Abstract
Introduction/Purpose:
Lower cardiorespiratory fitness and obesity may accelerate aging processes. The degree to which changes in fitness and body mass index (BMI) may alter the rate of aging may be important for planning treatment. We assessed cross-sectional and longitudinal associations that cardiorespiratory fitness and BMI had with a deficit accumulation frailty index.
Methods:
Fitness, based on standardized graded exercise tests, and weight to calculate body mass index at baseline and year 4 were collected from 3,944 participants, ages 45–76, in the Action for Health in Diabetes (Look AHEAD) randomized controlled clinical trial. A validated 38-item deficit accumulation frailty index (FI) was used as a marker of aging. Associations between baseline and changes in fitness and BMI with changes in FI were assessed using linear models.
Results:
Both baseline and 4-year changes in fitness and BMI were independently associated with 4-year changes in frailty (all p<0.001). Mean [95% confidence interval] changes in FI ranged from −0.001 [−0.005,0.002] for participants in the group with the greatest fitness increase and BMI loss to 0.017 [0.013,0.020] for participants in the group with the greatest fitness loss and BMI gain. Associations of 4-year changes in fitness and BMI with FI changes were similar across subgroups based on age, gender, baseline BMI, diabetes duration, and cardiovascular disease history. Increased fitness across 4 years was associated with less FI accumulation independent of baseline fitness.
Conclusions:
Adults with type 2 diabetes and overweight or obesity may slow aging processes captured by a FI by increasing their cardiorespiratory fitness and losing weight.
Keywords: WEIGHT LOSS, BIOLOGICAL AGING, LIFESTYLE INTERVENTION, TYPE 2 DIABETES MELLITUS, EPIDEMIOLOGY
INTRODUCTION
Lower cardiorespiratory fitness and physical capacity accelerate aging (1). This can be seen with associations with increased mortality (2,3) and multimorbidity (4), shortened healthspan (1), and with many biomarkers related to accelerated aging (3,5). Deficit accumulation frailty indices (FIs), which combine markers of age-related deficits in clinical characteristics, disease states, behaviors, and function, are increasingly used as markers of aging (6,7). Increases in FI scores are associated with subsequent increases in mortality and poorer trajectories of cognitive and physical function (8). While these indices are known to have cross-sectional and longitudinal associations with cardiorespiratory fitness (9,10), it is unknown whether individuals who improve their cardiorespiratory fitness through lifestyle changes may thereby slow aging processes captured by FIs.
Increases in fitness through changes in lifestyle are often accompanied by weight loss, and intentional weight loss and caloric restriction may slow aging processes (11–13) and the progression of FI (14). The degree to which increases in fitness additionally slow FI progression separately from weight loss is unknown.
We make use of data from the Action for Health in Diabetes randomized controlled clinical trial (15). Its participants had established type 2 diabetes and overweight or obesity, which placed them at increased risk for accelerated aging. Half were randomly assigned to an intensive lifestyle intervention (ILI) that was successful in inducing body mass index (BMI) loss and increased cardiorespiratory fitness compared with an intervention featuring diabetes support and education (DSE) (16). Our primary goal was to assess whether relative increases in fitness and BMI losses over four years independently or synergistically slowed progression of FI. We also examined the consistency of our findings across important clinical subgroups.
METHODS
The Look AHEAD protocol and CONSORT diagram have been published (15,17). Look AHEAD was a multi-site, single-masked randomized controlled clinical trial that recruited 5,145 individuals (during 2001 to 2004) from 16 U.S. centers. All had type 2 diabetes and met the following criteria: 45–76 years of age, BMI >25 kg/m2 (>27 kg/m2 if on insulin), glycated hemoglobin (HbA1c) <97mmol/mol (11%), systolic/diastolic blood pressure <160/<100 mmHg, triglycerides <600 mg/dl, a successful maximum graded exercise test. Protocols and consent forms were approved by local Institutional Review Boards and written informed consent was obtained from all participants.
