Real World Glycemic Effectiveness of Linagliptin Among Type 2 Diabetes Mellitus Adults by Age and Renal Function
Conditions: Diabetes Mellitus, Type 2
Sponsor: Boehringer Ingelheim

ABCD
001-MCS-90-124_RD-01 (4.0)
TITLE PAGE
Protocol for non-interventional studies based on existing data
TITLE PAGE
Document Number:
C20330622
BI Study Number:
1218.182
BI Investigational
Product(s):
Tradjenta
Title:
Real world glycemic effectiveness of linagliptin (Tradjenta®) 
among type 2 diabetes mellitus adults by age and renal function.
Protocol version 
identifier:
1.0
Date of last version of 
protocol:
20 October 2017
PASS:
No
EU PAS register 
number:
Not applicable
Active substance:
Linagliptin
Medicinal product:
Tradjenta
Product reference:
N/A
Procedure number:
N/A
Joint PASS:
No
Research question and 
objectives:
The primary study objective is to:

Describe change in HbA1c among adults with T2DM within 60 
to 180 days following initiation of linagliptin across pre-defined 
age and renal function categories.  Age categories will be defined 
as: 40 to 54 years, 55 to 64 years, 65 to 74 years, and ≥ 75 years.  
Renal function categories will be defined based on eGFR as: <30 
ml/min/1.73m2, 30 to 44 ml/min/1.73m2, 45 to 59 ml/min/1.73m2, 
60 to 89 ml/min/1.73m2, and ≥ 90 ml/min/1.73m2.
The secondary study objectives are to:

Compare change in HbA1c among adults with T2DM within 60 
to 180 days following initiation of linagliptin across pre-defined 
age and renal function categories.

Compare the proportion of adults with T2DM who achieve 
HbA1c < 7.0% within 60 to 180 days following initiation of 
linagliptin across the pre-defined age and renal function 
categories.

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BI Study Number 1218.182
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001-MCS-90-124_RD-01 (4.0)
 
 
 
 
 
 
 
Country(-ies) of study:
USA
Author:
Marketing authorisation 
holder(s):
Boehringer Ingelheim International GmbH
Binger Str. 173
D-55216 Ingelheim am Rhein
In case of PASS, add: 
<EU-QPV>
Not Applicable
In case of PASS, add: 
<Signature of EU-QPV>
Not Applicable
MAH contact person:
Not Applicable
Date:
20OCT17
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Proprietary confidential information
© 2017 Boehringer Ingelheim International GmbH or one or more of its affiliated companies. All rights reserved.
This document may not - in full or in part - be passed on, reproduced, published or otherwise used without prior written 
permission

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1.
TABLE OF CONTENTS
TITLE PAGE.................................................................................................................. 1
1.
TABLE OF CONTENTS....................................................................................... 3
2.
LIST OF ABBREVIATIONS................................................................................ 5
3.
RESPONSIBLE PARTIES.................................................................................... 6
4.
ABSTRACT........................................................................................................... 7
5.
AMENDMENTS AND UPDATES..................................................................... 11
6.
MILESTONES..................................................................................................... 12
7.
RATIONALE AND BACKGROUND................................................................ 13
8.
RESEARCH QUESTION AND OBJECTIVES ................................................. 15
9.
RESEARCH METHODS .................................................................................... 16
9.1
STUDY DESIGN............................................................................................ 16
9.2
SETTING........................................................................................................ 16
9.2.1
Observation Period.................................................................................. 16
9.2.2
Inclusion Criteria..................................................................................... 17
9.2.3
Exclusion Criteria.................................................................................... 17
9.2.4
Evaluation of Patient Sample.................................................................. 18
9.3
VARIABLES .................................................................................................. 18
9.3.1
Exposures ................................................................................................ 18
9.3.2
Outcomes................................................................................................. 18
9.3.2.1
Primary outcomes..................................................................................18
9.3.2.2
Secondary outcomes..............................................................................19
9.3.3
Covariates................................................................................................ 20
9.3.3.1
Demographics........................................................................................20
9.3.3.2
Clinical Characteristics..........................................................................21
9.4
DATA SOURCES........................................................................................... 24
9.5
STUDY SIZE.................................................................................................. 25
9.6
DATA MANAGEMENT................................................................................ 26
9.7
DATA ANALYSIS......................................................................................... 26
9.7.1
Feasibility Assessment ............................................................................ 26
9.7.2
Primary Analysis..................................................................................... 27
9.7.3
Secondary Analysis................................................................................. 27

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9.8
QUALITY CONTROL................................................................................... 30
9.9
LIMITATIONS OF THE RESEARCH METHODS...................................... 30
9.10
OTHER ASPECTS ......................................................................................... 32
9.11
SUBJECTS...................................................................................................... 32
9.12
BIAS................................................................................................................ 32
10.
PROTECTION OF HUMAN SUBJECTS .......................................................... 32
11.
MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE 
REACTIONS....................................................................................................... 32
12.
PLANS FOR DISSEMINATING AND COMMUNICATING STUDY 
RESULTS............................................................................................................ 33
13.
REFERENCES .................................................................................................... 33
13.1
PUBLISHED REFERENCES......................................................................... 33
13.2
UNPUBLISHED REFERENCES................................................................... 35
ANNEX 1. LIST OF STAND-ALONE DOCUMENTS.............................................. 36
ANNEX 2. ENCEPP CHECKLIST FOR STUDY PROTOCOLS .............................. 41
ANNEX 3. ADDITIONAL INFORMATION.............................................................. 42

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2.
LIST OF ABBREVIATIONS
CI
Confidence interval
CKD-EPI
Chronic Kidney Disease Epidemiology Collaboration
DPP-4
Dipeptidyl peptidase-4
eGFR
Estimated glomerular filtration rate
EHR
GLP-1RA
Electronic health record
Glucagon-like peptide-1 receptor agonist
HbA1c
Glycosylated hemoglobin
IRB
Institutional Review Board
Scr
Serum creatinine
SGLT2
T2DM
Sodium-glucose co-transporter 2
Type 2 diabetes mellitus
TZD
Thiazolidinediones

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001-MCS-90-124_RD-01 (4.0)
3.
RESPONSIBLE PARTIES

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001-MCS-90-124_RD-01 (4.0)
4.
ABSTRACT
Name of company:
Boehringer Ingelheim
Name of finished medicinal 
product:
Tradjenta
Name of active ingredient:
Linagliptin
Protocol date:
Study
number:
Version/Revision:
Version/Revision date:
20-OCT-2017
1218.182
1.0
Title of study:
Real world glycemic effectiveness of linagliptin (Tradjenta) among type 2 
diabetes mellitus adults by age and renal function
Rationale and 
background:
Patients with type 2 diabetes mellitus (T2DM) have progressive decline in 
renal function and this decline in function is greater than age-related decline 
in patients without diabetes.  Selection of glucose lowering agents is often 
affected by the level of renal function.  Dipeptidyl peptidase-4 (DPP-4) 
inhibitors are approved to treat hyperglycemia among adults with T2DM 
with a wide range of renal function.  In contrast to most other DPP-4 
inhibitors, linagliptin does not require dose adjustment as renal function 
declines.  Without further requirement of dose adjustment, linagliptin affords 
simplicity for chronic management of HbA1c.
 
 Current literature indicate that decline in renal 
function is also associated with glycemic control, hypertension, albuminuria 
and dyslipidemia.  However, there is limited evidence about the real world 
effectiveness and safety of linagliptin across different age and renal functions 
among adults with T2DM. 
For better management of hyperglycemia it is pertinent to understand the 
glycemic effectiveness of linagliptin among adults with T2DM with varying 
age and renal function who are treated in the real world setting.  With a 
potentially larger sample size, it is possible to compare the glycemic 
effectiveness of linagliptin across the range of ages and renal function.  
 
The aim of the current study is to compare real world glycemic effectiveness 
across a range of ages and renal function among adults with T2DM who are 
initiated on linagliptin.  
 
