Non-communicable disease, sociodemographic factors, and risk of death from infection: a UK Biobank observational cohort study.
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ABSTRACT:
SUBMITTER: Drozd M
PROVIDER: S-EPMC8323124 | biostudies-literature | 2021 Aug
REPOSITORIES: biostudies-literature
Publications
Non-communicable disease, sociodemographic factors, and risk of death from infection: a UK Biobank observational cohort study.
Drozd Michael M Pujades-Rodriguez Mar M Lillie Patrick J PJ Straw Sam S Morgan Ann W AW Kearney Mark T MT Witte Klaus K KK Cubbon Richard M RM
The Lancet. Infectious diseases 20210301 8
<h4>Background</h4>Non-communicable diseases (NCDs) have been highlighted as important risk factors for COVID-19 mortality. However, insufficient data exist on the wider context of infectious diseases in people with NCDs. We aimed to investigate the association between NCDs and the risk of death from any infection before the COVID-19 pandemic (up to Dec 31, 2019).<h4>Methods</h4>For this observational study, we used data from the UK Biobank observational cohort study to explore factors associate ...[more]
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Project description:BackgroundA greater understanding of the factors that are associated with favourable health may help increase longevity and healthy life expectancy. We examined sociodemographic, psychosocial, lifestyle and environmental exposures associated with multiple health indicators.MethodsUK Biobank recruited > 500,000 participants, aged 37-73, between 2006 and 2010. Health indicators examined were 81 cancer and 443 non-cancer illnesses used to classify participants' health status; long-standing illness; and self-rated health. Exposures were sociodemographic (age, sex, ethnicity, education, income and deprivation), psychosocial (loneliness and social isolation), lifestyle (smoking, alcohol intake, sleep duration, BMI, physical activity and stair climbing) and environmental (air pollution, noise and residential greenspace) factors. Associations were estimated using logistic and ordinal logistic regression.ResultsIn total, 307,378 participants (mean age = 56.1 years [SD = 8.07], 51.9% female) were selected for cross-sectional analyses. Low income, being male, neighbourhood deprivation, loneliness, social isolation, short or long sleep duration, low or high BMI and smoking were associated with poor health. Walking, vigorous-intensity physical activity and more frequent alcohol intake were associated with good health. There was some evidence that airborne pollutants (PM2.5, PM10 and NO2) and noise (Lden) were associated with poor health, though findings were not consistent across all models.ConclusionsOur findings highlight the multifactorial nature of health, the importance of non-medical factors, such as loneliness, healthy lifestyle behaviours and weight management, and the need to examine efforts to improve the health outcomes of individuals on low incomes.
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Project description:BackgroundWomen with peripheral artery disease (PAD) often have atypical symptoms, late hospital presentations, and worse prognosis. Risk factor identification and management are important. We assessed sex differences in associations of risk factors with PAD.Methods500,207 UK Biobank participants (54.5% women, mean age 56.5 years) without prior hospitalisation of PAD at baseline were included. Examined risk factors included blood pressure, smoking, diabetes, lipids, adiposity, history of stroke or myocardial infarction (MI), socioeconomic status, kidney function, C-reactive protein, and alcohol consumption. Poisson and Cox regressions were used to estimate sex-specific incidence of PAD hospitalisation or death, hazard ratios (HRs), and women-to-men ratios of HRs (RHR) with confidence intervals (CIs).ResultsOver a median of 12.6 years, 2658 women and 5002 men had a documented PAD. Age-adjusted incidence rates were higher in men. Most risk factors were associated with a higher risk of PAD in both sexes. Compared with men, women who were smokers or had a history of stroke or MI had a greater excess risk of PAD (relative to those who never smoked or had no history of stroke or MI): RHR 1.18 (95%CI 1.04, 1.34), 1.26 (1.02, 1.55), and 1.50 (1.25, 1.81), respectively. Higher high-density lipoprotein cholesterol (HDL-C) was more strongly associated with a lower risk of PAD in women than men, RHR 0.81 (0.68, 0.96). Compared to HDL-C at 40 to 60 mg/dL, the lowest level of HDL-C (≤40 mg/dL) was related to greater excess risk in women, RHR 1.20 (1.02, 1.41), whereas the highest level of HDL-C (>80 mg/dL) was associated with lower risk of PAD in women, but higher risk in men, RHR 0.50 (0.38, 0.65).ConclusionsWhile the incidence of PAD was higher in men, smoking and a history of stroke or MI were more strongly associated with a higher risk of PAD in women than men. HDL-C was more strongly associated with a lower risk of PAD in women than men.
