A microbiome-directed therapeutic food for children recovering from severe acute malnutrition.

Severe acute malnutrition (SAM), defined anthropometrically as a weight-for-length z-score more than 3 standard deviations below the mean (WLZ<-3), affects 19 million children under 5-years-old worldwide. Complete anthropometric recovery after standard inventions is rare with children often left with moderate acute malnutrition (MAM; WLZ -2 to -3). Here we conduct a randomized controlled trial (RCT), involving 12-18-month-old Bangladeshi children from urban and rural sites, who after hospital-based treatment for SAM received a 3-month intervention with a microbiota-directed complementary food (MDCF-2) or a ready-to-use supplementary food (RUSF) as they transitioned to MAM. The rate of WLZ improvement was significantly greater with MDCF-2 than the more calorically-dense RUSF, as we observed in a previous RCT of Bangladeshi children with MAM without antecedent SAM. A correlated meta-analysis of aptamer-based measurements of 4,520 plasma proteins in this and the prior RCT revealed 215 proteins positively-associated with WLZ (prominently those involved in musculoskeletal and CNS development) and 44 negatively-associated proteins (related to immune activation), with a significant enrichment in levels of the positively WLZ-associated proteins in the MDCF-2 arm. Characterizing changes in 754 bacterial metagenome-assembled genomes in serially collected fecal samples disclosed the effects of acute rehabilitation for SAM on the microbiome, its transition as each child achieves a state of MAM, and how specific strains of Prevotella copri function at the intersection between MDCF-2 glycan metabolism and the rescue of growth faltering. These results provide a rationale for further testing the generalizability of the efficacy of MDCF and identify biomarkers for defining treatment responses.

Novel Loci (EIF4A2, ADIPOQ, TPRG1) for Triglyceride / High-density Lipoprotein Cholesterol Ratio Longitudinal Change (ΔTHR) among Subjects without Type 2 Diabetes: Evidence from the Long Life Family Study (LLFS) and the Framingham Heart Study (FHS) Offspring Cohort (OS).

Aims/hypothesis: Triglyceride (TG) /High density lipoprotein cholesterol (HDL-C) ratio (THR) represents a single surrogate predictor of hyperinsulinemia or insulin resistance that is associated with premature aging processes, risk of diabetes and increased mortality. To identify novel genetic loci for THR change over time (ΔTHR), we conducted genome-wide association study (GWAS) and genome-wide linkage scan (GWLS) among subjects of European ancestry who had complete data from two exams collected about seven years apart from the Long Life Family Study (LLFS, n=1384), a study with familial clustering of exceptional longevity in the US and Denmark. Methods: Subjects with diabetes or using medications for dyslipidemia were excluded from this analysis. ΔTHR was derived using growth curve modeling, and adjusted for age, sex, field centers, and principal components (PCs). GWAS was conducted using a linear mixed model accounted for familial relatedness. Our linkage scan was built on haplotype-based IBD estimation with 0.5 cM average spacing. Results: Heritability of ΔTHR was moderate (46%). Our GWAS identified a significant locus at the LPL (p=1.58e-9) for ΔTHR; this gene locus has been reported before influencing baseline THR levels. Our GWLS found evidence for a significant linkage with a logarithm of the odds (LODs) exceeding 3 on 3q28 (LODs=4.1). Using a subset of 25 linkage enriched families (pedigree-specific LODs>0.1), we assessed sequence elements under 3q28 and identified two novel variants (EIF4A2/ADIPOQ-rs114108468, p=5e-6, MAF=1.8%; TPRG1-rs16864075, p=3e-6, MAF=8%; accounted for ~28% and ~29% of the linkage, respectively, and 57% jointly). While the former variant was associated with EIF4A2 (p=7e-5) / ADIPOQ (p=3.49e-2) RNA transcriptional levels, the latter variant was not associated with TPRG1 (p=0.23) RNA transcriptional levels. Replication in FHS OS observed modest effect of these loci on ΔTHR. Of 188 metabolites from 13 compound classes assayed in LLFS, we observed multiple metabolites (e.g., DG.38.5, PE.36.4, TG.58.3) that were significantly associated with the variants (p<3e-4). Conclusions: our linkage-guided sequence analysis approach permitted our discovery of two novel gene variants EIF4A2/ADIPOQ-rs114108468 and TPRG1-rs16864075 on 3q28 for ΔTHR among subjects without diabetes selected for exceptional survival and healthy aging.

