) 1.8 (1.4e2.4) 16.0 (14.6e17.4) 2.1 (1.6e2.7) 81.6 (80.1e83.0) 3.9 (3.3e4.7) 11.4 (10.2e12.6) 3.1 (2.5e3.8) By sex Female 1658 83.1 (81.2e84.9) 1.9 (1.3e2.6) 13.3 (11.7e15.0) 1.7 (1.2e2.5) 84.7 (82.9e86.4) 4.7 (3.7e5.8) 8.4 (7.1e9.8) 2.2 (1.6e3.1) Male 1210 76.0 (73.4e78.3) 1.8 (1.1e2.7) 19.7 (17.5e22.0) 2.6 (1.7e3.6) 77.4 (74.9e79.7) 2.9 (2.0e4.0) 15.5 (13.5e17.6) 4.3 (3.2e5.6) Age group (years) 35-44 1063 87.2 (85.0e89.2) 2.5 (1.7e3.7) 8.7 (7.1e10.6) 1.5 (0.9e2.4) 84.4 (82.1e86.5) 5.2 (3.9e6.7) 7.3 (5.8e9.1) 3.1 (2.1e4.3) 45-54 922 82.4 (79.8e84.8) 1.6 (0.9e2.7) 14.1 (11.9e16.5) 1.8 (1.1 e2.9) 80.8 (78.1e83.3) 3.0 (2.0e4.4) 12.8 (10.7e15.1) 3.4 (2.3e4.7) 55-64 642 70.7 (67.0e74.2) 1.4 (0.6e2.6) 24.8 (21.5e28.3) 3.1 (1.9e4.8) 80.4 (77.1e83.4) 3.3 (2.0e5.0) 12.9 (10.4e15.8) 3.4 (2.2e5.1) 65-75 241 64.7 (58.3e70.8) 0.8 (0.1e3.0) 31.5 (25.7e37.8) 2.9 (1.2e5.9) 75.5 (69.6e80.8) 3.7 (1.7e7.0) 19.5 (14.7e25.1) 1.2 (0.3e3.6) MHO, metabolically healthy obese; MHNO, metabolically healthy non-obese; MUO, metabolically unhealthy obese; MUNO, metabolically unhealthy non-obese. Results are expressed as percentageand (95% confidence interval). Metabolically healthy obesity10 41 participants (Supplemental table 3). Also, participants devoid of follow-up were significantly older, more frequently men, current smokers and of lower educational level, were less physically active and had a higher BMI. Hence, it is likely that the incidence of cardiovascular risk factors (and thus the change in status from MHO to MUO) would have been higher had these participants been included in the analyses. Further, the retained sample can no longer be considered as representative of the original population, so generalizability to other populations is limited. Still, our results apply to participants with MHO who are within the age range studied, as other studies that focused on this condition report similarfindings[23,24]. Overall, and despite the use of a propensity score to correct for selection bias, our results might underestimate the true impact of MHO on the incidence of cardiovascular and metabolic risk factors. We also did not collect information regarding bariatric surgery, which could explain why some MHO lost weight, becoming MHNO or MUNO. However, the relatively low number of MHO subjects becoming MHNO or MUNO at the second follow-up would likely not affect the significance of our results. Additionally, when calculating the incidence of T2DM, we did not use HbA 1c values due its lack of availability; instead, we defined thoseparticipants with a fasting plasma glucose 7.0 mmol/L to have T2DM. Yet, studies have shown that there exists a significant correlation between FPG and HbA 1c values so our conclusions would likely remain had we used HbA 1c values[32,33]. Finally, this study enrolled only Caucasian participants, which might limit the generalization of our findings to other ethnic groups. However, studies across multiple other ethnic groups also revealed a higher inci- dence of CVRFs in the MHO group[23,24,29]and insta- bility of the MHO phenotype[5]. Conclusion Being obese yet metabolically healthy leads to a higher risk of developing CVRFs as compared with being metabolically healthy and not obese. Our study supports that the MHO state is transient and should be regarded by clinicians as a warning sign. Funding The CoLaus study was and is supported by research grants from GlaxoSmithKline, the Faculty of Biology and Medicine Table 3Incidence of hypertension, type 2 diabetes mellitus, dyslipidemia, high triglycerides and low HDL-cholesterol or hypolipidemic drugtreatment at thefirst and the second follow-up, in metabolically healthy obese and metabolically healthy non-obese participants, Colaus study,Lausanne, Switzerland. N First follow-up Second follow-up Bivariate Multivariable MultivariablexBivariate Multivariable Multivariablex HTN ( 140/90 mm Hg) MHNO 2310 347 (15.0) 1.0 (ref.) 1.0 (ref.) 519 (22.5) 1.0 (ref.) 1.0 (ref.) MHO 111 22 (19.8) 1.39 (0.84e2.31) 1.34 (0.79e2.26) 31 (27.7) 1.19 (0.76e1.85) 1.11 (0.70e1.74) p-value 0.169 0.201 0.280 0.200 0.445 0.665 HTN ( 130/85 mm Hg) MHNO 1928 394 (20.4) 1.0 (ref.) 1.0 (ref.) 510 (26.5) 1.0 (ref.) 1.0 (ref.) MHO 94 26 (27.7) 1.43 (0.88e2.33) 1.44 (0.87e2.38) 32 (34.0) 1.29 (0.82e2.05) 1.30 (0.80e2.09) p-value 0.092 0.154 0.161 0.105 0.272 0.286 Type 2 diabetes MHNO 2834 62 (2.2) 1.0 (ref.) 1.0 (ref.) 62 (2.2) 1.0 (ref.) 1.0 (ref.) MHO 164 8 (4.9) 2.31 (1.05e5.08) 2.42 (1.06e5.50) 14 (8.5) 4.01 (2.12e7.58) 4.45 (2.23e8.88) p-value 0.027 0.037 0.036<0.001<0.001<0.001 Dyslipidemia MHNO 2339 445 (19.0) 1.0 (ref.) 1.0 (ref.) 508 (21.7) 1.0 (ref.) 1.0 (ref.) MHO 134 42 (31.3) 1.83 (1.24e2.70) 1.90 (1.27e2.85) 43 (32.1) 1.61 (1.10e2.37) 1.65 (1.10e2.47) p-value<0.001 0.002 0.002 0.005 0.015 0.015 Low HDL or Rx MHNO 2639 334 (12.7) 1.0 (ref.) 1.0 (ref.) 482 (18.3) 1.0 (ref.) 1.0 (ref.) MHO 150 27 (18.0) 1.52 (0.98e2.38) 1.66 (1.04e2.66) 40 (26.7) 1.59 (1.08e2.33) 1.66 (1.11e2.50) p-value 0.058 0.063 0.034 0.010 0.018 0.014 High triglycerides MHNO 2532 258 (10.2) 1.0 (ref.) 1.0 (ref.) 201 (7.9) 1.0 (ref.) 1.0 (ref.) MHO 151 31 (20.5) 2.05 (1.33e3.15) 1.94 (1.27e2.97) 19 (12.6) 1.52 (0.91e2.54) 1.40 (0.82e2.39) p-value<0.001 0.001 0.002 0.043 0.110 0.212 MHO, metabolically healthy obese; MHNO, metabolically healthy non-obese; HTN, hypertension; Rx, hypolipidemic drug treatment. Type 2diabetes was defined as fasting plasma glucose of 7.0 mmol/L and/or anti-diabetic drug treatment. Dyslipidemia was defined as fasting HDL-C<1.30 mmol/L in women and<1.00 mmol/L in men and/or high triglycerides (defined as fasting TG 1.7 mmol/L) and/or hypolipidemic drugtreatment. Results are expressed as number of participants (%) or as odds-ratio [95% confidence interval]. For each risk factor, analyses wereperformed on participants devoid of the condition at baseline. Bivariate analysis was done using chi-square; multivariable analysis was doneusing logistic regression adjusting for baseline age, sex, smoking status, education, and physical activity.xSensitivity analyses were conductedusing inverse probability weighting for non-participation due to lack of follow-up. P-values relate to the chi-square for bivariate analyses and tothe odds-ratio for multivariable analyses. 10 4 2M. Fingeret et al. of Lausanne, and the Swiss National Science Foundation (grants 33CSCO-122661, 33CS30-139468 and 33CS30- 148401). The funding sources had no involvement in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication. Ethics The institutional Ethics Committee of the University of Lausanne, which afterwards became the Ethics Commis- sion of Canton Vaud (www.cer-vd.ch) approved the baseline CoLaus study (reference 16/03, decisions of 13th January and 10th February 2003); the approval was renewed for thefirst (reference 33/09, decision of 23rd February 2009) and the second (reference 26/14, decision of 11th March 2014) follow-ups. The study was per- formed in agreement with the Helsinki declaration and its former amendments, and in accordance with the appli- cable Swiss legislation. All participants gave their signed informed consent before entering the study. Authors’contributions MF completed the bibliographic search, performed part of the statistical analyses, and wrote the manuscript. PMV devised the study, conducted most of the statistical ana- lyses, and revised the manuscript. PV devised the study and revised the manuscript for important intellectual content. All authors have read and approved this version of the manuscript. Conflicts of interest The authors report no conflict of interest. Appendix A. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.numecd.2018.06.011. References [1] Kelly T, Yang W, Chen CS, Reynolds K, He J. Global burden of obesity in 2005 and projections to 2030. Int J Obes 2008;32: 14 31e7.https://doi.org/10.1038/ijo.2008.102. [2] Mozaffarian D. Global scourge of cardiovascular disease: time for health care systems feform and precision population health. J Am Coll Cardiol 2017;70:26e8.https://doi.org/10.1016/j.jacc.2017. 05.007. [3] Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, Lee A, et al. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med 2017;377:13e27.https://doi.org/10.1056/ NEJMoa1614362. [4] Hamer M, Bell JA, Sabia S, Batty GD, Kivimaki M. Stability of metabolically healthy obesity over 8 years: the English Longitu- dinal Study of Ageing. Eur J Endocrinol 2015;173:703e8.https: //doi.org/10.1530/eje-15-0449. [5] Eshtiaghi R, Keihani S, Hosseinpanah F, Barzin M, Azizi F. Natural course of metabolically healthy abdominal obese adults after 10 years of follow-up: the Tehran Lipid and Glucose Study. Int J Obes 2015;39:514e9.https://doi.org/10.1038/ijo.2014.176.[6] Calori G, Lattuada G, Piemonti L, Garancini MP, Ragogna F, Villa M, et al. Prevalence, metabolic features, and prognosis of metaboli- cally healthy obese Italian individuals: the Cremona Study. Dia- betes Care 2011;34:210e5.https://doi.org/10.2337/dc10-0665. [7] Velho S, Paccaud F, Waeber G, Vollenweider P, Marques-Vidal P. Metabolically healthy obesity: different prevalences using different criteria. Eur J Clin Nutr 2010;64:1043e51.https: //doi.org/10.1038/ejcn.2010.114. [8]Karelis AD, Brochu M, Rabasa-Lhoret R. Can we identify meta- bolically healthy but obese individuals (MHO)? Diabetes Metab 2004;30:569e72. [9] Ranasinghe P, Mathangasinghe Y, Jayawardena R, Hills AP, Misra A. Prevalence and trends of metabolic syndrome among adults in the asia-pacific region: a systematic review. BMC Public Health 2017; 17:101.https://doi.org/10.1186/s12889-017-4041-1. [10] Khosravi-Boroujeni H, Sarrafzadegan N, Sadeghi M, Roohafza H, Talaei M, Ng SK, et al. Secular trend of metabolic syndrome and its components in a cohort of Iranian adults from 2001 to 2013. Metab Syndrome Relat Disord 2017;15:137e44.https://doi.org/10. 1089/met.2016.0073. [11] Kramer CK, Zinman B, Retnakaran R. Are metabolically healthy overweight and obesity benign conditions?: A systematic review and meta-analysis. Ann Intern Med 2013;159:758e69.https: //doi.org/10.7326/0003-4819-159-11-201312030-00008. [12] Fan J, Song Y, Chen Y, Hui R, Zhang W. Combined effect of obesity and cardio-metabolic abnormality on the risk of cardiovascular disease: a meta-analysis of prospective cohort studies. Int J Cardiol 2013;168:4761e8.https://doi.org/10.1016/j.ijcard.2013. 07.230. [13] Achilike I, Hazuda HP, Fowler SP, Aung K, Lorenzo C. Predicting development of the metabolically healthy obese phenotype. Int J Obes 2015;39:228e34.https://doi.org/10.1038/ijo.2014.113. [14] Hinnouho GM, Czernichow S, Dugravot A, Nabi H, Brunner EJ, Kivimaki M, et al. Metabolically healthy obesity and the risk of cardiovascular disease and type 2 diabetes: the Whitehall II cohort study. Eur Heart J 2015;36:551e9.https://doi.org/10.1093/ eurheartj/ehu123. [15] Kim CH, Kim HK, Bae SJ, Kim EH, Park JY. Independent impact of body mass index and metabolic syndrome on the risk of type 2 diabetes in Koreans. Metab Syndrome Relat Disord 2012;10: 321e 5.https://doi.org/10.1089/met.2011.0143. [16] Kuk JL, Ardern CI. Are metabolically normal but obese individuals at lower risk for all-cause mortality? Diabetes Care 2009;32: 2297e9.https://doi.org/10.2337/dc09-0574. [17] Hamer M, Stamatakis E. Metabolically healthy obesity and risk of all-cause and cardiovascular disease mortality. J Clin Endocrinol Metab 2012;97:2482e8.https://doi.org/10.1210/jc.2011-3475. [18] Firmann M, Mayor V, Vidal PM, Bochud M, Pecoud A, Hayoz D, et al. The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome. BMC Cardiovasc Disord 2008;8:6. https://doi.org/10.1186/1471-2261-8-6. [19]World Health Organization. Obesity: preventing and managing the global epidemic. World Health Organ Tech Rep Ser 2000;894(i- xii):1e253. [20] Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; american heart association; World heart federa- tion; international atherosclerosis society; and international as- sociation for the study of obesity. Circulation 2009;120:1640e5. https://doi.org/10.1161/circulationaha.109.192644. [21] Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 1998;15:539e53.https://doi.org/10. 1002/(sici)1096-9136(199807)15:7<539::aid-dia668>3.0.co;2-s. [22]Narduzzi S, Golini MN, Porta D, Stafoggia M, Forastiere F. Inverse probability weighting (IPW) for evaluating and“correcting”se- lection bias. Epidemiol Prev 2014;38:335e41. [23] Espinosa De Ycaza AE, Donegan D, Jensen MD. Long-term meta- bolic risk for the metabolically healthy overweight/obese pheno- type. Int J Obes (Lond) 2018;42:302e9.https://doi.org/10.1038/ijo. 2017.233. Metabolically healthy obesity1043 [24] Bradshaw PT, Reynolds KR, Wagenknecht LE, Ndumele CE, Stevens J. Incidence of components of metabolic syndrome in the metabolically healthy obese over 9 years follow-up: the Athero- sclerosis Risk in Communities study. Int J Obes (Lond) 2018;42: 295e301.https://doi.org/10.1038/ijo.2017.249. [25] Soriguer F, Gutierrez-Repiso C, Rubio-Martin E, Garcia-Fuentes E, Almaraz MC, Colomo N, et al. Metabolically healthy but obese, a matter of time? Findings from the prospective Pizarra study. J Clin Endocrinol Metab 2013;98:2318e25.https://doi.org/10.1210/jc. 2012-4253. [26] Hwang YC, Hayashi T, Fujimoto WY, Kahn SE, Leonetti DL, McNeely MJ, et al. Visceral abdominal fat accumulation predicts the conversion of metabolically healthy obese subjects to an unhealthy phenotype. Int J Obes 2015;39:1365e70.https://doi.org/10. 1038/ijo.2015.75. [27] Appleton SL, Seaborn CJ, Visvanathan R, Hill CL, Gill TK, Taylor AW, et al. Diabetes and cardiovascular disease outcomes in the meta- bolically healthy obese phenotype: a cohort study. Diabetes Care 2013;36:2388e94.https://doi.org/10.2337/dc12-1971.[28] Arnlov J, Sundstrom J, Ingelsson E, Lind L. Impact of BMI and the metabolic syndrome on the risk of diabetes in middle-aged men. Diabetes Care 2011;34:61e5.https://doi.org/10.2337/dc10-0955. [29] Bell JA, Kivimaki M, Hamer M. Metabolically healthy obesity and risk of incident type 2 diabetes: a meta-analysis of prospective cohort studies. Obes Rev 2014;15:504e15 .https://doi.org/10.1111/obr.12157. [30] Chang Y, Ryu S, Suh BS, Yun KE, Kim CW, Cho SI. Impact of BMI on the incidence of metabolic abnormalities in metabolically healthy men. Int J Obes 2012;36:1187e94.https://doi.org/10.1038/ijo.2011.247. [31] Lee SK, Kim SH, Cho GY, Baik I, Lim HE, Park CG, et al. Obesity phenotype and incident hypertension: a prospective community- based cohort study. J Hypertens 2013;31:145e51.https: //doi.org/10.1097/HJH.0b013e32835a3637. [32]Bozkaya G, Ozgu E, Karaca B. The association between estimated average glucose levels and fasting plasma glucose levels. Clinics 2010;65:1077e80. [33]Ito C, Maeda R, Ishida S, Sasaki H, Harada H. Correlation among fasting plasma glucose, two-hour plasma glucose levels in OGTT and HbA1c. Diabetes Res Clin Pract 2000;50:225e30. 10 4 4M. Fingeret et al.
Include references and avoid plagiarism when answering this, it is a graduate research NON-EXPERIMENTAL STUDY paper: Review the following attached articles that have been provided as example studies
