Potential Association Between Atherogenic Coefficient, Prognostic Nutritional Index, and Various Obesity Indices in Diabetic Nephropathy

Abstract

Background/Objectives: Type 2 diabetes mellitus (T2DM), is a rapidly growing global health concern, often accompanied by chronic kidney disease (CKD) and metabolic disturbances. Obesity-related indices, such as the visceral adiposity index (VAI) and body adiposity index (BAI), have been linked to cardiovascular and renal complications in diabetic patients. However, studies integrating both the atherogenic coefficient (AC) and prognostic nutritional index (PNI) for evaluating diabetic nephropathy (DN) remain limited. This study aimed to assess the associations of obesity-related indices with immunological and nutritional factors in patients with T2DM and prediabetes (PreDM). Methods: A retrospective, cross-sectional study was conducted over six months at a university clinical hospital in Dolj County, Romania. The study enrolled 268 newly diagnosed T2DM patients and 150 PreDM patients. Anthropometric parameters, laboratory tests, and demographic data were collected. AC and PNI were calculated using standard formulas, and statistical analyses were performed to determine their associations with metabolic and inflammatory markers. Results: Our study found that T2DM patients had significantly lower PNI values, indicating mild malnutrition, while PreDM patients maintained a normal nutritional status. AC was significantly higher in T2DM patients, correlating with lipid profile alterations and systemic inflammation. Obesity indices, particularly VAI, were significantly elevated in T2DM patients with higher AC values. Statistically significant differences in total cholesterol, low-density lipoprotein cholesterol (LDL-c), and triglycerides were observed between AC subgroups, reinforcing its role in cardiovascular risk assessment. Conclusions: The findings highlight the potential of AC and PNI as biomarkers for assessing nutritional, inflammatory, and lipemic status in diabetic patients. The significant associations between obesity-related indices, lipid profiles, and inflammation markers suggest that early assessment of these parameters may potentially aid in predicting diabetic complications. Further studies are needed to explore the clinical utility of AC and PNI in managing T2DM and CKD progression. Future research should investigate how the lipidic spectrum alters the progression of DN across various patient groups with diabetes and prediabetes.

Keywords: atherogenic coefficient; chronic kidney disease; diabetic nephropathy; metabolic syndrome; prognostic nutritional index; type 2 diabetes mellitus; visceral adiposity index.

Figure 1

The AC levels for patients with prediabetes (represented in white) or diabetes (indicated in gray) differ across various quarters of obesity-related indices: (A) BMI; (B) WHR; (C) WHtR; (D) BAI; (E) VAI. The violin plot illustrates the distribution of these indices, with the horizontal blue lines that indicate the median values, while the quartiles are represented by the horizontal red lines.

Figure 1

The AC levels for patients with prediabetes (represented in white) or diabetes (indicated in gray) differ across various quarters of obesity-related indices: (A) BMI; (B) WHR; (C) WHtR; (D) BAI; (E) VAI. The violin plot illustrates the distribution of these indices, with the horizontal blue lines that indicate the median values, while the quartiles are represented by the horizontal red lines.

Figure 2

The PNI levels for patients with prediabetes (represented in white) or diabetes (indicated in gray) differ across various quarters of obesity-related indices: (A) BMI; (B) WHR; (C) WHtR; (D) BAI; (E) VAI. The violin plot illustrates the distribution of these indices, with the horizontal blue lines that indicate the median values, while the quartiles are represented by the horizontal red lines.

Figure 2

The PNI levels for patients with prediabetes (represented in white) or diabetes (indicated in gray) differ across various quarters of obesity-related indices: (A) BMI; (B) WHR; (C) WHtR; (D) BAI; (E) VAI. The violin plot illustrates the distribution of these indices, with the horizontal blue lines that indicate the median values, while the quartiles are represented by the horizontal red lines.

Figure 3

Correlation matrix between AC, lipid spectrum, and obesity-related indices in the T2DM cohort. The correlation heatmap illustrates the relationships between the measured indicators. Strong positive correlations are represented by bright blue, while strong negative correlations are depicted in bright red.

Figure 4

Correlation matrix of AC, lipid spectrum, and obesity-related indices in the PreDM cohort. The correlation heatmap illustrates the relationships between the measured indicators. Strong positive correlations are represented by bright blue, while strong negative correlations are depicted in bright red.

Figure 5

Receiver operating characteristic (ROC) curve for HbA1c (A), ALB (B), FPG (C), PNI (D), CKD-EPI (E), 2hPG (F), AC (G), Creatinine (H), VAI (I), BAI (J), HDL-c (K), LDL-c (L) and TC (M).

Figure 5

Receiver operating characteristic (ROC) curve for HbA1c (A), ALB (B), FPG (C), PNI (D), CKD-EPI (E), 2hPG (F), AC (G), Creatinine (H), VAI (I), BAI (J), HDL-c (K), LDL-c (L) and TC (M).

Figure 5

Receiver operating characteristic (ROC) curve for HbA1c (A), ALB (B), FPG (C), PNI (D), CKD-EPI (E), 2hPG (F), AC (G), Creatinine (H), VAI (I), BAI (J), HDL-c (K), LDL-c (L) and TC (M).