Background: Nutritional weight-loss interventions are known to reduce bone mineral density (BMD), which can be prevented by adding (resistance) exercise training. However, this combined effect is not well studied in non-obese adults. In addition, the association between biomarkers and metabolite-based composite health markers with changes in BMD in such an intervention has not been studied as thoroughly. Objective: The aims of the current study were to investigate the effect of a combined nutritional and activity lifestyle intervention on lumbar spine and total body BMD in healthy middle-aged to older adults, and to relate these effects to a selection of immune-metabolic biomarkers, muscle mass and fat mass measurements, and two composite metabolite-based health scores. Methods: In this ancillary study of the single-arm Growing Old TOgether (GOTO) trial (trial registration number GOTNL3301 [https://onderzoekmetmensen.nl/nl/trial/27183], NL-OMON27183), 134 participants (mean age 62.9 years, 49% female) undertook a 13-week lifestyle modification, incorporating 12.5% caloric restriction and 12.5% increase in physical activity. The impact on lumbar spine and total body BMD was evaluated using dual-energy X-ray absorptiometry (DEXA). The intervention effect on BMD was related to changes in immune-metabolic biomarkers and two metabolite-based immune-metabolic health scores. Results: The trial significantly reduced bodyweight with 3.3 and 3.4 kg, consisting of 1.4 and 1.1 kg lean mass, in males (fdr < 0.001) and females (fdr < 0.001), respectively. Lean mass reduced by 1.4 kg in males (fdr < 0.001) and 1.1 kg in females (fdr < 0.001), whereas total body fat% reduced significantly with −1.5% (fdr < 0.001) in males and −1.5% (fdr < 0.001) in females. In males, lumbar spine BMD increased with 3.0% (fdr < 0.001) and total body BMD with 0.7% (fdr = 0.002). In females, the lumbar spine BMD had a trend in the upwards direction (1.2%, fdr = 0.09) and the total body BMD remained stable (0.4%, fdr = 0.07). In males, the increase in lumbar spine BMD was significantly associated with decreased weight (fdr = 0.001) and with decreased body and trunk fat% (fdr = 0.001, fdr = 0.001) and improved immune-metabolic health (fdr = 0.02). Males with higher BMD but a poor metabolite-based health score at baseline had a stronger increase in lumbar spine BMD (fdr = 0.03). Conclusions: A combined nutritional and activity lifestyle intervention significantly improved BMD of males with good bone health at baseline while at the same time improving metabolic health. Nutritional weight-loss interventions may not harm BMD when combined with exercise.

Many molecular aging biomarkers have been developed to capture heterogeneity in individual aging rates. Yet, systematic comparison of the modeling choices underlying these biomarkers has been limited. In this study, we trained aging biomarkers on the Rockwood frailty index (FI) and all-cause mortality using UK Biobank Olink proteomics and metabolomics (¹H-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 two aging biomarkers, ProteinFrailty (ProtFI) and ProteinMortality (ProtMort), which are both ElasticNet models that use a minimal set of proteins to predict FI and mortality, respectively. In particular, 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.

Human longevity, which is coupled to compression of age-related disease, is a heritable trait. However, only few common genetic variants have been linked to longevity, suggesting that rare, family-specific variants may also play a role. We therefore investigated whole-genome sequencing data of long-lived individuals from the Leiden Longevity Study and identified family-specific variants residing in genes involved in the mitogen-activated protein kinase (MAPK) cascade, a lifespan-associated and evolutionarily conserved pathway emerging from studies in model organisms. We subsequently generated and functionally characterised mouse embryonic stem cells (mESCs) harbouring these variants. Two variants, located in NF1 (Phe1112Leu) and RAF1 (Asp633Tyr), reduce MAPK/extracellular signal-regulated kinase (ERK) signalling pathway activity in mESCs. At the proteomic and transcriptomic level, we observed prominent changes that were shared (e.g. upregulation of ribosomal proteins and Foxo3 expression) and opposing between the variants (e.g. downregulation of mTORC1 signalling-related proteins and Ets2 expression in the RAF1^Asp633Tyr variant cell line versus upregulation in the NF1^Phe1112Leu variant cell lines). These changes were accompanied by opposing effects on proliferation. Moreover, the RAF1^Asp633Tyr variant improved resistance to replication stress, while this was not the case for the NF1^Phe1112Leu variant. In conclusion, we identified two rare genetic variants in long-lived families that influence MAPK/ERK signalling in a manner that has previously been linked to increased lifespan in model organisms. Our findings suggest that mESCs offer a suitable starting point for studying rare genetic variants linked to human longevity, allowing for the identification of promising variants to pursue in in vivo studies using model organism.

