Next Article in Journal
Effects of the COVID-19 Pandemic on Physical Activity in Chronic Diseases: A Systematic Review and Meta-Analysis
Previous Article in Journal
Subjective Experience of Speech Depending on the Acoustic Treatment in an Ordinary Room
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 18
Issue 23
10.3390/ijerph182312277
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Factors Associated with Chronic Kidney Disease in Patients with Type 2 Diabetes in Bangladesh

by
Sheikh Mohammed Shariful Islam
1,*,
Masudus Salehin
2,
Sojib Bin Zaman
3,
Tania Tansi
4,
Rajat Das Gupta
5,
Lingkan Barua
6,
Palash Chandra Banik
6 and
Riaz Uddin
1
1
Institute for Physical Activity and Nutrition, Deakin University, Burwood, VIC 3125, Australia
2
School of Health, Federation University Australia, Berwick, VIC 3806, Australia
3
Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC 3168, Australia
4
Bangladesh College of Home Economics, University of Dhaka, Dhaka 1215, Bangladesh
5
Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA
6
Department of Noncommunicable Diseases, Bangladesh University of Health Sciences, Dhaka 1216, Bangladesh
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(23), 12277; https://doi.org/10.3390/ijerph182312277
Submission received: 21 October 2021 / Revised: 18 November 2021 / Accepted: 20 November 2021 / Published: 23 November 2021
Versions Notes

Abstract

:
Diabetes and chronic kidney disease (CKD) are a major public health burden in low- and middle-income countries. This study aimed to explore factors associated with CKD in patients with type 2 diabetes (T2D) in Bangladesh. A cross-sectional study was conducted among 315 adults with T2D presenting at the outpatient department of Bangladesh Institute of Health Sciences (BIHS) hospital between July 2013 to December 2013. CKD was diagnosed based on the estimated glomerular filtration rate using the ‘Modification of Diet in Renal Disease’ equations and the presence of albuminuria estimated by the albumin-to-creatinine ratio. Multivariate logistic regression analysis was used to determine the factors associated with CKD. The overall prevalence of CKD among patients with T2D was 21.3%. In the unadjusted model, factors associated with CKD included age 40–49 years (OR: 5.7, 95% CI: 1.3–25.4), age 50–59 years (7.0, 1.6–39), age ≥60 years (7.6, 1.7–34), being female (2.2, 1.2–3.8), being hypertensive (1.9, 1.1–3.5), and household income between 10,001 and 20,000 Bangladeshi taka, BDT (2.9, 1.0–8.2) compared with income ≤10,000 BDT. However, after adjustment of other covariates, only the duration of hypertension and household income (10,001–20,000 BDT) remained statistically significant. There is a need to implement policies and programs for early detection and management of hypertension and CKD in T2D patients in Bangladesh.

1. Introduction

Chronic kidney disease (CKD) has been recognized as a global public health burden with increasing prevalence and mortality [1]. In 2017, 1.2 million deaths were due to CKD worldwide [1]. In South Asia, the prevalence of CKD was reported as 13.6% in Thailand [2], 10.8% in China [3], and 15.0% in India [4]. CKD often leads to progressive end-stage renal disease (ESRD), increased hospitalization, and premature deaths [5]. The prevalence of CKD has been increasing in many low- and middle-income countries (LMICs) where there is a lack of health systems’ capacity for prevention and management. CKD is more prevalent among the aged population and is also a common complication for people with diabetes and hypertension. In South Asia, the increasing aging population and rapid urbanization have led to an increase in obesity and diabetes mellitus [6,7]. If this trend continues to rise, it will likely spike the proportion of people with diabetes at a faster rate over the next two decades [6]. Particularly, CKD related to type 2 diabetes (T2D) will rise in parallel [8].
Globally, the prevalence of diabetes has increased in many countries over the past 30 years and inevitably, there are proportionally more people with T2D and CKD now than 30 years ago [9]. Diabetes is a major cause of CKD, accounting for about 40% of new patients [10]. Large-scale epidemiologic surveys have identified several important risk factors for CKD, including old age, presence of T2D, female sex, low socioeconomic status, and use of herbal medicine [11]. Diabetes is a significant risk factor for accelerated progression of CKD towards ESRD, kidney failure, and renal replacement therapy [12] and is considered to be a major contributor to chronic vascular diseases. The presence of diabetes in people with CKD deteriorates cardiovascular events [13] and worsens the survival for patients with dialysis or who have undergone kidney transplantation [14].
In recent years, the prevalence of T2D and hypertension has grown rapidly and is expected to increase the prevalence of CKD at an epidemic rate in South Asia [8,15,16]. Moreover, CKD causes an increased risk of myocardial infarction, stroke, and cardiovascular-related mortality in patients with T2D in comparison with non-diabetic patients [17]. Early detection of diabetic patients with CKD is important for monitoring cardiovascular risk factors and initiating therapy, which can slow down the progression of kidney failure [18]. There have been several reports on the prevalence and risk factors of CKD in patients with T2D in developed countries [11,19]. However, studies on factors associated with CKD in T2D patients are scarce in some developing countries like Bangladesh.
A recent systematic review found that the overall pooled prevalence of CKD among Bangladeshi adults was 17.3%, ranging from 12.8% to 26.0% [20]. Another study reported that people with type 2 diabetes in Bangladesh had a high proportion of self-reported kidney disease [21]. A number of studies in Bangladesh have also reported the prevalence of kidney disease in different population groups [22,23,24]. A few studies have reported on CKD in patients with T2D and its determinants in Bangladesh at community levels [25], in slums [26], in the rural population [27], or during screening [28]. However, information on CKD and its associated factors in T2D patients in urban hospitals is limited. Understanding the factors associated with CKD among people with T2D in urban hospital settings in Bangladesh is essential to develop preventive strategies and improve management practices. Therefore, the aim of this study was to determine the prevalence of CKD among patients with T2D and its associated factors in an urban hospital in Bangladesh.

