bmj · 2018-07-05 · confidential: for review only 2 abstract 5 objective: to investigate the...

Confidential: For Review Only

Predicted lean body mass, fat mass, and all-cause and

cause-specific mortality in men: results from a prospective

US cohort study

Journal: BMJ

Manuscript ID BMJ.2018.043414.R1

Article Type: Research

BMJ Journal: BMJ

Date Submitted by the Author: 01-Apr-2018

Complete List of Authors: Lee, Dong Hoon; Harvard University T H Chan School of Public Health, Nutrition Keum, NaNa; Harvard University T H Chan School of Public Health, Nutrition; Dongguk University, Department of food science and Biotechnology Hu, Frank; Harvard University T H Chan School of Public Health, Nutrition and Epidemiology; Brigham and Women’s Hospital and Harvard Medical School, Channing Division of Network Medicine, Department of Medicine Orav, Endel; Harvard University T H Chan School of Public Health, Biostatistics; Brigham and Women’s Hospital, Department of Medicine Rimm, Eric; Harvard University T H Chan School of Public Health, Nutrition and Epidemiology; Brigham and Women’s Hospital and Harvard Medical

School, Channing Division of Network Medicine, Department of Medicine Willett, Walter; Harvard University T H Chan School of Public Health, Nutrition and Epidemiology; Brigham and Women’s Hospital and Harvard Medical School, Channing Division of Network Medicine, Department of Medicine Giovannucci, Edward; Harvard University T H Chan School of Public Health, Nutrition and Epidemiology; Brigham and Women’s Hospital and Harvard Medical School, Channing Division of Network Medicine, Department of Medicine

Keywords: body mass index, body composition, lean body mass, fat mass, mortality, obesity paradox

https://mc.manuscriptcentral.com/bmj

BMJ


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Predicted lean body mass, fat mass, and all-cause and cause-specific mortality in men:

results from a prospective US cohort study

Dong Hoon Lee, NaNa Keum, Frank B. Hu, E. John Orav, Eric B. Rimm, Walter C. Willett,

Edward L. Giovannucci

Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA,

Dong Hoon Lee, post-doctoral research fellow, NaNa Keum, post-doctoral research fellow,

Frank B. Hu, professor, Eric B. Rimm, professor, Walter C. Willett, professor, Edward L.

Giovannucci, professor

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115,

USA, Frank B. Hu, professor, Eric B. Rimm, professor, Walter C. Willett, professor, Edward L.


Department of food science and Biotechnology, Dongguk University, Goyang, South Korea,

NaNa Keum, assistant professor, Channing Division of Network Medicine

Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston,

MA 02115, USA, Frank B. Hu, professor, Eric B. Rimm, professor, Walter C. Willett, professor,

Edward L. Giovannucci, professor

Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA, E. John

Orav, associate professor

Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115,

USA, E. John Orav, associate professor.

Corresponding author:

Edward L. Giovannucci, Department of Nutrition, Harvard T.H Chan School of Public Health,

665 Huntington Avenue, Bldg. 2, Room 371, Boston, MA 02115

Phone: 617-432-4648, Fax: 617-432-2435, Email: [email protected]

Word count: 4,229

Number of tables and figures: 4 tables and 1 figure

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ABSTRACT

Objective: To investigate the association of predicted lean body mass (LBM), fat mass (FM) and

body mass index (BMI) with all-cause and cause-specific mortality in men.

Design: Prospective cohort study.

Setting: Health professionals in the United States

Participants: 38,021 men (aged 40-75 years) from the Health Professionals Follow-up Study

were followed-up for death (1987-2012).

Main outcome measures: All-cause and cause-specific mortality.

Results: Using validated anthropometric prediction equations developed from the National

Health and Nutrition Examination Survey, LBM and FM were estimated for all participants.

During a mean of 21.4 years of follow-up, we identified 12,356 deaths. We consistently observed

a J-shaped association between BMI and mortality. Multivariable-adjusted Cox models including

both predicted FM and LBM showed a strong positive monotonic association between predicted

FM and mortality. Compared to those in the lowest quintile of predicted FM, men in the highest

quintile had 35% (95% confidence interval (CI): 26 to 46%), 67% (95% CI: 47 to 90%), and 24%

(95% CI: 8 to 42%) increased risk of mortality due to all causes, cardiovascular disease, and

cancer. In contrast, a U-shaped association was found between predicted LBM and mortality due

to all causes, cardiovascular disease, and cancer (P for non-linearity<0.001). However, there was

a strong inverse association between predicted LBM and mortality due to respiratory disease (P

for trend<0.001). Compared to those in the lowest quintile of predicted LBM, men in the highest

quintile had 50% (95% CI: 39 to 65%) decreased risk of death due to respiratory disease.

Conclusions: The shape of the relationship between BMI and mortality was determined by the

relationship between two body components (LBM and FM) and mortality. Our finding suggests

that the ‘obesity paradox’ controversy may be largely explained by low LBM, rather than low

FM, in the lower range of BMI.

Keywords: body mass index, body composition, lean body mass, fat mass, mortality, obesity

paradox

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“What this paper adds” box

Section 1: What is already known on this topic

• Numerous epidemiological studies have shown unexpected J-or U-shaped relationship

between body mass index (BMI) and mortality (‘obesity paradox’).

• The controversial issue of ‘obesity paradox’ may have arisen in part due to

underappreciation of different contributions of lean body mass (LBM) and fat mass (FM)

to BMI.

• Direct measure of body composition is difficult in large epidemiological settings, thus the

relationship between body composition and mortality is still unknown.

Section 2: What this study adds

• Using validated anthropometric prediction equations for body composition, this study

represents the first effort to comprehensively examined the association between lean

body mass, fat mass and mortality in a large prospective cohort study.

• Predicted fat mass showed a strong positive monotonic association with mortality, while

predicted lean body mass showed a strong U-shaped association with mortality.

• The ‘obesity paradox’ controversy may be explained largely by low LBM, rather than

low FM, in the lower range of BMI

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INTRODUCTION

Obesity is a major public health challenge in the United States and around the world.1 In 2013-

2014, more than two thirds of Americans were classified as overweight (defined as body mass

index (BMI) of 25-29.9 kg/m2) or obese (BMI of ≥30 kg/m

2).

2 BMI is known as a reasonably

good measure of general adiposity3, and many epidemiologic studies have provided evidence

supporting that obesity, assessed by BMI, is a significant risk factor for increased risk of many

chronic diseases as well as mortality.4-6

However, details of the shape of the association between

BMI and mortality has been a topic of considerable discussion as epidemiologic studies have

found various types of J-shaped, U-shaped, and linear relationships between BMI and mortality.7

For instance, in some studies, overweight was associated with increased mortality8, but in others,

the lowest mortality was observed among overweight individuals and mortality tended to

increase with lower BMI, even after accounting for smoking (residual confounding) and

preexisting disease (reverse causation).9 10

This pattern has come to be known as the “obesity

paradox”.11

Given the existing and rising number of overweight and obese adults in the US, these

divergent findings could cause a great deal of confusion among researchers, policy makers, and

the general public.

One important but underexplored methodological limitation in the current obesity

research is that BMI is an imperfect measure of adiposity.12-15

While BMI indicates overweight

relative to height, it does not discriminate between fat mass (FM) and lean body mass (LBM).16-

18 Given the same BMI, body composition is highly variable among individuals. This is

particularly important because FM and LBM may act differently on health outcomes including

mortality. Excess FM has shown to be detrimental for health,19

while growing evidence suggests

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that skeletal muscle, which accounts for majority of LBM, may be beneficial for health.20 21

Therefore, understanding different contributions of LBM and FM to BMI may provide new

insights on the ‘obesity paradox’ and deliver important clinical and public health messages

regarding healthy body composition beyond BMI. However, direct measurement of LBM is

particularly difficult in large epidemiological studies because it requires expensive and

sophisticated technologies like dual-energy X-ray absorptiometry (DXA) or imaging

technologies. Therefore, little is known about the influence of body composition, particularly

LBM, on mortality. A limited number of studies have used less accurate surrogate measures (e.g.,

arm circumference,22 23

total body potassium,24

skinfold,25

and bioelectrical impedance26

) or

direct measures27-33

to estimate body composition but these studies had relatively small sample

size, short period of follow-up, restricted study population (e.g., elderly) and/or potential biases

(e.g., confounding and reverse causation). Moreover, the association of LBM and FM with

cause-specific mortality is largely unknown.

Therefore, we used validated anthropometric prediction equations to estimate body

composition and examine the association of predicted LBM, FM and BMI with all-cause and

cause-specific mortality in a large prospective US cohort study of men. Application of validated

equations in a large cohort allowed us to estimate LBM and FM and examine the independent

roles of two different body components in relation to mortality, accounting for potential biases.

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METHODS

Study population

The Health Professionals Follow-up Study was initiated in 1986 when 51,529 male health

professionals aged 40–75 were enrolled. Participants were mailed questionnaires at baseline and

every two years thereafter to collect updated demographics, lifestyle, and medical information.

For the analysis, we included participants who had information on age, race, height, weight and

waist circumference, which were required to create predicted LBM and FM (N=40,764). We

excluded participants previously diagnosed with cancer or cardiovascular diseases (N=2,118)

and those with BMI <12.5 or >60 kg/m2

(N=625) at baseline. The final sample size was 38,021

men.

Exposure assessments

Derivation and validation of the predicted LBM and FM has been described in detail

previously.34

Briefly, we used a large US representative sample of 7,531 men who had measured

DXA from the National Health and Nutrition Examination Survey (NHANES). With DXA-

measured LBM and FM each as a dependent variable, a linear regression was performed using

age, race, height, weight, and waist circumference as independent predictors. Then, we validated

the developed equations in an independent validation group of 2,292 men and using obesity-

related biomarkers (i.e., triglycerides, total cholesterol, high-and low-density lipoprotein

cholesterol, glucose, insulin and C-reactive protein). The anthropometric prediction equations

had high predictive ability for LBM (R2=0.91, standard error of estimate (SEE)=2.6 kg) and FM

(R2=0.90, SEE=2.6 kg). Cross-validation in the validation group showed robustly high

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agreement between the actual and predicted LBM and FM with no evidence of bias. In an

additional validation, DXA-measured values and predicted values showed consistently high

agreement with similar errors across the range of LBM and FM. Scatter plots of the difference

between DXA and predicted values against DXA values showed no strong non-linear pattern

(Supplementary figure 1 and 2 and supplementary table 2 and 3). Moreover, the developed

equations performed well across different subgroups of the validation group (i.e., age, BMI, race,

smoking status, and disease status), and predicted FM showed similar correlations with obesity-

related biomarkers as DXA-measured FM.34

For a sensitivity analysis, we also used different

prediction equations that include additional polynomial terms of anthropometric measures. These

equations had similar R2 and SEEs but slightly improved fit in the extreme range of LBM and

FM (Supplementary figure 1 and 2 and supplementary table 2 and 3). The anthropometric

prediction equations are shown in the supplement (Supplementary table 1). Using the equations,

predicted LBM and FM were calculated for each cohort member based on their age, race, height,

weight, and waist circumference. Predicted LBM and FM were available in 1987, 1996, and

2008.

We collected information on height at enrollment in 1986, and weight from biennial

questionnaires.35 36

Distinct from the biennial questionnaire, participants were asked to measure

and report their waist circumferences to the nearest one-quarter inch using provided tape

measures and following the same instructions in 1987, 1996, and 2008. Non-responders received

follow-up mailings to increase the response rate. In our validation study, the correlation between

self-reported and technician-measured height, weight, and waist circumference were 0.94, 0.97,

and 0.95, respectively.35

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Ascertainment of outcomes

Deaths were identified by reports from the next of kin, postal authorities, or by searching the

National Death Index. More than 98% of deaths were ascertained from the follow up. Cause of

death was determined by physician review of medical records and death certificates. ICD-8

codes (International Classification of Diseases, 8th revision) were used to classify death due to

cardiovascular disease (codes 390-459, 795), cancer (codes 140-239), respiratory disease (codes

460-519), and other causes.

Ascertainment of covariates

Detailed information on age, race, smoking, and physical activity were collected in 1986 and

updated every two years from biannual questionnaires. Family history of cardiovascular disease

and cancer were assessed periodically. Dietary information was collected via validated food

frequency questionnaires every four years. The Alternate Healthy Eating Index (AHEI) was

calculated as an overall measure of diet quality.37

Statistical analyses

A Spearman correlation was calculated between predicted LBM and FM. Person-time of follow-

up was calculated from the age when the baseline predicted LBM and FM were available until

the age at death or the end of study (January 2012), whichever came first. Cox proportional

hazards models were used to estimate hazard ratios and 95% confidence interval (CI)s. We

stratified the analysis by age in months and calendar year of the questionnaire cycle.

Predicted FM and LBM were categorized into quintiles on the basis of the distribution of

exposures. We used predefined cut points for BMI (<18.5, 18.5-20.4, 20.5-22.4, 22.5-24.9, 25-

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27.4, 27.5-29.9, 30-34.9, and ≥35 kg/m2). For the main analysis, we used predicted FM, LBM,

and BMI measured at baseline to minimize the impact of underlying diseases on mortality. To

account for variation in body size, which is particularly important for LBM, we adjusted for

height by using residuals from the regression of LBM on height for LBM and by including

height as a continuous variable for FM in the models. In multivariable models, we adjusted for

potential confounders including race, family history of cardiovascular disease, family history of

cancer, smoking status, physical activity, total energy intake, alcohol consumption, and AHEI.

To examine the independent association of predicted LBM and FM in relation to mortality, we

further ran a multivariable model including both predicted LBM and FM. Test for trend was

conducted by treating the categorical predicted scores and BMI as continuous variables in the

model after assigning a median value for each category.

We also used restricted cubic splines with 5 knots at 5th

, 35th

, 50th

, 65th

, and 95th

percentiles to flexibly model the association between LBM and FM and mortality. We tested for

potential non-linearity using a likelihood ratio test comparing the model with only a linear term

to the model with linear and cubic spline terms.38-40

Given our a priori hypothesis that people

with low LBM in the lower BMI range cause the J-or U-shaped relationship between BMI and

mortality, we examined how the shape of BMI-mortality relationship changes after excluding

those with low LBM. For a sensitivity analysis, we additionally examined the shape of BMI-

mortality relationship after excluding those with low FM.

To evaluate the latency between predicted LBM and FM and mortality, we conducted

analyses using different lag times (approximately 0, 4+, 8+, and 12+ years). For each lagged

analysis, the baseline was shifted to 1987, 1990, 1994, and 1998, respectively, and predicted

LBM and FM were updated using three repeated measures accordingly. For example, for no lag

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time analysis (simple updated), we used the most updated predicted LBM and FM that were

closest to the time of death. For a lag time of 4+ years, we used predicted measures in 1987 for

follow-up from 1990 to 2000 and predicted measures in 1996 for follow-up from 2000 to 2012.

