Saturday, May 24, 2025

HLA-DQA1/DQB1 Genes Typing and Exosomes Characterization for the Assessment of Celiac Disease Risk in a Chilean Population

 

HLA-DQA1/DQB1 Genes Typing and Exosomes Characterization for the Assessment of Celiac Disease Risk in a Chilean Population

Introduction

Health is a state of complete physical, mental, and social wellbeing and not merely the absence of disease or infirmity [1]. Over the past few years, and after the human genome sequence completion, emerging of pharmacogenomics/pharmacogenetics was a very bold step toward personalized medicine intended to develop new and practical pharmacological approaches [2]. In this regard, nutrigenetics has been defined as the effect of genetic variation on dietary response [3]. This genetic heterogeneity is expressed in terms of a combination of specific single nucleotide polymorphisms (SNPs) (at both monogenic and polygenic levels) affecting the nutrient metabolism traits within different ethnic groups [4]. One of the most representative examples of this field is Coeliac disease (CD) associated to SNPs in the human leukocyte antigen (HLA) class II genes. HLA DQ2.5 (encoded by HLA‑DQA1*05 and DQB1*02 alleles) and HLA‑DQ8 proteins (encoded by HLA‑DQA1*03 and DQB1*03:02 alleles) are known as genetic predisposing factors for CD [5].

a) Upon digestion, partially digested gluten peptides (mainly gliadin fragments) interact with the epithelium and increase intestinal permeability triggering gliadin translocation into the lamina propria.

b) Once there, a series of events lead to transglutaminase (TG2) release and the deamination of gliadin peptides.

c) Antigen presenting cells (ACPs) recognize then the deaminated gliadin and present it via HLA-DQ receptors (mainly DQ2.5/ DQ8 phenotype) to CD4+T lymphocytes, which constitute the key step in the CD pathogenesis.

d) This triggers the activation and proliferation of both CD4 T and B cells, and proinflammatory cytokines secretion (IFN-ꭹ, TNF-α, IL-6, IL-10), which ultimately lead to metalloproteinases secretion and villous atrophy and crypt hyperplasia (not illustrated, since we are evoking here only the CD onset).

e) Exosomes participate in cell-cell communication during this inflammatory process and their content reflects that of parental cells, which make them a very promising biomarkers of CD inflammation.

CD is a multisystemic dietary, gluten-induced auto-immune disorder of the small intestine mucosa [6]. CD is a worldwide health problem occurring at any age in genetically predisposed individuals [7]. The worldwide incidence of CD is about 1%, which can change due to the interplay of genetic, environmental, and immunological factors [7]. CD is associated with chronic inflammation, diarrhea, mal-absorption, and gastrointestinal malignancies [5,6]. With vast clinical manifestations or even asymptomatic disease, undiagnosed and, therefore, untreated CD is mostly associated with other health complications like gastrointestinal cancers, which constitute a heavy burden for health-care systems and decrease life quality [7,8]. Also, it has been recognized that early treatment with a gluten-free diet could prevent CD-associated complications [7].

The personalized intervention is based on the 4P principles, personalized, predictive, preventative, and participative [2]. The investigation of new preventive and predictive approaches for CD also goes through innovative findings from other scientific fields as cellular and extracellular vehicles (EVs) biology, mainly small EVs called exosomes (EXs). Exosomes (EXs) are virus-sized particles (30-150 nm) delimited by a lipid bilayer and commonly released from endosomal multivesicular bodies [9]. They have emerged as new intercellular signaling pathways and biomolecules delivering agents in different physiological and pathological cases, including inflammatory responses [10-12]. In this regard, we have previously demonstrated that inflammatory molecules (TNF-a, IL-6, IL-1ra, and IL-10) are cell-to-cell transported via EXs (unpublished data). Among the studied cytokines, we found that the anti-inflammatory IL-1ra was the most abundant molecule in EXs. Therefore, we suggest that the exosomes’ size and content in cytokines (more particularly IL-1ra) could be a promising marker of inflammation state associated with auto-immune diseases like CD, which could be a milestone toward the prevention of a heavily under-diagnosed disease worldwide. This ongoing study is primarily structured around

(i) The screening of the frequency of CD-predisposing genetic mutations and

(ii) The Exssome content characterization as new tools for CD prevention and diagnosis to mitigate the associated complications of undiagnosed and/or asymptomatic CD. In this paper, we present our preliminary results regarding exosomes’ size and their IL-1ra content when isolated from individuals carrying different alleles of HLA-DQA1 and HLA-DQB1 class II genes.

Methods

Population and Study Design

The current study is designed as a cross‑sectional, population‑based study to investigate the frequency of CD predisposing HLA‑DQ genotypes and the role of exosomes in CD prevention among Chilean individuals. Healthy males and females aged between 20 and 70 years old were voluntarily recruited during February-June 2019 among students and staff of the Medicine Faculty, University of Santiago (Santiago, Chile). The study population includes subjects with average dietary intake of gluten, subjects who self-reported a gluten sensitivity, subjects who self-instituted and strictly adhered to a gluten-free diet, and CD-diagnosed subjects with and without treatment. Participants were also asked to complete a baseline questionnaire, including information about family history of CD, food intolerances history, and inflammatory disease history. Study details were thoroughly explained to all volunteers, and all participants prior to study enrollment signed a written consent. 8 mL fasting blood samples were collected from each participant’s antecubital vein in EDTA tubes (according to the GenoChip Food kit manufacturer). Blood specimens were freshly used for DNA extraction and then centrifuged at 2000 rpm for 10 min. Plasma specimens and DNA aliquots were collected and stored at -20°C and 4°C, respectively.

