Klemera-Doubal Method Research Articles

Clinical biomarker-based biological aging and risk of benign prostatic hyperplasia: A large prospective cohort study.

Chronological age (CAge), biological age (BAge), and accelerated age (AAge) are all important for aging-related diseases. CAge is a known risk factor for benign prostatic hyperplasia (BPH); However, the evidence of association of BAge and AAge with BPH is limited. This study aimed to evaluate the association of CAge, Bage, and AAge with BPH in a large prospective cohort. A total of 135,933 males without BPH at enrolment were extracted from the UK biobank. We calculated three BAge measures (Klemera-Doubal method, KDM; PhenoAge; homeostatic dysregulation, HD) based on 16 biomarkers. Additionally, we calculated KDM-BAge and PhenoAge-BAge measures based on the Levine method. The KDM-AAge and PhenoAge-AAge were assessed by the difference between CAge and BAge and were standardized (mean = 0 and standard deviation [SD] = 1). Cox proportional hazard models were applied to assess the associations of CAge, Bage, and AAge with incident BPH risk. During a median follow-up of 13.150 years, 11,811 (8.690%) incident BPH were identified. Advanced CAge and BAge measures were associated with an increased risk of BPH, showing threshold effects at a later age (all P for nonlinearity <0.001). Nonlinear relationships between AAge measures and risk of BPH were also found for KDM-AAge (P = 0.041) and PhenoAge-AAge (P = 0.020). Compared to the balance comparison group (-1 SD < AAge < 1 SD), the accelerated aging group (AAge > 2 SD) had a significantly elevated BPH risk with hazard ratio (HR) of 1.115 (95% CI, 1.000-1.223) for KDM-AAge and 1.180 (95% CI, 1.068-1.303) for PhenoAge-AAge, respectively. For PhenoAge-AAge, subgroup analysis of the accelerated aging group showed an increased HR of 1.904 (95% CI, 1.374-2.639) in males with CAge <50 years and 1.233 (95% CI, 1.088-1.397) in those having testosterone levels <12 nmol/L. Moreover, AAge-associated risk of BPH was independent of and additive to genetic risk. Biological aging is an independent and modifiable risk factor for BPH. We suggest performing active health interventions to slow biological aging, which will help mitigate the progression of prostate aging and further reduce the burden of BPH.

Joint association of diabetes mellitus and inflammation status with biological ageing acceleration and premature mortality

BackgroundWe aimed to investigate the associations of diabetes mellitus (DM) and C-reactive protein (CRP) with biological ageing acceleration and mortality risk. MethodsWe analyzed data from 41,634 adults with CRP and DM at baseline. Subjects were categorized into high CRP (>3 mg/L) and low CRP (≤3 mg/L) groups. The cross-sectional endpoints of the study were biological ageing indicators Klemera-Doubal method BioAge acceleration (KDMAccel) and Phenotypic age acceleration (PhenoAgeAccel), and the follow-up endpoints were all-cause mortality and cardiovascular mortality. ResultsIn adults with high CRP, compared with those without DM, PhenoAgeAccel increased by 1.66 years (95 % CI: 1.38–1.93), and 8.74 years (95 % CI: 8.25–9.22) in adults with prediabetes and DM, respectively (p for interaction <0.001). Using the CRPlow/non-DM group as a reference, adults in the CRPhigh/non-DM, CRPlow/DM, and CRPhigh/DM groups had significantly advanced biological ageing. Compared to adults without DM, low CRP, and no ageing acceleration, the multivariable-adjusted HRs (95%CIs) of all-cause and cardiovascular mortality in those with DM, CRP, and ageing acceleration were 3.22 (2.79–3.72), and 3.57 (2.81–4.54), respectively. ConclusionsThese findings suggest that the joint presence of low-grade inflammation and DM might be associated with higher odds of biological ageing acceleration and premature mortality.

