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Abstract Identifying biomarkers able to discriminate individuals on different health trajectories is crucial to understand the molecular basis of age-related morbidity. We investigated multi-omics signatures of general health and organ-specific morbidity, as well as their interconnectivity. We examined cross-sectional metabolome and proteome data from 3,142 adults of the Cooperative Health Research in South Tyrol (CHRIS) study, an Alpine population study designed to investigate how human biology, environment, and lifestyle factors contribute to people’s health over time. We had 174 metabolites and 148 proteins quantified from fasting serum and plasma samples. We used the Cumulative Illness Rating Scale (CIRS) Comorbidity Index (CMI), which considers morbidity in 14 organ systems, to assess health status (any morbidity vs. healthy). Omics-signatures for health status were identified using random forest (RF) classifiers. Linear regression models were fitted to assess directionality of omics markers and health status associations, as well as to identify omics markers related to organ-specific morbidity. Next to age, we identified 21 metabolites and 10 proteins as relevant predictors of health status and results confirmed associations for serotonin and glutamate to be age-independent. Considering organ-specific morbidity, several metabolites and proteins were jointly related to endocrine, cardiovascular, and renal morbidity. To conclude, circulating serotonin was identified as a potential novel predictor for overall morbidity.

Background Ventricular repolarization time (ECG QT and JT intervals) is associated with malignant arrhythmia. Genome‐wide association studies have identified 230 independent loci for QT and JT; however, 50% of their heritability remains unexplained. Previous work supports a causal effect of lower serum calcium concentrations on longer ventricular repolarization time. We hypothesized calcium interactions with QT and JT variant associations could explain a proportion of the missing heritability. Methods and Results We performed genome‐wide calcium interaction analyses for QT and JT intervals. Participants were stratified by their calcium level relative to the study distribution (top or bottom 20%). We performed a 2‐stage analysis (genome‐wide discovery [N=62 532] and replication [N=59 861] of lead variants) and a single‐stage genome‐wide meta‐analysis (N=122 393, [European ancestry N=117 581, African ancestry N=4812]). We also calculated 2‐degrees of freedom joint main and interaction and 1‐degree of freedom interaction P values. In 2‐stage and single‐stage analyses, 50 and 98 independent loci, respectively, were associated with either QT or JT intervals (2‐degrees of freedom joint main and interaction P value

Abstract Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.

Summary: Complement is a fundamental innate immune response component. Its alterations are associated with severe systemic diseases. To illuminate the complement’s genetic underpinnings, we conduct genome-wide association studies of the functional activity of the classical (CP), lectin (LP), and alternative (AP) complement pathways in the Cooperative Health Research in South Tyrol study (n = 4,990). We identify seven loci, encompassing 13 independent, pathway-specific variants located in or near complement genes (CFHR4, C7, C2, MBL2) and non-complement genes (PDE3A, TNXB, ABO), explaining up to 74% of complement pathways’ genetic heritability and implicating long-range haplotypes associated with LP at MBL2. Two-sample Mendelian randomization analyses, supported by transcriptome- and proteome-wide colocalization, confirm known causal pathways, establish within-complement feedback loops, and implicate causality of ABO on LP and of CFHR2 and C7 on AP. LP causally influences collectin-11 and KAAG1 levels and the risk of mouth ulcers. These results build a comprehensive resource to investigate the role of complement in human health.

