Elevated Fasting Insulin Level Significantly Increases the Risk of Microalbuminuria

Abstract

Background: Microalbuminuria is significantly associated with long-term prognosis in the general population as well as in diabetic patients. It is well known that insulin resistance (IR) can induce microalbuminuria, but an elevated fasting insulin level, which is an early clinical manifestation of IR, as a risk factor for microalbuminuria has not been clarified, so we investigated the association between fasting insulin level and the development of microalbuminuria in a general population.

Methods and Results: A total of 1,192 non-diabetic Korean men without microalbuminuria in 2005 were followed until 2010. They were categorized into 3 groups according to their fasting insulin levels and monitored for the development of microalbuminuria. The incidence of microalbuminuria was compared among groups, and Cox proportional hazards models were used to calculate the hazard ratios for microalbuminuria according to the fasting insulin levels. During 4,013.0 person-years of follow-up, 51 incident cases of microalbuminuria developed between 2006 and 2010. The incidence of microalbuminuria increased in proportion to the fasting insulin levels (tertile 1: 1.8%, tertile 2: 4.5%, tertile 3: 6.5%, P<0.001). Hazard ratios for microalbuminuria also increased in proportion to the fasting insulin levels [tertile 1: reference, tertile 2: 2.44 (1.01–5.89), tertile 3: 3.30 (1.40–7.78), respectively, P for trend 0.013].

Conclusions: Elevated fasting insulin level was associated with the future development of microalbuminuria. (Circ J 2015; 79: 210–215)

Microalbuminuria, defined as a urine albumin:creatinine ratio (UACR) of 30–299 mg/g, is an early clinical manifestation of chronic kidney disease.¹ Microalbuminuria has been highlighted for its clinical implication in the long-term prognosis of diabetic patients. Microalbuminuria is known not only as an early indicator of diabetic nephropathy but also as a significant predictor for cardiovascular (CV) events in diabetic patients.^2–4 Accordingly, microalbuminuria is routinely checked in diabetic patients in order to evaluate renal function and predict long-term prognosis.⁵ However, even in the general population, microalbuminuria has been reported as significantly associated with CV morbidity and mortality. Previous studies demonstrated that a general population with microalbuminuria had higher risks of CV diseases and all-cause mortality,^6,7 and microalbuminuria in patients with metabolic syndrome resulted in a higher prevalence of subclinical atherosclerosis and CV events.⁸ These reports suggest clinical significance of microalbuminuria as a meaningful predictor for CV events in the general population, but there are only limited data about inducible conditions or risk factors for microalbuminuria in the general population.

On the other hand, the role of hyperinsulinemia, a possible risk factor of microalbuminuria, in the development of microalbuminuria in the general population is still debatable. To the best of our knowledge, there have been only a few cohort studies with large populations examining the relationship between fasting insulin level and microalbuminuria. Therefore, we investigated the association between fasting insulin level microalbuminuria and if they are related, whether we can predict future development of microalbuminuria in the general population.

Methods

Study Design

A prospective cohort study was conducted in order to investigate the association between fasting serum insulin levels and the development of microalbuminuria. Study participants consisted of Korean men undergoing a medical health check-up at the Health Promotion Center of Kangbuk Samsung Hospital, Sungkyunkwan University, Seoul, Korea. The study methods have been described in detail.⁹ The purpose of the medical health check-up program is to promote the health of employees and to enhance early detection of existing diseases. All employees participate in either an annual or biennial health check-up, as required by Korea’s Industrial Safety and Health law. Most of the study population comprised employees and family members from various companies around the country. The costs of the medical examinations are largely paid by the employers. We took advantage of this opportunity to conduct a follow-up study.

Study Population

A total of 3,454 men, aged from 30 to 59 years old, who had their UACR examined for a medical check-up in 2005 participated in this study. Of these participants, 646 were excluded for various reasons: 17 had a past history of malignancy; 27 had a past history of CV disease; 290 were receiving lipid-lowering medication; 242 had baseline type 2 diabetes mellitus; 142 had baseline microalbuminuria (30≤UACR<299 μg/mg) and 12 had baseline overt albuminuria (UACR ≥300 μg/mg) at initial examinations. The total number of eligible participants was 2,808 and we excluded a further 1,616 participants who did not attend any follow-up visits between 2006 and 2010. Without the follow-up visit, we could not identify the development of microalbuminuria or calculate the individual person-years. Eventually, 1,192 participants were enrolled in the final analysis and monitored for the development of microalbuminuria. The total follow-up period was 4,013.0 person-years and average follow-up period was 3.37 (standard deviation [SD], 1.46) person-years. Ethics approval for the study protocol and analysis of the data were obtained from the Institutional Review Board of Kangbuk Samsung Hospital. Written informed consent was given by all participants.

