Prevalence of Metabolic Syndrome among middle-aged patients and its association with chronic kidney disease: A Cross-sectional study

doi:10.21203/rs.3.rs-4006719/v1

Background

Previous surveys suggest that obesity, hypertension, and diabetes mellitus may be positively related to the development of chronic kidney disease (CKD). However, this association might be altered by metabolic syndrome. Chronic kidney disease has become a worldwide health problem among aging populations. Hence, epidemiological information on middle-aged patients with metabolic syndrome is still lacking.

Objectives

The study aimed to assess the prevalence of metabolic syndrome among middle-aged patients and its association with chronic kidney disease.

Methodology:

The hospital-based cross-sectional study was carried out on 317 participants aged 40–59 years. All participants received a standardized personal interview, including a structured questionnaire, anthropometric measurements, and blood samples collected for laboratory testing. Metabolic syndrome was identified based on the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III). The estimated glomerular filtration rate (eGFR) was calculated by using the Cockroft-Gault formula, which in turn is utilized to predict the stages of chronic kidney disease based on the eGFR range based on NKF-KDOQI.

Result and discussion:

We investigated the association between metabolic syndrome and chronic kidney disease (CKD) in 317 participants aged 40-59 years. We found that metabolic syndrome was prevalent in more than half of the participants (54.2%) and increased with the worsening of CKD stages. We also identified waist circumference, fasting blood sugar, and triglycerides as significant metabolic factors associated with CKD. Furthermore, we observed that longer durations of diabetes mellitus and hypertension, especially when combined, increased the risk of CKD.

Conclusion:

Our findings suggest that metabolic syndrome is a major contributor to CKD and that early detection and management of metabolic factors are essential to prevent kidney damage.

Epidemiology

Metabolic syndrome

Chronic kidney disease

Middle aged patients

NCEP/ATP III

NKF-KDOQI

The escalating prevalence of obesity and diabetes, driven by shifts in dietary habits and lifestyle behaviors, is a significant global health concern.^1,2,3 This rise has been paralleled by an increase in metabolic syndrome, a condition that heightens the risk of mortality from chronic kidney disease (CKD), as reported by LaGuardia HA et al.⁴ CKD, characterized by a gradual and progressive loss of renal function over at least three months, is a primary cause of end-stage renal disease (ESRD).⁵ The global prevalence of CKD ranges from 8–16%, varying across different regions.⁶ CKD is categorized into five stages based on the degree of kidney damage or the estimated glomerular filtration rate (eGFR).^7,8 Regrettably, the majority of patients remain unaware of their condition until it has advanced to a later stage.⁹ Early cardiovascular disease (CVD), anemia, metabolic acidosis, and bone diseases are among the major complications associated with impaired kidney function.¹⁰ The leading risk factors and predictors for CKD include impaired fasting plasma glucose, hypertension, and a high body mass index (BMI).¹¹

There is a growing body of evidence suggesting a link between obesity, hypertension, diabetes mellitus, and the development of CKD.^12,13 Systematic reviews and meta-analyses have indicated that metabolically unhealthy individuals are at an increased risk for CKD.^14,15 However, it is important to note that previous studies investigating the association between metabolic health status phenotypes and renal disorders have primarily focused on eGFR measurements to define CKD.¹⁶

Given the contradictory findings and the scarcity of studies examining the association between CKD and metabolic syndrome in middle-aged patients, we aim to conduct cross-sectional studies to elucidate the relationship between CKD and metabolic health status. This research will contribute to a more comprehensive understanding of these complex health issues and inform future preventative and treatment strategies.

Study Design and participants' characteristics

This research was a cross-sectional, population-based study carried out in the medical wards of a tertiary care hospital in Erode. The participants comprised individuals who sought care at health centers in Erode. The study included individuals of all genders, aged 40–59 years, who were residents of Erode, willing to participate, and diagnosed with non-communicable diseases such as Diabetes Mellitus, Hypertension, Dyslipidemia, and Obesity.

The exclusion criteria encompassed incomplete questionnaires, unwillingness to participate in the tests, and pre-existing renal diseases or other conditions that could potentially impact renal function. Ultimately, a total of 317 eligible middle-aged individuals were included in the study. The study was conducted by the Declaration of Helsinki and was approved by the Institutional Ethics Committee of JKKN College of Pharmacy (JKKNCP/IEC-CER/0522123/38). Before the commencement of the study, all participants provided their written informed consent.

