Biogerontology

Healthspan, the period of life where organisms are without frailty and/or disease, is a major focus of biogerontological research. To understand late-life decline and increased mortality risk, short-lived organisms such as nematode worms are commonly used. Pristionchus nematodes are established models for evolutionary developmental genetics research and show promise as systems for comparative and experimental study of ageing. To support this, we developed phenotypic ageing profiles for the evo-devo model Pristionchus pacificus and its little-studied congener Pristionchus fissidentatus. We find that various life history traits differ between P. pacificus and P. fissidentatus (lifespan, brood size, and reproductive period), demonstrating their utility for studying divergent ageing trajectories. Further, several traits are consistently impacted by age, including intestinal barrier function, body size, and locomotory ability. Additionally, in P. pacificus, rupture avoidance, cuticle integrity, and feeding rate decline with age, indicating dysregulation across many tissue types. Several age-linked patterns resemble those documented for Caenorhabditis elegans despite considerable evolutionary distance, suggesting conserved senescent processes across the Rhabditida family of nematodes. This work highlights similarities and differences in the impact of ageing in two Pristionchus nematodes and supports their development as models for evolutionary genetic study of senescence.

Metals and per- and polyfluoroalkyl substances (PFAS) represent a significant environmental concern, yet their association with epigenetic age acceleration (EAA) remain largely understudied in marine mammals. Here, associations between EAA in common dolphins (Delphinus delphis) and life history (sex and sexual maturity), trace metals, and PFAS were investigated. EAA was calculated as the residual in the regression of epigenetic age vs chronological age, hence providing a direct measure of the deviation of the epigenetic age of an organism (positive or negative) by comparison with expectation, given their actual chronological age. Sixteen trace elements were quantified in hepatic and renal tissues (n = 53). In addition, 28 PFAS were quantified in hepatic tissue (n = 58). Associations between EAA and explanatory variables were assessed using regression-based and multivariate modelling approaches (linear models and canonical analysis of principal coordinates). No effect of sex was observed, although sexual maturity did significantly increase EAA. Exposure to metals was significantly associated with EAA, explaining 55.4% of the variation, with hepatic metals (Se, Zn, Cu, Al, Mn) driving this relationship. Although EAA was not significantly related to the total PFAS exposure overall, a subset of PFAS variables (PFBA, PFDA, PFHxS-B, PFNA) showed significant association with EAA after adjusting for sex and sexual maturity. Together, these subsets of metal and PFAS variables, in addition to the selenium-to-mercury (Se:Hg) molar ratio, explained 66.7% of the variation in EAA. Our results identify sexual maturity and specific contaminant mixtures as key potential drivers of EAA in common dolphins, highlighting the possible use of EAA as a biomarker of environmental and physiological stress in marine mammals.

Cancer, one of the worlds leading causes of death, is characterized by a complex metabolic reprogramming that features the Warburg effect as one of its hallmarks. The Warburg effect involves increased glucose and amino acid metabolism, which promotes tumor proliferation and progression. Although cancer has historically been attributed to genetic mutations, recent studies suggest a possible metabolic origin. However, a key characteristic of cancer cells is their greater adaptability than normal cells, as evidenced by their resistance to chemotherapy, which stems from their high mutability. This underscores the need to examine the relationship between metabolic reprogramming and cancer development from both metabolic and evolutionary perspectives. In this context, Saccharomyces cerevisiae snf1{Delta} strain has emerged as an ideal cellular model for studying the Warburg effect. This study aimed to determine whether deletion of the SNF1 gene in S. cerevisiae affects its chronological aging and competitiveness in a glucose and amino acid-dependent manner. Herein, we provide evidence that the snf1{Delta} strain changes the chronological aging depending on nutrimental condition, under low-nutrient levels shortens (0.1% glucose + 0.1x amino acids), and increases under high-nutrient levels (5% glucose + 3x amino acids). Competitiveness of the snf1{Delta} strain in co-cultivation with wild-type was also improved in 5% glucose + 3x amino acids, by approximately 2 Log10. These results indicate that snf1{Delta} strain aging and competitiveness are also sensitive to nutrimental status, as was observed in cancer cells.

