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Commentary

Found in Translation: A Type 1 Diabetes Genetic Risk Score Applied to Clinical Diagnosis

  1. Jose C. Florez⇑
  1. Diabetes Unit and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA; Department of Medicine, Harvard Medical School, Boston, MA; and Metabolism Program and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
  1. Corresponding author: Jose C. Florez, jcflorez{at}partners.org.
Diabetes Care 2016 Mar; 39(3): 330-332. https://doi.org/10.2337/dci15-0029
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We are entering a new era in which the explosion of genomic and biological information across multiple developmental and metabolic states is poised to transform the practice of medicine (1). It is now possible to center scientific investigation in the key model organism, the human, and to deploy modern technologies to acquire an unprecedented trove of biological data at multiple time points in the life of an individual (2).

The genomic revolution illustrates how big data sets can begin to yield biological and clinical insight. Concurrent advances in genotyping and sequencing technologies, in our understanding of human genetic variation, and in the statistical methodologies needed to interpret genetic findings have all led to a meteoric expansion in genetic knowledge (3). Meta-analyses of genome-wide association studies (GWAS) and ongoing comprehensive sequencing experiments are yielding a plethora of genetic associations from an agnostic vantage point, which can open unsuspected windows into the pathophysiology of metabolic phenotypes, including type 1 (4) and type 2 (5,6) diabetes.

However, whether this emerging body of knowledge will deepen our understanding of health and disease and lead to improved outcomes in public health remains to be seen. A frequent criticism of large-scale genomic studies is that so far they have failed to deliver on the hyped hopes that the human genome would usher in personalized medicine. Given the empirical observation that most genetic effects for complex phenotypes are modest, skeptics rightly argue that their utility for individualized prediction has not been proven, and they jump to conclude that the substantial public investment in resources, effort, and human capital in this line of investigation has been a wash.

Geneticists may counter that skeptics seem to ignore how genetic studies have illuminated pathophysiological mechanisms that remained obscure, such as the seminal involvement of neuronal circuits in the genesis of obesity (7), the primary role of genetically driven β-cell failure in type 2 diabetes (8), or the significant overlap of gene sets in autoimmunity (9). Genetic studies have identified drug targets that were either known (e.g., PPARG for thiazolidinediones [10], ABCC8/KCNJ11 for sulfonylureas [11], and HMGCR for statins [12]) or completely novel (PCSK9 [13]), illustrating that actionable biology can be detected through these approaches. Human geneticists also argue that not enough time for clinical translation has elapsed since many of these findings came to light, in comparison with the time scales required for the translation of other fundamental discoveries such as the cause of AIDS or the contribution of dyslipidemia to coronary artery disease.

Nevertheless, it is fair to admit that the penetration of nascent genetic findings into the clinic, particularly for prediction and diagnosis, has been timid at best. Our track record for the past decade does raise the question of whether precision medicine will ever become a reality, or it will remain science fiction.

The article by Oram et al. (14) in this issue of Diabetes Care is an elegant first step in the right direction. Cognizant of the modest effect of single variants on disease risk but aware of the growing list of single nucleotide polymorphisms (SNPs) associated with type 1 (15) and type 2 (16) diabetes, the authors aggregated the risk alleles at all diabetes-associated SNPs to construct genetic risk scores (GRSs) that together explain a larger proportion of the variance in each of the two types, and thereby increase statistical power. This is particularly pertinent for type 1 diabetes, which is somewhat unique among complex diseases in that its genetic architecture combines a very strong effect at the HLA region with a multitude of weaker effects at other loci. Thus, a GRS for type 1 diabetes, which includes two SNPs that tag the high-risk HLA haplotypes DR3 and DR4-DQ8 (and thus goes beyond classic HLA typing), achieves an area under the receiver operating characteristic curve (c-statistic) as high as 0.88 (with 1.0 indicating 100% sensitivity and specificity). In contrast, the GRS for type 2 diabetes, which does not include genetic markers as strong as HLA for type 1 diabetes, only achieves a c-statistic of 0.64, consistent with prior publications (17,18). Although the mean type 1 diabetes GRS was significantly higher in type 1 diabetes cases than in type 2 diabetes cases (0.279 vs. 0.229), there was substantial overlap between the two distributions.

Having established the technical and clinical validity of the GRS for type 1 diabetes, Oram et al. (14) point the way to its clinical utility by estimating sensitivity and specificity rates at various thresholds in an attempt to improve separation. A GRS of >0.280 to diagnose type 1 diabetes would exclude 95% of those without type 1 diabetes (specificity) but would only capture 50% of all type 1 diabetes cases (sensitivity); similarly, when dialed to >0.234 the same GRS would detect 95% of all type 1 diabetes cases but only exclude 53% of those without.

