Exploring the Relationships Between Obesity, Cholesterol, and Hypertension: a Cross-Sectional Study in Indonesia

Exploring the Relationships Between Obesity, Cholesterol, and Hypertension: a Cross-Sectional Study in Indonesia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the Relationships Between Obesity, Cholesterol, and Hypertension: a Cross-Sectional Study in Indonesia Sri Winarni, Abdul Mughni, Pradipa Winandika, Nisrina Ocktalifa Chumair, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7854297/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Obesity is a non-communicable disease closely related to increased cholesterol levels and hypertension. According to the Ministry of Health, the prevalence of obesity in Indonesia is expected to grow from 1.6% to 23.4% by 2023. Obesity is a risk factor for cardiovascular diseases. This study aimed to analyze the relationship between obesity, cholesterol levels, and blood pressure. The methodology employed for the research was descriptive and utilized a cross-sectional approach. The sample for the study included 285 individuals. Data were collected using a purposive sampling method with BMI ≥ 25. Data analysis used the chi-square test for categorical data and the Spearman test for numerical data. According to the findings from the analysis of categorical data, no meaningful connection was found between BMI, cholesterol (p = 0.918), and blood pressure (p = 0.139). In the numerical analysis, a notable inverse relationship was discovered between BMI and cholesterol levels (p = 0.037). There was a positive correlation between cholesterol levels and blood pressure categories (p = 0.002) and between cholesterol levels and systolic pressure (p = 0.001). A significant relationship was found between age and BMI (p = 0.019), cholesterol levels (p = 0.028), and blood pressure (p < 0.001). Additionally, a significant relationship was observed between sex and cholesterol levels (p < 0.001). Obesity can be harmful to young people, adults, and the elderly. While obesity was not strongly linked, it was identified as a risk factor for higher cholesterol levels and high blood pressure. Obesity Hypercholesterolemia Hypertension INTRODUCTION Obesity is not only prevalent in developed countries but has increasingly affected populations in developing nations over time. [ 1 ] In Indonesia, the incidence of obesity has increased annually. This is evidenced by the obesity prevalence of 21.8% in 2018, which has increased to 23.4% by 2023. In 2018, 20% of school-aged children, 14.8% of adolescents, and 35.5% of adults in Indonesia were living with overweight or obesity. [ 2 ] Obesity results in compromised delivery of oxygen and nutrients to bodily tissues, leading to an increase in blood volume and cardiac output, which subsequently causes elevated blood pressure.[ 3 ] It is a non-communicable disease that is strongly linked to high blood cholesterol and elevated blood pressure, as well as other cardiovascular risk factors.[ 1 ] Information from the World Health Organization (WHO) shows that approximately 1.13 billion people worldwide suffer from high blood pressure. [ 4 ] In Indonesia, the occurrence of hypertension is on the rise. According to the Basic Health Research (Riskesdas), the rate of hypertension in Indonesia has risen to 34. 1%, an increase from 25. 8% noted in the 2013 Riskesdas report.[ 5 ] The Monica I study and the Multinational Monitoring of Trends and Determinants in Cardiovascular Disease study showed a link between cholesterol levels and obesity. A rise of 1 kg/m² in Body Mass Index (BMI) is linked to an increase in serum total cholesterol of 7.7 mg/dL and a decrease in HDL of 0.8 mg/dL. [ 6 ] Age and sex are characteristics related to non-communicable diseases, such as obesity, cholesterol levels, and hypertension. According to data from Riskesdas (2018), the age group with the highest rate of obesity is 40–44 years, with a prevalence of 29.6%, and the incidence is also notably high among females, at 29.3%. The highest proportion of cholesterol levels occurred in the age group 55–64 years (29.2%) and among females (24%). The highest incidence of hypertension occurs in individuals aged < 75 years (69.53%) and among females (36.85%) [ 7 ] This research aimed to investigate the connection among obesity, cholesterol, and high blood pressure by analyzing body mass index (BMI), cholesterol metrics, and blood pressure readings collected from public health assessments. MATERIALS AND METHODS This study utilized a quantitative method featuring a cross-sectional design. The research included 285 subjects who participated in health assessments and treatments as components of community service initiatives in Grobogan Regency, Demak Regency, and Semarang City. Sampling was performed using a non-probability technique with purposive sampling. The inclusion criteria were respondents who participated in the treatment and were classified as overweight or obese. The variables included BMI, BMI categories, cholesterol levels, cholesterol categories, systolic blood pressure, and blood pressure categories. Data distribution was non-normal and non-parametric; therefore, data analysis used correlation approaches or relationships with Crosstabs for categorical data and Spearman's test for numeric data, namely, BMI, cholesterol levels, and systolic blood pressure. The categories were created based on the threshold values for body mass index (BMI), cholesterol levels, and systolic blood pressure as specified by the Ministry of Health. In this study, the Body Mass Index (BMI) categories were "Overweight" with BMI ≥ 25 and < 30, and "Obesity, " with BMI ≥ 30. Cholesterol categories were divided into "Normocholesterolemic" if the measurement is < 200 and "Hypercholesterolemia" if the measurement was ≥ 200. Blood pressure categories were divided into "Hypertension" if ≥ 140 and "Non-Hypertension" if 60 years 36 12.6 Gender Male 83 29.1 Female 202 70.9 BMI Category Obesity 71 24.9 Overweight 214 75.1 Cholesterol Category Hypercholesterolemia 144 50.5 Normocholesterolemia 141 49.5 Blood Pressure Category Hypertension 132 46.3 Non-Hypertension 153 53.7 Indicates that the majority of respondents were aged 36–60 years (65.6%). In addition, 70.9% of the respondents were female. The proportion of respondents categorized as obese was 24.9%. Half of the respondents fall into the "Hypercholesterolemia" category (50.5%). 46.3% of respondents have a blood pressure categorized as "Hypertension." The lowest BMI was 25.0, and the highest was 48.6, with a mean of 28.848. The lowest cholesterol level was 105, and the highest was 386, with a mean of 210.70. The lowest systolic pressure was 87 mmHg and the highest was 205 mmHg, with a mean of 140.33. Table 2 Relationship between the BMI category and the cholesterol category BMI Category Cholesterol Category P-value Hypercholesterolemia Normocholesterolemia f % F % Obesity 35 49.3 36 50.7 0.918 Overweight 109 50.9 105 49.1 Table 2 shows that 49. 3% of the obese participants had elevated cholesterol levels (hypercholesterolemia), whereas 50%. 9% of the overweight participants also had high cholesterol levels. A bivariate analysis performed through the chi-square test revealed that there was no significant statistical relationship between the body mass index (BMI) category and the cholesterol category (p = 0. 918, which is greater than 0.05). Table 3 Relationship between cholesterol category and blood pressure category Independent Variable Blood Pressure Category Hypertension Normal P-value f % F % BMI Category Obesity 27 38.0 44 62.0 0.139 Overweight 105 49.1 109 50.9 Cholesterol Category Hypercholesterolemia 80 55.6 64 44.4 0.002 Normocholesterolemia 52 36.9 89 63.1 According to Table 3 , 38% of individuals classified as obese reported having a history of hypertension, whereas 49.1% of those who were overweight also had a history of hypertension. The chi-square test for bivariate analysis revealed no significant association between BMI category and blood pressure category, as indicated by a p-value of 0.139, which is greater than 0.05. In total, 55.6% of the respondents with a history of hypercholesterolemia also had a history of hypertension. In contrast, 36.9% of the respondents with normal cholesterol levels had a history of hypertension. Bivariate analysis conducted through the chi-square test revealed a significant relationship between the cholesterol category and the blood pressure category (p = 0.002). Table 4 provides information on BMI categories, showing that 33.9% of respondents aged ≤ 35 years had a history of obesity. Among the respondents aged 36–60 years, the majority had a history of being overweight (74.9%). In contrast, 91.7% of respondents aged > 60 years had a history of being overweight. The bivariate analysis conducted with the chi-square test produced a p-value of 0.019, which signifies a statistically significant relationship between age and BMI. Among respondents aged ≤ 35 years, 64.5% had normal cholesterol levels. In the 36–60 years age group, 54.5% had high cholesterol/hypercholesterolemia. Meanwhile, 55.6% of the respondents aged > 60 years had high cholesterol levels. The chi-square test conducted for bivariate analysis resulted in a p-value of 0.028, suggesting a significant association between age and the category of cholesterol levels. Most respondents aged ≤ 35 years had a normal blood pressure (79%). In the 36–60 years age group, 48.7% had a history of hypertension. Among the respondents aged > 60 years, 77.8% had a history of hypertension. The chi-square test employed in the bivariate analysis produced a p-value of 0. 