{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:37:24Z","timestamp":1787020644861,"version":"build-2736575974"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Lipids Health Dis"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Subclinical hypothyroidism (SCH) often occurs in association with the emergence of the metabolic disorder like insulin resistance (IR). This study aimed to determine the relationship between the Triglyceride Glucose (TyG) index and thyroid function in patients with subclinical hypothyroidism (SCH), to identify metabolic predictors of thyroid dysfunction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This cross-sectional study used convenience sampling, and data were collected after written informed consent. This study was conducted at tertiary care hospitals in Peshawar, Pakistan, and included 2024 subclinical hypothyroid patients with an age\u2009&gt;\u200919 years. Individuals with any thyroid condition, diabetes, cardiovascular disorders or chronic liver conditions were excluded. Regression, ANOVA, and long short-term memory (LSTM) models were used to predict the TyG index, TSH, T3, and FT4 levels. All analyses were performed using R version 4.3.0 and Python. The result was considered statistically significant with\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.05.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The male-to-female ratio was 1:2, and the highest group included 41 50-year-olds (40.3%). Regression analysis revealed an inverse association between the TyG index and T3 level (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      = -0.313,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.0001) and a positive association with HbA1c (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u2009=\u20090.198;\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.0001), indicating a relationship between a higher TyG index and IR and poor glycemic control. The values of HDL were negatively correlated with the TyG index (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      = -0.221,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.0001); conversely, LDL was positively correlated to TyG (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u2009=\u20090.234,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.0001). The LSTM model presented high predictive accuracy with small mean squared errors, 0.00034 for the TyG index, 0.0015 for T3, and 0.0113 for T4.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The findings of this study demonstrated that the TyG index can be an effective and important parameter of metabolic health and a predictor of thyroid function in subclinical hypothyroid patients. These findings underscore the importance of early identification of metabolic risk factors for thyroid dysfunction, which can contribute to improved health outcomes and reduce the long-term burden of endocrine and cardiovascular diseases at the population level. Moreover, the results of the current research cannot be generalized, as it is a cross-sectional study.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12944-025-02740-1","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T07:10:28Z","timestamp":1761030628000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Exploring the associations of the triglyceride\u2012glucose index with thyroid function in subclinical hypothyroid patients: a cross-sectional study"],"prefix":"10.1186","volume":"24","author":[{"given":"Fawad","family":"Inayat","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sulaiman","family":"Shams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noor","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arsalan Waqas","family":"Ahmad Shah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ihtesham Ul","family":"Haq","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"issue":"1","key":"2740_CR1","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1038\/s41392-022-01073-0","volume":"7","author":"M Li","year":"2022","unstructured":"Li M, Chi X, Wang Y, Setrerrahmane S, Xie W, Xu H. 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