DPP-4 Inhibitors and Rheumatoid Arthritis Risk: An Epidemiological Study

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1 Table of contents

eMethods 1. Marginal structural Cox proportional hazards model ... 3

eMethods 2. Disease risk score ... 4

eMethods 3. Multiple imputation ... 5

eMethods 4. Sensitivity analysis without assumptions ... 6

eTable 1. List of Read codes for rheumatoid arthritis ... 7

eTable 2. Description of supporting events for the diagnosis of rheumatoid arthritis ... 9

eTable 3. Crude and adjusted HRs for the association between the use of single DPP-4 inhibitors and the risk of rheumatoid arthritis ... 10

eTable 4. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (stratified by sex) ... 11

eTable 5. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (1-year lag period) ... 12

eTable 6. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (3-month lag period) ... 13

eTable 7. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (restricted outcome) ... 14

eTable 8. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (competing risk) ... 15

eTable 9. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (exposure defined by receiving four prescriptions within a 12- month moving window) ... 16

eTable 10. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (exclude and censor on use of thiazolidinediones) ... 17

eTable 11. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (reclassify exposure) ... 18

eTable 12. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (marginal structural model) ... 19

eTable 13. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (disease risk score) ... 20

eTable 14. Parameter estimates for the disease risk score model ... 21

eTable 15. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (multiple imputation) ... 23

eTable 16. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (interaction term between year of cohort entry and use of statins) ... 24

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2 eTable 17. Bounds on corrected estimates and 95% confidence intervals for unmeasured

confounding (sensitivity analysis without assumptions)* ... 25 eTable 18. Baseline demographics and clinical characteristics of the cohort after propensity score trimming and stratified by drug use at cohort entry for the ancillary analysis ... 26 eFigure 1. Diagram depicting cohort entry, time-dependent exposure definition, and six-month exposure lagged analysis... 28 eFigure 2. Flowchart describing the construction of base and study cohorts for the second

ancillary analysis ... 30 eFigure 3. Cumulative incidence of incident rheumatoid arthritis among users of dipeptidyl peptidase-4 inhibitors and other non-insulin second-to-third line antidiabetic drugs*... 31

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3 eMethods 1. Marginal structural Cox proportional hazards model

To address the possibility of residual time-dependent confounding associated with time-varying exposures over the 10.5-year follow-up period, we repeated the analysis using a marginal structural Cox proportional hazards model.^1,2 We used two pooled logistic regression models (numerator and denominator of the stabilized inverse-probability-of-treatment weights [IPTWs]) to estimate the conditional probability of being exposed to dipeptidyl peptidase (DPP)-4 inhibitors given previous treatment history in the 30-days prior. The numerator model included baseline covariates (listed in the manuscript) and follow-up time, and the denominator model included covariates (listed in the manuscript) measured at each 30-day interval and follow-up time. Follow-up was modelled using a restricted cubic spline with five knots to avoid biases deriving from the linearity assumption.³ Similar methods were used to estimate inverse- probability-of-censoring weights (IPCWs). Thus, using predicted probabilities from treatment and censoring models, we calculated stabilized IPTW and IPCW for each patient. The product of these weights was used to reweigh the cohort, and to estimate the hazard ratios of rheumatoid arthritis associated with the use DPP-4 inhibitors, with 95% confidence intervals calculated using robust variance estimators.²

References

1. Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 2000;11:561-70.

2. Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000;11:550-60.

3. Cole SR, Hernan MA. Constructing inverse probability weights for marginal structural models. Am J Epidemiol. 2008;168:656-64.

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4 eMethods 2. Disease risk score

To address residual confounding, we performed a disease risk score (DRS) analysis, which depicts an alternative to the propensity score method.^1,2 To fit the DRS, we constructed a historical cohort³ with data from the United Kingdom Clinical Practice Research Datalink, from January 1, 1997 to December 31, 2006 (before dipeptidyl peptidase [DPP]-4 inhibitors were available). To enter the historical cohort, patients were required to have a new prescription for a non-insulin antidiabetic drug. All exclusions for the study cohort listed in the manuscript were applied to the historical cohort. As DPP-4 inhibitors were not available during the period in the historical cohort, the DRS estimated the probability of rheumatoid arthritis conditional on being unexposed to DPP-4 inhibitors. To calculate the DRS, we fitted a Cox proportional hazards model including all potential confounders (listed in the manuscript) and baseline exposure. The DRS was then applied to the study cohort to calculate the probability of rheumatoid arthritis conditional on being unexposed to DPP-4 inhibitors. We used the DRS stratified on deciles as a summary statistic in place of all individual potential confounders to estimate hazard ratios of incident rheumatoid arthritis associated with the use of DPP-4 inhibitors.

