Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers.

Yousef H, Feng SF, Jelinek HF

Scientific reports22 Jun 2024Cited by 6 (Europe PMC)

PMID38909127PMCIDPMC11193708DOI10.1038/s41598-024-65044-xPepBankPUB-38909127

Summary

Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore, it is of utmost importance to identify individuals at risk as early as possible to avoid long-term T2DM complications. In this study, we developed an interpretable machine learning model leveraging baseline levels of biomarkers of oxidative stress (OS), inflammation, and mitochondrial dysfunction (MD) for identifying individuals at risk of developing T2DM. In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. Two iForest models were trained and evaluated through ten-fold cross-validation, the first on traditional biomarkers (BMI, blood glucose levels (BGL) and triglycerides) alone and the second including the additional aforementioned biomarkers. The second model outperformed the first across all evaluation metrics, particularly for F1 score and recall, which were increased from 0.61 ± 0.05 to 0.81 ± 0.05 and 0.57 ± 0.06 to 0.81 ± 0.08, respectively. The feature importance scores identified a novel combination of biomarkers, including interleukin-10 (IL-10), 8-isoprostane, humanin (HN), and oxidized glutathione (GSSG), which were revealed to be more influential than the traditional biomarkers in the outcome prediction. These results reveal a promising method for simultaneously predicting and understanding the risk of T2DM development and suggest possible pharmacological intervention to address inflammation and OS early in disease progression.

CC BYText from Europe PMC. Highlighted sentences are the source of results.

Results from this study1

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Natural levels measured1

These results measure the body's own peptide. Nobody was given the peptide. They are not evidence about taking it. They are left out of key results, evidence counts and charts.

01HumanEndogenous levelObservational studyOther
In a machine-learning model for type 2 diabetes risk, baseline humanin was among the biomarkers that were more influential for the prediction than traditional biomarkers.
In plain wordsIn a computer model that predicts type 2 diabetes risk, the body's own humanin level counted more than some traditional markers.
Peptide
Humanin
Outcome
Not applicableHumanin importance in T2DM risk prediction model
Study
n: not reported in source
Outcome group
Cardiometabolic risk factors · Metabolic and weight
Source sentences
Summary
The feature importance scores identified a novel combination of biomarkers, including interleukin-10 (IL-10), 8-isoprostane, humanin (HN), and oxidized glutathione (GSSG), which were revealed to be more influential than the traditional biomarkers in the outcome prediction.
This result measures the body's own Humanin. Nobody was given the peptide. It is not evidence about taking it.
Show in the study text Report a problemFND-3890912701

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Version history

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Version 2Current
13 Oct 2026, 21:44 UTC
Study results updated
Version 1
13 Oct 2026, 16:31 UTC
First added · source update from Europe PMC

Subjects

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