Diabetes Care
1
Association Between
Cardiorespiratory Fitness and the
Determinants of Glycemic Control
Across the Entire Glucose
Tolerance Continuum
Thomas P.J. Solomon,1,2 Steven K. Malin,3
Kristian Karstoft,2 Sine H. Knudsen,2
Jacob M. Haus,4 Matthew J. Laye,2 and
John P. Kirwan3,5
DOI: 10.2337/dc14-2813
OBJECTIVE
Cardiorespiratory fitness (VO2max) is associated with glycemic control, yet the relationship between VO2max and the underlying determinants of glycemic control is less clear.
Our aim was to determine whether VO2max is associated with insulin sensitivity, insulin
secretion, and the disposition index, a measure of compensatory pancreatic b-cell
insulin secretion relative to insulin sensitivity, in subjects representing the entire range
of the glucose tolerance continuum.
RESEARCH DESIGN AND METHODS
RESULTS
A low VO2max was associated with high HbA1c (r = 20.33), high fasting glucose
(r = 20.34), high 2-h OGTT glucose (r = 20.33), low SiOGTT (r = 0.73), and high
early-phase (r = 20.34) and late-phase (r = 20.36) GSISOGTT. Furthermore, a low
VO2max was associated with low early- and late-phase DIOGTT (both r = 0.41).
Interestingly, relationships between VO2max and either glycemic control or latephase GSISOGTT deteriorated across the glucose tolerance continuum.
CONCLUSIONS
The association between poor cardiorespiratory fitness and compromised pancreatic b-cell compensation across the entire glucose tolerance continuum provides additional evidence highlighting the importance of fitness in protection
against the onset of a fundamental pathophysiological event that leads to type
2 diabetes.
Type 2 diabetes (T2D) is characterized by chronic hyperglycemia that develops when
pancreatic b-cell insulin secretion fails to compensate for the deterioration in insulin
sensitivity (1). Physical activity aimed at improving cardiorespiratory fitness is prescribed as part of standard-of-care treatment for T2D (2), primarily because
1
Department of Biomedical Sciences, Panum Institute, University of Copenhagen, Copenhagen,
Denmark
2
Center of Inflammation and Metabolism and
Center for Physical Activity Research, Rigshospitalet, Copenhagen, Denmark
3
Department of Pathobiology, Lerner Research
Institute, Cleveland Clinic, Cleveland, OH
4
Department of Kinesiology and Nutrition, University of Illinois at Chicago, IL
5
Metabolic Translational Research Center, Endocrinology & Metabolism Institute, Cleveland
Clinic, Cleveland, OH
Corresponding author: Thomas P.J. Solomon,
[email protected].
Received 27 November 2014 and accepted 7
February 2015.
© 2015 by the American Diabetes Association.
Readers may use this article as long as the work
is properly cited, the use is educational and not
for profit, and the work is not altered.
Diabetes Care Publish Ahead of Print, published online March 17, 2015
CARDIOVASCULAR AND METABOLIC RISK
A cohort of subjects (N = 313) with heterogeneous age, sex, BMI, and glycemic control
underwent measurements of body composition, HbA1c, fasting glucose, oral glucose
tolerance (OGTT), and VO2max. OGTT-derived insulin sensitivity (SiOGTT), glucosestimulated insulin secretion (GSISOGTT), and the disposition index (DIOGTT) (the product of SiOGTT and GSISOGTT) were measured, and associations between VO2max and
these determinants of glycemic control were examined.
2
VO2max and Glycemic Control
randomized controlled clinical trials
show that exercise training reduces hyperglycemia in patients with T2D (3–5)
and delays the onset of T2D in at-risk
individuals (6). Interestingly, in a longitudinal study of 8,633 nondiabetic
men, Blair and colleagues showed that
high cardiorespiratory fitness (as determined by maximal oxygen consumption
[VO2max], measured during exhaustive
incremental workload exercise) confers
protection against developing T2Drelated hyperglycemia (7). A further
longitudinal study by Church et al. (8),
examining 2,316 men with T2D, reported that high cardiorespiratory fitness reduced cardiovascular disease
mortality. Consequently, poor fitness
is considered a key determinant of
the pathophysiological progression of
glucose intolerance. However, because poor glucose disposition, driven
by inadequate b-cell insulin secretory
function in the presence of poor insulin sensitivity, is the fundamental
cause of hyperglycemia in T2D, it is
prudent to determine whether cardiorespiratory fitness is related to these
pathophysiological factors. Indeed, we
and others have shown that aerobic
exercise training that improves cardiorespiratory fitness also increases insulin sensitivity (9–14) and improves
b-cell insulin secretory function
(10,14,15) in patients with T2D. Nonetheless, whether the predictive value
of cardiorespiratory fitness for determining longitudinal glycemic control is
explained by an association between
fitness and the underlying determinants of glycemic control (insulin sensitivity and/or insulin secretory function)
is not clear.