000, showing a significant connection between age and blood pressure category. Table 4 Relationship between Age and BMI Category, Cholesterol Category, and Blood Pressure Category Age BMI category P-value Obesity Overweight f % f % ≤ 35 years 21 33.9 41 66.1 0.019 36–60 years 47 25.1 140 74.9 > 60 years 3 8.3 33 91.7 cholesterol category Hypercholesterolemia Normocholesterolemia P-value f % f % ≤ 35 tahun 22 35.5 40 64.5 0.028 36–60 tahun 102 54.5 85 45.5 > 60 tahun 20 55.6 16 44.4 Blood Pressure Category Hypertension Non-Hypertension P-value f % f % ≤ 35 years 13 21.0 49 79.0 0.000 36–60 years 91 48.7 96 51.3 > 60 years 28 77.8 8 22.2 Gender BMI Category Obesity Overweight P-value f % f % Male 14 16.9 69 83.1 0.063 Female 57 28.2 145 71.8 Cholesterol Category Hypercholesterolemia Normocholesterolemia P-value f % f % Male 22 26.5 61 73.5 0.000 Female 122 60.4 80 39.6 Blood Pressure Category Hypertension Non-Hypertension P-value f % f % Male 42 50.6 41 49.4 0.424 Female 90 44.6 112 55.4 Table 4 shows that most male respondents had a history of being overweight (83.1%). Similarly, most female respondents had a history of being overweight (71.8%). The chi-square test for analysing two variables resulted in a p-value of 0. 063, indicating that there is no significant connection between gender and BMI. Male respondents with a history of hypercholesterolemia accounted for 26.5%, while 60.4% of the female respondents had a history of hypercholesterolemia. Bivariate analysis using the chi-square test revealed a p-value of 0.000, indicating that sex was significantly associated with the cholesterol category. Among the male participants, 50.6% reported having a history of hypertension, whereas 44.6% of the female participants indicated the same. The bivariate analysis conducted with the chi-square test resulted in a p-value of 0. 424, suggesting that there is no significant relationship between sex and blood pressure category. The results of a multivariate analysis of categorical data showed that the cholesterol category influenced the blood pressure category (p = 0.002) with a positive coefficient of 0.185. Age affected the BMI category (p-value = 0.020) with a positive coefficient of 0.176, and the blood pressure group (p-value = 0.000) shows a negative coefficient of -0.367. Gender influenced the BMI category (p-value = 0.029), showing a negative coefficient of -0.130, and the cholesterol category (p-value = 0.000) shows a negative coefficient of -0.281. DISCUSSION In the univariate analysis of the characteristics of respondents, individuals aged between 36 and 60 years showed the highest rate of obesity at 65.6%. Additionally, 70.9% of the respondents were found to have the highest obesity prevalence. These findings are consistent with those of Sugiarti (2011), who reported that among individuals aged 30–39 years, 22% were classified as overweight, with 53% being female and 47% male. In terms of obesity classification, 36% were obese, with 77% females and 33% males. In the age group of 40 to 49 years, 28% were identified as overweight, comprising 54% females and 46% males. In the age group of 50 to 59 years, no individuals were identified as overweight; however, 67% were identified as obese, comprising 60% females and 40% males. [ 8 ] Many studies have shown that the highest prevalence of obesity is found among middle-aged adults. For example, in Saudi Arabia, the prevalence of obesity and overweight among individuals over 25 years old reaches 66%, peaking in the middle-aged group (76%). A study in Iran also found the highest obesity prevalence in the 55–64 age group (31.5%). Similarly, in Europe and the United States, the 40–59 age group shows the highest prevalence of obesity. In this study, 49.3% of obese respondents had hypercholesterolemia, whereas 50.9% of overweight respondents had hypercholesterolemia. The Chi-Square bivariate test revealed that there is no meaningful connection between BMI categories and cholesterol categories, as indicated by a p-value of 0.918. These findings align with Rasyid (2019), who reported that 21.54% of respondents with obesity exhibited total cholesterol levels ≥ 200 mg/dL. The Pearson test produced a p-value of 0.470, suggesting that there is no meaningful relationship between BMI and cholesterol levels.[ 9 ] Similarly, Wahyuni (2020) found a non-significant relationship between obesity and cholesterol, indicated by a p-value of 0.576 and a negative coefficient. [ 6 ] These findings are in contrast to the research conducted by Dana (2022), the study found a notable link between BMI and cholesterol; this is backed by a p-value of 0. 0007 and indicates a moderate level of correlation. People with a higher body mass index often experience increased blood cholesterol levels. In particular, a rise in BMI is associated with elevated LDL and triglyceride levels. Those classified as overweight or obese typically have higher total cholesterol, LDL, and triglyceride levels.[ 10 ] However, obesity is not always the sole cause of elevated cholesterol levels. Engaging in regular physical exercise can assist in lowering body weight and reducing cholesterol buildup in the blood vessels. Engaging in physical exercise or activity can increase HDL cholesterol levels while decreasing LDL cholesterol and triglyceride levels.[ 6 ] In this study, 38% of the respondents with obesity and 49.1% of those who were overweight also had hypertension. The chi-Square bivariate analysis revealed that there was no significant relationship between BMI categories and blood pressure categories, as indicated by a p-value of 0.139. Similarly, the Chi-Square test for numerical data found no significant link between BMI and systolic blood pressure, as evidenced by a p-value of 0.160. The results align with Sukmawaty (2022), who found no significant link between obesity and hypertension (p-value = 0.461).[ 11 ] Similarly, Sopiah (2020) found no strong relationship between hypertension in the elderly and BMI, with Pearson correlation tests showing p-values of 0.465 for systolic blood pressure and 0.58 for diastolic blood pressure.[ 12 ] Additionally, Sadiman (2024) found no noteworthy connection between obesity and hypertension in women who have reached menopause, indicated by a p-value of 0.080. [ 13 ] Nevertheless, these results contradict the findings of Asyfah (2020), which indicated a notable connection between obesity and hypertension. In this study, 56. 5% of individuals with obesity and 18. 5% of those classified as pre-obese reported experiencing hypertension. [ 8 ] Yogeswara (2023), a notable association was discovered between BMI and hypertension among 30 participants who met the inclusion criteria, evidenced by a p-value of 0. 000 and a correlation coefficient of 0.671.[ 3 ] Research consistently shows that being overweight is a major risk factor for hypertension across all age groups including children, adolescents, adults, and the elderly. Individuals who are overweight are 1.7–3.3 times more likely to develop hypertension compared to those with normal body weight, and this risk increases further in cases of obesity. Among adolescents, being overweight increases the risk of hypertension by 2.4–3.3 times. In adults, overweight and obesity contribute to approximately 40% of new hypertension cases. In the elderly, the prevalence of hypertension is also higher among those who are overweight.[ 1 ] Overweight causes metabolic changes such as activation of the sympathetic nervous system, increased sodium retention, and hormonal dysfunction, all of which contribute to elevated blood pressure. Weight loss has been proven to lower blood pressure and serves as a key strategy for the prevention and management of hypertension. Hypertension in obese patients results from a complex interaction of physiological and molecular mechanisms. The accumulation of fat, particularly visceral fat, leads to hormonal alterations, inflammation, and endothelial dysfunction that activate both the sympathetic nervous system and the renin–angiotensin–aldosterone system (RAAS). Activation of these systems increases sodium retention, fluid volume, and vascular tone, resulting in elevated blood pressure. Additionally, insulin resistance and hyperinsulinemia, commonly found in obesity, exacerbate sympathetic stimulation and renal sodium retention while reducing nitric oxide (NO) production, a key vasodilator, thereby promoting vasoconstriction and further raising blood pressure.(5) Adipose tissue in obesity also undergoes changes in adipokine secretion, characterized by increased leptin and decreased adiponectin levels. Elevated leptin stimulates sympathetic activity through hypothalamic pathways, while proinflammatory adipokines such as TNF-α and IL-6 worsen endothelial dysfunction and vascular inflammation. Moreover, compression of the kidneys by perirenal and visceral fat impairs sodium excretion and causes glomerular hyperfiltration, contributing to hypertension. Alterations in perivascular adipose tissue (PVAT), gut microbiota imbalance, elevated uric acid, and changes in incretin hormone activity further aggravate the condition. Overall, hypertension in obesity is a multifactorial disorder involving neural, hormonal, renal, and adipose mechanisms, requiring a comprehensive and individualized therapeutic approach. Various elements, including autonomic dysfunction, insulin resistance, and abnormalities in the structure and function of blood vessels, contribute to the relationship between obesity and hypertension. Obesity can cause insulin resistance and problems with the endothelial cells, which may lead to the narrowing of blood vessels and increased sodium retention in the kidneys. This ultimately contributes to higher blood pressure levels. [ 14 ] In this research, 55.6% of participants diagnosed with hypercholesterolemia were also found to have hypertension. A statistically significant relationship existed between cholesterol levels and blood pressure categories, supported by a p-value of 0.002. In the same manner, the numerical information showed a significant relationship between cholesterol levels and systolic blood pressure, with a p-value of 0.001. Furthermore, multivariate analysis revealed a notable link between cholesterol levels and both categories of blood pressure, as well as systolic blood pressure, demonstrated by a p-value of less than 0.05. The findings are consistent with Nugroho (2018), who identified a notable correlation between cholesterol levels and hypertension (p = 0.000). Individuals with elevated cholesterol levels were 4.450 times more likely to experience hypertension compared to those with lower cholesterol levels.