Spatial functional reorganizations can serve as potential biomarkers of post facial palsy synkinesis.

Facial palsy can result in a serious complication known as facial synkinesis, causing both physical and psychological harm to the patients. There is growing evidence that patients with facial synkinesis have brain abnormalities, but the brain mechanisms and underlying imaging biomarkers remain unclear. Here, we employed functional magnetic resonance imaging (fMRI) to investigate brain function in 31 unilateral post facial palsy synkinesis patients and 25 healthy controls during different facial expression movements and at rest. Combining surface-based mass-univariate analysis and multivariate pattern analysis, we identified diffused activation and intrinsic connection patterns in the primary motor cortex and the somatosensory cortex on the patient's affected side. Further, we classified post facial palsy synkinesis patients from healthy subjects with favorable accuracy using the support vector machine based on both task-related and resting-state functional magnetic resonance imaging data. Together, these findings indicate the potential of the identified functional reorganizations to serve as neuroimaging biomarkers for facial synkinesis diagnosis.

Unravelling the metabolomic diversity of pigmented and non-pigmented traditional rice from Tamil Nadu, India.

Rice metabolomics is widely used for biomarker research in the fields of pharmacology. As a consequence, characterization of the variations of the pigmented and non-pigmented traditional rice varieties of Tamil Nadu is crucial. These varieties possess fatty acids, sugars, terpenoids, plant sterols, phenols, carotenoids and other compounds that plays a major role in achieving sustainable development goal 2 (SDG 2). Gas-chromatography coupled with mass spectrometry was used to profile complete untargeted metabolomics of Kullkar (red colour) and Milagu Samba (white colour) for the first time and a total of 168 metabolites were identified. The metabolite profiles were subjected to data mining processes, including principal component analysis (PCA), Orthogonal Partial Least Square Discrimination Analysis (OPLS-DA) and Heat map analysis. OPLS-DA identified 144 differential metabolites between the 2 rice groups, variable importance in projection (VIP) ≥ 1 and fold change (FC) ≥ 2 or FC ≤ 0.5. Volcano plot (64 down regulated, 80 up regulated) was used to illustrate the differential metabolites. OPLS-DA predictive model showed good fit (R2X = 0.687) and predictability (Q2 = 0.977). The pathway enrichment analysis revealed the presence of three distinct pathways that were enriched. These findings serve as a foundation for further investigation into the function and nutritional significance of both pigmented and non-pigmented rice grains thereby can achieve the SDG 2.

No association between error-related ERPs and trait anxiety in a nonclinical sample: Convergence across analytical methods including mass-univariate statistics.

Enhanced error monitoring, as indexed by increased amplitude of the error-related negativity (ERN) event-related potential (ERP) component, has been suggested to reflect a vulnerability neuro-marker of anxiety disorders. Another error-related ERP component is the error positivity (Pe), which reflects late-stage error processing. The associations between heightened ERN and Pe amplitudes and anxiety levels in the nonclinical population have been inconsistent. In this preregistered study, we examined the association between anxiety, ERN, and Pe, using different analytical methods (mass-univariate analyses, MUAs and conventional analyses), self-reported anxiety scales (STAI and STICSA), and trial numbers (all correct trials and equal numbers of correct and error trials). In a sample of 82 healthy adults, both conventional and MUAs demonstrated a robust enhancement of the ERN and Pe to errors relative to the correct-response ERPs. However, the mass-univariate approach additionally unveiled a wider array of electrodes and a longer effect duration for this error enhancement. Across the analytic methods, the results showed a lack of consistent correlation between trait anxiety and error-related ERPs. Findings were not modulated by trial numbers, analyses, or anxiety scales. The present results suggest a lack of enhancement of error monitoring by anxious traits in individuals with subclinical anxiety and those with clinical anxiety but without a clinical diagnosis. Importantly, the absence of such correlation questions the validity of the ERN as a neural marker for anxiety disorders. Future studies that investigate neuro-markers of anxiety may explore alternative task designs and employ robust statistics to provide a more comprehensive understanding of anxiety vulnerability.

TES and SLC40A1 as Potential Biomarkers for Predicting Survival in T-Cell Acute Lymphoblastic Leukemia.

