Introduction
Older adults are frequently counseled to lose weight,
even though there is little evidence that overweight is
associated with increased mortality in those over age 65.
Six large controlled population-based studies of
non-smoking older adults have investigated the association
between body mass index (BMI) and mortality, controlling
for relevant covariates [ 1 2 3 4 5 6 ] . All studies found
excess risk for persons with very low BMI, but that persons
with moderately high BMI had little or no extra risk except
in certain small subsets. A review of 13 studies of older
adults drew similar conclusions [ 7 ] .
Many healthy older adults report gradual weight gain
throughout adult life. It may be that a small amount of
gradual weight gain is normative and associated with the
most robust health as we age. It has been suggested that
weight standards be adjusted upwards for age [ 8 ] . Such
recommendations remain controversial, however, because the
number of studies of older persons is fairly small, and
because few studies have examined the relation of BMI to
quality of life or years of healthy life (YHL) in the
elderly [ 9 ] .
In older adults, risk factors may have a greater effect
on health than on mortality. If so, then behavior change
trials of weight modification might be more successful if
they were evaluated on improved health, rather than on
decreased mortality. Clinical trials powered to detect
differences in YHL would often require fewer subjects than
trials to detect survival differences or cardiovascular
events [ 10 ] . In this paper we study whether BMI at
baseline is associated with living longer, and/or with more
years of being healthy, in a cohort of older adults for
whom risk factors, subclinical disease, and morbidity are
well characterized. The goal is to determine whether
analyses based on years of life (YOL) or on YHL would
provide substantively different results, and which measure
would yield more powerful evaluations of weight
modification interventions in older adults.
Materials and methods
Study design: The Cardiovascular Health
Study
The Cardiovascular Health Study (CHS) is a
population-based longitudinal study of 5,888 adults aged
65 and older at baseline [ 11 ] . Subjects were recruited
from a random sample of the Medicare eligibility lists in
four US counties. Extensive baseline data were collected
for all subjects using a baseline home interview, an
annual mail questionnaire, and annual clinic
examinations. Additional information was collected in a
brief telephone interview 6 months after each scheduled
visit. Two cohorts were followed, one with 7 years of
follow-up (n = 5,201) and the second (all African
American, n = 687) with 4 years of follow-up to date.
Data collection began in 1989, and follow-up is virtually
complete for all surviving subjects [ 12 ] .
Body mass index
BMI was calculated as measured weight in kilograms
divided by the square of measured height in meters. A
report from the National Heart Lung and Blood Institute
classifies normal weight (without reference to age) as a
BMI of 18.5 to 24.9; overweight as 25 to 29.9; and
obesity as 30.0 and higher [ 13 ] . We consider
separately the group with BMI between 18.5 and 20, which
was associated with lower survival in studies cited
above.
Years of life and years of healthy life
YOL is the number of years that a person lived in the
7 years after baseline. YHL is the number of years in
which the person was 'healthy', and is similar in concept
to quality-adjusted life-years, healthy year equivalents,
or active life expectancy [ 14 ] . We based YHL on
self-rated health (is your health excellent, very good,
good, fair, or poor?) (EVGFP) which was collected every 6
months. EVGFP is a simple but well-known measure, which
has been studied in detail [ 15 16 ] , and is predictive
of health events in many studies [ 17 ] . Because we are
examining health status over time, we added a sixth
health state, dead. Data were available about 93% of the
time. We used linear interpolation to estimate missing
data when there were known values before and after the
missing value, bringing the percent complete to 95% [ 18
] .
For this analysis we defined YHL as the number of
years (of 7) in which a person reported excellent, very
good, or good health (were 'healthy'). YHL ranges from 0
(for persons who were never in excellent, very good, or
good health) to 7 years (for persons who were healthy
throughout). Since people reported their health every 6
months, YHL has a reasonably continuous distribution. A
drawback of this simple definition of 'healthy' is that
it does not distinguish between fair or poor health and
death, since all are considered 'not healthy'. We also
used an alternative approach, which assigns a different
value to each level of EVGFP [ 19 ] . Preliminary results
were similar for the two approaches, however, and we
report results using only the simpler definition.
