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The Risk of Lymphoproliferative Disorders In Patients with Diabetes Mellitus: A Meta-Analysis

2010· article· en· W2979678735 on OpenAlexaboutno aff
Nikhil K. Mull, Jorge J. Castillo

Bibliographic record

VenueBlood · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineMultiple myelomaChronic lymphocytic leukemiaRelative riskMeta-analysisFunnel plotIncidence (geometry)Diabetes mellitusCohort studyPublication biasLeukemiaConfidence intervalEndocrinology

Abstract

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Abstract Abstract 3108 Introduction: The incidence and prevalence of lymphoproliferative disorders (LPDs) has been increasing over the past few decades. The epidemiology and risk factors associated with these malignancies continue to be in need of further research. Diabetes mellitus type 2 (DM2) has emerged as a potential risk factor for the development of LPDs, including non-Hodgkin lymphoma (NHL), chronic lymphocytic leukemia (CLL) and plasma cell myeloma (PCM). However, the association of diabetes with specific histological subtypes of LPD is unclear. The present study evaluates the association of DM2 to common subtypes of LPD using a meta-analytical approach. Methods: MEDLINE and the Cochrane Database were searched from January 1, 1950 to December 31, 2009 for observational retrospective case-control and prospective cohort studies reporting outcomes with 95% confidence intervals (CI) on the association between DM2 and incidence of LPDs. The search keyword was “diabetes AND (chronic lymphocytic leukemia OR lymphoma OR myeloma)”. Data were gathered independently by two investigators. Disagreements were addressed by consensus. Relative risk (RR) and 95% CI were calculated using a random-effects model (REM). Heterogeneity among studies was assessed using the I2 and the Cochrane Q statistics, the quality of the studies was assessed with the Newcastle-Ottawa scale (NOS), and the dissemination bias was assessed by direct examination of a funnel plot and by the trim-and-fill analysis. Results: From a total of 1,009 returns, 12 case-control (average NOS 5.8) and 8 cohort studies (average NOS 7.6) were included in our final analysis. Sixteen studies were included for the association of NHL; four for follicular lymphoma (FL), four for diffuse large B-cell lymphoma (DLBCL), four for small lymphocytic lymphoma (SLL)/CLL, and three for peripheral T-cell lymphoma (PTCL). Seven studies were included for PCM. The overall RR for NHL in DM2 patients was 1.2 (95% CI 1.1 to 1.4; p=0.002). There was moderate heterogeneity among studies (I2=42%; Q=29.4, p=0.03). There was also minimal dissemination bias; the trim-and-fill analysis found one imputed study, which would not have affected our results. The RR for patients with FL was 0.8 (95% CI 0.6 to 1.1; p=0.15), 1.1 (0.8 to 1.4; p=0.19) for DLBCL, 1.2 (95% CI 0.9 to 1.5; p=0.33) for CLL/SLL, and 2.4 (95% CI 1.3 to 4.6; p=0.007) for PTCL. The RR for PCM in DM2 patients was 1.1 (95% CI 0.8 to 1.4; p=0.67). There was a high degree of heterogeneity among studies (I2=59%; Q=26.9, p=0.005) but there was no evidence of dissemination bias. Conclusion: The findings of this study corroborate the 20% increased risk of NHL associated with DM2. Among NHL subtypes, FL, DLBCL, nor CLL/SLL were associated with increased risk from DM2. Similarly, there was not an association between DM2 and PCM. Although a small subset, we found a 40% increased risk of PTCL in DM2 patients. Large population-based studies focusing specifically on common chronic conditions, such as DM2, and their relationship with specific LPD subtypes are needed to confirm the results of this study. Disclosures: No relevant conflicts of interest to declare.

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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)0.015
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.028
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0130.058
Bibliometrics0.0050.007
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.004
GPT teacher head0.204
Teacher spread0.200 · 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 designMeta-analysis
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
Published2010
Admission routes1
Has abstractyes

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