Introduction

Diabetes mellitus (DM) is a rapidly expanding healthcare burden due to its significant influence on morbidity, mortality, and healthcare expenditures1. According to the International Diabetes Federation (IDF), 537 million people worldwide were estimated to have diabetes in 2021. By 2045, it is anticipated that this number will reach 783 million2. Globally, the prevalence of DM is increasing rapidly, and 80% of DM patients live in low- and middle-income nations (LMICs)3. Type 2 diabetes mellitus (T2DM), which accounts for approximately 90% of all cases of DM, is a diverse collection of illnesses that arise from decreased insulin production and/or resistance, both of which lead to hyperglycemia2. A significant portion of people with type 2 diabetes are obese, and a rise in body fat percentage continues to be a key risk factor for insulin resistance4. Apart from the impact of nutrition and lifestyle, there has been a recent focus on the importance of genetic background in T2DM risk assessment, and several loci were shown to be linked to T2DM risk in many patient cohorts5.

Vitamin D, traditionally recognized for its crucial role in calcium and phosphate homeostasis and bone health, has gained considerable attention in recent years for its pleiotropic effects extending beyond skeletal metabolism6. Emerging evidence from epidemiological and clinical studies increasingly links vitamin D deficiency to an elevated risk of various chronic diseases, including cardiovascular diseases, certain cancers, autoimmune disorders, and notably, T2DM7. Vitamin D is thought to play a role in regulating insulin secretion by directly affecting pancreatic beta-cell function and proliferation. Furthermore, it may enhance insulin sensitivity in peripheral tissues and reduce systemic inflammation, both of which are critical factors in the development and progression of insulin resistance and T2DM8. Vitamin D exerts its effects by attaching to the vitamin D receptor (VDR), a nuclear receptor family ligand-induced transcription factor implicated in several pathological processes9. The vitamin D receptor gene is located on the long arm of chromosome 12 (12q13.1) and is composed of 11 exons10. The 5′ coding region of the VDR gene contains the polymorphic site of the restriction enzyme FokI, and the genotypes of the FokI polymorphisms were found to be FF, Ff, and ff11. The polymorphic form (f) of the polymorphism results in the translation of a longer variant of the VDR protein (427 amino acids), which may function less well than the shorter variant (424 amino acids) translated by the F allele12.

Although VDR FokI gene polymorphisms have been widely studied in the context of vitamin D deficiency and T2DM risk, the conclusions of the study on the role of these polymorphisms are inconsistent and provide contradictory data13. Some studies have found strong associations between gene polymorphisms and these disorders, while others have not14. Given the unique genetic makeup of Ethiopian communities, including different ethnic groups, studying the link between the VDR FokI gene polymorphism and T2DM in this context is essential. While earlier research has investigated the relationship between VDR FokI polymorphisms and T2DM, results have been inconsistent. These discrepancies may be due to numerous factors, including differences in study design, sample size, demographic characteristics, and environmental factors15. In spite of the rising prevalence of T2DM in Ethiopia, inadequate studies have explored the role of VDR FokI polymorphisms in this population16. Given the unique genetic makeup of Ethiopian communities, including different ethnic groups, studying the link between the VDR FokI gene polymorphism and T2DM in this context is essential. This study aimed to investigate the association between FokI gene polymorphisms and type 2 diabetes risk in the Ethiopian population.

Materials and methods

Ethics approval and consent to participate

The institutional review board at the University of Gondar gave the study protocol approval (IOB/327/07/2023) on July 18, 2023. Study participants were enrolled only after informed written consent was obtained from each participant. All the information was collected in an anonymous manner and handled carefully. The guiding principles of the Helsinki Declaration were followed throughout the entire data-gathering process.

Study participants

A hospital-based matched case-control study was carried out at the University of Gondar Comprehensive Specialized Hospital from August to December 2023. Treatment and patient follow-up at the chronic follow-up clinic (CFC) are recommended for severe chronic illnesses, including T2DM. The source population consisted of all CFC patients, and the study participants were T2DM patients who were being monitored. Any nondiabetic, healthy volunteers who were available and matched for age and sex during the research period served as the study’s controls.

