Abstract
Objectives
To examine the association between BMI and all-cause mortality in the oldest old (≥80 years).
Design
The study used a prospective cohort study design.
Setting
Chinese Longitudinal Healthy Longevity Survey (CLHLS) between 1998/99 and 2011.
Population
8026 participants aged 80 years and older were followed every two to three years.
Measurements
Body weight and knee height were measured. Height was calculated based on knee height using a validated equation. Deaths were ascertained from family members during follow-up.
Results
The mean BMI was 19.8 (SD 4.5) kg/m2. The prevalence of underweight, overweight and obese was 37.5%, 10.2% and 4.4%, respectively. There were 5962 deaths during 29503 person-years of follow-up. Compared with normal weight, underweight was associated with a higher mortality risk (HRs: 1.20 (95%CI 1.13-1.27) but overweight (HR 0.89 (95%CI 0.81-0.99)) were associated with a lower risk. Obesity had a HR 0.91 (95%CI 0.78-1.05) for mortality.
Conclusion
Among oldest old Chinese, underweight is associated with an increased risk of all-cause mortality but overweight is associated with a reduced risk. Interventions to reduce undernutrition should be given priority among the oldest old Chinese.
Key words: Body mass index, all-cause mortality, Chinese, older adults, cohort study
Introduction
Obesity is a risk factor for many chronic diseases and its prevalence has increased over the past decades worldwide (1, 2). There is a growing interest in the association between body mass index (BMI) and mortality as shown by the number of publications in the area. The findings are mixed with the associations between BMI and mortality reported to be U shaped (3, 4), J shaped or reversed J shaped (5). Several systematic reviews and pooled analyses of prospective studies of adults in the general population have been conducted in recent years (6, 7, 8, 9, 10) and suggest a U shaped association. One review synthesized the evidence from Asian countries and found that underweight was associated with substantial increased risk of death in all study populations with a mean age at entry below 60 years (6). In East Asia, the association between BMI and all-cause mortality was found to be U-shaped (6). However, most of the studies were conducted in adults in the general populations. One meta-analysis specifically focused on older adults aged 65 years and above (7). In the study, a U shaped association between BMI and mortality was found and the lowest risk was among those with BMI between 24.0 and 30.9. However, the review included 32 studies conducted only in Europe, North America, Canada and Australia. In a meta-analysis among older people living in nursing homes in Europe, America, Canada, and Asia (including China) , it has been shown that there was an inverse association between BMI and all-cause mortality (11), supporting the ‘obesity paradox'. In China, studies on older adults aged 65 years and above suggest that being overweight/obese was not associated with an increased all-cause mortality (12, 3). In general, studies among adults aged 80 years and above are limited.
The current study aimed to assess the association between BMI (both as a categorical and continuous variable) and mortality among oldest adults in China. We hypothesized that there is an inverse association between BMI and all-cause mortality.
Methods
Data source
The study used data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). A detailed description of the study population has been previously published (13, 14). In brief, the baseline survey was conducted in 1998/1999 in 631 randomly selected counties and cities of 22 of China's 31 provinces. Participants were followed up in 2000, 2002, 2005, 2008-09 and 2011. In total, 9093 participants participated in the 1998/99 baseline survey. In the analysis, we excluded 134 participants aged below 80 years and 923 without complete anthropometric measurements or who had BMI<10 or >50 (n=10), leaving 8026 participants for the current analysis. In total 1943 (24.2%, 983 urban, 960 rural) participants were lost to follow-up during the study. The mean follow-up duration was 4.3(SD 2.9) years among those lost to follow-up. The detailed characteristics of those lost to follow-up have been published elsewhere [15]. Those lost to follow-up were in general younger and more likely to be living in an urban area.
This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human subjects/patients were approved by Human Research Ethics Committees in China and the United States. All participants signed a consent form.
Data collection and measurements
Participants were interviewed in their homes by health workers using a standard questionnaire (available online: http://centerforaging.duke.edu/documentation). All the interviewers were intensively trained before the survey.
