ABSTRACT
Background and Aim
Vitamin D, a fat‐soluble vitamin, has various vital roles in physiological processes. Recent evidence suggests that optimal vitamin D levels may be associated with improved immune function and reduced systemic inflammation. The present study aims to evaluate associations between serum vitamin D levels and various hematological inflammatory indices in participants of the MASHAD cohort study, Mashhad, Iran.
Methods
This study was designed as a cross‐sectional analysis conducted within the framework of phase I of the MASHAD cohort study. Participants (aged ≥ 18 years old and signed an informed consent) were recruited from a defined population, ensuring the representativeness of the sample for evaluation of potential association between serum vitamin D levels and hematological inflammatory indices including Red Cell Distribution Width (RDW‐CV), high‐sensitivity C‐reactive protein (hs‐CRP), systemic immune‐inflammation index (SII), platelet‐to‐lymphocyte ratio (PLR), Neutrophil‐to‐Lymphocyte Ratio (NLR), Mean Platelet Volume (MPV), and Platelet Distribution Width (PDW).
Results
A total of 458 participants were included in the current study, comprising 229 males and 229 females. Serum vitamin D had weak correlation with hs‐CRP (r = −0.02), SII (r = −0.01), PLR (r = 0.05), NLR (r = 0.01), PDW (r = −0.05), and RDW‐CV (r = −0.04). For males, no significant differences were observed in RDW‐CV, PDW, MPV, NLR, or PLR across the vitamin D quartiles (p > 0.05 for all comparisons). In contrast, among female participants, significant differences in PDW (p = 0.026) between quartile groups 1 and 4, and marginal significance between quartile 1 and 3 for RDW (p = 0.062) were observed.
Conclusion
The current findings revealed a weak association between serum vitamin D concentrations and inflammatory indicators. Among hematological parameters, gender‐based differences were observed, with PDW and RDW levels significantly varying between vitamin D quartiles in women.
Keywords: hematologic inflammatory biomarkers, inflammatory indices, metabolic parameters, vitamin D
1. Introduction
Vitamin D is a fat‐soluble vitamin that contributes to diverse vital physiological processes, including calcium homeostasis and bone metabolism. Vitamin D has been shown to exert potential association as an immunomodulatory agent, suggesting that its optimal levels are associated with an improved immune function and reduced systemic inflammation [1, 2]. Particularly, the relationship between vitamin D levels and hematological inflammatory indices, as markers of systemic inflammation, has garnered considerable attention [3, 4].
The Neutrophil‐to‐Lymphocyte Ratio (NLR), Platelet‐to‐Lymphocyte Ratio (PLR), Red Cell Distribution Width (RDW), Platelet Distribution Width (PDW), Systemic Immune‐Inflammation Index (SII), and Mean Platelet Volume (MPV) have been identified as indicators of inflammation and linked to various clinical conditions, including cardiovascular diseases and autoimmune disorders [5, 6]. Elevated levels of these indices are often associated with poor clinical outcomes, suggesting that they may reflect an inflammatory state [7, 8].
Despite the growing body of evidence supporting the role of vitamin D in modulating inflammatory responses, the specific relationship between serum vitamin D levels and hematological inflammatory indices remains inadequately explored. Previous studies have reported an association between higher vitamin D concentrations and lower inflammatory markers, while others report no significant association [9, 10, 11, 12]. These inconsistencies may be due to geographical and demographic variations among studies, necessitating further investigation to clarify such associations. This understanding is particularly relevant given the increasing prevalence of vitamin D deficiency globally and its potential impact on public health [9, 10, 11, 12].
The present study aims to evaluate associations between serum vitamin D levels and various hematological inflammatory indices in participants of phase I of the MASHAD cohort study.
2. Methods
2.1. Study Design and Participants
This study was designed as a cross‐sectional analysis conducted within the framework of phase I of the MASHAD cohort study. Participants were recruited from a defined population, ensuring the representativeness of the sample to investigate associations between serum vitamin D levels and hematological inflammatory indices. The study included individuals aged ≥ 18, who provided informed consent prior to participation. The Ethics Committee of Mashhad University of Medical Sciences approved the study (approval number: IR.MUMS.MEDICAL.REC.1402.001).
2.2. Inclusion and Exclusion Criteria
Inclusion criteria for the study encompassed being an adult aged 18 years or older, providing informed consent, and having complete demographic and laboratory data required for the analysis.
Exclusion criteria included creatinine levels greater than 1.2 mg/dL, hs‐CRP levels greater than 5 mg/L, or white blood cell (WBC) counts greater than 10 × 103/µL, in order to eliminate participants with evidence of overt acute or chronic inflammatory disorders and severe organ dysfunction.
