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Trying to Lose Weight, Losing Weight, and 9-Year Mortality in Overweight U.S. Adults With Diabetes

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Abstract

The aim of this study was to examine the relationships between intention to lose weight, actual weight loss, and all-cause mortality among overweight individuals with diabetes. We performed a prospective analysis among 1,401 overweight diabetic adults aged > or =35 years sampled in the National Health Interview Survey. The previous year intention to lose weight and weight change were assessed by self-report. Nine-year mortality rates were examined according to intent to lose weight and weight loss, which were adjusted for age, sex, education, ethnicity, smoking, initial body weight, and diabetes complications. Individuals trying to lose weight had a 23% lower mortality rate (hazard rate ratio [HRR] 0.77, 95% CI 0.61-0.99) than those who reported not trying to lose weight. This association was as strong for those who failed to lose weight (0.72, 0.55-0.96) as for those who succeeded in losing weight (0.83, 0.63-1.08). Trying to lose weight was beneficial for overweight (BMI 25-30 kg/m2) individuals (0.62, 0.46-0.83) but not for obese (BMI>30) individuals (1.17, 0.72-1.92). Overall weight loss, without regard to intent, was associated with an increase of 22% (1.22, 0.99-1.50) in the mortality rate. This increase was largely explained by unintentional weight loss, which was associated with a 58% (1.58, 1.08-2.31) higher mortality rate. Overweight diabetic adults trying to lose weight have a reduced risk of all-cause mortality, independent of whether they lose weight. Actual weight loss is associated with increased mortality only if the weight loss is unintentional.
Trying to Lose Weight, Losing Weight,
and 9-Year Mortality in Overweight U.S.
Adults With Diabetes
EDWARD W. GREGG,
PHD
ROBERT B. GERZOFF,
MS
THEODORE J. THOMPSON,
MS
DAVID F. WILLIAMSON,
PHD
OBJECTIVE The aim of this study was to examine the relationships between intention to
lose weight, actual weight loss, and all-cause mortality among overweight individuals with
diabetes.
RESEARCH DESIGN AND METHODS We performed a prospective analysis among
1,401 overweight diabetic adults aged 35 years sampled in the National Health Interview
Survey. The previous year intention to lose weight and weight change were assessed by self-
report. Nine-year mortality rates were examined according to intent to lose weight and weight
loss, which were adjusted for age, sex, education, ethnicity, smoking, initial body weight, and
diabetes complications.
RESULTS Individuals trying to lose weight had a 23% lower mortality rate (hazard rate
ratio [HRR] 0.77, 95% CI 0.61–0.99) than those who reported not trying to lose weight. This
association was as strong for those who failed to lose weight (0.72, 0.55–0.96) as for those who
succeeded in losing weight (0.83, 0.63–1.08). Trying to lose weight was beneficial for overweight
(BMI 25–30 kg/m
2
) individuals (0.62, 0.460.83) but not for obese (BMI 30) individuals
(1.17, 0.72–1.92). Overall weight loss, without regard to intent, was associated with an increase
of 22% (1.22, 0.99–1.50) in the mortality rate. This increase was largely explained by uninten-
tional weight loss, which was associated with a 58% (1.58, 1.08–2.31) higher mortality rate.
CONCLUSIONS Overweight diabetic adults trying to lose weight have a reduced risk of
all-cause mortality, independent of whether they lose weight. Actual weight loss is associated
with increased mortality only if the weight loss is unintentional.
Diabetes Care 27:657– 662, 2004
W
eight loss is considered a key
strategy to manage people with
type 2 diabetes because even
modest weight loss is associated with im-
proved blood pressure, lipid concentra-
tions, insulin sensitivity, and glycemic
control (1,2). By reducing these risk fac-
tors, weight loss may reduce the high risk
of vascular complications and death
among individuals with diabetes (3).
However, the physiological benefits of
weight loss have been observed primarily
in short-term studies, and little evidence
exists showing that these benefits trans-
late into increased longevity for people with
type 2 diabetes. Even more troubling, stud-
ies that have examined the association of
weight change with subsequent mortality,
without assessing weight loss intention,
generally find that losing weight is associ-
ated with increased rather than decreased
mortality risk (4–11).
The primary limitation of the obser-
vational literature on weight change and
mortality is the lack of information about
weight loss intention (10,11). The weight-
losing population includes an admixture
of individuals losing weight on purpose
and those who lose weight unintention-
ally. Unintentional weight loss is fre-
quently associated with poor health.
Thus, it is difficult to conclude from most
studies of weight loss whether overweight
adults with diabetes will lower their mor-
tality risk by embarking on weight loss
programs. In the only prospective study
to assess intentional weight loss among
individuals with diabetes, intentionally
losing up to 20 lb was associated with
25% lower all-cause and cardiovascular
disease mortality (12). We recently found
in the general population that intentional
weight loss was associated with reduced
mortality and that attempted weight loss
was associated with reduced mortality in-
dependent of actual weight change (13).
However, three other studies in the gen-
eral population found equivocal associa-
tions between intentional weight loss and
mortality (14–16).
In 1989, a special questionnaire mod-
ule in the National Health Interview Sur-
vey (NHIS) examined weight loss
practices and recent weight change
among a nationally representative sample
of individuals with diabetes (17). Vital
status was followed through 1997 (18),
providing an opportunity to examine the
relationship between weight change and
mortality rates while stratifying by weight
loss intention.
RESEARCH DESIGN AND
METHODS The NHIS is an ongo-
ing nationwide survey of the health status,
conditions, and behaviors of the U.S.
noninstitutionalized population (17).
The core NHIS uses multistage probabil-
ity sampling to select 45,000 house-
holds and 120,000 individuals annually.
We used data from the 1989 supplement,
in which 2,531 individuals age 18 years
who reported physician-diagnosed diabe-
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
From the Division of Diabetes Translation, the National Center for Chronic Disease Prevention and Health
Promotion Centers for Disease Control and Prevention, Atlanta, Georgia.
Address correspondence and reprint requests to Edward W. Gregg, PhD, Division of Diabetes Translation
Centers for Disease Control and Prevention, 4770 Buford Hwy., N.E. Mailstop K-10, Atlanta, GA 30341.
