A longevity-specific bank of induced pluripotent stem cells from centenarians and their offspring.

Centenarians provide a unique lens through which to study longevity, healthy aging, and resiliency. Moreover, models of human aging and resilience to disease that allow for the testing of potential interventions are virtually non-existent. We obtained and characterized over 50 centenarian and offspring peripheral blood samples including those connected to functional independence data highlighting resistance to disability and cognitive impairment. Targeted methylation arrays were used in molecular aging clocks to compare and contrast differences between biological and chronological age in these specialized subjects. Isolated peripheral blood mononuclear cells (PBMCs) were then successfully reprogrammed into high-quality induced pluripotent stem cell (iPSC) lines which were functionally characterized for pluripotency, genomic stability, and the ability to undergo directed differentiation. The result of this work is a one-of-a-kind resource for studies of human longevity and resilience that can fuel the discovery and validation of novel therapeutics for aging-related disease.

Cross-platform proteomics signatures of extreme old age.

In previous work we used a Somalogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals.

Whole genome-wide sequence analysis of long-lived families (Long-Life Family Study) identifies MTUS2 gene associated with late-onset Alzheimer's disease.

INTRODUCTION: Late-onset Alzheimer's disease (LOAD) has a strong genetic component. Participants in Long-Life Family Study (LLFS) exhibit delayed onset of dementia, offering a unique opportunity to investigate LOAD genetics. METHODS: We conducted a whole genome sequence analysis of 3475 LLFS members. Genetic associations were examined in six independent studies (N = 14,260) with a wide range of LOAD risk. Association analysis in a sub-sample of the LLFS cohort (N = 1739) evaluated the association of LOAD variants with beta amyloid (Aß) levels. RESULTS: We identified several single nucleotide polymorphisms (SNPs) in tight linkage disequilibrium within the MTUS2 gene associated with LOAD (rs73154407, p = 7.6 × 10-9). Association of MTUS2 variants with LOAD was observed in the five independent studies and was significantly stronger within high levels of Aß42/40 ratio compared to lower amyloid. DISCUSSION: MTUS2 encodes a microtubule associated protein implicated in the development and function of the nervous system, making it a plausible candidate to investigate LOAD biology. HIGHLIGHTS: Long-Life Family Study (LLFS) families may harbor late onset Alzheimer's dementia (LOAD) variants. LLFS whole genome sequence analysis identified MTUS2 gene variants associated with LOAD. The observed LLFS variants generalized to cohorts with wide range of LOAD risk. The association of MTUS2 with LOAD was stronger within high levels of beta amyloid. Our results provide evidence for MTUS2 gene as a novel LOAD candidate locus.

Signatures of Neuropsychological Test Results in the Long Life Family Study: A Cluster Analysis.

BACKGROUND: Discovering patterns of cognitive domains and characterizing how these patterns associate with other risk factors and biomarkers can improve our understanding of the determinants of cognitive aging. OBJECTIVE: To discover patterns of cognitive domains using neuropsychological test results in Long Life Family Study (LLFS) and characterize how these patterns associate with aging markers. METHODS: 5,086 LLFS participants were administered neuropsychological tests at enrollment. We performed a cluster analysis of six baseline neuropsychological test scores and tested the association between the identified clusters and various clinical variables, biomarkers, and polygenic risk scores using generalized estimating equations and the Chi-square test. We used Cox regression to correlate the clusters with the hazard of various medical events. We investigated whether the cluster information could enhance the prediction of cognitive decline using Bayesian beta regression. RESULTS: We identified 12 clusters with different cognitive signatures that represent profiles of performance across multiple neuropsychological tests. These signatures significantly correlated with 26 variables including polygenic risk scores, physical and pulmonary functions, and blood biomarkers and were associated with the hazard of mortality (p < 0.01), cardiovascular disease (p = 0.03), dementia (p = 0.01), and skin cancer (p = 0.03). CONCLUSION: The identified cognitive signatures capture multiple domains simultaneously and provide a holistic vision of cognitive function, showing that different patterns of cognitive function can coexist in aging individuals. Such patterns can be used for clinical intervention and primary care.

Multi-modal profiling of peripheral blood cells across the human lifespan reveals distinct immune cell signatures of aging and longevity.