Research question 
and objectives:
The primary study objective is to:

Describe change in HbA1c among adults with T2DM within 60 to 180 
days following initiation of linagliptin across pre-defined  age and renal 
function categories.  Age categories will be defined as: 40 to 54 years, 55 
to 64 years, 65 to 74 years, and ≥ 75 years.  Renal function categories will 

Boehringer Ingelheim
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001-MCS-90-124_RD-01 (4.0)
be defined based on eGFR as: <30 ml/min/1.73m2, 30 to 44 
ml/min/1.73m2, 45 to 59 ml/min/1.73m2, 60 to 89 ml/min/1.73m2, and ≥ 
90 ml/min/1.73m2.
The secondary study objectives are to:

Compare change in HbA1c among adults with T2DM within 60 to 180 
days following initiation of linagliptin across pre-defined age and renal 
function categories. 

Compare the proportion of adults with T2DM who achieve HbA1c < 7.0% 
within 60 to 180 days following initiation of linagliptin across the pre-
defined age and renal function categories.
 
 
 
 
 

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001-MCS-90-124_RD-01 (4.0)
Study design:
Non-interventional study based on existing data 
Population:
Patients with T2DM newly initiated on linagliptin, in the Optum electronic 
health record (EHR) database, aged 40 and older, and with HbA1c values 
during the both the 180 days before and the 60 to 180 days after starting 
linagliptin  
Variables:
Outcomes:

Primary: Change in HbA1c within 60 to 180 days after initiation of 
linagliptin

Secondary: HbA1c goal (< 7.0%) attainment during the 60 to 180 
days after initiation of linagliptin
Covariates:

Age categories: 40 to 54 years, 55 to 64 years, 65 to 74 years, and ≥ 
75 years

Sex

Race

Ethnicity

Geographic region

Pre-index comorbidity score

Pre-index HbA1c value

Pre-index renal function categories (based on the eGFR): <30 
ml/min/1.73m2, 30 to 44 ml/min/1.73m2, 45 to 59 ml/min/1.73m2, 60 
to 89 ml/min/1.73m2, and ≥ 90 ml/min/1.73m2
Data sources:
Optum electronic health record (EHR) data
Study size:
This study is exploratory and not characterized with a priori hypotheses.  All 
patients identified in the data that meet the inclusion criteria and do not meet 
the exclusion criteria will be included in the study population.
A sample size calculation was conducted to determine the minimum number 
of patients that would be required to detect a change in HbA1c of at least 
0.5% among the cohort groups.  Based on previously published studies, it 
was assumed that the standard deviation for the 24-week mean change in 
HbA1c from baseline could range between 1.2  and 1.5.  Assuming a SD of 
1.2 for the change in HbA1c, at least 92 patients would be needed in each of 
the two smallest groups of the cohort of interest (e.g., age, renal function, age 
by renal function) in order to have 80% power to detect a mean difference in 
HbA1c of at least 0.5% using a two-sided test at an alpha of 0.05.  If the 
standard deviation for the change in HbA1c is 1.5, then 143 patients would 
be needed in each of the two smallest groups.
Data analysis:
Primary analysis
All study variables, including pre-index and outcome measures, will be 
analyzed descriptively. Pre-index and outcome measures will be reported for 
the overall study population as well as stratified by predefined subgroups of
patients. 
The following stratifications will be conducted among the overall study 
population:

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
By age categories defined as 40 to 54 years, 55 to 64 years, 65 to 74 
years, and ≥ 75 years

By renal function categories (based on eGFR) defined as: < 30 
ml/min/1.73m2, 30 to 44 ml/min/1.73m2, 45 to 59 ml/min/1.73m2, 60 
to 89 ml/min/1.73 m2, ≥ 90 ml/min/1.73m2, and not available

By race defined as White, African American, Asian, or Other/
Unknown
Secondary analysis 
Comparisons of pre-index characteristics and outcome measures will be 
provided, and appropriate tests will be used based on the distribution of the 
measure.  Continuous measures will be compared using t-tests and 
categorical measures will be compared using chi-square tests. P-values and 
95% confidence intervals will be reported. Reported p-values will not be 
adjusted for multiple comparisons.
In addition to the descriptive analyses described above, multivariable 
modelling will be considered to assess change in HbA1c, controlling for an a 
priori list of covariates. The use of multivariable modelling will be dependent 
on having robust sample size distribution across age and eGFR categories.
 
 
 
 
 
 
 
 
 
 
 
 
 
  
Milestones:
Protocol Completion
Data Extraction, Preparation & QA
Data Analysis
Final Report

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5.
AMENDMENTS AND UPDATES
None

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6.
MILESTONES
Milestone
Planned Date
Optum to deliver first draft of protocol
14 July 2017
BIPI to provide comments on first draft of protocol
28 July 2017
Optum to deliver second draft of protocol
11 August 2017
BI to provide comments on second draft of protocol
25 August 2017
Optum to deliver final protocol
01 September 2017
BIPI approval of final protocol
19 October 2017
Optum to deliver count of qualifying patients in table 
format following delivery of final protocol 
30 November 2017
Optum to deliver descriptive analysis results tables 
12 January 2018
Optum to deliver multivariable model results tables
2 February 2018
Optum to deliver draft report 
2 March 2018
BIPI to provide comments following delivery of draft 
report
16 March 2018
Optum to deliver final report
30 March 2018

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7.
RATIONALE AND BACKGROUND
Patients with type 2 diabetes mellitus (T2DM) have progressive decline in renal function and 
this decline in function is greater than age-related decline in patients without diabetes (1-4).  
Selection of glucose lowering agents is often affected by the level of renal function.  
Dipeptidyl peptidase-4 (DPP-4) inhibitors are approved to treat hyperglycemia among adults 
with T2DM with a wide range of renal function.  In contrast to most other DPP-4 inhibitors
(5), linagliptin does not require dose adjustment as renal function declines.  Without further 
requirement of dose adjustment, linagliptin affords simplicity for chronic management of 
HbA1c. 
 
  Current 
literature indicate that decline in renal function is also associated with glycemic control, 
hypertension, albuminuria and dyslipidemia (9-11). However, there is limited evidence about 
the real world effectiveness and safety of linagliptin across different age and renal functions 
among adults with T2DM. 
Data from clinical studies demonstrate that the expected reduction in HbA1c should be 
similar across the range of ages, but direct comparisons with younger patients are not 
available.  Barnett et al reported on 238 patients aged 70 or older randomized to linagliptin 5 
mg daily (n = 160) or placebo (n=78).  The placebo adjusted mean difference in HbA1c after 
24 weeks was -0.64% (95% CI -0.81 to -0.48) (12).  Inzucchi et al reported on 247 patients 
aged 70 or older randomized to linagliptin (n = 126) or placebo (n = 121) added to basal 
insulin (13).  The placebo adjusted mean difference in HbA1c after 24 weeks was -0.77 (94% 
CI -0.95 to -0.59).  These values are consistent with other studies of linagliptin, but do not 
provide direct comparison data with younger patients.  
Limited data from studies evaluating linagliptin in patients with renal impairment suggest that 
glycemic control is maintained across different levels of renal function.  Groop et al analyzed 
data from three 24 week linagliptin clinical trials (n = 2,262) in which data from 2,143 
subjects were available to calculate renal function using the Modified Diet Renal Disease 
(MDRD) equation (14).  Subjects were divided by renal function into normal renal function 
(eGFR > 90 ml/min/1.73 m2), mild renal impairment (eGFR 60 to < 90 ml/min/1.73 m2) and 
moderate renal impairment (eGFR 30 to < 60 ml/min/1.73m2).  Distribution of subjects 
assigned to linagliptin vs. placebo by renal function category was as follows:  1) normal renal 
function: 870 linagliptin subjects and 342 placebo subjects; 2) mild renal function: 620
linagliptin subjects and 218 placebo subjects; and 3) moderate renal function: 68 linagliptin 
subjects and 25 placebo subjects.  The reduction in HbA1c was consistent across ranges of 
renal function.  However, the number of patients in the moderate renal dysfunction category 
was quite small, so there is greater uncertainty about the magnitude of the HbA1c reduction 
in this group.  These observations support the current proposal to demonstrate comparable 
effects in glycemic effectiveness across the range of renal function categories and provide 
more information in patients with low renal function especially in patients with eGFR < 45 
ml/min/1.73 m2.

