Diabetes, Hypertension, and Incidence of Chronic Kidney Disease: Is There any Multiplicative or Additive Interaction?

AUTHORS

Saeed Erfanpoor 1 , Koorosh Etemad 2 , Sara Kazempour 3 , Farzad Hadaegh 4 , Jalil Hasani 5 , Fereidoun Azizi 6 , Donna Parizadeh 4 , Davood Khalili 4 , *

1 Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran

2 Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran

3 Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran

4 Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

5 Kashmar School of Nursing, Mashhad University of Medical Sciences, Mashhad, Iran

6 Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

How to Cite: Erfanpoor S, Etemad K, Kazempour S, Hadaegh F, Hasani J, et al. Diabetes, Hypertension, and Incidence of Chronic Kidney Disease: Is There any Multiplicative or Additive Interaction?, Int J Endocrinol Metab. 2021 ; 19(1):e101061. doi: 10.5812/ijem.101061.

ARTICLE INFORMATION

International Journal of Endocrinology and Metabolism: 19 (1); e101061
Published Online: November 2, 2020
Article Type: Research Article
Received: January 15, 2020
Revised: July 15, 2020
Accepted: October 13, 2020
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Abstract

Background: The burden of chronic kidney disease (CKD) is on the rise worldwide; diabetes and hypertension are mentioned as the main contributors.

Objectives: The current study aimed to investigate the multiplicative and additive interaction of diabetes and hypertension in the incidence of CKD.

Methods: In this population-based cohort study, 7342 subjects aged 20 years or above (46.8% male) were divided into four groups: no diabetes and hypertension; diabetes and no hypertension; hypertension and no diabetes; and both diabetes and hypertension. The multivariable Cox regression was used to determine the effect of diabetes, hypertension, and their multiplicative interaction on CKD. The following indices were used to determine the additive interaction of diabetes and hypertension: the relative excess risk of interaction, the attributable proportion due to interaction, and the synergism index.

Results: Diabetes and hypertension had no significant multiplicative interaction in men (hazard ratio of 0.93, P value: 0.764) and women (hazard ratio of 0.79, P value: 0.198); furthermore, no additive interaction was found in men (relative excess risk due to interaction of 0.79, P value: 0.199; attributable proportion due to interaction of 0.22, P value: 0.130; synergy index of 1.44, P value: 0.183) and women (relative excess risk due to interaction of -0.26, P value: 0.233, attributable proportion due to interaction of -0.21, P value: 0.266; synergy index of 0.48, P value: 0.254).

Conclusions: This study demonstrated no synergic effect between diabetes and hypertension on the incidence of CKD.

1. Background

Chronic kidney disease (CKD) is a common disorder and an important growing public health problem worldwide. Various factors contribute to this increase, including increased prevalence of diabetes and hypertension (1-3). Moreover, comorbidity of diabetes and hypertension produces an increased risk of some complications (4). As per the global burden of diseases study in 2016, the global disability-adjusted life years (DALY) of CKD was 21 million in 1990, which reached 35 million in 2016, a 62% increase (5). CKD is more common among those aged 65 years or older (23.4-35.8%). However, its prevalence among those 30 years or older is considerable (7.2%) (6). In both developed and developing countries, diabetes and hypertension are major causes of CKD (7). Type 2 diabetes mellitus (T2DM) is a major risk factor for CKD (8), and various studies have investigated its role as an independent risk factor in the incidence of CKD (3, 9, 10). The prevalence of T2DM has been increasing continuously all around the world. Currently, approximately 450 million individuals are affected by T2DM, and it's expected to reach 690 million by 2045 (11). Hypertension is a cause of the incidence and intensification of CKD. It's the second leading cause of kidney failure in the US. Hypertension is the most common chronic disease in developed societies, which each year claims nearly 7.1 million lives globally. According to the annual reports of the US renal data system, up to 25% of CKD cases could be attributed to hypertension in 2013 (12-14). Although some studies have surveyed risk factors and their interaction on CKD progression (15), to the best of our knowledge, no study has investigated the potential interaction of diabetes and hypertension in the incidence of CKD. On the other hand, based on experimental research, there is a potential interaction between these two conditions in causing kidney damage (16).

2. Objectives

Hence, regarding the ascending trend of the prevalence and incidence of diabetes and hypertension, and the expected rise in the incidence of chronic complications such as CKD, the current study aimed to determine the interactive effect (multiplicative and additive) of these two risk factors on CKD incidence.