Genotyping

Genomic DNA was isolated from the blood samples using a QIAamp mini kit DNA extraction protocol, according to the manufacturer’s instructions (QIAGEN GmbH, Hilden, Germany). Samples were then genotyped using the GenoChip Food kit, following the manufacturer guidelines (Pharmgenomics Gmbf, Mainz, Germany). The GenoChip is permanently integrated onto the bottom of a 1.5 mL reaction tube, spanning only 3x3 mm and pre-coated by sequence-specific oligonucleotide probes. This kit is designed to detect SNPs related to some food intolerances, including but not limited to gluten, alcohol, fructose, lactose, and sucrose. The genotyping process consists of:

Multiplex PCR

The target regions in the HLA-DQA1 and HLA-DQB1 genes, including HLA DQA1*05 (145 bp), HLA DQA1*03 (125 bp), HLA DQB1*03:02 (404 bp), and HLA DQB1*02 (147 bp) were amplified by multiplex polymerase chain reaction (PCR) using the Primus 25 advanced® Thermocycler (PEQLAB Biotechnologie GmbH, Erlangen, Germany). Primers and probes were designed and provided by Pharmgenomics Gmbh. Microarray protocol- After a denaturation step for 2 min at 95°C, 5 μL of each amplification product is transferred to the array tube and bind to the corresponding immobilized oligonucleotide probes for 30 min at 55 °C and 550 rpm. A washing step removes unspecific bound fragments. Next, the Conjugation Mix is added, which binds to the probe-PCR fragment complexes for 5 min at 52°C and 550 rpm. Another washing step removing the unbound Conjugation Mix residues is required. The subsequent adding of the substrate leads to a precipitation reaction at those spots where DNA is bound after 15 min at 21°C and 550 rpm. All incubation steps were performed using the Thriller® Thermoshaker Incubator (PEQLAB Biotechnologie GmbH, Erlangen, Germany). The precipitation pattern is detected with the image reader (Alere Technologies Gmbh, Jena, Germany) and interpreted by the GenoChip Food software.

Exosomes Isolation

Exosomes were isolated by ultracentrifugation according to the protocol described by Theiry ,et al. with slight modifications. Briefly, blood samples were subjected to consecutive centrifugation steps (2,000g and 12,000g) to remove cellular debris and large vesicles (J.S 4.3 rotor and the Avanti J-20XP centrifuge, Beckman Coulter, USA). Exosomes were then pelleted two times with ultracentrifugation at 110,000g for 70 and 120 min (SW32 Ti rotor and the Optima LE-80K ultracentrifuge, Beckman Coulter, USA), separated by a filtration step through a 0.22 μm filter (Sarstedt AG & Co. KG, Nümbrecht, Germany). The supernatant was removed, and pellets were resuspended in 200 μL of PBS and stored at -20°C.

Western Blot Analysis

The Exssome preparation has been treated with RIPA lysis buffer and protease inhibitor cocktail. Samples were vortexed for 15s and incubated for 5 min at room temperature to allow complete lysis. Total Exssomal proteins were measured with Bradford assay (Bio-Rad, Germany). About 20 μg of proteins were loaded, resolved, transferred onto the Immuno-blot PVDF membrane, and subsequently blocked with 5% dry milk in TBS-T (Tris Buffered Saline with 0.05% Tween-20) for 1 hour. The membranes were then incubated with rabbit polyclonal CD63 and CD9 (EXSAB-kit-1, System Biosciences, CA, USA) (1:1000) antibodies overnight at 4 °C. The blots were vigorously washed whit TBS-T and then incubated with Goat anti-rabbit antibody (EXSAB-kit-1) (1:20,000) for 1 hour at room temperature. Membranes were detected with Pierce ECL plus Western blotting substrate (Thermo Scientific), and images were taken on Thermo Scientific™ MYECL™ Imager (Thermo Scientific).

ELISA Assay

According to the manufacturer’s instructions, the quantitative sandwich enzyme immunoassay technique was used to quantify exosomes’ IL-1ra content (R&D Systems, USA).

Transmission Electron Microscopy

The typical size and shape of purified exosomes were determined by TEM analysis (Prof. Dr. Osuna’s laboratory, University of Granada, Spain, and their related protocol was used). In brief, exosomes were pelleted down by ultracentrifugation at 100 000 g for an hour using the FiberliteTM F50L-24 x 1.5 FA rotor and Sorvall WX ultra series ultracentrifuge (Thermo Fisher Scientific, USA). Afterward, the supernatants were discarded while the Exssome pellet was fixed with 50 μL of 2% glutaraldehyde and 2% formaldehyde in cacodylate buffer with 0.1 M Saccharose. Fixed samples were then negatively stained using uranyl acetate and analyzed via TEM (Libra® 120, Carl Zeiss AG, Germany).

