The mean and standard deviation were used to describe the continuous measured variables and the frequencies and percentages were used to describe the categorical variables . The Kolmogrove-Smirnov statistical normality test and the histograms were used to assess the statistical normality assumptions for metric variables . The patients mean measured repeated parameters and outcomes ( before pandemic and after pandemic) were compared for statistically significant differences using the Non-Parameric  Wilcoxon's signed Rank test  and the Repeated measured categorical variables were also assessed for statistically significant changes across time using the Non-Parametric Cochran's Q chi-squared test. The independent samples t-test was used to assess the statistical significance of mean differences on metric variables between the levels of binary factors . The Pearson's correlations test was used to assess the correlations between metric measured variables . In order to account for the effect of time factor ( before vs post pandemic lockwon) into the multivariable analysis the data was restructured into a wide dataset with a matrix size equal to 121X2 repeated measuers = 242 data records  .  The restructured data was subjected to the Multivariable Binary Logistic Regression Analysis (MBLR) under the Generalized Linear Mixed models accounting for all the relevant predictor variables in the analysis. The associations between predictor variables with the analysed outcomes in the Logistic Regression analysis was expressed as Multivariable adjusted Odds Ratios (OR) with their associated 95% confidence intervals . The SPSS IBM statistical computing program version 21 was used for the statistical data analysis and the alpha significance value was considered at 0.050 level.

Table 1: Descriptive analysis of the patients sociodemographic characteristics and measured clinical outcomes and telemedicine.

The medical records of one hundred and twenty one diabetic patients were reviewed retrospectively and data was compiled on their glycemic control and the Albumin-Creatinine Ratio  (ACR) during two different time points ( before and post the COVID  lockdown . The resulted descriptive analysis for the patients sociodemographic characteristics are shown in the table-1. Most of the patients 57% were diabetic female patients and the remainder 43% of them were males . The mean ±SD age for the sample of patients was equal to 60.68 ±10.50 years , however 4.1% of the patients were aged between 35-40 years, another 9.1% of them were aged between 41-50 years and 36.4% of them were aged between 51-60 years but most of them 50.4% were aged >=61 years .  Most of the patients in the sample 81.8% had received a tele-medicine follow up care during the lockdown period while they stayed at their homes , Also, the resulted findings showed that most of the patients 74.4% were priori diagnosed with Hypertension that required treatment , and 71.9% were treated with either an ARB /ACE inhibitor medications . Less than the third (30%) of the patients were found to have high serum Albumin:Creatinine Ratio >=30 mg/gm during the study period and the remainder 70% of the patients had ACR within expected bounds.

Table 2: Bivariate comparison between patients with normal versus high ACR index

The bivariate comparison analysis findings between patients with Normal Versus abnormally high ACR ratio are shown in the table-3 and the resulted findings showed that the patients age and sex did not correlate significantly with their risk of having high serum ACR score above normal thresholds, however male patients were found slightly more inclined to have high ACR compared to females but the difference between the two genders on their abnormal ACR state was insignificant statistically , p-value=0.081 according to the chi-squared test of association.  Also, the resulted findings from the bivariate analysis showed that the patients care via telemedicine did not correlate significantly with their risk of  High ACR state , p-value=0.755, but hypertensive patients were found to be slightly  though not significantly statistically more inclined to have  high ACR score, p-value=0.075 according to the chi-squared test of independence .  The patients treatment with ACE/ARB agents did not converge significantly on their ACR state.

According to an Independent sample's t-test, the patients with high ACR (>=30 mg/gm) measured slightly higher mean glycated hemoglobin before the pandemic ( M=8.37) compared to patients with normal/low ACR ( Mean Hba1c=7.71) , the difference between the two groups was not significantly statistically nevertheless, p-value=0.055. The patients risk of having poor glycemic control ( having hba1c>=7.5% )  before the pandemic did not correlate significantly however with their risk of having high ACR levels, p-value=0.188 . But, the patients with high ACR ratio  measured a significantly higher mean Glycated hemoglobin hba1c score ( M=8.50) post the lockdown  compared to those patients who had normal/low ACR ( M=7.80) on average according to an independent samples t-test, p-value=0.043 .  Also, a chi-squared test of association showed that the patients with abnormally high  hba1c >=7.5%  were found to be significantly more predicted to  have high ACR score well above 30 mg/gm compared to patients with hba1c of less than 7.5% , p-value=0.018 . The patients mean body mass index did not differ significantly between the patients with normal or high ACR ratio neither before the lockdown or post the lockdown.

