Unveiling the link between physical activity levels and dementia risk: Insights from the UK Biobank study

BACKGROUND

There is limited information on the mixture effect and weights of light physical activity (LPA), moderate physical activity (MPA), and vigorous physical activity (VPA) on dementia risk.

METHODS

A prospective cohort study was conducted based on the UK Biobank dataset. We included participants aged at least 45 years old without dementia at baseline between 2006-2010. The weighted quantile sum regression was used to explore the mixture effect and weights of three types of physical activity on dementia risk.

RESULTS

This study includes 354,123 participants, with a mean baseline age of 58.0-year-old and 52.4 % of female participants. During a median follow-up time of 12.5 years, 5,136 cases of dementia were observed. The mixture effect of LPA, MPA, and VPA on dementia was statistically significant (β: -0.0924, 95 % Confidence Interval (CI): (-0.1402, -0.0446), P < 0.001), with VPA (weight: 0.7922) contributing most to a lower dementia risk, followed by MPA (0.1939). For Alzheimer's disease, MPA contributed the most (0.8555); for vascular dementia, VPA contributed the most (0.6271).

CONCLUSION

For Alzheimer's disease, MPA was identified as the most influential factor, while VPA stood out as the most impactful for vascular dementia.

A new hip fracture risk index derived from FEA-computed proximal femur fracture loads and energies-to-failure

Hip fracture risk assessment is an important but challenging task. Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur. We developed a global FEA-computed fracture risk index to increase the prediction accuracy of hip fracture incidence.

PURPOSE

Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur to compute the force (fracture load) and energy necessary to break the proximal femur in a particular loading condition. The fracture loads and energies-to-failure are individually associated with incident hip fracture, and provide different structural information about the proximal femur.

METHODS

We used principal component analysis (PCA) to develop a global FEA-computed fracture risk index that incorporates the FEA-computed yield and ultimate failure loads and energies-to-failure in four loading conditions of 110 hip fracture subjects and 235 age- and sex-matched control subjects from the AGES-Reykjavik study. Using a logistic regression model, we compared the prediction performance for hip fracture based on the stratified resampling.

RESULTS

We referred the first principal component (PC1) of the FE parameters as the global FEA-computed fracture risk index, which was the significant predictor of hip fracture (p-value < 0.001). The area under the receiver operating characteristic curve (AUC) using PC1 (0.776) was higher than that using all FE parameters combined (0.737) in the males (p-value < 0.001).

CONCLUSIONS

The global FEA-computed fracture risk index increased hip fracture risk prediction accuracy in males.

Mendelian randomization and transcriptome analysis identified immune-related biomarkers for osteoarthritis

Background

The immune microenvironment assumes a significant role in the pathogenesis of osteoarthritis (OA). However, the current biomarkers for the diagnosis and treatment of OA are not satisfactory. Our study aims to identify new OA immune-related biomarkers to direct the prevention and treatment of OA using multi-omics data.

Methods

The discovery dataset integrated the GSE89408 and GSE143514 datasets to identify biomarkers that were significantly associated with the OA immune microenvironment through multiple machine learning methods and weighted gene co-expression network analysis (WGCNA). The identified signature genes were confirmed using two independent validation datasets. We also performed a two-sample mendelian randomization (MR) study to generate causal relationships between biomarkers and OA using OA genome-wide association study (GWAS) summary data (cases n = 24,955, controls n = 378,169). Inverse-variance weighting (IVW) method was used as the main method of causal estimates. Sensitivity analyses were performed to assess the robustness and reliability of the IVW results.

Results

Three signature genes (FCER1G, HLA-DMB, and HHLA-DPA1) associated with the OA immune microenvironment were identified as having good diagnostic performances, which can be used as biomarkers. MR results showed increased levels of FCER1G (OR = 1.118, 95% CI 1.031-1.212, P = 0.041), HLA-DMB (OR = 1.057, 95% CI 1.045 -1.069, P = 1.11E-21) and HLA-DPA1 (OR = 1.030, 95% CI 1.005-1.056, P = 0.017) were causally and positively associated with the risk of developing OA.

Conclusion

The present study identified the 3 potential immune-related biomarkers for OA, providing new perspectives for the prevention and treatment of OA. The MR study provides genetic support for the causal effects of the 3 biomarkers with OA and may provide new insights into the molecular mechanisms leading to the development of OA.

Social and Behavior Factors of Alzheimer's Disease and Related Dementias: A National Study in the U.S

INTRODUCTION

Considerable research has linked many risk factors to Alzheimer's Disease and Related Dementias (ADRD). Without a clear etiology of ADRD, it is advantageous to rank the known risk factors by their importance and determine if disparities exist. Statistical-based ranking can provide insight into which risk factors should be further evaluated.

METHODS

This observational, population-based study assessed 50 county-level measures and estimates related to ADRD in 3,155 counties in the U.S. using data from 2010 to 2021. Statistical analysis was performed in 2022-2023. The machine learning method, eXtreme Gradient Boosting, was utilized to rank the importance of these variables by their relative contribution to the model performance. Stratified ranking was also performed based on a county's level of disadvantage. Shapley Additive exPlanations (SHAP) provided marginal contributions for each variable.

RESULTS

The top three ranked predictors at the county level were insufficient sleep, consuming less than one serving of fruits/vegetables per day among adults, and having less than a high school diploma. In both disadvantaged and non-disadvantaged counties, demographic variables such as sex and race were important in predicting ADRD. Lifestyle factors ranked highly in non-disadvantaged counties compared to more environmental factors in disadvantaged counties.

CONCLUSIONS

This ranked list of factors can provide a guided approach to ADRD primary prevention strategies in the U.S., as the effects of sleep, diet, and education on ADRD can be further developed. While sleep, diet, and education are important nationally, differing prevention strategies could be employed based on a county's level of disadvantage.

Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics

BACKGROUND

Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies.

METHOD

In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-view variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information.

RESULTS

We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R^2-scores > 0.01 for 71.55% of metabolites.

CONCLUSION

The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

Somatomotor-visual resting state functional connectivity increases after 2 years in the UK Biobank longitudinal cohort

Purpose

Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, in which high connectivity among all brain regions changes to a more modular structure with maturation. We examine FC changes in older adults after 2 years of aging in the UK Biobank (UKB) longitudinal cohort.

Approach

We process fMRI connectivity data using the Power264 atlas and then test whether the average internetwork FC changes in the 2722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t -test. We also compare the ability of Power264 and UKB-provided, independent component analysis (ICA)-based FC to determine which of a longitudinal scan pair is older. Finally, we investigate cross-sectional FC changes as well as differences due to differing scanner tasks in the UKB, Philadelphia Neurodevelopmental Cohort, and Alzheimer's Disease Neuroimaging Initiative datasets.

Results

We find a 6.8% average increase in somatomotor network (SMT)-visual network (VIS) connectivity from younger to older scans (corrected p < 10 - 15 ) that occurs in male, female, older subject (> 65 years old), and younger subject (< 55 years old) groups. Among all internetwork connections, the average SMT-VIS connectivity is the best predictor of relative scan age. Using the full FC and a training set of 2000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC.

Conclusions

We conclude that SMT-VIS connectivity increases with age in the UKB longitudinal cohort and that resting state FC increases with age in the UKB cross-sectional cohort.

