Spss v28

To address objective 1, linear regression analysis was applied. To address objective 2, the supervised learning algorithm classification decision tree was applied. To address objective 3, the k-means unsupervised learning algorithm was used. Beforehand, the elbow method was used to check the number of clusters. Also, a cross-tabulation table was applied to check the relationship between the assignment node or cluster of membership and the research group (Group 1 vs. Group 2). These analyses were carried out using SPSS v.28 [45 ]. In addition, Cronbach’s alpha and omega reliability indicators for this study were examined using SPSS v.28 [45 ]. Orange data mining software was used to obtain the visualisation images [46 ]. Table 2 presents an outline of the analyses conducted with respect to the study objectives.

All variables were analyzed using descriptive statistics. For the continuous and categorical variables, we computed the median and interquartile ranges (IQR) and the frequencies, respectively. We analyzed the differences between two groups and three or more groups using the nonparametric Mann–Whitney U test (MW-U) and Kruskal–Wallis test (KWT), respectively. Moreover, we performed Spearman’s correlation analysis to examine the relationship between SARM1 expression and various variables. Multinomial logistic regression (MLR) was performed where the T2DMN groups were the dependent variable and the serum SARM1 parameter was the independent variable. Also, we used MLR where the duration of T2DM (years) > 3 groups was the dependent variable and where the serum SARM1 parameter was the independent variable. The confounder factors were adjusted by the number of COVID-19 vaccine doses. Healthy participants were used as the reference group to compute the odds ratio (OR) and 95% confidence interval (CI). Every statistical test was two-sided. The significance threshold was chosen at p < 0.05. SPSS v28.0 (Chicago, Armonk, NY, USA) was used for the overall analyses.

First, we conducted preliminary analyses to examine bivariate associations between C-DD and criterion variables of interest (e.g., delay discounting, impulsivity, psychopathology symptoms). Next, we conducted a partial correlation to test whether C-DD was associated with behavioral delay discounting, after accounting for age, biological sex, and Freesurfer quality control motion parameters in Sample 1. Finally, Generalized Linear Modeling was used to test whether C-DD was associated with youth psychopathology in both samples. This analytics approach was chosen to accommodate the non-normal distributions of key study variables (see S1 and S2 Figs). Specifically, we specified a gamma distribution with log link model, with bootstrapping (1000 samples) and 95% confidence intervals. Age and biological sex were included as covariates of no interest in all multivariate analyses. All analyses were conducted in SPSS v.28.0 [56 ]. It should be noted that all external correlates of C-DD were measured at the same time point, and thus our findings reflect concurrent associations. A portion of these data used in the preparation of this article (Study 1) were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). All relevant data for Study 2 are within the manuscript and its Supporting Information files.