Table 3: Descriptive bivariate analysis of the  measured diabetic control and clinical outcomes throughout the study period-before and after the lockdown .  N=121

To assess the statistical significance of the change of patients measured glycemic control index and their ACR from before to after the lockdown the bivariate analysis methods were used to compare these score between the two time points. The resulted findings from the analysis, table-3, showed there was no statistically significant difference between the patients measured serum glycated hemoglobin (hba1c) index before and after the lockdown  according to the Non-parametric Wilcoxon's signed rank  paired samples test, p-value=0.165 . Also, the patients risk of having hba1c above 7.5% did not differ significantly between the two periods of time according to the Cochran's Q chi-squared test, p-value=0.180 . Also, the analysis  yielded findings showed that there has been no significant change in patients mean measured ACR from before to post lockdown periods , p-value=0.248 . Indeed the patients with high ACR level continued to have abnormally high ACR ratio during the two time periods which are at least 6 months apart, p-value =1, according to Cochran's Q chi-squared test , therefore the  patients with high ACR level before the lockdown also measured abnormally high ACR during the lockdown . The patients mean measured BMI score also did not differ significantly between before and after the pandemic lockdown periods , p-value=0.057.

The bivariate Pearson's correlations test findings, table-4, showed that the patients mean serum Hba1c score did Not correlate significantly with their mean measured ACR ratio score before the pandemic, p-value>0.050  . However, the patients age had correlated significantly but Negatively with their mean measured serum hba1c scorem r=-0.266, p-value<0.010, older patients measured significantly lower serum hba1c score on average . But,  according to the Pearson's correlations test the patients age had correlated positively and significantly with their mean measured serum ACR score, older patients tended to measured significantly higher mean serum ACR score in general , r=0.221, p-value<0.050.

Table 5: Multivariable Binary Logistic Regression Analysis of the patients odds of having high A/C ratio (>=30 mg/gm) during the study period.

To assess the impact of patients glycemic control  and the effect of  telemedicine on the patients odds of having abnormally high ACR ratio the Multivariable Binary Logistic Regression Analysis (MLBR) was used to regress the patients odds of having abnormally high ACR ratio against their sociodemographic factors, telemedicine care, serum hba1c score  comorbodity and ACR ratio . The resulted findings , table6, showed that male patients were found to be significantly more predicted to have abnormally high ACR ratio ( 3.571 times more ) compared to female patients on average, p-value<0.001 . The patients age ( in years) and their care with telemedicine and receiving ACE/ARB medications did not correlated significantly with their  odds of having abnormally high ACR ratio throughout the study time ( before and after lockdown) . Also, the patients history of hypertension did not correlate significantly with odds of having abnormally high ACR ratio, p-value=0.771, and their odds of having abnormally high ACR ratio did not differ significantly  between the two time points ( before vs after the lockdown) , p-value=0.909  . But , the patients mean measured serum glycated hemoglobin ( hba1c) index score had correlated significantly and positively with their odds of having abnormally high ACR ratio, for each additional one percent rise in the patients measured serum hba1c score their mean predicted odds of having abnormally high ACR score (>=30 mg/gm)  tended to rise significantly by a factor equal to 1.267 times higher or 26.7% times higher on average, p-value= 0.015 , higher serum hba1c predicted higher odds of having abnormally high ACR ratio as such , note figure -A it is clear from the illustration that patients with higher hba1c above normal acceptable levels measured significantly higher mean predicted probability of having high ACR score for both genders, but males indeed exceeded females with respect to their probability of  having high ACR with high Hba1c compared to females , an interaction effect between patients sex and their serum hba1c score and / state was tested in the iterative competing analysis models and it was found to be statistically non-significant as such the interaction effect was dismissed from the analysis model .  Not only that but also  the patients mean Body Mass Index ( BMI) score had correlated positively and significantly with their odds of having abnormally high ACR ratio, for each additional one percent rise in the patients mean BMI score the patients predicted odds of having abnormally high ACR tended to rise by a factor equal to 1.06 times higher or 6% times higher  on average , p-value=0.033.