Clinical and Laboratory Measurements

Study data included medical history, physical examination, information provided by a questionnaire, anthropometric measurements and laboratory measurements. The medical and drug prescription histories were assessed by the examining physicians. All the participants were asked to respond to a health-related behavior questionnaire, which included the topics of alcohol consumption, smoking and exercise. The questions about alcohol intake included the frequency of alcohol consumption on a weekly basis and the typical amount consumed on a daily basis (≥20 g/day). We considered persons reporting that they smoked at the time of the questionnaire to be current smokers. In addition, the participants were asked about the frequency per week of physical activities they engaged in that lasted long enough to produce perspiration, such as jogging, bicycling and swimming (≥1 time/week). Diabetes mellitus was defined as fasting serum glucose level ≥126 mg/dl, or the current use of blood glucose-lowering agents. Hypertension was defined as either the current use of antihypertensive medication or as having a measured blood pressure (BP) ≥140/90 mmHg at initial examinations. Trained nurses obtained seated BP levels using a standard mercury sphygmomanometer. The 1st and 5th Korotkoff sounds were used in order to estimate the systolic and diastolic BP.

Blood samples from an antecubital vein were collected after more than 12 h of fasting. Serum levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and γ-glutamyltransferase (GGT) were measured using Bayer Reagent Packs (Bayer HealthCare, Tarrytown, NY, USA) on an automated chemistry analyzer (ADVIA 1650 Autoanalyzer; Bayer Diagnostics, Leverkusen, Germany). Insulin levels were measured with immunoradiometric assays (Biosource, Nivelles, Belgium). Insulin resistance (IR) was calculated using the homeostasis model assessment of IR (HOMA-IR) as described by Matthews et al: fasting serum insulin (uU/ml)×fasting serum glucose (mmol/L)/22.5.¹⁰

Serum creatinine (SCr) was measured using the alkaline picrate (Jaffe) method. Kidney function was estimated using the glomerular filtration rate (eGFR) calculated using the Chronic Kidney Disease Epidemiology Collaboration equation: eGFR=141×min(SCr/K, 1)^a×max(SCr/K, 1)^−1.209×0.993^age×1.018 [if female]×1.159 [if Black], where SCr is serum creatinine, K is 0.7 for females and 0.9 for males, a is −0.329 for females and −0.411 for males, min indicates the minimum of SCr/K or 1 and max indicates the maximum of SCr/K or 1.¹¹ A single morning voided urine sample at baseline was used to measure the UACR. The urinary albumin concentration was determined by immunoradiometry (Radioimmunological competition assay, Immunotech Co, Prague, Czech Republic), and the urinary creatinine concentration was measured by a modified Jaffe method. The UACR measured in spot urine samples has been determined to be highly correlated with the 24-h urine albumin excretion.¹² The development of microalbuminuria was assessed from the annual records of all participants and defined as UACR between 30 and 300 μg/mg. The presence of overt albuminuria was routinely defined as UACR >300 μg/mg.

The fasting serum glucose was measured using the hexokinase method. Total cholesterol and triglyceride levels were measured using enzymatic colorimetric tests, low-density lipoprotein (LDL) cholesterol was measured using the homogeneous enzymatic colorimetric test, and high-density lipoprotein (HDL) cholesterol was measured using the selective inhibition method (Bayer Diagnostics, Leverkusen, Germany).

Statistical Analysis

Data are expressed as mean±(SD) or median (interquartile range) for continuous variables and percentages of the number for categorical variables.

One-way ANOVA and chi-square test were used to analyze the statistical differences among the characteristics of the study participants at the time of enrollment in relation to the tertile groups of fasting serum insulin levels. When we conducted analysis, we were using General Linear Model to calculate one-way ANOVA test, and Mantel Hanzel chi-square test to verify the result. Categories of fasting serum insulin were the following tertiles: <6.99, 6.99–9.36 and ≥9.36. The distributions of continuous variables were evaluated, and log transformations were used in the analysis as required.

