Cross-European Patterns of Obesity: Where Does Croatia Stand?—Descriptive Analysis of Waves 2015–2022 of the Survey of Health, Ageing and Retirement in Europe (SHARE) Including Adults Aged Over 50

Abstract

This paper investigates the prevalence of obesity and its links to health and dietary habits in middle-aged and older populations in Europe (50+), with a particular focus on Croatia. In Croatia, only 33.9% of adults have a normal BMI, while almost two-thirds (64.8%) are classified as overweight or obese, placing Croatia among the EU countries with the highest prevalence of overweight. Obesity significantly increases the risk of serious health complications, including cardiovascular disease (CVD) and type 2 diabetes. Therefore, we used data from the SHARE (Survey of Health, Ageing and Retirement in Europe), for the last four waves (wave 6–wave 9). The nutritional status was investigated (using the body mass index, BMI) as well as its relationship with cardiovascular disease and dietary habits. Different BMI categorizations were used (i) for the population under and (ii) over 65 years of age, and the results show that a significant proportion of the middle-aged and older European population is overweight or obese. When it comes to dietary habits, statistically significant differences in meat, fish, or chicken consumption were noted (p < 0.001): the Croatian population, especially men, consumes them significantly more often on a daily basis compared to the EU average. Similar patterns of dairy, legumes/eggs, and fruit/vegetable consumption were observed between the EU and Croatia, although there are some statistically significant differences in daily dairy consumption among the older population and in consumption of legumes/eggs and fruit/vegetables 3–6 times a week among the older population. The prevalence of CVD generally increases with increasing BMI in both regions and age groups. However, Croatia has a statistically significantly lower prevalence of high cholesterol compared to the EU in both age groups. Also, the trend of diabetes is more pronounced in the middle-aged population in Croatia compared to the EU. These results indicate specific differences in dietary habits and the association of BMI with certain CVDs in Croatia compared to the European Union average.

1. Introduction

Obesity is characterized by an excessive accumulation of body fat (BF) resulting from a prolonged imbalance between energy intake and expenditure. The mechanisms underlying this imbalance are multifactorial, involving complex interactions among biological, psychological, socioeconomic, behavioral, and environmental factors. Consequently, obesity should be viewed as a multifaceted condition influenced by an array of determinants beyond individual behavior alone [1].

1.1. Trends in Obesity Prevalence in Europe and Croatia

By 2030, nearly one-third of men (29.42%) and women (29.97%) across the European region are expected to have a body mass index (BMI) of 30 kg/m² or higher. This projection translates to about 102 million men and 113 million women at increased risk for obesity-related health complications in the region [2].

According to the data from the European Health Interview Survey (EHIS) conducted in Croatia, only one-third (33.9%) of adults aged 18 and over have a normal BMI, defined as between 18.5 and 24.9 kg/m². Nearly two-thirds (64.8%) of the adult population are classified as overweight or obese, while undernutrition affects 1.4% of the respondents. Croatia, along with Malta, ranks highest among the European Union (EU) countries in terms of the proportion of adults who are overweight or obese. When analyzed by sex, Croatia has the highest percentage of both men and women in the EU with excess body mass (BM)—73.2% of men and 58.5% of women. The prevalence of obesity among Croatian adults stands at 23.0%, with a slightly higher rate observed in men (23.7%) than in women (22.6%). According to the 2019 European Health Interview Survey (EHIS), among older adults in Croatia, 25.4% had a normal BM. Overweight was observed in 45.7% of this age group, while 28.4% were classified as obese. Undernutrition was found in only 0.5% of respondents in this demographic. When comparing data by sex, the proportion of men and women with normal weight was relatively similar—24.2% and 26.1%, respectively. However, overweight was more prevalent among men (51.4%) compared to women (41.9%), whereas obesity was more common among women (31.2%) than men (24.4%) [3,4].

1.2. Association of Obesity with Health Risks

Obesity in adults contributes to a broad spectrum of health complications, many of which significantly reduce the quality of life and increase mortality risk. A systematic review confirms that obesity elevates the risk of premature death, especially when central adiposity is considered alongside BMI [5]. Metabolic disorders such as type 2 diabetes, dyslipidemia, and hypertension are among the most common consequences, often leading to cardiovascular disease—the leading cause of death in this population [6,7]. In the older adults, excess weight increases the risk of osteoarthritis, mobility limitations, and frailty due to added mechanical stress on joints and muscles [8]. Obesity leads to a reduction in both static and dynamic lung volumes, with expiratory reserve volume being one of the earliest affected. It is linked to decreased airflow, increased airway hyperresponsiveness, and a higher risk of developing pulmonary hypertension, pulmonary embolism, respiratory infections, obstructive sleep apnea, and obesity hypoventilation syndrome. These physiological changes can ultimately result in hypoxic or hypercapnic respiratory failure. The underlying causes involve the physical burden of excess adipose tissue on the respiratory system as well as a systemic inflammatory state [9,10].