[ 15 ] Sulastri (2020) discovered a statistically significant link between cholesterol levels and the intensity of hypertension at Puskesmas Simbarwaringin, with a p-value of 0.000 (p-value < 0.05).[ 16 ] In 2023, Yogeswara discovered a statistically significant link between total cholesterol levels and hypertension at Puskesmas Gerung, as indicated by a p-value of 0.001 and a correlation coefficient of 0.599.[ 3 ] Conversely, Astannudinsyah (2020) observed no meaningful connection between cholesterol levels and hypertension at RSUD Ulin Banjarmasin, reporting a p-value of 0.129. This research classified hypertension into four categories: normal, pre-hypertension, stage 1 hypertension, and stage 2 hypertension, resulting in various outcomes.[ 17 ] This is consistent with the results of Saputra (2019), who reported no notable relationship between blood cholesterol levels and hypertension, indicated by a p-value of 0.129.[ 18 ] High-fat foods can increase cholesterol levels, leading to plaque atherosclerosis buildup in blood vessels, which in turn raises the workload on the heart and increases blood pressure.[ 8 ] Other risk factors for hypertension include stress, lifestyle, diet, age, occupation, education, and more.[ 17 ] Bivariate analysis of categorical variables revealed meaningful associations between age and several health indicators, including BMI (p = 0.019), cholesterol (p = 0.028), and blood pressure (p = 0.000). These outcomes were supported by numerical assessments, which further confirmed significant correlations between age and BMI (p = 0.001), cholesterol concentration (p = 0.000), and systolic blood pressure (p = 0.000). Additionally, multivariate analysis of categorical variables highlighted strong connections between age, BMI classifications, and blood pressure levels. Overall, the data consistently demonstrated that age was significantly related to BMI, cholesterol, and systolic blood pressure (p < 0.05). The results of this study are consistent with the work of Sugiarti et al., which showed a significant relationship between age and cholesterol levels. As people age, it is common for their blood cholesterol levels to rise.[ 19 ] According to Intan (2021), there is a significant relationship between age and obesity, indicated by a p-value of 0.000. People older than 45 are 1.4 times more likely to be obese than those younger than 45. In this study, 65. 5% of the participants identified as overweight or obese were within the age range of 36 to 60 years.[ 20 ] Weight gain typically begins after age 40, with factors such as diet, lifestyle, physical activity, occupation, and psychological conditions contributing to obesity risk at this age. This is consistent with Makmun (2021), which found a significant relationship between obesity and age (p = 0.016).[ 21 ] Siregar (2020) also found that age was a dominant determinant of total cholesterol levels in the Indonesian population (p = 0.000). Individuals aged 45–59 years had the highest risk compared to the other age groups, with an odds ratio of 4.770. Age is directly related to total cholesterol levels due to physiological changes that occur as people age.[ 22 ] As people age, their arteries tend to widen, become stiffer, and exhibit reduced activity of low-density lipoprotein (LDL) receptors. This combination leads to elevated LDL levels and blockages in the coronary arteries.[ 23 ] However, studies by Kurnaiawan (2019) and Ujani (2015) found no significant relationship between age and cholesterol levels, attributing fluctuations in cholesterol levels to diet and lifestyle factors.[ 24 , 25 ] Khasanah (2022) found a significant relationship between age and hypertension, indicated by a p-value of 0.000. People older than 45 years were observed to have a 5. 499 times higher probability of developing hypertension when compared to those younger than 45.[ 26 ] Sadiman (2024) found a notable connection between age and hypertension in menopausal women, with a p-value of 0.045 and an odds ratio of 3.215 (95% CI: 1.150–8.987). This suggests that older women are 3.2 times more likely to experience hypertension compared to their younger counterparts. [ 13 ] Aging from 51 to 60 years increases the risk of decreased organ function due to aging, and the immune system is also less effective, making individuals more susceptible to diseases. As age increases, blood pressure tends to rise due to reduced elasticity of blood vessels and decreased kidney function in blood pressure regulation.[ 14 ] An examination utilizing chi-square tests on categorical data revealed a meaningful link between gender and cholesterol levels (p = 0.000). In contrast, gender did not show a significant association with BMI or blood pressure categories (p < 0.05). Similarly, numerical data indicated a significant relationship between sex and cholesterol levels (p = 0.000). Multivariate analysis of categorical variables indicated that sex was significantly associated with both BMI (p = 0.029) and cholesterol levels (p = 0.000). Moreover, notable associations were also identified between sex and BMI, cholesterol, as well as systolic blood pressure (p < 0.05). Intan (2021) reported a significant relationship between sex and obesity and found that females are three times more likely to experience obesity than males. This may be due to differences in energy intake, physical activity, and hormonal changes between genders.[ 20 ] Conversely, Maharani (2020) found no significant relationship between sex and obesity (p = 0.092 > α = 0.05). This lack of a significant relationship may be attributed to other factors, such as genetics, physical activity, and diet, influencing obesity risk regardless of gender.[ 27 ] Siregar (2020) identified a notable correlation between gender and total cholesterol levels (p = 0.007). Women tended to have higher total cholesterol levels compared to men. This research revealed that women of all ages exhibited a greater occurrence of abnormal cholesterol levels in comparison to men.[ 22 ] Cholesterol levels are influenced by gender-specific hormones, with post-menopausal women typically having higher cholesterol levels than men. Therefore, cholesterol levels are also associated with age[ 28 ]. However, Kurnaiawan (2019) and Ujiani (2015) found no significant relationship between gender and cholesterol levels, noting that pre-menopausal women tend to have lower total cholesterol levels compared to age-matched men. Post-menopausal women are at higher risk of increased cholesterol due to the loss of estrogen, which helps prevent arterial plaque formation and boosts HDL (High-Density Lipoprotein) levels.[ 24 , 25 ] According to Yunus (2021), there was no notable link between gender and hypertension (p = 0.841). However, men are more prone to hypertension than women due to unhealthy lifestyle choices. However, the prevalence of hypertension in women increases after menopause owing to hormonal changes. This is consistent with Khasanah (2022), which found no significant relationship between gender and hypertension (p-value = 0.300).[ 26 ] According to the P2PTM of the Indonesian Ministry of Health, men are 2.3 times more likely to experience elevated systolic blood pressure than women. However, post-menopausal women show higher hypertension prevalence compared to men over 60 years old due to hormonal factors.[ 29 ] Epidemiological and interventional studies indicate that high consumption of saturated fats is associated with an increased risk of cardiovascular diseases, including hypertension and atherosclerosis, especially when not balanced by adequate intake of unsaturated fats. A high-fat diet can trigger dyslipidemia (blood lipid imbalance), inflammation, and oxidative stress, all of which accelerate plaque formation and vascular damage. Research in both animals and humans also shows that a high-fat diet, even without excess calorie intake, can cause insulin resistance, hypertension, and fat accumulation, thereby worsening cardiovascular risk. Therefore, limiting saturated fat intake and replacing it with unsaturated fats such as polyunsaturated (PUFA) and monounsaturated fatty acids (MUFA) is recommended to lower cholesterol levels, reduce plaque formation, decrease blood pressure, and ultimately reduce the risk of heart and vascular diseases. The relationship between age, cholesterol levels, and hypertension is closely interconnected. As individuals age, the risk of hypertension increases significantly, accompanied by changes in lipid profiles particularly an increase in total and LDL cholesterol and a decrease in HDL cholesterol. Large population studies have shown that systolic blood pressure (SBP) and total cholesterol tend to rise with age, with a sharper increase observed in women after midlife. Higher levels of cholesterol, especially LDL and non-HDL, are consistently associated with a greater risk of hypertension, particularly among younger and middle-aged adults; however, this association tends to weaken in older populations. Moreover, several studies have found that the combination of advanced age and elevated cholesterol levels synergistically increases the risk of hypertension and cardiovascular disease. In the elderly, however, the predictive value of cholesterol for hypertension and cardiovascular events becomes more complex, influenced by factors such as overall health status and comorbidities. Overall, advancing age and uncontrolled cholesterol levels are key risk factors for hypertension, highlighting the importance of monitoring and managing both factors to prevent cardiovascular diseases across different age groups. CONCLUSION The findings of this study indicate that there is no notable connection between Body Mass Index (BMI) and blood pressure. There was a notable link found between cholesterol levels and the occurrence of