Uncovering predictors of low hippocampal volume: Evidence from a large-scale machine-learning-based study in the UK Biobank.

INTRODUCTION: Hippocampal atrophy is an established biomarker for conversion from the normal ageing process to developing cognitive impairment and dementia. This study used a novel hypothesis-free machine-learning approach, to uncover potential risk factors of lower hippocampal volume using information from the world's largest brain imaging study. METHODS: A combination of machine learning and conventional statistical methods were used to identify predictors of low hippocampal volume. We run gradient boosting decision tree modelling including 2891 input features measured before magnetic resonance imaging assessments (median 9.2 years, range 4.2-13.8 years) using data from 42,152 dementia-free UK Biobank participants. Logistic regression analyses were run on 87 factors identified as important for prediction based on Shapley values. False discovery rate adjusted P-value <0.05 was used to declare statistical significance. RESULTS: Older age, male sex, greater height, and whole-body fat free mass were the main predictors of low hippocampal volume with the model also identifying associations with lung function and lifestyle factors including smoking, physical activity, and coffee intake (corrected P<0.05 for all). Red blood cell count and several red blood cell indices such as haemoglobin concentration, mean corpuscular haemoglobin, mean corpuscular volume, mean reticulocyte volume, mean sphered cell volume, and red blood cell distribution width were among many biomarkers associated with low hippocampal volume. CONCLUSION: Lifestyles, physical measures, and biomarkers may affect hippocampal volume, with many of the characteristics potentially reflecting oxygen supply to the brain. Further studies are required to establish causality and clinical relevance of these findings.

Alcohol consumption and the risk of all-cause and cause-specific mortality-a linear and nonlinear Mendelian randomization study.

BACKGROUND: Many observational studies support light-to-moderate alcohol intake as potentially protective against premature death. We used a genetic approach to evaluate the linear and nonlinear relationships between alcohol consumption and mortality from different underlying causes. METHODS: We used data from 278 093 white-British UK Biobank participants, aged 37-73 years at recruitment and with data on alcohol intake, genetic variants, and mortality. Habitual alcohol consumption was instrumented by 94 variants. Linear Mendelian randomization (MR) analyses were conducted using five complementary approaches, and nonlinear MR analyses by the doubly-ranked method. RESULTS: There were 20 834 deaths during the follow-up (median 12.6 years). In conventional analysis, the association between alcohol consumption and mortality outcomes was 'J-shaped'. In contrast, MR analyses supported a positive linear association with premature mortality, with no evidence for curvature (Pnonlinearity ≥ 0.21 for all outcomes). The odds ratio [OR] for each standard unit increase in alcohol intake was 1.27 (95% confidence interval [CI] 1.16-1.39) for all-cause mortality, 1.30 (95% CI 1.10-1.53) for cardiovascular disease, 1.20 (95% CI 1.08-1.33) for cancer, and 2.06 (95% CI 1.36-3.12) for digestive disease mortality. These results were consistent across pleiotropy-robust methods. There was no clear evidence for an association between alcohol consumption and mortality from respiratory diseases or COVID-19 (1.32, 95% CI 0.96-1.83 and 1.46, 95% CI 0.99-2.16, respectively; Pnonlinearity ≥ 0.21). CONCLUSION: Higher levels of genetically predicted alcohol consumption had a strong linear association with an increased risk of premature mortality with no evidence for any protective benefit at modest intake levels.

Phenome-wide association study of ovarian cancer identifies common comorbidities and reveals shared genetics with complex diseases and biomarkers.

