Consideration of differential treatment

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Original Investigation | Nutrition, Obesity, and Exercise
Association of Sex or Race With the Effect of Weight Loss on Physical Function
A Secondary Analysis of 8 Randomized Clinical Trials
Kristen M. Beavers, PhD; Rebecca H. Neiberg, MS; Stephen B. Kritchevsky, PhD; Barbara J. Nicklas, PhD; Dalane W. Kitzman, MD; Stephen P. Messier, PhD;
W. Jack Rejeski, PhD; Jamy D. Ard, MD; Daniel P. Beavers, PhD
Abstract
IMPORTANCE Consideration of differential treatment effects among subgroups in clinical trial
research is a topic of increasing interest. This is an especially salient issue for weight loss trials.
OBJECTIVE To determine whether stratification by sex and race is associated with meaningful
differences in physical function response to weight loss among older adults.
DESIGN, SETTING, AND PARTICIPANTS This pooled analysis used individual-level data from 8
completed randomized clinical trials of weight loss conducted at Wake Forest University or Wake
Forest School of Medicine, Winston-Salem, North Carolina. Data were housed within the Wake Forest
Older Americans Independence Center data repository and provided complete exposure, outcome,
and covariate information. Data were collected from November 1996 to March 30, 2017, and
analyzed from August 15, 2019, to June 10, 2020.
EXPOSURES Treatment arms within each study were collapsed into caloric restriction (CR
[n = 734]) and non-CR (n = 583) categories based on whether caloric restriction was specified in the
original study protocol.
MAIN OUTCOMES AND MEASURES Objectively measured 6-month change in weight, fast-paced
gait speed (meters per second), and Short Physical Performance Battery (SPPB) score.
RESULTS A total of 1317 adults (mean [SD] age, 67.7 [5.4] years; 920 [69.9%] female; 275 [20.9%]
Black) with overweight or obesity (mean [SD] body mass index [calculated as weight in kilograms
divided by height in meters squared], 33.9 [4.4]) were included at baseline. Six-month weight change
achieved among those randomized to CR was -7.7% (95% CI, -8.3% to -7.2%), with no difference
noted by sex; however, White individuals lost more weight than Black individuals assigned to CR
(-9.0% [95% CI, -9.6% to -8.4%] vs -6.0% [95% CI, -6.9% to 5.2%]; P < .001), and all CR groups
lost a significantly greater amount from baseline compared with non-CR groups (Black participants in
CR vs non-CR groups, -5.3% [95% CI, -6.4% to -4.1%; P < .001]; White participants in CR vs non-CR
groups, -7.2% [95% CI, -7.8% to -6.6%; P < .001]). Women experienced greater weight loss–
associated improvement in SPPB score (CR group, 0.35 [95% CI, 0.18-0.52]; non-CR group, 0.08
[95% CI, -0.11 to 0.27]) compared with men (CR group, 0.23 [95% CI, 0.00-0.46]; non-CR group,
0.34 [95% CI, 0.09-0.58]; P = .03). Black participants experienced greater weight loss–associated
improvement in gait speed (CR group, 0.08 [95% CI, 0.05-0.10] m/s; non-CR group, 0.02 [95% CI,
-0.01 to 0.05] m/s) compared with White participants (CR group, 0.07 [95% CI, 0.06-0.09] m/s;
non-CR group, 0.06 [95% CI, 0.04-0.08] m/s; P = .02).
(continued)
Key Points
Question Is sex or race associated with
the physical function response to a
weight loss intervention among
older adults?
Findings In this pooled secondary
analysis of 1317 individuals participating
in 8 randomized clinical trials of weight
loss, including 397 (30.1%) men and 275
(20.9%) Black participants, greater
weight loss–associated improvement in
short physical performance battery
score was observed in women vs men,
and greater gait speed improvement in
Black vs White participants.
Meaning These findings suggest that
the benefits of weight loss on physical
function in older adults differ by sex and
race, underscoring the need to consider
relevant biological variables in clinical
research design.
