Description
Multianalyte assay with algorithm analysis (MAAA) tests have been developed to predict diabetes risk. The PreDx^® Diabetes Risk Score (DRS) is an MAAA that is intended to predict the 5-year risk of Type 2 diabetes via a composite of 7 serum biomarkers that are combined via a proprietary algorithm to generate a risk score. The proposed use is to identify patients at greater risk of developing Type 2 diabetes and to potentially target preventive interventions at patients with the highest risk.

The PreDx Diabetes Risk Score has been evaluated in predicting risk of diabetes. In reports of 2 patient cohorts, the area under the curve for predicting progression to diabetes ranged from 0.78 to 0.84. This suggests good overall predictive ability, but conclusions about the predictive value of the diabetes risk score are limited by the lack of validation by independent research groups and testing in a wider variety of patient populations. The evidence is insufficient to determine the comparative accuracy of the PreDx^® DRS with other formal prediction models for diabetes.

There is a lack of evidence on the clinical utility of the PreDx^® DRS. No published studies were identified that used the risk score to select patients for preventive interventions. As a result, it is not known how this instrument will perform in targeting preventive interventions to patients who will benefit the most, nor is it known how this risk score compares with other methods for selecting high-risk patients. No published literature was found on MAAAs other than the PreDx DRS. Therefore, the use of MAAAs to predict diabetes risk, including, but not limited to, the PreDx^® DRS, is considered investigational.

Background
Type 2 diabetes mellitus is a highly prevalent disorder that is associated with an extremely high degree of morbidity and mortality. The true prevalence of Type 2 diabetes in the United States is uncertain due to a lack of population screening, but an estimated prevalence of 8.2% was reported in 2006.¹ The incidence has been increasing rapidly over the last several decades, and current trends indicate that this increase will continue.² Projections have estimated that the prevalence in the United States will reach 11.5% in 2011, 13.5% in 2021 and 14.5% in 2031.³

Therefore, there is an urgent public health need to counter this trend. The potential to improve outcomes and reduce costs by preventing the onset of diabetes is vast. To accomplish this, accurate risk prediction methods may be helpful to identify populations with the highest risk of diabetes. Identification of patients at high risk could then be followed by preventive interventions targeted at high-risk individuals.

Predicting Risk of Type 2 Diabetes
There are a variety of known factors that predict risk of Type 2 diabetes. The most direct are measures of glucose metabolism, such as fasting glucose, oral glucose tolerance testing and hemoglobin A1C (HgA1C). For patients with impaired fasting glucose or impaired glucose tolerance, there is a high rate of progression to diabetes. Approximately 10% of these patients will progress to diabetes each year, and by 10 years, more than 50% will have progressed to diabetes.⁴ 

Other risk factors for diabetes include family history, ethnicity, lifestyle factors, dietary patterns and numerous different laboratory parameters. A history of diabetes in the immediate family has long been recognized as one of the strongest predictors of diabetes. Regarding ethnicity, the risk of diabetes is increased 1.34 times for blacks, 1.86 times for Hispanics and 2.26 times for Asians.⁵ A sedentary lifestyle, cigarette smoking and dietary patterns that include sweetened foods and beverages have all been positively associated with the development of diabetes. In addition, there are numerous nonglucose laboratory parameters that are associated with the risk of diabetes. These include inflammatory markers, lipid markers, measures of endothelial dysfunction, sex hormones and many others.^6,7

Formal risk prediction instruments have combined clinical, laboratory and genetic information to improve and refine the predictive ability of single factors. Many different formal risk prediction models have been developed. These models vary in the number and type of factors examined and in the intended use of the instrument.⁸ For example, some prediction instruments consider the full range of clinical, biochemical and genetic factors to derive the most accurate predictive model.⁹ Others, such as the Indian Risk Score, the Griffin Risk Score and the FINDRISC score, use easily available clinical information without any laboratory markers to facilitate implementation as a widespread screening tool in areas of low resources.^10,11,12

In general, the available models have been shown to have good predictive ability, but most of them have not been externally validated. There is some evidence that directly compares the predictive accuracy of different measures, but there is insufficient comparative research to determine the optimal model. There is evidence that different models have different accuracy depending on the population tested. Also, relatively simple models have performed similarly to more complex models, and genetic information seems to add little over readily available clinical and metabolic parameters.¹³ 

Interventions to Prevent Type 2 Diabetes
A number of intervention trials have established that both lifestyle interventions and medications are effective in preventing the onset of Type 2 diabetes in high-risk individuals. These trials have selected patients at high risk for diabetes, but have used single or several clinical factors, such as impaired glucose metabolism, as selection factors, rather than formal risk prediction instruments. The largest reduction in diabetes incidence has been found for intensive lifestyle interventions that combine exercise and diet. There is a lesser effect for interventions with a single component and for interventions with medications.