The characteristics of the cohort at baseline have been published (18). Briefly, at enrollment the cohort was 60% women, with mean (SD) BMI 36.0 (5.9) kg/m2, age 58.7 (6.8) years, diabetes duration 6.8 (6.5) years, and HbA1c 7.3 (1.2)%.
Interventions
Participants were randomly assigned to ILI or DSE. The ILI targeted reducing caloric intake and increasing physical activity to induce weight loss >7% and maintaining this over time (19). Caloric consumption goals of 1200–1800 kilocalories/day were based on initial weight. Physical activity of >175 minutes/week through activities similar in intensity to brisk walking was targeted as was improved diet (<30% calories from fat, <10% calories from saturated fat, >15% calories from protein). Cardiometabolic risk factors (lipids, HbA1c, blood pressure) were monitored and participants were provided with results. During the first six months, ILI participants attended three group meetings and one individual session per month. For the remainder of the first year, they were provided with two group and one individual meeting per month. The intensity of the intervention gradually decreased thereafter.
DSE participants were invited to attend group sessions focused on diet, physical activity, and social support (20). Four meetings were offered during year 1, three per year during years 2–4, and one annually thereafter. Participants did not receive specific diet, activity, or weight goals or information on behavioral strategies, however the protocol for sharing risk factor information with participants and their physicians was the same as for ILI.
Cardiorespiratory fitness and BMI
A graded exercise treadmill test was used to assess cardiorespiratory fitness at baseline and years 1, 2 (25% subset) and 4 (16,21). In this report, we only use data from baseline and year 4 assessments. Cardiorespiratory fitness was defined as the estimated metabolic equivalent (MET) level based on the treadmill workload (speed and grade) using the criteria of attaining 80% of maximal heart rate for participants not taking a β-blocker or the criteria of attaining a rating of 16 on the rating of perceived exertion (RPE) scale. Change in cardiorespiratory fitness was defined as the difference in estimated submaximal METs attained at year 4 and the submaximal METs attained at baseline using the same termination criteria of attaining either 80% of maximal heart rate or attaining a rating of 16 on the RPE scale.
Assessment procedures involved setting the speed of the treadmill at 1.5, 2.0, 2.5, 3.0, 3.5 or 4.0 m.p.h. for the baseline test based on preferred speed of the participant and heart rate response during the first minute of the test, and this speed remained constant throughout the test. The grade of the treadmill was initially set at 0% and increased by 1% at 1-minute intervals throughout the test. Heart rate was assessed at rest, during the last 10seconds of each exercise stage, and at the point of test termination using a 12-lead electrocardiogram (ECG). RPE was assessed using the Borg 15-category scale (range is from 6 to 20) during the last 15seconds of each stage and at the point of test termination. Blood pressure was assessed using a manual sphygmomanometer and stethoscope during the last 45seconds of each even-minute stage (e.g., 2 and 4minutes).
The baseline test was terminated at the point of volitional exhaustion or at the point when American College of Sports Medicine test termination criteria were observed. A baseline test was considered valid if the maximal heart rate was ≥85% of age-predicted maximal heart rate (HRMax=220−age) if the participant was not taking a β-adrenergic blocking medication (β-blocker). If the participant was taking a β-blocking medication, the baseline test was considered valid if RPE was ≥18 at the point of termination. To be eligible for participation in Look AHEAD, participants needed to achieve ≥4 METs on the baseline graded exercise test, where one MET is equal to 3.5mlkg−1 per min of oxygen uptake.
The test at year 4 to assess cardiorespiratory fitness was a submaximal test, performed at the same walking speed as the baseline assessment. This submaximal test was terminated when the participant first achieved or exceeded 80% of age-predicted maximal heart rate (HRMax=220−age) if the participant was not taking a β-blocker at either the baseline or year 4 assessment period. If the participant was taking a β-blocker at either the baseline or year 4 assessment the submaximal test was terminated at the point when the participant first reported achieving or exceeding a rating of 16 on the RPE scale.
Weight was measured at baseline and year 4 using a digital scale by masked staff. Height was measured at baseline using a standard stadiometer and used to calculate BMI at both baseline and year 4 so that percent change in BMI was equal to percent change in weight.
