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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="systematic-review">
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">Explor Endocr Metab Dis</journal-id>
<journal-id journal-id-type="publisher-id">EEMD</journal-id>
<journal-title-group>
<journal-title>Exploration of Endocrine and Metabolic Diseases</journal-title>
</journal-title-group>
<issn pub-type="epub">2998-2456</issn>
<publisher>
<publisher-name>Open Exploration Publishing</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.37349/eemd.2025.101438</article-id>
<article-id pub-id-type="manuscript">101438</article-id>
<article-categories>
<subj-group>
<subject>Meta-Analysis</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Bidirectional relationship between vitamin D deficiency and type 1/type 2 diabetes mellitus: a systematic review and meta-analysis</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0009-0009-0482-1897</contrib-id>
<name>
<surname>Huang</surname>
<given-names>Huiling</given-names>
</name>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing—original draft</role>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing—review &amp; editing</role>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
<role content-type="https://credit.niso.org/contributor-roles/methodology/">Methodology</role>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
<role content-type="https://credit.niso.org/contributor-roles/formal-analysis/">Formal analysis</role>
<role content-type="https://credit.niso.org/contributor-roles/investigation/">Investigation</role>
<role content-type="https://credit.niso.org/contributor-roles/resources/">Resources</role>
<role content-type="https://credit.niso.org/contributor-roles/supervision/">Supervision</role>
<xref ref-type="aff" rid="I1">
<sup>1</sup>
</xref>
<xref ref-type="corresp" rid="cor1">
<sup>*</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0009-0008-3518-7684</contrib-id>
<name>
<surname>Shi</surname>
<given-names>Shaomei</given-names>
</name>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
<role content-type="https://credit.niso.org/contributor-roles/formal-analysis/">Formal analysis</role>
<role content-type="https://credit.niso.org/contributor-roles/investigation/">Investigation</role>
<xref ref-type="aff" rid="I2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0009-0007-7572-6542</contrib-id>
<name>
<surname>Li</surname>
<given-names>Dongsheng</given-names>
</name>
<role content-type="https://credit.niso.org/contributor-roles/formal-analysis/">Formal analysis</role>
<role content-type="https://credit.niso.org/contributor-roles/investigation/">Investigation</role>
<xref ref-type="aff" rid="I3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="editor">
<name>
<surname>Khan</surname>
<given-names>Dawood</given-names>
</name>
<role>Academic Editor</role>
<aff>Ulster University, United Kingdom</aff>
</contrib>
</contrib-group>
<aff id="I1">
<sup>1</sup>Independent Researcher, Shenzhen 518000, Guangdong, China</aff>
<aff id="I2">
<sup>2</sup>Department of Obstetrics, Dongguan Huangjiang Hospital, Dongguan 523000, Guangdong, China</aff>
<aff id="I3">
<sup>3</sup>Department of Surgery, Dongguan Huangjiang Hospital, Dongguan 523000, Guangdong, China</aff>
<author-notes>
<corresp id="cor1">
<bold>
<sup>*</sup>Correspondence:</bold> Huiling Huang, Independent Researcher, Shenzhen 518000, Guangdong, China. <email>huilinghuangsz@163.com</email></corresp>
</author-notes>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>17</day>
<month>07</month>
<year>2025</year>
</pub-date>
<volume>2</volume>
<elocation-id>101438</elocation-id>
<history>
<date date-type="received">
<day>10</day>
<month>04</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>17</day>
<month>06</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>© The Author(s) 2025.</copyright-statement>
<license xlink:href="https://creativecommons.org/licenses/by/4.0/">
<license-p>This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.</license-p>
</license>
</permissions>
<abstract>
<sec>
<title>Background:</title>
<p id="absp-1">Diabetes mellitus and vitamin D deficiency (VDD) are widespread global health concerns with overlapping metabolic risks. Emerging evidence suggests a bidirectional relationship: VDD exacerbates insulin resistance, whereas diabetes mellitus disrupts vitamin D metabolism.</p>
</sec>
<sec>
<title>Methods:</title>
<p id="absp-2">This meta-analysis was registered prospectively (PROSPERO CRD42025639951). We conducted a comprehensive search of PubMed, Embase, Web of Science, and the Cochrane Library from their inception to January 2025 for observational studies examining the bidirectional associations between VDD and diabetes mellitus. Studies were eligible if they (1) employed cohort or case-control designs, (2) defined VDD as serum 25-hydroxyvitamin D [25(OH)D] &lt; 20 ng/mL, and (3) diagnosed diabetes mellitus according to the American Diabetes Association (ADA) criteria. Two reviewers independently extracted data and assessed study quality using the Newcastle-Ottawa scale. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using random-effects models (STATA 15.1 and RevMan 5.4).</p>
</sec>
<sec>
<title>Results:</title>
<p id="absp-3">Among 53 studies (<italic>n</italic> = 552,032), individuals with VDD had a 53% increased risk of developing type 2 diabetes mellitus (T2DM) (OR = 1.53, 95% CI: 1.38–1.70). Conversely, individuals with type 1 diabetes mellitus (T1DM) and T2DM had a 2.02-fold and 2.62-fold increased risk of VDD, respectively. Subgroup analyses demonstrated stronger associations in Asian populations (T1DM: OR = 2.21; Europe: OR = 1.65; <italic>P</italic> &lt; 0.05 for regional difference) and among normal-weight T2DM patients (OR = 7.68, compared to obese: OR = 5.21).</p>
</sec>
<sec>
<title>Discussion:</title>
<p id="absp-4">This meta-analysis reveals a bidirectional link between VDD and diabetes mellitus, emphasizing subtype- and phenotype-specific risk profiles. Clinically, routine monitoring of serum 25(OH)D levels is recommended for diabetic patients, particularly in high-risk subgroups such as individuals with T1DM or lean T2DM phenotypes, and suggests targeted vitamin D supplementation for high-risk groups. On a public health scale, fortifying staple foods with vitamin D in regions with high deficiency rates, such as Asia, could alleviate the dual burden of VDD and diabetes mellitus.</p>
</sec>
</abstract>
<abstract abstract-type="graphical">
<p>
<fig id="F0">
<label>Graphical abstract.</label>
<caption>
<p>
<bold> Vitamin D deficiency and diabetes: a bidirectional pathogenic interplay.</bold> ↑: increase/elevation; ↓: decrease/reduction. GLUT4: glucose transporter type 4; IL-6: interleukin 6; NF-κB: nuclear factor kappaB; Th17: T helper 17; TNF-α: tumor necrosis factor-alpha; Treg: regulatory T</p>
<p>
<italic>Note.</italic> Created with MedPeer (<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.medpeer.cn/" ext-link-type="uri">medpeer.cn</ext-link>)</p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="eemd-02-101438-g000.tif" />
</fig>
</p>
</abstract>
<kwd-group>
<kwd>Meta-analysis</kwd>
<kwd>vitamin D deficiency</kwd>
<kwd>prevalence</kwd>
<kwd>systematic review</kwd>
<kwd>diabetes</kwd>
</kwd-group>
</article-meta>
</front>
<body>
<sec id="s1">
<title>Introduction</title>
<p id="p-1">Diabetes mellitus and vitamin D deficiency (VDD) represent significant global public health issues. Approximately 537 million adults have diabetes, with 90–95% diagnosed with type 2 diabetes mellitus (T2DM), and type 1 diabetes mellitus (T1DM) is rising by 3–5% annually [<xref ref-type="bibr" rid="B1">1</xref>]. The International Diabetes Federation projects that the global population aged 20–79 with diabetes will reach 700 million by 2045 [<xref ref-type="bibr" rid="B2">2</xref>]. Concurrently, about 1 billion people suffer from VDD, particularly prevalent in Asia and Africa due to limited sun exposure and dietary factors [<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B4">4</xref>]. Fu et al. [<xref ref-type="bibr" rid="B5">5</xref>] demonstrated that individuals with serum 25-hydroxyvitamin D [25(OH)D] levels ≥ 75 nmol/L had a multivariable-adjusted hazard ratio of 0.62 [95% confidence interval (CI): 0.56–0.70] for T2DM compared to those with levels &lt; 25 nmol/L. Notably, within the sub-50 nmol/L range, increasing 25(OH)D concentration significantly reduces T2DM risk [<xref ref-type="bibr" rid="B5">5</xref>]. Khudayar et al. [<xref ref-type="bibr" rid="B6">6</xref>] demonstrated that 54.1% of patients with T2DM exhibited VDD, compared to 28.6% in the control group. This deficiency was significantly linked to the onset of T2DM (<italic>P</italic> &lt; 0.0001) [<xref ref-type="bibr" rid="B6">6</xref>]. Vitamin D, a steroid hormone, plays a critical role in regulating bone mineral homeostasis and calcium metabolism. The predominant circulating form of vitamin D, 25(OH)D, determines an individual’s vitamin D status [<xref ref-type="bibr" rid="B7">7</xref>]. In this study, VDD was defined as 25(OH)D levels below 20 ng/mL (50 nmol/L) as deficient, levels between 20–29 ng/mL as insufficient, and levels equal to or greater than 30 ng/mL as adequate, consistent with Endocrine Society Clinical Practice Guideline [<xref ref-type="bibr" rid="B8">8</xref>]. Experimental and observational evidence indicates that vitamin D signaling plays a role in insulin secretion and sensitivity, suggesting that VDD may elevate the risk of diabetes [<xref ref-type="bibr" rid="B9">9</xref>]. Shared modifiable risk factors, such as obesity, physical inactivity, and advanced age, may confound the VDD-T1DM/T2DM relationship.</p>
