Dipeptidyl Peptidase-4 Inhibitors and Risk of Inflammatory Bowel Disease among Patients with Type 2 Diabetes Mellitus

Study type

• Study type:
  Non-interventional study
• Scope of the study:
  Assessment of risk minimisation measure implementation or effectiveness

Non-interventional study

• Non-interventional study design:
  Cohort

Study drug and medical condition

• Anatomical Therapeutic Chemical (ATC) code:
  (A10BB) Sulfonylureas

  Sulfonylureas

  (A10BG) Thiazolidinediones

  Thiazolidinediones

  (A10BH) Dipeptidyl peptidase 4 (DPP-4) inhibitors

  Dipeptidyl peptidase 4 (DPP-4) inhibitors
• Medical condition to be studied:
  Inflammatory bowel disease

  Diabetes mellitus

Population studied

• Age groups:
  Adults (18 to < 46 years)

  Adults (46 to < 65 years)

  Adults (65 to < 75 years)

  Adults (75 to < 85 years)

  Adults (85 years and over)
• Estimated number of subjects:
  916285

Study design details

• Main study objective:
  To evaluate the association between the initiation of Dipeptidyl peptidase-4 inhibitors versus the initiation of clinically relevant second-line glucose lowering therapies (thiazolidinediones and sulfonylureas) and the short-term risk of inflammatory bowel disease, based on an active comparator, new user study design.
• Outcomes:
  Primary outcome is incident inflammatory bowel disease (IBD), will be defined by the first IBD diagnosis (ICD-9-CM codes 555.x and 556.x), during follow-up which is preceded by a colonoscopy/sigmoidoscopy and biopsy within 30 days before diagnosis, and followed by a prescription claim for IBD medication treatment within 30 days after diagnosis. Chrohn's disease (CD) and ulcerative colitis (UC), respectively, which will be identified by the first diagnosis for CD or UC, respectively, with a colonoscopy/sigmoidoscopy and biopsy within 30 days before diagnosis and an inflammatory bowel disease treatment within 30 days after diagnosis.
• Data analysis plan:
  We will assess this balance by looking at the crude distribution of claims data based covariates across treatment cohorts from ACNU design, will then use propensity scores and standardized mortality/morbidity ratio weighting to remove remaining imbalances in measured potential confounders between study cohorts. We will estimate and compare the cumulative incidence of both primary and secondary outcomes for each study cohort using weighted Kaplan-Meier methods. Crude and adjusted hazard ratios for both primary and secondary outcomes will be estimated using weighted Cox proportional hazards models, controlling for age, sex, as well as any potential confounders that remain unbalanced after propensity score implementation. If the estimates in MarketScan and Medicare are compatible (I2 >75%), we will perform a meta-analysis using random-effects models with inverse variance weighting and the DerSimonian and Laird method to pool both estimates from MarketScan and Medicare data.