Association of Blood Chemistry Quantifications of Biological Aging With Disability and Mortality in Older Adults

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

BACKGROUND

The 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.

METHODS

A 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.

RESULTS

After 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.

CONCLUSIONS

Biological aging was positively associated with both all-cause and cause-specific mortality among people with NAFLD, particularly among younger individuals.

Retirement Makes You Old? Causal Effect of Retirement on Biological Age

Retirement is a critical life event for older people. Health scholars have scrutinized the health effects of retirement, but its consequences on age-related diseases and mortality are unclear. We extend this body of research by integrating measurements of biological age, representing the physiological decline preceding disease onset. Using data from the UK Biobank and a fuzzy regression discontinuity design, we estimated the effects of retirement on two biomarker-based biological age measures. Results showed that retirement significantly increases biological age for those induced to retire by the State Pension eligibility by 0.871-2.503 years, depending on sex and specific biological age measurement. Given the emerging scientific discussion about direct interventions to biological age to achieve additional improvements in population health, the positive effect of retirement on biological age has important implications for an increase in the State Pension eligibility age and its potential consequences on population health, public health care policy, and older people's labor force participation. Overall, this study provides novel empirical evidence contributing to the question of what social factors make people old.

From cup to clock: exploring coffee's role in slowing down biological aging

Background: Previous research has proposed that coffee consumption may have potential health benefits, yet the effect of coffee on one's biological age has not been determined to date. The purpose of this study is to investigate the influence of coffee drinking on biological aging. Methods: Participants were chosen from the National Health and Nutrition Examination Survey (NHANES) and had to meet the selection criteria. Coffee consumption was evaluated through two 24-hour dietary questionnaires. Biological age was measured using both the PhenoAge and KDM-BA algorithms. Multiple linear and logistic regression models were adopted to analyze the association of coffee consumption with biological aging. Results: A total of 13 384 participants with an average daily coffee consumption of 1.73 cups were included. Participants with higher coffee consumption tended to be older, male, non-Hispanic white; had a higher educational level beyond high school; were more likely to be married; had better financial status; and were less likely to smoke or engage in excessive drinking. These individuals with higher coffee consumption exhibited a younger biological age in relation to their chronological age, as indicated by lower mean advancements in PhenoAge and KDM-BA scores. Furthermore, coffee intake was found to be inversely related to PhenoAge and KDM-BA progressions, as well as to the chances of accelerated biological aging, both in unadjusted and adjusted models. These associations remained consistent across all age and gender groups. Additionally, some heterogeneity was also observed among body mass index and physical activity categories. Conclusions: Coffee drinking was inversely related to biological age advancements and the likelihood of accelerated biological aging. Moderate coffee consumption may offer substantial benefits in reducing biological aging.

Decreased cobalamin sensitivity and biological aging acceleration in the general population

BACKGROUND

The evidence on the association between cobalamin (Cbl) and aging or relevant outcomes is limited and controversial. We aimed to investigate the relationships between cobalamin intake- and function-related biomarkers and biological aging.

METHODS

The study encompassed 22,812 participants aged 20 years and older from the National Health and Nutrition Examination Survey. A panel of biomarkers or algorithms was used to assess biological aging, including Klemera-Doubal Age Acceleration (KDMAccel), Phenotypic age acceleration (PhenoAgeAccel), telomere length, α-Klotho, and PhenoAge advancement. Weighted generalized linear regression analysis was used to assess the associations between cobalamin-intake biomarkers (serum cobalamin, cobalamin intake from food, cobalamin supplement use, serum methylmalonic acid [MMA], and homocysteine [Hcy]) and function-related biomarkers (functional cobalamin deficiency and cobalamin insensitivity index).

RESULTS

Among the 22,812 individuals, the weighted mean (SE) age was 48.3 (0.2) years and 48.0% were males. Unexpectedly, serum and dietary cobalamin as well as serum MMA and Hcy levels were positively associated with most indicators of biological aging. Cobalamin sensitivity was assessed by the combination of binary Cbllow/high and MMAlow/high or Hcylow/high (cutoff values: 400 pg/mL for cobalamin, 250 nmol/L for MMA, and 12.1 μmol/l for Hcy) and a newly constructed cobalamin insensitivity index (based on the multiplicative term of serum cobalamin and serum MMA or Hcy). The multivariable-adjusted β (95%CIs) of KDMAccel in the MMAlowCbllow, MMAlowCblhigh, MMAhighCbllow, and MMAhighCblhigh groups were reference, 0.27 (0.03 to 0.51), 0.85 (0.41 to 1.29), and 7.97 years (5.77 to 10.17) respectively, which were consistent for the combination of serum Hcy and cobalamin. Both cobalamin insensitivity indices were robustly associated with biological aging acceleration in a dose-response pattern (each p < 0.001).

CONCLUSIONS

Decreased cobalamin sensitivity but not cobalamin insufficiency might be associated with biological aging acceleration. Further studies would improve understanding of the underlying mechanisms between decreased cobalamin sensitivity and biological aging acceleration.

Association between Blood Parameters of Nutritional Status and Functional Status in Extreme Longevity

BACKGROUND

The relationship between functional and nutritional status in the geriatric population remains an issue of debate and there is a gap in the knowledge regarding this field in long-lived individuals.

AIM

The main aim of this study was to assess the association between selected blood parameters of nutritional status and functional status in extreme longevity.

METHODS

The inclusion criteria were centenarians above 100 years of age who were examined at their homes, and blood samples were collected. The study group consisted of 170 individuals (25 men and 145 women, median age 100.75 years [100.29-101.58]).

RESULTS

Total protein and albumin serum concentration was significantly lower in long-lived individuals with severe functional decline compared to individuals with preserved functional status, p = 0.000001 and p = 0.0000, respectively. Iron serum level was significantly higher in the group with preserved functional status, p = 0.04. Preserved functional status was positively correlated with total protein serum concentration (p = 0.000), albumin concentration (p = 0.000), and iron serum level (p = 0.029). A negative correlation was stated between c-reactive protein (CRP) and functional status (p = 0.032). Univariable logistic regression analysis showed that the functional status of long-lived individuals depends on total protein (OR 2.89, CI 95% [1.67-5.0]) and albumin concentrations (OR 2.34, CI 95% [1.39-3.92]). Multivariable backward stepwise logistic regression analysis showed that a total protein concentration was the only variable independently related to the preserved functional status (OR 3.2, 95% Cl [1.8-5.67]).

CONCLUSIONS

In long-lived individuals, the total serum protein and albumin levels are lower in centenarians with severe functional decline, and they correlate with functional status. Total protein serum concentration is the only factor independently related to the preserved functional status in extreme longevity.

Early-life risk factors, accelerated biological aging and the late-life risk of mortality and morbidity

BACKGROUND

Early-life exposure increases health risks throughout an individual's lifetime. Biological aging is influenced by early-life risks as a key process of disease development, but whether early-life risks could accelerate biological aging and elevate late-life mortality and morbidity risks remains unknown. Knowledge is also limited on the potential moderating role of healthy lifestyle.