References

1. Arbogast PG, Ray WA. Use of disease risk scores in pharmacoepidemiologic studies. Stat Methods Med Res. 2009;18:67-80.

2. Arbogast PG, Ray WA. Performance of disease risk scores, propensity scores, and traditional multivariable outcome regression in the presence of multiple confounders. Am J Epidemiol.

2011;174:613-20.

3. Glynn RJ, Gagne JJ, Schneeweiss S. Role of disease risk scores in comparative effectiveness research with emerging therapies. Pharmacoepidemiol Drug Saf. 2012;21 Suppl 2:138-47.

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5 eMethods 3. Multiple imputation

We repeated the primary analysis using multiple imputation for variables with missing values (i.e., hemoglobin A1c, body mass index, smoking).^1,2 First, an ordinal logistic regression model was used to impute variables with missing information with explanatory variables and cumulative hazard,³ and one of the exposure groups (at cohort entry), along with all confounders considered in the primary analysis. We used multiple imputations methods for variables with missing information, and the results were combined using Rubin’s rules.⁴

References

1. Rubin DB. Multiple imputation for nonresponse in surveys: John Wiley & Sons; 2004.

2. Schafer JL. Analysis of incomplete multivariate data: CRC press; 1997.

3. White IR, Royston P. Imputing missing covariate values for the Cox model. Stat Med.

2009;28:1982-98.

4. Sterne JA, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ 2009;338:b2393.

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6 eMethods 4. Sensitivity analysis without assumptions

We performed a post-hoc sensitivity analysis to assess the impact of residual confounding on our point estimates using the model proposed by Ding and VanderWeele.¹ This model does not impose any assumptions on the unmeasured confounder or confounders such as having an unmeasured confounder that is binary, having no interaction between the effects of the exposure and the confounder on the outcome, or having only one unmeasured confounder. The model derives a ‘joint bounding factor’ and a sharp inequality such that the sensitivity analysis parameters must satisfy the inequality if an unmeasured confounder is to explain away the observed effect estimate, or reduce it to a particular level. Hypothesizing a ‘true’ hazard ratio (95% confidence interval) of 1.5 (1.2-1.8), we found that in order to completely explain away this increased risk of incident rheumatoid arthritis associated with dipeptidyl peptidase-4 inhibitors (i.e., to reduce the risk to hazard ratio = 1), an unmeasured confounder would need to be strongly associated with both the exposure and the outcome (eTable 17). For example, given an association between a potential unmeasured confounder and the exposure of 2.0, the association between this confounder and the outcome would need to be at least 3.0.

References

1. Ding P, VanderWeele TJ. Sensitivity Analysis Without Assumptions. Epidemiology (Cambridge, Mass). 2016;27(3):368-377.

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7 eTable 1. List of Read codes for rheumatoid arthritis

Read code Description

N040.00 Rheumatoid arthritis

N040P00 Seronegative rheumatoid arthritis N040T00 Flare of rheumatoid arthritis

N04X.00 Seropositive rheumatoid arthritis, unspecified N042200 Rheumatoid nodule

N04..00 Rheumatoid arthritis and other inflammatory polyarthropathy N047.00 Seropositive erosive rheumatoid arthritis

N041.00 Felty's syndrome

N040S00 Rheumatoid arthritis - multiple joint

N04y012 Fibrosing alveolitis associated with rheumatoid arthritis H570.00 Rheumatoid lung

N040N00 Rheumatoid vasculitis N040D00 Rheumatoid arthritis of knee N04y000 Rheumatoid lung

N040700 Rheumatoid arthritis of wrist N040R00 Rheumatoid nodule

N040800 Rheumatoid arthritis of MCP joint N040F00 Rheumatoid arthritis of ankle N040Q00 Rheumatoid bursitis

N040100 Other rheumatoid arthritis of spine N040900 Rheumatoid arthritis of PIP joint of finger N040200 Rheumatoid arthritis of shoulder

N042z00 Rheumatoid arthropathy + visceral / systemic involvement NOS Nyu1G00 [X] Seropositive rheumatoid arthritis, unspecified

