ORIGINAL
E n d o c r i n e
ARTICLE
R e s e a r c h
Fat Quality and Incident Cardiovascular Disease,
All-Cause Mortality, and Cancer Mortality
Klara J. Rosenquist, Joseph M. Massaro, Alison Pedley, Michelle T. Long,
Bernard E. Kreger, Ramachandran S. Vasan, Joanne M. Murabito,
Udo Hoffmann, and Caroline S. Fox
Division of Endocrinology and Metabolism (K.J.R., C.S.F.), Brigham and Women’s Hospital and Harvard
Medical School, Boston, Massachusetts, 02115; National Heart, Lung, and Blood Institute (NHLBI)
Framingham Heart Study (K.J.R., A.P., M.T.L., B.E.K., R.S.V., J.M.Mu., C.S.F.) and Division of Intramural
Research and the Center for Population Studies (K.J.R., A.P., M.T.L., C.S.F.), Framingham, Massachusetts,
01702; Department of Biostatistics (J.M.Ma.), Boston University School of Public Health; Department of
Medicine (M.T.L.), Section of Gastroenterology; Department of Medicine (B.E.K., J.M.Mu.), Section of
General Internal Medicine; and Department of Medicine (R.S.V., U.H.), Section of Preventive Medicine
and Epidemiology and Cardiology, Boston Medical Center and Boston University School of Medicine,
Boston, Massachusetts, 02118; and Departments of Medicine and Radiology, Massachusetts General
Hospital and Harvard Medical School, Boston, Massachusetts, 02114
Context: Cellular characteristics of fat quality have been associated with cardiometabolic risk and
can be estimated by computed tomography (CT) attenuation.
Objective: The aim was to determine the association between CT attenuation (measured in
Hounsfield units [HU]) and clinical outcomes.
Methods: This was a prospective community-based cohort study using data from the Framingham
Heart Study (n ⫽ 3324, 48% women, mean age 51 years) and Cox proportional hazard models.
Main Outcomes: The primary outcomes of interest were incident cardiovascular disease (CVD) and
all-cause mortality. The secondary outcomes of interest were incident cancer, non-CVD death, and
cancer death.
Results: There were 111 incident CVD events, 137 incident cancers, 85 deaths including 69 non-CVD
deaths, and 45 cancer deaths in up to 23 047 person-years of follow-up. A 1-SD increment in visceral
adipose tissue (VAT) HU was inversely associated with incident CVD in the age- and sex-adjusted
model (hazard ratio [HR] 0.78, P ⫽ .02) but not after multivariable adjustment (HR 0.83, P ⫽ .11).
VAT HU was directly associated with all-cause mortality (multivariable HR 1.40, P ⫽ .003), which
maintained significance after additional adjustment for body mass index (HR 1.53, P ⬍ .001) and
VAT volume (HR 1.99, P ⬍ .001). Non-CVD death remained significant in all 3 models, including after
adjustment for VAT volume (HR 1.97, P ⬍ .001). VAT HU was also associated with cancer mortality
(HR 1.93, P ⫽ .002). Similar results were obtained for sc adipose tissue HU.
Conclusions: Fat quality, as estimated by CT attenuation, is associated with all-cause mortality,
non-CVD death, and cancer death. These associations highlight how indirect indices of fat quality
can potentially add to a better understanding of obesity-related complications. (J Clin Endocrinol
Metab 100: 227–234, 2015)
ISSN Print 0021-972X ISSN Online 1945-7197
Printed in U.S.A.
Copyright © 2015 by the Endocrine Society
Received December 3, 2013. Accepted September 2, 2014.
First Published Online September 16, 2014
doi: 10.1210/jc.2013-4296
Abbreviations: Ang-2, angiopoietin-2; BMI, body mass index; BP, blood pressure; CI, confidence interval; CT, computed tomography; CTGF, connective tissue growth factor; CVD,
cardiovascular disease; HDL, high-density lipoprotein; HR, hazard ratio; HU, Hounsfield
unit; MDCT, multidetector CT; sFlt-1, soluble fms-like tyrosine kinase-1; SAT, sc adipose
tissue; VAT, visceral adipose tissue.
J Clin Endocrinol Metab, January 2015, 100(1):227–234
jcem.endojournals.org
227
228
Rosenquist et al
Fat Quality and Clinical Outcomes
he prevalence of obesity in the United States continues
to increase and has reached epidemic proportions (1).
Obesity is associated with an increased risk of multiple
metabolic risk factors, including insulin resistance and diabetes (2– 4), and is associated with increased overall mortality (5), cardiovascular disease (CVD) (6), and obesityrelated cancers (6). However, not all individuals with
obesity experience the same metabolic complications (7),
and obesity has been shown to be a heterogeneous condition with individual variability in fat depot deposition
(8) and differential metabolic risk (2, 4, 8).
Visceral adipose tissue (VAT) and sc adipose tissue
(SAT) are considered to be unique pathogenic fat depots
(2, 8) that can be imaged using computed tomography
(CT). CT imaging uses Hounsfield units (HU) based on
radiographic pixels to differentiate tissue subtype and
quantify radiodensity. Adipose tissue with relatively lower
lipid content and higher vascularity has a less negative (ie,
higher) HU (9, 10). In addition, adipose tissue with higher
HU has been associated with smaller adipocytes (11), suggesting that relative levels of adipose tissue extracellular
matrix may also be contributing to CT attenuation of adipose tissue. Thus, the HU provides a noninvasive and
indirect measure of fat composition and quality. We have
recently shown that higher adipose tissue HU levels are
associated with less adverse cardiometabolic risk factor
profiles, suggesting that this modality may capture unique
metabolic aspects of fat quality (12).