Deficit accumulation frailty index
As we noted in the introduction, FIs have become widely used measures of health status and aging. While these indices vary depending on data sources, a standard algorithm has been adopted for creating FI, which the score is a fraction of 30–40 evaluable health-related deficits that are present of the total evaluated, ranging from a possible score of 0 to 1 (22). In practice, scores above 0.40 are fairly rare and identify individuals with very poor health prognoses. While increases in FI over time are correlated with increases in calendar age, FIs are designed to align more with biologic aging than calendar age (23).
We previously constructed a FI with 38 components based on annual medical histories, clinic-based assessments, behaviors, functions, and abilities (11,24). This FI has been validated in the Look AHEAD cohort: changes in the FI are strongly predictive of subsequent trajectories of cognitive and physical function and mortality (8).
Statistical analysis
Our analyses are drawn from 3,944 (77%) of the 5,145 Look AHEAD participants who had graded exercise tests and FI scores at baseline and year 4. Compared with the 1,201 not included in the analyses, this subset of participants comprising our analysis dataset tended to be younger, less heavy, and have no history of cardiovascular disease (all p<0.001; see Supplemental Table 1, Supplemental Digital Content, Comparison of baseline characteristics of Look AHEAD participants included and not included in our analytic database). There was a modest imbalance between intervention groups: 51% of those included had been assigned to ILI compared with 46% of those not included (p=0.006). At baseline, those included had mean (SE) FI 0.201 (0.001) and those not included had mean FI 0.222 (0.002), p<0.001.
We grouped baseline fitness and 4-year changes in fitness according to tertiles. For baseline fitness, the tertile ranges were: [3.3–6.1] METs Tertile 1 (Least Fit), [6.2–7.8] METs Tertile 2 (Moderate Fit), and [7.9–16.7] METs Tertile 3 (Most Fit). For 4-year percent changes in fitness, these tertiles’s ranges were ≤ −10.0% Tertile 1 (Fitness Decline), −10.0% to 8.4% Tertile 2 (Fitness Stable), and ≥8.4% Tertile 3 (Fitness Increase). We grouped baseline BMI as 25–29 kg/m2; 30–39 kg/m2, and ≥40 kg/m2 as has commonly been done in other Look AHEAD publications. We defined 4-year changes in BMI within ±2.5% as stable. These correspond to changes of about ±2.5 kgs, which others have used to define stable weight (25,26). We labeled decreases in BMI >2.5% as loss and increases in BMI >2.5% as gain. Chi-squared and t-tests were used to compare these groups with respect to baseline characteristics, intervention assignment, and baseline FI scores.
Associations that baseline levels of fitness and BMI had with 4-year changes in FI were assessed using analyses of covariance, first with adjustment for age and intervention assignment, and then with additional adjustment for baseline fitness or BMI. Associations that 4-year changes in fitness and BMI had with changes in FI were assessed similarly. Beta-blocker use necessitated using participant’s rating of perceived exertion rather than the age-predicted maximal heart rate to define the threshold used for the submaximal exercise testing (16,27). To assess whether this influenced our findings, we repeated these analyses after removing all individuals taking beta-blockers at baseline and year 4 from the datasets. The consistency of relationships among subgroups based on age, gender, diabetes duration, history of cardiovascular disease and intervention assignment were assessed by including interaction terms in models. The consistency of associations between 4-year changes in fitness and 4-year changes in FI among subgroups based on baseline fitness levels was also assessed using interaction terms.
RESULTS
As seen in Table 1, baseline fitness tended to be greater among participants who were relatively younger, male, who had lower BMI, who had shorter durations of diabetes, who did not have a history of cardiovascular disease, and who had lower FI scores. At baseline, those with the greatest level of obesity tended to be younger, female, and less fit. They also had higher mean FI scores. As expected, there was little difference in baseline fitness and BMI levels between intervention groups due to randomization.
