Vitamin D Deficiency, Inflammatory Biomarkers, and MASLD in Older Adults with Type 2 Diabetes: A Cross-Sectional Study

Abstract

Purpose

This study aimed to investigate the association between serum 25-hydroxyvitamin D (25[OH]D) levels and metabolic dysfunction-associated steatotic liver disease (MASLD) in older patients with type 2 diabetes mellitus.

Patients and Methods

This retrospective cross-sectional study included 1044 older patients with T2DM who were treated at the Department of Endocrinology of Gansu Provincial Hospital between April 2021 and June 2024. MASLD was diagnosed by abdominal ultrasonography. Demographic, metabolic, and biochemical data were collected, including high-sensitivity C-reactive protein (hs-CRP), sialic acid (SA), and homocysteine (HCY). Binary logistic regression analysis was performed to assess the independent association between 25(OH)D levels and the presence of MASLD.

Results

MASLD was present in 64.0% of participants. Serum 25(OH)D levels were significantly lower in the MASLD group than in the non-MASLD group. After adjusting for age, sex, diabetes duration, body mass index, glycemic parameters, lipid profiles, and inflammatory markers, each 1 ng/mL decrease in 25(OH)D level was associated with 27.6% higher odds of MASLD (odds ratio [OR] = 0.724, 95% confidence interval [CI]: 0.686–0.765, P < 0.001). Receiver operating characteristic (ROC) analysis demonstrated the discriminatory performance of 25(OH)D for MASLD, yielding an area under the curve of 0.856 (95% CI: 0.828–0.884, P < 0.001) and an optimal cutoff value of 19.95 ng/mL.

Conclusion

Low serum 25(OH)D levels were independently associated with higher odds of MASLD in older patients with T2DM. Vitamin D status may have potential value for MASLD risk stratification, but prospective studies are required before treatment recommendations can be made.

Introduction

Metabolic dysfunction–associated steatotic liver disease (MASLD), formerly known as MAFLD or NAFLD, is a prevalent chronic liver disease strongly associated with type 2 diabetes mellitus (T2DM).^1,2 It is driven by insulin resistance, chronic inflammation, and metabolic dysregulation.³ MASLD may progress from simple hepatic steatosis to steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma.⁴ It is also associated with an increased risk of cardiovascular disease and mortality.⁵

Vitamin D deficiency, characterized by low serum 25(OH)D levels, is common among older adults and individuals with chronic metabolic disorders. Vitamin D status is influenced by multiple factors, including age, obesity, sunlight exposure, and hepatorenal function.^6,7 Cross-sectional studies and meta-analyses have demonstrated an inverse association between vitamin D levels and MASLD risk in patients with T2DM, suggesting that vitamin D deficiency may serve as an independent predictor of disease risk.⁸ However, findings vary considerably across populations and clinical settings. In patients with type 1 diabetes mellitus, vitamin D levels were not associated with NAFLD after multivariate adjustment.⁹ Similarly, studies in pediatric populations with NAFLD have reported a high prevalence of vitamin D deficiency but no significant association with disease severity.¹⁰ These conflicting findings highlight the complex relationship between vitamin D status and hepatic steatosis, which is likely modulated by underlying metabolic abnormalities and population-specific characteristics.¹¹

Several biological mechanisms may link vitamin D deficiency to MASLD. The vitamin D receptor (VDR) is expressed not only in classical calcium-regulating tissues but also in hepatocytes, hepatic stellate cells, adipocytes, pancreatic β-cells, and immune cells, and the vitamin D/VDR signaling pathway has been implicated in multiple stages of fatty liver pathogenesis.¹² In hepatocytes, VDR activation regulates key steps of lipid homeostasis, including lipid droplet accumulation, de novo lipogenesis, fatty-acid oxidation, and cholesterol metabolism, providing a direct molecular link between vitamin D status and hepatic steatosis. In hepatic stellate cells, ligand-bound VDR suppresses transforming growth factor-β1 (TGF-β1)–driven cell activation through a VDR/SMAD genomic circuit, thereby restraining the hepatic fibrotic response; conversely, Vdr knockout mice spontaneously develop hepatic fibrosis.¹³ Vitamin D may also attenuate insulin resistance by supporting pancreatic β-cell function and calcium-dependent insulin secretion, by enhancing insulin receptor expression and insulin-stimulated glucose uptake in peripheral tissues, and by modulating adipokines such as adiponectin; consistent with these mechanisms, systematic reviews have reported an inverse association between vitamin D status, insulin resistance, and incident T2DM, although causality remains debated.¹⁴ Finally, calcitriol, the hormonally active form of vitamin D, modulates innate and adaptive immunity by acting on B cells, T cells, and antigen-presenting cells in an autocrine and paracrine manner, thereby dampening pro-inflammatory responses.¹⁵ Together, these mechanisms converge on the chronic low-grade inflammation and metabolic dysregulation that link T2DM to MASLD.

