Challenges and recommendations for the translation of biomarkers of aging.

Biomarkers of aging (BOA) are quantitative parameters that predict biological age and ideally its changes in response to interventions. In recent years, many promising molecular and omic BOA have emerged with an enormous potential for translational geroscience and improving healthspan. However, clinical translation remains limited, in part due to the gap between preclinical research and the application of BOA in clinical research and other translational settings. We surveyed experts in these areas to better understand current challenges for the translation of aging biomarkers. We identified six key barriers to clinical translation and developed guidance for the field to overcome them. Core recommendations include linking BOA to clinically actionable insights, improving affordability and availability to broad populations and validation of biomarkers that are robust and responsive at the level of individuals. Our work provides key insights and practical recommendations to overcome barriers impeding clinical translation of BOA.

Development and validation of cardiometabolic risk predictive models based on LDL oxidation and candidate geromarkers from the MARK-AGE data.

The predictive value of the susceptibility to oxidation of LDL particles (LDLox) in cardiometabolic risk assessment is incompletely understood. The main objective of the current study was to assess its relationship with other relevant biomarkers and cardiometabolic risk factors from MARK-AGE data. A cross-sectional observational study was carried out on 1089 subjects (528 men and 561 women), aged 40-75 years old, randomly recruited age- and sex-stratified individuals from the general population. A correlation analysis exploring the relationships between LDLox and relevant biomarkers was undertaken, as well as the development and validation of several machine learning algorithms, for estimating the risk of the combined status of high blood pressure and obesity for the MARK-AGE subjects. The machine learning models yielded Area Under the Receiver Operating Characteristic Curve Score ranging 0.783-0.839 for the internal validation, while the external validation resulted in an Under the Receiver Operating Characteristic Curve Score between 0.648 and 0.787, with the variables based on LDLox reaching significant importance within the obtained predictions. The current study offers novel insights regarding the combined effects of LDL oxidation and other ageing markers on cardiometabolic risk. Future studies might be extended on larger patient cohorts, in order to obtain reproducible clinical assessment models.

Secondary integrated analysis of multi-tissue transcriptomic responses to a combined lifestyle intervention in older adults from the GOTO nonrandomized trial.

Molecular effects of lifestyle interventions are typically studied in a single tissue. Here, we perform a secondary analysis on the sex-specific effects of the Growing Old TOgether trial (GOTO, trial registration number GOT NL3301 ( https://onderzoekmetmensen.nl/nl/trial/27183 ), NL-OMON27183 , primary outcomes have been previously reported in ref. 1), a moderate 13-week combined lifestyle intervention on the transcriptomes of postprandial blood, subcutaneous adipose tissue (SAT) and muscle tissue in healthy older adults, the overlap in effect between tissues and their relation to whole-body parameters of metabolic health. The GOTO intervention has virtually no effect on the postprandial blood transcriptome, while the SAT and muscle transcriptomes respond significantly. In SAT, pathways involved in HDL remodeling, O2/CO2 exchange and signaling are overrepresented, while in muscle, collagen and extracellular matrix pathways are significantly overexpressed. Additionally, we find that the effects of the SAT transcriptome closest associates with gains in metabolic health. Lastly, in males, we identify a shared variation between the transcriptomes of the three tissues. We conclude that the GOTO intervention has a significant effect on metabolic and muscle fibre pathways in the SAT and muscle transcriptome, respectively. Aligning the response in the three tissues revealed a blood transcriptome component which may act as an integrated health marker for metabolic intervention effects across tissues.

Association between bilirubin and biomarkers of metabolic health and oxidative stress in the MARK-AGE cohort.

Recent studies have shown that elevated concentrations of unconjugated bilirubin (UCB) may be a protective host factor against the development of noncommunicable diseases (NCDs), whereas low levels of UCB are associated with the opposite effect. The results of this European study, in which 2,489 samples were tested for their UCB concentration using high-performance liquid chromatography (HPLC) and additional data from the MARK-AGE database were used for analysis, provide further evidence that elevated UCB concentrations are linked to a lower risk of developing NCDs and may act as a predictive marker of biological aging as individuals with elevated UCB concentrations showed favorable outcomes in metabolic health and oxidative-stress-related biomarkers. These findings underline the significance of studying individuals with moderate hyperbilirubinemia and investigate UCB routinely, also in the setting of aging, since this condition affects millions of people worldwide but has been underrepresented in clinical research and practice until now.

