Objective:To understand the current situation and influencing factors of functional constipation among elderly people in nursing homes, and construct a risk prediction model.Methods:Conveniently select 542 elderly people from 8 nursing homes in Tangshan urban area from July to November 2023 as the research subjects, use binary logistic regression analysis to construct a risk prediction model, and conduct internal validation of the model.Results:Among the 542 elderly people who were included in the study, there were 250 males and 292 females with an average age of 78.00 (70.00, 86.00) years. The incidence of functional constipation among elderly people in nursing homes was 54.06%(293/542). The predictive model includes six predictive factors: age, Barthel index, water intake, daily vegetable intake, insomnia, and perianal disease. The model AUC was 0.885 (95% CI 0.858-0.913), the Youden index was 0.628, the best critical value was 0.585, sensitivity was 0.819, specificity was 0.809. The Hosmer-Lemeshow test χ2=6.38, P=0.605. The internal validation results of the Bootstrap method showed that the AUC of the model was 0.876, the calibration curve was close to the standard line, and the Brier score was 0.135. The DCA results showed that the threshold was 0.1-0.9, and the model had good clinical net benefits. Conclusions:The incidence of functional constipation in elderly care institutions is relatively high. The functional constipation risk prediction model constructed in this study has good predictive efficacy and applicability, which can provide reference for nursing home staff.

Objective:To understand the current situation and influencing factors of functional constipation among elderly people in nursing homes, and construct a risk prediction model.Methods:Conveniently select 542 elderly people from 8 nursing homes in Tangshan urban area from July to November 2023 as the research subjects, use binary logistic regression analysis to construct a risk prediction model, and conduct internal validation of the model.Results:Among the 542 elderly people who were included in the study, there were 250 males and 292 females with an average age of 78.00 (70.00, 86.00) years. The incidence of functional constipation among elderly people in nursing homes was 54.06%(293/542). The predictive model includes six predictive factors: age, Barthel index, water intake, daily vegetable intake, insomnia, and perianal disease. The model AUC was 0.885 (95% CI 0.858-0.913), the Youden index was 0.628, the best critical value was 0.585, sensitivity was 0.819, specificity was 0.809. The Hosmer-Lemeshow test χ2=6.38, P=0.605. The internal validation results of the Bootstrap method showed that the AUC of the model was 0.876, the calibration curve was close to the standard line, and the Brier score was 0.135. The DCA results showed that the threshold was 0.1-0.9, and the model had good clinical net benefits. Conclusions:The incidence of functional constipation in elderly care institutions is relatively high. The functional constipation risk prediction model constructed in this study has good predictive efficacy and applicability, which can provide reference for nursing home staff.

OBJECTIVE: The barks, leaves, and branches of Cinnamomum cassia have been historically used as a traditional Chinese medicine, spice, and food preservative, in which phenylpropanoids are responsible compounds. However phenylpropanoid biosynthesis pathways are not clear in C. cassia. We elucidated the pathways by descriptive analyses of differentially expressed genes related to phenylpropanoid biosynthesis as well as to identify various phenylpropanoid metabolites. METHODS: Chemical analysis, metabolome sequencing, and transcriptome sequencing were performed to investigate the molecular mechanisms underlying the difference of active components content in the barks, branches and leaves of C. cassia. RESULTS: Metabolomic analysis revealed that small amounts of flavonoids, coumarine, and cinnamaldehyde accumulated in both leaves and branches. Transcriptome analysis showed that genes associated with phenylpropanoid and flavonoid biosynthesis were downregulated in the leaves and branches relative to the barks. The observed differences in essential oil content among the three tissues may be attributable to the differential expression of genes involved in the phenylpropanoid and flavonoid metabolic pathways. CONCLUSION This study identified the key genes in the phenylpropanoid pathway controling the flavonoid, coumarine, and cinnamaldehyde contents in the barks, branches and leaves by comparing the transcriptome and metabolome. These findings may be valuable in assessing phenylpropanoid and flavonoid metabolites and identifying specific candidate genes that are related to the synthesis of phenylpropanoids and flavonoids in C. cassia.