Adherence to healthy dietary guidelines and future depressive symptoms: evidence for sex differentials in the Whitehall II study.

It has been suggested that dietary patterns are associated with future risk of depressive symptoms. However, there is a paucity of prospective data that have examined the temporality of this relation

Underweight as a risk factor for respiratory death in the Whitehall cohort study: exploring reverse causality using a 45-year follow-up

Underweight adults have higher rates of respiratory death than the normal weight but it is unclear whether this association is causal or reflects illness-induced weight loss (reverse causality). Evidence from a 45-year follow-up of underweight participants for respiratory mortality in the Whitehall study (N=18 823; 2139 respiratory deaths) suggests that excess risk among the underweight is attributable to reverse causality. The age-adjusted and smoking-adjusted risk was 1.55-fold (95% CI 1.32 to 1.83) higher among underweight compared with normal weight participants, but attenuated in a stepwise manner to 1.14 (95% CI 0.76 to 1.71) after serial exclusions of deaths during the first 5-35 years of follow-up (Ptrend<0.001)

Validity of Cardiovascular Disease Event Ascertainment Using Linkage to UK Hospital Records

BACKGROUND: Use of electronic health records for ascertainment of disease outcomes in large population-based studies holds much promise due to low costs, diminished study participant burden, and reduced selection bias. However, the validity of cardiovascular disease endpoints derived from electronic records is unclear. METHODS: Participants were 7860 study members of the UK Whitehall II cohort study. We compared cardiovascular disease ascertainment using linkage to the National Health Service's Hospital Episode Statistics database records (hereafter, 'HES-ascertainment') against repeated biomedical examinations - our gold-standard ascertainment method ('Whitehall-ascertainment'). Follow-up for both methods was from 1997 to 2013 for coronary heart disease and from 1997 to 2009 for stroke. RESULTS: We identified 950 prevalent or incident non-fatal coronary heart disease cases and 118 prevalent or incident non-fatal stroke cases using Whitehall-ascertainment. The corresponding figures for HES ascertainment were 926 and 107. For coronary heart disease, the sensitivity of HES-ascertainment was 70%, positive predictive value 72%, specificity 96%, and the negative predictive value 96%. The pattern of results for stroke was similar. These statistics did not differ in analyses stratified by age, sex, baseline risk factor status, or after exclusion of prevalent cases. Estimates of risk factor-disease associations were similar between the two ascertainment methods. Including fatal cardiovascular disease in the outcomes improved the agreement between the methods. CONCLUSION: Our analyses support the validity of cardiovascular disease ascertainment using linkage to the UK Hospital Episode Statistics database records by showing agreement with high resolution disease data collected in the Whitehall II cohort.This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Association of change in cognitive function from early adulthood to middle age with risk of cause-specific mortality: the Vietnam Experience Study

BACKGROUND: Studies with single baseline measurements of cognitive function consistently reveal inverse relationships with mortality risk. The relation of change in functioning, particularly from early in the life course, which may offer additional insights into causality, has not, to the best of our knowledge, been tested. AIMS: To examine the association of change in cognition between late adolescence and middle age with cause-specific mortality using data from a prospective cohort study. METHODS: The analytical sample consisted of 4289 former US male military personnel who were administered the Army General Technical Test in early adulthood (mean age 20.4 years) and again in middle age (mean age 38.3 years). RESULTS: A 15-year period of mortality surveillance subsequent to the second phase of cognitive testing gave rise to 237 deaths. Following adjustment for age, a 10-unit increase in cognitive function was related to a reduced risk of death from all causes (HR 0.84; 95% CI 0.75 to 0.93) and cardiovascular disease (HR 0.78; 95% CI 0.64 to 0.95) but not from all cancers (HR 1.14; 95% CI 0.88 to 1.47) nor injury (HR 1.02; 95% CI 0.81 to 1.29). Adjustment for markers of socioeconomic status in middle age resulted in marked attenuation in the magnitude of these associations and statistical significance at conventional levels was lost in all analyses. CONCLUSIONS: In the present study, the apparent link between increased cognition and mortality was mediated by socioeconomic status

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 (1997–1999) 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 2007–2009, 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 prefrail. 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: 1.09–1.27) and 1.27 (1.17–1.37), 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

Validating a widely used measure of frailty: are all sub-components necessary? Evidence from the Whitehall II cohort study

There is growing interest in the measurement of frailty in older age. The most widely used measure (Fried) characterizes this syndrome using five components: exhaustion, physical activity, walking speed, grip strength, and weight loss. These components overlap, raising the possibility of using fewer, and therefore making the device more time- and cost-efficient. The analytic sample was 5,169 individuals (1,419 women) from the British Whitehall II cohort study, aged 55 to 79 years in 2007–2009. Hospitalization data were accessed through English national records (mean follow-up 15.2 months). Age- and sex-adjusted Cox models showed that all components were significantly associated with hospitalization, the hazard ratios (HR) ranging from 1.18 (95 % confidence interval = 0.98, 1.41) for grip strength to 1.60 (1.35, 1.90) for usual walking speed. Some attenuation of these effects was apparent following mutual adjustment for frailty components, but the rank order of the strength of association remained unchanged. We observed a dose–response relationship between the number of frailty components and the risk for hospitalization [1 component—HR = 1.10 (0.96, 1.26); 2—HR = 1.52 (1.26, 1.83); 3–5—HR = 2.41 (1.84, 3.16), P trend <0.0001]. A concordance index used to evaluate the predictive power for hospital admissions of individual components and the full scale was modest in magnitude (range 0.57 to 0.58). Our results support the validity of the multi-component frailty measure, but the predictive performance of the measure is poor

