Abstract
Objective
We aimed to investigate the risk of long-term mortality associated with weight and waist circumference (WC) change among older adults, particularly the overweight and obese ones.
Design
Cohort Study.
Setting
The Bambuí (Brazil) Cohort Study of Aging.
Participants
Community-dwelling elderly (n=1138).
Measurements
Weight and WC were reassessed three years after baseline. Mortality risk associated with a 5% weight/WC loss and gain was compared to that of weight/WC stability by Cox models adjusted for clinical, behavioral and social known risk factors for death (age, gender, BMI, smoking, diabetes, total cholesterol, hypertension, Chagas disease, major electrocardiographic changes, physical activity, B-type natriuretic peptide, C-reactive protein, creatinine, education and household income).
Results
Female sex was predominant (718; 63.1%). Mean age was 68 (6.7) years. Weight stability (696; 61.1%) was more common than weight loss (251; 22.1%) or gain (191; 16.8%). WC remained stable in 422 (37.3%), decreased in 418 (37.0%) and increased in 291 (25.7%) participants. There were 334 (29.3%) deaths over a median follow-up time of 8.0 (6.4-8.0) years from weight/WC reassessment. Weight loss (HR 1.69; 95% CI 1.30-2.21) and gain (HR 1.37; 95% CI 1.01-1.85) were associated with increased mortality, except in those who were physically active in which weight gain was associated with decreased mortality. Results were similar for participants who were overweight/ obese or with abdominal obesity at baseline (HR 1.41; 95%CI 1.02-1.97 and HR 2.01; 95%CI 1.29-3.12, for weight loss and gain, respectively). WC change was not significantly associated with mortality.
Conclusion
Although weight loss has been recommended for adults with excessive weight regardless of age, weight change might be detrimental in older adults. Rather than weight loss, clinical interventions should target healthy lifestyle behaviors that contribute to weight stability, particularly physical activity in overweight and obese older adults.
Key words: Mortality, elderly, obesity, abdominal obesity, overweight
Introduction
Conditions associated with obesity, such as cardiovascular and metabolic disorders and functional limitation, are prevalent among the elderly. However, several studies have reported that overweight (body mass index, BMI, 25-29.9 kg/m2) and class I obese (BMI 30-35 kg/m2) older individuals have comparable or decreased risk of death in comparison to the ones within the normal weight range (BMI 18.5-24.9 kg/m2) (1). This phenomenon, which is widely known as the ‘obesity paradox', has been reported both in community-dwelling elderly (2) as well as in those selected by specific conditions, such as heart failure (3), coronary artery disease (CAD) (4) and chronic kidney disease (5).
Additionally, the net effect of weight loss and changes in waist circumference (WC) in older adults are not well established. Controlled trials of weight loss are scarce in this population, particularly with a long-term follow-up for clinical rather than intermediate outcomes (6). Likewise, results from observational studies are heterogeneous with several reports of an increased risk of death associated with weight loss in older adults (7). Despite this, current guidelines based mainly on evidence from younger and middle-aged adults recommend that individuals aged 18 years or older with overweight and complicating risk factors, such as waist increase, diabetes, hypertension, dyslipidemia, smoking and established CAD or obesity (BMI ≥30 kg/m2) should lose 5-10% weight (8, 9). Also, despite evidence that abdominal adiposity increases morbidity and mortality and that waist changes may occur regardless of weight stability only a few studies have evaluated the influence of changes in WC on mortality in the elderly (10, 11).
As evidence from controlled clinical trials regarding the best strategy to manage excessive weight at older age is scarce, long-term observational studies might aid in clinical decisions in this population. Thus, our objective was to investigate the influence of weight and WC changes over the first three years of follow-up on subsequent long-term mortality of older adults in the Bambuí (Brazil) Cohort Study of Aging (BHAS), particularly of those with any indication to lose weight according to current guidelines (general overweight/obesity or abdominal adiposity).
