Articles |
From the Medical College of Pennsylvania, Philadelphia, and Hahnemann University, Philadelphia, Pa.
| Abstract |
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Key Words: blood pressure insulin lipids blacks African Americans
| Introduction |
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Elevated plasma insulin concentration (hyperinsulinemia) and impaired insulin-stimulated glucose utilization, or insulin resistance, are strongly linked with cardiovascular diseases, including essential hypertension, NIDDM, and atherosclerosis.5 Insulin resistance is a clinical phenomenon arising from a defect in the cellular response to insulin stimulation. Due to a peripheral cell defect in insulin-mediated glucose utilization, a greater quantity of insulin is necessary to achieve metabolic control of glucose, resulting in hyperinsulinemia.5 Hyperinsulinemia, a characteristic feature of obesity and NIDDM, is also observed in hypertension independent of obesity and NIDDM.6 7 8
Several clinical investigations in young adult whites have demonstrated a direct correlation of plasma insulin concentration with BP suggesting that insulin could be a mediator of BP.9 10 However, in a study by Saad et al11 of obese normotensive whites, blacks, and Pima Indians, a significant correlation of insulin resistance with BP was present in the whites but not in the blacks or Pima Indians. In contrast, we have previously demonstrated a significant correlation of insulin resistance with BP in young adult blacks that was independent of obesity.12 13
In white and Hispanic populations, higher plasma insulin concentration is associated with an atherogenic lipid pattern including elevated LDL-C and VLDL-TG and decreased HDL-C.14 15 The relation of plasma lipids with BP in the context of insulin resistance has not been examined in young blacks. The purpose of this study was to determine if alterations in plasma lipid concentration are present in young adult blacks with only borderline hypertension and if plasma lipid alterations are linked with insulin resistance.
| Methods |
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135 and <150 mm Hg systolic or
85 and
<96 mm Hg diastolic), on the basis of repeated
measurements of BP.12 18 Individuals with either IDDM or
NIDDM were excluded. No subject was taking antihypertensive medication
at the time of study. We used an OGTT to measure plasma glucose and
insulin concentration after a standard glucose challenge. A
euglycemic hyperinsulinemic clamp procedure
was used to quantify insulin-stimulated glucose utilization. Plasma
lipids were assayed from blood samples obtained after an overnight
fast. The protocol for this study was approved by the Institutional
Review Board of the Medical College of Pennsylvania. Written informed
consent was obtained from all participants at the time of
enrollment. Enrollment assessment consisted of physical examination, anthropometric measurements (height, weight, skinfold thickness, and circumference of arm, hips, thigh, and waist), and BP determination. Casual systolic (first phase) and diastolic (fifth phase) BP measurements were obtained by auscultation with a mercury column sphygmomanometer with subjects in the seated position after a 10-minute rest period. The average of two determinations was used as the BP at the time of the metabolic evaluation. From the anthropometric measurements, percent body fat19 and fat free mass20 were calculated. A history of the subject's diet was taken and each was asked to continue usual dietary patterns through completion of the protocol. For this population, the dietary average consisted of protein 14%, fat 31%, and carbohydrate 55%. No subject had a diet that deviated significantly from this average. After the enrollment assessment, each subject returned to the clinical research unit for an OGTT that was scheduled in the morning after a 12-hour fast. After a fasting blood sample was obtained, a 75-g glucose solution (Glucola, Ames Laboratories) was taken orally. Blood samples were obtained at 30, 60, and 120 minutes after ingestion of the glucose load. Each blood sample was immediately centrifuged. Plasma was removed and stored at -80°C until the samples were assayed for glucose and insulin concentration. Fasting blood was also obtained for measurement of serum lipids. The sample was sent to the Lipid Research Laboratory of the Hospital of the Medical College of Pennsylvania, where total cholesterol, HDL-C, and total TG levels were analyzed by standard enzymatic methods and an automated analyzer (Hitachi 704). HDL-C was isolated according to the method of Bachorik et al.21 LDL-C was calculated by the Friedewald22 equation. ApoA-I, apoB, and Lp(a) were assayed turbidimetrically by using commercial antibodies (Boehringer Mannheim).