Abstract Background The recent progress in molecular biology generates an increasing interest in investigating molecular biomarkers as markers of response to treatments. The present work is motivated by a study, where the objective was to explore the potential of the molecular biomarkers of renin-angiotensin-aldosterone system (RAAS) to identify the undertaken antihypertensive treatments in the general population. Population-based studies offer an opportunity to assess the effectiveness of treatments in real-world scenarios. However, lack of quality documentation, especially when electronic health record linkage is unavailable, leads to inaccurate reporting and classification bias. Method We present a machine learning clustering technique to determine the potential of measured RAAS biomarkers for the identification of undertaken treatments in the general population. The biomarkers were simultaneously determined through a novel mass-spectrometry analysis in 800 participants of the Cooperative Health Research In South Tyrol (CHRIS) study with documented antihypertensive treatments. We assessed the agreement, sensitivity and specificity of the resulting clusters against known treatment types. Through the lasso penalized regression, we identified clinical characteristics associated with the biomarkers, accounting for the effects of cluster and treatment classifications. Results We identified three well-separated clusters: cluster 1 (n = 444) preferentially including individuals not receiving RAAS-targeting drugs; cluster 2 (n = 235) identifying angiotensin type 1 receptor blockers (ARB) users (weighted kappa κw = 74%; sensitivity = 73%; specificity = 83%); and cluster 3 (n = 121) well discriminating angiotensin-converting enzyme inhibitors (ACEi) users (κw = 81%; sensitivity = 55%; specificity = 90%). Individuals in clusters 2 and 3 had higher frequency of diabetes as well as higher fasting glucose and BMI levels. Age, sex and kidney function were strong predictors of the RAAS biomarkers independently of the cluster structure. Conclusions Unsupervised clustering of angiotensin-based biomarkers is a viable technique to identify individuals on specific antihypertensive treatments, pointing to a potential application of the biomarkers as useful clinical diagnostic tools even outside of a controlled clinical setting.

The spatial and frontal QRS-T angles are electrocardiographic (ECG) predictors for arrhythmia. This work used genetic analyses to identify associated loci and pathways, and explore their relationships with other ECG traits and cardiovascular disease.

The QT interval is a heritable electrocardiographic measure associated with arrhythmia risk when prolonged. Here, the authors used a series of genetic analyses to identify genetic loci, pathways, therapeutic targets, and relationships with cardiovascular disease.

Objectives The continuous monitoring of SARS-CoV-2 infection waves and the emergence of novel pathogens pose a challenge for effective public health surveillance strategies based on diagnostics. Longitudinal population representative studies on incident events and symptoms of SARS-CoV-2 infection are scarce. We aimed at describing the evolution of the COVID-19 pandemic during 2020 and 2021 through regular monitoring of self-reported symptoms in an Alpine community sample.Design To this purpose, we designed a longitudinal population representative study, the Cooperative Health Research in South Tyrol COVID-19 study.Participants and outcome measures A sample of 845 participants was retrospectively investigated for active and past infections with swab and blood tests, by August 2020, allowing adjusted cumulative incidence estimation. Of them, 700 participants without previous infection or vaccination were followed up monthly until July 2021 for first-time infection and symptom self-reporting: COVID-19 anamnesis, social contacts, lifestyle and sociodemographic data were assessed remotely through digital questionnaires. Temporal symptom trajectories and infection rates were modelled through longitudinal clustering and dynamic correlation analysis. Negative binomial regression and random forest analysis assessed the relative importance of symptoms.Results At baseline, the cumulative incidence of SARS-CoV-2 infection was 1.10% (95% CI 0.51%, 2.10%). Symptom trajectories mimicked both self-reported and confirmed cases of incident infections. Cluster analysis identified two groups of high-frequency and low-frequency symptoms. Symptoms like fever and loss of smell fell in the low-frequency cluster. Symptoms most discriminative of test positivity (loss of smell, fatigue and joint-muscle aches) confirmed prior evidence.Conclusions Regular symptom tracking from population representative samples is an effective screening tool auxiliary to laboratory diagnostics for novel pathogens at critical times, as manifested in this study of COVID-19 patterns. Integrated surveillance systems might benefit from more direct involvement of citizens’ active symptom tracking.