Methods
Study design and population
The study used data of the 10 year follow-up of a populationbased cohort study of aging conducted in the Bambuí City (approximately 15,000 inhabitants), located in the state of Minas Gerais in the southeast region of Brazil. Procedures used in the Bambui cohort study of aging have been described in detail elsewhere (12). Briefly, the baseline cohort population comprised 1,606 (92.2%) of all the 1,742 residents aged 60 years or more on January 1st , 1997, who were identified by means of a complete census carried out in the city. Baseline data collection was performed from February to May 1997, comprising standardized interviews, anthropometric and blood pressure measurements, blood tests, and electrocardiograms (ECGs). For the present analysis, we selected participants who had weight or WC measured at baseline and reassessed in 2000.
Outcome Ascertainment
Deaths assigned to any cause occurring from reassessment of anthropometric measures in 2000 to December 31st, 2007 were included in this analysis. Deaths reported by next of kin during the annual follow-up interview were ascertained through the Brazilian mortality information system (Sistema de Informações sobre Mortalidade) with the permission of the Ministry of Health.
Anthropometric measures, weight and WC changes definitions
Two high-precision digital scales (range 0–150 kg×0.1 kg) were used for the measurement of weight (kg) and a portable stadiometer kit was used for measurements of height (cm). WC was measured at umbilicus height using inelastic tapes. The reliability of these measurements was determined by repeating them in a 5% cohort of all of the study participants. Both at baseline and reassessment, all measures were performed with individuals wearing light clothing and no shoes (13). BMI was calculated using the conventional formula of weight in kilograms divided by the square of the height in meters. We created three categories of weight change based on the proportional differences between baseline measurement and reassessment three years later-stability (reference), loss (reduction of at least 5% in baseline weight) and gain (increase of at least 5% in baseline weight). Intention of weight loss and of change in dietary habits within the last 12-month period was evaluated by questionnaire at baseline.
Additionally, the absolute difference between WC measures at the two time points was used to create three categories according to whether a variation (decrease or increase) of at least 3 cm did or not (stable) occur.
Other measurements and definitions
A standardized interview at baseline investigated lifestyle habits and social conditions. Current smoking was defined as consumption of > 100 cigarettes in lifetime with persistent consumption at the time of the interview. Physical activity was defined as leisure physical activity, such as walking and/ or practicing any other physical exercise, for at least 20-30min within the last 90 days. Monthly household income was verified according to Brazilian minimum wages (lower category 1-4, intermediate category 4-10, higher category ≥ 10). Education was verified in years and stratified in three categories (never, < 4years, ≥4 years). through standardized interview at baseline (12).
BNP was measured in blood samples collected in tubes containing ethylenediaminetetraacetic acid and stored at -80°C until used. Subjects were asked to fast for 12 hours prior to early-morning (6:30–8:30 AM) phlebotomy. A microparticlebased immunoassay (MEIA/AxSYM; Abbott Laboratories) with 15-5,000 pg/mL as limits of detection and average interassay coefficients of variation of 12% was used. C-reactive protein (CRP) was measured after 12-hours fast and serum samples were stored at -80º C. Measurements were taken by the CRP immunonephelometric method, Dade-Behring N Latex CRP particle-enhanced immunoassay on an automatic nephelometer (BNII™,Dade Behring, Marburg, Germany) traceable to the international reference standard CRM 470 (14). The limit of detection of the CRP assay as provided by the manufacturer is 0.175-500mg/L and the CV is 2.2 – 5.8%. Total cholesterol, fasting blood glucose and creatinine levels were measured by traditional enzymatic methods. 12-lead ECGs were digitally recorded at rest using standardized procedures, analyzed by experienced cardiologists at the ECG Reading Center (EPICARE Center, Wake Forest University School of Medicine, Winston-Salem, NC), codified according to the Minnesota Code (MC) (15).
