Body mass index, waist circumference, and mortality in subjects older than 80 years: a Mendelian randomization study.

BACKGROUND AND AIMS: Emerging evidence has raised an obesity paradox in observational studies of body mass index (BMI) and health among the oldest-old (aged ≥80 years), as an inverse relationship of BMI with mortality was reported. This study was to investigate the causal associations of BMI, waist circumference (WC), or both with mortality in the oldest-old people in China. METHODS: A total of 5306 community-based oldest-old (mean age 90.6 years) were enrolled in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) between 1998 and 2018. Genetic risk scores were constructed from 58 single-nucleotide polymorphisms (SNPs) associated with BMI and 49 SNPs associated with WC to subsequently derive causal estimates for Mendelian randomization (MR) models. One-sample linear MR along with non-linear MR analyses were performed to explore the associations of genetically predicted BMI, WC, and their joint effect with all-cause mortality, cardiovascular disease (CVD) mortality, and non-CVD mortality. RESULTS: During 24 337 person-years of follow-up, 3766 deaths were documented. In observational analyses, higher BMI and WC were both associated with decreased mortality risk [hazard ratio (HR) 0.963, 95% confidence interval (CI) 0.955-0.971 for a 1-kg/m2 increment of BMI and HR 0.971 (95% CI 0.950-0.993) for each 5 cm increase of WC]. Linear MR models indicated that each 1 kg/m2 increase in genetically predicted BMI was monotonically associated with a 4.5% decrease in all-cause mortality risk [HR 0.955 (95% CI 0.928-0.983)]. Non-linear curves showed the lowest mortality risk at the BMI of around 28.0 kg/m2, suggesting that optimal BMI for the oldest-old may be around overweight or mild obesity. Positive monotonic causal associations were observed between WC and all-cause mortality [HR 1.108 (95% CI 1.036-1.185) per 5 cm increase], CVD mortality [HR 1.193 (95% CI 1.064-1.337)], and non-CVD mortality [HR 1.110 (95% CI 1.016-1.212)]. The joint effect analyses indicated that the lowest risk was observed among those with higher BMI and lower WC. CONCLUSIONS: Among the oldest-old, opposite causal associations of BMI and WC with mortality were observed, and a body figure with higher BMI and lower WC could substantially decrease the mortality risk. Guidelines for the weight management should be cautiously designed and implemented among the oldest-old people, considering distinct roles of BMI and WC.

Improving the second-tier classification of methylmalonic acidemia patients using a machine learning ensemble method.

INTRODUCTION: Methylmalonic acidemia (MMA) is a disorder of autosomal recessive inheritance, with an estimated prevalence of 1:50,000. First-tier clinical diagnostic tests often return many false positives [five false positive (FP): one true positive (TP)]. In this work, our goal was to refine a classification model that can minimize the number of false positives, currently an unmet need in the upstream diagnostics of MMA. METHODS: We developed machine learning multivariable screening models for MMA with utility as a secondary-tier tool for false positives reduction. We utilized mass spectrometry-based features consisting of 11 amino acids and 31 carnitines derived from dried blood samples of neonatal patients, followed by additional ratio feature construction. Feature selection strategies (selection by filter, recursive feature elimination, and learned vector quantization) were used to determine the input set for evaluating the performance of 14 classification models to identify a candidate model set for an ensemble model development. RESULTS: Our work identified computational models that explore metabolic analytes to reduce the number of false positives without compromising sensitivity. The best results [area under the receiver operating characteristic curve (AUROC) of 97%, sensitivity of 92%, and specificity of 95%] were obtained utilizing an ensemble of the algorithms random forest, C5.0, sparse linear discriminant analysis, and autoencoder deep neural network stacked with the algorithm stochastic gradient boosting as the supervisor. The model achieved a good performance trade-off for a screening application with 6% false-positive rate (FPR) at 95% sensitivity, 35% FPR at 99% sensitivity, and 39% FPR at 100% sensitivity. CONCLUSIONS: The classification results and approach of this research can be utilized by clinicians globally, to improve the overall discovery of MMA in pediatric patients. The improved method, when adjusted to 100% precision, can be used to further inform the diagnostic process journey of MMA and help reduce the burden for patients and their families.

diseaseGPS: auxiliary diagnostic system for genetic disorders based on genotype and phenotype.