The calculations had to be modified to include the 438
persons in the second African American cohort, who have
been followed only 4 years to date. For those persons,
and for 70 persons in the first cohort who did not have
complete data, we estimated the last 4 years of YOL and
YHL from their age, sex, and health at the end of 3
years, using validated methods presented elsewhere [ 20 ]
. That article showed that estimated 4-year YOL and YHL
were unbiased for the African American cohort. In the
primary analysis we used observed 7-year YOL and YHL when
they were available, and observed 3-year YOL and YHL plus
4-year estimated YOL and YHL when they were not (about
10% of the sample). We performed all analyses with and
without the persons who had partially estimated data, to
ensure that the estimation had not distorted the
findings.
Covariates
The goal is to examine the association of YOL and YHL
with BMI. To adjust for possible confounding we chose
baseline covariates that were prevalent in the elderly,
related to mortality and morbidity in previous studies,
and likely to be related to BMI. Self-reported covariates
include age, gender, smoking (never or former), history
of arthritis, cancer, diabetes, fair or poor self-rated
health status, limitations in activities of daily living
or in instrumental activities of daily living, and 10
pounds or more unintended weight loss in the year before
baseline. Clinical covariates include hypertension,
cardiovascular disease (prevalent heart disease,
peripheral vascular disease, or cerebrovascular disease),
maximum thickness of the internal carotid artery,
depression (CESD score), serum albumin, serum
cholesterol, and serum creatinine. These measures are
explained in more detail elsewhere [ 21 22 23 24 ] . We
excluded 697 current smokers and 313 others with
incomplete covariate data, leaving 4,878 persons on whom
this analysis is based.
Analysis
All analyses were performed separately for men and
women. We calculated two sets of adjusted values, as
follows. We regressed YOL and YHL first on age, age
squared, race, and smoking history (former or never), and
second on all of the covariates listed above. We
calculated adjusted YOL as a person's observed YOL minus
predicted YOL (from the regression) plus the mean YOL
(6.52 years for women or 6.06 for men). That is, a
person's adjusted YOL is his residual from the regression
plus the grand mean. The mean of this new variable, for a
group of subjects, is the adjusted mean YOL for that
group. Adjusted YHL was calculated in a similar manner.
We calculated two sets of adjusted variables because of
the possibility of 'over-adjustment', controlling
inappropriately for factors (such as diabetes) which may
have been causally affected by the person's weight. We
plotted mean adjusted YOL and YHL against BMI, and tested
for difference among BMI groups using confidence
intervals or analysis of variance. Finally we calculated
the effect size for each measure, comparing each BMI
subgroup to the 'normal' group. The effect size is the
difference in mean YOL (or YHL) in two groups divided by
their common standard deviation. Since the sample size
required to detect an effect of this magnitude is
proportional to the inverse of the squared effect size,
large effect sizes are desirable.
Results
Table 1shows the distribution of key variables by sex
and race. Mean age at baseline was 73.1 and about two
thirds of the men and a third of the women were former
smokers. Black women had a higher mean BMI and higher
percent obese (BMI ≥ 30) than the other three groups. Black
men were most likely to have unintentionally lost more than
10 pounds in the past year; white women were least
likely.
About 78% of the subjects were healthy at baseline,
declining to 57% at the end of 7 years; 20% had died (data
not shown). Of the 22% who were unhealthy (fair or poor) at
baseline, about 24% were healthy 7 years later. There was
thus substantial change in EVGFP over time, in both
directions. Table 1shows the mean YOL and YHL (calculated
from EVGFP) in the first seven years of the study, adjusted
to age 73. For example, black women averaged 6.3 YOL, but
only 4.2 YHL of a maximum possible 7. We calculated some
additional descriptive statistics, shown in the final two
lines: years of unhealthy life (YOL minus YHL) and years
lost to death (7 minus YOL). White women had the most YHL
and black men the fewest; black women had the most years of
unhealthy life, and white men the fewest; black men lost
the most years to death (1.3 out of 7) while white women
lost only 0.4 years. For blacks, about 68% of their YOL
were healthy (YHL/YOL, not shown); for whites, about 75%
were healthy.
