A Population-Based Screening for Microalbuminuria Among Relatives of CKD Patients: The Kidney Evaluation and Awareness Program in Sheffield (KEAPS)

A Population-Based Screening for Microalbuminuria Among Relatives of CKD Patients: The Kidney Evaluation and Awareness Program in Sheffield (KEAPS) Aminu K. Bello, MMedSci, MRCP,1 Jean Peters, PhD, FFPH,2 Jeremy Wight, MD, FRCP, FFPH,3 Dick de Zeeuw, MD, PhD,4 and Meguid El Nahas, PhD, FRCP,1 on behalf of the European Kidney Institute Background: Microalbuminuria has been used to detect subjects at risk of cardiovascular disease and chronic kidney disease (CKD) in patients with diabetes, those with hypertension, and the general population. However, relatives of patients with CKD have not been investigated for microalbuminuria in the United Kingdom. Study Design: A cross-sectional study evaluating the prevalence of microalbuminuria in relatives of patients with CKD compared with the general population of Sheffield, England. Setting & Participants: Participants in the Kidney Evaluation and Awareness Program in Sheffield, a population-based screening program for microalbuminuria. 274 relatives of patients with CKD were studied and compared with an age- and sex-matched control group from the general population. Predictor: Family history of CKD. Measurement & Outcomes: Screening tools included a questionnaire collating information for demographics, lifestyle, and medical and family history of diabetes, hypertension, and CKD. Urine samples were collected for microalbuminuria estimation. Microalbuminuria measurements were obtained by using immunonephelometry. Microalbuminuria thresholds were defined using albumincreatinine ratio. Results: The prevalence of microalbuminuria was 9.5% in those with a family history of CKD. This was significantly greater than the prevalence of 1.4% in the age- and sex-matched control group with no family history of CKD (P ⫽ 0.001). Independent determinants of microalbuminuria in the study population in an adjusted logistic regression model were family history of diabetes (odds ratio [OR], 2.88; 95% confidence interval, 1.17 to 7.04), obesity (OR, 3.29; 95% confidence interval, 1.61 to 6.69), and family history of CKD (OR, 6.96; 95% confidence interval, 3.48 to 13.92). Limitations: Cross-sectional snapshot analysis, microalbuminuria measured once. Conclusions: The prevalence of microalbuminuria in relatives of patients with CKD is greater than in an age- and sex-matched control group from the general population. The prognostic value of microalbuminuria in this category of at-risk population remains to be determined in longitudinal studies. Am J Kidney Dis 52:434-443. © 2008 by the National Kidney Foundation, Inc. INDEX WORDS: Albuminuria; relatives; chronic kidney disease (CKD); cardiovascular disease (CVD); early detection; prevention.

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ecent decades have witnessed a marked interest in the study of microalbuminuria as a cardiovascular (CVD) and/or chronic kidney disease (CKD) risk marker.1,2 Prevalence estimates of microalbuminuria and associated factors were studied mainly in the general population3-5 and certain categories of at-risk populations, such as individuals with diabetes and hypertension, as well as ethnic minorities.6-8

A family history of CKD was associated with increased risk of the development of CKD.9-11 However, it is uncertain whether: (1) the increased risk is mediated by familial aggregation of risk factors for CKD, such as hypertension, obesity, and diabetes; (2) the increased risk is caused by innate genetic susceptibility to develop CKD; and (3) any increased susceptibility holds true to both markers of CKD (albuminuria and kidney insufficiency;

From the 1Sheffield Kidney Institute and 2School of Health and Related Research, The University of Sheffield; 3Directorate of Public Health, Sheffield Primary Care Trust and City Council, Sheffield, UK; and 4University Medical Centre Groningen, Groningen, The Netherlands. Received September 10, 2007. Accepted in revised form December 26, 2007. Originally published online as doi: 10.1053/j.ajkd.2007.12.034 on March 24, 2008.