| S-EPMC10584119 | biostudies-literature
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| S-EPMC7440632 | biostudies-literature
Project description:Simple Summary The study explored pancreatic cancer (PaCa) risk factors in the UK Biobank cohort. The risk factors included non-modifiable risk factors: age, gender, and modifiable risk factors: cigarette smoking, overweight and obesity, increased waist circumstance, abdominal obesity, Diabetic Mellitus (DM), and pancreatitis history. The study findings suggested that stopping cigarette smoking, avoiding overweight or obesity, abdominal obesity, Diabetic Mellitus, and pancreatitis history could contribute to a significant reduction in future PaCa cases if these exposures are avoided. Abstract Evidence on pancreatic cancer (PaCa) risk factors from large population-based cohort studies is limited. This study investigated the PaCa risk factors and the population attributable fraction (PAF) of modifiable risk factors in the UK Biobank cohort. The UK Biobank is a prospective cohort consisting of 502,413 participants with a mean follow-up time of 8.2 years. A binomial generalized linear regression model was used to calculate relative risks for PaCa risk factors. PAF was calculated to estimate the proportional reduction in PaCa if modifiable risk factors were to be eliminated. A total of 728 (0.14%) PaCa incident cases and 412,922 (82.19%) non-PaCa controls were analyzed. The non-modifiable risk factors included age and gender. The modifiable risk factors were cigarette smoking, overweight and obesity, increased waist circumstance, abdominal obesity, Diabetic Mellitus (DM), and pancreatitis history. The PAF suggested that eliminating smoking and obesity can contribute around a 16% reduction in PaCa cases while avoiding abdominal obesity can eliminate PaCa cases by 22%. Preventing pancreatitis and DM could potentially reduce PaCa cases by 1% and 6%, respectively. This study has identified modifiable and non-modifiable PaCa risk factors in the UK population. The PAF of modifiable risk factors can be applied to inform PaCa prevention programs.
| S-EPMC9599736 | biostudies-literature
Project description:Background Therapeutic advances have reduced cardiovascular death rates in people with cardiovascular diseases (CVD). We aimed to define the rates of cardiovascular and noncardiovascular death in people with specified CVDs or accruing cardiovascular multimorbidity. Methods and Results We studied 493 280 UK residents enrolled in the UK Biobank cohort study. The proportion of deaths attributed to cardiovascular, cancer, infection, or other causes were calculated in groups defined by 9 distinct self-reported CVDs at baseline, or by the number of these CVDs at baseline. Poisson regression analyses were then used to define adjusted incidence rate ratios for these causes of death, accounting for sociodemographic factors and comorbidity. Of 27 729 deaths, 20.4% were primarily attributed to CVD, 53.6% to cancer, 5.0% to infection, and 21.0% to other causes. As cardiovascular multimorbidity increased, the proportion of cardiovascular and infection-related deaths was greater, contrasting with cancer and other deaths. Compared with people without CVD, those with 3 or more CVDs experienced adjusted incidence rate ratios of 7.0 (6.2-7.8) for cardiovascular death, 4.4 (3.4-5.6) for infection death, 1.5 (1.4-1.7) for cancer death, and 2.0 (1.7-2.4) for other causes of death. There was substantial heterogeneity in causes of death, both in terms of crude proportions and adjusted incidence rate ratios, among the 9 studied baseline CVDs. Conclusions Noncardiovascular death is common in people with CVD, although its contribution varies widely between people with different CVDs. Holistic and personalized care are likely to be important tools for continuing to improve outcomes in people with CVD.
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Project description:BACKGROUND:Non-communicable diseases (NCDs) are rapidly becoming leading causes of morbidity and mortality in low- and middle-income countries, including those in sub-Saharan Africa. In contrast to high-income countries, the sociodemographic distribution, including socioeconomic inequalities, of NCDs and their risk factors is unclear in sub-Saharan Africa, particularly among rural populations. METHODS:We undertook a cross-sectional population-based survey of 7809 residents aged 13 years or older in the General Population Cohort in south-western rural Uganda. Information on behavioural, physiological and biochemical risk factors was obtained using standardized methods as recommended by the WHO STEPwise Approach to Surveillance. Socioeconomic status (SES) was determined by principal component analysis including household features, ownership, and occupation and education of the head of household. RESULTS:SES was found to be associated with NCD risk factors in this rural population. Smoking, alcohol consumption (men only) and low high-density lipoprotein (HDL) cholesterol were more common among those of lower SES. For example, the prevalence of smoking decreased 4-fold from the lowest to the highest SES groups, from 22.0% to 5.7% for men and 2.2% to 0.4% for women, respectively. In contrast, overweight, raised blood pressure, raised HbA1c (women only) and raised cholesterol were more common among those of higher SES. For example, the prevalence of overweight increased 5-fold from 2.1% to 10.1% for men, and 2-fold from 12.0% to 23.4% for women, from the lowest to highest SES groups respectively. However, neither low physical activity nor fruit, vegetable or staples consumption was associated with SES. Furthermore, associations between NCD risk factors and SES were modified by age and sex. CONCLUSIONS:Within this rural population, NCD risk factors are common and vary both inversely and positively across the SES gradient. A better understanding of the determinants of the sociodemographic distribution of NCDs and their risk factors in rural sub-Saharan African populations will help identify populations at most risk of developing NCDs and help plan interventions to reduce their burden.
