Drug-food Interactions in the Era of Molecular Big Data, Machine Intelligence, and Personalized Health

Development of method using language processing techniques for extracting information on drug-health food product interactions

AIMS

Health food products (HFPs) are foods and products related to maintaining and promoting health. HFPs may sometimes cause unforeseen adverse health effects by interacting with drugs. Considering the importance of information on the interactions between HFPs and drugs, this study aimed to establish a workflow to extract information on Drug-HFP Interactions (DHIs) from open resources.

METHODS

First, Information on drugs, enzymes, their interactions, and known DHIs was collected from multiple public databases and literature sources. Next, a network consisted of enzymes, HFP, and drugs was constructed, assuming enzymes as candidates for hubs in Drug-HFP interactions (Method 1). Furthermore, we developed methods to analyze the biomedical context of each drug and HFP to predict potential DHIs out of the DHIs obtained in Method 1 by applying BioWordVec, a widely used biomedical terminology quantifier (Method 2-1 and 2-2).

RESULTS

44,965 DHIs (30% known) were identified in Method 1, including 38 metabolic enzymes, 157 HFPs, and 1256 drugs. Method 2-1 selected 7401 DHIs (17% known) from the DHIs of Method 1, while Method 2-2 chose 2819 DHIs (30% known). Based on the different assumptions in these methods where Method 2-1 specifically selects HFPs interacting with specific enzymes and Method 2-2 specifically selects HFPs with similar function with drugs, the propsed methods resulted in extracting a wide variety of DHIs.

CONCLUSIONS

By integrating the results of language processing techniques with those of the network analysis, a workflow to efficiently extract unknown and known DHIs was constructed.

Utilization of smart devices and the evolution of customized healthcare services focusing on big data: a systematic review

Background

Currently, smart devices can prevent diseases by continuously collecting user information and providing health-related feedback. Smart devices big data provide personalized, faster, and more accurate health care. By examining existing studies, we suggest a new healthcare evolution and health promotion through information technology (IT) convergence. A big data systematic review examined the evolution of new health care and their potential for health promotion by monitoring physical activities, preventing diseases, and analyzing health data smart devices.

Methods

Therefore, this evaluates whether a new healthcare industry combining smart devices and big-data-based customized health care services can promote health. This study searched PubMed, Google Scholar, Scopus, and Research Information Sharing Service (RISS) for keywords related to big data, smart devices, healthcare, customized health services, health apps, and mobile health. This study comprised 43 of 453 publications from 2007 to 2023. Among them, a total of 43 articles were successfully completed in this study using the PRISMA flowchart in the final stage.

Results

Smart devices centered on big data enable personalized health care, and app technologies that promote well-being to prepare for aging society have many applications in clinical, prevention, public health, and rehabilitation settings. Smart devices and tailored healthcare services using big data to inform individuals about exercise, health status, diagnosis, and health information will expand into major sectors. By reviewing previous studies, the convergence of the IT technology field, which allows you to easily identify individual health and receive faster and more accurate medical services through customized health care services, has future-oriented values as, new health care services evolve. The systematic review of big data herein can monitor physical activity and prevent diseases using smart devices, thus promoting a healthy lifestyle.

Conclusions

Smart devices that analyze data to provide personal exercise and health conditions, checkups, and information, are making our lives easier. The information service using big data will continue to evolve into a personalized management service and provide basic healthcare data as it grows into an expected industry in the future.

Association of free-living diet composition with plasma lipoprotein(a) levels in healthy adults

BACKGROUND

Lipoprotein (a) [Lp(a)] is an apoB100-containing lipoprotein with high levels being positively associated with atherosclerotic cardiovascular disease. Lp(a) levels are genetically determined. However, previous studies report a negative association between Lp(a) and saturated fatty acid intake. Currently, apoB100 lowering therapies are used to lower Lp(a) levels, and apheresis therapy is FDA approved for patients with extreme elevations of Lp(a). The current study analyzed the association of free-living diet components with plasma Lp(a) levels.

METHODS

Dietary composition data was collected during screening visits for enrollment in previously completed lipid and lipoprotein metabolism studies at Columbia University Irving Medical Center via a standardized protocol by registered dietitians using 24 hour recalls. Data were analyzed with the Nutrition Data System for Research (Version 2018). Diet quality was calculated using the Healthy Eating Index (HEI) score. Fasting plasma Lp(a) levels were measured via an isoform-independent ELISA and apo(a) isoforms were measured using gel electrophoresis.

RESULTS

We enrolled 28 subjects [Black (n = 18); Hispanic (n = 7); White (n = 3)]. The mean age was 48.3 ± 12.5 years with 17 males. Median level of Lp(a) was 79.9 nmol/L (34.4-146.0) and it was negatively associated with absolute (grams/day) and relative (percent of total calories) intake of dietary saturated fatty acids (SFA) (R = -0.43, P = 0.02, SFA …(% CAL): R = -0.38, P = 0.04), palmitic acid intake (R = -0.38, P = 0.05), and stearic acid intake (R = -0.40, P = 0.03). Analyses of associations with HEI score when stratified based on Lp(a) levels > or ≤ 100 nmol/L revealed no significant associations with any of the constituent factors.

CONCLUSIONS

Using 24 hour recall, we confirm previous findings that Lp(a) levels are negatively associated with dietary saturated fatty acid intake. Additionally, Lp(a) levels are not related to diet quality, as assessed by the HEI score. The mechanisms underlying the relationship of SFA with Lp(a) require further investigation.