Institute of Endocrinology, Metabolism and Hypertension, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel; The Sackler Faculty of Medicine Tel-Aviv University, Israel; Robert H Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel; The Sagol Center for Epigenetics of Aging and Metabolism, Institute of Endocrinology, Metabolism and Hypertension, Tel Aviv-Sourasky Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Israel.
The Department of Health System Management, Peres Academic Center, Rehovot, Israel.
Exp Gerontol. 2020 Dec;142:111112. doi: 10.1016/j.exger.2020.111112. Epub 2020 Oct 13.
Less attention has been given to the detection and nutritional status and needs of obese frail/sarcopenic older subjects. The aim of this study was to characterize the nutritional composition in older (≥65 years), frail-prone, obese subjects (defined by either waist circumference [WC] or body mass index [BMI]).
A cross-sectional study with analysis of the national survey "Mabat Zahav". Random sample of 1751 community dwelling Israeli older adults (≥65 years). Eleven nutritional factors formerly linked to frailty were a-priori selected based on the current literature. Data was extracted from a 24-hour dietary recall. Adherence for each nutritional factor was defined using the Dietary Reference Intakes (DRI), and aggregated into a sum score of the overall adherence (ranging from "0" to "11", where "fair" adherence was defined as ≥6; inadequate adherence otherwise). Frailty likelihood was estimated using a validated non-direct model, and associations of nutritional factors with frailty-likelihood in obese vs non-obese individuals were examined. Additionally, a decision tree procedure based on machine learning was applied in order to capture nutritional factors related to frailty, stratified by gender, as well as by WC and/or BMI.
Overall, the prevalence rates of frailty and pre-frailty were 7.1 and 57.6%, respectively. A "fair nutritional adherence" was less common among frail-prone compared to robust subjects (23.1% vs. 32.1%; p < 0.0001). The intake of most frailty-related nutritional factors did not co-segregate according to the presence of abdominal or BMI-defined obesity. Still, compared to robust normal/overweight subjects, frail-prone obese (by BMI) individuals had a higher rate of inadequate nutritional adherence (odds-ratio 1.842; p < 0.05). Of all 11 nutritional factors, folate in obese women and vitamin A (as retinol) and calcium in non-obese and obese men, respectively, were recognized as the most prominent predictors of frail-prone prevalence by the machine learning process. Although BMI was more closely associated with impaired intake of the 11 selected nutritional components than WC, this association was eliminated when frailty status, low income and education were considered.
Frail-prone subjects differed from robust subjects in their nutritional intake. Nutritional inadequacies related to frailty-likelihood were mostly seen among obese women and non-obese men. In the prediction of inadequate adherence to the DRI of 11 nutritional components, obesity is a weaker predictor than frailty, lower education and low income in older Israeli adults.
对于肥胖虚弱/肌少症老年人的检测以及营养状况和需求，人们的关注度较低。本研究的目的是描述易患虚弱的肥胖老年人（定义为腰围[WC]或体重指数[BMI]）的营养成分。
这是一项横断面研究，对国家调查“Mabat Zahav”进行了分析。从以色列的 1751 名社区居住的老年成年人（≥65 岁）中随机抽取样本。根据现有文献，我们预先选择了 11 个与虚弱相关的营养因素。从 24 小时膳食回忆中提取数据。使用膳食参考摄入量（DRI）定义每个营养因素的依从性，并将其聚合为总体依从性的总和得分（范围从“0”到“11”，其中“良好”的依从性定义为≥6；否则为依从性不足）。使用经过验证的非直接模型估计虚弱可能性，并检查肥胖个体与非肥胖个体中营养因素与虚弱可能性的关联。此外，还应用了基于机器学习的决策树程序，以便根据性别以及 WC 和/或 BMI 分层，捕获与虚弱相关的营养因素。
总体而言，虚弱和虚弱前期的患病率分别为 7.1%和 57.6%。与健壮者相比，易患虚弱者的“良好营养依从性”较少（23.1%对 32.1%；p<0.0001）。大多数与虚弱相关的营养因素的摄入量并未根据腹部肥胖或 BMI 定义的肥胖而共同分离。尽管如此，与健壮的正常/超重者相比，易患虚弱的肥胖者（按 BMI 计算）的营养依从性不足率更高（比值比 1.842；p<0.05）。在所有 11 种营养因素中，肥胖女性的叶酸以及非肥胖和肥胖男性的维生素 A（视黄醇）和钙，被机器学习过程识别为易患虚弱者患病率的最主要预测因子。尽管 BMI 与 11 种选定营养成分的摄入受损更为密切相关，但当考虑到虚弱状况、低收入和教育程度时，这种关联就会消除。
易患虚弱的受试者与健壮的受试者在营养摄入方面存在差异。与虚弱可能性相关的营养不足主要见于肥胖女性和非肥胖男性。在预测 11 种营养成分的 DRI 依从性不足方面，肥胖在预测以色列老年人的虚弱、低教育程度和低收入方面是一个较弱的预测因子。
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