BACKGROUND: Age-related changes in immune cell composition and functionality are associated with multimorbidity and mortality. However, many centenarians delay the onset of aging-related disease suggesting the presence of elite immunity that remains highly functional at extreme old age. METHODS: To identify immune-specific patterns of aging and extreme human longevity, we analyzed novel single cell profiles from the peripheral blood mononuclear cells (PBMCs) of a random sample of 7 centenarians (mean age 106) and publicly available single cell RNA-sequencing (scRNA-seq) datasets that included an additional 7 centenarians as well as 52 people at younger ages (20-89 years). FINDINGS: The analysis confirmed known shifts in the ratio of lymphocytes to myeloid cells, and noncytotoxic to cytotoxic cell distributions with aging, but also identified significant shifts from CD4+ T cell to B cell populations in centenarians suggesting a history of exposure to natural and environmental immunogens. We validated several of these findings using flow cytometry analysis of the same samples. Our transcriptional analysis identified cell type signatures specific to exceptional longevity that included genes with age-related changes (e.g., increased expression of STK17A, a gene known to be involved in DNA damage response) as well as genes expressed uniquely in centenarians' PBMCs (e.g., S100A4, part of the S100 protein family studied in age-related disease and connected to longevity and metabolic regulation). INTERPRETATION: Collectively, these data suggest that centenarians harbor unique, highly functional immune systems that have successfully adapted to a history of insults allowing for the achievement of exceptional longevity. FUNDING: TK, SM, PS, GM, SA, TP are supported by NIH-NIAUH2AG064704 and U19AG023122. MM and PS are supported by NIHNIA Pepper center: P30 AG031679-10. This project is supported by the Flow Cytometry Core Facility at BUSM. FCCF is funded by the NIH Instrumentation grant: S10 OD021587.

Mosaic Chromosomal Alterations and Human Longevity.

Mosaic chromosomal alterations (mCAs) are structural alterations associated with aging, cancer, cardiovascular disease, infectious diseases, and mortality. The distribution of mCAs in centenarians and individuals with familial longevity is poorly understood. We used MOsaic CHromosomal Alteration (MoChA) to discover mCAs in 2050 centenarians, offspring, and 248 controls from the New England Centenarian Study (NECS) and in 3 642 subjects with familial longevity and 920 spousal controls from the Long-Life Family Study (LLFS). We analyzed study-specific associations of somatic mCAs with age, familial longevity, the incidence of age-related diseases, and mortality and aggregated the results by meta-analysis. We show that the accumulation of mCAs > 100 KB increased to 102 years and plateaued at older ages. Centenarians and offspring accumulated fewer autosomal mCAs compared with controls (relative risk 0.637, p = .0147). Subjects with the APOE E4 allele had a 35.3% higher risk of accumulating autosomal mCAs (p = .002). Males were at higher risk for mCAs compared to females (male relative risk 1.36, p = 5.15e-05). mCAs were associated with increased hazard for cancer (hazard ratio 1.2) and dementia (hazard ratio 1.259) at a 10% false discovery rate. We observed a borderline significant association between mCAs and risk for mortality (hazard ratio 1.07, p = .0605). Our results show that the prevalence of individuals with mCAs does not continue to increase at ages >102 years and factors promoting familial longevity appear to confer protections from mCAs. These results suggest that limited mCA accumulation could be an important mechanism for extreme human longevity that needs to be investigated.

A metabolomic signature of the APOE2 allele.

With the goal of identifying metabolites that significantly correlate with the protective e2 allele of the apolipoprotein E (APOE) gene, we established a consortium of five studies of healthy aging and extreme human longevity with 3545 participants. This consortium includes the New England Centenarian Study, the Baltimore Longitudinal Study of Aging, the Arivale study, the Longevity Genes Project/LonGenity studies, and the Long Life Family Study. We analyzed the association between APOE genotype groups E2 (e2e2 and e2e3 genotypes, N = 544), E3 (e3e3 genotypes, N = 2299), and E4 (e3e4 and e4e4 genotypes, N = 702) with metabolite profiles in the five studies and used fixed effect meta-analysis to aggregate the results. Our meta-analysis identified a signature of 19 metabolites that are significantly associated with the E2 genotype group at FDR < 10%. The group includes 10 glycerolipids and 4 glycerophospholipids that were all higher in E2 carriers compared to E3, with fold change ranging from 1.08 to 1.25. The organic acid 6-hydroxyindole sulfate, previously linked to changes in gut microbiome that were reflective of healthy aging and longevity, was also higher in E2 carriers compared to E3 carriers. Three sterol lipids and one sphingolipid species were significantly lower in carriers of the E2 genotype group. For some of these metabolites, the effect of the E2 genotype opposed the age effect. No metabolites reached a statistically significant association with the E4 group. This work confirms and expands previous results connecting the APOE gene to lipid regulation and suggests new links between the e2 allele, lipid metabolism, aging, and the gut-brain axis.

