Authors propose an efficient and simple way to screen for monogenic diabetes. All individuals, diagnosed with type 1 diabetes before age 30, should be tested for serum or urinary C-peptide levels. If C-peptide is present then GAD and IA1 antibodies are measured. If antibodies are undetectable, then patients should undergo genetic testing.
This simple protocol has a 20% chance of identifying monogenic diabetes. In other words, it improves positive predictive value from baseline 3.6% to 20%, with an impressive negative predictive value 99.9%. Identifying this rare form of diabetes is important, as patients could switch from insulin to oral sulfonylurea. In addition, family members could benefit from genetic screening and counseling.
ADA on monogenic diabetes and MODY
Monogenic diabetes: more common than we think
Objective: Monogenic diabetes, a young-onset form of diabetes, is often misdiagnosed as type 1 diabetes, resulting in unnecessary treatment with insulin. A screening approach for monogenic diabetes is needed to accurately select suitable patients for expensive diagnostic genetic testing. We used C-peptide and islet autoantibodies, highly sensitive and specific biomarkers for discriminating type 1 from non–type 1 diabetes, in a biomarker screening pathway for monogenic diabetes.
Research Design and Methods: We studied patients diagnosed at age 30 years or younger, currently younger than 50 years, in two U.K. regions with existing high detection of monogenic diabetes. The biomarker screening pathway comprised three stages: 1) assessment of endogenous insulin secretion using urinary C-peptide/creatinine ratio (UCPCR); 2) if UCPCR was ≥0.2 nmol/mmol, measurement of GAD and IA2 islet autoantibodies; and 3) if negative for both autoantibodies, molecular genetic diagnostic testing for 35 monogenic diabetes subtypes.
Results: A total of 1,407 patients participated (1,365 with no known genetic cause, 34 with monogenic diabetes, and 8 with cystic fibrosis–related diabetes). A total of 386 out of 1,365 (28%) patients had a UCPCR ≥0.2 nmol/mmol, and 216 out of 386 (56%) were negative for GAD and IA2 and underwent molecular genetic testing.
Seventeen new cases of monogenic diabetes were diagnosed (8 common Maturity Onset Diabetes of the Young [Sanger sequencing] and 9 rarer causes [next-generation sequencing]) in addition to the 34 known cases (estimated prevalence of 3.6%, 51/1,407). The positive predictive value was 20%, suggesting a 1-in-5 detection rate for the pathway. The negative predictive value was 99.9%.
The biomarker screening pathway for monogenic diabetes is an effective, cheap, and easily implemented approach to systematically screening all young-onset patients. The minimum prevalence of monogenic diabetes is 3.6% of patients diagnosed at age 30 years or younger.
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Correct classification of a patient’s diabetes is important to ensure he or she receives the most appropriate treatment and ongoing management. The most common form of diabetes in children and young adults is type 1 diabetes, accounting for >90% of cases. Other forms of diabetes in this age group, such as monogenic diabetes (including Maturity Onset Diabetes of the Young [MODY]), or young-onset type 2, are not often considered. It is estimated that at least 80% of patients with MODY are misdiagnosed, and other rarer forms of monogenic diabetes often go unrecognized because of lack of awareness. Patients with MODY or type 2 diabetes misclassified as type 1 diabetes will be treated with insulin, whereas noninsulin therapy would be more appropriate. Diet and metformin are the treatment of choice in young type 2 diabetes. Patients with MODY because of mutations in the HNF1A or HNF4A genes respond well to low-dose sulphonylureas, and those with MODY because of mutations in the GCK gene require no pharmacological treatment. Getting a correct diagnosis for all forms of monogenic diabetes has important implications for management of an individual’s diabetes, a prognosis, and recognition of associated clinical features; it also allows appropriate counseling of other family members regarding likely inheritance.
Identifying patients with monogenic diabetes, particularly MODY, can be challenging. Monogenic diabetes is confirmed by molecular genetic testing, but this is expensive, so testing all patients is not feasible. An approach that could be used to enrich for monogenic diabetes, increasing the proportion identified in those who undergo genetic testing, would be helpful. Clinical features can aid identification of those who may have an alternative diagnosis, and a probability calculator has been developed to help determine which patients are likely to have the most common forms of MODY. However, this will not pick up other forms of monogenic diabetes, and its performance is weaker for detecting MODY in insulin-treated patients compared with non–insulin-treated patients.
An alternative approach to enrich for monogenic diabetes is to use biomarkers that have been shown to discriminate well between type 1 and other forms of young-onset diabetes. Type 1 diabetes is characterized by autoimmune destruction of the β-cells in the pancreas, leading to absolute insulin deficiency, so two tests that could be used to diagnose type 1 diabetes are islet autoantibodies (markers of the autoimmune process) and C-peptide (a marker of insulin deficiency). C-peptide has been shown to be a highly sensitive and specific biomarker for discriminating between type 1 and type 2 diabetes and MODY 3–5 years after diagnosis. Urine C-peptide/creatinine ratio (UCPCR) can be used to remove the need for blood samples, which may be of particular concern in the pediatric population, and means that the sample can easily be taken at home and posted to the laboratory. GAD and IA2 islet autoantibodies also discriminate well between type 1 and MODY, with cross-sectional studies showing they are present in 80% of patients with type 1 diabetes and in <1% of patients with MODY. These biomarkers have been used to screen for MODY in other studies, but have been limited to pediatric cases only.
