Population-Based Assessmentof a Biomarker-Based ScreeningPathway to Aid Diagnosisof Monogenic Diabetes inYoung-Onset PatientsDiabetes Care 2017;40:1017–1025 | https://doi.org/10.2337/dc17-0224

OBJECTIVE

Monogenic diabetes, a young-onset formof diabetes, is oftenmisdiagnosed as type 1diabetes, resulting in unnecessary treatment with insulin. A screening approach formonogenic diabetes is needed to accurately select suitable patients for expensivediagnostic genetic testing. We used C-peptide and islet autoantibodies, highly sen-sitive and specific biomarkers for discriminating type 1 from non–type 1 diabetes, ina biomarker screening pathway for monogenic diabetes.

RESEARCH DESIGN AND METHODS

We studied patients diagnosed at age 30 years or younger, currently younger than50 years, in twoU.K. regions with existing high detection ofmonogenic diabetes. Thebiomarker screening pathway comprised three stages: 1) assessment of endogenousinsulin secretion using urinary C-peptide/creatinine ratio (UCPCR); 2) if UCPCRwas ‡0.2 nmol/mmol, measurement of GAD and IA2 islet autoantibodies; and 3) ifnegative for both autoantibodies, molecular genetic diagnostic testing for 35 mono-genic diabetes subtypes.

RESULTS

A total of 1,407 patients participated (1,365 with no known genetic cause, 34 withmonogenic diabetes, and 8with cystic fibrosis–related diabetes). A total of 386 out of1,365 (28%) patients had a UCPCR ‡0.2 nmol/mmol, and 216 out of 386 (56%) werenegative for GAD and IA2 and underwent molecular genetic testing. Seventeen newcases ofmonogenic diabeteswere diagnosed (8 commonMaturityOnset Diabetes ofthe Young [Sanger sequencing] and 9 rarer causes [next-generation sequencing]) inaddition to the 34 known cases (estimated prevalence of 3.6% [51/1,407] [95% CI2.7–4.7%]). The positive predictive value was 20%, suggesting a 1-in-5 detection ratefor the pathway. The negative predictive value was 99.9%.

CONCLUSIONS

The biomarker screening pathway formonogenic diabetes is an effective, cheap, andeasily implemented approach to systematically screening all young-onset patients.Theminimumprevalence ofmonogenic diabetes is 3.6%of patients diagnosed at age30 years or younger.

1Institute of Biomedical and Clinical Science, Uni-versity of Exeter Medical School, Exeter, U.K.2NIHR Exeter Clinical Research Facility, RoyalDevon and Exeter NHS Foundation Trust, Exeter,U.K.3Blood Sciences, Royal Devon and Exeter NHSFoundation Trust, Exeter, U.K.4Molecular Genetics Diagnostic Laboratory,Royal Devon and Exeter NHS Foundation Trust,Exeter, U.K.5Exeter Test Group, University of Exeter MedicalSchool, Exeter, U.K.6Division of Molecular & Clinical Medicine,School of Medicine, University of Dundee,Dundee, U.K.

Corresponding author: Andrew T. Hattersley,a.t.hattersley@exeter.ac.uk.

Received 30 January 2017 and accepted 26 April2017.

Clinical trial reg. no. NCT01238380, clinicaltrials.gov.

This article contains Supplementary Data onlineat http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc17-0224/-/DC1.

© 2017 by the American Diabetes Association.Readers may use this article as long as the workis properly cited, the use is educational and notfor profit, and the work is not altered. More infor-mation is available at http://www.diabetesjournals.org/content/license.

Beverley M. Shields,1,2

Maggie Shepherd,1,2 Michelle Hudson,1

Timothy J. McDonald,1,3 Kevin Colclough,4

Jaime Peters,5 Bridget Knight,1,2

Chris Hyde,5 Sian Ellard,1,4

Ewan R. Pearson,6 and

Andrew T. Hattersley,1,2 on behalf of the

UNITED study team

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Correct classification of a patient’s diabe-tes is important to ensure he or she re-ceives the most appropriate treatmentand ongoing management. The mostcommon form of diabetes in childrenand young adults is type 1 diabetes, ac-counting for .90% of cases (1,2). Otherforms of diabetes in this age group,such as monogenic diabetes (includingMaturity Onset Diabetes of the Young[MODY]), or young-onset type 2, are notoften considered. It is estimated that atleast 80% of patients withMODY aremis-diagnosed (3), and other rarer forms ofmonogenic diabetes often go unrecog-nized because of lack of awareness (4).Patients with MODY or type 2 diabetesmisclassified as type 1 diabetes willbe treated with insulin, whereas noninsu-lin therapy would be more appropriate.Diet and metformin are the treatment ofchoice in young type 2 diabetes (5). Pa-tients with MODY because of mutationsin the HNF1A or HNF4A genes respondwell to low-dose sulphonylureas (6,7),and those with MODY because of muta-tions in the GCK gene require no pharma-cological treatment (8). Getting a correctdiagnosis for all forms of monogenic di-abetes has important implications forman-agement of an individual’s diabetes, aprognosis, and recognition of associatedclinical features; it also allows appropriatecounseling of other family members re-garding likely inheritance (4).Identifying patientswithmonogenic di-

