ORIGINAL
ARTICLE
E n d o c r i n e
C a r e
Adolescence BMI and Trends in Adulthood Mortality:
A Study of 2.16 Million Adolescents
Gilad Twig, Arnon Afek, Ari Shamiss, Estela Derazne, Moran Landau Rabbi,
Dorit Tzur, Barak Gordon, and Amir Tirosh
Department of Medicine (G.T.), The Dr Pinchas Bornstein Talpiot Medical Leadership Program (G.T.,
A.T.), The Chaim Sheba Medical Center Management (A.A., A.S.), Tel-Hashomer 52621, Israel; The Israel
Defense Forces Medical Corps (G.T., E.D., M.L.R., D.T., B.G.), Israel. The Sackler School of Medicine
(A.A., A.S., E.D., M.L.R., B.G.), Tel-Aviv University, Tel-Aviv 69978, Israel; The Division of Endocrinology,
Diabetes, and Hypertension (A.T.), Brigham and Women’s Hospital and Harvard Medical School, Boston,
Massachusetts 02115
Context: The consequence of elevated body mass index (BMI) at adolescence on early adulthood
mortality rate and on predicted life expectancy is unclear.
Objective: The objective of the investigation was to study the relationship between BMI at adolescence and mortality rate as well as the mortality trend over the past 4 decades across the entire
BMI range.
Design and Setting: The study included a nationwide longitudinal cohort.
Participants: A total of 2 159 327 adolescents (59.1% males) born between 1950 and 1993, who
were medically evaluated for compulsory military service in Israel, participated in the study.
Interventions: Height and weight were measured at age 17 years, and BMI was stratified based on
the Centers for Disease Control and Prevention-established percentiles for age and sex.
Main Outcome Measure: Incident cases of all-cause mortality before age 50 years were recorded.
Cox-proportional hazard models were used to assess mortality rates and its trend overtime.
Results: During 43 126 211 person-years of follow-up, 18 530 deaths were recorded. As compared
with rates observed in the 25th to 50th BMI percentiles, all-cause mortality continuously increased
across BMI range, reaching rates of 8.90/104 and 2.90/104 person-years for men and women with
BMI greater than the 97th percentile, respectively. A multivariate analysis adjusted for age, socioeconomic status, education, and ethnicity demonstrated a significant increase in mortality at
BMI greater than the 50th percentile (BMI ⬎ 20.55 kg/m2) for men and the 85th percentile or
greater in women (BMI ⬎ 24.78 kg/m2). During the last 4 decades, a significant decrease in mortality
rates was documented in normal-weight participants born between 1970 and 1980 vs those born
between 1950 and 1960 (3.60/104 vs 4.99/104 person-years, P ⬍ .001). However, no improvement
in the survival rate was observed among overweight and obese adolescents during the same time
interval. Significant interaction between BMI and birth year was observed (P ⫽ .007).
Conclusions: BMI at adolescence, within the normal range, is associated with all-cause mortality in
adulthood. Mortality rates among overweight and obese adolescents did not improve in the last
40 years, suggesting that preadulthood obesity may attenuate the progressive increase in life
expectancy. (J Clin Endocrinol Metab 99: 2095–2103, 2014)
ISSN Print 0021-972X ISSN Online 1945-7197
Printed in U.S.A.
Copyright © 2014 by the Endocrine Society
Received January 23, 2014. Accepted February 26, 2014.
First Published Online March 6, 2014
doi: 10.1210/jc.2014-1213
Abbreviations: BMI, body mass index; BP, blood pressure; CDC, Centers for Disease Control
and Prevention; CI, confidence interval; HR, hazard ratio; SES, socioeconomic status; USSR,
Union of Soviet Socialist Republics.
J Clin Endocrinol Metab, June 2014, 99(6):2095–2103
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Twig et al
Adolescent BMI and All-Cause Mortality
he increase in life expectancy in the second half of the
20th century is explained primarily by a change over
time from a dominance of child mortality reductions to a
dominance of adult mortality reductions (1), with a profound decrease in mortality rates above age 70 years demonstrated as the main cause for the recent increase in life
expectancy (2). Yet less attention has been given to mortality that occurs in midlife, ie, before 50 years of age.
Death in this age group, although occurring with low incidence, clearly has an enormous personal, family and societal-economic impact. The main causes of death before
age 50 years vary between sexes more than later in life and
include, for women, malignancies, followed by unintentional injuries, and with much lower rates of cardiovascular diseases. In men, unintentional injuries are the leading cause of death before age 50 years, followed by
cardiovascular disease and intentional (self-inflicted) injuries (as recorded for the US population) (3). Of interest,
increased body mass index (BMI) in adulthood has been
shown to be associated with each of these causes of premature death (4 –11).
Although a growing body of evidence has linked obesity with an increased risk of death primarily in middleaged or elderly subjects (6, 7, 12–14), less is known about
the relationship between BMI in preadulthood and early
occurrence of these diseases, leading to premature death.
In a longitudinal study of American Indians, obesity in
childhood and adolescence was strongly associated with
increased rates of premature death from endogenous
causes (9). In another study, overweight among adolescents was associated with increased risk of all-cause mortality among men but not among women (11). Moreover,
although all-cause mortality rates are decreasing steadily
in recent decades (15, 16), it is unclear what the impact of
the increase in preadulthood adiposity on these trends is.
Several models have predicted that a continuous increase
in obesity rates may result in a potential decline in life
expectancy during the 21st century (17–20), and it has
been shown that obesity appears to markedly lessen life
expectancy in adulthood, especially among younger
adults (20). However, this has never been demonstrated
directly for overweight and obesity among children and
adolescents.
In this study we used a national database of 2.16 million
Israeli adolescents with measured height and weight between the years 1967 and 2011 to assess the relationship
between BMI at adolescence and mortality later in life as
well as the trend in mortality rates during the last 40 years
across the entire BMI range.
T
J Clin Endocrinol Metab, June 2014, 99(6):2095–2103
Materials and Methods
Study population
The study population consists of all Israeli adolescents born
between the years 1950 and 1993 who were medically evaluated
for compulsory military service. One year prior to conscription,
all eligible adolescents undergo a medical evaluation, which includes a review of medical records and physical examination.
