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Urinary albumin excretion is independently associated with carotid and femoral artery atherosclerosis in the general population

Martin Furtner, Stefan Kiechl, Artur Mair, Klaus Seppi, Siegfried Weger, Friedrich Oberhollenzer, Werner Poewe, Johann Willeit
DOI: http://dx.doi.org/10.1093/eurheartj/ehi014 279-287 First published online: 30 November 2004


Aims In diabetic patients, increased urinary albumin excretion (UAE), termed microalbuminuria when in the range between 30 and 300 mg/dL per day, is associated with a higher risk of atherosclerosis and its complications. Whether or not this notion applies to the general population is a matter of ongoing controversy because none of the few previous investigations among non-diabetics strictly represent the general community.

Methods and results Urinary albumin-to-creatinine ratio (uACR), a measure of UAE, was assessed from overnight spot urine samples in a population-based cohort of 684 individuals. The ratio was significantly related to age, gender, blood pressure, diabetes, markers of systemic inflammation, liver enzymes, and parathyroid hormone levels (P<0.001 each). Moreover, uACR emerged as a highly significant risk predictor of carotid and femoral artery atherosclerosis in the general community and the non-diabetic subpopulation alike (age/sex-adjusted P<0.001 each). In multivariable logistic regression analyses, odds ratios (95% CI) of carotid and femoral atherosclerosis amounted to 1.28 (1.01–1.61) and 1.44 (1.15–1.81) for a one unit increase in loge-transformed uACR (P=0.040 and 0.002). Corresponding odds ratios in non-diabetic subjects were 1.41 (1.09–1.84) and 1.54 (1.19–1.99) (P=0.010 and 0.001). Multivariable linear regression analyses yielded significant, or near significant, relations with carotid and femoral artery intima–media thickness and atherosclerosis scores (P=0.058–0.001).

Conclusion The uACR is significantly and independently associated with the presence and severity of atherosclerosis in the general population. The relation obtained was of a dose–response type and extended to levels far below what is termed microalbuminuria. The novel aspects of our study are its focus on various vascular territories and representivity of the general healthy population.

  • Urinary albumin excretion
  • Atherosclerosis
  • Peripheral vascular disease
  • Risk factors
  • Epidemiology


Urinary albumin excretion (UAE), often referred to as microalbuminuria when ranging between 30 and 300 mg/dL per day (20–200 µg/min) and as macroalbuminuria when exceeding 300 mg/dL per day,1,2 is an easily measurable parameter assumed to reflect systemic endothelial leakiness.35 It has found broad application in the routine care of diabetic patients as a predictor of cardiovascular disease,6,7 end organ damage, especially diabetic nephropathy,8,9 and mortality.1012

The association between UAE and atherosclerotic vascular disease in non-diabetic subjects is less well established.13,14 Most available data derive from selected subgroups1517 or healthy volunteers18 and are therefore not representative of the general community. A prospective investigation conducted in the general community found UAE and peripheral artery disease to be independent risk predictors of all-cause mortality.19 However, this study does not report a correlation between the two variables.

Moreover, most of these studies focused on UAE as defined by a urinary albumin-to-creatinine ratio (uACR) greater than 2.0 mg/mmol (=17.7 mg/g)14,17,19 or timed urinary albumin excretion exceeding 20 µg/min.18 Recently, evidence has emerged that predictive significance of UAE levels for vascular disease extends to levels well below these stringent thresholds.6,20,21

Further studies have been called upon to resolve the controversies surrounding the relation between UAE and atherosclerosis in the general population and to reconsider the usefulness of a microalbuminuric threshold value in this context.22 The Bruneck Study is appropriate for these purposes given that it is representative of the general community, its assessment of many conventional as well as novel vascular risk factors, its comprehensive quantification of atherosclerosis in various vascular territories by high-resolution duplex ultrasound, and availability of urinary albumin and creatinine measurements.


Study subjects

The Bruneck Study was a prospective population-based survey of the epidemiology and pathogenesis of atherosclerosis.2328 At the 1990 baseline evaluation, the study population was recruited as a sex- and age-stratified random sample of all inhabitants of Bruneck (125 women and 125 men in each of the 5th to 8th decades, 1000 in all). A total of 93.6% participated, with data assessment completed in 919 subjects. All participants were Caucasian. Between 1990 and the re-evaluations in 1995 and 2000 a total of 63 and 93 individuals died, respectively. Among survivors, ultrasonographic follow-up was 96.5% complete in 1995 and 93.3% in 2000 (n=684). The data presented in this publication pertain to the second follow-up in 2000. The study protocol was approved by the pertinent ethics committees, and all study subjects gave their written informed consent.