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Project description:ObjectivesTo investigate the sociodemographic patterning of non-communicable disease risk factors in rural India.DesignCross sectional study.SettingAbout 1600 villages from 18 states in India. Most were from four large states due to a convenience sampling strategy.Participants1983 (31% women) people aged 20-69 years (49% response rate).Main outcome measuresPrevalence of tobacco use, alcohol use, low fruit and vegetable intake, low physical activity, obesity, central adiposity, hypertension, dyslipidaemia, diabetes, and underweight.ResultsPrevalence of most risk factors increased with age. Tobacco and alcohol use, low intake of fruit and vegetables, and underweight were more common in lower socioeconomic positions; whereas obesity, dyslipidaemia, and diabetes (men only) and hypertension (women only) were more prevalent in higher socioeconomic positions. For example, 37% (95% CI 30% to 44%) of men smoked tobacco in the lowest socioeconomic group compared with 15% (12% to 17%) in the highest, while 35% (30% to 40%) of women in the highest socioeconomic group were obese compared with 13% (7% to 19%) in the lowest. The age standardised prevalence of some risk factors was: tobacco use (40% (37% to 42%) men, 4% (3% to 6%) women); low fruit and vegetable intake (69% (66% to 71%) men, 75% (71% to 78%) women); obesity (19% (17% to 21%) men, 28% (24% to 31%) women); dyslipidaemia (33% (31% to 36%) men, 35% (31% to 38%) women); hypertension (20% (18% to 22%) men, 22% (19% to 25%) women); diabetes (6% (5% to 7%) men, 5% (4% to 7%) women); and underweight (21% (19% to 23%) men, 18% (15% to 21%) women). Risk factors were generally more prevalent in south Indians compared with north Indians. For example, the prevalence of dyslipidaemia was 21% (17% to 33%) in north Indian men compared with 33% (29% to 38%) in south Indian men, while the prevalence of obesity was 13% (9% to 17%) in north Indian women compared with 24% (19% to 30%) in south Indian women.ConclusionsThe prevalence of most risk factors was generally high across a range of sociodemographic groups in this sample of rural villagers in India; in particular, the prevalence of tobacco use in men and obesity in women was striking. However, given the limitations of the study (convenience sampling design and low response rate), cautious interpretation of the results is warranted. These data highlight the need for careful monitoring and control of non-communicable disease risk factors in rural areas of India.
| S-EPMC2946988 | biostudies-literature
Project description:Most studies of severe/fatal COVID-19 risk have used routine/hospitalisation data without detailed pre-morbid characterisation. Using the community-based UK Biobank cohort, we investigate risk factors for COVID-19 mortality in comparison with non-COVID-19 mortality. We investigated demographic, social (education, income, housing, employment), lifestyle (smoking, drinking, body mass index), biological (lipids, cystatin C, vitamin D), medical (comorbidities, medications) and environmental (air pollution) data from UK Biobank (N = 473,550) in relation to 459 COVID-19 and 2626 non-COVID-19 deaths to 21 September 2020. We used univariate, multivariable and penalised regression models. Age (OR = 2.76 [2.18-3.49] per S.D. [8.1 years], p = 2.6 × 10-17), male sex (OR = 1.47 [1.26-1.73], p = 1.3 × 10-6) and Black versus White ethnicity (OR = 1.21 [1.12-1.29], p = 3.0 × 10-7) were independently associated with and jointly explanatory of (area under receiver operating characteristic curve, AUC = 0.79) increased risk of COVID-19 mortality. In multivariable regression, alongside demographic covariates, being a healthcare worker, current smoker, having cardiovascular disease, hypertension, diabetes, autoimmune disease, and oral steroid use at enrolment were independently associated with COVID-19 mortality. Penalised regression models selected income, cardiovascular disease, hypertension, diabetes, cystatin C, and oral steroid use as jointly contributing to COVID-19 mortality risk; Black ethnicity, hypertension and oral steroid use contributed to COVID-19 but not non-COVID-19 mortality. Age, male sex and Black ethnicity, as well as comorbidities and oral steroid use at enrolment were associated with increased risk of COVID-19 death. Our results suggest that previously reported associations of COVID-19 mortality with body mass index, low vitamin D, air pollutants, renin-angiotensin-aldosterone system inhibitors may be explained by the aforementioned factors.
