Genome-wide characterization of circulating metabolic biomarkers.

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

Role of Gut Microbiota in Statin-Associated New-Onset Diabetes-A Cross-Sectional and Prospective Analysis of the FINRISK 2002 Cohort.

BACKGROUND: Dyslipidemia is treated effectively with statins, but treatment has the potential to induce new-onset type-2 diabetes. Gut microbiota may contribute to this outcome variability. We assessed the associations of gut microbiota diversity and composition with statins. Bacterial associations with statin-associated new-onset type-2 diabetes (T2D) risk were also prospectively evaluated. METHODS: We examined shallow-shotgun-sequenced fecal samples from 5755 individuals in the FINRISK-2002 population cohort with a 17+-year-long register-based follow-up. Alpha-diversity was quantified using Shannon index and beta-diversity with Aitchison distance. Species-specific differential abundances were analyzed using general multivariate regression. Prospective associations were assessed with Cox regression. Applicable results were validated using gradient boosting. RESULTS: Statin use associated with differing taxonomic composition (R2, 0.02%; q=0.02) and 13 differentially abundant species in fully adjusted models (MaAsLin; q<0.05). The strongest positive association was with Clostridium sartagoforme (ß=0.37; SE=0.13; q=0.02) and the strongest negative association with Bacteroides cellulosilyticus (ß=-0.31; SE=0.11; q=0.02). Twenty-five microbial features had significant associations with incident T2D in statin users, of which only Bacteroides vulgatus (HR, 1.286 [1.136-1.457]; q=0.03) was consistent regardless of model adjustment. Finally, higher statin-associated T2D risk was seen with [Ruminococcus] torques (ΔHRstatins, +0.11; q=0.03), Blautia obeum (ΔHRstatins, +0.06; q=0.01), Blautia sp. KLE 1732 (ΔHRstatins, +0.05; q=0.01), and beta-diversity principal component 1 (ΔHRstatin, +0.07; q=0.03) but only when adjusting for demographic covariates. CONCLUSIONS: Statin users have compositionally differing microbiotas from nonusers. The human gut microbiota is associated with incident T2D risk in statin users and possibly has additive effects on statin-associated new-onset T2D risk.

Author Correction: Exome sequencing of Finnish isolates enhances rare-variant association power.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

Exome sequencing of Finnish isolates enhances rare-variant association power.

Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power.

C-reactive protein modifies lipoprotein(a)-related risk for coronary heart disease: the BiomarCaRE project.

BACKGROUND AND AIMS: Recent investigations have suggested an interdependence of lipoprotein(a) [Lp(a)]-related risk for cardiovascular disease with background inflammatory burden. The aim the present analysis was to investigate whether high-sensitive C-reactive protein (hsCRP) modulates the association between Lp(a) and coronary heart disease (CHD) in the general population. METHODS: Data from 71 678 participants from 8 European prospective population-based cohort studies were used (65 661 without/6017 with established CHD at baseline; median follow-up 9.8/13.8 years, respectively). Fine and Gray competing risk-adjusted models were calculated according to accompanying hsCRP concentration (<2 and ≥2 mg/L). RESULTS: Among CHD-free individuals, increased Lp(a) levels were associated with incident CHD irrespective of hsCRP concentration: fully adjusted sub-distribution hazard ratios [sHRs (95% confidence interval)] for the highest vs. lowest fifth of Lp(a) distribution were 1.45 (1.23-1.72) and 1.48 (1.23-1.78) for a hsCRP group of <2 and ≥2 mg/L, respectively, with no interaction found between these two biomarkers on CHD risk (Pinteraction = 0.82). In those with established CHD, similar associations were seen only among individuals with hsCRP ≥ 2 mg/L [1.34 (1.03-1.76)], whereas among participants with a hsCRP concentration <2 mg/L, there was no clear association between Lp(a) and future CHD events [1.29 (0.98-1.71)] (highest vs. lowest fifth, fully adjusted models; Pinteraction = 0.024). CONCLUSIONS: While among CHD-free individuals Lp(a) was significantly associated with incident CHD regardless of hsCRP, in participants with CHD at baseline, Lp(a) was related to recurrent CHD events only in those with residual inflammatory risk. These findings might guide adequate selection of high-risk patients for forthcoming Lp(a)-targeting compounds.

