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(Circulation. 1995;91:831-837.)
© 1995 American Heart Association, Inc.

Articles

Hyperinsulinemic Microalbuminuria

A New Risk Indicator for Coronary Heart Disease

Johanna Kuusisto, MD; Leena Mykkänen, MD; Kalevi Pyörälä, MD; Markku Laakso, MD

From the Department of Medicine, Kuopio University Hospital, Kuopio, Finland.

Correspondence to Markku Laakso, MD, Department of Medicine, University of Kuopio, 70210 Kuopio, Finland.

	Abstract

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Background Both hyperinsulinemia and microalbuminuria have been shown to increase coronary heart disease (CHD) risk, but the interaction among hyperinsulinemia, microalbuminuria, and the risk for CHD has not been investigated in previous studies.

Methods and Results The risk of CHD in relation to hyperinsulinemia and microalbuminuria was examined in a cohort of 1069 elderly nondiabetic subjects from Kuopio, east Finland, during 3.5 years of follow-up. The overall incidence of CHD death was 2.8%, and 6.9% of study subjects died of CHD or had a nonfatal myocardial infarction (later referred to as all CHD events). In the highest fasting-insulin quintile (fasting insulin 114.0 pmol/L), there was a slightly but insignificantly higher incidence rate of both CHD mortality and all CHD events compared with lower quintiles. The incidence rates of CHD mortality and all CHD events were significantly higher in the highest urinary albumin/creatinine ratio (ACR) quintile (ACR 3.22 mg/mmol) compared with lower quintiles (P<.05 and P<.01, respectively). Hyperinsulinemic microalbuminuria (simultaneous presence of fasting insulin 114.0 pmol/L and ACR 3.22 mg/mmol) markedly increased the risk of CHD mortality (12.5%, P<.001) and all CHD events (18.8%, P<.001) compared with normoinsulinemic subjects without microalbuminuria (2.2% and 5.8%, respectively). In univariate logistic regression analyses, hyperinsulinemic microalbuminuria was a strong predictor of both CHD death (odds ratio [OR], 5.93; P<.001) and all CHD events (OR, 3.39; P=.002). Multivariate logistic regression analyses were also performed, including sex, current smoking, waist-hip ratio, systolic blood pressure, and HDL cholesterol, with insulin, ACR, or both as independent variables. Even after adjustment for these variables, hyperinsulinemic microalbuminuria remained a strong predictor of CHD death (OR, 7.91; P<.001) and all CHD events (OR, 2.95; P=.014). The group with hyperinsulinemic microalbuminuria was characterized by the most adversely affected risk factor pattern (high triglycerides 2.3 mmol/L, low HDL cholesterol 0.9 mmol/L in men and 1.20 mmol/L in women, and hypertension).

Conclusions Simultaneous occurrence of hyperinsulinemia and microalbuminuria identifies a group of subjects with a highly increased risk for CHD in elderly nondiabetic subjects.

Key Words: insulin • microalbuminuria • coronary disease

	Introduction

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Coronary heart disease (CHD) is the most important cause of mortality and morbidity in western countries. The significance of smoking, hypertension, and high serum cholesterol as risk factors for CHD is well established. Recently, other risk factors for CHD have been suggested. Hyperinsulinemia^{1 2 3} and microalbuminuria,^{4 5 6} among others, have been linked to CHD.

Although both hyperinsulinemia and microalbuminuria have been shown to increase CHD risk, the interaction among hyperinsulinemia, microalbuminuria, and the risk for CHD has not been investigated previously. Therefore, we examined the risk of CHD in relation to hyperinsulinemia and microalbuminuria in a cohort of 1069 nondiabetic elderly subjects from Kuopio, east Finland, during 3.5 years of follow-up.

	Methods

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Research Design and Methods at Baseline Study
Study Population at Baseline
The baseline examination of this study was conducted in Kuopio between February 1986 and April 1988. The formation⁷ and representativeness⁸ of the study population have been described in detail previously. Briefly, 1910 subjects born between 1912 and 1921 were randomly selected from the population register of all inhabitants of Kuopio. A postal questionnaire containing questions about diagnosis of diabetes, ability to move, and willingness to participate in the study was sent to each subject. Eighty-three subjects from this sample were excluded because they were too ill to participate. Eventually, 1299 from 1827 eligible subjects participated in the examination at baseline, giving an overall participation rate of 71%. In the present study, all subjects with a previous history of diabetes or with glucose tolerance diagnostic of diabetes were excluded from all statistical analyses. Consequently, the final study population at baseline consisted of 1069 nondiabetic subjects.

