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      Overweight, obesity, and risk of cardiometabolic multimorbidity: pooled analysis of individual-level data for 120 813 adults from 16 cohort studies from the USA and Europe

      , Prof, PhD a , b , c , * , , MSc b , , PhD a , d , , PhD b , , MSc b , , Prof, PhD e , f , , PhD a , , PhD a , , PhD f , g , h , , Prof, MD i , , Prof, PhD j , , MD k , , PhD g , l , , PhD c , , MSc c , , Prof, PhD m , n , , MSc a ,   , PhD a , o , , Prof, PhD a , , MD p , q , r , , Prof, MD i , , Prof, MD p , s , , Prof, PhD c , , Prof, MD t , , Prof, PhD g , , MD i ,   , Prof, PhD a , u , , PhD a , v , , PhD a , w , , PhD x

      The Lancet. Public Health

      Elsevier, Ltd

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          Although overweight and obesity have been studied in relation to individual cardiometabolic diseases, their association with risk of cardiometabolic multimorbidity is poorly understood. Here we aimed to establish the risk of incident cardiometabolic multimorbidity (ie, at least two from: type 2 diabetes, coronary heart disease, and stroke) in adults who are overweight and obese compared with those who are a healthy weight.


          We pooled individual-participant data for BMI and incident cardiometabolic multimorbidity from 16 prospective cohort studies from the USA and Europe. Participants included in the analyses were 35 years or older and had data available for BMI at baseline and for type 2 diabetes, coronary heart disease, and stroke at baseline and follow-up. We excluded participants with a diagnosis of diabetes, coronary heart disease, or stroke at or before study baseline. According to WHO recommendations, we classified BMI into categories of healthy (20·0–24·9 kg/m 2), overweight (25·0–29·9 kg/m 2), class I (mild) obesity (30·0–34·9 kg/m 2), and class II and III (severe) obesity (≥35·0 kg/m 2). We used an inclusive definition of underweight (<20 kg/m 2) to achieve sufficient case numbers for analysis. The main outcome was cardiometabolic multimorbidity (ie, developing at least two from: type 2 diabetes, coronary heart disease, and stroke). Incident cardiometabolic multimorbidity was ascertained via resurvey or linkage to electronic medical records (including hospital admissions and death). We analysed data from each cohort separately using logistic regression and then pooled cohort-specific estimates using random-effects meta-analysis.


          Participants were 120  813 adults (mean age 51·4 years, range 35–103; 71 445 women) who did not have diabetes, coronary heart disease, or stroke at study baseline (1973–2012). During a mean follow-up of 10·7 years (1995–2014), we identified 1627 cases of multimorbidity. After adjustment for sociodemographic and lifestyle factors, compared with individuals with a healthy weight, the risk of developing cardiometabolic multimorbidity in overweight individuals was twice as high (odds ratio [OR] 2·0, 95% CI 1·7–2·4; p<0·0001), almost five times higher for individuals with class I obesity (4·5, 3·5–5·8; p<0·0001), and almost 15 times higher for individuals with classes II and III obesity combined (14·5, 10·1–21·0; p<0·0001). This association was noted in men and women, young and old, and white and non-white participants, and was not dependent on the method of exposure assessment or outcome ascertainment. In analyses of different combinations of cardiometabolic conditions, odds ratios associated with classes II and III obesity were 2·2 (95% CI 1·9–2·6) for vascular disease only (coronary heart disease or stroke), 12·0 (8·1–17·9) for vascular disease followed by diabetes, 18·6 (16·6–20·9) for diabetes only, and 29·8 (21·7–40·8) for diabetes followed by vascular disease.


          The risk of cardiometabolic multimorbidity increases as BMI increases; from double in overweight people to more than ten times in severely obese people compared with individuals with a healthy BMI. Our findings highlight the need for clinicians to actively screen for diabetes in overweight and obese patients with vascular disease, and pay increased attention to prevention of vascular disease in obese individuals with diabetes.


          NordForsk, Medical Research Council, Cancer Research UK, Finnish Work Environment Fund, and Academy of Finland.