Within the MASHAD cohort, information on several lifestyle and clinical factors potentially related to vitamin D status (such as smoking status, basic anthropometric indices, habitual physical activity or regular exercise, and menopausal status) was collected. However, detailed data on seasonal variation in sampling, quantitative sun exposure, and vitamin D supplementation were either not available or not sufficiently complete for inclusion in the present analysis. Similarly, subclinical inflammatory conditions (such as diabetes mellitus, metabolic syndrome, rheumatologic or other inflammatory autoimmune diseases, malignancies, malignant tumors, connective tissue diseases, hematological diseases, and severe hepatorenal dysfunction, inflammatory bowel disease, and chronic inflammatory dermatoses, including psoriasis and hidradenitis suppurativa) that might influence hematological inflammatory markers could not be systematically identified in all participants. As a result, these variables were not incorporated into the primary statistical models and may represent potential sources of residual confounding.
2.3. Biochemical and Other Measurements
In this study, serum vitamin D levels, specifically 25‐hydroxyvitamin D [25(OH)D], were measured. 25(OH)D is the primary circulating form of vitamin D in the blood and is considered the standard indicator of vitamin D status, as it reflects both vitamin D obtained from sunlight exposure and dietary sources. Measurements were performed using standardized immunoassay methods, which are laboratory techniques that use specific antibodies to detect and quantify target molecules in biological samples. This method is widely used in clinical and epidemiological studies due to its accuracy, reproducibility, and standardization.
Hematological indices, including NLR, PLR, RDW, PDW, and MPV, were measured using automated hematology analyzers as part of a complete blood count (CBC). Blood samples were collected in EDTA tubes to prevent clotting, and platelet size was determined by electrical impedance or optical methods, which calculate MPV as the average platelet volume and PDW as the variation in platelet size. Additionally, inflammatory markers such as hs‐CRP were measured to provide an overview of the inflammatory status of the participants.
2.4. Statistical Analysis
Participants were divided into quartiles based on their serum 25‑hydroxyvitamin D (25(OH)D) distribution. The quartile cut‑offs (ng/mL) were: Q1: < 12, Q2: 12–18, Q3: 18–28, Q4: > 28, derived from the distribution of the study population and consistent with previously reported vitamin D categories in Iranian adults.
Statistical analyses were performed using SPSS software to evaluate associations between serum vitamin D levels and hematological inflammatory indices. Categorical variables are presented as frequencies and percentages, while continuous variables are reported as means ± standard deviations (SD). The χ 2 test compared categorical variables, and one‐way ANOVA was used for continuous variables. The multiple comparisons analysis, utilizing Tukey's honestly significant difference (HSD) test, was performed to evaluate differences in hematological indices across different quartile groups of vitamin D levels for both male and female participants. Participants were divided into quartiles based on the distribution of the variable, with each quartile representing 25% of the study population. This method allows for comparisons across different levels of the variable and helps reduce the influence of extreme values. To further evaluate the association between serum vitamin D level and each hematological inflammatory index while accounting for potential confounding, crude and multivariable‐adjusted linear regression models were additionally constructed for men and women separately, with hematological indices as dependent variables and serum vitamin D as the independent variable. Adjusted models controlled for covariates that showed a statistically significant imbalance across vitamin D quartiles in Table 1 (age and menopausal status among women). A p value of < 0.05 was considered statistically significant. A correlation heat map of the variables was plotted using Pearson's correlation coefficients, with color intensity representing the magnitude and direction of associations.
