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Keywords:

  • diabetes;
  • epidemiology;
  • vitamin D

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and methods
  5. Results
  6. Discussion
  7. Competing interests
  8. Acknowledgements
  9. References

Diabet. Med. 27, 1107–1115 (2010)

Abstract

Aims  We wanted to test the hypothesis that low serum 25-hydroxyvitamin D (25(OH)D) concentrations are associated with increased risk of developing Type 2 diabetes mellitus (DM) in a population-based cohort during 11 years of follow-up.

Methods  The analyses included 4157 non-smokers and 1962 smokers from the Tromsø Study 1994–95 without diabetes at baseline. Subsequent Type 2 DM was defined using a hospital journal-based end-point registry, completed through the year 2005. Participants were allocated into quartiles of serum 25(OH)D within each month to account for seasonal variation, and serum 25(OH)D values both as a continuous variable and in quartiles were used in Cox regression models. The analyses were stratified by smoking. Adjustments were made for age, sex, body mass index (BMI), physical activity and, in non-smokers, former smoking.

Results  Type 2 DM was registered in 183 non-smoking and 64 smoking participants. Using the fourth (highest) quartile of serum 25(OH)D as the reference, non-smoking participants in the third, second and first quartiles had age- and sex-adjusted hazard ratios (95% confidence intervals) of incident Type 2 DM of 1.00 (0.62–1.61), 1.50 (0.97–2.31) and 1.89 (1.25–2.88), respectively, whereas the corresponding values for smokers were 1.79 (0.77–4.19), 2.33 (1.02–5.35) and 2.68 (1.18–6.08). Adjustment for BMI attenuated the hazard ratios, and they were no longer significant.

Conclusions  Baseline serum 25(OH)D was inversely associated with subsequent Type 2 DM in a population-based 11 year follow-up study, but not after adjustment for BMI. Randomized trials are needed to define the possible role of serum 25(OH)D status, and thereby the role of supplementation, in the prevention of Type 2 DM.


Abbreviations
1,25(OH)2D

1,25-dihydroxyvitamin D

25(OH)D

25-hydroxyvitamin D

PTH

parathyroid hormone

VDR

vitamin D receptor

Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and methods
  5. Results
  6. Discussion
  7. Competing interests
  8. Acknowledgements
  9. References

The prevalence of Type 2 diabetes mellitus (DM) is increasing worldwide, implying huge challenges for the health systems in the future [1,2]. Effective means for prevention and treatment of this condition are therefore needed. The basis of the development of the disease is insulin resistance combined with a relative deficit of insulin secretion from pancreatic B cells [1,3].

Serum 25-hydroxyvitamin D (25(OH)D) is the commonly used biomarker of a person's vitamin D status, and exists in two forms; 25-hydroxyvitamin D3 (25(OH)D3 and 25-hydroxyvitamin D2 (25(OH)D2). The presence of vitamin D receptors (VDRs) and the enzyme 25-hydroxyvitamin D-1α-hydroxylase, which converts 25(OH)D into its active form, 1,25-dihydroxyvitamin D (1,25(OH)2D), in B cells have been well described [4,5]. Studies suggest that high concentrations of 1,25(OH)2D lead to an increase in insulin secretion [6,7], possibly mediated through increased calcium influx into the B cells [4]. Systemic inflammation contributes to the development of insulin resistance and eventually Type 2 DM [8], and an anti-inflammatory effect of vitamin D may also possibly affect the development of Type 2 DM, although this has been little studied [8]. In line with this, cross-sectional data from a number of epidemiological studies show an inverse association between serum 25(OH)D concentrations and glucose concentrations [9–12], insulin resistance [10,12–16] and prevalence of Type 2 DM [16]. There are, however, few prospective studies evaluating this relationship [10,17,18], and a recent review pointed out the need for more prospective studies to clarify and quantify the association between serum 25(OH)D concentrations and the risk of Type 2 DM [19]. We wanted to test the hypothesis that low serum 25(OH)D concentrations are associated with increased risk of subsequent Type 2 DM in a population-based cohort with 11 years of follow-up.

Subjects and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and methods
  5. Results
  6. Discussion
  7. Competing interests
  8. Acknowledgements
  9. References

Study population

The Tromsø study is a repeated population-based study conducted in the municipality of Tromsø, Norway, situated at 69°N (current population 67 000). The study was initiated in 1974, focusing originally on cardiovascular diseases. However, the study has evolved through the years, with increasing emphasis on other conditions and chronic diseases, for example osteoporosis, diabetes, chronic obstructive pulmonary disease, cancer and dementia. The latest (sixth) health survey was conducted in 2007–2008.

In 1994 all individuals aged 25 years or older were invited to participate in the Tromsø Study. A total number of 27 158 persons went to the first visit, providing an attendance rate of 77%. Owing to capacity, only a subsample of the participants (all men aged 55–74 years, all women aged 50–74 years, and a sample of 5–10% of the remaining age groups between 25 and 84 years) were invited for a more extensive clinical examination (second visit), and 7965 persons, or 78% of those invited, attended [20]. The great majority of the participants were Caucasian. Sera from the second visit were stored for later analyses. The study was conducted by the University of Tromsø in co-operation with the National Health Screening Service, recommended by the Regional Committee for Medical and Health Research Ethics, North Norway, and approved by the Norwegian Data Inspectorate. Each participant provided written informed consent prior to the examinations.

Measurements

At both visits, the participants filled in questionnaires on medical history and lifestyle factors. Physical activity was registered through the following questions.

  • 1
     Light activity (not sweating or out of breath). How has your physical activity in leisure time been during the last year? Think of your weekly average for the year. Time spent going to work counts as leisure time (hours per week).
  • 2
     Vigorous physical activity (sweating/out of breath). How has your physical activity in leisure time been during the last year? Think of your weekly average for the year. Time spent going to work counts as leisure time (hours per week).