Discovery of genomic and transcriptomic pleiotropy between kidney function and soluble receptor for advanced glycation end products using correlated meta-analyses: The Long Life Family Study.

Patients with chronic kidney disease (CKD) have increased oxidative stress and chronic inflammation, which may escalate the production of advanced glycation end-products (AGEs). High soluble receptor for AGE (sRAGE) and low estimated glomerular filtration rate (eGFR) levels are associated with CKD and aging. We evaluated whether eGFR calculated from creatinine and cystatin C share pleiotropic genetic factors with sRAGE. We employed whole-genome sequencing and correlated meta-analyses on combined genome-wide association study (GWAS) p-values in 4182 individuals (age range: 24-110) from the Long Life Family Study (LLFS). We also conducted transcriptome-wide association studies (TWAS) on whole blood in a subset of 1209 individuals. We identified 59 pleiotropic GWAS loci (p < 5 × 10-8) and 17 TWAS genes (Bonferroni-p < 2.73 × 10-6) for eGFR traits and sRAGE. TWAS genes, LSP1 and MIR23AHG, were associated with eGFR and sRAGE located within GWAS loci, lncRNA-KCNQ1OT1 and CACNA1A/CCDC130, respectively. GWAS variants were eQTLs in the kidney glomeruli and tubules, and GWAS genes predicted kidney carcinoma. TWAS genes harbored eQTLs in the kidney, predicted kidney carcinoma, and connected enhancer-promoter variants with kidney function-related phenotypes at p < 5 × 10-8. Additionally, higher allele frequencies of protective variants for eGFR traits were detected in LLFS than in ALFA-Europeans and TOPMed, suggesting better kidney function in healthy-aging LLFS than in general populations. Integrating genomic annotation and transcriptional gene activity revealed the enrichment of genetic elements in kidney function and aging-related processes. The identified pleiotropic loci and gene expressions for eGFR and sRAGE suggest their underlying shared genetic effects and highlight their roles in kidney- and aging-related signaling pathways.

A Novel Gene ARHGAP44 for Longitudinal Changes in Glycated Hemoglobin (HbA1c) in Subjects without Type 2 Diabetes: Evidence from the Long Life Family Study (LLFS) and the Framingham Offspring Study (FOS).

Glycated hemoglobin (HbA1c) indicates average glucose levels over three months and is associated with insulin resistance and type 2 diabetes (T2D). Longitudinal changes in HbA1c (ΔHbA1c) are also associated with aging processes, cognitive performance, and mortality. We analyzed ΔHbA1c in 1,886 non-diabetic Europeans from the Long Life Family Study to uncover gene variants influencing ΔHbA1c. Using growth curve modeling adjusted for multiple covariates, we derived ΔHbA1c and conducted linkage-guided sequence analysis. Our genome-wide linkage scan identified a significant locus on 17p12. In-depth analysis of this locus revealed a variant rs56340929 (explaining 27% of the linkage peak) in the ARHGAP44 gene that was significantly associated with ΔHbA1c. RNA transcription of ARHGAP44 was associated with ΔHbA1c. The Framingham Offspring Study data further supported these findings on the gene level. Together, we found a novel gene ARHGAP44 for ΔHbA1c in family members without T2D. Follow-up studies using longitudinal omics data in large independent cohorts are warranted.

PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications.

