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Visceral adipose tissue (VAT)—fat stored around the internal organs—has been suggested as an independent risk factor for cardiovascular and metabolic disease 1 – 3 , as well as all-cause, cardiovascular-specific and cancer-specific mortality 4 , 5 . Yet, the contribution of genetics to VAT, as well as its disease-related effects, are largely unexplored due to the requirement for advanced imaging technologies to accurately measure VAT. Here, we develop sex-stratified, nonlinear prediction models (coefficient of determination = 0.76; typical 95% confidence interval (CI) = 0.74–0.78) for VAT mass using the UK Biobank cohort. We performed a genome-wide association study for predicted VAT mass and identified 102 novel visceral adiposity loci. Predicted VAT mass was associated with increased risk of hypertension, heart attack/angina, type 2 diabetes and hyperlipidemia, and Mendelian randomization analysis showed visceral fat to be a causal risk factor for all four diseases. In particular, a large difference in causal effect between the sexes was found for type 2 diabetes, with an odds ratio of 7.34 (95% CI = 4.48–12.0) in females and an odds ratio of 2.50 (95% CI = 1.98–3.14) in males. Our findings bolster the role of visceral adiposity as a potentially independent risk factor, in particular for type 2 diabetes in Caucasian females. Independent validation in other cohorts is necessary to determine whether the findings can translate to other ethnicities, or outside the UK. Analysis of the UK Biobank reveals new genetic loci associated with estimated visceral adipose tissue (VAT) mass, and suggests that VAT is potentially an independent risk factor for various cardiovascular and metabolic diseases, such as hypertension and type 2 diabetes.

Body mass and body fat composition are of clinical interest due to their links to cardiovascular- and metabolic diseases. Fat stored in the trunk has been suggested to be more pathogenic compared to fat stored in other compartments. In this study, we perform genome-wide association studies (GWAS) for the proportion of body fat distributed to the arms, legs and trunk estimated from segmental bio-electrical impedance analysis (sBIA) for 362,499 individuals from the UK Biobank. 98 independent associations with body fat distribution are identified, 29 that have not previously been associated with anthropometric traits. A high degree of sex-heterogeneity is observed and the effects of 37 associated variants are stronger in females compared to males. Our findings also implicate that body fat distribution in females involves mesenchyme derived tissues and cell types, female endocrine tissues as well as extracellular matrix maintenance and remodeling. Obesity and the distribution of fat within the body are risk factors for cardiometabolic diseases. Here, Rask-Andersen et al. perform GWAS for bio-electrical impedance measurements in UK Biobank participants and identify 29 novel independent loci for fat distribution and a high degree of sex-heterogeneity.

Previous genome-wide association studies (GWAS) have identified hundreds of genetic loci to be associated with body mass index (BMI) and risk of obesity. Genetic effects can differ between individuals depending on lifestyle or environmental factors due to gene-environment interactions. In this study, we examine gene-environment interactions in 362,496 unrelated participants with Caucasian ancestry from the UK Biobank resource. A total of 94 BMI-associated SNPs, selected from a previous GWAS on BMI, were used to construct weighted genetic scores for BMI (GSBMI). Linear regression modeling was used to estimate the effect of gene-environment interactions on BMI for 131 lifestyle factors related to: dietary habits, smoking and alcohol consumption, physical activity, socioeconomic status, mental health, sleeping patterns, as well as female-specific factors such as menopause and childbirth. In total, 15 lifestyle factors were observed to interact with GSBMI, of which alcohol intake frequency, usual walking pace, and Townsend deprivation index, a measure of socioeconomic status, were all highly significant (p = 1.45*10-29, p = 3.83*10-26, p = 4.66*10-11, respectively). Interestingly, the frequency of alcohol consumption, rather than the total weekly amount resulted in a significant interaction. The FTO locus was the strongest single locus interacting with any of the lifestyle factors. However, 13 significant interactions were also observed after omitting the FTO locus from the genetic score. Our analyses indicate that many lifestyle factors modify the genetic effects on BMI with some groups of individuals having more than double the effect of the genetic score. However, the underlying causal mechanisms of gene-environmental interactions are difficult to deduce from cross-sectional data alone and controlled experiments are required to fully characterise the causal factors.