Air pollution exposure, accelerated biological aging, and increased thyroid dysfunction risk: Evidence from a nationwide prospective study

BackgroundLong-term air pollution exposure is a major health concern, yet its associations with thyroid dysfunction (hyperthyroidism and hypothyroidism) and biological aging remain unclear. We aimed to determine the association of long-term air pollution exposure with thyroid dysfunction and to investigate the potential roles of biological aging. MethodsA prospective cohort study was conducted on 432,340 participants with available data on air pollutants including particulate matter (PM2.5, PM10, and PM2.5-10), nitrogen dioxide (NO2), and nitric oxide (NO) from the UK Biobank. An air pollution score was calculated using principal component analysis to reflect joint exposure to these pollutants. Biological aging was assessed using the Klemera-Doubal method biological age and the phenotypic age algorithms. The associations of individual and joint air pollutants with thyroid dysfunction were estimated using the Cox proportional hazards regression model. The roles of biological aging were explored using interaction and mediation analyses. ResultsDuring a median follow-up of 12.41 years, 1,721 (0.40 %) and 9,296 (2.15 %) participants developed hyperthyroidism and hypothyroidism, respectively. All air pollutants were observed to be significantly associated with an increased risk of incident hypothyroidism, while PM2.5, PM10, and NO2 were observed to be significantly associated with an increased risk of incident hyperthyroidism. The hazard ratios (HRs) for hyperthyroidism and hypothyroidism were 1.15 (95 % confidence interval: 1.00–1.32) and 1.15 (1.08–1.22) for individuals in the highest quartile compared with those in the lowest quartile of air pollution score, respectively. Additionally, we noticed that individuals with higher pollutant levels and biologically older generally had a higher risk of incident thyroid dysfunction. Moreover, accelerated biological aging partially mediated 1.9 %–9.4 % of air pollution-associated thyroid dysfunction. ConclusionsDespite the possible underestimation of incident thyroid dysfunction, long-term air pollution exposure may increase the risk of incident thyroid dysfunction, particularly in biologically older participants, with biological aging potentially involved in the mechanisms.

Association between biological aging and the risk of mortality in individuals with non-alcoholic fatty liver disease: A prospective cohort study

BackgroundThe biological process of aging plays an important role in nonalcoholic fatty liver disease (NAFLD) development. However, epidemiological evidence about the association of biological aging with mortality risk among people with NAFLD is limited. MethodsA total of 2199 participants with NAFLD from the National Health and Nutrition Examination Surveys (NHANES) III were included. The outcomes were all-cause and cause-specific (cardiovascular disease [CVD], cancer, and diabetes) mortality. We computed three BA measures, the Klemera-Doubal method (KDM) age, Phenotypic age, and homeostatic dysregulation (HD), by using 18 age-associated clinical biomarkers, and assessed their associations with mortality risk using Cox proportional hazards (CPH) models. ResultsAfter a median follow-up of 16 years, a total of 1077 deaths occurred. People with NAFLD who died during follow-up period exhibited higher baseline biological age (BA) and biological age accelerations (BAAs). The multivariate-adjusted CPH suggested that a one-standard deviation (SD) increase in KDM age acceleration, Phenotypic age acceleration, or HD was associated with a 3 %, 7 %, or 39 % elevated risk of all-cause mortality, respectively. The results of age-varying HRs showed that the associations of KDM age accelerations (AAs) and Phenotypic AAs with all-cause mortality appeared to be stronger in people with NAFLD younger than 45 years. ConclusionsBiological aging was positively associated with both all-cause and cause-specific mortality among people with NAFLD, particularly among younger individuals.

Association between biological aging and periodontitis using NHANES 2009–2014 and mendelian randomization

Aging is a recognized risk factor for periodontitis, while biological aging could provide more accurate insights into an individual's functional status. This study aimed to investigate the potential association between biological aging and periodontitis. Epidemiological data from 9803 participants in the 2009–2014 National Health and Nutrition Examination Survey were analyzed at a cross-sectional level to assess this link. Three biological ages [Klemera–Doubal method (KDM), PhenoAge, and homeostatic dysregulation (HD)] and two measures of accelerated biological aging (BioAgeAccel and PhenoAgeAccel) were set as primary exposure and were calculated. Logistic regression and restricted cubic spline regression were employed to examine the relationship between biological aging and periodontitis. Additionally, Mendelian randomization analysis was conducted to explore the causal connection between accelerated biological aging and periodontitis. After adjusting for age, gender, race, educational level, marital status, ratio of family income, and disease conditions, this study, found a significant association between subjects with older higher biological ages, accelerated biological aging, and periodontitis. Specifically, for a per year increase in the three biological ages (HD, KDM, and PhenoAge), the risk of periodontitis increases by 15%, 3%, and 4% respectively. Individuals who had positive BioAgeAccel or PhenoAgeAccel were 20% or 37% more likely to develop periodontitis compared with those who had negative BioAgeAccel or PhenoAgeAccel. Furthermore, a significant non-linear positive relationship was observed between the three biological ages, accelerated biological aging, and periodontitis. However, the Mendelian randomization analysis indicated no causal effect of accelerated biological aging on periodontitis. Our findings suggest that biological aging may contribute to the risk of periodontitis, highlighting the potential utility of preventive strategies targeting aging-related pathways in reducing periodontitis risk among older adults.