Table 1:
Baseline characteristics at Look AHEAD enrollment by fitness groups: N (percent) or mean (standard deviation).
| Baseline characteristic | Baseline Fitness (METs) | Baseline Body Mass Index (kg/m2) | ||||||
|---|---|---|---|---|---|---|---|---|
| 1st Tertile [3.3–6.1] N=1181 |
2nd Tertile [6.2–7.8] N=1400 |
3rd Tertile [7.9–16.7] N=1363 |
p-value* | 25–29 N=622 |
30–39 N=2497 |
40+ N=825 |
p-value | |
| Age, years | ||||||||
| Gender | ||||||||
| BMI, kg/m2 | ||||||||
| Fitness, METS | NA | NA | NA | NA | ||||
| Diabetes duration, yrs | ||||||||
| History of CVD** | ||||||||
| Intervention group | ||||||||
| Frailty Index | 0.22 (0.002) | 0.20 (0.002) | 0.18 (0.002) | <0.001 | 0.17 (0.06) | 0.20 (0.07) | 0.22 (0.07) | <0.001 |
Chi-squared test or analysis of variance
History of cardiovascular disease: self-report of prior myocardial infarction, coronary artery bypass, angioplasty/stent procedures, peripheral vascular disease, stroke, stable angina, or class I/II heart failure
Four-year increases in fitness tended to occur more often among participants who were younger and had higher BMI, lower fitness, and no history of cardiovascular disease at baseline (Table 2). Women tended to be more likely to have stable fitness than men. Random assignment to the ILI was associated with less decline in fitness. Four-year BMI gains were more common among younger individuals, those with longer durations of diabetes, and those assigned to DSE.
Table 2:
Differences in 4-year changes in fitness and body mass index among participants grouped by baseline characteristics.
| Baseline characteristic | 4-year Change in Fitness | 4-year Change in BMI | ||||||
|---|---|---|---|---|---|---|---|---|
| Lowest Tertile N=1317 |
Midle Tertile N=1322 |
Highest Tertile N=1305 |
p-value | Loss >2.5% N=1903 |
Stable N=1147 |
Gain > 2.5% N=894 |
p-value | |
| Age, years | ||||||||
| Gender | ||||||||
| BMI, kg/m2 | ||||||||
| Fitness, METS | ||||||||
| Diabetes duration, years | ||||||||
| History of CVD | ||||||||
| Intervention group | ||||||||
| Frailty Index | 0.202 (0.069) | 0.204 (0.069) | 0.197 (0.064) | 0.03 | 0.201 (0.07) | 0.197 (0.07) | 0.205 (0.07) | 0.04 |
Table 3 describes associations that baseline fitness and BMI had with 4-year changes in FI. With adjustment for baseline age and intervention assignment, baseline BMI and fitness levels were each associated with FI changes (p<0.001). Compared with those in the highest tertile of baseline fitness, those in the lowest tertile had nearly three times greater 4-year mean progression in FI. Similarly, compared with those with overweight (BMI 25–29 kg/m2) at baseline, those with Class 3 obesity (BMI≥40 kg/m2) had over twice the mean worsening of FI. Adjustment for baseline BMI attenuated the association between baseline fitness and 4-year FI changes, but it still remained significant (p<0.001). Similarly, adjustment for baseline fitness modestly attenuated the relationship between baseline BMI and 4-year FI changes.
Table 3:
Relationship of baseline fitness and body mass index with 4-year changes in frailty index scores.
| Baseline Fitness or BMI | 4-Year Change in FI With Adjustment for Baseline Age and Intervention Assignment |
4-Year Change in FI With Adjustment for Baseline Age, Intervention Assignment, and Either BMI or Fitness |
||
|---|---|---|---|---|
| Mean | 95% Confidence Interval | Mean | 95% Confidence Interval | |
| Fitness, METS | ||||
| BMI, kg/m2 | ||||
Four-year changes in fitness and BMI were correlated in both intervention groups (r= −0.26 for DSE and r= −0.32 for ILI, both p<0.001). Table 4 describes associations that 4-year percent changes in fitness and BMI had with changes in FI. There was a strong graded inverse association between change in fitness and change in FI scores (p<0.001), which was essentially unchanged with adjustment for changes in BMI. FI scores were essentially stable over 4 years among participants whose fitness increased. Four-year changes in BMI had a strong direct association with changes in FI, which was independent of changes in fitness. FI was also essentially stable over 4 years among individuals whose BMI decreased >2.5%.