Given that the median age at diagnosis for MODY is 20 years (from U.K. referrals data), and there is on average a delay of 13 years from diabetes diagnosis to a confirmed genetic diagnosis, it is crucial to study adults as well. Furthermore, the combined diagnostic performance of the two biomarkers as a screening pathway has not been formally assessed.
By excluding those with type 1 diabetes using these two biomarkers, we can obtain a smaller percentage of patients in whom diagnostic molecular testing for monogenic diabetes could be performed. We tested a screening pathway using both C-peptide and islet autoantibodies to exclude type 1 diabetes in two populations with previously high pickup rates of MODY and performed genetic testing on all patients with significant endogenous insulin and absence of islet autoantibodies. This allowed us to determine the prevalence of all monogenic diabetes subtypes in those diagnosed at 30 years or younger and to calculate the positive predictive values (PPVs) and negative predictive values (NPVs) for the pathway.
We found that 3.6% of patients diagnosed at younger than 30 years of age have monogenic diabetes. In areas in which no cases have been identified, we estimate that 1 in 5 patients referred for genetic testing because of the pathway will have monogenic diabetes, which is a 5.6-fold higher detection rate than if all patients in this age range received genetic testing. The high NPV of 99.9% indicates it is an extremely effective approach for ruling out monogenic diabetes.
There have been relatively few studies that have systematically screened whole populations for monogenic diabetes. The majority of studies have been in pediatric populations only, with only two studies that have screened adults. No other study has systematically screened a whole population of both adults and children together.
Only 8 out of 51 (16%) of patients with a genetic diagnosis of monogenic diabetes in our cohort were in the pediatric age range (younger than 20 years) at the time of recruitment, highlighting the importance of looking for monogenic diabetes in adult diabetes clinics. This may explain why the prevalence we find is higher than in any of the previous pediatric studies.
The strength of our pathway is the integration of two biomarkers (C-peptide and islet autoantibodies [both GAD and IA2]), rather than relying on clinical features. This offers a simple approach that does not require specific clinician interpretation or complex algorithms of different combinations of features. We showed that by using clinical features alone, over half of the cases of monogenic diabetes would be missed. By combining the two biomarkers, we increase the discriminatory ability and allow the clinician to pick up even atypical cases and rarer forms of monogenic diabetes, which traditional criteria may miss. The use of clinical features, however, results in fewer cases being sent for genetic testing that are negative, which clearly has cost implications. The most cost-effective approach is likely to involve a combination of biomarkers and clinical features. Further studies are needed to determine whether the pickup rate could be further improved by integrating the pathway with clinical features, such as the MODY calculator, or whether this would result in more missed patients because of reduced testing.
In this study, we also systematically tested all known genes for monogenic diabetes, rather than just the most common MODY genes (GCK, HNF1A, and HNF4A). Nine out of 17 (53%) of the cases identified as part of our cohort had mutations identified through additional testing on the targeted capture, and 17 out of 51 (33%) of all of the monogenic diabetes cases found in total had mutations in other genes, highlighting the advantage of further testing using targeted next-generation sequencing.
Treatment change from insulin to sulphonylureas is still possible in cases diagnosed with ABCC8 and KCNJ11, and for other genes for which treatment change is not an option, a confirmed diagnosis can still help with management, prognosis, and advice on risk to other family members. The decision whether to pay for the more expensive, but more comprehensive, next-generation sequencing, rather than Sanger sequencing for MODY genes only, would depend on assessing the tradeoffs of additional costs with long-term benefits to the patient. The presence of additional clinical features (e.g., renal cysts associated with HNF1B) may also point to specific monogenic diagnoses and increase the likelihood of a positive genetic test result.
A further limitation is that despite screening using C-peptide and antibody testing, the PPV is still fairly low at 20%, indicating that four out of five screened will not have a monogenic cause identified on diagnostic molecular genetic testing. However, the aim of our screening pathway is that it is used purely as a tool to narrow down those individuals who would be more appropriate for genetic testing. This approach is a vast improvement over no screening (which would represent a PPV at the background prevalence rate of 3.6%), misses fewer cases than using clinical features alone, and is at a level that has been shown to be cost-effective.
Finally, this study comprised a 98% white population and assesses patients at a median of 14 years after diagnosis. Assessment of the pathway in other racial groups and in patients close to diagnosis is needed.
In conclusion, we have demonstrated a simple, cheap, effective screening pathway that could be implemented at a population level to help correctly diagnose patients with monogenic diabetes.