1.3. Obesity and Cardiovascular Disease (CVD)

Obesity contributes to cardiovascular disease (CVD). In 2021, Croatia had a higher standardized mortality of CVD with rate of 572.8 per 100,000 people, which exceeds the EU average of 367.6 per 100,000, placing it among the countries with elevated mortality rates compared to other EU nations [11]. An excess of BF leads to endothelial dysfunction, changes in small blood vessels, and damage to heart muscle cells. These effects increase the risk of developing atherosclerotic and vasospastic coronary artery disease, irregular heart rhythms, cardiomyopathy, and heart failure. Obesity is believed to contribute directly to the development of coronary atherosclerotic plaques. This link is largely attributed to inflammation and elevated oxidative stress caused by excess BF, which leads to the oxidation of apo-B lipoproteins and impairs endothelial function [12]. Coronary atherosclerosis and ischemic heart disease are likely driven by a combination of factors, including the direct impact of excess BF on vascular function and damage, as well as the indirect effects of obesity-related metabolic disturbances [13]. Findings from the ARIC study indicate that obesity poses a particularly strong risk for developing heart failure, even when other metabolic risk factors are accounted for. Compared to individuals with normal weight, those with severe obesity (BMI ≥ 35) had a significantly higher risk of heart failure, with a hazard ratio of 3.74. In contrast, the risk was lower for coronary heart disease (HR 2.00) and stroke (HR 1.75), highlighting heart failure as the condition most strongly associated with obesity (p < 0.0001 for comparison between heart failure and other outcomes) [14]. Obesity causes both structural and functional alterations in the heart, negatively impacting hemodynamics as well as the left ventricular (LV) size and performance. It results in elevated total blood volume and stroke volume. Among individuals with normal blood pressure, systemic vascular resistance tends to decrease, leading to an overall rise in cardiac output in the early stages [15]. Over time, the increased blood volume and preload in obesity cause a leftward shift in the Frank–Starling curve, leading to negative remodeling of the left ventricle, such as chamber enlargement and hypertrophy. These changes, along with raised filling pressures and pulmonary artery pressure, increase the risk of both diastolic and systolic heart failure in individuals with obesity [15,16]. The Framingham Heart Study found that each 1 kg/m² rise in BMI corresponds to a 5% increase in the risk of developing heart failure for men and a 7% increase for women [17]. The link between obesity and hypertension is well recognized, with studies estimating that excess BM is responsible for approximately 65% to 78% of essential hypertension cases [18]. Multiple clinical and population-based studies have identified key mechanisms behind obesity-related hypertension. These include insulin and leptin-driven stimulation of the sympathetic nervous system, which in turn activates the renin–angiotensin–aldosterone system (RAAS) and promotes renal sodium retention. Additionally, excess adipose tissue directly contributes to RAAS activation by increasing levels of angiotensinogen, angiotensin II, aldosterone, and pro-inflammatory cytokines [19]. Increased sodium reabsorption by the kidneys causes a shift of the pressure natriuresis curve to the right, meaning that higher blood pressure is required to eliminate sodium and maintain fluid balance. This process helps explain why obese individuals with hypertension often have sodium sensitivity and why many benefit from diuretic treatment [19]. Dyslipidemia plays a major role in the onset of atherosclerosis and the related development of cardiovascular disease in people with obesity [20]. The entrance of apo-B lipoprotein particles within the arterial wall is the fundamental step that initiates and drives the atherosclerotic process from beginning to end [21]. Lipid abnormalities result from increased delivery of free fatty acids and triglycerides to the liver due to excess fat tissue, combined with insulin resistance and impaired adipocyte function, characterized by lower adiponectin levels and elevated production of pro-inflammatory cytokines [22]. Around 60–70% of individuals with obesity exhibit abnormal lipid profiles, such as increased serum triglycerides, very low-density lipoprotein, apolipoprotein B, and non-HDL cholesterol, along with reduced HDL cholesterol levels. Although LDL cholesterol levels may not always be significantly raised, there is a notable rise in small dense LDL particles, which are more likely to promote atherosclerosis because they oxidize easily, are more readily absorbed by macrophages, penetrate arterial walls more effectively, and have a lower affinity for LDL receptors, causing them to remain longer in the bloodstream [23].

1.4. Obesity and Type 2 Diabetes

Type 2 diabetes is commonly linked with obesity, affecting about 40% of individuals with excess BM [24]. Abdominal obesity, often measured by the waist-to-hip ratio, independently increases the risk of developing hypertension and high fasting glucose levels, even in individuals who are overweight but do not qualify as obese based on their BMI [25]. Gastaldelli and colleagues demonstrated that insulin resistance increases in direct relation to the amount of visceral fat, independent of BMI [26]. A meta-analysis examining the effects of lifestyle and weight loss interventions on HbA1c found that, among 19 groups of individuals with type 2 diabetes and obesity, 17 experienced improvements in blood glucose, lipid levels, and blood pressure over a 12-month period following a 5% reduction in BM from their starting point [27]. Obesity is a complex condition driven by multiple factors beyond individual behavior, with its prevalence rising sharply across Europe and particularly high in Croatia, where most adults are overweight or obese. This condition significantly increases the risk of serious health issues, including cardiovascular disease and type 2 diabetes, largely due to metabolic disturbances and structural changes in the body caused by excess fat.

Considering the above non-communicable diseases associated with overweight and obesity, the aim of this paper is to investigate, through the data from the Survey of Health, Ageing and Retirement in Europe (SHARE), the prevalence of overweight and obesity, of adults aged over 50 years, in Europe, with a special focus on Croatia. Croatia (infamously) ranks at the top in terms of the proportion of overweight and obese population; therefore, we investigated the middle-aged and older population through a descriptive analysis of the waves from 2015 to 2022 of the SHARE study.

2. Materials and Methods

2.1. Data

Data were utilized from the Survey of Health, Ageing and Retirement in Europe [28], a cross-national longitudinal survey that gathers comprehensive information on health, socio-economic status, and social and family networks of older adults. To date, SHARE has completed nine regular waves of data collection, using probability sampling methods to select participants. The SHARE study collects data from people over the age of 50, through interviews on health, lifestyle, and socioeconomic data in European countries. The study began in 2004, and the first wave of data was collected during 2004 and 2005. The study has a longitudinal design and data are collected approximately every 2 years, with the latest published data being for wave 9, which includes the data collected during 2021/2022. Croatia has been participating since wave 6 (

Table 1. Data profile used in this study.

Data Collected in Year.	Number of Countries (Included Individuals)	Wave of Data Collection
2015	18 (66,907)	Wave 6 (W6)
2017	27 (76,106)	Wave 7 (W7)
2019/20	27 (46,466)	Wave 8 (W8)
2021/22	28 (69,447)	Wave 9 (W9)

For monitoring trends, data from all four mentioned waves of the SHARE study were used.

). Detailed information on sampling procedures and survey participation can be found in the SHARE documentation [29]. The survey targets non-institutionalized individuals aged 50 and older who reside regularly in the participating countries and speak the national language(s).

2.2. Nutritional Status Assesment

Based on the recommendations, different distributions were used in the following nutrition categories: (i) younger than 65 and (ii) older than 65 (

Table 2. Nutritional status indicator according to the body mass index (BMI) based on age.

Nutritional Status.	BMI (kg/m2)
	Under 65 Years #	Age 65+ *
Underweighted	<18.5	<18.5
		18.5–20.9
Normal	18.5–24.9	21–27.49
Overweight	25–29.9	27.5–30.9
Obese	>30	31–39.9
Morbid obesity		>40

^# CDC recommendations [30]; * ESPEN: BMI categories for adults over 65 years old [31].

).

2.3. Basic Characteristics of the Population

In order to investigate trends in the latest survey, data from wave 9 (collected in 2021/22) were used. Thus,

Table 3. Basic characteristics of the population (wave 9).

