Nutrient-response curve

The first prerequisite to interpretation of nutrient-response in terms of the metabolic fate of a given nutrient is a reasonable understanding of the nutrient-response curve itself. Anyone who has looked at a nutrient-response curve should have been convinced that the curve is nonlinear provided the curve encompasses a reasonable range of nutrient intake. Typical nutrient-response curves are shown in Fig. 1. At higher levels of nutrient intake, inhibition of the response may be observed in any of the foregoing curves. The equation and parameter constraints will be referred to later in the text. 

There has been significant speculation on the reason why nutrient-response curves appear to be described by rational polynomials. It has been... 

FIG. 1. Possible shapes of nutrient-response curves and the parameter constraints which allow these curves to be described by a 2 2 rational polynomial. 

Estimates of the parameters of the equation for the nutrient-response curve can be obtained by graphical analysis (Schulz, 1987) or by statistical analysis of the rational polynomial (Press et al, 1992). Figure 4 shows the response of rats in terms of accumulation of body nitrogen to three different sources of dietary protein (Phillips, 1981). The sources of dietary protein were casein, peanut protein, and wheat gluten. The parameters were estimated graphically, and these estimates were fine tuned by simulation... 

FIG. 4, Nutrient-response curves for three different proteins. The dots represent the data obtained experimentally. The solid line is the line generated for casein, the broken line represents peanut protein, and the dashed line represents wheat gluten. 

Fig. 11.2 A hypothetical dose - response curve for an essential nutrient.

Nutrients enter into biological processes that are not characterized by a well-defined dose-response relationship. Therefore, in many cases, the dietary supplement itself is not expected to exhibit a characteristic dose-response curve. 

Estimation of the relative effectiveness of nutrient sources can be a useful way in which to estimate their efficacy as fertilisers (Barrow 1985). The relative effectiveness of alternative nutrient sources is usually calculated by comparing the yield plateau of the response curve of the fertiliser in question to a soluble source of the same nutrients (Barrow 1985). For minerals used as nutrient inputs in organic farming systems their relative effectiveness is almost always <1 due to low solubility in soil. Organic matter inputs can also be evaluated in terms of their relative effectiveness based on their recalcitrance, but of equal importance is the extent to which they are physically protected from degradation in soil aggregates (Strong et al. 1999), which would be different in different soil types. 

Figure 1 Biological response dependence on tissue concentration of an essential nutrient (solid curve) and of a deleterious substance (dashed curve). The relative position of the two curves on the concentration axis is arbitrary and one of convenience. (Reprinted from Ref. 2 by permission of Marcel Dekker, Inc)...

NUTRIENT-DEFICIENT ANIMALS. In this assay, animals, such as the rat or the chick, are fed diets deficient in a specific nutrient. Growth response curves are developed by the feeding of known amounts of the nutrient to some of the deficient animals. Other deficient animals are given the product to be assayed, and their responses are compared to the growth curves. In addition, the evaluator can observe changes in specific tissues as various levels of the specific nutrients are supplied. 

An inadequate intake in the diet of those food chemicals that are essential nutrients results in health risks. Indeed these risks are by far the most important in terms of the world s population where malnutrition is a major public health problem. But, unlike the toxic chemicals, they would show a very different dose-response if they were subject to similar animal bioassays. At very low doses there would be a high risk of disease that would decrease as the dose was increased, the curve would then plateau until exposure was at such a level that toxicity could occur. Figure 11.2 shows this relationship which is U- or J-shaped rather than the essentially linear dose-response that is assumed for chemicals that are only toxic. The plateau region reflects what is commonly regarded as the homeostatic region where the cell is able to maintain its function and any excess nutrient is excreted, or mechanisms are induced that are completely reversible. 

Moreover, DCQAs (1,5-, 3,4- and 4,5-DCQAs), with antioxidative activity, have been isolated from the leaves of garland (Chrysanthemum coronarium L.) [113], The garland Chrysanthemum coronarium L.) has been regarded as a health food in East Asia because the edible portions, such as leaf and stem, contain abundant -carotene, iron potassium, calcium, and dietary fiber. In addition to these common nutrients, some compounds responsible for the chemoprevention of cancers and other diseases are thought to be contained in garland. The antioxidative activity of DCQAs has been assayed by the decay curves of P-carotene. The antioxidative ability of 1 pg/ml these compounds are nearly equal to that of 0.1 pg/ml 3-/er/-butyl-4-hydroxyanisole (BHA). 

The dashed curve in Figme 1 for a toxic substance shows a lag region under the assumption that an organism can cope with some amount of a substance before toxic effects become evident (threshold). For both essential nutrients and toxic substances. Figure 1 shows general characteristics each substance has its own specific curve of biological response versus concentration. 

Discrete analyses for nitrate, nitrite, phosphate, and silicate were performed on samples drawn from the flow with a modified Hitachi autoanalyzer system the full-scale ranges for these measurements are 0-30, 0-10, 0-2.4, and 0-45 /xmol/L, respectively. The precision and accuracy of these measurements are 1 % of full-scale values. Both Sagami standard nutrient solutions and standard solutions prepared from preweighed standards were used to standardize the nutrient measurements. Beer s law curves were run throughout the experiment to establish the output response of the spectrophotometers. The response functions were linear for the concentrations measured. A complete description of the analytical methods used for nutrients is given in References 15 and 16. 

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