Given this framework, we sought to examine whether
this measure of fat quality is associated with clinical outcomes to better understand how abdominal fat depot composition contributes to obesity-related CVD, cancer, and
mortality. Given our previous findings showing that
higher HU levels are associated with less adverse CVD risk
factor levels, we hypothesized that adipose tissue characterized by higher HU would be associated with a lower
incidence of CVD and lower all-cause mortality.
T
Subjects and Methods
Study participants
Data for the present study were obtained from offspring and
third-generation cohort participants in the Framingham Heart
Study who had previously undergone multidetector CT (MDCT)
from 2002 to 2005 (2). Inclusion criteria included age ⬎35 years
for men and ⬎40 years for nonpregnant women and weight
⬍160 kg. A total of 3529 participants (1418 from offspring and
2111 from the third generation) underwent MDCT scanning,
resulting in a final sample size of 3324 after excluding participants with missing data including missing covariates, SAT or
VAT volumes, or SAT or VAT HU. The mean length of follow-up
was 6.9 years; the maximum length of follow-up was 8.5 years.
J Clin Endocrinol Metab, January 2015, 100(1):227–234
Measurement of SAT and VAT
Participants had previously undergone supine MDCT scanning of the abdomen, obtaining 25 contiguous 5-mm slices. SAT
and VAT volumes were acquired by manually outlining the visceral and sc fat depots. An image display window of ⫺195 to
⫺45 HU was applied. The mean HU of the window was recorded. This method has been previously documented and has
shown excellent interreader correlations (r ⫽ 0.99 for both VAT
and SAT) (12).
Measurement of covariates
Body mass index (BMI) was calculated by weight (kilograms)
divided by the square of height (meters). Blood pressure (BP) was
measured twice at rest with the average of the two measurements
used in analysis; hypertension was defined as systolic BP ⱖ140
mm Hg, diastolic BP ⱖ90 mm Hg, or the use of hypertensive
medications. Diabetes was classified as fasting plasma glucose
ⱖ126 mg/dL or treatment with an oral hypoglycemic agent or
insulin. Total and high-density lipoprotein (HDL) cholesterol
were measured on the fasting plasma sample. Current smoking
was defined as having smoked at least 1 cigarette per day during
the previous year.
Measurement of angiogenic growth factors and
systemic markers of fibrosis
Measurement of the biomarkers were determined from the
fasting blood draw. Blood samples were centrifuged immediately
and stored at ⫺80°C until biomarkers were measured. Serum
concentrations of angiogenic growth factors were assayed using
commercial kits (R&D Systems Inc) as previously reported (13,
14). Angiogenic growth factors measured were vascular endothelial growth factor, the soluble vascular endothelial growth
factor receptor (soluble fms-like tyrosine kinase-1 [sFlt-1]), hepatocyte growth factor, angiopoietin-2 (Ang-2) and its soluble
receptor (soluble tyrosine kinase with Ig-like and epidermal
growth factor-like domains 2). Urinary connective tissue growth
factor (CTGF) was used as a biomarker of fibrosis. CTGF was
measured using the human kidney Tox 1 assay (Rules-Based
Medicine, Inc; www.rulesbasedmedicine.com), a panel that uses
antigen-specific antibodies and a Luminex 100 Analyzer (Luminex
Corp; www.luminexcorp.com) to measure secreted CTGF protein (15).
Ascertainment of CVD, cancer, and all-cause
mortality
All participants in the Framingham Heart Study undergo continuous surveillance for incident cardiovascular events, cancer
diagnoses, and death. Incident CVD is assessed according to
previously reported standardized criteria (including coronary
heart disease (recognized or unrecognized myocardial infarction,
angina pectoris, coronary insufficiency, or coronary heart disease death), cerebrovascular disease (stroke or transient ischemic
attack), or congestive heart failure). Outcome events are adjudicated by a panel of 3 physicians after review of all available
information, hospitalization records, and physician charts. Cancer cases were identified at routine examinations and health updates, through surveillance of admissions at local Framingham
hospitals, or from death records. The cases were confirmed by
pathology reports, and 2 independent investigators reviewed the
medical records. Nonmalignant neoplasms and nonmelanoma
doi: 10.1210/jc.2013-4296
skin cancers were not included in cancer cases. A cause of death
was obtained from death certificates, hospital admission records, medical records, and family members. All deaths were
adjudicated by a panel of 3 investigators.
Statistical analysis
The primary outcomes of interest were incident CVD and
all-cause mortality. Cox proportional hazards models were used
to determine the longitudinal association between VAT HU and
SAT HU and incident CVD and all-cause mortality. For incident
CVD, we excluded all individuals with prevalent CVD at baseline. The secondary outcomes of interest were incident cancer,
non-CVD mortality, and cancer mortality. Cox proportional
hazards models were used to determine the longitudinal association
between VAT HU and SAT HU and these secondary outcomes of
interest. For incident cancer, we excluded all individuals with a
diagnosis of malignant cancer, other than nonmelanoma skin
cancer, at baseline. Non-CVD mortality included all causes of
mortality except for CVD. Cancer mortality included all individuals identified with cancer and in whom cancer was identified
as the primary cause of death. Subjects who died, not due to the
reason of interest (ie, non-CVD or cancer), were censored at the
time of death.
