Biochemical changes in patients during hypothyroid phase after thyroidectomy

Al-Janabi, Ghusoon; Hassan, Hayder Neamah; Al-Fahham, Ali A.

doi:10.25122/jml-2021-0297

Cited by 10 publications

(7 citation statements)

References 18 publications

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“…There are also some studies that serve to illustrate the effects of hypothyroidism of relatively short duration on the lipid profile. In one study 60 patients underwent thyroidectomy for an unspecified reason, were not started on thyroid hormone, and had lipid profiles measured 3 weeks later [ 36 ]. Compared with a group of control individuals, those with post-surgical hypothyroidism (serum TSH of approximately 70 mIU/L) had higher TC, LDL-C, TG, and TC/HDL-C ratio [ 36 ].…”

Section: Lipid Changes With Overt Hypothyroidismmentioning

confidence: 99%

“…In one study 60 patients underwent thyroidectomy for an unspecified reason, were not started on thyroid hormone, and had lipid profiles measured 3 weeks later [ 36 ]. Compared with a group of control individuals, those with post-surgical hypothyroidism (serum TSH of approximately 70 mIU/L) had higher TC, LDL-C, TG, and TC/HDL-C ratio [ 36 ]. There was no significant change in HDL-C. Three other studies examined lipid profiles in thyroidectomized patients who were hypothyroid while awaiting radioactive iodine treatment [ 37 – 39 ].…”

Section: Lipid Changes With Overt Hypothyroidismmentioning

confidence: 99%

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Hypothyroidism, lipids, and lipidomics

Jonklaas

2023

Endocrine

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Purpose Hypothyroidism is a relatively common endocrine disorder and is well documented to be associated with lipid abnormalities. Methods A narrative review was conducted of studies describing the alterations in the lipid profile accompanying both subclinical and overt hypothyroidism. Results Lipid abnormalities are seen with TSH values in the upper end of the accepted reference range, as well as with subclinical and overt hypothyroidism. The degree of lipid derangement is generally proportional to the degree of TSH elevation. Other factors such as age, sex, and body mass index can also influence the pattern of the lipid abnormalities seen. The most robust finding with TSH elevation is increases in the low density lipoprotein cholesterol. Thyroid hormone treatment is efficacious in reversing the lipid abnormalities in both subclinical and overt hypothyroidism. Conclusion Given the association of lipid abnormalities with metabolic and cardiovascular disease, consideration of hypothyroidism as an important non-communicable disease may facilitate studies that test the hypothesis that thyroid hormone treatment to reverse hypothyroidism-associated lipid abnormalities may improve metabolic and cardiovascular outcomes.

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Section: Lipid Changes With Overt Hypothyroidismmentioning

confidence: 99%

Section: Lipid Changes With Overt Hypothyroidismmentioning

confidence: 99%

Hypothyroidism, lipids, and lipidomics

Jonklaas

2023

Endocrine

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“…Top features, such as a high TSH level, short treatment duration, low FT4 level, advanced age, and a higher cumulative amiodarone dose, have been well explained by previous epidemiological studies [ 6 , 7 , 16 ]. Low alkaline phosphatase and high alanine aminotransferase levels were also found to be associated with hypothyroidism [ 60 ]. However, HDL and LDL, which were selected by RFE in this research, were not statistically significantly correlated with thyroid function in previous pharmacoepidemiological reports [ 61 ].…”

Section: Discussionmentioning

confidence: 99%

Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation

Chao

Chiang

et al. 2023

J Med Internet Res

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Background Machine learning offers new solutions for predicting life-threatening, unpredictable amiodarone-induced thyroid dysfunction. Traditional regression approaches for adverse-effect prediction without time-series consideration of features have yielded suboptimal predictions. Machine learning algorithms with multiple data sets at different time points may generate better performance in predicting adverse effects. Objective We aimed to develop and validate machine learning models for forecasting individualized amiodarone-induced thyroid dysfunction risk and to optimize a machine learning–based risk stratification scheme with a resampling method and readjustment of the clinically derived decision thresholds. Methods This study developed machine learning models using multicenter, delinked electronic health records. It included patients receiving amiodarone from January 2013 to December 2017. The training set was composed of data from Taipei Medical University Hospital and Wan Fang Hospital, while data from Taipei Medical University Shuang Ho Hospital were used as the external test set. The study collected stationary features at baseline and dynamic features at the first, second, third, sixth, ninth, 12th, 15th, 18th, and 21st months after amiodarone initiation. We used 16 machine learning models, including extreme gradient boosting, adaptive boosting, k-nearest neighbor, and logistic regression models, along with an original resampling method and 3 other resampling methods, including oversampling with the borderline-synthesized minority oversampling technique, undersampling–edited nearest neighbor, and over- and undersampling hybrid methods. The model performance was compared based on accuracy; Precision, recall, F1-score, geometric mean, area under the curve of the receiver operating characteristic curve (AUROC), and the area under the precision-recall curve (AUPRC). Feature importance was determined by the best model. The decision threshold was readjusted to identify the best cutoff value and a Kaplan-Meier survival analysis was performed. Results The training set contained 4075 patients from Taipei Medical University Hospital and Wan Fang Hospital, of whom 583 (14.3%) developed amiodarone-induced thyroid dysfunction, while the external test set included 2422 patients from Taipei Medical University Shuang Ho Hospital, of whom 275 (11.4%) developed amiodarone-induced thyroid dysfunction. The extreme gradient boosting oversampling machine learning model demonstrated the best predictive outcomes among all 16 models. The accuracy; Precision, recall, F1-score, G-mean, AUPRC, and AUROC were 0.923, 0.632, 0.756, 0.688, 0.845, 0.751, and 0.934, respectively. After readjusting the cutoff, the best value was 0.627, and the F1-score reached 0.699. The best threshold was able to classify 286 of 2422 patients (11.8%) as high-risk subjects, among which 275 were true-positive patients in the testing set. A shorter treatment duration; higher levels of thyroid-stimulating hormone and high-density lipoprotein cholesterol; and lower levels of free thyroxin, alkaline phosphatase, and low-density lipoprotein were the most important features. Conclusions Machine learning models combined with resampling methods can predict amiodarone-induced thyroid dysfunction and serve as a support tool for individualized risk prediction and clinical decision support.

