2026 ECNP Congress

PS01-1196

Neuropsychiatric characterization and psychopharmacological profiles of type 2 diabetes phenotypes: a cross-sectional cluster analysis

María Teresa Alcántara Garcés — A.M.E. Rodríguez Ramírez, M.A. Melgarejo Hernández · Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Centro de Atención Integral del Paciente con Diabetes, Mexico City, Mexico

2026 ECNP Congress

Background

Type 2 diabetes (T2D) is a heterogeneous condition frequently associated with neuropsychiatric comorbidities [1] and [2]. Recent evidence suggests that T2D can be classified into four distinct clusters via self-normalizing neural networks (SNNN): mild obesity-related (MORD), severe insulin-deficient (SIDD), severe insulin-resistant (SIRD), and mild age-related diabetes (MARD) [3] and [4]. However, the specific neuropsychiatric profiles of these phenotypes remain poorly defined.

Aims

To identify the neuropsychiatric comorbidities associated with specific T2D phenotypes and to calculate the probabilities of psychopharmacological treatment for each condition.

Methods

We conducted a comparative cross-sectional study of 1735 outpatients with T2D within a multidisciplinary care model [5]. SNNN was utilized for cluster classification [3]. Psychiatric diagnoses were established using the Mini International Neuropsychiatric Interview (M.I.N.I.)[5]. Logistic regression analyses, adjusted for age and sex, were employed to calculate odds ratios (ORs) for psychopathology and the likelihood of psychopharmacological prescription.

Results

A total of 1735 participants were included (58% female), with a median age of 59 years (IQR 52–66). The median time since diagnosis was 1.5 years (IQR 0–3), and the median HbA1c was 7.3% (IQR 6.2–9.5%). Prevalence rates for anxiety (19.1%), affective (17.9%), and eating disorders (23.7%) were identified; psychotropic medication was prescribed to 24.6% of the sample. The cohort was distributed among SIDD (36.5%), MORD (31.7%), MARD (22.3%), and SIRD (9.3%). MORD exhibited the highest neuropsychiatric comorbidity (anxiety disorders 24.4%, affective disorders 20.4%, eating disorders 33.8%, psychotropic medication indication 29.8%;p<0.001), and psychopathology burden, with significant associations with anxiety (OR 1.4 [95% CI 1-1.8; p=0.019]) and eating disorders (OR 1.9 [95% CI 1.5-2.5; p<0.001]). SIRD had the second highest percentage of eating disorders (30.4%), and psychotropic medication indication at 28.4% (anxiety disorders 17.3%, affective disorders 16%; p<0.001); it was notably associated with eating disorders (OR 1.8 [95% CI 1.2-2.5; p<0.001]), while SIDD had a higher percentage of affective disorders at 20.4% (anxiety disorders 17.9%, eating disorders 19.8%, psychotropic medication indication 23.1%; p<0.001), and was uniquely linked to affective disorders (OR 1.5 [95% CI 1-1.8; p=0.014]). Conversely, MARD was the cluster with the lowest percentages of psychopathology (anxiety disorders 14.5%, affective disorders 10.9%, eating disorders 13.2%, psychotropic medication indication 17.8%; p<0.001), and it appeared to be protective against both affective (OR 0.6 [95% CI 0.4-0.8; p=0.009]) and eating disorders (OR 0.5 [95% CI 0.3-0.7; p=0.003]). Treatment probabilities varied significantly by phenotype: MORD patients carried the highest odds for anxiety disorder treatment (OR 38.8 [95% CI 23-63], p<0.001), whereas SIRD and MARD subjects showed the strongest associations with treatment for affective disorders (OR 45.7 [95% CI 13-154]) and (OR 30.7 [95% CI 13-70]), respectively; p<0.001).

Conclusion: This study demonstrates that psychiatric comorbidities in T2D are cluster-specific, suggesting that neuropsychiatric symptoms are integral to the syndromic spectrum of each metabolic phenotype. Recognizing these phenotypes allows for a paradigm shift towards precision psychiatry in metabolic care.

References

  1. Milstein, J.L., Ferris, H.A., 2021. The brain as an insulin-sensitive metabolic organ. Molecular Metabolism 5(2):101-234.
  2. Fanelli, G., Franke, B., De Witte, W., et al., 2022. Insulinopathies of the brain? Genetic overlap between somatic insulin-related and neuropsychiatric disorders. Transl Psychiatry 12(1):59.
  3. Bello-Chavolla, O.Y., Bahena-López, J.P., Vargas-Vázquez, A., et al., 2020. Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach. BMJ Open Diab Res Care 8(1):e001550.
  4. Ahlqvist, E., Storm, P., Käräjämäki, A., Martinell, M., Dorkhan, M., Carlsson, A., et.al., 2018. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol 6(5):361-369.
  5. Hernandez-Jimenez, S., Garcia-Ulloa, C., Mehta, R., Aguilar-Salinas, C.A., Kershenobich-Stalnikowitz, D., 2014. Innovative models for the empowerment of patients with type 2 diabetes: the CAIPaDi program. Recent Pat Endocr Metab Immune Drug Discov 8(3):202-9.