Dataset Information

Reclassification of diabetes etiology in a family with multiple diabetes phenotypes.

ABSTRACT:

Background

Maturity-onset diabetes of the young (MODY) is uncommon; however, accurate diagnosis facilitates personalized management and informs prognosis in probands and relatives.

Objective

The objective of the study was to highlight that the appropriate use of genetic and nongenetic investigations leads to the correct classification of diabetes etiology.

Case discussion

A 30-year-old European female was diagnosed with insulin-treated gestational diabetes. She discontinued insulin after delivery; however, her fasting hyperglycemia persisted. β-Cell antibodies were negative and C-peptide was 0.79 nmol/L. Glucokinase (GCK)-MODY was suspected and confirmed by the identification of a GCK mutation (p.T206M).

Methods

Systematic clinical and biochemical characterization and GCK mutational analysis were implemented to determine the diabetes etiology in five relatives. Functional characterization of GCK mutations was performed.

Results

Identification of the p.T206M mutation in the proband's sister confirmed a diagnosis of GCK-MODY. Her daughter was diagnosed at 16 weeks with permanent neonatal diabetes (PNDM). Mutation analysis identified two GCK mutations that were inherited in trans-p. [(R43P);(T206M)], confirming a diagnosis of GCK-PNDM. Both mutations were shown to be kinetically inactivating. The proband's mother, other sister, and daughter all had a clinical diagnosis of type 1 diabetes, confirmed by undetectable C-peptide levels and β-cell antibody positivity. GCK mutations were not detected.

Conclusions

Two previously misclassified family members were shown to have GCK-MODY, whereas another was shown to have GCK-PNDM. A diagnosis of type 1 diabetes was confirmed in three relatives. This family exemplifies the importance of careful phenotyping and systematic evaluation of relatives after discovering monogenic diabetes in an individual.

SUBMITTER: Kavvoura FK

PROVIDER: S-EPMC4186945 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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Publications

Reclassification of diabetes etiology in a family with multiple diabetes phenotypes.

Kavvoura Fotini K FK Raimondo Anne A Thanabalasingham Gayathiry G Barrett Amy A Webster Amanda L AL Shears Debbie D Mann Nicholas P NP Ellard Sian S Gloyn Anna L AL Owen Katharine R KR

The Journal of clinical endocrinology and metabolism 20140227 6

<h4>Background</h4>Maturity-onset diabetes of the young (MODY) is uncommon; however, accurate diagnosis facilitates personalized management and informs prognosis in probands and relatives.<h4>Objective</h4>The objective of the study was to highlight that the appropriate use of genetic and nongenetic investigations leads to the correct classification of diabetes etiology.<h4>Case discussion</h4>A 30-year-old European female was diagnosed with insulin-treated gestational diabetes. She discontinued ...[more]

PMID: 24606082

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Project description:ImportanceUnderstanding the causes of infant mortality shapes public health, surveillance, and research investments. However, the association of single-locus (mendelian) genetic diseases with infant mortality is poorly understood.ObjectiveTo determine the association of genetic diseases with infant mortality.Design, setting, and participantsThis cohort study was conducted at a large pediatric hospital system in San Diego County (California) and included 546 infants (112 infant deaths [20.5%] and 434 infants [79.5%] with acute illness who survived; age, 0 to 1 year) who underwent diagnostic whole-genome sequencing (WGS) between January 2015 and December 2020. Data analysis was conducted between 2015 and 2022.ExposureInfants underwent WGS either premortem or postmortem with semiautomated phenotyping and diagnostic interpretation.Main outcomes and measuresProportion of infant deaths associated with single-locus genetic diseases.ResultsAmong 112 infant deaths (54 girls [48.2%]; 8 [7.1%] African American or Black, 1 [0.9%] American Indian or Alaska Native, 8 [7.1%] Asian, 48 [42.9%] Hispanic, 1 [0.9%] Native Hawaiian or Pacific Islander, and 34 [30.4%] White infants) in San Diego County between 2015 and 2020, single-locus genetic diseases were the most common identifiable cause of infant mortality, with 47 genetic diseases identified in 46 infants (41%). Thirty-nine (83%) of these diseases had been previously reported to be associated with childhood mortality. Twenty-eight death certificates (62%) for 45 of the 46 infants did not mention a genetic etiology. Treatments that can improve outcomes were available for 14 (30%) of the genetic diseases. In 5 of 7 infants in whom genetic diseases were identified postmortem, death might have been avoided had rapid, diagnostic WGS been performed at time of symptom onset or regional intensive care unit admission.Conclusions and relevanceIn this cohort study of 112 infant deaths, the association of genetic diseases with infant mortality was higher than previously recognized. Strategies to increase neonatal diagnosis of genetic diseases and immediately implement treatment may decrease infant mortality. Additional study is required to explore the generalizability of these findings and measure reduction in infant mortality.

Project description:Genome-wide association studies (GWAS) have been successful in identifying genetic variants associated with complex diseases. However, association analyses between genotypes and phenotypes are not straightforward due to the complex relationships between genetic and environmental factors. Moreover, multiple correlated phenotypes further complicate such analyses. To resolve this complexity, we present an analysis using structural equation modeling (SEM). Unlike current methods that focus only on identifying direct associations between diseases and genetic variants such as single-nucleotide polymorphisms (SNPs), our method introduces the effects of intermediate phenotypes, which are related phenotypes distinct from the target, into the systematic genetic study of diseases. Moreover, we consider multiple diseases simultaneously in a single model. The procedure can be summarized in four steps: 1) selection of informative SNPs, 2) extraction of latent variables from the selected SNPs, 3) investigation of the relationships among intermediate phenotypes and diseases, and 4) construction of an SEM. As a result, a quantitative map can be drawn that simultaneously shows the relationship among multiple SNPs, phenotypes, and diseases. In this study, we considered two correlated diseases, hypertension and type 2 diabetes (T2D), which are known to have a substantial overlap in their disease mechanism and have significant public health implications. As intermediate phenotypes for these diseases, we considered three obesity-related phenotypes-subscapular skin fold thickness, body mass index, and waist circumference-as traits representing subcutaneous adiposity, overall adiposity, and abdominal adiposity, respectively. Using GWAS data collected from the Korea Association Resource (KARE) project, we applied the proposed SEM process. Among 327,872 SNPs, 24 informative SNPs were selected in the first step (p<1.0E-05). Ten latent variables were generated in step 2. After an exploratory analysis, we established a path diagram among phenotypes and diseases in step 3. Finally, in step 4, we produced a quantitative map with paths moving from specific SNPs to hypertension through intermediate phenotypes and T2D. The resulting model had high goodness-of-fit measures (χ2 = 536.52, NFI = 0.997, CFI = 0.998, GFI = 0.995, AGFI = 0.993, RMSEA = 0.012).

Dataset Information

Reclassification of diabetes etiology in a family with multiple diabetes phenotypes.

Background

Objective

Case discussion

Methods

Results

Conclusions

Publications

Reclassification of diabetes etiology in a family with multiple diabetes phenotypes.

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