Correlation Between Serum 25-Hydroxyvitamin D Levels in Albuminuria Progression of Diabetic Kidney Disease and Underlying Mechanisms By Bioinformatics Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35634488", "kind": "science-record", "revision": 5, "content_hash": "sha256:d9d97b8e7ced8b23c7ab1f8fde9546cfa93d02e27bd633912e376f1c09172dbf", "created_at": "2026-11-01T18:14:33Z", "updated_at": "2026-11-01T22:49:10Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Correlation Between Serum 25-Hydroxyvitamin D Levels in Albuminuria Progression of Diabetic Kidney Disease and Underlying Mechanisms By Bioinformatics Analysis.", "doi": "10.3389/fendo.2022.880930", "pmid": "35634488", "authors": ["Bin Huang", "Wenjie Wen", "Shandong Ye"], "journal": "Frontiers in endocrinology", "year": 2022, "abstract": "Aim: This study aimed to assess the correlation between serum concentration of 25-hydroxyvitamin D and albuminuria progression of diabetic kidney disease (DKD) and to use bioinformatics methods to determine the potential mechanism in the pathological process of advanced DKD.\nMethods: A total of 178 type 1 diabetes mellitus (T1DM) patients with microalbuminuria complications who were hospitalized at least twice (with an interval > 24 months) in the Department of Endocrinology of The First Affiliated Hospital of USTC were included in this study. According to the urinary albumin creatinine ratio (UACR), we classified DKD stages as follows: microalbuminuria (UACR, 30-300 mg/g), and macroalbuminuria (UACR, >300 mg/g). We divided the patients into DKD progression (N=44) and stable group (N=134) on account of urinary albumin-to-creatinine ratio (UACR) by at least two randomized measurements. Stable group was defined as UACR between 30 and 300 mg/g, whereas progression group was defined as UACR >300 mg/g at the end of follow-up. Data were obtained from participants' medical records, and the 25-hydroxyvitamin D level was categorized into three groups as follows: G1 (N=45), <10 ng/mL; G2 (N=80), 10-20 ng/ml; and G3 (N=53), ≥20 ng/mL. The Nephroseq database (http://v5.nephroseq.org) was used to identify VDR expression in diabetic nephropathy. The dataset GSE142025 from GEO (http://www.ncbi.nlm.nih.gov/geo) was downloaded. After stratification by the median-centered log2 VDR expression value, the 21 advanced DKD samples were divided into two groups (low VDR expression group and high VDR expression group). Gene set enrichment analysis (GSEA) (http://software.broadinstitute.org/gsea/index.jsp). Differentially expressed genes (DEGs) were screened by the limma package (adjusted p < 0.05, |logFC| > 1). The Gene Ontology (GO; http://www.geneontology.org/) database and pathway analysis within the Kyoto Encyclopedia of Genes and Genomes (KEGG; https://www.kegg.jp/) were performed using the R package ClusterProfile. The CIBERSORT (Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts) algorithm was utilized for calculating the infiltrated immune cells in advanced kidney tissues.\nResults: 1) A multivariate Cox regression analysis revealed that DR (diabetic retinopathy), eGFR (estimated glomerular filtration rate), and 25-hydroxyvitamin D were significant independent predictors of DKD progression (HR: 2.57, 95% CI: 1.44.4.24, p=0.007; HR: 2.13, 95% CI: 1.58.3.79, p = 0.011; HR: 0.732, 95% CI: 0.232-0.816, p = 0.023, respectively). 2) Kaplan-Meier survival curves of DKD progression by serum 25-hydroxyvitamin D stratification showed that the G2 and G3 groups were significantly different when compared with the G1 group (log-rank χ2 = 14.69, p <0.001; χ2 = 28.26, p <0.001, respectively). 3) There was a weak negative correlation between 25-hydroxyvitamin D level and UACR at baseline,and the overall mean rate of change in eGFR was 1.121 ± 0.19 ml/min/1.73 m2/year. Neither crude nor adjusted rate of decline in eGFR was significantly different among patients classified according to baseline serum 25-hydroxyvitamin D levels (all p<0.05). 4) The high expression of VDR group was most positively correlated with enriched gene sets like reactome innate immune system and reactome G alpha I signaling events when compared with the low expression of VDR group. 5) The CIBERSORT algorithm showed decreased M2 macrophage infiltration in advanced kidneys in comparison to low VDR expression and high VDR expression.\nConclusion: This study concluded that low 25-hydroxyvitamin D levels can predict an increased risk of DKD albuminuria progression and eGFR decline. Decreased M2 macrophage infiltration may be a potential mechanism involved in this pathogenesis.", "claims": [{"id": "claim-method", "statement": "Comparison of rate of change in eGFR among groups classified according to baseline serum 25-hydroxyvitamin D levels: In (A) the crude model using ANOVA and in (B) the adjusted model (After adjusting for diabetic retinopathy, hemoglobin and TBIL in baseline, ANCOVA). There were significant difference in the rate in the both crude and adjusted model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35634488", "locator": "sections[13] \"Comparison of Rate of Change in eGFR Among Groups According to Baseline Serum 25-Hydroxyvitamin D Levels\" paragraph 3", "quote": "Comparison of rate of change in eGFR among groups classified according to baseline serum 25-hydroxyvitamin D levels: In (A) the crude model using ANOVA and in (B) the adjusted model (After adjusting for diabetic retinopathy, hemoglobin and TBIL in baseline, ANCOVA). There were significant difference in the rate in the both crude and adjusted model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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There were significant difference in the rate in the both crude and adjusted model."}}, {"id": "method-2", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Kaplan-Meier"}}, {"id": "method-3", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {"hazard_ratio": "ors of DKD progression (HR: 2"}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Cox regression"}}, {"id": "method-4", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "log-rank"}}, {"id": "method-5", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.7, "band": "medium", "quote": "limma"}}, {"id": "method-6", "family": "contingency_analysis", "specific": "chi_square", "label": "Categorical (contingency table) analysis", "dsl_operations": ["chi_square_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["categorical_cross_tabulation"], "evidence": {"score": 0.7, "band": "medium", "quote": "χ2"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: anova."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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