abetes, particularly MODY, can be chal-lenging. Monogenic diabetes is confirmedby molecular genetic testing, but this isexpensive, so testing all patients is notfeasible. An approach that could beused to enrich for monogenic diabetes,increasing the proportion identified inthose who undergo genetic testing,would be helpful. Clinical features canaid identification of those who mayhave an alternative diagnosis, and aprobability calculator has been devel-oped to help determine which patientsare likely tohave themost common formsof MODY (9). However, this will not pickup other forms of monogenic diabetes,and its performance is weaker for detect-ing MODY in insulin-treated patientscompared with non–insulin-treatedpatients.An alternative approach to enrich for

monogenic diabetes is to use biomarkersthat have been shown to discriminatewell between type 1 and other forms of

young-onset diabetes. Type 1 diabetes ischaracterized by autoimmune destruc-tion of the b-cells in the pancreas, lead-ing to absolute insulin deficiency, so twotests that could be used to diagnosetype 1 diabetes are islet autoantibodies(markers of the autoimmune process)and C-peptide (a marker of insulin de-ficiency). C-peptide has been shown tobe a highly sensitive and specific bio-marker for discriminating betweentype 1 and type 2 diabetes and MODY3–5 years after diagnosis (10,11). UrineC-peptide/creatinine ratio (UCPCR) canbe used to remove the need for bloodsamples, which may be of particular con-cern in the pediatric population, andmeans that the sample can easily betaken at home and posted to the labora-tory (12). GAD and IA2 islet autoanti-bodies also discriminate well betweentype 1 and MODY, with cross-sectionalstudies showing they are present in 80%of patients with type 1 diabetes andin ,1% of patients with MODY (13).These biomarkers have been used toscreen for MODY in other studies (14,15),but have been limited to pediatric casesonly. Given that the median age at di-agnosis for MODY is 20 years (from U.K.referrals data [3]), and there is onaverage a delay of 13 years from diabe-tes diagnosis to a confirmed genetic

diagnosis (16), it is crucial to study adultsas well. Furthermore, the combined diag-nostic performance of the two bio-markers as a screening pathway has notbeen formally assessed.

By excluding thosewith type1 diabetesusing these two biomarkers, we canobtain a smaller percentage of patientsin whom diagnostic molecular testingfor monogenic diabetes could be per-formed. We tested a screening pathwayusing both C-peptide and islet autoanti-bodies to exclude type 1 diabetes in twopopulations with previously high pickuprates ofMODY (3) and performed genetictesting on all patients with significant en-dogenous insulin and absence of isletautoantibodies. This allowed us to deter-mine the prevalence of all monogenic di-abetes subtypes in those diagnosed at30 years or younger and to calculate thepositive predictive values (PPVs) and neg-ative predictive values (NPVs) for thepathway.

RESEARCH DESIGN AND METHODS

SubjectsPatients diagnosed at age 30 years oryounger, and currently aged youngerthan 50 years, in the catchment areas ofthe Royal Devon and Exeter NHS Founda-tion Trust (Exeter, U.K.) and NinewellsHospital (Dundee, U.K.) were invited to

Figure 1—The UNITED biomarker screening pathway to investigate etiology of diabetes in patientsdiagnosed at age 30 years or younger. Genetic testing is carried out on all patients who haveendogenous insulin (UCPCR $0.2 nmol/mmol) and do not have either GAD or IA2 islet autoanti-bodies. Patientswithout endogenous insulin orwithGADand/or IA2 islet autoantibodies are classedas having type 1 diabetes.

1018 Monogenic Diabetes Biomarker Screening Pathway Diabetes Care Volume 40, August 2017

take part in the study via the doctors look-ing after their medical care. All patientswith diabetes in this age group were eli-gible regardless of cause. Both regionshad existing high pickup rates for MODYprior to the study because of researchinterests (3). Patients who consentedprovided samples as part of the bio-markers screening pathway (the UsingpharmacogeNetics to Improve Treatmentin Early-onset Diabetes [UNITED] study;clinicaltrials.gov NCT01238380).

Biomarker Screening PathwayAll recruited patients followed the bio-marker screening pathway (Fig. 1).

