Windows v25

Chi-square test and independent t test were used to compare participants’ baseline characteristics, and descriptive statistics included the means with standard deviations.
A “Groups” (CBCR and HBCR) × “Times” (1- and 6-month) repeated measure by ANOVA was used to determine the statistical difference in CPX variables (VO2/kg, metabolic equivalents [METs], minute ventilation/carbon dioxide production slope [VE/VCO2 slope], breathing reserve [BR], VT, heart rate recovery [HRR], oxygen consumption per heart rate [VO2/HR]). Group × Time's significant interaction would indicate that the observed change between 1- and 6-month intervals is significantly different between CBCR and HBCR groups. Tukey post-hoc test was performed if the interaction and main effects were observed. Laboratory data (HDL, LDL, total cholesterol, Cr, EF) at both time-intervals for the groups were included in the comparative analysis, and statistical significance was set at P < .05. Statistical Package for the Social Science, Windows v25.0, was used for all statistical analyses.

Descriptive statistics were provided for continuous and categorical variables. Continuous variables were expressed as mean with standard deviation. Chi-square test was used to compare the categorical variables and were expressed as frequencies and percentages. One-way ANOVA was used to compare groups for continuous variables and p < 0.05 was considered statistically significant.
Receiver operating curves (ROC) were plotted using sensitivity and 1- specificity using T1D and T2D as a gold standard. The ROC curves were used to determine the best cut-offs for sensitivity and specificity. The area under the curve (AUC) helped to study the overall performance of the cut-off points. Sensitivity was defined by the proportion of MODY participants with a given risk factor who were identified correctly by the given variable greater or equal to the cut-off point. Specificity was defined as the proportion of MODY participants without the risk factor who were identified below the cut-off point. The point closest to the upper left-hand corner which maximized sensitivity and specificity was selected as the optimal cut point. Positive and negative predictive values and accuracy for MODY predictors for different cut-off points were calculated.
All statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS, Inc., Windows V 25.0, Chicago).