Latest Review Date: October 2024

POLICY:

The use of multianalyte panels with algorithmic analysis (MAAA) for the prediction of Type II diabetes is considered investigational.

DESCRIPTION OF PROCEDURE OR SERVICE:

Multianalyte assays with algorithmic analyses (MAAAs) are tests that incorporate results from a panel of tests (molecular or non-molecular), with or without other clinical information, into an algorithm in order to generate a risk or probability score.

MAAAs have been developed for use as in screening, risk assessment, diagnosis, staging and prognosis, and therapy selection for a number of diseases. For the purposes of this literature review, MAAAs for the use of risk assessment of Type II Diabetes is evaluated.

There are a variety of known factors that predict risk of developing Type II diabetes. The most direct are measures of glucose metabolism, such as fasting glucose, oral glucose tolerance testing (OGTT), 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 ten years more than 50% will have progressed to diabetes.

Other risk factors for Type II 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. 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 non-glucose 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.

Formal risk prediction instruments have combined clinical, laboratory, and genetic information to improve and refine upon 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.

The PreDx® Diabetes Risk Score™ (DRS) is a commercially available MAAA that is intended to determine the five-year risk of developing Type II diabetes based on seven biomarkers in fasting blood: HgA1C, Glucose, Insulin, C-reactive protein, Ferritin, Adiponectin, Interleukin-2 receptor alpha. The results of these biomarkers are combined with age and gender to produce a quantitative risk score that varies from zero to ten. The proposed use of this technology is to identify patients at greater risk of developing Type II diabetes and to potentially target preventive interventions at patients with the highest risk.

KEY POINTS:

This policy is updated with review of literature through October 3, 2024.

Summary of Evidence

The evidence includes an identified comparative study suggesting that DRS may allow clinicians to focus on prevention. Another identified study evaluated the changes in cardiovascular risk factors in patients whose physicians used the PreDx® risk score. However, there are no published studies that evaluate use of the risk score to target preventive interventions.

There is a paucity of well-designed scientific evidence regarding the use of MAAAs to develop a risk score for developing Type II diabetes. There is a need for evidence based research in a wide variety of patient populations to prove the utility of this technology. Scientific evidence, preferably, randomized controlled trials, are needed that prove the technology results in an improvement in net health outcomes, therefore, this technology in 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 HgA1C. None of the identified guidelines discuss use of a risk score as a replacement for glucose-based screening measures.

U.S. Preventive Services Task Force Recommendations

To update its 2015 recommendation, the U. S. Preventative Services Task Force (USPSTF) commissioned a systematic review to evaluate screening for prediabetes and type II diabetes in asymptomatic, nonpregnant adults and preventive interventions for those with prediabetes. The updated recommendation, published in 2021, is as follows:

• Nonpregnant adults aged 35 to 70 years who have overweight or obesity and no symptoms of diabetes: Screen for prediabetes and type II diabetes, and offer or refer patients with prediabetes to effective preventive interventions. (Grade B recommendation)

KEY WORDS:

Diabetes Risk, PreDx Diabetes Risk Score, PreDx DRS, Pre-Dx, MAAA, diabetes, multianalyte panels with algorithmic analyses

APPROVED BY GOVERNING BODIES:

Clinical laboratories may develop and validate tests in-house and market them as a laboratory service; laboratory-developed tests must meet the general regulatory standards of the Clinical Laboratory Improvement Act. Laboratories that offer laboratory-developed tests must be licensed by the Clinical Laboratory Improvement Act for high-complexity testing. To date, the U.S. Food and Drug Administration has chosen not to require any regulatory review of this technology.

BENEFIT APPLICATION:

Coverage is subject to member’s specific benefits. Group-specific policy will supersede this policy when applicable.

ITS: Home Policy provisions apply.

FEP contracts: Special benefit consideration may apply. Refer to member’s benefit plan.