Growing evidence supports a critical role for the gut-brain axis in regulating metabolic health, inflammation,and cognitive function during aging. Age-associated gut dysbiosis has been linked to metabolic dysfunction and cognitive decline, with females exhibiting increased susceptibility to these age-related impairments. Diet is a major determinant of gut microbiome composition and function. Previous studies from our laboratory demonstrated that dietary protein restriction (DPR) induces fibroblast growth factor 21 (FGF21), improves metabolic health, and extends lifespan in male mice. However, the effects of DPR on the gut microbiome and associated health outcomes in aged female mice remain poorly understood. Female mice were assigned at 16 months of age to either a normal-protein (NP) or low-protein (LP) diet for 26 weeks. Metabolic assessments included food intake, fasting glucose concentrations, and glucose tolerance testing. Senescence-associated markers in mesenteric white adipose tissue (mWAT), fecal microbiome composition, and behavioral outcomes were evaluated to determine relationships among dietary protein intake, microbial communities, metabolic health, and cognitive function. Low-protein diet significantly improved metabolic health in aged female mice, as evidenced by improved glucose regulation. Microbiome analyses revealed increased abundance of Akkermansia at 17 months and Faecalibaculum in LP-fed animals at 22 months of age. More so, functional profiling and gene set enrichment analyses indicated enrichment of microbial pathways associated with membrane integrity and metal ion binding. Lastly, LP-fed female mice displayed improved memory performance at 22 months of age compared with age-matched NP-fed controls. Collectively, these findings demonstrate that DPR remodels the gut microbiome and improves metabolic and cognitive health in aged female mice. The observed microbial adaptations may contribute to the beneficial effects of DPR on aging related physiology, highlighting the gut microbiome as a potential mediator of dietary interventions that promote healthy aging.

Geroscience clinical trials need biomarker surrogate endpoints for healthspan. Leading candidates are omics-based composites developed from machine learning analysis of aging phenotypes including calendar age, survival, functional capacity, and Pace of Aging. Existing Pace of Aging biomarkers were developed in the Dunedin Longitudinal Study, limiting inference about strengths/weaknesses of the method as distinct from the Study, a unique single-year birth cohort followed through midlife with near-perfect retention and uniform measurement of multi-organ-system function across two decades of follow-up. We adapted our Pace of Aging method for mixed-age cohorts with variable follow-up of organ-function measures and applied it to develop a novel DNA methylation biomarker of Pace of Aging in data from the Framingham Heart Study Offspring Cohort, FraminghamPACE. Validation analyses across four independent cohorts and one clinical trial establish advantages for the Pace of Aging method in developing biomarkers that are both predictive of healthspan and responsive to geroprotective intervention.

Lipid peroxidation (LPO) is widely used as a biomarker of oxidative stress in coral bleaching research, yet its measurement remains poorly standardized across the field. A systematic review of the coral LPO literature reveals substantial variation in methodological approaches, including tissue fraction analysis, lysis protocols, assay choice, and normalization metrics, confounding cross-study comparison and obscuring the biological interpretation of results. We experimentally investigate two key sources of variation: the use of bulk holobiont vs separated host and algal symbiont fractions, and the choice of normalization metric. To do so, we used Montastraea cavernosa (n = 6 colonies) exposed to ambient (28C), heat stress (30.5C), and heat stress + artificial upwelling (AU; heat stress intermitted by daily pulses of cooler water, 30.5/27.5C) conditions in a controlled mesocosm experiment. Using a TBARS-based MDA assay with a lysis buffer optimized for coral tissue, we measured LPO separately in coral host and algal symbiont fractions across four time points throughout the day. Host MDA remained stable across all treatments and time points, consistent with either sufficient antioxidant buffering capacity or thermal acclimation over the experimental period. Algal symbiont MDA, in contrast, exhibited pronounced diel and treatment-specific dynamics, and the two fractions responses were decoupled from one another. Normalizing MDA to coral surface area instead of total protein content produced largely consistent diel and treatment patterns, but the two metrics diverged at specific time points, indicating that normalization choice is not interchangeable and can itself affect interpretation. Together, our literature review and empirical results demonstrate that host and algal symbiont LPO dynamics are not comparable when aggregated and argue for host-symbiont fraction separation and consistent, explicitly reported normalization as minimum standards for interpretable and cross-comparable coral LPO measurement.