Address correspondence to Meguid El Nahas, PhD, FRCP, Sheffield Kidney Institute, Northern General Hospital Campus (Sorby Wing), Sheffield S5 7AU, UK. E-mail: m.el-nahas@ sheffield.ac.uk © 2008 by the National Kidney Foundation, Inc. 0272-6386/08/5203-0009$34.00/0 doi:10.1053/j.ajkd.2007.12.034

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American Journal of Kidney Diseases, Vol 52, No 3 (September), 2008: pp 434-443

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Figure 1. Flow chart of recruitment of relatives of patients with chronic kidney disease (CKD).

glomerular filtration rate ⬍ 60 mL/min/1.73 m2 [⬍1.00 mL/s/1.73 m2]). Published data about the prevalence and determinants of microalbuminuria in relatives of patients with CKD are limited worldwide and lacking in the United Kingdom. The Kidney Evaluation and Awareness Program in Sheffield (KEAPS) is a population-based research program aimed at identifying the prevalence of microalbuminuria and its determinants in the general population and in relatives of patients with CKD living in Sheffield, England. We investigated whether relatives of patients treated for CKD carry a greater burden of microalbuminuria than the general population. We report prevalence estimates for microalbuminuria and its determinants in a group of relatives of patients in an end-stage renal disease (ESRD) program and compare the findings with a similar ageand sex-matched group recruited from a general population in the same English community.

METHODS Design and Setting KEAPS is a cross-sectional study designed to investigate the prevalence of early markers and risk factors for CKD in the general population and in relatives of patients in an ESRD program living in Sheffield and surrounding towns in South Yorkshire, in the north of England.

Study Population Index Cases (patients with CKD whose relatives were recruited) A computer-generated random sample (n ⫽ 609) stratified by primary ESRD cause (diabetes, hypertension/vascular,

chronic glomerulonephritides [CGNs] and others/unspecific) of all patients in the ESRD program in the Sheffield Kidney Institute, Sheffield, UK, as of October 31, 2004, were contacted about the study and asked to contact their first-degree relatives and identify those interested in participating in and eligible for the study. Of these patients, 76.6% (n ⫽ 467) responded. Of responders, 62.7% (n ⫽ 293) forwarded names of relatives interested in participating, whereas the remaining (n ⫽ 174) cited lack of first-degree relatives or relatives unwilling to participate. Each individual patient with ESRD is considered a family unit. Primary ESRD causes in these 293 patients (index cases) with each representing a family unit were diabetes, 18% (n ⫽ 53); hypertension/vascular-related causes, 20% (n ⫽ 58); CGNs, 22% (n ⫽ 64); and others (unknown/ unspecified), 40% (n ⫽ 118).

Relatives of Patients With CKD Names of 754 relatives were returned from the 293 individual families. From October 2004 to April 2005, the 754 relatives were sent by postal mail a questionnaire, consent form, and vial to collect an early-morning urine sample, along with relevant instructions. Of these, 447 responded; 424 responders were found eligible according to the following inclusion criteria for the study: (1) 18 years or older, (2) family history of CKD, and (3) no personal history of CKD. We subsequently selected 1 individual only from each of the 293 family units represented, giving a sample of 293 subjects for this analysis. In cases in which there were more than 1 relative per family, we randomly selected 1 from each family unit. Nineteen subjects were excluded because of missing data for age and/or sex, thus leaving 274 subjects for this analysis (Fig 1). Of this group selected for analysis, 53 were relatives of patients with CKD caused by underlying diabetic nephropathy, 58 were relatives of patients with CKD attributed to hypertension/renovascular diseases, 64 were relatives of patients with a CKD etiologic category

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Figure 2. Flow chart of recruitment of nonrelatives of patients with chronic kidney disease (CKD).

within CGNs, and the remaining (n ⫽ 99) were relatives of those with CKD from unknown and/or unspecified causes.

Nonrelatives of Patients With CKD A second sample of subjects with no family history of CKD was selected from a pool of the general population of 1,137 participants in a parallel and similar cross-sectional study being undertaken in the general population of Sheffield. In this general population study, stratified randomly selected adult inhabitants of Sheffield from the population register were sent an invitation to complete a postal questionnaire about their lifestyle and medical and family health history. In addition, respondents were asked to provide urine samples for urinary albumin estimation. Selection of nonrelatives of patients with CKD was based on matching with relatives of patients with CKD on the basis of age (within 5 years) and sex. One subject was matched per CKD relative, giving 274 subjects in this group for analysis (Fig 2).