Variables	All Countries				Croatia
	Female (N = 39,872)	Male (N = 29,575)	Total (N = 69,447)	p-Value	Female (N = 2647)	Male (N = 2040)	Total (N = 4687)	p-Value
Age (%)
51–64	31	29.7	30.4	0.8907	39.1	36.4	37.9	0.7439
65–74	34.7	38.2	36.1		34.3	39.9	36.7
75–85	24.1	24.5	24.3		18.9	18.6	18.8
>85	8.9	7.5	8.3		6,0	4.9	5.5
BMI (%)
Normal (under 65 y)	35.1	28.5	32.3	0.1869	31.1	22.5	27.3	0.0646
Normal (>65 y)	47.8	51.1	49.2		47.3	44.9	46.2
Marital status (%)
Living with partner	57.1	72.4	63.7	0.0006	62.4	78.6	69.5	0.0001
Not living with a partner	42.9	27.6	26.3		37.6	21.4	30.5
Physical inactivity (%)
Other *	85.1	88	86.3	0.3542	89.8	91.9	90.7	0.4539
Never vigorous nor moderate physical activity	14.8	11.8	13.5		9.8	7.8	8.9
Current job situation (%)
Retired	62	70.4	65.6	0.0658	57.8	73	64.4	0.0006
Not retired	38	29.6	34.4		42.2	27	35.6
Ever diagnosed/currently having
Heart attack	10.7	15.5	12.7	0.6510	11.8	16.2	13.7	0.3260
High blood pressure or hypertension	47.2	47.3	47.2		52.9	50.1	51.7
High blood cholesterol	27.8	27.3	27.6		23.6	19.7	21.9
Stroke	3.5	5.1	4.2		3.8	6.3	4.9
Diabetes or high blood sugar	14	16.8	15.2		13.7	19.1	16

Note: It is possible that the total does not add up to 100 because some respondents did not know the answer or refused to give it. The comparison across groups was conducted by use of chi-square test. * Other: occasional and regular physical activity. Red p-values: statistically significant (p < 0.05) by use of chi-squared test.

lists basic data on respondents over 50 years of age, for all participants and those from Croatia. Key data are age groups, their level of nutrition, the 5 diseases associated with obesity and their frequency, marital and employment status, and physical activity. The database also offers data on the frequency of food consumption from four food groups, namely (i) dairy products, (ii) legumes or eggs, (iii) meat, fish, or chicken, and (iv) fruits and vegetables. In wave 9, the question on why respondents do not eat meat appears for the first time (cannot afford it, vegetarianism, or other).

2.4. Statistical Analyses

Since the database also contains missing data, prior to data processing, an analysis was conducted for all observed parameters. The analysis is reliable if the proportion of missing data is below 5%; however, it is advised to additionally examine each parameter with and without missing data, using the t-test. If the p-value is greater than 0.05, the data can be used without fear for the objectivity of the outcome of the analysis of the aforementioned data [32]. The range of values for the observed set of variables was 0.11–0.74, and the data set was acceptable for drawing objective conclusions. The data in the SHARE study include coded nominal and scalar variables [28,29] and the test is applied depending on their type. Categorical variables are compared across groups by the use of chi-square test. Continuous variables were tested for normality using the Shapiro–Wilk test and are presented as mean values with the corresponding standard deviation (SD). Comparisons between groups were performed using a two-tailed paired t-test. The association between dietary patterns and the prevalence of cardiovascular disease with BMI above the normal weight cutoff (25 kg/m² for those younger than 65, and 27.4 kg/m² for the population over 65) was assessed using multivariate logistic regression to estimate adjusted odds ratios (AOR) and 95% confidence intervals (95% CI). All models were adjusted for the following covariates: age, sex, education level, and marital status. By the use of SPSS v 19 (version 19.0, SPSS Inc., Chicago, IL, USA), all analyses were performed. To avoid incomplete and inadequate reporting for this observational research, the STROBE Statement was used [33] and added as supplementary material (Table S1).

3. Results

As it can be seen from the basic characteristics, the frequency of the diseases is high; therefore, the aim was to investigate their relationship with obesity and excess body weight. CVDs such as heart attack, high blood pressure or hypertension, high blood cholesterol stroke and diabetes, or high blood sugar did not show significant differences depending on gender; therefore, an overview of the proportion of normally nourished (Figure 1) and EU population over 50 years, with excessive body weight and obesity (Figure 2) is presented. Additionally, the potential relationship between BMI and cardiovascular diseases with the frequency of consumption of a certain group of foods was investigated.

Figure 1 and Figure 2 show the percentages of the population with a normal body mass index (BMI) and overweight and obesity in the middle-aged (50–65 years) and older (65+) European population, across different data collection waves (labeled as W6, W7, W8, and W9).

Thus, Figure 1 shows that on average, the proportion of the population with a normal BMI ranges from 32.1% to 35.4% for the age group from 50 to 65 years, while for the population older than 65 years this percentage is generally higher, ranging from 49.5% to 51.8%. This suggests that the older population (65+) has a higher proportion of people with a normal BMI compared to the middle-aged population (50–65 years), which is solely the result of the different BMI categorization for people older than 65 (according to Table 2). The differences between countries for the age group 50–65 show that countries such as Switzerland (e.g., 47.7% in W6 and 51.2% in W7) and Italy (e.g., 45.5% in W6) have high percentages of people with a normal BMI, while Malta (e.g., 22.8% in W6 and 14.5% in W7), Latvia (e.g., 24.7% in W6), and the Czech Republic (e.g., 27.0% in W6) have significantly lower percentages of the population in the range of the normal BMI over 50. When observing differences between countries for respondents over 65, countries with high shares of normal BMI stand out, such as Sweden (e.g., 59.4% in W6 and 61.6% in W8), the Netherlands (e.g., 60.3% in W6 and 61.1% in W7), and Italy (e.g., 60.2% in W7).

The results for the overweight and obese population (Figure 2) show that for the age group from 50 to 65 years, the percentage of overweight and obese people ranges from 39.0% to 62.1%, while for the population older than 65 years, these percentages range from 40.1% to 64.6%. Overall, in most categories W6–W9, a significant proportion of both the middle-aged and older population is overweight or obese. Analysis of both figures shows that while the older population (65+) has a higher proportion of normal BMI compared to the middle-aged population (50–65 years), a significant proportion of both the middle-aged and older population in Europe is overweight or obese. There are clear differences in the prevalence of BMI between different European countries and across different data collection categories (W6–W9).

The frequency of consumption of four food groups was analyzed and the results of the European average were compared with the data of respondents in Croatia. Based on presented diagrams (Figure 3), which show the frequency of consumption of certain food groups (dairy products; legumes or eggs; meat, fish, or chicken; fruit and vegetables) comparing the European Union (EU) and Croatia (HR) by gender and for the total population, similar patterns in the consumption of dairy products, fruit and vegetables, and legumes and eggs are evident. Approximately 55–59% of women in the EU and 46–52% of men in the EU consume dairy products every day. In Croatia, these percentages are similar, with 57.6% of women and 49.7% of men consuming them daily. When looking at the total population, about 55.5% in the EU and 49.7% in HR consume them every day. About 23–26% of the population in both regions consume dairy products 3–6 times a week. Similar to dairy products, there are no significant differences in the frequency of consumption of legumes or eggs between the EU and Croatia. Only about 10–11% of the population in the EU and Croatia, regardless of gender, consume legumes or eggs every day, while the largest share of the population, about 30–34%, consumes these foods 3–6 times a week or twice a week. Fruit and vegetable consumption shows a very high daily frequency in both regions, with slight differences, but without statistical significance. The vast majority of the population consumes fruit and vegetables every day; therefore, in Croatia, 81.7% of women and 74.2% of men consume fruit and vegetables every day, while in the EU this percentage is 77.2% for women and 68.6% for men.