<p id="p-2">The available evidence suggests a bidirectional relationship between vitamin D and glucose homeostasis. Suboptimal vitamin D levels can impair pancreatic β-cell insulin secretion, a key process in regulating blood glucose. Additionally, VDD may diminish peripheral insulin sensitivity, thereby compromising glucose uptake by tissues, while chronic hyperglycemia and inflammation in diabetics can disrupt vitamin D metabolism [<xref ref-type="bibr" rid="B10">10</xref>]. Supporting this, Fu et al. [<xref ref-type="bibr" rid="B5">5</xref>] demonstrated a negative association between serum 25(OH)D levels and T2DM risk, even below the diabetes diagnostic threshold, and Nóvoa-Medina et al. [<xref ref-type="bibr" rid="B11">11</xref>] observed seasonal vitamin D variations and a higher deficiency prevalence in T1DM patients.</p>
<p id="p-3">Prior meta-analyses were limited by their emphasis on one-way relationships. Dominguez et al. [<xref ref-type="bibr" rid="B12">12</xref>] focused solely on the impact of VDD on the onset of T2DM in older individuals, neglecting the occurrence of VDD in those already suffering from diabetes. Similarly, Yu et al. [<xref ref-type="bibr" rid="B13">13</xref>] exclusively investigated the effect of VDD on the development of T1DM across different populations, without taking into account the prevalence of VDD in individuals already diagnosed with diabetes. Recent clinical studies have expanded this understanding: Khudayar et al. [<xref ref-type="bibr" rid="B6">6</xref>] identified a 2.95-fold increased VDD risk in T2DM patients (95% CI: 2.28–3.80; <italic>P</italic> &lt; 0.0001), while Chen et al. [<xref ref-type="bibr" rid="B14">14</xref>] found a 2.04-fold increased risk in the T1DM population (95% CI: 1.31–3.15; <italic>P</italic> = 0.001). This meta-analysis synthesizes data from 53 studies (<italic>n</italic> = 552,032) to systematically evaluate the bidirectional association between VDD and diabetes mellitus, stratified by subtypes (T1DM vs. T2DM) and phenotypic subgroups.</p>
</sec>
<sec id="s2">
<title>Materials and methods</title>
<sec id="t2-1">
<title>Literature searches</title>
<p id="p-4">The meta-analysis was registered in PROSPERO (CRD42025639951). Comprehensive searches were conducted in PubMed, Embase, Web of Science, and the Cochrane Library for articles published from database inception to January 2025. The search strategy employed keywords such as VDD, diabetes mellitus, type 1 diabetes mellitus, type 2 diabetes mellitus, T1DM, and T2DM. <xref ref-type="sec" rid="s-suppl">Table S1</xref> details the search strategy. This study aimed to: (1) assess the prevalence of diabetes mellitus in individuals with VDD and vice versa in those with T1DM/T2DM; (2) compare diabetes mellitus prevalence in VDD patients with non-VDD controls; and (3) compare VDD prevalence in T1DM/T2DM patients with non-diabetic controls.</p>
</sec>
<sec id="t2-2">
<title>Study selection</title>
<p id="p-5">Articles were selected based on the following criteria: (1) cohort or case-control studies (there were no limits on sample size when including case-control studies); (2) reporting the bidirectional prevalence of VDD (&lt; 20 ng/mL or 50 nmol/L) [<xref ref-type="bibr" rid="B8">8</xref>] versus diabetes mellitus (T1DM/T2DM), and diagnosed diabetes mellitus according to the American Diabetes Association (ADA) criteria [<xref ref-type="bibr" rid="B15">15</xref>]; (3) evaluating the clinical characteristics of patients with VDD and diabetes mellitus, providing detailed data for analysis. Studies were excluded if they: (1) lacked data convertible to vitamin D &lt; 20 ng/mL (e.g., only reported ≥ 30 ng/mL subgroup); (2) involved pre-diabetic populations (impaired fasting glycemia, glucose intolerance, and/or glycated hemoglobin A1c 5.7–6.4% or 39–47 mmol/mol) [<xref ref-type="bibr" rid="B15">15</xref>] or gestational diabetes; (3) involved duplicate populations; (4) were conference abstracts, letters, reviews, animal studies, cellular experiments, non-English papers, or unavailable data; (5) lacked access to the full publication. Two researchers (HH and SS) independently screened titles, abstracts, and full texts for eligibility, and a third investigator (DL) independently decided on the inclusion of any relevant articles cited within the retrieved articles.</p>
</sec>
<sec id="t2-3">
<title>Data extraction and quality assessment</title>
<p id="p-6">Two authors (HH and SS) independently reviewed the full-text articles and extracted data on (1) authorship, (2) publication year, (3) country or region, (4) study design, (5) diagnostic criteria for VDD and diabetes mellitus, (6) population, and (7) baseline characteristics. Key variables included (1) total participants, (2) participants with T1DM/T2DM, (3) participants with VDD, and (4) mean and standard deviation of clinical characteristics. Discrepancies in data extraction, such as differing interpretations of outcome definitions, were resolved through consensus discussions, with unresolved issues adjudicated by a third investigator (DL). Data were recorded on standardized forms and verified by two investigators (HH and SS). Study quality was evaluated using the Newcastle-Ottawa scale [<xref ref-type="bibr" rid="B16">16</xref>], assessing selection, exposure/outcome, and comparability; a score of 7–9 denotes high quality. Using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework, we evaluated the evidence for each outcome, categorizing it as “high”, “moderate”, “low”, or “very low” quality [<xref ref-type="bibr" rid="B17">17</xref>].</p>
</sec>
<sec id="t2-4">
<title>Data synthesis</title>
<p id="p-7">The combined prevalence and 95% CIs of diabetes mellitus and VDD were analyzed using generalized inverse variance statistics [<xref ref-type="bibr" rid="B18">18</xref>]. This method addresses between-study heterogeneity by inversely weighting individual study estimates to their variance, making it ideal for meta-analyses with diverse sample sizes and measurement scales, enabling robust pooling under a random-effects model. Mantel-Haenszel statistics calculated odds ratios (ORs) and 95% CIs to compare diabetes mellitus prevalence in VDD patients versus non-VDD controls, VDD prevalence in T1DM patients versus non-T1DM controls, and VDD prevalence in T2DM patients versus non-T2DM controls. Data were pooled using a random effects model. Heterogeneity was assessed with the Higgins <italic>I</italic><sup>2</sup> statistic, where <italic>I</italic><sup>2</sup> &gt; 50% indicated significant heterogeneity. Sensitivity analyses involved removing each study individually to re-estimate effect sizes. Publication bias was evaluated using funnel plots and Egger et al. [<xref ref-type="bibr" rid="B19">19</xref>] test, with <italic>P</italic> ≤ 0.05 indicating statistical significance. If significant bias was detected, Duval and Tweedie [<xref ref-type="bibr" rid="B20">20</xref>] trim and fill method was applied for adjustment. Subgroup analyses considered region, study design, follow-up duration, sample size, mean/median age, mean/median body mass index (BMI), assay method, and latitude climate zone. Analyses were conducted using RevMan 5.4 [Review Manager (RevMan) Version 5.4, The Cochrane Collaboration, 2020] and STATA 15.1 (StataCorp Stata MP 15.1, United States).</p>
</sec>
</sec>
<sec id="s3">
<title>Results</title>
<sec id="t3-1">
<title>Characteristics of included studies and quality assessment</title>
<p id="p-8">Fifty-three studies met the inclusion criteria. Of these, five examined the prevalence of T2DM in patients with VDD [<xref ref-type="bibr" rid="B5">5</xref>, <xref ref-type="bibr" rid="B21">21</xref>–<xref ref-type="bibr" rid="B24">24</xref>], 27 assessed VDD prevalence in patients with T2DM [<xref ref-type="bibr" rid="B6">6</xref>, <xref ref-type="bibr" rid="B25">25</xref>–<xref ref-type="bibr" rid="B50">50</xref>], 23 focused on VDD in T1DM patients [<xref ref-type="bibr" rid="B11">11</xref>, <xref ref-type="bibr" rid="B14">14</xref>, <xref ref-type="bibr" rid="B30">30</xref>, <xref ref-type="bibr" rid="B40">40</xref>, <xref ref-type="bibr" rid="B51">51</xref>–<xref ref-type="bibr" rid="B69">69</xref>], and two examined VDD prevalence in patients with both T1DM and T2DM [<xref ref-type="bibr" rid="B30">30</xref>, <xref ref-type="bibr" rid="B40">40</xref>], with data extracted separately. Clinical characteristics were extracted from all studies. Ten studies were conducted in Western countries, six in African countries, and 37 in Asia. <xref ref-type="fig" rid="fig1">Figure 1</xref> presents the article screening flowchart [<xref ref-type="bibr" rid="B70">70</xref>], while <xref ref-type="table" rid="t1">Table 1</xref> details the basic characteristics. All studies were observational, comprising six cohort studies and 47 case-control studies. Among them, 23 were high-quality studies with scores ranging from 7 to 9, and 30 had a score of 6 (see <xref ref-type="sec" rid="s-suppl">Tables S2</xref> and <xref ref-type="sec" rid="s-suppl">S3</xref>). The assessment revealed that most of the 51 outcomes were of low or very low quality. Specifically, two outcomes (3.9%) were rated as moderate quality, 19 outcomes (37.25%) as low quality, and 30 outcomes (58.8%) as very low quality (see <xref ref-type="sec" rid="s-suppl">Table S4</xref>).</p>
