Designing for caregiving networks: a case study of primary caregivers of children with medical complexity.

OBJECTIVE: The study aimed to characterize the experiences of primary caregivers of children with medical complexity (CMC) in engaging with other members of the child's caregiving network, thereby informing the design of health information technology (IT) for the caregiving network. Caregiving networks include friends, family, community members, and other trusted individuals who provide resources, information, health, or childcare. MATERIALS AND METHODS: We performed a secondary analysis of two qualitative studies. Primary studies conducted semi-structured interviews (n = 50) with family caregivers of CMC. Interviews were held in the Midwest (n = 30) and the mid-Atlantic region (n = 20). Interviews were transcribed verbatim for thematic analysis. Emergent themes were mapped to implications for the design of future health IT. RESULTS: Thematic analysis identified 8 themes characterizing a wide range of primary caregivers' experiences in constructing, managing, and ensuring high-quality care delivery across the caregiving network. DISCUSSION: Findings evidence a critical need to create flexible and customizable tools designed to support hiring/training processes, coordinating daily care across the caregiving network, communicating changing needs and care updates across the caregiving network, and creating contingency plans for instances where caregivers are unavailable to provide care to the CMC. Informaticists should additionally design accessible platforms that allow primary caregivers to connect with and learn from other caregivers while minimizing exposure to sensitive or emotional content as indicated by the user. CONCLUSION: This article contributes to the design of health IT for CMC caregiving networks by uncovering previously underrecognized needs and experiences of CMC primary caregivers and drawing direct connections to design implications.

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

Differences in Cardiometabolic Proteins in Pregnancy Prioritize Relevant Targets of Preeclampsia.

BACKGROUND: Preeclampsia is a hypertensive disorder of pregnancy characterized by widespread vascular inflammation. It occurs frequently in pregnancy, often without known risk factors, and has high rates of maternal and fetal morbidity and mortality. Identification of biomarkers that predict preeclampsia and its cardiovascular sequelae before clinical onset, or even before pregnancy, is a critical unmet need for the prevention of adverse pregnancy outcomes. METHODS: We explored differences in cardiovascular proteomics (Olink Explore 384) in 256 diverse pregnant persons across 2 centers (26% Hispanic, 21% Black). RESULTS: We identified significant differences in plasma abundance of markers associated with angiogenesis, blood pressure, cell adhesion, inflammation, and metabolism between individuals delivering with preeclampsia and controls, some of which have not been widely described previously and are not represented in the preeclampsia placental transcriptome. While we observed a broadly similar pattern in early (<34 weeks) versus late (≥34 weeks) preeclampsia, several proteins related to hemodynamic stress, hemostasis, and immune response appeared to be more highly dysregulated in early preeclampsia relative to late preeclampsia. CONCLUSIONS: These results demonstrate the value of performing targeted proteomics using a panel of cardiovascular biomarkers to identify biomarkers relevant to preeclampsia pathophysiology and highlight the need for larger multiomic studies to define modifiable pathways of surveillance and intervention upstream to preeclampsia diagnosis.

Genetic risk converges on regulatory networks mediating early type 2 diabetes.

Type 2 diabetes mellitus (T2D), a major cause of worldwide morbidity and mortality, is characterized by dysfunction of insulin-producing pancreatic islet ß cells1,2. T2D genome-wide association studies (GWAS) have identified hundreds of signals in non-coding and ß cell regulatory genomic regions, but deciphering their biological mechanisms remains challenging3-5. Here, to identify early disease-driving events, we performed traditional and multiplexed pancreatic tissue imaging, sorted-islet cell transcriptomics and islet functional analysis of early-stage T2D and control donors. By integrating diverse modalities, we show that early-stage T2D is characterized by ß cell-intrinsic defects that can be proportioned into gene regulatory modules with enrichment in signals of genetic risk. After identifying the ß cell hub gene and transcription factor RFX6 within one such module, we demonstrated multiple layers of genetic risk that converge on an RFX6-mediated network to reduce insulin secretion by ß cells. RFX6 perturbation in primary human islet cells alters ß cell chromatin architecture at regions enriched for T2D GWAS signals, and population-scale genetic analyses causally link genetically predicted reduced RFX6 expression with increased T2D risk. Understanding the molecular mechanisms of complex, systemic diseases necessitates integration of signals from multiple molecules, cells, organs and individuals, and thus we anticipate that this approach will be a useful template to identify and validate key regulatory networks and master hub genes for other diseases or traits using GWAS data.

