Life Family Study Research Articles

Novel Loci for Triglyceride / High-density Lipoprotein Cholesterol Ratio Longitudinal Change among Subjects without Type 2 Diabetes

Aims/hypothesisTriglyceride (TG) /High density lipoprotein cholesterol (HDL-C) ratio (THR) is a surrogate predictor of hyperinsulinemia. 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 non-diabetic Europeans from the Long Life Family Study (LLFS, n=1384). MethodsSubjects with diabetes or on dyslipidemia medications were excluded. ΔTHR was derived using growth curve modeling, and adjusted for age, sex, field centers, and principal components (PCs). GWAS used a linear mixed model accounting for familial relatedness. GWLS employed haplotype-based IBD estimation with 0.5 cM average spacing. ResultsHeritability of ΔTHR was moderate (46%). Our GWAS identified a significant locus at the LPL (p=1.58e-9) for ΔTHR; this locus has been reported before influencing baseline THR levels. Our GWLS found significant linkage with a logarithm of the odds (LODs) exceeding 3 on 3q28 (LODs=4.1). Using a subset of 25 linkage enriched families, 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). While the former variant was associated with EIF4A2 (p=7e-5) / ADIPOQ (p=3.49e-2) transcriptional levels, the latter variant was not associated with TPRG1 (p=0.23) transcriptional levels. Replication in the Framingham Heart Study Offspring Cohort (FOS) observed modest effect of these loci on ΔTHR. ConclusionsOur approach discovered two novel gene variants EIF4A2/ADIPOQ-rs114108468 and TPRG1-rs16864075 on 3q28 for ΔTHR among subjects without diabetes. Our findings provided novel insights into the molecular regulation of insulin resistance.

Metabolite signatures of chronological age, aging, survival, and longevity

Metabolites that mark aging are not fully known. We analyze 408 plasma metabolites in Long Life Family Study participants to characterize markers of age, aging, extreme longevity, and mortality. We identify 308 metabolites associated with age, 258 metabolites that change over time, 230 metabolites associated with extreme longevity, and 152 metabolites associated with mortality risk. We replicate many associations in independent studies. By summarizing the results into 19 signatures, we differentiate between metabolites that may mark aging-associated compensatory mechanisms from metabolites that mark cumulative damage of aging and from metabolites that characterize extreme longevity. We generate and validate a metabolomic clock that predicts biological age. Network analysis of the age-associated metabolites reveals a critical role of essential fatty acids to connect lipids with other metabolic processes. These results characterize many metabolites involved in aging and point to nutrition as a source of intervention for healthy aging therapeutics.

A methodology for gene level omics-WAS integration identifies genes influencing traits associated with cardiovascular risks: the Long Life Family Study.

The Long Life Family Study (LLFS) enrolled 4953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that affect cardiovascular risks in the LLFS population, we developed a multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from rare-variant analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 64 significant genes after Bonferroni correction (p ≤ 2.8 × 10-7), 29 of which replicated in the Framingham Heart Study (FHS) cohort. Notably, 20 of the 29 replicated genes do not have a previously known trait-associated variant in the GWAS Catalog within 50kb. Thirteen modules in Protein-Protein Interaction (PPI) networks are significantly enriched in genes with low meta-analysis p-values for at least one trait, three of which are replicated in the FHS cohort. The functional annotation of genes in these modules showed a significant over-representation of trait-related biological processes including sterol transport, protein-lipid complex remodeling, and immune response regulation. Among major findings, our results suggest a role of triglyceride-associated and mast-cell functional genes FCER1A, MS4A2, GATA2, HDC, and HRH4 in atherosclerosis risks. Our findings also suggest that lower expression of ATG2A, a gene we found to be associated with BMI, may be both a cause and consequence of obesity. Finally, our results suggest that ENPP3 may play an intermediary role in triglyceride-induced inflammation. Our pipeline is freely available and implemented in the Nextflow workflow language, making it easily runnable on any compute platform ( https://nf-co.re/omicsgenetraitassociation ).

All-cause mortality and cause-specific death in U.S. long-lived siblings: data from the Long Life Family Study.