Chronic low-grade inflammation is now recognized as a key pathophysiological link between T2DM and MASLD.¹⁶ Patients with T2DM exhibit persistent innate immune activation with elevated circulating acute-phase reactants, making circulating inflammatory biomarkers particularly relevant in this population. In the present study, three such biomarkers—high-sensitivity C-reactive protein (hs-CRP), sialic acid (SA), and homocysteine (HCY)—were selected for the regression models, as each reflects a different aspect of the inflammatory and metabolic burden associated with MASLD. hs-CRP is an acute-phase reactant produced by the liver, mainly in response to interleukin-6. Unlike conventional CRP assays, the high-sensitivity assay can detect the low-grade inflammation that is typical of cardiometabolic disease.¹⁶ Elevated hs-CRP has been identified as an independent clinical feature of nonalcoholic steatohepatitis and fibrosis severity, and it correlates with histological, elastographic, and ultrasonographic measures of fatty liver disease.¹⁷ SA is the terminal monosaccharide of glycoprotein glycans, and its circulating levels rise during acute-phase responses and tissue injury. Elevated serum SA has been associated with cardiometabolic risk, and experimental studies have shown that disruption of hepatocyte sialylation directly drives fatty liver disease and hepatic inflammation;¹⁸ more recently, a sialic acid (Neu5Ac)–PPARα signaling axis has been implicated in the pathogenesis of MASLD.¹⁹ HCY is a sulfur-containing intermediate of methionine metabolism that, when elevated, promotes oxidative stress and endothelial dysfunction; elevated HCY has been linked to NAFLD in meta-analyses and Mendelian randomization studies.^20,21 However, these biomarkers have so far been studied mainly in isolation,^22,23 and their combined contribution to the association between vitamin D status and MASLD remains unclear, particularly in older Chinese patients with T2DM. Glycemic markers (fasting blood glucose and glycated hemoglobin), lipid parameters (total cholesterol, triglycerides, HDL-C, and LDL-C), and liver enzymes (alanine and aspartate aminotransferases) were additionally included in the adjusted models as prespecified covariates, accounting for glycemic burden, atherogenic dyslipidemia, and baseline hepatocellular injury, respectively.

To address these knowledge gaps, a large-scale cross-sectional analysis of 1044 older patients with T2DM was conducted at Gansu Provincial People’s Hospital. Multivariate-adjusted analyses were performed to assess the independent association between serum 25(OH)D levels and prevalence of MASLD. ROC curve analysis further evaluated the diagnostic performance of 25(OH)D and inflammatory markers for MASLD and identified optimal clinical cutoff values. These analyses provide evidence supporting early risk stratification, screening, and targeted intervention in this high-risk population.

Materials and Methods

Patient Selection

This retrospective cross-sectional study was conducted in the Department of Endocrinology at Gansu Provincial Hospital from April 2021 to June 2024 and included 1044 older patients with T2DM. Patients aged ≥60 years who fulfilled the World Health Organization (WHO) diagnostic criteria for T2DM and underwent both hepatic ultrasonography and serum 25-hydroxyvitamin D [25(OH)D] testing during hospitalization were analyzed. Conversely, those with concurrent viral hepatitis, alcohol-related liver disease, autoimmune liver disease, or other known causes of chronic liver disease; advanced kidney disease (estimated glomerular filtration rate <15 mL/min/1.73 m²) or dialysis treatment; use of medications affecting bone metabolism or liver enzyme levels within the last 3 months; or missing clinical data or key variables that precluded accurate determination of exposure or outcome status were excluded. Data were obtained exclusively from the electronic medical records system and routine clinical laboratory assessments, with systematic collection and verification performed by trained research staff.

Biochemical Measurements

Participants’ weight and height were measured while wearing lightweight clothing and without shoes. Body mass index (BMI) was calculated as weight divided by height in meters squared (kg/m²). Following a 5-minute rest, systolic and diastolic blood pressure measurements were obtained twice at 10-minute intervals using a standardized mercury sphygmomanometer, and the mean value was recorded. Venous blood and 24-hour urine samples were collected in the morning after an overnight fast. Urinary biochemical parameters included 24-hour urinary protein, 24-hour urinary microalbumin, urinary creatinine, and the urine albumin-to-creatinine ratio (UACR).

The following biochemical parameters were assessed: 25(OH)D, glycated hemoglobin A1c (HbA1c), HCY, fasting blood glucose (FBG), SA, total cholesterol (TC), triglycerides (TG), high-sensitivity C-reactive protein (hs-CRP), high-density lipoprotein cholesterol (HDL-C), blood urea nitrogen (BUN), low-density lipoprotein cholesterol (LDL-C), serum creatinine (SCr), uric acid (UA), alanine aminotransferase (ALT), and aspartate aminotransferase (AST). LDL-C and HDL-C levels were measured using an automatic biochemical analyzer (AU5400; Beckman Coulter, Brea, CA, USA). HbA1c levels were determined by high-performance liquid chromatography using a Thermos system. Fasting insulin levels were measured using a chemiluminescent immunoassay (Abbott, Chicago, IL, USA). Serum UA, ALT, and AST levels were quantified using an automated biochemical analyzer (Modular E170; Roche, Basel, Switzerland). Serum 25(OH)D levels were measured through an electrochemiluminescence immunoassay (Cobas E601; Roche, Basel, Switzerland). SCr and BUN levels were determined using the urease method with a creatinine kit (Beijing Leadman Biochemistry, Beijing, China) and a urea nitrogen kit (Ningbo Medical System, Ningbo, China), respectively, on a Siemens ADVIA 2400 automatic biochemical analyzer. Serum hs-CRP levels were measured by latex-enhanced immunoturbidimetry using an hs-CRP assay kit (Beijing Leadman Biochemistry, Beijing, China) on an AU5400 automatic biochemical analyzer (Beckman Coulter, Brea, CA, USA). Serum SA levels were determined by an enzymatic colorimetric method using a sialic acid assay kit (Ningbo Medical System, Ningbo, China) on the same analyzer. Serum HCY levels were quantified by an enzymatic cycling method using a homocysteine assay kit (Beijing Leadman Biochemistry, Beijing, China) on a Siemens ADVIA 2400 automatic biochemical analyzer. Urinary protein, 24-hour urinary microalbumin excretion, and the urine albumin-to-creatinine ratio (UACR) were also measured at baseline to characterize renal involvement, given the reported association between albuminuria and MASLD.²⁴