+ Supplemental content
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Abstract (continued)
CONCLUSIONS AND RELEVANCE The association of weight loss on physical function in older adults
appears to differ by sex and race. These findings affirm the need to consider biological variables in
clinical trial design.
JAMA Network Open. 2020;3(8):e2014631. doi:10.1001/jamanetworkopen.2020.14631
Introduction
Consideration of relevant biological variables, such as sex and race, in clinical research is a topic of
increasing interest.1 Research funded by—and intended to benefit—the public should be all inclusive,
and implementation of effective evidence-based medicine depends on appropriate representation
of diverse groups in research studies.2 Disparities in health and longevity are well documented,3,4
and intervention efficacy can vary significantly by subgroup. For instance, results associated with
many of the compounds tested through the Interventions Testing Program (a National Institute on
Aging–sponsored study investigating treatments to extend lifespan in mice) demonstrate major
discrepancies by sex.5 Analyses from the cardiology literature also show discordant pharmacological
treatment effects in men vs women6 and Black vs White individuals.7 Although acceptance of federal
research policies designed to enhance diversity among clinical trial participants is increasing,
negative attitudes regarding practical implementation persist,8 with few randomized clinical trials
sufficiently designed to delineate treatment effects by sex and race subgroups, despite a growing call
to action.9
Appropriate representation of diverse populations in clinical trials is an especially salient issue
for weight loss interventions. Although data show that White men and women are equally affected
by obesity,10 men are historically underrepresented in weight loss trials.11 Similarly, non-Hispanic
Black individuals—for whom obesity is more prevalent compared with their White
counterparts10—typically constitute less than 20% of all study participants.12 Furthermore,
responsivity to weight loss interventions is variable, and data indicate that men and minorities
respond differently than non-Hispanic White women (who represent most weight loss study
participants). For example, men tend to lose more weight than women when given the same weight
loss intervention.11 Conversely, non-Hispanic Black participants typically lose 2 to 3 kg less than
non-Hispanic White participants in the same 6- to 12-month behavioral weight loss intervention; yet
interestingly, cardiometabolic risk factor modification appears similar across groups.13 This
observation implies that the magnitude of weight loss may interact with race/ethnicity to influence
health outcomes and raises the question whether weight loss recommendations should be subgroup
specific.
During the past decade, a number of weight loss trials were devoted to testing the effects of
diet and exercise on physical function among older adults with obesity.14 More than one-third of US
residents 65 years and older report some degree of physical disability,15 which can result in
dependency, institutionalization, and high rates of use of health care services.16 Although obesity has
a strong association with disability onset,17 Black individuals are more likely to have18 or develop19
physical impairment than White individuals for the same body mass index (BMI). Women also display
a greater susceptibility to obesity-associated disability than men20; however, older women preserve
physical capacity better than men over time,21 which may translate into differential patterns of
treatment response. As with the larger weight loss literature, underrepresentation of men and
minorities in aging research makes it challenging to establish whether physical function responses to
intentional weight loss are generalizable across all groups.
Data collected through the Wake Forest Older Americans Independence Center, WinstonSalem, North Carolina, provide a unique opportunity to address this important question. Individual
participant level data from 1317 middle-aged and older adults (397 male [30.1%] and 275 Black
[20.9%]) enrolled in 8 weight loss interventions who completed a standardized physical function
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assessment at baseline and 6-month follow-up provide an infrastructure to examine whether sex and
race are associated with the effect of weight loss on physical function. We hypothesize that
stratification by sex and race will reveal meaningful differences in aggregate treatment responses.
Methods
Study and Participants Descriptions
Individual participant data from 8 randomized clinical trials of weight loss conducted at Wake Forest
University or Wake Forest School of Medicine and housed within the Wake Forest Older Americans
Independence Center data repository were eligible for inclusion in the pooled analysis. All studies
assessed common measures of physical function before and 6 months after assignment to a caloric
restriction (CR) intervention or to a non-CR control condition, with or without exercise. The Wake
Forest Health Sciences institutional review board approved secondary analyses pertaining to the
pooled project. All participants provided written informed consent to participate in the 8 trials. We
followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting
guideline.
