ONLINE SUPPLEMENTARY MATERIAL
Table S1: Summary of selected studies investigating the association between basal fuel oxidation with ageing, adiposity and cardio-metabolic risk factors
Study [Citation]
Study Characteristics
Methodology
Primary Results
Comments
Bergouignan, 2014[1]
Cross sectional; 10 lean (M/F=5/5, mean
age=29y, BMI=21.5 kg/m2), 9 obese
(M/F=4/5, mean age=34y, BMI=33.6 kg/m2), 7
reduced obese sedentary (M/F=3/4, mean
age=34y, BMI=26.9 kg/m2), 12 reduced obese
exercise (M/F=6/6, mean age=33y, BMI=27.3
kg/m2)
IC: Metabolic chamber
BC: ID, BIA
Exercise testing
Measurement of glucose, insulin, FFAs and
triglycerides
Under resting conditions, no differences were found in 24-hr
RQ and FATOx. Twenty-four hour carbohydrate oxidation
was significantly higher in the obese group. Exercise
increased 24-hr FATOx.
Weight-reduced subjects who
exercised regularly had a level of
aerobic fitness and a metabolic
profile that were similar to that
observed in lean individuals. Does
not support the idea of a
mitochondrial defect in FATOx.
Beneficial effects observed
predominantly during sleep
Solomon, 2008[2]
Intervention; 2 groups of older obese allocated
to eucaloric diet plus exercise (group 1, N=12,
age=66y, BMI=34kg/m2) or hypocaloric plus
exercise (group 2, N=11, age=67y,
BMI=34kg/m2). Duration of intervention: 12
weeks
IC: Ventilated Hood
BC: Hydrostatic Weighing
Exercise testing
Insulin sensitivity (clamp) and muscle histology
Improvements in body composition, leptin, and basal fat
oxidation were greater in the hypocaloric group. There was
a correlation between the increase in basal fat oxidation and
the decrease in IMCL. In addition, basal fat oxidation was
associated with circulating leptin after but not before the
intervention
Exercise training improves resting
substrate oxidation and creates a
metabolic milieu that appears to
promote lipid utilization in skeletal
muscle, thus facilitating a reversal of
insulin and leptin resistance.
Solomon, 2008[3]
Cross sectional; 10 older (60y, BMI=31kg/m2)
and 10 young (35y, BMI=32kg/m2) subjects
IC: Ventilated Hood
BC: Hydrostatic Weighing
No differences between groups for RQ and CHOOx. Basal
FATOx adjusted for FFM was lower in older subjects.
FATOx not associated with FM, FFM
or WC
Melanson, 2007[4]
Cross sectional; 7 older (60-75y,
BMI=26kg/m2) and 7 young (20-30y,
BMI=25kg/m2) men
IC: Ventilated hood and metabolic chamber
BC: DXA
Fasting blood samples for measurement of
glucose, A and NA
The 24-h RQ was significantly lower in older subjects.
Sleep RQ did not differ between age groups. Adjusted
CHOOx was not significant between age groups. Adjusted
FATOx was significantly higher in older men
Association of RQ with metabolic
biomarkers not reported. Greater IR
and catecholamine concentrations
may have increased FFAs
bioavailability and increased FATOx
Rizzo, 2005[5]
Cross sectional; 81 women; 3 groups based on
age: adults<65y; aged 66-94y and long-lived
>95y. BMI range: 21.6 – 23.2kg/m2
IC: Face mask
BC: BMI, WHR, SFT
RQ was negatively correlated with age, WHR, FM and
glucose levels. In long lived subjects RQ negatively
associated with FFA
Long lived had higher RQ and gas
exchanges higher than aged subjects.
The indirect association of RQ with
FFA levels is counterintuitive.
Justified by authors by low levels of
circulating FFAs.
Kunz, 2002[6]
Cross sectional; 96 obese and non-obese
subjects (M/F=32/64; 52.5y, Mean BMI: non
obese= 26kg/m2, obese=35kg/m2)
IC: Ventilated Hood
BC: BIA
Significant effects of body FM and sex on basal
FATOx. Habitual fat intake was significantly related to
FATOx
Large variability in FATOx for a given
level of fat intake, which is in part
determined by genetic factors
Davy 2001[7]
Cross sectional; 5 older (63y, mean
weight=82kg) and 10 young (25y, mean
weight=79kg) subjects. Substrate oxidation
after changes in macronutrient composition
IC: Metabolic chamber
Insulin sensitivity by IVGTT
No significant difference in the 24hr NPRQ between young
and older groups. PROOx was higher in the older group. No
difference in CHOOx and FATOx between age groups
Ability to adjust macronutrient
oxidation in response to isocaloric
changes in diet composition was not
impaired in young and older adults.
