Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

No association between the vitamin D-binding protein (DBP) gene polymorphisms (rs7041 and rs4588) and multiple sclerosis and type 1 diabetes mellitus: A meta-analysis

  • Xin Zhang,

    Roles Data curation, Formal analysis, Methodology, Resources, Software, Visualization, Writing – original draft

    Affiliation Department of Neurology, Shenyang First People’s Hospital, Shenyang, Liaoning Province, China

    ⨯
  • Bai Gao,

    Roles Investigation, Methodology, Validation, Writing – original draft

    Affiliation Department of Nerve Function, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China

    ⨯
  • Bing Xu

    Roles Conceptualization, Project administration, Supervision, Writing – original draft, Writing – review & editing

    xb1968131@163.com

    Affiliation Department of Neurology, Shenyang First People’s Hospital, Shenyang, Liaoning Province, China

    ⨯

Abstract

Background

The association between polymorphisms in vitamin D-binding protein (DBP) gene and the risk of multiple sclerosis (MS) and type 1 diabetes mellitus (T1DM) has been investigated in many studies, but the studies showed controversial results. The rationale for this meta-analysis was to determine whether DBP polymorphisms increases the risk of MS and T1DM by pooling data.

Methods

Potentially relevant studies were searched using GWAS Catalog, PubMed, Embase, CNKI and WANFANG databases up to November 2019. The pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were performed to estimate the associations in a fixed-effects or random-effects model.

Results

A total of 13 studies were enrolled in this meta-analysis, including eight studies for MS and five for T1DM. The overall results showed that there was no significant association of DBP rs7041 and rs4588 polymorphisms with the risk of MS and T1DM under any genetic model. Similarly, subgroup analysis by ethnicity revealed that no significant association of rs7041 and rs4588 polymorphisms with the risk of MS and T1DM was observed in white or non-white racial groups.

Conclusions

This meta-analysis provides evidence that DBP rs7041 and rs4588 polymorphisms may not be associated with an increased risk in MS and T1DM. However, these findings need further validation by larger-scale epidemiological studies and genome-wide association studies (GWASs) in different populations.

Citation: Zhang X, Gao B, Xu B (2020) No association between the vitamin D-binding protein (DBP) gene polymorphisms (rs7041 and rs4588) and multiple sclerosis and type 1 diabetes mellitus: A meta-analysis. PLoS ONE 15(11): e0242256. https://doi.org/10.1371/journal.pone.0242256

Editor: Joseph Devaney, GeneDx, UNITED STATES

Received: March 4, 2020; Accepted: October 29, 2020; Published: November 12, 2020

Copyright: © 2020 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: CIs, confidence intervals; CNS, central nervous system; DBP, vitamin D-binding protein; GC, group-specific component; GWASs, genome-wide association studies; HWE, Hardy-Weinberg equilibrium; MESH, medical subjective headings; MS, multiple sclerosis; NOS, Newcastle-Ottawa Scale; ORs, odds ratios; PRISMA, Systematic Reviews and Meta-Analyses; SNPs, single nucleotide polymorphisms; T1DM, type 1 diabetes mellitus; Th17, T helper 17

Introduction

Autoimmune diseases are multifactorial with combination of genetic susceptibility and environmental factors leading to the etiology of diseases [1, 2]. The hypothesis that these diseases may share common genetic susceptibility loci has been supported by the epidemiological observation of co-occurrence of multiple autoimmune diseases within a single family [3, 4]. Multiple sclerosis (MS) and Type 1 diabetes mellitus (T1DM) are classified as autoimmune diseases caused by immune response against self-antigens, which lead to immune-mediated damage of self-tissues and organs [5, 6]. MS is an autoimmune demyelinating disease of the central nervous system (CNS) [5]. T1DM is a metabolic disease characterized by absolute insulin deficiency resulting from autoimmune destruction of pancreatic β-cell that produce insulin [6]. Although the etiologies of these autoimmune diseases are not well understood, complex interactions between the environmental triggers and genetic factors have been determined.