<fig id="fig1" position="float">
<label>Figure 1</label>
<caption>
<p id="fig1-p-1">
<bold>Flowchart of the selection of studies.</bold> DM: diabetes mellitus; T1DM: type 1 diabetes mellitus; T2DM: type 2 diabetes mellitus; VDD: vitamin D deficiency</p>
<p id="fig1-p-2">
<italic>Note.</italic> Adapted from [<xref ref-type="bibr" rid="B70">70</xref>]. CC BY</p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="eemd-02-101438-g001.tif" />
</fig>
<table-wrap id="t1">
<label>Table 1</label>
<caption>
<p id="t1-p-1">
<bold>The baseline characteristics of the included studies</bold>
</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th rowspan="2">
<bold>Authors</bold>
</th>
<th rowspan="2">
<bold>Year</bold>
</th>
<th rowspan="2">
<bold>Study period</bold>
</th>
<th rowspan="2">
<bold>Country</bold>
</th>
<th rowspan="2">
<bold>Study design</bold>
</th>
<th rowspan="2">
<bold>Population</bold>
</th>
<th colspan="2">
<bold>Number of patients</bold>
</th>
<th rowspan="2">
<bold>Follow-up (median/mean)</bold>
</th>
<th colspan="2">
<bold>Mean/median age</bold>
</th>
<th colspan="2">
<bold>Male (<italic>n</italic>)</bold>
</th>
<th colspan="2">
<bold>BMI (kg/m<sup>2</sup>)</bold>
</th>
</tr>
<tr>
<th>
<bold>On camp</bold>
</th>
<th>
<bold>Off camp</bold>
</th>
<th>
<bold>On camp</bold>
</th>
<th>
<bold>Off camp</bold>
</th>
<th>
<bold>On camp</bold>
</th>
<th>
<bold>Off camp</bold>
</th>
<th>
<bold>On camp</bold>
</th>
<th>
<bold>Off camp</bold>
</th>
</tr>
</thead>
<tbody>
<tr>
<td>Nóvoa-Medina et al. [<xref ref-type="bibr" rid="B11">11</xref>]</td>
<td>2023</td>
<td>2016–2022</td>
<td>Spain</td>
<td>Retrospective</td>
<td>T1DM patients age &lt; 15 years</td>
<td>146</td>
<td>346</td>
<td>NA</td>
<td>8.4 ± 3.9</td>
<td>10 ± 2.7</td>
<td>83</td>
<td>149</td>
<td>17.3 ± 4.5</td>
<td>17.6 ± 6.1</td>
</tr>
<tr>
<td>Boyraz et al. [<xref ref-type="bibr" rid="B29">29</xref>]</td>
<td>2016</td>
<td>2013.10.1–2014.4.30</td>
<td>Turkey</td>
<td>Retrospective</td>
<td>Patients with T2DM</td>
<td>126</td>
<td>62</td>
<td>NA</td>
<td>58.90 ± 9.436</td>
<td>48.016 ± 9.32</td>
<td>NA</td>
<td>NA</td>
<td>28.60 ± 3.71</td>
<td>26.764 ± 4.317</td>
</tr>
<tr>
<td>Khudayar et al. [<xref ref-type="bibr" rid="B6">6</xref>]</td>
<td>2022</td>
<td>2020.10–2021.9</td>
<td>Pakistan</td>
<td>Retrospective</td>
<td>Patients with T2DM aged 30 to 60 years</td>
<td>525</td>
<td>525</td>
<td>NA</td>
<td>50 ± 5.5</td>
<td>52.1 ± 5.7</td>
<td>284</td>
<td>280</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Alqudsi et al. [<xref ref-type="bibr" rid="B52">52</xref>]</td>
<td>2019</td>
<td>2017.5–2017.9</td>
<td>Saudi Arabia</td>
<td>Retrospective</td>
<td>Teenagers with T1DM aged 12 to 18 years</td>
<td>49</td>
<td>49</td>
<td>NA</td>
<td>14.52 ± 2.06</td>
<td>13.92 ± 2.03</td>
<td>24</td>
<td>24</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Parveen et al. [<xref ref-type="bibr" rid="B42">42</xref>]</td>
<td>2019</td>
<td>NA</td>
<td>India</td>
<td>Retrospective</td>
<td>Patients with T2DM aged between 19 and 65 years </td>
<td>44</td>
<td>44</td>
<td>NA</td>
<td>45.7</td>
<td>44.91</td>
<td>23</td>
<td>26</td>
<td>26.1</td>
<td>25.33</td>
</tr>
<tr>
<td>Razip et al. [<xref ref-type="bibr" rid="B43">43</xref>]</td>
<td>2021</td>
<td>2021.5.21–2021.7.1</td>
<td>Malaysia</td>
<td>Retrospective</td>
<td>Patients with T2DM aged 30 to 65 years</td>
<td>50</td>
<td>50</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>26</td>
<td>28</td>
<td>28.7 ± 4.80</td>
<td>27.3 ± 4.63</td>
</tr>
<tr>
<td>Tsur et al. [<xref ref-type="bibr" rid="B23">23</xref>]</td>
<td>2013</td>
<td>2008.7–2012.6</td>
<td>Israel</td>
<td>Prospective</td>
<td>People aged 40 to 70 years</td>
<td>62,226</td>
<td>55,734</td>
<td>2 years</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Ma et al. [<xref ref-type="bibr" rid="B41">41</xref>]</td>
<td>2020</td>
<td>2015.3–2019.5</td>
<td>China</td>
<td>Retrospective</td>
<td>Patients with T2DM</td>
<td>674</td>
<td>521</td>
<td>NA</td>
<td>62.92 ± 2.0</td>
<td>62.4 ± 3.3</td>
<td>319</td>
<td>246</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Thrailkill et al. [<xref ref-type="bibr" rid="B66">66</xref>]</td>
<td>2011</td>
<td>NA</td>
<td>USA</td>
<td>Retrospective</td>
<td>Patients with T1DM aged 14 to 40 years</td>
<td>115</td>
<td>55</td>
<td>NA</td>
<td>20.86</td>
<td>24.3</td>
<td>54</td>
<td>24</td>
<td>25.04</td>
<td>25.2</td>
</tr>
<tr>
<td>Kumar et al. [<xref ref-type="bibr" rid="B25">25</xref>]</td>
<td>2017</td>
<td>NA</td>
<td>India</td>
<td>Retrospective</td>
<td>Patients with T2DM aged 30 to 70 years</td>
<td>78</td>
<td>69</td>
<td>NA</td>
<td>48.50 </td>
<td>44</td>
<td>33</td>
<td>33</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Yadavelli et al. [<xref ref-type="bibr" rid="B68">68</xref>]</td>
<td>2023</td>
<td>2021.1–2022.5</td>
<td>India</td>
<td>Retrospective</td>
<td>Children with T1DM aged 6 to 15 years</td>
<td>50</td>
<td>50</td>
<td>NA</td>
<td>11.36</td>
<td>NA</td>
<td>21</td>
<td>38</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Lari et al. [<xref ref-type="bibr" rid="B39">39</xref>]</td>
<td>2022</td>
<td>2013–2017</td>
<td>Kuwait</td>
<td>Retrospective</td>
<td>Patients with T2DM</td>
<td>203</td>
<td>162</td>
<td>NA</td>
<td>57.3</td>
<td>29.26</td>
<td>60</td>
<td>51</td>
<td>33.57</td>
<td>29.03</td>
</tr>
<tr>
<td>Daga et al. [<xref ref-type="bibr" rid="B30">30</xref>]</td>
<td>2012a</td>
<td>NA</td>
<td>India</td>
<td>Retrospective</td>
<td>Newly diagnosed young patients with T1DM (under 25 years old)</td>
<td>13</td>
<td>41</td>
<td>NA</td>
<td>15.15</td>
<td>16.98</td>
<td>6</td>
<td>NA</td>
<td>16.81</td>
<td>17.47</td>
</tr>
<tr>
<td>Daga et al. [<xref ref-type="bibr" rid="B30">30</xref>]</td>
<td>2012b</td>
<td>NA</td>
<td>India</td>
<td>Retrospective</td>
<td>Young patients with newly diagnosed diabetes (under 25 years old)</td>
<td>58</td>
<td>41</td>
<td>NA</td>
<td>16.93</td>
<td>16.98</td>
<td>26</td>
<td>NA</td>
<td>17.23</td>
<td>17.47</td>
</tr>
<tr>
<td>Salih et al. [<xref ref-type="bibr" rid="B47">47</xref>]</td>
<td>2021</td>
<td>2018.2.1–2018.7.30</td>
<td>Iraq</td>
<td>Retrospective</td>
<td>Patients with T2DM who are over 25 years old</td>
<td>155</td>
<td>155</td>
<td>NA</td>
<td>49.94</td>
<td>48.95</td>
<td>68</td>
<td>70 </td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Tang et al. [<xref ref-type="bibr" rid="B49">49</xref>]</td>
<td>2023</td>
<td>2019.1–2021.1</td>
<td>China</td>
<td>Retrospective</td>
<td>Patients with T2DM, with or without foot ulcers</td>
<td>256</td>
<td>100</td>
<td>NA</td>
<td>55.3</td>
<td>55.1</td>
<td>142</td>
<td>57</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Bae et al. [<xref ref-type="bibr" rid="B54">54</xref>]</td>
<td>2018</td>
<td>2011</td>
<td>South Korea</td>
<td>Retrospective</td>
<td>Children and adolescents aged 6 to 20 years diagnosed with T1DM</td>
<td>85</td>
<td>518</td>
<td>NA</td>
<td>14.5 ± 4.4</td>
<td>13.8 ± 3.8</td>
<td>37</td>
<td>239</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Borkar et al. [<xref ref-type="bibr" rid="B56">56</xref>]</td>
<td>2010</td>
<td>2007.7–2008.12</td>
<td>India</td>
<td>Retrospective</td>
<td>Children aged 6 to 12 years who are newly diagnosed with T1DM</td>
<td>50</td>
<td>50</td>
<td>NA</td>
<td>8.63 ± 2.01</td>
<td>8.48 ± 1.58</td>
<td>29</td>
<td>35</td>
<td>15.74 ± 1.88</td>
<td>16.25 ± 2.43</td>
</tr>
<tr>
<td>Devaraj et al. [<xref ref-type="bibr" rid="B57">57</xref>]</td>
<td>2011</td>
<td>NA</td>
<td>USA</td>
<td>Retrospective</td>
<td>T1DM patients aged 18 years or older</td>
<td>50</td>
<td>36</td>
<td>NA</td>
<td>33.5 ± 11.1</td>
<td>31 ± 11</td>
<td>17</td>
<td>14</td>
<td>25.5 ± 5.5</td>
<td>25 ± 5</td>
</tr>
<tr>
<td>Rodrigues et al. [<xref ref-type="bibr" rid="B45">45</xref>]</td>
<td>2019</td>
<td>2012.6–2013.9</td>
<td>Brazil</td>
<td>Retrospective</td>
<td>T2DM patients aged 32 to 70 years</td>
<td>101</td>
<td>62</td>
<td>NA</td>
<td>56 ± 13</td>
<td>53 ± 18</td>
<td>19</td>
<td>12</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Reddy et al. [<xref ref-type="bibr" rid="B44">44</xref>]</td>
<td>2015</td>
<td>2010.10–2013.3</td>
<td>India</td>
<td>Retrospective</td>
<td>Patients with T2DM</td>
<td>164</td>
<td>99</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>94</td>
<td>54</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Durgarao et al. [<xref ref-type="bibr" rid="B32">32</xref>]</td>
<td>2017</td>
<td>2016.12–2017.1</td>
<td>India</td>
<td>Retrospective</td>
<td>T2DM patient aged 40 to 60 years</td>
<td>100</td>
<td>100</td>
<td>NA</td>
<td>49.76 ± 5.21</td>
<td>48.52 ± 6.03</td>
<td>63</td>
<td>66</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Jayashri et al. [<xref ref-type="bibr" rid="B38">38</xref>]</td>
<td>2020</td>
<td>2012–2013</td>
<td>India</td>
<td>Prospective</td>
<td>Diabetes patients aged between 20 and 80 years</td>
<td>300</td>
<td>900</td>