A Cochrane review on the efficacy of lifestyle interventions to prevent Type 2 diabetes was published in 2008.¹⁴ This review included 8 randomized trials that compared exercise and dietary interventions to standard therapy in patients at high risk for diabetes. There was a 37% reduction in the incidence of diabetes for the intervention cohort when a combined diet/exercise intervention was used, but there were not significant effects noted for an exercise-only or a diet-only intervention.

Another systematic review and meta-analysis evaluated the efficacy of medications for preventing progression to Type 2 diabetes.¹⁵ This review included 10 studies of oral hypoglycemic agents and 15 studies of injectable agents. Oral hypoglycemic agents and orlistat were found to be effective in reducing progression to diabetes compared with usual care. In the largest trials, with follow-up of greater than 2 years, metformin (relative risk [RR], 0.69; 95% confidence interval [CI], 0.57 to 0.83), acarbose (RR = 0.75; 95% CI, 0.63 to 0.90), troglitazone (RR = 0.45; 95% CI, 0.25 to 0.83) and orlistat (hazard ratio, 0.63; 95% CI, 0.46 to 0.86) were efficacious in decreasing diabetes incidence compared with placebo. Evidence for other medication such as statins, fibrates, antihypertensive agents and estrogen was inconclusive. 

The largest randomized trial of preventive interventions was the Diabetes Prevention Program trial.¹⁶ This trial enrolled 3,234 obese patients with a high risk of diabetes as defined by body mass index level, fasting glucose and 2-hour postprandial glucose levels. Participants were randomized to 1 of 3 groups: an intensive lifestyle intervention, a medication intervention consisting of metformin (850 mg twice per day) or a placebo control with information provided on diet and exercise. After a mean follow-up of 3 years, the incidence of diabetes was significantly reduced by 58% in the intensive lifestyle intervention group and by 31% in the metformin group. A follow-up observational study concluded that the bulk of the benefit persisted for at least 10 years following completion of the trial.¹⁷

PreDx^® DRS
The PreDx^® DRS^™ (Tethys Bioscience^®, Emeryville, CA) is a commercially available MAAA that is intended to determine the 5-year risk of developing Type 2 diabetes. The risk score is based on 7 biomarkers that are obtained by a peripheral blood draw:

• HgA1C
• Glucose 
• Insulin 
• C-reactive protein 
• Ferritin 
• Adiponectin 
• Interleukin-2 receptor alpha

The results of these biomarkers are combined with age and sex to produce a quantitative risk score that varies from 0 to 10. Results are reported as the absolute 5-year risk of developing Type 2 diabetes and the relative risk compared with age- and sex-matched controls. 

As of the most recent update, the PreDx DRS is no longer commercially available.

Regulatory Status
The biomarkers included in the Pre-Dx^® Diabetes Risk Score are not subject to U.S. Food and Drug Administration (FDA) approval. Laboratories performing these tests are subject to Clinical Laboratory Improvement Amendment (CLIA) standards for laboratory testing.

Policy
The use of multianalyte panels with algorithmic analysis (MAAA) for the prediction of Type 2 diabetes is investigational/unproven therefore considered NOT MEDICALLY NECESSARY.

Policy Guidelines
There is a CPT code for this MAAA —

81506: Endocrinology (Type 2 diabetes), biochemical assays of seven analytes (glucose, HbA1C, insulin, hs-CRP, adiponectin, ferritin, interleukin 2-receptor alpha), utilizing serum or plasma, algorithm reporting a risk score.