This study compared the mortality risk of long-lived siblings with the U.S. population average and their spouse controls, and investigated the leading causes of death and the familial effect in death pattern. In the Long Life Family Study (LLFS), 1,264 proband siblings (Mean age 90.1, SD 6.4) and 172 spouses (83.8, 7.2) from 511 U.S.-based families were recruited and followed over 12 years. Their survival function was compared with a birth cohort-, baseline age-, sex-, and race-matched pseudo sample from U.S. census data. To examine underlying and contributing causes, we examined in detail 338 deaths with complete death adjudication at the University of Pittsburgh Field Center through the year 2018. A familial effect on survival and death pattern was examined using mixed effect models. The LLFS siblings had better survival than the matched U.S. population average. They also had slightly but not significantly better survival than their spouses' (HR=1.18 [95%CI 0.94-1.49]) after adjusting for age and sex. Age at death ranged from 75-104 years, mean 91.4. The leading causes of death were cardiovascular disease (33.1%), dementia (22.2%), and cancer (10.7%). Mixed effect model shows a significant random effect of family in survival, with adjustment of baseline age and sex. There was no significant familial effect in the underlying cause of death or conditions directly contributing to death among siblings recruited by the University of Pittsburgh Field Center. Our findings demonstrate a higher survival in the LLFS siblings than the U.S. census data, with a familial component of survival. We did not find significant correspondence in causes of death between siblings within families.

Methods for joint modelling of longitudinal omics data and time-to-event outcomes: Applications to lysophosphatidylcholines in connection to aging and mortality in the Long Life Family Study.

Studying relationships between longitudinal changes in omics variables and risks of events requires specific methodologies for joint analyses of longitudinal and time-to-event outcomes. We applied two such approaches (joint models [JM], stochastic process models [SPM]) to longitudinal metabolomics data from the Long Life Family Study focusing on understudied associations of longitudinal changes in lysophosphatidylcholines (LPC) with mortality and aging-related outcomes (23 LPC species, 5,790 measurements of each in 4,011 participants, 1,431 of whom died during follow-up). JM analyses found that higher levels of the majority of LPC species were associated with lower mortality risks, with the largest effect size observed for LPC 15:0/0:0 (hazard ratio: 0.715, 95% CI (0.649, 0.788)). SPM applications to LPC 15:0/0:0 revealed how the association found in JM reflects underlying aging-related processes: decline in robustness to deviations from optimal LPC levels, better ability of males' organisms to return to equilibrium LPC levels (which are higher in females), and increasing gaps between the optimum and equilibrium levels leading to increased mortality risks with age. Our results support LPC as a biomarker of aging and related decline in robustness/resilience, and call for further exploration of factors underlying age-dynamics of LPC in relation to mortality and diseases.

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.

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).

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. 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. 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). 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.

Whole Genome Linkage and Association Analyses Identify DLG Associated Protein-1 as a Novel Positional and Biological Candidate Gene for Muscle Strength: The Long Life Family Study.

Grip strength is a robust indicator of overall health, is moderately heritable, and predicts longevity in older adults. Using genome-wide linkage analysis, we identified a novel locus on chromosome 18p (mega-basepair region: 3.4-4.0) linked to grip strength in 3755 individuals from 582 families aged 64 ± 12 years (range 30-110 years; 55% women). There were 26 families that contributed to the linkage peak (cumulative logarithm of the odds [LOD] score = 10.94), with 6 families (119 individuals) accounting for most of the linkage signal (LOD = 6.4). In these 6 families, using whole genome sequencing data, we performed association analyses between the 7312 single nucleotide (SNVs) and insertion deletion (INDELs) variants in the linkage region and grip strength. Models were adjusted for age, age2, sex, height, field center, and population substructure. We found significant associations between genetic variants (8 SNVs and 4 INDELs, p < 5×10-5) in the Disks Large-associated Protein 1 (DLGAP1) gene and grip strength. Haplotypes constructed using these variants explained up to 98.1% of the LOD score. Finally, RNAseq data showed that these variants were significantly associated with the expression of nearby Myosin Light Chain 12A (MYL12A), Structural Maintenance of Chromosomes Flexible Hinge Domain Containing 1 (SMCHD1), Erythrocyte Membrane Protein Band 4.1 Like 3 (EPB41L3) genes (p < .0004). The DLGAP1 gene plays an important role in the postsynaptic density of neurons; thus, it is both a novel positional and biological candidate gene for follow-up studies aimed at uncovering genetic determinants of muscle strength.

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.

Genome-Wide Epistatic Network Analyses of Semantic Fluency in Older Adults.