Statistical Analysis

Data were analyzed using descriptive statistics: arithmetic means, standard deviation, frequency, and percentage (GraphPad Prism version 6.00, GraphPad Prism Inc, San Diego, California). Data were presented as means ± standard deviation, and a oneway analysis of variance (ANOVA) test was used for multigroup (between CD risk groups) comparison followed by Student– Newman–Keuls test. Results with two-sided p-values of <0.05 were considered statistically significant.

Results

HLA-DQ Genotypes

Table 1 shows the frequency of each HLA DQ genotype. As shown in Figure 1, six participants carry alleles that were not detected by the used method. Of the 24 analyzed participants, about 46% carried the CD related HLA DQ molecules, DQ2.5 and DQ8, DQ2.5 (with a double dose of DQB1*02), and DQ2.5/DQ2.2, which are associated with extremely high risk to develop CD. More than 37% carry HLA-DQ2 and HLA-DQ8 haplotypes (DQ2.5 with a single dose of DQB1*02, DQ2.5-heterogenous, DQ8-homozygous, DQ8/DQ2.2, and DQ2.2 with a double dose of DQB1*02), making them highly susceptible to develop CD. 16.6% carried only one of the alleles of the risk HLA DQ2 heterodimers known as “half heterodimer” (HLA DQA1*05 and HLA DQB1*02). Celiac autoimmunity occurs almost exclusively in the presence of DQ molecules. Here, we found that almost all participants were carrying at least one of the DQ heterodimers. However, the calculated general population CD risk was above 3, indicating a high population risk to develop the disease.

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Figure 1: Study design. Six participants are carrying alleles that could not be detected by the used method.

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Table 1: The HLA-DQA1 and -DQB1 genotype frequencies were observed in the studied population and the associated risk to develop celiac disease.

Note: *CD risk score has been calculated according a 4-point scale (1 = extremely low, 2 = low, 3 = high, 4 = very high)

Exosomes Characterization

The study of circulating exosomes has emerged as a promising alternative for invasive biopsies, mainly in cancer diagnosis. However, before proceeding with a specific analysis, the characterization of exosomes is a critical step. Nevertheless, not standardized, the identification of isolated particles as exosomes relies on various criteria following a defined working flow. Accordingly, SDS-PAGE and Western blotting firstly characterized the isolated particles; and subsequently, the size, shape, and particles/mL were determined with the help of TEM. SDS-PAGE analysis (data not shown) reveals the presence of intense bands with a molecular weight of about 58 kDa, which could correspond to the Exssome marker CD63, while the slight bands around 25 KDa illustrate the presence of CD9 protein. The presence of CD63 and CD9 as primary Exssome markers was then confirmed on the western blotting membrane (Figure 2a). Furthermore, isolated particles display the expected cup-shaped morphology consistent with pure Exssome preparations (Figure 2b). Despite the genetic background of the donors, almost all isolated particles showed exosomes characteristic size (<150 nm), starting from 23.79 up to 211.22 nm (recorded only once). Taken together, these results demonstrate that the isolated particles certainly belong to the exosome subpopulation.

Exosomes as Potential Biomarkers for CD

All blood samples have been collected in EDTA tubes and used for plasma preparation; the exosomes were subsequently isolated by ultracentrifugation. Figure 2d shows the average size of isolated exosomes according to the donors’ genetic predisposition to CD. EXs’ size differs significantly (p< 0.05) when they are isolated from participants with extremely low (44.58 ± 7.88; n=4) and high (126.6 ± 9.334; n=8) or extremely high (118.3 ± 19.09; n=9) risk for CD. Overall, exosomes’ populations were highly homogenous in individuals of each risk group (low risk: 30.02 ± 5.64 to 57.07 ± 7.88; high risk: 120.03 ± 16.73 to 133.20 ± 22.88; E. high risk: 104.80 ± 15.74 to 131.81 ± 22.94) with a shift towards average bigger size when the risk to develop CD increases. When it comes to the exosomes’ content in IL-1ra, a similar pattern has also been observed (Figure 2e). EXs from donors with high to extremely high CD risk showed high IL-1ra content than those isolated from persons with extremely low CD risk. However, this difference has not been statistically borne due to the high heterogeneity of individuals within each group. Besides normal individuals, the study population also includes CD-diagnosed (treated and not treated) and self-reported intolerance to gluten subjects. EXs’ IL- 1ra was very high (108.8 ± 15.91 to 148.8 ± 12.37 in the E. highrisk group), as the CD-diagnosed subjects were not following any treatment. However, values of IL-1ra (in the same risk group) decrease (52.50 ± 3.54 and 48.75 ± 6.52) in exosomes isolated from persons receiving treatment for CD. The difference in exosomes’ IL- 1ra between treated and non-treated CD patients could open new treatment monitoring perspectives towards a fully personalized intervention. Our preliminary findings suggest that both exosomes seize and their content in IL-1ra could serve as biomarkers for CD diagnosis and prognosis in genetically susceptible subjects.