Key Points Currently marketed drugs mediate their effects via only a limited number of molecular targets. The recent trends in drug development were analysed by extensively matching drugs with drug targets and correlating these with drug approval dates. We have identified 435 effect-mediating targets that were encoded by single positions on the human genome. We have also observed a steady rate of introduction of new drugs. Furthermore, in our data set there has been no substantial decrease in the number of new drugs approved by the US Food and Drug Administration each year. On average, approximately 18 new drugs that act on targets that are encoded by the human genome are approved for the US market every year. The majority of new drugs target previously exploited structures that are encoded by the human genome. On average, approximately 4.3 novel target drugs (NTDs) — that is, new drugs that target a previously unexploited molecular target that is encoded by the human genome — are approved for the US market every year. Our drug–target network analysis shows a connection between the majority of drugs that form a giant interconnected network that we have termed the 'giant component'. However, NTDs have a greater tendency to be disconnected from the giant component and form small isolated networks. These smaller networks are of particular interest as they not only represent novel drug targets but also often represent new molecular mechanisms for treatment. Schiöth and colleagues examine the drugs approved by the US Food and Drug Administration over the past 30 years and analyse the interactions of these drugs with therapeutic targets encoded by the human genome, identifying 435 effect-mediating drug targets. They also analyse trends in the introduction of drugs that modulate previously unexploited targets, and discuss the network pharmacology of the drugs in the data set. The discovery and exploitation of new drug targets is a key focus for both the pharmaceutical industry and academic biomedical research. To provide an insight into trends in the exploitation of new drug targets, we have analysed the drugs that were approved by the US Food and Drug Administration during the past three decades and examined the interactions of these drugs with therapeutic targets that are encoded by the human genome, using the DrugBank database and extensive manual curation. We have identified 435 effect-mediating drug targets in the human genome, which are modulated by 989 unique drugs, through 2,242 drug–target interactions. We also analyse trends in the introduction of drugs that modulate previously unexploited targets, and discuss the network pharmacology of the drugs in our data set.

Background Sensorineural hearing loss is one of the most common sensory deficiencies. However, the molecular contribution to age-related hearing loss is not fully elucidated. Methods We performed genome-wide association studies (GWAS) for hearing loss-related traits in the UK Biobank ( N = 362,396) and selected a high confidence set of ten hearing-associated gene products for staining in human cochlear samples: EYA4, LMX1A, PTK2/FAK, UBE3B, MMP2, SYNJ2, GRM5, TRIOBP, LMO-7, and NOX4. Results All proteins were found to be expressed in human cochlear structures. Our findings illustrate cochlear structures that mediate mechano-electric transduction of auditory stimuli, neuronal conductance, and neuronal plasticity to be involved in age-related hearing loss. Conclusions Our results suggest common genetic variation to influence structural resilience to damage as well as cochlear recovery after trauma, which protect against accumulated damage to cochlear structures and the development of hearing loss over time.

Advanced psychiatric treatments remain uncertain in preventing suicide among adolescents. Across the 21 Swedish regions, using nationwide registers between 2016–2020, we found negative correlation between adolescent excess suicide mortality (AESM) and regional frequencies of clozapine, ECT, and lithium (CEL) usage among adolescents (β = −0.613, p  = 0.0003, 95% CI: −0.338, −0.889) and males (β = −0.404, p  = 0.009, 95% CI: −0.130, −0.678). No correlation was found among females ( p  = 0.197). Highest CEL usage among male adolescents was seen in regions with lowest quartile (Q1) AESM (W = 74, p  = 0.012). Regional CEL treatment frequency in 15–19-year-olds was related to lower AESM in males, reflecting potential treatment efficacy, treatment compliance or better-quality mental health care. Suicide prevention may benefit from early recognition and CEL treatment for severe mental illness in male adolescents. The results indicate association but further research, using independent samples and both prospective and observational methodologies, is needed to confirm causality. There are conflicting results on the effectiveness of pharmacologic interventions for suicide prevention in adolescence. Here, the authors show, in a retrospective registry study from Sweden during 2016–2020, that regional utilization rates of clozapine, electroconvulsive therapy and lithium in 15–19-year-olds were associated with lower excess suicide death rates in male adolescents

Associations between epigenetic alterations and disease status have been identified for many diseases. However, there is no strong evidence that epigenetic alterations are directly causal for disease pathogenesis. In this study, we combined SNP and DNA methylation data with measurements of protein biomarkers for cancer, inflammation or cardiovascular disease, to investigate the relative contribution of genetic and epigenetic variation on biomarker levels. A total of 121 protein biomarkers were measured and analyzed in relation to DNA methylation at 470,000 genomic positions and to over 10 million SNPs. We performed epigenome-wide association study (EWAS) and genome-wide association study (GWAS) analyses, and integrated biomarker, DNA methylation and SNP data using between 698 and 1033 samples depending on data availability for the different analyses. We identified 124 and 45 loci (Bonferroni adjusted P < 0.05) with effect sizes up to 0.22 standard units' change per 1% change in DNA methylation levels and up to four standard units' change per copy of the effective allele in the EWAS and GWAS respectively. Most GWAS loci were cis-regulatory whereas most EWAS loci were located in trans. Eleven EWAS loci were associated with multiple biomarkers, including one in NLRC5 associated with CXCL11, CXCL9, IL-12, and IL-18 levels. All EWAS signals that overlapped with a GWAS locus were driven by underlying genetic variants and three EWAS signals were confounded by smoking. While some cis-regulatory SNPs for biomarkers appeared to have an effect also on DNA methylation levels, cis-regulatory SNPs for DNA methylation were not observed to affect biomarker levels. We present associations between protein biomarker and DNA methylation levels at numerous loci in the genome. The associations are likely to reflect the underlying pattern of genetic variants, specific environmental exposures, or represent secondary effects to the pathogenesis of disease.

