Summary Background To our knowledge, a systematic comparison of predictors of mortality in middle-aged to elderly individuals has not yet been done. We investigated predictors of mortality in UK ...Biobank participants during a 5 year period. We aimed to investigate the associations between most of the available measurements and 5 year all-cause and cause-specific mortality, and to develop and validate a prediction score for 5 year mortality using only self-reported information. Methods Participants were enrolled in the UK Biobank from April, 2007, to July, 2010, from 21 assessment centres across England, Wales, and Scotland with standardised procedures. In this prospective population-based study, we assessed sex-specific associations of 655 measurements of demographics, health, and lifestyle with all-cause mortality and six cause-specific mortality categories in UK Biobank participants using the Cox proportional hazard model. We excluded variables that were missing in more than 80% of the participants and all cardiorespiratory fitness test measurements because summary data were not available. Validation of the prediction score was done in participants enrolled at the Scottish centres. UK life tables and census information were used to calibrate the score to the overall UK population. Findings About 500 000 participants were included in the UK Biobank. We excluded participants with more than 80% variables missing (n=746). Of 498 103 UK Biobank participants included (54% of whom were women) aged 37–73 years, 8532 (39% of whom were women) died during a median follow-up of 4·9 years (IQR 4·33–5·22). Self-reported health (C-index including age 0·74 95% CI 0·73–0·75) was the strongest predictor of all-cause mortality in men and a previous cancer diagnosis (0·73 0·72–0·74) was the strongest predictor of all-cause mortality in women. When excluding individuals with major diseases or disorders (Charlson comorbidity index >0; n=355 043), measures of smoking habits were the strongest predictors of all-cause mortality. The prognostic score including 13 self-reported predictors for men and 11 for women achieved good discrimination (0·80 0·77–0·83 for men and 0·79 0·76–0·83 for women) and significantly outperformed the Charlson comorbidity index (p<0·0001 in men and p=0·0007 in women). A dedicated website allows the interactive exploration of all results along with calculation of individual risk through an online questionnaire. Interpretation Measures that can simply be obtained by questionnaires and without physical examination were the strongest predictors of all-cause mortality in the UK Biobank population. The prediction score we have developed accurately predicts 5 year all-cause mortality and can be used by individuals to improve health awareness, and by health professionals and organisations to identify high-risk individuals and guide public policy. Funding Knut and Alice Wallenberg Foundation and the Swedish Research Council.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
IMPORTANCE: Individuals with mental disorders often develop comorbidity over time. Past studies of comorbidity have often restricted analyses to a subset of disorders and few studies have provided ...absolute risks of later comorbidity. OBJECTIVES: To undertake a comprehensive study of comorbidity within mental disorders, by providing temporally ordered age- and sex-specific pairwise estimates between the major groups of mental disorders, and to develop an interactive website to visualize all results and guide future research and clinical practice. DESIGN, SETTING, AND PARTICIPANTS: This population-based cohort study included all individuals born in Denmark between January 1, 1900, and December 31, 2015, and living in the country between January 1, 2000, and December 31, 2016. The analyses were conducted between June 2017 and May 2018. MAIN OUTCOMES AND MEASURES: Danish health registers were used to identify mental disorders, which were examined within the broad 10-level International Statistical Classification of Diseases and Related Health Problems, 10th Revision, subchapter groups (eg, codes F00-F09 and F10-F19). For each temporally ordered pair of disorders, overall and lagged hazard ratios and 95% CIs were calculated using Cox proportional hazards regression models. Absolute risks were estimated using competing risks survival analyses. Estimates for each sex were generated. RESULTS: A total of 5 940 778 persons were included in this study (2 958 293 men and 2 982 485 women; mean SD age at beginning of follow-up, 32.1 25.4 years). They were followed up for 83.9 million person-years. All mental disorders were associated with an increased risk of all other mental disorders when adjusting for sex, age, and calendar time (hazard ratios ranging from 2.0 95% CI, 1.7-2.4 for prior intellectual disabilities and later eating disorders to 48.6 95% CI, 46.6-50.7 for prior developmental disorders and later intellectual disabilities). The hazard ratios were temporally patterned, with higher estimates during the first year after the onset of the first disorder, but with persistently elevated rates during the entire observation period. Some disorders were associated with substantial absolute risks of developing specific later disorders (eg, 30.6% 95% CI, 29.3%-32.0% of men and 38.4% 95% CI, 37.5%-39.4% of women with a diagnosis of mood disorders before age 20 years developed neurotic disorders within the following 5 years). CONCLUSIONS AND RELEVANCE: Comorbidity within mental disorders is pervasive, and the risk persists over time. This study provides disorder-, sex-, and age-specific relative and absolute risks of the comorbidity of mental disorders. Web-based interactive data visualization tools are provided for clinical utility.
