Response to: ‘Correspondence on ‘Variants in urate transporters, ADH1B, GCKR and MEPE genes associated with transition from asymptomatic hyperuricaemia to gout: results of the first gout versus asymptomatic hyperuricaemia GWAS in Caucasians using data from the UK Biobank’’ by Takei et al

Abstract Background It now established that social factors impact the quality of ageing, through the lifecourse stimulation/dysregulation of key physiological systems. Composite scores such as allostatic load, focusing on the response to stress, can be used to measure individual physiological wear-and-tear. Methods Using data from the Understanding Society study, a cross-sectional panel study including 9,088 participants representative of the UK population, we defined a synthetic biological health score (BHS) capturing the wear-and-tear of four physiological systems (endocrine, inflammatory, cardiovascular, and metabolic systems), and of two key organs (liver and kidney). We used 16 established blood-derived biomarkers of these systems to calculate the BHS and explored the relative contribution of socio-economic position to the BHS and its main components across age groups. Using data from UK biobank, including over 400,000 UK participants in whom similar biomarkers have been assayed in blood, we sought validation of our results and investigated the role of the BHS on all-cause and disease specific mortality, and disease incidence. Results We identified a systematic decreasing education-related gradient of the BHS (p < 0·001) leading to lower biological risk in participants with higher educational attainment. Education-related differences in the BHS were detected early in life, and were not attributable to lifestyle and behavioural factors. Analyses of the UK biobank data validated these findings and also showed that the BHS contributed in turn, irrespective of established health risk factors, to all-cause and disease specific mortality. Interpretation Our findings highlight the social-to-biological processes ultimately leading to health inequalities, and suggest that such disparities can already be detected in the 20-40 year age group.

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Phenotypic and genetic characterization of lower LDL-C and increased type-2 diabetes risk in the UK Biobank

10.2337/figshare.12389273 ◽

2020 ◽

Author(s):

Ada Admin ◽

Yann C. Klimentidis ◽

Amit Arora ◽

Michelle Newell ◽

Jin Zhou ◽

...

Keyword(s):

Type 2 Diabetes ◽

Uk Biobank ◽

Alcoholic Fatty Liver ◽

Genome Wide ◽

Using Data ◽

Underlying Mechanisms ◽

Phenotypic And Genetic Characterization ◽

The Uk

Although hyperlipidemia is traditionally considered a risk factor for type-2 diabetes (T2D), evidence has emerged from statin trials and candidate gene investigations suggesting that lower LDL-C increases T2D risk. We thus sought to more comprehensively examine the phenotypic and genotypic relationships of LDL-C with T2D. Using data from the UK Biobank, we found that levels of circulating LDL-C were negatively associated with T2D prevalence (OR=0.41[0.39, 0.43] per mmol/L unit of LDL-C), despite positive associations of circulating LDL-C with HbA1c and BMI. We then performed the first genome-wide exploration of variants simultaneously associated with lower circulating LDL-C and increased T2D risk, using data on LDL-C from the UK Biobank (n=431,167) and the GLGC consortium (n=188,577), and T2D from the DIAGRAM consortium (n=898,130). We identified 31 loci associated with lower circulating LDL-C and increased T2D, capturing several potential mechanisms. Seven of these loci have previously been identified for this dual phenotype, and 9 have previously been implicated in non-alcoholic fatty liver disease. These findings extend our current understanding of the higher T2D risk among individuals with low circulating LDL-C, and of the underlying mechanisms, including those responsible for the diabetogenic effect of LDL-C-lowering medications.

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O3B.5 The mental health of police, firefighters and paramedics in the UK biobank: a comparison with the general working population

Occupational and Environmental Medicine ◽

10.1136/oem-2019-epi.65 ◽

2019 ◽

Vol 76 (Suppl 1) ◽

pp. A24.3-A25

Author(s):

Sharon Stevelink ◽

Nicola Fear ◽

Matthew Hotopf

Keyword(s):