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Figure 2: Characterization of serum-derived exosomes enriched from healthy and CD donors.

a) Immunoblots showing expression levels of CD63 and CD9 in the purified exosomes.

b) Representative TEM images of pure exosomes preparations (Scale bar– 200 nm). The graphs represent c) Proteins concentration

d) Size, and interleukin-1Ra content in exosomes enriched from individuals belonging to each CD risk group. For boxplots the center lines mark the median, box limits indicate minimal and maximal values. Data are represented as means ± standard deviation, *p < 0.05, **p < 0.01, ns: no significant difference. S, sample; PC, positive control.

Exosomes characterization

The study of circulating exosomes has emerged as a promising alternative for invasive biopsies, mainly in cancer diagnosis. However, before proceeding with a specific analysis, the characterization of exosomes is a critical step. Nevertheless, not standardized, the identification of isolated particles as exosomes relies on various criteria following a defined working flow. Accordingly, SDS-PAGE and Western blotting firstly characterized the isolated particles; and subsequently, the size, shape, and particles/mL were determined with the help of TEM. SDS-PAGE analysis (data not shown) reveals the presence of intense bands with a molecular weight of about 58 kDa, which could correspond to the Exssome marker CD63, while the slight bands around 25 KDa illustrate the presence of CD9 protein. The presence of CD63 and CD9 as primary Exssome markers was then confirmed on the western blotting membrane (Figure 2a). Furthermore, isolated particles display the expected cup-shaped morphology consistent with pure Exssome preparations (Figure 2b). Despite the genetic background of the donors, almost all isolated particles showed exosomes characteristic size (<150 nm), starting from 23.79 up to 211.22 nm (recorded only once). Taken together, these results demonstrate that the isolated particles certainly belong to the exosome subpopulation.

Exosomes as Potential Biomarkers for CD

All blood samples have been collected in EDTA tubes and used for plasma preparation; the exosomes were subsequently isolated by ultracentrifugation. (Figure 2d) shows the average size of isolated exosomes according to the donors’ genetic predisposition to CD. EXs’ size differs significantly (p< 0.05) when they are isolated from participants with extremely low (44.58 ± 7.88; n=4) and high (126.6 ± 9.334; n=8) or extremely high (118.3 ± 19.09; n=9) risk for CD. Overall, exosomes’ populations were highly homogenous in individuals of each risk group (low risk: 30.02 ± 5.64 to 57.07 ± 7.88; high risk: 120.03 ± 16.73 to 133.20 ± 22.88; E. high risk: 104.80 ± 15.74 to 131.81 ± 22.94) with a shift towards average bigger size when the risk to develop CD increases. When it comes to the exosomes’ content in IL-1ra, a similar pattern has also been observed (Figure 2e). EXs from donors with high to extremely high CD risk showed high IL-1ra content than those isolated from persons with extremely low CD risk. However, this difference has not been statistically borne due to the high heterogeneity of individuals within each group. Besides normal individuals, the study population also includes CD-diagnosed (treated and not treated) and self-reported intolerance to gluten subjects. EXs’ IL- 1ra was very high (108.8 ± 15.91 to 148.8 ± 12.37 in the E. highrisk group), as the CD-diagnosed subjects were not following any treatment. However, values of IL-1ra (in the same risk group) decrease (52.50 ± 3.54 and 48.75 ± 6.52) in exosomes isolated from persons receiving treatment for CD. The difference in exosomes’ IL- 1ra between treated and non-treated CD patients could open new treatment monitoring perspectives towards a fully personalized intervention. Our preliminary findings suggest that both exosomes seize and their content in IL-1ra could serve as biomarkers for CD diagnosis and prognosis in genetically susceptible subjects.

Discussion

Here, we present the first results regarding exosomes’ size and IL-1ra content when isolated from individuals carrying different alleles of HLA-DQA1 and HLA-DQB1 class II genes. CD is a multifactorial autoimmune disorder triggered by gluten in the small intestine mucosa of genetically susceptible persons. As described in the introduction, the CD is a heavily under-diagnosed disease in which developing complications such as lymphomas and autoimmune disorders is very likely (refractory celiac disease is associated with a 50% risk to develop lymphoma) [7]. However, CD develops rarely in the absence of specific HLA class II haplotypes, making the genetic testing of great importance. So far, only variations in HLA-DQA1 (DQA1*05:01 and DQA1*03) and HLA-DQB1 (DQB1*02, DQB1*03, and DQB1*03:02) alleles have been recognized in the clinical practice of CD [13]. Here, we found that almost all participants are carrying at least one of these alleles leading to genetic predisposition for celiac autoimmunity, which is similar to previous observations on the same population [14-16]. DQ2.5/DQ8 and DQ2.5/DQ2.2 genotypes were the most detected in normal as well as CD participants enrolled in this study. DQ2 homozygosity and DQ2/DQ8 double heterozygosity are associated with the highest CD risk, indicating a very high CD risk in the studied population [16-19]. Despite the CD genetic risk, HLA-DQA1*0501 and HLA-DQB1*02:01 (and their variations: HLADQA1* 05/02 and HLA-DQB1*02) encoding for DQ2 heterodimers (DQ2, DQ2.5, and DQ2.2) were the most dominant CD alleles. In this specific regard, Araya, et al. Have reported that DQ2 was present in 53.9% and 43.9.0% of Chilean CD cases and first-degree relatives [16]. However, Pérez-Bravo, et al. Have earlier found that CD is primarily associated with DQ8 conformation in Chilean children [14]. Therefore, the predominance of DQ2.5/DQ8 and DQ2.5/ DQ2.2 haplotypes were more or less consistent with the previous investigations. Nevertheless, the genotyping method herein used is different, which may explain some discrepancies from previous studies, even with a small sample size [20]. On the other hand, a positive HLA-DQ test means only a genetic predisposition for celiac autoimmunity, whereas the absent HLA-DQ predisposing alleles have an absolute diagnostic value. Along with other markers like anti-transglutaminase (anti-tTGA), HLA-typing would help in the CD screening process to avoid invasive diagnosis methods in the future. Accordingly, more research should also be addressed towards identifying new markers for a complete prediction of CD in genetically susceptible persons.