CURRENT CODING:

CPT Codes:

REFERENCES:

1. Abbasi A, Peelen LM, Corpeleijn E, van der Schouw YT, Stolk RP, Spijkerman AM, van der A DL, Moons KG, Navis G, Bakker SJ, Beulens JW. Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ. 2012 Sep 18; 345.
2. American Diabetes Association. Standards of medical care in diabetes--2014. Diabetes Care. 2014 Jan; 37 Suppl 1:S14-80.
3. Balkau B, Lange C, Fezeu L, Tichet J, de Lauzon-Guillain B, Czernichow S, Fumeron F, Froguel P, Vaxillaire M, Cauchi S, Ducimetière P, Eschwège E. Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR). Diabetes Care. 2008 Oct; 31(10):2056-61.
4. Colon-Franco J. Mainstream Clinical Adoption of Multianalyte Assays with Algorithmic Analyses. https://www.aacc.org/community/aacc-academy/publications/scientific-shorts/2019/mainstream-clinical-adoption-of-multianalyte-assays-with-algorithmic-analyses. 2019 Aug 13.
5. Colón-Franco JM, Bossuyt PMM, Algeciras-Schimnich A, Bird C, Engstrom-Melnyk J, Fleisher M, Kattan M, Lambert-Messerlian G. Current and Emerging Multianalyte Assays with Algorithmic Analyses-Are Laboratories Ready for Clinical Adoption? Clin Chem. 2018 Jun; 64(6):885-891.
6. Deberneh HM, Kim I. Prediction of Type 2 Diabetes Based on Machine Learning Algorithm. Int J Environ Res Public Health. 2021 Mar 23; 18(6):3317.
7. Griffin SJ, Little PS, Hales CN et al. Diabetes risk score: towards earlier detection of Type II diabetes in general practice. Diabetes Metab Res Rev 2000; 16(3):164-71.
8. IOM (Institute of Medicine). 2011. Clinical Practice Guidelines We Can Trust. Washington, DC: The National Academies Press.
9. Kengne AP, Beulens JW, Peelen LM, Moons KG, van der Schouw YT, Schulze MB, Spijkerman AM, Griffin SJ, Grobbee DE, Palla L, Tormo MJ, Arriola L, Barengo NC, Barricarte A, Boeing H, Bonet C, Clavel-Chapelon F, Dartois L, Fagherazzi G, Franks PW, Huerta JM, Kaaks R, Key TJ, Khaw KT, Li K, Mühlenbruch K, Nilsson PM, Overvad K, Overvad TF, Palli D, Panico S, Quirós JR, Rolandsson O, Roswall N, Sacerdote C, Sánchez MJ, Slimani N, Tagliabue G, Tjønneland A, Tumino R, van der A DL, Forouhi NG, Sharp SJ, Langenberg C, Riboli E, Wareham NJ. Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. Lancet Diabetes Endocrinol. 2014 Jan; 2(1):19-29.
10. Kolberg JA, Jørgensen T, Gerwien RW, Hamren S, McKenna MP, Moler E, Rowe MW, Urdea MS, Xu XM, Hansen T, Pedersen O, Borch-Johnsen K. Development of a type 2 diabetes risk model from a panel of serum biomarkers from the Inter99 cohort. Diabetes Care. 2009 Jul; 32(7):1207-12.
11. Lindström J, Tuomilehto J. The Diabetes Risk Score: A practical tool to predict type 2 diabetes risk. Diabetes Care 1 March 2003; 26 (3): 725–731.
12. Lyssenko V, Jørgensen T, Gerwien RW, Hansen T, Rowe MW, McKenna MP, Kolberg J, Pedersen O, Borch-Johnsen K, Groop L. 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. 2012 Jan; 9(1):59-67.
13. Mainous AG 3rd, Baker R, Koopman RJ, Saxena S, Diaz VA, Everett CJ, Majeed A. Impact of the population at risk of diabetes on projections of diabetes burden in the United States: an epidemic on the way. Diabetologia. 2007 May; 50(5):934-40.
14. Mohan V, Goldhaber-Fiebert JD, Radha V, Gokulakrishnan K. 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. 2011 Mar; 133(3):294-9.
15. Noble D, Mathur R, Dent T, Meads C, Greenhalgh T. Risk models and scores for type 2 diabetes: systematic review. BMJ. 2011 Nov 28; 343:d7163.
16. Schwarz PE, Li J, Lindstrom J et al. Tools for predicting the risk of Type II diabetes in daily practice. Horm Metab Res 2009; 41(2):86-97.
17. Shafizadeh TB, Moler EJ, Kolberg JA, Nguyen UT, Hansen T, Jorgensen T, Pedersen O, Borch-Johnsen K. Comparison of accuracy of diabetes risk score and components of the metabolic syndrome in assessing risk of incident type 2 diabetes in Inter99 cohort. PLoS One. 2011; 6(7):e22863.
18. Shah BR, Cox M, Inzucchi SE, Foody JM, Zimmer LO, Jorge CB, Ratner RE, Barringer TA, McGuire DK, Peterson ED. A quantitative measure of diabetes risk in community practice impacts clinical decisions: the PREVAIL initiative. Nutr Metab Cardiovasc Dis. 2014 Apr; 24(4):400-7.
19. Urdea M, Kolberg J, Wilber J, Gerwien R, Moler E, Rowe M, Jorgensen P, Hansen T, Pedersen O, Jørgensen T, Borch-Johnsen K. 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. 2009 Jul 1; 3(4):748-55.
20. US Preventive Services Task Force. Screening for Prediabetes and Type 2 Diabetes: US Preventive Services Task Force Recommendation Statement. JAMA. 2021; 326(8):736–743.