3. Methods

3.1. Study Population

This study was conducted on the framework of a population-based cohort study in Tehran, named Tehran Lipid and Glucose Study (TLGS), as secondary data analysis. The sampling technique and the methods of measurements in the TLGS have been published before (17). Briefly, the first phase of the TLGS started between 1999 and 2001 on a sample of 15005 individuals aged ≥ 3 years old who were selected using a multi-stage cluster sampling from the east of Tehran. All participants were invited for repeated measurements every three years for the other four consecutive phases up to 2014 (17). A total of 12808 participants aged ≥ 20 years at the baseline, phase 1, (5612 males and 7196 females) were included; exclusion criteria were: individuals with CKD (2061 people), those who developed hypertension or diabetes besides CKD during the follow-up (135 people), those with incomplete information on their baseline serum creatinine or covariates (fasting plasma glucose (FPG), 2-hour post-challenge plasma glucose (2h-PCPG), education, smoking status, body mass index (BMI), triglyceride (TG), total cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C) (1423 individuals), those with unknown outcome status due to leaving the study or death before follow-ups (1692 individuals), or those with no data on serum creatinine during the study period (155 individuals); eventually 7342 eligible subjects aged 20 years or older (3440 males and 3902 females) were fully followed-up. The study proposal was approved by the Research Council of the Research Institute for Endocrine Sciences (RIES) at Shahid Beheshti Medical Sciences University. Written informed consent was obtained from all participants.

Clinical, anthropometric, and laboratory measurements in the TLGS have been published before (3, 17). In the present study, GFR was calculated using the abbreviated predictive formula developed by the Modification of Diet in Renal Disease (MDRD) study and based on serum creatinine (18).

3.2. Definition of Variables and Outcomes

CKD was defined as eGFR < 60 mL/min/1.73 m2. CKD's event date was defined as the mid-time between the first diagnosis of CKD and the last follow-up visit; the follow-up period was calculated as the difference between the mid-time date and the date the subject entered the study. Survival time was considered as the interval between the first and last observation dates for the subjects who were censored and were categorized as lost to follow-up. Diabetes was defined as FPG ≥ 7 mmol/L, 2-h PCPG ≥ 11.1 mmol/L, or history of taking anti-diabetic drugs (19). Hypertension is described as systolic blood pressure (SBP) above 140 mmHg or diastolic blood pressure (DBP) above 90-mmHg or drug use (20).

3.3. Statistical Analyses

To analyze the data, first, the participants were separated into four groups according to their status regarding diabetes and hypertension: Group I: no diabetes and hypertension; Group II: diabetes and no hypertension; Group III: no diabetes but suffer from hypertension; and Group IV both diabetes and hypertension. Then, the baseline variables were compared for each group, separated by gender. Data were presented as mean ± standard deviation for normal continuous variables, median (interquartile range, IQR) for skewed continuous variables, and number (percentage) for classified variables. Continuous variables were compared by ANOVA test if normally distributed and by the Kruskal-Wallis test, in case of skewness. Classified variables were compared by the X2 test.

A significant interaction was found between “diabetes- hypertension” and “gender” in Group IV. Hence, the subsequent analyses were stratified by gender. Three models were built using the Cox Multivariable regression to calculate the hazard ratio of incident CKD for each group in reference to Group I. The first model was run to calculate the crude hazard ratios. The second model was age-adjusted, and the third model was also adjusted for education level, smoking status, BMI, TG, TC, HDL-C, eGFR, and propensity score for being lost to follow-up.

To reduce selection bias (21), propensity score (the estimated probability that a participant would have been followed in the study) was computed using the logistic regression for both genders. For this reason, the baseline measures (including FPG, 2h-PCPG, TG, TC, HDL-C, SBP, DBP, BMI, education level, age, eGFR, taking diabetes-related drugs, lipid and hypertension, and smoking status) were included in a logistic model as exposures, with participation in the follow-up as the outcome; the probability of participation in follow-up was then estimated for every participant.

Finally, using the Cox Multivariable model and by adjusting the variables (Body Mass Index; Total Cholesterol; Triglycerides; High-Density Lipoprotein cholesterol; eGFR; Smoking status; Age; Education Level; Propensity score) the multiplicative interaction of diabetes and hypertension was investigated in both genders. Further, the additive interaction of diabetes and hypertension was also examined by modifying the abovementioned variables and using the following equations:

1- The relative excess risk due to interaction (RERI):

RERI= HR++-HR+- -HR-++1

2- The attributable proportion due to interaction (AP):

AP=RERI/HR++

3- The synergy index (S):

S=HR++-1/[HR+--1+HR-+-1]

In the absence of an interactive effect, RERI and AP were equal to zero, and S was considered as one (22).

Schoenfeld's global residual test was used to evaluate the proportional hazards assumption. All analyses were performed using SPSS version 19 and STATA version 14 (Stata Corp LP, TX, USA). Statistical significance was considered when P value < 0.05.