In this respect, EXs have been widely studied as cancer biomarkers focusing on their protein and nucleic acid content, while little is known about their role(s) in pathologies like CD [21,22]. Starting from our previous observations on EXS’ behavior during inflammation, we hypothesize that EXS’ cytokines alongside their size could serve as a promising marker of CD inflammation. As stated in Figure 3, deaminated gluten peptides presentation (to CD4+T cells) via HLA-DQ (mainly DQ2.5/DQ8) is the crucial step determining the magnitude of the inflammatory response in CD. The cytokines network (IL-6/1ra/10, TNF-α, IFN-ꭹ) that is released directly or packed in EXs, leads to the metalloproteinase secretion and triggers villous atrophy and crypt hyperplasia. It has been recently demonstrated that serum cytokines rise upon gluten ingestion, and this could differentiate CD patients from selfreported gluten sensitivity [22,23]. Indeed, both studies have only focused on pro-inflammatory cytokines release (mainly IL-2), and no information about IL-1Ra or even IL-1 has been evoked. We, for the first time, report that EXS carries different amounts of IL-1Ra depending on genetic predisposition to CD and pathological status (treated or not), suggesting their role(s) in cell-cell communication during CD inflammation. IL-1Ra is a natural anti-inflammatory cytokine that tightly regulates the IL-1 activity creating a balance between innate immunity amplification and uncontrolled inflammation [24,25]. However, IL-1Ra has a short half-life, and a high concentration is required to inhibit IL-1 activity [25]. However, preliminary, these findings may provide clues about using EXS (by cells) as an alternative but stable way for cell-to-cell carrying of vulnerable IL-1Ra, reinforcing the idea to use them as stable biomarkers in the clinic.

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Figure 3: Immune dysregulation in CD onset and potential use of exosomes as new trends for diagnosis and screening.

Despite the growing interest in EXS, many secrets related to the releasing mechanism(s), like the variety of EXS size, remain unrevealed. Many microscopic methods are now widely used to study the physical features of EXS as an essential step in the characterization process of pure EXS preparations. To our knowledge, the EXS size itself was not yet considered as a marker since different EXS populations enriched from body fluids or cell cultures originate from cells with different morphological characteristics as it has been accepted. By contrast to that, Sokolova, et al. Have reported that EXS size was comparable when even isolated from cell lines that are morphologically highly heterogeneous [26]. This may indicate that cells release different subpopulations (regarding size) of EXS depending on their physiological status. Here, EXS populations were found to be highly homogenous in individuals belonging to the same CD risk group, while the size was significantly more prominent in favor of high CD risk groups. We should also underline that size distribution depend significantly on the isolation method [27,28]. However, the classical EXS isolation technique of ultracentrifugation herein utilized enriches particles up to a diameter of 250 nm without affecting their shape and integrity [26-28]. Accordingly, CD genetic predisposition seems to affect not only the IL-1Ra in EXS but also their size, suggesting the potential use of these two parameters (alongside HLA-DQ typing) as biomarkers for CD diagnostic and prognostic.

Conclusion

Celiac disease can be diagnosed at any age, and apparently, its worldwide prevalence is increasing due to environmental changes and the absence of non-invasive diagnostic and screening methods. In this paper, we have provided results (yet preliminary) supporting our starting hypothesis and demonstrating that HLA-DQA1/DQB1 gene typing, and exosomes characterization could be useful tools for CD assessment. EXS can be found in almost all body fluids, and their content seems to be more stable than circulating biomarkers due to protection by a lipid bilayer. Hence, developing a system for rapid characterization of active EXS (with specific cargo like the IL-1Ra) could open up possibilities of performing “liquid biopsy” in CD patients to avoid serious complications like lymphoma and colorectal cancer.


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Friday, May 23, 2025

Factors Affecting the Academic Performance among Students at Faculty of Medical Laboratory Science

 

Factors Affecting the Academic Performance among Students at Faculty of Medical Laboratory Science

Introduction

Students’ academic performance is very important and attracts the attention of all those involved in the higher education system [1- 3]. For any educational institute, students are the most important asset. Universities and colleges have no value without students. The economic and social development of a country is directly associated with the academic performance of students. The student’s academic performance plays a vital role in creating the finest quality alumnae who will become leaders and manpower of a particular country, consequently responsible for the country’s social and economic development [4]. The academic performance of the students’ has gained significant attention in past research. The performance of students is affected by psychological, economic, social, personal and environmental factors. Though these factors strongly influence the performance of the students, these factors differ from country to country and person to person. In this era of globalization and technological revolution, education is considered the first step for every human activity. It plays a vital role in the development of human capital and is linked with an individual’s well-being and opportunities for better living [5]. It ensures the acquisition of knowledge and skills that enable individuals to increase their productivity and improve their quality of life.