Recently, large-scale scRNA-seq datasets have been generated to understand the complex signaling mechanisms within the microenvironment of Alzheimer's Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. However, the background signaling networks are highly complex and interactive. It remains challenging to infer the core intra- and inter-multi-cell signaling communication networks using scRNA-seq data. In this study, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy. This model divides complex signaling networks into signaling paths, which are then scored and ranked using a novel graph transformer architecture to infer intra- and inter-cell signaling communications. We evaluated the performance of PathFinder using two scRNA-seq data cohorts. The first cohort is an APOE4 genotype-specific AD, and the second is a human cirrhosis cohort. The evaluation confirms the promising potential of using PathFinder as a general signaling network inference model.

A Paradigm For Calling Sequence In Families: The Long Life Family Study.

Over Several years, we have developed a system for assuring the quality of whole genome sequence (WGS) data in the LLFS families. We have focused on providing data to identify germline genetic variants with the aim of releasing as many variants on as many individuals as possible. We aim to assure the quality of the individual calls. The availability of family data has enabled us to use and validate some filters not commonly used in population-based studies. We developed slightly different procedures for the autosomal, X, Y, and Mitochondrial (MT) chromosomes. Some of these filters are specific to family data, but some can be used with any WGS data set. We also describe the procedure we use to construct linkage markers from the SNP sequence data and how we compute IBD values for use in linkage analysis.

mosGraphGen: a novel tool to generate multi-omic signaling graphs to facilitate integrative and interpretable graph AI model development.

Multi-omic data, i.e., genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data. Graph neural network (GNN) AI models have been widely used to analyze graph-structure datasets and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data by node and edge ranking analysis for signaling flow/cascade inference. However, it is non-trivial for graph-AI model developers to pre-analyze multi-omics data and convert them into graph-structure data for individual samples, which can be directly fed into graph-AI models. To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), a novel computational tool that generates multi-omics signaling graphs of individual samples by mapping the multi-omics data onto a biologically meaningful multi-level background signaling network. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. We evaluated the mosGraphGen using both multi-omics datasets of cancer and Alzheimer's disease (AD) samples. The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/Multi-OmicGraphBuilder/mosGraphGen.

Genetic risk accentuates dietary effects on hepatic steatosis, inflammation and fibrosis in a population-based cohort.

BACKGROUND & AIMS: Steatotic liver disease (SLD), characterized by elevated liver fat content (LFC), is influenced by genetics and diet. However, whether diet has a differential effect based on genetic risk is not well-characterized. We aimed to determine how genetic factors interact with diet to affect SLD in a large national biobank. METHODS: We included UK Biobank participants with dietary intake measured by 24-hour recall and genotyping. The primary predictors were dietary pattern, PNPLA3-rs738409-G, TM6SF2-rs58542926-T, a 16-variant hepatic steatosis polygenic risk score (PRS), and gene-environment interactions. The primary outcome was LFC, and secondary outcomes were iron-controlled T1 time (cT1, a measure of liver inflammation and fibrosis) and liver-related events/mortality. RESULTS: A total of 21,619 participants met inclusion criteria. In non-interaction models, Mediterranean diet and intake of fruit/vegetables/legumes and fish associated with lower LFC, while higher red/processed meat intake and all genetic predictors associated with higher LFC. In interaction models, all genetic predictors interacted with Mediterranean diet and fruit/vegetable/legume intake, while the steatosis PRS interacted with fish intake and the TM6SF2 genotype interacted with red/processed meat intake, to affect LFC. Dietary effects on LFC were up to 3.8-fold higher in PNPLA3-rs738409-GG vs. -CC individuals, and 1.4-3.0-fold higher in the top vs. bottom quartile of the steatosis PRS. Gene-diet interactions were stronger in participants with vs. without overweight. The steatosis PRS interacted with Mediterranean diet and fruit/vegetable/legume intake to affect cT1 and most dietary and genetic predictors associated with risk of liver-related events or mortality by age 70. CONCLUSIONS: Effects of diet on LFC and cT1 were markedly accentuated in patients at increased genetic risk for SLD, implying dietary interventions may be more impactful in these populations. IMPACT AND IMPLICATIONS: Genetic variants and diet both influence risk of hepatic steatosis, inflammation/fibrosis, and hepatic decompensation; however, how gene-diet interactions influence these outcomes has previously not been comprehensively characterized. We investigated this topic in the community-based UK Biobank and found that genetic risk and dietary quality interacted to influence hepatic steatosis and inflammation/fibrosis on liver MRI, so that the effects of diet were greater in people at elevated genetic risk. These results are relevant for patients and medical providers because they show that genetic risk is not fixed (i.e. modifiable factors can mitigate or exacerbate this risk) and realistic dietary changes may result in meaningful improvement in liver steatosis and inflammation/fibrosis. As genotyping becomes more routinely used in clinical practice, patients identified to be at high baseline genetic risk may benefit even more from intensive dietary counseling than those at lower risk, though future prospective studies are required.

PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications.

Recently, large-scale scRNA-seq datasets have been generated to understand the complex and poorly understood signaling mechanisms within microenvironment of Alzheimer's Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. Though a set of targets have been identified, however, it remains a challenging to infer the core intra- and inter-multi-cell signaling communication networks using the scRNA-seq data, considering the complex and highly interactive background signaling network. Herein, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and signaling communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy, which divides the complex signaling networks into signaling paths, and then score and rank them using a novel graph transformer architecture to infer the intra- and inter-cell signaling communications. We evaluated PathFinder using scRNA-seq data of APOE4-genotype specific AD mice models and identified novel APOE4 altered intra- and inter-cell interaction networks among neurons, astrocytes, and microglia. PathFinder is a general signaling network inference model and can be applied to other omics data-driven signaling network inference.

The Protective Effect of Familial Longevity Persists After Age 100: Findings From the Danish National Registers.

BACKGROUND: A recent study suggested that the protective effect of familial longevity becomes negligible for centenarians. However, the authors assessed the dependence on familial longevity in centenarians by comparing centenarians with 1 parent surviving to age 80+ to centenarians whose same-sexed parent did not survive to age 80. Here we test whether the protective effect of familial longevity persists after age 100 using more restrictive definitions of long-lived families. METHODS: Long-lived sibships were identified through 3 nationwide, consecutive studies in Denmark, including families with either at least 2 siblings aged 90+ or a Family Longevity Selection Score (FLoSS) above 7. Long-lived siblings enrolled in these studies and who reached age 100 were included. For each sibling, 5 controls matched on sex and year of birth were randomly selected among centenarians in the Danish population. Survival time from age 100 was described with Kaplan-Meier curves for siblings and controls separately. Survival analyses were performed using stratified Cox proportional hazards models. RESULTS: A total of 340 individuals from long-lived sibships who survived to age 100 and 1 700 controls were included. Among the long-lived siblings and controls, 1 650 (81%) were women. The results showed that long-lived siblings presented better overall survival after age 100 than sporadic long-livers (hazard ratio [HR]  = 0.80, 95% confidence interval [CI]  = 0.71-0.91), with even lower estimate (HR = 0.65, 95% CI = 0.50-0.85) if familial longevity was defined by FLoSS. CONCLUSIONS: The present study, with virtually no loss to follow-up, demonstrated a persistence of protective effect of familial longevity after age 100.

Highly accurate disease diagnosis and highly reproducible biomarker identification with PathFormer.

Biomarker identification is critical for precise disease diagnosis and understanding disease pathogenesis in omics data analysis, like using fold change and regression analysis. Graph neural networks (GNNs) have been the dominant deep learning model for analyzing graph-structured data. However, we found two major limitations of existing GNNs in omics data analysis, i.e., limited-prediction/diagnosis accuracy and limited-reproducible biomarker identification capacity across multiple datasets. The root of the challenges is the unique graph structure of biological signaling pathways, which consists of a large number of targets and intensive and complex signaling interactions among these targets. To resolve these two challenges, in this study, we presented a novel GNN model architecture, named PathFormer, which systematically integrate signaling network, priori knowledge and omics data to rank biomarkers and predict disease diagnosis. In the comparison results, PathFormer outperformed existing GNN models significantly in terms of highly accurate prediction capability (~30% accuracy improvement in disease diagnosis compared with existing GNN models) and high reproducibility of biomarker ranking across different datasets. The improvement was confirmed using two independent Alzheimer's Disease (AD) and cancer transcriptomic datasets. The PathFormer model can be directly applied to other omics data analysis studies.

