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NCT Number: NCT03988764

Monogenic Diabetes Misdiagnosed as Type 1

The study has two aims:

1. To (1a) determine the frequency of monogenic diabetes misdiagnosed as type 1 diabetes (T1D) and (2) to define an algorithm for case selection. 2. To discover novel genes whose mutations cause monogenic diabetes misdiagnosed as T1D.

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Key information

Conditions

Diabetes Mellitus, Type 1 Abnormalities, Multiple Autoimmune Diseases Blindness Congenital Abnormalities Congenital, Hereditary, and Neonatal Diseases and Abnormalities Cranial Nerve Diseases Deaf-Blind Disorders Deafness Diabetes Insipidus Diabetes Mellitus Ear Diseases Endocrine System Diseases Eye Diseases Eye Diseases, Hereditary Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Genetic Diseases, Inborn Glucose Metabolism Disorders Hearing Disorders Hearing Loss Heredodegenerative Disorders, Nervous System Immune System Diseases Kidney Diseases Male Urogenital Diseases Maturity-onset Diabetes in the Young (MODY) Metabolic Diseases Mitochondrial Diabetes Monogenic Diabetes Neonatal Diabetes Nervous System Diseases Neurodegenerative Diseases Neurologic Manifestations Nutritional and Metabolic Diseases Optic Atrophies, Hereditary Optic Atrophy Optic Nerve Diseases Otorhinolaryngologic Diseases Pituitary Diseases Sensation Disorders Urogenital Diseases Urologic Diseases Vision Disorders Wolcott-Rallison Syndrome Wolfram Syndrome

Age range

1 day–25 year

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

Aim 1. The investigators will recruit 5,000 cases diagnosed as T1D under the age of 25, from 17 participating clinics across Canada. All cases will be tested for four antibodies (against proinsulin, GAD65, islet antigen 2 (IA-2), and ZnT8). Cases negative for all four will be exome-sequenced.

  • Variant annotation will be focused on known monogenic diabetes genes. Variants rated as pathogenic, likely pathogenic or of unknown significance whose zygosity fits the genetic model, will be confirmed in a clinically certified laboratory and communicated to the treating health care team. End-point is the frequency of such variants compared to their frequency in control, non-T1D exomes.
  • The following variables will be examined for the ability to predict monogenic diabetes: Negativity for all autoantibodies tested, family history, polygenic T1D risk score, age of onset, sex, glycosylated hemoglobin (HbA1c), insulin dose, and presence of syndromic features. Predictors will be analyzed by multiple regression and results subjected to jackknife (leave-one-out) validation. Machine-learning techniques may be used.

Aim 2. Variants outside known genes in non-diagnostic exomes will be annotated and examined under autosomal dominant, recessive, X-linked and mitochondrial inheritance models. Corresponding frequency cutoffs will be 0.0005, 0.01, 0.001 and 0.0005 (if heteroplasmy >70%). Formal mutation-burden analysis will be based on depth-adjusted data from the Genome Aggregation Database (gnomAD). Genes mutated in more than one unrelated proband will be examined by a statistical approach taking into account the presence of a large number of phenocopies (Akawi et al., Nat Genet. 2015;47:1363-1369). Genes that achieve statistical significance will be tested in additional cohorts with international collaborations.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Diagnosis of diabetes under the age of 25 as either type 1 or undetermined type.

Exclusion criteria

  • Existing T1D autoantibody testing with a positive result

Treatment and study plan

None AHT

Other

No further intervention planned for either group as part of the current study.

Primary outcomes

  1. Proportion of monogenic diabetes among patients diagnosed as type 1 diabetes.

    Time frame: 6 years

    The exomes of all patients negative for four T1D autoantibodies will be sequenced and pathogenic variants in genes known to cause monogenic diabetes will be called and annotated. The frequency of genes carrying such variants among these patients will be compared to control exomes from public databases.

  2. Proportion of patients carrying mutations in previously unstudied genes that meet statistical criteria of pathogenicity for monogenic diabetes.

    Time frame: 7 years

    Exomes not found to carry a mutation (per outcome 1) will be analyzed to discover pathogenic variants in novel genes. Genes mutated in more than one unrelated probands will be statistically evaluated to see if variants in these gene occur more frequently than in control exomes. The number of probands that is needed to fulfill this criterion will depend on the gene's tolerance to protein-altering mutations.

Secondary outcomes

  1. Risk-prediction score for monogenic diabetes mutation in antibody negative T1D patients

    Time frame: 5 years

    Composit score with a statistically significant ROC curve for predicting monogenic diabetes in individuals previously diagnosed as T1D. It will be based on age of onset, T1D polygenic risk score. The risk score will aim to predict monogenic diabetes in cases with clinical T1D diagnosis and known to be antibody negative. The scale will be calculated as follows: From the exome sequencing, the investigators will be able to determine genotype at the three most important loci determining risk for autoimmune T1D (HLA, INS and PTPN22).The composite risk score, along with family history, age of onset, HbA1c+4*insulin dose/kg (as proxy for residual beta cell function) will be subjected to logistic regression for an overall risk. The ROC curve will be used to select a point likely to capture most cases unlikely to have autoimmune T1D, sacrificing specificity to maximize sensitivity. Data will be validated with jackknife cross-validation.

Study contacts

Contact information is provided by the study sponsor or research team.

Angeliki Makri, MD

CONTACT

[email protected]

5144124400 ext. 22623

Constantin Polychronakos, MD

CONTACT

[email protected]

5144124400 ext. 22866

Sponsors and collaborators

Lead sponsor

McGill University Health Centre/Research Institute of the McGill University Health Centre

Other

Registry information

Official study title

Accurate Diagnosis of Diabetes for Appropriate Management

Acronym: ADDAM

Important dates

Study start
2019
Primary completion
2025
Study completion
2025
First posted
Jun 17, 2019
Registry last updated
Dec 10, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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