In-silico functional analyses identify TMPRSS15-mediated intestinal absorption of lithium as a modulator of lithium response in bipolar disorder.

BACKGROUND: The therapeutic response to lithium in patients with bipolar disorder is highly variable and has a polygenic basis. Genome-wide association studies investigating lithium response have identified several relevant loci, though the precise mechanisms driving these associations are poorly understood. We aimed to prioritise the most likely effector gene and determine the mechanisms underlying an intergenic lithium response locus on chromosome 21 identified by the International Consortium of Lithium Genetics (ConLi+Gen). METHODS: We conducted in-silico functional analyses by integrating and synthesising information from several publicly available functional genetic datasets and databases including the Genotype-Tissue Expression (GTEx) project and HaploReg. RESULTS: The findings from this study highlighted TMPRSS15 as the most likely effector gene at the ConLi+Gen lithium response locus. TMPRSS15 encodes enterokinase, a gastrointestinal enzyme responsible for converting trypsinogen into trypsin and thus aiding digestion. Convergent findings from gene-based lookups in human and mouse databases as well as co-expression network analyses of small intestinal RNA-seq data (GTEx) implicated TMPRSS15 in the regulation of intestinal nutrient absorption, including ions like sodium and potassium, which may extend to lithium. LIMITATIONS: Although the findings from this study indicated that TMPRSS15 was the most likely effector gene at the ConLi+Gen lithium response locus, the evidence was circumstantial. Thus, the conclusions from this study need to be validated in appropriately designed wet-lab studies. CONCLUSIONS: The findings from this study are consistent with a model whereby TMPRSS15 impacts the efficacy of lithium treatment in patients with bipolar disorder by modulating intestinal lithium absorption.

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.

Cross-ancestry genetic architecture and prediction for cholesterol traits.

While cholesterol is essential, a high level of cholesterol is associated with the risk of cardiovascular diseases. Genome-wide association studies (GWASs) have proven successful in identifying genetic variants that are linked to cholesterol levels, predominantly in white European populations. However, the extent to which genetic effects on cholesterol vary across different ancestries remains largely unexplored. Here, we estimate cross-ancestry genetic correlation to address questions on how genetic effects are shared across ancestries. We find significant genetic heterogeneity between ancestries for cholesterol traits. Furthermore, we demonstrate that single nucleotide polymorphisms (SNPs) with concordant effects across ancestries for cholesterol are more frequently found in regulatory regions compared to other genomic regions. Indeed, the positive genetic covariance between ancestries is mostly driven by the effects of the concordant SNPs, whereas the genetic heterogeneity is attributed to the discordant SNPs. We also show that the predictive ability of the concordant SNPs is significantly higher than the discordant SNPs in the cross-ancestry polygenic prediction. The list of concordant SNPs for cholesterol is available in GWAS Catalog. These findings have relevance for the understanding of shared genetic architecture across ancestries, contributing to the development of clinical strategies for polygenic prediction of cholesterol in cross-ancestral settings.

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.

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.

A Metabolome-Wide Mendelian Randomization Study Identifies Dysregulated Arachidonic Acid Synthesis as a Potential Causal Risk Factor for Bipolar Disorder.

BACKGROUND: Bipolar disorder (BPD) is a debilitating mood disorder with an unclear etiology. A better understanding of the underlying pathophysiological mechanisms will help to identify novel targets for improved treatment options and prevention strategies. In this metabolome-wide Mendelian randomization study, we screened for metabolites that may have a causal role in BPD. METHODS: We tested a total of 913 circulating metabolite exposures assessed in 14,296 Europeans using a mass spectrometry-based platform. For the BPD outcome, we used summary data from the largest and most recent genome-wide association study reported to date, including 41,917 BPD cases. RESULTS: We identified 33 metabolites associated with BPD (padjusted < 5.48 × 10-5). Most of them were lipids, including arachidonic acid (ß = -0.154, SE = 0.023, p = 3.30 × 10-11), a polyunsaturated omega-6 fatty acid, along with several complex lipids containing either an arachidonic or a linoleic fatty acid side chain. These associations did not extend to other closely related psychiatric disorders like schizophrenia or depression, although they may be involved in the regulation of lithium response. These lipid associations were driven by genetic variants within the FADS1/2/3 gene cluster, which is a robust BPD risk locus encoding a family of fatty acid desaturase enzymes that are responsible for catalyzing the conversion of linoleic acid into arachidonic acid. Statistical colocalization analyses indicated that 27 of the 33 metabolites shared the same genetic etiology with BPD at the FADS1/2/3 cluster, demonstrating that our findings are not confounded by linkage disequilibrium. CONCLUSIONS: Overall, our findings support the notion that arachidonic acid and other polyunsaturated fatty acids may represent potential targets for BPD.