The answer alternatives for both questions were as follows: 1 = none, 2 = <1 h, 3 = 1–2 h, 4 = 3 h or more. This was recorded as 0–1–2–3 h, and a physical activity score was calculated by adding together hours of light and vigorous physical activity per week. Each hour of vigorous activity was given a double score compared with each hour of light activity, up to a maximum of nine points. To identify current smokers, we used the question, ‘Do you smoke cigarettes daily?’ (yes/no). Participants answering yes to ‘Do you smoke a pipe daily?’ or ‘Do you smoke cigars/cigarillos daily?’ were also coded as current smokers. Former smokers were identified from the question, ‘If you previously smoked daily, how long is it since you stopped (years)?’ In smokers, the following questions were also asked: ‘For previous or current smokers: how many cigarettes do you or did you smoke daily?’ and ‘If you currently smoke, or have smoked before, how many years in all have you smoked daily?’ Height and weight were measured wearing light clothing and no shoes, and body mass index (BMI) was defined as weight (in kilograms) divided by height squared (m2).

Blood samples were drawn in a non-fasting state. Glycated haemoglobin (HbA1c) concentrations were measured in the haemolysate by a latex-enhanced turbidimetric immunoassay on a Cobras Mira Plus instrument (Unimate 5 HbA1c; F. Hoffmann-La Roche AG, Basel, Switzerland). The day-to-day coefficient of variation (CV) was 3.2% for an HbA1c value of 5.3% (34 mmol/mol) and 5.1% for an HbA1c value of 11.4% (101 mmol/mol). Sera from the second visit were stored at −70°C and, after a median storage time of 13 years, thawed in March 2008 and analysed for 25(OH)D3 by an electrochemiluminescence immunoassay, using an automated clinical chemistry analyser (Modular E170, Roche Diagnostics®, Mannheim, Germany). According to the manufacturer, the assay has, for total analytical precision, a CV ≤ 7.8% as judged at any of three different concentrations (48.6, 73.8 and 177.0 nmol/l). The cross-reactivity with 25(OH)D2 was < 10%, and the analytical sensitivity was 10 nmol/l. Five participants had 25(OH)D3 below the detection limit, and their values were set to 5 nmol/l. At present, the laboratory has no reference values for 25(OH)D3, but the manufacturer provides a population-based reference range of 27.7–107.0 nmol/l for adults as a guideline. This analysis has been approved by the Norwegian Accreditation Authority. Our own previous findings suggest that smoking, for unknown reasons, interferes with the 25(OH)D assay in a dose-dependent manner, leading to a possible overestimation of serum 25(OH)D in smokers [21]. Serum parathyroid hormone (PTH) was analysed in 2001 in a subgroup of 1965 participants using an automated clinical chemical analyser (Immunlite 2000, Los Angeles, CA, USA). The assay measures intact PTH, with a reference range of 1.1–6.8 pmol/l (≤ 50 years) and 1.1–7.5 pmol/l (> 50 years).

Definition and ascertainment of diabetes

The possible cases of diabetes mellitus were identified through self-reported diabetes in questionnaires in 1994/5, 2001 and 2008, through elevated HbA1c > 6.5% (48 mmol/mol) in one of the two former health surveys, and through linkage of the Tromsø Study participant list to diabetes-related discharge diagnoses in the digital patient records at the only local hospital (ICD-9 codes 250, 357.2, 362.0, 583.8, 648.0, 648.8 and 790.2; ICD-10 codes E10–E14, O24 and R73). Some cases of hospital-confirmed diabetes, but with no diabetes-related discharge diagnosis, were detected through our adjudication process for cardiovascular diseases. We validated all possible cases of diabetes by checking medical records. Based on glucose measurements (non fasting glucose ≥ 11.1 mmol/l, fasting glucose ≥ 7.0 mmol/l or 2 h glucose load ≥ 11.1 mmol/l), HbA1c≥ 7.0.% (53 mmol/mol), or recorded use of insulin or oral anti-diabetic drugs, these were classified accordingly as having no diabetes, Type 1 or Type 2 DM. Measurement of C-peptide was the common method used at the hospital during the follow-up period to differentiate between Type 1 and Type 2 DM, while glutamic acid decarboxylase antibody measurements were performed in a minority of cases. The year of diagnosis was defined when possible [22]. The diabetes end-point registry was completed up to the end of 2005. To identify prevalent diabetes at baseline in 1994–95, we used a combination of the diabetes registry and self-reported diabetes in the questionnaire from baseline, so that all participants registered with a diagnosis of diabetes from 1994–95 or before, and/or reported having diabetes in the questionnaire in 1994–95, were excluded from the analyses. We also excluded all participants with baseline HbA1c > 6.5% (48 mmol/mol) in the Tromsø Study 1994–95. Incident diabetes in the follow-up period was identified through the diabetes registry only. Information on death and migration from the municipality was obtained from the Norwegian Registry of Vital Statistics [22].

Statistical analyses

Participants were allocated into quartiles based on serum 25(OH)D concentrations within each month to account for the seasonal variation [23]. Baseline data are presented as means (SD) or medians (interquartile range). Test for trend across serum 25OH)D quartiles was assessed in a linear regression model, using serum 25(OH)D quartiles as the independent variable. All significance tests were two-tailed, and P < 0.05 was considered statistically significant. Owing to the possible effects of smoking on the 25(OH)D measurements in our assay, non-smokers and smokers were analysed separately.

