Northwestern Type 2 Diabetes Mellitus (T2DM) Phenotype: Case and Control Selection from the NUgene Population
Introduction
The cases and controls have been defined to avoid confounding by inclusion of cases with type 1 diabetes and, as much as possible, of
controls at risk for type 2 diabetes which has not, as yet, manifested itself. By doing this, a potential source of bias has been introduced in
that type 2 diabetic subjects who are treated with insulin alone have been excluded, although diabetic subjects on insulin together with one of
the diabetes medications listed above are eligible for inclusion. This approach may select against type 2 diabetic subjects with more
significant degrees of pancreatic beta cell failure.
Challenges
1) Potential case contamination with T1DM (Type 1 Diabetes Mellitus) and Mature Onset Diabetes of the Young (MODY) patients.
2) Potential control contamination with cases. However, an ICD9 code for T2D is likely for diet controlled patients. Also, the family history
exclusion was added to reduce likelihood that patients were too young to have developed the disease yet.
3) Restrictions imposed by inclusion criteria for cases. One difficult area is the problems presented by patients on insulin alone with an
ICD9 code for type 2 diabetes, as some of these patients could represent individuals with type 1 diabetes which has been misclassified as
type 2 diabetes because of age of onset, etc. To address this, we have identified as cases, patients who are on insulin alone, AND: have been
on a type 2 diabetes medication in the past, or do not have a type 1 DM diagnosis, but have at least two visits (on different dates) with the
type 2 DM diagnosis in the problem list or in the encounter diagnosis.
4) Avoiding cases who have medication (e.g., steroid)-induced hyperglycemia.
Patient population
Patients with DNA samples will be selected from the NUgene population. From this population, cases and matched controls will be selected
according to the following parameters. Cases and controls will be Caucasian, African-American, and possibly other races, and matched by
gender, age (± 5 years), and BMI (± 2 units).
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Algorithm created by Bill Lowe, MD, last updated on 8/19/08 by Abel Kho, MD
Document last updated by Jennifer S. Allen & May Law on 8/27/2008
Algorithm for the Identification of Subjects with Type 2 Diabetes
Patient Population
T2DM ICD9 code(s)
Treated with insulin
medication
Never on T2DM
medication
T1DM ICD9
code(s) & <2
diagnosis^ date
No T2DM and
T1DM ICD9 codes
Treated with T2DM
medication
No DM medication
but abnormal lab*
Treated with T2DM
medication & have an
abnormal lab*
On T2DM
medication in past
No T1DM ICD9
code & ≥2
diagnoses^ dates
Type 2 Diabetes Cases
* Random glucose > 200mg/dl, Fasting glucose > 125 mg/dl, or hemoglobin A1c ≥6.5%.
^ Encounter or problem list diagnoses only (all other diagnoses in this chart could also include diagnoses in the medical history)
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Algorithm created by Bill Lowe, MD, last updated on 8/19/08 by Abel Kho, MD
Document last updated by Jennifer S. Allen & May Law on 8/27/2008
Identification of T2D Cases: 2 groups
1) Identification of patients who already have a T2D diagnosis
Step 1: Include patients with Type 2 Diabetes diagnosis based on ICD9 codes
(excluding those with ketoacidosis codes)
Table 1: Type 2 Diabetes ICD9 codes meeting inclusion criteria.
Description
ICD9 Code
Diabetes with other coma
250.30
250.32
Diabetes with hyperosmolarity
250.20
250.22
Diabetes with unspecified complication
250.90
250.92
Diabetes with other unspecified manifestation
250.80
250.82
Diabetes with peripheral circulatory disorder
250.70
250.72
Diabetes with neurological manifestations
250.60
250.62
Diabetes with opthalmic manifestations
250.50
250.52
Diabetes with renal manifestations
250.40
250.42
Diabetes mellitus without mention of complication
250.00
250.02
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Algorithm created by Bill Lowe, MD, last updated on 8/19/08 by Abel Kho, MD
Document last updated by Jennifer S. Allen & May Law on 8/27/2008
Step 2: Exclude patients (currently) treated only with insulin AND have never been on a
type 2 diabetes medication, and: diagnosed with T1DM, or even if not diagnosed with
T1DM, diagnosed with T2DM on <2 dates in an encounter or problem list.