Assessment of Endogenous Insulin, in

Insulin-Treated Patients, Using UCPCR

A key determinant of requirement forinsulin treatment is lack of endogenousinsulin secretion, and UCPCR is an easyscreening test that can be done at home.UCPCRwas used to rule out themajority ofpatients with type 1 diabetes in the firststage of screening, with minimal patientburden.Insulin-treated patients were asked to

collect a urine sample 2 h after the largestcarbohydrate-containing meal of the dayand to post this direct to the laboratoryin a pack provided to allow analysis within72 h of sample collection, in line withassay stability (12).Urinary C-peptide was measured by an

electrochemiluminescence immunoassay(intra-assay coefficient of variation, 3.3%;and interassay coefficient of variation,4.5%) on anE170 analyzer (RocheDiagnos-tics,Mannheim, Germany) (12). The lowerlimit of the C-peptide assay was 0.03nmol/L. Urinary creatinine was analyzedon the Roche P800 platform using creat-inine Jaffe reagent (standardized againstisotope dilution mass spectrometry) andused to calculate UCPCR (nmol/mmol).Patients with UCPCR $0.2 nmol/mmolwere considered to have significant en-dogenous insulin secretion (10).

Islet Autoantibody Measurement in Patients

With Significant Endogenous Insulin

Islet autoantibodies (GAD and IA2) weremeasured in patients who tested positivefor UCPCR ($0.2 nmol/mmol) or whowere noninsulin treated. In order to min-imize taking blood samples, particularly inchildren, the local pathology databaseswere checked for previous GAD and IA2results, and these were used if available.Patients with no previous islet autoanti-body results were invited to attend an

appointment with the study’s researchnurse to provide blood samples for isletautoantibody testing and DNA.

GADand IA2 antibody analysiswas per-formed using commercial ELISA assays(RSR Ltd., Cardiff, U.K.) and a Dynex DSXautomated ELISA system (Launch Diag-nostics, Longfield, U.K.) (13). Both meth-ods are highly specific and sensitive (GADantibodies, 98 and 84%, and IA-2 anti-bodies, 99 and 74%, respectively). Thelaboratory participates in the DiabetesAutoantibody Standardization Program.Patients were considered positive for an-tibodies if their resultswere.99th centile(64 World Health Organization units/mLfor GAD and 15 World Health Organiza-tion units/mL for IA2) (13).

Diagnostic Molecular Genetic Testing for

Monogenic Diabetes in Patients With

Significant Endogenous Insulin and

Negative Antibody Results

Sequencing of Three MODY Genes, the Most

Common Forms of Monogenic Diabetes. Forall patients who were negative for bothGAD and IA2 antibodies with significantendogenous insulin, DNA sequencing ofHNF1A, HNF4A, and GCK was performedby PCR amplification of purified genomicDNA, followedby Sanger DNA sequencingof each gene’s exons and flanking intronicregions. Dosage analysis of HNF1A,HNF4A, and GCK for partial- and whole-gene deletions was also performed bymultiplex ligation-dependent probe am-plification using the MRC-Holland MODYmultiplex ligation-dependent probe am-plification kit (P241-B1).Targeted Next-Generation Sequencing for

35 Genes in Which Mutations Are Known

to Cause Monogenic Diabetes. If no patho-genic mutation was identified in HNF1A,HNF4A, or GCK, further targeted next-generation sequencing was performedfor mutations in 35 monogenic diabetesgenes (all genes in which mutations areknown to cause MODY, neonatal dia-betes, and other genetic diabetic syn-dromes) using a custom SureSelectexon-capture assay (Agilent Technolo-gies, Santa Clara, CA) (17) (see Supple-mentary Data and Supplementary Table 1for methodology, sensitivity, and detailsof genes tested).

Statistical AnalysisFor comparing new cases diagnosedthrough the screening pathway to knowncases of monogenic diabetes and for

comparing the biomarker screening path-way with an approach using clinical fea-tures (including the MODY probabilitycalculator [9]) to detect monogenic dia-betes, variables were categorical, andtherefore, x2 and Fisher exact testswere used.Prevalence of MODY

The prevalence of MODY in this popula-tion was determined as the proportion ofpositive cases (including both knownMODY who were recruited and thoseidentified through the study) out of thetotal recruited.

To determine whether there was anypotential bias in recruitment of MODYpatients that may affect our prevalenceestimate, we also obtained summarydata on the number of patients with pre-viously confirmed monogenic diabetes ineach study area who had not been re-cruited into this study.Positive and Negative Predictive Values of

Pathway

Calculating the prevalence in this popula-tion allows us to determine the PPVs andNPVs for the pathway, the most impor-tant statistics for the clinician. PPVs andNPVs were calculated as posttest odds =pretest odds 3 positive likelihood ratio,in which pretest odds is prevalence/(12prevalence), and positive likelihoodratio is sensitivity/(1 2 specificity). PPV(equivalent to posttest probability) isposttest odds/(1 + posttest odds). NPVwas calculated similarly, but using a neg-ative likelihood ratio (1 2 sensitivity/specificity), with negative posttest proba-bility equal to 12 NPV. Number neededto test was calculated as 1/PPV.Performance of the Pathway: Sensitivity and