The euglycemic hyperinsulinemic clamp was used to measure insulin-stimulated glucose utilization.23 24 During steady state hyperinsulinemia, the glucose infusion rate required to maintain euglycemia quantifies insulin-stimulated glucose metabolism (M in mg · kg-1 · min-1). In both nonobese and obese young black men, we have demonstrated that with steady state hyperinsulinemia at a level of 70 to 80 µU/mL greater than fasting insulin level, endogenous glucose production is completely suppressed during the final 60 minutes of the procedure.25 Because the target level of steady state hyperinsulinemia in the present study was at least 70 to 80 µU/mL greater than fasting insulin level, endogenous glucose production is completely suppressed, and the glucose infusion rate required to maintain euglycemia (M) is an adequate index of total insulin-stimulated glucose utilization.24
Each subject returned to the clinical research unit for the euglycemic clamp procedure at 8 AM after a 12-hour overnight fast. The euglycemic clamp procedure was conducted according to methods previously described.12 In brief, the subject rested for at least 20 minutes after placement of venous catheters for infusion and sample withdrawal. Before the onset of euglycemic hyperinsulinemia, three samples were withdrawn for determination of fasting plasma glucose and fasting plasma insulin concentration. Euglycemic hyperinsulinemia was established with a primed constant infusion of insulin using the method of Rizza et al24 to compute the priming dose and infusion rate of insulin. The target clamped-insulin concentration was 70 to 80 µU/mL of insulin above fasting concentration, which was achieved with an infusion rate of 40 mU · m-2 · min-1.24 Glucose infusion was administered as 20% dextrose. The precise glucose concentration in the 20% dextrose stock solution was measured, and this value was used in the calculation of the glucose infusion rate with the negative feedback equation of DeFronzo et al.23 A personal computer was programmed to use this iterative negative feedback equation, which was amended for 10-minute plasma glucose sampling. Euglycemic hyperinsulinemia was maintained for 120 minutes. During the final 60 minutes of steady state hyperinsulinemia, insulin-stimulated glucose utilization was determined from the glucose infusion rate. The coefficient of variation for clamped plasma glucose concentration was less than 5% during the final 60 minutes of the procedure. Insulin-stimulated glucose utilization was computed as the mean glucose infusion rate during the final 60 minutes of hyperinsulinemia and expressed as mg · kg-1 · min-1 (M). Using the anthropometric measures, we computed fat free mass for each subject, and insulin-stimulated glucose utilization was also expressed as mg · kg-1 fat-free mass · min-1 (M').
Glucose was administered as 20% dextrose in water (Abbott). Insulin (Eli Lilly) was mixed with normal saline to a concentration of 1000 mU/mL. All solutions were delivered by syringe pumps (Harvard model 22). Plasma glucose concentration was analyzed with the glucose oxidase technique (YSI model 27, Glucostat). Plasma insulin concentration was determined with a solid-phase radioimmunoassay (Coat-A-Count, Diagnostic Products Corp). Coefficients of variation for interassay and intraassay variability for glucose, insulin, and the above lipid assays are less than 5%.
Data Analysis
Two-way ANOVA was used to test for significant differences
in means (normotensive subjects versus hypertensive subjects, and men
versus women). Tests for interactions were conducted between BP groups
and gender groups. Instead of using a repeated-measures ANOVA and
post hoc t tests for the OGTT data, we used the sum of
the insulin levels during the OGTT as the parameter in the
categorical or continuous data analysis. Differences in means
and interactions were considered statistically significant at
P<.05. Univariate correlations among
numerically continuous variables were examined by using Pearson
correlation coefficients. Stepwise multiple linear regressions were
used to examine multiple correlations among variables and to build
a regression model for insulin-stimulated glucose utilization (M)
by the other variables. On the basis of a theoretical model that
variables in several categories will significantly determine both M
and M', a stepwise multiple linear regression analysis was used
to determine the model of best fit of M and M' by independent
variables in several categories. All variables in all
categories were entered simultaneously in the stepwise
regression analysis. The anthropometric variables were BMI,
height, weight, and percent body fat. The hemodynamic
variables examined were systolic BP, diastolic
BP, and mean BP. Metabolic variables were fasting
plasma insulin concentration, fasting plasma glucose concentration, the
ratio of fasting plasma insulin to glucose concentration, and the sum
of plasma insulin concentration during the OGTT. The plasma lipid
parameters examined included total cholesterol,
HDL-C, LDL-C, TGs, apoA-I, apoB, and Lp(a). The stepwise computer
algorithm for the regression equation selects at the first step the
highest correlated variable with the dependent variable. At the
second step, the algorithm selects the variable that produces the
highest canonical correlation based on two independent variables
with the dependent variable. Therefore, variables that are
highly correlated with the first independent variable entered are
usually not entered into the regression. The computer algorithm
continues until there are no additional statistically significant
(P<.05) increases in the prediction of the single dependent
variable on the best linear combination of independent
variables. There are some highly correlated independent
variables such as weight, height, and BMI. The algorithm is not
disrupted or negated by this multicolinearity, but once one of a set of
highly correlated parameters is entered into the model, and
it is usually the strongest correlate with the dependent variable,
there is no additional predictive value for others in that set.