4. Results

In total, 7342 participants were studied, that 3440 (46.8%) of them were male. The mean age of males and females was 44.88 ± 13.84 and 38.01 ± 11.72 years, respectively. At the baseline, 374 subjects (5.09%) were diagnosed with diabetes, 963 (13.12%) were suffering from hypertension, and 269 (3.66%) had both conditions. During the following up period (11.31 years; IQR: 7.49-12.98), 1864 participants developed CKD, giving rise to a cumulative incidence of 17.81% in males and 32.06% in females. The baseline characteristics of participants separated by gender are provided in Table 1. Accordingly, a significant difference was found between the groups concerning social, clinical, and biochemical factors for both genders.

Table 1. Baseline Clinical Characteristics of Subjectsa
VariableTOTALGROUP 1bGROUP 2GROUP 3GROUP 4P Value
Men
Number34402646189485120
Age (years)44.88 ± 13.8438.96 ± 12.5751.95 ± 12.2650.37 ± 14.1756.13 ± 11.03< 0.001
Education Level (%)< 0.001
Higher than diploma640 (18.6)522 (19.7)24 (12.7)80 (16.5)14 (11.7)
Diploma/High school1990 (57.8)1622 (61.3)90 (47.6)228 (47.0)50 (41.7)
Illiterate/Primary School810 (23.5)502 (19.0)75 (39.7)177 (36.5)56 (46.7)
Smoking status (%) (current smoker)1076 (31.3)903 (34.1)55 (29.1)89 (18.4)29 (24.2)< 0.001
FPG (mg/dl)96.32 ± 26 ± 3889.75 ± 8.84158.05 ± 52.7493.48 ± 10.40155.39 ± 54.52< 0.001
2-hPG (mg/dl)110.48 ± 53.9798.42 ± 28.06269.70 ± 103.37112.94 ± 33.20271.69 ± 85.73< 0.001
SBP (mmHg)118.73 ± 16.87113.23 ± 10.80118.78 ± 10.72141.20 ± 17.76149.35 ± 22.61< 0.001
DBP (mmHg)77.08 ± 10.8373.83 ± 8.1976.29 ± 7.9091.57 ± 9.3091.51 ± 12.24< 0.001
BMI (kg/m2)25.83 ± 4.1125.31 ± 3.9427.06 ± 4.0227.45 ± 4.2329.01 ± 4.00< 0.001
TC (mg/dl)199.97 ± 42.40197.01 ± 42.26215.97 ± 44.62206.81 ± 40.49212.48 ± 38.75< 0.001
TG (mg/dl) (IQR)154 (104-224)144 (98-214)205 (144-301)169 (116-244)202 (155-257)< 0.001
HDL-C (mg/dl)37.83 ± 9.3037.88 ± 9.3336.62 ± 9.3438.21 ± 9.4837.08 ± 7.86< 0.001
eGFR (ml/min/1.73m2)76.78 ± 10.6177.91 ± 10.6274.04 ± 9.8973.19 ± 9.9270.78 ± 8.09< 0.001
CKD incidence (%)613 (17.8)359 (13.6)57 (30.2)145 (29.9)52 (43.3)< 0.001
Women
Number39023090185478149
Age (years)38.01 ± 11.7235.12 ± 10.1446.91 ± 9.1948.61 ± 11.5352.88 ± 9.15< 0.001
Education Level (%)< 0.001
Higher than diploma460 (11.8)428 (13.9)7 (3.8)22 (4.6)3 (2.0)
Diploma/High school2250 (57.7)1969 (63.7)72 (38.9)179 (37.4)30 (20.1)
Illiterate/Primary School1192 (30.5)693 (22.4)106 (57.3)277 (57.9)116 (77.9)
Smoking status (%) (current smoker)160 (4.1)123 (4.0)18 (9.7)11 (2.3)8 (5.4)< 0.001
FPG (mg/dl)94.71 ± 29.4487.52 ± 8.71164.48 ± 62.0292.39 ± 10.65164.58 ± 63.41< 0.001
2-hPG (mg/dl)114.05 ± 45.69104.09 ± 25.75264.92 ± 73.93121.20 ± 29.00261.72 ± 71.03< 0.001
SBP (mmHg)114.99 ± 17.23109.39 ± 11.01119.40 ± 10.83138.92 ± 18.14148.90 ± 20.02< 0.001
DBP (mmHg)76.08 ± 10.4973.07 ± 8.1277.89 ± 6.8290.51 ± 9.5189.96 ± 10.94< 0.001
BMI (kg/m2)27.22 ± 4.9126.48 ± 4.6629.21 ± 4.4729.90 ± 4.7631.33 ± 5.30< 0.001
TC (mg/dl)201.59 ± 45.16194.36 ± 41.37225.93 ± 47.71226.38 ± 46.85241.68 ± 51.87< 0.001
TG (mg/dl) (IQR)123 (85-183)110 (79-161)216 (146-285)163 (121-223)214 (165-301)< 0.001
HDL-C (mg/dl)44.60 ± 11.1944.92 ± 11.2640.94 ± 9.4944.60 ± 11.0842.59 ± 11.09< 0.001
eGFR (ml/min/1.73m2)73.56 ± 9.3174.55 ± 9.5070.26 ± 7.3870.07 ± 7.7568.16 ± 6.32< 0.001
CKD incidence1251 (32.1)853 (27.6)80 (43.2)234 (49.0)84 (56.4)< 0.001