Because the vast majority of non-Jewish minorities (primarily
Israeli Arabs) is exempt from military service, the study population consisted largely of the Israeli Jewish population.
Included were male and female adolescents medically eligible for
military service, who were born between 1950 and 1993 and were
16 –20 years old at the time of medical evaluation. Available for
analysis were 2 315 418 adolescents. Excluded were 22 510 males
and 7435 females with preexisting significant medical conditions
(such as malignancies, cardiovascular disease, diabetes, psychiatric
disorders, and mental retardation), a follow-up shorter than 2
years, or age younger than 21 years at the end of follow-up (66 675
males and 53 112 females). Deaths due to trauma during military
service (5977 males and 382 females) were also excluded. For final
analyses included were 2 159 327 participants (59.1% males). The
Institutional Review Board of the Israel Defense Forces Medical
Corps approved this study and waived the need for informed consent with assurance of strict maintenance of participants’ anonymity during database analyses.
Data collection
At baseline, right arm blood pressure (BP) in the sitting position (21), weight (in kilograms) and height (in centimeters)
were measured (barefoot and in underwear) by trained medics.
History and physical examination were performed by military
physicians who also reviewed the participants’ medical records.
Data regarding education, socioeconomic status (SES), country
of birth, and country of origin were also recorded.
Follow-up and outcome definition
The primary outcome was all-cause mortality. The Ministry
of Interior routinely reports to the Israel Defense Forces all
deaths of Israeli citizens, previously enrolled in military service.
The cause of death is not reported. Follow-up ended at the time
of reported death, age 50 years, or June 30, 2011, whichever
came first. Mean follow-up was 20.5 ⫾ 10.1 and 19.2 ⫾ 9.7 years
for males and females, respectively.
Statistical analysis
The cohort was stratified based on the current Centers for Disease Control and Prevention (CDC)-established BMI percentiles for
age (by month) and sex (Table 1) (22). For survival rate analysis, BMI was reclassified to four subgroups: normal weight (5th ⱕ
BMI ⬍ 85th), underweight (BMI ⬍ 5th), overweight (85th ⱕ
BMI ⬍ 95th), and obese (BMI ⱖ 95th percentile) (23, 24). Education was divided into low and high level at a threshold of 10 full
years of school education. SES based on place of residence was
obtained from records of the Israeli Ministry of Interior, which is
coded on a 1–10 scale based on the Israeli Central Bureau of Statistics. The variables that affect SES include age distribution, available workforce, level of unemployment, level of education (fraction
of undergraduate students and those entitled to a high school diploma), average income per capita, and percentage of residents receiving welfare support. As reported previously (23–25), SES was
doi: 10.1210/jc.2014-1213
Table 1.
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Baseline Characteristics of the Male Cohort Population
BMI Percentiles
< 3rd
3rd to 5th
5th to 10th
10th to
25th
25th to
50th
50th to
75th
75th to
85th
85th to
90th
90th to
97th
> 97th
Total
n
Age ⫾ SD, y
71 128
17.47 ⫾
0.52
173.08 ⫾
7.18
78.4
33 966
17.43 ⫾
0.49
173.1 ⫾
6.85
78
78 002
17.4 ⫾
0.47
173.22 ⫾
6.85
78.1
209 553
17.38 ⫾
0.46
173.33 ⫾
6.83
78.3
332 416
17.35 ⫾
0.44
173.39 ⫾
6.72
79.3
296 951
17.33 ⫾
0.43
173.58 ⫾
6.81
81.5
105 640
17.32 ⫾
0.41
173.78 ⫾
6.8
83.2
48 231
17.32 ⫾
0.42
173.88 ⫾
6.86
83.2
70 237
17.33 ⫾
0.42
173.98 ⫾
6.96
83.6
30 411
17.35 ⫾
0.43
173.97 ⫾
7.57
84.3
1 276 535
17.36 ⫾
0.44
173.49 ⫾
6.85
80.3%
27.6
50.8
21.6
27.6
50.9
21.4
27.4
50.9
21.7
27.3
50.9
21.8
27
51
22
26.2
51.7
22.1
26.1
52.5
21.4
26.7
52.5
20.8
27.4
52.8
19.8
29.4
52.9
17.7
26.9%
51.5%
21.6%
5
10.8
34.1
26.2
23.9
5
10.6
31.7
27.5
25.2
5.3
10.8
30.3
27.6
26.1
5.1
11.2
28.1
28.2
27.4
5.2
11.9
25.7
28
29.2
5.4
13.1
23.6
26.9
31
5.4
14.3
22.6
25.1
32.6
5.5
15.1
22.2
24.7
32.5
5.9
15.9
21.7
24
32.5
6.2
16.7
21.2
25.1
30.8
5.3%
12.5%
25.8%
27%
29.4%
82.2
6.8
2.5
5.8
2.7
115.1 ⫾
11.8
71.3 ⫾
8.4
82.8
6.5
1.9
5.6
3.2
116 ⫾
82.7
6.6
2
5.5
3.3
116.5 ⫾
11.6
71.7 ⫾
8.3
82.4
6.8
1.7
5.5
3.6
117.4 ⫾
11.5
72 ⫾
8.3
82.2
7.1
1.6
5.1
4.1
118.6 ⫾
11.4
72.4 ⫾
8.2
82.2
7.8
1.4
4.2
4.5
120.1 ⫾
11.4
73 ⫾
8.2
82.5
8.6
1.1
3
4.7
121.4 ⫾
11.4
73.6 ⫾
8.1
82.9
9.1
0.9
2.4
4.7
122.3 ⫾
11.5
73.9 ⫾
8.2
83
9.7
0.8
1.9
4.6
123.7 ⫾
11.6
74.7 ⫾
8.1
83.6
10.4
0.6
1.3
4.1
126.4 ⫾
11.8
76.3 ⫾
8.4
82.4%
7.6%
1.5%
4.5%
4.1%
119.2 ⫾
11.7
72.7 ⫾
8.3
Height ⫾ SD, cm
Education more
than 10 y, %
SES
Low
Intermediate
High
Family origin, %
Israel
USSR
Asia
Africa
West
Birth country, %
Israel
USSR
Asia
Africa
West
Systolic BP, mm Hg
Diastolic BP, mm Hg
11.6
71.6 ⫾
8.4
Anthropometric, ethnicity, sociodemographic data of 1 276 535 males distributed into BMI percentiles stratified by the CDC table.
coded into three groups: low (SES ⫽ 1– 4), medium (SES ⫽ 5–7),
and high (SES ⫽ 8 –10). Countries of origin (classified by the father’s
or grandfather’s country of birth) were grouped into five geographical areas: former Union of Soviet Socialist Republics (USSR) countries, Asia (non-USSR), Africa (excluding South Africa), Western
(non-USSR Europe, North and South America, South Africa, Australia and New Zealand), and Israel (25). The same geographical
areas were used for the participants’ country of birth.