Statistically significant differences were found between the EU and Croatia for the consumption of meat, fish, or chicken. In Croatia, 47.7% of women and 56.2% of men consume meat, fish, or chicken every day, while in the EU this percentage is significantly lower: 28.6% for women and 34.9% for men. Overall, 51.4% of the population in Croatia eats these foods daily, compared to 31.2% in the EU. In the EU, a higher proportion of the population consumes these foods 3–6 times a week (49.5% of women and 49.2% of men), compared to Croatia (41.6% of women and 36.9% of men). The p-values (p < 0.001) clearly show high statistical significance of the differences, suggesting that the patterns of meat, fish, and chicken consumption differ significantly between the EU and Croatia, with Croatia tending to consume them much more frequently every day.

Table 4. Frequency of consumption of foods from the four main groups in the middle-aged population.

Frequency of Food Consumption	EU, BMI * (kg/m2)				HR, BMI * (kg/m2)				p-Value
	<18.5	18.5–24.9	25–29.9	≥30	<18.5	18.5–24.9	25–29.9	≥30
Dairy products
Every day	62.0	60.1	57.2	55.6	47.8	52.7	50.0	49.3	0.0694
3–6 times a week	19.3	22.5	25.0	25.2	23.9	24.9	26.0	25.5	0.7583
Twice a week	8.6	8.9	9.7	10.0	15.2	10.3	13.0	12.5	0.2214
Once a week	3.5	3.7	3.8	4.4	2.2	5.2	5.0	6.1	0.5811
Less than once a week	6.2	4.6	4.3	4.7	10.9	6.8	6.0	6.5	0.2980
Legumes or eggs
Every day	12.1	11.2	10.6	10.7	6.5	12.1	9.6	9.1	0.1595
3–6 times a week	31.1	33.7	34.5	33.1	39.1	32.7	31.3	30.0	0.5115
Twice a week	31.1	29.2	29.5	29.8	32.6	32.4	35.9	37.3	0.3891
Once a week	15.6	18.1	18.2	18.6	10.9	17.0	18.5	19.3	0.5472
Less than once a week	9.4	7.6	7.1	7.7	10.9	5.6	4.7	4.2	0.1733
Meat, fish, or chicken
Every day	29.7	29.0	31.8	34.4	26.1	46.3	54.1	55.9	<0.0001
3–6 times a week	42.9	48.7	50.3	48.7	56.5	42.7	38.6	36.2	0.0074
Twice a week	15.4	14.5	12.5	11.5	8.7	7.2	5.3	6.4	<0.0001
Once a week	6.5	5.1	3.8	3.8	2.2	2.7	1.3	1.0	<0.0001
Less than once a week	5.1	2.8	1.6	1.5	6.5	1.1	0.7	0.5	0.1003
Fruits and vegetables
Every day	72.9	76.3	73.5	71.7	65.2	79.3	79.4	79.8	0.5137
3–6 times a week	19.0	17.6	20.3	21.2	21.7	13.9	15.0	14.0	0.0749
Twice a week	3.9	3.9	4.1	4.7	8.7	3.9	3.7	4.2	0.4414
Once a week	1.4	1.2	1.3	1.5	2.2	2.1	1.2	1.5	0.8868
Less than once a week	2.2	0.9	0.8	0.9	2.2	0.7	0.6	0.4	0.8560

* BMI: below 18.5—underweight; 18.5–24.9—normal BMI; 25–29.9—overweight; 30 and above—obese; red p-values: statistically significant (p < 0.05) by use of chi-squared test.

,

Table 5. Frequency of consumption of foods from the four main groups in the older population (65+).

Frequency of Food Consumption	EU, BMI # (kg/m2)						HR, BMI # (kg/m2)
	<18.5	18.5–20.9	21–27.4	27.5–30.9	31–39.9	≥40	<18.5	18.5–20.9	21–27.4	27.5–30.9	31–39.9	≥40	p-Value
Dairy products
Every day	54.6	61.2	59.0	56.1	55.3	58.0	30.9	56.0	51.2	50.2	48.0	59.0	0.0006
3–6 times a week	25.4	20.0	23.7	25.5	25.3	24.1	27.3	23.6	25.9	24.8	26.8	14.8	0.2326
Twice a week	10.2	9.0	9.2	9.9	10.2	8.7	20.5	8.2	12.0	11.8	13.1	14.8	0.0965
Once a week	3.0	3.6	3.7	4.0	4.5	4.4	4.5	2.2	5.2	5.8	6.1	3.3	0.6839
Less than once a week	4.6	6.0	4.3	4.4	4.6	4.9	8.2	9.3	5.8	7.3	5.8	8.2	0.3206
Legumes or eggs
Every day	11.1	11.9	10.9	10.5	10.5	13.0	6.8	13.7	10.7	9.8	8.7	11.5	0.6185
3–6 times a week	36.3	31.1	34.2	34.5	33.1	33.4	27.7	32.4	32.6	30.1	30.5	19.7	0.0219
Twice a week	27.7	29.6	29.4	29.8	29.6	26.7	27.7	28.6	34.1	36.5	38.0	34.4	0.3589
Once a week	13.8	18.4	18.2	17.8	19.1	18.1	14.1	18.1	17.5	18.7	18.9	27.9	0.6249
Less than once a week	8.6	8.9	7.2	7.3	7.5	8.7	15.0	6.6	5.0	5.0	3.8	6.6	0.0734
Meat, fish, or chicken
Every day	26.7	26.8	30.3	32.9	33.8	39.6	31.8	35.2	50.9	56.4	53.8	60.7	<0.0001
3–6 times a week	49.8	45.8	49.8	49.8	49.1	43.7	48.2	46.7	40.7	36.8	37.3	31.1	0.0087
Twice a week	13.8	15.6	13.4	12.3	11.7	10.3	7.3	8.2	6.0	5.2	7.1	6.6	<0.0001
Once a week	4.6	7.4	4.3	3.6	3.8	4.0	1.8	5.5	1.8	1.0	1.2		0.0007
Less than once a week	2.9	4.3	2.1	1.5	1.4	2.4	2.3	3.8	0.6	0.6	0.4	1.6	0.1995
Fruits and vegetables
Every day	64.3	78.4	75.0	72.8	71.3	71.7	56.8	76.9	78.9	80.8	79.5	83.6	0.4757
3–6 times a week	25.1	14.4	18.9	20.7	21.6	21.5	26.8	13.2	14.8	14.0	14.5	11.5	0.0049
Twice a week	5.9	4.2	3.9	4.4	4.7	3.8	5.5	6.0	3.8	3.3	4.3	3.3	0.9574
Once a week	1.4	1.6	1.3	1.2	1.4	1.5	1.4	2.2	1.8	1.3	1.2	1.6	0.9962
Less than once a week	1.1	1.2	0.8	0.8	0.9	1.4	0.9	1.1	0.6	0.6	0.3		0.9502

^# below 18.5—severe underweight; 18.5–20.9—underweight; 21–27.4—normal; 27.5–30.9—overweight; 31–39.9—obese; 40 and above—morbid obesity; red p-values: statistically significant (p < 0.05) by use of chi-squared test.