Research Governance and Ethics Both studies were conducted in line with the Declaration of Helsinki for health care research in humans and the Good Clinical Practice guidelines of the European Union. Each study subject provided informed consent before inclusion in the study. Research governance and ethical approval were obtained from the local research ethics committee and hosting organizations (Sheffield Teachings Hospitals National Health Service Trust and the Primary Care Trusts in Sheffield).

Data Collection and Evaluation Study participants received a mailed self-administered questionnaire and a urine vial for their urine sample. The 6-page questionnaire contained questions about sociodemographic (age, sex, and educational attainment), physical (weight and height), personal, and family health and lifestyle history. Age was categorized using UK population structure census categories.12 This was grouped broadly into youngerthan-50 and older-than-50-years groups for analysis. Educa-

tional attainment was grouped into 4 categories based on level of qualification achieved as a proxy for number of years in education. These qualifications include Ordinary (O) and Advanced (A) level qualifications, which are national subject-based examinations taken in secondary school. Group 1 comprises individuals who have no qualification or less than an O level (⬍11 years of education); 2 denotes individuals who hold at least 1 O level (equivalent to 11 years of education); 3 includes individuals who earned at least 1 A level (equivalent to 13 years of education); and 4 contains individuals who have a degree or equivalent (⬎13 years of education). Groups 1 and 2 were classified as low level of educational attainment, whereas groups 3 and 4 were classified as high level of educational attainment. Body mass index was calculated as weight/(height)2; in kilograms per square meter and divided into 4 categories on the basis of defined criteria13: less than 18.5, 18.5 to 24.9, 25 to 29.9, and 30 kg/m2 or greater, representing underweight, normal weight, overweight, and obese, respectively. Participants who reported a personal history of diabetes mellitus and/or diabetic medication use were categorized as diabetic. Similarly, those with a reported personal history of hypertension and/or use of antihypertensive agents were categorized as hypertensive. A history of CVD (coronary heart disease, stroke, or peripheral vascular disease) was taken as reported in the questionnaire. Family history of diabetes or hypertension was taken as reported. Family history of an underlying cause of CKD was evaluated from the hospital-based database of siblings and categorized into 4 distinct groups: diabetic, hypertensive/vascular, CGNs, and other (congenital, unknown/unspecified, pyelonephritis, obstructive, drugs, and toxins). Self-reported smoking status was categorized as current smoker (yes/no).

Urinary Albumin Measurement and Evaluation Morning urinary albumin concentration was determined by means of nephelometry with a threshold of 2.3 mg/L and interassay and intra-assay coefficients of variation of 2.6%

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Table 1. Sociodemographic and Health Characteristics of Relatives of Patients With Chronic Kidney Disease (n ⴝ 424) by Selection in the Study Selected

No. of relatives Age (y) Men High level of education Body mass index (kg/m2) Current smoker Family history of hypertension Family history of diabetes Hypertension Diabetes Prevalence of microalbuminuria

Not Selected

P

293 131 43.7 ⫾ 16.7 50.1 ⫾ 16.8 126 (43.0) 63 (48.0) 146 (49.8) 65 (49.6)

0.05 0.4 0.4

26.1 ⫾ 5.4 111 (37.8)

26.7 ⫾ 4.1 61 (46.6)

0.2 0.2

101 (34.5)

40 (30.5)

0.3

46 (15.7) 38 (13.0) 10 (3.4)

19 (14.5) 22 (16.8) 7 (5.3)

0.4 0.3 0.5

26 (8.8)

10 (7.6)

0.3

Note: Values expressed as mean ⫾ SD or number (percent).