METHODS

We investigate associations of three early-life risks around birth, breastfeeding, maternal smoking and birth weight, with biological aging of 202 580 UK Biobank participants (54.9 ± 8.1 years old). Biological aging was quantified as KDM-BA, PhenoAge and frailty. Moderate alcohol intake, no current smoking, healthy diet, BMI <30 kg/m2 and regular physical activity were considered as healthy lifestyles. Mortality and morbidity data were retrieved from health records.

RESULTS

Individual early-life risk factors were robustly associated with accelerated biological aging. A one-unit increase in the 'early-life risk score' integrating the three factors was associated with 0.060 (SE=0.0019) and 0.036-unit (SE = 0.0027) increase in z-scored KDM-BA acceleration and PhenoAge acceleration, respectively, and with 22.3% higher odds (95% CI: 1.185-1.262) of frailty. Increased chronological age and healthy lifestyles could mitigate the accelerations of KDM-BA and PhenoAge, respectively. Associations of early-life risk score with late-life mortality and morbidity were mediated by biological aging (proportions: 5.66-43.12%). KDM-BA and PhenoAge accelerations could significantly mediate the impact on most outcomes except anxiety, and frailty could not mediate the impact on T2D.

CONCLUSION

Biological aging could capture and mediate the late-life health risks stemming from the early-life risks, and could be potentially targeted for healthy longevity promotion.

Toward a prognostic model for all-cause mortality among old people with disability in long-term care in China

BACKGROUND

Current prognostic model of all-cause mortality may not be applicable for old people with disability in long-term care due to the absence of injury- and care-related predictors. We aimed to develop a prognostic model specifically tailored to this population, based on comprehensive predictors.

METHOD

We conducted a prospective study involving 41,004 participants aged ≥60 with disability in long-term care across 16 study sites in Southwest China from 2017 to 2021. Participants' demographics, clinical characteristics, disability status, and injury- and care-related information at baseline were used as candidate predictors. We employed a LASSO Cox regression model to develop the prognostic model using the training set (70 % of participants), and the predictive performance was validated in the validation set (30 % of participants). The prognostic index (PI) scores of the prognostic model were used to quantify mortality risk.

RESULTS

At the end of the 4-year follow-up, 17,797 deaths (43.4 %) were observed. The prognostic model revealed several powerful and robust predictors of mortality across the total sample and subgroups, including higher age, living with comorbidities, physical and perceptual disability, and living with pressure sores. Non-professional care was an additional predictor in older participants. The risk of death for participants in the highest quartile of PI scores was approximately four-fold higher compared to those in the lowest quartile.

CONCLUSIONS

We developed and validated a prognostic model that can be practically utilized to identify individuals and populations at risk of death among old people with disability in long-term care.

The biological age model for evaluating the degree of aging in centenarians

BACKGROUND

Biological age (BA) has been used to assess individuals' aging conditions. However, few studies have evaluated BA models' applicability in centenarians.

METHODS

Important organ function examinations were performed in 1798 cases of the longevity population (80∼115 years old) in Hainan, China. Eighty indicators were selected that responded to nutritional status, cardiovascular function, liver and kidney function, bone metabolic function, endocrine system, hematological system, and immune system. BA models were constructed using multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal method (KDM), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and light gradient boosting machine (lightGBM) methods. A tenfold crossover validated the efficacy of models.

RESULTS

A total of 1398 participants were enrolled, of whom centenarians accounted for 49.21%. Seven aging markers were obtained, including estimated glomerular filtration rate, albumin, pulse pressure, calf circumference, body surface area, fructosamine, and complement 4. Eight BA models were successfully constructed, namely MLR, PCA, KDM1, KDM2, RF, SVM, XGBoost and lightGBM, which had the worst R2 of 0.45 and the best R2 of 0.92. The best R2 for cross-validation was KDM2 (0.89), followed by PCA (0.62).

CONCLUSION

In this study, we successfully applied eight methods, including traditional methods and machine learning, to construct models of biological age, and the performance varied among the models.

Relationship between dietary macronutrients intake and biological aging: a cross-sectional analysis of NHANES data

PURPOSE

This study aimed to investigate the association between macronutrient intake and biological age.

METHODS

Data were collected from 26,381 adults who participated in the United States National Health and Nutrition Examination Survey (NHANES). Two biological ages were estimated using the Klemera-Doubal method (KDM) and PhenoAge algorithms. Biological age acceleration (AA) was computed as the difference between biological age and chronological age. The associations between macronutrient intakes and AA were investigated.

RESULTS

After fully adjusting for confounding factors, negative associations were observed between AA and fiber intake (KDM-AA: β - 0.53, 95% CI - 0.62, - 0.43, P < 0.05; PhenoAge acceleration: β - 0.30, 95% CI - 0.35, - 0.25, P < 0.05). High-quality carbohydrate intake was associated with decreased AA (KDM-AA: β - 0.57, 95% CI - 0.67, - 0.47, P < 0.05; PhenoAge acceleration: β - 0.32, 95% CI - 0.37, - 0.26, P < 0.05), while low-quality carbohydrate was associated with increased AA (KDM-AA: β 0.30, 95% CI 0.21, 0.38, P < 0.05; PhenoAge acceleration: β 0.16, 95% CI 0.11, 0.21, P < 0.05). Plant protein was associated with decreased AA (KDM-AA: β - 0.39, 95% CI - 0.51, - 0.27, P < 0.05; PhenoAge acceleration: β - 0.21, 95% CI - 0.26, - 0.15, P < 0.05). Long-chain SFA intake increased AA (KDM-AA: β 0.16, 95% CI 0.08, 0.24, P < 0.05; PhenoAge acceleration: β 0.11, 95% CI 0.07, 0.15, P < 0.05). ω-3 PUFA was associated with decreased KDM-AA (β - 0.18, 95% CI - 0.27, - 0.08, P < 0.05) and PhenoAge acceleration (β - 0.09, 95% CI - 0.13, - 0.04, P < 0.05).

CONCLUSION

Our findings suggest that dietary fiber, high-quality carbohydrate, plant protein, and ω-3 PUFA intake may have a protective effect against AA, while low-quality carbohydrate and long-chain SFA intake may increase AA. Therefore, dietary interventions aimed at modifying macronutrient intakes may be useful in preventing or delaying age-related disease and improving overall health.

Impact of gut microbiota on nonalcoholic fatty liver disease: insights from a leave-one-out cross-validation study

Introduction

Enteric dysbacteriosis is strongly associated with nonalcoholic fatty liver disease (NAFLD). However, the underlying causal relationship remains unknown. Thus, the present study aimed to investigate the relationship between gut microbiota and NAFLD using Mendelian randomization (MR) and analyze the target genes potentially regulated by specific microbiota.

Methods

Bidirectional two-sample MR analysis was performed using inverse variance weighted (IVW) supplemented by MR-Egger, weighted median, simple mode, and weighted mode methods. Data were pooled from gut microbiota and NAFLD association studies. The least absolute shrinkage, selection operator regression, and the Support Vector Machine algorithm were used to identify genes regulated by these intestinal flora in NAFLD. The liver expression of these genes was verified in methionine choline-deficient (MCD) diet-fed mice.