Specificity

The key question is: how well, if appliedto a whole population, do the biomarkersperform in a pathway for identifying newcases? Screening literature emphasizesthe difference between program sensi-tivity/specificity and test sensitivity/specificity, inwhich assessing the sensitiv-ity/specificity of a screening programsuch as this necessarily requires approxi-mation using multiple data sources (18).As this was a population-based studyrather than a case-control study, formalassessments of sensitivity and specificity(as normally conducted using a 23 2 ta-ble) of the pathway were limited becauseof the rarity of monogenic diabetes(meaning a small sample size of true pos-itive cases of monogenic diabetes) and

care.diabetesjournals.org Shields and Associates 1019

the expense of genetic testing (restrictingconfirmation of all the true negative non-monogenic cases).Assessments of sensitivity of the com-

ponents of the pathway for detectingmonogenic diabetes have been carriedout in larger case-control cohorts (n =508 monogenic diabetes cases for isletautoantibodies [99% sensitivity] [13];and n = 160 for UCPCR [99% sensitivity,both studies combined] [10,11]), so it ismore appropriate to use these estimates.We assumed a 98% sensitivity for bothcombined, based on these larger studies(assuming 1% missed because of false-negative UCPCR and 1% because offalse-positive islet autoantibodies). How-ever, the detection rate in all true mono-genic cases in this pathway will becalculated for comparison.Calculation of the specificity is limited, as

we have not performed genetic testing onall C-peptide–negative patients. Previouslarger studies have shown ,1% of pa-tients are missed (10,11,13). However,specificity of the biomarkers in thesestudies was assessed using gold-standardtype 1 diabetes as the comparison group,rather than all non-MODY patients in thisage range, and thus likely overestimates

the performance because of spectrumbias (19). We therefore calculated spec-ificity based on one minus the false-positive rate of thepathway (i.e., proportionUCPCR-positive/antibody-negative, butnot having a confirmed diagnosis ofmonogenic diabetes on subsequent ge-netic testing). This assumes that all patientsnegative according to the pathway aretrue negatives. As an additional test ofthis assumption, a subset of patients neg-ative for islet autoantibodies received ge-netic testing for the three main MODYgenes, and the proportion of MODY wascalculated.

Health economic evaluation of thepathway is addressed in a separate proj-ect (20,21).

RESULTS

SubjectsThe flow of subjects through the study isshown in Fig. 2. A total of 2,288 patientswere eligible in area, and 1,418 subjects(62%) in total consented to the study andwere recruited: 716 from the Exeter areaand 702 from Dundee. A total of 11 pa-tients dropped out (9 did not provideblood samples for antibody testing, and2did not provide samples forDNA testing).

Of the 1,407 remaining patients, 1,365 hadno known genetic cause for their diabetes.Characteristics of thesepatients are shownin Supplementary Table 2, and subse-quent results on the screening pathwayare based on these patients. A total of42 patients had a known genetic causefor their diabetes prior to participatingin this pathway: 34 patients had con-firmed monogenic diabetes (Table 1),and 8 patients had cystic fibrosis–relateddiabetes.

Biomarker Screening PathwayIdentifies 17 New Cases of MonogenicDiabetesExcluding dropouts, 1,281 (94%) of 1,365patients with no known genetic cause fortheir diabeteswere insulin treatedandpro-vided a sample for UCPCR testing. Two pa-tients were anuric because of renal failureand thereforewent straight on to antibodytesting. A total of 979 of these patients(76%) had minimal endogenous insulinsecretion (UCPCR ,0.2 nmol/mmol),indicating a diagnosis of type 1 diabetes,and received no further testing.

Islet autoantibodies were tested inthe 84 non–insulin-treated patients,300UCPCR-positive patients, and 2 anuricpatients. A total of 170 out of 386 (44%)tested positive for GAD and/or IA2 anti-bodies, confirming islet autoimmunityand hence a diagnosis of type 1 diabetes.Therefore, these patients received no fur-ther testing.

Sanger sequencing for the three mostcommonMODY genes was undertaken in216 patients (16% of the whole cohort).Eight patients tested positive, confirminga diagnosis of MODY: five HNF1A, twoHNF4A, and one GCK (Fig. 2 and Table 1).

Of the 208 who tested negative for thecommon MODY genes, additional testingby targeted next-generation sequencingidentified mutations in genes associatedwith monogenic diabetes in a further8 patients, and 1 patient had a mutationin POLD1 identified through exome se-quencing (Fig. 2 and Table 1).

New Cases of Monogenic DiabetesIdentifiedWere More Likely to Be RarerCauses and AtypicalMore of the new cases of monogenic di-abetes identified had mutations in genesother than the threemost common formsof MODY (25 out of 34 [74%] of thosediagnosed prior to the study had muta-tions inHNF1A, HNF4A, orGCK comparedwith 8 out of 17 [47%] identified from

Figure 2—Flow chart of patients recruited as part of UNITED. Biomarker screening pathway in 1,376patients with no known genetic cause for their diabetes in Exeter and Tayside. Eleven dropped out.Seventeen new cases of monogenic diabetes detected (*one case identified through exomesequencing).