Table 6: To assess the statistical significance of the predictors that may help explain the patients glycemic control the Multivariable Binary Logistic Regression Analysis was also applied to patients odds of having poor glycemic control ( i.e., having hba1c score <7.5% versus hba1c>=7.5%) .  The resulted findings from the multivariable analysis  , table6, showed that the patients sex did not correlate significantly with their odds of having high hba1c score (>=7.5%), p-value=0.488 . However, the patients age (years) had correlated significantly but negatively with their odds of having poor glycemic control , for each additional year rise in the patients age their mean predicted odds of having poorer glycemic control ( i.e. having hba1c>=7.5%) declined by a factor equal to to 4.5% times less on average, p-value=0.004, see the figure-B . The patients measured other factors ( BMI score, History of hypertension, being  treatmed with ACE/ARB agents) did not correlate significantly with their odds of having poorer glycemic control, but patients who received telemedicine care were found to be slightly more inclined to have poorer glycemic control compared to patients who had not received a tele-medicine care on average, the difference between them was not statistically significant however, p-value=0.090 . The patients odds of  having poor glycemic control did not differ significantly between before and after the pandemic lockdown periods , p-value=0.442, according to the multivariable adjusted findings, but the analysis findings showed that the patients with higher than normal serum ACR index ( ACR>=30 mg/gm) were found to be significantly more predicted (3.358 times more ) to have poor glycemic control  compared to patients with normal or low serum albumin-creatinine ACR ratio  on average, p-value=0.003, the patients with higher ACR were significantly more inclined to  poor glycemic control as such.

Figure1: The association between patients sex and high hba1c score with their mean predicted probability of having abnormally high ACR ratio .

Figure 2: The association between patience age group with their model mean predicted probability of having abnormally high hbaclc >=7.5%.

4. Discussion

The COVID-19 pandemic had presented significant challenges for the management of diabetic patients, with restrictions and lockdown measures limiting access to essential healthcare services and medications [9,11]. In response to these challenges, telehealth and eHealth platforms had emerged as promising solutions to ensure the timely and effective delivery of care to individuals with diabetes [11,12].

Telemedicine has proven to be invaluable during the COVID-19 era, benefiting patients with diabetes in primary and secondary care. It has been particularly useful for managing various types of diabetes, including T1D, T2D, gestational diabetes, and new cases. TM not only reduces the risk of COVID-19 infection but also improves glycemic control and alleviates diabetes distress [26].

Over the past two years, telemedicine has experienced significant growth in various medical fields, including diabetes care, thanks to the integration of new technological tools for data collection, transfer, and analysis. However, the widespread adoption of telemedicine has been hindered by several barriers, such as the absence of clear guidelines for data management and privacy, insufficient reimbursement policies, limited access to diabetes technologies, and the need for both patients and healthcare providers to become familiar with diverse digital tools [28].

This discussion aimed to provide a comprehensive analysis of the impact of the COVID-19 pandemic on diabetic patient outcomes and the utilization of telemedicine. It would draw on existing literature and statistical data analysis to shed light on the current state of affairs and identify areas for future research and improvement.

The present study analyzed a dataset comprising 121 diabetic patients to investigate the effects of the pandemic on patient outcomes and the adoption of telemedicine. The sample population consisted of 57% female and 43% male participants, with a mean age of 60.68 years. This demographic profile aligned with previous research indicating a higher prevalence of diabetes among women and the influence of age as a risk factor for diabetes and its complications [29].

The study revealed that 81.8% of the patients received telemedicine care during the pandemic, reflecting the widespread acceptance and utilization of telehealth technologies as an alternative means of providing healthcare services in times of restricted in-person care. This finding supported the recommendations for the use of telemedicine in ensuring the delivery of essential healthcare to diabetic patients in Saudi Arabia [21].

Interestingly, no significant differences were observed in the albumin/creatinine ratio (ACR) based on sex, age, receipt of telemedicine care, or treatment with ACE/ARB. However, male patients exhibited significantly higher odds of having a high ACR compared to female patients. This suggested a potential gender disparity in the development or progression of renal complications in diabetic individuals.

Moreover, the analysis demonstrated a significant increase in mean glycated hemoglobin (HbA1c) levels among patients with a high ACR after the pandemic compared to those with a normal ACR. This finding implies that the COVID-19 lockdown may have negatively affected glycemic control in diabetic patients with renal dysfunction. Previous research had highlighted the adverse effects of social isolation and limited access to healthcare on glycemic management in diabetic individuals [25]. While the utilization of telemedicine during the pandemic might have mitigated some of these negative effects, as evidenced by the high proportion of patients receiving telemedicine care, further investigation was necessary to assess the effectiveness of telemedicine in improving glycemic control and patient satisfaction in the specific context of Saudi Arabia.