Table 4:
Relationship of 4-year changes in fitness and body mass index with 4-year changes in frailty – with adjustment for baseline age and intervention assignment.
| Change in fitness and BMI |
4-Year Change in FI With Adjustment for Baseline Age and Intervention Assignment |
4-Year Change in FI With Adjustment for Baseline Age, Intervention Assignment, and Either BMI or Fitness |
||
|---|---|---|---|---|
| Mean | 95% Confidence Interval | Mean | 95% Confidence Interval | |
| 4-year change in fitness [METS] | ||||
| 4-year change in BMI | ||||
We repeated analyses underlying Tables 3 and 4, omitting the 29.6% of DSE participants and 29.1% of ILI participants (p=0.70) who were recorded as taking beta-blockers at baseline and/or year 4. As seen in Supplemental Tables 2 and 3 (Supplemental Digital Content, Relationship of baseline and 4-year-change fitness and body mass index), while table entries varied and some associations were attenuated, all remained statistically significant in this subset of participants who were not using beta-blockers.
We examined whether there was an interaction between changes in fitness and BMI with respect to changes in FI. As seen in Figure 1, there was no evidence for an interaction (p=0.30). FI increased more slowly among individuals with better profiles of fitness, irrespective of the benefits associated with BMI loss. Similarly, FI increased more slowly among individuals who had BMI loss, irrespective of changes in fitness. Mean [95% confidence interval] changes in FI ranged from −0.019 [−0.024 to −0.013] for participants in the group with the highest fitness increase and BMI loss to 0.029 [0.024 to 0.034] for participants in the group with the greatest fitness loss and BMI gain.
Figure 1.
Mean 4-year change in deficit accumulation frailty by change in fitness and body mass index
There was no evidence that the associations between changes in fitness and FI varied among subgroups at baseline based on gender, cardiovascular disease history, age, fitness, BMI, and intervention assignment (all interaction terms p>0.05, Table 5). As seen in Table 6, associations between changes in BMI and FI did not vary between these subgroups (p>0.05), with one exception: the association between BMI loss and FI changes appeared to be steeper among ILI compared with DSE participants (p=0.02).
Table 5:
Consistency of association that 4-year changes in fitness have with 4-year changes in FI across subgroups defined by characteristics at baseline: mean (SE) and interaction p-value.
| Subgroup | Tertile 4-Year Changes in Fitness | Interaction p-value | ||
|---|---|---|---|---|
| Decline Lowest Tertile < −10% N=1311 |
Stable Mid Tertile −10% to 8% N=1309 |
Increase Highest Tertile >8% N=1296 |
||
| Age | ||||
| 45–54 | 0.017 (0.004) | 0.005 (0.003) | −0.001 (0.003) | 0.94 |
| 55–64 | 0.016 (0.002) | 0.005 (0.002) | −0.002 (0.002) | |
| 65–76 | 0.019 (0.004) | 0.012 (0.004) | 0.001 (0.004) | |
| Gender | ||||
| Female | 0.017 (0.002) | 0.008 (0.007) | −0.000 (0.002) | 0.73 |
| Male | 0.017 (0.003) | 0.004 (0.003) | −0.002 (0.003) | |
| BMI, kg/m2 | ||||
| 25–29 | 0.015 (0.004) | 0.004 (0.004) | −0.001 (0.004) | 0.19 |
| 30–39 | 0.013 (0.002) | 0.004 (0.002) | −0.002 (0.002) | |
| ≥40 | 0.032 (0.004) | 0.014 (0.004) | 0.002 (0.004) | |
| Diabetes duration | ||||
| < 5 years | 0.014 (0.003) | 0.005 (0.003) | −0.000 (0.003) | 0.35 |
| ≥5 years | 0.019 (0.002) | 0.008 (0.002) | −0.002 (0.002) | |
| CVD History | ||||
| No | 0.016 (0.002) | 0.007 (0.002) | −0.002 (0.002) | 0.25 |
| Yes | 0.021 (0.005) | 0.001 (0.005) | 0.004 (0.006) | |
| Intervention | ||||
| DSE | 0.024 (0.002) | 0.011 (0.002) | 0.008 (0.003) | 0.17 |
| ILI | 0.010 (0.003) | 0.002 (0.002) | −0.010 (0.002) | 0.17 |
Table 6.