Outcome variable: Death ascertainment
In each follow-up survey, information on deaths and indicators of pre-death health status were collected through interviews with a close family member.
Exposure variable: BMI
Weight and knee height were measured during the survey.
Height was estimated based on knee height using the equations developed by Zhang et al (16).
Men: height=67.78+2.01 × knee height
Women: height=74.08+1.81 × knee height
Based on BMI (kg/m2), participants were was categorized as underweight (<18.5), normal (18.5-23.9), overweight (24-27.9) and obese (≥28). In the analysis, normal BMI was used as reference group.
Covariates
Sociodemographic factors: Education was grouped into four duration categories: 0 years, 1-5 years, 6-9 years, >9 years. Occupation before age of 60 was recoded into manual or nonmanual based on a question with nine occupational categories. Participants were asked with whom they were living.
Lifestyle factors: Cigarette smoking status was categorized into non-smokers (representing never smokers), ex-smokers and current smokers based on current and past history of smoking. The participants were asked whether they drink alcohol (yes/no) as well as the type and amount of alcohol they consumed. Information on regular physical activity was collected using question “Do you do exercise regularly at present, including jogging, playing ball, running and Qigong? and recoded as yes or no. Participants were asked about whether they undertook a list of other eight activities including housework, growing vegetables/other field work, gardening, reading newspapers/books, raising domestic animals, playing cards and/or mah-jong, watching TV and or/listening to radio, and undertaking religious activities. A score was given to each activity based on weekly frequency: almost every day (7), sometimes (3), never (0). A summary score of all these eight activities was created (range from 0 to 56) and recoded into quartiles.
The participants were asked to report their frequency intake of fruit, vegetable, meat, fish, and tea.
Health conditions: Total number of chronic diseases (NCD) was calculated as the sum of 13 conditions including hypertension, diabetes, heart disease, cardiovascular disease, bronchitis/emphysema/pneumonia/asthma, tuberculosis, cataracts, glaucoma, cancer, prostate tumour, gastric/duodenal ulcer, Parkinson's disease and bedsores. The Katz Activities of Daily Living (ADL) Scale was used to assess participants' disability (17). Having difficulty performing any one or more of the ADL tasks (bathing, dressing, toileting, transfers, continence and eating) was defined having an ADL disability.
Statistical analysis
The Chi square test was used to compare differences in categorical variables and ANOVA in continuous variables. For each participant, person-years of follow-up were calculated from the date of the baseline survey to the date of death, lost to follow-up or the date of last follow-up in 2011, whichever came first. The association between BMI categories and all-cause mortality was analysed using Cox proportional hazard models, adjusting for multiple covariates. We provide both crude and adjusted hazard ratios (HRs). Two models assessed the association between BMI categories and mortality. The first model controlled for age (continuous) and gender; the second model further adjusted for socio-demographic and lifestyle factors (smoking, alcohol drinking, physical activity, intake of fruit, vegetable, meat, fish and tea), residence (urban/rural) and education. As the sample size in the full model was above 7625 (95.0% of the whole sample), we did not impute the missing data. In the sensitivity analyses we excluded those who died within the first year of the baseline survey. The proportional hazards assumption in the Cox model was assessed with graphical methods and with models including time-by-covariate interactions. For BMI categories, the proportionality assumption was appropriate. Kaplan-Meier survival curve by BMI categories was presented.
We used restricted cubic spline regressions (18) to graphically model the associations between BMI (continuous) and the risk of all-cause mortality. Three knots were put at the 5, 50 and 95 percentiles of BMI.
Tests for interactions between BMI categories and sex, smoking, having NCD and ADL disability were conducted by adding a multiplicative term between these variables and the BMI categories in the fully adjusted models. Statistical significance was considered when P < 0.05 (two-sided). All analyses were performed using Stata 14 (Stata Corp., College Station, TX, USA).