Data were acquired from November 1996 to March 30, 2017. Primary outcome studies from
included trials, including study design details, have been published.22-29 Table 1 provides a brief
description of each study (ordered by study acronym), including sample size, distribution by sex and
race, age, health status, intervention strategy (and associated sample size), and available physical
function data. Of the 1590 baseline visits across all studies, 1382 had a 6-month follow-up visit, and
1359 had 6-month weight change data. Of these participants, 42 were excluded from the primary
analysis owing to missing at least 1 covariate (race [n = 17], educational level [n = 8], diabetes status
[n = 14], hypertension status [n = 15], and cardiovascular disease [n = 6]; some individuals were
missing >1 covariate), yielding the final sample of 1317 participants.
Exposure Measures: CR and Weight Change Categories
For the primary analysis, arms within each study were collapsed into CR (n = 734) and non-CR
(n = 583) categories based on whether weight loss via CR was specified in the original study protocol.
Table 1. Descriptive Summary of Weight Loss RCTs Included in the Pooled Analysis
Source (trial name)
No. of
participants
Male,
No. (%)
Black,
No. (%)
Mean
age, y Health status Intervention (No. of participants)
No. with
complete
gait speed
No. with
complete
SPPB
Messier et al,22 2004 (ADAPT) 228 66 (28) 53 (23) 69 Overweight/obese;
OA
CR (n = 62); AE (n = 57); CR plus AE
(n = 53); control (n = 56)
200 0
Normandin et al, 201828 (APPLE)
(PI, Nicklas)
33 8 (24) 6 (18) 70 Obese; OA CR (n = 15) CR plus vest (n = 18)a 33 33
Rejeski et al,23 2011 (CLIP) 262 88 (34) 44 (17) 67 Overweight/obese;
CVD/METS
CR plus AE (n = 95); AE (n = 83); control
(n = 84)
257 256
Nicklas et al, 201525 (I’M FIT) 110 50 (45) 13 (12) 70 Overweight/obese;
at-risk for
disability
CR plus RE (n = 55); RE (n = 55) 109 110
Messier et al, 201324 (IDEA) 356 106 (30) 62 (17) 66 Overweight/obese;
OA
CR (n = 107); AE (n = 118); CR plus AE
(n = 131)
338 131
Nicklas et al,26 2019 (INFINITE) 154 39 (25) 36 (23) 69 Obese Low CR plus AE (n = 58); high CR plus AE
(n = 52) AE (n = 44)
140 153
Beavers et al,29 2019 (Medifast)b 81 22 (27) 20 (25) 70 Obese/at-risk for
disability
CR (n = 42); control (n = 39) 79 81
Kitzman et al,27 2016 (SECRET) 93 18 (19) 41 (44) 67 Overweight/obese;
HFPEF
CR (n = 24); AE (n = 24); CR plus AE
(n = 22); control (n = 23)
89 89
Abbreviations: ADAPT, Arthritis, Diet, and Activity Promotion Trial; AE, aerobic exercise;
APPLE, Arthritis Pilot for Preserving Muscle While Losing Weight; CLIP, Cooperative
Lifestyle Intervention Program; CR, caloric restriction; CVD/METS, cardiovascular disease
or metabolic syndrome; HFPEF, heart failure with preserved ejection fraction; I’M FIT,
Improving Muscle for Functional Independence Trial; IDEA, Intensive Diet and Exercise
for Improving Knee Osteoarthritis in Obese and Overweight Older Adults; INFINITE,
Investigating Fitness Interventions in the Elderly; OA, osteoarthritis; PI, principal
investigator; RCT, randomized clinical trial; RE, resistance exercise; SECRET, Study of the
Effect of Caloric Restriction and Exercise Training in Patients With Heart Failure and a
Normal Ejection Fraction; SPPB, Short Physical Performance Battery.
a Intervention included weighted vest use during activities of daily living.
b Effect of High Protein Weight Loss for Seniors study using the Medifast plan.