For incident microalbuminuria cases, the time of microalbuminuria occurrence was assumed to be the midpoint between the visit at which microalbuminuria was first diagnosed and the baseline visit (2005). The person-years were calculated as the sum of follow-up times from the baseline until an assumed time of microalbuminuria development or until the final examination of each individual. We used Cox proportional hazards models to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CI) for incident microalbuminuria comparing the highest 2 tertiles of baseline fasting serum insulin levels vs. the lowest tertile. The data were adjusted for multiple covariates. In the multivariate models, we included variables that might confound the relationship between fasting serum insulin and microalbuminuria, which include age, GGT, triglyceride, fasting serum glucose, eGFR, recent smoking status, alcohol intake, regular exercise and hypertension. For the linear trends of risk, the number of tertiles was used as a continuous variable and tested on each model. To use the Cox proportional hazards models, we checked the validity of the proportional hazards assumption using 2 approaches. First, the assumption was assessed by log-minus-log survival function and found to graphically hold. Second, to confirm the validity of the proportional hazards assumption, time-dependent covariate analysis was used, but it was not statistically significant, suggesting that the proportional hazards assumption was not violated (P=0.283). P values <0.05 were considered to be statistically significant. Statistical analyses were performed using PASW Statistics 18 (SPSS Inc, Chicago, IL, USA).

Results

During 4,013.0 person-years of follow-up, 51 (4.3%) incident cases of microalbuminuria developed between 2006 and 2010. Table 1 shows the baseline characteristics of the excluded and included study participants.

Table 1. Comparison of Participants Excluded and Included in an Analysis of Microalbuminuria in a General Population

Characteristic	Exclusion from analysis (n=1,616)	Inclusion in analysis (n=1,192)	P value*
Age (years)	45.4±(7.5)	48.0±(7.0)	<0.001
Systolic BP (mmHg)	115.7±(14.0)	115.9±(13.9)	0.738
Diastolic BP (mmHg)	79.1±(9.8)	79.5±(9.7)	0.306
Total cholesterol (mg/dl)	194.3±(32.4)	196.1±(31.3)	0.128
Triglyceride (mg/dl)	153.9±(88.0)	149.9±(106.5)	0.285
HDL-cholesterol (mg/dl)	51.0±(10.6)	51.2±(10.8)	0.478
LDL-cholesterol (mg/dl)	113.2±(27.5)	115.1±(26.5)	0.062
Fasting serum glucose (mg/dl)	96.9±(9.1)	97.6±(9.0)	0.036
HOMA-IR	2.14±(0.94)	2.10±(0.87)	0.325
Insulin (uU/dl)	8.8±(3.6)	8.6±(3.4)	0.129
SCr (mg/dl)	1.12±(0.11)	1.13±(0.11)	0.052
eGFR (ml·min−1·1.73 m−2)	79.6±(10.4)	77.4±(10.0)	<0.001
GGT (U/L)	50.1±(57.0)	50.1±(59.2)	0.994
AST (U/L)	27.2±(23.7)	26.7±(13.1)	0.430
ALT (U/L)	32.9±(38.0)	30.4±(19.3)	0.022
Current smoker (%)	49.1	39.1	<0.001
Alcohol intake (%)	24.7	26.2	0.371
Regular exercise (%)	16.7	20.8	<0.001
Hypertension (%)	24.8	27.4	0.123

Data are expressed as mean±(standard deviation) or percentage. *P value by t-test for continuous variables and chi-square test for categorical variables. ALT, alanine aminotransferase; AST, aspartate aminotransferase; BP, blood pressure; eGFR, estimated glomerular filtration rate; GGT, γ-glutamyltransferase; HDL, high-density lipoprotein; HOMA-IR, homeostasis model assessment of insulin resistance; LDL, low-density lipoprotein; SCr, serum creatinine.

The baseline characteristics of the study participants in relation to tertile groups of fasting serum insulin levels are presented in Table 2. At baseline, the mean (SD) age and fasting serum insulin of the study participants were 48.0 (7.0) years and 8.6 (3.4) (μU/ml), respectively. There were clear dose-response relationships between all of the listed variables and tertile groups of fasting serum insulin levels except for eGFR, current smoking status and alcohol intake. Systolic and diastolic BP, total cholesterol, triglycerides, LDL-cholesterol, HOMA-IR, fasting serum glucose, SCr, GGT, AST, ALT, hypertension and development of microalbuminuria were positively associated with tertile groups of fasting serum insulin levels; whereas average person-years, age, HDL-cholesterol and regular exercise were inversely associated with tertile groups of fasting serum insulin levels.

