Genetics and biobanks converge to resolve a vexing knowledge gap in diabetes
Authors: Jose C Florez
Journal: The Lancet Diabetes & Endocrinology (2017)

The outdated designations of type 1 diabetes as “juvenile-onset diabetes” and type 2 diabetes as “adult-onset diabetes” reflect the incorrect categorical paradigm that has hindered the ability of clinicians to correctly diagnose outlier cases, causing unnecessary delays in instituting the appropriate therapy. The age-based distinction between the two most common forms of diabetes has become increasingly blurred, as type 2 diabetes becomes more frequent at younger ages and autoimmune diabetes in adulthood is better recognised. However, identification of autoimmune diabetes (whether type 1 diabetes or latent autoimmune diabetes of adults [LADA]) is largely dependent on the astuteness of clinicians and the availability of autoantibody testing. Thus, the diagnosis of autoimmune diabetes is all too frequently made post-hoc, after a patient with absolute insulin deficiency secondary to autoimmune β-cell destruction has developed life-threatening ketoacidosis while inadequately managed on oral antihyperglycaemic drugs. This historical barrier to the diagnosis of autoimmune diabetes in adulthood is compounded by the widespread use of insulin in severe or advanced diabetes, which can mask the presence of an autoimmune process in adult patients by protecting against ketoacidosis. Furthermore, autoantibody testing is not always done at the time of diagnosis because of its low positive predictive value in the context of the relatively low prevalence of autoimmune diabetes in adults. Thus, the true prevalence of autoimmune diabetes in adults is not known with confidence because many occult cases might exist among patients believed to have type 2 diabetes. In The Lancet Diabetes & Endocrinology, Nicholas Thomas and colleagues1Thomas NJ Jones SE Weedon MN Shields BM Oram RA Hattersley AT Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetic susceptibility analysis from UK Biobank.Lancet Diabetes Endocrinol. 2018; 6: 122-129Summary Full Text Full Text PDF PubMed Scopus (220) Google Scholar describe an innovative genetic method to estimate the prevalence of autoimmune diabetes in people older than 30 years. Their approach was facilitated by the explosion of genetic discovery over the past decade, which has yielded more than 50 loci that are robustly associated with type 1 diabetes.2Redondo MJ Steck AK Pugliese A Genetics of type 1 diabetes.Pediatr Diabetes. 2017; (published online Nov 2.)DOI:10.1111/pedi.12597Crossref Scopus (90) Google Scholar Identification of these loci enables the construction of genetic risk scores, which provide a fairly accurate estimate of overall genetic risk through summation of the aggregate of type 1 diabetes risk alleles in an individual (including variants correlated with the HLA risk haplotypes).3Oram RA Patel K 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: 337-344Crossref PubMed Scopus (155) Google Scholar, 4Winkler C Krumsiek J Buettner F et al.Feature ranking of type 1 diabetes susceptibility genes improves prediction of type 1 diabetes.Diabetologia. 2014; 57: 2521-2529Crossref PubMed Scopus (89) Google Scholar, 5Redondo MJ Oram RA Steck AK Genetic risk scores for type 1 diabetes prediction and diagnosis.Curr Diab Rep. 2017; 17: 129Crossref PubMed Scopus (26) Google Scholar Indeed, in a previous study,3Oram RA Patel K 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: 337-344Crossref PubMed Scopus (155) Google Scholar these researchers showed that a genetic risk score for type 1 diabetes, depending on where thresholds were set along the quantitative scale, could either exclude 95% of people without type 1 diabetes or capture 95% of those with type 1 diabetes. Because, for complex diseases, genetic burden has incomplete penetrance, the genetic risk score simply serves as a probability metric. The method used by Thomas and colleagues1Thomas NJ Jones SE Weedon MN Shields BM Oram RA Hattersley AT Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetic susceptibility analysis from UK Biobank.Lancet Diabetes Endocrinol. 2018; 6: 122-129Summary Full Text Full Text PDF PubMed Scopus (220) Google Scholar is deployed as follows. Consider a population of people with diabetes who are older than 30 years. This population is probably composed of a mixture of people with type 2 diabetes (the vast majority) and an unrecognised group of people with autoimmune diabetes (a small minority). One property of the type 1 diabetes genetic risk score is that a specific cutoff essentially separates the population without type 1 diabetes into two equal halves, whereas the same cutoff captures more than 95% of people with type 1 diabetes. In other words, for people without type 1 diabetes, that threshold divides the group into halves with genetic risk below and above the median, but those with type 1 diabetes will almost always fall above the threshold (see figure 1 in the report1Thomas NJ Jones SE Weedon MN Shields BM Oram RA Hattersley AT Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetic susceptibility analysis from UK Biobank.Lancet Diabetes Endocrinol. 