Departments of 1 Food Science and Human Nutrition, 2 Health and Exercise Science, and 3 Biochemistry and Molecular Biology, Colorado State University, Fort Collins, Colorado 80523
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ABSTRACT |
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We determined whether lower insulin
sensitivity persists in young, nonobese, nondiabetic Mexican-American
[MA; n = 13, 27.0 ± 2.0 yr, body mass index
(BMI) 23.0 ± 0.7] compared with non-Hispanic white (NHW;
n = 13, 24.8 ± 1.5 yr, BMI 22.8 ± 0.6)
males and females after accounting for cardiorespiratory fitness
(maximal O2 uptake), abdominal fat distribution (computed
tomography scans), dietary intake (4-day records), and skeletal muscle
insulin-signaling protein abundance from muscle biopsies (Western blot
analysis). MA were significantly less insulin sensitive compared with
their NHW counterparts when estimated by homeostatic model assessment of insulin resistance (MA: 1.53 ± 0.22 vs. NHW: 0.87 ± 0.16, P < 0.05) and the revised quantitative insulin
sensitivity check index (MA: 0.45 ± 0.08 vs. NHW: 0.58 ± 0.19, P = 0.05). However, skeletal muscle protein
abundance of insulin receptor- (IR
), phosphatidylinositol
3-kinase p85 subunit, Akt1, Akt2, and GLUT4 were not significantly
different. Differences in indexes of insulin sensitivity lost
significance after percent dietary intake of palmitic acid, palmitoleic
acid, and skeletal muscle protein abundance of IR
were accounted
for. We conclude that differences in insulin sensitivity between
nonobese, nondiabetic MA and NHW persist after effects of chronic and
acute exercise and total and abdominal fat distribution are accounted
for. These differences may be mediated, in part, by dietary fat intake.
cardiorespiratory fitness; insulin-signaling proteins
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INTRODUCTION |
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MEXICAN-AMERICANS (MA) exhibit a two- to threefold excess prevalence of type 2 diabetes mellitus (T2DM) compared with non-Hispanic white (NHW) Americans (60). Insulin resistance plays a central role in the pathophysiology of T2DM (56). Studies have shown that, across a wide range of ages, MA males and females both demonstrate greater levels of insulin resistance compared with NHW (1, 5, 30, 64). Although reasons for this discrepancy are not clear, it has been suggested that genetic factors could explain the higher prevalence of insulin resistance in MA, possibly owing to the fact that up to 35% of their genetic make-up is attributable to Native American ancestry (34). It is also possible that lifestyle factors, including diet and exercise, contribute to the ethnic differences in insulin resistance. Visceral adiposity (43), exercise (26), and dietary fat (51, 63) have all been shown to impact peripheral insulin resistance.
In several previous studies, MA have been shown to exhibit lower insulin sensitivity independently of body fat and body fat patterning (1, 5, 60, 64). However, this issue is not entirely resolved given that studies that have documented diminished insulin sensitivity in nonobese, nondiabetic MA compared with NHW utilized less sensitive methods [skinfolds, body mass index (BMI), waist-to-hip ratio] to estimate body fat and central adiposity (1, 32, 33). Furthermore, the possible contribution of lower physical activity and physical fitness in the MA as well as differences in dietary intake were not accounted for in these studies. Because MA compared with NHW tend to exhibit greater central adiposity (31, 61), are less physically active (14), and consume a more atherogenic diet (65), it is important to more carefully examine these factors as possible contributors to the lower insulin sensitivity in MA.