Levadoux, 2001[8]
Cross sectional; 20 older (>60y,
BMI=25kg/m2) and 20 young (<35y,
BMI=23kg/m2)
IC: Metabolic chamber
BC: ID, BIA
Exercise testing
Measurement of glucose, insulin, FFAs and
triglycerides
Fat and protein oxidation lower in women during sleeping.
FATOx was lower in older subjects during sleeping and 24hr.
Sleeping FATOx was correlated with VO2Max, FFM and
SMR adjustment for FFM and energy balance removed
association between age and FATOx.
Differences between results could be
to different adjustment of metabolic
variables
Ageing and Adiposity
Soares, 2000[9]
Cross sectional; 76 subjects, M/F=42/34; 28
older (>50y, BMI=26kg/m2) and 48 young (1835y, BMI=23kg/m2) men
IC: Ventilated Hood
BC: ID, DXA
Plasma leptin
There were no differences in RQ between gender and age
groups. No association between RQ and leptin in the whole
sample whereas a significant indirect association was found
in older men
Leptin may be involved in lipid
partitioning in older subjects
Weyer, 1999[10]
Cross sectional; 916 non-diabetic subjects
(M/F=561/355; 31.5y, weight range= 42.2215.2)
IC: Metabolic chamber
BC: Hydrostatic Weighing, DXA
24-hr RQ did not differ between males and females. WC
and percent body fat significant determinants of 24-hr RQ.
Basal substrate oxidation was not
reported
Toth, 1999[11]
Cross sectional; 59 middle aged
premenopausal women (age range: 47y, weight
range=47-104kg)
IC: Ventilated Hood
BC: DXA, BIA
Exercise Testing
Measurement of glucose, insulin, FFAs and
triglycerides, leptin
Significant correlations of REE and substrate oxidation to
fat-free mass, appendicular skeletal muscle mass, and leptin.
Leptin a predictor of CHOOx. Positive association of FATOx
with REE
FATOx may be determined by the
basal energy needs of the
metabolically active tissue. Leptin
may regulate CHOOx by affecting
hepatic glycogenolysis, peripheral
glucose utilization, or both.
Marra, 1998[12]
Longitudinal; 58 women (age range: 40y, mean
BMI=24.7kg/m2).
IC: Ventilated Hood
Changes in body weight after 3 years
Baseline RQ was not related to age, weight or BMI. Age
and RQ at baseline significantly predicted changes in either
weight or BMI.
Energy balance and diet composition
prior to RQ measurement not
controlled; hypothesis of defective
fat oxidation.
Toth, 1998[13]
Cross sectional; 12 older men (70y,
weight=73kg) and 12 older women (66y,
weight=65kg)
IC: Ventilated Hood
Measurement of FFAs availability and
noradrenaline appearance rate
FATOx was higher in men at rest compared with women and
remained higher after statistical control for resting oxygen
consumption. No gender differences in RQ were found
during exercise. These differences were independent of
noradrenaline appearance rate, FFAs availability
The role of gender as a modifier of
substrate oxidation and interaction
with ageing is still an open question
for metabolic research
Valtuena, 1997[14]
Longitudinal; 36 obese subjects (M/F=1/35;
37.2y, mean BMI=44.0kg/m2). Follow-up of 3,
6 and 12 months
IC: Ventilated Hood
BC: Hydrostatic Weighing
RQ was negatively associated with BMI and maximal
lifetime weight. High RQ was a significant predictor of
weight regain at 12 months. Basal RQ was lower in men
compared to women.
Hypotheses: patients with higher
lipolytic activity could be better able
to increase FATOx; an increased rate
of CHOOx/ FATOx may decrease
glycogen stores resulting in increased
appetite; a higher RQ could reflect a
deviation from the steady-state
Horber, 1996[15]
Cross sectional; 119 subjects (M/F=60/59, age
range: 20-81y, mean BMI=23.5kg/m2).
