Abstract
Background
The diabetes prevalence is escalating in Jordan; as a consequence, the risk of developing diabetic kidney diseases is also increasing.
Objective
This study evaluated the effect of risk factors and comorbidities on kidney function in patients with type 2 diabetes mellitus (T2DM).
Design
A cross-sectional, survey-based study.
Setting
Participants were recruited from the endocrinology and cardiology clinics of a tertiary hospital in Jordan.
Participants
Patients with T2DM aged 18 years and more who had undergone a kidney function test within a year before data collection.
Outcome measures
The estimated GFR (eGFR) mean values and proteinuria presence were used to evaluate the impact of risk factors on kidney function. Descriptive and analytical statistical approaches were used to calculate mean, prevalence and correlations. The SPSS software was used with a p value<0.05 for significance.
Results
Of the total 331 study participants, 54.1% were men and 45.9% were women. The mean age was 60 years. The eGFR mean values were significantly reduced in patients with T2DM with hypertension, hyperlipidaemia and proteinuria (p<0.01). The correlation analysis results showed that the eGFR was positively correlated with hypertension and hyperlipidaemia presence (rs=0.253, 0.220), and negatively correlated with age, body mass index and diabetes duration (rs=−0.395, –0.151, −0.221), respectively. However, the eGFR did not corelate with income, sex, smoking and anaemia. Of note, about 68% of the patients with T2DM had uncontrolled diabetes.
Conclusions
Kidney function were severely affected in patients with T2DM in the presence of risk factors and comorbidities. It is highly recommended to control diabetes through medications and life style, and to regularly check for kidney function to halt the deteriorations in kidney function.
Keywords: Diabetic nephropathy & vascular disease, General diabetes, Health policy, NEPHROLOGY
STRENGTHS AND LIMITATIONS OF THIS STUDY.
Collecting data from clinics at a tertiary hospital provides a broader and more diverse patient sample that enhances the external validity and real-world relevance of the findings.
Collecting data from both patients and the hospital’s electronic system strengthens the study by providing complementary information, enhancing data accuracy, and enabling triangulation of insights.
The design of the cross-sectional study does not allow causal relationships.
Patients seeking care at a tertiary hospital might have more severe cases or distinct characteristics, potentially limiting the generalisability of findings to the broader type 2 diabetes population.
Results are not representative of all patients with type 2 diabetes in Jordan.
Introduction
Diabetes is a disease of chronic hyperglycaemia resulting from insufficient production of insulin or defects in insulin action in the body due to resistance to its actions or a combination of both.1 A recent study from Jordan assessed the time trend in diabetes between 1994 and 2017; results showed an increase in diabetes prevalence from 14.2 to 32.4% through the years.2
Type 2diabetes mellitus (T2DM) can develop at any age; however, it is most common in people over 40.3 T2DM is increasing among younger people including children and adolescents.2 4 The risk factors associated with diabetes include: being overweight or obese, age 35 or older, having a family history of diabetes, lack of exercising and having a history of gestational diabetes.5 With time diabetes can severely damage macrovascular, which includes coronary, peripheral arterial disease and microvascular, which is associated with other DM-induced long-term complications such as neuropathy, retinopathy, nephropathy, diabetic foot, limbs amputation and cardiovascular diseases.3
T2DM is one of the primary causes of chronic kidney diseases (CKD) in the USA and it may progress to end-stage renal diseases.6 CKD is a worldwide public health problem, both the number of patients and the cost of treatment are involved.7 It is characterised by reduced estimated glomerular filtration rate (eGFR) and/or increased urine albumin-to-creatinine ratio (Alb/Cr).8 According to the National Kidney Foundation, the Kidney Disease Outcomes Quality Initiative guidelines used the following criteria for the diagnosis of CKD: eGFR value below 60 mL/min/ 1.73 m2 in a time period equal or more than 3 months or the presence of kidney damage with or without reduced eGFR during a time period equal or more than 3 months.4 Noteworthy, diabetes accounts for 9.1%–29.9% of CKD cases in the developing countries.9
Many of CKD complications can be prevented or delayed by early detection and treatment. Therefore, strict control of blood sugar levels is a significant factor in slowing the progression of CKD in T2DM. Hence, treatment to prevent diabetic kidney disease (DKD) should begin early before kidney damage develops.10
The importance of this study emerged from the fact that T2DM prevalence is high in Jordan.2 In addition, about 54% of the patients with T2DM in Jordan are with poor glycaemic control.11 As a consequence, the T2DM complications increase and may account for significant mortality.12 According to our knowledge, this study is the first that comprehensively evaluated kidney function and the associated risk factors among patients with T2DM in Jordan. Diabetes and its associated complications reduced the quality of life for patient with diabetes and created a heavy economic and social burden in Jordan. Therefore, the present study will provide a clear picture about the kidney function and associated risk factors among patients with T2DM in Jordan; this may guide the decision-maker to develop a better plan in order to improve the quality of patients’ lives. The aim of this study is to evaluate the effect of risk factors and comorbidities on kidney function in patients with T2DM in Jordan.
