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Background Diet may influence biological aging and the discrepancy (∆age) between a subject’s biological age (BA) and chronological age (CA). We aimed to investigate the correlation of dietary flavonoids with the ∆age of organs (heart, kidney, liver) and the whole body. Method A total of 3193 United States adults were extracted from the National Health and Nutrition Examination Survey (NHANES) in 2007–2008 and 2017–2018. Dietary flavonoids intake was assessed using 24-h dietary recall method. Multiple linear regression analysis was performed to evaluate the association of dietary flavonoids intake with the ∆age of organs (heart, kidney, liver) and the whole body. BA was computed based on circulating biomarkers, and the resulting ∆age was tested as an outcome in linear regression analysis. Results The ∆age of the whole body, heart, and liver was inversely associated with higher flavonoids intake (the whole body ∆age β = − 0.58, cardiovascular ∆age β = − 0.96, liver ∆age β = − 3.19) after adjustment for variables. However, higher flavonoids intake positively related to renal ∆age (β = 0.40) in participants with chronic kidney disease (CKD). Associations were influenced by population characteristics, such as age, health behavior, or chronic diseases. Anthocyanidins, isoflavones and flavones had the strongest inverse associations between the whole body ∆age and cardiovascular ∆age among all the flavonoids subclasses. Conclusion Flavonoids intake positively contributes to delaying the biological aging process, especially in the heart, and liver organ, which may be beneficial for reducing the long-term risk of cardiovascular or liver disease.

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.

Background Aging has been established as an independent risk factor for chronic obstructive pulmonary disease (COPD). Biological age (BA), a novel metric for gauging the extent of aging, has rarely been investigated in the context of acute exacerbation of COPD (AECOPD). Our study aimed to elucidate the association between BA and AECOPD, thereby highlighting the potential of BA as a predictive tool in clinical practice. Methods The dataset encompasses patients hospitalized at Chengdu Third People's Hospital between 2018 and 2022. The AECOPD patients enrolled in this study were hospitalized due to rapidly worsening symptoms, including cough, sputum production, and dyspnea, whereas the COPD patients were clinically stable. BA and biological age acceleration were ascertained through the Klemera-Doubale method (KDM). A multivariable logistic regression analysis was conducted to evaluate the correlation between BA, biological age acceleration, and the incidence of AECOPD, complemented by subgroup analyses to explore the dose‒response dynamics between biological age acceleration and the risk of AECOPD. The dataset was partitioned into training and validation sets at a 7:3 ratio, and LASSO regression was applied to refine the model's variable composition. To assess the ability of different variables to discriminate current disease status, we developed the initial model and three subsequent models, with the following variables added in the new model: Chronological age (CA), BA, and biological age acceleration. The models were subsequently evaluated within both datasets. Results The study cohort comprised 2,511 patients, through an analysis of the transect data, with 59.1% experiencing acute exacerbations. Both BA (79.14 ± 9.49 years) and biological age acceleration (1.04 ± 2.82 years) emerged as independent risk factors for AECOPD ( P  < 0.001). In Model 3, each year increment in BA and biological age acceleration corresponded to a 1.04-fold (95% CI = 1.027–1.048, P  < 0.001) and 1.18-fold (95% CI = 1.14–1.224, P  < 0.001) increase in exacerbation risk, respectively. The biological age of patients with stable COPD was significantly lower than the actual age (-0.36 ± 2.56 years), which suggests a significant inter-individual heterogeneity in the biological aging process of COPD patients. Subgroup analysis confirmed a pronounced dose‒response relationship between biological age acceleration and AECOPD risk(Q4 vs. Q1: OR = 2.7, 95% CI = 2.172–3.518). LASSO regression pinpointed BMI, Diabetes, Hypertensive heart disease, Cor pulmonale, Stroke, and Hyperlipidemia as critical variables within the model. The internal validation process revealed AUC values of 0.735 (95% CI = 0.7–0.77), 0.742 (95% CI = 0.707–0.777), 0.753 (95% CI = 0.719–0.787), and 0.766 (95% CI = 0.733–0.8) for the respective models. The HL test confirmed the models' good fit ( P  = 0.128, P  = 0.121, P  = 0.272, P  = 0.795), with Model 4 exhibiting the most precise calibration against the diagonal reference. Decision curve analysis (DCA) indicated that all the models provided a net benefit in disease outcome discrimination, with Model 4 yielding the most significant advantage. Conclusions The acceleration of aging portends an increased propensity for acute exacerbations, and a distinct dose–response relationship is observable between biological age acceleration and exacerbation events. BA and biological age acceleration outperform chronological age in discerning the likelihood of acute exacerbations, underscoring their enhanced ability to predict this critical health outcome.