E-mail: edg7@cdc.gov.
Received for publication 13 August 2003 and accepted in revised form 15 November 2003.
Abbreviations: NHIS, National Health Interview Survey; RCT, randomized controlled trial.
A table elsewhere in this issue shows conventional and Syste`me International (SI) units and conversion
factors for many substances.
© 2004 by the American Diabetes Association.
Clinical Care/Education/Nutrition
ORIGINAL ARTICLE
DIABETES CARE,VOLUME 27, NUMBER 3, MARCH 2004 657
tes were asked about weight loss and
other behaviors and services related to di-
abetes. The surveys response rate was
95%. Of the 2,531 respondents, we were
able to link 2,459 to the National Death
Index (18) through December 1997 (9
years). We used an algorithm provided by
the National Center for Health Statistics
to determine which matches should be
classied as deaths.
We excluded 852 individuals whose
BMI before weight loss was 25 kg/m
2
,
because weight loss is not typically indi-
cated for such individuals. We also ex-
cluded 55 individuals aged 35 years
because their mortality was extremely
low. Of the remaining 1,552 individuals,
we excluded 151 with missing data on
weight loss or other covariates, leaving
1,401 for the analysis.
Measurements
Interviewers determined participants
age, race, sex, education, smoking status,
self-rated health (5-point scale from ex-
cellent to poor), limitations in daily activ-
ity (unable, limited, not limited), and past
year hospitalizations and doctor visits.
Participants were also asked about insulin
use, symptoms of peripheral neuropathy,
history of physician diagnoses of heart
disease, stroke, and diabetic retinopathy,
and years since the diabetes diagnosis.
Self-reported height and weight were
used to compute their BMI before any
weight change. To assess intentional
weight loss, participants were asked,
Have you tried to lose weight in the past
year?(yes/no); Is your weight now
more, less, or about the same as a year
ago?(more/less/about the same); and In
the past year, about how much have you
gained/lost?(number of pounds).
Statistical analyses
To account for age differences across
weight-change groups, we computed age-
adjusted mortality rates using direct ad-
justment to the U.S. 2000 standard
population 35 years. We used Cox pro-
portional hazards regression to assess the
relationship between weight loss inten-
tion, self-reported weight change, and all-
cause mortality risk while adjusting for
potentially confounding variables. We
considered weight change as continuous
and categorical (lost 20 lb, lost 119 lb,
no change, gained 119 lb, gained 20
lb) variables. We also evaluated weight
change with quadratic terms but did not
nd that it improved the t of the models.
To compare mortality according to
intentional weight loss, we categorized in-
dividuals into four groups with the rst
serving as the referent group: 1) not trying
to lose weight and had either stable
weight or weight gain; 2) not trying to lose
weight but lost weight; 3) trying to lose
weight and had either stable weight or
weight gain; and 4) trying to lose weight
and lost weight. We also evaluated mor-
tality risk in which we merged the latter
two groups into one, such that all individ-
uals who were trying to lose weight were
compared with the rst group.
Multivariate models controlled for
age, race, sex, education, smoking, ini-
tialBMI (BMI before weight change),
measures of health status (self-rated
health, functional limitations, heart dis-
ease, stroke, retinopathy, and neuropa-
thy), measures of health care use (past
year hospitalizations and doctor visits),
insulin use, and years since diabetes diag-
nosis. We tested for interactions between
weight loss and age (3564 years; 65
years), sex, BMI (30 or 30 kg/m
2
), in-
sulin use, diabetes duration, and any vas-
cular disease (cardiovascular disease,
stroke, retinopathy, or neuropathy symp-
toms). Analyses were weighted using
SUDAAN (Research Triangle Institute,
Research Triangle Park, NC) so that study
estimates would statistically represent the
U.S. noninstitutionalized adult over-
weight population with diabetes.
RESULTS We estimate that 46% of
overweight individuals with diabetes re-
ported no weight change, 45% a weight
loss, and 9% a weight gain (Table 1).
Compared with individuals reporting no
weight change, those with weight loss
were younger, had higher BMI, were more
likely to be women, were more likely to
have been hospitalized in the past year,
had been diagnosed with diabetes more
recently, were less likely to use insulin
(and more likely to take oral medica-
tions), and were more likely to report hy-
pertension and neuropathy (P0.05).
Individuals with weight gain reported
worse health and were more likely to have
functional limitations than those with no
weight change, but these associations
were not statistically signicant. Race, ed-
ucation, smoking, number of doctor vis-
its, and stroke were not associated with
weight change.
Sixty-nine percent had tried to lose
weight during the previous year (Table 1).
Those trying to lose weight had a higher
median weight loss (5 lb) than those not
trying to lose weight (0 lb). They were also
more likely to be women, younger, had
higher BMI, and were more likely to be on
oral diabetic medications (and less likely
to take insulin) than those not trying to
lose weight. Additionally, they had been
diagnosed with diabetes more recently
and were more likely to have hyperten-
sion. There were no signicant differ-
ences in race, education, smoking, self-
rated health, functional limitations, or in
the frequency of hospitalizations and doc-
tor visits according to weight loss intent.
Similarly, there were no differences in the
prevalence of heart disease, stroke, retinal
disease, or neuropathy symptoms.
Weight change and mortality
Compared with individuals having no
weight change and controlled for age, sex,
race, initial BMI, smoking, and education,
those who lost any weight had a 22%
(hazard rate ratio [HRR] 1.22, 95% CI
0.991.50) higher mortality rate and
those losing at least 20 lb had a 40%
higher mortality rate (1.40, 1.051.87)
(Table 2). Findings were similar after ad-
ditional control for baseline health status,
health care use, diabetes-related compli-
cations, duration of disease, and insulin
use. When we included weight change as
a continuous variable in the Cox model,
each 10-lb decrease was associated with
an 8% (312%) increased mortality rate
(data not shown).
Weight gainers did not have an ap-
preciably higher mortality rate than those
with stable weight (HRR 1.11, 95% CI
0.741.66) (Table 2). Mortality rates
were nonsignicantly higher among those
with 20-lb weight gain. Their mortality
was 77% higher than those with stable
weight (1.77, 0.973.23). The excess
mortality in this group was attenuated in
fully adjusted analyses (1.48, 0.822.68).