Genome-wide association meta-analysis identifies 17 loci associated with nonalcoholic fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD) is common and partially heritable and has no effective treatments. We carried out a genome-wide association study (GWAS) meta-analysis of imaging (n = 66,814) and diagnostic code (3,584 cases versus 621,081 controls) measured NAFLD across diverse ancestries. We identified NAFLD-associated variants at torsin family 1 member B (TOR1B), fat mass and obesity associated (FTO), cordon-bleu WH2 repeat protein like 1 (COBLL1)/growth factor receptor-bound protein 14 (GRB14), insulin receptor (INSR), sterol regulatory element-binding transcription factor 1 (SREBF1) and patatin-like phospholipase domain-containing protein 2 (PNPLA2), as well as validated NAFLD-associated variants at patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily 2 (TM6SF2), apolipoprotein E (APOE), glucokinase regulator (GCKR), tribbles homolog 1 (TRIB1), glycerol-3-phosphate acyltransferase (GPAM), mitochondrial amidoxime-reducing component 1 (MARC1), microsomal triglyceride transfer protein large subunit (MTTP), alcohol dehydrogenase 1B (ADH1B), transmembrane channel like 4 (TMC4)/membrane-bound O-acyltransferase domain containing 7 (MBOAT7) and receptor-type tyrosine-protein phosphatase δ (PTPRD). Implicated genes highlight mitochondrial, cholesterol and de novo lipogenesis as causally contributing to NAFLD predisposition. Phenome-wide association study (PheWAS) analyses suggest at least seven subtypes of NAFLD. Individuals in the top 10% and 1% of genetic risk have a 2.5-fold to 6-fold increased risk of NAFLD, cirrhosis and hepatocellular carcinoma. These genetic variants identify subtypes of NAFLD, improve estimates of disease risk and can guide the development of targeted therapeutics.

Exogenous exposures shape genetic predisposition to lipids, Alzheimer's, and coronary heart disease in the MLXIPL gene locus.

Associations of single nucleotide polymorphisms (SNPs) of the MLXIPL lipid gene with Alzheimer's (AD) and coronary heart disease (CHD) and potentially causal mediation effects of their risk factors, high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG), were examined in two samples of European ancestry from the US (22,712 individuals 587/2,608 AD/CHD cases) and the UK Biobank (UKB) (232,341 individuals; 809/15,269 AD/CHD cases). Our results suggest that these associations can be regulated by several biological mechanisms and shaped by exogenous exposures. Two patterns of associations (represented by rs17145750 and rs6967028) were identified. Minor alleles of rs17145750 and rs6967028 demonstrated primary (secondary) association with high TG (lower HDL-C) and high HDL-C (lower TG) levels, respectively. The primary association explained ~50% of the secondary one suggesting partly independent mechanisms of TG and HDL-C regulation. The magnitude of the association of rs17145750 with HDL-C was significantly higher in the US vs. UKB sample and likely related to differences in exogenous exposures in the two countries. rs17145750 demonstrated a significant detrimental indirect effect through TG on AD risk in the UKB only (ßIE = 0.015, pIE = 1.9 × 10-3), which suggests protective effects of high TG levels against AD, likely shaped by exogenous exposures. Also, rs17145750 demonstrated significant protective indirect effects through TG and HDL-C in the associations with CHD in both samples. In contrast, rs6967028 demonstrated an adverse mediation effect through HDL-C on CHD risk in the US sample only (ßIE = 0.019, pIE = 8.6 × 10-4). This trade-off suggests different roles of triglyceride mediated mechanisms in the pathogenesis of AD and CHD.

Discovery of genomic and transcriptomic pleiotropy between kidney function and soluble receptor for advanced glycation end-products using correlated meta-analyses: The Long Life Family Study (LLFS).