Mental Health ◽

Random Sample ◽

Emergency Services ◽

Life Time ◽

Uk Biobank ◽

Post Traumatic Stress ◽

Emergency Services Personnel ◽

Working People ◽

Using Data ◽

The Uk

It is our responsibility to protect and look after the health of members of the emergency services as this directly impacts on the country’s readiness to respond to these disasters and is a critical part of our duty of care towards this important group of workers. This study examined the mental health outcomes and associations with individual and job characteristics among emergency services personnel compared to a random sample of working people, thereby using data from the UK Biobank. This data source contains data on over half a million adults in the UK, who were at the time of recruitment between 40–69 years. Over 2 80 000 reported being in work. Current emergency services personnel were identified based on Standard Occupational Classification (SOC) 2000 codes. A random sample of age and sex matched people working in other occupations were selected from the UK Biobank for comparative purposes. The prevalence of the outcomes of interest, based on current and life time measures of depression, anxiety, alcohol misuse, post-traumatic stress disorder, suicide and trauma will be presented. The findings will be discussed in the light of current policies and strategies and recommendations for further practice will be outlined.

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Abstract 5251: Prospective investigation of general and visceral obesity, body fat distribution and risk of common cancers using data from the UK Biobank cohort study

10.1158/1538-7445.am2018-5251 ◽

2018 ◽

Author(s):

Ying Liu ◽

Wei Zheng

Keyword(s):

Cohort Study ◽

Body Fat ◽

Visceral Obesity ◽

Fat Distribution ◽

Body Fat Distribution ◽

Uk Biobank ◽

Using Data ◽

The Uk ◽

Prospective Investigation

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Association between serum urate, gout and comorbidities: a case–control study using data from the UK Biobank

Rheumatology ◽

10.1093/rheumatology/keaa773 ◽

2020 ◽

Author(s):

Gabriela Sandoval-Plata ◽

Georgina Nakafero ◽

Mithun Chakravorty ◽

Kevin Morgan ◽

Abhishek Abhishek

Keyword(s):

Case Control Study ◽

Serum Urate ◽

Case Control ◽

Dietary Factors ◽

Uk Biobank ◽

Crystal Deposition ◽

Using Data ◽

Urate Lowering Treatment ◽

The Uk ◽

Control Study

Abstract Objectives To examine the association between comorbidities and serum urate (SU), gout and comorbidities, and to determine whether the association between gout and comorbidities is independent of SU. Methods We performed a case–control study using UK Biobank data. Two separate analyses were conducted: one excluding participants with gout to investigate the association between comorbidities and SU and the other with participants with gout as the index condition to examine the association between gout and comorbidities. SU was measured at the baseline visit. Self-reported physician-diagnosed illnesses were used to define gout and comorbidities, except for chronic kidney disease (CKD), which was defined using an estimated glomerular filtration rate cut-off. Participants prescribed urate-lowering treatment were also classified as gout. Logistic regression was used to examine associations. Odds ratios (ORs) and 95% CIs were calculated and adjusted for covariates including comorbidities and SU. Results Data for 458 781 UK Biobank participants were used to examine the association between comorbidities and SU. There was an association between hypertension, ischaemic heart disease (IHD), congestive cardiac failure (CCF), hyperlipidaemia, CKD and SU with and adjusted OR (aOR) of 1.10–3.14 for each 1 mg/dl SU increase. A total of 10 265 gout cases and 458 781 controls were included in the analysis of association between gout and comorbidities. Gout associated independently with hypertension, IHD, CCF, hyperlipidaemia and diabetes, with aORs of 1.21–4.15 after adjusting for covariates including SU. Conclusion Comorbidities associate with increasing SU. The association between gout and cardiometabolic comorbidities was independent of SU, suggesting separate SU-independent mechanisms such as inflammation driven by crystal deposition, pro-inflammatory genotype or non-purine dietary factors.

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Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases

10.1101/602516 ◽

2019 ◽

Cited By ~ 30

Author(s):

Ben Brumpton ◽

Eleanor Sanderson ◽

Fernando Pires Hartwig ◽

Sean Harrison ◽

Gunnhild Åberge Vie ◽

...

