Determinants of plasma levels of proglucagon and the metabolic impact of glucagon receptor signalling: a UK Biobank study.

AIMS/HYPOTHESES: Glucagon and glucagon-like peptide-1 (GLP-1) are derived from the same precursor; proglucagon, and dual agonists of their receptors are currently being explored for the treatment of obesity and metabolic dysfunction-associated steatotic liver disease (MASLD). Elevated levels of endogenous glucagon (hyperglucagonaemia) have been linked with hyperglycaemia in individuals with type 2 diabetes but are also observed in individuals with obesity and MASLD. GLP-1 levels have been reported to be largely unaffected or even reduced in similar conditions. We investigated potential determinants of plasma proglucagon and associations of glucagon receptor signalling with metabolic diseases based on data from the UK Biobank. METHODS: We used exome sequencing data from the UK Biobank for ~410,000 white participants to identify glucagon receptor variants and grouped them based on their known or predicted signalling. Data on plasma levels of proglucagon estimated using Olink technology were available for a subset of the cohort (~40,000). We determined associations of glucagon receptor variants and proglucagon with BMI, type 2 diabetes and liver fat (quantified by liver MRI) and performed survival analyses to investigate if elevated proglucagon predicts type 2 diabetes development. RESULTS: Obesity, MASLD and type 2 diabetes were associated with elevated plasma levels of proglucagon independently of each other. Baseline proglucagon levels were associated with the risk of type 2 diabetes development over a 14 year follow-up period (HR 1.13; 95% CI 1.09, 1.17; n=1562; p=1.3×10-12). This association was of the same magnitude across strata of BMI. Carriers of glucagon receptor variants with reduced cAMP signalling had elevated levels of proglucagon (ß 0.847; 95% CI 0.04, 1.66; n=17; p=0.04), and carriers of variants with a predicted frameshift mutation had higher levels of liver fat compared with the wild-type reference group (ß 0.504; 95% CI 0.03, 0.98; n=11; p=0.04). CONCLUSIONS/INTERPRETATION: Our findings support the suggestion that glucagon receptor signalling is involved in MASLD, that plasma levels of proglucagon are linked to the risk of type 2 diabetes development, and that proglucagon levels are influenced by genetic variation in the glucagon receptor, obesity, type 2 diabetes and MASLD. Determining the molecular signalling pathways downstream of glucagon receptor activation may guide the development of biased GLP-1/glucagon co-agonist with improved metabolic benefits. DATA AVAILABILITY: All coding is available through https://github.com/nicwin98/UK-Biobank-GCG.

Predicting Hearing Loss in Testicular Cancer Patients after Cisplatin-Based Chemotherapy.

Testicular cancer is predominantly curable, but the long-term side effects of chemotherapy have a severe impact on life quality. In this research study, we focus on hearing loss as a part of overall chemotherapy-induced ototoxicity. This is a unique approach where we combine clinical data from the acclaimed nationwide Danish Testicular Cancer (DaTeCa)-Late database. Clinical and genetic data on 433 patients were collected from hospital files in October 2014. Hearing loss was classified according to the FACT/GOG-Ntx-11 version 4 self-reported Ntx6. Machine learning models combining a genome-wide association study within a nested cross-validated logistic regression were applied to identify patients at high risk of hearing loss. The model comprising clinical and genetic data identified 67% of the patients with hearing loss; however, this was with a false discovery rate of 49%. For the non-affected patients, the model identified 66% of the patients with a false omission rate of 19%. An area under the receiver operating characteristic (ROC-AUC) curve of 0.73 (95% CI, 0.71-0.74) was obtained, and the model suggests genes SOD2 and MGST3 as important in improving prediction over the clinical-only model with a ROC-AUC of 0.66 (95% CI, 0.65-0.66). Such prediction models may be used to allow earlier detection and prevention of hearing loss. We suggest a possible biological mechanism for cisplatin-induced hearing loss development. On confirmation in larger studies, such models can help balance treatment in clinical practice.

Stratification of rheumatoid arthritis cohort using Ayurveda based deep phenotyping approach identifies novel genes in a GWAS.

BACKGROUND AND AIM: Genome wide association studies have scaled up both in terms of sample size and range of complex disorders investigated, but these have explained relatively little phenotypic variance. Of the several reasons, phenotypic heterogeneity seems to be a likely contributor for missing out genetic associations of large effects. Ayurveda, the traditional Indian system of medicine is one such tool which adopts a holistic deep phenotyping approach and classifies individuals based on their body constitution/prakriti. We hypothesized that Ayurveda based phenotypic stratification of healthy and diseased individuals will allow us to achieve much desired homogeneous cohorts which would facilitate detection of genetic association of large effects. In this proof of concept study, we performed a genome wide association testing of clinically diagnosed rheumatoid arthritis patients and healthy controls, who were re-phenotyped into Vata, Pitta and Kapha predominant prakriti sub-groups. EXPERIMENTAL PROCEDURE: Genotypes of rheumatoid arthritis cases (Vata = 49; Pitta = 117; Kapha = 78) and controls (Vata = 33; Pitta = 175; Kapha = 85) were retrieved from the total genotype data, used in a recent genome-wide association study performed in our laboratory. A total of 528461 SNPs were included after quality control. Prakriti-wise genome-wide association analysis was employed. RESULTS AND CONCLUSION: This study identified (i) prakriti-specific novel disease risk genes of high effect sizes; (ii) putative candidates of novel therapeutic potential; and (iii) a good correlation between genetic findings and clinical knowledge in Ayurveda. Adopting Ayurveda based deep phenotyping may facilitate explaining hitherto undiscovered heritability in complex traits and may propel much needed progress in personalized medicine.