A growing body of literature has investigated the relationship between age and biomolecular changes, leading to conclusions that aging occurs in discrete molecular "waves." Data summary tools such as LOESS and sliding window analyses like DE-SWAN are common approaches that have gained acceptance in recent years. We demonstrate via simple simulations that these tools can identify non-linear patterns of aging where they do not exist. Specifically, we show that (i) clustering of molecular trajectories using LOESS can lead to artifactual characteristic patterns of molecular aging, (ii) "waves" of aging identified using the combination of LOESS and DE-SWAN in real data are not robust to changes in the underlying age distribution and are not supported by valid permutation testing, and (iii) DE-SWAN alone can generate pronounced "waves" of nonlinear molecular aging in linear data due to differences in statistical power along the age continuum. Our results specifically challenge the statistical support for discrete aging crests inferred in the literature, but do not rule out nonlinear molecular aging or age-associated transitions that may be detectable using other cohorts and statistical models.

Biological age predicts health and lifespan better than chronological age, but remains difficult to measure. One leading molecular proxy for biological age is DNA methylation, which underlies age predictors known as "clocks". These clocks use penalized linear regression to predict chronological age from methylation levels using selected cytosine-guanine pairs (CpGs) along DNA. Although they predict chronological age within a few years and track mortality risk, there are several issues. Different clocks share a vanishingly small number of CpG sites, many of which show weak associations with age. Also, the clocks often do not transfer across methylation array platforms. This paper takes a network approach to better understand these issues. By using 12 public datasets from human blood, we build a co-methylation network of the sites that show the strongest age correlation. After pruning weak links, we find that it has a small number of large modules of covarying CpGs surrounded by many small modules and singleton sites. These modules are biologically interpretable, as they are associated with CpG island contexts and enriched for distinct Gene Ontology functions. We also map five established clocks onto this network (Horvath, Hannum, AltumAge, Skin & Blood, and Han) and find that they select some CpGs from the same module. This suggests that they are more similar than they appear. The network structure also suggests new ways to build clocks. A simple clock that retains one CpG per module matches the performance of established clocks. A second one, built from module-level principal components, outperforms all five established clocks in three validation cohorts and is transferable across array platforms (Illumina Infinium Methylation 450K or EPIC arrays). Overall, the network perspective shifts attention from individual CpG sites to modules of covarying sites. This perspective helps explain why DNA methylation clocks perform so well despite their differences and provides a more systematic approach for developing the next generation of aging biomarkers.

Survival analysis is a workhorse assay in Drosophila research to evaluate somatic fitness. It is indispensable in the study of ageing and insightful in immunity, metabolism, radiobiology, toxicology, ecology, and others. While conceptually simple, lifespan measurement is labour-intensive because it requires the continuous manual maintenance of large experimental cohorts. Here, we describe Drosben, an approach that combines a 3D-printed device to transfer flies from several vials simultaneously, a paper system for quick data recording and accompanying software that automatically digitalises life tables for analysis. We show that using Drosben reduces the time investment to perform lifespan assays by ~85%, with improved speed regardless of experience handling Drosophila vials. Using Drosben, we address the effects on longevity of chronic feeding of indole-acetic acid (IAA), naphthalene-acetic acid (NAA) and trimethoprim (TMP) -- compounds used to control heterologous targeted protein degradation systems. We find that IAA and NAA have noticeable deleterious effects while TMP has a small protective effect specifically in females. We further show that strong static magnetic fields do not affect Drosophila lifespan. Our work suggests that Drosben can cheaply accelerate research where lifespan is used as a life history trait.