CLCLSA: Cross-omics linked embedding with contrastive learning and self attention for integration with incomplete multi-omics data

Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding etiology of complex genetic diseases. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phenotypes. However, one obstacle faced when performing multi-omics data integration is the existence of unpaired multi-omics data due to instrument sensitivity and cost. Studies may fail if certain aspects of the subjects are missing or incomplete. In this paper, we propose a deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention (CLCLSA). Utilizing complete multi-omics data as supervision, the model employs cross-omics autoencoders to learn the feature representation across different types of biological data. The multi-omics contrastive learning is employed, which maximizes the mutual information between different types of omics. In addition, the feature-level self-attention and omics-level self-attention are employed to dynamically identify the most informative features for multi-omics data integration. Finally, a Softmax classifier is employed to perform multi-omics data classification. Extensive experiments were conducted on four public multi-omics datasets. The experimental results indicate that our proposed CLCLSA produces promising results in multi-omics data classification using both complete and incomplete multi-omics data.

Prenatal diagnosis and treatment for fetal angiotensin converting enzyme deficiency

OBJECTIVE

To elucidate an etiology in a case with persistent oligohydramnios by prenatal diagnosis and actively treat the case to achieve good prognosis.

METHODS

We performed whole exome sequencing (WES) of DNA from the fetus and parents. Serial amnioinfusions were conducted until birth. Pressors were required to maintain normal blood pressure. The infant angiotensin-converting enzyme (ACE) activity, angiotensin II (Ang II, a downstream product of ACE), and compensatory enzymes (CEs) activities were measured. Compensatory enzyme activities in plasma from age-matched healthy controls were also detected.

RESULTS

We identified a fetus with a severe ACE mutation prenatally. The infant was born prematurely without pulmonary dysplasia. Hypotension and anuria resolved spontaneously. He had almost no ACE activity, but his Ang II level and CE activity exceeded the upper limit of the normal range and the upper limit of the 95% confidence interval of controls, respectively. His renal function also largely recovered.

CONCLUSION

Fetuses with ACE mutations can be diagnosed prenatally through WES. Serial amnioinfusion permits the continuation of pregnancy in fetal ACE deficiency. Compensatory enzymes for defective ACE appeared postnatally. Renal function may be spared by preterm delivery; furthermore, for postnatal vasopressor therapy to begin, improving renal perfusion pressure before nephrogenesis has been completed.

Associations between genetically predicted plasma protein levels and Alzheimer's disease risk: a study using genetic prediction models

BACKGROUND

Specific peripheral proteins have been implicated to play an important role in the development of Alzheimer's disease (AD). However, the roles of additional novel protein biomarkers in AD etiology remains elusive. The availability of large-scale AD GWAS and plasma proteomic data provide the resources needed for the identification of causally relevant circulating proteins that may serve as risk factors for AD and potential therapeutic targets.

METHODS

We established and validated genetic prediction models for protein levels in plasma as instruments to investigate the associations between genetically predicted protein levels and AD risk. We studied 71,880 (proxy) cases and 383,378 (proxy) controls of European descent.

RESULTS

We identified 69 proteins with genetically predicted concentrations showing associations with AD risk. The drugs almitrine and ciclopirox targeting ATP1A1 were suggested to have a potential for being repositioned for AD treatment.

CONCLUSIONS

Our study provides additional insights into the underlying mechanisms of AD and potential therapeutic strategies.

Autosurv: interpretable deep learning framework for cancer survival analysis incorporating clinical and multi-omics data

Accurate prognosis for cancer patients can provide critical information for optimizing treatment plans and improving life quality. Combining omics data and demographic/clinical information can offer a more comprehensive view of cancer prognosis than using omics or clinical data alone and can also reveal the underlying disease mechanisms at the molecular level. In this study, we developed and validated a deep learning framework to extract information from high-dimensional gene expression and miRNA expression data and conduct prognosis prediction for breast cancer and ovarian-cancer patients using multiple independent multi-omics datasets. Our model achieved significantly better prognosis prediction than the current machine learning and deep learning approaches in various settings. Moreover, an interpretation method was applied to tackle the "black-box" nature of deep neural networks and we identified features (i.e., genes, miRNA, demographic/clinical variables) that were important to distinguish predicted high- and low-risk patients. The significance of the identified features was partially supported by previous studies.

Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC.

SIGNIFICANCE

PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. This dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions.

Single-cell RNA-seq identified novel genes involved in primordial follicle formation

Introduction

The number of primordial follicles (PFs) in mammals determines the ovarian reserve, and impairment of primordial follicle formation (PFF) will cause premature ovarian insufficiency (POI).

Methods

By analyzing public single-cell RNA sequencing performed during PFF on mice and human ovaries, we identified novel functional genes and novel ligand-receptor interaction during PFF. Based on immunofluorescence and in vitro ovarian culture, we confirmed mechanisms of genes and ligand-receptor interaction in PFF. We also applied whole exome sequencing (WES) in 93 cases with POI and whole genome sequencing (WGS) in 465 controls. Variants in POI patients were further investigated by in silico analysis and functional verification.

Results

We revealed ANXA7 (annexin A7) and GTF2F1 (general transcription factor IIF subunit 1) in germ cells to be novel potentially genes in promoting PFF. Ligand Mdk (midkine) in germ cells and its receptor Sdc1 (syndecan 1) in granulosa cells are novel interaction crucial for PFF. Based on immunofluorescence, we confirmed significant up-regulation of ANXA7 in PFs compared with germline cysts, and uniform expression of GTF2F1, MDK and SDC1 during PFF, in 25 weeks human fetal ovary. In vitro investigation indicated that Anxa7 and Gtf2f1 are vital for mice PFF by regulating Jak/Stat3 and Jnk signaling pathways, respectively. Ligand-receptor (Mdk-Sdc1) are crucial for PFF by regulating Pi3k-akt signaling pathway. Two heterozygous variants in GTF2F1, and one heterozygous variants in SDC1 were identified in cases, but no variant were identified in controls. The protein level of GTF2F1 or SDC1 in POI cases are significantly lower than that of controls, indicating the pathogenic effects of the two genes on ovarian function were dosage dependent.

Discussion

Our study identified novel genes and novel ligand-receptor interaction during PFF, and further expanding the genetic architecture of POI.

ImageNomer: Description of a functional connectivity and omics analysis tool and case study identifying a race confound

Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. FC in particular usually contains tens of thousands of features per subject, and can only be summarized and efficiently explored using visualizations. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and single nucleotide polymorphism (SNP) data of healthy adolescents. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer to visualize trends in achievement scores between races, we find a clear potential for confounding effects if race can be predicted using FC. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10-15% of WRAT score variation, this predictive ability disappears when controlling for race. We also use ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP) dataset. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.

Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load

Background

Hip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or "strength") and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion.

Results

We developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively.

Conclusion

The proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for predicting FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation exposure from QCT.

Metabolomic profiles associated with physical activity in White and African American adult men

BACKGROUND

Physical activity (PA) is associated with various health benefits, especially in improving chronic health conditions. However, the metabolic changes in host metabolism in response to PA remain unclear, especially in racially/ethnically diverse populations.

OBJECTIVE

This study is to assess the metabolic profiles associated with the frequency of PA in White and African American (AA) men.

METHODS

Using the untargeted metabolomics data collected from 698 White and AA participants (mean age: 38.0±8.0, age range: 20-50) from the Louisiana Osteoporosis Study (LOS), we conducted linear regression models to examine metabolites that are associated with PA levels (assessed by self-reported regular exercise frequency levels: 0, 1-2, and ≥3 times per week) in White and AA men, respectively, as well as in the pooled sample. Covariates considered for statistical adjustments included race (only for the pooled sample), age, BMI, waist circumstance, smoking status, and alcohol drinking.