Furthermore, the analysis examined the changes in measured outcomes before and after the pandemic lockdown. Surprisingly, no significant differences were found in mean HbA1c, ACR, or BMI between these time periods. This suggested that the COVID-19 pandemic and associated lockdown measures did not have a substantial impact on these parameters in the studied diabetic population. However, it was important to acknowledge that the analysis was limited to a specific dataset of 121 patients and might not fully represent the broader population of diabetic individuals.

The bivariate correlations at baseline showed no significant association between mean HbA1c and mean ACR levels. However, age exhibited a negative correlation with HbA1c levels, indicating that older patients had lower HbA1c levels, which was a positive finding in terms of glycemic control. Conversely, age correlated positively with ACR levels, suggesting that older patients had higher ACR levels, indicating a higher prevalence of renal complications in this population. These correlations highlighted f age as a potential risk factor for both glycemic control and renal function in diabetic patients.

Limitations of this study included the small sample size and the retrospective nature of the analysis, which might limit the generalizability of the findings. Additionally, the dataset relied on recorded information and might have been subject to errors or missing data. Future research should consider larger sample sizes, diverse populations, and prospective study designs to provide more robust evidence and address the limitations of the current analysis.

The findings of this study have important implications for future research in the field of telemedicine and diabetes management. Further investigation is needed to explore the long-term effects of telemedicine on glycemic control, patient satisfaction, and healthcare accessibility. Additionally, studies should focus on optimizing telemedicine platforms and interventions to enhance the overall management and outcomes of diabetic patients.

Based on this study, the following recommendations are suggested for future research and clinical practice:

1. Further research should be conducted to evaluate the long-term effects of telemedicine on glycemic control, patient adherence to treatment plans, and overall healthcare outcomes in diabetic patients. Longitudinal studies with larger sample sizes and diverse populations would provide more robust evidence in this regard.
2. Healthcare providers should be trained and educated on the effective utilization of telemedicine platforms for diabetes management. This includes the proper use of digital tools, communication strategies, and the integration of telemedicine into routine clinical practice.
3. Strategies should be developed to address the gender disparity observed in the development or progression of renal complications among diabetic patients. Future studies should explore the underlying factors contributing to this disparity and develop targeted interventions to improve outcomes in both male and female patients.
4. Efforts should be made to optimize telemedicine platforms and interventions to ensure equitable access to healthcare services for all diabetic patients, including those in rural or remote areas. This may involve addressing technological barriers, improving digital literacy, and ensuring the availability of telemedicine services in underserved communities.
5. Collaboration between healthcare providers, policymakers, and technology experts is essential to develop comprehensive telemedicine guidelines and policies for diabetes management. These guidelines should address issues such as privacy and security, reimbursement mechanisms, and standards for telemedicine platforms.
6. Patient education and empowerment should be emphasized in telemedicine interventions. Providing patients with the necessary knowledge and skills to actively participate in their own care, such as self-monitoring of blood glucose and medication management, can contribute to improved outcomes and self-management.
7. Continued monitoring and evaluation of telemedicine programs are necessary to assess their effectiveness, identify areas for improvement, and ensure quality care delivery. Regular feedback from patients and healthcare providers can help refine telemedicine interventions and address any barriers or challenges encountered.
8. By implementing these recommendations, healthcare systems can harness the potential of telemedicine to enhance diabetes management, improve patient outcomes, and increase access to quality care.

Author contributions

We would like to express our gratitude to all the participants who took part in this study and made this research possible. We also extend our appreciation to the healthcare professionals and staff at King Saud University Medical City for their support and assistance throughout the data collection process. Their contributions are greatly acknowledged.

Conflicts of interest

We declare that there are no conflicts of interest related to this research. We have no financial or personal relationships that could influence or bias the findings and interpretations presented in this study. Our objective is to provide unbiased and objective information to contribute to the scientific knowledge and understanding of telemedicine in diabetes management.

Conclusion

In conclusion, the present study contributes to the existing body of knowledge by providing insights into the impact of the COVID-19 pandemic on diabetic patient outcomes and the utilization of telemedicine. The findings underscore the importance of telehealth technologies in ensuring the continuity of care for diabetic patients during periods of restricted access to traditional healthcare services. The identified factors such as gender, glycemic control, and BMI can guide clinicians and researchers in tailoring interventions to mitigate the risk of renal complications in diabetic patients. However, further research is necessary to evaluate the effectiveness and long-term impact of telemedicine in the Saudi Arabian context and to explore additional factors that may influence patient outcomes.

