Patients’ awareness of the cardiovascular risk factors – results of the primary care arm of EUROASPIRE V study in Croatia

Abstract

Background

Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality globally and in Croatia. Effective prevention relies on patient awareness and management of modifiable risk factors, yet the extent of patient knowledge regarding their own risk factor values and guideline-recommended targets is unclear.

Methods

This study analysed data from the Croatian primary care arm of the EUROASPIRE V survey, which included adults aged 18–79 years without established atherosclerotic disease but receiving treatment for hypertension, dyslipidemia, or diabetes. Patients were interviewed and examined at least six months after initiation of therapy. Awareness was defined as knowing both current and target values for weight, waist circumference, blood pressure, total and LDL cholesterol, HbA1c, and fasting blood glucose.

Results

Among 203 Croatian participants (55.2% female, mean age 62.8 ± 10.2 years), awareness of risk factor values and targets was highest for blood pressure (80%) and weight (61.1%), but substantially lower for total cholesterol (25.6%), LDL cholesterol (17.1%), and waist circumference (16.7%). Notably, 82.6% of patients treated for diabetes were unaware of their HbA1c value or target. No significant differences in risk factor control were observed between aware and unaware patients.

Conclusions

A substantial proportion of high-risk primary care patients in Croatia lack awareness of their cardiovascular risk factor values and targets. Awareness alone appears insufficient to drive meaningful behavioral change or improve clinical outcomes. These findings highlight the need for more comprehensive, person-centered prevention strategies that go beyond risk communication to achieve effective CVD risk reduction.

Background

Cardiovascular disease (CVD) represents one of the major challenges of healthcare systems in Croatia and globally [1–3]. Healthy lifestyles and the control of the most common chronic diseases as arterial hypertension, diabetes and dyslipidemia prevent CVD events in people at high risk for developing CVD [4–6].

Cardiovascular disease (CVD) prevention aims to lessen disease burden by lowering morbidity and mortality rates, while enhancing both quality and duration of life [1]. Since 1994, the Joint European Societies (JES) have periodically revised their prevention guidelines, and their real-world uptake has been examined through a series of five multinational cross‑sectional studies—EUROASPIRE I to V—conducted between 1995. and 2018. under the auspices of the European Society of Cardiology’s EURObservational Research Programme (EORP) [2, 7–10].

Primary care arm of EUROASPIRE V determined to what extent the 2016 JES guidelines on CVD prevention had been implemented in clinical practice in people at high risk of developing CVD.

Aim of this research is to assess the level of patient awareness regarding their current and target cardiovascular risk factor values using data from the Croatian primary care arm of EUROASPIRE V study.

Methods

This study was conducted as part of the EUROASPIRE V primary care arm, coordinated by the European Society of Cardiology EURObservational Research Programme (EORP) that involved 16 countries including Croatia. The Croatian arm followed harmonized procedures established for the international research network to ensure comparability of data between countries [2]. According to the EUROASPIRE protocol, countries with fewer than 5 million inhabitants, such as Croatia, were permitted to recruit patients from only one geographic area with a population of at least 500,000. Zagreb was selected as the study location, and nine family medicine practices were chosen, each contributing equally to the overall recruitment of patients for the study. The participating physicians had prior research experience but were primarily engaged in regular clinical practice with patients.

Study design and participants

A cross-sectional study design was employed across selected primary care practices in Croatia. The study population comprised adults aged 18 to 79 years with no prior diagnosis of atherosclerotic cardiovascular disease. Eligible individuals were receiving pharmacological management for at least one major risk factor, specifically hypertension, dyslipidaemia, or hyperglycaemia, for a minimum of six months prior to enrolment. Patients were retrospectively identified via practice medical records and invited to participate.

Data collection procedures

Sociodemographic and clinical data, including health behaviors and relevant cardiovascular risk factors, were collected during dedicated study visits. Physical examinations and laboratory assessments followed standardized protocols and diagnostic definitions as outlined in the EUROASPIRE V methodology, extensively reported by Kotseva et al. [2]. In the EUROASPIRE design, eligible patients are identified from practice records rather than through experimental randomization. Each participating practice screened its records to find all patients meeting the inclusion criteria (e.g. no prior cardiovascular disease, on preventive medications). From this list, a predetermined number of patients were approached for participation, using consecutive invitation as guided by the study protocol.

