Still a geneticist's nightmare

Rich, Stephen S.

doi:10.1038/nature18906

Cited by 27 publications

(22 citation statements)

References 11 publications

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“…It is worth noting that not all genetic causes of diabetes can be ascribed simply to monogenic (single gene) mutations such as in MODY, or that there are a small number of rare genes inherited within generational lines that specifically affect an individual's risk of developing diabetes. This approach, although clinically useful, is simplistic and may inadvertently overlook the fact that, on the whole, the population carries multiple, common genetic alterations in a single gene (ie, variants) that may collectively increase the risk of developing diabetes 38 . A recent large analysis of exomes (protein‐coding regions which comprise a fraction, 1–2%, of the whole human genome), from 6500 subjects with T2D matched to an equal number of healthy controls from five ethnic groups, has cast doubt on the evidence that rare or low frequency disease‐associated variants affect the risk of developing diabetes.…”

Section: To Treat or Not To Treatmentioning

confidence: 99%

“…This approach, although clinically useful, is simplistic and may inadvertently overlook the fact that, on the whole, the population carries multiple, common genetic alterations in a single gene (ie, variants) that may collectively increase the risk of developing diabetes. 38 A recent large analysis of exomes (protein-coding regions which comprise a fraction, 1e2%, of the whole human genome), from 6500 subjects with T2D matched to an equal number of healthy controls from five ethnic groups, has cast doubt on the evidence that rare or low frequency diseaseassociated variants affect the risk of developing diabetes. The authors note that most of the diabetes-associated genetic variants are quite common in the population.…”

Section: Arguments For Treatmentmentioning

confidence: 99%

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A review of maturity onset diabetes of the young (MODY) and challenges in the management of glucokinase‐MODY

Bishay

Greenfield

2016

Medical Journal of Australia

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Maturity onset diabetes of the young (MODY), the most common monogenic form of diabetes, accounts for 1-2% of all diabetes diagnoses. Glucokinase (GCK)-MODY (also referred to as MODY2) constitutes 10-60% of all MODY cases and is inherited as an autosomal dominant heterozygous mutation, resulting in loss of function of the GCK gene. Patients with GCK-MODY generally have mild, fasting hyperglycaemia that is present from birth, are commonly leaner and diagnosed at a younger age than patients with type 2 diabetes, and rarely develop complications from diabetes. Hence, treatment is usually unnecessary and may be ceased. Therefore, genetic screening is recommended in all young patients (< 40 years) with an autosomal dominant family history of diabetes and who lack features of the metabolic syndrome and type 1 diabetes. Further, treatment discontinuation should be discussed with the patient as part of the informed consent process, as the realisation that prior treatment may have not been necessary - or that it could have been less burdensome - may have psychological implications for the patient. This is true for other forms of MODY, such as hepatocyte nuclear factor 1A mutations (MODY3) where hyperglycaemia is managed with low dose sulfonylurea rather than insulin. Patients with GCK-MODY, in line with trends in the general population, are becoming older and more overweight and obese, and are concomitantly developing features of insulin resistance and glucose intolerance. Therefore, controversy exists as to whether such "treatment-exempt" patients should be reassessed for treatment later in life. As testing becomes more accessible, clinicians and patients are likely to embrace genetic screening earlier in the course of diabetes, which may avert the consequences of delayed testing years after diagnosis and treatment initiation.

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Section: To Treat or Not To Treatmentioning

confidence: 99%

Section: Arguments For Treatmentmentioning

confidence: 99%

A review of maturity onset diabetes of the young (MODY) and challenges in the management of glucokinase‐MODY

Bishay

Greenfield

2016

Medical Journal of Australia

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“…Recently, new efforts have been undertaken to unravel the genetic background of type 2 diabetes by studying not only common gene variants, but also infrequent and rare variants . To date, only 10% of its heritability has been unveiled, which has been referred to as a “geneticist's nightmare” by some experts . Consequently, precision medicine based on a genotyping approach is still far away for type 2 diabetes.…”

