Joint association between body fat and its distribution with all-cause mortality: A data linkage cohort study based on NHANES (1988-2011)
Joint association between body fat and its distribution with all-cause mortality: A data linkage cohort study based on NHANES (1988- 2011)
Bin Dong 1 2
Yang Peng 1
Zhiqiang Wang 1
Odewumi Adegbija 1
Jie Hu 0 1
Jun Ma 1 2
Ying- Hua Ma 1 2
0 Institute for Urban Indigenous Health , Brisbane, Queensland , Australia
1 Editor: Laurie Twells, Memorial University , CANADA
2 School of Public Health, Peking University Health Science Center , Beijing , P. R. China , 2 Centre for Chronic Disease, School of Medicine, The University of Queensland , Herston, Queensland , Australia
Data Availability Statement: Data are available
from the NHANES, Centers for Disease Control and
Prevention, USA (https://wwwn.cdc.gov/nchs/
nhanes/nhanes3/datafiles.aspx#core). They are
third party data, and others would be able to
access these data in the same manner as the
authors. Authors confirm that we did not have any
special access privileges that others would not
A total of 4999 deaths occurred during 19-year follow-up. A U-shaped association between
BF% and mortality was found in both sexes, with the adjusted hazard ratios for other groups
between 1.02 (95% confidence interval: 0.89, 1.18) and 2.10 (1.47, 3.01) when BF% groups
of 25±30% in males and 30±35% in females were used as references. A non-linear
relationship between WHR and mortality was detected in males, with the adjusted hazard ratios
among other groups ranging from 1.05 (0.94, 1.18) to 1.52 (1.15, 2.00) compared with the
WHR category of 0.95±1.0. However in females, the death risk constantly increased across
the WHR spectrum. Joint impact of BF% and WHR suggested males with BF% of 25±30%
and WHR of 0.95±1.0 and females with BF% of 30±35% and WHR <0.9 were associated
with the lowest mortality risk and longest survival age compared with their counterparts in
Funding: This study was funded by the National
Health and Medical Research Council
(APP1042343), University of Queensland
International Scholarship (44078179) and the
Excellent Talents Fund Program of Peking
University Health Science Center
(BMU2017YJ002). The funders had no role in
study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
This study supported the use of body fat distribution in addition to fat amount in assessing
the risk of all-cause mortality.
Recent studies demonstrated the U-shaped association of body mass index (BMI) and
allcause mortality. These findings suggested both underweight and obesity were related with the
increased risk of mortality, and the lowest risk of mortality in the whole population was
observed with a BMI of around 25 kg/m2 [1±3]. However, BMI cannot discriminate between
lean tissue and body fat mass [
]. Some researchers used the skinfold thickness to estimate the
],but the skinfold thickness is a measure of subcutaneous fat mass and therefore it is
not able to measure intra-abdominal depots. It is unclear whether the U-shaped pattern of the
relationship between fat and mortality persists, when more direct measurement of body fat
percentage (BF%), rather than BMI and skinfold thickness, is used. Furthermore, the optimal
cut-off points for the BF% that associated with the lowest mortality risk remain unclear [
In addition, BMI cannot determine the patterns of fat distribution within the body which
were identified with other indicators, such as waist-hip ratio (WHR) [
]. There is increasing
evidence that variation in location of body fat is associated with different risk of cardiovascular
disease, diabetes and cancer [
]. Previous studies have also reported the association
between WHR and mortality.The mortality risk varies among individuals with the same
BF% but different body fat distribution has not been fully characterized. The joint association
between body fat amount and fat distribution with the risk of mortality requires further
In this study, the public released data of National Health and Nutrition Examination Survey
(NHANES) III from 1988±1994 which were linked to the mortality data until 31 December
2011 were used. We hypothesised that BF% and WHR have a combined impact on premature
death risk. These findings may improve our understanding on the association between body
fat and premature mortality, and contribute to current inconsistent recommendations
regarding optimal BF%.
