scholarly journals Walking pace improves all-cause and cardiovascular mortality risk prediction: A UK Biobank prognostic study

2019 ◽  
Vol 27 (10) ◽  
pp. 1036-1044 ◽  
Author(s):  
Stavroula Argyridou ◽  
Francesco Zaccardi ◽  
Melanie J Davies ◽  
Kamlesh Khunti ◽  
Thomas Yates
Keyword(s):  
Risk Factors ◽  
Risk Prediction ◽  
Cardiovascular Mortality ◽  
Smoking Status ◽  
Density Lipoprotein ◽  
Uk Biobank ◽  
All Cause Mortality ◽  
Lifestyle Measures ◽  
European Society Of Cardiology ◽  
The Uk

Aims The purpose of this study was to quantify and rank the prognostic relevance of dietary, physical activity and physical function factors in predicting all-cause and cardiovascular mortality in comparison with the established risk factors included in the European Society of Cardiology Systematic COronary Risk Evaluation (SCORE). Methods We examined the predictive discrimination of lifestyle measures using C-index and R2 in sex-stratified analyses adjusted for: model 1, age; model 2, SCORE variables (age, smoking status, systolic blood pressure, total and high-density lipoprotein cholesterol). Results The sample comprised 298,829 adults (median age, 57 years; 53.5% women) from the UK Biobank free from cancer and cardiovascular disease at baseline. Over a median follow-up of 6.9 years, there were 2174 and 3522 all–cause and 286 and 796 cardiovascular deaths in women and men, respectively. When added to model 1, self-reported walking pace improved C-index in women and men by 0.013 (99% CI: 0.007–0.020) and 0.022 (0.017–0.028) respectively for all-cause mortality; and by 0.023 (0.005–0.042) and 0.034 (0.020–0.048) respectively for cardiovascular mortality. When added to model 2, corresponding values for women and men were: 0.008 (0.003–0.012) and 0.013 (0.009–0.017) for all-cause mortality; and 0.012 (–0.001–0.025) and 0.024 (0.013–0.035) for cardiovascular mortality. Other lifestyle factors did not consistently improve discrimination across models and outcomes. The pattern of results for R2 mirrored those for C-index. Conclusion A simple self-reported measure of walking pace was the only lifestyle variable found to improve risk prediction for all-cause and cardiovascular mortality when added to established risk factors.

scholarly journals Baseline cardiometabolic profiles and SARS-CoV-2 infection in the UK Biobank

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0248602 ◽  
Author(s):  
Ryan J. Scalsky ◽  
Yi-Ju Chen ◽  
Karan Desai ◽  
Jeffery R. O’Connell ◽  
James A. Perry ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type Ii Diabetes ◽  
Cardiometabolic Risk ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Lipoprotein Cholesterol ◽  
Type Ii ◽  
Uk Biobank ◽  
The Uk ◽  
The Impact

Background SARS-CoV-2 is a rapidly spreading coronavirus responsible for the Covid-19 pandemic, which is characterized by severe respiratory infection. Many factors have been identified as risk factors for SARS-CoV-2, with much early attention being paid to body mass index (BMI), which is a well-known cardiometabolic risk factor. Objective This study seeks to examine the impact of additional baseline cardiometabolic risk factors including high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol (LDL-C), Apolipoprotein A-I (ApoA-I), Apolipoprotein B (ApoB), triglycerides, hemoglobin A1c (HbA1c) and diabetes on the odds of testing positive for SARS-CoV-2 in UK Biobank (UKB) study participants. Methods We examined the effect of BMI, lipid profiles, diabetes and alcohol intake on the odds of testing positive for SARS-Cov-2 among 9,005 UKB participants tested for SARS-CoV-2 from March 16 through July 14, 2020. Odds ratios and 95% confidence intervals were computed using logistic regression adjusted for age, sex and ancestry. Results Higher BMI, Type II diabetes and HbA1c were associated with increased SARS-CoV-2 odds (p < 0.05) while HDL-C and ApoA-I were associated with decreased odds (p < 0.001). Though the effect of BMI, Type II diabetes and HbA1c were eliminated when HDL-C was controlled, the effect of HDL-C remained significant when BMI was controlled for. LDL-C, ApoB and triglyceride levels were not found to be significantly associated with increased odds. Conclusion Elevated HDL-C and ApoA-I levels were associated with reduced odds of testing positive for SARS-CoV-2, while higher BMI, type II diabetes and HbA1c were associated with increased odds. The effects of BMI, type II diabetes and HbA1c levels were no longer significant after controlling for HDL-C, suggesting that these effects may be mediated in part through regulation of HDL-C levels. In summary, our study suggests that baseline HDL-C level may be useful for stratifying SARS-CoV-2 infection risk and corroborates the emerging picture that HDL-C may confer protection against sepsis in general and SARS-CoV-2 in particular.