1020 Monogenic Diabetes Biomarker Screening Pathway Diabetes Care Volume 40, August 2017

Table 1—Characteristics of patients diagnosed with monogenic diabetes and details of mutations found for those recruited butdiagnosed with monogenic diabetes prior to the study and those diagnosed as a result of the biomarker pathway

ID

Genetic characteristicsClinical characteristics

Gene Method ZygosityDNA leveldescription

Age atdiagnosis(years) Treatment

Parentwith

diabetes BMI HbA1c

Age atrecruitment(years)

Additionalclinicalfeatures

Recruited but diagnosed prior to the UNITED study211 GCK Sanger Het c.97_117dup 3 Diet Yes 34.0 48 16523 GCK Sanger Het c.97_117dup 27 Diet No 51.7 55 43537 GCK Sanger Het c.683C.T 11 Diet Yes 13538 GCK Sanger Het c.683C.T 9 Diet Yes 11542 GCK Sanger Het c.184G.A 29 Diet Yes 38.9 48 39543 GCK Sanger Het c.184G.A 4 Diet Yes 4544 GCK Sanger Het c.184G.A 3 Diet Yes 51155 GCK Sanger Het c.1343G.T 25 Diet Yes 19.3 50 2582095 GCK Sanger Het c.1019G.T 9 Diet Yes 21.9 45 14535 HNF1A Sanger Het c.379_381del 24 OHA Yes 25.9 51 47547 HNF1A Sanger Het c.1748G.A 22 Diet Yes 24.4 40 30 Low renal threshold554 HNF1A Sanger Het c.872dup 18 OHA Yes 30 86 39566 HNF1A Sanger Het c.872dup 17 OHA Yes 29.2 51 42 Sulphonylurea

sensitivity, low renalthreshold

603 HNF1A Sanger Het c.1420C.T 20 OHA Yes 26.5 56 42 Low renal threshold617 HNF1A Sanger Het c.779C.T 25 Diet Yes 25.4 44 26892 HNF1A Sanger Het c.476G.A 14 Insulin Yes 30.0 63 401370 HNF1A Sanger Het c.872dup 21 Diet Yes 36.1 83 211409 HNF1A Sanger Het c.872dup 21 OHA+Ins Yes 32.8 95 42 Sulphonylurea

sensitivity80480 HNF1A Sanger Het c.1093_1107+6del 19 OHA 22.9 73 4082261 HNF1A Sanger Het c.185del 12 OHA+Ins Yes 23.7 73 25 Low renal threshold82276 HNF1A Sanger Het c.434C.T 13 Insulin Yes 23.8 60 2782301 HNF1A Sanger Het c.1340C.T 20 OHA Yes 27.4 91 3782310 HNF1A Sanger Het c.185del 18 OHA Yes 24.4 48 45 Low renal threshold82374 HNF1A Sanger Het c.1093_1107+6del 19 OHA Yes 23.8 83 20 Sulphonylurea

sensitivity82258 HNF4A Sanger Het c.322G.A 28 Insulin Yes 20.9 60 31600 HNF1B Sanger Het c.982_986del 20 Insulin No 23.4 122 35 Renal cysts82033 HNF1B Sanger Het c.466A.G 17 Insulin No 25.3 54 35 Genital tract

malformations, renalhypoplasia

82006 KCNJ11 Sanger Het c.601C.T 0 OHA No 26.6 33 35 Diagnosed at 12 weeksof age

539 LMNA Sanger Het c.1930C.T 17 OHA+Ins Yes 24.2 114 49 Lipodystrophy595 LMNA Sanger Het c.1444C.T 21 OHA+Ins Yes 25.1 62 34604 3243 Hp m.3243A.G 27 Insulin Yes 26.9 54 3680541 3243 Hp m.3243A.G 28 Insulin Yes 26.4 83 4882399 3243 Hp m.3243A.G 29 Insulin Yes 26.4 56 41 Deafness540 NEUROD1 Sanger Het c.616dup 21 OHA+Ins Yes 49.8 83 36 Lipodystrophy and

necrobiosis

Identified as part of the biomarker pathway82372 GCK Sanger Het c.1340G.A 18 Diet No 25.5 46 1982316 HNF4A Sanger Het c.1064–5_1070del 14 Diet Yes 32.3 38 33377 HNF4A Sanger Het c.-12G.A 11 Insulin Yes 28.4 104 1480089 HNF1A Sanger Het c.1349dup 30 Insulin Yes 31.0 72 4880170 HNF1A Sanger Het c.391C.T 21 Insulin No 23.5 52 35 Low renal threshold80173 HNF1A Sanger Het c.495G.C 17 Insulin Yes 24.5 56 4682003 HNF1A Sanger Het c.28A.C 26 Diet Yes 29.8 73 2682352 HNF1A Sanger Het c.814C.T 13 Insulin Yes 32.3 91 4582013 HNF1A tNGS Het c.-258A.G 24 OHA Yes 39.6 75 43307 HNF1B tNGS Het c.1-?_1674+?del 29 Insulin No 22.7 62 31 Aspergersyndrome,renal

cysts, low fecal elastase,low magnesium

82014 NEUROD1 tNGS Het c.616dup 21 OHA No 35.3 88 31183 NEUROD1 tNGS Het c.616dup 29 Insulin No 27.1 55 4682010 3243 tNGS Hp m.3243A.G 27 OHA+Ins Yes 28.6 91 46

