Clinical Features and Unusual Heterozygous Mutations in Patients with Renal Hypokalemia.

BACKGROUND: Renal hypokalemia is associated with mutation. This study aimed to investigate the clinical features and pathogenic mutations in patients with renal hypokalemia. METHODS: The patients with hypokalemia were enrolled, and the renal function, thyroid function, renin-aldosterone system, urinary potassium excretion, and exome sequencing were performed. The correlation between the clinical phenotypes and causative genes was assessed. RESULTS: Five patients with hypokalemia were enrolled and diagnosed as tubular hypokalemia. The patients with common clinical manifestations were difficult to differentiate based on atypical laboratory findings. The results of the genetic analysis were as follows: both patient 1 and patient 2 were heterozygous for the c.C625T mutation of the KCNJ1 gene, which is responsible for Bartter syndrome. Patient 3 was heterozygous for the c.G298A mutation of the ATP6V1B1 gene, which is responsible for renal tubular acidosis. Patient 4 had a compound heterozygous mutation of c.G893A of the BSND gene, responsible for Bartter syndrome, and c.1029+5G>A, the ATP6V0A4 gene responsible for distal renal tubular acidosis. Patient 5 had Gitelman syndrome and carried the compound heterozygous mutations c.C1963T and c.G2029A of the SLC12A3 gene. All the above loci were known heterozygous mutations. CONCLUSIONS: The unusual heterozygous mutations were identified in five renal hypokalemia patients. Molecular diagnosis of tubular hypokalemia was conducive to accurate diagnosis and treatment.

Development and validation of a risk nomogram model for predicting peripheral neuropathy in patients with type 2 diabetes mellitus.

Objective: Diabetic peripheral neuropathy frequently occurs and presents severely in individuals suffering from type 2 diabetes mellitus, representing a significant complication. The objective of this research was to develop a risk nomogram for DPN, ensuring its internal validity and evaluating its capacity to predict the condition. Methods: In this retrospective analysis, Suqian First Hospital's cohort from January 2021 to June 2022 encompassed 397 individuals diagnosed with T2DM. A random number table method was utilized to allocate these patients into two groups for training and validation, following a 7:3 ratio. By applying univariate and multivariable logistic regression, predictive factors were refined to construct the nomogram. The model's prediction accuracy was assessed through metrics like the ROC area, HL test, and an analysis of the calibration curve. DCA further appraised the clinical applicability of the model. Emphasis was also placed on internal validation to confirm the model's dependability and consistency. Results: Out of 36 evaluated clinicopathological characteristics, a set of four, duration, TBIL, TG, and DPVD, were identified as key variables for constructing the predictive nomogram. The model exhibited robust discriminatory power, evidenced by an AUC of 0.771 (95% CI: 0.714-0.828) in the training cohort and an AUC of 0.754 (95% CI: 0.663-0.845) in the validation group. The congruence of the model's predictions with actual findings was corroborated by the calibration curve. Furthermore, DCA affirmed the clinical value of the model in predicting DPN. Conclusion: This research introduces an innovative risk nomogram designed for the prediction of diabetic peripheral neuropathy in individuals suffering from type 2 diabetes mellitus. It offers a valuable resource for healthcare professionals to pinpoint those at elevated risk of developing this complication. As a functional instrument, it stands as a viable option for the prognostication of DPN in clinical settings.