IC: Ventilated Hood (urine collection for
measurement of N excretion)
BC: DXA
Analyses stratified by age groups (young vs
older)
No differences between groups for RQ, PROOx and CHOOx.
FATOx adjusted for FFM was lower in older males. FATOx
adjusted for FFM decreased with increasing age in males
but not females. FATOx adjusted for FFM increased with FM
in females but not in males.
Dimorphism in adaptation of FATOx
with FM proposed as a mechanism
for greater propensity of males to
gain weight.
Nagy, 1996[16]
Cross sectional; 720 subjects (M/F=427/293,
age range: 17-90y, weight range=38.4 –
132.2kg)
IC: Ventilated Hood
BC: Hydrostatic Weighing
Measurement of glucose, insulin and
triglycerides
FATOx was negatively associated with age in both males and
females. It was also negatively correlated with FM, fasting
insulin and triglycerides and positively with FFM. In
multiple regression model, FATOx was predicted by Peak
VO2, FFM, thyroxin, and fasting insulin
RQ not reported. Fat oxidation not
greater in obese subjects. One of the
few studies to report a significant
role of FFM.
Rising, 1996[17]
Longitudinal; 7 non-diabetic Pima Indian men
(mean age=31y, mean weight= 111kg)
IC: Metabolic chamber
Over a 7-year period, mean unadjusted and adjusted 24-hour
RQ increased
Reduced fat utilization and decreased
BMR with age may both contribute
to increasing obesity in older
individuals
Roberts, 1996[18]
Overfeeding study; 9 older men (70y,
weight=73kg) and 7 older women (24y,
weight=76kg). Primary outcome: difference in
TEF; fasting and post-prandial RQ reported
IC: Face mask
BC: ID
There was no significant difference between age groups in
the change in TEF. There was a significant effect of agegroup on the fasting RQ (values for young men were lower),
and the increase in RQ following consumption of the test
meals was significantly lower in the older group.
Differences driven potentially by
lower activation of sympathetic tone.
Older individuals experience a
decreased ability to dissipate
excess energy intake during
overeating
Calles Escandon, 1995[19]
Cross sectional; 32 women, age range: 18-73y,
weight range=44 – 103kg)
IC: Ventilated Hood
BC: Hydrostatic Weighing
Measurement of glucose, FFAs and insulin
FATOx was negatively associated with age but positively
associated with FFM and aerobic fitness. No relationship
was found with FM and metabolic biomarkers.
Residual approach to test whether
FFM was main driver of FATOx. This
approach removed the association of
age and fitness with FATOx, which is
therefore explained by age related
decline in FFM
Astrup, 1994[20]
Cross sectional; 38 obese women (mean
age=34y; mean weight=93kg) and 35 lean
women (mean age=33y; mean weight=60kg)
IC: Metabolic chamber
BC: BIA
Obese women exhibited lower 24hr RQ. Positive association
between EE, FM and FATOx. Association between FM and
FATOx remained significant after adjustment for dietary fat,
age and EB. Basal RQ was again higher in obese women
and basal FATOx was positively correlated to FM.
For each 10-kg increase in FM there
is an expected increase in FATOx that
averages 11 g/d.
Seidell, 1992[21]
Longitudinal (BLSA); 775 men (age 18-98).
Follow up: 10 years
IC: Ventilated Hood
BC: weight change
RQ, but not REE, was positively related to weight change.
Major weight gain (from at least 5 kg to at least 15 kg) was
related to initial RQ in non-obese men.
Relatively high fasting RQ was a
weak but significant predictor of
substantial weight gain in non-obese
white men
Schutz, 1992[22]
Study 1 (cross sectional): 106 women divided
by FM (low FM: weight=71kg, FM=25kg;
high FM: weight=91kg, FM=38kg).
Study 2 (WL study): 24 obese, age=59y, preweight=74kg, post-weight=61kg.
IC: Ventilated Hood
BC: BIA
Study 1: FM was significantly associated with RQ (inverse)
and FATOx (direct)
Study 2: Fat oxidation fell an average of 42%
FATOx increases with changes in
body fat (gain, loss). For each 10-kg
increase in FM there is an expected
increase in FATOx that averages 20
g/d.
Zurlo, 1990[23]
Longitudinal; 152 Pima Indians (M/F=87/65,
age: 27y, mean weight=93.9kg). Mean follow
up: 25 months
IC: Metabolic chamber
BC: Hydrostatic Weighing
High 24-h RQ was significantly correlated with subsequent
increase in body weight and FM. Effect was independent of
energy expenditure
Family membership was the principal
determinant of the ratio of fat to
carbohydrate oxidation.