Results

IVW results confirmed a causal relationship between eight specific gut microbes and NAFLD. Notably, the order Actinomycetales, NB1n, the family Actinomycetaceae, Oxalobacteraceae and the genus Ruminococcaceae UCG005 were positively correlated, whereas Lactobacillaceae, the Christensenellaceae R7 group, and Intestinibacter were negatively correlated with NAFLD onset. In NAFLD, these eight bacteria regulated four genes: colony-stimulating factor 2 receptor β, fucosyltransferase 2, 17-beta-hydroxysteroid dehydrogenase 14, and microtubule affinity regulatory kinase 3 (MAPK3). All genes, except MARK3, were differentially expressed in the liver tissues of MCD diet-fed mice.

Discussion

The abundance of eight gut microbiota species and NAFLD progression displayed a causal relationship based on the expression of the four target genes. Our findings contributed to the advancement of intestinal microecology-based diagnostic technologies and targeted therapies for NAFLD.

Patterns and Life Course Determinants of Black-White Disparities in Biological Age Acceleration: A Decomposition Analysis

Despite the prominence of the weathering hypothesis as a mechanism underlying racialized inequities in morbidity and mortality, the life course social and economic determinants of Black-White disparities in biological aging remain inadequately understood. This study uses data from the Health and Retirement Study (n = 6,782), multivariable regression, and Kitagawa-Blinder-Oaxaca decomposition to assess Black-White disparities across three measures of biological aging: PhenoAge, Klemera-Doubal biological age, and homeostatic dysregulation. It also examines the contributions of racial differences in life course socioeconomic and stress exposures and vulnerability to those exposures to Black-White disparities in biological aging. Across the outcomes, Black individuals exhibited accelerated biological aging relative to White individuals. Decomposition analyses showed that racial differences in life course socioeconomic exposures accounted for roughly 27% to 55% of the racial disparities across the biological aging measures, and racial disparities in psychosocial stress exposure explained 7% to 11%. We found less evidence that heterogeneity in the associations between social exposures and biological aging by race contributed substantially to Black-White disparities in biological aging. Our findings offer new evidence of the role of life course social exposures in generating disparities in biological aging, with implications for understanding age patterns of morbidity and mortality risks.

Relationship between an ageing measure and chronic obstructive pulmonary disease, lung function: a cross-sectional study of NHANES, 2007-2010

OBJECTIVES

Chronic obstructive pulmonary disease (COPD) is a disease associated with ageing. However, actual age does not accurately reflect the degree of biological ageing. Phenotypic age (PhenoAge) is a new indicator of biological ageing, and phenotypic age minus actual age is known as phenotypic age acceleration (PhenoAgeAccel). This research aimed to analyse the relationship between PhenoAgeAccel and lung function and COPD.

DESIGN

A cross-sectional study.

PARTICIPANTS

Data for the study were obtained from the National Health and Nutrition Examination Survey (NHANES) 2007-2010. We defined people with forced expiratory volume in 1 s/forced vital capacity <0.70 after inhaled bronchodilators as COPD and the rest of the population as non-COPD. Adults aged 40 years or older were enrolled in the study.

PRIMARY AND SECONDARY OUTCOME MEASURES

Linear and logistic regression were used to investigate the relationship between PhenoAgeAccel, lung function and COPD. Subgroup analysis was performed by gender, age, ethnicity and smoking index COPD. In addition, we analysed the relationship between the smoking index, respiratory symptoms and PhenoAgeAccel. Multiple models were used to reduce confounding bias.

RESULTS

5397 participants were included in our study, of which 1042 had COPD. Compared with PhenoAgeAccel Quartile1, Quartile 4 had a 52% higher probability of COPD; elevated PhenoAgeAccel was also significantly associated with reduced lung function. Further subgroup analysis showed that high levels of PhenoAgeAccel had a more significant effect on lung function in COPD, older adults and whites (P for interaction <0.05). Respiratory symptoms and a high smoking index were related to higher indicators of ageing.

CONCLUSIONS

Our study found that accelerated ageing is associated with the development of COPD and impaired lung function. Smoking cessation and anti-ageing therapy have potential significance in COPD.

Aging measures and cancer: Findings from the Health and Retirement Study

Background

Compared to cancer-free persons, cancer survivors of the same chronological age (CA) have increased physiological dysfunction, i.e., higher biological age (BA), which may lead to higher morbidity and mortality. We estimated BA using eight aging metrics: BA computed by Klemera Doubal method (KDM-BA), phenotypic age (PhenoAge), five epigenetic clocks (ECs, Horvath, Hannum, Levine, GrimAge, and pace of aging (POA)), and subjective age (SA). We tested if aging constructs were associated with total cancer prevalence and all-cause mortality in cancer survivors and controls, i.e., cancer-free persons, in the Health and Retirement Study (HRS), a large population-based study.

Methods

In 2016, data on BA-KDM, PhenoAge, and SA were available for 946 cancer survivors and 4,555 controls; data for the five ECs were available for 582 cancer survivors and 2,805 controls. Weighted logistic regression was used to estimate the association between each aging construct and cancer prevalence (odds ratio, OR, 95%CI). Weighted Cox proportional hazards regression was used to estimate the associations between each aging construct and cancer incidence as well as all-cause mortality (hazard ratio, HR, 95%CI). To study all BA metrics (except for POA) independent of CA, we estimated age acceleration as residuals of BA regressed on CA.

Results

Age acceleration for each aging construct and POA were higher in cancer survivors than controls. In a multivariable-adjusted model, five aging constructs (age acceleration for Hannum, Horvath, Levine, GrimAge, and SA) were associated with cancer prevalence. Among all cancer survivors, age acceleration for PhenoAge and four ECs (Hannum, Horvath, Levine, and GrimAge), was associated with higher all-cause mortality over 4 years of follow-up. PhenoAge, Hannum, and GrimAge were also associated with all-cause mortality in controls. The highest HR was observed for GrimAge acceleration in cancer survivors: 2.03 (95% CI, 1.58-2.60). In contrast, acceleration for KDM-BA and POA was significantly associated with mortality in controls but not in cancer survivors. When all eight aging constructs were included in the same model, two of them (Levine and GrimAge) were significantly associated with mortality among cancers survivors. None of the aging constructs were associated with cancer incidence.

Conclusion

Variations in the associations between aging constructs and mortality in cancer survivors and controls suggests that aging constructs may capture different aspects of aging and that cancer survivors may be experiencing age-related physiologic dysfunctions differently than controls. Future work should evaluate how these aging constructs predict mortality for specific cancer types.

Biomarkers selection and mathematical modeling in biological age estimation

Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual's BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.