and 2.2%, respectively (Dade Behring Diagnostica, Marburg, Germany). Urinary creatinine level was determined by means of an automatic enzymatic method using Kodak Ektachem dry chemistry (Eastman Kodak, Rochester, NY), with intraassay and interassay coefficients of variation of 0.90% and 2.90%, respectively. Urinary leukocyte and erythrocyte evaluations were performed using Multistix 8 SG test strips (Bayer Diagnostics Mfg Ltd, Bridgend, UK). All samples that were clearly hematuric and/or leukocyturic were excluded from the analysis. Spot urine albumin-creatinine ratios (ACRs) were calculated for all subjects. To define microalbuminuria in morning urine specimens, we used the ACR cutoff value according to recommended standard guidelines.14,15 Microalbuminuria was defined as ACR of 20 to 200 mg/g in men and 30 to 300 mg/g in women.14,15

Statistical Analysis All analyses and calculations were performed using the SPSS statistical package, version 14.0 (SPSS Inc, Chicago, IL). Descriptive analyses were used to characterize participants by sociodemographic, lifestyle, and clinical factors. Continuous data are presented as mean ⫾ SD, and categorical variables, as proportions. Prevalence of microalbuminuria was expressed as percentage. Independent-sample ttests were applied for comparison of group means, and ␹2 tests were applied for proportions. Binomial logistic regression analyses were used to assess relationships between screened population demographic, health, and lifestyle characteristics with the presence of microalbuminuria in relatives and nonrelatives of patients with CKD combined. Factors associated with microalbuminuria were determined first by using univariate logistic regression analysis. Variables significant at P less than 0.50 on univariate analysis were presented further for the multivariate logistic regression model.

Adjustments were made in the multivariate model for diabetes status, hypertension status, CVD history, educational attainment, smoking, obesity, family history of diabetes, family history of hypertension, and family history of CKD. A 2-sided P less than 0.05 is considered statistically significant. Interactions between family history of CKD and other significant covariates associated with microalbuminuria were tested in the model. Interactions were considered significant at P less than 0.10. As a sensitivity analysis, we reexamined the data in the multivariate logistic models after missing values were imputed using the maximum likelihood estimation method.16

RESULTS General Characteristics Of the 754 relatives approached, there were 424 eligible respondents, giving an overall response rate of 56.2%. In the general population from which the control group was drawn, this was 51.7%. Only age was significantly different in demographic and clinical characteristics between relatives of patients with CKD (n ⫽ 293) selected for this analysis and those who were not selected (n ⫽ 131; Table 1). Population characteristics of relatives and nonrelatives of patients with CKD are listed in Table 2. Overall Prevalence of Microalbuminuria Overall, 36 subjects of the total screened population of 424 participants in KEAPS with a Table 2. Sociodemographic and Health Characteristics of the Study Population Relatives of Patients With CKD

Nonrelatives of Patients With CKD

No. of subjects 274 274 Age (y) 44.3 ⫾ 17.1 45.1 ⫾ 13.3 High level of education 146 (53.3) 144 (52.6) Body mass index 26.1 ⫾ 5.4 25.7 ⫾ 4.5 (kg/m2) Current smoker 110 (40.1) 61 (22.3) Hypertension 38 (13.9) 16 (5.8) Diabetes 10 (3.6) 9 (3.3) Cardiovascular disease history 9 (3.3) 7 (2.6) Family history of hypertension 101 (36.9) 56 (20.4) Family history of diabetes 46 (16.8) 21 (7.7) Prevalence of microalbuminuria 26 (9.5) 4 (1.4)

P

0.5 0.9 0.4 0.001 0.002 0.8 0.4 0.001 0.001 0.001

Note: Values expressed as mean ⫾ SD or number (percent). Abbreviation: CKD, chronic kidney disease.

