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BackgroundThe global incidence of hypertension, a condition of elevated blood pressure, is rising alarmingly. According to the World Health Organization’s Qatar Hypertension Profile for 2023, around 33% of adults are affected by hypertension. This is a significant public health concern that can lead to serious health complications if left untreated. Metabolic dysfunction is a primary cause of hypertension. By studying key biomarkers, we can discover new treatments to improve the lives of those with high blood pressure.AimsThis study aims to use explainable artificial intelligence (XAI) to interpret novel metabolite biosignatures linked to hypertension in Qatari Population.MethodsThe study utilized liquid chromatography-mass spectrometry (LC/MS) method to profile metabolites from biosamples of Qatari nationals diagnosed with stage 1 hypertension (n = 224) and controls (n = 554). Metabolon platform was used for the annotation of raw metabolite data generated during the process. A comprehensive series of analytical procedures, including data trimming, imputation, undersampling, feature selection, and biomarker discovery through explainable AI (XAI) models, were meticulously executed to ensure the accuracy and reliability of the results.ResultsElevated Vanillylmandelic acid (VMA) levels are markedly associated with stage 1 hypertension compared to controls. Glycerophosphorylcholine (GPC), N-Stearoylsphingosine (d18:1/18:0)*, and glycine are critical metabolites for accurate hypertension prediction. The light gradient boosting model yielded superior results, underscoring the potential of our research in enhancing hypertension diagnosis and treatment. The model’s classification metrics: accuracy (78.13%), precision (78.13%), recall (78.13%), F1-score (78.13%), and AUROC (83.88%) affirm its efficacy. SHapley Additive exPlanations (SHAP) further elucidate the metabolite markers, providing a deeper understanding of the disease’s pathology.ConclusionThis study identified novel metabolite biomarkers for precise hypertension diagnosis using XAI, enhancing early detection and intervention in the Qatari population.

Background Circadian rhythms disruption causes clustering of metabolic disorders and depression. The comorbidity of depression and obesity is common and could be attributable to shared genetic background. We aimed to examine the association between clock genes polygenic risk score (PRS) for depression and obesity among Qatari adults. The interaction between PRS and lifestyle factors was also examined. Method This cross-sectional study was conducted among 6,000 Qatari adults who participated in the Qatar Biobank Study. A polygenic risk scores (PRS) was constructed based on 185 single nucleotide polymorphisms (SNPs) within 18 clock genes. Dietary patterns were constructed using factor analysis based on food frequency intake. The association between exposure variables including clock genes PRS, lifestyle factors, and obesity was assessed by multivariable logistic regression models. Results The mean age of the participants was 40.2 (13.0). The prevalence of obesity was 42.9%. A positive association between clock genes PRS and obesity was found. After adjusting for sociodemographic and lifestyle factors, across quartiles of PRS, the odds ratio (95%CI) for obesity were 1.00, 1.24, 1.19, 1.26 (95%CI 1.08–1.39), respectively. There was a borderline significant interaction between PRS and a mixed dietary pattern in relation to obesity (p for interaction 0.072). The association between PRS and obesity was only observed among those with a high intake of mixed dietary pattern. No statistically significant interactions between PRS with smoking, physical activity and other dietary patterns were found. However, the association between PRS and obesity was only observed among non-smokers, those with moderate physical activity, a low intake of sweet/fast food pattern, and high intake of modern breakfast dietary pattern. Conclusion Clock genes PRS is positively associated with obesity and interacts with dietary pattern.

Background Hypertension is a critical public health issue worldwide. The identification of specific proteomic biomarkers in the Qatari population aims to advance personalized treatment strategies. Methods We conducted proteomic profiling on 778 Qatari individuals using an aptamer-based SOMAscan platform to analyze 1,305 biomarkers. Statistical analysis involved two-way ANOVA and association analyses with FDR correction, alongside pathway and gene-set enrichment analyses using Reactome and DisGeNET databases. Results The study identified 26 significant protein biomarkers associated with hypertension. Notably, QORL1 and BMP1 were identified as novel protein biomarkers. Enrichment analysis linked these biomarkers to critical pathways involved in vascular biology, immune system responses, and pathologies like arteriosclerosis and coronary artery disease. Correlation analyses highlighted robust interactions, particularly between QORL1 and various Apolipoprotein E isoforms, suggesting these biomarkers play pivotal roles in the molecular mechanisms underlying hypertension.

