Behavioral, metabolic, and molecular correlates of lower insulin sensitivity in Mexican-Americans

Richard C. Ho1, Kevin P. Davy2, Matthew S. Hickey2, Scott A. Summers3, and Christopher L. Melby1

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


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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-beta (IRbeta ), 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 IRbeta 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


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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 alpha -subunit of the insulin receptor (IR). This binding activates tyrosine kinase activity in the beta -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 IRbeta , PI3K p85, Akt1, Akt2, and GLUT4 compared with NHW after controlling for these same potential confounders.


    METHODS
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ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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 (VO2 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 VO2 max. VO2 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 VO2 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
HOMA-IR = (fasting insulin, &mgr;U/ml 

× fasting glucose, mmol/l)/22.5

QUICKI = 1/[log (glucose, mg/dl) + log (insulin, &mgr;U/ml)]

Revised QUICKI = 1/[log (glucose, mg/dl) 

+ log (insulin, &mgr;U/ml) + (log NEFA,mmol/l)]
where NEFA is nonesterified fatty acid. These estimates have been shown to correlate well with the more direct measures of insulin sensitivity such as the frequently sampled intravenous glucose tolerance test (FSIVGTT) and hyperinsulinemic euglycemic clamp (2, 8, 38, 50).

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 (approx 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 IRbeta , PI3K p85 alpha /beta -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 IRbeta , PI3K p85alpha , 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 IRbeta and Akt1 from Upstate Biotechnology, (Lake Placid, NY), PI3K p85alpha /beta , 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.


    RESULTS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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 VO2 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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Table 1.   Subject characteristics

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 2.   Dietary intake for MA and NHW subjects determined by 4-day diet record

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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Table 3.   Plasma concentrations of glucose, insulin, and NEFA and estimates of insulin sensitivity in MA and NHW study participants

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 IRbeta was accounted for. Correlations between HOMA-IR and these covariates are shown in Fig. 1.


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Fig. 1.   Relationships between homeostatic model assessment of insulin resistance (HOMA-IR) and insulin receptor-beta (IRbeta ) (n = 26), dietary palmitic acid (n = 24), and dietary palmitoleic acid (n = 24) in the entire sample of Mexican-Americans (MA) and non-Hispanic whites (NHW) combined.

Representative immunoblots are shown in Fig. 2. There were no significant differences between the two groups with regard to skeletal muscle protein abundance of IRbeta , PI3K p85, Akt1, Akt2, or GLUT4 (Fig. 3). Skeletal muscle protein abundance of IRbeta 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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Fig. 2.   Representative immunoblots of IRbeta , phosphatidylinositol 3-kinase (PI3K) p85, Akt1, Akt2, and GLUT4 from more fit (+) and less fit (-) MA and NHW men and women.



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Fig. 3.   Protein abundance of IRbeta , PI3K p85, Akt1, Akt2, and GLUT4 in MA and NHW men and women.

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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Table 4.   Relationship between dietary intake and estimates of insulin sensitivity in the entire sample of study participants (MA and NHW combined, n = 24)

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 VO2 relative to resting VO2, 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 IRbeta (r = 0.43, P < 0.05).


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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 IRbeta , 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 IRbeta 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 (VO2 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 p85alpha , 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 IRbeta 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 IRbeta , 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 beta -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, IKKbeta ) 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 IRbeta , 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.


    ACKNOWLEDGEMENTS

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.


    FOOTNOTES

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.


    REFERENCES
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

1.   Aguirre, MA, Jones CN, Pei D, Villa ML, and Reaven GM. Ethnic differences in insulin resistance and its consequences in older Mexican American and non-Hispanic white women. J Gerontol A Biol Sci Med Sci 52: M56-M60, 1997[Abstract].

2.   Anderson, RL, Hamman RF, Savage PJ, Saad MF, Laws A, Kades WW, Sands RE, and Cefalu W. Exploration of simple insulin sensitivity measures derived from frequently sampled intravenous glucose tolerance (FSIGT) tests. The Insulin Resistance Atherosclerosis Study. Am J Epidemiol 142: 724-732, 1995[Abstract].

3.   Andreelli, F, Laville M, Vega N, Riou JP, and Vidal H. Regulation of gene expression during severe caloric restriction: lack of induction of p85 alpha phosphatidylinositol 3-kinase mRNA in skeletal muscle of patients with type II (non-insulin-dependent) diabetes mellitus. Diabetologia 43: 356-363, 2000[ISI][Medline].