RESULTS

Of the 1133 untargeted compounds, we identified 7 metabolites associated with PA levels in the pooled sample after covariate adjustment with a false discovery rate of 0.15. Specifically, compared to participants who did not exercise, those who exercised at a frequency ≥3 times/week showed higher abundances in uracil, orotate, 1-(1-enyl-palmitoyl)-2-oleoyl-GPE (P-16:0/18:1) (GPE), threonate, and glycerate, but lower abundances in salicyluric glucuronide and adenine in the pooled sample. However, in Whites, salicyluric glucuronide and orotate were not significant. Adenine, GPE, and threonate were not significant in AAs. In addition, the seven metabolites were not significantly different between participants who exercised ≥3 times/week and 1-2 times/week, nor significantly different between participants with 1-2 times/week and 0/week in the pooled sample and respective White and AA groups.

CONCLUSIONS

Metabolite responses to PA are dose sensitive and may differ between White and AA populations. The identified metabolites may help advance our knowledge of guiding precision PA interventions. Studies with rigorous study designs are warranted to elucidate the relationship between PA and metabolites.

Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects

Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. We developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

CXADR promote epithelial-mesenchymal transition in endometriosis by modulating AKT/GSK-3β signaling

Endometriosis is a benign high prevalent disease exhibiting malignant features. However, the underlying pathogenesis and key molecules of endometriosis remain unclear. By integrating and analysis of existing expression profile datasets, we identified coxsackie and adenovirus receptor (CXADR), as a novel key gene in endometriosis. Based on the results of immunohistochemistry (IHC), we confirmed significant down-regulation of CXADR in ectopic endometrial tissues obtained from women with endometriosis compared with healthy controls. Further in vitro investigation indicated that CXADR regulated the stability and function of the phosphatases and AKT inhibitors PHLPP2 (pleckstrin homology domain and leucine-rich repeat protein phosphatase 2) and PTEN (phosphatase and tensin homolog). Loss of CXADR led to phosphorylation of AKT and glycogen synthase kinase-3β (GSK-3β), which resulted in stabilization of an epithelial-mesenchymal transition (EMT) factor, SNAIL1 (snail family transcriptional repressor 1). Therefore, EMT processs was induced, and the proliferation, migration and invasion of Ishikawa cells were enhanced. Over-expression of CXADR showed opposite effects. These findings suggest a previously undefined role of AKT/GSK-3β signaling axis in regulating EMT and reveal the involvement of a CXADR-induced EMT, in pathogenic progression of endometriosis.

Identification of potential genetic causal variants for obesity-related traits using statistical fine mapping

BACKGROUND

Obesity is highly influenced by heritability and variant effects. While previous genome-wide association studies (GWASs) have successfully identified numerous genetic loci associated with obesity-related traits [body mass index (BMI) and waist-to-hip ratio (WHR)], most causal variants remain unidentified. The high degree of linkage disequilibrium (LD) throughout the genome makes it extremely difficult to distinguish the GWAS-associated SNPs that exert a true biological effect.

OBJECTIVE

This study was to identify the potential causal variants having a biological effect on obesity-related traits.

METHODS

We used Probabilistic Annotation INTegratOR, a Bayesian fine-mapping method, which incorporated genetic association data (GWAS summary statistics), LD structure, and functional annotations to calculate a posterior probability of causality for SNPs across all loci of interest. Moreover, we performed gene expression analysis using the available public transcriptomic data to validate the corresponding genes of the potential causal SNPs partially.

RESULTS

We identified 96 SNPs for BMI and 43 SNPs for WHR with a high posterior probability of causality (> 99%), including 49 BMI SNPs and 24 WHR SNPs which did not reach genome-wide significance in the original GWAS. Finally, we partially validated some genes corresponding to the potential causal SNPs.

CONCLUSION

Using a statistical fine-mapping approach, we identified a set of potential causal variants to be prioritized for future functional validation and also detected some novel trait-associated variants. These results provided novel insight into our understanding of the genetics of obesity and also demonstrated that fine mapping may improve upon the results identified by the original GWASs.

Gut microbiota impacts bone via Bacteroides vulgatus-valeric acid-related pathways

Although the gut microbiota has been reported to influence osteoporosis risk, the individual species involved, and underlying mechanisms, remain largely unknown. We performed integrative analyses in a Chinese cohort of peri-/post-menopausal women with metagenomics/targeted metabolomics/whole-genome sequencing to identify novel microbiome-related biomarkers for bone health. Bacteroides vulgatus was found to be negatively associated with bone mineral density (BMD), which was validated in US white people. Serum valeric acid (VA), a microbiota derived metabolite, was positively associated with BMD and causally downregulated by B. vulgatus. Ovariectomized mice fed B. vulgatus demonstrated increased bone resorption and poorer bone micro-structure, while those fed VA demonstrated reduced bone resorption and better bone micro-structure. VA suppressed RELA protein production (pro-inflammatory), and enhanced IL10 mRNA expression (anti-inflammatory), leading to suppressed maturation of osteoclast-like cells and enhanced maturation of osteoblasts in vitro. The findings suggest that B. vulgatus and VA may represent promising targets for osteoporosis prevention/treatment.

ImageNomer: description of a functional connectivity and omics analysis tool and case study identifying a race confound

Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer, we find a clear potential for confounding effects of race. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10-15\% of WRAT score variation, this predictive ability disappears when controlling for race. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.

Deep Learning Methods for Omics Data Imputation

One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or non-monotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advanced imputation methods, including deep learning-based methods, have been proposed to address these challenges. Due to its capability of modeling complex patterns and relationships in large and high-dimensional datasets, many researchers have adopted deep learning models to impute missing omics data. This review provides a comprehensive overview of the currently available deep learning-based methods for omics imputation from the perspective of deep generative model architectures such as autoencoder, variational autoencoder, generative adversarial networks, and Transformer, with an emphasis on multi-omics data imputation. In addition, this review also discusses the opportunities that deep learning brings and the challenges that it might face in this field.

MIMOSA: A resource consisting of improved methylome imputation models increases power to identify DNA methylation-phenotype associations

Although DNA methylation has been implicated in the pathogenesis of numerous complex diseases, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified.However, current MWAS models are primarily trained by using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large, summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). With the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study in high cholesterol, pinpointing 146 putatively causal CpG sites.

Dissection of Cellular Communication between Human Primary Osteoblasts and Bone Marrow Mesenchymal Stem Cells in Osteoarthritis at Single-Cell Resolution

Background and Objectives

Osteoblasts are derived from bone marrow mesenchymal stem cells (BMMSCs) and play important role in bone remodeling. While our previous studies have investigated the cell subtypes and heterogeneity in osteoblasts and BMMSCs separately, cell-to-cell communications between osteoblasts and BMMSCs in vivo in humans have not been characterized. The aim of this study was to investigate the cellular communication between human primary osteoblasts and bone marrow mesenchymal stem cells.

Methods and Results

To investigate the cell-to-cell communications between osteoblasts and BMMSCs and identify new cell subtypes, we performed a systematic integration analysis with our single-cell RNA sequencing (scRNA-seq) transcriptomes data from BMMSCs and osteoblasts. We successfully identified a novel preosteoblasts subtype which highly expressed ATF3, CCL2, CXCL2 and IRF1. Biological functional annotations of the transcriptomes suggested that the novel preosteoblasts subtype may inhibit osteoblasts differentiation, maintain cells to a less differentiated status and recruit osteoclasts. Ligand-receptor interaction analysis showed strong interaction between mature osteoblasts and BMMSCs. Meanwhile, we found FZD1 was highly expressed in BMMSCs of osteogenic differentiation direction. WIF1 and SFRP4, which were highly expressed in mature osteoblasts were reported to inhibit osteogenic differentiation. We speculated that WIF1 and sFRP4 expressed in mature osteoblasts inhibited the binding of FZD1 to Wnt ligand in BMMSCs, thereby further inhibiting osteogenic differentiation of BMMSCs.