Effects of High-Intensity Interval Training on Melatonin Function and Cellular Lymphocyte Apoptosis in Sedentary Middle-Aged Men

Background: Physical performance increased by controlled interventions of high-intensity intermittent training (HIIT); however, little is known about their influence as anti-aging and antioxidant effects, or their role in mitochondrial biogenesis. Purpose: This study aimed to determine the effects of HIIT for 12 weeks on melatonin function, lymphocyte cell apoptosis, oxidative stress on aging, and physical performance. Methods: Eighty healthy male subjects aged 18-65 years randomly participated in a HIIT-exercise training program for 12 weeks. Anthropometric analysis, cardiovascular fitness, total antioxidant capacity (TAC), lymphocyte count and apoptosis, and serum melatonin and cytochrome c oxidase (COX), were estimated for all subjects before and after HIIT-exercise training. HIIT training was performed in subjects for 12 weeks. Results: Data analysis showed a significant increase in the expression levels of the melatonin hormone (11.2 ± 2.3, p < 0.001), TAC (48.7 ± 7.1, p < 0.002), COX (3.7 ± 0.75, p < 0.001), and a higher percentage of lymphocyte apoptosis (5.2 ± 0.31, p < 0.003). In addition, there was an improvement in fitness scores (W; 196.5 ± 4.6, VO₂max; 58.9 ± 2.5, p < 0.001), adiposity markers (p < 0.001); BMI, WHtR, and glycemic control parameters (p < 0.01); FG, HbA1c (%), FI, and serum C-peptide were significantly improved following HIIT intervention. Both melatonin and lymphocyte apoptosis significantly correlated with the studied parameters, especially TAC and COX. Furthermore, the correlation of lymphocyte apoptosis with longer exercise duration was significantly associated with increased serum melatonin following exercise training. This association supports the mechanistic role of melatonin in promoting lymphocyte apoptosis either via the extrinsic mediator pathway or via inhibition of lymphocyte division in the thymus and lymph nodes. Additionally, the correlation between melatonin, lymphocyte apoptosis, TAC, and COX activities significantly supports their role in enhancing physical performance. Conclusions: The main findings of this study were that HIIT exercise training for 12 weeks significantly improved adiposity markers, glycemic control parameters, and physical performance of sedentary older adult men. In addition, melatonin secretion, % of lymphocyte apoptosis, COX activities, and TAC as biological aging markers were significantly increased following HIIT exercise training interventions for 12 weeks. The use of HIIT exercise was effective in improving biological aging, which is adequate for supporting chronological age, especially regarding aging problems. However, subsequent studies are required with long-term follow-up to consider HIIT as a modulator for several cardiometabolic health problems in older individuals with obesity.

Wearable-based accelerometer activity profile as digital biomarker of inflammation, biological age, and mortality using hierarchical clustering analysis in NHANES 2011-2014

Repeated disruptions in circadian rhythms are associated with implications for health outcomes and longevity. The utilization of wearable devices in quantifying circadian rhythm to elucidate its connection to longevity, through continuously collected data remains largely unstudied. In this work, we investigate a data-driven segmentation of the 24-h accelerometer activity profiles from wearables as a novel digital biomarker for longevity in 7,297 U.S. adults from the 2011-2014 National Health and Nutrition Examination Survey. Using hierarchical clustering, we identified five clusters and described them as follows: "High activity", "Low activity", "Mild circadian rhythm (CR) disruption", "Severe CR disruption", and "Very low activity". Young adults with extreme CR disturbance are seemingly healthy with few comorbid conditions, but in fact associated with higher white blood cell, neutrophils, and lymphocyte counts (0.05-0.07 log-unit, all p < 0.05) and accelerated biological aging (1.42 years, p < 0.001). Older adults with CR disruption are significantly associated with increased systemic inflammation indexes (0.09-0.12 log-unit, all p < 0.05), biological aging advance (1.28 years, p = 0.021), and all-cause mortality risk (HR = 1.58, p = 0.042). Our findings highlight the importance of circadian alignment on longevity across all ages and suggest that data from wearable accelerometers can help in identifying at-risk populations and personalize treatments for healthier aging.

Accelerated biological aging and risk of depression and anxiety: evidence from 424,299 UK Biobank participants

Theory predicts that biological processes of aging may contribute to poor mental health in late life. To test this hypothesis, we evaluated prospective associations between biological age and incident depression and anxiety in 424,299 UK Biobank participants. We measured biological age from clinical traits using the KDM-BA and PhenoAge algorithms. At baseline, participants who were biologically older more often experienced depression/anxiety. During a median of 8.7 years of follow-up, participants with older biological age were at increased risk of incident depression/anxiety (5.9% increase per standard deviation [SD] of KDM-BA acceleration, 95% confidence intervals [CI]: 3.3%-8.5%; 11.3% increase per SD of PhenoAge acceleration, 95% CI: 9.%-13.0%). Biological-aging-associated risk of depression/anxiety was independent of and additive to genetic risk measured by genome-wide-association-study-based polygenic scores. Advanced biological aging may represent a potential risk factor for incident depression/anxiety in midlife and older adults and a potential target for risk assessment and intervention.

Biological age models based on a healthy Han Chinese population

BACKGROUND

Biological age (BA) may reflect the actual aging state in humans better than chronological age (CA). The study aimed to construct BA models suitable for the Chinese Han population by selecting appropriate aging markers and evaluation methods.

METHODS

A total of 1207 individuals (21∼91 years) from the Han Chinese population in Beijing were examined for essential organ functions, and 156 cardiovascular, pulmonary function, and atherosclerotic indices and clinical and genetic factors were used as candidate markers of aging. BA models were constructed using multiple linear regression (MLR), principal component analysis (PCA), and the Klemera and Doubal method (KDM). Models were internally and externally validated using cross-validation and disease populations.

RESULTS

Nine aging markers were selected. Two MLR, three PCA, and three KDM models were successfully constructed. External validation showed that the difference between CA and BA was most significant in the PCA3 and KDM2 models, while there was no significant difference in the MLR1 and MLR2 models; the fitted lines for BA in the disease population were higher than those in the healthy population in the MLR1, MLR2, KDM1, and KDM2 models, while the other models showed the opposite.

CONCLUSIONS

Based on a healthy population in Beijing, nine markers representing multiple organ/system functions were screened from the candidate markers, eight methods were successfully used to construct BA models, and the KDM2 model was found to potentially be more appropriate for assessing BA in the Chinese Han population.

Blunted Rest-Activity Circadian Rhythm Is Associated With Increased Rate of Biological Aging: An Analysis of NHANES 2011-2014

Impaired rest-activity circadian rhythm has been associated with increased risk for morbidity and mortality. Animals with mutations in clock genes display accelerated aging and shortened life span. Whether impaired rest-activity circadian rhythm is also associated with processes of aging in humans has not been explored. We analyzed accelerometry and physiological data from 7 539 adults participating in the 2011-2014 waves of the U.S. National Health and Nutrition Examination Surveys. We used accelerometry data to compute rest-activity rhythm measurements. We used physiological data to compute measurements of biological aging according to 3 published algorithms: Klemera-Doubal method (KDM) Biological Age, PhenoAge, and homeostatic dysregulation (HD). In the models adjusting multiple covariates, participants with higher relative amplitude (RA) and interdaily stability (IS) and lower intradaily variability (IV) exhibited less advanced biological aging indexed by KDM and PhenoAge (effect sizes for 1-quantile increase in these rest-activity measurements ranged from 0.54 to 0.57 "years" for RA, 0.24 to 0.28 "years" for IS, and 0.24 to 0.35 "years" for IV, ps < .001). Similar finding was observed for biological aging indexed by HD, but the significance was limited to RA with 1-quantile increase in RA associated with 0.09 log units decrease in HD (p < .001). The results indicate that blunted rest-activity circadian rhythm is associated with accelerated aging in the general population, suggesting that interventions aiming at enhancing circadian rhythm may be a novel approach for the extension of a healthy life span.

