Associations of the atherogenic index of plasma with insulin resistance and inflammation

SUMMARY

OBJECTIVE:  This study aimed to investigate the association between the atherogenic index of plasma and insulin resistance, while also evaluating atherogenic index of plasma's prognostic value in inflammation by examining its correlation with platelet/lymphocyte ratio and neutrophil/lymphocyte ratio.

METHODS:  Data collection involved accessing records from January 2022 to December 2022, with analyses including calculation of atherogenic index of plasma and homeostatic model assessment index.

RESULTS:  Data from 510 patients (mean age: 48.90±18.71 years; 69.4% female) showed a mean atherogenic index of plasma of 0.40±0.28, with 69% at high risk. Significant positive correlations were found between atherogenic index of plasma and age, lymphocyte, neutrophil, C-reactive protein, glucose, insulin, and homeostatic model assessment-insulin resistance, while the correlation with platelet/lymphocyte ratio was significantly negative.

CONCLUSION:  Atherogenic index of plasma presents as a promising predictor for insulin resistance, demonstrating robust associations with inflammation, thus offering valuable clinical insights and potential for early risk assessment and management strategies.

KEYWORDS:
Inflammation; Insulin resistance; Atherogenesis

INTRODUCTION

Insulin resistance (IR) is characterized by the reduced body's cells’ response to insulin and is a significant risk factor for cardiovascular disease (CVD), independent of diabetes. Beyond glucose metabolism, IR disrupts lipid metabolism, raising triglycerides (TGs) and low-density lipoprotein cholesterol (LDL-C), which are linked to atherosclerosis and CVD. IR often coexists with systemic inflammation and oxidative stress, worsening metabolic dysfunction and chronic diseases. Atherosclerosis, driven by lipid-induced immune-inflammatory processes, leads to CVD¹. Inflammation significantly contributes to atherosclerosis formation and progression, increasing the risk of dyslipidemia². Dyslipidemia, characterized by high LDL-C, total cholesterol, TGs, and low high-density lipoprotein cholesterol (HDL-C), contributes to atherosclerosis development³.

The atherogenic index of plasma (AIP) is determined by the logarithm of TG/HDL-C and is recognized as a strong predictor for CVD⁴. AIP assessment not only indicates coronary atherosclerosis and cardiovascular risk but is also an enhanced and effective measure of cardiovascular risk factors⁵. According to several studies, AIP reflects CVD risk better than other atherogenic indexes or lipoprotein cholesterol parameters alone⁶.

Type 2 diabetes mellitus (T2DM) is a major CVD risk factor, with higher CVD mortality compared to nondiabetic individuals⁷. IR, often linked to T2DM and measured by the homeostatic model assessment (HOMA-IR) with values above 2.5 indicating IR, heightens CVD risk⁸. Sharma et al. found a strong link between elevated fasting blood glucose (FBG) and increased AIP⁹. In AIP, values of −0.3 to 0.1 indicate low CVD risk, 0.1–0.24 moderate risk, and above 0.24 high risk¹⁰.

According to various studies, both AIP and IR are closely associated with inflammation, with AIP reflecting cardiovascular risk driven by inflammatory processes¹¹,¹². Platelet, lymphocyte, and neutrophil counts are key prognostic markers in clinical practice. The platelet/lymphocyte (PLR) and neutrophil/lymphocyte (NLR) ratios are used as biomarkers for inflammatory conditions like CVD, cancer, and autoimmune diseases. Elevated NLR and PLR are linked to higher inflammation severity and are strong, independent predictors of mortality risk in patients with comorbidities¹³.

This study aimed to investigate the relationship between AIP and IR and to assess AIP's role in diagnosing and monitoring T2DM. In addition, we evaluated AIP's prognostic values for inflammation by examining its correlation with inflammatory markers like NLR, PLR, and C-reactive protein (CRP).

