Racial and Ethnic Differences in Microalbuminuria ... - Semantic Scholar

Racial and Ethnic Differences in Microalbuminuria Prevalence in a Diabetes Population: The Pathways Study Bessie A. Young,*†‡ Wayne J. Katon,§ Michael Von Korff,储 Greg E. Simon,储 Elizabeth H. B. Lin,储 Paul S. Ciechanowski,§ Terry Bush,储 Malia Oliver,储 Evette J. Ludman,储 and Edward J. Boyko*† *Division of General Internal Medicine, Department of Medicine, University of Washington, and Primary and Specialty Medical Care Service, Veterans Affairs Puget Sound Health Care System; †Epidemiologic Research and Information Center, Veterans Affairs Puget Sound Health Care System; ‡Northwest Kidney Centers; §Department of Psychiatry & Behavioral Sciences, University of Washington School of Medicine; and 储Center for Health Studies, Group Health Cooperative, Seattle, Washington The objective of this study was to determine whether racial or ethnic differences in prevalence of diabetic microalbuminuria were observed in a large primary care population in which comparable access to health care exists. A cross-sectional analysis of survey and automated laboratory data 2969 primary care diabetic patients of a large regional health maintenance organization was conducted. Study data were analyzed for racial/ethnic differences in microalbuminuria (30 to 300 mg albumin/g creatinine) and macroalbuminuria (>300 mg albumin/g creatinine) prevalence among diabetes registry–identified patients who completed a survey that assessed demographics, diabetes care, and depression. Computerized pharmacy, hospital, and laboratory data were linked to survey data for analysis. Racial/ethnic differences in the odds of microalbuminuria and macroalbuminuria were assessed by unconditional logistic regression, stratified by the presence of hypertension. Among those tested, the unadjusted prevalence of micro- or macroalbuminuria was 30.9%, which was similar among the various racial/ethnic groups. Among those without hypertension, microalbuminuria was twofold greater (odds ratio [OR] 2.01; 95% confidence interval [CI] 1.14 to 3.53) and macroalbuminuria was threefold greater (OR 3.17; 95% CI 1.09 to 9.26) for Asians as compared with whites. Among those with hypertension, adjusted odds of microalbuminuria were greater for Hispanics (OR 3.82; 95% CI 1.16 to 12.57) than whites, whereas adjusted odds of macroalbuminuria were threefold greater for blacks (OR 3.32; 95% CI 1.26 to 8.76) than for whites. For most racial/ethnic minorities, hypertriglyceridemia was significantly associated with greater odds of micro- and macroalbuminuria. Among a large primary care population, racial/ethnic differences exist in the adjusted prevalence of microalbuminuria and macroalbuminuria depending on hypertension status. In this setting, racial/ ethnic differences in early diabetic nephropathy were observed despite comparable access to diabetes care. J Am Soc Nephrol 16: 219 –228, 2005. doi: 10.1681/ASN.2004030162

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iabetic nephropathy affects 20 to 40% of those who develop diabetes (1–3) and is associated with enormous morbidity, (4,5) mortality, (5) and health care costs (6). Racial and ethnic differences in prevalence and incidence of diabetic nephropathy (5) and diabetic renal failure or ESRD have been well described (5–7). Compared with whites, the prevalence of diabetic renal failure is two- to threefold greater in blacks (5,7,8), two-fold greater in Asians, (8) two- to threefold greater in Hispanics (9,10), and up to 18-fold greater in Native Americans (11). Microalbuminuria, or “incipient diabetic nephropathy,” is one of the initial clinical manifestations of early diabetic nephropathy (12), but it is unclear whether racial/ethnic differences exist in the prevalence of microalbuminuria when controlling for socioeco-

Received March 2, 2004. Accepted September 17, 2004. Published online ahead of print. Publication date available at www.jasn.org. Address correspondence to: Dr. Bessie A. Young, VA Puget Sound Health Care System (152-E), Epidemiologic Research and Information Center, 1660 S. Columbian Way, Seattle, WA 98108. Phone: 206-277-3586; Fax: 206-764-2563; E-mail: [email protected] Copyright © 2005 by the American Society of Nephrology

nomic status in a setting where access to health care is comparable across racial/ethnic groups. The limited population-based data describing racial/ethnic differences in the prevalence and incidence of microalbuminuria suggest that in the general population, blacks have greater odds of microalbuminuria compared with whites, but data that describe racial/ethnic differences in prevalence of diabetic microalbuminuria are lacking (5,13–15). Reasons for racial/ethnic differences in diabetic microalbuminuria may include socioeconomic disparities (16), disparities in access to health care (16 – 20), a greater prevalence of diabetes in racial or ethnic minority communities (21–26), greater proportion of uncontrolled hypertension in racial/ethnic minorities (16), differences in glycemic control (25), and possible biologic or genetic differences. However, there is controversy about the relative importance of these factors (27–36). Attainment of diabetes care guidelines, particularly those for early testing of urine for microalbuminuria, remain poor regardless of race or ethnicity (37). Data that compare prevalence of early diabetic nephropathy across racial/ethnic categories where comparable access to care and diabetes care quality exist are lacking. ISSN: 1046-6673/1601-0219

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Journal of the American Society of Nephrology

Using a large population-based sample of primary care patients with predominately type 2 diabetes, we sought to assess racial/ethnic differences in microalbuminuria and macroalbuminuria prevalence. We hypothesized that in the setting of a health maintenance organization (HMO) with relatively uniform access to health care and where diabetic treatment guidelines exist for diabetic nephropathy, racial/ethnic disparities in the prevalence of microalbuminuria will not be observed after adjusting for duration of diabetes and diabetes treatment practices.