Condence intervals were broad for these
comparisons because of the small number
of deaths.
Weight loss intent and mortality
Compared with those who were not try-
ing to lose weight and who had stable
weight or weight gain, individuals with
unintentional weight loss had a 58%
higher mortality rate (HRR 1.58, 95% CI
1.082.31) (Table 3). Among all individ-
Diabetes and weight loss
658 DIABETES CARE,VOLUME 27, NUMBER 3, MARCH 2004
uals not trying to lose weight, each 10 lb
of weight loss was associated with a 22%
increase in mortality rate (data not
shown).
Individuals trying to lose weight had
a 23% lower mortality rate (HRR 0.77,
95% CI 0.610.99) than those not trying
to lose weight (with stable weights or
weight gain) (Table 3). However, among
those trying to lose weight, weight change
itself was not associated with mortality. In
other words, the lower mortality rate as-
sociated with trying to lose weight was as
great for those who failed to lose weight
(0.72, 0.550.96) as for those who suc-
ceeded in losing weight (0.83, 0.63
1.08).
When we excluded individuals who
died during the rst 2 years of follow-up
(n84), we found similar associations
between weight loss intent and mortality,
although CIs for these analyses were
broader due to the smaller number of
deaths in the analysis. Of note, however,
the association of unintentional weight
loss and mortality was further attenuated
(HRR 1.28, 95% CI 0.841.94). Simi-
larly, when we excluded individuals with
particularly large weight loss (20% of
body weight, n68), there was little
change in mortality rates among those try-
ing to lose weight, but the excess mortal-
ity rate associated with unintentional
weight loss was attenuated (1.35, 0.91
2.00) relative to our primary analysis.
Exclusion of individuals more likely to
have type 1 diabetes, dened as being on
insulin since diagnosis, did not alter our
ndings.
Table 1Demographic and health status characteristics of overweight and obese persons with diabetes
Total
Actual weight change Trying to lose weight
None Loss Gain No Yes
n(%) 1,401 638 (45.6%) 629 (45.5%) 134 (8.9%) 427 (31.0%) 974 (69.0%)
Sex (% women) 57.6 52.5* 60.9 66.2 44.6 63.4
Mean age (years) 61.2 62.1* 60.1 62.4 64.2 59.9
Race (% nonwhite) 22.7 22.0 23.4 22.0 21.0 23.4
Current smoker (%) 19.7 17.7 22.5 16.1 23.2 18.2
Education (% high school) 33.0 33.7 32.5 32.6 35.8 31.8
Mean baseline weight (lb) 196.3 190.9* 203.8 185.8 185.8 201.0
Baseline BMI (kg/m
2
)31.6 30.5* 33.0 30.4 29.6 32.6
Median weight change (lb) 0 0 15 10 0 5
Self-rated health (% fair or worse) 49.7 47.6 50.6 56.6 51.3 49.0
Any functional limitations (%) 56.3 54.6 56.6 63.4 58.1 55.5
Hospitalized past year (%) 23.8 19.7* 28.8 19.7 25.5 23.0
Doctor visits past year (mean) 9.8 8.3 10.9 10.1 9.6 9.6
Duration of disease (mean years) 10.5 11.5* 9.4 11.0 12.0 9.9
Insulin use (%) 39.7 42.4* 36.1 44.3 44.0 37.8
Oral medications (%) 54.0 51.2* 57.9 48.6 47.8 56.8
Retinal disease (%) 24.5 21.8 27.1 25.2 21.5 25.8
Neuropathy symptoms (%) 39.0 33.7* 42.8 46.8 38.1 39.4
Hypertension (%) 65.6 61.8* 69.9 63.2 56.6 69.7
Heart disease (%) 32.7 30.4* 36.0 27.2 32.5 32.7
Stroke (%) 8.8 8.7 9.0 8.4 9.9 8.3
All gures except nare weighted to be representative of the U.S. diabetic population in 1989. *Signicant difference across weight change groups (P0.05);
signicantly different from those trying to lose weight (P0.05).
Table 2HRR for all-cause mortality associated with weight change and with degree of weight loss or gain among overweight and obese
persons with diabetes
Weight change
(median)
Prevalence
(%)
Death rate
(%/year)
Primary model*
HRR (95% CI)
Fully adjusted
HRR (95% CI)
No weight change 0 45.6 3.0 1.0 1.0
Weight loss (lb) 15 45.5 4.0 1.22 (0.991.50) 1.19 (0.961.47)
119 10 26.4 3.6 1.11 (0.871.41) 1.09 (0.851.40)
20 30 19.1 4.6 1.40 (1.051.87) 1.36 (1.031.80)
Weight gain (lb) 10 8.9 3.6 1.11 (0.741.66) 1.10 (0.721.67)
119 8 6.8 3.0 0.97 (0.621.53) 1.00 (0.611.63)
20 25 2.2 5.4 1.77 (0.973.23) 1.48 (0.822.68)
*Primary model adjusted for age, sex, race, smoking, education, and initial BMI; fully adjusted model: initial BMI, age, race, sex, education, self-rated health,
smoking, diabetes medications, duration of disease, functional limitations, hypertension, heart disease, stroke, retinal disease, neuropathy symptoms, hospital days,
and doctor visits.
Gregg and Associates
DIABETES CARE,VOLUME 27, NUMBER 3, MARCH 2004 659
We found no signicant (P0.05)
interactions of age, sex, obesity status, di-
abetes duration, insulin use, or vascular
disease on the weight lossmortality asso-
ciations. However, we found that associ-
ations of weight loss and mortality tended
to differ between overweight (BMI 2529
kg/m
2
) and obese (BMI 30 kg/m
2
) indi-
viduals (data not shown). Specically,
compared with the referent group (not
trying to lose weight with stable weight or
weight gain), unintentional weight loss
was associated with a higher relative mor-
tality rate among obese (BMI 30 kg/m
2
)
individuals (HRR 3.29, 95% CI 1.55
6.98) than overweight (BMI 2529 kg/
m
2
) individuals (1.20, 0.801.81).