<td>10 years</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Sarma et al. [<xref ref-type="bibr" rid="B48">48</xref>]</td>
<td>2018</td>
<td>2015.8–2016.12</td>
<td>India</td>
<td>Retrospective</td>
<td>Patients with T2DM</td>
<td>40</td>
<td>20</td>
<td>NA</td>
<td>49.65 ± 9.78</td>
<td>48.45 ± 8.73</td>
<td>NA</td>
<td>NA</td>
<td>24.51 ± 3.19</td>
<td>22.26 ± 1.32</td>
</tr>
<tr>
<td>Lin et al. [<xref ref-type="bibr" rid="B40">40</xref>]</td>
<td>2019a</td>
<td>NA</td>
<td>China</td>
<td>Retrospective</td>
<td>Patients with T1DM</td>
<td>56</td>
<td>42</td>
<td>NA</td>
<td>19.32 ± 4.59</td>
<td>20.79 ± 3.29</td>
<td>23</td>
<td>17</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Lin et al. [<xref ref-type="bibr" rid="B40">40</xref>]</td>
<td>2019b</td>
<td>NA</td>
<td>China</td>
<td>Retrospective</td>
<td>Patients with T2DM</td>
<td>41</td>
<td>28</td>
<td>NA</td>
<td>60.71 ± 16.26</td>
<td>54.07 ± 11.04</td>
<td>23</td>
<td>11</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Veronese et al. [<xref ref-type="bibr" rid="B24">24</xref>]</td>
<td>2014</td>
<td>1995–2009</td>
<td>Italy</td>
<td>Prospective</td>
<td>Community-dwelling elderly people aged 65 and older without diabetes</td>
<td>725</td>
<td>1,502</td>
<td>4.4 years</td>
<td>79.05</td>
<td>74.6</td>
<td>174</td>
<td>748</td>
<td>27.14</td>
<td>27.17</td>
</tr>
<tr>
<td>Hassan et al. [<xref ref-type="bibr" rid="B36">36</xref>]</td>
<td>2024</td>
<td>2022.3–2022.5</td>
<td>Sudan</td>
<td>Retrospective</td>
<td>Patients with T2DM from Sudan who are 18 years old or older</td>
<td>88</td>
<td>88</td>
<td>NA</td>
<td>55</td>
<td>55</td>
<td>40</td>
<td>42</td>
<td>27</td>
<td>26</td>
</tr>
<tr>
<td>Husemoen et al. [<xref ref-type="bibr" rid="B21">21</xref>]</td>
<td>2012</td>
<td>1993–2010.12.31</td>
<td>Denmark</td>
<td>Prospective</td>
<td>Men and women aged 41 to 71 years</td>
<td>11,423</td>
<td>25,430</td>
<td>16.4 years</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Liu et al. [<xref ref-type="bibr" rid="B61">61</xref>]</td>
<td>2018</td>
<td>NA</td>
<td>China</td>
<td>Retrospective</td>
<td>Children and adolescents with T1DM</td>
<td>296</td>
<td>295</td>
<td>NA</td>
<td>8.66</td>
<td>8.4</td>
<td>147</td>
<td>148</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Greer et al. [<xref ref-type="bibr" rid="B60">60</xref>]</td>
<td>2013</td>
<td>2007.3–2008.3</td>
<td>Australia</td>
<td>Retrospective</td>
<td>Children with T1DM</td>
<td>56</td>
<td>56</td>
<td>NA</td>
<td>11.55</td>
<td>8.67</td>
<td>28</td>
<td>30</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Bayani et al. [<xref ref-type="bibr" rid="B28">28</xref>]</td>
<td>2014</td>
<td>2011.10–2012.9</td>
<td>Iran</td>
<td>Retrospective</td>
<td>People aged 30 to 60 years with T2DM</td>
<td>120</td>
<td>120</td>
<td>NA</td>
<td>51.2 ± 7.98</td>
<td>50.6 ± 7.73</td>
<td>50</td>
<td>50</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Wang et al. [<xref ref-type="bibr" rid="B50">50</xref>]</td>
<td>2018</td>
<td>2013.6–2013.7</td>
<td>China</td>
<td>Retrospective</td>
<td>Patients with T2DM</td>
<td>397</td>
<td>397</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>26.34 ± 3.61</td>
<td>25.45 ± 3.58</td>
</tr>
<tr>
<td>Saleem et al. [<xref ref-type="bibr" rid="B46">46</xref>]</td>
<td>2017</td>
<td>2017.1.1–2017.8.31</td>
<td>Pakistan</td>
<td>Retrospective</td>
<td>Patients with T2DM</td>
<td>100</td>
<td>100</td>
<td>NA</td>
<td>48.55 ± 14.62</td>
<td>46.15 ± 12.43</td>
<td>53</td>
<td>51</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Majeed et al. [<xref ref-type="bibr" rid="B62">62</xref>]</td>
<td>2023</td>
<td>2013.3–2017.4</td>
<td>United Arab Emirates</td>
<td>Retrospective</td>
<td>Children and adolescents aged 4 to 19 years with T1DM</td>
<td>148</td>
<td>296</td>
<td>NA</td>
<td>12.5</td>
<td>11.9</td>
<td>66</td>
<td>123</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Esteghamati et al. [<xref ref-type="bibr" rid="B33">33</xref>]</td>
<td>2015</td>
<td>2012.3–2013.8</td>
<td>Iran</td>
<td>Retrospective</td>
<td>Adults or adolescents aged 15 years and above with T2DM</td>
<td>1,195</td>
<td>209</td>
<td>NA</td>
<td>55.8 ± 10.4</td>
<td>52.5 ± 11.1</td>
<td>555</td>
<td>97 </td>
<td>29.7 ± 5.2</td>
<td>28.5 ± 4.8</td>
</tr>
<tr>
<td>Alduraywish et al. [<xref ref-type="bibr" rid="B26">26</xref>]</td>
<td>2019</td>
<td>2016.11.1–2017.4.19</td>
<td>Saudi Arabia</td>
<td>Retrospective</td>
<td>Patients with T2DM</td>
<td>290</td>
<td>126</td>
<td>NA</td>
<td>44.45 ± 16.96</td>
<td>47.82 ± 13.29</td>
<td>NA</td>
<td>NA</td>
<td>27.66 ± 4.998</td>
<td>29.07 ± 4.595</td>
</tr>
<tr>
<td>Rochmah et al. [<xref ref-type="bibr" rid="B65">65</xref>]</td>
<td>2022</td>
<td>2019.3–2019.5</td>
<td>Indonesia</td>
<td>Retrospective</td>
<td>Children with T1DM under the age of 18</td>
<td>31</td>
<td>24</td>
<td>NA</td>
<td>11.22 ± 4.15</td>
<td>7.55 ± 3.18</td>
<td>18</td>
<td>10</td>
<td>17.35 ± 4.32</td>
<td>18.54 ± 5.23</td>
</tr>
<tr>
<td>Nam et al. [<xref ref-type="bibr" rid="B64">64</xref>]</td>
<td>2019</td>
<td>NA</td>
<td>South Korea</td>
<td>Retrospective</td>
<td>Patients with T1DM under the age of 20</td>
<td>96</td>
<td>156</td>
<td>NA</td>
<td>14.7 ± 4.3</td>
<td>14.0 ± 4.2</td>
<td>42</td>
<td>67</td>
<td>0.2 ± 0.1</td>
<td>–0.1 ± 0.1</td>
</tr>
<tr>
<td>Bajaj et al. [<xref ref-type="bibr" rid="B27">27</xref>]</td>
<td>2014</td>
<td>NA</td>
<td>India</td>
<td>Retrospective</td>
<td>Patients with T2DM aged 8 to 70 years</td>
<td>158</td>
<td>130</td>
<td>NA</td>
<td>52.85 ± 8.26</td>
<td>51.87 ± 6.43</td>
<td>95</td>
<td>80</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Chen et al. [<xref ref-type="bibr" rid="B14">14</xref>]</td>
<td>2022</td>
<td>2017–2019</td>
<td>China</td>
<td>Retrospective</td>
<td>Children with T1DM</td>
<td>141</td>
<td>200</td>
<td>NA</td>
<td>6.3 ± 3.3</td>
<td>5.6 ± 3.6</td>
<td>59</td>
<td>114</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Bin-Abbas et al. [<xref ref-type="bibr" rid="B55">55</xref>]</td>
<td>2011</td>
<td>2010.6–2010.9</td>
<td>Saudi Arabia</td>
<td>Retrospective</td>
<td>Saudi children diagnosed with T1DM for more than 5 months</td>
<td>100</td>
<td>100</td>
<td>NA</td>
<td>9.5 ± 3.2</td>
<td>8.9 ± 2.1</td>
<td>41</td>
<td>48</td>
<td>17.6 ± 3.6</td>
<td>17.9 ± 3.0</td>
</tr>
<tr>
<td>Gendy et al. [<xref ref-type="bibr" rid="B35">35</xref>]</td>
<td>2018</td>
<td>2016.12–2017.11</td>
<td>Egypt</td>
<td>Retrospective</td>
<td>Patients with T2DM</td>
<td>50</td>
<td>50</td>
<td>NA</td>
<td>52.48 ± 6.55</td>
<td>51 ± 8.2</td>
<td>10</td>
<td>16</td>
<td>33.03 ± 6.07</td>
<td>30.57 ± 7.68</td>
</tr>
<tr>
<td>Ahmed et al. [<xref ref-type="bibr" rid="B58">58</xref>]</td>
<td>2019</td>
<td>2017.12–2018.11</td>
<td>Egypt</td>
<td>Retrospective</td>
<td>Children with T1DM (before vitamin D treatment)</td>
<td>50</td>
<td>50</td>
<td>NA</td>
<td>11.16 ± 3.27</td>
<td>10.97 ± 2.77</td>
<td>24</td>
<td>25</td>
<td>17.50 ± 3.24</td>
<td>17.32 ± 2.92</td>
</tr>
<tr>
<td>Fondjo et al. [<xref ref-type="bibr" rid="B34">34</xref>]</td>
<td>2017</td>
<td>2015.10–2015.12</td>
<td>Ghana</td>
<td>Retrospective</td>
<td>Patients with T2DM</td>
<td>118</td>
<td>98</td>
<td>NA</td>
<td>58.81 ± 0.90</td>
<td>57.79 ± 1.49</td>
<td>25</td>
<td>22</td>
<td>26.05 ± 0.47</td>
<td>23.73 ± 0.51</td>
</tr>
<tr>
<td>Mihçioğlu and Hatun [<xref ref-type="bibr" rid="B63">63</xref>]</td>
<td>2022</td>
<td>2009.3.1–2010.9.1</td>
<td>Turkey</td>
<td>Retrospective</td>
<td>Patients newly diagnosed with T1DM</td>
<td>80</td>
<td>30</td>
<td>NA</td>
<td>8.38 ± 4.25</td>
<td>8.45 ± 5.39</td>
<td>44</td>
<td>15</td>
<td>16.08 ± 2.90</td>
<td>18.72 ± 5.68</td>
</tr>
<tr>
<td>Ghandchi et al. [<xref ref-type="bibr" rid="B59">59</xref>]</td>
<td>2012</td>
<td>2008–2009</td>
<td>Iran</td>
<td>Retrospective</td>
<td>Patients with T1DM aged 5 to 25 years</td>
<td>60</td>
<td>120</td>
<td>NA</td>
<td>14.4</td>
<td>15.15</td>
<td>32</td>
<td>52</td>
<td>21.4</td>
<td>21</td>
</tr>
<tr>
<td>Abd-Allah et al. [<xref ref-type="bibr" rid="B51">51</xref>]</td>
<td>2014</td>
<td>NA</td>
<td>Egypt</td>
<td>Retrospective</td>
<td>Patients with T1DM</td>
<td>120</td>
<td>120</td>
<td>NA</td>
<td>11.7 ± 2.8</td>
<td>11.1 ± 2.6</td>
<td>42</td>
<td>48</td>
<td>18.5 ± 4.3</td>
<td>21.6 ± 1.5</td>
</tr>
<tr>
<td>Ziaei-Kajbaf et al. [<xref ref-type="bibr" rid="B69">69</xref>]</td>
<td>2018</td>
<td>2015.10–2016.3</td>
<td>Iran</td>
<td>Retrospective</td>
<td>Children and teenagers aged 1 to 15 years</td>
<td>85</td>
<td>85</td>
<td>NA</td>
<td>8.73 ± 4.05</td>
<td>7.62 ± 3.68</td>
<td>40</td>
<td>44</td>
<td>16.52</td>
<td>15.92</td>
</tr>
<tr>
<td>Azab et al. [<xref ref-type="bibr" rid="B53">53</xref>]</td>
<td>2013</td>
<td>2012.1–2012.12</td>
<td>Egypt</td>
<td>Retrospective</td>
<td>Children and adolescents aged 6 to 16 years with T1DM</td>
<td>80</td>
<td>40</td>
<td>NA</td>
<td>11.4 ± 2.5</td>
<td>10.8 ± 2.3</td>
<td>34</td>
<td>24</td>
<td>23.6 ± 5.7</td>
<td>21.8 ± 4.7</td>
</tr>
<tr>
<td>Wierzbicka et al. [<xref ref-type="bibr" rid="B67">67</xref>]</td>
<td>2016</td>
<td>NA</td>
<td>Poland</td>
<td>Retrospective</td>
<td>Teenage patients with T1DM</td>
<td>60</td>
<td>40</td>
<td>NA</td>
<td>15.1 ± 1.9</td>
<td>15.6 ± 1.8</td>
<td>28</td>
<td>20</td>
<td>21.1 ± 3.1</td>
<td>21.7 ± 2.9</td>
</tr>
<tr>
<td>Pilz et al. [<xref ref-type="bibr" rid="B22">22</xref>]</td>