Cardio-Metabolic Risk Factors
Montalcini, 2014[24]
Cross sectional; 40 unrelated obese subjects;
20 affected by hypertriglyceridemia (mean
age=49y, mean BMI=35.7kg/m2) and 20
unaffected (mean age=49y, mean
BMI=36.3kg/m2)
IC: Ventilated Hood
Measurement of glucose, lipids, insulin
High TG levels are associated with greater RQ
Suggested role of insulin in
suppressing lipid oxidation
Montalcini, 2014[25]
Cross sectional; 35 overweight/obese subjects
(M/F=8/27, mean BMI=32kg/m2, mean
age=52y)
IC: Ventilated Hood
Ultrasound for assessment of LVCR and CIMT
RQ was higher in subjects with LVCR and high CIMT
The area under the receiver operating
characteristic (ROC) curve for RQ to
predict LVCR was 0.72
Galgani, 2014[26]
Cross sectional; 30 subjects (M/F=15/15, mean
age=35y, mean BMI=27kg/m2)
IC: Ventilated hood and metabolic chamber
BC: DXA
OGTT and euglycemic–hyperinsulinemic clamp:
Si and Beta Cell Function
Fasting non-protein RQs did not correlate with insulin
sensitivity but correlated with insulin rate sensitivity and
insulin secretion rate (i.e., 24-hr urinary C peptide
excretion)
Insulin secretion rate estimated from
urinary C-peptide was related to
carbohydrate balance and oxidation
Croci, 2013[27]
Cross sectional; 20 overweight with NAFLD
(M/F=12/8, age=48, BMI=34kg/m2) and 15
lean healthy controls (M/F=10/5, age=41,
BMI=23kg/m2)
IC: Ventilated Hood
BC: DXA, CT scan
Insulin Sensitivity (Clamp)
Graded exercise test
Ketone bodies, TG
NAFLD patients oxidised less fat compared to controls and
confirmed by a greater basal RQ in the NAFLD group.
Basal RQ directly correlated with liver steatosis but not with
visceral fat, FM or BMI of ketone bodies. Reduced basal
hepatic FATOx in NAFLD was associated with increased
fasting circulating TG.
NAFLD patients demonstrated
metabolic inflexibility, which was
defined as an impaired capacity to
adapt fuel oxidation to fuel
availability. Also a reduced ability to
increase fat oxidation during an acute
exercise session
Montalcini, 2013[28]
Cross sectional; 132 subjects (M/F=52/70,
mean age=48y, mean BMI=33.6kg/m2)
IC: Ventilated Hood
Ultrasound for assessment CIMT
RQ was significantly associated with CIMT. RQ was not
associated with age, WC and systolic BP
Role of insulin as a modulator of the
effects on intermediate metabolism
and connection with atherosclerosis
pathogenesis
Korenaga, K[29]
Cross sectional; 32 patients with NAFLD
(M/F=24/8; age=45y, BMI=27kg/m2)
IC: Ventilated Hood
Fibrosis associated with a decrease in basal RQ; RQ
Suggestion of use of RQ as marker of
OGTT
negatively associated with glucose AUC
disease severity
Liver biopsy
Ferro, 2013[30]
Cross sectional; 223 subjects (M/F=90/133,
IC: Ventilated Hood
In multivariate model, RQ only correlated with Systolic BP.
RQ not associated with indexes of
mean age=53y, mean BMI=31.5kg/m2)
Assessment of MetSyn and individual
RQ>0.90 greater prevalence of hypertension
adiposity and age.
biomarkers (WC, GLU, HDL, TG, Systolic BP,
Diastolic BP)
M= male; F= female; BMI= body mass index; IC= indirect calorimetry; BC= body composition; ID= isotope dilution; BIA= bioelectrical impedance; FFA= free fatty acids; RQ= respiratory quotient; FATOx= fat oxidation; CHOOx =
carbohydrate oxidation; FM= fat mass; FFM= fat free mass; WC= waist circumference; DXA= dual x ray absorptiometry; A= adrenaline; NA= nor-adrenaline; IR= insulin resistance; WHR= wait hip ratio; SFT= skinfold thickness; IVGTT=
intra venous glucose tolerance test; PROOx = protein oxidation; SMR = sleeping metabolic rate; REE = resting energy expenditure; TEF = thermic effect of food; EE = energy expenditure; EB= energy balance; TG = triglycerides; CIMT=
carotid intima media thickness; LVCR= Left ventricular concentric remodelling; OGTT= oral glucose tolerance test; NAFLD = non-alcoholic fatty liver disease; BP = blood pressure; GLU = glucose; HDL = high density lipoproteins;
IMCL= intra myocellular lipids; OGTT = oral glucose tolerance test.
