Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran.
Department of Nutrition, Fasa University of Medical Sciences, Fasa, Iran.
Sci Rep. 2023 Oct 18;13(1):17749. doi: 10.1038/s41598-023-44766-4.
Cardiovascular diseases (CVDs) have been among the most significant non-communicable diseases. Dietary risks account for the most cause of CVDs mortalities. Evaluating overall dietary patterns (through the Latent profile of dietary intake) can provide a more accurate prediction regarding the prevalence of CVDs. The present cross-sectional study aimed to investigate the relationship between the latent profile of dietary intake and CVDs prevalence. The population of the Fasa Adults Cohort Study was employed to gather the data (n = 8319). A modified FFQ was employed to assess eating behaviors. Minerals, as well as the energy intake and total fiber, were measured using Nutritionist IV software (version 7.0). To estimate the prevalence of CVDs, accurate records of patients' histories were made. Individuals were clustered according to their dietary intake using latent profile analysis. The mean age was 48.75 ± 9.59 years, and 53.28% (4430) were women. 63.9% of participants with low Socioeconomic Status (SES) were in the low-intake profile (P < 0.001), and high SES increases the odds of being in the high-intake profile (OR = 2.87, 95% CI 2.55-3.24). The low-intake group had the lowest amount of physical activity (Met) (P < 0.001). The result of multivariate logistic regression revealed that categorized in the low-intake group significantly increased the development of CVDs (OR = 1.32, 95% CI 1.07-1.63, P = 0.010). The mean micronutrients and total fiber, in individuals with a low intake profile, were significantly lower than other groups (P < 0.001). Overall, we estimated that a low intake of all food groups increases the odds of developing CVDs significantly.
心血管疾病(CVDs)一直是最主要的非传染性疾病之一。饮食风险是导致 CVD 死亡率的主要原因。评估整体饮食模式(通过饮食摄入的潜在特征分析)可以更准确地预测 CVD 的患病率。本横断面研究旨在探讨饮食摄入的潜在特征与 CVD 患病率之间的关系。采用法萨成年人队列研究的人群收集数据(n=8319)。采用改良的 FFQ 评估饮食行为。使用营养师 IV 软件(版本 7.0)测量矿物质以及能量摄入和总纤维。为了估计 CVD 的患病率,准确记录了患者的病史。采用潜在特征分析根据饮食摄入将个体聚类。平均年龄为 48.75±9.59 岁,53.28%(4430)为女性。63.9%社会经济地位(SES)较低的参与者处于低摄入量特征(P<0.001),高 SES 增加处于高摄入量特征的几率(OR=2.87,95%CI 2.55-3.24)。低摄入量组的体力活动(Met)最少(P<0.001)。多变量 logistic 回归的结果表明,低摄入量组显著增加了 CVD 的发展(OR=1.32,95%CI 1.07-1.63,P=0.010)。低摄入量组个体的微量营养素和总纤维的平均值明显低于其他组(P<0.001)。总的来说,我们估计所有食物组的低摄入量都会显著增加患 CVD 的几率。