Among whites, the gender differences in Table 1were
statistically significant (p <.05) except for BMI and
unintended weight loss. Among blacks, gender differences
were significant except for 10 pounds unintended weight
loss and weight loss since age 50. Among males, there were
significant differences between black and white for BMI,
unintended weight loss, YOL, YHL, years of unhealthy life,
and years lost to death. Whites in the sample had higher
income and education (data not shown). After adjusting for
income and education, as well as age and former smoking,
the difference in BMI was no longer statistically
significant. Among females, blacks and whites differed
significantly on BMI, BMI>30, weight loss since age 50,
YOL, YHL, years of unhealthy life, and years lost to death.
After adjustment for income and education, the difference
in weight loss since age 50 was no longer significant.
Blacks had significantly lower YOL and YHL than whites
after adjustment for age, but the difference disappeared
after adjustment for the entire set of health-related
baseline covariates (analyses not shown).
We next examined the relationship of BMI to YOL and YHL.
Table 2presents the mean values of YOL and YHL, adjusted
for age, race, and previous smoking (columns 1 and 3), and
also adjusted for the entire set of covariates (columns 2
and 4). For example, YOL for women, adjusted for age, race,
and smoking, averaged 6.0 years for women with a baseline
BMI below 18.5, but averaged 6.6 years for women with a BMI
from 25 to 29.9. The second column, which shows results
adjusted for all covariates, is not very different (the
only discrepancy is for men with BMI < 18.5, a category
containing only 14 men). Adjustment for extensive
covariates also made little difference for YHL (columns 3
and 4). Subsequent analyses are adjusted only for age,
race, and former smoking. As mentioned above, the group
with BMI from 18.5 to 20 would be considered 'normal' by
the NHLBI guidelines, but had lower YOL and YHL than those
with 20-24.9 in all comparisons. For this reason, and to
increase sample size for those with low BMI, we combined
the two lower categories, defining underweight as a BMI
under 20.
Figure 1is a plot of adjusted YOL and YHL by sex and
BMI. For each BMI category the mean and its 95% confidence
interval are plotted. Categories whose confidence intervals
do not overlap, or overlap only slightly, are significantly
different. The bars are slightly offset to permit all error
bars to be seen.
YOL for women (the uppermost curve on Figure 1) averaged
about 6.5 out of 7 years, and showed no evident association
between BMI and YOL for BMI above 20. Underweight women
averaged about .25 fewer YOL than other women (p < .05
compared with normal group). Underweight men also had lower
YOL, but this group was not significantly different from
the normal group, in part because of low sample size. Men
classified as normal, overweight or obese all had about the
same YOL.
The lowermost two lines in Figure 1show mean YHL for
women and men. Women who were normal or overweight averaged
about 4.9 YHL. The YHL for underweight or obese women was
about 4.5 years, which was significantly lower than the
normal group. The relationship of BMI to YHL for men is
similar, but differences among BMI groups were not
statistically significant. YHL was significantly higher for
women than for men in the normal and overweight groups, but
the sexes had similar YHL in the underweight and obese
groups.
We next present the effect size for comparing each group
to the normal BMI group. The effect sizes are shown in
Table 3, with the significance results of the associated
t-tests for the differences in means of the two groups
being compared. For example, underweight women averaged
4.50 YHL compared to 4.92 for normal women, and the common
standard deviation was 1.44. The effect size is thus
(4.92-4.50)/1.44 = .29. The two groups had significantly
different YHL, implying that the effect size is also
significantly greater than zero. A clinical trial of a
treatment to help underweight women achieve normal weight
(presumably by addressing the underlying cause) could be
expected to have 80% power with N = (1.96+.84) 2/.29 2=
about 93 women per treatment arm, if 7-year YHL were the
outcome measure.
The biggest effect sizes are in the first row, comparing
underweight to normal. YHL and YOL have similar effect
sizes for women, and are significantly different from zero.
The effect sizes are not significantly different from zero
for men, in part because there were only 42 men in the
underweight category. The effect size comparing overweight
to normal yielded small, non-significant effect sizes, with
inconsistent signs, suggesting extremely large sample sizes
would be needed. For comparing obese to normal, only YHL
for women showed a large and significant effect size. Thus,
an intervention to improve the health of underweight women
to that of their normal weight peers could be performed
using either YHL or YOL as the outcome variable. Trials to
make obese women comparable to normal women could be
evaluated using YHL, but not YOL. Trials to improve the
health of the other groups to that of the normals would
probably be fruitless since there is no evidence that being
overweight (for men or women) or obese (for men) affects
YOL or YHL.
