Adolescent Nonalcoholic Fatty Liver Disease and Type 2 Diabetes in Young Adulthood
Bibliographic record
Abstract
CONTEXT: The long-term risk of type 2 diabetes in adolescents with nonalcoholic fatty liver disease (NAFLD) is unclear. OBJECTIVE: To assess type 2 diabetes risk among adolescents with NAFLD. DESIGN AND SETTING: A nationwide, population-based study of Israeli adolescents who were examined before military service during 1997-2011 and were followed until December 31, 2016. PARTICIPANTS: A total of 1 025 796 normoglycemic adolescents were included. INTERVENTIONS: Biopsy or radiographic tests were prerequisite for NAFLD diagnosis. Data were linked to the Israeli National Diabetes Registry. MAIN OUTCOME MEASURES: Type 2 diabetes incidence. RESULTS: During a mean follow-up of 13.3 years, 12 of 633 adolescents with NAFLD (1.9%; all with high body mass index [BMI] at baseline) were diagnosed with type 2 diabetes compared with 2917 (0.3%) adolescents without NAFLD. The hazard ratio (HR) for type 2 diabetes was 2.59 (95% confidence interval [CI], 1.47-4.58) for the NAFLD vs. the non-NAFLD group after adjustment for BMI and sociodemographic confounders. The elevated risk persisted in several sensitivity analyses. These included an analysis of persons without other metabolic comorbidities (adjusted HR, 2.75 [95% CI, 1.48-5.14]) and of persons with high BMI; and an analysis whose outcome was type 2 diabetes by age 30 years (adjusted HR, 2.14 [95% CI, 1.02-4.52]). The results remained significant when a sex-, birth year-, and BMI-matched control group was the reference (adjusted HR, 2.98 [95% CI, 1.54-5.74]). CONCLUSIONS: Among normoglycemic adolescents, NAFLD was associated with an increased adjusted risk for type 2 diabetes, which may be apparent before age 30 years.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".