Alcohol consumption and accelerated biological ageing in middle-aged and older people: A longitudinal study from two cohorts.

The relationship between alcohol consumption and age-related diseases is inconsistent. Biological age (BA) serves as both a precursor and a predictor of age-related diseases; however, longitudinal associations between alcohol consumption and BA in middle-aged and older people remain unclear. We measured whether there was a longitudinal association between drinking frequency and pure alcohol intake with BA among middle-aged and older people. This study involved two prospective cohort studies, set in Southwestern China and the United Kingdom. A total of 8046 participants from the China Multi-Ethnic Cohort study (CMEC) and 5412 participants from the UK Biobank (UKB), aged 30-79 years, took part, with complete data from two waves of clinical biomarkers. BA was calculated by the Klemera Doubal's method. Accelerated BA equalled BA minus chronological age. Drinking frequency and pure alcohol intake were obtained through self-reported questionnaires. Drinking frequency in the past year was classified as current non-drinking, occasional (monthly drinking) and regular (weekly drinking). Compared with consistent current non-drinkers, more frequent drinkers [CMEC: β = 0.46, 95% confidence interval (CI) = 0.13-0.80; UKB: β = 0.65, 95% CI = 0.01-1.29)], less frequent drinkers (CMEC: β = 0.62, 95% CI = 0.37-0.87; UKB: β = 0.54, 95% CI = -0.01-1.09), consistent occasional drinkers (CMEC: β = 0.51, 95% CI = 0.23-0.79; UKB: β = 0.63, 95% CI = 0.13-1.13) and consistent regular drinkers (CMEC: β = 0.56, 95% CI = 0.17-0.95; UKB: β = 0.46, 95% CI = 0.00-0.91) exhibited increased accelerated BA. A non-linear relationship between pure alcohol intake and accelerated BA was observed among consistent regular drinkers. In middle-aged and older people, any change in drinking frequency and any amount of pure alcohol intake seem to be positively associated with acceleration of biological ageing, compared with maintaining abstinence.

Accelerated biological aging mediated associations of ammonium, sulfate in fine particulate matter with liver cirrhosis

BackgroundAlthough both air pollution and aging are related to the development of liver cirrhosis, the role of biological aging in association of the mixture of fine particulate matter (PM2.5) and its constituents with liver cirrhosis was unknown. MethodsThis case-control retrospective study included 100 liver cirrhosis patients and 100 control subjects matched by age and sex. The concentrations of PM2.5 and its constituents were estimated for patients using machine-learning methods. The clinical biomarkers were used to calculate biological age using the Klemera−Doubalmethod (KDM) algorithms. Individual associations of PM2.5 and its constituents or biological age with liver cirrhosis were analyzed by generalized linear models. WQS and BKMR were applied to analyze association of mixture of PM2.5 and its constituents with liver cirrhosis. The mediation effect of biological age on associations of PM2.5 and its constituents with liver cirrhosis was further explored. Resultswe found that each 1-unit increment in NH4+, NO3−, SO42− and biological age were related to 3.618-fold (95%CI: 1.896, 6.904), 1.880-fold (95%CI: 1.319, 2.680), 2.955-fold (95%CI: 1.656, 5.272) and 1.244-fold (95%CI: 1.093, 1.414) increased liver cirrhosis. Both WQS and BKMR models showed that the mixture of PM2.5 and its constituents was related to increased liver cirrhosis. Furthermore, the mediated proportion of biological age on associations of NH4+ and SO42− with liver cirrhosis were 14.7 % and 14.6 %, respectively. ConclusionsBiological aging may partly explain the exposure to PM2.5 and its constituents in association with increased risk for liver cirrhosis, implying that delaying the aging process may be a key step for preventing PM2.5-related liver cirrhosis risk.