Review Hypertension and diabetes mellitus as a predictive risk factors for stroke Aladeen Alloubani a,* , Abdulmoneam Saleh b, Ibrahim Abdelhafiz c aKing Hussein Cancer Center, Nursing Supervisor for Research & Evidence Based Practice, Amman, Jordan bUniversity of Tabuk, Family Medicine, Faculty of Medicine, Tabuk, Saudi Arabia cAl-Ghad International Health Sciences Colleges, Health Management, Najran, Saudi Arabia A R T I C L E I N F O Keywords: Hypertension Diabetes mellitus Stroke Risk factors Lifestyle A B S T R A C T Background: Stroke is becoming a major challenge in healthcare systems, and this has necessitated the study of the various risk factors. As the number of people with hypertension, diabetes mellitus and obesity increases, the problem is expected to worsen. This review paper evaluates what can be done to eliminate or reduce the risk of stroke. Objective: The aim of the research is to evaluate the risk factors for stroke. The paper also aims to understand how these risks can be handled to avoid incidences of stroke. Method: Published clinical trials of stroke risk factors studies were recognised by a search of EMBASE and MEDLINE databases with keywords hypertension, blood pressure, diabetes mellitus, stroke or cardiovascular disease, or prospective study, and meta-analysis. Results: The findings of this review are that the prevention of stroke starts with identifying risk factors for stroke, most of the patients diagnosed with stroke have various risk factors. Consequently, it is a very significant to identify all the risk factors for stroke as well as to teach the patient how to dominate them. Conclusion: after summarising all the studies mentioned in the paper, it can be established that hypertension and diabetes mellitus are a stroke risk factors and correlated in patients with atherosclerosis. © 2018 Diabetes India. Published by Elsevier Ltd. All rights reserved. Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577 2. Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 578 3. Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 578 3.1. Design and strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 578 3.2. Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 578 3.3. Ethical considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 578 4. Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 578 4.1. Hypertension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 578 4.2. Diabetes mellitus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 582 5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583 6. Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583 1. Introduction There are diverse classifications of risk factors concerning strokes, traditional and new, such as hyperhomocysteinemia and hyperco- agulable state. There are also risk factors that are modifiable and non- modifiable. Cerebrovascular illnesses or diseases adhere to risk factors that are non-modifiable for sex-based orientation, age, race or * Corresponding author. E-mail address: [email protected] (A. Alloubani). https://doi.org/10.1016/j.dsx.2018.03.009 1871-4021/© 2018 Diabetes India. Published by Elsevier Ltd. All rights reserved. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 12 (2018) 577–584 Contents lists available at ScienceDirect Diabetes & Metabolic Syndrome: Clinical Research & Reviews journal homepa ge: www.elsev ier.com/locate/dsx ethnic groups, and genotype prior to the myocardial infarction, to the stroke and or TIA, in addition to modifiable risk factors such as diabetes, hypertension and hyperlipidemia [1]. There are also coronary artery diseases along with physical immobility, consump- tion of alcoholic beverages, cigarette smoking and obesity [2]. Atherosclerosis is a key pathomorphological method that narrows the arterial walls across the body and within the brain, leading to cerebrovascular disease. It is believed that atheroscle- rosis arises from chronic inflammation and damage to the arterial wall within the peripheral or coronary vascular system. As a reaction to endothelial inflammation and damage, oxidised lipids from LDL (low-density lipoproteins) particles gather in the endothelial region of the vessel wall [2]. The oxidation of these particles may be brought about by angiotensin II. Monocyte then infiltrates the arterial wall and differentiates into macrophages, which accumulate oxidised lipids to form foam cells. After their creation, foam cells encourage spread of macrophage and drawing of T-lymphocytes. These T-lymphocytes consequently bring about smooth muscle propagation within the arterial walls and build-up of collagen. This process results in the creation of a lipid dense atherosclerotic lesion with a fibrous cap. When this lesion ruptures, there is severe vascular infarction which ruptures, causing frequent bleeding in the plaque in diabetic patients (diabetic patients also have a greater perioperative risk of carotid endarterectomy). Apart from atheroma creation, there is a greater proof of higher platelet adhesion, hypercoagulability, defected nitric oxide pro- duction and the higher creation of free radicals, in addition to altered calcium regulation in diabetic patients [3,4]. 2. Aim The aim of this research paper is to evaluate the hypertension and diabetes mellitus as a risk factors associated with stroke. Having understood the risk factors, the research evaluates how these risk factors have been handled in the past and what can be done in the future. The research aims to identify the major problems that have led to increased risks of stroke. This can help in recommending the steps that can be used to deal with the condition. Based on the information available, it will be possible to offer a guide to people who are already facing high-risk factors, so that they can avoid the worst scenario of suffering a stroke. Additionally, people who have not yet experienced risk factors can learn from this paper, steps that they can take to avoid increasing the risk of suffering from a stroke. The review will highlight gaps in healthcare that need to be closed to ensure that people receive better care and that mortalities resulting from stroke are reduced. 3. Method 3.1. Design and strategy Published clinical trials of stroke risk factors studies were recognised by a search of EMBASE and MEDLINE databases with keywords hypertension; blood pressure; diabetes mellitus; stroke or cardiovascular disease; or prospective study; and meta-analysis. Included are clinical trials involved patients with hypertension and diabetes mellitus as a predictive stroke risk factor. Date of birth or age, gender, blood pressure documented at baseline. Randomised controlled trials of hypertension as a stroke risk factor published before 2016 were eligible for inclusion. Random studies distribution of participants to a stroke risk factors lowering drug or placebo; random distribution of participants to different stroke risk factors lowering drugs; and randomdistribution of participants to different stroke risk factors lowering targets were eligible. To decrease the risk of small- study effects [5], all studies were needed to have at least 1000 patient-years of follow-up in each study group. Studies were involved if they were published or information were reachable before 2016, and if they provided information on inclusion criteria, regions and number of randomisation method, trial endpoints, duration of follow-up, trial interventions and methods of analyses. Results were independently extracted and summarised. Nevertheless, no further analyses were directed. The first step was the selection of relevant references that could be used to complete the research. This was based on the information that the resources provide the background of the authors, the publisher and the year the references were published. After selecting the needed references, the second step was to read through them and get the relevant information that could be useful for the research [6]. The research depended on primary sources that are reliable and address the issue of stroke risks in society. Various reputable organisations have completed research on the issue of stroke, and these resources are important for understanding the issue of stroke and its relationship with hypertension. Journal articles, books and websites were used in collecting reliable data published by authors in this field. Based on these sources, it was possible to make reductions as to how the problem of stroke affects society and how it can be handled. 3.2. Approach The sources were used in the literature review, where the various sources are critically evaluated to offer information about the topic. The findings from the sources were discussed and summarised in the tables to provide more information on the issue. Based on such findings, it was possible to make conclusions and recommendations [7]. 3.3. Ethical considerations This study was deemed IRB-exempt according to the university’s Human Subjects Protection guidelines since data were publicly available and individual patients were not identifiable. The research was based on ethical guidelines for carrying out research. The references used in the research are well cited and referenced to avoid plagiarism. The sources are paraphrased to ensure that the research is not just a duplicate of the previous research. There are also no incidences where personal ideas that may be biased are included in the research as for facts. This ensures that the research is reliable, hence important to the various targeted users [6]. 4. Results and discussion 4.1. Hypertension Hypertension is the most predominant modifiable risk factor for stroke with a prevalence of about thirty percent in developed nations. Hypertension is exposed more in elderly. The Framingham Stroke Risk Profile (FSRP) was established to have better and more rapid assessment of stroke risk factors [8]. The developers of FSRP employed information from thirty-six years of follow-up within the Framingham Stroke cohort study then verified them from other cohorts. Sex-specific approxima- tions of the probability of stroke are offered by the FSRP with the help of clinical information [9]. Hypertension was found in the Framingham Heart Research Study concerning specialists who 578 A. Alloubani et al. / Diabetes & Metabolic Syndrome: Clinical Research & Reviews 12 (2018) 577–584 know about lifetime risks of hypertension. They did find around 90 percent for women and men who found to be non-hypertensive from about 55 up to 65 years old and the lived ages range from around 80 to 85 years old [10–12]. Presumably, there was a deduction in a meta-analysis of 1 million grown-ups that are enlisted in 61 observation studies and noted dynamic and direct inclination concerning to deaths brought about by the ischemic heart illness as well as stroke. It is systolic levels of blood pressure of 115 mm Hg low and of the diastolic base of about 75 mm Hg high’. A research study found that for each 20 mm Hg systolic and as well as 10 mm Hg diastolic rise in circulatory strains, the death ratios resulting from stroke and ischemic illness increased twofold. Nevertheless, it was recom- mended that around 10 mm Hg decrease in systolic pressure and as well as the 5 mm Hg decrease in diastolic blood pressure may prompt a forty percent reduction in the risk of death caused by stroke and a 30 percent reduction in ischemic heart illness and other vascular associated deaths [10 ,13]. Blood pressure in patients having diagnosed stroke was assessed by the United Kingdom transient ischemic attack (TIA) trial. It was found that there was a direct and consistent relation between recurrent strokes and blood pressure. The data showed that a five mmHg lower diastolic blood pressure was related to a decrease in stroke for around one-third [14 ,15]. Furthermore, there was 2003 Joint National Committee on Prevention, Detection, Evaluation, and Treatment of high blood pressure or JNC – 7 that consider a category of hypertension. The new category did change the pressure of the blood of less than 12 0/ 80 mm Hg from the optimum down to what is standard, and prehyperten- sion was accessible for the systolic pressure of the blood of 12 0 up to 13 9 mm Hg and the diastolic pressure of the blood of 80 up to 89 mm Hg. This re-categorization was done to support modifications to lifestyles in the beginning phases of hypertension and decrease the occurrences of heart attack and stroke [16]. Changes in the lifestyle include dietary changes, which essentially involves consuming vegetables and fruits more (meta-analysis of 9 autonomous types of research has depicted that three to five servings every day decrease stroke risk for 0, 89) and eating less salt [17]. Further lifestyle changes include weight loss, aerobic activity and restricting alcohol intake. It is not suggested to undergo pharmacological treatment till systolic pressures increase to more than 14 0 mm Hg as well as diastolic increases to over 90 mm Hg brain perfusion. The significance of treating hypertension to decrease the stroke risk injuries is evident; however, the most optimal choice of antihypertensive medicine is not so evident [18]. It was depicted in the study of Heart Outcomes Prevention Evaluation (HOPE) that when angiotensin converting enzyme inhibitor (ACEI) Ramipril was used, there were better cardiovas- cular outcomes, further than its capacity to decrease blood pressure, which was not so intense in this trial (the mean decrease in systolic/diastolic blood pressure was 3/2 mm Hg) [9,19]. Consequently, The Losartan Intervention for the Endpoint reduction in hypertension (LIFE) study did assess impacts of Angiotensin Receptor Blocker (ARB) Losartan with the beta blocker, Atenolol toward cardiovascular-related failure, stroke and ‘myo- cardial infarction’ in patients that experienced hypertension and ‘left ventricular hypertrophy’. An evaluation demonstrates similar risk decline of 25% in fatal stroke and the backing of Losartan than Atenolol [20]. The foremost trial that was carried out only on patients diagnosed with a cerebrovascular disease or transient ischemic attack (TIA) was the perindopril protection against recurring stroke study (PROG- RESS). The patients with a background marked by a stroke were relegated to false treatment, perindopril alongside indapamide and a thiazide diuretic [21]. A normal blood pressure was noted as 147 /86 mm Hg, a combination of the indapamide the as well as perindopril produced a standard decrease in the blood pressure which is 12 /5 mm Hg and 43% reduction in relative risks of irregular stroke in opposition to perindopril that brought a simple decrease of 5/3 mm Hg in the blood pressure. The study did offer an increase to questions with respect to positive findings, and the findings indicate that ACEI and the thiazide [22,23]. The Study on Cognition and Prognosis in Elderly labelled as (SCOPE) that elderly patients experienced disconnected systolic hypertension and were risky into ARB antihypertensive treatment with Losartan and non-ARB treatment [10]. Notwithstanding, there was a comparable reduction in the blood pressure in ‘ARB arm (22/6 mm Hg)’ and ‘non-ARB arm (20/5 mm Hg)’ with 42 percent decrease in stroke risks. It is indicated through these findings that ACEI and ARB, particularly when mixed with a thiazide diuretic, may be better compared to other antihypertensive regimens in the secondary prevention of stroke (verified earlier findings of progress) [24,25]. It has been revealed by three randomized trials to compare the more severe blood pressure control against the less severe blood pressure control that the more severe blood pressure control was better at diminishing the magnitude of strokes, but mainly in diabetic patients and only affected diastolic blood pressures United Kingdom Prospective Diabetes Study (UKPDS) [26,27]. A thiazide diuretic chlorthalidone was found to be better than a calcium channel blocker (CCB) amlodipine, alpha-receptor antag- onist doxazosin, and angiotensin converting enzyme inhibitor (ACEI) lisinopril when avoiding one or main vascular conditions like stroke, in the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) [28]. On Going Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial (ONTARGET) was assess the correlation between the ACEI ramipril against the ARB telmisartan and against the combination of both to decrease the chance of main vascular conditions in the patient with high risk [29,30]. The European Lacidipine Study on Atherosclerosis (ELSA) assessed the influence of lacidipine (calcium antagonist) cantered medication as well as the beta-blocker (atenolol) cantered remedy on the advancement and evolution of carotid atherosclerosis (measured in individuals with the highly severe condition). The initial inference of this research involved the degree of alteration in the wideness of the wall of the carotid artery, which was appraised with the help of a B- mode ultrasound (in the individuals who were on lacidipine IMT was decreased by forty percent in five years’ development phase) Valsartan Antihypertensive Long-term Use Evaluation (VALUE) research. The main objective of this research was to decide if the hypertension being treated with the treatment that was instigated with the angiotensin receptor blocker (ARB) valsartan would offer a decrease of 15 % in the possibility of cardiac condition and death, as compared to the blood pressure being controlled with the treatment of calcium channel blocker (CCB) amlodipine. Amlodipine decreased the possibility of stroke but not significantly [31,32]. For Secondary Prevention of the disease and mortality after stroke, the Eprosartan compared against Nitrendipine revealed that the potential prospective randomized controlled study (MOSES) displays that the individuals who have endured cerebrovascular conditions in the past, a medication cantered on eprosartan to reduce the blood pressure (BP) as compared to the nitrendipine is more affecting towards the repetition of cerebro- vascular conditions and cardiac related issues [33]. The SPS3 or the “Secondary Prevention of Small Subcortical Strokes Trial” did explore differentiating the different antiplatelet medicines to maintain a strategic distance from stroke in people by method for lacunar strokes is additionally looking at the impact of a diverse blood pressure mark, ‘systolic blood pressure less than150 mm Hg in contrast to targeted blood pressure <13 0 mm Hg [10 , 3 4 , 3 5]. A. Alloubani et al. / Diabetes & Metabolic Syndrome: Clinical Research & Reviews 12 (2018) 577–584 579 Table 1 Summary of the included Hypertension studies. Year Study Participant Number of risk factors Findings 19 4 8 Framingham Heart Research Study5209 respondents (Men: 2336 Women: 2873) ages of 30 and 62 from the town of Framingham, MassachusettsHigh blood pressure, high blood cholesterol, smoking, obesity, diabetes, and physical inactivity. (blood triglyceride and HDL cholesterol levels, age, gender, and psychosocial issues)1960s Cigarette smoking increases risk of heart disease. Increased cholesterol and elevated blood pressure increase risk of heart disease Exercise decreases risk of heart disease, and obesity increases it. 1970s Elevated blood pressure increases risk of stroke. In women who are postmenopausal, risk of heart disease is increased, compared with women who are premenopausal. Psychosocial factors affect risk of heart disease. 