Benefit Application
BlueCard/National Account Issues
All Pre-Dx^® Diabetes Risk Score tests are processed at the Tethys Clinical Laboratory in Emeryville, CA. Providers receive Styrofoam coolers and ice packs to use for overnight shipping of the specimens to the laboratory.

Rationale
Prediction of Type 2 Diabetes
Does the PreDx^® Diabetes Risk Score improve the ability to predict progression to diabetes, compared with standard clinical measurements?

The development and validation of the PreDx^® Diabetes Risk Score (DRS) has been described in a series of manufacturer-sponsored studies.^18,19,20 Kolberg et al. first described the derivation of this risk score in 2009, using the Danish Inter99 patient cohort.¹⁸ This cohort consists of 61,031 subjects aged 30 to 60 years old and was intended to estimate the 5-year risk of progression to Type 2 diabetes. The authors identified 64 candidate biomarkers that had support in the literature and that met study quality control indicators. They applied multiple logistic regression approaches to select the biomarkers with the greatest predictive ability. Validation of the model with the same cohort was performed by the bootstrapping method.

The final model included 6 biomarkers: glucose, insulin, C-reactive protein, ferritin, adiponectin and Interleukin-2 receptor alpha. The area under the curve (AUC) for the final fitted model was 0.78, and the bootstrapping estimate for AUC was similar at 0.76. The risk score was compared with single variables and simple combinations of variables. The best single predictor was the oral glucose tolerance test with an AUC of 0.79, which was not significantly different from the DRS. For the other single or combined variables, the AUC varied from 0.65 to 0.75. The DRS was superior to other single or combination variables, except for the 2-hour insulin level, which was not significantly different.

In a separate publication by the same research group using the same overall population of the Inter99 cohort, the model was validated in a different way.²⁰ In this nested case-control design, 202 participants who progressed to Type 2 diabetes were compared with 597 controls randomly selected from all participants who did not progress. The PreDx^® logistic model in this study consisted of the previously derived 6 biomarkers with the addition of hemoglobin A1c (HgA1c). The AUC of the fitted model was 0.84. This was superior to single biomarkers, which had AUCs that ranged from 0.70 to 0.77, and was also superior to a noninvasive clinical model that had an AUC of 0.77. The absolute 5-year risk of progression to diabetes for patients with a low DRS (< 4.5) was 1%, which rose to 7% for patients with a moderate DRS (≥ 4.5 and < 8), and to 24% for patients with a high DRS (≥ 8.0). 

A second validation study used a separate cohort from the prospective Botnia study.¹⁹ This was a cohort of 2,770 people who were at increased risk of developing Type 2 diabetes due mainly to family history. Outcome data and biomarker data were available for 2,350 individuals. The AUC for the validation set was 0.78, which was lower than the AUC of 0.84 obtained for the training set. The absolute 5-year risk of progression to diabetes for patients with a low DRS (< 4.5) was 1.1% (95% CI, 0.5% to 1.6%), which rose to 4.0% (95% CI, 2.3% to 5.7%) for patients with a moderate DRS (≥ 4.5 and < 8) and to 12.7% (95% CI, 7.0% to 18.1%) for patients with a high DRS (≥ 8.0). Reclassification analysis was also performed using fasting glucose and oral glucose tolerance testing (OGTT) as baseline. The net reclassification index of 0.20 indicated that the DRS performed better than glucose and OGTT. The main advantage of the DRS was in reclassifying patients with abnormal glucose and OGTT into lower risk levels after application of the DRS.

How does the PreDx^® DRS compare with other diabetes risk scores in predicting the future risk of Type 2 diabetes?

There is a body of literature on the comparative accuracy of different DRSs. However, the most studies that directly compare different risk scores do not include the PreDx^® DRS as one of the comparators. For example, Abbasi et al. performed a systematic review and independent validation of 12 different risk models identified in the literature but did not include the PreDx^® score.¹³ In another publication evaluating the validity of different risk models, a total of 5 risk scores were reviewed, but the PreDx^® score was not included.⁴ Kegne et al. evaluated 12 noninvasive predictive models, not including the PreDx score, for diabetes in the EPIC-InterAct case-cohort sample, which included 27,770 individuals from 8 European countries, of whom 12,403 had incidence diabetes.²¹ The authors reported good discrimination overall, with C statistics ranging from 0.76 (95% confidence interval [CI], 0.72 to 0.80) to 0.81 (95% CI, 0.77 to 0.84). 