Four models were constructed. First, we examined the ageand sex-adjusted model. Second, we constructed a multivariable
model adjusted for age, sex, smoking status, systolic BP, hypertension treatment, total cholesterol, HDL cholesterol, and diabetes mellitus. For all-cause mortality, we additionally adjusted
for prevalent CVD and prevalent cancer at the time of the baseline examination; for CVD mortality, we adjusted for prevalent
CVD; and for cancer mortality, we adjusted for prevalent cancer.
The third model additionally adjusted the multivariable
model for BMI. The fourth model additionally adjusted the
multivariable model for fat volume (VAT or SAT depot volume, respectively).
Secondary analyses
Additional analyses investigated interactions between mean
HU and SAT or VAT volumes within each outcome (incident
CVD, incident cancer, all-cause mortality, non-CVD mortality,
and cancer mortality). The interaction of sex with HU variables
was also assessed for each outcome.
In a subgroup analysis, Cox proportional hazards models
were used to evaluate the relationship of fat variables to all-cause
mortality within clinical ranges of BMI: normal weight (BMI
18 –24.9 kg/m2), overweight (BMI 25–29.9 kg/m2), and obese
(BMI ⱖ30 kg/m2). The model was adjusted for age, sex, systolic
BP, hypertension treatment, diabetes, total cholesterol, HDL cholesterol, smoking status, and VAT or SAT volume, respectively.
All analyses were performed using SAS version 9.3 (SAS Institute). Hazard ratios (HR) are presented for a 1-SD increase in
the fat variable. A P value ⬍.05 was considered statistically significant for all analyses with the exception of the primary outcomes. Because there were 2 primary outcomes, a P value ⬍.025
(.05/2) was used for the primary analysis. Secondary outcomes
including incident cancer, non-CVD mortality, and cancer mortality were exploratory, and therefore, a P value ⬍.05 was considered statistically significant.
jcem.endojournals.org
Table 1.
229
Study Sample Characteristics
Continuous characteristics,
mean (SD)
Age, y
BMI, kg/m2
VAT, cm3
SAT, cm3
VAT HU
SAT HU
Systolic BP, mm Hg
Diastolic BP, mm Hg
Total cholesterol, mg/dL
HDL cholesterol, mg/dL
Categorical characteristics, n (%)
Hypertension
Diabetes
Hypertension treatment
Current smoker
Women
(n ⴝ 1603)
Men
(n ⴝ 1721)
52.2 (9.9)
27.1 (5.8)
1365 (834)
3150 (1515)
⫺92.5 (4.4)
⫺102.3 (5.1)
120 (18)
74 (9)
198 (37)
61 (17)
49.7 (10.6)
28.4 (4.5)
2237 (1017)
2637 (1204)
⫺95.2 (4.5)
⫺99.6 (4.4)
123 (15)
78 (9)
195 (34)
46 (12)
427 (26.7)
88 (5.5)
300 (18.7)
199 (12.4)
547 (31.8)
126 (7.3)
341 (19.8)
232 (13.5)
Results
Characteristics of the 3324 study participants are presented in Table 1. The sample was nearly half women, with
a mean age of 52.2 years in women and 49.7 years in men.
Women had a mean VAT volume of 1365 cm3 and VAT
HU of ⫺92.5 (range ⫺105.7 to ⫺80.5). Men had a mean
VAT volume of 2237 cm3 and VAT HU of ⫺95.2 (range
⫺104.5 to ⫺79.0). The SD of VAT HU was 4.4 for women
and 4.5 for men. The SD of SAT HU was 5.1 for women
and 4.4 for men. Based on our previous work, the correlation between VAT volume and VAT HU was ⫺0.75 for
women and ⫺0.72 for men in this study sample (12). The
correlation between SAT volume and SAT HU was ⫺0.49
for women and ⫺0.56 for men (12).
Incident CVD events
There were a total of 111 incident CVD events in
18 033 person-years of follow-up (Table 2 and Figure 1).
As hypothesized, a 1-SD increment increase in VAT HU
was associated with lower incident CVD in the age- and
sex-adjusted model (HR 0.78, 95% confidence interval
[CI] 0.64 – 0.98, P ⫽ .02). Upon multivariable adjustment,
the association between VAT HU and incident CVD was
attenuated and no longer statistically significant (HR
0.83, 95% CI 0.67–1.04, P ⫽ .11). Additional adjustment
for BMI (HR 0.88, 95% CI 0.70 –1.11, P ⫽ .29) and VAT
volume (HR 1.05, 95% CI 0.79 –1.40, P ⫽ .75) did not
materially change these results. Results for SAT HU were
not statistically significant.
All-cause mortality
There were a total of 85 deaths in 23 047 personyears of follow-up (Table 3 and Figure 1). Contrary to
230
Rosenquist et al
Table 2.
