Glycemic Control in Patients Treated with Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia: A Systematic Review
Bibliographic record
Abstract
Abstract Introduction Tyrosine kinase inhibitors (TKIs) have revolutionized the treatment of chronic myeloid leukemia (CML). Many endocrine receptors, such as insulin receptors, are tyrosine kinase receptors. Hence, TKIs may have effects that impact endocrine function. However, it is not clear to what extent glycemic outcomes are affected by TKI use in this patient population, including children (<18 years of age). A better understanding of these health outcomes and identification of populations at high risk for these effects is critical for choice of TKI and instituting appropriate long-term monitoring. Thus, the aim of this systematic review is to summarize the glycemic outcomes in patients with CML on TKI therapy. Methods A systematic review of the literature up until January 2018 was conducted, utilizing databases including Cochrane Central, EMBASE, and MEDLINE. We included randomized controlled trials (RCT), quasi-RCT, cohort studies, cross-sectional studies and case-series. Eligible studies included male and female CML patients of all ages receiving treatment with TKIs including imatinib, dasatinib, nilotinib, ponatinib, or bosutinib. We included studies that reported glycemic outcomes as a primary or secondary outcome. Markers of glycemia reported include fasting plasma glucose (FPG), random blood glucose, HbA1c, and Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) if reported. Additionally, a meta-analysis was planned if two or more studies with similar designs comparing the similar population and measuring the same outcome were identified. Titles, abstracts, and full text papers were reviewed by two independent reviewers and a third reviewer was consulted to resolve disputes. Results Out of 607 full-text studies retrieved, six studies met the inclusion criteria. These studies included 10 to 267 patients. None of these studies included children. Elevations in FPG levels were demonstrated across all trials using nilotinib. The prevalence of hyperglycemia in these studies ranged from 2.7% to 35.3% within the study populations. One cohort study comparing nilotinib, imatinib, and dasatinib demonstrated that increase in FPG levels with nilotinib treatment was more significant that with imatinib (p = 0.015), and dasatinib (p = 0.009), but no significant differences between dasatinib and imatinib (p = 0.95). A second cohort study comparing nilotinib and imatinib also revealed an increased prevalence of hyperglycemia with Nilotinib use (35.3%) compared to Imatinib use (27.3%). No studies were identified that evaluated glycemic control in bosutinib or ponatinib treated patients. Two studies reported an increase in HOMA-IR with nilotinib use, however, this was not associated with an increase in HbA1c levels, a diagnostic marker of pre-diabetes and diabetes. Conclusions Nilotinib use appears to be associated with dysglycemia to a greater extent than other TKIs in adult CML patients. However, the clinical significance of hyperglycemia while on TKI treatment and future diabetes risk remains unclear. There were no data outlining the glycemic effects of TKI in the pediatric age group. Glycemic and metabolic outcomes in CML patients should be closely monitored, and further research is needed to elucidate the glycemic control in pediatric patients with CML since they are likely to use lifelong TKI therapy. Disclosures Hillis: Bristol-Myers Squibb: Honoraria; Novartis: Honoraria.
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 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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".