Vitamin D status was categorized as follows: deficiency, 25(OH)D < 20 ng/mL; insufficiency, 20 ng/mL ≤ 25(OH)D < 30 ng/mL; and sufficiency, 25(OH)D ≥ 30 ng/mL.²⁵ Given that all participants had confirmed T2DM, MASLD was defined as the presence of hepatic steatosis on abdominal ultrasonography in the absence of other identifiable causes of liver disease, consistent with the current international consensus operational definition for MASLD in individuals with T2DM.^26,27 Ultrasound remains the first-line noninvasive imaging modality for detecting hepatic steatosis, relying on standardized qualitative features such as hepatorenal echo contrast, posterior beam attenuation, and intrahepatic vessel blurring.^28,29 For missing data, participants with missing key variables (25(OH)D levels or MASLD status) were excluded. Missingness in other covariates accounted for <5% of observations and was handled using a complete-case analysis without imputation.

Analysis

This retrospective cross-sectional study summarized continuous variables according to their distribution. Normally distributed variables were expressed as the mean ± standard deviation (SD), whereas non-normally distributed variables were presented as the median (interquartile range [IQR]). Categorical variables were reported as frequencies and percentages [n (%)]. Between-group comparisons were performed using the independent-samples t-test for normally distributed continuous variables, the Mann–Whitney U-test for non-normally distributed continuous variables, and the chi-square (χ²) test for categorical variables.

Spearman’s rank correlation analysis was performed to assess the associations between 25(OH)D levels and various metabolic parameters. To evaluate the independent associations of 25(OH)D and other biomarkers with the odds of MASLD, multivariate binary logistic regression models were constructed with sequential adjustment for potential confounders. Model 1 was unadjusted. Model 2 was adjusted for age and sex. Model 3 included additional adjustments for diabetes duration, BMI, smoking history, alcohol consumption, FBG, and HbA1c. Model 4 was further adjusted for liver function markers (ALT and AST) and lipid profiles (TC, TG, HDL-C, and LDL-C) in addition to the covariates included in Model 3. Results were presented as odds ratios (ORs) with corresponding 95% confidence intervals (CIs).

To further assess the discriminatory performance of independent predictors of MASLD, the area under the receiver operating characteristic curve (AUROC) was calculated with 95% CIs. The optimal cutoff value was determined using the maximum Youden index (sensitivity + specificity − 1),³⁰ and the corresponding sensitivity and specificity were reported. All statistical tests were two-sided, and a P value of <0.05 was considered statistically significant. Statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA).

Results

Clinical Characteristics and Biochemical Data

A total of 1044 hospitalized patients with T2DM were included, comprising 651 men (62.4%) and 393 women (37.6%), aged 60 to 91 years. Based on ultrasonographic findings and metabolic criteria, 668 patients (64.0%) were diagnosed with MASLD, whereas 376 (36.0%) were classified as non-MASLD.

As summarized in Table 1, no significant differences were observed between the MASLD and non-MASLD groups in sex; age; diabetes duration; BMI; blood pressure; or FBG, TG, HDL-C, SUA, SCr, 24-hour urinary protein, 24-hour urinary microalbumin, or UACR levels (all P > 0.05). However, patients with MASLD exhibited significantly higher HbA1c, TC, LDL-C, hs-CRP, SA, and HCY levels and significantly lower BUN levels than those without MASLD (all P < 0.05). Serum 25(OH)D levels were also significantly lower in the MASLD group (P < 0.001).

Table 1.