Cardio-metabolic conditions were defined by the presence of CAD, diabetes, hypertension, or dyslipidemia. CAD was defined as the presence of old myocardial infarction (major Q wave abnormalities (MC 1.1.x or 1.2.x) or minor Q waves abnormalities with ST segment or T-wave abnormalities (1.3.x and (4.1.x, 4.2, 5.1, or 5.2)). Diabetes was defined as a 12-h-fasting glucose ≥126 mg/dL and/or the use of insulin or oral hypoglycaemic agents. Hypertension was defined as systolic or diastolic pressure ≥ 140 or 90 mmHg using the mean of the two lowest blood pressure measurements out of three or use of anti-hypertensive medication. Dyslipidemia was defined as total cholesterol ≥ 200mg/dL.
Bambuí city was an endemic area of Chagas disease (Trypanosoma cruzi infection) and the infection remained highly prevalent in old persons due to a cohort effect, despite the successful interruption of the transmission by 1970. Thus, the presence of Chagas disease has been investigated and defined by seropositivity on three different assays performed concurrently (12).
Statistical analysis
We used histograms and Shapiro-Wilk tests to verify normality. Skewed variables (BNP, CRP, creatinine) were logtransformed. Characteristics of the participants were compared across the categories of weight change (stability as reference) by means of the Pearson Chi-square or Fisher's exact test (for frequencies), the ANOVA (for means) and the Kruskal-Wallis tests (for medians) for participants with general and abdominal obesity. We performed Kaplan-Meier analysis and compared survival across categories of weight change by log-rank test. Differences between measures at the two time points were compared by means of either paired t-test or Wilcoxon test. Pearson and Spearman coefficients were calculated to verify the correlation between changes in each measure.
Proportional hazards (PH) assumption was verified both by plotting the logarithm of the cumulative hazards against the logarithm of time to follow-up and by adding a time-dependent variable to the model (p=0.34) with both methods showing that there was not any violation of the assumption. Thus, in order to calculate hazard ratios (HR) and 95% confidence intervals (CI) for death associated with weight or WC change (stability as reference), Cox PH models were adjusted for age (continuous), gender and known risk factors for mortality derived both from results of the Bambuí cohort and other populations (16, 17, 18, 19) - BMI at baseline (continuous plus quadratic term), intentionality of weight loss (no, yes), current smoking (no, yes), physical activity (< or ≥ 3-5 times/week), Chagas disease, diabetes (no, yes), hypertension (no, yes), total cholesterol (continuous), major electrocardiographic abnormalities (no, yes), logtransformed serum B-type natriuretic peptide (continuous), log-transformed serum C-reactive protein (continuous), logtransformed creatinine (continuous), education (never, < 4, ≥4 school years), and monthly household income (1-4, 4-10, ≥ 10 minimum wage). Similarly to the association between BMI and mortality, the relation between weight/waist change and mortality might not be linear (e.g. a U-or J-shaped relation). One possible approach to incorporate non-linear effects of continuous predictors, such as weight and waist change, into the Cox model is to introduce smooth (continuous differentiable) polynomial functions known as natural splines. Thus, we performed regression splines analysis with five degrees of freedom, in order to detect a non-linear effect of weight/ waist change on the outcome. Subsequently, we stratified the analyses by BMI at baseline (< 25 versus ≥ 25 kg/m2) under the hypotheses that normal or underweight individuals might not benefit from weight loss or waist decrease differently from those with an increased BMI. Since gender, smoking, physical activity and the presence of Chagas disease might lead to different effects of weight loss on mortality (20), we also stratified by these variables. All data were analyzed using the R Statistical Environment version 3.1.2 (21) with the addition of the ‘smoothHR' package (22).
Results
Study population was comprised by 1138 participants, mostly women (718; 63.1%), with mean age of 68 (6.7) years. Among these, 681 individuals were overweight/obese, as defined by BMI or abdominal obesity, and 103 (8.8%) had normal weight with abdominal obesity. Median follow-up time from weight reassessment was 8.0 (6.4-8.0) years. From baseline to weight reassessment three years later, most of the participants had stable weight (696; 61.1%), whereas 251 (22.1%) and 191 (16.8%) had weight loss and gain, respectively. Weight loss was intentional in 46 (18.3%) of those who lost weight Median (IQR) loss and gain were 6.5 kg (3.0- 11.3) and 4.9 kg (3.5-6.6), respectively.
