Counterpoint: "Streamlined" Does Not Mean Simple

Association of early-life tobacco smoke exposure with chronic gastrointestinal diseases in adulthood and trajectory of chronic gastrointestinal multi-morbidity: A large prospective cohort study.

A low magnesium intake has been suggested to be associated with amyotrophic lateral sclerosis (ALS) in pathological and case-control studies, but prospective studies in humans are lacking.The relation between dietary intake of magnesium and ALS risk was explored in five large prospective cohort studies (the Nurses’ Health Study, the Health Professionals Follow-up Study, the Cancer Prevention Study II Nutrition Cohort, the Multiethnic Cohort Study, and the National Institutes of Health – AARP Diet and Health Study), comprising over 1,050,000 males and females contributing 1093 cases of ALS during a mean of 15 years of follow-up. Cox proportional hazards models were used within each cohort, and cohort-specific estimates were subsequently pooled using a random-effects model. Results demonstrated that dietary magnesium intake was not associated with ALS risk, relative risk 1.07, 95% confidence interval 0.88 − 1.31 comparing the highest quintile of intake with the lowest. This finding does not support a protective effect of magnesium intake on ALS risk. Further analyses should explore magnesium intake in combination with heavy metal exposure and genetic variants affecting magnesium absorption.

Large prospective cohort studies may offer an opportunity to study the etiology and natural history of rare cancers. Cancer diagnoses in observational cohort studies are often self-reported. Little information exists on the validity of self-reported cancer diagnosis, especially rare cancers, in Canada. This study evaluated the validity of self-reported cancer diagnosis in Alberta's Tomorrow Project (ATP), a provincial cohort in Canada. ATP data were linked to the Alberta Cancer Registry (ACR). The first instance of self-reported cancer in a follow-up survey was compared to the first cancer diagnosis in the ACR after enrollment. The sensitivity and positive predictive value (PPV) were estimated for the reporting of cancer status, reporting of common or rare cancer, and reporting of site-specific cancer. Logistic regression analysis explored factors associated with false positive, false negative, and incorrect cancer site reporting. In the 30,843 ATP participants who consented to registry linkage, there were 810 primary cancer diagnoses in the ACR and 959 self-reports of first cancer post-enrollment, for a cancer status sensitivity of 92.1% (95% CI: 90.0-93.9) and PPV of 77.8% (95% CI: 75.0-80.4). Compared to common cancers, rare cancers had a lower sensitivity (62.8% vs. 89.6%) and PPV (35.8% vs. 84.5%). Participants with a rare cancer were more likely to report an incorrect site than those with a common cancer. Rare cancers were less likely to be captured by active follow-up than common cancers. While rare cancer research may be feasible in large cohort studies, registry linkage is necessary to capture rare cancer diagnoses completely and accurately.

ObjectivesTo compare associations between the Gilbert syndrome genotype in European populations, measured bilirubin concentrations, genetically predicted bilirubin using this genotype, and a wide range of health outcomes in a large...

Large-scale biobanks provide important opportunities for mental health research, but selection biases raise questions regarding the comparability of individuals with those in clinical research settings. To compare the genetic liability to psychiatric disorders in individuals with schizophrenia in the UK Biobank with individuals in the Psychiatric Genomics Consortium (PGC) and to compare genetic liability and phenotypic features with participants recruited from clinical settings. This cross-sectional study included participants from the population-based UK Biobank and schizophrenia samples recruited from clinical settings (CLOZUK, CardiffCOGS, Cardiff F-Series, and Cardiff Affected Sib-Pairs). Data were collected between January 1993 and July 2021. Data analysis was conducted between July 2021 and June 2023. A genome-wide association study of UK Biobank schizophrenia case-control status was conducted, and the results were compared with those from the PGC via genetic correlations. To test for differences with the clinical samples, polygenic risk scores (PRS) were calculated for schizophrenia, bipolar disorder, depression, and intelligence using PRS-CS. PRS and phenotypic comparisons were conducted using pairwise logistic regressions. The proportions of individuals with copy number variants associated with schizophrenia were compared using Firth logistic regression. The sample of 517 375 participants included 1438 UK Biobank participants with schizophrenia (550 [38.2%] female; mean [SD] age, 54.7 [8.3] years), 499 475 UK Biobank controls (271 884 [54.4%] female; mean [SD] age, 56.5 [8.1] years), and 4 schizophrenia research samples (4758 [28.9%] female; mean [SD] age, 38.2 [21.0] years). Liability to schizophrenia in UK Biobank was highly correlated with the latest genome-wide association study from the PGC (genetic correlation, 0.98; SE, 0.18) and showed the expected patterns of correlations with other psychiatric disorders. The schizophrenia PRS explained 6.8% of the variance in liability for schizophrenia case status in UK Biobank. UK Biobank participants with schizophrenia had significantly lower schizophrenia PRS than 3 of the clinically ascertained samples and significantly lower rates of schizophrenia-associated copy number variants than the CLOZUK sample. UK Biobank participants with schizophrenia had higher educational attainment and employment rates than the clinically ascertained schizophrenia samples, lower rates of smoking, and a later age of onset of psychosis. Individuals with schizophrenia in the UK Biobank, and likely other volunteer-based biobanks, represent those less severely affected. Their inclusion in wider studies should enhance the representation of the full spectrum of illness severity.

