Endocrine

, Volume 44, Issue 2, pp 473–480

Vitamin D status and 5-year changes in urine albumin creatinine ratio and parathyroid hormone in a general population

Authors

    • Research Centre for Prevention and Health, Glostrup Hospital
  • Lise Lotte Nystrup Husemoen
    • Research Centre for Prevention and Health, Glostrup Hospital
  • Charlotta Pisinger
    • Research Centre for Prevention and Health, Glostrup Hospital
  • Torben Jørgensen
    • Research Centre for Prevention and Health, Glostrup Hospital
    • Faculty of Health Science, University of Copenhagen
    • Faculty of Medicine, Alborg University
  • Betina Heinsbæk Thuesen
    • Research Centre for Prevention and Health, Glostrup Hospital
  • Knud Rasmussen
    • Department of MedicineRoskilde University Hospital
  • Mogens Fenger
    • Department of Clinical BiochemistryHvidovre Hospital
  • Peter Rossing
    • Steno Diabetes Center
    • University of Copenhagen
    • University of Aarhus
  • Allan Linneberg
    • Research Centre for Prevention and Health, Glostrup Hospital
Original Article

DOI: 10.1007/s12020-013-9887-0

Cite this article as:
Skaaby, T., Husemoen, L.L.N., Pisinger, C. et al. Endocrine (2013) 44: 473. doi:10.1007/s12020-013-9887-0

Abstract

Vitamin D is associated with cardiovascular disease and renal function but the mechanisms are as yet unexplained. Microalbuminuria is associated with a higher risk of kidney function loss, cardiovascular disease, and mortality. Parathyroid hormone is a predictor of cardiovascular mortality and negatively correlated with glomerular filtration rate. We investigated the association between vitamin D status and 5-year changes in urine albumin creatinine ratio (UACR) and parathyroid hormone (PTH). A random sample of 6,784 individuals aged 30−60 years from a general population participated in the Inter99 study in 1999–2001. Vitamin D (serum-25-hydroxyvitamin D) was measured at baseline by high-performance liquid chromatography. UACR and PTH were measured at baseline and follow-up. Increased UACR was defined as UACR >4.0 mg/g reflecting the upper quartile at baseline. We included 4,330 individuals who participated at 5-year follow-up. In multivariable linear regression analysis, a 10-nmol/l higher baseline level of vitamin D was associated with a 5-year decrease in UACR by 0.92 % (95 % confidence interval, CI 0.13, 1.71). In multivariable logistic regression analysis, the odds ratio of developing increased UACR during follow-up was 0.96 (95 % CI 0.92, 0.98) per 10 nmol/l higher baseline vitamin D level. We found a significant inverse cross-sectional (p < 0.0001) but no prospective association (p = 0.6) between baseline vitamin D status and parathyroid hormone. We found low vitamin D status to be a predictor of long-term development of increased UACR. It remains to be proven whether vitamin D deficiency is a causal and reversible factor in the development of albuminuria.

Keywords

AlbuminuriaParathyroid hormoneProspectiveUrine albumin creatinine ratioVitamin D

Introduction

Vitamin D is a fat-soluble vitamin retrieved from diet and dietary supplements and through sun exposed skin. It is metabolized to its active form in the kidneys and liver. Vitamin D plays a key role in bone health but low vitamin D status has recently been found to be associated with a wide range of diseases including cardiovascular disease (CVD), kidney disease, and diabetes [1, 2]. The mechanisms are as yet unexplained.

Microalbuminuria occurs when the kidneys leak small amounts of albumin into the urine; i.e., when the glomerular albumin permeability is abnormally high. Microalbuminuria is an early marker of chronic kidney disease (CKD) and is associated with a higher risk of renal function loss, cardiovascular disease, and all-cause mortality [35]. Microalbuminuria is diagnosed from the urine excretion of albumin in a 24-h urine collection or, more commonly, from albumin creatinine ratio (UACR) in a spot sample. Since a decrease in urine albumin excretion is associated with a lower risk of cardiovascular and renal disease [6, 7], microalbuminuria is an important therapeutic target.

