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É. Kocsis Semmelweis University, Doktoral School of Health Sciences, H–1088 Budapest, Vas utca 17, Hungary

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H.J. Feith Department of Social Sciences, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary

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Abstract

Obesity and other unhealthy behaviors are behind cardiovascular morbidity and mortality, with the Roma population particularly at risk. The aim of our cross-sectional (questionnaire- and physical measurements-based) study was to compare the prevalence of obesity in Hungarian, Romanian, and Slovakian Hungarian-speaking Roma and non-Roma (N = 1893) in relation to lifestyle-related risk factors for cardiovascular diseases (CVD). In the total sample, the proportion of extreme obesity was higher in Roma (P < 0.001) than non-Roma. The mean waist circumference was the highest in Hungary (P < 0.001). Visceral fat was higher in the Hungarian Roma sample than in the Slovak (P = 0.006) or Romanian Roma samples (P = 0.005). Hungarian Roma total cholesterol levels were lower than in the Slovak (P < 0.001) or Romanian samples (P < 0.001). Hypertension and cholesterol levels were associated with a higher risk among non-Roma men (P < 0.001), and the presence of smoking increased CVD risk among both men (P = 0.024) and women (P < 0.001) in the Roma minority. The combined presence of several risk factors was found mainly in Roma. Overall, Roma scores were found to be worse, but ethnicity did not provide clear evidence for the questions examined, but rather the level of education, which is associated with socioeconomic status.

Abstract

Obesity and other unhealthy behaviors are behind cardiovascular morbidity and mortality, with the Roma population particularly at risk. The aim of our cross-sectional (questionnaire- and physical measurements-based) study was to compare the prevalence of obesity in Hungarian, Romanian, and Slovakian Hungarian-speaking Roma and non-Roma (N = 1893) in relation to lifestyle-related risk factors for cardiovascular diseases (CVD). In the total sample, the proportion of extreme obesity was higher in Roma (P < 0.001) than non-Roma. The mean waist circumference was the highest in Hungary (P < 0.001). Visceral fat was higher in the Hungarian Roma sample than in the Slovak (P = 0.006) or Romanian Roma samples (P = 0.005). Hungarian Roma total cholesterol levels were lower than in the Slovak (P < 0.001) or Romanian samples (P < 0.001). Hypertension and cholesterol levels were associated with a higher risk among non-Roma men (P < 0.001), and the presence of smoking increased CVD risk among both men (P = 0.024) and women (P < 0.001) in the Roma minority. The combined presence of several risk factors was found mainly in Roma. Overall, Roma scores were found to be worse, but ethnicity did not provide clear evidence for the questions examined, but rather the level of education, which is associated with socioeconomic status.

1 Introduction

In our fast-paced world, we are exposed to many risks, and we cope with stresses differently. According to Poirier (2014), we are living in an era where our cardiovascular health is setting the upper limit of the human lifespan.

According to HCSO (Hungarian Central Statistical Office) (2021), although the cardiovascular disease (CVD) mortality rate has declined somewhat in our country since 2000 (35.9%), myocardial infarction, ischaemic heart disease, or cerebrovascular disease are still the leading causes of deaths, accounting for 33.3% of deaths in 2019. Hungary is followed by Romania and Slovakia in the list of the three countries that are the focus of the study (WHO 2022).

The lifestyle, obesity, stress, chronic diseases, or even comorbidities are associated with an increased risk of developing CVD. The main risk factors for developing the disease are high blood pressure, high total cholesterol and triglycerides, smoking, physical inactivity, and obesity (Wallisch et al., 2021; An et al., 2023). These are basically all modifiable factors. The strongest non-modifiable risk factor is age, followed by gender and race (Kotseva et al., 2019; Jilani et al., 2021; Jancsó et al., 2022).

In the world ranking of obesity, Hungary ranks 41st with 26.5% of the population being obese. Romania ranks 75th with 22.5% and Slovakia 98th with 20.5% obesity (Obesity Rates by Country, 2022).

In the context of ethnicity in Europe, the Roma minority is the focus of our study. Even today they tend to live in large, closed communities, often segregated, and under poor sanitary conditions. Low educational attainment, resulting unemployment, poor nutrition, high levels of smoking and alcohol consumption, and lack of physical activity may all contribute to higher morbidity and mortality rates among the Roma minority (FRA, 2018). Their number is estimated at 10–12 million, half of the Roma population live in EU countries, and the majority are citizens of these countries. The European Commission (2020) estimates that the majority of Roma live in four Member States (Bulgaria, Slovakia, Romania, and Hungary). Due to their socio-economic status, many of them are disadvantaged, have very poor health, and often have difficulty accessing health care. CVD is thought to be associated with higher mortality among Roma than among the general population (Piko et al., 2021; Zajc Petranović et al., 2021), and similar patterns have been reported in Slovakia and Romania (Babinská et al., 2014; Orav, 2016).

The target population of our comparative research was the Hungarian-speaking Roma and non-Roma population living in rural areas in Hungary, Romania, and Slovakia. The Roma minority is growing in all three countries (Orav, 2016; Atlas, 2019).

The objective of this publication was to compare the prevalence of risk factors underlying CVD in the three countries. It is important to investigate whether there are parameters that are typical for a sample of the countries we examine, irrespective of gender, age, ethnicity, and country. The pandemic caused by the SARS-CoV-2 broke out before the start of our research, so our work was carried out in this setting. The presence and impact of the virus therefore motivated our research even more. Obesity and the associated chronic cardiovascular disease (CVD) are already a major concern in the population by default, whereas changing life circumstances in the context of the epidemiological response have further increased both the incidence and prevalence of obesity and CVD (Chocair et al., 2020; Stefan et al., 2021).