SUMMARY: The next-generation sequencing brought opportunities for the diagnosis of genetic disorders due to its high-throughput capabilities. However, the majority of existing methods were limited to only sequencing candidate variants, and the process of linking these variants to a diagnosis of genetic disorders still required medical professionals to consult databases. Therefore, we introduce diseaseGPS, an integrated platform for the diagnosis of genetic disorders that combines both phenotype and genotype data for analysis. It offers not only a user-friendly GUI web application for those without a programming background but also scripts that can be executed in batch mode for bioinformatics professionals. The genetic and phenotypic data are integrated using the ACMG-Bayes method and a novel phenotypic similarity method, to prioritize the results of genetic disorders. diseaseGPS was evaluated on 6085 cases from Deciphering Developmental Disorders project and 187 cases from Shanghai Children's hospital. The results demonstrated that diseaseGPS performed better than other commonly used methods. AVAILABILITY AND IMPLEMENTATION: diseaseGPS is available to freely accessed at https://diseasegps.sjtu.edu.cn with source code at https://github.com/BioHuangDY/diseaseGPS.

HBV-infected hepatocellular carcinoma can be robustly classified into three clinically relevant subgroups by a novel analytical protocol.

Liver cancer is the third leading cause of cancer-related death worldwide, and hepatocellular carcinoma (HCC) accounts for a relatively large proportion of all primary liver malignancies. Among the several known risk factors, hepatitis B virus (HBV) infection is one of the important causes of HCC. In this study, we demonstrated that the HBV-infected HCC patients could be robustly classified into three clinically relevant subgroups, i.e. Cluster1, Cluster2 and Cluster3, based on consistent differentially expressed mRNAs and proteins, which showed better generalization. The proposed three subgroups showed different molecular characteristics, immune microenvironment and prognostic survival characteristics. The Cluster1 subgroup had near-normal levels of metabolism-related proteins, low proliferation activity and good immune infiltration, which were associated with its good liver function, smaller tumor size, good prognosis, low alpha-fetoprotein (AFP) levels and lower clinical stage. In contrast, the Cluster3 subgroup had the lowest levels of metabolism-related proteins, which corresponded with its severe liver dysfunction. Also, high proliferation activity and poor immune microenvironment in Cluster3 subgroup were associated with its poor prognosis, larger tumor size, high AFP levels, high incidence of tumor thrombus and higher clinical stage. The characteristics of the Cluster2 subgroup were between the Cluster1 and Cluster3 groups. In addition, MCM2-7, RFC2-5, MSH2, MSH6, SMC2, SMC4, NCPAG and TOP2A proteins were significantly upregulated in the Cluster3 subgroup. Meanwhile, abnormally high phosphorylation levels of these proteins were associated with high levels of DNA repair, telomere maintenance and proliferative features. Therefore, these proteins could be identified as potential diagnostic and prognostic markers. In general, our research provided a novel analytical protocol and insights for the robust classification, treatment and prevention of HBV-infected HCC.

FVC as an adaptive and accurate method for filtering variants from popular NGS analysis pipelines.

The quality control of variants from whole-genome sequencing data is vital in clinical diagnosis and human genetics research. However, current filtering methods (Frequency, Hard-Filter, VQSR, GARFIELD, and VEF) were developed to be utilized on particular variant callers and have certain limitations. Especially, the number of eliminated true variants far exceeds the number of removed false variants using these methods. Here, we present an adaptive method for quality control on genetic variants from different analysis pipelines, and validate it on the variants generated from four popular variant callers (GATK HaplotypeCaller, Mutect2, Varscan2, and DeepVariant). FVC consistently exhibited the best performance. It removed far more false variants than the current state-of-the-art filtering methods and recalled ~51-99% true variants filtered out by the other methods. Once trained, FVC can be conveniently integrated into a user-specific variant calling pipeline.

Pan-Cancer Single-Cell Analysis Reveals the Core Factors and Pathway in Specific Cancer Stem Cells of Upper Gastrointestinal Cancer.

