大型人群队列遗传资源建设与利用

在大数据和精准医学的时代,大型人群队列研究是慢性病病因研究的首选设计之一,队列遗传资源已成为我国重要的战略资源。推动队列遗传资源的标准化建设与利用可有效促进生物医药领域研究与技术开发的原始创新。本文以“泰州队列”为例,介绍大型人群队列遗传资源建设过程中采集、保藏和共享利用的原则、方法、标准体系和具体实践经验等,以期为我国大型人群队列研究的遗传资源建设和利用提供参考与借鉴。

Circulating Lipoproteins Mediate the Association Between Cardiovascular Risk Factors and Cognitive Decline:A Community‑Based Cohort Study

Cardiovascular health metrics are now widely recognized as modifable risk factors for cognitive decline and dementia.Metabolic perturbations might play roles in the linkage of cardiovascular diseases and dementia.Circulating metabolites profling by metabolomics may improve understanding of the potential mechanism by which cardiovascular risk factors contribute to cognitive decline.In a prospective community-based cohort in China(n=725),312 serum metabolic phenotypes were quantifed,and cardiovascular health score was calculated including smoking,exercise,sleep,diet,body mass index,blood pressure,and blood glucose.Cognitive function assessments were conducted in baseline and follow-up visits to identify longitudinal cognitive decline.A better cardiovascular health was signifcantly associated with lower risk of concentration decline and orientation decline(hazard ratio(HR):0.84–0.90;p<0.05).Apolipoprotein-A1,high-density lipoprotein(HDL)cholesterol,cholesterol ester,and phospholipid concentrations were signifcantly associated with a lower risk of longitudinal memory and orientation decline(p<0.05 and adjusted-p<0.20).Mediation analysis suggested that the negative association between health status and the risk of orientation decline was partly mediated by cholesterol ester and total lipids in HDL-2 and-3(proportion of mediation:7.68–8.21%,both p<0.05).Cardiovascular risk factors were associated with greater risks of cognitive decline,which were found to be mediated by circulating lipoproteins,particularly the medium-size HDL components.These fndings underscore the potential of utilizing lipoproteins as targets for early stage dementia screening and intervention.

Investigating the Effect of Metabolic Phenotypes on Health Events in Alcoholic and Nonalcoholic Fatty Liver Disease

Background and Aims:Metabolic dysfunction and obe-sity commonly coexist with both alcoholic and nonalcoholic fatty liver disease(AFLD and NAFLD).The association of AFLD and NAFLD with incident diseases in individuals with different metabolic phenotypes are unclear.Methods:UK Biobank study participants were screened for the presence of fatty liver at baseline.Body mass index and metabolic dysfunction were used to define metabolic phenotypes.Cox regression model was performed to examine the associations of AFLD and NAFLD with incident significant liver diseases(SLDs),cardiovascular diseases(CVDs),chronic kidney dis-eases(CKDs),and cancers,respectively.Results:A total of 43,974 AFLD and 103,248 NAFLD cases were identified.Both AFLD and NAFLD were associated with an increased risk of diseases of interest.The effects were amplified by obesity and metabolic abnormalities and modified by metabolic phe-notypes.Compared to individuals free of fatty liver and with phenotype of metabolically healthy-normal weight,AFLD[hazard ratio(HR 3.27;95%CI:1.95-5.47)]and NAFLD(HR 2.25;95%CI:1.28-3.94)cases with phenotype of met-abolically obese-normal weight had the greatest risk of SLDs.For CVDs,CKDs,and cancer,the greatest risks were detected in AFLD and NAFLD cases with phenotype of metabolically obese-overweight/obesity.In this subpopulation,AFLD and NAFLD conferred a 2.75-fold(95%CI:2.32-3.25)and 4.02-fold 95%CI:(3.64-4.43)increased risk of CVDs,4.37-fold 95%CI:(3.38-5.64)and 6.55-fold 95%CI:(5.73-7.48)increased risk of CKDs,and 1.19-fold 95%CI:(1.08-1.27)and 1.21-fold 95%CI:(1.14-1.28)increased risk of cancers,respectively.Conclusions:Metabolic phenotypes modified the association of AFLD and NAFLD with intrahepatic and ex-trahepatic diseases.

