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The Journal of Clinical Endocrinology & Metabolism Vol. 85, No. 10 3557-3560
Copyright © 2000 by The Endocrine Society

Original Studies

Relationship between Hyperinsulinemia and Remnant Lipoprotein Concentrations in Patients with Impaired Glucose Tolerance

Masumi Ai, Akira Tanaka, Keiko Ogita, Masaaki Sekine, Fujie Numano, Fujio Numano and Gerald M. Reaven

Third Department of Internal Medicine, Tokyo Medical and Dental University, Tokyo 113-8519, Japan; Department of Internal Medicine, Tokyo Metropolitan Kiyose Hospital (K.O., M.S., F.N.), Tokyo 204-0023, Japan; and Stanford University School of Medicine (G.M.R.), Stanford, California 94305-5406

Address all correspondence and requests for reprints to: Akira Tanaka, M.D., Third Department of Internal Medicine, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan. address: tanaka.med3{at}med.tmd.ac.jp


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
This study was performed to explore further the association between insulin resistance and plasma remnant lipoprotein (RLP) concentration. For this purpose we used the sum of the plasma insulin concentrations before and 30, 60, 90, 120, and 180 min after a 75-g oral glucose load ({Sigma}IRI) as a surrogate measure of insulin resistance in 61 subjects with impaired glucose tolerance. {Sigma}IRI was determined on 2 occasions, before and 16 weeks after initiation of a diet and exercise program. At baseline, {Sigma}IRI correlated with the sum of the plasma glucose concentrations in response to the 75-g oral glucose load (r = 0.26; P < 0.04) as well as plasma concentrations of triglyceride (r = 0.21; P = 0.09), RLP-cholesterol (r = 0.41; P < 0.001), and RLP-triglyceride (r = 0.46; P < 0.001). In contrast, neither total (r = 0.07) nor high density lipoprotein (HDL) cholesterol (r = 0.04) concentrations correlated with {Sigma}IRI. {Sigma}IRI was lower in 42 subjects following life-style intervention, associated with significant (P < 0.005) reductions in {Sigma}glucose, and fasting glucose, insulin, triglyceride, RLP-cholesterol, and RLP-triglyceride concentrations. However, none of these variables decreased in the 19 subjects whose {Sigma}IRI did not fall. Finally, the change in {Sigma}IRI following intervention with diet and exercise was significantly associated with differences in {Sigma}glucose (r = 0.63; P < 0.001) and fasting glucose (r = 0.26; P < 0.05), insulin (r = 0.79; P < 0.001), triglyceride (r = 0.29; P < 0.03), RLP-cholesterol (r = 0.71; P < 0.001), and RLP-triglyceride (r = 0.49; P < 0.001) concentrations. These results demonstrate that variations in concentrations of RLPs are highly correlated with changes in {Sigma}IRI, consistent with the possibilities that 1) RLP measurements are useful estimates of insulin resistance; and 2) an increase in RLP concentrations may provide the mechanistic link between insulin resistance and coronary heart disease.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
THERE IS considerable evidence that insulin resistance and/or compensatory hyperinsulinemia increase the risk of coronary heart disease (CHD) in nondiabetic individuals (1, 2, 3, 4, 5, 6). However, the link between these defects and CHD is complicated because of the fact that multiple CHD risk factors exist in insulin-resistant individuals (7, 8). In this context, increasing attention has been given to the atherogenicity of the triglyceride (TG)-rich lipoproteins that accumulate in the postprandial state (9, 10, 11). As the magnitude of postprandial lipemia is accentuated in insulin-resistant and hyperinsulinemic individuals (12), it can be speculated that this change may play an important role in the increased risk of CHD seen in these individuals. Efforts to evaluate the relationship among insulin resistance, dyslipidemia, postprandial lipemia, and CHD have been aided by the description of a technique (13, 14, 15), based on the use of monoclonal antibodies to apoA-I and apoB-100, to isolate and determine the cholesterol and TG concentrations of remnant lipoprotein (RLPs). Using this technique, evidence has recently been published showing that fasting RLP concentrations are increased in patients with impaired glucose tolerance (16), as well as in insulin-resistant, nondiabetic individuals (17). The current study was an effort to extend these studies by defining the relationship in patients with impaired glucose tolerance (IGT) between the plasma insulin response to oral glucose and variables associated with insulin resistance, including RLP concentrations.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
The study population was selected from 200 individuals with the diagnosis of impaired fasting glucose (18) made at the time of an annual medical evaluation. A 75-g oral glucose tolerance test (OGTT) was performed on 160 of the original 200 individuals, and 92 were newly diagnosed as having IGT by the criteria of the American Diabetes Association (18). Twenty-four of the 92 subjects with IGT were excluded from further study because of their premenopausal status (n = 5), questions about excessive alcohol consumption (n = 12), or treatment with medications that might influence the experimental variables being evaluated (n = 7). The 68 remaining subjects were instructed on an energy-restricted diet (30 Cal/ideal wt·day) and an exercise program (at least 30 min of brisk walking everyday). Subjects were seen every 2 weeks for the next 16 weeks, and then compliance to the intervention program was evaluated, including estimates of the nutritional component of the diet reviewed. During this period 7 additional individuals voluntarily withdrew from the study, leaving 61 participants remaining for the final evaluation. At the end of the 16-week intervention period, a second OGTT was performed on those subjects who had persisted with the dietary exercise program for 16 weeks. The 61 individuals who finished the 16-week intervention programs had a mean (±SEM) age of 57 ± 1 yr; they consisted of 38 men and 23 postmenopausal women, with a mean (±SEM) body mass index of 23.8 ± 0.3 kg/m2. Thirty of the 61 subjects were taking antihypertensive medication (18 calcium channel blockers, 8 angiotensin-converting enzyme inhibitors, and 4 both).