References

1. Galiero R, Pafundi P C, Nevola R, et al. The importance of telemedicine during covid-19 pandemic: a focus on diabetic retinopathy. Journal of Diabetes Research (2020): 1-8.
2. Russo GT, Andreozzi F, Calabrese M, et al. Role of telemedicine during COVID-19 pandemic in type 2 diabetes outpatients: The AMD annals initiative. Diabetes Research and Clinical Practice 194 (2022): 110158.
3. Borries TM, Dunbar A, Bhukhen A, et al. The impact of telemedicine on patient self-management processes and clinical outcomes for patients with Types I or II Diabetes Mellitus in the United States: A scoping review. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 13 (2019): 1353-1357.
4. Dhediya R, Chadha M, Bhattacharya AD, et al. Role of Telemedicine in Diabetes Management. Journal of Diabetes Science and Technology (2022): 193229682210811.
5. De Kreutzenberg SV. (2022). Telemedicine for the Clinical Management of Diabetes; Implications and Considerations After COVID-19 Experience. High Blood Pressure & Cardiovascular Prevention 29 (2022): 319-326.
6. Wang H, Yuan X, Wang J, et al. Telemedicine maybe an effective solution for management of chronic disease during the COVID-19 epidemic. Primary Health Care Research & Development, 22 (2021): 48.
7. Kusuma CF, Aristawidya L, Susanti CP, et al. A review of the effectiveness of telemedicine in glycemic control in diabetes mellitus patients. Medicine 101 (2022): 32028.
8. Al-Sofiani ME, Alyusuf EY, Alharthi S, et al. Rapid Implementation of a Diabetes Telemedicine Clinic During the Coronavirus Disease 2019 Outbreak: Our Protocol, Experience, and Satisfaction Reports in Saudi Arabia. Journal of Diabetes Science and Technology (2020): 193229682094709.
9. Nachimuthu S, R Vijayalakshmi, M Sudha, et al. Coping with diabetes during the COVID e 19 lockdown in India: Results of an online pilot survey. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14 (2020): 579-582.
10. Tourkmani AM, ALHarbi TJ, Bin Rsheed AM, et al. (2020) The impact of telemedicine on patients with uncontrolled type 2 diabetes mellitus during the COVID-19 pandemic in Saudi Arabia: Findings and implications. Journal of Telemedicine and Telecare 29 (2023): 390-398.
11. Ghosh A, Gupta R, Misra AJD. Telemedicine for diabetes care in India during COVID19 pandemic and national lockdown period: Guidelines for physicians. Diabetes Metab Syndr 14 (2020): 273-276.
12. Bitar H and Alismail S. The role of eHealth, telehealth, and telemedicine for chronic disease patients during COVID-19 pandemic: A rapid systematic review. DIGITAL HEALTH 7 (2021): 1-19.
13. Lurie N and Carr BG. The role of telehealth in the medical response to disasters. JAMA Intern Med 178 (2018): 745-746.
14. Hollander JE and Carr BG. Virtually perfect? Telemedicine for COVID-19. N Engl J Med 382 (2020): 1679-1681.
15. Smith AC, Thomas E, Snoswell CL, et al. (2019) Telehealth for global emergencies: Implications for coronavirus disease 2019 (COVID-19). J Telemed Telecare 26 (2020): 309-313.
16. Zhou X, Snoswell CL, Harding LE, et al. (2020) The role of Telehealth in reducing the mental health burden from COVID-19. Telemed E Health 26 (2020): 377-379.
17. Stevens WJM, van der Sande R, Beijer LJ, et al. scoping review on adverse effects. J Med Internet Res 21 (2019): 10736.
18. Jahanshir A, Karimialavijeh E, Sheikh H, et al. Smartphones and medical applications in the emergency department daily practice. Emergency 5 (2017): 14.
19. Monaghesh E and Hajizadeh A. The role of telehealth during COVID-19 outbreak: a systematic review based on current evidence. BMC Public Health 20 (2020):1193.
20. Charles BL. Telemedicine can lower costs and improve access. Healthc Financ Manage 54 (2000): 66.
21. Tourkmani AM, ALHarbi TJ, Bin Rsheed AM, et al. The impact of telemedicine on patients with uncontrolled type 2 diabetes mellitus during the COVID-19 pandemic in Saudi Arabia: Findings and implications. Journal of Telemedicine and Telecare 29 (2023): 390-398.