2 Materials and methods

Our study was conducted among Hungarian-speaking residents aged 18, living in rural areas of the Carpathian Basin, of Roma and non-Roma origin in three countries. The inclusion criteria for Roma and non-Roma sample were, in addition to age, to participate in the research voluntarily and to state clearly whether they considered themselves to be Roma or non-Roma. Those who did not agree to participate in both the questionnaire and the physical survey were excluded from the study.

The survey was conducted in 21 settlements in Hungary, 15 in Romania, and 6 in Slovakia. The survey was carried out in five regions of Hungary, whereas in Romania the majority of the survey was conducted in the counties of Bihor, Satu Mare, Harghita, Mures, and Covasna, and in Slovakia in the districts of Nitra, Kosice, and Banska Bystrica. The subjects of our research were reached through social networks and local Roma leaders.

We started our study with a self-made questionnaire including questions on socio-economic status, health status, and health behaviour. As a second part of the study, we conducted physical measurements at the locations in 2021 and 2022. Physical examinations were carried out on all participants who completed the questionnaire, according to the measurement protocol. Blood parameters were measured on an empty stomach, no food was consumed at least 6 h before the measurement as the organisers had informed everyone in advance. We used OMRON 511 Bioimpedance body composition monitors and OMRON M7 Intelli IT blood pressure monitors for the physical examinations, and MuliCare IN 3-in-1 kits to measure blood glucose, triglycerides, and cholesterol levels for blood samples.

In the questionnaire survey, we listed examples related to the issue of physical activity (hiking, cycling, sports, gardening, or if you walk a lot, e.g. going to work or shopping, or running errands in the village).

The questionnaire survey was performed under the ethical approval of the ETT TUKEB (ETT TUKEB IV/5210-2/2020/EKU), and the physical examinations were performed under an official ethical research approval of the head of the National Public Health Center (NPHC) (20102-8/2021/EÜIG).

SPSS Statistics 25 was used for statistical analysis. It was used to perform descriptive statistics. Khi2 test, T-test, and ANOVA were used to examine associations and differences between variables. Normality test was performed using Kolmogorov–Smirnov test with 95% confidence interval and P < 0.05 significance level.

3 Results and discussion

Our sample consisted of 1,893 participants from Hungary, Romania, and Slovakia, approximately half of whom were of Roma origin, with women over-represented in the subsamples. The average age was similar in all strata, but it is also clear that the educational attainment of Roma participants was lower in all strata (Table 1).

Table 1.

Socio-demographic data of the sample

Hungary (n = 852)Romania (n = 631)Slovakia (n = 631)
(45.47 ± 14.96 year)(40.12 ± 15.69 year)(40.12 ± 15.69 year)
VariableRoma (n = 430)Non-Roma (n = 422)Roma (n = 330)Non-Roma (n = 301)Roma (n = 204)Non-Roma (n = 206)
(44.61 ± 14.52 year)(46.34 ± 15.38 year)(39.07 ± 14.48 year)(41.27 ± 16.87 year)(39.42 ± 15.51 year)(40.63 ± 14.55 year)
GenderMale (n = 108)Female (n = 322)Male (n = 128)Female (n = 294)Male (n = 72)Female (n = 258)Male (n = 118)Female (n = 183)Male (n = 58)Female (n = 146)Male (n = 43)Female (n = 163)
Age (year)44.35 ± 16.0744.70 ± 13.9846.45 ± 16.1246.29 ± 15.0738.00 ± 15.3539.37 ± 14.2542.31 ± 17.2640.60 ± 16.6339.66 ± 16.5539.32 ± 15.1344.26 ± 15.5039.67 ± 14.19
Education8 primary class or less50.0%60.2%21.1%15.0%70.8%84.5%23.7%29.0%36.2%61.0%2.3%5.5%
Secondary vocational school24.1%19.9%21.1%11.2%23.6%12.8%24.6%9.8%32.8%21.2%20.9%13.5%
Secondary school19.4%12.7%28.9%29.6%4.2%2.3%32.2%26.2%24.1%15.8%25.6%31.3%
College/University6.5%7.1%28.9%44.2%1.4%0.4%19.5%35.0%6.9%2.1%51.2%49.7%

At the time of the survey, Roma smoked at higher rates in all three countries. A notable finding was that in Slovakia, quit rates were higher among Roma than among non-Roma (Fig. 1).

Fig. 1.
Fig. 1.

Smoking habits in each sub-sample in % (N = 1893)

Citation: Acta Alimentaria 52, 2; 10.1556/066.2023.00006

Physical inactivity is predominantly particularly prevalent among Roma in Hungary and Romania. In Slovakia, however, Roma are reported to be more active (Fig. 2).

Fig. 2.
Fig. 2.

Physical activity characteristics in % (N = 1893)

Citation: Acta Alimentaria 52, 2; 10.1556/066.2023.00006

The results of the physical examinations are shown in Table 2.

Table 2.

Mean results of physical examinations in the study sample (N = 1893)

Hungary (n = 852)Romania (n = 631)Slovakia (n = 631)
VariableRoma (n = 430)Non-Roma (n = 422)Roma (n = 330)Non-Roma (n = 301)Roma (n = 204)Non-Roma (n = 206)
BMI (kg m−2)29.77 ± 7.4328.43 ± 6.629.22 ± 7.4728.19 ± 6.5728.07 ± 7.9227.25 ± 6.03
Waist circumference (cm)Male (n = 108)Female (n = 322)Male (n = 128)Female (n = 294)Male (n = 72)Female (n = 258)Male (n = 118)Female (n = 183)Male (n = 58)Female (n = 146)Male (n = 43)Female (n = 163)
♂<102 cm100.7494.6499.8091.27101.2394.3597.5191.5496.4191.4795.4689.48
±±±±±±±±±±±±
♀ <88 cm16.4818.3416.0015.0216.5017.5014.7818.0617.3915.3012.3515.90
Visceral fat (<9)10.35 ± 5.829.40 ± 4.769.23 ± 4.899.14 ± 5.309.08 ± 4.968.43 ± 4.78
Blood glucose (≤6.0 mmol L−1)5.78 ± 2.675.59 ± 1.995.87 ± 2.676.07 ± 2.175.79 ± 2.105.72 ± 2.47
Total cholesterol (≤5.2 mmol L−1)4.84 ± 1.295.23 ± 1.345.44 ± 1.525.23 ± 1.535.38 ± 1.435.20 ± 1.50
Triglyceride (≤1.7 mmol L−1)2.12 ± 1.132.01 ± 1.122.06 ± 1.201.85 ± 1.082.16 ± 1.192.17 ± 1.23
Blood pressure