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family history of CKD had microalbuminuria, giving a prevalence of 8.5%. By means of cluster analysis, using 1 subject per family unit, 26 of the 274 subjects representing the family units had microalbuminuria, giving a prevalence of 9.5%. In the 274 age- and sex-matched subjects from the general population sample, only 4 had microalbuminuria, giving a prevalence of 1.4% in this group. The prevalence of microalbuminuria was significantly different between relatives of patients with CKD and age- and sex-matched nonrelatives from the general population (P ⫽ 0.001). The overall prevalence of microalbuminuria in the general population from which nonrelatives of patients with CKD were selected was 7.0%. Stratifying the overall prevalence rate in this general population across age categories showed the prevalence of microalbuminuria to increase with age: 1.1%, 3.5%, 6.7%, 14.4%, 8.4%, and 12.0% in the 18- to 34-, 35- to 49-, 50to 59-, 60- to 64-, 65- to 74-, and older-than-75year age categories, respectively. Figure 3 shows the distribution of microalbuminuria in relatives of patients with CKD by type of underlying nephropathy in index cases (patients whose relatives were recruited). Distribution of Microalbuminuria in Relatives of Patients With CKD by Demographic and Clinical Variables The relationship between microalbuminuria in relatives of patients with CKD and age shows a nonlinear pattern because of the 26 subjects with microalbuminuria, 19 (73.0%) were younger than 50 years, 12.0% (n ⫽ 3) were aged 50 to 59 years, and the remaining 15.0% (n ⫽ 4) were older than 60 years. Prevalences of microalbuminuria across the various age categories of 18 to 34, 35 to 49, 50 to 59, 60 to 64, 65 to 74, and older than 75 years were 12.2%, 9.7%, 7.5%, 5.3%, 3.4%, and 18.2%. Looking at the prevalence of microalbuminuria by age categories of younger than 50 and 50 years and older shows prevalences of 10.9% in those younger than 50 years and 7.0% in those 50 years and older (Table 3). Of note, there was a significant trend in the pattern of the response rate to the study across age categories, with the majority of responders

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Figure 3. Distribution of microalbuminuria in relatives by type of underlying nephropathy in index cases (patients whose relatives were recruited). Of relatives of patients with chronic kidney disease (CKD) caused by underlying diabetic nephropathy (N ⫽ 53), 9 (17%) had microalbuminuria; 58 subjects were relatives of patients with CKD attributed to hypertension/renovascular diseases, and of these, 7 (12%) had microalbuminuria. An additional 64 subjects were relatives of patients with a CKD etiologic category in chronic glomerulonephritides (CGN); 4 (6%) had microalbuminuria and the remaining (n ⫽ 99) were relatives of those with CKD from unknown and/or unspecified causes, with 6 subjects having microalbuminuria, giving a prevalence of microalbuminuria of 6%.

and contributors to the study younger than 50 years (P ⫽ 0.002). The prevalence of microalbuminuria was significantly greater in women than men (P ⫽ 0.001). Of 124 male relatives of patients with CKD, 8 had microalbuminuria, giving a prevalence of 6.5%, whereas of 150 women, 18 had microalbuminuria, giving a prevalence of 12.0% (Table 3). There was a decreasing trend in prevalences of microalbuminuria with increasing levels of education attainment because the majority of those who screened positive for microalbuminuria were from a low level of educational attainment category. Microalbuminuria distribution by the 2 classes of educational level was 11.3% in those with low-level educational attainment and 7.5% in those with high-level education (P ⫽ 0.002). Prevalences of microalbuminuria were 18.2% in obese subjects and 9.4% in nonobese subjects (P ⫽ 0.001; Table 3). Only 1 individual of those with a personal history of diabetes and 1 of hypertensive subjects had microalbuminuria. The prevalence of microalbuminuria was significantly greater in those who reported a personal CVD history compared with who did not (P ⫽

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Table 3. Distribution of Microalbuminuria Across Sociodemographic and Clinical Characteristics in Relatives of Patients With CKD (N ⴝ 274)

Characteristic

Age categories (y) ⬍50 ⬎50 Sex Men Women Obese Yes No Smoking Yes No Low educational level Yes No Diabetes Yes No Hypertension Yes No Cardiovascular disease history Yes No Family history of diabetes Yes No Family history of hypertension Yes No

Subjects With MA/Total No. of Subjects in Category

Prevalence of MA (%)

19/175 7/99

10.9 7.0

8/124 18/150

6.5 12.0

8/44 18/191

18.2 9.4

9/110 17/161

8.2 10.6

15/133 11/146

11.3 7.5

1/10 25/261

10.0 9.6

1/38 25/236

2.6 10.6

P

0.05

0.001

0.001

0.3

0.002

0.6

0.01

0.01 2/9 24/218

22.2 11.0 0.01

9/46 17/221

19.6 7.7 0.04

15/101 11/157

14.9 7.0

Abbreviation: CKD, chronic kidney disease; MA, microalbuminuria.