<td>2012</td>
<td>2000–2009</td>
<td>Netherlands</td>
<td>Prospective</td>
<td>People aged 50 to 75 years</td>
<td>106</td>
<td>174</td>
<td>7.5 ± 0.5 years</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Fu et al. [<xref ref-type="bibr" rid="B5">5</xref>]</td>
<td>2024</td>
<td>2006–2023.3.30</td>
<td>UK</td>
<td>Prospective</td>
<td>Adults aged 40 to 69 years</td>
<td>205,609</td>
<td>174,090</td>
<td>14.1 years</td>
<td>55.53</td>
<td>57</td>
<td>111,440</td>
<td>94,487</td>
<td>27.62</td>
<td>26.4</td>
</tr>
<tr>
<td>Dhas et al. [<xref ref-type="bibr" rid="B31">31</xref>]</td>
<td>2019</td>
<td>2016.3–2018.2</td>
<td>India</td>
<td>Retrospective</td>
<td>Adults with T2DM aged 30 to 50 years</td>
<td>90</td>
<td>90</td>
<td>-</td>
<td>41.83 ± 5.91</td>
<td>37.97 ± 6.14</td>
<td>50</td>
<td>52</td>
<td>28.46 ± 3.68</td>
<td>25.59 ± 3.95</td>
</tr>
<tr>
<td>Iqbal et al. [<xref ref-type="bibr" rid="B37">37</xref>]</td>
<td>2017</td>
<td>2013.1–2015.7</td>
<td>Pakistan</td>
<td>Retrospective</td>
<td>Adult patients with T2DM</td>
<td>165</td>
<td>165</td>
<td>-</td>
<td>48.82</td>
<td>46.27</td>
<td>116</td>
<td>116</td>
<td>28.96</td>
<td>25.24</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p id="t1-fn-1">BMI: body mass index; NA: not available; T1DM: type 1 diabetes mellitus; T2DM: type 2 diabetes mellitus; UK: United Kingdom; USA: United States of America</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="t3-2">
<title>Prevalence of T2DM in patients with VDD</title>
<p id="p-9">Five prospective cohort studies examined T2DM prevalence in patients with VDD. Among VDD patients (<italic>n</italic> = 537,019), T2DM prevalence was significantly higher compared to non-VDD controls [OR: 1.53, 95% CI: 1.38–1.70, 95% prediction interval (PI): 1.16–2.02, <italic>I</italic><sup>2</sup> = 66%, <italic>P</italic> &lt; 0.00001; very low (the quality of evidence is expressed as “GRADE”); <xref ref-type="fig" rid="fig2">Figure 2</xref>]. Sensitivity analyses identified a source of heterogeneity (<xref ref-type="sec" rid="s-suppl">Figure S1</xref>): one study (Veronese et al. [<xref ref-type="bibr" rid="B24">24</xref>]) focused on community-dwelling older adults aged 65 and above. Excluding this study, the combined OR for T2DM in VDD patients was 1.65 (95% CI: 1.59–1.70, <italic>I</italic><sup>2</sup> = 0%, <italic>P</italic> &lt; 0.00001). Subgroup analysis (<xref ref-type="table" rid="t2">Table 2</xref>) showed a combined OR of 1.66 (95% CI: 1.60–1.71, <italic>I</italic><sup>2</sup> = 0%, <italic>P</italic> &lt; 0.00001; low) for VDD patients with follow-up ≥ 5 years, and 1.34 (95% CI: 0.95–1.88, <italic>I</italic><sup>2</sup> = 84%, <italic>P</italic> = 0.09; very low) for those with follow-up &lt; 5 years. The articles were categorized into two groups based on sample size (<italic>n</italic> ≥ 1,000 and <italic>n</italic> &lt; 1,000). For patients with VDD and sample sizes ≥ 1,000, the combined OR for T2DM was 1.54 (95% CI: 1.38–1.71, <italic>I</italic><sup>2</sup> = 73%, <italic>P</italic> &lt; 0.00001; low). For sample sizes &lt; 1,000, the combined OR was 1.24 (95% CI: 0.65–2.38, <italic>P</italic> = 0.51; very low). Three studies employing the chemiluminescence immunoassay (CLIA) test method were categorized, revealing a combined OR of 1.65 (95% CI: 1.60–1.70, <italic>I</italic><sup>2</sup> = 0%, <italic>P</italic> &lt; 0.00001; low) for diabetes. Subgroup analyses revealed no significant OR for T2DM in VDD patients across age, BMI, sample size &lt; 1,000, and follow-up &lt; 5 years compared to the non-VDD population, likely due to the limited number of studies. Egger’s test (<italic>P</italic> = 0.109) indicated no publication bias, and the funnel plot was symmetric.</p>
<fig id="fig2" position="float">
<label>Figure 2</label>
<caption>
<p id="fig2-p-1">
<bold>Comparison of the prevalence of T2DM in patients with and without VDD.</bold> CI: confidence interval; MH: Mantel-Haenszel; T2DM: type 2 diabetes mellitus; VDD: vitamin D deficiency</p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="eemd-02-101438-g002.tif" />
</fig>
<table-wrap id="t2">
<label>Table 2</label>
<caption>
<p id="t2-p-1">
<bold>Subgroup analyses</bold>
</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th rowspan="2">
<bold>Subgroup</bold>
</th>
<th colspan="6">
<bold>Effects of VDD on the risk of T2DM</bold>
</th>
<th colspan="6">
<bold>Effects of T1DM on the risk of VDD</bold>
</th>
<th colspan="6">
<bold>Effects of T2DM on the risk of VDD</bold>
</th>
</tr>
<tr>
<th>
<bold>Study</bold>
</th>
<th>
<bold>OR (95% CI)</bold>
</th>
<th>
<bold>
<italic>P</italic> value</bold>
</th>
<th>
<bold>
<italic>I</italic>
<sup>2</sup>
</bold>
</th>
<th>
<bold>Egger’s test</bold>
</th>
<th>
<bold>GRADE</bold>
</th>
<th>
<bold>Study</bold>
</th>
<th>
<bold>OR (95% CI)</bold>
</th>
<th>
<bold>
<italic>P</italic> value</bold>
</th>
<th>
<bold>
<italic>I</italic>
<sup>2</sup>
</bold>
</th>
<th>
<bold>Egger’s test</bold>
</th>
<th>
<bold>GRADE</bold>
</th>
<th>
<bold>Study</bold>
</th>
<th>
<bold>OR (95% CI)</bold>
</th>
<th>
<bold>
<italic>P</italic> value</bold>
</th>
<th>
<bold>
<italic>I</italic>
<sup>2</sup>
</bold>
</th>
<th>
<bold>Egger’s test</bold>
</th>
<th>
<bold>GRADE</bold>
</th>
</tr>
</thead>
<tbody>
<tr>
<td>Total</td>
<td>5</td>
<td>1.53 [1.38–1.70]</td>
<td>&lt; 0.00001</td>
<td>66%</td>
<td>0.109</td>
<td>Very low</td>
<td>23</td>
<td>2.02 [1.42–2.89]</td>
<td>0.0001</td>
<td>81%</td>
<td>0.055</td>
<td>Low</td>
<td>27</td>
<td>2.62 [1.85–3.70]</td>
<td>&lt; 0.00001</td>
<td>90%</td>
<td>0.038</td>
<td>Low</td>
</tr>
<tr>
<td colspan="19">Study design</td>
</tr>
<tr>
<td>Prospective</td>
<td>5</td>
<td>1.53 [1.38–1.70]</td>
<td>&lt; 0.00001</td>
<td>66%</td>
<td>0.109</td>
<td>Very low</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>1</td>
<td>1.64 [1.25–2.14]</td>
<td>0.0003</td>
<td>NA</td>
<td>NA</td>
<td>Very low</td>
</tr>
<tr>
<td>Retrospective</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>23</td>
<td>2.02 [1.42–2.89]</td>
<td>0.0001</td>
<td>81%</td>
<td>0.055</td>
<td>Low</td>
<td>26</td>
<td>2.71 [1.86–3.95]</td>
<td>&lt; 0.00001</td>
<td>90%</td>
<td>0.038</td>
<td>Low</td>
</tr>
<tr>
<td colspan="19">Follow-up</td>
</tr>
<tr>
<td>≥ 5 years</td>
<td>3</td>
<td>1.66 [1.60–1.71]</td>
<td>&lt; 0.00001</td>
<td>0%</td>
<td>NA</td>
<td>Low</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>1</td>
<td>1.64 [1.25–2.14]</td>
<td>0.0003</td>
<td>NA</td>
<td>NA</td>
<td>Very low</td>
</tr>
<tr>
<td>&lt; 5 years</td>
<td>2</td>
<td>1.34 [0.95–1.88]</td>
<td>0.09</td>
<td>84%</td>
<td>NA</td>
<td>Very low</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td colspan="19">Region</td>
</tr>
<tr>
<td>Asia</td>
<td>1</td>
<td>1.56 [1.43–1.70]</td>
<td>&lt; 0.00001</td>
<td>NA</td>
<td>NA</td>
<td>Very low</td>
<td>15</td>
<td>2.21 [1.55–3.15]</td>
<td>&lt; 0.0001</td>
<td>74%</td>
<td>0.165</td>
<td>Low</td>
<td>23</td>
<td>2.38 [1.67–3.39]</td>
<td>&lt; 0.00001</td>
<td>90%</td>
<td>0.049</td>
<td>Low</td>
</tr>
<tr>
<td>Europe</td>
<td>4</td>
<td>1.45 [1.17–1.78]</td>
<td>0.0005</td>
<td>71%</td>
<td>NA</td>
<td>Very low</td>
<td>2</td>
<td>1.65 [1.03–2.64]</td>
<td>0.04</td>
<td>0%</td>
<td>NA</td>
<td>Very low</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>North America</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>2</td>
<td>1.67 [0.83–3.37]</td>
<td>0.15</td>
<td>0%</td>
<td>NA</td>
<td>Very low</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>Africa</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>3</td>
<td>3.71 [0.26–53.46]</td>
<td>0.33</td>
<td>96%</td>
<td>NA</td>
<td>Very low</td>
<td>3</td>
<td>4.75 [0.55–41.06]</td>
<td>0.16</td>
<td>92%</td>
<td>NA</td>
<td>Very low</td>
</tr>
<tr>
<td>Australia</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>1</td>
<td>1.73 [0.39–7.62]</td>
<td>0.47</td>
<td>NA</td>
<td>NA</td>
<td>Very low</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td>South America</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>1</td>
<td>11.50 [4.76–27.75]</td>
<td>&lt; 0.00001</td>
<td>NA</td>
<td>NA</td>
<td>Low</td>
</tr>
<tr>
<td colspan="19">Sample size</td>
</tr>
<tr>
<td>≥ 1,000</td>
<td>4</td>
<td>1.54 [1.38–1.71]</td>
<td>&lt; 0.00001</td>
<td>73%</td>
<td>NA</td>
<td>Low</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>4</td>
<td>2.44 [1.74–3.42]</td>
<td>&lt; 0.00001</td>
<td>79%</td>
<td>NA</td>
<td>Low</td>
</tr>
<tr>
<td>&lt; 1,000</td>
<td>1</td>
<td>1.24 [0.65–2.38]</td>
<td>0.51</td>
<td>NA</td>
<td>NA</td>
<td>Very low</td>
<td>23</td>
<td>2.02 [1.42–2.89]</td>
<td>0.0001</td>
<td>81%</td>
<td>0.055</td>
<td>Low</td>
<td>23</td>
<td>2.80 [1.73–4.53]</td>
<td>&lt; 0.0001</td>
<td>91%</td>
<td>0.024</td>
<td>Low</td>
</tr>
<tr>
<td colspan="19">Mean/median age</td>
</tr>
<tr>
<td>≥ 50 years</td>
<td>2</td>
<td>1.38 [0.92–2.06]</td>
<td>0.12</td>
<td>90%</td>
<td>NA</td>
<td>Very low</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>12</td>
<td>3.29 [2.02–5.34]</td>
<td>&lt; 0.00001</td>
<td>86%</td>
<td>0.111</td>
<td>Low</td>
</tr>
<tr>
<td>&lt; 50 years</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>23</td>
<td>2.02 [1.42–2.89]</td>
<td>0.0001</td>
<td>81%</td>
<td>0.055</td>
<td>Low</td>
<td>11</td>
<td>2.87 [1.53–5.37]</td>
<td>0.001</td>
<td>90%</td>
<td>0.014</td>
<td>Very low</td>
</tr>