Background and objectives: In recent years, the need to develop analytical strategies for healthy aging has assumed great importance. In this study, we introduce DyViA, a generative artificial intelligence (genAI) platform that can construct personalized trajectories capable of predicting the plausible progression of selected phenotypes with advancing age. Research design and methods: DyViA presents a suite of deep learning models covering two major GenAI approaches: DyViA-Diff, a new diffusion model; and DyViA-mGAN, an improved version of a recent Generative Adversarial Network model. It demonstrated the dynamic progression of femoral neck bone mineral density (BMD) using data from a longitudinal cohort study of women in the U.S. of age 65 years or above. Results: Using very few initial measurements, DyViA generated individual-specific continuous trajectories of BMD, with a corresponding region of acceptable predictions, from 66 to 89 years. The results were subjected to rigorous quality-control and comparative analysis across multiple methods. While DyViA-Diff is the superior model with more coherent and accurate predictions, DyViA-mGAN allows for encoding population- and individual-level effects with a better control. Discussion and implications: Given the prevalence of osteoporosis in the aging population, the main impact of DyViAs genAI-driven contribution in the form of personalized, plausible models of BMD progression with age lies in the systematic yet rigorous transition from otherwise static models of inference about a clearly dynamic phenomenon to a continuous one. The foresight offered by DyViAs outputs empowers an individual by conferring a certain degree of strategic preparedness in the course of aging.

Erythropoiesis is the most replication-intensive process in the body. Its lifelong replicative demands may erode hematopoietic cells replicative capacity, leading to a decline in erythrocyte count (EC) in older individuals and limiting their lifespan. We examined the relationship between EC and mortality among 1,620 participants aged [≥]70 years in the Long Life Family Study, among whom lower EC further augmented the exponential age-dependent rise in mortality. We identified an EC threshold (ECT) ([~]3.8x1012/L) below which mortality was amplified (p=9.3x10-6). As EC declined with age (p=8.2x10-18), it fell below this threshold in many participants, sharply increasing their mortality risk. This mortality-based ECT in older individuals emerged from modeling, independent of the WHO anemia definition based on statistical thresholds (5th centiles) of hemoglobin distribution in populations [≤] 65 years. Thus, declining EC may be one of the biological factors imposing a natural lifespan limit on many older individuals.

Ambient temperature is emerging as an environmental factor that may influence cognitive performance in ageing populations. This is particularly relevant in Australia, where people live across diverse climatic regions spanning alpine to tropical conditions. We examined daily temperatures and cognitive performance in 1,873 midlife and older adults (1,297 women, mean age 61.0 years) who completed the Creyos online battery (formerly Cambridge Brain Sciences). Twelve tasks assessed memory, visuospatial processing, language, attention, and executive function. Task scores were linked to postcode-level contemporaneous weather data. The scores were analysed in relation to maximum and minimum air and wet-bulb temperatures and postcode- and month-relative temperature percentiles. Regression models adjusted for age, sex, education, socioeconomic status, and climate zone, with season included for air and wet-bulb measures. Higher minimum, but not maximum, temperature was associated with poorer performance on Paired Associates, a task assessing associative memory. This pattern was observed for air temperature, wet-bulb temperature, and temperature percentile, suggesting poorer memory performance after warmer nights, both in absolute terms and relative to local seasonal norms. Temperature was not significantly associated with performance on any other task, including measures of short-term/working memory, visuospatial processing, language, attention, or executive function. These findings suggest a task-specific association between higher overnight temperature and poorer associative memory performance, rather than a general reduction in cognition. Further studies incorporating personal exposure and sleep measures are needed to clarify whether night-time thermal conditions affect cognitive health in midlife and older populations.