Systolic

Diastolic (<140/90 Hgmm)
132.70 ± 21.98133.80 ± 20.53135.62 ± 23.38135.69 ± 20.23135.45 ± 26.51135.98 ± 28.50
83.59 ± 12.5883.74 ± 11.8686.30 ± 13.1485.29 ± 12.0385.95 ± 11.2785.88 ± 13.63

When comparing the BMI values of the Roma respondents, we found that the mean BMI value of Roma in Hungary was significantly higher than that of Roma in Slovakia (F (963) = 5.311, P = 0.005). The mean BMI value of Roma in Hungary was higher than that of the majority sample (t (850) = 3.098, P = 0.002). Categorising BMI values according to WHO recommendation, we found that there were significantly more extremely obese Roma in the Roma population (P = 0.004) than in the overall sample (P < 0.001).

For waist circumference, based on the average of the three countries (Roma, and non-Roma), we found that Hungary presented significantly higher values (F (1892) = 5.730, P < 0.003) than Slovakia. In Hungary, the mean waist circumference was significantly (t (850) = 1.993, P = 0.047) higher for Roma compared to non-Roma.

When examining visceral fat, we found that the mean of Hungarian (Roma, and non-Roma) participants was significantly (F (1866) = 7.397, P < 0.001) higher than that of Slovak or Romanian participants. Within countries, comparing the mean visceral fat of Roma and non-Roma subjects, we found a significant difference only in the Hungarian sample, where the mean was significantly higher (t (835) = 2.571, P = 0.010) for Roma than for non-Roma. When comparing the Roma groups, we found that the mean visceral fat of Hungarian Roma was significantly higher than that of Roma from Slovakia (F (954) = 5.751, P = 0.003) or Romania.

Blood glucose levels showed similar average results for Roma in all three countries. However, among non-Roma, Hungarian participants had a lower value than the non-Roma in Romania (F (928) = 4.499, P = 0.011).

Serum total cholesterol levels were significantly lower in the Hungarian Roma samples (F (963) = 19.938, P < 0.001) than in the Roma samples from Slovakia or Romania.

Significant differences were observed for diastolic values, with the Hungarian Roma sample having a significantly lower value (F (963) = 5.102, P = 0.006) than the Slovak and Romanian Roma samples. We were not able to investigate the exact background of this further, but it is assumed that a higher proportion of Roma in Hungary were diagnosed with hypertension and been taking prescribed medication regularly, but it is also possible that the so-called „Roma paradox” is more apparent in Hungary (Hu et al., 2012; Kósa et al., 2015; Soltész et al., 2020; Zajc Petranović et al., 2021).

Waist circumference (t (962) = –4.003, P < 0.001), BMI (t (962) = –5.524, P < 0.001), and visceral fat (t (953) = –6.279, P < 0.001) values were lower in total Roma than in smokers, as were blood glucose levels (t (962) = –2.680, P = 0.007). Total non-Roma smokers also had lower BMI (t (927) = 2.913, P = 0.004) and visceral fat (t (910) = 2.124, P = 0.034) than total non-Roma non-smokers. In the native Roma sample, the mean waist circumference (t (428) = –2.679, P = 0.008), BMI (t (428) = –3.950, P < 0.001), visceral fat (t (423) = –5.024, P < 0.001) and blood glucose (t (428) = –3.005, P = 0.007) were higher in Hungarian Roma participants who did not smoke. The mean BMI (t (420) = –3.481, P < 0.001) and visceral fat (t (410) = –2.235, P = 0.026) were higher in the native majority participants than in Hungarian majority non-smokers.

The mean BMI (t (962) = 2.308, P = 0.021) and triglyceride levels (t (962) = 4.455, P < 0.001) of Roma who regularly exercised were lower than in those Roma who never exercised. Non-Roma had higher waist circumference (t (927) = 3.992, P < 0.001), BMI (t (927) = 4.061, P < 0.001), visceral fat (t (910) = 2.706, P = 0.007), cholesterol (t (927) = 2.329, P = 0.020), and triglyceride levels (t (927) = 2.445, P = 0.015) than those non-Roma who were never physically active. Among Hungarian Roma, cholesterol (t (428) = 3.208, P < 0.001) and triglyceride levels (t (428) = 3.893, P < 0.001) of those who regularly exercised were also lower than in those who never exercised. In the majority sample, the mean waist circumference (t (420) = 3.453, P < 0.001) and BMI (t (420) = 3.096, P = 0.002) were higher in inactive participants.

We examined participants for CVD risk in relation to obesity.

The two main risk factors were high blood pressure and cholesterol levels. These were found to affect 23.3% of men and 20.5% of women in the total sample. When smoking was taken into account, 10.4% of men and 8.3% of women were affected by the risk. After adding gender and age, we found that 6.1% of men were affected by all five risk factors, while 5.6% of women were affected by four risk factors (female gender is not a risk factor here). Comparing Roma and non-Roma, smoking among the risk factors had a higher proportion for Roma, whereas majority participants were more affected by the two other factors (Table 3).

Table 3.

Prevalence of risk factors in the sample (%)

Summarising the prevalence of the five possible risk factors for men, we found that significantly (P = 0.002) more Roma men were affected by two of the risk factors for CVD than non-Roma men from the same country (Fig. 3). Among men for whom gender was the only risk factor, the majority (P = 0.007) had a tertiary education. For men, there was no risk factor 0, as being male alone is not a risk factor for CVD.