Vitamin D insufficiency may play a role in the development of albuminuria and kidney disease. A cross-sectional study found an association between a low vitamin D level and prevalence of albuminuria [8]. Likewise, a randomized controlled trial (RCT) of patients with diabetic nephropathy showed a decrease in albuminuria in the group treated with paracalcitol, a selective activator of the vitamin D receptor [9].

Parathyroid hormone (PTH) is an important hormone for bone health, maintaining normal serum concentrations of calcium and phosphate. PTH is regulated through levels of vitamin D and calcium, and vitamin D or calcium insufficiencies are generally associated with an increase in PTH [10]. PTH is a predictor of cardiovascular mortality [11] and is negatively correlated with glomerular filtration rate, GFR [12, 13]. It has been suggested that vitamin D deficiency can initiate secondary hyperparathyroidism even without overt hypocalcaemia [14].

Evidence from prospective studies of the association between vitamin D status and changes in UACR and PTH is limited. To the best of our knowledge, only one previous study has examined the prospective association between vitamin D status and development of albuminuria in the general population, and they found no association [15]. The objective of the present study was to investigate the association between vitamin D status as assessed by 25-hydroxyvitamin D (25-OH-D)—which reflects the total amount of vitamin D from dietary and cutaneous sources—at baseline and 5-year longitudinal changes in urine albumin creatinine ratio and parathyroid hormone in a large sample of adult Danes.

Methods

Study population

The Inter99 study carried out in 1999–2001 included 6,784 individuals aged 30–60 years drawn from an age- and sex-stratified random sample of the population [16]. The baseline participation rate was 52.5 %. The Inter99 study was a population-based randomized controlled trial (CT00289237, ClinicalTrials.gov) investigating the effects of lifestyle intervention on CVD. Details on the study and the intervention program are described elsewhere [16]. A total of 4,513 individuals participated in the 5-year follow-up study [17], and 4,330 individuals had baseline measurements of vitamin D. Inter99 data were considered observational, and analyses were adjusted for study group. The participants of the present study are thus a subgroup of the participants included in our study of vitamin D status, incident cardiovascular disease and all-cause mortality [18].

The health examination included a self-administered questionnaire, a physical examination, various blood tests, and a random urinary spot sample. The questionnaires provided information on education, diet, leisure time physical activity, smoking habits, and alcohol consumption. Measurements followed identical procedures at baseline and follow-up. Height and weight were measured without shoes and with light clothes. Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. Blood pressure (mmHg) was measured twice in the sitting position, and we used the average of the two systolic measurements.

Exposure

Vitamin D measurements: serum samples were stored at −20 °C, and the concentration of 25-OH-D was measured by high-performance liquid chromatography (HPLC) in 2009 as previously described [19, 20]. The accuracy of the HPLC method used was compared with a validated HPLC method using certified reference material (No. 968c; NIST, Gaithersburg, MD, USA) (ref) [20]. Participants were not informed of their vitamin D status (was measured at a later stage).

Outcome

The urine samples were cooled and analyzed within a week from sampling. An internal validation study confirmed the durability of urine albumin in urine samples kept at 2–8 °C. Urine albumin concentration was analyzed by a turbidimetric assay (Cobas Mira plus, Roche diagnostic systems, la Roche, Basel, Switzerland). Internal validation insured that the results were comparable throughout the study period. Urine creatinine concentration was analyzed by the Jaffé method (Hitachi 912 system, Roche diagnostic, Germany) until 2004 and then changed to the IDMS traceable enzymatic method (also Hitachi 912). Based on samples from 102 subjects, a correction factor of 1.14 for u-creatinine was developed so albumin/creatinine ratios were comparable throughout the study period. The urine albumin creatinine ratio was calculated by the formula: Urine albumin (mg/l)×8.84/urine creatinine (mmol/l) and expressed in mg/g. Increased UACR was defined as the upper quartile of the baseline data (>4 mg/g). In the prospective analyses, incidence of increased UACR was defined as proportion of persons with increased UACR at follow-up (UACR >4 mg/g) among those without increased UACR at baseline (persons with increased UACR at baseline were excluded, n = 1,158).

Baseline and follow-up blood samples for serum PTH measurements were stored at −20 °C until analysis in 2007–2008 and 2008–2009, respectively. PTH concentrations were measured as intact PTH by competitive chemiluminescent enzyme immunoassay (IMMULITE 2000 System; Siemens Healthcare Diagnostics, Deerfield, IL, USA) and expressed in pmol/l.