Similarly, for a lag times of 8+ years, we used predicted measures in 1987 for deaths in 1994-

2004 and predicted measures in 1996 for deaths in 2004-2012. Moreover, we conducted

stratified analyses to explore whether the association of predicted LBM and FM with mortality

varied across smoking status and age.

Several sensitivity analyses were conducted with no adjustment for physical activity,

exclusion of deaths that have occurred in the early follow-up period (2 years) and right-censoring

criteria for age (>85 years), and inclusion of baseline illness. We also conducted analyses using

different categories for predicted LBM, FM and BMI (i.e., quintiles and deciles). Lastly, we

tested the robustness of our findings using other prediction equations with polynomial terms. All

statistical tests were two-sided and P<0.05 was considered to determine statistical significance.

We used SAS 9.4 for all analyses (SAS institute).

Patient involvement

No patients were involved in setting the research question or the outcome measures, nor were

they involved in the design and implementation of the study. There are no plans to involve

patients in dissemination.

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RESULTS

Study participants

A total of 38,021 men were included in the analyses. Baseline characteristics of participants

according to BMI categories are presented in Table 1. The mean age was 54.4 years and the

mean BMI was 25.4 kg/m2. Predicted LBM increased with higher BMI. Predicted FM slightly

deceased in the second category of BMI (18.5-20.4 kg/m2) and then increased with higher BMI.

Moreover, men with lower BMI tended to have higher physical activity and AHEI score, peaking

in the third category of BMI (20.5-22.4 kg/m2). Although the number of men with underweight

(BMI<18 kg/m2) was small, they were taller and had higher waist circumference and lower

physical activity and AHEI score. The Spearman correlation between predicted LBM and FM

was 0.66 in men.

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Table 1 Age-standardized baseline characteristics according to body mass index in men (Health Professionals Follow-up

Study, 1987-2012) Body Mass Index (kg/m

2)

<18.5 18.5-20.4 20.5-24.9 22.5-24.9 25.0-27.4 27.5-29.9 30.0-34.9 ≥35.0

Person-years 1839 15337 92790 254122 243335 95023 52320 8275

Age (year)a 55.5 (10.4) 54.0 (10.8) 53.8 (10.2) 54.0 (9.9) 54.5 (9.7) 54.9 (9.6) 55.1 (9.4) 55.5 (10.1)

Height (cm) 185.4 (12.7) 179.4 (7.8) 178.7 (6.2) 178.5 (6.4) 178.2 (6.5) 178.7 (6.8) 178.5 (7.1) 176.4 (10.0)

Weight (kg) 60.8 (8.2) 64.1 (5.7) 69.6 (5.1) 75.9 (5.8) 82.9 (6.4) 91.4 (7.3) 101.2 (9.0) 118.2 (13.9)

Waist circumference (cm) 86.6 (12.4) 82.8 (5.3) 86.8 (5.1) 91.2 (5.6) 96.7 (6.1) 102.9 (6.8) 110.6 (7.9) 123.4 (11.4)

BMI (kg/m2) 17.6 (0.8) 19.8 (0.5) 21.7 (0.5) 23.7 (0.7) 26.0 (0.7) 28.5 (0.7) 31.7 (1.3) 37.9 (3.6)

Predicted fat mass (kg) 13.3 (5.0) 13.1 (2.5) 15.9 (2.4) 19.1 (2.6) 22.8 (2.9) 27.1 (3.3) 32.3 (4.1) 41.2 (6.5)

Predicted Lean body mass (kg) 40.4 (5.8) 47.5 (2.2) 50.6 (1.9) 53.9 (2.1) 57.4 (2.3) 61.2 (2.6) 65.9 (3.4) 75.2 (6.0)

Total energy intake (kcal/day) 2132 (610) 2023 (570) 2045 (599) 2002 (595) 1992 (609) 2002 (625) 2036 (639) 2089 (657)

Alcohol consumption (g/day) 14.2 (18.7) 9.7 (14.2) 10.9 (14.3) 11.5 (14.7) 11.8 (15.4) 11.7 (15.5) 10.9 (16.1) 8.9 (15.1)

AHEI (score) 51.4 (13.5) 54.1 (12.7) 54.3 (12.0) 53.8 (11.6) 52.4 (11.1) 51.5 (10.9) 50.7 (11.0) 49.3 (10.8)

Physical activity (MET-h/wk) 21.4 (35.8) 22.6 (27.0) 24.2 (28.6) 22.3 (27.4) 19.4 (23.9) 16.8 (22.0) 14.4 (20.9) 11.7 (14.9)

White (%) 98.4 99.2 99.5 99.3 99.2 98.8 98.7 99.4

Family history of CVD (%) 35.3 32.2 33.0 33.4 33.7 33.8 35.2 35.5

Family history of cancer (%) 17.6 16.8 17.2 16.8 17.5 16.9 16.8 15.4

Smoking status (%)

Never 47.4 56.5 56.0 50.5 45.8 44.1 42.3 41.1

Past 34.0 32.3 35.2 42.2 46.0 47.5 50.0 50.6

Current 18.6 11.2 8.9 7.3 8.2 8.4 7.8 8.3

Abbreviation: BMI, body mass index; AHEI, alternate healthy eating index; CVD, cardiovascular disease

Data are presented as means (SD) for continuous variables and percentages for categorical variables, unless otherwise indicated. a Value is not age adjusted

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All-cause mortality

During up to 25 years of follow-up (mean of 20.4 years), we identified 12,356 deaths. The

association of predicted FM and LBM with all-cause mortality in men is presented in Table 2. A

multivariable adjusted model showed a positive association between predicted FM and all-cause

mortality, while predicted LBM showed a U-shaped association with all-cause mortality. In a

mutually adjusted model including both predicted FM and LBM, we consistently observed a

strong positive association between predicted FM and all-cause mortality. Compared to those in

the lowest quintile of predicted FM, men in the highest quintiles had 35% (95% CI: 26 to 46%)

increased hazard of all-cause mortality. Moreover, predicted LBM showed a stronger U-shaped

association with all-cause mortality in the mutually adjusted model. Compared to those in the

lowest quintile of predicted LBM, men in the second to fourth quintiles had 8 to 10% decreased

hazard of all-cause mortality.

In Figure 1, we used restricted cubic splines to flexibly model and visualize the

relationship between predicted FM and LBM with all-cause mortality in men. The risk of all-

cause mortality was relatively flat and increased slightly until around 21 kg of predicted FM, and

then started to increase rapidly afterwards (P for non-linearity<0.001). The average BMI for men

with 21 kg of FM is 25 kg/m2. In respect to the strong U-shaped relationship between predicted

LBM and all-cause mortality, the plot showed a substantial reduction of the risk within the lower

range of predicted LBM, which reached the lowest risk around 55 kg and then increased

thereafter (P for non-linearity<0.001).

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Table 2. Hazard ratio (95% CI) of all-cause mortality according to predicted fat mass and lean body

mass in men (Health Professionals Follow-up Study)

Analysis

Hazard Ratio (95% CI)

No of

deaths

IR

/100,000py Model 1 Model 2 Model 3

Fat Massa,b

Quintile 1 1937 1265 1 (reference) 1 (reference) 1 (reference)

Quintile 2 2298 1504 1.09 (1.03 to 1.16) 1.06 (1.00 to 1.12) 1.08 (1.01 to 1.15)

Quintile 3 2297 1504 1.03 (0.97 to 1.09) 0.98 (0.92 to 1.04) 1.01 (0.94 to 1.07)

Quintile 4 2726 1789 1.23 (1.16 to 1.31) 1.13 (1.06 to 1.20) 1.16 (1.09 to 1.24)

Quintile 5 3098 2038 1.51 (1.42 to 1.60) 1.33 (1.25 to 1.41) 1.35 (1.26 to 1.46)

P-trend

<.001 <.001 <.001

Lean Body Massa,b


Quintile 2 2419 1585 0.93 (0.88 to 0.98) 0.93 (0.88 to 0.98) 0.92 (0.87 to 0.97)

Quintile 3 2324 1521 0.95 (0.90 to 1.01) 0.93 (0.88 to 0.98) 0.90 (0.85 to 0.96)

Quintile 4 2282 1494 1.03 (0.98 to 1.09) 1.00 (0.95 to 1.06) 0.92 (0.87 to 0.98)

Quintile 5 2335 1529 1.26 (1.20 to 1.34) 1.16 (1.10 to 1.23) 0.97 (0.91 to 1.04)

P-trend <.001 <.001 0.49

Model 1: adjusted for age.

Model 2: adjusted for age, race (white or non-white), family history of cardiovascular disease (yes or no), family history of cancer (yes or no),

physical activity (<3, 3-8.9, 9-17.9, 18-26.9, or >27 MET-hour/week), alcohol consumption (0, 0.1-4.9, 5-9.9, 10-14.9, or 15.0+ g/day), total

energy intake (quintiles), and smoking status (never, ever, 1-14, 15-24, ≥25 cigs/day), Alternate Healthy Eating Index (quintiles).

Model 3: additionally, mutually adjusted for predicted fat mass and predicted lean body mass. a Derived from validated anthropometric prediction equations.

b Height was adjusted by including height as a continuous variable for fat mass and by regressing out variation due to height for lean body mass.

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When we used BMI alone, we observed a J-shaped relationship between BMI and all-

cause mortality in men (Table 3 and Figure 1). We also examined the influence on BMI when we

excluded men with low predicted LBM. When we excluded those in the lowest 2.5th

percentiles

of predicted LBM, the J-shaped relationship between BMI and mortality disappeared. Upon

excluding more participants with low predicted LBM (5th

and 10th

percentiles), the BMI-

mortality relationship became more linear and slightly stronger. However, the J-shaped

relationship still existed when excluding those with low FM (Supplementary table 4).

We further examined how the association of predicted FM and LBM with all-cause

mortality changes by different lag times (Supplementary table 5). With shorter lag times,

predicted FM showed a less linear positive association with all-cause mortality, while predicted

LBM showed a stronger U-shaped association with all-cause mortality. We also examined the

associations stratified by smoking status and age (Supplementary table 6 and 7). The relationship

between predicted FM and all-cause mortality was stronger and more linear among never-

smokers compared to current-smokers and among younger adults compared to older adults. On

the other hand, we observed a stronger U-shaped association between predicted LBM and all-

cause mortality among current-smokers compared to never-or past-smokers. We observed a

similar U-shaped relationship for predicted LBM across all age groups.

Our findings remained robust in several sensitivity analyses (Supplementary table 8, 9

and 10). The results did not change with no adjustment for physical activity, exclusion of deaths

in the early follow-up period and right-censoring criteria for age, inclusion of baseline illness and

use of quintiles and deciles for exposures. Moreover, using other prediction equations with

polynomial terms showed consistent results (data not shown).

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Table 3. Hazard ratio (95% CI) of all-cause mortality according to body mass index in men (Health Professionals Follow-

up Study)

Analysis


No of

deaths

IR

/100,000py Model 1

a Model 2

a Model 3

b Model 4

c Model 5

d

BMI

<18.5 53 2883 1.74 (1.33 to 2.28) 1.65 (1.25 to 2.16) NA NA NA

18.5-20.4 269 1754 1.16 (1.03 to 1.32) 1.09 (0.96 to 1.24) 0.95 (0.76-1.18) 0.95 (0.68 to 1.32) 0.79 (0.38 to 1.66)

20.5-22.4 1358 1464 1.01 (0.95 to 1.08) 1.03 (0.97 to 1.09) 1.01 (0.94-1.08) 0.99 (0.92 to 1.06) 0.93 (0.85 to 1.03)

22.5-24.9 3740 1472 1 (reference) 1 (reference) 1 (reference) 1 (reference) 1 (reference)

25.0-27.4 3986 1638 1.09 (1.04 to 1.14) 1.04 (0.99 to 1.09) 1.04 (1.00-1.09) 1.05 (1.00 to 1.10) 1.06 (1.01 to 1.11)

27.5-29.9 1753 1845 1.31 (1.24 to 1.39) 1.21 (1.14 to 1.28) 1.22 (1.15-1.29) 1.23 (1.16 to 1.30) 1.24 (1.17 to 1.31)

30.0-34.9 1001 1913 1.48 (1.38 to 1.59) 1.31 (1.22 to 1.41) 1.31 (1.22-1.41) 1.33 (1.24 to 1.43) 1.34 (1.25 to 1.44)

≥35.0 196 2368 2.28 (1.98 to 2.64) 2.01 (1.74 to 2.33) 2.02 (1.75-2.34) 2.04 (1.76 to 2.36) 2.06 (1.78 to 2.38)

P-trend

<.001 <.001 <.001 <.001 <.001

Abbreviation: BMI, body mass index; NA, not available (no cases available after exclusion).


Model 2: adjusted for age, race (white or non-white), family history of cardiovascular disease (yes or no), family history of cancer (yes or no), physical activity (<3, 3-8.9, 9-

17.9, 18-26.9, or >27 MET-hour/week), alcohol consumption (0, 0.1-4.9, 5-9.9, 10-14.9, or 15.0+ g/day), total energy intake (quintiles), smoking status (never, ever, 1-14,

15-24, ≥25 cigs/day), and Alternate Healthy Eating Index (quintiles).

Model 3: additionally, excluded 2.5%ile of total participants with low lean body masse

Model 4: additionally, excluded 5%ile of total participants with low lean body masse

Model 5: additionally, excluded 10%ile of total participants with low lean body masse a Number of deaths/person-years for each category of BMI: 53/1839, 269/15337, 1358/92790, 3740/254122, 3986/243335, 1753/95023, 1001/52320, and 196/8275. b Number of deaths/person-years for each category of BMI: 0/26, 80/7196, 1147/85194, 3695/252987, 3980/243212, 1751/94960, 1000/52311, and 196/8275. c Number of deaths/person-years for each category of BMI: 0/0, 36/3402, 884/72686, 3603/250459, 3976/243080, 1751/94960, 1000/52311, and 196/8275. d Number of deaths/person-years for each category of BMI: 0/0, 7/781, 509/48989, 3295/239196, 3963/242606, 1751/94928, 1000/52311, and 196/8275. e For exclusion analyses, height-adjusted lean body mass was used after regressing out variation due to height.