,

Table 6. Frequency of cardiovascular disease prevalence, depending on body mass index for the middle-aged population.

Ever Diagnosed/Currently Having	EU, BMI * (kg/m2)				HR, BMI * (kg/m2)				p-Value
	<18.5	18.5–24.9	25–29.9	≥30	<18.5	18.5–24.9	25–29.9	≥30
Heart attack
Not selected	87.8	89.3	87.1	84.6	93.5	88.9	85.6	84.3	0.9454
Selected	11.9	10.6	12.8	15.4	6.5	11.0	14.4	15.7	0.1941
High blood pressure or hypertension
Not selected	70.4	64.9	51.6	37.4	63.0	59.5	48.9	36.2	0.6687
Selected	29.4	35.0	48.3	62.6	37.0	40.5	51.1	63.8	0.4793
High blood cholesterol
Not selected	82.6	77.3	70.7	67.4	91.3	80.9	78.0	73.7	0.5319
Selected	17.2	22.7	29.2	32.5	8.7	19.1	21.9	26.3	0.0050
Stroke
Not selected	94.0	96.1	96.0	95.3	87.0	95.2	95.1	95.6	0.8997
Selected	5.8	3.8	3.9	4.7	13.0	4.8	4.9	4.4	0.2195
Diabetes or high blood sugar
Not selected	92.9	90.9	85.3	75.5	97.8	90.3	85.6	74.3	0.9660
Selected	6.8	9.0	14.7	24.5	2.2	9.6	14.4	25.7	0.0175

* BMI: below 18.5—underweight; 18.5–24.9—normal BMI; 25–29.9—overweight; 30 and above—obese; red p-values: statistically significant (p < 0.05) by use of chi-squared test.

and

Table 7. Frequency of cardiovascular disease prevalence, depending on body mass index for the older population (65+).

Ever Diagnosed/Currently Having	EU, BMI * (kg/m2)						HR, BMI * (kg/m2)						p-Value
	<18.5	18.5–20.9	21–27.4	27.5–30.9	31–39.9	≥40	<18.5	18.5–20.9	21–27.4	27.5–30.9	31–39.9	≥40
Heart attack
Not selected	84.0	89.3	88.6	86.5	84.3	81.3	79.1	86.3	87.5	86.4	82.7	82.0	0.9933
Selected	14.0	10.5	11.4	13.5	15.6	18.7	12.3	13.7	12.4	13.6	17.3	18.0	0.9368
High blood pressure or hypertension
Not selected	52.5	71.8	59.6	46.5	36.8	26.6	38.2	61.0	54.8	45.6	35.2	31.1	0.1326
Selected	45.5	28.0	40.3	53.5	63.2	73.4	53.2	39.0	45.1	54.4	64.8	68.9	0.4071
High blood cholesterol
Not selected	74.5	80.5	74.3	69.7	67.0	64.6	78.6	84.6	79.8	76.2	73.1	73.8	0.7006
Selected	23.5	19.3	25.6	30.2	33.0	35.4	12.7	15.4	20.1	23.8	26.9	26.2	0.0030
Stroke
Not selected	91.8	95.7	96.1	96.0	95.2	93.6	83.6	94.0	95.3	95.0	95.9	90.2	0.9648
Selected	6.3	4.1	3.8	3.9	4.7	6.4	7.7	6.0	4.6	5.0	4.1	9.8	0.7752
Diabetes or high blood sugar
Not selected	82.8	93.2	88.8	82.7	75.3	59.7	77.7	90.7	88.7	82.5	74.4	55.7	0.9827
Selected	15.2	6.6	11.1	17.3	24.6	40.3	13.6	9.3	11.2	17.5	25.6	44.3	0.9253

* below 18.5—severe underweight; 18.5–20.9—underweight; 21–27.4—normal; 27.5–30.9—overweight; 31–39.9—obese; 40 and above—morbid obesity; red p-values: statistically significant (p < 0.05) by use of chi-squared test.

present in detail the frequency of consumption of certain food groups and the prevalence of cardiovascular diseases (CVDs) in relation to body mass index (BMI) for the middle-aged and older population in the European Union (EU) and Croatia (HR).

Table 4 presents the frequency of consumption of dairy products, legumes/eggs, meat/fish/chicken, and fruit and vegetables for the middle-aged population in the EU and HR, divided by BMI categories, and shows that in the EU, 60.1% of people with a normal BMI consume dairy products every day, while in Croatia this percentage is 52.7%. Among obese people, 55.6% consume them every day in the EU, compared to 49.3% in HR. As with dairy consumption, there is not a statistically significant difference in the consumption of legumes or eggs, with around 10–12% of people in both regions consuming legumes or eggs every day, with a slightly higher share of those consuming them 3–6 times a week (around 30–39%). There are very significant differences in the consumption of meat, fish, or chicken. In Croatia, a significantly higher share of the middle-aged population consumes meat, fish, or chicken every day compared to the EU. Considering their body mass index, 46.3% of people with a normal BMI in Croatia consume them every day, while in the EU this percentage is 29.0%. Among obese people, 55.9% in Croatia eat them every day, compared to 34.4% in the EU. These differences are statistically significant for daily consumption (p < 0.0001), consumption 3–6 times a week (p = 0.0074), twice a week (p < 0.0001), and once a week (p < 0.0001). A high proportion of the middle-aged population in both regions consumes fruit and vegetables every day (e.g., 76.3% with normal BMI in the EU and 79.3% in Croatia).

Speaking of frequency of food consumption in the older population EU vs. Croatia according to BMI, Table 5 shows the same food categories for the population older than 65 years. In the EU, daily consumption of dairy products ranges from 54.6% (severely malnourished) to 61.2% (malnourished). In Croatia, it ranges from 30.9% (severely malnourished) to 59.0% (obese). There is a statistically significant difference in daily consumption of dairy products (p = 0.0006), which suggests differences in consumption patterns between the EU and Croatia in this age group. For the consumption of legumes or eggs, the frequencies of daily consumption are low (around 6–13% in most categories), and there is a statistically significant difference in the consumption 3–6 times a week (p = 0.0219). Croatia has slightly lower percentages of the consumption 3–6 times a week compared to the EU. As with the frequency of consumption of meat, fish, or chicken among the middle-aged population, very significant differences were recorded here, even among those who do not consume it (

Table A1. Reasons for not consuming meat in the middle-aged population.