Table 2.
Nominal and ordinal variables were compared between BMI
percentiles using the c2 test and linear-by-linear association, respectively. Medians (continuous variables) were used to test for trend
across BMI percentiles. Cox proportional hazard models were used
to estimate the hazard ratios (HRs) and 95% confidence interval
(CI) for all-cause mortality. Log minus log plots for each variable
were assessed to verify the assumption of proportionality of the
hazards, which was confirmed for all variables studied. To assess
Baseline Characteristics of the Female Cohort Population
BMI Percentiles
< 3rd
3rd to 5th
5th to 10th
10th to
25th
25th to
50th
50th to
75th
75th to
85th
85th to
90th
90th to
97th
> 97th
Total
n
Age, y
25 274
17.42 ⫾
0.47
163.35 ⫾
6.59
94.1
14 654
17.39 ⫾
0.44
162.86 ⫾
6.19
94.4
38 195
17.38 ⫾
0.43
162.61 ⫾
6.11
94.2
125 203
17.36 ⫾
0.41
162.43 ⫾
6.07
94.5
238 029
17.35 ⫾
0.4
162.13 ⫾
5.99
94.6
244 976
17.34 ⫾
0.39
161.82 ⫾
6.05
94.3
93 830
17.33 ⫾
0.39
161.81 ⫾
6.1
94
41 805
17.33 ⫾
0.4
161.7 ⫾
6.18
93.7
50 955
17.32 ⫾
0.4
161.97 ⫾
6.26
93.7
9871
17.3 ⫾
0.41
162.28 ⫾
6.48
94.6
882 792
17.35 ⫾
0.4
162.09 ⫾
6.1
94.3%
20.1
53.9
26
20.4
53.5
26.1
19.8
54
26.2
20
53.7
26.4
20.3
53.4
26.3
20.7
53.8
25.5
21.1
54.5
24.4
22.1
54.8
23.1
22.4
56.2
21.4
23.2
57.1
19.7
20.7%
54%
25.3%
5.6
12.8
31.4
22.5
27.8
5.7
12
30.1
22.4
29.8
5.7
11.9
28.9
22.7
30.8
6
12.1
26.5
22.9
32.5
6
12.2
24.6
23.2
34.1
5.9
12.5
23
24
34.6
5.9
12.7
22.4
24.9
34.1
5.8
13.1
22
25.7
33.3
5.9
13.7
21.3
26.9
32.2
6.2
14.4
20.3
28.4
30.6
5.9%
12.5%
24.3%
23.9%
33.4%
84.5
8
0.9
3.7
2.9
108.9 ⫾
11.6
69.2 ⫾
8.3
86
7.3
0.9
2.9
2.9
109.3 ⫾
11.6
69.3 ⫾
8.2
86.9
6.7
0.9
2.6
3
109.8 ⫾
11.7
69.5 ⫾
8.2
86.7
6.7
0.7
2.3
3.6
110.3 ⫾
11.6
69.7 ⫾
8.2
86.6
6.5
0.7
2.2
4
111.2 ⫾
11.5
70.1 ⫾
8.2
86.1
6.6
0.7
2.1
4.5
112.6 ⫾
11.6
70.7 ⫾
8.2
86.1
6.9
0.6
1.8
4.6
114.1 ⫾
11.6
71.5 ⫾
8.2
86.1
7.2
0.5
1.7
4.6
115.3 ⫾
11.6
72.2 ⫾
8.2
86.4
7.9
0.4
1.3
4.1
117.4 ⫾
11.8
73.5 ⫾
8.2
86.7
8.6
0.3
0.7
3.6
120.8 ⫾
12.2
75.4 ⫾
8.4
86.3%
6.8%
0.7%
2.1%
4.1%
112.3 ⫾
11.8
70.7 ⫾
8.3
Height ⫾ SD, cm
Education more
than 10 y, %
SES
Low
Intermediate
High
Origin, %
Israel
USSR
Asia
Africa
West
Birth country, %
Israel
USSR
Asia
Africa
West
Systolic BP, mm Hg
Diastolic BP, mm Hg
Anthropometric, ethnicity, sociodemographic data of 882 792 females distributed into BMI percentiles stratified by the CDC table.
2098
Twig et al
Adolescent BMI and All-Cause Mortality
J Clin Endocrinol Metab, June 2014, 99(6):2095–2103
rollment was similar for males and
females (17.35 ⫾ 0.4 y). More than
80% of participants were born in Israel but with significant heterogeneity in ethnic origin. A negative association was found between BMI and
SES (P ⬍ .001), as previously described (23, 24).
BMI at adolescence and
mortality rates in adulthood
During 26 177 855 and 16 948
356 person-years, there were 15 469
and 3061 deaths recorded among
males and females, respectively. For
male adolescents, mortality rate was
lowest (5.59/104 person-years) in
those with BMI values between the
25th and 50th percentiles (defined as
the reference group). Higher and
lower BMIs were associated with an
increase in mortality, reaching 8.90/
104 and 5.91/104 person-years among
participants with BMI greater than
the 97th and BMI less than the third
percentile, respectively (Figure 1A).