Table 2. Baseline Characteristics of Participants in the Analysis of Microalbuminuria According to Tertile Groups of Fasting Serum Insulin Levels (n=1,192)

Characteristic	Overall	Fasting serum insulin (μU/ml)			P value for trend*
		Tertile 1	Tertile 2	Tertile 3
Total person-years	4,013.0	1,387.6	1,328.3	1,297.1
Average person-years	3.37±(1.46)	3.49±(1.44)	3.34±(1.45)	3.26±(1.49)	0.023
Age (years)	48.0±(7.0)	48.9±(6.5)	48.3±(6.9)	46.8±(7.5)	<0.001
Systolic BP (mmHg)	115.8±(13.9)	114.3±(13.5)	115.3±(13.7)	117.9±(14.2)	<0.001
Diastolic BP (mmHg)	79.5±(9.7)	78.6±(9.8)	79.1±(9.4)	80.8±(9.9)	<0.001
Total cholesterol (mg/dl)	196.1±(31.4)	192.6±(30.1)	195.8±(32.0)	199.9±(31.7)	<0.001
Triglyceride (mg/dl)	125 (90–180)	104 (81–148)	128 (91–172)	149 (109–217)	<0.001
HDL-cholesterol (mg/dl)	51.2±(10.8)	53.9±(11.6)	50.9±(10.4)	48.9±(9.7)	<0.001
LDL-cholesterol (mg/dl)	115.1±(26.5)	111.6±(26.0)	115.3±(26.7)	118.5±(26.9)	<0.001
Fasting serum glucose (mg/dl)	97.6±(8.9)	95.5±(8.6)	98.2±(8.8)	99.0±(9.1)	<0.001
HOMA-IR	1.95 (1.50–2.47)	1.35 (1.17–1.52)	1.95 (1.77–2.13)	2.80 (2.43–3.34)	<0.001
SCr (mg/dl)	1.13±(0.11)	1.12±(0.11)	1.12±(0.10)	1.15±(0.12)	0.005
eGFR (ml·min−1·1.73 m−2)	77.4±(10.0)	77.5±(9.6)	77.8±(9.4)	76.8±(10.9)	0.377
GGT (U/L)	33 (22–57)	28 (19–45)	30 (21–50)	45 (28–74)	<0.001
AST (U/L)	24 (21–30)	23 (20–28)	24 (20–29)	26 (22–32)	<0.001
ALT (U/L)	26 (20–35)	22 (17–28)	25 (20–33)	31 (23–45)	<0.001
Current smoker (%)	39.0	39.8	37.1	40.3	0.893
Alcohol intake (%)	26.2	27.9	23.5	27.3	0.824
Regular exercise (%)	20.9	23.4	22.5	16.7	0.022
Hypertension (%)	27.3	23.2	27.7	31.2	0.012
Development of microalbuminuria (%)	4.3	1.8	4.5	6.5	<0.001

Data are mean±(standard deviation), median (interquartile range), or percentage. *P value by ANOVA-test for continuous variables and chi-square test for categorical variables. Abbreviations as in Table 1.

Table 3 shows the HRs and 95% CIs for incident microalbuminuria according to the tertile groups of fasting serum insulin levels. (<6.99, 6.99–9.36 and ≥9.36). In the unadjusted model, the HRs and 95% CIs for incident microalbuminuria comparing the 2nd to 3rd tertile vs. the 1st tertile of fasting serum insulin were 2.73 (1.14–6.54) and 4.09 (1.77–9.41), respectively (P for trend <0.001). These associations were also significant, even after further adjustments for covariates in models 1 and 2. In model 2, the adjusted HRs and 95% CIs for incident microalbuminuria across baseline tertile groups of fasting serum insulin levels were 2.44 (1.01–5.89) and 3.30 (1.40–7.78), respectively (P for trend=0.013).