	EU, BMI * (kg/m2)				HR, BMI * (kg/m2)				p-Value
	<18.5	18.5–24.9	25–29.9	≥30	<18.5	18.5–24.9	25–29.9	≥30
Cannot afford to eat it more often	11.6	9.8	12.1	15.3	35.3	37.5	15.7	21.4	<0.0001
You follow a vegetarian diet	19.3	15.3	11.6	8.8		25.0	10.0	5.0	0.0777
For other reasons	68.7	74.5	75.9	75.7	64.7	37.5	74.3	73.6	<0.0001

* BMI: below 18.5—underweight; 18.5–24.9—normal BMI; 25–29.9—overweight; 30 and above—obese.

and

Table A2. Reasons for not consuming meat in the older population (65+).

	EU, BMI # (kg/m2)						HR, BMI # (kg/m2)						p-Value
	<18.5	18.5–20.9	21–27.4	27.5–30.9	31–39.9	≥40	<18.5	18.5–20.9	21–27.4	27.5–30.9	31–39.9	≥40
Cannot afford to eat it more often	14.6	8.3	10.6	12.9	16.1	20.2	36.0	12.5	18.7	20.5	22.8	20.0	0.0004
You follow a vegetarian diet	11.8	17.7	14.2	9.4	8.4	8.3	8.0	21.9	6.6	5.1	5.1	20.0	0.0002
For other reasons	72.3	73.6	74.8	77.5	75.3	71.4	56.0	65.6	74.7	74.4	72.2	60.0	0.1486

^# below 18.5—severe underweight; 18.5–20.9—underweight; 21–27.4—normal; 27.5–30.9—overweight; 31–39.9—obese; 40 and above—morbid obesity; red p-values: statistically significant (p < 0.05).

). The Croatian older population consumes meat, fish, or chicken “every day”, which is significantly more often than the EU. Thus, among people with a normal BMI (21–27.4), 50.9% in Croatia consume them every day, while in the EU this percentage is 30.3%. Among the morbidly obese (>40 BMI), 60.7% in Croatia eat them every day, compared to 39.6% in the EU. These differences are statistically significant for daily consumption (p < 0.0001), consumption 3-6 times a week (p = 0.0087), twice a week (p < 0.0001), and once a week (p = 0.0007).

A high proportion of the older population in both regions consumes fruit and vegetables every day (e.g., 78.9% with normal BMI in HR and 75.0% in the EU). However, there is a statistically significant difference in consumption 3-6 times a week (p = 0.0049), where the EU has a higher proportion.

Table 6 shows the prevalence in the middle-aged population divided by BMI categories.

The frequency of diagnosed heart attacks increases with increasing BMI in both regions. For example, in the EU 10.6% of people with a normal BMI had a heart attack, while among the obese this percentage is 15.4%. A similar trend is observed in Croatia (11.0% vs. 15.7%). The prevalence of hypertension increases significantly with increasing BMI in both regions. For example, in the EU 35.0% of people with a normal BMI have hypertension, while among the obese this percentage is 62.6%. In Croatia, the figures are similar (40.5% vs. 63.8%). The prevalence of high cholesterol also increases with BMI. For example, in the EU 22.7% of people with a normal BMI have high cholesterol, while among the obese it is 32.5%. In Croatia, the percentages are somewhat lower, especially at lower BMI (e.g., 19.1% with normal BMI vs. 26.3% with obese BMI) and there is a statistically significant difference (p = 0.0050), indicating that Croatia has a lower prevalence of diagnosed high cholesterol in the middle-aged population compared to the EU.

The prevalence of stroke is relatively low in both regions and is similar across BMI categories, while the prevalence of diabetes increases significantly with increasing BMI. In the EU, 9.0% of people with normal BMI have diabetes, while among the obese this percentage is 24.5%. In Croatia, this trend is even more pronounced (9.6% vs. 25.7%) and a statistically significant difference was found (p = 0.0175).

The prevalence of cardiovascular diseases in the older population (Table 7) shows that the prevalence of heart attack varies between BMI categories, but there is no clear pattern associated with BMI in both regions, while the prevalence of hypertension increases strongly with BMI in both regions.

For example, in the EU 28.0% of underweight individuals have hypertension, while among the morbidly obese (≥40 BMI) it is 73.4%. In Croatia the percentages are similar (39.0% vs. 68.9%). There is a statistically significant difference (p = 0.0030) for the prevalence of high cholesterol, which increases with increasing BMI in both regions. In the EU, 19.3% of underweight individuals have high cholesterol, while among the morbidly obese it is 35.4%. In Croatia the percentages are lower (15.4% vs. 26.2%). The prevalence of stroke is relatively low and similar in both regions, and the prevalence of diabetes increases significantly in those over 65, with increasing BMI. In the EU, 6.6% of the undernourished have diabetes, while among the morbidly obese it is 40.3%. In Croatia, the trend is similar and the percentages are comparable (9.3% vs. 44.3%).

Given that the shares of men and women and other covariates differ in the data, only comparing percentage frequencies may provide an unbiased view of the actual situation, and it is necessary to use one of the regression models with model adjustments.

Table 8. Association between dietary habits and the prevalence of cardiovascular diseases with overweight and obesity.