Results
The mean age of the 8026 participants at baseline was 90.1 (SD 6.9) years in men and 93.5 (SD 7.7) years in women. The mean BMI was 19.8 (SD 4.5) kg/m2. The prevalence of underweight, overweight and obese was 37.5%, 10.2% and 4.4%, respectively. Only 63 (0.78%) participants had BMI above 35. There was a significant decrease in age across BMI categories of underweight, normal, overweight and obesity. Women were more likely to be underweight than men. Dietary habits were different across categories of BMI: those who were underweight had a lower intake of fruit, meat, and fish than other groups.
During 29503 person-years of follow-up (a median follow-up of 3.6 years), there were 5962 deaths. Kaplan-Meier survival curve shows a clear difference in the proportion of survival according to BMI categories (Figure 1). Compared with normal weight, underweight was associated with increased risk of mortality (hazard ratio (HR) 1.22 (95%CI 1.15-1.29)) after adjusting for age and gender (Table 2). The association remained after further adjustment for sociodemographic and lifestyle factors with an HR of 1.20 (95% CI 1.13-1.27). Overweight (HR 0.89 (95%CI 0.81-0.99)) was associated with a lower risk of mortality than normal weight. Obesity had a HR 0.91 (95%CI 0.79-1.04) for mortality. The above associations were slightly attenuated after we excluded those (n=1025) who died within 1 year of follow-up. We conducted further analysis using small increments of BMI, and the results suggested that the optimal BMI range for survival was 22.0-25.9 kg/m2 (Supplemental Table 1). If we use BMI≥23 and ≥27 as cutoff cutoff for overweight and obesity, the HRs for mortality were 1.19 (1.12-1.26), 1.00, 0.90(0.82-0.98), 0.91(0.81-1.03) for underweight, normal, overweight and obesity, respectively.
Figure 1.
Kaplan-Meier survive curve by BMI categories
Table 2.
Hazard ratio (95% CI) for all-cause mortality according to BMI categories (n=8206)
| Underweight <18.5 | Normal 18.5-23.9 | Overweight 24-27.9 | Obese ≥28 | p for trend | |
|---|---|---|---|---|---|
| Participants a risk (n) | 3008 | 3846 | 822 | 350 | |
| Person-years | 9607 | 14849 | 3568 | 1478 | |
| Cases | 2439 | 2795 | 520 | 208 | |
| Model 1 | 1.22 (1.15-1.29) | 1.00 | 0.83 (0.75-0.91) | 0.86 (0.75-0.99) | <0.001 |
| Model 2 | 1.20 (1.13-1.27) | 1.00 | 0.89 (0.81-0.99) | 0.91 (0.79-1.04) | <0.001 |
| Model 3 | 1.16 (1.08-1.23) | 1.00 | 0.92 (0.83-1.02) | 0.92 (0.80-1.07) | <0.001 |
Model 1 adjusted for age and gender; Model 2 further adjusted for residence, education, smoking, alcohol drinking, physical activity, intake of fruit, vegetable, meat, fish and tea; Model 3 excluded those died within 1 years of follow-up.
Supplemental Table 1.
