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Two Risk-Scoring Systems for Predicting Incident Diabetes Mellitus in U.S. Adults Age 45 to 64 Years

Henry S. Kahn, MD; Yiling J. Cheng, MD, PhD; Theodore J. Thompson, MS; Giuseppina Imperatore, MD, PhD; and Edward W. Gregg, PhD
[+] Article and Author Information

From the Centers for Disease Control and Prevention, Atlanta, Georgia.


Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily reflect the opinions or views of the ARIC Study or the National Heart, Lung, and Blood Institute, nor do they necessarily represent the official position of the Centers for Disease Control and Prevention.

Acknowledgment: The authors thank the ARIC investigators, coordinating center, and volunteer participants and the staff of the National Heart, Lung, and Blood Institute, who authorized sharing of the limited-access longitudinal data sets.

Financial Support: By the Centers for Disease Control and Prevention.

Potential Financial Conflicts of Interest: None disclosed.

Reproducible Research Statement:Study protocol (ARIC): Available at http://www.cscc.unc.edu/aric/index.php. Statistical code (secondary analysis): Available from Dr. Kahn (hkahn@cdc.gov). Data set (ARIC): Available through a limited-access distribution agreement (http://www.cscc.unc.edu/aric/utility/docfilter.php?study=aric&filter_type=datadist).

Requests for Single Reprints: Henry S. Kahn, MD, CDC Mail Stop K-10, 4770 Buford Highway Northeast, Atlanta, GA 30341; e-mail, hkahn@cdc.gov.

Current Author Addresses: Drs. Kahn, Cheng, Imperatore, and Gregg and Mr. Thompson: Centers for Disease Control and Prevention, CDC Mail Stop K-10, 4770 Buford Highway Northeast, Atlanta, GA 30341.

Author Contributions: Conception and design: H.S. Kahn, Y.J. Cheng, G. Imperatore.

Analysis and interpretation of the data: H.S. Kahn, Y.J. Cheng, G. Imperatore, E.W. Gregg.

Drafting of the article: H.S. Kahn.

Critical revision of the article for important intellectual content: H.S. Kahn, Y.J. Cheng, G. Imperatore, E.W. Gregg.

Final approval of the article: H.S. Kahn, Y.J. Cheng, G. Imperatore, E.W. Gregg.

Provision of study materials or patients: H.S. Kahn, Y.J. Cheng.

Statistical expertise: Y.J. Cheng, T.J. Thompson.

Collection and assembly of data: H.S. Kahn, Y.J. Cheng.


Ann Intern Med. 2009;150(11):741-751. doi:10.7326/0003-4819-150-11-200906020-00002
Text Size: A A A

Background: Simple prediction scores could help identify adults at high risk for diabetes.

Objective: To derive and validate scoring systems by using longitudinal data from a study that repeatedly tested for incident diabetes.

Design: Prospective cohort, divided into derivation and validation samples.

Setting: The ARIC (Atherosclerosis Risk in Communities) study, which followed participants for 14.9 years beginning in 1987 to 1989.

Participants: 12 729 U.S. adults (baseline age, 45 to 64 years; 22.8% black). Follow-up was 96.1% at 5 years and 72.2% at 10 years.

Measurements: Anthropometry, blood pressure, and pulse (basic system) plus a fasting blood specimen assayed for common analytes (enhanced system). Diabetes was identified in 18.9% of participants. Risk score integer points were derived from proportional hazard coefficients associated with baseline categorical variables and quintiles of continuous variables.

Results: The basic scoring system included waist circumference (10 to 35 points); maternal diabetes (13 points); hypertension (11 points); and paternal diabetes, short stature, black race, age 55 years or older, increased weight, rapid pulse, and smoking history (≤8 points each). The enhanced system included glucose (6 to 28 points); waist circumference (5 to 21 points); maternal diabetes (8 points); and triglycerides, black race, paternal diabetes, low high-density lipoprotein cholesterol concentration, short stature, high uric acid, age 55 years or older, hypertension, rapid pulse, and nonuse of alcohol (≤7 points each). When applied to the validation sample, ascending quintiles of the basic system were associated with a 10-year incidence of diabetes of 5.3%, 8.7%, 15.5%, 24.5%, and 33.0%, respectively. Quintiles of the enhanced system were associated with a 10-year incidence of 3.5%, 6.4%, 11.5%, 19.3%, and 46.1%.