METHODS

Patients and design

Within the scope of the study, complete blood count, CRP, albumin, HDL-C, LDL-C, TGs, and FBG records of the patients who received service from the family medicine outpatient clinic between January 2022 and December 2022 were accessed by retrospective inquiry through the hospital information system. Age, gender, diagnoses, and test results were recorded on the data collection form. AIP and HOMA-IR were calculated from these data.

Ethical approval

The study was approved by the Nigde University Research Ethics Committee (Approval No. 2023/41).

Calculation of the parameters

Atherogenic index of plasma

Dobiásová et al. described AIP as a sensitive and potential index of the atherogenicity of lipoproteins. AIP is calculated as log¹⁰ (TG/HDL cholesterol)⁵.

Homeostatic model assessment-insulin resistance

HOMA-IR is an easier test to perform than other methods that can demonstrate both IR and β-cell functions. The calculation is performed by multiplying the insulin level in mIU/L by the glucose level in mg/dL and then dividing the result by 405¹⁴.

Data analysis

Data analysis was done in the SPSS v25 package program. The normality of continuous data was assessed using the Kolmogorov-Smirnov test. Independent-samples t-test was used in independent groups to compare two groups in continuous data. Relationships were examined with Spearman correlation analysis since parametric test conditions were not met. Binary logistic regression with Forward:LR was used to examine the factors affecting the presence of a high-risk AIP. Variables with p<0.25 in univariate analysis and variables that were thought to clinically affect the dependent variable were included in the model. The fit of the model was evaluated with the Hosmer-Lemeshow test and its descriptiveness with Nagelkerke R2. All analyses were conducted using a two-tailed approach, and significance was defined as p<0.05.

In the post hoc power analysis performed after the study, the effect size between the presence of IR and AIP was found to be 0.473 and the power of the study was calculated as 99%.

RESULTS

The data of 510 patients were examined. The mean age of the participants was 48.90±18.71 years, and 69.4% of them were women. The mean AIP of the participants was 0.40±0.28, with 69% classified as high risk for AIP. In addition, 50.8% of the participants had IR (HOMA-IR≥2.5). The mean values of the participants’ biochemical parameters are shown in Table 1.

Table 1
Distribution of biochemical parameters of patients.

	Mean±standard deviation	Median (minimum–maximum)
Age (years)	48.90±18.71	49.50 (14–93)
NLR	2.03±1.04	1.78 (0.54–10.27)
PLR	126.62±50.05	119.13 (15.95–582.05)
Lymphocyte (103/mcL)	2.30±0.73	2.28 (0.76–6)
Platelet (103/mcL)	269.78±71.11	267 (41–599)
Neutrophil (103/mcL)	4.29±1.53	4.09 (1.20–14.79)
CRP (mg/L)	4.50±10.13	1.90 (0–120.50)
Glucose (mg/dL)	110.16±48.58	94 (65–441)
HDL (mg/dL)	49.40±12.53	48 (22–107)
Triglyceride (mg/dL)	140.08±88.90	115 (25–735)
Insulin (mIU/L)	15.76±29.91	10.40 (0.40–591)
AIP	0.40±0.28	0.39 (-0.62 to 1.34)
HOMA-IR	4.72±15.03	2.54 (0.22–321.04)
		n (%)
Gender	Female	354 (69.4%)
	Male	156 (30.6%)
AIP risk classification	Low risk (API<0.11)	76 (14.9%)
	Medium risk (API=0.11–0.24)	82 (16.1%)
	High risk (API>0.24)	352 (69%)
Presence of IR	No IR (HOMA-IR<2.5)	251 (49.2%)
	IR (HOMA-IR≥2.5)	259 (50.8%)

• NLR: neutrophil/lymphocyte ratio; PLR: platelet/lymphocyte ratio; CRP: C-reactive protein; HDL: high-density lipoprotein; AIP: atherogenic index of plasma; HOMA-IR: homeostatic model assessment-insulin resistance; IR: insulin resistance.

