A national study exploring the association between triglyceride-glucose index and risk of hyperuricemia events in adults with hypertension.

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 < 0.001) and non-HTN participants (OR: 2.61, 95 % CI: 1.45-4.69, Ptrend < 0.001). Restricted cubic spline regression showed linearity of the associations between the TyG index and hyperuricemia (p-nonlinear > 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 < 0.05). Conclusions: TyG index, a practical surrogate of IR, was linearly and positively associated with hyperuricemia in HTN and non-HTN participants. Proactive measures are needed to prevent the comorbidity of IR-driven hyperuricemia in the future.

Social integration and utilization of national basic public health services among China's internal migrants with chronic diseases: A structural equation modelling approach.

Internal migrants with chronic diseases (IMCDs) are a specific subgroup of the internal migrants, but few studies have focused on health service utilization among this group. Social integration is an essential element in the maintenance of health and well-being in migrants. However, the measurement of social integration remains inconsistent. This study aimed to measure social integration more comprehensively and evaluate the association between social integration and National Basic Public Health Services (NBPHS) utilization among IMCDs in China, thereby providing theoretical support for health promotion among IMCDs. The data of this study were obtained from the China Migrants Dynamic Survey (CMDS) in 2017. A total of 9272 internal migrants who self-reported hypertension and/or type 2 diabetes were included in the analysis. Four factors were extracted through exploratory factor analysis to measure the social integration of IMCDs: psychological identity, community involvement, social security, and sociocultural adaptation. The results show the IMCDs underutilized NBPHS, with 26.80 % stating that they have not used any of the services in the NBPHS. We confirmed the positive association between social integration and NBPHS use among IMCDs. The social integration of IMCDs in developed regions was relatively worse than in developing regions, further exacerbating the underutilization of NBPHS in developed regions. Therefore, targeted government measures and supportive policies are necessary, especially in developed regions, to encourage IMCDs to participate in social organizations and community activities and stimulate their active participation in the NBPHS.

The association between the Short Physical Performance Battery and longitudinal trajectories of depressive symptoms among Chinese older adults.

BACKGROUND: In this study, the long-term trajectories of depressive symptoms in a 7-year prospective survey cohort of Chinese older adults were explored. Additionally, the study examined whether there was an independent association between scores on the Short Physical Performance Battery (SPPB) and the different trajectories of depressive symptoms. METHODS: A total of 2177 elderly individuals had their depressive symptoms assessed based on the Center for Epidemiological Studies-Depression (CES-D) scale in the years 2011, 2013, 2015, and 2018. In addition, their demographic characteristics, chronic diseases, and lifestyle factors were also assessed. The trajectories of depressive symptoms were analysed using the group-based trajectories analysis model. Furthermore, the relationship between the objectively measured SPPB scores and the long-term trajectory of depressive symptoms was explored using multinomial logistic regression. RESULTS: The group-based trajectory analysis model categorized the trajectories of depressive symptoms across four waves into four groups: persistent low depressive symptoms, increasing depressive symptoms, decreased depressive symptoms, and persistent high depressive symptoms. After controlling for confounding factors, it was observed that a higher baseline SPPB score was associated with an increased likelihood of persistent high depressive symptoms, OR (95% CI) = 0.724 (0.644, 0.814), for the persistent high depressive symptoms versus the persistent low depressive symptoms. CONCLUSIONS: Low levels of SPPB score are associated with persistent high depressive symptoms in older adults. Conversely, improving physical performance as measured by the SPPB can help reduce the risk of major depressive disorder and persistent depressive disorder in the elderly.

Construction of predictive model for osteoporosis related factors among postmenopausal women on the basis of logistic regression and Bayesian network.

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.