Association between BMI and all-cause mortality among Chinese old people aged 80+ years
| BMI (kg/m2) | case/N | HR(95% CI) | p |
|---|---|---|---|
| 12-12.9 | 92/102 | 2.20(1.71-2.84) | <0.001 |
| 13-13.9 | 190/216 | 1.74(1.41-2.13) | <0.001 |
| 14-14.9 | 362/428 | 1.38(1.16-1.66) | <0.001 |
| 15-15.9 | 463/566 | 1.45(1.22-1.72) | <0.001 |
| 16.16.9 | 620/787 | 1.36(1.16-1.61) | <0.001 |
| 17-17.9 | 599/762 | 1.43(1.21-1.69) | <0.001 |
| 18-18.9 | 616/819 | 1.32(1.12-1.56) | 0.001 |
| 19-19.9 | 585/786 | 1.30(1.10-1.54) | 0.002 |
| 20-20.9 | 493/680 | 1.23(1.04-1.46) | 0.017 |
| 21-21.9 | 357/508 | 1.16(0.97-1.38) | 0.109 |
| 22-22.9 | 301/414 | 1.03(0.86-1.24) | 0.719 |
| 23-23.9 | 243/359 | 1.14(0.94-1.38) | 0.185 |
| 24-24.9 | 185/285 | 1.00 | |
| 25-25.9 | 126/204 | 1.05(0.83-1.31) | 0.702 |
| 26-26.9 | 105/160 | 1.18(0.93-1.51) | 0.169 |
| 27-27.9 | 86/150 | 1.13(0.87-1.46) | 0.353 |
| 28-28.9 | 72/111 | 0.93(0.71-1.22) | 0.583 |
| 29-29.9 | 46/79 | 1.09(0.79-1.51) | 0.587 |
| 30-39.9 | 105/176 | 1.18(0.93-1.50) | 0.181 |
| 40+ | 12/18 | 1.39(0.78-2.50) | 0.266 |
Model adjusted for age, gender, residence, education, smoking, alcohol drinking, physical activity, intake of fruit, vegetable, meat, fish and tea.
In subgroup analyses (Table 3), we found a significant interaction between BMI and smoking. However, the main findings were similar in all subgroups with a higher all-cause mortality among those who underweight.
Table 3.
Hazard ratio (95% CI) for all-cause mortality according to BMI categories: subgroup analysesa
| Subgroup (even/n) | Underweight <18.5 | Normal 18.5-23.9 | Overweight 24-27.9 | Obesity ≥28 | p for interaction |
|---|---|---|---|---|---|
| Sex | |||||
| Men (2377/3225) | 1.22 (1.11-1.34) | 1.00 | 0.94 (0.83-1.07) | 0.93 (0.79-1.11) | 0.669 |
| Women (3585/4801) | 1.17 (1.09-1.26) | 1.00 | 0.84 (0.72-0.97) | 0.87 (0.69-1.08) | |
| Smoking | |||||
| Smokers (1921/2632) | 1.39 (1.26-1.54) | 1.00 | 0.92 (0.79-1.07) | 0.87 (0.71-1.07) | 0.002 |
| Non-smoker (4039/5392) | 1.12 (1.05-1.20) | 1.00 | 0.89 (0.78-1.01) | 0.96 (0.80-1.15) | |
| Having NCD b | |||||
| Yes (2943/4043) | 1.25 (1.15-1.36) | 1.00 | 0.92 (0.81-1.06) | 0.88 (0.73-1.05) | 0.261 |
| No (3019/3983) | 1.17 (1.08-1.26) | 1.00 | 0.86 (0.75-0.99) | 0.95 (0.78-1.16) | |
| ADL disability | |||||
| Yes (2124/2671) | 1.27 (1.15-1.40) | 1.00 | 0.83 (0.69-0.99) | 0.94 (0.73-1.21) | 0.206 |
| No (3838/5355) | 1.17 (1.09-1.26) | 1.00 | 0.91 (0.81-1.02) | 0.88 (0.75-1.04) |
a. Models adjusted for age, gender, residence, education, smoking, alcohol drinking, physical activity, intake of fruit, vegetable, meat, fish and tea. Stratification variables were not adjusted in corresponding models; b. Having any of the following conditions including hypertension, diabetes, heart disease, cardiovascular disease, bronchitis/emphysema/pneumonia/asthma, tuberculosis, cataracts, glaucoma, cancer, prostate tumour, gastric/duodenal ulcer, Parkinson’s disease and bedsores
We found a non-linear association between BMI and all-cause mortality in both men and women (Figure 2). The shapes of the curve were different between smokers and non-smokers (Figure 3). The risk of mortality for low BMI was much higher for smokers than non-smokers.
Table 1.