Efficient computation of Faith's phylogenetic diversity with applications in characterizing microbiomes.

The number of publicly available microbiome samples is continually growing. As data set size increases, bottlenecks arise in standard analytical pipelines. Faith's phylogenetic diversity (Faith's PD) is a highly utilized phylogenetic alpha diversity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's phylogenetic diversity (SFPhD) enables calculation of this widely adopted diversity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting diversity differences between younger and older populations in the FINRISK study's metagenomic data.

Associations of plant-based foods, red and processed meat, and dairy with gut microbiome in Finnish adults.

PURPOSE: Population-based studies on the associations of plant-based foods, red meat or dairy with gut microbiome are scarce. We examined whether the consumption of plant-based foods (vegetables, potatoes, fruits, cereals), red and processed meat (RPM) or dairy (fermented milk, cheese, other dairy products) are related to gut microbiome in Finnish adults. METHODS: We utilized data from the National FINRISK/FINDIET 2002 Study (n = 1273, aged 25-64 years, 55% women). Diet was assessed with 48-hour dietary recalls. Gut microbiome was analyzed using shallow shotgun sequencing. We applied multivariate analyses with linear models and permutational ANOVAs adjusted for relevant confounders. RESULTS: Fruit consumption was positively (beta = 0.03, SE = 0.01, P = 0.04), while a dairy subgroup including milk, cream and ice-creams was inversely associated (beta=-0.03, SE 0.01, P = 0.02) with intra-individual gut microbiome diversity (alpha-diversity). Plant-based foods (R2 = 0.001, P = 0.03) and dairy (R2 = 0.002, P = 0.01) but not RPM (R2 = 0.001, P = 0.38) contributed to the compositional differences in gut microbiome (beta-diversity). Plant-based foods were associated with several butyrate producers/cellulolytic species including Roseburia hominis. RPM associations included an inverse association with R. hominis. Dairy was positively associated with several lactic producing/probiotic species including Lactobacillus delbrueckii and potentially opportunistic pathogens including Citrobacter freundii. Dairy, fermented milk, vegetables, and cereals were associated with specific microbial functions. CONCLUSION: Our results suggest a potential association between plant-based foods and dairy or their subgroups with microbial diversity measures. Furthermore, our findings indicated that all the food groups were associated with distinct overall microbial community compositions. Plant-based food consumption particularly was associated with a larger number of putative beneficial species.

ANGPTL8 protein-truncating variant associated with lower serum triglycerides and risk of coronary disease.

Protein-truncating variants (PTVs) affecting dyslipidemia risk may point to therapeutic targets for cardiometabolic disease. Our objective was to identify PTVs that were associated with both lipid levels and the risk of coronary artery disease (CAD) or type 2 diabetes (T2D) and assess their possible associations with risks of other diseases. To achieve this aim, we leveraged the enrichment of PTVs in the Finnish population and tested the association of low-frequency PTVs in 1,209 genes with serum lipid levels in the Finrisk Study (n = 23,435). We then tested which of the lipid-associated PTVs were also associated with the risks of T2D or CAD, as well as 2,683 disease endpoints curated in the FinnGen Study (n = 218,792). Two PTVs were associated with both lipid levels and the risk of CAD or T2D: triglyceride-lowering variants in ANGPTL8 (-24.0[-30.4 to -16.9] mg/dL per rs760351239-T allele, P = 3.4 × 10-9) and ANGPTL4 (-14.4[-18.6 to -9.8] mg/dL per rs746226153-G allele, P = 4.3 × 10-9). The risk of T2D was lower in carriers of the ANGPTL4 PTV (OR = 0.70[0.60-0.81], P = 2.2 × 10-6) than noncarriers. The odds of CAD were 47% lower in carriers of a PTV in ANGPTL8 (OR = 0.53[0.37-0.76], P = 4.5 × 10-4) than noncarriers. Finally, the phenome-wide scan of the ANGPTL8 PTV showed that the ANGPTL8 PTV carriers were less likely to use statin therapy (68,782 cases, OR = 0.52[0.40-0.68], P = 1.7 × 10-6) compared to noncarriers. Our findings provide genetic evidence of potential long-term efficacy and safety of therapeutic targeting of dyslipidemias.

