Hashimoto’s thyroiditis in children and adolescents: analysis of long-term course
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Ayşe Yaşar
,
Heves Kırmızıbekmez
,
Esma Ebru Altun
,
Fatma Güliz Atmaca
,
Şerife Ece Ulu
,
Gülcan Seymen
,
Burcu Cüce
,
Zehra Sucu Saymaz
und
Fatma Dursun
Abstract
Objectives
This study retrospectively evaluated long-term clinical and biochemical findings in children and adolescents diagnosed with Hashimoto thyroiditis (HT).
Methods
A total of 233 patients aged 2–18 years, diagnosed with HT and followed for at least three years, were included. Patients were classified into five subgroups based on thyroid stimulating hormone (TSH) and free thyroxine (fT4) levels at diagnosis. Clinical, laboratory, and radiological findings at baseline and during follow-up were analyzed.
Results
The most common initial thyroid status was subclinical hypothyroidism (SCH) (39.5 %), followed by euthyroidism (36.5 %), overt hypothyroidism (OH) (18.5 %), subclinical hyperthyroidism (3.9 %), and overt hyperthyroidism (1.7 %). Data from 173 patients were available at the third and fifth years. Total of 48.4 % of patients were monitored without treatment, 47.6 % were initiated on levothyroxine and 3 % were initiated on methimazole. Among OH patients, 9 % became euthyroid over time and discontinued treatment. Levothyroxine was initiated in 73.6 % of SCH patients, but only one discontinued treatment due to normalization. Among 63 untreated patients following for five years, no functional deterioration was observed. While autoantibody levels remained stable across all groups at year three, both TPO-Ab and TG-Ab levels significantly decreased by year five (p=0.000).
Conclusions
Given its dynamic nature, HT requires continuous monitoring through regular clinical assessments and thyroid function testing.
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Research ethics: The study was performed according to the Helsinki II decloration and approved by the local Ethical Comittee.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: No competing interst.
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Research funding: None declared.
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Data availability: The raw data can be obtained on request from the corresponding author.
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