0.01). The prevalence of microalbuminuria in smokers was 8.2% compared with 10.6% in nonsmokers (P ⫽ 0.3; Table 3). The prevalence of microalbuminuria was significantly greater in those with a family history of diabetes (P ⫽ 0.01) or hypertension (P ⫽ 0.04; Table 3). Determinants of Microalbuminuria in Relatives and Nonrelatives of Patients With CKD Table 4 lists unadjusted odds ratios (ORs) for the association of various factors univariately with the presence of microalbuminuria in relatives and nonrelatives of patients with CKD

combined. Independent predictor variables associated with the presence of microalbuminuria in a mutually adjusted logistic regression model are listed in Table 5: obesity (OR, 3.29; 95% confidence interval, 1.61 to 6.69), family history of diabetes (OR, 2.88; 95% confidence interval, 1.17 to 7.04), and family history of CKD (OR, 6.96; 95% confidence interval, 3.48 to 13.92). No significant interactions were found between the variable family history of CKD and other significant covariates associated with microalbuminuria tested in the model. Odds ratios were basically unchanged when we examined multivariate logistic models after missing values were imputed.

DISCUSSION This is the first study to our knowledge to specifically screen first-degree relatives of patients being treated for CKD in the United Kingdom for the presence of microalbuminuria, a risk marker/factor for CKD and CVD.17-20 The prevalence of microalbuminuria in the relatives of patients with CKD studied was greater than that in the age- and sex-matched group from a general population without a family history of CKD. A number of studies evaluated the prevalence of CKD in family members of patients with ESRD.9-11 Such studies showed increased risk of CKD in subjects with a family history of CKD.9-11 Table 4. Results From Univariate Logistic Regression Analysis: Unadjusted ORs for Association Between Various Demographic and Clinical Variables With the Presence of Microalbuminuria in Study Population Variable

OR

95% CI

P

Age (/1 y) Men (v women) Diabetes (yes/no) Hypertension (yes/no) Cardiovascular disease history (yes/no) Low educational level (yes/no) Smoking (yes/no) Obesity (yes/no) Family history of diabetes (yes/no) Family history of hypertension (yes/no) Family history of CKD (yes/no)

0.99 0.92 1.53 0.45

0.98-1.02 0.54-1.56 0.43-5.40 0.22-0.93

0.8 0.8 0.5 0.03

1.46 1.09 1.55 4.15

0.78-2.73 0.64-1.85 0.90-2.67 2.27-7.58

0.2 0.6 0.1 0.001

4.28 2.50-7.33

0.001

2.71 1.37-5.38 0.004 7.68 2.54-23.25 0.001

Abbreviation: OR, odds ratio; CI, confidence interval; CKD, chronic kidney disease.

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Table 5. Results From Multivariate Logistic Regression Analysis: Adjusted ORs for Association Between Various Demographic and Clinical Variables With the Presence of Microalbuminuria in Study Population Variable

Diabetes (yes/no) Hypertension (yes/no) Cardiovascular disease history (yes/no) Smoking (yes/no) Obesity (yes/no ) Family history of diabetes (yes/no) Family history of hypertension (yes/no) Family history of CKD (yes/no)

OR

95% CI

P

1.38 0.23-8.17 1.13 0.40-3.19

0.7 0.8

1.02 0.39-2.66 0.87 0.42-1.80 3.29 1.61-6.69

0.9 0.7 0.01

2.88 1.17-7.04

0.02

1.95 0.93-4.12 0.08 6.96 3.48-13.92 0.001

Abbreviation: OR, odds ratio; CI, confidence interval; CKD, chronic kidney disease.