Assessment of patient awareness of cardiovascular risk factors

We conducted a focused analysis of the Croatian cohort from the EUROASPIRE V primary care study, with particular emphasis on patient responses to question of the EUROASPIRE questionnaire: “Are you aware of your latest weight, waist circumference, blood pressure, total and LDL cholesterol, HbA1c or blood glucose level, and what your target is?” For each modifiable cardiovascular risk factor, patients were categorized into two groups: those who were aware (i.e., answered “yes” to knowing both their current measured value and the recommended target) and those who were unaware (i.e., uncertain or did not know either their current value, target value, or both).

Statistical analysis

Data were entered into a secure database and analyzed using SPSS software from SPSS Inc., headquartered in Chicago, Illinois. Continuous variables are reported as mean ± SD and categorical variables as counts and percentages. Descriptive statistics were used to estimate the prevalence rates of risk factors and medication use at interview. Prevalences were compared between gender and age groups according to Fisher’s exact test. Comparisons between groups used Student’s t-test for normally distributed continuous variables or Mann–Whitney U test as appropriate, and chi-square tests for categorical variables. Spearman’s rank correlation was used for patient-reported and measured values. Differences between sex and age groups within the aware and unaware groups were assessed using two-way Analysis of Variance (ANOVA). Analyses were conducted separately for each awareness group. A p-value < 0.05 was considered statistically significant.

Results

Characteristics of the study population

Croatian part of EUROASPIRE V cohort was responsible for 203 or 7.4% of a total of 2759 subjects. Population of Croatian subjects were mostly female (N = 112; 55.2%) with mean age of 62.8 +/- 10.2 years and mean BMI of 29.3 +/- 5.3 kg/m². Female and male subjects were comparable by age (p = 0.215), but not by BMI (p = 0.038; Student’s t test for both analyses) with men having on average somewhat higher mean BMI (30.2 +/- 4.99 kg/m²) than women (28.6 +/- 5.5 kg/m²).

Lifestyle characteristics

In Table 1 we presented the data for Croatian subjects on patients’ lifestyle characteristics by age and gender. The overall prevalence of smoking was 17.7%, with nearly identical rates observed in men (17.6%) and women (17.9%). Smoking was notably more common among participants younger than 60 years (25%) compared to those aged 60 years or older (13.7%). The prevalence of overweight and obesity was high in the study population, with a marked difference between men (90.1%) and women (72.3%). In contrast, rates were similar across age groups, with 79.2% among participants under 60 years and 80.9% among those aged 60 years or older.

Table 1.

Patients’ lifestyle characteristics by age and gender

		Gender		p valuea	Age		p valueb
	All n = 203%	Men n = 91%	Women n = 112%		< 60 years n = 72%	≥ 60 years n = 131%
Smoking	17.7	17.6	17.9	0.407	25.0	13.7	0.020
Current smokers not having been offered professional advice to quit in past 3 years	11.1	12.5	10.0	0.813	10.5	11.8	0.906
Current smokers not having attempted to quit smoking in past 3 years	69.4	68.8	70.0	0.192	57.9	82.4	0.906
Current smokers not having the intention to quit within the next 6 months	88.9	87.5	90.0	0.696	84.2	94.1	0.247
Overweight and obesity	80.3	90.1	72.3	0.002	79.2	80.9	0.764
Obesity	40.4	44.0	37.5	0.351	36.1	42.7	0.357
Central obesity	64.0	61.5	66.1	0.503	58.3	67.2	0.209
Obese patients never been told to be overweight	9.8	7.5	11.9	0.502	3.8	12.5	0.219
Obese patients not having attempted actively to lose weight in last month	47.6	45.0	50.0	0.332	34.6	53.6	0.264
Obese patients not seriously considering weight loss in next 6 months	35.4	37.5	33.3	0.874	30.8	37.5	0.801
Obese patients not being aware of their weight target	31.7	35.0	28.5	0.804	38.5	28.6	0.160
Obese patients not having been advised to follow dietary guidelines	23.1	15.0	31.0	0.148	26.9	21.4	0.787
Regular physical activity ≥30 min on average 5 times a week	27.6	30.8	25.0	0.658	33.3	24.4	0.364
Vigorous physical activity for ≥20 min 3 or more times a week	8.9	9.9	8.0	0.068	12.5	6.9	0.669
Performing planned physical activity to increase physical fitness	18.7	22.0	16.1	0.283	22.2	16.8	0.343
Not performing planned physical activity and no intention to do so in next 6 months	30.5	27.5	33.0	0.334	20.8	35.9	0.074
Not having received personal advice to do more general everyday activities	30.5	23.1	36.6	0.102	27.8	32.1	0.592

