OBJECTIVE: Understanding the amounts of intensity-specific movement needed to attenuate the association between sedentary time and mortality may help to inform personalized prescription and behavioral counselling. Herein, we examined the joint associations of sedentary time and intensity-specific physical activity with all-cause and cardiovascular disease (CVD) mortality.
METHODS: Prospective cohort study including 73,729 adults from the UK Biobank who wore an Axivity AX3 accelerometer on their dominant wrist for at least 3 days, being one a weekend day, between June 2013 and December 2015. We considered the median tertile values of sedentary time and physical activity in each intensity band to determine the amount of physical activity needed to attenuate the association between sedentary time and mortality.
RESULTS: During a median of 6.9 years of follow-up (628,807 person-years), we documented 1521 deaths, including 388 from CVD. Physical activity of any intensity attenuated the detrimental association of sedentary time with mortality. Overall, at least a median of 6 min/day of vigorous physical activity, 30 min/day of MVPA, 64 min/day of moderate physical activity, or 163 min/day of light physical activity (mutually-adjusted for other intensities) attenuated the association between sedentary time and mortality. High sedentary time was associated with higher risk of CVD mortality only among participants with low MVPA (HR 1.96; 95% CI 1.23 to 3.14).
CONCLUSIONS: Different amounts of each physical activity intensity may attenuate the association between high sedentary time and mortality.

Computational variant effect predictors offer a scalable and increasingly reliable means of interpreting human genetic variation, but concerns of circularity and bias have limited previous methods for evaluating and comparing predictors. Population-level cohorts of genotyped and phenotyped participants that have not been used in predictor training can facilitate an unbiased benchmarking of available methods. Using a curated set of human gene-trait associations with a reported rare-variant burden association, we evaluate the correlations of 24 computational variant effect predictors with associated human traits in the UK Biobank and All of Us cohorts.
AlphaMissense outperformed all other predictors in inferring human traits based on rare missense variants in UK Biobank and All of Us participants. The overall rankings of computational variant effect predictors in these two cohorts showed a significant positive correlation.
We describe a method to assess computational variant effect predictors that sidesteps the limitations of previous evaluations. This approach is generalizable to future predictors and could continue to inform predictor choice for personal and clinical genetics.

OBJECTIVE: To explore the relationships of multiple reproductive factors with type 2 diabetes mellitus (T2DM) risk and the joint effects of reproductive factors and genetic susceptibility.
METHODS: We included 262,368 women without prevalent T2DM from the UK biobank. Cox proportional hazards regression models were employed to estimate the relationships of reproductive factors with T2DM risk and the joint effects of reproductive factors and genetic susceptibility.
RESULTS: During a mean follow-up of 12.2 years, 8,996 T2DM cases were identified. Early menarche (< 12 years, hazard ratio (HR) 1.08 [95% confidence interval (CI) 1.02;1.13]), late menarche (≥ 15 years, HR 1.11 [1.04;1.17]), early menopause (< 45 years, HR 1.20 [1.12;1.29]), short reproductive lifespan (< 30 years, HR 1.25 [1.16;1.35]), hysterectomy (1.31, HR [1.23;1.40]), oophorectomy (HR 1.28 [1.20;1.36]), high parity (≥ 4, HR 1.25 [1.17;1.34]), early age at first live birth (< 20 years, HR 1.23 [1.16;1.31]), miscarriage (HR 1.13 [1.07;1.19]), stillbirth (HR 1.14 [1.03;1.27]), and ever used hormonal replacement therapy (HR 1.19 [1.14;1.24]) were related to a higher T2DM risk, while ever used oral contraceptives (HR 0.93 [0.89;0.98]) was related to a lower T2DM risk. Furthermore, women with reproductive risk factors and high genetic risk had the highest T2DM risk compared to those with low genetic risk and without reproductive risk factors.
CONCLUSIONS: Our findings show that multiple reproductive factors are related to T2DM risk, particularly in women with high genetic risk.

We present new models and methods for the posterior drift problem where the regression function in the target domain is modelled as a linear adjustment, on an appropriate scale, of that in the source domain, and study the theoretical properties of our proposed estimators in the binary classification problem. The core idea of our model inherits the simplicity and the usefulness of generalized linear models and accelerated failure time models from the classical statistics literature. Our approach is shown to be flexible and applicable in a variety of statistical settings, and can be adopted for transfer learning problems in various domains including epidemiology, genetics and biomedicine. As concrete applications, we illustrate the power of our approach (i) through mortality prediction for British Asians by borrowing strength from similar data from the larger pool of British Caucasians, using the UK Biobank data, and (ii) in overcoming a spurious correlation present in the source domain of the Waterbirds dataset.