This increase in productivity also leads to new sources of earning which enhances the economic growth of a country [6]. The quality of students’ performance remains a top priority for educators. It is meant for making a difference locally, regionally, nationally and globally. Educators, trainers, and researchers have long been interested in exploring variables contributing effectively to the quality of performance of learners. These variables are inside and outside universities that affect students’ quality of academic achievement. These factors may be termed student factors, family factors, school factors and peer factors [7]. Generally, these factors include age, gender, geographical belongingness, ethnicity, marital status, Socioeconomic Status (SES), parent’s education level, parental profession, language, income and religious affiliations. Besides other factors, socioeconomic status is one of the most researched and debated factors among educational professionals that contribute to the academic performance of students. The most prevalent argument is that the socioeconomic status of learners affects the quality of their academic performance. Most experts argue that the low socioeconomic status has a negative effect on the academic performance of students because the basic needs of students remain unfulfilled and hence, they do not perform better academically [8-10]. The low socioeconomic status causes environmental deficiencies which results in the low self-esteem of students [9]. More specifically, this study aims to identify and analyze factors that affect the students’ academic performance in medical laboratory sciences.

Rationale

In medical college when students are enrolled and start learning activities, their academic performance varies widely. Some students try to do their best to stay at the top level, while others barely try to pass; finding out why some students perform well academically and the factors that affect students’ performance is important, as this understanding can then be used to promote the factors that contribute to high academic performance and achieve institution/ universities desired outcome. Some currently study stated the obvious effect of pre-admission criteria, socio-demographic factors, study habits and learning styles on medical laboratory students’ performance.

Materials and Methods

Study Design

A descriptive cross-sectional study.

Study Area

The study has been conducted in the Faculty of the medical laboratory science/ international university of Africa a private university in Khartoum, Sudan. It is a member of the federation of the universities of the Islamic world. The university has faculties of education and humanities, shariah and Islamic studies, of pure and applied science, medicine and engineering.

Study Population

It included all the Students of medical laboratory science/ international university of Africa batch 3 and 4.

Inclusion Criteria

• Students in the third and fourth year

• Students with excellent academic performance (The Cumulative Grade Point Average (CGPA) equals or more than 3.5 was considered as excellent academic performance).

• Students with low performance (The Cumulative Grade Point Average (CGPA) less than 2.5 was considered as poor academic performance)

• Final year (4th year) students have the greatest undergraduate academic experience. The Cumulative Grade Point Average (CGPA) will reflect the academic performance during the whole period.

• Exclusion Criteria

• Students in first, second batch.

• Students with moderate performance.

• Students who refuse to participate in the study.

Sample Size

Total covers for all students fit in inclusion criteria at the time of the study.

Data Collection Tool

In this study, the tool of data collection used was a Selfadministered Questionnaire by Google form. The questionnaire includes sex, age, CGPA, academic performance, study habits and learning style, motivation in studying medicine, medical illness, economic and social factors.

Designing & Validation of Questionnaire

A prior literature search was done for factors that affect the academic performance of students [10,11]. The questionnaire had 30 questions in English by Google form focusing on important factors associated with the academic performance of the students:

• Individual factors like interest, problems related to language/ understanding.

• Factors related to pre-admission.

• Impact of teaching-learning methods used.

• Factors related to the learning style.

• Factors pertaining to family.

The majority of questions had responses graded on a Likert scale of 1 to 5, while some had clear options given to identify specific factors.

Data Management and Analysis

The collected data was cleaned, coded, entered in a master sheet and analyzed by statistical package for social science (IBM SPSS Inc.Chicago, version no 23) software. Chi-square test will be used for comparing categorical data; the level of significance will be set at 0.05, the table will be constructed using Microsoft word.

Results

The total number of students induced in this study in the clerkship period was 525 students. 267 students were in the semester [8], while 258 were in the semester [12]. The number of students with low academic performance CGPA of less than 2.5 was 52. 45 of them accepted to participate in the study (response rate is 17.2%) Which is enough samples of the 52 students with 95%. Confidence level and 25.7% confidence interval 281 students with CGPA 3.5 or more, 216 of them accept to participate in the study with (82.8%) response. Out of the 525 students, 171 were males which is (32.6%) while 354 (67.4%) were females. It was approximately the same percentage of participants in the study (20 out of 73 were male 28.1%). Also out of the 45 students with a CGPA of less than 2.5, four were male (17.3%). This ratio of male to female students is reflective of the overall student population. Gender does not affect the academic performance of the participants in this study (Figure 1). 260 students responded to the questions about social, family and marital status. Out of them only two students (2.7%) were married both of them were females. 3 divorced medical students were found (1.2%). The following factors were assessed (Marital status, family size whether the student now lives with his family or in the university hostel or not and the presence of family problems such as disease or disability, divorced parents, death of a family member). They were found not significantly affect the academic performance of the study population. (P-value < 0.05) see Tables 1-3 and Figure 2. In contrast, chronic medical problems and diseases during the study period of the medical laboratory student her\himself significantly affect their academic performance. (P value=0.023) (Table 3).