Table S2: Description of the main characteristics of the study population stratified by sex (male, female) and age (18-39y, 40-59y and ≥60y)
All
Female
18-39y
40-59y
≥60y
18-39y
40-59y
≥60y
992
1463
364
733
963
229
N
Smoking
Yes
No
Former
BMI Category (N)
Normal Weight
Overweight
Obese
Male
18-39y
259
40-59y
500
≥60y
135
P Value
p<0.001
292
551
149
241
834
388
54
179
131
207
420
106
152
589
222
35
137
57
85
131
43
89
245
166
19
61
135
304
375
313
304
589
570
42
119
203
273
256
204
268
395
300
36
81
112
31
119
109
36
194
270
6
38
91
Disease Count (N)
1.5 (1.4)
2.3 (1.8)
3.7 (2.2)
1.4 (1.3)
2.3 (1.8)
3.7 (2.2)
1.6 (1.4)
2.5 (1.7)
3.7 (2.0)
PAL (METs/week)*
1062 (297, 2491)
876 (198, 1986)
1074 (297, 2612)
990 (279, 2346)
838 (198, 1980)
1032 (297, 2520)
1356 (396, 3012)
918 (199, 2023)
1158 (297, 2655)
FM (kg)
27.21 (11.87)
28.92 (11.42)
32.92 (11.23)
27.24 (11.61)
28.68 (11.10)
32.98 (11.20)
27.33 (12.49)
29.52 (12.07)
32.99 (11.45)
FFM (kg)
51.64 (11.26)
52.82 (11.67)
50.44 (11.17)
46.18 (5.63)
45.91 (5.81)
43.30 (5.19)
66.97 (8.45)
66.14 (7.99)
62.48 (7.48)
Height (cm)
166.4 (8.7)
166.3 (9.0)
163.5 (9.0)
162.8 (6.0)
161.6 (6.3)
158.2 (5.7)
176.6 (6.6)
175.3 (6.2)
172.6 (6.0)
Mediterranean Diet ScoreA
6.1 (1.63)
N=826
6.6 (1.65)
N=1208
7.6 (1.61)
N=259
6.1 (1.6)
N=609
6.6 (1.6)
N=789
7.6 (1.6)
N=149
6.1 (1.6)
N=217
6.5 (1.7)
N=419
7.5 (1.6)
N=110
921
71
-
1385
78
338
26
-
683
50
-
919
44
208
21
-
238
21
-
466
34
-
130
5
-
Dieting
No
Yes
Menopause
No
Yes
-
0.003
A: p<0.001
S: p<0.001
A*S: 0.80
A: p<0.001
S: 0.04
A*S: 0.01
A: p<0.001
S: 0.56
A*S: 0.71
A: p<0.001
S: p<0.001
A*S: 0.16
A: p<0.001
S: p<0.001
A*S: 0.65
A: p<0.001
S: p=0.60
A*S: 0.85
Female: p=025
Male: p=016
-
624
338
Mean (SD) presented for normally distributed variables. Median (upper and lower quartiles) presented from not normally distributed variables. N= number of subjects; BMI= body mass index; PAL= physical activity level; MET= metabolic equivalent time. FM= fat mass;
FFM= fat free mass; *log transformed before analyses. A= Age; S= Sex; A*S= Age*Sex Interaction term. A Sample size was different from main analyses due to missing data for this variable .
Table S3: Multiple linear regression to identify predictors of total (fat mass index) and segmental adiposity (pre-peritoneal and abdominal
visceral and sub-cutaneous fat) measured by bioelectrical impedance and ultrasonography, respectively.