Conclusions

Our study provided a more systematic and comprehensive understanding of the heterogeneity of osteogenic cells. At the single cell level, this study provided insights into the cell-to-cell communications between BMMSCs and osteoblasts and mature osteoblasts may mediate negative feedback regulation of osteogenesis process.

Somatomotor-Visual Resting State Functional Connectivity Increases After Two Years in the UK Biobank Longitudinal Cohort

Purpose

Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, where high connectivity among all brain regions changes to a more modular structure with maturation. In this work, we examine changes in FC in older adults after two years of aging in the UK Biobank longitudinal cohort.

Approach

We process data using the Power264 atlas, then test whether FC changes in the 2,722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, ICA-based FC to determine which of a longitudinal scan pair is older.

Results

We find a 6.8% average increase in SMT-VIS connectivity from younger to older scan (from ρ = 0.39 to ρ = 0.42) that occurs in male, female, older subject (> 65 years old), and younger subject (< 55 years old) groups. Among all inter-network connections, this average SMT-VIS connectivity is the best predictor of relative scan age, accurately predicting which scan is older 57% of the time. Using the full FC and a training set of 2,000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC.

Conclusions

We conclude that SMT-VIS connectivity increases in the longitudinal cohort, while resting state FC increases generally with age in the cross-sectional cohort. However, we consider the possibility of a change in resting state scanner task between UKB longitudinal data acquisitions.

Somatomotor-Visual Resting State Functional Connectivity Increases After Two Years in the UK Biobank Longitudinal Cohort

Purpose

Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, where high connectivity among all brain regions changes to a more modular structure with maturation. In this work, we examine changes in FC in older adults after two years of aging in the UK Biobank longitudinal cohort.

Approach

We process data using the Power264 atlas, then test whether FC changes in the 2,722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, ICA-based FC to determine which of a longitudinal scan pair is older.

Results

We find a 6.8% average increase in SMT-VIS connectivity from younger to older scan (from ρ = 0.39 to ρ = 0.42 ) that occurs in male, female, older subject (> 65 years old), and younger subject (< 55 years old) groups. Among all inter-network connections, this average SMT-VIS connectivity is the best predictor of relative scan age, accurately predicting which scan is older 57% of the time. Using the full FC and a training set of 2,000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC.

Conclusions

We conclude that SMT-VIS connectivity increases in the longitudinal cohort, while resting state FC increases generally with age in the cross-sectional cohort. However, we consider the possibility of a change in resting state scanner task between UKB longitudinal data acquisitions.

Atp6i deficient mouse model uncovers transforming growth factor-β1 /Smad2/3 as a key signaling pathway regulating odontoblast differentiation and tooth root formation

The biomolecular mechanisms that regulate tooth root development and odontoblast differentiation are poorly understood. We found that Atp6i deficient mice (Atp6i-/-) arrested tooth root formation, indicated by truncated Hertwig's epithelial root sheath (HERS) progression. Furthermore, Atp6i deficiency significantly reduced the proliferation and differentiation of radicular odontogenic cells responsible for root formation. Atp6i-/- mice had largely decreased expression of odontoblast differentiation marker gene expression profiles (Col1a1, Nfic, Dspp, and Osx) in the alveolar bone. Atp6i-/- mice sample RNA-seq analysis results showed decreased expression levels of odontoblast markers. Additionally, there was a significant reduction in Smad2/3 activation, inhibiting transforming growth factor-β (TGF-β) signaling in Atp6i-/- odontoblasts. Through treating pulp precursor cells with Atp6i-/- or wild-type OC bone resorption-conditioned medium, we found the latter medium to promote odontoblast differentiation, as shown by increased odontoblast differentiation marker genes expression (Nfic, Dspp, Osx, and Runx2). This increased expression was significantly blocked by anti-TGF-β1 antibody neutralization, whereas odontoblast differentiation and Smad2/3 activation were significantly attenuated by Atp6i-/- OC conditioned medium. Importantly, ectopic TGF-β1 partially rescued root development and root dentin deposition of Atp6i-/- mice tooth germs were transplanted under mouse kidney capsules. Collectively, our novel data shows that the prevention of TGF-β1 release from the alveolar bone matrix due to OC dysfunction may lead to osteopetrosis-associated root formation via impaired radicular odontoblast differentiation. As such, this study uncovers TGF-β1 /Smad2/3 as a key signaling pathway regulating odontoblast differentiation and tooth root formation and may contribute to future therapeutic approaches to tooth root regeneration.

AUTOSURV: INTERPRETABLE DEEP LEARNING FRAMEWORK FOR CANCER SURVIVAL ANALYSIS INCORPORATING CLINICAL AND MULTI-OMICS DATA

Accurate prognosis for cancer patients can provide critical information for optimizing treatment plans and improving life quality. Combining omics data and demographic/clinical information can offer a more comprehensive view of cancer prognosis than using omics or clinical data alone and can reveal the underlying disease mechanisms at the molecular level. In this study, we developed a novel deep learning framework to extract information from high-dimensional gene expression and miRNA expression data and conduct prognosis prediction for breast cancer and ovarian cancer patients. Our model achieved significantly better prognosis prediction than the conventional Cox Proportional Hazard model and other competitive deep learning approaches in various settings. Moreover, an interpretation approach was applied to tackle the "black-box" nature of deep neural networks and we identified features (i.e., genes, miRNA, demographic/clinical variables) that made important contributions to distinguishing predicted high- and low-risk patients. The identified associations were partially supported by previous studies.

Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results

Functional magnetic resonance (fMRI) is an invaluable tool in studying cognitive processes in vivo. Many recent studies use functional connectivity (FC), partial correlation connectivity (PC), or fMRI-derived brain networks to predict phenotypes with results that sometimes cannot be replicated. At the same time, FC can be used to identify the same subject from different scans with great accuracy. In this paper, we show a method by which one can unknowingly inflate classification results from 61% accuracy to 86% accuracy by treating longitudinal or contemporaneous scans of the same subject as independent data points. Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10,000 training subjects without double-dipping. We replicate this effect in four different datasets: the UK Biobank (UKB), the Philadelphia Neurodevelopmental Cohort (PNC), the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP), and an OpenNeuro Fibromyalgia dataset (Fibro). The unintentional improvement ranges between 7% and 25% in the four datasets. Additionally, we find that by using dynamic functional connectivity (dFC), one can apply this method even when one is limited to a single scan per subject. One major problem is that features such as ROIs or connectivities that are reported alongside inflated results may confuse future work. This article hopes to shed light on how even minor pipeline anomalies may lead to unexpectedly superb results.

