Development of a Patient-Reported Outcome Measure for Youth Receiving Gender-Affirming Care: The GENDER-Q Youth Module.

Prediabetes and type 2 diabetes mellitus (prediabetes/diabetes) are multifaceted conditions influenced by various biological and epidemiological factors, dietary patterns, physical activity, and socioeconomic status. Recent data reveal a surge in prediabetes/diabetes prevalence among youth in the United States from 4.1% in 1999 to 22% in 2018, and the trend is only projected to continue. This is especially troubling due to its disproportionate impact on racially and ethnically minoritized groups and those with limited socioeconomic resources, exacerbating existing health disparities. Early onset of prediabetes/diabetes poses heightened health and economic burdens due to prolonged disease duration and increased susceptibility to other cardiometabolic conditions. Addressing this pressing public health crisis requires intensified research into the interplay of multiple prediabetes/diabetes risk factors. There is a large body of research addressing individual prediabetes/diabetes risk factors, such as physical activity, body mass index (BMI), diet, and screen time. Notably, studies have shown strong associations between increased physical activity and reduced risk of prediabetes/diabetes as well as benefits of physical activity in managing these conditions. Higher BMI and unhealthy dietary patterns have emerged as significant risk factors for prediabetes/diabetes. The impact of screen time on prediabetes/diabetes risk remains unclear, with mixed findings in the literature. While most studies indicate a potential correlation between excessive screen time and prediabetes/diabetes risk, others have found inconclusive or contradictory evidence. These studies, while useful, are limited because they examine risk factors individually, despite recognition that multiple factors likely work together to affect the risk of prediabetes/diabetes. Moreover, many of the cited works are not youth-specific, further limiting our understanding of these risk factors. To address this gap, we applied latent class analysis (LCA) to a nationally representative sample of youth with data on physical activity, diet, screen time, BMI, and sociodemographic factors. LCA is a statistical method that categorizes individuals into latent groups with similar observable characteristics. LCA is thus advantageous in identifying underlying subpopulations based on multiple prediabetes/diabetes risk factors.