N040B00 Rheumatoid arthritis of hip N042100 Rheumatoid lung disease N040500 Rheumatoid arthritis of elbow

N042.00 Other rheumatoid arthropathy + visceral / systemic involvement Nyu1200 [X] Other specified rheumatoid arthritis

G5yA.00 Rheumatoid carditis

F396400 Myopathy due to rheumatoid arthritis F371200 Polyneuropathy in rheumatoid arthritis N040000 Rheumatoid arthritis of cervical spine N040K00 Rheumatoid arthritis of 1st MTP joint

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8 Read code Description

Nyu1100 [X] Other seropositive rheumatoid arthritis N040A00 Rheumatoid arthritis of DIP joint of finger N040G00 Rheumatoid arthritis of subtalar joint G5y8.00 Rheumatoid myocarditis

N040H00 Rheumatoid arthritis of talonavicular joint N040J00 Rheumatoid arthritis of other tarsal joint N040M00 Rheumatoid arthritis of IP joint of toe N04y011 Caplan's syndrome

N040400 Rheumatoid arthritis of acromioclavicular joint N040L00 Rheumatoid arthritis of lesser MTP joint N040600 Rheumatoid arthritis of distal radioulnar joint N040C00 Rheumatoid arthritis of sacroiliac joint N040300 Rheumatoid arthritis of sternoclavicular joint N040E00 Rheumatoid arthritis of tibiofibular joint

Nyu1000 [X] Rheumatoid arthritis + involvement / other organs or systems

Abbreviations: MCP,metacarpophalangeal; NOS, not otherwise specified; MTP, metatarsophalangeal; DIP, distal interphalangeal; IP, interphalangeal

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9 eTable 2. Description of supporting events for the diagnosis of rheumatoid arthritis

Number of supporting events N = 464

None, n (%) 56 (12)

One, n (%) 145 (31)

Use of DMARDs 69 (15)

Referral to / feedback from a rheumatologist 76 (16) Two, n (%)

Use of DMARDs, referral to / feedback from a rheumatologist 263 (57)

Abbreviations: DMARDs, disease-modifying antirheumatic drugs

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10 eTable 3. Crude and adjusted HRs for the association between the use of single DPP-4 inhibitors and the risk of rheumatoid arthritis

Exposure Events Person-

years

Incidence rate^a

(95% CI) Crude HR Adjusted HR ^b (95% CI) Other antidiabetic drugs 389 472,623 82 (74, 91) 1.00 [Reference] 1.00 [Reference]

Sitagliptin use only 57 68,297 84 (63, 108) 1.0 1.0 (0.8, 1.4)

Saxagliptin use only 6 9549 63 (23, 137) 0.8 0.8 (0.4, 1.8)

Linagliptin use only 5 5690 88 (29, 205) 1.1 1.1 (0.5, 2.7)

Other DPP-4 inhibitors 7 11,010 64 (26, 131) 0.8 0.8 (0.4, 1.7)

Abbreviations: HR, hazard ratio; CI, confidence interval; DPP-4, dipeptidyl peptidase-4

a Per 100,000 person-years.

b Adjusted for year of cohort entry, age, sex, alcohol-related disorders (including alcoholism, alcoholic cirrhosis of the liver, alcoholic hepatitis and hepatic failure), smoking status, body mass index category, hemoglobin A1c level, duration of treated diabetes, presence of

microvascular (nephropathy, neuropathy, retinopathy) and macrovascular (myocardial infarction, ischemic stroke, peripheral arteriopathy) complications of diabetes, use of antidiabetic drugs at baseline, presence of other autoimmune conditions, use of statins, and total number of non- antidiabetic drugs in the year before cohort entry.

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11 eTable 4. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (stratified by sex)

Male

Adjusted HR ^a (95% CI)

Female

Adjusted HR ^a (95% CI) P for interaction Other antidiabetic drugs 1.0 (reference) 1.0 (reference) 0.56

DPP-4 inhibitors 1.1 (0.7, 1.5) 0.9 (0.6, 1.3)

a Adjusted for year of cohort entry, age, sex, alcohol-related disorders (including alcoholism, alcoholic cirrhosis of the liver, alcoholic hepatitis and hepatic failure), smoking status, body mass index category, hemoglobin A1c level, duration of treated diabetes, presence of

microvascular (nephropathy, neuropathy, retinopathy) and macrovascular (myocardial infarction, ischemic stroke, peripheral arteriopathy) complications of diabetes, use of antidiabetic drugs at baseline, presence of other autoimmune conditions, use of statins, and total number of non- antidiabetic drugs in the year before cohort entry

