Biology subjects

Iniesta, R.

Publications and source records attributed to Iniesta, R..

3 recordsLinked to original sources

bioRxiv2019

Working Towards a Blood-Derived Gene Expression Biomarker Specific for Alzheimer’s Disease

BackgroundA significant number of studies have investigated the use of blood-derived gene expression profiling as a biomarker for Alzheimers Disease (AD). However, the typical approach of developing classification models trained on subjects with AD and complimentary cognitive healthy controls may result in markers of general illness rather than being AD-specific. Incorporating additional related neurological and age-related disorders during the classification model development process may lead to the discovery of an AD-specific expression signature.\n\nMethodsTwo XGBoost classification models were developed and optimised. The first used the typical approach, training on 160 AD and 160 cognitively normal controls, while the second was trained in 6318 AD and 6318 mixed controls. Up-sampling was performed in each training set to the minority classes to avoid sampling bias, and both classification models were evaluated in an independent dataset consisting of 127 AD and 687 mixed controls. The mixed control group represents a heterogeneous ageing population consisting of Parkinsons Disease, Multiple Sclerosis, Amyotrophic Lateral Sclerosis, Bipolar Disorder, Schizophrenia, Coronary Artery Disease, Rheumatoid Arthritis, Chronic Obstructive Pulmonary Disease, and cognitively healthy subjects.\n\nResultsThe typical approach resulted in a 74 gene classification model with a validation performance of 58.3% sensitivity, 30.3% specificity, 13.4% PPV and 79.7% NPV. In contrast, the second approach resulted in a 28 gene classification model with an overall improved validation performance of 46.5% sensitivity, 95.6% specificity, 66.3% PPV and 90.6% NPV.\n\nConclusionsThe addition of related neurological and age-related disorders into the AD classification model developmental process identified a more AD-specific expression signature, with improved ability to distinguish AD from other related diseases and cognitively healthy controls. However, this was at the cost of sensitivity. Further improvement is still required to identify a robust blood transcriptomic signature specific to AD.

genetics↗

bioRxiv2019

SPiQE: an automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis

OBJECTIVESFasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). Compared to concentric needle EMG, high-density surface EMG (HDSEMG) is non-invasive and records fasciculation potentials (FPs) from greater muscle volumes over longer durations. To detect and characterise FPs from vast data sets generated by serial HDSEMG, we developed an automated analytical tool.\n\nMETHODSSix ALS patients and two control patients (one with benign fasciculation syndrome and one with multifocal motor neuropathy) underwent 30-minute HDSEMG from biceps and gastrocnemius monthly. In MATLAB we developed a novel, innovative method to identify FPs amidst fluctuating noise levels. One hundred repeats of 5-fold cross validation estimated the models predictive ability.\n\nRESULTSBy applying this method, we identified 5,318 FPs from 80 minutes of recordings with a sensitivity of 83.6% (+/-0.2 SEM), specificity of 91.6% (+/-0.1 SEM) and classification accuracy of 87.9% (+/-0.1 SEM). An amplitude exclusion threshold (100V) removed excessively noisy data without compromising sensitivity. The resulting automated FP counts were not significantly different to the manual counts (p=0.394).\n\nCONCLUSIONWe have devised and internally validated an automated method to accurately identify FPs from HDSEMG, a technique we have named Surface Potential Quantification Engine (SPiQE).\n\nSIGNIFICANCELongitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.\n\nHighlightsO_LISPiQE combines serial high-density surface EMG with an innovative signal-processing methodology\nC_LIO_LISPiQE identifies fasciculations in ALS patients with high sensitivity and specificity\nC_LIO_LIThe optimal noise-responsive model achieves an average classification accuracy of 88%\nC_LI

neuroscience↗

bioRxiv2017

Predictors of intestinal inflammation in asymptomatic first-degree relatives of patients with Crohn’s disease

ObjectiveRelatives of individuals with Crohns disease (CD) carry an increased number of CD-associated genetic variants and are at increased risk of developing the disease. Multiple environmental and genetic factors contribute to this increased risk. We aimed to estimate the utility of genotype, smoking, family history, and a panel of biomarkers to predict risk in asymptomatic first-degree relatives (FDRs) of CD patients.\n\nDesignWe calculated a combined genotype (72 CD-associated genetic markers) and smoking relative risk score in 454 FDRs, and performed capsule endoscopy and collected 22 biomarkers in individuals from the highest and lowest risk quartiles. We then predicted small intestinal inflammation using genetic risk score, smoking status, number of relatives with CD, capsule transit time, and the panel of biomarkers in 124 individuals with complete data. Our principal analysis was to calculate the predictive utility from two machine learning classifiers: an elastic net and a random forest.\n\nResultsBoth classifiers successfully predicted FDRs with intestinal inflammation: elastic net (AUC=0.80, 95% CI: 0.62-0.98), random forest (AUC=0.87, 95% CI: 0.75-1.00). The elastic net selected a 3-predictor solution: CD family history (OR=1.31), genetic risk score (OR=1.14), and faecal calprotectin (OR=1.04). The same 3 variables were among the top 5 most important predictors as ranked by the random forest.\n\nConclusionA readily collectable panel of genetic risk variants, added to family history and faecal calprotectin, predicts those at greatest risk for developing CD with a good degree of accuracy.

epidemiology↗