Mitigating type 1 error inflation and power loss in GxE PRS: Genotype-environment interaction in polygenic risk score models.

The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an individual's genetic profile. However, the impact of genotype-environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing genotype-environment interaction polygenic risk score (GxE PRS) models are often inappropriately used, which can result in inflated type 1 error rates and compromised results. In this study, we propose novel GxE PRS models that jointly incorporate additive and interaction genetic effects although also including an additional quadratic term for nongenetic covariates, enhancing their robustness against model misspecification. Through extensive simulations, we demonstrate that our proposed models outperform existing models in terms of controlling type 1 error rates and enhancing statistical power. Furthermore, we apply the proposed models to real data, and report significant GxE effects. Specifically, we highlight the impact of our models on both quantitative and binary traits. For quantitative traits, we uncover the GxE modulation of genetic effects on body mass index by alcohol intake frequency. In the case of binary traits, we identify the GxE modulation of genetic effects on hypertension by waist-to-hip ratio. These findings underscore the importance of employing a robust model that effectively controls type 1 error rates, thus preventing the occurrence of spurious GxE signals. To facilitate the implementation of our approach, we have developed an innovative R software package called GxEprs, specifically designed to detect and estimate GxE effects. Overall, our study highlights the importance of accurate GxE modeling and its implications for genetic risk prediction, although providing a practical tool to support further research in this area.

Unraveling phenotypic variance in metabolic syndrome through multi-omics.

Complex multi-omics effects drive the clustering of cardiometabolic risk factors, underscoring the imperative to comprehend how individual and combined omics shape phenotypic variation. Our study partitions phenotypic variance in metabolic syndrome (MetS), blood glucose (GLU), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and blood pressure through genome, transcriptome, metabolome, and exposome (i.e., lifestyle exposome) analyses. Our analysis included a cohort of 62,822 unrelated individuals with white British ancestry, sourced from the UK biobank. We employed linear mixed models to partition phenotypic variance using the restricted maximum likelihood (REML) method, implemented in MTG2 (v2.22). We initiated the analysis by individually modeling omics, followed by subsequent integration of pairwise omics in a joint model that also accounted for the covariance and interaction between omics layers. Finally, we estimated the correlations of various omics effects between the phenotypes using bivariate REML. Significant proportions of the MetS variance were attributed to distinct data sources: genome (9.47%), transcriptome (4.24%), metabolome (14.34%), and exposome (3.77%). The phenotypic variances explained by the genome, transcriptome, metabolome, and exposome ranged from 3.28% for GLU to 25.35% for HDL-C, 0% for GLU to 19.34% for HDL-C, 4.29% for systolic blood pressure (SBP) to 35.75% for TG, and 0.89% for GLU to 10.17% for HDL-C, respectively. Significant correlations were found between genomic and transcriptomic effects for TG and HDL-C. Furthermore, significant interaction effects between omics data were detected for both MetS and its components. Interestingly, significant correlation of omics effect between the phenotypes was found. This study underscores omics' roles, interaction effects, and random-effects covariance in unveiling phenotypic variation in multi-omics domains.

Genome-Wide Analyses of Vocabulary Size in Infancy and Toddlerhood: Associations With Attention-Deficit/Hyperactivity Disorder, Literacy, and Cognition-Related Traits.