Subjects and methods
Public released data of NHANES III were used in this cohort study, which were collected by
National Center for Health Statistics of U.S. Centers for Disease Control and Prevention. The
detail of the survey design was described elsewhere [
]. In brief, NHANES III was a nationally
representative cross-sectional survey conducted from 1988 until 1994. This survey included a
national, complex multistage, clustered, stratified probability sample of the civilian,
noninstitutionalized American population. Data collection occurred during a home interview and a
health examination conducted in a mobile examination center. The Institutional Review
Board at the Centers for Disease Control and Prevention approved the study design and all
participants provided written informed consent [
]. Our project of analysing the survey data
was approved by the School of Low Risk Ethical Review Committee of the University of
A total of 30818 adolescents and adults were examined in NHANES III with an overall
examined response rate of 78%. Among 19370 adults aged between 18 and 89 years, pregnant
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women were excluded because they were not eligible for the bioelectrical impedance analysis
(BIA) procedure (n = 322). Another 25 individuals without follow-up data were also removed
from the sample. Additionally, participants with missing data of either WHR or BIA resistance
were excluded as well (n = 2608), which lead to the final sample of 16415 participants for analysis.
During the home interview, self-reported sex, age, ethnicity (non-Hispanic white,
non-Hispanic blacks, Mexican American, and the other),year of education, urbanization (metropolitan
area, and other), and family income (< $20000/year, and $20000/year) were recorded.
Smoking status, including current, former, and never smoking, was identified according to the
use of cigarettes, cigars, or pipe tobacco. Participants who smoked at least 100 cigarettes, 20
cigars, or 20 pipes of tobacco in their lifetime, but no longer smoked in the home interview
were considered as former smokers. Leisure-time physical activity, including jogging or
running, riding a bicycle or exercise bicycle, swimming, aerobic dancing, other dancing,
calisthenics or floor exercises, gardening or yard work, weight lifting, and other activities, and the
frequency of doing these activities was recorded. These information were used to calculate the
physical activity, which was further classified as 6 groups (0, 1±5, 6±10, 11±20, 21±30, and
31 times/month). The consumption frequency and unit of alcoholic beverages (beer, wine,
liquor) over the past month also were assessed, which was additionally classified into 6 groups
(0, > 0 ± 5, > 5 ± 10, > 10 ± 20, > 20 ± 30, and > 30 drinks/month). A participant
was identified as hypertensive if he/she had an average seated blood pressure 140/90 mm Hg
], or was told had hypertension/ high blood pressure by a doctor or a health professional.
History of pre-existing chronic diseases, including diabetes, heart attack, heart failure,
stroke, chronic bronchitis, emphysema, and cancer, was also collected. Individuals with
elevated plasma glucose ( 7 mmol/l) or elevated HbA1c ( 6.5%), as well as those who were
diagnosed by doctors, were identified as diabetic patients [
]. All other chronic conditions
were diagnosed by doctors.
Trained technicians measured height to the nearest 0.1cm using a fixed stadiometer, and
measured circumference measures to the nearest 0.1 cm using a steel measuring tape. Waist
circumference (WC) was measured at the level of iliac crest at the end of a normal expiration.
Hip circumference (HC) was measured at the widest circumference around the buttocks. The
WHR was calculated as WC (cm) divided by HC (cm).