Blood samples were removed at the time of both OGTTs before and 30, 60, 90, 120, and 180 min after a 75-g oral glucose load for determination of plasma glucose (19) and insulin (20) concentrations. The sum of these six measurements of plasma glucose ({Sigma}glucose) and insulin ({Sigma}IRI) concentrations were calculated, and this value was used for all further data analyses.

In addition, the fasting blood samples were used for measurement of plasma cholesterol (21), high density lipoprotein cholesterol (22), and TG (23). Chylomicron and very low density lipoprotein remnant particles were isolated by an immunoseparation method using monoclonal antibodies to apolipoprotein A-1 (apoA-1) and apoB-100 that recognize TG-rich lipoproteins containing apoB-48 and a population of apoB-100 enriched in apoE. Briefly, plasma is added to an immunoaffinity gel suspension containing the two monoclonal antibodies, and the reaction mixture is shaken for 60 min at room temperature, then left to stand for 10 min. Aliquots of the supernatant are then used for the measurement of RLP-cholesterol and TG concentrations (13, 14, 15).

Results are expressed as the mean ± SEM. Changes observed in response to the 16-week intervention period of diet and exercise were evaluated by comparing the differences between values at baseline and those at the end of the study using the Mann-Whitney nonpaired rank test. Finally, correlation coefficients were determined between {Sigma}IRI and the other variables at baseline using Spearman’s correlation test as well as between the changes in the {Sigma}IRI after the interventions and changes in the other variables. In each analysis, NS refers to P > 0.10.


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
Table 1Go presents the correlations between the sum of the insulin concentrations ({Sigma}IRI) at baseline and the other experimental variables. These results show that the greater the {Sigma}IRI, the older the subject (P < 0.05), and the higher their concentrations of fasting and {Sigma} glucose (P < 0.05). In addition, fasting insulin (P < 0.001), TG (P = 0.09), RLP-cholesterol (P < 0.001), and RLP-TG (P < 0.001) concentrations were significantly correlated with {Sigma}IRI.