For survival analyses, we used the Cox regression model. Survival time was set from the date of attendance to the end of follow-up, diagnosis of Type 2 DM, migration out of the municipality or death, whichever occurred first. In the basic model, we included age and sex as covariates. In the multivariable models, we also included the possible confounders BMI, physical activity and, in non-smokers, former smoking. In current smokers, adjustments were made for number of cigarettes smoked and years of smoking. Cox regression analyses models were also performed with serum 25(OH)D as a continuous variable, adding possible confounders one by one. To examine further the effect of BMI on the relation between serum 25(OH)D and diabetes risk, we performed the same analyses with the participants stratified by BMI quartiles.

Participants where year of diabetes diagnosis could not be set with certainty were given a year of diagnosis midway between baseline and end of follow-up. The analyses were also performed with these participants excluded. To study how the participants with missing year of diagnosis influenced the analysis, we finally performed logistic regression models in order to evaluate the effect of year of diagnosis. Statistical analyses were performed with SPSS version 16.0 (SPSS Inc., Chicago, IL, USA).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and methods
  5. Results
  6. Discussion
  7. Competing interests
  8. Acknowledgements
  9. References

There were 4829 non-smoking and 2336 smoking participants with valid serum 25(OH)D measurements in the baseline examination of the Tromsø Study 1994–95. After exclusion of persons registered with Type 1 DM (n = 15), participants reporting diabetes at baseline and/or having journal-based previous diagnosis of Type 2 DM (n = 159), participants with baseline HbA1c > 6.5% (48 mmol/mol) (n = 42) or missing (n = 375) at baseline, participants registered as moved out of Tromsø before baseline (n = 14) or with missing information on any of the variables used in the models (n = 67), 4157 non-smoking participants were available for the final analyses. Among smokers, 1962 participants were included after exclusion of participants with Type 1 DM (n = 4), self-reported and/or registered Type 2 DM at baseline (n = 48), baseline HbA1c > 6.5% (48 mmol/mol) (n = 11) or missing (n = 239), participants registered as moved out of Tromsø before baseline (n = 2) or with missing information on any of the variables used in the models (n = 70). This group also included exclusively cigar (n = 15) and pipe smokers (n = 19).

Table 1 shows baseline characteristics for the participants according to serum 25(OH)D quartile in non-smokers and smokers. In non-smokers, age, BMI and serum PTH tended to decrease with increasing quartiles, while the proportion of former smokers and physical activity score increased slightly with increasing quartiles of serum 25(OH)D. In smokers, BMI and serum PTH decreased across serum 25(OH)D quartiles, while number of cigarettes increased.

Table 1.   Baseline characteristics of participants with serum 25(OH)D measurements in the Tromsø Study 1994–95 followed for development of Type 2 diabetes mellitus in a period of 11 years after baseline
 AllSerum 25(OH)D
Quartile* 1Quartile 2Quartile 3Quartile 4Test for trend (P-value)
  1. Abbreviations: BMI, body mass index; 25(OH)D, 25-hydroxyvitamin D; NA, not assessed; and PTH, parathyroid hormone.

  2. The data are presented as means (SD) if not otherwise stated.

  3. *To account for seasonal variations, quartiles of serum 25(OH)D were created within each month of blood sampling and thereafter pooled.

Non-smokers
 n41571030104210401045  —
 Serum 25(OH)D (nmol/l, range)5.0–192.25.0–53.134.8–62.343.4–73.552.3–192.2  —
 Serum 25(OH)D (nmol/l)52.8 (16.8)34.5 (8.0)47.2 (6.4)56.6 (6.8)72.8 (13.9)  NA
 Sex (% females/males)62/3862/3862/3862/3862/381.00
 Age (years)59.6 (10.1)60.8 (10.6)60.0 (9.7)59.1 (10.6)58.6 (9.2)< 0.01
 BMI (kg/m2)26.3 (3.8)27.0 (4.4)26.5 (3.9)26.0 (3.6)25.7 (3.3)< 0.01
 Former smokers (%)5046494955< 0.01
 Physical activity score 3.4 (2.4)3.2 (2.4)3.3 (2.3)3.5 (2.3)3.7 (2.4)< 0.01
 Serum PTH measured (n)1938563518446411  NA
 Serum PTH [pmol/l, median (interquartile range)]2.7 (1.5)3.0 (1.9)2.7 (1.4)2.5 (1.3)2.3 (1.4)< 0.01
Smokers
 n1962473488507494  —
 Serum 25(OH)D (nmol/l, range)5.0–179.55.0–67.252.9–79.364.4–91.376.0–179.5  —
 Serum 25(OH)D (nmol/l)73.0 (20.3)49.9 (9.3)65.4 (5.9)77.1 (6.5)98.6 (15.2)  NA
 Sex (% females/males)60/4060/4055/4559/4164/360.13
 Age (years)57.3 (10.3)57.7 (11.6)57.2 (10.6)57.3 (9.6)56.8 (9.4)0.22
 BMI (kg/m2)24.7 (3.7)25.1 (4.3)24.9 (3.7)24.7 (3.4)24.1 (3.2)< 0.01
 Physical activity score 3.1 (2.4)3.0 (2.4)3.0 (2.3)3.2 (2.4)3.2 (2.4)0.12
 Number of years smoked34.2 (11.5)33.3 (12.6)34.1 (11.7)34.8 (11.1)34.4 (10.8)0.08
 Number of cigarettes/day11.3 (6.1)10.3 (5.6)11.4 (6.5)11.8 (6.0)11.9 (6.2)< 0.01
 Serum PTH measured (n)890252212225201  NA
 Serum PTH [pmol/l, median (interquartile range)]2.3 (1.4)2.6 (1.9)2.3 (1.6)2.1 (1.2)2.0 (1.3)< 0.01

Type 2 DM was registered in 183 participants (4.4%) of the non-smokers and in 64 (3.3%) of the smokers during the follow-up period. Year of diagnosis was known for 144 of the non-smokers and 47 of the smokers. Age- and sex-adjusted survival analyses revealed an increased hazard ratio for being diagnosed with Type 2 DM throughout the follow-up period for both non-smokers and smokers in the first quartile compared with the fourth, and for smokers also in the second quartile (Tables 2 and 3). However, after additional adjustment for BMI, the hazard ratios were attenuated and no longer significant. Further adjustment for physical activity score and, for non-smokers, former smoking status did not change the results, nor did adjustment for serum PTH in the subgroup where this was measured (data not shown). There was no statistical interaction between sex and quartile of serum 25(OH)D in relation to Type 2 DM (= 0.65), and the results were, as expected, similar when the analyses were performed stratified by sex (data not shown).

