Hu Emily A, Scharen Jared, Nguyen Viet, Langheier Jason
Zipongo Inc, DBA Foodsmart, San Francisco, CA, United States.
JMIR Cardio. 2021 Jun 10;5(1):e28392. doi: 10.2196/28392.
A strong association exists between consuming a healthy diet and lowering cholesterol levels among individuals with high cholesterol. However, implementing and sustaining a healthy diet in the real world is a major challenge. Digital technologies are at the forefront of changing dietary behavior on a massive scale, as they can reach broad populations. There is a lack of evidence that has examined the benefit of a digital nutrition intervention, especially one that incorporates nutrition education, meal planning, and food ordering, on cholesterol levels among individuals with dyslipidemia.
The aim of this observational longitudinal study was to examine the characteristics of people with dyslipidemia, determine how their status changed over time, and evaluate the changes in total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), non-HDL-C, and triglycerides among individuals with elevated lipids who used Foodsmart, a digital nutrition platform that integrates education, meal planning, and food ordering.
We included 653 adults who used Foodsmart between January 2015 and February 2021, and reported a lipid marker twice. Participants self-reported age, gender, weight, and usual dietary intake in a 53-item food frequency questionnaire, and lipid values could be provided at any time. Dyslipidemia was defined as total cholesterol ≥200 mg/dL, HDL-C ≤40 mg/dL, LDL-C ≥130 mg/dL, or triglycerides ≥150 mg/dL. We retrospectively analyzed distributions of user characteristics and their associations with the likelihood of returning to normal lipid levels. We calculated the mean changes and percent changes in lipid markers among users with elevated lipids.
In our total sample, 54.1% (353/653) of participants had dyslipidemia at baseline. Participants with dyslipidemia at baseline were more likely to be older, be male, and have a higher weight and BMI compared with participants who had normal lipid levels. We found that 36.3% (128/353) of participants who had dyslipidemia at baseline improved their lipid levels to normal by the end of follow-up. Using multivariate logistic regression, we found that baseline obesity (odds ratio [OR] 2.57, 95% CI 1.25-5.29; P=.01) and Nutriscore (OR 1.04, 95% CI 1.00-1.09; P=.04) were directly associated with achieving normal lipid levels. Participants with elevated lipid levels saw improvements as follows: HDL-C increased by 38.5%, total cholesterol decreased by 6.8%, cholesterol ratio decreased by 20.9%, LDL-C decreased by 12.9%, non-HDL-C decreased by 7.8%, and triglycerides decreased by 10.8%.
This study characterized users of the Foodsmart platform who had dyslipidemia and found that users with elevated lipid levels showed improvements in the levels over time.
在高胆固醇人群中，健康饮食与降低胆固醇水平之间存在密切关联。然而，在现实世界中实施并维持健康饮食是一项重大挑战。数字技术正处于大规模改变饮食行为的前沿，因为它们能够覆盖广泛人群。目前缺乏证据来检验数字营养干预，尤其是包含营养教育、膳食计划和食物订购的干预，对血脂异常个体胆固醇水平的益处。
这项观察性纵向研究的目的是研究血脂异常人群的特征，确定他们的状况随时间如何变化，并评估使用Foodsmart（一个整合了教育、膳食计划和食物订购功能的数字营养平台）的血脂升高个体的总胆固醇、高密度脂蛋白胆固醇（HDL-C）、低密度脂蛋白胆固醇（LDL-C）、非HDL-C和甘油三酯的变化。
我们纳入了2015年1月至2021年2月期间使用Foodsmart且报告了两次血脂指标的653名成年人。参与者在一份包含53项内容的食物频率问卷中自行报告年龄、性别、体重和日常饮食摄入量，并且可以随时提供血脂值。血脂异常的定义为总胆固醇≥200mg/dL、HDL-C≤40mg/dL、LDL-C≥130mg/dL或甘油三酯≥150mg/dL。我们回顾性分析了用户特征的分布及其与恢复正常血脂水平可能性的关联。我们计算了血脂升高用户血脂指标的平均变化和百分比变化。
在我们的总样本中，54.1%（353/653）的参与者在基线时患有血脂异常。与血脂水平正常的参与者相比，基线时患有血脂异常的参与者更可能年龄较大、为男性，且体重和BMI更高。我们发现，基线时患有血脂异常的参与者中有36.3%（128/353）在随访结束时血脂水平改善至正常。使用多变量逻辑回归分析，我们发现基线肥胖（优势比[OR]2.57，95%CI 1.25 - 5.29；P = 0.01）和营养评分（OR 1.04，95%CI 1.00 - 1.09；P = 0.04）与达到正常血脂水平直接相关。血脂升高的参与者有以下改善：HDL-C升高38.5%，总胆固醇降低6.8%，胆固醇比率降低20.9%，LDL-C降低12.9%，非HDL-C降低7.8%，甘油三酯降低10.8%。
本研究对Foodsmart平台上患有血脂异常的用户进行了特征分析，发现血脂升高的用户血脂水平随时间有所改善。