Inclusion and exclusion criteria

Patients with type 2 diabetes whose blood glucose test results were confirmed were recruited for this study. The study comprised patients who had been getting follow-up care at the CFC for a minimum of one year, in line with our earlier research17,18. Age- and sex-matched healthy persons without diabetes who had normal blood glucose test results and had the same socioeconomic position and geographic region served as the controls. Individuals with long-term noncommunicable illnesses or persistent infections with viruses or bacteria were not included. Additionally, this study did not include patients who were unwilling or unable to give informed consent.

Sample size determination and sampling technique

Through the use of an independent t test, the sample size was determined using G* Power version 3.1.9.419. The following parameters were used: alpha = 0.05, power (1−β) = 0.8 (80%), effect size (d) = 0.5, allocation ratio N2/N1 = 1, and as similar studies were not conducted in the Ethiopian population. A total of 140 people of both sexes participated in the study; 70 of them were T2DM patients, and the remaining 70 were healthy, non-diabetic controls. From among all registered patients, participants were chosen using basic random sampling techniques using a table of random numbers (TRN) as outlined in our previous research works17,18.

Data collection and laboratory methods

The socio-demographic and behavioral characteristics of both patients and healthy control subjects were taken through a semi-structured interviewer questionnaire. The questionnaire was adapted from the “WHO step-wise approach to chronic disease risk factor surveillance (STEPS)”20. The questionnaire was initially prepared in English, translated into the local language (Amharic) in order to obtain the required information from the respondents, and translated back to English to check for any inconsistencies in the meaning of words by language experts21. A pretest was done among 5% of the sample population among individuals in other hospitals that were not included in the main study. Biochemical tests for glucose and other relevant markers were performed using standardized laboratory methods with established reliability and validity20. Quality control measures were implemented to ensure the accuracy of the laboratory results. The data were collected by professional health workers, such as nurses and laboratory technologists, under the supervision of the principal investigator. The laboratory personnel collected a five milliliter blood sample from the median cubital vein of each participant, including patients and healthy controls, in accordance with safety protocols. Out of the 5 ml sample, 3 ml was maintained in the test tube to allow the blood to coagulate. After the serum was extracted via centrifugation, the tubes were replaced with fresh tubes for biochemical analysis. Using the Dimension EXL 200 completely automated analyzer, enzymatic analyses of glucose were carried out on each test in the diagnostic laboratory of the University of Gondar Comprehensive Specialized Hospital. Participants were categorized as diabetic if their fasting blood sugar (FBG) was ≥ 126 mg/dl, if their fasting blood sugar (RBG) was ≥ 200 mg/dl, or if they were treated with insulin or oral hypoglycemic agents; prediabetic if their FBG was 100–125 mg/dl or if their RBG was 140–199 mg/dl; and normal if their FBG was < 100 mg/dl or if their RBG was < 140 mg/dl21.

Genomic DNA extraction and genotyping

Genomic deoxyribonucleic acid (DNA) was extracted from the final 2 milliliters of blood samples that were collected in ethylenediamine tetra acetic acid (EDTA)-containing tubes from each participant using the nonenzymatic salting-out method17,18. Spectrophotometric measurements were made at 260 and 280 nm to assess the purity and concentration of the extracted genomic DNA, and the quality of the DNA was confirmed using 1% agarose gel electrophoresis22. The FokI genotypes were identified using the forward primer 5′-AGC TGG CCC TGG CAC TGA CTC TGG CTC T-3′ and the reverse primer 5′-ATG GAA ACA CCT TGC TTC TTC TCC CTC-3′. A 25 µl reaction mixture was used for the amplification, including 12.5 µl of the master mix (which included MgCl2, dNTPs, PCR buffer, and Taq polymerase), 1 µl each of the forward and reverse primers, 2 µl of each sample, and 8.5 µl of PCR-grade water to round out the volume. 95 °C was chosen as the starting point for the amplification’s denaturation step. for 5 min, followed by 30 cycles of denaturation at 94 °C for 60 s, annealing at 69 °C for 45 s, extension at 72 °C for 60 s, and a final extension at 72 °C for 7 min21 (Fig. 1).