A Mendelian randomization study for drug repurposing reveals bezafibrate and fenofibric acid as potential osteoporosis treatments

Background: Lipid pathways have been implicated in the pathogenesis of osteoporosis (OP). Lipid-lowering drugs may be used to prevent and treat OP. However, the causal interpretation of results from traditional observational designs is controversial by confounding. We aimed to investigate the causal association between genetically proxied lipid-lowering drugs and OP risk. Methods: We conducted two-step Mendelian randomization (MR) analyses to investigate the causal association of genetically proxied lipid-lowering drugs on the risk of OP. The first step MR was used to estimate the associations of drug target genes expression with low-density lipoprotein cholesterol (LDL-C) levels. The significant SNPs in the first step MR were used as instrumental variables in the second step MR to estimate the associations of LDL-C levels with forearm bone mineral density (FA-BMD), femoral neck BMD (FN-BMD), lumbar spine BMD (LS-BMD) and fracture. The significant lipid-lowering drugs after MR analyses were further evaluated for their effects on bone mineralization using a dexamethasone-induced OP zebrafish model. Results: The first step MR analysis found that the higher expression of four genes (HMGCR, NPC1L1, PCSK9 and PPARG) was significantly associated with a lower LDL-C level. The genetically decreased LDL-C level mediated by the PPARG was significantly associated with increased FN-BMD (BETA = -1.38, p = 0.001) and LS-BMD (BETA = -2.07, p = 3.35 × 10-5) and was marginally significantly associated with FA-BMD (BETA = -2.36, p = 0.008) and reduced fracture risk (OR = 3.47, p = 0.008). Bezafibrate (BZF) and Fenofibric acid (FBA) act as PPARG agonists. Therefore genetically proxied BZF and FBA had significant protective effects on OP. The dexamethasone-induced OP zebrafish treated with BZF and FBA showed increased bone mineralization area and integrated optical density (IOD) with alizarin red staining. Conclusion: The present study provided evidence that BZF and FBA can increase BMD, suggesting their potential effects in preventing and treating OP. These findings potentially pave the way for future studies that may allow personalized selection of lipid-lowering drugs for those at risk of OP.

Enteric nervous system damage caused by abnormal intestinal butyrate metabolism may lead to functional constipation

Functional constipation (FC) is a high morbidity gastrointestinal disease for which dysfunction in the enteric nervous system is a major pathogenesis mechanism. To enhance our understanding of the involvement of intestinal microbiota and its metabolites in the pathogenesis of FC, we conducted a shotgun metagenomic sequencing analysis of gut microbiota and serum short-chain fatty acids (SCFAs) analysis in 460 Chinese women with different defecation frequencies. We observed that the abundance ofFusobacterium_varium, a butyric acid-producing bacterium, was positively correlated (P = 0.0096) with the frequency of defecation; however, the concentrations of serum butyric acid was negatively correlated (P = 3.51E-05) with defecation frequency. These results were verified in an independent cohort (6 patients with FC and 6 controls). To further study the effects of butyric acid on intestinal nerve cells, we treated mouse intestinal neurons in vitro with various concentrations of butyrate (0.1, 0.5, 1, and 2.5 mM). We found that intestinal neurons treated with 0.5 mM butyrate proliferated better than those in the other treatment groups, with significant differences in cell cycle and oxidative phosphorylation signal pathways. We suggest that the decreased butyrate production resulting from the reduced abundance of Fusobacterium in gut microbiota affects the proliferation of intestinal neurons and the energy supply of intestinal cells. However, with FC disease advancing, the consumption and excretion of butyric acid reduce, leading to its accumulation in the intestine. Moreover, the accumulation of an excessively high amount of butyric acid inhibits the proliferation of nerve cells and subsequently exacerbates the disease.

CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data

Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phenotypes. However, one obstacle faced when performing multi-omics data integration is the existence of unpaired multi-omics data due to instrument sensitivity and cost. Studies may fail if certain aspects of the subjects are missing or incomplete. In this paper, we propose a deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention (CLCLSA). Utilizing complete multi-omics data as supervision, the model employs cross-omics autoencoders to learn the feature representation across different types of biological data. The multi-omics contrastive learning, which is used to maximize the mutual information between different types of omics, is employed before latent feature concatenation. In addition, the feature-level self-attention and omics-level self-attention are employed to dynamically identify the most informative features for multi-omics data integration. Extensive experiments were conducted on four public multi-omics datasets. The experimental results indicated that the proposed CLCLSA outperformed the state-of-the-art approaches for multi-omics data classification using incomplete multiomics data.

Potential Molecular Mechanisms of Alzheimer's Disease from Genetic Studies

The devastating effects of Alzheimer's disease (AD) are yet to be ameliorated due to the absence of curative treatment options. AD is an aging-related disease that affects cognition, and molecular imbalance is one of its hallmarks. There is a need to identify common causes of molecular imbalance in AD and their potential mechanisms for continuing research. A narrative synthesis of molecular mechanisms in AD from primary studies that employed single-cell sequencing (scRNA-seq) or spatial genomics was conducted using Embase and PubMed databases. We found that differences in molecular mechanisms in AD could be grouped into four key categories: sex-specific features, early-onset features, aging, and immune system pathways. The reported causes of molecular imbalance were alterations in bile acid (BA) synthesis, PITRM1, TREM2, olfactory mucosa (OM) cells, cholesterol catabolism, NFkB, double-strand break (DSB) neuronal damage, P65KD silencing, tau and APOE expression. What changed from previous findings in contrast to results obtained were explored to find potential factors for AD-modifying investigations.

CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data

Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phenotypes. However, one obstacle faced when performing multi-omics data integration is the existence of unpaired multi-omics data due to instrument sensitivity and cost. Studies may fail if certain aspects of the subjects are missing or incomplete. In this paper, we propose a deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention (CLCLSA). Utilizing complete multi-omics data as supervision, the model employs cross-omics autoencoders to learn the feature representation across different types of biological data. The multi-omics contrastive learning, which is used to maximize the mutual information between different types of omics, is employed before latent feature concatenation. In addition, the feature-level self-attention and omics-level self-attention are employed to dynamically identify the most informative features for multi-omics data integration. Extensive experiments were conducted on four public multi-omics datasets. The experimental results indicated that the proposed CLCLSA outperformed the state-of-the-art approaches for multi-omics data classification using incomplete multi-omics data.

A variability in response of osteoclasts to zoledronic acid is mediated by smoking-associated modification in the DNA methylome

BACKGROUND

Clinical trials have shown zoledronic acid as a potent bisphosphonate in preventing bone loss, but with varying potency between patients. Human osteoclasts ex vivo reportedly displayed a variable sensitivity to zoledronic acid > 200-fold, determined by the half-maximal inhibitory concentration (IC50), with cigarette smoking as one of the reported contributors to this variation. To reveal the molecular basis of the smoking-mediated variation on treatment sensitivity, we performed a DNA methylome profiling on whole blood cells from 34 healthy female blood donors. Multiple regression models were fitted to associate DNA methylation with ex vivo determined IC50 values, smoking, and their interaction adjusting for age and cell compositions.

RESULTS

We identified 59 CpGs displaying genome-wide significance (p < 1e-08) with a false discovery rate (FDR) < 0.05 for the smoking-dependent association with IC50. Among them, 3 CpGs have p < 1e-08 and FDR < 2e-03. By comparing with genome-wide association studies, 15 significant CpGs were locally enriched (within < 50,000 bp) by SNPs associated with bone and body size measures. Furthermore, through a replication analysis using data from a published multi-omics association study on bone mineral density (BMD), we could validate that 29 out of the 59 CpGs were in close vicinity of genomic sites significantly associated with BMD. Gene Ontology (GO) analysis on genes linked to the 59 CpGs displaying smoking-dependent association with IC50, detected 18 significant GO terms including cation:cation antiporter activity, extracellular matrix conferring tensile strength, ligand-gated ion channel activity, etc. CONCLUSIONS: Our results suggest that smoking mediates individual sensitivity to zoledronic acid treatment through epigenetic regulation. Our novel findings could have important clinical implications since DNA methylation analysis may enable personalized zoledronic acid treatment.

