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Record W4403149744 · doi:10.1210/jendso/bvae163.1957

12217 Exploring Thyroid-stimulating Hormone As A Potential Cardiovascular Disease Risk Marker In Euthyroid Individuals

2024· article· en· W4403149744 on OpenAlexaff
Alfredo Pinheiro Neto, Henrique Luca Lucchesi, Carolina Castro Porto Silva Janovsky, Isabela M Benseñor, Laura Sterian Ward, Lucas Leite Cunha

Bibliographic record

VenueJournal of the Endocrine Society · 2024
Typearticle
Languageen
FieldMedicine
TopicThyroid Disorders and Treatments
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsEuthyroidMedicineThyroid-stimulating hormoneThyroidInternal medicineHormoneThyroid diseaseDiseaseEndocrinology

Abstract

fetched live from OpenAlex

Abstract Disclosure: A.P. Neto: None. H. Lucchesi: None. C.C. Silva Janovsky: None. I. Bensenor: None. L.S. Ward: None. L.L. Cunha: None. Previous studies have suggested that TSH levels may predict longevity primarily in elderly individuals. Aging is associated with a high prevalence of cardiovascular and metabolic diseases that reduce survival. However, the relationship between serum TSH levels and these conditions, especially traditional cardiovascular risk markers, remains poorly explored. We employed data from the longitudinal multicenter cohort study ELSA-Brasil, which includes workers from five Brazilian Universities, to evaluate thyroid function in 8,452 participants aged between 34 and 75 years, classified as euthyroid and followed for 8.9 ± 0.6 years. The participants were evaluated for the presence of diabetes, hypertension, angina, peripheral arterial disease, and cardiovascular mortality. Additionally, we measured cardiovascular markers, including global cardiovascular risk score, HbA1c, microalbuminuria (mg/dl), LDL levels, and triglyceride levels. Statistical analysis was used to compare TSH levels with clinically relevant outcomes and classical cardiovascular risk markers. We observed that higher TSH levels correlated negatively with global cardiovascular risk (Spearman rank -0.023, p-value=0.038), microalbuminuria (Spearman rank -0.055, p<0.000), and HbA1c (Spearman rank -0.041, p<0.000). However, TSH levels were positively correlated with hypertriglyceridemia (Spearman rank +0.125, p<0.000) but had no relationship with LDL (p >0.05). The appraisal of cardio-metabolic diseases showed that individuals without diabetes had higher TSH levels (1.79 mIU/L, IQR 1.07) than individuals with diabetes (1.71 mIU/L, IQR 1.07). Patients with arteriosclerosis diseases, such as coronary and peripheral arterial disease, had lower TSH levels (1.66, IQR 0.98 and 1.67, IQR 0.94, respectively) than those who did not have these comorbidities (1.79, IQR 1.07, and 1.79, IQR 1.07). Furthermore, TSH levels did not correlate with the incidence of hypertension (p >0.05). Finally, we divided the population into two groups: higher and lower TSH levels based on the median TSH levels (1.78 mIU/L, IQR 1.06). The Kaplan-Meier curve did not show a difference between these groups (p=0.758) in survival after cardiovascular events (08 events in total) during follow-up. In conclusion, our results suggest that higher serum TSH levels in euthyroid individuals are associated with a lower prevalence of metabolic and cardiovascular disease. However, the relatively short observation duration and low frequency of cardiovascular events in our cohort made it difficult to statistically analyze several endpoints, such as survival. Further investigation is necessary to completely understand whether TSH is a cardiovascular risk indicator. Presentation: 6/2/2024

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.019
GPT teacher head0.266
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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