BACKGROUND: Ovarian cancer (OC) is commonly diagnosed among older women who have comorbidities. This hypothesis-free phenome-wide association study (PheWAS) aimed to identify comorbidities associated with OC, as well as traits that share a genetic architecture with OC. METHODS: We used data from 181,203 white British female UK Biobank participants and analysed OC and OC subtype-specific genetic risk scores (OC-GRS) for an association with 889 diseases and 43 other traits. We conducted PheWAS and colocalization analyses for individual variants to identify evidence for shared genetic architecture. RESULTS: The OC-GRS was associated with 10 diseases, and the clear cell OC-GRS was associated with five diseases at the FDR threshold (p = 5.6 × 10-4 ). Mendelian randomizaiton analysis (MR) provided robust evidence for the association of OC with higher risk of "secondary malignant neoplasm of digestive systems" (OR 1.64, 95% CI 1.33, 2.02), "ascites" (1.48, 95% CI 1.17, 1.86), "chronic airway obstruction" (1.17, 95% CI 1.07, 1.29), and "abnormal findings on examination of the lung" (1.51, 95% CI 1.22, 1.87). Analyses of lung spirometry measures provided further support for compromised respiratory function. PheWAS on individual OC variants identified five genetic variants associated with other diseases, and seven variants associated with biomarkers (all, p ≤ 4.5 × 10-8 ). Colocalization analysis identified rs4449583 (from TERT locus) as the shared causal variant for OC and seborrheic keratosis. CONCLUSIONS: OC is associated with digestive and respiratory comorbidities. Several variants affecting OC risk were associated with other diseases and biomarkers, with this study identifying a novel genetic locus shared between OC and skin conditions.

Hypothesis-free discovery of novel cancer predictors using machine learning.

BACKGROUND: Cancer is a leading cause of morbidity and mortality worldwide, and better understanding of the risk factors could enhance prevention. METHODS: We conducted a hypothesis-free analysis combining machine learning and statistical approaches to identify cancer risk factors from 2828 potential predictors captured at baseline. There were 459,169 UK Biobank participants free from cancer at baseline and 48,671 new cancer cases during the 10-year follow-up. Logistic regression models adjusted for age, sex, ethnicity, education, material deprivation, smoking, alcohol intake, body mass index and skin colour (as a proxy for sun sensitivity) were used for obtaining adjusted odds ratios, with continuous predictors presented using quintiles (Q). RESULTS: In addition to smoking, older age and male sex, positively associating features included several anthropometric characteristics, whole body water mass, pulse, hypertension and biomarkers such as urinary microalbumin (Q5 vs. Q1 OR 1.16, 95% CI = 1.13-1.19), C-reactive protein (Q5 vs. Q1 OR 1.20, 95% CI = 1.16-1.24) and red blood cell distribution width (Q5 vs. Q1 OR 1.18, 95% CI = 1.14-1.21), among others. High-density lipoprotein cholesterol (Q5 vs. Q1 OR 0.84, 95% CI = 0.81-0.87) and albumin (Q5 vs. Q1 OR 0.84, 95% CI = 0.81-0.87) were inversely associated with cancer. In sex-stratified analyses, higher testosterone increased the risk in females but not in males (Q5 vs. Q1 ORfemales 1.23, 95% CI = 1.17-1.30). Phosphate was associated with a lower risk in females but a higher risk in males (Q5 vs. Q1 ORfemales 0.94, 95% CI = 0.90-0.99 vs. ORmales 1.09, 95% CI 1.04-1.15). CONCLUSIONS: This hypothesis-free analysis suggests personal characteristics, metabolic biomarkers, physical measures and smoking as important predictors of cancer risk, with further studies needed to confirm causality and clinical relevance.

Schizophrenia and co-morbidity risk: Evidence from a data driven phenomewide association study.

Schizophrenia is a chronic debilitating psychiatric disorder with significant morbidity and mortality. In this study, we used information from 337,484 UK Biobank participants and performed PheWAS using schizophrenia genetic risk score on 1135 disease outcomes. Signals that passed the false discovery rate threshold were further analyzed for evidence on the causality of the association. We extended the analysis to 30 serum, four urine, and six neuroimaging biomarkers to identify biomarkers that could be affected by schizophrenia. Schizophrenia GRS was associated with 54 (39 distinct) disease outcomes including schizophrenia in the PheWAS analysis. Of these, a causal association were found with 10 distinct diseases in the MR analysis. Schizophrenia causally linked with higher odds of anxiety (OR = 1.41, 95%CI 1.12 to 1.21), bipolar disorder (OR = 1.52, 95%CI 1.36 to 1.70), major depressive disorder (OR = 1.12, 95%CI 1.08 to 1.16) and suicidal ideation (OR = 1.30, 95%CI 1.19 to 1.42). Lower odds were found for several diseases including type 1 diabetes, coronary atherosclerosis and some musculoskeletal disorders. In analyses using biomarkers, schizophrenia was associated with lower serum 25(OH)D, gamma glutamyltransferase, cystatin C, serum creatinine. However, we did not find association with any of the brain imaging markers. Our analyses confirmed the co-existence of schizophrenia with other mental health disorders but did not otherwise suggest strong effects on disease risk. Biomarker analyses reflected associations which could be explained by unhealthy lifestyles, suggesting patients with schizophrenia may benefit from screening for and managing broader health aspects.