<tr>
<td colspan="19">Mean/median BMI</td>
</tr>
<tr>
<td>≥ 30 kg/m<sup>2</sup></td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>2</td>
<td>5.21 [0.08–332.64]</td>
<td>0.44</td>
<td>88%</td>
<td>NA</td>
<td>Very low</td>
</tr>
<tr>
<td>≥ 25, &lt; 30 kg/m<sup>2</sup></td>
<td>2</td>
<td>1.38 [0.92–2.06]</td>
<td>0.12</td>
<td>90%</td>
<td>NA</td>
<td>Very low</td>
<td>2</td>
<td>1.67 [0.83–3.37]</td>
<td>0.15</td>
<td>0%</td>
<td>NA</td>
<td>Very low</td>
<td>9</td>
<td>1.30 [0.71–2.37]</td>
<td>0.4</td>
<td>92%</td>
<td>NA</td>
<td>Very low</td>
</tr>
<tr>
<td>≥ 18.5, &lt; 25 kg/m<sup>2</sup></td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>4</td>
<td>1.23 [0.32–4.63]</td>
<td>0.76</td>
<td>90%</td>
<td>NA</td>
<td>Very low</td>
<td>2</td>
<td>7.68 [3.94–14.95]</td>
<td>&lt; 0.00001</td>
<td>0%</td>
<td>NA</td>
<td>Moderate</td>
</tr>
<tr>
<td>&lt; 18.5 kg/m<sup>2</sup></td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>9</td>
<td>2.91 [1.71–4.94]</td>
<td>&lt; 0.0001</td>
<td>67%</td>
<td>NA</td>
<td>Low</td>
<td>1</td>
<td>40.38 [5.08–320.74]</td>
<td>0.0005</td>
<td>NA</td>
<td>NA</td>
<td>Low</td>
</tr>
<tr>
<td colspan="19">Assay method</td>
</tr>
<tr>
<td>CLIA</td>
<td>3</td>
<td>1.65 [1.60–1.70]</td>
<td>&lt; 0.00001</td>
<td>0%</td>
<td>NA</td>
<td>Low</td>
<td>7</td>
<td>1.98 [1.02–3.83]</td>
<td>0.04</td>
<td>80%</td>
<td>NA</td>
<td>Very low</td>
<td>3</td>
<td>1.83 [0.86–3.90]</td>
<td>0.12</td>
<td>74%</td>
<td>NA</td>
<td>Very low</td>
</tr>
<tr>
<td>RIA</td>
<td>2</td>
<td>1.12 [0.88–1.42]</td>
<td>0.37</td>
<td>0%</td>
<td>NA</td>
<td>Very low</td>
<td>3</td>
<td>2.61 [1.86–3.68]</td>
<td>&lt; 0.00001</td>
<td>0%</td>
<td>NA</td>
<td>Moderate</td>
<td>2</td>
<td>7.79 [0.43–141.44]</td>
<td>0.16</td>
<td>87%</td>
<td>NA</td>
<td>Very low</td>
</tr>
<tr>
<td>ELISA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>6</td>
<td>2.18 [0.72–6.55]</td>
<td>0.17</td>
<td>92%</td>
<td>NA</td>
<td>Very low</td>
<td>12</td>
<td>2.64 [1.50–4.64]</td>
<td>0.0008</td>
<td>90%</td>
<td>0.004</td>
<td>Low</td>
</tr>
<tr>
<td>HPLC</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>4</td>
<td>1.94 [0.87–4.34]</td>
<td>0.11</td>
<td>63%</td>
<td>NA</td>
<td>Very low</td>
<td>4</td>
<td>2.91 [1.38–6.11]</td>
<td>0.005</td>
<td>77%</td>
<td>NA</td>
<td>Very low</td>
</tr>
<tr>
<td>LC-MS/MS</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>1</td>
<td>2.04 [1.31–3.15]</td>
<td>0.001</td>
<td>NA</td>
<td>NA</td>
<td>Very low</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
</tr>
<tr>
<td colspan="19">Latitude climate zone</td>
</tr>
<tr>
<td>Temperate zone</td>
<td>5</td>
<td>1.53 [1.38–1.70]</td>
<td>&lt; 0.00001</td>
<td>66%</td>
<td>0.109</td>
<td>Very low</td>
<td>19</td>
<td>1.94 [1.35–2.80]</td>
<td>0.0004</td>
<td>81%</td>
<td>0.055</td>
<td>Very low</td>
<td>17</td>
<td>2.54 [1.59–4.05]</td>
<td>&lt; 0.0001</td>
<td>92%</td>
<td>0.038</td>
<td>Low</td>
</tr>
<tr>
<td>Tropical zone</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>NA</td>
<td>4</td>
<td>2.55 [0.56–11.61]</td>
<td>0.23</td>
<td>83%</td>
<td>NA</td>
<td>Very low</td>
<td>10</td>
<td>2.89 [1.69–4.94]</td>
<td>0.0001</td>
<td>83%</td>
<td>0.147</td>
<td>Low</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p id="t2-fn-1">BMI: body mass index; CI: confidence interval; CLIA: chemiluminescence immunoassay; ELISA: enzyme-linked immunosorbent assay; GRADE: Grading of Recommendations, Assessment, Development, and Evaluation; HPLC: high performance liquid chromatography; LC-MS/MS: Liquid chromatography-tandem mass spectrometry; NA: not available; ORs: odds ratios; RIA: radioimmunoassay; T1DM: type 1 diabetes mellitus; T2DM: type 2 diabetes mellitus; VDD: vitamin D deficiency</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="t3-3">
<title>Prevalence of VDD in people with T1DM</title>
<p id="p-10">Twenty-three case-control studies were included to assess the prevalence of VDD in individuals with T1DM. The overall analysis, involving 4,816 T1DM patients, revealed a significantly higher prevalence of VDD in comparison to non-T1DM controls (OR: 2.02, 95% CI: 1.42–2.89, 95% PI: 0.43–9.50, <italic>I</italic><sup>2</sup> = 81%, <italic>P</italic> = 0.0001; low; <xref ref-type="fig" rid="fig3">Figure 3</xref>). Sensitivity analyses, excluding six specific studies [<xref ref-type="bibr" rid="B51">51</xref>, <xref ref-type="bibr" rid="B58">58</xref>, <xref ref-type="bibr" rid="B62">62</xref>, <xref ref-type="bibr" rid="B63">63</xref>, <xref ref-type="bibr" rid="B68">68</xref>, <xref ref-type="bibr" rid="B69">69</xref>], demonstrated that the combined OR for VDD in T1DM patients was 2.19 (95% CI: 1.86–2.58, <italic>I</italic><sup>2</sup> = 0%, <italic>P</italic> &lt; 0.00001), indicating a potential source of heterogeneity (<xref ref-type="sec" rid="s-suppl">Figure S2</xref>). There was substantial heterogeneity among the studies (<italic>I</italic><sup>2</sup> = 81%, 95% PI: 0.43–9.50). This variation could potentially be explained by differences in population characteristics (e.g., age, geographic latitude) and inconsistencies in the detection methodologies. Subgroup analyses (<xref ref-type="table" rid="t2">Table 2</xref>) indicated a significantly elevated risk of VDD in Asian and European populations with T1DM compared to non-T1DM controls, with combined ORs of 2.21 (95% CI: 1.55–3.15, <italic>I</italic><sup>2</sup> = 74%, <italic>P</italic> &lt; 0.0001; low) and 1.65 (95% CI: 1.03–2.64, <italic>I</italic><sup>2</sup> = 0%, <italic>P</italic> = 0.04; very low), respectively. Subgroup analysis revealed that among the studies utilizing the radioimmunoassay (RIA) method for detection, three studies reported a combined OR for diabetes of 2.61 (95% CI: 1.86–3.68, <italic>I</italic><sup>2</sup> = 0%, <italic>P</italic> &lt; 0.00001; moderate). In contrast, among the studies employing the CLIA method, seven studies indicated a combined OR for diabetes of 1.98 (95% CI: 1.02–3.83, <italic>I</italic><sup>2</sup> = 80%, <italic>P</italic> = 0.04; very low). Nineteen studies conducted in temperate regions showed a combined OR of 1.94 (95% CI: 1.35–2.80, <italic>I</italic><sup>2</sup> = 81%, <italic>P</italic> = 0.0004; very low) for VDD in patients with T1DM. Assessment using the Egger test (<italic>P</italic> = 0.055) revealed no publication bias, and funnel plots exhibited symmetry.</p>
<fig id="fig3" position="float">
<label>Figure 3</label>
<caption>
<p id="fig3-p-1">
<bold>Comparison of the prevalence of VDD in patients with and without T1DM.</bold> CI: confidence interval; MH: Mantel-Haenszel; T1DM: type 1 diabetes mellitus; VDD: vitamin D deficiency</p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="eemd-02-101438-g003.tif" />
</fig>
</sec>
<sec id="t3-4">
<title>Prevalence of VDD in people with T2DM</title>
<p id="p-11">A prospective cohort study and 26 case-control studies examined the prevalence of VDD in patients with T2DM. Among T2DM patients (<italic>n</italic> = 10,197), VDD prevalence was significantly higher compared to non-T2DM controls (OR: 2.62, 95% CI: 1.85–3.70, 95% PI: 0.48–14.33, <italic>I</italic><sup>2</sup> = 90%, <italic>P</italic> &lt; 0.00001; low; <xref ref-type="fig" rid="fig4">Figure 4</xref>). Sensitivity analyses confirmed stable results after excluding one study (<xref ref-type="sec" rid="s-suppl">Figure S3</xref>). Subgroup analysis (<xref ref-type="table" rid="t2">Table 2</xref>) of the 26 case-control studies yielded a combined OR of 2.71 (95% CI: 1.86–3.95, <italic>I</italic><sup>2</sup> = 90%, <italic>P</italic> &lt; 0.00001; low) for VDD in the T2DM group. Studies were categorized by sample size (<italic>n</italic> ≥ 1,000 and <italic>n</italic> &lt; 1,000). For sample sizes ≥ 1,000, the combined OR was 2.44 (95% CI: 1.74–3.42, <italic>I</italic><sup>2</sup> = 79%, <italic>P</italic> &lt; 0.00001; low), while for sizes &lt; 1,000, it was 2.80 (95% CI: 1.73–4.53, <italic>I</italic><sup>2</sup> = 91%, <italic>P</italic> &lt; 0.0001; low). In temperate regions, VDD patients with T2DM exhibited a combined OR of 2.54 (95% CI: 1.59–4.05, <italic>I</italic><sup>2</sup> = 92%, <italic>P</italic> &lt; 0.0001; low), whereas in tropical regions, the combined OR was 2.89 (95% CI: 1.69–4.94, <italic>I</italic><sup>2</sup> = 83%, <italic>P</italic> = 0.0001; low). The elevated risk of VDD in individuals with T2DM remained statistically significant irrespective of sample size and age. Publication bias was detected through Egger’s test (<italic>P</italic> = 0.038) and confirmed by the asymmetry observed in the funnel plots. However, the trim and fill method demonstrated that there was no substantial alteration in the effect size (pre-trim: 2.62 [1.85–3.70], post-trim: 2.379 [1.675–3.378]), indicating that publication bias had minimal impact on the outcomes of the meta-analysis. Please refer to <xref ref-type="sec" rid="s-suppl">Figure S4</xref> for the trim and fill funnel plot.</p>
<fig id="fig4" position="float">
<label>Figure 4</label>
<caption>
<p id="fig4-p-1">
<bold>Comparison of the prevalence of VDD in patients with and without T2DM.</bold> CI: confidence interval; MH: Mantel-Haenszel; T2DM: type 2 diabetes mellitus; VDD: vitamin D deficiency</p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="eemd-02-101438-g004.tif" />
</fig>
</sec>
</sec>
<sec id="s4">