Changes in the fine-scale genetic structure of Finland through the 20th century.

Information about individual-level genetic ancestry is central to population genetics, forensics and genomic medicine. So far, studies have typically considered genetic ancestry on a broad continental level, and there is much less understanding of how more detailed genetic ancestry profiles can be generated and how accurate and reliable they are. Here, we assess these questions by developing a framework for individual-level ancestry estimation within a single European country, Finland, and we apply the framework to track changes in the fine-scale genetic structure throughout the 20th century. We estimate the genetic ancestry for 18,463 individuals from the National FINRISK Study with respect to up to 10 genetically and geographically motivated Finnish reference groups and illustrate the annual changes in the fine-scale genetic structure over the decades from 1920s to 1980s for 12 geographic regions of Finland. We detected major changes after a sudden, internal migration related to World War II from the region of ceded Karelia to the other parts of the country as well as the effect of urbanization starting from the 1950s. We also show that while the level of genetic heterogeneity in general increases towards the present day, its rate of change has considerable differences between the regions. To our knowledge, this is the first study that estimates annual changes in the fine-scale ancestry profiles within a relatively homogeneous European country and demonstrates how such information captures a detailed spatial and temporal history of a population. We provide an interactive website for the general public to examine our results.

The gut microbiome is a significant risk factor for future chronic lung disease.

BACKGROUND: The gut-lung axis is generally recognized, but there are few large studies of the gut microbiome and incident respiratory disease in adults. OBJECTIVE: We sought to investigate the association and predictive capacity of the gut microbiome for incident asthma and chronic obstructive pulmonary disease (COPD). METHODS: Shallow metagenomic sequencing was performed for stool samples from a prospective, population-based cohort (FINRISK02; N = 7115 adults) with linked national administrative health register-derived classifications for incident asthma and COPD up to 15 years after baseline. Generalized linear models and Cox regressions were used to assess associations of microbial taxa and diversity with disease occurrence. Predictive models were constructed using machine learning with extreme gradient boosting. Models considered taxa abundances individually and in combination with other risk factors, including sex, age, body mass index, and smoking status. RESULTS: A total of 695 and 392 statistically significant associations were found between baseline taxonomic groups and incident asthma and COPD, respectively. Gradient boosting decision trees of baseline gut microbiome abundance predicted incident asthma and COPD in the validation data sets with mean area under the curves of 0.608 and 0.780, respectively. Cox analysis showed that the baseline gut microbiome achieved higher predictive performance than individual conventional risk factors, with C-indices of 0.623 for asthma and 0.817 for COPD. The integration of the gut microbiome and conventional risk factors further improved prediction capacities. CONCLUSIONS: The gut microbiome is a significant risk factor for incident asthma and incident COPD and is largely independent of conventional risk factors.

Prognostic Value of Cardiovascular Biomarkers in the Population.