Clinical history and examination

Body mass index was calculated as weight divided by height squared (kg/m2). Smoking status and alcohol consumption were recorded as detailed previously.28 Diabetes was diagnosed according to American Diabetes Association (ADA) criteria.29 Chronic infections were assessed by means of an extensive screening procedure.28 Hypertension was defined as blood pressure ≥140/90 mmHg (mean of three independent measurements obtained with a standard mercury sphygmomanometer after at least 10 min of rest) or the use of anti-hypertensive drugs.

Laboratory methods

Blood samples were drawn after an overnight fast and 12 h of abstinence from smoking. All laboratory parameters were assessed by standard procedures as detailed previously.24,25 A first-voided, early-morning spot urine sample was gained for calculation of uACR. This method has a high sensitivity for detecting abnormal UAE values.30,31 uACR values (mg/g) can be converted into UAE levels (µg/min) (approximation) by means of Bakker's formula.32 Urinary creatinine was measured using a modified Jaffé method (Merck). Urinary albumin was determined using an immunonephelometry method (Dade Behring).

Assessment of atherosclerosis

The ultrasound protocol involves the scanning of the internal (bulbous and distal segments) and common carotid (proximal and distal segments) and femoral arteries (40 mm proximal and 10 mm distal to the bifurcation into the superficial and deep branches) of either side, with a 10 MHz imaging probe. Atherosclerotic lesions were defined according to two ultrasound criteria: (i) wall surface (protrusion or roughness of the arterial boundary) and (ii) wall texture (echogenicity). The maximum axial diameters of plaques were measured in each vessel segment, and atherosclerosis scores were calculated by summing all the diameters. Separate scores were assessed for the carotid and femoral arteries. The intima–media thickness (IMT) was quantified at the far wall of plaque-free sections of the common carotid and femoral arteries as the distance between the lumen–intima and media–adventitia interfaces. Details about the variability of the ultrasound methods applied have been extensively described previously.2326


All calculations were performed using the SPSS 11.5 software package. In Tables 1 and 2, continuous variables are presented as mean±SD or median (interquartile range), and dichotomous variables as absolute numbers and percentages. Standard linear regression analysis (continuous variables) and logistic regression analysis (dichotomous variables) were applied to explore the associations of vascular risk factors, lifestyle, demographic, and other variables with uACR. In these analyses uACR was treated as a continuous variable. Levels of C-reactive protein, lipoprotein(a), and uACR were loge-transformed to satisfy the assumption of normality and constant variance of the residuals. The P-values presented are from models adjusted for age, sex, and diabetes. Separate equations fitted in the entire study population and in non-diabetic subjects yielded similar results (Table 1). In Table 1, a Bonferroni correction was performed to account for multiple (30 in total) comparisons. Accordingly, a P-value <0.0017 was considered statistically significant. In all other analyses a two-sided P-value of <0.05 was considered to indicate significance.

View this table:
Table 1

Association of uACR with demographic and lifestyle parameters, vascular risk factors, and other variables (n=684)