INTRODUCTION: Identifying patients with high-risk T-cell acute lymphoblastic leukemia (T-ALL) is crucial for personalized therapy; however, the lack of robust biomarkers hinders prognosis assessment. To address this issue, our study aimed to screen and validate genes whose expression may serve as predictive indicators of outcomes in T-ALL patients while also investigating the underlying molecular mechanisms. METHODS: Differentially expressed genes (DEGs) between T-ALL patients and healthy controls were identified by integrating data from three independent public datasets. Functional annotation of these DEGs and protein-protein interactions were also conducted. Further, we enrolled a prospective cohort of T-ALL patients (n = 20) at our center, conducting RNA-seq analysis on their bone marrow samples. Survival-based univariate Cox analysis was employed to identify gene expressions related to survival, and an intersection algorithm was sequentially applied. Furthermore, we validated the identified genes using cases from the Therapeutically Applicable Research to Generate Effective Treatments database, plotting Kaplan-Meier curves for secondary validation. RESULTS: Through the integration of survival-related genes with DEGs identified in T-ALL, our analysis revealed six T-ALL-specific genes, the expression levels of which were linked to prognostic value. Notably, the independent prognostic value of SLC40A1 and TES expression levels was confirmed in both an external cohort and a prospective cohort at our center. CONCLUSION: In summary, our preliminary study indicates that the expression levels of TES and SLC40A1 genes show promise as potential indicators for predicting survival outcomes in T-ALL patients.

From big data to big insights: statistical and bioinformatic approaches for exploring the lipidome.

The goal of lipidomic studies is to provide a broad characterization of cellular lipids present and changing in a sample of interest. Recent lipidomic research has significantly contributed to revealing the multifaceted roles that lipids play in fundamental cellular processes, including signaling, energy storage, and structural support. Furthermore, these findings have shed light on how lipids dynamically respond to various perturbations. Continued advancement in analytical techniques has also led to improved abilities to detect and identify novel lipid species, resulting in increasingly large datasets. Statistical analysis of these datasets can be challenging not only because of their vast size, but also because of the highly correlated data structure that exists due to many lipids belonging to the same metabolic or regulatory pathways. Interpretation of these lipidomic datasets is also hindered by a lack of current biological knowledge for the individual lipids. These limitations can therefore make lipidomic data analysis a daunting task. To address these difficulties and shed light on opportunities and also weaknesses in current tools, we have assembled this review. Here, we illustrate common statistical approaches for finding patterns in lipidomic datasets, including univariate hypothesis testing, unsupervised clustering, supervised classification modeling, and deep learning approaches. We then describe various bioinformatic tools often used to biologically contextualize results of interest. Overall, this review provides a framework for guiding lipidomic data analysis to promote a greater assessment of lipidomic results, while understanding potential advantages and weaknesses along the way.

Effects of Inversion and Fixation Location on the Processing of Face and House Stimuli - A Mass Univariate Analysis.

Most Event Related Potential studies investigating the time course of visual processing have focused mainly on the N170 component. Stimulus orientation affects the N170 amplitude for faces but not for objects, a finding interpreted as reflecting holistic/configural processing for faces and featural processing for objects. Furthermore, while recent studies suggest where on the face people fixate impacts the N170, fixation location effects have not been investigated in objects. A data-driven mass univariate analysis (all time points and electrodes) was used to investigate the time course of inversion and fixation location effects on the neural processing of faces and houses. Strong and widespread orientation effects were found for both faces and houses, from 100-350ms post-stimulus onset, including P1 and N170 components, and later, a finding arguing against a lack of holistic processing for houses. While no clear fixation effect was found for houses, fixation location strongly impacted face processing early, reflecting retinotopic mapping around the C2 and P1 components, and during the N170-P2 interval. Face inversion effects were also largest for nasion fixation around 120ms. The results support the view that facial feature integration (1) depends on which feature is being fixated and where the other features are situated in the visual field, (2) occurs maximally during the P1-N170 interval when fixation is on the nasion and (3) continues past 200ms, suggesting the N170 peak, where weak effects were found, might be an inflexion point between processes rather than the end of a feature integration into a whole process.

