J Atherosclerosis Prev Treat. 2020 May-Aug;11(2):39–47 | doi:10.53590/japt.02.1009
Matina Kouvari1, Demosthenes B. Panagiotakos1,2, Christina Chrysohoou3, Ekavi N. Georgousopoulou1,4,5, Dimitrios Tousoulis3, Christos Pitsavos3, The ATTICA study Investigators
1Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
2Faculty of Health, University of Canberra, Australia
3First Cardiology Clinic, School of Medicine, University of Athens, Greece
4School of Medicine, Sydney, The University of Notre Dame, Sydney, Australia
5Medical School, Australian National University, Canberra, Australia
Aim: The sex-specific effect of lipid-related biomarkers on 10-year first fatal/non fatal cardiovascular disease (CVD) incidence was evaluated. Material and Methods: ATTICA study was conducted during 2001-2012. N=1,514 men and n=1,528 women (>18 years) from greater Athens area, Greece were recruited. Follow-up (2011-2012) was achieved in n=2,020 participants.Baseline lipid profile was measured. In particular, total cholesterol (TC), high density lipoprotein cholesterol (HDL-C) and triglycerides (TGL), apolipoprotein B100 and A1 (ApoB100 and ApoA1) were measured. Low density lipoprotein cholesterol (LDL-C) was assessed through the Friedewald formula. Results: Overall CVD event was 15.5% (n=317) (19.7% in men and 11.7% in women, p<0.001). HDL-C and TGL were independently associated with CVD in women; per 10mg/dL HDL-C increase, Hazard Ratio (HR)=0.73,95% Confidence Interval (95%CI)(0.53, 1.00) and per 10mg/dLTGL increase, HR=1.10,95%CI(1.00, 1.21). ApoA1 (per 10mg/dL increase, HR=0.90,95%CI(0.81, 0.99)) was inversely associated with CVD in women while a positive association with apoB100 was observed only in men (per 10mg/dL increase, HR=1.10,95%CI(1.00, 1.21)). Conclusions: Beyond the common cholesterol-adjusted risk scores, reclassifying total CVD risk according to other lipid markers may contribute to early CVD prevention.
Key words: Heart disease, sex, woman, lipoproteins, apoliproteins, primary prevention
Corresponding author: Prof. Demosthenes B. Panagiotakos, DrMed, FRSPH, FACE School of Health Science and Education, Department of Nutrition and Dietetics, Harokopio University, Athens, Greece, E-mail: email@example.com
Submission: 07.04.2020, Acceptance: 27.05.2020
Cardiovascular disease (CVD) is responsible for >4 million deaths in Europe each year killing more women (i.e. 2.2 million) than men (i.e. 1.8 million).1 In addition to this,within the last year a transformation in women’s risk-factor profile towards unhealthy lifestyle habits and obesity has been observed followed by an acceleration in coronary heart disease and acute myocardial infarction incidence, even in younger –pre-menopause– life stages.2 Despite this epidemiological evidence as well as the increasing evidence that predictive risk factors differ between sexes, the vast majority of the obtained knowledge on prevention and risk prediction in CVD spectrum is still based on studies conducted predominately in men.3
Dyslipidemia remains a well-established CVD risk factor.4 Numerous cohorts, Mendelian randomization studies, and randomized clinical trials have consistently demonstrated a log-linear relationship between the absolute changes in plasma LDL-C and CVD risk, providing compelling evidence for a causal relation.5 On the other side, the inverse association between plasma high density lipoprotein cholesterol (HDL-C) and risk for atherosclerotic CVDstands among the most reproducible associations in observational epidemiology yet with weaker evidence regarding its added value as a therapeutic target.6,7 In this regard, a sex-related heterogeneity has been previously suggested with total cholesterol (TC) and low density lipoprotein cholesterol (LDL-C) being stronger CVD predictors in case of men8-10 and HDL-C in case of women.11,12 What is more, traditional risk markers explain only a proportion of total CVD risk. To this issue, beyond the strong documentation and commentary on these conventional lipid biomarkers, their apo-lipoproteins (apolipoproteins A1 (ApoA1) and apolipoproteins B100 (ApoB100)) are lastly investigated as potentially strong independent CVD predictors. Nevertheless, no robust conclusions exist while their clinical use is often overlooked.4 On the other hand, the hitherto literature lacks in the aforementioned need for a sex-specific orientation.13,14
Thereby, the aim of the present work was to evaluate the effect ofconventional lipid-related markers (TC, LDL-C, HDL-C, triglycerides (TGL)) as well as their apolipoproteins on 10-year first fatal/non fatal CVD incidence,in apparently healthy men and women using the sample of ATTICA study.15
The ATTICA study is a prospective, observational cohort investigation which was initiated in 2001.15 At baseline (2001-2002), n=3,042 apparently healthy volunteers residing in the greater metropolitan Athens area, Greece, agreed to participate (75% participation rate). Of the enrolled participants, n=1,514 (49.8%) were men (46±13 years) and n=1,528 (50.2%) were women (45±14 years). During baseline examination, a detailed clinical evaluation was performed by trained physicians; all participants were free of CVD and other chronic diseases, according to the study protocol. For the scope of the present work, we initially used the n=2,020 participants with complete CVD evaluation in the follow-up assessment.