Systematic metabolomic studies identified adult adiposity biomarkers with acetylglycine associated with fat loss in vivo

Obesity is associated with various adverse health outcomes. Body fat (BF) distribution is recognized as an important factor of negative health consequences of obesity. Although metabolomics studies, mainly focused on body mass index (BMI) and waist circumference, have explored the biological mechanisms involved in the development of obesity, these proxy composite measures are not accurate and cannot reflect BF distribution, and thus may hinder accurate assessment of metabolic alterations and differential risk of metabolic disorders among individuals presenting adiposity differently throughout the body. Thus, the exact relations between metabolites and BF remain to be elucidated. Here, we aim to examine the associations of metabolites and metabolic pathways with BF traits which reflect BF distribution. We performed systematic untargeted serum metabolite profiling and dual-energy X-ray absorptiometry (DXA) whole body fat scan for 517 Chinese women. We jointly analyzed DXA-derived four BF phenotypes to detect cross-phenotype metabolite associations and to prioritize important metabolomic factors. Topology-based pathway analysis was used to identify important BF-related biological processes. Finally, we explored the relationships of the identified BF-related candidate metabolites with BF traits in different sex and ethnicity through two independent cohorts. Acetylglycine, the top distinguished finding, was validated for its obesity resistance effect through in vivo studies of various diet-induced obese (DIO) mice. Eighteen metabolites and fourteen pathways were discovered to be associated with BF phenotypes. Six of the metabolites were validated in varying sex and ethnicity. The obesity-resistant effects of acetylglycine were observed to be highly robust and generalizable in both human and DIO mice. These findings demonstrate the importance of metabolites associated with BF distribution patterns and several biological pathways that may contribute to obesity and obesity-related disease etiology, prevention, and intervention. Acetylglycine is highlighted as a potential therapeutic candidate for preventing excessive adiposity in future studies.

Simultaneous detection of novel genes and SNPs by adaptive p-value combination

Combining SNP p-values from GWAS summary data is a promising strategy for detecting novel genetic factors. Existing statistical methods for the p-value-based SNP-set testing confront two challenges. First, the statistical power of different methods depends on unknown patterns of genetic effects that could drastically vary over different SNP sets. Second, they do not identify which SNPs primarily contribute to the global association of the whole set. We propose a new signal-adaptive analysis pipeline to address these challenges using the omnibus thresholding Fisher’s method (oTFisher). The oTFisher remains robustly powerful over various patterns of genetic effects. Its adaptive thresholding can be applied to estimate important SNPs contributing to the overall significance of the given SNP set. We develop efficient calculation algorithms to control the type I error rate, which accounts for the linkage disequilibrium among SNPs. Extensive simulations show that the oTFisher has robustly high power and provides a higher balanced accuracy in screening SNPs than the traditional Bonferroni and FDR procedures. We applied the oTFisher to study the genetic association of genes and haplotype blocks of the bone density-related traits using the summary data of the Genetic Factors for Osteoporosis Consortium. The oTFisher identified more novel and literature-reported genetic factors than existing p-value combination methods. Relevant computation has been implemented into the R package TFisher to support similar data analysis.

Polygenic risk score improves the accuracy of a clinical risk score for coronary artery disease

BACKGROUND

The value of polygenic risk scores (PRSs) towards improving guideline-recommended clinical risk models for coronary artery disease (CAD) prediction is controversial. Here we examine whether an integrated polygenic risk score improves the prediction of CAD beyond pooled cohort equations.  METHODS: An observation study of 291,305 unrelated White British UK Biobank participants enrolled from 2006 to 2010 was conducted. A case-control sample of 9499 prevalent CAD cases and an equal number of randomly selected controls was used for tuning and integrating of the polygenic risk scores. A separate cohort of 272,307 individuals (with follow-up to 2020) was used to examine the risk prediction performance of pooled cohort equations, integrated polygenic risk score, and PRS-enhanced pooled cohort equation for incident CAD cases. The performance of each model was analyzed by discrimination and risk reclassification using a 7.5% threshold.

RESULTS

In the cohort of 272,307 individuals (mean age, 56.7 years) used to analyze predictive accuracy, there were 7036 incident CAD cases over a 12-year follow-up period. Model discrimination was tested for integrated polygenic risk score, pooled cohort equation, and PRS-enhanced pooled cohort equation with reported C-statistics of 0.640 (95% CI, 0.634-0.646), 0.718 (95% CI, 0.713-0.723), and 0.753 (95% CI, 0.748-0.758), respectively. Risk reclassification for the addition of the integrated polygenic risk score to the pooled cohort equation at a 7.5% risk threshold resulted in a net reclassification improvement of 0.117 (95% CI, 0.102 to 0.129) for cases and - 0.023 (95% CI, - 0.025 to - 0.022) for noncases [overall: 0.093 (95% CI, 0.08 to 0.104)]. For incident CAD cases, this represented 14.2% correctly reclassified to the higher-risk category and 2.6% incorrectly reclassified to the lower-risk category.

CONCLUSIONS

Addition of the integrated polygenic risk score for CAD to the pooled cohort questions improves the predictive accuracy for incident CAD and clinical risk classification in the White British from the UK Biobank. These findings suggest that an integrated polygenic risk score may enhance CAD risk prediction and screening in the White British population.

An autoencoder-based deep learning method for genotype imputation

Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a single batch loss rather than the average loss over batches. This modified AE imputation model was evaluated using a yeast dataset, the human leukocyte antigen (HLA) data from the 1,000 Genomes Project (1KGP), and our in-house genotype data from the Louisiana Osteoporosis Study (LOS). Our modified AE imputation model has achieved comparable or better performance than the existing SCDA model in terms of evaluation metrics such as the concordance rate (CR), the Hellinger score, the scaled Euclidean norm (SEN) score, and the imputation quality score (IQS) in all three datasets. Taking the imputation results from the HLA data as an example, the AE model achieved an average CR of 0.9468 and 0.9459, Hellinger score of 0.9765 and 0.9518, SEN score of 0.9977 and 0.9953, and IQS of 0.9515 and 0.9044 at missing ratios of 10% and 20%, respectively. As for the results of LOS data, it achieved an average CR of 0.9005, Hellinger score of 0.9384, SEN score of 0.9940, and IQS of 0.8681 at the missing ratio of 20%. In summary, our proposed method for genotype imputation has a great potential to increase the statistical power of GWAS and improve downstream post-GWAS analyses.

Identification of genetic loci shared between Alzheimer's disease and hypertension

Alzheimer's disease (AD) and high blood pressure (BP) are prevalent age-related diseases with significant unexplained heritability. A thorough analysis of genetic pleiotropy between AD and BP will lay a foundation for the study of the associated molecular mechanisms, leading to a better understanding of the development of each phenotype. We used the conditional false discovery rate (cFDR) method to identify novel genetic loci associated with both AD and BP. The cFDR approach improves the effective sample size for association testing by combining GWAS summary statistics for correlated phenotypes. We identified 50 pleiotropic SNPs for AD and BP, 7 of which are novel and have not previously been reported to be associated with either AD or BP. The novel SNPs located at STK3 are particularly noteworthy, as this gene may influence AD risk via the Hippo signaling network, which regulates cell death. Bayesian colocalization analysis demonstrated that although AD and BP are associated, they do not appear to share the same causal variants. We further performed two sample Mendelian randomization analysis, but could not detect a causal effect of BP on AD. Despite the inability to establish a causal link between AD and BP, our findings report some potential novel pleiotropic loci that may influence disease susceptibility. In summary, we identified 7 SNPs that annotate to 4 novel genes which have not previously been reported to be associated with AD nor with BP and discuss the possible role of one of these genes, STK3 in the Hippo signaling network.