Noble et al. conducted a systematic review of DRSs and included the Inter99 Danish cohort study score from which the PreDx^® score was derived, but the authors do not specify that the PreDx^® score is specifically used.⁸ The authors make no direct comparisons between risk scores, noting that direct comparisons are precluded by heterogeneity in the patient populations, clinical outcomes reported and intended context of use, among other factors. 

Within the manufacturer-sponsored validation studies, there were limited comparisons of the PreDx^® score with other risk models.^19,20 PreDx^® score was compared with single markers and simple combinations of markers and was superior to most of these comparators. Lyssenko et al.¹⁹ compared the performance of the DRS with 2 other risk prediction scores, the Framingham score and the San Antonio Heart risk score. The Framingham score had an AUC of 0.76, while the San Antonio score had an AUC of 0.77, neither of which were significantly different from the DRS, which had an AUC of 0.78.

Subsequently, Rowe et al. compared the PreDx^® score with other clinical variables used for diabetes-risk prediction in a different patient population. The Insulin Resistance Atherosclerosis Study cohort was a multiethnic U.S. cohort, including 722 patients,²² which was designed to evaluate insulin resistance and cardiovascular risk factors and disease states in different U.S. ethnic groups and varying states of glucose tolerance. This study compared the 5-year risk of Type 2 diabetes as estimated by the DRS with other risk-assessment tools, including fasting glucose, body mass index (BMI), fasting insulin, the homeostasis model assessment-estimated insulin resistance (HOMA-IR) index and the OGTT. Test performance was assessed by receiver operator characteristic curve analysis, with AUC reported. In the whole cohort, the DRS had a significantly higher AUC than fasting glucose alone (0.762 vs. 0.711, p = 0.003), fasting insulin alone (0.690, p = 0.003), HOMA-IR (0.716, p = 0.03) and BMI (0.671, p < 0.001). It was not statistically different from the 2-hour glucose tolerance test. 

Section Summary
The PreDx^® DRS has been tested in 2 different prospective cohorts of patients, with reported AUCs for prediction of diabetes of 0.78 and 0.84, indicating good overall accuracy for predicting progression to diabetes. It has been evaluated in 1 U.S. cohort, but has otherwise not been tested in a wide range of patient populations. As a result, there is some uncertainty in the predictive accuracy and generalizability of the risk score.

The evidence is insufficient to determine the comparative efficacy of the PreDx^® score compared with other diabetes risk scores. The single study that compared the PreDx^® score with 2 established measures (Framingham diabetes risk score, San Antonio Heart diabetes risk score) reported that the overall accuracy, as defined by AUC for predicting progression to diabetes, did not differ significantly among the 3 measures. A study in a U.S. cohort of patients suggested that the PreDx^® score may better predict diabetes than several individual risk factors alone. However, this comparative evidence is incomplete, and more comprehensive comparative studies are needed.

Prevention of Type 2 Diabetes
Does use of multianalyte assays (multianalyte assay with algorithm analysis) lead to targeted interventions that reduce the incidence of Type 2 diabetes?

In 2013, Shah et al. published results from the industry-sponsored Provision of Evidence-based Therapies Among Individuals at High Risk for Type 2 Diabetes (PREVAIL) initiative, a retrospective cohort study designed to evaluate the influence of the PreDx^® score on the use of interventions related to prediabetes and diabetes risk factors.²³ The study included 30 sites across the United States, each of which retrospectively abstracted chart information for up to 50 consecutive patients who had undergone PreDx^® testing at a baseline visit and a follow-up visit, for a total of 913 patients. The PreDx^® test score was stratified into low-, moderate- and high-risk groups. From baseline to follow-up, all patients demonstrated small reductions in systolic blood pressure (128 to 126.5, p = 0.039), increased antihypertensive use among those with hypertension (73.1% to 77.2%, p < 0.001), decreased median low-density lipoprotein (LDL) (104 mg/dL to 100 mg/dL, p = 0.009) and increased median high-density lipoprotein (HDL) (48 mg/dL to 50 mg/dL, p < 0.001). A similar proportion of patients received lifestyle counseling at follow-up as at baseline. The PreDx^® risk group was not significantly associated with changes in systolic blood pressure, antihypertensive use or changes in LDL or HDL. However, patients with higher PreDx^® risk groups were more likely to undergo lifestyle counseling. Limitations of this study include a lack of standardized inclusion criteria, lack of a comparison group and nonstandardized use of the PreDx^® test for clinical decision-making. Any interventions were left up to physician and/or patient discretion. These limitations make it impossible to determine what changes are attributable to the PreDx test.  

