Atherogenic Plasma Index in a Young Non-Obese Caucasian Population

Abstract

Cardiovascular risk is based on age, gender, smoking, systolic blood pressure and total cholesterol. Although it is well documented that the atherosclerosis process starts early in life, the risk is very clearly defined only for fatal events and only for persons above 40 years old. Besides the Guidelines for the Management of Dyslipidaemias, other factors such as atherogenic plasma index (AIP) or adipokine levels have certain predictive information about this risk. The aim of our study was to assess the gender differences in AIP in a young non-obese adult population. We selected 85 young adults without a diagnosis of metabolic syndrome or related medical conditions. The metabolic syndrome components were measured and those (2 subjects) with 3 established metabolic syndrome criteria were excluded. The average AIP value on the whole group was 0.07. When stratifying the risk according to the AIP value, the majority of the participants were in the low risk range (51 participants representing 61.4% of the total), 18 (21.6%) were included in the high risk, and 15 (18%), in the medium risk category. The average AIP in women’s group was 0.02, and in men’s group, 0.18 (p<0.001). Our preliminary results suggest that, for screening purposes, the AIP threshold should be different according to gender. To confirm this finding, a large longitudinal study is needed.

Keywords: Cardiovascular riskatherogenic plasma indexgenderyoung adults

Introduction

Problem Statement

The risk for cardiovascular diseases is very well defined for people over 40 years old (Bala, Craciun, & Hancu, 2016; Gao et al., 2016). However, the atherogenic process starts much earlier, presumably in childhood. In the last decades, with the increasing trend in obesity among adolescents and young adults, the cardiovascular risk has become a very important public health topic. Obesity is not sufficient to characterise the risk, as not all the obese population is metabolically obese (Guo & Garvey, 2016; Gherman et al., 2010). Therefore, assessing the appropriate screening methods for the young non-obese persons needs further research (Srikanthan et al., 2016).

Research Questions

Are there specific screening tests for cardiovascular risk in young non-obese adults?

Is there a gender-specific difference in the definition of cardiovascular risk in young non-obese adults?

Purpose of the Study

To better characterise the cardiovascular risk in non-obese young adults, defined by BMI criteria, taking into consideration the gender differences.

Research Methods

We designed a cross-sectional study involving 85 young adults (average age = 23.78, StDev = 1.31 years) without a diagnosis of metabolic syndrome or related diseases (hypertension, ischemic heart disease, diabetes, polycystic ovary syndrome). We checked for the metabolic risk factors by measuring the abdominal circumference, the blood pressure and the lipid profile. Blood was collected after minimum 8 hours of fasting, in the morning. The lipid profile was measured using standard laboratory methods.

Atherogenic plasma index was defined using the formula: log (TG/HDLc), where TG is the serum triglycerides and HDLc is the high-density lipoprotein cholesterol.

We stratified the risk, according to previously reported thresholds (Dobiasova & Frohlich, 2001), in low, medium and high. The statistical analysis was performed using StatPlus: mac v6. As the atherogenic plasma index was normally distributed, we used the ANOVA test for the analysis of variance between the women and men groups.

Findings

Both groups had similar ages (men’s average age = 23.88, women’s average age = 23.78, p = 0.79). As mentioned, none of the subjects included in the analysis met the IDF metabolic syndrome criteria.

The average AIP value on the whole group was 0.07. When stratifying the risk according to the AIP value, the majority of the participants were in the low risk range (51 participants representing 61.4% of the total); there were 18 (21.6%) that matched the inclusion category of the high risk and 15 (18%), of the medium risk. The distribution of AIP by gender is presented in Figure 01 and Figure 02 .

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The average AIP in women’s group was 0.02, and in the men’s group, 0.18. The comparison between the means was statistically significant (p<0.001).

Our result is in line not only with the previously recognized lower risk in women before menopause (Rossi et al., 2002; Pitha et al., 2014), but also with different nutrition influences related to gender (Morselli et al., 2015). Therefore, gender differences should be considered for both primary prevention and screening tests.