Fig. 3.
Fig. 3.

Comparison of Roma and non-Roma men by number of risk factors by country (n = 527)

Citation: Acta Alimentaria 52, 2; 10.1556/066.2023.00006

When looking at women in Hungary, we found that Roma women had a higher proportion of four risk factors (P = 0.032) (Fig. 4). We found a similar result for Roma women in Romania, where more of them had four risk factors (P < 0.001). For women, five risk factors are not possible, as one of the risk factors is being a male, so only four factors can be considered for women.

Fig. 4.
Fig. 4.

Comparison of Roma and non-Roma women by number of risk factors by country (n = 1,366)

Citation: Acta Alimentaria 52, 2; 10.1556/066.2023.00006

Among Hungarian women, we found that the majority (P < 0.001) of women without risk factors had a tertiary education. Among Hungarian Roma women, three risk factors were already present in the 18–24 age group (P < 0.001) compared to non-Roma women in this age group.

Among domestic non-Roma women in the 25–39 age category, only two risk factors appeared in a higher proportion, while in the case of Roma women of a similar age, more risk factors were observed, but here the difference was not significant.

Overall, we found that both men and women in our study during the pandemic had unfavourably high mean values for both body weight and waist circumference. The proportion of people with high blood pressure was common among the study participants, total cholesterol levels were also elevated, and triglycerides were clearly higher than the recommended levels in all three countries. The elevated parameters measured were in themselves a significant cardiovascular risk. The obesity rate was more pronounced in women than in men. We obtained similar results in a previous study in Hungary (Barna et al., 2020).

Cardiovascular risk factors were significantly present in both male and female samples, with a high number of those with two or three factors. When only hypertension and cholesterol were included as risk factors, the proportion of those affected was higher in the non-Roma sample in all three countries. Smoking as another risk factor reversed the prevalence rates in the sample, and Roma were at a higher risk.

Our data above provide clear evidence that obesity, increased visceral fat, waist circumference, cholesterol and blood glucose levels, higher smoking rate, and thus CVD risk rates were higher in Roma minorities in all three countries, and we have confirmed this in line with our previous research. Smoking was the risk factor responsible for the higher CVD risk rate for Roma. On the other hand, the “Roma paradox” was also well confirmed in the sample, as Roma had lower blood pressure, so when this was one of the risk factors, the non-Roma sample was more affected (Hu et al., 2012; Kósa et al., 2015; Soltész et al., 2020; Zajc Petranović et al., 2021).

One limitation of our research was that women were over-represented in the sample we studied. This was partly because men were working at the time of the screening and partly because they were much less willing to participate in the study than women. A further complicating factor for us was the COVID-19 pandemic, as restrictive measures prevented pre-arranged fieldwork in many cases. Another factor, particularly among Roma, was the needle marks for blood sampling being often used to cover up the COVID-19 vaccination. A further complicating factor was the phrase “what we do not know, we do not have”. If they are not aware of their particular health problem, then it does not exist for them, and therefore does not need to be treated.

4 Conclusions

In conclusion, obesity and CVD risk measures were significantly present in the study sample in all three countries. However, as expected, the Hungarian sample came out on top among the three countries studied (WHO, 2022; Obesity, 2022). The Hungarian sample showed the highest proportion of obesity, as well as the highest proportion of risk factors.

We hypothesised that the Roma sample would have higher rates of obesity, but we found no significant difference between the Roma and non-Roma samples when comparing the total Roma sample with the total non-Roma sample, regardless of country. However, women, whether of Roma or non-Roma origin, were primarily at risk for abdominal obesity. In terms of CVD risk factors, smoking was found to be the most influential factor, and the combined presence of several risk factors was found to be more prevalent in Roma.

Our results also call for a reduction in smoking rates, especially in the Roma population, requiring a broader and more effective education programme (Stauder et al., 2010; Cselkó et al., 2018; Kékes et al., 2019; Eurostat, 2022a, 2022b). On the other hand, it can be seen that initiatives to reduce obesity rates are ineffective, although data suggest that CVD incidence is decreasing, it is still the leading cause of death (Rurik et al., 2021; HCSO, 2022; WHO, 2022).

Further detailed research is needed on the knowledge of the population of good lifestyle choices, including issues related to nutrition, physical activity, and smoking, which may be closely related to obesity.

Acknowledgement

Predoctoral fellowship grant (EFOP-3.6.3-VEKOP-16-2017-00009).

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    • Export Citation
  • Kékes, E., Barna, I., Daiko, T., and Dankovics, G. (2019). Nemi különbségek a dohányzás gyakoriságában hazánkban 2010 és 2018 között (Gender differences in smoking frequency in Hungary between 2010 and 2018). Orvosi Hetilap, 160(52): 20472053. https://doi.org/10.1556/650.2019.31637(abstract in English).

    • Search Google Scholar
    • Export Citation
  • Kósa, Z., Moravcsik-Kornyicki, Á., Diószegi, J., Bayard R., Szabó Z., Sándor J., and Ádány R., (2015). Prevalence of metabolic syndrome among Roma: a comparative health examination survey in Hungary. European Journal of Public Health, 25(2): 299304. https://doi.org/10.1093/eurpub/cku157.