Trying to lose weight was associated with
a lower mortality rate among overweight
individuals (0.62, 0.460.83) but was
not associated with mortality among
obese individuals (1.17, 0.721.92). The
association between trying to lose weight
and lowered mortality among overweight
individuals existed for those who suc-
ceeded (0.58, 0.430.84) as well as those
who failed to lose weight (0.64, 0.42
0.97).
CONCLUSIONS Using a national
sample of overweight and obese adults
with diabetes, we found a large difference
in the mortality rate between individuals
with unintentional weight loss (58% in-
crease in mortality rate) and those with
intentional weight loss (a nonsignicant
17% decrease). Our ndings suggest that
intentional weight loss is not harmful
among individuals with diabetes, and the
large body of research relating weight loss
to increased mortality (411) may be
spuriously inuenced by unintentional
weight loss.
An unexpected nding of our study
was that intention to lose weight was as-
sociated with reduced mortality regard-
less of whether weight loss occurred.
Individuals who reported trying to lose
weight had a 23% lower mortality rate
than those not trying to lose weight, and
this benet was as great for those who
failed to lose weight as for those who suc-
ceeded. There are several possible expla-
nations for this nding. First, trying to
lose weight may be a marker of healthy
behaviors, such as being more physically
active or eating healthier foods, and these
lifestyle behaviors may be more important
determinants of health status than weight
loss per se. Unfortunately, we had little
information on the ways in which people
lose weight in this population, but previ-
ous ndings relating physical activity,
lower fat intake, and higher ber intake to
better health support this idea (1921).
A second possibility is that people
who report trying to lose weightmay be
more likely to engage in positive health
behaviors unrelated to weight (e.g., using
seat belts, not smoking) or have more fre-
quent contact with health care providers
and preventive care practices, such as ear-
lier screening and treatment for disease.
Although our analyses controlled for
smoking, health status, and health care
use, as well as for exposure to diabetes
education and nutritional counseling,
there may be other fundamental differ-
ences between people who try to lose
weight that we could not detect.
Our nding that losing weight per se
was not associated with mortality reduc-
tion compared with those who failed to
lose weight may be a reection of poor
long-term weight loss efcacy. In other
words, weight loss attempts in an obser-
vational, population-based study such as
this are likely to be heterogeneous and
may not reect what could be achieved
with structured, clinical weight loss pro-
grams. Our study only assessed past year
weight loss at one point in time, and thus,
we were unable to compare people who
succeeded in long-term weight mainte-
nance with those who regained their
weight the following year. Nevertheless,
our nding demonstrates the need to con-
sider weight loss intent and to continue to
explore the possibility that lifestyle
changes may be more important clinical
and public health messages than weight
loss itself.
We found a strong association be-
tween trying to lose weight and lower
mortality rates among overweight (BMI
2529.9 kg/m
2
) individuals but surpris-
ingly, no association with mortality
among obese (BMI 30 kg/m
2
) individu-
als. We suspect that because obese adults
have greater levels of risk factors for car-
diovascular or other early mortality than
overweight individuals, typical weight
loss attempts may not be powerful
enough to inuence mortality in this
group. Alternatively, obese individuals
may be more likely to receive other types
of medical treatments that outweigh the
effects of lifestyle-based weight loss at-
tempts. We are unaware of data to sup-
port this, however, and recent ndings
from the Diabetes Prevention Program
(22) found that the benets of lifestyle-
induced weight loss for diabetes preven-
tion do not differ by baseline obesity
status. Thus, our ndings of differential
effects by weight status should be exam-
ined in other studies.
Few studies have examined the rela-
tionship between intentional weight loss
and mortality. In the rst epidemiologic
study of weight loss and mortality among
diabetic individuals undergoing dietary
counseling, Lean et al. (23) found that
greater weight loss was associated with
lower mortality. A subsequent study of
Table 3HRR for all-cause mortality associated with weight loss intent and weight change among overweight and obese persons with diabetes
Weight change
(median)
Prevalence
(%)
Death rate
(%/year)
Primary model*
HRR (95% CI)
Fully adjusted
HRR (95% CI)
Excluding rst 2 years
of mortality
Not trying to lose weight
Stable weight/weight gain 0 23.0 3.6 1.0 1.0 1.0
Lost weight 15 8.0 6.3 1.73 (1.202.48) 1.58 (1.082.31) 1.28 (0.841.94)
Trying to lose weight overall 5 69.0 3.2 0.80 (0.631.01) 0.77 (0.610.99) 0.77 (0.581.01)
Stable weight/weight gain 0 31.5 2.8 0.74 (0.570.98) 0.72 (0.550.96) 0.77 (0.571.05)
Lost weight 15 37.5 3.4 0.85 (0.661.11) 0.83 (0.631.08) 0.76 (0.561.04)
*Primary model adjusted for age, sex, race, smoking, education and initial BMI; fully adjusted model: initial BMI, age, race, sex, education, self-rated health,
smoking, diabetes medications, duration of disease, functional limitations, hypertension, heart disease, stroke, retinal disease, neuropathy symptoms, hospital days,
and doctor visits.
Diabetes and weight loss
660 DIABETES CARE,VOLUME 27, NUMBER 3, MARCH 2004
diabetic adults in the Cancer Prevention
Study I associated moderate intentional
weight loss with lower mortality (12).
Similarly, we recently published ndings
from the general U.S. population (13) in
which trying to lose weight was also asso-
ciated with lower mortality independent
of weight loss, and mortality rates were
lowest among individuals with modest
weight loss (19 lb). However, several
other studies have found adverse or null
associations of intentional weight loss
(1416). Previous studies, however, have
not examined the independent associa-
tion of weight loss intention, and it is pos-
sible that by categorizing such individuals
along with individuals who did not try to
lose weight, the benet of weight loss was
underestimated.
Our study has several limitations.
Both body weight and weight loss were
based on self-report, which is known to
overestimate height, and underestimate
weight (and BMI) compared with physi-
cal measurements. Previous studies, how-
ever, have found self-reports of weight
change and intentional weight loss to be
reliable and accurate (24,25). Addition-
ally, we know of no data indicating that
misclassication of body weight is associ-
ated with weight loss intent. If recall error
were not associated with either weight
loss intent or mortality, this would either
have no effect or bias results toward the
null, possibly leading to an underestimate
of the benets of intentional weight loss
on mortality.