Prediction of Nephrotoxicity Associated With Cisplatin-Based Chemotherapy in Testicular Cancer Patients.

BACKGROUND: Cisplatin-based chemotherapy may induce nephrotoxicity. This study presents a random forest predictive model that identifies testicular cancer patients at risk of nephrotoxicity before treatment. METHODS: Clinical data and DNA from saliva samples were collected for 433 patients. These were genotyped on Illumina HumanOmniExpressExome-8 v1.2 (964 193 markers). Clinical and genomics-based random forest models generated a risk score for each individual to develop nephrotoxicity defined as a 20% drop in isotopic glomerular filtration rate during chemotherapy. The area under the receiver operating characteristic curve was the primary measure to evaluate models. Sensitivity, specificity, and positive and negative predictive values were used to discuss model clinical utility. RESULTS: Of 433 patients assessed in this study, 26.8% developed nephrotoxicity after bleomycin-etoposide-cisplatin treatment. Genomic markers found to be associated with nephrotoxicity were located at NAT1, NAT2, and the intergenic region of CNTN6 and CNTN4. These, in addition to previously associated markers located at ERCC1, ERCC2, and SLC22A2, were found to improve predictions in a clinical feature-trained random forest model. Using only clinical data for training the model, an area under the receiver operating characteristic curve of 0.635 (95% confidence interval [CI] = 0.629 to 0.640) was obtained. Retraining the classifier by adding genomics markers increased performance to 0.731 (95% CI = 0.726 to 0.736) and 0.692 (95% CI = 0.688 to 0.696) on the holdout set. CONCLUSIONS: A clinical and genomics-based machine learning algorithm improved the ability to identify patients at risk of nephrotoxicity compared with using clinical variables alone. Novel genetics associations with cisplatin-induced nephrotoxicity were found for NAT1, NAT2, CNTN6, and CNTN4 that require replication in larger studies before application to clinical practice.

Association between brown eye colour in rs12913832:GG individuals and SNPs in TYR, TYRP1, and SLC24A4.

The genotype of a single SNP, rs12913832, is the primary predictor of blue and brown eye colours. The genotypes rs12913832:AA and rs12913832:GA are most often observed in individuals with brown eye colours, whereas rs12913832:GG is most often observed in individuals with blue eye colours. However, approximately 3% of Europeans with the rs12913832:GG genotype have brown eye colours. The purpose of the study presented here was to identify variants that explain brown eye colour formation in individuals with the rs12913832:GG genotype. Genes and regulatory regions surrounding SLC24A4, TYRP1, SLC24A5, IRF4, TYR, and SLC45A2, as well as the upstream region of OCA2 within the HERC2 gene were sequenced in a study comprising 40 individuals with the rs12913832:GG genotype. Of these, 24 individuals were considered to have blue eye colours and 16 individuals were considered to have brown eye colours. We identified 211 variants within the SLC24A4, TYRP1, IRF4, and TYR target regions associated with eye colour. Based on in silico analyses of predicted variant effects we recognized four variants, TYRP1 rs35866166:C, TYRP1 rs62538956:C, SLC24A4 rs1289469:C, and TYR rs1126809:G, to be the most promising candidates for explanation of brown eye colour in individuals with the rs12913832:GG genotype. Of the 16 individuals with brown eye colours, 14 individuals had four alleles, whereas the alleles were rare in the blue eyed individuals. rs35866166, rs62538956, and rs1289469 were for the first time found to be associated with pigmentary traits, whilst rs1126809 was previously found to be associated with pigmentary variation. To improve prediction of eye colours we suggest that future eye colour prediction models should include rs35866166, rs62538956, rs1289469, and rs1126809.

Data integration for prediction of weight loss in randomized controlled dietary trials.

Diet is an important component in weight management strategies, but heterogeneous responses to the same diet make it difficult to foresee individual weight-loss outcomes. Omics-based technologies now allow for analysis of multiple factors for weight loss prediction at the individual level. Here, we classify weight loss responders (N = 106) and non-responders (N = 97) of overweight non-diabetic middle-aged Danes to two earlier reported dietary trials over 8 weeks. Random forest models integrated gut microbiome, host genetics, urine metabolome, measures of physiology and anthropometrics measured prior to any dietary intervention to identify individual predisposing features of weight loss in combination with diet. The most predictive models for weight loss included features of diet, gut bacterial species and urine metabolites (ROC-AUC: 0.84-0.88) compared to a diet-only model (ROC-AUC: 0.62). A model ensemble integrating multi-omics identified 64% of the non-responders with 80% confidence. Such models will be useful to assist in selecting appropriate weight management strategies, as individual predisposition to diet response varies.