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Among 13 study-specific arms collapsed into the CR arm, 5 included participants randomized to CR
only (n = 250), and 8 included participants randomized to CR combined with exercise (n = 484).
Among 10 study-specific arms collapsed into the non-CR arm, 4 included participants randomized to
attention control (n = 202), and 6 included participants randomized to exercise only (n = 381).
Categorical amount of weight change from baseline to 6 months (weight gain/stability, <3% loss
[n = 596]; moderate weight loss, 3%-7% [n = 262]; and high weight loss, 7% [n = 459]) was used
as a secondary exposure variable among all participants.
Outcome Measures: Objectively Measured Physical Function
All physical function measures were assessed by trained and blinded assessors, using standardized
protocols at baseline and 6 months. All studies collected fast-paced gait speed, and 7 of the 8 studies
(excluding the Arthritis, Diet, and Activity Promotion Trial22) included the Short Physical
Performance Battery (SPPB). Time recorded from the 6-minute walk (685 [52.0%] of the study
sample) or fast-paced 400-m walk (632 [48.0%]) was used to derive fast-paced gait speed. During
the 6-minute walk test,30 participants were asked to walk as far as they could around a circular track
in 6 minutes. During the 400-m walk test,31 participants were asked to walk 10 laps of a 40-m course
and were given a maximum of 15 minutes to complete the test. The SPPB is a standardized measure
of physical performance that assesses standing balance, usual gait velocity for a 4-m course, and time
to sit down and rise from a chair 5 times as quickly as possible.32 Each task is scored on a scale of 0
to 4, with 0 indicating the inability to complete the task and 1 to 4 indicating the level of performance.
The total SPPB score ranges from 0 (lowest function) to 12 (highest function).
Covariate and Exploratory Measures
All studies captured self-reported demographic characteristics (age, sex, race, and educational level)
and presence of select comorbidities (diabetes, hypertension, or cardiovascular disease) via
questionnaire at baseline. Specifically for sex and race, participants selected options defined by the
investigator. For the present pooled analysis, sex was categorized as male or female, and the National
Institutes of Health race format was used to categorize individuals as Black or African American
(henceforth referred to as Black) or White. Standing height was measured using a clinical
stadiometer, and BMI was measured with a standard scale (with shoes and outer garments removed).
Body mass index was calculated as weight in kilograms divided by height in meters squared. Baseline
and follow-up whole-body fat and lean mass were also measured in 4 studies using dual-energy x-ray
absorptiometry (DXA) on the same machine (Hologic Discovery) and following a standardized
protocol.25-28
Statistical Analysis
Data were analyzed from August 15, 2019, to June 10, 2020. Baseline data were analyzed using
descriptive statistics, with means and SDs computed for continuous variables and counts and
proportions for discrete variables. The primary analytic model sample sizes differed by outcome
measure (percentage weight change [n = 1317], gait speed [n = 1245], and SPPB [n = 853]).
Six-month pooled treatment effects on weight, gait speed, and SPPB score by group were estimated
using general linear models adjusted for age, sex, race, study, educational level, BMI (gait speed and
SPPB models only), comorbid status, and baseline value of the outcome. Tests of heterogeneity of
change were first examined among CR or weight loss category, sex, and race as a 3-way interaction
term. Subsequently, heterogeneity between CR or weight loss category and sex or race were then
investigated through 2-way interaction terms. Nonsignificant interactions were dropped from the
models to generate final estimates. Sensitivity analyses examining the potential influence of exercise
were performed by: (1) testing 3-way interactions among CR or weight loss category, sex or race, and
a binary indicator for exercise assignment (if structured exercise was included in the original study
protocol); and (2) including the binary indicator for exercise assignment as a covariate in significant
2-way interaction term models. Last, exploratory analyses using change in total body fat and lean
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mass as outcome measures were conducted on the subset of participants with DXA data collected at
baseline and 6-month visits (n = 360). All analyses were generated using SAS software, version 9.4
(SAS Institute, Inc), using 2-sided hypothesis tests and assuming a type I error rate of 0.05 for all
comparisons. P < .05 indicated significance.