No other studies were identified that used the PreDx^® DRS as a method to select patients for interventions to prevent Type 2 diabetes. 

Other risk prediction instruments have been used for this purpose, demonstrating the potential for the use of risk prediction instruments to target preventive interventions. The AusDiab study used the Australian Type 2 Diabetes Risk Assessment Tool, based on 9 self-assessed measures, to evaluate the efficiency of detecting individuals at high risk of diabetes.²⁴ This study used data from 5,814 participants in the Australian Diabetes, Obesity and Lifestyle Study to model 4 different screening strategies. The optimal strategy, defined as resulting in the greatest number of patients entered into preventive interventions at the least total costs, was noninvasive screening with the Diabetes Risk Assessment Tool, followed by measurement of fasting plasma glucose. 

Section Summary
The evidence is insufficient to determine whether the PreDx^® risk score can improve outcomes by targeting preventive interventions to patients who will benefit most. One study evaluated changes in cardiovascular risk factors in patients whose physicians used the PreDx^® score, but there are no published studies that evaluate use of the risk score to target preventive interventions. It is not known whether the PreDx^® risk score is as good as or better than other methods for identifying individuals at high risk for diabetes.

Ongoing and Unpublished Clinical Trials
An online search of ClinicalTrials.gov in January 2015 found one unpublished trial using the PreDx risk score:

• Assessing the Risk of Developing Type II Diabetes Using Serum Biomarkers in Patients Diagnosed With Obstructive Sleep Apnea (OSA & DM) (NCT01447251): This is a randomized, open-label trial to evaluate the impact of patient receipt of information about their PreDx diabetes risk score device in patients with newly diagnosed obstructive sleep apnea requiring continuous positive airway pressure. The primary outcome is the number of participants with a change in their 7 serum biomarker panel results. Enrollment is planned for 70 subjects; the estimated study completion date was July 2014, but no published results were identified. 

Summary of Evidence
The PreDx Diabetes Risk Score, a multianalyte assay with algorithm analysis (MAAA) that uses 7 biomarkers, has been evaluated in predicting risk of diabetes. In reports of 2 patient cohorts, the area under the curve for predicting progression to diabetes ranged from 0.78 to 0.84. This suggests good overall predictive ability, but conclusions about the predictive value of the diabetes risk score are limited by the lack of validation by independent research groups and testing in a wider variety of patient populations. The evidence is insufficient to determine the comparative accuracy of the PreDx^® DRS with other formal prediction models for diabetes.

There is a lack of evidence on the clinical utility of the PreDx^® score. No published studies were identified that used the risk score to select patients for preventive interventions. As a result, it is not known how this instrument will perform in targeting preventive interventions to patients who will benefit the most, nor is it known how this risk score compares with other methods for selecting high-risk patients. No published literature was found on MAAAs other than the PreDx diabetes risk score. Therefore, use of MAAAs to predict diabetes risk, including, but not limited to, the PreDx^® diabetes risk score, is considered investigational.

Practice Guidelines and Position Statements
There are no clinical practice guidelines that specifically address the use of diabetes risk scores such as the PreDx^® score. However, there are a number of clinical practice guidelines that address screening for diabetes in high-risk individuals. These guidelines specify that screening is performed by glucose-based measurements, either by fasting glucose, oral glucose tolerance test or hemoglobin A1c. None of the available guidelines discuss use of a risk score as a replacement for glucose-based screening measures. 