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Cause-specific mortality

We further examined the association of predicted FM and LBM with cause-specific mortality

(Table 4). Mutually adjusted models showed a linear positive association between predicted FM

and mortality due to cardiovascular disease and cancer. Compared to those in the lowest quintile

of predicted FM, men in the highest quintile had 67% (95% CI: 47 to 90%) and 24% (95% CI: 8

to 42%) increased hazard of death due to cardiovascular disease and cancer, respectively. In

contrast, predicted LBM showed a U-shaped association with mortality due to cardiovascular

disease and cancer in the mutually adjusted models. However, predicted LBM showed a strong

inverse association with mortality due to respiratory disease (P for trend<.001). Compared to

those in the lowest quintile of predicted LBM, men in the highest quintile had 50% (95% CI: 39

to 65%) decreased hazard of death due to respiratory disease. When we examined the association

between BMI and cause-specific mortality, we observed a U-shaped association for

cardiovascular disease death but a positive association for cancer death and an inverse

association for respiratory disease death.

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Table 4. Hazard ratio (95% CI) of cause-specific mortality according to predicted fat mass and lean

body mass in men (Health Professionals Follow-up Study)

Analysis Hazard Ratio (95% CI)

CVD death Cancer death Respiratory death Other death

No. of deaths 4296 3723 960 3377

IR/100,000py 558 483 124 438

Fat Massa,b

Quintile 1 1 (reference) 1 (reference) 1 (reference) 1 (reference)

Quintile 2 1.11 (1.00 to 1.24) 1.15 (1.03 to 1.29) 0.92 (0.74 to 1.14) 1.01 (0.90 to 1.14)




P-trend <.001 0.01 0.03 0.05

Lean Body Massa,b






P-trend 0.10 0.86 <.001 0.99

BMI

<18.5 1.45 (0.87 to 2.41) 0.66 (0.32 to 1.40) 5.33 (3.10 to 9.17) 1.86 (1.15 to 3.01)

18.5-20.4 1.12 (0.90 to 1.38) 0.99 (0.78 to 1.25) 1.93 (1.36 to 2.73) 0.92 (0.72 to 1.19)

20.5-22.4 0.95 (0.85 to 1.06) 0.97 (0.87 to 1.09) 1.30 (1.06 to 1.60) 1.09 (0.97 to 1.22)

22.5-24.9 1 (reference) 1 (reference) 1 (reference) 1 (reference)

25.0-27.4 1.16 (1.08 to 1.26) 1.01 (0.93 to 1.09) 0.91 (0.78 to 1.08) 0.98 (0.92 to 1.06)

27.5-29.9 1.40 (1.27 to 1.54) 1.13 (1.02 to 1.25) 1.09 (0.89 to 1.35) 1.10 (0.98 to 1.23)

30.0-34.9 1.75 (1.56 to 1.96) 1.12 (0.98 to 1.28) 0.81 (0.60 to 1.09) 1.18 (1.02 to 1.35)

≥35.0 2.66 (2.11 to 3.36) 1.55 (1.17 to 2.04) 0.90 (0.43 to 1.92) 2.13 (1.63 to 2.77)

P-trend <.001 <.001 <.001 0.002

Abbreviation: BMI, body mass index; CVD, cardiovascular disease; NA, not available (no cases available after exclusion).

All models were adjusted for age, race (white or non-white), family history of cardiovascular disease (yes or no), family history of cancer (yes or

no), physical activity (<3, 3-8.9, 9-17.9, 18-26.9, or >27 MET-hour/week), alcohol (0, 0.1-4.9, 5-9.9, 10-14.9, or 15.0+ g/day), total energy

intake (quintiles), smoking status (never, ever, 1-14, 15-24, ≥25 cigs/day), and Alternate Healthy Eating Index (quintiles). Fat mass and lean

body mass were mutually adjusted in the model. a Derived from validated anthropometric prediction equations.


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DISCUSSION

Principal findings

In a large prospective cohort study of men, we used validated anthropometric prediction

equations to examine the association of LBM and FM with all-cause and cause-specific mortality.

We found a strong positive association between predicted FM and mortality due to all causes,

cardiovascular disease, and cancer. In contrast, predicted LBM showed a U-shaped association

with mortality due to all causes, cardiovascular disease, and cancer, and an inverse association

with mortality due to respiratory disease.

Comparison with other studies

Numerous epidemiological studies have examined the relationship between BMI and mortality,

but controversy and confusion exist around the unexpected J- or U-shaped association between

BMI and mortality.9 A systematic review and meta-analysis of 141 prospective studies in 2013

reported that, relative to normal weight, both grade 2 and grade 3 obesity (BMI≥30 kg/m2) were

associated with higher all-cause mortality but overweight (BMI 25-29.9 kg/m2) was associated

with lower all-cause mortality. In contrast, an individual participant-data meta-analysis of 239

prospective studies by the Global BMI Mortality Collaboration in 2016 showed evidence that

increased risk of all-cause mortality among overweight was largely due to confounding by

aspects of smoking and reverse causation from underlying disease and frailty at older ages.8 41 42

There are ongoing controversies around the ‘obesity paradox’43-45

with many studies reporting

excess mortality at the lower BMI range. More importantly, these studies acknowledged the

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major limitation of BMI as a measure of adiposity but did not directly investigate two distinct

compartments of body composition (LBM and FM) in relation to mortality.8 9

Our findings on BMI were in line with the previous findings, whereby we consistently

observed a J-shaped relationship with mortality even after accounting for age, smoking, and

baseline diseases. Two different shapes in mortality risk for FM and LBM taken together can

explain the observed J-shaped relationship between BMI and mortality in our study. The

increased risk of mortality in the lower BMI range (<25 kg/m2) could be attributed to a

combination of the high risk among men with low predicted LBM, which over-rides the modest

positive association between predicted FM and mortality in this lower range of BMI. The

increase of mortality risk at the BMI range of 25-30 kg/m2 is likely due to the high risk

associated with predicted FM in combination with only a moderate risk associated with predicted

LBM. Lastly, the rapid increase of mortality risk in the higher BMI range (>30kg/m2) could be

due to a very high risk associated with both predicted FM and LBM. Of note, at the high end of

BMI (>30kg/m2), the vast majority of individuals have high predicted FM and LBM. Those with

high predicted LBM almost invariably have high FM; for example, the average predicted FM for

those in the highest decile of predicted LBM was 31 kg (Supplementary table 10).

These observed patterns for FM and LBM were further supported from our additional

analyses of BMI and mortality after excluding those in the lower end of predicted LBM, which

resulted in a strong linear positive relationship between BMI and mortality. This shows that

separating lean and healthy (low BMI and normal LBM) vs. lean and unhealthy (low BMI and

low LBM) individuals could be a key to explain the ‘obesity paradox’ phenomenon. Our data

directly address the controversial hypothesis that accumulating excess fat may be causally

beneficial for survivor, and show that this is not likely to be true.46

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To date, only a limited number of studies have examined mortality in relation to directly

measured body composition using DXA or computed tomography.27-33

Most studies have been

done among elderly populations with approximate mean age of 75 years.27-29 31 33

The findings

showed inconsistent and various shapes of the relationship. An Italian study of 934 older people

showed no association of calf muscle and fat mass area with 6-year mortality28

, while another

study of 2292 elderly from the US found that low leg muscle area was associated with increased

risk of 6-year mortality in men only.29

In contrast, other studies found that lower fat percent23 27

or leg fat mass29

was associated with increased mortality27 31 33

but among these studies, only one

study of 477 community-dwelling older people from Netherlands reported that lower

appendicular skeletal muscle mass was associated with increased risk of 12-year mortality.33

Although these studies used a direct measure of body composition, the study samples were

restricted to elderly population which limits the generalizability of the findings. Moreover, they

also had other limitations such as small sample size, short follow-up, exposure measured at one-

time point, lack of information on important confounders (especially smoking) and no

examination on cause-specific mortality. Nonetheless, our finding was consistent with a recent

large-scale Canadian study that measured DXA from participants referred for bone mineral

density testing.30

That study found that high percent fat and low BMI were independently

associated with increased risk of mortality when percent fat and BMI were simultaneously

adjusted in the models. However, the observed associations might have been confounded by

smoking or physical activity due to lack of information on those variables, and the study did not

directly use LBM in the analysis.

The BMI-mortality relationship is prone to reverse causation by preexisting diseases that

can cause weight loss and also increase risk of mortality, and this is more likely to be a concern

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with shorter lag times. We found that, with shorter lag time periods, the positive association

between predicted FM and mortality was attenuated, while the U-shaped association between

predicted LBM and mortality tended to be strengthened. Therefore, the stronger U-shaped

relationship between BMI and mortality with shorter lag time periods can be mostly attributed to

the pronounced U-shaped association for predicted LBM, which may be an indicator of health

status capturing any preexisting undiagnosed medical condition, including frailty at older ages.

The influence of smoking is particularly important in investigating the obesity-mortality

relationship. Not only is smoking a strong risk factor for death, but it also affects body weight

and body composition.47-50

Similar to the BMI-mortality association, predicted FM showed

weaker and less linear association with mortality among current-smokers than past-or never-

smokers. Interestingly, we found a stronger U-shaped relationship between predicted LBM and

mortality among current-smokers than past-or never-smokers. Although we cannot completely

rule out the residual confounding by smoking, our findings showed some evidence that the

frequently observed U-shaped relationship between BMI and mortality among smokers may be

affected by the strong U-shaped association between LBM and mortality.

Strengths and limitations of the study

Our study has several strengths. First, the innovative approach of validated anthropometric

prediction equations allowed us to practically estimate LBM and FM in large epidemiological

settings. This is the first and one of the most comprehensive analyses to examine the association

of predicted body composition with all-cause and cause-specific mortality in a large prospective

cohort study. Second, the Health Professionals Follow-up Study is a well-established prospective

cohort study that has a large number of deaths over long-term follow-up period. Third, detailed

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and updated information on lifestyle and health-related factors allowed adequate control for

confounding. Fourth, repeated measures on exposures (i.e., predicted scores) allowed prospective

analyses of different lag time periods to examine reverse causality in the obesity-mortality

relationship.

There are several limitations as well. First, predicted LBM and FM are not perfect

measures of actual LBM and FM. Nonetheless, the validation results from the NHANES showed

high predictive ability of the anthropometric equations with no systematic bias. In fact, the very

high R2 between FM and LBM (>0.90) for direct DXA measurements and predicted measures in

an independent dataset indicate that a direct DXA measure would give very similar answers to

ours; this is further supported by the equal predictive ability of the predicted measures and DXA

measures for various obesity-related biomarkers. Moreover, given the prospective study design,

any mismeasurement in the exposures would likely be random with respect to endpoints,

resulting in conservative associations. Second, we cannot entirely rule out the possibility of

unmeasured or unknown confounding factors that may account for the associations observed in

this study. However, the homogeneity of the study population and comprehensive data on the

risk factors minimized potential confounding. Third, the generalizability of the findings may be

limited given that the study participants were restricted to health professionals and

predominantly White men. However, we believe that our main findings will be broadly

applicable.

Clinical and public health implications

The current study provides strong evidence that excess FM increases the risk of mortality.

Increased FM was not protective for mortality which is counter to the premise of the ‘obesity

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paradox’. On the other hand, low LBM was associated with increased risk of mortality in the

lower range of BMI. Our study suggests that understanding different contributions of LBM and

FM to BMI may explain the shape of the relationship between BMI and mortality as well as the

controversial issue of ‘obesity paradox’. Our findings support the current recommendations on

body weight for prevention of chronic diseases to keep a normal body weight defined by BMI of

18.5 to 24.9 kg/m2, and further suggest that the lowest mortality risk can be observed at the lower

normal range of BMI if the influence of LBM on mortality is accounted for. The current

recommendations should highlight not only the importance of normal body weight but also

healthy body composition (e.g., healthy lean) to reduce confusion around the optimal weight

(e.g., ‘obesity paradox’) for overall health. Interventions and strategies to promote healthy body

composition via lifestyle modification (e.g., physical activity and diet) may be an important next

step to improve population health.

Conclusions

We found a strong positive association between predicted FM and mortality, and a U-shaped

association between predicted LBM and mortality in men. Low LBM, rather than low FM, may

be driving the increased risk of mortality in the lower BMI range. Understanding the independent

role of LBM and FM has important implications for clarifying the ‘obesity paradox’

phenomenon in the relationship between BMI and mortality.

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Acknowledgements: We thank the participants and staff of the HSPF for their valuable

contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO,

CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH,

OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for

analyses and interpretation of these data.

Contributors: DHL and ELG had full access to all of the data in the study and take

responsibility for the integrity of the data and the accuracy of the data analysis. DHL and ELG

conceived and designed the study. NK, FBH, EJO, EBR, WCW, and ELG acquired the data.

DHL and ELG drafted the manuscript. All the authors critically revised the manuscript for

important intellectual content. DHL did the statistical analysis. FBH, EBR, WCW, and ELG

obtained funding. DHL and ELG were responsible for administrative, technical, or material

support. ELG was responsible for study supervision. DHL is the guarantor.

Funding: This work was supported by the National Institutes of Health (UM1 CA167552 and

R01 HL35464). The funders had no role in the design and conduct of the study; collection,

management, analysis, and interpretation of the data; and preparation, review, or approval of the

manuscript; and decision to submit the manuscript for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at

http://www.icmje.org/coi_disclosure.pdf and declare: no support from any organization for the

submitted work other than those described above; no financial relationships with any

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organizations that might have an interest in the submitted work in the previous three years; no

other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: This investigation was approved by the Institutional Review Board of the

Harvard T.H. Chan School of Public Health and Brigham and Women’s Hospital.

Transparency declaration: The lead author (the manuscript's guarantor) affirms that the

manuscript is an honest, accurate, and transparent account of the study being reported; that no

important aspects of the study have been omitted; and that any discrepancies from the study as

planned (and, if relevant, registered) have been explained.

Data sharing: No additional data available.

Copyright/License for Publication: The Corresponding Author has the right to grant on behalf

of all authors and does grant on behalf of all authors, a worldwide licence to the Publishers and

its licensees in perpetuity, in all forms, formats and media (whether known now or created in the

future), to i) publish, reproduce, distribute, display and store the Contribution, ii) translate the

Contribution into other languages, create adaptations, reprints, include within collections and

create summaries, extracts and/or, abstracts of the Contribution, iii) create any other derivative

work(s) based on the Contribution, iv) to exploit all subsidiary rights in the Contribution, v) the

inclusion of electronic links from the Contribution to third party material where-ever it may be

located; and, vi) licence any third party to do any or all of the above.

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Figure legends

Figure 1. The association of predicted body composition* and body mass index with all-

cause mortality in men. 1a. Fat mass and all-cause mortality. 2b. Lean body mass and all-

cause mortality. 3c. Body mass index and all-cause mortality.

Hazard ratios are indicated by solid lines and 95 % CIs by dashed lines. The reference point is

the lowest value for each fat mass and lean body mass and 25 kg/m2 for body mass index, with

knots placed at the 5th

, 35th

, 50th

, 65th

and 95th

percentiles of each fat mass and lean body mass

distribution. The models adjusted for the same cofounders in Table 2 plus mutually adjusted for

predicted fat mass and predicted lean body mass.