P3823Body mass index or waist and hip circumference as predictors of outcome in the UK biobank

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
P Pellicori ◽  
B Stanley ◽  
S Iliodromiti ◽  
C A Celis-Morales ◽  
D M Lyall ◽  
...  
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Heart Failure ◽  
Body Fat ◽  
Uk Biobank ◽  
Anthropometric Indices ◽  
All Cause Mortality ◽  
History Of ◽  
Cv Disease ◽  
The Uk

Abstract Background Controversies exist about the relationship between body habitus and mortality, especially for patients with cardiovascular disease. Purpose We evaluated the relations between different anthropometric indices and mortality amongst participants with and without cardiovascular (CV) risk factors, or established CV disease (stroke, myocardial infarction and/or heart failure), enrolled in the UK Biobank. Methods The UK Biobank is a large prospective study which, between 2006 and 2010, enrolled 502,620 participants aged 38–73 years. Participants filled questionnaires and had a medical history recorded, physical measurements done and biological samples taken. The UK Biobank is routinely linked to national death registries and updated on a quarterly basis. Data on death were coded according to the International Classification of Diseases, 10th Revision (ICD-10). The primary end-point was all-cause mortality (ACM) across three subgroups of men and women: those with, or without, one or more CV risk factors (smoking, diabetes and/or hypertension), and those with CV disease (history of stroke, myocardial infarction and/or heart failure) at recruitment. Presence, or absence, of CV risk factors and diagnoses of CV disease were self-reported by participants at enrolment. Associations between anthropometric indices (body mass index (BMI), waist circumference (WC), waist to hip ratio (WHiR), and waist to height ratio (WHeR)) and the risk of all-cause mortality were analysed using Cox regression models. Results After excluding those with history of cancer at baseline (n=45,222), 453,046 participants were included (median age: 58 (interquartile range: 50 - 63) years; 53% women), of whom 150,732 had at least one CV risk factor, and 17,884 established CV disease. During a median follow-up of 5 years, 6,319 participants died. Baseline BMI had a U-shaped relationship with ACM, with higher nadir-values for those with CV risk factors or CV disease, for both sexes (figure). WC, WHiR and WHeR (measures of central distribution of body fat) had more linear associations with ACM, regardless of CV risk factors, CV disease and sex. Conclusions For adults with or without CV risk factors or established CV disease, measures of central distribution of body fat are more strongly and more linearly associated with ACM than BMI. WC, or WHiR, rather than BMI, appear to be more appropriate variables for risk stratification.

scholarly journals Consumption of coffee and tea and risk of developing stroke, dementia, and poststroke dementia: A cohort study in the UK Biobank

PLoS Medicine ◽  
2021 ◽  
Vol 18 (11) ◽  
pp. e1003830
Author(s):  
Yuan Zhang ◽  
Hongxi Yang ◽  
Shu Li ◽  
Wei-dong Li ◽  
Yaogang Wang
Keyword(s):  
Cohort Study ◽  
Lower Risk ◽  
Smoking Status ◽  
Density Lipoprotein ◽  
Tea Consumption ◽  
Uk Biobank ◽  
Daily Consumption ◽  
History Of ◽  
Poststroke Dementia ◽  
The Uk