Significant positive correlations exist between AIP and various factors, including age, lymphocyte, neutrophil, CRP, glucose, insulin, and HOMA-IR (p<0.05). Despite these results, the correlation between AIP and PLR was significantly negative (p<0.001). The AIP of the patients was also compared based on their gender and IR values. The results indicated significant differences in AIP among genders and IR values (p<0.001) (Table 2).

Table 2
Correlation of atherogenic index of plasma with other variables.

Variables	r	p*
Age	0.363	<0.001
NLR	-0.003	0.942
PLR	-0.223	<0.001
Lymphocyte	0.190	<0.001
Platelet	-0.081	0.068
Neutrophil	0.151	0.001
CRP	0.242	<0.001
Glucose	0.301	<0.001
Insulin	0.241	<0.001
HOMA-IR	0.330	<0.001
		p**
Gender	Female	<0.001
	Male
Presence of IR	No IR (HOMA-IR<2.5)	<0.001
	IR (HOMA-IR≥2.5)

• ^*
  Spearman correlation test;
• ^**
  Independent samples’ t-test. AIP: atherogenic index of plasma; NLR: neutrophil/lymphocyte ratio; PLR: platelet/lymphocyte ratio; CRP: C-reactive protein; HOMA-IR: homeostatic model assessment-insulin resistance; IR: insulin resistance.

Gender, age, lymphocyte, and IR had the highest explanatory values in the multivariate logistic regression model. Being male increased the likelihood of high-risk AIP by 2.97 times and IR by 2.23 times. Each unit increase in age and lymphocyte also increased the likelihood of high-risk AIP by 1.03 and 1.85 times, respectively (Table 3).

Table 3
Evaluation of factors affecting the status of having a high-risk atherogenic index of plasma.

Variables	OR (95%CI)	p
Gender (ref: Female)	2.97 (1.79–4.93)	<0.001
Age	1.03 (1.02–1.04)	<0.001
Lymphocyte	1.85 (1.34–2.56)	<0.001
Presence of IR (ref: no IR)	2.23 (1.45–3.42)	0.001

• R²=0.26, −2 loglikelihood=526.34, Omnibus test:<0.001, Hosmer-Lemeshow test: 0.191. CI: confidence interval; OR: odds ratio; IR: insulin resistance.

DISCUSSION

AIP, which comprehensively measures lipid levels, serves as a potent biomarker for dyslipidemia and atherosclerosis. Furthermore, it is considered indicative of coronary and metabolic syndromes¹⁵. Elevated AIP values are associated with coronary artery disease (CAD) and its risk factors, such as hyperlipidemia, metabolic syndrome, and hypertension¹⁶. In our study, the average AIP was 0.40, with 69% of the participants classified as high risk. Significant correlations were observed between AIP and both age and gender. Furthermore, the AIP values of males were higher than those of females, similar to the findings of a study conducted in the Czech Republic¹⁰. In a study by Cai et al., AIP was significantly positively associated with age¹⁵. Older age and male gender are considered at risk in terms of AIP values, indicating an increased risk of CVD. These results could have a meaningful impact on comprehending the influence of age and gender on AIP levels and should be considered when assessing the cardiovascular health profiles of patients.

IR is considered a factor connecting impaired carbohydrate metabolism to a higher risk of CVD¹⁷. IR, a possible cause of T2DM, is significantly and positively correlated with AIP, as indicated in previous studies¹⁸. In an Australian cohort study with 161 participants, AIP was significantly higher in men and patients with T2DM (p=0.009)¹⁹. Akbaş et al. found that HbA1c has the strongest correlation with AIP and stated that high AIP levels may be indicative of prediabetes, diabetes, and possibly CAD²⁰. Similarly, a study of 4,252 patients reported a parallel increase in AIP with an increase in HbA1c⁷. Li et al. found that AIP was associated with HOMA-IR, HbA1c, and FBG and that metabolic syndrome had a significantly higher prevalence in the group with high AIP²¹. Similar to these studies, we observed a significant positive association between AIP and HOMA-IR. This suggests that AIP could serve as a valuable marker for assessing IR, prediabetes, and the potential development of diabetes.