Patients with chronic kidney disease (CKD) have increased oxidative stress and chronic inflammation, which may escalate the production of advanced glycation end-products (AGE). High soluble receptor for AGE (sRAGE) and low estimated glomerular filtration rate (eGFR) levels are associated with CKD and aging. We evaluated whether eGFR calculated from creatinine and cystatin C share pleiotropic genetic factors with sRAGE. We employed whole-genome sequencing and correlated meta-analyses on combined genomewide association study (GWAS) p -values in 4,182 individuals (age range: 24-110) from the Long Life Family Study (LLFS). We also conducted transcriptome-wide association studies (TWAS) on whole blood in a subset of 1,209 individuals. We identified 59 pleiotropic GWAS loci ( p <5×10 -8 ) and 17 TWAS genes (Bonferroni- p <2.73×10 -6 ) for eGFR traits and sRAGE. TWAS genes, LSP1 and MIR23AHG , were associated with eGFR and sRAGE located within GWAS loci, lncRNA- KCNQ1OT1 and CACNA1A/CCDC130 , respectively. GWAS variants were eQTLs in the kidney glomeruli and tubules, and GWAS genes predicted kidney carcinoma. TWAS genes harbored eQTLs in the kidney, predicted kidney carcinoma, and connected enhancer-promoter variants with kidney function-related phenotypes at p <5×10 -8 . Additionally, higher allele frequencies of protective variants for eGFR traits were detected in LLFS than in ALFA-Europeans and TOPMed, suggesting better kidney function in healthy-aging LLFS than in general populations. Integrating genomic annotation and transcriptional gene activity revealed the enrichment of genetic elements in kidney function and kidney diseases. The identified pleiotropic loci and gene expressions for eGFR and sRAGE suggest their underlying shared genetic effects and highlight their roles in kidney- and aging-related signaling pathways.

Multi-omics insights into the biological mechanisms underlying statistical gene-by-lifestyle interactions with smoking and alcohol consumption.

Though both genetic and lifestyle factors are known to influence cardiometabolic outcomes, less attention has been given to whether lifestyle exposures can alter the association between a genetic variant and these outcomes. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium's Gene-Lifestyle Interactions Working Group has recently published investigations of genome-wide gene-environment interactions in large multi-ancestry meta-analyses with a focus on cigarette smoking and alcohol consumption as lifestyle factors and blood pressure and serum lipids as outcomes. Further description of the biological mechanisms underlying these statistical interactions would represent a significant advance in our understanding of gene-environment interactions, yet accessing and harmonizing individual-level genetic and 'omics data is challenging. Here, we demonstrate the coordinated use of summary-level data for gene-lifestyle interaction associations on up to 600,000 individuals, differential methylation data, and gene expression data for the characterization and prioritization of loci for future follow-up analyses. Using this approach, we identify 48 genes for which there are multiple sources of functional support for the identified gene-lifestyle interaction. We also identified five genes for which differential expression was observed by the same lifestyle factor for which a gene-lifestyle interaction was found. For instance, in gene-lifestyle interaction analysis, the T allele of rs6490056 (ALDH2) was associated with higher systolic blood pressure, and a larger effect was observed in smokers compared to non-smokers. In gene expression studies, this allele is associated with decreased expression of ALDH2, which is part of a major oxidative pathway. Other results show increased expression of ALDH2 among smokers. Oxidative stress is known to contribute to worsening blood pressure. Together these data support the hypothesis that rs6490056 reduces expression of ALDH2, which raises oxidative stress, leading to an increase in blood pressure, with a stronger effect among smokers, in whom the burden of oxidative stress is greater. Other genes for which the aggregation of data types suggest a potential mechanism include: GCNT4×current smoking (HDL), PTPRZ1×ever-smoking (HDL), SYN2×current smoking (pulse pressure), and TMEM116×ever-smoking (mean arterial pressure). This work demonstrates the utility of careful curation of summary-level data from a variety of sources to prioritize gene-lifestyle interaction loci for follow-up analyses.