1980s High levels of HDL cholesterol reduce risk of heart disease. 1990s Having an enlarged left ventricle of the heart (left ventricular hypertrophy) increases risk of stroke. Elevated blood pressure can progress to heart failure. 2000s So called “high normal blood pressure” increases risk of cardiovascular disease (high normal blood pressure is called prehypertension in medicine; it is defined as a systolic pressure of 12 0–13 9 mm Hg and/or a diastolic pressure of 80–89 mm Hg). From 197 9 to 1985United Kingdom transient ischemic attack (TIA) aspirin trial2435 patient Treatment with aspirin 600 mg twice daily (n = 815), aspirin 300 mg once daily (n = 806) or placebo (n = 814).transient ischemic attack or minor ischemic strokeThere was no definite difference between responses to the 300 mg of aspirin and 120 0 mg daily doses, except that the lower dose was significantly less gastro toxic. 2006 Fruit and vegetable consumption and stroke: meta-analysis of cohort studies257,551 individuals (4917 stroke events) with an average follow-up of 13 yearsEight studies, consisting of nine independent cohorts, met the inclusion criteria. Increased fruit and vegetable intake in the range commonly consumed is associated with a reduced risk of stroke Subgroup analyses showed that fruit and vegetables had a significant protective effect on both ischemic and hemorrhagic stroke. Increased fruit and vegetable intake in the range commonly consumed is asso- ciated with a reduced risk of stroke. 19 97 Losartan Intervention for Endpoint reduction in hypertension (LIFE)9218 hypertensive patients Hypertension study is a double-blind, prospective, parallel group study designed to compare the effects of losartan with those of the beta-blocker atenolol on the reduction of cardiovascular morbidity and mortality.There was no significant difference between the losartan and atenolol treatment groups in adjusted relative risk of cardiovascular mortality, which was one of the primary endpoint components. 2001 perindopril protection against recurring stroke study (PROGRESS)6105 individuals’ active treatment (n = 3051) or placebo (n = 3054)“Randomized trial of a perindopril-based blood-pressure-lowering regimen” Blood- pressure-lowering regimen reduced the risk of stroke among both hypertensive and non-hypertensive individuals with a history of stroke or transient ischemic attack. Combination therapy with perindopril plus indapamide reduced blood pressure by 12/5 mm Hg and stroke risk by 43% (30–54). Single-drug therapy reduced blood pres- sure by 5/3 mm Hg and produced no discernable reduction in the risk of stroke. 2000 Heart Outcomes Prevention Evaluation (HOPE)9297 high-risk patients, 55 years’ oldvascular disease or diabetes plus one other cardiovascular risk factor and who were not known to have a low EF or heart failureRamipril, a long-acting angiotensin- converting–enzyme inhibitor, reduces the rates of death, myocardial infarction, stroke, revascularization, cardiac arrest, heart failure, complications related to diabetes, and new cases of diabetes in a broad spectrum of high-risk patients. 2002 The Antihypertensive and Lipid-Lowering Treatment To Prevent Heart Attack Trial (ALLHAT)33357 participants Antihypertensive treatment trial and the second largest lipid-lowering trial. Participants were men and women aged 55 years or older who had stage 1 or stage 2 hypertension with at least 1 additional risk factor for CHD events. Thiazide-type diuretics are superior in preventing 1 or more major forms of CVD and are less expensive. They should be preferred for first-step antihypertensive therapy. 580 A. Alloubani et al. / Diabetes & Metabolic Syndrome: Clinical Research & Reviews 12 (2018) 577–584 In individuals with a high-risk condition, the On-Going Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial (ONTARGET), is contrasting the ACEI ramipril against the ARB telmisartan against a blend of both to avoid main vascular episodes [30]. The telmisartan 80 mg with control in ACEI sensitive individuals is deliberated by the Telmisartan Randomized Assessment Study in ACE-intolerant Subjects with Cardiovascular Disease (TRANSCEND) [36] with similar risk elements and endings as ONTARGET.The influence of the medications of high blood pressure is restricted by the absence of knowledge in the individuals regarding high blood pressure (past research have revealed that only around 60% of the victims were conscious of suffering from high blood pressure), only a few of them acquire the standard applicable medication, while one-third of them are regulated to control the blood pressure (which is usually because of infrequent medication use). The instructions to handle high blood pressure to avoid stroke signifies that initial avoidance of stroke implies that the typical blood Table 1 (Continued) Year Study Participant Number of risk factors Findings 2008 On Going Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial (ONTARGET)150,000 patients Telmisartan was equivalent to Ramipril in patients with vascular disease or high-risk diabetes and was associated with less angioedema.Telmisartan appears to have a benefit beyond blood pressure reduction that matches a representative ACEI, Ramipril, against major clinical events in high–CV risk patients. 2008 Telmisartan Randomized Assessment Study in ACE- intolerant Subjects with Cardiovascular Disease (TRANSCEND)5926 patients Telmisartan 80 mg/ day (n = 295) placebo n = 2972 5776 patients (out of a projected total of 6000 High-risk patients with coronary, peripheral, or cerebrovascular disease or diabetes with end-organ damage are being recruitedA randomized controlled trial Angiotensin- converting enzyme (ACE) inhibitors reduce major cardiovascular events 1. Cardiovascular death 2. Non-fatal myocardial infarction 3. Non-fatal stroke Mean blood pressure was lower in the Telmisartan group than in the placebo group Telmisartan was well tolerated in patients unable to tolerate ACE inhibi- tors. Although the drug had no significant effect on the primary outcome of this study, which included hospitalizations for heart failure, it modestly reduced the risk of the composite outcome of car- diovascular death, myocardial infarction, or stroke. 2002 The European Lacidipine Study on Atherosclerosis (ELSA)2334 patients Hypertension The greater efficacy of lacidipine on carotid IMT progression and number of plaques per patient, despite a smaller ambulatory blood pressure reduction, indicates an ant atherosclerotic action of lacidipine independent of its antihypertensive action 2004 Valsartan Antihypertensive Long-term Use Evaluation (VALUE)15245 patients Aged 50 years or older with treated or untreated hypertension and high risk of cardiac events participated in a randomized, double-blind, parallel-group comparison of therapy based on valsartan or amlodipine. The baseline BP in the amlodipine group was 1.0/0.5 mm Hg higher than in the valsartan group. Risk and disease factors were well balanced in the 2 monotherapy groups with exception of the LV strain pattern, which was more prevalent in the amlo- dipine group. Monotherapy patients had significantly lower values in 11 of 15 demographic, risk, and disease categories. Prevalence of smoking and coronary heart disease was higher, and there were fewer women in the monotherapy group. 2005 prospective randomized controlled study (MOSES)1405 well-defined high-risk hypertensives with cerebral event during the last 24 months Eprosartan Compared with Nitrendipine for Secondary Prevention (MOSES) study was the first to compare an angiotensin II type 1 receptor antagonist with a calci- um antagonist in secondary stroke pre- vention. The combined primary end point was significantly lower in the Eprosartan group. MOSES does reveal protective effects of Eprosartan over Nitrendipine in high- risk patients. 2012 The Secondary Prevention of Small Subcortical Strokes (SPS3) trial3000 patients Symptomatic small subcortical strokes and two levels of systolic blood pressure targets –‘intensive’ (<13 0 mm Hg) vs. ‘usual’ (130–14 9 mm Hg). Secondary Prevention of Small Subcor- tical Strokes will address several im- portant clinical and scientific questions by testing two interventions in patients with recent magnetic resonance imag- ing-defined lacunar infarcts, which are likely due to small vessel disease. The results will inform the management of millions of patients with this common vascular disorder. A. Alloubani et al. / Diabetes & Metabolic Syndrome: Clinical Research & Reviews 12 (2018) 577–584 581 pressure must be fixed at a level less than 14 9 mm Hg/90 mmHg and less than 13 0 mm Hg/80 mmHg in diabetic patients [37]. The daily routine should be altered in a way to achieve the normal blood pressure marks, and in case this does not work, the patient should contemplate getting on medication. Presently, the blood pressure medication is suggested to all the sufferers of stroke or TIA. The normal blood pressure marks must be set after considering the age, sex and other conditions that the patient might be suffering from. The advantages are witnessed after a normal decrease in the blood pressure of around 10/5 mm Hg. The medication selected in based on the description of the sufferer, and in individuals prone to stroke, the ACEI and ARB to a diuretic are preferred, along with the information from the past examinations (AB/CD rule). In the individuals suffering from an acute case of precerebral artery stenosis, in the secondary prevention of stroke, if the high blood pressure is decreased, it should be initiated carefully [38,39]. Table 1 summarises all hypertension studies that included in this review.4.2. Diabetes mellitus Diabetes is a long-term chronic disease which needs constant medical care and treatment not only by doctors but also by patient knowledge about self-care to prevent critical secondary illness caused by diabetes. Diabetes is categorized according to previous diagnostic criteria which includes: DM type 1, DM type 2, illnesses of exocrine pancreas (cystic fibrosis), prediabetes (consistent high glucose level in fasting and impaired glucose tolerance), Diabetes because of genetic disorder of b cells of pancreas, secondary diabetes caused by drugs and chemicals and Gestational Diabetes Diagnosis of diabetes must be set up according to signs of diabetes and (Fasting plasma glucose, Glycosylated haemoglobin, Oral glucose tolerance tests). A large population of patients who faced strokes sooner or later has diabetes (16–24%). People with diabetes have 1. 5–3 times chance of having strokes and high death rates compared to the common population with no diabetes. The main reason of metabolic abnormalities is due to Table 2 Summary of the included DM studies. Year Study Participant Number of risk factors Findings 1995 Insulin Resistance Atherosclerosis Study (IRAS)Over 16 0 0 men and women were recruited from four geographic areas to represent a range of glucose tolerance (normal, impaired, and diabetic) and ethnicity (hispanic, non- Hispanic white, and African-American)Assess the relationships between insulin resistance, insulinemia, glycemia, other components of the insulin resistance syndrome, and prevalent cardiovascular disease (CVD) in a large multiethnic cohort.Improve association between insulin resistance and CVD, apart from the concomitant hyper insulinemia and related CVD risk factors. 2012 Stop Atherosclerosis in Native Diabetics Study (SANDS)499 people with type 2 diabetes age 40, without known CVD, were recruited for a randomized 3-year trialIntervention strategies to reduce CVD in diabetic individuals. Understanding the effects of intensive risk-factor reduction on atherosclerosis burden and cardiac function in diabetic individuals in all US populations and provide evidence for determining LDL and blood pressure treatment goals for diabetic patients.The baseline characteristics of the SANDS cohort are like those of a population based sample of American Indian diabetic men and women and closely resemble diabetic men and women of other ethnic groups. Results from this study can be used to identify appropriate targets for LDL-C and BP lowering in diabetic American Indians and diabetic patients in other ethnic groups. 1998 United Kingdom Prospective Diabetes study (UKPDS)510 2 patients with newly diagnosed type 2 diabetes. It ran for twenty years (1977 to 19 97 ) in 23 UK clinical sites.Randomised, multicentre trial of glycaemic therapies. Complications of type 2 diabetes, previously often regarded as inevitable, could be reduced by improving blood glucose and/or blood pressure control.Initial insulin therapy induced more hypoglycemic reactions and weight gain without necessarily providing better control, it may be reasonable to start with oral agents and change to insulin if goals for glycemic levels are not achieved. 1998 Study to Prevent Non-Insulin- Dependent Diabetes Mellitus (STOP NIDDM trial)1418 subjects diagnosed with impaired glucose tolerance (IGT)Diabetic patients taking acarbosa there was very low stroke incidenceScreening of a high-risk population yields one eligible subject per every 10 volunteers screened. This study should answer the question of whether acarbose can prevent or delay the progression of IGT to type 2 diabetes mellitus 2004 (Prospective pioglitazone Clinical Trial in macro Vascular Events (PROACTIVE trial)5238 patients have been randomized from 19 countries.Patients with type 2 diabetes managed with diet and/or oral blood glucose-lowering drugs that have a history of macrovascular disease. Pioglitazon reduces risk of stroke and cardiovascular risk in type 2 diabetic patients at high risk for strokeThe cohort of patients enrolled in PROactive is a typical type 2 diabetic population at high risk of further macrovascular events. The characteristics of this population are ideal for assessing the ability of pioglitazone to reduce the cardiovascular risk of patients with type 2 diabetes 2007 the UK Glucose Insulin in Stroke Trial (GIST-UK).933 patients were recruited Patients presenting within 24 h of stroke onset and with admission plasma glucose concentration between 6.0-17.0 mmol/LGKI infusions significantly reduced plasma glucose concentrations and blood pressure. Hyperglycaemia after acute stroke is a common finding that has been associated with an increased risk of death. 