Background: We propose the biological drift framework for the interpretation of biological test results: a z-score-like framework based on optimized and personalized reference populations and a distance-to-optimum drift metric for longitudinal interpretation relative to an estimated individual optimum. We benchmarked biological drifts against Reference Change Values (RCVs), which are used to interpret serial laboratory results by defining the minimum change expected to exceed normal within-subject biological variation CVi. Objectives: To benchmark biological drifts against the classical biological-variation framework and assess their consistency with RCV thresholds across routine biomarkers. Methods: For 62 routine biomarkers, biological drift levels were compared with RCVs after transformation to test the consistency between the two frameworks. Results: Severe biological drifts mostly exceeded the 95% RCV threshold, indicating changes unlikely to be explained by short-term biological variation alone. In contrast, moderate drifts reached the 95% RCV threshold for approximately one in two biomarkers, suggesting that many moderate distance-to-optimum deviations may remain within expected variability, particularly for biomarkers with large within-subject variation CVi. Results are particularly interesting for the follow-up of people with diabetes and for the management of thyroid and hepatic disorders. Conclusions: Biological drifts derived from optimized personalized reference populations are broadly consistent with the RCV framework for identifying biologically meaningful deviations from the optimum and may therefore be relevant for the monitoring of certain biomarkers across several medical conditions in clinical practice.

The pace of aging can be delayed by mutations, dietary manipulations, and drugs, yet the metabolic mechanisms underlying longevity interventions remain poorly understood. Here we present a multi-tissue metabolomic analysis of male UM-HET3 mice treated from 4 to 12 months of age with five validated longevity interventions: rapamycin, acarbose, 17-estradiol, canagliflozin, or caloric restriction. Using a feature-stabilized XGBoost pipeline applied to seven tissues, we show that metabolomic profiles can identify treated mice as likely recipients of a lifespan-extending intervention well before survival differences emerge. A leave-one-intervention-out procedure confirmed that models trained on any four interventions successfully classified mice from a fifth, unseen intervention, implying shared metabolic alterations across mechanistically distinct treatments. The most influential metabolites -- defined as the minimum set explaining 50% of cumulative model gain -- differed substantially across tissues. Only ergothioneine, a dietary antioxidant, ranked highly in more than two tissues: it was elevated by all five interventions in plasma and brain, and by four of five in muscle. Enrichment analyses further identified coordinated remodeling of lipid classes in plasma, perigonadal fat, and kidney. These findings reveal tissue-specific metabolic reprogramming shared across mechanistically distinct longevity interventions and, pending validation against interventions that do not extend lifespan, suggest a path toward metabolomic screening of candidate anti-aging drugs.

Loss of chromosome Y (LOY) is the most common aneuploidy in aging men and is increasingly recognized as a marker of aging and genomic instability. Because LOY occurs in mosaic form, its degree reflects the fraction of cells lacking the Y chromosome. Existing SNP-array- and sequencing-based methods rely largely on single genomic features and indirect transformations to estimate this fraction. We developed BaySeq-Y, a Bayesian method that directly estimates LOY mosaicism from sequencing data using VCF files with read depth (DP) and allelic depth (AD). Within a rigorous Bayesian framework, BaySeq-Y integrates complementary LOY-associated genomic features, including decreased read depth and allelic imbalance, and can additionally leverage haplotype phasing to improve precision. In simulations and fluorescence in situ hybridization validation (FISH), BaySeq-Y provided accurate estimates and outperformed existing methods. Applications to ROSMAP and GTEx supported its biological relevance through transcriptomic validation, demonstrating its utility for quantifying LOY across diverse sequencing datasets.

Organisms have spent their life histories exposed to background levels of natural ionizing radiation. To document the role that radiation plays, the deprivation of these natural levels has been studied by incubating organisms in the shielded space of underground laboratories. We report here on two studies (Study I and Study II) using Aedes aegypti for the first time as a model organism incubated 655 meters underground at the Waste Isolation Pilot Plant (WIPP) outside of Carlsbad, New Mexico, U.S.A. Male mosquitos were incubated at the surface exposed to natural background radiation, and were compared to two underground treatments in which incubators were supplemented with radiation sources used to mimic background and these groups were compared to the underground, radiation-deprived treatment. In Study I, the mosquitos incubated underground in the absence of natural radiation had higher levels of mortality compared to those incubated at the surface and PCA plots of the two transcriptomes were clearly differentiated. Study II was conducted the following year and the experiment was narrowed to include only the surface control and underground, radiation-deprived treatment which allowed for four biological replicates. Again, there was a higher level of mortality in the mosquitos grown underground compared mosquitos grown at the surface. Transcriptomes were not as clearly differentiated by PCA analysis and fecundity data were similar between the two groups. Functional analysis of transcriptomic DEGs from two independent studies suggested there are stress responses in radiation deprived mosquitoes. The absence of a secondary stressor in Study II is discussed as an explanation for the transcriptome differences in the two experiments.

