Omar M. E. Albagha

Objective: To determine if common genetic variants at the chromosome 1p13 locus which predisposes to Paget’s Disease of Bone (PDB) regulate Macrophage Colony stimulating factor (M-CSF) expression in bone cells and to determine if there is dysregulation of M-CSF in patients with PDB. Methods: The regions surrounding single nucleotide polymorphisms (SNP) previously associated with PDB at a genome wide significant level were deleted in clones of MG63 osteoblast-like cells using CRISPR/Cas and levels of M-CSF mRNA and protein were evaluated in cells with and without deletions. Serum M-CSF was measured by ELISA in PDB and controls and values in patients were related to clinical features of PDB and biochemical markers of bone remodelling. Results: Deletion of the region surrounding rs484959 increased expression of M-CSF mRNA by 1.31 fold [95%,1.04-1.58], p = 0.026 and M-CSF protein by 1.91 fold [1.25-2.57], p = 0.013, compared with control cells. Serum M-CSF was significantly elevated in PDB versus controls (mean ± SD = 781.7 ± 678 vs. 541 ± 491 pg/mL, p < 0.001). Furthermore, M-CSF levels were associated with number of affected bones (p = 0.029), bodily pain assessed by SF-36 (p = 0.026) and biochemical markers of bone turnover. Conclusion: The rs484959 SNP is a regulatory variant which modulates M-CSF mRNA and protein expression in osteoblasts. Serum M-CSF is increased in PDB, is associated with pain, disease extent and biochemical markers of bone remodelling. This suggest that there is dysregulation of M-CSF in PDB and that this cytokine may play a role in pathogenesis of the disease.

Plasma proteomic scores for body mass index have been derived primarily in European populations, and it is unclear whether these scores are generalizable and useful in other ancestries. Here, we show that plasma proteomic BMI scores developed in participants of European, East Asian, South Asian and African ancestries from the UK Biobank (UKBB; N=50,621) and the South African Middle-Aged Soweto Cohort (MASC; N=948) are portable across ancestries, explain up to 48.7% of trait variance and significantly predict incident obesity. We identified eight protein biomarkers common across all scores, six of which showed causal associations with BMI in Mendelian randomization analyses and were associated with BMI across the Olink (UKBB, MASC) and SomaScan platforms (Qatar Biobank; N= 2410). Discrepancy analysis between the high predicted proteomic BMI and low measured BMI revealed metabolically unhealthy normal weight (MUNW) individuals, with higher visceral fat, elevated triglycerides, and low insulin sensitivity. Thus, plasma proteomic BMI scores may enhance the precision of cardiometabolic risk stratification across diverse populations

Plasma proteomic scores for body mass index have been derived primarily in European populations, and it is unclear whether these scores are generalizable and useful in other ancestries. Here, we show that plasma proteomic BMI scores developed in participants of European, East Asian, South Asian and African ancestries from the UK Biobank (UKBB; N = 50,621) and the South African Middle-Aged Soweto Cohort (MASC; N = 948) are portable across ancestries, explain up to 48.7% of trait variance and significantly predict incident obesity. We identified eight protein biomarkers common across all scores, six of which showed causal associations with BMI in Mendelian randomization analyses and were associated with BMI across the Olink (UKBB, MASC) and SomaScan platforms (Qatar Biobank; N = 2410). Discrepancy analysis between the high predicted proteomic BMI and low measured BMI revealed metabolically unhealthy normal weight (MUNW) individuals, with higher visceral fat, elevated triglycerides, and low insulin sensitivity. Thus, plasma proteomic BMI scores may enhance the precision of cardiometabolic risk stratification across diverse populations.

Background and Objectives: Human populations show striking phenotypic disparities in traits and diseases, including responses to infectious diseases (IDs). The impact of host genetic diversity on infection susceptibility and outcomes is increasingly recognized, yet remains largely unknown in Qatar. Here, we explored the distribution of infection-related host genetic variants in Qatar. Materials and Methods: Infection-related genetic variants from the GWAS Catalog were analyzed in 6047 Qatari whole genomes from the Qatar Genome Program (QGP) and compared with populations from the 1000 Genomes Project (1000G). Results: Out of 272,610 GWAS Catalog associations, 1086 were related to ID susceptibility, resistance, severity, progression, clearance, response to treatment, or vaccination, and hence included in the subsequent analysis. A significant heterogeneity in the allelic frequencies (AFs) between Qatari (n = 6047) and the 1000G populations (n = 2504) was observed. The QGP cohort carries significantly lower AFs of most risk variants associated with susceptibility to tuberculosis, malaria, hepatitis, diarrheal disease, and shingles (up to 222-fold, p < 0.0001). Contrarily, an enrichment in the AFs of variants that increase the risk of chickenpox, plantar warts, pneumonia, urinary tract infections (UTIs), and leprosy was observed among Qatari individuals, yet with much smaller-fold differences (≤5-fold, p < 0.0001). In addition, most severity/chronicity-related variants were considerably less prevalent in the Qatari population (up to 20.5-fold). Analysis of variants associated with antibody response revealed a distinct genetic distribution, especially in Epstein–Barr virus (EBV) and Chlamydia pneumoniae infections (27.8- and 3-fold, respectively). Moreover, a variable inter-population allelic distribution was observed in SNPs related to measles, mumps, and rubella (MMR), smallpox, and hepatitis B virus (HBV) vaccine response, as well as variants linked to viral clearance, viral load, virus-induced progression to cancer, and response to treatment. Conclusions: These findings reveal substantial differences in pathogen-associated host variants in a diverse Qatari cohort and highlight the need for follow-up validation and future GWAS discovery efforts.

