EFFECT OF LEVOTHYROXINE ON COGNITIVE FUNCTIONS AND CORRELATION WITH NT-PROBNP IN SUBCLINICAL HYPOTHYROIDISM
Bibliographic record
Abstract
ABSTRACT Background: Subclinical hypothyroidism (SCH) affects cardiovascular and neurocognitive function. N-terminal pro-B-type natriuretic peptide (NT-proBNP) serves as both a cardiac biomarker and cognitive function marker, reflecting cerebrovascular dysfunction impacting cognitive performance. Objective: To evaluate levothyroxine treatment effects on cognitive function, thyroid functions and NT-proBNP levels in SCH. Methods: A prospective observational study included 40 SCH patients stratified into levothyroxine-treated (n=21) and untreated (n=19) groups. Cognitive function was assessed using Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Addenbrooke’s Cognitive Examination (ACE) scales over a minimum of 3 months follow-up or when the patient became euthyroid for the treatment arm. Results: Both groups showed comparable baseline cognitive scores. Baseline differences included higher TSH levels (8.10 ± 1.44 vs 6.53 ± 1.42 mcIU/mL, p=0.001), elevated NT-proBNP concentrations (111.33 ± 46.84 vs 84.05 ± 35.40 pmol/L, p=0.046), and increased anti-thyroid peroxidase antibodies (421.52 ± 281.52 vs 84.95 ± 102.16 IU/mL, p=0.001) in the treated group. Following treatment, the levothyroxine group demonstrated TSH normalization (8.10 ± 1.44 to 2.38 ± 0.86 mcIU/mL, p=0.001) and significant NT-proBNP reduction (111.33 ± 46.84 to 91.10 ± 39.67 pmol/L, p=0.001). The untreated group showed no significant changes in TSH or NT-proBNP levels on follow-up. Despite biochemical improvements, treated group did not demonstrate statistically significant cognitive function changes across assessment tools. Conclusion: Levothyroxine therapy in subclinical hypothyroidism significantly reduced NT-proBNP, while standard cognitive assessment scales showed no significant improvement. This suggests these cognitive scales may lack sensitivity and are subjective, not detecting early cognitive changes, hence more detailed cognitive assessment scales are needed for detecting subtle cognitive improvement. However, NT-proBNP proved to be a more sensitive and objective biomarker, indicating potential early cardiovascular and neurocognitive benefits of therapy that precede measurable changes on traditional cognitive tests.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".