Q2 Solutions Q2 Solutions Actionable Insights for Better Health and Lab Solutions
Blog
| October 30, 2017

Genomic Testing for Insulin Resistance

Learn how genomic testing helps identify patients at-risk for diabetes.
Authors: Marc Edwards & Victor Weigman

As we celebrate the efforts of so many researchers, providers, patients, and advocates on World Diabetes Day, we have much to be excited about today.  New discoveries are being made and new hope is being given to patients living with diabetes across the globe. However, this excitement is tempered by some very sobering statistics:
  • Worldwide prevalence figures estimate that there were 415 million adults living with Type 1 Diabetes (T1D) and Type 2 Diabetes (T2D) in 2015 and that by 2035, this number will have risen to 642 million, especially in middle and low income families.1 
  • Diabetes affects 30 million children and adults in the US (1 in 11 Americans).2 
  • The global prevalence of diabetes among adults (ages 20-79 years) has risen from 4.7% in 1980 to 8.8% in 2015.1
  •  Lifetime risk of diagnosed diabetes from age 20 years was 40.2% for men and 39.6% for women, representing increases of 20 percentage points and 13 percentage points, respectively, since 1985–89.3 
  • There will be a continued need for health services and extensive costs to manage the disease, and emphasize the need for effective interventions to reduce incidence.3 

Diabetes subgroups
In response to the growing incidence and prevalence, researchers continue to work toward finding new ways to identify, treat, and care for patients at-risk for and living with diabetes. Recently, efforts have been made to more precisely identify diabetes subgroups based upon genetic markers and biomarkers.4   Figure 1 shows results from the All New Diabetics in Scania (ANDIS) project in Sweden illustrating the spectrum of diabetes subgroups. These subgroups may provide insight into identification of patients at risk for developing diabetes. One of Q2 Solutions’ parent companies, Quest Diagnostics, offers a test menu that uses knowledge of diabetes subgroups to improve diagnosis and management.

world diabetes day

Figure 1: The spectrum of diabetes subgroups (ANDIS April 2012)4 
Image made available through Creative Commons license 4.0 
T2D: Type 2 Diabetes; MODY: maturity-onset diabetes of the young; LADA: latent autoimmune diabetes in adults


Insulin Resistance 
Insulin resistance is a significant factor in T2D, and there are several important ideas developing in this area that contribute to the development of better risk assessment, diagnosis and treatments. In T2D, there is a relative decrease in insulin action either through decreased relative production or through receptors that are not responding to insulin stimulation. Insulin inhibits glucose production by the liver, but not in patients with T2D. Dr. Gerald Shulman, Professor of Medicine and Professor of Cellular and Molecular Physiology at Yale University has been quoted as saying, “None of the drugs we currently use to treat type 2 diabetes target the root cause…By understanding the molecular basis for hepatic insulin resistance we now can design better and more effective drugs for its treatment.”

In T1D, the lack of insulin relates more to immune or autoimmune attack and destruction of the pancreas cells producing insulin. Thus, research focusing on immunological processes can advance the field.  The body creates antibodies against glutamic acid decarboxylase, or GAD and antibody testing can be used to detect presence of autoimmune diabetes mellitus.7

Insulin resistance is also a significant factor in metabolic syndrome which has been called “a cluster of the most dangerous heart attack risk factors: diabetes and prediabetes, abdominal obesity, high cholesterol and high blood pressure.”8

Genome Wide Studies (GWAS) and the role of the central lab
The central laboratory plays an important role in the discovery of new ways to identify patients at-risk for diabetes.  For example, Q2 Solutions is actively involved in the standardization of insulin assays including circulating insulin concentration in serum or plasma in order to provide important information for the estimation of insulin secretion and insulin resistance.  Currently, the lack of standardization of insulin assays hinders efforts to achieve consistent measures for treatment guidelines.9 

Diabetes is very complex, and known to be caused not just by genetics and epigenetics (factors indirectly affecting DNA) but environment as well.  Currently, more than 120 loci (~153 variants) have been repeatedly associated with type II diabetes but its heritability is still not fully understood. (Click here to see the table of genetic loci associated with risk of T2D.10)

Since 2007 (where Science Magazine named GWAS as “Breakthrough of the Year”), there has been an explosion of genome-wide association studies (GWAS), where microchips designed to assay hundreds of thousands to millions of well-known single nucleotide polymorphisms (SNPs) were used to identify locations linked to disease.  GWAS maps compare frequencies of variants across the genome between disease cases and matched controls. (Click here to see the history of GWAS. Access to Nature reviews: Genetics required.11

Until recently, genomic testing of diabetes was limited to microarrays, both for broad, genome-wide content and smaller content.  Since this time, the commonality of these studies across different groups has yielded smaller genetic loci lists, which can be readily and cheaply tested to better genetically define diagnosis.

