The aim of this study was to examine the association between body mass index and weight changes on disability transitions and mortality among Brazilian older adults. Longitudinal data from the Health, Well-Being, and Aging in Latin America and the Caribbean Study conducted in São Paulo, Brazil (2000 and 2006), were used to examine the impact of obesity on disability and mortality and of weight changes on health transitions related to disability. Logistic and multinomial regression models were used in the analyses. Individuals who were obese were more likely than those of normal weight to have limitations on activities of daily living (ADL), instrumental activity of daily living (IADL), and Nagi's limitations. Obesity was associated with higher incidence of ADL and IADL limitations and with lower recovery from Nagi's limitations. Compared to those who maintained their weight, those who gained weight experienced higher incidence of ADL and Nagi's limitations, even after controlling for initial body mass index. Higher mortality among overweight individuals was only found when the reference category was “remaining free of Nagi limitations.” The findings of the study underline the importance of maintaining normal weight for preventing disability at older ages.
Brazil is among the 25 countries in the world with the fastest aging rates [
Fast changes in the population’s nutritional intake that have occurred in Brazil in recent decades [
Few studies focus on the impact of BMI on mortality and disability in the Latin American and the Caribbean (LAC) region. Based on the baseline for the Health, Well-Being, and Aging in Latin America and the Caribbean Study (SABE), Al Snih and colleagues [
Obesity has been associated with higher prevalence of disability in cross-sectional and longitudinal studies [
Large systematic reviews have shown that the relationship between BMI and mortality seems to follow a J-shaped (sometimes U-shaped) curve [
The use of BMI categories has been criticized for not reproducing well the complexities of the BMI and mortality relationship [
Our study uses data a large cohort study conducted in São Paulo, Brazil, to examine the association between BMI and body weight changes on disability transitions and mortality, while controlling for a series of demographic, socioeconomic, and health determinants. We investigate these associations on three types of disability (activities of daily living, instrumental activities of daily living, and Nagi’s limitations) transitions.
Data from the two waves (2000 and 2006) of the SABE cohort study conducted in São Paulo, Brazil, were used in this study. SABE is a multicenter survey with respondents in seven major cities throughout LAC countries that have been investigating the health and well-being of older adults (age 60 and over). The study was approved by the Institutional Review Boards at the collaborating institutions [
The baseline sample was obtained using a two-stage stratified sampling based on the 1995 National Household Survey master sampling frame. The data in the first wave were collected in two stages. The first stage was a household interview conducted by a single interviewer using a standardized questionnaire that included several questions about the living conditions and health status of the subjects. The second stage of data collection consisted of a household visit by a pair of interviewers who completed anthropometric and physical-performance measurements. At baseline, the response rates reached 84.6% in São Paulo. In the first stage, information on 2,143 individuals was collected. Additional characteristics of the baseline data collection process have been described elsewhere [
In 2006, to reestablish contact, trained interviewers visited the addresses and neighborhoods of surviving participants from the 2000 survey. For those not found during these visits, interviewers used the additional contact information collected at baseline (e.g., telephone numbers of children or other relatives) to obtain further information about the subjects’ current location. In 2006, researchers collected data via face-to-face interviews using a standardized questionnaire. The 2006 questionnaire was very similar to the one used in 2000 but included additional questions that complemented the previous study. Vital statistics records were used to identify subjects who had died between 2000 and 2006. The search was based on the names, sex, dates of birth, and addresses listed in the 2000 database.
Of the 2,143 participants in the first wave of SABE São Paulo, 355 (16.6%) had missing data on selected variables. Most of them
Self-reported disability in six ADL measures (dressing, bathing, eating, getting in and out of a bed, toileting, and getting across a room) were used to measure disability. Individuals were given the following introduction: “Here are a few everyday activities. Please tell me if you have any difficulty with these because of a health problem. Exclude any difficulties you expect to last less than three months.” After this introduction, they were asked “Do you have difficulty…?” And the possible answers were: “yes,” “no,” “does not know,” and “no response” for each one of the six ADL measures. Participants who answered “does not know” and “no response” were classified as missing. IADL questions followed the ADL ones. No additional introduction was made. Individuals were asked “Do you have difficulty…?” The IADL items included were preparing a hot meal, managing money, shopping, using of transportation within the community, ability to use the telephone, and responsibility for one’s own medications. The possible answers were “yes,” “no,” “cannot do it,” “does not know,” and “no response.” Those who answered “cannot do it” were classified as having difficulty performing the activity, whereas those answering “does not know” and “no response” were classified as missing. The Nagi physical performance measure included lifting or carrying objects that weighed five kilograms or more; lifting a coin; pulling or pushing a large object, such as a living-room chair; stooping, kneeling, or crouching; and reaching or extending the arms above shoulder level. Each of the three disability measures was converted into binary form, in which respondents scored “0” if they did not indicate any limitations and “1” if they reported having difficulty performing one or more activity in the scale.