Lifestyle, genetic risk and incidence of cancer: a prospective cohort study of 13 cancer types.

BACKGROUND: Genetic and lifestyle factors are associated with cancer risk. We investigated the benefits of adhering to lifestyle advice by the World Cancer Research Fund (WCRF) with the risk of 13 types of cancer and whether these associations differ according to genetic risk using data from the UK Biobank. METHODS: In 2006-2010, participants aged 37-73 years had their lifestyle assessed and were followed up for incident cancers until 2015-2019. Analyses were restricted to those of White European ancestry with no prior history of malignant cancer (n = 195 822). Polygenic risk scores (PRSs) were computed for 13 cancer types and these cancers combined ('overall cancer'), and a lifestyle index was calculated from WCRF recommendations. Associations with cancer incidence were estimated using Cox regression, adjusting for relevant confounders. Additive and multiplicative interactions between lifestyle index and PRSs were assessed. RESULTS: There were 15 240 incident cancers during the 1 926 987 person-years of follow-up (median follow-up = 10.2 years). After adjusting for confounders, the lifestyle index was associated with a lower risk of overall cancer [hazard ratio per standard deviation increase (95% CI) = 0.89 (0.87, 0.90)] and of eight specific cancer types. There was no evidence of interactions on the multiplicative scale. There was evidence of additive interactions in risks for colorectal, breast, pancreatic, lung and bladder cancers, such that the recommended lifestyle was associated with greater change in absolute risk for persons at higher genetic risk (P < 0.0003 for all). CONCLUSIONS: The recommended lifestyle has beneficial associations with most cancers. In terms of absolute risk, the protective association is greater for higher genetic risk groups for some cancers. These findings have important implications for persons most genetically predisposed to those cancers and for targeted strategies for cancer prevention.

Milk consumption and risk of twelve cancers: A large-scale observational and Mendelian randomisation study.

BACKGROUND & AIMS: Milk consumption is a modifiable lifestyle factor that has been associated with several cancer types in observational studies. Limited evidence exists regarding the causality of these relationships. Using a genetic variant (rs4988235) near the lactase gene (LCT) locus that proxies milk consumption, we conducted a comprehensive survey to assess potential causal relationships between milk consumption and 12 types of cancer. METHODS: Our analyses were conducted using white British participants of the UK Biobank (n = up to 255,196), the FinnGen cohort (up to 260,405), and available cancer consortia. We included cancers with previous evidence of an association with milk consumption in observational studies, as well as cancers common in both UK Biobank and FinnGen populations (>1000 cases). We evaluated phenotypic associations of milk intake and cancer incidence in the UK Biobank, and then used a Mendelian randomisation (MR) approach to assess causality in the UK Biobank, FinnGen consortium, and combined analyses incorporating additional consortia data for five cancers. In MR meta-analyses, case numbers for cancers of breast, ovary, uterus, cervix, prostate, bladder and urinary tract, colorectum, and lung ranged between 6000 and 148,000 cases, and between 780 and 1342 cases for cancers of the liver, mouth, stomach and diffuse large B-cell lymphoma. RESULTS: In observational analyses, milk consumption was associated with higher risk of bladder and urinary tract cancer (OR 1.23, 95% CI 1.03-1.47), but not with any other cancer. This association was not confirmed in the MR analysis, and genetically predicted milk consumption showed a significant association only with lower risk of colorectal cancer (0.89, 0.81-0.98 per additional 50 g/day). In the MR analyses conducted among individual cohorts, genetically predicted milk consumption provided evidence for an association with lower colorectal cancer in the FinnGen cohort (0.85, 0.74-0.97), and in the UK Biobank greater risk of female breast cancer (1.12, 1.03-1.23), and uterine cancer in pre-menopausal females (3.98, 1.48-10.7). CONCLUSION: In a comprehensive survey of milk-cancer associations, we confirm of a protective role of milk consumption for colorectal cancer. Our analyses also provide some suggestion for higher risks of breast cancer and premenopausal uterine cancer, warranting further investigation.