With the a priori knowledge (9–16)
that exercise training improves VO2max
and b-cell insulin secretory compensation for changing insulin sensitivity (the
glucose disposition index) and the evidence that both variables are reduced in
normoglycemic first-degree relatives of
T2D patients (17), we hypothesized that
low cardiorespiratory fitness would be
associated with low disposition index,
the underlying pathophysiological determinant of glucose intolerance. Therefore, our aim was to examine this
relationship in a large cohort representing the entire glucose tolerance continuum from normal glucose tolerance
(NGT) to T2D.
Diabetes Care
RESEARCH DESIGN AND METHODS
Subjects
Potential participants underwent medical
screening to determine their eligibility for
the study. This included a medical history
assessment, an electrocardiogram, and
blood chemistry screening. Evidence of
prior or current chronic pulmonary, hepatic, renal, gastrointestinal, or hematological disease; weight loss (.2 kg in the
last 6 months); smoking; pregnancy; and
contraindication to an exercise test were
used as exclusion criteria. Subjects were
recruited by newspaper/radio advertisement from the local municipal areas in
Copenhagen, Denmark, and Cleveland,
OH. All subjects provided oral and written
informed consent prior to participation,
and the methods were approved by
ethics committees at both locations (Institutional Review Board, Cleveland
Clinic, and Scientific Ethics Committee of
the Capital Region of Denmark). Data
from some subjects participating in this
work have previously been published
(3,9,10,18,19). Subjects included in the
study were stratified by glucose tolerance
status (i.e., NGT, impaired glucose tolerance, or T2D) according to their 2-h glucose on an oral glucose tolerance test
(OGTT) (2). The final study cohort included N = 313 subjects.
Pretest Control Period
Tests took place at the Clinical Research
Unit at the Cleveland Clinic and at the
clinical research laboratory of the Centre of Inflammation and Metabolism,
Rigshospitalet. Subjects being treated
with antidiabetic drugs (metformin,
N = 38; sulfonylureas, N = 14; GLP-1 analogs, N = 12; and dipeptidyl peptidase-4
inhibitors, N = 2) withheld their medications for 5 days prior to metabolic
testing, during which time diet and
physical activity records were used to
ensure there were no pretesting variations in dietary or activity habits. Subjects also abstained from structured
exercise for at least 24 h prior to metabolic testing.
Clinical Procedures
Height and weight were measured using
standard techniques. Whole-body adiposity was estimated using DEXA (Lunar
iDXA; GE Healthcare, Madison, WI). Subjects performed an exercise test to determine their VO2max during exhaustive
incremental workload exercise to
volitional exhaustion, which provides a
measure of cardiorespiratory fitness.
Heart rate was measured using a chestworn monitor (Polar Electro, Kempele,
Finland) and VO2 was measured online
using an automated system (Oxycon Pro,
Erich Jaeger, Hoechberg, Germany;
CPET, Cosmed, Rome, Italy). VO 2max
was determined when at least two of
the following criteria were met: respiratory exchange ratios .1.1; no further
increase in VO2 despite increasing workloads, with heart rate greater than
age-predicted maximum; or volitional fatigue. The VO2max test was conducted
48–72 h prior to subsequent metabolic
assessments to avoid the acute effects of
physical exercise on glycemic control.
On a separate day, after an 8- to 10-h
overnight fast, subjects came to the laboratory and an antecubital venous cannula was placed. Baseline blood samples
were collected and subjects ingested
75 g anhydrous glucose dissolved in
300 mL water (standard OGTT). After
glucose ingestion, venous blood samples were collected every 15 min for
2 h. Venous blood was collected into
tubes containing EDTA and 500 kiU/mL
aprotinin (serine protease inhibitor;
Sigma) or into plain tubes containing
no anticoagulant. Plain tubes were allowed to clot for 30 min at room temperature, while EDTA tubes were kept
on ice. Blood was centrifuged at 2,000g
for 15 min at room temperature, and
respective serum/plasma was stored
at 2808C until analysis.