We aimed at examining the shared and unique associations of metabolites with multiple cardiometabolic diseases (CMD), i.e. type 2 diabetes (T2D), coronary heart disease (CHD) and stroke. In this study, a total of 168 plasma metabolites were measured by targeted high-throughput nuclear magnetic resonance spectroscopy among 98,162 participants free of T2D, CHD, and stroke at baseline. Cox proportional hazard models estimated hazard ratios for one SD increase in metabolite concentration levels, and false discovery rate (at 10%) was used to correct for multiple comparisons. Over 12.1 years of follow-up on average, 3,463 T2D, 6,186 CHD, and 1,892 stroke events were recorded. Most lipoprotein metabolites were associated with risks of T2D and CHD but not with the risk of stroke, with stronger associations for T2D than for CHD. Phospholipids within intermediate-density lipoprotein or large low-density lipoprotein particles showed positive associations with CHD and inverse associations with T2D. Metabolites indicating very small very low-density lipoprotein, histidine, creatinine, albumin, and glycoprotein acetyls were associated with risks of all three conditions. This large-scale metabolomics study revealed common and distinct metabolic biomarkers for T2D, CHD and stroke, providing instrumental information to possibly implement precision medicine for preventing and treating these conditions.

UNASSIGNED: Heterozygous familial hypercholesterolemia (HeFH) is a monogenic disorder characterized by increased circulating low-density lipoprotein cholesterol and accelerated atherosclerosis. Even among this high-risk group, prior studies note considerable variability in risk of coronary artery disease (CAD).
UNASSIGNED: The purpose of this study was to evaluate the cumulative impact of many common DNA variants-as quantified by a polygenic score-on incident CAD among individuals carrying a HeFH variant.
UNASSIGNED: We analyzed data from a prospective cohort study of 1,315 individuals who carried a HeFH variant and 1,315 matched family noncarriers derived from a nationwide screening program in the Netherlands, with subsequent replication in 151,009 participants of the UK Biobank.
UNASSIGNED: Despite identification and lipid management within the Dutch screening program, 84 (6.4%) of HeFH variant carriers developed CAD as compared to 45 (3.4%) of matched family members (median follow-up 10.2 years, HR 1.88, 95% CI: 1.31-2.70). Among HeFH variant carriers, a polygenic score was associated with CAD with an effect size similar to low-density lipoprotein cholesterol - HR of 1.35 (95% CI: 1.07-1.70) and 1.41 (95% CI: 1.17-1.70) per standard deviation increase, respectively. When compared to noncarriers, CAD risk increased from 1.24-fold (95% CI: 0.64-2.34) to 3.37-fold (95% CI: 2.11-5.36) across quintiles of the polygenic score. A similar risk gradient, 1.36-fold (95% CI: 0.65-2.85) to 2.88-fold (95% CI: 1.59-5.20), was observed in 429 carriers in the UK Biobank.
UNASSIGNED: In 2 cohort studies involving 1,744 individuals with genetically confirmed HeFH - the largest study to date - risk of CAD varied according to polygenic background, in some cases approaching the risk observed in noncarriers.

UNASSIGNED: People with monogenic familial hypercholesterolemia (FH) are at an increased risk of premature coronary heart disease and death. With a prevalence of 1:250, FH is relatively common; but currently there is no population screening strategy in place and most carriers are identified late in life, delaying timely and cost-effective interventions.
UNASSIGNED: The purpose of this study was to derive an algorithm to identify people with suspected monogenic FH for subsequent confirmatory genomic testing and cascade screening.
UNASSIGNED: A least absolute shrinkage and selection operator logistic regression model was used to identify predictors that accurately identified people with FH in 139,779 unrelated participants of the UK Biobank. Candidate predictors included information on medical and family history, anthropometric measures, blood biomarkers, and a low-density lipoprotein cholesterol (LDL-C) polygenic score (PGS). Model derivation and evaluation were performed in independent training and testing data.
UNASSIGNED: A total of 488 FH variant carriers were identified using whole-exome sequencing of the low-density lipoprotein receptor, apolipoprotein B, apolipoprotein E, proprotein convertase subtilisin/kexin type 9 genes. A 14-variable algorithm for FH was derived, with an area under the curve of 0.77 (95% CI: 0.71-0.83), where the top 5 most important variables included triglyceride, LDL-C, apolipoprotein A1 concentrations, self-reported statin use, and LDL-C PGS. Excluding the PGS as a candidate feature resulted in a 9-variable model with a comparable area under the curve: 0.76 (95% CI: 0.71-0.82). Both multivariable models (w/wo the PGS) outperformed screening-prioritization based on LDL-C adjusted for statin use.
UNASSIGNED: Detecting individuals with FH can be improved by considering additional predictors. This would reduce the sequencing burden in a 2-stage population screening strategy for FH.