Table 2. Prescribed type 2 diabetes medications meeting patient inclusion criteria:
Drug class
Brand name
Generic name
Sulfonylureas
acetohexamide
Sulfonylureas
tolazamide
Sulfonylureas
Diabinese
chlorpropamide
Sulfonylureas
Glucotrol
glipizide
Sulfonylureas
Glucotrol XL
glipizide
Sulfonylureas
Micronase
glyburide
Sulfonylureas
Glynase
glyburide
Sulfonylureas
Diabeta
glyburide
Sulfonylureas
Amaryl
glimepiride
Meglitinides
Prandin
repaglinide
Meglitinides
Starlix
nateglinide
Biguanides
Glucophage
metformin
Thiazoldinediones
Avandia
rosiglitazone
Thiazoldinediones
ACTOS
pioglitazone
Thiazoldinediones
troglitazone
Alpha-glucosidase inhibitors
Precose
acarbose
Alpha-glucosidase inhibitors
Glyset
miglitol
DPPIV inhibitor
Januvia
sitagliptin
Injectables
Byetta
exenatide
Table 3*. Prescribed medications meeting patient exclusion criteria unless one or more of the
medications listed above is also prescribed:
Drug class
Brand name
Generic name
Injectables
Insulin
Insulin
Injectables
Symlin**
Pramlintide
Diabetic Insulin Supplies
* Limits potential case contamination with T1D patients.
** Exclude if patient is on this alone or in combination with insulin only.
2) Identification of patients who do not yet have a T2D diagnosis
Step 1: Include patients with hemoglobin A1C lab value ≥ 6.5%, fasting glucose > 125
mg/dl or random glucose > 200 mg/dl AND prescribed one of the medications (or combinations
thereof) listed in Table 2.
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Algorithm created by Bill Lowe, MD, last updated on 8/19/08 by Abel Kho, MD
Document last updated by Jennifer S. Allen & May Law on 8/27/2008
Identification of T2D Controls:
Patients must meet all of the following criteria:
1) Have had at least 2 clinic visits (face-to-face outpatient clinic encounters).
2) Have not been assigned an ICD9 code for diabetes (type 1 or type 2) or any diabetesrelated condition (See codes from Tables 1 and 4)
Table 4. ICD9 codes to exclude potential controls (in addition to Table 1).
Description
ICD9 Code
Diabetes mellitus type 1 & 2
250.xx
Impaired fasting glucose
790.21
Impaired oral glucose tolerance test
790.22
Abnormal glucose not otherwise specified
790.2, 790.29
Abnormal glucose during pregnancy
648.8x
Gestational diabetes
648.0x
Glycosuria
791.5
Dysmetabolic syndrome X
277.7
Family history of diabetes mellitus
V18.0
Screening for diabetes mellitus
V77.1
3) Have not been prescribed insulin or Pramlintide (See Table 3), or any medications for
diabetes treatment (See Table 2), or diabetic supplies such as those for medication
administration or glucose monitoring.
4) Do not have a reported (random or fasting) blood glucose ≥ 110mg/dl and have had at
least 1 glucose measurement
5) Do not have a reported hemoglobin A1c ≥ 6.0%
6) Do not have a reported family history of diabetes (type 1 or type 2) (Note – this
information will be available in the EMR for some patients, but not others. However, this
data would be available on the entire NUgene population via participant questionnaires.)
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Algorithm created by Bill Lowe, MD, last updated on 8/19/08 by Abel Kho, MD
Document last updated by Jennifer S. Allen & May Law on 8/27/2008
Additional data to be collected
-
Alcohol use
Smoking
Pulse
Blood pressure
Random glucose values
Possible variables for quantitative trait analysis:
- Weight
- BMI
- Fasting glucose levels
- HbA1C
- Cholesterol levels
- Triglyceride level
- WBCs
- Hematocrit levels
6 of 6
Algorithm created by Bill Lowe, MD, last updated on 8/19/08 by Abel Kho, MD
Document last updated by Jennifer S. Allen & May Law on 8/27/2008