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

Background 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. Methods 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/m²), overweight (25·0–29·9 kg/m²), class I (mild) obesity (30·0–34·9 kg/m²), and class II and III (severe) obesity (≥35·0 kg/m²). We used an inclusive definition of underweight (<20 kg/m²) 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. Findings Participants were 120 813 adults (mean age 51·4 years, range 35–103; 71445 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. Interpretation 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

Long working hours as a risk factor for atrial fibrillation: A multi-cohort study

Aims Studies suggest that people who work long hours are at increased risk of stroke, but the association of long working hours with atrial fibrillation, the most common cardiac arrhythmia and a risk factor for stroke, is unknown. We examined the risk of atrial fibrillation in individuals working long hours (>55 per week) and those working standard 35-40 hours per week. Methods In this prospective multi-cohort study from the Individual-Participant-Data Meta-analysis in and results Working Populations (IPD-Work) Consortium, the study population was 85,494 working men and women (mean age 43.4 years) with no recorded atrial fibrillation. Working hours were assessed at study baseline (1991-2004). Mean follow-up for incident atrial fibrillation was 10 years and cases were defined using data on electrocardiograms, hospital records, drug reimbursement registers, and death certificates. We identified 1061 new cases of atrial fibrillation (10-year cumulative incidence 12.4 per 1000). After adjustment for age, sex and socioeconomic status, individuals working long hours had a 1.4-fold increased risk of atrial fibrillation compared to those working standard hours (hazard ratio=1.42, 95%CI=1.13-1.80, P=0.003). There was no significant heterogeneity between the cohortspecific effect estimates (I2=0%, P=0.66) and the finding remained after excluding participants with coronary heart disease or stroke at baseline or during the follow-up (N=2006, hazard ratio=1.36, 95%CI=1.05-1.76, P=0. 0180). Adjustment for potential confounding factors, such as obesity, risky alcohol use and high blood pressure, had little impact on this association. Conclusion Individuals who worked long hours were more likely to develop atrial fibrillation than those working standard hours

Hazard ratios (95% CI) for the association of cumulative socioeconomic score with type 2 diabetes incidence (<i>n</i> = 6,387; 731 incident diabetes cases).

a<p>The cumulative SES score is entered as a continuous 3-level variable into the models. HR is for the lowest versus highest score.</p>b<p>All risk factors are updated at phases 3, 5, and7 and additionally adjusted for the risk factor at the previous phase.</p>c<p>Additional contribution of CRP and IL-6 to the model adjusted for age, sex, ethnicity, family history of diabetes, prevalent conditions, smoking, physical activity, BMI, and diet.</p><p>ref., reference; Δ, attenuation.</p

Hazard ratios (95% CI) for the association of lifecourse socioeconomic trajectories with type 2 diabetes incidence (<i>n</i> = 6,387; 731 incident diabetes cases).

a<p>All risk factors are updated at phases 3, 5, and 7 and additionally adjusted for the risk factor at the previous phase.</p>b<p>Additional contribution of CRP and IL-6 to the model adjusted for age, sex, ethnicity, family history of diabetes, prevalent conditions, smoking, physical activity, BMI, and diet.</p><p>N/A, not applicable; ref., reference; Δ, attenuation.</p

Odds ratios (95% CI) for the association of indicators of socioeconomic status across the lifecourse with type 2 diabetes risk factors at baseline (Whitehall II phase 3), <i>n</i> = 6,387.

a<p>Cumulative SES score includes father's occupational position, participants' education, and participants' occupational position at phase 3. Each SES measure was a 3-level variable with values ranging from 0 (high) to 2 (low). A score was calculated by summing each SES measure (range 0–6). The final cumulative SES score was categorized as high (score = 0–2), middle (score = 3–5), and low (score = 6).</p>b<p>Lifecourse SES trajectory refers to father's occupational position and participants' occupational position at phase 3.</p>c<p>Model adjusted for age, sex, ethnicity, family history of diabetes, and prevalent conditions.</p>d<p>Model adjusted for age, sex, ethnicity, family history of diabetes, prevalent conditions, smoking, physical activity, diet.</p><p>β, Beta coefficient; ref., reference.</p

Diabetes risk factors, diabetes risk algorithms, and the prediction of future frailty: the Whitehall II prospective cohort study

