Frontiers in endocrinology

Improving Type 2 Diabetes Treatment by Identifying Different Subtypes

Updated

Abstract

Essence

This review argues that subclassifying type 2 diabetes by pathophysiology could better match existing drugs to patient profiles.

Evidence

A review of available studies proposes seven recently diagnosed type 2 diabetes subgroups and a stratified diagnostic-treatment algorithm for oral antidiabetic selection.

Caveat

The proposed drug matching is based on reviewed evidence rather than a prospective validation trial of the seven-subgroup algorithm.

Simplified

Key figures

Figure 1
Pathophysiological processes causing and in type 2 diabetes
Frames how and altered expression contribute to insulin resistance in type 2 diabetes
fendo-16-1710511-g001
  • Panel 1
    Type 2 diabetes is linked to defects in insulin secretion and altered () levels
  • Panel 2
    Inflammatory processes and are present alongside insulin secretion defects
  • Panel 3
    Insulin resistance (IR) involves increased of on serine/threonine residues
  • Panel 4
    Alterations in expression and function of GLUT4 transporter reduce glucose uptake in muscle and fat
Figure 2
Factors influencing type 2 diabetes and its associated chronic complications
Highlights key factors and serious complications that frame the complexity of type 2 diabetes progression
fendo-16-1710511-g002
  • Panel 1
    Non-modifiable factors linked to type 2 diabetes: obesity, ethnicity, and family history
  • Panel 2
    Metabolic disorders contributing to type 2 diabetes: and
  • Panel 3
    Chronic complications associated with type 2 diabetes: severe , kidney disease, heart failure, and or dementia
Figure 3
How and lipid metabolism from adipose tissue influence inflammation and
Frames how increased and link adipose tissue to inflammation and metabolic disease progression
fendo-16-1710511-g003
  • Panel Adipokines
    Shows adipokines leptin (increased) and (decreased) released from adipose tissue
  • Panel Lipid metabolism
    Shows lipid metabolism changes with increased free fatty acids (FFA) and decreased (TG)
  • Panel Inflammatory Activation
    Lists inflammatory markers TNF-α, , , and macrophages involved in activation
  • Panel Metabolic Dysfunction
    Includes and as key metabolic problems
  • Panel Disease Progression
    Lists diseases linked to progression: cardiovascular disease (), non-alcoholic fatty liver disease (), type 2 diabetes, and obesity
Figure 4
Clinical algorithm classifying type 2 diabetes subgroups by age, , , , and
Frames a clear classification system highlighting distinct type 2 diabetes subgroups based on metabolic and hereditary markers
fendo-16-1710511-g004
  • Entire diagram
    Algorithm flowchart categorizes patients based on insulin secretion deficiency, , inheritance, and age-related factors using specific thresholds for HbA1c, BMI, HOMA-IR, and HOMA-B
  • Left section
    Classifies deficiency in insulin secretion using HOMA-B or HOMA2-B ≤ 104, HbA1c ≥ 7, and age ≥ 50 years to define MIDD, EOIDD, SIDD, and LOIDD subgroups
  • Center section
    Defines insulin resistance with HOMA-IR or HOMA2-IR ≥ 1.9 and BMI ≥ 30 kg/m² to identify SOIRD, UARD, LOIRD, SIDRD, and CIRDD subgroups, with further distinctions by HOMA-IR or HOMA2-IR 3–5
  • Right section
    Incorporates family history and inheritance-related subgroups IRD, EOIRD, MOD, IROD 1, IROD 2, and age-related diabetes for patients with HOMA-B or HOMA2-B > 104 and age > 65 years
  • Bottom section
    Shows age-related diabetes subgroups MDH, MARD, and MD distinguished by HDL levels
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Full Text

What this is

  • () is a complex, heterogeneous disease with significant complications.
  • This review proposes subclassification of into seven distinct subgroups based on pathophysiology.
  • The goal is to tailor treatment strategies to individual patient profiles, optimizing management and outcomes.

Essence

  • Subclassifying type 2 diabetes into seven groups based on pathophysiology can improve treatment personalization. Specific pharmacological strategies are recommended for each subgroup to enhance patient outcomes.

Key takeaways

  • Subclassifying into seven groups allows for tailored treatment plans. Each subgroup has distinct characteristics and risks, which can guide specific pharmacological interventions.
  • Metformin remains the first-line treatment for all subgroups. Additional therapies should be chosen based on individual pathophysiological profiles and associated risks.

Caveats

  • Current subclassification methods require further validation before widespread clinical adoption. There is a need for prospective studies to confirm the effectiveness of tailored treatments.

Definitions

  • Type 2 Diabetes Mellitus (T2D): A chronic metabolic disorder characterized by insulin resistance and impaired insulin secretion, leading to hyperglycemia.
  • Insulin Resistance: A condition where cells fail to respond effectively to insulin, resulting in reduced glucose uptake and increased blood sugar levels.

Simplified

Funding

Competing interests

No commercial or financial ties reported.
PubMed

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