The OAS1/2/3 cluster has been identified as a risk locus for severe COVID-19 among individuals of European ancestry, with a protective haplotype of approximately 75 kilobases (kb) derived from ...Neanderthals in the chromosomal region 12q24.13. This haplotype contains a splice variant of OAS1, which occurs in people of African ancestry independently of gene flow from Neanderthals. Using trans-ancestry fine-mapping approaches in 20,779 hospitalized cases, we demonstrate that this splice variant is likely to be the SNP responsible for the association at this locus, thus strongly implicating OAS1 as an effector gene influencing COVID-19 severity.
Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs ...genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution.
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Accurate risk assessment of an individuals' propensity to develop cardiovascular diseases (CVDs) is crucial for the prevention of these conditions. Numerous published risk prediction models used for ...CVD risk assessment are based on conventional risk factors and include only a limited number of biomarkers. The addition of novel biomarkers can boost the discriminative ability of risk prediction models for CVDs with different pathogenesis. The present study reports the development of risk prediction models for a range of heterogeneous CVDs, including coronary artery disease (CAD), stroke, deep vein thrombosis (DVT), and abdominal aortic aneurysm (AAA), as well as for Type 2 diabetes mellitus (DM2), a major CVD risk factor. In addition to conventional risk factors, the models incorporate various blood biomarkers and comorbidities to improve both individual and population stratification. An automatic variable selection approach was developed to generate the best set of explanatory variables for each model from the initial panel of risk factors. In total, up to 254,220 UK Biobank participants (ranging from 215,269 to 254,220 for different CVDs and DM2) were included in the analyses. The derived prediction models utilizing Cox proportional hazards regression achieved consistent discrimination performance (C-index) for all diseases: CAD, 0.794 (95% CI, 0.787-0.801); DM2, 0.909 (95% CI, 0.903-0.916); stroke, 0.778 (95% CI, 0.756-0.801); DVT, 0.743 (95% CI, 0.737-0.749); and AAA, 0.893 (95% CI, 0.874-0.912). When validated on various subpopulations, they demonstrated higher discrimination in healthier and middle-age individuals. In general, calibration of a five-year risk of developing the CVDs and DM2 demonstrated incremental overestimation of disease-related conditions amongst the highest decile of risk probabilities. In summary, the risk prediction models described were validated with high discrimination and good calibration for several CVDs and DM2. These models incorporate multiple shared predictor variables and may be integrated into a single platform to enhance clinical stratification to impact health outcomes.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Common variant heritability has been widely reported to be concentrated in variants within cell-type-specific non-coding functional annotations, but little is known about low-frequency variant ...functional architectures. We partitioned the heritability of both low-frequency (0.5%≤ minor allele frequency <5%) and common (minor allele frequency ≥5%) variants in 40 UK Biobank traits across a broad set of functional annotations. We determined that non-synonymous coding variants explain 17 ± 1% of low-frequency variant heritability (Formula: see text) versus 2.1 ± 0.2% of common variant heritability (Formula: see text). Cell-type-specific non-coding annotations that were significantly enriched for Formula: see text of corresponding traits were similarly enriched for Formula: see text for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain dorsolateral prefrontal cortex explain 57 ± 12% of Formula: see text versus 12 ± 2% of Formula: see text for neuroticism. Forward simulations confirmed that low-frequency variant enrichment depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict effect size variance of causal rare variants (minor allele frequency <0.5%).