Alcohol consumption was determined according to the subject's estimate of the average number of glasses of alcoholic drinks ingested per week. In statistical analyses, subjects were classified as alcohol users or nonusers. Smoking habits were defined as current smoking.

Weight and height were measured with subjects in light clothing without shoes. Body mass index (BMI) was calculated by weight per height squared (kilograms per meter squared). Waist circumference (centimeters) was measured at the level of the umbilicus and hip circumference at the level of the greatest hip girth with the subject standing and breathing normally. Body fat distribution was measured by waist-hip ratio (WHR).

Blood pressure was measured on the right arm with subjects in the supine position with a mercury sphygmomanometer after a 5-minute rest. Two readings were taken (1.5-minute interval); the latter reading was used in statistical analyses. In each measurement, blood pressure was read to the nearest 2 mm Hg. Subjects were defined as having hypertension if systolic blood pressure was 160 mm Hg, or diastolic blood pressure 95 mm Hg, or if they were receiving drug treatment for hypertension.

Diagnosis of Previous CHD Events
A conventional 12-lead resting ECG was recorded, and all ECGs were classified according to the Minnesota code.⁹ The ECGs were coded without knowledge of the glucose tolerance status and other characteristics of the study subjects.

Symptoms suggestive of angina pectoris or myocardial infarction (MI) were recorded with the Rose Cardiovascular Questionnaire¹⁰ by a specially trained nurse. All medical records of subjects who reported that they had been admitted to the hospital for chest pain or symptoms suggestive of MI before the baseline examination were reviewed by one of the authors (L.M.). World Health Organization (WHO) MONICA project criteria for verified definite and possible MI,¹¹ as modified by the FINMONICA AMI Register Study Group,¹² based on chest pain symptoms, ECG changes, and enzyme determinations were used to ascertain previous MI. Previous MI was defined to be present if a subject had had a possible or definite MI according to hospital records before the baseline examination or if there was a major Q wave (Minnesota code 1.1 or 1.2) on ECG at baseline.

Glucose Tolerance
WHO diagnostic criteria for diabetes mellitus were used to classify subjects without previously known diabetes.¹³ The criteria are as follows: (1) diabetes mellitus, fasting venous plasma glucose 7.8 mmol/L, or 2-hour venous plasma glucose 11.1 mmol/L in a 75-g oral glucose tolerance test; (2) impaired glucose tolerance (IGT), fasting venous plasma glucose <7.8 mmol/L, and 2-hour venous plasma glucose 7.8 to 11.0 mmol/L; and (3) normal glucose tolerance, fasting and 2-hour venous plasma glucose <7.8 mmol/L. Previously known diabetes was considered to be present if a physician had made the diagnosis.

Laboratory Methods
Blood samples were taken between 7:30 and 9:30 AM after a 12-hour fast. All subjects underwent a 75-g oral glucose tolerance test. Venous blood samples for glucose and insulin determinations were taken before and 2 hours after the glucose load. Plasma glucose was determined by the glucose oxidase method (Glucose Auto & Stat HGA-1120 analyzer, Daiichi). Plasma insulin concentration measured as milliunits per liter (1 mU/L=6.0 pmol/L) was determined from samples stored at -70°C by a commercial double-antibody solid-phase radioimmunoassay (Phadeseph Insulin RIA 100, Pharmacia Diagnostics, AB).¹⁴ Serum HDL cholesterol was determined after precipitation of LDL and VLDL with dextran sulfate and MgCl₂.¹⁵ Commercial enzymatic methods were used to determine cholesterol (Monotest, Boehringer Mannheim)¹⁶ and triglycerides (Peridocrome, Boehringer Mannheim).¹⁷ Commercial control sera were used to standardize the measurements of cholesterol and triglycerides (Seronorm and Seronorm Lipid, Nycomed).