Introduction: Gestational diabetes mellitus (GDM) is a condition characterized by glucose intolerance that is first identified during pregnancy and typically resolves after childbirth. This condition can lead to various complications, both prenatal and postnatal, including type 2 diabetes (T2D). However, not all women with GDM progress to T2D, and the molecular mechanisms underlying this heterogeneity remain poorly understood. Here, we hypothesized that GDM participants could be clustered into T2D-like subtypes. Methods: To derive T2D-like subtypes, we applied K-means clustering to GDM participants using the predefined T2D subtype cluster centers established in the QPHI cohort. Differential methylation analysis was performed, and the top-ranked sites were used for further analysis. Additionally, we estimated system-specific age acceleration and its association with subtype-specific complications. Results: Our study demonstrated the effectiveness of the novel clustering approach, originally developed for T2D, in GDM. Additionally, the exploratory analysis of the top-ranked CpG sites suggested potential subtype-related methylation patterns. The identified pathways also suggested overlapping molecular mechanisms underlying both GDM and T2D. Conclusion: These findings support the feasibility of classifying GDM into T2D-like clinical subtypes and suggest potential epigenetic differences that warrant validation in larger longitudinal cohorts.

Background: Type 2 diabetes (T2D) is a complex and highly heterogeneous disease driven in part by genetic predisposition and can be stratified into clinical subgroups to aid disease management. We recently grouped T2D subjects in the Qatar Biobank (QBB) cohort into Severe Insulin‐Deficient Diabetes (SIDD), Severe Insulin‐Resistant Diabetes (SIRD), Mild Obesity‐Related Diabetes (MOD) and Mild Age‐Related Diabetes (MARD) subtypes. Herein, we focused on the genetic makeup of these subtypes. Methods: We used the QBB cohort ( n = 13,808), of whom 2687 were with T2D, and comprehensively assessed polygenic risk scores (PGS) across T2D subtypes, investigated genetic loci associated with each subtype by leveraging the most recent and largest GWAS for T2D, evaluated SNP associations across T2D genetic clusters, and identified protein interaction pathways associated with these distinct T2D subtypes. Results: MOD showed consistently lower PGS compared with other T2D subtypes across all tested scores. SIDD showed more associations with SNPs mapping to residual glycemic cluster compared with other T2D subtypes. The incremental analysis of PGS004838 demonstrated a high ΔAUC of 0.101 for SIDD and a moderate ΔAUC of 0.068 for SIRD, but not for MOD and MARD. Protein interaction analyses identified candidate subtype‐associated gene networks linked to pathways related to glucose homeostasis in SIDD, insulin signalling and hepatic metabolism in SIRD, body fat distribution in MOD and vascular‐related processes in MARD. Conclusion: We found heterogeneous genetic architectures across clinically defined T2D subtypes in a Middle Eastern population. Our findings provide evidence supporting differential polygenic burden, subtype genetic associations and subtype‐associated biological pathways across T2D subtypes. These observations support the utility of subtype‐based genetic analyses for improving biological understanding of T2D heterogeneity.

N-lactoyl-amino acids (Lac-AA) are emerging as important metabolites with diverse physiological roles. This study integrates metabolomics and genomics to investigate the genetic determinants and clinical relevance of three Lac-AA: N-Lactoyl phenylalanine (Lac-Phe), N-Lactoyl tyrosine (Lac-Tyr), and N-Lactoyl valine (Lac-Tyr). We conducted a metabolome-wide association study (mGWAS) on 2811 participants followed by a phenome-wide association study (PheWAS) and pathway enrichment analysis. Our mGWAS revealed modest genetic contributions to Lac-AA levels, with genome-wide significant loci identified for Lac-Tyr and Lac-Val, but not for Lac-Phe. PheWAS analysis linked these genetic variants to key clinical traits, including white blood cell count, platelet count, and glucose levels. Pathway enrichment highlighted the involvement of Lac-AA in immune-metabolic crosstalk, particularly in inflammation and energy metabolism. These findings suggest that Lac-AA levels are primarily influenced by dynamic metabolic or inflammatory states rather than fixed genetic factors. Our results underscore the potential of Lac-AA as metabolic sensors and biomarkers at the intersection of cellular energy states and systemic inflammation, opening new avenues for research in metabolic and inflammatory disorders.

Vitamin D deficiency is highly prevalent in the Middle East despite abundant sunlight; however, most genetic studies have focused on common variants in Europeans only. We analyzed whole-genome sequences from 13,808 Qatar Biobank participants, evaluating rare variants (minor allele frequency 0.01–0.0001) for associations with serum 25-hydroxyvitamin D (25(OH)D) levels and deficiency risk (≤20 ng/mL) in independent discovery (n = 5885) and replication (n = 7767) cohorts, followed by meta-analyses. In quantitative analyses, the discovery cohort identified 41 genome-wide significant signals, including CD36 rs192198195 (p = 2.48 × 10⁻⁸), and replication found 46, including SLC16A7 rs889439631 (p = 2.19 × 10⁻⁸), implicating lipid metabolism pathways. In binary analyses, replication revealed POTEB3 rs2605913 (p = 2.8 × 10⁻⁸), while meta-analysis (n = 13,652) uncovered SLC25A37 rs952825245 (p = 5.15 × 10⁻¹²), a locus associated with cancer and vitamin D signaling. Rare-variant polygenic scores derived from discovery significantly predicted continuous (R² = 0.146, p = 9.08 × 10⁻¹²) and binary traits (AUC = 0.548, OR = 0.99, p = 9.22 × 10⁻⁶) in replication. This first rare-variant GWAS of vitamin D in Middle Easterners identifies novel loci and pathways, underscores the contribution of ancestry-specific rare alleles, and supports integrating rare and common variants to guide precision management in high-burden populations.