2016 Insulin Resistance Intervention After Stroke Trial (IRIS)3936 subjects at approximately 17 0 hospitals in Australia, Canada, Germany, Israel, Italy, the United Kingdom (UK) and the US.Treatment with an approved antidiabetic drug at the prediabetic stage of insulin resistance (IR) improves outcomes in patients with cerebrovascular disease. After 5 years, pioglitazone-treated patients had 24% reduction in cardiovascular outcomes above and beyond a generally modern approach to secondary stroke prevention.Pioglitazone therapy was associated with reduced vascular events and new diabetes, and an increase in weight, oedema and bone fractures. Pioglitazone may add to the strategies for preventing further events in patients with stroke or transient ischaemic attack. 2004 The Collaborative Atorvastatin Diabetes Study (CARDS)2838 patients aged 40–75 years in 13 2 centers in the UK and Ireland were randomized to placebo (n = 1410 ) or atorvastatin 10 mg daily (n = 1428).Type 2 diabetes Multicenter randomized placebo-controlled trialAtorvastatin 10 mg daily is safe and efficacious in reducing the risk of first cardiovascular disease events, including stroke, in patients with type 2 diabetes without high LDL-cholesterol. 582 A. Alloubani et al. / Diabetes & Metabolic Syndrome: Clinical Research & Reviews 12 (2018) 577–584 proatherogenic risk factors-abnormal fat deposition in arteries, hypertension, hyperglycaemia (high blood glucose) also have 1.5- fold increased risk of strokes. Atherosclerotic changes in extracra- nial and intracranial vessels are caused due to insulin resistance by the cells and hyperinsulinemia which cause diabetes and not due to high glucose levels or other risk factors. Diabetes vascular complications divided into microvascular complications (neuropathy, retinopathy and diabetic nephropathy) and macrovascular complications (stroke, peripheral artery disease and coronary vascular disease). There are solid indications of enlarged aggregation of platelet, hypercoagulability as well as raised free radical formation and altered calcium regulation in diabetic patients. Consequently, diabetes mellitus can fasten atherosclerosis even process in younger age. Table 2 summarises all diabetes mellitus studies included in this review. Clinical evidence has proven the evidence of atherosclerosis (subclinical forms) while IRAS (The Insulin Resistance Atheroscle- rosis Study) provides that glucose uptake and diabetes not be linked with each other in an increase in intima-media thickness (IMT) [40]. Control in cerebrovascular risk factors (hypertension, hypolipo- proteinaemia) decrease the further thickening of IMT in patients who have diabetes as stated by SANDS proofs (The Stop Atherosclerosis in Native Diabetics Study). To lower strokes in a patient, UKPDS (United Kingdom Prospective Diabetes Study) states that change in the way of living and diabetic therapies is important a 1% decrease in HbA1c reduce the risk of strokes to 4%. While at early stages of diabetes mellitus, if glucose control (HbA1c = 7 mmol/L) with proper diet is ensured and per oral diabetic agents or insulin is taken than the risk of atherosclerosis is reduced [26,41] STOP NIDDM (Study to Prevent Non-Insulin-Dependent Diabe- tes Mellitus) lays evidence that stroke rates will reduce if patients take acarbose drugs (2/682 pts). Moreover, it is proposed that it was connected with a decrease in hypertension and cardiovascular disease [42,43]. Experimental research/control measures and use of insulin lowers the strokes by 41 % while PROACTIVE (Prospective Piogli- tazone Clinical Trial in macroVascular Events) provide evidence via double blinded experimental studies; 5238 patients, 34.5 months, 45 mg pioglitazone vs. placebo) in which pioglitazone lowers the chances of stroke in DM 2. Hypoglycemia correction was confirmed as a significant parameter in acute stroke treatment. According to Meta-analyses have revealed (32 studies) that high glucose levels have dangerous consequences (6–8 mmol/L) and its control is more important as it results in raising 28 days’ death rate in non-diabetic (RR 3.1 CI 95% 2.5–3.8) and diabetic (RR 1. 3 CI 95% 0.5–3.4). According to Insulin Resistance Intervention after Stroke Trial (IRIS), Glucose Insulin in Stroke Trial-Pilot (GIST), Insulin in Acute Ischemic Stroke (INSULINFARCT), control of glucose levels and regulation of different metabolic factors can lead to the treatment of strokes in diabetic patients. 1/5 of diabetics are not able to respond to medicines like aspirins to reduce clotting which causes bleeding in them compared to normal populations [44]. The Collaborative Atorvastatin Diabetes Study (CARDS): 2838 patients with type 2 diabetes without increased cholesterol levelswere selected for this study to atorvastatin 10 mg/day against placebo group in the primary prevention of stroke and coronary artery disease. A significant reduction of 52% in the relative risk of stroke was observed in patients taking atorvastatin (RR 48%; 95% CI: 11–69%). Patients with coronary artery disease, hypertension or DM are suggested a cholesterol-lowering therapy. Also, Patients with a history of ischemic stroke might be favoured for control as they are undergoing on ‘statin treatment’ within the care facility [45]. Table 3 compares stroke risk factors for diabetic and nondiabetic patients. 5. Conclusion In conclusion, after summarising of all the studies mentioned in the paper, it can be established that hypertension and diabetes mellitus are a stroke risk factors and correlated in patients with atherosclerosis. The significance of primary, secondary prevention and monitoring of risk factors is also highlighted in this study. The risk factors for stroke can be eliminated if individuals change their lifestyles and engage in simple exercise every day. This can reduce hyperglycaemia levels and reduce blood pressure. 6. Limitations The research findings are limited to the articles that were selected for evaluation. There are many other researchers that have addressed the topic, and it would have been interesting to compare their findings to gain a greater understanding of the issue. Since there is not the time to study all the relevant resources, only the selected resources shaped the findings of this research. References [1] Arboix A. Cardiovascular risk factors for acute stroke: risk profiles in the different subtypes of ischemic stroke. World J Clin Cases 20153(5), doi:http:// dx.doi.org/10.12998/wjcc.v3.i5.418. [2] LaRosa J, Grundy S, Waters D. Statin therapy after stroke or transient ischemic attack. N Engl J Med 2006;355(22):2368–71, doi:http://dx.doi.org/10.1056/ NEJMc062456. [3] Demarin V, Lovren9 ci c-Huzjan A, Trkanjec Z, Vukovi c V, Vargek Solter V, Šeri c V, et al. Recommendations for stroke management 2006 update. Acta Clin Croat 2006;45(3):219–85. [4] Spiess BD, Horrow J, Kaplan JA. Transfusion Medicine and Coagulation Disorders. Philadelphia: Saunders Elsevier; 2006, doi:http://dx.doi.org/ 10.1016/B978-1-4160-3786-6.10024-5. [5] Gluud L, Thorlund K, Gluud C, Woods L. Reported methodologic quality and discrepancies between large and small randomized trials in meta-analyses. Ann Intern Med 2008149(8). . Accessed 17 April 2017 http://cat.inist.fr/? aModele=afficheN&cpsidt=21778685. [6] Research Arora H. Methodology: a step-by-step guide for beginners. Abhigyan 2011;29(3):62–4. [7] Brink H, Van WC, Van RG. In: Brink H, Van der Walt C, Van Rensburg G, editors. Fundamentals of Research Methodology for Health Care Professionals. Kenwyn: Juta: Google Books; 2018. . Published 2007 https://books.google.com.sa/books? hl=en&lr=&id=YZnPYoA4Jk0C&oi=fnd&pg=PA1&dq=Fundamentals+of +research+methodology+for+health+care+professionals.+Kenwyn:+Juta. &ots=9UGjyItF_T&sig=1tCkTrpJf9ni3qcnoizBLXCY-IU&redir_esc=y#v= onepage&q&f=false. [8] Seshadri S, Debette S. Risk Factors for Cerebrovascular Disease and Stroke. Oxford University Press; 2016. . Accessed 16 October 2016 https://books. google.com/books?hl=en&lr=&id=HVlxCwAAQBAJ&oi=fnd&pg=PP1&dq=Risk +Factors+for+Cerebrovascular+Disease+and+Stroke&ots=s2- 3e0V3tW&sig=KecSH6HNoXAVkPyHKMbIZe3zyYs. Table 3 Patients diagnosed with Diabetes. Stroke Patients diagnosed with Diabetes Patient without Diabetes Hemorrhagic or Ischemic stroke 20% persons were alive 40% persons were alive Lacunar stroke Common Not common Infratentorial stroke Common Not common Risk for stroke at the age more than 55 Increase Decrease relative risk women/men Women more than men Women less than men A. Alloubani et al. / Diabetes & Metabolic Syndrome: Clinical Research & Reviews 12 (2018) 577–584 583 [9] Romero JR, Morris J, Pikula A. Review Stroke prevention: modifying risk factors. Ther Adv Cardiovasc Dis 2008;2(4):287–303, doi:http://dx.doi.org/ 10.1177/1753944708093847. [10] Acharya T, Huang J, Tringali S, Frei CR, Mortensen EM, Mansi IA. Statin use and the risk of kidney disease with long-term follow-Up (8.4-Year study). Am J Cardiol 2016;117(4):647–55, doi:http://dx.doi.org/10.1016/j.amjcard.2015.11.031. [11] Lionakis N, Mendrinos D, Sanidas E, Favatas G, Georgopoulou M. Hypertension in the elderly. World J Cardiol 2012;4(5):135–47, doi:http://dx.doi.org/ 10.4330/wjc.v4.i5.135. [12] Afifi RM, Omar SR, El. Eaggal AA. A community screening plan for the prevalence of some chronic diseases in specified adult populations in Saudi Arabia-II: pre-hypertension and hypertension. Int J Biomed Res 2013;4(5):205, doi:http://dx.doi.org/10.7439/ijbr.v4i5.258. [13] Lewington S, Clarke R, Qizilbash N, et al. Prospective Studies Collaboration, Age-specific relevance of usual blood pressure to vascular mortality: a meta- analysis of individual data for one million adults in 61 prospective studies. The Lancet 2002;360(9349):1903–13 , doi:http://dx.doi.org/10.1016/S0140-6736 (02)11911-8. [14] Rodgers A, MacMahon S, Gamble G, Slattery J. Blood pressure and risk of stroke in patients with cerebrovascular disease. BMJ 1996. . Accessed October 19 , 2016 http://www.bmj.com/content/313/7050/147.short. [15] Sacco RL, Adams R, Albers G, Alberts MJ, Benavente O, Furie K, et al. Guidelines for prevention of stroke in patients with ischemic stroke or transient ischemic attack. Stroke 200637(2). [16] Chobanian A, Bakris G, Black H, Cushman W. Seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure. Natl Hear Lung, Blood Inst 2003. . Accessed 19 October 2016 http://www.nhlbi.nih.gov/files/docs/guidelines/jnc7full.pdf. [17] He FJ, Nowson CA, MacGregor GA. Fruit and vegetable consumption and stroke: meta-analysis of cohort studies. Lancet (London, England) 2006;367 (9507):320–6, doi:http://dx.doi.org/10.1016/S0140-6736(06)68069-0. [18] Ravenni R, Jabre JF, Casiglia E, Mazza A. Primary stroke prevention and hypertension treatment: which is the first-line strategy? Neurol Int. 20113(2), doi:http://dx.doi.org/10.4081/ni.2011.e12. [19] Sleight P. The HOPE study (Heart outcomes prevention evaluation). J Renin Angiotensin Aldosterone Syst 2000;1(1):18–20, doi:http://dx.doi.org/10.3317/ jraas.2000.002. [20] Dahlöf B, Devereux R, Faire D, Fyhrquist F, Hedner T, Ibsen H, et al. The losartan intervention for endpoint reduction (LIFE) in hypertension study: rationale, design, and methods. the LIFE study group. Am J Hypertens 1997;10(7 Pt. 1) 705–13 . . Accessed 30 October 2016 http://www.ncbi.nlm.nih.gov/pubmed/ 9234823. [21] PROGRESS Collaborative Group. Randomised trial of a perindopril-based blood-pressure-lowering regimen among 6105 individuals with previous stroke or transient ischaemic attack. Lancet 2001;358(9287):1033–41, doi: http://dx.doi.org/10.1016/S0140-6736(01)06178-5. [22] Ratnasabapathy Y, Lawes CMM, Anderson CS. The perindopril protection against recurrent stroke study (PROGRESS): clinical implications for older patients with cerebrovascular disease. Drugs Aging 2003;20(4)241–51. . Accessed 16 October 2016 http://www.ncbi.nlm.nih.gov/pubmed/12641480. [23] Daskalopoulou S, Daskalopoulos M, Perrea D, Nicolaides A, Liapis C. Carotid artery atherosclerosis: what is the evidence for drug action? Curr Pharm Des 2007;13(11):1141–59, doi:http://dx.doi.org/10.2174/138161207780619019. [24] Lithell H, Hansson L, Skoog I, Elmfeldt D, Hofman A, Olofsson B, et al. The Study on Cognition and Prognosis in the Elderly (SCOPE): principal results of a randomized double-blind intervention trial. J Hypertens 2003;21(5):875–86, doi:http://dx.doi.org/10.1097/01.hjh.0000059028.82022.89. [25] Trenkwalder P. The study on COgnition and prognosis in the elderly (SCOPE)– recent analyses. J Hypertens Suppl 2006;24(1):S107–14 , doi:http://dx.doi.org/ 10.1097/01.hjh.0000220415.99610.22. [26] Holman R, Cull C, Fox C. United Kingdom prospective diabetes study (UKPDS) 13 : relative efficacy of randomly allocated diet, sulphonylurea, insulin, or metformin in patients with newly. Br Med J 1995. . Accessed 20 October 2016 http://search.proquest.com/openview/ 0d99293421472d81ca6de5fcbc08ee80/1?pq-origsite=gscholar&cbl=40566. [27] Alva ML, Gray A, Mihaylova B, Leal J, Holman RR. The impact of diabetes- related complications on healthcare costs: new results from the UKPDS (UKPDS 84). Diabetic 2015;32(4):459–66. http://onlinelibrary.wiley.com/doi/ 10.1111/dme.12647/full. [28] ALLHAT Officers, Coordinators for the ALLHAT Collaborative Research Group. The antihypertensive and lipid-Lowering treatment to prevent heart attacktrial. Major outcomes in moderately hypercholesterolemic, hypertensive patients randomized to pravastatin vs usual care: the antihypertensive and lipid-lowering treatment to prevent heart attack trial (ALLHAT-LLT). JAMA 2016;288(23):459–66. http://www.ncbi.nlm.nih.gov/pubmed/12479764. [29] The ONTARGET Investigators. Telmisartan, ramipril, or both in patients at high risk for vascular events. N Engl J Med 2008;358(15):1547–59, doi:http://dx. doi.org/10.1056/NEJMoa0801317. [30] Liebson PR, Amsterdam EA. Ongoing telmisartan alone and in combination with ramipril global endpoint trial (ONTARGET): implications for reduced cardiovascular risk. Prev Cardiol 2009;12(1)43–50. . Accessed 25 October 2016 http://www.ncbi.nlm.nih.gov/pubmed/1930169. [31] Zanchetti A, Bond MG, Hennig M, Neiss A, Mancia G, Dal Palù C, et al. Calcium antagonist lacidipine slows down progression of asymptomatic carotid atherosclerosis: principal results of the European Lacidipine Study on Atherosclerosis (ELSA), a randomized, double-blind, long-term trial. Circulation 2002;106(19)2422–7. . Accessed 26 October 2016 http://www. ncbi.nlm.nih.gov/pubmed/12417537. [32] Mancia G, Grassi G, Redon J. Manual of Hypertension of the European Society of Hypertension. CRC Press; 2014. . Accessed 3 November 2016 https://books. google.com/books?hl=en&lr=&id=sTncBQAAQBAJ&oi=fnd&pg=PP1&dq= Manual+of+Hypertension+of+the+European+Society+of+Hypertension, +Second+Edition&ots=JnImld_P-Z&sig=teDi_RLqV1MHbWmZR0zHADDp5QY. [33] Schrader J, Lüders S, Kulschewski A, Hammersen F, Plate K, Berger J, et al. Morbidity and mortality after stroke, eprosartan compared with nitrendipine for secondary prevention: principal results of a prospective randomized controlled study (MOSES). Stroke 2005;36(6):1218–24, doi:http://dx.doi.org/ 10.1161/01.STR.0000166048.35740.a9. [34] Benavente OR, White CL, Pearce L, Pergola P, Roldan A, Benavente MF, et al. The secondary prevention of small subcortical strokes (SPS3) study. Int J Stroke 2011;6(2):164–75, doi:http://dx.doi.org/10.1111/j.1747-4949.2010.00573.x. [35] Graves JW, White CL, Szychowski JM, Pergola PE, Benavente OR, Coffey CS, et al. Predictors of lowering SBP to assigned targets at 12 months in the Secondary Prevention of Small Subcortical Strokes study. J Hypertens 2012;30(6):1233– 40, doi:http://dx.doi.org/10.1097/HJH.0b013e328353968d. [36] Telmisartan Randomised AssessmeNt Study in ACE iNtolerant subjects with cardiovascular Disease (TRANSCEND) Investigators, Yusuf S, Te o K, et al. Effects of the angiotensin-receptor blocker telmisartan on cardiovascular events in high-risk patients intolerant to angiotensin-converting enzyme inhibitors: a randomised controlled trial. Lancet (London, England) 2008;372(9644):1174– 83, doi:http://dx.doi.org/10.1016/S0140-6736(08)61242-8. [37] Rundek T, Sacco RL. Risk factor management to prevent first stroke. Neurol Clin 2008;26(4)1007–45, doi:http://dx.doi.org/10.1016/j.ncl.2008.09.001 ix. [38] Appleton JP, Sprigg N, Bath PM. Blood pressure management in acute stroke. Stroke Vasc Neuro 1 2016;e000020:72–82, doi:http://dx.doi.org/10.1136/svn- 2016-000020. [39] Shimada K. SY 08-2 hypertension management for secondary prevention of stroke. J Hypertens 2016;34:e183, doi:http://dx.doi.org/10.1097/01.hjh. 0000500397.25140.82. [40] Wagenknecht LE, Mayer EJ, Rewers M, Haffner S, Selby J, Borok GM, et al. The insulin resistance atherosclerosis study (IRAS). Ann Epidemiol 1995;5(6):464– 72, doi:http://dx.doi.org/10.1016/1047-2797(95)00062-3. [41] UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet 1998;352 (9131):837–53, doi:http://dx.doi.org/10.1016/S0140-6736(98)07019-6. [42] Nishimura R. The study TO prevent non-Insulin dependent diabetes mellitus trial (acarbose). Nihon Rinsho 2005;63:478–82. http://europepmc.org/ abstract/med/15779425. [43] Chiasson J-L, Gomis R, Hanefeld M, Josse RG, Karasik A, Laakso M. The STOP- NIDDM Trial: an international study on the efficacy of an a-glucosidase inhibitor to prevent type 2 diabetes in a population with impaired glucose tolerance: rationale, design, and preliminary screening data. Diabetes Care 199821(10). http://care.diabetesjournals.org/content/21/10/1720.short. [44] Bath P, Appleton J, Sprigg N. The Insulin Resistance Intervention after Stroke trial: a perspective on future practice and research. Int J 2016;11(7):741–3. http://journals.sagepub.com/doi/abs/10.1177/1747493016660099. [45] Colhoun H, Betteridge D, Durrington P, Hitman G. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised. Lancet 2004;364(9435):685–96. http://www.sciencedirect.com/science/article/pii/ S0140673604168955. 584 A. Alloubani et al. / Diabetes & Metabolic Syndrome: Clinical Research & Reviews 12 (2018) 577–584