    • Search Google Scholar
    • Export Citation
  • Kotseva, K., De Backer, G., De Bacquer, D., Rydén, L., Hoes, A., Grobbee, D., Maggioni, A.,Marques-Vidal, P., Jennings, C., Abreu, A., Aguiar, C., Badariene, J., Bruthans, J.,Castro Conde, A., Cifkova, R., Crowley, J., Davletov, K., Deckers, J., De Smedt, D., De Sutter, J., Dilic, M., Dolzhenko, M., Dzerve, V., Erglis, A., Fras, Z., Gaita, D., Gotcheva, N., Heuschmann, P., Hasan-Ali, H., Jankowski, P., Lalic, N., Lehto, S., Lovic, D., Mancas, S., Mellbin, L., Milicic, D., Mirrakhimov, E., Oganov, R., Pogosova, N., Reiner, Z., Stöerk, S., Tokgözoğlu, L., Tsioufis, C., Vulic, D., Wood, D., and EUROASPIRE Investigators* (2019). Lifestyle and impact on cardiovascular risk factor control in coronary patients across 27 countries: results from the European Society of Cardiology ESC-EORP EUROASPIRE V registry. European Journal of Preventive Cardiology, 26(8): 824835. https://doi.org/10.1177/2047487318825350.

    • Search Google Scholar
    • Export Citation
  • Obesity Rates by Country. (2022). World population rewiew ,available at: https://worldpopulationreview.com/country-rankings/obesity-rates-by-country. (Last accessed: 08 February 2023).

    • Search Google Scholar
    • Export Citation
  • Orav A. (2016). EU policy for Roma inclusion, Available at: https://www.europarl.europa.eu/RegData/etudes/ATAG/2016/579094/EPRS_ATA(2016)579094_EN.pdf. (Last accessed: 14 January 2023).

    • Search Google Scholar
    • Export Citation
  • Piko, P., Kosa, Z., Sandor, J., and Ádány R. (2021). Comparative risk assessment for the development of cardiovascular diseases in the Hungarian general and Roma population. Scientific Reports ,11(1): 3085. https://pubmed.ncbi.nlm.nih.gov/33542357/.

    • Search Google Scholar
    • Export Citation
  • Poirier, P. (2014). Exercise, heart rate variability, and longevity: the cocoon mystery? Circulation, 129(21): 20852087. https://doi.org/10.1161/CIRCULATIONAHA.114.009778.

    • Search Google Scholar
    • Export Citation
  • Rurik, I., Apor, P., Barna, M., Barna, I., Bedros, R., Kempler, P., Martos, É., Mohos, E., Pavlik, G., Pados, Gy., Pucsok, J., Simonyi, G., and Biró, Gy. (2021). Az elhízás kezelés és megelőzése: táplálkozás, testmozgás, orvosi lehetőségek (Treatment and prevention of obesity: nutrition, exercise, medical options). Orvosi Hetilap, 162(9): 323335. https://akjournals.com/view/journals/650/162/9/article-p323.xmlabstract in English.

    • Search Google Scholar
    • Export Citation
  • Soltész, B., Pikó, P., Sándor, J., Kósa, Z., Ádány, R., and Fiatal, S. (2020). The genetic risk for hypertension is lower among the Hungarian Roma population compared to the general population. Plos One, 15(6): e0234547. https://doi.org/10.1371/journal.pone.0234547.

    • Search Google Scholar
    • Export Citation
  • Stauder, A., Konkoly, T., Kovács, M., Balog, P., William, V., and William R. (2010). Worldwide stress: different problems, similar solutions? Cultural adaptation and evaluation of a standardized stress management program in Hungary. International Journal of Behavioral Medicine, 17(1): 2532. https://link.springer.com/article/10.1007/s12529-009-9054-4.

    • Search Google Scholar
    • Export Citation
  • Stefan, N., Birkenfeld, A.L., and Schulze, M.B. (2021). Global pandemics interconnected - obesity, impaired metabolic health and COVID-19. Nature Reviews. Endocrinology ,17(3): 135149. https://doi.org/10.1038/s41574-020-00462-1.

    • Search Google Scholar
    • Export Citation
  • Wallisch, C., Agibetov, A., Dunkler, D., Haller, M., Samwald, M., Dorffner, G., and Heinze, G. (2021). The roles of predictors in cardiovascular risk models - a question of modeling culture? BMC Medical Research Methodollogy, 21(1): 284. https://doi.org/10.1186/s12874-021-01487-4.

    • Search Google Scholar
    • Export Citation
  • WHO (2022). WHO mortality database, cardiovascular diseases, Available at: https://platform.who.int/mortality/themes/theme-details/MDB/noncommunicable-diseases. (Last accessed: 22 February 2023).

    • Search Google Scholar
    • Export Citation
  • Zajc Petranović, M., Rizzieri, M., and Sivaraj, A.E. (2021). CVD risk factors in the Ukrainian Roma and meta-analysis of their prevalence in Roma populations worldwide. Journal of Personalized Medicine, 11(11): 1138. https://www.mdpi.com/2075-4426/11/11/1138.

    • Search Google Scholar
    • Export Citation
  • An, J., Zhang, Y., Zhou, H., Zhou, M., Safford, M., Muntner, P., Morgan, A.E., and Reynolds, K. (2023). Incidence of atherosclerotic cardiovascular disease in young adults at low short-term but high long-term risk. Journal of the American College of Cardiology, 81(7): 623632. https://doi.org/10.1016/j.jacc.2022.11.051 (Last accessed: 23 February 2023).

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  • Atlas Rómskych Komunít na Slovensku (2019). (Atlas of Roma communities in Slovakia 2019) Office of the Slovak government commissioner for Roma communities; Pozsony, Slovakia: 2019, Available at: https://www.minv.sk/?atlas-romskych-komunit-2019 (Last accessed: 23 February 2023).

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  • Babinská, I., Madarasová-Gecková, A., and Jarčuška, P. (2014). Does the population living in Roma settlements differ in physical activity, smoking and alcohol consumption from the majority population in Slovakia? Central European Journal of Public Health, 22(Supplement): S22S27. https://doi.org/10.21101/cejph.a3897.