Materials and Methods Setting We conducted a cross-sectional analysis of baseline data collected as part of the Pathways Study, described elsewhere (38). In brief, the Pathways Study is an epidemiologic evaluation of the prevalence and impact of depression conducted in a population-based sample of primary care diabetic patients at Group Health Cooperative (GHC). GHC is a large nonprofit HMO that services ⬎400,000 people in 30 primary care clinics located in Western Washington State. GHC maintains a diabetes registry of approximately 18,000 patients, from which patients were identified and targeted for enrollment into the study. Local GHC Human Subjects Review Committee approval was obtained before study initiation. Since 1995, GHC has implemented a quality improvement program that includes algorithm-based diabetic nephropathy guidelines that detail treatment recommendations for those with diabetes and early diabetic nephropathy. Primary care clinicians are provided with diabetic nephropathy guidelines, and the diabetic registry maintains a record of guideline-recommended test results by patient.

Data Source and Patients Nine primary care clinics were considered for patient recruitment and were chosen on the basis of the following criteria: (1) large numbers of diabetic primary care patients, (2) ethnic diversity, and (3) located within a 40-mile geographic proximity to the Seattle/King County area. Cases were identified using any of the following criteria: (1) currently prescribed any diabetic treatment medication, (2) a fasting glucose ⱖ126 mg/dl or a random glucose ⱖ200 mg/dl confirmed by a second test during the year before ascertainment, (3) a hospital discharge diagnosis of diabetes, or (4) two outpatient diagnoses of diabetes at any time during GHC enrollment. Using these criteria, approximately 9000 patients were identified and sent a survey that assessed demographics (age, gender, income, and selfreported ethnicity), diabetes characteristics (type, duration of diabetes, self-care characteristics, and complications), depression status, and satisfaction with health care. Of those, a total of 7841 patients were eligible for inclusion. Exclusion criteria included the following: Diabetes not present (n ⫽ 289), gestational diabetes (n ⫽ 8), cognitive impairment (n ⫽ 80), severe illness (n ⫽ 202), deceased (n ⫽ 128), disenrollment (n ⫽ 444), language or hearing problems (n ⫽ 99), or other reasons (n ⫽ 2). Of those who were eligible, 4839 (61.7%) returned the survey, 4467 (92.3%) of whom gave permission for linkage of survey data to automated data that GHC maintains on all of its enrollees for an overall response rate of 57%. To assess for the possibility of nonresponse bias, differences in deidentified data were examined between survey respondents and nonrespondents. Propensity scores (39) were estimated on the basis of the following variables: age, gender, hemoglobin A1c (HbA1c), Rx-Risk score (a measure of medical comorbidity), number of primary care visits, number of specialty visits, and patient’s primary clinic location. Weighted and nonweighted analyses were conducted, producing similar results; thus, we report nonweighted data. Because race is not recorded in automated data at GHC, racial/ethnic differences in nonresponse variables were not available for nonrespondents. Of the 4467 eligible patients, 2969 (66.5%) had microalbu-

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minuria tested. Because patients who were tested for microalbuminuria did not differ from those who were not tested in regard to demographic, laboratory, medication, or diabetes care characteristics, subsequent analyses were restricted to those who were tested for microalbuminuria.

Exposures Potential risk factors for microalbuminuria or macroalbuminuria were determined a priori and included race/ethnicity, duration of diabetes, diabetes type, glycemic control, and treatment for hypertension. Race or ethnicity was obtained by self-report and includes the following categories: White/Caucasian, black/African American, Asian or Pacific Islander, American Indian or Alaskan Native, or other. A separate question asked respondents whether they were of Hispanic or Spanish ethnicity. Those who self-reported being of Hispanic or Spanish origin were analyzed separately from all other racial/ethnic categories and categorized as being of “Hispanic” origin. Race and ethnicity were available for 97.1% of the population. Because Native Americans or Alaskan Natives (1.5%) and those of other race/ethnicity (3.0%) composed ⬍5.0% of the population, results for these populations were excluded. Automated data were used to assess laboratory results, medication use, hospitalizations, and outpatient visits. HbA1c, triglyceride, and LDL levels were ascertained closest to the date of the baseline epidemiologic survey for up to 18 mo before study enrollment. Medication use was self-reported and validated using automated pharmacy data. The presence of comorbid conditions, such as cardiovascular disease, hypertension, stroke, amputation, and heart failure, was identified by the use of International Classification of Diseases, Ninth Revision diagnosis codes (40) from outpatient visits and hospitalizations that occurred in the 18 mo before study entry. Computerized pharmacy records were used to identify patients who were taking angiotensinconverting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARB). HCG-CoA reductase inhibitor (statin) use was also categorically determined for the 18 mo before date of study enrollment.