Include references and avoid plagiarism when answering this, it is a graduate research NON-EXPERIMENTAL STUDY paper: Review the following attached articles that have been provided as example studies
CORONARY HEART DISEASE (S. VIRANI AND S. NADERI, SECTION EDITORS) Obesity and Cardiovascular Disease: a Risk Factor or a Risk Marker? Ta h e r M a n d v i w a l a 1&Umair Khalid 1&Anita Deswal 1,2 Published online: 14 March 2016 #Springer Science+Business Media New York (outside the USA) 2016 Abstract In the USA, 69 % of adults are either overweight or obese and 35 % are obese. Obesity is associated with an increased incidence of various cardiovascular disorders. Obesity is a risk marker for cardiovascular disease, in that it is associated with a much higher prevalence of comor- bidities such as diabetes, hypertension, and metabolic syndrome, which then increase the risk for cardiovascular disease. However, in addition, obesity may also be an in- dependent risk factor for the development of cardiovascu- lar disease. Furthermore, although obesity has been shown to be an independent risk factor for several cardiovascular diseases, it is often associated with improved survival once the diagnosis of the cardiovas cular disease has been made, leading to the term “obesity paradox. ”Several pathways linking obesity and cardiovascular disease have been described. In this review, we attempt to summarize the complex relationship between obesity and cardiovascular disorders, in particular coronary atherosclerosis, heart fail- ure, and atrial fibrillation. Keywords Obesity. Cardiovascular disease . Hypertension . Atherosclerosis . Coronary artery disease . Heart failure Introduction Over the past two decades, obesity has become a global epi- demic affecting both pediatric and adult populations. In the USA, 69 % of adults are either overweight or obese and 35 % are obese [ 1]. Obesity has been associated with a de- creased life expectancy as well as with increased morbidity [ 2 ]. The relationship between cardiovascular disease (CV) and obesity has been widely studied, but a number of questions still remain. For instance, obesity has been linked to develop- ment of cardiovascular diseases including atherosclerosis and symptomatic coronary artery disease (CAD), heart failure (HF), and atrial fibrillation (Fig. 1). Obesity does increase the risk of CV disease secondarily through its influence on the development and severity of comorbidities such as hyperten- sion, dyslipidemia, and glucose intolerance or diabetes [ 3]. However, the increase in various CV diseases may also occur in the absence of other comorbidities and may be due to struc- tural and functional changes of the myocardium through ex- cess adipose tissue deposition or through other mechanisms related to obesity [ 4]. Furthermore, obesity has been known to be an independent risk factor for the development of CV diseases such as HF but has also been shown to be associated with improved survival once the diagnosis of HF is established (the so-called “obesity paradox ”)[ 5, 6, 7]. This obesity para- dox has also been observed in other CV disease states [ 8]. In this review, we attempt to emphasize the association of obesity with CV disease, focusing on whether obesity is an independent risk factor for the development of CV disease states such as CAD, HF, and atrial fibrillation. The fact that obesity is a risk marker, in that it is associated with a much higher prevalence of comorbidities such as diabetes, hyperten- sion, and metabolic syndrome, which themselves are risk fac- tors for the development of CV disease, is well established and will not be discussed in any detail. This article is part of the Topical Collection on Coronary Heart Disease * Anita Deswal [email protected] 1 Baylor College of Medicine, Houston, TX, USA 2 Michael E. DeBakey VA Medical Center, (111B) 2002 Holcombe Blvd, Houston, TX 77030, USA Curr Atheroscler Rep (2016) 18: 21 DOI 10.1007/s11883-016-0575-4 Measures of Obesity Several measures other than just body weight are used to measure obesity, including body mass index (BMI), waist circumference, and waist-to-hip ratio. Body weight indexed to height to measure the BMI is a commonly used index of obesity and overweight. An adult is considered normal weight if the BMI is 18.5 to <25 kg/m 2, overweight if the BMI is 25.0 to 29.9 kg/m 2, and obese if the BMI is ≥30.0 kg/m 2[9 ]. Waist circumference has been shown to be a better index of abdom- inal obesity than waist-to-hip ratio. It also correlates better with BMI than waist-to-hip ratio [ 10]. A recent study showed overall age-adjusted obesity prevalence to be 35.7 %. There were, however, race-specific age-adjusted differences noted; specifically, 36.2 % non-Hispanic white men, 38.8 % non- Hispanic black men, 32.2 % non-Hispanic white women, and 58.5 % non-Hispanic black women met the BMI criteria for obesity [ 1]. Obesity and Atherosclerotic Disease The understanding of the pathophysiology of atherosclerosis and obesity has dramatically changed over the past few de- cades. Historically, obesity and atherosclerosis were thought to be simply lipid storage disorders involving triglycerides in adipose tissue and cholesteryl ester in atheromata. However, now they are both also viewed as chronic inflammatory dis- ease states, with activation of both innate and adaptive immu- nity [ 11, 12 ]. Atherosclerotic disease and obesity share several common pathophysiological features. First, lipids contribute to both atherosclerosis and obesity; oxidized low-density lipoprotein (LDL) and free fatty acids can trigger inflammation and initiate disease. Inflammation mediates all stages of athero- genesis —from early lesion development to atheroma compli- cation —and is associated with obesity, insulin resistance, and type 2 diabetes mellitus. Inflammation may form the major link between obesity and atherosclerosis: Adipokines released by adipose tissue induce insulin resistance, endothelial dys- function, hypercoagulability, and systemic inflammation, all of which can promote atherosclerosis. The accumulation of heterogeneous macrophage populations, T cell activation, cell death, and the effects of numerous cytokines and chemokines characterize both atherosclerosis and obesity. Inflammatory biomarkers, such as high-sensitivity C-reactive protein, IL-6, and IL-18, can predict cardiovascular events, may be used to guide therapy, and reflect the pathophysiological links be- tween obesity and its associ ated metabolic disorders [9]. When evaluated in a clinical trial, a multidisciplinary program, which aimed to reduce body weight in obese women through lifestyle changes, was associated with a reduction in markers of vascular inflammation (CRP, IL-6, and IL-18) and in insu- lin resistance along with an increase in levels of adiponectin, an adipocytokine with anti-in flammatory and insulin- sensitizing properties [ 13]. It is important to understand the link between inflammation and atherosclerosis and how obesity accelerates this process. Obese individuals have a higher propensity toward inflamma- tion compared to non-obese individuals. Patients with visceral obesity have been found to have higher levels of proinflam- matory adipokines including TNF-alpha, IL-6, MCP-1, resistin, and leptin [ 14]. The higher level of inflammation has been correlated with observations from several large- scale prospective studies that demonstrated elevated levels of CRP in obese patients [ 15]. Elevated CRP levels indepen- dently predict a higher risk of future myocardial infarction, 1 unit increment in BMI 4% increased risk of ischemic stroke 6% increased risk of hemorrhagic stroke 4% increasedrisk of Atrial Fibrillation 5% and 7% increasedrisk of HF in men & women respectively 10 kg increment in body weight 12% increased risk of CAD 3mmHg higher SBP 2.3mmHg higher DBP Fig. 1Increased risk of cardiovascular diseases with increase in body mass index (BMI) and body weight. CAD = coronary artery disease; HF = heart failure; SBP = systolic blood pressure; DBP = diastolic blood pressure 21 Page 2 of 10 Curr Atheroscler Rep (2016) 18: 21 peripheral arterial disease, and the risk of developing type II diabetes mellitus [16–18 ]. Furthermore, CRP levels correlate with increased visceral adiposity independent of overall BMI [ 19 ]. This has led to the use of CRP levels in patients with intermediate risk of coronary artery disease to improve risk stratification for major adverse cardiovascular events (MACEs) [ 20]. As the evidence of atherosclerosis being a chronic inflam- matory state increased, so did the therapeutic approach to its treatment. HMG-CoA reductase inhibitors or statins were ini- tially developed to decrease LDL cholesterol, but many stud- ies have shown that they also provide an anti-inflammatory effect including the reduction of CRP levels [ 21,22 ]. Using this background, the Justification for the Use of Statins in Primary Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) trial demonstrated that the number of cardiovascular events was significantly decreased in healthy individuals with an LDL cholesterol less than 130 mg/dl but with CRP levels of 2 mg/l or higher, through the use of 20 mg rosuvastatin daily compared with placebo [ 23 ]. Similarly, salicylates such as aspirin (which of course also have additional anti-platelet action) have been known to decrease inflammation and have been used to treat many dif- ferent conditions ranging from rheumatoid arthritis to primary prevention of myocardial infarction. Studies, however, have found that the use of non-acetylated salicylate derivative, salsalates, which do not increase bleeding times, decreases inflammation and improves glycemic control in obese indi- viduals [ 24–26 ]. Anti-inflammatory drugs could be beneficial in improving CV outcomes in obese atherosclerotic patients, but further long-term studies are necessary. The relationship between obesity and CAD can be assessed in two settings. The first is obesity ’s role in the pathogenesis of coronary atherosclerosis and stable ischemic heart disease and, second, its implications in acute coronary syndromes (ACSs) and coronary revascularization. It is known that obe- sity is an independent risk factor for CAD [ 3], as well as for ACS when CAD is present [ 27]. The Pathobiological Determinants of Atherosclerosis in Youth (PDAY) study allowed a better understanding of the relationship between obesity and CAD [ 28]. In this study, the investigators conducted a post-mortem study on the arteries of young individuals who had died from accidental causes or suicides. They concluded that the onset of CAD occurs de- cades before the actual clinical manifestations. They also de- termined that higher BMI was associated with more complex coronary lesions, as shown by an increased number of fatty streaks, raised lesions, and high-grade complex lesions, when compared to normal weight individuals. Another study showed that the complexity of coronary lesions was directly related to panniculus thickness in all patients with BMI >30 kg/m 2[ 28 ]. In addition, obesity likely needs to be long-standing and pres- ent for at least two decades to become an independent risk factor for clinically significant coronary artery disease, as sug- gested by several long-term cohort studies [ 29–31 ]. One study showed that a 10-kg increase in body weight was associated with 12 % increased risk of CAD and 3 mmHg higher systolic and 2.3 mmHg higher diastolic blood pressure [ 32]. As detailed earlier, the pathogenesis of CAD in obese indi- viduals is mediated via adipose tissue, which serves as an ac- tive paracrine and endocrine organ, and produces a wide array of adipokines that are directly involved in atherosclerosis [ 33]. These adipokines interact and influence several other metabol- ic and immunologic cascades, such as vascular reactivity (e.g., leptin, TNF- α, and adiponectin), inflammation (e.g., monocyte chemotactic protein 1 and IL-8) and coagulation (e.g., plasmin- ogen activator inhibitor). These pathways often overlap and activate more bioactive factors, following a positive feedback course [ 34–37 ]. In comparison to normal weight individuals, circulating platelets in obese counterparts show increased acti- vation as well as diminished sensitivity to anti-platelet agents such as aspirin [ 38,39 ]. Despite all this knowledge, the com- prehensive cellular and molecular pathways linking obesity to development of CAD and triggering of ACS are not complete- ly understood. Fat distribution appears to play a key role. Abdominal obesity is associated with worse outcomes in pa- tients with known CVD, as demonstrated by data from the Trandolapril Cardiac Eval uation (TRACE) registry [ 40]. Moreover, this relationship remained significant after adjust- ment for comorbidities associated with obesity, such as diabe- tes and hypertension. In the INTERHEART study, waist-to-hip ratio, waist circumference, and waist-to-height ratio were all stronger predictors of myocardial infarction (MI) compared to BMI alone [ 41]. This indicates that the “abdominal ”compo- nent of obesity has the most detrimental effects pertaining to CAD in obese individuals. Obesity and Acute Cardiovascular Syndromes In a large study including more than 100,000 patients who presented with non-ST elevation myocardial infarction (NSTEMI), obe- sity was found to be the strongest factor linked to NSTEMI events in younger patients, followed by tobacco use. The higher the BMI, the lower the mean age at which the patients presented with NSTEMI [ 42]. This inverse correlation with age also holds true for ST elevation myocardial infarction (STEMI) patients [ 43]. Based on the available data, obesity is considered an independent risk factor for STEMI in younger patients [ 44,45 ]. Moreover, obesity is also linked to develop- ment of other acute vascular events; for each 1-unit increase in BMI, there is a 4 % increased risk of ischemic stroke and 6 % increased risk for hemorrhagic stroke [ 32,46 ]. Obesity and Revascularization for CAD In addition to obe- sity being a risk factor for the development of ACS, obesity may also increase the risk of adverse events after treatment for these conditions. In a follow-up study of patients who Curr Atheroscler Rep (2016) 18: 21 Page 3 of 1021 received bare metal stents, obesity was found to be an inde- pendent predictor of increased in-stent restenosis, 