The serum vitamin D-binding protein (DBP, group-specific component, GC) is known to function as an immunomodulatory factor, as well as the important carrier protein for vitamin D and its biologically active metabolite, 1,25(OH)2D3 [7]. It has now been shown that vitamin D has a wide range of immune actions [8, 9]. The DBP binds to vitamin D and its metabolites and transports them to various target tissues [10]. When the DBP gene mutations occur, serum vitamin D level will be decreased though the patient having sufficient sun exposure or vitamin D supplement. Two single nucleotide polymorphisms (SNPs) of the DBP gene, rs7041 and rs4588, have been widely studied [11–13]. GAT→GAG substitution at rs7041 leads to aspartic acid to glutamic acid and ACG→AAG substitution at rs4588 leads to changes of amino acid threonine for Lysine. These two mutations have lower binding capacity for vitamin D [14], and vitamin D deficiency can be linked to the pathophysiology of autoimmune diseases [15]. Although the precise etiology of MS remains unclear, the subset of CD4+ T cells, T helper 17 (Th17) cells have been proposed to play a significant role in inflammatory response [16]. Deficiency of vitamin D and 1,25(OH)2D3 fails to inhibit Th17 cells proliferation, which produce IL-17, TNF-α. Macrophages into the CNS ultimately lead to the pathogenesis of MS [17]. T1DM results from the destruction of insulin-producing β-cells within the islets of Langerhans in the pancreas [18]. 1,25(OH)2D3 inhibits antigen-induced T-cell proliferation and Th1-associated cytokine production, which regulate CD8+ lymphocytes and macrophages [19]. Macrophages and CD8+ T cells infiltrates the islets’ interstitium [20], leading to β-cells loss [21]. Insufficient insulin production results in impaired blood glucose regulation, which ultimately causes T1DM.

Up to now, the association between DBP rs7041 and rs4588 polymorphisms and risk of MS and T1DM has been reported in several studies, whereas the results remains controversial. In addition, the association between rs7041 and rs4588 DBP polymorphisms and MS and T1DM risk has not been included in genome-wide association studies (GWASs) or meta-analysis studies. The rationale for this meta-analysis was to determine whether DBP polymorphisms increases the risk of MS and T1DM by pooling data. The meta-analysis was a powerful way to effectively increase the sample size to provide a more valid pooled estimate. Therefore, we performed the comprehensive meta-analysis to derive a more precise estimations of the association between DBP polymorphism and the risk of MS and T1DM.

Methods

This meta-analysis was conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 checklist (S1 Checklist) [22].

Search strategy

A comprehensive literature search was performed in GWAS Catalog, PubMed, Embase, CNKI and WANFANG databases to explore eligible literature up to November 2019. Terms “diabetes mellitus” or “multiple sclerosis”, “rs7041” or “rs4588” were used in GWAS Catalog. And in other databases, the combination of medical subjective headings (MESH) and text words were as follows: “polymorphism, genetic” or “polymorphism” or “polymorphisms” or “genome-wide association study” or “genome-wide association studies” or “GWAS” or “rs7041” or “rs4588”, “vitamin D-binding protein” or “DBP” or “group-specific component” or “GC”, “multiple sclerosis” or “diabetes mellitus”. The literature language was limited to English or Chinese and subjects were limited to humans. References from these papers and reviews were manually retrieved for additional eligible studies.

Inclusion and exclusion criteria

Inclusion criteria were: (1) studies on the associations between DBP rs7041 or rs4588 polymorphism and MS or T1DM; (2) sufficient data for calculating odds ratios (ORs) with accompanying 95% confidence intervals (CIs); (3) published case-control studies in English or Chinese. Exclusion criteria were: (1) reviews, case reports, meta-analyses, letters, and editorials; (2) studies lacking detailed genotype data; (3) overlapped data.

Data extraction and quality assessment

Two investigators extracted the following data independently from each study: first author’s name, year of publication, ethnicity, the genotype frequencies, the P-value of Hardy-Weinberg equilibrium (HWE) of controls, and Newcastle-Ottawa Scale (NOS) score [23]. Discrepancies were resolved by discussion. The NOS was carried out to assess the methodological quality of eligible case-control studies on 3 aspects: selection, comparability, and exposure. The total NOS score ranges from 0 to 9 stars, and study was assumed to be high methodological quality if the score was 6 or more.