In females, mortality rates followed
a similar pattern but were approximately 3-fold lower than rates
among males, reaching 2.90/104 and
Figure 1. The relationship between Hazard ratio for mortality of all-cause and BMI. A, The rate of
1.94/104 person-years for BMI
death (total deaths divided by total person-years). B, HRs of multivariate model (adjusted to birth year,
age at examination, education, SES, birth country, and country of origin) are shown for males and
greater than the 97th and BMI less
females (blue and red curves, respectively). Blue and red asterisks indicate significant difference as
than the third percentile, respeccompared with the reference group (BMI values of 25th-50th percentiles) for males and females,
tively, as compared with 1.60/104
respectively. C and D, Multivariate Cox survival functions assorted by weight category adjusted to the
model in panel B. The subgroups of BMI were classified according to the CDC-established BMI
person-years in the reference group
percentiles as follows: normal weight (N; 5th ⱕ BMI ⬍ 85th), underweight (U; BMI ⬍ 5th),
(25th ⱕ BMI ⬍ 50th percentiles).
overweight (Ov; 85th ⱕ BMI ⬍ 95th) and obese (Ob; BMI ⱖ 95th percentile). Number of participants
Mean age at death was 36.0 ⫾ 9.0 and
at risk is shown for the different follow-up duration.
34.7 ⫾ 9.4 years for males and femortality trends, population was stratified into four groups based on
males, respectively (Tables 3 and 4).
theparticipants’birthyear:1950–1960,1961–1970,1971–1980,and
In males, survival rate analysis adjusted for age and year
1981–1991. Sensitivity analysis for different follow-up intervals was
used to detect the earliest time interval associated with significant difof birth (model 1) demonstrated a significant increase in
ference in mortality rates among participants with abnormal BMI as
mortality rate among participants with an adolescence
comparedwithparticipantswithnormalBMI.Alogranktestwasused
BMI at the 75th percentile or greater, reaching an HR of
to compare Kaplan-Meir survival curves at 10 years (among all four
2.007 (CI 1.812–2.222, P ⬍ .001) for BMI greater than the
groups), 20 years (1950s, 1960s, and 1970s) and 30 years (1950s and
1960s). Subjects with missing data (⬃7.9%) were excluded from the
97th percentile (⬎31.93 kg/m2; Table 3). Further adjustanalysis (relevant only for the multivariate models). Analyses were perment for SES, education, and ethnicity minimally affected
formed using SPSS version 19.0. Values are presented as mean ⫾ SD,
the association between BMI and mortality (models 2 and
unless mentioned otherwise.
3). Of note, in the multivariate model (model 3), BMI as
low as the 50th percentile (⬎20.55 kg/m2) was already
Results
associated with a 5.4% increase in all-cause mortality in
Baseline characteristics of male and female study partici- adults younger than 50 years (HR 1.054; CI 1.005–1.106,
pants are presented in Tables 1 and 2. Mean age at en- P ⫽ .032) as compared with teenagers with 25th less than
doi: 10.1210/jc.2014-1213
Table 3.
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2099
Hazard Ratio for All-Cause Mortality Across BMI Percentiles Among Males
BMI Percentiles
< 3rd
3rd to
5th
5th to
10th
10th to
25th
25th to
50th
50th to
75th
75th to
85th
85th to
90th
90th to
97th
> 97th
Total
Total deaths
Age at death, y
Mean follow-up ⫾ SD, y
Median follow-up, y
Cumulative follow-up,
person-years
Mortality rate,
per 10 000
person-years
Model 1: birth year, age
HR
CI
P value
Model 2: birth
year, age,
education, SES
HR
CI
P value
Model 3: birth year,
847
35.5 ⫾ 8.9
20.1 ⫾ 9.7
19.73
1 432 196
449
35.8 ⫾ 9
20.7 ⫾ 9.8
20.61
704 181
918
35.1 ⫾ 8.8
20.9 ⫾ 9.8
20.9
1 634 135
2555
35.7 ⫾ 9
21.2 ⫾ 9.9
21.28
4 440 505
3968
35.8 ⫾ 9
21.3 ⫾ 10.1
21.5
7 092 498
3535
36.1 ⫾ 9.1
20.7 ⫾ 10.2
20.52
6 169 631
1329
36.5 ⫾ 9.2
19.7 ⫾ 10.2
19.12
2 087 246
564
37.1 ⫾ 8.8
18.8 ⫾ 10.2
17.89
909 825
896
37.1 ⫾ 9.4
17.8 ⫾ 10.2
16.26
1 249 355
408
37.5 ⫾ 8.7
15 ⫾ 9.4
12.53
458 283
15 469
36 ⫾ 9
20.5 ⫾ 10.1
20.13
26 177, 55
5.91
6.37
5.61
5.75
5.59
5.72
6.36
6.198
7.17
8.9
5.9
1.1
1.021–1.184
.012
1.169
1.06 –1.288
.002
1.024
0.953–1.1
.525
1.036
0.986 –1.089
.166
1
1.038
0.992–1.086
.108
1.191
1.119 –1.267
⬍.001
1.189
1.089 –1.299
⬍.001
1.434
1.334 –1.542
⬍.001
2.007
1.812–2.222
⬍.001
1.048
0.97–1.133
.233
1.133
1.023–1.254
.017
0.992
0.92–1.07
.837
1.013
0.961–1.067
.633
1
1.06
1.011–1.112
.016
1.198
1.122–1.28
⬍.001
1.179
1.074 –1.295
⬍.001
1.422
1.317–1.535
⬍.001
1.887
1.696 –2.099
⬍.001
1.072
0.99 –1.16
.058
1.155
1.042–1.28
.006
1.002
0.929 –1.082
.951
1.022
0.969 –1.077
.429
1
1.054
1.005–1.106
.032
1.195
1.118 –1.278
⬍.001
1.187
1.08 –1.304
⬍.001
1.416
1.31–1.531
⬍.001
1.899
1.704 –2.115
⬍.001
age, education,
SES, birth country,
country of origin
HR
CI
P value
Cox regression multivariate analysis was used to derive the HRs under different models. Results summarized 1 276 535 males during 26 177 855
persons-years of follow-up. P of trend for age of death was less than .001.
BMI less than the 50th percentiles. BMI less than the third percentile (⬍17.43 kg/m2) was not significantly associated with
increased mortality (HR 1.072; CI 0.990–1.160, P ⫽ .058).
