This person most resembles the diabetes subtype:

The DDZ Diabetes-Cluster-Tool assigns people with diabetes to one of the five diabetes subtypes (diabetes clusters). In addition, it graphically depicts the degree of similarity to each of the five subtypes.

The diabetes subtypes are:
  • 1/SAID: Severe autoimmune diabetes
  • 2/SIDD: Severe insulin-deficient diabetes
  • 3/SIRD: Severe insulin-resistant diabetes
  • 4/MOD: Mild obesity-related diabetes
  • 5/MARD: Mild age-related diabetes

References
Ahlqvist, E., Storm, P., Käräjämäki, A., Martinell, M., Dorkhan, M., Carlsson, A., ... & Groop, L. (2018). Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. The Lancet Diabetes & Endocrinology 6(5), 361-369.

Zaharia, O. P., Strassburger, K., Strom, A., Bönhof, G. J., Karusheva, Y., Antoniou, S., ... & Roden, M. (2019). Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes: a 5-year follow-up study. The Lancet Diabetes & Endocrinology 7(9), 684-694.
We record the number of page visits of the DDZ Diabetes-Cluster-Tools website. The entered data are not recorded. When interpreting the data, please keep in mind that the clustering was originally developed for newly diagnosed individuals with adult-onset diabetes.

Disclaimer:
The web-tool available on this website is designed to assign patients to the adult-onset diabetes clusters proposed by Ahlqvist et al 2018, for research purposes only. It is not intended to replace the medical judgment of a qualified healthcare professional, nor is it intended to be used as a diagnostic tool or to guide treatment decisions. The use of this tool should not substitute for seeking professional medical advice, diagnosis, or treatment. The results obtained from this tool should be interpreted in conjunction with a thorough medical evaluation and in consultation with a qualified healthcare professional. By using this web-tool, you acknowledge that the results generated are not a substitute for professional medical advice or diagnosis and that you assume full responsibility for the use of this tool. In no event shall the creators or operators of this web-tool be liable for any damages arising from the use of this tool or any information contained within.

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The required information for the cluster assignment are:
  • Presence of antibodies to glutamic acid decarboxylase (GAD)
  • Age at diagnosis (years)
  • Body-Mass-Index (BMI)
  • Fasting plasma Glucose (mmol/l or mg/dl)
  • Fasting C-Peptide (ng/ml, nmol/l or pmol/l)
  • HbA1c (%)
  • Sex
The algorithm for the cluster assignment was developed by Ahlqvist et al. (2018) and consists of three steps:
  1. The information of the person are standardized with respect to the mean and the standard deviation observed for their sex in the original Swedish cohort of people with diabetes.
  2. The person is subsequently compared with each of the five subtypes by computing the difference to the sex-specific average values of that subtype in the original Swedish cohort (Euclidian Distance).
  3. Finally the person is assigned to the cluster to which the computed distance was the smallest.
To determine the degree of similarity to each of the five subtypes the computed distances are subsequently inverted and scaled such that their values add up to 1.
The computations for the DDZ Diabetes-Cluster-Tool were implemented in the statistical software R version 4.2.0 (R Core Team, 2023).

References
Ahlqvist, E., Storm, P., Käräjämäki, A., Martinell, M., Dorkhan, M., Carlsson, A., ... & Groop, L. (2018). Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. The Lancet Diabetes & Endocrinology 6(5), 361-369.

Zaharia, O. P., Strassburger, K., Strom, A., Bönhof, G. J., Karusheva, Y., Antoniou, S., ... & Roden, M. (2019). Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes: a 5-year follow-up study. The Lancet Diabetes & Endocrinology 7(9), 684-694.

R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.