Main mechanisms and clinical implications of alterations in energy expenditure state among patients with pheochromocytoma and paraganglioma: A review.

Pheochromocytoma and paraganglioma (PPGL) are rare neuroendocrine tumors with diverse clinical presentations. Alterations in energy expenditure state are commonly observed in patients with PPGL. However, the reported prevalence of hypermetabolism varies significantly and the underlying mechanisms and implications of this presentation have not been well elucidated. This review discusses and analyzes the factors that contribute to energy consumption. Elevated catecholamine levels in patients can significantly affect substance and energy metabolism. Additionally, changes in the activation of brown adipose tissue (BAT), inflammation, and the inherent energy demands of the tumor can contribute to increased resting energy expenditure (REE) and other energy metabolism indicators. The PPGL biomarker, chromogranin A (CgA), and its fragments also influence energy metabolism. Chronic hypermetabolic states may be detrimental to these patients, with surgical tumor removal remaining the primary therapeutic intervention. The high energy expenditure of PPGL has not received the attention it deserves, and an accurate assessment of energy metabolism is the cornerstone for an adequate understanding and treatment of the disease.

Risk Assessment of Sarcopenia in Patients With Type 2 Diabetes Mellitus Using Data Mining Methods.

Purpose: Sarcopenia is a geriatric syndrome, and it is closely related to the prevalence of type 2 diabetes mellitus (T2DM). Until now, the diagnosis of sarcopenia requires Dual Energy X-ray Absorptiometry (DXA) scanning. This study aims to make risk assessment of sarcopenia with support vector machine (SVM) and random forest (RF) when DXA is not available. Methods: Firstly, we recruited 132 patients aged over 65 and diagnosed with T2DM in Changchun, China. Clinical data were collected for predicting sarcopenia. Secondly, we selected 3, 5, and 7 features out of over 40 features of patient's data with backward selection, respectively, to train SVM and RF classification models and regression models. Finally, to evaluate the performance of the models, we performed leave one out and 5-fold cross validation. Results: When training the model with 5 features, the sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) were favorable, and it was better than the models trained with 3 features and 7 features. Area under the receiver operating characteristic (ROC) curve (AUC) were over 0.7, and the mean AUC of SVM models was higher than that of RF. Conclusions: Using SVM and RF to make risk assessment of sarcopenia in the elderly is an option in clinical setting. Only 5 features are needed to input into the software to run the algorithm for a primary assessment. It cannot replace DXA to diagnose sarcopenia, but is a good tool to evaluate sarcopenia.

[Birth weight distribution among premature infants and related social factors].

OBJECTIVE: To understand the distribution of birth weight among premature infants and the associated social factors. METHODS: The study population consisted of 97 537 women who delivered singleton live birth of 20 to 41 gestational weeks in 4 counties/cities, Jiangsu and Zhejiang provinces, China from 1995 to 2000. Chi-square test was employed to test the difference of proportions between respective groups. One- way ANOVA was used to test the differences regarding the mean of gestational weeks at the first prenatal visit and the mean of prenatal visits between the two groups. Multivariate logistic regression was conducted to examine the factors associated with premature birth. RESULTS: Women aged 35 years had higher (8.8%) premature incidence than those aged less than 24 years (5.6%), 25 - 29 years (4.6%), or 30 - 34 years (4.5%, P < 0.001). Women with height less than 149 cm had higher (6.8%) premature incidence than those with height taller than 150 cm (5.0%). Women whose BMI were at least 28 and 24 - 28 had higher (5.5%, 5.5%) premature incidences than those whose BMI were 18.5 - 24.0 (5.0%), < 18.5 (4.6%, P < 0.001). The incidence of premature birth was 6.0% among women without previous pregnancy, higher than that among those women with 4 times of pregnancies (5.7%), 2 times of pregnancies (4.3%), and 3 times of pregnancies (4.0%). Parous women with at least two deliveries had higher (9.3%) premature incidence than the primiparous women (5.2%) and whose women with only one delivery (4.5%, P < 0.001). Women who received early prenatal care had lower 4.7% premature incidence than those who did not receive the service (6.1%). The mean times of prenatal visits among women with premature births was 8.53, less than that of those with full term delivery (10.97). Women with less than four times of prenatal visit had higher (18.9%) premature incidence than those with at least five prenatal visits (4.9%). Multivariate logistic regression showed that premature delivery risk was associated with age, height, BMI, gravidity, parity, early prenatal care, the mean of gestational weeks at first prenatal visit and the mean number of prenatal visits etc. CONCLUSION: Premature delivery risk was associated with factors as age, height, BMI, gravidity, parity, early prenatal care, the mean of gestational weeks at first prenatal visit, the mean number of prenatal visits etc.