Metabolic profile predicts incident cancer: A large-scale population study in the UK Biobank.

BACKGROUND AND AIMS: Analyses to predict the risk of cancer typically focus on single biomarkers, which do not capture their complex interrelations. We hypothesized that the use of metabolic profiles may provide new insights into cancer prediction. METHODS: We used information from 290,888 UK Biobank participants aged 37 to 73 years at baseline. Metabolic subgroups were defined based on clustering of biochemical data using an artificial neural network approach and examined for their association with incident cancers identified through linkage to cancer registry. In addition, we evaluated associations between 38 individual biomarkers and cancer risk. RESULTS: In total, 21,973 individuals developed cancer during the follow-up (median 3.87 years, interquartile range [IQR] = 2.03-5.58). Compared to the metabolically favorable subgroup (IV), subgroup III (defined as "high BMI, C-reactive protein & cystatin C") was associated with a higher risk of obesity-related cancers (hazard ratio [HR] = 1.26, 95 % CI = 1.21 to 1.32) and hematologic-malignancies (e.g., lymphoid leukemia: HR = 1.83, 95%CI = 1.44 to 2.33). Subgroup II ("high triglycerides & liver enzymes") was strongly associated with liver cancer risk (HR = 5.70, 95%CI = 3.57 to 9.11). Analysis of individual biomarkers showed a positive association between testosterone and greater risks of hormone-sensitive cancers (HR per SD higher = 1.32, 95%CI = 1.23 to 1.44), and liver cancer (HR = 2.49, 95%CI =1.47 to 4.24). Many liver tests were individually associated with a greater risk of liver cancer with the strongest association observed for gamma-glutamyl transferase (HR = 2.40, 95%CI = 2.19 to 2.65). CONCLUSIONS: Metabolic profile in middle-to-older age can predict cancer incidence, in particular risk of obesity-related cancer, hematologic malignancies, and liver cancer. Elevated values from liver tests are strong predictors for later risk of liver cancer.

Metabolic profile-based subgroups can identify differences in brain volumes and brain iron deposition.

AIMS: To evaluate associations of metabolic profiles and biomarkers with brain atrophy, lesions, and iron deposition to understand the early risk factors associated with dementia. MATERIALS AND METHODS: Using data from 26 239 UK Biobank participants free from dementia and stroke, we assessed the associations of metabolic subgroups, derived using an artificial neural network approach (self-organizing map), and 39 individual biomarkers with brain MRI measures: total brain volume (TBV), grey matter volume (GMV), white matter volume (WMV), hippocampal volume (HV), white matter hyperintensity (WMH) volume, and caudate iron deposition. RESULTS: In metabolic subgroup analyses, participants characterized by high triglycerides and liver enzymes showed the most adverse brain outcomes compared to the healthy reference subgroup with high-density lipoprotein cholesterol and low body mass index (BMI) including associations with GMV (ßstandardized -0.20, 95% confidence interval [CI] -0.24 to -0.16), HV (ßstandardized -0.09, 95% CI -0.13 to -0.04), WMH volume (ßstandardized 0.22, 95% CI 0.18 to 0.26), and caudate iron deposition (ßstandardized 0.30, 95% CI 0.25 to 0.34), with similar adverse associations for the subgroup with high BMI, C-reactive protein and cystatin C, and the subgroup with high blood pressure (BP) and apolipoprotein B. Among the biomarkers, striking associations were seen between basal metabolic rate (BMR) and caudate iron deposition (ßstandardized 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV (ßstandardized -0.15, 95% CI -0.16 to -0.14) and HV (ßstandardized -0.11, 95% CI -0.12 to -0.10), and between BP and WMH volume (ßstandardized 0.13, 95% CI 0.12 to 0.14 for diastolic BP). CONCLUSIONS: Metabolic profiles were associated differentially with brain neuroimaging characteristics. Associations of BMR, BP and other individual biomarkers may provide insights into actionable mechanisms driving these brain associations.