Late reproduction is associated with extended female survival but not with familial longevity.

RESEARCH QUESTION: Are age at last childbirth and number of children, as facets of female reproductive health, related to individual lifespan or familial longevity? DESIGN: This observational study included 10,255 female participants from a multigenerational historical cohort, the LINKing System for historical family reconstruction (LINKS), and 1258 female participants from 651 long-lived families in the Leiden Longevity Study (LLS). Age at last childbirth and number of children, as outcomes of reproductive success, were compared with individual and familial longevity using the LINKS dataset. In addition, the genetic predisposition in the form of a polygenic risk score (PRS) for age at menopause was studied in relation to familial longevity using the LLS dataset. RESULTS: For each year increase in the age of the birth of the last child, a woman's lifespan increased by 0.06 years (22 days; P = 0.002). The yearly risk for having a last child was 9% lower in women who survived to the oldest 10% of their birth cohort (hazard ratio 0.91, 95% CI 0.86-0.95). Women who came from long-living families did not have a higher mean age of last childbirth. There was no significant association between familial longevity and genetic predisposition to age at menopause. CONCLUSIONS: Female reproductive health associates with a longer lifespan. Familial longevity does not associate to extended reproductive health. Other factors in somatic maintenance that support a longer lifespan are likely to have an impact on reproductive health.

The performance of metabolomics-based prediction scores for mortality in older patients with solid tumors.

Prognostic information is needed to balance benefits and risks of cancer treatment in older patients. Metabolomics-based scores were previously developed to predict 5- and 10-year mortality (MetaboHealth) and biological age (MetaboAge). This study aims to investigate the association of MetaboHealth and MetaboAge with 1-year mortality in older patients with solid tumors, and to study their predictive value for mortality in addition to established clinical predictors. This prospective cohort study included patients aged ≥ 70 years with a solid malignant tumor, who underwent blood sampling and a geriatric assessment before treatment initiation. The outcome was all-cause 1-year mortality. Of the 192 patients, the median age was 77 years. With each SD increase of MetaboHealth, patients had a 2.32 times increased risk of mortality (HR 2.32, 95% CI 1.59-3.39). With each year increase in MetaboAge, there was a 4% increased risk of mortality (HR 1.04, 1.01-1.07). MetaboHealth and MetaboAge showed an AUC of 0.66 (0.56-0.75) and 0.60 (0.51-0.68) for mortality prediction accuracy, respectively. The AUC of a predictive model containing age, primary tumor site, distant metastasis, comorbidity, and malnutrition was 0.76 (0.68-0.83). Addition of MetaboHealth increased AUC to 0.80 (0.74-0.87) (p = 0.09) and AUC did not change with MetaboAge (0.76 (0.69-0.83) (p = 0.89)). Higher MetaboHealth and MetaboAge scores were associated with 1-year mortality. The addition of MetaboHealth to established clinical predictors only marginally improved mortality prediction in this cohort with various types of tumors. MetaboHealth may potentially improve identification of older patients vulnerable for adverse events, but numbers were too small for definitive conclusions. The TENT study is retrospectively registered at the Netherlands Trial Register (NTR), trial number NL8107. Date of registration: 22-10-2019.

Unraveling interindividual variation of trimethylamine N-oxide and its precursors at the population level.

Trimethylamine N-oxide (TMAO) is a circulating microbiome-derived metabolite implicated in the development of atherosclerosis and cardiovascular disease (CVD). We investigated whether plasma levels of TMAO, its precursors (betaine, carnitine, deoxycarnitine, choline), and TMAO-to-precursor ratios are associated with clinical outcomes, including CVD and mortality. This was followed by an in-depth analysis of their genetic, gut microbial, and dietary determinants. The analyses were conducted in five Dutch prospective cohort studies including 7834 individuals. To further investigate association results, Mendelian Randomization (MR) was also explored. We found only plasma choline levels (hazard ratio [HR] 1.17, [95% CI 1.07; 1.28]) and not TMAO to be associated with CVD risk. Our association analyses uncovered 10 genome-wide significant loci, including novel genomic regions for betaine (6p21.1, 6q25.3), choline (2q34, 5q31.1), and deoxycarnitine (10q21.2, 11p14.2) comprising several metabolic gene associations, for example, CPS1 or PEMT. Furthermore, our analyses uncovered 68 gut microbiota associations, mainly related to TMAO-to-precursors ratios and the Ruminococcaceae family, and 16 associations of food groups and metabolites including fish-TMAO, meat-carnitine, and plant-based food-betaine associations. No significant association was identified by the MR approach. Our analyses provide novel insights into the TMAO pathway, its determinants, and pathophysiological impact on the general population.