^acomparison of difference between gender

 ^bcomparison of difference between age groups

Table 2 summarizes the lifestyle modifications undertaken by patients within the past three years to reduce their risk of cardiovascular disease, stratified by age and gender. Notably, none of the patients reported participation in structured smoking cessation programs, nor the use of medications such as bupropion or varenicline.

Table 2.

Reported lifestyle changes taken by patients to reduce their risk of heart disease within the last three years by age and gender

		Gender		p valuea	Age		p valueb
	All n = 203	Men n = 91%	Women n = 112%		< 60 years n = 72%	≥60 years n = 131%
Smokingc
Abstinence	15.5	25.7	5.6	0.192	14.3	16.3	0.906
Reduction	14.1	14.3	13.9	0.654	28.6	4.7	0.026
Smoking cessation clinic	0	0	0	/	0	0	/
Nicotine replacement therapy	1.4	2.9	0	/	3.6	0	/
Bupropion	0	0	0	/	0	0	/
Varenicline	0	0	0	/	0	0	/
In patients with BMI ≥ 30 kg/m2
Reduction of fat	67.1	70	64.3	0.570	65.4	67.9	0.748
Reduction of calories	58.5	57.5	59.5	0.819	61.5	57.1	0.921
Participating in regular physical activity	51.2	57.5	45.2	0.527	46.2	53.6	0.737
In patients using BP-lowering medication
Special dietd	55.1	61	49.4	0.249	59.3	52.9	0.689
Reduction of salt	74.4	74	74.7	0.996	81.5	70.6	0.249
Increased everyday physical activity	62.2	62.3	62	0.999	59.8	66.7	0.490
In patients using lipid-lowering medications
Special dietd	75.9	70.6	79.6	0.372	75	76.3	0.788
Reduction of fat	66.3	67.6	65.3	0.443	62.5	67.8	0.692
More fruit and vegetables	74.7	76.5	73.5	0.528	70.8	76.3	0.875
More fish	53	50	55.1	0.412	41.7	57.6	0.413
Increased everyday physical activity	61.4	73.5	53.1	0.061	50	66.1	0.198
In patients with diabetes
Reduction of fat	73.9	72.2	75.8	0.235	77.8	72.5	0.682
More fruit and vegetables	81.2	75	87.9	0.172	77.8	82.4	0.670
Less sugar	81.2	77.8	84.8	0.375	66.7	86.3	0.032*
Less alcohol	39.1	41.7	36.4	0.829	27.8	43.1	0.064
Increased everyday physical activity	55.1	50	60.6	0.658	55.6	54.9	0.047

BMI body mass index, BP blood pressure

^acomparison of difference between gender

^bcomparison of difference between age groups

^cChange during the last three years reported by smokers

^dprescribed by a doctor or other health professional

The analysis of assessment of patient awareness of cardiovascular risk factors was stratified by the presence of each individual risk factor and shown in Fig. 1. From the public health perspective, interesting are subjects who don’t know or are unsure of either actual or target value for any of the mentioned parameters (“unaware” group). Proportions of such subjects were comparable by gender (p > 0.100 for all parameters; chi-square test). Same was not true for age, where mean age of aware subjects for target fasting glucose levels was significantly (p = 0.007) higher (65.2 +/- 9.01 years), compared to unaware (61.4 +/- 10.53 years). Similar, but borderline statistically significant differences were also seen for total cholesterol (p = 0.079; aware were older with 65.5 +/- 9.02 compared to 62.2 +/- 10.32 years in unaware) and blood pressure (p = 0.057; aware were older with 63.3 +/- 9.61 compared to 60.6 +/- 11.26 years in unaware.

