Evaluation of potential drug-drug interactions among medications prescribed in primary health-care centers for type 2 diabetes mellitus patients. A cross-sectional study from Palestine

Evaluation of potential drug-drug interactions among medications prescribed in primary health-care centers for type 2 diabetes mellitus patients. 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A cross-sectional study from Palestine Asma Radwan, Naser Shraim, Rowa' AlRamahi, Sabrine Athamneh, Iyad Ali, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8088372/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background and Aim: Drug-drug interactions (DDIs) pose a serious threat to patient safety, especially among type 2 diabetes mellitus (T2DM) patients who frequently experience comorbidities and polypharmacy. This study aimed to assess the prevalence and types of potential DDIs in T2DM patients attending primary healthcare centers and to identify associated risk factors. Methods: A cross-sectional observational study was conducted on 400 T2DM patients from primary health-care centers in Hebron and Bethlehem (July–September 2018). Patient demographics, comorbidities, and medications were recorded. Lexi-Comp® was used to detect DDIs and classify them by severity: A (no known interaction), B (minor), C (moderate), D (major), and X (contraindicated). Results: Among 114 drugs prescribed, 96% of patients had at least one potential DDI (n = 2,627 total; average: 6.6 per prescription). The majority were category C (76.7%), followed by B (11.7%), D (10%), A (1.3%), and X (0.3%). DDIs were significantly associated with older age, comorbidities, polypharmacy, complications, and higher education level (p < 0.05), but not gender, marital status, or smoking. Conclusions: Potential DDIs are common among T2DM patients, particularly those with complex medication regimens. Targeted interventions for high-risk groups and use of interaction screening tools are essential to improve medication safety. Health sciences/Diseases Health sciences/Endocrinology Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Drug-drug interactions type 2 diabetes mellitus polypharmacy prescriptions Palestine Figures Figure 1 1. Introduction Recently, the increasing prevalence of complex treatment regimens and polypharmacy has contributed to a rise in potential drug-drug interactions (DDIs), particularly among chronic disease patients like those with type 2 diabetes mellitus (T2DM) [ 1 ]. Several types of interactions exist: drug-drug, drug-disease, drug-food, drug-alcohol, drug-herbal products, and drug-nutritional status [ 2 ]. A DDI is an alteration of a drug therapy’s effect which leads to an increase or a decrease in the efficacy of one drug caused by the presence of another drug [ 3 ]. Substantial risk of adverse DDIs increased within patients aged over 50 years and receiving three or more medications [ 4 ]. Most of them are likely to be preventable, due to their clearly pharmacologic effect and well documented DDIs in previous clinical studies and reports [ 5 , 6 ]. Generally, DDIs may necessitate dosage adjustment or other medical intervention [ 7 , 8 ]. DDIs can be categorized into two main groups : pharmacokinetic and pharmacodynamics interactions [ 7 ]. T2DM is a global public health challenge, commonly associated with hypertension and dyslipidemia. These comorbidities require long-term medication use, increasing the risk of DDIs. Evidence suggests that T2DM patients are particularly vulnerable to interactions between antidiabetic agents and cardiovascular medications.[ 9 ]. Commonly, it has multiple concomitant disorders including hypertension and dyslipidemia. Recent statistics indicate that there is a two to six times greater risk of cardiovascular death in T2DM patients compared with people without the disease [ 10 , 11 ]. These patients are most susceptible to interference between their anti-diabetic medications and antihypertensive agents, that is, lipid lowering agents interacting with the anti-diabetic agents themselves [ 12 ]. Thus, implementation of appropriate therapeutic intervention, controlling comorbidities and disease management are considerably more complicated and increasingly challenging [ 10 ]. Currently, there is a lack of published research in Palestine that focuses on evaluating potential DDIs among T2DM patients. This study is believed to be the first of its kind to examine and discuss this issue. To minimize the occurrence and consequences of DDIs, it is essential for pharmacists, clinicians, healthcare professionals, and researchers to have greater knowledge and experience with these interactions. By avoiding DDIs, healthcare providers can improve patients' quality of life and achieve more effective patient care, while also reducing healthcare costs. The primary objective of this study was to assess potential DDIs among medications prescribed for T2DM patients at primary healthcare centres in the southern West Bank. 2. Methods 2.1 Study design A cross-sectional observational study design was conducted to evaluate potential DDIs among prescribed medications in primary healthcare centers among T2DM patients. 2.2 Study setting The study enrolled 400 patients diagnosed with T2DM who visited government primary healthcare centres, including outpatient clinics for DM disease in Hebron and Bethlehem, located in the southern region of the West Bank, between July and September 2018. 2.3 Population of the study and selection criteria The study recruited all T2DM patients who visited the primary healthcare centers in the Southern part of the West Bank, specifically Hebron and Bethlehem districts, over a three-month period. These patients attended the centres on a monthly basis to obtain their medications for their chronic condition, free of charge if they had a governmental health insurance. This study included T2DM patients who had at least three months of consecutive follow-up and visited the primary healthcare centers in the Southern part of the West Bank for their medication. Patients who were mentally unstable, critically ill, pregnant, or did not visit the primary healthcare centers for their condition were excluded from the study. 2.4 Sample size To determine the appropriate sample size for this study, the researchers referred to the Ministry of Health records of 2017 in Palestine which reported that the total number of T2DM patients in the Southern districts of West Bank was 13,470, representing approximately one-third of the total T2DM patients in the West Bank (i.e., 41,555). Using these figures, the researchers used the Raosoft sample size calculator ( http://www.raosoft.com/samplesize.html ) to estimate the required sample size with a 5% margin of error and a 95% confidence level. The target sample size for this study was set at approximately 400 T2DM patients. 2.5 Data collection form and management The study recruited patients who regularly visited primary healthcare centers and were using at least one diabetic medication chronically. Participants were asked to participate in the study prior to the beginning of the interview. Data was collected using a questionnaire that was divided into three parts: socio-demographic characteristics, medical history, and management and treatment information. Part one collected information on gender, age, marital status, education, and employment status. Part two collected information on patients' check-ups, complications, and tests. Part three asked patients about their co-morbidities, history of medication use, indications, and frequency. The study only recorded prescribed medications and their dosage regimens from the most recent prescription obtained from MOH clinics. Over-the-counter medications that were not mentioned in the prescriptions were excluded as they were considered to have widely safe use, and it would be difficult to document them. Different types of insulin were grouped as one drug and documented under a single insulin category. Medical records were also consulted to gather additional data about the patients' comorbidities and specific checkups, in addition to the patient interviews. In order to identify potential drug-drug interactions (DDIs), the study utilized the Lexi-Comp® electronic database ("lexi-Comp", 2019), which is an online software interaction checker. This database was used to screen all prescribed medications submitted into the application for potential DDIs, and categorize them according to severity based on their mechanism of action. Each interaction was given a risk rating of A, B, C, D, or X, depending on its clinical significance. For example, a rating of A indicated no known interaction, while a rating of X indicated that the combination of drugs should be avoided. 2.6 Statistical analysis The Statistical Package for Social Sciences (SPSS software version 21) was used for statistical analysis in this study. Continuous data was reported as mean ± standard deviation or median, while frequencies and percentages were used for categorical variables. The Mann-Whitney-U test was used to assess significant differences between two groups of an independent variable on a continuous dependent variable. On the other hand, the Kruskal-Wallis test was used to determine differences between two or more independent variables on a continuous dependent variable. Univariate analysis, which involves exploring single variables, was used to identify factors associated with DDIs. This analysis included descriptive statistics such as frequencies, mean, count, and standard deviation. A P-value of < 0.05 was considered statistically significant for all analyses. 2.7 Ethical compliance and consent Before the study began, the study protocol was approved by the local institutional review boards (IRB) of An-Najah National University (April 4, 2018) and the Palestinian MOH (June 12, 2018). All methods were performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki. The ethical standards were explained to all participants, and they were informed of their right to refuse or discontinue their participation in the study. Informed verbal consent was obtained from all participants before the interviews began. 