Keyword(s):

Population Stratification ◽

Mendelian Randomization ◽

Genetic Data ◽

Population Based ◽

Health Study ◽

Uk Biobank ◽

Family Effects ◽

Family Based ◽

Using Data ◽

The Uk

AbstractMendelian randomization (MR) is a widely-used method for causal inference using genetic data. Mendelian randomization studies of unrelated individuals may be susceptible to bias from family structure, for example, through dynastic effects which occur when parental genotypes directly affect offspring phenotypes. Here we describe methods for within-family Mendelian randomization and through simulations show that family-based methods can overcome bias due to dynastic effects. We illustrate these issues empirically using data from 61,008 siblings from the UK Biobank and Nord-Trøndelag Health Study. Both within-family and population-based Mendelian randomization analyses reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while MR estimates from population-based samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects largely disappeared in within-family MR analyses. We found differences between population-based and within-family based estimates, indicating the importance of controlling for family effects and population structure in Mendelian randomization studies.

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Phenome-Wide Association Study of Actigraphy in the UK Biobank

10.1101/2021.12.09.21267558 ◽

2021 ◽

Author(s):

Thomas G. Brooks ◽

Nicholas F. Lahens ◽

Gregory R. Grant ◽

Yvette I. Sheline ◽

Garret A. FitzGerald ◽

...

Keyword(s):

Physical Activity ◽

Airway Obstruction ◽

Large Scale ◽

Diurnal Rhythms ◽

Uk Biobank ◽

Daily Lives ◽

Individualized Approach ◽

Using Data ◽

The Uk ◽

Chronic Airway

AbstractWrist-worn accelerometer actigraphy devices present the opportunity for large-scale data collection from people during their daily lives. Using data from approximately 100,000 participants in the UK Biobank, actigraphy-derived measures of physical activity, sleep, and diurnal rhythms were associated in exploration and validation cohorts with a full phenome-wide set of diagnoses, biomarkers and metadata. Rhythmicity was captured by two independent models based on accelerometer and skin temperature harnessing behavioral (diurnal) and molecular (circadian) components. We found that robust rhythms significantly with biomarkers, survival, and phenotypes including diabetes, hypertension, mood disorders, and chronic airway obstruction; these associations were comparable to those with physical activity and sleep. Surprisingly, associations were mostly consistent between the sexes, while modulation by age was significant. More importantly, rhythms were found to be powerful predictors of future diseases: a two standard deviation difference in wrist temperature rhythms corresponded to increases in rate of diagnosis of 61% in diabetes, 38% in chronic airway obstruction, 27% in anxiety disorders, and 22% in hypertension. Our PheWAS of actigraphy data in the UK Biobank establishes that rhythmicity is fundamental to modeling disease trajectories, as are physical activity and sleep. Integration of long-term remote biosensing into patient care could thus afford an individualized approach to risk management.

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Interaction-based Mendelian randomization with measured and unmeasured gene-by-covariate interactions

10.1101/2020.07.27.20162909 ◽

2020 ◽

Cited By ~ 1

Author(s):

Wes Spiller ◽

Fernando Pires Hartwig ◽

Eleanor Sanderson ◽

George Davey Smith ◽

Jack Bowden

Keyword(s):

Blood Pressure ◽

Body Mass Index ◽

Mendelian Randomization ◽

Sensitivity Analyses ◽

Uk Biobank ◽

Gene Environment ◽

Gxe Interactions ◽

Using Data ◽

The Uk ◽

Positive Effect

Studies leveraging gene-environment (GxE) interactions within Mendelian randomization (MR) analyses have prompted the emergence of two methodologies: MR-GxE and MR-GENIUS. Such methods are attractive in allowing for pleiotropic bias to be corrected when using individual instruments. Specifically, MR-GxE requires an interaction to be explicitly identified, while MR-GENIUS does not. We critically examine the assumptions of MR-GxE and MR-GENIUS, and propose sensitivity analyses to evaluate their performance. Finally, we explore the association between body mass index (BMI) and systolic blood pressure (SBP) using data from the UK Biobank. We find both approaches share similar assumptions, though differences between the approaches lend themselves to differing research settings. Where interactions are identified, MR-GxE relies on weaker assumptions and allows for further sensitivity analyses. MR-GENIUS circumvents the need to identify interactions, but relies on the MR-GxE assumptions holding globally. Through applied analyses we find evidence of a positive effect of BMI upon SBP.

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