Continued on p. 1022

care.diabetesjournals.org Shields and Associates 1021

Sanger sequencing as part of the bio-marker screening pathway; P = 0.06)(Supplementary Fig. 1). Those diagnosedwith monogenic diabetes as part of thestudy were less likely to have a parentknown to be affected than those with aprevious known monogenic diagnosis(8 out of 17 [47%] vs. 29 out of 34 [85%];P = 0.007).

Minimum Prevalence of MonogenicDiabetes of 3.6% in Those Diagnosed at30 Years or Younger, CurrentlyYounger Than 50 YearsWe found 51 cases of monogenic diabe-tes (which represents a further 50% [n =17] in addition to the 34 previously diag-nosed) out of 1,407 recruited patients,providing a prevalence of 3.6% (95% CI2.7% to 4.7%) in patients diagnosed at30 years or younger and currently youn-ger than 50 years.From the database of U.K. referrals, we

identified 26 patients with a diagnosis ofmonogenic diabetes in the Exeter andTayside regions who met study inclusioncriteria but were not recruited to theUNITED study. Therefore, the proportionof known monogenic diabetes prior tothe study in the recruited population(34 out of 1,407 [2.4%]) was similar tothe proportion in the nonrecruited pop-ulation (26 out of 870 [3.0%]) (P = 0.4),suggesting no overall bias in recruitment.More of the nonrecruited cases hadMODY caused by mutations in the GCK

gene, but this was not significant giventhe small numbers (46 vs. 26%; P = 0.1).Therewas nodifference in terms of age atdiagnosis (mean 18 vs. 19 years; P = 0.5),age at time of recruitment (using 2011 fornonrecruited patients) (32 vs. 32 years;P = 0.98), or sex (35 female vs. 45%male; P = 0.4).

Performance of the PathwayIn line with what was expected givenlarger studies of the diagnostic accuracyof UCPCR and islet autoantibodies andthe known pathophysiology of mono-genic diabetes, all case subjects with pre-viously diagnosed monogenic diabeteswhoprovided all samples for the pathway(n = 21) were UCPCR positive and anti-body negative. Similarly, all antibody-pos-itive patients with DNA available (n = 47)tested negative for the three mainMODYgenes, so no additionalMODY cases werepicked up in this group.

A total of 199 out of 1,348 (15%) pa-tients were put forward for genetic testingwho were not found to have monogenicdiabetes (i.e., 15% false-positive rate, so85% specificity). Assuming a 98% sensitiv-ity and 85% specificity, the PPV for thepathway is 20%, suggesting a 1-in-5pickup rate for monogenic diabetes, a5.6-fold increase in probability over thebackground prevalence alone (Table 2).The NPV was 99.9%, indicating that theprobability of having monogenic diabe-tes if a patient is UCPCR negative or

islet autoantibody positive is 0.1% (1 in1,000) (Table 2).

Comparison of Biomarker ScreeningPathway With Clinical FeaturesIf genetic testing had been limited to thestandard clinical criteria forMODY (age atdiagnosis younger than 25 years, non–insulin-requiring, and a parent known to beaffected with diabetes), fewer patientswould have required testing (n = 33),leading to a higher pickup rate and PPV(57.6%) than the biomarker pathway, butthe majority of monogenic cases wouldhave been missed (63% compared with0% for the biomarker pathway) (Table2). The MODY probability calculator alsohad a higher PPV (40.4%), but missedmore cases (55%) compared with thebiomarker pathway.

CONCLUSIONS

The biomarker screening pathway formonogenic diabetes is a systematic,cheap (U.K. UCPCR cost of £10.80 andantibodies cost of £20), and easily imple-mented approach to screening all pa-tients with young-onset diabetes in aclinic or population that helps identifysuitable patients for molecular diagnosticgenetic testing. The pathway picked upnew cases of monogenic diabetes, evenin areas of existing high detection be-cause of research interests in the regions.We found that 3.6% of patients diag-nosed at younger than 30 years of age

Table 1—Continued

ID

Genetic characteristicsClinical characteristics

Gene Method ZygosityDNA leveldescription

Age atdiagnosis(years) Treatment

Parentwith

diabetes BMI HbA1c

Age atrecruitment(years)

