Chronic kidney disease (CKD) encompasses a range of pathophysiological processes, characterized by impaired renal function and a progressive decline in glomerular filtration rate (GFR). This condition, which represents a significant global health concern, is increasingly prevalent, particularly among individuals with metabolic disorders such as dysglycemia. The concentration of glycated hemoglobin (HbA1c) serves as both an indicator of dysglycemia, including prediabetic states, and a marker of average blood glucose levels over the preceding three months [1-3].

Diabetic nephropathy remains the leading cause of end-stage renal disease (ESRD), and tight glycemic control is known to slow the onset and progression of diabetic complications. Moreover, evidence suggests that patients with diabetes and advanced CKD benefit from improved metabolic regulation. However, dialysis and renal failure can disrupt glucose and insulin homeostasis. This raises concerns about the reliability of HbA1c as a measure of long-term glycemic control in patients with CKD or ESRD. Recent investigations have focused on assessing the accuracy of long-term glycemic markers in this patient population. The HbA1c test differs from daily finger-stick blood glucose testing, with a target HbA1c level of 7% for individuals with diabetes, while those without diabetes typically have levels between 4% and 5.9% [5,6].

Dyslipidemia (DLP), although not always present, is a common complication associated with worsening renal disease. DLP is characterized by elevated triglyceride levels, low HDL cholesterol, and the presence of small, dense LDL particles, all of which contribute to renal and cardiovascular risk. The high cardiovascular morbidity and mortality seen in CKD patients are partially attributed to dyslipidemia. The oxidative modification of LDL and HDL particles leads to the formation of smaller, oxidized LDL particles, further exacerbating cardiovascular risks [7-9].

The management of lipid disorders begins with therapeutic lifestyle changes (TLC), as outlined by the National Cholesterol Education Program (NCEP) Expert Panel in the Third Report on the Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (ATP III). These changes are believed to reduce cardiovascular risk through mechanisms beyond lowering LDL cholesterol. According to ATP III, LDL cholesterol is the primary target for cholesterol-lowering therapies. Lipid measurements typically require a 9-12 hour fasting period. However, few studies have collectively explored the relationship between CKD, HbA1c, dyslipidemia, and electrolyte disturbances in renal failure patients [10]. This study aimed to investigate these associations in patients with renal failure.

A hospital-based case-control study was conducted, enrolling 136 CKD patients as cases and 120 non-CKD individuals as controls. Both groups comprised adult participants over the age of 20, of either gender. CKD cases were defined as individuals with a confirmed diagnosis of chronic kidney disease, while controls were age- and gender-matched participants without a CKD diagnosis. 

The inclusion criteria for the study comprised adult participants aged over 20 years who were diagnosed with CKD, characterized by progressive deterioration in kidney function and a declining glomerular filtration rate (GFR). Conversely, individuals under 20 years of age or those unwilling to provide informed consent were excluded from participation.

Serum lipid profiles were assessed using a semi-auto analyzer. Total cholesterol was measured using the CHOD/PAP method with a reagent kit consisting of Enzyme Reagent 1 (L1), Enzyme Reagent 2 (L2), and a Cholesterol Standard (200 mg/dL). Triglycerides were assessed by the Triglycerides (DST) kit using the GPO method. HDL-cholesterol was quantified via an enzymatic colorimetric assay performed on an automated analyzer. LDL and VLDL cholesterol levels were calculated using the following formulas: VLDL-C = TG/5 and LDL-C = TC − (HDL-C + VLDL-C). HbA1c was determined using High-Performance Liquid Chromatography (HPLC) with affinity columns to isolate HbA1c molecules from other hemoglobins. The proportion of HbA1c peak area to total hemoglobin peak areas was used to calculate its concentration. Urea levels were measured using the GLDH Kinetic method in serum samples. Creatinine was determined by Jaffe’s Alkaline Picrate Method, wherein creatinine reacts with picric acid in an alkaline medium to form a red tautomer, which was then quantified at 520 nm. Creatinine clearance, an indicator of GFR, was estimated using the 4-variable MDRD formula.

Ultrasonography (USG) was employed to evaluate renal structure and detect CKD. Common findings included reduced kidney size, thinning of the renal parenchyma, and increased cortical reflectivity compared to the adjacent liver. Cortico-medullary differentiation was enhanced, revealing renal pyramids more clearly. 

Data were compiled into master charts using Microsoft Office Excel 2010 and analyzed with SPSS version 23. Mean and standard deviations for each parameter were computed separately for cases and controls. Differences between group means were evaluated using the unpaired, two-tailed Student's t-test. Univariate analysis was applied to compare HbA1c and dyslipidemia proportions between cases and controls. Statistical significance was set at a p-value < 0.05.

The distribution of chronic kidney disease (CKD) cases and controls across various age groups is summarized in Table 1. Among cases, the majority were males aged 36–50 years (29 males, 11 females), followed by males in the 66–80 years age group (27 males, 7 females). No significant difference was observed in the age distribution between cases and controls (P = 0.35).