Baseline Sample characteristics by BMI categories (n=8026)
| Underweight <18.5 | Normal 18.5-23.9 | Overweight 24-27.9 | Obese ≥28 | p-value | |
|---|---|---|---|---|---|
| N | 3008 | 3846 | 822 | 350 | |
| Age, mean (SD) | 94.2 (7.3) | 91.4 (7.4) | 89.7 (7.2) | 87.9 (7.2) | <0.001 |
| Sex | <0.001 | ||||
| Men | 772 (25.7%) | 1759 (45.7%) | 470 (57.2%) | 224 (64.0%) | |
| Women | 2236 (74.3%) | 2087 (54.3%) | 352 (42.8%) | 126 (36.0%) | |
| Years of education | <0.001 | ||||
| No | 2356 (78.3%) | 2458 (63.9%) | 425 (51.7%) | 165 (47.1%) | |
| 1-5 | 449 (14.9%) | 908 (23.6%) | 249 (30.3%) | 111 (31.7%) | |
| 6-9 | 118 (3.9%) | 274 (7.1%) | 72 (8.8%) | 39 (11.1%) | |
| >9 | 70 (2.3%) | 187 (4.9%) | 74 (9.0%) | 35 (10.0%) | |
| Missing | 15 (0.5%) | 19 (0.5%) | 2 (0.2%) | 0 (0.0%) | |
| Co-residence of interviewee | 0.28 | ||||
| with household member(s) | 2599 (86.4%) | 3300 (85.8%) | 711 (86.5%) | 299 (85.4%) | |
| alone | 315 (10.5%) | 400 (10.4%) | 84 (10.2%) | 45 (12.9%) | |
| in an institution | 94 (3.1%) | 146 (3.8%) | 27 (3.3%) | 6 (1.7%) | |
| ADL disability | <0.001 | ||||
| No | 1849 (61.5%) | 2665 (69.3%) | 585 (71.2%) | 256 (73.1%) | |
| Yes | 1153 (38.3%) | 1162 (30.2%) | 234 (28.5%) | 92 (26.3%) | |
| Missing | 6 (0.2%) | 19 (0.5%) | 3 (0.4%) | 2 (0.6%) | |
| Intake of fruit | <0.001 | ||||
| Never | 973 (32.4%) | 1017 (26.4%) | 215 (26.2%) | 79 (22.6%) | |
| Occasionally | 1671 (55.6%) | 2185 (56.8%) | 406 (49.4%) | 185 (52.9%) | |
| Almost daily | 361 (12.0%) | 644 (16.7%) | 201 (24.5%) | 86 (24.6%) | |
| Intake of vegetable | 0.006 | ||||
| Never | 155 (5.2%) | 158 (4.1%) | 27 (3.3%) | 12 (3.4%) | |
| Occasionally | 563 (18.7%) | 659 (17.1%) | 127 (15.5%) | 74 (21.1%) | |
| Almost daily | 2289 (76.1%) | 3028 (78.8%) | 668 (81.3%) | 264 (75.4%) | |
| Intake of meat | 0.012 | ||||
| Never | 592 (19.8%) | 721 (18.8%) | 166 (20.3%) | 59 (16.9%) | |
| Occasionally | 1569 (52.4%) | 1969 (51.5%) | 382 (46.8%) | 169 (48.3%) | |
| Almost daily | 832 (27.8%) | 1136 (29.7%) | 268 (32.8%) | 122 (34.9%) | |
| Intake of fish | <0.001 | ||||
| Never | 1008 (33.8%) | 1142 (30.0%) | 250 (30.6%) | 98 (28.1%) | |
| Occasionally | 1635 (54.9%) | 2200 (57.7%) | 442 (54.1%) | 194 (55.6%) | |
| Almost daily | 335 (11.2%) | 468 (12.3%) | 125 (15.3%) | 57 (16.3%) | |
| Intake of tea | <0.001 | ||||
| Never | 1795 (62.0%) | 2045 (55.4%) | 379 (47.9%) | 150 (45.2%) | |
| Occasionally | 490 (16.9%) | 672 (18.2%) | 156 (19.7%) | 57 (17.2%) | |
| Almost daily | 612 (21.1%) | 976 (26.4%) | 256 (32.4%) | 125 (37.7%) | |
| Residence | <0.001 | ||||
| Urban (city/town) | 858 (28.5%) | 1479 (38.5%) | 402 (48.9%) | 161 (46.0%) | |
| Rural | 2150 (71.5%) | 2367 (61.5%) | 420 (51.1%) | 189 (54.0%) | |
| Smoking | <0.001 | ||||
