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Luigi Ferrucci

Publications and source records attributed to Luigi Ferrucci.

7 recordsLinked to original sources
PubMed2026

Sex-specific biological aging clocks across organs and omics.

Sex differentially shapes aging, neurodevelopment and neurodegenerative diseases such as Alzheimer's disease (AD). However, most biological aging clocks (artificial intelligence-predicted age minus chronological age) were trained on sex-pooled samples and implicitly assume sex invariance.Here we developed 38 sex-specific biological aging clocks across 15 organ systems. We first demonstrate the importance of sex-stratified training for constructing sex-specific healthy normative references and then reveal marked divergence between female and male clocks. Key genetic parameters and Mendelian randomization results indicate that organ-specific aging liability and its relationships to cardiometabolic, endocrine and mental traits are configured differently in females and males. Proteomic analyses identify distinct, organ-resolved synaptic, immune, vascular and metabolic networks that differentially track female and male biological aging. In longitudinal survival analyses, sex-specific clocks predict whole-body systemic diseases and all-cause mortality in a sex-dependent and organ-dependent manner. Further analyses reveal sex-dependent associations between the brain aging clock and cognitive decline trajectory during a preclinical AD clinical trial. Sex-stratified clocks may offer distinct value by defining biological age against sex-appropriate normative references and revealing sex-dependent genetic, molecular and clinical signatures that pooled models may obscure. Meanwhile, sex-pooled and sex-interaction approaches remain valuable, as human aging and disease also share fundamental biological similarities between females and males. Together, these findings reveal sex-specific biological aging signatures in aging, AD and systemic health, highlighting the need for explicitly sex-stratified modeling approaches.

Journal Article↗
PubMed2026

Characterization of DNA methylation in PBMCs and donor-matched iPSCs shows age-related methylation is reset during stem cell reprogramming.

DNA methylation is an important epigenetic mechanism that helps define and maintain cellular functions. It is influenced by many factors, including environmental exposures, genotype, cell type, sex, and aging. Since age is the primary risk factor for developing neurodegenerative diseases, it is important to determine if age-related DNA methylation is retained when cells are reprogrammed to an induced Pluripotent Stem Cell (iPSC) state. Here, we selected peripheral blood mononuclear cells (PBMCs; n = 99) from a cohort of diverse and healthy individuals enrolled in the Genetic and Epigenetic Signatures of Translational Aging Laboratory Testing (GESTALT) study to reprogram to iPSCs. After reprogramming, the resulting iPSCs were evaluated for DNA methylation signatures to determine if they reflect the confounding factors of aging and environmental effects. Data from genome-wide DNA methylation arrays in both cell types showed that age-related methylation measured by epigenetic clocks is largely reset to an early methylation age after reprogramming of PBMCs to iPSCs. We further examined the epigenetic age of each cell type using an Epigenome-wide Association Study (EWAS) and identified a set of methylation Quantitative Trait Loci in each cell type. Our results show that age-related DNA methylation is largely reset in iPSCs, and each cell type has a unique set of methylation sites that are modified by population-level genetic variation.

DNA Methylation↗
PubMed2026

Epigenetic Clocks of Biological Aging and Cognitively Healthy Longevity: The Women's Health Initiative Memory Study.

BACKGROUND: Little is known about whether epigenetic age acceleration (EAA) clocks are capable of predicting exceptional longevity with or without preserved cognitive function. METHODS: We examined 5844 women from the Women's Health Initiative Memory Study. Fifteen epigenetic clocks were measured at baseline (1996-1999). Longevity outcomes were defined as: 1) survival to age 90 with preserved cognition (n = 1726, 29.5%); or 2) survival to age 90 with cognitive impairment (n = 956, 16.4%); vs. 3) death before age 90 (n = 2611, 44.7%). Logistic regression models examined associations between the 15 clocks and survival to age 90 (vs. death before age 90), adjusting for covariates. Multinomial logistic regression models examined associations with survival to age 90 without cognitive impairment and survival to age 90 with cognitive impairment (each vs. death before age 90), also adjusting for covariates. RESULTS: Each standard deviation increase in EAA for the first-generation clocks was associated with 7%-18% reduced odds of survival to age 90 vs. earlier death. Stronger associations were observed for second- and third-generation clocks, including AgeAccelGrim2 (OR = 0.66; 95% CI 0.61-0.71), PCGrimAge (OR = 0.64; 95% CI 0.59-0.69), PCPhenoAge (OR = 0.73; 95% CI 0.68-0.78) and DunedinPACE (OR = 0.77; 95% CI 0.72-0.82). None of the clocks was more strongly associated with survival to age 90 with preserved cognition than with survival to age 90 with cognitive impairment, relative to death before age 90. CONCLUSION: All epigenetic clocks were associated with exceptional longevity, but none were associated with cognitive healthspan. Developing clocks that can differentiate long survival with and without preserved cognitive function is critical.

Healthspan↗
PubMed2026

Macrophage regulation of extracellular matrix remodeling in aging skeletal muscle.

The extracellular matrix (ECM) is a dynamic structural network that supports tissue architecture and regulates cell function. It is primarily composed of collagens, elastin, proteoglycans, and glycoproteins, which are synthesized by canonical and non-canonical ECM-producing cells. During aging, the ECM undergoes progressive changes in structure and composition, a process recently recognized as the 13th hallmark of aging. In skeletal muscle (SKM), age-associated ECM remodeling, largely regulated by immune system-ECM crosstalk, contributes to sarcopenia and impaired regeneration. Macrophages (MΦs), as key innate immune cells, regulate ECM dynamics both indirectly by activating canonical ECM-producing cells and directly by synthesizing ECM components. Notably, a distinct subset of ECM-producing MΦs that express collagen (COL+ MΦs) has been identified across multiple tissues, although their function in SKM homeostasis and aging remains poorly understood. Here, we review current knowledge of ECM production and remodeling, with special emphasis on MΦ involvement, including COL+ MΦs, as critical regulators of fibrogenesis, especially during SKM aging and regeneration.