The ATTICA study was approved by the Bioethics Committee of Athens Medical School. The study was carried out in accordance with the Declaration of Helsinki (1989) of the World Medical Association. All participants were informed about the study aims and procedures and provided written informed consent.
Lipid-related markers measurements at baseline examination
Blood samples were collected from an antecubital vein, between 8 to 10 a.m., in a sitting position after 12h fasting and alcohol avoidance. Serum for blood lipid measurements was prepared immediately after collection. The biochemical evaluation was carried out in a laboratory that followed the criteria of the World Health Organization Lipid Reference Laboratories. Serum total cholesterol, HDL-C and triglycerides were measured using chromatographic enzymic method using a Technicon automatic analyzer RA-1000 (Dade Behring, Marburg, Germany). HDL-C was determined after precipitation of the ApoB-containing lipoproteins with dextran-magnesium-chloride. LDL-C (mg/dL) was calculated using the Friedewald formula: (TC) – (HDL-C) – 1/5*(TGL) (only for participants with TGL<400 mg/dL). ApoB100 and apoA1 were measured by rate immunonephelometry. An internal quality control was in place for assessing the validity of TC, TGL and HDL-C methods. The intra and inter-assay coefficients of variation of cholesterol levels did not exceed 9%, triglycerides 4% and HDL-C 4%. Cut-off values of LDL-C, TGL and TC were defined according to the most updated guidelines for dylipidaemias.4 In case of HDL-C the sex-specific cut-off values suggested in NCEP ATP III (revised) criteria for metabolic syndrome were used.
Other baseline measurements
Baseline assessment regarding sociodemographic and lifestyle factors included among others age, sex, body mass index, level of adherence to Mediterranean diet, physical activity level and smoking habits. Height was measured to the nearest 0.5 cm and weight to the nearest 100 g. Body mass index was calculated as weight (in kg) divided by squared height (in m2). Level of adherence to Mediterranean diet was evaluated through the MedDietScore (range 0-55). Current smokers were defined as those who smoked at least one cigarette per day. Physical activity level was recorded through a translated, validated, version of International Physical Activity Questionnaire.
Further details regarding the methods and measurements applied have been previously described.15
Endpoint and follow-up evaluation
During 2011-12, the ATTICA Study’s investigators performed the 10-year follow-up (median follow-up time 8.41 years). In order to participate in the follow-up all participants were initially appointed through telephone calls. Afterwards, the investigators of the ATTICA study (physicians, nurses, nutritionists) approached the participants and performed a detailed evaluation of their medical records. For the participants who died during the follow-up (i.e. n=99), the information achieved from their relatives, as well as death certificates. The combined endpoint studied in this work was the development of a fatal or non-fatal CVD event. A CVD event was defined as the development of: acute myocardial infarction, or unstable angina, or other identified forms of ischemia (WHO-ICD coding 410-414.9, 427.2, 427.6), or heart failure of different types and chronic arrhythmias (WHO-ICD coding 400.0-404.9, 427.0 -427.5, 427.9-) or stroke (WHO-ICD coding 430-438).
Categorical variables are presented as absolute (n) and relative frequencies (%). Continuous variables are presented as mean values ± standard deviation or median (Interquartile Range) if normality was not met. Associations between normally distributed variables and sex were evaluated through Student’s t-test for independent samples. Whether these variables were normally distributed was tested through P-P plot and equality of variances through Levene’s test. For non-normally distributed variables, the Mann-Whitney test was used. Associations between categorical variables and sex were tested with the chi-squared test. Hazard Ratios (HR) and their corresponding 95% Confidence Intervals (95%CI) for lipid-related markers in relation to 10-year CVD event were evaluated through multivariable Cox-regression analysis in the total sample, as well as in subgroups. Proportional hazards’ assumption was graphically tested. The STATA software, version 14 (MP & Associates, Sparta, Greece) was used for all statistical analyses. Two sided level of significance was set at p<0.05.
