We aimed to develop a calculator to determine the probability of having HNF1A-MODY (hepatocyte nuclear factor 1 alpha-maturity-onset diabetes of the young) or HNF4A (hepatocyte nuclear factor 4 alpha)-MODY (the commonest forms of MODY) in Asian Indians using clinical and biochemical criteria.
Methods:
We extracted data on individuals with young-onset diabetes aged <30 years (n = 29 191) from electronic records. Genetically confirmed HNF1A- and HNF4A-MODY (n = 55) were selected along with 1000 individuals each of type 1 diabetes (T1D) and type 2 diabetes (T2D). These data sets were used to develop a classification model using logistic regression. The model’s performance was evaluated using receiver operating characteristic (ROC) curves in an internal data set and validated in an external data set.
Results:
Eight predictive models were constructed, beginning with a basic model that included variables, such as age at diagnosis, body mass index (BMI), parental history, and glycated hemoglobin (HbA1c) (models 1 and 5). High-density lipoprotein (HDL) cholesterol was added in models 2 and 6, stimulated C-peptide in models 3 and 7, and all predictors were combined in models 4 and 8. Models 1 to 4, designed to distinguish MODY from T1D, achieved an ROC-area under the curve (AUC) value ranging from 0.884 to 0.957, while models 5 to 8, aimed at differentiating MODY from T2D, achieved an ROC-AUC value ranging from 0.914 to 0.936. All models demonstrated excellent performance in internal validation, with high five-fold cross-validation c-statistics. An online calculator using these models estimates MODY probability that is accessible at https://mdrf-t1d-calculator.shinyapps.io/MODY/.
Conclusion:
We developed an ethnicity-specific calculator to help identify individuals with possible HNF1A-MODY or HNF4A-MODY in Asian Indians. This user-friendly, web-based tool would be helpful to select candidates for genetic testing in this population.
JangKM.Maturity-onset diabetes of the young: update and perspectives on diagnosis and treatment. Yeungnam Univ J Med. 2020;37(1):13-21. doi:10.12701/yujm.2019.00409.
2.
MohanVRamachandranASnehalathaCMohanRBharaniGViswanathanM.High prevalence of maturity-onset diabetes of the young (MODY) among Indians. Diabetes Care. 1985;8(4):371-374. doi:10.2337/diacare.8.4.371.
3.
FirdousPNissarKAliS, et al. Genetic testing of maturity-onset diabetes of the young current status and future perspectives. Front Endocrinol (Lausanne). 2018;9:253. doi:10.3389/fendo.2018.00253.
4.
TattersallRBFajansSS.A difference between the inheritance of classical juvenile-onset and maturity-onset type diabetes of young people. Diabetes. 1975;24(1):44-53. doi:10.2337/diab.24.1.44.
5.
RiddleMCPhilipsonLHRichSS, et al. Monogenic diabetes: from genetic insights to population-based precision in care. Reflections from a diabetes care editors’ expert forum. Diabetes Care. 2020;43(12):3117-3128. doi:10.2337/dci20-0065.
6.
da Silva SantosTFonsecaLSantos MonteiroS, et al. MODY probability calculator utility in individuals’ selection for genetic testing: its accuracy and performance. Endocrinol Diabetes Metab. 2022;5(5):e00332. doi:10.1002/edm2.332.
7.
ShieldsBMMcDonaldTJEllardSCampbellMJHydeCHattersleyAT.The development and validation of a clinical prediction model to determine the probability of MODY in patients with young-onset diabetes. Diabetologia. 2012;55(5):1265-1272. doi:10.1007/s00125-011-2418-8.
8.
AlarconGNguyenAJonesAShieldsBRedondoMJTosurM.The maturity-onset diabetes of the young (MODY) calculator overestimates MODY probability in Hispanic youth. J Clin Endocrinol Metab. 2025;110(7):e2191-e2197. doi:10.1210/clinem/dgae770.
9.
MisraSShieldsBColcloughK, et al. South Asian individuals with diabetes who are referred for MODY testing in the UK have a lower mutation pick-up rate than white European people. Diabetologia. 2016;59(10):2262-2265. doi:10.1007/s00125-016-4056-7.
10.
NarayanKMVKondalDKobesS, et al. Incidence of diabetes in South Asian young adults compared to Pima Indians. BMJ Open Diabetes Res Care. 2021;9(1):e001988. doi:10.1136/bmjdrc-2020-001988.
11.
KantRDavisAVermaV.Maturity-onset diabetes of the young: rapid evidence review. Am Fam Physician. 2022;105:162-167.
12.
SampathkumarGValiyaparambilPPKumarH, et al. Low genetic confirmation rate in South Indian subjects with a clinical diagnosis of maturity-onset diabetes of the young (MODY) who underwent targeted next-generation sequencing for 13 genes. J Endocrinol Invest. 2022;45(3):607-615. doi:10.1007/s40618-021-01698-y.