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12 eTable 5. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (1-year lag period)

years

DPP-4 inhibitors 66 79,555 83 (64, 106) 1.0 1.0 (0.8, 1.4)

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13 eTable 6. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (3-month lag period)

years

DPP-4 inhibitors 80 102,488 78 (62, 97) 1.0 1.0 (0.8, 1.3)

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14 eTable 7. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (restricted outcome)

years

DPP-4 inhibitors 66 94,533 70 (54, 89) 1.1 1.1 (0.8, 1.5)

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15 eTable 8. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (competing risk)

years

DPP-4 inhibitors 75 94,546 79 (62, 99) 1.0 1.0 (0.8, 1.3)

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16 eTable 9. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (exposure defined by receiving four prescriptions within a 12-month moving window)

years

DPP-4 inhibitors 56 76,233 74 (56, 95) 0.9 0.9 (0.7, 1.2)

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17 eTable 10. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (exclude and censor on use of thiazolidinediones)

years

DPP-4 inhibitors 51 64,428 79 (59, 104) 1.0 1.1 (0.8, 1.5)

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18 eTable 11. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (reclassify exposure)

Exposure^b Events Person-

years

(95% CI) Crude HR Adjusted HR^b (95% CI) Non-incretin antidiabetic drugs 376 457,097 82 (74, 91) 1.0 [Reference] 1.0 [Reference]

DPP-4 inhibitors 62 82,475 75 (58, 96) 0.9 0.9 (0.7, 1.3)

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19 eTable 12. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (marginal structural model)

months

(95% CI) Crude marginal HR Adjusted marginal HR ^b (95% CI) Other antidiabetic drugs 389 5,793,811 6.7 (6.1, 7.4) 1.0 [Reference] 1.0 [Reference]

DPP-4 inhibitors 75 1,176,693 6.4 (5.0, 8.0) 0.9 0.9 (0.7, 1.2)

a Per 100,000 person-months.

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20 eTable 13. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (disease risk score)

years

DPP-4 inhibitors 75 94,546 79 (62, 99) 1.0 1.0 (0.8, 1.3)

b Stratified on disease risk score deciles.

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21 eTable 14. Parameter estimates for the disease risk score model

Variables HR (95% CI)

Age in years 1.0 (1.0, 1.1)

Spline variable1 age 1.0 (0.9, 1.2)

Male 0.5 (0.4, 0.6)

Year of cohort entry 1.0 (0.9, 1.1)

Excessive alcohol use 1.1 (0.6, 1.8)

Smoking status

Never 1.0 (reference)

Current 1.6 (1.2, 2.2)

Past 1.2 (0.9, 1.8)

Unknown 0.7 (0.4, 1.4)

Body mass index

< 25 kg/m² 1.0 (reference)

25-30 kg/m² 1.0 (0.7, 1.5)

≥30 kg/m² 0.8 (0.6, 1.2)

Unknown 1.2 (0.7, 2.1)

Hemoglobin A1c

≤7.0% (≤53 mmol/mol) 1.0 (reference)

7.1%-8.0% (54–64 mmol/mol) 0.7 (0.4, 1.1)

>8.0% (>64 mmol/mol) 0.7 (0.4, 1.0)

Unknown 0.9 (0.6, 1.3)

Duration of treated diabetes in years 1.0 (1.0, 1.0) Spline variable1 duration of treated diabetes 1.0 (1.0, 1.0) Spine variable2 duration of treated diabetes 1.0 (1.0, 1.1) Spline variable3 duration of treated diabetes 1.0 (0.9, 1.1)

Nephropathy 1.0 (0.5, 2.2)

Neuropathy 0.5 (0.2, 1.6)

Retinopathy 0.6 (0.3, 1.2)

Myocardial infarction 0.9 (0.5, 1.6)

Ischemic stroke 0.5 (0.2, 1.2)

Peripheral arteriopathy 1.2 (0.7, 2.1)

Other autoimmune conditions 1.9 (1.0, 3.5)

Statins 0.7 (0.5, 1.0)

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22

Variables HR (95% CI)

Number of non-antidiabetic drugs

0 1.0 (reference)