Urinary albumin excretion was assessed in an early-morning spot urine sample. Urinary albumin was measured from samples stored at -20°C by a commercial immunoturbidimetry assay with sensitivity of 4 mg/L and intra-assay and interassay coefficients of variation 7.1% (Orion Diagnostica). Urinary creatinine was determined by a modified Jaffe method.¹⁸ The urinary albumin (milligrams per liter)/creatinine (millimoles per liter) ratio (ACR) was used as a measure of albumin excretion.

Research Design and Methods at Follow-up Study
Study Population and Follow-up Period
The follow-up study was conducted between March 1990 and June 1991. Of 1069 nondiabetic subjects who participated in the baseline study, 75 died during the follow-up and 99 were not willing or were too ill to participate in the follow-up study. Thus, 895 subjects participated, giving an overall participation rate of 90%.

The follow-up period was defined as the period between the baseline and follow-up studies for those who participated. The mean follow-up period for the participants was 3.5 years (range, 2.7 to 5.2 years). Subjects were invited to the follow-up study in the same order in which they participated in the baseline study. For nonparticipants, the follow-up period was defined as the period between baseline study and June 30, 1991 (the day when the last subject participated in the follow-up study), and deaths and cardiovascular events during this time period were recorded.

Diagnosis of New CHD Events
A conventional 12-lead ECG was taken, and ECG findings were classified according to the Minnesota code.⁹

Symptoms suggestive of angina pectoris or MI were recorded with the Rose Cardiovascular Questionnaire¹⁰ by the same specially trained nurse as in the baseline study. Medical records of those participants who reported hospitalization for chest pain or other symptoms suggestive of MI during the follow-up, of all nonparticipants, and of those who died during the follow-up were reviewed by one of the authors (J.K.). FINMONICA criteria for definite and possible MI based on chest pain symptoms, ECG changes, and enzyme determinations were used to ascertain a new MI during the follow-up period.^{11 12} All death certificates of those who died during the follow-up were reviewed (J.K.). Hospital and autopsy records were used in the final classification of the causes of death. All deaths were coded according to the ninth revision of the International Classification of Diseases (ICD9).¹⁹

A CHD death during the follow-up was defined as a death caused by CHD (ICD9 codes 410 through 414). For participants, a new nonfatal MI during the follow-up was defined as follows: (1) a definite or possible MI verified at the hospital by the FINMONICA criteria^{11 12} or (2) a new major Q-QS change on the ECG (progression from no Minnesota Q-QS code to code 1.1 or 1.2 or from Minnesota Q-QS code 1.3 to 1.1). For nonparticipants, a new nonfatal MI was defined as a definite or possible MI verified at the hospital by the WHO criteria (because these subjects did not participate in the follow-up study, no new ECG was available for coding). All CHD events included CHD deaths or nonfatal MIs. If a subject had more than one CHD event during the follow-up, only one was included in each of the statistical analyses.

Statistical Methods
Data analyses were conducted with the SPSS/PC+ program. Data are given as mean±SEM or percentages. Pearson's correlation coefficient was used to show linear correlation. Student's two-tailed t test for independent samples, ² test, Fisher's exact test, or ANOVA was used to assess the differences between the groups when appropriate. The comparison between the two groups in Table 6 was performed only if the probability value for ANOVA over the four groups was <.05. Univariate and multiple logistic regression analyses based on the maximum-likelihood method were used to investigate the association of cardiovascular risk factors with the incidence of CHD events. Logarithmic transformations of fasting insulin, ACR, and total triglycerides were performed because of skewed distribution in all statistical analyses including these variables as continuous variables.

View this table: [in this window] [in a new window]   Table 6. Baseline Characteristics of Study Subjects by Fasting Insulin and Urinary ACR

Approval of the Ethics Committee
This study was approved by the Ethics Committee of the Kuopio University Hospital. All study subjects gave informed consent.

	Results

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Table 1 shows the clinical characteristics of the study subjects by sex. In total, 1069 nondiabetic subjects (396 men and 673 women) participated in the baseline study. IGT was observed in approximately 20% of both men and women. Hypertension was very common, particularly in women. No patients had overt clinical nephropathy (maximum serum creatinine concentration in study subjects was 249 µmol/L). The range of urinary albumin excretion was 0 to 500 mg/L in men and 0 to 1128 mg/L in women.