hypertension, supported by both categorical and numerical data, as well as through bivariate and multivariate analyses. Variations were also noted in BMI and cholesterol variables. In the bivariate categorical test, no significant connection was detected between BMI and cholesterol levels; however, the bivariate numerical test did reveal a relationship between BMI and cholesterol levels. Moreover, there was a notable correlation between age and factors such as obesity, cholesterol, and hypertension, as well as a significant link between sex and cholesterol levels. In both bivariate categorical and numerical analyses, no significant association was found between sex and BMI or between sex and blood pressure. However, multivariate analysis revealed a significant connection between sex and BMI, as well as between sex and blood pressure. These results suggest that further detailed research is necessary to explore factors like obesity, cholesterol, and hypertension in both categorical and numerical contexts. Declarations CONSENT TO PARTICIPATE Before we gained information and data, patients were informed of consent through informed consent documents, which included consent for giving needed data and publishing the data for scientific purposes without disclosing their specific personal information. CONSENT FOR PUBLICATION As stated before, the informed consent documents also stated the possibility of using their data for publication and scientific purposes. ALVAILABILITY OF DATA AND MATERIALS The datasets generated and/or analyzed during the current study are available from the first author upon reasonable request. Data is provided within the manuscript or supplementary information files. If anyone would like to request data from this study, please contact us via email at [email protected] . COMPETING INTERESTS The authors declare no competing interests. FUNDING Funding for this research was fully provided by the Institute for Research and Community Service of Universitas Diponegoro for providing research funding under number 609 − 50/UN7.D2/PP/VII/2024. ACKNOWLEDGEMENT Acknowledgements to the Institute for Research and Community Service of Universitas Diponegoro for providing research funding. Acknowledgements to the people of the Grobogan Regency, Demak Regency, and Semarang City who have supported the sustainability of this research. Author Contribution Sri Winarni, Pradipa Winandika, Nisrina Ocktalifa Chumair, Ahla Hulaila, and Gabrina Selvi Yanuarista wrote the main manuscript text and Abdul Mughni and Siti Fatimah,prepared table. All authors reviewed the manuscript Acknowledgement Acknowledgements to the Institute for Research and Community Service of Universitas Diponegoro for providing research funding. Acknowledgements to the people of the Grobogan Regency, Demak Regency, and Semarang City who have supported the sustainability of this research. Data Availability Availability of Data and Materials. Data is provided within the manuscript or supplementary information files. If anyone would like to request data from this study, please contact us via email at [email protected] ETHICS AND GUIDELINES This research was granted ethical approval from the health research ethics committee of the Faculty of Public Health, Diponegoro University with license number 465/EA/KEPK-FKM/2024. Riset team commit to the primacy of research participants health and well-being and must offer care in the research participants’s best interest. Protocol was performed in accordance with the relevant guidelines and regulations from Declaration of Helsinki. References Supriatiningrum DN. Faktor Resiko Wanita Obesitas Pada Status Sosial Ekonomi Menengah Ke Bawah. Ghidza Media J. 2021;2(2):163. Unicef. 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Ghidza J Gizi dan Kesehat [Internet]. 2019;2(2):44. Available from: https://jurnal.fkm.untad.ac.id/index.php/ghidza/article/view/7/7 Sulastri D. Hubungan Kadar Kolesterol Dengan Derajat Hipertensi. J Ilmu Keperawatan Indones. 2020;1(2). Astannudinsyah R, Negara CK. Jurnal Medika Karya Ilmiah Kesehatan Vol 5, No.2. 2020 ISSN: Med Karya Ilm Kesehat [Internet]. 2020;5(2). Available from: http://jurnal.itkeswhs.ac.id/index.php/medika/article/download/129/128 Muhammad Saputra CK, Negara A, Martiana, Henny Puspasari AM. Correlation of blood cholesterol levels and hypertension with the incidence of stroke in The Provincial. Hosp Banjarmasin. 2014;55–60. Sugiarti L, Latifah L, Hubungan, Obesitas, Umur Dan Jenis Kelamin Terhadap Kadar Kolesterol Darah. J Sains Nat [Internet]. 2017;1(1):84. Available from: https://www.ejournalunb.ac.id/index.php/JSN/article/view/16/15 Intan SEN, Palupi NS, Prangdimurti E. 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Hubungan antara usia dan jenis kelamin dengan kadar kolesterol penderita obesitas RSUD Abdul Moloek Provinsi Lampung. J Kesehat. 2015;VI. Khasanah NAH, Hubungan Usia, Jenis Kelamin Dan Status Obesitas Dengan Kejadian Hipertensi Di Wilayah Puskesmas Sumbang Ii Kabupaten Banyumas. J Bina Cipta Husada [Internet]. 2022;XVIII(1):43–55. Available from: https://jurnal.stikesbch.ac.id/index.php/jurnal/article/view/60/76 Maharani S, Hernanda R. Faktor Yang Berhubungan Dengan Kejadian Obesitas Pada Anak Usia Sekolah. J Ilm Multi Sci Kesehat [Internet]. 2020;12(2):285–99. Available from: http://jurnal.stikes-aisyiyah-palembang.ac.id/index.php/Kep/article/view/513/0 Rasyid RA, Triawanti T, Homeostasis AR. 2019 U. Korelasi Indeks Massa Tubuh, Waist Hip Ratio terhadap Tekanan Darah Sistol, Diastol, dan Kadar Kolesterol Total Serum. Homeostatis [Internet]. 2019;2(1):161–8. Available from: http://ppjp.ulm.ac.id/journals/index.php/hms/article/view/444 Choi HM, Kim HC, Kang DR. Sex differences in hypertension prevalence and control: Analysis of the 2010–2014 Korea national health and nutrition examination survey. PLoS ONE. 2017;12(5):1–12. Additional Declarations No competing interests reported. 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[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] In Indonesia, the incidence of obesity has increased annually. This is evidenced by the obesity prevalence of 21.8% in 2018, which has increased to 23.4% by 2023. In 2018, 20% of school-aged children, 14.8% of adolescents, and 35.5% of adults in Indonesia were living with overweight or obesity. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eObesity results in compromised delivery of oxygen and nutrients to bodily tissues, leading to an increase in blood volume and cardiac output, which subsequently causes elevated blood pressure.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] It is a non-communicable disease that is strongly linked to high blood cholesterol and elevated blood pressure, as well as other cardiovascular risk factors.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Information from the World Health Organization (WHO) shows that approximately 1.13\u0026nbsp;billion people worldwide suffer from high blood pressure. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] In Indonesia, the occurrence of hypertension is on the rise. According to the Basic Health Research (Riskesdas), the rate of hypertension in Indonesia has risen to 34. 1%, an increase from 25. 8% noted in the 2013 Riskesdas report.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThe Monica I study and the Multinational Monitoring of Trends and Determinants in Cardiovascular Disease study showed a link between cholesterol levels and obesity. A rise of 1 kg/m\u0026sup2; in Body Mass Index (BMI) is linked to an increase in serum total cholesterol of 7.7 mg/dL and a decrease in HDL of 0.8 mg/dL. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAge and sex are characteristics related to non-communicable diseases, such as obesity, cholesterol levels, and hypertension. According to data from Riskesdas (2018), the age group with the highest rate of obesity is 40\u0026ndash;44 years, with a prevalence of 29.6%, and the incidence is also notably high among females, at 29.3%. The highest proportion of cholesterol levels occurred in the age group 55\u0026ndash;64 years (29.2%) and among females (24%). The highest incidence of hypertension occurs in individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;75 years (69.53%) and among females (36.85%) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThis research aimed to investigate the connection among obesity, cholesterol, and high blood pressure by analyzing body mass index (BMI), cholesterol metrics, and blood pressure readings collected from public health assessments.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eThis study utilized a quantitative method featuring a cross-sectional design. The research included 285 subjects who participated in health assessments and treatments as components of community service initiatives in Grobogan Regency, Demak Regency, and Semarang City. Sampling was performed using a non-probability technique with purposive sampling. The inclusion criteria were respondents who participated in the treatment and were classified as overweight or obese.\u003c/p\u003e\u003cp\u003eThe variables included BMI, BMI categories, cholesterol levels, cholesterol categories, systolic blood pressure, and blood pressure categories. Data distribution was non-normal and non-parametric; therefore, data analysis used correlation approaches or relationships with Crosstabs for categorical data and Spearman's test for numeric data, namely, BMI, cholesterol levels, and systolic blood pressure.\u003c/p\u003e\u003cp\u003eThe categories were created based on the threshold values for body mass index (BMI), cholesterol levels, and systolic blood pressure as specified by the Ministry of Health. In this study, the Body Mass Index (BMI) categories were \"Overweight\" with BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 and \u0026lt;\u0026thinsp;30, and \"Obesity, \" with BMI\u0026thinsp;\u0026ge;\u0026thinsp;30. Cholesterol categories were divided into \"Normocholesterolemic\" if the measurement is \u0026lt;\u0026thinsp;200 and \"Hypercholesterolemia\" if the measurement was \u0026ge;\u0026thinsp;200. Blood pressure categories were divided into \"Hypertension\" if\u0026thinsp;\u0026ge;\u0026thinsp;140 and \"Non-Hypertension\" if\u0026thinsp;\u0026lt;\u0026thinsp;140. The clinical trial number in this research is not applicable.