3. Results 3.1 Socio-demographic characteristics of participating patients All 400 patients who were approached to participate in the study completed the questionnaire, resulting in a response rate of 100%. The majority of patients were over 60 years old (183, 45.8%), while the youngest participant was 38 years old and the oldest was 89. Females accounted for the majority of participants (248, 62.0%), and the majority lived in villages (247, 61.8%). In terms of educational level, the highest percentage of patients had a primary school education (185, 46.3%), while only (47, 11.8%) had a college or university degree (Table 1 ). Table 1 here Table 1 Socio-demographic characteristics of the participant patients Characteristics Frequency Percentage Age 31–40 2 0.5% 41–50 54 13.5% 51–60 161 40.3% > 60 183 45.8% Gender Male 152 38.0% Female 248 62.0% Living place City 153 38.3% Village 247 61.8% Educational level Primary 185 46.3% High school 65 16.3% University 47 11.8% None 103 25.8% 3.2 Drug prescribed Table 2 here Table 2 Top 20 prescribed medications used by patients included in the study: No. Medication Frequency Percentage 1. Metformin 342 85.5% 2. Atorvastatin 298 74.5% 3. Acetyl salicylic acid 296 74.0% 4. Glimepiride 177 44.3% 5. Enalapril 129 32.3% 6. Amlodipine 122 30.5% 7. Ranitidine 112 28.0% 8. Bisoprolol 107 26.8% 9. Furosemide 106 26.5% 10. Carbamazepine 83 20.8% 11. Alfacalcidol 80 20.0% 12. Calcium 53 13.3% 13. Losartan 47 11.8% 14. Allopurinol 44 11.0% 15. Clopidogrel 43 10.8% 16. Hydrochlorothiazide 35 8.8% 17. Omeprazole 35 8.8% 18. Atenolol 32 8.0% 19. Spironolactone 23 5.8% 20. Valsartan 21 5.3% 3.3 Co-morbid conditions The DM participating patients had a range of co-morbid conditions, with cardiovascular diseases, dyslipidemia, gout, infectious diseases caused by pathogenic microorganisms, and thyroid disease being the most commonly diagnosed conditions. The prevalence of DDIs in patients with comorbidities was 90.5%, with the majority of these patients having cardiovascular diseases (77.3%) and dyslipidemia (64.5%). Gout, infectious diseases, thyroid disease, and asthma also contributed to the prevalence of DDIs, with 12.0%, 9.5%, 5.5%, and 3.5% of patients respectively. Table 3 provides a detailed list of the most commonly occurring co-morbidities among DM patients. Table 3 Concomitant medical conditions and comorbidities among DM patients No. Disease Frequency Percentage (%) 1. Cardiovascular disease 309 77.3% 2. Dyslipidemia 258 64.5% 3. Gout 48 12.0% 4. Infectious disease 38 9.5% 5. Thyroid 22 5.5% 6. Asthma 14 3.5% 7. Benign Prostatic Hypertrophy 12 3.0% 8. GI Upset (chronic or transient) 10 2.5% 9. Allergy (chronic or transient) 10 2.5% 10. Neurologic disorders * 7 1.8% 11. Cancer 3 0.8% 12. Surgery 2 0.5% 13. Renal disease 1 0.3% * All diseases of the brain, spine, and the nerves that connect them, affect millions of people each year Table 3 here 3.4 Most common combination The primary drug interaction observed in this study was the combination of Acetyl salicylic acid with Metformin, which accounted for 61.5% of all potential drug interactions. The second most common interaction was Glimepiride with Metformin, which occurred in 40.5% of cases. Further details on the top 10 potential drug interactions and their descriptions can be found in Table 4 . Table 4 Top 10 potential DDIs. No. Drug-drug interactions Percent Risk rating Severity Cause and effect 1. ASA/ Metformin 61.5% C Moderate Salicylates may enhance the hypoglycemic effect of blood glucose lowering agents. 2. Glimepiride/Metformin 40.5% C Moderate Antidiabetic agents may enhance the hypoglycemic effect of hypoglycemia-associated agents. 3. Glimepiride/ASA 33.0% C Moderate Salicylates may enhance the hypoglycemic effect of blood glucose lowering agents. 4. Metformin/ Enalapril 28.3% C Moderate ACE inhibitors may enhance the adverse / toxic effect of metformin. This includes both a risk for hypoglycemia and for lactic acidosis. 5. Enalapril/ ASA 24.3% C Moderate Salicylates may enhance the nephrotoxic effect of ACE inhibitors. Salicylates may diminish the therapeutic effect of ACE inhibitors. 6. Insulin/ Metformin 20.5% C Moderate Antidiabetic agents may enhance the hypoglycemic effect of hypoglycemia associated agents. 7. ASA/ Furosemide 20.5% C Moderate Salicylates may diminish the diuretic effect of Loop Diuretics. Loop Diuretics may increase the serum concentration of Salicylates. 8. Metformin /Furosemide 19.3% C Moderate Hyperglycemia associated agents may diminish the therapeutic effect of antidiabetic agents. 9. Atorvastatin/Carbamazepine 15.8% D Slightly severe Carbamazepine (CYP3A4 Inducers / strong) may increase the metabolism of Atorvastatin (CYP3A4 Substrates). 10. Glimepiride/Enalapril 14.5% B Minor ACE inhibitors may enhance the hypoglycemic effect of blood glucose lowering agents Table 4 here 3.5 Evaluation of potential DDIs During the study, a total of 2627 potential interactions were identified. Among the 400 patients, 96% had at least one potential DDI, while 16 patients had no potential DDI. Figure 1 shows the percentage of the number of DDIs per prescription. Table 5 presents the prevalence of DDIs among the participating patients and explains how the percentage of DDIs (96%) was distributed among associated factors. The study found that about 60% of DDIs occurred in female patients, and a little less than half of DDIs were associated with patients over 60 years old. One-third of DDIs were associated with patients with nephropathy as a complication of T2DM. The majority of DDIs occurred in patients who had polypharmacy and comorbidities in their cases (73.0% and 90.5%, respectively). Out of the total number of identified potential interactions (2627), 1.33% were classified as A, 11.72% were classified as B, 76.67% were classified as C, 10.01% were classified as D, and 0.27% were classified as X, according to risk rating classification. Table 5 The prevalence of DDIs among associated factors. Characteristic Category Frequency of DDIs Prevalence of DDIs Gender Men Female 145 239 36.2% 59.8% Age 31–40 41–50 51–60 > 60 2 46 156 180 0.5% 11.5% 39.0% 45.0% Complications N.A Nephropathy Neuropathy Retinopathy Diabetic foot 160 122 78 22 2 40.0% 30.5% 19.5% 5.5% 0.5% Polypharmacy 10 61 291 32 15.0% 73.0% 8.0% Comorbidities Yes No 362 22 90.5% 5.5% Figure 1 here Table 5 : here 3.6 Factors associated with potential DDIs The results of the univariate analysis indicated that there was a statistically significant correlation between age and the number of potential interactions (p value < 0.001). Educational level, comorbidities, number of medications, and complications were also found to be significantly associated with the number of potential DDIs (p value < 0.001 for each one). However, no significant relationship was observed between gender and the number of potential DDIs (p value = 0.404), marital status and the number of potential DDIs (p value = 0.088), or smoking and the number of potential DDIs (p value = 0.279) as shown in Table 6 . Furthermore, this prospective observational study demonstrated a positive correlation between age and two variables: the number of prescribed medications and complications. Table 6 Factors associated with potential DDIs. Characteristics Frequency N = 400 Number of DDIs Median (Q1 – Q3) P-value Gender Male Female 152 (38%) 248 (62%) 5 (3–8.75) 6 (3–9) 0.404 Age category 31–40 41–50 51–60 > 60 2 (0.5%) 54 (13.5%) 161 (40.25%) 183 (45.75%) 2.5 (2–3) 3 (1–6) 5 (3–8) 6 (3–10) 0.000** Marital status Single Married widowed Divorced 4 (1%) 357 (89.25%) 37 (9.25%) 2 (0.5%) 6 (4 -11.5) 5 (3–9) 7 (3–14) 2 (1–3) 0.088 Educational level Primary High school Bachelor None 185 (46.25%) 65 (16.25%) 47 (11.75%) 103 (25.75% ) 6 (3–9) 4 (1–8) 4 (1–6) 6 (3–10) 0.000** Smoking Smoker Non smoker 347 (86.75%) 53 (13.25%) 5 (2.5–8) 5 (3–9) 0.279 Comorbidities Yes No 373 (93.3%) 6 (3–9) 0.000** 27 (6.8%) 1 (1–3) Number of medications 10 76 (19%) 292 (73%) 32 (8%) 1 (1–3) 6 (4–9) 16.5 (13.25–20) 0.000** Complications Yes No 227 (65.8%) 173 (43.2%) 7 (3–10) 4 (1.5–6) 0.000** Table 6 here 3.7 Controlled vs uncontrolled HbA1c Out of the 400 T2DM patients included in the study, it was found that 396 (99.0%) had undergone HbA1c% testing in the past 3 months, while the remaining 4 (1.0%) patients had not. Among the 396 patients who had undergone the HbA1c% test, only 107 (26.8%) cases had controlled glycemia, while 289 (72.3%) cases had uncontrolled glycemia. Table 7 illustrates that there is a significant association between the presence of HbA1c and complications (P-value = 0.000), indicating that patients with uncontrolled glycemia were more likely to experience complications. However, there were no significant differences observed between the HbA1c test and the presence of potential DDIs (P-value = 0.03). Table 7 Association relationship between HbA1c and complication / DDIs HbA1c Sig (p- value) Controlled A1C 7% No. (%) 107 (26.8) 289 (72.3) Complications 39 (36.4) 186 (64.4) 0.000 No Complications 68 (63.6) 103 (35.6) DDIs 100 (93.5) 280 (96.9) 0.03 No DDIs 7 (6.5) 9 (3.1) 4. Discussion In our study, the highest incidence of DDIs was observed in patients over 60 years old, accounting for 45.0% of cases. This finding is consistent with a previous study conducted by Dinesh K U et al. (2007), which reported a high prevalence of DDIs among patients over 50 years old [ 13 ]. This finding is also consistent with a large population-based study conducted in Sweden by Astrand, Bengt et al., in 2006 [ 14 ]. The incidence of DDIs showed a positive correlation with age groups, likely due to changes in pharmacokinetic and pharmacodynamic interactions that occur as individuals age. Moreover, elderly patients often have multiple diseases, which require them to take more medications to manage their conditions, thereby increasing their susceptibility to potential DDIs. The present study showed that the most commonly prescribed medications were metformin, atorvastatin, acetyl salicylic acid, glimepiride, and enalapril. These findings were similar to those reported in a study by Dinesh et al. conducted on 182 patients and 685 different medications, where metformin was also found to be the most commonly prescribed medication, followed by enalapril and atenolol [ 13 ]. The current findings were consistent with a study conducted by the Stage research team in 2015, which also reported that metformin was the most commonly prescribed medication. This could be attributed to the fact that metformin is considered the mainstay drug for T2DM patients and the first-choice treatment, as it has been shown to decrease the mortality rate associated with diabetic patients [ 15 ]. Our study revealed a significant association between the presence of comorbidities and DDIs in T2DM patients, as 90.5% of the identified potential interactions were found in patients with comorbidities. Among the comorbidities, the most common were cardiovascular disease, dyslipidemia, gout, infectious diseases, and thyroid disease, in descending order of frequency.Sanker et al. reported that infections were the most common disease associated with T2DM patients, followed by hypertension and dyslipidemia. [ 1 ] The observed difference in the prevalence of