Observational studies have inherent
limitations. We controlled for health sta-
tus and health care use at baseline and
attempted to account for underlying dis-
ease by excluding individuals who died in
the initial years of follow-up or who had
signicant weight losses. However, we
still cannot rule out selection bias or re-
sidual confounding due to improper or
inadequate assessment of underlying
health status. If present, this could lead to
underestimates of both the benetofin-
tentional weight loss and the mortality
risk associated with unintentional weight
loss. Given that cardiovascular disease is
the most common cause of death among
individuals with diabetes, it is unfortu-
nate that there have been no adequately
controlled randomized controlled trials
(RCTs) to examine the effects of weight
loss on cardiovascular disease outcomes
and mortality. We are reminded by the
recent controversy related to the long-
term effects of hormone replacement
therapy (26,27) that RCTs are important
to conrm the value of broadly used pub-
lic health interventions.
In summary, we found that uninten-
tional weight loss was associated with in-
creased mortality among overweight
diabetic U.S. adults and that trying to lose
weight is associated with decreased mor-
tality independent of actual weight
change. Our study challenges previous
concerns that intentional weight loss
causes increased mortality but still leaves
the relative importance of weight loss per
se unclear. Instead, trying to lose weight
may be benecial even if such attempts
are not successful. Our study highlights
the importance of independently assess-
ing weight loss intent in observational
studies of weight loss. Further examina-
tion of this question may help determine
whether changes in lifestyle behaviors are
more important determinants of health
status than weight loss itself. A clear an-
swer to this question would have impor-
tant implications for clinical as well as
public health messages.
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Diabetes and weight loss
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... Individuals with overweight or obesity would be assigned into diet and/or physical activity regimens. Since such RCTs are rare, expensive and difficult to conduct, there is an increasing interest in using observational studies [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Observational data usually include a larger, more diverse and representative sample, with longer follow up compared to RCTs, and analysis can be conducted in a timely fashion [12][13][14]. ...
... Nevertheless, questions such as 'What is the effect of bodyweight reduction on cardiovascular disease?' can still be answered using EHR or data from cohorts [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Unfortunately, these questions can introduce ambiguity to the definition of 'baseline', where eligibility criteria determining who is selected into the study should be met, as well as to the choices of confounders that should be controlled for, i.e. from enrolment or the 1st follow-up? ...
... Other methodologies in the literature There have also been papers that have combined methods 2 and 3, e.g. in the analysis of Wannamethee et al. [24], the authors practically used method 2 but adjusted for one confounder from time zero, smoking status (like in method 3). Moreover, some papers [5][6][7][8] have set baseline at the time of the 1st follow-up and have considered BMI change amongst individuals at a certain BMI group, measured after the (hypothetical) intervention, i.e. at the 1st follow-up visit. In other words, the research question this method addresses is awkward, i.e. 'what is the effect of healthy weight change among individuals at a given BMI group measured after weight change', which is definitely not suitable to estimate the causal effect of weight change [5][6][7][8]. ...
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Background/Objectives When studying the effect of weight change between two time points on a health outcome using observational data, two main problems arise initially (i) ‘when is time zero?’ and (ii) ‘which confounders should we account for?’ From the baseline date or the 1st follow-up (when the weight change can be measured)? Different methods have been previously used in the literature that carry different sources of bias and hence produce different results. Methods We utilised the target trial emulation framework and considered weight change as a hypothetical intervention. First, we used a simplified example from a hypothetical randomised trial where no modelling is required. Then we simulated data from an observational study where modelling is needed. We demonstrate the problems of each of these methods and suggest a strategy. Interventions weight loss/gain vs maintenance. Results The recommended method defines time-zero at enrolment, but adjustment for confounders (or exclusion of individuals based on levels of confounders) should be performed both at enrolment and the 1st follow-up. Conclusions The implementation of our suggested method [adjusting for (or excluding based on) confounders measured both at baseline and the 1st follow-up] can help researchers attenuate bias by avoiding some common pitfalls. Other methods that have been widely used in the past to estimate the effect of weight change on a health outcome are more biased. However, two issues remain (i) the exposure is not well-defined as there are different ways of changing weight (however we tried to reduce this problem by excluding individuals who develop a chronic disease); and (ii) immortal time bias, which may be small if the time to first follow up is short.
... A given follow up period would be defined during which the CVD events would be collected. Since such randomised trials are rare, expensive and difficult to conduct, there is an increasing interest in using observational studies [2][3][4][5][6][7][8][9][10][11][12][13][14]. The situation is further complicated as data on physical activity or diet are typically not recorded. ...
... However, questions such as "What is the effect on cardiovascular disease of bodyweight reduction?" can be answered using EHR or data from consented cohorts [2][3][4][5][6][7][8][9][10][11][12][13][14]. Undeniably, to assess the effect of weight/BMI change using observational data requires information on individuals' bodyweight for two time points; ...
... Individuals are allocated to a BMI change group based on their BMI at the 1 st follow-up and their observed weight/ BMI trajectories (from enrolment till the 1 st follow-up) b. Baseline confounders: Confounders measured at the 1 st follow-upThere are many studies that have adopted this approach to estimate the relationship between weight/BMI change and a health outcome[5][6][7][8]. However, the estimand (i.e. the parameter of interest we end up estimating) here is awkward. ...
Article
Estimating the effect of a change in a particular risk factor and a chronic disease requires information on the risk factor from two time points; the enrolment and the first follow-up. When using observational data to study the effect of such an exposure (change in risk factor) extra complications arise, namely (i) when is time zero? and (ii) which information on confounders should we account for in this type of analysis? From enrolment or the 1st follow-up? Or from both?. The combination of these questions has proven to be very challenging. Researchers have applied different methodologies with mixed success, because the different choices made when answering these questions induce systematic bias. Here we review these methodologies and highlight the sources of bias in each type of analysis. We discuss the advantages and the limitations of each method ending by making our recommendations on the analysis plan.
