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Introduction: Metabolic Syndrome (MetS), diabetes, and other noncommunicable diseases (NCDs) have been a major focus of research in recent decades as the prevalence of these conditions continues to rapidly increase globally. However, the timing and patterns of development from metabolic risk factors to disease states are less well understood and are especially critical to understand in low- and middle-income countries (LMICs) and populations undergoing epidemiological transitions. Methods: Nationally representative sociodemographic, anthropometric, and point-of-care biomarker data from the 2016 Tunisian Health Examination Survey (n = 8170) were used to determine the prevalence of diabetes and MetS components in Tunisia and to investigate associations between glycated hemoglobin (HbA1c) and MetS components (blood pressure [BP], HDL cholesterol [HDL], triglycerides [TG], and waist circumference [WC]) in participants aged 15-97 years old. To better understand how sustained metabolic dysregulation and disease states impact these associations, diabetic and nondiabetic groups were analyzed separately. Results: The overall prevalence of diabetes based on measured HbA1c was 18.2%. The diabetic groups had a higher prevalence of each individual MetS component, and significantly higher (BP, TG, WC, and HbA1c) and lower (HDL) values than the nondiabetic groups. Yet, there were a higher number of significant associations between HbA1c and MetS components found in nondiabetic women and men when compared to diabetic women and men. HbA1c was positively associated with the cumulative number of MetS components, irrespective of diabetes status in men and women. Conclusions: The prevalence of both diabetes and MetS components (particularly low HDL cholesterol and elevated TG) is high among the Tunisian population. More MetS components were associated with HbA1c in nondiabetic individuals, showing a strong connection between the development of MetS components and diabetes. However, once the diabetes disease state manifests, there is more variability in the relationships. These results show the potential for HbA1c to be an indicator of metabolic health below clinical disease cutoffs, which may allow insights into the physiological changes that precipitate the emergence of diabetes. [ABSTRACT FROM AUTHOR]

Objective: The standard method of generating disorder‐specific disability scores has lay raters make rankings between pairs of disorders based on brief disorder vignettes. This method introduces bias due to differential rater knowledge of disorders and inability to disentangle the disability due to disorders from the disability due to comorbidities. Methods: We propose an alternative, data‐driven, method of generating disorder‐specific disability scores that assesses disorders in a sample of individuals either from population medical registry data or population survey self‐reports and uses Generalized Random Forests (GRF) to predict global (rather than disorder‐specific) disability assessed by clinician ratings or by survey respondent self‐reports. This method also provides a principled basis for studying patterns and predictors of heterogeneity in disorder‐specific disability. We illustrate this method by analyzing data for 16 disorders assessed in the World Mental Health Surveys (n = 53,645). Results: Adjustments for comorbidity decreased estimates of disorder‐specific disability substantially. Estimates were generally somewhat higher with GRF than conventional multivariable regression models. Heterogeneity was nonsignificant. Conclusions: The results show clearly that the proposed approach is practical, and that adjustment is needed for comorbidities to obtain accurate estimates of disorder‐specific disability. Expansion to a wider range of disorders would likely find more evidence for heterogeneity. [ABSTRACT FROM AUTHOR]