comorbidities with DMT2 could be attributed to various factors such as differences in study participants, physiological factors, and pharmacological aspects. The present study demonstrated that the combination of acetyl salicylic acid with metformin had the highest prevalence of DDIs among T2DM patients. This finding is consistent with the fact that acetyl salicylic acid can enhance the hypoglycemic effect of blood glucose lowering agents, such as metformin[ 16 ]. Sanker et al. also reported that the combination of acetyl salicylic acid and insulin was associated with a high prevalence of moderate DDIs [ 1 ]. Metformin and enalapril was the most common combination was appeared in another study conducted in Palestine [ 17 ]. These medications are typically recommended as the initial treatment options for patients with diabetes and cardiovascular disease. The current study included 400 patients who were prescribed a total of 114 different medications. It was observed that 96% of the patients had at least one potential DDI. These findings were consistent with previous studies conducted by Otachi in Kenya, where 96% of prescriptions had at least one potential DDI [ 18 ], and by Rodrigues et al. in Saudi Arabia, where approximately 90% of prescriptions had at least one potential DDI [ 19 ]. However, Sanker et al. (2015) reported a lower percentage, with only approximately two-thirds of included patients having at least one potential DDI. The differences in the prevalence of DDIs may be attributed to healthcare providers' poor knowledge of basic information associated with DDIs, as well as differences in patients' compliance with their prescriptions in various countries [ 1 ]. The current study revealed the presence of all five classes of risk rating for DDIs, with moderate and minor interactions being the most common. These interactions only require appropriate rational drug therapy and continuous monitoring as clinical management actions. Additionally, the prevalence of moderate DDIs (76.67%) in this study was consistent with a previous study published by Rodrigues et al., where 74% of reported DDIs were classified as moderate [ 19 ]. A study conducted by Dinesh et al. in 2007 reported a result that was similar to ours, with 92.1% of potential DDIs being moderate in severity[ 20 ]. Our study revealed a low prevalence of potential major DDIs, accounting for only 0.27% of all identified DDIs, which involved seven out of 400 T2DM patients. Although even a single major DDI can pose a significant risk to the patient's health and wellbeing, the therapeutic effects of drugs may be altered if given together. This finding is in contrast to the results of a previous study conducted by Otachi in 2016, which reported a higher prevalence of major DDIs at 4%[ 18 ]. In contrast to our finding, a study conducted in 2015 by Samardzic et al. [ 21 ] indicated a complete absence of Category D and X DDIs, This outcome may have resulted from the exclusion of certain drugs that typically cause these severe interactions or heightened prescriber vigilance regarding potential DDIs with co-prescribed anti-diabetic medications.. The results of our study showed a significant association between the number of medications per prescription and the incidence of potential DDIs. Specifically, we observed that prescriptions containing a larger number of medications were more likely to have potential DDIs. About 73% of the potential DDIs in our study were found in prescriptions containing 5–10 medications, with an average of 6 interactions per prescription. These findings are consistent with previous studies, such as the study conducted in Nairobi by Otachi in 2016, which reported an average of 5 potential DDIs per prescription [ 18 ]. The high average of 6 drugs in polypharmacy was not unexpected, given that T2DM is a commonly coexisting disease with other conditions, which increases the likelihood of potential DDIs in their prescriptions. Johnell and Klarin, reported a strong relationship between the number of prescribed drugs and the possibility of DDI incidences [ 22 ]. The findings of our study are consistent with the results reported by Dookeeram and colleagues, who found that patients with T2DM and other comorbidities tend to have multiple medications prescribed, leading to polypharmacy. This is because many of these chronic conditions require long-term management and patients may require several medications to control their symptoms. Furthermore, Dookeeram and co-authors also reported that polypharmacy and certain common chronic diseases such as hypertension, diabetes mellitus, and psychiatric diseases were significantly associated with potential DDIs [ 23 ]. Out of the 400 patients, 396 had regular HbA1c tests, revealing that only 107 (26.8%) had controlled glycemia, while 289 (72.3%) had uncontrolled glycemia. Our study showed a correlation between HbA1c levels and the occurrence of complications, with controlled glycemia associated with fewer complications, and higher HbA1c levels correlated with increased risk of complications. A study conducted in 2017 by Hammad MA et al. reported that 52.9% of patients had controlled glycemia while 47.1% had uncontrolled glycemia, which differs from our study where only 26.8% had controlled glycemia[ 24 ]. The differences in the results between the two studies may be attributed to several factors, such as poor adherence of the patients to the prescribed doses and regimens, as well as the insufficient adherence of healthcare providers to the IDF guidelines for managing T2DM patients. Based on our data analysis, we found no correlation between HbA1c levels and potential DDIs in T2DM patients. Our results show that the prevalence of potential DDIs was similar for patients with both low and high HbA1c levels. Therefore, it can be concluded that there is no direct relationship between the level of HbA1c and the occurrence of potential DDIs in T2DM patients. This finding is consistent with a study conducted by Samardzic and Bacic-Vrca in 2015 [ 21 ]. Our study showed that patients with uncontrolled glycemia (> 7%) had more complications and comorbidities, leading to a higher number of medications in addition to their anti-diabetic drugs. On the other hand, patients with controlled glycemia (≤ 7%) had fewer medications, but more anti-diabetic drugs requiring caution due to their complex mechanism of action. This could explain why there was no difference in the occurrence of DDIs with respect to HbA1c levels. These findings are in line with the complexity of managing T2DM and the need for individualized treatment regimens. 5. Limitations and strength of the study As a prospective study, we faced challenges in obtaining accurate information from patients during interviews, which could potentially lead to misinformation about their prescribed medications and decrease the accuracy of the research. Additionally, we did not clinically monitor the occurrence of DDIs in patients. Furthermore, the generalization of the results is limited as the sample was taken only from the Southern part of the West Bank and may not be representative of all Palestinian DM patients. However, it should be noted that this study is the first of its kind in Palestine and the Arab world, and one of the few worldwide. These findings provide valuable baseline data that can be used to determine the prevalence of potential DDIs in DM patients and identify associated factors, as well as design and implement suitable interventions, educational programs, and additional related studies. 6. Conclusions T2DM patients at primary healthcare centers are at high risk of potential DDIs, particularly those prescribed for their comorbidities and complications. Metformin and ASA were the most commonly implicated drugs. Most interactions were of moderate severity (class C). Factors associated with DDIs among T2DM patients include age, comorbidities, complications, number of medications, and education level. This high prevalence underscores the need for increased awareness among healthcare providers about DDIs. Updating data, ongoing research, educational programs, and improved counseling can help prevent improper use of medications and minimize DDIs in diabetic patients. Declarations Ethics approval and consent to participate The procedures involving human participants were reviewed and approved by the IRBs of An-Najah National University and the Palestinian MOH, as detailed in Section 2.7 . All methods were performed in accordance with the relevant guidelines and regulations. Consent for publication Not applicable Competing interests The authors declare no competing interests. Additional information A thesis has previously been published [25] This study was based on a master's thesis in clinical pharmacy titled "Evaluation of Potential Drug-Drug Interactions among Medications Prescribed in Primary Health-Care Centers for Type 2 Diabetes Mellitus Patients: A Cross-Sectional Study from Palestine." The thesis is available at https://repository.najah.edu/items/a8108736-a266-44ac-b3be-c08e8660279e Funding Not applicable Author Contribution NS and RA were involved in the conception and design of the work, analysis and interpretation of data, drafting and final approval the manuscript. SA was involved in collecting data, analysis and interpretation of data, and drafting of the manuscript. AR, IA, MA, AE and FS drafted the manuscript. NS has corresponding author status NS can be reached at [email protected] . Acknowledgement The study authors express their gratitude to the participants who took part in the study. 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Pharmacological and Therapeutic analysis of anti-diabetic and antihypertensive drugs among diabetic hypertensive patients in Palestine. J. Islamic Univ. Gaza (Natural Sci. Series) . 12 (2), 35–57 (2004). Otachi, E. O. Potential drug-drug interactions among patients with type 2 diabetes and hypertension in Kisii Teaching and Referral Hospital, Kenya (University of Nairobi, 2016). Rodrigues, A. T. et al. Clinical relevancy and risks of potential drug–drug interactions in intensive therapy. Saudi Pharm. J. 23 (4), 366–370 (2015). Dinesh, K. U. et al. Pattern of potential drug-drug interactions in diabetic out-patients in a tertiary care teaching hospital in Nepal. Med. J. Malay. 62 (4), 294–298 (2007). Samardzic, I. & Bacic-Vrca, V. Incidence of potential drug-drug interactions with antidiabetic drugs. Die Pharmazie . 70 (6), 410–415 (2015). Johnell, K. & Klarin, I. The relationship between number of drugs and potential drug-drug interactions in the elderly: a study of over 600,000 elderly patients from the Swedish Prescribed Drug Register. Drug Saf. 30 (10), 911–918 (2007). Dookeeram, D. et al. Polypharmacy and potential drug-drug interactions in emergency department patients in the Caribbean. Int. J. Clin. Pharm. 