Events)a
Fat Quality and Clinical Outcomes
J Clin Endocrinol Metab, January 2015, 100(1):227–234
Multivariable Cox Proportional Hazard Models of VAT and SAT Attenuation and Incident CVD (Death Plus
Incident CVDb (111 Events/18 033
Person-Years Follow-up)
Incident Cancerc (137 Events/20 870
Person-Years Follow-up)
VAT HU
VAT HU
SAT HU
SAT HU
Adjusted For
HR (95% CI)
P
Value HR (95% CI)
P
Value HR (95% CI)
P
Value HR (95% CI)
P
Value
Age and sex
Multivariabled
Multivariable and BMI
Multivariable and
fat depot volumee,f,g
0.78 (0.64 – 0.98)
0.83 (0.67–1.04)
0.88 (0.70 –1.11)
1.05 (0.79 –1.40)
.02
.11
.29
.75
.46
.66
.95
.78
.63
.48
.46
.27
.67
.59
.59
.44
a
0.92 (0.75–1.14)
0.95 (0.76 –1.19)
0.99 (0.79 –1.24)
1.04 (0.81–1.32)
0.96 (0.80 –1.14)
0.93 (0.77–1.13)
0.93 (0.57–1.14)
1.16 (0.89 –1.50)
0.96 (0.79 –1.16)
0.95 (0.78 –1.15)
0.95 (0.78 –1.16)
0.92 (0.73–1.14)
Data are presented as HR (95% CI) per 1-SD increase in HU.
b
Sex interaction for incident CVD in the age- and sex-adjusted model: VAT HU, P ⫽ .65; SAT HU, P ⫽ .60.
c
Sex interaction for incident cancer in the age- and sex-adjusted model: VAT HU, P ⫽ .03; SAT HU, P ⫽ .03.
d
Adjusted for age, sex, systolic BP, hypertension treatment, diabetes, total cholesterol, HDL cholesterol, and smoking.
e
Multivariable-adjusted P value for interaction between VAT HU and VAT volume: P ⫽ .09.
f
Multivariable-adjusted P value for interaction between SAT HU and SAT volume: P ⫽ .25.
g
Adjusted for VAT or SAT volume.
our hypothesis, per 1-SD increase in VAT HU, there was
a 40% increase in the multivariable-adjusted risk of
all-cause mortality (HR 1.4, 95% CI 1.12–1.75, P ⫽
.003). This association was similar after additional ad-
justment for BMI (HR1.53, 95% CI 1.21–1.93, P ⬍
.001) and VAT volume (HR 1.99, 95% CI 1.47–2.69,
P ⬍ .001).
A 1-SD increment increase in SAT HU was associated
with a 30% increase in the multivariable risk for all-cause
mortality of 1.27 (95% CI 1.05–1.54, P ⫽ .01), which
remained significant after additional adjustment for BMI
(HR 1.32, 95% CI 1.09 –1.61, P ⫽ .005) or SAT volume
(HR 1.36, 95% CI 1.08 –1.70, P ⫽ .008).
Within clinical categories of BMI, VAT HU was associated with all-cause mortality among normal-weight,
overweight, and obese individuals (Figure 2).
CVD mortality
There were 16 incident CVD deaths. A 1-SD increment
increase in VAT was not associated with incident CVD
death in either the age- and sex-adjusted model (HR 0.86,
95% CI 0.50 –1.46, P ⫽ .6) or the model additionally
adjusted for BMI or VAT volume. Similar negative results
were obtained for SAT (data not shown).
Figure 1. Event rates for all-cause mortality, cancer mortality, and
incident CVD by quartiles of VAT HU and SAT HU. Quartile 1 (Q1)
indicates the most negative HU quartile, whereas Q4 indicates the least
negative HU quartile.
Non-CVD mortality
The observation of an association between VAT and
SAT HU and all-cause mortality, but not CVD mortality,
raised the question of whether there is an association between HU and non-CVD mortality. There were 69 nonCVD deaths in 23 047 person-years of follow-up (Table
3). Indeed, a 1-SD increment increase in VAT HU was
associated with a multivariable adjusted HR for non-CVD
mortality of 1.49 (95% CI 1.17–1.90, P ⫽ .001). This
association remained significant after additional adjust-
doi: 10.1210/jc.2013-4296
Table 3.
jcem.endojournals.org
Multivariable Cox Proportional Hazard Models of VAT and SAT Attenuation and Mortalitya
All-Cause Mortalityb (85 Events/23 047
Person-Years Follow-up)
Adjusted For VAT
Age and Sex
Multivariablee
Multivariable
and BMI
Multivariable
and fat
depot
volumef,g,h
231
P
Value
SAT
Non-CVD Mortalityc (69 Events/23 047
Person-Years Follow-up)
P
Value VAT
P
Value
SAT
Cancer Mortalityd (45 Events/23 047
Person-Years Follow-up)
P
Value VAT
P
Value SAT
P
Value
1.30 (1.05–1.60) .02
1.31 (1.09 –1.57) .004
1.40 (1.12–1.75) .003
1.27 (1.05–1.54) .01
1.53 (1.21–1.93) ⬍.001 1.32 (1.09 –1.61) .005
1.42 (1.13–1.78) .003
1.34 (1.10 –1.63) .004
1.49 (1.17–1.90) .001
1.29 (1.05–1.58) .01
1.62 (1.25–2.10) ⬍.001 1.34 (1.08 –1.65) .007
1.38 (1.04 –1.84) .03
1.50 (1.10 –2.03) .01
1.73 (1.25–2.40 .001
1.29 (1.01–1.65) .04
1.29 (1.00 –1.66) .05
1.39 (1.07–1.81) .01
1.99 (1.47–2.69) ⬍.001 1.36 (1.08 –1.70) .008
1.97 (1.42–2.75) ⬍.001 1.36 (1.06 –1.73) .01
1.93 (1.27–2.94)
1.45 (1.08 –1.97) .01
a
Data are presented as HR (95% CI) per 1-SD increase in HU.