U-shaped association between sleep duration and biological aging: Evidence from the UK Biobank study.

Previous research on sleep and aging largely has failed to illustrate the optimal dose-response curve of this relationship. We aimed to analyze the associations between sleep duration and measures of predicted age. In total, 241,713 participants from the UK Biobank were included. Habitual sleep duration was collected from the baseline questionnaire. Four indicators, homeostatic dysregulation (HD), phenoAge (PA), Klemera-Doubal method (KDM), and allostatic load (AL), were chosen to assess predicted age. Multivariate linear regression models were utilized. The association of sleep duration and predicted age followed a U-shape (All p for nonlinear <0.05). Compared with individuals who sleep for 7 h/day, the multivariable-adjusted beta of ≤5 and ≥9 h/day were 0.05 (95% CI 0.03, 0.07) and 0.03 (95% CI 0.02, 0.05) for HD, 0.08 (95% CI 0.01, 0.14) and 0.36 (95% CI 0.31, 0.41) for PA, and 0.21 (95% CI 0.12, 0.30) and 0.30 (95% CI 0.23, 0.37) for KDM. Significant independent and joint effects of sleep and cystatin C (CysC) and gamma glutamyltransferase (GGT) on predicted age metrics were future found. Similar results were observed when conducting stratification analyses. Short and long sleep duration were associated with accelerated predicted age metrics mediated by CysC and GGT.

Accelerated biological aging as potential mediator mediates the relationship between pro-inflammatory diets and the risk of depression and anxiety: A prospective analysis from the UK biobank

BackgroundThe relationship between inflammatory dietary patterns and the risk of depression/anxiety has not been clearly established due to differences in study populations, geographic regions, sex, and methods of calculating the inflammatory index. MethodsWe drew upon a prospective cohort in the UK Biobank and calculated the energy-adjusted dietary inflammatory index (E-DII). The follow-up time was defined from the date of completing the last dietary survey questionnaire to the date of diagnosis of depression, anxiety, phobic anxiety, other types of anxiety, death, loss to follow-up, or the respective censoring dates for England (September 30, 2021), Scotland (July 31, 2021), and Wales (February 28, 2018). The final follow-up times end on September 30, 2021, July 31, 2021, and February 28, 2018, for England, Scotland, and Wales, respectively. During the follow-up process, if a participant develops the condition, dies, or is lost to follow-up, the follow-up is terminated. We used Cox regression to evaluate the connection between E-DII and depression/anxiety. We employed restricted cubic spline curves for nonlinear relationships. We also conducted mediation analyses to explore whether biological age mediated the relationship between E-DII and depression. Additionally, we investigated whether genetic susceptibility modified the relationship between E-DII and depression through interaction modeling. ResultsIn the final analysis, we included a total of 151,295, 159,695, 165,649, and 160,097 participants for the analysis of depression, all types of anxiety, specific phobia anxiety, and other types of anxiety, respectively. For every one-unit increase in E-DII, the risk of experiencing depression and anxiety increased by 5 % and 4 %, respectively. We identified a “J”-shaped nonlinear relationship (P for nonlinear = 0.003) for both depression and anxiety. A significant association with an elevated risk of depression was observed when E-DII exceeded 0.440, and an increased risk of anxiety was noted when E-DII was more than −0.196. Mediation analysis demonstrated that PhenoAge age acceleration (AA) (For depression, proportion of mediation = 9.6 %; For anxiety, proportion of mediation = 10.1 %) and Klemera-Doubal method Biological Age (KDM AA) (For depression, proportion of mediation = 2.9 %; For anxiety, proportion of mediation = 5.1 %) acted as mediators between E-DII and the development of depression and anxiety (P < 0.05). ConclusionsDiets with pro-inflammatory characteristics are associated with a heightened risk of depression and anxiety. Furthermore, the association of pro-inflammatory diets and depression is mediated by biological age.