Table 1: Age distribution of CKD cases and controls

The majority of CKD cases were diagnosed at advanced stages, as shown in Table 2. Stage 4 CKD was the most prevalent, accounting for 38.97% of cases (n = 53), followed by Stage 5 at 30.15% (n = 41). Early stages (Stages 1 and 2) were less frequent.

Table 2: Frequency of cases according to CKD stage

HbA1c distribution among cases and controls is presented in Table 3. CKD cases exhibited a higher proportion of diabetic HbA1c levels (≥6.5%), with 43.38% (n = 59) of cases compared to 20.00% of controls. Conversely, normal HbA1c levels (≤5.6%) were more frequent in controls (65.83%) than in cases (46.32%). This difference was statistically significant (P < 0.01).

Table 3: HbA1c categories among CKD cases and controls

As detailed in Table 4, dyslipidemia was significantly more prevalent among CKD cases (43.38%, n = 59) compared to controls (26.67%, n = 32; P < 0.01). The specific patterns of dyslipidemia are outlined in Table 5.

Among CKD cases, the most frequent abnormalities were high low-density lipoprotein (LDL) levels (28.81%) and lower high-density lipoprotein (HDL) levels (27.12%). These were followed by high triglycerides (TG; 25.42%) and high total cholesterol (TC; 18.64%). In controls, similar trends were observed, with the most common abnormalities being high TC (28.13%) and lower HDL (25.00%).

Table 4: Frequency of dyslipidemia among CKD cases and controls

Table 5: Details of dyslipidemia among CKD cases and controls

Although CKD, diabetes, and dyslipidemia are prevalent in India, the limited availability of comprehensive studies examining their interrelationship within the Indian context prompted the initiation of this research. The present study aimed to evaluate the association between CKD, dysglycemia, and dyslipidemia in Indian patients. Statistical analysis was employed to extrapolate findings to the broader Indian population.

This study included 136 CKD patients (non-edematous cases) and 120 age- and sex-matched controls. Gender distribution did not differ significantly between the groups. There was no significant difference in mean age or distribution among the study groups. Levin et al. [11] identified aging as a non-regulatory factor in CKD development. Among male CKD cases, the predominant age groups were 36–50 years and 66–80 years, whereas most female cases fell within the 51–65-year range. A history of alcohol use was noted in 27.52% of cases versus 30.48% of controls, showing no significant difference. However, smoking history was present in 33.94% of cases compared to 19.05% of controls, a significant association. Similarly, the CHOICE study found that 28% of dialysis patients were current smokers, and research by Orth et al. [12] linked smoking to CKD progression and increased cardiovascular risk. Bryson et al. [7] observed a two-fold higher adjusted risk of a glomerular filtration rate (GFR) <60 mL/min/1.73 m² in current smokers versus non-smokers.

In this study, HbA1c levels differed significantly between cases and controls. Diabetic status (HbA1c >6.5%) was noted in 59 CKD patients (43.38%) versus 24 controls (20%), with an extremely significant p-value. Tonelli et al. [13] reported diabetes in 38.5% of CKD patients, with an association between diabetes and cardiovascular disease (CVD) across CKD stages 1–4. Cummings et al. [14] demonstrated that HbA1c levels >7% strongly predicted changes in estimated GFR. Prior research indicated that long-term glycemic variability, expressed as HbA1c SD, was a significant predictor of renal and cardiovascular complications.

Significant differences were observed in lipid profiles between CKD cases and controls. Dyslipidemia was present in CKD patients. Triglycerides, HDL cholesterol, and LDL cholesterol levels were significantly elevated in CKD patients, while total cholesterol did not differ significantly between groups. Sowers et al. [15] observed that routine lipid parameters such as total and LDL cholesterol are typically within high-normal or low ranges in CKD but hypertriglyceridemia often emerges early in CKD and persists in up to 70% of end-stage renal disease (ESRD) patients. Hemodialysis, however, may improve triglyceridemia in non-diabetic individuals. Reduced HDL cholesterol levels in CKD patients have been attributed to low lecithin-cholesterol acyltransferase activity and the pro-inflammatory state associated with uremia.

The MMKD study, which followed 227 patients with primary kidney disease over seven years, documented early decreases in HDL cholesterol alongside increases in triglycerides and reductions in lipoprotein A. Indian studies on lipid profiles in CKD have yielded inconsistent results. While Sharma et al. [16] and Kunde et al. [17] reported no hyperlipidemia, Gupta et al. [18] and Das et al. [19] observed patterns similar to Western studies, including hypertriglyceridemia and low HDL cholesterol. These lipid abnormalities, often due to increased hepatic VLDL synthesis and impaired triglyceride clearance, were consistent with findings in recent Indian and Western studies.

Considering the atherogenic lipid profile observed in Asian populations, the significant relationship between dyslipidemia and CKD identified in this study aligns with findings from other ethnic groups, underscoring the validity of the current results.

Our study demonstrated a significant association between elevated HbA1c levels and the presence of CKD compared to individuals without CKD. The prevalence of dyslipidemia was markedly higher among CKD patients relative to the control group, characterized by increased triglycerides (TG), elevated LDL cholesterol, and reduced HDL cholesterol levels. These findings align with national trends, highlighting the growing burden of diabetes and hypertension as emerging epidemics.

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