Development and Validation of a Nomogram Model for Individualizing the Risk of Osteopenia in Abdominal Obesity.

OBJECTIVE: This study was aimed to create and validate a risk prediction model for the incidence of osteopenia in individuals with abdominal obesity. METHODS: Survey data from the National Health and Nutrition Examination Survey (NHANES) database for the years 2013-2014 and 2017-2018 was selected and included those with waist circumferences ≥102 m in men and ≥88 cm in women, which were defined as abdominal obesity. A multifactor logistic regression model was constructed using LASSO regression analysis to identify the best predictor variables, followed by the creation of a nomogram model. The model was then verified and evaluated using the consistency index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA). Results Screening based on LASSO regression analysis revealed that sex, age, race, body mass index (BMI), alkaline phosphatase (ALP) and Triglycerides (TG) were significant predictors of osteopenia development in individuals with abdominal obesity (P < 0.05). These six variables were included in the nomogram. In the training and validation sets, the C indices were 0.714 (95 % CI: 0.689-0.738) and 0.701 (95 % CI: 0.662-0.739), respectively, with corresponding AUCs of 0.714 and 0.701. The nomogram model exhibited good consistency with actual observations, as demonstrated by the calibration curve. The DCA nomogram showed that early intervention for at-risk populations has a net positive impact. CONCLUSION: Sex, age, race, BMI, ALP and TG are predictive factors for osteopenia in individuals with abdominal obesity. The constructed nomogram model can be utilized to predict the clinical risk of osteopenia in the population with abdominal obesity.

Development and validation of a cognitive dysfunction risk prediction model for the abdominal obesity population.

Objectives: This study was aimed to develop a nomogram that can accurately predict the likelihood of cognitive dysfunction in individuals with abdominal obesity by utilizing various predictor factors. Methods: A total of 1490 cases of abdominal obesity were randomly selected from the National Health and Nutrition Examination Survey (NHANES) database for the years 2011-2014. The diagnostic criteria for abdominal obesity were as follows: waist size ≥ 102 cm for men and waist size ≥ 88 cm for women, and cognitive function was assessed by Consortium to Establish a Registry for Alzheimer's Disease (CERAD), Word Learning subtest, Delayed Word Recall Test, Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). The cases were divided into two sets: a training set consisting of 1043 cases (70%) and a validation set consisting of 447 cases (30%). To create the model nomogram, multifactor logistic regression models were constructed based on the selected predictors identified through LASSO regression analysis. The model's performance was assessed using several metrics, including the consistency index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA) to assess the clinical benefit of the model. Results: The multivariate logistic regression analysis revealed that age, sex, education level, 24-hour total fat intake, red blood cell folate concentration, depression, and moderate work activity were significant predictors of cognitive dysfunction in individuals with abdominal obesity (p < 0.05). These predictors were incorporated into the nomogram. The C-indices for the training and validation sets were 0.814 (95% CI: 0.875-0.842) and 0.805 (95% CI: 0.758-0.851), respectively. The corresponding AUC values were 0.814 (95% CI: 0.875-0.842) and 0.795 (95% CI: 0.753-0.847). The calibration curves demonstrated a satisfactory level of agreement between the nomogram model and the observed data. The DCA indicated that early intervention for at-risk populations would provide a net benefit, as indicated by the line graph. Conclusion: Age, sex, education level, 24-hour total fat intake, red blood cell folate concentration, depression, and moderate work activity were identified as predictive factors for cognitive dysfunction in individuals with abdominal obesity. In conclusion, the nomogram model developed in this study can effectively predict the clinical risk of cognitive dysfunction in individuals with abdominal obesity.

A systematic review and meta-analysis of the correlation between polycystic ovary syndrome and irritable bowel syndrome.

BACKGROUND: Research on the prevalence of irritable bowel syndrome (IBS) among polycystic ovary syndrome (PCOS) patients has gained significant momentum over the years. However, it remains unclear whether PCOS is related to a higher prevalence of IBS. The objective of this systematic review and meta-analysis was to fully study IBS correlation with PCOS. METHODS: From inception until October 16th, 2022, all observational studies documenting IBS prevalence in PCOS patients were collected from the China national knowledge infrastructure(CNKI), China Science and Technology Journal Database(VIP), Wanfang database, PubMed, Embase, Web of Science, and Cochrane databases. The quality of case-control studies was assessed with Newcastle-Ottawa Scale. Review Manager 5.3 was used to determine the pooled odds ratio (OR) and 95% confidence interval (CI). RESULTS: 5 case-control studies involving 1268 individuals and one cross-sectional study involving 291 participants were included in our qualitative analysis. The quantitative analysis was conducted based on five case-control studies. Four case-control studies involving 1063 participants showed a higher prevalence of IBS in PCOS This meta-analysis revealed an almost twice higher risk of IBS in comparison with controls (OR = 2.23, 95%CI:1.58-3.14, p < 0.001; I2=41%, p = 0.150). Four sensitivity analyses validated the consistency of the aggregated findings. CONCLUSION: This meta-analysis and systematic review demonstrated a significant association between PCOS and increased odds of IBS. However, more high-quality and well-controlled research is essential to increase the robustness of our conclusions.