Cardiovascular diseases (CVD) are the leading cause of death worldwide. People affected by CVDs may go undiagnosed until the occurrence of a serious heart failure event such as stroke, heart attack, and myocardial infraction. In Qatar, there is a lack of studies focusing on CVD diagnosis based on non-invasive methods such as retinal image or dual-energy X-ray absorptiometry (DXA). In this study, we aimed at diagnosing CVD using a novel approach integrating information from retinal images and DXA data. We considered an adult Qatari cohort of 500 participants from Qatar Biobank (QBB) with an equal number of participants from the CVD and the control groups. We designed a case-control study with a novel multi-modal (combining data from multiple modalities—DXA and retinal images)—to propose a deep learning (DL)-based technique to distinguish the CVD group from the control group. Uni-modal models based on retinal images and DXA data achieved 75.6% and 77.4% accuracy, respectively. The multi-modal model showed an improved accuracy of 78.3% in classifying CVD group and the control group. We used gradient class activation map (GradCAM) to highlight the areas of interest in the retinal images that influenced the decisions of the proposed DL model most. It was observed that the model focused mostly on the centre of the retinal images where signs of CVD such as hemorrhages were present. This indicates that our model can identify and make use of certain prognosis markers for hypertension and ischemic heart disease. From DXA data, we found higher values for bone mineral density, fat content, muscle mass and bone area across majority of the body parts in CVD group compared to the control group indicating better bone health in the Qatari CVD cohort. This seminal method based on DXA scans and retinal images demonstrate major potentials for the early detection of CVD in a fast and relatively non-invasive manner.

Background T2D is of high prevalence in the middle east and thus studying its mechanisms is of a significant importance. Using 1026 Qatar BioBank samples, epigenetics, whole genome sequencing and metabolomics were combined to further elucidate the biological mechanisms of T2D in a population with a high prevalence of T2D. Methods An epigenome-wide association study (EWAS) with T2D was performed using the Infinium 850K EPIC array, followed by whole genome-wide sequencing SNP-CpG association analysis (> 5.5 million SNPs) and a methylome-metabolome (CpG-metabolite) analysis of the identified T2D sites. Results A total of 66 T2D-CpG associations were identified, including 63 novel sites in pathways of fructose and mannose metabolism, insulin signaling, galactose, starch and sucrose metabolism, and carbohydrate absorption and digestion. Whole genome SNP associations with the 66 CpGs resulted in 688 significant CpG-SNP associations comprising 22 unique CpGs (33% of the 66 CPGs) and included 181 novel pairs or pairs in novel loci. Fourteen of the loci overlapped published GWAS loci for diabetes related traits and were used to identify causal associations of HK1 and PFKFB2 with HbA1c. Methylome-metabolome analysis identified 66 significant CpG-metabolite pairs among which 61 pairs were novel. Using the identified methylome-metabolome associations, methylation QTLs, and metabolic networks, a multi-omics network was constructed which suggested a number of metabolic mechanisms underlying T2D methylated genes. 1-palmitoyl-2-oleoyl-GPE (16:0/18:1) – a triglyceride-associated metabolite, shared a common network with 13 methylated CpGs, including TXNIP, PFKFB2, OCIAD1, and BLCAP. Mannonate – a food component/plant shared a common network with 6 methylated genes, including TXNIP, BLCAP, THBS4 and PEF1, pointing to a common possible cause of methylation in those genes. A subnetwork with alanine, glutamine, urea cycle (citrulline, arginine), and 1-carboxyethylvaline linked to PFKFB2 and TXNIP revealed associations with kidney function, hypertension and triglyceride metabolism. The pathway containing STYXL1-POR was associated with a sphingosine-ceramides subnetwork associated with HDL-C and LDL-C and point to steroid perturbations in T2D. Conclusions This study revealed several novel methylated genes in T2D, with their genomic variants and associated metabolic pathways with several implications for future clinical use of multi-omics associations in disease and for studying therapeutic targets.