Comparison of univariate and bivariate Poisson regression methods in the analysis of determinants of female schooling and fertility in Malawi.

Recent research has established existence of a correlation between women's education and fertility, suggesting that they share similar risk factors. However, in many studies, the two variables were analysed separately, which could bias the conclusions by undermining the apparent correlations of such paired outcomes. In this article, the univariate and bivariate Poisson regression models were applied to nationally representative sample of 24,562 women from the 2015-16 Malawi demographic and health survey to examine the risk factors of women's education levels and fertility. The R software version 4.1.2 was used for the analyses. The results showed that estimates from the bivariate Poisson model were consistent with those obtained from the separate univariate Poisson models. The sizes of estimates of coefficients, their standard errors, p-values, and directions were comparable in both bivariate and univariate Poisson models. Using either the univariate or bivariate Poisson model, it was found that the age of a woman at first sexual experience, her current age, household wealth index, and contraceptive usage were significantly associated with both the woman's schooling and fertility. The study further revealed that ethnicity, religion, and region of residence impacted education level only and not fertility. Similarly, marital status and occupation impacted fertility only and not education. The study also found that higher education levels were linked to a lower number of children, with a strong negative correlation of -0.62 between the two variables. The study recommends using bivariate Poisson regression for analysing paired count response data, when there is an apparent covariance between the outcome variables. The results suggest that efforts by policymakers to achieve the desired women's sexual and reproductive health in sub-Saharan Africa should be intertwined with improving women's and girls' education attainment in the region.

Measuring spatial inequalities in maternal and child mortalities in Pakistan: evidence from geographically weighted regression.

BACKGROUND: In developing countries, the death probability of a child and mother is more significant than in developed countries; these inequalities in health outcomes are unfair. The present study encompasses a spatial analysis of maternal and child mortalities in Pakistan. The study aims to estimate the District Mortality Index (DMI), measure the inequality ratio and slope, and ascertain the spatial impact of numerous factors on DMI scores across Pakistani districts. METHOD: This study used micro-level household datasets from multiple indicator cluster surveys (MICS) to estimate the DMI. To find out how different the DMI scores were, the inequality ratio and slope were used. This study further utilized spatial autocorrelation tests to determine the magnitude and location of the spatial dependence of the clusters with high and low mortality rates. The Geographically Weighted Regression (GWR) model was also applied to examine the spatial impact of socioeconomic, environmental, health, and housing attributes on DMI. RESULTS: The inequality ratio for DMI showed that the upper decile districts are 16 times more prone to mortalities than districts in the lower decile, and the districts of Baluchistan depicted extreme spatial heterogeneity in terms of DMI. The findings of the Local Indicator of Spatial Association (LISA) and Moran's test confirmed spatial homogeneity in all mortalities among the districts in Pakistan. The H-H clusters of maternal mortality and DMI were in Baluchistan, and the H-H clusters of child mortality were seen in Punjab. The results of GWR showed that the wealth index quintile has a significant spatial impact on DMI; however, improved sanitation, handwashing practices, and antenatal care adversely influenced DMI scores. CONCLUSION: The findings reveal a significant disparity in DMI and spatial relationships among all mortalities in Pakistan's districts. Additionally, socioeconomic, environmental, health, and housing variables have an impact on DMI. Notably, spatial proximity among individuals who are at risk of death occurs in areas with elevated mortality rates. Policymakers may mitigate these mortalities by focusing on vulnerable zones and implementing measures such as raising public awareness, enhancing healthcare services, and improving access to clean drinking water and sanitation facilities.

Analyzing the factors associated with efficacy among teriparatide treatment in postmenopausal women with osteoporosis.