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For better management of hyperglycemia it is pertinent to understand the glycemic 
effectiveness of linagliptin among adults with T2DM with varying age and renal function 
who are treated in the real world setting.  With a potentially larger sample size, it is possible 
to compare the glycemic effectiveness of linagliptin across the range of ages and renal 
function. 
 
The aim of the current study is to compare real world glycemic effectiveness across a range 
of ages and renal function among adults with T2DM who are initiated on linagliptin.  
 
 

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001-MCS-90-124_RD-01 (4.0)
8.
RESEARCH QUESTION AND OBJECTIVES
The purpose of this study is to assess glycemic control among adults with T2DM during the 
60 to 180 days following initiation of linagliptin.  
The primary study objective is to:

Describe change in HbA1c among adults with T2DM within the 60 to 180 days following 
initiation of linagliptin across pre-defined age and renal function categories.  Age 
categories will be defined as: 40 to 54 years, 55 to 64 years, 65 to 74 years, and ≥ 75 
years.  Renal function categories will be defined based on eGFR as: <30 ml/min/1.73m2,
30 to 44 ml/min/1.73m2, 45 to 59 ml/min/1.73m2, 60 to 89 ml/min/1.73m2, and ≥ 90 
ml/min/1.73m2.
The secondary study objectives are to:

Compare change in HbA1c among adults with T2DM within 60 to 180 days following 
initiation of linagliptin across pre-defined age and renal function categories.  

Compare the proportion of adults with T2DM who achieve HbA1c < 7.0% within 60 to 
180 days following initiation of linagliptin across the pre-defined age and renal function 
categories.
 
 
 
 
 
 

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9.
RESEARCH METHODS
9.1
STUDY DESIGN
This will be a non-interventional cohort study using existing data from patients in the Optum 
Clinical Database which contains electronic health record (EHR) data from providers across 
the United States.
Real-world EHR data will be used to determine whether there is comparable effectiveness of 
linagliptin 5 mg daily on glycemic effectiveness as determined by the change in HbA1c and
the percentage of patients achieving an HbA1c goal of < 7.0% during the 60 to 180 days
after initiation of linagliptin across a range of age and renal function categories. 
 
 
  
9.2
SETTING
9.2.1
Observation Period
This will be a non-interventional study using existing Optum EHR data for the period of July 
1, 2011 through March 31, 2017. Patients with a written prescription for linagliptin will be 
identified during an identification period starting on January 1, 2012 and ending on 
September 30, 2016.  The date of the first written prescription for linagliptin during the 
identification period will be designated as the index date without a prior prescription for 
linagliptin in the pre-index period.
Baseline characteristics will be evaluated during the 180-day period prior to the index date 
(pre-index period) and on the index date. Outcomes will be assessed during the 180-day
period following the index date (follow-up period). A diagram of the study observation 
period is provided in Figure 1.
Figure 1.  Study Observation Period

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9.2.2
Inclusion Criteria
To be included in the study sample, patients must meet all of the following criteria:

≥ 1 written prescription for linagliptin (Tradjenta®, Jentadueto®, or Jentadueto XR®) 
in the EHR data during the identification period (medication codes for index 
medications in Table 1 of Annex 1, Appendix B)

≥ 40 years of age based on the year of the index date

First active date in the EHR is ≥ 180 days prior to the index date

≥ 1 diagnosis code representing T2DM in the EHR data during the 180-day pre-index 
period or on the index date (diagnosis codes in Annex 1, Appendix A)

≥ 1 HbA1c value during the 180-day pre-index period or on the index date

≥ 1 HbA1c value 60 to 180 days after the index date
9.2.3
Exclusion Criteria
Patients will be excluded from the study sample if they have any of the following:

≥ 1 written prescription, medication administration or medication history record for 
linagliptin or other DPP-4 inhibitor in the EHR data during the 180-day pre-index 
period (medication codes for DDP-4 inhibitors in Table 2 of Annex 1, Appendix B)

≥ 1 written prescription or medication administration for a new antihyperglycemic 
medication other than linagliptin on the index date
New antihyperglycemic medication will be defined as a written prescription or 
medication administration for any antihyperglycemic medication that was not present 
in the patient’s written prescription, medication administration, or medication history 
records during the 180-day pre-index period. Medication codes for antihyperglycemic 
medications are shown in Tables 2 through 13 of Annex 1, Appendix B.  Individual 
antihyperglycemic medications will be distinguished by generic name using the 
column labelled “Medication Name” in Tables 2 through 13.  Combination products 
containing two generic ingredients will be considered as two distinct 
antihyperglycemic medications. 
Note:  This exclusion criterion is designed to exclude patients from the study 
sample if they start a new antihyperglycemic medication other than linagliptin
on the index date.  Patients that start a new antihyperglycemic medication in 
the follow-up period will not be removed from the study sample to avoid 
creating a biased sample.  Addition of a new antihyperglycemic medication 
during follow-up will be evaluated through the sensitivity analysis described in 
Section 9.7.4.4.  While we will be capturing additions of new therapies, 
discontinuation of linagliptin is not able to be accurately measured in 
electronic record data.  The electronic record data capture prescriptions written 
by a prescriber, but it is not possible to know if patients received and adhered 
to their medication and there are no structured data fields to identify if and 
when a medication was discontinued by the patient or the provider.  

≥ 1 diagnosis code or procedure code representing renal transplant, solid organ 
transplant, or bone marrow transplant in the EHR data during the 180-day pre-index 
period or on the index date (diagnosis and procedure codes in Annex 1, Appendix A)

≥ 1 diagnosis code representing malignancy in the EHR data during the 180-day pre-
index period or on the index date (diagnosis codes in Annex 1, Appendix A)

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9.2.4
Evaluation of Patient Sample
Before finalizing the study sample, Optum will assess the sample to determine the number of
patients excluded and remaining due to the exclusion criterion that requires patients to have 
no new antihyperglycemic medications other than linagliptin on the index date.  
If this evaluation shows that the study sample size would be negatively impacted by 
excluding these patients, patients with new antihyperglycemic medications on the index date
will be kept in the sample and a binary variable will be created to flag the patients for further 
subanalysis.  
9.3
VARIABLES
9.3.1
Exposures
An exposure variable will not need to be created for this study since all the study patients will 
have linagliptin exposure on the index date.
9.3.2
Outcomes
All outcomes will be measured in the follow-up period.
9.3.2.1
Primary outcomes
The primary outcome variable, change in HbA1c, as defined below, will be evaluated among 
the overall study sample and stratified across pre-defined age and renal function categories.  
Age categories will be defined as: 40 to 54 years, 55 to 64 years, 65 to 74 years, and ≥ 75
years.  Renal function categories will be defined based on eGFR, , calculated using the 
Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation (17), and 
categorized as:  < 30 ml/min/1.73m2, 30 to 44 ml/min/1.73m2, 45 to 59 ml/min/1.73m2, 60 to 
89 ml/min/1.73m2, and ≥ 90 ml/min/1.73m2.  
The HbA1c variable definitions assume that the majority of patients will have only one 
HbA1c measure during the follow-up period because providers are not likely to perform 
multiple HbA1c tests during a 180-day period.  However, if this assumption turns out to be 
false after examining the data, Section 9.7.3 defines alternative analyses that will be 
conducted.

Change in HbA1c—The change in HbA1c will be calculated for each patient by 
subtracting the patient’s last (most recent) HbA1c value during the pre-index period
(including the index date) from the patient’s last (most recent) HbA1c value 60 to 180 
days after the index date.

Follow-up HbA1c—Follow-up HbA1c will be defined as the last (most recent)
HbA1c value 60 to 180 days after the index date. The number of days from the index 
date to the last (most recent) HbA1c 60 to 180 days after the index date will also be 
recorded.