Characteristics of Study Participants with or without MASLD

Variables	MASLD Group (n=668)	Non-MASLD Group (n=376)	P
Sex			0.125
Male [n (%)]	405 (60.6)	246 (65.4)
Female [n (%)]	263 (39.4)	130 (34.6)
Age (years)	70 (64–77)	69 (65–75)	0.256
Duration of diabetes (years)	7.0 (2.0–11.3)	7.5 (2.3–13.0)	0.067
BMI (kg/m2)	24.6 (22.5–26.6)	24.3 (22.1–26.0)	0.105
SBP (mmHg)	128.0 (120.0–140.0)	125.0 (120.0–138.0)	0.282
DBP (mmHg)	80.0 (70.0–84.0)	80.0 (70.0–82.0)	0.060
FBG (mmol/L)	8.9 (7.2–12.2)	8.9 (6.9–11.7)	0.569
HbA1c (%)	8.3 (7.0–10.3)	8.3 (6.7–9.7)	0.034*
25(OH)D (ng/mL)	14.6 (12.3–17.1)	22.5 (20.2–26.1)	<0.001*
hs-CRP (mg/L)	5.9 (2.2–12.6)	0.9 (0.3–1.9)	<0.001*
SA (mg/L)	578.0 (533.0–661.0)	499.0 (448.3–539.0)	<0.001*
HCY (μmol/L)	13.0 (10.0–18.0)	12.0 (10.0–15.0)	<0.001*
TC (mmol/L)	4.6 (3.8–5.4)	4.4 (3.7–5.1)	0.014*
TG (mmol/L)	1.6 (1.2–2.3)	1.5 (1.1–2.4)	0.316
HDL-C (mmol/L)	1.2 (1.0–1.4)	1.2 (1.0–1.4)	0.887
LDL-C (mmol/L)	3.1±0.9	2.9±0.9	0.006*
ALT (U/L)	24.0 (17.0–34.0)	23.0 (16.3–33.0)	0.930
AST (U/L)	18.0 (15.0–24.0)	19.0 (16.0–24.0)	0.298
SUA (μmol/L)	308.5 (257.0–373.0)	317.1 (260.3–377.8)	0.239
BUN (mmol/L)	5.4 (4.5–6.6)	5.8 (4.8–7.0)	0.007*
SCr (μmol/L)	68.0 (57.3–79.6)	69.0 (58.8–77.6)	0.725
24-hour urinary protein (g/24h)	0.096 (0.070–0.150)	0.102 (0.070–0.166)	0.301
24-hour urinary microalbumin (mg/24h)	12.42 (8.36–27.36)	13.00 (8.00–45.21)	0.564
UACR (mg/mmol)	1.37 (0.87–3.01)	1.43 (0.81–4.72)	0.815

Notes: *P<0.05 was considered statistically significant.

Abbreviations: MASLD,metabolic dysfunction–associated steatotic liver disease; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting plasma glucose; HbA1c, glycated hemoglobin; 25(OH)D, 25-hydroxy-vitaminD3; hs-CRP, high-sensitivity C-reactive protein; SA, sialic acid; HCY, homocysteine; TG, total triglyceride; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; ALT, alanine transaminase; AST, aspartate aminotransferase; SUA, serum uric acid; BUN, blood urea nitrogen; SCr, serum creatinine;HDL-C, high-density lipoprotein cholesterol.

Association Between Vitamin D Status and MASLD Prevalence

As shown in Table 2, vitamin D deficiency was observed in 78.0% of patients with MASLD, significantly higher than the 60.6% observed in the non-MASLD group. Conversely, vitamin D sufficiency was present in only 1.6% of patients with MASLD compared with 10.1% of those without MASLD (χ² = 196.5, P < 0.001). A similar pattern was observed when participants were stratified into tertiles based on 25 (OH)D levels (Table 3). The prevalence of MASLD increased progressively as vitamin D status declined from sufficient to insufficient to deficient, with corresponding prevalences of 22.4%, 55.3%, and 69.6%, respectively (P < 0.001). Furthermore, patients with vitamin D deficiency were older and had a longer diabetes duration; higher BMI; and significantly elevated levels of HbA1c, hs-CRP, SA, HCY, TG, and LDL-C (all P < 0.05).

Table 2.

Prevalence of Vitamin D Deficiency in MASLD Group and Non-MASLD Group

	MASLD Group (n=668)	Non-MASLD Group (n=376)	X2	P-value
25(OH)D
Sufficiency	11(1.6%)	38(10.1%)	196.5	<0.001*
Insufficiency	136(20.4%)	110(29.3%)
Deficiency	521(78.0%)	228(60.6%)

Notes: *P<0.05 was considered statistically significant.

Abbreviations: MASLD, metabolic dysfunction–associated steatotic liver disease; 25(OH)D, 25-hydroxy-vitaminD3.

Table 3.