2. Materials and Methods

2.1. Study Design and Population

This cross-sectional analysis was based on the baseline data of the Mobile Phone Intervention for Diabetes (MPID) study in Bangladesh [29]. The data were collected from July 2013 to December 2013. Details on participant selection, assessment process, and clinical data measurement were described in the study protocol, which was published previously [30]. Briefly, the MPID was a single-center randomized controlled intervention trial to measure the effects of a six-month mobile phone text messaging intervention program on glycemic control in people with T2D. The inclusion criteria were adult patients with T2D attending the outpatient department of Bangladesh Institute of Health Sciences Hospital (BIHS), who owned a mobile phone, and were willing to receive a text messaging program on lifestyle behavior change for six months. The BIHS hospital serves more than 2 million people living in Dhaka city and the surrounding areas in Bangladesh. All potential eligible patients referred by an attending BIHS physician and providing written informed consent were screened for eligibility and baseline data and blood samples were collected.

2.2. Data Collection and Process

All patients underwent a structured interview to collect information on socio-demographics, including age, sex, household income, and education; time of diabetes onset and duration; presence and duration of hypertension; status of fasting plasma glucose (FPG), two-hour post prandial glucose (2h PPG) and glycated hemoglobin (HbA1c); and blood pressure (BP) readings, and lipid-lowering therapy, with an indication of the type/class of drug/medication (antihypertensive, anti-dyslipidemia, oral antihyperglycemic agents, etc.).

2.3. Measurements and Definitions

The body weight and height of the participants were measured to the nearest 0.1 kg and 0.1 cm, respectively, using a standard protocol [30] and were used to calculate the body mass index (BMI; in kg/m2). BP was measured three times by a trained physician using a digital sphygmomanometer (Omron, SEM-1, Omron Corp, Osaka, Japan) in a sitting position after patients had taken five minutes to rest and a five-minute interval between the records. The mean of the last two measures was used to define BP status. Hypertension was defined by systolic BP ≥140 mmHg and/or diastolic BP ≥90 mmHg and/or ongoing anti-hypertensive treatment. Glycated hemoglobin (HbA1c) was measured by high-performance liquid chromatography using Diabetes Control and Complications Trial (DCCT) aligned methods. Serum triglycerides (TGs), total cholesterol (TC), and high-density lipoprotein (HDL) cholesterol were determined by colorimetric enzymatic methods; LDL cholesterol was calculated by the Friedwald formula, i.e., LDL-cholesterol (in mmol/L) = TC − [HDL-cholesterol + (TG/2.17)]. Diabetes was defined according to WHO criteria as fasting plasma glucose values of ≥7.0 mmol/L (126 mg/dl), 2-h post-prandal plasma glucose ≥11.1 mmol/L (200 mg/dl), HbA1c ≥6.5% (48 mmol/L), or a random plasma glucose ≥11.1 mmol/L (200 mg/dl) in the presence of signs and symptoms of diabetes [31].
The presence of CKD was assessed using the most recent urinary albumin and serum creatinine reports extracted from the patient’s medical report during the last 12 months. We included reports where the tests were performed at the BIHS hospital to avoid measurement bias. The estimated glomerular filtration rate (eGFR) was calculated using the Modification of Diet in Renal Disease (MDRD) equations [32]. Patients with eGFR <60 mL/min/1.73 m2 present for three months or more and/or markers of kidney damage determined by the presence of albuminuria estimated by the albumin-to-creatinine ratio were defined as CKD according to The National Kidney Foundation’s Kidney Disease Outcomes Quality Initiative [33].

2.4. Data Analysis

Statistical analyses were conducted using SPSS 21 (IBM Corporation, Armonk, NY, USA). Continuous data were expressed as the mean ± standard deviation (SD), or median and interquartile ranges, and categorical data were presented as a number with percentages. The Kolmogorov–Smirnov test was used to check normality for the continuous variables. We compared the mean and median between groups using the t-test and Mann–Whitney U test, respectively. We used multiple logistic regressions to explore the factors associated with CKD. We identified factors that had a bivariate association with CKD at the 20% level of significance (p ≤ 0.20). We checked these variables for collinearity issues, and those without any collinearity were entered into the multivariable model. We expressed associations as an odds ratio (OR) with 95% confidence intervals (CIs). We considered a p-value of <0.05 as significant.

2.5. Ethics

The study was conducted following the Declaration of Helsinki and the protocol was approved by the Research Review Committee and Ethical Review Committee of the International Center for Diarrhoeal Disease Research, Bangladesh (icddr,b). Written informed consent was obtained from all the participants. Permission was granted from the Director of BIHS hospital to use their facility for data collection.

3. Results

Overall, 500 patients were recruited, of which 185 (37%) had missing data on renal function and were excluded from the present analyses. Therefore, the analytical sample for this analysis was 315 participants with complete information on renal function as assessed by serum creatinine and albuminuria measurements.

3.1. Characteristics of the Patients

The mean ± SD age of the patients was 50.7 ± 9.9 years. Patients with CKD were on average 4.6 years older than non-CKD patients (p < 0.001). CKD were more common in females with T2D than males: 54.3% and 68.7%, respectively. The median income of the patients with T2D was 35,000 BDT (equivalent to US$448.7). The education level of the majority of the patients with T2D (41.0%) was a higher secondary certificate or above. CKD was more common in patients with a secondary level of education (37.3%), and most non-CKD patients had a higher secondary level of education (44.4%) (Table 1).
More than one-third (38.4) of the patients had diabetes for ≥5 years; however, CKD was more common (55.2%) among those who had diabetes for <5 years. The mean FPG was 7.6 ± 2.1 mmol/L among the T2D patients; however, the average FPG in CKD patients was about 0.6 mmol/L less than the non-CKD patients’ group. This study found that 2-h PPG was uncontrolled in the majority of patients with T2D (53.8%). Moreover, about half (45.9%) of the patients were in a poorly controlled glycemic state (HbA1c > 8%). Most of the T2D patients were hypertensive (60.0%). The duration of HTN was >5 years in the majority of T2D patients (54.6%) and CKD was more common among this group. Most of the patients with T2D were obese (87.8%).

3.2. Prevalence of CKD

The overall prevalence of CKD among patients with T2D was 21.3%, which was higher among females (68.7%) compared to males (31.3%). FBS was optimal (<6.7 mmol/L) in most of the diabetic patients with CKD (47.7%). However, there was no significant difference in 2hABF in these patients. Around 97.0% of CKD patients with T2D were obese and 71.6% were hypertensive. CKD was more common in patients with a secondary level of education (37.3%) than patients who studied less than or higher than the secondary level.