Progress in biological age research

Biological age (BA) is a common model to evaluate the function of aging individuals as it may provide a more accurate measure of the extent of human aging than chronological age (CA). Biological age is influenced by the used biomarkers and standards in selected aging biomarkers and the statistical method to construct BA. Traditional used BA estimation approaches include multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal's method (KDM), and, in recent years, deep learning methods. This review summarizes the markers for each organ/system used to construct biological age and published literature using methods in BA research. Future research needs to explore the new aging markers and the standard in select markers and new methods in building BA models.

Familial aggregation of the aging process: biological age measured in young adult offspring as a predictor of parental mortality

Measures of biological age (BA) integrate information across organ systems to quantify "biological aging," i.e., inter-individual differences in aging-related health decline. While longevity and lifespan aggregate in families, reflecting transmission of genes and environments across generations, little is known about intergenerational continuity of biological aging or the extent to which this continuity may be modified by environmental factors. Using data from the Jerusalem Perinatal Study (JPS), we tested if differences in offspring BA were related to mortality in their parents. We measured BA using biomarker data collected from 1473 offspring during clinical exams in 2007-2009, at age 32 ± 1.1. Parental mortality was obtained from population registry data for the years 2004-2016. We fitted parametric survival models to investigate the associations between offspring BA and parental all-cause and cause-specific mortality. We explored potential differences in these relationships by socioeconomic position (SEP) and offspring sex. Participants' BAs widely varied (SD = 6.95). Among those measured to be biologically older, parents had increased all-cause mortality (HR = 1.10, 95% CI: 1.08, 1.13), diabetes mortality (HR = 1.19, 95% CI: 1.08, 1.30), and cancer mortality (HR = 1.07, 95% CI: 1.02, 1.13). The association with all-cause mortality was stronger for families with low compared with high SEP (P_interaction = 0.04) and for daughters as compared to sons (P_interaction < 0.001). Using a clinical-biomarker-based BA estimate, observable by young adulthood prior to the onset of aging-related diseases, we demonstrate intergenerational continuity of the aging process. Furthermore, variation in this familial aggregation according to household socioeconomic position (SEP) at offspring birth and between families of sons and daughters proposes that the environment alters individuals' aging trajectory set by their parents.

Causal analysis identifies small HDL particles and physical activity as key determinants of longevity of older adults

BACKGROUND

The hard endpoint of death is one of the most significant outcomes in both clinical practice and research settings. Our goal was to discover direct causes of longevity from medically accessible data.

METHODS

Using a framework that combines local causal discovery algorithms with discovery of maximally predictive and compact feature sets (the "Markov boundaries" of the response) and equivalence classes, we examined 186 variables and their relationships with survival over 27 years in 1507 participants, aged ≥71 years, of the longitudinal, community-based D-EPESE study.

FINDINGS

As few as 8-15 variables predicted longevity at 2-, 5- and 10-years with predictive performance (area under receiver operator characteristic curve) of 0·76 (95% CIs 0·69, 0·83), 0·76 (0·72, 0·81) and 0·66 (0·61, 0·71), respectively. Numbers of small high-density lipoprotein particles, younger age, and fewer pack years of cigarette smoking were the strongest determinants of longevity at 2-, 5- and 10-years, respectively. Physical function was a prominent predictor of longevity at all time horizons. Age and cognitive function contributed to predictions at 5 and 10 years. Age was not among the local 2-year prediction variables (although significant in univariable analysis), thus establishing that age is not a direct cause of 2-year longevity in the context of measured factors in our data that determine longevity.

INTERPRETATION

The discoveries in this study proceed from causal data science analyses of deep clinical and molecular phenotyping data in a community-based cohort of older adults with known lifespan.

FUNDING

NIH/NIA R01AG054840, R01AG12765, and P30-AG028716, NIH/NIA Contract N01-AG-12102 and NCRR 1UL1TR002494-01.

Biological aging in maltreated children followed up into middle adulthood

BACKGROUND

Childhood adversity has been linked to many indicators of shorter healthy lifespan, including earlier onset of disease and disability as well as early mortality. These observations suggest the hypothesis that childhood maltreatment may accelerate aging.

OBJECTIVE

To characterize the relationship between childhood maltreatment and accelerated biological aging in a prospective cohort of 357 individuals with documented cases of childhood maltreatment and 250 controls matched on demographic and socioeconomic factors.

METHODS

Cases were drawn from juvenile and adult court records from the years 1967 through 1971 in a large Midwest metropolitan geographic area. Cases were defined as having court-substantiated cases of childhood physical or sexual abuse, or neglect occurring at age 11 or younger. Controls were selected from the same schools and hospitals of birth and matched on age, sex, race, and approximate socioeconomic status. We compared biological aging in these two groups using two blood-chemistry algorithms, the Klemera-Doubal method Biological Age (KDM BA) and the PhenoAge. Algorithms were developed and validated in data from the National Health and Nutrition Examination Surveys (NHANES) using published methods and publicly available software.

RESULTS

Participants (55% women, 49% non-White) had mean age of 41 years (SD=4). Those with court substantiated childhood maltreatment history exhibited more advanced biological aging as compared with matched controls, although this difference was statistically different for only the KDM BA measure (KDM BA Cohen's D=0.20, 95% CI=[0.03,0.36], p = 0.02; PhenoAge Cohen's D=0.09 95% CI=[-0.08,0.25], p = 0.296). In subgroup analyses, maltreatment effect sizes were larger for women as compared to men and for White participants as compared to non-White participants, although these differences were not statistically significant at the α= 0.05 level.

CONCLUSIONS AND RELEVANCE

As of midlife, effects of childhood maltreatment on biological aging are small in magnitude but discernible. Interventions to treat psychological and behavioral sequelae of exposure to childhood maltreatment, including in midlife adults, have potential to protect survivors from excess burden of disease, disability, and mortality in later life.

Role of sleep quality in the acceleration of biological aging and its potential for preventive interaction on air pollution insults: Findings from the UK Biobank cohort

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 (PM_2.5 and NO₂ ) on AAs. Sleep quality also showed a modifying effect on the associations of elevated PM_2.5 and NO₂  levels with accelerated AAs. For instance, an interquartile range increase in PM_2.5  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.

All-Subset Analysis Improves the Predictive Accuracy of Biological Age for All-Cause Mortality in Chinese and U.S. Populations

BACKGROUND

Klemera-Doubal's method (KDM) is an advanced and widely applied algorithm for estimating biological age (BA), but it has no uniform paradigm for biomarker processing. This article proposed all subsets of biomarkers for estimating BAs and assessed their association with mortality to determine the most predictive subset and BA.