Fig. 1
figure 1

A sample agarose gel (1%) electrophoresis demonstrating the extracted genomic DNA’s purity. Lanes 2–15 contain isolated genomic DNA; Lane 1 is a 100 bp DNA ladder.

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The polymerase chain reaction (PCR) products were electrophoretically separated for 50 min at 120 V on a 2% agarose gel. The PCR-amplified products (12 µl) were injected into the agarose gel wells after being combined with 3 µl of loading dye. To assess the sizes of the pieces of interest, 3 µl of 2% ethidium bromide was employed for staining, and DNA ladders (100 bp) that represent molecular weight markers were electrophoresed with the DNA fragments. After electrophoresis in 1× Tris-acetate (TAE) EDTA buffer, the gel was seen under a ultraviolet transilluminator17,18. The PCR product of the FokI gene was 265 bp in length (Fig. 2). The PCR-RFLP was conducted by digesting the PCR products using 0.5 µl of Fok1 restriction enzyme at 37 °C for 4 h. For electrophoresis, 5 µl of the digested reaction mixture was then loaded into a 2% agarose gel containing ethidium bromide, run for 1 h at 120 V, and then visualized under UV light. A 100-bp DNA ladder was used for the determination of the digested fragments. The normal F allele remains intact while the mutant F allele splits into two segments measuring 169 and 96 bp. Finally, the 169 bp band (ff), 265 bp band (FF), and both 265 and 169 bp band (Ff) PCR products were generated for the FokI genotype (Fig. 3).

Fig. 2
figure 2

An example of a 2% agarose gel electrophoresis displaying the FokI gene PCR products: Lane 1 has a 100 bp DNA ladder; Lanes 2–24 include FokI PCR products.

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Fig. 3
figure 3

Representative 2% agarose gel electrophoresis showing PCR products of the FokI gene: Lane 1: 100 bp DNA ladder; Lanes 2, 3, 5, 7, 9, 11, 12, and 16–20: homozygous ff genotypes; Lanes 4, 8, 10, 15 and 21: heterozygous Ff genotypes; Lanes 6, 13 and 14: homozygous FF genotypes.

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Statistical analysis

The data was analyzed using STATA version 14. Quantitative data was presented using means and standard deviations (x + s). The continuous variables between patients and controls were compared using a t test for independent samples. To compare the genotype and allele frequency distributions, the chi-square test was employed. Using logistic regression with a 95% confidence interval, the risk correlations between FokI gene polymorphisms and T2DM were assessed. Statistical significance was defined as a p value less than 0.05.

Results

Sociodemographic and behavioral characteristics

The general characteristics of the study participants are summarized in Table 1. The distributions of T2DM patients and nondiabetic healthy controls by sex and age were similar. Thirty-four (48.6%) of the 70 T2DM patients were female, and 36 (51.4%) were male. Similarly, 35 (50.0%) of the 70 healthy controls were female, and 35 (50.0%) were male. The mean ages of the patients in the case and control groups were 58.2 ± 12.1 and 57.6 ± 6.7 years, respectively. The behavioral characteristics of the study participants in relation to alcohol intake, physical exercise, diet, and smoking habits were not significantly different between the cases and controls (p > 0.05).

Table 1 Sociodemographic and behavioral characteristics of the study participants in university of Gondar comprehensive specialized hospital, Northwest ethiopia, 2023.
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Distribution of FokI genotypes and allele frequencies

The FokI genotype frequency distribution in patients with T2DM and healthy controls is given in Table 2. The FokI ff genotype and f allele were more frequent in T2DM patients than in nondiabetic controls. Compared to the control group, the frequency of the homozygous ff genotype in the patients was more than three times higher (odds ratio [OR] 3.16; 95% confidence level [CL] 1.25–7.75; P = 0.012). In T2DM patients compared to nondiabetic controls, the frequency of the f allele was more than twice as high as that of the F allele (OR 2.21; 95% CL 1.35–3.62; P = 0.0015). The genotype distributions were in accordance with the Hardy–Weinberg equilibrium (p > 0.05) in the control groups (X^2 = 3.61), with the f and F allele frequencies recorded at 0.52 0.48, respectively. This suggests that the genotype distribution in the control group supports the reliability of the genotypic data. In the patient group, the frequencies of the ff, Ff, and FF genotypes were 57.1%, 27.1%, and 15.1%, respectively, whereas in the control group, the corresponding values were 32.8%, 38.5%, and 28.5%, respectively. However, compared to those in healthy controls, FokI genotypes Ff and FF were less frequent in T2DM patients (Fig. 4).