Abbreviations: FBS, Fasting Blood Sugare; 2-hPG, 2-hour Plasma Glucose; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; BMI, Body Mass Index; TC, Total Cholesterol; TG, Total Triglycerides ; HDL-C, High-Density Lipoprotein cholesterol; eGFR, estimated Glomerular Filtration Rate; CKD, Chronic Kidney Disease.

aValues are Mean ± S.D. or frequency (%). IQR is 25th to 75th percentile.

bGROUP 1, Diabetes−/Hypertension−; GROUP 2, Diabetes +/ Hypertension −; GROUP 3, Diabetes−/ Hypertension +; GROUP 4 Diabetes+/ Hypertension +.

According to the results of Cox regression models, the hazard ratio of CKD for males was greater in group 4 than groups 2 and 3, in all crude, age-adjusted, and Multivariable-adjusted models. For females, in crude and age-adjusted models, the hazard ratio of CKD for females in group 4 was higher than groups 2 and 3; in the multivariable-adjusted model, diabetes and hypertension showed a modest effect on CKD with around 25% increase in the risk (Table 2).

Table 2. Results of Crude and Multivariable Cox Regression Analysis Relating Diabetes and Hypertension to CKDa
VariableGROUP 1bGROUP 2GROUP 3GROUP 4
Men
CrudeReference3.13 (2.33-4.19)2.67 (2.19-3.26)5.99 (4.40-8.15)
Adjusted for ageReference1.91 (1.42-2.57)1.66 (1.35-2.05)3.23 (2.36-4.42)
Multivariable-adjustedcReference2.16 (1.60-2.93)1.63 (1.29-2.05)3.59 (2.59-4.99)
Women
CrudeReference2.00 (1.57-2.55)2.20 (1.89-2.56)2.69 (2.15-3.37)
Adjusted for ageReference1.28 (0.99-1.65)1.35 (1.15-1.59)1.44 (1.14-1.82)
Multivariable-adjustedcReference1.24 (0.96-1.60)1.28 (1.08-1.51)1.25 (0.98-1.59)

aValues are the Hazard ratio (95% CI).

bGROUP 1, Diabetes−/Hypertension−; GROUP 2, Diabetes +/ Hypertension −; GROUP 3, Diabetes−/ Hypertension +; GROUP 4 Diabetes+/ Hypertension +.

cAdjusted for Body Mass Index; Total Cholesterol; Total Triglycerides; High-Density Lipoprotein cholesterol; estimated Glomerular Filtration Rate; Smoking status; Age; Education Level; Propensity score for being lost to follow-up.

The incidence of CKD in males and females separated by health status (diabetes and/or hypertension) is shown in Figure 1, which depicts no quantitative (i.e. different slops) or qualitative (i.e. crossed lines) interactions between diabetes and hypertension. After introducing diabetes, hypertension, and their multiplicative interaction in the Cox Multivariable regression model, the final result indicated that the multiplicative interaction of diabetes with hypertension, concerning CKD incidence, was not significant either in males (hazard ratio of 1.01 and P value = 0.943) or females (hazard ratio of 0.78 and P value = 0.186) (Table 3).

Figure 1. Incidence of CKD in females and males with and without diabetes and/or hypertension. CKD, Chronic Kidney Disease; DM, Diabetes Mellitus; HTN, Hypertension.
Table 3. Multiplicative Interaction of Diabetes and Hypertension in Incidence of Chronic Kidney Diseasea
Multivariable adjusted ModelbMenP ValueWomenP Value
Diabetes2.16 (1.60-2.93)< 0.0011.24 (0.96-1.60)0.096
Hypertension1.63 (1.29-2.05)< 0.0011.28 (1.08-1.51)0.003
Diabetes × Hypertension1.01 (0.65-1.58)0.9430.78 (0.55-1.12)0.186

aValues are Hazard ratio.

bAdjusted for Body Mass Index; Total Cholesterol; Triglycerides; High-Density Lipoprotein cholesterol; estimated Glomerular Filtration Rate; Smoking status; Age; Education Level; Propensity score for being lost to follow-up.

