Effect of Diet, Exercise, or Both on Biological Age and Healthy Aging in Older Adults with Obesity: Secondary Analysis of a Randomized Controlled Trial

Abstract

Objective:

To determine the effect of diet, exercise, and diet-exercise in combination on measures of biological age.

Design:

Secondary analysis of a 1-year randomized, controlled trial

Setting:

University-based Medical Center

Participants:

One-hundred-seven older (age≥65 yrs.) adults with obesity (BMI≥30 kg/m²) were randomized and 93 completed the study. Analyses used intention-to-treat.

Interventions:

Participants were randomized to a control group, a weight-management (diet) group, an exercise group, or a weight-management-plus-exercise (diet-exercise group).

Main outcome measures:

We calculated Klemera-Doubal Method (KDM) biological age, Homeostatic Dysregulation (HD) score, and Health Aging Index (HAI) score at baseline, and changes at 6- and 12-months.

Results:

Diet and diet-exercise decreased KDM biological age more than exercise and control (−2.4±0.4, −2.2±0.3, −0.2±0.4, and 0.2±0.5, respectively, P<0.05 for the between group-differences). Diet and diet-exercise also decreased HD score more than exercise and control (−1.0±0.3, −1.1±0.3, 0.1±0.3, and 0.3±0.3 respectively, P<0.05). Moreover, diet-exercise decreased HAI score more than exercise, diet, or control (−1.1±0.2, −0.5±0.2, −0.5±0.2, and 0.00±0.2, respectively, P<0.05).

Conclusions:

These findings suggest that diet and diet-exercise are both effective methods of improving biological age, and that biological age may be a valuable method of assessing geroprotective interventions in older humans.

INTRODUCTION

The proportion of individuals in the United States who are older than 65 years and individuals who are obese have been steadily rising for many decades (1). As the intersection between the two cohorts expands, obesity in older adults is becoming an increasingly common public health problem that impacts frailty, quality of life, and physical function (2). We have reported that diet and exercise in combination is an effective method to improve functional status in this population (3). However, the effect of these geroprotective therapies has not been shown to prolong healthy lifespan, which is difficult and impractical to measure due to the costs and difficulties associated with studies that have longer follow-up.

Biological age may be able to provide a surrogate measure by which to measure extension of healthy lifespan (4). Aging is a complex process in which molecular and cellular damage accumulate, resulting in reduced functioning and ultimately death. Biological age represents another method to index the degree to which an individual has aged. While chronological age is a commonly used index that is able to predict morbidity and mortality, there is a large degree of heterogeneity among older individuals of the same chronological age (5). Additionally, chronological age progresses at a fixed rate and thus is impervious to any interventions, making it less useful as a dependent variable in assessing various interventions. Biological age attempts to more accurately characterize aging by using various biomarkers which represent aging itself rather than chronic disease (6). These biomarkers represent function across a wide variety of organ systems to holistically capture the state of an individual’s health and account for some of the heterogeneity in morbidity, mortality, and other outcomes among individuals of the same chronological age. Various methods for calculating biological age have been proposed, including the Klemera-Doubal algorithm (7, 8) and homeostatic dysregulation (9-11), both of which have been shown to predict mortality risk and to be associated with differences in physical and cognitive function (8, 11, 12). The healthy aging index is another index that has been shown to predict morbidity and mortality independent of chronological age and comorbidities in the general older population (13).

We analyzed data from our previous study demonstrating the independent and combined geroprotective effects of diet-induced weight loss and exercise in older individuals who were obese over the course of a year (3). We hypothesized that among these lifestyle interventions, the combination of weight loss and exercise would result in the least deterioration of the biological age in older adults with obesity. Specifically, we calculated three different indices of aging—Klemera-Doubal biological age, Homeostatic Dysregulation, and Healthy Aging Index.

METHODS

This study was a secondary analysis derived from a 1-year randomized controlled trial that evaluated the independent and combined effects of diet and exercise on physical function in older adults. Details of the trial have been described in the original paper (3). Briefly, 107 participants who were older (age ≥65 yrs.) and obese (body mass index ≥30 kg/m²) were randomized into four arms: control, diet alone, exercise alone, and diet-exercise in combination. Other inclusion criteria included: mild-moderate frailty (Physical Performance Test score of 18 to 32) (14), stable body weight for the past year (no fluctuations greater than 2 kg), stable medications for at least 6 months prior to enrollment, and a sedentary lifestyle (regular exercise < 1 hour per week). Exclusion criteria included: inability to exercise due to neurological or musculoskeletal conditions, a history of cancer, a history of severe cardiopulmonary disease, visual, hearing, or cognitive impairments, current tobacco use, and usage of drugs affecting bone metabolism. Participants were assessed at baseline, 6 months, and 12 months during the interventions.