Body weight and height were measured without shoes and with light clothing by trained examiners. BMI was calculated as kg/m2. Four BMI categories were defined according to the criteria adopted by the Pan American Health Organization for the SABE study [
The following sociodemographic characteristics were included in the analysis: age (in years), gender, smoking status (never, former, or current smoker), schooling (in years of formal education), and household arrangement (living alone or accompanied). All regression analysis also included a measure of number of chronic conditions at the baseline. Health status based on the number of self-reported chronic conditions included diabetes, hypertension, cardiovascular disease, stroke, cancer, arthritis, and osteoporosis.
STATA S.E. 12.1 for Windows (StataCorp, College Station, TX) was used for all the statistical analyses. Descriptive statistics were conducted first. Weighted logistic regressions were then used to assess the influence of BMI on disability prevalence. Multinomial logistic regressions were used to assess the influence of BMI on disability transitions and mortality. For those free of disability, four outcomes were considered in the multinomial logistic regressions: remained free of disability (reference category), became disabled (incidence), died, or were lost to followup. For those who were disabled at the baseline, four outcomes were included in the multinomial logistic regressions: remained disabled (reference category), recovered from disability, died, or were lost to followup. Multinomial logistic regressions were used to analyze the role of weight change on health transitions as discussed above, excluding mortality, as we do not have information on weight change prior to death in between waves.
In the baseline, there were 1,420 individuals free of ADL and 368 individuals with ADL. In 2006, among those free of ADL, 606 individuals had remained free of ADL, 226 had developed ADL, 329 had died, and 259 were lost in the followup or had missing data on ADL status. Among those who had ADL in the baseline, 99 remained with ADL, 75 recovered from ADL, 144 died and 50 were lost in the followup or had missing data on ADL in 2006. For IADL limitations, 1,207 were free of IADL, and 581 had IADL in the baseline. Among those who were free of IADL in the baseline, 491 remained free of limitations, 257 developed IADL, 230 had died, and 229 were lost in the followup or had missing data on IADL in the second wave. Among those with IADL in the baseline, 220 remained with IADL, 36 recovered from IADL, 243 had died, and 82 were lost in the followup or had missing data on IADL status in 2006. Regarding the Nagi, 654 participants were free of it in 2000, and 1,134 had at least one Nagi limitation. Among those free of Nagi, 192 remained free of it, 210 developed Nagi, 129 died, and 123 were lost in the followup or had missing data on the Nagi variable in 2006. Among those who had at least one Nagi limitation in 2000, 539 remained with Nagi’s limitations, 70 recovered from Nagi’s limitations, 344 died, and 181 were lost in the followup or had missing data on Nagi’s limitations in the second wave.
In the final sample, 23.4% were underweight, 43.3% had normal weight, 12.4% were overweight, and 21.1% of the participants were classified as obese. Table
Prevalence of ADL, IADL, and Nagi’s limitations, by sex and BMI categories, São Paulo, 2000 (weighted estimates).
Total | Underweight | Normal | Overweight | Obese |
| |
---|---|---|---|---|---|---|
Total |
|
|
|
|
|
|
ADL | 16.7 | 14.7 | 13.9 | 18.2 | 23.4 | ** |
IADL | 24.4 | 28.9 | 21.0 | 25.0 | 26.7 | ** |
Nagi | 57.8 | 52.7 | 50.7 | 61.5 | 74.9 | *** |
Females |
|
|
|
|
|
|
ADL | 19.8 | 14.8 | 17.2 | 22.2 | 25.4 | ** |
IADL | 30.5 | 36.5 | 26.7 | 33.8 | 30.4 | |
Nagi | 67.2 | 59.7 | 60.5 | 73.9 | 78.3 | *** |
Males |
|
|
|
|
|
|
ADL | 12.3 | 14.5 | 10.3 | 13.4 | 15.7 | |
IADL | 15.6 | 19.9 | 14.6 | 13.9 | 12.7 | |
Nagi | 44.1 | 44.4 | 39.6 | 45.9 | 61.8 | ** |
ADL: activities of daily living; IADL: instrumental activities of daily living.