Results
Participant Characteristics
Table 2 presents baseline characteristics for the participants included in the pooled study sample, by
CR and weight change category. Overall, participants had a mean age of 67.7 (5.4) years and had class
I obesity (mean BMI, 33.9 [4.4]). A total of 920 participants (69.9%) were women and 397 (30.1%)
were men; 275 were Black (20.9%). No differences in any baseline characteristic were noted by CR
category, except SPPB score, which was higher in the CR group (10.5 [1.4] vs 10.3 [1.6]; P = .01).
Similar uniformity was noted when stratifying by weight change category, with only 2 significant
differences noted: those in the high weight loss category were more likely to be White (394 of 459
[85.8%] vs 65 of 459 [14.2%]; P < .001) and presented with a faster mean baseline gait speed (1.3
[0.2] vs 1.2 [0.2] m/s; P = .02). Participant characteristics, stratified by sex and race, are also
presented in eTables 1 and 2 in the Supplement. Baseline characteristics of the subset of participants
with DXA (n = 360) showed that compared with the sample without DXA, they were more likely to
be Black (88 of 360 [24.4%] vs 187 of 957 [19.5%]) and have diabetes (70 of 360 [19.4%] vs 118 of
957 [12.3%]) or hypertension (231 of 360 [64.2%] vs 537 of 957 [56.1%]).
Table 2. Demographic Characteristics by Caloric Restriction and Weight Change Category
Variable
Treatment groupa Weight change categorya,b
Non-CR
(n = 583)
CR
(n = 734)
Gain/stability
(n = 596)
Moderate loss
(n = 262)
High loss
(n = 459)
Age, mean (SD), y 67.6 (5.3) 67.7 (5.5) 67.6 (5.3) 67.7 (5.7) 67.7 (5.4)
Sex
Female 399 (68.4) 521 (71.0) 413 (69.3) 188 (71.8) 319 (69.5)
Male 184 (31.6) 213 (29.0) 183 (30.7) 74 (28.2) 140 (30.5)
Race
White 468 (80.3) 574 (78.2) 450 (75.5) 198 (75.6) 394 (85.8)
Black 115 (19.7) 160 (21.8) 146 (24.5) 64 (24.4) 65 (14.2)
Educational level
Primary/secondary
only
118 (20.2) 137 (18.7) 119 (20.0) 43 (16.4) 93 (20.3)
College graduate 342 (58.7) 438 (59.7) 366 (61.4) 155 (59.2) 259 (56.4)
Postcollege graduate 123 (21.1) 159 (21.7) 111 (18.6) 64 (24.4) 107 (23.3)
BMI, mean (SD) 33.8 (4.7) 34.0 (4.2) 33.8 (4.4) 33.9 (4.5) 34.1 (4.4)
Comorbidities
Diabetes 89 (15.3) 99 (13.5) 89 (14.9) 41 (15.6) 58 (12.6)
Hypertension 336 (57.6) 432 (58.9) 348 (58.4) 156 (59.5) 264 (57.5)
CVD history 204 (35.0) 244 (33.2) 223 (37.4) 84 (32.1) 141 (30.7)
Physical function
assessments
Fast gait speed, m/sc 1.23 (0.23) 1.24 (0.22) 1.22 (0.22) 1.23 (0.22) 1.26 (0.22)
SPPB (0-12 score)d 10.27 (1.59) 10.52 (1.39) 10.34 (1.51) 10.30 (1.60) 10.54 (1.40)
DXA body composition
measures, mean (SD)e
Total fat mass, kg 37.7 (8.7) 38.2 (8.6) 37.4 (8.4) 38.1 (8.5) 38.6 (9.0)
Total lean mass, kg 53.8 (11.3 52.8 (11.0) 53.7 (11.3) 52.2 (10.4) 53.3 (11.4)
Body fat, % 40.2 (7.2) 41.1 (7.0) 40.2 (7.1) 41.2 (6.7) 41.1 (7.2)
Abbreviations: BMI, body mass index (calculated as
weight in kilograms divided by square of height in
meters); CVD, cardiovascular disease; DXA, dual
energy x-ray absorptiometry; SPPB, short physical
performance battery.
a Unless otherwise indicated, data are expressed as
number (percentage) of participants.
b Weight gain/stability indicates less than 3% loss;
moderate weight loss, 3% to 7%; high weight loss, at
least 7%.
c Includes 1245 participants.
d Includes 853 participants. Scores range from 0 to 12,
with higher scores indicating highest function.
e Includes 360 participants.