Participants in the control group were not given specific recommendations regarding diet or exercise, and only provided general information about a healthy diet every month. Participants in the diet group were given a diet that was approximately 500 to 750 kcal below their daily energy requirements and met weekly with a dietitian to adjust the diet and assess progress. Participants in the exercise group were instructed to attend three exercise sessions per week of approximately 90 min duration (15-min flexibility exercise, 30-min aerobic exercise, 30-min progressive resistance exercise, and 15-min balance exercise). Aerobic exercise was performed on a treadmill, bike, or elliptical trainer at approximately 65% of their peak heart rate, which was gradually increased to 70%–85% of peak heart rate. Resistance exercise was performed on weight-lifting machines consisting of 9 upper-body and lower-body exercises. The initial sessions were 1–2 sets of 8–12 repetitions at 65% of the one-repetition maximum (1-RM; the maximum weight a participant can lift, in one attempt), which was increased progressively to 2–3 sets at approximately 80% of the 1-RM. All exercise sessions were supervised by exercise physiologists at our facility. Participants in the diet-exercise group were assigned both the diet and exercise plans as detailed above.

Klemera-Doubal Method Biological Age Algorithm

Biological age was computed following the Klemera-Doubal method (KDM) (7). The KDM calibration curves were obtained using publicly available National Health and Nutrition Examination Survey (NHANES) 2005-2006 data to match the data collection period of our study. Pregnant women and individuals under 30 were excluded. Calibration for nine biomarkers (albumin, alkaline phosphatase, creatinine, hemoglobin A1c, blood urea nitrogen, white blood cell count, C-reactive protein, total cholesterol, systolic blood pressure) used data from the NHANES. Calibration for VO₂max used data from the Fitness Registry and the Importance of Exercise National Database (FRIEND) study (15). Hemoglobin A1c was not collected as part of the original study, and was instead calculated from fasting glucose levels using formula developed by Nathan et al (16). This panel of biomarkers differs from past panels (8) in its usage of white blood cell count as a marker of immune function and VO₂max as a marker of cardiovascular fitness, both of which show an association with mortality (17, 18). The KDM consists of computation of biological ages for each of our ten predictors using the linear relationship between predictor and chronological age as classical calibration. The individual estimates of biological age are BA_i = (y_i − q_i)/k_i , i = 1 to 10, where y_i = individual biomarker, q_i = intercept, and k_i = regression coefficient. These 10 estimates were combined as a weighted average $BA={\sum }_1^{10}{w}_{i}B{A}_i+w_{11}\text{CA}$, where the addition of chronological age (CA) provides “shrinkage” of BA back toward CA, and weights w_i = (k²/s²)/D and $w_{11}=(1∕s_B^2)∕D$, and $s_B^2$ is computed from the reference data and denominator $D={\sum }_1^{11}{w}_i$ (for $s_B^2$, refer to the original method outlined by Klemera and Doubal (7). Since the relationship of some biomarkers to chronological age differed by sex, regression parameters were sex-specific. Since our experimental design means repeated measures ANOVA analysis involves differences of biological age over visits, the relevant formula was $\Delta BA={\sum }_1^{10}{w}_i\Delta y_i∕k_i+0$ but D in w_i is unchanged. When using the baseline chronological age in this formulation, ΔCA=0, we trimmed ΔBA at ±20 years for these subsequent analyses. We noted that Klemera-Doubal’s derivation was partly geometric where the ten linear regression lines formed a single line parameterized by age in ten-dimensional space and the biological age estimate was obtained as a projection of each ten-dimensional point (of the ten biomarkers measured for each participant) onto this single line. Statistically, the ten biological age markers were considered statistically independent measures and in combination gave the form of the overall variance, denoted as D above. Higher values represent accelerated biological age.