Importance: Identification of individuals at high risk for atherosclerotic cardiovascular disease within the population is important to inform primary prevention strategies. Objective: To evaluate the prognostic value of routinely available cardiovascular biomarkers when added to established risk factors. Design, Setting, and Participants: Individual-level analysis including data on cardiovascular biomarkers from 28 general population-based cohorts from 12 countries and 4 continents with assessments by participant age. The median follow-up was 11.8 years. Exposure: Measurement of high-sensitivity cardiac troponin I, high-sensitivity cardiac troponin T, N-terminal pro-B-type natriuretic peptide, B-type natriuretic peptide, or high-sensitivity C-reactive protein. Main Outcomes and Measures: The primary outcome was incident atherosclerotic cardiovascular disease, which included all fatal and nonfatal events. The secondary outcomes were all-cause mortality, heart failure, ischemic stroke, and myocardial infarction. Subdistribution hazard ratios (HRs) for the association of biomarkers and outcomes were calculated after adjustment for established risk factors. The additional predictive value of the biomarkers was assessed using the C statistic and reclassification analyses. Results: The analyses included 164 054 individuals (median age, 53.1 years [IQR, 42.7-62.9 years] and 52.4% were women). There were 17 211 incident atherosclerotic cardiovascular disease events. All biomarkers were significantly associated with incident atherosclerotic cardiovascular disease (subdistribution HR per 1-SD change, 1.13 [95% CI, 1.11-1.16] for high-sensitivity cardiac troponin I; 1.18 [95% CI, 1.12-1.23] for high-sensitivity cardiac troponin T; 1.21 [95% CI, 1.18-1.24] for N-terminal pro-B-type natriuretic peptide; 1.14 [95% CI, 1.08-1.22] for B-type natriuretic peptide; and 1.14 [95% CI, 1.12-1.16] for high-sensitivity C-reactive protein) and all secondary outcomes. The addition of each single biomarker to a model that included established risk factors improved the C statistic. For 10-year incident atherosclerotic cardiovascular disease in younger people (aged <65 years), the combination of high-sensitivity cardiac troponin I, N-terminal pro-B-type natriuretic peptide, and high-sensitivity C-reactive protein resulted in a C statistic improvement from 0.812 (95% CI, 0.8021-0.8208) to 0.8194 (95% CI, 0.8089-0.8277). The combination of these biomarkers also improved reclassification compared with the conventional model. Improvements in risk prediction were most pronounced for the secondary outcomes of heart failure and all-cause mortality. The incremental value of biomarkers was greater in people aged 65 years or older vs younger people. Conclusions and Relevance: Cardiovascular biomarkers were strongly associated with fatal and nonfatal cardiovascular events and mortality. The addition of biomarkers to established risk factors led to only a small improvement in risk prediction metrics for atherosclerotic cardiovascular disease, but was more favorable for heart failure and mortality.

A Platelet Function Modulator of Thrombin Activation Is Causally Linked to Cardiovascular Disease and Affects PAR4 Receptor Signaling.

Dual antiplatelet therapy reduces ischemic events in cardiovascular disease, but it increases bleeding risk. Thrombin receptors PAR1 and PAR4 are drug targets, but the role of thrombin in platelet aggregation remains largely unexplored in large populations. We performed a genome-wide association study (GWAS) of platelet aggregation in response to full-length thrombin, followed by clinical association analyses, Mendelian randomization, and functional characterization including iPSC-derived megakaryocyte and platelet experiments. We identified a single sentinel variant in the GRK5 locus (rs10886430-G, p = 3.0 × 10-42) associated with increased thrombin-induced platelet aggregation (ß = 0.70, SE = 0.05). We show that disruption of platelet GRK5 expression by rs10886430-G is associated with enhanced platelet reactivity. The proposed mechanism of a GATA1-driven megakaryocyte enhancer is confirmed in allele-specific experiments. Utilizing further data, we demonstrate that the allelic effect is highly platelet- and thrombin-specific and not likely due to effects on thrombin levels. The variant is associated with increased risk of cardiovascular disease outcomes in UK BioBank, most strongly with pulmonary embolism. The variant associates with increased risk of stroke in the MEGASTROKE, UK BioBank, and FinnGen studies. Mendelian randomization analyses in independent samples support a causal role for rs10886430-G in increasing risk for stroke, pulmonary embolism, and venous thromboembolism through its effect on thrombin-induced platelet reactivity. We demonstrate that G protein-coupled receptor kinase 5 (GRK5) promotes platelet activation specifically via PAR4 receptor signaling. GRK5 inhibitors in development for the treatment of heart failure and cancer could have platelet off-target deleterious effects. Common variants in GRK5 may modify clinical outcomes with PAR4 inhibitors, and upregulation of GRK5 activity or signaling in platelets may have therapeutic benefits.