BACKGROUND: Teriparatide (TPTD) is a widely used anabolic agent for the treatment of osteoporosis. Several factors have been identified to be related to bone mineral density (BMD) increase in anti-osteoporosis treatment with other agents; however, there has been no systematic analysis to summarize the associated determinants of BMD reaction to daily teriparatide treatment. METHODS: In this retrospective study, we performed a comprehensive investigation involving not only clinical data but also several relevant lifestyle factors to be examined for their potential contribution to BMD response. This post-hoc analysis included 258 post-menopaused patients with osteoporosis who received TPTD at 20 µg/day for 12 months. Univariate and multivariate analyses were conducted to distinguish the response variables of lumbar spine (LS) BMD transformation, the principal outcome measure of efficacy, from the baseline at 12 months. RESULTS: Twelve months of TPTD treatment resulted in an absolute 0.39 ± 0.37 increase in T-score of LS BMD. Gastrointestinal disease, prior bisphosphonate or glucocorticoid treatment, no vitamin K2 supplementation, low levels of serum 25(OH)D and PINP, weak increment of PINP and ß-CTX at 3 months, unhealthy lifestyle (excessive smoking, tea, coffee, and drinking), vegetarian diet pattern, low ALT level, and high BMD at baseline were determined by univariate analyses to be related to the weak reaction of TPTD treatment (P < 0.10). In the multiple regression model, postmenopausal women with vitamin K2 supplementation, higher baseline serum 25(OH)D level, and higher PINP concentration at 3 months indicated a good reaction of LS BMD at 12 months (P < 0.05). Patients with gastrointestinal disease, prior bisphosphonate and glucocorticoid treatment, vegetarian diet pattern, and higher baseline BMD were significantly more likely to have a lower absolute LS BMD response compared to patients without these characteristics (P < 0.05). Further analysis confirmed the negative effect of unhealthy lifestyle on TPTD treatment. CONCLUSION: Our results emphasize the significance of a comprehensive assessment of clinical or lifestyle-related characteristics of postmenopausal women with osteoporosis in the management of TPTD therapy in routine care.

Estimation of direct and maternal covariance along with genetic and phenotypic trends of reproduction traits in Murrah buffalo.

The research utilized data from 662 Murrah buffaloes meticulously recorded over 24 years (1996-2019) from historical pedigree sheets maintained at the buffalo farm of the Department of Livestock Production and Management (LPM) at Lala Lajpat Rai University of Veterinary and Animal Sciences (LUVAS), Hisar. A series of six univariate animal models were employed to calculate estimates of (co)variance components and heritability for first lactation reproduction traits. Among these models, Model 2 was identified as the best fit for age at first calving (AFC) and calving interval (CI), while Model 1 proved optimal for service period (SP) and conception rate (CR). The heritability estimates for AFC, SP, CR and CI across the models were ranged between 0.11 and 0.32, 0.01 and 0.03, 0.05 and 0.06, and 0.01 and 0.06, respectively. Maternal effects (m2 ) were observed in AFC and CI, ranging from 0.10 to 0.20 and 0.01 to 0.03, respectively. Across all three traits, there was a consistent negative genetic correlation (-0.75 to -0.92) between direct additive and maternal effects. The breeding values for AFC, SP, CR and CI varied within specific ranges from -32.85 to 44.33 days, -15.61 to 28.42 days, -7.41 to 6.48% and -20.64 to 35.79 days, respectively. Significantly, Spearman's rank correlation analysis revealed highly significant relationships (p < .01) between the breeding values of different models, indicating strong and consistent associations within these traits. These findings underscore the stable and reliable connections observed within the breeding values for these essential reproductive traits across the various models used in the study. The majority of reproductive traits showed favourable negative trends, indicating a positive outcome. A decrease in AFC, SP and CI suggests an extended economic life for the animals. Additionally, the upward trends in CR reflect positive indications of effective management practices and skilled operational procedures.

Estimation of additive and maternal covariance of production traits in Murrah buffalo.