***
Table
Relative risk ratios of the impact of body mass index categories on disability transitions and mortality among those who were free of disability in the baseline, São Paulo, Brazil—2000–2006.
Variables | ADL |
|
IADL |
|
NAGI |
| |||
---|---|---|---|---|---|---|---|---|---|
RRRa | 95% CI | RRR | 95% CI | RRR | 95% CI | ||||
|
|||||||||
Age | 1.10 |
|
* * * | 1.11 |
|
* * * | 1.05 |
|
* * |
Female | 1.70 |
|
* | 1.48 |
|
2.37 |
|
* * | |
Smoking status | |||||||||
Former smoker | 0.94 |
|
1.15 |
|
1.36 |
|
|||
Current smoker | 1.58 |
|
1.52 |
|
0.93 |
|
|||
Number of chronic conditions | 1.56 |
|
* * * | 1.33 |
|
* * | 1.23 |
|
|
Schooling | 0.95 |
|
0.91 |
|
0.88 |
|
* | ||
Live alone | 0.81 |
|
0.61 |
|
0.93 |
|
|||
BMI categories | |||||||||
Underweight | 1.16 |
|
1.92 |
|
* * | 1.03 |
|
||
Overweight | 0.93 |
|
1.57 |
|
1.86 |
|
|||
Obese | 2.07 |
|
* * | 2.42 |
|
* * * | 1.19 |
|
|
|
|||||||||
Age | 1.11 |
|
* * * | 1.13 |
|
* * * | 1.11 |
|
* * * |
Female | 0.60 |
|
* | 0.41 |
|
* * * | 0.68 |
|
|
Smoking status | |||||||||
Former smoker | 1.25 |
|
1.17 |
|
0.90 |
|
|||
Current smoker | 2.89 |
|
* * * | 2.69 |
|
* * | 2.68 |
|
* |
Number of chronic conditions | 1.40 |
|
* * * | 1.41 |
|
* * * | 1.82 |
|
* * |
Schooling | 0.95 |
|
0.94 |
|
0.93 |
|
|||
Live alone | 1.01 |
|
1.16 |
|
1.36 |
|
|||
BMI categories | |||||||||
Underweight | 1.21 |
|
1.22 |
|
1.87 |
|
|||
Overweight | 1.30 |
|
1.41 |
|
2.50 |
|
* | ||
Obese | 1.07 |
|
1.29 |
|
0.68 |
|
|||
|
|||||||||
Age | 1.03 |
|
* | 1.05 |
|
* * * | 1.03 |
|
|
Female | 1.38 |
|
1.24 |
|
2.19 |
|
** | ||
Smoking status | |||||||||
Former smoker | 1.36 |
|
1.54 |
|
2.01 |
|
* | ||
Current smoker | 1.05 |
|
1.02 |
|
1.32 |
|
|||
Number of chronic conditions | 1.12 |
|
1.12 |
|
1.22 |
|
|||
Schooling | 1.02 |
|
1.01 |
|
0.99 |
|
|||
Live alone | 1.37 |
|
1.42 |
|
0.93 |
|
|||
BMI categories | |||||||||
Underweight | 1.58 |
|
2.07 |
|
* * | 1.44 |
|
||
Overweight | 1.25 |
|
1.33 |
|
1.41 |
|
|||
Obese | 1.36 |
|
1.55 |
|
* | 1.32 |
|
||
|
1,420 | 1,207 | 654 | ||||||
BIC′ | −104.55 | −82.451 | 47.73 |
ADL: activities of daily living; IADL: instrumental activities of daily living; RRR: relative risk ratio; CI: confidence interval; BMI: body mass index.
aRemaining free of disability is the reference category. Normal weight is the reference category for BMI, living accompanied is the baseline category for household arrangement, and never smoked is the reference category for smoking status.
***
Table
Relative risk ratios of the impact of body mass index categories on disability transitions and mortality among those who had disability in the baseline, São Paulo, Brazil—2000–2006.