Renal cell carcinoma (RCC) is a heterogeneous group of cancers where the clear cell (ccRCC) is the most common and the most lethal. The absence of accurate diagnostic and follow-up biomarkers along with the time-limited response to therapies may explain the lethality and shows the necessity of new sensitive and specific biomarkers. One of the most studied molecules are the telomeres: specialized ribonucleoprotein structures that keep the structural integrity of the genome. Among other features, telomere length (TL) has been widely studied in several tumor models regarding its biomarker potential, due to the easy detection and quantification. The scope of this review was to analyze all the information about this parameter in RCC. There was some disparity in the results of the studies, since some pointed to an association between short TL and risk or poor outcome of RCC; others between long TL and RCC outcome and some did not find any association. We propose some epidemiological and biological explanations to these differences. The telomeres may play a dual role during RCC carcinogenesis in the early stages, short telomeres may increase RCC risk and in late carcinogenesis, long telomeres seem to be associated with tumor prognosis. However, the controversy of the results along with the lack of specificity are some problems that need to be clarified for the usage of TL as a prognostic biomarker.

DNA methylation, validated as a surrogate for biological age, is a potential tool for predicting future morbidity and mortality outcomes. This study aims to explore how lifestyle patterns are associated with epigenetic changes in British men. Five biological age clocks were utilised to investigate the relationship between these epigenetic markers and lifestyle-related factors in a prospective study involving 221 participants. Spearman’s correlation test, Pearson’s correlation test, and univariate linear regression were employed for analysis. The results indicate that higher consumption of saturated fat and total daily calories, and a higher body mass index (BMI) are associated with accelerated biological aging. Conversely, higher vitamin D intake and a higher healthy lifestyle index (HLI) are linked to decelerated biological aging. These findings highlight the potential impact of specific lifestyle-related factors on biological aging and can serve as a reference for applying healthy lifestyle improvements in future disease prevention studies.

Background: There is a growing consensus that chronological age (CA) is not an accurate indicator of the aging process and that biological age (BA) instead is a better measure of an individual’s risk of age-related outcomes and a more accurate predictor of mortality than actual CA. In this context, BA measures the “true” age, which is an integrated result of an individual’s level of damage accumulation across all levels of biological organization, along with preserved resources. The BA is plastic and depends upon epigenetics. Brain state is an important factor contributing to health- and lifespan. Methods and Objective: Quantitative electroencephalography (qEEG)-derived brain BA (BBA) is a suitable and promising measure of brain aging. In the present study, we aimed to show that BBA can be decelerated or even reversed in humans (N = 89) by using customized programs of nutraceutical compounds or lifestyle changes (mean duration = 13 months). Results: We observed that BBA was younger than CA in both groups at the end of the intervention. Furthermore, the BBA of the participants in the nutraceuticals group was 2.83 years younger at the endpoint of the intervention compared with their BBA score at the beginning of the intervention, while the BBA of the participants in the lifestyle group was only 0.02 years younger at the end of the intervention. These results were accompanied by improvements in mental–physical health comorbidities in both groups. The pre-intervention BBA score and the sex of the participants were considered confounding factors and analyzed separately. Conclusions: Overall, the obtained results support the feasibility of the goal of this study and also provide the first robust evidence that halting and reversal of brain aging are possible in humans within a reasonable (practical) timeframe of approximately one year.

Sleep has been associated with aging and relevant health outcomes, but the causal relationship remains inconclusive. In this study, we investigated the associations of sleep behaviors with biological ages (BAs) among 363,886 middle and elderly adults from UK Biobank. Sleep index (0 [worst]–6 [best]) of each participant was retrieved from the following six sleep behaviors: snoring, chronotype, daytime sleepiness, sleep duration, insomnia, and difficulties in getting up. Two BAs, the KDM‐biological age and PhenoAge, were estimated by corresponding algorithms based on clinical traits, and their residual discrepancies with chronological age were defined as the age accelerations (AAs). We first observed negative associations between the sleep index and the two AAs, and demonstrated that the change of AAs could be the consequence of sleep quality using Mendelian randomization with genetic risk scores of sleep index and BAs. Particularly, a one‐unit increase in sleep index was associated with 0.104‐ and 0.119‐year decreases in KDM‐biological AA and PhenoAge acceleration, respectively. Air pollution is another key driver of aging. We further observed significant independent and joint effects of sleep and air pollution (PM2.5 and NO2) on AAs. Sleep quality also showed a modifying effect on the associations of elevated PM2.5 and NO2 levels with accelerated AAs. For instance, an interquartile range increase in PM2.5 level was associated with 0.009‐, 0.044‐, and 0.074‐year increase in PhenoAge acceleration among people with high (5–6), medium (3–4), and low (0–2) sleep index, respectively. Our findings elucidate that better sleep quality could lessen accelerated biological aging resulting from air pollution. Our study explored the causal relationship between sleep and aging, which remains inconclusive. Sleep quality was negatively associated with the accelerations of two biological ages (BAs) in 363,886 UK adults. Mendelian randomization demonstrated that worsening sleep quality could accelerate BAs. Sleep and air pollution (i.e., PM2.5 and NO2) could accelerate two BAs independently. Better sleep quality could lessen the accelerations of two BAs resulting from exogenous exposures such as PM2.5 and NO2.