Background and objectives Metabolic syndrome (MetS) and its constituent comorbidities, along with mineral imbalances, pose a significant health burden in the Qatari population. Although Magnesium (Mg) and Calcium (Ca) have been individually linked to MetS, the impact of the calcium-to-magnesium ratio (Ca: Mg) on MetS remains unclear, especially in the adult population of Qatar. In this study, we aim to investigate the association between the total serum concentrations of Ca, Mg and Ca: Mg ratio with the outcome of MetS. Methods This comprehensive cross-sectional study included data on 9693 participants collected by Qatar Biobank (QBB). The serum levels of Mg and Ca, in addition to recorded metabolic parameters for the study participants, were used in the analyses. The presence of MetS was deemed as our primary outcome and its components as secondary outcomes. Logistic regression models were run to examine these associations. Results and conclusion MetS was present in more than 19% of the population. The mean serum Mg was higher in the non-MetS group 0.83 ± 0.06 mmol/L compared to the MetS group 0.81 ± 0.08 mmol/L. Conversely, the mean serum Ca and Ca: Mg ratio were higher in the MetS group (2.33 ± 0.09 mmol/L, 2.92 ± 0.36 mmol/L) compared to the non-MetS group (2.30 ± 0.08 mmol/L, 2.77 ± 0.23 mmol/L) respectively. In the context of MetS, we observed a negative dose-response relationship between Mg quartiles and MetS. In contrast, we found a positive association between Ca as well as Ca: Mg ratio and MetS.

Anthropometric indicators can predict the development of diabetes among adults. Among them, a new indicator (Body Shape Index) was developed. Several cohort observational studies have demonstrated that A Body Shape Index (ABSI) is a prominent indicator for mortality and morbidity. Nevertheless, the predictive level of ABSI for diabetes varied among different ethnicities. This study aimed to assess the predictive level of ABSI for diabetes compared to BMI in the Qatari population. Date from 2536 Qatari adults aged 20-79 years attending the Qatar Biobank Study were used. Body height, weight, and waist circumference were measured. Blood samples were measured for glucose. The association between ABSI, BMI, and diabetes was assessed using a logistic regression. Both ABSI and BMI were positively associated with diabetes after adjusting for potential confounding factors. ABSI had a stronger association with diabetes than BMI. Per 1 SD increment of ABSI and BMI, the z-score had an odds ratios of 1.85 (1.54-2.23) and 1.34 (1.18-1.51) for diabetes, respectively. ABSI and BMI are significantly associated with diabetes in the Qatari population. ABSI is a better predictor for the risk of diabetes than BMI after the adjustment for age, gender, education, and physical activity.

Background The role of the human microbiome in human health and disease has been studied in various body sites. However, compared to the gut microbiome, where most of the research focus is, the salivary microbiome still bears a vast amount of information that needs to be revealed. This study aims to characterize the salivary microbiome composition in the Qatari population, and to explore specific microbial signatures that can be associated with various lifestyles and different oral conditions. Materials and methods We characterized the salivary microbiome of 997 Qatari adults using high-throughput sequencing of the V1–V3 region of the 16S rRNA gene. Results In this study, we have characterized the salivary microbiome of 997 Qatari participants. Our data show that Bacteroidetes , Firmicutes , Actinobacteria and Proteobacteria are the common phyla isolated from the saliva samples, with Bacteroidetes being the most predominant phylum. Bacteroidetes was also more predominant in males versus females in the study cohort, although differences in the microbial diversity were not statistically significant. We also show that, a lower diversity of the salivary microbiome is observed in the elderly participants, with Prevotella and Treponema being the most significant genera. In participants with oral conditions such as mouth ulcers, bleeding or painful gum, our data show that Prevotella and Capnocytophaga are the most dominant genera as compared to the controls. Similar patterns were observed in participants with various smoking habits as compared to the non-smoking participants. Our data show that Streptococcus and Neisseria are more dominant among denture users, as compared to the non-denture users. Our data also show that, abnormal oral conditions are associated with a reduced microbial diversity and microbial richness. Moreover, in this study we show that frequent coffee drinkers have higher microbial diversity compared to the non-drinkers, indicating that coffee may cause changes to the salivary microbiome. Furthermore, tea drinkers show higher microbial richness as compared to the non-tea drinkers. Conclusion This is the first study to assess the salivary microbiome in an Arab population, and one of the largest population-based studies aiming to the characterize the salivary microbiome composition and its association with age, oral health, denture use, smoking and coffee-tea consumption.

