The treatment of type 2 diabetes (T2D) necessitates a multifaceted approach that combines behavioral and pharmacological interventions to mitigate complications and sustain a high quality of life. Treatment encompasses the management of glucose levels, weight, cardiovascular risk factors, comorbidities, and associated complications through medication and lifestyle adjustments. Metformin, a standard in diabetes management, continues to serve as the primary, first-line oral treatment across all age groups due to its efficacy, versatility in combination therapy, and cost-effectiveness. Glucagon-like peptide-1 receptor agonists (GLP-1 RA) offer notable benefits for HbA1c and weight reduction, with significant cardiovascular benefits. Sodium-glucose cotransporter inhibitors (SGLT-2i) lower glucose levels independently of insulin while conferring notable benefits for cardiovascular, renal, and heart-failure outcomes. Combined therapies emphasizing early and sustained glycemic control are promising options for diabetes management. As insulin therapy remains pivotal, metformin and non-insulin agents such as GLP-1 RA and SGLT-2i offer compelling options. Notably, exciting novel treatments like the dual GLP-1/ glucose-dependent insulinotropic polypeptide (GIP) agonist show promise for substantially reducing glycated hemoglobin and body weight. This comprehensive review highlights the evolving landscape of pharmacotherapy in diabetes, the drugs currently available for treating diabetes, their effectiveness and efficacy, the impact on target organs, and side effects. This work also provides insights that can support the customization of treatment strategies.

Aims: To describe the development and report the first‐stage validation of a digital version of the digit symbol substitution test (DSST), for assessment of cognitive function in older people with diabetes. Materials and Methods: A multidisciplinary team of experts was convened to conceptualize and build a digital version of the DSST and develop a machine‐learning (ML) algorithm to analyse the inputs. One hundred individuals with type 2 diabetes (aged ≥ 60 years) were invited to participate in a one‐time meeting in which both the digital and the pencil‐and‐paper (P&P) versions of the DSST were administered. Information pertaining to demographics, laboratory measurements, and diabetes indices was collected. The correlation between the digital and P&P versions of the test was determined. Additionally, as part of the validation process, the performance of the digital version in people with and without known risk factors for cognitive impairment was analysed. Results: The ML model yielded an overall accuracy of 89.1%. A strong correlation was found between the P&P and digital versions ( r = 0.76, p < 0.001) of the DSST, as well as between the ML model and the manual reading of the digital DSST ( r = 0.99, p < 0.001). Conclusions: This study describes the development of and provides first‐stage validation data for a newly developed digital cognitive assessment tool that may be used for screening and surveillance of cognitive function in older people with diabetes. More studies are needed to further validate this tool, especially when self‐administered and in different clinical settings.