Department of Epidemiology and Public Health, Faculty of Medicine and Health Sciences, Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK.
Faculty of Health, School of Allied Health, Education, Medicine and Social Care, Anglia Ruskin University, Chelmsford CM1 1SQ, UK.
Nutrients. 2021 May 31;13(6):1883. doi: 10.3390/nu13061883.
Malnutrition (undernutrition) in older adults is often not diagnosed before its adverse consequences have occurred, despite the existence of established screening tools. As a potential method of early detection, we examined whether readily available and routinely measured clinical biochemical diagnostic test data could predict poor nutritional status. We combined 2008-2017 data of 1518 free-living individuals ≥50 years from the United Kingdom National Diet and Nutrition Survey (NDNS) and used logistic regression to determine associations between routine biochemical diagnostic test data, micronutrient deficiency biomarkers, and established malnutrition indicators (components of screening tools) in a three-step validation process. A prediction model was created to determine how effectively routine biochemical diagnostic tests and established malnutrition indicators predicted poor nutritional status (defined by ≥1 micronutrient deficiency in blood of vitamins B, B and C; selenium; or zinc). Significant predictors of poor nutritional status were low concentrations of total cholesterol, haemoglobin, HbA1c, ferritin and vitamin D status, and high concentrations of C-reactive protein; except for HbA1c, these were also associated with established malnutrition indicators. Additional validation was provided by the significant association of established malnutrition indicators (low protein, fruit/vegetable and fluid intake) with biochemically defined poor nutritional status. The prediction model (including biochemical tests, established malnutrition indicators and covariates) showed an AUC of 0.79 (95% CI: 0.76-0.81), sensitivity of 66.0% and specificity of 78.1%. Clinical routine biochemical diagnostic test data have the potential to facilitate early detection of malnutrition risk in free-living older populations. However, further validation in different settings and against established malnutrition screening tools is warranted.
老年人营养不良(营养不足)在其不良后果发生之前通常未被诊断出来,尽管已经存在既定的筛查工具。作为早期检测的一种潜在方法,我们研究了是否可以利用现成的、常规测量的临床生化诊断测试数据来预测营养不良状况。我们合并了来自英国国家饮食与营养调查(NDNS)的 2008-2017 年 1518 名 50 岁以上的自由生活个体的数据,并使用逻辑回归来确定常规生化诊断测试数据、微量营养素缺乏生物标志物与既定营养不良指标(筛查工具的组成部分)之间的关联,这一过程经过了三步验证。创建了一个预测模型,以确定常规生化诊断测试和既定的营养不良指标在多大程度上可以预测营养不良状况(定义为血液中维生素 B、B 和 C、硒或锌缺乏 1 种或多种微量营养素)。营养不良状况的显著预测因子为总胆固醇、血红蛋白、HbA1c、铁蛋白和维生素 D 状态浓度低,C 反应蛋白浓度高;除 HbA1c 外,这些也与既定的营养不良指标相关。既定的营养不良指标(蛋白质、水果/蔬菜和液体摄入低)与生化定义的营养不良状况显著相关,这为进一步提供了验证。预测模型(包括生化测试、既定的营养不良指标和协变量)的 AUC 为 0.79(95%CI:0.76-0.81),灵敏度为 66.0%,特异性为 78.1%。临床常规生化诊断测试数据有可能促进对自由生活的老年人群营养不良风险的早期检测。但是,在不同的环境中以及针对既定的营养不良筛查工具进行进一步验证是必要的。