Table 3. HRs and 95% CIs for the Incidence of Microalbuminuria According to the Tertile Groups of Fasting Serum Insulin Levels in the Analysis of Microalbuminuria in a General Population

	Person- years	Incidence (cases)	Incidence density (per 1,000 person-year)	HR (95% CI)
				Unadjusted	Model 1	Model 2
Fasting serum insulin level
Tertile 1	1,387.6	7	5.0	1.00 (reference)	1.00 (reference)	1.00 (reference)
Tertile 2	1,328.3	18	13.5	2.73 (1.14–6.54)	2.46 (1.02–5.93)	2.44 (1.01–5.89)
Tertile 3	1,297.1	26	20.0	4.09 (1.77–9.41)	3.61 (1.54–8.45)	3.30 (1.40–7.78)
P for trend				<0.001	0.005	0.013
Age					1.02 (0.98–1.07)	1.02 (0.97–1.07)
Systolic BP					1.01 (0.99–1.03)	1.00 (0.97–1.03)
GGT					1.00 (1.00–1.01)	1.00 (1.00–1.01)
Triglyceride					1.00 (1.00–1.01)	1.00 (1.00–1.01)
Fasting serum glucose					1.04 (1.01–1.07)	1.04 (1.01–1.08)
eGFR					1.01 (0.98–1.03)	1.00 (0.97–1.03)
Smoking status						1.41 (0.77–2.56)
Regular exercise						0.76 (0.40–1.46)
Alcohol intake						1.20 (0.61–2.36)
Hypertension						1.63 (0.77–3.43)

Model 1 adjusted for age, GGT, systolic BP, triglyceride, fasting serum glucose and eGFR. Model 2 adjusted for model 1 plus current smoking status, regular exercise, alcohol intake and hypertension. CI, confidence interval; HR, hazard ratio. Other abbreviations as in Table 1.

Discussion

The present study, which was designed to investigate the association between fasting insulin level and the development of microalbuminuria, had several novel findings. First, the incidence and the HR of microalbuminuria increased in proportion to the baseline fasting insulin level, independent of age, fasting glucose, GGT, triglyceride, eGFR, physical activity, and hypertension. Second, even in this general population, an elevated fasting insulin level was significantly associated with the development of microalbuminuria. Third, mean values of HOMA-IR, an indicator of IR, increased from 1st to 3rd tertile group similarly to the incidence of microalbuminuria. These findings could have significant clinical implications.

Most of all, our study showed the clinical utility of the fasting insulin level as an independent predictor of microalbuminuria, which suggests the necessity for careful surveillance for microalbuminuria even in non-diabetic patients with hyperinsulinemia. As mentioned earlier, microalbuminuria is a predictive marker for poor prognosis in the general population as well as in diabetic patients.^1–8 Urinary albumin excretion was a definite predictor for all-cause of mortality in the Prevention of Renal and Vascular End Stage Disease (PREVEND) Study, which showed that higher normal range of albumin excretion (<20 mg/L) was a significant risk factor for CV and non-CV mortality.¹³ In addition, population-based epidemiologic research, such as the EPIC-Norfolk¹⁴ and HUNT studies,¹⁵ also support the hypothesis that microalbuminuria increases CV and non-CV mortality.

Nevertheless, a risk factor or predictor for microalbuminuria in the general population has not yet been clarified. In particular, there have been conflicting results for the association between fasting insulin level and microalbuminuria. In a previous study of non-diabetic native Americans, microalbuminuria had no association with metabolic factors such as fasting insulin level, fasting glucose, 2-h blood glucose, body mass index (BMI) or waist to hip ratio except the IR syndrome itself.¹⁶ Several cross-sectional studies such as IRAS (Insulin Resistance Atherosclerosis Study) also did not show a significant relation between fasting insulin level and microalbuminuria.¹⁷

However, there have been studies that support our study findings. A cross-sectional study of young healthy African American demonstrated that albuminuria was significantly correlated with fasting plasma insulin concentration.¹⁸ In addition, considering the fasting insulin level as an early clinical manifestation of IR, previous studies showing a significant association between microalbuminuria and IR may be well consistent with our study. Nevertheless, to the best of our knowledge, there are only a few cohort studies that have investigates the longitudinal association between fasting insulin level and the development of microalbuminuria. Because our study was designed for a prospective cohort study with a large sample size, the clinical significance of the fasting insulin level for the development of microalbuminuria may have been more clearly shown.