Methods
Study design
This is an observational cross-sectional study. The sociodemographic, anthropometric and clinical information for the patients with T2DM were collected by interviewing patients and accessing their electronic medical records. The sociodemographic information includes patients age, sex, monthly income and the place of sample collection (clinic). The anthropometric information includes: patients’ weight, height and body mass index (BMI). The clinical information includes: diabetes onset information, glycated haemoglobin (HbA1C), Fasting blood sugar (FBS), proteinuria and the eGFR values. The risk factors include: smoking status, hypertension, hyperlipidaemia, anaemia and BMI information. Dipstick analysis of urine was used to identify the presence of proteinuria, with a threshold of at least +1 (equivalent to 30 mg of protein/dL of urine) or greater.
The allocation of patients seen at the hospital was intended to create a sample that is reflective of individuals seeking medical care for T2DM within our target population. We employed several approaches to patient selection to ensure that the characteristics of this sample are representative of those typically seeking medical attention for type 2tdiabetes. First, participants were recruited from King Abdulla University Hospital, which serves as the largest medical facility in northern Jordan and offers comprehensive healthcare services to both urban and rural areas, making it an ideal setting for our study’s inclusive approach. The hospital’s extensive reach aligns with our research objectives and enhances representation from various demographic and socioeconomic backgrounds of Jordanian patients with type 2 diabetes. Second, the exclusion criteria were kept to the minimum by only excluding patients with conditions that might introduce bias to the outcome measures.
A written consent form was collected from the patients on participation. The research questionnaire was developed by the research team and addressed the different risk factors and comorbidities associated with diabetes.
Inclusion and exclusion criteria
The patients with T2DM who participated in this study were on antidiabetic medications, at least 18 years old, and they also performed a kidney function test within the last year before the samples were collected. However, the following patients were excluded from the study, patients with T2DM who undergo acute renal failure, pregnant women, patients who were on a vegetarian or protein-rich diet, patients who had imputations and patients with impaired mental health that affect their answers in the interview.
Statistical analysis
The responses of patients with T2DM were analysed using IBM SPSS Statistics (Version 27) software (2020) (SPSS). Descriptive statistics were used to calculate the mean and the prevalence of the different risk factors in patients with T2DM. Spearman’s coefficient was used to determine the strength and type of relationship between risk factors and eGFR. A p value<0.05 was considered significant. The estimated GFR was calculated using the Modification of Diet in Renal Disease (MDRD) equation (GFR=175 * standardised serum Creatinine1.154 * age-0.203 * 1.212 [if black] * 0.742 [if female]).13 We opted for the MDRD equation to estimate eGFR due to MDRD’s accuracy in older age groups, aligning with our participant demographic (mean age: 60 years).14 The following electronic calculator was used to calculate the eGFR (https://www.mdcalc.com/mdrd-gfr-equation); the eGFR was expressed in mL/min per 1.73 m2.
Patients and public involvement
Patients or the public were not involved in the design, reporting or dissemination of this study.
Results
The sociodemographic information of the patients with T2DM
The sociodemographic information for the 331 patients with T2DM showed that the mean age was 60.28 years. 54.1% were men and 45.9% were women. The majority of the patients with T2DM (75%) had medium-to-low income (less than US$1400). Most of the data (87.6%) were collected at the endocrinology clinic and the rest (12.4%) were from the cardiology clinic at King Abdullah University Hospital in the northern of Jordan. The anthropometric measurements of the patients with T2DM indicated that the mean weight was 86.8 kg and the mean height was 166.8 cm; the BMI mean was 31.2, reflecting obesity in most of the patients with T2DM.