Homeostatic Dysregulation Algorithm

Homeostatic dysregulation (HD) was computed using the same NHANES reference data obtaining means μ_i, standard deviation σ_i, and the correlation matrix S for the same nine biomarkers. Reference data were computed from healthy individuals who had biomarkers within age and sex-specific reference ranges (19). PROC FACTOR in SAS was used to compute the principal components (eigenvectors) of the correlation matrix and PROC SCORE in SAS was used to compute the Mahalanobis distance of the standardized biomarkers from the reference mean: z_i = (y_i − μ_i)/σ_i. MD is $d=\sqrt{z^T{S}^{-1}z+z_{10}^2}$, where S⁻¹ was computed from the principal component decomposition of S. For the tenth biomarker, VO₂max, the standardized biomarker z₁₀ = (y₁₀ − μ₁₀)/σ₁₀, where μ₁₀, and σ₁₀ were obtained from the FRIEND reference data; and the squared distance $z_{10}^2$ was added into the distance d. Higher values represent accelerated biological age.

Healthy Aging Index

Healthy Aging Index (HAI) scores were calculated following Sanders et al. (13) by assigning tertile scores of 0, 1, or 2 for five different measures of physiological function: systolic blood pressure, creatinine, fasting glucose, Modified Mini-Mental Status Examination (3MS) score, and VO₂max. VO₂max was used to substitute for forced vital capacity as a measure of cardiopulmonary function. VO₂max tertiles were determined using FRIEND data (15). Scores from all five measures were then summed, and a final score was determined with 0 representing the healthiest score and 10 representing the unhealthiest. HAI was computed for each patient and each visit. Higher values represent accelerated biological age.

Missing Values

Based on a method outlined by Belsky et al. (10), for participants with one biomarker measurement missing out of the three measurements, the missing value was imputed based on the linear regression of the other two observed measurements. Participants for which this was not possible had pro-rated biological age calculated and had a missing HD and HAI.

Statistical Analysis

Intention-to-treat analyses were performed using SAS software, version 9.4 (SAS Institute, Cary, NC, USA), by analyzing data from all participants originally randomly assigned. Baseline characteristics were compared by using analyses of variance or Fisher’s exact test. Longitudinal changes between groups were carried out with the use of mixed model-repeated measures analyses of covariance. Change from baseline was used as the dependent variable with group, time, and group x time as independent effects and baseline values and sex as covariates. When the overall P value for the interaction between group and time was <0.05, the appropriate contrasts were used to test the null hypothesis that changes in 1 group were equal to corresponding changes in another group. Analyses testing for within-group changes also were performed using mixed-model repeated-measures analysis of variance. Pearson’s correlation was used to assess associations between changes in selected outcomes. Data for change scores are presented as least-squares-adjusted means (± SE). P values <0.05 were considered to indicate statistical significance.

RESULTS

Study Population

A total of 107 older adults with obesity were randomized and 93 completed the 1-year lifestyle interventions (3). The median attendance at diet therapy sessions was 83% in the diet group, and 82% in the diet-exercise group. The median attendance at exercise sessions was 88% in the exercise group and 83% in the diet-exercise group. Body weight decreased by 9.2±0.6 kg in the diet group and by 8.9±0.6 kg in the diet-exercise group. Body weight did not significantly change in the exercise and control groups (0.6±0.7 kg and 1.7±0.7 kg, respectively). There were no significant differences in baseline characteristics among the four groups (Table 1). Participants’ biological ages were older than their chronological age, with a mean biological age of 72.4 years at baseline and a mean chronological age of 69.7 years at baseline

Table 1.