Cardiovascular disease risk scores in identifying future frailty: the Whitehall II prospective cohort study

Objectives: To examine the capacity of existing cardiovascular disease (CVD) risk algorithms widely used in primary care, to predict frailty. / Design: Prospective cohort study. Risk algorithms at baseline (1997–1999) were the Framingham CVD, coronary heart disease and stroke risk scores, and the Systematic Coronary Risk Evaluation. / Setting: Civil Service departments in London, UK. / Participants: 3895 participants (73% men) aged 45–69 years and free of CVD at baseline. / Main outcome measure: Status of frailty at the end of follow-up (2007–2009), based on the following indicators: self-reported exhaustion, low physical activity, slow walking speed, low grip strength and weight loss. / Results: At the end of the follow-up, 2.8% (n=108) of the sample was classified as frail. All four CVD risk scores were associated with future risk of developing frailty, with ORs per one SD increment in the score ranging from 1.35 (95% CI 1.21 to 1.51) for the Framingham stroke score to 1.42 (1.23 to 1.62) for the Framingham CVD score. These associations remained after excluding incident CVD cases. For comparison, the corresponding ORs for the risk scores and incident cardiovascular events varied between 1.36 (1.15 to 1.61) and 1.64 (1.50 to 1.80) depending on the risk algorithm. / Conclusions: The use of CVD risk scores in clinical practice may also have utility for frailty prediction

Does adding information on job strain improve risk prediction for coronary heart disease beyond the standard Framingham risk score? The Whitehall II study

Guidelines for coronary heart disease (CHD) prevention recommend using multifactorial risk prediction algorithms, particularly the Framingham risk score. We sought to examine whether adding information on job strain to the Framingham model improves its predictive power in a low-risk working population

Association between change in cardiovascular risk scores and future cardiovascular disease: analyses of data from the Whitehall II longitudinal, prospective cohort study

BACKGROUND: Evaluation of cardiovascular disease risk in primary care, which is recommended every 5 years in middle-aged and older adults (typical age range 40-75 years), is based on risk scores, such as the European Society of Cardiology Systematic Coronary Risk Evaluation (SCORE) and American College of Cardiology/American Heart Association Atherosclerotic Cardiovascular Disease (ASCVD) algorithms. This evaluation currently uses only the most recent risk factor assessment. We aimed to examine whether 5-year changes in SCORE and ASCVD risk scores are associated with future cardiovascular disease risk. METHODS: We analysed data from the Whitehall II longitudinal, prospective cohort study for individuals with no history of stroke, myocardial infarction, coronary artery bypass graft, percutaneous coronary intervention, definite angina, heart failure, or peripheral artery disease. Participants underwent clinical examinations in 5-year intervals between Aug 7, 1991, and Dec 6, 2016, and were followed up for incident cardiovascular disease until Oct 2, 2019. Levels of, and 5-year changes in, cardiovascular disease risk were assessed using the SCORE and ASCVD risk scores and were analysed as predictors of cardiovascular disease. Harrell's C index, continuous net reclassification improvement, the Akaike information criterion, and calibration analysis were used to assess whether incorporating change in risk scores into a model including only a single risk score assessment improved the predictive performance. We assessed the levels of, and 5-year changes in, SCORE and ASCVD risk scores as predictors of cardiovascular disease and disease-free life-years using Cox proportional hazards and flexible parametric survival models. FINDINGS: 7574 participants (5233 [69·1%] men, 2341 [30·9%] women) aged 40-75 years were included in analyses of risk score change between April 24, 1997, and Oct 2, 2019. During a mean follow-up of 18·7 years (SD 5·5), 1441 (19·0%; 1042 [72·3%] men and 399 [27·7%] women) participants developed cardiovascular disease. Adding 5-year change in risk score to a model that included only a single risk score assessment improved model performance according to Harrell's C index (from 0·685 to 0·690, change 0·004 [95% CI 0·000 to 0·008] for SCORE; from 0·699 to 0·700, change 0·001 [0·000 to 0·003] for ASCVD), the Akaike information criterion (from 17 255 to 17 200, change -57 [95% CI -97 to -13] for SCORE; from 14 739 to 14 729, change -10 [-28 to 7] for ASCVD), and the continuous net reclassification index (0·353 [95% CI 0·234 to 0·447] for SCORE; 0·232 [0·030 to 0·344] for ASCVD). Both favourable and unfavourable changes in SCORE and ASCVD were associated with cardiovascular disease risk and disease-free life-years. The associations were seen in both sexes and all age groups up to the age of 75 years. At the age of 45 years, each 2-unit improvement in risk scores was associated with an additional 1·3 life-years (95% CI 0·4 to 2·2) free of cardiovascular disease for SCORE and an additional 0·9 life-years (95% CI 0·5 to 1·3) for ASCVD. At age 65 years, this same improvement was associated with an additional 0·4 life-years (95% CI 0·0 to 0·7) free of cardiovascular disease for SCORE and 0·3 life-years (95% CI 0·1 to 0·5) for ASCVD. These models were developed into an interactive calculator, which enables estimation of the number of cardiovascular disease-free life-years for an individual as a function of two risk score measurements. INTERPRETATION: Changes in the SCORE and ASCVD risk scores over time inform cardiovascular disease risk prediction beyond a single risk score assessment. Repeat data might allow more accurate cardiovascular risk stratification and strengthen the evidence base for decisions on preventive interventions. FUNDING: UK Medical Research Council, British Heart Foundation, Wellcome Trust, and US National Institute on Aging