Imputation of Human Primary Osteoblast Single Cell RNA-Seq Data Identified Three Novel Osteoblastic Subtypes

BACKGROUND

Recently, single-cell RNA sequencing (scRNA-seq) technology was increasingly used to study transcriptomics at a single-cell resolution, scRNA-seq analysis was complicated by the "dropout", where the data only captures a small fraction of the transcriptome. This phenomenon can lead to the fact that the actual expressed transcript may not be detected. We previously performed osteoblast subtypes classification and dissection on freshly isolated human osteoblasts.

MATERIALS AND METHODS

Here, we used the scImpute method to impute the missing values of dropout genes from a scRNA-seq dataset generated on freshly isolated human osteoblasts.

RESULTS

Based on the imputed gene expression patterns, we discovered three new osteoblast subtypes. Specifically, these newfound osteoblast subtypes are osteoblast progenitors, and two undetermined osteoblasts. Osteoblast progenitors showed significantly high expression of proliferation related genes (FOS, JUN, JUNB and JUND). Analysis of each subtype showed that in addition to bone formation, these undetermined osteoblasts may involve osteoclast and adipocyte differentiation and have the potential function of regulate immune activation.

CONCLUSIONS

Our findings provided a new perspective for studying the osteoblast heterogeneity and potential biological functions of these freshly isolated human osteoblasts at the single-cell level, which provides further insight into osteoblasts subtypes under various (pathological) physiological conditions.

Gut microbiota accelerates obesity in peri-/post-menopausal women via Bacteroides fragilis and acetic acid

OBJECTIVE

Many animal experiments and epidemiological studies have shown that the gut microbiota (GM) plays an important role in the development of obesity, but the specific biological mechanism involved in the pathogenesis of disease remain unknown. We aimed to examine the relationships and functional mechanisms of GM on obesity in peri- and post-menopausal women.

METHODS

We recruited 499 Chinese peri- and post-menopausal women and performed comprehensive analyses of the gut microbiome, targeted metabolomics for short-chain fatty acids in serum, and host whole-genome sequencing by various association analysis methods.

RESULTS

Through constrained linear regression analysis, we found that an elevated abundance of Bacteroides fragilis (B. fragilis) was associated with obesity. We also found that serum levels of acetic acid were negatively associated with obesity, and that B. fragilis was negatively associated with serum acetic acid levels by partial Spearman correlation analysis. Mendelian randomization analysis indicated that B. fragilis increases the risk of obesity and may causally down-regulate acetic acid levels.

CONCLUSIONS

We found the gut with B. fragilis may accelerate obesity, in part, by suppressing acetic acid levels. Therefore, B. fragilis and acetic acid may represent important therapeutic targets for obesity intervention in peri- and post-menopausal women.

Genome-wide meta-analysis of alcohol use disorder in East Asians

Alcohol use disorder (AUD) is a leading cause of death and disability worldwide. Genome-wide association studies (GWAS) have identified ~30 AUD risk genes in European populations, but many fewer in East Asians. We conducted GWAS and genome-wide meta-analysis of AUD in 13,551 subjects with East Asian ancestry, using published summary data and newly genotyped data from five cohorts: (1) electronic health record (EHR)-diagnosed AUD in the Million Veteran Program (MVP) sample; (2) DSM-IV diagnosed alcohol dependence (AD) in a Han Chinese-GSA (array) cohort; (3) AD in a Han Chinese-Cyto (array) cohort; and (4) two AD Thai cohorts. The MVP and Thai samples included newly genotyped subjects from ongoing recruitment. In total, 2254 cases and 11,297 controls were analyzed. An AUD polygenic risk score was analyzed in an independent sample with 4464 East Asians (Genetic Epidemiology Research in Adult Health and Aging (GERA)). Phenotypes from survey data and ICD-9-CM diagnoses were tested for association with the AUD PRS. Two risk loci were detected: the well-known functional variant rs1229984 in ADH1B and rs3782886 in BRAP (near the ALDH2 gene locus) are the lead variants. AUD PRS was significantly associated with days per week of alcohol consumption (beta = 0.43, SE = 0.067, p = 2.47 × 10^-10) and nominally associated with pack years of smoking (beta = 0.09, SE = 0.05, p = 4.52 × 10^-2) and ever vs. never smoking (beta = 0.06, SE = 0.02, p = 1.14 × 10^-2). This is the largest GWAS of AUD in East Asians to date. Building on previous findings, we were able to analyze pleiotropy, but did not identify any new risk regions, underscoring the importance of recruiting additional East Asian subjects for alcohol GWAS.

Metagenomic study of the gut microbiota associated with cow milk consumption in Chinese peri-/postmenopausal women

Cow milk consumption (CMC) and alterations of gut bacterial composition are proposed to be closely related to human health and disease. Our research aims to investigate the changes in human gut microbial composition in Chinese peri-/postmenopausal women with different CMC habits. A total of 517 subjects were recruited and questionnaires about their CMC status were collected; 394 subjects were included in the final analyses. Fecal samples were used for studying gut bacterial composition. All the subjects were divided into a control group (n = 248) and a CMC group (n = 146) according to their CMC status. Non-parametric tests and LEfSe at different taxonomic levels were used to reveal differentially abundant taxa and functional categories across different CMC groups. Relative abundance (RA) of one phylum (p_Actinobacteria), three genera (g_Bifidobacterium, g_Anaerostipes, and g_Bacteroides), and 28 species diversified significantly across groups. Specifically, taxa g_Anaerostipes (p < 0.01), g_Bacteroides (p < 0.05), s_Anaerostipes_hadrus (p < 0.01), and s_Bifidobacterium_pseudocatenulatum (p < 0.01) were positively correlated with CMC levels, but p_Actinobacteria (p < 0.01) and g_Bifidobacterium (p < 0.01) were negatively associated with CMC levels. KEGG module analysis revealed 48 gut microbiome functional modules significantly (p < 0.05) associated with CMC, including Vibrio cholerae pathogenicity signature, cholera toxins (p = 9.52e-04), and cephamycin C biosynthesis module (p = 0.0057), among others. In conclusion, CMC was associated with changes in gut microbiome patterns including beta diversity and richness of some gut microbiota. The alterations of certain bacteria including g_Anaerostipes and s_Bifidobacterium_pseudocatenulatum in the CMC group should be important for human health. This study further supports the biological value of habitual cow milk consumption.

A bi-directional Mendelian randomization study of the sarcopenia-related traits and osteoporosis

Both sarcopenia and osteoporosis are common geriatric diseases causing huge socioeconomic burdens, and clinically, they often occur simultaneously. Observational studies have found a controversial correlation between sarcopenia and osteoporosis and their causal relationship is not clear. Therefore, we performed a bi-directional two-sample Mendelian randomization (MR) analysis to assess the potential causal relationship between sarcopenia-related traits (hand grip strength, lean mass, walking pace) and osteoporosis. Our analysis was performed by applying genetic variants obtained from the UK Biobank and the GEnetic Factors for OSteoporosis (GEFOS) datasets. We used inverse-variance weighted (IVW) and several sensitivity analyses to estimate and cross-validate the potential causal relationship in this study. We found that bone mineral density (BMD) was causally positively associated with left-hand grip strength (β = 0.017, p-value = 0.001), fat-free mass (FFM; right leg FFM, β = 0.014, p-value = 0.003; left arm FFM, β = 0.014, p-value = 0.005), but not walking pace. Higher hand grip strength was potentially causally associated with increased LS-BMD (right-hand grip strength, β = 0.318, p-value = 0.001; left-hand grip strength, β = 0.358, p-value = 3.97 × 10-4). In conclusion, osteoporosis may be a risk factor for sarcopenia-related traits and muscle strength may have a site-specific effect on BMD.