The American Diabetes Association published guidelines in 2014 on testing for diabetes in asymptomatic patients.²⁵ The following parameters for testing were recommended for adults: 

• Testing to detect diabetes and assess future risk for diabetes should be considered in adults who are overweight or obese (body mass index [BMI] ≥ 5 kg/m²) and who have at least 1 additional risk factor for diabetes among the following: 
  • Physical inactivity 
  • First-degree relative with diabetes 
  • High-risk ethnicity (African-American, Latino, Native American, Asian/Pacific Islander) 
  • Women with polycystic ovarian syndrome
  • Women who delivered a baby weighing > 9 pounds or were diagnosed with gestational diabetes mellitus 
  • Hypertension (≥ 40/90 or on therapy for hypertension) 
  • High-density lipoprotein cholesterol < 35 mg/dL and/or a triglyceride level > 250 mg/dL 
  • HgbA1C ≥ 7%, impaired glucose tolerance or impaired fasting glucose on previous testing 
  • Other clinical conditions associated with insulin resistance, e.g., severe obesity and acanthosis nigricans) 
  • History of cardiovascular disease 
• Among adults without risk factors, testing should begin at age 45

The following parameters for testing were recommended for children: Testing to detect diabetes should be considered for children who are overweight (BMI > 85th percentile for age/sex, or weight > 120% of ideal for height) and have any 2 of the following risk factors: 

• Family history of diabetes in first- or second-degree relative 
• High-risk race/ethnicity (African-American, Latino, Native American, Asian American, Pacific Islander) 
• Signs of insulin resistance or conditions associated with insulin resistance (acanthosis nigricans, hypertension, dyslipidemia, polycystic ovarian syndrome or small-for-gestational-age birth weight)
• Maternal history of diabetes or gestational diabetes during the child's gestation 

U.S. Preventive Services Task Force Recommendations
The U.S. Preventive Services Task Force (USPSTF) published guidelines on screening for diabetes in adults in 2008.²⁶ The following recommendations were made for screening: 

• USPSTF recommends screening for Type 2 diabetes in asymptomatic adults with sustained blood pressure (either treated or untreated) greater than 135/80 mm Hg (Grade B recommendation). 
• USPSTF concluded that the current evidence is insufficient to assess the balance of benefits and harms of routine screening for Type 2 diabetes in asymptomatic adults with blood pressure of 135/80 mm Hg or lower (I statement — insufficient evidence).