<title>Discussion</title>
<p id="p-12">The bidirectional relationship between diabetes mellitus and VDD has become a significant focus in metabolic disease research. Salih et al. [<xref ref-type="bibr" rid="B47">47</xref>] identified a substantial prevalence of vitamin D insufficiency and deficiency in individuals with T2DM (71%), especially in those with inadequate glycemic control and prolonged disease duration. This was established through a study involving 155 patients with T2DM and 155 non-exposed control subjects [<xref ref-type="bibr" rid="B47">47</xref>]. Similarly, Majeed et al. [<xref ref-type="bibr" rid="B62">62</xref>] observed a high prevalence of 25(OH)D deficiency among children and adolescents in the United Arab Emirates, with rates decreasing with age. Pittas et al. [<xref ref-type="bibr" rid="B71">71</xref>] reported that daily supplementation with 400 International Unit (IU) of vitamin D<sub>3</sub> did not significantly lower the risk of diabetes in high-risk populations not screened for VDD. Furthermore, they noted a higher prevalence of VDD in females compared to males, and a greater risk of VDD in obese individuals compared to those with normal weight [<xref ref-type="bibr" rid="B62">62</xref>]. Chen et al. [<xref ref-type="bibr" rid="B72">72</xref>] reported a negative correlation between serum 25(OH)D levels and the homeostatic model assessment of insulin resistance, specifically in females with VDD. This indicates a gender-specific effect of VDD on insulin resistance [<xref ref-type="bibr" rid="B72">72</xref>].</p>
<p id="p-13">This study systematically examined the complex interaction between VDD and diabetes mellitus, including its subtypes (T1DM/T2DM), by analyzing 53 observational studies (<italic>n</italic> = 552,032). The findings revealed a 53% increased risk of T2DM (OR = 1.53) in individuals with VDD. This association is likely mediated by vitamin D’s influence on pancreatic β-cell function and insulin signaling pathways. Studies show that vitamin D enhances insulin secretion by binding to vitamin D receptors (VDRs) on β-cells, which regulate insulin gene transcription [<xref ref-type="bibr" rid="B41">41</xref>]. VDD, meanwhile, promotes insulin resistance by upregulating inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α) and interleukin 6 (IL-6) and impairing glucose transporter type 4 (GLUT4) activity in skeletal muscle and adipose tissue [<xref ref-type="bibr" rid="B73">73</xref>, <xref ref-type="bibr" rid="B74">74</xref>]. Conversely, the risk of VDD was elevated by 2.02-fold in patients with T1DM and 2.62-fold in those with T2DM. These results corroborate previous studies by Lucato et al. [<xref ref-type="bibr" rid="B75">75</xref>] and Yang et al. [<xref ref-type="bibr" rid="B76">76</xref>], underscoring the pivotal role of disrupted vitamin D metabolism in diabetes development. Importantly, this bidirectional relationship exhibited significant heterogeneity across populations. For instance, the risk of VDD was notably higher in T1DM patients in Asia (OR = 2.21) compared to European populations (OR = 1.65), while the risk in T2DM patients in Africa showed greater variability (OR = 4.75, 95% CI: 0.55–41.06). This suggests that genetic factors, environmental exposures, and healthcare access may contribute to these geographical differences [<xref ref-type="bibr" rid="B77">77</xref>]. Socioeconomic factors, including limited access to vitamin D-rich foods, healthcare disparities, and cultural practices (e.g., sun avoidance), may contribute to the observed geographic heterogeneity in VDD risk [<xref ref-type="bibr" rid="B78">78</xref>]. Polymorphisms in vitamin D metabolism genes, like the BsmI variant, are linked to a heightened risk of T1DM, especially prevalent in Asian populations, and may impact 25-hydroxylation and vitamin D binding [<xref ref-type="bibr" rid="B79">79</xref>]. Cultural practices, including skin-whitening norms and indoor lifestyles, along with high urban air pollution, further limit cutaneous vitamin D synthesis in these regions [<xref ref-type="bibr" rid="B78">78</xref>]. In contrast to Europe, where vitamin D management is often integrated into diabetes care, many Asian healthcare systems lack standardized screening and supplementation for VDD [<xref ref-type="bibr" rid="B78">78</xref>, <xref ref-type="bibr" rid="B80">80</xref>].</p>
<p id="p-14">Study design differences significantly influenced the results. Prospective cohort studies demonstrated a stronger effect of VDD on T2DM (<italic>I</italic><sup>2</sup> = 66%), while retrospective case-control studies indicated a higher estimated risk of VDD in T2DM patients (OR = 2.71) but exhibited substantial heterogeneity (<italic>I</italic><sup>2</sup> = 90%). The bidirectional analysis primarily relied on retrospective case-control studies (47 out of 53), whereas only 6 prospective cohort studies were included. It is important to highlight that the relationship between VDD and the development of T2DM was predominantly based on data from 5 prospective cohorts. Conversely, the reverse association, where diabetes mellitus leads to VDD, was mainly supported by retrospective data. This disparity in study designs could impact the ability to establish causality, given that case-control studies are susceptible to recall bias and residual confounding. Analysis of follow-up duration revealed a critical insight: the T2DM risk associated with VDD increased 1.66-fold (<italic>I</italic><sup>2</sup> = 0%) when follow-up exceeded 5 years. This finding aligns with the ‘cumulative metabolic damage’ hypothesis by Schöttker et al. [<xref ref-type="bibr" rid="B81">81</xref>], which posits that persistent VDD can irreversibly impair islet function via chronic inflammation and oxidative stress. Conversely, short-term studies (&lt; 5 years) showed negligible and non-significant effect sizes, implying that earlier vitamin D interventions might be necessary to prevent pathological processes.</p>
<p id="p-15">The metabolic intricacy is further underscored by BMI stratification results: obese (BMI ≥ 30 kg/m<sup>2</sup>) T2DM patients exhibit a lower VDD risk (OR = 5.21) compared to their normal-weight counterparts (OR = 7.68). This paradox may arise from vitamin D accumulation in subcutaneous adipose tissue in obese individuals, leading to seemingly lower circulating 25(OH)D levels [<xref ref-type="bibr" rid="B82">82</xref>], while lean patients might have an inherent defect in vitamin D absorption or metabolism. In obesity, excess subcutaneous adipose tissue may sequester lipid-soluble vitamin D, creating a “sink effect” that lowers circulating 25(OH)D levels despite adequate systemic availability [<xref ref-type="bibr" rid="B82">82</xref>]. This hypothesis is supported by studies showing serum vitamin D elevation following weight loss without supplementation [<xref ref-type="bibr" rid="B83">83</xref>]. The CI for the OR in the obese group was wide (0.08–332.64), with a non-significant <italic>P</italic> value (<italic>P</italic> = 0.44), possibly due to the small sample size (<italic>n</italic> = 2) or outliers. Interpretation of this result should be approached with caution. Conversely, the significantly increased VDD risk in lean T2DM patients suggests intrinsic metabolic defects, potentially involving genetic polymorphisms in vitamin D hydroxylating enzymes, such as <italic>CYP2R1</italic> variants impairing hepatic 25-hydroxylation [<xref ref-type="bibr" rid="B84">84</xref>], gut dysbiosis-mediated suppression of intestinal VDR expression [<xref ref-type="bibr" rid="B85">85</xref>]. These findings advocate for personalized supplementation strategies: higher doses to counteract adipose sequestration in obesity [<xref ref-type="bibr" rid="B86">86</xref>] and targeted therapies for absorption or metabolism defects in lean patients.</p>
<p id="p-16">A complex interaction network links VDD and diabetes mellitus through multiple pathophysiological mechanisms. The high expression of the VDR in pancreatic β-cells and its regulatory role in insulin gene transcription underlie VDD’s direct impact on insulin secretion [<xref ref-type="bibr" rid="B87">87</xref>–<xref ref-type="bibr" rid="B89">89</xref>]. Norman et al. [<xref ref-type="bibr" rid="B90">90</xref>] found that insulin secretion from pancreatic islets in rat models decreased by 40–50% in the VDD group. This function was partially restored after supplementation with 1,25(OH)<sub>2</sub>D<sub>3</sub>. In the endocrine pathway, vitamin D directly influences β-cell function by maintaining intracellular calcium homeostasis. It regulates L-type calcium channels and calcium-binding proteins, essential for glucose-stimulated insulin secretion [<xref ref-type="bibr" rid="B91">91</xref>]. Deficiency in vitamin D can disrupt calcium signaling, diminishing β-cell sensitivity to glucose and impairing insulin synthesis and release [<xref ref-type="bibr" rid="B91">91</xref>]. Furthermore, vitamin D activates VDR, enhancing GLUT4 expression and membrane transport capacity in skeletal muscle and adipose tissue [<xref ref-type="bibr" rid="B74">74</xref>, <xref ref-type="bibr" rid="B91">91</xref>]. VDD leads to compromised glucose uptake in peripheral tissues. VDR gene polymorphisms, including BsmI, FokI, and ApaI loci, influence receptor activity [<xref ref-type="bibr" rid="B74">74</xref>]. Pro-inflammatory