{"doi":"10.1210/clinem/dgae294","title":"TSH Trajectories During Levothyroxine Treatment in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) Cohort","abstract":"CONTEXT: Thyroid-stimulating hormone (TSH) trajectory classification represents a novel approach to defining the adequacy of levothyroxine (LT4) treatment for hypothyroidism over time. OBJECTIVE: This is a proof of principle study that uses longitudinal clinical data, including thyroid hormone levels from a large prospective study to define classes of TSH trajectories and examine changes in cardiovascular (CV) health markers over the study period. METHODS: Growth mixture modeling (GMM), including latent class growth analysis (LCGA), was used to classify LT4-treated individuals participating in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) based on serial TSH levels. Repeated measure analyses were then utilized to assess within-class changes in blood pressure, lipid levels, hemoglobin A1c, and CV-related medication utilization. RESULTS: From the 621 LT4-treated study participants, the best-fit GMM approach identified 4 TSH trajectory classes, as defined by their relationship to the normal TSH range: (1) high-high normal TSH, (2) normal TSH, (3) normal to low TSH, and (4) low to normal TSH. Notably, the average baseline LT4 dose was lowest in the high-high normal TSH group (77.7 µg, P < .001). There were no significant differences in CV health markers between the classes at baseline. At least 1 significant difference in CV markers occurred in all classes, highlighted by the low to normal class, in which total and high-density lipoprotein cholesterol, triglycerides, and A1c all increased significantly (P = .049, P < .001, P < .001, and P = .001, respectively). Utilization of antihypertensive, antihyperlipidemic, and antidiabetes medications increased in all classes. CONCLUSION: GMM/LCGA represents a viable approach to define and examine LT4 treatment by TSH trajectory. More comprehensive datasets should allow for more complex trajectory modeling and analysis of clinical outcome differences between trajectory classes.","journal":"The Journal of Clinical Endocrinology & Metabolism","year":2024,"id":435295,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9292,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1242454,"name":"Gustavo Penna","orcid":"0000-0003-2125-7051","position":1,"is_corresponding":false},{"id":352757,"name":"Wen Wan","orcid":"0000-0002-7464-6563","position":2,"is_corresponding":false},{"id":42485,"name":"Isabela M. Benseñor","orcid":"0000-0002-6723-5678","position":3,"is_corresponding":false},{"id":677060,"name":"Neda Laiteerapong","orcid":"0000-0003-0124-4325","position":4,"is_corresponding":false},{"id":254116,"name":"Antônio C. Bianco","orcid":"0000-0001-7737-6813","position":5,"is_corresponding":false},{"id":302143,"name":"Matthew D Ettleson","orcid":"0000-0003-0328-1752","position":0,"is_corresponding":true}],"reference_count":39,"raw_metadata":null,"created_at":"2026-07-19T02:00:03.406367Z","pmid":"38780968","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}