Fig. 1.

Proportion of patients aware and unaware of their current and target values for individual cardiovascular risk factors. Data shown as percentages for each risk factor

All cardiovascular risk factors presented in Table 3, with the exception of diastolic blood pressure, demonstrated at least a moderate correlation between patient-reported and measured values, all reaching statistical significance.

Table 3.

Correlation between patient-reported and measured actual cardiovascular risk factors

	Spearman’s rho	p
Total cholesterol (mmol/L)	0.807	< 0.001
Blood pressure systolic (mmHg)	0.527	< 0.001
Blood pressure diastolic (mmHg)	0.338	< 0.001
Weight (kg)	0.953	< 0.001
Weight target (kg)*	0.665	< 0.0.001
Waist circumference (cm)	0.997	< 0.001
LDL cholesterol (mmol/L)	0.836	< 0.001
Fasting blood glucose (mmol/L)	0.657	< 0.001
HbA1c (%)	0.707	0.005

*Reported target weight correlated with target calculated from BMI 24.9

The patients’ self-reported target values for cardiovascular risk factors presented in Table 4 closely correspond to the target values recommended by the 2016 JES guidelines [1].

Table 4.

Patients’ self-reported target cardiovascular risk factor values

	Mean target value reported	SD
Total cholesterol (mmol/L)	4.87	0.501
Blood pressure systolic (mmHg)	126.9	7.76
Blood pressure diastolic (mmHg)	78.5	5.55
Waist circumference in men (cm)	92.9	8.02
Waist circumference in women (cm)	84.0	7.75
LDL cholesterol (mmol/L)	2.76	0.435
Fasting blood glucose (mmol/L)	6.00	0.633
HbA1c (%)	6.47	0.287

The data presented in Table 5 revealed no significant differences in measured cardiovascular risk factor values between patients who were aware of their current and target risk factor levels and those who were unaware. Mean total cholesterol levels were slightly higher in the unaware group (5.33 mmol/L) compared to the aware group (5.04 mmol/L). Systolic and diastolic blood pressure values were comparable between aware and unaware groups. Weight and waist circumference were also similar overall and LDL cholesterol showed no significant difference between groups but was higher in women than men among the unaware (3.29 vs. 3.04 mmol/L, p < 0.05). Fasting blood glucose was paradoxically higher in the aware group, but same as for HbA1c levels, there was no significant differences between groups. Age stratification showed some variation but no consistent pattern of better control among any group.

Table 5.

Mean values of measured cardiovascular risk factors in aware and unaware group, substratified by sex and age

	Unaware group					Aware group
	All	Sex		Age		All	Sex		Age
	Mean	Men	Women	< 60 years	≥60 years	Mean	Men	Women	< 60 years	≥60 years
Total cholesterol (mmol/L)	5.33	5.12	5.47 (p < 0.05)	5.78	5.06 (p < 0.05)	5.04	4.81	5.25	5.28	4.93
Blood pressure systolic (mmHg)	138	143	130	128	144	137	137	137	135	138
Blood pressure diastolic (mmHg)	85	86.7	82.5	83.9	85.7	82.7	83.4	82.2	84.3	82
Weight (kg)	91.5	94	86.8	95.1	88.7	92.8	102	85.2 (p < 0.05)	99.8	89.2
Waist circumference (cm)	105	110	99.5 (p < 0.05)	106	104	110	112	105	116	106
LDL cholesterol (mmol/L)	3.19	3.04	3.29 (p < 0.05)	3.56	2.98 (p < 0.05)	3.14	3.24	3.04	3.42	3.01
Fasting blood glucose (mmol/L)	7.3	7.44	7.07	7.14	7.37	8.16	8.25	8.08	8.95	7.91
HbA1c (%)	6.52	6.36	6.68	6.01	6.69	6.46	6.6	6.17	7.10	6.14