BACKGROUND: The number of words children produce (expressive vocabulary) and understand (receptive vocabulary) changes rapidly during early development, partially due to genetic factors. Here, we performed a meta-genome-wide association study of vocabulary acquisition and investigated polygenic overlap with literacy, cognition, developmental phenotypes, and neurodevelopmental conditions, including attention-deficit/hyperactivity disorder (ADHD). METHODS: We studied 37,913 parent-reported vocabulary size measures (English, Dutch, Danish) for 17,298 children of European descent. Meta-analyses were performed for early-phase expressive (infancy, 15-18 months), late-phase expressive (toddlerhood, 24-38 months), and late-phase receptive (toddlerhood, 24-38 months) vocabulary. Subsequently, we estimated single nucleotide polymorphism-based heritability (SNP-h2) and genetic correlations (rg) and modeled underlying factor structures with multivariate models. RESULTS: Early-life vocabulary size was modestly heritable (SNP-h2 = 0.08-0.24). Genetic overlap between infant expressive and toddler receptive vocabulary was negligible (rg = 0.07), although each measure was moderately related to toddler expressive vocabulary (rg = 0.69 and rg = 0.67, respectively), suggesting a multifactorial genetic architecture. Both infant and toddler expressive vocabulary were genetically linked to literacy (e.g., spelling: rg = 0.58 and rg = 0.79, respectively), underlining genetic similarity. However, a genetic association of early-life vocabulary with educational attainment and intelligence emerged only during toddlerhood (e.g., receptive vocabulary and intelligence: rg = 0.36). Increased ADHD risk was genetically associated with larger infant expressive vocabulary (rg = 0.23). Multivariate genetic models in the ALSPAC (Avon Longitudinal Study of Parents and Children) cohort confirmed this finding for ADHD symptoms (e.g., at age 13; rg = 0.54) but showed that the association effect reversed for toddler receptive vocabulary (rg = -0.74), highlighting developmental heterogeneity. CONCLUSIONS: The genetic architecture of early-life vocabulary changes during development, shaping polygenic association patterns with later-life ADHD, literacy, and cognition-related traits.

Vitamin D Status in Women with a History of Infertility and Decreased Fecundability: A Population-Based Study.

BACKGROUND: Infertility and fecundability problems have been linked with lower 25-hydroxyvitamin D (25(OH)D) concentrations, but studies conducted with small, heterogenous or selected populations have shown inconsistent results. METHODS: This study included women at age 31 from prospective population-based Northern Finland Birth Cohort 1966. Serum 25(OH)D concentrations were evaluated between women with or without previous infertility examinations or treatments (infertility group, n = 375, reference group, n = 2051) and time to pregnancy (TTP) of over 12 months (decreased fecundability group, n = 338) with a wide range of confounders. Furthermore, 25(OH)D concentrations were also compared among reproductive outcomes. RESULTS: The mean 25(OH)D concentration was lower and 25(OH)D < 30 nmol/L was more frequent in women with a history of infertility compared to reference group. Moreover, 25(OH)D > 75 nmol/L was more frequent in the reference group. The mean 25(OH)D concentration was lower in women who had had multiple miscarriages. Both history of infertility (ß = -2.7, 95% confidence interval (CI) -4.6, -0.7) and decreased fecundability associated with lower 25(OH)D concentration (ß = -4.1, 95% CI -7.4, -0.8) after adjustments. In conclusion, this population-based study demonstrated that previous infertility and decreased fecundability were associated with lower 25(OH)D.

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.

Muscle Traits, Sarcopenia, and Sarcopenic Obesity: A Vitamin D Mendelian Randomization Study.