Blood Chemistry Analysis
Glucose concentrations were measured
using a bedside analyzer (YSI Stat, Yellow
Springs; ABL, Radiometer Medical,
Brønshøj, Denmark); insulin and Cpeptide concentrations were determined by electrochemiluminescence
immunoassay (E-modular; Roche, Basel,
Switzerland); total cholesterol and triglycerides were determined by colorimetric
assays (P-modular; Roche). A further
venous blood sample was collected
into an EDTA tube so that hemoglobin
A1c (HbA1c) levels could be determined
by high-performance liquid chromatography (HPLC) (Tosoh G7 analyzer; San
Francisco, CA).
Calculations
Insulin sensitivity during OGTT (SiOGTT)
was calculated using a model previously
care.diabetesjournals.org
Solomon and Associates
validated against the gold-standard
hyperinsulinemic-euglycemic clamp
method (19): Si OGTT (mmol/kg/min/
pmol z L) = 0.138 2 (0.00231 3 BMI) 2
(0.00118 3 G120) 2 (0.0000135 3 I30) 2
(0.00000678 3 I90), where BMI is measured as weight in kilograms divided by
the square of height in meters, G120 is
glucose at 120 min, and I30 and I90 are
insulin at 30 and 90 min during OGTT.
GSIS OGTT was determined from the
OGTT plasma C-peptide response rather
than the insulin response to overcome
the confounding effects of hepatic insulin extraction, which removes up to 80%
of insulin at first pass prior to measurement in the peripheral venous blood
(20). Early- and late-phase oral glucose–
stimulated insulin secretion (GSISOGTT)
was calculated as the area under the
C-peptide curve from 0 to 30 and 30 to
120 min during OGTT, respectively. We
previously showed that the log-log relationship between these measures of
GSISOGTT and SiOGTT is described by an
inverse linear model (19); therefore,
the oral disposition index (DI OGTT ),
which is a measure of glucose disposal
and estimates pancreatic b-cell insulin
secretory compensation for changing
insulin sensitivity during OGTT, was
calculated as the product of GSISOGTT
and SiOGTT.
Statistics
Relationships between VO 2max and
measures of glycemic control (HbA1c,
fasting glucose, and 2-h OGTT glucose)
and GSISOGTT were best fit by log-log
curvilinear regression. Relationships
between VO 2max and measures of
SiOGTT and DIOGTT were best fit by linear
regression. For testing of the difference
between correlation coefficients, r values were converted to z scores using
Fisher r-to-z transformation and were
then compared using the Cohen test
(21). Forced-entry multiple regression
was used to determine the relative contributions of VO2max and other independent variables (age, sex, body weight,
BMI, and whole-body fat percentage)
to the variance in glycemic control
(HbA1c, fasting glucose, 2-h OGTT glucose), Si OGTT , GSIS OGTT , and DI OGTT .
One-way ANOVA and Bonferroni post
hoc tests were used to assess differences between glucose tolerance groups.
Statistical significance was set at P ,
0.05 and determined using Prism v6
(GraphPad, La Jolla, CA) and SPSS v20
(IBM, New York, NY).
RESULTS
Glycemic Control, Insulin Sensitivity,
Insulin Secretion, and Disposition
Index
Table 1 shows subject characteristics
stratified by glucose tolerance status. Glycemic control (HbA1c, fasting glucose, and
2-h glucose) was progressively reduced
across the glucose tolerance continuum.
SiOGTT was markedly lower in subjects
with impaired glucose tolerance and
T2D compared with those with NGT.
Early- and late-phase GSIS OGTT were
greatest in impaired glucose tolerant
subjects, and lowest in the T2D group.
Early- and late-phase DI OGTT showed
progressive deterioration across the
glucose tolerance groups.