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  • Barna, I., Kékes, E., Halmy, E., Balogh, Z., Kubányi, J., Szőts, G., Németh, J., Pécsvárady, Zs., Majoros, A., Daiki, T., Erdei, O., and Dankovics, G. (2020). Magyarország átfogó egészségvédelmi szűrőprogramjának (MÁÉSZ) 2019. évi és 2010–2019 közötti összefoglaló adatai (Comprehensive health care screening program of Hungary 2010–2019 (MÁÉSZ)). Lege Artis Medicinae, 30(3): 89102. https://doi.org/10.336/Iam.30.009 (abstract in English).

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  • Chocair, P.R., Neves, P, Pereira, L., Mohrbacher, S., Souza Oliveira, E., Loureiro Nardotto, L., Martins Bales, A., Hamamoto Sato, V.A., Coelho Ferreira, B.M., and Cuvello Neto, A.L. (2020). Covid-19 and metabolic syndrome. Revista da Associacao Medica Brasileira, 66(7): 871875. https://doi.org/10.1590/1806-9282.66.7.871.

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  • Cselkó, Z., Kovács, G., and Horváth, I. (2018). The smoking situation in Hungary. Tobacco Induced Diseases, 16(1): 265. https://doi.org/10.18332/tid/84120.

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  • Eurostat (2022a). Smoking prevalence, by country, 2014 and 2020. Available at: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=File:Smoking_prevalence,_by_country,_2014_and_2020_(%25_of_population_aged_15_or_over).png.

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  • Eurostat (2022b). SDG 3 - Good Health and well-being. Smoking prevalence. Available at: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=SDG_3_-_Good_Health_and_well-being#Smoking_prevalence.

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  • FRA (2018). EU-MIDIS II. Second European Union minorities and discrimination survey Roma – selected findings, available at: http://fra.europa.eu/en/publication/2016/second-european-union-minorities-and-discrimination-survey-roma-selected-findings. (Last accessed: 08 February 2023).

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  • HCSO (2021). KSH: 1.5. Halálozások a leggyakoribb halálokok szerint (1990-) Deaths according to the most common causes of death (1990-), Avavilable at: https://www.ksh.hu/docs/hun/xstadat/xstadat_eves/i_wnh001.html. (Last accessed: 08 February 2023).

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  • HCSO (2022). KSH: 22.1.1.9. Halálozások száma és aránya, csecsemőhalandóság, születéskor várható élettartam, halálozás főbb okok szerint. (Number and proportion of deaths, infant mortality, life expectancy at birth, death by main causes) Available at: https://www.ksh.hu/stadat_files/nep/hu/nep0009.html?utm_source=kshhu&utm_medium=banner&utm_campaign=theme-nepesseg-es-nepmozgalom (Accessed: 12 June 2022).

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  • Hu, S.S., Kong, L.Z., Gao, R.L., Zhu, M.L., Wang, W., Wang, Y.J., Wu, Z.S., Chen, W.W., and Liu, M.B. (2012). Outline of the report on cardiovascular disease in China, 2010. Biomedical and Environmental Sciences, 25(3): 251256. https://doi.org/10.3967/0895-3988.2012.03.001.

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  • Jancsó, Z., Csenteri, O., Szőllősi, G.J., Vajer, P., and Andréka, P. (2022). Cardiovascular risk management: the success of target level achievement in high- and very high-risk patients in Hungary. BMC Primary Care, 23(1): 305. https://doi.org/10.1186/s12875-022-01922-5.

    • Search Google Scholar
    • Export Citation
  • Jilani, M.H., Javed, Z., Yahya, T., Valero-Elizondo, J., Khan S., Kash, B., Blankstein, R., Virani, S., Blaha, M., Dubey, P., Hyder, A., Vahidy, F., Cainzos-Achirica, M., and Nasir, K. (2021). Social determinants of health and cardiovascular disease: current state and future directions towards healthcare equity. Current Atherosclerosis Reports, 23: 55. https://doi.org/10.1007/s11883-021-00949-w.

    • Search Google Scholar
    • Export Citation
  • Kékes, E., Barna, I., Daiko, T., and Dankovics, G. (2019). Nemi különbségek a dohányzás gyakoriságában hazánkban 2010 és 2018 között (Gender differences in smoking frequency in Hungary between 2010 and 2018). Orvosi Hetilap, 160(52): 20472053. https://doi.org/10.1556/650.2019.31637(abstract in English).

    • Search Google Scholar
    • Export Citation
  • Kósa, Z., Moravcsik-Kornyicki, Á., Diószegi, J., Bayard R., Szabó Z., Sándor J., and Ádány R., (2015). Prevalence of metabolic syndrome among Roma: a comparative health examination survey in Hungary. European Journal of Public Health, 25(2): 299304. https://doi.org/10.1093/eurpub/cku157.

    • Search Google Scholar
    • Export Citation
  • Kotseva, K., De Backer, G., De Bacquer, D., Rydén, L., Hoes, A., Grobbee, D., Maggioni, A.,Marques-Vidal, P., Jennings, C., Abreu, A., Aguiar, C., Badariene, J., Bruthans, J.,Castro Conde, A., Cifkova, R., Crowley, J., Davletov, K., Deckers, J., De Smedt, D., De Sutter, J., Dilic, M., Dolzhenko, M., Dzerve, V., Erglis, A., Fras, Z., Gaita, D., Gotcheva, N., Heuschmann, P., Hasan-Ali, H., Jankowski, P., Lalic, N., Lehto, S., Lovic, D., Mancas, S., Mellbin, L., Milicic, D., Mirrakhimov, E., Oganov, R., Pogosova, N., Reiner, Z., Stöerk, S., Tokgözoğlu, L., Tsioufis, C., Vulic, D., Wood, D., and EUROASPIRE Investigators* (2019). Lifestyle and impact on cardiovascular risk factor control in coronary patients across 27 countries: results from the European Society of Cardiology ESC-EORP EUROASPIRE V registry. European Journal of Preventive Cardiology, 26(8): 824835. https://doi.org/10.1177/2047487318825350.