Numerous studies have provided evidence that skeletal muscle insulin
resistance seen in T2DM is primarily isolated to the insulin-stimulated
phosphatidylinositol 3-kinase (PI3K)-signaling cascade (6,
15). Under normal physiological conditions, insulin binds to the
-subunit of the insulin receptor (IR). This binding activates
tyrosine kinase activity in the
-subunit and causes tyrosine
phosphorylation of various IR substrates (IRS). Once activated, IRS-1
docks with the p85 regulatory subunit of PI3K and activates its p110
catalytic subunit. Catalytic activity of PI3K phosphorylates
PI(4,5)P2 to
PI(3,4,5)P3. PIP3 is necessary to
activate 3-phosphoinositide-dependent protein kinase-1, which phosphorylates Akt (serine/threonine kinase, a central regulator of
anabolic metabolism) on Thr308. Subsequent phosphorylation
of Akt on Ser473 further activates the enzyme. Unknown
downstream steps enhance the translocation of GLUT4 from intracellular
vesicles to the plasma membrane, resulting in glucose entry into the
cell. Some studies have documented diminished abundance of some of
these proteins in association with insulin resistance, whereas
others have not (7, 41, 57, 58). Despite the well
recognized differences in insulin sensitivity between MA and NHW, there
are no data available regarding possible differences in the expression of these skeletal muscle-signaling proteins, which could account for
the lower insulin sensitivity in MA.
We undertook the present study to accomplish two specific aims. First,
we sought to determine whether or not differences in insulin
sensitivity persist between these two groups after controlling for the
effects of acute and chronic exercise, abdominal fat distribution, and
dietary intake. Second, we sought to determine whether MA exhibit lower
skeletal muscle protein concentrations of IR, PI3K p85, Akt1, Akt2,
and GLUT4 compared with NHW after controlling for these same potential confounders.
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METHODS |
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Subjects.
A total of 13 nonobese (BMI < 30) MA (7 females, 6 males) were
matched to 13 nonobese NHW (7 females, 6 males) aged 18-40 yr, on
the basis of gender, age, and aerobic fitness [maximal O2
uptake (O2 max)
5 ml · kg
1 · min
1] as
measured by a graded exercise test. Subjects were eligible for
participation on the basis of the following characteristics: being
nonsmoking, apparently healthy individuals with no overt signs or
symptoms of disease as determined by a medical history, and having
normal fasting blood glucose (<110 mg/dl), no past or present history
of endocrine disorders, and resting blood pressure <140/90 mmHg.
Individuals were excluded from participation for the following reasons:
pregnancy, oral contraceptive use, tobacco use, diabetes mellitus,
history of any eating disorders, history of menstrual cycle
irregularities, history of hypo- or hyperthyroidism, and use of any
medications that could influence insulin sensitivity. In addition,
subjects had no orthopedic problems that prohibited them from engaging
in the maximal-exercise test. Subjects had been weight stable (±2.5
kg) for the previous 6 mo. To be appropriately identified as MA, each
participant traced his/her ethnicity to all four grandparents.
The study protocol was approved by the Colorado State University Human
Research Committee. Verbal and written informed consent was obtained
from each volunteer.
Body mass, height, and composition. Body weight was measured on a balance scale to the nearest 100 g. Body height was measured with a wall-mounted stadiometer to the nearest 0.1 cm. The percentage of body fat, absolute fat mass, and fat-free mass were measured in all subjects by means of dual-energy X-ray absorptiometry using software version 4.5c (model DPX-IQ, Lunar, Madison, WI).
Abdominal visceral fat.
The measurement of total, visceral, and subcutaneous fat in the
abdominal region was performed using a General Electric high-speed computed tomography (CT) scanner (Milwaukee, WI) with helical capability (21). A cross-sectional scan 10 mm thick,
centered at the L4-L5 intervertebral space, was obtained using 170 mA
with a scanning time of 2 s and a 512 × 512 matrix.
Abdominal visceral fat (AVF) was measured by sectioning the
intra-abdominal area by pixel density. Abdominal subcutaneous fat (ASF)
was calculated by determining the area of adipose tissue (AT) within
the abdominal wall. The areas of deep (DSF) and superficial
subcutaneous (SSF) AT were determined by dividing the two compartments
on the basis of the fascial delineation (37). SSF was
calculated by subtracting DSF from total subcutaneous AT. Within each
of the three compartments, the cross-sectional area of AT was measured
in pixels (0.6 mm) in the attenuation range of 190 to
30 HU by use
of commercially available software (sliceOmatic; Tomovision, Montreal, Canada).