Table 2.   The hazard ratios for developing Type 2 diabetes mellitus during 11 years of follow-up in relation to serum 25(OH)D quartile at baseline in the Tromsø Study 1994–95 in 4157 non-smokers
 Cox regression, model 1Cox regression, model 2Cox regression, model 3
HR95% CIP-valueHR95% CIP-valueHR95% CIP-value
  1. Abbreviations: BMI, body mass index; CI, confidence interval; HR, hazard ratio; and 25(OH)D, 25-hydroxyvitamin D.

  2. *To account for seasonal variations, quartiles of serum 25(OH)D were created within each month of blood sampling and thereafter pooled.

Quartiles* of serum 25(OH)D (events/person years at risk)
 Quartile 4 (ref) (34/10 201)111
 Quartile 3 (34/10 160) 1.000.62–1.611.000.950.59–1.530.850.940.59–1.510.81
 Quartile 2 (51/10 035)1.500.97–2.310.071.290.83–1.990.261.270.82–1.970.28
 Quartile 1 (64/9827)1.891.25–2.88< 0.011.400.91–2.140.121.370.89–2.100.15
Age (years)1.021.00–1.040.021.021.00–1.030.071.011.00–1.030.19
 Sex (female = 0, male = 1)1.310.97–1.750.081.531.14–2.07< 0.011.611.16–2.24< 0.01
 BMI (kg/m2)1.171.14–1.21< 0.011.171.13–1.20< 0.01
 Physical activity score 0.910.84–0.97< 0.01
Former smoking (0 = no, 1 = yes)1.120.82–1.540.48
Table 3.   The hazard ratios for developing Type 2 diabetes mellitus during 11 years of follow-up in relation to serum 25(OH)D quartile at baseline in the Tromsø Study 1994–95 in 1962 smokers
 Cox regression, model 1*Cox regression, model 2*Cox regression, model 3*
HR95% CIP-valueHR95% CIP-valueHR95% CIP-value
  1. Abbreviations: BMI, body mass index; CI, confidence interval; HR, hazard ratio; and 25(OH)D, 25-hydroxyvitamin D.

  2. *Owing to an apparent dose-dependent interference with smoke in the serum 25(OH)D assay, all the models were adjusted for self-reported number of cigarettes and years of smoking.

  3. †To account for seasonal variations, quartiles of serum 25(OH)D were created within each month of blood sampling and thereafter pooled.

Quartiles† of serum 25(OH)D (events/person years at risk)
 Quartile 4 (ref) (8/4705)111
 Quartile 3 (16/4859)1.790.77–4.190.181.500.66–3.610.321.550.66–3.640.31
 Quartile 2 (19/4441)2.331.02–5.350.051.840.80–4.230.151.760.76–4.050.19
 Quartile 1 (21/4395)2.681.18–6.080.021.500.64–3.550.361.470.62–3.480.38
Age (years)1.030.99–1.060.111.020.99–1.060.271.020.98–1.050.39
 Sex (female = 0, male = 1)2.681.55–4.62< 0.012.731.57–4.73< 0.012.971.71–5.20< 0.01
 BMI (kg/m2)1.251.19–1.32< 0.011.241.18–1.31< 0.01
 Physical activity score 0.890.78–1.010.06

Exclusion of the participants with diabetes having unknown year of diagnosis did not change the results substantially (data not shown). When performing logistic regression, the risk estimates (odds ratios) and significance levels were closely similar to what we found in the Cox regression model presented in the tables (data not shown).

Using serum 25(OH)D as a continuous variable with adjustment for month of blood sampling, age- and sex-adjusted analyses again revealed a 14 and 15% reduction in incident Type 2 DM per 10 nmol/l increase in serum 25(OH)D in non-smokers and smokers, respectively (Table 4). However, after additional adjustment for BMI, the estimates were again reduced and no longer significant (Table 4).

Table 4.   The hazard ratios for developing Type 2 diabetes mellitus during 11 years of follow-up in relation to serum 25(OH)D at baseline in the Tromsø Study 1994–95 in 4157 non-smokers and 1962 smokers; Cox regression model with serum 25(OH)D as continuous variable
 HR (95% CI) for developing Type 2 DM, non-smokersHR (95% CI) for developing Type 2 DM, smokers†
  1. Abbreviations: BMI, body mass index; CI, confidence interval; DM, diabetes mellitus; HR, hazard ratio; 25(OH)D, 25-hydroxyvitamin D.

  2. *P < 0.05.

  3. **P < 0.01.

  4. †Owing to an apparent dose-dependent interference with smoking in the serum 25(OH)D assay, all the models are adjusted for self-reported number of cigarettes and years of smoking in smokers.