Genome-wide Association Study Identifies Novel Risk Loci for Apical Periodontitis.

INTRODUCTION: Apical periodontitis (AP) is a common consequence of root canal infection leading to periapical bone resorption. Microbial and host genetic factors and their interactions have been shown to play a role in AP development and progression. Variations in a few genes have been reported in association with AP; however, the lack of genome-wide studies has hindered progress in understanding the molecular mechanisms involved. Here, we report the first genome-wide association study of AP in a large and well-characterized population. METHODS: Male and female adults (n = 932) presenting with deep caries and AP (cases), or deep caries without AP (controls) were included. Genotyping was performed using the Illumina Expanded Multi-Ethnic Genotyping Array (MEGA). Single-variant association testing was performed adjusting for sex and 5 principal components. Subphenotype association testing, analyses of genetically regulated gene expression, polygenic risk score, and phenome-wide association (PheWAS) analyses were also conducted. RESULTS: Eight loci reached near genome-wide significant association with AP (P < 5 × 10-6); gene-focused analyses replicated 3 previously reported associations (P < 8.9 × 10-5). Sex-specific and subphenotype-specific analyses revealed additional significant associations with variants genome-wide. Functionally oriented gene-based analyses revealed 8 genes significantly associated with AP (P < 5 × 10-5), and PheWAS analysis revealed 33 phecodes associated with AP risk score (P < 3.08 × 10-5). CONCLUSIONS: This study identified novel genes/loci contributing to AP and specific contributions to AP risk in men and women. Importantly, we identified additional systemic conditions significantly associated with AP risk. Our findings provide strong evidence for host-mediated effects on AP susceptibility.

The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes.

AIMS/HYPOTHESIS: The Latino population has been systematically underrepresented in large-scale genetic analyses, and previous studies have relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) released the largest multi-ancestry genotype reference panel representing a unique opportunity to analyse rare genetic variations in the Latino population. We hypothesise that a more comprehensive analysis of low/rare variation using the TOPMed panel would improve our knowledge of the genetics of type 2 diabetes in the Latino population. METHODS: We evaluated the TOPMed imputation performance using genotyping array and whole-exome sequence data in six Latino cohorts. To evaluate the ability of TOPMed imputation to increase the number of identified loci, we performed a Latino type 2 diabetes genome-wide association study (GWAS) meta-analysis in 8150 individuals with type 2 diabetes and 10,735 control individuals and replicated the results in six additional cohorts including whole-genome sequence data from the All of Us cohort. RESULTS: Compared with imputation with 1000G, the TOPMed panel improved the identification of rare and low-frequency variants. We identified 26 genome-wide significant signals including a novel variant (minor allele frequency 1.7%; OR 1.37, p=3.4 × 10-9). A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improved the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance. CONCLUSIONS/INTERPRETATION: Our results demonstrate the utility of TOPMed imputation for identifying low-frequency variants in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores. DATA AVAILABILITY: Full summary statistics are available through the Common Metabolic Diseases Knowledge Portal ( https://t2d.hugeamp.org/downloads.html ) and through the GWAS catalog ( https://www.ebi.ac.uk/gwas/ , accession ID: GCST90255648). Polygenic score (PS) weights for each ancestry are available via the PGS catalog ( https://www.pgscatalog.org , publication ID: PGP000445, scores IDs: PGS003443, PGS003444 and PGS003445).

Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance (P<5×10-8) that map to 611 loci, of which 145 loci are previously unreported. We define eight non-overlapping clusters of T2D signals characterised by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care.

Exploring the genetics of rhythmic perception and musical engagement in the Vanderbilt Online Musicality Study.