* Percentiles (0, 2.5, 5, 10, 25, 50, 75, 90, and 100%ile): 7, 13, 14, 15, 18, 21, 25, 29, and 66 kg

for fat mass, 24, 48, 49, 51, 53, 56, 59, 63, and 103 kg for lean body mass and 14.2, 21.5, 21.2,

22.0, 23.4, 25.1, 27.0, 31.0 and 62.0 kg/m2.

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Figure 1. The association of predicted body composition* and body mass index with all-cause mortality in men. 1a. Fat mass and all-cause mortality. 2b. Lean body mass and all-cause mortality. 3c. Body mass

index and all-cause mortality.

311x116mm (96 x 96 DPI)

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Supplementary online contents

Supplementary figure 1. Scatter plots for LBM in an independent validation group from the NHANES 1999-

2006

Supplementary figure 2. Scatter plots for FM in an independent validation group from the NHANES 1999-

2006

Supplementary table 1. Anthropometric prediction equations for lean body mass and fat mass in men

Supplementary table 2. Validation of predicted LBM with DXA-measured LBM in an independent validation

group from the NHANES 1999-2006

Supplementary table 3. Validation of predicted FM with DXA-measured FM in an independent validation

group from the NHANES 1999-2006

Supplementary table 4. Hazard ratio (95% CI) of all-cause mortality according to body mass index in men

(exclusion of low fat mass).

Supplementary table 5. Hazard ratio (95% CI) of all-cause mortality according to predicted fat mass, lean

body mass and body mass index by different lag-time periods


body mass and body mass index stratified by smoking status.


body mass and body mass index stratified by age

Supplementary table 8. Sensitivity analysis of predicted fat mass, lean body mass and body mass index in

relation to all-cause mortality in men

Supplementary table 9. Hazard ratio (95% CI) of all-cause and cause-specific mortality according to quintiles

of body mass index in men

Supplementary table 10. Hazard ratio (95% CI) of all-cause mortality according to deciles of predicted fat

mass, lean body mass and body mass index in men

This supplementary material has been provided by the authors to give readers additional information about their

work.

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Supplementary figure 1. Scatter plots for LBM in an independent validation group from the NHANES 1999-2006

(n=2292). Scatter plots of DXA-measured LBM (y-axis) against predicted LBM (x-axis) (A. Original equations and B.

Equations with polynomial terms). Scatter plots of difference between DXA and predicted LBM (y-axis) against DXA-

measured LBM (x-axis) (C. Original equation and D. Equations with polynomial terms). For A and B, solid lines

represent regression and dotted lines present 95% CI. For C and D, dotted lines represent mean difference of 0. LBM, lean

body mass; DXA, dual-energy X-ray absorptiometry; NHANES, National Health and Nutrition Examination Survey

1999-2006

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Supplementary figure 2. Scatter plots for FM in an independent validation group from the NHANES 1999-2006

(n=2292). Scatter plots of DXA-measured FM (y-axis) against predicted FM (x-axis) (A. Original equations and B.

Equations with polynomial terms). Scatter plots of difference between DXA and predicted FM (y-axis) against DXA-

measured FM (x-axis) (C. Original equation and D. Equations with polynomial terms). For A and B, solid lines represent

regression and dotted lines present 95% CI. For C and D, dotted lines represent mean difference of 0. Abbreviation: FM,

fat mass; DXA, dual-energy X-ray absorptiometry; NHANES, National Health and Nutrition Examination Survey

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Supplementary table 1. Anthropometric prediction equations for lean body mass and

fat mass in menƚ

Original equations

LBM (kg)

= 19.363 + 0.001*age (yr) + 0.064*height (cm) + 0.756*weight (kg) - 0.366*waist (cm) -

0.066*Mexican + 0.231*Hispanic + 0.432*Black - 1.007*Other ethnicity

R2=0.914

SEE=2.548

FM (kg)

= -18.592 - 0.009*age (yr) - 0.080*height (cm) + 0.226*weight (kg) + 0.387*waist (cm) +

0.080*Mexican - 0.188*Hispanic - 0.483*Black + 1.050*Other ethnicity

R2=0.900

SEE=2.603

Equations with polynomial terms

LBM (kg)

= 12.052 – 0.003*age (yr) + 0.049*height (cm) + 1.133*weight (kg) – 0.486*waist (cm) –

0.112*Mexican + 0.135*Hispanic + 0.359*Black - 0.849*Other -0.006*(weight (kg)*waist (cm))

+ 0.001*(weight (kg))2 + 0.003*(waist (cm))

2

R2=0.918

SEE=2.494

FM (kg)

= -14.321 – 0.005*age (yr) – 0.064*height (cm) – 0.236*weight (kg) + 0.639*waist (cm) +

0.112*Mexican – 0.099*Hispanic – 0.363*Black + 0.888*Other + 0.008*(weight (kg)*waist

(cm)) – 0.002*(weight (kg))2 – 0.005*(waist (cm))

2

R2=0.905

SEE=2.543

Abbreviation: LBM, lean body mass; FM, fat mass. Race variables are binary variables (1 if yes, 0 if no), and White is the reference group.

ƚ Equations were developed and validated using the National Health and Nutrition Examination Survey 1999-2006

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Supplementary table 2. Validation of predicted LBM with DXA-measured LBM in an independent validation group from the NHANES

1999-2006 (n=2292)

Deciles of DXA-measured LBM

Mean (SD) 1 2 3 4 5 6 7 8 9 10

DXA-measured LBM (kg) 42.70 (2.85) 47.70 (0.98) 50.40 (0.72) 52.78 (0.64) 54.91 (0.58) 57.25 (0.69) 59.69 (0.70) 62.32 (0.82) 65.80 (1.21) 72.60 (4.20)

Original equations

Predicted LBM (kg) 44.37 (2.76) 48.64 (2.38) 50.96 (2.21) 52.95 (2.34) 54.78 (2.11) 57.23 (2.13) 59.31 (2.52) 61.62 (2.75) 65.16 (2.95) 71.96 (5.44)

Diff (DXA-predicted) (kg) -1.67 (1.98) -0.94 (2.14) -0.56 (2.13) -0.18 (2.28) 0.13 (2.02) 0.02 (2.12) 0.39 (2.41) 0.70 (2.53) 0.64 (2.69) 0.64 (3.18)


Predicted LBM (kg) 43.83 (3.06) 48.5 (2.37) 50.97 (2.28) 53.06 (2.37) 54.94 (2.16) 57.46 (2.18) 59.57 (2.51) 61.86 (2.74) 65.36 (2.82) 71.85 (5.03)

Diff (DXA-predicted) (kg) -1.13 (1.95) -0.80 (2.09) -0.57 (2.19) -0.28 (2.30) -0.04 (2.06) -0.21 (2.18) 0.13 (2.41) 0.47 (2.52) 0.44 (2.56) 0.75 (2.97)

Abbreviation: LBM, lean body mass; DXA, dual-energy X-ray absorptiometry; NHANES, National Health and Nutrition Examination Survey 1999-2006

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Supplementary table 3. Validation of predicted FM with DXA-measured FM in an independent validation group from the NHANES

1999-2006 (n=2292)

Deciles of DXA-measured FM

Mean (SD) 1 2 3 4 5 6 7 8 9 10

DXA-measured FM (kg) 9.80 (1.32) 13.21 (0.87) 15.93 (0.73) 18.34 (0.67) 20.55 (0.62) 22.46 (0.57) 24.52 (0.65) 27.00 (0.82) 30.28 (1.14) 38.55 (5.42)

Original equations

Predicted FM (kg) 9.63 (2.71) 13.79 (2.22) 16.39 (2.38) 18.82 (2.34) 21.1 (2.24) 22.79 (2.31) 24.77 (2.39) 26.74 (2.76) 29.63 (2.70) 36.38 (4.84)

Diff (DXA-predicted) (kg) 0.17 (2.17) -0.59 (2.03) -0.47 (2.30) -0.49 (2.15) -0.54 (2.14) -0.33 (2.27) -0.25 (2.34) 0.26 (2.59) 0.65 (2.57) 2.17 (3.07)


Predicted FM (kg) 10.22 (2.24) 13.95 (2.00) 16.40 (2.15) 18.73 (2.15) 20.86 (2.13) 22.45 (2.18) 24.4 (2.36) 26.4 (2.85) 29.33 (2.79) 36.71 (5.56)

Diff (DXA-predicted) (kg) -0.42 (1.76) -0.75 (1.84) -0.48 (2.07) -0.39 (1.97) -0.30 (2.03) 0.01 (2.14) 0.12 (2.32) 0.60 (2.67) 0.95 (2.66) 1.84 (3.17)

Abbreviation: FM, fat mass; DXA, dual-energy X-ray absorptiometry; NHANES, National Health and Nutrition Examination Survey

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Supplementary table 4. Hazard ratio (95% CI) of all-cause mortality according to body mass index in men (exclusion of low fat mass*).


No of

deaths

IR

/100,000py Model 1 Model 2 Model 3 Model 4 Model 5

BMI

<18.5 53 2883 1.74 (1.33 to 2.28) 1.64 (1.25 to 2.15) 2.24 (1.16 to 4.32) 1.66 (0.75 to 3.71) 1.75 (0.79 to 3.91)

18.5-22.4 1626 1504 1.04 (0.98 to 1.10) 1.03 (0.98 to 1.10) 1.04 (0.97 to 1.10) 1.03 (0.97 to 1.10) 1.10 (1.03 to 1.19)


25.0-27.4 3987 1639 1.09 (1.04 to 1.14) 1.04 (0.99 to 1.09) 1.04 (0.99 to 1.09) 1.04 (0.99 to 1.09) 1.05 (1.00 to 1.09)

27.5-29.9 1753 1845 1.31 (1.24 to 1.39) 1.21 (1.14 to 1.28) 1.21 (1.14 to 1.28) 1.21 (1.15 to 1.29) 1.22 (1.15 to 1.29)

30.0-34.9 1001 1913 1.48 (1.38 to 1.59) 1.31 (1.22 to 1.40) 1.31 (1.22 to 1.41) 1.32 (1.22 to 1.41) 1.32 (1.23 to 1.42)

≥35.0 196 2368 2.28 (1.98 to 2.64) 1.99 (1.72 to 2.30) 2.02 (1.75 to 2.33) 2.02 (1.75 to 2.34) 2.03 (1.76 to 2.35)

P-trend

<.001 <.001 <.001 <.001 <.001 Abbreviation: BMI, body mass index. Model 1: adjusted for age

Model 2: adjusted for age, race (white or non-white), family history of cardiovascular disease (yes or no), family history of cancer (yes or no), alcohol (0, 0.1-4.9, 5-9.9, 10-14.9, or 15.0+ g/day), total daily energy intake

(quintiles), and smoking (never, ever, 1-14, 15-24, ≥25 cigs/day), physical activity (<3, 3-8.9, 9-17.9, 18-26.9, or >27 MET-hour/week), Alternate Healthy Eating Index (quintiles). Model 3: additionally, excluded 2.5%ile of total participants with low fat mass (height-adjusted)

Model 4: additionally, excluded 5%ile of total participants with low fat mass (height-adjusted)

Model 5: additionally, excluded 10%ile of total participants with low fat mass (height-adjusted) * Derived from validated anthropometric prediction equations.

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Supplementary table 5. Hazard ratio (95% CI) of all-cause mortality according to body mass index,

predicted fat mass, lean body mass and body mass by different lag-time periods


0-12y 4-16y 8-18y 12-22y

No. of deaths 12356 10726 8214 5982

IR/100,000py 1419 1580 1694 1856

Fat massa,b






P-trend <.001 <.001 <.001 <.001

Lean body massa,b


Quintile 2 0.87 (0.83 to 0.92) 0.87 (0.83 to 0.93) 0.90 (0.84 to 0.96) 0.93 (0.86-1.01)

Quintile 3 0.84 (0.80 to 0.89) 0.88 (0.83 to 0.94) 0.88 (0.82 to 0.94) 0.93 (0.85-1.01)

Quintile 4 0.85 (0.80 to 0.91) 0.89 (0.83 to 0.95) 0.90 (0.84 to 0.97) 0.93 (0.85-1.02)

Quintile 5 0.90 (0.84 to 0.97) 0.95 (0.88 to 1.03) 0.94 (0.87 to 1.03) 1.00 (0.91-1.11)

P-trend 0.001 0.21 0.21 0.91

BMI

<18.5 2.32 (2.00 to 2.70) 1.45 (1.11 to 1.90) 1.46 (1.06 to 2.02) 1.55 (1.06 to 2.27)

18.5-22.4 1.20 (1.13 to 1.26) 1.06 (1.00 to 1.13) 1.02 (0.95 to 1.09) 0.99 (0.91 to 1.08)


25.0-27.4 0.94 (0.90 to 0.99) 0.98 (0.93 to 1.03) 1.01 (0.95 to 1.07) 1.03 (0.97 to 1.10)

27.5-29.9 1.09 (1.03 to 1.15) 1.16 (1.09 to 1.23) 1.17 (1.09 to 1.26) 1.24 (1.14 to 1.35)

30.0-34.9 1.20 (1.12 to 1.29) 1.28 (1.19 to 1.38) 1.35 (1.24 to 1.46) 1.36 (1.22 to 1.50)

≥35.0 1.64 (1.44 to 1.87) 1.61 (1.39 to 1.86) 1.65 (1.39 to 1.95) 1.92 (1.58 to 2.34)

P-trend 0.27 <.001 <.001 <.001 Abbreviation: BMI, body mass index; NA, not available (no cases available after exclusion).

All models were adjusted for age, race (white or non-white), family history of cardiovascular disease (yes or no), family history of cancer (yes or no), physical

activity (<3, 3-8.9, 9-17.9, 18-26.9, or >27 MET-hour/week), alcohol (0, 0.1-4.9, 5-9.9, 10-14.9, or 15.0+ g/day), total energy intake (quintiles), smoking status (never, ever, 1-14, 15-24, ≥25 cigs/day), and Alternate Healthy Eating Index (quintiles). Fat mass and lean body mass were mutually adjusted in the model. a Derived from validated anthropometric prediction equations.


