Fasting intact insulin by mass spectrometry is associated with metabolic dysfunction–associated steatotic liver disease in youth
Bibliographic record
Abstract
BACKGROUND: Fasting intact insulin concentrations can predict metabolic dysfunction-associated steatotic liver disease (MASLD) in adults without diabetes; however, research in youth is limited. We sought to determine whether fasting intact insulin, measured by liquid chromatography-tandem mass spectrometry, is associated with MASLD in children. METHODS: This retrospective cross-sectional analysis used data and samples from children who participated in studies across 3 universities between 2014 and 2022. Key measurements included fasting intact insulin, ALT, and hepatic steatosis assessed by MRI techniques. MASLD was defined as hepatic steatosis ≥5% by MRI with at least 1 cardiometabolic risk factor. The optimal cutoff points to identify MASLD were determined by maximizing the Youden index, and the AUROC curves were compared using the DeLong test. RESULTS: The analysis included 184 children (28% male; 14.9 ± 2.6 y; 57% Hispanic race/ethnicity; body mass index 32.5 ± 8.1 kg/m2; 64% with MASLD, 43% with polycystic ovary syndrome, and 5% with other liver diseases). Fasting intact insulin and ALT levels were significantly higher in children with MASLD (p < 0.05). Fasting intact insulin was strongly associated with MASLD with an AUROC of 0.83 (0.77-0.90), sensitivity of 71%, and specificity of 85%. When combined with ALT (intact insulin × ALT [μU/mL × U/L]), the AUROC was 0.88 (0.83-0.94), with a sensitivity of 89% and specificity of 81%. The improvement in AUROC over intact insulin alone was not statistically significant (p = 0.089) but was statistically significant from ALT (p = 0.022). Optimal cutoff points for intact insulin and intact insulin × ALT were 20 μU/mL and 522 μU/mL × U/L, respectively. CONCLUSIONS: In pediatric patients, measurements of fasting intact insulin alone and combined with ALT provide a noninvasive strategy for identifying the presence of MASLD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".