Evaluation of GENESIS, SAIGE, REGENIE and fastGWA-GLMM for genome-wide association studies of binary traits in correlated data.

Performing a genome-wide association study (GWAS) with a binary phenotype using family data is a challenging task. Using linear mixed effects models is typically unsuitable for binary traits, and numerical approximations of the likelihood function may not work well with rare genetic variants with small counts. Additionally, imbalance in the case-control ratios poses challenges as traditional statistical methods such as the Score test or Wald test perform poorly in this setting. In the last couple of years, several methods have been proposed to better approximate the likelihood function of a mixed effects logistic regression model that uses Saddle Point Approximation (SPA). SPA adjustment has recently been implemented in multiple software, including GENESIS, SAIGE, REGENIE and fastGWA-GLMM: four increasingly popular tools to perform GWAS of binary traits. We compare Score and SPA tests using real family data to evaluate computational efficiency and the agreement of the results. Additionally, we compare various ways to adjust for family relatedness, such as sparse and full genetic relationship matrices (GRM) and polygenic effect estimates. We use the New England Centenarian Study imputed genotype data and the Long Life Family Study whole-genome sequencing data and the binary phenotype of human extreme longevity to compare the agreement of the results and tools' computational performance. The evaluation suggests that REGENIE might not be a good choice when analyzing correlated data of a small size. fastGWA-GLMM is the most computationally efficient compared to the other three tools, but it appears to be overly conservative when applied to family-based data. GENESIS, SAIGE and fastGWA-GLMM produced similar, although not identical, results, with SPA adjustment performing better than Score tests. Our evaluation also demonstrates the importance of adjusting by full GRM in highly correlated datasets when using GENESIS or SAIGE.

Association between late maternal age and age-related endophenotypes in the Long Life Family Study.

Extended maternal age has been suggested as marker of delayed age-associated disabilities. We use the Long Life Family Study (LLFS) offspring generation to investigate the association between extended maternal age at last childbirth and healthy-aging endophenotypes. We hypothesize that women with extended maternal age at last childbirth will exhibit healthier endophenotype profiles compared to younger mothers. The association between maternal age and age-related endophenotypes previously derived in LLFS was assessed using Generalized Estimating Equations to adjust for relatedness. The quartiles of the maternal age at last childbirth were modeled as the independent variables. Univariate analyses tested the association between maternal age at last childbirth and age at clinical assessment, education, field center, Apolipoprotein E (APOE) genotype, depression, stress, smoking and successful pregnancies. Only the variables significantly associated in the univariate analyses were considered in secondary multivariate analyses. Univariate analyses showed that compared to older mothers (age at last birth ≥35), mothers 30 years old or younger at last childbirth are less educated (12 ± 3 years versus 13 ± 3 years) and have a higher frequency of smoking (9% versus 3% for maternal age ≥35). Results showed that older mothers (age at last birth ≥31-34 or ≥ 35) demonstrated significantly better cognitive profiles (p = 0.017 and p = 0.021 respectively) compared with mothers with last childbirth age ≤30. Later maternal age among women from long-life families is associated with a better cognitive profile, supporting the hypothesis that later age at childbirth may be a marker for healthy aging.

Digitally generated Trail Making Test data: Analysis using hidden Markov modeling.

The Trail Making Test (TMT) is a neuropsychological test used to assess cognitive dysfunction. The TMT consists of two parts: TMT-A requires connecting numbers 1 to 25 sequentially; TMT-B requires connecting numbers 1 to 12 and letters A to L sequentially, alternating between numbers and letters. We propose using a digitally recorded version of TMT to capture cognitive or physical functions underlying test performance. We analyzed digital versions of TMT-A and -B to derive time metrics and used Bayesian hidden Markov models to extract additional metrics. We correlated these derived metrics with cognitive and physical function scores using regression. On both TMT-A and -B, digital metrics associated with graphomotor processing test scores and gait speed. Digital metrics on TMT-B were additionally associated with episodic memory test scores and grip strength. These metrics provide additional information of cognitive state and can differentiate cognitive and physical factors affecting test performance.

A Genome-Wide Association Study of 2304 Extreme Longevity Cases Identifies Novel Longevity Variants.