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariable Frequency Distribution\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;35 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e36\u0026ndash;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI Category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCholesterol Category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypercholesterolemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormocholesterolemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBlood Pressure Category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIndicates that the majority of respondents were aged 36\u0026ndash;60 years (65.6%). In addition, 70.9% of the respondents were female. The proportion of respondents categorized as obese was 24.9%. Half of the respondents fall into the \"Hypercholesterolemia\" category (50.5%). 46.3% of respondents have a blood pressure categorized as \"Hypertension.\"\u003c/p\u003e\u003cp\u003eThe lowest BMI was 25.0, and the highest was 48.6, with a mean of 28.848. The lowest cholesterol level was 105, and the highest was 386, with a mean of 210.70. The lowest systolic pressure was 87 mmHg and the highest was 205 mmHg, with a mean of 140.33.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelationship between the BMI category and the cholesterol category\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eBMI Category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eCholesterol Category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eHypercholesterolemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eNormocholesterolemia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.918\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOverweight\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that 49. 3% of the obese participants had elevated cholesterol levels (hypercholesterolemia), whereas 50%. 9% of the overweight participants also had high cholesterol levels. A bivariate analysis performed through the chi-square test revealed that there was no significant statistical relationship between the body mass index (BMI) category and the cholesterol category (p\u0026thinsp;=\u0026thinsp;0. 918, which is greater than 0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelationship between cholesterol category and blood pressure category\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eIndependent Variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eBlood Pressure Category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI Category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOverweight\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCholesterol Category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypercholesterolemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormocholesterolemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, 38% of individuals classified as obese reported having a history of hypertension, whereas 49.1% of those who were overweight also had a history of hypertension. The chi-square test for bivariate analysis revealed no significant association between BMI category and blood pressure category, as indicated by a p-value of 0.139, which is greater than 0.05.\u003c/p\u003e\u003cp\u003eIn total, 55.6% of the respondents with a history of hypercholesterolemia also had a history of hypertension. In contrast, 36.9% of the respondents with normal cholesterol levels had a history of hypertension. Bivariate analysis conducted through the chi-square test revealed a significant relationship between the cholesterol category and the blood pressure category (p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides information on BMI categories, showing that 33.9% of respondents aged\u0026thinsp;\u0026le;\u0026thinsp;35 years had a history of obesity. Among the respondents aged 36\u0026ndash;60 years, the majority had a history of being overweight (74.9%). In contrast, 91.7% of respondents aged\u0026thinsp;\u0026gt;\u0026thinsp;60 years had a history of being overweight. The bivariate analysis conducted with the chi-square test produced a p-value of 0.019, which signifies a statistically significant relationship between age and BMI.\u003c/p\u003e\u003cp\u003eAmong respondents aged\u0026thinsp;\u0026le;\u0026thinsp;35 years, 64.5% had normal cholesterol levels. In the 36\u0026ndash;60 years age group, 54.5% had high cholesterol/hypercholesterolemia. Meanwhile, 55.6% of the respondents aged\u0026thinsp;\u0026gt;\u0026thinsp;60 years had high cholesterol levels. The chi-square test conducted for bivariate analysis resulted in a p-value of 0.028, suggesting a significant association between age and the category of cholesterol levels.\u003c/p\u003e\u003cp\u003eMost respondents aged\u0026thinsp;\u0026le;\u0026thinsp;35 years had a normal blood pressure (79%). In the 36\u0026ndash;60 years age group, 48.7% had a history of hypertension. Among the respondents aged\u0026thinsp;\u0026gt;\u0026thinsp;60 years, 77.8% had a history of hypertension. The chi-square test employed in the bivariate analysis produced a p-value of 0. 000, showing a significant connection between age and blood pressure category.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelationship between Age and BMI Category, Cholesterol Category, and Blood Pressure Category\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e\u003cp\u003eBMI category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003ef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003ef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;35 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e33.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e66.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e36\u0026ndash;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e25.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e74.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e91.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003echolesterol category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eHypercholesterolemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003eNormocholesterolemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003ef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003ef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;35 tahun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e35.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e64.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e36\u0026ndash;60 tahun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e54.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e45.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60 tahun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e55.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e44.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eBlood Pressure Category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003eNon-Hypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003ef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003ef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;35 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e21.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e79.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e36\u0026ndash;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e48.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e51.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e77.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003e22.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBMI Category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eObesity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003eOverweight\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c13\" namest=\"c11\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003ef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e16.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e83.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c13\" namest=\"c11\" rowspan=\"2\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e28.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e71.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCholesterol Category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eHypercholesterolemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003eNormocholesterolemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003ef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003ef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e26.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e73.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c13\" namest=\"c11\" rowspan=\"2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e60.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e39.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBlood Pressure Category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003eNon-Hypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003ef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003ef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e50.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e49.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c13\" namest=\"c11\" rowspan=\"2\"\u003e\u003cp\u003e0.424\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e44.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003e55.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that most male respondents had a history of being overweight (83.1%). Similarly, most female respondents had a history of being overweight (71.8%). The chi-square test for analysing two variables resulted in a p-value of 0. 