Genetic pleiotropy between pulmonary function and age-related traits: The Long Life Family Study.

BACKGROUND: Pulmonary function (PF) progressively declines with aging. Forced expiratory volume in the first second (FEV1) and forced vital capacity (FVC) are predictors of morbidity of pulmonary and cardiovascular diseases and all-cause mortality. In addition, reduced PF is associated with elevated chronic low-grade systemic inflammation, glucose metabolism, body fatness, and low muscle strength. It may suggest pleiotropic genetic effects between PF with these age-related factors. METHODS: We evaluated whether FEV1 and FVC share common pleiotropic genetic effects factors with interleukin-6, high-sensitivity C-reactive protein, body mass index, muscle (grip) strength, plasma glucose, and glycosylated hemoglobin in 3,888 individuals (age range: 26-106). We employed sex-combined and sex-specific correlated meta-analyses to test whether combining genome-wide association p-values from two or more traits enhances the ability to detect variants sharing effects on these correlated traits. RESULTS: We identified 32 loci for PF, including 29 novel pleiotropic loci associated with pulmonary function and (i) body fatness (CYP2U1/SGMS2), (ii) glucose metabolism (CBWD1/DOCK8 and MMUT/CENPQ), (iii) inflammatory markers (GLRA3/HPGD, TRIM9, CALN1, CTNNB1/ZNF621, GATA5/SLCO4A1/NTSR1, and NPVF/C7orf31/CYCS), and (iv) muscle strength (MAL2, AC008825.1/LINC02103, AL136418.1). CONCLUSIONS: The identified genes/loci for PF and age-related traits suggest their underlying shared genetic effects, which can explain part of their phenotypic correlations. Integration of gene expression and genomic annotation data shows enrichment of our genetic variants in lung, blood, adipose, pancreas, and muscles, among others. Our findings highlight the critical roles of identified gene/locus in systemic inflammation, glucose metabolism, strength performance, PF, and pulmonary disease, which are involved in accelerated biological aging.

NIA Long Life Family Study: Objectives, Design, and Heritability of Cross-Sectional and Longitudinal Phenotypes.

The NIA Long Life Family Study (LLFS) is a longitudinal, multicenter, multinational, population-based multigenerational family study of the genetic and nongenetic determinants of exceptional longevity and healthy aging. The Visit 1 in-person evaluation (2006-2009) recruited 4 953 individuals from 539 two-generation families, selected from the upper 1% tail of the Family Longevity Selection Score (FLoSS, which quantifies the degree of familial clustering of longevity). Demographic, anthropometric, cognitive, activities of daily living, ankle-brachial index, blood pressure, physical performance, and pulmonary function, along with serum, plasma, lymphocytes, red cells, and DNA, were collected. A Genome Wide Association Scan (GWAS) (Ilumina Omni 2.5M chip) followed by imputation was conducted. Visit 2 (2014-2017) repeated all Visit 1 protocols and added carotid ultrasonography of atherosclerotic plaque and wall thickness, additional cognitive testing, and perceived fatigability. On average, LLFS families show healthier aging profiles than reference populations, such as the Framingham Heart Study, at all age/sex groups, for many critical healthy aging phenotypes. However, participants are not uniformly protected. There is considerable heterogeneity among the pedigrees, with some showing exceptional cognition, others showing exceptional grip strength, others exceptional pulmonary function, etc. with little overlap in these families. There is strong heritability for key healthy aging phenotypes, both cross-sectionally and longitudinally, suggesting that at least some of this protection may be genetic. Little of the variance in these heritable phenotypes is explained by the common genome (GWAS + Imputation), which may indicate that rare protective variants for specific phenotypes may be running in selected families.

Obesity Partially Mediates the Diabetogenic Effect of Lowering LDL Cholesterol.

