Beyond the pancreas: Endocrine crosstalk of Bone–Vitamin D–Parathyroid Hormone Axis in Type 2 diabetes mellitus – An emerging tri-hormonal model for glycemic regulation and novel perspectives in management
Bibliographic record
Abstract
ABSTRACT Type 2 diabetes mellitus (T2DM) has traditionally been explained by insulin resistance and β-cell dysfunction, but emerging evidence indicates that it is also influenced by multi-organ endocrine interactions. This systematic review consolidates current findings on the roles of osteocalcin, Vitamin D, and parathyroid hormone (PTH) in T2DM, presenting a Tri-Hormonal Axis model for glycemic regulation. A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, Embase, and Google Scholar in accordance with PRISMA guidelines. Eligible human and animal studies reporting associations of one or more of these biomarkers with T2DM were included. Fifty studies met the criteria and were evaluated using the Newcastle–Ottawa Scale and the Cochrane Risk of Bias 2 tool. The findings demonstrated that osteocalcin levels are consistently reduced in uncontrolled T2DM and show inverse associations with HbA1c and HOMA-IR, suggesting a role in insulin sensitivity and secretion. Vitamin D deficiency was found in 55%–85% of patients, and supplementation trials indicated modest but significant improvements in glycemic indices, particularly among those with baseline deficiency. Elevated PTH levels were consistently associated with insulin resistance, poor glycemic control, and an increased risk of diabetic complications. While only a limited number of studies examined all three biomarkers simultaneously, the triad of low osteocalcin, low Vitamin D, and high PTH strongly predicted adverse metabolic outcomes. These findings suggest that T2DM extends beyond pancreatic dysfunction to involve endocrine crosstalk between bone, Vitamin D, and parathyroid pathways, highlighting new opportunities for biomarker-driven risk prediction and adjunctive therapeutic strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".