The research took into account a variety of characteristics of the participants, including their age, gender, place of residence, level of physical activity, dietary habits, and medical history. The age of the participants was divided into four categories: 40–44, 45–49, 50–54, and 55–59 years. The International Physical Activity Questionnaire (IPAQ) was used to classify physical activity levels as high, moderate, or low.¹⁷ Dietary habits were categorized as healthy, moderate, or unhealthy based on the Healthy Eating Index (HEI) score.^18,19 The duration of conditions such as diabetes and hypertension was determined through direct patient interviews. Information on current smoking habits and alcohol consumption was also collected.

Metabolic Syndrome Assessment

Risk factors for renal failure, including hypertension, dyslipidemia, waist circumference, smoking, blood pressure, fasting blood glucose, lipid analysis, BUN/creatinine, and BMI were analyzed. Metabolic syndrome was diagnosed based on the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III).²⁰ Participants were diagnosed with metabolic syndrome if they met three or more of the following five criteria: waist circumference ≥ 90 cm (men) or ≥ 85 cm (women), fasting blood sugar ≥ 100 mg/dL, triglycerides > 150 mg, HDL –C level < 40 mg/dL (men) or < 50 mg/dL (women), and blood pressure ≥ 130/85 mmHg. The data from participants diagnosed with metabolic syndrome were then analyzed.

Biochemical Analysis

Blood samples were primarily drawn from the median cubital and cephalic veins after a minimum of 8 hours of fasting. The samples were refrigerated and sent to a diagnostic medical laboratory for analysis within 24 hours. The levels of triglycerides, HDL-C, and fasting blood glucose were measured using enzymatic methods on Lipid Biosensor (TamilNadu, India).²¹

Chronic Kidney Disease Assessment

The assessment of chronic kidney disease (CKD) was conducted using the estimated Glomerular Filtration Rate (eGFR), calculated with the Cockcroft-Gault formula.²² This formula takes into account the patient’s creatinine levels, age, gender, and ethnicity: CrCl = 72×serum creatinine in mg/dL (140 − age in years) weight in kg

[For females, the result is multiplied by a factor of 0.85].

Following the National Kidney Foundation- Kidney Disease Outcomes Quality Initiative (NKF-KDOQI), CKD was categorized into five stages: normal (stage 1), mildly decreased (stage 2), mildly to moderately decreased (stage 3a), moderately to severely decreased (stage 3b), and severely decreased (stage 4) (stage 1, eGFR ≥ 90 mL/min/1.73 m2; stage 2, 89 to 60 mL/min/1.73 m2; stage 3a, 59 to 45 mL/min/1.73 m2; stage 3b, 44 to 30 mL/min/1.73 m2; and stage 4, < 30 mL/min/1.73 m2, respectively).²³

Our research aimed to understand the effects of metabolic syndrome on the progression of renal function deterioration. To achieve this, we categorized participants into five distinct groups based on their renal function: normal, mildly decreased, mildly to moderately decreased, moderately to severely decreased, and severely decreased. We then proceeded to analyze various factors that could potentially influence the deterioration of renal function.

Statistical Analysis

Data were collated in an Excel spreadsheet and analyzed using SPSS version 27.0.1.0. The mean ± standard deviation (SD) for continuous variables was calculated, and the number of participants was expressed as a percentage for categorical variables. Hypothesis testing for continuous variables was performed using an independent t-test and One-way ANOVA to examine differences in the mean values between groups. For categorical variables, the Chi-squared test and X2-trend test were used to identify differences in proportions. Multiple logistic regression models were developed to investigate the association between the metabolic components of metabolic syndrome and chronic kidney disease (CKD). A p-value of less than 0.05 was considered statistically significant.

Proportion of participants with chronic kidney disease

According to Table 1, more than half of the participants (54.2%) had metabolic syndrome, while the rest (45.8%) did not. Among those with metabolic syndrome, the distribution of chronic kidney disease (CKD) stages was as follows: Stage 2, n = 64 (95% CI: 38.3–55); Stage 3a, n = 41 (95% CI: 44.7–67.5); Stage 3b, n = 20 (95% CI: 57.5–90.6); Stage 4, n = 9 (95% CI: 59-104.6). The data indicate that the prevalence of metabolic syndrome increases with the worsening of CKD, especially in Stages 3b, 4, and 5. This implies a potential causal link between the severity of CKD and the occurrence of metabolic syndrome. The p-value confirms the statistical significance of the association between metabolic syndrome status and different glomerular filtration rate (GFR) levels. This highlights the need for regular screening and management of metabolic syndrome in CKD patients to prevent further deterioration of kidney function.