Emotional unstable personality disorder (EUPD; previously borderline personality disorder, BPD) is associated with excess natural-cause mortality, comorbid medical conditions, poor health habits and stress related epigenomic alterations. Previous studies demonstrated that GrimAge – a state-of-the-art epigenetic age (EA) estimator – strongly predicts mortality risk and physiological dysregulation. Herein, we utilize the GrimAge algorithm to investigate whether women with EUPD and a history of recent suicide attempts exhibit EA acceleration (EAA) in comparison to healthy controls. Genome-wide methylation patterns were measured using the Illumina Infinum Methylation Epic BeadChip in whole blood from 97 EUPD patients and 32 healthy controls. The control group was significantly older (p < 0.0001) and reported lesser exposure to violent behavior in both youth and adulthood (p < 0.0001). Groups were otherwise comparable regarding gender, BMI, or tobacco usage (p > 0.05). EA estimator DNAmGrimAge exceeded chronological age by 8.8 and 2.3 years in the EUPD and control group, respectively. Similarly, EAA marker AgeAccelGrim was substantially higher in EUPD subjects when compared to controls, in both univariate and multivariate analyzes (p < 0.00001). Tobacco usage conferred substantial within-group effects on the EA-chronological age difference, i.e., 10.74 years (SD = 4.19) compared to 6.00 years (SD = 3.10) in the non-user EUPD group (p < 0.00001). Notably, past alcohol and substance abuse, use of psychotropic medications, global assessment of functioning, self-reported exposure to violent behavior in youth and adulthood, later completed suicide (N = 8) and age at first suicide attempt did not predict EAA in the EUPD group (p > 0.05). These results underscore the importance of addressing medical health conditions along with low-cost preventative interventions aimed at improving somatic health outcomes in EUPD, such as efforts to support cessation of tobacco use. The independency of GrimAge to other EA algorithms in this group of severely impaired EUPD patients, suggest it may have unique characteristics to evaluate risk of adverse health outcomes in context of psychiatric disorders.

Background Sixty percent of eating disorders do not meet criteria for anorexia- or bulimia nervosa, as defined by the Diagnostic and Statistical Manual version 4 (DSM-IV). Instead they are diagnosed as ‘eating disorders not otherwise specified’ (EDNOS). Discrepancies between criteria and clinical reality currently hampering eating disorder diagnoses in the DSM-IV will be addressed by the forthcoming DSM-V. However, future diagnoses for eating disorders will rely on current advances in the fields of neuroimaging and genetics for classification of symptoms that will ultimately improve treatment. Discussion Here we debate the classification issues, and discuss how brain imaging and genetic discoveries might be interwoven into a model of eating disorders to provide better classification and treatment. The debate concerns: a) current issues in the classification of eating disorders in the DSM-IV, b) changes proposed for DSM-V, c) neuroimaging eating disorder research and d) genetic eating disorder research. Summary We outline a novel evidence-based ‘impulse control’ spectrum model of eating disorders. A model of eating disorders is proposed that will aid future diagnosis of symptoms, coinciding with contemporary suggestions by clinicians and the proposed changes due to be published in the DSM-V.

Suicide attempts (SA) are associated with excess non-suicidal mortality, putatively mediated in part by premature cellular senescence. Epigenetic age (EA) estimators of biological age have been previously demonstrated to strongly predict physiological dysregulation and mortality risk. Herein, we investigate if violent SA with high intent-to-die is predictive of epigenetics-derived estimates of biological aging. The genome-wide methylation pattern was measured using the Illumina Infinium Methylation EPIC BeadChip in whole blood of 88 suicide attempters. Subjects were stratified into two groups based on the putative risk of later committed suicide (low- [ n  = 58] and high-risk [ n  = 30]) in dependency of SA method (violent or non-violent) and/or intent-to-die (high/low). Estimators of intrinsic and extrinsic EA acceleration, one marker optimized to predict physiological dysregulation (DNAmPhenoAge/AgeAccelPheno) and one optimized to predict lifespan (DNAmGrimAge/AgeAccelGrim) were investigated for associations to severity of SA, by univariate and multivariate analyses. The study was adequately powered to detect differences of 2.2 years in AgeAccelGrim in relation to SA severity. Baseline DNAmGrimAge exceeded chronological age by 7.3 years on average across all samples, conferring a mean 24.6% increase in relation to actual age. No individual EA acceleration marker was differentiated by suicidal risk group ( p  > 0.1). Thus, SA per se but not severity of SA is related to EA, implicating that excess non-suicidal mortality in SA is unrelated to risk of committed suicide. Preventative healthcare efforts aimed at curtailing excess mortality after SA may benefit from acting equally powerful to recognize somatic comorbidities irrespective of the severity inherent in the act itself.