Statistical analysis

A χ2 test was performed to measured deviation from the HWE in controls. The pooled ORs and 95% CIs were used to estimate the association between the polymorphisms in the DBP gene and risk of MS and T1DM. For DBP rs7041 polymorphism, the allele (G versus T), dominant (GG+GT versus TT), recessive (GG versus GT+TT), homozygous (GG versus TT), and heterozygous model (GG versus GT) were evaluated. For the DBP rs4588 polymorphism, the allele (A versus C), dominant (AA + AC versus CC), recessive (AA versus AC + CC), homozygous (AA versus CC), and heterozygous model (AA versus AC) were evaluated. Heterogeneity between studies was determined using χ2 based Q-test and quantified using the I2 statistics [24]. When P value> 0.1 or I2<50%, pooled ORs were calculated by fixed-effects model (Mantel-Haenszel method). Otherwise, the random-effects model (DerSimonian and Laird method) was applied [25]. Furthermore, subgroup analyses were conducted based on ethnicity (white and non-white) to obtain the sources of heterogeneity. The white group included Caucasian and European, and the non-white group consisted of Hispanic, Black, Asian and Bengali. To determine potential publication bias, the Egger’s linear regression test was used for overall genetic models [26]. If there was publication bias, we recalculated the adjusted ORs using the trim-and-fill method [27] to evaluate the possible impact of publication bias. Sensitivity analyses were carried out by removing studies deviated from HWE to assess the robustness of the results. The 2-tailed P value<0.05 was considered statistically significant. Statistical analyses were performed using Stata 15.0 software (Stata Corporation, College Station, TX, USA). Moreover, statistical power analyses were conducted using G*Power software (version 3.1.9.2) [28] to determine whether the meta-analysis had sufficient power (≥80%).

Results

Literature research and study characteristics

After a comprehensive literature search, 901 articles were initially identified, and 140 duplicate articles were removed. After checking titles and abstracts, 738 articles were excluded. A further 12 articles were excluded after checking the full text. Overall, a total of 11 articles (consisting of 13 studies) met our inclusion criteria were enrolled in our meta-analysis [29–39]. Of these 13 studies: 8 studies (6 articles) assessed the association of DBP gene polymorphisms with MS risk [29–34] and 5 studies (5 articles) investigated the association between DBP gene polymorphisms and risk of T1DM [35–39]. Furthermore, there were no GWASs and meta-analyses published in this area. GWAS catalog search yielded 805 and 323 results for the term “multiple sclerosis” and “type I diabetes mellitus” respectively. However, none of them presented results of DBP polymorphisms examined in genetic association studies. The NOS scores of studies ranged from 3 to 8 stars. The characteristics and NOS scores of included studies are shown in Table 1. Fig 1 showed the detailed screening process for the articles involved in the meta-analysis.

thumbnail
Download:
Fig 1. PRISMA flow diagram.

https://doi.org/10.1371/journal.pone.0242256.g001

thumbnail
Download:
Table 1. Characteristics of the included studies in this meta-analysis.

https://doi.org/10.1371/journal.pone.0242256.t001

DBP rs7041 and rs4588 polymorphism and MS risk

To determine the association of DBP gene polymorphism with the MS risk, 8 studies (involving 1600 cases and 1770 controls) about DBP rs7041 polymorphism and 8 studies (involving 1600 cases and 1771 controls) about DBP rs4588 polymorphism were enrolled in our meta-analysis. As shown in Table 2, no association was observed between DBP rs7041 and rs4588 gene polymorphisms and risk of MS in overall population under any genetic model. Furthermore, stratification by ethnicity failed to explore any association of these polymorphisms with risk of MS in the white and non-white racial group.

thumbnail
Download:
Table 2. Results of meta-analysis for DBP polymorphism and MS and T1DM.

https://doi.org/10.1371/journal.pone.0242256.t002

DBP rs7041 and rs4588 polymorphism and T1DM risk

To determine the potential association between DBP gene polymorphism and the risk of T1DM, 5 studies (involving 1843 cases and 2151 controls) about DBP rs7041 polymorphism and 4 studies (involving 1712 cases and 2056 controls) about DBP rs4588 polymorphism were enrolled in this meta-analysis. As shown in Table 2, no association was found between polymorphisms (rs7041 and rs4588) in the DBP gene and risk of T1DM in overall population under overall genetic models. Furthermore, stratification by ethnicity failed to explore any association between these polymorphisms and risk of T1DM in the white and non-white racial group.