063, indicating that there is no significant connection between gender and BMI.\u003c/p\u003e\u003cp\u003eMale respondents with a history of hypercholesterolemia accounted for 26.5%, while 60.4% of the female respondents had a history of hypercholesterolemia. Bivariate analysis using the chi-square test revealed a p-value of 0.000, indicating that sex was significantly associated with the cholesterol category.\u003c/p\u003e\u003cp\u003eAmong the male participants, 50.6% reported having a history of hypertension, whereas 44.6% of the female participants indicated the same. The bivariate analysis conducted with the chi-square test resulted in a p-value of 0. 424, suggesting that there is no significant relationship between sex and blood pressure category.\u003c/p\u003e\u003cp\u003eThe results of a multivariate analysis of categorical data showed that the cholesterol category influenced the blood pressure category (p\u0026thinsp;=\u0026thinsp;0.002) with a positive coefficient of 0.185. Age affected the BMI category (p-value\u0026thinsp;=\u0026thinsp;0.020) with a positive coefficient of 0.176, and the blood pressure group (p-value\u0026thinsp;=\u0026thinsp;0.000) shows a negative coefficient of -0.367. Gender influenced the BMI category (p-value\u0026thinsp;=\u0026thinsp;0.029), showing a negative coefficient of -0.130, and the cholesterol category (p-value\u0026thinsp;=\u0026thinsp;0.000) shows a negative coefficient of -0.281.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn the univariate analysis of the characteristics of respondents, individuals aged between 36 and 60 years showed the highest rate of obesity at 65.6%. Additionally, 70.9% of the respondents were found to have the highest obesity prevalence. These findings are consistent with those of Sugiarti (2011), who reported that among individuals aged 30\u0026ndash;39 years, 22% were classified as overweight, with 53% being female and 47% male. In terms of obesity classification, 36% were obese, with 77% females and 33% males. In the age group of 40 to 49 years, 28% were identified as overweight, comprising 54% females and 46% males. In the age group of 50 to 59 years, no individuals were identified as overweight; however, 67% were identified as obese, comprising 60% females and 40% males. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eMany studies have shown that the highest prevalence of obesity is found among middle-aged adults. For example, in Saudi Arabia, the prevalence of obesity and overweight among individuals over 25 years old reaches 66%, peaking in the middle-aged group (76%). A study in Iran also found the highest obesity prevalence in the 55\u0026ndash;64 age group (31.5%). Similarly, in Europe and the United States, the 40\u0026ndash;59 age group shows the highest prevalence of obesity.\u003c/p\u003e\u003cp\u003eIn this study, 49.3% of obese respondents had hypercholesterolemia, whereas 50.9% of overweight respondents had hypercholesterolemia. The Chi-Square bivariate test revealed that there is no meaningful connection between BMI categories and cholesterol categories, as indicated by a p-value of 0.918. These findings align with Rasyid (2019), who reported that 21.54% of respondents with obesity exhibited total cholesterol levels\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL. The Pearson test produced a p-value of 0.470, suggesting that there is no meaningful relationship between BMI and cholesterol levels.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Similarly, Wahyuni (2020) found a non-significant relationship between obesity and cholesterol, indicated by a p-value of 0.576 and a negative coefficient. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThese findings are in contrast to the research conducted by Dana (2022), the study found a notable link between BMI and cholesterol; this is backed by a p-value of 0. 0007 and indicates a moderate level of correlation. People with a higher body mass index often experience increased blood cholesterol levels. In particular, a rise in BMI is associated with elevated LDL and triglyceride levels. Those classified as overweight or obese typically have higher total cholesterol, LDL, and triglyceride levels.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eHowever, obesity is not always the sole cause of elevated cholesterol levels. Engaging in regular physical exercise can assist in lowering body weight and reducing cholesterol buildup in the blood vessels. Engaging in physical exercise or activity can increase HDL cholesterol levels while decreasing LDL cholesterol and triglyceride levels.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eIn this study, 38% of the respondents with obesity and 49.1% of those who were overweight also had hypertension. The chi-Square bivariate analysis revealed that there was no significant relationship between BMI categories and blood pressure categories, as indicated by a p-value of 0.139. Similarly, the Chi-Square test for numerical data found no significant link between BMI and systolic blood pressure, as evidenced by a p-value of 0.160. The results align with Sukmawaty (2022), who found no significant link between obesity and hypertension (p-value\u0026thinsp;=\u0026thinsp;0.461).[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Similarly, Sopiah (2020) found no strong relationship between hypertension in the elderly and BMI, with Pearson correlation tests showing p-values of 0.465 for systolic blood pressure and 0.58 for diastolic blood pressure.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Additionally, Sadiman (2024) found no noteworthy connection between obesity and hypertension in women who have reached menopause, indicated by a p-value of 0.080. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eNevertheless, these results contradict the findings of Asyfah (2020), which indicated a notable connection between obesity and hypertension. In this study, 56. 5% of individuals with obesity and 18. 5% of those classified as pre-obese reported experiencing hypertension. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Yogeswara (2023), a notable association was discovered between BMI and hypertension among 30 participants who met the inclusion criteria, evidenced by a p-value of 0. 000 and a correlation coefficient of 0.671.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eResearch consistently shows that being overweight is a major risk factor for hypertension across all age groups including children, adolescents, adults, and the elderly. Individuals who are overweight are 1.7\u0026ndash;3.3 times more likely to develop hypertension compared to those with normal body weight, and this risk increases further in cases of obesity. Among adolescents, being overweight increases the risk of hypertension by 2.4\u0026ndash;3.3 times. In adults, overweight and obesity contribute to approximately 40% of new hypertension cases. In the elderly, the prevalence of hypertension is also higher among those who are overweight.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eOverweight causes metabolic changes such as activation of the sympathetic nervous system, increased sodium retention, and hormonal dysfunction, all of which contribute to elevated blood pressure. Weight loss has been proven to lower blood pressure and serves as a key strategy for the prevention and management of hypertension.\u003c/p\u003e\u003cp\u003eHypertension in obese patients results from a complex interaction of physiological and molecular mechanisms. The accumulation of fat, particularly visceral fat, leads to hormonal alterations, inflammation, and endothelial dysfunction that activate both the sympathetic nervous system and the renin\u0026ndash;angiotensin\u0026ndash;aldosterone system (RAAS). Activation of these systems increases sodium retention, fluid volume, and vascular tone, resulting in elevated blood pressure. Additionally, insulin resistance and hyperinsulinemia, commonly found in obesity, exacerbate sympathetic stimulation and renal sodium retention while reducing nitric oxide (NO) production, a key vasodilator, thereby promoting vasoconstriction and further raising blood pressure.(5)\u003c/p\u003e\u003cp\u003eAdipose tissue in obesity also undergoes changes in adipokine secretion, characterized by increased leptin and decreased adiponectin levels. Elevated leptin stimulates sympathetic activity through hypothalamic pathways, while proinflammatory adipokines such as TNF-α and IL-6 worsen endothelial dysfunction and vascular inflammation. Moreover, compression of the kidneys by perirenal and visceral fat impairs sodium excretion and causes glomerular hyperfiltration, contributing to hypertension. Alterations in perivascular adipose tissue (PVAT), gut microbiota imbalance, elevated uric acid, and changes in incretin hormone activity further aggravate the condition. Overall, hypertension in obesity is a multifactorial disorder involving neural, hormonal, renal, and adipose mechanisms, requiring a comprehensive and individualized therapeutic approach.\u003c/p\u003e\u003cp\u003eVarious elements, including autonomic dysfunction, insulin resistance, and abnormalities in the structure and function of blood vessels, contribute to the relationship between obesity and hypertension. Obesity can cause insulin resistance and problems with the endothelial cells, which may lead to the narrowing of blood vessels and increased sodium retention in the kidneys. This ultimately contributes to higher blood pressure levels. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eIn this research, 55.6% of participants diagnosed with hypercholesterolemia were also found to have hypertension. A statistically significant relationship existed between cholesterol levels and blood pressure categories, supported by a p-value of 0.002. In the same manner, the numerical information showed a significant relationship between cholesterol levels and systolic blood pressure, with a p-value of 0.001. Furthermore, multivariate analysis revealed a notable link between cholesterol levels and both categories of blood pressure, as well as systolic blood pressure, demonstrated by a p-value of less than 0.05. The findings are consistent with Nugroho (2018), who identified a notable correlation between cholesterol levels and hypertension (p\u0026thinsp;=\u0026thinsp;0.000). Individuals with elevated cholesterol levels were 4.450 times more likely to experience hypertension compared to those with lower cholesterol levels.