# Functional Characterization of Glucokinase Variants to Aid Clinical Interpretation of Monogenic Diabetes

## TL;DR

This study helps diagnose a rare type of diabetes by testing how genetic changes affect a key enzyme in glucose metabolism.

## Contribution

The study provides functional data for 25 glucokinase variants to improve clinical diagnosis of monogenic diabetes.

## Key findings

- Functional analysis of 25 glucokinase variants revealed their impact on enzyme activity and stability.
- Integration of functional data with clinical evidence improves variant classification accuracy.
- The study demonstrates how functional evidence can lead to diagnostic certainty in monogenic diabetes.

## Abstract

Precision medicine starts with a precision diagnosis. Yet up to 80% of cases of monogenic diabetes, a form of diabetes characterized by mutations in a single gene, are either overlooked or misdiagnosed. A genetic test for monogenic diabetes does not always lead to a precise diagnosis, as novel variants are often classified as variants of unknown significance. Variant interpretation requires collation of a framework of evidence, including population, computational, and segregation data, and can be assisted by functional analysis. The inclusion of functional data can be challenging, depending on the number of benign and pathogenic variants available for benchmarking assays. Glucokinase is the rate-limiting step for glucose metabolism in the pancreatic beta-cell and governs the threshold for glucose-stimulated insulin release. Loss-of-function alleles in the glucokinase (GCK) gene are a cause of stable fasting hyperglycemia from birth and/or diabetes. In this study, we functionally characterized 25 variants identified during diagnostic testing or in exome sequencing studies. We assessed their kinetic characteristics, stability, and interaction with pharmacological and physiological regulators. We integrated our functional data with existing data from the ClinGen Monogenic Diabetes Variant Curation Expert Review panel using a gene-specific framework to assist variant classification. We show how functional evidence can aid variant classification, thus enabling diagnostic certainty.