Vitamin D Deficiency Increases Mortality Risk in the UK Biobank : A Nonlinear Mendelian Randomization Study.

BACKGROUND: Low vitamin D status is associated with increased mortality, but randomized trials on severely deficient participants are lacking. OBJECTIVE: To assess genetic evidence for the causal role of low vitamin D status in mortality. DESIGN: Nonlinear Mendelian randomization analyses. SETTING: UK Biobank, a large-scale, prospective cohort from England, Scotland, and Wales with participants recruited between March 2006 and July 2010. PARTICIPANTS: 307 601 unrelated UK Biobank participants of White European ancestry (aged 37 to 73 years at recruitment) with available measurements of 25-hydroxyvitamin D (25-(OH)D) and genetic data. MEASUREMENTS: Genetically predicted 25-(OH)D was estimated using 35 confirmed variants of 25-(OH)D. All-cause and cause-specific mortality (cardiovascular disease [CVD], cancer, and respiratory) were recorded up to June 2020. RESULTS: There were 18 700 deaths during the 14 years of follow-up. The association of genetically predicted 25-(OH)D with all-cause mortality was L-shaped (P for nonlinearity < 0.001), and risk for death decreased steeply with increasing concentrations until 50 nmol/L. Evidence for an association was also seen in analyses of mortality from cancer, CVD, and respiratory diseases (P ≤ 0.033 for all outcomes). Odds of all-cause mortality in the genetic analysis were estimated to increase by 25% (odds ratio, 1.25 [95% CI, 1.16 to 1.35]) for participants with a measured 25-(OH)D concentration of 25 nmol/L compared with 50 nmol/L. LIMITATIONS: Analyses were restricted to a White European population. A genetic approach is best suited to providing proof of principle on causality, whereas the strength of the association is approximate. CONCLUSION: Our study supports a causal relationship between vitamin D deficiency and mortality. Additional research needs to identify strategies that meet the National Academy of Medicine's guideline of greater than 50 nmol/L and that reduce the premature risk for death associated with low vitamin D levels. PRIMARY FUNDING SOURCE: National Health and Medical Research Council.

Healthy Lifestyle, Genetic Risk and Brain Health: A Gene-Environment Interaction Study in the UK Biobank.

Genetic susceptibility and lifestyle affect the risk of dementia but there is little direct evidence for their associations with preclinical changes in brain structure. We investigated the association of genetic dementia risk and healthy lifestyle with brain morphometry, and whether effects from elevated genetic risk are modified by lifestyle changes. We used prospective data from up to 25,894 UK Biobank participants (median follow-up of 8.8 years), and defined healthy lifestyle according to American Heart Association criteria as BMI < 30, no smoking, healthy diet and regular physical activity). Higher genetic risk was associated with lower hippocampal volume (beta −0.16 cm3, 95% CI −0.22, −0.11) and total brain volume (−4.34 cm3, 95% CI −7.68, −1.01) in participants aged ≥60 years but not <60 years. Healthy lifestyle was associated with higher total brain, grey matter and hippocampal volumes, and lower volume of white matter hyperintensities, with no effect modification by age or genetic risk. In conclusion, adverse effects of high genetic risk on brain health were only found in older participants, while adhering to healthy lifestyle recommendations is beneficial regardless of age or genetic risk.

A genome-wide association study of total child psychiatric problems scores.