Higher levels of NLR and PLR have been shown to correlate with the severity of inflammation. Researchers have found that NLR and PLR are closely associated with T2DM and its microvascular complications²². Allahverdiyev et al. studied patients with myocardial infarction and found that the NLR and PLR values of patients in the high AIP group were lower than those in the group low AIP²³. A study conducted on patients with chronic kidney disease found a positive correlation between AIP and NLR and PLR, with AIP also associated with CVD²⁴. We found that AIP is correlated with neutrophil and lymphocyte values, and a significant negative correlation exists between AIP and PLR. PLR has been observed to increase in various inflammatory conditions such as thyroid disorders, gastrointestinal diseases, thyroiditis, cancer, diabetes mellitus, and irritable bowel disease²⁵. This finding suggests that AIP not only correlates with cardiovascular risk but also exhibits a strong association with inflammation. Similarly, our findings revealed a noteworthy positive correlation between AIP and CRP, indicating a potential relationship between AIP and inflammatory processes. This observation supports the notion that AIP could be a valuable marker for assessing inflammatory conditions.

This study had limitations. Since this study included a limited number of patients, larger studies are required for more comprehensive results. Conducted at a single center in Turkey, the findings may not be widely applicable, necessitating validation through larger, multicenter studies for broader relevance. In addition, only baseline AIP data were available, preventing analysis of changes over time and their impact on outcomes.

In conclusion, we found strong relationships between AIP and both HOMA-IR and PLR. AIP is a promising predictor of IR and T2DM, with strong links to inflammation. AIP is easily measurable and valuable for diagnosing and monitoring diabetes and inflammation. Lowering AIP by correcting lipid abnormalities may reduce the risk of IR and T2DM, making it a useful tool in routine clinical practice for early risk assessment and management.

• Funding:
  none.