Dietary intake. Subjects were instructed to accurately record food intake (e.g., portion sizes, food preparation methods, brand names of products) over a 4-day period by using two-dimensional food models. Records were checked for completion and sufficiency of detail. Subjects were asked to provide food labels for products used to determine appropriate substitutions when actual items consumed were not in the software database. Food intake records were analyzed using the Food Intake Analysis System (FIAS 3.98 nutrient analysis program, University of Texas School of Public Health, 1998). The FIAS database consists of the Primary Data Set and the Survey Nutrient Data Base of the National Nutrient Data Bank, developed and maintained by the US Department of Agriculture. Two MA, one male and one female, did not complete their 4-day dietary records; thus all analyses conducted involving dietary variables are for 24 subjects only.
Cardiorespiratory fitness.
Acute and chronic exercise is related to insulin sensitivity, and MA
have been reported to exhibit lower levels of physical activity
compared with NHW. Thus we sought to control for this potential
confounder by recruiting individuals from both groups who
were similar in O2 max.
O2 max was measured during incremental
treadmill exercise to volitional exhaustion as previously described
by Balke and Ware (4). Measurements of oxygen
consumption, carbon dioxide production, pulmonary ventilation, and the
respiratory exchange ratio were determined by on-line computer-assisted
open-circuit spirometry (CPX Express; MedGraphics, Minnesota, MN). To
ensure that
O2 max was obtained, at least three of the four following criteria were satisfied:
1) a plateau in oxygen consumption with increasing workload;
2) a respiratory exchange ratio
1.15; 3)
achievement of 100% of the age-predicted maximal heart rate; and
4) a maximal rating of perceived exertion of
18 on the
Borg scale.
Insulin sensitivity.
For the determination of insulin sensitivity, subjects were instructed
to remain fasted for 12 h before blood collection. Furthermore,
subjects refrained from participation in any form of exercise for
48 h before the study. Female subjects were tested during the
early follicular phase of their menstrual cycles (days 3-10). Four estimates of insulin sensitivity were
used: fasting plasma insulin, the homeostatic model assessment of
insulin resistance (HOMA-IR) (28), the quantitative
insulin sensitivity check index (QUICKI) (38), and the
revised QUICKI (54) by use of the following formulas
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Muscle biopsies.
Muscle biopsies were obtained from all subjects to examine
insulin-signaling pathway intermediates and glucose transporters in
skeletal muscle. Subjects reported to the laboratory after an overnight
fast and 48 h after any exercise. Percutaneous muscle biopsies
(75 mg) were obtained from the belly of the vastus lateralis by use
of a 5-mm Bergstrom needle with suction applied as described previously
(36). Muscle obtained from the subjects was immediately frozen in liquid nitrogen and subsequently analyzed for the
determination of IR
, PI3K p85
/
-subunits, Akt1, Akt2, and
GLUT4 protein abundance.
Immunoblotting.
