ABSTRACT

Background: Chronic kidney disease (CKD) is one of the most serious kidney diseases characterized by poor filtration. Over the past few years, there has been an upward trend in the severity of CKD, which has been associated with a rapid increase in cases of non-communicable chronic diseases, particularly diabetes. However, little is known about when this problem may occur, the prevalence of chronic kidney disease in patients with type ii diabetes, and the predictors. Therefore, the aim of this study was to determine the incidence, timing, and predictors of chronic kidney disease in type 2 diabetic patients attending referral hospitals in the Amhara region, Ethiopia.

Methods: A retrospective follow-up study was conducted involving 415 patients with type II diabetes who participated in chronic follow-up between 2012 and 2017. Considering hospital as a clustering variable, a multivariate shared frailty Weibull (Gamma) survival model was used. The applicability of the model was checked by Akaike Information Criteria (AIC) and log-likelihood. Factors with P-values ≤0.2 in bivariate analysis were considered in the multivariate model. variables with P-values <0.05 and their corresponding 95% confidence levels were considered to be significant predictors of chronic kidney disease.

Results: The overall cumulative incidence of chronic kidney disease was 10.8% [95%; CI: 7.7–14.0%] with a median occurrence time of 5 years. The annual incidence rate was 193/10,000 [95%; CI: 144.28–258.78]. Having cardiovascular disease/s [AHR = 3.82; 95%CI:1.4470–10.1023] and hypercholesterolemia [AHR = 3.31; 95% CI: 1.3323–8.2703] were predictors of chronic kidney disease.

Conclusion: One in 10 people with diabetes has chronic kidney disease. The median time to develop chronic kidney disease is 5 years. Hypercholesterolaemia and cardiovascular disease increase the risk of developing CKD. Therefore, it is recommended to enhance health education for diabetic patients to improve their cholesterol levels and prevent cardiovascular disease in order to reduce the incidence of this life-threatening disease.

Click here to get the effects of Cistanche and what is the Cistanche extract

Introduction

Diabetes mellitus (DM) is a metabolic disorder associated with either the failure of the pancreatic islet beta cells that produce insulin or insulin resistance where the human body cannot uptake the available insulin effectively. DM is alarmingly increasing and becoming one of the pressing public health problems among other non-communicable chronic diseases (NCD) globally. The prevalence of DM was 8.8% among people between the age group of 20 and 79 years, which indicates almost 440 million people are affected by the problem. It is predicted that more than 550 million people will develop DM by the end of 2035. Different vascular and neural damages, including kidney disease, are attributable to DM that might pose danger in the renal capillaries and subsequently lead to the reduction of glomerular filtration rate (GFR). Once the kidney is damaged, it could not filter properly, and difficult to remove waste products that interfere with the normal physiological function of the body which progressively leads the body to shut down.

CKD is a progressive loss of kidney function resulting from the vascular and neural complications of DM that incite several adults to die prematurely. In the world, around 13.3 million people are affected by CKD yearly, of which 85% of the cases are from developing countries Approximately, 1.7 million annual deaths are ascribed to kidney disease. The annual incidence of CKD among type-II diabetic patients ranges from 20-58/1000. Moreover, the time-to-occurrence of CKD (the time at which patients developed CKD since they were diagnosed with type II DM) varies across different studies. The median occurrence time of CKD was around 3.8–12 years worldwide. In Africa, the incidence of CKD is estimated to be between 13.3–25%. Particularly, in Sub-Saharan Africa, the burden of CKD is much greater and associated with additional risk factors like poverty, infections, low level of health literacy, and the high cost of medical fees for screening and treatment, which collectively aggravate the risk and progression of the problem with declined probability of survival.