Current Challenges 
Q2 Solutions is interested in identifying and helping to address the current challenges with genomic testing for diabetes. For example, these GWAS studies are as varied across experimental factors and statistical design as possible, making interpretation difficult for clinical trial testing and identifying patient response to therapy, particularly among ethnic, age and clinical covariates (e.g., weight, family history).  Moreover, knowing genomic loci associated with glycemic traits in a complex disease only gets to part of the problem of diagnosis and less to the problem of precision treatment. There is still murkiness around the T2D genetic architecture and understanding how the disease biologically progresses, given the genetic, epigenetic and environmental factors.  

With so many studies and consortia [DIAGRAM (Diabetes Genetics Replication and Meta-analysis Consortium) and MAGIC (Meta-Analyses of Glucose-and Insulin-related traits Consortium)] involved in research, the overall findings are not convergent.  Currently, there are 629 insulin resistance clinical trials in progress.12 If the field is to benefit from such investment and enthusiasm, there is a need for standardization of the organization and oversight of clinical trials to look into biomarkers and their relevance to treatment decisions, genetic risk and larger exploratory biomarker collections across molecular types to ensure multiple facets are assayed. This means that as new biomarkers surface for assessment of risk or status, these biomarkers would become consideration for clinical trials for new therapies. 

Traditional screening and efficacy biomarkers in use and standardized across the Q2 Solutions laboratories include glucose in serum or plasma, National Glycohemoglobin Standardization Program (NGSP) Level 1 certified Hemoglobin A1c, insulin, C-peptide, and CDC lipid panels. Safety biomarkers also are supported with the additional precision of enzymatic serum creatinine that is standardized to isotope dilution mass spectrometry (IDMS), urine “microalbumin” and urine albumin to creatinine ratios, and other standardized calculations supported by the medical and scientific community. These include the estimated glomerular filtration rate (eGFR) and the measures of the status of kidney disease such as the Cockcroft-Gault equation, Modification of Diet in Renal Disease (MDRD) equation(s), and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations.

Reducing the global burden of type 2 diabetes requires early action
One of the aims of World Diabetes Day is to keep diabetes “firmly in the public and political spotlight.” Progress requires both economic and political support. Diabetes-related costs should continue to drive action from governments to reduce the disease within their populations.  For example, the total cost of diabetes and prediabetes care in the U.S. is $322 billion. Insulin prices increased nearly 300% between 2002 and 2013. In fact, healthcare costs for people with diabetes are more than twice as high as those without diabetes.2  With the WHO reporting 1 in 3 people as overweight and 1 in 10 as obese,13 the overall healthcare costs related to diabetes are sure to increase even further in the years to come.  Governments can help prevent costs by supporting a few key actions, namely, creating healthy environments, supporting better diagnosis and treatment and bolstering efforts to improve the quality of diabetes-related data.

Diabetes Collaboration programs
As the potential of genomic testing for diabetes continues to become clearer, it is still important for people to maintain healthy weight, exercise, and get regular medical check-ups. Quest Diagnostics has an ongoing partnership through their Health and Wellness offering with the American Diabetes Association to help identify people at risk for T2D by promoting awareness and increasing participation in employer wellness screenings.14 The efforts target those who are unaware they have diabetes with messages and tools to encourage and facilitate healthy lifestyle choices.

On World Diabetes Day, let’s all remain vigilant in the fight against this disease, but celebrate the excitement of new developments and the diagnostic and therapeutic possibilities for patients and those at risk.

Related Services:


References: 
1. www.diabetesatlas.org
2. http://www.diabetes.org/diabetes-basics/statistics/infographics/adv-staggering-cost-of-diabetes.html
3. http://www.sciencedirect.com/science/article/pii/S2213858714701615
4. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4377835/
5. http://www.questdiagnostics.com/home/physicians/testing-services/condition/diabetes/test-menu.html
6. https://medicine.yale.edu/ycci/researchspectrum/t1t4/t1/shulman.aspx
7. https://www.questdiagnostics.com/home/physicians/testing-services/condition/diabetes/test-menu.html
8. http://onlinelibrary.wiley.com/doi/10.1111/j.1464-5491.2006.01858.x/full
9. https://www.ncbi.nlm.nih.gov/pubmed/17272483
10. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4377835/table/genes-06-00087-t001/
11. http://www.nature.com/nrg/journal/v17/n9/full/nrg.2016.56.html?foxtrotcallback=true
12. https://clinicaltrials.gov/ct2/results?cond=Insulin+Resistance,+Diabetes
13. http://www.who.int/diabetes/global-report/WHD2016_Diabetes_Infographic_v2.pdf?ua=1
14. http://newsroom.questdiagnostics.com/2017-03-06-Quest-Diagnostics-and-American-Diabetes-Association-R-to-Help-Identify-People-at-Risk-for-Type-2-Diabetes