Approximately 50 mg of frozen muscle were homogenized in ice-cold
buffer (in mM: 50 HEPES, pH 7.4, 100 NaF, 10 Na4P2O7, 2.5 EDTA, 2 Na3VO4, and 2 phenylmethylsulfonyl fluoride and
1 µM leupeptin, 1 µM pepstatin, and 0.2 µM aprotinin). Tissue
lysates were solubilized with gentle mixing in 1% Triton X-100 for
1.5 h at 4°C and centrifuged at 14,000 g for 15 min
at 4°C. Total protein concentration was determined by the
bicinchoninic acid assay (Pierce, Rockford, IL). Positive controls (fat
cell lysate, Jurkat cell lysate) were used as internal standards for
IR, PI3K p85
, Akt1, Akt2, and GLUT4. Data are expressed as
protein abundance in arbitrary units, with one group compared relative
to the other. Solubilized proteins (50 µg) were resolved by 7.5%
SDS-PAGE and transferred to a PVDF membrane. The membrane was blocked
with 5% nonfat dry milk containing Tris-buffered saline (TBS)
containing 0.05% Tween 20 for 1 h at room temperature. The
membrane was then incubated with 3% milk for 1 h in TBS
containing 0.05% Tween 20 at room temperature with antibodies to IR
and Akt1 from Upstate Biotechnology, (Lake Placid, NY), PI3K
p85
/
, Akt2, and GLUT4. Anti-p85 antibodies were raised by
Rockland (Gilbertsville, PA) against a protein in which glutathione S-transferase (GST) is fused to the NH2-terminal
SH2 domain of p85, kindly provided by Jonathan Backer
(Albert Einstein College of Medicine). Polyclonal rabbit anti-Akt2
antibodies were raised against the COOH-terminal sequence
CDQTHFPQFSYSASIRE in Akt2. Polyclonal sheep anti-GLUT4 antibodies were
raised against a GST fusion protein containing the last 31 amino acids
of the GLUT4 COOH terminus (GST-ISATFRRTPSLLEQEVKPSTELEY-LGPDEND).
Membranes were incubated with the horseradish peroxidase-conjugated
secondary antibody for 1 h at room temperature and then washed
with TBS containing 0.05% Tween 20. The bands were visualized using
the Enhanced Chemiluminescence Plus System (Amersham, Arlingtom
Heights, IL) and quantified by phosphoimager.
Assays. Blood glucose was measured by the glucose oxidase method with an autoanalyzer (YSI 2300 Stat Plus, Yellow Springs Instrument, Yellow Springs, OH). Plasma insulin was measured using a two-step sandwich ELISA (DSL, Webster, TX). This assay demonstrates a 2.6% intra-assay and 6.2% interassay variation. Total plasma NEFA concentrations were analyzed by colorimetric assay (Waco Chemicals, Richmond, VA).
Statistical analysis. Data were analyzed for normality and homogeneity of variance. Dependent variables were initially subjected to normal testing by use of both qualitative (data plots) and quantitative (Kolmogorov-Smirnov test) approaches. Group differences were analyzed using a 2 × 2 ANOVA (ethnicity × gender). When gender differences failed to reach significance, males and females were analyzed together within ethnic groups by independent t-tests. Pearson product-moment correlations and partial correlations were used to determine associations between independent and dependent variables. Analysis of covariance was performed when potentially confounding variables exhibited a significant relationship to the dependent variables. The significance level was set a priori at P < 0.05.
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RESULTS |
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Subject characteristics are given in Table
1. On average, both groups were composed
of young, normal-weight men and women of moderate cardiorespiratory
fitness. There were expected gender differences in several of the
physical characteristics such as height, weight, body composition, fat
patterning, and O2 max. However, there
were no ethnic differences in any of these physical characteristics,
except for the lower stature of MA compared with NHW. CT revealed that
total AVF, total ASF, SSF, and DSF were not different between the two
groups (Table 1). The MA exhibited a stronger family history of T2DM
compared with NHW, with 75% of the former reporting either
grandparents or parents with diabetes compared with only 10% of the
Caucasians (data not shown in table format).
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Dietary intake of energy and macronutrients is shown in Table
2. Percent total energy intake from
palmitoleic acid was significantly higher among MA, with a trend toward
higher percent total energy intake from palmitic acid and oleic acid
and lower fiber intake among MA (Table 2). After gender was co-varied
for, total intake of palmitic acid was significantly higher among MA
(11.39 ± 1.53 vs. 15.74 ± 2.07 g/day, P < 0.05).