Analyses of circulating metabolites in large prospective epidemiological studies could lead to improved prediction and better biological understanding of coronary heart disease (CHD). We performed a ...mass spectrometry-based non-targeted metabolomics study for association with incident CHD events in 1,028 individuals (131 events; 10 y. median follow-up) with validation in 1,670 individuals (282 events; 3.9 y. median follow-up). Four metabolites were replicated and independent of main cardiovascular risk factors lysophosphatidylcholine 18∶1 (hazard ratio HR per standard deviation SD increment = 0.77, P-value<0.001), lysophosphatidylcholine 18∶2 (HR = 0.81, P-value<0.001), monoglyceride 18∶2 (MG 18∶2; HR = 1.18, P-value = 0.011) and sphingomyelin 28∶1 (HR = 0.85, P-value = 0.015). Together they contributed to moderate improvements in discrimination and re-classification in addition to traditional risk factors (C-statistic: 0.76 vs. 0.75; NRI: 9.2%). MG 18∶2 was associated with CHD independently of triglycerides. Lysophosphatidylcholines were negatively associated with body mass index, C-reactive protein and with less evidence of subclinical cardiovascular disease in additional 970 participants; a reverse pattern was observed for MG 18∶2. MG 18∶2 showed an enrichment (P-value = 0.002) of significant associations with CHD-associated SNPs (P-value = 1.2×10-7 for association with rs964184 in the ZNF259/APOA5 region) and a weak, but positive causal effect (odds ratio = 1.05 per SD increment in MG 18∶2, P-value = 0.05) on CHD, as suggested by Mendelian randomization analysis. In conclusion, we identified four lipid-related metabolites with evidence for clinical utility, as well as a causal role in CHD development.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Understanding the causal impact that clinical risk factors have on healthcare-related costs is critical to evaluate healthcare interventions. Here, we used a genetically-informed design, Mendelian ...Randomization (MR), to infer the causal impact of 15 risk factors on annual total healthcare costs. We calculated healthcare costs for 373,160 participants from the FinnGen Study and replicated our results in 323,774 individuals from the United Kingdom and Netherlands. Robust causal effects were observed for waist circumference (WC), adult body mass index, and systolic blood pressure, in which a standard deviation increase corresponded to 22.78% 95% CI: 18.75-26.95, 13.64% 10.26-17.12, and 13.08% 8.84-17.48 increased healthcare costs, respectively. A lack of causal effects was observed for certain clinically relevant biomarkers, such as albumin, C-reactive protein, and vitamin D. Our results indicated that increased WC is a major contributor to annual total healthcare costs and more attention may be given to WC screening, surveillance, and mitigation.
There is a limited understanding about the impact of rare protein-truncating variants across multiple phenotypes. We explore the impact of this class of variants on 13 quantitative traits and 10 ...diseases using whole-exome sequencing data from 100,296 individuals. Protein-truncating variants in genes intolerant to this class of mutations increased risk of autism, schizophrenia, bipolar disorder, intellectual disability, and ADHD. In individuals without these disorders, there was an association with shorter height, lower education, increased hospitalization, and reduced age at enrollment. Gene sets implicated from GWASs did not show a significant protein-truncating variants burden beyond what was captured by established Mendelian genes. In conclusion, we provide a thorough investigation of the impact of rare deleterious coding variants on complex traits, suggesting widespread pleiotropic risk.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
OBJECTIVE—Current guidelines do not support the use of genetic profiles in risk assessment of coronary heart disease (CHD). However, new single nucleotide polymorphisms associated with CHD and ...intermediate cardiovascular traits have recently been discovered. We aimed to compare several multilocus genetic risk score (MGRS) in terms of association with CHD and to evaluate clinical use.
APPROACH AND RESULTS—We investigated 6 Swedish prospective cohort studies with 10 612 participants free of CHD at baseline. We developed 1 overall MGRS based on 395 single nucleotide polymorphisms reported as being associated with cardiovascular traits, 1 CHD-specific MGRS, including 46 single nucleotide polymorphisms, and 6 trait-specific MGRS for each established CHD risk factors. Both the overall and the CHD-specific MGRS were significantly associated with CHD risk (781 incident events; hazard ratios for fourth versus first quartile, 1.54 and 1.52; P<0.001) and improved risk classification beyond established risk factors (net reclassification improvement, 4.2% and 4.9%; P=0.006 and 0.017). Discrimination improvement was modest (C-index improvement, 0.004). A polygene MGRS performed worse than the CHD-specific MGRS. We estimate that 1 additional CHD event for every 318 people screened at intermediate risk could be saved by measuring the CHD-specific genetic score in addition to the established risk factors.
CONCLUSIONS—Our results indicate that genetic information could be of some clinical value for prediction of CHD, although further studies are needed to address aspects, such as feasibility, ethics, and cost efficiency of genetic profiling in the primary prevention setting.
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