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

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

Does pattern mixture modelling reduce bias due to informative attrition compared to fitting a mixed effects model to the available cases or data imputed using multiple imputation?: a simulation study.

BACKGROUND: Informative attrition occurs when the reason participants drop out from a study is associated with the study outcome. Analysing data with informative attrition can bias longitudinal study inferences. Approaches exist to reduce bias when analysing longitudinal data with monotone missingness (once participants drop out they do not return). However, findings may differ when using these approaches to analyse longitudinal data with non-monotone missingness. METHODS: Different approaches to reduce bias due to informative attrition in non-monotone longitudinal data were compared. To achieve this aim, we simulated data from a Whitehall II cohort epidemiological study, which used the slope coefficients from a linear mixed effects model to investigate the association between smoking status at baseline and subsequent decline in cognition scores. Participants with lower cognitive scores were thought to be more likely to drop out. By using a simulation study, a range of scenarios using distributions of variables which exist in real data were compared. Informative attrition that would introduce a known bias to the simulated data was specified and the estimates from a mixed effects model with random intercept and slopes when fitted to: available cases; data imputed using multiple imputation (MI); imputed data adjusted using pattern mixture modelling (PMM) were compared. The two-fold fully conditional specification MI approach, previously validated for non-monotone longitudinal data under ignorable missing data assumption, was used. However, MI may not reduce bias because informative attrition is non-ignorable missing. Therefore, PMM was applied to reduce the bias, usually unknown, by adjusting the values imputed with MI by a fixed value equal to the introduced bias. RESULTS: With highly correlated repeated outcome measures, the slope coefficients from a mixed effects model were found to have least bias when fitted to available cases. However, for moderately correlated outcome measurements, the slope coefficients from fitting a mixed effects model to data adjusted using PMM were least biased but still underestimated the true coefficients. CONCLUSIONS: PMM may potentially reduce bias in studies analysing longitudinal data with suspected informative attrition and moderately correlated repeated outcome measurements. Including additional auxiliary variables in the imputation model may also reduce any remaining bias

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

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

Chronic inflammation as a determinant of future aging phenotypes

Background: The importance of chronic inflammation as a determinant of aging phenotypes may have been underestimated in previous studies that used a single measurement of inflammatory markers. We assessed inflammatory markers twice over a 5-year exposure period to examine the association between chronic inflammation and future aging phenotypes in a large population of men and women. Methods: We obtained data for 3044 middle aged adults (28.2% women) who were participating in the Whitehall II study and had no history of stroke, myocardial infarction or cancer at our study’s baseline (1997–1999). Interleukin-6 was measured at baseline and 5 years earlier. Cause-specific mortality, chronic disease and functioning were ascertained from hospital data, register linkage and clinical examinations. We used these data to create 4 aging phenotypes at the 10-year follow-up (2007–2009): successful aging (free of major chronic disease and with optimal physical, mental and cognitive functioning), incident fatal or nonfatal cardiovascular disease, death from noncardiovascular causes and normal aging (all other participants). Results: Of the 3044 participants, 721 (23.7%) met the criteria for successful aging at the 10-year follow-up, 321 (10.6%) had cardiovascular disease events, 147 (4.8%) died from noncardiovascular causes, and the remaining 1855 (60.9%) were included in the normal aging phenotype. After adjustment for potential confounders, having a high interleukin-6 level (> 2.0 ng/L) twice over the 5-year exposure period nearly halved the odds of successful aging at the 10-year follow-up (odds ratio [OR] 0.53, 95% confidence interval [CI] 0.38–0.74) and increased the risk of future cardiovascular events (OR 1.64, 95% CI 1.15–2.33) and noncardiovascular death (OR 2.43, 95% CI 1.58–3.80). Interpretation: Chronic inflammation, as ascertained by repeat measurements, was associated with a range of unhealthy aging phenotypes and a decreased likelihood of successful aging. Our results suggest that assessing long term chronic inflammation by repeat measurement of interleukin-6 has the potential to guide clinical practice