13.
NjølstadPRMolvenA.To test, or not to test: time for a MODY calculator?Diabetologia. 2012;55:1231-1234. doi:10.1007/s00125-012-2514-4.
14.
KarczewskiKJFrancioliLCTiaoG, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581:434-443. doi:10.1038/s41586-020-2308-7.
15.
RichardsSAzizNBaleS, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405-424. doi:10.1038/gim.2015.30.
16.
AarthyRAston-MourneyKAmuthaA, et al. Identification of appropriate biochemical parameters and cut points to detect Maturity Onset Diabetes of Young (MODY) in Asian Indians in a clinic setting. Sci Rep. 2023;13:11408. doi:10.1038/s41598-023-37766-x.
17.
ViswanathanMSnehalathaCRamachandranA, et al. Glycaemic and insulin responses to some breakfast items in diabetic subjects. Nutr Rep Int. 1988;37:409-418.
18.
AlbertiKGZimmetPZ.Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998;15(7):539-553. doi:10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S.
19.
AmuthaAAnjanaRMVenkatesanU, et al. Incidence of complications in young-onset diabetes: comparing type 2 with type 1 (the young diab study). Diabetes Res Clin Pract. 2017;123:1-8. doi:10.1016/j.diabres.2016.11.006.
20.
AarthyRAston-MourneyKAmuthaA, et al. Prevalence, clinical features and complications of common forms of Maturity Onset Diabetes of the Young (MODY) seen at a tertiary diabetes centre in south India. Prim Care Diabetes. 2023;17(4):401-407. doi:10.1016/j.pcd.2023.04.004.
21.
VasileiouGHoyerJThielCT, et al. Prenatal diagnosis of HNF1B-associated renal cysts: is there a need to differentiate intragenic variants from 17q12 microdeletion syndrome?Prenat Diagn. 2019;39(12):1136-1147. doi:10.1002/pd.5556.
22.
TaghaviSMFatemiSSRafatpanahHGanjaliRTavakolafshariJValizadehN.Mutations in the coding regions of the hepatocyte nuclear factor 4 alpha in Iranian families with maturity onset diabetes of the young. Cardiovasc Diabetol. 2009;8:63. doi:10.1186/1475-2840-8-63.
23.
TintoNZagariACapuanoM, et al. Glucokinase gene mutations: structural and genotype-phenotype analyses in MODY children from South Italy. PLoS ONE. 2008;3:e1870. doi:10.1371/journal.pone.0001870.
24.
LimDMHuhNParkKY.Hepatocyte nuclear factor 1-alpha mutation in normal glucose-tolerant subjects and early-onset type 2 diabetic patients. Korean J Intern Med. 2008;23(4):165-169. doi:10.3904/kjim.2008.23.4.165.
25.
EllardSFlanaganSEGirardCA, et al. Permanent neonatal diabetes caused by dominant, recessive, or compound heterozygous SUR1 mutations with opposite functional effects. Am J Hum Genet. 2007;81(2):375-382. doi:10.1086/519174.
26.
van SmedenMMoonsKGde GrootJA, et al. Sample size for binary logistic prediction models: beyond events per variable criteria. Stat Methods Med Res. 2019;28(8):2455-2474. doi:10.1177/0962280218784726.
27.
LynamAMcDonaldTHillA, et al. Development and validation of multivariable clinical diagnostic models to identify type 1 diabetes requiring rapid insulin therapy in adults aged 18-50 years. BMJ Open. 2019;9:e031586. doi:10.1136/bmjopen-2019-031586.
28.
VenkatesanUAmuthaAAnjanaRMUnnikrishnanRMappillairajuBMohanV.Predictive modeling for diabetes subtype classification in India: a machine learning approach. J Diabetol. 2025;16:165. doi:10.4103/jod.jod_233_24.
29.
VenkatesanUAmuthaAJonesAG, et al. Performance of European prediction models for classification of type 1 and type 2 diabetes in Indians. Diabetes Metab Syndr. 2024;18(4):103007. doi:10.1016/j.dsx.2024.103007.
30.
LukAWildSHJonesS, et al. Early-onset type 2 diabetes: the next major diabetes transition. Lancet. 2025;405:2313-2326. doi:10.1016/S0140-6736(25)00830-X.
31.
KreienkampRJShieldsBMPollinTI, et al. MODY calculator and clinical features routinely used to distinguish MODY from type 2 diabetes in adults perform poorly for youth clinically diagnosed with type 2 diabetes. Diabetes Care. 2024;48:e3-e5. doi:10.2337/dc24-1565.
32.
ColcloughKPatelK.How do I diagnose Maturity Onset Diabetes of the Young in my patients?Clin Endocrinol (Oxf). 2022;97(4):436-447. doi:10.1111/cen.14744.