Cistanche Tubulosa

Ethiopia is one of the developing countries with a high burden of CKD due to the emergence of NCD associated with swift changes in lifestyle. About 10.4–19.1% of the population has exhibited CKD in the country. The median time to develop CKD among type-II DM was also estimated to be 5.9 years. Likewise, 39% of annual deaths in the country are owing to NCD, of which diabetes-associated kidney failure makes up 10 to 40 percent of all deaths. CKD in type II DM patients is attributable to multifaceted factors such as dyslipidemia, overweight/obesity, co-morbidity (hypertension), cardiovascular diseases, and uncontrolled blood glucose levels. Ethiopia has signed to achieve the Sustainable Development Goal (SDG) from 2016 to 2030 which includes reducing premature death from NCD by a third. However, little is known related to the incidence, time to occurrence of CKD, and its predictors, particularly among type II DM patients.

Therefore, the aim of this study was tripled (i) to estimate the time-to-occurrence of CKD; (ii) to determine the incidence of CKD; and (iii) to identify factors predicting CKD among T2DM patients attending the Amhara region referral hospitals. Understanding the aforementioned research problems will help decision-makers to cut the morbidity and mortality rates associated with CKD. Furthermore, clinicians may also use this evidence and strengthen the care provided to diabetic patients.

Study design and period: A retrospective follow-up study was conducted by reviewing medical recordings of type-II DM adult patients who have had follow up from the 1st of May 2012 to the 1st of May 2017 in the Amhara region referral hospitals, in Ethiopia. The data were collected between April 1st and May 1st, 2021.

Study setting: Northwest Amhara regional state is situated to the north of Addis Ababa, the capital of Ethiopia. In the region, there are 6 referral hospitals, namely the University of Gondar comprehensive specialized hospital, Felege Hiwot referral hospital, Tibeb Gione referral hospital, DebereMarkos referral hospital, Debre Berhan referral hospital, Debre Tabor referral hospital. These referral hospitals have a system to provide care for chronically ill patients having different forms of illnesses, including patients with DM. Currently; roughly 12,300 clients with type-II DM visit these hospitals to receive regular follow-up care.

Sampling technique and procedure: Of the five referral hospitals, four hospitals (i.e the University of Gondar comprehensive specialized hospital, Felege Hiwot referral hospital, Debere Markos referral hospital, and Debre tabor referral hospital) were selected randomly. Proportional sample allocation was done for each referral hospital based on the monthly number of DM patients who were on follow-up. Finally, the required number of participants was selected by simple random sampling technique using the list of diabetic patients enrolled between the 1st of May 2012 and the 1st of May 2017 as a sampling frame. Then, using a computer-generating random sample technique, 420 participants were chosen.

Cistanche Tubulosa extract

Data collection tool and procedure: The data were gathered from the medical recordings of patients enrolled in chronic follow care from the 1st of May 2012 to the 1st of May 2017. A data extraction checklist was used to abstract the required data. The socio-demographic characteristics, the presence of co-morbidity, treatment modality, blood glucose status, and lipid profiles of the clients were extracted. The data were gathered by 4 BSc nurses supervised by two MSc nurses.

Quality assurance: To maintain the quality of the data, a one-day training session was conducted for data collectors and supervisors, focusing on the data extraction system, data collection tools, and study objectives. An attempt was made to pre-test 5% (21 patient records) of all participants before data collection began to ensure the quality of the data. After analyzing the pre-test data, a number of variables were added to remove linguistic ambiguity. Close follow-up and monitoring were carried out by both the principal investigator and the mentor during data collection. Necessary feedback was forwarded to the data collectors on a daily basis. The data collected were reviewed and checked for completeness prior to data entry. Uniform confirmation criteria were used for clinical variables with more than one diagnostic criterion in an attempt to reduce misclassification bias. Follow-up was conducted using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist.

Data processing and analysis:

Data were entered, cleaned, and coded using Epi Info version 7 and exported to STATA version 14 for further analysis. Continuous variables were described in terms of mean and median along with the appropriate measure of dispersions.

The incidence rate of CKD was also calculated for the entire cohort by dividing the total number of incident cases of CKD by the total person-years of follow-up. To estimate the median survival time and compare it across groups of key characteristics, the survival curves and Kaplan-Meier were used.