Clinical Characteristics of Patients with T2DM by 25(OH)D Levels Category

Variables	Sufficiency (n=49)	Insufficiency (n=246)	Deficiency (n=749)	P
Sex				0.075
Male [n (%)]	33 (67.3)	167 (67.9)	451 (60.2)
Female [n (%)]	16 (32.7)	79 (32.1)	298 (39.8)
MASLD [n (%)]	11 (22.4)	136 (55.3)	521 (69.6)	<0.001*
Age (years)	69.0 (63.0–73.0)	69.0 (64.0–74.2)	70.0 (65.0–77.0)	0.005*
Duration of diabetes(years)	7.0 (2.0–11.3)	7.0 (2.0–11.3)	7.5 (2.3–13.0)	0.041*
BMI (kg/m2)	22.8 (21.0–25.9)	24.4 (22.0–26.0)	24.5 (22.5–26.6)	0.019*
SBP (mmHg)	126.0 (118.0–137.5)	124.5 (118.8–138.0)	128.0 (120.0–140.0)	0.229
DBP (mmHg)	78.0 (70.0–82.0)	78.0 (70.0–80.0)	80.0 (70.0–84.0)	0.068
FBG (mmol/L)	8.4 (7.0–11.2)	8.7 (6.7–11.5)	9.0 (7.1–12.4)	0.086
HbA1C (%)	7.5 (6.9–9.4)	8.3 (6.7–9.7)	8.4 (7.0–10.3)	0.025*
25(OH)D(ng/ml)	33.0 (31.4–39.3)	23.2 (21.2–25.3)	14.6 (12.2–17.0)	<0.001*
hs-CRP (mg/L)	0.5 (0.3–1.8)	0.9 (0.3–1.9)	5.0 (1.8–12.0)	<0.001*
SA (mg/L)	475.0 (422.0–522.5)	501.0 (452.8–542.3)	573.0 (522.5–649.5)	<0.001*
HCY (umol/L)	12.0 (9.0–14.5)	12.0 (10.0–15.0)	13.0 (10.0–17.0)	0.005*
TC (mmol/L)	4.1 (3.4–4.7)	4.4 (3.8–5.1)	4.6 (3.8–5.3)	0.011*
TG (mmol/L)	1.1 (0.9–1.9)	1.6 (1.0–2.2)	1.7 (1.2–2.4)	0.001*
HDL-C (mmol/L)	1.2 (1.1–1.4)	1.2 (1.0–1.4)	1.2 (1.0–1.4)	0.455
LDL-C (mmol/L)	2.7±0.9	3.0±0.9	3.1±0.9	0.015*
ALT (U/L)	25.0 (16.0–37.0)	22.5 (16.0–33.0)	23.0 (17.0–34.0)	0.785
AST (U/L)	19.0 (16.0–27.0)	19.0 (15.0–24.0)	19.0 (15.0–24.0)	0.171
SUA (umol/L)	303.0 (263.0–364.8)	316.8 (257.8–377.3)	311.2 (259.0–374.8)	0.919
BUN (mmol/L)	6.1 (4.8–7.0)	5.8 (4.8–7.0)	5.4 (4.5–6.6)	0.082
SCr (umol/L)	70.5 (58.7–77.7)	68.6 (59.1–77.6)	68.6 (57.5–79.9)	0.916

Notes: *P<0.05 was considered statistically significant.

Abbreviations: MASLD,metabolic dysfunction–associated steatotic liver disease; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting plasma glucose; HbA1c, glycated hemoglobin; 25(OH)D, 25-hydroxy-vitaminD3; hs-CRP, high-sensitivity C-reactive protein; SA, sialic acid; HCY, homocysteine; TG, total triglyceride; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; ALT, alanine transaminase; AST, aspartate aminotransferase; SUA, serum uric acid; BUN, blood urea nitrogen; SCr, serum creatinine;HDL-C, high-density lipoprotein cholesterol.

Correlations Between 25(OH)D and Other Metabolic Parameters

Spearman’s correlation analyses (Table 4) revealed that 25(OH)D levels were significantly and inversely correlated with SBP, DBP, FBG, HbA1c, hs-CRP, SA, HCY, TC, TG, and LDL-C (all P < 0.05), and positively correlated with diabetes duration, SUA, BUN, and SCr (all P < 0.05). No significant correlations were observed between 25(OH)D and age, BMI, ALT, AST, or HDL-C (all P > 0.05). The strongest inverse associations were observed for hs-CRP and SA, suggesting a potential association between lower vitamin D status and increased systemic inflammation.

Table 4.

Correlations Between 25(OH)D and Other Metabolic Parameters

Variables	R-value	P-value
Age (years)	−0.031	0.318
Duration of diabetes (years)	0.083	0.007*
BMI (kg/m2)	−0.043	0.168
SBP (mmHg)	−0.085	0.006*
DBP (mmHg)	−0.114	<0.001*
FBG (mmol/L)	−0.090	0.004*
HbA1C (%)	−0.107	<0.001*
hs-CRP (mg/L)	−0.454	<0.001*
SA (mg/L)	−0.376	<0.001*
HCY (umol/L)	−0.107	<0.001*
TC (mmol/L)	−0.093	0.003*
TG (mmol/L)	−0.078	0.012*
HDL-C (mmol/L)	0.016	0.602
LDL-C (mmol/L)	−0.088	0.004*
ALT (U/L)	0.027	0.377
AST (U/L)	0.061	0.059
SUA (umol/L)	0.065	0.037*
BUN (mmol/L)	0.072	0.020*
SCr (umol/L)	0.077	0.013*

Notes: *P<0.05 was considered statistically significant.

Abbreviations: MASLD, metabolic dysfunction–associated steatotic liver disease; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting plasma glucose; HbA1c, glycated hemoglobin; 25(OH)D, 25-hydroxy-vitaminD3; hs-CRP, high-sensitivity C-reactive protein; SA, sialic acid; HCY, homocysteine; TG, total triglyceride; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; ALT, alanine transaminase; AST, aspartate aminotransferase; SUA, serum uric acid; BUN, blood urea nitrogen; SCr, serum creatinine;HDL-C, high-density lipoprotein cholesterol.

Binary Logistic Regression Analysis

As shown in Table 5, in Model 1 (unadjusted), each 1 ng/mL decrease in 25(OH)D level was associated with 26.8% higher odds of MASLD (OR = 0.732, 95% CI: 0.695–0.771, P < 0.001). This association remained consistent after sequential adjustment for demographic characteristics (Model 2), metabolic and diabetes-related factors (Model 3), and liver function markers and lipid profiles (Model 4). In the fully adjusted Model 4, each 1 ng/mL reduction in 25(OH)D level remained independently associated with 27.6% higher odds of MASLD (OR = 0.724, 95% CI: 0.686–0.765, P < 0.001). Furthermore, hs-CRP (OR = 1.294, 95% CI: 1.204–1.390, P < 0.001), SA (OR = 1.015, 95% CI: 1.011–1.018, P < 0.001), and HCY (OR = 1.041, 95% CI: 1.010–1.074, P = 0.011) were independently associated with MASLD.