b
Sex interaction for all-cause mortality in the age- and sex-adjusted model: VAT HU, P ⫽ .13; SAT HU, P ⫽ .60.
c
Sex interaction for non-CVD mortality in the age- and sex-adjusted model: VAT HU, P ⫽ .14; SAT HU, P ⫽ .10.
d
Sex interaction for cancer mortality in the age- and sex-adjusted model: VAT HU, P ⫽ .15; SAT HU, P ⫽ .09.
.002
e
Adjusted for age, sex, systolic BP, hypertension treatment, diabetes, total cholesterol, HDL cholesterol, and smoking. All-cause mortality was
adjusted for incident CVD, and cancer mortality was adjusted for incident cancer.
f
Multivariable-adjusted P value for interaction between VAT HU and VAT volume: all-cause mortality, P ⫽ .50; non-CVD mortality, P ⫽ .14; cancer
death, P ⫽ .90.
g
Multivariable-adjusted P-value for interaction between SAT HU and SAT volume: all-cause mortality, P ⫽ .47; non-CVD mortality, P ⫽ .48; cancer
death, P ⫽ .80.
h
Adjusted for VAT or SAT volume.
ment for BMI (HR 1.62, 95% CI 1.25–2.10, P ⬍ .001) and
VAT volume (HR 1.97, 95% CI 1.42–2.75, P ⬍ .001).
Similar but somewhat less striking results were observed
with SAT HU (Table 3).
Figure 2. Cox proportional hazards models of all-cause mortality
stratified by clinical BMI categories: normal weight (BMI 18 –24.9 kg/
m2), overweight (BMI 25–29.9 kg/m2), and obese (BMI ⱖ30
kg/m2).The model was adjusted for age, sex, systolic BP, hypertension
treatment, diabetes, total cholesterol, HDL cholesterol, smoking status,
and VAT or SAT volume, respectively. P values reflect the statistical
significance of the HR within each category, and error bars reflect the
95% CI.
Cancer mortality
To detail the association between adipose tissue attenuation and non-CVD mortality, a secondary analysis of
cancer mortality was completed. There were a total of 45
cancer deaths in 23 047 person-years of follow-up (Figure
1). A 1-SD increment increase in VAT HU was associated
with a multivariable adjusted HR for cancer mortality of
1.50 (95% CI 1.10 –2.03, P ⫽ .01), which remained significant after additional adjustment for BMI (HR 1.73,
95% CI 1.25–2.40, P ⫽ .001) and VAT volume (HR 1.93,
95% CI 1.28 –2.94, P ⫽ .002, Table 3). Similar results
were seen with SAT HU (Table 3).
Secondary analyses
There were no major interactions between SAT or VAT
HU and respective fat volumes (outlined in Tables 2 and
3, all P values ⬎.05).
We further excluded individuals with cancer deaths
within 2 years of follow-up. The number of cancer deaths
decreased from 45 to 37. In this analysis, a 1-SD increment
in VAT HU showed a slightly attenuated association for
cancer mortality in the multivariable model (HR 1.24,
95% CI 0.88 –1.75, P ⫽ .23) compared with the analysis
not excluding cancer death within 2 years. Similar findings
were seen in the BMI-adjusted model (HR 1.42, 95% CI
0.98 –2.07, P ⫽ .07) and VAT volume-adjusted model
(HR 1.64, 95% CI 1.03–2.63, P ⫽ .038). Similar results
were also seen with SAT HU.
232
Rosenquist et al
Fat Quality and Clinical Outcomes
Additional multivariable linear regression models were
constructed to determine the association between biomarkers of systemic fibrosis (CTGF), angiogenic growth
factors, and SAT HU. A 1-SD increment increase in CTGF
was associated with a 0.36 increase in SAT HU (95% CI
0.09 – 0.62, P ⫽ .0079) and a 1-SD increment increase in
Ang-2 was associated with a 0.70 increase in SAT HU
(95% CI 0.51– 0.90, P ⬍ .0001). There was an inverse
relationship with sTie-2 and positive correlation with
sFlt-1 (outlined in Supplemental Table 1).
Discussion
Our main findings are 4-fold. First, as we hypothesized,
higher CT HU was associated with lower incident CVD in
the age- and sex-adjusted model. Second, contrary to our
initial hypothesis, higher CT HU was associated with an
increased risk of all-cause mortality. Third, the association
between CT HU and all-cause mortality persisted even
after additional adjustment for generalized adiposity and
absolute adipose tissue volume. Finally, we observed an
association between higher CT HU and non-CVD mortality, specifically cancer mortality, suggesting that fat
quality characteristics of abdominal fat depots may be
associated with obesity-related cancer mortality.
In the context of the current literature
Studies focusing on fat distribution have found central
adiposity to be more closely associated with adverse cardiometabolic risk and increased CVD than generalized
adiposity (16, 17). More specifically, studies using CT
imaging to directly quantify abdominal fat have found
that VAT volume in particular is associated with an adverse risk factor profile and all-cause mortality (2, 3, 8,
18). In addition, we have recently shown that VAT volume
is associated with incident CVD and cancer above and
beyond BMI and waist circumference (19).
