BACKGROUND:The relationship between thyroid hormones and body anthropometric measures in type 2 diabetes mellitus (T2DM) patients with normal thyroid function is unclear. The purpose of this study was to evaluate the association between thyroid hormones and body composition in euthyroid T2DM patients in men and women. MATERIAL AND METHODS:This was a cross-sectional study that included 561 euthyroid T2DM patients. Fasting venous blood was collected to test laboratory indexes. Bioelectric impedance analysis (BIA) was used to measure body composition. Propensity score matching (PSM) was used to enroll patients with similar baseline characteristics. The least absolute shrinkage and selection operator (LASSO) algorithm was used to establish a linear regression model of thyroid hormone and body composition. PSM was performed to match 159 men and 159 women. RESULTS:The LASSO regression analysis suggested that thyroid-stimulating hormone (TSH) level was not correlated with body composition parameters in females. In females, free triiodothyronine (FT3) level was positively correlated with body mass index (BMI), fat-free mass index (FFMI), and skeletal muscle index (SMI), and was negatively correlated with extracellular water fraction (EWF). In males, FT3 level was positively correlated with waist circumference (WC) and SMI and negatively correlated with EWF. Free thyroxine (FT4) level in both women and men was positively correlated with body fat mass (BFM) and left lower-limb muscle mass (LLLMM). Moreover, in males, FT4 level was correlated with more body composition parameters. In euthyroid T2DM patients, FT3 level was positively correlated with SMI and negatively correlated with EWF, while FT4 level was positively correlated with BFM and LLLMM. CONCLUSIONS:Thyroid function can affect body composition in euthyroid T2DM patients. Thyroid function is more likely to affect the fat and muscle distribution of males than females.

Objective: To research the association among body composition and the presence or absence of comorbid hypertension in postmenopausal women with osteoporosis. Methods: Postmenopausal osteoporosis sufferers according to whether they are combined with hypertension or not were divided into two groups: hypertension-negative group and hypertension-positive group. Compare the indicators of the two groups, find out the independent influencing factors, and test the test effect of influencing factors. Establish a prediction model and analyse the relationship between the prediction model and blood pressure. Results: There were statistical differences in age, 25-hydroxyvitamin D, uric acid, homocysteine, history of diabetes, high-density lipoprotein cholesterol, body mass index (BMI), waist circumference (WC), body fat mass (BFM), body fat percentage (PBF) and visceral fat area (VFA) between two groups. Logistic analysis showed that BFM and VFA were independent influencing factors for hypertension, with for BFM (OR, 0.46; 95% CI, 0.24– 0.90; p=0.024) and VFA (OR, 1.06; 95% CI, 1.01– 1.13; p=0.031). Based on the body composition parameters of BMI, WC, BFM, PBF and VFA, the area under the curve of the prediction model for detecting hypertension was 0.694 by receiver-operating characteristic test (p < 0.001). Using generalized additive model, the predictor were found to have a significant dose-response relationship with systolic blood pressure (SBP), but not with diastolic blood pressure. Conclusion: BFM and VFA are independent influencing factors for hypertension in postmenopausal osteoporosis patients. In postmenopausal osteoporosis patients, the predictive model composed of body composition related parameters has certain significance in predicting whether postmenopausal osteoporosis is complicated with hypertension. The effect of the prediction model on blood pressure was mainly reflected in SBP.

Background: Metabolic abnormalities and body mass index (BMI) are apparent risk factors for recurrent stroke. For the prevention of recurrent stroke, which one is more important remains uncertain. This study aimed to compare metabolic phenotypes and BMI as indicators of recurrent stroke in Chinese hospitalized stroke patients. Methods: In this cross-sectional population-based study, 856 hospitalized stroke patients from the First People’s Hospital of Changzhou, the Third Affiliated Hospital of Soochow University, were enrolled. We implemented the standardized questionnaire and biochemical measurements to collect participants’ data. Recurrent stroke was defined as new-onset stroke in patients with a definite history of previous stroke. Metabolic phenotypes were categorized based on the Adult Treatment Panel III criteria. Obesity was defined as BMI ≥25 kg/m². Multivariate logistic regression analyses were used for the association of recurrent stroke with metabolic abnormalities and BMI. Results: Among the hospitalized stroke patients, the prevalence of recurrent stroke was 22.4%. Metabolic abnormalities, rather than BMI, were significantly associated with recurrent stroke. Compared with metabolically healthy patients, metabolically unhealthy patients had a 72% [odds ratio (OR) =1.72, 95% confidence interval (CI): 1.10–2.68] increased risk of recurrent stroke, regardless of BMI and other confounding factors. No statistical association between BMI and recurrent stroke was found. Furthermore, metabolic status significantly improved the risk prediction of recurrent stroke when combined with conventional risk factors (net reclassification index 17.6%, P=0.0047; integrated discrimination improvement 0.7%, P=0.014), whereas BMI did not. Conclusions: Recurrent stroke is likely associated with metabolic abnormalities rather than BMI. For the secondary prevention of stroke, controlling metabolic abnormalities is more crucial than controlling BMI in stroke patients. The longitudinal study and intervention study are warranted in the further.