METHODS

Clinical biomarkers, including those from physical examinations and blood assays, were assessed in the China Health and Nutrition Survey (CHNS) 2009 wave. Those correlated with chronological age (CA) were combined to produce complete subsets, and BA was estimated by KDM from each subset of biomarkers. A Cox proportional hazards regression model was used to examine and compare each BA's effect size and predictive capacity for all-cause mortality. Validation analysis was performed in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and National Health and Nutrition Examination Survey (NHANES). KD-BA and Levine's BA were compared in all cohorts.

RESULTS

A total of 130 918 panels of BAs were estimated from complete subsets comprising 3-17 biomarkers, whose Pearson coefficients with CA varied from 0.39 to 1. The most predictive subset consisted of 5 biomarkers, whose estimated KD-BA had the most predictive accuracy for all-cause mortality. Compared with Levine's BA, the accuracy of the best-fitting KD-BA in predicting death varied among specific populations.

CONCLUSION

All-subset analysis could effectively reduce the number of redundant biomarkers and significantly improve the accuracy of KD-BA in predicting all-cause mortality.

An objective metric of individual health and aging for population surveys

BACKGROUND

We have previously developed and validated a biomarker-based metric of overall health status using Mahalanobis distance (DM) to measure how far from the norm of a reference population (RP) an individual's biomarker profile is. DM is not particularly sensitive to the choice of biomarkers; however, this makes comparison across studies difficult. Here we aimed to identify and validate a standard, optimized version of DM that would be highly stable across populations, while using fewer and more commonly measured biomarkers.

METHODS

Using three datasets (the Baltimore Longitudinal Study of Aging, Invecchiare in Chianti and the National Health and Nutrition Examination Survey), we selected the most stable sets of biomarkers in all three populations, notably when interchanging RPs across populations. We performed regression models, using a fourth dataset (the Women's Health and Aging Study), to compare the new DM sets to other well-known metrics [allostatic load (AL) and self-assessed health (SAH)] in their association with diverse health outcomes: mortality, frailty, cardiovascular disease (CVD), diabetes, and comorbidity number.

RESULTS

A nine- (DM9) and a seventeen-biomarker set (DM17) were identified as highly stable regardless of the chosen RP (e.g.: mean correlation among versions generated by interchanging RPs across dataset of r = 0.94 for both DM9 and DM17). In general, DM17 and DM9 were both competitive compared with AL and SAH in predicting aging correlates, with some exceptions for DM9. For example, DM9, DM17, AL, and SAH all predicted mortality to a similar extent (ranges of hazard ratios of 1.15-1.30, 1.21-1.36, 1.17-1.38, and 1.17-1.49, respectively). On the other hand, DM9 predicted CVD less well than DM17 (ranges of odds ratios of 0.97-1.08, 1.07-1.85, respectively).

CONCLUSIONS

The metrics we propose here are easy to measure with data that are already available in a wide array of panel, cohort, and clinical studies. The standardized versions here lose a small amount of predictive power compared to more complete versions, but are nonetheless competitive with existing metrics of overall health. DM17 performs slightly better than DM9 and should be preferred in most cases, but DM9 may still be used when a more limited number of biomarkers is available.

Testing Black-White Disparities in Biological Aging Among Older Adults in the United States: Analysis of DNA-Methylation and Blood-Chemistry Methods

Biological aging is a proposed mechanism through which social determinants drive health disparities. We conducted proof-of-concept testing of 8 DNA-methylation (DNAm) and blood-chemistry quantifications of biological aging as mediators of disparities in healthspan between Black and White participants in the 2016 wave of the Health and Retirement Study (n = 9,005). We quantified biological aging from 4 DNAm "clocks" (Horvath, Hannum, PhenoAge, and GrimAge clock), a DNAm pace-of-aging measure (DunedinPoAm), and 3 blood-chemistry measures (PhenoAge, Klemera-Doubal method biological age, and homeostatic dysregulation). We quantified Black-White disparities in healthspan from cross-sectional and longitudinal data on physical performance tests, self-reported limitations in activities of daily living, and physician-diagnosed chronic diseases, self-rated health, and survival. DNAm and blood-chemistry quantifications of biological aging were moderately correlated (Pearson's r = 0.1-0.4). The GrimAge clock, DunedinPoAm, and all 3 blood-chemistry measures were associated with healthspan characteristics (e.g., mortality effect-size hazard ratios were 1.71-2.32 per standard deviation of biological aging) and showed evidence of more advanced/faster biological aging in Black participants than in White participants (Cohen's d = 0.4-0.5). These measures accounted for 13%-95% of Black-White differences in healthspan-related characteristics. Findings suggest that reducing disparities in biological aging can contribute to building health equity.

Association Between Functional Limitations and Incident Cardiovascular Diseases and All-Cause Mortality Among the Middle-Aged and Older Adults in China: A Population-Based Prospective Cohort Study

Background

The prevalence of functional limitations is relatively high among the middle-aged and older adults. However, the contribution of functional limitations to subsequent incident cardiovascular diseases (CVD) and death is unclear. This study aims to examine the association between functional limitations and incident CVD and all-cause mortality among the middle-aged and older adults.

Methods

This is a nationally representative prospective cohort study. Participants were middle-aged and older Chinese adults from The China Health and Retirement Longitudinal Study. Functional limitations were measured using activities of daily living (ADL) scale and instrumental activities of daily living (IADL) scale. Incident CVD and death were recorded at followed-up from June 1, 2011, up until August 31, 2018. Cox proportional hazards model was used to assess the association between functional limitations and incident CVD and all-cause mortality.

Results

A total of 11,013 participants were included in this study. During the 7 years of follow-up, 1,914 incident CVD and 1,182 incident deaths were identified. Participants with functional limitations were associated with a 23% increased risk of incident CVD (HR, 1.23, 95% CI:1.08,1.39) after adjusting for age, gender, residential area, marital status, education, smoking, alcohol drinking, sleep duration, nap duration, depression symptoms, social participation, history of hypertension, diabetes, dyslipidemia, use of hypertension medications, diabetes medications, and lipid-lowering therapy. Moreover, participants with functional limitations were associated with a 63% increased risk of all-cause mortality (HR,1.63, 95%CI: 1.41,1.89) after adjusting for potential confounders.

Conclusions

Functional limitations were significantly associated with subsequent incident CVD and death among the middle-aged and older Chinese adults.

DunedinPACE, a DNA methylation biomarker of the pace of aging

Background

Measures to quantify changes in the pace of biological aging in response to intervention are needed to evaluate geroprotective interventions for humans. Previously, we showed that quantification of the pace of biological aging from a DNA-methylation blood test was possible (Belsky et al., 2020). Here, we report a next-generation DNA-methylation biomarker of Pace of Aging, DunedinPACE (for Pace of Aging Calculated from the Epigenome).

Methods

We used data from the Dunedin Study 1972-1973 birth cohort tracking within-individual decline in 19 indicators of organ-system integrity across four time points spanning two decades to model Pace of Aging. We distilled this two-decade Pace of Aging into a single-time-point DNA-methylation blood-test using elastic-net regression and a DNA-methylation dataset restricted to exclude probes with low test-retest reliability. We evaluated the resulting measure, named DunedinPACE, in five additional datasets.