T helper 1 (Th1) and Th17 cells are critical in mediating autoreactivity against β-cells [<xref ref-type="bibr" rid="B92">92</xref>]. In animal models, 1,25(OH)<sub>2</sub>D, the active form of vitamin D, enhances regulatory T (Treg) cell activity and inhibits Th17 and Th1 cells via VDR [<xref ref-type="bibr" rid="B93">93</xref>, <xref ref-type="bibr" rid="B94">94</xref>]. VDD disrupts the balance between Th17 and Treg cells [<xref ref-type="bibr" rid="B95">95</xref>]. It reduces pro-inflammatory Th17 cell differentiation by inhibiting the STAT3/RORγt pathway, thereby decreasing cytokine-mediated autoimmune attacks, such as IL-17, on pancreatic islet β-cells, notably in T1DM. Conversely, deficiency leads to excessive IL-12 and IL-23 secretion by dendritic cells, promoting Th1/Th17 polarization and weakening Treg cells’ immunosuppressive effects, exacerbating chronic low-grade inflammation [<xref ref-type="bibr" rid="B96">96</xref>]. This state upregulates inflammatory factors like TNF-α and IL-6 via nuclear factor kappaB (NF-κB) and Jun N-terminal kinase (JNK) pathways, disrupts insulin receptor substrate-1 (IRS-1) phosphorylation, and induces insulin resistance in skeletal muscle and adipose tissue [<xref ref-type="bibr" rid="B97">97</xref>]. Vitamin D supplementation can inhibit the NF-κB pathway, reducing pro-inflammatory factors, and may more effectively prevent and treat diabetes mellitus [<xref ref-type="bibr" rid="B73">73</xref>, <xref ref-type="bibr" rid="B98">98</xref>], as demonstrated by Mohammad et al. [<xref ref-type="bibr" rid="B99">99</xref>] who observed a dose-dependent positive correlation between 25(OH)D levels and insulin sensitivity. Conversely, diabetes mellitus exacerbates VDD via several pathways: obesity-related fat accumulation decreases vitamin D bioavailability [<xref ref-type="bibr" rid="B82">82</xref>], diabetic nephropathy impairs 1,25(OH)<sub>2</sub>D activation [<xref ref-type="bibr" rid="B100">100</xref>], and autonomic neuropathy limits outdoor activity, reducing skin synthesis of vitamin D.</p>
<p id="p-17">While this study confirmed the robustness of its findings through sensitivity analyses and the trim and fill method, several limitations persist. The observational design cannot fully account for confounding factors like sunshine duration and dietary intake. Additionally, the predominance of studies from Asia (70%) may limit the global applicability of the results. Despite efforts to harmonize VDD diagnostic thresholds through data conversion, variations in 25(OH)D measurement methods across original studies may introduce residual bias. These limitations underscore the need for more prospective cohort studies, particularly standardized ones in underrepresented regions such as Africa and South America.</p>
<p id="p-18">This study examines the integrated management of diabetes mellitus and VDD through clinical transformation. For Asian patients with T1DM and chronic VDD, routine clinical procedures should include regular monitoring of serum 25(OH)D levels, with intervention thresholds raised from the standard &lt; 20 ng/mL to &lt; 30 ng/mL. At the public health level, promoting vitamin D-fortified foods, such as dairy products, is suggested as an effective prevention strategy in regions with high prevalence, like South Asia and the Middle East. This study underscores the heightened risk of VDD in normal-weight T2DM patients, prompting clinicians to focus on lean individuals and tailor vitamin D supplementation plans. Gender differences, particularly in postmenopausal women with reduced estrogen affecting vitamin D binding protein function, should also be considered [<xref ref-type="bibr" rid="B72">72</xref>, <xref ref-type="bibr" rid="B101">101</xref>]. Maintaining 25(OH)D levels at or above 25 ng/mL is recommended to enhance insulin sensitivity. Future research should investigate the metabolic benefits of VDR agonists in specific populations and determine the optimal timing and dosage of supplementation through randomized controlled trials to develop an evidence-based strategy for addressing the “metabolism-vitamin D disproportionation cycle”.</p>
<sec id="t4-1">
<title>Conclusion</title>
<p id="p-19">This meta-analysis indicates a robust bidirectional link between diabetes mellitus and VDD. Individuals with VDD exhibited a 53% higher risk of developing diabetes, while those with T1DM and T2DM faced a 2.02-fold and 2.62-fold increased risk of VDD, respectively. Subgroup analyses highlighted geographic and metabolic variations, with elevated risks in Asian populations (OR = 2.21 for T1DM) and normal-weight T2DM patients (OR = 7.68 vs. obese: OR = 5.21). However, these findings should be interpreted with caution due to limitations such as significant heterogeneity (e.g., <italic>I</italic><sup>2</sup> = 90% for VDD in T2DM), regional overrepresentation (70% of studies from Asia), and observational design constraints that preclude causal inference. The bidirectional analysis relied mostly on retrospective case-control studies (47 out of 53), which might lead to recall bias and residual confounding, especially in the diabetes mellitus to VDD pathway. Variations in 25(OH)D measurement methods and unmeasured confounds (sun, diet) may further bias the results. Routine screening of serum 25(OH)D levels in diabetic patients, especially in high-risk subgroups such as Asian individuals with T1DM and lean T2DM, is essential for guiding timely vitamin D supplementation. Vitamin D fortification programs for staple foods like dairy and cereals should be prioritized in regions with high deficiency rates, such as South Asia and sub-Saharan Africa. Incorporating 25(OH)D screening into diabetes management guidelines is advisable. Additionally, a thorough evaluation of the cost-effectiveness of vitamin D-fortified dairy products is warranted. Future research should focus on large, multi-regional prospective cohort studies with standardized vitamin D assays to confirm causality and determine clinical intervention thresholds.</p>
</sec>
</sec>
</body>
<back>
<glossary>
<title>Abbreviations</title>
<def-list>
<def-item>
<term>25(OH)D</term>
<def>
<p>25-hydroxyvitamin D</p>
</def>
</def-item>
<def-item>
<term>BMI</term>
<def>
<p>body mass index</p>
</def>
</def-item>
<def-item>
<term>CI</term>
<def>
<p>confidence interval</p>
</def>
</def-item>
<def-item>
<term>CLIA</term>
<def>
<p>chemiluminescence immunoassay</p>
</def>
</def-item>
<def-item>
<term>GLUT4</term>
<def>
<p>glucose transporter type 4</p>
</def>
</def-item>
<def-item>
<term>GRADE</term>
<def>
<p>Grading of Recommendations, Assessment, Development, and Evaluation</p>
</def>
</def-item>
<def-item>
<term>IL-6</term>
<def>
<p>interleukin 6</p>
</def>
</def-item>
<def-item>
<term>NF-κB</term>
<def>
<p>nuclear factor kappaB</p>
</def>
</def-item>
<def-item>
<term>ORs</term>
<def>
<p>odds ratios</p>
</def>
</def-item>
<def-item>
<term>PI</term>
<def>
<p>prediction interval</p>
</def>
</def-item>
<def-item>
<term>T1DM</term>
<def>
<p>type 1 diabetes mellitus</p>
</def>
</def-item>
<def-item>
<term>T2DM</term>
<def>
<p>type 2 diabetes mellitus</p>
</def>
</def-item>
<def-item>
<term>Th1</term>
<def>
<p>T helper 1</p>
</def>
</def-item>
<def-item>
<term>TNF-α</term>
<def>
<p>tumor necrosis factor-alpha</p>
</def>
</def-item>
<def-item>
<term>Treg</term>
<def>
<p>regulatory T</p>
</def>
</def-item>
<def-item>
<term>VDD</term>
<def>
<p>vitamin D deficiency</p>
</def>
</def-item>
<def-item>
<term>VDRs</term>
<def>
<p>vitamin D receptors</p>
</def>
</def-item>
</def-list>
</glossary>
<sec id="s-suppl" sec-type="supplementary-material">
<title>Supplementary materials</title>
<p>The supplementary figures for this article are available at: <uri xlink:href="https://www.explorationpub.com/uploads/Article/file/101438_sup_1.pdf">https://www.explorationpub.com/uploads/Article/file/101438_sup_1.pdf</uri>. The supplementary tables for this article are available at: <uri xlink:href="https://www.explorationpub.com/uploads/Article/file/101438_sup_2.pdf">https://www.explorationpub.com/uploads/Article/file/101438_sup_2.pdf</uri>.</p>
<supplementary-material id="SD1" content-type="local-data">
<media xlink:href="101438_sup_1.pdf" mimetype="application" mime-subtype="pdf"></media>
</supplementary-material>
<supplementary-material id="SD2" content-type="local-data">
<media xlink:href="101438_sup_2.pdf" mimetype="application" mime-subtype="pdf"></media>
</supplementary-material>
</sec>
<sec id="s6">
<title>Declarations</title>
<sec id="t-6-1">
<title>Author contributions</title>
<p>HH: Writing—original draft, Writing—review &amp; editing, Conceptualization, Methodology, Data curation, Formal analysis, Investigation, Resources, Supervision. SS: Data curation, Formal analysis, Investigation. DL: Formal analysis, Investigation. All authors read and approved the submitted version.</p>
</sec>
<sec id="t-6-2" sec-type="COI-statement">
<title>Conflicts of interest</title>
<p>The authors declare that they have no conflicts of interest.</p>
</sec>
<sec id="t-6-3">
<title>Ethical approval</title>
<p>Not applicable.</p>
</sec>
<sec id="t-6-4">
<title>Consent to participate</title>
<p>Not applicable.</p>
</sec>
<sec id="t-6-5">
<title>Consent to publication</title>
<p>Not applicable.</p>
</sec>
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