Tea consumption has been associations with a lower risk of mortality and numerous health benefits. However, it is still unclear whether consuming tea with or without sugar or sweeteners has different effects on mortality. It is necessary to investigate the associations of unsweetened, sugar-sweetened, and artificially sweetened tea consumption with all-cause mortality and cause-specific mortality. In this population-based cohort study of 195,361 UK Biobank participants who completed at least one 24-h dietary recall, we examined tea consumption by type (unsweetened, sugar-sweetened, artificially sweetened). Cox proportional hazards models and restricted cubic splines were used to assess nonlinear associations between tea intake and the risks of all-cause, cancer, and cardiovascular disease (CVD) mortality. We also conducted subgroup analyses stratified by genetic score for caffeine metabolism. After a median follow-up of 13.6 years, 11,718 all-cause deaths were recorded, including 2,202 deaths from CVD and 6,415 from cancer. A U-shaped association was observed between tea consumption and mortality risk. Compared with non-consumers, individuals consuming 3.5-4.5 drinks per day of unsweetened tea had the lowest risks of all-cause (HR, 0.80; 95% CI: 0.75-0.86), cancer (HR, 0.86; 95% CI: 0.77-0.97), and CVD (HR, 0.73; 95% CI: 0.60-0.89). Sugar-sweetened tea showed no consistent or statistically significant associations with all-cause, cancer, or CVD mortality across different levels of consumption. Similarly, no significant associations were found for artificially sweetened tea. The observed associations between tea consumption and mortality were not modified by genetic predisposition to caffeine metabolism. Unsweetened tea consumption was significantly associated with a lower risk of all-cause, cancer, and CVD mortality. No consistent or statistically significant associations were observed for sugar-sweetened or artificially sweetened tea. The potential attenuation of tea's protective effects by added sugar or artificial sweeteners warrants further investigation. Given current evidence, it may be advisable to consume tea without added sweeteners to optimize health benefits and longevity.

Background and Aims: Epidemiological evidence has shown the association between nutritional habits and liver disease. However, results remain conflicting. This study investigated the influence of dietary factors on the risk of incident non-alcoholic fatty liver disease (NAFLD), cirrhosis, and liver cancer. Methods: Data from the UK Biobank database were analyzed (n = 372,492). According to baseline data from the food frequency questionnaire, two main dietary patterns (Western and prudent) were identified using principal component analysis. We used cox proportional hazards models to explore the associations of individual food groups and dietary patterns with NAFLD, cirrhosis, and liver cancer. Results: During a median follow-up of 12 years, 3527 hospitalized NAFLD, 1643 cirrhosis, and 669 liver cancer cases were recorded among 372,492 participants without prior history of cancer or chronic liver diseases at baseline. In multivariable adjusted analysis, participants in the high tertile of Western dietary pattern score had an 18% (95%CI = 1.09–1.29), 21% (95%CI = 1.07–1.37), and 24% (95%CI = 1.02–1.50) higher risk of incident NAFLD, liver cirrhosis, and liver cancer, respectively, compared with the low tertile. Participants in the high tertile of prudent scores had a 15% (95%CI = 0.75–0.96) lower risk of cirrhosis, as compared with those in the low tertile. In addition, the higher consumption of red meat and the lower consumption of fruit, cereal, tea, and dietary fiber were significantly associated with a higher risk of NAFLD, cirrhosis, and liver cancer (ptrend < 0.05). Conclusions: This large prospective cohort study showed that an increased intake of food from the Western dietary pattern could be correlated with an increased risk of chronic liver diseases, while the prudent pattern was only correlated with a reduced liver cirrhosis risk. These data may provide new insights into lifestyle interventions for the prevention of chronical liver diseases.