Substantial genetic correlations have been reported across psychiatric disorders and numerous cross-disorder genetic variants have been detected. To identify the genetic variants underlying general psychopathology in childhood, we performed a genome-wide association study using a total psychiatric problem score. We analyzed 6,844,199 common SNPs in 38,418 school-aged children from 20 population-based cohorts participating in the EAGLE consortium. The SNP heritability of total psychiatric problems was 5.4% (SE = 0.01) and two loci reached genome-wide significance: rs10767094 and rs202005905. We also observed an association of SBF2, a gene associated with neuroticism in previous GWAS, with total psychiatric problems. The genetic effects underlying the total score were shared with common psychiatric disorders only (attention-deficit/hyperactivity disorder, anxiety, depression, insomnia) (rG > 0.49), but not with autism or the less common adult disorders (schizophrenia, bipolar disorder, or eating disorders) (rG < 0.01). Importantly, the total psychiatric problem score also showed at least a moderate genetic correlation with intelligence, educational attainment, wellbeing, smoking, and body fat (rG > 0.29). The results suggest that many common genetic variants are associated with childhood psychiatric symptoms and related phenotypes in general instead of with specific symptoms. Further research is needed to establish causality and pleiotropic mechanisms between related traits.

Egg and Dietary Cholesterol Intake and Risk of All-Cause, Cardiovascular, and Cancer Mortality: A Systematic Review and Dose-Response Meta-Analysis of Prospective Cohort Studies.

Objective: This systematic review and meta-analysis of prospective cohort studies examined the associations between egg and dietary cholesterol intake and the risk of mortality from all causes, including cardiovascular disease (CVD) and cancer. Methods: We searched PubMed, Scopus, ISI Web of Knowledge, and Google Scholar until April 2021, as well as references to the relevant articles retrieved. Random-effects models were used to calculate summary relative risk (RR) and 95% confidence intervals (CIs) for the highest vs. lowest categories of egg and dietary cholesterol intake. Also, linear and non-linear dose-response analyses were conducted to examine the dose-response relationships. Results: We included 55 studies, comprising data from 2,772,486 individuals with 228,425, 71,745, and 67,211 cases of all-cause, CVD, and cancer mortality, respectively. Intake of each additional egg per day was associated with a 7% higher risk of all-cause (1.07, 95% CI: 1.02-1.12, I2 = 84.8%) and a 13% higher risk of cancer mortality (1.13, 95% CI: 1.06-1.20, I2 = 54.2%), but was not associated with CVD mortality (1.00, 95% CI: 0.92-1.09, I2 = 81.5%). Non-linear analyses showed increased risks for egg consumption of more than 1.5 and 0.5 eggs/day, respectively. Each 100 mg/day increment in dietary cholesterol intake was associated with a 6% higher risk of all-cause mortality (1.06, 95% CI: 1.03-1.08, I2 = 34.5%) and a 6% higher risk of cancer mortality (1.06, 95% CI: 1.05-1.07, I2 = 0%), but was not associated with CVD mortality (1.04, 95% CI: 0.99-1.10, I2 = 85.9%). Non-linear analyses demonstrated elevated risks of CVD and cancer mortality for intakes more than 450 and 250 mg/day, respectively. Conclusions and Relevance: High-dietary intake of eggs and cholesterol was associated with all-cause and cancer mortality. Little evidence for elevated risks was seen for intakes below 0.5 egg/day or 250 mg/day of dietary cholesterol. Our findings should be considered with caution because of small risk estimates and moderate between-study heterogeneity. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=252564, PROSPERO, identifier: CRD42021252564.

Considering hormone-sensitive cancers as a single disease in the UK biobank reveals shared aetiology.

Hormone-related cancers, including cancers of the breast, prostate, ovaries, uterine, and thyroid, globally contribute to the majority of cancer incidence. We hypothesize that hormone-sensitive cancers share common genetic risk factors that have rarely been investigated by previous genomic studies of site-specific cancers. Here, we show that considering hormone-sensitive cancers as a single disease in the UK Biobank reveals shared genetic aetiology. We observe that a significant proportion of variance in disease liability is explained by the genome-wide single nucleotide polymorphisms (SNPs), i.e., SNP-based heritability on the liability scale is estimated as 10.06% (SE 0.70%). Moreover, we find 55 genome-wide significant SNPs for the disease, using a genome-wide association study. Pair-wise analysis also estimates positive genetic correlations between some pairs of hormone-sensitive cancers although they are not statistically significant. Our finding suggests that heritable genetic factors may be a key driver in the mechanism of carcinogenesis shared by hormone-sensitive cancers.