Table 5.

Binary Logistic Regression Model for MASLD in Patients with T2DM

		B	SE	Wald	P	OR	95% CI
Model 1	25(OH)D (ng/ml)	−0.312	0.026	139.520	<0.001	0.732	0.695–0.771
	hs-CRP (mg/L)	0.238	0.033	51.197	<0.001	1.269	1.189–1.354
	SA (mg/L)	0.014	0.002	74.607	<0.001	1.014	1.011–1.017
	HCY (umol/L)	0.036	0.015	5.568	0.018	1.036	1.006–1.068
Model 2	25(OH)D (ng/ml)	−0.318	0.027	138.941	<0.001	0.728	0.690–0.767
	hs-CRP (mg/L)	0.236	0.033	49.731	<0.001	1.266	1.186–1.352
	SA (mg/L)	0.014	0.002	74.969	<0.001	1.014	1.011–1.017
	HCY (umol/L)	0.035	0.015	5.427	0.020	1.036	1.006–1.067
Model 3	25(OH)D (ng/ml)	−0.320	0.027	137.264	<0.001	0.726	0.688–0.766
	hs-CRP (mg/L)	0.253	0.036	50.053	<0.001	1.288	1.201–1.382
	SA (mg/L)	0.015	0.002	77.076	<0.001	1.015	1.011–1.018
	HCY (umol/L)	0.039	0.016	6.248	0.012	1.040	1.009–1.073
Model 4	25(OH)D (ng/ml)	−0.323	0.028	135.511	<0.001	0.724	0.686–0.765
	hs-CRP (mg/L)	0.257	0.037	49.526	<0.001	1.294	1.204–1.390
	SA (mg/L)	0.015	0.002	75.877	<0.001	1.015	1.011–1.018
	HCY (umol/L)	0.041	0.016	6.543	0.011	1.041	1.010–1.074

Notes: Model 1: no parameter was adjusted; Model 2: adjusted for age, sex; Model 3: Model 2 plus duration of DM, BMI, smoking history, FBG, HbA1C; Model 4: Model 3 plus hepatic function and serum lipids.

Abbreviations: 25(OH)D, 25-hydroxyvitamin D; hs-CRP, high-sensitivity C-reactive protein; SA, sialic acid; HCY, homocysteine.

Predictive Performance and Clinical Cutoff Values

ROC curve analysis (Table 6 and Figure 1) demonstrated that 25 (OH)D exhibited strong discriminatory performance for MASLD, with an AUROC of 0.856 (95% CI: 0.828–0.884, P < 0.001). The optimal cutoff value identified by the maximum Youden index was 19.95 ng/mL, corresponding to a sensitivity of 96.9% and a specificity of 75.8%. hs-CRP (AUROC = 0.844, 95% CI: 0.821–0.867; cutoff = 1.95 mg/L), SA (AUROC = 0.812, 95% CI: 0.785–0.838; cutoff = 547.5 mg/L), and HCY (AUROC = 0.586, 95% CI: 0.551–0.621; cutoff = 20.50 umol/L) are shown in Table 6 and Figure 2. Although HCY showed high specificity (95.7%), its low sensitivity (19.0%) limited its potential clinical applicability.

Table 6.

Diagnostic Performance of Independent Predictors for MASLD in Patients with T2DM

Parameters	Area Under the ROC Curve	Lower 95% CI	Upper 95% CI	P	Cut off value	Sensitivity (%)	Specificity (%)
25(OH)D (ng/ml)	0.856	0.828	0.884	<0.001	19.950	96.9	75.8
hs-CRP (mg/L)	0.844	0.821	0.867	<0.001	1.950	78.0	75.5
SA (mg/L)	0.812	0.785	0.838	<0.001	547.50	68.3	78.7
HCY (umol/L)	0.586	0.551	0.621	<0.001	20.50	19.0	95.7

Notes: The optimal cutoff value was determined using the maximum Youden index. P values refer to the test of whether the AUROC is significantly different from 0.50 (no discrimination).

Abbreviations: AUROC, area under the receiver operating characteristic curve; CI, confidence interval; 25(OH)D, 25-hydroxyvitamin D; hs-CRP, high-sensitivity C-reactive protein; SA, sialic acid; HCY, homocysteine.

Figure 1.

Receiver operating characteristic (ROC) curve of 25-hydroxyvitamin D [25(OH)D] for predicting metabolic dysfunction-associated steatotic liver disease (MASLD) in patients with type 2 diabetes mellitus. The red solid curve represents the ROC curve of 25(OH)D, and the black dotted diagonal line represents the reference line of no discrimination (AUROC = 0.50). The area under the ROC curve (AUROC) was 0.856 (95% CI: 0.828–0.884, P < 0.001). The optimal cutoff value determined by the maximum Youden index was 19.95 ng/mL, with a sensitivity of 96.9% and a specificity of 75.8%.

Figure 2.