To investigate the additive interaction of diabetes and hypertension, the previously mentioned equations 1-3 (RERI, AP, S) were used. The additive interaction values for males and females were as follow: RERI= 0.79 (males) and -0.26 (females) using equation 1; AP=0.22 (males) and -0.21 (females), using equation 2; and S=1.44 (males) and S=0.48 (females), using equation 3 (Table 4). Accordingly, no significant additive interaction was detected between diabetes and hypertension concerning the incidence of CKD in either gender.

Table 4. Additive Interaction of Diabetes and Hypertension in Incidence of Chronic Kidney Disease
EstimatesaP ValueCI (95%)
Mens
RERIb0.790.199-0.42 – 2.01
APc0.220.130-0.06 – 0.50
Sd1.440.1830.84 – 2.47
Women
RERIb-0.260.233-0.71 – 0.17
APc-0.210.266-0.59 – 0.16
Sd0.480.2540.14 – 1.67

aAdjusted for Body Mass Index; Total Cholesterol; Triglycerides; High-Density Lipoprotein cholesterol; estimated Glomerular Filtration Rate; Smoking status; Age; Education Level; Propensity score for being lost to follow-up. Interaction exists if RERI > 0, AP > 0, and S > significantly.

bRelative Excess Risk due to Interaction,

cAttributable Proportion due to Interaction,

dSynergy index

5. Discussion

In the present study, the incidence of CKD was almost twice in females than males, which is similar to the findings of previous studies conducted in Iran (3, 23) and a number of other studies (24, 25). Although hypertension and diabetes are independent risk factors of CKD, no significant additive or multiplicative interaction was found between these risk factors concerning CKD incidence. In the present study, after 10 years of follow-up, more than 2% of adults (≥ 20 y/o) develop CKD (stages 3 to 5) each year.

In this study, diabetes was an independent risk factor for CKD in males; however, the evidence was not sufficient to prove such an association for females. Most studies on the association between diabetes and CKD incidence mentioned diabetes as an independent risk factor for CKD incidence (10, 26). Meanwhile, not all studies reported a similar association. Khajehdehi et al. (2014), in a study conducted in Iran, reported no significant association between diabetes and CKD (27). Yamagata et al. also mentioned diabetes as a protective factor for the incidence of CKD stage 3-5 in males and females (28). This difference can be attributed to the time since diagnosis of diabetes in various participants. Al-Rubeaan et al. (2014) showed that a mean diagnosis time of more than 15 years is an important risk factor for developing CKD (odds ratio= 6.30: 5.46-7.27) (10). There are other studies that reported an association between the duration of diabetes and the incidence of CKD, for example, in South Korea, India, and Thailand (29-31). In the present study, the mean age of females and males diagnosed with diabetes was 47 and 52 years, respectively. The lower mean age of females with diabetes, which in turn indicates the probability of lower duration of diabetes diagnosis, can be considered as the main reason for observed differences between females and males. Moreover, since female sex hormones have protective effects for renal function, which is reversed by hyperglycemia, renal damages may present later in females than males (32). In this line, based on TLGS's previous investigations, the present study revealed that diabetes is an independent risk factor for CKD in females, whereas newly diagnosed diabetes, probably with a shorter duration, was not associated with the incidence of CKD (3, 33).

We also investigated the effect of hypertension on CKD incidence. For both females and males, hypertension was an independent risk factor for CKD incidence, which is consistent with the results of previous studies, both in Iran and other countries (1, 3, 23, 27, 34, 35). The lower risk of hypertension for CKD development in females, compared to males, can be attributed to different factors such as female sexual hormones, under-controlled hypertension, and less compliance with treatment in males compared to females (36). Different studies have indicated a close association between diabetes and hypertension, whereby there is considerable overlap in the etiology and influence of these two diseases (37). In the US, 30% of individuals with type I diabetes, along with 50 to 80% of those with type II diabetes, suffer from hypertension (38). In the present study, around 42% of diabetic patients had hypertension, as well. Diabetes and hypertension are two chronic diseases affecting a large portion of the population. The comorbidity of diabetes and hypertension suggests common predisposing factors that can be either genetic or environmental (39). Several studies have reported an association between genetics and developing diabetes and hypertension (40-42). In addition to genetic-related factors, environmental factors are an important arena that needs further investigation (i.e. consumption of high levels of sodium, alcohol and non-saturated fats, smoking, no physical exercise, psychological stress are examples of unhealthy lifestyle) (37). Based on the aforementioned issues, it seems that to investigate the interaction between diabetes and hypertension in the incidence of different diseases, particularly the diseases of interest here, as well as paying attention to the high overlap of these two conditions, the role of genetic and environmental factors driving these two diseases should also be taken in to account. In other words, several genetic, environmental, and behavioral factors contribute to the comorbidity of diabetes and hypertension. Moreover, their interaction is associated with an increased risk of chronic diseases such as CKD and cardiovascular diseases. Nevertheless, to investigate the incidence of these outcomes, studies with longer periods should be implemented.