1 0.9 (0.3, 3.1)

2 1.8 (0.6, 4.9)

3 1.3 (0.4, 3.6)

≥4 2.7 (1.1, 6.7)

Class of unique antidiabetic drugs

Metformin 0.9 (0.4, 2.3)

Sulfonylureas 0.8 (0.3, 2.3)

Insulin 0.0

Other 1.9 (0.6, 6.3)

Abbreviations: HR, hazard ratio; CI, confidence interval

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23 eTable 15. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (multiple imputation)

years

DPP-4 inhibitors 75 94,546 79 (62, 99) 1.0 1.0 (0.8, 1.3)

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24 eTable 16. Crude and adjusted HRs for the association between the use of DPP-4 inhibitors and the risk of rheumatoid arthritis (interaction term between year of cohort entry and use of statins)

Exposure Events Person- years

DPP-4 inhibitors 75 94,546 79 (62, 99) 1.0 1.0 (0.8, 1.3)

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25 eTable 17. Bounds on corrected estimates and 95% confidence intervals for unmeasured confounding (sensitivity analysis without assumptions)*

1.2 1.3 1.5 1.8 2.0 2.5 3.0 4.0 5.0 6.0 8.0 10.0

1.2 1.46 (1.17, 1.75) 1.44 (1.15, 1.73) 1.42 (1.13, 1.70) 1.39 (1.11, 1.67) 1.38 (1.10, 1.65) 1.35 (1.08, 1.62) 1.33 (1.07, 1.60) 1.31 (1.05, 1.58) 1.30 (1.04, 1.56) 1.29 (1.03, 1.55) 1.28 (1.03, 1.54) 1.28 (1.02, 1.53)

1.3 1.44 (1.15, 1.73) 1.42 (1.14, 1.70) 1.38 (1.11, 1.66) 1.35 (1.08, 1.62) 1.33 (1.06, 1.59) 1.29 (1.03, 1.55) 1.27 (1.02, 1.52) 1.24 (0.99, 1.49) 1.22 (0.98, 1.47) 1.21 (0.97, 1.45) 1.20 (0.96, 1.44) 1.19 (0.95, 1.43)

1.5 1.42 (1.13, 1.70) 1.38 (1.11, 1.66) 1.33 (1.07, 1.60) 1.28 (1.02, 1.53) 1.25 (1.00, 1.50) 1.20 (0.96, 1.44) 1.17 (0.93, 1.40) 1.13 (0.90, 1.35) 1.10 (0.88, 1.32) 1.08 (0.87, 1.30) 1.06 (0.85, 1.28) 1.05 (0.84, 1.26)

1.8 1.39 (1.11, 1.67) 1.35 (1.08, 1.62) 1.28 (1.02, 1.53) 1.20 (0.96, 1.44) 1.17 (0.93, 1.40) 1.10 (0.88, 1.32) 1.06 (0.84, 1.27) 1.00 (0.80, 1.20) 0.97 (0.77, 1.16) 0.94 (0.76, 1.13) 0.92 (0.73, 1.10) 0.90 (0.72, 1.08)

2.0 1.38 (1.10, 1.65) 1.33 (1.06, 1.59) 1.25 (1.00, 1.50) 1.17 (0.93, 1.40) 1.13 (0.90, 1.35) 1.05 (0.84, 1.26) 1.00 (0.80, 1.20) 0.94 (0.75, 1.13) 0.90 (0.72, 1.08) 0.88 (0.70, 1.05) 0.84 (0.68, 1.01) 0.83 (0.66, 0.99)

2.5 1.35 (1.08, 1.62) 1.29 (1.03, 1.55) 1.20 (0.96, 1.44) 1.10 (0.88, 1.32) 1.05 (0.84, 1.26) 0.96 (0.77, 1.15) 0.90 (0.72, 1.08) 0.83 (0.66, 0.99) 0.78 (0.62, 0.94) 0.75 (0.60, 0.90) 0.71 (0.57, 0.86) 0.69 (0.55, 0.83)

3.0 1.33 (1.07, 1.60) 1.27 (1.02, 1.52) 1.17 (0.93, 1.40) 1.06 (0.84, 1.27) 1.00 (0.80, 1.20) 0.90 (0.72, 1.08) 0.83 (0.67, 1.00) 0.75 (0.60, 0.90) 0.70 (0.56, 0.84) 0.67 (0.53, 0.80) 0.63 (0.50, 0.75) 0.60 (0.48, 0.72)

