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Results for “Biological age”

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At least 19 recordsLinked to original sources
PubMed2026

Early life sugar rationing and ageing related diseases, biological ageing and mortality.

Early-life nutrition may influence lifelong ageing, yet human evidence is scarce. Using Britain's postwar sugar rationing as a natural experiment, we examine its long-term effects in 64,809 United Kingdom Biobank participants. Exposure to sugar rationing during the first 1,000 days of life is associated with a 9% lower incidence of hallmark-related disease, with a hazard ratio of 0.91 and a 95% confidence interval of 0.88-0.94, and a 19% lower risk of all-cause mortality, with a hazard ratio of 0.81 and a 95% confidence interval of 0.69-0.93. Mediation analysis indicates that the survival association is statistically mediated, by approximately 60%, through differences in incident hallmark-related disease. Rationed individuals show 1.0-1.2-year younger biological ages across multiple clocks and lower organ ages, particularly in the lung, heart, and liver. Proteomic profiling identifies 47 altered proteins, with enrichment of adenosine monophosphate-activated protein kinase and longevity pathways and suppression of mechanistic target of rapamycin signaling. These findings are consistent with international recommendations to limit free or added sugars from the World Health Organization, United States Dietary Guidelines, and American Heart Association, and may inform policy discussions related to sugar taxation and infant food and marketing policies under the United Nations 2030 Agenda.

Humans↗
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↗
PubMed1976

[Assessment of the biological age in the animal-experiment (author's transl)].

Experimental investigations of internal and external factors presumably influencing the aging process require an objective assessment of the biological age or vitality respectively by means of as many age parameters as possible. Using the rat, a valuable test animal in experimental gerontology, whose life expectancy of about 40 month allows longitudinal studies, a standard test programm for the estimation of the biological age has been developed. The age parameters used originate from investigations of 1. the tail tendon collagen, 2. the skin, 3. the aorta, 4. the ECG, 5. the lipofuscin content of brain and heart, 6. the tissue respiration of various organs, 7, the motor activity and 8. learning and memory. Using the above-mentioned age parameters a statistical measure for the biological age will be calculated by means of multivariate analysis and will allow the comparison of differeent age-and experimental-groups.

Aging↗
PubMed1979

[Degree of sexual maturation as an index of biological age].

Attempt was made to establish some characteristics of sexual maturation, depending upon chronologic and biologic age. A total of 1277 girls from 8 to 17 years of age in the city of Sofia are included in the study. There were no cases of precocious of considerably delayed pubertal development. Essential differences were found in the body dimensions, in connection with the increase in the degree of sexual maturation in one and the same chronologic age. The importance of biologic age for the individual approach to teen-agers girls and boys is emphasized.

Adolescent↗
PubMed2026

Accelerated Biological Aging Increases the Risk of Head and Neck Cancer: Insights From Genetic Instruments of Epigenetic Clocks.

Epigenetic clocks are robust biomarkers of biological aging and have been associated with cancer susceptibility. However, the relationship between genetically predicted epigenetic age acceleration and head and neck cancer risk remains unclear. Using a large case-control study of 2189 head and neck squamous cell carcinoma (HNSCC) cases and 2189 age- and sex-matched controls, we investigated the associations between polygenic scores (PGSs) for multiple epigenetic clocks and HNSCC risk, and evaluated their potential causal roles using two-sample Mendelian randomization (MR). Genome-wide association study (GWAS)-identified single nucleotide polymorphisms (SNPs) associated with four epigenetic clocks (HannumAge, HorvathAge, GrimAge, and PhenoAge) were used to construct clock-specific PGSs. Logistic regression models were applied to assess associations between PGSs and HNSCC risk, while MR analyses, including inverse-variance weighted (IVW), weighted median, and MR-Egger methods, were used to infer potential causal relationships. Among the 48 epigenetic clock-associated SNPs, 12 showed nominal associations with HNSCC risk, and one variant (rs2275558 in PBX1) remained significant after Bonferroni correction (OR = 0.67, 95% CI: 0.60-0.76). PGSs for all four epigenetic clocks were higher in cases than in controls. In logistic regression analyses, each standard deviation increase in HannumAge PGS was associated with a 25% higher risk of HNSCC (OR = 1.25, 95% CI: 1.10-1.41), whereas HorvathAge, GrimAge, and PhenoAge PGSs showed weaker positive associations (ORs ranging from 1.06 to 1.10). Individuals in the highest PGS quartile for all four epigenetic clocks exhibiting 14%-25% higher risk than those in the lower three quartiles. MR analyses supported potential causal effects of genetically predicted HannumAge (IVW OR = 1.24 per SD increase, 95% CI: 1.09-1.42) and GrimAge (IVW OR = 1.23 per SD increase, 95% CI: 0.98-1.56) on HNSCC risk, with consistent estimates in weighted median analyses. Our results highlight biological aging as a potential etiologic mechanism for HNSCC and suggest that epigenetic clock-related genetic profiles may improve HNSCC risk stratification.