In conclusion, this study demonstrated that hypertension, regardless of the presence or absence of diabetes, is an independent risk factor for CKD incidence in both genders. The findings also indicated that diabetes is an independent risk factor for CKD incidence in males, while such association was not found for females. Moreover, diabetes has no multiplicative or additive interaction with hypertension in the incidence of CKD.

Acknowledgements

Footnotes

References

  • 1.

    Hosseinpanah F, Kasraei F, Nassiri AA, Azizi F. High prevalence of chronic kidney disease in Iran: a large population-based study. BMC Public Health. 2009;9:44. doi: 10.1186/1471-2458-9-44. [PubMed: 19183493]. [PubMed Central: PMC2658666].

  • 2.

    James MT, Hemmelgarn BR, Tonelli M. Early recognition and prevention of chronic kidney disease. The Lancet. 2010;375(9722):1296-309. doi: 10.1016/s0140-6736(09)62004-3.

  • 3.

    Tohidi M, Hasheminia M, Mohebi R, Khalili D, Hosseinpanah F, Yazdani B, et al. Incidence of chronic kidney disease and its risk factors, results of over 10 year follow up in an Iranian cohort. PLoS One. 2012;7(9). e45304. doi: 10.1371/journal.pone.0045304. [PubMed: 23028919]. [PubMed Central: PMC3459968].

  • 4.

    Long AN, Dagogo-Jack S. Comorbidities of diabetes and hypertension: mechanisms and approach to target organ protection. J Clin Hypertens (Greenwich). 2011;13(4):244-51. doi: 10.1111/j.1751-7176.2011.00434.x. [PubMed: 21466619]. [PubMed Central: PMC3746062].

  • 5.

    Hay SI, Abajobir AA, Abate KH, Abbafati C, Abbas KM, Abd-Allah F, et al. Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet. 2017;390(10100):1260-344. doi: 10.1016/s0140-6736(17)32130-x.

  • 6.

    Pyram R, Kansara A, Banerji MA, Loney-Hutchinson L. Chronic kidney disease and diabetes. Maturitas. 2012;71(2):94-103. doi: 10.1016/j.maturitas.2011.11.009. [PubMed: 22137331].

  • 7.

    Jha V, Garcia-Garcia G, Iseki K, Li Z, Naicker S, Plattner B, et al. Chronic kidney disease: global dimension and perspectives. The Lancet. 2013;382(9888):260-72. doi: 10.1016/s0140-6736(13)60687-x.

  • 8.

    Koro CE, Lee BH, Bowlin SJ. Antidiabetic medication use and prevalence of chronic kidney disease among patients with type 2 diabetes mellitus in the United States. Clin Ther. 2009;31(11):2608-17. doi: 10.1016/j.clinthera.2009.10.020. [PubMed: 20110005].

  • 9.

    Lee SJ, Chung CW. Health behaviors and risk factors associated with chronic kidney disease in Korean patients with diabetes: the Fourth Korean National Health and Nutritional Examination Survey. Asian Nurs Res (Korean Soc Nurs Sci). 2014;8(1):8-14. doi: 10.1016/j.anr.2013.11.001. [PubMed: 25030487].

  • 10.

    Al-Rubeaan K, Youssef AM, Subhani SN, Ahmad NA, Al-Sharqawi AH, Al-Mutlaq HM, et al. Diabetic nephropathy and its risk factors in a society with a type 2 diabetes epidemic: a Saudi National Diabetes Registry-based study. PLoS One. 2014;9(2). e88956. doi: 10.1371/journal.pone.0088956. [PubMed: 24586457]. [PubMed Central: PMC3931705].

  • 11.

    IDF. IDF Diabetes Atlas. 8th ed. Belgium: International Diabetes Federation; 2017.

  • 12.

    Sarafidis PA, Li S, Chen SC, Collins AJ, Brown WW, Klag MJ, et al. Hypertension awareness, treatment, and control in chronic kidney disease. Am J Med. 2008;121(4):332-40. doi: 10.1016/j.amjmed.2007.11.025. [PubMed: 18374693].

  • 13.

    Horowitz B, Miskulin D, Zager P. Epidemiology of hypertension in CKD. Adv Chronic Kidney Dis. 2015;22(2):88-95. doi: 10.1053/j.ackd.2014.09.004. [PubMed: 25704344].

  • 14.