OBJECTIVE: LDL cholesterol (LDLc)-lowering drugs modestly increase body weight and type 2 diabetes risk, but the extent to which the diabetogenic effect of lowering LDLc is mediated through increased BMI is unknown. RESEARCH DESIGN AND METHODS: We conducted summary-level univariable and multivariable Mendelian randomization (MR) analyses in 921,908 participants to investigate the effect of lowering LDLc on type 2 diabetes risk and the proportion of this effect mediated through BMI. We used data from 92,532 participants from 14 observational studies to replicate findings in individual-level MR analyses. RESULTS: A 1-SD decrease in genetically predicted LDLc was associated with increased type 2 diabetes odds (odds ratio [OR] 1.12 [95% CI 1.01, 1.24]) and BMI (ß = 0.07 SD units [95% CI 0.02, 0.12]) in univariable MR analyses. The multivariable MR analysis showed evidence of an indirect effect of lowering LDLc on type 2 diabetes through BMI (OR 1.04 [95% CI 1.01, 1.08]) with a proportion mediated of 38% of the total effect (P = 0.03). Total and indirect effect estimates were similar across a number of sensitivity analyses. Individual-level MR analyses confirmed the indirect effect of lowering LDLc on type 2 diabetes through BMI with an estimated proportion mediated of 8% (P = 0.04). CONCLUSIONS: These findings suggest that the diabetogenic effect attributed to lowering LDLc is partially mediated through increased BMI. Our results could help advance understanding of adipose tissue and lipids in type 2 diabetes pathophysiology and inform strategies to reduce diabetes risk among individuals taking LDLc-lowering medications.

Combined Effect of Lead Exposure and Allostatic Load on Cardiovascular Disease Mortality-A Preliminary Study.

This study explores the combined effect of lead (Pb) exposure and an index of chronic physiological stress on cardiovascular disease mortality using data from the National Health and Nutrition Examination Survey (NHANES) 1999-2008 linked to 1999-2014 National Death Index data. Chronic physiological stress was measured using the allostatic load (AL) index, which was formed by analyzing markers from the cardiovascular, inflammatory, and metabolic systems, with Pb levels, assessed using blood lead levels (BLL). The dataset was analyzed with statistical techniques to explore (a) the relationship between Pb exposure and AL, and (b) the combined role of Pb and AL on cardiovascular disease mortality. Results indicated that AL was more elevated in those with BLLs above the 50th percentile in the US population and that those with elevated AL were more likely to have high BLL. Finally, the interaction of AL and BLL significantly increased the likelihood of cardiovascular disease mortality. These findings highlight the need for considering the totality of exposures experienced by populations to build holistic programs to prevent Pb exposure and reduce stressors to promote optimal health outcomes and reduce cardiovascular mortality risk.

Allele-specific variation at APOE increases nonalcoholic fatty liver disease and obesity but decreases risk of Alzheimer's disease and myocardial infarction.

Nonalcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease and is highly correlated with metabolic disease. NAFLD results from environmental exposures acting on a susceptible polygenic background. This study performed the largest multiethnic investigation of exonic variation associated with NAFLD and correlated metabolic traits and diseases. An exome array meta-analysis was carried out among eight multiethnic population-based cohorts (n = 16 492) with computed tomography (CT) measured hepatic steatosis. A fixed effects meta-analysis identified five exome-wide significant loci (P < 5.30 × 10-7); including a novel signal near TOMM40/APOE. Joint analysis of TOMM40/APOE variants revealed the TOMM40 signal was attributed to APOE rs429358-T; APOE rs7412 was not associated with liver attenuation. Moreover, rs429358-T was associated with higher serum alanine aminotransferase, liver steatosis, cirrhosis, triglycerides and obesity; as well as, lower cholesterol and decreased risk of myocardial infarction and Alzheimer's disease (AD) in phenome-wide association analyses in the Michigan Genomics Initiative, United Kingdom Biobank and/or public datasets. These results implicate APOE in imaging-based identification of NAFLD. This association may or may not translate to nonalcoholic steatohepatitis; however, these results indicate a significant association with advanced liver disease and hepatic cirrhosis. These findings highlight allelic heterogeneity at the APOE locus and demonstrate an inverse link between NAFLD and AD at the exome level in the largest analysis to date.