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Overall Treatment Effects on Achieved Weight Loss and Physical Function
In pooled analyses, mean 6-month weight change among CR participants was -7.7% (95% CI, -8.3%
to -7.2%), whereas non-CR participants lost a more modest amount of weight (-1.0% [95% CI, -1.6%
to -0.3%]; P < .001). In general agreement with individual trial findings, pooled physical function
treatment effect estimates showed improvements in fast-paced gait speed (0.02 [95% CI, 0.01-
0.04] m/s) in the CR vs non-CR groups (P = .01). A marginal, albeit nonsignificant, improvement in
SPPB score (0.15 [(95% CI, -0.01 to 0.32]; P = .06) in the CR vs non-CR groups was also observed.
Association of Sex or Race With the Effect of Weight Loss on Physical Function
No difference in weight change was noted by sex; however, White individuals lost more weight than
Black individuals assigned to CR (-9.0% [95% CI, -9.6% to -8.4%] vs -6.0% [95% CI, -6.9% to
-5.2%]; P < .001), and all CR groups lost a significantly greater amount from baseline in comparison
with the non-CR groups (Black participants in CR vs non-CR groups, -5.3% [95% CI, -6.4% to -4.1%;
P < .001]; White participants in CR vs non-CR groups, -7.2% [95% CI, -7.8% to -6.6%; P < .001]). No
significant 3-way interactions were observed among CR or weight change category, sex, and race;
thus, 2-way interaction term models were pursued. Three significant (P < .05) 2-way interaction
terms were observed between sex or race and CR or weight change category, with stratified results
presented in the Figure.
A sex by CR category interaction was observed for SPPB (P = .03), with women assigned to CR
experiencing greater improvement in SPPB score (CR group, 0.35 [95% CI, 0.18-0.52]; non-CR group,
0.08 [95% CI, -0.11 to 0.27]) compared with men (CR group, 0.23 [95% CI, 0.00-0.46]; non-CR
group, 0.34 [95% CI, 0.09-0.58]). A race by CR category interaction was observed for gait speed
(P = .01), with Black participants assigned to CR experiencing greater improvement (CR group, 0.08
[95% CI, 0.05-0.10] m/s; non-CR group, 0.02 [95% CI, -0.01 to 0.05] m/s) compared with White
participants (CR group, 0.07 [95% CI, 0.06-0.09] m/s; non-CR group, 0.06 [95% CI, 0.04-0.08]
m/s). A race by weight change category interaction was also observed for gait speed (P = .006), with
greater weight loss associated with greater improvement in White participants (high weight loss,
0.09 [95% CI, 0.07-0.11] m/s compared with weight gain/stability, 0.05 [95% CI, 0.03-0.07] m/s),
although gains were most apparent in Black participants experiencing high weight loss (0.12 [95% CI,
0.08-0.16] m/s) compared with those experiencing weight gain/stability (0.01 [95% CI, -0.02 to
0.04] m/s). Sensitivity analysis revealed no significant 3-way interactions among treatment arm or
weight change category, sex or race, and exercise assignment; likewise, further adjustment for
exercise assignment did not significantly alter results (see eTable 3 in the Supplement).
Because body composition changes may underlie observed associations, exploratory models
were fit with change in fat mass and lean mass as the outcome among the subset of individual with
complete baseline and follow-up DXA data (n = 360). Across all CR groups, fat mass loss was similar.
