Background: Despite the achievements of the national program for the prevention and control of diabetes (NPPCD) over the past two decades, the available evidence indicates a high prevalence of this disease in Iran. This qualitative study aims to investigate barriers to the NPPCD by pursuing the perspectives of relevant policymakers, planners, and healthcare workers. Methods: A grounded theory approach was used to analyze participants' perceptions and experiences. Semi-structured interviews (n=23) and eight focus groups (n=109) were conducted with relevant policymakers, planners, and healthcare workers in charge of Iran's national diabetes management program. Of the 132 participants, ages ranged from 25 to 56 years, and 53% were female. Constant comparative analysis of the data was conducted manually, and open, axial, and selective coding was applied to the data. Results: Two main themes emerged from data analysis: implementation barriers and inefficient policy-making/planning. Insufficient financial resources, staff shortage and insufficient motivation, inadequate knowledge of some healthcare workers, and defects in the referral system were recognized as the NPPCD implementation barriers. Inappropriate program prioritizing, the lack of or poor intersectoral collaboration, and the lack of an effective evaluation system were the inefficient policy-making/planning problems. Conclusion: Current results highlighted that inefficient policy-making and planning have led to several implementation problems. Moreover, the key strategies to promote this program are prioritizing the NPPCD, practical intersectoral collaboration, and utilizing a more efficient evaluation system to assess the program and staff performance.

The Tehran cardiometabolic genetic study (TCGS) is a large population-based cohort study that conducts periodic follow-ups. TCGS has created a comprehensive database comprising 20,367 participants born between 1911 and 2015 selected from four main ongoing studies in a family-based longitudinal framework. The study's primary goal is to identify the potential targets for prevention and intervention for non-communicable diseases that may develop in mid-life and late life. TCGS cohort focuses on cardiovascular, endocrine, metabolic abnormalities, cancers, and some inherited diseases. Since 2017, the TCGS cohort has augmented by encoding all health-related complications, including hospitalization outcomes and self-reports according to ICD11 coding, and verifying consanguineous marriage using genetic markers. This research provides an update on the rationale and design of the study, summarizes its findings, and outlines the objectives for precision medicine. Supplementary Information The online version contains supplementary material available at 10.1007/s10654-023-01008-1.

Background: To investigate the association between Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) and Homeostasis Model Assessment of Beta-cell function (HOMA-B) with the incidence of diabetes and pre-diabetes subtypes.Methods: A total of 3101 normoglycemic people aged 20-70 years were included in the 6-year follow-up study. Multinomial logistic regression was used to calculate the incidence possibility of isolated Impaired Fasting Glucose (iIFG), isolated Impaired Glucose Tolerance (iIGT), Combined impaired fasting glucose & impaired glucose tolerance (CGI), and Diabetes Mellitus (DM) per standard deviation (SD) increment in HOMA-IR and HOMA-B in the crude and multivariable model. Results: In the multivariate model, per unit SD increase in HOMA-IR increased the odds of iIFG, iIGT, CGI, and DM by 43%, 42%, 75%, and 92%, respectively. There was a positive correlation between the increase in HOMA-B and incidence of iIGT; however, after adjusting the results for metabolic syndrome components, it was inversely correlated with the incidence of iIFG [Odds Ratio = 0.86(0.75-0.99)]. Conclusions: HOMA-IR is positively correlated with diabetes and pre-diabetes subtypes’ incidence, and HOMA-B is inversely correlated with the incidence of iIFG but positively correlated with iIGT incidence. However, none of these alone is a good criterion for predicting diabetes and pre-diabetes.