Editorial: The role of vitamin D in metabolic and cardiovascular health
Bibliographic record
Abstract
The role of vitamin D in metabolic and cardiovascular healthIn the last few decades, interest in vitamin D (VitD) has grown significantly since numerous studies have suggested that besides its well-established roles in bone metabolism (1), it could have other important roles in organism, including roles in immunity, endocrine, cardiovascular, and reproductive system (2-7).Many studies indicated that VitD status is inversely associated with the incidence of several metabolic diseases and conditions, including obesity (8, 9), insulin resistance (10-14), metabolic syndrome (15-17), dyslipidemia (13, 18, 19), diabetes (10, 11, 20-22), non-alcoholic fatty liver disease (NAFLD) (23-25), and cardiovascular diseases (7,(26)(27)(28).However, the findings were often inconsistent, and the cause/effect relationships particularly remained to be confirmed, as well as the molecular pathways of these associations.Moreover, the relationship of VitD with metabolic and cardiovascular disorders seems to be bidirectional: e.g., obesity could worsen VitD deficiency (8), and vice versa, VitD deficiency could aggravate obesity and related metabolic and cardiovascular complications (insulin resistance, defects in insulin secretion, disordered metabolism of lipids, hepatic steatosis, and gut dysbiosis) (7, 9) by multiple mechanisms, many of which are still undiscovered and unclarified.Additionally, since in the above-mentioned disorders and diseases there is also chronic inflammation and increased oxidative stress, the role of VitD as immunomodulator and anti-oxidative agent has been proposed as one of the mechanisms by which VitD can influence these conditions (29-34).This Frontiers Research Topic "The role of vitamin D in metabolic and cardiovascular health" focused on epidemiological research on associations of VitD with metabolic and cardiovascular health, particularly in specific population groups, and pathophysiological pathways of these associations, as well as possible confounding factors which modulate these associations.The Research Topic welcomed also articles on the role of VitD as one of supporting therapeutics in cardiovascular and metabolic diseases, as well as immunomodulator.In this Research Topic there are 13 papers covering the above-mentioned aspects.Chen Y. C. et al. compared the risk for VitD deficiency across different categories of metabolically healthy normal weight (MHNW) to metabolically unhealthy overweight/obese insulin resistant (MUO) subjects, by studding 6,655 Chinese adults.The study confirmed the
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".