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“…В большинстве случаев врачи различных специальностей (кардиологи, неврологи, участковые терапевты, врачи общей практики) сталкиваются и анализируют уже клинические кардиальные и церебральные исходы и осложнения у пациентов с патологией щитовидной железы, часто рассматривая эндокринную патологию в качестве сопутствующего, но не потенцирующего атерогенез состояния. В отношении стратификации групп кардиоваскулярного и церебрального риска, необходимости выбора сроков начала и способов антиатерогенной терапии у пациентов с различным гормональным статусом щитовидной железы мнения ученых весьма противоречивы [14,[29][30][31][32][33][34][35][36][37][38][39]. Кроме того, в случае подозрения на вторичную гиперлипидемию необходимо учитывать и другие обстоятельства, обусловленные коморбидностью патологии.…”

Section:  вторичная гиперлипидемия и атеросклероз у пациентов с пато...unclassified

“…Некомпенсированный гипотиреоз характеризуется ухудшением качества жизни и серьезными кардиометаболическими нарушениями. Вместе с тем риск развития ССЗ может повышаться и при его субклинической форме [14,[28][29][30][31][32][33][34][35][36][37][38][39]52].…”

Section:  роль гормонов щитовидной железы в развитии гиперлипидемииunclassified

Secondary Hyperlipidemia and Atherosclerosis in Patients with Thyroid Pathology

Петрова¹,

N.²,

Т.В.³

et al. 2023

Кардиология В Беларуси

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Несмотря на достижения отечественного и зарубежного здравоохранения, патология сердечно-сосудистой системы (ССС), ассоциированная с атеросклерозом, вносит существенный вклад в статистику общей смертности в Европе и Республике Беларусь. Жизнеугрожающие осложнения со стороны ССС, трактуемые как кардиоваскулярные риски, и их предупреждение находятся в фокусе приоритетных направлений мирового здравоохранения. Каждый второй представитель развитых стран в возрасте 40 лет и старше погибает от последствий атеросклероза. Ранняя инвалидизация и высокая смертность пациентов трудоспособного возраста, масштабы финансовых затрат на этапах амбулаторного и стационарного лечения, потребность рационального использования средств бюджета стран на реабилитацию, в том числе льготное обеспечение данной категории лиц, проведение дорогостоящих интервенционных и кардиохирургических лечебно-диагностических процедур наглядно демонстрируют необходимость поиска новых научно обоснованных основ стратификации групп кардиоваскулярного риска и профилактических подходов. Неослабевающий научный и практический интерес представляет изучение вклада коморбидных состояний в механизмы развития вторичной гиперлипидемии и атерогенеза. На сегодняшний день, по данным Всемирной организации здравоохранения, поражение щитовидной железы занимает второе место в перечне эндокринных заболеваний после нарушения обмена углеводов. Гиперлипидемия у пациентов с нарушением функции щитовидной железы (гипо- или гипертиреоз) является сложной мультидисциплинарной проблемой, требующей углубленного изучения. В настоящем обзоре представлено современное видение проблемы, отражены основные вопросы метаболизма тиреоидных гормонов, их роль в развитии дислипидемии и структурно-функционального поражения миокарда, новые взгляды на профилактику кардиоваскулярных рисков у пациентов с дисфункцией щитовидной железы. Despite the achievements of national and foreign health care, atherosclerosis-related cardiovascular diseases (CVD) contribute significantly to the total mortality in Europe and the Republic of Belarus. Life-threatening complications of CVD, interpreted as cardiovascular risks, and their prevention are in the focus of the global health care priorities. Every second inhabitant of developed countries aged 40 years and older dies due to atherosclerosis outcomes. Early disability and high mortality of patients of working age, extent of financial costs at outpatient and inpatient stages of treatment, needs for rational use of national budgets funds for rehabilitation, including preferential provision for this category of people, expensive interventional and cardiac surgery treatment and diagnostic procedures, clearly demonstrate the necessity of searching for a new evidence- based stratification of cardiovascular risk groups and preventive approaches. The contribution of comorbid conditions in the mechanisms of secondary hyperlipidemia and atherogenesis development is of ongoing scientific and clinical interest. According to the World Health Organization, thyroid gland involvement occupies the second place in the list of endocrine diseases after carbohydrate metabolism disorders. Dyslipidemia in patients with thyroid dysfunction (hypo- or hyperthyroidism) is a complex multidisciplinary problem that requires extensive study. This review presents the current view of the problem, reflects the main issues of thyroid hormones metabolism, in development of dyslipidemia and structural and functional myocardial involvement, and current views on prevention of cardiovascular risks in patients with thyroid dysfunction.

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Biochemical changes in patients during hypothyroid phase after thyroidectomy

Cited by 10 publications

References 18 publications

Hypothyroidism, lipids, and lipidomics

Hypothyroidism, lipids, and lipidomics

Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation

Secondary Hyperlipidemia and Atherosclerosis in Patients with Thyroid Pathology

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