These scores were initially tested in the data set from the Wellcome Trust Case Control Consortium. To the extent that this consortium contributed to the GWAS meta-analyses that identified some of the same SNPs included in the GRS, the discovery and validation data sets are not completely independent, allowing for the potential of overfitting. To allay this concern and provide additional clinical insight, Oram et al. (14) tested their type 1 diabetes GRS in the independent South West England cohort of 223 participants with diabetes diagnosed between the ages of 20 and 40 years. Their clinical outcome was severe insulin deficiency, defined as the need for continuous insulin treatment earlier than 3 years from diagnosis in the setting of a documented low C-peptide. In this context, the type 1 diabetes GRS performed similarly as before, with a c-statistic of 0.87 and sensitivity and specificity rates comparable to those attained in the Wellcome Trust Case Control Consortium. Interestingly, the GRS provided information that was orthogonal to that furnished by clinical predictors such as autoantibody status, BMI, and age at diagnosis.

It thus appears that these forward-looking investigators have generated an innovative genetic tool that may be helpful in specific clinical situations (Fig. 1). The unmet medical need arises from the critical differences in the therapeutic approaches to newly diagnosed type 1 versus type 2 diabetes, and the risks involved in making the wrong prescription decision. A person with type 1 diabetes who is misdiagnosed may not receive insulin at first and thus be exposed to life-threatening diabetic ketoacidosis; conversely, a person with type 2 diabetes who is mistakenly thought to have type 1 diabetes will receive insulin injections unnecessarily (but not unsafely, as his or her diabetes could still be well treated). Such diagnostic challenges include the obese, antibody-negative patient with type 1 diabetes and the lean, antibody-positive young patient with type 2 or monogenic diabetes. Such cases do exist, in that obesity has become much more prevalent (and can therefore affect patients with autoimmune diabetes) and autoantibody testing in clinical practice, where only one autoantibody might be checked at a single point in time, is not 100% discriminatory. In this sense, the age bracket examined by the authors (20–40 years) is particularly pertinent, and the finding that genetic testing complements other clinical features is appealing. Further advantages of genetic testing include that it only needs to be done once in the individual’s lifetime, it is not dependent on the disease time course, and it can be done cheaply and accurately. Capitalizing on its technical validity and having proven its clinical validity, studies that demonstrate further clinical utility (e.g., in other ethnic groups or in specific clinical scenarios) would help advance the field.

Figure 1
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Figure 1

Potential use of the type 1 diabetes GRS described here in challenging clinical scenarios. The typical situation involves newly diagnosed diabetes in a young patient (20–40 years of age). A lean body habitus, particularly in people of European descent, would usually point toward type 1 diabetes (or more rarely monogenic diabetes), whereas an obese patient, particularly of African, Hispanic, Middle Eastern, or South Asian ancestry, is more likely to have type 2 diabetes. The presence of autoantibodies and low or undetectable C-peptide would confirm type 1 diabetes in the lean patient, and the absence of autoantibodies and normal or high levels of C-peptide would confirm type 2 diabetes in the obese patient. Where autoantibodies and C-peptide results seem counterintuitive, the T1D GRS may help guide the diagnosis toward or away from type 1 diabetes, particularly if very high (>0.280) or very low (<0.234) (see text for details). T1D, type 1 diabetes; T2D, type 2 diabetes; MODY, maturity-onset diabetes of the young.

In the end, the clinician’s ability to absorb, assimilate, and translate genomic information will likely determine the extent of its impact on public health. Currently, clinicians trained in the most advanced settings are still woefully unprepared to make meaningful use of the data accumulating at a dizzying scale. Which pieces of information are clinically actionable and should be incorporated into medical practice need to be tested in a rigorous fashion, weighed rationally, acted upon, and disseminated in a didactic manner. The article by Oram et al. (14) exemplifies how this can be done.

Article Information

Funding. J.C.F. is supported by a Massachusetts General Hospital Research Scholars Award.

Duality of Interest. J.C.F. has received a consulting honorarium from Sanofi. No other potential conflicts of interest relevant to this article were reported.

Footnotes

  • See accompanying article, p. 337.