Additionalclinicalfeatures

82038 PPARG tNGS Het c.1154G.A 22 OHA No 26.6 53 36 Lipodystrophy,acanthosis

80925 TRMT10A tNGS Hom c.79G.T 23 OHA+Ins No 33.0 69 28 Microcephaly, learningdifficulties, epilepsy

17 WFS1 tNGS C/Het c.874C.T & c.877del 20 Insulin n/k 21.8 42 24 Bilateral optic atrophy,neurogenic bladder, diettreatment, muscle pain

on exercise175 POLD1 Exome Het c.1812–1814del 14 OHA No 18.6 30 21 Total lipodystrophy,

sensorineuraldeafness, mandibular

hypoplasia,hypogonadism,

undescended testes,severe insulin resistance

References for the genes and further details of the mutations are in Supplementary Table 3. C/Het, compound heterozygous; Het, heterozygous; Hom,homozygous; Hp, Hp gene deletion; Ins, insulin; OHA, oral hypoglycemic agent; tNGS, targeted next-generation sequencing.

1022 Monogenic Diabetes Biomarker Screening Pathway Diabetes Care Volume 40, August 2017

have monogenic diabetes. In areas inwhich no cases have been identified, weestimate that 1 in 5 patients referred forgenetic testing because of the pathwaywill have monogenic diabetes, which is a5.6-fold higher detection rate than if allpatients in this age range received genetictesting. The highNPVof 99.9% indicates itis an extremely effective approach for rul-ing out monogenic diabetes.There have been relatively few studies

that have systematically screened wholepopulations for monogenic diabetes. Themajority of studies have been in pediatricpopulations only (14,15,22–26), with onlytwo studies that have screened adults(27,28). No other study has systematicallyscreened a whole population of bothadults and children together. Only 8 outof 51 (16%) of patients with a geneticdiagnosis of monogenic diabetes in ourcohort were in the pediatric age range(younger than 20 years) at the time ofrecruitment, highlighting the importanceof looking formonogenic diabetes in adultdiabetes clinics. This may explain why theprevalence we find is higher than in any ofthe previous pediatric studies.The strength of our pathway is the in-

tegration of two biomarkers (C-peptideand islet autoantibodies [both GAD andIA2]), rather than relying on clinical fea-tures. This offers a simple approach thatdoes not require specific clinician inter-pretation or complex algorithms of differ-ent combinations of features.We showedthat by using clinical features alone, overhalf of the cases of monogenic diabeteswould be missed. By combining the twobiomarkers, we increase the discrimina-tory ability and allow the clinician topick up even atypical cases and rarer

forms of monogenic diabetes, which tra-ditional criteria may miss. The use of clin-ical features, however, results in fewercases being sent for genetic testing thatare negative, which clearly has cost impli-cations. Themost cost-effective approachis likely to involve a combination of bio-markers and clinical features. Furtherstudies are needed to determinewhetherthepickup rate could be further improvedby integrating the pathway with clini-cal features, such as the MODY calcula-tor, or whether this would result inmoremissed patients because of reducedtesting.

In this study, we also systematicallytested all known genes for monogenic di-abetes, rather than just the most com-mon MODY genes (GCK, HNF1A, andHNF4A). Nine out of 17 (53%) of the casesidentified as part of our cohort had mu-tations identified through additional test-ing on the targeted capture, and 17 out of51 (33%) of all of the monogenic diabetescases found in total had mutations inother genes, highlighting the advantageof further testing using targeted next-generation sequencing.

Health economic evaluation of thepathway for detecting the common formsof MODY (GCK, HNF1A, and HNF4A) hasbeen carried out as a separate project,which has shown the pathway to becost-saving (20,21). The cost-effectivenessof additional testing for other forms ofmonogenic diabetes has not been as-sessed. Because of the rarity of othermonogenic diabetes, there are few dataavailable to inform such analyses. Treat-ment change from insulin to sulphonylur-eas is still possible in cases diagnosedwithABCC8 and KCNJ11 (29,30), and for other

genes for which treatment change is notan option, a confirmed diagnosis can stillhelp with management, prognosis, andadvice on risk to other family members(4). The decision whether to pay for themore expensive, but more comprehen-sive, next-generation sequencing, ratherthan Sanger sequencing for MODY genesonly, would depend on assessing thetradeoffs of additional costs with long-term benefits to the patient. The pres-ence of additional clinical features (e.g.,renal cysts associated with HNF1B) mayalso point to specific monogenic diagno-ses and increase the likelihood of a posi-tive genetic test result.