Other covariates

Age was divided into three categories: 30–35, 40–50, and 55–60 years. Time of blood sample was divided into season: March–May, June–August, September–November, or December–February. Dietary habits were classified according to intake of vegetables, fruit, fish, and saturated fat in healthy, average, or unhealthy [17]. Education was classified as none, low, medium, high, or missing value including students. Physical activity during leisure time was divided into sedentary, light, or moderate/vigorous. Smoking habits were divided into never smokers, ex-smokers, occasional smokers, current smokers <15 g/day, 15–< 25 g/day, or ≥25 g of tobacco/day. BMI was divided into the following groups: <18.5, ≥18.5–25, ≥25–30, or ≥30 kg/m2 [21]. Alcohol consumption was classified as consumption of 0, >0–7, >7–14, or >14 standard drinks per week.

The concentration of HbA1C was analyzed from capillary blood by ion exchange HPLC using Bio-Rad VARIANT™ Hemoglobin A1C (BioRad, USA). Type 2 diabetes was defined as Hba1c ≥6.5 % [22] or self-reported diabetes. Hypertension was defined as systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg or self-reported use of antihypertensive medication.

According to 5-year changes in physical activity, participants were classified as less active, unchanged, or more active. According to 5-year changes in dietary habits, they were classified as unhealthier, unchanged, or healthier. According to 5-year changes in smoking habits, they were classified as smoking less, smoking the same, or smoking more.

Statistical analyses

All analyses were performed with SAS, version 9.2 (SAS Institute Inc. Cary, NC USA). We did a complete case analysis; only participants with complete information on all considered variables were included for each outcome. p values were two-tailed, and statistical significance was defined as p < 0.05. Descriptive characteristics of the participants presented as percent (total number) and median (inter quartile range, IQR) or geometric mean (95 % confidence interval, CI) were compared with non-parametric statistics (Kruskal–Wallis test).

We used linear regression analysis to model cross-sectional associations and the changes in UACR and PTH during follow-up as a function of baseline vitamin D status (Tables 2, 3). The outcomes were transformed by the natural logarithm to obtain a normal distribution. All prospective models were adjusted for the baseline value of the outcome to adjust for regression to the mean and allow changes in explanatory variables to be interpreted as effects on outcome changes from baseline to follow-up [23]. The β-coefficients were back-transformed and reported as percent with 95 % confidence intervals (CI). The prospective association between baseline vitamin D status and the dichotomous outcome increased UACR was analyzed by logistic regression models and expressed as odds ratios with 95 % CI (Table 3).

For both the linear and logistic regressions, model 1 reflects the unadjusted estimate (adjusted for the baseline value of the outcome in the prospective linear regressions). Model 2 was (further) adjusted for gender; age; randomisation status (group A or B); season of blood sample; dietary habits; education; physical activity; smoking habits; BMI; alcohol consumption; baseline systolic blood pressure (continuous variable); 5-year changes in physical activity; 5-year changes in dietary habits; 5-year changes in smoking habits; and 5-year changes in total intake of alcohol (continuous variable) and BMI (continuous variable).

We investigated the validity of the linear models by checking residual plots, leverage and Cook’s distance. Likelihood ratio tests were used to test for statistical significance in all logistic regression analyses. The logistic regression models were checked with the Hosmer–Lemeshow test. In both linear and logistic regression models, the linearity assumption was checked by adding the term squared and cubed to the model and checking for significance. We also divided vitamin D into quartiles and quintiles to test for a non-linear relationship between vitamin D status and UACR.

In additional analyses (data not shown) of the associations between vitamin D status and changes of UACR and increased UACR in follow-up, we excluded persons with diabetes or hypertension at baseline, and further adjusted for baseline PTH status and diabetes and hypertension at follow-up.