  • © 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

References

  1. ↵
    1. Collins FS
    . Exceptional opportunities in medical science: a view from the National Institutes of Health. JAMA 2015;313:131–132pmid:25585318
    OpenUrlCrossRefPubMed
  2. ↵
    1. McCarthy MI
    . Genomic medicine at the heart of diabetes management. Diabetologia 2015;58:1725–1729pmid:25860651
    OpenUrlCrossRefPubMed
  3. ↵
    1. Altshuler D,
    2. Daly MJ,
    3. Lander ES
    . Genetic mapping in human disease. Science 2008;322:881–888pmid:18988837
    OpenUrlAbstract/FREE Full Text
  4. ↵
    1. Pociot F,
    2. Akolkar B,
    3. Concannon P, et al
    . Genetics of type 1 diabetes: what’s next? Diabetes 2010;59:1561–1571pmid:20587799
    OpenUrlFREE Full Text
  5. ↵
    1. Billings LK,
    2. Florez JC
    . The genetics of type 2 diabetes: what have we learned from GWAS? Ann N Y Acad Sci 2010;1212:59–77pmid:21091714
    OpenUrlCrossRefPubMedWeb of Science
  6. ↵
    1. McCarthy MI
    . Genomics, type 2 diabetes, and obesity. N Engl J Med 2010;363:2339–2350pmid:21142536
    OpenUrlCrossRefPubMedWeb of Science
  7. ↵
    1. Locke AE,
    2. Kahali B,
    3. Berndt SI, et al.; LifeLines Cohort Study; ADIPOGen Consortium; AGEN-BMI Working Group; CARDIOGRAMplusC4D Consortium; CKDGen Consortium; GLGC; ICBP; MAGIC Investigators; MuTHER Consortium; MIGen Consortium; PAGE Consortium; ReproGen Consortium; GENIE Consortium; International Endogene Consortium
    . Genetic studies of body mass index yield new insights for obesity biology. Nature 2015;518:197–206pmid:25673413
    OpenUrlCrossRefPubMed
  8. ↵
    1. Dimas AS,
    2. Lagou V,
    3. Barker A, et al.; MAGIC Investigators
    . Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity. Diabetes 2014;63:2158–2171pmid:24296717
    OpenUrlAbstract/FREE Full Text
  9. ↵
    1. Cotsapas C,
    2. Voight BF,
    3. Rossin E, et al.; FOCiS Network of Consortia
    . Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet 2011;7:e1002254pmid:21852963
    OpenUrlCrossRefPubMed
  10. ↵
    1. Altshuler D,
    2. Hirschhorn JN,
    3. Klannemark M, et al
    . The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet 2000;26:76–80pmid:10973253
    OpenUrlCrossRefPubMedWeb of Science
  11. ↵
    1. Gloyn AL,
    2. Weedon MN,
    3. Owen KR, et al
    . Large-scale association studies of variants in genes encoding the pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes 2003;52:568–572pmid:12540637
    OpenUrlAbstract/FREE Full Text
  12. ↵
    1. Kathiresan S,
    2. Melander O,
    3. Guiducci C, et al
    . Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet 2008;40:189–197pmid:18193044
    OpenUrlCrossRefPubMed
  13. ↵
    1. Cohen JC,
    2. Boerwinkle E,
    3. Mosley TH Jr,
    4. Hobbs HH
    . Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N Engl J Med 2006;354:1264–1272pmid:16554528
    OpenUrlCrossRefPubMedWeb of Science
  14. ↵
    1. Oram RA,
    2. Patel K,
    3. Hill A, et al
    . A type 1 diabetes genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults. Diabetes Care 2016;39:337–344pmid:26577414
    OpenUrlAbstract/FREE Full Text
  15. ↵
    1. Onengut-Gumuscu S,
    2. Chen WM,
    3. Burren O, et al.; Type 1 Diabetes Genetics Consortium
    . Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat Genet 2015;47:381–386pmid:25751624
    OpenUrlCrossRefPubMed
  16. ↵
    1. Morris AP,
    2. Voight BF,
    3. Teslovich TM, et al.; Wellcome Trust Case Control Consortium; Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) Investigators; Genetic Investigation of ANthropometric Traits (GIANT) Consortium; Asian Genetic Epidemiology Network–Type 2 Diabetes (AGEN-T2D) Consortium; South Asian Type 2 Diabetes (SAT2D) Consortium; DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium
    . Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet 2012;44:981–990pmid:22885922
    OpenUrlCrossRefPubMed
  17. ↵
    1. Meigs JB,
    2. Shrader P,
    3. Sullivan LM, et al
    . Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med 2008;359:2208–2219pmid:19020323
    OpenUrlCrossRefPubMedWeb of Science
  18. ↵
    1. Walford GA,
    2. Porneala BC,
    3. Dauriz M, et al
    . Metabolite traits and genetic risk provide complementary information for the prediction of future type 2 diabetes. Diabetes Care 2014;37:2508–2514pmid:24947790
    OpenUrlAbstract/FREE Full Text
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Found in Translation: A Type 1 Diabetes Genetic Risk Score Applied to Clinical Diagnosis
Jose C. Florez
Diabetes Care Mar 2016, 39 (3) 330-332; DOI: 10.2337/dci15-0029

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Found in Translation: A Type 1 Diabetes Genetic Risk Score Applied to Clinical Diagnosis
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Diabetes Care Mar 2016, 39 (3) 330-332; DOI: 10.2337/dci15-0029
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