Consistency of association that 4-year changes in body mass index have with 4-year changes in FI across subgroups defined by characteristics at baseline: mean (SE) and interaction p-value.
| Subgroup | Tertile 4-Year Changes in Body Mass Index | Interaction p-value | ||
|---|---|---|---|---|
| Loss >2.5% Loss |
Stable ±2.5% Change |
Gain >2.5% Gain |
||
| Age | ||||
| Gender | ||||
| BMI | ||||
| Diabetes duration | ||||
| CVD History | ||||
| Intervention | ||||
With adjustment for baseline BMI and 4-year changes in BMI, improvements in cardiorespiratory fitness slowed FI progression similarly across all baseline levels of fitness (p=0.89), as seen in Figure 2.
Figure 2.
Mean four year changes in frailty for participants grouped by baseline fitness tertile and four year change in fitness tertile, with covariate adjustment for baseline body mass index and four year change in body mass index.
DISCUSSION
At baseline, both cardiorespiratory fitness and BMI were strongly correlated in the Look AHEAD cohort (28) and poorer levels of both were associated with elevated FI at baseline and worsening in FI over time. These associations were to be expected based on prior reports, as noted in the introduction (9,10,14). The finding that changes in BMI and fitness were both independently associated with changes in FI over 4-years is more novel: few studies have examined these associations over time. There are important clinical implications of this finding because it suggests that individuals who increase their fitness or decrease their BMI can slow the progression of FI. Because the benefits appear to be additive (given the lack of a significant interaction), the greatest benefit would be expected in those who have positive changes in both fitness and BMI. It is noteworthy that increases in fitness appeared to benefit FI for individuals irrespective of their level of fitness at baseline and even after statistical adjustment for both baseline and 4-year change in BMI. It follows that prevention of either condition may contribute to slowing aging processes, however prevention of either low fitness or obesity does not make up for the deficits associated with the other condition, as is similar to what has been reported about all-cause mortality (29).
There is considerable evidence linking lower cardiorespiratory fitness to accelerated aging. For example, Kokkinos, et al. report that among U.S. veterans aged 30–95 years, being in the lowest quintile of fitness based on a standardized treadmill test was associated with a hazard ratio of 4.09 [95% CI 3.90, 4.20] for mortality across 10.2 years of follow-up compared with being in the highest quintile (3). Poorer fitness based on exercise tests is associated with increased vascular aging (30), greater levels of multimorbidity (4), poorer profiles of brain structure and function (31–33), and poorer biomarkers of aging (5). More generally, markers related to greater physical capacity are associated with many biomarkers of aging (1). Separately, there is a vast literature establishing that obesity is related to accelerated aging (34,35). The Look AHEAD study has contributed to this. Within this cohort, participants with obesity at baseline had greater increases in FI scores over time (8,14).
Previous research suggests that weight loss without increased physical activity may lead to losses in cardiorespiratory fitness relative to weight loss with increased physical activity (36). However, we found that weight loss and cardiorespiratory fitness in Look AHEAD were correlated in both intervention groups, as has been reported earlier (21), suggesting that individuals in both intervention groups were following recommendations to maintain adequate levels of physical activity. Despite this correlation, both increases in fitness and BMI loss over four years were independently associated with slower increases in FI, even after covariate adjustment for intervention assignment and age. Both appear to be important strategies for slowing the accumulation of health-related deficits. The benefits of BMI loss on FI may be larger than those for increased fitness, as seen in Figure 1 and Table 4, however benefits for slowing the progression of FI associated with increased fitness were similar among those with BMI losses and BMI gains (based on the non-significant interactions). Across the full cohort, FI increased at a mean of about 0.01 units per year (37). Thus, the 4-year differences seen in Figure 1, which range from about −0.02 to 0.03 units, may translate to 5-year differences in “usual” aging in the Look AHEAD cohort.