Section: Introductionmentioning

confidence: 99%

“…8 To date, only 10% of its heritability has been unveiled, which has been referred to as a "geneticist's nightmare" by some experts. 9 Consequently, precision medicine based on a genotyping approach is still far away for type 2 diabetes. Shifting to a phenotyping approach of precision medicine seems a more promising alternative, in particular in the short-term, to improve patients' health outcomes.…”

mentioning

confidence: 99%

A risk score including body mass index, glycated haemoglobin and triglycerides predicts future glycaemic control in people with type 2 diabetes

Hertroijs

Elissen

Brouwers

et al. 2017

Diabetes Obesity Metabolism

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AimTo identify, predict and validate distinct glycaemic trajectories among patients with newly diagnosed type 2 diabetes treated in primary care, as a first step towards more effective patient‐centred care.MethodsWe conducted a retrospective study in two cohorts, using routinely collected individual patient data from primary care practices obtained from two large Dutch diabetes patient registries. Participants included adult patients newly diagnosed with type 2 diabetes between January 2006 and December 2014 (development cohort, n = 10 528; validation cohort, n = 3777). Latent growth mixture modelling identified distinct glycaemic 5‐year trajectories. Machine learning models were built to predict the trajectories using easily obtainable patient characteristics in daily clinical practice.ResultsThree different glycaemic trajectories were identified: (1) stable, adequate glycaemic control (76.5% of patients); (2) improved glycaemic control (21.3% of patients); and (3) deteriorated glycaemic control (2.2% of patients). Similar trajectories could be discerned in the validation cohort. Body mass index and glycated haemoglobin and triglyceride levels were the most important predictors of trajectory membership. The predictive model, trained on the development cohort, had a receiver‐operating characteristic area under the curve of 0.96 in the validation cohort, indicating excellent accuracy.ConclusionsThe developed model can effectively explain heterogeneity in future glycaemic response of patients with type 2 diabetes. It can therefore be used in clinical practice as a quick and easy tool to provide tailored diabetes care.

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“…We calculated AUC of ROC curves (Figure 4, Table 2) followed by categorical reclassification (NRI) and discrimination slopes (IDI) ( Table 3) T2DM has been so-called geneticists' nightmare. 24 Since the introduction of GWAS, many previous studies have tried to find the genetic factors leading to T2DM. However, very strict criteria have been used to identify associated genetic loci in GWAS to ensure discovery of biologically meaningful genetic variants.…”

Section: Resultsmentioning

confidence: 99%

Genetic prediction of type 2 diabetes using deep neural network

2018

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Type 2 diabetes (T2DM) has strong heritability but genetic models to explain heritability have been challenging. We tested deep neural network (DNN) to predict T2DM using the nested case-control study of Nurses' Health Study (3326 females, 45.6% T2DM) and Health Professionals Follow-up Study (2502 males, 46.5% T2DM). We selected 96, 214, 399, and 678 single-nucleotide polymorphism (SNPs) through Fisher's exact test and L1-penalized logistic regression. We split each dataset randomly in 4:1 to train prediction models and test their performance. DNN and logistic regressions showed better area under the curve (AUC) of ROC curves than the clinical model when 399 or more SNPs included. DNN was superior than logistic regressions in AUC with 399 or more SNPs in male and 678 SNPs in female. Addition of clinical factors consistently increased AUC of DNN but failed to improve logistic regressions with 214 or more SNPs. In conclusion, we show that DNN can be a versatile tool to predict T2DM incorporating large numbers of SNPs and clinical information. Limitations include a relatively small number of the subjects mostly of European ethnicity. Further studies are warranted to confirm and improve performance of genetic prediction models using DNN in different ethnic groups.

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Still a geneticist's nightmare

Cited by 27 publications

References 11 publications

A review of maturity onset diabetes of the young (MODY) and challenges in the management of glucokinase‐MODY

A review of maturity onset diabetes of the young (MODY) and challenges in the management of glucokinase‐MODY

A risk score including body mass index, glycated haemoglobin and triglycerides predicts future glycaemic control in people with type 2 diabetes

Genetic prediction of type 2 diabetes using deep neural network

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