Discussion

This study offers a comprehensive analysis of patient awareness of cardiovascular risk factors in a Croatian primary care cohort, using standardized EUROASPIRE V methodology. Our findings show that a substantial proportion of patients treated for hypertension, dyslipidaemia, or diabetes are not aware of their own risk factor values or guideline-recommended targets. These findings are in line with the broader EUROASPIRE V results from 16 European countries, which revealed significant gaps in the implementation of preventive measures and risk factor control among high-risk individuals [2]. Direct comparison between Croatian and broader European data presented in the article from Kotseva et al. [2] was not possible, as Croatian data are already incorporated within the aggregated European dataset. Nevertheless, given that the Croatian population constitutes only 7.4% of the total European dataset, we considered it methodologically acceptable to include this comparison, as the influence of Croatian data on the overall European estimates is minimal. From a public health perspective, the high rates of smoking, overweight/obesity, physical inactivity, and low dietary compliance observed in our population and presented in Table 1 further emphasize the need for integrated prevention strategies. Croatian cohort mirrors trends seen throughout European data, where cardiovascular risk factor burdens remain high despite decades of guidelines and campaigns [1, 2, 7–10].

The proportion of current smokers without an intention to quit within six months was higher in the Croatian data (69.4%) compared to the European data (58.5%), with this difference most pronounced among individuals aged 60 and older (82.4% in Croatian cohort vs. 64.1% in EUROASPIRE V overall). It is a good trend but Croatia still has weak anti-smoking policies [11]. Both bupropion and varenicline are available in Croatia through prescription. However, structured smoking cessation programs and counseling services remain limited, available only in secondary care settings. The complete absence of structured smoking cessation support shown in Table 2 (e.g. no reported use of bupropion, varenicline, or referral to cessation programs) in observed Croatian cohort is concerning specially regarding facts that studies have shown that professional smoking cessation interventions significantly increase quit rates [12–14]. Overweight and obesity rates among men were higher in the Croatian dataset (90.1% vs. 83.3% in EUROASPIRE V overall), while women in EUROASPIRE V overall population showed a higher prevalence of obesity (44.9% vs. 37.5% in Croatia). Central obesity was also more frequently reported among men in Croatian cohort (61.5% vs. 53.8% in EUROASPIRE V overall). This could be influenced by differences in gender roles, societal expectations, and access to health resources across European countries. Socioeconomic inequalities, such as lower education and income, are linked to higher obesity rates among women in both Croatia and Europe, but the magnitude and distribution of these factors vary by country [15].

Despite its geographical and cultural classification as a Mediterranean country, Croatia exhibits CVD mortality rates that are comparable to those observed in Eastern and Central European countries, deviating from the typically lower CVD mortality associated with Mediterranean populations. Variations among countries are mainly attributable to different levels of preventive care, disease patterns, and distinctive characteristics of their healthcare system [16].

The European data revealed greater gaps in awareness and counseling. For example, a higher percentage of obese patients in EUROASPIRE V overall population reported never being told they were overweight (18.6% vs. 9.8% in Croatian cohort), and more were unaware of their weight targets (37.6% vs. 31.7%), with the difference particularly notable among women (42.8% in EUROASPIRE V overall vs. 28.5% in Croatian cohort). Additionally, more men in EUROASPIRE V overall population had been advised to follow dietary guidelines (36.5% in EUROASPIRE V overall vs. 15.0% in Croatian cohort).

Physical activity levels were consistently higher in the European dataset, with 36.4% of patients engaging in regular physical activity (≥ 30 min, 5x/week) compared to 27.6% in the Croatian data. Vigorous physical activity (≥ 20 min, 3x/week) was also more common in EUROASPIRE V overall (16.1% vs. 8.9% in Croatian cohort), and a greater proportion reported performing planned physical activity (31.6% in EUROASPIRE V overall vs. 18.7% in Croatian cohort). This could be due to stronger promotion of exercise, greater access to recreational facilities, and cultural norms that support active lifestyles in some European countries [17]. However, the European data also showed a higher percentage of patients not performing planned activity and having no intention to start, particularly among women (42.1% in EUROASPIRE V overall vs. 33.0% in Croatian cohort).

