Methylome-wide studies of six metabolic traits.

Exploring the molecular correlates of metabolic health measures may identify the shared and unique biological processes and pathways that they track. Here, we performed epigenome-wide association studies (EWASs) of six metabolic traits: body mass index (BMI), body fat percentage, waist-hip ratio (WHR), and blood-based measures of glucose, high-density lipoprotein (HDL) cholesterol, and total cholesterol. We considered blood-based DNA methylation (DNAm) from >750,000 CpG sites in over 17,000 volunteers from the Generation Scotland (GS) cohort. Linear regression analyses identified between 304 and 11,815 significant CpGs per trait at P<3.6×10-8, with 37 significant CpG sites across all six traits. Further, we performed a Bayesian EWAS that jointly models all CpGs simultaneously and conditionally on each other, as opposed to the marginal linear regression analyses. This identified between 3 and 27 CpGs with a posterior inclusion probability ≥ 0.95 across the six traits. Next, we used elastic net penalised regression to train epigenetic scores (EpiScores) of each trait in GS, which were then tested in the Lothian Birth Cohort 1936 (LBC1936; European ancestry) and Health for Life in Singapore (HELIOS; Indian-, Malay- and Chinese-ancestries). A maximum of 27.1% of the variance in BMI was explained by the BMI EpiScore in the subset of Malay-ancestry Singaporeans. Four metabolic EpiScores were associated with general cognitive function in LBC1936 in models adjusted for vascular risk factors (Standardised ßrange: 0.08 - 0.12, PFDR < 0.05). EpiScores of metabolic health are applicable across ancestries and can reflect differences in brain health.

Whole Exome Sequencing of Multi-Regional Biopsies from Metastatic Lesions to Evaluate Actionable Truncal Mutations Using a Single-Pass Percutaneous Technique.

We investigate the feasibility of obtaining multiple spatially-separated biopsies from a single lesion to explore intratumor heterogeneity and identify actionable truncal mutations using whole exome sequencing (WES). A single-pass radiologically-guided percutaneous technique was used to obtain four spatially-separated biopsies from a single metastatic lesion. WES was performed to identify putative truncal variants (PTVs), defined as a non-synonymous somatic (NSS) variant present in all four spatially separated biopsies. Actionable truncal mutations-filtered using the FoundationOne panel-were defined as clinically relevant PTVs. Mutational landscapes of each biopsy and their association with patient outcomes were assessed. WES on 50 biopsied samples from 13 patients across six cancer types were analyzed. Actionable truncal mutations were identified in 9/13 patients; 31.1 ± 5.12 more unique NSS variants were detected with every additional multi- region tumor biopsy (MRTB) analyzed. The number of PTVs dropped by 16.1 ± 17.9 with every additional MRTB, with the decrease most pronounced (36.8 ± 19.7) when two MRTB were analyzed compared to one. MRTB most reliably predicted PTV compared to in silico analysis of allele frequencies and cancer cell fraction based on one biopsy sample. Three patients treated with actionable truncal mutation-directed therapy derived clinical benefit. Multi-regional sampling for genomics analysis is feasible and informative to help prioritize precision-therapy strategies.

A novel neural-inspired learning algorithm with application to clinical risk prediction.

Clinical risk prediction - the estimation of the likelihood an individual is at risk of a disease - is a coveted and exigent clinical task, and a cornerstone to the recommendation of life saving management strategies. This is especially important for individuals at risk of cardiovascular disease (CVD) given the fact that it is the leading causes of death in many developed counties. To this end, we introduce a novel learning algorithm - a key factor that influences the performance of machine learning-based prediction models - and utilities it to develop CVD risk prediction tool. This novel neural-inspired algorithm, called the Artificial Neural Cell System for classification (ANCSc), is inspired by mechanisms that develop the brain and empowering it with capabilities such as information processing/storage and recall, decision making and initiating actions on external environment. Specifically, we exploit on 3 natural neural mechanisms responsible for developing and enriching the brain - namely neurogenesis, neuroplasticity via nurturing and apoptosis - when implementing ANCSc algorithm. Benchmark testing was conducted using the Honolulu Heart Program (HHP) dataset and results are juxtaposed with 2 other algorithms - i.e. Support Vector Machine (SVM) and Evolutionary Data-Conscious Artificial Immune Recognition System (EDC-AIRS). Empirical experiments indicate that ANCSc algorithm (statistically) outperforms both SVM and EDC-AIRS algorithms. Key clinical markers identified by ANCSc algorithm include risk factors related to diet/lifestyle, pulmonary function, personal/family/medical history, blood data, blood pressure, and electrocardiography. These clinical markers, in general, are also found to be clinically significant - providing a promising avenue for identifying potential cardiovascular risk factors to be evaluated in clinical trials.

The effect of sample age and prediction resolution on myocardial infarction risk prediction.

Myocardial infarction (MI) is one of the leading causes of death in many developed countries. Hence, early detection of MI events is critical for effective preventative therapies, potentially reducing avoidable mortality. One approach for early disease prediction is the use of risk prediction models developed using machine learning techniques. One important component of these models is to provide clinicians with the flexibility to customize (e.g., the prediction range) and use the risk prediction model that they deemed most beneficial for their patients. Therefore, in this paper, we develop MI prediction models and investigate the effect of sample age and prediction resolution on the performance of MI risk prediction models. The cardiovascular health study dataset was used in this study. Results indicate that the prediction model developed using SVM algorithm is capable of achieving high sensitivity, specificity, and balanced accuracy of 95.3%, 84.8%, and 90.1%, respectively, over a time span of 6 years. Both sample age and prediction resolution were found not to have a significant impact on the performance of MI risk prediction models developed using subjects aged 65 and above. This implies that risk prediction models developed using different sample age and prediction resolution is a feasible approach. These models can be integrated into a computer aided screening tool which clinicians can use to interpret and predict the MI risk status of the individual patients after performing the necessary clinical assessments (e.g., cognitive function, physical function, electrocardiography, general changes to health/lifestyle, and medications) required by the models. This could offer a means for clinicians to screen the patients at risk of having MI in the near future and prescribe early medical intervention to reduce the risk.

A biological continuum based approach for efficient clinical classification.

Clinical feature selection problem is the task of selecting and identifying a subset of informative clinical features that are useful for promoting accurate clinical diagnosis. This is a significant task of pragmatic value in the clinical settings as each clinical test is associated with a different financial cost, diagnostic value, and risk for obtaining the measurement. Moreover, with continual introduction of new clinical features, the need to repeat the feature selection task can be very time consuming. Therefore to address this issue, we propose a novel feature selection technique for diagnosis of myocardial infarction - one of the leading causes of morbidity and mortality in many high-income countries. This method adopts the conceptual framework of biological continuum, the optimization capability of genetic algorithm for performing feature selection and the classification ability of support vector machine. Together, a network of clinical risk factors, called the biological continuum based etiological network (BCEN), was constructed. Evaluation of the proposed methods was carried out using the cardiovascular heart study (CHS) dataset. Results demonstrate a significant speedup of 4.73-fold can be achieved for the development of MI classification model. The key advantage of this methodology is the provision of a reusable (feature subset) paradigm for efficient development of up-to-date and efficacious clinical classification models.