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9.3.2.2
Secondary outcomes
The secondary outcome variable, HbA1c goal attainment, as defined below, will be evaluated 
among the overall study sample and stratified across pre-defined age and renal function 
categories.  Age categories will be defined as: 40 to 54, 55 to 64, 65 to 74, and ≥ 75 years.  
Renal function categories will be defined based on eGFR as:  < 30 ml/min/1.73m2, 30 to 44
ml/min/1.73m2, 45 to 59 ml/min/1.73m2, 60-89 ml/min/1.73m2, and ≥ 90 ml/min/1.73m2.

HbA1c goal attainment—A binary variable will be created to designate HbA1c goal 
attainment 60 to 180 days after the index date.  HbA1c goal attainment will be defined 
as the presence of an HbA1c value < 7.0% any time 60 to 180 days after the index 
date.

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9.3.3
Covariates
Covariates will include demographic and clinical characteristic variables measured during the 
pre-index period (including the index date) unless otherwise noted. 
9.3.3.1
Demographics

Age—Age will be defined as of the index year.

Age categories—Patients will be categorized to one of the following age groups: 
•
40 to 54 years
•
55 to 64 years
•
65 to 74 years
•
≥ 75 years

Sex—Sex will be captured and recorded.

Race—Race will be categorized as follows:
•
White
•
African American (AA)
•
Asian
•
Other/ Unknown

Ethnicity
•
Hispanic
•
Non-Hispanic
•
Unknown
•
Not Available 

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
Geographic region—The U.S. region will be determined and reported. States will be 
categorized into five geographic regions in accordance with the U.S. Census Bureau’s 
region designations as presented in the table below.
Table 1.  United States Geographic Regions
Region
Division
State
Northeast
New England
CT, MA, ME, NH, RI,  VT
Mid Atlantic
NJ, NY, PA
Midwest
East North Central
IL, IN, MI, OH, WI
West North Central
IA, KS, MN, MO, ND, NE, SD
South
South Atlantic
DC, DE, FL, GA, MD, NC, SC, VA, WV
East South Central
AL, KY, MS, TN
West South Central
AR, LA, OK, TX
West
Mountain
AZ, CO, ID, MT, NM, NV, UT, WY
Pacific
AK, CA, HI, OR, WA
Other
Other
Armed Forces Americas (except Canada), Armed Forces 
(Europe, Canada, Middle East, Africa), Armed Forces 
Pacific, American Samoa, Federated State of Micronesia, 
Guam, Marshall Islands, Commonwealth of the Northern 
Mariana Islands, Puerto Rico, Palau, Virgin Islands
9.3.3.2
Clinical Characteristics

Pre-index Quan-Charlson comorbidity score—Quan-Charlson comorbidity score will 
be calculated based on the presence of diagnosis codes in the pre-index period
(including the index date) (16).  The Quan-Charlson comorbidity score will also be 
categorized into the following groups: zero, one to two, three to four, and five or 
more.

Pre-index comorbid conditions-General comorbid conditions will be defined using the 
Clinical Classifications Software managed by the Agency for Healthcare Research 
and Quality (AHRQ) (17).  This measure generates indicator variables for specific 
disease conditions based on diagnosis codes. The top 20 comorbid conditions will be 
presented.

Pre-index antihyperglycemic medications— Pre-index antihyperglycemic
medications will be identified based on written prescription, medication 
administration, and medication history records in the EHR data during the pre-index 
period. Binary flags, one per medication class and another per distinct medication 
based on generic name, will be created for the antihyperglycemic medications.  
Medication classes will be determined using the “Medication Class” column in Tables 
2 through 13 of Annex 1, Appendix B and will include metformin, sulfonylureas,
meglitinides, thiazolidinediones (TZDs), DPP-4 inhibitors other than linagliptin, 
glucagon-like peptide-1 receptor agonists (GLP-1RA), sodium-glucose co-transporter 
2 (SGLT2) inhibitors, basal insulins, bolus insulins, mixed insulins, alpha glucosidase 
inhibitors, and amylin analogs.  Combination products containing two medication 
classes will be considered as two distinct medication classes.  
Distinct medications will be determined using the “Medication Name” column in
Tables 2 through 13 of Annex 1, Appendix B.  Combination products containing two 
generic ingredients will be considered as two distinct antihyperglycemic medications.

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
Pre-index antihyperglycemic therapy categories—Patients will be categorized into 
one of the following groups based on the medication class for their pre-index 
antihyperglycemic therapies:
•
Metformin only
•
Sulfonylurea only
•
Megilitinde only
•
TZD only
•
GLP-1RA only
•
SGLT2 inhibitor only
•
Basal insulin only
•
Metformin + sulfonylurea
•
Other combination (additional groups will be created for any medication 
class combinations that are present among least 5% of the study sample) 

New antihyperglycemic medication on the index date – This variable will be created 
only if the evaluation of the patient sample described in Section 9.2.4 determines that,
in order to preserve sample size, it is necessary to retain patients in the study if they 
have new antihyperglycemic medications other than linagliptin on the index date.  A 
binary flag will be created to indicate whether or not the patient had ≥ 1 new 
antihyperglycemic medication other than linagliptin on the index date.  A new 
antihyperglycemic medication will be defined as a written prescription or medication 
administration for any antihyperglycemic medication that was not present in the 
patient’s written prescription, medication administration, or medication history 
records during the pre-index period (based on the antihyperglycemic medication 
codes in Tables 2 through 13 of Annex 1, Appendix B).  Individual antihyperglycemic 
medications will be distinguished by generic name using the column labelled 
“Medication Name” in Tables 2 through 13.  Combination products containing two 
generic ingredients will be considered as two distinct antihyperglycemic medications.  
In addition, the specific new medications and medication classes on the index date 
will be captured and reported.  

Number of HbA1c values during the pre-index period (including the index date)—
The number of HbA1c values each patient had during the pre-index period (including
the index date) will be captured for each patient and categorized as follows: only 1 
value, 2 values, or 3 or more values.

Pre-index HbA1c value—The last (most recent) HbA1c value in the pre-index period
(including the index date) will be captured and reported. The number of days from the 
last (most recent) pre-index HbA1c to the index date will also be recorded. 

Pre-index HbA1c category based on 8% threshold—The last (most recent) HbA1c 
value in the pre-index period (including the index date) will be used to categorize 
patients into one of the following categories:  HbA1c < 8% or HbA1c ≥ 8%.

Pre-index HbA1c category based on 9% threshold—The last (most recent) HbA1c 
value in the pre-index period (including the index date) will be used to categorize 
patients into one of the following categories:  HbA1c < 9% or HbA1c ≥ 9%.

Pre-index serum creatinine—The last (most recent) serum creatinine value in the pre-
index period (including the index date) will be captured and reported. 

Pre-index renal function—Pre-index renal function will be assessed using estimated 
glomerular filtration rate (eGFR) which will be calculated using the Chronic Kidney 
Disease Epidemiology Collaboration (CKD-EPI) equation (18).    This equation 

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requires variables for serum creatinine, age, sex, and race but weight and height are 
not required because the results are normalized to 1.73 m2 body surface area (an
accepted average adult surface area) (19).  The last (most recent) serum creatinine 
value in the pre-index period (including the index date) will be used for the serum 
creatinine (Scr) variable.  The CKD-EPI equation (18) to estimate GFR in units of 
ml/min/1.73m2 after incorporating multiplication factors for race and sex into the 
intercept is displayed in Table 2.  For purposes of this eGFR calculation, race will be 
categorized as either AA or non-AA (includes White, Asian and Other/Unknown).
Table 2.  CKD-EPI equation (Adapted from Reference 18)
Race
Sex
Scr 
(mg/dL)
Equation for eGFR in units of ml/min/1.73m2
AA
Female
≤0.7
 = 166  
0.7
.
 (0.993)
AA
Female
> 0.7
 = 166  
0.7
 .
 (0.993)
AA
Male
≤0.9
 = 163  
0.9
.!  
 (0.993)
AA
Male
> 0.9
 = 163  
0.9
 .
 (0.993)
Non-AA
Female
≤0.7
 = 144  
0.7
.
 (0.993)
Non-AA
Female
> 0.7
 = 144  
0.7
 .
 (0.993)
Non-AA
Male
≤0.9
 = 141  
0.9
.!  
 (0.993)
Non-AA
Male
>0.9
 = 141  
0.9
 .
 (0.993)

