Development and validation of a clinical score for identifying patients with high risk of latent autoimmune adult diabetes (LADA): The LADA primary care-protocol study

Abstract

Background Latent autoimmune diabetes in adults (LADA) is a type of diabetes mellitus showing overlapping characteristics between type 1 Diabetes Mellitus and type 2 Diabetes Mellitus (T2DM), and autoimmunity against insulin-producing pancreatic cells. For its diagnosis, at least one type of anti-pancreatic islet antibody (GADAb is the most common) is required. Many authors recommend performing this measure in all newly diagnosed patients with DM, but it is not possible in Primary Health Care (PHC) due to its high cost. Currently, a relevant proportion of patients diagnosed as T2DM could be LADA. Confusing LADA with T2DM has clinical and safety implications, given its different therapeutic approach. The main objective of the study is to develop and validate a clinical score for identifying adult patients with DM at high risk of LADA in PHC. Methods This is an observational, descriptive, cross-sectional study carried out in Primary Care Health Centers with a centralized laboratory. All people over 30 years of age diagnosed with diabetes within a minimum of 6 months and a maximum of 4 years before the start of the study will be recruited. Individuals will be recruited by consecutive sampling. The study variables will be obtained through clinical interviews, physical examinations, and electronic medical records. The following variables will be recorded: those related to Diabetes Mellitus, sociodemographic, anthropometric, lifestyle habits, laboratory parameters, presence of comorbidities, additional treatments, personal or family autoimmune disorders, self-perceived health status, Fourlanos criteria, and LADA diagnosis (as main variable) according to current criteria. Discussion The study will provide an effective method for identifying patients at increased risk of LADA and, therefore, candidates for antibody testing. However, a slight participation bias is to be expected. Differences between participants and non-participants will be studied to quantify this potential bias.