Figure. Sex- and Race-Stratified Associations Between Caloric Restriction (CR) or Weight Change Categories and Physical Function
0.5
0.4
0.3
0.2
0.1

–0.1
0.10
0.08
0.06
0.04
0.09
0.07
0.05
0.03
0.02
0.01

0.16
0.14
0.10
0.06
0.12
0.08
0.04
0.02

–0.02
Change in SPPB score
Patient sex
A Interaction of sex and CR
Female Male
Change in gait speed, m/s
Patient race
B Interaction of race and CR
Black White
Change in gait speed, m/s
Patient race
C Interaction of race and weight change
Black White
Caloric restriction
No caloric restriction
Caloric restriction
No caloric restriction
Weight gain/stability
Moderate weight loss
High weight loss
Data are shown when the interaction term was P < .05. Model statements are adjusted for age, sex, or race, as appropriate, and study, educational level, body mass index, comorbid
status, and baseline value of the outcome. Error bars indicate 95% CI.
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Lean mass loss was similar in White and Black participants; however, women lost more lean mass
compared with men (-2.7 [95% CI, -2.2 to -3.2] vs -1.2 [95% CI, -0.3 to -2.0] kg).
Discussion
Results from this analysis suggest that the functional benefit of weight loss in older adults may differ
by sex and race. Specifically, weight loss–associated improvement in SPPB score was greater in
women than men, and the beneficial effect of weight loss on gait speed was greater in Black than
White participants and augmented with greater weight loss. Importantly, the degree of difference we
observed between subgroups aligns with clinically meaningful thresholds (ie, 0.30-point change in
SPPB33 and a 0.05-m/s change in gait speed34). This not only confers domain-specific pragmatic
information to the geriatrician recommending weight loss to patients but also underscores the need
to consider relevant biological variables—such as sex and race—in clinical research design.
A logical ensuing question from our results is, what is driving the differential treatment effects?
Although inferential capability of our data set is limited, we draw on the larger literature to aid in
interpretation. Observational data show women have greater susceptibility to obesity-associated
disability than men20; therefore, greater weight loss–associated improvements in SPPB score in
women could be expected, despite greater lean mass losses noted in our exploratory DXA analysis.
Indeed, trial data in older adults demonstrate that fat mass loss is a more significant covariate
associated with change in physical function than lean mass loss.35 In contrast, all men in the pooled
analysis—regardless of weight loss assignment—experienced modest improvement in SPPB score,
which suggests a different mechanism of action. Social engagement is an important determinant of
functional status in older adults,36 with men typically reporting less social connectedness than
women.37 Because included trials were behavioral based, opportunity for social engagement would
have increased for all participants and could underlie the universal improvement in SPPB score we
observed in men. Sex differences are also noted in the health benefits derived from exericse,38 with
data pointing to greater muscle strength and quality gains experienced by men compared with
women.39 Although we adjusted for exercise assignment, doing so affects the relative, not absolute,
treatment responses. Thus, the residual effect of exercise may explain the observed SPPB
improvement in men across CR categories.
Black participants are more likely to have18 or develop19 physical impairment than White
participants for the same BMI; thus, as with women, greater weight loss–associated gait speed
improvement in Black participants could be expected. Limited trial data examining the effects of
weight loss on physical function in Black and White participants suggest similar functional
improvement with similar weight loss40; however, the weight loss threshold beyond which
functional benefit is conferred may differ. In the present analysis, Black individuals experienced less
weight loss, yet greater improvement in gait speed compared with White individuals. Similarly, a
systematic review of National Institutes of Health–funded, multicenter, behavioral lifestyle
interventions also suggests the health effects of weight reduction are more profound for Black than
White individuals, with Blacks experiencing greater improvement in cardiometabolic risk factors per
unit of weight lost.13 Exploratory body composition results from the present analysis reveal similar
absolute lean mass loss for both racial groups, despite larger absolute weight loss achieved in White
vs Black participants, which may contribute to differential gait speed response. That said,
improvement in gait speed was noted in White individuals regardless of CR assignment category. As
with men and SPPB response, we speculate that this difference could be owing to social facilitation
and/or exercise, because these variables were present in all categories, and we implore the larger
research community to help explain the phenomenology presented in this report.