Awareness presented in Fig. 1 was highest for blood pressure (80%) and weight (61.1%), but fell sharply for other parameters: only 25.6% were aware of their total cholesterol values and targets, 17.1% for LDL-C, and 16.7% for waist circumference. Alarmingly, 82.6% of patients on treatment (medications or diet) for diabetes did not know either their HbA1c value or target. These results are consistent with previous EUROASPIRE surveys, which have shown limited patient understanding of modifiable CVD risk factors despite treatment. All cardiovascular risk factors shown in Table 3, except for diastolic blood pressure, exhibited at least a moderate and statistically significant correlation between patient-reported and measured values. These findings indicate that patients’ reported awareness reflects genuine understanding rather than merely an attitudinal response. Notably nearly nine out of 10 patients after coronary event (secondary prevention) were aware of their weight (93.4%) and blood pressure (86.7%). However, less than one-half of them knew their total cholesterol (48.9%), fasting glucose (49.6%) and waist circumference (29.3%) [10]. In Table 4, patients’ self-reported target values for cardiovascular risk factors are presented, which align with the target values recommended in the JES 2016 guidelines. This similarity indicates that patients who reported awareness of their target risk factor levels demonstrate an accurate understanding of clinically established goals. Therefore, these findings support the conclusion that patient-reported awareness reflects true knowledge of appropriate cardiovascular risk targets rather than misconception or arbitrary estimates. This alignment underscores the validity of using patient-reported target values as indicators of their informed engagement in risk factor management. While it is true that the recent ESC/EAS guidelines do not provide a specific target for total cholesterol, it is important to note that in Croatia, laboratory reports indicate a target total cholesterol value of 5.0 mmol/L. Patients often see and remember this figure in their lab results, which explains their awareness of total cholesterol targets. The comprehensive global monitoring framework of the WHO recognizes elevated total cholesterol levels as a biological risk target for prevention and control of non-communicable diseases [18].

Research also shows disparities between high and low- and middle-income countries regarding awareness and control of CVD risk factors. From 2000 to 2010, the age-standardized prevalence of hypertension decreased by 2.6% in high-income countries but increased by 7.7% in low- and middle-income countries. During the same period, the proportions of awareness (58.2% versus 67.0%), treatment (44.5% versus 55.6%), and control (17.9% versus 28.4%) increased substantially in high-income countries, whereas awareness (32.3% versus 37.9%) and treatment (24.9% versus 29.0%) increased less, and control (8.4% versus 7.7%) even slightly decreased in low- and middle-income countries [19].

The lack of difference in risk factor levels between aware and unaware patients shown in Table 5 is noteworthy. While it might be expected that greater awareness leads to better control, our findings show that awareness alone is not sufficient. As shown in Table 5, we observed a paradoxical trend of higher fasting plasma glucose levels among patients who reported awareness of their treatment goals compared to those who were unaware. This difference did not reach statistical significance but similar findings were demonstrated in the research by Brockmeyer et al., in the context of HbA1c. In this study, patients who knew their HbA1c target were somewhat less likely to achieve the goal compared to those who did not know it. However, this relationship was not statistically significant [20]. Psychological and behavioral barriers—rather than lack of knowledge—drive inadequate control, and addressing emotion, motivation, and coping skills is crucial alongside education [21, 22]. According to the Health belief model, a change in belief typically precedes a change in behavior [23]. Awareness of risk factors and understanding their personal relevance (susceptibility and severity) are essential first steps, but they must be coupled with perceived benefits, low barriers, and strong self-efficacy to result in actual behavioral change [24]. In Transtheoretical model of health behavior which is found applicable in CVD prevention [25], awareness alone does not guarantee action, it must be coupled with readiness and motivation to change [26]. A recent meta-analysis demonstrated that CVD risk communication strategies in the context of primary prevention do not produce significant clinical improvements in blood pressure, fasting blood glucose, smoking cessation, or cholesterol levels [27]. In line with previous research, Alzaman et al. found that awareness of CVD risk was modestly associated with healthier behavior [28] and Rountree et al. found that the perception of CVD severity in women 25–55 years old was associated with reduced intention to change behavior [29]. In other study, although more than half of the participants had high knowledge and attitude about CVD, their behaviors were not satisfactory [30] and Negesa et al. found that there was no statistically significant association between knowledge of cardiovascular risk factors and actual cumulative risk behaviour [31]. These observations are consistent with our findings, suggesting that awareness of health risks alone is insufficient to prompt meaningful behavioral change, potentially due to underlying sociological or psychological barriers. Person-centred care, a core concept in family medicine is especially useful in these circumstances. It can be used to provide the patients with the information and tools they need to take an active role in their own care. This includes clear explanations of diagnoses, treatment options and outcomes, as well as offering resources and support to patients who want to take control of their own health [32]. Trust and shared responsibility are central to this concept, aiming to empower patients in managing their health effectively. Despite its significance, family medicine doctors encounter barriers in implementing person-centred care effectively, many of which remain unclear [33].