Results

DunedinPACE showed high test-retest reliability, was associated with morbidity, disability, and mortality, and indicated faster aging in young adults with childhood adversity. DunedinPACE effect-sizes were similar to GrimAge Clock effect-sizes. In analysis of incident morbidity, disability, and mortality, DunedinPACE and added incremental prediction beyond GrimAge.

Conclusions

DunedinPACE is a novel blood biomarker of the pace of aging for gerontology and geroscience.

Funding

This research was supported by US-National Institute on Aging grants AG032282, AG061378, AG066887, and UK Medical Research Council grant MR/P005918/1.

Associations between life course socioeconomic conditions and the Pace of Aging

BACKGROUND

Socioeconomic disadvantage is a well-established predictor of morbidity and mortality, and is thought to accelerate the aging process. This study examined associations between life course socioeconomic conditions and the Pace of Aging, a longitudinal measure of age-related physiological decline.

METHODS

Data were drawn from a Swiss population-based cohort of individuals originally recruited between 2003 and 2006, and followed up for 11 years (2834 women, 2475 men aged 35 - 75 years (mean 52)). Pace of Aging was measured using three repeated assessments of 12 biomarkers reflecting multiple body systems. Analysis tested associations of socioeconomic conditions with physiological status at baseline and with the Pace of Aging.

RESULTS

Participants with more life course socioeconomic disadvantage were physiologically older at baseline and experienced faster Pace of Aging. Effect-sizes (β) for associations of childhood socioeconomic disadvantage with baseline physiological status ranged from 0.1-0.2; for adulthood socioeconomic disadvantage, effect-sizes ranged from 0.2-0.3. Effect-sizes were smaller for associations with the Pace of Aging (< 0.05 for childhood disadvantage, 0.05-0.1 for adulthood disadvantage). Those who experienced disadvantaged socioeconomic conditions from childhood to adulthood aged 10% faster over the 11 years of follow-up as compared with those who experienced consistently advantaged socioeconomic conditions. Covariate adjustment for health behaviors attenuated associations, but most remained statistically significant.

CONCLUSIONS

Socioeconomic inequalities contribute to a faster Pace of Aging, partly through differences in health behaviors. Intervention to slow aging in at risk individuals is needed by midlife, before aetiology of aging-related diseases become established.

A Machine Learning-Based Aging Measure Among Middle-Aged and Older Chinese Adults: The China Health and Retirement Longitudinal Study

Objective: Biological age (BA) has been accepted as a more accurate proxy of aging than chronological age (CA). This study aimed to use machine learning (ML) algorithms to estimate BA in the Chinese population. Materials and methods: We used data from 9,771 middle-aged and older Chinese adults (≥45 years) in the 2011/2012 wave of the China Health and Retirement Longitudinal Study and followed until 2018. We used several ML algorithms (e.g., Gradient Boosting Regressor, Random Forest, CatBoost Regressor, and Support Vector Machine) to develop new measures of biological aging (ML-BAs) based on physiological biomarkers. R-squared value and mean absolute error (MAE) were used to determine the optimal performance of these ML-BAs. We used logistic regression models to examine the associations of the best ML-BA and a conventional aging measure-Klemera and Doubal method-BA (KDM-BA) we previously developed-with physical disability and mortality, respectively. Results: The Gradient Boosting Regression model performed the best, resulting in an ML-BA with an R-squared value of 0.270 and an MAE of 6.519. This ML-BA was significantly associated with disability in basic activities of daily living, instrumental activities of daily living, lower extremity mobility, and upper extremity mobility, and mortality, with odds ratios ranging from 1 to 7% (per 1-year increment in ML-BA, all P < 0.001), independent of CA. These associations were generally comparable to that of KDM-BA. Conclusion: This study provides a valid ML-based measure of biological aging for middle-aged and older Chinese adults. These findings support the application of ML in geroscience research and may help facilitate preventive and geroprotector intervention studies.

A toolkit for quantification of biological age from blood chemistry and organ function test data: BioAge

Methods to quantify biological aging are emerging as new measurement tools for epidemiology and population science and have been proposed as surrogate measures for healthy lifespan extension in geroscience clinical trials. Publicly available software packages to compute biological aging measurements from DNA methylation data have accelerated dissemination of these measures and generated rapid gains in knowledge about how different measures perform in a range of datasets. Biological age measures derived from blood chemistry data were introduced at the same time as the DNA methylation measures and, in multiple studies, demonstrate superior performance to these measures in prediction of healthy lifespan. However, their dissemination has been slow by comparison, resulting in a significant gap in knowledge. We developed a software package to help address this knowledge gap. The BioAge R package, available for download at GitHub ( http://github.com/dayoonkwon/BioAge ), implements three published methods to quantify biological aging based on analysis of chronological age and mortality risk: Klemera-Doubal biological age, PhenoAge, and homeostatic dysregulation. The package allows users to parametrize measurement algorithms using custom sets of biomarkers, to compare the resulting measurements to published versions of the Klemera-Doubal method and PhenoAge algorithms, and to score the measurements in new datasets. We applied BioAge to safety lab data from the CALERIE™ randomized controlled trial, the first-ever human trial of long-term calorie restriction in healthy, non-obese adults, to test effects of intervention on biological aging. Results contribute evidence that CALERIE intervention slowed biological aging. BioAge is a toolkit to facilitate measurement of biological age for geroscience.

Comparing Biological Age Estimates Using Domain-Specific Measures From the Canadian Longitudinal Study on Aging

Many studies have shown that estimates of biological age (BA) can predict health-related outcomes in older adults. Often, researchers employ multiple measures belonging to a variety of biological/physiological systems, and assess the validity of BA estimates by how well they approximate chronological age (CA). However, it is not clear whether this is the best approach for judging a BA estimate, or whether certain groups of measures are more informative to this end. Using data from the Canadian Longitudinal Study on Aging, we composed panels of biological measures based on the physiological systems/domains they belong to (blood, organ function, physical/cognitive performance), and also composed a panel of measures that optimized the association of BA with CA. We then compared BA estimates for each according to their association with CA and health-related outcomes, including frailty, multimorbidity, chronic condition domains, disability, and health care utilization. Although BA estimated using all 40 measures (r = 0.74) or our age-optimized panel (r = 0.77) most closely approximated CA, the strength of associations to health-related outcomes was comparable or weaker than that of our panel composed only of physical performance measures (CA r = 0.59). All BA estimates were significantly associated to the outcomes considered, with exception to the neurological and musculoskeletal disease domains, and only varied slightly by sex. In summary, while the approximation of CA is important to consider when estimating BA, the strength of associations to prospective outcomes may be of greater importance. Hence, the context in which BA is estimated should be influenced by an investigator's specific research goals.

A Predictive Model for Progression of CKD to Kidney Failure Based on Routine Laboratory Tests

RATIONALE & OBJECTIVE

Stratification of chronic kidney disease (CKD) patients at risk for progressing to end-stage kidney disease (ESKD) requiring kidney replacement therapy (KRT) is important for clinical decision-making and trial enrollment.