Outcomes The primary outcomes of interest were the presence of microalbuminuria and macroalbuminuria obtained from values averaged for the 18-mo period before study enrollment. Microalbuminuria, identified in automated laboratory data by specific laboratory data codes and defined by an abnormal albumin-to-creatinine ratio of 30 to 300 mg albumin/g creatinine (mg alb/g cr), was obtained from a random spot urine collection. Macroalbuminuria was defined as an albumin-tocreatinine ratio of ⬎300 mg alb/g cr. Laboratory tests were conducted via a centralized GHC laboratory.

Statistical Analyses Statistical analyses were conducted to compare the distribution of covariates by the presence or absence of microalbuminuria or macroalbuminuria and by race/ethnicity. Data analyses were performed using STATA-SE version 8 (Stata, College Station, TX) (41). Statistical significance was determined using independent t test for continuous data and the ␹2 tests for categorical data (42). The associations between exposures and outcomes of interest were measured as the unadjusted and adjusted odds ratios (OR) (43). These estimates and the 95% confidence intervals (CI) were determined using unconditional logistic regression for dichotomous outcomes (44). Modeling techniques included logistic regression with forced entry of covariates of interest. Potential confounders were identified by a change of 10% or more in the primary outcome of interest in multivariate analyses (43). Because hypertension is more likely to be associated with microalbuminuria, analyses were stratified for the presence or absence of an International Classification of Diseases, Ninth Revision

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Racial/Ethnic Differences in Microalbuminuria

diagnosis of hypertension as a surrogate for actual BP levels. Potential interactions between race/ethnicity and hypertension and race/ethnicity and hypertriglyceridemia were assessed in multivariate models and not found to be significant.

Results Baseline Cohort Of the 4467 patients for whom data were available, 2969 had laboratory data available to determine the primary outcomes of interest (Table 1). Microalbuminuria was identified in 731 (24.6%) enrollees, whereas macroalbuminuria was identified in 187 (6.3%) enrollees. Compared with those with

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normal urinary albumin excretion, those with microalbuminuria were older, had longer duration diabetes, had worse glycemic control, and had higher mean serum triglyceride levels (Table 1). In addition, those with microalbuminuria were more likely to have been on oral hypoglycemic agents, insulin, statins, and ACEI/ARB than those with normal urinary albumin excretion. These patients also had lower mean income level and educational attainment. Likewise, those with macroalbuminuria were older, were more likely to have hypertension, had longer duration diabetes, had more primary and specialty visits, had lower income and

Table 1. Population characteristics by the presence of microalbuminuria and macroalbuminuriaa Characteristics

Demographics age, mean (SD) diabetes duration, yr (SD) gender, % male Race (%) white black Asian Hispanic Native American other Current smoker (%) Primary care visits, mean (SD) Specialty visits, mean (SD) Income ⬎$20,000, % Married, % High school ⫹, % Hypertension, % Laboratory studies, mean (SD) HbA1c, % (SD) serum creatinine, mg/dl (SD) triglycerides, mg/dl (SD) LDL, mg/dl (SD) urine spot albumin:creatinine ratio, mg alb/gr creat (SD) Medications (%) oral diabetes medication insulin prescribed statin prescribed ACEI/ARB Diabetes care, n (%)c HbA1c tested LDL tested triglycerides tested a

Baseline Microalbuminuria Cohort (n ⫽ 2969)

Normal Albuminuria (n ⫽ 2051; 69.1%)

60.1 (12.6) 9.0 (8.9) 50.3

59.3 (12.3) 8.4 (8.7) 49.0

2197 (74.0) 253 (8.5) 287 (9.7) 98 (3.3) 46 (1.5) 88 (3.0) 9.6 5.8 (5.4) 3.1 (3.5) 61.1 67.1 77.8 37.9

1538 (76.4) 168 (8.3) 199 (9.9) 65 (3.2) 32 (1.6) 12 (0.6) 9.6 5.5 (4.9) 2.9 (3.3) 63.0 68.1 79.2 33.3

7.9 (1.6) 1.0 (0.4) 221.5 (219.0) 112.6 (34.7) 117.3 (510.7)

61.0 30.1 29.4 55.6 2826 (95.2) 2194 (73.9) 2190 (73.8)

7.7 (1.5) 1.0 (0.3) 204.9 (201.2) 113.2 (33.7) 9.7 (7.8)

58.6 27.2 26.4 49.3 1946 (94.9) 1493 (72.8) 1496 (72.9)

Microalbuminuria (n ⫽ 731; 24.6%)

62.0 (12.9)b 9.8 (9.0)b 52.9 538 (76.4) 61 (8.7) 65 (9.2) 27 (3.8) 11 (1.6) 2 (0.3) 8.6 6.3 (6.1)b 3.2 (3.5)b 57.8 66.5 75.1 44.9 8.3 (1.8)b 1.1 (0.5)b 256.3 (261.0)b 111.7 (36.6) 90.0 (63.7)b

68.0 33.2 34.9 69.8 701 (95.9) 552 (75.5) 553 (75.6)

Macroalbuminuria (n ⫽ 187; 6.3%)