Serum 25(OH)D (per 10 nmol/l)0.89 (0.81–0.97)*0.86 (0.75–0.98)*
+ Month of blood sampling0.86 (0.78–0.94)**0.83 (0.73–0.96)*
+ Age (years)0.87 (0.78–0.96)**0.84 (0.73–0.96)*
+ Sex (female = 0, male = 1)0.86 (0.78–0.95)**0.85 (0.74–0.98)*
+ Physical activity score0.87 (0.78–0.96)**0.86 (0.75–0.98)*
+ BMI (kg/m2)0.95 (0.86–1.05)0.96 (0.83–1.12)

When we stratified the participants into quartiles of BMI, we found that for both smokers and non-smokers, the hazard ratio for developing Type 2 DM decreased significantly with increasing serum 25(OH)D in the lowest (leanest) BMI quartile, while there was no effect in the other BMI quartiles (Table 5). Additional adjustment for BMI did not change the results (data not shown).

Table 5.   The hazard ratios for developing Type 2 diabetes mellitus during 11 years of follow-up in relation to serum 25(OH)D at baseline in the Tromsø Study 1994–95 in 4157 non-smokers and 1962 smokers, stratified by BMI quartiles
 BMI quartile
1234
  1. Abbreviations: BMI, body mass index; and 25(OH)D, 25-hydroxyvitamin D.

  2. If not otherwise stated, data shown are hazard ratios (95% confidence intervals) for developing Type 2 diabetes mellitus for each 10 nmol/l increase in serum 25(OH)D.

  3. *P < 0.05.

  4. †Owing to an apparent dose-dependent interference with smoke in the serum 25(OH)D assay, all the models are adjusted for self-reported number of cigarettes and years of smoking in smokers.

BMI range (kg/m2) 11.9–23.1 23.2–25.425.5–28.228.3–54.7
Non-smokers
 Events/subjects7/85020/104342/1142114/1122
 Serum 25(OH)D0.67 (0.42–1.07)1.06 (0.83–1.36)1.01 (0.83–1.36)0.94 (0.83–1.06)
 + Month of blood sampling0.62 (0.40–0.97)*1.05 (0.82–1.36)0.95 (0.78–1.17)0.92 (0.81–1.05)
 + Age (years)0.65 (0.42–1.02)1.07 (0.83–1.36)0.96 (0.78–1.18)0.92 (0.82–1.05)
 + Sex (female = 0, male = 1)0.54 (0.33–0.90)*1.05 (0.81–1.34)0.95 (0.78–1.17)0.91 (0.80–1.04)
 + Physical activity score0.56 (0.34–0.92)*1.06 (0.83–1.36)0.96 (0.78–1.18)0.91 (0.80–1.04)
Smokers†
 Events/subjects6/7055/524 21/40532/328
 Serum 25(OH)D0.64 (0.42–0.98)*0.89 (0.56–1.42)1.07 (0.84–1.34)0.87 (0.71–1.07)
 + Month of blood sampling0.61 (0.39–0.96)*0.94 (0.59–1.49)1.09 (0.84–1.41)0.83 (0.67–1.02)
 + Age (years)0.60 (0.38–0.94)*0.96 (0.60–1.52)1.08 (0.84–1.41)0.83 (0.67–1.03)
 + Sex (female = 0, male = 1)0.62 (0.39–0.98)*0.96 (0.61–1.52)1.08 (0.84–1.45)0.81 (0.64–1.02)
 + Physical activity score0.62 (0.39–0.98)*0.99 (0.61–1.58)1.09 (0.84–1.42)0.83 (0.65–1.04)

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and methods
  5. Results
  6. Discussion
  7. Competing interests
  8. Acknowledgements
  9. References

In this prospective observational study, we found an increased 11 year risk of Type 2 DM for both smoking and non-smoking participants with serum 25(OH)D concentrations in the lower quartiles and, conversely, the risk of incident Type 2 DM during follow-up decreased with increasing serum 25(OH)D concentrations. These associations were attenuated, and no longer significant, after adjustment for BMI. However, stratified analyses within BMI quartiles still showed a significant decrease in risk of developing Type 2 DM with increasing serum 25(OH)D concentration in the lowest BMI quartile in both smokers and non-smokers.

An advantage of our study is the inclusion of a large number of both men and women in a wide age range. A high attendance rate of nearly 80% at both visits of the Tromsø Study 1994–95 minimized the risk of selection bias and thereby increased the external validity, at least among Caucasians. Attendance rate was lower in the youngest and oldest age groups, as only 52 and 54% of the invited participants in the age ranges 25–34 and 75–84 years, respectively, attended the second visit of the study. However, previous reports from the Tromsø Study have not found any significant differences regarding self-reported health between participants who only attended the first visit and participants attending both the first and second visit [20].

The diabetes registration was thorough, and included also non-pharmacologically treated participants with Type 2 DM. However, we must expect some misclassification of diabetes because of the limitation of the registry for validation through hospital medical records. Since the local hospital provides the only laboratory in the municipality, the diabetes registration committee also had HbA1c measurements taken by the family doctors available when validating the possible cases, thus increasing the quality of the registry.

The use of serum 25(OH)D as a predictor reflects both nutritional and cutaneous sources of vitamin D, and is therefore a better marker of available vitamin D in the body than self-reported intake of vitamin D [19]. The immunometric method used for analyses of vitamin D does not recognize ergocalciferol (25(OH)D2) [24]. This is not a concern in this study, as food fortification and over-the-counter vitamin D supplements for adults in Norway consist of cholecalciferol (vitamin D3) exclusively. Of greater concern is that compounds from cigarette smoking seem to interfere with the assay in a dose-dependent way [21], which makes the serum 25(OH)D measurements in smokers uncertain. Although attempts were made to adjust for this bias, the results in smokers must be interpreted with caution. Another limitation of the study was the relatively high serum 25(OH)D concentration in the population, which might have hidden a possible effect of low serum 25(OH)D concentrations on risk of diabetes. We also had only one serum 25(OH)D measurement available, and although the tracking of serum 25(OH)D has been shown to be of the same magnitude as for other risk factors, such as serum lipids and blood pressure [25], repeated serum 25(OH)D measurements would have strengthened the study.

