Dataset Information

Recognition of maturity-onset diabetes of the young in China.

ABSTRACT:

Aims/introduction

Given that mutations related to maturity-onset diabetes of the young (MODY) are rarely found in Chinese populations, we aim to characterize the mutation spectrum of MODY pedigrees.

Materials and methods

Maturity-onset diabetes of the young candidate gene- or exome-targeted capture sequencing was carried out in 76 probands from unrelated families fulfilling the clinical diagnostic criteria for MODY. MAF <0.01 in the GnomAD or ExAC database was used to filter significant variants. Sanger sequencing was then carried out to validate findings. Function prediction by SIFT, PolyPhen-2 and PROVEAN or CADD was carried out in missense mutations.

Results

A total of 32 mutations in six genes were identified in 31 families, accounting for 40.79% of the potential MODY families. The MODY subtype detection rate was 18.42% for GCK, 15.79% for HNF1A, 2.63% for HNF4A, and 1.32% for KLF11, PAX4 and NEUROG3. Seven nonsense/frameshift mutations and four missense mutations with damaging prediction were newly identified novel mutations. The clinical features of MODY2, MODY3/1 and MODYX are similar to previous reports. Clinical phenotype of NEUROG3 p.Arg55Glufs*23 is characterized by hyperglycemia and mild intermittent abdominal pain.

Conclusions

This study adds to the emerging pattern of MODY epidemiology that the proportion of MODY explained by known pathogenic genes is higher than that previously reported, and found NEUROG3 as a new causative gene for MODY.

SUBMITTER: Liang H

PROVIDER: S-EPMC8015824 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:It is important to identify patients with Maturity-onset diabetes of the young (MODY) as a molecular diagnosis determines both treatment and prognosis. Genetic testing is currently expensive and many patients are therefore not assessed and are misclassified as having either type 1 or type 2 diabetes. Biomarkers could facilitate the prioritisation of patients for genetic testing. We hypothesised that patients with different underlying genetic aetiologies for their diabetes could have distinct metabolic profiles which may uncover novel biomarkers. The aim of this study was to perform metabolic profiling in urine from patients with MODY due to mutations in the genes encoding glucokinase (GCK) or hepatocyte nuclear factor 1 alpha (HNF1A), type 2 diabetes (T2D) and normoglycaemic control subjects. Urinary metabolic profiling by Nuclear Magnetic Resonance (NMR) and ultra performance liquid chromatography hyphenated to Q-TOF mass spectrometry (UPLC-MS) was performed in a Discovery set of subjects with HNF1A-MODY (n = 14), GCK-MODY (n = 17), T2D (n = 14) and normoglycaemic controls (n = 34). Data were used to build a valid partial least squares discriminate analysis (PLS-DA) model where HNF1A-MODY subjects could be separated from the other diabetes subtypes. No single metabolite contributed significantly to the separation of the patient groups. However, betaine, valine, glycine and glucose were elevated in the urine of HNF1A-MODY subjects compared to the other subgroups. Direct measurements of urinary amino acids and betaine in an extended dataset did not support differences between patients groups. Elevated urinary glucose in HNF1A-MODY is consistent with the previously reported low renal threshold for glucose in this genetic subtype. In conclusion, we report the first metabolic profiling study in monogenic diabetes and show that, despite the distinct biochemical pathways affected, there are unlikely to be robust urinary biomarkers which distinguish monogenic subtypes from T2D. Our results have implications for studies investigating metabolic profiles in complex traits including T2D.