Table 1.
Baseline characteristics in different serum vitamin D quartiles stratified by gender.
| Parameter | 1st quartile | 2nd quartile | 3rd quartile | 4th quartile | p value | ||
|---|---|---|---|---|---|---|---|
| Male (n = 229) | |||||||
| Age (year) | 46.57 ± 8.12 | 46.76 ± 7.63 | 46.01 ± 7.49 | 47.57 ± 8.91 | 0.756 | ||
| Smoking | Never | 24 (60.0%) | 35 (55.6%) | 43 (61.4%) | 30 (53.6%) | 0.978 | |
| Ex | 5 (12.5%) | 10 (15.9%) | 9 (12.9%) | 8 (14.3%) | |||
| Current | 11 (27.5%) | 18 (28.6%) | 18 (25.7%) | 18 (32.1%) | |||
| Height (m) | 1.69 ± 0.06 | 1.69 ± 0.06 | 1.69 ± 0.06 | 1.7 ± 0.06 | 0.852 | ||
| Weight (kg) | 75.15 ± 15.42 | 74.8 ± 12.76 | 75.05 ± 14.34 | 77.27 ± 13.59 | 0.763 | ||
| BMI | 26.14 ± 4.65 | 26.16 ± 4.09 | 26.1 ± 4.49 | 26.67 ± 4.19 | 0.884 | ||
| PAL | Inactive | 22 (56.4%) | 39 (60.9%) | 29 (48.3%) | 30 (53.6%) | 0.951 | |
| Low activity | 8 (20.5%) | 13 (20.3%) | 13 (21.7%) | 11 (19.6%) | |||
| Moderate activity | 6 (15.4%) | 6 (9.4%) | 10 (16.7%) | 9 (16.1%) | |||
| High activity | 3 (7.7%) | 6 (9.4%) | 8 (13.3%) | 6 (10.7%) | |||
| DM | 5 (12.8%) | 7 (10.4%) | 2 (3.1%) | 7 (12.3%) | 0.200 | ||
| MetS | 12 (30.8%) | 17 (25.0%) | 19 (29.7%) | 13 (22.8%) | 0.762 | ||
| Female (n = 229) | |||||||
| Age (year) | 44.83 ± 6.95 | 45.76 ± 7.23 | 45.76 ± 7.61 | 49.29 ± 8.14 | 0.007 | ||
| Smoking | Never | 55 (74.3%) | 44 (84.6%) | 32 (69.6%) | 44 (77.2%) | 0.063 | |
| Ex | 1 (1.4%) | 4 (7.7%) | 4 (8.7%) | 6 (10.5%) | |||
| Current | 18 (24.3%) | 4 (7.7%) | 10 (21.7%) | 7 (12.3%) | |||
| Height (m) | 1.56 ± 0.06 | 1.55 ± 0.04 | 1.56 ± 0.05 | 1.55 ± 0.04 | 0.450 | ||
| Weight (kg) | 70.53 ± 12.67 | 68.55 ± 10.27 | 70.2 ± 8.64 | 68.8 ± 12.82 | 0.723 | ||
| BMI | 28.78 ± 4.67 | 28.4 ± 4.06 | 28.8 ± 4.23 | 28.55 ± 5.07 | 0.960 | ||
| PAL | Inactive | 2 (2.8%) | 0 (0.0%) | 1 (2.4%) | 5 (9.1%) | 0.319 | |
| Low activity | 24 (33.8%) | 19 (39.6%) | 13 (31.7%) | 13 (23.6%) | |||
| Moderate activity | 34 (47.9%) | 25 (52.1%) | 20 (48.8%) | 30 (54.5%) | |||
| High activity | 11 (15.5%) | 4 (8.3%) | 7 (17.1%) | 7 (12.7%) | |||
| Menstruation status | Pre‐menopause | 54 (73.0%) | 34 (65.4%) | 25 (56.8%) | 23 (41.1%) | 0.009 | |
| Natural menopause | 17 (23.0%) | 15 (28.8%) | 13 (29.5%) | 28 (50.0%) | |||
| Surgical menopause | 3 (4.1%) | 3 (5.8%) | 6 (13.6%) | 5 (8.9%) | |||
| DM | 3 (4.1%) | 3 (5.8%) | 5 (11.4%) | 5 (8.9%) | 0.443 | ||
| MetS | 23 (31.1%) | 18 (34.6%) | 15 (34.1%) | 28 (50.0%) | 0.142 | ||
Abbreviations: BMI, body mass index; DM, diabetes mellitus; Mets, metabolic syndrome; PAL, physical activity.
3. Results
3.1. Population Demographics
A total of 458 participants were included in the study, comprising 229 males and 229 females. The mean age, height, weight, and BMI were 46.7 ± 7.9 years, 1.69 ± 0.06 m, 75.5 ± 13.8 kg, and 26.1 ± 4.3 among males, and 44.8 ± 7.6 years, 1.56 ± 0.05 m, 69.6 ± 11.4 kg, and 28.6 ± 4.5 among females, respectively. Analysis revealed a statistically significant difference in age between males and females (p = 0.007). Within the population, non‐smokers, ex‐smokers, and current smokers constituted 57.6%, 14.0%, and 28.4% of males, and 76.4%, 6.6%, and 17.0% of females, respectively (Table 1). The marginally significant difference in smoking status was observed among females (p = 0.063).
3.2. Metabolic, Hematological, and Inflammatory Indices
Primary endpoints were NLR, PLR, RDW, PDW, MPV, and hsCRP. Secondary endpoints included metabolic parameters (LDL, cholesterol, glucose, triglycerides) and other hematological indices (WBC, platelet count).
Hematological indices were compared between vitamin D quartiles. Among males, only LDL cholesterol levels exhibited significant differences across vitamin D groups (F (3, 225) = 2.775, p = 0.042). Among females, only total cholesterol levels demonstrated significant differences (F (3, 225) = 3.399, p = 0.019), while other indices such as glucose and triglycerides did not show significant variations in either sex (Tables 2A and 2B).
Table 2A.