This study does not confirm the findings from a Finnish nested case–control study with 17 years of follow-up, where the authors reported an adjusted odds ratio for developing Type 2 DM of 0.28 [95% confidence interval (CI) 0.10–0.81] in men when comparing the highest vs. the lowest quartile of baseline serum 25(OH)D [18]. In women, there was, however, no association (odds ratio 1.14; 95% CI 0.60–2.17) [18]. Their diabetes registration was based on prescription registers only, and thus did not include diet-controlled diabetes. Compared with the Finnish study, our cohort was older and had higher serum 25(OH)D concentrations (a mean serum 25(OH)D concentration of 34.5 nmol/l in the first quartile compared with 22.3 nmol/l in the Finnish study). Their follow-up time was also longer (17 vs. 11 years), and the number of cases higher (412 vs. 183). However, in a previous work that included only one of the two cohorts used in the nested case–control study, the results were similar to ours, as the lower risk of Type 2 DM in the highest serum 25(OH)D quartile was attenuated and no longer significant after adjustment for BMI [17]. Our loss of significance after adjustment for BMI might therefore be due to lack of power.

The inverse relationship between serum 25(OH)D and BMI is well known and consistently described, and it is generally believed to be due to sequestration and/or storage of vitamin D and its metabolites in fat tissue [26]. It has been argued that vitamin D deficiency may be the cause and not the result of overweight [27] and, in that case, it might be reasonable not to adjust for BMI, as the BMI could be an intermediate step between vitamin D and diabetes and thus, not a confounder. However, two recently published randomized controlled trials with vitamin D supplementation in overweight patients do not support a causal effect of vitamin D on weight [28,29]. Our results showing that serum 25(OH)D was associated with decreased risk of Type 2 DM in the leanest quartile suggest that although much of the association between serum 25OH)D and subsequent Type 2 DM is mediated through BMI, there might still be an impact of 25(OH)D independent of BMI. This is consistent with the results of two minor studies where insulin sensitivity improved after vitamin D supplementation without any concomitant change in BMI [30,31]. One could also speculate that the well-known association between increased BMI and elevated risk of Type 2 DM is so strong that it excludes the possibility of finding an effect of serum 25(OH)D in the higher BMI quartiles. However, as the number of events was low in the leanest BMI quartiles, these results must be interpreted with caution.

Several epidemiological studies have reported an inverse cross-sectional relationship persisting after adjustment for BMI between serum 25(OH)D concentrations and prevalent diabetes [16], metabolic syndrome [9,10,32–34], fasting glucose [9–12] and insulin resistance [10,13–16]. Also, in the Ely Study, including 524 British non-diabetic men and women, baseline serum 25(OH)D was inversely associated with 10 year risk of hyperglycaemia and insulin resistance independent of baseline BMI [10]. However, stratified analyses by high/low insulin-like growth factor binding protein 1 showed that the association between baseline serum 25(OH)D and hyperglycaemia was only significant in participants with lower insulin-like growth factor binding protein 1 [10]. No association between serum 25(OH)D and metabolic syndrome was found in the Rancho Bernardo Study, where an inverse association between serum 25(OH)D and glucose was described in men (n = 410) only, and not in women (n = 660) [35].

In the few intervention studies performed, which are mostly small, short-term and, in most cases, without control groups, the results are inconsistent regarding insulin sensitivity and secretion after treatment with various forms of vitamin D with or without calcium [7,36–40]. A larger study, including 314 participants, reported improvement in fasting glucose concentrations and homeostasis model assessment of insulin resistance scores in participants randomized to 500 mg calcium and 700 IU cholecalciferol vs. placebo, but only in women with baseline impaired fasting glucose [41]. In the Women’s Health Initiative trial, there was no effect of a daily dose of 1000 mg calcium and 400 IU vitamin D vs. placebo regarding 7 years risk of incident diabetes in a total of 33 951 women [42], nor did the RECORD study show any beneficial effect of intake of 800 IU vitamin D for 24–62 months compared with placebo on incident Type 2 DM in 5292 elderly participants [43]. Those two studies were originally designed for skeletal outcomes, and the dosages used might be too low to reach a substantial increase in serum 25(OH)D concentration. Smaller controlled trials, using high-dosage vitamin D supplementation among diabetic patients, have so far shown no effect on glycaemic control or insulin sensitivity [44]; however, in high-risk patients, a significant decrease in insulin resistance has been reported [30,31].

How can these inconsistent results be explained? Epidemiological studies on vitamin D are challenging. One well-known problem is the use of different laboratory methods for serum 25(OH)D measurements, with no universal gold standard. Another problem is how to handle the seasonality of serum 25(O)D, as it is strongly influenced by UVB radiation from sunlight. Most studies, like ours, have only one serum 25(OH)D measurement available per participant. In many studies, the whole cohort is stratified into quartiles of vitamin D, and the authors thereafter try to account for seasonal variation by adjusting for season or month of blood sampling. However, a recently published study using simulated models reported that this method might result in a bias away from null [23]. This could be avoided by using season- or month-specific quartiles, thereby putting more weight on the relative serum 25(OH)D concentrations instead of the absolute concentrations [23]. This is debatable, as we cannot assume that the seasonal variation will be the same for all participants. Also, we do not know what is the most important for different health outcomes, the mean serum 25(OH)D concentration over time or the length of time with serum 25(OH)D concentrations below a critical threshold. The results of the studies may also differ due to different populations included, with different serum 25(OH)D concentrations and diabetes risk profiles. Finally, residual confounding might explain the positive results in a number of observational studies, as vitamin D status or supplementation might be considered as a marker of a health-conscious lifestyle, so that other, not measured, factors are the real protective ones.