Background Previous studies have revealed the involvement of coffee and tea in the development of stroke and dementia. However, little is known about the association between the combination of coffee and tea and the risk of stroke, dementia, and poststroke dementia. Therefore, we aimed to investigate the associations of coffee and tea separately and in combination with the risk of developing stroke and dementia. Methods and findings This prospective cohort study included 365,682 participants (50 to 74 years old) from the UK Biobank. Participants joined the study from 2006 to 2010 and were followed up until 2020. We used Cox proportional hazards models to estimate the associations between coffee/tea consumption and incident stroke and dementia, adjusting for sex, age, ethnicity, qualification, income, body mass index (BMI), physical activity, alcohol status, smoking status, diet pattern, consumption of sugar-sweetened beverages, high-density lipoprotein (HDL), low-density lipoprotein (LDL), history of cancer, history of diabetes, history of cardiovascular arterial disease (CAD), and hypertension. Coffee and tea consumption was assessed at baseline. During a median follow-up of 11.4 years for new onset disease, 5,079 participants developed dementia, and 10,053 participants developed stroke. The associations of coffee and tea with stroke and dementia were nonlinear (P for nonlinear <0.01), and coffee intake of 2 to 3 cups/d or tea intake of 3 to 5 cups/d or their combination intake of 4 to 6 cups/d were linked with the lowest hazard ratio (HR) of incident stroke and dementia. Compared with those who did not drink tea and coffee, drinking 2 to 3 cups of coffee and 2 to 3 cups of tea per day was associated with a 32% (HR 0.68, 95% CI, 0.59 to 0.79; P < 0.001) lower risk of stroke and a 28% (HR, 0.72, 95% CI, 0.59 to 0.89; P = 0.002) lower risk of dementia. Moreover, the combination of coffee and tea consumption was associated with lower risk of ischemic stroke and vascular dementia. Additionally, the combination of tea and coffee was associated with a lower risk of poststroke dementia, with the lowest risk of incident poststroke dementia at a daily consumption level of 3 to 6 cups of coffee and tea (HR, 0.52, 95% CI, 0.32 to 0.83; P = 0.007). The main limitations were that coffee and tea intake was self-reported at baseline and may not reflect long-term consumption patterns, unmeasured confounders in observational studies may result in biased effect estimates, and UK Biobank participants are not representative of the whole United Kingdom population. Conclusions We found that drinking coffee and tea separately or in combination were associated with lower risk of stroke and dementia. Intake of coffee alone or in combination with tea was associated with lower risk of poststroke dementia.

scholarly journals Quantifying the Predictive Performance of Objectively Measured Physical Activity on Mortality in the UK Biobank

2020 ◽  
Author(s):  
Andrew Leroux ◽  
Shiyao Xu ◽  
Prosenjit Kundu ◽  
John Muschelli ◽  
Ekaterina Smirnova ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Cox Regression ◽  
Predictive Performance ◽  
Prediction Performance ◽  
Objective Measures ◽  
Uk Biobank ◽  
All Cause Mortality ◽  
Traditional Risk Factors ◽  
The Uk

Abstract Background Objective measures of physical activity (PA) derived from wrist-worn accelerometers are compared with traditional risk factors in terms of mortality prediction performance in the UK Biobank. Method A subset of participants in the UK Biobank study wore a tri-axial wrist-worn accelerometer in a free-living environment for up to 7 days. A total of 82 304 individuals over the age of 50 (439 707 person-years of follow-up, 1959 deaths) had both accelerometry data that met specified quality criteria and complete data on a set of traditional mortality risk factors. Predictive performance was assessed using cross-validated Concordance (C) for Cox regression models. Forward selection was used to obtain a set of best predictors of mortality. Results In univariate Cox regression, age was the best predictor of all-cause mortality (C = 0.681) followed by 12 PA predictors, led by minutes of moderate-to-vigorous PA (C = 0.661) and total acceleration (C = 0.661). Overall, 16 of the top 20 predictors were objective PA measures (C = 0.578–0.661). Using a threshold of 0.001 improvement in Concordance, the Concordance for the best model that did not include PA measures was 0.735 (9 covariates) compared with 0.748 (12 covariates) for the best model with PA variables (p-value &lt; .001). Conclusions Objective measures of PA derived from accelerometry outperform traditional predictors of all-cause mortality in the UK Biobank except age and substantially improve the prediction performance of mortality models based on traditional risk factors. Results confirm and complement previous findings in the National Health and Nutrition Examination Survey (NHANES).

scholarly journals 699 Sleep Health Traits and COVID-19: Mortality Risk from the UK Biobank

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A273-A273
Author(s):  
Xi Zheng ◽  
Ma Cherrysse Ulsa ◽  
Peng Li ◽  
Lei Gao ◽  
Kun Hu
Keyword(s):  
Risk Factors ◽  
Sleep Apnea ◽  
Sleep Duration ◽  
Sex Education ◽  
Mortality Risk ◽  
Self Report ◽  
Severe Illness ◽  
Uk Biobank ◽  
Sleep Health ◽  
The Uk