We performed a genome-wide association study (GWAS) of human extreme longevity (EL), defined as surviving past the 99th survival percentile, by aggregating data from four centenarian studies. The combined data included 2304 EL cases and 5879 controls. The analysis identified a locus in CDKN2B-AS1 (rs6475609, p = 7.13 × 10-8) that almost reached genome-wide significance and four additional loci that were suggestively significant. Among these, a novel rare variant (rs145265196) on chromosome 11 had much higher longevity allele frequencies in cases of Ashkenazi Jewish and Southern Italian ancestry compared to cases of other European ancestries. We also correlated EL-associated SNPs with serum proteins to link our findings to potential biological mechanisms that may be related to EL and are under genetic regulation. The findings from the proteomic analyses suggested that longevity-promoting alleles of significant genetic variants either provided EL cases with more youthful molecular profiles compared to controls or provided some form of protection from other illnesses, such as Alzheimer's disease, and disease progressions.

Slower Decline in Processing Speed Is Associated with Familial Longevity.

INTRODUCTION: Cross-sectional analyses have associated familial longevity with better cognitive function and lower risk of cognitive impairment in comparison with individuals without familial longevity. The extent to which long-lived families also demonstrate slower rates of cognitive aging (i.e., change in cognition over time) is unknown. This study examined longitudinally collected data among 2 generations of the Long Life Family Study (LLFS) to compare rates of cognitive change across relatives and spouse controls. METHODS: We analyzed change in 6 neuropsychological test scores collected approximately 8 years apart among LLFS family members (n = 3,972) versus spouse controls (n = 1,092) using a Bayesian hierarchical model that included age, years of follow-up, sex, education, generation, and field center and all possible pairwise interactions. RESULTS: At a mean age of 88 years at enrollment in the older generation and 60 years in the younger generation, LLFS family members performed better than their spouses on the Digit Symbol Substitution Test (DSST) and the Logical Memory test. At follow-up, family members in the younger generation also showed slower decline than spouses on the DSST, whereas rates of change of Digit Span, fluency, and memory were similar between the 2 groups. DISCUSSION/CONCLUSION: Individuals in families with longevity appear to have better cognitive performance than their spouses for cognitive processes including psychomotor processing, episodic memory, and retrieval. Additionally, they demonstrate longer cognitive health spans with a slower decline on a multifactorial test of processing speed, a task requiring the integration of processes including organized visual search, working and incidental memory, and graphomotor ability. Long-lived families may be a valuable cohort for studying resilience to cognitive 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.

Association of fatigue, inflammation, and physical activity on gait speed: the Long Life Family Study.

BACKGROUND: Fatigue, inflammation, and physical activity (PA) are all independently associated with gait speed, but their directionality is not fully elucidated. AIMS: Evaluate the bidirectional associations amongst fatigue, inflammation, and PA on gait speed. METHODS: This cross sectional study included probands (n = 1280, aged 49-105) and offspring (n = 2772, aged 24-88) in the Long Life Family Study. We assessed gait speed, fatigue with the question "I could not get going", inflammation using fasting interleukin-6 (IL-6) and high sensitivity C-reactive protein (CRP), and self-reported PA as walking frequency in the past two weeks. The two generations were examined separately using linear mixed modeling. RESULTS: Lower fatigue, lower IL-6, and greater PA were all associated with faster gait speed in both generations (all p < 0.05); lower CRP was only associated with faster gait speed in the offspring. PA explained the association of fatigue and gait speed via a 16.1% (95% CI 9.7%, 26.7%) attenuation of the direct associations for the probands and 9.9% (95% CI 6.3%, 18.8%) in the offspring. In addition, IL-6 explained more of the association of fatigue and gait speed than the association between PA and gait speed, via a 14.9% (95% CI 9.2%, 23.4%) attenuation of the direct association in the offspring only. DISCUSSION: Results revealed a potential directionality from fatigue to IL-6 to PA that may lead to faster gait speed. Future work should examine these relationships longitudinally to establish temporality and causality. CONCLUSIONS: Our findings support a signal that lowering fatigue and inflammation and increasing physical activity may delay functional decline.

APOE E2/E2 Is Associated with Slower Rate of Cognitive Decline with Age.

BACKGROUND: The E4 allele of the APOE gene is known to be associated with cognitive impairment. However, a limited number of studies have examined the association between the E2 allele and longitudinal changes of cognitive function. OBJECTIVE: To determine whether rates of cognitive change differ in carriers of the APOE E2 allele compared to other genotypes. METHODS: We conducted a secondary analysis of data from two ongoing longitudinal cohort studies, the Long Life Family Study (LLFS) and New England Centenarian Study (NECS). We included participants who had APOE genotyping data, data from longitudinal administrations of the Telephone Interview for Cognitive Status (TICS), and age, sex, and education available. We assessed whether cognitive change as measured by rate of decline in TICS score differed among people with different APOE genotypes. We used a hierarchical mixed effect model with APOE genotypes, their interactions with age, and potential confounders. RESULTS: After adjusting for sex and education, in carriers of the common E3/E3 genotype, TICS score decreased by 0.15 points per year of age. In those with the E2/E2 genotype, TICS score decreased by 0.05 points per year of age, a significantly slower rate of decline (p = 0.017). We observed no protective effect of the E2/E3 genotype on cognitive decline. CONCLUSION: These results suggest a protective effect of the E2/E2 genotype on a measure of global cognitive function.