... (Fig. 1, Ref. [50]). Data on individuals with T2D and BMI >25 kg/m 2 in the National Health Interview Survey [51] showed that self-reported intentional weight loss was not associated with lower risk of mortality during 9 years of follow-up (Hazard Ratio, HR = 0.83, 95% CI, 0.63-1.08). Interest- ...
... For patients with BMI <30 kg/m 2 , intentional weight loss was not associated with mortality risk. In contrast to studies in which weight loss intention and amount were selfreported [49,51], subjects in this study were weighed regularly during the physician-supervised 6-year monitoring period, with a median of 13 weights recorded during the monitoring period. Lowest mortality was observed in patients who maintained their weight during the follow-up period. ...
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Cardiometabolic diseases, including cardiovascular disease (CVD) and type 2 diabetes (T2D), are the leading cause of death globally. Because T2D and obesity are strongly associated, weight loss is the cornerstone of treatment. However, weight loss is rarely sustained, which may lead to weight cycling, which is associated with increased mortality risk in patients with T2D. Meta-analyses show that weight loss is not generally associated with reduced mortality risk in T2D, whereas weight cycling is associated with increased all-cause and CVD mortality. This may be attributable in part to increased variability in CVD risk factors that often accompany weight cycling, which studies show is consistently associated with adverse CVD outcomes in patients with T2D. The inconsistent associations between weight loss and mortality risk in T2D, and consistent findings of elevated mortality risk associated with weight cycling, present a conundrum for a weight-loss focused T2D prevention and treatment strategy. This is further complicated by the findings that among patients with T2D, mortality risk is lowest in the body mass index (BMI) range of ~25–35 kg/m2. Because this “obesity paradox” has been consistently demonstrated in 7 meta-analyses, the lower mortality risk for individuals with T2D in this BMI range may not be all that paradoxical. Physical activity (PA), cardiorespiratory fitness (CRF), and muscular fitness (MF) are all associated with reduced risk of T2D, and lower risk of CVD and all-cause mortality in individuals with T2D. Reducing sedentary behavior, independent of PA status, also is strongly associated with reduced risk of T2D. Improvements in cardiometabolic risk factors with exercise training are comparable to those observed in weight loss interventions, and are largely independent of weight loss. To minimize risks associated with weight cycling, it may be prudent to adopt a weight-neutral approach for prevention and treatment of individuals with obesity and T2D by focusing on increasing PA and improving CRF and MF without a specific weight loss goal.
... Prior studies have demonstrated that weight loss through diet and exercise can lead to a greater reduction in waist circumference compared to overall weight loss, resulting in a decrease in ABSI (36)(37)(38). Therefore, it's worth examining whether mortality reductions reported in some studies (39,40) for individuals wanting to lose weight regardless of weight change can be linked to a decrease in ABSI. Future studies will determine whether changes in ABSI can serve as a distinctive and clinically valuable biomarker for lifestyle adjustments. ...
Article
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Objectives The current study aimed to examine how the trajectory of a body shape index (ABSI) could predict mortality in a prospective cohort of 5587 participants. Methods A Growth Mixture Model (GMM) was employed to identify ABSI and body shape trajectories spanning from 2000 to 2018. Multivariate Cox regression models with hazard ratio (HR) and 95% confidence intervals (CIs) were built to assess the association of death from all-cause and cardiovascular disease (CVD) with ABSI and body shape trajectories. Results We found that individuals with a low ABSI–marked increase (Class II) and high ABSI–marked increase trajectory (Class III) had a higher risk of all-cause (adjusted HR for Class II, 1.37; 95%CI, 1.04-1.79; adjusted HR for Class III, 1.42; 95%CI, 1.05-1.91) and non- CVD mortality (adjusted HR for Class II, 1.38; 95%CI, 1.00-1.91; adjusted HR for Class III, 1.42; 95%CI, 1.00-2.05) as well as an increased risk of CVD (adjusted HR for Class II, 1.40; 95%CI, 1.14-1.71; adjusted HR for Class III, 1.42; 95%CI, 1.13-1.78) and coronary heart disease (CHD) (adjusted HR for Class II, 1.52; 95%CI, 1.18-1.96; adjusted HR for Class III, 1.47; 95%CI, 1.11-1.95. The trajectories of body shape phenotypes did not show any significant associations with mortality, CVD, or CHD events. Conclusions ABSI trajectories might be associated with subsequent risk of mortality and CVD events.
... A prospective analysis in people with both overweight and T2DM examining 9-year mortality rates showed that people who tried intentionally tried to lose weight had a reduced risk of all-cause mortality independent of whether they lost weight, whereas those who lost weight unintentionally were at higher mortality risk. 21 Unintentional weight loss might be pathologically driven as a result of another illness or disease, and the weight loss might not in fact correlate with a reduction in adipose tissue, but in skeletal mass, instead or might be related to a decompensation in diabetes control. The limitations of most studies examining whether weight loss can confer mortality benefits are potential confounders, such as age, multiple health conditions, and duration of diabetes. ...
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Diabetes and obesity are closely interlinked. Obesity is a major risk factor for the development of type 2 diabetes mellitus and appears to be an important risk factor for diabetic micro- and macrovascular complications. Management of hyperglycaemia in people with diabetes is important to reduce diabetes-related complications. Previously, there was a significant tension between management of hyperglycaemia and mitigating weight gain. Older drugs, such as sulfonylureas, glitazones, and insulin, although effective antihyperglycaemic agents, tend to induce weight gain. There is now an increasing recognition in people with obesity and diabetes that the focus should be on aiding weight loss, initially with improvements in diet and physical activity, possibly with the use of low-calorie diet programmes. Subsequent addition of metformin and newer agents, such as sodium-glucose transporter-2 inhibitors and glucagon-like peptide-1 analogues, will aid glucose control and weight reduction, and offer cardiovascular and renal protection. These drugs are now much higher in the therapeutic pathway in many national and international guidelines. Bariatric surgery may also be an effective way to manage hyperglycaemia or induce remission in individuals with both obesity and diabetes.