Low levels of awareness are not limited to the general public, healthcare professionals also exhibit suboptimal levels of knowledge regarding CVD risk factors. In PERCRO-DOC survey conducted in 2010, only 53.3% of the physicians knew the LDL-cholesterol goal value for high-risk patients and only 56.2% knew which HDL-cholesterol level is the marker of increased risk [34]. Other study has shown healthcare professionals with high cholesterol generally do not engage in physical activity [35]. This lack of awareness among healthcare professionals may contribute to low levels of awareness among patients [34].

Strengths and limitations

The major strengths of this study include the use of a standardized, internationally validated methodology and high-quality data collection across multiple sites. The linkage with the EUROASPIRE network allows contextualization of findings in a wider European framework. Limitations include inability to perform a direct comparison between Croatian and broader European data presented in the article from Kotseva et al. [2], as Croatian data are already incorporated within the aggregated European dataset. Additionally, awareness was self-reported, and not objectively verified through documentation or patient logs, introducing potential recall or social desirability bias. In the questionnaire patients could write a number corresponding to their target or actual values for each cardiovascular risk factor. The numerical responses for cardiovascular risk factors were not analyzed because the study focused on patients’ subjective perception of their care engagement rather than exact numerical knowledge. Additionally, the low number of patients aware of each risk factor limited the robustness of further subgroup analyses. Similarily, socioeconomic status was not analyzed due to subgroup size limitations, though its influence is acknowledged for future research. Finally, as a cross-sectional study, causal relationships between awareness and control cannot be reliably inferred. Therefore, future research should explore in depth how patients’ awareness influences the management of CVR factors.

Conclusions

These findings reinforce the need to shift preventive care in Croatia and similar settings toward a more person-centered approach. Clinicians should consistently share clear, understandable information about risk factors and targets, respecting each patient’s unique situation and preferences. Repeating these messages over time, engaging in shared decisions, and offering practical advice can help patients reach their goals. Public health institutions should integrate structured patient education and counseling into routine care pathways. Ultimately, improving awareness is a necessary, though not sufficient, step toward reducing the burden of cardiovascular disease.

Abbreviations

CVD

Cardiovascular disease

EUROASPIRE

European action on secondary and primary prevention by intervention to reduce events

ESC

European Society of Cardiology

EORP

EURObservational Research Programme

JES

Joint european societies

BMI

Body mass index

SBP

Systolic blood pressure

DBP

Diastolic blood pressure

SD

Standard deviation

HDL-C

High-density lipoprotein cholesterol

HbA1c

Glycated hemoglobin

LDL-C

Low-density lipoprotein cholesterol

IFCC

International federation of clinical chemistry

BP

Blood pressure

SES

Socioeconomic status

WHO

World health organization

EACPR

European association for cardiovascular prevention & rehabilitation

Authors’ contributions

IK and VC contributed to concept and design, data analysis, interpretation, preparation of draft manuscript. ZOA, GP, MH, LK, NB, PH contributed to the data analysis, data interpretation and providing critique. DM, ZR, JS contributed to the conception and design, data interpretation and providing critique. All authors have read and approved the final manuscript.

Funding

Since the start of EORP, the following companies have supported the EUROASPIRE V survey through research grants to the European Society of Cardiology: Amgen, Daiichi Sankyo, Eli Lilly, Pfizer, Sanofi, Ferrer and Novo Nordisk. The sponsors of the EUROASPIRE surveys had no role in the design, data collection, data analysis, data interpretation, decision to publish, or writing the manuscript.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to their inclusion in a European dataset, but are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

We have obtained ethical approval from Health centre Zagreb – Centar and Health centre Zagreb - West. Patients, prior to participating in the study and after having the opportunity to ask questions, signed two copies of the informed consent form, one of which was given to them and the other was stored in the patient file.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.