Abstract Introduction While there is emerging evidence for acute sleep disruption in the aftermath of coronavirus disease 2019 (COVID-19), it is unknown whether sleep traits contribute to mortality risk. In this study, we tested whether earlier-life sleep duration, chronotype, insomnia, napping or sleep apnea were associated with increased 30-day COVID-19 mortality. Methods We included 34,711 participants from the UK Biobank, who presented for COVID-19 testing between March and October 2020 (mean age at diagnosis: 69.4±8.3; range 50.2–84.6). Self-reported sleep duration (less than 6h/6-9h/more than 9h), chronotype (“morning”/”intermediate”/”evening”), daytime dozing (often/rarely), insomnia (often/rarely), napping (often/rarely) and presence of sleep apnea (ICD-10 or self-report) were obtained between 2006 and 2010. Multivariate logistic regression models were used to adjust for age, sex, education, socioeconomic status, and relevant risk factors (BMI, hypertension, diabetes, respiratory diseases, smoking, and alcohol). Results The mean time between sleep measures and COVID-19 testing was 11.6±0.9 years. Overall, 5,066 (14.6%) were positive. In those who were positive, 355 (7.0%) died within 30 days (median = 8) after diagnosis. Long sleepers (&gt;9h vs. 6-9h) [20/103 (19.4%) vs. 300/4,573 (6.6%); OR 2.09, 95% 1.19–3.64, p=0.009), often daytime dozers (OR 1.68, 95% 1.04–2.72, p=0.03), and nappers (OR 1.52, 95% 1.04–2.23, p=0.03) were at greater odds of mortality. Prior diagnosis of sleep apnea also saw a two-fold increased odds (OR 2.07, 95% CI: 1.25–3.44 p=0.005). No associations were seen for short sleepers, chronotype or insomnia with COVID-19 mortality. Conclusion Data across all current waves of infection show that prior sleep traits/disturbances, in particular long sleep duration, daytime dozing, napping and sleep apnea, are associated with increased 30-day mortality after COVID-19, independent of health-related risk factors. While sleep health traits may reflect unmeasured poor health, further work is warranted to examine the exact underlying mechanisms, and to test whether sleep health optimization offers resilience to severe illness from COVID-19. Support (if any) NIH [T32GM007592 and R03AG067985 to L.G. RF1AG059867, RF1AG064312, to K.H.], the BrightFocus Foundation A2020886S to P.L. and the Foundation of Anesthesia Education and Research MRTG-02-15-2020 to L.G.

Advanced cardiometabolic & inflammatory markers for prediction of cardiovascular disease and cancer

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
D Radenkovic ◽  
S.C Chawla ◽  
G Botta ◽  
A Boli ◽  
M.B Banach ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Risk Score ◽  
Cancer Incidence ◽  
Risk Function ◽  
Patient Characteristics ◽  
Risk Scores ◽  
Uk Biobank ◽  
Training Set ◽  
All Cause Mortality ◽  
The Uk

Abstract   The two leading causes of mortality worldwide are cardiovascular disease (CVD) and cancer. The annual total cost of CVD and cancer is an estimated $844.4 billion in the US and is projected to double by 2030. Thus, there has been an increased shift to preventive medicine to improve health outcomes and development of risk scores, which allow early identification of individuals at risk to target personalised interventions and prevent disease. Our aim was to define a Risk Score R(x) which, given the baseline characteristics of a given individual, outputs the relative risk for composite CVD, cancer incidence and all-cause mortality. A non-linear model was used to calculate risk scores based on the participants of the UK Biobank (= 502548). The model used parameters including patient characteristics (age, sex, ethnicity), baseline conditions, lifestyle factors of diet and physical activity, blood pressure, metabolic markers and advanced lipid variables, including ApoA and ApoB and lipoprotein(a), as input. The risk score was defined by normalising the risk function by a fixed value, the average risk of the training set. To fit the non-linear model &gt;400,000 participants were used as training set and &gt;45,000 participants were used as test set for validation. The exponent of risk function was represented as a multilayer neural network. This allowed capturing interdependent behaviour of covariates, training a single model for all outcomes, and preserving heterogeneity of the groups, which is in contrast to CoxPH models which are traditionally used in risk scores and require homogeneous groups. The model was trained over 60 epochs and predictive performance was determined by the C-index with standard errors and confidence intervals estimated with bootstrap sampling. By inputing the variables described, one can obtain personalised hazard ratios for 3 major outcomes of CVD, cancer and all-cause mortality. Therefore, an individual with a risk Score of e.g. 1.5, at any time he/she has 50% more chances than average of experiencing the corresponding event. The proposed model showed the following discrimination, for risk of CVD (C-index = 0.8006), cancer incidence (C-index = 0.6907), and all-cause mortality (C-index = 0.7770) on the validation set. The CVD model is particularly strong (C-index &gt;0.8) and is an improvement on a previous CVD risk prediction model also based on classical risk factors with total cholesterol and HDL-c on the UK Biobank data (C-index = 0.7444) published last year (Welsh et al. 2019). Unlike classically-used CoxPH models, our model considers correlation of variables as shown by the table of the values of correlation in Figure 1. This is an accurate model that is based on the most comprehensive set of patient characteristics and biomarkers, allowing clinicians to identify multiple targets for improvement and practice active preventive cardiology in the era of precision medicine. Figure 1. Correlation of variables in the R(x) Funding Acknowledgement Type of funding source: None