Uncovering the genetic underpinnings of musical ability and engagement is a foundational step for exploring their wide-ranging associations with cognition, health, and neurodevelopment. Prior studies have focused on using twin and family designs, demonstrating moderate heritability of musical phenotypes. The current study used genome-wide complex trait analysis and polygenic score (PGS) approaches utilizing genotype data to examine genetic influences on two musicality traits (rhythmic perception and music engagement) in N = 1792 unrelated adults in the Vanderbilt Online Musicality Study. Meta-analyzed heritability estimates (including a replication sample of Swedish individuals) were 31% for rhythmic perception and 12% for self-reported music engagement. A PGS derived from a recent study on beat synchronization ability predicted both rhythmic perception (ß = 0.11) and music engagement (ß = 0.19) in our sample, suggesting that genetic influences underlying self-reported beat synchronization ability also influence individuals' rhythmic discrimination aptitude and the degree to which they engage in music. Cross-trait analyses revealed a modest contribution of PGSs from several nonmusical traits (from the cognitive, personality, and circadian chronotype domains) to individual differences in musicality (ß = -0.06 to 0.07). This work sheds light on the complex relationship between the genetic architecture of musical rhythm processing, beat synchronization, music engagement, and other nonmusical traits.

IMMerge: merging imputation data at scale.

SUMMARY: Genomic data are often processed in batches and analyzed together to save time. However, it is challenging to combine multiple large VCFs and properly handle imputation quality and missing variants due to the limitations of available tools. To address these concerns, we developed IMMerge, a Python-based tool that takes advantage of multiprocessing to reduce running time. For the first time in a publicly available tool, imputation quality scores are correctly combined with Fisher's z transformation. AVAILABILITY AND IMPLEMENTATION: IMMerge is an open-source project under MIT license. Source code and user manual are available at https://github.com/belowlab/IMMerge.

Genetic variants associated with circulating liver injury markers in Mexican Americans, a population at risk for non-alcoholic fatty liver disease.

Objective: Mexican Americans are disproportionally affected by non-alcoholic fatty liver disease (NAFLD), liver fibrosis and hepatocellular carcinoma. Noninvasive means to identify those in this population at high risk for these diseases are urgently needed. Approach: The Cameron County Hispanic Cohort (CCHC) is a population-based cohort with high rates of obesity (51%), type 2 diabetes (28%) and NAFLD (49%). In a subgroup of 564 CCHC subjects, we evaluated 339 genetic variants previously reported to be associated with liver injury markers aspartate aminotransferase (AST) and alanine aminotransferase (ALT) in United Kingdom and Japanese cohorts. Results: Association was confirmed for 86 variants. Among them, 27 had higher effect allele frequency in the CCHC than in the United Kingdom and Japanese cohorts, and 16 had stronger associations with AST and ALT than rs738409 (PNPLA3). These included rs17710008 (MYCT1), rs2519093 (ABO), rs1801690 (APOH), rs10409243 (S1PR2), rs1800759 (LOC100507053) and rs2491441 (RGL1), which were also associated with steatosis and/or liver fibrosis measured by vibration-controlled transient elastography. Main contributors to advanced fibrosis risk were rs11240351 (CNTN2), rs1800759 (LOC100507053), rs738409 (PNPLA3) and rs1801690 (APOH), with advanced fibrosis detected in 37.5% of subjects with 3 of these 4 variants [AOR = 11.6 (95% CI) = 3.8-35.3]. AST- and ALT-associated variants implicated distinct pathways (ethanol and galactose degradation versus antigen presentation and B cell development). Finally, 8 variants, including rs62292950 (DNAJC13), were associated with gut microbiome changes. Conclusion: These genotype-phenotype findings may have utility in risk modeling and disease prevention in this high-risk population.

Genetic pleiotropy underpinning adiposity and inflammation in self-identified Hispanic/Latino populations.