Relationships With Cardiorespiratory
Fitness (VO2max)
Table 2 and Figs. 1 and 2 show the correlation coefficients for the associations between VO2max and glycemic control,
SiOGTT, GSISOGTT, and DIOGTT. VO2max was
inversely associated with clinical markers
of glycemic control: HbA1c (Fig. 1A), fasting glucose (Fig. 1B), and 2-h glucose during OGTT (Fig. 1C); however, these
relationships were strongest in NGT subjects, while significant correlations were
Table 1—Characteristics of subjects representing the glucose tolerance continuum
N (male/female)
Age (years)
Weight (kg)
NGT
IGT
T2D
137 6 84/53
85 6 44/41
91 6 44/47
51 6 1
82.7 6 1.3
61 6 1
93.9 6 1.7
59 6 1
87.9 6 1.6
PNGT vs. T2D
PIGT vs. T2D
d
d
d
,0.001
,0.0001
,0.001
,0.05
ns
,0.05
PNGT vs. IGT
BMI (kg/m2)
26.8 6 0.4
32.5 6 0.3
30.6 6 0.6
,0.001
,0.001
,0.05
Adiposity (%)
28.6 6 1.0
38.2 6 1.2
37.1 6 1.1
,0.001
,0.001
ns
VO2max (mL/kg/min)
37.0 6 1.1
25.1 6 0.9
26.2 6 0.8
,0.0001
,0.0001
ns
VO2max (L/min)
3.02 6 0.08
2.32 6 0.07
2.28 6 0.07
,0.0001
,0.0001
ns
Fasting plasma total cholesterol (mmol/L)
5.00 6 0.08
5.15 6 0.11
5.17 6 0.10
ns
ns
ns
Fasting plasma triglycerides (mmol/L)
1.15 6 0.06
1.60 6 0.09
1.89 6 0.23
,0.05
,0.001
ns
131 6 2
78 6 1
133 6 2
80 6 2
141 6 2
85 6 1
ns
ns
,0.001
,0.0001
,0.05
,0.05
5.54 6 0.08
37.3 6 0.8
5.16 6 0.04
6.05 6 0.09
5.69 6 0.07
39.0 6 0.7
5.63 6 0.07
9.11 6 0.09
6.59 6 0.11
48.9 6 1.2
7.43 6 0.22
15.08 6 0.32
ns
ns
,0.01
,0.0001
,0.0001
,0.0001
,0.0001
,0.0001
,0.0001
,0.0001
,0.0001
,0.0001
0.0606 6 0.0012
0.0415 6 0.0018
0.0409 6 0.0015
,0.0001
,0.0001
ns
36,083 6 1,331
249,414 6 8,325
45,802 6 2,661
351,893 6 19,681
41,153 6 1,520
255,493 6 11,131
,0.001
,0.0001
ns
ns
ns
,0.001
2,085 6 66
14,470 6 430
1,677 6 104
12,840 6 724
1,665 6 69
10,202 6 560
,0.001
ns
,0.001
,0.0001
ns
,0.01
Systolic blood pressure (mmHg)
Diastolic blood pressure (mmHg)
Glycemic control
HbA1c (%)
HbA1c (mmol/mol)
Fasting plasma glucose (mmol/L)
2-h OGTT plasma glucose (mmol/L)
SiOGTT (mmol/kg/min/pmol z L)
GSISOGTT (AUC C-peptide [pmol/L z min])
Early phase
Late phase
DIOGTT (arbitrary units)
Early phase
Late phase
Data are means 6 SEM. Differences between group means were compared by one-way ANOVA adjusted for multiple comparisons. AUC, area under
the curve; early phase, measures made during 0–30 min of OGTT; late-phase, measures made during 30–120 min of OGTT.
3
4
VO2max and Glycemic Control
Diabetes Care
Table 2—Relationships between VO2max and OGTT-derived variables
NGT
Glycemic control
HbA1c (%)
Fasting plasma glucose (mmol/L)
2-h OGTT plasma glucose (mmol/L)
T2D
All subjects
r
P
r
P
r
P
r
P
20.392
20.475
20.436
,0.0001
,0.0001
,0.0001
20.217
20.288
20.343
ns
,0.05
,0.001
20.015
20.175
20.097
ns
ns
ns
20.333
20.336
20.325
,0.0001
,0.0001
,0.0001
0.733
,0.0001
0.609
,0.0001
0.525
,0.0001
0.734
,0.0001
20.325
20.406
,0.001
,0.0001
20.309
20.312
,0.05
,0.05
20.233
20.166
,0.05
ns
20.336
20.361
,0.0001
,0.0001
0.399
0.404
,0.01
,0.01
0.361
0.360
,0.01
,0.01
0.390
0.354
,0.01
,0.01
0.405
0.411
,0.0001
,0.0001
SiOGTT (mmol/kg/min/pmol z L)
GSISOGTT (AUC C-peptide [pmol/L z min])
Early phase
Late phase
IGT
DIOGTT (arbitrary units)
Early phase
Late phase
In a cohort representative of the whole glucose tolerance continuum, we determined whether statistically significant associations existed between
cardiorespiratory fitness (VO2max) (mL/kg/min) and HbA1c (total N = 218; n = 102 NGT, n = 47 IGT, and n = 69 T2D), fasting glucose and 2-h OGTT (total
N = 313; n = 137 NGT, n = 85 IGT, and n = 91 T2D), SiOGTT (total N = 304; n = 134 NGT, n = 83 IGT, and n = 87 T2D), GSISOGTT (total N = 240; n = 111 NGT,
n = 54 IGT, and n = 75 T2D), and DIOGTT (total N = 235; n = 110 NGT, n = 53 IGT, and n = 72 T2D). Data indicate correlation coefficients (r) for
comparisons between variables. AUC, area under the curve.