The importance of fat quality has been increasingly recognized as an important correlate of cardiometabolic risk.
Adipocyte hypertrophy has been associated with diabetes,
independent of insulin resistance (20), and is a predictor of
macrophage accumulation in adipocytes (21). Macrophage accumulation has in turn been associated with insulin resistance (22). Additionally, individuals with metabolic syndrome have increased levels of proinflammatory
adipokines and macrophage accumulation in SAT than
matched controls (23). Given the invasive techniques,
these studies have been limited to small clinical samples
and animal models.
Based on recent work that has shown that higher fat
attenuation defines fat tissue with decreased lipid content
J Clin Endocrinol Metab, January 2015, 100(1):227–234
and increased vascularity (9, 10), we exploited existing
measurements of CT attenuation as a noninvasive measure of abdominal fat quality. Our study found an inverse
association between fat quality estimated by CT attenuation and cardiometabolic risk (12). An increase in CT
attenuation was associated with more favorable cardiometabolic risk factors (12). On the basis of this previous
work, we hypothesized that higher fat attenuation would
be associated with a lower risk of CVD. Indeed, we did
find that incident CVD event rates were lower with increasing VAT HU. However, we observed this only in the
age- and sex-adjusted model, and additional adjustment
for CVD risk factors attenuated this relationship, suggesting that the association between VAT attenuation and
incident CVD in the age- and sex-adjusted model may be
due to shared CVD risk factors that are associated with
both fat quality and CVD.
Contrary to our a priori hypothesis, we observed that
the higher HU attenuation was associated with an increased risk of all-cause mortality, non-CVD mortality,
and cancer mortality. This is similar to findings from a
study completed in the Health ABC and AGES-Reykjavik
cohorts in which they found that higher-density adipose
tissue was associated with all-cause mortality (11). However, compared with that study, which specifically evaluated these outcomes in individuals 65 years and older, our
study evaluated CT attenuation in all individuals in the
Framingham Heart Study MDCT substudy (mean age ⫽
52.2 years in women and 49.7 years in men). As individuals age, they typically lose weight with a preferential loss
of SAT volume (24). Weight loss, changes in fat distribution, and other age-related fat quality changes distinguish
these 2 study populations.
There are several potential physiologic mechanisms to
explain our findings. Two leading possibilities may be adipose tissue fibrosis and vascularity. Invasive biopsy techniques in animal models and human studies have determined that adipose tissue from obese, insulin-resistant
individuals is characterized by increased inflammatory
macrophages, fibrosis, and increases in components of the
extracellular matrix (25–27). Higher CT attenuation of fat
depots would be concordant with extracellular matrix fibrosis in underlying fat tissue. In a secondary analysis, we
did find that higher levels of CTGF, a marker of systemic
fibrosis, was associated with an increase in SAT HU.
Furthermore, characteristics that promote fibrosis formation in adipose tissue, inflammation, and cytokine release (28, 29) are also microenvironment characteristics
that promote tumor growth (30, 31). In sum, inflammation may be a shared pathway contributing to both adipose tissue fibrosis and cancer tumorigenesis. Higher CT
attenuation of adipose tissue may reflect higher adipose
doi: 10.1210/jc.2013-4296
jcem.endojournals.org
233
tissue fibrosis, providing potential mechanistic insight
into the observed association between CT attenuation and
cancer mortality. Taken together with the findings of our
current study, the association of higher HU and non-CVD
mortality may in part be due to a higher level of extracellular collagen fibrosis.
Second, higher HU may also reflect an increase in adipose tissue vascularity. Highly vascularized tissue has a
higher HU on CT studies due to the tissue properties of
blood (32). Several recent studies have highlighted a role
of decreased angiogenesis in association with dysfunctional
adipose tissue (33, 34). In a secondary analysis, we found that
increases in levels of Ang-2 and sFlt-1, angiogenic growth
factors, were associated with an increase in SAT HU.
Adipose tissue vascularity also contributes to the cellular microenvironment in such a way as to make it more
conducive to unregulated growth (35) as seen in cancer.
This suggests a level of cross talk between adipose tissue
and cancer cells such that surrounding adipose tissue may
be altered to provide a supportive microenvironment in
which cancer cells can take hold.
mortality. Specifically, within normal-weight and overweight ranges, CT HU was associated with increased risk
of all-cause mortality, suggesting that fat quality contributes
to mortality risk above that of the BMI category alone. However, the underlying cellular mechanisms that link fat attenuation with incident CVD, all-cause mortality, and nonCVD death remain uncertain, and future mechanistic
research is needed to further elucidate this association.
Implications for further research
Our findings suggest the importance of both fat distribution and fat quality studies in the study of obesity outcomes. Most previous studies on mortality outcomes have
shown a positive association between obesity and mortality (5, 6, 36, 37). However, a small number of studies have
also shown that overweight BMI has only a modest association with all-cause mortality (38 – 40). For example, a
recent study found an association between all-cause mortality and BMI in the grade 2 and 3 obesity range (BMI
ⱖ35 kg/m2) but not BMI in the overweight range (BMI 25
to ⬍30 kg/m2) (39). Investigators have postulated that the
variability in these findings may be due in part to the use
of measurements of generalized adiposity rather than
more specific measures of body fat distribution (16).