    Saran R, Robinson B, Abbott KC, Agodoa LYC, Bhave N, Bragg-Gresham J, et al. US Renal Data System 2017 Annual Data Report: Epidemiology of Kidney Disease in the United States. Am J Kidney Dis. 2018;71(3 Suppl 1). A7. doi: 10.1053/j.ajkd.2018.01.002. [PubMed: 29477157]. [PubMed Central: PMC6593155].

  • 15.

    Su SL, Lin C, Kao S, Wu CC, Lu KC, Lai CH, et al. Risk factors and their interaction on chronic kidney disease: A multi-centre case control study in Taiwan. BMC Nephrol. 2015;16:83. doi: 10.1186/s12882-015-0065-x. [PubMed: 26077152]. [PubMed Central: PMC4469431].

  • 16.

    Wang Z, do Carmo JM, Aberdein N, Zhou X, Williams JM, da Silva AA, et al. Synergistic Interaction of Hypertension and Diabetes in Promoting Kidney Injury and the Role of Endoplasmic Reticulum Stress. Hypertension. 2017;69(5):879-91. doi: 10.1161/HYPERTENSIONAHA.116.08560. [PubMed: 28348018]. [PubMed Central: PMC5395247].

  • 17.

    Azizi F, Ghanbarian A, Momenan AA, Hadaegh F, Mirmiran P, Hedayati M, et al. Prevention of non-communicable disease in a population in nutrition transition: Tehran Lipid and Glucose Study phase II. Trials. 2009;10:5. doi: 10.1186/1745-6215-10-5. [PubMed: 19166627]. [PubMed Central: PMC2656492].

  • 18.

    Yamanaka G, Kawashima H, Miyajima T, Oana S, Ishida Y, Kashiwagi Y, et al. CEREBROSPINAL FLUID LEVELS OF CYTOKINES AND CHEMOKINES IN PATIENTS WITH WEST SYNDROME. NEUROSCIENCE RESEARCH PROGRESS.

  • 19.

    American Diabetes A. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2013;36 Suppl 1:S67-74. doi: 10.2337/dc13-S067. [PubMed: 23264425]. [PubMed Central: PMC3537273].

  • 20.

    Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JJ, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289(19):2560-72. doi: 10.1001/jama.289.19.2560. [PubMed: 12748199].

  • 21.

    Rubin DB. Estimating causal effects from large data sets using propensity scores. Ann Intern Med. 1997;127(8 Pt 2):757-63. doi: 10.7326/0003-4819-127-8_part_2-199710151-00064. [PubMed: 9382394].

  • 22.

    de Mutsert R, Jager KJ, Zoccali C, Dekker FW. The effect of joint exposures: examining the presence of interaction. Kidney Int. 2009;75(7):677-81. doi: 10.1038/ki.2008.645. [PubMed: 19190674].

  • 23.

    Najafi I, Attari F, Islami F, Shakeri R, Malekzadeh F, Salahi R, et al. Renal function and risk factors of moderate to severe chronic kidney disease in Golestan Province, northeast of Iran. PLoS One. 2010;5(12). e14216. doi: 10.1371/journal.pone.0014216. [PubMed: 21151983]. [PubMed Central: PMC2997063].

  • 24.

    Zhang QL, Rothenbacher D. Prevalence of chronic kidney disease in population-based studies: systematic review. BMC Public Health. 2008;8:117. doi: 10.1186/1471-2458-8-117. [PubMed: 18405348]. [PubMed Central: PMC2377260].

  • 25.

    Ong-Ajyooth L, Vareesangthip K, Khonputsa P, Aekplakorn W. Prevalence of chronic kidney disease in Thai adults: a national health survey. BMC Nephrol. 2009;10:35. doi: 10.1186/1471-2369-10-35. [PubMed: 19878577]. [PubMed Central: PMC2781792].

  • 26.

    Bash LD, Selvin E, Steffes M, Coresh J, Astor BC. Poor glycemic control in diabetes and the risk of incident chronic kidney disease even in the absence of albuminuria and retinopathy: Atherosclerosis Risk in Communities (ARIC) Study. Arch Intern Med. 2008;168(22):2440-7. doi: 10.1001/archinte.168.22.2440. [PubMed: 19064828]. [PubMed Central: PMC2766035].

  • 27.

    Khajehdehi P, Malekmakan L, Pakfetrat M, Roozbeh J, Sayadi M. Prevalence of Chronic Kidney Disease and Its Contributing Risk Factors in Southern Iran A Cross-sectional Adult Population-based Study. IJKD. 2014.

  • 28.