Weight loss in older individuals can be a marker of impending age-related chronic diseases (38,39). While we found benefit for BMI loss in slowing increases in FI scores, this may relate to the cohort’s age range (45–76 years) and the intentionality of weight loss advocated within both intervention groups. Importantly, the benefits of both increases in cardiorespiratory fitness and BMI loss on FI were statistically similar across the subgroups based on age, gender, baseline BMI, diabetes duration, and history of cardiovascular disease. Even among individuals in the highest age range (65–76 years) of the Look AHEAD cohort, benefits of both BMI decreases and cardiorespiratory fitness gains were evident. We have previously reported that these older participants assigned to ILI achieved weight losses and increases in fitness that were at least as large as those achieved by younger participants (40).
Limitations
Our study benefited from the size of the cohort and standardized assessments in the Look AHEAD study. We acknowledge several limitations. As volunteers who met eligibility criteria for a clinical trial of behavioral weight loss, the findings may not generalize to other cohorts and extend to individuals without type 2 diabetes and/or overweight or obesity. The analyses we report were not pre-specified in the study protocol and thus should be viewed as exploratory. The study utilized submaximal fitness testing to assess cardiorespiratory fitness which is less accurate than maximal graded fitness testing but considered safer, particularly in participants with musculoskeletal impairments, cardiovascular risk factors and older adults (41). Further, the current study assessed within subject change in cardiorespiratory fitness which can be adequately quantified despite the limitations of the submaximal approach. The FI index that we use, while validated in the Look AHEAD cohort, is not replicated elsewhere. We describe associations that changes in fitness and BMI have with changes in FI but cannot rule out the possibility of reverse causality.
CONCLUSIONS
Among adults with type 2 diabetes and overweight or obesity, losing weight and increasing aerobic fitness may slow the rate of aging as captured by a FI.
Supplementary Material
Funding
The Action for Health in Diabetes is supported through the following cooperative agreements from the National Institutes of Health: DK57136, DK57149, DK56990, DK57177, DK57171, DK57151, DK57182, DK57131, DK57002, DK57078, DK57154, DK57178, DK57219, DK57008, DK57135, and DK56992. Additional support was provided by AG058571 and AG073697. JR was funded through a diversity supplement to AG073697.
The following federal agencies contributed support to Look AHEAD: National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; National Institute of Nursing Research; National Center on Minority Health and Health Disparities; Office of Research on Women’s Health; the Centers for Disease Control and Prevention; and the Department of Veterans Affairs. This research was supported in part by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases. The Indian Health Service (I.H.S.) provided personnel, medical oversight, and use of facilities. The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the I.H.S. or other funding sources.
Additional support was received from the University of Pittsburgh General Clinical Research Center (GCRC) (M01RR000056), the Clinical Translational Research Center (CTRC) funded by the Clinical & Translational Science Award (UL1 RR 024153) and NIH grant (DK 046204); Frederic C. Bartter General Clinical Research Center (M01RR01346); and the Wake Forest Alzheimer’s Disease Core Center (P30AG049638–01A1).
The following organizations have committed to make major contributions to Look AHEAD: FedEx Corporation; Health Management Resources; LifeScan, Inc., a Johnson & Johnson Company; OPTIFAST® of Nestle HealthCare Nutrition, Inc.; Hoffmann-La Roche Inc.; Abbott Nutrition; and Slim-Fast Brand of Unilever North America.
Clinicaltrials.gov Identifier: NCT00017953
Conflict of Interest
The authors have no conflicts of interest to declare beyond the funding sources listed above. The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.The results of the present study do not constitute endorsement by the American College of Sports Medicine.
Footnotes
SUPPLEMENTAL DIGITAL CONTENT
SDC 1: Supplemental Digital Content.docx
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