STUDY DESIGN

Four independent prospective observational cohort studies.

SETTING & PARTICIPANTS

The development cohort was comprised of 4,915 CKD patients and three independent validation cohorts were comprised of a total of 3,063. Patients were followed-up for approximately five years.

NEW PREDICTORS & ESTABLISHED PREDICTORS

22 demographic, anthropometric and laboratory variables commonly assessed in CKD patients.

OUTCOMES

Progression to ESKD requiring KRT.

ANALYTICAL APPROACH

A Least Absolute Shrinkage and Selection Operator (LASSO) Cox proportional hazards model was fit to select laboratory variables that best identified patients at high risk for ESKD. Model discrimination and calibration were assessed and compared against the 4-variable Tangri (T4) risk equation. Both used a resampling approach within the development cohort and in the validation cohorts using cause-specific concordance (C) statistics, net reclassification improvement, and calibration graphs.

RESULTS

The newly derived 6-variable (Z6) risk score included serum creatinine, albumin, cystatin C and urea, as well as hemoglobin and the urine albumin-to-creatinine ratio. Based on the resampling approach, Z6 achieved a median C value of 0.909 (95% CI, 0.868-0.937) at two years after the baseline visit, whereas the T4 achieved a median C value of 0.855 (95% CI, 0.799-0.915). In the three independent validation cohorts, Z6 C values were 0.894, 0.921, and 0.891, whereas the T4 C values were 0.882, 0.913, and 0.862.

LIMITATIONS

The Z6 was both derived and tested only in White European cohorts.

CONCLUSIONS

A new risk equation, based on six routinely available laboratory tests facilitates identification of patients with CKD who are at high risk of progressing to ESKD.

Associations of Loneliness and Social Isolation with Healthspan and Lifespan in the US Health and Retirement Study

BACKGROUND

Loneliness and social isolation are emerging public health challenges for aging populations.

METHODS

We followed N=11,302 US Health and Retirement Study (HRS) participants aged 50-95 from 2006-2014 to measure persistence of experiences of loneliness and exposure to social isolation. We tested associations of longitudinal loneliness and social isolation phenotypes with disability, morbidity, mortality, and biological aging through 2018.

RESULTS

During follow-up, 18% of older adults met criteria for loneliness, with 6% meeting criteria at two or more follow-up assessments. For social isolation, these fractions were 21% and 8%. HRS participants who experienced loneliness and were exposed to social isolation were at increased risk for disease, disability, and mortality. Those experiencing persistent loneliness were at a 57% increased hazard of mortality compared to those who never experienced loneliness. For social isolation, the increase was 28%. Effect-sizes were somewhat larger for counts of prevalent activity limitations and somewhat smaller for counts of prevalent chronic diseases. Covariate adjustment for socioeconomic and psychological risks attenuated but did not fully explain associations. Older adults who experienced loneliness and were exposed to social isolation also exhibited physiological indications of advanced biological aging (Cohen's-d for persistent loneliness and social isolation=0.26 and 0.21, respectively). For loneliness, but not social isolation, persistence was associated with increased risk.

CONCLUSION

Deficits in social connectedness prevalent in a national sample of US older adults were associated with morbidity, disability, and mortality and with more advanced biological aging. Bolstering social connectedness to interrupt experiences of loneliness may promote healthy aging.

Disparities in the pace of biological aging among midlife adults of the same chronological age have implications for future frailty risk and policy

Some humans age faster than others. Variation in biological aging can be measured in midlife, but the implications of this variation are poorly understood. We tested associations between midlife biological aging and indicators of future frailty-risk in the Dunedin cohort of 1037 infants born the same year and followed to age 45. Participants' Pace of Aging was quantified by tracking declining function in 19 biomarkers indexing the cardiovascular, metabolic, renal, immune, dental, and pulmonary systems across ages 26, 32, 38, and 45 years. At age 45 in 2019, participants with faster Pace of Aging had more cognitive difficulties, signs of advanced brain aging, diminished sensory-motor functions, older appearance, and more pessimistic perceptions of aging. People who are aging more rapidly than same-age peers in midlife may prematurely need supports to sustain independence that are usually reserved for older adults. Chronological age does not adequately identify need for such supports.

Quest for a summary measure of biological age: the health and retirement study

Measures of biological age and its components have been shown to provide important information about individual health and prospective change in health as there is clear value in being able to assess whether someone is experiencing accelerated or decelerated aging. However, how to best assess biological age remains a question. We compare prediction of health outcomes using existing summary measures of biological age with a measure created by adding novel biomarkers related to aging to measures based on more conventional clinical chemistry and exam measures. We also compare the explanatory power of summary biological age measures compared to the individual biomarkers used to construct the measures. To accomplish this, we examine how well biological age, phenotypic age, and expanded biological age and five sets of individual biomarkers explain variability in four major health outcomes linked to aging in a large, nationally representative cohort of older Americans. We conclude that different summary measures of accelerated aging do better at explaining different health outcomes, and that chronological age has greater explanatory power for both cognitive dysfunction and mortality than the summary measures. In addition, we find that there is reduction in the variance explained in health outcomes when indicators are combined into summary measures, and that combining clinical indicators with more novel markers related to aging does best at explaining health outcomes. Finally, it is hard to define a set of assays that parsimoniously explains the greatest amount of variance across the range of health outcomes studied here. All of the individual markers considered were related to at least one of the health outcomes.

Development and Validation of 2 Composite Aging Measures Using Routine Clinical Biomarkers in the Chinese Population: Analyses From 2 Prospective Cohort Studies

BACKGROUND

This study aimed to: (i) develop 2 composite aging measures in the Chinese population using 2 recent advanced algorithms (the Klemera and Doubal method and Mahalanobis distance); and (ii) validate the 2 measures by examining their associations with mortality and disease counts.

METHODS

Based on data from the China Nutrition and Health Survey (CHNS) 2009 wave (N = 8119, aged 20-79 years, 53.5% women), a nationwide prospective cohort study of the Chinese population, we developed Klemera and Doubal method-biological age (KDM-BA) and physiological dysregulation (PD, derived from Mahalanobis distance) using 12 biomarkers. For the validation analysis, we used Cox proportional hazard regression models (for mortality) and linear, Poisson, and logistic regression models (for disease counts) to examine the associations. We replicated the validation analysis in the China Health and Retirement Longitudinal Study (CHARLS, N = 9304, aged 45-99 years, 53.4% women).

RESULTS

Both aging measures were predictive of mortality after accounting for age and gender (KDM-BA, per 1-year, hazard ratio [HR] = 1.14, 95% confidence interval [CI] = 1.08, 1.19; PD, per 1-SD, HR = 1.50, 95% CI = 1.33, 1.69). With few exceptions, these mortality predictions were robust across stratifications by age, gender, education, and health behaviors. The 2 aging measures were associated with disease counts both cross-sectionally and longitudinally. These results were generally replicable in CHARLS although 4 biomarkers were not available.

CONCLUSIONS

We successfully developed and validated 2 composite aging measures-KDM-BA and PD, which have great potentials for applications in early identifications and preventions of aging and aging-related diseases in China.