3.3. Results of Bivariate Analysis

Table 2 presents the bivariable regression analysis of factors associated with CKD. Older patient groups had significantly greater chances of having CKD compared to the young patients group. Compared to patients aged <40 years, patients in the age group 40–49 years had more than a 5 times higher odds of developing CKD (p = 0.02) while for patients in the age group ≥60 years, the odds were 7.6 times higher (p = 0.01). Females had a 2.2 times higher odds of having CKD when compared with male patients (p < 0.01). This study found that the odds of developing CKD among hypertensive diabetic patients were higher than the normotensive diabetic patients (p = 0.03). However, the duration of diabetes (<5 or ≥5 years) was not significantly associated with CKD in our patient groups (p = 0.09).

3.4. Results of Multivariate Analysis

Table 3 presents the multivariate analysis of factors associated with CKD. We found only two factors that were statistically and significantly associated with CKD. Diabetic patients with a household income of 10,000–20,000 BDT had a higher odds of having CKD (OR = 4.12; 95% CI 1.12–15.20) than those with a lesser income. The duration of hypertension was found to be associated with a higher odds of CKD, i.e., with a one-year increase in the duration of hypertension among patients with T2D, the odds of developing CKD were 6% higher (OR 1.06; CI 1.00–1.12).

4. Discussion

To the best of our knowledge, this is the first study that has estimated the prevalence of CKD and its associated factors in patients with T2D attending an urban hospital in Bangladesh. Almost one in five people with T2D in urban Bangladesh had CKD, which was almost twice as common in females than males. The key finding of our study is that household income and the duration of hypertension were significantly associated with CKD in our study population. The CKD prevalence in our study is similar to a previous study conducted in Thailand, where one-fifth of the participants with T2D attending a district hospital for diabetic care had CKD [34]. In Nepal, 20.3% of patients with T2D reported having CKD [35]. Studies conducted in other South Asian and developing countries reported a relatively higher prevalence. For example, CKD prevalence was 31.5% in Pakistan [36] and 24.7% in Tanzania [37]. However, our finding was in contrast with the DEMAND study, which assessed markers of CKD in 32,208 patients in a referred, clinic-based, or office-based setting across 33 countries and found ~50% of patients with T2D without a known history of proteinuria and/or diabetic kidney disease had CKD [38]. These discrepancies might be due to the differences in population health, geographic locations, methods of CKD diagnosis, quality of diabetes care, and available resources for management of T2D.
Hypertension is common in patients with CKD and those with T2D. Hypertension is a classical risk factor of kidney injury, and uncontrolled hypertension plays an important role in the progression of CKD by increasing glomerular filtration pressure and subsequent renal damage [39]. Patients with T2D may have hypertension for years prior to the onset of overt diabetes. Furthermore, people with diabetes have shown low medication adherence in Bangladesh, which exposes them to increased risks of diabetes complications [40,41,42]. Still, if BP rises further, these patients are subsequently likely to develop CKD and diabetic nephropathy. The community-based study of CKD among T2D patients in Taiwan showed that the presence and duration of hypertension were both significantly associated with proteinuria and renal impairment after controlling for covariates [43]. Apart from showing a higher prevalence of CKD in patients with hypertension (71.6%) in our study, the duration of hypertension among our population was also significantly associated with CKD after adjustment of all covariates, which is supported by the study of Thakur et al. in Nepal [35], which showed a significant association of high BP with microalbuminuria. Previous studies also showed a positive correlation between these two variables [44,45].
CKD is increasingly common in low- and middle-income countries, mostly due to a lack of healthcare infrastructure and resources for diagnosis and management, health insurance, and limited healthcare access [46]. Poverty is one of the most important markers of poor health and chronic diseases, including CKD [47]. Our results showed that participants with household incomes between BDT 10,001 and 20,000 had almost four times the odds of having CKD compared to those with household incomes <BDT 10,000. This might be due to the fact that low income groups avoid healthcare services as they cannot afford the high costs of treatment and also higher prevalence of hypertension among high-income groups in Bangladesh [48,49]. Our results contrast with a large registry-based study in India, where CKD was significantly lower in the highest income group [50].
Ageing is considered the most common cause of renal impairment in patients with T2D and with a global increase in life expectancy, the prevalence of CKD is expected to rise [51]. Our study found a non-significant association between aging and CKD. Previous studies have shown a higher prevalence of CKD in females [43,52], consistent with our findings. In our study, CKD was more prevalent (55.2%) in patients with diabetes <5 years, reflecting the early onset of CKD in patients with T2D, although the relationship was not statistically significant. However, a study in Nepal [35] reported a positive correlation between microalbuminuria and the duration (>5 years) of diabetes mellitus. Similar findings were observed by Idowu et al. and Varghese et al., who reported the duration of diabetes as a major predictor of microalbuminuria [53,54].
Our study had a number of limitations. First, we used a cross-sectional study design; therefore, we could not establish any causal relationship between CKD and its risk factors. Second, data were collected from a single hospital setting in Dhaka city and are not representative of the Bangladeshi population. Moreover, the study had a relatively small sample size, which may have limited the generalizability of the findings to other T2D patients in Bangladesh; hence, the results should be interpreted with caution. Third, data for this study were collected in 2013. There have been considerable changes in the sociodemographic and economic conditions of the country and the health behaviors of its population, which may have impacted on T2D and CKD in Bangladesh. Therefore, studies with more recent data are required. Fourth, urinary albumin and serum creatinine data were obtained from patients’ medical reports for tests conducted at the BIHS hospital only and thus, participants who tested in other facilities or did not have updated reports were excluded. Fifth, in the current study, other factors, such as history of atrial fibrillation, coronary artery disease, or heart failure, which may contribute to T2D and CKD, were not assessed. Finally, as with any cross-sectional study with self-reported data, recall bias or reporting bias could be a problem. Nevertheless, the findings of this study could shed light on future study design and more extensive studies with more robust measurements and follow-up are required.
Our study was conducted in an urban tertiary-level hospital, where one-fifth of the diabetic patients had CKD despite the availability of a wide range of healthcare services and facilities, for example, multidisciplinary specialists, counsellors, and research labs. This might also reflect the harsh scenario of the rural population in Bangladesh or similar country settings, where patients with T2D remain undiagnosed for an extended period of time and eventually present to the hospital when they have already developed macro- or microvascular complications [42,55]. Strategies for early detection of CKD and hypertension with innovative approaches to management are needed in patients with T2D in Bangladesh [56,57,58,59,60,61,62].

