Performing meta-analysis with incomplete statistical information in clinical trials

<p>Abstract</p> <p>Background</p> <p>Results from clinical trials are usually summarized in the form of sampling distributions. When full information (mean, SEM) about these distributions is given, performing meta-analysis is straightforward. However, when some of the sampling distributions only have mean values, a challenging issue is to decide how to use such distributions in meta-analysis. Currently, the most common approaches are either ignoring such trials or for each trial with a missing SEM, finding a similar trial and taking its SEM value as the missing SEM. Both approaches have drawbacks. As an alternative, this paper develops and tests two new methods, the first being the prognostic method and the second being the interval method, to estimate any missing SEMs from a set of sampling distributions with full information. A merging method is also proposed to handle clinical trials with partial information to simulate meta-analysis.</p> <p>Methods</p> <p>Both of our methods use the assumption that the samples for which the sampling distributions will be merged are randomly selected from the same population. In the prognostic method, we predict the missing SEMs from the given SEMs. In the interval method, we define intervals that we believe will contain the missing SEMs and then we use these intervals in the merging process.</p> <p>Results</p> <p>Two sets of clinical trials are used to verify our methods. One family of trials is on comparing different drugs for reduction of low density lipprotein cholesterol (LDL) for Type-2 diabetes, and the other is about the effectiveness of drugs for lowering intraocular pressure (IOP). Both methods are shown to be useful for approximating the conventional meta-analysis including trials with incomplete information. For example, the meta-analysis result of Latanoprost versus Timolol on IOP reduction for six months provided in <abbrgrp><abbr bid="B1">1</abbr></abbrgrp> was 5.05 ± 1.15 (Mean ± SEM) with full information. If the last trial in this study is assumed to be with partial information, the traditional analysis method for dealing with incomplete information that ignores this trial would give 6.49 ± 1.36 while our prognostic method gives 5.02 ± 1.15, and our interval method provides two intervals as Mean ∈ [4.25, 5.63] and SEM ∈ [1.01, 1.24].</p> <p>Conclusion</p> <p>Both the prognostic and the interval methods are useful alternatives for dealing with missing data in meta-analysis. We recommend clinicians to use the prognostic method to predict the missing SEMs in order to perform meta-analysis and the interval method for obtaining a more cautious result.</p

NeuroGrid: recording action potentials from the surface of the brain.

Recording from neural networks at the resolution of action potentials is critical for understanding how information is processed in the brain. Here, we address this challenge by developing an organic material-based, ultraconformable, biocompatible and scalable neural interface array (the 'NeuroGrid') that can record both local field potentials(LFPs) and action potentials from superficial cortical neurons without penetrating the brain surface. Spikes with features of interneurons and pyramidal cells were simultaneously acquired by multiple neighboring electrodes of the NeuroGrid, allowing for the isolation of putative single neurons in rats. Spiking activity demonstrated consistent phase modulation by ongoing brain oscillations and was stable in recordings exceeding 1 week's duration. We also recorded LFP-modulated spiking activity intraoperatively in patients undergoing epilepsy surgery. The NeuroGrid constitutes an effective method for large-scale, stable recording of neuronal spikes in concert with local population synaptic activity, enhancing comprehension of neural processes across spatiotemporal scales and potentially facilitating diagnosis and therapy for brain disorders

The genetic susceptibility to type 2 diabetes may be modulated by obesity status: implications for association studies

