Hidden Complication of Obesity and Diabetes: Is It Time to Put More Focus on Fatty Liver?
Bibliographic record
Abstract
Since the release of the EMPA-REG trial in 2015, the focus in diabetes management has been shifted from a glucocentric approach to a more organ-protective approach. Much of the focus has been on cardiorenal protections, thanks to the numerous landmark trials being published in recent years. However, the significance of what seems to be an innocuous fatty deposition in the liver has received less attention than it deserves for many years, especially in people living with diabetes and obesity, but its impact on health has slowly been highlighted more in the last decade. Bodies of research are now suggesting that non-alcoholic fatty liver disease (NAFLD) is a significant independent risk factor for cardiovascular disease, including myocardial infarction, heart failure, and atrial fibrillation, while it carries the increased risk of cirrhosis, hepatocellular carcinoma, and extrahepatic cancers. Numerous organisations have begun to publish guidelines focusing on screening and treating NAFLD in recent years in an effort to combat this underappreciated, underdiagnosed, and undertreated complication of diabetes and obesity. This review paper will provide an overview of NAFLD, highlighting the argument that NAFLD is indeed an independent cardiovascular risk factor, discussing the proposed pathophysiology of NAFLD being a cardiovascular risk factor, and suggesting a highly validated hepatic fibrosis screening tool, which is a simple, easy-to-use tool to screen for hepatic fibrosis, and can be used in primary care offices.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".