Results

Descriptive characteristics of the participants are shown in Table 1. In baseline analyses, UACR and PTH were significantly associated with vitamin D status in the crude and adjusted model (Table 2). In the adjusted model, a 10 nmol/l higher vitamin D status at baseline was associated with a 0.96 % (p = 0.032) and a 1.45 % (p < 0.0001) lower UACR and PTH level, respectively. Among the 4,330 persons included in the present study, the percentages (number) of hypertensive and diabetic persons at baseline were 37.6 % (1,629) and 6.9 % (298), respectively. The percentages (number) of persons who developed hypertension or diabetes during the 5-year follow-up were 8.9 % (384) and 2.8 % (120), respectively. The number of persons with self-reported use of antihypertensive medication was 283 and 635 at baseline and 5-year follow-up, respectively.
Table 1

Serum 25-hydroxyvitamin D status, parathyroid hormone, and urine albumin creatinine ratio according to baseline characteristics

Characteristic

All participants % (n)

Median (IQR)

Median (IQR)

Geometric mean (95 % CI)

25-OH-D (nmol/l)

PTH (pmol/l)

UACR (mg/g)

Gender

 Male

50.5 (2,189)

47.0 (32.0, 64.0)

5.2 (4.1, 6.6)

3.3 (3.2, 3.4)

 Female

49.5 (2,141)

50.0 (34.0, 67.0)

4.8 (3.7, 6.2)

3.8 (3.7, 3.9)

 p valuea

 

0.007

<0.0001

<0.0001

Age (years)

 30–35

12.5 (542)

51.0 (36.0, 68.0)

4.8 (3.8, 6.2)

3.4 (3.2, 3.7)

 40–50

62.9 (2,722)

48.0 (33.0, 65.0)

5.0 (3.8, 6.4)

3.4 (3.3, 3.5)

 55–60

24.6 (1,066)

48.0 (33.0, 66.0)

5.2 (4.0, 6.6)

3.8 (3.7, 4.0)

 p valuea

 

0.08

0.001

<0.0001

Education

 None

12.9 (560)

46.0 (32.5, 64.0)

5.2 (4.0, 6.6)

3.6 (3.4, 3.8)

 Low

27.7 (1,198)

51.0 (34.0, 68.0)

4.9 (3.8, 6.3)

3.5 (3.4, 3.7)

 Medium

42.9 (1,857)

48.0 (33.0, 65.0)

5.0 (3.8, 6.4)

3.5 (3.4, 3.7)

 High

9.8 (424)

47.0 (32.0, 62.0)

4.9 (3.9, 6.2)

3.2 (3.0, 3.4)

 p valuea

 

0.01

0.088

0.003

Body mass index (kg/m2)

 <18.5

0.81 (35)

44.0 (32.0, 76.0)

4.9 (3.8, 5.6)

4.4 (3.5, 5.4)

 18.5–24.9

43.9 (1,901)

51.0 (35.0, 68.0)

4.8 (3.8, 6.0)

3.4 (3.3, 3.5)

 25–29.9

40.0 (1,733)

48.0 (33.0, 65.0)

5.1 (4.0, 6.6)

3.4 (3.3, 3.5)

 ≥30

15.2 (659)

43.0 (30.0, 59.0)

5.6 (4.2, 7.0)

4.2 (3.9, 4.5)

 p valuea

 

<0.0001

<0.0001

<0.0001

Season (blood sample)

 Mar–May

30.0 (1,297)

43.0 (29.0, 59.0)

5.1 (3.9, 6.5)

3.6 (3.5, 3.7)

 Jun–Aug

23.1 (1,002)

54.0 (36.0, 79.0)

4.7 (3.7, 6.2)

3.5 (3.4, 3.7)

 Sep–Nov

25.5 (1,104)

57.0 (43.0, 72.0)

4.9 (3.8, 6.2)

3.5 (3.3, 3.6)

 Dec–Feb

21.4 (927)

41.0 (28.0, 54.0)

5.4 (4.3, 6.8)

3.5 (3.3, 3.6)

 p valuea

 

<0.0001

<0.0001

0.29

Physical activity

 Sedentary

19.4 (825)

44.0 (30.0, 63.0)

5.3 (4.1, 6.8)

3.8 (3.6, 4.0)

 Light

62.5 (2,663)

49.0 (34.0, 66.0)

5.0 (3.8, 6.4)

3.5 (3.4, 3.6)

 Moderate/vigorous

18.1 (771)

50.0 (36.0, 67.0)

4.7 (3.7, 6.1)

3.2 (3.0, 3.3)

 p valuea

 