| Current smoker | 437 (14.5%) | 730 (19.0%) | 178 (21.7%) | 71 (20.3%) | |
| Ex-smoker | 363 (12.1%) | 594 (15.4%) | 173 (21.0%) | 86 (24.6%) | |
| Non-smoker | 2207 (73.4%) | 2521 (65.6%) | 471 (57.3%) | 193 (55.1%) | |
| Alcohol drink or not at present? | 0.007 | ||||
| Yes | 680 (22.6%) | 940 (24.5%) | 222 (27.0%) | 101 (28.9%) | |
| No | 2327 (77.4%) | 2903 (75.5%) | 599 (73.0%) | 248 (71.1%) | |
| Exercise or not at present? | <0.001 | ||||
| No physical activity | 2407 (80.1%) | 2703 (70.3%) | 486 (59.1%) | 209 (59.7%) | |
| Having physical activity | 598 (19.9%) | 1141 (29.7%) | 336 (40.9%) | 141 (40.3%) | |
| Quantiles of other activities score | <0.001 | ||||
| Q1 (0) | 1000 (33.2%) | 816 (21.2%) | 117 (14.2%) | 45 (12.9%) | |
| Q2 (3-7) | 943 (31.3%) | 1176 (30.6%) | 242 (29.4%) | 94 (26.9%) | |
| Q3 (9-14) | 608 (20.2%) | 910 (23.7%) | 216 (26.3%) | 96 (27.4%) | |
| Q4 (15-56) | 457 (15.2%) | 944 (24.5%) | 247 (30.0%) | 115 (32.9%) | |
| Number of chronic diseases, mean (SD) | 0.7 (0.9) | 0.7 (1.0) | 0.9 (1.1) | 1.1 (1.2) | <0.001 |
Figure 2.
Association between body mass index (BMI) and all-cause mortality
Figure 3.
Association between body mass index (BMI) and all-cause mortality
Discussion
In the large prospective cohort study of oldest older Chinese, we found that underweight was associated with an increased risk of mortality. However, overweight/obesity was inversely associated with mortality. The shape of the association between BMI and mortality differs by smoking status. The risk of mortality was the highest among smokers with low BMI. Our study provides additional support for the obesity paradox among oldest older adults. The findings are independent of smoking and chronic diseases.
Comparison with other studies
Our findings are different from Western countries. We did not find a U shaped association between BMI and mortality (7). It could be that our sample is relatively low in BMI. The prevalence of BMI above 35 is very low. In studies with U-shaped association, the increased mortality happened when BMI was above 35 (4). However, the shape of the association between BMI and mortality was similar to studies conducted in older adults in China (3, 12).
To the best of our knowledge, this is the first study in China with enough sample power to assess the association between BMI and mortality among those aged 80 years and above with more than five years follow-up. Several potential mechanism for obesity paradox have been hypothesized (19) including 1) excess fat may act as a metabolic reserve during illness or injury; 2) overweight/obesity protects against osteoporotic fracture and cognitive decline.
Gender and mortality
Although women had a lower risk of mortality in the sample (15), the association between BMI and mortality was similar between men and women.