5. Conclusions

In conclusion, we found that more than one in five adults with T2D attending an urban hospital in Bangladesh had CKD. Higher household income and hypertension were associated with CKD in patients with T2D. There is an urgent need for collective efforts for the prevention, early detection, and management of hypertension and CKD in T2D patients in Bangladesh.

Author Contributions

Conceptualization, S.M.S.I.; formal analysis, S.M.S.I. and R.U.; funding acquisition, S.M.S.I.; investigation, S.M.S.I.; methodology, S.M.S.I. and R.U.; project administration, S.M.S.I.; resources, S.M.S.I.; writing—original draft, S.M.S.I., R.U., M.S., R.D.G. and S.B.Z.; writing—review and editing, T.T., R.D.G., L.B. and P.C.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research study was supported by the icddr,b and the Center for International Health (CIH), Ludwig-Maximilians-Universitat, Munich, Germany through DAAD, BMZ and Exceed.

Institutional Review Board Statement

The study was approved by the Research Review Committee and Ethical Review Committee of the International Center for Diarrhoeal Disease Research, Bangladesh (icddr,b) (approval number PR #13018).

Informed Consent Statement

Written informed consent was obtained from all the participants.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We acknowledge with gratitude the commitment of DAAD, BMZ and Exceed to support our research. We would like to thank the BIHS staff for their overall support. We also thank study participants and the study team for their valuable support for this research.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bikbov, B.; Purcell, C.A.; Levey, A.S.; Smith, M.; Abdoli, A.; Abebe, M.; Adebayo, O.M.; Afarideh, M.; Agarwal, S.K.; Agudelo-Botero, M. Global, regional, and national burden of chronic kidney disease, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 2020, 395, 709–733. [Google Scholar] [CrossRef] [Green Version]
  2. Jha, V. Current status of chronic kidney disease care in southeast Asia. Semin. Nephrol. 2009, 29, 487–496. [Google Scholar] [CrossRef] [PubMed]
  3. Zhang, L.; Wang, F.; Wang, L.; Wang, W.; Liu, B.; Liu, J.; Chen, M.; He, Q.; Liao, Y.; Yu, X. Prevalence of chronic kidney disease in China: A cross-sectional survey. Lancet 2012, 379, 815–822. [Google Scholar] [CrossRef]
  4. Varma, P.P.; Raman, D.K.; Ramakrishnan, T.; Singh, P.; Varma, A. Prevalence of early stages of chronic kidney disease in apparently healthy central government employees in India. Nephrol. Dial. Transplant. 2010, 25, 3011–3017. [Google Scholar] [CrossRef] [Green Version]
  5. Mills, K.T.; Xu, Y.; Zhang, W.; Bundy, J.D.; Chen, C.-S.; Kelly, T.N.; Chen, J.; He, J. A systematic analysis of worldwide population-based data on the global burden of chronic kidney disease in 2010. Kidney Int. 2015, 88, 950–957. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Ebrahim, S.; Kinra, S.; Bowen, L.; Andersen, E.; Ben-Shlomo, Y.; Lyngdoh, T.; Ramakrishnan, L.; Ahuja, R.; Joshi, P.; Das, S.M. The effect of rural-to-urban migration on obesity and diabetes in India: A cross-sectional study. PLoS Med. 2010, 7, e1000268. [Google Scholar] [CrossRef]
  7. Islam, S.M.S.; Purnat, T.D.; Phuong, N.T.A.; Mwingira, U.; Schacht, K.; Fröschl, G. Non-Communicable Diseases (NCDs) in developing countries: A symposium report. Glob. Health 2014, 10, 1–8. [Google Scholar] [CrossRef] [Green Version]
  8. Abraham, G.; Varughese, S.; Thandavan, T.; Iyengar, A.; Fernando, E.; Naqvi, S.; Sheriff, R.; Ur-Rashid, H.; Gopalakrishnan, N.; Kafle, R.K. Chronic kidney disease hotspots in developing countries in South Asia. Clin. Kidney J. 2016, 9, 135–141. [Google Scholar] [CrossRef] [Green Version]
  9. Guariguata, L.; Whiting, D.R.; Hambleton, I.; Beagley, J.; Linnenkamp, U.; Shaw, J.E. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res. Clin. Pract. 2014, 103, 137–149. [Google Scholar] [CrossRef]
  10. Levey, A.S.; Coresh, J. Chronic kidney disease. Lancet 2012, 379, 165–180. [Google Scholar] [CrossRef]
  11. Jha, V.; Garcia-Garcia, G.; Iseki, K.; Li, Z.; Naicker, S.; Plattner, B.; Saran, R.; Wang, A.Y.-M.; Yang, C.-W. Chronic kidney disease: Global dimension and perspectives. Lancet 2013, 382, 260–272. [Google Scholar] [CrossRef]
  12. Ortiz, A.; Covic, A.; Fliser, D.; Fouque, D.; Goldsmith, D.; Kanbay, M.; Mallamaci, F.; Massy, Z.A.; Rossignol, P.; Vanholder, R. Epidemiology, contributors to, and clinical trials of mortality risk in chronic kidney failure. Lancet 2014, 383, 1831–1843. [Google Scholar] [CrossRef]
  13. Go, A.S.; Chertow, G.M.; Fan, D.; McCulloch, C.E.; Hsu, C.-Y. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N. Engl. J. Med. 2004, 351, 1296–1305. [Google Scholar] [CrossRef]