REFERENCES

• ¹ Boudoulas KD, Triposciadis F, Geleris P, Boudoulas H. Coronary atherosclerosis: pathophysiologic basis for diagnosis and management. Prog Cardiovasc Dis. 2016;58(6):676-92. https://doi.org/10.1016/j.pcad.2016.04.003
  » https://doi.org/10.1016/j.pcad.2016.04.003
• ² Zhan Y, Xu T, Tan X. Two parameters reflect lipid-driven inflammatory state in acute coronary syndrome: atherogenic index of plasma, neutrophil-lymphocyte ratio. BMC Cardiovasc Disord. 2016;16:96. https://doi.org/10.1186/s12872-016-0274-7
  » https://doi.org/10.1186/s12872-016-0274-7
• ³ Sniderman AD, Williams K, Contois JH, Monroe HM, McQueen MJ, Graaf J, et al. A meta-analysis of low-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol, and apolipoprotein B as markers of cardiovascular risk. Circ Cardiovasc Qual Outcomes. 2011;4(3):337-45. https://doi.org/10.1161/CIRCOUTCOMES.110.959247
  » https://doi.org/10.1161/CIRCOUTCOMES.110.959247
• ⁴ Dobiásová M, Frohlich J, Sedová M, Cheung MC, Brown BG. Cholesterol esterification and atherogenic index of plasma correlate with lipoprotein size and findings on coronary angiography. J Lipid Res. 2011;52(3):566-71. https://doi.org/10.1194/jlr.P011668
  » https://doi.org/10.1194/jlr.P011668
• ⁵ Dobiásová M, Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER(HDL)). Clin Biochem. 2001;34(7):583-8. https://doi.org/10.1016/s0009-9120(01)00263-6
  » https://doi.org/10.1016/s0009-9120(01)00263-6
• ⁶ Essiarab F, Taki H, Lebrazi H, Sabri M, Saïle R. Usefulness of lipid ratios and atherogenic index of plasma in obese Moroccan women with or without metabolic syndrome. Ethn Dis. 2014;24(2):207-12. PMID: 24804368
• ⁷ Bozkur E, Esen A, Polat O, Okuturlar Y, Akdeniz YS, Piskinpasa H, et al. Relationship of HbA1c with plasma atherogenic index and non-HDL cholesterol in patients with type 2 diabetes mellitus. Int J Diabetes Dev Ctries. 2021;41:302-6. https://doi.org/10.1007/s13410-020-00884-7
  » https://doi.org/10.1007/s13410-020-00884-7
• ⁸ Tahapary DL, Pratisthita LB, Fitri NA, Marcella C, Wafa S, Kurniawan F, et al. Challenges in the diagnosis of insulin resistance: focusing on the role of HOMA-IR and tryglyceride/glucose index. Diabetes Metab Syndr. 2022;16(8):102581. https://doi.org/10.1016/j.dsx.2022.102581
  » https://doi.org/10.1016/j.dsx.2022.102581
• ⁹ Sharma N, Baliarsingh S. High fasting serum glucose in non-diabetic subjects ≥45 years is an indicator of future cardiovascular events as it is positively associated with atherogenic index of plasma. Arch Physiol Biochem. 2012;118(2):43-6. https://doi.org/10.3109/13813455.2012.677455
  » https://doi.org/10.3109/13813455.2012.677455
• ¹⁰ Dobiásová M. [AIP--atherogenic index of plasma as a significant predictor of cardiovascular risk: from research to practice]. Vnitr Lek. 2006;52(1):64-71. PMID: 16526201
• ¹¹ Mazidi M, Katsiki N, Mikhailidis DP, Banach M. Association of ideal cardiovascular health metrics with serum uric acid, inflammation and atherogenic index of plasma: a population-based survey. Atherosclerosis. 2019;284:44-9. https://doi.org/10.1016/j.atherosclerosis.2018.09.016
  » https://doi.org/10.1016/j.atherosclerosis.2018.09.016
• ¹² Aktas G, Alcelik A, Ozlu T, Tosun M, Tekce BK, Savli H, et al. Association between omentin levels and insulin resistance in pregnancy. Exp Clin Endocrinol Diabetes. 2014;122(3):163-6. https://doi.org/10.1055/s-0034-1370917
  » https://doi.org/10.1055/s-0034-1370917
• ¹³ Nur Eke R, Ozen M, Altun Y, Ellidag HY. Investigation of the relationship between mortality and red cell distribution width, mean platelet volume, platelet-to-lymphocyte ratio, and neutrophil-to-lymphocyte ratio in geriatric patients. Turkish J Geriatrics. 2020;23(4):424-33. https://doi.org/10.31086/tjgeri.2020.180
  » https://doi.org/10.31086/tjgeri.2020.180
• ¹⁴ Sama S, Jain G, Kant R, Bhadoria AS, Naithani M, Kumar A. Quantifying the homeostatic model assessment of insulin resistance to predict mortality in multi-organ dysfunction syndrome. Indian J Crit Care Med. 2021;25(12):1364-9. https://doi.org/10.5005/jp-journals-10071-24043
