Exploring food contents in scientific literature with FoodMine

FoodAtlas: Automated knowledge extraction of food and chemicals from literature

Automated generation of knowledge graphs that accurately capture published information can help with knowledge organization and access, which have the potential to accelerate discovery and innovation. Here, we present an integrated pipeline to construct a large-scale knowledge graph using large language models in an active learning setting. We apply our pipeline to the association of raw food, ingredients, and chemicals, a domain that lacks such knowledge resources. By using an iterative active learning approach of 4120 manually curated premise-hypothesis pairs as training data for ten consecutive cycles, the entailment model extracted 230,848 food-chemical composition relationships from 155,260 scientific papers, with 106,082 (46.0 %) of them never been reported in any published database. To augment the knowledge incorporated in the knowledge graph, we further incorporated information from 5 external databases and ontology sources. We then applied a link prediction model to identify putative food-chemical relationships that were not part of the constructed knowledge graph. Validation of the 443 hypotheses generated by the link prediction model resulted in 355 new food-chemical relationships, while results show that the model score correlates well (R2 = 0.70) with the probability of a novel finding. This work demonstrates how automated learning from literature at scale can accelerate discovery and support practical applications through reproducible, evidence-based capture of latent interactions of diverse entities, such as food and chemicals.

Decoding the Foodome: Molecular Networks Connecting Diet and Health

Diet, a modifiable risk factor, plays a pivotal role in most diseases, from cardiovascular disease to type 2 diabetes mellitus, cancer, and obesity. However, our understanding of the mechanistic role of the chemical compounds found in food remains incomplete. In this review, we explore the "dark matter" of nutrition, going beyond the macro- and micronutrients documented by national databases to unveil the exceptional chemical diversity of food composition. We also discuss the need to explore the impact of each compound in the presence of associated chemicals and relevant food sources and describe the tools that will allow us to do so. Finally, we discuss the role of network medicine in understanding the mechanism of action of each food molecule. Overall, we illustrate the important role of network science and artificial intelligence in our ability to reveal nutrition's multifaceted role in health and disease.

GroceryDB: Prevalence of Processed Food in Grocery Stores

The offering of grocery stores is a strong driver of consumer decisions, shaping their diet and long-term health. While highly processed food like packaged products, processed meat, and sweetened soft drinks have been increasingly associated with unhealthy diet, information on the degree of processing characterizing an item in a store is not straightforward to obtain, limiting the ability of individuals to make informed choices. Here we introduce GroceryDB, a database with over 50,000 food items sold by Walmart, Target, and Wholefoods, unveiling how big data can be harnessed to empower consumers and policymakers with systematic access to the degree of processing of the foods they select, and the potential alternatives in the surrounding food environment. The wealth of data collected on ingredient lists and nutrition facts allows a large scale analysis of ingredient patterns and degree of processing stratified by store, food category, and price range. We find that the nutritional choices of the consumers, translated as the degree of food processing, strongly depend on the food categories and grocery stores. Moreover, the data allows us to quantify the individual contribution of over 1,000 ingredients to ultra-processing. GroceryDB and the associated http://TrueFood.Tech/ website make this information accessible, guiding consumers toward less processed food choices while assisting policymakers in reforming the food supply.

An overview of food lipids toward food lipidomics

Increasing evidence regarding lipids' beneficial effects on human health has changed the common perception of consumers and dietary officials about the role(s) of food lipids in a healthy diet. However, lipids are a wide group of molecules with specific nutritional and bioactive properties. To understand their true nutritional and functional value, robust methods are needed for accurate identification and quantification. Specific analytical strategies are crucial to target specific classes, especially the ones present in trace amounts. Finding a unique and comprehensive methodology to cover the full lipidome of each foodstuff is still a challenge. This review presents an overview of the lipids nutritionally relevant in foods and new trends in food lipid analysis for each type/class of lipids. Food lipid classes are described following the LipidMaps classification, fatty acids, endocannabinoids, waxes, C8 compounds, glycerophospholipids, glycerolipids (i.e., glycolipids, betaine lipids, and triglycerides), sphingolipids, sterols, sercosterols (vitamin D), isoprenoids (i.e., carotenoids and retinoids (vitamin A)), quinones (i.e., coenzyme Q, vitamin K, and vitamin E), terpenes, oxidized lipids, and oxylipin are highlighted. The uniqueness of each food group: oil-, protein-, and starch-rich, as well as marine foods, fruits, and vegetables (water-rich) regarding its lipid composition, is included. The effect of cooking, food processing, and storage, in addition to the importance of lipidomics in food quality and authenticity, are also discussed. A critical review of challenges and future trends of the analytical approaches and computational methods in global food lipidomics as the basis to increase consumer awareness of the significant role of lipids in food quality and food security worldwide is presented.