Among female adolescents, age- and birth year-adjusted BMI greater than the 85th percentile (BMI 24.78
kg/m2) was associated with an increased risk for all-cause
mortality in adulthood (HR 1.313; CI 1.109 –1.554, P ⫽
.002, model 1, Table 4). Further adjustment for SES, education, and ethnicity (models 2 and 3) did not significantly attenuate this association. Figure 1B demonstrates
the HRs for death by the age of 50 years for each of the
BMI percentiles at adolescence in both males and females.
Survival rate analysis for underweight, normalweight, overweight, and obese male and female adolescents is demonstrated in Figure 1 (C and D, respectively). In both sexes, overweight adolescents exhibited
higher mortality rates than normal-weight participants
at relatively young ages. This effect was more pronounced among obese subjects with significant decrease
in survival observed already after 15 and 10 years of
follow-up (corresponding to approximate ages of 32
and 27 y) in males and females, respectively.
Changes in survival rate patterns in the last 50
years
A significant improvement in global life expectancy and
continuous decline in mortality rates have been observed
in recent decades. Accordingly, life expectancy in Israel has
increased between 1949 and 2012 from 64.9 to 80.6 years in
men and from 67.6 to 84 years in women (Israel Bureau
of Statistics, http://www1.cbs.gov.il/reader/cw_usr_view_
SHTML?ID⫽591). Given this trend, we next stratified the
study population to four groups based on the participants’
year of birth. Indeed, in normal-weight adolescents, mortality rates after 10 years of follow-up declined from 3.46/
104 among participants born in the 1950s to 2.05/104
person-years for participants born in the 1980s, corresponding to a 41% reduction in mortality rate (P ⬍ .001).
Similarly, mortality rates among adolescents born in the
1960s and in the 1970s was 19% and 28% lower, respectively, as compared with participants born in the 1950s
(Supplemental Tables 1A and 2). In contrast, no significant improvement in survival rate was observed among
overweight and obese male adolescents in the last 40 years
(log rank; P ⱖ .093 for all follow-up intervals; Figure 2, C
and D, and Supplemental Table 1). A similar result was also
observed using a univariate (age adjusted) and multivariate
(birth year, age, education, SES, birth country, and country
of origin) (the Cox regression models (Figure 3A). No improvement in survival rates during the last 40 years was observed, even when overweight and obese participants were
merged to a single group to increase detection power (Figure
3B). Among males, a test of interaction between birth decade
and BMI subgroups was significant in univariate and multivariate models either when used as categorical or continuous variables (P ⱕ .002 for all models in either form of
variable).
2100
Twig et al
Table 4.
Adolescent BMI and All-Cause Mortality
J Clin Endocrinol Metab, June 2014, 99(6):2095–2103
Hazard Ratio for All-Cause Mortality Across BMI Percentiles Among Females
BMI Percentiles
< 3rd
3rd to
5th
5th to
10th
10th to
25th
25th to
50th
50th to
75th
75th to
85th
85th to
90th
90th to
97th
> 97th
Total
Total death
Age at death, y
88
33.4 ⫾
9.1
17.92 ⫾
9.56
16.88
452 807
51
33.3 ⫾
9.9
18.57 ⫾
9.57
17.78
272 129
130
32.3 ⫾
9.4
18.86 ⫾
9.62
18.03
720 319
386
33.9 ⫾
9.4
19.26 ⫾
9.7
18.58
2 411 048
786
34.6 ⫾
9.4
19.72 ⫾
9.82
19.14
4 694 609
871
35.2 ⫾
9.3
19.72 ⫾
9.83
19.14
4 830 443
334
35.8 ⫾
9.5
19.17 ⫾
9.78
18.42
1 798 679
163
34.6 ⫾
9.2
18.46 ⫾
9.69
17.64
771 634
213
34.6 ⫾
9.2
16.93 ⫾
9.37
15.55
862 721
39
37.5 ⫾
10.3
13.59 ⫾
8.2
11.69
134 145
3061
34.7 ⫾
9.4
19.2 ⫾
9.78
18.51
16 948 536
1.94
1.87
1.8
1.6
1.67
1.8
1.85
2.11
2.46
2.9
1.8
1.22
0.979 –
1.522
.077
1.162
0.875–
1.542
.299
1.109
0.921–
1.336
.274
0.972
0.861–
1.098
.65
1
1.078
0.979 –
1.187
.127
1.129
0.993–
1.283
.064
1.313
1.109 –
1.554
.002
1.619
1.391–
1.884
⬍.001
2.182
1.58 –
3.012
⬍.001
1.145
0.907–
1.444
.254
1.129
0.84 –
1.516
.420
1.106
0.914 –
1.339
.301
0.955
0.842–
1.084
.479
1
1.082
0.979 –
1.196
.124
1.119
0.979 –
1.278
.098
1.311
1.101–
1.562
.002
1.572
1.343–
1.841
⬍.001
2.134
1.532–
2.973
⬍.001
1.151
0.921–
1.452
.237
1.117
0.829 –
1.505
.468
1.108
0.914 –
1.342
.297
0.956
0.842–
1.085
.485
1
1.088
0.984 –
1.203
.101
1.123
0.982–
1.283
.09
1.325
1.113–
1.579
.002
1.56
1.331–
1.829
⬍.001
2.157
1.549 –
3.005
⬍.001
Mean follow-up ⫾
SD, years]
Median follow-up, y
Cumulative follow-up,
person-years
Rate
Model 1: birth
year, age
HR
CI
P value
Model 2: birth year,
age, education,
SES
HR
CI
P value
Model 3: birth year,
age, education,
SES, birth country,
country of origin
HR
CI
P value
Cox regression multivariate analysis was used to derive the HRs under different models. Results summarized 882 792 females during 16 948 536
persons-years of follow-up. P of trend for age of death was .004.
Among females, survival rates were significantly higher
than males in all BMI categories (Figure 2 and Supplemental Tables 1B and 2). In the normal-weight group,
mortality rate after 10 years decreased from 1.19/104
among participants born in the 1950s to 0.77/104 personyears for participants born in the 1980s, corresponding to
a 35% reduction in mortality rate (log rank; P ⫽ .007).