Humans↗
PubMed2026

An open benchmark and language models for AI in aging biology.

Over the past two decades, human aging has been characterized across DNA methylation, transcriptomic, proteomic, and clinical modalities, yet no benchmark evaluates whether AI systems can interpret these heterogeneous data types in the context of aging biology. We introduce LongevityBench, an open suite of 17 tasks spanning five biodata domains, and use it to assess 18 frontier AI systems from six developer teams. Despite recent advances in AI, no single model dominates all tasks, with omics-based age prediction being the hardest task regardless of scale. To test whether these gaps can be closed without frontier-scale resources, we fine-tuned a family of five multitask Longevity-LLMs on domain-specific aging data. The compact (0.6B-9B parameters) Longevity-LLMs matched or exceeded far larger frontier systems on LongevityBench, showing that general-purpose language models can be adapted to structured-omics tasks. We publicly release the benchmark, models, and Longevity Claw, an agentic research interface for aging researchers.

Aging↗
PubMed2026

Investigation of the Causal Association Between Biological Aging Indicators and Vascular Disease Through Two-Sample Mendelian Randomization Analysis.

ObjectiveThis study used two-sample Mendelian Randomization to investigate the causal link between multiple biological aging indicators and vascular disease.MethodsSummary genetic data was obtained from genome-wide association studies (GWAS) focusing on aging-related exposures and various vascular disease outcomes. The exposures included granulocyte proportions, PAI-1 (plasminogen activator inhibitor-1), telomere lengths, and the Frailty Index. The primary analysis employed the Inverse Variance Weighted (IVW) method to estimate causal relationships, supported by MR-Egger, weighted median, and weighted mode methods. Sensitivity analyses, including Cochran's Q test, MR-Egger regression, leave-one-out test, and the MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) test, were conducted to evaluate heterogeneity and pleiotropy.ResultsThe analysis revealed distinct pathways after sensitivity adjustments. A higher genetically predicted Frailty Index was associated with an increased risk of abdominal aortic aneurysm (OR=2.5935, 95% CI: 1.3936-4.8268, P=0.0026, false discovery rate (FDR)=0.0475), atherosclerosis excluding cerebral and coronary sclerosis (OR=2.0262, 95% CI:1.5179-2.705, P=1.66×10-6, FDR=1×10-4), and arterial thromboembolic events (OR = 4.0306, 95% CI: 1.7133-9.4818, P = 0.0014, FDR = 0.0337). Conversely, longer telomere length demonstrated a strong, specific protective effect against abdominal aortic aneurysm (OR=0.5008, 95% CI:0.4111-0.6100, P=6.42×10-12, FDR=9.25×10-10), indicating that shorter telomere length is associated with an increased risk of AAA. Furthermore, a lower granulocyte proportion was causally linked to an increased risk of thoracic aortic aneurysm (OR=0.0181, 95% CI: 0.0014-0.2376, P=0.0023, FDR=0.0465).ConclusionThis study identifies three genetic pathways linking biological aging to vascular disease, offering new molecular targets for its prevention and treatment.

Humans↗
PubMed1975

Biological age and habitual physical activity in relation to physical fitness in 12- and 13-year-old schoolboys.

PURPOSE: The purpose of this study was to investigate the relationship between biological age, habitual physical activity and anthropometrical and physiological characteristics in 12- and 13-year-old schoolboys (n = 70). METHODS: At the beginning and the end of the school year 1971/2 biological age was determined by measuring skeletal age from left hand X-ray photographs. Habitual physical activity was determined by questionnaire interview and pedometers. RESULTS: All anthropometrical characteristics showed significant correlations (P less than 0.05) with skeletal age except for bicipital and tricipital skinfolds. Out of 9 physical fitness tests handgrip was the only test that showed a significant correlation (0.52) with skeletal age. Pedometer scores gave significant negative correlations (P less than 0.05) with anthropometrical characteristics except for tricipital skinfold. The fitness tests bent arm hang, 12 min run walk, sit and reach and W-170 showed significant correlations (P less than 0.05) with pedometer scores.

Activities of Daily Living↗
PubMed2025

Genome-wide methylation biomarkers and biological aging in patients with bipolar disorder characterized for lithium response.