From data to insight: Exploring contaminants in different food groups with literature mining and machine learning techniques

Food remains a major source of human exposure to chemical contaminants that are unintentionally present in commodities globally, despite strict regulation. Scientific literature is a valuable source of quantification data on those contaminants in various foods, but manually summarizing the information is not practicable. In this review, literature mining and machine learning techniques were applied in 72 foods to obtain relevant information on 96 contaminants, including heavy metals, polychlorinated biphenyls, dioxins, furans, polycyclic aromatic hydrocarbons (PAHs), pesticides, mycotoxins, and heterocyclic aromatic amines (HAAs). The 11,723 data points collected from 254 papers from the last two decades were then used to identify the patterns of contaminants distribution. Considering contaminant categories, metals were the most studied globally, followed by PAHs, mycotoxins, pesticides, and HAAs. As for geographical region, the distribution was uneven, with Europe and Asia having the highest number of studies, followed by North and South America, Africa and Oceania. Regarding food groups, all contained metals, while PAHs were found in seven out of 12 groups. Mycotoxins were found in six groups, and pesticides in almost all except meat, eggs, and vegetable oils. HAAs appeared in only three food groups, with fish and seafood reporting the highest levels. The median concentrations of contaminants varied across food groups, with citrinin having the highest median value. The information gathered is highly relevant to explore, establish connections, and identify patterns between diverse datasets, aiming at a comprehensive view of food contamination.

Food composition databases in the era of Big Data: Vegetable oils as a case study

Understanding the population's dietary patterns and their impacts on health requires many different sources of information. The development of reliable food composition databases is a key step in this pursuit. With them, nutrition and health care professionals can provide better public health advice and guide society toward achieving a better and healthier life. Unfortunately, these databases are full of caveats. Focusing on the specific case of vegetable oils, we analyzed the possible obsolescence of the information and the differences or inconsistencies among databases. We show that in many cases, the information is limited, incompletely documented, old or unreliable. More importantly, despite the many efforts carried out in the last decades, there is still much work to be done. As such, institutions should develop long-standing programs that can ensure the quality of the information on what we eat in the long term. In the face of climate change and complex societal challenges in an interconnected world, the full diversity of the food system needs to be recognized and more efforts should be put toward achieving a data-driven food system.

MilkyBase, a database of human milk composition as a function of maternal-, infant- and measurement conditions

This study describes the development of a database, called MilkyBase, of the biochemical composition of human milk. The data were selected, digitized and curated partly by machine-learning, partly manually from publications. The database can be used to find patterns in the milk composition as a function of maternal-, infant- and measurement conditions and as a platform for users to put their own data in the format shown here. The database is an Excel workbook of linked sheets, making it easy to input data by non-computationally minded nutritionists. The hierarchical organisation of the fields makes sure that statistical inference methods can be programmed to analyse the data. Uncertainty quantification and recording dynamic (time-dependent) compositions offer predictive potentials.

Measurement(s)	Concentration of biochemical compounds in human milk or/and derived quantities, like their sums or ratios.
Technology Type(s)	Data mining, by means of Machine Learning and targeted manual literature search within available scientific publications in the internet.
Factor Type(s)	Georgaphical region • Cohort size • Measurement Method • Various characteristics (including history) of mother, child, breast milk and measurement
Sample Characteristic - Organism	Human milk
Sample Characteristic - Environment	Standard birth environment
Sample Characteristic - Location	Various regions of the world

Nutrient concentrations in food display universal behaviour

Extensive programmes around the world endeavour to measure and catalogue the composition of food. Here we analyse the nutrient content of the full US food supply and show that the concentration of each nutrient follows a universal single-parameter scaling law that accurately captures the eight orders of magnitude in nutrient content variability. We show that the universality is rooted in the biochemical constraints obeyed by the metabolic pathways responsible for nutrient modulation, allowing us to confirm the empirically observed scaling law and to predict its variability in agreement with the data. We propose that the natural nutrient variability in food can be quantitatively formalized. This provides a mathematical rationale for imputing missing values in food composition databases and paves the way towards a quantitative understanding of the impact of food processing on nutrient balance and health effects.