This difference was validated by a multivariate Cox-regression analysis (adjusted for birth year, age, education,
SES, birth country and country of origin) with an HR of
0.724 (95% CI 0.558 – 0.938, P ⫽ .015). Follow-up of 20
years (participants born in 1970s vs 1950s) demonstrated
an 18% reduction in mortality rates (log rank; P ⬍ .001).
Similar to males, obese females did not exhibit improved
survival rate across birth decades independent of follow-up duration (log rank; P ⱖ .504). A test of interaction
between BMI and birth year was significant for univariate
and multivariate models (P ⬍ .007). However, when modeled as categorical variables, the P values of interaction did
not reach statistical significance (P ⫽ .063 in the univariate
and P ⫽ .100 in the multivariate models).
Discussion
In this large population-based study, we report the association between preadulthood BMI values and mortality
rates from adolescence to midlife (17–50 y). During more
than 43 million person-years, there were more than
18 500 recorded deaths among men and women. The
study’s main findings are that regardless of BMI, mortality
rates among young men are about 3-fold higher than in
young women. In addition, BMI values at the middle to
high normal range in men are associated with increased
mortality, whereas in women, only overweight and obesity are associated with higher mortality rates. Moreover,
consistent with the marked increase in life expectancy observed in Israel during the follow-up period, both underweight and normal-weight teenagers exhibited a significant decline in mortality rates over time. However, no
significant improvement in survival rates was observed
among overweight and obese adolescents. In fact, the mortality rate associated with adolescence obesity recorded
between the years 2000 –2011 were found to be as high as
the rate observed during the 1960s and 1970s.
Although the cause of death is not available to us, frequencies of the different etiologies for death have been well
described in various ages. Similar to the CDC reports for
the US population (3), the leading causes of death among
young Israeli men aged 25– 44 years are malignant neoplasms and cardio/cerebrovascular. In addition, motor vehicle accidents account for additional 16% of cases in this
age group (Supplemental Figure 1). Interestingly, al-
doi: 10.1210/jc.2014-1213
jcem.endojournals.org
2101
Figure 3. The effect of progressing birth decade on all-cause mortality
for different BMI groups. Mortality hazard ratios for normal-weight (A)
and overweight-obese adolescents (B; merged to a single group). Model 1
and model 3 adjust for the variables listed in Tables 3 and 4. The survival
rate of adolescents born in the 1950s (1950 –1960) is used as a reference
for survival in all models and is compared after 30 years with adolescents
born in the 1960s (1961–1970), 20 years to adolescents born in the
1970s (1971–1980), and after 10 years for participants born in the 1980s
(1981–1991). Note that obese and overweight adolescents do not benefit
the time-dependent decrease in overall mortality. Values of HRs, 95% CIs,
and P values are detailed in Supplemental Table 3.
Figure 2. Survival curves by BMI groups and decade of birth. KaplanMeir curves stratified by gender and BMI for each decade of birth are
shown. The color key in the lower panel describes the distribution of
decades for all of the panels. Follow-up data including participants at
risk are available in Supplemental Tables 1 and 2.
though obesity has been linked to increased mortality primarily from cardiovascular diseases and malignancies (26,
27), increased risk for death due to motor vehicle crashes
was also reported among obese men (8, 28), likely due to
obstructive sleep apnea. As for malignancies, although the
exact mechanisms are not fully clear, increased body
weight was associated not only with increased incidence
rates of cancers but also with a worse prognosis as compared with lean patients with the same conditions. Increased mortality associated with obesity was reported for
all cancers combined and for cancers at multiple specific
sites (29, 30).
Our results confirm a U-shaped relationship between
BMI and mortality as was previously described (4, 6, 7, 10,
12). The increased mortality risk seen in underweight was
suggested to arise, at least in part, from reverse causation
with low BMI being a consequence of preexisting illness
(12, 31). The exclusion of participants with preexisting
significant medical conditions, such as malignancies, in
the current study minimizes, but does not eliminate, the
potential contribution of reverse causation. Therefore,
these results may suggest a more direct relationship between low BMI and increased early mortality. Further
studies and careful assessment of the causes of death are
needed to better characterize association.
In light of the marked increase in life expectancy in the
last 50 years, it is somewhat surprising that no improvement in survival was observed among overweight and
obese participants. This is despite a dramatic decline in
mortality from cardiovascular diseases, a leading cause of
obesity-related deaths, in recent decades (15, 16). This
decline in cardiovascular mortality may be attributed both
to reductions in major risk factors and to evidence-based
medical therapies (32). One potential explanation for the
lack of survival benefit associated with obesity is the relatively young age of our study population. Indeed, as opposed to the trend in older age groups, death from cardiovascular cause among young adults remained stable or
even increased in recent years (16, 33). Young adults present both a significant increase in many of the obesityrelated cardiometabolic risk factors and may also be less
likely to be prescribed with primary prevention therapies
given an absolute low cardiovascular disease risk when
common risk scores are being used. For example, when
2102
Twig et al
Adolescent BMI and All-Cause Mortality
using the Framingham risk score, at the age of 25 years, the
absolute risk for a cardiovascular event will almost always
be low, regardless of the presence of additional risk factors. Thus, the impressive advances in primary prevention
of cardiovascular disease leading to decreased mortality in
middle-aged and elderly patients may have limited benefits
in young adults in the absence of specific tools designed to
better predict the risk for an early cardiovascular event.
Our results are consistent with previous studies detecting
a signal for premature mortality associated with increased
BMI in childhood and adolescence in smaller cohorts and
among populations of different ethnic backgrounds (9, 11).
The significantly lower absolute mortality rate observed in
women at this age group, and its association with higher
preadulthood BMI values, may explain why previous smaller
studies could not demonstrate a link between elevated BMI
among girls and premature death (11).