Abstract 11: Biological Age as a Predictor of Arterial Stiffness in Midlife Women

Introduction: Black women develop hypertension (HTN) earlier in life and experience higher cardiovascular disease (CVD) morbidity and mortality rates at younger ages compared to White women. Growing evidence suggests this may be due to Black women experiencing premature physiological deterioration and accelerated biological aging, true global state of an aging organism. Yet, the relationship between biological aging and arterial stiffness, a precursory marker of subclinical CVD, in Black women has not been studied. We investigated the association between biological age (BA) and arterial stiffness in Black and White women. Hypothesis: BA will be associated with arterial stiffness and this association will be modified by race. Methods: In 219 women (n=140 White, n=79 Black) enrolled in the Morehouse-Emory Center for Health Equity and Predictive Health Institute cohorts (age range 25-49), arterial stiffness was assessed using carotid-femoral pulse wave velocity (CFPWV), measured by applanation tonometry (SphygmoCor®). BA was estimated using the Klemera-Doubal method from 10-select clinical biomarkers (BUN, Creatinine, Total Cholesterol, Alkaline Phosphatase, MCV, RDW, Albumin, Lymphocytes, WBC, CRP) and chronological age. Overall and race-specific associations between BA and arterial stiffness adjusting for demographics (race, education, income, marital status), lifestyle (alcohol, smoking), clinical (body mass index, HTN, diabetes history, SBP), and mental health factors (depressive symptoms) were estimated using multiple linear regression. Results: Mean chronological and BA age was 40 (SD=6.1) and 43 (SD=7.2) years, respectively. Black women had a BA that was 3.4 (SD=5.7) years older than their chronological age, while the difference was 1.3 (SD=4.8) years older for White women. Thus, Black women’s BA was 2.1 (95%CI: -3.5, -0.6) years older than White women. Mean (SD) CFPWV was 7.3 (1.1) in Black women and 6.3 (0.8) in White women. Overall, BA was associated with higher (worse) CFPWV in the minimal (β=0.06 m/s per year, 95%CI: 0.03, 0.08) and fully adjusted models (β=0.04 m/s, 95%CI: 0.01, 0.08). There was no significant interaction between race and BA in the association with arterial stiffness. Conclusions: Black women had older BA and worse CFPWV than White women. BA may partially account for the higher arterial stiffness in Black women.

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

BackgroundThere 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 ∆.MethodsWe 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.ResultsThe 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.ConclusionsCross-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.

Accelerated biological aging elevates the risk of cardiometabolic multimorbidity and mortality.

Associations of biological aging with the development and mortality of cardiometabolic multimorbidity (CMM) remain unclear. Here we conducted a multistate analysis in 341,159 adults of the UK Biobank. CMM was defined as the coexistence of two or three cardiometabolic diseases (CMDs), including type 2 diabetes, ischemic heart disease and stroke. Biological aging was measured using the Klemera-Doubal Method Biological Age and PhenoAge algorithms. Over a median follow-up of 8.84 years, biologically older participants demonstrated robust higher risks from first CMD to CMM and then to death. In particular, adjusted hazard ratios for first CMD to CMM and for CMM to death were 1.15 (95% confidence interval (CI): 1.12, 1.19) and 1.26 (95% CI: 1.17, 1.35) per 1 s.d. increase in PhenoAge acceleration, respectively. Compared with frailty, Framingham Risk Score and Systematic Coronary Risk Evaluation 2 (SCORE2), biological aging measures yielded consistent substantial associations with CMM development. Accelerated biological aging may help identify individuals with CMM risks, potentially enabling early intervention and subclinical prevention.

The mixed effect of Endocrine-Disrupting chemicals on biological age Acceleration: Unveiling the mechanism and potential intervention target

IntroductionAlthough previous studies investigated the potential adverse effects of endocrine-disrupting chemicals (EDCs) on biological age acceleration and aging-related diseases, the mixed effect of multiple types of EDCs on biological age acceleration, including its potential underlying mechanism, remains unclear. MethodsData from the National Health and Nutrition Examination Survey (NHANES) were used to analyze biological age measures, including Klemera-Doubal method biological age (KDM-BA), phenotypic age, and homeostatic dysregulation (HD). Weight quantile sum (WQS) regression was performed to screen biological age-related EDCs (BA-EDCs) and assess the mixed effect of BA-EDCs on biological age acceleration and aging-related disease. Targets of BA-EDCs were obtained from three databases, while heart aging-related genes were obtained from the Aging Anno database. Protein–protein interaction (PPI) network and MCODE algorithm were applied to identify potential interactions between BA-EDC targets and heart aging-related genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to identify related pathways. ResultsThis cross-sectional study included 1,439 participants. A decile increase in BA-EDCs co-exposure was associated with 0.31 years and 0.17 years of KDM-BA and phenotypic age acceleration, respectively. The mixed effect of BA-EDCs was associated with an increased prevalence of atherosclerotic cardiovascular disease (ASCVD). Vitamins C and E demonstrated a significant interaction effect on the association between BA-EDCs and KDM-BA acceleration. PPI network and functional enrichment analysis indicated that the AGE-RAGE signaling pathway in diabetic complications was significantly enriched. ConclusionOur results showed that the co-exposure effect of BA-EDCs was associated with biological age acceleration and ASCVD, with the AGE-RAGE signaling pathway being the underlying mechanism. Vitamins C and E may also be an actionable target for preventing EDC-induced biological aging.