Imaging techniques can directly visualize fat depots and
distinguish between pathogenic and nonpathogenic fat depots to clarify some of the concern regarding BMI. For
example, VAT is more strongly associated with all-cause
mortality than generalized adiposity (16) and SAT (18).
However, if the cellular characteristics of SAT changes
such as to limit expansion, as is seen in fibrosis of SAT,
excess fat will be increasingly stored in VAT and other
ectopic fat depots (26). Therefore, fat distribution studies
may be further strengthened by the study of underlying fat
composition as both fat quantity and fat quality studies
may provide synergistic information regarding the metabolic consequences of obesity. In the present study, we
found that within categories of BMI, the evaluation of CT
HU provides additional information regarding all-cause
Conclusions
Fat quality estimated by CT attenuation is associated
with age- and sex-adjusted incident CVD, all-cause mortality, non-CVD mortality, and cancer mortality. These
findings highlight how indirect indices of fat quality can
potentially add to our understanding of the complications
of obesity.
Strengths and limitations
There are several strengths of this study including a
large, well-defined community-dwelling cohort with comprehensive phenotyping. We used a novel measure to estimate fat quality from CT scans. Some limitations warrant mention. The sample is predominately Caucasian,
and generalizability to other populations is uncertain.
These findings are derived from an observational study,
and causality cannot be inferred. Finally, the mechanistic
underpinning explaining the association between higher
CT HU and mortality outcomes remains to be determined.
Acknowledgments
Address all correspondence and requests for reprints to: Caroline
S. Fox, MD, MPH, 73 Mt Wayte Avenue Suite 2, Framingham,
Massachusetts 01702. E-mail: [email protected].
This research was conducted in part using data and resources
from the Framingham Heart Study (FHS) of the National Heart,
Lung, and Blood Institute (NHLBI) of the National Institutes of
Health and Boston University School of Medicine. This work
was supported by the NHLBI’s FHS (Contract N01-HC-25195)
and ROI-HL-077477 (to R.S.V.). K.J.R. is supported through funding from the Whitaker Cardiovascular Institute (T32 HL007224).
K.J.R. and CSF designed the study, led the statistical analysis,
and wrote the manuscript. K.J.R. and C.S.F. are the guarantors
of the study. J.M.Ma. and A.P. completed the statistical analyses
and reviewed and edited the manuscript. M.T.L. contributed to
the analysis. B.K., J.M.Mu., R.S.V., and U.H. contributed to the
discussion and reviewed and edited the manuscript. U.H. provided expertise in imaging techniques, and R.S.V. provided expertise in serum biomarkers.
Disclosure Summary: A.P. is an employee of Merck. The rest
of the authors have nothing to disclose.
234
Rosenquist et al
Fat Quality and Clinical Outcomes
J Clin Endocrinol Metab, January 2015, 100(1):227–234
References
1. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and
trends in obesity among US adults, 1999 –2008. JAMA. 2010;
303(3):235–241.
2. Fox CS, Massaro JM, Hoffmann U, et al. Abdominal visceral and
subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation.
2007;116:39 – 48.
3. Goodpaster BH, Krishnaswami S, Harris TB, et al. Obesity, regional
body fat distribution, and the metabolic syndrome in older men and
women. Arch Intern Med. 2005;165(7):777–783.
4. McLaughlin T, Abbasi F, Lamendola C, Reaven G. Heterogeneity in
the prevalence of risk factors for cardiovascular disease and type 2
diabetes mellitus in obese individuals: effect of differences in insulin
sensitivity. Arch Intern Med. 2007;167(7):642– 648.
5. Calle EE, Thun MJ, Petrelli JM, Rodriguez C, Heath CW. Bodymass index and mortality in a prospective cohort of U.S. adults.
N Engl J Med. 1999;341(15):1097–1105.
6. Prospective Studies Collaboration, 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(9669):1083–1096.
7. Klöting N, Fasshauer M, Dietrich A, et al. Insulin-sensitive obesity.
Am J Physiol Endocrinol Metab. 2010;299(3):E506 –E515.
8. McLaughlin T, Lamendola C, Liu A, Abbasi F. Preferential fat deposition in subcutaneous versus visceral depots is associated with insulin
sensitivity. J Clin Endocrinol Metab. 2011;96(11):E1756 –E1760.
9. Baba S, Jacene HA, Engles JM, Honda H, Wahl RL. CT Hounsfield
units of brown adipose tissue increase with activation: preclinical
and clinical studies. J Nuclear Med. 2010;51(2):246 –250.
10. Hu HH, Chung SA, Nayak KS, Jackson HA, Gilsanz V. Differential
computed tomographic attenuation of metabolically active and inactive adipose tissues: preliminary findings. J Comput Assist Tomogr. 2011;35:65–71.
11. Murphy RA, Register TC, Shively CA, et al. Adipose tissue density,
a novel biomarker predicting mortality risk in older adults. J Gerontol A Biol Sci Med Sci. 2014;69(1):109 –117.
12. Rosenquist KJ, Pedley A, Massaro JM, et al. Visceral and subcutaneous fat quality is associated with cardiometabolic risk. JACC Cardiovasc Imaging. 2013;6(7):762–771.