Conclusion

Our study shows highly significant differences of the average AIP in young non-obese men compared to women. This raises the question of a possible different threshold of the AIP value for risk definition according to gender. To confirm this finding, a large longitudinal study is needed.

References

• Bala, C., Craciun, A. E., & Hancu, N. (2016). Updating the concept of metabolically healthy obesity. Acta Endo (Buc), 12(2), 197-205. doi: 10.4183/aeb.2016.19710
• Bridger, T. (2009). Childhood obesity and cardiovascular disease. Paediatrics & Child Health, 14(3), 177-182.
• Dobiasova, M., & Frohlich, J. (2001). The plasma parameter log (TG/HDL-C) as an atherogenic index: Correlation with lipoprotein particle size and esterification rate in apo B-lipoprotein-depleted plasma. Clin Biochem., 34(7), 583-588.
• Fang, J., Zhang, J. P., Luo, C. X., Yu, X. M., & Lv, L. Q. (2010). Carotid Intima-media thickness in childhood and adolescent obesity relations to abdominal obesity, high triglyceride level and insulin resistance. Int J Med Sci., 7(5), 278-283. doi:10.7150/ijms.7.278
• Gao, Z., Khoury, P. R., McCoy, C. E., Shah, A. S., Kimball, T. R., Dolan, L. M., & Urbina, E. M. (2016). Adiposity has no direct effect on carotid intima-media thickness in adolescents and young adults: Use of structural equation modelling to elucidate indirect & direct pathways. Atherosclerosis, 246, 29-35. doi:10.1016/j.atherosclerosis.2015.11.033
• Gherman, C., Mironiuc, A., Palcau, L., Cristea, A., Muresan, A., Filip, A., … Micula, S. (2010). Adipocytokines and their relationship with symptomatic atherosclerotic peripheral arterial disease. Rev Romana Med Lab., 18(1), 23-32.
• Gidding, S. S., Rana, J. S., Prendergast, C., McGill, H., Carr, J. J., Liu, K., …McMahan, K. (2016). Pathobiological determinants of atherosclerosis in youth (PDAY) risk score in young adults predicts coronary artery and abdominal aorta calcium in middle age: The CARDIA study. Circulation, 133(2), 139-146.
• Guo, F., & Garvey, W. T. (2016). Cardiometabolic disease risk in metabolically healthy and unhealthy obesity: Stability of metabolic health status in adults. Obesity, 24(2), 516-525. doi:10.1002/oby.21344
• Leeder, S., Raymond, S., & Greenberg, H. (2004). A race against time: The challenge of cardiovascular disease in developing economics. New York: Columbia University.
• Morselli, E., Frank, A. P., Rodriguez-Navas, C., Criollo, A., & Clegg, D. J. (2015). Sexual dimorphic response to chronic high fat diet. Int J Obes., 40(2), 206-209.
• Park, M. H., Skow, A., De Matteis, S., Kessel, A., Saxena, S., Viner, R., & Kinra, S. (2015). Adiposity and carotid-intima media thickness in children and adolescents: A systematic review. BMC Pediatrics, 15:161. doi: 10.1186/s12887-015-0478-5
• Pitha, J., Auzký, O., Kovář, J., Lejsková, M., Adámková, S., Babková, E., … Mrázková, J. (2014). Changes in cardiovascular risk profile in women after menopause (Prague Pre- and Post- Menopausal Female study). Cor et Vasa, 56(2), e113-e117. https://doi.org/10.1016/ j.crvasa.2014.01.004
• Rossi, R., Grimaldi, T., Origliani, G., Fantini, G., Coppi, F., & Modena, M. G. (2002). Menopause and cardiovascular risk. Pathophysiol Haemos Thromb., 32, 325-328.
• Srikanthan, K., Feyh, A., Visweshwar, H., Shapiro, J. I., & Sodhi, K. (2016). Systematic review of metabolic syndrome biomarkers: A panel for early detection, management, and risk stratification in the West Virginian population. Int J Med Sci., 13(1), 25-38. doi:10.7150/ijms.13800