Background: The knowledge of factors influencing disease progression in patients with established coronary heart disease (CHD) is still relatively limited. One potential pathway is related to peroxisome proliferator–activated receptor gamma coactivator-1 alpha (PPARGC1A), a transcription factor linked to energy metabolism which may play a role in the heart function. Thus, its associations with subsequent CHD events remain unclear. We aimed to investigate the effect of three different SNPs in the PPARGC1A gene on the risk of subsequent CHD in a population with established CHD.Methods: We employed an individual-level meta-analysis using 23 studies from the GENetIcs of sUbSequent Coronary Heart Disease (GENIUS-CHD) consortium, which included participants (n = 80,900) with either acute coronary syndrome, stable CHD, or a mixture of both at baseline. Three variants in the PPARGC1A gene (rs8192678, G482S; rs7672915, intron 2; and rs3755863, T528T) were tested for their associations with subsequent events during the follow-up using a Cox proportional hazards model adjusted for age and sex. The primary outcome was subsequent CHD death or myocardial infarction (CHD death/myocardial infarction). Stratified analyses of the participant or study characteristics as well as additional analyses for secondary outcomes of specific cardiovascular disease diagnoses and all-cause death were also performed.Results: Meta-analysis revealed no significant association between any of the three variants in the PPARGC1A gene and the primary outcome of CHD death/myocardial infarction among those with established CHD at baseline: rs8192678, hazard ratio (HR): 1.01, 95% confidence interval (CI) 0.98–1.05 and rs7672915, HR: 0.97, 95% CI 0.94–1.00; rs3755863, HR: 1.02, 95% CI 0.99–1.06. Similarly, no significant associations were observed for any of the secondary outcomes. The results from stratified analyses showed null results, except for significant inverse associations between rs7672915 (intron 2) and the primary outcome among 1) individuals aged ≥65, 2) individuals with renal impairment, and 3) antiplatelet users.Conclusion: We found no clear associations between polymorphisms in the PPARGC1A gene and subsequent CHD events in patients with established CHD at baseline.

On the electrocardiogram, the PR interval reflects conduction from the atria to ventricles and also serves as risk indicator of cardiovascular morbidity and mortality. Here, the authors perform genome-wide meta-analyses for PR interval in multiple ancestries and identify 141 previously unreported genetic loci.

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is characterized by triglyceride accumulation in the hepatocytes in the absence of alcohol overconsumption, commonly associated with insulin resistance and obesity. Both NAFLD and type 2 diabetes (T2D) are characterized by an altered microbiota composition, however the role of the microbiota in NAFLD and T2D is not well understood. To assess the relationship between alteration in the microbiota and NAFLD while dissecting the role of T2D, we established a nested study on T2D and non-T2D individuals within the Cooperative Health Research In South Tyrol (CHRIS) study, called the CHRIS-NAFLD study. Here, we present the study protocol along with baseline and follow-up characteristics of study participants. Methods Among the first 4979 CHRIS study participants, 227 individuals with T2D were identified and recalled, along with 227 age- and sex-matched non-T2D individuals. Participants underwent ultrasound and transient elastography examination to evaluate the presence of hepatic steatosis and liver stiffness. Additionally, sampling of saliva and faeces, biochemical measurements and clinical interviews were carried out. Results We recruited 173 T2D and 183 non-T2D participants (78% overall response rate). Hepatic steatosis was more common in T2D (63.7%) than non-T2D (36.3%) participants. T2D participants also had higher levels of liver stiffness (median 4.8 kPa, interquartile range (IQR) 3.7, 5.9) than non-T2D participants (median 3.9 kPa, IQR 3.3, 5.1). The non-invasive scoring systems like the NAFLD fibrosis score (NFS) suggests an increased liver fibrosis in T2D (mean − 0.55, standard deviation, SD, 1.30) than non-T2D participants (mean − 1.30, SD, 1.17). Discussion Given the comprehensive biochemical and clinical characterization of study participants, once the bioinformatics classification of the microbiota will be completed, the CHRIS-NAFLD study will become a useful resource to further our understanding of the relationship between microbiota, T2D and NAFLD.