Baseline Characteristics of Participants¹

	Control (n-27)	Exercise (n=26)	Diet (n=26)	Diet-Exercise (n=28)	P value
Age (years)	68.9 ± 4.3	69.5 ± 3.8	69.8 ± 4.0	69.5 ± 4.1	0.85
Female, n (%)	18 (67)	16 (62)	17 (65)	16 (57)	0.89
White, n (%)	22 (81)	21 (81)	23 (88)	25 (89)	0.66
Weight (kg)	101.0 ± 16.3	99.2 ± 17.4	104.1 ± 15.3	99.1 ± 16.8	0.93
Body mass index (kg/m2)	37.3 ± 4.7	36.9 ± 5.4	37.2 ± 4.5	37.2 ± 5.4	0.44
3MS score	96.3 ± 3.7	94.5 ± 5.2	96.0 ± 2.9	95.7 ± 4.4	0.67
Systolic blood pressure (mm Hg)	133.3 ± 18.6	132.7 ± 13.9	135.3 ± 19.1	138.6 ± 23.6	0.55
VO2max (mL/kg/min)	16.3 ± 3.8	17.4 ± 3.5	17.6 ± 2.2	17.3 ± 3.5	0.49
Albumin (g/dL)	4.1 ± 0.2	6.5 ± 10.6	4.0 ± 0.8	5.9 ± 9.2	0.33
Alkaline phosphatase (U/L)	80.2 ± 23.0	103.3 ± 133.8	69.9 ± 17.7	75.1 ± 19.1	0.40
Blood urea nitrogen (mg/dL)	17.0 ± 4.5	18.2 ± 5.8	23.6 ± 30.8	17.1 ± 4.7	0.87
Cholesterol (mg/dL)	178.7 ± 27.3	176.3 ± 34.5	183.8 ± 36.6	178.4 ± 31.0	0.81
Creatinine (mg/dL)	0.9 ± 0.2	0.9 ± 0.2	0.9 ± 0.1	0.9 ± 0.2	0.72
C-reactive protein (mg/L)	3.8 ± 3.9	4.0 ± 4.0	3.5 ± 3.0	4.8 ± 6.3	0.31
Hemoglobin A1c (%)*	5.2 ± 0.4	5.1 ± 0.3	5.0 ± 0.4	5.1 ± 0.4	0.19
White blood cell count (109 cells/L)	6.3 ± 1.5	6.7 ± 1.6	6.1 ± 1.5	5.8 ± 1.7	0.31
Glucose, fasting (mg/dL)	101.7 ± 10.1	99.7 ± 8.4	96.2 ± 12.2	99.3±11.3	0.89

¹Values are mean ± standard deviation; 3MS, Modified Mini-Mental State Examination

^*Calculated from fasting blood glucose values using a formula developed by Nathan et al.(16)

Klemera Doubal Method Biological Age

Participants in the diet and diet-exercise groups had significantly improved KDM biological age at 12 months compared to participants in the exercise and control groups (Table 2 and Figure 1A). The diet group had a decrease in biological age of 2.4 ± 0.4 yrs. which significantly differed from both exercise and control groups (P =0.003 and P <0.001, respectively). The diet-exercise group had a decrease in biological age of 2.2 ± 0.3 yrs. which significantly differed from both exercise and control groups (P =0.01 and P <0.001, respectively). The effect of both diet and diet-exercise in improving biological age was achieved by the 6-month mark, at which point the biological age in both groups significantly differed from the control group (P = 0.046 and P = 0.006, respectively). Both the control and exercise groups had no significant changes in biological age over the 12-month period. Changes in body mass correlated with changes in KDM biological age (R=0.35, P<0.001).

Table 2.

Effect of Diet, Exercise, or Both on Biological Age, Homeostatic Dysregulation, and Healthy Aging Index¹

	Control (n-27)	Exercise (n=26)	Diet (n=26)	Diet-Exercise (n=28)
Klemera Doubal biological age (yr)
Baseline	72.0 ± 1.0	72.6 ± 1.0	72.5 ± 0.8	72.6 ± 0.9
Change at 6 months	−0.9 ± 0.3	−0.4 ± 0.2§	−1.4 ± 0.2*§	−1.7 ± 0.3*§
Change at 1 year	0.2 ± 0.5	−0.7 ± 0.4	−2.4 ± 0.4*†§	−2.2 ± 0.3*†§
Homeostatic Dysregulation (score)
Baseline	10.2 ± 0.2	10.4 ± 0.3	10.6 ± 0.5	11.2 ± 0.5
Change at 6 months	0.2 ± 0.3	0.0 ± 0.3	−1.0 ± 0.2*†§	−0.9 ± 0.3*§
Change at 1 year	0.3 ± 0.3	0.1 ± 0.3	−1.0 ± 0.3*†§	−1.1 ± 0.3*†§
Healthy Aging Index (score)
Baseline	4.6 ± 0.3	5.0 ± 0.2	4.5 ± 0.2	4.7 ± 0.2
Change at 6 months	0.2 ± 0.2	−0.4 ± 0.2	−0.3 ± 0.2	−0.7 ± 0.2*§
Change at 1 year	0.0 ± 0.2	−0.5 ± 0.2	−0.5 ± 0.2	−1.1 ± 0.2*†‡§

¹Plus-minus values for the change scores are the least-squares adjusted means± SE from the repeated measures analyses of variance plus-minus values for the baseline values are the observed means ± SE. Decrease in biological age or scores indicates improvement.