1-year tar- get revascularization, and MACE [47]. The Limus Eluted from a Durable Versus Erodable Stent Coating (LEADERS) trial showed that obesity was an independent risk factor for stent thrombosis [ 48]. However, a large European cohort of over 7000 patients did not show any association of BMI and stent thrombosis or adverse cardiac events [ 49]. The neutral effect of obesity in patients with drug eluting stents has been shown in other studies as well [ 50,51 ]. Based on this data, whether drug-eluting stents should be given even greater pref- erence in all obese patients remains debatable. There is also controversial data regarding obesity and resistance to thienopyridines, and the clinical relevance of studies linking obesity with clopidogrel resistance is yet to be confirmed [ 52]. Obesity Paradox in Patients with CAD Surprisingly, despite the observations linking obesity with increased incidence of acute myocardial infarction (NSTEMI or STEMI), several large studies have shown obese patients to have better survival when examining mortality after MI (obesity paradox). Data from large ACS trials such as the Superior Yield of the New Strategy of Enoxaparin, Revascularization, and Glycoprotein IIb/IIIa Inhibitors (SYNERGY) [ 53] and Metabolic Efficiency with Ranolazine for Less Ischaemia in NSTE-ACS (MERLIN)-TIMI 36 [ 54] have shown an independent inverse correlation between BMI and overall mortality. A recent meta- analysis of 10 post –percutaneous coronary intervention (PCI) and 12 post –coronary artery bypass grafting (CABG) studies further substantiates the obesity paradox after coronary revas- cularization. In both post-PCI and post-CABG subgroup of patients, the 30-day all-cause mortality was lower in obese patients compared to their normal weight counterparts [ 55]. The protective effect of obesity, however, seems to decline when severe or morbid obesity is present (J-shaped relation- ship), and the lowest mortality may be seen in overweight patients [ 56]. Obesity and Heart Failure Over the last few decades, there have been significant ad- vancements in available medical and interventional therapies in cardiology. Despite the use of several mortality-reducing therapies, the number of cases of incident HF and of acute decompensated HF hospitalizations has increased [57 ]. During the same time, incidence of obesity has also been on the rise. As noted previously, 35 % of the US population is obese, and approximately 69 % are either overweight or obese [ 1 ]. There exists a well-established link between obesity and HF; hence, the rising prevalence of both obesity and HF may make this association an important target for prevention. One of the first published reports linking obesity and HF in 1956 was a case report of a morbidly obese female who had “heart failure ”from the compressive effects of the physical mass of the adipose tissue underneath her chest wall [ 58]. We now know that obesity has several complex mechanisms by which it may lead to HF. The term “obesity cardiomyopa- thy ”was first used in 1992, when an analysis of 519 pa- tients, using right heart catheterization and endomyocardial biopsy, showed that individuals with higher BMI were more likely to have dilated cardiomyopathy than their lean counterparts [ 59]. Epidemiological Association of Obesity with Heart Failure Kenchaiah et al. reported the first large epidemiological study showing obesity to be an independent risk factor for develop- ment of HF, analyzing 5881 individuals from the Framingham Heart Study. It was concluded that for each increment of 1 kg/ m 2in BMI, there was an increase in the risk of HF of 7 % for women and 5 % for men [ 60]. As compared with subjects with a normal BMI, obese individuals had a doubling of the risk of HF. For women, the hazard ratio was 2.17 (95 % confidence interval (CI), 1.51 to 2.97); for men, the hazard ratio was 1.90 (95 % CI, 1.30 to 2.79). The risk for development of HF was independent of age, alcohol, and cigarette use and comorbid- ities including but not limited to diabetes, hypertension, and history of myocardial infarction. Similarly, an analysis from the Physician ’s Health Study of 21,094 men without known coronary artery disease showed that compared to normal weight, overweight and obesity were independently associat- ed with an increase of HF [ 61]. Loehr et al. examined the Atherosclerosis Risk in Communities cohort of over 14,000 individuals and showed obesity to be an independent risk factor for development of HF, after adjusting the covariates [ 62 ]. Similarly, another large study of over 59,000 individuals from Finland showed a graded association between BMI and HF risk, with adjusted hazard ratios of HF for overweight and obese patients as compared to normal weight of 1.25 (95 % CI = 1.12 –1.39) and 1.99 (95 % CI = 1.74 –2.27) in males, and 1.33 (95 % CI = 1.16 –1.51) and 2.06 (95 % CI = 1.80 –2.37) in females [ 63]. In addition to BMI, other anthropometric indices of obesity including waist circumference, waist-to-hip ratio, and waist-to-height ratio have also been independently asso- ciated with incident HF in large, population-based studies; however, indices such as waist circumference and waist-to- hip ratio have not been shown to perform better than BMI as predictors of HF [ 62,64 ]. Protective Effect of Physical Activity It should be noted that physical activity appears to influence the relationship between obesity and HF. In the Physicians Health Study, in addition to increasing BMI, lower level of physical activity was also as- sociated with an increased risk of HF, with the highest relative risk of HF seen in obese men who were also physically inac- tive compared to men who were lean and physically active 21 Page 4 of 10 Curr Atheroscler Rep (2016) 18: 21 [61 ]. Similarly, in the Finnish cohort discussed above, partic- ipants demonstrated the protective effect of physical activity at all levels of obesity, reducing risk of HF in fully adjusted models by 21 –32 % [ 63]. Mechanisms of Obesity-Associated Heart Failure There are several possible theories to explain the effect of higher BMI leading to development of HF (Fig. 2). Obesity can pro- duce a range of hemodynamic changes that can predispose to changes in cardiac morphology and ventricular function, in- cluding left ventricular (LV) dilation, eccentric or concentric LV hypertrophy, LV systolic and diastolic dysfunction, and RV dysfunction [ 65,66 ]. Obesity has also been described as a state of hemodynamic overload associated with increase in cardiac output and blood pressure [ 67]. Over a long duration, this could lead to left ventricular hypertrophy, depressed systolic function, and impaired relaxation [ 68,69]. While these changes occur in all classes of obesity, they are most pronounced in the severely obese and could potentially lead to HF in such individuals. Another mechanism in- volves a direct effect of excess body fat on the myocardium causing cardiac adaptation and remodeling, which has of- ten been termed obesity cardiomyopathy [ 70,71]. Although the concept of a cardiomyopathy related to obe- sity has previously been described, severe left ventricular systolic dysfunction occurs uncommonly due to obesity alone and its presence should trigger an investigation for other contributory factors, before attributing it to obesity alone. On the other hand, HF with preserved left ventricular ejection fraction (HFpEF) is seen much more frequently in obese patients [ 72,73]. Although the relationship between obesity and incident HF may be related to hemodynamic and anatomic cardiac changes related to excess body mass, evidence suggests that the rela- tionship is also mediated by obesity-related metabolic, inflam- matory, and neurohormonal changes. Obesity and insulin re- sistance are highly correlated, which may in part potentiate the link between obesity and HF. In the Uppsala study, insulin sensitivity but not anthropometric indices of obesity was in- dependently predictive of HF risk when both were evaluated together in a fully adjusted model [ 74]. Abnormalities in the adipokine pathway, as well as other inflammatory cytokines seen in obese individuals, may contribute to the pathophysi- ology of HF [ 70,75 ]. The adipokine resistin, expressed by adipocytes and associated with insulin resistance and inflam- mation, was associated with the risk of developing HF inde- pendent of coronary heart disease and other risk factors in a Framingham offspring analysis [ 76]. Of note, leptin has been studied in great detail. Produced by adipocytes, its levels have been found to directly correlate with BMI and waist circum- ference. Higher levels have been associated with an increased risk for incident HF in patients without pre-existing coronary artery disease, although this association was absent in patients with pre-existing CAD [ 75]. The role of inflammation is also supported by an analysis from the Multi-Ethnic Study of Atherosclerosis (MESA). Although the risk of HF was 83 % higher in obese compared to that in non-obese subjects after adjustment for traditional risk factors, the relationship be- tween obesity and incident HF was no longer significant after OBESITY Fat Metabolic Neurohormonal Hemodynamic SVR Blood volume CO LV hypertrophyLV dilatation Leptin, resistin Adiponectin deficiency Inflammtory cytokines Sympathetic tone Systolic and diastolic dysfunction HEART FAILURE Cardiac steatosis: lipotoxicity Insulin resistance Associated comorbidities Hypertension DiabetesCAD/MI Sleep disordered breathing, hypoxia Pulmonary arterial hypertension: RVH & dilation Fig. 2 Interplay of various mechanisms in obese individuals that may lead to the development of heart failure. SVR = systemic vascular resistance; CO = cardiac output; CAD = coronary artery disease; MI = myocardial infarction; RVH = right ventricular hypertrophy; LV = left ventricular Curr Atheroscler Rep (2016) 18: 21 Page 5 of 1021 adjustment for the inflammatory biomarkers, IL-6 or C- reactive protein [77]. Finally, as is well known, obesity is associated with the other comorbid risk factors that contribute to incident HF, such as DM, HTN, and CAD [ 68]. Obesity Paradox in HF Several studies have shown that despite being an independent risk factor for the development of HF, obesity is associated with lower mortality in patients with established HF, the “obesity paradox in HF ”[5 , 6, 78 ]. Several hypotheses have been suggested to explain this appar- ent paradox [ 6, 79 ]. Severe HF due to its catabolic effect may be associated with weight loss, known as “cardiac cachexia, ” and was initially thought to be the explanation for better sur- vival in overweight or obese HF patients. Although the obesity paradox in established HF has been demonstrated in several studies, a more recent study found that that even over- weight and obesity prior to incident HF (i.e., pre-morbid or pre-HF higher body mass index) are associated with improved survival compared to normal weight up to 10 years after de- velopment of HF, suggesting that weight loss due to advanced HF may not completely explain the protective effect of higher body mass index (BMI) in HF patients [ 80]. Several neuro-humoral pathways have also been postulated to explain the obesity paradox in HF. In obese patients, high levels of serum lipoproteins may neutralize bacterial toxins or circulating cytokines, such as TNF- α[81 ]. Obese patients also have decreased adiponectin levels and low catecholamine re- sponse, both of which may be associated with improved HF survival [ 79]. Levels of circulating stem cells are also increased in obese patients, which may lead to improved out- comes [ 82]. Furthermore, obese patients are likely to be diag- nosed early with HF due to the symptoms produced by excess body weight, such as dyspnea and edema. This may lead to an apparently improved life span, i.e., lead time bias [ 80]. Obese patients have multiple comorbidities, such as diabetes mellitus and hypertension, and hence represent a high-risk screening population. Moreover, given higher blood pressures in the overweight and obese population groups, greater up-titration of disease modifying therapies could be possible. All these hypotheses could explain the reasons behind the observed obesity paradox in HF. Interestingly, a recent study found that the obesity paradox was not seen in overweight male patients after adjustment for confounding variables but remained in overweight female patients [ 83]. The protective effect in fe- male patients could be partially explained by recent research which suggests that female hearts have greater myocardial fatty acid uptake and lesser myocardial glucose utilization in advanced HF [ 83]. Future studies are needed to further under- stand the pathways involved. Currently, the most recent HF guidelines by the American College of Cardiology/American Heart Association do not specifically recommend weight reduction in obese patients with HF [ 84]. Large, prospective, randomized control trials with long follow-up would be able to definitively answer the effectiveness of intentional weight loss interventions in obese HF patients. Nevertheless, based on the data available, obese HF patients appear to benefit from exercise interventions. Atrial Fibrillation Atrial fibrillation (AF) is one of the most common arrhythmias worldwide and is more commonly observed in obese individ- uals [ 85]. A report from the Framingham study showed that after adjustment for CV disease risk factors, and the occur- rence of interim myocardial infarction or HF, every 1-unit Obesity Genetics Comorbidities (DM, HTN, OSA etc) Inflammation Ventricular remodeling CAD Atrial Fibrillation Atrial enlargement, fibrosis & remodeling Fig. 3 Interplay of various mechanisms in obese individuals that may lead to the development of atrial fibrillation. CAD = coronary artery disease; DM = diabetes mellitus; OSA = obstructive sleep apnea; HTN = hypertension 21 Page 6 of 10 Curr Atheroscler Rep (2016) 18: 21 increment in BMI was independently associated with a 4 % increase in risk of AF in both men and women [85](Fig. 1). When examined by BMI categories of obesity, the investiga- tors found that compared to normal weight individuals, obese individuals had a 50 % increased risk of AF. The Danish Diet, Cancer, and Health Cohort Study also demonstrated a higher risk of AF associated with increased anthropometric measure- ments such as height, weight, BMI, hip circumference, and waist circumference, as well as with increased bioimpedance- derived measures of body fat mass, body fat percentage, and lean body mass [ 86]. Mechanisms for Atrial Fibrillation with Obesity The Framingham study demonstrated that the excess risk of AF associated with obesity appeared to be mediated by left atrial dilatation. [ 87]. The investigators observed a graded increase in left atrial size as BMI category increased from normal to overweight to obese, consistent with previous observations of obesity as a major risk factor for the development of left atrial enlargement [ 88,89 ]. After adjusting for left atrial diameter, obesity was no longer associated with an increased risk for the development of AF, whereas left atrial diameter remained strongly associated with risk of AF. Left atrial dilation/ remodeling which is an established mechanistically important factor in the pathogenesis of AF is likely multifactorial in obesity and occurs as a result of increased central blood vol- ume, and secondary to elevated left ventricular diastolic pres- sures both due to obesity-driven load with increase in left ventricular mass and HF, as well as secondary to obesity- associated hypertension, diabetes, and obstructive sleep apnea (Fig. 3)[ 90 ]. Interestingly, genetic studies have suggested a link between non-valvular AF and obesity. Specifically, polymorphisms of TaqIB cholesteryl transfer protein gene and 1444 C/T of CRP gene have been shown to be associated with an increased susceptibility in obese males [ 91]. Additionally, a G protein- coupled potassium channel known as GIRK4 (G protein- coupled inward rectifier K channel), which is found prevalent- ly in the heart, has been shown to be abnormally expressed in obese individuals with AF [ 92]. Several recent studies have described the relationship