Genome-wide Association Study and Meta-analysis on Alcohol-Associated Liver Cirrhosis Identifies Genetic Risk Factors

International audienceBackground and aims - Only a minority of heavy drinkers progress to alcohol-associated cirrhosis (ALC). The aim of this study was to identify common genetic variants that underlie risk for ALC. Approach and results - We analyzed data from 1,128 subjects of European ancestry with ALC and 614 heavy-drinking subjects without known liver disease from Australia, the United States, the United Kingdom, and three countries in Europe. A genome-wide association study (GWAS) was performed, adjusting for principal components and clinical covariates (alcohol use, age, sex, body mass index, and diabetes). We validated our GWAS findings using UK Biobank. We then performed a meta-analysis combining data from our study, the UK Biobank, and a previously published GWAS. Our GWAS found genome-wide significant risk association of rs738409 in patatin-like phospholipase domain containing 3 (PNPLA3) (odds ratio [OR] = 2.19 [G allele], P = 4.93 × 10 ) and rs4607179 near HSD17B13 (OR = 0.57 [C allele], P = 1.09 × 10 ) with ALC. Conditional analysis accounting for the PNPLA3 and HSD17B13 loci identified a protective association at rs374702773 in Fas-associated factor family member 2 (FAF2) (OR = 0.61 [del(T) allele], P = 2.56 × 10 ) for ALC. This association was replicated in the UK Biobank using conditional analysis (OR = 0.79, P = 0.001). Meta-analysis (without conditioning) confirmed genome-wide significance for the identified FAF2 locus as well as PNPLA3 and HSD17B13. Two other previously known loci (SERPINA1 and SUGP1/TM6SF2) were also genome-wide significant in the meta-analysis. GeneOntology pathway analysis identified lipid droplets as the target for several identified genes. In conclusion, our GWAS identified a locus at FAF2 associated with reduced risk of ALC among heavy drinkers. Like the PNPLA3 and HSD17B13 gene products, the FAF2 product has been localized to fat droplets in hepatocytes. Conclusions - Our genetic findings implicate lipid droplets in the biological pathway(s) underlying ALC

Sleep in the Natural Environment: A Pilot Study

Sleep quality has been directly linked to cognitive function, quality of life, and a variety of serious diseases across many clinical domains. Standard methods for assessing sleep involve overnight studies in hospital settings, which are uncomfortable, expensive, not representative of real sleep, and difficult to conduct on a large scale. Recently, numerous commercial digital devices have been developed that record physiological data, such as movement, heart rate, and respiratory rate, which can act as a proxy for sleep quality in lieu of standard electroencephalogram recording equipment. The sleep-related output metrics from these devices include sleep staging and total sleep duration and are derived via proprietary algorithms that utilize a variety of these physiological recordings. Each device company makes different claims of accuracy and measures different features of sleep quality, and it is still unknown how well these devices correlate with one another and perform in a research setting. In this pilot study of 21 participants, we investigated whether sleep metric outputs from self-reported sleep metrics (SRSMs) and four sensors, specifically Fitbit Surge (a smart watch), Withings Aura (a sensor pad that is placed under a mattress), Hexoskin (a smart shirt), and Oura Ring (a smart ring), were related to known cognitive and psychological metrics, including the n-back test and Pittsburgh Sleep Quality Index (PSQI). We analyzed correlation between multiple device-related sleep metrics. Furthermore, we investigated relationships between these sleep metrics and cognitive scores across different timepoints and SRSM through univariate linear regressions. We found that correlations for sleep metrics between the devices across the sleep cycle were almost uniformly low, but still significant (p < 0.05). For cognitive scores, we found the Withings latency was statistically significant for afternoon and evening timepoints at p = 0.016 and p = 0.013. We did not find any significant associations between SRSMs and PSQI or cognitive scores. Additionally, Oura Ring’s total sleep duration and efficiency in relation to the PSQI measure was statistically significant at p = 0.004 and p = 0.033, respectively. These findings can hopefully be used to guide future sensor-based sleep research

A genetic risk score and diabetes predict development of alcohol-related cirrhosis in drinkers