Association of accelerated aging with the incidence risk of Cardiometabolic diseases in people with severe mental illnesses: A prospective analysis in UK Biobank

<h3>Context:</h3> UK Biobank is increasingly used to study causes, associations, and implications of multimorbidity. However, UK Biobank is criticised for lack of representativeness and ‘healthy volunteer bias’. Selection bias can lead to spurious or biased estimates of associations between exposures and outcomes. <h3>Objectives:</h3> To compare association between multimorbidity and adverse health outcomes in UK Biobank and a nationally representative sample. <h3>Design:</h3> Cohorts identified from linked routine healthcare data from UK Biobank and from the Secure Anonymised Information Linkage (SAIL) databank. <h3>Setting:</h3> Community. <h3>Participants:</h3> UK Biobank participants (n=211,597, age 40-70) with linked primary care data and a sample from a nationally representative routine data source (SAIL) (n=852,055, age 40-70). <h3>Main outcome measures:</h3> Multimorbidity (n=40 long-term conditions [LTCs]) was identified from primary care Read codes and quantified using a simple count and a weighted score. Individual LTCs and LTC combinations were also assessed. Associations with all-cause mortality, unscheduled hospitalisation, and major adverse cardiovascular events (MACE) were assessed using Weibull or Poisson models and adjusted for age, sex, and socioeconomic status. <h3>Results:</h3> Multimorbidity was less common in UK Biobank than SAIL. This difference was attenuated, but persisted, after standardising by age, sex and socioeconomic status. The effect of increasing multimorbidity count on mortality, unscheduled hospitalisation, and MACE was similar between UK Biobank and SAIL at LTC counts of ≤3, however above this level UK Biobank underestimated the risk associated with multimorbidity. Absolute risk of mortality, hospitalisation and MACE, at all levels of multimorbidity, was lower in UK Biobank than SAIL (adjusting for age, sex, and socioeconomic status). Both cohorts produced similar hazard ratios for some LTCs (e.g. hypertension and coronary heart disease) but underestimated the risk for others (e.g. alcohol problems or mental health conditions). Similarly hazard ratios for some LTC combinations were similar between the cohorts (e.g. cardiovascular, respiratory conditions), UK Biobank underestimated the risk for combinations including pain or mental health conditions. <h3>Conclusions:</h3> UK Biobank accurately estimates risk of outcomes associated with LTC counts ≤3. However, for counts ≥4 estimates of magnitude of association from UK Biobank are likely to be conservative.

<h3>Introduction</h3> Cardiovascular magnetic resonance (CMR)-derived aortic distensibility (AoD) is a validated tool to measure arterial stiffness¹. This local stiffness marker is an independent predictor of cardiovascular events and all-cause mortality²....

Response to: ‘Correspondence to ‘Associations of regular glucosamine use with all-cause and cause-specific mortality: a large prospective cohort study’ by Li et al’ by Yueh et al

A population-based recalibration method for updating survival neural networks models for cardiovascular risk prediction in United Kingdom and China.

Familial partial lipodystrophy (FPLD) is a heterogenous group of syndromes associated with a high prevalence of cardiometabolic diseases. Prior work has proposed DEXA-derived fat mass ratio (FMR) - defined as trunk fat percentage (trunk fat %) divided by leg fat percentage (leg fat %) - as a biomarker of FPLD, but this metric has not previously been characterized in large cohort studies. We set out to (1) understand the cardiometabolic burden of individuals with high FMR in up to 40,796 participants in the UK Biobank and 9,408 participants in the Fenland study, (2) characterize the common variant genetic underpinnings of FMR, and (3) build and test a polygenic predictor for FMR. Participants with high FMR were at higher risk for type 2 diabetes (OR = 2.30, p = 3.5 × 10-41) and MASLD/MASH (OR = 2.55, p = 4.9 × 10-7) in UK Biobank, and had higher fasting insulin (difference = +19.8 pmol/L, p = 5.7 × 10-36) and fasting triglycerides (difference = +36.1 mg/dL, p = 2.5 × 10-28) in the Fenland Study. Across FMR and its component traits, 61 conditionally independent variant-trait pairs were discovered, including 13 newly-identified pairs. A polygenic score for FMR was associated with increased risk of cardiometabolic diseases. This work establishes the cardiometabolic significance of high FMR - a biomarker for FPLD - in two large cohort studies and may prove useful in increasing diagnosis rates of patients with metabolically unhealthy fat distribution to enable treatment or a preventive therapy.

