Nonalcoholic Fatty Liver Disease in The Rotterdam Study: About Muscle Mass, Sarcopenia, Fat Mass, and Fat Distribution
Bibliographic record
Abstract
ABSTRACT Nonalcoholic fatty liver disease (NAFLD) is the most prevalent liver disease worldwide. Obesity is a major risk factor for NAFLD and recently, low skeletal muscle mass emerged as additional risk factor for NAFLD. However, the different contributions of body mass index (BMI) to the risk of NAFLD are not yet well-known. We therefore studied body composition and muscle function with NAFLD in an elderly population-based study. Participants of European descent underwent dual-energy X-ray absorptiometry (DXA) and hepatic ultrasonography. NAFLD was defined as liver steatosis in absence of secondary causes for steatosis. Skeletal muscle index (SMI) was defined as appendicular lean mass/height2 and (pre)sarcopenia was defined using the European Working Group on Sarcopenia in Older People (EWGSOP) consensus guidelines. All analyses were stratified by sex and BMI (cut point: 25 kg/m2) and adjusted for age, weight, height, homeostasis model assessment of insulin resistance (HOMA-IR), triglycerides, and android-fat-to-gynoid-fat ratio (AGR). We included 4609 participants, of whom 1623 had NAFLD (n = 161 normal-weight and n = 1462 overweight). Presarcopenia and sarcopenia prevalence was low (5.9% and 4.5%, respectively) and both were not associated with NAFLD. SMI was associated with less NAFLD in normal-weight women (OR, 0.48; 95% CI, 0.29 to 0.80). A similar association for SMI and NAFLD was seen in normal-weight men, but significance dissipated after adjustment for AGR (OR, 0.63; 95% CI, 0.39 to 1.02). Generally, fat mass was a better predictor for NAFLD than lean mass. In particular, android fat mass was associated with all NAFLD subgroups (OR from 1.77 in overweight men to 8.34 in normal-weight women, pmax = 0.001), whereas substitution of gynoid fat mass for other body components had a significant protective association with NAFLD in every subgroup, but normal-weight men. Likewise, AGR was the best performing predictor for NAFLD prevalence (OR from 1.97 in normal-weight men to 4.81 in normal-weight women, pmax < 0.001). In conclusion, both high fat mass and low SMI were associated with normal-weight NAFLD. However, fat distribution (as assessed by AGR) could best predict NAFLD prevalence. © 2019 The Authors. Journal of Bone and Mineral Research Published by Wiley Periodicals, Inc.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".