View this table: [in this window] [in a new window]   Table 1. Baseline Characteristics of the Study Subjects by Sex

Table 2 shows the correlations of fasting insulin and ACR with baseline characteristics. The correlation between fasting insulin and ACR was low but statistically significant (r=.10, P<.05). Fasting insulin also correlated significantly with BMI (P<.001), WHR (P<.001), fasting glucose (P<.001), 2-hour glucose (P<.001), urinary albumin (P<.01), and urinary creatinine (P<.05) and inversely with HDL cholesterol (P<.001). ACR correlated significantly with age (P<.01), systolic blood pressure (P<.001), diastolic blood pressure (P<.01), and urinary albumin (P<.001) and inversely with urinary creatinine (P<.001).

View this table: [in this window] [in a new window]   Table 2. Correlations of Fasting Insulin and Urinary ACR With Baseline Characteristics

During 3.5 years of follow-up, the overall incidence of CHD death was 2.8%, and 6.9% of study subjects died of CHD or had a nonfatal MI (later referred to as all CHD events). Table 3 shows the incidence of CHD mortality and all CHD events in subjects with (1) low (quintiles I through IV) and high (quintile V) baseline fasting insulin and (2) with low (quintiles I through IV) and high (quintile V) baseline ACR. In the highest fasting-insulin quintile (fasting insulin 114.0 pmol/L), there was a slight but insignificantly higher incidence rate of both CHD mortality and all CHD events compared with lower quintiles. The incidence rates of CHD mortality and all CHD events were significantly higher in the highest ACR quintile (ACR 3.22 mg/mmol) compared with lower quintiles (P<.05 and P<.01, respectively).

View this table: [in this window] [in a new window]   Table 3. Incidence of CHD Mortality and All CHD Events (CHD Mortality or Nonfatal MI) during 3.5 Years by Insulin and ACR Quintiles

The Figure shows incidence rates of CHD mortality and all CHD events in the whole study population stratified by both fasting insulin and ACR. The study population was divided into the following groups: (1) both fasting insulin and ACR in quintiles 1 through 4, (n=688), (2) fasting insulin in the highest quintile but ACR in quintiles 1 through 4 (n=168), (3) ACR in the highest quintile but insulin in quintiles 1 through 4 (n=165), and (4) both insulin and ACR in the highest quintile, "hyperinsulinemic microalbuminuria" (n=48). Compared with normoinsulinemic subjects without microalbuminuria, the hyperinsulinemic normoalbuminuric group did not have a clearly increased risk of CHD mortality (2.4% versus 2.2%, P=NS) or all CHD events (5.4% versus 5.8%, P=NS). High ACR without a concomitant high fasting insulin increased the risk of CHD mortality (3.0%, P=NS) and all CHD events (9.7%, P=NS) slightly but insignificantly. In contrast, hyperinsulinemic microalbuminuria markedly increased the risk of CHD mortality (12.5%, P<.001) and all CHD events (18.8%, P<.001). The results remained unchanged also after the exclusion of subjects with a previous MI at baseline. The incidence rates for CHD death and all CHD events in the hyperinsulinemic microalbuminuric group compared with those in normoinsulinemic subjects without microalbuminuria were 10.5% versus 1.6% (P<.01) and 15.8% versus 5.0% (P<.01). To exclude the possibility that hypertension could be an important confounding factor, all statistical analyses shown in the Figure were performed in normotensive subjects (n=470). Normotensive subjects with hyperinsulinemic microalbuminuria had a significantly increased risk for CHD death and all CHD events compared with normoinsulinemic subjects without microalbuminuria (23.1% versus 1.8%, P<.01 and 23.1% versus 4.4%, P<.05, respectively). To confirm that IGT was not the cause of increased CHD in hyperinsulinemic microalbuminuric subjects, we repeated the analysis in normoglycemic subjects (n=827). In subjects with a completely normal glucose tolerance, hyperinsulinemic microalbuminuria also was associated with significantly increased CHD risk compared with normoinsulinemic subjects without microalbuminuria (CHD mortality, 11.5% versus 2.1%, P<.05; all CHD events, 19.2% versus 5.4%, P<.01).