WC stability, decrease and increase was observed in 422 (37.3%), 418 (37.0%) and 291 (25.7%) participants, respectively. Median waist variation was 7.0 (4.4-11.0) cm among participants who decreased WC over time and 5.7 (4.0-7.8) cm among those who increased. Men (159; 38.3%) increased WC more commonly than women (132; 17.8%, p<0.001). Changes in weight and WC were moderately correlated (r Spearman=0.54; p<0.001). Baseline characteristics for the whole population and according to weight change subgroups are depicted in Table 1.
Table 1.
Baseline characteristics of participants according to weight change over the first three years of follow-up
| Characteristics | Weight change | ||||
|---|---|---|---|---|---|
| Total (n=1138) | Stability (n=696; 61.1%) | Loss (n =251; 22.1%) | Gain (n=191; 16.8%) | p value* | |
| Deaths, n (%) | 334 (29.3) | 176 (25.3) | 99 (39.4) | 59 (30.9) | <0.001 |
| Age, mean (SD) | 68 (6.7) | 68 (6.6) | 69 (7.4) | 66 (6.0) | 0.07 |
| Female sex, n (%) | 718 (63.1) | 398 (57.2) | 190 (75.7) | 130 (68.1) | <0.001 |
| Smoking, n (%) | 191 (16.8) | 114 (16.4) | 41 (16.3) | 36 (18.8) | 0.70 |
| Physically active‡, n (%) | 266 (23.4) | 176 (25.3) | 56 (22.3) | 34 (17.8) | 0.09 |
| Chagas disease, n (%) | 429 (37.7) | 261 (37.5) | 97 (38.6) | 71 (37.2) | 0.94 |
| Hypertension, n (%) | 424 (37.3) | 243 (34.9) | 110 (47.8) | 71 (37.2) | 0.04 |
| Diabetes mellitus, n (%) | 153 (13.4) | 79 (11.4) | 54 (21.5) | 20 (10.5) | <0.001 |
| BMI at baseline in kg/m2, median (IQR) | 25.1 (21.9-28.1) | 25.2 (22.1-28.1) | 25.9 (22.9-28.9) | 23.7 (20.6-26.9) | <0.001 |
| Waist at baseline in cm, median (IQR) | 92 (84-99) | 92 (84-99) | 93 (87-100) | 88 (82-96) | <0.001 |
| BNP in pg/ml, median (IQR) | 77 (41-141) | 70 (39-134) | 93 (48-158) | 81 (47-140) | 0.002 |
| C-reactive protein in mg/L, median (IQR) | 3.1 (1.4-6.1) | 2.9 (1.2-5.7) | 3.7 (1.7-6.9) | 3.1 (1.6-7.0) | 0.003 |
| Serum creatinine in mg/dL, median (IQR) | 0.84 (0.75-0.96) | 0.85 (0.75-0.97) | 0.83 (0.74-0.95) | 0.81 (0.72-0.95) | 0.06 |
| Total cholesterol, median (IQR) | 230 (202-268) | 230 (202-264) | 237 (208-279) | 223 (197-263) | 0.02 |
| Education, n (%) | |||||
| Lower | 344 (30.2) | 207 (29.8) | 82 (32.7) | 55 (28.8) | 0.87 |
| Intermediate | 664 (58.5) | 409 (58.8) | 143 (57.0) | 112 (58.6) | |
| Higher | 129 (11.3) | 79 (11.4) | 26 (10.3) | 24 (12.6) | |
| Monthly household income, n (%) | |||||
| Lower | 749 (65.8) | 453 (65.5) | 172 (69.1) | 124 (65.6) | 0.86 |
| Intermediate | 285 | ||||
| (25.0) | 178 (25.7) | 59 (23.7) | 48 (25.4) | ||
| Higher | 96 (9.2) | 61 (8.8) | 18 (7.2) | 17 (9.0) | |
| Intention to lose weight, n (%) | 229 (20.1) | 145 (20.9) | 46 (18.3) | 38 (19.9) | 0.08 |
| Normal weight and abdominal obesity, n(%) |
104 (9.1) |
53 (7.6) |
29 (11.6) |
22 (11.5) |
0.69 |
P value: ANOVA, Pearson’s chi-square or Fisher’s exact test, and the Kruskal Wallis test for differences between means, frequencies and medians, respectively † Leisure physical activity (walking or any other physical exercise) for at least 20-30min, ≥3-5 times/week ‡ Education: lower category-never studied, intermediate category -< 4 school years, higher category -≥4 school years) § Monthly household income in minimum wages (lower category 1-4, intermediate category 4-10, higher category ≥ 10)