(1) Background: Observational studies associate vitamin D deficiency with muscle disorders, while some clinical trial data support a minor association between the vitamin and skeletal muscle performance in healthy subjects. Vitamin D receptor knockout mice studies confirm the relationship between vitamin D and skeletal muscle; however, causal inference in humans is challenging due to the ethical implications of including vitamin D-deficient participants in randomized trials. This study uses genetic methods to safely explore causal underpinnings for the relationship between 25(OH)D concentrations and skeletal muscle-related traits, including grip strength and combined arm skeletal muscle mass, and extends this analysis to suspected pathophysiology in the form of probable sarcopenia and sarcopenic obesity. (2) Methods: We conducted Mendelian randomization (MR) analyses in up to 307,281 participants from the UK Biobank of whom 25,414 had probable sarcopenia and 16,520 had sarcopenic obesity. In total, 35 variants were used to instrument 25(OH)D and MR analyses conducted using multiple approaches. (3) Results: Genetic analyses provided support for a relationship between genetically predicted higher 25(OH)D and skeletal muscle traits, with linear MR analyses for grip strength showing 0.11 kg (95% CI 0.04, 0.19) greater contractile force per 10 unit higher 25(OH)D, while there was a modest association with skeletal muscle mass (0.01 kg (95% CI 0.003, 0.02) greater muscle mass). For probable sarcopenia risk, there was suggestive evidence for lower odds by higher 25(OH)D (OR 0.96 (95% CI 0.92, 1.00)); however, this did not reflect an association with sarcopenic obesity (OR 0.97 (95% CI 0.93, 1.02)), but was seen in probable sarcopenia cases who were not obese (OR 0.92 (95% CI 0.86, 0.98)). Results were similar across multiple MR approaches. (4) Conclusions: Our study supports a causal relationship between 25(OH)D and skeletal muscle health. While evidence for benefit did not extend to lower risk of sarcopenic obesity, effective vitamin D-deficiency prevention strategies may help reduce age-related muscle weakness.

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.

Genetic effects on the timing of parturition and links to fetal birth weight.

The timing of parturition is crucial for neonatal survival and infant health. Yet, its genetic basis remains largely unresolved. We present a maternal genome-wide meta-analysis of gestational duration (n = 195,555), identifying 22 associated loci (24 independent variants) and an enrichment in genes differentially expressed during labor. A meta-analysis of preterm delivery (18,797 cases, 260,246 controls) revealed six associated loci and large genetic similarities with gestational duration. Analysis of the parental transmitted and nontransmitted alleles (n = 136,833) shows that 15 of the gestational duration genetic variants act through the maternal genome, whereas 7 act both through the maternal and fetal genomes and 2 act only via the fetal genome. Finally, the maternal effects on gestational duration show signs of antagonistic pleiotropy with the fetal effects on birth weight: maternal alleles that increase gestational duration have negative fetal effects on birth weight. The present study provides insights into the genetic effects on the timing of parturition and the complex maternal-fetal relationship between gestational duration and birth weight.

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.

Do Chronic Low Back Pain and Chronic Widespread Pain differ in their association with Depression Symptoms in the 1958 British Cohort?

OBJECTIVE: Depression frequently coexists with chronic pain. Contemporary models suggest that these conditions share pathobiological mechanisms, prompting a need to investigate their temporal association. This investigation aimed to explore two distinctly different chronic pain conditions, and their cross-sectional and prospective associations with depression. METHODS: Self-reported information was available on chronic widespread pain (CWP), chronic low back pain (CLBP) (45 years), and depression symptoms (45 and 50 years) from up to 9,377 participants in the 1958 British cohort. Depression symptom outcomes were derived by "Clinical Interview Schedule-Revised" (45 years) and "Short Form-36" (50 years). Relationships between both chronic pain conditions and depression symptoms were investigated by fitting four separate logistic regression models, each with varying levels of covariate adjustment, including depression at baseline. RESULTS: CWP was associated with depression symptoms cross-sectionally (odds ratio [OR] = 2.04, 95% confidence interval [CI] 1.65, 2.52; P < 0.001, n = 7,629), and prospectively when fully adjusted for baseline, sociodemographic, lifestyle, and health covariates (OR = 1.45, 95% CI 1.17, 1.80; P = < 0.001, n = 6,275). CLBP was associated with depression symptoms prospectively (full model: OR = 1.28, 95% CI 1.01, 1.61; P = 0.04, n = 6,288). In fully adjusted models the prospective association of CWP with depression symptoms was more heavily influenced by our covariates than CLBP with depression symptoms. CONCLUSION: Pain may be a stressor from which depression can arise. Development of depression may be differentially dependant upon the type of pain experienced. Screening for depression symptoms among individuals with both chronic pain conditions is indicated and should be repeated over time.

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.