<p>Abstract</p> <p>Background</p> <p>Considering that a portion of the heterogeneity amongst previous replication studies may be due to a variable proportion of obese subjects in case-control designs, we assessed the association of genetic variants with type 2 diabetes (T2D) in large groups of obese and non-obese subjects.</p> <p>Methods</p> <p>We genotyped <it>RETN</it>, <it>KCNJ11</it>, <it>HNF4A</it>, <it>HNF1A</it>, <it>GCK</it>, <it>SLC30A8</it>, <it>ENPP1</it>, <it>ADIPOQ</it>, <it>PPARG</it>, and <it>TCF7L2 </it>polymorphisms in 1,283 normoglycemic (NG) and 1,581 T2D obese individuals as well as in 3,189 NG and 1,244 T2D non-obese subjects of European descent, allowing us to examine T2D risk over a wide range of BMI.</p> <p>Results</p> <p>Amongst non-obese individuals, we observed significant T2D associations with <it>HNF1A </it>I27L [odds ratio (OR) = 1.14, <it>P </it>= 0.04], <it>GCK </it>-30G>A (OR = 1.23, <it>P </it>= 0.01), <it>SLC30A8 </it>R325W (OR = 0.87, <it>P </it>= 0.04), and <it>TCF7L2 </it>rs7903146 (OR = 1.89, <it>P </it>= 4.5 × 10^-23), and non-significant associations with <it>PPARG </it>Pro12Ala (OR = 0.85, <it>P </it>= 0.14), <it>ADIPOQ </it>-11,377C>G (OR = 1.00, <it>P </it>= 0.97) and <it>ENPP1 </it>K121Q (OR = 0.99, <it>P </it>= 0.94). In obese subjects, associations with T2D were detected with <it>PPARG </it>Pro12Ala (OR = 0.73, <it>P </it>= 0.004), <it>ADIPOQ </it>-11,377C>G (OR = 1.26, <it>P </it>= 0.02), <it>ENPP1 </it>K121Q (OR = 1.30, <it>P </it>= 0.003) and <it>TCF7L2 </it>rs7903146 (OR = 1.30, <it>P </it>= 1.1 × 10^-4), and non-significant associations with <it>HNF1A </it>I27L (OR = 0.96, <it>P </it>= 0.53), <it>GCK </it>-30G>A (OR = 1.15, <it>P </it>= 0.12) and <it>SLC30A8 </it>R325W (OR = 0.95, <it>P </it>= 0.44). However, a genotypic heterogeneity was only found for <it>TCF7L2 </it>rs7903146 (<it>P </it>= 3.2 × 10^-5) and <it>ENPP1 </it>K121Q (<it>P </it>= 0.02). No association with T2D was found for <it>KCNJ11</it>, <it>RETN</it>, and <it>HNF4A </it>polymorphisms in non-obese or in obese individuals.</p> <p>Conclusion</p> <p>Genetic variants modulating insulin action may have an increased effect on T2D susceptibility in the presence of obesity, whereas genetic variants acting on insulin secretion may have a greater impact on T2D susceptibility in non-obese individuals.</p

The evolving place of incretin-based therapies in type 2 diabetes

Treatment options for type 2 diabetes based on the action of the incretin hormone glucagon-like peptide-1 (GLP-1) were first introduced in 2005. These comprise the injectable GLP-1 receptor agonists solely acting on the GLP-1 receptor on the one hand and orally active dipeptidyl-peptidase inhibitors (DPP-4 inhibitors) raising endogenous GLP-1 and other hormone levels by inhibiting the degrading enzyme DPP-4. In adult medicine, both treatment options are attractive and more commonly used because of their action and safety profile. The incretin-based therapies stimulate insulin secretion and inhibit glucagon secretion in a glucose-dependent manner and carry no intrinsic risk of hypoglycaemia. GLP-1 receptor agonists allow weight loss, whereas DPP-4 inhibitors are weight neutral. This review gives an overview of the mechanism of action and the substances and clinical data available

Pharmacology and therapeutic implications of current drugs for type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is a global epidemic that poses a major challenge to health-care systems. Improving metabolic control to approach normal glycaemia (where practical) greatly benefits long-term prognoses and justifies early, effective, sustained and safety-conscious intervention. Improvements in the understanding of the complex pathogenesis of T2DM have underpinned the development of glucose-lowering therapies with complementary mechanisms of action, which have expanded treatment options and facilitated individualized management strategies. Over the past decade, several new classes of glucose-lowering agents have been licensed, including glucagon-like peptide 1 receptor (GLP-1R) agonists, dipeptidyl peptidase 4 (DPP-4) inhibitors and sodium/glucose cotransporter 2 (SGLT2) inhibitors. These agents can be used individually or in combination with well-established treatments such as biguanides, sulfonylureas and thiazolidinediones. Although novel agents have potential advantages including low risk of hypoglycaemia and help with weight control, long-term safety has yet to be established. In this Review, we assess the pharmacokinetics, pharmacodynamics and safety profiles, including cardiovascular safety, of currently available therapies for management of hyperglycaemia in patients with T2DM within the context of disease pathogenesis and natural history. In addition, we briefly describe treatment algorithms for patients with T2DM and lessons from present therapies to inform the development of future therapies

Mechanisms of progression of chronic kidney disease

Chronic kidney disease (CKD) occurs in all age groups, including children. Regardless of the underlying cause, CKD is characterized by progressive scarring that ultimately affects all structures of the kidney. The relentless progression of CKD is postulated to result from a self-perpetuating vicious cycle of fibrosis activated after initial injury. We will review possible mechanisms of progressive renal damage, including systemic and glomerular hypertension, various cytokines and growth factors, with special emphasis on the renin–angiotensin–aldosterone system (RAAS), podocyte loss, dyslipidemia and proteinuria. We will also discuss possible specific mechanisms of tubulointerstitial fibrosis that are not dependent on glomerulosclerosis, and possible underlying predispositions for CKD, such as genetic factors and low nephron number

Genome-wide physical activity interactions in adiposity. A meta-analysis of 200,452 adults

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by similar to 30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.Peer reviewe