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Figure 1: Correlation between gender of participants and academic performance of medical laboratory students.

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Figure 2: Correlation between family size of participants and academic performance of medical laboratory students.

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Table 1: Correlation between social status of participants and academic performance of medical laboratory students.

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Table 2: Correlation between presence with family of participants and academic performance of medical laboratory students.

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Table 3: Correlation between family suffering of participants and academic performance of medical laboratory students.

The social-economic status of the student’s families was not found to significantly influence their academic performance (P-value > 0.05). Whether the student is financially supported by parents or another family member, relative or supported her\himself was not found to significantly influence their academic performance (P-value > 0.05) (Table 4). Parents’ level of education and the mother’s profession and the presence of physicians as guidance in the family were assessed and found not statistically affect medical students’ academic performance. The profession of the father both influences the academic performance of the medical student. (P values = 0.021 and 0.180 respectively) (Tables 5-7) and (Figure 3). Preadmission criteria like admission to the university whether general or private influence the academic performance of the students. (P values = 0.002) (Table 8). Secondary school examination type and the number of secondary school examination attempts were both not statistically influence the academic performance of the students. P values = (0.241 and 0.345 respectively) (Table 9). English language proficiency was assessed and it had no significant effect on the academic performance (P-value = 0.222) (Table 10). Time spent on TV has no significance (p values =0.957 value for no watching) (Table11). Networking, chatting social has a significant influence on the academic performance of the students when more than 4 hours (p values =0.035) (Table 11). Sleeping hours per day showed no a significant correlation with academic performance when less than 6 hours (p value=0.541) and a significant correlation with academic performance when sleeping between 6-8 hours (p value= 0.048) (Table 12).

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Figure 3: Correlation between Physician in family as guidance of participants and academic performance of medical laboratory students.

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Table 4: Correlation between financial support of participants and academic performance of medical laboratory students (Financial support).

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Table 5: Correlation between economic status of participants and academic performance of medical laboratory students.

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Table 6: Correlation between parent education of participants and academic performance of medical laboratory students.

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Table 7: Correlation between parent occupation of participants and academic performance of medical laboratory students.

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Table 8: Correlation between admission to the University (higher secondary school) of Participants and academic performance of medical laboratory students.

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Table 9: Correlation between preadmission criteria Secondary School exam attempted and academic performance of medical laboratory students.

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Table 10: Correlation between preadmission criteria of English language proficiency and academic performance of medical laboratory students.

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Table 11: Correlation between preadmission criteria of Time extended on T.V & social network /chatting and academic performance of medical laboratory students.

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Table 12: Correlation between preadmission criteria Sleeping hours a day and academic performance of medical laboratory students.

There was no significant difference between those who had higher or lower CGPA when it came to motivation to study, studying in a group alone or with one colleague, studying hours per day, studying hours at weekends and the use of the following while studying (Mapping, Note Forming, Highlighting, Summarizing, recording, etc) (P-value > 0.05) (Tables 13 & 14). What students did when facing difficulty in studying had no influence on their academic performance (P-value = 0.696) (Table 15). How they spend their vacation had no influence on their academic performance (P-value = 0.447) (Table 16). The most commonly used reading resources among students are the internet and other resources like watching video lectures. While the least used were Using textbooks as the primary source of knowledge, handouts and notes were not correlated to the academic performance, while using the internet showed a positive influence on the outcome (p-value 0.011) (Table 17). Using other reading resources like textbook notes as the primary resource was associated with poor academic performance (P-value 0.005) (Figure 4). Attendance of the students is excellent in all activities in practical sessions, lectures, tutorials, PBL and clinical teaching (Figure 5). Significant differences were observed between the two groups with regard to the attendance of lectures, tutorials, practical sessions, PBL activities and clinical teaching. P values = (0.011- 0.000- 0.000- 0.013- 0.000 respectively) (Table 5).

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Table 13: Correlation between preadmission criteria Motivation to study and academic performance of medical laboratory students.

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Table 14: Correlation between preadmission criteria studying habits and academic performance of medical laboratory students.

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Table 15: Correlation between preadmission criteria Facing difficulty and academic performance of medical laboratory students.

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Table 16: Correlation between preadmission criteria activity during vacation and academic performance of medical laboratory students.

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Table 17: Correlation between preadmission criteria Weekend studying hours and academic performance of medical laboratory students.

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Figure 4: Correlation between Physician in family as guidance of participants and academic performance of medical laboratory students.

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Figure 5: Correlation between the percentage of attendance of academic activities and academic performance of medical laboratory students.

Discussion

Identifying the factors that can lead to the good academic performance of medical laboratory students is an interesting element of medical education. This study shows that there is a statistically significant link between good performance and the medical student’s own condition or handicap, the Father’s occupation, presence with family, and a chronic medical condition during medical studying (Bronchial asthma, Migraine, Insulindependent Diabetes Mellitus and corrected congenital heart defect are some examples). admittance to the faculty, whether general or private, secondary school certificate type, sleeping hours per day between 6-8 hours, attendance to all academic and clinical activities and students ؛s own notes and textbooks as a primary source of studying (P-value < 0.05). In this study, 45 out of 260 students had the academic performance of 50% of these medical laboratory students was low. As a result, the existence of chronic medical disease has an impact on a medical laboratory student’s academic performance during the period of the study (p-value 0.023).