The potential implications of the study findings on expected life expectancy are worth consideration. It has been
speculated that the rising prevalence of obesity, especially
in childhood and adolescence, is expected to lead to an
elevated risk of fatal conditions as these teenagers age (17,
34). Although it is not possible to predict exactly when
obesity among the young will have its largest negative
effect on life expectancy, it has been estimated to be evident already at the first half of this century, when overweight and obese adolescents reach the ages of greatest
vulnerability (17). Although the absolute mortality rates
are relatively low in the young adult population, the increased obesity-related deaths before the age of 50 years
may have a profound effect on life expectancy. This is of
special importance, given the continuous rise in overweight and obesity rates that were tripled among Israeli
adolescents during the last 40 years (Supplemental Figure
2). Moreover, and in addition to increased mortality rates
among underweight male adolescents, the significant increase in mortality rate attributed to overweight was noticeable at BMI values as low as the 50th percentile or
greater, which are well below the currently considered
threshold for overweight. This may have led to an underestimation of the actual mortality risk associated with an
increased BMI in the past because most previous studies
used the predefined CDC criteria for normal weight as the
reference group (14). Indeed, a statistically significant decline or stagnation in life expectancy in several counties in
the United States was reported to be consistent with the
geographical patterns of obesity (35).
Our study has certain limitations. First, cause of death is not
available, preventing us from studying the association of causespecific mortality across the BMI range. Nevertheless, because
the study participants consisted largely of the Jewish population
in Israel, the Israel Central Bureau of Statistics data on age-
J Clin Endocrinol Metab, June 2014, 99(6):2095–2103
stratified causes of death (Supplemental Figure 1 and http://
www1.cbs.gov.il/reader/cw_usr_view_SHTML?ID ⫽ 593)
provides a reliable estimation of the leading causes of death in
this population. Second, several concerns arise regarding potential biases in epidemiological analyses studying the relationshipbetweenweightandmortality.Mostcohortstudiesexclude
peopleatahigherriskofmortality,suchasthoseinhospitalsand
nursing homes, for whom the obesity-associated relative risk
may be lower, resulting in overestimation of the true effect of
obesity (36). Alternatively, regression dilution and reverse causation were suggested as potential biases leading to underestimation of the true relative risk of obesity-attributable deaths
(37, 38). Given the size of this population-based study, in which
BMI was measured, rather than self-reported (39), as early as in
adolescence, it is unlikely that either of these possibilities significantly biased our observations. Third, as previously discussed
(40), given the association between obesity and early puberty,
by age 17years, participants with higher BMI values are more
likely to have completed puberty and to have reached final
height. Thus, being placed in a lower decile for BMI during
adolescencemaybeassociatedwithhavingalowerriskofbodyweight related morbidity and mortality in early adulthood, reflecting a later onset of puberty and a later increase in BMI. The
strengths of the study include the standardized direct (rather
than reported) height and weight measurements and its large
size and follow-up duration, which provided us with sufficient
power to assess mortality rates in a younger population within
narrower BMI subgroups.
In summary, BMI at adolescence, within the currently
considered normal range, is a risk factor for all-cause mortality at early adulthood. As opposed to the trends in the
entire population, mortality rates among overweight and
obese adolescents did not decrease in the last 40 years. The
current increase in preadulthood obesity may therefore
attenuate the progressive increase in life expectancy.
Acknowledgments
We are grateful to Professor Assaf Rudich (Faculty of Health
Sciences at Ben-Gurion University) and Professor Avraham
Karasik and Dr Tali Cukierman-Yaffe [Sheba Medical Center
and Tel-Aviv University (both in Israel)] for critical review of the
manuscript and thoughtful discussions.
Author contributions included the following: study concept
and design (G.T., A.T., and M.L.R.), acquisition of the data
(G.T., E.D., and D.T.), research methods and statistical analyses
(G.T., E.D., and A.T.), interpretation of the data (G.T., E.D., and
A.T.), drafting of the manuscript (G.T. and A.T.), and critical
revision of the manuscript for important intellectual content
(A.A., A.S., D.T., E.D., M.L.R., and B.G.).
This work was performed in partial fulfillment of the MD
thesis requirements (M.L.R.) of the Sackler Faculty of Medicine,
Tel Aviv University. G.T. and E.D. had full access to all the data
doi: 10.1210/jc.2014-1213
in the study and take responsibility for the integrity of the data
and the accuracy of the data analysis.
Address all correspondence and requests for reprints to: Amir
Tirosh, MD, PhD, Department of Endocrinology, Diabetes, and
Hypertension, Brigham and Women’s Hospital and Harvard
Medical School, 221 Longwood Avenue, Boston, MA 02115.
E-mail: [email protected].
This work was supported by a grant from the Talpiot Medical
Leadership Program, Chaim Sheba Medical Center, TelHashomer, Israel (G.T., A.T.), and by the Israel Defense Forces
Medical Corps.
Disclosure Summary: The authors have nothing to disclose.
References
1. Canudas-Romo V. Three measures of longevity: time trends and
record values. Demography. 2010;47:299 –312.
2. Wilmoth JR, Deegan LJ, Lundstrom H, Horiuchi S. Increase of maximum life-span in Sweden, 1861–1999. Science. 2000;289:2366 –
2368.
3. Minino AM, Arias E, Kochanek KD, Murphy SL, Smith BL. Deaths:
final data for 2000. Natl Vital Stat Rep. 2002;50:1–119.
4. Bjorge T, Engeland A, Tverdal A, Smith GD. Body mass index in
adolescence in relation to cause-specific mortality: a follow-up of
230 000 Norwegian adolescents. Am J Epidemiol. 2008;168:30 –37.
5. Carpenter KM, Hasin DS, Allison DB, Faith MS. Relationships between obesity and DSM-IV major depressive disorder, suicide ideation, and suicide attempts: results from a general population study.
Am J Public Health. 2000;90:251–257.
6. Flegal KM, Graubard BI, Williamson DF, Gail MH. Cause-specific
excess deaths associated with underweight, overweight, and obesity.
JAMA. 2007;298:2028 –2037.
7. Whitlock G, Lewington S, Sherliker P, et al. Body-mass index and
cause-specific mortality in 900 000 adults: collaborative analyses of
57 prospective studies. Lancet. 2009;373:1083–1096.
8. Zhu S, Layde PM, Guse CE, et al. Obesity and risk for death due to
motor vehicle crashes. Am J Public Health. 2006;96:734 –739.