Variables | ADL |
|
IADL |
|
NAGI |
| |||
---|---|---|---|---|---|---|---|---|---|
RRRa | 95% CI | RRR | 95% CI | RRR | 95% CI | ||||
|
|||||||||
Age | 0.93 |
|
** | 0.92 |
|
** | 0.96 |
|
* |
Female | 0.65 |
|
0.39 |
|
0.56 |
|
|||
Smoking status | |||||||||
Former smoker | 0.39 |
|
1.04 |
|
1.54 |
|
|||
Current smoker | 1.43 |
|
0.55 |
|
1.01 |
|
|||
Number of chronic conditions | 0.72 |
|
* | 0.93 |
|
0.59 |
|
*** | |
Schooling | 1.03 |
|
0.99 |
|
1.05 |
|
|||
Live alone | 0.55 |
|
1.11 |
|
3.24 |
|
** | ||
BMI categories | |||||||||
Underweight | 0.37 |
|
0.14 |
|
0.62 |
|
|||
Overweight | 1.01 |
|
0.80 |
|
0.82 |
|
|||
Obese | 0.48 |
|
0.77 |
|
0.46 |
|
* | ||
|
|||||||||
Age | 1.12 |
|
*** | 1.07 |
|
*** | 1.10 |
|
*** |
Female | 0.40 |
|
* | 0.65 |
|
0.48 |
|
** | |
Smoking status | |||||||||
Former smoker | 1.33 |
|
1.75 |
|
1.75 |
|
* | ||
Current smoker | 3.35 |
|
* | 3.09 |
|
** | 2.30 |
|
** |
Number of chronic conditions | 0.87 |
|
0.97 |
|
0.98 |
|
|||
Schooling | 0.98 |
|
1.04 |
|
0.95 |
|
|||
Live alone | 1.03 |
|
0.88 |
|
1.23 |
|
|||
BMI categories | |||||||||
Underweight | 0.97 |
|
1.28 |
|
0.9 |
|
|||
Overweight | 0.35 |
|
0.75 |
|
0.79 |
|
|||
Obese | 0.54 |
|
0.66 |
|
0.78 |
|
|||
|
|||||||||
Age | 1.00 |
|
1.00 |
|
1.01 |
|
|||
Female | 0.50 |
|
1.40 |
|
0.93 |
|
|||
Smoking status | |||||||||
Former smoker | 0.40 |
|
0.69 |
|
1.09 |
|
|||
Current smoker | 0.62 |
|
0.86 |
|
0.66 |
|
|||
Number of chronic conditions | 0.73 |
|
0.88 |
|
0.88 |
|
|||
Schooling | 1.16 |
|
1.07 |
|
1.03 |
|
|||
Live alone | 1.73 |
|
1.43 |
|
2.38 |
|
** | ||
BMI categories | |||||||||
Underweight | 1.67 |
|
1.42 |
|
1.85 |
|
* | ||
Overweight | 0.43 |
|
0.90 |
|
1.27 |
|
|||
Obese | 0.92 |
|
1.14 |
|
1.24 |
|
|||
|
368 | 581 | 1,134 | ||||||
BIC′ | 30.33 | 62.47 | −42.91 |
ADL: activities of daily living; IADL: instrumental activities of daily living; RRR: relative risk ratio; CI: confidence interval; BMI: body mass index.
aRemaining with disability is the reference category. Normal weight is the reference category for BMI, living accompanied is the baseline category for household arrangement, and never smoked is the reference category for smoking status.
***
In the last set of analyses, we focus on the role of weight gain between waves on disability transitions (Table
Relative risk ratios of the impact of body mass index categories and body mass index changes on disability transitions, São Paulo, Brazil—2000–2006.