<0.0001

<0.0001

<0.0001

Diet

 Unhealthy

14.1 (590)

46.0 (31.0, 64.0)

5.2 (3.9, 6.5)

3.5 (3.3, 3.7)

 Average

70.5 (2,957)

48.0 (33.0, 65.0)

5.0 (3.9, 6.4)

3.5 (3.4, 3.6)

 Healthy

15.5 (650)

51.0 (35.0, 68.0)

4.8 (3.8, 6.3)

3.5 (3.3, 3.7)

 p valuea

 

0.003

0.067

0.90

Alcohol (drinks/week)

 0

8.4 (352)

44.0 (30.0, 65.0)

5.4 (4.1, 6.7)

4.0 (3.7, 4.4)

 ≤7

45.1 (1,882)

49.0 (34.0, 65.0)

5.0 (3.9, 6.4)

3.4 (3.3, 3.6)

 ≤14

22.8 (950)

50.0 (35.0, 65.0)

4.9 (3.9, 6.4)

3.4 (3.2, 3.6)

 >14

23.6 (985)

49.0 (32.0, 67.0)

4.8 (3.8, 6.2)

3.5 (3.4, 3.7)

 p valuea

 

0.11

0.001

0.005

Smoking habits (g/day)

 Never smoker

40.2 (1,726)

50.0 (34.0, 66.0)

5.2 (4.1, 6.6)

3.4 (3.3, 3.5)

 Former smoker

27.9 (1,197)

50.0 (35.0, 68.0)

5.2 (4.0, 6.5)

3.6 (3.4, 3.7)

 Occasional smoker

167 (3.9)

47.0 (32.0, 64.0)

4.8 (4.0, 6.5)

3.6 (3.2, 4.1)

 Current smoker, <15

9.4 (403)

48.0 (32.0, 64.0)

4.7 (3.6, 6.0)

3.6 (3.3, 3.8)

 Current smoker, <25

13.9 (595)

45.0 (30.0, 61.0)

4.6 (3.6, 5.8)

3.5 (3.3, 3.7)

 Current smoker, ≥25

4.8 (205)

42.0 (28.0, 60.0)

4.8 (3.7, 5.9)

3.8 (3.4, 4.3)

 p valuea

 

<0.0001

<0.0001

0.32

a Kruskal–Wallis test

25-OH-D 25-hydroxyvitamin D, CI confidence interval, IQR interquartile range, PTH parathyroid hormone, UACR urine albumin creatinine ratio

Table 2

Multivariable linear regression of the cross-sectional association between serum 25-hydroxyvitamin D status and urine albumin creatinine ratio and parathyroid hormone

Risk factors

Number

Difference per 10 nmol/l higher vitamin D level

Model 1a

Model 2b

Difference in  % (95 % CI)

Difference in  % (95 % CI)

Urine albumin creatinine ratio

4,013

−1.01 (−1.86, −0.16)

−0.96 (−1.83, −0.085)

p = 0.020

p = 0.032

Parathyroid hormone

3,997

−1.88 (−2.33, −1.43)

−1.45 (−1.91, −0.98)

p < 0.0001

p < 0.0001

BMI body mass index, CI confidence interval, UACR urine albumin creatinine ratio

aUnadjusted

bAdjusted for gender, age, season, randomisation status, physical activity, alcohol consumption, diet, waist circumference, BMI, smoking habits, and education. For UACR also systolic blood pressure

In the adjusted model, UACR level decreased significantly during follow-up by 0.92 % (p = 0.022) per 10 nmol/l higher vitamin D status (Table 3). Vitamin D status at baseline was not associated with changes in PTH during follow-up (p = 0.60).
Table 3

Multivariable linear regression of the prospective association between 25-hydroxyvitamin D status and 5-year changes in urine albumin creatinine ratio and parathyroid hormone

Risk factors

Number

Changes per 10 nmol/l higher vitamin D level

Model 1a

Model 2b

Changes in  % (95 % CI)

Changes in  % (95 % CI)

Urine albumin creatinine ratio

3,493

−0.87 (−1.62, −0.12)

−0.92 (−1.71, −0.13)

p = 0.023

p = 0.022

Parathyroid hormone

3,440

0.026 (−0.13, 0.66)