Metabolic parameters across vitamin D quartiles (by gender).
| Gender | Quartile | LDL (mg/dL) | TC (mg/dL) | p value (LDL) | p value (TC) |
|---|---|---|---|---|---|
| Male | Q1 | 109.98 ± 41.18 | 182.73 ± 42.58 | 0.042 | 0.076 |
| Male | Q2 | 122.46 ± 38.25 | 193.7 ± 43.55 | ||
| Male | Q3 | 112.29 ± 30.82 | 181.97 ± 44.64 | ||
| Male | Q4 | 104.08 ± 33.17 | 173.45 ± 36.79 | ||
| Female | Q1 | 117.52 ± 29.82 | 186.59 ± 32.27 | 0.083 | 0.019 |
| Female | Q2 | 119.77 ± 35.63 | 192.71 ± 39.04 | ||
| Female | Q3 | 119.65 ± 39.2 | 192.52 ± 42.35 | ||
| Female | Q4 | 133.26 ± 43.86 | 207.77 ± 41.55 |
Note: Vitamin D quartile ranges (ng/mL): Q1 < 12, Q2 = 12–18, Q3 = 18–28, Q4 > 28. Statistical method: One‑way ANOVA; post‑hoc Tukey HSD for pairwise comparisons. Bold p values indicate statistical significance (p < 0.05).
Abbreviations: LDL, low‑density lipoprotein; TC, total cholesterol.
Table 2B.
Hematological inflammatory indices across vitamin D quartiles (by gender).
| Gender | Quartile | RDW (%) | PDW (%) | p value (RDW) | p value (PDW) |
|---|---|---|---|---|---|
| Male | Q1 | 13.87 ± 2.7 | 12.4 ± 2.13 | 0.871 | 0.871 |
| Male | Q2 | 13.64 ± 1.02 | 12.62 ± 1.55 | ||
| Male | Q3 | 13.73 ± 1.38 | 13.18 ± 2.1 | ||
| Male | Q4 | 13.85 ± 1.49 | 12.96 ± 1.82 | ||
| Female | Q1 | 14.2 ± 1.47 | 13.5 ± 2.41 | 0.027 | 0.026 |
| Female | Q2 | 14.15 ± 2.14 | 13.12 ± 2.35 | ||
| Female | Q3 | 13.51 ± 0.91 | 13.13 ± 1.78 | ||
| Female | Q4 | 13.67 ± 0.66 | 12.38 ± 2.04 |
Note: Vitamin D quartile ranges (ng/mL): Q1 < 12, Q2 = 12–18, Q3 = 18–28, Q4 > 28. Post‑hoc significant pairs (females): PDW: Q1 vs. Q4 (p = 0.026); RDW: Q1 vs. Q4 (p = 0.027).
Abbreviations: PDW, platelet distribution width; RDW, red cell distribution width.
3.3. Correlation Between Serum Vitamin D and Inflammatory Indices
In the whole sample, serum vitamin D had a weak correlation with hs‐CRP (r = −0.02), SII (r = −0.01), PLR (r = 0.05), NLR (r = 0.01), PDW (r = −0.05), and RDW‐CV (r = −0.04) (Figure 1). Based on the multiple comparisons analysis across different quartile groups of vitamin D levels, revealed no significant differences in RDW‐CV, PDW, MPV, NLR, or PLR in males (p > 0.05 for all comparisons). However, among female participants, significant differences in PDW between the first and fourth groups (p = 0.026) and significant differences in RDW between the first and fourth groups (p = 0.027) were found (Table 3 and Supporting Information S1). In multivariable‐adjusted linear regression models controlling for baseline covariates that differed significantly across vitamin D quartiles (age and menopausal status among women), the association between vitamin D and PDW remained statistically significant (adjusted p= 0.004), whereas the association with RDW was attenuated to borderline significance (adjusted p= 0.051), suggesting that part of the crude RDW association may be explained by age and menopausal status (Table 4).
Figure 1.
Heatmap presentation of correlations between serum vitamin D, sex, age, WBC, and inflammatory indexes. BMI, body mass index; MPV, mean platelet volume; NLR, neutrophil‐lymphocyte ratio; PDW, Platelet distribution width; PLR, platelet‐to‐lymphocyte ratio; RDW, red cell distribution width; SII, systemic immune‐inflammation index.
Table 3.
Pairwise comparisons of RDW and PDW (only significant or borderline).
| Parameter | Gender | Comparison | p value |
|---|---|---|---|
| RDW | Female | Q1 vs. Q3 | 0.062 |
| RDW | Female | Q1 vs. Q4 | 0.027 |
| PDW | Female | Q1 vs. Q4 | 0.026 |
Note: Only comparisons with p < 0.10 are shown. The full pairwise matrix (including Q2–Q3, Q2–Q4, Q3–Q4) is available as supporting material.
Table 4.