9. Franks PW, Hanson RL, Knowler WC, Sievers ML, Bennett PH,
Looker HC. Childhood obesity, other cardiovascular risk factors,
and premature death. N Engl J Med. 2010;362:485– 493.
10. Ma J, Flanders WD, Ward EM, Jemal A. Body mass index in young adulthood and premature death: analyses of the US National Health Interview
Survey linked mortality files. Am J Epidemiol. 2011;174:934–944.
11. Must A, Jacques PF, Dallal GE, Bajema CJ, Dietz WH. Long-term
morbidity and mortality of overweight adolescents. A follow-up of
the Harvard Growth Study of 1922 to 1935. N Engl J Med. 1992;
327:1350 –1355.
12. Berrington de Gonzalez A, Hartge P, Cerhan JR, et al. Body-mass
index and mortality among 1.46 million white adults. N Engl J Med.
2010;363:2211–2219.
13. Boggs DA, Rosenberg L, Cozier YC, et al. General and abdominal
obesity and risk of death among black women. N Engl J Med. 2011;
365:901–908.
14. Flegal KM, Kit BK, Orpana H, Graubard BI. Association of allcause mortality with overweight and obesity using standard body
mass index categories: a systematic review and meta-analysis.
JAMA. 2013;309:71– 82.
15. Ford ES. Trends in mortality from all causes and cardiovascular
disease among hypertensive and nonhypertensive adults in the
United States. Circulation. 2011;123:1737–1744.
16. Taylor R, Page A, Danquah J. The Australian epidemic of cardiovascular mortality 1935–2005: effects of period and birth cohort.
J Epidemiol Commun Health. 2012;66:e18.
jcem.endojournals.org
2103
17. Olshansky SJ, Passaro DJ, Hershow RC, et al. A potential decline in
life expectancy in the United States in the 21st century. N Engl J Med.
2005;352:1138 –1145.
18. Peeters A, Barendregt JJ, Willekens F, Mackenbach JP, Al Mamun
A, Bonneux L. Obesity in adulthood and its consequences for life
expectancy: a life-table analysis. Ann Intern Med. 2003;138:24 –32.
19. Preston SH, Stokes A. Contribution of obesity to international differences
in life expectancy. Am J Public Health. 2011;101:2137–2143.
20. Fontaine KR, Redden DT, Wang C, Westfall AO, Allison DB. Years
of life lost due to obesity. JAMA. 2003;289:187–193.
21. Tirosh A, Afek A, Rudich A, et al. Progression of normotensive
adolescents to hypertensive adults: a study of 26 980 teenagers.
Hypertension. 2010;56:203–209.
22. Kuczmarski RJ, Ogden CL, Grummer-Strawn LM, et al. 2000 CDC
growth charts for the United States: methods and development. Vital
Health Stat. 2002;11:1–190.
23. Gross R, Brammli-Greenberg S, Gordon B, Rabinowitz J, Afek A.
Population-based trends in male adolescent obesity in Israel 1967–
2003. J Adolesc Health. 2009;44:195–198.
24. Meydan C, Afek A, Derazne E, et al. Population-based trends in
overweight and obesity: a comparative study of 2 148 342 Israeli
male and female adolescents born 1950 –1993. Pediatr Obes. 2013;
8(2):98 –111.
25. Twig G, Livneh A, Vivante A, et al. Mortality risk factors associated
with familial Mediterranean fever among a cohort of 1.25 million
adolescents. Ann Rheum Dis. 2014;73(4):704 –709.
26. Klenk J, Nagel G, Ulmer H, et al. Body mass index and mortality:
results of a cohort of 184 697 adults in Austria. Eur J Epidemiol.
2009;24:83–91.
27. Sasazuki S, Inoue M, Tsuji I, et al. Body mass index and mortality from
all causes and major causes in Japanese: results of a pooled analysis of 7
large-scale cohort studies. J Epidemiol. 2011;21:417–430.
28. Al Lawati NM, Patel SR, Ayas NT. Epidemiology, risk factors, and
consequences of obstructive sleep apnea and short sleep duration.
Prog Cardiovasc Dis. 2009;51:285–293.
29. Calle EE, Rodriguez C, Walker-Thurmond K, Thun MJ. Overweight, obesity, and mortality from cancer in a prospectively studied
cohort of U.S. adults. N Engl J Med. 2003;348:1625–1638.
30. Parekh N, Chandran U, Bandera EV. Obesity in cancer survival.
Annu Rev Nutr. 2012;32:311–342.
31. Zheng W, McLerran DF, Rolland B, et al. Association between
body-mass index and risk of death in more than 1 million Asians.
N Engl J Med. 2011;364:719 –729.
32. Ford ES, Ajani UA, Croft JB, et al. Explaining the decrease in US
deaths from coronary disease, 1980 –2000. N Engl J Med. 2007;
356:2388 –2398.
33. Ford ES, Capewell S. Coronary heart disease mortality among young
adults in the US from 1980 through 2002: concealed leveling of
mortality rates. J Am Coll Cardiol. 2007;50:2128 –2132.
34. Koplan J. Preventing Childhood Obesity: Health in the Balance.
Washington, DC: National Academies Press; 2005.
35. Ezzati M, Friedman AB, Kulkarni SC, Murray CJ. The reversal of
fortunes: trends in county mortality and cross-county mortality disparities in the United States. PLoS Med. 2008;5:e66.
36. Flegal KM, Graubard BI, Williamson DF. Methods of calculating
deaths attributable to obesity. Am J Epidemiol. 2004;160:331–338.
37. Greenberg JA. Correcting biases in estimates of mortality attributable to obesity. Obesity (Silver Spring). 2006;14:2071–2079.
38. Greenberg JA, Fontaine K, Allison DB. Putative biases in estimating
mortality attributable to obesity in the US population. Int J Obes
(Lond). 2007;31:1449 –1455.
39. Keith SW, Fontaine KR, Pajewski NM, Mehta T, Allison DB. Use of
self-reported height and weight biases the body mass index-mortality association. Int J Obes (Lond). 2011;35:401– 408.
40. Tirosh A, Shai I, Afek A, et al. Adolescent BMI trajectory and risk of
diabetes versus coronary disease. N Engl J Med. 2011;364:1315–1325.