not evident in the subgroup of subjects
with T2D (Table 2). Furthermore, these
relationships were significantly weaker
in subjects with T2D compared with those
with NGT (NGT vs. T2D z scores for HbA1c,
fasting glucose, and 2-h glucose were
22.51 [P , 0.05], 22.48 [P , 0.05],
and 22.67 [P , 0.01], respectively). Fig.
2A demonstrates that VO2max was directly
correlated with SiOGTT. This significant relationship was evident across the entire
glucose tolerance continuum (Table 2),
although with decreasing r values across
the continuum where the relationship in
T2D subjects was significantly weaker
than in NGT subjects (NGT vs. T2D z
score = 2.52, P , 0.05). Fig. 2B and C indicate that VO2max was inversely related
to early- and late-phase GSISOGTT; nevertheless, these relationships were weaker
in subjects with T2D, particularly during
late phase (NGT vs. T2DM z score =
21.73, P = 0.08), where the relationship
was not significant (Table 2). Fig. 2D and E
show that VO2max was directly related to
early- and late-phase DIOGTT. This finding
was also consistent across the whole glucose tolerance continuum (Table 2).
These significant relationships persisted
when analyzing absolute VO2max (liters
per minute) vs. HbA1c (r = 20.37, P ,
0.0001), fasting glucose (r = 20.22, P ,
0.001), 2-h OGTT glucose (r = 20.34,
P , 0.0001), SiOGTT (r = 0.44, P , 0.0001),
GSISOGTT (r = 20.26, P , 0.05; r = 20.21,
P , 0.05 [early and late phase, respectively), and DIOGTT (r = 0.25, P , 0.0001;
r = 0.26, P , 0.0001). However, r values
were lower compared with the analyses
made against relative VO2max (milliliters
per kilogram per minute) indicating that
differing body mass accounts for some
of the variance in these relationships.
Multiple regression analysis (Table 3)
where VO 2max was entered simultaneously with the other independent
variables (age, sex, weight, BMI, and %
body fat) showed that although VO2max
was significantly associated with glycemic control (HbA1c, fasting glucose, and
2-h OGTT glucose), SiOGTT, GSISOGTT, and
DIOGTT, the other independent variables
also explained some of the variance. By
comparing significant b-values in Table
3, these analyses also indicate that the
majority of the variance in glycemic
control was related to VO2max, while
the majority of variance in GSISOGTT ,
SiOGTT, and DIOGTT was related to body
weight and BMI, respectively.
CONCLUSIONS
In the current study, we demonstrate
that cardiorespiratory fitness is inversely related with clinical markers of
glycemic control, such that high HbA1c,
high fasting glucose, and impaired oral
glucose tolerance are associated with
low VO2max. We further found that cardiorespiratory fitness is positively related to insulin sensitivity and inversely
related to GSISOGTT. However, the major
novel finding of this work is that cardiorespiratory fitness is associated with
pancreatic b-cell insulin secretory compensation for changing insulin sensitivity (DIOGTT) in a cohort representing the
entire glucose tolerance continuum.
The Italian Diabetes and Exercise
Study (IDES [22]) demonstrated that
changes in fitness after aerobic training
predicted improvements in HbA1c (and
other cardiovascular disease risk factors). In this study, we show that
VO 2max is inversely associated with
HbA1c (and other markers of glycemic
control such as fasting and 2-h OGTT
glucose) in a large cohort representing
the entire glucose tolerance continuum, supporting the findings from
Blair’s group, who showed that high
VO2max confers protection against the
development of T2D over a 6-year period
even when adjusted for age and parental
diabetes (7). These data provide
strength to the notion that maintaining
cardiorespiratory fitness will help prevent T2D. That said, it should be noted
that when the T2D subgroup was analyzed in isolation, the relationship between VO 2max and glycemic control
(HbA1c and fasting and OGTT glucose)
was not evident (Table 2), possibly suggesting that once T2D is present cardiorespiratory fitness is a lesser determinant
of the state of glycemia.