A limitation of our study was that wehad small numbers of patients withmonogenic diabetes onwhich to evaluatethe sensitivity of the pathway. Consider-ably larger studies have shown thebiomarkers individually to be highly sen-sitive for monogenic diabetes (99% forUCPCR [10,11] and .99% for islet auto-antibodies [13]), and by using both ofthese markers in a pathway, the numberof missed cases should be minimal ata population level (2% of 3.6% = 0.07%,reflected in the NPV of 99.9%). Althoughthere have been reports of MODY pa-tients who are positive for islet autoanti-bodies (reviewed in Ref. 13), these arerare and likely to be cases with coinciden-tal type 1 diabetes. Previous studies re-porting high prevalence of positiveautoantibodies in their cohort have in-cluded clinically defined, rather than ge-netically confirmed, MODY (31) or uselow cutoffs for antibody positivity, whichcan be inappropriate (32), and are likelyto represent an overestimate. There isalso the potential for missed cases basedonUCPCR, but again, the number of thesepatients will be small, and as they haveinsulin levels suggestive of type 1 diabe-tes (33), they are unlikely to be able totransfer off insulin even if a genetic diag-nosis is made.

A further limitation is that despitescreening using C-peptide and antibodytesting, the PPV is still fairly low at 20%,indicating that four out of five screenedwill not have a monogenic cause identi-fied on diagnostic molecular genetic test-ing. However, the aim of our screeningpathway is that it is used purely as atool to narrow down those individualswho would be more appropriate for ge-netic testing. This approach is a vast im-provement over no screening (which

Table 2—PPV and NPV values for the biomarker pathway, traditional MODY criteria(age at diagnosis younger than 25 years, non–insulin-treated, and parent affectedwith diabetes), and the MODY probability calculator (using a probability >25%, thepickup rate for the diagnostic laboratory)

N

Prevalence ofmonogenicdiabetes PPV (%) NPV (%)

Percentage ofmonogeniccases missed

Numberneededto test

Biomarkerpathway 1,407 3.6% (51/1,407) 20.0 99.91 0 5

Traditional MODYcriteria 1,362 3.6% (49/1,362) 57.6 97.7 63 2

MODY probabilitycalculator 1,347 3.3% (45/1,347) 40.4 98.3 55 3

Prevalence is the proportion of diagnosed monogenic diabetes, percentage of monogenic casesmissed is the proportion ofmonogenic cases not picked up by the approach, and number needed totest is 1/PPV.

care.diabetesjournals.org Shields and Associates 1023

would represent a PPV at the backgroundprevalence rate of 3.6%), misses fewercases than using clinical features alone,and is at a level that has been shown tobe cost-effective (20,21). Furthermore,the screening pathway still provides use-ful test results for this age group that of-fer additional information to supportpatient care. Patients with severe insulindeficiency, as determined by very lowC-peptide values, will not respond to non-insulin therapy (33). Positive C-peptideand negative antibody results are impor-tant clinically to highlight atypical cases oftype 1 diabetes or inwhich other forms ofdiabetes, such as young-onset type 2 di-abetes, should be considered. Patientswith very high endogenous insulin with-out islet autoantibodies andnomutationsin monogenic diabetes genes are likely tohave type 2 diabetes and may be able tomanage on noninsulin treatment.Finally, this study comprised a 98%

white population and assesses patientsat a median of 14 years after diagnosis.Assessment of the pathway in other racialgroups and in patients close to diagnosisis needed.In conclusion, we have demonstrated

a simple, cheap, effective screening path-way that could be implemented at a pop-ulation level to help correctly diagnosepatients with monogenic diabetes.

Funding. This study was supported by the De-partment of Health and Wellcome Trust HealthInnovation Challenge Award (HICF-1009-041 andWT-091985). B.M.S., M.H., B.K., S.E., and A.T.H.are core members of the National Institute forHealth Research (NIHR) Exeter Clinical ResearchFacility. T.J.M. is supported by an NIHR Chief Sci-entist Office Fellowship. J.P. is partly supported bythe NIHR Collaboration for Leadership in AppliedHealth Research and Care for the South WestPeninsula (PenCLAHRC). S.E. and A.T.H. are Well-come Trust Senior Investigators. E.R.P. is a Well-comeTrustNew Investigator. A.T.H. is an NIHRSenior Investigator.Duality of Interest. No potential conflicts of in-terest relevant to this article were reported.Author Contributions. B.M.S. carried out allanalyses and drafted the manuscript. M.S. col-lected data and reviewed and edited the man-uscript. M.H. coordinated the UNITED study andreviewed and edited the manuscript. T.J.M.coordinated the C-peptide and islet autoanti-body testing and reviewed and edited the man-uscript. K.C. coordinated the genetic testing andreviewed and edited the manuscript. J.P. pro-vided input on analysis and reviewed and editedthe manuscript. B.K. wrote the protocol andethics application and reviewed and edited themanuscript. C.H. provided input on study designand analysis and reviewed and edited the man-

uscript. S.E. led the genetic testing and reviewedand edited the manuscript. E.R.P. designed thestudy, led the Tayside arm of the project, andreviewed and edited the manuscript. A.T.H.designed the study, led the Exeter arm of theproject, and reviewed and edited the manu-script. B.M.S. is theguarantorof thisworkand,assuch, had full access to all the data in the studyand takes responsibility for the integrity of thedata and the accuracy of the data analysis.

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