We extend these findings by investigating individuals with a family history of CKD from a UK community-based sample for microalbuminuria, an early marker of CVD and CKD. The prevalence of microalbuminuria in the studied relatives of patients with CKD is less than previously reported in some studies of this category of an at-risk population, such as in the Kidney Early Evaluation Program (KEEP) in the United States.6 Different population characteristics could account for differences in prevalence. The population we studied was relatively younger, lean, predominantly white, and included fewer patients with diabetes and hypertension than those encountered in KEEP. More than half the KEEP participants had hypertension compared with only 13.0% in our study population. Similarly, about a quarter of KEEP participants had diabetes compared with only 3.4% in our study population. Moreover, about a third of KEEP participants were of African American descent, whereas most of our subjects were white. Differences in the preponderance of risk factors for microalbuminuria therefore might have accounted for some of the noted differences in prevalence of microalbuminuria between this study in the United Kingdom and KEEP in the United States. There are varied opinions about the impact of a positive family history on risk of CKD, evolving from studies with several variations in such demographic and clinical characteristics as sex, race mix, diabetes, hypertension, obesity, smok-

ing, and other coexisting cardiovascular morbidities. In addition, differences in methods of specimen collections, definitions, categorization of albuminuria, and even laboratory evaluations would lead to different results and therefore conclusions.21 Several factors were associated with microalbuminuria in our multivariate model; among them is a family history of diabetes. Could this imply that microalbuminuria precedes the development of diabetes? Increased urinary albumin excretion is associated with increased risk of the development of diabetes.22 Microalbuminuria also is a feature of metabolic syndrome that often precedes type 2 diabetes.22 Microalbuminuria was linked to generalized vascular endothelial dysfunction, which was shown to predict the development of diabetes.22 Another independent determinant of microalbuminuria in this population is a family history of hypertension, although the effect was attenuated and made only marginally significant (P ⫽ 0.08) in the multivariate model. It may simply be that the relatives we tested had increased blood pressure and undiagnosed hypertension with associated microalbuminuria. Of interest, the Framingham Offspring Study showed that increased urinary albumin concentration was associated with risk of developing hypertension.23 This also was linked to generalized endothelial dysfunction.23,24 Increased body weight and obesity are early and independent risk factors for increased albuminuria, even in nondiabetic and nonhypertensive populations.25 We found obesity in this study to be a strong and independent predictor of microalbuminuria. Obesity is known to cluster with several cardiovascular risk factors and markers suggested to be causally related to increased urinary albumin excretion, such as high blood pressure and increased serum cholesterol, glucose, and high-sensitivity C-reactive protein levels. It also is a key component of metabolic syndrome, associated with microalbuminuria. We recently showed that weight loss and a decrease in associated inflammation led to reversal of albuminuria.26 We did not find age, sex, personal history of diabetes or hypertension, CVD history, low education, or smoking to be independent determinants of microalbuminuria in this population.

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That very few individuals with a personal history of diabetes or hypertension had microalbuminuria is surprising, but may be explainable because those with diagnosed diabetes/hypertension consequently would be treated, unlike those with a family history who may have undiagnosed/ subclinical or preclinical diabetes or hypertension. The small number of patients in these categories also may give false-negative results. We observed a steady increase in prevalence of microalbuminuria with age in the general population until the age of 64 years. Subsequently, changes in microalbuminuria may have been affected by the age-related decrease in glomerular filtration rate. Such an age-related increase in microalbuminuria was not observed in relatives of patients with CKD. This may be caused by the small sample size of individuals with microalbuminuria in advanced age categories. Predominantly, relatives of patients with CKD were young adults and only a few were 50 years and older, making the analysis difficult to interpret. Another potential explanation is that older people among relatives with microalbuminuria might be more likely to have this detected already because of several comorbid conditions associated with aging and thus undergoing close health care monitoring and possibly treatment. This coupled with the low prevalence of diabetes and hypertension might have masked the effect of age on microalbuminuria, as reported in some general population studies.3,4 However, a study in Denmark showed a linear relationship between age and microalbuminuria in males, but not females.27 Similarly, the prevalence of microalbuminuria is much less in the selected nonrelative sample compared with the total general population from which they were selected. This group of nonrelatives of patients with CKD was much younger than the total population. Moreover, the sample was not a random sample of the overall population from which it was drawn and therefore may be biased by selection by age and sex to match relatives of patients with CKD. However, the overall prevalence of 7.0% noted in the total general population is similar to findings in some other general population studies.3-5 Of note, the prevalence of microalbuminuria was greater in women in this study. It may be argued that use of ACR may have contributed to