4.   Balke, B, and Ware WW. An experimental study of physical fitness in Air Force personnel. US Armed Forces Medicine 10: 675-688, 1959.

5.   Batey, LS, Goff DC, Jr, Tortolero SR, Nichaman MZ, Chan W, Chan FA, Grunbaum J, Hanis CL, and Labarthe DR. Summary measures of the insulin resistance syndrome are adverse among Mexican-American versus non-Hispanic white children: the Corpus Christi Child Heart Study. Circulation 96: 4319-4325, 1997[Abstract/Free Full Text].

6.   Bjornholm, M, Kawano Y, Lehtihet M, and Zierath JR. Insulin receptor substrate-1 phosphorylation and phosphatidylinositol 3-kinase activity in skeletal muscle from NIDDM subjects after in vivo insulin stimulation. Diabetes 46: 524-527, 1997[Abstract].

7.   Bonini, JA, Colca JR, Dailey C, White M, and Hofmann C. Compensatory alterations for insulin signal transduction and glucose transport in insulin-resistant diabetes. Am J Physiol Endocrinol Metab 269: E759-E765, 1995[Abstract/Free Full Text].

8.   Bonora, E, Targher G, Alberiche M, Bonadonna RC, Saggiani F, Zenere MB, Monauni T, and Muggeo M. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Diabetes Care 23: 57-63, 2000[Abstract].

9.   Boyce, VL, and Swinburn BA. The traditional Pima Indian diet. Composition and adaptation for use in a dietary intervention study. Diabetes Care 16: 369-371, 1993[Abstract].

10.   Budohoski, L, Panczenko-Kresowska B, Langfort J, Zernicka E, Dubaniewicz A, Ziemlanski S, Challiss RA, and Newsholme EA. Effects of saturated and polyunsaturated fat enriched diet on the skeletal muscle insulin sensitivity in young rats. J Physiol Pharmacol 44: 391-398, 1993[Medline].

11.   Caro, JF, Sinha MK, Raju SM, Ittoop O, Pories WJ, Flickinger EG, Meelheim D, and Dohm GL. Insulin receptor kinase in human skeletal muscle from obese subjects with and without noninsulin dependent diabetes. J Clin Invest 79: 1330-1337, 1987[ISI][Medline].

12.   Carvalho, E, Eliasson B, Wesslau C, and Smith U. Impaired phosphorylation and insulin-stimulated translocation to the plasma membrane of protein kinase B/Akt in adipocytes from Type II diabetic subjects. Diabetologia 43: 1107-1115, 2000[ISI][Medline].

13.   Ciaraldi, TP, Carter L, Mudaliar S, Kern PA, and Henry RR. Effects of tumor necrosis factor-alpha on glucose metabolism in cultured human muscle cells from nondiabetic and type 2 diabetic subjects. Endocrinology 139: 4793-4800, 1998[Abstract/Free Full Text].

14.   Crespo, CJ, Keteyian SJ, Heath GW, and Sempos CT. Leisure-time physical activity among US adults. Results from the Third National Health and Nutrition Examination Survey. Arch Intern Med 156: 93-98, 1996[Abstract].

15.   Cusi, K, Maezono K, Osman A, Pendergrass M, Patti ME, Pratipanawatr T, DeFronzo RA, Kahn CR, and Mandarino LJ. Insulin resistance differentially affects the PI 3-kinase- and MAP kinase-mediated signaling in human muscle. J Clin Invest 105: 311-320, 2000[Abstract/Free Full Text].

16.   DeLany, JP, Windhauser MM, Champagne CM, and Bray GA. Differential oxidation of individual dietary fatty acids in humans. Am J Clin Nutr 72: 905-911, 2000[Abstract/Free Full Text].

17.   Despres, JP, Lemieux S, Lamarche B, Prud'homme D, Moorjani S, Brun LD, Gagne C, and Lupien PJ. The insulin resistance-dyslipidemic syndrome: contribution of visceral obesity and therapeutic implications. Int J Obes Relat Metab Disord 19, Suppl1: S76-S86, 1995[Medline].

18.   Devlin, JT. Effects of exercise on insulin sensitivity in humans. Diabetes Care 15: 1690-1693, 1992[Abstract].

19.   Devlin, JT, Hirshman M, Horton ED, and Horton ES. Enhanced peripheral and splanchnic insulin sensitivity in NIDDM men after single bout of exercise. Diabetes 36: 434-439, 1987[Abstract].