  14. Foley, R.N.; Murray, A.M.; Li, S.; Herzog, C.A.; McBean, A.M.; Eggers, P.W.; Collins, A.J. Chronic kidney disease and the risk for cardiovascular disease, renal replacement, and death in the United States Medicare population, 1998 to 1999. J. Am. Soc. Nephrol. 2005, 16, 489–495. [Google Scholar] [CrossRef]
  15. Jha, V. ESRD burden in South Asia: The challenges we are facing. Glob. Health 2015, 219, 221. [Google Scholar] [CrossRef] [PubMed]
  16. Al-Zubayer, M.A.; Ahammed, B.; Sarder, M.A.; Kundu, S.; Majumder, U.K.; Islam, S.M.S. Double and triple burden of non-communicable diseases and its determinants among adults in Bangladesh: Evidence from a recent demographic and health survey. Int. J. Clin. Pract. 2021, 75, e14613. [Google Scholar] [CrossRef] [PubMed]
  17. Gerstein, H.C.; Mann, J.F.; Yi, Q.; Zinman, B.; Dinneen, S.F.; Hoogwerf, B.; Hallé, J.P.; Young, J.; Rashkow, A.; Joyce, C. Albuminuria and risk of cardiovascular events, death, and heart failure in diabetic and nondiabetic individuals. JAMA 2001, 286, 421–426. [Google Scholar] [CrossRef] [Green Version]
  18. Hahr, A.J.; Molitch, M.E. Management of diabetes mellitus in patients with chronic kidney disease. Clin. Diabetes Endocrinol. 2015, 1, 2. [Google Scholar] [CrossRef] [Green Version]
  19. Islam, S.; Islam, M.; Rawal, L.; Mainuddin, A.; Wahiduzzaman, M.; Niessen, L. Clinical profile of patients with diabetic nephropathy in a tertiary level hospital in Dhaka, Bangladesh. Arch. Med. Health Sci. 2015, 3, 191–198. [Google Scholar] [CrossRef]
  20. Hasan, M.; Sutradhar, I.; Gupta, R.D.; Sarker, M. Prevalence of chronic kidney disease in South Asia: A systematic review. BMC Nephrol. 2018, 19, 291. [Google Scholar] [CrossRef] [Green Version]
  21. Islam, S.M.S.; Alam, D.S.; Wahiduzzaman, M.; Niessen, L.W.; Froeschl, G.; Ferrari, U.; Seissler, J.; Rouf, H.; Lechner, A. Clinical characteristics and complications of patients with type 2 diabetes attending an urban hospital in Bangladesh. Diabetes Metab. Syndr. Clin. Res. Rev. 2015, 9, 7–13. [Google Scholar] [CrossRef] [PubMed]
  22. Hasan, M.J.; Muqueet, A.; Sharmeen, A.; Rahman, M.; Ahmed, T.U.; Haque, A.; Quddush, A.R.; Bari, M.A. Prevalence of Diabetes mellitus, Hypertension and Proteinuria in a Rural area of Bangladesh. Community Based Med J. 2012, 1, 8–13. [Google Scholar] [CrossRef]
  23. Karim, A.; Das, D.; Salahuddin, M.; Marjan, G.; Islam, M.; Shaha, A.; Gupta, R.; Islam, S.; Amin, R.; Rashid, H. Prevalence of microalbuminuria and overt proteinuria in hypertension and their relations with renal function in a rural population of Bangladesh. Bangladesh J. Med. 2013, 24, 59–64. [Google Scholar] [CrossRef] [Green Version]
  24. Feng, L.; de Silva, H.A.; Jehan, I.; Naheed, A.; Kasturiratne, A.; Himani, G.; Hasnat, M.A.; Jafar, T.H. Regional variation in chronic kidney disease and associated factors in hypertensive individuals in rural South Asia: Findings from control of blood pressure and risk attenuation—Bangladesh, Pakistan and Sri Lanka. Nephrol. Dial. Transplant. 2019, 34, 1723–1730. [Google Scholar] [CrossRef] [Green Version]
  25. Anand, S.; Khanam, M.A.; Saquib, J.; Saquib, N.; Ahmed, T.; Alam, D.S.; Cullen, M.R.; Barry, M.; Chertow, G.M. High prevalence of chronic kidney disease in a community survey of urban Bangladeshis: A cross-sectional study. Glob. Health 2014, 10, 9. [Google Scholar] [CrossRef] [Green Version]
  26. Huda, M.N.; Alam, K.S. Prevalence of chronic kidney disease and its association with risk factors in disadvantageous population. Int. J. Nephrol. 2012, 2012, 7. [Google Scholar] [CrossRef]
  27. Rahim, M.A.; Mitra, P.; Haque, H.F.; Samdani, T.S.; Zaman, S.; Uddin, K.N. Prevalence of chronic kidney disease stages 3-5 among patients with type 2 diabetes mellitus in Bangladesh. IMC J. Med Sci. 2017, 11, 19–24. [Google Scholar] [CrossRef] [Green Version]
  28. Fatema, K.; Abedin, Z.; Mansur, A.; Rahman, F.; Khatun, T.; Sumi, N.; Kobura, K.; Akter, S.; Ali, L. Screening for chronic kidney diseases among an adult population. Saudi J. Kidney Dis. Transplant. 2013, 24, 534. [Google Scholar]
  29. Islam, S.M.S.; Niessen, L.W.; Ferrari, U.; Ali, L.; Seissler, J.; Lechner, A. Effects of Mobile Phone SMS to Improve Glycemic Control Among Patients with Type 2 Diabetes in Bangladesh: A Prospective, Parallel-Group, Randomized Controlled Trial. Diabetes Care 2015, 38, e112–e113. [Google Scholar] [CrossRef] [Green Version]
  30. Islam, S.M.S.; Lechner, A.; Ferrari, U.; Froeschl, G.; Alam, D.S.; Holle, R.; Seissler, J.; Niessen, L.W. Mobile Phone Intervention for Increasing Adherence to Treatment for Type 2 Diabetes in an Urban Area of Bangladesh: Protocol for a Randomized Controlled Trial. BMC Health Serv. Res. 2014, 14, 1–9. [Google Scholar] [CrossRef] [Green Version]