Background German cockroach (Blattella germanica) infestation is an important urban environmental health menace associated with food contamination, allergic disease, and reduced quality of life. Long-term control depends not only on professional pest management, but also on residents knowledge and preventive behaviors. This study assessed the knowledge, Health belief model (HBM) constructs, self-efficacy, and preventive practices related to German cockroach infestation among urban residents in Tehran, Iran. Methods In this cross-sectional study, 120 adults with professionally confirmed household German cockroach infestation were recruited from licensed pest-control companies in Tehran. Data were collated using a 39-item HBM-based questionnaire assessing knowledge, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, and preventive practices. Descriptive statistics, Pearson correlation, and multiple linear regression were performed. Results Participants demonstrated modest knowledge regarding German cockroach biology (mean score: 0.538) and moderate preventive practices (3.157). Preventive practices were positively correlated with knowledge (r = 0.256, P = 0.005), perceived benefits (r = 0.292, P = 0.001), and self-efficacy (r = 0.244, P = 0.007). Regression analysis showed that the model explained 17.3% of the variance in preventive practices (R2 = 0.173, P = 0.001). Knowledge ({beta} = 0.191, P = 0.036), perceived benefits ({beta} = 0.231, P = 0.010), and self-efficacy ({beta} = 0.229, P = 0.012) were significant predictors. Conclusions Urban residents with confirmed German cockroach infestation showed limited knowledge and moderate preventive behaviors. Knowledge, perceived benefits, and self-efficacy were independently associated with preventive practices and demonstrated modest predictive value. Interventions targeting these behavioral factors, alongside environmental and structural improvements, may enhance sustainable household cockroach control.

The APOE gene is a critical determinant of human healthspan and longevity, with the rare{varepsilon} 2 allele traditionally viewed as a universal protective factor against Alzheimer s disease (AD) and a driver of exceptional lifespan. However, this protective paradigm is predominantly derived from European-centric cohorts, leaving the evolutionary and clinical impacts of{varepsilon} 2 across diverse, highly admixed populations largely unknown due to a lack of local ancestry (LA) resolution. To investigate how local genomic backgrounds modulate APOE survival dynamics we analyzed two Brazilian sample collection of older adults from Sao Paulo city: the Biobank for Aging Studies (BAS, n = 716), a post-mortem autopsy study of naturally deceased individuals; and the Health, Well-being and Aging Study (SABE, n = 952), a census-based elderly sample collection. We evaluated deviations from Hardy-Weinberg equilibrium (HWE) using robust permutation-based models to capture ongoing selective and mortality pressures at the APOE locus. While global APOE frequencies adhered to HWE, integrating LA unveiled striking, mirrored ancestral deviations. Our findings reveal that APOE {varepsilon}2 homozygotes with African ancestry significantly contribute to deviations from HWE in the BAS, with an excess of {varepsilon}2AFR/{varepsilon}2AFR homozygotes observed (p = 0.0196). These distinct HWE deviations demonstrate that an African LA background acts as a genetic buffer, attenuating the phenotypic extreme effects of APOE alleles. Furthermore, we observed an excess of the{varepsilon} 4 European haplotypes in the BAS, which is consistent with a mortality pressure allelic effect in the European LA context. Conversely, the{varepsilon} 4AFR/{varepsilon}4AFR combination was overrepresented in the SABE. While this buffering mechanism mitigates{varepsilon} 4 toxicity, it simultaneously dampens the exceptional longevity advantage typically conferred by the{varepsilon} 2 allele, leading to its neutral accumulation in the post-mortem cohort. Our study challenges the "one-size-fits-all" assumption of APOE biomarkers, demonstrating that{varepsilon} 2 protective mechanisms are context-dependent and modulated by local genomic backgrounds in admixed populations.

