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The observed (non missing) variables of each column were randomly split into ten sets.
Missing variables of the PROMs were addressed using the respective authors' instructions.
Missing variables of partly completed questionnaires were defined as missing values.
Our study sample was limited to 1,600 participants (227 with a history of diabetes and 1,323 without) after excluding those who fasted less than 8 h (N = 20) or who were missing variables of interest (N = 402).
We excluded participants who identified race/ethnicity as other than white or black, who had a self-reported physician diagnosis of diabetes or diabetes medication use (visit 1 or visit 2), who were nonfasting, or who were missing variables of interest; thus, the final sample size for our main analyses was 12,485 individuals.
Because the analyses were restricted to complete cases, which can introduce bias if the data are not missing completely at random, we used a multiple-imputation method to replace the missing variables of our study.
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Vitals signs accounted for the majority of missing variables for both methods (10/22 for paper; 3/5 for PDA).
The missing variable for the estimation of P g is the probability of having at least one PDV in gene g (π g).
Due to missing variables, questionnaires of 147 parents (125 mothers, 17 fathers, 1 both parents, and 4 other caretakers) were eventually used for the data analyses.
Choice among collinear variables was performed considering clinical relevance, rate of missing variables and reproducibility of definitions.
Additionally, previously collected values of the missing variables and background characteristics of the sample previously shown to affect missing data were included in the imputation model (Fig. DS1).
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