| Results |
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The parameters of insulin-regulated glucose
metabolism and plasma lipids for the BP and gender groups
are presented in Table 2
. The fasting plasma
insulin concentration is greater in the borderline hypertensive
subjects of both sexes compared with the normotensive subjects, and
this difference is statistically significant by two-way ANOVA
(P=.003). Similarly, the ratio of fasting plasma insulin
concentration to fasting plasma glucose concentration (I/G) is greater
in the borderline hypertensive subjects than in the normotensive
subjects; this difference is also statistically significant
(P=.003). There are no statistically significant sex
differences in these two variables. The sums of plasma insulin
concentrations during the OGTT are not significantly different between
the BP groups. While the mean value for the sum of plasma insulin
concentration is greater in the women, the sex difference does not
reach statistical significance (P=.068).
Insulin-stimulated glucose utilization (M) determined by the
insulin clamp procedure demonstrates a lower M, or relative insulin
resistance in the borderline hypertensive subjects compared with the
normotensive subjects (P=.006). M is also significantly
lower in the women compared with men in each BP group
(P=.040). When the rate of insulin-stimulated glucose
utilization is corrected for adiposity and expressed as
mg · kg-1 fat free mass · min-1 (M'),
the insulin sensitivity remains significantly lower in the borderline
hypertensive subjects compared with the normotensive subjects
(P=.006), and there are no sex differences in M'. Also shown
in Table 2
are the results of the two-way ANOVA of plasma lipid
concentrations by BP group and sex. There were no statistically
significant differences between normotensive subjects and borderline
hypertensive subjects in any of the plasma lipids. There were
significant sex differences, with men demonstrating higher TG levels
(P=.006) and higher apoB levels (P=.03) compared
with women. Women demonstrated higher Lp(a) compared with men
(P=.01).
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When the relation of BP with insulin sensitivity was examined by correlation analysis on the entire study population, there was a statistically significant correlation of insulin-stimulated glucose utilization, corrected for adiposity, in mg · kg-1 fat free mass · min-1 (M') with systolic BP (r=-.39, P<.0001), diastolic BP (r=-.22, P=.018), and mean BP (r=-.31, P=.001). The total insulin response to glucose challenge on the OGTT did not show significant differences by BP classification by the two-way ANOVA. However, the univariate analysis on the entire study group demonstrated a statistically significant correlation of the sum of insulin during OGTT with the insulin sensitivity corrected for adipose mass, M' (r=-.56, P<.0001).
The relation of plasma lipids with BP and metabolic
parameters was examined by univariate
correlation analysis. Table 3
provides a summary
of these results. There was a statistically significant correlation of
mean arterial BP with TGs (P=.02) and with apoB
(P=.019). Total cholesterol correlated
significantly with fasting insulin (P=.005) and the ratio of
fasting insulin to glucose concentration (P=.004) but did
not correlate with sum of insulin or the measures of insulin
sensitivity. There was no significant correlation of Lp(a) with the BPs
or any of the metabolic parameters. However,
HDL-C, LDL-C, TG levels, apoA-I, and apoB demonstrated statistically
significant correlations with insulin, both fasting and during the
OGTT, and with insulin sensitivity. These correlations remained
statistically significant with the insulin sensitivity corrected for
adiposity (M'). The lipid fraction that demonstrated the strongest
relation with insulin sensitivity was HDL-C (r=.437 for M
and r=.382 for M', both P<.0001). The relations
of apoA-I with M and M' paralleled those of HDL-C; however, the
correlation coefficients were not as high. The Figure
depicts the correlation of HDL-C with the corrected
insulin-stimulated glucose utilization in
mg · kg-1 fat free mass · min-1. This
significant correlation indicates that the association of lower HDL-C
with insulin resistance is independent of body fat.