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Sulastri (2020) discovered a statistically significant link between cholesterol levels and the intensity of hypertension at Puskesmas Simbarwaringin, with a p-value of 0.000 (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] In 2023, Yogeswara discovered a statistically significant link between total cholesterol levels and hypertension at Puskesmas Gerung, as indicated by a p-value of 0.001 and a correlation coefficient of 0.599.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eConversely, Astannudinsyah (2020) observed no meaningful connection between cholesterol levels and hypertension at RSUD Ulin Banjarmasin, reporting a p-value of 0.129. This research classified hypertension into four categories: normal, pre-hypertension, stage 1 hypertension, and stage 2 hypertension, resulting in various outcomes.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] This is consistent with the results of Saputra (2019), who reported no notable relationship between blood cholesterol levels and hypertension, indicated by a p-value of 0.129.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eHigh-fat foods can increase cholesterol levels, leading to plaque atherosclerosis buildup in blood vessels, which in turn raises the workload on the heart and increases blood pressure.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Other risk factors for hypertension include stress, lifestyle, diet, age, occupation, education, and more.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eBivariate analysis of categorical variables revealed meaningful associations between age and several health indicators, including BMI (p\u0026thinsp;=\u0026thinsp;0.019), cholesterol (p\u0026thinsp;=\u0026thinsp;0.028), and blood pressure (p\u0026thinsp;=\u0026thinsp;0.000). These outcomes were supported by numerical assessments, which further confirmed significant correlations between age and BMI (p\u0026thinsp;=\u0026thinsp;0.001), cholesterol concentration (p\u0026thinsp;=\u0026thinsp;0.000), and systolic blood pressure (p\u0026thinsp;=\u0026thinsp;0.000). Additionally, multivariate analysis of categorical variables highlighted strong connections between age, BMI classifications, and blood pressure levels. Overall, the data consistently demonstrated that age was significantly related to BMI, cholesterol, and systolic blood pressure (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eThe results of this study are consistent with the work of Sugiarti et al., which showed a significant relationship between age and cholesterol levels. As people age, it is common for their blood cholesterol levels to rise.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] According to Intan (2021), there is a significant relationship between age and obesity, indicated by a p-value of 0.000. People older than 45 are 1.4 times more likely to be obese than those younger than 45. In this study, 65. 5% of the participants identified as overweight or obese were within the age range of 36 to 60 years.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Weight gain typically begins after age 40, with factors such as diet, lifestyle, physical activity, occupation, and psychological conditions contributing to obesity risk at this age. This is consistent with Makmun (2021), which found a significant relationship between obesity and age (p\u0026thinsp;=\u0026thinsp;0.016).[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eSiregar (2020) also found that age was a dominant determinant of total cholesterol levels in the Indonesian population (p\u0026thinsp;=\u0026thinsp;0.000). Individuals aged 45\u0026ndash;59 years had the highest risk compared to the other age groups, with an odds ratio of 4.770. Age is directly related to total cholesterol levels due to physiological changes that occur as people age.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] As people age, their arteries tend to widen, become stiffer, and exhibit reduced activity of low-density lipoprotein (LDL) receptors. This combination leads to elevated LDL levels and blockages in the coronary arteries.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] However, studies by Kurnaiawan (2019) and Ujani (2015) found no significant relationship between age and cholesterol levels, attributing fluctuations in cholesterol levels to diet and lifestyle factors.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eKhasanah (2022) found a significant relationship between age and hypertension, indicated by a p-value of 0.000. People older than 45 years were observed to have a 5. 499 times higher probability of developing hypertension when compared to those younger than 45.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] Sadiman (2024) found a notable connection between age and hypertension in menopausal women, with a p-value of 0.045 and an odds ratio of 3.215 (95% CI: 1.150\u0026ndash;8.987). This suggests that older women are 3.2 times more likely to experience hypertension compared to their younger counterparts. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAging from 51 to 60 years increases the risk of decreased organ function due to aging, and the immune system is also less effective, making individuals more susceptible to diseases. As age increases, blood pressure tends to rise due to reduced elasticity of blood vessels and decreased kidney function in blood pressure regulation.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAn examination utilizing chi-square tests on categorical data revealed a meaningful link between gender and cholesterol levels (p\u0026thinsp;=\u0026thinsp;0.000). In contrast, gender did not show a significant association with BMI or blood pressure categories (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, numerical data indicated a significant relationship between sex and cholesterol levels (p\u0026thinsp;=\u0026thinsp;0.000). Multivariate analysis of categorical variables indicated that sex was significantly associated with both BMI (p\u0026thinsp;=\u0026thinsp;0.029) and cholesterol levels (p\u0026thinsp;=\u0026thinsp;0.000). Moreover, notable associations were also identified between sex and BMI, cholesterol, as well as systolic blood pressure (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eIntan (2021) reported a significant relationship between sex and obesity and found that females are three times more likely to experience obesity than males. This may be due to differences in energy intake, physical activity, and hormonal changes between genders.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Conversely, Maharani (2020) found no significant relationship between sex and obesity (p\u0026thinsp;=\u0026thinsp;0.092\u0026thinsp;\u0026gt;\u0026thinsp;α\u0026thinsp;=\u0026thinsp;0.05). This lack of a significant relationship may be attributed to other factors, such as genetics, physical activity, and diet, influencing obesity risk regardless of gender.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eSiregar (2020) identified a notable correlation between gender and total cholesterol levels (p\u0026thinsp;=\u0026thinsp;0.007). Women tended to have higher total cholesterol levels compared to men. This research revealed that women of all ages exhibited a greater occurrence of abnormal cholesterol levels in comparison to men.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Cholesterol levels are influenced by gender-specific hormones, with post-menopausal women typically having higher cholesterol levels than men. Therefore, cholesterol levels are also associated with age[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, Kurnaiawan (2019) and Ujiani (2015) found no significant relationship between gender and cholesterol levels, noting that pre-menopausal women tend to have lower total cholesterol levels compared to age-matched men. Post-menopausal women are at higher risk of increased cholesterol due to the loss of estrogen, which helps prevent arterial plaque formation and boosts HDL (High-Density Lipoprotein) levels.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAccording to Yunus (2021), there was no notable link between gender and hypertension (p\u0026thinsp;=\u0026thinsp;0.841). However, men are more prone to hypertension than women due to unhealthy lifestyle choices. However, the prevalence of hypertension in women increases after menopause owing to hormonal changes. This is consistent with Khasanah (2022), which found no significant relationship between gender and hypertension (p-value\u0026thinsp;=\u0026thinsp;0.300).[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] According to the P2PTM of the Indonesian Ministry of Health, men are 2.3 times more likely to experience elevated systolic blood pressure than women. However, post-menopausal women show higher hypertension prevalence compared to men over 60 years old due to hormonal factors.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eEpidemiological and interventional studies indicate that high consumption of saturated fats is associated with an increased risk of cardiovascular diseases, including hypertension and atherosclerosis, especially when not balanced by adequate intake of unsaturated fats. A high-fat diet can trigger dyslipidemia (blood lipid imbalance), inflammation, and oxidative stress, all of which accelerate plaque formation and vascular damage. Research in both animals and humans also shows that a high-fat diet, even without excess calorie intake, can cause insulin resistance, hypertension, and fat accumulation, thereby worsening cardiovascular risk. Therefore, limiting saturated fat intake and replacing it with unsaturated fats such as polyunsaturated (PUFA) and monounsaturated fatty acids (MUFA) is recommended to lower cholesterol levels, reduce plaque formation, decrease blood pressure, and ultimately reduce the risk of heart and vascular diseases.