Significance Because obese people are at an increased risk of many age-related diseases, it is a plausible hypothesis that obesity increases the biological age of some tissues and cell types. However, it has been difficult to detect such an accelerated aging effect because it is unclear how to measure tissue age. Here we use a recently developed biomarker of aging (known as “epigenetic clock”) to study the relationship between epigenetic age and obesity in several human tissues. We report an unexpectedly strong correlation between high body mass index and the epigenetic age of liver tissue. This finding may explain why obese people suffer from the early onset of many age-related pathologies, including liver cancer. Because of the dearth of biomarkers of aging, it has been difficult to test the hypothesis that obesity increases tissue age. Here we use a novel epigenetic biomarker of aging (referred to as an “epigenetic clock”) to study the relationship between high body mass index (BMI) and the DNA methylation ages of human blood, liver, muscle, and adipose tissue. A significant correlation between BMI and epigenetic age acceleration could only be observed for liver ( r = 0.42, P = 6.8 × 10 ⁻⁴ in dataset 1 and r = 0.42, P = 1.2 × 10 ⁻⁴ in dataset 2). On average, epigenetic age increased by 3.3 y for each 10 BMI units. The detected age acceleration in liver is not associated with the Nonalcoholic Fatty Liver Disease Activity Score or any of its component traits after adjustment for BMI. The 279 genes that are underexpressed in older liver samples are highly enriched (1.2 × 10 ⁻⁹) with nuclear mitochondrial genes that play a role in oxidative phosphorylation and electron transport. The epigenetic age acceleration, which is not reversible in the short term after rapid weight loss induced by bariatric surgery, may play a role in liver-related comorbidities of obesity, such as insulin resistance and liver cancer.

Attaining personalized healthy aging requires accurate monitoring of physiological changes and identifying subclinical markers that predict accelerated or delayed aging. Classic biostatistical methods most rely on supervised variables to estimate physiological aging and do not capture the full complexity of inter‐parameter interactions. Machine learning (ML) is promising, but its black box nature eludes direct understanding, substantially limiting physician confidence and clinical usage. Using a broad population dataset from the National Health and Nutrition Examination Survey (NHANES) study including routine biological variables and after selection of XGBoost as the most appropriate algorithm, we created an innovative explainable ML framework to determine a Personalized physiological age (PPA). PPA predicted both chronic disease and mortality independently of chronological age. Twenty‐six variables were sufficient to predict PPA. Using SHapley Additive exPlanations (SHAP), we implemented a precise quantitative associated metric for each variable explaining physiological (i.e., accelerated or delayed) deviations from age‐specific normative data. Among the variables, glycated hemoglobin (HbA1c) displays a major relative weight in the estimation of PPA. Finally, clustering profiles of identical contextualized explanations reveal different aging trajectories opening opportunities to specific clinical follow‐up. These data show that PPA is a robust, quantitative and explainable ML‐based metric that monitors personalized health status. Our approach also provides a complete framework applicable to different datasets or variables, allowing precision physiological age estimation. Personalized physiological age (PPA) is a robust, quantitative and explainable machine learning‐based metric that monitors health status. Our approach provides a complete framework applicable to different datasets or variables, allowing precision physiological age estimation.

Background DNA methylation changes at a discrete set of sites in the human genome are predictive of chronological and biological age. However, it is not known whether these changes are causative or a consequence of an underlying ageing process. It has also not been shown whether this epigenetic clock is unique to humans or conserved in the more experimentally tractable mouse. Results We have generated a comprehensive set of genome-scale base-resolution methylation maps from multiple mouse tissues spanning a wide range of ages. Many CpG sites show significant tissue-independent correlations with age which allowed us to develop a multi-tissue predictor of age in the mouse. Our model, which estimates age based on DNA methylation at 329 unique CpG sites, has a median absolute error of 3.33 weeks and has similar properties to the recently described human epigenetic clock. Using publicly available datasets, we find that the mouse clock is accurate enough to measure effects on biological age, including in the context of interventions. While females and males show no significant differences in predicted DNA methylation age, ovariectomy results in significant age acceleration in females. Furthermore, we identify significant differences in age-acceleration dependent on the lipid content of the diet. Conclusions Here we identify and characterise an epigenetic predictor of age in mice, the mouse epigenetic clock. This clock will be instrumental for understanding the biology of ageing and will allow modulation of its ticking rate and resetting the clock in vivo to study the impact on biological age.