BACKGROUND: Epigenetic mechanisms might play a role in modulating susceptibility to bipolar disorder (BD) and response to lithium, the mainstay treatment for BD. Additionally, individuals with BD experience accelerated biological aging. METHODS: We compared blood DNA methylation profiles measured with EPIC v.2.0 arrays between patients with BD (33 lithium responders and 31 nonresponders) and nonpsychiatric controls (n = 32), as well as based on long-term lithium response. In addition, we compared cellular aging between these groups using epigenetic age, pace of aging, and, for the first time, transcriptional age acceleration based on bulk RNA sequencing in 93 patients and 56 controls. RESULTS: We identified 191 differentially methylated positions (DMPs) and 8 differentially methylated regions between patients with BD and controls, located in genes enriched for "Postsynaptic Density" (odds ratio = 6.81, p = 0.001). No DMP was significantly associated with lithium response after multiple testing correction. Patients showed a significantly higher biological age acceleration than controls based on two epigenetic clocks (GrimAge, Mann-Whitney U = 551, p = 0.0009; GrimAge2: U = 477, p = 9.0E-05) and pace of aging (DunedinPACE, t = 3.01, p = 0.003), but not on transcriptional age. While we observed no significant difference in epigenetic aging based on lithium response, lithium responders showed lower epigenetic acceleration using all clocks, with a trend observed using the PhenoAge clock (t = 1.97, p = 0.053). CONCLUSIONS: Our findings point to methylation patterns characterizing BD and support the hypothesis of accelerated cellular aging in BD.

Humans↗
PubMed2026

Genetic and epigenetic underpinnings of biological aging: a multi-omics study integrating Mendelian randomization, spatial transcriptomics, and drug target discovery.

Inflammaging represents a hallmark of biological aging, yet the causal inflammatory mediators driving multi-dimensional epigenetic aging and their effector genes remain poorly characterized at the genetic level. We developed a four-tier analytical framework integrating causal screening, multi-omics effector gene mapping, spatial transcriptomics, and drug target evaluation. Two-sample Mendelian randomization (MR) of 91 circulating inflammatory proteins against six aging phenotypes identified IL-12B, IFNG, and IL-2 as the most robust pro-aging mediators with consistent effects across independent outcomes. Using multi-omics summary-based MR (SMR) as the core analytical engine, we integrated four-layer whole-blood molecular QTL resources eQTL (eQTLGen, n = 31,684), sQTL (GTEx, n = 755), pQTL (INTERVAL + SCALLOP, n = 34,232), and mQTL (McRae et al., n = 1,980) - with GWAS summary statistics for four epigenetic age acceleration measures. At a stringent threshold (P_SMR < 1&#xd7;10&#x207b;&#xb9;&#xb2;), seven high-confidence effector genes were identified: NHLRC1, TPMT, SELP, and RIPPLY3 for IEAA; ZNF373A and PLDN for HannumAA; and EDARADD for PhenoAA. The chromosome 6p21 NHLRC1-TPMT locus, overwhelmingly driven by methylation QTL signals (-log&#x2081;&#x2080;P = 26.06), emerged as the dominant genetic node of epigenetic aging. Spatial projection via gsMap onto a mouse E16.5 embryo atlas (121,767 cells) revealed preferential enrichment in smooth muscle and lung, with EDARADD showing marked specificity in mucosal epithelium. Cross-database drug target mining classified TPMT and SELP as repurposable known targets and NHLRC1 as a high-priority novel druggable candidate. This study provides multi-omics convergent causal evidence for inflammation-driven epigenetic aging and delivers genetically anchored targets for precision anti-aging intervention.

Aging↗
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&#x2009;=&#x2009;1726, 29.5%); or 2) survival to age 90 with cognitive impairment (n&#x2009;=&#x2009;956, 16.4%); vs. 3) death before age 90 (n&#x2009;=&#x2009;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&#x2009;=&#x2009;0.66; 95% CI 0.61-0.71), PCGrimAge (OR&#x2009;=&#x2009;0.64; 95% CI 0.59-0.69), PCPhenoAge (OR&#x2009;=&#x2009;0.73; 95% CI 0.68-0.78) and DunedinPACE (OR&#x2009;=&#x2009;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↗
PubMed1979

[Biological age in children with clefts].

The skeletal and the dental maturity in a group of 189 children with cleft lip and/or cleft palate were determined by means of X-rays of the wrist joint and orthopantomogrammes. In 60 children, anthropometric determinations were also performed. 486 normal children involved in the Nijmegen growth study served as control subjects. The ages ranged from 4 to 14 years. A retardation in dental development was evidenced in boys with clefts. There was no remarkable difference in dental development between girls with clefts and girls without clefts. The progression in skeletal maturity was greater in girls with clefts than in girls without clefts. There was no remarkable difference in skeletal maturity between boys with clefts and normal boys. The determination of 10 anthropometric values revealed only a few remarkable differences between children with clefts and normal children.

Adolescent↗