There had been little previous research on the impact of medical diseases on academic performance, but it was obvious from this study that medical diseases had an impact on students’ attendance and sleep (P value less than 0.05). Another issue that was not considered was stress. Anxiety and tension levels were extremely high, and the percentage of medical laboratory students (92%) was the same [13]. Regarding the father profession, 27 (10.4%) mentioned that their fathers were laborer’s. All of them had an excellent academic performance. They may feel motivated or pressed by family. Another study in Sudan found eight medical students mentioned that their fathers were laborer’s. All of them had an excellent academic performance. They may feel motivated or pressed by family. P-value 0.002. No reason was suggested in the previous studies [14]. In 90 (34.6%) of the participants’ families, doctors were present. The presence of physicians in the family as guidance has no good effect on medical students’ performance. Al Shawwa et al. discovered the opposite of this result [2].

Admissions to universities, whether general or private, have an impact on students’ academic performance, according to our study. In contrast to Mohamed’s study in Sudan, eleven medical students were granted private entrance to medical school. Six (54.5%) of them had poor academic performance. It is apparent that medical students who received private admission performed worse than those who received general admission one p-value of 0.002. Increasing the number of SSCE tries also had a detrimental impact on performance [15]. At Nile Valley University, a study was done to compare the academic performance of private admission medical students to their public admission colleagues. Between private and public admission students, there were statistically significant variations in academic performance. While only 78.4% of private admission students progressed without delay, 90% of the public admission students did so. The pass rate in all phases of medical study as well as the Cumulative Grade Point Average (CGPA) was lower among private admission students. Of re-sits [16]. This difference is proportionate to the difference in grades obtained at SSCE and the number of re-sits [17].

In addition, Better performance was found in students with non-Sudanese non-Arabic secondary school certificates, while the lowest performance was observed among students with Arabic secondary school certificates p-value of 0.000 Similar to the result conducted at the University of Gezira, Sudan [18]. The majority of the medical students with high CGPA sleep between six to eight hours 129 (49.6%) (Both sleeping more than eight hours and sleeping less than six hours per day is associated with poor academic performance. Bahmamm et al stated that decreased nocturnal sleep time, late bedtimes during weekdays and weekends and increased daytime sleepiness are negatively associated with academic performance in medical students [19,20]. Partial sleep impecunious (less than 6 hours of sleep per night) can lead to a lack of attention, attentiveness, remembrance, and judgmental thinking in a person’s day to day life [21]. Attending each one of the activities is strongly linked to excellent academic performance in our study. The negative correlation for poor performance suggests the value of monitoring attendance and identifying students at risk for poor performance [22]. This study with Mohamed in the University of Gezira, Sudan showed different results [18] that medical laboratory students had less attending lectures and tutorials compared with clinical and practical sessions.

It is concluded that making lecture attendance mandatory by educators may adversely affect the performance of some students not attending lectures since some students with poor attendance achieved outstanding academic performance [23]. So making lectures and tutorials more interesting for medical students may be better than mandatory attendance which sometimes seems the only solution. Attendance could be enhanced by combining classroom instruction with hospital and laboratory training. There would be a significant increase in attendance if there were signin sheets, fewer gaps between sessions, and numerous lectures on the same day. Medical laboratory schools could examine these variables in the future to improve student motivation to attend classes [24]. In our study, no other social or family characteristics were identified to have an impact on the academic performance of medical laboratory students. Gender, marital status, family size, socioeconomic condition of the family, degree of education of the parents, and the mother’s career are some examples of these characteristics. Whether the student lives with his family is affect academic performance and significant relationships. English language proficiency was one of the pre-admission criteria that had no effect on academic success. According to the same result by Al Shawwa et al, a poor command of the English language appears to be a significant negative factor in medical laboratory students’ academic performance [2].

Video tutorials and the internet were the least used resources among pupils. Multimedia learning has been found to be beneficial in the training of clinical laboratory skills. Learners, on the other hand, have both opportunities and obstacles when using technology. The purpose of this study was to look into how students used and perceived online clinical videos for learning laboratory skills [25]. E-learning laboratory education is offered in a variety of ways. Offering online films on laboratory skills is a popular format among them. Although medical laboratory videos have been shown to improve learning outcomes, there is a lack of study on how to make them more effective. Furthermore, there is little guidance on how to integrate e-learning into the curriculum despite the recommendation that information technology resources be an integral part of supporting the clinical and laboratory skills curriculum [25-44].

Conclusion

This study set out to detect the factors affecting the performance of undergraduate students in the international universities of Africa in a medical laboratory with a view to understanding some of the factors for success that may lead to innovative ways of providing a more successful academic atmosphere for students and university. We found in this study the Father’s occupation, presence with family, and a chronic medical condition during medical studying (Bronchial asthma, Migraine, Insulin-dependent Diabetes Mellitus and corrected congenital heart defect are some examples). admittance to the faculty, whether general or private, secondary school certificate type, sleeping hours per day between 6-8 hours, attendance to all academic and clinical activities and students discovered a statistically significant link between good performance and the medical laboratory students.


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