Replication of GWAS Coding SNPs Implicates MMEL1 as a Potential Susceptibility Locus among Saudi Arabian Celiac Disease Patients

Celiac disease (CD), a gluten intolerance disorder, was implicated to have 57 genetic susceptibility loci for Europeans but not for culturally and geographically distinct ethnic populations like Saudi Arabian CD patients. Therefore, we genotyped Saudi CD patients and healthy controls for three polymorphisms, that is, Phe196Ser in IRAK1, Trp262Arg in SH2B3, and Met518Thr in MMEL1 genes. Single locus analysis identified that carriers of the 518 Thr/Thr (MMEL1) genotype conferred a 1.6-fold increased disease risk compared to the noncarriers (OR = 2.6; 95% CI: 1.22–5.54;P<0.01). This significance persisted even under allelic (OR = 1.55; 95% CI: 1.05–2.28;P=0.02) and additive (OR = 0.35; 95% CI: 0.17–0.71;P=0.03) genetic models. However, frequencies for Trp262Arg (SH2B3) and Phe196Ser (IRAK1) polymorphisms were not significantly different between patients and controls. The overall best MDR model included Met518Thr and Trp262Arg polymorphisms, with a maximal testing accuracy of 64.1% and a maximal cross-validation consistency of 10 out of 10 (P=0.0156). Allelic distribution of the 518 Thr/Thr polymorphism in MMEL1 primarily suggests its independent and synergistic contribution towards CD susceptibility among Saudi patients. Lack of significant association of IRAK and SH2B3 gene polymorphisms in Saudi patients but their association in European groups suggests the genetic heterogeneity of CD.

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Genetic Association of HLA-A26, -A31, and -B*51 with Behcet's Disease in Saudi Patients

Clinical Medicine Insights Arthritis and Musculoskeletal Disorders ◽

10.4137/cmamd.s39879 ◽

2016 ◽

Vol 9 ◽

pp. CMAMD.S39879 ◽

Cited By ~ 7

Author(s):

Fahda Al-Okaily ◽

Seham Al-Rashidi ◽

Maysoon Al-Balawi ◽

MD. Mustafa ◽

Misbahul Arfin ◽

...

Keyword(s):

Behçet’S Disease ◽

Behcet’S Disease ◽

Behçet's Disease ◽

Active Form ◽

Susceptibility Loci ◽

Behcet's Disease ◽

Ethnic Populations ◽

Significant Difference ◽

Hla Genotyping ◽

Saudi Patients

Background HLA-B*51 has been universally associated with Behcet's disease (BD) susceptibility, while different alleles of HLA-A have also been identified as independent BD susceptibility loci in various ethnic populations. The objective of this study was to investigate associations of HLA-A and - B alleles with BD in Saudi patients. Materials and Methods Genotyping for HLA-A and HLA-B was performed using HLA genotyping kit (Lab type(R) SSO) in 120 Saudi subjects, including 60 BD patients and 60 matched healthy controls. Results Our results revealed that frequencies of HLA-A*26, -A*31, and - B*51 were significantly higher in BD patients than in controls, suggesting that HLA-A*26, -A*31, and - B*51 are associated with BD. The frequency of HLA-B*15 was significantly lower in BD patients than in controls. Stratification of genotyping results into active and nonactive forms of BD revealed that the frequency of HLA-A*31 was significantly higher in the nonactive form than in the active form of BD, while there was no significant difference in the distribution of other alleles between the two forms of BD. Conclusion This study suggests that HLA-A*26, -A*31, and - B*51 are associated with susceptibility risk to BD, while HLA-B*15 may be protective in Saudi patients. However, larger scale studies are needed to confirm these findings.

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A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

Scientific Reports ◽

10.1038/s41598-021-82098-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Shaker El-Sappagh ◽

Jose M. Alonso ◽

S. M. Riazul Islam ◽

Ahmad M. Sultan ◽

Kyung Sup Kwak

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cross Validation ◽

Disease Risk ◽

Binary Classification ◽

Fuzzy Rule ◽

Large Set ◽

Detection Model ◽

Multi Class Classification ◽

Clinical Measures

AbstractAlzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.

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Prevalence of physical inactivity in Saudi Arabia: a brief review

Eastern Mediterranean Health Journal ◽

10.26719/2004.10.4-5.663 ◽

2004 ◽

Vol 10 (4-5) ◽

pp. 663-670

Author(s):

H. M. Al Hazzaa

Keyword(s):

Saudi Arabia ◽

Physical Inactivity ◽

Disease Risk ◽

Activity Patterns ◽

National Policy ◽

Saudi Arabian ◽

Lifestyle Changes ◽

High Prevalence ◽

Arabian Population ◽

Heart Disease Risk

Major lifestyle changes in recent years in Saudi Arabia may be leading to physical inactivity and a low level of physical fitness. This paper reviews the current literature about physical inactivity in the Saudi Arabian population and discusses its implications for health. Available data from a small number of studies suggests a high prevalence [43.3%-99.5%] of physical inactivity among Saudi children and adults alike. Furthermore, the proportion of Saudi children and adults who are at risk due to inactivity is much higher than for any other coronary heart disease risk factor. It is recommended that a national policy encouraging activity in daily life be established and more studies are carried out to address physical activity patterns with representative samples of the Saudi Arabian population

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