BACKGROUND: Concurrent variation in adiposity and inflammation suggests potential shared functional pathways and pleiotropic disease underpinning. Yet, exploration of pleiotropy in the context of adiposity-inflammation has been scarce, and none has included self-identified Hispanic/Latino populations. Given the high level of ancestral diversity in Hispanic American population, genetic studies may reveal variants that are infrequent/monomorphic in more homogeneous populations. METHODS: Using multi-trait Adaptive Sum of Powered Score (aSPU) method, we examined individual and shared genetic effects underlying inflammatory (CRP) and adiposity-related traits (Body Mass Index [BMI]), and central adiposity (Waist to Hip Ratio [WHR]) in HLA participating in the Population Architecture Using Genomics and Epidemiology (PAGE) cohort (N = 35,871) with replication of effects in the Cameron County Hispanic Cohort (CCHC) which consists of Mexican American individuals. RESULTS: Of the > 16 million SNPs tested, variants representing 7 independent loci were found to illustrate significant association with multiple traits. Two out of 7 variants were replicated at statistically significant level in multi-trait analyses in CCHC. The lead variant on APOE (rs439401) and rs11208712 were found to harbor multi-trait associations with adiposity and inflammation. CONCLUSIONS: Results from this study demonstrate the importance of considering pleiotropy for improving our understanding of the etiology of the various metabolic pathways that regulate cardiovascular disease development.

Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation.

We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10-9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.

Gut Microbiome Alterations Associated with Diabetes in Mexican Americans in South Texas.

Mexican Americans have a high prevalence of diabetes and burden of diabetes-related complications, highlighting the need for novel preventive strategies and noninvasive predictors of diabetes risk tailored to this population. Changes in the gut microbiome have the potential to predict diabetes. Here, we aimed to identify alterations in the gut microbiome associated with diabetes in the high-risk population of Mexican Americans in South Texas. Stool samples were collected from 216 subjects from the population-based Cameron County Hispanic Cohort. Among them, 75 had type 2 diabetes. Taxonomic and functional profiling of the stool samples were assessed by 16S and shotgun metagenomic sequencing, and the influence of genetic factors was explored. The gut microbiome of subjects with diabetes was enriched with proinflammatory Proteobacteria members (Enterobacteriaceae, Escherichia-Shigella) and depleted of butyrate-producing Clostridiales members (Faecalibacterium prausnitzii, Peptostreptococcaceae, and Clostridium sensu stricto 1). The accompanying metagenomic changes in subjects with diabetes suggested dysregulated amino acid metabolism, reduced galacturonate and glucuronate catabolism (correlating with Faecalibacterium prausnitzii abundance), and enriched heme biosynthesis (correlating with Enterobacteriaceae abundance). Polymorphism rs7129790 near MMP27 was strongly associated with high Proteobacteria abundance and was more frequent in this cohort and in individuals of Mexican ancestry than in Europeans. In conclusion, Mexican Americans in South Texas with diabetes display distinct gut microbiome and metagenomic signatures. These signatures may have utility in risk modeling and disease prevention in this high-risk population. IMPORTANCE The gut microbiome composition varies across ethnicities and geographical locations, yet studies on diabetes-associated microbiome changes specific to high-risk Mexican Americans are lacking. Here, we aimed to identify specific alterations associated with diabetes in this population, as well as host genetic factors that may explain increased disease susceptibility in this ethnic group. Using samples from a population-based cohort of Mexican Americans with a high prevalence of obesity and diabetes, we confirmed findings from studies on other ethnicities that suggested promotion of a chronic proinflammatory environment, loss of butyrate production, and compromised intestinal barrier integrity. High abundance of proinflammatory Proteobacteria was associated with a polymorphism that was more frequent in this cohort and in individuals of Mexican ancestry than in Europeans. Validation of microbiome-based risk models for diabetes should be evaluated in prospective cohort studies.

Identification of genetic effects underlying type 2 diabetes in South Asian and European populations.

South Asians are at high risk of developing type 2 diabetes (T2D). We carried out a genome-wide association meta-analysis with South Asian T2D cases (n = 16,677) and controls (n = 33,856), followed by combined analyses with Europeans (neff = 231,420). We identify 21 novel genetic loci for significant association with T2D (P = 4.7 × 10-8 to 5.2 × 10-12), to the best of our knowledge at the point of analysis. The loci are enriched for regulatory features, including DNA methylation and gene expression in relevant tissues, and highlight CHMP4B, PDHB, LRIG1 and other genes linked to adiposity and glucose metabolism. A polygenic risk score based on South Asian-derived summary statistics shows ~4-fold higher risk for T2D between the top and bottom quartile. Our results provide further insights into the genetic mechanisms underlying T2D, and highlight the opportunities for discovery from joint analysis of data from across ancestral populations.