The identification of genetic variants that predispose individuals to cardiovascular disease and a better understanding of their targets would be highly advantageous. Genome-wide association studies have identified variants that associate with QT-interval length (a measure of myocardial repolarization). Three of the strongest associating variants (single-nucleotide polymorphisms) are located in the putative promotor region of CNOT1, a gene encoding the central CNOT1 subunit of CCR4-NOT: a multifunctional, conserved complex regulating gene expression and mRNA stability and turnover. We isolated the minimum fragment of the CNOT1 promoter containing all three variants from individuals homozygous for the QT risk alleles and demonstrated that the haplotype associating with longer QT interval caused reduced reporter expression in a cardiac cell line, suggesting that reduced CNOT1 expression might contribute to abnormal QT intervals. Systematic siRNA-mediated knockdown of CCR4-NOT components in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) revealed that silencing CNOT1 and other CCR4-NOT genes reduced their proliferative capacity. Silencing CNOT7 also shortened action potential duration. Furthermore, the cardiac-specific knockdown of Drosophila orthologs of CCR4-NOT genes in vivo (CNOT1/Not1 and CNOT7/8/Pop2) was either lethal or resulted in dilated cardiomyopathy, reduced contractility or a propensity for arrhythmia. Silencing CNOT2/Not2, CNOT4/Not4 and CNOT6/6L/twin also affected cardiac chamber size and contractility. Developmental studies suggested that CNOT1/Not1 and CNOT7/8/Pop2 are required during cardiac remodeling from larval to adult stages. To summarize, we have demonstrated how disease-associated genes identified by GWAS can be investigated by combining human cardiomyocyte cell-based and whole-organism in vivo heart models. Our results also suggest a potential link of CNOT1 and CNOT7/8 to QT alterations and further establish a crucial role of the CCR4-NOT complex in heart development and function. This article has an associated First Person interview with the first author of the paper.

Atrial fibrillation (AF) is a supraventricular arrhythmia deriving from uncoordinated electrical activation with considerable associated morbidity and mortality. To expand the limited understanding of AF biological mechanisms, we performed two screenings, investigating the genetic and metabolic determinants of AF in the Cooperative Health Research in South Tyrol study. We found 110 AF cases out of 10,509 general population individuals. A genome-wide association scan (GWAS) identified two novel loci (p-value < 5 × 10−8) around SNPs rs745582874, next to gene PBX1, and rs768476991, within gene PCCA, with genotype calling confirmed by Sanger sequencing. Risk alleles at both SNPs were enriched in a family detected through familial aggregation analysis of the phenotype, and both rare alleles co-segregated with AF. The metabolic screening of 175 metabolites, in a subset of individuals, revealed a 41% lower concentration of lysophosphatidylcholine lysoPC a C20:3 in AF cases compared to controls (p-adj = 0.005). The genetic findings, combined with previous evidence, indicate that the two identified GWAS loci may be considered novel genetic rare determinants for AF. Considering additionally the association of lysoPC a C20:3 with AF by metabolic screening, our results demonstrate the valuable contribution of the combined genomic and metabolomic approach in studying AF in large-scale population studies.

BackgroundHeart rate variability (HRV) reflects the autonomous nervous system modulation on heart rate and is associated with several pathologies, including cardiac mortality. While mechanistic studies show that smoking is associated with lower HRV, population-based studies present conflicting results.MethodsWe assessed the mutual effects of active smoking status, cumulative smoking history, and current smoking intensity, on HRV among 4751 adults from the Cooperative Health Research In South Tyrol (CHRIS) study. The HRV metrics standard deviation of normal-to-normal (NN) inter-beat intervals (SDNN), square root of the mean squared differences of consecutive NN intervals (RMSSD), total power (TP), low (LF) and high frequency (HF) power, and their ratio (LF/HF), were derived from 20-minute electrocardiograms. Smoking status, pack-years (PY), and tobacco grams/day from standardized questionnaires were the main exposures. We fitted linear mixed models to account for relatedness, non-linearity, and moderating effects, and including fractional polynomials.ResultsPast smokers had higher HRV levels than never smokers, independently of PY. The association of HRV with current smoking became apparent when accounting for the interaction between smoking status and PY. In current smokers, but not in past smokers, we observed HRV reductions between 2.0% (SDNN) and 4.9% (TP) every 5 PY increase. Furthermore, current smokers were characterized by dose-response reductions of 9.8% (SDNN), 8.9% (RMSSD), 20.1% (TP), 17.7% (LF), and 19.1% (HF), respectively, every 10 grams/day of smoked tobacco, independently of common cardiometabolic conditions and HRV-modifying drugs. The LF/HF ratio was not associated with smoking status, history, or intensity.ConclusionsSmoking cessation was associated with higher HRV levels. In current smokers, heavier smoking intensity appears gradually detrimental on HRV, corroborating previous evidence. By affecting both the sympathetic and parasympathetic nervous system indexes, but not the LF/HF balance, smoking intensity seems to exert a systemic dysautonomic effect.