  31. World Health Organization. Classification of Diabetes Mellitus. 2019. Available online: https://apps.who.int/iris/rest/bitstreams/1233344/retrieve (accessed on 23 November 2021).
  32. Levey, A. A simplified equation to predict glomerular filtration rate from serum creatinine. J. Am. Soc. Nephrol. 2000, 11, A0828. [Google Scholar]
  33. Levey, A.S.; Coresh, J.; Balk, E.; Kausz, A.T.; Levin, A.; Steffes, M.W.; Hogg, R.J.; Perrone, R.D.; Lau, J.; Eknoyan, G. National Kidney Foundation practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. Ann. Intern. Med. 2003, 139, 137–147. [Google Scholar] [CrossRef]
  34. Zaman, S.B.; Gupta, R.D.; Pramual, P.; Khan, R.K.; Sujimongkol, C.; Hossain, N.; Haider, M.R.; Karim, M.N.; Kibria, G.M.; Islam, S.M.S. The burden of chronic kidney disease among people with diabetes by insurance schemes: Findings from a primary referral hospital in Thailand. Diabetes Epidemiol. Manag. 2021, 4, 100026. [Google Scholar] [CrossRef]
  35. Thakur, S.K.; Dhakal, S.P.; Parajuli, S.; Sah, A.K.; Nepal, S.P.; Paudel, B.D. Microalbuminuria and its risk factors in type 2 diabetic patients. J. Nepal Health Res. Counc. 2019, 17, 61–65. [Google Scholar] [CrossRef]
  36. Ahmad, T.; Ulhaq, I.; Mawani, M.; Islam, N. Microalbuminuria in Type-2 Diabetes Mellitus; the tip of iceberg of diabetic complications. Pak. J. Med. Sci. 2017, 33, 519. [Google Scholar] [CrossRef]
  37. Janmohamed, M.N.; Kalluvya, S.E.; Mueller, A.; Kabangila, R.; Smart, L.R.; Downs, J.A.; Peck, R.N. Prevalence of chronic kidney disease in diabetic adult out-patients in Tanzania. BMC Nephrol. 2013, 14, 183. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Parving, H.-H.; Lewis, J.; Ravid, M.; Remuzzi, G.; Hunsicker, L. Prevalence and risk factors for microalbuminuria in a referred cohort of type II diabetic patients: A global perspective. Kidney Int. 2006, 69, 2057–2063. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Chen, J.; Gu, D.; Chen, C.-S.; Wu, X.; Hamm, L.L.; Muntner, P.; Batuman, V.; Lee, C.-H.; Whelton, P.K.; He, J. Association between the metabolic syndrome and chronic kidney disease in Chinese adults. Nephrol. Dial. Transplant. 2007, 22, 1100–1106. [Google Scholar] [CrossRef] [Green Version]
  40. Islam, S.M.S.; Islam, M.T.; Uddin, R.; Tansi, T.; Talukder, S.; Sarker, F.; Mamun, K.A.A.; Adibi, S.; Rawal, L.B. Factors associated with low medication adherence in patients with Type 2 diabetes mellitus attending a tertiary hospital in Bangladesh. Lifestyle Med. 2021, 2, e47. [Google Scholar] [CrossRef]
  41. Islam, S.M.S.; Biswas, T.; Bhuiyan, F.A.; Mustafa, K.; Islam, A. Patients’ perspective of disease and medication adherence for type 2 diabetes in an urban area in Bangladesh: A qualitative study. BMC Res. Notes 2017, 10, 1–8. [Google Scholar] [CrossRef] [Green Version]
  42. Islam, S.M.S.; Ahmed, S.; Uddin, R.; Siddiqui, M.U.; Malekahmadi, M.; Al Mamun, A.; Alizadehsani, R.; Khosravi, A.; Nahavandi, S. Cardiovascular diseases risk prediction in patients with diabetes: Posthoc analysis from a matched case-control study in Bangladesh. J. Diabetes Metab. Disord. 2021, 20, 417–425. [Google Scholar] [CrossRef] [PubMed]
  43. Lin, C.-H.; Yang, W.-C.; Tsai, S.-T.; Tung, T.-H.; Chou, P. A community-based study of chronic kidney disease among type 2 diabetics in Kinmen, Taiwan. Diabetes Res. Clin. Pract. 2007, 75, 306–312. [Google Scholar] [CrossRef] [PubMed]
  44. Afkhami-Ardekani, M.; Modarresi, M.; Amirchaghmaghi, E. Prevalence of microalbuminuria and its risk factors in type 2 diabetic patients. Indian J. Nephrol. 2008, 18, 112. [Google Scholar] [CrossRef] [PubMed]
  45. Ko, G.T.; Chan, J.C.; Lau, M.; Cockram, C.S. Diabetic microangiopathic complications in young Chinese diabetic patients: A clinic-based cross-sectional study. J. Diabetes Its Complicat. 1999, 13, 300–306. [Google Scholar] [CrossRef]
  46. George, C.; Mogueo, A.; Okpechi, I.; Echouffo-Tcheugui, J.B.; Kengne, A.P. Chronic kidney disease in low-income to middle-income countries: The case for increased screening. BMJ Global Health 2017, 2, e000256. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Hossain, M.P.; Goyder, E.C.; Rigby, J.E.; El Nahas, M. CKD and poverty: A growing global challenge. Am. J. Kidney Dis. 2009, 53, 166–174. [Google Scholar] [CrossRef]
  48. Islam, S.M.S.; Mainuddin, A.; Islam, M.S.; Karim, M.A.; Mou, S.Z.; Arefin, S.; Chowdhury, K.N. Prevalence of risk factors for hypertension: A cross-sectional study in an urban area of Bangladesh. Glob. Cardiol. Sci. Pract. 2015, 2015, 43. [Google Scholar] [CrossRef] [PubMed]
  49. Islam, S.M.S.; Lechner, A.; Ferrari, U.; Laxy, M.; Seissler, J.; Brown, J.; Niessen, L.W.; Holle, R. Healthcare use and expenditure for diabetes in Bangladesh. BMJ Glob. Health 2017, 2, e000033. [Google Scholar] [CrossRef] [Green Version]