20.   Dunaif, A, Wu X, Lee A, and Diamanti-Kandarakis E. Defects in insulin receptor signaling in vivo in the polycystic ovary syndrome (PCOS). Am J Physiol Endocrinol Metab 281: E392-E399, 2001[Abstract/Free Full Text].

21.   Ferland, M, Despres JP, Tremblay A, Pinault S, Nadeau A, Moorjani S, Lupien PJ, Theriault G, and Bouchard C. Assessment of adipose tissue distribution by computed axial tomography in obese women: association with body density and anthropometric measurements. Br J Nutr 61: 139-148, 1989[ISI][Medline].

22.   Ferrannini, E, Haffner SM, Mitchell BD, and Stern MP. Hyperinsulinaemia: the key feature of a cardiovascular and metabolic syndrome. Diabetologia 34: 416-422, 1991[ISI][Medline].

23.   Garvey, WT, Maianu L, Hancock JA, Golichowski AM, and Baron A. Gene expression of GLUT4 in skeletal muscle from insulin-resistant patients with obesity, IGT, GDM, and NIDDM. Diabetes 41: 465-475, 1992[Abstract].

24.   Gaster, M, Staehr P, Beck-Nielsen H, Schroder HD, and Handberg A. GLUT4 is reduced in slow muscle fibers of type 2 diabetic patients: is insulin resistance in type 2 diabetes a slow, type 1 fiber disease? Diabetes 50: 1324-1329, 2001[Abstract/Free Full Text].

25.   Goodyear, LJ, Giorgino F, Sherman LA, Carey J, Smith RJ, and Dohm GL. Insulin receptor phosphorylation, insulin receptor substrate-1 phosphorylation, and phosphatidylinositol 3-kinase activity are decreased in intact skeletal muscle strips from obese subjects. J Clin Invest 95: 2195-2204, 1995[ISI][Medline].

26.   Goodyear, LJ, and Kahn BB. Exercise, glucose transport, and insulin sensitivity. Annu Rev Med 49: 235-261, 1998[ISI][Medline].

27.   Haffner, SM, Katz MS, and Dunn JF. The relationship of insulin sensitivity and metabolic clearance of insulin to adiposity and sex hormone binding globulin. Endocr Res 16: 361-376, 1990[ISI][Medline].

28.   Haffner, SM, Kennedy E, Gonzalez C, Stern MP, and Miettinen H. A prospective analysis of the HOMA model. The Mexico City Diabetes Study. Diabetes Care 19: 1138-1141, 1996[Abstract].

29.   Haffner, SM, Miettinen H, and Stern MP. The homeostasis model in the San Antonio Heart Study. Diabetes Care 20: 1087-1092, 1997[Abstract].

30.   Haffner, SM, Stern MP, Dunn J, Mobley M, Blackwell J, and Bergman RN. Diminished insulin sensitivity and increased insulin response in nonobese, nondiabetic Mexican Americans. Metabolism 39: 842-847, 1990[ISI][Medline].

31.   Haffner, SM, Stern MP, Hazuda HP, Pugh J, Patterson JK, and Malina R. Upper body and centralized adiposity in Mexican Americans and non-Hispanic whites: relationship to body mass index and other behavioral and demographic variables. Int J Obes 10: 493-502, 1986[ISI][Medline].

32.   Haffner, SM, Stern MP, Hazuda HP, Rosenthal M, Knapp JA, and Malina RM. Role of obesity and fat distribution in non-insulin-dependent diabetes mellitus in Mexican Americans and non-Hispanic whites. Diabetes Care 9: 153-161, 1986[Abstract].

33.   Haffner, SM, Stern MP, Mitchell BD, Hazuda HP, and Patterson JK. Incidence of type II diabetes in Mexican Americans predicted by fasting insulin and glucose levels, obesity, and body-fat distribution. Diabetes 39: 283-288, 1990[Abstract].

34.   Hanis, CL, Hewett-Emmett D, Bertin TK, and Schull WJ. Origins of U.S. Hispanics. Implications for diabetes. Diabetes Care 14: 618-627, 1991[Abstract].