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Table 3 provides information on indexes
of insulin sensitivity and plasma NEFA. HOMA-IR was significantly
higher, and the revised QUICKI was significantly lower, among MA
compared with NHW subjects, indicating lower insulin sensitivity in the
former group. Group differences in fasting glucose (P = 0.11), insulin (P = 0.07), and QUICKI
(P = 0.06) approached statistical significance. Although the higher plasma NEFA concentration in the MA compared with
the NHW subjects was not statistically significant, it also approached
significance (P = 0.09).
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Despite the possible confounding factors shown in Table 1 (i.e.,
fitness and fatness) being taken into account, differences in insulin
sensitivity between MA and NHW subjects persisted. Differences in
HOMA-IR between the groups remained significant after percent intakes
of total fat, saturated fat, and all fatty acids except for palmitate
and palmitoleate were accounted for. Differences in HOMA-IR between the
groups lost significance when percent energy intake from palmitic acid,
percent energy intake from palmitoleic acid, or protein abundance of
IR was accounted for. Correlations between HOMA-IR and these
covariates are shown in Fig. 1.
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Representative immunoblots are shown in Fig.
2. There were no significant differences
between the two groups with regard to skeletal muscle protein abundance
of IR, PI3K p85, Akt1, Akt2, or GLUT4 (Fig.
3). Skeletal muscle protein abundance of
IR
was significantly associated with fasting plasma insulin (r
=
0.46, P < 0.05) and HOMA-IR (r =
0.43, P < 0.05). Skeletal muscle Akt2 protein levels
were associated with HOMA-IR after gender was accounted for, although
this relationship did not reach statistical significance (r =
0.37, P = 0.07). Abundance of
the other signaling proteins studied was not related to estimates of
insulin sensitivity.
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Pearson product-moment correlations were used to analyze relationships
between estimates of insulin sensitivity and dietary variables (Table
4). When the groups were analyzed in
aggregate, fasting insulin was positively associated with percent
energy intake from fat (r = 0.43, P < 0.05) and percent energy intake from monounsaturated fat and inversely
associated with fiber intake. The degree of insulin resistance
estimated by HOMA-IR was associated with percent energy intake from
fat, percent energy intake from monounsaturated fat, and percent energy
intake from saturated fat. HOMA-IR was significantly associated with
the intake of individual fatty acids, palmitate, stearate,
palmitoleate, and oleate. HOMA-IR was also associated with percent
energy intake from carbohydrate and fiber intake. Correlations of these
dietary variables with QUICKI and revised QUICKI were of similar
magnitude to the correlations with HOMA-IR.
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Pearson product-moment correlations were used to analyze relationships
among insulin sensitivity, adiposity measures, plasma NEFA, and
cardiorespiratory fitness. Indexes of insulin sensitivity (fasting
insulin, HOMA-IR) were not related to BMI, percent body fat, AVF, ASF,
SSF, or DSF (not shown). Fasting plasma NEFA concentrations were not
related to fasting insulin or HOMA-IR. Fasting plasma NEFA was
significantly associated with both fasting glucose (r = 0.49, P < 0.05) and percent total calories from fat
(r = 0.44, P < 0.05). Among the entire
sample, cardiorespiratory fitness expressed as maximal metabolic
equivalents (maximal exercise O2 relative to resting
O2, i.e., multiples
of resting metabolic rate) was associated with indexes of insulin
sensitivity (HOMA-IR: r = 0.48, P < 0.02; fasting insulin: r = 0.49, P < 0.02) and IR
(r = 0.43, P < 0.05).
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DISCUSSION |
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There are several important findings from this study that warrant
discussion. First, nonobese, nondiabetic MA adults were less insulin
sensitive compared with NHW adults, even when the potential roles of
cardiorespiratory fitness, acute exercise, and total and regional
adiposity were accounted for. Second, skeletal muscle protein abundance
of IR, PI3K p85, Akt1, Akt2, and GLUT4 was not significantly
different between the two groups and therefore does not account for the
group differences in insulin sensitivity. Finally, group differences in
insulin sensitivity were attenuated to losing statistical significance
after dietary intakes of palmitic acid, palmitoleic acid or skeletal
muscle IR
protein content were accounted for.