A Catalog of Natural Products Occurring in Watermelon-Citrullus lanatus

Sweet dessert watermelon (Citrullus lanatus) is one of the most important vegetable crops consumed throughout the world. The chemical composition of watermelon provides both high nutritional value and various health benefits. The present manuscript introduces a catalog of 1,679 small molecules occurring in the watermelon and their cheminformatics analysis for diverse features. In this catalog, the phytochemicals are associated with the literature describing their presence in the watermelon plant, and when possible, concentration values in various plant parts (flesh, seeds, leaves, roots, rind). Also cataloged are the chemical classes, molecular weight and formula, chemical structure, and certain physical and chemical properties for each phytochemical. In our view, knowing precisely what is in what we eat, as this catalog does for watermelon, supports both the rationale for certain controlled feeding studies in the field of precision nutrition, and plant breeding efforts for the development of new varieties with enhanced concentrations of specific phytochemicals. Additionally, improved and comprehensive collections of natural products accessible to the public will be especially useful to researchers in nutrition, cheminformatics, bioinformatics, and drug development, among other disciplines.

Emergent expansion of clinical epigenetics in patients with cardiovascular diseases

PURPOSE OF REVIEW

Cardiovascular diseases (CVDs) are typically caused by multifactorial events including mutations in a large number of genes. Epigenetic-derived modifications in the cells are normal but can be amended by aging, lifestyle, and exposure to toxic substances. Major epigenetic modifications are DNA methylation, histone modification, chromatin remodeling as well as the noncoding RNAs. These pivotal players are involved in the epigenetic-induced modifications observed during CVDs. Nevertheless, despite impressive efforts capitalized in epigenetic research in the last 50 years, clinical applications are still not satisfactory.

RECENT FINDINGS

Briefly, we present some of the recent steps forward in the epigenetic studies of CVDs. There is an increased appreciation for the contribution of epigenetic alterations in the development of CVDs. Now, we have novel epigenetic biomarkers and therapeutic trials with the use of statins, metformin, and some compounds affecting epigenetic pathways including a BET inhibitor apabetalone. The new knowledge of epigenetic regulation is also discussed in the light of precision medicine of CVDs.

SUMMARY

Epigenetic studies of CVDs have the promise to yield both mechanistic insights as well as adjunct treatments (repurposed drugs and apabetalone). The overall concept of precision medicine is not widely recognized in routine medical practice and the so-called reductionist approach remains the most used way to treat CVD patients.

ScanBious: Survey for Obesity Genes Using PubMed Abstracts and DisGeNET

We used automatic text-mining of PubMed abstracts of papers related to obesity, with the aim of revealing that the information used in abstracts reflects the current understanding and key concepts of this widely explored problem. We compared expert data from DisGeNET to the results of an automated MeSH (Medical Subject Heading) search, which was performed by the ScanBious web tool. The analysis provided an overview of the obesity field, highlighting major trends such as physiological conditions, age, and diet, as well as key well-studied genes, such as adiponectin and its receptor. By intersecting the DisGeNET knowledge with the ScanBious results, we deciphered four clusters of obesity-related genes. An initial set of 100+ thousand abstracts and 622 genes was reduced to 19 genes, distributed among just a few groups: heredity, inflammation, intercellular signaling, and cancer. Rapid profiling of articles could drive personalized medicine: if the disease signs of a particular person were superimposed on a general network, then it would be possible to understand which are non-specific (observed in cohorts and, therefore, most likely have known treatment solutions) and which are less investigated, and probably represent a personalized case.

A systematic comprehensive longitudinal evaluation of dietary factors associated with acute myocardial infarction and fatal coronary heart disease

Environmental factors, and in particular diet, are known to play a key role in the development of Coronary Heart Disease. Many of these factors were unveiled by detailed nutritional epidemiology studies, focusing on the role of a single nutrient or food at a time. Here, we apply an Environment-Wide Association Study approach to Nurses' Health Study data to explore comprehensively and agnostically the association of 257 nutrients and 117 foods with coronary heart disease risk (acute myocardial infarction and fatal coronary heart disease). After accounting for multiple testing, we identify 16 food items and 37 nutrients that show statistically significant association - while adjusting for potential confounding and control variables such as physical activity, smoking, calorie intake, and medication use - among which 38 associations were validated in Nurses' Health Study II. Our implementation of Environment-Wide Association Study successfully reproduces prior knowledge of diet-coronary heart disease associations in the epidemiological literature, and helps us detect new associations that were only marginally studied, opening potential avenues for further extensive experimental validation. We also show that Environment-Wide Association Study allows us to identify a bipartite food-nutrient network, highlighting which foods drive the associations of specific nutrients with coronary heart disease risk.