Genome-wide Association Analysis in Humans Links Nucleotide Metabolism to Leukocyte Telomere Length.

Leukocyte telomere length (LTL) is a heritable biomarker of genomic aging. In this study, we perform a genome-wide meta-analysis of LTL by pooling densely genotyped and imputed association results across large-scale European-descent studies including up to 78,592 individuals. We identify 49 genomic regions at a false dicovery rate (FDR) < 0.05 threshold and prioritize genes at 31, with five highlighting nucleotide metabolism as an important regulator of LTL. We report six genome-wide significant loci in or near SENP7, MOB1B, CARMIL1, PRRC2A, TERF2, and RFWD3, and our results support recently identified PARP1, POT1, ATM, and MPHOSPH6 loci. Phenome-wide analyses in >350,000 UK Biobank participants suggest that genetically shorter telomere length increases the risk of hypothyroidism and decreases the risk of thyroid cancer, lymphoma, and a range of proliferative conditions. Our results replicate previously reported associations with increased risk of coronary artery disease and lower risk for multiple cancer types. Our findings substantially expand current knowledge on genes that regulate LTL and their impact on human health and disease.

Longitudinal metabolomics of increasing body-mass index and waist-hip ratio reveals two dynamic patterns of obesity pandemic.

BACKGROUND/OBJECTIVE: This observational study dissects the complex temporal associations between body-mass index (BMI), waist-hip ratio (WHR) and circulating metabolomics using a combination of longitudinal and cross-sectional population-based datasets and new systems epidemiology tools. SUBJECTS/METHODS: Firstly, a data-driven subgrouping algorithm was employed to simplify high-dimensional metabolic profiling data into a single categorical variable: a self-organizing map (SOM) was created from 174 metabolic measures from cross-sectional surveys (FINRISK, n = 9708, ages 25-74) and a birth cohort (NFBC1966, n = 3117, age 31 at baseline, age 46 at follow-up) and an expert committee defined four subgroups of individuals based on visual inspection of the SOM. Secondly, the subgroups were compared regarding BMI and WHR trajectories in an independent longitudinal dataset: participants of the Young Finns Study (YFS, n = 1286, ages 24-39 at baseline, 10 years follow-up, three visits) were categorized into the four subgroups and subgroup-specific age-dependent trajectories of BMI, WHR and metabolic measures were modelled by linear regression. RESULTS: The four subgroups were characterised at age 39 by high BMI, WHR and dyslipidemia (designated TG-rich); low BMI, WHR and favourable lipids (TG-poor); low lipids in general (Low lipid) and high low-density-lipoprotein cholesterol (High LDL-C). Trajectory modelling of the YFS dataset revealed a dynamic BMI divergence pattern: despite overlapping starting points at age 24, the subgroups diverged in BMI, fasting insulin (three-fold difference at age 49 between TG-rich and TG-poor) and insulin-associated measures such as triglyceride-cholesterol ratio. Trajectories also revealed a WHR progression pattern: despite different starting points at the age of 24 in WHR, LDL-C and cholesterol-associated measures, all subgroups exhibited similar rates of change in these measures, i.e. WHR progression was uniform regardless of the cross-sectional metabolic profile. CONCLUSIONS: Age-associated weight variation in adults between 24 and 49 manifests as temporal divergence in BMI and uniform progression of WHR across metabolic health strata.

An arrhythmogenic metabolite in atrial fibrillation.