Crude and adjusted linear regression analysis of the association between serum vitamin D level and hematological inflammatory indices, stratified by sex.
| Crude | Adjusteda | |||||
|---|---|---|---|---|---|---|
| B | 95%CI | p value | B | 95% CI | p value | |
| Male | ||||||
| WBC (103/mcL) | −0.106 | −0.283 to 0.072 | 0.242 | −0.106 | −0.282 to 0.069 | 0.233 |
| Lymphocytes (%) | −0.551 | −1.416 to 0.307 | 0.206 | −0.581 | −1.444 to 0.281 | 0.186 |
| Neutrophils (%) | −0.737 | −5.193 to 1.044 | 0.745 | −0.724 | −5.204 to 3.757 | 0.820 |
| Platelets (103/mcL) | −3.320 | −9.790 to 3.150 | 0.313 | −3.373 | −9.821 to 3.076 | 0.304 |
| RDW (%) | 0.003 | −0.204 to 0.210 | 0.977 | 0.002 | −0.206 to 0.210 | 0.982 |
| PDW (%) | 0.211 | −0.029 to 0.451 | 0.085 | 0.217 | −0.024 to 0.458 | 0.077 |
| MPV (fL) | 0.136 | 0.017 to 0.256 | 0.026 | 0.139 | 0.019 to 0.259 | 0.023 |
| NLR | 0.035 | −0.045 to 0.144 | 0.391 | 0.036 | −0.043 to 0.116 | 0.369 |
| PLR | 1.311 | −2.800 to 5.422 | 0.530 | 1.365 | −2.757 to 5.486 | 0.515 |
| SII (109 cells/L) | −0.369 | −19.908 to 19.169 | 0.970 | −0.445 | −20.071 to 19.180 | 0.964 |
| Female | ||||||
| WBC (103/mcL) | −0.062 | −0.207 to 0.083 | 0.400 | −0.040 | −0.189 to 0.108 | 0.593 |
| Lymphocytes (%) | 0.191 | −0.625 to 1.007 | 0.645 | −0.103 | −0.939 to 0.733 | 0.809 |
| Neutrophils (%) | −0.013 | −1.071 to 1.044 | 0.980 | 0.346 | −0.732 to 1.424 | 0.528 |
| Platelets (103/mcL) | 2.172 | −4.894 to 9.239 | 0.545 | 4.594 | −2.579 to 11.767 | 0.208 |
| RDW (%) | −0.212 | −0.37 to 0.048 | 0.012 | −0.168 | −0.336 to 0.001 | 0.051 |
| PDW (%) | −0.326 | −0.577 to 0.075 | 0.011 | −0.382 | −0.639 to 0.125 | 0.004 |
| MPV (fL) | −0.111 | −0.220 to 0.001 | 0.047 | −0.123 | −0.236 to 0.010 | 0.033 |
| NLR | −0.037 | −0.105 to 0.031 | 0.285 | −0.010 | −0.080 to 0.059 | 0.766 |
| PLR | 1.308 | −3.461 to 6.076 | 0.589 | 3.118 | −1.778 to 8.014 | 0.211 |
| SII (109 cells/L) | −5.710 | −26.207 to 14.787 | 0.583 | 4.900 | −15.753 to 25.554 | 0.640 |
Adjusted for variables with p < 0.05 in Table 1 (age and menopausal status among women). Diabetes mellitus and metabolic syndrome were not entered as covariates because their prevalence did not differ significantly across vitamin D quartiles in either sex (Table 1: DM, p= 0.200 men/0.443 women; MetS, p= 0.762 men/0.142 women); this remains acknowledged as a limitation (see Section 2.2).
4. Discussion
The study included 458 participants to evaluate the association between serum vitamin D levels and various hematological inflammatory indices. Results suggested that serum vitamin D levels had weak correlation with inflammatory indices, including hs‐CRP, SII, PLR, NLR, PDW, and RDW‐CV. Among males, no significant differences were observed in hematological indices across vitamin D groups, while among females, significant differences in PDW and RDW between specific quartile groups were observed.
4.1. Association Between Vitamin D and Metabolic Parameters
Although significant differences in LDL among males and total cholesterol among females were observed, these metabolic findings were secondary outcomes of the present study. Therefore, they should be interpreted cautiously. Previous studies have reported inconsistent associations between vitamin D status and lipid profiles, and the clinical significance of these observations remains uncertain. Further prospective studies are warranted [13, 14].
4.2. Association Between Vitamin D and Hematologic Parameters
In general, we observed weak associations between vitamin D concentrations and hematological inflammatory parameters in the entire cohort studied. The vast majority of indices based on leukocytes and platelets, such as WBC, neutrophil granulocytes, lymphocytes, NLR, PLR, SII, and platelets, showed no significant differences among vitamin D quartiles by gender. This suggested that, in the community setting, hematological inflammatory biomarkers widely utilized in research practice are not good indicators of vitamin D deficiency or insufficiency on the population level.
Notably, we observed sex‐specific associations between vitamin D concentration and morphological indices of erythrocytes and platelets. We found that, in females, RDW and PDW decreased significantly in higher quartiles of vitamin D compared to the lowest quartile.