Variable	EU, Overweighed and Obese		HR, Overweighed and Obese
	AOR (95% CI)	p-Value	AOR (95% CI)	p-Value
Dairy products
Every day	1.16 (0.99–1.35)	0.064	1.55 (1.07–2.25)	0.020
3–6 times a week	1.22 (1.04–1.44)	0.015	1.42 (0.96–2.09)	0.078
Twice a week	1.35 (1.13–1.62)	0.001	1.59 (1.03–2.43)	0.035
Once a week	1.25 (1–1.56)	0.047	1.48 (0.88–2.49)	0.139
Less than once a week	1.00		1.00
Legumes or eggs
Every day	0.88 (0.74–1.04)	0.129	0.91 (0.56–1.49)	0.719
3–6 times a week	1.01 (0.87–1.16)	0.912	1.45 (0.93–2.26)	0.102
Twice a week	1.03 (0.89–1.19)	0.711	1.57 (1–2.44)	0.048
Once a week	1.08 (0.93–1.26)	0.331	1.83 (1.15–2.94)	0.011
Less than once a week	1.00		1.00
Meat, fish, or chicken
Every day	2.26 (1.79–2.86)	<0.001	1.35 (0.52–3.5)	0.537
3–6 times a week	1.94 (1.54–2.45)	<0.001	0.97 (0.38–2.52)	0.955
Twice a week	1.62 (1.27–2.07)	<0.001	0.83 (0.31–2.26)	0.720
Once a week	1.34 (1.01–1.77)	0.041	0.55 (0.17–1.71)	0.300
Less than once a week	1.00		1.00
Fruits and vegetables
Every day	1.1 (0.74–1.63)	0.651	1.03 (0.41–2.61)	0.954
3–6 times a week	1.22 (0.82–1.83)	0.321	1.05 (0.41–2.72)	0.917
Twice a week	1.2 (0.79–1.84)	0.392	1.3 (0.46–3.72)	0.618
Once a week	1.1 (0.68–1.78)	0.691	0.62 (0.2–1.92)	0.409
Less than once a week	1.00		1.00
CVDs
Heart attack
Yes	0.94 (0.84–1.06)	0.340	0.94 (0.71–1.26)	0.686
No	1.00		1.00
High blood pressure or hypertension
Yes	2.04 (1.89–2.21)	<0.001	1.84 (1.5–2.24)	<0.001
No	1.00		1.00
High blood cholesterol
Yes	1.13 (1.04–1.24)	0.005	1.13 (0.88–1.44)	0.347
No	1.00		1.00
Stroke
Yes	0.96 (0.8–1.16)	0.670	0.7 (0.46–1.06)	0.092
No	1.00		1.00
Diabetes or high blood sugar
Yes	1.73 (1.54–1.95)	<0.001	1.71 (1.27–2.28)	<0.001
No	1.00		1.00

CI, confidence interval; AOR, adjusted odds ratio for age, gender, marital status, and education level.

shows the associations between dietary habits and the prevalence of cardiovascular disease among overweight and obese individuals in the EU and Croatia, after adjustment for age, sex, marital status, and education. Frequent consumption of meat, fish, or chicken was most strongly associated with increased risk in the EU population (AOR = 2.26 for daily consumption, p < 0.001), while in Croatia this association was weaker and statistically insignificant. Significant, although heterogeneous, associations were observed for dairy products and legumes/eggs, with more frequent consumption of legumes or eggs twice a week (AOR = 1.57, p = 0.048) and once a week (AOR = 1.83, p = 0.011) being associated with higher risk in Croatia. Fruit and vegetable intake did not show significant relationships in either population. Of the chronic conditions, hypertension and diabetes were consistently strongly associated with obesity in both countries (e.g., EU: AOR = 2.04 and 1.73; HR: AOR = 1.84 and 1.71; all p < 0.001), while myocardial infarction and stroke did not show significant associations.

4. Discussion

The global prevalence and nature of obesity in older adults is an extremely important topic because obesity is a chronic, complex, and multifactorial disease whose prevalence has doubled globally since 1980 [6]. A global meta-analysis of 45.7 million older adults (defined as ≥60 years) showed an overall prevalence of obesity of 25.3%, with the highest prevalence recorded in Latin America (40.4%) and continental Europe (33.6%), and the lowest in Asia (14.6%) [4]. Obesity is associated with a number of serious health problems, including (i) sarcopenia and frailty, (ii) osteoporosis and fractures, (iii) inflammation and dysfunction, (iv) metabolic disorders, (v) neuromuscular communication disorders, and (vi) decreased quality of life.

Osteosarcopenic obesity is a specific body composition phenotype defined as the accumulation of excess fat tissue with a reduction in muscle and bone mass. This phenotype is particularly common in postmenopausal women (6–41%, and up to 83% in the older population) and strongly predisposes to frailty syndrome. Frailty, which is more common in women, is associated with reduced functional reserve, increased risk of falls, fractures, sarcopenia, and disability [32]. BMI alone is insufficient to assess health risks because it does not take into account the amount and distribution of fat, as well as muscle and bone mass. A more comprehensive understanding of body composition is needed, especially in the context of aging [32]. Men generally have more muscle mass, while women have more fat tissue. With age, hormonal changes such as reduced estrogen in women lead to the accumulation of visceral fat, which is more typical for men and increases the risk of metabolic syndrome and cardiovascular disease. Visceral fat has been recognized as an early indicator of frailty, especially in women. Fat infiltration into skeletal muscle (myosteatosis) is associated with adverse effects on muscle mass and strength. There is a strong relationship between adipose tissue, muscle tissue, and bone tissue [33].

Obesity is classified as a pre-diabetic stage and significantly increases the risk of T2D. Patients with diabetes have reduced muscle strength and a higher risk of falls and fractures, and risk factors for diabetes in addition to older age (5.5 times higher risk) are overweight and obesity (2 times higher risk) and abdominal obesity (1.5 times higher risk) significantly increase the chances of diabetes. Hypertension (1.6 times higher risk) is also strongly associated with diabetes.

While obesity was once considered a protective factor for bones due to mechanical stress and estrogen production, recent studies show an increased risk of fractures and delayed bone healing in older obese patients [32,34] because the functions of bone cells (osteoclasts, osteoblasts, and osteocytes) and bone marrow stem cells are impaired.

Analysis of these data indicates that the patterns of dairy products, legumes/eggs, and fruit and vegetables consumption in the European Union and Croatia are very similar and without statistically significant differences [35,36]. However, there is a clear and statistically significant difference in the consumption of meat, fish, and chicken, where the Croatian population, especially men, consumes these foods significantly more often on a daily basis compared to the EU average. This data may be relevant for understanding dietary habits and potential health outcomes in Croatia compared to the EU average. Personalized nutritional interventions are key for early recognition of frailty syndrome and establishment of personalized treatments. Thus, adequate intake of 1.2–1.5 g/kg BM per day is crucial for maintaining muscle mass and bone health. Insufficient protein intake can increase the risk of sarcopenia and fractures. The recommended daily intake of calcium is 1000–1200 mg for strong bones, and vitamin D in the range of 800–2000 IU per day (target >30 ng/mL serum levels). Omega-3 fatty acids have anti-inflammatory properties and are beneficial for chronic inflammation associated with obesity and sarcopenia and can improve muscle protein synthesis. Regular exercise is recommended (strength training, aerobic exercise, yoga, Pilates, etc., e.g., 300 min/week of moderate intensity or 150 min of high intensity) which is effective in reducing visceral fat. Speaking of dietary patterns, the Mediterranean diet is associated with a reduction in visceral fat and a lower prevalence of frailty [24,37], the ketogenic diet can reduce visceral fat [23,35], and high-protein diets are effective in reducing visceral fat and preserving muscle mass [32,38].