Extracellular Matrix↗
PubMed2026

Human Immunodeficiency Virus-Associated Proteomic Signature of Myocardial Fibrosis and Incident Heart Failure.

BACKGROUND: People with human immunodeficiency virus (HIV) (PWH) are at higher risk of myocardial fibrosis and subsequent heart failure (HF) compared to people without HIV (PWOH). Mechanisms underlying this risk and its specificity to PWH are unclear. METHODS: We measured 2594 proteins in plasma obtained concurrently with cardiovascular magnetic resonance imaging among 342 PWH and PWOH. We estimated associations with HIV serostatus and myocardial fibrosis (elevated extracellular volume fraction [ECV] &#x2265;30% among women, &#x2265;28% among men) using multivariable regression. Among an independent community-based cohort, we estimated associations between the identified signature and time to incident HF. RESULTS: Mean age of participants was 55 (standard deviation [SD], 6) years, 25% were female, 61% were PWH (88% on antiretroviral therapy, 74% with undetectable HIV RNA), and 52% had elevated ECV. We identified 39 proteins and 1 cluster of 42 proteins that were higher among PWH versus PWOH and positively associated with elevated ECV, independent of risk factors (false discovery rate <0.05). Among an independent cohort of 3223 PWOH (mean age, 68 [SD, 9] years; 52% female; 118 incident HF cases over a mean of 9.8 [SD, 1.4] years), we found that this protein cluster and 34 of 39 individual proteins were associated with time to incident HF. This signature was statistically enriched for T-cell activation, tumor necrosis factor signaling, ephrin signaling, and tissue maintenance and repair. CONCLUSIONS: We identified an HIV-related proteomic signature associated with myocardial fibrosis regardless of HIV serostatus and that predicted incident HF among the general population. Our results identify several novel associations related to specific immune processes that may contribute to risk of myocardial fibrosis and subsequent HF among both PWH and PWOH.

Humans↗
PubMed2026

Genetic modifiers of APOE-&#x3b5;4-associated cognitive decline.

The APOE-&#x3b5;4 allele is the strongest genetic risk factor for late-onset Alzheimer's disease. However, APOE-&#x3b5;4 is not deterministic, highlighting the need to identify additional genetic and environmental factors. APOE-&#x3b5;4 has been linked to accelerated cognitive decline, so we sought to investigate genetic factors that modify APOE-&#x3b5;4-associated cognitive decline. We conduct cross-ancestry APOE-&#x3b5;4-stratified and interaction GWAS using harmonized cognitive data from 32,778 participants, including 29,354 non-Hispanic White and 3,424 non-Hispanic Black individuals. Our primary outcome is late-life cognition, measured using harmonized composite scores for memory, executive function, and language, modeled as continuous traits reflecting both normative cognitive aging and disease-related decline. We identify two genome-wide significant loci in APOE-&#x3b5;4 carriers, reaching genome-wide significance for executive function. These loci also demonstrate nominal associations across the other domains, suggesting broad effects on cognition. In non-carriers, we identify a genome-wide significant association at ITGB8 restricted to executive function, and another locus associated with language. We further link these loci to SEMA6D, GRIN3A, and ITGB8 through expression and methylation databases. Post-GWAS analyses implicate additional genes including SLCO1A2, and DNAH11. Genetic correlation analyses reveal differences by APOE-&#x3b5;4 status for immune-related traits, suggesting immune-related predispositions may exacerbate cognitive risk in APOE-&#x3b5;4 carriers.

Humans↗
PubMed2024

Assessment of Gene Set Enrichment Analysis using curated RNA-seq-based benchmarks.

Pathway enrichment analysis is a ubiquitous computational biology method to interpret a list of genes (typically derived from the association of large-scale omics data with phenotypes of interest) in terms of higher-level, predefined gene sets that share biological function, chromosomal location, or other common features. Among many tools developed so far, Gene Set Enrichment Analysis (GSEA) stands out as one of the pioneering and most widely used methods. Although originally developed for microarray data, GSEA is nowadays extensively utilized for RNA-seq data analysis. Here, we quantitatively assessed the performance of a variety of GSEA modalities and provide guidance in the practical use of GSEA in RNA-seq experiments. We leveraged harmonized RNA-seq datasets available from The Cancer Genome Atlas (TCGA) in combination with large, curated pathway collections from the Molecular Signatures Database to obtain cancer-type-specific target pathway lists across multiple cancer types. We carried out a detailed analysis of GSEA performance using both gene-set and phenotype permutations combined with four different choices for the Kolmogorov-Smirnov enrichment statistic. Based on our benchmarks, we conclude that the classic/unweighted gene-set permutation approach offered comparable or better sensitivity-vs-specificity tradeoffs across cancer types compared with other, more complex and computationally intensive permutation methods. Finally, we analyzed other large cohorts for thyroid cancer and hepatocellular carcinoma. We utilized a new consensus metric, the Enrichment Evidence Score (EES), which showed a remarkable agreement between pathways identified in TCGA and those from other sources, despite differences in cancer etiology. This finding suggests an EES-based strategy to identify a core set of pathways that may be complemented by an expanded set of pathways for downstream exploratory analysis. This work fills the existing gap in current guidelines and benchmarks for the use of GSEA with RNA-seq data and provides a framework to enable detailed benchmarking of other RNA-seq-based pathway analysis tools.

Humans↗