Fat Mass Index (kg/m2)
VAT (cm)*
SAT (cm)*
b
SE
P
b
SE
P
b
SE
p-value
0.34
0.48
0.10
R²
-13.089
1.199
-1.149
0.124
-0.509
0.152
Intercept
<0.001
<0.001
<0.001
0.054
0.006
0.011
0.001
-0.003
0.001
Age (years)
<0.001
<0.001
<0.001
-6.726
0.214
0.067
0.022
-0.362
0.027
Sex (Male, Female)
<0.001
<0.001
<0.001
0.043
0.104
0.67
-0.004
0.011
0.67
-0.008
0.013
0.53
Smoking (yes, no)
-1.803
1.203
0.13
0.088
0.124
0.47
0.163
0.153
0.28
RQ
0.302
0.040
0.030
0.004
0.015
0.005
Disease Count (n)
<0.001
<0.001
<0.001
-0.016
0.003
-0.001
0.0003
-0.002
0.0004
PAL (METs/week)*
<0.001
<0.001
<0.001
1.306
0.040
0.111
0.004
0.086
0.005
FFMI (kg/m2)
<0.001
<0.001
<0.001
B= raw regression coefficient; SE= standard error; R 2= coefficient of determination; FMI= fat mass index; FFMI= fat free mass index; RQ= respiratory quotient;
PAL= physical activity level; MET= metabolic equivalent time; SAT= sub cutaneous adipose tissue; VAT= visceral adipose tissue. Significant results are
highlighted in bold.
*log transformed before analyses.
Figure S2: Probability of metabolic syndrome (MetSyn) with fat free mass index (FFMI). Logistic models were represented unadjusted (dotted line, ∙ ∙ ∙ ∙ ∙ ∙ ∙ ),
partially adjusted for lifestyle factors and health status [age, smoking, disease count and physical activity (solid black like, ───)] and fully adjusted for lifestyle
factors and health status and metabolic (RQ, REE) and body composition variables [fat free mass index (FFMI), FMI, visceral and subcutaneous adiposity (solid
grey line, ───)]. Bubble plots were used to indicate the frequency of distribution of each independent variable in the two metabolic syndrome groups (0= no
metabolic syndrome; 1= metabolic syndrome).
Table S4: Multiple linear regression to identify lifestyle and body composition predictors of cardiovascular risk factors and cumulative metabolic risk (N=2293)
Glucose
Systolic BP
Diastolic BP
HDL
Triglycerides
(mg/dL)*
(mmHg)*
(mmHg)*
(mg/dL)*
(mg/dL)*
b
SE
P
b
SE
P
SE
P
0.33
b
SE
P
0.35
b
Z Score
SE
P
0.24
b
SE
P
R²
0.23
Intercept
4.20
0.04
<0.001
4.39
0.03
<0.001
3.93
0.04
<0.001
4.64
0.08
<0.001
3.17
0.17
<0.001
-13.62
0.90
<0.001
Age (years)
0.002
0.0002
<0.001
0.002
0.0002
<0.001
0.002
0.0002
<0.001
0.002
0.0004
<0.001
0.002
0.001
<0.001
0.05
0.005
<0.001
Sex (Male, Female)
0.009
0.01
0.35
0.02
0.007
0.004
0.04
0.009
<0.001
-0.15
0.01
<0.001
0.10
0.04
0.007
1.20
0.20
<0.001
Smoking (yes, no)
0.007
0.003
0.07
-0.005
0.002
0.08
-0.002
0.003
0.52
0.02
0.006
<0.001
-0.04
0.01
0.002
-0.20
0.07
<0.001
Mediterranean Diet Score
0.009
0.001
0.56
-0.0004
0.001
0.71
-0.001
0.001
0.14
-0.001
0.002
0.66
0.009
0.005
0.10
0.005
0.02
0.85
Dieting (yes, no)
0.002
0.001
0.84
0.006
0.007
0.44
-0.003
0.009
0.67
0.04
0.03
0.06
0.0001
0.03
0.99
-0.11
0.20
0.56
RQ
-0.07
0.04
0.12
-0.007
0.03
0.81
0.01
0.03
0.68
-0.002
0.08
0.97
0.32
0.12
0.05
0.86
0.56
0.56
REE (kcal/day)
0.0005
0.00001
0.001
0.0001
0.00001
<0.001
0.00010
0.00001
<0.001
-0.000001
0.00003
0.63
0.0002
0.0007
0.003
0.003
0.0003
<0.001
Disease Count (n)
0.006
0.001
<0.001
0.003
0.001
<0.001
0.002
0.001
0.11
-0.007
0.002