| S-EPMC7882869 | biostudies-literature
Project description:BackgroundMultimorbidity is associated with higher mortality, but the relationship with cancer and cardiovascular mortality is unclear. The influence of demographics and type of condition on the relationship of multimorbidity with mortality remains unknown. We examine the relationship between multimorbidity (number/type) and cause of mortality and the impact of demographic factors on this relationship.MethodsData source: the UK Biobank; 500,769 participants; 37-73 years; 53.7% female. Exposure variables: number and type of long-term conditions (LTCs) (N = 43) at baseline, modelled separately. Cox regression models were used to study the impact of LTCs on all-cause/vascular/cancer mortality during median 7-year follow-up. All-cause mortality regression models were stratified by age/sex/socioeconomic status.ResultsAll-cause mortality is 2.9% (14,348 participants). Of all deaths, 8350 (58.2%) were cancer deaths and 2985 (20.8%) vascular deaths. Dose-response relationship is observed between the increasing number of LTCs and all-cause/cancer/vascular mortality. A strong association is observed between cardiometabolic multimorbidity and all three clinical outcomes; non-cardiometabolic multimorbidity (excluding cancer) is associated with all-cause/vascular mortality. All-cause mortality risk for those with ≥ 4 LTCs was nearly 3 times higher than those with no LTCs (HR 2.79, CI 2.61-2.98); for ≥ 4 cardiometabolic conditions, it was > 3 times higher (HR 3.20, CI 2.56-4.00); and for ≥ 4 non-cardiometabolic conditions (excluding cancer), it was 50% more (HR 1.50, CI 1.36-1.67). For those with ≥ 4 LTCs, morbidity combinations that included cardiometabolic conditions, chronic kidney disease, cancer, epilepsy, chronic obstructive pulmonary disease, depression, osteoporosis and connective tissue disorders had the greatest impact on all-cause mortality. In the stratified model by age/sex, absolute all-cause mortality was higher among the 60-73 age group with an increasing number of LTCs; however, the relative effect size of the increasing number of LTCs on higher mortality risk was larger among those 37-49 years, especially men. While socioeconomic status was a significant predictor of all-cause mortality, mortality risk with increasing number of LTCs remained constant across different socioeconomic gradients.ConclusionsMultimorbidity is associated with higher all-cause/cancer/vascular mortality. Type, as opposed to number, of LTCs may have an important role in understanding the relationship between multimorbidity and mortality. Multimorbidity had a greater relative impact on all-cause mortality in middle-aged as opposed to older populations, particularly males, which deserves exploration.
| S-EPMC6456941 | biostudies-literature
Project description:BackgroundConflicting evidence has emerged regarding the relevance of smoking on risk of COVID-19 and its severity.MethodsWe undertook large-scale observational and Mendelian randomisation (MR) analyses using UK Biobank. Most recent smoking status was determined from primary care records (70.8%) and UK Biobank questionnaire data (29.2%). COVID-19 outcomes were derived from Public Health England SARS-CoV-2 testing data, hospital admissions data, and death certificates (until 18 August 2020). Logistic regression was used to estimate associations between smoking status and confirmed SARS-CoV-2 infection, COVID-19-related hospitalisation, and COVID-19-related death. Inverse variance-weighted MR analyses using established genetic instruments for smoking initiation and smoking heaviness were undertaken (reported per SD increase).ResultsThere were 421 469 eligible participants, 1649 confirmed infections, 968 COVID-19-related hospitalisations and 444 COVID-19-related deaths. Compared with never-smokers, current smokers had higher risks of hospitalisation (OR 1.80, 95% CI 1.26 to 2.29) and mortality (smoking 1-9/day: OR 2.14, 95% CI 0.87 to 5.24; 10-19/day: OR 5.91, 95% CI 3.66 to 9.54; 20+/day: OR 6.11, 95% CI 3.59 to 10.42). In MR analyses of 281 105 White British participants, genetically predicted propensity to initiate smoking was associated with higher risks of infection (OR 1.45, 95% CI 1.10 to 1.91) and hospitalisation (OR 1.60, 95% CI 1.13 to 2.27). Genetically predicted higher number of cigarettes smoked per day was associated with higher risks of all outcomes (infection OR 2.51, 95% CI 1.20 to 5.24; hospitalisation OR 5.08, 95% CI 2.04 to 12.66; and death OR 10.02, 95% CI 2.53 to 39.72).InterpretationCongruent results from two analytical approaches support a causal effect of smoking on risk of severe COVID-19.
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