62.5 (13.2)b 12.8 (9.8)b 54.5 121 (67.2) 24 (13.3) 23 (12.8) 6 (3.3) 3 (1.7) 3 (1.7) 13.1 7.6 (6.9)b 4.4 (4.2)b 53.0c 57.8c 73.2c 59.9c 8.1 1.5 260.0 110.4 1404.4 59.9c 50.3c 40.1c 69.0c 179 145 145

HbA1c, hemoglobin A1c; ACEI, angiotensin-converting enzyme inhibitors; ARB, angiotensin receptor blockers Missing lab data, n ⫽ 1870, microalbuminuria tests available in 2969. P ⬍ 0.01, means of microalbuminuria and macroalbuminuria groups compared independently with normal albuminuria group using the independent t test. c P ⬍ 0.05 categorical groups compared using ␹2. b

(7.8)b (0.8)b (199.3)b (38.2) (1532.8)b

(95.7) (77.5) (77.5)

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educational attainment, had worse glycemic control, and had higher mean triglyceride levels than those with normal urinary albumin excretion (Table 1). Those with macroalbuminuria were also more likely to have been on oral hypoglycemic medications or insulin, statins, and ACEI/ARB than those with normal urinary albumin.

Race and Ethnicity Differences in the baseline population were evident by race/ ethnicity and are shown in Table 2. Racial and ethnic minorities tended to be younger than whites, had similar or higher levels of income, were similarly educated, and were more likely to be employed full or part time (Table 2). Blacks had the highest levels of baseline microalbuminuria and were as likely to be on ACEI/ ARB, oral hypoglycemic agents, and insulin but were less likely to be receiving a statin drug. Hypertension was more likely to be present in blacks than in whites, Asians, or Hispanics. Racial and ethnic minorities had similar levels of HbA1c and creatinine but lower mean levels of LDL and triglycerides. The proportion of patients who were on an ACEI/ARB or those who had testing for microalbuminuria did not differ by race/ethnicity.

Microalbuminuria Racial and ethnic differences in the odds of microalbuminuria were assessed and are shown in Table 3. Among those

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with hypertension, Hispanics had 3.8-fold greater odds (OR 3.82; 95% CI 1.16 to 12.57), whereas blacks trended toward higher odds of microalbuminuria (OR 1.59; 95% CI 0.83 to 3.03) compared with whites after adjusting for diabetes type, glycemic control, diabetes duration, statin use, and body mass index (BMI; Table 3). In addition, patients with triglyceride levels ⱖ200 mg/dl had a twofold higher risk of microalbuminuria compared with those with levels ⬍200 mg/dl (OR 2.03; 95% CI 1.37 to 3.01). For every year of duration of diabetes, there was a 3% greater chance of microalbuminuria. In addition, there was a 23% greater chance of microalbuminuria associated with every percentage elevation of HbA1c (Table 3). Among those without hypertension, Asians had a twofold greater adjusted odds of microalbuminuria compared with whites (OR 2.01; 95% CI 1.14 to 3.53), whereas the odds of microalbuminuria for blacks and Hispanics did not reach significance. In addition, hypertriglyceridemia was associated with 1.9-fold greater odds of microalbuminuria (OR 1.89; 95% CI 1.34 to 2.68; Table 3). Every year of diabetes was associated with a 2% increase in the odds of microalbuminuria, whereas each percentage elevation of HbA1c was associated with a 17% greater chance of microalbuminuria. BMI ⬎30 kg/m2 was associated with 1.74-fold increase in odds of microalbuminuria.

Table 2. Racial and ethnic differences in characteristics of those tested for microalbuminuriaa Characteristics

White (n ⫽ 2197)

Black (n ⫽ 253)

Asian (n ⫽ 287)

Hispanic (n ⫽ 98)

Age Gender, male (%) Duration if diabetes, yr (SD) Smoking, % Married, % Income ⬎$20,000, % Education, % Employed full/part time, % BMI, average kg/m2 (SD) Hypertension present, % HbA1c, average (%) LDL, mg/dl Triglycerides, mg/dl Microalbuminuria, mg/g Cr Microalbuminuria test, n (SD) Creatinine, mg/dl Creatinine tests, n (SD) Medications ACEI/ARB, % Oral medications, % Insulin prescribed, % Statins prescribed, %

60.9 (12.3) 50.6 9.4 (9.2) 8.6 67.4 59.4 79.0 47.1 31.9 (7.5) 37.0 7.8 (1.6) 112.4 (35.0) 226.6 (227.4) 106.3 (472.3) 1.6 (0.9) 1.0 (0.4) 1.5 (2.7)

57.0 (13.6)b 48.2 8.6 (8.1) 17.0 56.6 72.1 70.1 56.6 32.1 (6.9) 53.8 8.3 (1.9) 119.9 (36.2)b 160.5 (152.2)b 139.2 (439.8) 1.7 (0.9) 1.1 (0.4) 1.4 (1.4)

58.7 (13.1)b 47.0 8.3 (8.5) 8.5 76.0 59.4 77.1 57.2 26.9 (5.0)b 34.8 7.8 (1.6) 109.9 (33.4) 209.8 (146.0) 187.2 (833.8) 1.7 (0.8) 1.0 (0.6) 1.3 (1.4)