39 (5), 1119–1127 (2017). Hammad, M. A. et al. Drug-drug Interaction-related Uncontrolled Glycemia. J Pharm. Bioallied Sci 2017 , 9(4):221–228 . Additional Declarations No competing interests reported. Supplementary Files Supplementary1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 31 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers invited by journal 06 Mar, 2026 Editor assigned by journal 03 Mar, 2026 Editor invited by journal 20 Nov, 2025 Submission checks completed at journal 13 Nov, 2025 First submitted to journal 13 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8088372","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":602770004,"identity":"abe625a4-7cb5-4efc-b81e-d48cc05c571a","order_by":0,"name":"Asma Radwan","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Asma","middleName":"","lastName":"Radwan","suffix":""},{"id":602770008,"identity":"8ebe221b-f295-41bd-a8e6-5a589006fac9","order_by":1,"name":"Naser Shraim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYHACNiCWYOAHkgaMDUDyALFaJBtI1AJUDlJJlBZzBvZnjytqLKKNbzc/KPi5g0GO70YCfi2WDTzmhmeOSeRuu3PMwLD3DIOxJCEtBgd42CQb2IBabiQYGPC2MSRuIKyF/Zlkwz+J3M0z0j8Y/m1jqCdCC4OZZGObRO4GiRwDY6AtCQZEOAyopU8id8aNnAJj2TYJw5lnHhDjsG91uf0z0rcZvm2zkec7TsAWBnmEmWwGoDglCTATcNEoGAWjYBSMVAAA6upFrViLTMYAAAAASUVORK5CYII=","orcid":"","institution":"An-Najah National University","correspondingAuthor":true,"prefix":"","firstName":"Naser","middleName":"","lastName":"Shraim","suffix":""},{"id":602770011,"identity":"275bf5e6-3615-4c35-8c53-3ecb2b655fb2","order_by":2,"name":"Rowa' AlRamahi","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Rowa'","middleName":"","lastName":"AlRamahi","suffix":""},{"id":602770013,"identity":"ee7385a8-9437-4a38-9a64-b75cf31c8612","order_by":3,"name":"Sabrine Athamneh","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Sabrine","middleName":"","lastName":"Athamneh","suffix":""},{"id":602770015,"identity":"0583b8e4-bade-4678-9c91-3990577f2694","order_by":4,"name":"Iyad Ali","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Iyad","middleName":"","lastName":"Ali","suffix":""},{"id":602770020,"identity":"68d4cb4f-1b5a-4e87-9a21-3f636b53c6c9","order_by":5,"name":"Fairouz Abu Shaikha","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Fairouz","middleName":"Abu","lastName":"Shaikha","suffix":""},{"id":602770036,"identity":"4cc0a1e8-4a46-4974-9284-e73fabbe8427","order_by":6,"name":"Ahmad Eid","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Eid","suffix":""},{"id":602770045,"identity":"22d4c38f-7940-4bd7-b70a-3968304f8bfc","order_by":7,"name":"Murad Abualhasan","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Murad","middleName":"","lastName":"Abualhasan","suffix":""}],"badges":[],"createdAt":"2025-11-11 15:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8088372/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8088372/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104414076,"identity":"d6bad330-aa90-4104-92b8-f7da8174dfec","added_by":"auto","created_at":"2026-03-11 13:06:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53728,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentages of the number of potential DDIs for patients\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8088372/v1/8d3047801c2c94c2f9417369.png"},{"id":104417201,"identity":"88562414-c635-4b44-a368-877d48fc9581","added_by":"auto","created_at":"2026-03-11 13:18:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1441790,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8088372/v1/68f0adf5-4560-488e-8b9e-6e61a72f9d73.pdf"},{"id":104414147,"identity":"53cb0632-a4c2-4b08-990e-df0edeca3406","added_by":"auto","created_at":"2026-03-11 13:06:55","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":71718,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8088372/v1/a081230f032cb2aae12d687d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of potential drug-drug interactions among medications prescribed in primary health-care centers for type 2 diabetes mellitus patients. A cross-sectional study from Palestine","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRecently, the increasing prevalence of complex treatment regimens and polypharmacy has contributed to a rise in potential drug-drug interactions (DDIs), particularly among chronic disease patients like those with type 2 diabetes mellitus (T2DM) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Several types of interactions exist: drug-drug, drug-disease, drug-food, drug-alcohol, drug-herbal products, and drug-nutritional status [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA DDI is an alteration of a drug therapy\u0026rsquo;s effect which leads to an increase or a decrease in the efficacy of one drug caused by the presence of another drug [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Substantial risk of adverse DDIs increased within patients aged over 50 years and receiving three or more medications [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Most of them are likely to be preventable, due to their clearly pharmacologic effect and well documented DDIs in previous clinical studies and reports [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenerally, DDIs may necessitate dosage adjustment or other medical intervention [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. DDIs can be categorized into two main groups : pharmacokinetic and pharmacodynamics interactions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eT2DM is a global public health challenge, commonly associated with hypertension and dyslipidemia. These comorbidities require long-term medication use, increasing the risk of DDIs. Evidence suggests that T2DM patients are particularly vulnerable to interactions between antidiabetic agents and cardiovascular medications.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Commonly, it has multiple concomitant disorders including hypertension and dyslipidemia. Recent statistics indicate that there is a two to six times greater risk of cardiovascular death in T2DM patients compared with people without the disease [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These patients are most susceptible to interference between their anti-diabetic medications and antihypertensive agents, that is, lipid lowering agents interacting with the anti-diabetic agents themselves [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Thus, implementation of appropriate therapeutic intervention, controlling comorbidities and disease management are considerably more complicated and increasingly challenging [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, there is a lack of published research in Palestine that focuses on evaluating potential DDIs among T2DM patients. This study is believed to be the first of its kind to examine and discuss this issue. To minimize the occurrence and consequences of DDIs, it is essential for pharmacists, clinicians, healthcare professionals, and researchers to have greater knowledge and experience with these interactions. By avoiding DDIs, healthcare providers can improve patients' quality of life and achieve more effective patient care, while also reducing healthcare costs. The primary objective of this study was to assess potential DDIs among medications prescribed for T2DM patients at primary healthcare centres in the southern West Bank.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eA cross-sectional observational study design was conducted to evaluate potential DDIs among prescribed medications in primary healthcare centers among T2DM patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study setting\u003c/h2\u003e \u003cp\u003eThe study enrolled 400 patients diagnosed with T2DM who visited government primary healthcare centres, including outpatient clinics for DM disease in Hebron and Bethlehem, located in the southern region of the West Bank, between July and September 2018.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Population of the study and selection criteria\u003c/h2\u003e \u003cp\u003eThe study recruited all T2DM patients who visited the primary healthcare centers in the Southern part of the West Bank, specifically Hebron and Bethlehem districts, over a three-month period. These patients attended the centres on a monthly basis to obtain their medications for their chronic condition, free of charge if they had a governmental health insurance.\u003c/p\u003e \u003cp\u003eThis study included T2DM patients who had at least three months of consecutive follow-up and visited the primary healthcare centers in the Southern part of the West Bank for their medication. Patients who were mentally unstable, critically ill, pregnant, or did not visit the primary healthcare centers for their condition were excluded from the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sample size\u003c/h2\u003e \u003cp\u003eTo determine the appropriate sample size for this study, the researchers referred to the Ministry of Health records of 2017 in Palestine which reported that the total number of T2DM patients in the Southern districts of West Bank was 13,470, representing approximately one-third of the total T2DM patients in the West Bank (i.e., 41,555). Using these figures, the researchers used the Raosoft sample size calculator (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.raosoft.com/samplesize.html\u003c/span\u003e\u003cspan address=\"http://www.raosoft.com/samplesize.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to estimate the required sample size with a 5% margin of error and a 95% confidence level. The target sample size for this study was set at approximately 400 T2DM patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data collection form and management\u003c/h2\u003e \u003cp\u003eThe study recruited patients who regularly visited primary healthcare centers and were using at least one diabetic medication chronically. Participants were asked to participate in the study prior to the beginning of the interview. Data was collected using a questionnaire that was divided into three parts: socio-demographic characteristics, medical history, and management and treatment information. Part one collected information on gender, age, marital status, education, and employment status. Part two collected information on patients' check-ups, complications, and tests. Part three asked patients about their co-morbidities, history of medication use, indications, and frequency.