Table 2 Distribution of FokI genotypes and allele frequencies of the study participants at the university of Gondar comprehensive specialized hospital, Northwest ethiopia, 2023.
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Fig. 4
figure 4

Distribution of the FokI genotype in T2DM patients and nondiabetic control.

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Associations between FokI genotypes and sociodemographic characteristics

The sociodemographic and behavioral variables of both patients and controls in relation to the FokI genotype distributions are given in Table 3. The FokI genotypes (ff, Ff and FF) in the study groups were assessed for age, sex, residential area, marital status, educational status, family history, religion, occupation, alcohol intake, physical exercise, diet, and smoking habits. None of the variables were significantly related to the genotypes in the study groups (p > 0.05).

Table 3 Association of the FokI genotypes with sociodemographic and behavioral characteristics at the university of Gondar comprehensive specialized hospital, Northwest ethiopia, 2023.
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Discussion

The FokI gene polymorphism is potentially associated with T2DM and could support the progression of diabetes complications24. However, there are conflicting reports regarding FokI gene polymorphisms and T2DM risk13. In the present study, FokI gene polymorphisms were examined in T2DM patients and nondiabetic controls. The frequencies of the ff genotype and f allele were significantly greater in the T2DM patients (57.1% and 70.7%, respectively; P < 0.05) than in the nondiabetic controls (32.8% and 52.1%, respectively; P < 0.05) (Table 2; Fig. 4). Compared with FF genotype carriers, ff genotype carriers had a more than threefold increased risk of developing T2DM (OR 3.16; 95% CL 1.25–7.75; P = 0.12). The frequency of the f allele was more than twice as high as that of the F allele in patients (OR 2.21; 95% CL 1.35–3.62; P = 0.0015) (Table 2). The observed differences in genotype and allele frequency distributions between cases and controls may be attributed to several factors. First, the VDR FokI polymorphism may be associated with altered vitamin D receptor function, leading to differences in insulin sensitivity and glucose metabolism. Second, genetic variations in the VDR gene may interact with other genetic factors or environmental factors, such as dietary habits and physical activity, to influence T2DM risk. Third, the VDR FokI polymorphism may be linked to specific ethnic or geographic subgroups within the Ethiopian population, contributing to the observed differences in allele frequencies.

The results of this study are in agreement with those of case-control studies conducted in Egyptian populations, which included 156 patients with T2DM and 145 healthy control subjects. The study revealed that patients with the ff genotype and f allele (P = 0.001) were significantly more likely to have a high risk of developing T2DM2. Similarly, studies carried out in populations from Sudan25, Saudi Arabia26, Emirate27, Iraq28, India29, Chile30, Egypt31, and Brazil32 showed that the FokI gene ff genotype and f allele were associated with a high incidence of T2DM. Only a portion of the molecular basis for the suggested association between FokI gene polymorphisms and T2DM is known. The gene encoding the VDR gene is located on chromosome 12q12-q1425. VDR expression in preadipocytes suggests a potential mechanism for this impact, which involves the direct interaction of the vitamin D metabolic pathway with cell adiposity development33. The presence of the FokI polymorphism is indicated by the presence or absence of a FokI restriction site inside the ATG transcriptional start site of the VDR gene34. In the presence of the restriction site (f allele), the gene’s transcription proceeds normally; in the absence of the restriction site, the gene’s transcription proceeds at a shorter length. A longer VDR protein seems to have less transcriptional activity, which decreases target cell activation35.

