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Omega-3 and Omega-6 Fatty Acids*
Published in Nathalie Bergeron, Patty W. Siri-Tarino, George A. Bray, Ronald M. Krauss, Nutrition and Cardiometabolic Health, 2017
William S. Harris, Nathalie Bergeron, Patty W. Siri-Tarino, George A. Bray, Ronald M. Krauss
The classic tool of nutritional epidemiology has been the prospective cohort study in which large numbers of healthy subjects are recruited, their diets analyzed by a variety of techniques [24-h recall, 3-day food record, food frequency questionnaire, etc. (Shim, Oh, and Kim, 2014)], and a wide range of biometric and health-related measures are collected. In some studies, biological samples are taken for biomarker measurement. The cohort is then followed without any prescribed interventions for a number of years, and the incidence of different diseases is tracked. With these data, sophisticated statistical analysis is applied to explore the question of how food/nutrient/pattern is associated with an outcome of interest. The strengths of such studies are their “real world” setting and the ability to include many thousands of subjects. Their weaknesses include not being able to control every aspect of a person’s life, and the very real possibility that, even though nutrient X (whether from dietary data or biomarker level) is strongly associated with incident disease, it may be other factors that track with nutrient X that are the real reason for the observed relationships. This “unmeasured confounding” makes it impossible to conclude that a cause and effect relationship exists between the nutrient and the disease outcome. Nevertheless, such associations (if they are seen in multiple studies conducted under a variety of conditions) build a strong circumstantial case for a link between nutrient and disease.
Use of Intake Biomarkers in Nutritional Epidemiology
Published in Dale A. Schoeller, Margriet S. Westerterp-Plantenga, Advances in the Assessment of Dietary Intake, 2017
There have been more than 40 years of study by multiple research groups of associations between self-reported intake of foods and nutrients and subsequent chronic disease incidence. In spite of strong suggestions from animal feeding studies, international correlational analyses, and migrant studies, and in spite of consistent analytic epidemiologic findings of elevated risks of cardiovascular diseases, major cancers and diabetes among overweight and obese persons, rather few diet and disease associations have been identified by expert panels convened to review the worldwide evidence (World Cancer Research Fund/American Institute for Cancer Research 1997; 2007; World Health Organization 2003). An important limitation of the self-report approach, whether based on food frequency questionnaires (FFQs), food records or diaries (e.g., four-day food records), or dietary recalls (e.g., one or more 24-hour dietary recalls) is the inability to adequately measure total energy intake (Neuhouser et al. 2008; Prentice et al. 2011; Subar et al. 2003). In fact, self-reported energy not only is highly variable, but it also has systematic biases, with overweight and obese persons tending to substantially underestimate energy intake. Hence, there are major limitations to nutritional epidemiology as currently practiced. The incorporation of objective intake measures, usually referred to as intake biomarkers, into the nutritional epidemiology research agenda has potential for a fresh and penetrating study of a broad range of dietary intake associations with subsequent chronic disease risk.
Personalization of Nutrition Advice
Published in David Heber, Zhaoping Li, Primary Care Nutrition, 2017
Many in the food industry and the dietetic community view the dietary guidelines issued by the government as laws that must not be violated. Clearly, they behave like laws for the marketing and regulatory departments of food companies and public agencies. However, these guidelines are not unchanging laws, but have evolved over many decades, largely based on data derived from the field of nutritional epidemiology (Willett 1998). This field depends on self-reported dietary intake, and measurement of a limited number of biomarkers, such as height and weight. Nutritional epidemiology has resulted in some of the most influential publications in nutrition science in humans. While there is a large nutrition science literature in animal nutrition, the data on human intervention studies are usually conducted on small numbers of people, with the exceptions of a few large studies, such as the Women’s Health Initiative (WHI) and the Dietary Approaches to Stop Hypertension (DASH) (Sacks et al. 1995; Howard et al. 2006; Prentice et al. 2006).
Baseline dietary patterns of children enrolled in an urban family weight management study: associations with demographic characteristics
Published in Child and Adolescent Obesity, 2021
Parisa Assassi, Beatrice J. Selwyn, David Lounsbury, Wenyaw Chan, Melissa Harrell, Judith Wylie-Rosett
Traditional approaches to nutritional epidemiology have contributed to an understanding of the cause and prevention of individual nutrient deficiency without explicitly linking findings to chronic diseases, such as cancer and cardiovascular disease (Hu 2002; Kant 2004). More recently, nutritional epidemiology has begun to focus on dietary pattern analysis which describes the overall diet, represents a broader picture of food and nutrient intake, and predicts health outcomes more precisely than assessing individual foods or nutrients (Hu 2002; Slattery 2008; Ocke 2013). Dietary pattern represents complex sets of highly correlated dietary exposures, and detects joint effects of foods by considering the entire eating pattern (Jaconson and Stanton 1986; Jacques and Tucker 2001). Dietary patterns can be influenced by acculturation, adherence to traditional eating patterns, and the heterogeneity of populations with diverse socioeconomic, cultural and ethnic backgrounds (Satia et al. 2001; Arredondo et al. 2006; Nettleton et al. 2008; Davis et al. 2013).
100% Fruit Juice in Child and Adolescent Dietary Patterns
Published in Journal of the American College of Nutrition, 2020
In a recent commentary, Ioannidis (5) sharply criticized the way that data from observational cohort studies are reported, stating: “Some nutrition scientists and much of the public often consider epidemiologic associations of nutritional factors to represent causal effects that can inform public health policy and guidelines. However, the emerging picture of nutritional epidemiology is difficult to reconcile with good scientific principles.” Attributing disease causality to individual nutrients or foods oversimplifies and misrepresents highly complex eating behaviors, dietary intake trends, social influences on food choices, and metabolic responses that can confound cohort studies. Residual confounding— the inability to accurately measure potentially confounding variables—can introduce bias into the interpretation of findings. As Ioannidis points out, there are 250,000 different foods, containing thousands of different chemicals that can contribute to an individual’s dietary pattern (DP) (5). The difficulties in studying individual food items consumed within a complex dietary pattern were clearly demonstrated by Nicklas et al. (6). Using common statistical approaches to examine the effect of egg consumption on health, the authors demonstrated remarkable variability in outcomes as egg consumption was consumed within different food and lifestyle patterns. The ramifications of a reductionist perspective may be far-reaching in terms of the public’s understanding of nutrition and health (5–7).