4.0 1.31 (1.05, 1.58) 1.24 (0.99, 1.49) 1.13 (0.90, 1.35) 1.00 (0.80, 1.20) 0.94 (0.75, 1.13) 0.83 (0.66, 0.99) 0.75 (0.60, 0.90) 0.66 (0.53, 0.79) 0.60 (0.48, 0.72) 0.56 (0.45, 0.68) 0.52 (0.41, 0.62) 0.49 (0.39, 0.59)

5.0 1.30 (1.04, 1.56) 1.22 (0.98, 1.47) 1.10 (0.88, 1.32) 0.97 (0.77, 1.16) 0.90 (0.72, 1.08) 0.78 (0.62, 0.94) 0.70 (0.56, 0.84) 0.60 (0.48, 0.72) 0.54 (0.43, 0.65) 0.50 (0.40, 0.60) 0.45 (0.36, 0.54) 0.42 (0.34, 0.50)

6.0 1.29 (1.03, 1.55) 1.21 (0.97, 1.45) 1.08 (0.87, 1.30) 0.94 (0.76, 1.13) 0.88 (0.70, 1.05) 0.75 (0.60, 0.90) 0.67 (0.53, 0.80) 0.56 (0.45, 0.68) 0.50 (0.40, 0.60) 0.46 (0.37, 0.55) 0.41 (0.33, 0.49) 0.38 (0.30, 0.45)

8.0 1.28 (1.03, 1.54) 1.20 (0.96, 1.44) 1.06 (0.85, 1.28) 0.92 (0.73, 1.10) 0.84 (0.68, 1.01) 0.71 (0.57, 0.86) 0.63 (0.50, 0.75) 0.52 (0.41, 0.62) 0.45 (0.36, 0.54) 0.41 (0.33, 0.49) 0.35 (0.28, 0.42) 0.32 (0.26, 0.38)

10.0 1.28 (1.02, 1.53) 1.19 (0.95, 1.43) 1.05 (0.84, 1.26) 0.90 (0.72, 1.08) 0.83 (0.66, 0.99) 0.69 (0.55, 0.83) 0.60 (0.48, 0.72) 0.49 (0.39, 0.59) 0.42 (0.34, 0.50) 0.38 (0.30, 0.45) 0.32 (0.26, 0.38) 0.29 (0.23, 0.34)

* Rows correspond to increasing strength of the risk ratio of unmeasured confounding on the outcome and columns correspond to increasing strength of risk ratio of unmeasured confounding on the exposure

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26 eTable 18. Baseline demographics and clinical characteristics of the cohort after propensity score trimming and stratified by drug use at cohort entry for the ancillary analysis

Characteristic Use at cohort entry

Entire Cohort DPP-4 inhibitors

Other non-insulin second-to- third line antidiabetic drugs

Total 25,585 6381 19,204

Age in years, (mean, SD) 65 (12) 66 (11) 64 (12)

Male, n (%) 15,109 (59) 3667 (58) 11,442 (60)

Alcohol-related disorders, n (%) 4274 (17) 1249 (20) 3025 (16) Smoking status, n (%)

Current 3513 (14) ¥ 2688 (14)

Past 10,083 (39) 2489 (39) 7594 (40)

Never 11,976 (47) 3065 (48) 8911 (46)

Unknown 13 (0.1) ¥ 11 (0.1)

Body mass index, n (%)

< 25 kg/m² 2670 (10) 633 (10) 2037 (11)

25-30 kg/m² 8083 (32) 1920 (30) 6163 (32)

≥30 14,792 (58) 3822 (60) 10,970 (57)

Unknown 40 (0.2) 6 (0.1) 34 (0.2)

Hemoglobin A1c, n (%)

≤7.0% 3411 (13) 1157 (18) 2254 (12)

7.1%-8.0% 8891 (35) 2299 (36) 6592 (34)

>8.0% 13,230 (52) 2918 (46) 10,312 (54)

Unknown 53 (0.2) 7 (0.1) 46 (0.2)

Duration of treated diabetes in years (mean, SD) 5.9 (3.4) 8.3 (3.4) 5.1 (3.0) Diabetic complications

Nephropathy, n (%) 8252 (32) 2409 (38) 5843 (30)

Neuropathy, n (%) 5931 (23) 1723 (27) 4208 (22)