−0.11 (−0.51, 0.30)

p = 0.19

p = 0.60

BMI body mass index, CI confidence interval, UACR urine albumin creatinine ratio

aAdjusted for the baseline value

bFurther adjusted for gender, age, season, randomisation status, physical activity, alcohol consumption, diet, BMI, smoking habits, and education, and 5-year changes in BMI, smoking habits, alcohol consumption, diet, and physical activity. For UACR also systolic blood pressure

The development of increased UACR (n = 1,371) was significantly associated with baseline vitamin D status in the fully adjusted analysis with an odds ratio of 0.96 (p = 0.0006) for a 10 nmol/l higher vitamin D status (Table 4).
Table 4

Multivariable logistic regression analyses of the longitudinal association between baseline 25-hydroxyvitamin D status and development of increased UACR during follow-up

Outcome

Incidence per 10 nmol/l higher vitamin D level

n (total n)

Model 1a

Model 2b

OR (95 % CI)

OR (95 % CI)

Increased UACRc

1,371 (2,603)

0.96 (0.93, 0.99)

0.96 (0.92, 0.98)

p = 0.004

p = 0.0006

BMI body mass index, CI confidence interval, OR odds ratio, UACR urine albumin creatinine ratio

aUnadjusted

bAdjusted for gender, age, season, randomisation status, physical activity, alcohol consumption, diet, BMI, smoking habits, and education, systolic blood pressure, and 5-year changes in BMI, smoking habits, alcohol consumption, diet, and physical activity

cIncreased UACR was defined as the upper quartile of the baseline data. In the longitudinal analyses, incidence of increased UACR was defined as proportion of persons with increased UACR at follow-up among those without increased UACR at baseline (persons with increased UACR at baseline were excluded, n = 1,158)

In additional analyses (data not shown), we removed blood pressure and BMI stepwise as covariates from the fully adjusted models to explore whether they could be mediators of the association between vitamin D status and UACR. The estimates were, however, essentially unchanged (<5 % change), as were the p values. Also, excluding persons with diabetes or hypertension at baseline, and further adjusting for baseline PTH status and diabetes and hypertension at follow-up did not change the strength of the associations between vitamin D status and changes of UACR and increased UACR at follow-up.

Among the 6,784 persons participating in the Inter99 study at baseline, 52 persons (18 females) died before the 5-year follow-up. They had a lower vitamin D status (median: 42.0, IQR: 24.0, 57.0 nmol/l), and higher UACR (geometric mean: 5.6, CI 3.8, 8.2 mg/g) levels compared with the survivors who participated in the 5-year follow-up study (Table 1). PTH status was approximately the same (median: 5.0, IQR: 4.0, 6.9 pmol/l).

Discussion

We found a statistically significant association between vitamin D status and both cross-sectional and prospective levels of urine albumin creatinine ratio (UACR). Likewise, lower vitamin D status at baseline was a significant predictor of the development of an increased UACR at 5-year follow-up. We found an inverse cross-sectional but no prospective association between vitamin D status at baseline and parathyroid hormone.

Our cross-sectional results are in line with a previous cross-sectional study of vitamin D status and albuminuria [8]. Regarding our prospective results, a recent prospective study found no association between vitamin D status and development of albuminuria [15]. Possible explanations for the lack of association in their study are the use of other cut-offs for defining albuminuria and fewer events at follow-up (mean follow-up time was 7.8 years). An argument for using different cut-offs than the traditional or for analyzing UACR as a continuous variable—is the fact that albuminuria well below what is usually defined as microalbuminuria is a strong predictor of cardiovascular morbidity and mortality [7].

Microalbuminuria is a marker of vascular damage and endothelial dysfunction [5] and could be affected by vitamin D status in a number of ways. First, vitamin D could affect UACR through direct cellular effects preventing podocyte loss and glomerulosclerosis [8]. Second, vitamin D status could affect UACR through an effect on diabetes and insulin resistance which are known risk factors of albuminuria, since vitamin D deficiency has been associated with diabetes markers and insulin sensitivity [2426]. Third, insufficient vitamin D may contribute to albuminuria by activating the renin–angiotensin–aldosterone system which may lead to albuminuria through both hemodynamic and non-hemodynamic mechanisms. Our analyses do, however, not support blood pressure as an intermediate in the pathway between vitamin D and UACR as our estimates were essentially unchanged before and after adjusting for blood pressure.