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Supplementary table 6. Hazard ratio (95% CI) of all-cause mortality according to

predicted fat mass, lean body mass and body mass stratified by smoking status


Never-smokers Past-smoker Current-smoker

No. of deaths 6791 4947 618

IR/100,000py 1457 1377 1366

Fat massa,b

Quintile 1 1 (reference) 1 (reference) 1 (reference)

Quintile 2 1.06 (0.97 to 1.15) 1.14 (1.03 to 1.26) 0.95 (0.72 to 1.26)

Quintile 3 1.01 (0.93 to 1.11) 1.02 (0.92 to 1.13) 0.92 (0.68 to 1.24)

Quintile 4 1.17 (1.07 to 1.28) 1.16 (1.04 to 1.29) 1.22 (0.90 to 1.65)

Quintile 5 1.40 (1.26 to 1.54) 1.34 (1.19 to 1.51) 1.17 (0.84 to 1.65)

P-trend <.001 <.001 0.18

Lean body massa,b


Quintile 2 0.95 (0.88 to 1.02) 0.91 (0.83 to 0.99) 0.66 (0.50 to 0.86)

Quintile 3 0.90 (0.83 to 0.98) 0.94 (0.86 to 1.03) 0.72 (0.54 to 0.94)

Quintile 4 0.92 (0.85 to 1.00) 0.94 (0.85 to 1.03) 0.81 (0.61 to 1.08)

Quintile 5 0.97 (0.89 to 1.07) 0.98 (0.88 to 1.10) 0.86 (0.63 to 1.19)

P-trend 0.46 0.97 0.61

BMI

<18.5 1.57 (1.08 to 2.29) 1.67 (1.07 to 2.60) 1.96 (0.78 to 4.94)

18.5-22.4 1.01 (0.93 to 1.09) 1.05 (0.95 to 1.15) 1.33 (1.04 to 1.70)

22.5-24.9 1 (reference) 1 (reference) 1 (reference)

25.0-27.4 1.05 (0.99 to 1.11) 1.02 (0.95 to 1.10) 1.15 (0.92 to 1.42)

27.5-29.9 1.23 (1.14 to 1.33) 1.20 (1.10 to 1.31) 1.24 (0.95 to 1.63)

30.0-34.9 1.32 (1.19 to 1.45) 1.27 (1.14 to 1.42) 1.56 (1.12 to 2.17)

≥35.0 2.02 (1.65 to 2.48) 2.06 (1.66 to 2.56) 1.89 (0.86 to 4.14)

P-trend <.001 <.001 0.19 Abbreviation: BMI, body mass index; NA, not available (no cases available after exclusion).

All models were adjusted for age, race (white or non-white), family history of cardiovascular disease (yes or no), family history of cancer (yes or no), physical activity (<3, 3-8.9, 9-17.9, 18-26.9, or >27 MET-hour/week), alcohol (0, 0.1-4.9, 5-9.9, 10-14.9, or 15.0+

g/day), total energy intake (quintiles), smoking status (never, ever, 1-14, 15-24, ≥25 cigs/day), and Alternate Healthy Eating Index

(quintiles). Fat mass and lean body mass were mutually adjusted in the model. a Derived from validated anthropometric prediction equations.


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Supplementary table 7. Hazard ratio (95% CI) of all-cause mortality according to

predicted fat mass, lean body mass and body mass stratified by age


Age <70yrs Age 70-84yrs Age ≥85yrs

No. of deaths 2406 6845 3105

IR/100,000py 402 2789 11983

Fat massa,b


Quintile 2 1.14 (0.99 to 1.31) 1.08 (0.99 to 1.18) 1.02 (0.91 to 1.15)

Quintile 3 0.95 (0.81 to 1.11) 1.01 (0.93 to 1.10) 1.03 (0.91 to 1.16)

Quintile 4 1.22 (1.04 to 1.42) 1.17 (1.07 to 1.28) 1.10 (0.97 to 1.25)

Quintile 5 1.52 (1.28 to 1.80) 1.39 (1.25 to 1.53) 1.15 (0.99 to 1.33)

P-trend <.001 <.001 0.04

Lean body massa,b


Quintile 2 0.85 (0.73 to 0.97) 0.92 (0.85 to 0.99) 0.97 (0.88 to 1.07)

Quintile 3 0.87 (0.76 to 1.01) 0.90 (0.83 to 0.97) 0.94 (0.84 to 1.05)

Quintile 4 0.94 (0.81 to 1.09) 0.88 (0.81 to 0.96) 1.03 (0.91 to 1.16)

Quintile 5 0.98 (0.84 to 1.15) 0.92 (0.84 to 1.01) 1.05 (0.90 to 1.22)

P-trend 0.55 0.08 0.58

BMI

<18.5 1.80 (0.96 to 3.37) 1.69 (1.19 to 2.39) 1.45 (0.80 to 2.63)

18.5-22.4 1.05 (0.91 to 1.21) 1.04 (0.96 to 1.13) 1.03 (0.92 to 1.14)

22.5-24.9 1 (reference) 1 (reference) 1 (reference)

25.0-27.4 1.12 (1.00 to 1.24) 1.03 (0.97 to 1.09) 1.02 (0.94 to 1.12)

27.5-29.9 1.37 (1.21 to 1.56) 1.18 (1.09 to 1.27) 1.18 (1.04 to 1.33)

30.0-34.9 1.59 (1.38 to 1.84) 1.28 (1.16 to 1.40) 1.12 (0.94 to 1.34)

≥35.0 2.33 (1.80 to 3.03) 2.05 (1.70 to 2.47) 1.29 (0.79 to 2.13)

P-trend <.001 <.001 0.04 Abbreviation: BMI, body mass index; NA, not available (no cases available after exclusion).

All models were adjusted for age, race (white or non-white), family history of cardiovascular disease (yes or no), family history of cancer (yes or no), physical activity (<3, 3-8.9, 9-17.9, 18-26.9, or >27 MET-hour/week), alcohol (0, 0.1-4.9, 5-9.9, 10-14.9, or 15.0+

g/day), total energy intake (quintiles), smoking status (never, ever, 1-14, 15-24, ≥25 cigs/day), and Alternate Healthy Eating Index

(quintiles). Fat mass and lean body mass were mutually adjusted in the model. a Derived from validated anthropometric prediction equations.


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Supplementary table 8. Sensitivity analysis of predicted fat mass, lean body mass and body

mass in relation to all-cause mortality in men


Model 1* Model 2

† Model 3

‡ Model 4

§

Fat massa,b






P-trend <.001 <.001 <.001 <.001

Lean body massa,b






P-trend 0.33 0.62 0.27 0.29

BMI

<18.5 1.68 (1.28 to 2.20) 1.36 (1.00 to 1.84) 1.85 (1.39 to 2.46) 1.48 (1.14 to 1.92)

18.5-22.4 1.04 (0.98 to 1.10) 1.03 (0.97 to 1.09) 1.05 (0.98 to 1.12) 1.04 (0.98 to 1.10)


25.0-27.4 1.05 (1.00 to 1.10) 1.03 (0.99 to 1.08) 1.04 (0.99 to 1.10) 1.03 (0.99 to 1.08)

27.5-29.9 1.23 (1.16 to 1.31) 1.21 (1.14 to 1.28) 1.23 (1.15 to 1.31) 1.21 (1.14 to 1.28)

30.0-34.9 1.35 (1.26 to 1.45) 1.31 (1.22 to 1.41) 1.35 (1.25 to 1.46) 1.32 (1.23 to 1.41)

≥35.0 2.10 (1.81 to 2.42) 2.04 (1.76 to 2.36) 2.09 (1.79 to 2.43) 1.86 (1.61 to 2.14)

P-trend <.001 <.001 <.001 <.001 Abbreviation: BMI, body mass index.

All models were adjusted for age, race (white or non-white), family history of cardiovascular disease (yes or no), family history of cancer (yes or no), physical activity (<3, 3-8.9, 9-17.9, 18-26.9, or >27 MET-hour/week), alcohol (0, 0.1-4.9, 5-9.9, 10-14.9, or 15.0+ g/day), total energy intake (quintiles),

smoking status (never, ever, 1-14, 15-24, ≥25 cigs/day), and Alternate Healthy Eating Index (quintiles). Fat mass and lean body mass were mutually

adjusted in the model. *

Model 1: no adjustment for physical activity (number of deaths/person-years: 12356/763041). †

Model 2: exclusion of deaths occurred in the early follow-up period (2 years) (number of deaths/person-years: 11940/760100). ‡

Model 3: exclusion of right censoring criteria for age (>85 years) (number of deaths/person-years: 9764/743476). §

Model 4: inclusion of baseline illness (number of deaths/person-years: 13377/788198).

a Derived from validated anthropometric prediction equations. b Height was adjusted by including height as a continuous variable for fat mass and by regressing out variation due to height for lean body mass.

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Supplementary table 9. Hazard ratio (95% CI) of all-cause and cause-specific mortality according to quintiles of

body mass index in men


All cause death CVD death Cancer death Respiratory death Other death

No. of deaths 12356 4296 3723 960 3377

IR/100,000py 1619 558 483 124 438

BMI

Quintile 1 1 (reference) 1 (reference) 1 (reference) 1 (reference) 1 (reference)

Quintile 2 0.98 (0.93 to 1.04) 1.01 (0.91 to 1.12) 1.05 (0.95 to 1.17) 0.74 (0.61 to 0.90) 0.97 (0.87 to 1.09)




P-trend <.001 <.001 0.01 0.03 0.05


All models were adjusted for age, race (white or non-white), family history of cardiovascular disease (yes or no), family history of cancer (yes or no), physical

activity (<3, 3-8.9, 9-17.9, 18-26.9, or >27 MET-hour/week), alcohol (0, 0.1-4.9, 5-9.9, 10-14.9, or 15.0+ g/day), total energy intake (quintiles), smoking status

(never, ever, 1-14, 15-24, ≥25 cigs/day), and Alternate Healthy Eating Index (quintiles). Fat mass and lean body mass were mutually adjusted in the model. a Derived from validated anthropometric prediction equations.


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Supplementary table 10. Hazard ratio (95% CI) of all-cause mortality according to deciles of predicted fat mass,

lean body mass and body mass index in men


FM (kg)

Mean (SD)

LBM (kg)

Mean (SD)

BMI (kg/m2)

Mean (SD)

No of

deaths Model 1 Model 2 Model 3

Fat massa,b

Decile 1 13.7 (1.5) 51.9 (3.3) 21.6 (1.4) 1009 1 (reference) 1 (reference) 1 (reference)

Decile 2 16.5 (0.5) 53.3 (3.2) 22.9 (1.2) 928 0.85 (0.78 to 0.93) 0.87 (0.79 to 0.95) 0.89 (0.81 to 0.98)

Decile 3 18.0 (0.4) 54.1 (3.3) 23.6 (1.3) 1094 0.97 (0.89 to 1.06) 0.95 (0.87-1.04) 0.99 (0.90 to 1.08)

Decile 4 19.3 (0.4) 54.6 (3.3) 24.1 (1.3) 1204 1.04 (0.96 to 1.14) 1.02 (0.94-1.11) 1.07 (0.98 to 1.17)

Decile 5 20.5 (0.4) 55.5 (3.5) 24.8 (1.4) 1147 0.94 (0.86 to 1.02) 0.89 (0.82-0.97) 0.94 (0.86 to 1.03)

Decile 6 21.8 (0.4) 56.0 (3.6) 25.3 (1.4) 1150 0.96 (0.88 to 1.04) 0.93 (0.85-1.01) 0.98 (0.90 to 1.08)

Decile 7 23.2 (0.5) 56.9 (3.7) 26.0 (1.5) 1303 1.08 (0.99 to 1.17) 1.00 (0.92-1.08) 1.06 (0.97 to 1.16)

Decile 8 25.0 (0.6) 57.8 (4.1) 26.7 (1.6) 1423 1.20 (1.10 to 1.30) 1.11 (1.02-1.20) 1.18 (1.07 to 1.29)

Decile 9 27.5 (1.0) 59.4 (4.3) 27.9 (1.8) 1515 1.28 (1.18 to 1.39) 1.17 (1.08-1.27) 1.24 (1.13 to 1.37)

Decile 10 33.8 (4.5) 64.6 (6.3) 31.3 (3.2) 1583 1.52 (1.40 to 1.65) 1.32 (1.21-1.43) 1.36 (1.23 to 1.51)

P-trend <.001 <.001 <.001

Lean body massa,b

Decile 1 17.4 (4.3) 48.5 (2.4) 21.5 (1.4) 1656 1 (reference) 1 (reference) 1 (reference)

Decile 2 18.3 (3.7) 51.6 (0.5) 22.8 (1.1) 1340 0.89 (0.83 to 0.96) 0.90 (0.84 to 0.97) 0.91 (0.84 to 0.97)

Decile 3 19.2 (3.7) 53.0 (0.4) 23.5 (1.1) 1242 0.88 (0.82 to 0.95) 0.89 (0.82 to 0.95) 0.88 (0.82 to 0.95)

Decile 4 20.0 (3.7) 54.2 (0.3) 24.2 (1.1) 1177 0.88 (0.82 to 0.95) 0.88 (0.82 to 0.95) 0.87 (0.81 to 0.94)

Decile 5 20.5 (3.7) 55.3 (0.3) 24.7 (1.1) 1163 0.87 (0.81 to 0.94) 0.87 (0.80 to 0.93) 0.85 (0.78 to 0.92)

Decile 6 21.3 (3.9) 56.4 (0.4) 25.3 (1.2) 1161 0.94 (0.87 to 1.01) 0.92 (0.85 to 0.99) 0.88 (0.81 to 0.95)

Decile 7 22.2 (3.8) 57.6 (0.4) 26.0 (1.2) 1099 0.92 (0.86 to 1.00) 0.91 (0.84 to 0.99) 0.85 (0.79 to 0.93)

Decile 8 23.5 (4.1) 59.1 (0.5) 26.8 (1.2) 1183 1.04 (0.96 to 1.12) 1.00 (0.93 to 1.08) 0.90 (0.83 to 0.98)

Decile 9 25.6 (4.5) 61.3 (0.9) 28.1 (1.4) 1137 1.08 (1.00 to 1.17) 1.03 (0.96 to 1.11) 0.88 (0.81 to 0.96)

Decile 10 31.2 (6.3) 67.0 (4.4) 31.5 (3.1) 1198 1.33 (1.23 to 1.44) 1.20 (1.11 to 1.30) 0.94 (0.85 to 1.03)

P-trend <.001 <.001 0.08

BMI

Decile 1 14.9 (2.6) 49.4 (2.7) 21.0 (0.9) 1221 1 (reference) 1 (reference) NA

Decile 2 17.1 (2.2) 52.0 (1.8) 22.5 (0.3) 1112 0.93 (0.86 to 1.01) 0.95 (0.87 to 1.03) NA

Decile 3 18.6 (2.4) 53.3 (1.9) 23.4 (0.2) 1188 0.96 (0.89 to 1.04) 0.97 (0.89 to 1.05) NA

Decile 4 19.4 (2.4) 54.3 (1.9) 24.0 (0.2) 884 0.94 (0.86 to 1.03) 0.94 (0.86 to 1.02) NA

Decile 5 20.7 (2.5) 55.2 (1.9) 24.6 (0.2) 1175 0.98 (0.90 to 1.06) 0.96 (0.89 to 1.04) NA

Decile 6 21.6 (2.6) 56.2 (2.0) 25.3 (0.2) 1222 0.95 (0.87 to 1.02) 0.92 (0.85 to 0.99) NA

Decile 7 22.9 (2.7) 57.3 (2.0) 26.0 (0.3) 1408 1.07 (0.99 to 1.16) 1.02 (0.94 to 1.10) NA

Decile 8 24.3 (2.9) 58.8 (2.2) 27.0 (0.3) 1306 1.10 (1.02 to 1.19) 1.04 (0.96 to 1.13) NA

Decile 9 26.7 (3.2) 60.8 (2.6) 28.3 (0.5) 1387 1.23 (1.14 to 1.33) 1.14 (1.06 to 1.24) NA

Decile 10 32.6 (5.4) 66.3 (5.0) 31.9 (2.8) 1453 1.46 (1.35 to 1.57) 1.29 (1.19 to 1.40) NA

P-trend <.001 <.001

Abbreviation: BMI, body mass index; FM, fat mass; LBM, lean body mass; NA, not available.