be- tween thickness of epicardial adipose tissue (EAT) and AF burden [ 91]. A significant correlation exists between EAT and BMI, waist circumference, or visceral adipose tissue [ 93 ]. The Framingham study reported that pericardial fat vol- ume predicted AF, independently of other measures of adipos- ity, including BMI [ 94]. Furthermore, studies indicate that pericardial fat is associated with the increased prevalence and severity of AF, independent of traditional risk factors, including left atrial dilation [ 95], suggesting that EAT volume may have prognostic significance besides traditional measures of obesity [ 96,97 ]. It has been speculated that the direct im- pact of obesity on atrial substrate may be mediated via EAT [ 96 ]. As detailed before, adipose tissue produces a variety of bioactive molecules, such as inflammatory mediators and adipocytokines that have been shown to mediate the effect of visceral fat on other tissues. Among others, EAT also se- cretes activin A and matrix metalloproteinases (MMPs), which may mediate the fibrotic effect of EAT on the atrial wall [ 98 ]. Inflammatory markers such as CRP, TNF- α, IL-2, IL-6, IL-8, and monocyte chemoattractant protein (MCP)-1 have been associated with AF [ 99] and are produced and secreted by EAT, specifically in the setting of obesity, diabetes, and ischemic cardiomyopathy. There is also increasing evidence that implicates inflammation in AF development and perpet- uation. In addition, EAT accumulation is associated with fatty infiltration from the epicardial layer, which advances deep into the myocardium, contributing to myocardial functional disor- ganization and the formation of local arrhythmogenic substrate [ 100]. EAT may also affect the atrial cellular com- ponents by providing a source for precursor cells that can differentiate into myofibroblasts and thus contribute to atrial remodeling, a substrate for AF [ 101]. Furthermore, patients with lower BMI and a single episode of new-onset AF have a lower risk of progressing to perma- nent AF compared to obese individuals [ 98]. Interestingly, a recent trial in Australia comparing the Long-Term Effect of Goal Directed Weight Management in an Atrial fibrillation Cohort (LEGACY) found that long-term weight loss is asso- ciated with a significant decrease in the incidence of recurrent AF [ 102]. In LEGACY, the individuals with ≥10 % weight loss resulted in a sixfold greater probability of AF-free surviv- al [ 102 ]. The risk of developing hypertension, diabetes, met- abolic syndrome, coronary artery disease, and sleep apnea is expectedly significantly hi gher in obese individuals. Therefore, the risk of AF is also increased in obese patients through the various mechanisms that contribute to the devel- opment of AF in patients with these comorbidities [ 90]. Conclusion and Future Directions In summary, we have discussed the evidence that supports obesity as both an independent risk factor and a risk marker for the development of asymptomatic and symptomatic coro- nary artery disease, heart failure, and atrial fibrillation. Future studies exploring the role of weight loss as a positive disease modifier in these conditions are urgently needed. Compliance with Ethical Standards Conflict of Interest Taher Mandviwala and Umair Khalid declare that they have no conflict of interest. Anita Deswal declares research support from Novartis as site-PI of multicenter clinical trial, and grant support from the NIH as site-PI for clinical trials. Curr Atheroscler Rep (2016) 18: 21 Page 7 of 1021 Human and Animal Rights and Informed ConsentThis article does not contain any studies with human or animal subjects performed by any of the authors. References Papers of particular interest, published recently, have been highlighted as: Of importance 1. Flegal K, Carroll M, Kit B, Ogden C. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999 –2010. JAMA. 2012;307:491 –7. 2. Fontaine K, Redden D, Wang C, Westfall A, Allison D. Years of life lost due to obesity. JAMA. 2003;289:187 –93. 3. Poirier P, Giles T, Bray G, et al. Obesity and cardiovascular dis- ease: pathophysiology, evaluation, and effect of weight loss. Circulation. 2006;113:898 –918. 4. Poirier P, Martin J, Marceau P, Biron S, Marceau S. Impact of bariatric surgery on cardiac structure, function and clinical mani- festations in morbid obesity. Expert Rev Cardiovasc Ther. 2004;2: 193–201. 5. Horwich TB, Fonarow GC, Hamilton M, MacLellan W, Woo M, Tillisch J. The relationship between obesity and mortality in pa- tients with heart failure. J Am Coll Cardiol. 2001;38:789 –95. 6. Lavie CJ, Alpert MA, Arena R, Mehra MR, Milani RV, Ventura HO. Impact of obesity and the obesity paradox on prevalence and prognosis in heart failure. JACC Heart Fail. 2013;1(2):93 –102. Lavie et al. review the data on obesity and cardiopulmonary fitness in heart failure patients. The article also includes infor- mative data on obesity and its impact on treatments such as heart transplantation and LV assist devices . 7. Shah R, Gayat E, Januzzi Jr JL, et al. Mebazaa A Body mass index and mortality in acutely decompensated heart failure across the world: a global obesity paradox. J Am Coll Cardiol. 2014;63: 778–85. 8. Akin I, Nienaber C. “Obesity paradox ”in coronary artery disease. World J Cardiol. 2015;7(10):603 –8. 9. Clinical Guidelines on the Ide ntification, Evaluation, and Treatment of Overweight and Obesity in Adults: The Evidence Report: National Institutes of Health. Obes Res. 1998;2:51S– 209S. 10. Kurpad SS, Tandon H, Srinivasan K. Waist circumference corre- lates better with body mass index than waist-to-hip ratio in Asian Indians. Natl Med J India. 2003;16(4):189 –92. 11. Rocha V, Libby P. Obesity, inflammation, and atherosclerosis. Nat Rev Cardiol. 2009;6:399 –409. Rocha et al. review in detail the interplay between obesity and inflammation leading to the de- velopment of atherosclerosis . 12. Ross R. Atherosclerosis –an inflammatory disease. N Engl J Med. 1999;340(2):115 –26. 13. Esposito K, Pontillo A, Di Palo C, et al. Effect of weight loss and lifestyle changes on vascular inflammatory markers in obese women: a randomized trial. JAMA. 2003;289:1799 –804. 14. Shoelson SE, Herrero L, Naaz A. Obesity, inflammation, and in- sulin resistance. Gastroenterology. 2007;132:2169 –80. 15. Ridker P. C-reactive protein and the prediction of cardiovas- cular events among those at intermediate risk: moving an in- flammatory hypothesis toward consensus. J Am Coll Cardiol. 2007;49:2129 –38. 16. Everett B, Kurth T, Buring J, Ridker P. The relative strength of C- reactive protein and lipid levels as determinants of ischemic stroke compared with coronary heart disease in women. J Am Coll Cardiol. 2006;48:2235 –42. 17. Ridker P, Stampfer M, Rifai N. Novel risk factors for systemic atherosclerosis: a comparison of C-reactive protein, fibrinogen, homocysteine, lipoprotein(a), and standard cholesterol screening as predictors of peripheral arterial disease. JAMA. 2001;285(19): 2481–5. 18. Pradhan A, Manson J, Rifai N, Buring J, Ridker P. C-reactive protein, interleukin 6, and risk of developing type 2 diabetesmellitus. JAMA. 2001;286:327 –34 . 19. Visser M, Bouter L, McQuillan G, Wener M, Harris T. Elevated C- reactive protein levels in overweight and obese adults. JAMA. 1999;282:2131 –5. 20. Pearson T, Mensah G, Alexander R, et al. Markers of inflamma- tion and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation. 2003;107:499 –511. 21. Ridker P, Rifai N, Pfeffer M, et al. Inflammation, pravastatin, and the risk of coronary events after myocardial infarction in patients with average cholesterol levels. Cholesterol and recurrent events (CAre) investigators. Circulation. 1998;98:839 –44. 22. Ridker P, Rifai N, Clearfield M, et al. Measurement of C-reactive protein for the targeting of statin therapy in the primary prevention of acute coronary events. N Engl J Med. 2001;344:1959 –65. 23. Ridker P, Danielson E, Fonseca F, et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive pro- tein. N Engl J Med. 2008;359:2195 –207. 24. Yuan M, Konstantopoulos N, Lee J, et al. Reversal of obesity and diet-induced insulin resistance with salicylates or targeted disrup- tion of ikkbeta. Science. 2001;293:1673 –7. 25. Fleischman A, Shoelson S, Bernier R, Goldfine A. Salsalate im- proves glycemia and inflammatory parameters in obese young adults. Diabetes Care. 2008;31:289 –94. 26. Koska J, Ortega E, Bunt J, et al. The effect of salsalate on insulin action and glucose tolerance in obese non-diabetic patients: results of a randomised double-blind placebo-controlled study. Diabetologia. 2009;52:385 –93. 27. Wolk R, Berger P, Lennon J, Brilakis E, Somers V. Body mass index: a risk factor for unstable angina and myocardial infarction in patients with angiographically confirmed coronary artery dis- ease. Circulation. 2003;108:2206 –11. 28. McGill H, McMahan C, Herderick E, et al. Obesity accelerates the progression of coronary atherosclerosis in young men. Circulation. 2002;105:2712 –8. 29. Rabkin S, Mathewson F, Hsu P. Relation of body weight to devel- opment of ischemic heart disease in a cohort of young North American men after a 26 year observation period: the Manitoba Study. Am J Cardiol. 1977;39:452 –8. 30. Manson J, Colditz G, Stampfer M, et al. A prospective study of obesity and risk of coronary heart disease in women. N Engl J Med. 1990;322:882 –9. 31. Wilson P, D ’Agostino R, Sullivan L, Parise H, Kannel W. Overweight and obesity as determinants of cardiovascular risk: the Framingham experience. Arch Intern Med. 2002;16:1867 –72. 32. Din-Dzietham R, Liu Y, Bielo M, Shamsa F. High blood pressure trends in children and adolescents in national surveys, 1963 to 2002. Circulation. 2007;116(13):1488 –96. 33. Lau D, Dhillon B, Yan H, Szmitko P, Verma S. Adipokines: mo- lecular links between obesity and atheroslcerosis. Am J Physiol Heart Circ Physiol. 2005;288:2031 –41. 34. Juge-Aubry C, Henrichot E, Meier C. Adipose tissue: a regulator of inflammation. Best Pract Res Clin Endocrinol Metab. 2005;19: 547–66. 21 Page 8 of 10 Curr Atheroscler Rep (2016) 18: 21 35. Knudson J, Dincer U, Zhang C, et al. Leptin receptors areexpressed in coronary arteries and hyperleptinemia causes signif- icant coronary endothelial dysfunction. Am J Physiol Heart Circ Physiol. 2005;289:48 –56. 36. Trayhurn P, Wood I. Signaling role of adipose tissue: adipokines and inflammation in obesity. Biochem Soc Trans. 2005;33:1078 –81. 37. Payne G, Kohr M, Tune J. Epicardial perivascular adipose tissue as a therapeutic target in obesity-related coronary artery disease. Br J Pharmacol. 2012;165(3):659 –69. 38. Anfossi G, Russo I, Trovati M. Platelet dysfunction in central obesity. Nutr Metab Cardiovasc Dis. 2009;19:440 –9. 39. Salama M, Morad A, Saleh M, Sabri N, Zaki M, ElSafady L. Resistance to low-dose aspirin therapy among patients with acute coronary syndrome in relation to associated risk factors. J Clin Pharm Ther. 2012;37:630 –6. 40. Kragelund C, Hassager C, Hildebrandt P, Torp-Pedersen C, Kober L. Impact of obesity on long-term prognosis following acute myo- cardial infarction. Int J Cardiol. 2005;98:123 –31. 41. Yusuf S, Hawken S, Ounpuu S, et al. Effect of potentially modi- fiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case –control study. Lancet. 2004;364:937 –52. 42. Madala M, Franklin B, Chen A, et al. Obesity and age of first non- ST-segment elevation myocardial infarction. J Am Coll Cardiol. 2008;52:979 –85. 43. Das S, Alexander K, Chen A, et al. Impact of body weight and extreme obesity on the presentation, treatment, and in-hospital outcomes of 50,149 patients with ST-Segment elevation myocar- dial infarction results from the NCDR (National Cardiovascular Data). J Am Coll Cardiol. 2011;58(25):2642 –50. 44. Bajaj S, Shamoon F, Gupta N, et al. Acute ST-segment elevation myocardial infarction in young adults: who is at risk? Coron Artery Dis. 2011;22:238 –44. 45. Jamil G, Jamil M, Alkhazraji H, et al. Risk factor assessment of young patients with acute myocardial infarction. Am J Cardiovasc Dis. 2013;3:170 –4. 46. Lavie C, Milani R, Ventura H. Obesity and cardiovascular disease: risk factor, paradox and impact of weight loss. J Am Coll Cardiol. 2009;53(21):1925 –32. 47. Nikolsky E, Kosinski E, Mishkel GJ, et al. Impact of obesity on revascularization and restenosis rates after bare-metal and drug- eluting stent implantation (from the TAXUS-IV trial). Am J Cardiol. 2005;95:709 –15. 48. Sarno G, Garg S, Onuma Y, et al. The impact of body mass index on the one year outcomes of patients treated by percutaneous coronary intervention with biolimus- and sirolimus-eluting stents (from the LEADERS Trial). Am J Cardiol. 2010;105:475 –9. 49. Sarno G, Raber L, Onuma Y, et al. Impact of body mass index on the five-year outcome of patients having percutaneous coronary interventions with drug-eluting stents. Am J Cardiol. 2011;108: 195–201. 50. Akin I, Tolg R, Hochadel M, et al. No evidence of “obesity para- dox ”after treatment with drug-eluting stents in a routine clinical practice: results from the prospective multicenter German DES.DE (German Drug-Eluting Stent) Registry. JACC Cardivasc Interv. 2012;5(2):162 –9. 51. Wang Z, Zhou Y, Zhao Y, et al. Effect of obesity on repeat revascularization in patients undergoing percutaneous coro- nary intervention with drug-eluting stents. Obesity (Silver Spring). 2012;20:141 –6. 52. Feher G, Koltai K, Alkonyi B, et al. Clopidogrel resistance: role of body mass and concomitant medications. Int J Cardiol. 2007;120: 188–92. 53. Mahaffey K, Tonev S, Spinler S, et al. Obesity in patients with non-ST-segment elevation acute coronary syndromes: results from the SYNERGY trial. Int J Cardiol. 2010;139:123 –33. 54. Kadakia M, Fox C, Scirica B, Murphy S, Bonaca M, Morrow D. Central obesity and cardiovascular outcomes in patients with acute coronary syndrome: observations from the MERLIN-TIMI 36 tri- al. Heart. 2011;97:1782 – 7. 5 5. OreopoulosA,PadwalR,NorrisC,MullenJ,PretoriusV, Kalantar-Zadeh K. Effect of obesity on short- and long-term mor- tality postcoronary revascularization: a meta-analysis. Obesity (Silver Spring). 2008;16:442 –50. 56. Li Y, Lin G, Lin C, Wang J, Han C. Relation of body mass index to mortality among patients with percutaneous coronary intervention longer than 5 years follow-up: a meta-analysis. Int J Cardiol. 2013;168:4315 –8. 57. Bui A, Horwich T, Fonarow G. Epidemiology and risk profile of heart failure. Nat Rev Cardiol. 2011;8(1):30 –41. 58. Counihan T. Heart failure due to extreme obesity. Br Heart J. 1956;18(3):425 –6. 59. Kasper EK, Hruban RH, Baughman KL. Cardiomyopathy of obe- sity: a clinicopathologic evaluation of 43 obese patients with heart failure. Am J Cardiol. 1992;70(9):921 –4. 60. Kenchaiah S, Evans J, Levy D, et al. Obesity and the risk of heart failure. N Engl J Med. 2002;347:305 –13. 61. Kenchaiah S, Sesso H, Gaziano J. Body mass index and vigorous physical activity and the risk of heart failure among men. Circulation. 2009;119(1):44 –52. 62. Loehr L, Rosamond W, Poole C, et al. Association of multiple anthropometrics of overweight and obesity with incident heart failure: the Atherosclerosis Risk in Communities study. Circ Heart Fail. 2009;2(1):18 –24. 63. Hu G, Jousilahti P, Antikainen R, Katzmarzyk PT, Tuomilehto J. Joint effects of physical activity, body mass index, waist circum- ference, and waist-to-hip ratio on the risk of heart failure. Circulation. 2010;121(2):237 –44. 64. Hu G, Jousilahti P, Antikainen R, Katzmarzyk P, Tuomilehto J. Joint effects of physical activity, body mass index, waist circum- ference, and waist-to-hip ratio on the risk of heart failure. Circulation. 2010;121:237 –44. 65. Pascual M, Pascual D, Soria F, et al. Effects of isolated obesity on systolic and diastolic left ventricular function. Heart. 2003;89(10): 1152–6. 66. Alpert M, Alexander J. Obesity and ventricular function in man: systolic function. In: Alpert MA, Alexander JK, editors. The heart and lung in obesity. Armonk, New York: Futura Publishing Company; 1998. p. 77 –94. 67. Alexander J, Dennis E, Smith W, Amad K, Duncan W, Austin R. Blood volume, cardiac output, and distribution of systemic blood flow in extreme obesity. Cardiovasc Res Cent Bull. 1962;1:39 –44. 