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aime-Casas,3 Aida Jiménez-Corona,2,4 David Rivera-De La Parra,1,2 Enrique Octavio Graue-Hernández,2 Sergio Hernández-Jiménez1 On behalf of the Group of Study CAIPaDi1Centro de Atención Integral del Paciente con Diabetes (CAIPaDi) Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico; 2Instituto de Oftalmología Fundación Conde de Valenciana IAP, Mexico City, Mexico; 3Escuela de Medicina, Universidad Panamericana, Mexico City, Mexico; 4Dirección General de Epidemiología, Secretaría de Salud, Mexico City, MexicoCorrespondence: Liliana Pérez-Peralta, Centro de Atención Integral del Paciente con Diabetes, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de Quiroga No. 15. Colonia Sección XVI, Tlalpan, Mexico City, 14080, Mexico, Tel +52 55 54870900 (5045), Email [email protected] Purpose: To evaluate the risk factors associated with diabetic macular edema (DME) in patients with a recent type 2 diabetes mellitus (T2DM) diagnosis.Patients and Methods: We conducted a case-control study at a third-level hospital in Mexico City. We enrolled patients ≥ 18 years old, with T2DM less than five years of diagnosis, without disabling complications, and non-smokers. The control group was patients with diabetic retinopathy and without macular edema (DR-DME). Cases were patients with DR+DME. We measured fasting glucose, creatinine, lipid profile, urinary albumin/creatinine ratio (ACR), and HbA1c. An ophthalmological examination consisted of visual acuity measurement, digital three-field fundus photography with an automatic non-mydriatic camera, slit lamp, and Optical coherence tomography (OCT) examination.Results: 183 and 61 patients with DR-DME and DR+DME, respectively, were included in the analysis. The prevalence of mild DR was higher in the DR-DME group, but the frequencies of moderate and severe retinopathy were higher in the DR+DME group. Patients in the DR-DME group had better vision than those in the DR+DME group. Logistic regression analysis revealed that age (OR, 1.07), HbA1c (OR, 1.19), and Albumin-to-Creatinine Ratio (ACR) > 30 mg/g (OR, 3.37) were associated with an increased possibility of DME compared to DR-DME.Conclusion: Our study provides insights into the association between risk factors and DME. We found a statistically strong association between HbA1c levels, age, and ACR. Patients with poor metabolic control should undergo an extensive medical examination to screen for DME, which may be related to the chronicity of DM and renal damage. [ABSTRACT FROM AUTHOR]

Background: Nutritional diagnosis involves identifying a nutritional problem, its cause, and the signs that indicate it to guide appropriate treatment. Few studies report on the most prevalent nutritional diagnoses in people living with type 2 diabetes (T2D)., Objective: To define nutritional diagnoses across different domains and their association with metabolic, anthropometric, and dietary parameters in individuals with T2D., Methods: A personalized nutritional intervention was conducted using the Nutrition Care Process (NCP) model, which encompasses assessment, diagnosis, intervention, and evaluation, utilizing standardized terminology from the Nutrition Care Process Terminology (NCPT). Two dietitians, trained and standardized in applying the NCP and NCPT, performed patient assessments and established the diagnoses. Patients over 18 years old with a diagnosis of T2D for less than 5 years were included., Results: Data from 2,050 patients were analyzed, of whom 55.3% were women, and 44.7% were men, with a median age of 57 and 54 years, respectively. The most prevalent nutritional diagnosis was excessive energy and carbohydrate intake. Diagnoses were distributed across domains: Intake (55.9%), Behavioral/Environmental (32.7%), Clinical (10.2%), and 1.2% without nutritional diagnosis. Significant intergroup differences were observed in anthropometric variables such as BMI, waist circumference, and body fat percentage ( p  < 0.05). HbA1c and glucose levels were significantly higher in the Intake and Behavioral groups ( p  < 0.001). Albumin/creatinine ratio (ACR) was higher in the ingestion group ( p  = 0.007). Caloric and carbohydrate intake were higher in the ingestion group, while protein and fat intake were higher in the clinical and behavioral groups ( p  < 0.001)., Conclusion: Nutritional diagnoses in the intake domain, followed by behavioral/ environmental and clinical domains, are highly prevalent in people with T2D and are associated with worse metabolic control, higher BMI, and increased energy and carbohydrate intake. Timely identification of issues in these domains can support targeted nutritional therapy to improve disease management and promote a healthy lifestyle., Clinical Trial Registration: https://clinicaltrials.gov/study/NCT02836808?term=caipadi&rank=2, Identifier (NCT02836808)., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Landa-Anell, Del Razo-Olvera, Bodnar, Cordova-Isidro, Lagunas-Valdepeña, Arias-Marroquín, García-Ulloa, Melgarejo-Hernández and Hernández-Jiménez.)

Introduction: The burden of multimorbidity is recognised increasingly in low- and middle-income countries (LMICs), creating a strong emphasis on the need for effective evidence-based interventions. Core outcome sets (COS) appropriate for the study of multimorbidity in LMICs do not presently exist. These are required to standardise reporting and contribute to a consistent and cohesive evidence-base to inform policy and practice. We describe the development of two COS for intervention trials aimed at preventing and treating multimorbidity in adults in LMICs., Methods: To generate a comprehensive list of relevant prevention and treatment outcomes, we conducted a systematic review and qualitative interviews with people with multimorbidity and their caregivers living in LMICs. We then used a modified two-round Delphi process to identify outcomes most important to four stakeholder groups (people with multimorbidity/caregivers, multimorbidity researchers, healthcare professionals and policymakers) with representation from 33 countries. Consensus meetings were used to reach agreement on the two final COS., Registration: https://www.comet-initiative.org/Studies/Details/1580., Results: The systematic review and qualitative interviews identified 24 outcomes for prevention and 49 for treatment of multimorbidity. An additional 12 prevention and 6 treatment outcomes were added from Delphi round 1. Delphi round 2 surveys were completed by 95 of 132 round 1 participants (72.0%) for prevention and 95 of 133 (71.4%) participants for treatment outcomes. Consensus meetings agreed four outcomes for the prevention COS: (1) adverse events, (2) development of new comorbidity, (3) health risk behaviour and (4) quality of life; and four for the treatment COS: (1) adherence to treatment, (2) adverse events, (3) out-of-pocket expenditure and (4) quality of life., Conclusion: Following established guidelines, we developed two COS for trials of interventions for multimorbidity prevention and treatment, specific to adults in LMIC contexts. We recommend their inclusion in future trials to meaningfully advance the field of multimorbidity research in LMICs., Prospero Registration Number: CRD42020197293., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Introduction: Previous reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings., Research Design and Methods: We trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999-2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup., Results: SNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89)., Conclusions: Diabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications., Competing Interests: Competing interests: JPB-L, AV-V and NEA-V are enrolled at the PECEM program of the Faculty of Medicine at UNAM. JPB-L and AV-V are supported by CONACyT., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)