Jahangir Shahraz, Farahnaz Joukar, Fateme Sheida, Sara Yeganeh, Saman Maroufizadeh, Massood Baghaee, Mohammadreza Naghipour, Fariborz Mansour-Ghanaei
{"title":"体质指数(BMI)与血脂异常之间的关系:来自波斯吉兰队列研究(PGCS)的结果。","authors":"Jahangir Shahraz, Farahnaz Joukar, Fateme Sheida, Sara Yeganeh, Saman Maroufizadeh, Massood Baghaee, Mohammadreza Naghipour, Fariborz Mansour-Ghanaei","doi":"10.1002/osp4.70055","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Obesity and dyslipidemia are interconnected complex conditions and their prevalence differs across different geographical regions. As a major risk factor for cardiovascular diseases, dyslipidemia is often misdiagnosed and inadequately treated, highlighting the need for region-specific public health policies. Therefore, the objective of this study was to examine the associations between BMI and dyslipidemia in the Prospective Epidemiological Research Studies in Iran (PERSIAN) Guilan Cohort study (PGCS) population.</p><p><strong>Methods: </strong>This cross-sectional study analyzed the demographic and biochemical data from 10,519 participants of the PGCS population. Participants were divided into two groups with and without dyslipidemia and were compared based on BMI. Data analysis was performed in SPSS v16 with a significance level of < 0.05.</p><p><strong>Results: </strong>The average age of the participants was 51.52 ± 8.90 years. The prevalence of dyslipidemia in all participants was equal to 75.83%. Among those with dyslipidemia, 41.18% and 35.39% had overweight and obesity, respectively. There was a positive association between BMI and the prevalence of dyslipidemia (unadjusted OR = 1.09, 95% confidence interval (CI): 1.08-1.10) (<i>p</i> < 0.01), indicating that for a one-unit increase in participants' BMI, the probability of having dyslipidemia increased by 9%, which remained statistically significant even after adjusting. Analysis of dyslipidemia components and BMI revealed a significant association between elevated TG and cholesterol, as well as low HDL levels and higher BMI (unadjusted OR = 1.04, 1.01, and 1.09, respectively) (<i>p</i> < 0.01). However, this was not statistically significant for high LDL levels (unadjusted OR = 1.01) (<i>p</i> = 0.05).</p><p><strong>Conclusion: </strong>Given the high prevalence of dyslipidemia in our studied region and its strong association with obesity, prioritizing obesity management in public health decision-making is vital. Greater focus should be given on accessing and modifying the components of dyslipidemia, particularly LDL particles, as a potentially significant research target to prevent the mismanagement of dyslipidemia in individuals with obesity.</p>","PeriodicalId":19448,"journal":{"name":"Obesity Science & Practice","volume":"11 1","pages":"e70055"},"PeriodicalIF":1.9000,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11802237/pdf/","citationCount":"0","resultStr":"{\"title\":\"Associations Between Body Mass Index (BMI) and Dyslipidemia: Results From the PERSIAN Guilan Cohort Study (PGCS).\",\"authors\":\"Jahangir Shahraz, Farahnaz Joukar, Fateme Sheida, Sara Yeganeh, Saman Maroufizadeh, Massood Baghaee, Mohammadreza Naghipour, Fariborz Mansour-Ghanaei\",\"doi\":\"10.1002/osp4.70055\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Obesity and dyslipidemia are interconnected complex conditions and their prevalence differs across different geographical regions. As a major risk factor for cardiovascular diseases, dyslipidemia is often misdiagnosed and inadequately treated, highlighting the need for region-specific public health policies. Therefore, the objective of this study was to examine the associations between BMI and dyslipidemia in the Prospective Epidemiological Research Studies in Iran (PERSIAN) Guilan Cohort study (PGCS) population.</p><p><strong>Methods: </strong>This cross-sectional study analyzed the demographic and biochemical data from 10,519 participants of the PGCS population. Participants were divided into two groups with and without dyslipidemia and were compared based on BMI. Data analysis was performed in SPSS v16 with a significance level of < 0.05.</p><p><strong>Results: </strong>The average age of the participants was 51.52 ± 8.90 years. The prevalence of dyslipidemia in all participants was equal to 75.83%. Among those with dyslipidemia, 41.18% and 35.39% had overweight and obesity, respectively. 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引用次数: 0
摘要
背景:肥胖和血脂异常是相互关联的复杂疾病,其患病率在不同的地理区域有所不同。作为心血管疾病的一个主要危险因素,血脂异常经常被误诊和治疗不当,这突出表明需要制定针对特定区域的公共卫生政策。因此,本研究的目的是在伊朗(波斯)Guilan队列研究(PGCS)人群的前瞻性流行病学研究中检查BMI与血脂异常之间的关系。方法:本横断面研究分析了10,519名PGCS人群的人口学和生化数据。参与者被分为有和没有血脂异常的两组,并根据BMI进行比较。数据分析采用SPSS v16软件,显著性水平< 0.05。结果:参与者平均年龄为51.52±8.90岁。所有参与者的血脂异常患病率为75.83%。血脂异常人群中超重和肥胖分别占41.18%和35.39%。BMI与血脂异常患病率呈正相关(未经调整的OR = 1.09, 95%可信区间(CI): 1.08-1.10) (p p p = 0.05)。结论:鉴于血脂异常在我们研究地区的高患病率及其与肥胖的密切关系,在公共卫生决策中优先考虑肥胖管理是至关重要的。应将更多的注意力放在获取和修改血脂异常的成分上,特别是LDL颗粒,作为防止肥胖个体血脂异常管理不当的潜在重要研究目标。
Associations Between Body Mass Index (BMI) and Dyslipidemia: Results From the PERSIAN Guilan Cohort Study (PGCS).
Background: Obesity and dyslipidemia are interconnected complex conditions and their prevalence differs across different geographical regions. As a major risk factor for cardiovascular diseases, dyslipidemia is often misdiagnosed and inadequately treated, highlighting the need for region-specific public health policies. Therefore, the objective of this study was to examine the associations between BMI and dyslipidemia in the Prospective Epidemiological Research Studies in Iran (PERSIAN) Guilan Cohort study (PGCS) population.
Methods: This cross-sectional study analyzed the demographic and biochemical data from 10,519 participants of the PGCS population. Participants were divided into two groups with and without dyslipidemia and were compared based on BMI. Data analysis was performed in SPSS v16 with a significance level of < 0.05.
Results: The average age of the participants was 51.52 ± 8.90 years. The prevalence of dyslipidemia in all participants was equal to 75.83%. Among those with dyslipidemia, 41.18% and 35.39% had overweight and obesity, respectively. There was a positive association between BMI and the prevalence of dyslipidemia (unadjusted OR = 1.09, 95% confidence interval (CI): 1.08-1.10) (p < 0.01), indicating that for a one-unit increase in participants' BMI, the probability of having dyslipidemia increased by 9%, which remained statistically significant even after adjusting. Analysis of dyslipidemia components and BMI revealed a significant association between elevated TG and cholesterol, as well as low HDL levels and higher BMI (unadjusted OR = 1.04, 1.01, and 1.09, respectively) (p < 0.01). However, this was not statistically significant for high LDL levels (unadjusted OR = 1.01) (p = 0.05).
Conclusion: Given the high prevalence of dyslipidemia in our studied region and its strong association with obesity, prioritizing obesity management in public health decision-making is vital. Greater focus should be given on accessing and modifying the components of dyslipidemia, particularly LDL particles, as a potentially significant research target to prevent the mismanagement of dyslipidemia in individuals with obesity.