Digital Technology Differentiates Graphomotor and Information Processing Speed Patterns of Behavior.

BACKGROUND: Coupling digital technology with traditional neuropsychological test performance allows collection of high-precision metrics that can clarify and/or define underlying constructs related to brain and cognition. OBJECTIVE: To identify graphomotor and information processing trajectories using a digitally administered version of the Digit Symbol Substitution Test (DSST). METHODS: A subset of Long Life Family Study participants (n = 1,594) completed the DSST. Total time to draw each symbol was divided into 'writing' and non-writing or 'thinking' time. Bayesian clustering grouped participants by change in median time over intervals of eight consecutively drawn symbols across the 90 s test. Clusters were characterized based on sociodemographic characteristics, health and physical function data, APOE genotype, and neuropsychological test scores. RESULTS: Clustering revealed four 'thinking' time trajectories, with two clusters showing significant changes within the test. Participants in these clusters obtained lower episodic memory scores but were similar in other health and functional characteristics. Clustering of 'writing' time also revealed four performance trajectories where one cluster of participants showed progressively slower writing time. These participants had weaker grip strength, slower gait speed, and greater perceived physical fatigability, but no differences in cognitive test scores. CONCLUSION: Digital data identified previously unrecognized patterns of 'writing' and 'thinking' time that cannot be detected without digital technology. These patterns of performance were differentially associated with measures of cognitive and physical function and may constitute specific neurocognitive biomarkers signaling the presence of subtle to mild dysfunction. Such information could inform the selection and timing of in-depth neuropsychological assessments and help target interventions.

Effect of longevity genetic variants on the molecular aging rate.

We conducted a genome-wide association study of 1320 centenarians from the New England Centenarian Study (median age = 104 years) and 2899 unrelated controls using >9 M genetic variants imputed to the HRC panel of ~65,000 haplotypes. The genetic variants with the most significant associations were correlated to 4131 proteins that were profiled in the serum of a subset of 224 study participants using a SOMAscan array. The genetic associations were replicated in a genome-wide association study of 480 centenarians and ~800 controls of Ashkenazi Jewish descent. The proteomic associations were replicated in a proteomic scan of approximately 1000 Ashkenazi Jewish participants from a third cohort. The analysis replicated a protein signature associated with APOE genotypes and confirmed strong overexpression of BIRC2 (p < 5E-16) and under-expression of APOB in carriers of the APOE2 allele (p < 0.05). The analysis also discovered and replicated associations between longevity variants and slower changes of protein biomarkers of aging, including a novel protein signature of rs2184061 (CDKN2A/CDKN2B in chromosome 9) that suggests a genetic regulation of GDF15. The analyses showed that longevity variants correlate with proteome signatures that could be manipulated to discover healthy-aging targets.

Protein signatures of centenarians and their offspring suggest centenarians age slower than other humans.

Using samples from the New England Centenarian Study (NECS), we sought to characterize the serum proteome of 77 centenarians, 82 centenarians' offspring, and 65 age-matched controls of the offspring (mean ages: 105, 80, and 79 years). We identified 1312 proteins that significantly differ between centenarians and their offspring and controls (FDR < 1%), and two different protein signatures that predict longer survival in centenarians and in younger people. By comparing the centenarian signature with 2 independent proteomic studies of aging, we replicated the association of 484 proteins of aging and we identified two serum protein signatures that are specific of extreme old age. The data suggest that centenarians acquire similar aging signatures as seen in younger cohorts that have short survival periods, suggesting that they do not escape normal aging markers, but rather acquire them much later than usual. For example, centenarian signatures are significantly enriched for senescence-associated secretory phenotypes, consistent with those seen with younger aged individuals, and from this finding, we provide a new list of serum proteins that can be used to measure cellular senescence. Protein co-expression network analysis suggests that a small number of biological drivers may regulate aging and extreme longevity, and that changes in gene regulation may be important to reach extreme old age. This centenarian study thus provides additional signatures that can be used to measure aging and provides specific circulating biomarkers of healthy aging and longevity, suggesting potential mechanisms that could help prolong health and support longevity.