Implications of Total Energy Intake for Epidemiologic Analyses Nutritional Epidemiology Walter Willet. Three reasons that total energy intake deserves special consideration in nutritional epidemiology. 1. The level of energy may be a primary determinant of disease.
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1.The level of energy may be a primary determinant of disease.
2. Individual difference in total energy intake produce variation in intake of specific nutrients unrelated to dietary composition because the consumption of most nutrients is positively correlated with total energy intake. This added variation may be extraneous, and thus a source of error, in many analyses.
3. When energy intake is associated w/ disease but is not a direct cause, the effects of specific nutrients may be distorted or confounded by total energy intake.
- RMRs are quantitatively the most important, representing ~60% of total energy expenditure in most individuals.
- Thermogenic effect of food (the metabolic cost of absorbing and processing CHO, protein, and fat) varies w/ the sources of energy, but is only ~10% of the total.
- Adaptive thermogenesis represents the capacity of an individual to conserve or expend energy in response to variable intake of food or temperature extremes. In humans it is defined differently by various investigators and is difficult to measure. It is estimated to be < 10% of calories.
- Physical activity accounts for ~30% of energy intake in a moderately active individual.
- Factors influencing energy intake can generally be considered as 3 categories: body size, metabolic efficiency, and physical activity. Departures from energy balance, that is, change in body energy stores due to intake above or below expenditure, also account for part of the observed variation among persons (Fig 11-2).
needed for resting metabolic
Physical activity Intake:
Metabolic efficiency Intake:
Energy balance Intake:
it seems appropriate to interpret a lack of association w/ relative weight in a specific study as evidence against a direct causal effect of total energy intake on risk of disease.
Model 1: increased energy intake is fully compensated by adaptive thermo-genesis up to a certain point, and weight gain occurs only after a threshold increase in caloric intake is exceeded.
Model 2: any long-term increase in energy intake causes weight gain; any compensatory increase in thermogenesis occurs only in conjunction with weight gain.
- (Table 11-3) women w/ lower energy intake tend to have a proportionally higher intake of fiber than women w/ higher energy intake.
To the extent that energy intake reflects body size, adjustment for total energy intake is usually appropriate as an absolute amount of a specific nutrient tends to have less of an effect for a larger, higher energy-consuming person than for a smaller person.
ex. Case-control study of large bowel cancer (Jain M et al., 1980)
- cancer patients reported higher caloric intake than did controls but did not weight more than the controls (crude intake in Table 11-5); cancer patients also consistently reported higher fat intakes than did noncases.
- it is useful to consider possible explanations for the difference in caloric intake between cases and controls in interpreting the above findings:
the possibility that cases have a metabolic abnormality that renders them less efficient in their utilization of food energy cannot be dismissed.
1 physical activity, size, and metabolic efficiency
- recall bias cannot be dismissed as an explanation in the above findings.
- when calculated as nutrient densities, the association w/ total fat intake essentially disappears for men and is largely eliminated for women (nutrient density intake in Table 11-5). Strong inverse associations are seen for fiber and vitamin C intakes expressed as nutrient densities, which had not association w/ cancer in the crude analysis.
- the nutrient density analysis overstates the protective association of fiber and vitamin C and underestimates the effect of fat, because dividing by caloric intake produces inverse associations even when these nutrients are not independently associated w/ disease.
- subsequent prospective findings are consistent w/ the clear evidence of a protective effect of physical activity against colon cancer and strongly suggest that the case-control findings w/ total energy intake were due to methodologic bias. This discordance raises serious concerns regarding the validity of case-control studies of diet and cancer.
Disease=b1Nutrient residual+b2Calories (model 2, Table 11-6).
Disease=b3Calories + b4Nutrient (model 3, Table 11-6)
- In model 3, calories and nutrient are entered as separate terms, the coefficient for calories (b3) represents calories independent of the specific nutrient, which may have a meaning distinctly different from total energy intake. Thus the inclusion of a specific nutrient together w/ calories in a model fundamentally changes the biologic meaning of calories. The coefficient for calories in model 3 may fail to attain significance when total energy intake has a significant and important relation w/ disease.
- The two terms in model address two distinct and clear questions: (1) is total energy intake associated w/ disease? (2) is the nutrient composition of the diet related to disease?.
- The use of the standard multivariate model can also create confusion between the distributions of crude nutrient intake and the nutrient intake independent of energy intake.
- The issue of collinearity should be concerned when strongly correlated variables are simultaneously included in the same model, which will frequently occur using the standard multivariate model.
Disease=b5CalNutrient1 + b6Calother2 (model 4, Table 11-6)
1 calories provided by the specific nutrient; 2 calories from sources other than the specific nutrient.
Disease=b7Nutrient/Calories + b8Calories (model 5, Table 11-6)
- An alternative approach, in theory, would be to include the major determinants of energy intake (body size, physical activity, and metabolic efficiency) as separate variables in a multivariate model. It could be informative to include as many of these variables as possible along w/ total energy intake as independent variables.
- Because energy intake and disease outcome may differ in their relationships w/ body components such as lean mass and fat, it would be desirable to include both height and a measure of fatness uncorrelated w/ height as separate terms in a multivariate model.
- Actual dietary data generally do not have the simple, approximately normal distributions.
- Typically, energy intake and the nutrient intake are skewed toward higher values, and the variation in nutrient intake (and thus the residuals) is greater at higher total energy intake (Fig 11-7, for example, using saturated fat). The lack of constant variation in the residuals across level of the independent variable (heteroscedasticity) is in principle a violation of usual regression assumptions and, if ignored, has serious implications for the various methods of energy adjustment.
- It has been pointed out that if the residuals from heteroscedastic data are divided into categories, subjects in both the highest and lowest categories will tend to have the highest energy intake.
- Transformations, such as taking logarithms of the variables, are typically used to create residuals w/ a more constant variance across the independent variable (Fig 11-8). As a result, subjects will contribute similarly to information on dietary composition and disease risk regardless of their energy intake.
- The effects of heteroscedasticity are most transparent in the use of residuals as a measure of dietary composition. The same issues exist with the standard multivariate model, but may not be appreciated because the residuals from one independent variable regressed on another are not typically examined.
- The impact of non-normally distributed variables is less clear in the “energy decomposition” model, but it is likely that variability in the nutrient of interest could differ by level of energy intake from other sources in some circumstances.
- In principle, the analytic approaches listed above could be extended to include other nutrients as well. For example, one could use the energy-adjustment approach (model 2) to compute calorie-adjusted residuals for both protein and fat and include both along w/ total calories in the same model, or one can use the energy decomposition method to enter energy from fat, protein, and CHO as three separate terms.
- The capacity to include multiple energy-adjusted nutrients in a model simultaneously will be limited by their intercorrelations and the size of the dataset.
- The inclusion of additional nutrient terms to these models should be done w/ caution as the interpretation of even the two-variable models can be complex.
- Reasons for analyzing nutrients in categories: (1) the capacity to compute relative risks for actual groups of subjects; (2) the avoidance of imposing a dose-response relationship (such as linear) that does not actually exist; and (3) the ability to avoid undue influences of outlier values.
- Alternative arguments exist for using continuous variables, including the maximization of statistical power; a thorough analysis will usually involve both approaches.
- In conducting categorical nutrient analyses, it is important to recognize that the statistical interchangeability of the standard multivariate, energy partition, and residual models does not apply.