Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes
Identifying prediabetes subphenotype before determination of diabetes could improve the treatment approach to diabetes and its complication. The recent definition of prediabetes lacks the knowledge to identify subphenotypes of the pathophysiology of type 2 diabetes and its future complication. The author Wagner and colleagues published a study titled “Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes” in Nature Medicine Journal. The summary of findings is given below:
Objective:
To describe subphenotyping approach of metabolic risk before diabetes manifestation
Method:
The variables obtained from oral glucose tolerance tests, liver fat content, MRI measured body fat distribution, and the genetic risk was segregated in a cohort of extensively phenotyped individuals. To identify six distinct clusters of sub-phenotypes, the individuals at increased risk for type 2 diabetes are included in the study. The six clusters are 1) Low risk 2) Very-low risk 3) Beta-cell failure 4) Low risk obese 5) High-risk insulin resistant fatty liver 6) High risk visceral fat nephropathy.
Using simpler markers of similar anthropometric and glycemic constructs the clusters identified by the sophisticated phenotypes in the TUEF/TULIP cohort were replicated in a large prospective occupational cohort (the Whitehall II study).
Findings:
Individuals categorized under clusters 5 and 6 constitute obese, high-risk subpopulations with different glycemic, renal, cardiovascular, and all-cause mortality risk profiles. Additionally, individuals in cluster 6 were found to have elevated renal sinus fat and exercise induced-microalbuminuria, which leads to increased risk of nephropathy. Uniquely, glucose was not the driver for such adverse event in cluster 6. Whereas clusters 3, 5, and 6 are considered as high risk, cluster 4 participants were found with obesity but low glycemic deterioration. These individuals can be categorized under metabolically healthy obesity. They are also associated with a lower risk of type 2 diabetes, independently from sex, age, and BMI. The study suggests that phenotype characterized by elevated genetic risk and low insulin secretion can be a reason behind high diabetes incidence seen in this group. Cluster 3 individuals had high intima-media thickness (IMT), independent of sex, age, and BMI. These individuals who were not at increased cardiovascular risk had a moderately elevated risk of chronic kidney disease.
Such subphenotype identification suggests further potential therapeutic implications. Lifestyle intervention aiming for weight loss and liver fat reduction could benefit individuals in cluster 5. Individuals characterized under cluster 3 might benefit from dietary caloric restriction and a standard aerobic exercise for reduction of visceral fat.
Limitations:
The findings can’t be generalized due to the lack of ethnic diversity. Authors acknowledge that there is uncertainty concerning the optimal number of clusters, variable selection, and whether these approaches are inferior to conventional predictions from multivariable modelling. Additionally, the different feature variable set and the moderate reassignment rate of the original clusters to the feature set of Whitehall II is another limitation. The clinical use of variables in TUEF/TULIP cohort for metabolic classification could be limited due to the sophisticated nature of these variables. A potential underestimation of the risk for diabetes and nephropathy in the TUEF/TULIP cohort might have affected due to low follow-up rates. Lastly, the nephropathy models in Whitehall II are not adjusted for baseline eGFR due to a lack of baseline measurements and the absolute risks being low.
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