Cross-sectional metabolic subgroups and 10-year follow-up of cardiometabolic multimorbidity in the UK Biobank.

We assigned 329,908 UK Biobank participants into six subgroups based on a self-organizing map of 51 biochemical measures (blinded for clinical outcomes). The subgroup with the most favorable metabolic traits was chosen as the reference. Hazard ratios (HR) for incident disease were modeled by Cox regression. Enrichment ratios (ER) of incident multi-morbidity versus randomly expected co-occurrence were evaluated by permutation tests; ER is like HR but captures co-occurrence rather than event frequency. The subgroup with high urinary excretion without kidney stress (HR = 1.24) and the subgroup with the highest apolipoprotein B and blood pressure (HR = 1.52) were associated with ischemic heart disease (IHD). The subgroup with kidney stress, high adiposity and inflammation was associated with IHD (HR = 2.11), cancer (HR = 1.29), dementia (HR = 1.70) and mortality (HR = 2.12). The subgroup with high liver enzymes and triglycerides was at risk of diabetes (HR = 15.6). Multimorbidity was enriched in metabolically favorable subgroups (3.4 ≤ ER ≤ 4.0) despite lower disease burden overall; the relative risk of co-occurring disease was higher in the absence of obvious metabolic dysfunction. These results provide synergistic insight into metabolic health and its associations with cardiovascular disease in a large population sample.

Lifestyle Modifies the Diabetes-Related Metabolic Risk, Conditional on Individual Genetic Differences.

Metabolic syndrome is a group of heritable metabolic traits that are highly associated with type 2 diabetes (T2DM). Classical interventions to T2DM include individual self-management of environmental risk factors, such as improving diet quality, increasing physical activity, and reducing smoking and alcohol consumption, which decreases the risk of developing metabolic syndrome. However, it is poorly understood how the phenotypes of diabetes-related metabolic traits change with respect to lifestyle modifications at the individual level. In the analysis, we used 12 diabetes-related metabolic traits and eight lifestyle covariates from the UK Biobank comprising 288,837 white British participants genotyped for 1,133,273 genome-wide single nucleotide polymorphisms. We found 16 GxE interactions. Modulation of genetic effects by physical activity was seen for four traits (glucose, HbA1c, C-reactive protein, systolic blood pressure) and by alcohol and smoking for three (BMI, glucose, waist-hip ratio and BMI and diastolic and systolic blood pressure, respectively). We also found a number of significant phenotypic modulations by the lifestyle covariates, which were not attributed to the genetic effects in the model. Overall, modulation in the metabolic risk in response to the level of lifestyle covariates was clearly observed, and its direction and magnitude were varied depending on individual differences. We also showed that the metabolic risk inferred by our model was notably higher in T2DM prospective cases than controls. Our findings highlight the importance of individual genetic differences in the prevention and management of diabetes and suggest that the one-size-fits-all approach may not benefit all.

Vitamin D supplementation and prevention of cardiovascular disease and cancer in the Finnish Vitamin D Trial: a randomized controlled trial.

BACKGROUND: Vitamin D insufficiency is associated with risks of cardiovascular diseases (CVD) and cancer in observational studies, but evidence for benefits with vitamin D supplementation is limited. OBJECTIVES: To investigate the effects of vitamin D3 supplementation on CVD and cancer incidences. METHODS: The study was a 5-year, randomized, placebo-controlled trial among 2495 male participants ≥60 years and post-menopausal female participants ≥65 years from a general Finnish population who were free of prior CVD or cancer. The study had 3 arms: placebo, 1600 IU/day, or 3200 IU/day vitamin D3. Follow-up was by annual study questionnaires and national registry data. A representative subcohort of 551 participants had more detailed in-person investigations. The primary endpoints were incident major CVD and invasive cancer. Secondary endpoints included the individual components of the primary CVD endpoint (myocardial infarction, stroke, and CVD mortality), site-specific cancers, and cancer death. RESULTS: During the follow-up, there were 41 (4.9%), 42 (5.0%), and 36 (4.3%) major CVD events in the placebo, 1600 IU/d (compared with placebo: HR: 0.97; 95% CI: 0.63-1.49; P = 0.89), and 3200 IU/d (HR: 0.84; 95% CI: 0.54-1.31; P = 0.44) arms, respectively. Invasive cancer was diagnosed in 41 (4.9%), 48 (5.8%), and 40 (4.8%) participants in the placebo, 1600 IU/d (HR: 1.14; 95% CI: 0.75-1.72; P = 0.55), and 3200 IU/d (HR: 0.95; 95% CI: 0.61-1.47; P = 0.81) arms, respectively. There were no significant differences in the secondary endpoints or total mortality. In the subcohort, the mean baseline serum 25-hydroxyvitamin D concentration was 75 nmol/L (SD, 18 nmol/L). After 12 months, the concentrations were 73 nmol/L (SD, 18 nmol/L), 100 nmol/L (SD, 21 nmol/L), and 120 nmol/L (SD, 22 nmol/L) in the placebo, 1600 IU/d, and 3200 IU/d arms, respectively. CONCLUSIONS: Vitamin D3 supplementation did not lower the incidences of major CVD events or invasive cancer among older adults, possibly due to sufficient vitamin D status in most participants at baseline.