Publication bias

The Egger’s linear regression test was conducted to detect the publication biases under all genetic models. For DBP rs7041 polymorphism and MS risk, the results indicated that there was evidence for publication bias in the dominant model (GG+GT vs. TT: P = 0.034). The trim and fill method revealed no difference between the unadjusted (OR = 0.93, 95% CI 0.77–1.13, P = 0.034) and adjusted (OR = 0.91, 95% CI 0.75–1.10, P = 0.322) results, which indicated that the stability of the results was not affected by the presence of publication bias. For DBP rs4588 polymorphism and MS risk, and DBP rs7041 or rs4588 polymorphism and T1DM risk, no evidence of publication bias was found.

Sensitivity analysis

The genotype distribution in the control subjects in two studies [30, 34] significantly deviated from the HWE in our meta-analysis. Sensitivity analysis was carried out by omitting the HWE-violating studies to estimate the robustness of our conclusions, and the results were materially changed after excluding the two studies (Table 2).

Statistical power analysis

Statistical power analyses were calculated to detect the powers of the association of the DBP rs7041 and rs4588 polymorphism with the risk of MS and T1DM with α = 0.05 and β = 0.2. The power of each pooled result ranged from 5 to 17%, and the specific power values are summarized in Table 2.

Discussion

Our study demonstrated that there not be any statistically significant difference for rs7041 or rs4588 alleles and genotypes of DBP gene between MS or T1DM and control.

There should be differences between Asian and the other ethnicities in non-white racial groups. Unfortunately, there were insufficient number of studies in different ethnicities. For DBP polymorphism and MS risk, 1 study involving Hispanic, 1 study involving Black, 2 studies involving Asian, and 4 studies involving white were enrolled. For DBP polymorphism and T1DM risk, 5 studies involving white were enrolled. Because of insufficient number of studies in different ethnicities, study population was categorized as being white and non-white racial groups on basis of ethnicity in this study. The former group included Caucasian and European, and the latter group consisted of Hispanic, Black, Asian and Bengali. The findings of our meta-analysis should be interpreted with caution in the case of limited number of studies, more studies on different ethnicities are needed in the future.

Several limitations involved in our results should be addressed. First, our study included eight articles for MS and five for T1DM, the meta-analysis may be unable to have sufficient power to identify real association because of the limited study number, especially when grouped by ethnicity. Since the power of most genetic models ranged from 5 to 17%, less than 80%, the findings of the meta-analysis may be insufficiently confirmed. Owing to the low statistical power, conclusions drawn from this meta-analysis should be interpreted cautiously. Second, the studies included in this meta-analysis can be organized into two categories according to ethnicity: white and non-white racial groups, however, the latter group consisted of different ethnicities, including Hispanic, Black, Asian and Bengali. Thus, our conclusions are only applicable to white racial groups, further studies in other ethnicities are needed. Third, selection bias may occur due to the inclusion of only English or Chinese literature. Forth, MS and T1DM are multifactorial disorders resulted from complex interactions between genetic, epigenetic, and environmental factors, suggesting that the DBP polymorphism may only partially contribute to the pathogenesis of these chronic diseases, this may lead to bias in our results. Finally, our study only determined the associations between two single loci in the DBP gene, rs7041 and rs4588 polymorphisms and the risk of MS and T1DM, but we did not examine associations between DBP gene haplotypes and these diseases due to insufficient haplotype data. It remains unclear whether other DBP gene mutations can particularly lead to changes in its expression. In terms of the genetic causes of disease, haplotypes can provide more critical information than the corresponding single SNP.

In conclusion, our meta-analysis suggests that neither rs7041 nor rs4588 polymorphism of DBP is associated with the MS and T1DM risk. Further well-designed studies with larger sample sizes and different ethnicities are needed to validate our conclusion.

Supporting information

S1 Checklist. PRISMA 2009 checklist.

https://doi.org/10.1371/journal.pone.0242256.s001

(DOC)

S2 Checklist. Meta-analysis on genetic association studies checklist.

https://doi.org/10.1371/journal.pone.0242256.s002

(DOCX)

Acknowledgments

The authors thank all the participants in this study.