  50. Rajapurkar, M.M.; John, G.T.; Kirpalani, A.L.; Abraham, G.; Agarwal, S.K.; Almeida, A.F.; Gang, S.; Gupta, A.; Modi, G.; Pahari, D. What do we know about chronic kidney disease in India: First report of the Indian CKD registry. BMC Nephrol. 2012, 13, 10. [Google Scholar] [CrossRef] [Green Version]
  51. Thomas, M.C.; Cooper, M.E.; Zimmet, P. Changing epidemiology of type 2 diabetes mellitus and associated chronic kidney disease. Nat. Rev. Nephrol. 2016, 12, 73. [Google Scholar] [CrossRef]
  52. Chadban, S.J.; Briganti, E.M.; Kerr, P.G.; Dunstan, D.W.; Welborn, T.A.; Zimmet, P.Z.; Atkins, R.C. Prevalence of kidney damage in Australian adults: The AusDiab kidney study. J. Am. Soc. Nephrol. 2003, 14, S131–S138. [Google Scholar] [CrossRef] [Green Version]
  53. Idowu, A.A.; Ajose, A.O.; Adedeji, A.T.; Adegoke, A.O.; Jimoh, K.A. Microalbuminuria, Other Markers of Nephropathy and Biochemical Derangements in Type 2 Diabetes Mellitus: Relationships and Determinants. Ghana Med. J. 2017, 51, 56–63. [Google Scholar] [PubMed]
  54. Varghese, A.; Deepa, R.; Rema, M.; Mohan, V. Prevalence of microalbuminuria in type 2 diabetes mellitus at a diabetes centre in southern India. Postgrad. Med. J. 2001, 77, 399–402. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Safita, N.; Islam, S.M.S.; Chow, C.K.; Niessen, L.; Lechner, A.; Holle, R.; Laxy, M. The impact of type 2 diabetes on health related quality of life in Bangladesh: Results from a matched study comparing treated cases with non-diabetic controls. Health Qual. Life Outcomes 2016, 14, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Moses, J.C.; Adibi, S.; Shariful Islam, S.M.; Wickramasinghe, N.; Nguyen, L. Application of Smartphone Technologies in Disease Monitoring: A Systematic Review. Healthcare 2021, 9, 889. [Google Scholar] [CrossRef] [PubMed]
  57. Islam, S.M.S.; Maddison, R. Digital health approaches for cardiovascular diseases prevention and management: Lessons from preliminary studies. Mhealth 2021, 7, 41. [Google Scholar] [CrossRef]
  58. Islam, S.M.S.; Farmer, A.J.; Bobrow, K.; Maddison, R.; Whittaker, R.; Dale, L.A.P.; Lechner, A.; Lear, S.; Eapen, Z.; Niessen, L.W. Mobile phone text-messaging interventions aimed to prevent cardiovascular diseases (Text2PreventCVD): Systematic review and individual patient data meta-analysis. Open Heart 2019, 6, e001017. [Google Scholar]
  59. Islam, F.M.A.; Lambert, E.A.; Islam, S.M.S.; Islam, M.A.; Biswas, D.; McDonald, R.; Maddison, R.; Thompson, B.; Lambert, G.W. Lowering blood pressure by changing lifestyle through a motivational education program: A cluster randomized controlled trial study protocol. Trials 2021, 22, 1–11. [Google Scholar] [CrossRef]
  60. Frost, D.; Mahmud, M.; Kaiser, M.S.; Musoke, D.; Henry, P.; Islam, S.M.S. Innovative Approaches to Strengthening Health Systems in Low-and Middle-Income countries: Current Models, Developments, and Challenges. Health Policy Technol. 2021, 10, 100567. [Google Scholar] [CrossRef]
  61. Islam, S.; Chow, C.; Redfern, J.; Kok, C.; Rådholm, K.; Stepien, S.; Rodgers, A.; Hackett, M. Effect of text messaging on depression in patients with coronary heart disease: A sub study analysis from the TEXT ME randomised controlled trial. BMJ Open 2019, 9, e022637. [Google Scholar] [CrossRef] [Green Version]
  62. Islam, S.; Tabassum, R.; Liu, Y.; Chen, S.; Redfern, J.; Kim, S.; Ball, K.; Maddison, R.; Chow, C. The role of social media in preventing and managing non-communicable diseases in low-and-middle income countries: Hope or Hype? Health Policy Technol. 2019, 8, 96–101. [Google Scholar]
Table
Table 1. Basic characteristics of the study participants (N = 315).
Table 1. Basic characteristics of the study participants (N = 315).
VariablesN (%)
Age (years)
Mean ± SD, years50.7 ± 9.9
 <4044 (14.0)
 40–49103 (32.7)
 50–59104 (33.0)
 ≥6064 (20.3)
Gender
 Male144 (45.7)
 Female171 (54.3)
Household income (BDT)
 ≤10,000 BDT36 (11.8)
 10,001–20,000 BDT94 (30.8)
 20,001–30,000 BDT52 (17.0)
 30,001–40,000 BDT29 (9.5)
 40,001–50,000 BDT39 (12.8)
 >50,000 BDT55 (18.0)
Education
 No academic education40 (12.7)
 Primary (year 5 completed)53 (16.8)
 Secondary (year 10 completed)93 (29.5)
 Higher secondary and above (year 12 and above completed)129 (41.0)
Duration of diabetes (years)
Median (IQR) duration: 3.0 (1.0–7.0)
 <5 Years194 (61.6)
 ≥5 Years121 (38.4)
Duration of hypertension (years)
Median (IQR) duration: 5.0 (2.0–10.0)
 <5 Years83 (45.4)
 ≥5 Years100 (54.6)
FPG (mmol/L)
 Mean ± SD.7.6 ± 2.1
 Optimal < 6.7 mmol/L114 (36.9)
 Fair 6.7–7.8 mmol/L84 (27.2)
 Poor > 7.8 mmol/L111 (35.9)
2h AFB (mmol/L)
 Control < 10.0 mmol/L141 (46.2)
 Uncontrolled ≥ 10.0 mmol/L164 (53.8)
HbA1c (%)
 Optimal < 7%56 (28.9)
 Fair 7–8%49 (25.3)
 Poor > 8%89 (45.9)