Body composition was also examined. Participants had a single, tetrapolar BIA
measurement of resistance (Res) and reactance at 50 kHz taken between the right wrist and ankle while
in a supine position, using Valhalla 1990B Bio-Resistance Body Composition Analyzer
(Valhalla Scientific, San Diego, CA, USA) [
]. Because the equations for calculating fat-free mass
(FFM) were developed based on RJL bioelectrical impedance analyzers (RJL, Clinton Twp, MI,
USA), the Valhalla resistance value for each NHANES III subject was converted to an
equivalent RJL resistance value according to the equations developed by WC Chumlea et al. as
For males: RJL resistance = 2.5 + 0.98 Val resistance (r2 = 0.996, root-mean-square error
(RMSE) = 5.0 ohms)
For females: RJL resistance = 9.6+ 0.96 Val resistance (r2 = 0.993, RMSE = 5.3 ohms)
The FFM prediction equations developed based on RJL resistance data were listed below,
which were further used to estimate the percentage of body fat (BF% = ((weightÐFFM)/
weight) × 100%) [
For males: FFM = -10.678 + 0.262 weight + 0.652 height2/RJL resistance + 0.015 RJL
resistance (r2 = 0.90, RMSE = 3.9kg)
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For females FFM = -9.529 + 0.168 weight + 0.696 height2/RJL resistance + 0.016 RJL
resistance (r2 = 0.83, RMSE = 2.9kg)
Mortality status and data of death were recorded in the NHANES linked National Death Index
(NDI) public-access files through December 31, 2011. We merged the baseline data from
NHANES III with follow-up data from the National Death Index. For those who died before
December 31, 2011, survival time ended at the age of death, otherwise they were censored at
the age attained by the end of 2011.
To explore the non-linear association between BF% and WHR with all-cause mortality,
adiposity indicators (BF% and WHR) were converted into restricted cubic spline variables with 4
knots. The values of these knots were 5th, 35th, 65th, and 95th percentiles. The partial
correlations between BF% and different adiposity indicators were investigated when age and ethnicity
were controlled. As previous studies suggested the relationships between BF% and WHR with
mortality and other adverse outcomes may differ between sexes [
7, 17, 18
], the analyses were
performed separately for both sexes. Cox proportional hazard models were performed to
obtain the hazard ratios and their 95% confidence intervals (CIs) in males and females, after
adjustment for baseline age and ethnicity. For the purpose of comparison, both BF% and
WHR were classified into 7 categories. Hazard ratios and their 95% CIs were calculated when
the corresponding median groups were used as the reference group. We adjusted for baseline
age and ethnicity in initial models, and further adjusted for household income, year of
education, urbanization, physical activity, alcohol intake, smoking status, and hypertension. These
results were presented graphically. To assess the joint influence of BF% and WHR on risk of
mortality, participants were categorized into 9 groups based on BF% and WHR to estimate the
hazard ratios when the groups with lowest risk of mortality were used as the reference. In
addition, the survival ages, as well as their differences, were also evaluated using parametric survival
models with the Stata commands of streg, when the data were set with the ªorigin timeº of zero
and the ªenter timeº as the age of baseline examination. The Bonferroni method was used
when multiple comparisons were conducted. In sensitivity analysis, to limit the effects of
reverse causality, we assessed the relationship in non-smokers who had follow-up duration
5 years and without pre-existing chronic diseases, because these factors can themselves affect
adiposity . Statistical relationships were considered significant for P values < 0.05. All
analyses were performed using Stata 14 (College Station, Texas, USA).
A total of 16415 individuals were included in this study. During a median follow-up time of 19
years, 2673 and 2326 deaths occurred in males and females, respectively. Table 1 shows the
baseline characteristics by sex. While the mean BF% of females was 12.3% higher than that in
males, males had a larger WHR than females.
WHR moderately correlated with BF%, and the partial correlation coefficients were 0.54 for
males and 0.37 for females when age and ethnicity were controlled for (Table 2). Stronger
correlations were found between BF% and other adiposity indicators, including BMI, WC, HC
and waist-height ratio, with coefficients ranging between 0.64 and 0.85.
When adiposity indicators were used as continuous variables, a non-linear (U-sharped)
relationship between BF% and all-cause mortality was observed in males and females (Fig 1)
after adjusting for age and ethnicity. The BF% related to the lowest mortality was around 25%
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Mean (SD)/n (percentage)
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Note: BIA, bioelectrical impedance analysis; BMI, body mass index; NH, non-Hispanic; SD, standard deviation; WHR, waist-hip ratio.
§, values are numbers (percentages, %).
Mean (SD)/n (percentage)
in males, and 35% in females. However, the association between WHR and mortality risk
differed between males and females when age and ethnicity were adjusted for. While a non-linear
association was detected in males with the nadir around 0.97, the mortality risk consistently
increased with the increase of WHR in females (Fig 1).
