Diabetes and Metabolic Dysfunction–associated Steatotic Liver Disease in Adults: A Clinical Practice Guideline
Bibliographic record
Abstract
Key Messages for Health-care ProvidersMetabolic dysfunctioneassociated steatotic liver disease (MASLD) affects around 70% of people living with type 2 diabetes (T2D).Liver fibrosis is the primary determinant of the hepatic and nonhepatic adverse outcomes of people living with MASLD.Consider screening for MASLD-related liver fibrosis in all individuals living with prediabetes or type 2 diabetes by using the Fibrosis-4 Index (FIB-4) score, which can assist in ruling out the likelihood of advanced liver fibrosis.If the FIB-4 score is <1.3, then the management of metabolic syndrome, especially diabetes and weight, should be done in the primary care clinic.Additional tests may be required if the FIB-4 score is between 1.3 and 2.67.If the FIB-4 score is >2.67, then referral to hepatology is warranted due to the high risk of advanced fibrosis.Sustained weight reduction of at least 5% to 10% is recommended, but, if possible, weight loss of >10% is preferred as it can assist with reversing liver fibrosis.Pioglitazone and subcutaneous semaglutide may be considered to improve glycemia and may reduce the liver fat content and progression of liver fibrosis.Cardiovascular protection is crucial in people living with MASLD, as most people with MASLD have cardiovascular disease (CVD).Therefore, statins can be maintained unless decompensated cirrhosis is developed. Key Messages for People Living With DiabetesLiver disease is common in people with diabetes.Many people living with diabetes also have a liver condition called MASLD, and many of them have not received a formal diagnosis.MASLD affects more than just the liver.Although it can cause liver damage over time, most people with MASLD are more likely to have heart disease, which is the leading cause of illness in this group.Simple blood tests can help check the liver.Everyone with prediabetes or T2D should be checked for liver scarring (also called fibrosis), which is a key predictor of complications.A simple calculation called the FIB-4 score, based on age and routine blood tests, can help rule out serious damage.Weight loss is one of the best treatments.Losing at least 5% to 10% of body weight can reduce fat in the liver.A 10% reduction in body weight may even help reverse some liver scarring.Some diabetes medications may help the liver.Certain treatments for diabetes may also reduce liver fat and slow down liver damage.Heart health matters too.Because heart disease is common in people with MASLD, statins (cholesterol-lowering medications) are usually safe and should be continuedexcept for cases of advanced liver disease like decompensated cirrhosis.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".