Plasma trimethylamine N-oxide (TMAO): associations with cognition, neuroimaging, and dementia.

BACKGROUND: The gut-derived metabolite Trimethylamine N-oxide (TMAO) and its precursors - betaine, carnitine, choline, and deoxycarnitine - have been associated with an increased risk of cardiovascular disease, but their relation to cognition, neuroimaging markers, and dementia remains uncertain. METHODS: In the population-based Rotterdam Study, we used multivariable regression models to study the associations between plasma TMAO, its precursors, and cognition in 3,143 participants. Subsequently, we examined their link to structural brain MRI markers in 2,047 participants, with a partial validation in the Leiden Longevity Study (n = 318). Among 2,517 participants, we assessed the risk of incident dementia using multivariable Cox proportional hazard models. Following this, we stratified the longitudinal associations by medication use and sex, after which we conducted a sensitivity analysis for individuals with impaired renal function. RESULTS: Overall, plasma TMAO was not associated with cognition, neuroimaging markers or incident dementia. Instead, higher plasma choline was significantly associated with poor cognition (adjusted mean difference: -0.170 [95% confidence interval (CI) -0.297;-0.043]), brain atrophy and more markers of cerebral small vessel disease, such as white matter hyperintensity volume (0.237 [95% CI: 0.076;0.397]). By contrast, higher carnitine concurred with lower white matter hyperintensity volume (-0.177 [95% CI: -0.343;-0.010]). Only among individuals with impaired renal function, TMAO appeared to increase risk of dementia (hazard ratio (HR): 1.73 [95% CI: 1.16;2.60]). No notable differences were observed in stratified analyses. CONCLUSIONS: Plasma choline, as opposed to TMAO, was found to be associated with cognitive decline, brain atrophy, and markers of cerebral small vessel disease. These findings illustrate the complexity of relationships between TMAO and its precursors, and emphasize the need for concurrent study to elucidate gut-brain mechanisms.

Physical activity and fiber intake beneficial for muscle mass and strength preservation during aging: A comprehensive cross-sectional study in the UK biobank cohort.

BACKGROUND: Aging triggers intricate physiological changes, particularly in whole-body fat-free mass (FFM) and handgrip strength, affecting overall health and independence. Despite existing research, the broader significance of how muscle health is affected by the intricate interplay of lifestyle factors simultaneously during aging needs more exploration. This study aims to examine how nutrition, physical activity, and sleep impact on FFM and handgrip strength in middle-aged men and women, facilitating future personalized recommendations for preserving muscle health. METHODS: The cross-sectional analysis of the UK Biobank involved 45,984 individuals (54 % women) aged 40-70 years with a complete dataset. Multiple linear regression explored determinants of FFM and handgrip strength, considering traditional, socio-demographics, medication use and smoking as covariates, with sex and age (younger and older than 55 years) stratifications. RESULTS: In older men and women, higher physical activity beneficially affect both FFM (respectively Β = 3.36 × 10-3, p-value = 1.66 × 10-3; Β = 2.52 × 10-3, p-value = 3.57 × 10-4) and handgrip strength (Β = 6.05 × 10-3, p-value = 7.99 × 10-5, Β = 8.98 × 10-3, p-value = 2.95 × 10-15). Similar results were found in fiber intake for FFM in older men and women (respectively B = 3.00 × 10-2, p-value = 2.76 × 10-5; B = 2.68 × 10-2, p-value = 1.78 × 10-9) and handgrip strength (Β = 3.27 × 10-2, p-value = 1.40 × 10-3; Β = 3.12 × 10-2, p-value = 1.34 × 10-5). Other lifestyle factors influence FFM and handgrip strength differently. Key determinants influencing handgrip strength included higher protein intake, lower water intake, higher alcohol intake, and extended sleep duration whereas mainly higher water intake is associated with higher FFM. CONCLUSIONS: In both men and women, the main factors associated with FFM and handgrip strength are physical activity and fiber intake, which may underlie a connection between gut and muscle health. Given the observed complexity of muscle health in the age and sex strata, further longitudinal research is needed to provide personalized lifestyle recommendations.

A plant-based diet index to study the relation between diet and disease risk among adults: a narrative review.