Objective: To examine whether established diabetes risk factors and diabetes risk algorithms are associated with future frailty. Design: Prospective cohort study. Risk algorithms at baseline (1997e1999) were the Framingham Offspring, Cambridge, and Finnish diabetes risk scores. Setting: Civil service departments in London, United Kingdom. Participants: There were 2707 participants (72% men) aged 45 to 69 years at baseline assessment and free of diabetes. Measurements: Risk factors (age, sex, family history of diabetes, body mass index, waist circumference, systolic and diastolic blood pressure, antihypertensive and corticosteroid treatments, history of high blood glucose, smoking status, physical activity, consumption of fruits and vegetables, fasting glucose, HDL-cholesterol, and triglycerides) were used to construct the risk algorithms. Frailty, assessed during a resurvey in 2007e2009, was denoted by the presence of 3 or more of the following indicators: self reported exhaustion, low physical activity, slow walking speed, low grip strength, and weight loss; “prefrailty” was defined as having 2 or fewer of these indicators. Results: After a mean follow-up of 10.5 years, 2.8% of the sample was classified as frail and 37.5% as pre-frail. Increased age, being female, stopping smoking, low physical activity, and not having a daily consumption of fruits and vegetables were each associated with frailty or prefrailty. The Cambridge and Finnish diabetes risk scores were associated with frailty/prefrailty with odds ratios per 1 SD increase (disadvantage) in score of 1.18 (95% confidence interval: 1.09e1.27) and 1.27 (1.17e1.37), respectively. Conclusion: Selected diabetes risk factors and risk scores are associated with subsequent frailty. Risk scores may have utility for frailty prediction in clinical practice

Hazard ratios (95% CI) for the association of indicators of socioeconomic status in early and adult life with type 2 diabetes incidence (<i>n</i> = 6,387; 731 incident diabetes cases).

a<p>Adjusted for age, sex, ethnicity, family history of diabetes, and prevalent conditions.</p>b<p>All risk factors are updated at phases 3, 5, and 7 and additionally adjusted for the risk factor at the previous phase.</p>c<p>Additional contribution of CRP and IL-6 to the model adjusted for age, sex, ethnicity, family history of diabetes, prevalent conditions, smoking, physical activity, BMI, and diet.</p><p>Δ, attenuation; N/A, not applicable; ref., reference.</p

Study participant characteristics at baseline (Whitehall II phase 3) and type 2 diabetes incidence at a mean 14.3-y follow-up (from phase 3 to phase 9) according to indicators of socioeconomic status in early and adult life (<i>n</i> = 6,387; 731 incident diabetes cases).

a<p><i>p</i> for linear trend across socioeconomic categories.</p>b<p>Prevalent conditions considered are coronary heart disease, stroke, cancer, and hypertension.</p>c<p>Age, sex, and ethnicity adjusted diabetes incidence rate per 1,000 person-year over a 14.3-y mean follow-up.</p><p>CI, 95% CI.</p

Contribution of smoking, physical activity, diet, BMI, CRP, and IL-6 to the association between lifecourse socioeconomic status and type 2 diabetes incidence.

<p>The first bar shows explanatory factors for the associations of low cumulative SES score (ref. high cumulative SES score) (A) and adverse SES-trajectory (ref. high-high SES trajectory) (B) with type 2 diabetes (T2D). Inflammatory markers, in combination, explain 26% (95% CI 16%–46%) of the first association (A) and 34% (95% CI 20%–62%) of the latter association (B). All associations are adjusted for age, sex, ethnicity, family history of T2D, and prevalent conditions. Cumulative SES score includes father's occupational position, participants' education, and participants' occupational position at phase 3. Each SES measure was a 3-level variable with values ranging from 0 (high) to 2 (low). A score was calculated by summing each SES measure (range 0–6). The final cumulative SES score was categorized as high (score = 0–2), middle (score = 3–5), and low (score = 6). Lifecourse SES trajectory refers to father's occupational position and participants' occupational position at phase 3.</p

Simplified conceptual framework for the potential role of inflammatory processes in explaining social inequalities in type 2 diabetes.

<p>Socioeconomic adversity over the lifetime is hypothesized to be associated with type 2 diabetes risk. Part of this association might be mediated by the elevated inflammatory states resulting from altered gene expression and/or unhealthy lifestyles, both related to socioeconomic adversity. Other factors (e.g., low birth weight) may also mediate part of the association between SES and type 2 diabetes (arrow A). Furthermore, SES is hypothesized to contribute to elevated inflammation because of comorbid conditions (arrow B).</p