55.5 (11.9)b 55.1 6.5 (5.9)b 8.5c 71.4c 77.9c 79.6c 75.3c 31.1 (7.8) 28.6c 8.0 (1.8) 110.0 (32.0) 244.0 (330.2) 112.8 (500.4) 1.8 (0.9) 0.9 (0.3) 1.3 (2.1)

56.6 59.7 31.8 31.5

56.5 63.6 35.2 19.0

49.1 66.6 17.1 26.8

46.9 56.1 19.4c 17.3c

All racial and ethnic categories are mutually exclusive. Data for Native Americans (n ⫽ 46) and other (n ⫽ 88) are not shown. b P ⬍ 0.05 independent t test compared each individual minority group with white. c P ⬍ 0.005, for ␹2 test comparing all groups. a

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Table 3. Racial or ethnic differences in odds of microalbuminuriaa Overall

Hypertension

No Hypertension

Covariate Ethnicity white black Asian Hispanic Age Diabetes type (type 1 versus type 2) Duration, yr HbA1c, % Oral diabetes medications Insulin Triglycerides ⱖ 200 mg/dl BMI ⱖ 30 kg/m2 Hypertension

Unadjusted

Adjusted

Unadjusted

Adjusted

Unadjusted

Adjusted

1.0 0.98 (0.72–1.34) 0.90 (0.67–1.21) 1.20 (0.76–1.19) 1.02 (1.01–1.03) 0.92 (0.72–1.19)

1.0 1.32 (0.81–2.18) 1.38 (0.90–2.12) 1.93 (1.02–3.64) 1.01 (0.99–1.02) 1.08 (0.70–1.67)

1.0 0.98 (0.64–1.49) 0.79 (0.48–1.30) 1.50 (0.68–3.31) 1.01 (0.99–1.02) 1.26 (0.81–1.95)

1.0 1.59 (0.83–3.03) 0.88 (0.45–1.72) 3.82 (1.16–12.57) 1.02 (1.00–1.05) 1.29 (0.65–2.58)

1.0 0.82 (0.51–1.33) 0.99 (0.68–1.44) 1.15 (0.65–2.03) 1.02 (1.01–1.03) 0.86 (0.63–1.19)

1.0 1.13 (0.49–2.61) 2.01 (1.14–3.53) 1.55 (0.68–3.53) 1.00 (0.98–1.01) 0.88 (0.49–1.60)

1.02 (1.00–1.03) 1.24 (1.18–1.30) 1.47 (1.23–1.75)

1.03 (1.01–1.04) 1.20 (1.11–1.31) 1.07 (0.80–1.42)

1.02 (1.00–1.04) 1.31 (1.20–1.43) 1.19 (0.90–1.57)

1.03 (1.00–1.05) 1.23 (1.07–1.42) 1.02 (0.65–1.58)

1.01 (1.00–1.03) 1.22 (1.15–1.30) 1.63 (1.29–2.06)

1.02 (1.00–1.05) 1.17 (1.05–1.31) 1.12 (0.72–1.66)

1.35 (1.13–1.62) 1.59 (1.30–1.93)

0.90 (0.65–1.27) 1.98 (1.54–2.55)

1.71 (1.29–2.26) 1.39 (1.04–1.88)

1.01 (0.61–1.67) 2.03 (1.37–3.01)

1.15 (0.91–1.47) 1.74 (1.34–2.27)

0.90 (0.56–1.43) 1.89 (1.34–2.68)

1.28 (0.95–1.73) 1.61 (1.36–1.92)

1.32 (1.01–1.72) 1.25 (0.97–1.62)

1.24 (0.82–1.87) —

0.92 (0.61–1.38) —

1.03 (0.65–1.64) —

1.74 (1.20–2.52) —

a Models adjusted for above covariates and marital status, salary, education, employment status, ACEI/ARB use, and smoking history. Microalbuminuria was identified in automated laboratory data by specific laboratory data codes and was defined by an abnormal albumin-to-creatinine ratio 30 to 300 mg albumin/g creatinine. Data for Native American patients and those of other race/ethnicity are not shown because the small number of patients. Sample size for models were as follows: microalbuminuria with hypertension, n ⫽ 986; microalbuminuria without hypertension, n ⫽ 1731.

Table 4. Racial or ethnic differences in odds of macroalbuminuriaa Overall

Hypertension

No Hypertension

Covariate

Ethnicity white black Asian Hispanic Age Duration (yr) Oral diabetes medications Insulin HbA1c (%) Triglycerides ⱖ 200 mg/dl BMI ⱖ 30 kg/ m2 Hypertension

Unadjusted

Adjusted

Unadjusted

Adjusted

Unadjusted

Adjusted

1.0 1.87 (1.18–2.96) 1.45 (0.91–2.32) 1.18 (0.50–2.78) 1.02 (1.01–1.04) 1.04 (1.03–1.06) 1.06 (0.78–1.44)

1.0 2.61 (1.20–5.65) 2.24 (1.09–4.61) 2.15 (0.60–7.74) 1.01 (0.99–1.04) 1.06 (1.03–1.09) 1.32 (0.78–2.24)