Table A1 and Table A2 detail the reasons for not consuming meat in the middle-aged and older population, comparing the data from the European Union (EU) with those from Croatia (HR), divided by body mass index (BMI). The three possible choices for not consuming meat are (i) lack of financial resources, (ii) following a vegetarian diet, and (iii) "other reasons". "You can‘t afford to eat it more often" in the EU ranges from 9.8% (normal BMI) to 15.3% (obese BMI). However, in Croatia (HR), this reason is significantly more pronounced, especially in people with malnutrition (<18.5 BMI) with 35.3% and normal BMI (18.5–24.9) with 37.5%. For overweight and obesity, the percentages in HR are around 15.7% and 21.4%, respectively. A statistically significant difference (p < 0.0001) was also found, indicating that the financial unavailability of meat is a significantly greater problem in the middle-aged population of Croatia compared to the EU average. The share of vegetarian diet followers in the EU varies from 8.8% (obese) to 19.3% (malnourished), while in Croatia, the percentage ranges from 5.0% (overweight) to 25.0% (malnourished); however, there is not a statistically significant difference (p = 0.0777) between the EU and HR, despite some numerical variations. The shares of the frequency of the choice "For other reasons" are statistically significantly different (p < 0.0001), suggesting that the structure of "other reasons" for not consuming meat differs significantly between the EU and HR.

Reasons for not consuming meat in the older population (Table A2), again in Croatia, are significantly more pronounced, especially among severely malnourished (<18.5 BMI) with 36.0%. The percentages are generally higher in HR in most BMI categories (e.g., 18.7% for normal BMI and 22.8% for obese 31–39.9 BMI). The difference is statistically significant (p = 0.0004), confirming that the financial unavailability of meat remains a significant factor in the Croatian older population compared to the EU. In the population older than 65 years, vegetarianism in the EU ranges from 8.3% (morbidly obese) to 17.7% (underweight, 18.5–20.9 BMI), while in HR a greater variation is observed, from 5.1% (overweight and obese 31–39.9 BMI) to 21.9% (underweight, 18.5–20.9 BMI) and 20.0% (morbidly obese, ≥40 BMI). Based on the study of Gianfredi et al. [34], contribution to higher BMI is the food insecurity which is related to increased consumption of nutrient-poor foods and energy-dense food. The difference is statistically significant (p = 0.0002), suggesting that there are specific differences in the prevalence of vegetarian diets between the older EU and Croatian populations, with a slightly higher share in some Croatian BMI categories and lower in others, compared to the EU average. The Croatian population, both middle-aged and older, consumes meat, fish, or chicken on a daily basis more often than the EU average. There are also statistically significant differences in the daily consumption of dairy products in the older population and in the consumption of legumes/eggs and fruits/vegetables 3–6 times a week in the older population, with different trends between the EU and HR. In both age groups (middle-aged and older), the prevalence of high blood cholesterol is statistically significantly lower in Croatia compared to the EU, regardless of BMI. This study showed specific differences in dietary habits and the association of BMI with certain CVDs in Croatia compared to the European Union average. Research on long COVID risk factors is ongoing [39]. High body mass index (BMI) may increase long COVID risk, yet no evidence has been established regarding sex differences in the relationship between BMI and the risk of long COVID [39]. Targeted strategies promoting healthy eating and physical activity, particularly among men in urban settings, are essential to address these disparities [39]. Cognitive maintenance—defined as a capacity to maintain good or excellent cognitive functioning—is a valuable aging outcome [2]. Socio-demographic dementia risk and protective factors may contribute differently to it across social classes [40]. The prevalence of heart attack, hypertension, stroke, and diabetes generally increases with increasing BMI in both regions and age groups [41,42]. However, it is not enough to compare the proportions in the population, which in each country may have a different proportion of men/women as well as other covariates, and the presented results have shown that gender, level of education, marital status, etc., show significant changes in some cases. Therefore, it is necessary to minimize the influence of covariates through a binary or multivariate logistic regression model. Thus, in this paper we see a clear trend of association between body mass index and increased cardiovascular diseases, which is in line with the research of Larsson et al. [43], who conducted a Mendelian randomization study and concluded that a higher BMI is associated with most cardiovascular conditions, and they examined fourteen. Results indicate specific differences in dietary habits and the association of BMI with certain CVDs in Croatia compared to the European Union average. Studies on the effectiveness of protein, vitamin D, and calcium supplementation have shown contradictory results [5,7]. This heterogeneity may be due to differences in population (e.g., healthy older population vs. frail), doses, and duration of studies [36]. However, it is inevitable that future studies should investigate pharmacological therapies for osteosarcopenic obesity [7,8,9]. A growing area of research is the role of the gut microbiota in sarcopenia and frailty [5]. Publicly available studies (such as the SHARE study) that are designed and randomized with clearly defined participants, considering gender and specific conditions (diseases, activities, etc.) are increasingly emphasized. The need for personalized and gender-sensitive approaches in research and clinical practice is increasingly emphasized.

5. Conclusions

This study, using the data from the SHARE survey, confirms that obesity is a complex and multifactorial public health problem across Europe, with a particular focus on Croatia, which stands out for its high proportion of overweight and obese people in middle and older age. Almost two-thirds (64.8%) of adults in Croatia are classified as overweight or obese. The study showed (i) a high prevalence of obesity and its association with health risks, and (ii) statistically significant differences in dietary habits.

Statistically significant differences were recorded in the consumption of meat, fish, or chicken, with the Croatian population, especially men, consuming them significantly more often on a daily basis compared to the EU average, and the financial unavailability of meat proved to be a significant factor in the frequency of its consumption. In the EU population, frequent consumption of meat, fish, or chicken was strongly associated with obesity risk, while this association was weaker and non-significant in Croatia. Legumes and eggs showed heterogeneous effects, with higher risk observed in Croatia, whereas fruit and vegetable intake showed no significant associations. Hypertension and diabetes consistently emerged as strong correlates of obesity in both populations, while myocardial infarction and stroke were not associated. Overweight and obesity significantly increase the risk of serious health complications, including cardiovascular disease (CVD) and type 2 diabetes.

These findings highlight the need for the development of targeted public health interventions and policies, given that obesity is a complex condition influenced by multiple determinants beyond individual behavior.

It is important to note that this study has certain limitations. For example, different BMI categorizations for the population younger and older than 65 years may affect direct comparisons of the prevalence of "normal BMI" across age groups, which requires caution in interpretation. Also, data from the SHARE study rely on self-reporting by participants, which can introduce biases such as recall bias or social desirability bias. The dietary habits analysis focused on the frequency of consumption of four general food groups, which does not provide a sufficiently detailed insight into the complexity of the overall dietary pattern (e.g., portion size and specific types of foods within categories). However, despite these limitations, the findings of this study provide valuable insights into the unique patterns of obesity, dietary habits, and health outcomes in Croatia compared to the rest of Europe, offering a solid basis for future public health strategies and further research.