VariablesAllQuintile group for uACRPP*
uACR range (mg/g)0.28–14250.28–3.473.48–5.085.09–8.268.27–17.41>17.41
Demographic variables
 Age (years)a66.0±10.262.2±9.063.4±9.665.1±9.367.1±9.472.2±10.7<0.001<0.001
 Female sex, n (%)a354 (51.8)42 (31.1)76 (55.9)75 (53.6)78 (57.4)83 (60.6)0.0020.001
Glucose metabolism
 Diabetes mellitus, n (%)a83 (12.1)9 (6.7)9 (6.6)15 (10.7)16 (11.8)34 (24.8)<0.001
 Fasting glucose, mmol/L5.64±1.355.45±0.885.30±0.805.62±0.945.71±1.556.18±2.000.0030.002
 HbA1c, %4.06±0.643.95±0.453.91±0.373.98±0.464.13±0.754.34±0.900.0210.194
 HDL cholesterol, mmol/L1.49±0.401.41±0.361.49±0.401.49±0.351.54±0.451.47±0.400.6360.201
 LDL cholesterol, mmol/L3.86±0.953.87±0.993.89±0.893.88±0.963.79±1.033.84±0.910.3380.437
 Lipoprotein(a), µmol/Lb0.44 (0.16–1.38)0.49 (0.16–1.45)0.54 (0.21–1.57)0.40 (0.12–1.18)0.41 (0.16–1.42)0.39 (0.15–1.20)0.0370.046
Life-style/body composition
 Smoking, n (%)a112 (16.4)25 (18.5)24 (17.6)29 (20.7)21 (15.4)13 (9.5)0.8430.867
 Alcohol consumption, g/day23.1±30.731.5±36.018.4±26.021.4±29.121.0±30.323.2±30.00.0560.042
 Body mass index, kg/m225.7±3.925.8±3.625.1±3.825.4±3.726.0±3.326.2±4.70.5010.908
 Waist to hip ratio0.93±0.070.94±0.060.91±0.060.93±0.070.93±0.080.93±0.080.5850.656
Blood pressure
 Hypertension, n (%)a381 (55.7)54 (40.0)62 (45.6)74 (52.9)86 (63.2)105 (76.6)<0.001<0.001
 Systolic, mm Hg139.4±18.5132.7±14.8135.4±17.1137.8±15.9143.0±19.6148.2±20.5<0.001<0.001
 Diastolic, mm Hg83.9±8.282.3±7.183.2±7.783.1±8.384.6±8.186.3±9.3<0.001<0.001
 Fibrinogen, µmol/L8.53±1.768.20±1.578.27±1.458.24±1.688.67±1.719.30±2.10<0.0010.001
 Leukocyte count, ×1000/µL6.20±1.686.06±1.566.09±1.676.13±1.716.22±1.756.50±1.70<0.0010.001
 Granulocyte count, ×1000/µL3.50±1.283.36±1.183.32±1.213.40±1.273.55±1.343.85±1.35<0.001<0.001
 C-reactive protein, mg/Lb1.83 (0.91–3.94)1.63 (0.87–3.68)1.62 (0.86–3.22)1.54 (0.80–3.46)1.98 (0.83–4.25)2.74 (1.29–6.89)<0.0010.009
Infection parameters
 Chronic infection, n (%)a160 (23.4)23 (17.0)20 (14.7)23 (16.4)38 (27.9)56 (40.8)0.0520.052
Chlamydomonas pneumoniae IgG, titre2.80±1.612.51±1.532.80±1.602.60±1.592.81±1.683.30±1.560.0060.133
Iron status
 Ferritin, pmol/L270.1±311.2261.6±230.2219.8±210.7341.3±464.7243.2±245.9282.3±314.60.1220.017
 Soluble transferrin receptor, mg/dL1.35±0.321.29±0.291.28±0.201.33±0.291.35±0.281.49±0.45<0.001<0.001
Hepatic parameters
 γGT, IU/L40.7±92.532.4±21.830.4±25.936.5±33.835.4±41.468.5±194.9<0.001<0.001
 Alanine aminotransferase, IU/L25.8±16.525.6±13.723.0±10.027.6±17.925.7±14.827.3±22.90.0010.001
Other parameters
 Total serum protein, g/L72.5±4.471.9±4.172.1±3.872.8±5.073.0±4.472.7±4.6<0.001<0.001
 Thyroid stimulating hormone, mU/L1.85±1.851.73±1.442.04±1.881.93±2.321.77±1.931.76±1.550.0930.300
 Parathyroid hormone, ng/L55.2±28.550.6±20.048.3±18.352.1±23.059.0±33.466.0±38.8<0.001<0.001
 Folate, nmol/L13.1±5.612.9±4.513.2±5.613.9±6.812.8±5.112.9±5.50.9020.848
 Homocysteine, µmol/L13.3±8.112.1±3.412.6±5.913.6±14.112.8±5.215.2±6.60.1780.003

P-values are from linear and logistic regression analyses in which loge-transformed uACR was entered as a continuous variable. The analyses focusing on age, sex, and diabetes are unadjusted whereas all others were adjusted for these three variables. When accounting for the multiple comparisons performed, P-values <0.0017 are considered statistically significant (Bonferroni correction).

*P-value after exclusion of 83 diabetic subjects. Analyses were adjusted for age and sex.

uACR in mg/g (×0.123 for conversion to mg/mmol); HDL, high density lipoprotein; LDL, low density lipoprotein; AS, atherosclerosis; γGT, gamma glutamyl transferase.

aNumber (%).

bMedian (IQR).