As mentioned above, we repeated these analyses excluding
the persons with partially estimated data, and using two
different ways of coding YHL. The only substantive change
was that some of the differences between blacks and whites
shown in Table 1were no longer statistically significant,
due to a smaller sample size.
Discussion
Optimal weight and overweight
Recent studies have defined obesity without reference
to age [ 6 13 30 ] . Andres
et al proposed a desirable BMI of
24-30 for persons aged 60 to 69 [ 8 ] . Allison
et al [ 31 ] proposed 27-30 for
older men and 30-35 for older women. In Figure 1, the
overweight (as opposed to the obese) are no different
from those of normal weight, suggesting that these two
categories could be combined for older adults. Since
future improvements in life expectancy may be limited [
32 ] , the greatest advances may be made by improving
people's YHL. This suggests that the development of
future guidelines should take YHL or other measures of
quality of life into account.
Implications for clinical trials
Based on these findings, trials to address obesity in
older women could be efficient if YHL (but not YOL) was
the outcome measure. That is, women who changed from
being obese to being normal would likely show changes in
YHL, but not YOL. Clinical trials of weight modification
interventions for older adults who were merely overweight
would appear to be fruitless since the interventions
would probably not have a direct effect on either YOL or
YHL.
Weight or weight change are sometimes used as the
outcome in evaluations of interventions such as diet or
exercise programs. The fact that weight is not associated
in a consistent way with health suggests that such
evaluations should be considered critically when older
adults are the subjects. This is particularly important
in the light of recent findings, which found that
interventions such as weight-loss drugs may be harmful [
33 34 ] . For older adults, the risks associated with
higher weight are especially unclear, and the optimal
outcome for a trial of weight loss in older adults
requires specific attention to improved health and
mortality.
Interestingly, the strongest health relationships were
found for underweight older adults. Clinical trials whose
objective was to make the underweight as healthy as their
normal-weight peers (presumably by addressing the
underlying conditions that caused the low weight) could
be performed efficiently using either YOL or YHL as the
outcome measure. Both YOL and YHL would be clinically
significant in this patient group.
Potential limitations
CHS participants were somewhat healthier than the
average older adult; however, adjustment for detailed
covariates made little difference in the findings. We
estimated the last four years of health data for about
10% of the sample, but results with and without this
group were similar. Analysis of mean YOL instead of the
more traditional survival analysis survival analysis was
appropriate here, since virtually no persons were lost to
follow-up. Biases caused by over-adjustment are probably
not large, since the findings were not sensitive to the
number of variables adjusted for.
These results are for a 7-year follow-up. The relative
superiority of YHL to YOL would probably hold in trials
with shorter follow-up. The effect sizes in Table 3might
also be appropriate in shorter trials, since lengthy
trials often add little information [ 10 ] .
EVGFP, on which YHL was based, might have missed some
effects of obesity on risk factors for future health. A
person who is depressed because of a poor self-image
related to obesity or who has osteo-arthritis related to
obesity and limits to activities to successfully avoid
pain would surely have worse EVGFP than others, based on
results from many studies. However, health measures
designed specifically to measure those conditions might
be more sensitive to change in weight than EVGFP. If YHL
were based on such measures, the superiority of YHL to
YOL would likely be even greater than that shown here.
These more sensitive measures might also have detected
differences between the overweight and normal weight
persons, but we think this is unlikely given the absence
of any differences in EVGFP.
Conclusion
Recommendations for desirable weight have been
criticized for emphasizing mortality rather than health. We
found associations between YHL and obesity that were not
present in the mortality analysis, suggesting that YHL may
be a more sensitive measure of the burden of obesity in
older adults, especially for women. Future efforts to
determine desirable weight guidelines should include
measures of YHL. Using either YOL or YHL, however, we found
no excess risk for older adults who would be classified as
'overweight' by the NHLBI guidelines. This suggests using
YHL as the outcome measure in clinical trials involving
obese or underweight older adults, and discouraging trials
that address older adults who are merely overweight.
Competing interests
None declared
Abbreviations
BMI Body mass index
CESD Center for Epidemiologic Studies Depression
Scale
CHS Cardiovascular Health Study
EVGFP Is your health excellent, very good, good, fair or
poor?
QALY Quality-adjusted life years
YHL Years of healthy life
YOL Years of life
