In preparation for personalized nutrition, an accurate assessment of dietary intakes on key essential nutrients using smartphones can help promote health and reduce health risks across vulnerable populations. We, therefore, validated the accuracy of a mobile application (app) against Food Frequency Questionnaire (FFQ) using artificial intelligence (AI) machine-learning-based analytics, assessing key macro- and micro-nutrients across various modern diets. We first used Bland and Altman analysis to identify and visualize the differences between the two measures. We then applied AI-based analytics to enhance prediction accuracy, including generalized regression to identify factors that contributed to the differences between the two measures. The mobile app underestimated most macro- and micro-nutrients compared to FFQ (ranges: −5% for total calories, −19% for cobalamin, −33% for vitamin E). The average correlations between the two measures were 0.87 for macro-nutrients and 0.84 for micro-nutrients. Factors that contributed to the differences between the two measures using total calories as an example, included caloric range (1000–2000 versus others), carbohydrate, and protein; for cobalamin, included caloric range, protein, and Chinese diet. Future studies are needed to validate actual intakes and reporting of various diets, and to examine the accuracy of mobile App. Thus, a mobile app can be used to support personalized nutrition in the mHealth era, considering adjustments with sources that could contribute to the inaccurate estimates of nutrients.
Glutathione S transferase mu 1 (GSTM1) gene has been associated with lung cancer (LC) risk, for GSTM1 enzyme playing a vital role in detoxification pathway and protective against toxic insults. The major objective of this study was to investigate GSTM1 deletion pattern and its association with LC in the world’s population by using meta-prediction techniques. The secondary objective was to examine the effects of air pollution, smoking status, and other factors for gene-environment interactions with GSTM1 deletion and LC risk. We completed a comprehensive search to yield a total of 170 studies (40,296 cases and 48,346 controls) published from 1999 to 2017 for meta-analyses. The results revealed that GSTM1 deletion type was associated with increased risk of LC, while GSTM1 present type provided protective effect for all populations combined worldwide. Subgroup analysis on the rank order of risks from highest to lowest, among racial–ethnic groups, were Chinese, South East Asian, other North Asian, European, and finally American. Additional predictive analyses presented that air pollution played a significant role with increased risks of GSTM1 deletion and LC susceptibility, and the risks increased for smokers with higher levels of air pollution. Based on the findings of meta-predictive analysis, increased air pollution levels and smoking status presented additive effects to the LC risk susceptibilities and GSTM1 gene polymorphisms, for gene-environment interactions. Future studies are needed to examine gene-environment interactions for GSTM1 interacting with environmental factors and dietary interventions to mitigate the toxic effects, for LC prevention.
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