\u003c/p\u003e \u003cp\u003eThe study only recorded prescribed medications and their dosage regimens from the most recent prescription obtained from MOH clinics. Over-the-counter medications that were not mentioned in the prescriptions were excluded as they were considered to have widely safe use, and it would be difficult to document them. Different types of insulin were grouped as one drug and documented under a single insulin category. Medical records were also consulted to gather additional data about the patients' comorbidities and specific checkups, in addition to the patient interviews.\u003c/p\u003e \u003cp\u003eIn order to identify potential drug-drug interactions (DDIs), the study utilized the Lexi-Comp\u0026reg; electronic database (\"lexi-Comp\", 2019), which is an online software interaction checker. This database was used to screen all prescribed medications submitted into the application for potential DDIs, and categorize them according to severity based on their mechanism of action. Each interaction was given a risk rating of A, B, C, D, or X, depending on its clinical significance. For example, a rating of A indicated no known interaction, while a rating of X indicated that the combination of drugs should be avoided.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe Statistical Package for Social Sciences (SPSS software version 21) was used for statistical analysis in this study. Continuous data was reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median, while frequencies and percentages were used for categorical variables. The Mann-Whitney-U test was used to assess significant differences between two groups of an independent variable on a continuous dependent variable. On the other hand, the Kruskal-Wallis test was used to determine differences between two or more independent variables on a continuous dependent variable. Univariate analysis, which involves exploring single variables, was used to identify factors associated with DDIs. This analysis included descriptive statistics such as frequencies, mean, count, and standard deviation. A P-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant for all analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Ethical compliance and consent\u003c/h2\u003e \u003cp\u003eBefore the study began, the study protocol was approved by the local institutional review boards (IRB) of An-Najah National University (April 4, 2018) and the Palestinian MOH (June 12, 2018). All methods were performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki. The ethical standards were explained to all participants, and they were informed of their right to refuse or discontinue their participation in the study. Informed verbal consent was obtained from all participants before the interviews began.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Socio-demographic characteristics of participating patients\u003c/h2\u003e \u003cp\u003eAll 400 patients who were approached to participate in the study completed the questionnaire, resulting in a response rate of 100%. The majority of patients were over 60 years old (183, 45.8%), while the youngest participant was 38 years old and the oldest was 89. Females accounted for the majority of participants (248, 62.0%), and the majority lived in villages (247, 61.8%). In terms of educational level, the highest percentage of patients had a primary school education (185, 46.3%), while only (47, 11.8%) had a college or university degree (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of the participant patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving place\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVillage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Drug prescribed\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 20 prescribed medications used by patients included in the study:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtorvastatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcetyl salicylic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlimepiride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnalapril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmlodipine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRanitidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBisoprolol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFurosemide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbamazepine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlfacalcidol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLosartan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAllopurinol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClopidogrel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydrochlorothiazide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOmeprazole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtenolol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpironolactone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValsartan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Co-morbid conditions\u003c/h2\u003e \u003cp\u003eThe DM participating patients had a range of co-morbid conditions, with cardiovascular diseases, dyslipidemia, gout, infectious diseases caused by pathogenic microorganisms, and thyroid disease being the most commonly diagnosed conditions. The prevalence of DDIs in patients with comorbidities was 90.5%, with the majority of these patients having cardiovascular diseases (77.3%) and dyslipidemia (64.5%). Gout, infectious diseases, thyroid disease, and asthma also contributed to the prevalence of DDIs, with 12.0%, 9.5%, 5.5%, and 3.5% of patients respectively. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides a detailed list of the most commonly occurring co-morbidities among DM patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConcomitant medical conditions and comorbidities among DM patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfectious disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThyroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenign Prostatic Hypertrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGI Upset (chronic or transient)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAllergy (chronic or transient)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeurologic disorders *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRenal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* All diseases of the brain, spine, and the nerves that connect them, affect millions of people each year\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Most common combination\u003c/h2\u003e \u003cp\u003eThe primary drug interaction observed in this study was the combination of Acetyl salicylic acid with Metformin, which accounted for 61.5% of all potential drug interactions. The second most common interaction was Glimepiride with Metformin, which occurred in 40.5% of cases. Further details on the top 10 potential drug interactions and their descriptions can be found in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 potential DDIs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrug-drug interactions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eRisk rating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSeverity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eCause and effect\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASA/ Metformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eSalicylates may enhance the hypoglycemic effect of blood glucose lowering agents.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlimepiride/Metformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e40.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eAntidiabetic agents may enhance the hypoglycemic effect of hypoglycemia-associated agents.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlimepiride/ASA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e33.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eSalicylates may enhance the hypoglycemic effect of blood glucose lowering agents.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetformin/ Enalapril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e28.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eACE inhibitors may enhance the adverse / toxic effect of metformin. This includes both a risk for hypoglycemia and for lactic acidosis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnalapril/ ASA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e24.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eSalicylates may enhance the nephrotoxic effect of ACE inhibitors. Salicylates may diminish the therapeutic effect of ACE inhibitors.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsulin/ Metformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e20.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eAntidiabetic agents may enhance the hypoglycemic effect of hypoglycemia associated agents.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASA/ Furosemide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e20.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eSalicylates may diminish the diuretic effect of Loop Diuretics. Loop Diuretics may increase the serum concentration of Salicylates.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetformin /Furosemide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eHyperglycemia associated agents may diminish the therapeutic effect of antidiabetic agents.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtorvastatin/Carbamazepine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSlightly severe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eCarbamazepine (CYP3A4 Inducers / strong) may increase the metabolism of Atorvastatin (CYP3A4 Substrates).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlimepiride/Enalapril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e14.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eACE inhibitors may enhance the hypoglycemic effect of blood glucose lowering agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e here\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Evaluation of potential DDIs\u003c/h2\u003e \u003cp\u003eDuring the study, a total of 2627 potential interactions were identified. Among the 400 patients, 96% had at least one potential DDI, while 16 patients had no potential DDI. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the percentage of the number of DDIs per prescription. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the prevalence of DDIs among the participating patients and explains how the percentage of DDIs (96%) was distributed among associated factors. The study found that about 60% of DDIs occurred in female patients, and a little less than half of DDIs were associated with patients over 60 years old. One-third of DDIs were associated with patients with nephropathy as a complication of T2DM. The majority of DDIs occurred in patients who had polypharmacy and comorbidities in their cases (73.0% and 90.5%, respectively). Out of the total number of identified potential interactions (2627), 1.33% were classified as A, 11.72% were classified as B, 76.67% were classified as C, 10.01% were classified as D, and 0.27% were classified as X, according to risk rating classification.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe prevalence of DDIs among associated factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency of DDIs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrevalence of DDIs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145\u003c/p\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.2%\u003c/p\u003e \u003cp\u003e59.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;40\u003c/p\u003e \u003cp\u003e41\u0026ndash;50\u003c/p\u003e \u003cp\u003e51\u0026ndash;60\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e46\u003c/p\u003e \u003cp\u003e156\u003c/p\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5%\u003c/p\u003e \u003cp\u003e11.5%\u003c/p\u003e \u003cp\u003e39.0%\u003c/p\u003e \u003cp\u003e45.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComplications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN.A\u003c/p\u003e \u003cp\u003eNephropathy\u003c/p\u003e \u003cp\u003eNeuropathy\u003c/p\u003e \u003cp\u003eRetinopathy\u003c/p\u003e \u003cp\u003eDiabetic foot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003cp\u003e122\u003c/p\u003e \u003cp\u003e78\u003c/p\u003e \u003cp\u003e22\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.0%\u003c/p\u003e \u003cp\u003e30.5%\u003c/p\u003e \u003cp\u003e19.5%\u003c/p\u003e \u003cp\u003e5.5%\u003c/p\u003e \u003cp\u003e0.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePolypharmacy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003cp\u003e5\u0026ndash;10\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003cp\u003e291\u003c/p\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.0%\u003c/p\u003e \u003cp\u003e73.0%\u003c/p\u003e \u003cp\u003e8.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e362\u003c/p\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.5%\u003c/p\u003e \u003cp\u003e5.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e: here\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Factors associated with potential DDIs\u003c/h2\u003e \u003cp\u003eThe results of the univariate analysis indicated that there was a statistically significant correlation between age and the number of potential interactions (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Educational level, comorbidities, number of medications, and complications were also found to be significantly associated with the number of potential DDIs (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for each one). However, no significant relationship was observed between gender and the number of potential DDIs (p value\u0026thinsp;=\u0026thinsp;0.404), marital status and the number of potential DDIs (p value\u0026thinsp;=\u0026thinsp;0.088), or smoking and the number of potential DDIs (p value\u0026thinsp;=\u0026thinsp;0.279) as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Furthermore, this prospective observational study demonstrated a positive correlation between age and two variables: the number of prescribed medications and complications.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors associated with potential DDIs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;400\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of DDIs\u003c/p\u003e \u003cp\u003eMedian (Q1 \u0026ndash; Q3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152 (38%)\u003c/p\u003e \u003cp\u003e248 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3\u0026ndash;8.75)\u003c/p\u003e \u003cp\u003e6 (3\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e31\u0026ndash;40\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e41\u0026ndash;50\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e51\u0026ndash;60\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.5%)\u003c/p\u003e \u003cp\u003e54 (13.5%)\u003c/p\u003e \u003cp\u003e161 (40.25%)\u003c/p\u003e \u003cp\u003e183 (45.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5 (2\u0026ndash;3)\u003c/p\u003e \u003cp\u003e3 (1\u0026ndash;6)\u003c/p\u003e \u003cp\u003e5 (3\u0026ndash;8)\u003c/p\u003e \u003cp\u003e6 (3\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSingle\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eMarried\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ewidowed\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eDivorced\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1%)\u003c/p\u003e \u003cp\u003e357 (89.25%)\u003c/p\u003e \u003cp\u003e37 (9.25%)\u003c/p\u003e \u003cp\u003e2 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (4 -11.5)\u003c/p\u003e \u003cp\u003e5 (3\u0026ndash;9)\u003c/p\u003e \u003cp\u003e7 (3\u0026ndash;14)\u003c/p\u003e \u003cp\u003e2 (1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eHigh school\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eBachelor\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eNone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185 (46.25%)\u003c/p\u003e \u003cp\u003e65 (16.25%)\u003c/p\u003e \u003cp\u003e47 (11.75%)\u003c/p\u003e \u003cp\u003e103 (25.75% )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (3\u0026ndash;9)\u003c/p\u003e \u003cp\u003e4 (1\u0026ndash;8)\u003c/p\u003e \u003cp\u003e4 (1\u0026ndash;6)\u003c/p\u003e \u003cp\u003e6 (3\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoker\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eNon smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e347 (86.75%)\u003c/p\u003e \u003cp\u003e53 (13.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (2.5\u0026ndash;8)\u003c/p\u003e \u003cp\u003e5 (3\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373 (93.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (3\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of medications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;5\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e5\u0026ndash;10\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (19%)\u003c/p\u003e \u003cp\u003e292 (73%)\u003c/p\u003e \u003cp\u003e32 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1\u0026ndash;3)\u003c/p\u003e \u003cp\u003e6 (4\u0026ndash;9)\u003c/p\u003e \u003cp\u003e16.5 (13.25\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComplications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227 (65.8%)\u003c/p\u003e \u003cp\u003e173 (43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (3\u0026ndash;10)\u003c/p\u003e \u003cp\u003e4 (1.5\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e here\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Controlled vs uncontrolled HbA1c\u003c/h2\u003e \u003cp\u003eOut of the 400 T2DM patients included in the study, it was found that 396 (99.0%) had undergone HbA1c% testing in the past 3 months, while the remaining 4 (1.0%) patients had not. Among the 396 patients who had undergone the HbA1c% test, only 107 (26.8%) cases had controlled glycemia, while 289 (72.3%) cases had uncontrolled glycemia.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates that there is a significant association between the presence of HbA1c and complications (P-value\u0026thinsp;=\u0026thinsp;0.000), indicating that patients with uncontrolled glycemia were more likely to experience complications. However, there were no significant differences observed between the HbA1c test and the presence of potential DDIs (P-value\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation relationship between HbA1c and complication / DDIs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSig (p- value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControlled A1C\u0026thinsp;\u0026lt;\u0026thinsp;7%\u003c/p\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUncontrolled A1C\u0026thinsp;\u0026gt;\u0026thinsp;7%\u003c/p\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (26.8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e289 (72.3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComplications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e186 (64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo Complications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (35.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDDIs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e280 (96.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo DDIs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn our study, the highest incidence of DDIs was observed in patients over 60 years old, accounting for 45.0% of cases. This finding is consistent with a previous study conducted by Dinesh K U et al. (2007), which reported a high prevalence of DDIs among patients over 50 years old [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This finding is also consistent with a large population-based study conducted in Sweden by Astrand, Bengt et al., in 2006 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The incidence of DDIs showed a positive correlation with age groups, likely due to changes in pharmacokinetic and pharmacodynamic interactions that occur as individuals age. Moreover, elderly patients often have multiple diseases, which require them to take more medications to manage their conditions, thereby increasing their susceptibility to potential DDIs.\u003c/p\u003e \u003cp\u003eThe present study showed that the most commonly prescribed medications were metformin, atorvastatin, acetyl salicylic acid, glimepiride, and enalapril. These findings were similar to those reported in a study by Dinesh et al. conducted on 182 patients and 685 different medications, where metformin was also found to be the most commonly prescribed medication, followed by enalapril and atenolol [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The current findings were consistent with a study conducted by the Stage research team in 2015, which also reported that metformin was the most commonly prescribed medication. This could be attributed to the fact that metformin is considered the mainstay drug for T2DM patients and the first-choice treatment, as it has been shown to decrease the mortality rate associated with diabetic patients [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study revealed a significant association between the presence of comorbidities and DDIs in T2DM patients, as 90.5% of the identified potential interactions were found in patients with comorbidities. Among the comorbidities, the most common were cardiovascular disease, dyslipidemia, gout, infectious diseases, and thyroid disease, in descending order of frequency.Sanker et al. reported that infections were the most common disease associated with T2DM patients, followed by hypertension and dyslipidemia. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] The observed difference in the prevalence of comorbidities with DMT2 could be attributed to various factors such as differences in study participants, physiological factors, and pharmacological aspects.\u003c/p\u003e \u003cp\u003eThe present study demonstrated that the combination of acetyl salicylic acid with metformin had the highest prevalence of DDIs among T2DM patients. This finding is consistent with the fact that acetyl salicylic acid can enhance the hypoglycemic effect of blood glucose lowering agents, such as metformin[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Sanker et al. also reported that the combination of acetyl salicylic acid and insulin was associated with a high prevalence of moderate DDIs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Metformin and enalapril was the most common combination was appeared in another study conducted in Palestine [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These medications are typically recommended as the initial treatment options for patients with diabetes and cardiovascular disease.\u003c/p\u003e \u003cp\u003eThe current study included 400 patients who were prescribed a total of 114 different medications. It was observed that 96% of the patients had at least one potential DDI. These findings were consistent with previous studies conducted by Otachi in Kenya, where 96% of prescriptions had at least one potential DDI [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and by Rodrigues et al. in Saudi Arabia, where approximately 90% of prescriptions had at least one potential DDI [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, Sanker et al. (2015) reported a lower percentage, with only approximately two-thirds of included patients having at least one potential DDI. The differences in the prevalence of DDIs may be attributed to healthcare providers' poor knowledge of basic information associated with DDIs, as well as differences in patients' compliance with their prescriptions in various countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current study revealed the presence of all five classes of risk rating for DDIs, with moderate and minor interactions being the most common. These interactions only require appropriate rational drug therapy and continuous monitoring as clinical management actions. Additionally, the prevalence of moderate DDIs (76.67%) in this study was consistent with a previous study published by Rodrigues et al., where 74% of reported DDIs were classified as moderate [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA study conducted by Dinesh et al. in 2007 reported a result that was similar to ours, with 92.1% of potential DDIs being moderate in severity[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our study revealed a low prevalence of potential major DDIs, accounting for only 0.27% of all identified DDIs, which involved seven out of 400 T2DM patients. Although even a single major DDI can pose a significant risk to the patient's health and wellbeing, the therapeutic effects of drugs may be altered if given together. This finding is in contrast to the results of a previous study conducted by Otachi in 2016, which reported a higher prevalence of major DDIs at 4%[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In contrast to our finding, a study conducted in 2015 by Samardzic et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] indicated a complete absence of Category D and X DDIs, This outcome may have resulted from the exclusion of certain drugs that typically cause these severe interactions or heightened prescriber vigilance regarding potential DDIs with co-prescribed anti-diabetic medications..\u003c/p\u003e \u003cp\u003eThe results of our study showed a significant association between the number of medications per prescription and the incidence of potential DDIs. Specifically, we observed that prescriptions containing a larger number of medications were more likely to have potential DDIs. About 73% of the potential DDIs in our study were found in prescriptions containing 5\u0026ndash;10 medications, with an average of 6 interactions per prescription. These findings are consistent with previous studies, such as the study conducted in Nairobi by Otachi in 2016, which reported an average of 5 potential DDIs per prescription [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The high average of 6 drugs in polypharmacy was not unexpected, given that T2DM is a commonly coexisting disease with other conditions, which increases the likelihood of potential DDIs in their prescriptions. Johnell and Klarin, reported a strong relationship between the number of prescribed drugs and the possibility of DDI incidences [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings of our study are consistent with the results reported by Dookeeram and colleagues, who found that patients with T2DM and other comorbidities tend to have multiple medications prescribed, leading to polypharmacy. This is because many of these chronic conditions require long-term management and patients may require several medications to control their symptoms. Furthermore, Dookeeram and co-authors also reported that polypharmacy and certain common chronic diseases such as hypertension, diabetes mellitus, and psychiatric diseases were significantly associated with potential DDIs [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOut of the 400 patients, 396 had regular HbA1c tests, revealing that only 107 (26.8%) had controlled glycemia, while 289 (72.3%) had uncontrolled glycemia. Our study showed a correlation between HbA1c levels and the occurrence of complications, with controlled glycemia associated with fewer complications, and higher HbA1c levels correlated with increased risk of complications.\u003c/p\u003e \u003cp\u003eA study conducted in 2017 by Hammad MA et al. reported that 52.9% of patients had controlled glycemia while 47.1% had uncontrolled glycemia, which differs from our study where only 26.8% had controlled glycemia[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The differences in the results between the two studies may be attributed to several factors, such as poor adherence of the patients to the prescribed doses and regimens, as well as the insufficient adherence of healthcare providers to the IDF guidelines for managing T2DM patients.\u003c/p\u003e \u003cp\u003eBased on our data analysis, we found no correlation between HbA1c levels and potential DDIs in T2DM patients. Our results show that the prevalence of potential DDIs was similar for patients with both low and high HbA1c levels. Therefore, it can be concluded that there is no direct relationship between the level of HbA1c and the occurrence of potential DDIs in T2DM patients. This finding is consistent with a study conducted by Samardzic and Bacic-Vrca in 2015 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study showed that patients with uncontrolled glycemia (\u0026gt;\u0026thinsp;7%) had more complications and comorbidities, leading to a higher number of medications in addition to their anti-diabetic drugs. On the other hand, patients with controlled glycemia (\u0026le;\u0026thinsp;7%) had fewer medications, but more anti-diabetic drugs requiring caution due to their complex mechanism of action. This could explain why there was no difference in the occurrence of DDIs with respect to HbA1c levels. These findings are in line with the complexity of managing T2DM and the need for individualized treatment regimens.\u003c/p\u003e"},{"header":"5. Limitations and strength of the study","content":"\u003cp\u003eAs a prospective study, we faced challenges in obtaining accurate information from patients during interviews, which could potentially lead to misinformation about their prescribed medications and decrease the accuracy of the research. Additionally, we did not clinically monitor the occurrence of DDIs in patients. Furthermore, the generalization of the results is limited as the sample was taken only from the Southern part of the West Bank and may not be representative of all Palestinian DM patients. However, it should be noted that this study is the first of its kind in Palestine and the Arab world, and one of the few worldwide. These findings provide valuable baseline data that can be used to determine the prevalence of potential DDIs in DM patients and identify associated factors, as well as design and implement suitable interventions, educational programs, and additional related studies.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eT2DM patients at primary healthcare centers are at high risk of potential DDIs, particularly those prescribed for their comorbidities and complications. Metformin and ASA were the most commonly implicated drugs. Most interactions were of moderate severity (class C). Factors associated with DDIs among T2DM patients include age, comorbidities, complications, number of medications, and education level. This high prevalence underscores the need for increased awareness among healthcare providers about DDIs. Updating data, ongoing research, educational programs, and improved counseling can help prevent improper use of medications and minimize DDIs in diabetic patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe procedures involving human participants were reviewed and approved by the IRBs of An-Najah National University and the Palestinian MOH, as detailed in Section \u003cspan class=\"InternalRef\"\u003e2.7\u003c/span\u003e. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eAdditional information\u003c/h2\u003e\n\u003cp\u003eA thesis has previously been published [25] This study was based on a master\u0026apos;s thesis in clinical pharmacy titled \u0026quot;Evaluation of Potential Drug-Drug Interactions among Medications Prescribed in Primary Health-Care Centers for Type 2 Diabetes Mellitus Patients: A Cross-Sectional Study from Palestine.\u0026quot; The thesis is available at\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u0026nbsp;\u003cspan class=\"RefSource\"\u003ehttps://repository.najah.edu/items/a8108736-a266-44ac-b3be-c08e8660279e\u003c/span\u003e \u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eNS and RA were involved in the conception and design of the work, analysis and interpretation of data, drafting and final approval the manuscript. SA was involved in collecting data, analysis and interpretation of data, and drafting of the manuscript. AR, IA, MA, AE and FS drafted the manuscript. NS has corresponding author status NS can be reached at [email protected].