Variables | ADL |
|
IADL |
|
NAGI |
| |||
---|---|---|---|---|---|---|---|---|---|
RRRa | 95% CI | RRR | 95% CI | RRR | 95% CI | ||||
|
|||||||||
Age | 1.10 |
|
*** | 1.11 |
|
*** | 1.05 |
|
* |
Female | 1.75 |
|
* | 1.53 |
|
2.44 |
|
** | |
Smoking status | |||||||||
Former smoker | 0.85 |
|
1.14 |
|
1.47 |
|
|||
Current smoker | 1.38 |
|
1.45 |
|
0.97 |
|
|||
Number of chronic conditions | 1.56 |
|
*** | 1.33 |
|
** | 1.25 |
|
|
Schooling | 0.97 |
|
0.92 |
|
0.90 |
|
|||
Live alone | 0.85 |
|
0.60 |
|
1.01 |
|
|||
BMI categories | |||||||||
Underweight | 0.92 |
|
1.73 |
|
* | 0.93 |
|
||
Overweight | 0.72 |
|
1.39 |
|
1.87 |
|
|||
Obese | 1.99 |
|
* | 2.38 |
|
*** | 1.22 |
|
|
BMI change | |||||||||
Loss | 1.23 |
|
0.99 |
|
0.85 |
|
|||
Gain | 2.30 |
|
* | 1.97 |
|
2.15 |
|
* | |
|
800 | 737 | 389 | ||||||
BIC′ | 27.46 | 9.40 | 28.56 | ||||||
|
|||||||||
Age | 0.92 |
|
* | 0.93 |
|
* | 0.95 |
|
* |
Female | 0.43 |
|
0.41 |
|
0.53 |
|
|||
Smoking status | |||||||||
Former smoker | 0.34 |
|
1.40 |
|
1.46 |
|
|||
Current smoker | 1.11 |
|
0.69 |
|
0.97 |
|
|||
Number of chronic conditions | 0.64 |
|
* | 0.94 |
|
0.60 |
|
*** | |
Schooling | 1.05 |
|
1.05 |
|
1.03 |
|
|||
Live alone | 0.50 |
|
1.03 |
|
3.27 |
|
** | ||
BMI categories | |||||||||
Underweight | 0.49 |
|
0.16 |
|
0.71 |
|
|||
Overweight | 0.89 |
|
0.76 |
|
0.86 |
|
|||
Obese | 0.42 |
|
0.68 |
|
0.43 |
|
* | ||
BMI change | |||||||||
Loss | 0.52 |
|
0.53 |
|
1.07 |
|
|||
Gain | 0.18 |
|
* | 0.64 |
|
0.53 |
|
||
|
161 | 224 | 572 | ||||||
BIC′ | 31.87 | 91.45 | 85.22 |
ADL: activities of daily living; IADL: instrumental activities of daily living; RRR: relative risk ratio; CI: confidence interval; BMI: body mass index.
aRelative risk ratios were adjusted by smoking status. Remaining free of disability is the reference category for those who started without disability, and remaining with disability is the reference category for those who had disability in the baseline. Normal weight is the reference category for BMI. Stable weight is the baseline category for weight change. Results for lost in the followup were omitted (available upon request).
***
Most previous studies have focused on the association between BMI and disability [
There is growing interest in the role of weight changes on health transitions [
In additional analyses (not shown and available upon request), we have explored additional models to test whether BMI and weight changes influence changes in the number of disabilities over time. We found that obesity was associated with the increases in the number of Nagi’s limitations. Weight loss and weight gain were associated with an increase in the number of ADL and Nagi’s limitations over time. Changes in the number of IADL limitations were not statistically associated with BMI categories or weight changes. As expected, older age was associated with the increases in the number of ADL, IADL, and Nagi’s limitations over time. A higher number of chronic conditions were also associated with an increase in the number of ADL, IADL and Nagi’s limitations over time. Being female was also positively associated with increases in the number of Nagi limitations. We also tested fractional polynomial models following the approach suggested by Wong and colleagues [
The only mortality differential by BMI categories was found among overweight participants who were more likely to die than to remain free of Nagi’s limitations. In further analyses (not shown), results from a logistic regression that controlled for the same covariates included in this study, revealed no differences in mortality among underweight, normal weight, overweight, and obese participants. This is consistent with previous studies suggesting that the association between BMI and mortality becomes less U-shaped at older ages [
Our findings also contribute to a growing debate in the field about whether greater life expectancy implies better health for the expanding surviving elderly female population in Latin America [
Aging is related to the increase of fat mass, and there is growing evidence of the detrimental impact of obesity on disability at older ages. There is evidence as well that changes in lifestyle, such as walking, have positive effects on preventing mobility limitations [
This study advances the literature on the impact of body weight and body weight changes on disability and mortality. This study, however, has some limitations. First, the data used in the study on disability measures were self-reported. Although this could be a possible source of bias, methodological studies have shown that self-reported data on functional disability are consistent with medical diagnoses [
This study confirms previous studies that have found obesity to be associated with increased disability in Brazilian older adults. Historically, Brazil has mainly been concerned with curbing malnutrition; however, in recent years, new policies have targeted the marketing of highly processed and unhealthy foods [
F. C. D. Andrade planned the study, supervised the data analysis, and wrote the paper. A. I. N. M. Nazan contributed to the paper writing. M. L. Lebrão and Y. A. de O. Duarte collected the data, helped plan the study, including instrumentation, and revised the paper.
This study was supported by grants from FAPESP/Brazil, the Brazilian Ministry of Health, and the Lemann Institute for Brazilian Studies at the University of Illinois at Urbana-Champaign. The authors gratefully acknowledge the feedback received at the 2012 Population Association of America Annual Meeting. They also thank Fernão Dias de Lima for the careful management of the database.