View larger version (68K): [in this window] [in a new window]   Figure 1. Bar graphs showing incidence (%) of coronary heart disease (CHD) deaths (top) and all CHD events (CHD death or nonfatal myocardial infarction; bottom) by fasting insulin (INS) and urinary albumin/creatinine ratio (ACR) in the whole study population.

We also investigated the effect of hyperinsulinemic microalbuminuria on CHD incidence separately in men and women. Compared with normoinsulinemic men without microalbuminuria, men with hyperinsulinemic microalbuminuria had a significantly increased risk for CHD death (31.3% versus 4.6%, P<.001) and all CHD events (43.8% versus 10.0%, P<.001). In women, there were only six CHD deaths, but subjects with hyperinsulinemic microalbuminuria tended to have higher CHD mortality compared with normoinsulinemic women without microalbuminuria (3.1% versus 0.7%, P=NS). Similarly, the risk for all CHD events in women with hyperinsulinemic microalbuminuria tended to be higher compared with that in normoinsulinemic women without microalbuminuria (6.3% versus 3.3%, P=NS).

The association among insulin, ACR, and the risk of CHD events was further investigated by logistic regression analyses, including insulin, ACR, or their combination as a categorial variable (Table 4). High insulin alone did not predict CHD mortality (odds ratio [OR], 2.02; P=NS) or all CHD events (OR, 1.29; P=NS). High ACR was predictive of both CHD mortality (OR, 2.40; P=.024) and all CHD events (OR, 2.19; P=.003). However, hyperinsulinemic microalbuminuria was a strong predictor of both CHD death (OR, 5.93; P<.001) and all CHD events (OR, 3.39; P=.002). Multivariate logistic regression analyses were also performed, including sex, current smoking, WHR, systolic blood pressure, and HDL cholesterol, with insulin, ACR, or both as independent variables. These variables were found to be predictive of CHD mortality or all CHD events in our nondiabetic population.²⁰ In addition, total cholesterol was included in multivariate models to cover all classic cardiovascular risk factors, even though it was not associated with CHD events in univariate logistic regression analysis. In the multivariate model, both high fasting insulin and high ACR were predictive of CHD death but with only borderline statistical significance (OR, 2.57; P=.035 and OR, 2.29; P=.053, respectively). High ACR but not high insulin was predictive of all CHD events, also with borderline statistical significance (OR, 1.76; P=.047). However, hyperinsulinemic microalbuminuria was a strong predictor of CHD death (OR, 7.91; P<.001) and all CHD events (OR, 2.95; P=.014). In fact, as shown in Table 5, which reports the results of stepwise multivariate logistic regression analyses, hyperinsulinemic microalbuminuria was the only variable besides male sex to predict CHD death. Simultaneous occurrence of high insulin and high ACR was the fourth most important predictor for all CHD events after male sex, high systolic blood pressure, and smoking. HDL cholesterol was also significantly associated with all CHD events.

View this table: [in this window] [in a new window]   Table 4. ORs and Their 95% CIs Based on Logistic Regression Analyses for Fasting Insulin and Urinary ACR as Risk Factors for CHD Mortality and All CHD Events (CHD Mortality or Nonfatal MI) During 3.5 Years of Follow-up

View this table: [in this window] [in a new window]   Table 5. Forward Stepwise Logistic Regression Analysis of Variables Significantly Associated With CHD Mortality and All CHD Events (CHD Mortality or Nonfatal MI)