Strengths and Limitations
Strengths of this study include the unique ability to generate a large sample by pooling individuallevel data from randomized clinical trials with similar major design elements. In addition,
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standardized protocols were used to collect all physical function data (including training/certification
of functional assessors and use of standardized script language), with gait speed and SPPB well
represented across studies. Although some heterogeneity among the trials can be acknowledged as
a limitation, it also broadens the generalizability of our findings and protects against
overinterpretation of idiosyncratic results from any single study. Although weight loss is the central
process measure of interest, we are unable to adjust for other measures of compliance, such as
intervention attendance, which may also be important drivers of variability in treatment response.
Similarly, we did not fully explore the effect of exercise on change in absolute physical function,
although sensitivity analyses adjusting for exercise did not materially affect study findings. Finally,
while our findings are provocative, they are certainly not definitive, and replication is warranted.
Conclusions
This secondary analysis of 8 randomized clinical trials found that women and Black participants were
more likely to experience functional benefit from a weight loss intervention than men or White
participants. These findings affirm the need to consider relevant biological variables in clinical
research—with the important caveat that this burden should not fall solely on individual
investigators. Fundamentally, the problem is one of sample size and speaks to the need to have data
sharing mechanisms in place to pool studies of similar interventions, as well as a repository of
stratified results. Future work seeking to clarify the extent and correlates of interindividual variability
to treatment response in additional scientific domains has major implications for patients, clinicians,
and the larger scientific community.
ARTICLE INFORMATION
Accepted for Publication: June 12, 2020.
Published: August 21, 2020. doi:10.1001/jamanetworkopen.2020.14631
Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Beavers KM
et al. JAMA Network Open.
Corresponding Author: Kristen M. Beavers, PhD, Department of Health and Exercise Science, Wake Forest
University, PO Box 7868, Winston-Salem, NC 27106 (beaverkm@wfu.edu).
Author Affiliations: Department of Health and Exercise Science, Wake Forest University, Winston-Salem, North
Carolina (K. M. Beavers, Messier, Rejeski); Department of Department of Biostatistics and Data Science, Wake
Forest School of Medicine, Winston-Salem, North Carolina (Neiberg, D. P. Beavers); Sections of Gerontology and
Cardiovascular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem,
North Carolina (Kritchevsky, Nicklas, Kitzman); Department of Epidemiology and Prevention, Wake Forest School
of Medicine, Winston-Salem, North Carolina (Ard).
Author Contributions: Drs K. Beavers and D. Beavers had full access to all the data in the study and take
responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: K. Beavers, Kritchevsky, Nicklas, Kitzman, Rejeski, Ard, D. Beavers.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: K. Beavers, Neiberg, Ard, D. Beavers.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Neiberg, D. Beavers.
Obtained funding: K. Beavers, Kritchevsky, Nicklas, Kitzman, Messier, Rejeski, D. Beavers.
Administrative, technical, or material support: Neiberg, Nicklas, Messier, Ard, D. Beavers.
Supervision: K. Beavers, Nicklas, Messier.
Conflict of Interest Disclosures: None reported.
Funding/Support: This study was supported by grants P60 AG10484 (Dr Messier), R01 HL076441 (Dr Rejeski),
R01 AG020583 (Dr Nicklas), R01 AR052528 (Dr Kritchevsky), R01 HL093713 (Dr Nicklas), R01 AG018915 (Dr
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Kitzman) , P30 AG21332 (Dr Kritchevsky), and R21 AG061344 (Dr K. Beavers/Dr D. Beavers) from the National
Institutes of Health.
Role of the Funder/Sponsor: The sponsor had no role in the design and conduct of the study; collection,
management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and
decision to submit the manuscript for publication.
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SUPPLEMENT.
eTable 1. Baseline Demographic Characteristics by Sex
eTable 2. Baseline Demographic Characteristics by Race
eTable 3. Type III Tests of 3-Way Interactions for Sensitivity Analyses Involving Exercise
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