Previous studies have reported that elevated RDW and PDW are associated with increased inflammatory activity, adverse cardiovascular outcomes, and autoimmune diseases. However, the small differences observed in the present study should be interpreted cautiously and are not sufficient to support any immediate clinical implications [15, 16]. This means that the lower RDW and PDW were observed among women with higher vitamin D levels, but due to the cross‐sectional study design, causation cannot be attributed [17, 18].
There are several possible explanations for this sex‐stratified pattern, which should be interpreted with caution, as a formal statistical test of a sex × vitamin D interaction term in a unified regression model was not performed in the present analysis. Firstly, sex hormones, particularly estrogen, are known to affect bone marrow activity, erythropoiesis, and thrombocytosis. Estrogen may also be related to the metabolism of vitamin D receptors [19].
Additionally, differences in body fat distribution and iron levels between the sexes may differentially affect RDW and PDW in their associations with vitamin D concentrations; for instance, subclinical iron deficiency or iron loss through menstruation among females may act synergistically in influencing RDW and PDW with the associated vitamin D concentration on erythropoiesis [20]. Furthermore, unrecognized variables like varying levels of sun exposure, dietary intake of vitamin D, usage of vitamin D or multivitamins, and the presence of chronic, low‐grade inflammation could be responsible for the sex‐specific trends found. Further studies incorporating hormonal, nutritional, and other lifestyle measures, including follow‐up analyses, will be required to clarify these mechanisms behind the gender differences [21].
Although statistically significant differences in RDW and PDW were observed among female participants across vitamin D quartiles, the absolute magnitude of these differences was small. Therefore, these findings should not be considered clinically meaningful or interpreted as evidence supporting the use of RDW or PDW as diagnostic or screening markers for vitamin D status. Rather, they may reflect modest associations that warrant further investigation in larger prospective studies [22, 23].
While previous studies have demonstrated associations between vitamin D and hematological inflammatory biomarkers, our study is unique in that it expands these results to a large population‐based sample in northeastern Iran, an area where vitamin D deficiency is very prevalent along with cardiometabolic risks [24, 25].
Through the careful analysis of a comprehensive panel of hematological parameters and categorization according to sex and vitamin D quartiles, we highlight the finding that most of the ratios obtained from the leukocytes and platelets demonstrate weak correlations with vitamin D levels, while RDW and PDW present a unique pattern among females [26, 27]. This study expands the current knowledge by suggesting that the associations between serum vitamin D concentrations and hematological inflammatory markers are generally weak and may be limited to specific hematological parameters observed among female participants rather than being consistently observed across all inflammatory indices.
In addition, contrary to earlier large‐scale studies that have described a general association of vitamin D deficiency with inflammatory markers, our findings reveal a selective effect based on sex.
It is important to recognize several limitations of the current study when assessing its results. First, the cross‐sectional nature of the study cannot establish causality for any of the associations found between vitamin D levels and hematological inflammatory parameters. Second, while we investigated data collected from a relatively large community sample, this research does not offer a novel biological pathway or an innovative method; rather, it offers further support to previous findings from different locations. One additional limitation to acknowledge is the absence of extensive data on factors that may affect vitamin D levels and inflammatory responses. For instance, data related to seasonality in blood collection, sun exposure, and vitamin D supplementation, which could significantly impact vitamin D serum levels, were insufficiently provided. In addition, although some statistically significant associations were identified, the magnitude of these differences was small, limiting their immediate clinical applicability.
Moreover, some subclinical states of inflammation that might not be suggested by any significant changes in common laboratory tests but could be related to hematologic inflammatory biomarkers could not be considered in this analysis. This lack of adjustment for potential confounders may be associated with residual confounding and restricts the validity and applicability of the obtained results regarding the connection between vitamin D and inflammatory biomarkers. Further research that considers a larger number of variables related to individual lifestyle, environmental factors, and subclinical health states is necessary to establish clearer relationships. Additionally, the sex‐specific patterns reported here were identified through separate sex‐stratified analyses rather than through a formal statistical test of a sex × vitamin D interaction term in a unified regression model; therefore, these findings should be regarded as exploratory and hypothesis‐generating, and future studies with adequate statistical power to formally test this interaction are warranted. Lastly, our study involves individuals residing in a single region of Iran and cannot be generalized to people with a distinct ethnicity or living under different environmental and health care circumstances.
5. Conclusion
It can be concluded from this study that there is very little association between vitamin D levels in the blood and most inflammatory markers studied. However, sex‐stratified analyses suggested a differential pattern between men and women with respect to inflammatory parameters measured through hematological analysis, with vitamin D quartiles associated with significant differences in PDW and RDW values in females but not in males; this pattern should be confirmed by formal interaction testing in future studies. From this, it can be said that in this particular Middle Eastern adult population, the common inflammatory markers measured through blood test may not be of much use in determining vitamin D deficiency, though some red cells/platelets markers may reveal vitamin D deficiency in women.