Hyperglycemia in individuals with
T2D develops when insulin secretion
no longer compensates for poor insulin
sensitivity, indicating a state of b-cell
dysfunction. Indeed, exercise training
improves glycemic control (3–6,23),
and it is traditionally thought that this
benefit is driven entirely by increases in
muscle glucose uptake and/or increased
insulin sensitivity (rev. in 24,25). Furthermore, VO 2max is associated with
care.diabetesjournals.org
Figure 1—Cardiorespiratory fitness is associated with markers of glycemic control. VO2max was
measured during incremental workload and exhaustive aerobic exercise in subjects representative of a heterogeneous population with respect to age, BMI, adiposity, and glucose tolerance
status (white circles, NGT; light-gray squares, IGT; and dark-gray triangles, T2D). Regression
analysis demonstrated inverse curvilinear log-log relationships between VO2max and HbA1c
(log10y = 20.14 log10x + 0.98) (A), fasting glucose (log10y = 20.17 log10x + 1.03) (B), and 2-h
glucose during OGTT (log10y = 20.39 log10x + 1.55) (C). Solid and dotted lines represent the
regression curves and 95% CI, respectively, and show unadjusted data.
Solomon and Associates
insulin sensitivity, as first shown in 1983
by Rosenthal et al. (26), and we now
confirm this finding across the entire
continuum of glucose tolerance. We
also found VO2max to be inversely associated with GSISOGTT; however, as for
variables of glycemic control (HbA 1c
and fasting and 2-h glucose), such associations were less evident in subjects
with T2D. Given that VO2max is well correlated with insulin sensitivity even in
subjects with T2D, the poor relationship
between VO2max and variables of glycemic control in subjects with diabetes is
potentially explained by the fact that
the solid inverse relationship between
insulin sensitivity and insulin secretion
is also diminished in subjects with T2D,
as evidenced by the deterioration in glucose disposition. Thus, again, in T2D
once the disease is established, cardiorespiratory fitness is a lesser determinant of insulin secretion. However, it is
important to note that glycemic control
is not entirely governed by insulin sensitivity. The ability to restore b-cell function is particularly relevant for the
treatment of T2D, a disease in which
b-cell insulin secretory function is no
longer able to overcome the poor underlying degree of insulin sensitivity.
Thus, it is of importance to examine insulin secretory compensation (i.e., the
disposition index). Interestingly, aerobic
exercise training increases the disposition index in subjects without (16,27)
and in subjects with (10) T2D, and
Chen et al. (28) have demonstrated
that self-reported weekly minutes of
physical activity are directly associated
with disposition index. In support of
such findings, we now demonstrate
that high VO2max is also predictive of
high b-cell insulin secretory compensation for poor insulin sensitivity (DIOGTT)
across the entire glucose tolerance continuum. While these relationships do
not indicate causality, our findings do
highlight an important association between cardiorespiratory fitness and the
key determinant of T2D-related hyperglycemia. However, it should also be
noted that the genetic component of
VO2max (29) has not been addressed in
this study and should be examined in
future work.
The underlying mechanisms as to why
VO2max (or aerobic training) may be positively associated with b-cell compensation are not well defined. Physical
5
6
VO2max and Glycemic Control
Diabetes Care
Figure 2—Cardiorespiratory fitness is associated with SiOGTT, GSISOGTT, and DIOGTT. VO2max was measured during incremental workload and
exhaustive aerobic exercise in subjects representative of a heterogeneous population with respect to age, BMI, adiposity, and glucose tolerance
status (white circles, NGT; light-gray squares, IGT; and dark-gray triangles, T2D). VO2max was directly associated with SiOGTT (y = 0.00117x + 0.0119)
(A) but had an inverse curvilinear log-log relationship with early-phase (log10y = 20.39 log10x + 5.18) (B) and late-phase (log10y = 20.42 log10x + 6.07)
(C) GSISOGTT. Finally, there were direct linear relationships between VO2max and early-phase (y = 27.8x + 916) (D) and late-phase (y = 200x + 5,966) (E)
DIOGTT, a measure of pancreatic b-cell insulin secretory compensation for changing insulin sensitivity. Solid and dotted lines represent the regression
curves and 95% CIs, respectively, and show unadjusted data.
activity per se, which is a major determinant of VO2max, decreases blood glucose and lipids and may thereby relieve
glucolipotoxicity in b-cells, allowing restoration of appropriate insulin secretory
function (30). Further to this, increased
physical activity lowers circulating levels
of proinflammatory cytokines (tumor
necrosis factor-a, leptin) and increases
levels of various growth factors, hormones, and cytokines (growth hormone, insulin-like growth factor-1,
GLP-1, interleukin-6, and interleukin-1
receptor antagonist) that have direct
beneficial effects on b-cells (30). Irrespective of the speculated mechanisms,
our findings support the need for future
work examining the role of exercise in
b-cell health.
