Longevity Papers

Understanding metabolic changes across the human lifespan is essential for addressing age-related health challenges. However, comprehensive metabolomic and lipidomic analyses, particularly in human plasma, remain underexplored. Herein, we performed untargeted metabolomics and lipidomics profiling of plasma collected from 136 individuals aged 0-84 years. This analysis reveals distinct metabolic signatures across life stages, with newborns displaying unique sphingosine (SPH) profiles, while aging was found to be characterized by elevated amino acid levels and lipid imbalances. Notably, we identified linear and nonlinear metabolic trajectories across the lifespan, highlighting critical transition points reflecting the key stages of metabolic reprogramming. By integrating these metabolic patterns, we developed an "aging clock" based on plasma metabolite profiling, thus providing a powerful tool to predict biological age. These findings offer new insights into the dynamic metabolic landscape of aging, paving the way for targeted interventions to improve healthspan and prevent age-related diseases.

The insulin/IGF-1 signaling (IIS) pathway is an evolutionary conserved regulator of longevity, and its modulation is a hallmark of aging research. The 1993 ground-breaking report of a daf-2 mutation (e1370) that reduced IIS and doubled C. elegans lifespan in hermaphrodite worms paved the way for molecular approaches to modulating aging. However, the impact of that mutation on the male sex has remained largely unstudied. Here we report that the same mutation extends male lifespan by fourfold, to over 110 days. This extreme longevity is coupled with a dramatic extension of healthspan as well. These findings establish sex not as a secondary variable but as a primary determinant of longevity potential, capable of amplifying the output of a core aging pathway to an astonishing degree. This work provides a new approach or dissecting the interplay between sex and aging and suggests that sex-specific interventions may be critical for developing future anti-aging therapeutics.

Lysosomes are critical hubs for both cellular degradation and signal transduction, yet their function declines with age. Aging is also associated with significant changes in lysosomal morphology, but the physiological significance of these alterations remains poorly understood. Here, we find that a subset of aged lysosomes undergo enlargement resulting from lysosomal dysfunction in C. elegans. Importantly, this enlargement is not merely a passive consequence of functional decline but represents an active adaptive response to preserve lysosomal degradation capacity. Blocking lysosomal enlargement exacerbates the impaired degradation of dysfunctional lysosomes. Mechanistically, lysosomal enlargement is a transcriptionally regulated process governed by the longevity transcription factor SKN-1, which responds to lysosomal dysfunction by restricting fission and thereby induces lysosomal enlargement. Furthermore, in long-lived germline-deficient animals, SKN-1 activation induces lysosomal enlargement, thereby promoting lysosomal degradation and contributing to longevity. These findings unveil a morphological adaptation that safeguards lysosomal homeostasis, with potential relevance for lysosomal aging and life span.

There is still relatively little known about the genetic underpinnings of proteomic aging clocks. Here, we describe a genome-wide association study of proteomic aging in the UK Biobank (n=38,865), identifying 27 loci associated with participants' proteomic age gap (ProtAgeGap). ProtAgeGap exhibits a strong genetic correlation with longevity (rg = -0.83), and in FinnGen a ProtAgeGap polygenic score (PGS) was associated with significantly increased odds of achieving longevity (n=500,348; OR = 1.43). Additional PGS analyses in All of Us (n=117,415), China Kadoorie Biobank (n=100,640), and ABCD Study (n=5,204) demonstrate reproducible associations across biobanks of ProtAgeGap PGS with obesity, cardiometabolic disease, and osteoarthritis in adults, and with developmental timing in children. Finally, colocalization analysis identified FTO as an obesity-related mechanism uniting diverse aging traits. Our results demonstrate a shared genetic architecture across the life course of ProtAgeGap with longevity, early developmental biology, and cardiometabolic and musculoskeletal diseases.

Biological brain ageing is a major risk factor for neurodegenerative diseases, which are characterized by selective degeneration of particular neuron types. We analyzed the impact of ageing on the transcriptome of neurons in the ventral tegmental area (VTA), substantia nigra pars compacta (SNc) and locus coeruleus (LC), that show differential vulnerabilities to Parkinson disease. Neurons were isolated from human post mortem brain tissues originating from 48 individuals ranging from 17 to 102 years of age and subjected to Smart-seq2 RNA sequencing. We identified 2,764 genes that were correlated with chronological ageing. This gene expression data was used to develop a feature selection-based Time Traversal algorithm, utilizing functionally grouped gene sets, GO terms, with high predictive accuracy of biological brain ageing. We identified 59 GO terms that can predict biological age using a linear regression model, where leave-one-out cross validation demonstrated a strong correlation between chronological age and predicted biological age (Pearson correlation coefficient = 0.946; adjusted R2 = 0.771). The algorithm was validated on five independent datasets with high predictive performance, demonstrating shared ageing features across the human brain. Nonetheless, our analysis also highlights brain region and neuron type specificity in particular ageing features. Resilient neurons showed a weaker association with age-related transcriptional changes, indicating that they age slower than their vulnerable counterparts, thus revealing targets that may be used to slow down ageing and prevent disease development.

Traditional epigenetic aging clocks are limited because they do not incorporate clinical information and functional tests, and rely on DNA samples and methylation profiling infrastructure which are not easily accessible. To address these limitations, we built a new framework, FusionAge, with which we trained 26 aging clocks using interpretable nonlinear models, including deep neural networks (DNNs). Our results show that multimodal clocks built with DNNs significantly outperform clocks derived from single modalities or traditional linear models. FusionAge-derived biological age is more strongly associated with incident disease and mortality compared to chronological age in UK Biobank individuals. We validated these findings in the National Health and Nutrition Examination Survey, confirming that cardiorespiratory fitness is a major, consistent driver of biological age. Finally, we applied FusionAge to demonstrate its utility in detecting biological age changes in astronauts following spaceflight. Together, we demonstrate a powerful, portable framework for assessing biological age that captures the complex, multifactorial nature of human aging.

Cellular reprogramming with transient OSKM expression can reverse aging phenotypes, but genetic factor delivery introduces heterogeneous expression, reprogramming-associated stress, and barriers for therapeutic use. Small-molecule chemical reprogramming is an alternative, yet its performance relative to genetic approaches in human cells is unresolved. We directly compared a human-optimized chemical reprogramming protocol with doxycycline-inducible OSKM in progerin-induced aged fibroblasts and primary fibroblasts from donors over 85. Both reprogramming methods reduced senescence, mitochondrial ROS, and age-associated gene expression, with chemical reprogramming matching or exceeding OSKM efficacy. The two approaches, however, followed distinct trajectories. OSKM generated heterogeneous populations, including subsets acquiring pluripotency markers while others retained fibroblast identity. Chemical reprogramming produced uniform CD13-low populations without pluripotency marker induction. OSKM induced acute senescence that required a chase period to resolve, whereas chemical reprogramming lowered senescence during active treatment. In old fibroblasts, chemical reprogramming reversed multiple aging hallmarks while preserving fibroblast identity and avoiding telomerase activation. These results show that human-optimized chemical reprogramming can rejuvenate aged human fibroblasts with comparable efficacy to OSKM while generating more homogeneous outcomes and lower cellular stress, supporting small-molecule approaches as promising avenues for therapeutic rejuvenation.