Author Contributions
Abbas Mohtashamian: writing – Original draft. MohammadReza Latifi: writing – Original draft. Reza Nezhad Shahrokh Abadi: writing – review and editing. Rana Kolahi‐Ahari: data curation. Toktam Sahranavard: supervision. Sara Saffar Soflaei: formal analysis. Negar Morovatdar: validation. Mohaddeseh Sedaghati Noghabi: investigation. Gordon A. Ferns: funding acquisition. Ramin Rezaee: conceptualization. Majid Ghayour‐Mobarhan: project administration.
Ethics Statement
This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the Ethics Committee of Mashhad University of Medical Sciences, Mashhad, Iran. Written informed consent was obtained from all patients.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Table S1: Statistical significance in comparison of vitamin D percentiles with various parameters based on gender.
Acknowledgments
This study was supported by the Vice‐Chancellor for Research and Technology of Mashhad University of Medical Sciences, Mashhad, Iran (Project No. 4012126), approved by the ethics committee of the university (Approval No. IR.MUMS.MEDICAL.REC.1402.001) and presented as a thesis for a medical doctorate degree by Mohaddeseh Sedaghati Noghabi. We thank the Research Center for Biochemistry and Nutrition in Metabolic Diseases, Mashhad University of Medical Sciences, Mashhad, Iran. All authors read and approved the final manuscript. This work was supported by the Vice‐Chancellor for Research and Technology of Mashhad University of Medical Sciences, Mashhad, Iran.
Mohtashamian A., Latifi M., Nejad Shahrokh Abadi R., et al., “Association of Serum Vitamin D level and Hematological Inflammatory Indices in Participants of the MASHAD study,” Health Science Reports 9 (2026): e73142. 10.1002/hsr2.73142.
Abbas Mohtashamian and MohammadReza Latifi contributed equally as the first author.
Contributor Information
Ramin Rezaee, Email: Raminrezaee1983@gmail.com.
Majid Ghayour‐Mobarhan, Email: GhayourM@mums.ac.ir.
Data Availability Statement
The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.
References
- 1. Amrein K., Scherkl M., Hoffmann M., et al., “Vitamin D Deficiency 2.0: An Update on the Current Status Worldwide,” European Journal of Clinical Nutrition 74, no. 11 (2020): 1498–1513. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Christakos S., Hewison M., Gardner D. G., et al., “Vitamin D: Beyond Bone,” Annals of the New York Academy of Sciences 1287, no. 1 (2013): 45–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Greten F. R. and Grivennikov S. I., “Inflammation and Cancer: Triggers, Mechanisms, and Consequences,” Immunity 51, no. 1 (2019): 27–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Ma L., Zeng A., Chen B., Chen Y., and Zhou R., “Neutrophil to Lymphocyte Ratio and Platelet to Lymphocyte Ratio in Patients With Systemic Lupus Erythematosus and Their Correlation With Activity: A Meta‐Analysis,” International Immunopharmacology 76 (2019): 105949. [DOI] [PubMed] [Google Scholar]
- 5. Konuksever D., Yücel Karakaya S. P., Bölük O., Koçak M., Kılıç B. O., and Saç R. Ü., “The Association of Vitamin D Deficiency With Hemogram‐Derived Inflammatory Biomarkers in Children,” Nutrition, Metabolism, and Cardiovascular Diseases 32, no. 10 (2022): 2418–2423. [DOI] [PubMed] [Google Scholar]
- 6. Miroliaee A. E., Salamzadeh J., Shokouhi S., and Sahraei Z., “The Study of Vitamin D Administration Effect on CRP and Interleukin‐6 as Prognostic Biomarkers of Ventilator Associated Pneumonia,” Journal of Critical Care 44 (2018): 300–305. [DOI] [PubMed] [Google Scholar]
- 7. Demir M., Demir C., and Keçeoğlu S., “The Relationship Between Vitamin D Deficiency and Coronary Artery Ectasia,” Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej 10, no. 4 (2014): 238–241. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Akbas E. M., Gungor A., Ozcicek A., Akbas N., Askin S., and Polat M., “Vitamin D and Inflammation: Evaluation With Neutrophil‐to‐Lymphocyte Ratio and Platelet‐to‐Lymphocyte Ratio,” Archives of Medical Science 12, no. 4 (2016): 721–727. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Ghrahani R., Ghozali M., Sugianli A. K., Yuniati T., Judistiani R. T. D., and Setiabudiawan B., “Positive Correlation Between Monocyte‐to‐Lymphocyte Ratio and C‐Reactive Protein in Vitamin D Deficient Preterm Infants With Respiratory Distress Syndrome,” IIUM Medical Journal Malaysia 20, no. 2 (2021): 11–18. [Google Scholar]