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To examine the relations among the BP, lipid, and metabolic
variables, multiple regression analysis was applied to all
variables to delineate the major determinants of insulin
sensitivity. When M is used as the dependent variable, the
statistically significant variables in the regression equation were
BMI, systolic BP, sum of insulin, HDL-C, and apoA-I, with a
multiple R=.739, P<.0001. To correct for the
effect of body fat on insulin resistance, the corrected measure of
insulin sensitivity, M', was used as the dependent variable. There
was then a statistically significant regression of M' on
systolic BP, sum of insulin, HDL-C, and apoA-I. The multiple
R for this model is R=.684, P<.0001.
The statistics for these multiple regressions are listed in Table 4
. The two regression models are markedly similar. M',
corrected for body fat, is no longer significantly correlated with BMI,
and therefore BMI is not a significant variable in the multiple
regression on M'. All other variables remain in the regression
model for M'. The magnitude of the slopes, SE of slopes, and the
t ratio of slope divided by SE of slope of each of the
variables are quite similar between the two regression models. The
largest difference between the two models is the value of the constant,
which is a function of using M' instead of M as the dependent
variable. Overall, highly correlated parameters from
each of the four areas (anthropometric, BP, insulin, lipids) contribute
to and significantly explain the insulin resistance (M).
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| Discussion |
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Obesity is generally associated with the insulin-resistant syndrome, and hyperinsulinemia is observed under conditions of excessive adiposity. The high percentage of obese subjects in this study, particularly the borderline hypertensive women, would indicate that the insulin resistance may not be entirely independent of obesity. However, we have previously demonstrated relative insulin resistance in lean borderline hypertensive black men compared with lean normotensive black men,12 and we have also demonstrated in young blacks that the quantitative measures of insulin resistance are significantly correlated with BP in nonobese subjects having a BMI less than 28.0 kg/m2.13 Thus, it is unlikely that obesity per se is a sole causal factor for the linkage of reduced insulin sensitivity and lower HDL-C in those young adults with borderline to mild hypertension.
We have previously reported sex-linked differences in insulin resistance with lower levels of insulin-stimulated glucose utilization, demonstrated in black women compared with black men.28 Again in this study, both normotensive and borderline hypertensive women had lower insulin sensitivity (M) than the men in the respective BP group. However, when the M value was corrected for adiposity in each subject by expressing insulin-stimulated glucose utilization in mg/kg fat free mass, the sex differences disappeared. Despite somewhat higher plasma insulin concentrations during the OGTT in women, the multiple regression models did not detect sex as a significant variable contributing to insulin resistance in this population.