There are some limitations to our
study. Firstly, it is important to note
care.diabetesjournals.org
Solomon and Associates
Age
VO2max
Constant
20.03
20.30
20.02
20.06
11.29
B
0.01
0.21
0.01
0.01
1.04
SE B
20.43
20.16
20.29
20.83
0.006
0.15
0.001
4.8 3 1029
5.8 3 10222
P
0.09
20.04
21.03
20.02
20.10
14.06
B
0.02
0.04
0.01
0.32
0.01
0.02
1.66
SE B
20.29
0.30
20.41
20.32
20.17
20.68
0.01
0.05
0.003
0.002
0.04
3.7 3 1028
1.3 3 10215
P
20.10
0.18
20.12
22.84
20.08
20.32
37.02
B
0.04
0.11
0.03
0.82
0.03
0.04
4.20
SE B
20.26
0.23
20.48
20.34
20.21
20.87
0.02
0.10
0.0003
0.0006
0.006
7.3 3 10213
1.1 3 10216
P
20.0001
20.003
0.0001
0.007
0.0002
0.0005
0.08
B
0.00007
0.0002
0.00006
0.001
0.00005
0.00007
0.007
SE B
SiOGTT
20.09
20.81
0.13
0.20
0.13
0.34
b
(R2 = 0.86)
0.05
1.5 3 10231
0.02
4.0 3 1026
7.6 3 1025
1.1 3 10210
HbA1c
Table 3—Multiple regression analyses
Sex
20.04
Independent variables
Weight
0.03
(R2 = 0.20)
b
FPG (R2 = 0.15)
b
2-h OGTT glucose (R2 = 0.22)
b
P
2.4 3 10224
P
0.14
b
0.25
b
20.29
SE B
b
0.0006
0.01
B
5,346
0.03
P
18,623
0.04
SE B
0.0001
20.02
B
770
0.36
0.04
Fat %
P
3,130
0.08
0.23
BMI
SE B
0.05
36.3
55.5
Late-phase DIOGTT (R2 = 0.32)
B
100,528
33.6
96.7
Early-phase DIOGTT (R2 = 0.33)
P
195,511
0.84
0.04
Late-phase GSISOGTT (R2 = 0.18)
SE B
0.04
0.22
b
B
14,130
20.02
Early-phase GSISOGTT(R2 = 0.18)
29,034
5.2
8.0
Independent variables
Constant
16.8
0.65
0.06
21.1
0.05
0.26
0.05
9.4 3 1028
0.14
1133
44.4
0.30
20.85
0.03
513
83.4
66
142
20.13
0.29
0.007
132
2785
20.24
0.11
0.38
0.25
1.4 3 1028
899
163
6.4
0.17
20.90
16,306
21
21,339
172
17.3
9.4
231,946
0.87
0.03
10.9
2121
0.06
0.86
0.008
20.02
0.35
0.30
20.32
28,840
1178
0.16
20.03
20.16
24,922
2,574
1,579
3,571
126
0.69
0.001
1,640
2649
2,278
0.05
0.51
0.92
0.66
2240
4,057
166
20.02
20.07
26,057
1,644
538
220
502
VO2max
Sex
Weight
223
2223
Age
Fat %
BMI
In a cohort representative of the whole glucose tolerance continuum, we determined the relative contributions of VO2max and other independent variables such as age, sex, body weight, BMI, and whole-body
fat percentage to the variance of glycemic control (HbA1c, fasting glucose [FPG], 2-h OGTT glucose), SiOGTT, GSISOGTT, and DIOGTT using a forced-entry multiple regression method. Dependent variables are the
actual values and not log transformed. The statistical significance of b values is indicated by the individual P values. B, the unstandardized b, which represents the change in the dependent variable resulting
from one unit change in the independent variable; SE B, the SE of b; b, standardized b, which represents the change in the dependent variable resulting from 1 SD change in the independent variable.