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  		Table 1. Correlation coefficients between the total integrated insulin response ({Sigma}IRI) and other relevant variables at baseline

 
The changes in the experimental variables after the period of life-style intervention are presented in Table 2Go as a function of whether the {Sigma}IRI increased or decreased. In 19 of the 61 volunteers, {Sigma}IRI was higher after the intervention, whereas it decreased in the remaining 42. It should be noted that the ages of those in whom {Sigma}IRI increased (57 ± 2 yr) were similar to those of the individuals showing the decrease in {Sigma}IRI (57 ± 1 yr). Furthermore, the difference in insulin response was unrelated to the amount of weight loss, which averaged about 0.7 kg in both groups. In fact, about 80% of the subjects in each group lost at least some weight in response to the intervention. It can be seen from these results that the group with lower {Sigma}IRI after the period of intervention also had significant (P < 0.001) decreases in the {Sigma} glucose response, as well as in fasting plasma glucose, insulin, TG, RLP-cholesterol, and RLP-TG concentrations. In contrast, the values of all of these variables increased in subjects in whom {Sigma}IRI was also higher.


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  		Table 2. Difference ({Delta}) in experimental variables between the two glucose tolerance tests as a function of the change in total integrated insulin response ({Sigma}IRI)

 
To further assess the relationship between {Sigma}IRI and the other variables, correlation coefficients (r) between changes ({Delta}) in {Sigma}IRI between the two glucose tolerance tests ({Sigma}IRI2 - {Sigma}IRI1) were correlated with changes ({Delta}) in the other measurements. These results are shown in Table 3Go and indicate that changes in {Sigma}IRI were significantly correlated with those in the fasting plasma glucose concentration (P < 0.05) and the {Sigma} glucose response (P < 0.001), with those in fasting plasma insulin (P < 0.001) and TG (P < 0.02) concentrations, as well with those in RLP-cholesterol and RLP-TG concentrations (P < 0.001).


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  		Table 3. Correlation coefficients (r) between differences ({Delta}) in the total integrated insulin response ({Sigma}IRI) and changes in the other variables

 

   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
The results of this study provide further evidence of the close relationship between insulin resistance and the fasting plasma RLP concentration. By examining this relationship at two points in time, we were able to demonstrate that a change in one of the two variables was highly correlated with the predicted change in the other. Indeed, it appeared that this relationship was as close, if not closer, than that between other variables previously shown to be associated with insulin resistance.

Before discussing the physiological and clinical implications of our findings, it would be useful to address the manner in which we have used the sum of the insulin response to oral glucose, i.e. {Sigma}IRI, as a surrogate measure of insulin resistance. We have assumed that {Sigma}IRI in nondiabetic individuals provides a reasonable estimate of resistance to insulin-mediated glucose disposal by muscle, a view supported by previous reports in which we observed correlation coefficients of 0.75 and 0.81 between specific estimates of insulin resistance and plasma insulin concentrations in 50 (24) and 62 (25) nondiabetic individuals. More recently, in a study of 490 nondiabetic volunteers (26), using a different method to measure insulin action, we demonstrated a correlation coefficient of approximately 0.81 between insulin-mediated glucose disposal and plasma insulin response to oral glucose. Thus, our use of insulin response as an estimate of insulin resistance seems reasonable.

In addition to serving as a surrogate marker of insulin resistance, the change in IRI has been used as a dynamic index to define the degree to which all of the measured variables are related. To accomplish this goal, patients with IGT were enrolled in a diet and exercise program. When studied 16 weeks later, 42 of the 61 subjects who finished the program had a decrease in {Sigma}IRI. In contrast, the {Sigma}IRI was higher in 19 individuals. There were no obvious differences in the baseline characteristics of these 2 groups. The average weight loss was also similar in the 2 groups, and about 80% of the individuals in either group lost weight. We can only speculate on why the responses of the 2 groups were so disparate, but the lack of an explanation does not obviate our ability to use the individual changes in {Sigma}IRI between the studies to illuminate the relationship between insulin resistance and related variables.

If we now focus attention on the relationship between the experimental variables in all 61 subjects before any intervention, the results in Table 1Go clearly indicate that there were highly significant correlation coefficients (P < 0.001) between {Sigma}insulin and fasting insulin and both RLP-cholesterol (r = 0.41) and RLP-TG (r = 0.46) concentrations. In addition, correlations of a lesser degree existed between {Sigma}IRI and age (P < 0.04), fasting plasma glucose (P < 0.05), {Sigma}glucose (P < 0.04), and plasma TG concentrations (P < 0.09).