0.01
0.03
0.005
<0.001
0.17
0.03
<0.001
PAL (METs/week)*
-0.0002
0.00009
0.01
0.0001
0.00007
0.04
0.00001
0.00009
0.55
0.0002
0.0002
0.38
0.0002
0.0003
0.49
0.0003
0.0002
0.86
FFMI (kg/m2)
0.005
0.002
0.007
0.001
0.001
0.33
0.002
0.0001
0.14
-0.02
0.003
<0.001
-0.001
0.01
0.87
0.15
0.04
<0.001
FMI (kg/m2)
0.002
0.009
0.01
0.003
0.0007
<0.001
0.004
0.0007
<0.001
-0.002
0.003
0.10
-0.005
0.003
0.16
0.08
0.01
<0.001
VAT (cm)
0.03
0.008
<0.001
0.01
0.006
0.02
0.01
0.007
0.06
-0.08
0.01
<0.001
0.27
0.03
<0.001
1.19
0.15
<0.001
SAT (cm)*
-0.01
0.006
0.04
0.009
0.004
0.07
0.02
0.005
<0.001
-0.003
0.02
0.66
0.05
0.02
0.03
0.10
0.12
0.41
*
0.38
b
Metabolic Risk
0.53
B= raw regression coefficient; SE= standard error; R2= coefficient of determination; REE= resting energy expenditure; FMI= fat mass index; FFMI= fat free mass index; RQ= respiratory quotient; BP= blood pressure;
PAL= physical activity level; MET= metabolic equivalent time; SAT= sub cutaneous adipose tissue; VAT= visceral adipose tissue. Significant results are highlighted in bold.
*log transformed before analyses.
Table S5: Logistic regression to evaluate the risk for Metabolic Syndrome (dependent variable, binary) associated with metabolic and body composition measurements (N=2293)
β (SE)
OR (95% CI)
P value
RQ
0.24 (0.99)
1.27 (0.18 – 8.98)
0.80
REE (kcal/day)
0.001 (0.004)
1.00 (1.00 – 1.01)
0.001
0.07 (0.02)
1.07 (1.03 – 1.12)
<0.001
Model
A
2
FMI (kg/m )
0.10 (0.04)
1.11 (1.01 – 1.21)
0.02
*
1.39 (0.20)
4.05 (2.73 – 6.01)
<0.001
*
0.18 (0.16)
1.20 (0.87 – 1.67)
0.72
2
FFMI (kg/m )
VAT (cm)
SAT (cm)
β= Regression Coefficient; SE= standard error; OR= odds ratio (95% Confidence Intervals); FMI= fat mass index; FFMI= fat free mass index; RQ= respiratory quotient; REE= resting energy expenditure; SAT= sub cutaneous adipose
tissue; VAT= visceral adipose tissue. Significant results are highlighted in bold. *log transformed before analyses. AModel adjusted for age, sex, smoking, disease count, Mediterranean Diet Score, dieting and physical activity level.
Table S6: Logistic regression to evaluate the risk for Metabolic Syndrome (dependent variable, binary) in middle aged women (N=962, 40-59y) associated with metabolic and body
composition measurements
β (SE)
OR (95% CI)
P value
ModelA
RQ
REE (kcal/day)
2
FMI (kg/m )
1.27 (0.18 – 8.98)
0.80
0.002 (0.0007)
1.00 (1.00 – 1.01)
0.002
0.03 (0.03)
1.03 (0.97 – 1.10)
0.26
0.02 (0.07)
1.02 (0.87 – 1.19)
0.76
*
2.01 (0.30)
7.46 (4.09– 13.60)
<0.001
*
0.22 (0.28)
1.25 (0.72 – 2.17)
0.72
2
FFMI (kg/m )
VAT (cm)
SAT (cm)
0.82 (0.99)
β= Regression Coefficient; SE= standard error; OR= odds ratio (95% Confidence Intervals); FMI= fat mass index; FFMI= fat free mass index; RQ= respiratory quotient; REE= resting energy expenditure; SAT= sub cutaneous adipose
tissue; VAT= visceral adipose tissue. Significant results are highlighted in bold. *log transformed before analyses. AModel adjusted for age, sex, smoking, disease count, Mediterranean Diet Score, dieting, menopausal status, and physical
activity level.
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