The proportional hazard assumption test was checked graphically, by using Schoenfeld residual test and Cox-Snell residual test, and the assumption was not violated. Variables having a p-value ≤ of 0.2 were entered into the models. Considering the clustering effect by the hospital (the heterogeneity of incidence of CKD across hospitals), a multivariate shared frailty model with Weibull distribution and Gamma frailty term was applied. This model has been chosen after comparing it with Cox and other parametric models with different distributions and frailty terms. Model fitness was checked by both the Akaike information criteria (AIC)and log-likelihood. The model was selected considering the AIC and log-likelihood estimates. The presence of multicollinearity was checked by using the variance inflation factor and correlation coefficient. Variables having a p-value < 0.05 in the final model were considered to be significantly associated with CKD. The risk estimate was expressed using an adjusted hazard ratio with its 95% CI.

Cistanche supplement

Ethical consideration： Ethical approval was obtained from the institutional review board (IRB) of the University of Gondar (Ref.No:-S/N164/7/2013). A permission letter to conduct the study was received from the medical director’s office of the included Hospitals. The data clerks of the hospital were adequately informed about the purpose, method, and anticipated benefits of the study by the data collector. Since the source of data was secondary (chart review), consent was not applicable and the waiver has been permitted by the IRB.

Cistanche Tubulosa extract is a herb that is extracted from Cistanche Tubulosa after a number of rigorous operations to benefit the kidney function of the human body. It contains a large number of active substances of Phenylethanoid total glycoside, Echinacoside, and Verbascoside, they can increase the proliferation rate of kidney cells up to 8-10 times; it can even improve the repair ability of damaged kidney cells and inhibit apoptosis of kidney cells.

REFERENCES

1. Eizirik D.L., Pasquali L., and Cnop M., Pancreatic β-cells in type 1 and type 2 diabetes mellitus: different pathways to failure. Nature Reviews Endocrinology, 2020. 16(7): p. 349–362.

2. Ogurtsova K., et al., IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes research and clinical practice, 2017. 128: p. 40–50.

3. Atlas D., International diabetes federation. IDF Diabetes Atlas, 7th ed. Brussels, Belgium: International Diabetes Federation, 2015.

4. Misra A., et al., Diabetes, cardiovascular disease, and chronic kidney disease in South Asia: current status and future directions. BMJ, 2017. 357.

5. Chaurasia V., Pal S., and Tiwari B., Chronic kidney disease: a predictive model using a decision tree. International Journal of Engineering Research and Technology, 2018.

6. Couser W.G., et al., The contribution of chronic kidney disease to the global burden of major non-communicable diseases. Kidney international, 2011. 80(12): p. 1258–1270.

7. Ruiz-Ortega M., et al., Targeting the progression of chronic kidney disease. Nature Reviews Nephrology, 2020. 16(5): p. 269–288.

8. Lv J.-C. and Zhang L.-X., Prevalence and disease burden of chronic kidney disease. Renal Fibrosis: Mechanisms and Therapies, 2019: p. 3–15.

9. Gerber C., et al., Incidence and progression of chronic kidney disease in black and white individuals with type 2 diabetes. Clinical Journal of the American Society of Nephrology, 2018. 13(6): p. 884–892.

10. Gonza´lez-Pe´rez A., et al., Impact of chronic kidney disease definition on assessment of its incidence and risk factors in patients with newly diagnosed type 1 and type 2 diabetes in the UK: A cohort study using primary care data from the United Kingdom. Primary care diabetes, 2020. 14(4): p. 381–387.

11. Koye D.N., et al., Trends in the incidence of ESKD in people with type 1 and type 2 diabetes in Australia, 2002–2013. American Journal of Kidney Diseases, 2019. 73(3): p. 300–308.

12. Tsai M.-H., et al., Incidence, prevalence, and duration of chronic kidney disease in Taiwan: Results from a community-based screening program of 106,094 individuals. Nephron, 2018. 140(3): p. 175– 184.

13. Zoppini G., et al., Serum uric acid levels and incident chronic kidney disease in patients with type 2 diabetes and preserved kidney function. Diabetes care, 2012. 35(1): p. 99–104.

14. Luk A.O., et al., Metabolic syndrome predicts the new onset of chronic kidney disease in 5,829 patients with type 2 diabetes: a 5-year prospective analysis of the Hong Kong Diabetes Registry. Diabetes care, 2008. 31(12): p. 2357–2361.

15. Salinero-Fort M.A., et al., Five-year incidence of chronic kidney disease (stage 3–5) and associated risk factors in a Spanish cohort: the MADIABETES Study. PLoS One, 2015. 10(4): p. e0122030.

16. Sukkar L., et al., Incidence and Associations of Chronic Kidney Disease in Community Participants With Diabetes: A 5-Year Prospective Analysis of the EXTEND45 Study. Diabetes care, 2020. 43(5): p. 982–990.

17. Shurraw S., et al., Association between glycemic control and adverse outcomes in people with diabetes mellitus and chronic kidney disease: a population-based cohort study. Archives of internal medicine, 2011. 171(21): p. 1920–1927.

18. Seck S.M., Diallo I.M., and Diagne S.I.L., Epidemiological patterns of chronic kidney disease in black African elders: a retrospective study in West Africa. Saudi Journal of Kidney Diseases and Transplantation, 2013. 24(5): p. 1068.

19. Stanifer J.W., et al., The epidemiology of chronic kidney disease in sub-Saharan Africa: a systematic review and meta-analysis. The Lancet Global Health, 2014. 2(3): p. e174–e181.

20. Tanner E.K., et al., Prevalence and predictors of chronic kidney disease among Ghanaian patients with hypertension and diabetes mellitus: A multicenter cross-sectional study. The Journal of Clinical Hypertension, 2019. 21(10): p. 1542–1550.

21. Katchunga P., et al., Hypertension, insulin resistance and chronic kidney disease in type 2 diabetes patients from South Kivu, DR Congo. Nephrologie & therapeutique, 2010. 6(6): p. 520–525.

22. Liyanage T., et al., Worldwide access to treatment for end-stage kidney disease: a systematic review. The Lancet, 2015. 385(9981): p. 1975–1982.

23. Norton J.M., et al., Social determinants of racial disparities in CKD. Journal of the American Society of Nephrology, 2016. 27(9): p. 2576–2595.

24. Wolde H.F., et al., High Hidden Burden of Diabetes Mellitus among Adults Aged 18 Years and Above in Urban Northwest Ethiopia. Journal of Diabetes Research, 2020. 2020.

25. Fiseha T., Kassim M., and Yemane T., Chronic kidney disease and underdiagnosis of renal insufficiency among diabetic patients attending a hospital in Southern Ethiopia. BMC nephrology, 2014. 15 (1): p. 1–5.

26. Kore C. and Yohannes H., Prevalence of chronic kidney disease and associated factors among patients with kidney problems in public hospitals in Addis Ababa, Ethiopia. J Kidney, 2018. 4(162): p. 2472– 1220.1000162.

27. Geletu A.H., et al., Incidence and predictors of chronic kidney diseases among type 2 diabetes mellitus patients at St. Paul’s Hospital, Addis Ababa, Ethiopia. BMC research notes, 2018. 11(1): p. 1–6.

28. Organization, W.H., Noncommunicable diseases country profiles 2018. 2018.

29. Koye D., et al., Incidence of chronic kidney disease among people with diabetes: a systematic review of observational studies. Diabetic Medicine, 2017. 34(7): p. 887–901.

30. Damtie S., et al., Chronic kidney disease, and associated risk factors assessment among diabetes mellitus patients at a tertiary hospital, Northwest Ethiopia. Ethiopian Journal of Health Sciences, 2018. 28(6).

31. United Nations E., Sustainable development Goal 3, Good health and well-being. 2016.

32. Ghaferi A.A., Schwartz T.A., and Pawlik T.M., STROBE Reporting Guidelines for Observational Studies. JAMA surgery, 2021.