Prediabetes Phenotypes and NAFLD Risk in a Cohort of 2,127 Adults

A clinical cohort of 2,127 prediabetic individuals aged 18–59 years was analyzed to classify phenotypes and assess non-alcoholic fatty liver disease risk. The data was created by Baojia Zheng and last updated on June 3, 2026. Latent Profile Analysis identified four distinct phenotypes with NAFLD prevalence ranging from 18.39% to 87.93%.

Use Cases

• Classifying prediabetes phenotypes based on age, insulin resistance, and BMI as described in the methods.
• Investigating pathogenic pathways for NAFLD, such as the metabolism-immunity-inflammation axis, using mediation analysis.
• Training machine learning models to predict NAFLD risk across different demographic and metabolic profiles.
• Analyzing the association between hepatocellular damage and NAFLD in specific high-risk subgroups.

Strengths

• The cohort includes 2,127 individuals, providing a substantial sample size for analysis.
• The study identifies four distinct phenotypes with clear, quantified differences in NAFLD prevalence (e.g., 87.93% vs. 18.39%).
• The description details the use of advanced analytical methods like Latent Profile Analysis and machine learning algorithms.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small at 23.4 KB, suggesting limited scope or summary-level data.

Provenance

Source

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Collection Method

A cohort study employing Latent Profile Analysis, machine learning, and mediation analysis.

Freshness

Last updated 2026-06-03 04:47:22; freshness should be verified.