Semantic fluency impairment has been attributed to a wide range of neurocognitive and psychiatric conditions, especially in the older population. Moderate heritability estimates on semantic fluency were obtained from both twin and family-based studies suggesting genetic contributions to the observed variation across individuals. Currently, effort in identifying the genetic variants underlying the heritability estimates for this complex trait remains scarce. Using the semantic fluency scale and genome-wide SNP genotype data from the Long Life Family Study (LLFS), we performed a genome-wide association study (GWAS) and epistasis network analysis on semantic fluency in 2289 individuals aged over 60 years from the American LLFS cohorts and replicated the findings in 1129 individuals aged over 50 years from the Danish LLFS cohort. In the GWAS, two SNPs with genome-wide significance (rs3749683, p = 2.52 × 10-8; rs880179, p = 4.83 × 10-8) mapped to the CMYAS gene on chromosome 5 were detected. The epistasis network analysis identified five modules as significant (4.16 × 10-5 < p < 7.35 × 10-3), of which two were replicated (p < 3.10 × 10-3). These two modules revealed significant enrichment of tissue-specific gene expression in brain tissues and high enrichment of GWAS catalog traits, e.g., obesity-related traits, blood pressure, chronotype, sleep duration, and brain structure, that have been reported to associate with verbal performance in epidemiological studies. Our results suggest high tissue specificity of genetic regulation of gene expression in brain tissues with epistatic SNP networks functioning jointly in modifying individual verbal ability and cognitive performance.

Systemic inflammation in relation to exceptional memory in the Long Life Family Study (LLFS)

Background and objectivesWe previously found a substantial familial aggregation of healthy aging phenotypes, including exceptional memory (EM) in long-lived persons. In the current study, we aim to assess whether long-lived families with EM and without EM (non-EM) differ in systemic inflammation status and trajectory. MethodsThe current study included 4333 participants of the multi-center Long Life Family Study (LLFS). LLFS families were classified as EM (556 individuals from 28 families) or non-EM (3777 individuals from 416 families), with 2 or more offspring exhibiting exceptional memory performance (i.e. having baseline composite z-score representing immediate and delayed story memory being 1.5 SD above the mean in the nondemented offspring sample) considered as EM. Blood samples from baseline were used to measure inflammatory biomarkers including total white blood cell (WBC) and its subtypes (neutrophils, lymphocytes, monocytes) count, platelet count, high sensitivity C-reactive protein, and interleukin-6. Generalized linear models were used to examine cross-sectional differences in inflammatory biomarkers at baseline. In a sub-sample of 2227 participants (338 subjects from 24 EM families and 1889 from 328 non-EM families) with repeated measures of immune cell counts, we examined whether the rate of biomarker change differed between EM and non-EM families. All models were adjusted for family size, relatedness, age, sex, education, field center, APOE genotype, and body mass index. ResultsLLFS participants from EM families had a marginally higher monocyte count at baseline (b = 0.028, SE = 0.0110, p = 0.010) after adjusting for age, sex, education, and field site, particularly in men (p < 0.0001) but not in women (p = 0.493) (p-interaction = 0.003). Over time, monocyte counts increased (p < 0.0001) in both EM and non-EM families, while lymphocytes and platelet counts decreased over time in the non-EM families (p < 0.0001) but not in the EM families. After adjusting for multiple variables, there was no significant difference in biomarker change over time between the EM and non-EM families. DiscussionCompared with non-EM families, EM families had significantly higher monocyte count at baseline but had similar change over time. Our study suggests that differences in monocyte counts may be a pathway through which EM emerges in some long-lived families, especially among men.

Is being overweight a causal factor in better survival among the oldest old? a Mendelian randomization study.

Overweight, defined by a body mass index (BMI) between 25 and 30, has been associated with enhanced survival among older adults in some studies. However, whether being overweight is causally linked to longevity remains unclear. To investigate this, we conducted a Mendelian randomization (MR) study of lifespan 85+ years, using overweight as an exposure variable and data from the Health and Retirement Study and the Long Life Family Study. An essential aspect of MR involves selecting appropriate single nucleotide polymorphisms (SNPs) as instrumental variables (IVs). This is challenging due to the limited number of SNP candidates within biologically relevant genes that can satisfy all necessary assumptions and criteria. To address this challenge, we employed a novel strategy of creating additional IVs by pairing SNPs between candidate genes. This strategy allowed us to expand the pool of IV candidates with new "composite" SNPs derived from eight candidate obesity genes. Our study found that being overweight between ages 75 and 85, compared to having a normal weight (BMI 18.5-24.9), significantly contributes to improved survival beyond age 85. Results of this MR study thus support a causal relationship between overweight and longevity in older adults.