Pre-index renal function categories—Patients will be categorized into one of the 
following renal function groups based on their eGFR during the pre-index period
(including the index date):
•
< 30 ml/min/1.73m2
•
30 to 44 ml/min/1.73m2
•
45 to 59 ml/min/1.73m2
•
60 to 89 ml/min/1.73m2
•
≥ 90 ml/min/1.73m2
•
Not available

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9.4
DATA SOURCES
This study will use data from Optum’s Clinical Electronic Health Record (EHR) database. 
This database aggregates clinical treatment data from a provider network of over 140,000
physicians at more than 600 hospitals and more than 6,500 clinics. The EHR database 
currently has more than 60 million unique patients across the United States and Puerto Rico.  
The demographics of patients within the Optum EHR data are representative of the United 
States population (Table 3).
The data are provided by more than 46 contributors, including the nation’s leading medical 
groups, integrated delivery networks (IDNs), and hospital chains. Using deterministic 
matching technology, Optum collects the provider data from different platforms and IT 
systems (e.g., Cerner, Epic, GE, McKesson), and then normalizes, validates, and integrates it. 
The result is a longitudinal record that delivers a comprehensive spectrum of health and 
medical information.  Records in the database are updated quarterly.
Table 3.  Optum EHR Data Patient Demographics Compared with the US Population 
Attribute
Optum Lives, 2015
US Healthcare 
Utilizers1
US Population2
Gender
Male
43%
47%
49%
Female 
57%
53%
51%
Age Group
0-9
10%
13%
13%
10-17
7%
10%
11%
18-24
7%
8%
10%
25-34
12%
13%
14%
35-44
12%
12%
13%
45-54
14%
13%
14%
55-64
15%
14%
13%
                                                
1 Source of US estimate of healthcare utilizers by age & sex: US Department of Health & Human Services, Agency for 
Healthcare Research & Quality, Medical Expenditure Panel Survey (MEPS), Projected Expenditure Data File for 2015. 
Percent of US population with non-zero medical expenditures found from MEPS and applied to US population estimate for 
2015 from US Census Bureau, Current Population Survey.
2 Source of estimate of US population totals alone and by insurance type: US Census Bureau, Current Population Survey, 
Annual Social and Economic Supplement, 2015.

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Attribute
Optum Lives, 2015
US Healthcare 
Utilizers1
US Population2
65-74
12%
9%
9%
75+
10%
8%
6%
US Region
Northeast 
13%
18%
Midwest
35%
21%
South
38%
37%
West 
14%
24%
Race/Ethnicity
White
62%
66%
62%
African American
9%
11%
12%
Hispanic
6%
16%
18%
Asian
2%
5%
5%
Other/Unknown
21%
3%
3%
Insurance Coverage
Commercial
66%
61%
58%
Medicare
14%
17%
16%
Medicaid/ Other 
gov't
12%
13%
15%
Uninsured
8%
9%
10%
9.5
STUDY SIZE
This study is exploratory and not characterized with a priori hypotheses.  All patients 
identified in the data that meet the inclusion criteria and do not meet the exclusion criteria 
will be included in the study population.
A sample size calculation was conducted to determine the minimum number of patients that 
would be required to detect a change in HbA1c of at least 0.5% among the cohort groups.  
Based on previously published studies, it was assumed that the standard deviation for the 24-

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week mean change in HbA1c from baseline could range between 1.2 (based on the placebo 
adjusted value in the MARLINA-T2D trial (20)) and 1.5 (based on retrospective studies of 
diabetes treatments using electronic medical record data from across the United States (21).  
Assuming a SD of 1.2 for the change in HbA1c, at least 92 patients would be needed in each 
of the two smallest groups of the cohort of interest (e.g., age, renal function, age by renal 
function) in order to have 80% power to detect a mean difference in HbA1c of at least 0.5%
using a two-sided test at an alpha of 0.05.  If the standard deviation for the change in HbA1c 
is 1.5, then 143 patients would be needed in each of the two smallest groups.
9.6
DATA MANAGEMENT
Optum stores all extracted data on secure servers.  Data files for patients who meet the 
inclusion criteria will be extracted from the database using Oracle® and SAS® -based tools 
and housed on Optum UNIX® servers for programming and analysis by Optum staff.
9.7
DATA ANALYSIS
9.7.1
Feasibility Assessment
Feasibility analyses will include determinations of the number of patients in each potential 
category of age and renal function. Some cells may be too small (e.g. eGFR 30 to 44 
ml/min/1.73m2), but as long as most of the key groups have sufficient numbers (as described 
in Section 9.5 Study Size) of patients this will not detract from the overall results.  The 
number of AA individuals will not be as large as the full data set and as such some of the age 
and eGFR groups will be much smaller than for the whole group. Since the AA subgroup 
analysis is exploratory, having some small groups will not significantly limit the overall 
clinical interpretation even if statistical comparability cannot be confirmed. 
Optum conducted a feasibility analysis using data from the Optum EHR database. There were 
63,499 patients identified with a written prescription for linagliptin between January 1, 2012 
and June 30, 2016. After excluding patients without data available for a six-month pre-index 
period, patients with a prescription for linagliptin or other DPP-4 inhibitor, and patients less 
than 40 years of age, the patient count was reduced to 38,868 patients.
A total of 16,566 patients had at least one HbA1c result during the six-month period prior to 
the index date and at least one HbA1c result during the six-month period after the index date. 
Of these, 16,566 adult patients were newly initiated on linagliptin with baseline and follow-
up HbA1c results, race/ethnicity was available in 15,289 patients, a serum creatinine value 
was available for 15,130 patients, and both race/ethnicity and serum creatinine were available 
for 13,962 patients.
Among the 13,962 patients meeting the criteria and with a baseline serum creatinine, the 
smallest age group cell (40-54 years) had more than 2,500 patients and the cell with age > 75 
years (which is of special interest in this protocol) had more than 3,000 patients.  Thus, for 
the primary end point to assess HbA1c across age ranges, each category is of sufficient size 
to conduct this study.   
 

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9.7.2
Primary Analysis
For the primary analysis, study variables, including pre-index and outcome measures, will be 
analyzed descriptively. Numbers and percentages will be provided for dichotomous and 
categorical variables. Means, medians, and standard deviations will be provided for 
continuous variables. 
Results will be stratified by age, renal function, and race/ethnicity. Pre-index and outcome 
measures will be reported for the overall study population as well as stratified by predefined 
subgroups of patients. 
The following stratifications will be conducted among the overall study population:

By age categories defined as 40 to 54 years, 55 to 64 years, 65 to 74 years, and ≥ 75 
years

By renal function categories (based on eGFR) defined as: < 30 ml/min/1.73m2, 30 to 
44 ml/min/1.73m2, 45 to 59 ml/min/1.73m2, 60 to 89 ml/min/1.73 m2, ≥ 90
ml/min/1.73m2, and not available

By race defined as White, African American, Asian, or Other/Unknown
9.7.3
Secondary Analysis
For the secondary analysis, results will be stratified by age, renal function, and race/ethnicity. 
Comparisons of pre-index characteristics and outcome measures across age, renal function, 
and race/ethnicity strata will be provided, and appropriate tests will be used based on the 
distribution of the measure.  Continuous measures will be compared using t-tests and 
categorical measures will be compared using chi-square tests.  P-values and 95% confidence 
intervals will be reported. Reported p-values will not be adjusted for multiple comparisons.
In addition, as part of the secondary analysis, multivariable modelling will be considered to 
assess change in HbA1c, controlling for an a priori list of covariates. The use of multivariable 
modelling will be dependent on having robust sample size distribution (as defined in Section 
9.5 Study Size) across age and eGFR categories.  
Since the primary outcome of change in HbA1c is calculated using just two timepoints, pre-
index and follow-up, multivariable analysis will be conducted using ordinary least squares 
(OLS).  In the event that the assumption that the majority of patients have only one HbA1c 
measure during the follow-up period turns out to be false and there are multiple HbA1c 
values per patient during this timeframe, mixed models repeated measures (MMRM) will be 
used to control for the correlation of multiple HbA1c observations from the same patient. 
Following standard procedure, regression diagnostics will be performed for each model to 
assess goodness of fit and violations of model assumptions (e.g., multicollinearity, 