The HuaBiao project:whole-exome sequencing of 5000 Han Chinese individuals

Next-generation sequencing technologies have significantly accelerated the identification of disease-causing mutations and facilitated the emergence of personalized medicine(Genomes Project Consortium et al.,2015;Goodwin et al.,2016;Sirugo et al.,2019).In comparison with whole-genome sequencing,whole-exome sequencing(WES),which covers the coding regions of the genome,offers a cost-efficacy balance.WES provides deeper sequencing depth(>100)and allows the more accurate detection of rare variants that are tailored for clinical applications(Lek et al.,2016).

Using Composite Phenotypes to Reveal Hidden Physiological Heterogeneity in High-Altitude Acclimatization in a Chinese Han Longitudinal Cohort

Altitude acclimatization is a human physiological process of adjusting to the decreased oxygen availability.Since several physiological processes are involved and their correlations are complicated,the analyses of single traits are insufficient in revealing the complex mechanism of high-altitude acclimatization.In this study,we examined these physiological responses as the composite phenotypes that are represented by a linear combination of physiological traits.We developed a strategy that combines both spectral clustering and partial least squares path modeling(PLSPM)to define composite phenotypes based on a cohort study of 883 Chinese Han males.In addition,we captured 14 composite phenotypes from 28 physiological traits of high-altitude acclimatization.Using these composite phenotypes,we applied k-means clustering to reveal hidden population physiological heterogeneity in high-altitude acclimatization.Furthermore,we employed multivariate linear regression to systematically model(Models 1 and 2)oxygen saturation(SpO_(2))changes in high-altitude acclimatization and evaluated model fitness performance.Composite phenotypes based on Model 2 fit better than single trait-based Model 1 in all measurement indices.This new strategy of using composite phenotypes may be potentially employed as a general strategy for complex traits research such as genetic loci discovery and analyses of phenomics.

Phenome-Wide Association Analysis Reveals Novel Links Between Genetically Determined Levels of Liver Enzymes and Disease Phenotypes

Serum liver enzymes(alanine aminotransferase[ALT],aspartate aminotransferase[AST],λ-glutamyl transferase[GGT]and alkaline phosphatase[ALP])are the leading biomarkers to measure liver injury,and they have been reported to be associated with several intrahepatic and extrahepatic diseases in observational studies.We conducted a phenome-wide association study(PheWAS)to identify disease phenotypes associated with genetically predicted liver enzymes based on the UK Biobank cohort.Univariable and multivariable Mendelian randomization(MR)analyses were performed to obtain the causal esti-mates of associations that detected in PheWAS.Our PheWAS identified 40 out of 1,376 pairs(16,17,three and four pairs for ALT,AST,GGT and ALP,respectively)of genotype-phenotype associations reaching statistical significance at the 5%false discovery rate threshold.A total of 34 links were further validated in Mendelian randomization analyses.Most of the disease phenotypes that associated with genetically determined ALT level were liver-related,including primary liver cancer and alcoholic liver damage.The disease outcomes associated with genetically determined AST involved a wide range of phenotypic categories including endocrine/metabolic diseases,digestive diseases,and neurological disorder.Genetically predicted GGT level was associated with the risk of other chronic non-alcoholic liver disease,abnormal results of function study of liver,and cholelithiasis.Genetically determined ALP level was associated with pulmonary heart disease,phlebitis and thrombophlebitis of lower extremities,and hypercholesterolemia.Our findings reveal novel links between liver enzymes and disease phenotypes providing insights into the full understanding of the biological roles of liver enzymes.