In conclusion, we have demonstrated that lower baseline serum 25(OH)D concentrations are associated with a higher 11 year risk of Type 2 DM in a prospective population-based study; however, this finding was no longer significant after adjustment for BMI. Epidemiological studies in this particular field are challenging for a number of reasons, and long-term randomized controlled trials, as well as prospective follow-up studies from a young age, using repeated serum 25(OH)D measurements, are needed to assess the possible role of vitamin D status over time and of long-term vitamin D supplementation in the prevention of Type 2 DM.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and methods
  5. Results
  6. Discussion
  7. Competing interests
  8. Acknowledgements
  9. References

We are grateful to the Tromsø Study for providing us with access to the data material. This work was supported by grants from the Norwegian Council of Cardiovascular Diseases and from the Northern Norway Regional Health Authority.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Subjects and methods
  5. Results
  6. Discussion
  7. Competing interests
  8. Acknowledgements
  9. References
  • 1
    Stumvoll M, Goldstein BJ, Van Haeften T. Type 2 diabetes: principles of pathogenesis and therapy. Lancet 2005; 365: 13331346.
  • 2
    Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 2004; 27: 10471053.
  • 3
    Fujimoto WY. The importance of insulin resistance in the pathogenesis of type 2 diabetes mellitus. Am J Med 2000; 108: 9S14S.
  • 4
    Bland R, Markovic D, Hills CE, Hughes SV, Chan SL, Squires PE et al. Expression of 25-hydroxyvitamin D3-1α-hydroxylase in pancreatic islets. J Steroid Biochem Mol Biol 2004; 90: 121125.
  • 5
    Johnson JA, Grande JP, Roche PC, Kumar R. Immunohistochemical localization of the 1,25(OH)2D3 receptor and calbindin D28k in human and rat pancreas. Am J Physiol 1994; 267: E356E360.
  • 6
    Cade C, Norman AW. Vitamin D3 improves impaired glucose tolerance and insulin secretion in the vitamin D-deficient rat in vivo. Endocrinology 1986; 119: 8490.
  • 7
    Gedik O, Akalin S. Effects of vitamin D deficiency and repletion on insulin and glucagon secretion in man. Diabetologia 1986; 29: 142145.
  • 8
    Palomer X, González-Clemente JM, Blanco-Vaca F, Mauricio D. Role of vitamin D in the pathogenesis of type 2 diabetes mellitus. Diabetes Obes Metab 2007; 10: 185197.
  • 9
    Ford ES, Ajani UA, McGuire LC, Liu S. Concentrations of vitamin D and the metabolic syndrome among U.S. adults. Diabetes Care 2005; 28: 12281230.
  • 10
    Forouhi NG, Luan J, Cooper A, Boucher BJ, Wareham NJ. Baseline serum 25-hydroxy vitamin D is predictive of future glycemic status and insulin resistance. Diabetes 2008; 57: 26192625.
  • 11
    Need AG, O’Loughlin PD, Horowitz M, Nordin BE. Relationship between fasting serum glucose, age, body mass index and serum 25 hydroxyvitamin D in postmenopausal women. Clin Endocrinol 2005; 62: 738741.
  • 12
    Liu E, Meigs JB, Pittas AG, McKeown NM, Economos CD, Booth SL et al. Plasma 25-hydroxyvitamin D is associated with markers ofthe insulin resistant phenotype in nondiabetic adults. J Nutr 2009; 139: 329334.
  • 13
    Baynes KC, Boucher BJ, Feskens EJ, Kromhout D. Vitamin D, glucose tolerance and insulinaemia in elderly men. Diabetologia 1997; 40: 344347.
  • 14
    Chiu KC, Chu A, Go VL, Saad MF. Hypovitaminosis D is associated with insulin resistance and β cell dysfunction. Am J Clin Nutr 2004; 79: 820825.
  • 15
    Kamycheva E, Jorde R, Figenschau Y, Haug E. Insulin sensitivity in subjects with secondary hyperparathyroidism and the effect of a low serum 25-hydroxyvitamin D level on insulin sensitivity. J Endocrinol Invest 2007; 30: 126132.
  • 16
    Scragg R, Sowers M, Bell C. Serum 25-hydroxyvitamin D, diabetes, and ethnicity in the Third National Health and Nutrition Examination Survey. Diabetes Care 2004; 27: 28132818.
  • 17
    Mattila C, Knekt P, Männistö S, Rissanen H, Laaksonen MA, Montonen J et al. Serum 25-hydroxyvitamin D concentration and subsequent risk of type 2 diabetes. Diabetes Care 2007; 30: 25692570.
  • 18
    Knekt P, Laaksonen M, Mattila C, Härkänen T, Marniemi J, Heliövaara M et al. Serum vitamin D and subsequent occurrence of type 2 diabetes. Epidemiology 2008; 19: 666671.
  • 19
    Pittas AG, Lau J, Hu FB, Dawson-Hughes B. The role of vitamin D and calcium in type 2 diabetes. A systematic review and meta-analyses. J Clin Endocrinol Metab 2007; 92: 20172029.
  • 20
    Berntsen GK, Fønnebø V, Tollan A, Søgaard AJ, Magnus JH. Forearm bone mineral density by age in 7,620 men and women. The Tromsø study, a population-based study. Am J Epidemiol 2001; 153: 365373.
  • 21
    Grimnes G, Almås B, Eggen AE, Emaus N, Figenschau Y, Hopstock L et al. Effect of smoking on the serum levels of 25-hydroxyvitamin D depends on the assay employed. Eur J Endocrinol 2010 May 17. (Epub ahead of print).