1.0 1.69 (0.96–2.95) 1.58 (0.84–2.97) 0.94 (0.21–4.17) 1.01 (0.99–1.03) 1.05 (1.03–1.06) 0.68 (0.46–1.07)

1.0 3.32 (1.26–8.76) 1.68 (0.62–4.57) 2.21 (0.22–22.23) 1.02 (0.83–1.07) 1.06 (1.02–1.10) 0.92 (0.45–1.84)

1.0 1.38 (0.58–3.31) 1.35 (0.65–2.80) 1.67 (0.58–4.82) 1.01 (1.00–1.03) 1.04 (1.01–1.06) 1.75 (1.06–2.90)

1.0 2.20 (0.54–8.99) 3.17 (1.09–9.26) 2.62 (0.52–13.17) 1.00 (0.96–1.04) 1.07 (1.02–1.12) 3.10 (1.19–8.12)

2.76 (2.04–3.73) 2.20 (1.27–3.79) 3.04 (2.02–4.57) 1.61 (0.76–3.38) 1.15 (1.05–1.26) 1.09 (0.93–1.27) 1.15 (1.01–1.31) 1.10 (0.88–1.38) 2.08 (1.48–2.93) 3.76 (2.31–6.14) 1.64 (1.05–2.57) 3.70 (1.86–7.39)

2.64 (1.65–4.21) 3.40 (1.46–7.89) 1.22 (1.07–1.39) 1.07 (0.86–1.34) 2.84 (1.64–4.94) 3.70 (1.80–7.63)

1.28 (0.95–1.73) 1.15 (0.71–1.87) 1.24 (0.82–1.87) 1.28 (0.65–2.52)

1.03 (0.65–1.64) 1.04 (0.49–2.22)

3.02 (2.22–4.10) 1.94 (1.20–3.13)

—

—

—

—

a Models adjusted for above covariates and marital status, salary, education, employment status, ACEI/ARB use, and smoking history. Macroalbuminuria was identified in automated laboratory data by specific laboratory data codes and was defined by an abnormal albumin to creatinine ratio ⬎300 mg albumin/g creatinine. Native American data are not shown because of small numbers. Sample size for models were as follows: macroalbuminuria with hypertension, n ⫽ 775; macroalbuminuria without hypertension, n ⫽ 1381.

Macroalbuminuria The odds of macroalbuminuria were similarly assessed and are shown in Table 4. Among those with hypertension, blacks had threefold greater odds of macroalbuminuria compared

with whites (OR 3.32; 95% CI 1.26 to 8.76), whereas Asians and Hispanics trended toward greater odds of macroalbuminuria. Duration of diabetes conferred 6% greater odds of macroalbuminuria for each year of diabetes. Hypertriglyceridemia was

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associated with 3.7-fold greater odds of macroalbuminuria (OR 3.70; 95% CI 1.86 to 7.39). Among those without hypertension, Asian ethnicity was associated with threefold greater odds of macroalbuminuria (OR 3.17; 95% CI 1.09 to 9.26). Hypertriglyceridemia (OR 3.70; 95% CI 1.80 to 7.63) and duration of diabetes (OR 1.07; 95% CI 1.02 to 1.12, per year of duration) were associated with greater odds of macroalbuminuria.

Factors Associated with Racial/Ethnic Differences in Microalbuminuria or Macroalbuminuria We sought to determine which factors (race/ethnicity, socioeconomic characteristics, diabetes characteristics, use of ACEI/ ARB, BMI ⬎30 kg/m2, triglyceride level ⬎200 mg/dl, or LDL level/statin use) contributed more toward the difference in odds of micro- and macroalbuminuria between individual racial/ethnic minority groups and whites (Table 5) (18). Combining micro- and macroalbuminuria outcomes for greater power, we found that among those with hypertension, hypertriglyceridemia resulted in the greatest change in odds for micro- or macroalbuminuria among blacks compared with whites. In contrast, controlling for socioeconomic status, diabetes care characteristics (diabetes type, glycemic control, and diabetes duration), and hypertriglyceridemia resulted in the greatest change in the in odds of micro- or macroalbuminuria for Hispanics compared with whites. OR did not reach significance for hypertensive Asian patients. Similarly, among those without hypertension, BMI ⬎30 kg/m2 and hypertriglyceridemia contributed more toward the differences in micro- or macroalbuminuria found for Asian patients compared with whites. Ethnic differences in the odds of micro- or macroalbuminuria were less apparent for nonhypertensive blacks and Hispanics.