\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe study authors express their gratitude to the participants who took part in the study. They also acknowledge An-Najah National University for providing support and making this research possible.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe manuscript contains all the relevant data related to this study, including the supplementary materials. If required, the datasets used and/or analyzed during the current study can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSankar, V., Saaed, Y., Joseph, R. M., Azizi, H. \u0026amp; Thomas, P. M. Serious drug-drug interactions in the prescriptions of diabetic patients. \u003cem\u003eMed. Sci.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e (4), 93\u0026ndash;103 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMallet, L., Spinewine, A. \u0026amp; Huang, A. The challenge of managing drug interactions in elderly people. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e370\u003c/b\u003e (9582), 185\u0026ndash;191 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaren Baxter, I. H. S. \u003cem\u003eStockley's Drug Interactions\u003c/em\u003e 8th edn (Pharmaceutical, 2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldberg, R. M., Mabee, J., Chan, L. \u0026amp; Wong, S. Drug-drug and drug-disease interactions in the ED: analysis of a high-risk population. \u003cem\u003eAm. J. Emerg. Med.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (5), 447\u0026ndash;450 (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePirmohamed, M. et al. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients. \u003cem\u003eBmj\u003c/em\u003e \u003cb\u003e329\u003c/b\u003e (7456), 15\u0026ndash;19 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJuurlink, D. N., Mamdani, M., Kopp, A., Laupacis, A. \u0026amp; Redelmeier, D. A. Drug-drug interactions among elderly patients hospitalized for drug toxicity. \u003cem\u003eJAMA\u003c/em\u003e \u003cb\u003e289\u003c/b\u003e (13), 1652\u0026ndash;1658 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMay, M. \u0026amp; Schindler, C. Clinically and pharmacologically relevant interactions of antidiabetic drugs. \u003cem\u003eTherapeutic Adv. Endocrinol. metabolism\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e (2), 69\u0026ndash;83 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams, D. \u0026amp; Feely, J. Pharmacokinetic-pharmacodynamic drug interactions with HMG-CoA reductase inhibitors. \u003cem\u003eClin. Pharmacokinet.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e (5), 343\u0026ndash;370 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, L., Magliano, D. J. \u0026amp; Zimmet, P. Z. The worldwide epidemiology of type 2 diabetes mellitus\u0026mdash;present and future perspectives. \u003cem\u003eNat. Reviews Endocrinol.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (4), 228 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreeman, J. S. \u0026amp; Gross, B. Potential drug interactions associated with treatments for type 2 diabetes and its comorbidities: a clinical pharmacology review. \u003cem\u003eExpert Rev. Clin. Pharmacol.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (1), 31\u0026ndash;42 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaede, P. et al. Multifactorial intervention and cardiovascular disease in patients with type 2 diabetes. \u003cem\u003eN. Engl. J. Med.\u003c/em\u003e \u003cb\u003e348\u003c/b\u003e (5), 383\u0026ndash;393 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaman Huri, H. \u0026amp; Chai Ling, L. Drug-related problems in type 2 diabetes mellitus patients with dyslipidemia. \u003cem\u003eBMC public. health\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 1192 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDurga, B. \u0026amp; Pharm, B. Pattern of potential drug-drug interactions in diabetic out-patients in a tertiary care teaching hospital in Nepal. \u003cem\u003eMed. J. Malaysia\u003c/em\u003e. \u003cb\u003e62\u003c/b\u003e (4), 295 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Aring;strand, B., \u0026Aring;strand, E., Antonov, K. \u0026amp; Petersson, G. Detection of potential drug interactions\u0026ndash;a model for a national pharmacy register. \u003cem\u003eEur. J. Clin. Pharmacol.\u003c/em\u003e \u003cb\u003e62\u003c/b\u003e (9), 749\u0026ndash;756 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStage, T. B., Brosen, K. \u0026amp; Christensen, M. M. A Comprehensive Review of Drug-Drug Interactions with Metformin. \u003cem\u003eClin. Pharmacokinet.\u003c/em\u003e \u003cb\u003e54\u003c/b\u003e (8), 811\u0026ndash;824 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003elexi-Comp In.; (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSweileh, W. M., Aker, O. A. \u0026amp; Jaradat, N. A. Pharmacological and Therapeutic analysis of anti-diabetic and antihypertensive drugs among diabetic hypertensive patients in Palestine. \u003cem\u003eJ. Islamic Univ. Gaza (Natural Sci. Series)\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e (2), 35\u0026ndash;57 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOtachi, E. O. \u003cem\u003ePotential drug-drug interactions among patients with type 2 diabetes and hypertension in Kisii Teaching and Referral Hospital, Kenya\u003c/em\u003e (University of Nairobi, 2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodrigues, A. T. et al. Clinical relevancy and risks of potential drug\u0026ndash;drug interactions in intensive therapy. \u003cem\u003eSaudi Pharm. J.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (4), 366\u0026ndash;370 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDinesh, K. U. et al. Pattern of potential drug-drug interactions in diabetic out-patients in a tertiary care teaching hospital in Nepal. \u003cem\u003eMed. J. Malay.\u003c/em\u003e \u003cb\u003e62\u003c/b\u003e (4), 294\u0026ndash;298 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamardzic, I. \u0026amp; Bacic-Vrca, V. Incidence of potential drug-drug interactions with antidiabetic drugs. \u003cem\u003eDie Pharmazie\u003c/em\u003e. \u003cb\u003e70\u003c/b\u003e (6), 410\u0026ndash;415 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnell, K. \u0026amp; Klarin, I. The relationship between number of drugs and potential drug-drug interactions in the elderly: a study of over 600,000 elderly patients from the Swedish Prescribed Drug Register. \u003cem\u003eDrug Saf.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e (10), 911\u0026ndash;918 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDookeeram, D. et al. Polypharmacy and potential drug-drug interactions in emergency department patients in the Caribbean. \u003cem\u003eInt. J. Clin. Pharm.\u003c/em\u003e \u003cb\u003e39\u003c/b\u003e (5), 1119\u0026ndash;1127 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHammad, M. A. et al. Drug-drug Interaction-related Uncontrolled Glycemia. \u003cem\u003eJ Pharm. Bioallied Sci\u003c/em\u003e \u003cb\u003e2017\u003c/b\u003e, 9(4):221\u0026ndash;228 .\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Drug-drug interactions, type 2 diabetes mellitus, polypharmacy, prescriptions, Palestine","lastPublishedDoi":"10.21203/rs.3.rs-8088372/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8088372/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aim:\u003c/h2\u003e \u003cp\u003eDrug-drug interactions (DDIs) pose a serious threat to patient safety, especially among type 2 diabetes mellitus (T2DM) patients who frequently experience comorbidities and polypharmacy. This study aimed to assess the prevalence and types of potential DDIs in T2DM patients attending primary healthcare centers and to identify associated risk factors.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA cross-sectional observational study was conducted on 400 T2DM patients from primary health-care centers in Hebron and Bethlehem (July\u0026ndash;September 2018). Patient demographics, comorbidities, and medications were recorded. Lexi-Comp\u0026reg; was used to detect DDIs and classify them by severity: A (no known interaction), B (minor), C (moderate), D (major), and X (contraindicated).\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eAmong 114 drugs prescribed, 96% of patients had at least one potential DDI (n\u0026thinsp;=\u0026thinsp;2,627 total; average: 6.6 per prescription). The majority were category C (76.7%), followed by B (11.7%), D (10%), A (1.3%), and X (0.3%). DDIs were significantly associated with older age, comorbidities, polypharmacy, complications, and higher education level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but not gender, marital status, or smoking.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003ePotential DDIs are common among T2DM patients, particularly those with complex medication regimens. Targeted interventions for high-risk groups and use of interaction screening tools are essential to improve medication safety.\u003c/p\u003e","manuscriptTitle":"Evaluation of potential drug-drug interactions among medications prescribed in primary health-care centers for type 2 diabetes mellitus patients. A cross-sectional study from Palestine","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 12:04:34","doi":"10.21203/rs.3.rs-8088372/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-31T19:20:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15027938932348071094417296585416416015","date":"2026-03-23T18:11:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T13:06:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-03T16:07:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-20T08:46:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-13T17:28:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-13T17:25:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"31ec3387-6773-48fc-9f65-be654590f95a","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64136575,"name":"Health sciences/Diseases"},{"id":64136576,"name":"Health sciences/Endocrinology"},{"id":64136577,"name":"Health sciences/Health care"},{"id":64136578,"name":"Health sciences/Medical research"},{"id":64136579,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-03-11T12:04:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 12:04:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8088372","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8088372","identity":"rs-8088372","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}