^*P <0.05 for the comparison of the value from control group

^†P <0.05 for the comparison of the value from exercise group

^‡P < 0.05 for the comparison of the value from diet group

^§P <0.05 for the comparison of the value from baseline

Figure 1.

Mean changes in Klemera Doubal biological age (A), Homeostatic Dysregulation score (B), and Healthy Aging Index score (C) during the Interventions. In A, the changes in Klemara Doubal biological age in the Diet-exercise group and Diet group differed significantly from the changes in the Control group at 6 months and from the changes in the Control and Exercise groups at 12 months. In B, the changes in Homeostatic Dysregulation score in the Diet-exercise group and Diet group differed significantly from the changes in the Control group at 6 months and from the changes in the Control and Exercise groups at 12 months. In C, the changes in Healthy Aging Index score in the Diet-exercise group differed significantly from the changes in the Control, Diet, and Exercise groups at 12 months. Changes are presented as least-squares-adjusted means; T bars indicate standard errors.

Homeostatic Dysregulation

Participants in the diet and diet-exercise groups had significantly improved HD scores at 12 months compared to participants in the exercise and control groups (Table 2 and Fig 1B). The diet group had a decrease in HD score of 1.0 ± 0.3 which significantly differed from both the exercise and control groups (P =0.008 and P =0.03, respectively). The diet-exercise group had a decrease in HD score of 1.1 ± 0.3 which significantly differed from both the control and exercise groups (P =0.02 and P =0.049, respectively). The effect of both diet and diet-exercise in improving HD score was achieved by the 6-month mark, at which point the scores in both groups significantly differed from the control group (P =0.003 and P =0.007, respectively). Both the control and exercise groups had no significant changes in HD scores over the 12-month period. Changes in body mass correlated with changes in HD score (R=0.23, P=0.006).

Healthy Aging Index

Participants in the diet-exercise group had significantly improved HAI scores at 12 months compared to participants in the control, exercise, and diet groups (Table 2 and Figure 1C). The diet-exercise group had a decrease in HAI score of 1.1 ± 0.1 which significantly differed from the control, exercise, and diet groups (P <0.001, P =0.02, and P =0.03, respectively). The effect of diet-exercise in improving HAI score was achieved by the 6-month mark, at which point the score in the diet-exercise group significantly differed from the control group (P <0.001). Both the control and exercise groups had no significant changes in HD scores over the 12-month period. Changes in body mass correlated with changes in HAI score (R=0.27, P<0.001).

DISCUSSION

To our knowledge, this is the first study to analyze the effect of diet, exercise, and diet-exercise interventions on different indices of aging in older adults with obesity. Diet and diet-exercise interventions significantly improved KDM biological age and HD score compared to both exercise and control, suggesting that diet and diet-exercise interventions resulting in weight loss are effective measures of improving lifespan in older adults with obesity. Differences in KDM biological age can account for variability in both morbidity and mortality risk in older adults and KDM predicts mortality better than using biological age alone (8, 12). The HD score also significantly predicts mortality when controlling for chronological age (11). The results of our study are consistent with literature demonstrating the ability of calorie restriction to improve cardiovascular and metabolic risk factors in young, non-obese adults (20), and improve cellular function by reducing oxidative stress and inflammation in human and animal models (21, 22). Our results are also consistent with studies showing similar lifespan extensions in sedentary calorie-restricted rats and exercising calorie-restricted rats, suggesting that exercise does not interfere with the extension of lifespan by calorie restriction (23). On average, our participants’ biological ages (72.4 y) were older than their chronological ages (69.7 y) at baseline, which may be explained by the fact that participants in the NHANES database were recruited from the general population, while participants in our study were specifically persons who were obese and had a sedentary lifestyle. Diet and diet-exercise interventions demonstrated a decrease in biological age, indicating the possibility of not only preventing aging in this population, but also reversing it. In contrast, calorie restriction in young, healthy adults was shown to slow but not reverse biological age (10). However, the reduction in biological age of older adults shown in our study likely has a limit that will be reached as obese and sedentary adults approach a state of health similar to that of the general population. This reversal in biological age can also be seen in HD scores, with only diet and diet-exercise showing decreased scores compared to baseline. In both KDM biological age and HD, significant effects for both diet and diet-exercise compared to control were achieved by the 6-month mark, suggesting that six months of therapy may already be sufficient to see many of the benefits of these interventions. The 12-month diet and diet-exercise interventions brought in continued improvement in biological age compared to control and exercise alone, suggesting the importance of prolonged calorie restriction for improving lifespan.