68. Alpert M, Lambert C, Panayiotou H, et al. Relation of duration of morbid obesity to left ventricular mass, systolic function, and di- astolic filling, and effect of weight loss. Am J Cardiol. 1995;76: 1194–7. 69. Nakajima T, Fujioka S, Tokunaga K, Hirobe K, Matsuzawa Y, Tarui S. Noninvasive study of left ventricular performance in obese patients: influence of duration of obesity. Circulation. 1985;71:481 –6. 70. McGavock J, Victor R, Unger R, Szczepaniak L. Adiposity of the heart, revisited. Ann Intern Med. 2006;144:517 –24. 71. Poirier P, Giles T, Bray G, et al. Obesity and cardiovascular dis- ease: pathophysiology, evaluation, and effect of weight loss. Arterioscler Thromb Vasc Biol. 2006;26:968 –76. 72. Kapoor J, Heidenreich P. Obesity and survival in patients with heart failure and preserved systolic function: a U-shaped relation- ship. Am Heart J. 2010;159:75 –80. Curr Atheroscler Rep (2016) 18: 21 Page 9 of 1021 73. Ather S, Chan W, Bozkurt B, et al. Impact of noncardiac comor-bidities on morbidity and mortality in a predominantly male pop- ulation with heart failure and preserved versus reduced ejection fraction. J Am Coll Cardiol. 2012;59(11):998 –1005. 74. Ingelsson E, Sundstrom J, Arnlöv J, Zethelius B, Lind L. Insulin resistance and risk of congestive heart failure. JAMA. 2005;294(3):334 –41. 75. Wannamethee S, Shaper A, Whincup P, Lennon L, Sattar N. Obesity and risk of incident heart failure in older men with and without pre-existing coronary heart disease: does leptin have a role? J Am Coll Cardiol. 2011;58:1870 –7. 76. Frankel D, Vasan R, D ’Agostino R, et al. Resistin, adiponectin, and risk of heart failure the Framingham offspring study. J Am Coll Cardiol. 2009;53(9):754 –62. 77. Bahrami H, Bluemke D, Kronmal R, et al. Novel metabolic risk factors for incident heart failure and their relationship with obesity: the MESA (Multi-Ethnic Study of Atherosclerosis) study. J Am Coll Cardiol. 2008;51(18):1775 –83. 78. Bozkurt B, Deswal A. Obesity as a prognostic factor in chronic symptomatic heart failure. Am Heart J. 2005;150(6):1233 –9. 79. Kistorp C, Faber J, Galatius S, et al. Plasma adiponectin, body mass index, and mortality in patients with chronic heart failure. Circulation. 2005;112:1756 –62. 80. Khalid U, Ather S, Bavishi C, et al. Pre-morbid body mass index and mortality after incident heart failure: the ARIC Study. J Am Coll Cardiol. 2014;64(25):2743 –9. This is the first study that describes the obesity paradox in heart failure, based on pre- heart failure (i.e. premorbid) BMI. The authors found that obesity and underweight compared to normal weight prior to the development of heart failure was associated with im- proved survival after the development of heart failure . 81. Lavie C, Ventura H. Weighing in on obesity and the obesity par- adox in heart failure. J Card Fail. 2011;17:381 –3. 82. Blogowski W, Serwin K, Budkowska M, et al. Clinical analysis of systemic and adipose tissue levels of selected hormones/ adipokines and stromal-derived factor-1. J Biol Regul Homeost Agents. 2012;26:607 –15. 83. Vest A, Wu Y, Hachamovitch R, Young J, Cho L. The heart failure overweight/obesity survival paradox: the missing sex link. J Am Coll Cardiol HF. 2015;3:917 –26. 84. Yancy C, Jessup M, Bozkurt B, et al. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2013;62: 147–239. 85. Wang T, Parise H, Levy D, et al. Obesity and the risk of new-onset atrial fibrillation. JAMA. 2004;292:2471 –7. 86. Frost L, Benjamin E, Fenger-Grøn M, Pedersen A. Tjønneland A, Overvad K. Body fat, body fat distribution, lean body mass, and atrial fibrillation and flutter. A Danish cohort study. Obesity (Silver Spring). 2014;22:1546 –52. 87. Wanahita N, Messerli F, Bangalore S, Gami A, Somers V, Steinberg J. Atrial fibrillation and obesity: results of a meta-anal- ysis. Am Heart J. 2008;155:310 –5. 88. Iacobellis G, Ribaudo M, Leto G, et al. Influence of excess fat on cardiac morphology and function: study in uncomplicated obesity. Obes Res. 2002;10:767 –73. 89. Gottdiener J, Reda D, Williams D, Materson B. Left atrial size in hypertensive men: influence of obesity, race and age. J Am Coll Cardiol. 1997;29:651 –8. 90. Goudis C, Korantzopoulos P, Ntalas I, Kallergis E, Ketikoglou D. Obesity and atrial fibrillation: a comprehensive review of the path- ophysiological mechanisms and links. J Cardiol. 2015;66(5):361 – 9. Goudis et al. review the most recent clinical and experimen- tal studies linking obesity to atrial fibrillation . 91. Yang W, Xu L, Zhang H. Association of CETP and CRP gene polymorphisms with non-valvular atrial fibrillation in ChineseHan population. Zhonghua Yi Xue Yi Chuan Xue Za Zhi. 20 08;25:225 –229. 92. Kang Y, Hu Y, Li N. Advances in research on G protein-coupled inward rectifier K(+) channel gene. Zhongguo Yi Xue Ke Xue Yuan Xue Bao. 2012;34:426 –430. 93. Rabkin S. The relationship between epicardial fat and indices of obesity and the metabolic syndrome: a systematic review and me- ta-analysis. Metab Syndr Relat Disord. 2014;12:31 –42. 94. Thanassoulis G, Massaro J, O ’Donnell CJ, et al. Pericardial fat is associated with prevalent atrial fibrillation: the Framingham Heart Study. Circ Arrhythm E. 2010;3(4):345 –50. 95. Al Chekakie M, Welles C, Metoyer R, et al. Pericardial fat is independently associated with human atrial fibrillation. J Am Coll Cardiol. 2010;56:784 –8. 96. Wong C, Abed H, Molaee P, et al. Pericardial fat is associated with atrial fibrillation severity and ablation outcome. J Am Coll Cardiol. 2011;57(17):1745 –51. 97. Shin S, Yong H, Lim H, et al. Total and interatrial epicardial adipose tissues are independently associated with left atrial remod- eling in patients with atrial fibrillation. J Cardiovasc Electrophysiol. 2011;22(6):647 –55. 98. Hatem S, Sanders P. Epicardial adipose tissue and atrial fibrilla- tion. Cardiovasc Res. 2014;102:205 –13. 99. Guo Y, Lip G, Apostolakis S. Inflammation in atrial fibrillation. J Am Coll Cardiol. 2012;60:2263 –70. 100. Mahajan R, Brooks A, Shipp N. Epicardial fat infiltration of atrial musculature creates the substrate for atrial fibrillation in obesity. Heart Rhythm. 2012;9:124. 101. Zuk P, Zhu M, Ashjian P, et al. Human adipose tissue is a source of multipotent stem cells. Mol Biol Cell. 2002;13:4279 –95. 102. Pathak R, Middeldorp M, Meredith M, Mehta A, MActSt, MahajanR,WongC,TwomeyD,ElliottA,KalmanJ, Abhayaratna W, Lau D, Sanders P. Long-Term Effect of Goal- Directed Weight Management in an Atrial Fibrillation Cohort. J Am Coll Cardiol. 2015;65:2159 –2169 21 Page 10 of 10 Curr Atheroscler Rep (2016) 18: 21
Include references and avoid plagiarism when answering this, it is a graduate research NON-EXPERIMENTAL STUDY paper: Review the following attached articles that have been provided as example studies
Chinwe Eluagu 1 Association between body mass index, hypertension, and diabetes S tudy objectives Engagement of the young adults in a physical exercise program will have numerous objectives, firstly is to understand the relationship between obesity and physical exercise. If the subjects’ overall weight reduces due to exercise, then t here will be some significant changes . ( Tsimihodimos et al. 2018 ). The second objective is a comparison between the body mass index of people that participate and others th at do not participate in physical exercise to understand the changes that physical exercise has on people. The third objective is observing the long – term impacts of physical activity on individ uals since, although the process takes longer, most scientists acknowledge that it is the most significan t. Background Over the past few years, a gradual increase in the body mass index among the young adults between 25 and 40 years old has increased significantly due to a lack of physical exercise and nutrition patterns. An example is , due to the economic nature of our society, people take their vehicles t o work and remain seated for long hours at work . This affects the quality of physical exercise; young adults are also taking sugary and fast foods associated with a huge number of calories, thus increasing their overall weight. For this study, we will seek to unde rstand the importance of physical exercising among the young adults in reducing the body mass index, thus reducing cases of hypertension and diabetes. This will create a long – term method of solving the obesity issue since if a culture of physical exercising is i ntroduced for young adults, it will be adhered to throughout their adulthood. 2 Research question and hypothesis How does physical exercise intervention among obese young adults help reduce body mass index compared to those not undertaking the program for six months? The alternative hypothesis is after engaging in phys ical exercises for six months, the young adult ’ s overall body mass index will reduce significantly tha n those who do not participate in the physical exercise programs. While the null hypothesis is there will be no difference in the body mass index for young adults who undergo the exercise program for six months. The hypothesis will be testable since the young adults will be weighed before engaging in the research process with the ir weight recorded at every interval . B y every four weeks of physical exercising, the subject will be cons idered to track the progress. The physical exercise will be mild, including walking to work instead of driving and visiting the gym for one hour daily. Patient eligibility The patients involved in this study should be between 25 and 40 years old, and they should consent to the study by agreeing to regular exercise and programs r elating to reducing weight. They should have flexible work schedules since the program involves one hour of gym attendance per day. The data to be collected from the patient will include their height and weight after four weeks to observe and no te the differences. ( Pribis et al. 2010). The variables The physical exercise intervention will be the independent variable that can be changed and manipulated by the author to reach the required results. An example is , the control group will 3 not engage in physical exercising while the experimental group will engage in a physical exercise program, thus noting the differences between the groups . While the body mass index wi ll be the dependent variable which will change upon engagement in continuous physical exercising, evaluation of participants will occur monthly (four weeks) to note the changes and effectiveness of the study. S tudy design The randomized control trial will be the most effective in this study since the participants in the intervention group will be regularly involved in physical exercise programs, and changes will be noted to ensure there is significance while in the control group. This will be the most effective method since the differences and changes in weight between the two groups will be observed and recorded gradually to ensure there are effects on the experimen tal group. Recruitment Numerous methods would be used to recruit participants; however, electronic health records will be the most significant since data is already available regarding the patient’s BMI. Therefore, those eligible will receive messages but consent to the study. Before consent, everyone will have to understand the risks and issues relating to participation, such as discomfort when one must regularly attend the gym and walk instead of driving for physical fitness. Outcomes, intervention, an d covariates With t he experimental group, there would be a significant reduction of their BMI with consistent and continuous engagement in physical exercises. As participants continued to engage, their overall health was better. However, for the control group, the results were often 4 similar since the results were no change in weight. There were other covariates in the study; nutrition was highly significant since engagement in an excellent physical exercising program was not successful without proper diets. Gender affected the responses of d ifferent genders to exercise programs; for example, the males were more proactive in the programs than the females. ( Wang et al. 2004). E – protocol tool use & Reflection The electronic health records informatics tool was highly significant since, in our healthcare facility, there are different records and charts a bout patients; therefore, locating participants was not difficult. It only required seeking authorization from the management and sending emails and messages to patients for consent. Most patients were will ing to participate since they were aware of physical exercise and nutrition impacts on their healthcare. The method was more significant than physically locating patients and participants, which would be difficult considering the need to protect privacy. There are different features assessed; firstly, there are patient demographics, including their insurance, age, names, contacts, and gender. Since, in the study, young adults between 25 and 40 years were required, we only selected such participants. We also neede d to balance the two genders; therefore, while sending messages, the two were informed. ( Evans, 2016). We also focused on the clinical notes feature of the electronic health records; for example, in every encounter with physicians, there are records of phy sical examination results, treatments etc. therefore, the notes helped ensure the involved patients did not have underlining conditions that would jeopardize their wellbeing. 5 Other essential features are not provided, such as the patient’s occupation and nutrition patterns, which would have been successful in the study. An example like patient flexibility was required for the intervention group to ensure they had adequate time for physical exercising and walking to work. Additionally, the nutrition behavior wa s significant in the project’s success. ( Kohli & Tan 2016). An example was, patients taking healthy food like carbs and fruits regularly had a higher possibility of excellent results. The significant difficulty was patient privacy concerns since when patients give their information to the healthcare facility, they expect it to be protected from third – party access. Therefore, most respondents wondered how we accessed the information; however, after explaining that we were part of the healthcare facility researc h and development team, they were willing to participate . Therefore, I would prefer to use the electronic health records informatics tools other than physically looking for participants, which would take a longer duration and mos t would resist engagement. 6 References Evans, R. S. (2016). Electronic health records: then, now, and in the future. Yearbook of medical informatics , 25 (S 01), S48 – S61. https://www.thieme – connect.com/products/ejournals/html/10.15265/IYS – 2016 – s006 Garg, H., Batra, N., Singh, G., & Mujral, A. (2022). Hypertension and Diabetes Mellitus: Coprediction and Time Trajectories. Indian Journal of Public Health Research & De velopment , 13 (3), 102 – 106. https://revistaamplamente.com/index.php/ijphrd/article/download/18178/15904 Kohli, R., & Tan, S. S. L. (2016). Electronic Health Records . Mis Quarterly , 40 (3), 553 – 574. https://www.jstor. org/stable/pdf/26629027.pdf?casa_token=_0xv4Vh5lowAAAAA:HQG biEY3haJ2rq9IlSHMh4Nv – Ww0aH9CcwNEZ2CNRSIjTJs70jNTqfKc2b8njo5wpMurup9I9OWESE6ybRRkgOxF0 O6MenFimo0ToWrA9e9aptoDH88 Pribis, P., Burtnack, C. A., McKenzie, S. O., & Thayer, J. (2010). Trends in body fat, body mass index and physical fitness among male and female college students. Nutrients , 2 (10), 1075 – 1085. https://www.mdpi.com/2072 – 6643/2/10/1075/pdf Tsimihodimos, V., Gonzalez – Villalpando, C., Meigs, J. B., & Ferrannini, E. (2018). Hypertension and diabetes mellitus: coprediction and time trajectories. Hypertension , 71 (3), 422 – 428. https://www.ahajournals. org/doi/full/10.1161/HYPERTENSIONAHA.117.10546 7 Wang, F., McDonald, T., Champagne, L. J., & Edington, D. W. (2004). Relationship of body mass index and physical activity to health care costs among employees. Journal of Occupational and Environmental Medici ne , 428 – 436. https://www.jstor.org/stable/pdf/44996 617.pdf?casa_token=rj2 – cn_R – BgAAAAA:yd0m4uaVZ1kJtQUhbY6AXC2xjx5z6dBnMLRDwgcsfn0IfUaFBDp2vYw WbahtOCnO62nE8N4p6k5yIPW_KYwK6lfx6KjxHEMIdoXfkCkHYU_f – UgMFEs