References

1. Fox CS, Pencina MJ, Meigs JB, et al. Trends in the incidence of type 2 diabetes mellitus from the 1970s to the 1990s: the Framingham Heart Study. Circulation. Jun 27 2006;113(25):2914-2918. PMID 16785337
2. Blonde L. State of diabetes care in the United States. Am J Manag Care. Apr 2007;13 Suppl 2:S36-40. PMID 17417931
3. Mainous AG, 3rd, Baker R, Koopman RJ, et al. Impact of the population at risk of diabetes on projections of diabetes burden in the United States: an epidemic on the way. Diabetologia. May 2007;50(5):934-940. PMID 17119914
4. Schwarz PE, Li J, Lindstrom J, et al. Tools for predicting the risk of type 2 diabetes in daily practice. Horm Metab Res. Feb 2009;41(2):86-97. PMID 19021089
5. Shai I, Jiang R, Manson JE, et al. Ethnicity, obesity, and risk of type 2 diabetes in women: a 20-year follow-up study. Diabetes Care. Jul 2006;29(7):1585-1590. PMID 16801583
6. Liu S, Tinker L, Song Y, et al. A prospective study of inflammatory cytokines and diabetes mellitus in a multiethnic cohort of postmenopausal women. Arch Intern Med. Aug 13-27 2007;167(15):1676-1685. PMID 17698692
7. Meigs JB, Hu FB, Rifai N, et al. Biomarkers of endothelial dysfunction and risk of type 2 diabetes mellitus. JAMA. Apr 28 2004;291(16):1978-1986. PMID 15113816
8. Noble D, Mathur R, Dent T, et al. Risk models and scores for type 2 diabetes: systematic review. BMJ. 2011-11-28 12:18:32 2011;343.
9. Balkau B, Lange C, Fezeu L, et al. Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR). Diabetes Care. Oct 2008;31(10):2056-2061. PMID 18689695
10. Mohan V, Goldhaber-Fiebert JD, Radha V, et al. Screening with OGTT alone or in combination with the Indian diabetes risk score or genotyping of TCF7L2 to detect undiagnosed type 2 diabetes in Asian Indians. Indian J Med Res. Mar 2011;133:294-299. PMID 21441683
11. Griffin SJ, Little PS, Hales CN, et al. Diabetes risk score: towards earlier detection of type 2 diabetes in general practice. Diabetes Metab Res Rev. May-Jun 2000;16(3):164-171. PMID 10867715
12. Zhang L, Zhang Z, Zhang Y, et al. Evaluation of Finnish Diabetes Risk Score in screening undiagnosed diabetes and prediabetes among U.S. adults by gender and race: NHANES 1999-2010. PLoS One. 2014;9(5):e97865. PMID 24852786
13. Abbasi A, Peelen LM, Corpeleijn E, et al. Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ. 2012;345:e5900. PMID 22990994
14. Orozco LJ, Buchleitner AM, Gimenez-Perez G, et al. Exercise or exercise and diet for preventing type 2 diabetes mellitus. Cochrane Database Syst Rev. 2008(3):CD003054. PMID 18646086
15. Padwal R, Majumdar SR, Johnson JA, et al. A systematic review of drug therapy to delay or prevent type 2 diabetes. Diabetes Care. Mar 2005;28(3):736-744. PMID 15735219
16. Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. Feb 7 2002;346(6):393-403. PMID 11832527
17. Diabetes Prevention Program Research G, Knowler WC, Fowler SE, et al. 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet. Nov 14 2009;374(9702):1677-1686. PMID 19878986
18. Kolberg JA, Jorgensen T, Gerwien RW, et al. Development of a type 2 diabetes risk model from a panel of serum biomarkers from the Inter99 cohort. Diabetes Care. Jul 2009;32(7):1207-1212. PMID 19564473
19. Lyssenko V, Jorgensen T, Gerwien RW, et al. Validation of a multi-marker model for the prediction of incident type 2 diabetes mellitus: combined results of the Inter99 and Botnia studies. Diab Vasc Dis Res. Jan 2012;9(1):59-67. PMID 22058089
20. Urdea M, Kolberg J, Wilber J, et al. Validation of a multimarker model for assessing risk of type 2 diabetes from a five-year prospective study of 6784 Danish people (Inter99). J Diabetes Sci Technol. Jul 2009;3(4):748-755. PMID 20144324
21. Kengne AP, Beulens JW, Peelen LM, et al. Non-invasive risk scores for prediction of type 2 diabetes (EPICInterAct): a validation of existing models. Lancet Diabetes Endocrinol. Jan 2014;2(1):19-29. PMID 24622666
22. Rowe MW, Bergman RN, Wagenknecht LE, et al. Performance of a multi-marker diabetes risk score in the Insulin Resistance Atherosclerosis Study (IRAS), a multi-ethnic US cohort. Diabetes Metab Res Rev. Sep 2012;28(6):519-526. PMID 22492485
23. Shah BR, Cox M, Inzucchi SE, et al. A quantitative measure of diabetes risk in community practice impacts clinical decisions: The PREVAIL initiative. Nutr Metab Cardiovasc Dis. Nov 1 2013. PMID 24374006
24. Chen L, Magliano DJ, Balkau B, et al. Maximizing efficiency and cost-effectiveness of Type 2 diabetes screening: the AusDiab study. Diabet Med. Apr 2011;28(4):414-423. PMID 21392062
25. American Diabetes Association. Standards of Medical Care in Diabetes — 2014. Diabetes Care. January 1, 2014 2014;37(Supplement 1):S14-S80. 
26. U. S. Preventive Services Task Force. Screening for type 2 diabetes mellitus in adults: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. Jun 3 2008;148(11):846-854. PMID 18519930

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Procedure and diagnosis codes on Medical Policy documents are included only as a general reference tool for each policy. They may not be all-inclusive.  

This medical policy was developed through consideration of peer-reviewed medical literature generally recognized by the relevant medical community, U.S. FDA approval status, nationally accepted standards of medical practice and accepted standards of medical practice in this community, Blue Cross Blue Shield Association technology assessment program (TEC) and other nonaffiliated technology evaluation centers, reference to federal regulations, other plan medical policies and accredited national guidelines.

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