The Impact of Oral Hypoglycemics and Statins on Outcomes in Myelodysplastic Syndromes
Bibliographic record
Abstract
Abstract Background: Observational studies suggest an anti-neoplastic effect associated with statins, metformin, dipeptidyl peptidase-4 inhibitors (DPP4i), while sulfonylureas may have a neutral or detrimental effect. The aim of this study was to determine the impact of these medications in patients with myelodysplastic syndromes (MDS). Methods: A prospective Canadian national registry containing disease and patient-related characteristics enrolled patients with MDS between January 1, 2006 and December 31, 2019. The Ontario subset of the registry was linked to population-based health system administrative databases. The primary outcome was the impact of statin/oral hypoglycemic medication exposure on overall survival (OS). Cumulative medication exposure (in months) was evaluated from 1-year prior to registry enrollment to date of death or end of follow-up (March 31, 2020) as a time-varying covariate. Cox regression analysis controlling for comorbid disease burden (Aggregated Diagnosis Groups; ADG) and sociodemographic factors (age, sex, rurality, income quintile) examined the relationship between medication exposure and OS. Our secondary outcome was leukemia-free survival (LFS), in which cause-specific hazard model was used to evaluate the association between medication exposure and LFS, taking death as the competing event. Results: In total, 533 patients aged >66 years were included (395 lower-risk IPSS, 130 higher-risk IPSS). The median age was 76.0 years (IQR 72.0-81.0), 65.1% were male and 9% had secondary MDS. The mean follow-up was 2.6 years (SD+ 2.4). Starting one year prior to registry enrollment and until the end of follow-up, 49.3% used a statin, 18.9% used metformin, 9.0% used a sulfonylurea and 6.4% used a DPP4i. On univariate analysis, we identified an improved OS in the lower-risk IPSS group using DPP4i (HR 0.98, 95% CI 0.95-1.00, p=0.05), while there was no significant difference in the higher-risk IPSS group for users of DPP4i (HR 1.03, 95% CI 0.99-1.07, p=0.21). In both lower and higher-risk IPSS groups, there was no difference in mortality for statins (HR 1.00, CI 1.00-1.01, p=0.93), metformin (HR 1.00, CI 0.99-1.01, p=0.81) and sulfonylureas (HR 1.00, CI 0.99-1.02, p=0.43). Increased age (p<0.01), higher Charlson score (p<0.01), greater comorbid disease burden (p=0.04), higher-risk disease (IPSS p<0.01, IPSS-R p<0.01), history of heart failure (p<0.01) and hypertension (p=0.01) were predictive of inferior OS on univariate analysis. There was no association between exposure to the studied medications and LFS in the lower-risk group: metformin (HR 0.99, 95% CI 0.96-1.02, p=0.32), sulfonylureas (HR 0.99, 95% CI 0.94-1.04, p=0.57) and statins (HR 0.99, 95% CI 0.98-1.01, p=0.41). The impact of DPP4i exposure on LFS could not be assessed in lower risk disease due to infrequent events. There was also no association in the higher-risk IPSS group: DPP4i (HR 1.06, 95% CI 1.00-1.12, p=0.05), metformin (HR 1.02, CI 0.98-1.05, p=0.34), sulfonylureas (HR 1.04, 1.00-1.08, p=0.07) and statins (HR 1.02, 1.00-1.04, p=0.12). On multivariable analysis in the lower-risk IPSS group, no associations were identified between the use of medications of interest and all-cause mortality: DPP4i (HR 0.98, 95% CI 0.95-1.00, p=0.06), metformin (HR 1.00, 95% CI 0.99-1.01, p=0.99), sulfonylurea (HR 1.00, 95% CI 0.99-1.02, p=0.65) and statins (HR 1.00, 95% CI 0.99-1.00, p=0.56). In those with known cause of death, the main cause of death amongst DPP4i users was infection (35.7%) followed by acute myeloid leukemia (AML) (21.4%) and cardiac disease (14.3%), while the main cause of death in patients not on DPP4i was progressive MDS (23.2%) followed by AML (22.5%) and infections (22.1%). Conclusions: DPP4i may confer a survival benefit in lower-risk but not higher-risk MDS that cannot be explained by a reduction in cardiovascular deaths. Metformin, sulfonylureas and statins showed no impact on OS and LFS in lower and higher-risk groups. Improved bone marrow function through exosome inhibition and increased granulocyte-macrophage colony stimulating activity is the suggested mechanism for the potential survival benefit with DPP4i in lower-risk IPSS patients. This represents the first study evaluating the impact of oral hypoglycemic mediations and statins in a large cohort of patients with MDS. Further prospective studies are required to further evaluate the effects of DPP4i in MDS. Disclosures Leber: AMGEN: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; TaiHo: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Otsuka: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Astellas: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Jazz: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Khalaf: Pfizer: Honoraria; Novartis: Honoraria; Paladin: Honoraria. Sabloff: TaiHo: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Jaxx: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; ROCHE: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Yee: Astex: Membership on an entity's Board of Directors or advisory committees, Research Funding; Geron: Research Funding; Janssen: Research Funding; Jazz: Research Funding; MedImmune: Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Onconova: Research Funding; Tolero: Research Funding; AbbVie: Honoraria; Bristol-Myers Squibb/Celgene: Membership on an entity's Board of Directors or advisory committees; Otsuka: Membership on an entity's Board of Directors or advisory committees; Paladin: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Shattuck Labs: Membership on an entity's Board of Directors or advisory committees; TaiHo: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; F. Hoffmann La Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Forma Therapeutics: Research Funding. Buckstein: Celgene: Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; TAIHO: Research Funding; Otsuka: Research Funding.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".