Variable number tandem repeats (VNTRs) are highly polymorphic DNA regions harboring many potentially disease-causing variants. However, VNTRs often appear unresolved (\"dark\") in variation databases due to their repetitive nature. One particularly complex and medically relevant VNTR is the KIV-2 VNTR located in the cardiovascular disease gene LPA which encompasses up to 70% of the coding sequence.
Using the highly complex LPA gene as a model, we develop a computational approach to resolve intra-repeat variation in VNTRs from largely available short-read sequencing data. We apply the approach to six protein-coding VNTRs in 2504 samples from the 1000 Genomes Project and developed an optimized method for the LPA KIV-2 VNTR that discriminates the confounding KIV-2 subtypes upfront. This results in an F1-score improvement of up to 2.1-fold compared to previously published strategies. Finally, we analyze the LPA VNTR in > 199,000 UK Biobank samples, detecting > 700 KIV-2 mutations. This approach successfully reveals new strong Lp(a)-lowering effects for KIV-2 variants, with protective effect against coronary artery disease, and also validated previous findings based on tagging SNPs.
Our approach paves the way for reliable variant detection in VNTRs at scale and we show that it is transferable to other dark regions, which will help unlock medical information hidden in VNTRs.

BACKGROUND: Liver disease and dementia are both highly prevalent and share common pathological mechanisms. We aimed to investigate the associations between metabolic dysfunction-associated fatty liver disease (MAFLD), metabolic dysfunction-associated steatotic liver disease (MASLD) and the risk of all-cause and cause-specific dementia.
METHODS: We conducted a prospective study with 403,506 participants from the UK Biobank. Outcomes included all-cause dementia, Alzheimer\'s disease, and vascular dementia. Multivariable Cox proportional hazards models were used for analyses.
RESULTS: 155,068 (38.4%) participants had MAFLD, and 111,938 (27.7%) had MASLD at baseline. During a median follow-up of 13.7 years, 5,732 participants developed dementia (2,355 Alzheimer\'s disease and 1,274 vascular dementia). MAFLD was associated with an increased risk of vascular dementia (HR 1.32 [95% CI 1.18-1.48]) but a reduced risk of Alzheimer\'s disease (0.92 [0.84-1.0]). Differing risks emerged among MAFLD subtypes, with the diabetes subtype increasing risk of all-cause dementia (1.8 [1.65-1.96]), vascular dementia (2.95 [2.53-3.45]) and Alzheimer\'s disease (1.46 [1.26-1.69]), the lean metabolic disorder subtype only increasing vascular dementia risk (2.01 [1.25-3.22]), whereas the overweight/obesity subtype decreasing risk of Alzheimer\'s disease (0.83 [0.75-0.91]) and all-cause dementia (0.9 [0.84-0.95]). MASLD was associated with an increased risk of vascular dementia (1.24 [1.1-1.39]) but not Alzheimer\'s disease (1.0 [0.91-1.09]). The effect of MAFLD on vascular dementia was consistent regardless of MASLD presence, whereas associations with Alzheimer\'s disease were only present in those without MASLD (0.78 [0.67-0.91]).
CONCLUSIONS: MAFLD and MASLD are associated with an increased risk of vascular dementia, with subtype-specific variations observed in dementia risks. Further research is needed to refine MAFLD and SLD subtyping and explore the underlying mechanisms contributing to dementia risk.

BACKGROUND: The C-reactive protein/albumin ratio (CAR) seems to mirror disease severity and prognosis in several acute disorders particularly in elderly patients, yet less is known about if CAR is superior to C-reactive protein (CRP) in the general population.
METHODS: Prospective study design on the UK Biobank, where serum samples of CRP and Albumin were used. Cox regression analyses were conducted to assess all-cause and cardiovascular mortality, myocardial infarction, ischemic stroke, and heart failure over a follow-up period of approximately 12.5 years. The Cox model was adjusted for established cardiovascular disease (CVD) risk factors, including age, sex, smoking habits, physical activity level, BMI level, systolic blood pressure, LDL-cholesterol, statin treatment, diabetes, and previous CVD, with hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). Analyses were also stratified by sex, CRP level (< 10 and ≥ 10 mg/ml) and age (< 60 and ≥ 60 years).
RESULTS: In total, 411,506 individuals (186,043 men and 225,463 women) were included. In comparisons between HRs for all adverse outcomes, the results were similar or identical for CAR and CRP. For example, both CAR and CRP, adjusted HRs for all-cause mortality were 1.13 (95% CI 1.12-1.14). Regarding CVD mortality, the adjusted HR for CAR was 1.14 (95% CI 1.12-1.15), while for CRP, it was 1.13 (95% CI 1.11-1.15).
CONCLUSIONS: Within this study CAR was not superior to CRP in predictive ability of mortality or CVD disorders.
BACKGROUND: Not applicable (cohort study).