Discussion In this population-based cohort of primary care patients who had diabetes and for whom medical care was provided in a setting in which guidelines for assessment and treatment of

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early diabetic nephropathy existed, we found racial/ethnic differences in prevalence of microalbuminuria and macroalbuminuria. Blacks and Hispanics were more likely than whites to have microalbuminuria or macroalbuminuria when they had an underlying diagnosis of hypertension, whereas Asians were more likely than whites to have microalbuminuria or macroalbuminuria in the absence of hypertension. Hypertension and duration of diabetes remained risk factors for micro- and macroalbuminuria, whereas hypertriglyceridemia had the strongest association with both microalbuminuria and macroalbuminuria after adjustment for diabetes care characteristics, BMI, and socioeconomic status. Although it is widely known that blacks and other ethnic minority groups have a greater propensity toward diabetic renal failure or ESRD (5–7), less is known about ethnic differences in early manifestations of diabetic nephropathy, particularly in representative population samples with equal access to care after controlling for socioeconomic status. In the current study, the prevalence of microalbuminuria was similar to that reported by small clinic-based studies (45– 47); however, the white population from the current study had a higher prevalence of microalbuminuria compared with recent treatment trials (48). Nationally, blacks and Hispanics have been shown to have greater odds of microalbuminuria in the general population, but ethnic differences in microalbuminuria have not been reported for those with a diagnosis of diabetes (15,49,50). Thus, the current study found that in a health care system in which enrollees have equal access to diabetes-associated care, ethnic differences in micro- and macroalbuminuria occurred after adjustment for clinical characteristics, socioeconomic status, and treatment practices. We found hypertriglyceridemia to be robustly associated with micro- and microalbuminuria. Unlike recent treatment trials (48), the current study did not find a strong association of elevated LDL levels with micro- and macroalbuminuria after

Table 5. Effect of various patient characteristics on odds of microalbuminuria or macroalbuminuriaa

Baseline Model

With hypertension white black Asian Hispanic Without hypertension white black Asian Hispanic a

Baseline, ⫹SES

Baseline, ⫹SES, ⫹Diabetes

Baseline, ⫹SES, ⫹Diabetes, ⫹ACEI

Baseline, ⫹SES, ⫹Diabetes, ⫹ACEI, ⫹BMI ⱖ30

Baseline, ⫹SES, ⫹ Diabetes, ⫹ACEI, ⫹BMI ⱖ30, ⫹Triglycerides

Baseline, ⫹SES, ⫹Diabetes, ⫹ACEI, ⫹BMI ⱖ30, ⫹Triglycerides, ⫹LDL/Statin

1.0 1.2 (0.8–1.7) 1.0 (0.7–1.5) 1.4 (0.7–3.0)

1.0 1.3 (0.9–1.2) 1.0 (0.6–1.6) 2.2 (0.3–4.0)

1.0 1.2 (0.8–1.9) 1.0 (0.6–1.6) 3.1 (0.3–44.0)

1.0 1.2 (0.8–1.9) 1.0 (0.6–1.7) 3.0 (1.1–8.8)

1.0 1.3 (0.8–2.0) 1.0 (0.6–1.7) 3.0 (1.1–8.8)

1.0 2.0 (1.1–3.6) 1.1 (0.6–2.0) 4.1 (1.3–13.2)

1.0 2.1 (1.1–3.7) 1.1 (0.6–2.0) 4.2 (1.3–13.6)

1.0 0.9 (0.6–1.4) 1.1 (0.8–1.5) 1.2 (0.7–2.0)

1.0 1.0 (0.6–1.7) 1.3 (0.9–1.9) 1.2 (0.7–2.2)

1.0 1.0 (0.6–1.7) 1.4 (1.0–2.1) 1.3 (0.4–2.7)

1.0 1.1 (0.6–1.9) 1.4 (1.0–2.2) 1.3 (0.7–2.5)

1.0 1.2 (0.7–2.1) 1.8 (1.2–2.8) 1.3 (0.7–2.6)

1.0 1.4 (0.7–2.3) 2.2 (1.3–3.8) 1.5 (0.3–2.4)

1.0 1.4 (0.7–2.9) 2.3 (1.3–3.9) 1.5 (0.7–3.3)

SES, socioeconomic status.

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controlling for statin use. However, the strong association with hypertriglyceridemia remained after adjusting for BMI, LDL, and statin use. Although it is widely known that diabetes (51) and proteinuria (52,53) are associated with elevated triglyceride levels, it is not known, in this case, whether hypertriglyceridemia preceded the onset of microalbuminuria or dyslipidemia was a result of the underlying renal dysfunction. Longitudinal results from the Coronary Artery Risk Development in Young Adults Study found that hypertriglyceridemia conferred a greater risk of microalbuminuria than elevated BP or other risk factors in people with and without diabetes (13). Furthermore, in a preliminary report from a recent randomized, controlled trial, treatment of hypertriglyceridemia with fenofibrate was shown to improve incipient diabetic nephropathy (54); however, long-term studies are lacking. A longitudinal study is needed to assess causal relationships of hypertriglyceridemia with development of microalbuminuria or with progression of microalbuminuria to macroalbuminuria in patients with diabetes. Because hypertension is known to affect the prevalence of micro- or macroalbuminuria and because hypertensive renal disease more often affects certain minority populations (6), we stratified analyses by the presence or absence of hypertension. Hypertension has been shown to increase the risk of microalbuminuria in the absence of diabetes (13,15), and racial and ethnic differences in the risk of hypertension are known to exist (45,46,55,56). Numerous mechanisms have been described to account for racial and ethnic differences in risk of hypertension (57–74), including increased salt sensitivity in blacks (75) and Asians (57), low renin-angiotensin responsiveness in blacks (58,65), decreased excretion of potassium (76), and differences in renal vasculature and nephron number, although controversy exists regarding some proposed mechanisms (77,78). The current study found that prevalent hypertension was associated with a 1.9-fold greater odds of macroalbuminuria (OR 1.94; 95% CI 1.20 to 3.13) and trended toward increased odds of microalbuminuria (OR 1.25; 95% CI 0.97 to 1.62); however, when models were stratified by diagnosis of hypertension, blacks and Hispanics were more likely to have micro- or macroalbuminuria in the presence of hypertension, whereas Asian patients were more likely to have micro- or macroalbuminuria in the absence of a diagnosis of hypertension. Reasons for differences among racial/ethnic minority groups may reside in a group’s propensity toward hypertension, differences in ease of BP control, or response to antihypertensive medications, all of which we were unable to ascertain using the current database. Adjustment for several factors resulted in an increase in the relative odds of micro- or macroalbuminuria among hypertensive patients, but contribution of specific factors differed by ethnicity (Table 5). Among blacks, adjustment for triglycerides resulted in the greatest change in the odds of micro- or macroalbuminuria, whereas among Hispanics, adjustment for socioeconomic status, diabetes characteristics, and hypertriglyceridemia resulted in the greatest change of micro- or macroalbuminuria compared with whites. Blacks in this HMO setting were more similar to their white counterparts in terms