There were 334 (29.3%) deaths throughout the followup. Among these, 176, (25.3%), 99, (39.4%) and 59 (30.9%) occurred in participants with stable weight, weight loss and weight gain categories (p<0.001), respectively. Conversely, the number of deaths did not differ across WC change categories) and (119, 28.2% versus 122, 29.2% versus 92, 31.6%; (p=0.61, for WC stability, decrease and increase, respectively). After adjustment for covariates, we found that both weight loss (HR 1.69; 95% CI 1.30-2.21) and gain (HR 1.37; 95% CI 1.01- 1.85) were significantly associated with an increased risk of mortality. Figure 1 displays this finding along the continuum of weight change. The lack of association between mortality and large weight variation is probably due to the small number of participants with weight change ≥25% (n=12; 1.0%). Neither was intention to lose weight associated with mortality (p=0.42) nor changed the association between weight change and mortaliy (p=0.69, for interaction). Stratification by sex, baseline BMI (overweight/obesity versus normal or underweight), smoking status or Chagas disease did not appreciably change these results either. Conversely, the association between weight change and risk of death was modified by physical activity with weight gain being associated with a decrease in the risk of death among participants who were physically active (Figure 2). Waist change was not significantly associated with mortality. These results did not change according to sex or BMI at baseline.
Figure 1.
Mortality risk associated with weight variation
Figure 2.
Mortality risk associated with weight variation according to physical activity status
Discussion
In this population of Brazilian elderly waist variation did not influence on subsequent mortality, whereas both weight loss and gain led to an increased mortality risk in the long-term follow-up in comparison to weight stability. This occurred also in older adults which were overweight/obese or had normal weight with abdominal obesity at baseline. However, in those who were physically active weight gain reduced the risk of death. Intention to lose weight did not modify the association between weight loss and mortality.
Although numerous studies have reported an increased risk of death associated with weight loss in older adults in general, only a small number of them addressed the issue in individuals which have an indication to lose weight according to current guidelines (23). The studies which selected only the elderly with prior excessive weight or abdominal obesity and cardio-metabolic disorders reported either an increased or non-significant risk of death associated with weight loss (24, 25, 26, 27, 28, 29). However, methodological differences make comparison to our findings difficult. The observational studies are very heterogeneous in regard to the approach to smoking status (stratification versus adjustment), the assessment of intentionality of weight change and assessment of the anthropometric measures (measured versus self-reported) as well as to the intervals of weight reassessment and follow-up periods. Both a reduced (30) and an increased risk (29)between voluntary weight loss and death have been previously reported. In our population, we did not find a significant effect of the intentionality of weight loss among participants who had weight or waist decrease.
Figure 3.
Mortality risk associated with waist circumference change
The increased mortality is probably related to the fact that weight loss does not necessarily mean fat loss. Harmful changes that accompany weight loss, such as malnutrition, loss of muscle (sarcopenia) and bone mass density as well as systemic inflammation, that can be particularly deleterious at older ages and overcome the potential metabolic and mechanical benefits of reducing fat mass (31, 32). Even among the overweight and obese elderly, weight loss might not beneficial for those who had not developed metabolic disorders throughout the lifespan. These individuals probably did not have an important clinically dysfunctional adipose tissue and consequently might have a greater benefit of its function as a reservoir in stressful events and crosstalk with the cardiovascular and gastrointestinal systems than those with cardio-metabolic abnormalities (33).