Only diet-exercise demonstrated a significantly improved HAI at 12 months compared to all other groups, and no other groups were significantly different at 12 months, suggesting a combined intervention may be necessary to significantly increase healthy lifespan in older adults with obesity. Other studies have demonstrated the ability of weight loss in combination with both aerobic and resistance exercise to improve multiple measures of physical and cognitive function (3, 24), which is associated with improvements in morbidity, mortality, and disability (25). The HAI was developed to be a noninvasive version of the physiologic index of comorbidity and measure function across different organ systems. Several studies have demonstrated its ability to predict mortality as well as cardiovascular disease and disability (13, 26-28). None of our groups demonstrated deterioration in HAI scores during the study, which may be explained by a study that showed HAI scores plateau following age 60 yrs. among men who are considered “early agers” (29). Individuals who were considered “early agers” tended to be obese and sedentary, matching our study population.

This study demonstrates the ability of three measures of aging to reflect improvements in functional status secondary to geroprotective interventions in older individuals who are also obese. This expands on existing literature showing associations between advanced biological age and worse physical function, cognitive function, and mortality in older individuals (8, 11-13, 26, 30). Exercise, diet, and diet-exercise have already been shown to improve physical function while reducing self-reported disability and loss of independence, all of which are associated with improvements in morbidity and mortality (3, 24). This study therefore serves as a proof of concept that KDM biological age, HD score, and HAI score can be used as outcomes in studies of geroprotective interventions, with specific indices of aging detecting varying proportions of morbidity and mortality. For example, HAI was most closely aligned with our previous study showing maximal benefit of combined diet-exercise intervention in improving physical function (3), showing HAI’s closer association to functional status. Future studies need to be conducted in order to verify the ability of biological age to serve as an outcome for geroprotective therapies.

Our study was strengthened by its randomized and controlled design, as well as the high adherence to treatment over the course of a year. Weight loss was significant and similar in both diet and diet-exercise groups, and stable in exercise and control groups, which reassures us that the effects we see are truly secondary to calorie restriction-induced weight loss. Additionally, collecting data from the six-month time point allowed us to see some of the trajectory of the effect of the interventions, and assess that the full effect may not be quite yet achieved at that point. Limitations of the study include that our study recruited volunteers who had the time and capability to participate in the diet or exercise regimens, which restricts the generalizability of our findings and the ability to translate them into real-world interventions. While our results suggest that the longevity benefits of calorie restriction extend to the obese and older population, practically achieving this in the real world without close follow up with a dietitian and access to healthy food options remains challenging. Additionally, the follow-up period of one year was sufficient to detect a significant effect in our results, but a longer follow-up of multiple years might show when diminishing returns occur, as well as long term effects of diet and exercise. Our study was also limited in the biomarkers and data we had available, which limited us to specific methods of calculating biological age. The KDM and HD algorithms were dependent on calibration with specific data from NHANES. Although we utilized three different methods of quantifying aging that were available through NHANES and our data, there are many methods of calculating biological age that involve molecular and genomic measurements not available to us (4, 31). These measurements, such as telomere length, DNA methylation, and T-cell DNA rearrangement could complement traditionally used clinical biomarkers as part of calculating biological age. As more accurate measures of biological age are developed and methods of testing individuals becomes more advanced, the specific index of aging used to assess geroprotective therapies will need to be updated as well.

In conclusion, our findings suggest that diet and diet-exercise combination are able to improve biological age in older adults with obesity. Diet-exercise combination was the only intervention which significantly improved healthy aging index scores, reflecting the necessity of both diet and exercise in improving functional status. Our study paves the way for future studies examining geroprotective therapies in older adults by demonstrating the ability of aging indices to reflect these interventions.