Genetically predicted childhood obesity and adult atrial fibrillation: A mendelian randomization study

Background and aimsIt is unclear whether the association of childhood obesity with adult atrial fibrillation observed in observational studies reflects causal effects. The aim of this study was to evaluate the association of childhood obesity with adult atrial fibrillation using genetic instruments.Methods and resultsWe used a two-sample Mendelian randomization (MR) design to evaluate the association between childhood obesity and adult atrial fibrillation. Two sets of genetic variants (15 single nucleotide polymorphisms [SNPs] for childhood body mass index [BMI] and 12 SNPs for dichotomous childhood obesity) were selected as instruments. Summary data on SNP-childhood obesity and SNP-atrial fibrillation associations were obtained from recently published genome-wide association studies. Effect estimates were evaluated using inverse-variance weighted (IVW) methods. Other MR analyses, including MR-Egger, simple and weighted median, weighted MBE and MR-PRESSO methods were performed in sensitivity analyses.The IVW models showed that both a genetically predicted one-standard deviation increase in childhood BMI (kg/m²) and higher log-odds of childhood obesity were associated with a substantial increase in the risk of atrial fibrillation (OR = 1.22, 95% CI: 1.11–1.34, P < 0.001; OR = 1.09, 95% CI: 1.04–1.14, P < 0.001). MR-Egger regression showed no evidence of genetic pleiotropy for childhood BMI (intercept = 0.000, 95% CI: ?0.024 to 0.023), but for childhood obesity (intercept = ?0.036, 95% CI: ?0.057 to ?0.015). Similar results were observed using leave-one-out and other MR methods in sensitivity analyses.ConclusionsThis MR analysis found a consistent association between genetically predicted childhood obesity and an increased risk of adult atrial fibrillation. Further research is warranted to validate our findings.     

Management of Type 2 Diabetes: Current Strategies,Unfocussed Aspects,Challenges, and Alternatives

Type 2 diabetes mellitus (T2DM) accounts for >90% of the cases of diabetes in adults. Resistance to insulin action is the major cause that leads to chronic hyperglycemia in diabetic patients. T2DM is the consequence of activation of multiple pathways and factors involved in insulin resistance and β-cell dysfunction. Also, the etiology of T2DM involves the complex interplay between genetics and environmental factors. This interplay can be governed efficiently by lifestyle modifications to achieve better management of diabetes. The present review aims at discussing the major factors involved in the development of T2DM that remain unfocussed during the anti-diabetic therapy. The review also focuses on lifestyle modifications that are warranted for the successful management of T2DM. In addition, it attempts to explain flaws in current strategies to combat diabetes. The employability of phytoconstituents as multitargeting molecules and their potential use as effective therapeutic adjuvants to first line hypoglycemic agents to prevent side effects caused by the synthetic drugs are also discussed.     

Genetically predicted insomnia and lung cancer risk: a Mendelian randomization study

BackgroundThe relationship between insomnia and lung cancer is scanty. The Mendelian randomization approach provides the rationale for evaluating the potential causality between genetically-predicted insomnia and lung cancer risk.MethodsWe extracted 148 insomnia-related single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) from published genome-wide association studies (GWASs). Summary data of individual-level genetic information of participants were obtained from the International Lung Cancer Consortium (ILCCO) (29,266 cases and 56,450 controls). MR analyses were performed using the inverse-variance-weighted approach, MR pleiotropy residual sum and outlier (MR-PRESSO) test, weighted median estimator, and MR-Egger regression. Sensitivity analyses were further performed using Egger intercept analysis, leave-one-out analysis, MR-PRESSO global test, and Cochran's Q test to verify the robustness of our findings.ResultsThe results of the MR analysis indicated an increased risk of lung cancer in insomnia patients (OR = 1.1671; 95% CI 1.0754–1.2666, p = 0.0002). The subgroup analyses showed increased risks of lung adenocarcinoma (OR = 1.1878; 95% CI 1.0594–1.3317, p = 0.0032) and squamous cell lung cancer (OR = 1.1595; 95% CI 1.0248–1.3119, p = 0.0188).ConclusionOur study indicated that insomnia is a causal risk factor in the development of lung cancer. Due to the lack of evidence on both the epidemiology and the mechanism level, more studies are needed to better elucidate the results of the study.     

Should we start integrating genetic data in decision-making on device-aided therapies in Parkinson disease? A point of view

Parkinson disease (PD) is a complex heterogeneous neurodegenerative disorder. Association studies have revealed numerous genetic risk loci and variants, and about 5–10% suffer from a monogenic form. Because the presentation and course of PD is unique to each patient, personalized symptomatic treatment should ideally be offered to treat the most disabling motor and non-motor symptoms. Indeed, clinical milestones and treatment complications that appear during disease progression are influenced by the genetic imprint. With recent advances in PD, more patients live longer to become eligible for device-aided therapies, such as apomorphine continuous subcutaneous infusion, levodopa duodenal gel infusion, and deep brain stimulation surgery, each with its own inclusion and exclusion criteria, advantages and disadvantages. Because genetic variants influence the expression of particular clinical profiles, factors for better or worse outcomes for device-aided therapies may then be proactively identified. For example, mutations in PRKN, LRRK2 and GBA express phenotypes that favor suitability for different device therapies, although with marked differences in the therapeutic window; whereas multiplications of SNCA express phenotypes that make them less desirable for device therapies.     

New generation genetic testing entering the clinic

New generation sequencing (NGS) genetic testing is a powerful diagnostic tool and is increasingly used in the clinical workup of patients, especially in unusual presentations or where a positive family history suggests heritable disease. This review addresses the NGS technologies Targeted sequencing (TS), Whole exome sequencing (WES), Whole genome sequencing (WGS), and the use of gene panels or gene lists for clinical diagnostic purposes. These methods primarily assess nucleotide sequence but can also detect copy number variants and many tandem repeat expansions, greatly simplifying diagnostic algorithms for movement disorders. Studies evaluating the efficacy of NGS in diagnosing movement disorders have reported a diagnostic yield of up to 10.1% for familial and 15.7% for early-onset PD, 11.7–37.5% for dystonia, 12.1–61.8% for ataxia/spastic paraplegia and 11.3–28% for combined movement disorders. Patient selection and stringency in the interpretation of the detected variants and genotypes affect diagnostic yield. Careful comparison of the patient's or family's disease features with the previously reported phenotype associated with the same variant or gene can avoid false-positive diagnoses, although some genes are implicated in various phenotypes. Moving from TS to WES and WGS increases the number of patients correctly diagnosed, but for many patients, a genetic cause cannot be identified today. However, new genetically defined entities are discovered at rapid pace, and genetic databases and our knowledge of genotype-phenotype correlations expand steadily. We discuss the need for clear communication of genetic results and suggest a list of aspects to consider when reporting neurogenetic disorders using NGS testing.