In contrast, the findings of other investigations conducted in Tunisia36, Malaysia37, England38, Italy34, Brazil39, and China40 contrasted with the results of the current investigation, which found no evidence of a substantial correlation between the risk of type 2 diabetes and variations in the FokI gene. Variations in experimental design, small sample size, low statistical power, clinical heterogeneity, patient diagnostic criteria, and interfaces between genetic background and environmental incentives according to geographic variations might all contribute to discrepancies across studies39.

The current evidence shows that gaps in the control of T2DM may be attributed to sociodemographic determinants and behavioral characteristics in addition to genetic risk factors41. An earlier study suggested that demographic and behavioral variables determine racial differences in T2DM incidence42. The findings of the current study showed that the levels of some sociodemographic and behavioral factors were high in diabetic patients. Being widowed, being unemployed, having a low income, having a lower education, having low physical activity, experiencing stress, smoking, and consuming alcohol were more strongly but not significantly associated with T2DM in the case group than in the control group (Table 1). This could be because patients and controls came from the same geographical location and had similar socioeconomic statuses.

The present study also examined the associations of FokI gene polymorphisms with the sociodemographic and behavioral characteristics of both diabetic patients and healthy controls. The results revealed no statistically significant differences among the ff, Ff, and FF genotypes of the FokI gene polymorphisms (Table 3). The findings of this study are in agreement with other studies conducted in populations from Jordan43, Brazil44, and Turkey45, as they were unable to detect any significant association between FokI genotypes and the impact of sociodemographic and behavioral characteristics. On the other hand, studies conducted in Indian46, South African47, and Chinese48 populations reported conflicting results and revealed a correlation between the FokI gene and sociodemographic risk factors such as sex, occupation, and residence. The exact mechanisms by which demographic factors influence FokI gene polymorphisms in T2DM development are complex and not yet fully understood47. However, several studies have proposed possible mechanisms that may explain this relationship, and sociodemographic and behavioral factors could influence FokI gene polymorphisms through gene‒environment interactions, epigenetic modifications, altered gene recombination, and influence on gene expression49.

This study has some limitations. First, in detecting the FokI genotype in individuals with T2DM, the relatively limited sample size may introduce bias. However, further studies with large sample sizes are needed to determine the correlation between FokI gene polymorphisms and disease risk. Second, there are no measurements of plasma vitamin D levels or other VDR genes that correlate directly with the genetic polymorphisms investigated in this study. Third, the absence of Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) testing. While HOMA-IR provides valuable insights into insulin resistance, a key characteristic of Type 2 Diabetes Mellitus, the necessary baseline insulin levels were not collected as part of our original study design due to resource constraints. We acknowledge that the inclusion of HOMA-IR data would have further strengthened our findings, and we recommend its consideration as an important analytical component for future research in this area. The strength of this study is that the risk of T2DM was linked to genetic, sociodemographic, and behavioral characteristics to emphasize the vital significance of its association with the disease. The results of this study will provide a baseline for these areas, but more investigations are required to identify other gene polymorphisms that could be trustworthy predictors of T2DM in this population.

Conclusion

In conclusion, this study provides evidence that the FokI gene polymorphism, specifically the ff genotype and the f allele, is significantly associated with an increased risk of T2DM in the Ethiopian population. These results significantly contribute to the existing body of knowledge by providing direct evidence of this genetic susceptibility in the population understudy, thereby enhancing the global understanding of T2DM pathogenesis. This study underscores the potential role of vitamin D receptor genetic variations in T2DM susceptibility, suggesting that genetic screening for FokI polymorphisms could potentially aid in identifying individuals at higher risk within the Ethiopian context. However, it is important to acknowledge certain limitations of this study, including its hospital-based case-control design and relatively modest sample size, which may limit the generalizability of these findings to the broader Ethiopian population. Additionally, while the FokI polymorphism was found to be significant, future research should explore the interplay of other VDR gene polymorphisms and environmental factors that may contribute to T2DM risk. Further research could also explore the potential for personalized prevention or intervention strategies tailored to individuals with specific genetic predispositions in this population. Such studies would provide deeper insights into the complex genetic architecture of T2DM and inform public health initiatives aimed at disease prevention and management.