    Yamagata K, Ishida K, Sairenchi T, Takahashi H, Ohba S, Shiigai T, et al. Risk factors for chronic kidney disease in a community-based population: a 10-year follow-up study. Kidney Int. 2007;71(2):159-66. doi: 10.1038/sj.ki.5002017. [PubMed: 17136030].

  • 29.

    Yang CW, Park JT, Kim YS, Kim YL, Lee YS, Oh YS, et al. Prevalence of diabetic nephropathy in primary care type 2 diabetic patients with hypertension: data from the Korean Epidemiology Study on Hypertension III (KEY III study). Nephrol Dial Transplant. 2011;26(10):3249-55. doi: 10.1093/ndt/gfr011. [PubMed: 21372264].

  • 30.

    Viswanathan V, Tilak P, Kumpatla S. Risk factors associated with the development of overt nephropathy in type 2 diabetes patients: a 12 years observational study. Indian J Med Res. 2012;136(1):46-53. [PubMed: 22885263]. [PubMed Central: PMC3461717].

  • 31.

    Shirzad M, Taghdisi MH, Dehdari T, Abolghasemi J. The effect of educational intervention in changing mothers’ attitudes, perceived self-efficacy and perceived barriers regarding oral health of preschool children. Iranian Journal of Health Education and Health Promotion. 2015;3(3):181-7.

  • 32.

    Maric C, Sullivan S. Estrogens and the diabetic kidney. Gend Med. 2008;5 Suppl A:S103-13. doi: 10.1016/j.genm.2008.03.010. [PubMed: 18395675]. [PubMed Central: PMC3178838].

  • 33.

    Parizadeh D, Rahimian N, Akbarpour S, Azizi F, Hadaegh F. Sex-specific clinical outcomes of impaired glucose status: A long follow-up from the Tehran Lipid and Glucose Study. Eur J Prev Cardiol. 2019;26(10):1080-91. doi: 10.1177/2047487319834396. [PubMed: 30862232].

  • 34.

    Parikh NI, Hwang SJ, Larson MG, Meigs JB, Levy D, Fox CS. Cardiovascular disease risk factors in chronic kidney disease: overall burden and rates of treatment and control. Arch Intern Med. 2006;166(17):1884-91. doi: 10.1001/archinte.166.17.1884. [PubMed: 17000946].

  • 35.

    Herget-Rosenthal S, Dehnen D, Kribben A, Quellmann T. Progressive chronic kidney disease in primary care: modifiable risk factors and predictive model. Prev Med. 2013;57(4):357-62. doi: 10.1016/j.ypmed.2013.06.010. [PubMed: 23783072].

  • 36.

    Duru OK, Li S, Jurkovitz C, Bakris G, Brown W, Chen SC, et al. Race and sex differences in hypertension control in CKD: results from the Kidney Early Evaluation Program (KEEP). Am J Kidney Dis. 2008;51(2):192-8. doi: 10.1053/j.ajkd.2007.09.023. [PubMed: 18215697]. [PubMed Central: PMC2866650].

  • 37.

    Cheung BM, Li C. Diabetes and hypertension: is there a common metabolic pathway? Curr Atheroscler Rep. 2012;14(2):160-6. doi: 10.1007/s11883-012-0227-2. [PubMed: 22281657]. [PubMed Central: PMC3314178].

  • 38.

    Landsberg L, Molitch M. Diabetes and hypertension: pathogenesis, prevention and treatment. Clin Exp Hypertens. 2004;26(7-8):621-8. doi: 10.1081/ceh-200031945. [PubMed: 15702616].

  • 39.

    Cheung BM. The hypertension-diabetes continuum. J Cardiovasc Pharmacol. 2010;55(4):333-9. doi: 10.1097/fjc.0b013e3181d26430. [PubMed: 20422737].

  • 40.

    Tso AW, Sham PC, Wat NM, Xu A, Cheung BM, Rong R, et al. Polymorphisms of the gene encoding adiponectin and glycaemic outcome of Chinese subjects with impaired glucose tolerance: a 5-year follow-up study. Diabetologia. 2006;49(8):1806-15. doi: 10.1007/s00125-006-0324-2. [PubMed: 16788799].

  • 41.

    Ong KL, Leung RY, Wong LY, Cherny SS, Sham PC, Lam TH, et al. Association of a polymorphism in the lipin 1 gene with systolic blood pressure in men. Am J Hypertens. 2008;21(5):539-45. doi: 10.1038/ajh.2008.21. [PubMed: 18437145].

  • 42.

    Chow WS, Cheung BM, Tso AW, Xu A, Wat NM, Fong CH, et al. Hypoadiponectinemia as a predictor for the development of hypertension: a 5-year prospective study. Hypertension. 2007;49(6):1455-61. doi: 10.1161/HYPERTENSIONAHA.107.086835. [PubMed: 17452504].

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