References

  1. 1. Boscolo P, Youinou P, Theoharides TC, Cerulli G, Conti P. Environmental and occupational stress and autoimmunity. Autoimmunity reviews. 2008;7(4):340–343. pmid:18295740
  2. 2. Invernizzi P, Gershwin ME. The genetics of human autoimmune disease. Journal of autoimmunity. 2009;33(3–4):290–299. pmid:19682858
  3. 3. Marrack P, Kappler J, Kotzin BL. Autoimmune disease: why and where it occurs. Nature medicine. 2001;7(8):899–905. pmid:11479621
  4. 4. Davies AJ. Immunological tolerance and the autoimmune response. Autoimmunity reviews. 2008;7(7):538–543. pmid:18625442
  5. 5. Miljkovic D, Spasojevic I. Multiple sclerosis: molecular mechanisms and therapeutic opportunities. Antioxidants & redox signaling. 2013;19(18):2286–2334. pmid:23473637
  6. 6. Cheon CK, Nam HK, Lee KH, Kim SY, Song JS, Kim C. Vitamin D receptor gene polymorphisms and type 1 diabetes mellitus in a Korean population. Pediatrics international: official journal of the Japan Pediatric Society. 2015;57(5):870–874.
  7. 7. Safadi FF, Thornton P, Magiera H, Hollis BW, Gentile M, Haddad JG, et al. Osteopathy and resistance to vitamin D toxicity in mice null for vitamin D binding protein. The Journal of clinical investigation. 1999;103(2):239–251. pmid:9916136
  8. 8. Deluca HF, Cantorna MT. Vitamin D: its role and uses in immunology. FASEB journal: official publication of the Federation of American Societies for Experimental Biology. 2001;15(14):2579–2585. pmid:11726533
  9. 9. Arnson Y, Amital H, Shoenfeld Y. Vitamin D and autoimmunity: new aetiological and therapeutic considerations. Annals of the rheumatic diseases. 2007;66(9):1137–1142. pmid:17557889
  10. 10. Chun RF. New perspectives on the vitamin D binding protein. Cell biochemistry and function. 2012;30(6):445–456. pmid:22528806
  11. 11. Baier LJ, Dobberfuhl AM, Pratley RE, Hanson RL, Bogardus C. Variations in the vitamin D-binding protein (Gc locus) are associated with oral glucose tolerance in nondiabetic Pima Indians. The Journal of clinical endocrinology and metabolism. 1998;83(8):2993–2996. pmid:9709981
  12. 12. Hirai M, Suzuki S, Hinokio Y, Chiba M, Kasuga S, Hirai A, et al. Group specific component protein genotype is associated with NIDDM in Japan. Diabetologia. 1998;41(6):742–743. pmid:9662062
  13. 13. Szathmary EJ. The effect of Gc genotype on fasting insulin level in Dogrib Indians. Human genetics. 1987;75(4):368–372. pmid:3552957
  14. 14. Arnaud J, Constans J. Affinity differences for vitamin D metabolites associated with the genetic isoforms of the human serum carrier protein (DBP). Human genetics. 1993;92(2):183–188. pmid:8370586
  15. 15. Mattila C, Knekt P, Mannisto 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(10):2569–2570. pmid:17626891
  16. 16. Weiner HL. The challenge of multiple sclerosis: how do we cure a chronic heterogeneous disease? Annals of neurology. 2009;65(3):239–248. pmid:19334069
  17. 17. Jadidi-Niaragh F, Mirshafiey A. Th17 cell, the new player of neuroinflammatory process in multiple sclerosis. Scandinavian journal of immunology. 2011;74(1):1–13. pmid:21338381
  18. 18. Atkinson MA, Maclaren NK. The pathogenesis of insulin-dependent diabetes mellitus. The New England journal of medicine. 1994;331(21):1428–1436. pmid:7969282
  19. 19. Giarratana N, Penna G, Amuchastegui S, Mariani R, Daniel KC, Adorini L. A vitamin D analog down-regulates proinflammatory chemokine production by pancreatic islets inhibiting T cell recruitment and type 1 diabetes development. Journal of immunology. 2004;173(4):2280–2287. pmid:15294940
  20. 20. Ellerman KE, Like AA. Susceptibility to diabetes is widely distributed in normal class IIu haplotype rats. Diabetologia. 2000;43(7):890–898. pmid:10952462