Relationship of dietary natural folate and synthetic folic acid co-exposure patterns with biological aging: findings from NHANES 2003-2018.

Background: The mandatory folic acid fortification program in the United States has inevitably exposed most Americans to both natural folate and synthetic folic acid. We aim to examine the association of dietary folate co-exposure patterns with biological aging indicators. Methods: A total of 18 889 participants were enrolled from 2003 to 2018. Dietary intake of folate from diverse sources was evaluated by 24-hour dietary recall. Biological aging indicators were developed based on age-related clinical indicators, including the phenotypic age (PA), Klemera-Doubal method (KDM), homeostatic dysregulation (HD), and allostatic load (AL). The unsupervised K-means clustering method, logistic regression model, and restricted cubic spline (RCS) regression model were used to explore the relationship of natural folate and synthetic folic acid co-exposure with biological aging indicators. Results: The results indicated that higher intake of total folate, dietary folate, and food natural folate was associated with lower PA [OR = 0.75 (0.64, 0.88); OR = 0.79 (0.70, 0.90); OR = 0.65 (0.57, 0.75)], KDM [OR = 0.63 (0.53, 0.75); OR = 0.80 (0.65, 0.98); OR = 0.62 (0.49, 0.77)], HD [OR = 0.69 (0.56, 0.84); OR = 0.78 (0.67, 0.92); OR = 0.78 (0.68, 0.90)], and AL [OR = 0.69 (0.58, 0.82); OR = 0.73 (0.63, 0.85); OR = 0.74 (0.62, 0.90)], consistently. Four co-exposure patterns were generated based on the intake of folate from diverse sources, as follows: "low folate exposure group" to cluster 1, "dietary folate exposure group" to cluster 2, "mixed source high folate exposure group" to cluster 3, and "mixed source excessive folate exposure group" to cluster 4. Compared with cluster 1, participants in cluster 2 are associated with lower biological age indicators (ORPA = 0.82 [0.72, 0.93]; ORKDM = 0.58 [0.47, 0.70]; ORHD = 0.85 [0.75, 0.97]; ORAL = 0.87 [0.77, 0.98]), while participants in cluster 3 and cluster 4 are not. Conclusion: For individuals subjected to folic acid fortification programs, a higher intake of dietary folate, especially natural folate, coupled with a lower consumption of folic acid supplements, was found to be associated with lower biological age indicators.

The association between accelerated biological aging and the risk of osteoarthritis: a cross-sectional study.

Biological age (BA) offers an effective assessment of true aging state. The progression of Osteoarthritis (OA) is closely associated with an increase in chronological age, the correlation between BA and OA has not been fully elucidated. This study analyzed data from the National Health and Nutrition Examination Survey (NHANES) 2005-2018. Thirteen commonly used clinical traits were employed to calculate two measures of BA: the Klemera-Doubal method age (KDM-Age) and phenotypic age (Pheno-Age). The residuals of the regression of these ages based on chronological age were calculated as KDM-Age or Pheno-Age acceleration, respectively. OA was determined through self-reported prior diagnoses. The prevalence of OA across different quartiles of BA was compared using weighted chi-square tests and linear trend tests. The association between BA and OA was assessed using weighted multivariate logistic regression models. A total of 30,547 participants aged ≥20 years were included in this study, 3,922 (14%) were diagnosed with OA. Participants with OA exhibited higher chronological age, KDM-Age, Pheno-Age, KDM-Age advance, and Pheno-Age advance compared to those without OA (p < 0.001). The prevalence of OA significantly increased with higher quartiles of KDM-Age advance and Pheno-Age advance (P for trend < 0.001). In the fully adjusted model, compared to the lowest quartile (Q1) of KDM-Age advance, the highest quartile (Q4) was associated with a 36.3% increased risk of OA (OR = 1.363; 95% CI = 1.213 to 1.532, p < 0.001). The highest quartile of Pheno-Age advance (Q4) was associated with a 24.3% increased risk of OA compared to Q1 (OR = 1.243; 95% CI = 1.113 to 1.389, p < 0.001). In males and young people, no statistical differences were found in OA risk between the highest and the lowest quartiles of KDM-Age advance (p = 0.151) and Pheno-Age advance (p = 0.057), respectively. Adults with accelerated biological aging have an increased risk of OA, particularly among females and older adults.