    • Search Google Scholar
    • Export Citation
  • Obesity Rates by Country. (2022). World population rewiew ,available at: https://worldpopulationreview.com/country-rankings/obesity-rates-by-country. (Last accessed: 08 February 2023).

    • Search Google Scholar
    • Export Citation
  • Orav A. (2016). EU policy for Roma inclusion, Available at: https://www.europarl.europa.eu/RegData/etudes/ATAG/2016/579094/EPRS_ATA(2016)579094_EN.pdf. (Last accessed: 14 January 2023).

    • Search Google Scholar
    • Export Citation
  • Piko, P., Kosa, Z., Sandor, J., and Ádány R. (2021). Comparative risk assessment for the development of cardiovascular diseases in the Hungarian general and Roma population. Scientific Reports ,11(1): 3085. https://pubmed.ncbi.nlm.nih.gov/33542357/.

    • Search Google Scholar
    • Export Citation
  • Poirier, P. (2014). Exercise, heart rate variability, and longevity: the cocoon mystery? Circulation, 129(21): 20852087. https://doi.org/10.1161/CIRCULATIONAHA.114.009778.

    • Search Google Scholar
    • Export Citation
  • Rurik, I., Apor, P., Barna, M., Barna, I., Bedros, R., Kempler, P., Martos, É., Mohos, E., Pavlik, G., Pados, Gy., Pucsok, J., Simonyi, G., and Biró, Gy. (2021). Az elhízás kezelés és megelőzése: táplálkozás, testmozgás, orvosi lehetőségek (Treatment and prevention of obesity: nutrition, exercise, medical options). Orvosi Hetilap, 162(9): 323335. https://akjournals.com/view/journals/650/162/9/article-p323.xmlabstract in English.

    • Search Google Scholar
    • Export Citation
  • Soltész, B., Pikó, P., Sándor, J., Kósa, Z., Ádány, R., and Fiatal, S. (2020). The genetic risk for hypertension is lower among the Hungarian Roma population compared to the general population. Plos One, 15(6): e0234547. https://doi.org/10.1371/journal.pone.0234547.

    • Search Google Scholar
    • Export Citation
  • Stauder, A., Konkoly, T., Kovács, M., Balog, P., William, V., and William R. (2010). Worldwide stress: different problems, similar solutions? Cultural adaptation and evaluation of a standardized stress management program in Hungary. International Journal of Behavioral Medicine, 17(1): 2532. https://link.springer.com/article/10.1007/s12529-009-9054-4.

    • Search Google Scholar
    • Export Citation
  • Stefan, N., Birkenfeld, A.L., and Schulze, M.B. (2021). Global pandemics interconnected - obesity, impaired metabolic health and COVID-19. Nature Reviews. Endocrinology ,17(3): 135149. https://doi.org/10.1038/s41574-020-00462-1.

    • Search Google Scholar
    • Export Citation
  • Wallisch, C., Agibetov, A., Dunkler, D., Haller, M., Samwald, M., Dorffner, G., and Heinze, G. (2021). The roles of predictors in cardiovascular risk models - a question of modeling culture? BMC Medical Research Methodollogy, 21(1): 284. https://doi.org/10.1186/s12874-021-01487-4.

    • Search Google Scholar
    • Export Citation
  • WHO (2022). WHO mortality database, cardiovascular diseases, Available at: https://platform.who.int/mortality/themes/theme-details/MDB/noncommunicable-diseases. (Last accessed: 22 February 2023).

    • Search Google Scholar
    • Export Citation
  • Zajc Petranović, M., Rizzieri, M., and Sivaraj, A.E. (2021). CVD risk factors in the Ukrainian Roma and meta-analysis of their prevalence in Roma populations worldwide. Journal of Personalized Medicine, 11(11): 1138. https://www.mdpi.com/2075-4426/11/11/1138.

    • Search Google Scholar
    • Export Citation
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The author instruction is available in PDF.
Please, download the file from HERE.