Similar patterns between BF% and WHR with mortality were found when the adiposity
indicators were used as categorical variables (Figs 2 and 3). For instance, risks of mortality in
males with BF% <15% increased by 55%, and corresponding risk increased by 108% for BF%
>40%, compared with their counterparts with BF% between 25% and 30% (Fig 2).
Furthermore, those estimates were minimally changed after further adjustment of demographic and
lifestyle factors (Fig 2). A sensitivity analysis was performed when the participants were limited
to non-smokers with follow-up duration 5 years and without the history of chronic
conditions. This restriction led to a large reduction in sample size (more than 70% participants were
deleted). Although the estimates declined and failed statistical significance, similar patterns
were observed (Tables A and B in S1 File).
The joint association between BF% and WHR with mortality and survival age is presented
in Table 3 by sex. After adjustment of the potential confounding factors, with the same BF%
category, males within either high or low WHR category were associated with an increased
risk of mortality. However, females with small WHR were related to a low risk of mortality.
Meanwhile, when the WHR category was given, either high or low BF% was associated with an
enhanced mortality risk, which was observed in both sexes. Consistent with the relationships
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Fig 1. Association between body fat percentage (A and B) and waist-hip ratio (C and D) with hazard ratio of all-cause mortality in males and females,
NHAHES 1988±2011. Notes: Solid lines and dash lines represent the hazard ratios and their 95% confidence intervals after adjusting for baseline age and
between BF% and WHR with mortality, males within the BF% of 25±30% and WHR of 0.95±
1.0 showed the longest survival age compared to their counterparts within other categories,
and the difference ranged between 2.2 (95% CI: -2.1, 6.6) and 5.7 (95% CI: 1.8, 9.6) years.
Although the differences were not statistically significant, females with BF% of 25±30% and
WHR< 0.9 were found to have survived longer than, if not similar with, their peers in other
This cohort study of 16415 American adults with 280584 person-years provides evidence that
both amount and distribution of body fat are associated with the risk of mortality. While a
Ushaped association between BF% and mortality was identified, sex-specific relationships
between WHR and mortality were also observed. Furthermore, patterns of these associations
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Fig 2. Hazard ratios of all-cause mortality in males (A and B) and females (C and D) with various body fat
percentage categories, NHAHES 1988±2011. Notes: CI, confidence interval. Body fat percentage categories of 25±30
in males and 30±35 in females were used as the reference groups. Model 1 (A and C) was adjusted for baseline age and
ethnicity. Model 2 (B and D) was adjusted for baseline age, ethnicity, household income, year of education,
urbanization, physical activity, alcohol intake, smoking status, and hypertension.
persist even when BF% and WHR were combined to predict the risk of mortality. Specifically,
males with BF% of 25±30% and WHR of 0.95±1 and females with BF% between 30% and 35%
and WHR < 0.9 had the lowest risk of mortality and the longest life expectancy. These findings
suggested that current recommendations regarding obesity assessment and management may
need to be improved in this population.
Since excess adiposity enhances the risk of adverse conditions, such as cardiovascular
diseases and cancer [
], one may expect increased BMI to be related with a high risk of
death. In addition, compared with normal weight, studies have also demonstrated that low
BMI is associated with chronic pulmonary disease and other respiratory diseases, and result in
an increased mortality risk [1±3]. However in most of the previous studies, indirect indicators,
such as BMI and skinfold thickness, were used to estimate overall adiposity, and more direct
measures, including BIA and dual-energy x-ray absorptiometry (DXA), were rarely applied
. Utilization of indirect adiposity indicators is subject to reduced ability to differentiate
levels of fatness and leanness among individuals [
]. Zong and colleagues assessed the
association between overall body fat measured by dual-energy X-ray absorptiometry (DXA) with
allcause mortality [
]. In their cohort of 9471 American adults with a follow-up duration of 8.8
years, participants in the second BF% quartile had the lowest risk of death compared with their
counterparts in either higher or lower quantile [
]. However, that study treated BF% as a
categorical variable, and gender disparities in those associations were not reported. In our study,
we illustrated the mortality risk across the whole BF% spectrum, and found BF% associated
with the lowest risk of mortality, around 25% in males and 35% in females. Additionally, this
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Fig 3. Hazard ratios of all-cause mortality in males (A and B) and females (C and D) with various waist-hip ratio
(WHR) categories, NHAHES 1988±2011. Notes: CI, confidence interval. WHR category of 0.95±1.00 in males and
0.90±0.95 in females was used as the reference group. Model 1 (A and C) was adjusted for baseline age and ethnicity.