Plant-based diets (PBD) may offer various health benefits and contribute to a sustainable way of life, but, if not planned correctly, may also confer risks, e.g., by focusing on plant foods with low nutrient density, such as foods primarily consisting of refined carbohydrates. A plant-based diet index (PDI) differentiating between a healthful, unhealthful, and overall PBD, offers a promising approach to standardize and compare studies and integrate results. In this review we (1) summarize current evidence on the PDI and disease risk of relevance to public health, (2) discuss the methodology of the PDI and how it can be sensibly applied in further studies and (3) indicate areas with a lack of knowledge, such as vulnerable populations. In summary, our amalgamation shows, that adherence to a healthier plant-based diet is associated with an 8-68% lower risk for metabolic risk factors, diabetes, and cardiovascular disease, while adherence to an unhealthier plant-based diet is associated with a 10-63% higher risk. Although differences in calculation methods and underlying diet patterns between populations should be accounted for, the PDI can be a useful tool to assess adherence to different plant-based diet patterns and their association with health outcomes in cohort studies across cultures.

Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data.

BACKGROUND: There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual's true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one's chronological age-independent aging divergence ∆. METHODS: We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold. RESULTS: The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging's association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random. CONCLUSIONS: Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.

Depressive Symptoms and Plasma Markers of Alzheimer's Disease and Neurodegeneration: A Coordinated Meta-Analysis of 8 Cohort Studies.

BACKGROUND: Depressive symptoms are associated with an increased risk of Alzheimer's disease (AD). There has been a recent emergence in plasma biomarkers for AD pathophysiology, such as amyloid-beta (Aß) and phosphorylated tau (p-tau), as well as for axonal damage (neurofilament light, NfL) and astrocytic activation (glial fibrillary acidic protein, GFAP). Hypothesizing that depressive symptoms may occur along the AD process, we investigated associations between plasma biomarkers of AD with depressive symptoms in individuals without dementia. METHODS: A two-stage meta-analysis was performed on 2 clinic-based and 6 population-based cohorts (N = 7210) as part of the Netherlands Consortium of Dementia Cohorts. Plasma markers (Aß42/40, p-tau181, NfL, and GFAP) were measured using Single Molecular Array (Simoa; Quanterix) assays. Depressive symptoms were measured with validated questionnaires. We estimated the cross-sectional association of each standardized plasma marker (determinants) with standardized depressive symptoms (outcome) using linear regressions, correcting for age, sex, education, and APOE ε4 allele presence, as well as subgrouping by sex and APOE ε4 allele. Effect estimates were entered into a random-effects meta-analysis. RESULTS: Mean age of participants was 71 years. The prevalence of clinically relevant depressive symptoms ranged from 1% to 22%. None of the plasma markers were associated with depressive symptoms in the meta-analyses. However, NfL was associated with depressive symptoms only in APOE ε4 carriers (ß 0.11; 95% CI: 0.05-0.17). CONCLUSIONS: Late-life depressive symptoms did not show an association to plasma biomarkers of AD pathology. However, in APOE ε4 allele carriers, a more profound role of neurodegeneration was suggested with depressive symptoms.

Validation of biomarkers of aging.

The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials.

Associations of circulating GDF15 with combined cognitive frailty and depression in older adults of the MARK-AGE study.

Growth differentiation factor-15 (GDF15) might be involved in the development of cognitive frailty and depression. Therefore, we evaluated cross-sectional associations of plasma GDF15 with combined cognitive-frailty-and-depression in older (i.e. ≥ 55 years) and younger adults of the MARK-AGE study. In the present work, samples and data of MARK-AGE ("European study to establish bioMARKers of human AGEing") participants (N = 2736) were analyzed. Cognitive frailty was determined by the global cognitive functioning score (GCF) and depression by the Self-Rating Depression Scale (SDS score). Adults were classified into three groups: (I) neither-cognitive-frailty-nor-depression, (II) either-cognitive-frailty-or-depression or (III) both-cognitive-frailty-and-depression. Cross-sectional associations were determined by unadjusted and by age, BMI, sex, comorbidities and hsCRP-adjusted linear and logistic regression analyses. Cognitive frailty, depression, age and GDF15 were significantly related within the whole study sample. High GDF15 levels were significantly associated with both-cognitive-frailty-and-depression (adjusted ß = 0.177 [0.044 - 0.310], p = 0.009), and with low GCF scores and high SDS scores. High GDF15 concentrations and quartiles were significantly associated with higher odds to have both-cognitive-frailty-and-depression (adjusted odds ratio = 2.353 [1.267 - 4.372], p = 0.007; and adjusted odds ratio = 1.414 [1.025 - 1.951], p = 0.035, respectively) independent of age, BMI, sex, comorbidities and hsCRP. These associations remained significant when evaluating older adults. We conclude that plasma GDF15 concentrations are significantly associated with combined cognitive-frailty-and-depression status and, with cognitive frailty and depressive symptoms separately in old as well as young community-dwelling adults.