All other data are mean±SD.

View this table:
Table 2

Prevalence and severity of carotid and femoral artery atherosclerosis and IMT in the study population and in quintile groups for uACR (n=684)

VariablesAllQuintile group for uACR
Prevalence of AS
 Carotid artery, n (%)a378 (55.3)56 (41.5)61 (44.9)77 (55.0)86 (63.2)98 (71.5)
 Femoral artery, n (%)a388 (56.7)59 (43.7)69 (50.7)80 (57.1)80 (58.8)100 (73.0)
AS score
 Carotid artery, mmb1.6 (0.0–5.6)0.0 (0.0–3.1)0.0 (0.0–4.2)1.4 (0.0–4.6)2.8 (0.0–6.6)3.3 (0.0–8.5)
 Femoral artery, mmb1.6 (0.0–4.2)0.0 (0.0–3.7)1.4 (0.0–1.7)1.6 (0.0–4.0)1.8 (0.0–4.5)3.3 (0.0–5.2)
 Carotid artery, mmc1.00±0.180.95±0.180.95±0.170.99±0.171.03±0.171.08±0.18
 Femoral artery, mmc1.02±0.200.97±0.200.98±0.191.01±0.201.04±0.211.11±0.20

uACR in mg/g (×0.123 for conversion to mg/mmol); AS, atherosclerosis.

aNumber (%).

bMedian (IQR).


The association of uACR with carotid and femoral artery atherosclerosis and IMT was tested by means of linear (IMT and loge-transformed atherosclerosis score) and logistic (presence of atherosclerosis) regression analysis (Table 3). Multivariable analyses were adjusted for age, sex, diabetes mellitus (ADA criteria), HDL and LDL cholesterol, loge-transformed lipoprotein(a), ferritin, homocysteine, systolic blood pressure, body mass index, smoking (yes/no), drug use (diuretics, calcium antagonists, statins, ACE-inhibitors, and acetylsalicylic acid), loge-transformed C-reactive protein, chronic infection, and fibrinogen. These covariates had either been identified as vascular risk predictors in previous analyses of the Bruneck Study,24,25,28 or are known determinants of UAE.12,14,3336 A separate equation was additionally adjusted for parathyroid hormone (parathormone), gamma glutamyl transferase and serum protein, which were significantly correlated with uACR in the Bruneck population but not yet described as determinants of UAE. Again, models were fitted in the entire and the non-diabetic population (Table 3). Regression coefficients (95% CI) and odds ratios (95% CI) were calculated for a 1-unit increase in loge-transformed uACR (Table 3). To examine a potential differential association between uACR and atherosclerosis in subgroups interaction terms were added to the regression models. Finally, analyses using uACR quintile groups (set of indicator variables) instead of the continuous uACR variable were employed to examine the scale of association between uACR and atherosclerosis. In Figure 1, results are exemplified for logistic regression analyses focusing on prevalent carotid and femoral atherosclerosis.

Figure 1 Odds ratios of carotid and femoral artery atherosclerosis (AS) according to quintile groups for uACR. *P<0.05; P<0.01.

View this table:
Table 3

Association of uACR with carotid and femoral artery atherosclerosis and IMT (n=684)