The clinical picture of the patients with T2DM
The clinical parameters of the patients with T2DM were introduced in table 1. The patients were classified according to the diabetes onset duration into five groups, more than 50% of the patients had diabetes for 1–10 years. The patient’s glucose level was monitored by measuring the HbA1C and the FBS levels. Results showed that most of the patients with T2DM (68.1%) had an HbA1C level of 7 and more, and the FBS values were higher than 130 mg/dL in most of the patients (67.4%) indicating uncontrolled diabetes. There were signs of proteinuria in the urine of patients with T2DM; 22.54% had proteinuria. The kidney function were evaluated by measuring the eGFR using the MDRD formula as indicated in the Method section, most of the patients with T2DM (68.57%) had their eGFR less than 90 mL/min per 1.73 m2, and about 22.35% had their eGFR less than 60 mL/min per 1.73 m2 suggesting a decline in kidney function.
Table 1.
The clinical parameters among the patients with T2DM
| Clinical parameter | Percentage (%) | N |
| Diabetes onset duration (years) | ||
| 1–5 | 31.7 | 331 |
| 6–10 | 27.8 | |
| 11–15 | 17.8 | |
| 16–20 | 12.4 | |
| More than 20 | 10.3 | |
| HbA1C | ||
| Less than 7 | 30.9 | 327 |
| 7 and more | 68.1 | |
| FBS (mg/dL) | ||
| 130 mg/dL or less | 32.6 | 193 |
| Higher than 130 mg/dL | 67.4 | |
| Proteinuria | ||
| Present | 22.54 | 284 |
| Absent | 77.46 | |
| eGFR | ||
| 90 mL/min per 1.73 m2 and more | 31.43 | 331 |
| Less than 90 mL/min per 1.73 m2 | 68.57 | |
| Less than 60 mL/min per 1.73 m2 | 22.35 |
The risk factors and comorbidities in patients with T2DM
The presence of risk factors and comorbidities that could increase the chance of having renal diseases were shown in table 2. The smoking status was provided for the patients with T2DM, about 31% of the patients with T2DM were current smokers, 13% were former smokers and 57% were non-smokers, among the current smokers about 25% were men and 6% were women. Hypertension affected 68.6% patients with T2DM, about 36% were men and 32% were women. Hyperlipidaemia presented in 60.1% patients with T2DM, of which 34% were men and 26% were women. Anaemia influenced 17.5% patients with T2DM, about 8% were men and 10% were women. The BMI values were classified as follows: underweight (below 18.5%) in 0.03% patients with T2DM. Healthy weight (18.5–24.9) in 16.5% patients with T2DM, overweight (25.0–29.9) in about 33% patients with T2DM and obese (30 and above) in about 50% patients with T2DM. Interestingly, 18.6% of men were overweight compared with 14.3% women whereas 27% of women were obese compared with 23% men (table 2).
Table 2.
Risk factors and comorbidities in male and female patients with type 2 diabetes mellitus
| Risk factors | Percentage (100%) | P value | N | ||
| All | Males | Female | |||
| Smoking status | |||||
| Current | 30.5 | 24.77 | 5.43 | 0.0001* | 331 |
| Former | 13 | 10.27 | 2.72 | ||
| Non-smoker | 56.5 | 19.03 | 37.76 | ||
| Hypertension | |||||
| Present | 68.6 | 36.25 | 32.32 | 0.514 | 331 |
| Absent | 31.4 | 17.82 | 13.59 | ||
| Hyperlipidaemia | |||||
| Present | 60.1 | 33.84 | 26.29 | 0.325 | 331 |
| Absent | 39.9 | 20.24 | 19.63 | ||
| Anaemia | |||||
| Present | 17.5 | 7.85 | 9.67 | 0.120 | 331 |
| Absent | 82.5 | 46.22 | 36.25 | ||
| BMI | |||||
| Underweight (below 18.5) | 0.03 | 0.03 | 0 | 0.001* | 328 |
| Healthy weight (18.5–24.9) | 16.4 | 11.56 | 4.88 | ||
| Overweight (25.0–29.9) | 32.9 | 18.59 | 14.33 | ||
| Obese (30 and above) | 50.3 | 23.17 | 27.13 | ||
* Significant (p value<0.01).
BMI, body mass index; N, number of participants.
The risk factors values for women were normalised to men and represented in figure 1 for the easy comparison between the risk factors in men and women. The differences between values of men and women were further analysed for significance. Results in table 2 and figure 1 showed that the difference in smoking status and BMI between men and women was highly significant (p<0.001). However, there was no significant difference between values of men and women regarding hypertension, hyperlipidaemia and anaemia onset (table 2, figure 1)
Figure 1.
Risk factors and comorbidities in female normalised to males patients with type 2 diabetes mellitus.