The study was done to determine additive, maternal and common permanent environmental effects and best-suited model for some production traits using six univariate animal models that differed in the (co)variance components fitted to assess the importance of maternal effect using likelihood ratio test in Murrah buffaloes. Data from 614 Murrah buffaloes related to production traits were collected from history pedigree sheets maintained at the buffalo farm, Department of Livestock Production and Management (LPM), LUVAS, Hisar. The production traits under this study were 305 days milk yield (305DMY), peak yield (PY), lactation length (LL), dry period (DP), lactation milk yield (LMY) and wet average (WA). The heritability estimates were in the range of 0.33-0.44 for 305DMY, 0.25-0.51 for PY, 0.05-0.13 for LL, 0.03-0.23 for DP, 0.17-0.40 for LMY and 0.37-0.66 for WA. Model 1 was considered best for most of the traits, viz., 305DMY, PY, LL, LMY and WA followed by model 2 for DP. Covariance and correlated values within the traits caused inflation of heritability in model 3 and model 6. The maximum covariance between the additive and maternal effect was found in trait LMY, which was 14,183.90 in model 3 and the minimum value was also reported in the same trait for model 6, valued at -3522.37. Multivariate analysis showed that all production traits were moderate to high and positively correlated with each other except for DP, which was low and negative genetic and phenotypic correlated. Spearman's rank correlation coefficients of breeding value among all six models were high and significant, ranged from 0.78 to 1.00 for all the traits except DP, therefore any of the models could be taken into account depending upon the availability of data.

Check your outliers! An introduction to identifying statistical outliers in R with easystats.

Beyond the challenge of keeping up to date with current best practices regarding the diagnosis and treatment of outliers, an additional difficulty arises concerning the mathematical implementation of the recommended methods. Here, we provide an overview of current recommendations and best practices and demonstrate how they can easily and conveniently be implemented in the R statistical computing software, using the {performance} package of the easystats ecosystem. We cover univariate, multivariate, and model-based statistical outlier detection methods, their recommended threshold, standard output, and plotting methods. We conclude by reviewing the different theoretical types of outliers, whether to exclude or winsorize them, and the importance of transparency. A preprint of this paper is available at: 10.31234/osf.io/bu6nt.

Statistical methods and resources for biomarker discovery using metabolomics.

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.

BrainStat: A toolbox for brain-wide statistics and multimodal feature associations.

Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here, we introduce BrainStat - a toolbox for (i) univariate and multivariate linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, task-based fMRI meta-analysis, as well as resting-state fMRI motifs across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox streamlines analytical processes and accelerates cross-modal research. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation.

Association between trans fatty acids and COVID-19: A multivariate Mendelian randomization study.

Traditional observational studies have suggested a potential association between trans fatty acids (TFAs), which are considered to be health-damaging fatty acids, and coronavirus disease 2019 (COVID-19). However, whether there is a causal relationship between them is currently unclear. We aimed to investigate the causal link between genetically determined TFAs and COVID-19. We performed univariate and multivariate Mendelian randomization (MR) studies using summary statistics from the European Pedigree TFAs (n = 8013), COVID-19 susceptibility (n = 159 840), COVID-19 hospitalization (n = 44 986), and COVID-19 severity (n = 18 152) genome-wide association studies (GWAS). The inverse variance weighted (IVW) method was used as the primary MR analysis, and several other methods were used as supplements. In univariate MR analysis, higher levels of circulating trans, cis-18:2 TFAs were positively associated with a higher COVID-19 hospitalization rate (p < 0.0033; odds ratio [OR] = 1.637; 95% confidence interval [CI]: 1.116-2.401) and COVID-19 severity (p < 0.0033; OR = 2.575; 95% CI: 1.412-4.698). Furthermore, in multivariate MR analysis, trans, cis-18:2 had an independent and significant causal association with a higher COVID-19 hospitalization rate (p = 0.00044; OR = 1.862; 95% CI = 1.316-2.636) and COVID-19 severity (p = 0.0016; OR = 2.268; 95% CI = 1.361-3.779) after the five TFAs were adjusted for each other. Together, our findings provide evidence that trans, cis-18:2 TFAs have an independent and robust causal effect on COVID-19 hospitalization and severity.

Clinicodemographic correlates of psychotic features in bipolar disorder - a multicenter study in China.