BACKGROUND: Long-chain acyl-carnitines (ACs) are potential arrhythmogenic metabolites. Their role in atrial fibrillation (AF) remains incompletely understood. Using a systems medicine approach, we assessed the contribution of C18:1AC to AF by analysing its in vitro effects on cardiac electrophysiology and metabolism, and translated our findings into the human setting. METHODS AND RESULTS: Human iPSC-derived engineered heart tissue was exposed to C18:1AC. A biphasic effect on contractile force was observed: short exposure enhanced contractile force, but elicited spontaneous contractions and impaired Ca2+ handling. Continuous exposure provoked an impairment of contractile force. In human atrial mitochondria from AF individuals, C18:1AC inhibited respiration. In a population-based cohort as well as a cohort of patients, high C18:1AC serum concentrations were associated with the incidence and prevalence of AF. CONCLUSION: Our data provide evidence for an arrhythmogenic potential of the metabolite C18:1AC. The metabolite interferes with mitochondrial metabolism, thereby contributing to contractile dysfunction and shows predictive potential as novel circulating biomarker for risk of AF.

Risk prediction of atrial fibrillation and its complications in the community using hs troponin I.

AIMS: Atrial fibrillation (AF) is becoming increasingly common. Traditional cardiovascular risk factors (CVRF) do not explain all AF cases. Blood-based biomarkers reflecting cardiac injury such as high-sensitivity troponin I (hsTnI) may help close this gap. METHODS: We investigated the predictive ability of hsTnI for incident AF in 45,298 participants (median age 51.4 years, 45.0% men) across European community cohorts in comparison to CVRF and established biomarkers (C-reactive protein, N-terminal pro B-type natriuretic peptide). RESULTS: During a median follow-up of 7.7 years, 1734 (3.8%) participants developed AF. Those in the highest hsTnI quarter (≥4.2 ng/L) had a 3.91-fold (95% confidence interval (CI) 3.30, 4.63; p < .01) risk for developing AF compared to the lowest quarter (<1.4 ng/L). In multivariable-adjusted Cox proportional hazards models a statistically significant association was seen between hsTnI and AF (hazard ratio (HR) per 1 standard deviation (SD) increase in log10(hsTnI) 1.08; 95% CI 1.01, 1.16; p = .03). Inclusion of hsTnI did improve model discrimination (C-index CVRF 0.811 vs. C-index CVRF and hsTnI 0.813; p < .01). Higher hsTnI concentrations were associated with heart failure (HR per SD 1.37; 95% CI 1.12, 1.68; p < .01) and overall mortality (HR per SD 1.24; 95% CI 1.09, 1.41; p < .01). CONCLUSION: hsTnI as a biomarker of myocardial injury does not improve prediction of AF incidence beyond classical CVRF and NT-proBNP. However, it is associated with the AF-related disease heart failure and mortality likely reflecting underlying subclinical cardiovascular impairment.

Correction: Lipoprotein signatures of cholesteryl ester transfer protein and HMG-CoA reductase inhibition.

[This corrects the article DOI: 10.1371/journal.pbio.3000572.].

Abdominal obesity and alcohol use modify the impact of genetic risk for incident advanced liver disease in the general population.

BACKGROUND & AIMS: Genetic variants, abdominal obesity and alcohol use are risk factors for incident liver disease (ILD). We aimed to study whether variants either alone or when aggregated into genetic risk scores (GRSs) associate with ILD, and whether waist-hip ratio (WHR) or alcohol use interacts with this risk. METHODS: Our study included 33 770 persons (mean age 50 years, 47% men) who participated in health-examination surveys (FINRISK 1992-2012 or Health 2000) with data on alcohol use, WHR and 63 genotypes associated with liver disease. Data were linked with national health registers for liver-related outcomes (hospitalizations, malignancies and death). Exclusions were baseline clinical liver disease. Mean follow-up time was 12.2 years. Cox regression analyses between variants and ILD were adjusted for age, sex and BMI. RESULTS: Variants in PNPLA3, IFNL4, TM6SF2, FDFT1, PPP1R3B, SERPINA1 and HSD17B13 were associated with ILD. GRSs calculated from these variants were not associated with WHR or alcohol use, but were exponentially associated with ILD (up to 25-fold higher risk in high versus low score). The risk of ILD in individuals with high GRS and high WHR or alcohol use compared with those with none of these risk factors was increased by up to 90-fold. GRSs provided new prognostic information particularly in individuals with high WHR. CONCLUSIONS: The effect of multiple genetic variants on the risk of ILD is potentiated by abdominal obesity and alcohol use. Simple GRSs may help to identify individuals with adverse lifestyle who are at a particularly high risk of ILD.