The results of our study extend previous findings in several important areas. Previous studies used crude anthropometric methods (BMI, skinfolds) to measure body adiposity (30, 60), whereas the present data suggest that differences in insulin sensitivity between nonobese, nondiabetic MA and NHW persist after both total adiposity and abdominal adiposity determined by CT are accounted for. It has recently been suggested that fat stored in various abdominal compartments (visceral vs. subcutaneous) confers different degrees of association with insulin resistance (39). Abdominal obesity is widely recognized as a strong correlate of insulin resistance. Visceral adiposity, independent of total adiposity, has been implicated in the etiology of skeletal muscle insulin resistance and T2DM (17, 27). Recently, it has been reported that both abdominal visceral adiposity and deep subcutaneous adiposity are strongly associated with peripheral insulin resistance, whereas superficial abdominal fat is not (39). The use of CT in the present study allowed us to characterize these specific aspects of fat patterning rather than using less accurate measures of fat patterning such as skinfold thickness, waist circumference, or waist-to-hip ratios. Why, then, given the use of the gold standard, was the magnitude of visceral adiposity not associated with the measures of insulin sensitivity in our study? One possible explanation is that, because all subjects were nonobese, the lack of adequate heterogeneity in both abdominal fat and insulin sensitivity may have masked a relationship. Nevertheless, it is evident that, even within a homogeneous sample, nonobese MA are less insulin sensitive than NHW, with this difference apparently unrelated to visceral adiposity.
Dietary fat has been considered to be a risk factor in the development of insulin resistance and T2DM (51), but many studies have failed to account for the higher intake of dietary fat commonly reported among MA. Here, we have shown that MA exhibit a tendency to consume higher amounts of dietary fat, which, from a statistical analysis approach, partially mediate differences in insulin sensitivity compared with NHW. Our findings are in agreement with numerous studies showing that high dietary fat and saturated fat intake is associated with reduced insulin sensitivity (10, 48, 49). MA children exhibiting a greater degree of insulin resistance compared with NHW have been reported to consume higher than recommended percent energy from fat and saturated fat (65, 67). There was a trend for MA in our study to ingest more total and saturated fatty acids than NHW, with differences reaching statistical significance for palmitate and palmitoleate. Previous reports have shown an inverse relationship between insulin sensitivity and the dietary intake of palmitate and palmitoleate (66). Presumably, dietary intake of palmitate and palmitoleate affects the concentrations of these fatty acids in plasma and muscle and may result in decreased insulin sensitivity. Interestingly, as has been documented in large, heterogeneous epidemiological studies, we were able to observe significantly lower insulin sensitivity coupled with higher dietary fat intake among our group of young, nonobese, nondiabetic MA compared with NHW individuals. Together, these data suggest the possibility that the slight yet significantly higher intake of specific dietary fatty acids among MA may contribute to their lower insulin sensitivity.
It has been clearly established that exercise improves insulin action
in skeletal muscle (18, 26, 35). These improvements are
typically observed within the first 24 h after an exercise bout
(19, 42, 68). Some studies have suggested that chronic exercise training is also associated with improved insulin
responsiveness (52, 59). A recent study found that
cardiorespiratory fitness levels
(O2 max) were associated with fasting
insulin concentrations after age, percent body fat, and waist
circumference were controlled for (44). Similarly, we
found a significant inverse relationship between cardiorespiratory
fitness and HOMA-IR in our entire sample after controlling for gender,
body fat, and total abdominal fat (data not shown). Despite the effect
of exercise on insulin action, some previous studies in MA have
neglected to account for the possible influences of acute and chronic
exercise on estimates of insulin sensitivity (1, 30). We
hypothesized that, when any confounding effects of acute exercise (by
eliminating all exercise for 48 h before blood sampling) and
matching MA and NHW for cardiorespiratory fitness were controlled for,
there would be no decrement in insulin sensitivity in MA. However, our
data indicate that the lower insulin sensitivity in MA persists even when fitness and acute exercise are rigorously controlled for.