GigaAssay - An adaptable high-throughput saturation mutagenesis assay platform

High-throughput assay systems have had a large impact on understanding the mechanisms of basic cell functions. However, high-throughput assays that directly assess molecular functions are limited. Herein, we describe the "GigaAssay", a modular high-throughput one-pot assay system for measuring molecular functions of thousands of genetic variants at once. In this system, each cell was infected with one virus from a library encoding thousands of Tat mutant proteins, with each viral particle encoding a random unique molecular identifier (UMI). We demonstrate proof of concept by measuring transcription of a GFP reporter in an engineered reporter cell line driven by binding of the HIV Tat transcription factor to the HIV long terminal repeat. Infected cells were flow-sorted into 3 bins based on their GFP fluorescence readout. The transcriptional activity of each Tat mutant was calculated from the ratio of signals from each bin. The use of UMIs in the GigaAssay produced a high average accuracy (95%) and positive predictive value (98%) determined by comparison to literature benchmark data, known C-terminal truncations, and blinded independent mutant tests. Including the substitution tolerance with structure/function analysis shows restricted substitution types spatially concentrated in the Cys-rich region. Tat has abundant intragenic epistasis (10%) when single and double mutants are compared.

Development of an approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms

Purpose: In stable coronary artery disease (CAD), reduction in mortality and/or myocardial infarction with revascularization over medical therapy has not been reliably achieved. Coronary arteries are usually extracted to perform stenosis detection. As such, developing accurate segmentation of vascular structures and quantification of coronary arterial stenosis in invasive coronary angiograms (ICA) is necessary. Approach: A multi-input and multiscale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation. The proposed model generated a refined prediction map with the following two training stages: (i) stage I coarsely segmented the major coronary arteries from preprocessed single-channel ICAs and generated the probability map of arteries; and (ii) during the stage II, a three-channel image consisting of the original preprocessed image, a generated probability map, and an edge-enhanced image generated from the preprocessed image was fed to the proposed MIMS U-Net to produce the final segmentation result. After segmentation, an arterial stenosis detection algorithm was developed to extract vascular centerlines and calculate arterial diameters to evaluate stenotic level. Results: Experimental results demonstrated that the proposed method achieved an average Dice similarity coefficient of 0.8329, an average sensitivity of 0.8281, and an average specificity of 0.9979 in our dataset with 294 ICAs obtained from 73 patients. Moreover, our stenosis detection algorithm achieved a true positive rate of 0.6668 and a positive predictive value of 0.7043. Conclusions: Our proposed approach has great promise for clinical use and could help physicians improve diagnosis and therapeutic decisions for CAD.

Integrative analysis of multi-omics data to detect the underlying molecular mechanisms for obesity in vivo in humans

BACKGROUND

Obesity is a complex, multifactorial condition in which genetic play an important role. Most of the systematic studies currently focuses on individual omics aspect and provide insightful yet limited knowledge about the comprehensive and complex crosstalk between various omics levels.

SUBJECTS AND METHODS

Therefore, we performed a most comprehensive trans-omics study with various omics data from 104 subjects, to identify interactions/networks and particularly causal regulatory relationships within and especially those between omic molecules with the purpose to discover molecular genetic mechanisms underlying obesity etiology in vivo in humans.

RESULTS

By applying differentially analysis, we identified 8 differentially expressed hub genes (DEHGs), 14 differentially methylated regions (DMRs) and 12 differentially accumulated metabolites (DAMs) for obesity individually. By integrating those multi-omics biomarkers using Mendelian Randomization (MR) and network MR analyses, we identified 18 causal pathways with mediation effect. For the 20 biomarkers involved in those 18 pairs, 17 biomarkers were implicated in the pathophysiology of obesity or related diseases.

CONCLUSIONS

The integration of trans-omics and MR analyses may provide us a holistic understanding of the underlying functional mechanisms, molecular regulatory information flow and the interactive molecular systems among different omic molecules for obesity risk and other complex diseases/traits.

Deepm5C: A deep learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

As one of the most prevalent post-transcriptional epigenetic modifications, N5-methylcytosine (m5C), plays an essential role in various cellular processes and disease pathogenesis. Therefore, it is important accurately identify m5C modifications in order to gain a deeper understanding of cellular processes and other possible functional mechanisms. Although a few computational methods have been proposed, their respective models have been developed using small training datasets. Hence, their practical application is quite limited in genome-wide detection. To overcome the existing limitations, we propose Deepm5C, a bioinformatics method to identify RNA m5C sites in the throughout human genome. To develop Deepm5C, we constructed a novel benchmarking dataset and investigated a mixture of three conventional feature encoding algorithms and a feature derived from word embedding approaches. Afterwards, four variants of deep learning classifiers and four commonly used conventional classifiers were employed and trained with the four encodings, ultimately obtaining 32 baseline models. A stacking strategy is effectively utilized by integrating the predicted output of the optimal baseline models and trained with a 1-D convolutional neural network. As a result, the Deepm5C predictor achieved excellent performance during cross-validation with a Matthews correlation coefficient and accuracy of 0.697 and 0.855, respectively. The corresponding metrics during the independent test were 0.691 and 0.852, respectively. Overall, Deepm5C achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, Deepm5C is expected to assist community-wide efforts in identifying putative m5Cs and formulate the novel testable biological hypothesis.

Associations of physical activity with sarcopenia and sarcopenic obesity in middle-aged and older adults: the Louisiana osteoporosis study

BACKGROUND

This study examined the associations between physical activity, obesity, and sarcopenia in middle-aged and older adults.

METHODS

We analyzed the data of 8, 919 study participants aged between 45 to 97 (mean age = 57.2 ± 8.8) from a Southern state in the United States. Self-reported physical activity was classified to regular exercise ≥ 3 times/week, < 3 times/week, and no regular exercise. Associations between physical activity, obesity and sarcopenia were explored with generalized linear models and ordinal logistic regressions stratified by age (middle-aged and older adults) and gender adjusting for covariates.

RESULTS

In middle-aged and older adults, all examined obesity related traits (e.g., body mass index, waist circumference) were inversely associated with physical activity levels (p < 0.01) in both genders. Exercising ≥ 3 times/week was negatively associated with lean mass indicators (e.g., appendicular lean mass) in middle-aged and older females (p < 0.01), while the negative associations become positive after adjusting for weight. Positive associations between physical activity and grip strength were only found in middle-aged males (p < 0.05). Ordinal logistic regression revealed that those exercising ≥ 3 times/week were less likely to have obesity, sarcopenia, and sarcopenia obesity in all groups (p < 0.01), except for sarcopenia in older males and females (p > 0.05). Positive associations of exercising < 3 times/week with sarcopenia and sarcopenia obesity were only found in middled adults.

CONCLUSION

The associations of exercise frequency with obesity and sarcopenia vary considerably across gender and age groups. Exercise programs need to be individualized to optimize health benefits. Future research exploring physical activity strategies to balance weight reduction and lean mass maintaining is warranted in middle-aged and especially older adults.