225

of socioeconomic characteristics, which may explain why adjustment for socioeconomic factors did not change the odds of microalbuminuria among blacks compared with whites. For nonhypertensive Asian patients, adjustment for BMI ⱖ30 kg/m2 and hypertriglyceridemia had the greatest affect on the magnitude of change in the odds of micro- or macroalbuminuria compared with whites. A longitudinal study of patients with similar duration of diabetes for whom baseline BMI, BP, diabetes characteristics, dietary patterns, and lipid levels were similar would be necessary to determine whether these risk factors precede microalbuminuria in time and therefore potentially cause this outcome or follow its development. In addition, genetic or environmental/cultural forces may explain the differences found, but because of the cross-sectional nature of the study and lack of BP levels or additional information on antihypertensive medications, causal inferences cannot be clarified from data in the current study. Besides differences in baseline diagnosis of hypertension, explanations for racial/ethnic disparities in the presence of microalbuminuria or macroalbuminuria are likely multifactorial, as a result in part of differences in access to care (14), differences in glycemic control (25), and differences in the processes and quality of care (26) or may involve biologic or genetic differences. In the current study, access to care was comparable among study participants, as were identified diabetes care characteristics. Ethnic minority groups may be more prone to metabolic syndrome (79), which may predispose them to microalbuminuria or macroalbuminuria once diabetes develops. Other genetic differences, such as ACE genotype polymorphisms, may also explain a relative lack of response to ACEI in certain ethnic groups (80,81); however, detailed genetic and long-term longitudinal studies are necessary to determine casual mechanisms. This study has several strengths, which include the use of a large population-based sample of diabetic patients who were enrolled in a setting with relatively uniform treatment and guidelines and with an active quality improvement program for treatment of diabetic nephropathy. Also, information on pertinent covariates, such as ACEI/ARB, that might be related to presence of albuminuria was available, allowing adjustment for this potential confounding effect. Limitations include the cross-sectional nature of this study, which precludes assessment of whether predictor covariates were present before the appearance of micro- and macroalbuminuria. In addition, the effect of BP level on micro- or macroalbuminuria could not be assessed in the current study, because BP measurements were not available within the automated database. However, Chaiken et al. (46) found that actual level of BP was less predictive of risk of micro- or macroalbuminuria in a black clinic cohort than having a diagnosis of hypertension. Also, because of the observational nature of the study, all laboratory data (including urine albumin determination) were assessed within an 18-mo time frame before the initiation of the survey and not at the same time period for all participants. Furthermore, HbA1c levels, triglyceride levels, and LDL levels were obtained at different time points for each individual. An additional limitation was the small sample of

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Native Americans/Alaskan Natives, for whom conclusions could not be determined. Finally, microalbuminuria levels were not available on all patients, which reduced the sample size and may have caused selection bias in the detection of an association between microalbuminuria and covariates. However, racial/ethnic minorities were as likely to have received microalbuminuria testing compared with whites, which argues against the possibility of differential selection bias by race or ethnicity. In summary, among individuals who have diabetes and similar access to clinical care in a large health management organization, ethnic differences in the prevalence of incipient diabetic nephropathy were found, which varied by presence of a diagnosis of hypertension. Poor glycemic control, hypertriglyceridemia, and longer duration of diabetes were independently associated with prevalent microalbuminuria and macroalbuminuria. Hypertriglyceridemia contributed significantly to ethnic differences in prevalent microalbuminuria and macroalbuminuria and merits further research as a potentially modifiable risk factor for diabetic nephropathy.

Acknowledgments Supported by grants #MH 4-1739 and #MH 01643 from the National Institute of Mental Health Services Division, Bethesda, MD. An American Diabetes Association Career Development Award and a Robert Wood Johnson Amos Medical Faculty Development Fellowship support Dr. Young currently. An abstract of this paper was submitted and accepted for an oral presentation at the 2004 American Diabetes Associations Annual meeting, June 7, 2004. We gratefully acknowledge the assistance of Heather Ross, BS, in the preparation of this manuscript.

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