The effect of risk factors and comorbidities on eGFR
To study the effect of risk factors and comorbidities on eGFR, we compared the mean values of eGFR in the presence and absence of risk factors (table 3). Results showed that the mean eGFR values were significantly (p value<0.01) decreased in patients with T2DM who had hypertension, hyperlipidaemia and proteinuria.
Table 3.
Risk factors and comorbidities’ effect on estimated glomerular filtration rate (eGFR)
| Risk factor | eGFR mean value | P value |
| Smoking status | ||
| Current | 81.07 | 0.124 |
| Former | 71.51 | |
| Non-smoker | 75.87 | |
| Hypertension | ||
| Present | 73.40 | 0.000* |
| Absent | 87.83 | |
| Hyperlipidaemia | ||
| Present | 72.51 | 0.000* |
| Absent | 81.83 | |
| Anaemia | ||
| Present | 74.03 | 0.226 |
| Absent | 78.71 | |
| Proteinuria | ||
| Present | 61.04 | 0.000* |
| Absent | 81.95 | |
| BMI | ||
| Less than 30 | 82.34 | 0.067 |
| 30 and more | 77.24 |
* Significance level (p value<0.01).
BMI, body mass index.
Of note, the eGFR values were not significant in the following groups: obese (BMI>30) and non-obese; current, formers and non-smokers; patients with and without anaemia (table 3).
The correlation of sociodemographic information and comorbidities with eGFR
We opted to study the correlation between the different variables and eGFR. Results in table 4 showed that age, BMI and diabetes duration negatively correlated with eGFR (rs=−0.395, −0.151, −0.221), respectively, whereas hypertension and hyperlipidaemia were positively correlated with eGFR (rs=0.253, 0.220) respectively. Nevertheless, other variables like income, smoking, sex and anaemia had no significant correlation with eGFR (table 4).
Table 4.
The correlations between variables and estimated glomerular filtration rate (eGFR) in patients with type 2 diabetes mellitus (T2DM)
| Variables (N range: 328–331) |
Spearman’s rho | eGFR |
| Income | rs | −0.035 |
| P | 0.529 | |
| Smoking | rs | −0.094 |
| P | 0.088 | |
| Age | rs | −0.395* |
| P | 0.000 | |
| BMI | rs | −0.151 |
| P | 0.006 | |
| Sex | rs | −0.020 |
| P | 0.719 | |
| Diabetes duration (years) | rs | −0.221* |
| P | 0.000 | |
| Hypertension | rs | 0.253* |
| P | 0.000 | |
| Hyperlipidaemia | rs | 0.220* |
| P | 0.000 | |
| Anaemia | rs | 0.067 |
| P | 0.226 |
rs: Spearman Correlation coefficient; P: significance level.
*Correlation is significant (p< 0.05).
N, number of patients with T2DM.
Discussion
The present study discussed the prevalence and the impact of risk factors and comorbidities among patients with T2DM in a tertiary hospital in Jordan. The eGFR values were used to reflect kidney function in the presence of risk factors and comorbidities.
A large share of patients with T2DM had uncontrolled diabetes and eGFR values less than 90 mL/min per 1.73 m2, about 22% had eGFR values less than 60 mL/min per 1.73 m2, which indicated a high risk of DKD. In addition, hypertension and hyperlipidaemia comorbidities were also prevalent among the patients with T2DM. Results showed correlations and a significant reduction in the eGFR values in the presence of hypertension, hyperlipidaemia and proteinuria. Age of patients with T2DM and diabetes onset duration were also risk factors of DKD.
Our findings are consistent with previous findings in which DKD was observed among the patients with T2DM. However, the prevalence of DKD was higher in other countries compared with Jordan; it was about 28% in Spain and Netherlands,15 16 31% in UK17 and about 40% in the USA and Japan.18 19 The difference in DKD prevalence might be due to the differences in the number of diabetes cases included in the studies, some of the studies included T2DM only, others included both T1DM and T2DM.
Diabetes onset duration was considered an important risk factor in developing DKD which was in consistent with previous reports showing that DKD was independently associated with T2DM duration. The development of DKD was greater among patients with longer duration of diabetes20 21; consequently, intensive and appropriate healthcare of patients with T2DM, especially in the old age group, may slow the progression of long-term complications and improve the quality of their lives.