PHYSICAL CAPACITY AS MARKER FOR RATE OF AGING IN MID LIFE

Abstract Biological age captures the heterogeneity of aging by providing an estimation for rate of aging. As an alternative to the widely investigated laboratory biomarkers of aging, behavioral functional markers of physical capacity may offer a feasible alternative for identifying individuals at risk for negative aging trajectory. We aimed to examine the associations of different functional tests both together and as a composite score of physical capacity with rate of aging. We conducted a cross-sectional study, with midlife adults (age ~45) reporting being able to perform leisure physical exercise. Biological age was estimated using the Klemera-Doubal method and a set of physiological biomarkers; rate of aging, ∆Age, was defined as the difference between age and estimated biological age. Physical capacity was measured using a testing battery of 15 functional tests linked with six physical capacity domains. 116 participants completed the testing battery. For women, better results in terms of strength, flexibility, cardiorespiratory fitness, and balance tests were negatively correlated with ∆Age (r=0.38-0.29, p&amp;lt;0.05); for men, beyond these tests, agility was also negatively correlated with ∆Age (r=0.27-0.59, p&amp;lt;0.05). A sex-standardized composite score of physical capacity was negatively associated with ∆Age after controlling for chronological age, smoking, and education (r=-0.437, p=0.007; r=-0.491, p&amp;lt;0.001 for women and men respectively). The suggested physical capacity battery offers a functional assessment for ∆Age. Higher physical capacity metrics correlate with smaller ∆Age, corresponding with younger biological age. Measuring physical capacity may help to assess aging trajectory and offer a suitable behavioral intervention goal.

THE LINK BETWEEN VIOLENCE EXPOSURE AND BIOLOGICAL AGING IN MID-LIFE MUSLIMS IN ISRAEL

Abstract Trauma and accumulated chronic stress throughout life are associated with negative health outcomes; more specifically, exposure to violence has been linked to an accelerated aging state. In Israel’s Arab sector, rates of violence have been steadily rising for the past several years. Homicide rates, relative to population size, are seven times higher among Arabs. We aimed to determine the effects of exposure to violence on estimation of biological age as a measure of rate of aging within the ethnic minority of Muslims in Israel. We have conducted a cross-sectional study that included 375 participants age 50.5±6.9 years, of which 61% women. Biological age was estimated using the Klemera-Doubal method and physiological biomarkers; exposure to violence was measured using the Screen for Adolescent Violence Exposure questionnaire followed by a principal component analysis. The average total score of violence exposure was 1.83±0.36 (range 1-5, 1 corresponds with lower exposure). We found a positive and significant relationship among biological age, the “direct evidence of violence” component, and the “evidence of severe violence in the immediate environment” component (r = 0.119, p&amp;lt;0.05; r = 0.111, p&amp;lt;0.05, respectively). These results demonstrate that exposure to violence negatively affects health and accelerates aging within Israel’s Muslim minority. Comprehensive, culturally- and gender-adapted programs are required to reduce violence and address its contribution to the negative aging trajectory in this population.