It may be speculated whether residual confounding or reverse causation could explain the observed inverse association between vitamin D status and UACR. To reduce the risk of reverse causation, we excluded persons with UACR in the upper quartile at baseline. Likewise, we adjusted for baseline value in the linear regression models. Strengthening causal inference of the association between vitamin D status and UACR, a recent RCT of patients with diabetic nephropathy treated with a selective activator of the vitamin D receptor showed a decrease in albuminuria [9].

Our results support the well-established inverse cross-sectional association between vitamin D status and PTH. To the best of our knowledge, no studies have examined the relationship between vitamin D status and long-term changes in PTH in the general population. A possible explanation for the observed lack of longitudinal association is that a single vitamin D measurement may lose predictive power over time. Thus, Grant et al. found a diminishing utility of a single measurement of 25-OH-D to determine the effect of vitamin D in cancer risk reduction as the follow-up time increases [27]. To overcome the problem of changing vitamin D status during a cohort study, repeated vitamin measurements would be preferable.

The strengths of our study include the longitudinal population-based design; the large general population sample used; the large amount of vitamin D insufficient participants [19]; a long-term follow-up; the detailed information on covariates and the multivariable analyses; and the use of the objective biomarker of vitamin D, 25-OH-D, which is well suited for epidemiological studies due to its long half-life and wide range of distribution in the population. Further, as opposed to 1,25-dihydroxyvitamin D which is known to be deficient in chronic kidney disease, renal metabolism is not required to generate 25-OH-D [8].

The limitations of our study include not having information on chronic kidney disease and the use of a single spot urine sample at baseline and follow-up to assess UACR, which is less accurate than 24-h urine collections [28, 29]. Both several acute (such as infections of the urinary tract) and chronic conditions may affect urinary albumin results [30]. If low vitamin D status is associated with a higher risk of e.g., infection, it could falsely strengthen the observed association. Serial measurements as opposed to single 5-year assessments would more rigorously trace the outline of the general trend. The fact that the persons who died before follow-up had a lower vitamin D status and higher UACR at baseline would tend to attenuate the association between vitamin D status and changes in UACR. Due to the low number of deaths, we expect the attenuation to be small.

The fact that determination of vitamin D status relied on a single baseline measurement would tend to attenuate any true association. Of note, the participants were not informed of their vitamin D status at baseline (analyzed at a later stage). Somewhat supportive of the use of a single vitamin D measurement in predicting future health outcomes, Jorde et al. found vitamin D status to be as stable a predictor as blood pressure and lipids [31]. Considering the interventional design of the study; if the participants with highest UACR and presumably also lowest vitamin D status participated in the interventions and changed UACR risk factors, it could attenuate the association between vitamin D status and UACR. It is recommended to store samples for measurement of 25-OH-D at −80 °C but studies have demonstrated stability of 25-OH-D in serum samples under different conditions [32, 33]. In general, measuring serum 25-OH-D is associated with methodological problems. As illustrated by the comparison of two HPLC methods [20], there are considerable variations between methods [3437]. There were, however, no systematic errors, and any misclassification is likely to be random and would have attenuated the associations toward the null value.

In conclusion, we found low vitamin D status to be a predictor of long-term development of increased UACR. Since prevention of albuminuria reduces loss of kidney function and development of cardiovascular disease, albuminuria is an important therapeutic target. It remains to be proven whether vitamin D deficiency is a causal and reversible factor in the development of albuminuria.

Acknowledgments

We would like to thank the participants and all members of the Inter99 staff at Research Centre for Prevention and Health. The Inter99 study was initiated by Torben Jørgensen, DMSci (principal investigator); Knut Borch-Johnsen, DMSci, (co-principal investigator); Troels Thomsen, PhD; and Hans Ibsen, DMSci. The Steering Committee comprises the former two and Charlotta Pisinger, PhD, MPH.

Ethical standards

Participants gave their informed consent, and the study was approved by the local Ethics Committees and the Danish Data Protection Agency. The recommendations of the Declaration of Helsinki were followed.

Copyright information

© Springer Science+Business Media New York 2013