We were also interested in examining whether or not nonobese,
nondiabetic MA displayed differences in several of the main proteins
involved in the PI3K insulin-signaling pathway. Although insulin
receptor numbers have been shown to be significantly decreased in
isolated adipocytes from obese T2DM patients (57), the
number has been shown to be only slightly reduced in skeletal muscle of
these individuals (11). One study found that protein
levels of PI3K were lower in diabetic vs. nondiabetic mice
(7), whereas Andreelli et al. (3) found that
mRNA levels of IRS-1, PI3K p85, and GLUT4 were not different in
skeletal muscle of controls and T2DM patients. Another study reported
that the amount of Akt2 in skeletal muscle of obese rats was 56% lower
compared with lean rats but that there were no differences in the
amount of IR or the p85 regulatory subunit of PI3K in insulin-resistant
vs. control skeletal muscle (20). Kim et al.
(41) found no differences in the amount of Akt1 between
insulin-resistant obese and lean rats. Although diabetic individuals
exhibit impaired Akt activity, no differences in Akt protein levels
between healthy volunteers and type 2 diabetics have been documented
(12, 46). Several studies have documented a reduction in
GLUT4 expression in isolated adipocytes (47, 58) and
skeletal muscle (25) of patients with T2DM. However, other
studies have failed to show reduced GLUT4 expression in skeletal muscle
of T2DM patients (13, 23, 53). One study has reported that
GLUT4 density is lower in slow-twitch skeletal muscle fibers of T2DM
individuals (24). In the present study, we found that, in
the entire sample, estimates of insulin sensitivity were related to
IR
and Akt2 protein abundance. However, the lack of group
differences in signaling proteins suggests that the lower insulin
sensitivity in MA is not the result of reduced skeletal muscle
expression of IR
, PI3K p85, Akt1, Akt2, or GLUT4.
We can only speculate on the mechanisms responsible for the lower
insulin sensitivity in MA compared with NHW in the present investigation. Although we documented a tendency for MA to consume higher intakes of dietary fat compared with NHW individuals, we did not
determine plasma fatty acid or intramuscular lipid profiles in these
subjects. Palmitate is one of the least preferred substrates for
skeletal muscle -oxidation (16) and has been implicated in the de novo synthesis of ceramide, a known inhibitor of
insulin-stimulated glucose uptake (62). The possible
relation of fatty acid intake, plasma fatty acids, intramuscular lipid,
ceramide production, and insulin sensitivity should be addressed in
future studies of MA.
There are some potential limitations of the present study that should be addressed. First, we used only fasting samples of glucose and insulin to determine surrogates of insulin sensitivity (i.e., HOMA-IR, QUICKI, and revised QUICKI) rather than the more clinically sophisticated FSIGTT or the hyperinsulinemic euglycemic clamp. The reasons we used this approach are as follows. Strong associations between these estimates and the hyperinsulinemic euglycemic clamp technique have been found in several studies (2, 8, 38). Haffner et al. (29) have previously shown the HOMA-IR method to be able to readily characterize the lower insulin sensitivity in MA compared with NHW. Furthermore, it has been suggested that estimates of insulin sensitivity based on fasting insulin and glucose may be the best markers of early insulin resistance in nondiabetic individuals (22, 47). Thus this approach seemed especially well suited for the study of young, nonobese, nondiabetic MA individuals, all of whom exhibited fasting blood glucose concentrations <110 mg/dl. Note that, although the MA subjects were less insulin sensitive on the basis of HOMA-IR and revised QUICKI, they are clearly not to be considered "insulin resistant." However, previous studies have implicated diminished insulin sensitivity among nondiabetic MA as a risk factor for future insulin resistance and development of T2DM (5, 30). Another limitation is the use of self-reported dietary intake records to characterize habitual intake of energy and macronutrients. Although such dietary assessment has inherent inaccuracies, we took painstaking efforts to accurately estimate serving sizes and used a research dietitian to review recorded information with each study subject. The fact that the group differences in insulin sensitivity were found and were statistically related to dietary fat intake, even in the face of less than perfect estimates of insulin sensitivity and dietary intake, suggests that this may be a very important finding. An obvious extension of this study will be to experimentally determine whether manipulations of dietary fatty acids in MA affect insulin action.