To investigate whether a more adverse cardiovascular risk factor profile explains significantly increased risk for CHD in subjects with hyperinsulinemic microalbuminuria, we determined the levels of baseline risk factors in similar groups presented earlier in the Figure. As Table 6 shows, the sex distribution was similar in all four groups. Subjects with high ACR with or without concomitant hyperinsulinemia were slightly older than those with both low fasting insulin and low ACR. The group with hyperinsulinemic microalbuminuria was characterized by the most adversely affected risk factor pattern. IGT, hypertension, high WHR, low HDL cholesterol, high triglycerides, high fasting and 2-hour glucose, and high fasting and 2-hour insulin were more common in subjects with hyperinsulinemic microalbuminuria than in normoinsulinemic subjects without microalbuminuria. In total, 12.5% of men and 25.0% of women in the hyperinsulinemic microalbuminuric group had a cluster of the following three risk factors: high total triglycerides (2.3 mmol/L), low HDL cholesterol (0.9 mmol/L in men, 1.20 mmol/L in women), or hypertension. Only 2 of 48 subjects (4.2%) had none of these risk factors. We previously estimated that the expected simultaneous occurrence of high triglycerides, low HDL cholesterol, and hypertension is 1.6% in men and 3.4% in women in our study population.²¹ In hyperinsulinemic microalbuminuric men and women, the clustering of these three risk factors was remarkably increased compared with that expected (ORs and their 95% CIs: 9.3 [1.3 to 67.9] and 9.4 [4.2 to 21.4], respectively). Thus, the clustering of high triglycerides, low HDL cholesterol, and hypertension was three times more common in hyperinsulinemic microalbuminuric men and women compared with all men (4.8%; OR, 3.3 [1.3 to 8.5]) and women (7.6%; OR, 2.3 [1.4 to 3.9]) in our study population.

	Discussion

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We have shown that in an elderly nondiabetic population, hyperinsulinemic microalbuminuria is a strong predictor of CHD events. In fact, in addition to male sex, hyperinsulinemic microalbuminuria was the only baseline variable predicting CHD death. The risks of CHD mortality and all CHD events in subjects with hyperinsulinemic microalbuminuria, who represent 4.5% of the study population, were 5.9- and 3.4-fold compared with subjects with normoinsulinemia and without microalbuminuria. The effect of hyperinsulinemic microalbuminuria was more pronounced in men than in women, probably because of the higher incidence of CHD deaths and all CHD events in men. Even after adjustment for conventional cardiovascular risk factors, hyperinsulinemic microalbuminuria was still highly predictive of CHD events, especially of CHD death. In contrast, hyperinsulinemia without concomitant microalbuminuria and microalbuminuria without concomitant hyperinsulinemia were much weaker predictors of CHD events.

Three large epidemiological prospective studies have shown that insulin level predicts CHD events in men.^{1 2 3} In the Helsinki Policeman Study, 1- and 2-hour post–oral glucose load insulin predicted cardiovascular events.¹ The Paris Prospective Study showed that fasting insulin predicted CHD mortality in obese men, but in the follow-up, 2-hour and nonfasting insulin were predictive of CHD mortality.² In the Busselton Study, 1-hour post–glucose load insulin predicted cardiovascular disease incidence in elderly men (age, 60 to 69 years) in univariate analysis and cardiovascular mortality in men over 40 years of age in multivariate analysis.³ However, in the Gothenburg Study on elderly men, insulin failed to predict CHD events,²² and in the Multiple Risk Factor Intervention Trial, men who had fatal or nonfatal MIs did not show any difference in baseline fasting insulin levels compared with corresponding men without MI.²³ There are no studies showing an association between insulin and CHD incidence in women.

Microalbuminuria is defined as urinary albumin excretion >20 but <200 µg/min. Microalbuminuria has been shown to predict not only diabetic nephropathy^{24 25} but also macrovascular complications in non–insulin-dependent diabetes.^{25 26 27} Moreover, two studies have shown that microalbuminuria predicts cardiovascular mortality in nondiabetic subjects.^{4 5} In the study by Yudkin and coworkers,⁴ microalbuminuria defined as urinary albumin excretion rate >20 µg/min predicted cardiovascular mortality in 167 subjects 40 years of age or older during 3.6 years of follow-up. In the Fredericia Study, urinary excretion rate >7.52 µg/min predicted cardiovascular and total mortality in 216 subjects 60 to 74 years of age.⁵