Polygenic risk impacts PDGFRA mutation penetrance in non-syndromic cleft lip and palate.

Non-syndromic cleft lip with or without cleft palate (NSCL/P) is a common, severe craniofacial malformation that imposes significant medical, psychosocial and financial burdens. NSCL/P is a multifactorial disorder with genetic and environmental factors playing etiologic roles. Currently, only 25% of the genetic variation underlying NSCL/P has been identified by linkage, candidate gene and genome-wide association studies. In this study, whole-genome sequencing and genome-wide genotyping followed by polygenic risk score (PRS) and linkage analyses were used to identify the genetic etiology of NSCL/P in a large three-generation family. We identified a rare missense variant in PDGFRA (c.C2740T; p.R914W) as potentially etiologic in a gene-based association test using pVAAST (P = 1.78 × 10-4) and showed decreased penetrance. PRS analysis suggested that variant penetrance was likely modified by common NSCL/P risk variants, with lower scores found among unaffected carriers. Linkage analysis provided additional support for PRS-modified penetrance, with a 7.4-fold increase in likelihood after conditioning on PRS. Functional characterization experiments showed that the putatively causal variant was null for signaling activity in vitro; further, perturbation of pdgfra in zebrafish embryos resulted in unilateral orofacial clefting. Our findings show that a rare PDGFRA variant, modified by additional common NSCL/P risk variants, have a profound effect on NSCL/P risk. These data provide compelling evidence for multifactorial inheritance long postulated to underlie NSCL/P and may explain some unusual familial patterns.

Novel diabetes gene discovery through comprehensive characterization and integrative analysis of longitudinal gene expression changes.

Type 2 diabetes is a complex, systemic disease affected by both genetic and environmental factors. Previous research has identified genetic variants associated with type 2 diabetes risk; however, gene regulatory changes underlying progression to metabolic dysfunction are still largely unknown. We investigated RNA expression changes that occur during diabetes progression using a two-stage approach. In our discovery stage, we compared changes in gene expression using two longitudinally collected blood samples from subjects whose fasting blood glucose transitioned to a level consistent with type 2 diabetes diagnosis between the time points against those who did not with a novel analytical network approach. Our network methodology identified 17 networks, one of which was significantly associated with transition status. This 822-gene network harbors many genes novel to the type 2 diabetes literature but is also significantly enriched for genes previously associated with type 2 diabetes. In the validation stage, we queried associations of genetically determined expression with diabetes-related traits in a large biobank with linked electronic health records. We observed a significant enrichment of genes in our identified network whose genetically determined expression is associated with type 2 diabetes and other metabolic traits and validated 31 genes that are not near previously reported type 2 diabetes loci. Finally, we provide additional functional support, which suggests that the genes in this network are regulated by enhancers that operate in human pancreatic islet cells. We present an innovative and systematic approach that identified and validated key gene expression changes associated with type 2 diabetes transition status and demonstrated their translational relevance in a large clinical resource.

Population-based genetic effects for developmental stuttering.

Despite a lifetime prevalence of at least 5%, developmental stuttering, characterized by prolongations, blocks, and repetitions of speech sounds, remains a largely idiopathic speech disorder. Family, twin, and segregation studies overwhelmingly support a strong genetic influence on stuttering risk; however, its complex mode of inheritance combined with thus-far underpowered genetic studies contribute to the challenge of identifying and reproducing genes implicated in developmental stuttering susceptibility. We conducted a trans-ancestry genome-wide association study (GWAS) and meta-analysis of developmental stuttering in two primary datasets: The International Stuttering Project comprising 1,345 clinically ascertained cases from multiple global sites and 6,759 matched population controls from the biobank at Vanderbilt University Medical Center (VUMC), and 785 self-reported stuttering cases and 7,572 controls ascertained from The National Longitudinal Study of Adolescent to Adult Health (Add Health). Meta-analysis of these genome-wide association studies identified a genome-wide significant (GWS) signal for clinically reported developmental stuttering in the general population: a protective variant in the intronic or genic upstream region of SSUH2 (rs113284510, protective allele frequency = 7.49%, Z = -5.576, p = 2.46 × 10-8) that acts as an expression quantitative trait locus (eQTL) in esophagus-muscularis tissue by reducing its gene expression. In addition, we identified 15 loci reaching suggestive significance (p < 5 × 10-6). This foundational population-based genetic study of a common speech disorder reports the findings of a clinically ascertained study of developmental stuttering and highlights the need for further research.