The human leukocyte antigen (HLA) complex on chromosome 6p21 has been unambiguously associated with multiple sclerosis (MS). The complex features of the HLA region, especially its high genic content, extreme polymorphism, and extensive linkage disequilibrium, has prevented to resolve the nature of HLA association in MS. We performed a family based association study on the isolated population of the Nuoro province (Sardinia) to clarify the role of HLA genes in MS. The main stage of our study involved an analysis of the ancestral haplotypes A2Cw7B58DR2DQ1 and A30Cw5B18DR3DQ2. On the basis of a multiplicative model, the effect of the first haplotype is protective with an odds ratio (OR) = 0.27 (95% confidence interval CI 0.13-0.57), while that of the second is deleterious, OR 1.78 (95% CI 1.26-2.50). We found both class I (A, Cw, B) and class II (DR, DQ) loci to have an effect on MS susceptibility, but we saw that they act independently from each other. We also performed an exploratory analysis on a set of 796 SNPs in the same HLA region. Our study supports the claim that Class I and Class II loci act independently on MS susceptibility and this has a biological explanation. Also, the analysis of SNPs suggests that there are other HLA genes involved in MS, but replication is needed. This opens up new perspective on the study of MS.

Multiple genome screens have been performed to identify regions in linkage or association with Multiple Sclerosis (MS, OMIM 126200), but little overlap has been found among them. This may be, in part, due to a low statistical power to detect small genetic effects and to genetic heterogeneity within and among the studied populations. Motivated by these considerations, we studied a very special population, namely that of Nuoro, Sardinia, Italy. This is an isolated, old, and genetically homogeneous population with high prevalence of MS. Our study sample includes both nuclear families and unrelated cases and controls. A multi-stage study design was adopted. In the first stage, microsatellites were typed in the 17q11.2 region, previously independently found to be in linkage with MS. One significant association was found at microsatellite D17S798. Next, a bioinformatic screening of the region surrounding this marker highlighted an interesting candidate MS susceptibility gene: the Amiloride-sensitive Cation Channel Neuronal 1 (ACCN1) gene. In the second stage of the study, we resequenced the exons and the 3' untranslated (UTR) region of ACCN1, and investigated the MS association of Single Nucleotide Polymorphisms (SNPs) identified in that region. For this purpose, we developed a method of analysis where complete, phase-solved, posterior-weighted haplotype assignments are imputed for each study individual from incomplete, multi-locus, genotyping data. The imputed assignments provide an input to a number of proposed procedures for testing association at a microsatellite level or of a sequence of SNPs. These include a Mantel-Haenszel type test based on expected frequencies of pseudocase/pseudocontrol haplotypes, as well as permutation based tests, including a combination of permutation and weighted logistic regression analysis. Application of these methods allowed us to find a significant association between MS and the SNP rs28936 located in the 3' UTR segment of ACCN1 with p = 0.0004 (p = 0.002, after adjusting for multiple testing). This result is in tune with several recent experimental findings which suggest that ACCN1 may play an important role in the pathogenesis of MS.