  21. 21. Gepts W. Pathologic anatomy of the pancreas in juvenile diabetes mellitus. Diabetes. 1965;14(10):619–633. pmid:5318831
  22. 22. Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Journal of clinical epidemiology. 2009;62(10):1006–1012. pmid:19631508
  23. 23. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. European journal of epidemiology. 2010;25(9):603–605. pmid:20652370
  24. 24. Davey Smith G, Egger M. Meta-analyses of randomised controlled trials. Lancet. 1997;350(9085):1182. pmid:9343537
  25. 25. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. Journal of the National Cancer Institute. 1959;22(4):719–748. pmid:13655060
  26. 26. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. Bmj. 1997;315(7109):629–634. pmid:9310563
  27. 27. Duval S, Tweedie R. Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56(2):455–463. pmid:10877304
  28. 28. Faul F, Erdfelder E, Buchner A, Lang AG. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behavior research methods. 2009;41(4):1149–1160. pmid:19897823
  29. 29. Agliardi C, Guerini FR, Zanzottera M, Bolognesi E, Costa AS, Clerici M. Vitamin D-binding protein gene polymorphisms are not associated with MS risk in an Italian cohort. Journal of neuroimmunology. 2017;305:92–95. pmid:28284354
  30. 30. Agnello L, Scazzone C, Lo Sasso B, Bellia C, Bivona G, Realmuto S, et al. VDBP, CYP27B1, and 25-Hydroxyvitamin D Gene Polymorphism Analyses in a Group of Sicilian Multiple Sclerosis Patients. Biochemical genetics. 2017;55(2):183–192. pmid:27904983
  31. 31. Langer-Gould A, Lucas RM, Xiang AH, Wu J, Chen LH, Gonzales E, et al. Vitamin D-Binding Protein Polymorphisms, 25-Hydroxyvitamin D, Sunshine and Multiple Sclerosis. Nutrients. 2018;10(2):184. pmid:29414925
  32. 32. Simon KC, Munger KL, Xing Y, Ascherio A. Polymorphisms in vitamin D metabolism related genes and risk of multiple sclerosis. Multiple sclerosis (Houndmills, Basingstoke, England). 2010;16(2):133–138. pmid:20007432
  33. 33. Niino M, Kikuchi S, Fukazawa T, Yabe I, Tashiro K. No association of vitamin D-binding protein gene polymorphisms in Japanese patients with MS. Journal of neuroimmunology. 2002;127(1–2):177–179. pmid:12044990
  34. 34. Li XH, Li Y, Wang BQ, Liu SL. Association of DBP gene polymorphism and risk of multiple sclerosis in Han people of Shandong Province. Journal of Shandong University (Health Sciences). 2010; 48(4): 67–9,73(Chinese).
  35. 35. Kirac D, Dincer Yazan C, Gezmis H, Yaman A, Haklar G, Sirikci O, et al. VDBP, VDR Mutations and Other Factors Related With Vitamin D Metabolism May Be Associated With Type 1 Diabetes Mellitus. Cellular and molecular biology (Noisy-le-Grand, France). 2018;64(3):11–16. pmid:29506625
  36. 36. Blanton D, Han Z, Bierschenk L, Linga-Reddy MV, Wang H, Clare-Salzler M, et al. Reduced serum vitamin D-binding protein levels are associated with type 1 diabetes. Diabetes. 2011;60(10):2566–2570. pmid:21844098
  37. 37. Ongagna JC, Kaltenbacher MC, Sapin R, Pinget M, Belcourt A. The HLA-DQB alleles and amino acid variants of the vitamin D-binding protein in diabetic patients in Alsace. Clinical biochemistry. 2001;34(1):59–63. pmid:11239517
  38. 38. Ongagna JC, Pinget M, Belcourt A. Vitamin D-binding protein gene polymorphism association with IA-2 autoantibodies in type 1 diabetes. Clinical biochemistry. 2005;38(5):415–419. pmid:15820770
  39. 39. Klupa T, Malecki M, Hanna L, Sieradzka J, Frey J, Warram JH, et al. Amino acid variants of the vitamin D-binding protein and risk of diabetes in white Americans of European origin. European journal of endocrinology. 1999;141(5):490–493. pmid:10576765