35.   Henriksson, J. Influence of exercise on insulin sensitivity. J Cardiovasc Risk 2: 303-309, 1995[Medline].

36.   Hickey, MS, Weidner MD, Gavigan KE, Zheng D, Tyndall GL, and Houmard JA. The insulin action-fiber type relationship in humans is muscle group specific. Am J Physiol Endocrinol Metab 269: E150-E154, 1995[Abstract/Free Full Text].

37.   Johnson, D, Cormack GC, Abrahams PH, and Dixon AK. Computed tomographic observations on subcutaneous fat: implications for liposuction. Plast Reconstr Surg 97: 387-396, 1996[ISI][Medline].

38.   Katz, A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, and Quon MJ. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 85: 2402-2410, 2000[Abstract/Free Full Text].

39.   Kelley, DE, Thaete FL, Troost F, Huwe T, and Goodpaster BH. Subdivisions of subcutaneous abdominal adipose tissue and insulin resistance. Am J Physiol Endocrinol Metab 278: E941-E948, 2000[Abstract/Free Full Text].

40.   Kim, YB, Nikoulina SE, Ciaraldi TP, Henry RR, and Kahn BB. Normal insulin-dependent activation of Akt/protein kinase B, with diminished activation of phosphoinositide 3-kinase, in muscle in type 2 diabetes. J Clin Invest 104: 733-741, 1999[Abstract/Free Full Text].

41.   Kim, YB, Peroni OD, Franke TF, and Kahn BB. Divergent regulation of Akt1 and Akt2 isoforms in insulin target tissues of obese Zucker rats. Diabetes 49: 847-856, 2000[Abstract].

42.   Kirwan, JP, del Aguila LF, Hernandez JM, Williamson DL, O'Gorman DJ, Lewis R, and Krishnan RK. Regular exercise enhances insulin activation of IRS-1-associated PI3-kinase in human skeletal muscle. J Appl Physiol 88: 797-803, 2000[Abstract/Free Full Text].

43.   Kissebah, AH. Intra-abdominal fat: is it a major factor in developing diabetes and coronary artery disease? Diabetes Res Clin Pract 30, Suppl: 25-30, 1996[Medline].

44.   Kriska, AM, Hanley AJ, Harris SB, and Zinman B. Physical activity, physical fitness, and insulin and glucose concentrations in an isolated Native Canadian population experiencing rapid lifestyle change. Diabetes Care 24: 1787-1792, 2001[Abstract/Free Full Text].

45.   Krook, A, Bjornholm M, Galuska D, Jiang XJ, Fahlman R, Myers MG, Jr, Wallberg-Henriksson H, and Zierath JR. Characterization of signal transduction and glucose transport in skeletal muscle from type 2 diabetic patients. Diabetes 49: 284-292, 2000[Abstract].

46.   Krook, A, Roth RA, Jiang XJ, Zierath JR, and Wallberg-Henriksson H. Insulin-stimulated Akt kinase activity is reduced in skeletal muscle from NIDDM subjects. Diabetes 47: 1281-1286, 1998[Abstract].

47.   Laakso, M. How good a marker is insulin level for insulin resistance? Am J Epidemiol 137: 959-965, 1993[Abstract].

48.   Lovejoy, J, and DiGirolamo M. Habitual dietary intake and insulin sensitivity in lean and obese adults. Am J Clin Nutr 55: 1174-1179, 1992[Abstract].

49.   Marshall, JA, Hamman RF, and Baxter J. High-fat, low-carbohydrate diet and the etiology of non-insulin-dependent diabetes mellitus: the San Luis Valley Diabetes Study. Am J Epidemiol 134: 590-603, 1991[Abstract].

50.   Matthews, DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, and Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28: 412-419, 1985[ISI][Medline].

51.   Mayer-Davis, EJ, Monaco JH, Hoen HM, Carmichael S, Vitolins MZ, Rewers MJ, Haffner SM, Ayad MF, Bergman RN, and Karter AJ. Dietary fat and insulin sensitivity in a triethnic population: the role of obesity. The Insulin Resistance Atherosclerosis Study (IRAS). Am J Clin Nutr 65: 79-87, 1997[Abstract].

52.   Mikines, KJ, Sonne B, Farrell PA, Tronier B, and Galbo H. Effect of physical exercise on sensitivity and responsiveness to insulin in humans. Am J Physiol Endocrinol Metab 254: E248-E259, 1988[Abstract/Free Full Text].

53.   Pedersen, O, Bak JF, Andersen PH, Lund S, Moller DE, Flier JS, and Kahn BB. Evidence against altered expression of GLUT1 or GLUT4 in skeletal muscle of patients with obesity or NIDDM. Diabetes 39: 865-870, 1990[Abstract].