BACKGROUND: Psychotic symptoms are prevalent in patients with bipolar disorder (BD). However, nearly all previous studies on differences in sociodemographic and clinical factors between patients with (BD P +) and without (BD P-) psychotic symptoms were conducted in Western populations, and limited information is known in China. METHOD: A total of 555 patients with BD from seven centers across China were recruited. A standardized procedure was used to collect patients' sociodemographic and clinical characteristics. The patients were divided into BD P + or BD P- groups based on the presence of lifetime psychotic symptoms. Mann-Whitney U test or chi-square test was used to analyze differences in sociodemographic and clinical factors between patients with BD P + and BD P-. Multiple logistic regression analysis was conducted to explore factors that were independently correlated with psychotic symptoms in BD. All the above analyses were re-conducted after the patients were divided into BD I and BD II group according to their types of diagnosis. RESULTS: A total of 35 patients refused to participate, and the remaining 520 patients were included in the analyses. Compared with patients with BD P-, those with BD P + were more likely to be diagnosed with BD I and mania/hypomania/mixed polarity in the first mood episode. Moreover, they were more likely to be misdiagnosed as schizophrenia than major depressive disorder, were hospitalized more often, used antidepressants less frequently, and used more antipsychotics and mood stabilizers. Multivariate analyses revealed that diagnosis of BD I, more frequent misdiagnosis as schizophrenia and other mental disorders, less frequent misdiagnosis as major depressive disorder, more frequent lifetime suicidal behavior, more frequent hospitalizations, less frequent use of antidepressants, more frequent use of antipsychotics and mood stabilizers were independently correlated with psychotic symptoms in BD. After dividing the patients into BD I and BD II groups, we observed notable differences in sociodemographic and clinical factors, as well as clinicodemographic correlates of psychotic features between the two groups. CONCLUSIONS: Differences in clinical factors between patients with BD P + and BD P- showed cross-cultural consistency, but results on the clinicodemographic correlates of psychotic features were not. Notable differences between patients with BD I and BD II were found. Future work exploring the psychotic features of BD needs to take types of diagnosis and cultural differences into consideration. TRIAL REGISTRATION: This study was first registered on the website of the ClinicalTrials.gov ( https://clinicaltrials.gov/ ) on 18/01/2013. Its registration number is NCT01770704.

Spatial spillover impact of determinants on child mortality in Pakistan: evidence from Spatial Durbin Model.

BACKGROUND: Child mortality is a major challenge to public health in Pakistan and other developing countries. Reduction of the child mortality rate would improve public health and enhance human well-being and prosperity. This study recognizes the spatial clusters of child mortality across districts of Pakistan and identifies the direct and spatial spillover effects of determinants on the Child Mortality Rate (CMR). METHOD: Data of the multiple indicators cluster survey (MICS) conducted by the United Nations International Children's Emergency Fund (UNICEF) was used to study the CMR. We used spatial univariate autocorrelation to test the spatial dependence between contiguous districts concerning CMR. We also applied the Spatial Durbin Model (SDM) to measure the spatial spillover effects of factors on CMR. RESULTS: The study results showed 31% significant spatial association across the districts and identified a cluster of hot spots characterized by the high-high CMR in the districts of Punjab province. The empirical analysis of the SDM confirmed that the direct and spatial spillover effect of the poorest wealth quintile and MPI vulnerability on CMR is positive whereas access to postnatal care to the newly born child and improved drinking water has negatively (directly and indirectly) determined the CMR in Pakistan. CONCLUSION: The instant results concluded that spatial dependence and significant spatial spillover effects concerning CMR exist across districts. Prioritization of the hot spot districts characterized by higher CMR can significantly reduce the CMR with improvement in financial statuses of households from the poorest quintile and MPI vulnerability as well as improvement in accessibility to postnatal care services and safe drinking water.

Meta-Analysis and Multivariate GWAS Analyses in 77,850 Individuals of African Ancestry Identify Novel Variants Associated with Blood Pressure Traits.