Elevated plasma insulin concentration, a component of insulin resistance, has been detected in first-degree relatives of patients with NIDDM and is considered to be a significant risk factor for future development of NIDDM.29 30 31 That elevated plasma insulin also predicts essential hypertension is suggested by recent reports from the San Antonio Heart Study. In a prospective study, elevated fasting insulin levels were found on initial examination in those who were normotensive but who developed hypertension in an 8-year follow-up. In that study, the predictive value of insulin was greater in the nonobese cases.14 Although Saad et al11 did not detect a correlation of insulin resistance, measured by insulin clamp, with BP in obese normotensive blacks, the larger CARDIA study on young adults demonstrated a positive association of fasting insulin concentration with BP in both blacks and whites. The strongest correlate of fasting insulin was BMI, but after adjustment for BMI, there was still a significant correlation of BP with insulin in both racial groups.32
Insulin resistance is defined as impaired insulin-stimulated glucose utilization.8 Due to a peripheral cell defect in insulin-mediated glucose metabolism, a greater quantity of insulin is necessary to achieve metabolic control of glucose. Subsequent increases in insulin secretion result in hyperinsulinemia. Ferrannini et al8 33 have detected a reduction of insulin-stimulated glycogen synthesis in skeletal muscle in nonobese patients with essential hypertension. Subsequent hyperinsulinemia, expressed as a consequence of this defect, may mediate BP elevation through extrametabolic pathways, such as the effect of insulin on sodium transport.34 35 36 The results of this study are in agreement with this concept. Insulin sensitivity (M and M') as determined by the insulin clamp is significantly lower in the borderline hypertensive subjects compared with the normotensive subjects. Fasting insulin level and the ratio of fasting insulin to glucose are higher in the borderline hypertensive subjects. Although the sum of insulin levels during the OGTT is not significantly different between the two BP groups, a significant relation with BP is present when sum of insulin is examined as a continuous variable in correlation analysis. While insulin resistance and hyperinsulinemia cosegregate in this study, the weight of the statistical power is consistent with the concept that the hyperinsulinemia is a consequence of the insulin resistance.
Univariate analysis of the plasma lipid data in this study demonstrated significant correlations of HDL-C, LDL-C, TG levels, apoA-I, and apoB with plasma insulin concentration and with insulin sensitivity (M, M'). Total cholesterol, TG levels, and apoB correlated significantly with BP. Lemne et al37 reported a significant correlation of VLDL, HDL-C, and TG with plasma insulin level in both normotensive and borderline hypertensive men. When the plasma lipid levels were compared between the normotensive and borderline hypertensive groups, VLDL and TG were significantly higher, and HDL-C was significantly lower in the borderline hypertensive subjects than the normotensive subjects. However, most of the group differences in plasma lipoproteins disappeared when corrections were made for BMI or plasma insulin level. Our results are consistent with the CARDIA study on a biracial population of young adult blacks and whites. In a much larger sample of both blacks and whites, fasting plasma insulin level correlated with LDL-C, HDL-C, TG, apoA-I, and apoB after adjustment for covariates of BMI, alcohol intake, and physical activity.32 Overall, these reports suggest that it is the hyperinsulinemia that mediates the alterations in plasma lipids. In both the CARDIA study and the data reported here, the association of an atherogenic lipid profile with hyperinsulinemia suggests that insulin resistance may be contributing to the risk for cardiovascular disease beyond the risk of hypertension alone in this population.
When all lipid and lipoprotein variables in this study were entered into a stepwise multiple linear regression analysis, HDL-C emerged as the most significant lipid component in the model for insulin resistance. Investigations on racial difference in plasma lipids have reported higher HDL-C in blacks compared with whites.38 The CARDIA study, which examined young adults 18 to 24 years old, demonstrated higher HDL-C and apoA-I levels in the young black males compared with the white males.39 Similar racial differences of higher HDL-C levels in young black males compared with young white males were found in the Bogalusa Heart Study.40 Higher HDL-C was reported in both black males and black females compared with their white age- and sex-matched counterparts in the LRC Program Prevalence Study.41 Since plasma HDL-C concentration has been shown to be inversely related to coronary heart disease, it has been suggested that the relatively higher HDL-C could account for lower mortality from coronary artery disease in blacks even though blacks have higher rates of hypertension.38 The association of lower HDL-C with insulin resistance and hyperinsulinemia demonstrated in this study suggests that the cardiovascular protective effect of HDL-C may be lost in young adult blacks with borderline hypertension.
The insulin-resistant syndrome contributes a constellation of risk factors for cardiovascular disease. In this study, impaired insulin-stimulated glucose utilization correlated with BP, plasma insulin concentrations, and atherogenic alterations in plasma lipid levels. Despite the high prevalence of obesity in this black population, these significant relations persisted after adjustment for obesity. The data suggest that in blacks with mild hypertension the risk for cardiovascular disease may be augmented when the metabolic components of insulin resistance also are present.
| Selected Abbreviations and Acronyms |
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| Acknowledgments |
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| Footnotes |
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Received May 16, 1995; accepted September 13, 1995.
| References |
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