Obesity
 Not obese7 (2.2)
 Obese308 (97.8)
Hypertension
 Absent126 (40.0)
 Present189 (60.0)
Note FPG: Fasting Plasma Glucose; BDT: Bangladeshi Taka, 10,000 BDT = $128.2 [1$ = 78 BDT]; IQR: interquartile range; HbA1c: Glycated Hemoglobin; 2 h ABF: Blood sugar 2 h after breakfast.
Table
Table 2. Bivariate results of factors associated with CKD in T2D patients (N = 315).
Table 2. Bivariate results of factors associated with CKD in T2D patients (N = 315).
VariablesCKD
(n = 67)
n (%)		Non-CKD
(n = 248)
n (%)		Unadjusted OR
(95% CI)		p-Value
Age (years)
 <402 (3.0)42 (16.9)Reference
 40–4922 (32.8)81 (32.7)5.7 (1.3–25.4)0.02
 50–5926 (38.8)78 (31.5)7.0 (1.6–30.9)0.01
 ≥6017 (25.4)47 (19.0)7.6 (1.7–34.8)0.01
Gender
 Male21 (31.3)123 (49.6)Reference
 Female46 (68.7)125 (50.4)2.2 (1.2–3.8)0.01
Household income (BDT)
 ≤10,000 BDT5 (7.8)31 (12.9)Reference
 10,001–20,000 BDT30 (46.9)64 (26.6)2.9 (1.0–8.2)0.04
 20,001–30,000 BDT9 (14.1)43 (17.8)1.3 (0.4–4.3)0.67
 30,001–40,000 BDT5 (7.8)24 (10.0)1.3 (0.3–5.0)0.71
 40,001–50,000 BDT9 (14.1)30 (12.4)1.9 (0.6–6.2)0.31
 >50,000 BDT6 (9.4)49 (20.3)0.8 (0.2–2.7)0.67
Education
 No academic education7 (10.4)33 (13.3)Reference
 Primary16 (23.9)37 (14.9)2.0 (0.7–5.6)0.17
 Secondary25 (37.3)68 (27.4)1.7 (0.7–4.4)0.25
 Higher secondary and above19 (28.4)110 (44.4)0.8 (0.3–2.1)0.67
Duration of diabetes (years)
 <5 Years37 (55.2)157 (63.3)Reference
 ≥5 Years30 (44.8)91 (36.7)1.4 (0.8–2.4)0.23
Duration of hypertension (years)
 <5 Years18 (35.3)65 (49.2)Reference
 ≥5 Years33 (64.7)67 (50.8)1.8 (0.9–3.5)0.09
FPG (mmol/L)
 Optimal < 6.7 mmol/L31 (47.7)83 (34.0)Reference
 Fair 6.7–7.8 mmol/L18 (27.7)66 (27.0)0.7 (0.4–1.4)0.35
 Poor > 7.8 mmol/L16 (24.6)95 (38.9)0.5 (0.2–0.9)0.02
2-h PPG (mmol/L)
 Control < 10.0 mmol/L32 (50.8)109 (45.0)Reference
 Uncontrolled ≥ 10.0 mmol/L31 (49.2)133 (55.0)0.8 (0.5–1.4)0.42
HbA1c (%)
 Optimal < 7%12 (36.4)44 (27.3)Reference
 Fair 7–8%9 (27.3)40 (24.8)0.8 (0.3–2.2)0.70
 Poor > 8%12 (36.4)77 (47.8)0.6 (0.2–1.4)0.21
Obesity
 Not obese2 (3.0)5 (2.0)Reference
 Obese65 (97.0)243 (98.0)0.7 (0.1–3.5)0.64
Hypertension
 Absent19 (28.4)107 (43.1)Reference
 Present48 (71.6)141 (56.9)1.9 (1.1–3.5)0.03
Note CI: confidence interval; OR: odds ratio; FPG: Fasting Plasma Glucose; 2-h PPG: 2 h post prandal glucose; HbA1c: glycated hemoglobin; BDT: Bangladeshi Taka, 10,000 BDT = $128.2 [1$ = 78 BDT].
Table
Table 3. Multivariate analysis of factors associated with CKD in T2D patients.
Table 3. Multivariate analysis of factors associated with CKD in T2D patients.
VariablesOR (95% CI)p-Value
Age (years)
 <40Reference
 40–492.82 (0.52–15.32)0.231
 50–592.50 (0.45–13.96)0.295
 ≥602.51 (0.40–15.70)0.327
Gender
 MaleReference
 Female1.76 (0.78–3.97)0.177
Household income (BDT)
 ≤10,000 BDTReference
 10,001–20,000 BDT4.12 (1.12–15.20)0.033
 20,001–30,000 BDT2.06 (0.48–8.88)0.331
 30,001–40,000 BDT1.19 (0.21–6.65)0.842
 40,001–50,000 BDT1.43 (0.31–6.62)0.649
 >50,000 BDT0.71 (0.15–3.38)0.664
Duration of hypertension (years)1.06 (1.00–1.12)0.049
FPG (mmol/L)0.97 (0.81–1.17)0.763
Hypertension
 AbsentReference
 Present1.56 (0.56–4.35)0.392
Note CI: confidence interval; OR: odds ratio; FPG: Fasting Plasma Glucose; BDT: Bangladeshi Taka, 10,000 BDT = US$128.2 [1 US$ = 78 BDT]. Bold face indicates significance at 5% (p < 0.05) level.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Islam, S.M.S.; Salehin, M.; Zaman, S.B.; Tansi, T.; Gupta, R.D.; Barua, L.; Banik, P.C.; Uddin, R. Factors Associated with Chronic Kidney Disease in Patients with Type 2 Diabetes in Bangladesh. Int. J. Environ. Res. Public Health 2021, 18, 12277. https://doi.org/10.3390/ijerph182312277

AMA Style

Islam SMS, Salehin M, Zaman SB, Tansi T, Gupta RD, Barua L, Banik PC, Uddin R. Factors Associated with Chronic Kidney Disease in Patients with Type 2 Diabetes in Bangladesh. International Journal of Environmental Research and Public Health. 2021; 18(23):12277. https://doi.org/10.3390/ijerph182312277

Chicago/Turabian Style

Islam, Sheikh Mohammed Shariful, Masudus Salehin, Sojib Bin Zaman, Tania Tansi, Rajat Das Gupta, Lingkan Barua, Palash Chandra Banik, and Riaz Uddin. 2021. "Factors Associated with Chronic Kidney Disease in Patients with Type 2 Diabetes in Bangladesh" International Journal of Environmental Research and Public Health 18, no. 23: 12277. https://doi.org/10.3390/ijerph182312277

APA Style

Islam, S. M. S., Salehin, M., Zaman, S. B., Tansi, T., Gupta, R. D., Barua, L., Banik, P. C., & Uddin, R. (2021). Factors Associated with Chronic Kidney Disease in Patients with Type 2 Diabetes in Bangladesh. International Journal of Environmental Research and Public Health, 18(23), 12277. https://doi.org/10.3390/ijerph182312277

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop