Niels van Den Berg

Background: MetaboHealth represents a novel indicator of overall health in middle age and may potentially be suitable as actionable health check in prevention strategies. MetaboHealth is a blood-based metabolomic composite score that predicts a wide range of age-related conditions and mortality in large European cohorts. Methods: Here, we investigated whether MetaboHealth can be personalised and limited to clinically validated, i.e. CE-marked, metabolic biomarkers. Next, we assessed whether the updated MetaboHealth score predicts all-cause mortality and cardiometabolic disease incidence and can be improved by a lifestyle intervention. To personalise MetaboHealth, we scaled the metabolic biomarkers using a Dutch reference population (i.e. the Biobanking and BioMolecular Research Infrastructure Netherlands) and, in addition, based the score solely on clinically validated metabolic biomarkers. Results: The novel version of the score, Personal-MetaboHealth, retained predictive accuracy for all-cause mortality and showed an even stronger association with incident cardiometabolic disease in the Leiden Longevity Study (LLS) in which 2,404 participants were followed for up to 22 and 16 years for mortality and morbidity, respectively. The association of Personal-MetaboHealth with all-cause mortality remained robust after adjusting for smoking quantity, alcohol consumption, and medication use, while the cardiometabolic disease association was attenuated due to smoking quantity. Each unit increase in Personal-MetaboHealth was associated with a 13.69 year later onset of the first cardiometabolic disease in the LLS. Next, we showed that Personal-MetaboHealth can be improved by a 3-month combined lifestyle intervention in middle aged individuals (Growing Old Together study), specifically in those at risk, i.e. with an unhealthy score at baseline. Conclusions: Personal-MetaboHealth thus offers a potential actionable health check in middle age for early prevention and extension of healthy lifespan.

Familial longevity, quantified using the Longevity Relatives Count (LRC) score indicating the proportion of ancestral long-lived family members, associates with a pronounced 13 years delayed onset of cardiometabolic disease (CMD). Understanding the molecular basis of familial longevity therefore provides critical insights into mechanisms of cardiometabolic resilience. However, the combined metabolomics and proteomics profile associated with the delayed CMD onset observed in such long-lived family members is not understood yet. Hence, we integrated plasma metabolomics and proteomics in 495 participants from the Leiden Longevity Study to identify molecular signatures associated with (a contrast in) the LRC score. Metabolomics profiling captured 429 features, including amino acid derivatives, nucleosides, and lipid mediators, while proteomics quantified 374 proteins related to cardiovascular, metabolic, and inflammatory pathways. Three within-family analysis approaches were examined and overlapping findings were interpreted. We identified ten metabolites and nine proteins that are associated with increased familial longevity, exemplified by a high LRC score. High LRC scoring individuals exhibited lower levels of amino acid derivatives (prolylhydroxyproline, 5-hydroxy-tryptophan, asymmetric dimethylarginine), nucleosides (2-methylguanosine, 7-methylguanosine, pseudouridine), N-acetylneuraminic acid and quinolinic acid, indicating optimized extracellular matrix integrity, vascular function, and reduced neuroinflammatory activity. Lipid mediators, including elevated 6-keto-PGF1a and reduced 9-HOTrE/alpha-linolenic acid ratio, reflected preserved endothelial homeostasis and attenuated inflammatory signaling. At the proteome level, strong ancestral familial longevity is associated with immune regulators (RETN, NPPB, IGSF8), extracellular matrix components (EFEMP1, EPHB4), and adhesion/signaling molecules (LRP11, ICAM3, KIT, ADGRG2), highlighting coordinated regulation of inflammation, tissue remodeling, and regenerative capacity. Multi-omics pathway analyses indicated convergence on amino acid and nucleotide metabolism, lipid signaling, extracellular matrix remodeling, and receptor-mediated communication. Collectively, these multi-omics systemic signatures define a molecular framework of ancestral familial longevity characterized by reduced inflammation, preserved tissue integrity, and enhanced metabolic and regenerative processes. Our findings provide mechanistic insight into the biology of familial longevity and potentially cardiometabolic resilience.

Aging is a major risk factor for chronic diseases. Unlike the general population, members of long-lived families maintain exceptional health as they age, with over 10 years delayed onset of their first chronic disease. We therefore hypothesize that one of the key features explaining healthy survival up to high ages (longevity) is the absence of chronic disease risk alleles. We investigated this hypothesis in the Leiden Longevity Study, a cohort with data from more than 420 long-lived families in three generations and the Leiden 85-plus study. To analyze our data, we constructed a set of polygenic scores (PGS) covering the top diseases causing most deaths in the Netherlands. We showed that having an increasing number of long-lived ancestors is additively associated with lower genetic risk for coronary artery disease (CAD). Using accelerated failure time modelling, we further showed that a lower PGS for CAD explains up to 20% of the delay in cardiovascular disease incidence in descendants of long-lived families. Finally, we constructed a novel cholesterol-metabolism-PGS, based on gene-annotation enrichment analysis, that predicted time to all-cause mortality in two independent 90 + study populations. Our findings demonstrate that the absence of chronic disease risk alleles is one key feature linked to longevity and that alleles linked to cholesterol metabolism are a key component in healthy aging trajectories. Graphical abstract

The biomarker MetaboHealth represents a novel indicator of overall health in middle age and may potentially be suitable as actionable health check in prevention strategies. MetaboHealth is a blood-based metabolomic composite score that predicts a wide range of age-related conditions and mortality in large European cohorts. Here, we investigated whether MetaboHealth can be personalised and limited to clinically validated metabolomic markers. Next, we assessed whether the updated MetaboHealth score predicts all-cause mortality and cardiometabolic disease incidence and can be improved by a lifestyle intervention. To personalise MetaboHealth, we scaled the metabolomic markers using a Dutch reference population (i.e. the Biobanking and BioMolecular Research Infrastructure Netherlands) and, in addition, based the score solely on clinically validated metabolic markers. The novel version of the score, Personal-MetaboHealth, retained predictive accuracy for all-cause mortality and showed an even stronger association with incident cardiometabolic disease in the Leiden Longevity Study (LLS) in which 2,404 participants were followed for up to 22 and 16 years for mortality and morbidity, respectively. The association of Personal-MetaboHealth with all-cause mortality remained robust after adjusting for smoking, alcohol use, and medication, while the cardiometabolic disease association was partially driven by smoking. Each standard deviation decrease in Personal-MetaboHealth was associated with a 11.7 year earlier onset of the first cardiometabolic disease in the LLS. Next we showed that Personal-MetaboHealth can be improved by a 3-month combined lifestyle intervention in middle aged individuals (Growing Old Together study), specifically in those at risk with an unhealthy score at baseline. Personal-MetaboHealth thus offers a potential actionable health check in middle age for early prevention and extension of healthy lifespan.

Summary: Background: The risk of chronic diseases and multimorbidity increases with age, yet, individuals of the same age can strongly differ in healthspan, ranging from early manifestation of age-related disease to robust health into very old age. Plasma biomarkers, including metabolites and proteins, can capture intrinsic health status, thereby providing insights into the nature of this variation. These biomarkers have been widely explored to understand chronic and early disease risk but less so for disease resilience in mid- and late-life survival (i.e. after 90 years), or for multigenerational longevity. Methods: We quantified 326 plasma proteins using data-independent mass spectrometry in two generations of the Leiden Longevity Study cohort: F1 nonagenarian siblings (late-life; age ≥89; N=852) and F2 offspring and their partners (mid-life; age 30-80; N=2,282). Baseline plasma protein levels were tested for association with mid- and late-life survival, with up to 22 years of follow-up, and cardiometabolic healthspan, with up to 16 years of follow-up. By comparing F2 offspring and partners, we tested for plasma proteins associating with familial longevity. Findings: Four proteins: GSN, F2, CRTAC1, and HP, consistently associated with increased mid- and late-life survival, prolonged cardiometabolic healthspan, and familial longevity; representing overall resilience. Moreover, six proteins: APCS, C7, FCN2, HPR, GSN, and PIGR, associated with mortality independent of MetaboHealth, a well-established metabolomics-based mortality score. Interpretation: We identified GSN, F2, CRTAC1, and HP as promising candidate indicators of healthy aging and resilience, meriting further study. Funding: ZonMw, LUF, BBMRI-NL, VOILA, Jörg Bernards-Stiftung, Köln Fortune, and CECAD Research in context: Evidence before this study: As the population ages, implementing biomarkers that are able to distinguish vulnerable individuals from highly resilient ones may help to alleviate the burden on our healthcare system. Several studies have shown that plasma-derived proteins change with chronological age and can be used to discern those at risk of disease or early mortality. However, most plasma proteome studies to date have focused either on cross-sectional analyses, short follow-up periods, and/or cohorts with a narrow age range, often centered around mid-life. These studies therefore do not capture the biological factors that contribute to survival up to high ages (longevity), disease resilience and sustained health. In addition, it remains unclear whether the proteins and underlying mechanisms associated with increased survival are consistent across mid and late-life. Added value of this study: This study set out to investigate the potential of plasma proteins as markers for healthy aging and resilience, and examines the extent to which these associations are consistent across generations. Using the family-based Leiden Longevity Study cohort, including long-lived nonagenarian siblings, their middle-aged offspring, and their partners, we leveraged a unique design to study survival, mid-life health, and multigenerational longevity. We show that prospective (longevity) survival is associated with both age-dependent and age-independent protein effects. Most age-independent proteins displaying consistent associations across life stages. In addition, relative protein level differences were detectable on average ten years before the onset of cardiometabolic disease in mid-life, indicating signatures of future resilience or vulnerability. Four proteins (GSN, CRTAC1, F2, and HP) show consistent association with survival-related outcomes and are proposed as candidate markers of healthy aging. We further observe favorable differences in these proteins in families enriched for longevity, which is indicative of lower disease risk. These identified proteins implicate inflammatory and osteoarthritis related processes. Furthermore, we demonstrate that proteins capture survival domains distinct from those reflected by metabolomic measures, demonstrating the complementary value of plasma proteomics. Implications of all the available evidence: These findings highlight candidate protein biomarkers of healthy aging and resilience, which may provide improved insight into the mechanisms underlying healthy aging. They also facilitate the development of more sensitive indicators of health, whose implementation may enable earlier and more effective interventions in both population health and clinical care.

Defining and quantifying exceptional familial human survival is a persistent challenge in longevity research. Traditional approaches rely on binary thresholds, arbitrary cutoffs, or simple descriptive measures, which discard information on variation among the oldest individuals, ignore differences in background mortality, and yield unstable family-level summaries. We propose a principled, model-based framework that transforms survival times into percentiles relative to population life tables, standardizing across birth cohorts, sexes, and populations. We extend beta mixed-effects regression to accommodate intentional left-censoring, which downweights early deaths while retaining their contribution to the likelihood, thereby focusing inference on extreme survival. Family-specific random effects provide interpretable, statistically grounded longevity scores, overcoming the limitations of ad hoc measures and enabling robust identification of long-lived families. Simulation studies and application to a large multigenerational Dutch cohort demonstrate that the method reliably identifies families enriched for longevity. This framework provides a flexible, interpretable, and robust tool for analyzing familial survival, offering a paradigm shift in the statistical study of exceptional human lifespan.

Life expectancy has steadily increased in the last two centuries, while healthspan has been lagging behind. Survival into extreme ages strongly clusters within families which often exhibit a delayed onset of (multi)morbidity, yet the underlying protective genetic mechanisms are still largely undefined. We performed affected sib-pair linkage analysis in 212 sibships enriched for ancestral longevity and identified four genomic regions (LODmax ≥3.0) at 1q21.1, 6p24.3, 6q14.3, and 19p13.3. Within these regions, we prioritized 12 rare protein-altering variants in seven candidate genes (NUP210L, SLC27A3, CD1A, CGAS, IBTK, RARS2, and SH2D3A) located in longevity-associated loci. Notably, a missense variant in CGAS (rs200818241), was present in two sibships. Using human- and mouse-based cell models, we showed that rs200818241 reduced protein stability and attenuated activation of the canonical cGAS-STING pathway in a cell-type specific manner. This dampened signalling mitigated inflammation and delayed cellular senescence, mechanisms that may contribute to the survival advantage of CGAS variant carriers. Our findings indicate novel rare variants and candidate genes linked to familial longevity and highlight the cGAS-STING pathway as a potential contributor to the protective mechanisms underlying human longevity.

Descendants of exceptionally long-lived families have a delayed onset of their first chronic disease. We therefore hypothesize that one of the key features explaining healthy survival up to high ages is the absence of chronic disease risk alleles. Using the LLS and Leiden85+ multigenerational cohorts and Polygenic Scores (PGS) for chronic diseases, we showed that an increasing number of long-lived ancestors is additively associated with lower genetic risk for coronary artery disease (CAD). We further showed that a lower PGS for CAD explains up to 20% of the delay in cardiovascular disease incidence in long-lived families. Finally, we constructed a novel cholesterol-metabolism-PGS, based on gene-annotation enrichment analysis, that predicted time to all-cause mortality in two 90+ study populations. Our findings demonstrate that the absence of chronic disease risk alleles is one key-feature linked to longevity and that alleles linked to cholesterol metabolism are a key component in healthy aging trajectories.

Chronological age overlooks the heterogeneity in aging. In response, a wide range of molecular aging biomarkers has been developed to better capture an individual”s aging rate. Yet, a comprehensive comparison of modeling choices in the development of these biomarkers is lacking. In this study, we trained aging biomarkers on the Rockwood frailty index (FI) and all-cause mortality using UK Biobank Olink proteomics and metabolomics ( ¹ H-NMR) data ( n =40,696). We systematically established the impact of model choice, target outcome, and molecular data source on several age-related outcomes. From this, we developed ProteinFrailty (ProtFI), an elastic net model using a minimal set of proteins to predict FI. ProtFI outperformed established aging biomarkers in relation to diverse outcomes, including incident cardiovascular disease, handgrip strength, and self-rated health, both in internal validation and two Dutch external cohorts ( n =995, n =500). Our findings show that an efficient frailty-trained proteomic biomarker robustly predicts age-related decline.

Globally, the lifespan of populations increases but the healthspan is lagging behind. Previous research showed that survival into extreme ages (longevity) clusters in families as illustrated by the increasing lifespan of study participants with each additional long-lived family member. Here we investigate whether the healthspan in such families follows a similar quantitative pattern using three-generational data from two databases, LLS (Netherlands), and SEDD (Sweden). We study healthspan in 2143 families containing index persons with 26 follow-up years and two ancestral generations, comprising 17,539 persons. Our results provide strong evidence that an increasing number of long-lived ancestors associates with up to a decade of healthspan extension. Further evidence indicates that members of long-lived families have a delayed onset of medication use, multimorbidity and, in mid-life, healthier metabolomic profiles than their partners. We conclude that both lifespan and healthspan are quantitatively linked to ancestral longevity, making family data invaluable to identify protective mechanisms of multimorbidity.