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Background: The triglyceride-glucose (TyG) index has been recommended as a practical surrogate of insulin resistance (IR). However, the association between the TyG index and hyperuricemia among adults with hypertension remains to be elucidated. Methods: We included and analyzed 3134 HTN patients and 4233 non-HTN participants from the cross-sectional 2013–2018 U.S. National Health and Nutrition Examination Surveys (NHANES). Multivariable logistic regression and restricted cubic splines (RCS) were used to explore the association between the TyG index and hyperuricemia. Stratifed analyses were performed to assess the association in populations with different subgroups of hypertension. Results: The prevalence of hyperuricemia was higher in HTN patients (28.00 %) than in non-HTN participants (12.47 %). The multivariable logistic regression showed that the TyG index was significantly associated with hyperuricemia. After multivariable adjustment, higher TyG index levels were found to be associated with a higher prevalence of hyperuricemia in HTN patients (OR: 2.39, 95 % CI: 1.37–4.17, Ptrend 0.05). In the subgroup analysis suggested that the positive association seemed to be strong among male, alcohol use, and diabetes group (P for interaction

Osteoporosis is a prevalent chronic disease that often goes unnoticed in postmenopausal women. Early identification of risk factors for osteoporosis in postmenopausal women is essential. This study aimed to develop predictive models for osteoporosis-related factors among postmenopausal women in the U.S. and explore the influencing factors. In this cross-sectional study, we included 4417 postmenopausal women from the NHANES (2009–2010, 2013–2014, and 2017–2020). Through multiple regression analysis, we found that age, minutes of sedentary activity, prednisone or cortisone usage, arthritis, bone loss around teeth, and trouble sleeping were risk factors for osteoporosis after menopause. Conversely, height, BMI, and age at the last menstrual period were identified as protective factors. The findings from the Bayesian network analysis indicated that several factors influenced osteoporosis, including age, BMI, bone loss around teeth, prednisone or cortisone usage, arthritis, and age at the last menstrual period. On the other hand, minutes of sedentary activity and height might have indirect effects, while trouble sleeping may not have a significant impact. Both logistic regression and Bayesian network models demonstrated good predictive capabilities in predicting osteoporosis among postmenopausal women. In addition, Bayesian networks offer a more intuitive depiction of the intricate network risk mechanism between diseases and factors.