Aging is a multifaceted and intricate physiological process characterized by a gradual decline in functional capacity, leading to increased susceptibility to diseases and mortality. While chronological age serves as a strong risk factor for age-related health conditions, considerable heterogeneity exists in the aging trajectories of individuals, suggesting that biological age may provide a more nuanced understanding of the aging process. However, the concept of biological age lacks a clear operationalization, leading to the development of various biological age predictors without a solid statistical foundation. This paper addresses these limitations by proposing a comprehensive operationalization of biological age, introducing the “AccelerAge” framework for predicting biological age, and introducing previously underutilized evaluation measures for assessing the performance of biological age predictors. The AccelerAge framework, based on Accelerated Failure Time (AFT) models, directly models the effect of candidate predictors of aging on an individual’s survival time, aligning with the prevalent metaphor of aging as a clock. We compare predictors based on the AccelerAge framework to a predictor based on the GrimAge predictor, which is considered one of the best-performing biological age predictors, using simulated data as well as data from the UK Biobank and the Leiden Longevity Study. Our approach seeks to establish a robust statistical foundation for biological age clocks, enabling a more accurate and interpretable assessment of an individual’s aging status.

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.

Background: Little is known about the impact of timing as opposed to frequency and intensity of daily physical activity on metabolic health. Therefore, we assessed the association between accelerometery-based daily timing of physical activity and measures of metabolic health in sedentary older people. Methods: Hourly mean physical activity derived from wrist-worn accelerometers over a 6-day period was collected at baseline and after 3 months in sedentary participants from the Active and Healthy Ageing study. A principal component analysis (PCA) was performed to reduce the number of dimensions (e.g. define periods instead of separate hours) of hourly physical activity at baseline and change during follow-up. Cross-sectionally, a multivariable-adjusted linear regression analysis was used to associate the principal components, particularly correlated with increased physical activity in data-driven periods during the day, with body mass index (BMI), fasting glucose and insulin, HbA1c and the homeostatic model assessment for insulin resistance (HOMA-IR). For the longitudinal analyses, we calculated the hourly changes in physical activity and change in metabolic health after follow-up. Results: We included 207 individuals (61.4% male, mean age: 64.8 [SD 2.9], mean BMI: 28.9 [4.7]). Higher physical activity in the early morning was associated with lower fasting glucose (−2.22%, 95% CI: −4.19, −0.40), fasting insulin (−13.54%, 95%CI: −23.49, −4.39), and HOMA-IR (−16.07%, 95%CI: −27.63, −5.65). Higher physical activity in the late afternoon to evening was associated with lower BMI (−2.84%, 95% CI: −4.92, −0.70). Higher physical activity at night was associated with higher BMI (2.86%, 95% CI: 0.90, 4.78), fasting glucose (2.57%, 95% CI: 0.70, 4.30), and HbA1c (2.37%, 95% CI: 1.00, 3.82). Similar results were present in the prospective analysis. Conclusion: Specific physical activity timing patterns were associated with more beneficial metabolic health, suggesting particular time-dependent physical activity interventions might maximise health benefits.

Motivation: 1H-NMR metabolomics is rapidly becoming a standard resource in large epidemiological studies to acquire metabolic profiles in large numbers of samples in a relatively low-priced and standardized manner. Concomitantly, metabolomics-based models are increasingly developed that capture disease risk or clinical risk factors. These developments raise the need for user-friendly toolbox to inspect new 1H-NMR metabolomics data and project a wide array of previously established risk models. Results: We present MiMIR (Metabolomics-based Models for Imputing Risk), a graphical user interface that provides an intuitive framework for ad hoc statistical analysis of Nightingale Health's 1H-NMR metabolomics data and allows for the projection and calibration of 24 pre-trained metabolomics-based models, without any pre-required programming knowledge.

Background: Missing or incomplete phenotypic information can severely deteriorate the statistical power in epidemiological studies. High-throughput quantification of small-molecules in bio-samples, i.e. ‘metabolomics’, is steadily gaining popularity, as it is highly informative for various phenotypical characteristics. Here we aim to leverage metabolomics to impute missing data in clinical variables routinely assessed in large epidemiological and clinical studies. Methods: To this end, we have employed ∼26,000 ¹H-NMR metabolomics samples from 28 Dutch cohorts collected within the BBMRI-NL consortium, to create 19 metabolomics-based predictors for clinical variables, including diabetes status (AUC_{5-Fold CV} = 0·94) and lipid medication usage (AUC_{5-Fold CV} = 0·90). Findings: Subsequent application in independent cohorts confirmed that our metabolomics-based predictors can indeed be used to impute a wide array of missing clinical variables from a single metabolomics data resource. In addition, application highlighted the potential use of our predictors to explore the effects of totally unobserved confounders in omics association studies. Finally, we show that our predictors can be used to explore risk factor profiles contributing to mortality in older participants. Interpretation: To conclude, we provide ¹H-NMR metabolomics-based models to impute clinical variables routinely assessed in epidemiological studies and illustrate their merit in scenarios when phenotypic variables are partially incomplete or totally unobserved. Funding: BBMRI-NL, X-omics, VOILA, Medical Delta and the Dutch Research Council (NWO-VENI).