54.   Perseghin, G, Caumo A, Caloni M, Testolin G, and Luzi L. Incorporation of the fasting plasma FFA concentration into QUICKI improves its association with insulin sensitivity in nonobese individuals. J Clin Endocrinol Metab 86: 4776-4781, 2001[Abstract/Free Full Text].

55.   Ravussin, E, Valencia ME, Esparza J, Bennett PH, and Schulz LO. Effects of a traditional lifestyle on obesity in Pima Indians. Diabetes Care 17: 1067-1074, 1994[Abstract].

56.   Reaven, GM. Pathophysiology of insulin resistance in human disease. Physiol Rev 75: 473-486, 1995[Abstract/Free Full Text].

57.   Sinha, MK, Pories WJ, Flickinger EG, Meelheim D, and Caro JF. Insulin-receptor kinase activity of adipose tissue from morbidly obese humans with and without NIDDM. Diabetes 36: 620-625, 1987[Abstract].

58.   Sinha, MK, Raineri-Maldonado C, Buchanan C, Pories WJ, Carter-Su C, Pilch PF, and Caro JF. Adipose tissue glucose transporters in NIDDM. Decreased levels of muscle/fat isoform. Diabetes 40: 472-477, 1991[Abstract].

59.   Stallknecht, B, Larsen JJ, Mikines KJ, Simonsen L, JBülow, and Galbo H. Effect of training on insulin sensitivity of glucose uptake and lipolysis in human adipose tissue. Am J Physiol Endocrinol Metab 279: E376-E385, 2000[Abstract/Free Full Text].

60.   Stern, MP, Gaskill SP, Hazuda HP, Gardner LI, and Haffner SM. Does obesity explain excess prevalence of diabetes among Mexican Americans? Results of the San Antonio Heart Study. Diabetologia 24: 272-277, 1983[ISI][Medline].

61.   Stern, MP, and Haffner SM. Type II diabetes and its complications in Mexican Americans. Diabetes Metab Rev 6: 29-45, 1990[ISI][Medline].

62.   Summers, SA, Garza LA, Zhou H, and Birnbaum MJ. Regulation of insulin-stimulated glucose transporter GLUT4 translocation and Akt kinase activity by ceramide. Mol Cell Biol 18: 5457-5464, 1998[Abstract/Free Full Text].

63.   Swinburn, BA, Boyce VL, Bergman RN, Howard BV, and Bogardus C. Deterioration in carbohydrate metabolism and lipoprotein changes induced by modern, high fat diet in Pima Indians and Caucasians. J Clin Endocrinol Metab 73: 156-165, 1991[Abstract].

64.   Tortolero, SR, Goff DC, Jr, Nichaman MZ, Labarthe DR, Grunbaum JA, and Hanis CL. Cardiovascular risk factors in Mexican-American and non-Hispanic white children: the Corpus Christi Child Heart Study. Circulation 96: 418-423, 1997[Abstract/Free Full Text].

65.   Trevino, RP, Marshall RM, Jr, Hale DE, Rodriguez R, Baker G, and Gomez J. Diabetes risk factors in low-income Mexican-American children. Diabetes Care 22: 202-207, 1999[Abstract].

66.   Vessby, B, Aro A, Skarfors E, Berglund L, Salminen I, and Lithell H. The risk to develop NIDDM is related to the fatty acid composition of the serum cholesterol esters. Diabetes 43: 1353-1357, 1994[Abstract].

67.   Webber, LS, Osganian V, Luepker RV, Feldman HA, Stone EJ, Elder JP, Perry CL, Nader PR, Parcel GS, and Broyles SL. Cardiovascular risk factors among third grade children in four regions of the United States. The CATCH Study. Child and Adolescent Trial for Cardiovascular Health. Am J Epidemiol 141: 428-439, 1995[Abstract].

68.   Youngren, JF, Keen S, Kulp JL, Tanner CJ, Houmard JA, and Goldfine ID. Enhanced muscle insulin receptor autophosphorylation with short-term aerobic exercise training. Am J Physiol Endocrinol Metab 280: E528-E533, 2001[Abstract/Free Full Text].


Am J Physiol Endocrinol Metab 283(4):E799-E808
0193-1849/02 $5.00 Copyright © 2002 the American Physiological Society




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