Model 2: adjusted for age, race (white or non-white), family history of cardiovascular disease (yes or no), family history of cancer (yes or no), physical activity (<3, 3-8.9, 9-17.9, 18-26.9, or >27 MET-hour/week), alcohol consumption (0, 0.1-4.9, 5-9.9, 10-14.9, or 15.0+ g/day), total energy intake (quintiles), and smoking status (never, ever, 1-14, 15-24, ≥25

cigs/day), Alternate Healthy Eating Index (quintiles).



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Predicted lean body mass, fat mass, and all-cause and cause-specific mortality in men:

results from a prospective US cohort study

Dong Hoon Lee, NaNa Keum, Frank B. Hu, E. John Orav, Eric B. Rimm, Walter C. Willett,

Edward L. Giovannucci

Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA,

Dong Hoon Lee, post-doctoral research fellow, NaNa Keum, post-doctoral research fellow,

Frank B. Hu, professor, Eric B. Rimm, professor, Walter C. Willett, professor, Edward L.


Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115,

USA, Frank B. Hu, professor, Eric B. Rimm, professor, Walter C. Willett, professor, Edward L.


Department of food science and Biotechnology, Dongguk University, Goyang, South Korea,

NaNa Keum, assistant professor, Channing Division of Network Medicine

Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston,

MA 02115, USA, Frank B. Hu, professor, Eric B. Rimm, professor, Walter C. Willett, professor,

Edward L. Giovannucci, professor

Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA, E. John

Orav, associate professor

Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115,

USA, E. John Orav, associate professor.

Corresponding author:

Edward L. Giovannucci, Department of Nutrition, Harvard T.H Chan School of Public Health,

665 Huntington Avenue, Bldg. 2, Room 371, Boston, MA 02115

Phone: 617-432-4648, Fax: 617-432-2435, Email: [email protected]

Word count: 4,229

Number of tables and figures: 4 tables and 1 figure

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ABSTRACT

Objective: To investigate the association of predicted lean body mass (LBM), fat mass (FM) and

body mass index (BMI) with all-cause and cause-specific mortality in men.

Design: Prospective cohort study.

Setting: Health professionals in the United States

Participants: 38,021 men (aged 40-75 years) from the Health Professionals Follow-up Study

were followed-up for death (1987-2012).

Main outcome measures: All-cause and cause-specific mortality.

Results: Using validated anthropometric prediction equations previously developed from the

National Health and Nutrition Examination Survey, LBM and FM were estimated for all

participants. During a mean of 21.4 years of follow-up, we identified 12,356 deaths. We

consistently observed a J-shaped association between BMI and mortality. Multivariable-adjusted

Cox models including both predicted FM and LBM showed a strong positive monotonic

association between predicted FM and mortality. Compared to those in the lowest quintile of

predicted FM, men in the highest quintile had 35% (95% confidence interval (CI): 26 to 46%),

67% (95% CI: 47 to 90%), and 24% (95% CI: 8 to 42%) increased risk of mortality due to all

causes, cardiovascular disease, and cancer. In contrast, a U-shaped association was found

between predicted LBM and mortality due to all causes, cardiovascular disease, and cancer (P for

non-linearity<0.001). However, there was a strong inverse association between predicted LBM

and mortality due to respiratory disease (P for trend<0.001). Compared to those in the lowest

quintile of predicted LBM, men in the highest quintile had 50% (95% CI: 39 to 65%) decreased

risk of death due to respiratory disease.

Conclusions: The shape of the relationship between BMI and mortality was determined by the

relationship between two body components (LBM and FM) and mortality. Our finding suggests

that the ‘obesity paradox’ controversy may be largely explained by low LBM, rather than low

FM, in the lower range of BMI.

Keywords: body mass index, body composition, lean body mass, fat mass, mortality, obesity

paradox

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“What this paper adds” box

Section 1: What is already known on this topic

• Numerous epidemiological studies have shown unexpected J-or U-shaped relationship

between body mass index (BMI) and mortality (‘obesity paradox’).

• The controversial issue of ‘obesity paradox’ may have arisen in part due to

underappreciation of different contributions of lean body mass (LBM) and fat mass (FM)

to BMI.

• Direct measure of body composition is difficult in large epidemiological settings, thus the

relationship between body composition and mortality is still unknown.

Section 2: What this study adds

• Using validated anthropometric prediction equations for body composition, this study

represents the first effort to comprehensively examined the association between lean

body mass, fat mass and mortality in a large prospective cohort study.

• Predicted fat mass showed a strong positive monotonic association with mortality, while

predicted lean body mass showed a strong U-shaped association with mortality.

• The ‘obesity paradox’ controversy may be explained largely by low LBM, rather than

low FM, in the lower range of BMI

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INTRODUCTION

Obesity is a major public health challenge in the United States and around the world.(1) In 2013-

2014, more than two thirds of Americans were classified as overweight (defined as body mass

index (BMI) of 25-29.9 kg/m2) or obese (BMI of ≥30 kg/m

2).(2) BMI is known as a reasonably

good measure of general adiposity(3), and many epidemiologic studies have provided evidence

supporting that obesity, assessed by BMI, is a significant risk factor for increased risk of many

chronic diseases as well as mortality.(4-6) However, details of the shape of the association

between BMI and mortality has been a topic of considerable discussion as epidemiologic studies

have found various types of J-shaped, U-shaped, and linear relationships between BMI and

mortality.(7) For instance, in some studies, overweight was associated with increased

mortality(8), but in others, the lowest mortality was observed among overweight individuals and

mortality tended to increase with lower BMI, even after accounting for smoking (residual

confounding) and preexisting disease (reverse causation).(9, 10) This pattern has come to be

known as the “obesity paradox”.(11) Given the existing and rising number of overweight and

obese adults in the US, these divergent findings could cause a great deal of confusion among

researchers, policy makers, and the general public.

One important but underexplored methodological limitation in the current obesity

research is that BMI is an imperfect measure of adiposity.(12-15) While BMI indicates

overweight relative to height, it does not discriminate between fat mass (FM) and lean body mass

(LBM).(16-18) Given the same BMI, body composition is highly variable among individuals.

This is particularly important because FM and LBM may act differently on health outcomes

including mortality. Excess FM has shown to be detrimental for health,(19) while growing

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evidence suggests that skeletal muscle, which accounts for majority of LBM, may be beneficial

for health.(20, 21) Therefore, understanding different contributions of LBM and FM to BMI may

provide new insights on the ‘obesity paradox’ and deliver important clinical and public health

messages regarding healthy body composition beyond BMI. However, direct measurement of

LBM is particularly difficult in large epidemiological studies because it requires expensive and

sophisticated technologies like dual-energy X-ray absorptiometry (DXA) or imaging

technologies. Therefore, little is known about the influence of body composition, particularly

LBM, on mortality. A limited number of studies have used less accurate surrogate measures (e.g.,

arm circumference,(22, 23) total body potassium,(24) skinfold,(25) and bioelectrical

impedance(26)) or direct measures(27-33) to estimate body composition but these studies had

relatively small sample size, short period of follow-up, restricted study population (e.g., elderly)

and/or potential biases (e.g., confounding and reverse causation). Moreover, the association of

LBM and FM with cause-specific mortality is largely unknown.

Therefore, we used validated anthropometric prediction equations to estimate body

composition and examine the association of predicted LBM, FM and BMI with all-cause and

cause-specific mortality in a large prospective US cohort study of men. Application of validated

equations in a large cohort allowed us to estimate LBM and FM and examine the independent

roles of two different body components in relation to mortality, accounting for potential biases.

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METHODS

Study population

The Health Professionals Follow-up Study was initiated in 1986 when 51,529 male health

professionals aged 40–75 were enrolled. Participants were mailed questionnaires at baseline and

every two years thereafter to collect updated demographics, lifestyle, and medical information.

For the analysis, we included participants who had information on age, race, height, weight and

waist circumference, which were required to create predicted LBM and FM (N=40,764). We

excluded participants previously diagnosed with cancer or cardiovascular diseases (N=2,118)

and those with BMI <12.5 or >60 kg/m2

(N=625) at baseline. The final sample size was 38,021

men.

Exposure assessments

Derivation and validation of the predicted LBM and FM has been described in detail

previously.(34) Briefly, we used a large US representative sample of 7,531 men who had

measured DXA from the National Health and Nutrition Examination Survey (NHANES). With

DXA-measured LBM and FM each as a dependent variable, a linear regression was performed

using age, race, height, weight, and waist circumference as independent predictors. Then, we

validated the developed equations in an independent validation group of 2,292 men and using

obesity-related biomarkers (i.e., triglycerides, total cholesterol, high-and low-density lipoprotein

cholesterol, glucose, insulin and C-reactive protein). The anthropometric prediction equations

had high predictive ability for LBM (R2=0.91, standard error of estimate (SEE)=2.6 kg) and FM

(R2=0.90, SEE=2.6 kg). Cross-validation in the validation group showed robustly high

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agreement between the actual and predicted LBM and FM with no evidence of bias. In an

additional validation, DXA-measured values and predicted values showed consistently high

agreement with similar errors across the range of LBM and FM. Scatter plots of the difference

between DXA and predicted values against DXA values showed no strong non-linear pattern

(Supplementary figure 1 and 2 and supplementary table 2 and 3). Moreover, the developed

equations performed well across different subgroups of the validation group (i.e., age, BMI, race,

smoking status, and disease status), and predicted FM showed similar correlations with obesity-

related biomarkers as DXA-measured FM.(34) For a sensitivity analysis, we also used different

prediction equations that include additional polynomial terms of anthropometric measures. These

equations had similar R2 and SEEs but slightly improved fit in the extreme range of LBM and

FM (Supplementary figure 1 and 2 and supplementary table 2 and 3). The anthropometric

prediction equations are shown in the supplement (Supplementary table 1). Using the equations,

predicted LBM and FM were calculated for each cohort member based on their age, race, height,

weight, and waist circumference. Predicted LBM and FM were available in 1987, 1996, and

2008.

We collected information on height at enrollment in 1986, and weight from biennial

questionnaires.(35, 36) Distinct from the biennial questionnaire, participants were asked to

measure and report their waist circumferences to the nearest one-quarter inch using provided

tape measures and following the same instructions in 1987, 1996, and 2008. Non-responders

received follow-up mailings to increase the response rate. In our validation study, the correlation

between self-reported and technician-measured height, weight, and waist circumference were

0.94, 0.97, and 0.95, respectively.(35)

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Ascertainment of outcomes

Deaths were identified by reports from the next of kin, postal authorities, or by searching the

National Death Index. More than 98% of deaths were ascertained from the follow up. Cause of

death was determined by physician review of medical records and death certificates. ICD-8

codes (International Classification of Diseases, 8th revision) were used to classify death due to

cardiovascular disease (codes 390-459, 795), cancer (codes 140-239), respiratory disease (codes

460-519), and other causes.

Ascertainment of covariates

Detailed information on age, race, smoking, and physical activity were collected in 1986 and

updated every two years from biannual questionnaires. Family history of cardiovascular disease

and cancer were assessed periodically. Dietary information was collected via validated food

frequency questionnaires every four years. The Alternate Healthy Eating Index (AHEI) was

calculated as an overall measure of diet quality.(37)

Statistical analyses

A Spearman correlation was calculated between predicted LBM and FM. Person-time of follow-

up was calculated from the age when the baseline predicted LBM and FM were available until

the age at death or the end of study (January 2012), whichever came first. Cox proportional

hazards models were used to estimate hazard ratios and 95% confidence interval (CI)s. We

stratified the analysis by age in months and calendar year of the questionnaire cycle.

Predicted FM and LBM were categorized into quintiles on the basis of the distribution of

exposures. We used predefined cut points for BMI (<18.5, 18.5-20.4, 20.5-22.4, 22.5-24.9, 25-

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27.4, 27.5-29.9, 30-34.9, and ≥35 kg/m2). For the main analysis, we used predicted FM, LBM,

and BMI measured at baseline to minimize the impact of underlying diseases on mortality. To

account for variation in body size, which is particularly important for LBM, we adjusted for

height by using residuals from the regression of LBM on height for LBM and by including

height as a continuous variable for FM in the models. In multivariable models, we adjusted for

potential confounders including race, family history of cardiovascular disease, family history of

cancer, smoking status, physical activity, total energy intake, alcohol consumption, and AHEI.

To examine the independent association of predicted LBM and FM in relation to mortality, we

further ran a multivariable model including both predicted LBM and FM. Test for trend was

conducted by treating the categorical predicted scores and BMI as continuous variables in the

model after assigning a median value for each category.

We also used restricted cubic splines with 5 knots at 5th

, 35th

, 50th

, 65th

, and 95th

percentiles to flexibly model the association between LBM and FM and mortality. We tested for

potential non-linearity using a likelihood ratio test comparing the model with only a linear term

to the model with linear and cubic spline terms.(38-40) Given our a priori hypothesis that people

with low LBM in the lower BMI range cause the J-or U-shaped relationship between BMI and

mortality, we examined how the shape of BMI-mortality relationship changes after excluding

those with low LBM. For a sensitivity analysis, we additionally examined the shape of BMI-

mortality relationship after excluding those with low FM.

To evaluate the latency between predicted LBM and FM and mortality, we conducted

analyses using different lag times (approximately 0, 4+, 8+, and 12+ years). For each lagged

analysis, the baseline was shifted to 1987, 1990, 1994, and 1998, respectively, and predicted

LBM and FM were updated using three repeated measures accordingly. For example, for no lag

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time analysis (simple updated), we used the most updated predicted LBM and FM that were

closest to the time of death. For a lag time of 4+ years, we used predicted measures in 1987 for

follow-up from 1990 to 2000 and predicted measures in 1996 for follow-up from 2000 to 2012.