MaximLDNieboRUtellMJ.Screening tests: a review with examples. Inhal Toxicol. 2014;26(13):811-828. doi:10.3109/08958378.2014.955932.
35.
StevensRJPoppeKK.Validation of clinical prediction models: what does the “calibration slope” really measure?J Clin Epidemiol. 2020;118:93-99. doi:10.1016/j.jclinepi.2019.09.016.
36.
Van CalsterBMcLernonDJvan SmedenM, et al. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019;17:230. doi:10.1186/s12916-019-1466-7.
37.
ZieglerA.Clinical prediction models: a practical approach to development, validation, and updating Ewout W. Steyerberg (2019). Second Edition, Springer Series Statistics for Biology and Health. Cham: Springer. 558 pages. ISBN: 978-3-030-16398-3. https://doi.org/10.1007/978-3-030-16399-0. Biom J. 2020;62:1122-1123. doi:10.1002/bimj.202000088.
38.
SteyerbergEWVickersAJCookNR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128-138. doi:10.1097/EDE.0b013e3181c30fb2.
39.
SzopaMKlupaTKapustaM, et al. A decision algorithm to identify patients with high probability of monogenic diabetes due to HNF1A mutations. Endocrine. 2019;64(1):75-81. doi:10.1007/s12020-019-01863-7.
40.
BesserREJJonesJMcDonaldTJSmithRShepherdMHHattersleyAT. Using highly sensitive C-reactive protein measurement to diagnose MODY in a family with suspected type 2 diabetes. BMJ Case Rep. 2012;2012:bcr0120125612. doi:10.1136/bcr.01.2012.5612.
41.
Rama ChandranSBhalshankarJFarhad VasanwalaRZhaoYOwenKRSu-Lyn GardnerD.Traditional clinical criteria outperform high-sensitivity C-reactive protein for the screening of hepatic nuclear factor 1 alpha maturity-onset diabetes of the young among young Asians with diabetes. Ther Adv Endocrinol Metab. 2018;9(9):271-282. doi:10.1177/2042018818776167.
42.
Bellanné-ChantelotCCosteJCianguraC, et al. High-sensitivity C-reactive protein does not improve the differential diagnosis of HNF1A-MODY and familial young-onset type 2 diabetes: a grey zone analysis. Diabetes Metab. 2016;42:33-37. doi:10.1016/j.diabet.2015.02.001.
43.
McDonaldTJShieldsBMLawryJ, et al. High-sensitivity CRP discriminates HNF1A-MODY from other subtypes of diabetes. Diabetes Care. 2011;34:1860-1862. doi:10.2337/dc11-0323.
44.
NaitzaSPorcuESteriM, et al. A genome-wide association scan on the levels of markers of inflammation in Sardinians reveals associations that underpin its complex regulation. PLoS Genet. 2012;8(1):e1002480. doi:10.1371/journal.pgen.1002480.
45.
CurocichinGWuYMcDadeTW, et al. Single-nucleotide polymorphisms at five loci are associated with C-reactive protein levels in a cohort of Filipino young adults. J Hum Genet. 2011;56(12):823-827. doi:10.1038/jhg.2011.106.
46.
ToniattiCDemartisAMonaciPNicosiaACilibertoG.Synergistic trans-activation of the human C-reactive protein promoter by transcription factor HNF-1 binding at two distinct sites. EMBO J. 1990;9(13):4467-4475. doi:10.1002/j.1460-2075.1990.tb07897.x.
47.
MoraSMusunuruKBlumenthalRS.The clinical utility of high-sensitivity C-reactive protein in cardiovascular disease and the potential implication of JUPITER on current practice guidelines. Clin Chem. 2009;55(2):219-228. doi:10.1373/clinchem.2008.109728.
48.
JenkinsNPKeevilBGHutchinsonIVBrooksNH.Beta-blockers are associated with lower C-reactive protein concentrations in patients with coronary artery disease. Am J Med. 2002;112(4):269-274. doi:10.1016/s0002-9343(01)01115-9.
49.
RidkerPMCushmanMStampferMJTracyRPHennekensCH.Inflammation, aspirin, and the risk of cardiovascular disease in apparently healthy men. N Engl J Med. 1997;336:973-979. doi:10.1056/NEJM199704033361401.
50.
JohanssonBBIrgensHUMolnesJ, et al. Targeted next-generation sequencing reveals MODY in up to 6.5% of antibody-negative diabetes cases listed in the Norwegian Childhood Diabetes Registry. Diabetologia. 2017;60(4):625-635. doi:10.1007/s00125-016-4167-1.
51.
ZečevićKVolčanšekŠKatsikiN, et al. Maturity—onset diabetes of the young (MODY)—in search of ideal diagnostic criteria and precise treatment. Prog Cardiovasc Dis. 2024;85:14-25. doi:10.1016/j.pcad.2024.03.004.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