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heteroskedasticity). When there are violations of the model, Optum will note them and make 
appropriate corrections to the data (i.e., typically through transformation of either the 
independent or dependent variables) or in the method of estimation.
The following list of covariates will be included in the model provided sufficient sample size 
distribution:

Age categories (40 to 54 years, 55 to 64 hears, 65 to 74 years, and ≥ 75 years) 

Sex

Race

Ethnicity

Geographic region

Pre-index HbA1c

Pre-index renal function categories based on eGFR (< 30 ml/min/1.73m2, 30 to 44 
ml/min/1.73m2, 45 to 59 ml/min/1.73m2, 60 to 89 ml/min/1.73m2, and ≥ 90 
ml/min/1.73m2)

Pre-index Quan-Charlson comorbidity score

Pre-index individual comorbidities (each of the AHRQ top 20 comorbidities identified 
for this population will be included unless the comorbidity is already included in the 
Charlson Comorbidity Index)
Age and renal function categories will be assessed for adequate sample size across categories 
based on the sample size calculation reported in Section 9.5.  If the sample size distribution is 
not sufficient to evaluate age as a categorical variable, age will be included as a continuous 
variable in the model.  The eGFR categories of 45 to 59 ml/min/1.73m2 (Stage 3A kidney 
disease) and 30 to 44 ml/min/1.73m2 (Stage 3B kidney disease) will be collapsed into one 
category (Stage 3 kidney disease) if there is not sufficient sample to evaluate as separate 
categories..   If the sample size distribution is still not sufficient across the eGFR categories, 
then eGFR will be included as a continuous variable in the model.   

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9.8
QUALITY CONTROL
To generate an accurate dataset, Optum incorporates quality assurance checks during dataset 
construction. Multiple checks are used by project team members throughout the dataset 
construction process. An additional final overall verification is performed before the dataset 
is released for analysis. Quality checks used include: 

Verification of sample selection

Version control, when necessary

Checks for dataset merge or join problems

Review of raw data used for creation of constructed variables
•
Checks on record identifiers (e.g., uniqueness) 
•
Checks for missing or out-of-range variables 
•
Other edit or logic checks (e.g., verifying skip patterns, null values, flag 
versus continuous measures) 
•
Visual checks of text fields 

Careful review of analytic variable specifications 
•
Review of all constructed variable definitions by a second analyst 
•
Confirmation of complex variable definitions with study experts (i.e., 
medical director, pharmaceutical specialist)

Multiple checks on each constructed variable 
•
Checks for missing or out-of-range variables 
•
Record-level verification of all data elements for a sample of records 
•
Double programming of select variables 
•
Cross-tabs 
•
Logic checks and code review

Internal team review of the implementation of the dataset construction process
All amendments to the study protocol will be discussed with and agreed upon by the sponsor. 
Actual amendments to the study protocol will be made by the vendor and sent to BIPI for 
review and approval. The date of the amendments will be documented on the cover page of 
the study protocol. 
9.9
LIMITATIONS OF THE RESEARCH METHODS
This study has several limitations inherent to its non-interventional study design.  There may 
be baseline differences in the different age and renal function categories.  In randomized 
clinical trials, these differences are mitigated by randomization.  Among the important 
considerations is if there are substantial differences in baseline HbA1c.  It is well known that 
the change from HbA1c is a function of baseline HbA1c, ie, the higher the baseline HbA1c, 
the greater the change from baseline even with comparable doses of the glucose lowering 
medication.  This observation has been confirmed by Esposito et al (22) in their analyses of 
several DPP4 inhibitors.  
Second, we have selected a follow up window of 6 months rather than a longer window e.g. 
up to 1 year.  Maximal effectiveness is usually achieved by 6 months, although in 
randomized controlled trials HbA1c values may be lower at later time points.  This limitation 
is balanced against the observation that medication persistence at 6 months will likely be 

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better than at 12 months and the fact that we wish to maximize the numbers of patients in 
each of the age and renal function cells.  
Third, duration of T2DM is likely to be longer in patients with lower renal function. 
Increased duration of diabetes is also associated with greater reductions in beta cell function.  
Since one of the mechanisms of DPP4 inhibitor medications is to increase GLP-1 mediated 
insulin secretion, reduced renal function could theoretically be associated with diminished 
glycemic effectiveness.  If the current study supports this observation, this will confound the 
claim of comparable glycemic effectiveness across the range of renal function, but it will be 
an important observation for the management of T2DM patients.  Lajra et al reported on a 
retrospective analysis of 202 patients from two separate trials in which patients with > 10 
years of diabetes were randomized to linagliptin 5 mg (n = 122) or placebo (n = 80)(15).  The 
placebo adjusted mean difference in HbA1c was -0.66% (95% CI -0.85 to -0.38).  No direct 
comparisons were made with shorter duration of diabetes.  However, these data suggest that 
differences in duration of diabetes are unlikely to have a major effect on the data obtained in 
the current proposal.
Fourth, in contrast to many real world evidence studies, this study does not have an active 
comparator.  Since all other DPP4 inhibitors require dose adjustment for declining renal 
function, a comparator study to show similar/different effectiveness would be difficult to 
design to be able to account for all the patient factors that contribute to medication 
distribution, elimination and dosing and likely would require an inordinately large data base.
Fifth, due to the descriptive nature of this study, multiple different comparisons are being 
conducted to evaluate change in HbA1c by age, renal function, and race categories.  We have 
not adjusted our p values for multiplicity but we may perform post hoc adjustments if 
requested by reviewers during the publication process.    
Finally, the findings from this study are expected to have limitations that are common to 
analyses conducted with retrospective EHR data.  The possibility of misclassification exists 
in all studies that rely on retrospective data.  Evidence of conditions and events that are based 
on reported diagnosis codes or procedure codes may not reflect confirmed diagnoses.  While 
EHR data capture prescriptions written by a prescriber, it is not possible to know if patients 
received and adhered to their medication and there are no structured data fields to identify if 
and when a medication was discontinued by the patient or the provider. The EHR database 
does not include the entire medical chart for the patient, which raises the possibility of 
missing data.  Unlike claims data, EHR data do not contain eligibility information and 
continuous eligibility is not able to be assessed.  Services provided to the patient by facilities 
and providers outside of the healthcare systems providing EHR data to Optum will not be 
captured although this is not expected to be a major limitation in this study because the
primary outcome, HbA1c, is likely to be measured by the same provider who wrote the 
patient’s prescription for linagliptin.  However, there may be circumstances where a patient 
may receive care that falls outside of their provider’s electronic record system such as being 
treated at an out of network hospital.
Despite these limitations, EHR data are a powerful source of data for the examination of 
health outcomes in the “real world” setting away from the highly controlled environment of 

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clinical trials.  This study offers the advantage of a large sample of patients with diverse 
demographics and medical histories.
9.10
OTHER ASPECTS
Ethical Approval: This study will be conducted using a limited electronic health record
database which complies with HIPAA. Institutional Review Board and Independent Ethics 
Committee approvals will not be obtained for this study.
9.11
SUBJECTS
Subjects that meet the study inclusion and exclusion criteria defined in Sections 9.2.2 and 
9.2.3 will be selected from the Optum Clinical Electronic Health Record Database.  The 
inclusion and exclusion criteria are designed to result in a sample of patients geographically 
dispersed across the United States with T2DM and newly started on linagliptin.  
9.12
BIAS
As noted in Section 9.9, the possibility of bias exists in all studies that rely on retrospective 
observational data.  Information bias is inherent in the use of EHR databases since electronic 
health records are only available from those healthcare providers and facilities that contribute 
their data to the database.   The study design requires that patients have an HbA1c value 
during the pre-index period and during the follow-up period which could lead to selection 
bias since not all of the patients newly started on linagliptin are included. Further, patient 
selection will be based on diagnosis and procedure codes which may have misclassification 
errors as noted in Section 9.9.  Pre-index variables will be based on the 180-day period prior 
to the index date which may result in misclassification of clinical characteristic variables if 
complete medical history is not documented during the EHR data during this timeframe.  
These limitations will be acknowledged in the final study report.  
10.
PROTECTION OF HUMAN SUBJECTS
This study will be conducted using a limited electronic health record dataset which complies 
with HIPAA.  No patient’s identity or medical records will be disclosed for the purposes of 
this study except in compliance with applicable law.
11.
MANAGEMENT AND REPORTING OF ADVERSE 
EVENTS/ADVERSE REACTIONS
This is a retrospective observational study using secondary data, in which all patient data will 
be collected and analyzed in aggregate. Individual patient safety related information will not 
be captured during this study. Thus, individual safety reporting is not applicable for this 
study.