POLICY HISTORY:

Medical Policy Panel, February 2014

Medical Policy Group, September 2014 (1): New policy, previously only listed on Investigational Listing; remains investigational

Medical Policy Administration Committee, September 2014

Available for comment September 16 through October 31, 2014

Medical Policy Panel, February 2015

Medical Policy Group, February 2015 (6): 2015 Updates to Description, Key Points and References; no change to policy statement. Effective February 18, 2015: Active Policy but no longer scheduled for regular literature reviews and updates

Medical Policy Group, October 2019 (9): Updates to Description, Key Points, and References. Added key words: MAAA, diabetes, multianalyte panels with algorithmic analyses. No change to policy statement.

Medical Policy Group, August 2021 (9): Updates to References, Description, Key Points. Policy statement updated to remove “not medically necessary,” no change to policy intent.

Medical Policy Group, October 2021 (9): Reviewed by consensus. No new published peer-reviewed literature available that would alter the coverage statement in this policy.

Medical Policy Group, August 2022 (9): Updated Description, Key Points. Reviewed by consensus. References added. No new published peer-reviewed literature available that would alter the coverage statement in this policy.

Medical Policy Group, September 2023 (5): Updates to Key Points and Benefit Application. No change to Policy Statement. Reviewed by consensus. No new published peer-reviewed literature available that would alter the coverage statement in this policy.

Medical Policy Group, October 2024 (5): Reviewed by consensus. Minor update to Key Points. No change to Policy Statement. No new published peer-reviewed literature available that would alter the coverage statement in this policy.

This medical policy is not an authorization, certification, explanation of benefits, or a contract. Eligibility and benefits are determined on a case-by-case basis according to the terms of the member’s plan in effect as of the date services are rendered. All medical policies are based on (i) research of current medical literature and (ii) review of common medical practices in the treatment and diagnosis of disease as of the date hereof. Physicians and other providers are solely responsible for all aspects of medical care and treatment, including the type, quality, and levels of care and treatment.

This policy is intended to be used for adjudication of claims (including pre-admission certification, pre-determinations, and pre-procedure review) in Blue Cross and Blue Shield’s administration of plan contracts.

The plan does not approve or deny procedures, services, testing, or equipment for our members. Our decisions concern coverage only. The decision of whether or not to have a certain test, treatment or procedure is one made between the physician and his/her patient. The plan administers benefits based on the member’s contract and corporate medical policies. Physicians should always exercise their best medical judgment in providing the care they feel is most appropriate for their patients. Needed care should not be delayed or refused because of a coverage determination.

As a general rule, benefits are payable under health plans only in cases of medical necessity and only if services or supplies are not investigational, provided the customer group contracts have such coverage.

The following Association Technology Evaluation Criteria must be met for a service/supply to be considered for coverage:

1. The technology must have final approval from the appropriate government regulatory bodies;

2. The scientific evidence must permit conclusions concerning the effect of the technology on health outcomes;

3. The technology must improve the net health outcome;

4. The technology must be as beneficial as any established alternatives;

5. The improvement must be attainable outside the investigational setting.

Medical Necessity means that health care services (e.g., procedures, treatments, supplies, devices, equipment, facilities or drugs) that a physician, exercising prudent clinical judgment, would provide to a patient for the purpose of preventing, evaluating, diagnosing or treating an illness, injury or disease or its symptoms, and that are:

1. In accordance with generally accepted standards of medical practice; and

2. Clinically appropriate in terms of type, frequency, extent, site and duration and considered effective for the patient’s illness, injury or disease; and

3. Not primarily for the convenience of the patient, physician or other health care provider; and

4. Not more costly than an alternative service or sequence of services at least as likely to produce equivalent therapeutic or diagnostic results as to the diagnosis or treatment of that patient’s illness, injury or disease.