Technical Report: A Comprehensive Comparison between Different Quantification Versions of Nightingale Health's 1H-NMR Metabolomics Platform.

1H-NMR metabolomics data is increasingly used to track health and disease. Nightingale Health, a major supplier of 1H-NMR metabolomics, has recently updated the quantification strategy to further align with clinical standards. Such updates, however, might influence backward replicability, particularly affecting studies with repeated measures. Using data from BBMRI-NL consortium (~28,000 samples from 28 cohorts), we compared Nightingale data, originally released in 2014 and 2016, with a re-quantified version released in 2020, of which both versions were based on the same NMR spectra. Apart from two discontinued and twenty-three new analytes, we generally observe a high concordance between quantification versions with 73 out of 222 (33%) analytes showing a mean ρ > 0.9 across all cohorts. Conversely, five analytes consistently showed lower Spearman's correlations (ρ < 0.7) between versions, namely acetoacetate, LDL-L, saturated fatty acids, S-HDL-C, and sphingomyelins. Furthermore, previously trained multi-analyte scores, such as MetaboAge or MetaboHealth, might be particularly sensitive to platform changes. Whereas MetaboHealth replicated well, the MetaboAge score had to be retrained due to use of discontinued analytes. Notably, both scores in the re-quantified data recapitulated mortality associations observed previously. Concluding, we urge caution in utilizing different platform versions to avoid mixing analytes, having different units, or simply being discontinued.

Metabolomic biomarkers of habitual B vitamin intakes unveil novel differentially methylated positions in the human epigenome.

BACKGROUND: B vitamins such as folate (B9), B6, and B12 are key in one carbon metabolism, which generates methyl donors for DNA methylation. Several studies have linked differential methylation to self-reported intakes of folate and B12, but these estimates can be imprecise, while metabolomic biomarkers can offer an objective assessment of dietary intakes. We explored blood metabolomic biomarkers of folate and vitamins B6 and B12, to carry out epigenome-wide analyses across up to three European cohorts. Associations between self-reported habitual daily B vitamin intakes and 756 metabolites (Metabolon Inc.) were assessed in serum samples from 1064 UK participants from the TwinsUK cohort. The identified B vitamin metabolomic biomarkers were then used in epigenome-wide association tests with fasting blood DNA methylation levels at 430,768 sites from the Infinium HumanMethylation450 BeadChip in blood samples from 2182 European participants from the TwinsUK and KORA cohorts. Candidate signals were explored for metabolite associations with gene expression levels in a subset of the TwinsUK sample (n = 297). Metabolomic biomarker epigenetic associations were also compared with epigenetic associations of self-reported habitual B vitamin intakes in samples from 2294 European participants. RESULTS: Eighteen metabolites were associated with B vitamin intakes after correction for multiple testing (Bonferroni-adj. p < 0.05), of which 7 metabolites were available in both cohorts and tested for epigenome-wide association. Three metabolites - pipecolate (metabolomic biomarker of B6 and folate intakes), pyridoxate (marker of B6 and folate) and docosahexaenoate (DHA, marker of B6) - were associated with 10, 3 and 1 differentially methylated positions (DMPs), respectively. The strongest association was observed between DHA and DMP cg03440556 in the SCD gene (effect = 0.093 ± 0.016, p = 4.07E-09). Pyridoxate, a catabolic product of vitamin B6, was inversely associated with CpG methylation near the SLC1A5 gene promoter region (cg02711608 and cg22304262) and with SLC7A11 (cg06690548), but not with corresponding changes in gene expression levels. The self-reported intake of folate and vitamin B6 had consistent but non-significant associations with the epigenetic signals. CONCLUSION: Metabolomic biomarkers are a valuable approach to investigate the effects of dietary B vitamin intake on the human epigenome.

The association of blood biomarkers with treatment response and adverse health outcomes in older patients with solid tumors: A systematic review.