Median survival time was 9.7 years in men and 9.8 years in women (p=0.55). The 10-year fatal/non fatal CVD event rate in the ATTICA study participants was 15.7% (n=317) [19.7% (n=198) in men and 11.7% (n=119) in women, p<0.001]. Median survival time was 9.7 years in men and 9.8 years in women (p=0.55).Baseline sociodemographic and clinical characteristics of participants as well as their metrics for lipid-related markersseparately for men and women are summarized in Table 1.
Findings from nested Cox regression models that evaluated the association between lipid markers (i.e. TC, LDL-C, HDL-C and TGL)and CVD incidence in free-of-CVD men and women of ATTICA study are presented in Table 2. In the unadjusted models, a positive association was observed between all lipid-related factors and CVD in both men and women (all p-values<0.05). However, in the age-adjusted models several sex-specific associations were revealed. In particular, TC lost its independent aggravating effect in both men and women. In case of men only HDL-C in terms of continuous variable was inversely associated with CVD onset; per 10mg/dL HDL-C increase was associated with 20% lower CVD risk within the decade. However, when adjusting for other clinical and lifestyle factors this association remained yet without reaching the level of significance. As for women, the age-adjusted models revealed significant associations only in case of HDL-C and TGL. This was retained even in multi-adjusted models. In specific, per 10mg/dL increase in HDL-C 10% lower CVD risk was observed while women with HDL-C>45mg/dL had about 27% lower risk to develop CVD within the decade, Similarly, per 10mg/dL raise in TGL, 10% lower CVD risk was revealed with the risk for CVD onset being about 31% higher in women with TGL>150mg/dL, in the multi-adjusted model (Table 2).
The association between apolipoproteins and 10-year CVD incidence was as well evaluated in free-of-CVD men and women of ATTICA study through nested Cox regression analysis and results are summarized in Table 3. It was revealed that in case of women apo-lipoproteins A1 (ApoA1) was independently associated with 10-year CVD onset; particularly, per 10mg/dL increase in ApoA1 the risk to develop CVD was 19% lower. In case of men, besides the significant trends observed principally for apo-lipoprotein B100 (ApoB100) indicating an independent aggravating effect in age-adjusted models this was not the case after taking into account potential confounders (Table 3).
To the best of our knowledge, the sex-specific effect of lipid-related markers, conventional and novel, on long-term CVD onset has been inadequately investigated. This work stands among the very few in the Mediterranean region that suggested different predictive ability among various lipid markers and their apolipoproteins specifying the outcomes to men and women. Despite the potential limitations of the present study, the results presented here may confer to better understanding the role of lipid-related markers on CVD risk stratification, even more from the standpoint of a sex-specific approach highly suggested in primary health care spectrum.
Dyslipidemia stands among the most important predictors of CVD. In this context, LDL-C reduction remains the primary therapeutic target in CVD with preventive and prognostic potentials. Taking into account the updates on CVD prevention and dyslipidemia management guidelines from the European Society of Cardiology, the concept “less is more” regarding LDL-C values is strongly supported.4 Besides the high recognition of this lipid marker in therapeutic regimens to accurately prevent major cardiac episodes or achieve a better prognosis, its efficiency in the context of CVD risk prediction is still questioned. For instance, people with obesity or metabolic syndrome are assigned in high or even very high CVD risk category, even if their lipid profile is beyond the typical one e.g., high LDL-C.16 Indeed, such people are principally characterized by low HDL-C values, elevated levels of TGL and a high content of small dense pro-atherogenic ApoB100 particles.16 It is therefore possible that people with a high content of elevated small dense lipoprotein particles have near normal LDL-C values due to the discordance between the ApoB100 particle number and their cholesterol content. These people will have an underestimated risk prediction score. Hence, the high residual risk for CVD as well as the different phenotypes exist,have driven lipid research towards novel surrogate lipid markers such as ApoB100, ApoA1, non-HDL-C, small dense LDL-C particles, Lp(a)etc.
