The effect of socioeconomic deprivation on the association between an extended measurement of unhealthy lifestyle factors and health outcomes: a prospective analysis of the UK Biobank cohort

Background: Combinations of lifestyle factors interact to increase mortality. Combinations of traditional factors such as smoking and alcohol are well described, but the additional effects of emerging factors such as television viewing time are not. The effect of socioeconomic deprivation on these extended lifestyle risks also remains unclear. We aimed to examine whether deprivation modifies the association between an extended score of lifestyle-related risk factors and health outcomes. Methods: Data for this prospective analysis were sourced from the UK Biobank, a prospective population-based cohort study. We assigned all participants an extended lifestyle score, with 1 point for each unhealthy lifestyle factor (incorporating sleep duration and high television viewing time, in addition to smoking, excessive alcohol, poor diet [low intake of oily fish or fruits and vegetables, and high intake of red meat or processed meats], and low physical activity), categorised as most healthy (score 0–2), moderately healthy (score 3–5), or least healthy (score 6–9). Cox proportional hazards models were used to examine the association between lifestyle score and health outcomes (all-cause mortality and cardiovascular disease mortality and incidence), and whether this association was modified by deprivation. All analyses were landmark analyses, in which participants were excluded if they had an event (death or cardiovascular disease event) within 2 years of recruitment. Participants with non-communicable diseases (except hypertension) and missing covariate data were excluded from analyses. Participants were also excluded if they reported implausible values for physical activity, sleep duration, and total screen time. All analyses were adjusted for age, sex, ethnicity, month of assessment, history of hypertension, systolic blood pressure, medication for hypercholesterolaemia or hypertension, and body-mass index categories. Findings: 328 594 participants aged 40–69 years were included in the study, with a mean follow-up period of 4·9 years (SD 0·83) after the landmark period for all-cause and cardiovascular disease mortality, and 4·1 years (0·81) for cardiovascular disease incidence. In the least deprived quintile, the adjusted hazard ratio (HR) in the least healthy lifestyle category, compared with the most healthy category, was 1·65 (95% CI 1·25–2·19) for all-cause mortality, 1·93 (1·16–3·20) for cardiovascular disease mortality, and 1·29 (1·10–1·52) for cardiovascular disease incidence. Equivalent HRs in the most deprived quintile were 2·47 (95% CI 2·04–3·00), 3·36 (2·36–4·76), and 1·41 (1·25–1·60), respectively. The HR for trend for one increment change towards least healthy in the least deprived quintile compared with that in the most deprived quintile was 1·25 (95% CI 1·12–1·39) versus 1·55 (1·40–1·70) for all-cause mortality, 1·30 (1·05–1·61) versus 1·83 (1·54–2·18) for cardiovascular disease mortality, and 1·10 (1·04–1·17) versus 1·16 (1·09–1·23) for cardiovascular disease incidence. A significant interaction was found between lifestyle and deprivation for all-cause and cardiovascular disease mortality (both pinteraction<0·0001), but not for cardiovascular disease incidence (pinteraction=0·11). Interpretation: Wide combinations of lifestyle factors are associated with disproportionate harm in deprived populations. Social and fiscal policies that reduce poverty are needed alongside public health and individual-level interventions that address a wider range of lifestyle factors in areas of deprivation

13 C-, 15 N- and 31 P-NMR studies of oxidized and reduced low molecular mass thioredoxin reductase and some mutant proteins

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65853/1/j.1432-1033.2004.04043.x.pd

The influence of socioeconomic status on the association between unhealthy lifestyle factors and adverse health outcomes: a systematic review

Background: Combinations of lifestyle factors (LFs) and socioeconomic status (SES) are independently associated with cardiovascular disease (CVD), cancer, and mortality. Less advantaged SES groups may be disproportionately vulnerable to unhealthy LFs but interactions between LFs and SES remain poorly understood. This review aimed to synthesise the available evidence for whether and how SES modifies associations between combinations of LFs and adverse health outcomes. Methods: Systematic review of studies that examine associations between combinations of >3 LFs and health outcomes and report data on SES influences on associations. Databases (PubMed/EMBASE/CINAHL), references, forward citations, and grey-literature were searched from inception to December 2021. Eligibility criteria were analyses of prospective adult cohorts that examined all-cause mortality or CVD or cancer mortality/incidence. Results: Six studies (n=42,467–399,537; 46.5–56.8 years old; 54.6–59.3% women) of five cohorts were included. All examined all-cause mortality; three assessed CVD/cancer outcomes. Four studies observed multiplicative interactions between LFs and SES, but in opposing directions. Two studies tested for additive interactions; interactions were observed in one cohort (UK Biobank) and not in another (NHANES). All-cause mortality HRs (95% CIs) for unhealthy LFs (versus healthy LFs) from the most advantaged SES groups ranged from 0.68 (0.32–1.45) to 4.17 (2.27–7.69). Equivalent estimates from the least advantaged ranged from 1.30 (1.13–1.50) to 4.00 (2.22–7.14). In 19 analyses (including sensitivity analyses) of joint associations between LFs, SES, and all-cause mortality, highest all-cause mortality was observed in the unhealthiest LF-least advantaged suggesting an additive effect. Conclusions: Limited and heterogenous literature suggests that the influence of SES on associations between combinations of unhealthy LFs and adverse health could be additive but remains unclear. Additional prospective analyses would help clarify whether SES modifies associations between combinations of unhealthy LFs and health outcomes. Registration: Protocol is registered with PROSPERO (CRD42020172588; 25 June 2020)