Patient Demographics

A total of 317 participants were categorized into four different age groups: 40–44 years (n = 25), 45–49 years (n = 78), 50–54 years (n = 90) and 55–59 years old (n = 124). The gender, BMI, waist circumference, physical activity level, alcohol consumption status, previous history of dyslipidemia, metabolic syndrome presence, and CKD stages were significantly correlated between different age groups (Table 2).

Association between Metabolic Syndrome and Chronic kidney disease

The logistic regression analysis highlights several significant associations between certain metabolic factors and CKD (stages 3a to 5). It shows that for every unit increase in Waist Circumference (WC), there's a 3% decrease in the odds of having CKD, significant at a p-value of 0.007. On the other hand, each unit increase in Fasting Blood Sugar (FBS) and Triglycerides (TG) is associated with a 1% increase in the odds of having CKD, significant at p-values of 0.011 and 0.001, respectively. Moreover, individuals with metabolic syndrome have approximately 101% higher odds of having CKD (stages 3a to 5) compared to those without metabolic syndrome, as indicated by an Odds Ratio of 2.01. This association is statistically significant at the 5% significance level with a p-value of 0.004, providing strong evidence of a notable relationship between metabolic syndrome status and CKD (Table 3).

Interplay of DM and Hypertension duration in predictors of CKD

Research into Chronic Kidney Disease (CKD) predictors highlights the significant impact of Diabetes Mellitus (DM) and Hypertension (HT) durations. Specifically, durations of 5–10 years and over 10 years for both DM and HT show high significance (OR > 1) with increased risks for CKD. For instance, 5–10 years of DM and HT exhibit an Odds Ratio (OR) of 11.08, and over 10 years show an OR of 15.28, indicating substantially heightened risks for CKD. However, the duration of 5–10 years and over 10 years for HT alone display low significance (OR < 1) with reduced likelihoods of CKD (OR: 0.035 and 0.103, respectively). These findings emphasize the critical role of DM and longer durations of both DM and HT in the onset and progression of CKD. Vigilant monitoring and effective management strategies are crucial for populations at risk to mitigate these potential risks. (Table 4)

This study’s primary findings indicate that metabolic syndrome independently contributes to the development of chronic kidney disease (CKD). The results affirm the efficacy of the modified ATP III guideline in diagnosing metabolic syndrome and predicting CKD incidence.

In this cross-sectional study, we observed a significant positive correlation between the prevalence of CKD and Metabolic Syndrome. Furthermore, a decrease in the Glomerular Filtration Rate (GFR) was associated with Metabolic Syndrome. Our study adds to the growing body of evidence suggesting that individuals with Metabolic Syndrome are at an elevated risk of developing CKD.

While previous studies have reported a 65% prevalence of metabolic syndrome among CKD patients, this association could be significantly influenced by hypertension.²⁴ Prior research has proposed that the link between metabolic syndrome and CKD may be weakened by diabetes mellitus and obesity.²⁵ Conversely, our study aligns with the view that Metabolic Syndrome is a crucial risk factor for secondary chronic kidney damage, including mild renal dysfunction, with diabetes and increased Body Mass Index (BMI) being the most significant risk factors.²⁶

The literature presents inconsistent results regarding the association between metabolic syndrome and CKD. Contrary to our study, a cross-sectional study on middle-aged men and women reported that metabolic syndrome increases the risk of CKD.

Interpreting the results of this study requires considering several limitations. Due to the cross-sectional nature of this study, we cannot establish a causal relationship between CKD indicators and associated factors.

Our study demonstrated a strong positive correlation between the prevalence of Metabolic Syndrome and CKD. Moreover, we found that increased waist circumference, fasting blood glucose, and triglyceride levels were risk factors for CKD in the middle-aged population. Our results also showed that the duration of diabetes mellitus (DM) and hypertension (HT) for 5–10 years and more than 10 years were significantly associated with higher odds ratios (OR > 1) for CKD. These findings suggest that middle age is a critical stage for the development and progression of metabolic syndrome and CKD and that early detection and intervention are essential to prevent further complications.

Ethics Approval and Consent to Participate

Ethical approval for this study was granted by the Institutional Review Board of JKKN College of Pharmacy (JKKNCP/IEC-CER/0522123/38). Written informed consent was obtained from all participants before their inclusion in the study. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Consent for Publication

Individual consent for publication was obtained from all participants involved in the study. Participants were informed that the data collected would be used for research and potential publication, ensuring confidentiality and anonymity in the presentation of the research findings.