Receiver operating characteristic (ROC) curves of high-sensitivity C-reactive protein (hs-CRP), sialic acid (SA), and homocysteine (HCY) for predicting metabolic dysfunction-associated steatotic liver disease (MASLD) in patients with type 2 diabetes mellitus. The red curve represents hs-CRP (AUROC = 0.844, 95% CI: 0.821–0.867; optimal cutoff = 1.95 mg/L; sensitivity = 78.0%; specificity = 75.5%), the blue curve represents SA (AUROC = 0.812, 95% CI: 0.785–0.838; optimal cutoff = 547.5 mg/L; sensitivity = 68.3%; specificity = 78.7%), and the green curve represents HCY (AUROC = 0.586, 95% CI: 0.551–0.621; optimal cutoff = 20.50 μmol/L; sensitivity = 19.0%; specificity = 95.7%). The black dotted diagonal line represents the reference line of no discrimination (AUROC = 0.50). All P < 0.001.

Discussion

In this cross-sectional study involving 1044 older adults with T2DM, the association between 25(OH)D levels and MASLD was investigated. Vitamin D deficiency was significantly more prevalent among patients with MASLD than among those without MASLD. The prevalence of MASLD increased progressively with declining 25(OH)D levels. Multivariate logistic regression analysis demonstrated that lower 25(OH)D levels were independently associated with a higher likelihood of MASLD after adjustment for potential confounders. In addition, hs-CRP, SA, and HCY were positively associated with MASLD, whereas ROC curve analysis indicated that 25(OH)D had strong discriminatory ability for identifying MASLD.

Several baseline differences between the two groups deserve comment. The higher HbA1c in the MASLD group reflects poorer chronic glycemic control, which promotes hepatic de novo lipogenesis; biopsy-proven data link even mild deterioration of glycemic control to greater NAFLD activity and advanced fibrosis.³¹ The elevated total cholesterol and LDL-C are consistent with the atherogenic dyslipidemia of MASLD, in which insulin resistance enhances hepatic very-low-density lipoprotein production and increases cardiovascular risk.^32,33 The higher hs-CRP, sialic acid, and homocysteine levels reflect the chronic low-grade inflammatory state of MASLD and are consistent with their independent associations in our regression models. Conversely, BUN was lower in the MASLD group; this seemingly paradoxical finding may be explained by the urea cycle dysregulation and impaired ureagenesis reported in NAFLD, and low BUN has been epidemiologically linked to fatty liver, partly through qualitative changes in body composition.³⁴ Because the two groups differed by definition in several metabolic variables, residual confounding cannot be excluded, which is why we applied sequential adjustment across Models 1–4.

Our findings are consistent with previous cross-sectional studies in T2DM populations, which have reported an association between vitamin D deficiency and fatty liver disease.³⁵ Lower 25(OH)D levels have been linked to the presence of MASLD and progressive fibrosis, and recent evidence from Chinese patients with T2DM further supports this relationship.³⁶ All studies, including the present study, used ultrasonography to identify hepatic steatosis, ensuring methodological consistency.^37,38 Although previous studies have reported significant associations within subgroups stratified by BMI or age, the current analysis demonstrated a robust association across the entire cohort of older adults, potentially reflecting the substantial metabolic dysregulation in this population.^39,40 In the fully adjusted model, each 1 ng/mL increase in 25(OH)D was associated with lower odds of MASLD (OR = 0.724), corresponding to a 27.6% reduction in the odds of MASLD per unit increase in 25(OH)D levels. Urinary protein, 24-hour microalbumin, and UACR were not entered into the primary models, as the analysis was prespecified to focus on serum vitamin D status, metabolic factors, and inflammatory biomarkers; their balanced distribution between the two groups makes material confounding unlikely. Given the reported link between albuminuria and NAFLD severity and advanced fibrosis,²⁴ the renal implications of MASLD in this population warrant investigation in future longitudinal studies.

Previous studies have demonstrated that systemic inflammation contributes to the progression of hepatic fibrosis in fatty liver disease; however, limited evidence has specifically examined the roles of hs-CRP, HCY, or SA.^41–43 In the present study, hs-CRP, SA, and HCY were independently associated with MASLD, with hs-CRP and SA emerging as significant predictors in multivariate regression models.

Among the inflammatory biomarkers examined, the clinical significance of sialic acid (SA) relative to more established markers such as hs-CRP deserves specific comment. Circulating SA is derived mainly from the sialylated glycans of acute-phase glycoproteins and cell-surface glycoconjugates, and its concentration rises during systemic inflammation and tissue injury; SA therefore represents an integrated, albeit non-specific, index of inflammatory and glycation burden. In patients with T2DM, serum SA correlates strongly with glycemic control and HbA1c,⁴⁴ and elevated levels have been associated with diabetic retinopathy and cardiovascular risk factors,^45,46 suggesting that SA captures the interface between chronic hyperglycemia and vascular inflammation. Two cross-sectional studies have also linked serum SA to MASLD.¹⁸ Consistent with this background, SA showed moderate discriminatory performance for MASLD in our cohort (AUROC = 0.812, 95% CI: 0.785–0.838). However, compared with hs-CRP, which benefits from standardized high-sensitivity assays, validated clinical cutoffs, and extensive prospective evidence, SA lacks tissue specificity, assay standardization, and prospective validation. SA should therefore be regarded as an adjunctive marker within a broader metabolic and inflammatory risk-assessment framework rather than as a standalone screening tool, and prospective studies are needed to define its incremental value over established markers.⁴⁷

