The relationship between dietary inflammatory index and osteoporosis among chronic kidney disease population.

Dietary inflammation index (DII) is an epidemiological survey tool to evaluate dietary inflammation potential. Osteoporosis, whose development is deeply affected by inflammation, may be also affected by dietary inflammatory patterns. However, the relationship between DII and osteoporosis is unclear for chronic kidney disease (CKD) population. Our study involved 526 CKD patients from the US National Health and Nutrition Examination Survey (NHANES). DII levels were stratified into four quantile groups. Multivariable regression models were used to examine the association between DII and osteoporosis. Restricted cubic splines and subgroup analysis were additionally adopted. Results showed that the overall prevalence of osteoporosis among CKD patients was 25.3%. After fully adjusted, OR (95% confidence interval) for Q4 group compared with Q3 (reference group) in total and female population were 2.09 (1.05, 4.23) and 2.80 (1.14, 7.08), respectively. Subgroup analysis indicated that these results had no interaction with age, gender, body mass index (BMI), renal function, urinary protein, calcium, phosphorus and total 25-hydroxyvitamin D. DII was negatively correlated with lumbar spine bone mineral density (BMD) in CKD population (P < 0.05). Therefore, in CKD patients, higher DII was associated with higher osteoporosis risk and lower BMD of lumber spine, especially in female. Anti-inflammatory diet patterns may be a protective intervention for some CKD-related osteoporosis.

Study on the correlation between bioelectrical impedance analysis index and protein energy consumption in maintenance dialysis patients.

BACKGROUND: Protein-energy wasting (PEW) has been reported to be pretty common in maintenance dialysis patients. However, the existing PEW diagnostic standard is limited in clinical use due to the complexity of it. Bioelectrical impedance analysis (BIA), as a non-invasive nutritional assessment method, can objectively and quantitatively analyze the changes of body tissue components under different nutritional states. We aim to explore the association between PEW and BIA and establish a reliable diagnostic model of PEW. METHODS: We collected cross-sectional data of 609 maintenance dialysis patients at the First Affiliated Hospital, College of Medicine, Zhejiang University. PEW was diagnosed according to International Society of Renal Nutrition and Metabolism (ISRNM) criteria. Among them, 448 consecutive patients were included in the training set for the establishment of a diagnostic nomogram. 161 consecutive patients were included for internal validation. 52 patients from Zhejiang Hospital were included for external validation of the diagnostic model. Correlation analysis of BIA indexes with other nutritional indicators was performed. Logistic regression was used to examine the association of BIA indexes with PEW. 12 diagnostic models of PEW in maintenance dialysis patients were developed and the performance of them in terms of discrimination and calibration was evaluated using C statistics and Hosmer-Lemeshow-type χ2 statistics. After comparing to existing diagnostic models, and performing both internal and external validation, we finally established a simple but reliable PEW diagnostic model which may have great value of clinical application. RESULTS: A total of 609 individuals from First Affiliated Hospital, College of Medicine, Zhejiang University and 52 individuals from Zhejiang Hospital were included. After full adjustment, age, peritoneal dialysis (compared to hemodialysis), subjective global assessment (SGA, compared to non-SGA) and water ratio were independent risk factors, while triglyceride, urea nitrogen, calcium, ferritin, BCM, VFA and phase angle were independent protective factors of PEW. The model incorporated water ratio, VFA, BCM, phase angle and cholesterol revealed best performance. A nomogram was developed according to the results of model performance. The model achieved high C-indexes of 0.843 in the training set, 0.841 and 0.829 in the internal and external validation sets, respectively, and had a well-fitted calibration curve. The net reclassification improvement (NRI) showed 8%, 13%, 2%, 38%, 36% improvement of diagnostic accuracy of our model compared with "PEW score model", "modified PEW score model", "3-index model", "SGA model" and "BIA decision tree model", respectively. CONCLUSIONS: BIA can be used as an auxiliary tool to evaluate PEW risk and may have certain clinical application value.