The results of the comparison between the changes in experimental variables in those in whom {Sigma}IRI decreased vs. those in whom it increased were consistent with previous findings. Specifically, the results in Table 2Go show that a decrease in {Sigma}IRI was associated with a fall (P < 0.001) in {Sigma}glucose as well as in fasting plasma glucose, insulin, TG, RLP-cholesterol, and RLP-TG concentrations. These results are quite consistent with well established relationships between hyperinsulinemia and increased plasma levels of glucose, TG, and RLPs (4, 5, 6, 7, 8). The only somewhat unexpected finding was the lack of a significant difference between the changes in HDL cholesterol concentration in the two groups.

Based upon the relationships noted in Tables 1–3GoGoGo, it appeared that the variables most closely related to {Sigma}IRI were the concentrations of fasting plasma insulin, RLP-cholesterol, and RLP-TG. The fact that the fasting plasma insulin concentration was highly correlated with the plasma insulin response was to be expected, but we found somewhat surprising the fact that the relationship between {Sigma}IRI and RLP-cholesterol concentration was of a similar magnitude. For example, it can be seen from Table 3Go that 62% (0.792) of the variability in fasting plasma insulin concentration could be attributed to the time-related change in {Sigma}IRI seen after the life-style intervention, a value very similar to the 50% variability (0.712) in RLP-cholesterol concentration associated with the change in {Sigma}IRI. To put it more simply, these data demonstrate that plasma concentrations of RLPs are closely related to insulin resistance and compensatory hyperinsulinemia, a conclusion entirely consistent with previous reports of increased RLP concentrations in insulin-resistant, nondiabetic individuals (16), and patients with IGT and type 2 diabetes (17).

The implications of the strong association between hyperinsulinemia and RLP concentrations discerned in this study are 2-fold. It is impractical in large scale epidemiological studies to quantify insulin resistance in the population at large, and this is largely true of measuring the plasma glucose and/or insulin response to an oral glucose challenge. On the other hand, it appears that the RLP concentration is a more sensitive indicator of insulin resistance than is the glucose concentration and is comparable to the association between insulin resistance and plasma insulin concentration. Thus, it is possible that measurement of the fasting RLP concentration can serve as a surrogate estimate of the degree of insulin resistance in epidemiological studies comparable to that provided by determination of plasma insulin concentrations. Obviously, whether this is the case will depend upon the results of prospective studies evaluating the sensitivity and specificity of RLP as a surrogate measure of insulin resistance.

A second issue, and one of pathogenetic importance, is that determination of the fasting RLP concentration provides more than a surrogate estimate of insulin resistance. Arguments continue to exist concerning the mechanistic link between plasma insulin concentrations and CHD (27, 28, 29, 30). In contrast, evidence of the importance of postprandial lipemia as a risk factor for CHD (9, 10, 11), the association between insulin resistance and/or hyperinsulinemia and CHD (1, 2, 3, 4, 5, 6, 7, 8), and the relationship between hyperinsulinemia and RLP concentration noted in this and previous studies (16, 17) suggest that measurement of the plasma RLP concentration provides both a sensitive estimate of insulin resistance as well as a mechanistic explanation for the increase in CHD described in insulin-resistant and/or hyperinsulinemic, nondiabetic individuals. On the other hand, it should be emphasized that the correlation coefficients in Tables 2Go and 3Go identify associations; they do not necessarily define causality. However, evidence has been presented indicating that the relationship between insulin resistance and fasting RLP concentrations has been shown to be independent of differences in age, gender, and body mass index (17). Whether insulin resistance is the primary defect or whether insulin resistance, hyperinsulinemia, and RLP concentrations are secondary to a more fundamental abnormality remains to be decided.

Received February 8, 2000.

Revised July 3, 2000.

Accepted July 6, 2000.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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