scholarly journals Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 991
Author(s):  
Erik Widen ◽  
Timothy G. Raben ◽  
Louis Lello ◽  
Stephen D. H. Hsu
Keyword(s):  
Disease Risk ◽  
Smoking Status ◽  
Glycated Haemoglobin ◽  
European Ancestry ◽  
Risk Scores ◽  
Common Disease ◽  
Atherosclerotic Cardiovascular Disease ◽  
Uk Biobank ◽  
Lipoprotein A ◽  
The Uk

We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information); we call these predictors biomarker risk scores, BMRS. Individuals who are at high risk (e.g., odds ratio of >5× population average) can be identified for conditions such as coronary artery disease (AUC∼0.75), diabetes (AUC∼0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ∼10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: PRS) for common diseases to the risk predictors which result from the concatenation of learned functions BMRS and PGS, i.e., applying the BMRS predictors to the PGS output.

scholarly journals Sex differences in the association between major cardiovascular risk factors in midlife and dementia: a cohort study using data from the UK Biobank

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jessica Gong ◽  
Katie Harris ◽  
Sanne A. E. Peters ◽  
Mark Woodward
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Sex Differences ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Sex Difference ◽  
Incidence Rates ◽  
Cox Proportional Hazards ◽  
Uk Biobank ◽  
The Uk

Abstract Background Sex differences in major cardiovascular risk factors for incident (fatal or non-fatal) all-cause dementia were assessed in the UK Biobank. The effects of these risk factors on all-cause dementia were explored by age and socioeconomic status (SES). Methods Cox proportional hazards models were used to estimate hazard ratios (HRs) and women-to-men ratio of HRs (RHR) with 95% confidence intervals (CIs) for systolic blood pressure (SBP) and diastolic blood pressure (DBP), smoking, diabetes, adiposity, stroke, SES and lipids with dementia. Poisson regression was used to estimate the sex-specific incidence rate of dementia for these risk factors. Results 502,226 individuals in midlife (54.4% women, mean age 56.5 years) with no prevalent dementia were included in the analyses. Over 11.8 years (median), 4068 participants (45.9% women) developed dementia. The crude incidence rates were 5.88 [95% CI 5.62–6.16] for women and 8.42 [8.07–8.78] for men, per 10,000 person-years. Sex was associated with the risk of dementia, where the risk was lower in women than men (HR = 0.83 [0.77–0.89]). Current smoking, diabetes, high adiposity, prior stroke and low SES were associated with a greater risk of dementia, similarly in women and men. The relationship between blood pressure (BP) and dementia was U-shaped in men but had a dose-response relationship in women: the HR for SBP per 20 mmHg was 1.08 [1.02–1.13] in women and 0.98 [0.93–1.03] in men. This sex difference was not affected by the use of antihypertensive medication at baseline. The sex difference in the effect of raised BP was consistent for dementia subtypes (vascular dementia and Alzheimer’s disease). Conclusions Several mid-life cardiovascular risk factors were associated with dementia similarly in women and men, but not raised BP. Future bespoke BP-lowering trials are necessary to understand its role in restricting cognitive decline and to clarify any sex difference.

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