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S1349 Glucagon-Like Peptide-1 Receptor Agonist and Pioglitazone Therapy Have Superior Mortality and Liver Outcomes for Patients With Type 2 Diabetes and a Diagnosis or at Risk for Undiagnosed Non-alcoholic Fatty Liver Disease

2023· article· en· W4387749619 on OpenAlexaff
Benjamin D. Liu, Mohamed Aly, Cindy Hsin-Ti Lin, Noordeep Panesar, Kamran Qureshi

Bibliographic record

VenueThe American Journal of Gastroenterology · 2023
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicinePioglitazoneInternal medicineHazard ratioType 2 diabetesProportional hazards modelNonalcoholic fatty liver diseaseAdverse effectDiabetes mellitusFatty liverGastroenterologyDiseaseConfidence intervalEndocrinology

Abstract

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Introduction: Antidiabetic therapies Pioglitazone (PGZ) and Glucagon-Like Peptide-1 receptor Agonists (GLP1A) may have potential benefit in Nonalcoholic Fatty Liver Disease (NAFLD) and Nonalcoholic Steatohepatitis (NASH). We evaluated the efficacy of these agents in Type 2 Diabetes (T2DM) patients with NAFLD/NASH in reducing all-cause mortality and adverse liver outcomes. Methods: We queried TriNetX, a healthcare database that provides up to 20 years of de-identified aggregate data for over 123 million patients from 14 different countries. Patients were selected based on International Classification of Diseases-10th Edition (ICD-10) codes, with all patients having T2DM and either NAFLD or NASH, or a high risk of undiagnosed NAFLD/NASH (RISK) based on age > 50, ALT > 30, and T2DM. Patients could not have or be at risk for other chronic liver diseases or have a baseline high risk of mortality, according to the Charlson-Morbidity Index. We constructed 3 exclusive cohorts based on index event medication: Group 1 (GLP1A), Group 2 (PGZ), and Group 3 (other second-line T2DM medications). We matched Group 1 or Group 2 patients with Group 3 patients that had similar propensity scores on demographics, factors affecting the choice of second line T2DM medication, and factors predicting adverse liver outcomes. Matched patients were followed on an intention-to-treat basis for up to 20 years after medication initiation. Time-to-event analyses were performed using Kaplan-Meier analysis and Hazard ratios (HR) were estimated using Cox-proportional HR models with a P-value < 0.05 considered significant. The potential for unmeasured confounding was evaluated by E-value sensitivity analysis. We performed a sub-analysis on patients with an established diagnosis of cirrhosis. Results: All-cause mortality was noted to be lower among both Group 1 (HR 0.58; 95% CI 0.55 – 0.61) and Group 2 (HR 0.92; 95% CI 0.85 – 0.99) when compared to Group 3. A similar reduction was noted in Group 1 when cirrhotic patients were compared (HR 0.61; 95% CI 0.52 – 0.71). Incidence rates of liver decompensation and HCC were reduced in both Group 1 and 2 when compared with Group 3. Reduction in Liver Transplant was noted only in Group 1 (HR 0.62; 95% CI 0.44 – 0.87). E-value analyses suggested unmeasured confounders were unlikely to change any outcome associations. Conclusion: In a real-world long-term study using a large database, the use of GLP1A and PGZ for T2DM management in NAFLD/NASH population was associated with reduced overall mortality and incidence of hepatic decompensation and HCC (Figure 1 and Table 1).Figure 1.: Kaplan-Meier Curves for All-Cause Mortality and Liver Outcomes in Diabetic Patients with Fatty Liver Disease or At-risk for Undiagnosed Fatty Liver Disease Following Medication Initiation. GLP-1 RA = Glucagon-Like Peptide-1 Receptor Agonist. HR = Hazard Ratio. Table 1. - Hazard Ratios (HR) and E-Value Sensitivity Analysis for Unmeasured Confounding Cohort Outcome Hazard Ratio (95% CI) *E-Value for HR Estimate E-Value for Lower Limit of 95% CI GLP-1 RA All-Cause Mortality 0.579 (0.549 - 0.611) **7.76 8.55 New Cirrhosis 1.10 (0.98 - 1.22) 2.35 1.48 Liver Decompensation Events 0.723 (0.68 - 0.768) 4.85 5.59 Hepatocellular Carcinoma 0.633 (0.579 - 0.692) 6.52 7.76 Liver Transplant 0.615 (0.437 - 0.867) 6.91 12.45 Pioglitazone All-Cause Mortality 0.916 (0.847 - 0.991) 2.27 3.09 New Cirrhosis 0.874 (0.709 - 1.078) 2.77 5.08 Liver Decompensation Events 0.773 (0.695 - 0.861) 4.08 5.32 Hepatocellular Carcinoma 0.631 (0.533 - 0.748) 6.56 9.01 Liver Transplant 0.792 (0.413 - 1.517) 3.81 13.55 Prior Cirrhosis GLP-1 RA All-Cause Mortality 0.607 (0.52 - 0.709) 7.09 9.41 Liver Decompensation Events 0.782 (0.682 - 0.898) 3.95 5.56 Hepatocellular Carcinoma 0.796 (0.623 - 1.016) 3.76 6.73 Liver Transplant 0.587 (0.409 - 0.844) 7.56 13.75 Prior Cirrhosis Pioglitazone All-Cause Mortality 1.189 (0.85 - 1.663) 3.17 3.06 Liver Decompensation Events 1.212 (0.778 - 1.888) 3.37 4.01 Hepatocellular Carcinoma 0.703 (0.351 - 1.405) 5.18 17.05 Liver Transplant 0.815 (0.302 - 2.195) 3.50 20.76 *The E-value is the minimum strength of association to fully explain away a specific treatment–outcome association. If there exists an unmeasured covariate having a relative risk association at least as large as **7.76, then residual confounding could explain the observed association between GLP-1 RA and All-Cause Mortality. CI, Confidence Interval, GLP-1 RA 5 Glucagon-Like Peptide-1 Receptor Agonist.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.021
GPT teacher head0.276
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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