- 10. Cure C., Cure E., Yuce S., Yazici T., Karakoyun I., and Efe H., “Mean Platelet Volume and Vitamin D Level,” Annals of Laboratory Medicine 34, no. 2 (2014): 98–103. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Di Rosa M., Malaguarnera G., De Gregorio C., Palumbo M., Nunnari G., and Malaguarnera L., “Immuno‐Modulatory Effects of Vitamin D3 in Human Monocyte and Macrophages,” Cellular Immunology 280, no. 1 (2012): 36–43. [DOI] [PubMed] [Google Scholar]
- 12. Erkus E., Aktas G., Atak B. M., Kocak M. Z., Duman T. T., and Savli H., “Haemogram Parameters in Vitamin D Deficiency,” Journal of the College of Physicians and Surgeons—Pakistan: JCPSP 28, no. 10 (2018): 779–782. [PubMed] [Google Scholar]
- 13. Abudawood M., Tabassum H., Ansar S., et al., “Assessment of Gender‐Related Differences in Vitamin D Levels and Cardiovascular Risk Factors in Saudi Patients With Type 2 Diabetes Mellitus,” Saudi Journal of Biological Sciences 25, no. 1 (2018): 31–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Chen N., Wan Z., Han S. F., Li B. Y., Zhang Z. L., and Qin L. Q., “Effect of Vitamin D Supplementation on the Level of Circulating High‐Sensitivity C‐Reactive Protein: A Meta‐Analysis of Randomized Controlled Trials,” Nutrients 6, no. 6 (2014): 2206–2216. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Autier P., Boniol M., Pizot C., and Mullie P., “Vitamin D Status and Ill Health: A Systematic Review,” Lancet. Diabetes & Endocrinology 2, no. 1 (2014): 76–89. [DOI] [PubMed] [Google Scholar]
- 16. Pilz S., Verheyen N., Grübler M. R., Tomaschitz A., and März W., “Vitamin D and Cardiovascular Disease Prevention,” Nature Reviews Cardiology 13, no. 7 (2016): 404–417. [DOI] [PubMed] [Google Scholar]
- 17. Aranow C., “Vitamin D and the Immune System,” Journal of Investigative Medicine 59, no. 6 (2011): 881–886. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Forget P., Khalifa C., Defour J.‐P., Latinne D., Van Pel M.‐C., and De Kock M., “What Is the Normal Value of the Neutrophil‐to‐Lymphocyte Ratio?,” BMC Research Notes 10, no. 1 (2017): 12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Straub R. H., “The Complex Role of Estrogens in Inflammation,” Endocrine Reviews 28, no. 5 (2007): 521–574. [DOI] [PubMed] [Google Scholar]
- 20. Vanlint S., “Vitamin D and Obesity,” Nutrients 5, no. 3 (2013): 949–956. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Klein S. L. and Flanagan K. L., “Sex Differences in Immune Responses,” Nature Reviews Immunology 16, no. 10 (2016): 626–638. [DOI] [PubMed] [Google Scholar]
- 22. Elgormus Y., Okuyan O., and Uzun H., “The Relationship Between Hematological Indices as Indicators of Inflammation and 25‐Hydroxyvitamin D3 Status in Newborns,” BMC Pediatrics 23, no. 1 (2023): 83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Ustundag Y., “The Relationship Between Serum Vitamin D Levels and Hematological Inflammatory Indices in Patients With Heart Failure,” International Journal of Medical Biochemistry 7, no. 1 (2024): 1–5. [Google Scholar]
- 24. Dziedzic E. A., Gąsior J. S., Tuzimek A., Dąbrowski M., and Jankowski P., “The Association Between Serum Vitamin D Concentration and New Inflammatory Biomarkers‐Systemic Inflammatory Index (SII) and Systemic Inflammatory Response (SIRI)‐In Patients With Ischemic Heart Disease,” Nutrients 14, no. 19 (2022): 4212. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Park Y. C., Kim J., Seo M. S., Hong S. W., Cho E. S., and Kim J. K., “Inverse Relationship Between Vitamin D Levels and Platelet Indices in Korean Adults,” Hematology 22, no. 10 (2017): 623–629. [DOI] [PubMed] [Google Scholar]
- 26. Otero T. M. N., Monlezun D. J., Christopher K. B., Camargo C. A., and Quraishi S. A., “Vitamin D Status and Elevated Red Cell Distribution Width in Community‐Dwelling Adults: Results From the National Health and Nutrition Examination Survey 2001–2006,” Journal of Nutrition, Health and Aging 21, no. 10 (2017): 1176–1182. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Sha S., Gwenzi T., Chen L.‐J., Brenner H., and Schöttker B., “About the Associations of Vitamin D Deficiency and Biomarkers of Systemic Inflammatory Response With All‐Cause and Cause‐Specific Mortality in a General Population Sample of Almost 400,000 UK Biobank Participants,” European Journal of Epidemiology 38, no. 9 (2023): 957–971. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1: Statistical significance in comparison of vitamin D percentiles with various parameters based on gender.
Data Availability Statement
The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.