DNMTs are required for delayed genome instability caused by radiation

This is an open-access article licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License. The article may be redistributed, reproduced, and reused for non-commercial purposes, provided the original source is properly cited - Copyright @ 2012 Landes Bioscience.The ability of ionizing radiation to initiate genomic instability has been harnessed in the clinic where the localized delivery of controlled doses of radiation is used to induce cell death in tumor cells. Though very effective as a therapy, tumor relapse can occur in vivo and its appearance has been attributed to the radio-resistance of cells with stem cell-like features. The molecular mechanisms underlying these phenomena are unclear but there is evidence suggesting an inverse correlation between radiation-induced genomic instability and global hypomethylation. To further investigate the relationship between DNA hypomethylation, radiosensitivity and genomic stability in stem-like cells we have studied mouse embryonic stem cells containing differing levels of DNA methylation due to the presence or absence of DNA methyltransferases. Unexpectedly, we found that global levels of methylation do not determine radiosensitivity. In particular, radiation-induced delayed genomic instability was observed at the Hprt gene locus only in wild-type cells. Furthermore, absence of Dnmt1 resulted in a 10-fold increase in de novo Hprt mutation rate, which was unaltered by radiation. Our data indicate that functional DNMTs are required for radiation-induced genomic instability, and that individual DNMTs play distinct roles in genome stability. We propose that DNMTS may contribute to the acquirement of radio-resistance in stem-like cells.This study is funded by NOTE, BBSRC and the Royal Society Dorothy Hodgkin Research Fellowship

Coronary heart disease and risk factors as predictors of trajectories of psychological distress from midlife to old age.

OBJECTIVE: To examine coronary heart disease (CHD) and its risk factors as predictors of long-term trajectories of psychological distress from midlife to old age. METHODS: In the Whitehall II cohort study, 6890 participants (4814 men, 2076 women; mean age 49.5 years) had up to seven repeat assessments of psychological distress over 21 years (mean follow-up 19 years). CHD and its risk factors (lifestyle-related risk factors, diabetes, hypertension and cholesterol) were assessed at baseline. Group-based trajectory modelling was used to identify clusters of individuals with a similar pattern of psychological distress over time. RESULTS: We identified four trajectories of psychological distress over the follow-up: 'persistently low' (69% of the participants), 'persistently intermediate' (13%), 'intermediate to low' (12%) and 'persistently high' (7%). The corresponding proportions were 60%, 16%, 13% and 11% among participants with CHD; 63%, 15%, 12% and 10% among smokers and 63%, 16%, 12% and 10% among obese participants. In multivariable adjusted multinomial regression analyses comparing other trajectories to persistently low trajectory, prevalent CHD was associated with intermediate to low (OR 1.70, 95% CI 1.08 to 2.68) and persistently high (OR 1.92, 95% CI 1.16 to 3.19) trajectories. Smoking (OR 1.33, 95% CI 1.07 to 1.64; OR 1.55, 95% CI 1.19 to 2.04) and obesity (OR 1.33, 95% CI 1.04 to 1.70; OR 1.47, 95% CI 1.07 to 2.01) were associated with persistently intermediate and persistently high trajectories, respectively. CONCLUSION: CHD, smoking and obesity may have a role in the development of long-lasting psychological distress from midlife to old age

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

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

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)

Psychosocial functioning and intelligence both partly explain socioeconomic inequalities in premature death. A population-based male cohort study

The possible contributions of psychosocial functioning and intelligence differences to socioeconomic status (SES)-related inequalities in premature death were investigated. None of the previous studies focusing on inequalities in mortality has included measures of both psychosocial functioning and intelligence.The study was based on a cohort of 49 321 men born 1949-1951 from the general community in Sweden. Data on psychosocial functioning and intelligence from military conscription at ∼18 years of age were linked with register data on education, occupational class, and income at 35-39 years of age. Psychosocial functioning was rated by psychologists as a summary measure of differences in level of activity, power of initiative, independence, and emotional stability. Intelligence was measured through a multidimensional test. Causes of death between 40 and 57 years of age were followed in registers.The estimated inequalities in all-cause mortality by education and occupational class were attenuated with 32% (95% confidence interval: 20-45%) and 41% (29-52%) after adjustments for individual psychological differences; both psychosocial functioning and intelligence contributed to account for the inequalities. The inequalities in cardiovascular and injury mortality were attenuated by as much as 51% (24-76%) and 52% (35-68%) after the same adjustments, and the inequalities in alcohol-related mortality were attenuated by up to 33% (8-59%). Less of the inequalities were accounted for when those were measured by level of income, with which intelligence had a weaker correlation. The small SES-related inequalities in cancer mortality were not attenuated by adjustment for intelligence.Differences in psychosocial functioning and intelligence might both contribute to the explanation of observed SES-related inequalities in premature death, but the magnitude of their contributions likely varies with measure of socioeconomic status and cause of death. Both psychosocial functioning and intelligence should be considered in future studies

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