\u003c/p\u003e\u003cp\u003eThe relationship between age, cholesterol levels, and hypertension is closely interconnected. As individuals age, the risk of hypertension increases significantly, accompanied by changes in lipid profiles particularly an increase in total and LDL cholesterol and a decrease in HDL cholesterol. Large population studies have shown that systolic blood pressure (SBP) and total cholesterol tend to rise with age, with a sharper increase observed in women after midlife. Higher levels of cholesterol, especially LDL and non-HDL, are consistently associated with a greater risk of hypertension, particularly among younger and middle-aged adults; however, this association tends to weaken in older populations.\u003c/p\u003e\u003cp\u003eMoreover, several studies have found that the combination of advanced age and elevated cholesterol levels synergistically increases the risk of hypertension and cardiovascular disease. In the elderly, however, the predictive value of cholesterol for hypertension and cardiovascular events becomes more complex, influenced by factors such as overall health status and comorbidities. Overall, advancing age and uncontrolled cholesterol levels are key risk factors for hypertension, highlighting the importance of monitoring and managing both factors to prevent cardiovascular diseases across different age groups.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe findings of this study indicate that there is no notable connection between Body Mass Index (BMI) and blood pressure. There was a notable link found between cholesterol levels and the occurrence of hypertension, supported by both categorical and numerical data, as well as through bivariate and multivariate analyses. Variations were also noted in BMI and cholesterol variables. In the bivariate categorical test, no significant connection was detected between BMI and cholesterol levels; however, the bivariate numerical test did reveal a relationship between BMI and cholesterol levels. Moreover, there was a notable correlation between age and factors such as obesity, cholesterol, and hypertension, as well as a significant link between sex and cholesterol levels. In both bivariate categorical and numerical analyses, no significant association was found between sex and BMI or between sex and blood pressure. However, multivariate analysis revealed a significant connection between sex and BMI, as well as between sex and blood pressure. These results suggest that further detailed research is necessary to explore factors like obesity, cholesterol, and hypertension in both categorical and numerical contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCONSENT TO PARTICIPATE\u003c/h2\u003e\u003cp\u003e Before we gained information and data, patients were informed of consent through informed consent documents, which included consent for giving needed data and publishing the data for scientific purposes without disclosing their specific personal information.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION\u003c/strong\u003e\u003cp\u003eAs stated before, the informed consent documents also stated the possibility of using their data for publication and scientific purposes.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eALVAILABILITY OF DATA AND MATERIALS\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the first author upon reasonable request. Data is provided within the manuscript or supplementary information files. If anyone would like to request data from this study, please contact us via email at [email protected].\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCOMPETING INTERESTS\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e\u003cp\u003eFunding for this research was fully provided by the Institute for Research and Community Service of Universitas Diponegoro for providing research funding under number 609\u0026thinsp;\u0026minus;\u0026thinsp;50/UN7.D2/PP/VII/2024.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eACKNOWLEDGEMENT\u003c/h2\u003e\u003cp\u003eAcknowledgements to the Institute for Research and Community Service of Universitas Diponegoro for providing research funding. Acknowledgements to the people of the Grobogan Regency, Demak Regency, and Semarang City who have supported the sustainability of this research.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSri Winarni, Pradipa Winandika, Nisrina Ocktalifa Chumair, Ahla Hulaila, and Gabrina Selvi Yanuarista wrote the main manuscript text and Abdul Mughni and Siti Fatimah,prepared table. All authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAcknowledgements to the Institute for Research and Community Service of Universitas Diponegoro for providing research funding. Acknowledgements to the people of the Grobogan Regency, Demak Regency, and Semarang City who have supported the sustainability of this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAvailability of Data and Materials. Data is provided within the manuscript or supplementary information files. If anyone would like to request data from this study, please contact us via email at [email protected]\u003c/p\u003e\n\u003ch3\u003eETHICS AND GUIDELINES\u003c/h3\u003e\n\u003cp\u003e This research was granted ethical approval from the health research ethics committee of the Faculty of Public Health, Diponegoro University with license number 465/EA/KEPK-FKM/2024. Riset team commit to the primacy of research participants health and well-being and must offer care in the research participants\u0026rsquo;s best interest. Protocol was performed in accordance with the relevant guidelines and regulations from Declaration of Helsinki.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSupriatiningrum DN. Faktor Resiko Wanita Obesitas Pada Status Sosial Ekonomi Menengah Ke Bawah. Ghidza Media J. 2021;2(2):163.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUnicef. Analisis Lanskap Kelebihan Berat Badan dan Obesitas di Indonesia. 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PLoS ONE. 2017;12(5):1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Obesity, Hypercholesterolemia, Hypertension","lastPublishedDoi":"10.21203/rs.3.rs-7854297/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7854297/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObesity is a non-communicable disease closely related to increased cholesterol levels and hypertension. According to the Ministry of Health, the prevalence of obesity in Indonesia is expected to grow from 1.6% to 23.4% by 2023. Obesity is a risk factor for cardiovascular diseases. This study aimed to analyze the relationship between obesity, cholesterol levels, and blood pressure. The methodology employed for the research was descriptive and utilized a cross-sectional approach. The sample for the study included 285 individuals. Data were collected using a purposive sampling method with BMI\u0026thinsp;\u0026ge;\u0026thinsp;25. Data analysis used the chi-square test for categorical data and the Spearman test for numerical data. According to the findings from the analysis of categorical data, no meaningful connection was found between BMI, cholesterol (p\u0026thinsp;=\u0026thinsp;0.918), and blood pressure (p\u0026thinsp;=\u0026thinsp;0.139). In the numerical analysis, a notable inverse relationship was discovered between BMI and cholesterol levels (p\u0026thinsp;=\u0026thinsp;0.037). There was a positive correlation between cholesterol levels and blood pressure categories (p\u0026thinsp;=\u0026thinsp;0.002) and between cholesterol levels and systolic pressure (p\u0026thinsp;=\u0026thinsp;0.001). A significant relationship was found between age and BMI (p\u0026thinsp;=\u0026thinsp;0.019), cholesterol levels (p\u0026thinsp;=\u0026thinsp;0.028), and blood pressure (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, a significant relationship was observed between sex and cholesterol levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eObesity can be harmful to young people, adults, and the elderly. While obesity was not strongly linked, it was identified as a risk factor for higher cholesterol levels and high blood pressure.\u003c/p\u003e","manuscriptTitle":"Exploring the Relationships Between Obesity, Cholesterol, and Hypertension: a Cross-Sectional Study in Indonesia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-25 12:03:25","doi":"10.21203/rs.3.rs-7854297/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-11T10:09:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-08T13:23:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61196198243148246360880586880040090566","date":"2025-12-07T15:22:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-02T00:04:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50297903134029031097531516785228869761","date":"2025-11-28T02:11:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73203119297662848978137003600943560991","date":"2025-11-24T12:45:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243323431744855065447094647067441037603","date":"2025-11-14T10:21:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-14T09:36:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-03T14:21:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-25T07:47:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2025-10-25T07:45:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"42968c50-29d0-4633-b84b-d1660ae09aaf","owner":[],"postedDate":"November 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T09:23:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-25 12:03:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7854297","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7854297","identity":"rs-7854297","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}