Ageing is correlated with renal insufficiency and albuminuria.22 23 Our results are consistent with the current evidence which showed that the increase in age is accompanied with a gradual loss of nephrons and a reduced renal blood flow, which leads to DKD15 24 25; subsequently, screening of DKD in an old age group is a critical strategy required to take an appropriate intervention and control.
Proteinuria is used clinically as a marker of DKD in the patients with T2DM.26 In agreement with previous studies that considered the presence of proteinuria a major risk factor of DKD, we found a significant reduction in eGFR in the presence of proteinuria in patients with T2DM.27–29 Therefore, early intervention may reduce the risk of progression of kidney diseases and cardiovascular outcomes in patients with T2DM.
Hypertension is commonly associated with DKD; it was developed in more than 75% of patients with CKD at any stage.30 It is both a cause and a consequence of CKD. In consonance with a previous report, hypertension was independently associated with DKD.15 Hence, the control of blood pressure and the use of antihypertensive drugs will improve the kidney function and slowdown the rate of progression of renal disease in the patients with T2DM.31
The higher triglyceride level is associated with lower eGFR value. Our findings are in line with the previous findings that showed a correlation of hyperlipidaemia and the development of T2DM nephropathy; a reduction in the eGFR value was associated with the presence of hyperlipidaemia in patients with T2DM. 32–34 This might be explained by the increase in insulin resistance in patients with T2DM which resulted in an increase in hyperlipidaemia due to the free fatty acids flux resulting from lipolysis.35 36 Noteworthy, the increased cardiovascular morbidity and mortality in patients with T2DM have been attributed to the high prevalence of hyperlipidaemia.36 37 Interestingly, the lipid-lowering medications reduce the chance of DKD.36
Other risk factors may also participate in developing DKD. In line with previous findings, the eGFR decreased in the patients with T2DM with high BMI and smoking.38–40 Conversely, weight loss reduces urinary albumin excretion and prevents the decline in GFR.41 Therefore, preventive approaches to reduce and control the weight among those who are overweight, and obese may reduce DKD risk.
Finally, our findings had an important strength points that are relevant to usual clinical practice. To our knowledge, the current study is the first in Jordan that evaluated the risk factors and comorbidities associated with diabetes. The analysis approach we followed revealed significant findings. In addition, this study provided a guideline for clinical practitioners for implementing early and optimal intervention that is necessary to delay DKD progression in patients with T2DM. However, this study is limited by being a cross-sectional study which only provides the basis for associations rather than causality. There was some missing information for patient’s biochemical data. In addition, our samples were collected from a tertiary hospital located in the northern of Jordan; this may create a selection bias affecting the generalisability of the study findings.
In conclusion, the prevalence of DKD increased in the presence of risk factors and comorbidities such as older age, obesity, longer duration of diabetes, hypertension hyperlipidaemia and proteinuria. Thus, a planned evaluation of kidney function and associated risk factors would help in earlier diagnosis. This will hinder the progression to advanced stages of CKD and allowing an appropriate intervention in the early stages of the disease during which treatment is more effective.
The generalisability of our study results extends to the patients with T2DM with characteristics similar to our studied sample, particularly those seeking care for T2DM within comparable clinical contexts. While we acknowledge the need for cautious interpretation in different contexts, we believe that our approach to data collection and the diversity of our sample will contribute to the broader relevance and generalisability of our findings.
Supplementary Material
Acknowledgments
The authors would like to thank the Endocrinology and Cardiology Clinics at King Abdullah University Hospital, Jordan, for providing support in sample collection and patients’ data. Our gratitude is to Jordan University of Science and Technology for funding this research. A special thank is for patients who agreed to share their information in this study.
Footnotes
Contributors: AA-M: participated in the study design, writing the introduction and discussion sections, and editing the manuscript. EYA: study design, writing and editing the manuscript. HYA: study design, collecting data. SIA-A: ethical approval, collecting data. AQ: statistical analysis and writing the Method section. AA-M in the role of the corresponding author, designed the study and is the guarantor.
Funding: This research was funded by the deanship of scientific research of Jordan University of Science and Technology, Jordan. Grant no. 20200195.
Competing interests: None declared.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review: Not commissioned; externally peer reviewed.
Data availability statement
Data are available upon reasonable request.
Ethics statements
Patient consent for publication
Consent obtained directly from patient(s)
Ethics approval
This study involves human participants and was approved by Institutional Review Board committee at King Abdullah University Hospital (Reference number 48/132/2020), which was issued on 01.04.2020, Participants gave informed consent to participate in the study before taking part.
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Data Availability Statement
Data are available upon reasonable request.