Model 2 (B and D) was adjusted for baseline age, ethnicity, household income, year of education, urbanization,
physical activity, alcohol intake, smoking status, and hypertension.
association changed slightly when life-style behaviours and demographic factors were adjusted
Both overall and abdominal adiposity are recommended by clinical guidelines for assessing
obesity, and WC is suggested to be used as the indicator of abdominal adiposity [
the strong correlation between WC and BF% implies that WC is not only an indicator of
abdominal adiposity, but also for overall obesity. A number of studies have also observed that
WC largely represents variability in intra-abdominal and subcutaneous fat, while variability in
HC represents differences in subcutaneous fat, gluteal muscle, and pelvic width [
the consequence, the difference in WHR could be used to distinguish the fat distribution .
In the current study, a weaker correlation was found between BF% and WHR, compared with
correlations for other adiposity indicators, which suggested WHR may provide additional
information regarding body fat distribution. Czernichow et al. conducted a meta-analysis and
observed the risk of mortality increased across the WHR spectrum [
]. However, that study
did not present the association by sex. Using cohort datasets conducted in a Chinese
population, a study reported that the risk of mortality increased across the WHR spectrum in women,
while the difference in men's mortality risk was not significant until WHR 0.95 . Those
findings were consistent with our results which showed the sex distinction in WHR related
death risk. Although underlying mechanisms remain to be elucidated, research has
demonstrated that large HC is associated with the adverse cardio-metabolic profile when WC was
controlled for, and that association was stronger among women compared with men [
However, the relationship between BF%, as well as WHR, and mortality was largely attenuated
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(95% CI) §
-5.7 (-9.6, -1.8) ³
-2.2 (-6.6, 2.1)
-3.4 (-7.6, 0.7)
-2.9 (-7.6, 1.8)
-2.3 (-6.1, 1.5)
-5.1 (-11.4, 1.2)
-3.0 (-8.0, 2.0)
-3.9 (-7.7, -0.05) ²
-3.0 (-6.4, 0.5)
-2.8 (-8.2, 2.5)
-5.8 (-12.4, 0.7)
-0.03 (-4.5, 4.4)
-1.6 (-5.7, 2.5)
0.3 (-2.6, 3.2)
-2.3 (-5.3, 0.6)
-2.2 (-5.0, 0.7)
Note: CI, confidence interval; WHR, waist-hip ratio.
§, adjusted for baseline age, ethnicity, household income, year of education, urbanization, physical activity, alcohol intake, smoking status, and hypertension.
², P< 0.05
³, P< 0.01.
in the sensitivity analysis, inferring these relationships may partly be explained by pre-existing
conditions. As the sample size significantly declined in the sensitivity analysis, further study
with large sample is needed to clarify it.
Extending to above findings, this study found that BF% and WHR were independently
associated with mortality, and their joint impact on mortality risk showed males with BF% of
25±30% and WHR of 0.95±1 and females with BF% of 30±35% and small WHR had the longest
survival time. These observations were in line with previous studies which showed WHR was
associated with mortality independent of BMI [
8, 17, 22
]. Our findings contribute to the
current inconsistent recommendations for assessing adiposity. For instance, while National
Institutes of Health classified subjects as obese when BF% exceeds 25% in men and 30% in women
, World Health Organization expert committee stated ªthere is no agreement about cut-off
points for the percentage of body fat that constitutes obesityº [
]. Other recommendations
suggested that it would be beneficial to keep ªas lean as possible within the normal rangeº .
