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

Abstract

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.

Machine Learning Prediction of Food Processing

“…All continuous variables were transformed using Box-Cox transformation or logit function (applied to Framingham and ACC/AHA scores) to stabilize the variance and improve the validity of measures of association [16]. We then standardized all continuous variables, to bring their effect sizes on similar scale.…”

“…Second, the classification of composite recipes, products, and mixed meals including different types of processed items -a large proportion of the food supply -is not straightforward. Hence, current approaches have classified all foods with at least one ultra-processed ingredient as ultra-processed [16,22], reducing the discrimination of such foods in relation to health outcomes [23,24]. Third, all the observed risk for the NOVA classification is in the NOVA 4 class, representing a large and heterogeneous category of ultra-processed food that does not allow investigation of the health implications of different gradations of processing.…”

“…A landmark, widely used classification system for food processing is the NOVA system [11], which categorizes individual foods into four broad categories: unprocessed or minimally processed (NOVA 1), like fresh, dry or frozen fruits or vegetables, grains, legumes, meat, fish and milk; culinary ingredients (NOVA 2), like table sugars, oils, fats and salt; processed foods (NOVA 3), like canned food, simple bread and cheese; and ultra-processed products (NOVA 4), everything else including packaged breads, cookies, sweetened breakfast cereals, margarines, sauces and spreads, carbonated drinks, hot dogs, hamburgers and pizzas. Epidemiological studies have documented significant associations between greater consumption of the highest NOVA category of processing (NOVA 4, ultra-processed food) and diseases onset [12][13][14][15][16][17][18], including links to obesity [19], CHD [17,20], diabetes mellitus [21], cancer [16], and depression [15]. A randomized controlled metabolic trial has also confirmed short-term adverse effects of ultra-processed foods, compared with unprocessed foods, on caloric intake and weight gain [18].…”

Embracing the Complexity of Primary Health Care: System-Based Tools and Strategies for Researching the Case Management Process

“… 13 , 24 , 25 To date, where complexity principles have focused on health it has predominantly related to non-clinical applications, such as the social and organisational aspects of healthcare structures and networks, and health knowledge management, such as research networks, organisational affiliations, mentorship, peer support networks, and citation networks for example, 26 genomic and proteomic interactions for example, 27 and comorbidity and symptom relationships, identified using large datasets, for example. 28 It has been noted that there is generally limited research, education, and implementation of complexity approaches within the health care domain. 24 , 25 While the diagnosis of pathology using syndromic patterning reduces some of the complexity involved in clinical health care provision, it also reduces the capacity to individualise disease assessment and treatment, and to identify susceptibility and preclinical manifestations of disease.…”

Network percolation reveals adaptive bridges of the mobility network response to COVID-19

“…The ongoing pandemic continues to wreak havoc across the globe, and the U.S. has suffered the highest impact among all countries with over 20 million infections 350,000 deaths [ 1 , 2 ]. Despite the sustainable efforts in forecasting and containing the deadly virus, it has been challenging to predict COVID-19 spread.…”