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the observed sex differences in microalbuminuria distribution. However, use of different cutoff values for ACR in each sex to compensate for the presumably lower creatinine excretion in women might have corrected for this. Moreover, a US-based study4 and another from the United Kingdom18 showed a greater prevalence of microalbuminuria in females than males, similar to our findings. Another interesting finding in this study is the greater prevalence of microalbuminuria in participants with low levels of educational attainment. This needs further and intensive evaluation because a recent study from the United States showed that socioeconomic factors could modify microalbuminuria distribution in the general population.28 We are investigating these issues in our general UK population. The strengths of our study are as follows. First, the study population was a random sample of family members of patients with CKD and compared with an age- and sex-matched group from a general population-based sample in the same community. Second, relatives and nonrelatives were both drawn from a homogenous population sample. Third, data were obtained from a relatively large population of family members of patients with CKD. The response rate to the study was reasonable for a postal survey.29 Fourth, we used ACR in the definition of microalbuminuria and different cutoff values for both sexes. ACR has good reliability and was recommended for population screening.21 The study is not without limitations, the first of which relates to possible inaccuracies of urine albumin estimates because of the snapshot nature of our analysis (cross-sectional) and that urinary albumin was measured on only a single occasion. Thus, we cannot exclude the possibility of falsepositive/-negative outcomes because measurement of single urine specimens on 1 occasion may pick up individuals with both persistent and intermittent microalbuminuria.21 However, we corrected for some potential variability in urine concentrations by factoring for urinary creatinine excretion and used ACR in the analysis. Studies showed that early morning spot urine gave a reasonable estimate of 24-hour urinary excretion of albumin and was a reliable index of albuminuria in population-based studies.21,30 Second, we relied on self-reported information for

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the various demographic and clinical parameters evaluated. Self-reported histories might have introduced bias, such as recall bias leading to misclassification of relatives of patients with CKD with undiagnosed hypertension or diabetes. However, the efficacy of reported parameters in epidemiological research was shown.31-33 Third, we matched subjects in the study for only age and sex because attempting to match on many variables is more problematic. However, we controlled for other potential confounding variables that were significantly different between the 2 groups at baseline in a regression analysis. Finally, the low number of subjects older than 50 years might have limited our ability to assess the relationship between increasing age and microalbuminuria in this study. What is the relevance of our findings? This can be understood in the context of the current guidelines and recommendations.14,15,34 Early detection and prevention of such early markers of CKD and CVD risk as microalbuminuria was emphasized in these various guidelines and recommendations.14,15,34 This is all the more relevant in individuals with increased risk, such as those with a family history of CKD. Is it therefore worth screening relatives of patients with CKD for albuminuria? Our findings suggest that this presumably at-risk population category has a greater risk of microalbuminuria than an age- and sex-matched group from the general population. From our data, emphasis should be on screening those with potential risk factors, such as relatives of patients with CKD with hypertension and diabetes, as well as those with obesity. The impact of albuminuria in predicting CVD and CKD outcomes in this category of at-risk population remains to be determined in longitudinal studies.

ACKNOWLEDGEMENTS We thank the Prevention of Renal and Vascular End-stage Disease (PREVEND) study group in The Netherlands for support with the measurement of urinary albumin and creatinine concentrations and acknowledge the Sheffield Area Kidney Patient Association and the Sheffield Kidney Research Foundation. We thank Stephan Walters of the School of Health and Related Research of the University of Sheffield for support in the design and analysis of the study and Jean Russell from the Corporate Information and Statistical Services Unit of the University of Sheffield for support in the supervision and revision of the statistical analyses.

Support: Dr Bello was an International Society of Nephrology (ISN) Fellow at the Sheffield Kidney Institute and acknowledges the support of the ISN. Financial Disclosure: None.

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