LOW SENSORY RESPONSIVENESS IS ASSOCIATED WITH ACCELERATED AGING IN MID LIFE

Abstract The rate of aging is affected by genetic, behavioral, and emotional mechanisms; one possibly additional mechanism is the ability of the nervous system to sense and react appropriately to stimuli. This ability, called sensory responsiveness, may be a factor in shaping the aging mechanism and could be used as a marker of aging. The aim of this study was to examine the association between sensory responsiveness levels and rate of aging in midlife. A total of 97 participants born between 1974-1976 (age 45.63±0.67 at end point, 46% women) were included. Biological age was estimated at age ~32 and ~45 using the Klemera-doubal method and a set of biomarkers. Rate of aging was calculated by the difference in estimated biological age scores divided by passing years, reflecting temporal change in biological age. Sensory responsiveness was evaluated using the Sensory Responsiveness Questionnaire Scale, identifying high or low sensory responsiveness subtypes. We found that low sensory responsiveness was positively associated with rate of aging while controlling for chronological age, social engagement, and physical performance (linear regression, r=0.211; p=0.038). High sensory responsiveness subtype was not significantly associated with rate of aging (linear regression, r=-0.02; p=0.843). To conclude, low sensory responsiveness subtype may have a significant role in identifying the rate of aging in midlife. These findings highlight the importance of sensory responsiveness evaluations as a marker for rate of aging and the need for interventions focusing on sensory responsivity difficulties, as a mean of supporting healthier aging.

COMPARISON OF CLINICAL BIOMARKER AND DNAM-DERIVED BIOLOGICAL AGE MEASURES IN AN ELECTIVE SURGERY COHORT

Abstract Older adults face greater post-operative morbidity due to decreasing biological aging (BA)-associated physical resilience. BA measures may quantify these vulnerabilities. Common BA quantification approaches include measures derived from clinical biomarkers and whole blood DNA methylation. The Duke Surgery One Thousand Patient Cohort is a prospective biospecimen and data repository of patients undergoing elective surgery. Using a subset of this cohort (n=119; 61±13 years of age; 50% female; 21% African American), we sought to determine: 1) the inter-relationship of BA measures; 2) associations of race, sex, BMI, and surgery type with BA; and 3) associations of BA with American Society of Anesthesia (ASA) Classification Score . Clinical biomarker BA measures included Klemera-Doubal method (KDM), homeostatic dysregulation (HD), and PhenoAge. DNAm measures included the DNAm PhenoAge, Hannum Age, and GrimAge. We calculated BA advancement (BAA) as BA minus Chronological Age (CA). We calculated Pearson correlation coefficients and fitted regression models, adjusting for CA, sex, race, and BMI. Overall, participants were biologically older than their CA. Male sex and African American race were associated with greater BAA by most measures . Associations of surgery type with BAA were consistent across measures . Greater BAA by most measures was associated with a higher ASA Score, reflecting higher baseline surgical risk. In conclusion, BA measures using different quantitative approaches showed anticipated associations with demographic factors, surgery type, and surgical risk. Ongoing work will identify BA measures that predict adverse surgical outcomes, and that could be used to identify patients for perioperative prehabilitation.

Single- and combined-phthalate exposures are associated with biological ageing among adults

BackgroundPrevious research has emphasized the effects of lifestyle and genetics on ageing. However, the association between exposure to phthalates, which are extensively used in cosmetics and personal care products, and ageing is still unclear. MethodData for 4711 subjects from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2010 were incorporated in the present study. The acceleration of the Klemera-Doubal method–biological age (KDM–BA) and phenotypic Age (PhenoAge) were measured by the composite of 13 biomarkers. Multiple-linear and weighted-quantile sum (WQS) regression models were constructed to explore the relationships of single- and combined-phthalate exposures, as indicated by urinary phthalate metabolites, with KDM–BA and PhenoAge. A generalized additive model (GAM) was fitted to explore the potential nonlinear relationships among the above variables. ResultsExcept for mono-(carboxynonyl), all urinary phthalate metabolites were associated with biological ageing, with correlation coefficients ranging from 0.241 to 0.526; however, mono-ethyl presented a negative correlation. The WQS models revealed mixed effects of combined urinary phthalate metabolites on ageing, with a 0.22-year ((95 % CI) 0.09, 0.32) increase in KDM–BA acceleration and a 0.27-year ((95 % CI) 0.13, 0.37) increase in PhenoAge acceleration for each decile increase in urinary phthalate metabolites. Moreover, MCPP, MEOHP, and MBzP seemed to be the top three phthalates in terms of biological ageing, with weights of 33.3 % and 32.2 %, 29.2 % and 17.2 %, and 21.5 % and 30.1 % in KDM–BA and PhenoAge acceleration, respectively. ConclusionSingle-phthalate exposure was mostly associated with the ageing process, and combined-phthalate exposure presented mixed effects on biological ageing, emphasizing phthalate exposure as a significant risk factor for ageing.