In regard to the effects of weight gain on mortality, previous studies have also observed either a non-significant or an increased risk of death associated with weight gain in the elderly (24). At older age, weight gain is usually secondary to an increase in visceral (intra-abdominal, intrahepatic) and intramuscular fat, which are more frequently associated with deleterious inflammatory, atherogenic and metabolic effects than an increase in peripheral/subcutaneous fat (34). Additionally, weight gain is probably a marker of unhealthy lifestyle habits, poor cardiorespiratory fitness, reduced mobility and functional status (35). This seems not to be the case in individuals who are physically active, in which weight gain might reflect an increase in muscle mass and strength as well as bone mass which are associated with good mobility and function in the elderly (32).
Differently from studies with middle-aged adults (11), we did not find a significant association between changes in WC over time and mortality in this population of predominantly female Brazilian elderly. WC is a surrogate of both abdominal subcutaneous and visceral fat. Reduction in visceral fat is probably associated with reduction of the pro-atherogenic cardio-metabolic and inflammatory abnormalities associated with it (36). Although the use of WC change allowed us to partially overcome the inaccuracy of BMI as a surrogate of adiposity in the elderly and to take into account the role of changes in body fat distribution, which might occur even in the elderly whose weight remain stable (37), the lack of relationship may have occurred due to a non-perfect correlation between WC and visceral fat. These limitations of anthropometric measures highlight the need of research with more accurate measures of adiposity compartments, such as dual-energy X-ray absorptiometry. Moreover, controlled studies which assess the influence of anthropometric measures changes on muscle mass and functionality might provide insights to the issue of the effect of weight change on long-term mortality (36).
Our study provides data on the influence of weight change on death independently of known risk factors in a population of elderly with low educational and income levels. The longterm follow-up with a low proportion of losses to follow-up and the definition of overweight/obesity based on measures (not self-reported) of both BMI and waist circumference are major strengths of our study. However, the small number of overweight/obese subjects, particularly of those with BMI≥ 35 kg/m2 (n=33; 2.9%), comprises a limitation. Additionally, we acknowledge that, although we adjusted both for chronic diseases or makers of disease (e.g. diabetes, hypertension, Chagas diseases cholesterol, creatinine, CRP and BNP) that might confound the association between changes in anthropometric measures and mortality and performed sensitivity analysis for non-smokers, residual confounding might still be possible due incomplete data regarding other conditions, such as cancer.
In conclusion, we observed that weight change (either loss or gain) is a marker of worse prognosis in comparison to weight stability even in older adults with general or abdominal obesity. Weight gain, however, might not be harmful in the long-term in those who are physically active. Since weight stability increases survival even in overweight and obese elderly, promoting healthy habits to maintain weight rather than focusing on measures to lose weight might benefit older adults. These findings suggest that applying general recommendations of weight control in adults to the elderly might potentially lead to increased mortality.
Acknowledgments: This work was supported by Brazilian public research agencies: Financiadora de Estudos e Projetos, Rio de Janeiro, Brazil; Ministério da Saúde, Brasília, Brazil; Fundação de Amparo à Pesquisa do Estado de Minas Gerais, Belo Horizonte, Brazil. Alline M Beleigoli is supported by Conselho de Aperfeiçoamento de Profissional de Ensino Superior (CAPES), Belo Horizonte, Brazil.
Conflict of interest: All participating authors declare no conflict of interest. AMB declares no conflict of interest. MFHD declares no conflict of interest. EB declares no conflict of interest. JLS declares no conflict of interest. MFLC declares no conflict of interest. ALR declares no conflict of interest.
Ethical Statement: The study was approved by the Ethics Committee of the Fundação Oswaldo Cruz, Brazil, and all participants signed an informed consent form.
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