The current results advise that the lowest mortality is observed with the BF% around 25% in
males and 35% in females, though the possibility that the increased risk among individuals
with low BF% is non-causal cannot be ruled out entirely. In addition to stay within an optimal
BF%, our results also demonstrated that it is critical to maintain an appropriate body shape
which is sex-specific. In current analysis, the differences in expected survival time could be up
to 10.8 years among people within same BF% category but different WHR, suggesting WHR
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could be used to identify people with high risk of mortality in addition to BF% and guide the
interventions to reduce the risk of death.
The current study has limitations. Although this study used a large nationally representative
longitudinal sample with long duration of follow-up, it is difficult to tease out the confounding
effect, such as smoking, in this study. A sensitivity analysis excluding smokers and participants
with pre-existing conditions were conducted and the results were similar with current study.
However, the small sample size compromise the statistical power and most of those differences
were not statistically significant. A study with a larger sample size is desired. In addition, since
BF% and WHR were only measured at baseline, the relationship between one measurement
on future mortality should be interpreted with caution as it may be impacted by other events
and interventions. In addition, we cannot address the impact of any changes in %BF and
WHR on the risk of mortality. Finally as this study was conducted on adults living in the
United States, studies conducted in other populations are needed to clarify the generalization
of these findings.
Both the amount of body fat and its distribution have been shown to be associated with the
risk of disease and mortality. Research on the joint association between body fat amount and
fat distribution with mortality based on direct measurement of body fat helps to further
elucidate this relationship. Sex-specific optimal levels of BF% and WHR for the lowest risk of
mortality in general American adults were also provided. Future study is warranted to confirm
these relationships among different populations.
S1 File. Table A. [Association between body fat percentage and mortality in non-smokers
who had follow-up duration 5 years and without pre-existing chronic diseases, NHAHES
1988±2011. Note: CI, confidence interval. §, adjusted for baseline age, ethnicity, household
income, year of education, urban area, physical activity, alcohol intake, and hypertension. ²,
P< 0.05; ³, P< 0.01.] Table B. [Association between WHR and mortality in non-smokers
who had follow-up duration 5 years and without pre-existing chronic diseases, NHAHES
1988±2011. Note: CI, confidence interval; WHR, waist-hip ratio. §, adjusted for baseline age,
ethnicity, household income, year of education, urban area, physical activity, alcohol intake,
The authors gratefully acknowledge the Centers for Disease Control and Prevention, USA, for
access to the NHANES public release data (https://wwwn.cdc.gov/nchs/nhanes/nhanes3/
datafiles.aspx#core). The findings and conclusions in this report are those of the authors and
do not necessarily represent the official position of the Centers for Disease Control and
Conceptualization: Bin Dong, Yang Peng, Zhiqiang Wang, Jun Ma, Ying-Hua Ma.
Data curation: Bin Dong, Yang Peng, Zhiqiang Wang, Odewumi Adegbija, Jie Hu, Jun Ma,
11 / 13
Formal analysis: Bin Dong, Zhiqiang Wang, Jun Ma, Ying-Hua Ma.
Funding acquisition: Yang Peng, Zhiqiang Wang.
Methodology: Bin Dong, Yang Peng, Zhiqiang Wang, Odewumi Adegbija, Jie Hu, Jun Ma,
Project administration: Bin Dong, Zhiqiang Wang, Jun Ma, Ying-Hua Ma.
Resources: Yang Peng, Zhiqiang Wang, Odewumi Adegbija, Jie Hu, Jun Ma, Ying-Hua Ma.
Software: Zhiqiang Wang.
Supervision: Zhiqiang Wang, Jun Ma, Ying-Hua Ma.
Validation: Bin Dong, Yang Peng, Zhiqiang Wang, Odewumi Adegbija, Jie Hu, Jun Ma,
Visualization: Bin Dong, Yang Peng, Odewumi Adegbija, Jie Hu, Jun Ma, Ying-Hua Ma.
Writing ± original draft: Bin Dong.
Writing ± review & editing: Yang Peng, Zhiqiang Wang, Odewumi Adegbija, Jie Hu, Jun Ma,
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