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Purpose: Diabetes is a chronic metabolic disease that affects approximately 422 million people worldwide and leads to the death of 1.5 million people every year. The prevalence of diabetes among the population aged 30 or older in Korea has steadily increased since 2018, reaching 16.7% in 2020, with one in six adults having diabetes. This study was conducted to identify factors affecting weight management in overweight or obese patients with diabetes (OOPD) in Korea using data from the 2018-2022 National Health and Nutrition Examination Survey. Therefore, the goal of this study is to analyze weight perception and factors related to weight perception and to identify factors that influence weight loss efforts among OOPD in Korea. Methods: Socioeconomic characteristics, disease morbidity, weight perception, and weight loss efforts were investigated in 950 participants. Data were analyzed using descriptive statistics, cross-tabulation, and logistic regression. Results: Among the overweight or obese patients with diabetes, 24.4% perceived their weight to be normal, with a higher proportion among men (29.6%) than among women (14.6%). Weight loss efforts were 5.11 times (95% CI: 3.02-8.66) higher in people with overweight perceptions than in those with normal weight perceptions. Additionally, the rate was 1.54 times (95% CI: 1.06 2.25) higher in people with dyslipidemia than in those without dyslipidemia. Conclusion: These results suggest that weight management approaches for overweight or obese patients with diabetes should be designed individually based on weight perception and disease morbidity.
Article
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Background: This study investigated the effects of weight loss during follow-up on cardiovascular outcomes in a type 2 diabetes cohort and tested interactions with clinical and laboratory variables, particularly physical activity, that could impact the associations. Methods: Relative weight changes were assessed in 651 individuals with type 2 diabetes and categorized as ≥5% loss, <5% loss, or gain. Associations between weight loss categories and incident cardiovascular outcomes (total cardiovascular events [CVEs], major adverse cardiovascular events [MACEs], and cardiovascular mortality) were assessed using multivariable Cox regression with interaction analyses. Results: During the initial 2 years, 125 individuals (19.2%) lost ≥5% of their weight, 180 (27.6%) lost <5%, and 346 (53.1%) gained weight. Over a median additional follow-up of 9.3 years, 188 patients had CVEs (150 MACEs) and 106 patients died from cardiovascular causes. Patients with ≥5% weight loss had a significantly lower risk of total CVEs (hazard ratio [HR], 0.52; 95% confidence interval, 0.33 to 0.89; P=0.011) than those who gained weight, but non-significant lower risks of MACEs or cardiovascular deaths. Patients with <5% weight loss had risks similar to those with weight gain. There were interactions between weight loss and physical activity. In active individuals, ≥5% weight loss was associated with significantly lower risks for total CVEs (HR, 0.20; P=0.004) and MACEs (HR, 0.21; P=0.010), whereas in sedentary individuals, no cardiovascular protective effect of weight loss was evidenced. Conclusion: Weight loss ≥5% may be beneficial for cardiovascular disease prevention, particularly when achieved with regular physical activity, even in high-risk individuals with long-standing type 2 diabetes.
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This study assesses the relationship of body mass index to 5-year mortality in a cohort of 4317 nonsmoking men and women aged 65 to 100 years. Logistic regression analyses were conducted to predict mortality as a function of baseline body mass index, adjusting for demographic, clinical, and laboratory covariates. There was an inverse relationship between body mass index and mortality; death rates were higher for those who weighed the least. Inclusion of covariates had trivial effects on these results. People who had lost 10% or more of their body weight since age 50 had a relatively high death rate. When that group was excluded, there was no remaining relationship between body mass index and mortality. The association between higher body mass index and mortality often found in middle-aged populations was not observed in this large cohort of older adults. Over-weight does not seem to be a risk factor for 5-year mortality in this age group. Rather, the risks associated with significant weight loss should be the primary concern.
Article
Objective: To summarize published studies analyzing the effects of long-term change in body weight on all-cause mortality and have not been reported elsewhere in these proceedings. Data Sources: Thirteen reports from 11 diverse population studies, 7 from the United States and 4 from Europe. Study Selection: All studies included a weight change period of 4 or more years, followed by a mortality assessment period of 8 or more years. All weight changes occurred in persons 17 years or older
Article
Objective: To evaluate the relation between weight variability and death in high;risk, middle-aged men participating in the Multiple Risk Factor Intervention Trial (MRFIT). Design: Cohort study with 3.8 years of follow-up. Setting: Multicenter, collaborative, primary prevention trial conducted at 22 clinical centers in the United States. Participants: Men (n=10 529) who were 35 to 57 years old at baseline and who were in the upper 10% to 15% of risk for coronary heart disease because of smoking, high blood pressure, and elevated cholesterol level. Participants were seen at least annually for 6 to 7 years for medical evaluations in study clinical centers
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Several epidemiologic investigations have suggested that weight loss is associated with increased mortality risk but have not examined whether the weight loss was intentional or unintentional. The authors examined whether the association between weight loss and mortality differs by whether the weight loss was intentional or unintentional as part of the Iowa Women's Health Study, a prospective cohort study of health risk factors in postmenopausal women. Women aged 55-69 years completed questions about intentional and unintentional weight losses since age 18 years via mail survey in 1992 and were followed through 1995. One or more intentional weight loss episodes of 20 or more pounds (> or =9.1 kg) during adulthood was not significantly associated with higher total or cardiovascular disease mortality risk compared with never losing > or =20 pounds. One or more unintentional weight loss episodes of 20 or more pounds was associated with a 26-57% higher total mortality risk and a 51-114% higher cardiovascular disease mortality risk, compared with never losing 20 or more pounds. Associations between unintentional weight loss and increased mortality risk were confined mostly to women with prevalent disease, hypertension, or diabetes. Patterns of association did not vary by overweight status. These findings suggest that the association between weight loss and increased mortality risk observed in epidemiologic studies may be due to unintentional weight loss that reflects existing disease and not due to intentional weight loss.