Similarly, for a lag times of 8+ years, we used predicted measures in 1987 for deaths in 1994-

2004 and predicted measures in 1996 for deaths in 2004-2012. Moreover, we conducted

stratified analyses to explore whether the association of predicted LBM and FM with mortality

varied across smoking status and age.

Several sensitivity analyses were conducted with no adjustment for physical activity,

exclusion of deaths that have occurred in the early follow-up period (2 years) and right-censoring

criteria for age (>85 years), and inclusion of baseline illness. We also conducted analyses using

different categories for predicted LBM, FM and BMI (i.e., quintiles and deciles). Lastly, we

tested the robustness of our findings using other prediction equations with polynomial terms. All

statistical tests were two-sided and P<0.05 was considered to determine statistical significance.

We used SAS 9.4 for all analyses (SAS institute).

Patient involvement

No patients were involved in setting the research question or the outcome measures, nor were

they involved in the design and implementation of the study. There are no plans to involve

patients in dissemination.

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RESULTS

Study participants

A total of 38,021 men were included in the analyses. Baseline characteristics of participants

according to BMI categories are presented in Table 1. The mean age was 54.4 years and the

mean BMI was 25.4 kg/m2. Predicted LBM increased with higher BMI. Predicted FM slightly

deceased in the second category of BMI (18.5-20.4 kg/m2) and then increased with higher BMI.

Moreover, men with lower BMI tended to have higher physical activity and AHEI score, peaking

in the third category of BMI (20.5-22.4 kg/m2). Although the number of men with underweight

(BMI<18 kg/m2) was small, they were taller and had higher waist circumference and lower

physical activity and AHEI score. The Spearman correlation between predicted LBM and FM

was 0.66 in men.

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Table 1 Age-standardized baseline characteristics according to body mass index in men (Health Professionals Follow-up

Study, 1987-2012) Body mass index (kg/m

2)

<18.5 18.5-20.4 20.5-24.9 22.5-24.9 25.0-27.4 27.5-29.9 30.0-34.9 ≥35.0

Person-years 1839 15337 92790 254122 243335 95023 52320 8275

Age (year)a 55.5 (10.4) 54.0 (10.8) 53.8 (10.2) 54.0 (9.9) 54.5 (9.7) 54.9 (9.6) 55.1 (9.4) 55.5 (10.1)

Height (cm) 185.4 (12.7) 179.4 (7.8) 178.7 (6.2) 178.5 (6.4) 178.2 (6.5) 178.7 (6.8) 178.5 (7.1) 176.4 (10.0)

Weight (kg) 60.8 (8.2) 64.1 (5.7) 69.6 (5.1) 75.9 (5.8) 82.9 (6.4) 91.4 (7.3) 101.2 (9.0) 118.2 (13.9)

Waist circumference (cm) 86.6 (12.4) 82.8 (5.3) 86.8 (5.1) 91.2 (5.6) 96.7 (6.1) 102.9 (6.8) 110.6 (7.9) 123.4 (11.4)

BMI (kg/m2) 17.6 (0.8) 19.8 (0.5) 21.7 (0.5) 23.7 (0.7) 26.0 (0.7) 28.5 (0.7) 31.7 (1.3) 37.9 (3.6)

Predicted fat mass (kg) 13.3 (5.0) 13.1 (2.5) 15.9 (2.4) 19.1 (2.6) 22.8 (2.9) 27.1 (3.3) 32.3 (4.1) 41.2 (6.5)

Predicted Lean body mass (kg) 40.4 (5.8) 47.5 (2.2) 50.6 (1.9) 53.9 (2.1) 57.4 (2.3) 61.2 (2.6) 65.9 (3.4) 75.2 (6.0)

Total energy intake (kcal/day) 2132 (610) 2023 (570) 2045 (599) 2002 (595) 1992 (609) 2002 (625) 2036 (639) 2089 (657)

Alcohol consumption (g/day) 14.2 (18.7) 9.7 (14.2) 10.9 (14.3) 11.5 (14.7) 11.8 (15.4) 11.7 (15.5) 10.9 (16.1) 8.9 (15.1)

AHEI (score) 51.4 (13.5) 54.1 (12.7) 54.3 (12.0) 53.8 (11.6) 52.4 (11.1) 51.5 (10.9) 50.7 (11.0) 49.3 (10.8)

Physical activity (MET-h/wk) 21.4 (35.8) 22.6 (27.0) 24.2 (28.6) 22.3 (27.4) 19.4 (23.9) 16.8 (22.0) 14.4 (20.9) 11.7 (14.9)

White (%) 98.4 99.2 99.5 99.3 99.2 98.8 98.7 99.4

Family history of CVD (%) 35.3 32.2 33.0 33.4 33.7 33.8 35.2 35.5

Family history of cancer (%) 17.6 16.8 17.2 16.8 17.5 16.9 16.8 15.4

Smoking status (%)

Never 47.4 56.5 56.0 50.5 45.8 44.1 42.3 41.1

Past 34.0 32.3 35.2 42.2 46.0 47.5 50.0 50.6

Current 18.6 11.2 8.9 7.3 8.2 8.4 7.8 8.3

Abbreviation: BMI, body mass index; AHEI, alternate healthy eating index; CVD, cardiovascular disease

Data are presented as means (SD) for continuous variables and percentages for categorical variables, unless otherwise indicated. a Value is not age adjusted

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All-cause mortality

During up to 25 years of follow-up (mean of 20.4 years), we identified 12,356 deaths. The

association of predicted FM and LBM with all-cause mortality in men is presented in Table 2. A

multivariable adjusted model showed a positive association between predicted FM and all-cause

mortality, while predicted LBM showed a U-shaped association with all-cause mortality. In a

mutually adjusted model including both predicted FM and LBM, we consistently observed a

strong positive association between predicted FM and all-cause mortality. Compared to those in

the lowest quintile of predicted FM, men in the highest quintiles had 35% (95% CI: 26 to 46%)

increased hazard of all-cause mortality. Moreover, predicted LBM showed a stronger U-shaped

association with all-cause mortality in the mutually adjusted model. Compared to those in the

lowest quintile of predicted LBM, men in the second to fourth quintiles had 8 to 10% decreased

hazard of all-cause mortality.

In Figure 1, we used restricted cubic splines to flexibly model and visualize the

relationship between predicted FM and LBM with all-cause mortality in men. The risk of all-

cause mortality was relatively flat and increased slightly until around 21 kg of predicted FM, and

then started to increase rapidly afterwards (P for non-linearity<0.001). The average BMI for men

with 21 kg of FM is 25 kg/m2. In respect to the strong U-shaped relationship between predicted

LBM and all-cause mortality, the plot showed a substantial reduction of the risk within the lower

range of predicted LBM, which reached the lowest risk around 55 kg and then increased

thereafter (P for non-linearity<0.001).

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Table 2. Hazard ratio (95% CI) of all-cause mortality according to predicted fat mass and lean body

mass in men (Health Professionals Follow-up Study)

Analysis


No of

deaths

IR

/100,000py Model 1 Model 2 Model 3

Fat massa,b


Quintile 2 2298 1504 1.09 (1.03 to 1.16) 1.06 (1.00 to 1.12) 1.08 (1.01 to 1.15)

Quintile 3 2297 1504 1.03 (0.97 to 1.09) 0.98 (0.92 to 1.04) 1.01 (0.94 to 1.07)

Quintile 4 2726 1789 1.23 (1.16 to 1.31) 1.13 (1.06 to 1.20) 1.16 (1.09 to 1.24)

Quintile 5 3098 2038 1.51 (1.42 to 1.60) 1.33 (1.25 to 1.41) 1.35 (1.26 to 1.46)

P-trend

<.001 <.001 <.001

Lean body massa,b


Quintile 2 2419 1585 0.93 (0.88 to 0.98) 0.93 (0.88 to 0.98) 0.92 (0.87 to 0.97)

Quintile 3 2324 1521 0.95 (0.90 to 1.01) 0.93 (0.88 to 0.98) 0.90 (0.85 to 0.96)

Quintile 4 2282 1494 1.03 (0.98 to 1.09) 1.00 (0.95 to 1.06) 0.92 (0.87 to 0.98)

Quintile 5 2335 1529 1.26 (1.20 to 1.34) 1.16 (1.10 to 1.23) 0.97 (0.91 to 1.04)

P-trend <.001 <.001 0.49


Model 2: adjusted for age, race (white or non-white), family history of cardiovascular disease (yes or no), family history of cancer (yes or no),

physical activity (<3, 3-8.9, 9-17.9, 18-26.9, or >27 MET-hour/week), alcohol consumption (0, 0.1-4.9, 5-9.9, 10-14.9, or 15.0+ g/day), total

energy intake (quintiles), and smoking status (never, ever, 1-14, 15-24, ≥25 cigs/day), Alternate Healthy Eating Index (quintiles).



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When we used BMI alone, we observed a J-shaped relationship between BMI and all-

cause mortality in men (Table 3 and Figure 1). We also examined the influence on BMI when we

excluded men with low predicted LBM. When we excluded those in the lowest 2.5th

percentiles

of predicted LBM, the J-shaped relationship between BMI and mortality disappeared. Upon

excluding more participants with low predicted LBM (5th

and 10th

percentiles), the BMI-

mortality relationship became more linear and slightly stronger. However, the J-shaped

relationship still existed when excluding those with low FM (Supplementary table 4).

We further examined how the association of predicted FM and LBM with all-cause

mortality changes by different lag times (Supplementary table 5). With shorter lag times,

predicted FM showed a less linear positive association with all-cause mortality, while predicted

LBM showed a stronger U-shaped association with all-cause mortality. We also examined the

associations stratified by smoking status and age (Supplementary table 6 and 7). The relationship

between predicted FM and all-cause mortality was stronger and more linear among never-

smokers compared to current-smokers and among younger adults compared to older adults. On

the other hand, we observed a stronger U-shaped association between predicted LBM and all-

cause mortality among current-smokers compared to never-or past-smokers. We observed a

similar U-shaped relationship for predicted LBM across all age groups.

Our findings remained robust in several sensitivity analyses (Supplementary table 8, 9

and 10). The results did not change with no adjustment for physical activity, exclusion of deaths

in the early follow-up period and right-censoring criteria for age, inclusion of baseline illness and

use of quintiles and deciles for exposures. Moreover, using other prediction equations with

polynomial terms showed consistent results (data not shown).

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Table 3. Hazard ratio (95% CI) of all-cause mortality according to body mass index in men (Health Professionals Follow-

up Study)

Analysis


No of

deaths

IR

/100,000py Model 1

a Model 2

a Model 3

b Model 4

c Model 5

d

BMI

<18.5 53 2883 1.74 (1.33 to 2.28) 1.65 (1.25 to 2.16) NA NA NA

18.5-20.4 269 1754 1.16 (1.03 to 1.32) 1.09 (0.96 to 1.24) 0.95 (0.76-1.18) 0.95 (0.68 to 1.32) 0.79 (0.38 to 1.66)

20.5-22.4 1358 1464 1.01 (0.95 to 1.08) 1.03 (0.97 to 1.09) 1.01 (0.94-1.08) 0.99 (0.92 to 1.06) 0.93 (0.85 to 1.03)


25.0-27.4 3986 1638 1.09 (1.04 to 1.14) 1.04 (0.99 to 1.09) 1.04 (1.00-1.09) 1.05 (1.00 to 1.10) 1.06 (1.01 to 1.11)

27.5-29.9 1753 1845 1.31 (1.24 to 1.39) 1.21 (1.14 to 1.28) 1.22 (1.15-1.29) 1.23 (1.16 to 1.30) 1.24 (1.17 to 1.31)

30.0-34.9 1001 1913 1.48 (1.38 to 1.59) 1.31 (1.22 to 1.41) 1.31 (1.22-1.41) 1.33 (1.24 to 1.43) 1.34 (1.25 to 1.44)

≥35.0 196 2368 2.28 (1.98 to 2.64) 2.01 (1.74 to 2.33) 2.02 (1.75-2.34) 2.04 (1.76 to 2.36) 2.06 (1.78 to 2.38)

P-trend

<.001 <.001 <.001 <.001 <.001

Abbreviation: BMI, body mass index; NA, not available (no cases available after exclusion).


Model 2: adjusted for age, race (white or non-white), family history of cardiovascular disease (yes or no), family history of cancer (yes or no), physical activity (<3, 3-8.9, 9-

17.9, 18-26.9, or >27 MET-hour/week), alcohol consumption (0, 0.1-4.9, 5-9.9, 10-14.9, or 15.0+ g/day), total energy intake (quintiles), smoking status (never, ever, 1-14,

15-24, ≥25 cigs/day), and Alternate Healthy Eating Index (quintiles).

Model 3: additionally, excluded 2.5%ile of total participants with low lean body masse

Model 4: additionally, excluded 5%ile of total participants with low lean body masse

Model 5: additionally, excluded 10%ile of total participants with low lean body masse a Number of deaths/person-years for each category of BMI: 53/1839, 269/15337, 1358/92790, 3740/254122, 3986/243335, 1753/95023, 1001/52320, and 196/8275. b Number of deaths/person-years for each category of BMI: 0/26, 80/7196, 1147/85194, 3695/252987, 3980/243212, 1751/94960, 1000/52311, and 196/8275. c Number of deaths/person-years for each category of BMI: 0/0, 36/3402, 884/72686, 3603/250459, 3976/243080, 1751/94960, 1000/52311, and 196/8275. d Number of deaths/person-years for each category of BMI: 0/0, 7/781, 509/48989, 3295/239196, 3963/242606, 1751/94928, 1000/52311, and 196/8275. e For exclusion analyses, height-adjusted lean body mass was used after regressing out variation due to height.

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Cause-specific mortality

We further examined the association of predicted FM and LBM with cause-specific mortality

(Table 4). Mutually adjusted models showed a linear positive association between predicted FM

and mortality due to cardiovascular disease and cancer. Compared to those in the lowest quintile

of predicted FM, men in the highest quintile had 67% (95% CI: 47 to 90%) and 24% (95% CI: 8

to 42%) increased hazard of death due to cardiovascular disease and cancer, respectively. In

contrast, predicted LBM showed a U-shaped association with mortality due to cardiovascular

disease and cancer in the mutually adjusted models. However, predicted LBM showed a strong

inverse association with mortality due to respiratory disease (P for trend<.001). Compared to

those in the lowest quintile of predicted LBM, men in the highest quintile had 50% (95% CI: 39

to 65%) decreased hazard of death due to respiratory disease. When we examined the association

between BMI and cause-specific mortality, we observed a U-shaped association for

cardiovascular disease death but a positive association for cancer death and an inverse

association for respiratory disease death.