Abstract Background: The influence of taurine on biological ageing remains unclear, particularly whether it acts as a causal driver or a functional biomarker. We aimed to disentangle the distinct roles of plasma taurine relative abundance, dietary taurine supply, and genetic metabolic capacity on all-cause mortality and unhealthy ageing. Methods: This prospective study used data from the Korean Genome and Epidemiology Study (2001~2022). A subcohort of 2,321 participants (mean age 56.5 years; 51.4% female) with complete metabolomic, dietary, and genomic data was analyzed. Three independent pathways were evaluated: (1) plasma taurine/total amino acid (AA) ratio, (2) dietary taurine to protein ratio, and (3) a weighted genetic risk score (GRS) from 21 SNPs in taurine biosynthesis and transport genes. Primary outcomes were all-cause mortality and unhealthy ageing (Physiological Healthy Ageing Index [PHAI] score [≤] 25th percentile). Results: A higher plasma taurine/total AA ratio was consistently associated with improved ageing outcomes. Participants in the highest quartile showed 29% lower all-cause mortality (Hazard Ratio [HR], 0.71; 95% Confidence Interval [CI], 0.52-0.98; P for trend = .04) and lower risk of PHAI-based unhealthy ageing (HR, 0.77; 95% CI, 0.59-1.00; P for trend = .04) versus the lowest quartile. Dietary taurine-to-protein ratio was not associated with mortality (P for trend = .70), nor was the GRS (P for trend = .74). Conclusions: The protective association of taurine was linked to its relative abundance within the systemic amino acid pool, rather than dietary intake or genetic predisposition, supporting taurine as a functional biomarker of metabolic efficiency rather than a deterministic causal driver of ageing.

Background: The Dietary Inflammatory Index (DII) is widely used to assess the inflammatory potential of diet, but it relies on self-reported dietary assessment and does not directly capture individual differences in metabolism as an intermediate connection to inflammation. High-resolution metabolomics provides objective measurements that complement dietary assessment to support precision nutrition to control inflammation. Objective: We developed, tested, and applied a Dietary Metabolite Inflammatory Index (DMII) to assess diet-related chronic inflammation using metabolites measured by liquid chromatography high-resolution mass spectrometry. Methods: DII was calculated using dietaryindex R package with Block Food Frequency Questionnaire (FFQ) data. To develop the DMII, chronic inflammation-related dietary metabolites corresponding to the DII food parameters were found through a literature review. Dietary metabolites were identified and quantified by authentic standards by our established laboratory procedures. DMII uses the same inflammatory effect scores as the DII. Three DMII versions were developed: concentration-based, median-based, and quintile-based DMII. Mean and standard deviation of 29 dietary metabolites were calculated by using 3025 human plasma samples from 3 studies. DMII was tested in the Center for Health Discovery and Well-Being cohort (CHDWB) and the Atlanta African American Maternal and Child cohort (ATLAA) using chronic inflammation biomarkers, including high-sensitivity C-reactive protein (hsCRP), CRP, and IL6. The median-based DMII was further applied to four Alzheimers disease metabolomics datasets as a proof-of-concept application. Results: In the CHDWB study, concentration-based DMII had a weak positive correlation with Block FFQ-derived DII and strongly correlated with median-based and quintile-based DMII. In the same study, all three DMII versions had significant positive correlations with hsCRP and IL6. In the ATLAA study, only concentration-based DMII was positively associated with CRP and IL6. Higher median-based DMII was associated with higher odds of Alzheimers disease. Conclusions: DMII provides a metabolomics-based framework for assessing diet-related chronic inflammation using metabolomics data. This metabolomics approach may complement self-reported dietary assessment to use diet and nutrition to help protect against chronic disease linked to inflammation.