International audienceBackground & aims - Only a minority of excess alcohol drinkers develop cirrhosis. We developed and evaluated risk stratification scores to identify those at highest risk. Methods - Three cohorts (GenomALC-1: n = 1,690, GenomALC-2: n = 3,037, UK Biobank: relevant n = 6,898) with a history of heavy alcohol consumption (≥80 g/day (men), ≥50 g/day (women), for ≥10 years) were included. Cases were participants with alcohol-related cirrhosis. Controls had a history of similar alcohol consumption but no evidence of liver disease. Risk scores were computed from up to 8 genetic loci identified previously as associated with alcohol-related cirrhosis and 3 clinical risk factors. Score performance for the stratification of alcohol-related cirrhosis risk was assessed and compared across the alcohol-related liver disease spectrum, including hepatocellular carcinoma (HCC). Results - A combination of 3 single nucleotide polymorphisms (SNPs) (PNPLA3:rs738409, SUGP1-TM6SF2:rs10401969, HSD17B13:rs6834314) and diabetes status best discriminated cirrhosis risk. The odds ratios (ORs) and (95% CIs) between the lowest (Q1) and highest (Q2) score quintiles of the 3-SNP score, based on independent allelic effect size estimates, were 5.99 (4.18-8.60) (GenomALC-1), 2.81 (2.03-3.89) (GenomALC-2), and 3.10 (2.32-4.14) (UK Biobank). Patients with diabetes and high risk scores had ORs of 14.7 (7.69-28.1) (GenomALC-1) and 17.1 (11.3-25.7) (UK Biobank) compared to those without diabetes and with low risk scores. Patients with cirrhosis and HCC had significantly higher mean risk scores than patients with cirrhosis alone (0.76 ± 0.06 vs. 0.61 ± 0.02, p = 0.007). Score performance was not significantly enhanced by information on additional genetic risk variants, body mass index or coffee consumption. Conclusions - A risk score based on 3 genetic risk variants and diabetes status enables the stratification of heavy drinkers based on their risk of cirrhosis, allowing for the provision of earlier preventative interventions. Lay summary - Excessive chronic drinking leads to cirrhosis in some people, but so far there is no way to identify those at high risk of developing this debilitating disease. We developed a genetic risk score that can identify patients at high risk. The risk of cirrhosis is increased >10-fold with just two risk factors - diabetes and a high genetic risk score. Risk assessment using this test could enable the early and personalised management of this disease in high-risk patients

A genetic risk score and diabetes predict development of alcohol-related cirrhosis in drinkers

Background & aimsOnly a minority of excess alcohol drinkers develop cirrhosis. We developed and evaluated risk stratification scores to identify those at highest risk.MethodsThree cohorts (GenomALC-1: n = 1,690, GenomALC-2: n = 3,037, UK Biobank: relevant n = 6,898) with a history of heavy alcohol consumption (≥80 g/day (men), ≥50 g/day (women), for ≥10 years) were included. Cases were participants with alcohol-related cirrhosis. Controls had a history of similar alcohol consumption but no evidence of liver disease. Risk scores were computed from up to 8 genetic loci identified previously as associated with alcohol-related cirrhosis and 3 clinical risk factors. Score performance for the stratification of alcohol-related cirrhosis risk was assessed and compared across the alcohol-related liver disease spectrum, including hepatocellular carcinoma (HCC).ResultsA combination of 3 single nucleotide polymorphisms (SNPs) (PNPLA3:rs738409, SUGP1-TM6SF2:rs10401969, HSD17B13:rs6834314) and diabetes status best discriminated cirrhosis risk. The odds ratios (ORs) and (95% CIs) between the lowest (Q1) and highest (Q5) score quintiles of the 3-SNP score, based on independent allelic effect size estimates, were 5.99 (4.18-8.60) (GenomALC-1), 2.81 (2.03-3.89) (GenomALC-2), and 3.10 (2.32-4.14) (UK Biobank). Patients with diabetes and high risk scores had ORs of 14.7 (7.69-28.1) (GenomALC-1) and 17.1 (11.3-25.7) (UK Biobank) compared to those without diabetes and with low risk scores. Patients with cirrhosis and HCC had significantly higher mean risk scores than patients with cirrhosis alone (0.76 ± 0.06 vs. 0.61 ± 0.02, p = 0.007). Score performance was not significantly enhanced by information on additional genetic risk variants, body mass index or coffee consumption.ConclusionsA risk score based on 3 genetic risk variants and diabetes status enables the stratification of heavy drinkers based on their risk of cirrhosis, allowing for the provision of earlier preventative interventions.Lay summaryExcessive chronic drinking leads to cirrhosis in some people, but so far there is no way to identify those at high risk of developing this debilitating disease. We developed a genetic risk score that can identify patients at high risk. The risk of cirrhosis is increased >10-fold with just two risk factors - diabetes and a high genetic risk score. Risk assessment using this test could enable the early and personalised management of this disease in high-risk patients