NOVEL GENE VARIANTS ASSOCIATED WITH HBA1C CHANGES OVER TIME AMONG NONDIABETIC SUBJECTS IN THE LONG LIFE FAMILY STUDY

Abstract Longitudinal changes in HbA1c (ΔHbA1c) are associated with insulin resistance, aging, cognition, and mortality. To identify novel gene variants underlying ΔHbA1c, we conducted linkage-guided sequence analysis on 17p12 (LODs=3.59) in the Long Life Family Study, a study with familial clustering of exceptional longevity in the US and Denmark. Subjects with clinical diagnosis of diabetes or diabetes treatment and undiagnosed diabetes cases whose fasting glucose ≥ 126 mg/dl or HbA1c ≥ 6.5% were excluded. ΔHbA1c, collected from two exams 7 years apart, was derived using growth curve modeling, adjusting for age, sex, BMI, smoking, field centers and PCs, and was blom-transformed to approximate normality. We identified a significant variant under the linkage peak (ARHGAP44 rs56340929, p=1.77E-06, MAF=6%, accounting for linkage=26.5%). Taking advantage of our currently available RNAseq data, we found a significant association between quantification of ARHGAP44 RNA transcript and adjusted ΔHbA1c using 16 linkage enriched families (n=176, β = -0.24, SE=0.09, p=0.01). We also assessed currently available lipidomics data (188 metabolites, 13 compound classes) and found phosphatidylcholine (p=0.025) and lysophosphatidylcholine (p=0.02) were marginally associated with adjusted ΔHbA1c. ARHGAP44 is reportedly associated with glycemic traits and is mainly expressed in the brain. Further complete omics-data analyses in the LLFS and a replication study using the Framingham Offspring Study are underway.

LINGUISTIC PROFILES OF PARAGRAPH RECALL PREDICT DEPRESSION IN OLDER ADULTS

Abstract Our previous work identified linguistic markers from paragraph recall that predicted cognitive impairment. However, many features were related to psychological constructs, and mood disorders, such as depression, are also associated with cognitive impairment. Therefore, we aimed to identify linguistic markers that are associated with depression and examine their overlap with those for cognitive impairment. We used Linguistic Inquiry Word Count, a natural language processing tool, on Logical Memory transcriptions from the Long Life Family Study. Depression status was positive for participants with a self-reported depression diagnosis or high depressive symptoms (i.e., a CESD-10 score≥10) at testing. We used logistic regression models with Generalized Estimating Equations adjusted by age, sex, education, and within-family relatedness to identify linguistic features associated with depression status (Bonferroni-corrected threshold of p&amp;lt;.001). The sample included 61 participants with evidence of depression and 539 participants without (mean age=72.1±10.9 years, female=63%). A lower percentage of verb use in delayed recall was significantly associated with depression (Odds Ratio [OR]=0.71, CI 0.55-0.93, p=.0001). Additionally, 3 immediate recall features (verbs, OR=0.71; prepositions, OR=1.30; differentiation words, OR=1.30) and 5 delayed recall features (function words, OR=0.68; prepositions, OR=1.50; future tense, OR=0.48; cognitive process words, OR=0.78; past tense, OR=0.74) were significant at a nominal level (p=.009 to p=.035). We identified linguistic features from paragraph recall responses that predict depression and were largely distinct from those associated with cognitive impairment as only two features overlapped. Linguistic analyses of spoken responses may help us understand contributors to test performance and distinguish between cognitive impairment and depression.