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Table 4. Hazard ratio (95% CI) of cause-specific mortality according to predicted fat mass, lean body

mass and body mass index in men (Health Professionals Follow-up Study)

Analysis Hazard Ratio (95% CI)

CVD death Cancer death Respiratory death Other death

No. of deaths 4296 3723 960 3377

IR/100,000py 558 483 124 438

Fat massa,b






P-trend <.001 0.01 0.03 0.05

Lean body massa,b






P-trend 0.10 0.86 <.001 0.99

BMI

<18.5 1.45 (0.87 to 2.41) 0.66 (0.32 to 1.40) 5.33 (3.10 to 9.17) 1.86 (1.15 to 3.01)

18.5-20.4 1.12 (0.90 to 1.38) 0.99 (0.78 to 1.25) 1.93 (1.36 to 2.73) 0.92 (0.72 to 1.19)

20.5-22.4 0.95 (0.85 to 1.06) 0.97 (0.87 to 1.09) 1.30 (1.06 to 1.60) 1.09 (0.97 to 1.22)


25.0-27.4 1.16 (1.08 to 1.26) 1.01 (0.93 to 1.09) 0.91 (0.78 to 1.08) 0.98 (0.92 to 1.06)

27.5-29.9 1.40 (1.27 to 1.54) 1.13 (1.02 to 1.25) 1.09 (0.89 to 1.35) 1.10 (0.98 to 1.23)

30.0-34.9 1.75 (1.56 to 1.96) 1.12 (0.98 to 1.28) 0.81 (0.60 to 1.09) 1.18 (1.02 to 1.35)

≥35.0 2.66 (2.11 to 3.36) 1.55 (1.17 to 2.04) 0.90 (0.43 to 1.92) 2.13 (1.63 to 2.77)

P-trend <.001 <.001 <.001 0.002


All models were adjusted for age, race (white or non-white), family history of cardiovascular disease (yes or no), family history of cancer (yes or

no), physical activity (<3, 3-8.9, 9-17.9, 18-26.9, or >27 MET-hour/week), alcohol (0, 0.1-4.9, 5-9.9, 10-14.9, or 15.0+ g/day), total energy

intake (quintiles), smoking status (never, ever, 1-14, 15-24, ≥25 cigs/day), and Alternate Healthy Eating Index (quintiles). Fat mass and lean

body mass were mutually adjusted in the model. a Derived from validated anthropometric prediction equations.


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DISCUSSION

Principal findings

In a large prospective cohort study of men, we used validated anthropometric prediction

equations to examine the association of LBM and FM with all-cause and cause-specific mortality.

We found a strong positive association between predicted FM and mortality due to all causes,

cardiovascular disease, and cancer. In contrast, predicted LBM showed a U-shaped association

with mortality due to all causes, cardiovascular disease, and cancer, and an inverse association

with mortality due to respiratory disease.

Comparison with other studies

Numerous epidemiological studies have examined the relationship between BMI and mortality,

but controversy and confusion exist around the unexpected J- or U-shaped association between

BMI and mortality.(9) A systematic review and meta-analysis of 141 prospective studies in 2013

reported that, relative to normal weight, both grade 2 and grade 3 obesity (BMI≥30 kg/m2) were

associated with higher all-cause mortality, but overweight (BMI 25-29.9 kg/m2) was associated

with lower all-cause mortality. In contrast, an individual participant-data meta-analysis of 239

prospective studies by the Global BMI Mortality Collaboration in 2016 showed evidence that

increased risk of all-cause mortality among overweight was largely due to confounding by

aspects of smoking and reverse causation from underlying disease and frailty at older ages.(8, 41,

42) There are ongoing controversies around the ‘obesity paradox’(43-45) with many studies

reporting excess mortality at the lower BMI range. More importantly, these studies

acknowledged the major limitation of BMI as a measure of adiposity but did not directly

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investigate two distinct compartments of body composition (LBM and FM) in relation to

mortality.(8, 9)

Our findings on BMI were in line with the previous findings, whereby we consistently

observed a J-shaped relationship with mortality even after accounting for age, smoking, and

baseline diseases. Two different shapes in mortality risk for FM and LBM taken together can

explain the observed J-shaped relationship between BMI and mortality in our study. The

increased risk of mortality in the lower BMI range (<25 kg/m2) could be attributed to a

combination of the high risk among men with low predicted LBM, which over-rides the modest

positive association between predicted FM and mortality in this lower range of BMI. The

increase of mortality risk at the BMI range of 25-30 kg/m2 is likely due to the high risk

associated with predicted FM in combination with only a moderate risk associated with predicted

LBM. Lastly, the rapid increase of mortality risk in the higher BMI range (>30kg/m2) could be

due to a very high risk associated with both predicted FM and LBM. Of note, at the high end of

BMI (>30kg/m2), the vast majority of individuals have high predicted FM and LBM. Those with

high predicted LBM almost invariably have high FM; for example, the average predicted FM for

those in the highest decile of predicted LBM was 31 kg (Supplementary table 10).

These observed patterns for FM and LBM were further supported from our additional

analyses of BMI and mortality after excluding those in the lower end of predicted LBM, which

resulted in a strong linear positive relationship between BMI and mortality. This shows that

separating lean and healthy (low BMI and normal LBM) vs. lean and unhealthy (low BMI and

low LBM) individuals could be a key to explain the ‘obesity paradox’ phenomenon. Our data

directly address the controversial hypothesis that accumulating excess fat may be causally

beneficial for survivor, and show that this is not likely to be true.(46)

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To date, only a limited number of studies have examined mortality in relation to directly

measured body composition using DXA or computed tomography.(27-33) Most studies have

been done among elderly populations with approximate mean age of 75 years.(27-29, 31, 33)

The findings showed inconsistent and various shapes of the relationship. An Italian study of 934

older people showed no association of calf muscle and fat mass area with 6-year mortality(28),

while another study of 2292 elderly from the US found that low leg muscle area was associated

with increased risk of 6-year mortality in men only.(29) In contrast, other studies found that

lower fat percent23 27

or leg fat mass29

was associated with increased mortality(27, 31, 33) but

among these studies, only one study of 477 community-dwelling older people from Netherlands

reported that lower appendicular skeletal muscle mass was associated with increased risk of 12-

year mortality.(33) Although these studies used a direct measure of body composition, the study

samples were restricted to elderly population which limits the generalizability of the findings.

Moreover, they also had other limitations such as small sample size, short follow-up, exposure

measured at one-time point, lack of information on important confounders (especially smoking)

and no examination on cause-specific mortality. Nonetheless, our finding was consistent with a

recent large-scale Canadian study that measured DXA from participants referred for bone

mineral density testing.(30) That study found that high percent fat and low BMI were

independently associated with increased risk of mortality when percent fat and BMI were

simultaneously adjusted in the models. However, the observed associations might have been

confounded by smoking or physical activity due to lack of information on those variables, and

the study did not directly use LBM in the analysis.

The BMI-mortality relationship is prone to reverse causation by preexisting diseases that

can cause weight loss and also increase risk of mortality, and this is more likely to be a concern

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with shorter lag times. We found that, with shorter lag time periods, the positive association

between predicted FM and mortality was attenuated, while the U-shaped association between

predicted LBM and mortality tended to be strengthened. Therefore, the stronger U-shaped

relationship between BMI and mortality with shorter lag time periods can be mostly attributed to

the pronounced U-shaped association for predicted LBM, which may be an indicator of health

status capturing any preexisting undiagnosed medical condition, including frailty at older ages.

The influence of smoking is particularly important in investigating the obesity-mortality

relationship. Not only is smoking a strong risk factor for death, but it also affects body weight

and body composition.(47-50) Similar to the BMI-mortality association, predicted FM showed

weaker and less linear association with mortality among current-smokers than past-or never-

smokers. Interestingly, we found a stronger U-shaped relationship between predicted LBM and

mortality among current-smokers than past-or never-smokers. Although we cannot completely

rule out the residual confounding by smoking, our findings showed some evidence that the

frequently observed U-shaped relationship between BMI and mortality among smokers may be

affected by the strong U-shaped association between LBM and mortality.

Strengths and limitations of the study

Our study has several strengths. First, the innovative approach of validated anthropometric

prediction equations allowed us to practically estimate LBM and FM in large epidemiological

settings. This is the first and one of the most comprehensive analyses to examine the association

of predicted body composition with all-cause and cause-specific mortality in a large prospective

cohort study. Second, the Health Professionals Follow-up Study is a well-established prospective

cohort study that has a large number of deaths over long-term follow-up period. Third, detailed

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and updated information on lifestyle and health-related factors allowed adequate control for

confounding. Fourth, repeated measures on exposures (i.e., predicted scores) allowed prospective

analyses of different lag time periods to examine reverse causality in the obesity-mortality

relationship.

There are several limitations as well. First, predicted LBM and FM are not perfect

measures of actual LBM and FM. Nonetheless, the validation results from the NHANES showed

high predictive ability of the anthropometric equations with no systematic bias. In fact, the very

high R2 between FM and LBM (>0.90) for direct DXA measurements and predicted measures in

an independent dataset indicate that a direct DXA measure would give very similar answers to

ours; this is further supported by the equal predictive ability of the predicted measures and DXA

measures for various obesity-related biomarkers. Moreover, given the prospective study design,

any mismeasurement in the exposures would likely be random with respect to endpoints,

resulting in conservative associations. Second, we cannot entirely rule out the possibility of

unmeasured or unknown confounding factors that may account for the associations observed in

this study. However, the homogeneity of the study population and comprehensive data on the

risk factors minimized potential confounding. Third, the generalizability of the findings may be

limited given that the study participants were restricted to health professionals and

predominantly White men. However, we believe that our main findings will be broadly

applicable.

Clinical and public health implications

The current study provides strong evidence that excess FM increases the risk of mortality.

Increased FM was not protective for mortality which is counter to the premise of the ‘obesity

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paradox’. On the other hand, low LBM was associated with increased risk of mortality in the

lower range of BMI. Our study suggests that understanding different contributions of LBM and

FM to BMI may explain the shape of the relationship between BMI and mortality as well as the

controversial issue of ‘obesity paradox’. Our findings support the current recommendations on

body weight for prevention of chronic diseases to keep a normal body weight defined by BMI of

18.5 to 24.9 kg/m2, and further suggest that the lowest mortality risk can be observed at the lower

normal range of BMI if the influence of LBM on mortality is accounted for. The current

recommendations should highlight not only the importance of normal body weight but also

healthy body composition (e.g., healthy lean) to reduce confusion around the optimal weight

(e.g., ‘obesity paradox’) for overall health. Interventions and strategies to promote healthy body

composition via lifestyle modification (e.g., physical activity and diet) may be an important next

step to improve population health.

Conclusions

We found a strong positive association between predicted FM and mortality, and a U-shaped

association between predicted LBM and mortality in men. Low LBM, rather than low FM, may

be driving the increased risk of mortality in the lower BMI range. Understanding the independent

role of LBM and FM has important implications for clarifying the ‘obesity paradox’

phenomenon in the relationship between BMI and mortality.

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Acknowledgements: We thank the participants and staff of the HSPF for their valuable

contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO,

CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH,

OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for

analyses and interpretation of these data.

Contributors: DHL and ELG had full access to all of the data in the study and take

responsibility for the integrity of the data and the accuracy of the data analysis. DHL and ELG

conceived and designed the study. NK, FBH, EJO, EBR, WCW, and ELG acquired the data.

DHL and ELG drafted the manuscript. All the authors critically revised the manuscript for

important intellectual content. DHL did the statistical analysis. FBH, EBR, WCW, and ELG

obtained funding. DHL and ELG were responsible for administrative, technical, or material

support. ELG was responsible for study supervision. DHL is the guarantor.

Funding: This work was supported by the National Institutes of Health (UM1 CA167552 and

R01 HL35464). The funders had no role in the design and conduct of the study; collection,

management, analysis, and interpretation of the data; and preparation, review, or approval of the

manuscript; and decision to submit the manuscript for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at

http://www.icmje.org/coi_disclosure.pdf and declare: no support from any organization for the

submitted work other than those described above; no financial relationships with any

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organizations that might have an interest in the submitted work in the previous three years; no

other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: This investigation was approved by the Institutional Review Board of the

Harvard T.H. Chan School of Public Health and Brigham and Women’s Hospital.

Transparency declaration: The lead author (the manuscript's guarantor) affirms that the

manuscript is an honest, accurate, and transparent account of the study being reported; that no

important aspects of the study have been omitted; and that any discrepancies from the study as

planned (and, if relevant, registered) have been explained.

Data sharing: No additional data available.

Copyright/License for Publication: The Corresponding Author has the right to grant on behalf

of all authors and does grant on behalf of all authors, a worldwide licence to the Publishers and

its licensees in perpetuity, in all forms, formats and media (whether known now or created in the

future), to i) publish, reproduce, distribute, display and store the Contribution, ii) translate the

Contribution into other languages, create adaptations, reprints, include within collections and

create summaries, extracts and/or, abstracts of the Contribution, iii) create any other derivative

work(s) based on the Contribution, iv) to exploit all subsidiary rights in the Contribution, v) the

inclusion of electronic links from the Contribution to third party material where-ever it may be

located; and, vi) licence any third party to do any or all of the above.

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This is an Open Access article distributed in accordance with the Creative Commons Attribution

Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt,

build upon this work non-commercially, and license their derivative works on different terms,

provided the original work is properly cited and the use is non-commercial. See:

http://creativecommons.org/licenses/by-nc/4.0/.

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Figure legends

Figure 1. The association of predicted body composition and body mass index* with all-

cause mortality in men. 1a. Fat mass and all-cause mortality. 2b. Lean body mass and all-

cause mortality. 3c. Body mass index and all-cause mortality.

Hazard ratios are indicated by solid lines and 95 % CIs by dashed lines. The reference point is

the lowest value for each fat mass and lean body mass and 25 kg/m2 for body mass index, with

knots placed at the 5th

, 35th

, 50th

, 65th

and 95th

percentiles of each fat mass and lean body mass

distribution. The models adjusted for the same cofounders in Table 2 plus mutually adjusted for

predicted fat mass and predicted lean body mass.

* Percentiles (0, 2.5, 5, 10, 25, 50, 75, 90, and 100%ile): 7, 13, 14, 15, 18, 21, 25, 29, and 66 kg

for fat mass, 24, 48, 49, 51, 53, 56, 59, 63, and 103 kg for lean body mass and 14.2, 21.5, 21.2,

22.0, 23.4, 25.1, 27.0, 31.0 and 62.0 kg/m2.

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