Regression coefficient95% CIP
IMT carotid artery
 Age- and sex-adjusted0.0230.011–0.035<0.001
 Multivariable modela0.0130.001–0.0250.041
 Multivariable modelb0.0150.002–0.0270.020
 Age- and sex-adjusted (non-diabetics)0.0260.013–0.039<0.001
 Multivariable modela (non-diabetics)0.0160.003–0.0290.016
IMT femoral artery
 Age- and sex-adjusted0.0310.017–0.045<0.001
 Multivariable modela0.0220.008–0.0360.002
 Multivariable modelb0.0220.008–0.0360.002
 Age- and sex-adjusted (non-diabetics)0.0320.016–0.047<0.001
 Multivariable modela (non-diabetics)0.0250.010–0.0410.001
AS score carotid arteryc
 Age- and sex-adjusted0.2170.089–0.344<0.001
 Multivariable modela0.128−0.004–0.2590.058
 Multivariable modelb0.134−0.001–0.2680.051
 Age- and sex-adjusted (non-diabetics)0.2800.139–0.421<0.001
 Multivariable modela (non-diabetics)0.2060.062–0.3490.005
AS score femoral arteryc
 Age- and sex-adjusted0.2400.116–0.364<0.001
 Multivariable modela0.2040.081–0.3260.001
 Multivariable modelb0.1980.072–0.3250.002
 Age- and sex-adjusted (non-diabetics)0.2590.120–0.398<0.001
 Multivariable modela (non-diabetics)0.2330.098–0.3690.001
Odds ratio95% CI P
Presence of carotid AS
 Age- and sex-adjusted1.431.16–1.780.001
 Multivariable modela1.281.01–1.610.040
 Multivariable modelb1.311.03–1.660.028
 Age- and sex-adjusted (non-diabetics)1.551.22–1.97<0.001
 Multivariable modela (non-diabetics)1.411.09–1.840.010
Presence of femoral artery AS
 Age- and sex-adjusted1.421.17–1.73<0.001
 Multivariable modela1.441.15–1.810.002
 Multivariable modelb1.421.13–1.790.003
 Age- and sex-adjusted (non-diabetics)1.481.18–1.850.001
 Multivariable modela (non-diabetics)1.541.19–1.990.001

Regression co-efficients were derived from linear regression analysis. Odds ratios were derived from logistic regression analysis, calculated for a 1 unit increase in loge-transformed uACR.

AS, atherosclerosis.

aAdjustment for age, sex, diabetes mellitus, HDL and LDL cholesterol, loge-transformed lipoprotein(a), ferritin, homocysteine, systolic blood pressure, body mass index, smoking (yes/no), drug use (diuretics, calcium antagonists, statins, ACE-inhibitors, acetylsalicylic acid), loge-transformed C-reactive protein, chronic infection, and fibrinogen.

bAdditional adjustment for parathormone, gamma glutamyl transferase and serum protein.

cThe atherosclerosis score was loge-transformed.


Main descriptive characteristics of the uACR (mg/g) and its distributions for males and females are shown in Figure 2. Sex-specific uACR cut-off values for microalbuminuria given by the National Kidney Foundation (NKF) are 17–250 mg/g for men and 25–355 mg/g for women.1 Totals for 51 out of 330 men (15.5%) and 41 out of 354 women (11.6%) were microalbuminuric according to NKF criteria, while three men (<1%) and six women (<2%) exhibited macroalbuminuria defined as a uACR of more than 250 (355) mg/g.

Figure 2 Distribution and main descriptive characteristics of uACR in the participants of the Bruneck Study.

Table 1 shows the descriptive statistics of clinical characteristics, lifestyle variables, and vascular risk factors in the entire study population and in quintile groups for uACR. As expected, age, female sex, and the proportion of diabetics increased across the uACR quintile groups (P for trend <0.001 each). After adjustment for these variables, further significant or nearly significant associations emerged for measures of blood pressure, inflammation, infection, iron status, and liver function, as well as levels of total serum protein, parathormone, and homocysteine. All these variables steadily increased across quintile groups (dose–response relation), and the associations persisted after exclusion of 83 subjects with diabetes mellitus.

Prevalence and severity of carotid and femoral artery atherosclerosis, as well as the level of IMT in the study population and in quintile groups for uACR, are depicted in Table 2. All measures of atherosclerosis were significantly related to uACR in age- and sex-adjusted analyses (P-value ≤0.001 each, Table 3). In multivariable analyses the associations decreased in strength but in most instances remained independently significant (Table 3). Of note, atherosclerosis risk steadily increased across quintile groups for uACR (Table 2, Figure 2) and, in line, levels of uACR increased along with the number of vascular territories involved in the atherosclerotic process (Figure 3). There was no consistent evidence for differential associations in men and women, middle-aged and elderly individuals or other subgroups according to main vascular risk factors. All findings were very similar after exclusion of diabetics (Table 3).

Figure 3 uACR according to the number of territories with atherosclerosis. Means presented are geometric.