Background Identifying determinants of low bone mineral density (BMD) is crucial for understanding the underlying pathobiology and developing effective prevention and management strategies. Here we applied machine learning (ML) algorithms to predict low femoral neck BMD using standard demographic and laboratory parameters. Methods Data from 4829 healthy individuals enrolled in the Qatar Biobank were studied. The cohort was split 60% and 40% for training and validation, respectively. Logistic regression algorithms were implemented to predict femoral neck BMD, and the area under the curve (AUC) was used to evaluate model performance. Features associated with low femoral neck BMD were subjected the statistical analysis to establish associated risk. Results The final predictive model had an AUC of 86.4% (accuracy 79%, 95%CI: 77.98–80.65%) for the training set and 85.9% (accuracy 78%, 95% CI: 75.92–80.61%) for the validation set. Sex, body mass index, age, creatinine, alkaline phosphatase, total cholesterol, and magnesium were identified as informative features for predicting femoral neck BMD. Age (odds ratio (OR) 0.945, 95%CI: 0.945–0.963, p  < 0.001), alkaline phosphatase (OR 0.990, 95%CI: 0.986–0.995, p  < 0.001), total cholesterol (OR 0.845, 95%CI: 0.767–0.931, p  < 0.001), and magnesium (OR 0.136, 95%CI: 0.034–0.571, p  < 0.001) were inversely associated with BMD, while BMI and creatinine were positively associated with BMD (OR 1.116, 95%CI: 1.140–1.192, p  < 0.001 and OR 1.031, 95%CI: 1.022–1.039, p  < 0.001, respectively). Conclusion Several biological determinants were found to have a significant global effect on BMD with a reasonable effect size. By combining standard demographic and laboratory variables, our model provides proof-of-concept for predicting low BMD. This approach suggests that, with further validation, an ML-driven model could complement or potentially reduce the need for imaging when assessing individuals at risk for low BMD, which is an important component of fracture risk prediction. Clinical trial number Not applicable.

Cardiovascular disease (CVD) remains a leading cause of global morbidity and mortality. Timely diagnosis is important in reducing both short and long-term health complications. Saliva has emerged as a potential source for biomarker discovery, offering a non-invasive tool for early detection of individuals at elevated risk for CVD, yet large-scale extensive proteomic analysis using saliva for a comprehensive biomarker discovery remains limited. In an effort to develop a diagnostic tool using saliva samples, our study aims to assess the salivary and plasma proteomes in subjects with high risk of developing CVD using a large-scale proteomic approach. Leveraging on the SOMAscan platform, we analyzed 1,317 proteins in saliva and plasma collected from subjects at a high risk of CVD (HR-CVD) and compared the profiles to subjects with low risk of CVD (LR-CVD). Our analysis revealed significant differences in the plasma and salivary proteins between the two groups. Pathway enrichment analysis of the differentially detected proteins revealed that the immune system activation and extracellular matrix remodeling are the most enriched pathways in the CVD-HR group. Comparing proteomic signatures between plasma and saliva, we found approximately 42 and 17 differentially expressed proteins associated with CVD-HR uniquely expressed in plasma and saliva respectively. Additionally, we identified eight common CVD-risk biomarkers shared between both plasma and saliva, demonstrating promising diagnostic tools for identifying individuals at high risk of developing CVD. In conclusion, saliva proteomics holds a significant promise to identify subjects with a high risk to develop CVD. Further studies are needed to validate our findings.