Retinopathy, n (%) 6147 (24) 2140 (34) 4007 (21)

Myocardial infarction, n (%) 1866 (7.3) 473 (7.4) 1393 (7.3)

Ischemic stroke, n (%) 1384 (5.4) 368 (5.8) 1016 (5.3)

Peripheral arteriopathy, n (%) 1259 (4.9) 363 (5.7) 896 (4.7) Class of unique antidiabetic drugs, n (%)§

Metformin 23,884 (93) 6260 (98) 17,624 (92)

Sulfonylureas 9073 (36) 3814 (60) 5259 (27)

Thiazolidinediones 5391 (21) 2345 (37) 3046 (16)

Insulin 0 (0.0) 0 (0.0) 0 (0.0)

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27

Characteristic Use at cohort entry

Entire Cohort DPP-4 inhibitors

Other non-insulin second-to- third line antidiabetic drugs

Other 511 (2.0) 220 (3.5) 291 (1.5)

Other autoimmune conditions, n (%) 712 (2.8) 182 (2.9) 530 (2.8)

Statins, n (%) 21,551 (84) 5413 (85) 16,138 (84)

Number of non-antidiabetic drugs (mean, SD) 9.6 (5.6) 10.2 (5.9) 9.5 (5.6)

0 108 (0.4) 20 (0.3) 88 (0.5)

1 329 (1.3) 77 (1.2) 252 (1.3)

2 761 (3.0) 151 (2.4) 610 (3.2)

3 1100 (4.3) 235 (3.7) 865 (4.5)

≥4 23,287 (91) 5898 (92) 17,389 (91)

Abbreviations: SD, standard deviation; DPP-4, dipeptidyl peptidase-4

§Non-mutually exclusive groups measured any time before (not including) cohort entry.

¥ Numbers <5 are not displayed, as per the confidentiality policies of the Clinical Practice Research Datalink.

(28)

28 eFigure 1. Diagram depicting cohort entry, time-dependent exposure definition, and six- month exposure lagged analysis

Orange line represents person-time exposed to dipeptidyl peptidase-4 (DPP-4) inhibitors, violet line represents person-time exposed to other, non-DPP-4 inhibitor, antidiabetic drugs, and blue line represents excluded person-time (lag period imposed at the stage of study design). Cohort entry corresponded to the initiation of a new class of antidiabetic drugs (either DPP-4 inhibitors or not) in or after 2007. Exposure to DPP-4 inhibitors was lagged by six months (lag period imposed at the stage of exposure definition) to account for minimum latency period and minimize detection bias. Scenario a corresponds to an excluded event occurring in the first six months after cohort entry (blue solid square). Scenarios b and d correspond to an event exposed to other antidiabetic drugs (violet solid square), while scenario c corresponds to an event exposed to DPP-4 inhibitors (orange solid square).

(29)

29

(30)

30 eFigure 2. Flowchart describing the construction of base and study cohorts for the second ancillary analysis

Abbreviations: DPP-4, dipeptidyl peptidase-4

117,766 Excluded

113,366 Patients who don’t fail on their first-line treatment at study cohort entry

4400 Patients with previous use of insulin

6384 New users of DPP-4 inhibitors

1249 Excluded

1249 Propensity score trimming

20,453 New users of other non-insulin second-to-third line antidiabetic drugs

6381 New users of DPP-4 inhibitors

19,204 New users of other non-insulin second-to-third line

antidiabetic drugs 3 Excluded

3 Propensity score trimming

144,603 Patients included in the study cohort for the primary analysis

26,837 Patients who failed on first- line treatment and haven’t started insulins

(31)

31 0.0%

0.1%

0.2%

0.3%

0.4%

0.5%

0.6%

0.7%

0.8%

0 1 2 3 4 5 6 7 8 9 10

Duration of follow-up (in years) Log -rank p-value: 0.80

Number at risk

19,204 16,759 14,090 11,474 8883 6688 4717 3058 1716 776 0 6381 5552 4726 3895 2953 2118 1346 603 205 38 0

eFigure 3. Cumulative incidence of incident rheumatoid arthritis among users of dipeptidyl peptidase-4 inhibitors and other non-insulin second-to-third line antidiabetic drugs*

* This is part of the second ancillary analysis.

Cu m ul ati ve

in ci de nc e

Use of other second-line antidiabetic drugs

Use of dipeptidyl peptidase-4 inhibitors