Purpose: To explore the association between the water distribution in the human body and 25-hydroxyvitamin D among patients with type 2 diabetes mellitus (T2DM), and to analyze whether water distribution is a mediator between vitamin D and T2DM complications. Patients and Methods: In total, 533 T2DM inpatients were included from August 1, 2016 to April 1, 2023. Water distribution indicators, whether from the whole body, arms, trunk, or legs, were measured to calculate the association with vitamin D using linear regression analysis. Subgroups based on age, sex, body mass index, and macrovascular complications were established to clarify changes in the association between vitamin D and water distribution in different populations. Mediation analysis was applied to evaluate the correlation between vitamin D, water distribution, and T2DM complications. Results: There was a negative correlation between 25-hydroxyvitamin D and the ratio of extracellular water (ECW)/total body water (TBW) both in the whole body (P=0.045, β=− 0.008) and in certain parts. A U-shaped restricted cubic spline curve further presented an inflection point (approximately 23 ng/mL 25-hydroxyvitamin D) in the relationship between 25-hydroxyvitamin D and the ECW/TBW of the whole body. A negative correlation was observed between ECW/TBW and vitamin D in the obese subgroup (P=0.015, β=− 0.038). In the total effect of vitamin D on diabetic nephropathy (DN), the mediation effect of ECW/TBW accounted for 15.44%. Conclusion: A correlation between vitamin D and water distribution was observed, and a high ECW/TBW was one of the pathways through which low vitamin D levels might affect DN.

Background Metabolic abnormalities and body mass index (BMI) are known as apparent risk factors of recurrent stroke, but which one is more likely related to recurrent stroke remains uncertain. This study aimed to compare the metabolic phenotypes and BMI as indicators of recurrent stroke in Chinese hospitalized stroke patients. Methods In this retrospective population-based study, 856 hospitalized stroke patients from the Third Affiliated Hospital of Soochow University were enrolled. Recurrent stroke was defined as newly-onset stroke patients with a history of previous stroke. Metabolic phenotypes were categorized based on Adult Treatment Panel III criteria. BMI ≥ 25kg/m² was defined as obesity. Results Among the hospitalized stroke patients, the prevalence of recurrent stroke was 21.9%. Metabolic abnormalities rather than BMI were significantly associated with recurrent stroke. Compared with metabolically healthy patients, metabolically unhealthy ones had 72% (odds ratio [OR] = 1.72, 95% confidence interval [CI] 1.01–2.68) increased risk of recurrent stroke, regardless of BMI and other confounding factors. Whereas, no statistical association between BMI and recurrent stroke were found. Metabolic status significantly improved risk prediction of recurrent stroke when adding to the conventional-risk-factor model (net reclassification index 17.6%, P = 0.0047; integrated discrimination improvement 0.7%, P = 0.014), while BMI did not. Conclusions Recurrent stroke is likely associated with metabolic abnormalities rather than with BMI. For the secondary prevention of stroke, controlling metabolic abnormalities is a more crucial method then BMI controlling in stroke patients.

Background Metabolic status and body mass index (BMI) are known as apparent risk factors of recurrent stroke, but which one is more likely related to recurrent stroke remains uncertain. This study aimed to compare the metabolic phenotypes and BMI as indicators of recurrent stroke in Chinese hospitalized stroke patients. Methods In this retrospective population-based study, 856 hospitalized stroke patients from the Third Affiliated Hospital of Soochow University were enrolled. Recurrent stroke was defined as newly-onset stroke patients with a history of previous stroke. Metabolic phenotypes were based on Adult Treatment Panel III criteria. BMI ≥ 25kg/m² was defined as obesity. Results Among the hospitalized stroke patients, the prevalence of recurrent stroke was 21.9%. Metabolic phenotypes rather than BMI were significantly associated with recurrent stroke. Compared with metabolically healthy patients, metabolically unhealthy ones had 72% (odds ratio [OR] = 1.72, 95% confidence interval [CI] 1.01–2.68) increased recurrent stroke, regardless of BMI and other confounding factors. Whereas, no statistical association between BMI and recurrent stroke were found. Metabolic status improved risk prediction of recurrent stroke when adding to conventional risk factors (net reclassification index 17.6%, P = 0.0047; integrated discrimination improvement 0.7%, P = 0.014), while BMI did not. Conclusions Recurrent stroke was likely associated with poor metabolic status rather than with BMI, suggesting that controlling metabolic abnormalities could be an important method for recurrent stroke prevention.