Population-scale expression profiling studies can provide valuable insights into biological and disease-underlying mechanisms. The availability of phenotypic traits is essential for studying clinical effects. Therefore, missing, incomplete, or inaccurate phenotypic information can make analyses challenging and prevent RNA-seq or other omics data to be reused. A possible solution are predictors that infer clinical or behavioral phenotypic traits from molecular data. While such predictors have been developed based on different omics data types and are being applied in various studies, metabolomics-based surrogates are less commonly used than predictors based on DNA methylation profiles.In this study, we inferred 17 traits, including diabetes status and exposure to lipid medication, using previously trained metabolomic predictors. We evaluated whether these metabolomic surrogates can be used as an alternative to reported information for studying the respective phenotypes using expression profiling data of four population cohorts. For the majority of the 17 traits, the metabolomic surrogates performed similarly to the reported phenotypes in terms of effect sizes, number of significant associations, replication rates, and significantly enriched pathways.The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets. In studies where availability of clinical metadata is limited, missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Additionally, the availability of such surrogates could be used to correct for potential biological confounding. In the future, it would be interesting to further investigate the use of molecular predictors across different omics types and cohorts.

To determine the small vessel disease spectrum associated with cysteine-altering NOTCH3 variants in community-dwelling individuals by analyzing the clinical and neuroimaging features of UK Biobank participants harboring such variants. The exome and genome sequencing datasets of the UK Biobank (n = 50,000) and cohorts of cognitively healthy elderly (n = 751) were queried for cysteine-altering NOTCH3 variants. Brain MRIs of individuals harboring such variants were scored according to Standards for Reporting Vascular Changes on Neuroimaging criteria, and clinical information was extracted with ICD-10 codes. Clinical and neuroimaging data were compared to age- and sex-matched UK Biobank controls and clinically diagnosed patients from the Dutch cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) registry. We identified 108 individuals harboring a cysteine-altering NOTCH3 variant (2.2 of 1,000), of whom 75% have a variant that has previously been reported in CADASIL pedigrees. Almost all variants were located in 1 of the NOTCH3 protein epidermal growth factor-like repeat domains 7 to 34. White matter hyperintensity lesion load was higher in individuals with NOTCH3 variants than in controls (p = 0.006) but lower than in patients with CADASIL with the same variants (p < 0.001). Almost half of the 24 individuals with brain MRI had a Fazekas score of 0 or 1 up to age 70 years. There was no increased risk of stroke. Although community-dwelling individuals harboring a cysteine-altering NOTCH3 variant have a higher small vessel disease MRI burden than controls, almost half have no MRI abnormalities up to age 70 years. This shows that NOTCH3 cysteine altering variants are associated with an extremely broad phenotypic spectrum, ranging from CADASIL to nonpenetrance.

Background: The positive relationship between cognitive and physical performance has been widely established. The influence of brain structure on both domains has been shown as well. Objective: We studied whether the relationship between brain structure and physical performance is independent of cognitive performance. Methods: This was a cross-sectional analysis of 297 middle-aged to older adults (mean age ± SD 65.4 ± 6.8 years). Memory function, executive function and physical performance measured by the Tandem Stance Test, Chair Stand Test, 4-meter walk and 25-meter walk were assessed. Magnetic resonance imaging was available in 237 participants and used to determine the (sub)cortical gray matter, white matter, hippocampal and basal ganglia volumes and the presence of cerebral small-vessel disease, i.e. white matter hyperintensities, cerebral microbleeds (CMBs) and lacunar infarcts (LIs). Regression analysis was used adjusting for age, gender, education and whole-brain volume. A Bonferroni correction was applied considering p values <0.017 as statistically significant. Results: Poor memory function was associated with a slower 4-meter walking speed (p < 0.01). No association was found between brain structure and cognitive performance. The presence of CMBs and LIs was associated with a slower 25-meter walking speed (p < 0.001). This result did not change after additional adjustment for cognitive performance. Conclusions: In middle-aged to older adults, CMBs and LIs are associated with walking speed independent of cognitive performance. This emphasizes the clinical relevance of identifying each of the possible underlying mechanisms of physical performance, which is required for the development of timely and targeted therapies.