TRANSCRIPTOMIC SIGNATURES OF AGING, GENETICS, AND MORTALITY RISK: FINDINGS FROM THE LONG LIFE FAMILY STUDY

Abstract The Long Life Family Study (LLFS), funded by the National Institute on Aging, is an international collaborative study of the genetics and familial components of exceptional longevity and healthy aging. We phenotyped 4,953 individuals from 539 two-generational families (1,727 proband; 3,226 offspring) at baseline (2006-2009). A second visit (2014-2017) was conducted for 2,904 (478 proband; 2,426 offspring) participants, with a third visit ongoing. The longitudinal, comprehensive in-person visits measured domains of healthy aging, including physical performance, cognition, and blood markers. Extensive genetic analyses were performed using the baseline blood draw, including genotyping with the Illumina 2.5M Human Omni array, linkage analyses with the families, whole genome sequencing using the TopMED protocol, metabolomic assays, and trascriptomics (RNA-seq). Collectively, this symposium will present novel findings that examined differences in gene expression between those of extreme old age compared to younger participants, elucidate potential new genomic regions and variants associated with ankle brachial index, examine rare variants under linkage peaks for perceived physical fatigability, and compare mortality and cause of death between LLFS and the US population. Specifically, Ms. Li will share results on gene expression profiles in aging. Then, Ms. Ressler will share findings on the genetics of ankle brachial index with linkage and association results. Next, Ms. Gay will discuss associations under the linkage peak for perceived physical fatigability. Lastly, Ms. Yao will present mortality and patterns of death in the LLFS. As Discussant, Dr. Nalini Raghavachari from the NIA will provide insights and propose future directions for LLFS.

GENETIC PLEIOTROPY OF KIDNEY FUNCTION AND SOLUBLE RECEPTOR FOR AGE: THE LONG LIFE FAMILY STUDY

Abstract Chronic kidney disease (CKD) patients have increased inflammation and oxidative stress that may escalate the production of advanced glycation end-products (AGEs), a biomarker implicated in aging. Circulate soluble receptor for AGE (sRAGE) is associated with CKD and inversely associated with the estimated glomerular filtration rate (eGFR). In the Long Life Family Study, eGFR measured by creatinine (eGFRcr) and cystatin-C (eGFRcys) correlated with sRAGE (r~ -0.27, p&amp;lt; 1x10-30). We investigated genetic pleiotropy conducting GWAS for eGFRcr, eGFRcys, and sRAGE and correlated meta-analysis (CMA) on GWAS p-values from whole-genome sequence variants in 4,182 subjects (age range: 24–110). Of the 59 loci identified (p&amp;lt; 5x10-8), 42 were novel eGFR discoveries. We performed TWAS and CMA-TWAS for eGFRcr, eGFRcys, and sRAGE on blood RNA-seq in 1,209 subjects and identified 17 genes with pleiotropic expressions for eGFRs and sRAGE (Bonferroni-p&amp;lt; 2.73x10-6). Several CMA-GWAS gene variants (e.g., SH3GLB1/SELENOF, CENPP) were eQTLs in glomeruli and tubule kidney (Human Kidney eQTL Atlas, HK-Atlas) and other tissues (GTEx-v8). The bioinformatics analyses identified (i)genes harboring eQTLs in glomeruli and tubule kidney (HK-Atlas), (ii)enhancer-promoter gene variants associated with kidney function-related phenotypes at genome-wide significance (GeneHancer/GeneCards/GWAS-catalog), and (iii)enhancer regions predicting gene expressions in kidney related-function (EPRI, enhancer-promoter-RNA maps). Some functional genes were found among bioinformatics (e.g., MTX1, TOPORS, SNTB1, LSP1, SNTB1, ATG2A, TP53INP2) and others were unique (e.g., TEC, CCN1, and ERGIC1, GeneHancer/GeneCards/GWAS-catalog). Genomic, transcriptomic, and bioinformatics results demonstrated evidence that some novel variants and genes have functional regulatory features in the kidney and are likely involved in biological and clinical aging.

METABOLOMIC SIGNATURES OF AGE, EXTREME OLD AGE, AND LONGEVITY IN LONG LIFE FAMILY STUDY PARTICIPANTS

Abstract Serum metabolomics of aging has been a growing area of interest and several studies have identified metabolites that correlate with chronological age. The Long Life Family Study (LLFS) has generated serum metabolomics of individuals aged 30 to 110 years and several years of follow up, and offers a unique opportunity to identify (1) a robust signature of age; (2) a signature of extreme old age that differs from the age-related signature; and (3) a signature that predict survival. We analyzed 409 general metabolites and lipid species in approximately 2700 LLFS participants and used a state-of-the-art computational approach with mixed effect models and whole genome sequence data to model within family relation. The analysis identified 305 metabolites that correlate with age at 5% false discovery rate (FDR), 30 metabolites that are not age related and differ in centenarians compared to younger individuals, and 144 metabolites that predict survival at 5%FDR. The aging signature included well known markers: eg ergothioneine and tryptophan that decreased with older age, and was enriched for carbohydrates, organic acids, and several lipid classes. The extreme old age signature was enriched for glycerolipids and glycerophospholipids. The metabolomics signature of survival was enriched for nucleic and organic acids. Comparison with other studies showed strong agreement of results and also highlighted unique finding in extreme old individuals. The analysis also showed substantial variability of serum metabolomics at different ages, thus confirming the heterogeneity of molecular signatures of age and the opportunity to discover specific molecular profiles that promote heathy aging.