Senior editors

Editor(s)-in-Chief: András Salgó

Co-ordinating Editor(s) Marianna Tóth-Markus

Co-editor(s): A. Halász

       Editorial Board

  • L. Abrankó (Szent István University, Gödöllő, Hungary)
  • D. Bánáti (University of Szeged, Szeged, Hungary)
  • J. Baranyi (Institute of Food Research, Norwich, UK)
  • I. Bata-Vidács (Agro-Environmental Research Institute, National Agricultural Research and Innovation Centre, Budapest, Hungary)
  • F. Békés (FBFD PTY LTD, Sydney, NSW Australia)
  • Gy. Biró (National Institute for Food and Nutrition Science, Budapest, Hungary)
  • A. Blázovics (Semmelweis University, Budapest, Hungary)
  • F. Capozzi (University of Bologna, Bologna, Italy)
  • M. Carcea (Research Centre for Food and Nutrition, Council for Agricultural Research and Economics Rome, Italy)
  • Zs. Cserhalmi (Food Science Research Institute, National Agricultural Research and Innovation Centre, Budapest, Hungary)
  • M. Dalla Rosa (University of Bologna, Bologna, Italy)
  • I. Dalmadi (Szent István University, Budapest, Hungary)
  • K. Demnerova (University of Chemistry and Technology, Prague, Czech Republic)
  • M. Dobozi King (Texas A&M University, Texas, USA)
  • Muying Du (Southwest University in Chongqing, Chongqing, China)
  • S. N. El (Ege University, Izmir, Turkey)
  • S. B. Engelsen (University of Copenhagen, Copenhagen, Denmark)
  • E. Gelencsér (Food Science Research Institute, National Agricultural Research and Innovation Centre, Budapest, Hungary)
  • V. M. Gómez-López (Universidad Católica San Antonio de Murcia, Murcia, Spain)
  • J. Hardi (University of Osijek, Osijek, Croatia)
  • H. He (Henan Institute of Science and Technology, Xinxiang, China)
  • K. Héberger (Research Centre for Natural Sciences, ELKH, Budapest, Hungary)
  • N. Ilić (University of Novi Sad, Novi Sad, Serbia)
  • D. Knorr (Technische Universität Berlin, Berlin, Germany)
  • H. Köksel (Hacettepe University, Ankara, Turkey)
  • K. Liburdi (Tuscia University, Viterbo, Italy)
  • M. Lindhauer (Max Rubner Institute, Detmold, Germany)
  • M.-T. Liong (Universiti Sains Malaysia, Penang, Malaysia)
  • M. Manley (Stellenbosch University, Stellenbosch, South Africa)
  • M. Mézes (Szent István University, Gödöllő, Hungary)
  • Á. Németh (Budapest University of Technology and Economics, Budapest, Hungary)
  • P. Ng (Michigan State University,  Michigan, USA)
  • Q. D. Nguyen (Szent István University, Budapest, Hungary)
  • L. Nyström (ETH Zürich, Switzerland)
  • L. Perez (University of Cordoba, Cordoba, Spain)
  • V. Piironen (University of Helsinki, Finland)
  • A. Pino (University of Catania, Catania, Italy)
  • M. Rychtera (University of Chemistry and Technology, Prague, Czech Republic)
  • K. Scherf (Technical University, Munich, Germany)
  • R. Schönlechner (University of Natural Resources and Life Sciences, Vienna, Austria)
  • A. Sharma (Department of Atomic Energy, Delhi, India)
  • A. Szarka (Budapest University of Technology and Economics, Budapest, Hungary)
  • M. Szeitzné Szabó (National Food Chain Safety Office, Budapest, Hungary)
  • S. Tömösközi (Budapest University of Technology and Economics, Budapest, Hungary)
  • L. Varga (University of West Hungary, Mosonmagyaróvár, Hungary)
  • R. Venskutonis (Kaunas University of Technology, Kaunas, Lithuania)
  • B. Wróblewska (Institute of Animal Reproduction and Food Research, Polish Academy of Sciences Olsztyn, Poland)

 

Acta Alimentaria
E-mail: Acta.Alimentaria@uni-mate.hu

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2022  
Web of Science  
Total Cites
WoS
892
Journal Impact Factor 1.1
Rank by Impact Factor

Food Science and Technology (Q4)
Nutrition and Dietetics (Q4)

Impact Factor
without
Journal Self Cites
1.1
5 Year
Impact Factor
1
Journal Citation Indicator 0.22
Rank by Journal Citation Indicator

Food Science and Technology (Q4)
Nutrition and Dietetics (Q4)

Scimago  
Scimago
H-index
32
Scimago
Journal Rank
0.231
Scimago Quartile Score

Food Science (Q3)

Scopus  
Scopus
Cite Score
1.7
Scopus
CIte Score Rank
Food Science 225/359 (37th PCTL)
Scopus
SNIP
0.408

2021  
Web of Science  
Total Cites
WoS
856
Journal Impact Factor 1,000
Rank by Impact Factor Food Science & Technology 130/143
Nutrition & Dietetics 81/90
Impact Factor
without
Journal Self Cites
0,941
5 Year
Impact Factor
1,039
Journal Citation Indicator 0,19
Rank by Journal Citation Indicator Food Science & Technology 143/164
Nutrition & Dietetics 92/109
Scimago  
Scimago
H-index
30
Scimago
Journal Rank
0,235
Scimago Quartile Score

Food Science (Q3)

Scopus  
Scopus
Cite Score
1,4
Scopus
CIte Score Rank
Food Sciences 222/338 (Q3)
Scopus
SNIP
0,387

 

2020
 
Total Cites
768
WoS
Journal
Impact Factor
0,650
Rank by
Nutrition & Dietetics 79/89 (Q4)
Impact Factor
Food Science & Technology 130/144 (Q4)
Impact Factor
0,575
without
Journal Self Cites
5 Year
0,899
Impact Factor
Journal
0,17
Citation Indicator
 
Rank by Journal
Nutrition & Dietetics 88/103 (Q4)
Citation Indicator
Food Science & Technology 142/160 (Q4)
Citable
59
Items
Total
58
Articles
Total
1
Reviews
Scimago
28
H-index
Scimago
0,237
Journal Rank
Scimago
Food Science Q3
Quartile Score
 
Scopus
248/238=1,0
Scite Score
 
Scopus
Food Science 216/310 (Q3)
Scite Score Rank
 
Scopus
0,349
SNIP
 
Days from
100
submission
 
to acceptance
 
Days from
143
acceptance
 
to publication
 
Acceptance
16%
Rate
2019  
Total Cites
WoS
522
Impact Factor 0,458
Impact Factor
without
Journal Self Cites
0,433
5 Year
Impact Factor
0,503
Immediacy
Index
0,100
Citable
Items
60
Total
Articles
59
Total
Reviews
1
Cited
Half-Life
7,8
Citing
Half-Life
9,8
Eigenfactor
Score
0,00034
Article Influence
Score
0,077
% Articles
in
Citable Items
98,33
Normalized
Eigenfactor
0,04267
Average
IF
Percentile
7,429
Scimago
H-index
27
Scimago
Journal Rank
0,212
Scopus
Scite Score
220/247=0,9
Scopus
Scite Score Rank
Food Science 215/299 (Q3)
Scopus
SNIP
0,275
Acceptance
Rate
15%

 

Acta Alimentaria
Publication Model Hybrid
Submission Fee none
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Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription fee 2023 Online subsscription: 776 EUR / 944 USD
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Acta Alimentaria
Language English
Size B5
Year of
Foundation
1972
Volumes
per Year
1
Issues
per Year
4
Founder Magyar Tudományos Akadémia    
Founder's
Address
H-1051 Budapest, Hungary, Széchenyi István tér 9.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 0139-3006 (Print)
ISSN 1588-2535 (Online)

 

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