Upper gastrointestinal cancer (UGIC) is an aggressive carcinoma with increasing incidence and poor outcomes worldwide. Here, we collected 39,057 cells, and they were annotated into nine cell types. By clustering cancer stem cells (CSCs), we discovered the ubiquitous existence of sub-cluster CSCs in all UGICs, which is named upper gastrointestinal cancer stem cells (UGCSCs). The identification of UGCSC function is coincident with the carcinogen of UGICs. We compared the UGCSC expression profile with 215,291 single cells from six other cancers and discovered that UGCSCs are specific tumor stem cells in UGIC. Exploration of the expression network indicated that inflammatory genes (CXCL8, CXCL3, PIGR, and RNASE1) and Wnt pathway genes (GAST, REG1A, TFF3, and ZG16B) are upregulated in tumor stem cells of UGICs. These results suggest a new mechanism for carcinogenesis in UGIC: mucosa damage and repair caused by poor eating habits lead to chronic inflammation, and the persistent chronic inflammation triggers the Wnt pathway; ultimately, this process induces UGICs. These findings establish the core signal pathway that connects poor eating habits and UGIC. Our system provides deeper insights into UGIC carcinogens and a platform to promote gastrointestinal cancer diagnosis and therapy.

A meta-learning approach to improving radiation response prediction in cancers.

PURPOSE: Predicting the efficacy of radiotherapy in individual patients has drawn widespread attention, but the limited sample size remains a bottleneck for utilizing high-dimensional multi-omics data to guide personalized radiotherapy. We hypothesize the recently developed meta-learning framework could address this limitation. METHODS AND MATERIALS: By combining gene expression, DNA methylation, and clinical data of 806 patients who had received radiotherapy from The Cancer Genome Atlas (TCGA), we applied the Model-Agnostic Meta-Learning (MAML) framework to tasks consisting of pan-cancer data, to obtain the best initial parameters of a neural network for a specific cancer with smaller number of samples. The performance of meta-learning framework was compared with four traditional machine learning methods based on two training schemes, and tested on Cancer Cell Line Encyclopedia (CCLE) and Chinese Glioma Genome Atlas (CGGA) datasets. Moreover, biological significance of the models was investigated by survival analysis and feature interpretation. RESULTS: The mean AUC (Area under the ROC Curve) [95% confidence interval] of our models across nine cancer types was 0.702 [0.691-0.713], which improved by 0.166 on average over other the four machine learning methods on two training schemes. Our models performed significantly better (p < 0.05) in seven cancer types and performed comparable to the other predictors in the rest of two cancer types. The more pan-cancer samples were used to transfer meta-knowledge, the greater the performance improved (p < 0.05). The predicted response scores that our models generated were negatively correlated with cell radiosensitivity index in four cancer types (p < 0.05), while not statistically significant in the other three cancer types. Moreover, the predicted response scores were shown to be prognostic factors in seven cancer types and eight potential radiosensitivity-related genes were identified. CONCLUSIONS: For the first time, we established the meta-learning approach to improving individual radiation response prediction by transferring common knowledge from pan-cancer data with MAML framework. The results demonstrated the superiority, generalizability, and biological significance of our approach.

Integrative Analysis of Membrane Proteome and MicroRNA Reveals Novel Lung Cancer Metastasis Biomarkers.

Lung cancer is one of the most common human cancers both in incidence and mortality, with prognosis particularly poor in metastatic cases. Metastasis in lung cancer is a multifarious process driven by a complex regulatory landscape involving many mechanisms, genes, and proteins. Membrane proteins play a crucial role in the metastatic journey both inside tumor cells and the extra-cellular matrix and are a viable area of research focus with the potential to uncover biomarkers and drug targets. In this work we performed membrane proteome analysis of highly and poorly metastatic lung cells which integrated genomic, proteomic, and transcriptional data. A total of 1,762 membrane proteins were identified, and within this set, there were 163 proteins with significant changes between the two cell lines. We applied the Tied Diffusion through Interacting Events method to integrate the differentially expressed disease-related microRNAs and functionally dys-regulated membrane protein information to further explore the role of key membrane proteins and microRNAs in multi-omics context. Has-miR-137 was revealed as a key gene involved in the activity of membrane proteins by targeting MET and PXN, affecting membrane proteins through protein-protein interaction mechanism. Furthermore, we found that the membrane proteins CDH2, EGFR, ITGA3, ITGA5, ITGB1, and CALR may have significant effect on cancer prognosis and outcomes, which were further validated in vitro. Our study provides multi-omics-based network method of integrating microRNAs and membrane proteome information, and uncovers a differential molecular signatures of highly and poorly metastatic lung cancer cells; these molecules may serve as potential targets for giant-cell lung metastasis treatment and prognosis.