Another clinical implication of our study is the relation of IR to microalbuminuria. The fasting insulin level is significantly associated with IR. Fasting insulin level is one of factors necessary to calculate HOMA-IR, the indicator of IR, and hyperinsulinemia is an early clinical manifestation of IR.¹⁹ Our study also showed an increasing trend of HOMA-IR from the 1st to the 3rd tertile, similarly to the incidence of microalbuminuria, which implies a role of IR in the development of microalbuminuria. Therefore, considering that IR is the definite cause of type 2 diabetes, people with incident microalbuminuria might have a higher potential risk for diabetes. However, even with a comparatively short follow-up period, not a few cases of microalbuminuria might be detected before the diagnosis of diabetes, which suggests that the fasting insulin level could be a predictive marker for prognosis in the high-risk group of diabetes patients. As is well known, microalbuminuria is not only an early clinical manifestation of diabetic nephropathy but also a significant clinical indicator of prognosis of diabetic patients.^1–4 Diabetic patients with microalbuminuria have a higher risk of diabetic nephropathy and CV diseases. Accordingly, if the group at high risk of diabetic complications can be predicted by the fasting insulin level before the diagnosis of diabetes, it may be helpful to implement a preventive strategy for diabetic complications.

In addition, the pathophysiologic mechanism of this study can be explained by the elevated insulin level being related to IR. Even though the underlying molecular pathway is not clearly defined yet, it is widely believed that the underlying mechanisms of IR play a significant role in the development of microalbuminuria.²⁰ Hyperinsulinemia, especially, is characterized as a compensatory response of IR and is noted for its adverse effect on the renal system. It has been observed that hyperinsulinemia increases renal sodium retention by increasing tubular reabsorption of sodium.^21,22 This sodium-retaining property of insulin increases glomerular pressure, which could increase the urinary albumin excretion.^21,22 In parallel, hyperinsulinemia could increase glomerular permeability to albumin by accelerating mesangial cell proliferation and glomerulosclerosis.²³ These alterations in glomerular structure and renal hemodynamics might play a significant role in the development of microalbuminuria. Increasing evidence has also established that inflammatory and metabolic effects of IR are linked to renal injury. IR promotesa the release of inflammatory cytokines, such as insulin-like growth factor²⁴ and tumor necrosis factor-β,^25,26 which contribute to glomerular mesangial expansion, basement membrane thickening, and the loss of slit pore diaphragm integrity, ultimately leading to progression of microalbuminuria. Angiopoietin-like protein 2, which is a recently studied inflammatory cytokine associated with IR, could be linked to increased chronic renal disease in the general population.²⁷ Some anti-inflammatory drugs can improve endothelial dysfunction in diabetic patients.²⁸ We think that these findings have clinical implications for our research. Additionally, hyperinsulinemia is associated with impaired endothelial-dependent vascular relaxation and endothelial cell derangement.^22,29 Poor vasodilatory response in vascular smooth muscle cells increases the glomerular pressure and eventually results in urinary albumin excretion. Thus, this could be the mechanical background for the role of elevated insulin levels in the development of microalbuminuria.

Study Limitations

First, urinary albumin was assessed in only a single urine specimen from study participants. Prior studies have suggested that urinary albumin levels may exhibit considerable intra-individual variability.³⁰ Nonetheless, the national practice guidelines recommend the use of spot specimens for the UACR because the test is easily performed in the clinic and the results correlate well with those of 24-h collections.³¹ Second, indicators of obesity such as BMI or waist circumference could not be included as an adjusting covariate. Obesity is strongly related to metabolic diseases that affect the development of microalbuminuria. In addition, previous study showed the causative effect of obesity in the development of microalbuminuria.³² Therefore, obesity should be considered as an adjusting covariate; however, because we were missing a significant amount of BMI or waist circumference values in our raw data, we could not conduct the analysis with these factors. Accordingly, this limitation warrants another study reflecting the influence of obesity in the development of microalbuminuria according to insulin levels.

Third, the study population of this study was restricted to Korean men, so our study findings can not be extrapolated to women or other ethnicities. Considering that previous studies of women have shown a somewhat different pattern of results from ours,^16,32–34 additional studies including women are warranted.

In conclusion, our study findings indicate that, besides IR, an elevated fasting insulin level per se can be a risk factor or early predictor for developing microalbuminuria. Understanding the clinical significance of the fasting insulin level in predicting microalbuminuria may provide important insight into preventing medical problems related to microalbuminuria.

Disclosures

Financial Support and Disclosure Statement: The authors have nothing to disclose. No potential conflicts of interest relevant to this article are reported.

Author Contributions: All authors had access to the data used in this study and participated in writing the manuscript. Jae-Hong Ryoo and Sung Keun Park participated equally in collecting the data, coordinating the study, analyzing the data, interpreting the results, and writing the manuscript and therefore should be considered as first authors. Ju Young Jung is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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