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12.
PLANS FOR DISSEMINATING AND COMMUNICATING 
STUDY RESULTS
Example table shells showing the data layout for the results for the overall population and for 
the AA subanalysis are provided in the analysis plan.  
Results will be shared with the internal medical/marketing team as soon as they have been 
completed and validated.  Manuscript of all of the results will be considered.  If the AA data 
are robust, this may be a separate publication.
If abstract submission would not hold up publication this may be a consideration.  However, 
getting the data into the public domain so that it can be used to answer external questions is 
important.
Authorship of any publications resulting from this study will be determined on the basis of 
the International Committee of Medical Journal Editors (ICJME) Recommendations for the 
Conduct, Reporting, Editing and Publication of Scholarly Work in Medical Journals.  The 
Uniform Requirements state that all persons designated as authors should qualify for 
authorship, and all those who qualify should have participated sufficiently in the work to take 
public responsibility for appropriate portions of the content. One or more authors should take 
responsibility for the integrity of the work as a whole, from inception to published article.
Authorship credit should be based on:

Substantial contributions to conception and design, or acquisition of data, or analysis 
and interpretation of data

Drafting the article or revising it critically for important intellectual content; and 

Final approval of the version to be published
Authors should meet all three conditions.
13.
REFERENCES
13.1
PUBLISHED REFERENCES
1.
http://www.ajkd.org/issues?issue_key=S0272-6386(16)X0002-9. USRDS (United 
States Renal Data System) 2015 report. accessed 18Oct2016.
2.
https://www.usrds.org/adr.aspx. 2015 CDC report on CKD. Accessed 18Oct2015.
3.
www2.kidney.org/...ckd/p4_class_g1.htm. KDOQI Clinical Practice Guidelines for 
Chronic Kidney Disease. accessed 20 Oct 2016.
4.
Stanton RC. Frontiers in diabetic kidney disease: introduction. Am J Kidney Dis. 
2014;63(2 Suppl 2):S1-2.
5.
https://www.merck.com/product/usa/pi_circulars/j/.../januvia_pi.pdf. accessed 
18Oct2016.
6.
Bryson CL, Ross HJ, Boyko EJ, Young BA. Racial and ethnic variations in 
albuminuria in the US Third National Health and Nutrition Examination Survey 
(NHANES III) population: associations with diabetes and level of CKD. Am J Kidney 
Dis. 2006;48(5):720-6.

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Proprietary confidential information © 2017 Boehringer Ingelheim International GmbH or one or more of its affiliated companies
001-MCS-90-124_RD-01 (4.0)
7.
Derose SF, Rutkowski MP, Crooks PW, Shi JM, Wang JQ, Kalantar-Zadeh K, et al.
Racial differences in estimated GFR decline, ESRD, and mortality in an integrated
health system. Am J Kidney Dis. 2013;62(2):236-44.
8.
Tarver-Carr ME, Powe NR, Eberhardt MS, LaVeist TA, Kington RS, Coresh J, et al.
Excess risk of chronic kidney disease among African-American versus white subjects 
in the United States: a population-based study of potential explanatory factors. J Am 
Soc Nephrol. 2002;13(9):2363-70.
9.
American Diabetes A. 5. Glycemic Targets. Diabetes Care. 2016;39 Suppl 1:S39-46.
10.
American Diabetes A. 7. Approaches to Glycemic Treatment. Diabetes Care. 2016;39 
Suppl 1:S52-9.
11.
American Diabetes A. 9. Microvascular Complications and Foot Care. Diabetes Care. 
2016;39 Suppl 1:S72-80.
12.
Barnett AH, Huisman H, Jones R, von Eynatten M, Patel S, Woerle HJ. Linagliptin 
for patients aged 70 years or older with type 2 diabetes inadequately controlled with 
common antidiabetes treatments: a randomised, double-blind, placebo-controlled trial. 
Lancet. 2013;382(9902):1413-23.
13.
Inzucchi SE, Nauck MA, Hehnke U, Woerle HJ, von Eynatten M, Henry RR. 
Improved glucose control with reduced hypoglycaemic risk when linagliptin is added 
to basal insulin in elderly patients with type 2 diabetes. Diabetes Obes Metab. 
2015;17(9):868-77.
14.
Groop PH, Del Prato S, Taskinen MR, Owens DR, Gong Y, Crowe S, et al.
Linagliptin treatment in subjects with type 2 diabetes with and without mild-to-
moderate renal impairment. Diabetes Obes Metab. 2014;16(6):560-8.
15.
Lajara R, Aguilar R, Hehnke U, Woerle HJ, von Eynatten M. Efficacy and safety of 
linagliptin in subjects with long-standing type 2 diabetes mellitus (>10 years): 
evidence from pooled data of randomized, double-blind, placebo-controlled, phase III 
trials. Clin Ther. 2014;36(11):1595-605.
16.  
Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P, Januel JM, Sundararajan 
V. Updating and validating the Charlson comorbidity index and score for risk 
adjustment in hospital discharge abstracts using data from 6 countries. Am. J. 
Epidemiol. 2011;173(6):676-82.
17.
Clinical Classification Software (CCS) for ICD-9-CM. Agency for Healthcare 
Research and Quality, Rockville, MD. http://www.hcup-
us.ahrq.gov/toolssoftware/ccs/ccs.jsp
18.  
Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, 3rd, Feldman HI, et al. A 
new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604-
12.
19.
National Institue of Diabetes and Digestive and Kidney Diseases.  Estimating 
Glomerular Filtration Rate (GFR).  Accessed on July 3, 2017 at: 
https://www.niddk.nih.gov/health-information/health-communication-
programs/nkdep/lab-evaluation/gfr/estimating/Pages/estimating.aspx#goto-2
20.  
Groop PH, Cooper ME, Perkovic V et al.  Linagliptin and its effects on 
hyperglycemia and albuminuria in patients with type 2 diabetes and renal dysfunction: 
the randomized MARLINA-T2D trial.  Diabetes Obes Metab 2017; Jun 21 [Epub 
ahead of print]
21.
Singhal M, Unni S, Schaerhamer M et al.  Real-world glycemic control from GLP-
1RA therapy with and without concurrent insulin in patients with type 2 diabetes.  
JMCP 2017;23:267-77.