Limitations: The risk scoring systems had no question regarding previous gestational diabetes, and knowledge of parental diabetes may be uncertain. The analyzed cohort was restricted by age and race; the systems may be less effective in other samples.

Conclusion: Basic information identified adults at high risk for diabetes. Additional data from fasting blood tests better identified those at extreme risk.

Primary Funding Source: Centers for Disease Control and Prevention.

Figures

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Figure 1.
Study flow diagram.

ARIC = Atherosclerosis Risk in Communities.

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Figure 2.
Scoring sheet for the basic diabetes prediction model.

The model does not require blood analysis data. Developed from a derivation sample of 9587 persons age 45 to 64 years who did not have diabetes at baseline.

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Figure 3.
Scoring sheet for the enhanced diabetes prediction model.

The model includes data from a fasting blood sample. Developed from a derivation sample of 9587 persons age 45 to 64 years who did not have diabetes at baseline. HDL = high-density lipoprotein.

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Figure 4.
Receiver-operating characteristic curves generated from testing a risk-scoring system in an independent validation sample of 3142 adults from the ARIC Study.

The ARIC-derived basic system is compared with the DESIR clinical diabetes risk score (20) (neither required a blood sample), and the ARIC-derived enhanced system is compared with the Framingham simple clinical model (21) (both required a fasting blood sample). ARIC = Atherosclerosis Risk in Communities; DESIR = Epidemiologic Study on the Insulin Resistance Syndrome.

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Figure 5.
Estimated 10-year diabetes incidence (±SE) in the independent validation sample (n= 3142), by quintile of prediction scores in the basic or enhanced scoring system.
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Appendix Figure 1.
Scoring sheet for the basic diabetes prediction model.

See the Appendix for further information. The model does not require blood analysis data. Developed from the full derivation sample of the Atherosclerosis Risk in Communities Study: 12 729 persons age 45 to 64 years who did not have diabetes at baseline.

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Appendix Figure 2.
Scoring sheet for the enhanced diabetes prediction model.

See the Appendix for further information. The model includes data from a fasting blood sample. Developed from the full derivation sample of the Atherosclerosis Risk in Communities Study: 12 729 persons age 45 to 64 years who did not have diabetes at baseline. HDL = high-density lipoprotein.

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Comments

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Pulse Mass Index for Predicting Diabetes and Cardiovascular Risk
Posted on June 6, 2009
Enrique J. Sánchez-Delgado
Hospital Metropolitano Vivan Pellas, Managua, Nicaragua
Conflict of Interest: None Declared

Pulse Mass Index for Predicting Diabetes, Cardiovascular Risk and effects of anti diabetic drugs

Henry S. Kahn et al developed an scoring system for predicting Diabetes mellitus which includes, among other parameters, stature or height, increased weight and rapid pulse. These are the component of both, the Body Mass Index and the Pulse Mass Index.

I reported ten years ago in Lancet March 13, 1999, that the Pulse Mass Index (Resting Heart Rate multiplied by the Body Mass Index and divided by 1730) has a very high and direct correlation with the global cardiovascular risk as calculated by the Framingham Risk Score, and that persons with a Pulse Mass Index of 1.3 or over, have a high probability to be at high global cardiovascular risk.

Now, in this issue of Annals (June 2, 2009), the study by Kahn et al demonstrates that the elements of the Pulse Mass Index are also useful for predicting Diabetes mellitus.

In this issue I also comment that cardiovascular or metabolic drugs that reduce or not increase the Pulse Mass Index, like Beta blockers or metformin, tend to improve the long term cardiovascular prognosis, contrary to drugs like rapid acting vasodilators that increase pulse rate, retain water and do not reduce mortality, or like glitazones that increase weight or intensive glycemic control with glitazones and insulin, that do not improve the long term cardiovascular prognosis when associated with weight gain or hypoglycaemia (and related tachycardia), so that the Pulse Mass Index can also be of help to predict the potential benefits or adverse reactions of cardiovascular and anti diabetic drugs.

Prof. Enrique Sánchez-Delgado MD

Internist-Clinical Pharmacologist

Director of Medical Education

Hospital Metropolitano Vivian Pellas

Managua, Nicaragua

Conflict of Interest:

None declared

No Title
Posted on June 18, 2009
Matthias B. Schulze
Technische Universität München and German Institute of Human Nutrition Potsdam-Rehbruecke, Germany
Conflict of Interest: None Declared
No Comment
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