Longitudinal data are not available regarding the quantitative importance of dietary habits with respect to genetic background exhibited by MA. However, studies of this nature have been conducted with Pima Indians (9, 55), which may be relevant considering that up to 35% of the genetic background of MA is attributable to Native American ancestry (34). Pima Indians maintaining a traditional lifestyle (lower fat, high intake of low glycemic index carbohydrates) in Mexico are typically nonobese and exhibit a low prevalence of diabetes (55). On the other hand, Pimas living in Arizona who have adapted to a more Western lifestyle (higher dietary fat, higher glycemic index carbohydrates) are more obese and insulin resistant (55). One might speculate that slightly higher dietary fat intakes exhibited by the MA in our study could be quantitatively important, particularly if coupled with differences in fat oxidation, which could impact intramuscular fat accumulation.
The lack of group differences in skeletal muscle cytosolic
insulin-signaling proteins is a novel finding. However, we did not
evaluate functional activity or insulin-stimulated phosphorylation of
these intermediates. Clinical states of insulin resistance have been
associated with impairments in insulin-stimulated glucose uptake and
are associated with decreases in IRS-1 tyrosine phosphorylation, PI3K
activity, and Akt serine/threonine phosphorylation. Kim et al.
(40) and others (6, 25, 45) have reported
that insulin-stimulated IRS-1-associated PI3K activity is decreased in
skeletal muscle of diabetic subjects. Although impaired activities of
these proteins appear to be primary defects in insulin-stimulated GLUT4
translocation, reduced protein content may contribute to some models of
human insulin resistance. Additionally, a growing body of evidence
suggests that other mediators of insulin responsiveness (PKC, IKK)
may be involved in skeletal muscle insulin resistance. Future studies should investigate possible differences in basal and insulin-stimulated activity of these signaling intermediates.
In summary, the present study demonstrates that lower insulin
sensitivity persists in nonobese, nondiabetic Mexican-Americans compared with their non-Hispanic white counterparts, even after acute
and chronic effects of exercise and abdominal fat distribution are
accounted for. Furthermore, these differences are not explained by the
protein abundance of skeletal muscle IR, PI3K p85, Akt1, Akt2, or
GLUT4. Differences in insulin sensitivity are lost when dietary intakes
of palmitate and palmitoleate are accounted for, suggesting the
possibility that these factors may contribute to the lower insulin
sensitivity seen in Mexican-Americans. Future studies should determine
whether this relationship is coincidental or causal.
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ACKNOWLEDGEMENTS |
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We thank our research dietitians, Ashley Bubert, Lissa Heald, and Brenda Davy for their technical assistance, Rich Salas, director of the El Centro Student Organization for help in recruitment, and all the study participants for their cooperation.
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FOOTNOTES |
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The research presented here was supported by National Institutes of Health Grants National Institute of Diabetes and Digestive and Kidney Diseases HL-67227 and 62283, National Research Service Award F31 DK-10057-02, the Colorado Agricultural Experiment Station (Project 616), and the Gatorade Science Foundation.
Current address of R. C. Ho: Joslin Diabetes Center, Metabolism Section, Boston, MA 02215
Address for reprint requests and other correspondence: C. L. Melby, Dept. of Food Science and Human Nutrition, Nutrition and Metabolic Fitness Laboratory, Colorado State Univ., 226 Gifford, Fort Collins, CO 80523 (E-mail: melby{at}cahs.colostate.edu).
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
June 18, 2002;10.1152/ajpendo.00105.2002
Received 8 March 2002; accepted in final form 7 June 2002.
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