Multimorbidity, polypharmacy, and COVID-19 infection within the UK Biobank cohort

Background: It is now well recognised that the risk of severe COVID-19 increases with some long-term conditions (LTCs). However, prior research primarily focuses on individual LTCs and there is a lack of data on the influence of multimorbidity (≥2 LTCs) on the risk of COVID-19. Given the high prevalence of multimorbidity, more detailed understanding of the associations with multimorbidity and COVID-19 would improve risk stratification and help protect those most vulnerable to severe COVID-19. Here we examine the relationships between multimorbidity, polypharmacy (a proxy of multimorbidity), and COVID-19; and how these differ by sociodemographic, lifestyle, and physiological prognostic factors. Methods and findings: We studied data from UK Biobank (428,199 participants; aged 37–73; recruited 2006–2010) on self-reported LTCs, medications, sociodemographic, lifestyle, and physiological measures which were linked to COVID-19 test data. Poisson regression models examined risk of COVID-19 by multimorbidity/polypharmacy and effect modification by COVID-19 prognostic factors (age/sex/ethnicity/socioeconomic status/smoking/physical activity/BMI/systolic blood pressure/renal function). 4,498 (1.05%) participants were tested; 1,324 (0.31%) tested positive for COVID-19. Compared with no LTCs, relative risk (RR) of COVID-19 in those with 1 LTC was no higher (RR 1.12 (CI 0.96–1.30)), whereas those with ≥2 LTCs had 48% higher risk; RR 1.48 (1.28–1.71). Compared with no cardiometabolic LTCs, having 1 and ≥2 cardiometabolic LTCs had a higher risk of COVID-19; RR 1.28 (1.12–1.46) and 1.77 (1.46–2.15), respectively. Polypharmacy was associated with a dose response higher risk of COVID-19. All prognostic factors were associated with a higher risk of COVID-19 infection in multimorbidity; being non-white, most socioeconomically deprived, BMI ≥40 kg/m2, and reduced renal function were associated with the highest risk of COVID-19 infection: RR 2.81 (2.09–3.78); 2.79 (2.00–3.90); 2.66 (1.88–3.76); 2.13 (1.46–3.12), respectively. No multiplicative interaction between multimorbidity and prognostic factors was identified. Important limitations include the low proportion of UK Biobank participants with COVID-19 test data (1.05%) and UK Biobank participants being more affluent, healthier and less ethnically diverse than the general population. Conclusions: Increasing multimorbidity, especially cardiometabolic multimorbidity, and polypharmacy are associated with a higher risk of developing COVID-19. Those with multimorbidity and additional factors, such as non-white ethnicity, are at heightened risk of COVID-19

Prospective associations of coronary heart disease loci in African Americans using the MetaboChip

Background: Coronary heart disease (CHD) is a leading cause of morbidity and mortality in African Americans. However, there is a paucity of studies assessing genetic determinants of CHD in African Americans. We examined the association of publishe

The association between a lifestyle score, socioeconomic status, and COVID-19 outcomes within the UK Biobank cohort

Background: Infection with SARS-CoV-2 virus (COVID-19) impacts disadvantaged groups most. Lifestyle factors are also associated with adverse COVID-19 outcomes. To inform COVID-19 policy and interventions, we explored effect modification of socioeconomic-status (SES) on associations between lifestyle and COVID-19 outcomes. Methods: Using data from UK-Biobank, a large prospective cohort of 502,536 participants aged 37–73 years recruited between 2006 and 2010, we assigned participants a lifestyle score comprising nine factors. Poisson regression models with penalised splines were used to analyse associations between lifestyle score, deprivation (Townsend), and COVID-19 mortality and severe COVID-19. Associations between each exposure and outcome were examined independently before participants were dichotomised by deprivation to examine exposures jointly. Models were adjusted for sociodemographic/health factors. Results: Of 343,850 participants (mean age > 60 years) with complete data, 707 (0.21%) died from COVID-19 and 2506 (0.76%) had severe COVID-19. There was evidence of a nonlinear association between lifestyle score and COVID-19 mortality but limited evidence for nonlinearity between lifestyle score and severe COVID-19 and between deprivation and COVID-19 outcomes. Compared with low deprivation, participants in the high deprivation group had higher risk of COVID-19 outcomes across the lifestyle score. There was evidence for an additive interaction between lifestyle score and deprivation. Compared with participants with the healthiest lifestyle score in the low deprivation group, COVID-19 mortality risk ratios (95% CIs) for those with less healthy scores in low versus high deprivation groups were 5.09 (1.39–25.20) and 9.60 (4.70–21.44), respectively. Equivalent figures for severe COVID-19 were 5.17 (2.46–12.01) and 6.02 (4.72–7.71). Alternative SES measures produced similar results. Conclusions: Unhealthy lifestyles are associated with higher risk of adverse COVID-19, but risks are highest in the most disadvantaged, suggesting an additive influence between SES and lifestyle. COVID-19 policy and interventions should consider both lifestyle and SES. The greatest public health benefit from lifestyle focussed COVID-19 policy and interventions is likely to be seen when greatest support for healthy living is provided to the most disadvantaged groups

Semileptonic Meson Decays in the Quark Model: An Update

We present the predictions of ISGW2, an update of the ISGW quark model for semileptonic meson decays. The updated model incorporates a number of features which should make it more reliable, including the constraints imposed by Heavy Quark Symmetry, hyperfine distortions of wavefunctions, and form factors with more realistic high recoil behaviors.Comment: All text and tables contained in the ".latex" file and all figures (14) contained in the ".uu" file