Availability of Data and Materials

The datasets generated and analyzed during the current study are available from the corresponding author, Dr. Satheesh S., Associate Professor at JKKN College of Pharmacy, on reasonable request. The data are not publicly available due to them containing information that could compromise the privacy of research participants.

Competing Interests

The authors declare that they have no competing interests.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Acknowledgments

My sincere gratitude to Dr. S. Satheesh, Pharm.D., Associate Professor, Department of Pharmacy Practice, who supported and contributed to the creation of this research work. This job would not have been possible without her great support, advice, and encouragement. I would like to extend my sincere gratitude to Dr. N. Venkateswaramurthy, M.Pharm., Ph.D., Head of the Department, for his direction and experience in the preparation of this research. Lastly, my heartfelt gratitude goes to my family and friends for their endless encouragement throughout this project.

Authors Contributions

Study concept and design: Shankar Ganesh M; Acquisition of data: Aravindhan S; Analysis and interpretation of data: Satheesh S; Drafting of the manuscript: Shankar Ganesh M and Aravindhan S; Critical revision of the manuscript: Satheesh S; Statistical analysis: Shankar Ganesh M and Satheesh S; administrative, technical, or material support: Satheesh S; and study supervision: Satheesh S

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Table 1. Proportion of participants with chronic kidney disease

CKD Stage	Metabolic Syndrome Presence			Metabolic Syndrome Absence			P-value
CKD Stage	n	prevalence	95% CI	n	prevalence	95% CI	P-value
Stage 1	32	50.79	38.5-63.1	31	49.21	36.8-61.5	0.0011
Stage 2	64	46.7	38.3-55	73	53.2	44.9-61.6
Stage 3a	41	56.1	44.7-67.5	32	43.8	32.4-55.2
Stage 3b	20	74	57.5-90.6	7	25.9	9.4-42.4
Stage 4	9	81.8	59-104.6	2	18.18	4.61-40.97
Stage 5	6	100	100-100	0	0	0

Table 2. Demographics and biochemical characteristics

Demographic and Clinical Data		Age Range (in years)				P-value
		40-44	45-49	50-54	55-59
		Mean ± SD	Mean ± SD	Mean ± SD	Mean ± SD
Age (in years)		42.12±1.07	46.8±1.19	52.3±1.22	56.9±1.26	<0.001
Male		18 (5.67)	34(10.7)	55(17.3)	59(18.6)	0.039
Female		8 (2.5)	43(13.5)	36(11.3)	64(20.1)	0.039
Height		159.9±7.93	155.4±11.22	158.9±10.35	156.4±8.7	0.072
Weight		62.6±9.26	62.6±9.16	60.7±9.3	61.6±10	0.580
Body Mass Index		24.7±3.4	26.18±4.6	24.17±3.9	25.3±4.2	0.019
Waist Circumference		102.6±8.09	97.18±9.45	100.2±10.6	99.3±8.19	0.039
Systolic BP (mmHg)		133.69±21.15	137.7±23.7	141.3±27.7	141.6±28.6	0.436
Diastolic BP (mmHg)		84±11.52	86.8±11.37	89.3±15.9	88.16±14.3	0.334
Fasting Blood Sugar		161.9±48.25	152.9±60.9	154.4±54	154.4±61.9	0.926
High-Density Lipoprotein cholesterol		40.9±10.34	44.8±8.5	44.241±8.7	43.1±9.3	0.230
Post Prandial Blood Sugar		187±61.7	174±63.5	183±60.2	177.2±71.4	0.750
Low-Density Lipoprotein cholesterol		93.1±20.1	97.9±20.6	97±18.7	101±17.5	0.179
Very Low-Density Lipoprotein cholesterol		28.9±7.6	25.9±9.8	26.8±8.16	26.3±7.6	0.448
Blood Urea		41.5±38.1	36.7±28.8	34±7.3	34.7±18.4	0.482
Serum creatinine		1.42±1.54	1.18±0.79	1.2±0.9	1.23±0.8	0.694
eGFR		77.9±29.9	75.1±27	69.8±26.9	65.2±32.45	0.061
Residence, n (%)	Rural	14 (53.8)	41 (53.2)	48 (52.7)	65 (52.8)	0.996
Residence, n (%)	Urban	12 (46.15)	36 (46.7)	43 (47.2)	58 (47.15)	0.996
Physical activity, n (%)	High	4 (15.38)	23 (29.8)	21 (23)	17 (13.8)	0.030
	Moderate	22 (84.6)	54 (70.1)	66 (72.5)	98 (79.67)
	Low	0	0	4 (4.4)	8 (6.5)
Diet Intake, n (%)	Healthy	10 (38.4)	29 (37.6)	19 (20.8)	36 (29.2)	0.263
	Moderate	14 (53.8)	40 (51.9)	58 (63.7)	68 (55.2)
	Unhealthy	2 (7.6)	8 (10.39)	14 (15.3)	19 (15.4)
Smoking, n (%)	Yes	13 (50)	23 (29.8)	40 (43.9)	43 (34.9)	0.132
Smoking, n (%)	No	13 (50)	54 (70.1)	51 (56)	80 (65)	0.132
Alcohol, n (%)	Yes	15 (57.6)	25 (32.4)	41 (45)	42 (34.1)	0.049
Alcohol, n (%)	No	11 (42.3)	52 (67.5)	50 (54.9)	81 (65.8)	0.049
Diabetes Mellitus, n (%)	Yes	19 (73)	50 (64.9)	71 (78)	89 (72.3)	0.311
Diabetes Mellitus, n (%)	No	7 (26.9)	27 (35)	20 (21.9)	34 (27.6}	0.311
Hypertension, n (%)	Yes	13 (50)	56 (72.7)	60 (65.9)	82 (66.6)	0.209
Hypertension, n (%)	No	13 (50)	21 (27.2)	31 (34)	41 (33.3)	0.209
Dyslipidemia, n (%)	Yes	0	1 (1.3)	0	7 (5.6)	0.036
Dyslipidemia, n (%)	No	26 (100)	76 (98.7)	91 (100)	116 (94.3)	0.036
Metabolic syndrome, n (%)	Presence	16 (61.5)	31 (40.2)	54 (59.3)	71 (57.7)	0.042
Metabolic syndrome, n (%)	Absence	10 (38.5)	46 (59.7)	37 (40.6)	52 (42.2)	0.042
CKD stages, n (%)	Stage 1	10 (38.4)	22 (28.5)	18 (19.7)	13 (10.5)	0.053
	Stage 2	8 (30.7)	33 (42.8)	43 (47.2)	53 (43)
	Stage 3a	6 (23)	15 (19.4)	18 (19.7)	34 (27.6)
	Stage 3b	0	4 (5.1)	7 (7.6)	16 (13)
	Stage 4	1 (3.8)	1 (1.3)	4 (4.4)	5 (4)
	Stage 5	1 (3.8)	2 (2.6)	1 (1.1)	2 (1.6)