13. Lieb W, Safa R, Benjamin EJ, et al. Vascular endothelial growth
factor, its soluble receptor, and hepatocyte growth factor: clinical
and genetic correlates and association with vascular function. Eur
Heart J. 2009;30(9):1121–1127.
14. Lieb W, Zachariah JP, Xanthakis V, et al. Clinical and genetic correlates of circulating angiopoietin-2 and soluble Tie-2 in the community. Circ Cardiovasc Genet. 2010;3(3):300 –306.
15. O’Seaghdha CM, Hwang SJ, Bhavsar NA, et al. Lower urinary connective tissue growth factor levels and incident CKD stage 3 in the
general population. Am J Kidney Dis. 2011;57(6):841– 849.
16. Després JP, Moorjani S, Lupien PJ, Tremblay A, Nadeau A,
Bouchard C. Regional distribution of body fat, plasma lipoproteins,
and cardiovascular disease. Arteriosclerosis. 1990;10(4):497–511.
17. Nieves DJ, Cnop M, Retzlaff B, et al. The atherogenic lipoprotein
profile associated with obesity and insulin resistance is largely attributable to intra-abdominal fat. Diabetes. 2003;52(1):172–179.
18. Katzmarzyk PT, Mire E, Bouchard C. Abdominal obesity and mortality: The Pennington Center Longitudinal Study. Nutr Diabetes.
2012;2:e42.
19. Britton KA, Massaro JM, Murabito JM, Kreger BE, Hoffmann U,
Fox CS. Body fat distribution, incident cardiovascular disease, cancer,
and all-cause mortality. J Am Coll Cardiol. 2013;62(10):921–925.
20. Weyer C, Foley JE, Bogardus C, Tataranni PA, Pratley RE. Enlarged
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
subcutaneous abdominal adipocyte size, but not obesity itself, predicts Type II diabetes independent of insulin resistance. Diabetologia. 2000;43(12):1498 –1506.
Weisberg SP, McCann D, Desai M, Rosenbaum M, Leibel RL, Ferrante AW Jr. Obesity is associated with macrophage accumulation
in adipose tissue. J Clin Invest. 2003;112:1796 –1808.
Apovian CM, Bigornia S, Mott M, et al. Adipose macrophage infiltration is associated with insulin resistance and vascular endothelial dysfunction in obese subjects. Arterioscler Thromb Vasc Biol.
2008;28(9):1654 –1659.
Bremer AA, Jialal I. Adipose tissue dysfunction in nascent metabolic
syndrome. J Obes. 2013;2013:393192.
Shen W, Punyanitya M, Silva AM, et al. Sexual dimorphism of adipose
tissue distribution across the lifespan: a cross-sectional whole-body
magnetic resonance imaging study. Nutr Metab (Lond). 2009;6:17.
Spencer M, Unal R, Zhu B, et al. Adipose tissue extracellular matrix
and vascular abnormalities in obesity and insulin resistance. J Clin
Endocrinol Metab. 2011;96(12):E1990 –E1998.
Divoux A, Tordjman J, Lacasa D, et al. Fibrosis in human adipose
tissue: composition, distribution, and link with lipid metabolism
and fat mass loss. Diabetes 2010; 59(11):2817–2825.
Pasarica M, Gowronska-Kozak B, Burk D, et al. Adipose tissue collagen VI in obesity. J Clin Endocrinol Metab. 2009;94(12):5155–5162.
Divoux A, Clément K. Architecture and the extracellular matrix: the
still unappreciated components of the adipose tissue. Obes Rev.
2011;12(5):e494 – e503.
Duncan MR, Berman B. Stimulation of collagen and glycosaminoglycan production in cultured human adult dermal fibroblasts by
recombinant human interleukin 6. J Invest Dermatol. 1991;97(4):
686 – 692.
Nieman KM, Romero IL, Van Houten B, Lengyel E. Adipose tissue
and adipocytes support tumorigenesis and metastasis. Biochim Biophys Acta. 2013;1831(10):1533–1541.
Dirat B, Bochet L, Dabek M, et al. Cancer-associated adipocytes
exhibit an activated phenotype and contribute to breast cancer invasion. Cancer Res. 2011;71(7):2455–2465.
Furlan A, Fakhran S, Federle MP. Spontaneous abdominal hemorrhage: causes, CT findings, and clinical implications. AJR Am J
Roentgenol. 2009;193(4):1077–1087.
Gealekman O, Guseva N, Hartigan C, et al. Depot-specific differences and insufficient subcutaneous adipose tissue angiogenesis in
human obesity. Circulation. 2011;123(2):186 –194.
Pasarica M, Sereda OR, Redman LM, et al. Reduced adipose tissue
oxygenation in human obesity: evidence for rarefaction, macrophage chemotaxis, and inflammation without an angiogenic response. Diabetes. 2009;58(3):718 –725.
Folkman J. Tumor angiogenesis: therapeutic implications. N Engl
J Med. 1971;285(21):1182–1186.
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(17):1625–1638.
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(23):2211–2219.
Flegal KM, Graubard BI, Williamson DF, Gail MH. Cause-specific
excess deaths associated with underweight, overweight, and obesity.
JAMA. 2007;298(17):2028 –2037.
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(1):71– 82.
Orpana HM, Berthelot JM, Kaplan MS, Feeny DH, McFarland B,
Ross NA. BMI and mortality: results from a national longitudinal
study of Canadian adults. Obesity (Silver Spring). 2010;18(1):214 –
218.