that pausing antidiabetic drugs for 5
days prior to metabolic testing may induce acute increases in glycemia, potentially inducing glucotoxicity. On the
contrary, this time period may not be
sufficient to wash out all drugs. With
these issues in mind, care should be
taken when interpreting, for example,
the relationship between VO2max and
HbA1c and whether it would be altered
when pausing the antidiabetic drugs for
5 days. Therefore, future studies should
carefully consider the rationale for withholding drugs in the context of the primary outcomes of the study. Particular
attention should also be drawn to the
strength of the correlations. Despite
finding significant relationships in the
whole cohort, VO 2max explains just
;11% of the variance in measures of
glycemic control (HbA 1c and fasting
and OGTT glucose), which indicates
that other factors, such as the other independent variables included in the
multiple regression analyses (Table 3),
are also involved. Furthermore, for
all variables, the strength of the relationships with VO2max progressively diminishes with advancing glucose
intolerance. This is particularly interesting because it highlights the complexity
of the pathophysiology of T2D. Indeed,
prior work in subjects with T2D has
found a relationship between aerobic
exercise training–induced changes in fitness and glycemic control (31). Furthermore, we previously found that patients
randomized to a training intervention
that does not increase VO2max fail to improve glycemic control compared with
patients randomized to training that increases VO2max (3). However, we (32)
and others (33) have also shown that
exercise training–induced improvements in VO2max do not always extrapolate to improved glycemia. Accordingly,
while VO2max explains ;11% of the variance in measures of glycemic control, it
accounted for ;54% of the variance in
insulin sensitivity. Thus, although cardiorespiratory fitness is predictive of insulin
sensitivity, this does not extrapolate to
the same magnitude of prediction of glycemic control. On the other hand, ;17%
of the variance in DIOGTT was accounted
for by VO2max. As such, the underlying
mechanisms linking cardiorespiratory
fitness to improvements in glycemic
control require more detailed investigation. Lastly, it is important to note that
7
8
VO2max and Glycemic Control
body weight and BMI explain a large part
of the variance of GSISOGTT and of SiOGTT
and DIOGTT, respectively (Table 3). Consequently, it is likely that weight loss
combined with exercise training–
induced increases in cardiorespiratory
fitness is required to mediate the most
optimal beneficial adaptation in glycemic control.
In conclusion, our study has advanced the prior knowledge that high
cardiorespiratory fitness is associated
with good glycemic control by showing
that VO2max is also positively associated
with the disposition index in a cohort of
adults representing the entire glucose
tolerance continuum. While the large
contribution of body weight and BMI
must not be ignored, these findings
are clinically significant because they
also highlight the importance of an individual’s cardiorespiratory fitness level
in relation to the fundamental pathophysiological cause of hyperglycemiarelated chronic disease.
Acknowledgments. The authors thank Julianne
Filion, R.N. (Department of Pathobiology,
Cleveland Clinic), for her clinical nursing support and Lisbeth Andreasen, M.Sc. (Department of Clinical Biochemistry, Rigshospitalet),
for her assistance with biochemical assays.
Funding. Support for this study came from the
Danish Center for Strategic Research in Type 2
Diabetes (Danish Council for Strategic Research,
grant nos. 09-067009 and 09-075724), and the
National Institutes of Health (NIH) (RO1
AG12834) (to J.P.K.). S.K.M. was supported by
an NIH T32 grant (DK007319 [training grant] to
J.P.K.). The Centre of Inflammation and Metabolism is supported by the Danish National
Research Foundation (grant no. 02-512-55).
Duality of Interest. This work was funded by a
Paul Langerhans Program grant from the European Foundation for the Study of Diabetes/
Amylin Pharmaceuticals, Inc. (to T.P.J.S.). No
other potential conflicts of interest relevant to
this article were reported.
Author Contributions. T.P.J.S. designed the
study and drafted the manuscript. T.P.J.S., S.K.M.,
K.K., S.H.K., J.M.H., M.J.L., and J.P.K. implemented the study and edited the manuscript.
T.P.J.S., S.K.M., J.M.H., M.J.L., and J.P.K. had
access to and analyzed data. T.P.J.S., S.K.M.,
K.K., J.M.H., M.J.L., and J.P.K. discussed the data.
T.P.J.S. is the guarantor of this work and, as such,
had full access to all the data in the study and
takes responsibility for the integrity of the data
and the accuracy of the data analysis.
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