DEVELOPMENTAL ORIGINS OF EXCEPTIONAL HEALTH AND SURVIVAL: A FOUR-GENERATION COHORT STUDY

Abstract Little is known about the early life health trajectories associated with longevity, due to the length of follow-up required for such studies. In the Long Life Family Study (LLFS) we have identified 659 Danish long-lived sibships and their descendants across three generations and followed them through national civil and health registries. We have previously shown lower adult mortality and morbidity in both offspring and grandchildren generations. Here we compared perinatal, infant, and maternal health outcomes of third-generation grandchildren (n = 5637) and also fourth-generation great-grandchildren (14,908) born into these long-lived families between 1973-2018, to matched controls from the general population. Hazard ratios (HRs) and odds ratios (ORs) were estimated by Cox proportional hazards and conditional logistic regression models. A general pattern of reduced risk was observed in the grandchildren across a range of perinatal and infant health outcomes, most notably in infant mortality (HR = 0.53, 95% CI [0.36, 0.77]), preterm birth (OR = 0.82, [0.72, 0.93]), and low birth weight (OR = 0.83, [0.76, 0.90]). These associations were robust to further adjustment for parental education levels. Negligible infant survival advantage was observed in the great-grandchildren (HR = 0.90, [0.70, 1.17]). However, there were signals of benefit across some other perinatal outcomes, although much fewer and weaker in magnitude than in the grandchildren. Our findings suggest that phenotypic longevity may have developmental origins as early as the perinatal period, and that this effect persists over at least three generations.

METABOLOMICS OF LONGEVITY AND LIFESPAN

Abstract Serum metabolomics has been an important source of biomarkers of aging and longevity for years. This symposium will bring together investigators from large studies of human longevity to provide an overview of recent discoveries on serum metabolomics of aging and extreme human longevity, their connections to genetic variations, and highlight the challenges of correlating metabolomic profiles of aging in human studies and across multiple species. Dr. Sebastiani will describe results from analyses of serum metabolomics of participants enrolled in the Long Life Family Study, highlight similarities and differences between metabolomic profiles of old age and extreme old age, and some connections with genetics of extreme human longevity. Dr. Rappaport will connect specific variations of the APOE alleles to metabolomic profiles and describe a possible role of bioenergetics pathways in mediating the effect of APOE to longevity and resistance to Alzheimer’s disease. Dr. Monti will expand the characterization of metabolomics of aging and extreme human longevity in a large metabolomic study of very old centenarians by using traditional statistical analyses and novel machine learning techniques. His analysis identifies rich signatures of aging and longevity that include well known metabolites and point to bile acids and several classes of steroids as important marker of longevity. Analytical innovations will be taken further by Dr. Schork who will introduce a novel approach based on distance of profiles to analyze multiple metabolites simultaneously and show the value of this approach to analyze metabolomic profiles of maximum lifespan across multiple species. This is a Geroscience Interest Group Sponsored Symposium.

MULTI-OMICS INTEGRATION IDENTIFIES GENES INFLUENCING TRAITS ASSOCIATED WITH CARDIOVASCULAR RISKS

Abstract The Long Life Family Study (LLFS) enrolled 4,953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that protect LLFS participants against age-related cardiovascular risks, we developed a freely available multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from Rare-Variant Analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 51 significant genes after Bonferroni correction (P ≤ 2.8×10-7). CETP, NLRC5, SLC45A3, and TOMM40 lie within 50 Kb of a known trait-associated variant (previously associated genes). Analysis of protein-protein interaction (PPI) networks identified another 63 genes (passing genes) that (1) have CMA p-value ≤ 5×10-3, (2) lie in a PPI module (highly connected subnetwork) enriched for genes with low P-values, and (3) are annotated with a biological process that is enriched among module genes, ten of which were previously associated with the same traits. Permutation analysis showed that passing genes have a false positive rate of 1 in 14876 and are more likely to be previously known than non-passing genes with similar p-values. CMA improved on the 3 input analyses by producing the largest number of modules enriched for genes with low P-values and highly enriched for genes participating in shared biological processes. Overall, module analysis identified highly plausible candidate causal genes whose P-values after CMA alone were merely suggestive.