INTRODUCTION: Blood biomarkers are potentially useful prognostic markers and may support treatment decisions, but it is unknown if and which biomarkers are most useful in older patients with solid tumors. The aim of this systematic review was to evaluate the evidence on the association of blood biomarkers with treatment response and adverse health outcomes in older patients with solid tumors. MATERIALS AND METHODS: A literature search was conducted in five databases in December 2022 to identify studies on blood biomarkers measured before treatment initiation, not tumor specific, and outcomes in patients with solid tumors aged ≥60 years. Studies on any type or line of oncologic treatment could be included. Titles and abstracts were screened by three authors. Data extraction and quality assessment, using the Quality in Prognosis Studies (QUIPS) checklist, were performed by two authors. RESULTS: Sixty-three studies were included, with a median sample size of 138 patients (Interquartile range [IQR] 99-244) aged 76 years (IQR 72-78). Most studies were retrospective cohort studies (63%). The risk of bias was moderate in 52% and high in 43%. Less than one-third reported geriatric parameters. Eighty-six percent examined mortality outcomes, 37% therapeutic response, and 37% adverse events. In total, 77 unique markers were studied in patients with a large variety of tumor types and treatment modalities. Neutrophil-to-lymphocyte ratio (20 studies), albumin (19), C-reactive protein (16), hemoglobin (14) and (modified) Glasgow Prognostic Score ((m)GPS) (12) were studied most often. The vast majority showed no significant association of these biomarkers with outcomes, except for associations between low albumin and adverse events and high (m)GPS with mortality. DISCUSSION: Most studies did not find a significant association between blood biomarkers and clinical outcomes. The interpretation of current evidence on prognostic blood biomarkers is hampered by small sample sizes and inconsistent results across heterogeneous studies. The choice for blood biomarkers in the majority of included studies seemed driven by availability in clinical practice in retrospective cohort studies. Ageing biomarkers are rarely studied in older patients with solid tumors. Further research is needed in larger and more homogenous cohorts that combine clinical parameters and biomarkers before these can be used in clinical practice.

Increasing number of long-lived ancestors marks a decade of healthspan extension and healthier metabolomics profiles.

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.

Epigenetic and Metabolomic Biomarkers for Biological Age: A Comparative Analysis of Mortality and Frailty Risk.

Biological age captures a person's age-related risk of unfavorable outcomes using biophysiological information. Multivariate biological age measures include frailty scores and molecular biomarkers. These measures are often studied in isolation, but here we present a large-scale study comparing them. In 2 prospective cohorts (n = 3 222), we compared epigenetic (DNAm Horvath, DNAm Hannum, DNAm Lin, DNAm epiTOC, DNAm PhenoAge, DNAm DunedinPoAm, DNAm GrimAge, and DNAm Zhang) and metabolomic-based (MetaboAge and MetaboHealth) biomarkers in reflection of biological age, as represented by 5 frailty measures and overall mortality. Biomarkers trained on outcomes with biophysiological and/or mortality information outperformed age-trained biomarkers in frailty reflection and mortality prediction. DNAm GrimAge and MetaboHealth, trained on mortality, showed the strongest association with these outcomes. The associations of DNAm GrimAge and MetaboHealth with frailty and mortality were independent of each other and of the frailty score mimicking clinical geriatric assessment. Epigenetic, metabolomic, and clinical biological age markers seem to capture different aspects of aging. These findings suggest that mortality-trained molecular markers may provide novel phenotype reflecting biological age and strengthen current clinical geriatric health and well-being assessment.

Lipid-induced transcriptomic changes in blood link to lipid metabolism and allergic response.

Immune cell function can be altered by lipids in circulation, a process potentially relevant to lipid-associated inflammatory diseases including atherosclerosis and rheumatoid arthritis. To gain further insight in the molecular changes involved, we here perform a transcriptome-wide association analysis of blood triglycerides, HDL cholesterol, and LDL cholesterol in 3229 individuals, followed by a systematic bidirectional Mendelian randomization analysis to assess the direction of effects and control for pleiotropy. Triglycerides are found to induce transcriptional changes in 55 genes and HDL cholesterol in 5 genes. The function and cell-specific expression pattern of these genes implies that triglycerides downregulate both cellular lipid metabolism and, unexpectedly, allergic response. Indeed, a Mendelian randomization approach based on GWAS summary statistics indicates that several of these genes, including interleukin-4 (IL4) and IgE receptors (FCER1A, MS4A2), affect the incidence of allergic diseases. Our findings highlight the interplay between triglycerides and immune cells in allergic disease.