The interplay of serum cations, insulin resistance, and atherogenic indices in predicting depression in hypothyroid patients
Bibliographic record
Abstract
Background: A significant proportion of people with hypothyroidism (HT) is linked to affective disorders, including depression. The pathophysiology and factors affecting or predicating depression in HT patients is still to be elucidated. The current study intends to investigate serum levels of cations, insulin resistance parameters, trace elements and atherogenic indices, in HT+Dep, HT, and healthy control groups. Methods: We measured the biomarkers in the blood of sixty HT+Dep patients, sixty HT patients, and healthy controls who participated in the study. Selenium was measured using flameless atomic absorption spectrophotometry. While insulin level was measured using the ELISA technique. Results: We observed significant insulin resistance (IR) and dyslipidemia in HT patients, which were more pronounced in HT+Dep. Moreover, HT+Dep patients exhibited alterations in the blood concentrations of cations and trace elements. Artificial neural network analysis demonstrated that the atherogenic index of plasma (AIP) is the most precise predictor of depression in HT patients, with a success rate of 100%. This was followed by the distance from Castelli's risk index-I (CRI-I) (24.7%), ionized calcium (23.1%), the IR index (HOMA2IR) (22.4%), and the insulin sensitivity index (HOMA2S%) (21.8%). Selenium, conversely, was the most reliable biomarker for differentiating the HT group from the control group. Conclusion: Depression in HT patients is associated with alteration in the serum levels of cations, atherogenic indices, trace elements, and IR. AIP is the best predictor for depression in HT patients. It is essential to correct the amounts of blood biomarkers of HT patients to mitigate the severity of depression.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".