Persistent organic pollutants associate with liver disease in a Finnish general population sample.

BACKGROUND AND AIMS: Persistent organic pollutants (POPs) have multiple adverse effects on human health. Recent studies show a possible association with liver disease, but population-based data are scarce. In this population-based study, we studied the associations between POPs and biomarkers of liver disease and incident liver disease. METHODS: This study consisted of 2789 adults that participated in the environmental toxin subset of the Finnish health-examination survey, FINRISK 2007. Toxins were measured from serum samples, and standard liver tests and dynamic aspartate aminotransferase-alanine aminotransferase ratio (dAAR) were measured as biomarkers of liver function. Associations between POPs and the biomarkers were then analysed using linear regression. Associations between POPs and incident liver disease (n = 36) were analysed by Cox regression. RESULTS: Organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs) and several perfluorinated alkyl substances exhibited statistically significant positive associations with several biomarkers of liver injury (betacoefficient per SD 0.04-0.14, p < 0.05). These associations were stronger in subgroups of individuals with obesity or non-alcoholic fatty liver disease. OCPs, PCBs and perfluoro-octanoic acid also had significant positive associations with dAAR, which can be used to predict risk of incident severe liver outcomes (beta coefficient per SD 0.05-0.08, p < 0.05). OCPs and PCBs were also significantly and positively associated with incident liver disease (hazard ratio per SD 1.82 95% CI 1.21-2.73, p < 0.01 and hazard ratio per SD 1.69, 95% CI 1.07-2.68, p < 0.05 respectively). CONCLUSIONS: Several POPs show positive associations with markers of liver injury and incident liver disease, suggesting that environmental toxins are important risk factors for chronic liver disease.

Exploring the incremental utility of circulating biomarkers for robust risk prediction of incident atrial fibrillation in European cohorts using regressions and modern machine learning methods.

AIMS: To identify robust circulating predictors for incident atrial fibrillation (AF) using classical regressions and machine learning (ML) techniques within a broad spectrum of candidate variables. METHODS AND RESULTS: In pooled European community cohorts (n = 42 280 individuals), 14 routinely available biomarkers mirroring distinct pathophysiological pathways including lipids, inflammation, renal, and myocardium-specific markers (N-terminal pro B-type natriuretic peptide [NT-proBNP], high-sensitivity troponin I [hsTnI]) were examined in relation to incident AF using Cox regressions and distinct ML methods. Of 42 280 individuals (21 843 women [51.7%]; median [interquartile range, IQR] age, 52.2 [42.7, 62.0] years), 1496 (3.5%) developed AF during a median follow-up time of 5.7 years. In multivariable-adjusted Cox-regression analysis, NT-proBNP was the strongest circulating predictor of incident AF [hazard ratio (HR) per standard deviation (SD), 1.93 (95% CI, 1.82-2.04); P < 0.001]. Further, hsTnI [HR per SD, 1.18 (95% CI, 1.13-1.22); P < 0.001], cystatin C [HR per SD, 1.16 (95% CI, 1.10-1.23); P < 0.001], and C-reactive protein [HR per SD, 1.08 (95% CI, 1.02-1.14); P = 0.012] correlated positively with incident AF. Applying various ML techniques, a high inter-method consistency of selected candidate variables was observed. NT-proBNP was identified as the blood-based marker with the highest predictive value for incident AF. Relevant clinical predictors were age, the use of antihypertensive medication, and body mass index. CONCLUSION: Using different variable selection procedures including ML methods, NT-proBNP consistently remained the strongest blood-based predictor of incident AF and ranked before classical cardiovascular risk factors. The clinical benefit of these findings for identifying at-risk individuals for targeted AF screening needs to be elucidated and tested prospectively.