A polygenic risk score estimates the genetic risk of an individual for some disease or trait, calculated by aggregating the effect of many common variants associated with the condition. With the increasing availability of genetic data in large cohort studies such as the UK Biobank, inclusion of this genetic risk as a covariate in statistical analyses is becoming more widespread. Previously this required specialist knowledge, but as tooling and data availability have improved it has become more feasible for statisticians and epidemiologists to calculate existing scores themselves for use in analyses. While tutorial resources exist for conducting genome-wide association studies and generating of new polygenic risk scores, fewer guides exist for the simple calculation and application of existing genetic scores. This guide outlines the key steps of this process: selection of suitable polygenic risk scores from the literature, extraction of relevant genetic variants and verification of their quality, calculation of the risk score and key considerations of its inclusion in statistical models, using the UK Biobank imputed data as a model data set. Many of the techniques in this guide will generalize to other datasets, however we also focus on some of the specific techniques required for using data in the formats UK Biobank have selected. This includes some of the challenges faced when working with large numbers of variants, where the computation time required by some tools is impractical. While we have focused on only a couple of tools, which may not be the best ones for every given aspect of the process, one barrier to working with genetic data is the sheer volume of tools available, and the difficulty for a novice to assess their viability. By discussing in depth a couple of tools that are adequate for the calculation even at large scale, we hope to make polygenic risk scores more accessible to a wider range of researchers.

Background Right ventricular (RV) mass is an important predictor of outcome in patients with pulmonary arterial hypertension (PAH), and there is evidence that extremes of RV mass are associated with worse outcomes at a population level. Beyond PAH, the determinants of RV mass are poorly understood, and the relationships between RV mass and outcomes have not been explored. Methods UK Biobank is a large prospective cohort study. Demographics, lifestyle and medical history, and blood samples were obtained at enrolment. Health outcomes were ascertained by database linkage. Genotyping was performed using customised Affymetrix arrays. A subset of the cohort underwent cardiovascular magnetic resonance imaging. We used deep learning to derive left ventricular (LV) and RV volumes and mass, and segmentation accuracy was reviewed visually. We used linear and Cox proportional hazards regression to examine the relationships between cardiovascular risk factors and RV mass, and between RV mass and incident heart failure. We used two sample Mendelian randomisation (MR) to explore the causal associations between genetically determined exposures and outcomes. Results Baseline characteristics of the cohort are shown in Table 1. In a multivariable linear model (Table 2) age, non-white ethnicity and diabetes were associated with lower RV mass. Male sex, height, weight, physical activity and regular alcohol consumption were associated with higher RV mass. In a multivariable Cox model adjusted for age, sex, body mass index, socioeconomic deprivation, level of education, history of hypertension, diabetes and chronic lung disease, smoking status, alcohol consumption and physical activity, RV mass was associated with a higher hazard of heart failure (HR 1.10, 95%CI 1.01 to 1.19, p = 0.033) (Figure 1). Following additional adjustment for LV mass and LV end-diastolic volume, the direction of the relationship between RV mass and heart failure reversed (HR 0.86, 95%CI 0.78 to 0.95, p = 0.003). Genetically determined physical activity and alcohol consumption were associated with higher RV mass, although the statistical significance of the latter relationship was not robust across sensitivity analyses (Figure 2a). In separate MR models, genetically determined LV and RV mass were both associated with higher odds of developing heart failure. In a multivariable MR model controlling for the effect of LV mass, the direction of the relationship between genetically determined RV mass and heart failure reversed, and higher RV mass was associated with lower odds of heart failure (beta -1.058, 95% CI -1.76 to -0.35, p = 0.003), aligning with the observational findings (Figure 2b). Conclusions Physical activity is causally associated with higher RV mass. Alcohol consumption is correlated with higher RV mass, although the evidence for causation is less strong. After accounting for LV mass, higher RV mass appears to be protective against heart failure.