Gut microbiome features associated with liver fibrosis in Hispanics, a population at high risk for fatty liver disease.

BACKGROUND AND AIMS: Hispanics are disproportionately affected by NAFLD, liver fibrosis, cirrhosis, and HCC. Preventive strategies and noninvasive means to identify those in this population at high risk for liver fibrosis, are urgently needed. We aimed to characterize the gut microbiome signatures and related biological functions associated with liver fibrosis in Hispanics and identify environmental and genetic factors affecting them. APPROACH AND RESULTS: Subjects of the population-based Cameron County Hispanic Cohort (CCHC; n = 217) were screened by vibration-controlled transient elastography (FibroScan). Among them, 144 (66.7%) had steatosis and 28 (13.0%) had liver fibrosis. The gut microbiome of subjects with liver fibrosis was enriched with immunogenic commensals (e.g., Prevotella copri, Holdemanella, Clostridiaceae 1) and depleted of Bacteroides caccae, Parabacteroides distasonis, Enterobacter, and Marinifilaceae. The liver fibrosis-associated metagenome was characterized by changes in the urea cycle, L-citrulline biosynthesis and creatinine degradation pathways, and altered synthesis of B vitamins and lipoic acid. These metagenomic changes strongly correlated with the depletion of Parabacteroides distasonis and enrichment of Prevotella and Holdemanella. Liver fibrosis was also associated with depletion of bacterial pathways related to L-fucose biosynthesis. Alcohol consumption, even moderate, was associated with high Prevotella abundance. The single-nucleotide polymorphisms rs3769502 and rs7573751 in the NCK adaptor protein 2 (NCK2) gene positively associated with high Prevotella abundance. CONCLUSION: Hispanics with liver fibrosis display microbiome profiles and associated functional changes that may promote oxidative stress and a proinflammatory environment. These microbiome signatures, together with NCK2 polymorphisms, may have utility in risk modeling and disease prevention in this high-risk population.

Associations of carotid intima media thickness with gene expression in whole blood and genetically predicted gene expression across 48 tissues.

Carotid intima media thickness (cIMT) is a biomarker of subclinical atherosclerosis and a predictor of future cardiovascular events. Identifying associations between gene expression levels and cIMT may provide insight to atherosclerosis etiology. Here, we use two approaches to identify associations between mRNA levels and cIMT: differential gene expression analysis in whole blood and S-PrediXcan. We used microarrays to measure genome-wide whole blood mRNA levels of 5647 European individuals from four studies. We examined the association of mRNA levels with cIMT adjusted for various potential confounders. Significant associations were tested for replication in three studies totaling 3943 participants. Next, we applied S-PrediXcan to summary statistics from a cIMT genome-wide association study (GWAS) of 71 128 individuals to estimate the association between genetically determined mRNA levels and cIMT and replicated these analyses using S-PrediXcan on an independent GWAS on cIMT that included 22 179 individuals from the UK Biobank. mRNA levels of TNFAIP3, CEBPD and METRNL were inversely associated with cIMT, but these associations were not significant in the replication analysis. S-PrediXcan identified associations between cIMT and genetically determined mRNA levels for 36 genes, of which six were significant in the replication analysis, including TLN2, which had not been previously reported for cIMT. There was weak correlation between our results using differential gene expression analysis and S-PrediXcan. Differential expression analysis and S-PrediXcan represent complementary approaches for the discovery of associations between phenotypes and gene expression. Using these approaches, we prioritize TNFAIP3, CEBPD, METRNL and TLN2 as new candidate genes whose differential expression might modulate cIMT.