Differential prevalence and prognostic value of metabolic syndrome components among patients with MASLD
Bibliographic record
Abstract
Background & Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is becoming increasingly prevalent in the general population. This study aimed at describing the cardiometabolic burden of the MASLD population and to identify patients at the highest risk of all-cause mortality and liver fibrosis. Methods: We analysed individuals with MASLD enrolled in the National Health and Nutrition Survey (NHANES) III study (N = 3,628) and in the NHANES 2017-2020 study (n = 2,618). MASLD was defined as hepatic steatosis (by ultrasonography or controlled attenuation parameter), together with cardiometabolic dysfunction. Primary endpoints were all-cause mortality and liver fibrosis (liver stiffness measurement ≥8 kPa). Regression models were adjusted for age, sex, race, marital status, education, and smoking, and results were stratified by age groups (20-40, 40-60, 60-80 years). Results: Among the total MASLD population (median age = 48, [25th to 75th percentiles: 36-62] years, 44.8% males), 65% had three or more cardiometabolic disorders. The most frequent were obesity (89.1%), (pre-) diabetes (66.6%), and low-HDL (54.7%). During a median follow-up of 22.3 (25th to 75th percentiles: 16.9-24.2) years, 1,405 deaths occurred. Hypertension (adjusted hazard ratio [aHR] 1.42, 95% CI 1.26-1.61), (pre-)diabetes (aHR 1.28, 95% CI 1.09-1.49), and hypertriglyceridaemia (aHR 1.19, 95% CI 1.05-1.34) were the strongest predictors of all-cause mortality. Consistent results were obtained regarding the association between cardiometabolic disorders and fibrosis. Here, increased waist circumference (adjusted odds ratio [aOR] 3.45, 95% CI 1.44-8.25), (pre-)diabetes (aOR 1.90, 95% CI 1.44-2.25), and hypertension (aHR 1.84, 95% CI 1.40-2.43) showed the strongest associations. Conclusions: MASLD patients vary greatly in their cardiometabolic burden and consequently, in their prognosis. Our results highlight MASLD as a disease spectrum rather than as a single disease entity, necessitating an individualised treatment approach. Impact and implications: Understanding the disease burden of MASLD patients is key, but can be challenging for healthcare professionals. Results from the current study indicate that cardiometabolic risk management is particularly warranted in the younger adult population, with specific attention to hypertension and (pre-)diabetes.
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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 | 0.000 | 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) | 0.002 | 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".