There is circumstantial evidence for an association between UAE and diabetes mellitus,2,614,37 hypertension,4,6,12,14,15,17,21 and older age,6,7,12,21,34 and emerging evidence for associations with obesity,14,35,36 dyslipidaemia,35,36 hyperhomocysteinaemia,33 and smoking.36 UAE reflects systemic endothelial leakiness3,5 and may arise from an interaction between noxious influences of vascular risk attributes and a predisposing genetic background.38 The precise mechanisms underlying the development of UAE are still not fully understood, although a number of explanatory hypotheses linking risk factors with endothelial leakiness have been proposed. For example, in diabetics it is postulated that the endothelium is stressed by high glycosylation end products, thus becoming more permeable for albumin,9 and that disturbed proteoglycan synthesis causes the glomerular membrane to lose its charge sensitivity and integrity.3 In hypertensives, endothelial dysfunction is a common finding and high intraglomerular pressure causes increased albumin excretion.39

Our data are confirmatory of all the aforementioned associations between risk factors and UAE with the exception of dyslipidaemia (Table 1). The pattern of potential associations extends to a variety of novel vascular risk predictors such as chronic infection and systemic inflammation,40 both of which are well-established contributors to endothelial dysfunction. In addition, highly significant associations were observed to exist with parathormone levels and measures of liver function. The mechanisms underlying these associations are not immediately apparent. As a potential clue, parathormone was reported to be a sensitive marker for subclinical renal impairment.41 Hepatosteatosis and elevated liver enzymes, in turn, are features of insulin resistance and abnormal glucose metabolism42 that are related to UAE.

As an outstanding and novel finding, our study demonstrates that UAE is significantly related to atherosclerosis in the general healthy population. Of note, all our findings were virtually identical in separate analyses focusing on the entire population and on non-diabetic subjects only and apply to atherosclerosis in the carotid, as well as the femoral, arteries (Tables 2 and 3). Other comparable studies have focused on segments of the general population such as subjects with hypertension1517,43 or diabetes history,27,43 enrolled healthy volunteers,18 or recruited subjects from identified subgroups of ethnicity and glucose status.14 A majority of these evaluations revealed a significant association between microalbuminuria and atherosclerosis. Less consensus, however, prevails on whether the association between UAE and atherosclerosis is independent of other vascular risk attributes. In some studies, significance was lost when adjustment was performed for conventional vascular risk factors such as blood pressure and age.18,37 In our survey and the IRAS14 alike, the associations between UAE and various ultrasound measures of atherosclerosis remained significant in multivariable risk models controlling for effects of age, sex, and numerous vascular risk factors. The nature of this independent relation between UAE and atherosclerosis remains to be elucidated. It is possible that endothelial leakiness, as reflected by UAE, is in part a primary and possibly genetically determined vascular risk factor, or that it mirrors the endothelial dysfunction featuring the atherosclerotic process or arises from the action of yet unknown risk factors. Whatever the cause, further acceleration of the development of vessel pathology by the presence of elevated endothelial leakiness may be assumed, because it facilitates the entrance of lipoproteins and other pro-atherogenic mediators.

Of note, the associations between uACR and atherosclerosis or vascular risk attributes observed in our study did not show obvious thresholds, but were of a dose–response type. Accordingly, prominent relations were already detectable far below the commonly applied microalbuminuric thresholds.6,14,19 In fact, only 13.5% of the individuals in our study were microalbuminuric according to NKF criteria.1 A strict focus on this group as employed in many previous studies would clearly ignore the prominent association across the entire uACR range. In analogy, recent studies suggest that the predictive significance of UAE for cardiovascular risk extends to much lower cut-off values.6,20,21

A limitation of our study is the fact that UAE was assessed by uACR in a single morning spot urine sample. More precise methods such as collection of 24 h urine samples and/or repeated testing, however, are not practicable in large population studies, nor are they in daily clinical routine. Accordingly, the minor decline in assessment accuracy3032 may be outweighed by better applicability of the results obtained. Emergence of incorrect findings based on uACR measurements is unlikely, because misclassification of some individuals due to random assessment error tends to reduce the strength of associations rather than to create spurious ones. Another drawback of our study is the fact that the Bruneck population is from a geographically remote area and entirely Caucasian. Given the well-known ethnic and regional differences in the frequency and degree of UAE,44 extrapolation of our findings to populations of other origin and ethnicity requires caution. Finally, the study was cross-sectional in design and thus awaits confirmation in prospective evaluations.

In conclusion, uACR is an easily obtainable parameter that may find application in the routine assessment of vascular risk, as is already standard in the management of subjects with diabetes. In our population study, a highly significant relation between uACR and both carotid and femoral atherosclerosis emerged, which was at least partly independent of established vascular risk factors. The association was of a dose–response type without obvious thresholds and extended to levels far below what is currently termed microalbuminuria.


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