  • 22
    Vikan T, Schirmer H, Njølstad I, Svartberg J. Low testosterone levels and SHBG levels and high estradiol levels are independent predictors of type 2 diabetes in men. Eur J Endocrinol 2010; 162: 747754.
  • 23
    Wang Y, Jacobs EJ, McCullough ML, Rodriguez C, Thun MJ, Calle EE et al. Comparing methods for accounting for seasonal variability in a biomarker when only a single sample is available: insights from simulations based on serum 25-hydroxyvitamin D. Am J Epidemiol 2009; 170: 8894.
  • 24
    Leino A, Turpeinen U, Koskinen P. Automated measurement of 25-OH vitamin D3 on the Roche Modular E170 analyzer. Clin Chem 2008; 54: 20592062.
  • 25
    Jorde R, Sneve M, Hutchinson M, Emaus N, Figenschau Y, Grimnes G. Tracking of serum 25-hydroxyvitamin D levels during 14 years in a population-based study and during 12 months in an intervention study. Am J Epidemiol 2010; 171: 903908.
  • 26
    Wortsman J, Matsuoka LY, Chen TC, Lu Z, Holick MF. Decreased bioavailability of vitamin D in obesity. Am J Clin Nutr 2000; 72: 690693.
  • 27
    Foss YJ. Vitamin D deficiency is the cause of common obesity. Med Hypotheses 2009; 72: 314321.
  • 28
    Sneve M, Figenschau Y, Jorde R. Supplementation with cholecalciferol does not result in weight reduction in overweight and obese subjects. Eur J Endocrinol 2008; 159: 675684.
  • 29
    Zittermann A, Frisch S, Berthold HK, Götting C, Kuhn J, Kleesiek K et al. Vitamin D supplementation enhances the beneficial effects of weight loss on cardiovascular disease risk markers. Am J Clin Nutr 2009; 89: 13211327.
  • 30
    Von Hurst PR, Stonehouse W, Coad J. Vitamin D supplementation reduces insulin resistance in South Asian women living in New Zealand who are insulin resistant and vitamin D deficient – a randomised, placebo-controlled trial. Br J Nutr 2009; 28: 17.
  • 31
    Nagpal J, Pande JN, Bhartia A. A double-blind, randomized, placebo-controlled trial of the short-tern effect of vitamin D3 supplementation on insulin sensitivity in apparently healthy, middle-aged, centrally obese men. Diabet Med 2009; 26: 1927.
  • 32
    Reis JP, Von Mühlen D, Miller ER. Relation of 25-hydroxyvitamin D and parathyroid hormone levels with metabolic syndrome among US adults. Eur J Epidemiol 2008; 159: 4148.
  • 33
    Hyppönen E, Boucher BJ, Berry DJ, Power C. 25-Hydroxyvitamin D, IGF-1, and metabolic syndrome at 45 years of age: a cross-sectional study in the 1958 British Birth Cohort. Diabetes 2008; 57: 298305.
  • 34
    Reis JP, Von Mühlen D, Miller ER 3rd, Michos ED, Appel LJ. Vitamin D status and cardiometabolic risk factors in the United States adolescent population. Pediatrics 2009 Aug 3. (Epub ahead of print).
  • 35
    Reis JP, Von Mühlen D, Kritz-Silverstein D, Wingard DL, Barrett-Connor E. Vitamin D, parathyroid hormone levels, and the prevalence of metabolic syndrome in community-dwelling older adults. Diabetes Care 2007; 30: 15491555.
  • 36
    Borissova AM, Tankova T, Kirilov G, Dakovska L, Kovacheva R. The effect of vitamin D3 on insulin secretion and peripheral insulin sensitivity in type 2 diabetic patients. Int J Clin Pract 2003; 57: 258261.
  • 37
    Lind L, Pollare T, Hvarfner A, Lithell H, Sørensen OH, Ljunghall S. Long-term treatment with active vitamin D (alphacalcidiol) in middle-aged men with impaired glucose tolerance. Effects on insulin secretion and sensitivity, glucose tolerance and blood pressure. Diabetes Res 1989; 11: 141147.
  • 38
    Orwoll E, Riddle M, Prince M. Effects of vitamin D on insulin and glucagon secretion in non-insulin-dependent diabetes mellitus. Am J Clin Nutr 1994; 59: 10831087.
  • 39
    Tai K, Need AG, Horowitz M, Chapman IM. Glucose tolerance and vitamin D: effects of treating vitamin D deficiency. Nutrition 2008; 24: 950956.
  • 40
    Taylor AV, Wise PH. Vitamin D replacement in Asians with diabetes may increase insulin resistance. Postgrad Med J 1998; 74: 365366.
  • 41
    Pittas AG, Harris SS, Stark PC, Dawson-Hughes B. The effect of calcium and vitamin D supplementation on blood glucose and markers of inflammation in non-diabetic adults. Diabetes Care 2007; 30: 980986.
  • 42
    DeBoer IH, Tinker LF, Connelly S, Curb JD, Howard BV, Kestenbaum B et al. Calcium plus vitamin D supplementation and the risk of incident diabetes in the Women’s Health Initiative. Diabetes Care 2008; 31: 701707.
  • 43
    Avenell A, Cook JA, MacLennan GS, McPherson GC. Vitamin D supplementation and type 2 diabetes: a substudy of a randomised placebo-controlled trial in older people (RECORD trial, ISRCTN 51647438). Age Ageing 2009; 38: 606609.
  • 44
    Jorde R, Figenschau Y. Supplementation with cholecalciferol does not improve glycaemic control in diabetic subjects with normal serum 25-hydroxyvitamin D levels. Eur J Nutr 2009; 48: 349354.