2018; 6: 122-129Summary Full Text Full Text PDF PubMed Scopus (220) Google Scholar). Enter UK Biobank. This amazing resource contains more than 500 000 individuals in the UK who have provided DNA and allowed their genetic data to be linked to their electronic health record. This dataset allows for the generation of a type 1 diabetes genetic risk score for each participant, with particular attention paid to people aged 30–60 years and those with diabetes. Setting the genetic risk score threshold at the 50th centile divides the group of 379 511 participants into two halves of 189 508 individuals with a high type 1 diabetes genetic risk score and 190 003 individuals with a low type 1 diabetes genetic risk score. Focusing on the 13 250 individuals with diabetes, one sees that 7268 people are above the type 1 diabetes genetic risk score cutoff and 5982 people are below the same cutoff, which is significantly skewed from the 50:50 expectation under the null hypothesis of a single population. The investigators appropriately speculate that this excess of 1286 individuals (nearly 10%) in the higher type 1 diabetes genetic risk score group have autoimmune diabetes. Although they were more likely to be diagnosed at a younger age (58% with diabetes diagnosed aged ≤30 years), a sizeable number of people with autoimmune diabetes were diagnosed when aged 31–60 years (about 4% of all individuals with diabetes). Among individuals with a high type 1 diabetes genetic risk score, no major clinical differences were seen between those diagnosed age 30 years or younger and those diagnosed later. This ingenious application of genetics enabled the establishment of an approximate prevalence of autoimmune diabetes among adults, which highlights the need for clinicians to be alert to this possibility at the time of diagnosis irrespective of the patient's age. However, it does not suggest that the type 1 diabetes genetic risk score can be used in this manner to diagnose autoimmune diabetes in individuals: because type 2 diabetes is much more prevalent in adults than autoimmune diabetes, most older adults with a high type 1 diabetes genetic risk score will still have type 2 diabetes. However, if the type 1 diabetes genetic risk score is low, the patient is unlikely to have autoimmune diabetes, showing how a genetic tool (which is extremely accurate, will become inexpensive, and only needs to be tested once in the lifetime of an individual) might help to exclude a diagnosis more effectively than the use of autoantibody testing or HLA haplotyping. The investigators focus on “genetically defined type 1 diabetes” and do not directly address LADA, typically defined as diabetes with positive autoantibodies diagnosed after age 35 years and not requiring insulin at the time of diagnosis (although many patients with LADA do require insulin within 3 years of onset). Whether LADA is a distinct entity, a less aggressive form of type 1 diabetes on an autoimmune continuum, or an artificial construct that results from a mixture of patients with type 1 and type 2 diabetes remains a point of contention.6Gale EA Latent autoimmune diabetes in adults: a guide for the perplexed.Diabetologia. 2005; 48: 2195-2199Crossref PubMed Scopus (114) Google Scholar, 7Tuomi T Santoro N Caprio S Cai M Weng J Groop L The many faces of diabetes: a disease with increasing heterogeneity.Lancet. 2014; 383: 1084-1094Summary Full Text Full Text PDF PubMed Scopus (401) Google Scholar, 8Ostergaard JA Laugesen E Leslie RD Should there be concern about autoimmune diabetes in adults? Current evidence and controversies.Curr Diab Rep. 2016; 16: 82Crossref PubMed Scopus (15) Google Scholar, 9Buzzetti R Zampetti S Maddaloni E Adult-onset autoimmune diabetes: current knowledge and implications for management.Nat Rev Endocrinol. 2017; 13: 674-686Crossref PubMed Scopus (145) Google Scholar In view of the genetic similarities between LADA and type 1 diabetes,10Mishra R Chesi A Cousminer DL et al.Relative contribution of type 1 and type 2 diabetes loci to the genetic etiology of adult-onset, non-insulin-requiring autoimmune diabetes.BMC Med. 2017; 15: 88Crossref PubMed Scopus (62) Google Scholar it is likely that the type 1 diabetes genetic risk score provides a reasonable approximation for all autoimmune diabetes, although this would need to be directly tested in a well phenotyped cohort of patients with LADA. More generally, the study by Thomas and colleagues1Thomas NJ Jones SE Weedon MN Shields BM Oram RA Hattersley AT Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetic susceptibility analysis from UK Biobank.Lancet Diabetes Endocrinol. 2018; 6: 122-129Summary Full Text Full Text PDF PubMed Scopus (220) Google Scholar shows that a concerted effort to advance genetic discovery for complex traits can yield useful translational results once the findings reach critical mass, genetic material becomes available across clinical resources, data are made available to investigators, and intelligent and inquisitive users are empowered to test hypotheses. Hopefully, this type of inquiry will continue to facilitate progress across many other complex diseases. I declare no competing interests. I am a Massachusetts General Hospital Research Scholar and am supported by US National Institutes of Health grants NIDDK R01 DK072041, NIDDK U01 DK105554, NIGMS R01 GM117163, NIDDK R01 DK105154, and NIDDK K24 DK110550. Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetically stratified survival analysis from UK BiobankGenetic susceptibility to type 1 diabetes results in non-obesity-related, insulin-dependent diabetes, which presents throughout the first six decades of life. Our results highlight the difficulty of identifying type 1 diabetes after age 30 years because of the increasing background prevalence of type 2 diabetes. Failure to diagnose late-onset type 1 diabetes can have serious consequences because these patients rapidly develop insulin dependency. Full-Text PDF Open Access