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          Most cited references 53

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          Although more than 80% of the global burden of cardiovascular disease occurs in low-income and middle-income countries, knowledge of the importance of risk factors is largely derived from developed countries. Therefore, the effect of such factors on risk of coronary heart disease in most regions of the world is unknown. We established a standardised case-control study of acute myocardial infarction in 52 countries, representing every inhabited continent. 15152 cases and 14820 controls were enrolled. The relation of smoking, history of hypertension or diabetes, waist/hip ratio, dietary patterns, physical activity, consumption of alcohol, blood apolipoproteins (Apo), and psychosocial factors to myocardial infarction are reported here. Odds ratios and their 99% CIs for the association of risk factors to myocardial infarction and their population attributable risks (PAR) were calculated. Smoking (odds ratio 2.87 for current vs never, PAR 35.7% for current and former vs never), raised ApoB/ApoA1 ratio (3.25 for top vs lowest quintile, PAR 49.2% for top four quintiles vs lowest quintile), history of hypertension (1.91, PAR 17.9%), diabetes (2.37, PAR 9.9%), abdominal obesity (1.12 for top vs lowest tertile and 1.62 for middle vs lowest tertile, PAR 20.1% for top two tertiles vs lowest tertile), psychosocial factors (2.67, PAR 32.5%), daily consumption of fruits and vegetables (0.70, PAR 13.7% for lack of daily consumption), regular alcohol consumption (0.91, PAR 6.7%), and regular physical activity (0.86, PAR 12.2%), were all significantly related to acute myocardial infarction (p<0.0001 for all risk factors and p=0.03 for alcohol). These associations were noted in men and women, old and young, and in all regions of the world. Collectively, these nine risk factors accounted for 90% of the PAR in men and 94% in women. Abnormal lipids, smoking, hypertension, diabetes, abdominal obesity, psychosocial factors, consumption of fruits, vegetables, and alcohol, and regular physical activity account for most of the risk of myocardial infarction worldwide in both sexes and at all ages in all regions. This finding suggests that approaches to prevention can be based on similar principles worldwide and have the potential to prevent most premature cases of myocardial infarction.
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              Obesity as a medical problem.

               P Kopelman (2000)
              Obesity is now so common within the world's population that it is beginning to replace undernutrition and infectious diseases as the most significant contributor to ill health. In particular, obesity is associated with diabetes mellitus, coronary heart disease, certain forms of cancer, and sleep-breathing disorders. Obesity is defined by a body-mass index (weight divided by square of the height) of 30 kg m(-2) or greater, but this does not take into account the morbidity and mortality associated with more modest degrees of overweight, nor the detrimental effect of intra-abdominal fat. The global epidemic of obesity results from a combination of genetic susceptibility, increased availability of high-energy foods and decreased requirement for physical activity in modern society. Obesity should no longer be regarded simply as a cosmetic problem affecting certain individuals, but an epidemic that threatens global well being.

                Author and article information

                Lancet Public Health
                Lancet Public Health
                The Lancet. Public Health
                Elsevier, Ltd
                19 May 2017
                June 2017
                19 May 2017
                : 2
                : 6
                : e277-e285
                [a ]Department of Epidemiology and Public Health, University College London, London, UK
                [b ]Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
                [c ]Finnish Institute of Occupational Health, Helsinki, Finland
                [d ]School of Social and Community Medicine, University of Bristol, Bristol, UK
                [e ]Centre for Occupational and Environmental Medicine, Stockholm County Council, Sweden
                [f ]Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
                [g ]Stress Research Institute, Stockholm University, Stockholm, Sweden
                [h ]School of Health Sciences, Jönköping University, Jönköping, Sweden
                [i ]Inserm UMS 011, Population-Based Epidemiological Cohorts Unit, Villejuif, France
                [j ]Department of Health Sciences, Mid Sweden University, Sundsvall, Sweden
                [k ]Department of Public Health, University of Helsinki, Helsinki, Finland
                [l ]Department of Psychology, Umeå University, Umeå, Sweden
                [m ]Department of Public Health and Department of Psychology, University of Copenhagen, Copenhagen, Denmark
                [n ]National Research Centre for the Working Environment, Copenhagen, Denmark
                [o ]Inserm U1018, Centre for Research in Epidemiology and Population Health, Villejuif, France
                [p ]Department of Public Health, University of Turku, Turku, Finland
                [q ]Folkhälsan Research Center, Helsinki, Finland
                [r ]University of Skövde, Skövde, Sweden
                [s ]Turku University Hospital, Turku, Finland
                [t ]Department of Medical Sciences, Uppsala University, Uppsala, Sweden
                [u ]National Centre for Sport and Exercise Medicine, Loughborough University, Loughborough, UK
                [v ]MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
                [w ]1st Department of Medicine, Semmelweis University Faculty of Medicine, Budapest, Hungary
                [x ]Institute of Behavioral Sciences, University of Helsinki, Helsinki, Finland
                Author notes
                [* ]Correspondence to: Prof Mika Kivimäki, Department of Epidemiology and Public Health, University College London WC1E 6BT, UKCorrespondence to: Prof Mika KivimäkiDepartment of Epidemiology and Public HealthUniversity CollegeLondonWC1E 6BTUK m.kivimaki@
                © 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

                This is an open access article under the CC BY-NC-ND license (



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