Table 3. Association between Metabolic Syndrome and Chronic kidney disease

Metabolic syndrome factors	Odds Ratio	95% Confidence Interval	P-value
Waist Circumference	0.97	0.94-0.99	0.007
Systolic Blood Pressure	1.01	1.0-1.01	0.211
Diastolic Blood Pressure	1.01	0.99-1.02	0.431
Fasting Blood Sugar	1.01	1.0-1.01	0.011
Triglycerides	1.01	1.01-1.02	0.001
High-Density Lipoprotein-C	0.98	0.95-1.0	0.101
Metabolic Syndrome (Presence vs Absence)	2.01	1.25-3.21	0.004

Table 4. Interplay of DM and Hypertension duration in predictors of CKD

Duration		Duration of Hypertension OR (95% CI)
Duration		< 5 years	5-10 years	> 10 years	Nil
Duration of Diabetes Mellitus OR (95% CI)	< 5 years	4.75 (1.92-11.73)	3.09 (0.98-9.77)	4.51 (0.57-35.67)	0.761 (0.315-1.841)
	5-10 years	5.22 (2.03-13.40)	11.08 (4.65-26.40)	2.20 (0.67-7.24)	_
	> 10 years	Zero counts	6.73 (0.86-52.755)	15.28 (4.81-48.5)	2.19 (0.81-5.95)
	Nil	7.85 (3.05-20.21)	0.035 (0.012-0.098)	0.103 (0.027-0.393)	6.26 (1.74-22.51)

The authors declare no competing interests.

Prevalence of Metabolic Syndrome among middle-aged patients and its association with chronic kidney disease: A Cross-sectional study

Status:

Version 1

Abstract

Introduction

Materials and methods

Metabolic Syndrome Assessment

Biochemical Analysis

Chronic Kidney Disease Assessment

Statistical Analysis

Results

Patient Demographics

Association between Metabolic Syndrome and Chronic kidney disease

Interplay of DM and Hypertension duration in predictors of CKD

Discussion

Conclusion

Declarations

References

Tables

Additional Declarations

Status:

Version 1