22. Forde R, Arente L, Ausili D, et al. The impact of the COVID?19 pandemic on people with diabetes and diabetes services: A pan?European survey of diabetes specialist nurses undertaken by the Foundation of European Nurses in Diabetes survey consortium. Diabetic Medicine, 38 (2020): 14498.
23. Eberle C & Stichling S. Impact of COVID-19 lockdown on glycemic control in patients with type 1 and type 2 diabetes mellitus: a systematic review. Diabetology & Metabolic Syndrome 13 (2021): 95.
24. Hosomi Y, Munekawa C, Hashimoto Y, et al. The effect of COVID-19 pandemic on the lifestyle and glycemic control in patients with type 1 diabetes: a retrospective cohort study. Diabetology International 13 (2021): 85-90.
25. Stachteas P. The impact of the COVID-19 pandemic on the management of patients with chronic diseases in Primary Health Care 216 (2022): 445-448.
26. Papazafiropoulou A. (2022). Telemedicine and diabetes during the COVID-19 era. Archives of Medical Science – Atherosclerotic Diseases 7 (2022): 131-135.
27. Aberer F, Hochfellner DA & Mader JK. (2021). Application of Telemedicine in Diabetes Care: The Time is Now. Diabetes Therapy, 12 (2021): 629-639.
28. Giani E, Dovc K, Dos Santos TJ, et al. Telemedicine and COVID ?19 pandemic: The perfect storm to mark a change in diabetes care. Results from a world?wide cross?sectional web?based survey. Pediatric Diabetes, 22(2021), 1115-1119.
29. Zhang H, Ni J, Yu C, et al. Sex-Based Differences in Diabetes Prevalence and Risk Factors: A Population-Based Cross-Sectional Study Among Low-Income Adults in China. Frontiers in Endocrinology 10 (2019): 658.
30. Camargo, L.M.A., Silva, R.P.M. and de Oliveira Meneguetti, D.U., 2019. Research methodology topics: Cohort studies or prospective and retrospective cohort studies. Journal of Human Growth and Development, 29(3), pp.433-436.
31. Bikbov B, Purcell CA, Levey AS, et al. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The lancet 395 (2020): 709-733.
32. Brough HA, Kalayci O, Sediva A, et al. Managing childhood allergies and immunodeficiencies during respiratory virus epidemics – the 2020 COVID-19 pandemic: a statement from the EAACI-section on pediatrics. Pediatr Allergy Immunol 31 (2020): 442-448.
33. Deng SQ and Peng HJ. Characteristics of and public health responses to the coronavirus disease 2019 outbreak in China. J Clin Med 9 (2020): 575.
34. Gupta R, Hussain A, Misra AJD. Diabetes and COVID-19: Evidence, current status and unanswered research questions. Eur J Clin Nutr 74 (2020): 864-870.
35. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 395 (2020): 497-506.
36. Jiang F, Deng L, Zhang L, et al. Review of the clinical characteristics of coronavirus disease 2019 (COVID-19). J Gen Intern Med 35 (2020): 1545-1549.
37. Lipsitch M, Swerdlow DL, Finelli L. Defining the epidemiology of Covid-19—studies needed. N Engl J Med 382 (2020): 1194-1196.
38. Mousa D, Helal I, Alhejaili F, et al. Sun-137 prevalence of chronic kidney disease markers in Saudi Arabia: population based pilot study. Kidney International Reports 5 (2020): 258.
39. van der Hoek L, Pyrc K, Jebbink MF, et al. Identification of a new human coronavirus. Nat Med 10 (2004): 368-373.
40. Wu Z and McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA 323 (2020): 1239-1242.
41. Jun Min. “Managing cardiovascular risk in people with chronic kidney disease: a review of the evidence from randomized controlled trials.” Therapeutic advances in chronic disease 2,4 (2011): 265-278.
42. Alguwaihes AM, Al-Sofiani ME, Megdad M, et al. Diabetes and Covid-19 among hospitalized patients in Saudi Arabia: a single-centre retrospective study. Cardiovascular Diabetology, 19 (2020): 1-12.
43. Bain SC, Czernichow S, Bøgelund M, Costs of COVID-19 pandemic associated with diabetes in Europe: a health care cost model. Current Medical Research and Opinion, 37 (2020): 27-36.
44. Hafidh K, Abbas S, Khan A, et al. The Clinical Characteristics and Outcomes of COVID-19 Infections in Patients with Diabetes at a Tertiary Care Center in the UAE. Dubai Diabetes and Endocrinology Journal, 26 (2020): 158-163.