Interaction between smoking and BMI
Our finding of a significant increase of mortality among smokers with low BMI was consistent with other studies. In China, the prevalence of smoking among women is generally low (2.4% in women vs 52.8% in men) (20). In our sample, less than 8% of women were current smokers. The inverse association between BMI and all-cause mortality is independent of smoking status. It is unlikely caused by reverse causation due to smoking, especially among women.
Nutritional status among oldest older Chinese
The participants in our study are healthy survivors. Compared with other Western population, the mean BMI in our sample was much lower and the prevalence of overweight (10.2%) and obese (4.5%) was relatively low. It is possible that those who were overweight/obese died earlier. However, the obesity epidemic is limited to the last three decades in China and affects more of those with high socioeconomic status suggesting that premature death among overweight/obese is the sample is unlikely the explanation. Given that more than one third of the participants were underweight, this high prevalence needs to be addressed to reduce avoidable morbidity and mortality.
Unique characteristics of the cohort
Most of the participants were born in 1910s and experienced hardship during the early and mid-life. They survived wars, Chinese famine, and also experienced westernization of food and lifestyle. As a generation, it can be speculated that overweight/obesity in this cohort is rare till the age of 60 years (around 1970s). In the Framingham Cohort Study, it has been found that the duration of obesity was associated with the risk of mortality among adults aged 28-62 years (21). In our study, it could be that the duration of obesity is not long enough to cause adverse effects. Thus it may not be appropriate to conclude that obesity has no effect on survival among oldest older Chinese. In the context of obesity and mortality, generational effects should not be ignored. Different from Western countries, socioeconomic status is positively associated with obesity in China. In our sample, education level increases with the increase of BMI. Thus the observed beneficial effects of obesity may be confounded by socioeconomic status. Although we had adjusted for education, residual confounding is possible.
Strengths and limitations
The strengths of the study are the relatively large sample size and the detailed information on lifestyle and chronic conditions. It is representative of the oldest older Chinese and findings are generalizable. We have conducted several subgroup and sensitivity analyses by assessing the association between BMI and mortality among non-smokers, those without chronic diseases, and those without ADL disability. Furthermore, the risks were robust to the use of BMI as a categorical as well as continuous variable. The main limitation is the lack of measured height. It is difficult to measure height among individuals with disability or those who cannot stand, which is common among the oldest old. However, using knee height to predict body height is robust. The prediction formula we used was validated in Chinese aged 65 years and above (16). We compared the BMI in the current study with those with measured BMI (based on measured height and weight) in China Health and Nutrition Survey (CHNS) in 2000 (22) and found the mean values are similar among those aged 80-90 years (20.8 vs 21.1). Secondly, the lost to follow-up is high (about 25%) due to city construction and moving of residences. However, this may not cause substantial bias. Finally, the prevalence of obesity as well as the assumed duration of obesity is low in the sample. With the increasing burden of obesity at younger age in China, further research is needed.
In conclusion, low BMI is associated with increased risk of mortality among oldest older Chinese. Overweight/obesity is inversely associated with mortality as compared with normal weight. Intervention on undernutrition should be given priority among oldest old Chinese.
Acknowledgments
Data used for this research was provided by the study entitled “Chinese Longitudinal Healthy Longevity Survey (CLHLS) managed by the Center for Healthy Aging and Development Studies, Peking University. CLHLS is supported by funds from the U.S. National Institutes on Aging (NIA), China Natural Science Foundation, China Social Science Foundation, and UNFPA. The study sponsors had no role in the study design, analysis, interpretation of data, writing of the report, or in the decision to submit the paper for publication.
Author contributions
JW and ZS contributed to the conception, analysis, and interpretation of data; drafting of the report; and have given approval of the final version for publication. AWT, TZ and SA contributed to analysis and interpretation of the data, commented on the report, revising the manuscript and approving the final version for publication.
Conflicts of interest
We declare that we have no conflicts of interest.
Ethical standard
This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human subjects/patients were approved by Biomedical Ethics Committee of Peking University (IRB00001052-13074). All participants signed a consent form.
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