  » https://doi.org/10.5005/jp-journals-10071-24043
• ¹⁵ Cai G, Liu W, Lv S, Wang X, Guo Y, Yan Z, et al. Gender-specific associations between atherogenic index of plasma and the presence and severity of acute coronary syndrome in very young adults: a hospital-based observational study. Lipids Health Dis. 2019;18(1):99. https://doi.org/10.1186/s12944-019-1043-2
  » https://doi.org/10.1186/s12944-019-1043-2
• ¹⁶ Kammar-García A, López-Moreno P, Hernández-Hernández ME, Ortíz-Bueno AM, Martínez-Montaño MLC. Atherogenic index of plasma as a marker of cardiovascular risk factors in Mexicans aged 18 to 22 years. Proc (Bayl Univ Med Cent). 2020;34(1):22-7. https://doi.org/10.1080/08998280.2020.1799479
  » https://doi.org/10.1080/08998280.2020.1799479
• ¹⁷ Soares-Jr JM, Hayashida SAY, Marcondes JAM, Maciel GAR, Barcellos CRG, Maffazioli GN, et al. Influence of phenotypes on the metabolic syndrome of women with polycystic ovary syndrome over a six-year follow-up in Brazil. Biomedicines. 2023;11(12):3262. https://doi.org/10.3390/biomedicines11123262
  » https://doi.org/10.3390/biomedicines11123262
• ¹⁸ Kou H, Deng J, Gao D, Song A, Han Z, Wei J, et al. Relationship among adiponectin, insulin resistance and atherosclerosis in non-diabetic hypertensive patients and healthy adults. Clin Exp Hypertens. 2018;40(7):656-63. https://doi.org/10.1080/10641963.2018.1425414
  » https://doi.org/10.1080/10641963.2018.1425414
• ¹⁹ Yuvaraj J, Isa M, Che ZC, Lim E, Nerlekar N, Nicholls SJ, et al. Atherogenic index of plasma is associated with epicardial adipose tissue volume assessed on coronary computed tomography angiography. Sci Rep. 2022;12(1):9626. https://doi.org/10.1038/s41598-022-13479-5
  » https://doi.org/10.1038/s41598-022-13479-5
• ²⁰ Akbaş EM, Akbaş N, Timuroğlu A, Tahirler H, Demirtaş L, Özçiçek F, et al. Association between atherogenic index of plasma, blood pressure and impaired glucose metabolism; results of diabetes screening in a Tertiary Healthcare Center. Arch Basic Clin Res. 2020;2(3):73-9. https://doi.org/10.5152/ABCR.2020.20026
  » https://doi.org/10.5152/ABCR.2020.20026
• ²¹ Li Z, Huang Q, Sun L, Bao T, Dai Z. Atherogenic index in type 2 diabetes and its relationship with chronic microvascular complications. Int J Endocrinol. 2018;2018:1765835. https://doi.org/10.1155/2018/1765835
  » https://doi.org/10.1155/2018/1765835
• ²² Li L, Shen Q, Rao S. Association of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio with diabetic kidney disease in chinese patients with type 2 diabetes: a cross-sectional study. Ther Clin Risk Manag. 2022;18:1157-66. https://doi.org/10.2147/TCRM.S393135
  » https://doi.org/10.2147/TCRM.S393135
• ²³ Allahverdiyev A, Koyuncu IMA, Kuru B, Allahverdiyeva A, Ertas FS. The relationship of plasma aterogenity index and mean platelet volume with the risk of development of 1-year total major adverse cardiac event in patients with non-ST elevation myocardial infarction. Int J Angiol. 2023;32(2):81-7. https://doi.org/10.1055/s-0043-1764223
  » https://doi.org/10.1055/s-0043-1764223
• ²⁴ Mamven M, Ucha I, Adejumo O. Surrogate inflammatory markers and some correlates in pre-dialysis chronic kidney disease patients: a cross-sectional study: surrogate inflammatory markers in pre-dialysis, CKD. Babcock Univ Med J. 2022;5(1):45-53. https://doi.org/10.38029/babcockunivmedj.v5i1.125
  » https://doi.org/10.38029/babcockunivmedj.v5i1.125
• ²⁵ Atak B, Aktas G, Duman TT, Erkus E, Kocak MZ, Savli H. Diabetes control could through platelet-to-lymphocyte ratio in hemograms. Rev Assoc Med Bras (1992). 2019;65(1):38-42. https://doi.org/10.1590/1806-9282.65.1.38
  » https://doi.org/10.1590/1806-9282.65.1.38

Publication Dates

• Publication in this collection
  02 Dec 2024
• Date of issue
  2024

History

• Received
  09 July 2024
• Accepted
  18 Aug 2024