These findings may reflect the pleiotropic function of vitamin D in regulating glucose and lipid metabolism, insulin sensitivity, and inflammatory responses.⁴⁸ Vitamin D receptors (VDRs) are expressed in hepatocytes, and preclinical studies have suggested that VDR activation may attenuate hepatic steatosis by modulating lipid droplet accumulation, fatty acid oxidation, and cholesterol metabolism. Additionally, the immunomodulatory effects of vitamin D, including the promotion of anti-inflammatory regulatory T-cell responses, may help mitigate the chronic low-grade inflammation characteristic of MASLD.^49–51 This finding is consistent with the strong inverse correlations observed between 25(OH)D levels and hs-CRP/SA concentrations.^52,53 These results suggest that vitamin D deficiency may be associated with enhanced hepatic inflammation and may contribute to the development of steatosis.^54,55 Furthermore, the significant associations between 25(OH)D levels and metabolic parameters—including HbA1c, LDL-C, and TG—highlight the potential interplay among vitamin D status, glycemic regulation, and lipid metabolism, all of which are key factors in MASLD pathogenesis.^56–58

The AUROC for 25(OH)D was 0.856 (95% CI: 0.828–0.884), with an optimal cutoff value of 19.95 ng/mL, which closely corresponds to the established threshold for vitamin D deficiency (<20 ng/mL).^59,60 These findings have practical implications for the management of older patients with T2DM. Serum 25(OH)D is inexpensive and routinely measurable, and at the cutoff of 19.95 ng/mL it detected MASLD with a sensitivity of 96.9% in our cohort. It could therefore serve as a triage tool to identify vitamin D-deficient patients who should be prioritized for ultrasonography and non-invasive fibrosis assessment—a stepwise strategy that is particularly attractive in primary care and resource-limited settings, where systematic MASLD screening in patients with T2DM is recommended.⁶¹ However, its moderate specificity (75.8%) indicates that 25(OH)D should complement, rather than replace, imaging-based diagnosis. As for treatment, meta-analyses of randomized controlled trials suggest that vitamin D supplementation improves insulin resistance and selected metabolic parameters in patients with NAFLD,^62,63 but a histological benefit remains unproven. Correction of vitamin D deficiency may thus be a safe, low-cost adjunct to lifestyle intervention in older patients with T2DM and MASLD, although our cross-sectional design precludes causal inference and interventional studies in this population are warranted.

Strengths and Limitations

This study has several strengths. The large, well-characterized cohort of patients with T2DM provided sufficient statistical power to evaluate the association between vitamin D status and MASLD. Comprehensive metabolic and inflammatory profiling, along with adjustment for key covariates, minimized potential confounding and improved the reliability of the findings in this high-risk population.

This study has some limitations. First, the cross-sectional design precludes causal inference. Reverse causality cannot be excluded, as hepatic steatosis may influence 25(OH)D metabolism and contribute to lower vitamin D levels. Longitudinal studies are needed to clarify temporal relationships. Second, MASLD was diagnosed using ultrasonography, which is operator-dependent and has limited sensitivity for mild steatosis, potentially leading to underestimation of MASLD prevalence, particularly among lean individuals. Third, despite adjustment for major confounders, residual confounding from unmeasured factors—such as seasonal variation, sunlight exposure, and physical activity—may have affected the observed associations. Finally, the single-center hospital-based sample may limit the generalizability of the findings to community-dwelling older adults with T2DM.

Conclusion

This study demonstrated that vitamin D deficiency was independently associated with an increased likelihood of MASLD among older patients with T2DM, and serum 25(OH)D levels showed strong discriminatory performance for MASLD identification. These findings suggest that vitamin D assessment may serve as a potential tool for MASLD risk stratification in diabetes care. Future prospective studies are warranted to validate these findings and investigate whether interventions targeting vitamin D status can influence MASLD development or progression.

Funding Statement

This study was funded by the Soft Science Special Project of Gansu Provincial Science and Technology Program (26JRZA147), the Scientific Research Project of Gansu Provincial Health Commission (GSWSKY2025-06), the Longyuan Young Elite Talent Program of Gansu Province (LYQNYC2024-12), and the Gansu Provincial People’s Hospital In-hospital Fund Program (25GSSYG-3).

Data Sharing Statement

The datasets generated and/or analyzed during the current study are not publicly available due to privacy concerns, but are available from the corresponding author.

Ethics Approval and Informed Consent

This study was carried out following the principles set forth in the Declaration of Helsinki and was approved by the Ethics Committee of Gansu Provincial Hospital (2025-786). The requirement for informed consent was waived by the Ethics Committee of Gansu Provincial Hospital because of the retrospective nature of the study and the use of de-identified data obtained from electronic medical records.

Author Contributions

Conceptualization: Yuanjun Zhang and Jinxing Quan; Methodology: Juxiang Liu and Jinxing Quan; Formal analysis: Yuanjun Zhang; Investigation: Xiang Chang and Haiyan Tai; Data curation: Yuanjun Zhang; Writing – original draft: Yuanjun Zhang; Writing – review & editing: Juxiang Liu, Xiang Chang, Haiyan Tai and Jinxing Quan; Supervision: Jinxing Quan. All authors gave final approval of the version to be published, agreed on the journal to which the article was submitted, and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.