In the present study, insulin alone failed to predict CHD events, and microalbuminuria alone had only a weak association with CHD incidence. Then why did the combination of hyperinsulinemia and microalbuminuria markedly predict CHD events in the present study? Even if a definitive answer cannot be given, the following possibilities have to be considered. First, hyperinsulinemia and microalbuminuria could be independent of each other but potentiate each other's atherogenic effect. There is evidence from animal studies that insulin enhances the development of atherosclerotic plaque.²⁸ In vitro, insulin has been shown to cause smooth muscle cell proliferation²⁹ ; stimulate LDL binding to smooth muscle cells,³⁰ fibroblasts,³¹ and monocytes³² ; and stimulate cholesterol synthesis in monocytes.³³ In other words, insulin may promote atherogenesis by directly affecting the arterial wall. Microalbuminuria is associated with the accumulation of extracellular matrix in glomeruli and large vessel walls.³⁴ Changes in the quality of the extracellular matrix^{35 36} and proliferation of mesangial and myomedial cells³⁷ have been reported. Similar changes in the extracellular matrix of vessel walls have also been found in atherosclerosis.³⁸ It has been suggested that microalbuminuria is a marker of vascular damage and thus is an early finding in atherosclerosis.³⁹ In hyperinsulinemic microalbuminuria, changes in the extracellular matrix of vessel walls associated with microalbuminuria may enhance the effects of insulin on vessel wall cells, or simultaneous adverse changes in insulin level and urinary albumin excretion may enhance atherogenesis synergistically by some other mechanisms.

Second, hyperinsulinemic microalbuminuria could cause accelerated atherosclerosis indirectly because it identifies a group of subjects with exceptionally adverse patterns of cardiovascular risk factors. Indeed, hyperinsulinemic microalbuminuria was associated with a highly abnormal cardiovascular risk factor pattern, including a high prevalence of IGT and hypertension, high BMI and WHR, low HDL cholesterol, and a high triglyceride level (Table 6). In previous studies, both hyperinsulinemia²⁸ and microalbuminuria⁶ were separately associated with adverse cardiovascular risk factors, among them hypertension,^{40 41 42} a finding very similar to our study results.

Third, hyperinsulinemic microalbuminuria could be only a marker of insulin resistance, which in turn could be causally related to increased risk for CHD. Hyperinsulinemia is a compensatory mechanism for insulin resistance; microalbuminuria also has been associated with insulin resistance. In a recent study by Niskanen and Laakso,⁴³ microalbuminuria identified the most insulin-resistant subjects among subjects with non–insulin-dependent diabetes. Thus, hyperinsulinemic microalbuminuria might by itself⁴⁴ or through associated risk factors be associated with accelerated atherosclerosis.⁴⁴

The present study has some limitations. First, we used only a single spot urine sample to assess microalbuminuria instead of timed collections that would have been preferable. However, we calculated the urinary albumin excretion as a ratio of urinary albumin to urinary creatinine that markedly enhances the accuracy of the single spot urine sample in the assessment of microalbuminuria.^{45 46} In previous studies, ACR in early-morning spot urine samples has been shown to correlate reasonably well with 24-hour urinary albumin excretion, with 2.00 to 3.50 mg/mmol corresponding to a urinary albumin excretion rate of 20 µg/min.^{45 46} In the present study, the cutoff point used to define microalbuminuria was 3.22 mg/mmol and will probably identify the group with urinary albumin excretion rate >20 µg/min. Second, we did not estimate insulin resistance directly but measured fasting insulin level. In nondiabetic subjects, however, the fasting insulin level correlates at least moderately with insulin resistance measured by the euglycemic clamp technique.^{47 48} Nevertheless, our study cannot definitively prove that hyperinsulinemic microalbuminuria identifies the most insulin-resistant subjects in our population.

In conclusion, hyperinsulinemic microalbuminuria is a strong risk indicator for CHD events in elderly subjects; it also identifies patients with a high prevalence of adverse cardiovascular risk factors, particularly IGT, abdominal obesity, hypertension, high triglycerides, and low HDL cholesterol. Investigation of mechanisms contributing to the high CHD risk in subjects with hyperinsulinemic microalbuminuria should be a challenge for future studies.

	Acknowledgments

 
This work was supported by grants from the Academy of Finland, the Helena Vuorenmies Foundation, the Aarne and Aili Turunen Foundation, the Yrjö Jahnsson Foundation, the Finnish Heart Research Foundation, the Finnish Society of Cardiology, the Orion Corporation Research Foundation, and the Finnish Foundation of Medicine.

Received March 31, 1994; accepted September 18, 1994.

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