AGE challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography.

Angle closure glaucoma (ACG) is a more aggressive disease than open-angle glaucoma, where the abnormal anatomical structures of the anterior chamber angle (ACA) may cause an elevated intraocular pressure and gradually lead to glaucomatous optic neuropathy and eventually to visual impairment and blindness. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging provides a fast and contactless way to discriminate angle closure from open angle. Although many medical image analysis algorithms have been developed for glaucoma diagnosis, only a few studies have focused on AS-OCT imaging. In particular, there is no public AS-OCT dataset available for evaluating the existing methods in a uniform way, which limits progress in the development of automated techniques for angle closure detection and assessment. To address this, we organized the Angle closure Glaucoma Evaluation challenge (AGE), held in conjunction with MICCAI 2019. The AGE challenge consisted of two tasks: scleral spur localization and angle closure classification. For this challenge, we released a large dataset of 4800 annotated AS-OCT images from 199 patients, and also proposed an evaluation framework to benchmark and compare different models. During the AGE challenge, over 200 teams registered online, and more than 1100 results were submitted for online evaluation. Finally, eight teams participated in the onsite challenge. In this paper, we summarize these eight onsite challenge methods and analyze their corresponding results for the two tasks. We further discuss limitations and future directions. In the AGE challenge, the top-performing approach had an average Euclidean Distance of 10 pixels (10 µm) in scleral spur localization, while in the task of angle closure classification, all the algorithms achieved satisfactory performances, with two best obtaining an accuracy rate of 100%. These artificial intelligence techniques have the potential to promote new developments in AS-OCT image analysis and image-based angle closure glaucoma assessment in particular.

Promoter methylation of BRCA1 is associated with estrogen, progesterone and human epidermal growth factor receptor-negative tumors and the prognosis of breast cancer: A meta-analysis.

Aberrant methylation of the breast cancer susceptibility gene 1 (BRCA1) promoter is a mechanism for its functional inactivation. It may potentially be used as a prognostic marker in studies for patients with breast cancer and plays an important role in tumorigenesis. Numerous studies have suggested that the methylation of the BRCA1 promoter is associated with the prognosis of breast cancer. However, the prognosis of BRCA1 promoter methylation in breast cancer patients of different ethnicities remains ambiguous. The present meta-analysis was performed to adjust and augment a previously published study, which estimated the correlations between promoter methylation of BRCA1 and the clinical outcomes of breast cancer patients. These results indicated that BRCA1 methylation was significantly correlated with a poor prognosis of breast cancer, particularly for Asian patients, but the correlation was over-estimated in the previous study. The combined hazard ratios (HRs) in the present study were 1.76 (1.15-2.68) and 1.97 (1.12-3.44) for univariate and multivariate analysis of overall survival, which were different from 2.02 (1.35-3.03) and 1.38 (1.04-1.84) in the previous study. For studies of disease-free survival, the univariate and multivariate analyses also have different pooled HRs: 2.89 (1.73-4.83) and 3.92 (1.49-10.32) in the previously published study and 1.28 (0.68-2.43) and 1.64 (0.64-4.19) in the present study. In addition, the BRCA1 promoter regions used to detect the hypermethylation were different. All the studies using the Baldwin's primer reported that breast cancer patients with BRCA1 promoter methylation had a better prognosis. There were also correlations between BRCA1 promoter methylation and receptor-negativity of the estrogen receptors, progesterone receptor, human epidermal growth factor receptor 2 and a triple-negative status. Patients with the estrogen, progesterone and epidermal growth factor-related receptor-negative status were more likely to be negative for the BRCA1 protein.