Article
Considerable epidemiological evidence has accumulated regarding the effect of postmenopausal estrogens on coronary heart disease risk. Five hospital-based case-control studies yielded inconsistent but generally null results; however, these are difficult to interpret due to the problems in selecting appropriate controls. Six population-based case-control studies found decreased relative risks among estrogen users, though only 1 was statistically significant. Three cross-sectional studies of women with or without stenosis on coronary angiography each showed markedly less atherosclerosis among estrogen users. Of 16 prospective studies, 15 found decreased relative risks, in most instances, statistically significant. The Framingham study alone observed an elevated risk, which was not statistically significant when angina was omitted. A reanalysis of the data showed a nonsignificant protective effect among younger women and a nonsignificant increase in risk among older women. Overall, the bulk of the evidence strongly supports a protective effect of estrogens that is unlikely to be explained by confounding factors. This benefit is consistent with the effect of estrogens on lipoprotein subfractions (decreasing low-density lipoprotein levels and elevating high-density lipoprotein levels). A quantitative overview of all studies taken together yielded a relative risk of 0.56 (95% confidence interval 0.50–0.61), and taking only the internally controlled prospective and angiographic studies, the relative risk was 0.50 (95% confidence interval 0.43–0.56).
Article
Fluctuation in body weight is a common phenomenon, due in part to the high prevalence of dieting. In this study we examined the associations between variability in body weight and health end points in subjects participating in the Framingham Heart Study, which involves follow-up examinations every two years after entry. The degree of variability of body weight was expressed as the coefficient of variation of each subject's measured body-mass-index values at the first eight biennial examinations during the study and on their recalled weight at 25 years of age. Using the 32-year follow-up data, we analyzed total mortality, mortality from coronary heart disease, and morbidity due to coronary heart disease and cancer in relation to intraindividual variation in body weight, including only end points that occurred after the 10th biennial examination. We used age-adjusted proportional-hazards regression for the data analysis. Subjects with highly variable body weights had increased total mortality (P = 0.005 for men, P = 0.01 for women), mortality from coronary heart disease (P = 0.009 for men, P = 0.009 for women), and morbidity due to coronary heart disease (P = 0.0009 for men, P = 0.006 for women). Using a multivariate analysis that also controlled for obesity, trends in weight over time, and five indicators of cardiovascular risk, we found that the positive associations between fluctuations in body weight and end points related to mortality and coronary heart disease could not be attributed to these potential confounding factors. The relative risks of these end points in subjects whose weight varied substantially, as compared with those whose weight was relatively stable, ranged from 1.27 to 1.93. Fluctuations in body weight may have negative health consequences, independent of obesity and the trend of body weight over time.
Article
Care guidelines for people with non-insulin-dependent diabetes mellitus (NIDDM) emphasize the importance of weight loss in reducing mortality risk. However, existing evidence regarding the relationship between weight and mortality and the effects of weight change is conflicting. We examined these relationships in the World Health Organization Multinational Study of Vascular Disease in Diabetes. This was a cohort study of 1,416 men and 1,544 women. Baseline examinations were performed in 1975 through 1977, a morbidity follow-up was performed in 1983, and a mortality follow-up continued until 1988. Data were analyzed according to geographical groups: Europeans, East Asians, and Native Americans. The relationship between weight change and mortality was analyzed for Europeans only. Generally, body mass index (BMI) was positively associated with age, blood pressure, and cholesterol but was negatively associated with duration of diabetes, prevalence of retinopathy, and use of insulin. There was no clear relationship between BMI and mortality across the geographical groups. In Europeans, weight loss in the leanest subjects at baseline (BMI < 26 kg/m2) was associated with a threefold increase in mortality risk compared with those who had maintained a steady weight (relative risk [RR] 3.05, 95% confidence interval [CI] 1.26-7.36). Only in the most obese group was weight loss associated with a reduction in mortality risk (BMI > 29 kg/m2, RR 0.84, 95% CI 0.40-1.74). The positive association of BMI with age, blood pressure, and cholesterol and the negative association with duration of diabetes, retinopathy, and use of insulin may explain why there is no strong relationship between BMI and mortality in NIDDM. Weight loss, particularly in the relatively lean diabetic person, may be associated with an increased mortality risk.
Article
Six measures of weight variability were examined in a cohort of 29,015 postmenopausal women. Recalled weight at ages 18, 30, 40 and 50 years, current weight at baseline and at each of three biennial follow ups (approximate ages 62, 64, 66, 68 years), and recalled episodes of intentional and unintentional weight loss were used to construct (1) the coefficient of variation (CV) in body weight, (2) weight change categories (cycling, weight gain, weight loss and stable weight), (3) the root mean square error of variation (RMSE) around the slope of weight versus age, (4) the number of intentional weight loss episodes of 5 or more pounds, (5) the number of unintentional weight loss episodes of 20 or more pounds and (6) a categorical measure of intentional and unintentional weight loss episodes of > = 20 lb. The nine-month test-retest reliability correlations for the measures of lifetime history of intentional and unintentional weight loss were 0.80 and 0.62, respectively. Correlations between the different weight variability measures were positive but weak, suggesting that they reflect different aspects of weight variability. The RMSE discriminated categorically defined cyclers from weight gainers, but the CV did not. The weight change categories were more sensitive to age-related weight changes than the CV or RMSE. Studies examining the relationship between weight variability and health outcomes need to include measures that distinguish intentionality, short-term versus long term variability, and the magnitude, direction, and frequency of weight change.
Article
In order to determine whether weight loss explains high mortality rates in those with a low body mass index (BMI), the relationships between BMI, rate of weight gain and mortality were examined in Pima Indians. Subjects were 814 diabetic and 1814 nondiabetic participants in a longitudinal survey who had at least two examinations after age 20. Median duration of follow-up was 8.1 (range 0.03-25.1) years. BMI showed a U-shaped relationship with mortality rates in men with the lowest rates in the 30-35 kg/m2 category; an inverse relationship was seen in women. Subjects who were losing weight had higher mortality rates than those who were gaining. However, excess mortality among the lightest subjects was present among those who were gaining weight. Among nondiabetic subjects, the mortality ratio (MR) for BMI < 25 kg/m2 compared with 30-35 kg/m2 was 1.5 [95% confidence interval (CI) 1.0-2.2] unadjusted for weight gain, while the adjusted MR was 1.3 [95% CI 0.9-1.9]. Weight loss, which may reflect underlying illness, is associated with high mortality rates in Pima Indians but does not fully account for the high mortality in the lightest individuals.