To the best of our knowledge, this work stands among the very few that examined the role of lipid biomarkers against hard CVD-related endpoints, specifying the outcomes for men and women. Focusing on the traditional lipid markers, what we revealed was that LDL-C was an important CVD predictor only in men yet in case of women HDL-C seemed to contribute more to the overall CVD risk. Men have higher LDL-C levels compared with the age-matched women, till the menopause stage; since then a steep LDL-C raise occurs, predisposing women to a CVD-risk escalation.20 On the other side, HDL-C and triglycerides have been suggested as stronger lipid indicators in case of women which comes in line with findings arisen here. More specifically, HDL-C addition in SCORE risk stratification led to a modest improvement in its predictive ability for women.21 In Women’s Health Study, the inverse association between HDL-C and primary CVD incidence was retained across all LDL-C levels, with the exception of women with low total atherogenic particle burden.20 As for triglycerides, in a relevantmeta-analysis, a stronger association of fasting triglycerides with CVD mortality in women was revealed.22
Among the primary purposes of the present work was to evaluate the sex-specific effect of apolipoproteins against hard CVD endpoints.In the INTERHEART study, dyslipidemia, defined as (ApoB100)/(ApoA1) ratio possessed the highest population-attributed risk in both sexes.23 Here, we saw that ApoB100 was a stronger CVD predictor for men yet ApoA1 for women. However, apolipoproteins seem to have the lowest discriminative ability against CVD in both men and women which comes in line a meta-analysis of cohort studies implemented by the Emerging Risk Factors Collaboration.6
Strengths and limitations
Several limitations should be presented for better interpretation of the observed outcomes. Specifically, only baseline measurements were taken into account for our research hypothesis; hence misclassifications of transitions cannot be precluded due to the extended interim periods between follow-up assessments. Additionally, the ATTICA study participants presented a generally mild dyslipidemic profile at baseline along with relatively small variability of the lipid levels among individuals; this may contribute to –unexpectedly– non-significant trends in case of biomarkers such as TC and LDL-C; these characteristics of ATTICA study sample may modest the strength of the observed associations between lipid indices and 10-year CVD risk.
The aforementioned limitations are compensated for the fact that this is one of the very few works that evaluated the sex-based effect of lipid-related markers and apolipoproteins on 10-year CVD onset; to the best of our knowledge the evidence-based data regarding this issue are inadequate.
While ever increasing efforts have sought to elucidate the lipid-related biomarkers that contribute more or less to the CVD risk in the primary prevention, recommendations remain to be guided with appropriate conclusive evidence, mostly from a sex-oriented approach.23 The findings presented here partially address the literature gaps in the following key areas. Firstly, much as LDL-C is the key therapeutic target to achieve a healthier vascular system, its contribution to early CVD risk prediction seems to be questioned; here, high LDL-C levels were independently associated with increased CVD only in men. Secondly, HDL-C accompanied by or not with triglycerides seemed to contribute more to women’s CVD risk compared with other conventional lipid markers. Lastly, alternative lipid-related markers, predominately non-HDL-C particles, seemed to have an independent aggravating effect on long-term CVD principally in women. In this regard, beyond the commonly used TC-adjusted risk scores in primary prevention spectrum, more prospective studies are demanded to investigate the incremental value of reclassifying total CVD risk according to the most updated dyslipidaemia-related evidence. Sex differences should be an indispensable part of this process.
Conflict of interest
None to declare
The ATTICA study is supported by research grants from the Hellenic Cardiology Society [HCS2002] and the Hellenic Atherosclerosis Society [HAS2003]. The present work is also supported by a research grant from Hellenic Atherosclerosis Society.
The authors would like to thank the ATTICA study investigators.
1. Townsend N, Wilson L, Bhatnagar P, Wickramasinghe K, Rayner M, Nichols M. Cardiovascular disease in Europe: epidemiological update 2016. Eur Heart J. 2016 Nov;37(42):3232-45.
2. Mosca L, Benjamin EJ, Berra K, Bezanson JL, Dolor RJ, Lloyd-Jones DM, et al. Effectiveness-based guidelines for the prevention of cardiovascular disease in women–2011 update: a guideline from the American Heart Association [published correction appears in J Am Coll Cardiol. 2012 May;59(18):1663]. J Am Coll Cardiol. 2011 Mar; 57:1404-23.
3. Kouvari M, Yannakoulia M, Souliotis K, Panagiotakos DB. Challenges in sex- and gender-centered prevention and management of cardiovascular disease: Implications of genetic, metabolic, and environmental paths. Angiology. 2018 Nov; 69:843-53.
4. Mach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk. Eur Heart J. 2020 Jan; 41(1):111-88.
5. Cholesterol Treatment Trialists’ (CTT) Collaboration, Baigent C, Blackwell L, Emberson J, Holland LE, Reith C, et al. Efficacy and safety of more intensive lowering of LDL cholesterol: A meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet. 2010 Nov;376(9753):1670-81.