Estimating the influence of body mass index (BMI) on mortality using offspring BMI as an instrumental variable.

OBJECTIVE: High body mass index (BMI) is an important predictor of mortality but estimating underlying causality is hampered by confounding and pre-existing disease. Here, we use information from the offspring to approximate parental BMIs, with an aim to avoid biased estimation of mortality risk caused by reverse causality. METHODS: The analyses were based on information on 9674 offspring-mother and 9096 offspring-father pairs obtained from the 1958 British birth cohort. Parental BMI-mortality associations were analysed using conventional methods and using offspring BMI as a proxy, or instrument, for their parents' BMI. RESULTS: In the conventional analysis, associations between parental BMI and all-cause mortality were U-shaped (Pcurvature < 0.001), while offspring BMI had linear associations with parental mortality (Ptrend < 0.001, Pcurvature > 0.46). Curvature was particularly pronounced for mortality from respiratory diseases and from lung cancer. Instrumental variable analyses suggested a positive association between BMI and mortality from all causes [mothers: HR per SD of BMI 1.43 (95% CI 1.21-1.69), fathers: HR 1.17 (1.00-1.36)] and from coronary heart disease [mothers: HR 1.65 (1.15-2.36), fathers: HR 1.51 (1.17-1.97)]. These were larger than HR from the equivalent conventional analyses, despite some attenuation by adjustment for social indicators and smoking. CONCLUSIONS: Analyses using offspring BMI as a proxy for parental BMI suggest that the apparent adverse consequences of low BMI are considerably overestimated and adverse consequences of overweight are underestimated in conventional epidemiological studies.

Combining machine learning and conventional statistical approaches for risk factor discovery in a large cohort study.

We present a simple and efficient hypothesis-free machine learning pipeline for risk factor discovery that accounts for non-linearity and interaction in large biomedical databases with minimal variable pre-processing. In this study, mortality models were built using gradient boosting decision trees (GBDT) and important predictors were identified using a Shapley values-based feature attribution method, SHAP values. Cox models controlled for false discovery rate were used for confounder adjustment, interpretability, and further validation. The pipeline was tested using information from 502,506 UK Biobank participants, aged 37-73 years at recruitment and followed over seven years for mortality registrations. From the 11,639 predictors included in GBDT, 193 potential risk factors had SHAP values ≥ 0.05, passed the correlation test, and were selected for further modelling. Of the total variable importance summed up, 60% was directly health related, and baseline characteristics, sociodemographics, and lifestyle factors each contributed about 10%. Cox models adjusted for baseline characteristics, showed evidence for an association with mortality for 166 out of the 193 predictors. These included mostly well-known risk factors (e.g., age, sex, ethnicity, education, material deprivation, smoking, physical activity, self-rated health, BMI, and many disease outcomes). For 19 predictors we saw evidence for an association in the unadjusted but not adjusted analyses, suggesting bias by confounding. Our GBDT-SHAP pipeline was able to identify relevant predictors 'hidden' within thousands of variables, providing an efficient and pragmatic solution for the first stage of hypothesis free risk factor identification.