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001-MCS-90-124_RD-01 (4.0)
22.
Esposito K, Cozzolino D, Bellastella G et al.  Dipeptidyl peptidase-4 inhibitors and 
HbA1c target of < 7% in type 2 diabetes: meta-analysis of randomized controlled 
trials.  Diabtes Obes Metab 2011;13(7):594-603.
13.2
UNPUBLISHED REFERENCES

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001-MCS-90-124_RD-01 (4.0)
ANNEX 1. LIST OF STAND-ALONE DOCUMENTS
Appendix A.  Diagnosis and procedure codes for identifying study conditions
Code
Code 
Type
Description
Type
Comment
00868
CPT
Anesthesia for extraperitoneal procedures in 
lower abdomen, including urinary tract; renal 
transplant (recipient)
Allogenic
Anesthesia
50323
CPT
Backbench standard preparation of cadaver 
donor renal allograft prior to transplantation, 
including dissection and removal of 
perinephric fat, diaphragmatic and 
retroperitoneal attachments, excision of 
adrenal gland, and preparation of ureter(s), 
renal vein(s), and renal artery(s), ligating 
branches, as necessary
Allogenic
Associated 
Service
50325
CPT
Backbench standard preparation of living 
donor renal allograft (open or laparoscopic) 
prior to transplantation, including dissection 
and removal of perinephric fat and 
preparation of ureter(s), renal vein(s), and 
renal artery(s), ligating branches, as 
necessary
Allogenic
Associated 
Service
50327
CPT
Backbench reconstruction of cadaver or living 
donor renal allograft prior to transplantation; 
venous anastomosis, each
Allogenic
Associated 
Service
50328
CPT
Backbench reconstruction of cadaver or living 
donor renal allograft prior to transplantation; 
arterial anastomosis, each
Allogenic
Associated 
Service
50329
CPT
Backbench reconstruction of cadaver or living 
donor renal allograft prior to transplantation; 
ureteral anastomosis, each
Allogenic
Associated 
Service
50360
CPT
Renal allotransplantation, implantation of 
graft; without recipient nephrectomy
Allogenic
Transplant
ation
50365
CPT
Renal allotransplantation, implantation of 
graft; with recipient nephrectomy
Allogenic
Transplant
ation
76776
CPT
Ultrasound, transplanted kidney, real time 
and duplex Doppler with image 
documentation
Unknown
Imaging
S2065
HCPCS
Simultaneous pancreas kidney 
transplantation
Allogenic
Transplant
ation
T8610
ICD-10 
Dx
Unspecified complication of kidney transplant
Unknown
Complicatio
n
T8611
ICD-10 
Dx
Kidney transplant rejection
Unknown
Complicatio
n
T8612
ICD-10 
Dx
Kidney transplant failure
Unknown
Complicatio
n
T8613
ICD-10 
Kidney transplant infection
Unknown
Complicatio

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Dx
n
T8619
ICD-10 
Dx
Other complication of kidney transplant
Unknown
Complicatio
n
Z4822
ICD-10 
Dx
Encounter for aftercare following kidney 
transplant
Unknown
Aftercare
Z940
ICD-10 
Dx
Kidney transplant status
Unknown
Transplant 
Status
0TY00Z0
ICD-10 
PCS
Transplantation of Right Kidney, Allogeneic, 
Open Approach
Allogenic
Transplant
ation
0TY00Z1
ICD-10 
PCS
Transplantation of Right Kidney, Syngeneic, 
Open Approach
Syngenic
Transplant
ation
0TY00Z2
ICD-10 
PCS
Transplantation of Right Kidney, Zooplastic, 
Open Approach
Xenograft
Transplant
ation
0TY10Z0
ICD-10 
PCS
Transplantation of Left Kidney, Allogeneic, 
Open Approach
Allogenic
Transplant
ation
0TY10Z1
ICD-10 
PCS
Transplantation of Left Kidney, Syngeneic, 
Open Approach
Syngenic
Transplant
ation
0TY10Z2
ICD-10 
PCS
Transplantation of Left Kidney, Zooplastic, 
Open Approach
Xenograft
Transplant
ation
996.81
ICD-9 
Dx
Complications of transplanted kidney
Unknown
Complicatio
n
V42.0
ICD-9 
Dx
Kidney replaced by transplant
Unknown
Transplant 
Status
55.69
ICD-9 
Proc
Other kidney transplantation
Unknown
Transplant
ation
Appendix B.  Codes for identifying study medications
NDC
Product 
Name
Medication Name
Medication Class
50458014030
INVOKANA
CANAGLIFLOZIN
SGLT2 INHIBITOR
50458014090
INVOKANA
CANAGLIFLOZIN
SGLT2 INHIBITOR
50458014130
INVOKANA
CANAGLIFLOZIN
SGLT2 INHIBITOR
50458014190
INVOKANA
CANAGLIFLOZIN
SGLT2 INHIBITOR
50458054160
INVOKAMET
CANAGLIFLOZIN/METFORMIN 
SGLT2 
INHIBITOR/METFORMIN
50458054360
INVOKAMET
CANAGLIFLOZIN/METFORMIN 
SGLT2 
INHIBITOR/METFORMIN
50458054260
INVOKAMET
CANAGLIFLOZIN/METFORMIN 
SGLT2 
INHIBITOR/METFORMIN

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50458054060
INVOKAMET
CANAGLIFLOZIN/METFORMIN 
SGLT2 
INHIBITOR/METFORMIN
50458094001
INVOKAMET 
XR
CANAGLIFLOZIN/METFORMIN 
SGLT2 
INHIBITOR/METFORMIN
50458094101
INVOKAMET 
XR
CANAGLIFLOZIN/METFORMIN 
SGLT2 
INHIBITOR/METFORMIN
50458094201
INVOKAMET 
XR
CANAGLIFLOZIN/METFORMIN 
SGLT2 
INHIBITOR/METFORMIN
50458094301
INVOKAMET 
XR
CANAGLIFLOZIN/METFORMIN 
SGLT2 
INHIBITOR/METFORMIN
00003142811
FARXIGA
DAPAGLIFLOZIN
SGLT2 INHIBITOR
00310621030
FARXIGA
DAPAGLIFLOZIN
SGLT2 INHIBITOR
00003142711
FARXIGA
DAPAGLIFLOZIN
SGLT2 INHIBITOR
00310620530
FARXIGA
DAPAGLIFLOZIN
SGLT2 INHIBITOR
00310625030
XIGDUO XR
DAPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00310627030
XIGDUO XR
DAPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00310626030
XIGDUO XR
DAPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00310626060
XIGDUO XR
DAPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00310628030
XIGDUO XR
DAPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597015230
JARDIANCE
EMPAGLIFLOZIN
SGLT2 INHIBITOR
00597015237
JARDIANCE
EMPAGLIFLOZIN
SGLT2 INHIBITOR
00597015290
JARDIANCE
EMPAGLIFLOZIN
SGLT2 INHIBITOR
00597015330
JARDIANCE
EMPAGLIFLOZIN
SGLT2 INHIBITOR
00597015337
JARDIANCE
EMPAGLIFLOZIN
SGLT2 INHIBITOR
00597015390
JARDIANCE
EMPAGLIFLOZIN
SGLT2 INHIBITOR
00597018230
GLYXAMBI
EMPAGLIFLOZIN/LINAGLIPTIN
SGLT2 INHIBITOR/DPP4 
INHIBITOR
00597018239
GLYXAMBI
EMPAGLIFLOZIN/LINAGLIPTIN
SGLT2 INHIBITOR/DPP4 
INHIBITOR
00597018290
GLYXAMBI
EMPAGLIFLOZIN/LINAGLIPTIN
SGLT2 INHIBITOR/DPP4 
INHIBITOR
00597016430
GLYXAMBI
EMPAGLIFLOZIN/LINAGLIPTIN
SGLT2 INHIBITOR/DPP4 

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INHIBITOR
00597016439
GLYXAMBI
EMPAGLIFLOZIN/LINAGLIPTIN
SGLT2 INHIBITOR/DPP4 
INHIBITOR
00597016490
GLYXAMBI
EMPAGLIFLOZIN/LINAGLIPTIN
SGLT2 INHIBITOR/DPP4 
INHIBITOR
00597017518
SYNJARDY
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597017560
SYNJARDY
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597016818
SYNJARDY
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597016860
SYNJARDY
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597015918
SYNJARDY
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597015960
SYNJARDY
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597018018
SYNJARDY
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597018060
SYNJARDY
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597029020
SYNJARDY XR
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597029059
SYNJARDY XR
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597029074
SYNJARDY XR
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597030020
SYNJARDY XR
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597030045
SYNJARDY XR
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597030093
SYNJARDY XR
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597028036
SYNJARDY XR
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597028073
SYNJARDY XR
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597028090
SYNJARDY XR
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN

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00597029561
SYNJARDY XR
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597029578
SYNJARDY XR
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
00597029588
SYNJARDY XR
EMPAGLIFLOZIN/METFORMIN
SGLT2 
INHIBITOR/METFORMIN
Appendix C.  Example table shells for analysis of overall study population see the analysis 
plan

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ANNEX 2. ENCEPP CHECKLIST FOR STUDY PROTOCOLS
Not applicable

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ANNEX 3. ADDITIONAL INFORMATION
Not applicable