6. Emerging Risk Factors Collaboration, Di Angelantonio E, Gao P, Pennells L, Kaptoge S, Caslake M, et al. Lipid-related markers and cardiovascular disease prediction. JAMA. 2012 Jun;307(23):2499-506.
7. Estrada-Luna D, Ortiz-Rodriguez MA, Medina-Briseño L, Carreón-Torres E, Izquierdo-Vega JA, Sharma A, et al. Current therapies focused on high-density lipoproteins associated with cardiovascular disease. Molecules. 2018 Oct; 23(11):2730.
8. Albrektsen G, Heuch I, Løchen ML, Steinar Thelle D, Wilsgaard T, Njølstad I, et al. Risk of incident myocardial infarction by gender: Interactions with serum lipids, blood pressure and smoking. The Tromsø Study 1979-2012. Atherosclerosis. 2017 Jun;261:52-9.
9. Lu Y, Zhou S, Dreyer RP, Caulfield M, Spatz ES, Geda M, et al. Sex differences in lipid profiles and treatment utilization among young adults with acute myocardial infarction: Results from the VIRGO study. Am Heart J 2017 Jan; 183:74-84.
10. Madssen E, Laugsand LE, Wiseth R, Mørkedal B, Platou C, Vatten L, et al. Risk of acute myocardial infarction: dyslipidemia more detrimental for men than women. Epidemiology. 2013 Sep; 24(5):637–642.
11. Mehta LS, Beckie TM, DeVon HA, Grines CL, Krumholz HM, Johnson MN, et al. Acute Myocardial Infarction in Women: A Scientific Statement From the American Heart Association. Circulation. 2016 Mar;133(9):916–947.
12. Manson JE, Bassuk SS. Biomarkers of cardiovascular disease risk in women. Metabolism. 2015 Mar;64(3 Suppl 1):S33-9.
13. Calling S, Johansson SE, Wolff M, Sundquist J, Sundquist K. The ratio of total cholesterol to high density lipoprotein cholesterol and myocardial infarction in Women’s health in the Lund area (WHILA): A 17-year follow-up cohort study. BMC Cardiovasc Disord. 2019;19:239.
14. Matthan NR, Zhu L, Pencina M, D’Agostino RB, Schaefer EJ, Lichtenstein AH. Sex-specific differences in the predictive value of cholesterol homeostasis markers and 10-year cardiovascular disease event rate in Framingham Offspring Study participants. J Am Heart Assoc. 2013 Feb;2:e005066.
15. Pitsavos C, Panagiotakos DB, Chrysohoou C, Stefanadis C. Epidemiology of cardiovascular risk factors in Greece: Aims, design and baseline characteristics of the ATTICA study. BMC Public Health. 2003 Oct;3:32.
16. Drexel H, Aczel S, Marte T, Vonbank A, Saely CH. Factors predicting cardiovascular events in statin-treated diabetic and non-diabetic patients with coronary atherosclerosis. Atherosclerosis. 2010 Feb; 208(2):484-9.
17. Rader DJ, Hovingh GK. HDL and cardiovascular disease. Lancet. 2014 Aug;384(9943):618-25.
18. Toth PP, Barter PJ, Rosenson RS, Boden WE, Chapman MJ, Cuchel M, et al. High-density lipoproteins: A consensus statement from the National Lipid Association. J Clin Lipidol. 2013 Sep-Oct;7(5):484-525.
19. Carr SS, Hooper AJ, Sullivan DR, Burnett JR. Non-HDL-cholesterol and apolipoprotein B compared with LDL-cholesterol in atherosclerotic cardiovascular disease risk assessment. Pathology. 2019 Feb; 51(2):148-54.
20. Mora S, Buring JE, Ridker PM, Cui Y. Association of high-density lipoprotein cholesterol with incident cardiovascular events in women, by low-density lipoprotein cholesterol and apolipoprotein B100 levels: a cohort study. Ann Intern Med. 2011 Dec;155(11):742-50.
21. Cooney MT, Dudina A, De Bacquer D, Fitzgerald A, Conroy R, Sans S, et al. How much does HDL cholesterol add to risk estimation? A report from the SCORE Investigators. Eur J Cardiovasc Prev Rehabil. 2009 Jun;16(3):304-14.
22. Liu J, Zeng FF, Liu ZM, Zhang C-X, Ling W-H, Chen Y-M. Effects of blood triglycerides on cardiovascular and all-cause mortality: A systematic review and meta-analysis of 61 prospective studies. Lipids Health Dis. 2013 Oct;12:159.
23. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004 Sep;364(9438):937-52.