High blood pressure (HBP) has been implicated as a major risk factor for cardiovascular diseases in several populations, including individuals of African ancestry. Despite the elevated burden of HBP-induced cardiovascular diseases in Africa and other populations of African descent, limited genetic studies have been carried out to explore the genetic mechanism driving this phenomenon. We performed genome-wide association univariate and multivariate analyses of both systolic (SBP) and diastolic blood pressure (DBP) traits in 77, 850 individuals of African ancestry. We used summary statistics data from six independent cohorts, including the African Partnership for Chronic Disease Research (APCDR), the UK Biobank, and the Million Veteran Program (MVP). FUMA was used to annotate, prioritize, visualize, and interpret our findings to gain a better understanding of the molecular mechanism(s) underlying the genetics of BP traits. Finally, we undertook a Bayesian fine-mapping analysis to identify potential causal variants. Our meta-analysis identified 10 independent variants associated with SBP and 9 with DBP traits. Whilst our multivariate GWAS method identified 21 independent signals, 18 of these SNPs have been previously identified. SBP was linked to gene sets involved in biological processes such as synapse assembly and cell-cell adhesion via plasma membrane adhesion. Of the 19 independent SNPs identified in the BP meta-analysis, only 11 variants had posterior probability (PP) of > 50%, including one novel variant: rs562545 (MOBP, PP = 77%). To facilitate further research and fine-mapping of high-risk loci/variants in highly susceptible groups for cardiovascular disease and other related traits, large-scale genomic datasets are needed. Our findings highlight the importance of including ancestrally diverse populations in large GWASs and the need for diversity in genetic research.

Serum NMR-Based Metabolomics Profiling Identifies Lipoprotein Subfraction Variables and Amino Acid Reshuffling in Myeloma Development and Progression.

Multiple myeloma (MM) is an incurable hematological cancer. It is preceded by monoclonal gammopathy of uncertain significance (MGUS)-an asymptomatic phase. It has been demonstrated that early detection increases the 5-year survival rate. However, blood-based biomarkers that enable early disease detection are lacking. Metabolomic and lipoprotein subfraction variable profiling is gaining traction to expand our understanding of disease states and, more specifically, for identifying diagnostic markers in patients with hematological cancers. This study aims to enhance our understanding of multiple myeloma (MM) and identify candidate metabolites, allowing for a more effective preventative treatment. Serum was collected from 25 healthy controls, 20 patients with MGUS, and 30 patients with MM. 1H-NMR (Nuclear Magnetic Resonance) spectroscopy was utilized to evaluate serum samples. The metabolite concentrations were examined using multivariate, univariate, and pathway analysis. Metabolic profiles of the MGUS patients revealed lower levels of alanine, lysine, leucine but higher levels of formic acid when compared to controls. However, metabolic profiling of MM patients, compared to controls, exhibited decreased levels of total Apolipoprotein-A1, HDL-4 Apolipoprotein-A1, HDL-4 Apolipoprotein-A2, HDL Free Cholesterol, HDL-3 Cholesterol and HDL-4 Cholesterol. Lastly, metabolic comparison between MGUS to MM patients primarily indicated alterations in lipoproteins levels: Total Cholesterol, HDL Cholesterol, HDL Free Cholesterol, Total Apolipoprotein-A1, HDL Apolipoprotein-A1, HDL-4 Apolipoprotein-A1 and HDL-4 Phospholipids. This study provides novel insights into the serum metabolic and lipoprotein subfraction changes in patients as they progress from a healthy state to MGUS to MM, which may allow for earlier clinical detection and treatment.

Survival-related genes are diversified across cancers but generally enriched in cancer hallmark pathways.

BACKGROUND: Pan-cancer studies have disclosed many commonalities and differences in mutations, copy number variations, and gene expression alterations among cancers. Some of these features are significantly associated with clinical outcomes, and many prognosis-predictive biomarkers or biosignatures have been proposed for specific cancer types. Here, we systematically explored the biological functions and the distribution of survival-related genes (SRGs) across cancers. RESULTS: We carried out two different statistical survival models on the mRNA expression profiles in 33 cancer types from TCGA. We identified SRGs in each cancer type based on the Cox proportional hazards model and the log-rank test. We found a large difference in the number of SRGs among different cancer types, and most of the identified SRGs were specific to a particular cancer type. While these SRGs were unique to each cancer type, they were found mostly enriched in cancer hallmark pathways, e.g., cell proliferation, cell differentiation, DNA metabolism, and RNA metabolism. We also analyzed the association between cancer driver genes and SRGs and did not find significant over-representation amongst most cancers. CONCLUSIONS: In summary, our work identified all the SRGs for 33 cancer types from TCGA. In addition, the pan-cancer analysis revealed the similarities and the differences in the biological functions of SRGs across cancers. Given the potential of SRGs in clinical utility, our results can serve as a resource for basic research and biotech applications.