Authors | Chapter | Content |
---|---|---|
Gibson R.S. | 1. | Introduction |
Leclercq C., Troubat N., & Gibson R.S. | 2. |
Food available for consumption at National and Household Levels |
Gibson R.S. | 3. | Food consumption of individuals |
Gibson R.S. | 8a. | Nutrient reference values |
Gibson R.S. | 8b. | Evaluation of intakes and diets |
Arimond M., & Deitchler M. | 8c. | Dietary guidelines and quality |
Gibson R.S. | 9. | Introduction to Anthropometry |
Gibson R.S., & Meredith-Jones K. | 10. | Body size |
Gibson R.S. | 11. | Body composition |
Gibson R.S. | 13. | Evaluation of Anthropometric Data |
Gibson R.S. | 14. | Body Composition: Laboratory Methods |
Gibson R.S. | 15. | Biomarkers |
Gibson R.S. | 17. | Iron |
Whiting S.J. & Gibson R.S. | 18b. | Vitamin D |
Carr A.C. | 19. | Vitamin C |
Whitfield K.C. | 20a. | Thiamine |
Pentieva K. | 20b. | Riboflavin |
McNulty H. | 22a. | Folate |
Whiting S.J. & Gibson R.S. | 23a. | Calcium |
Calvo M.S., Whiting, S.J. & Uribarri J. | 23b. | Phosphorus |
Nielsen F. | 23c. | Magnesium |
McArdle, H.J. | 24b. | Copper |
Gibson R.S. | 24c. | Zinc |
Combs G.F. Jr. | 25b. | Selenium |
“the use of emerging information and communications technology, especially the internet, to improve or enable health and health care”whereas m-Health interventions are
“those designed for delivery through mobile phones” (Olson, 2016).Interventions using these communication technologies to assess, monitor and improve nutrition-related behaviors and body weight, appear to be efficacious across cognitive outcomes, and some behavioral and emotional outcomes, although changing dietary behaviors is a more challenging outcome. There is an urgent need for a rigorous scientific evaluation of e‑ and m‑health intervention technologies. To date their public health impact remains uncertain. Nutritional assessment is also an essential component of the nutritional care of the hospitalized patient. The important relationship between nutritional status and health, and particularly the critical role of nutrition in recovery from acute illness or injury, is well documented. Although it is many years since the prevalence of malnutrition among hospitalized patients was first reported (Bistrian et al., 1974, 1976), such malnutrition still persists (Barker et al., 2011). In the early 1990s, evidence-based medicine started as a movement in an effort to optimize clinical care. Originally, evidence-based-medicine focused on critical appraisal, followed by the development of methods and techniques for generating systematic reviews and clinical practice guidelines (Djulbegovic and Guyatt, 2017). For more details see Section 1.1.6. Point of care technology (POCT) is also a rapidly expanding health care approach that can be used in diverse settings, particularly those with limited health services or laboratory infrastructure, as the tests do not require specialized equipment and are simple to use. The tests are also quick, enabling prompt clinical decisions to be made to improve the patient’s health at or near the site of patient care. The development and evaluation of POC devices for the diagnosis of malaria, tuberculosis, HIV, and other infectious diseases is on-going and holds promise for low-resource settings (Heidt et al., 2020; Mitra and Sharma, 2021). Guidelines by WHO (2019) for the development of POC devices globally are available, but challenges with regulatory approval, quality assurance programs, and product service and support remain (Drain et al., 2014). Personalized nutrition is also a rapidly expanding approach that tailors dietary recommendation to the specific biological requirements of an individual on the basis of their health status and performance goals. See Setion 1.1.5 for more details. The approach has become possible with the increasing advances in “‑omic sciences” (e.g., nutrigenomics, proteomics and metabolomics). See Chapter 15 and van Ommen et al. (2017) for more details. Health-care administrators and the community in general, continue to demand demonstrable benefits from the investment of public funds in nutrition intervention programs. This requires improved techniques in nutritional assessment and the monitoring and evaluation of nutrition interventions. In addition, implementation research is now being recognized as critical for maximizing the benefits of evidence-based interventions. Implementation research in nutrition aims to build evidence-based knowledge and sound theory to design and implement programs that will deliver nutrition programs effectively. However, to overcome the unique challenges faced during the implementation of nutrition and health interventions, strengthening the capacity of practitioners alongside that of health researchers is essential. Dako-Gyke et al. (2020) have developed an implementation research course curriculum that targets both practitioners and researchers simultaneously, and which is focused on low‑ and middle-income countries. The aim of this 3rd edition of “Principles of Nutritional assessment” is to provide guidance on some of these new, improved techniques, as well as a comprehensive and critical appraisal of many of the classic, well-established methods in nutritional assessment.
Goal | Definition |
---|---|
Weight management | Maintaining (or attaining) an ideal body weight and/or body shaping that ties into heart, muscle, brain and metabolic health |
Metabolic health | Keeping metabolism healthy today and tomorrow |
Cholesterol | Reducing and optimizing the balance between
high-density lipoprotein and low-density lipoprotein cholesterol in individuals in whom this is disturbed |
Blood pressure | Reducing blood pressure in individuals who have
elevated blood pressure |
Heart health | Keeping the heart healthy today and tomorrow. |
Muscle | Having muscle mass and muscle functional abilities. This is the physiological basis or underpinning of the consumer goal of “strength” |
Endurance | Sustaining energy to meet the challenges of the day (e.g., energy to do that report at work, energy to play soccer with your children after work) |
Strength | Feeling strong within yourself,
avoiding muscle fatigue |
Memory | Maintaining and attaining an optimal short-term and/or working memory |
Attention | Maintaining and attaining optimal focused and
sustained attention (i.e., being “in the moment” and able to utilize information from that “moment”) |
Challenge | Approach |
---|---|
Baseline exposure | Unlike drug exposure,
most persons have some level of dietary exposure to the nutrient or dietary substance of interest, either from food or supplements, or by endogenous synthesis in the case of vitamin D. information on background intakes and the methodologies used to assess them should be captured in the SR so that any related uncertainties can be factored into data interpretation. |
Nutrient status | The nutrient status of an individual or population
can affect the response to nutrient supplementation. |
Chemical
form of the nutrient or dietary substance | If nutrients occur in multiple forms,
the forms may differ in their biological activity. Assuring bioequivalence or making use of conversion factors can be critical for appropriate data interpretation. |
Factors that influence bioavailability | Depending upon the
nutrient or dietary substance, influences such as nutrient-nutrient interactions, drug or food interactions, adiposity, or physiological state such as pregnancy may affect the utilization of the nutrient. Capturing such information allows these influences to be factored into conclusions about the data. |
Multiple and interrelated biological functions of a nutrient or dietary substance | Biological functions need to be
understood in order to ensure focus and to define clearly the nutrient- or dietary substance—specific scope of the review. |
Nature of nutrient or
dietary substance intervention | Food-based interventions require detailed
documentation of the approaches taken to assess nutrient or dietary substance intake. |
Uncertainties in assessing dose-response relationships | Specific documentation of measurement and assay
procedures is required to account for differences in health outcomes. |
“a biological characteristic that can be objectively measured and evaluated as an indicator of normal biological or pathogenic processes, and/or as an indicator of responses to nutrition interventions”.Nutritional biomarkers can be measurements based on biological tissues and fluids, on physiological or behavioral functions and, more recently, on metabolic and genetic data that in turn influence health, well-being, and risk of disease. Yetley and colleagues (2017b) have highlighted the difference between risk biomarkers and surrogate biomarkers. A risk biomarker is defined by the Institute of Medicine (2010) as a biomarker that indicates a component of an individual’s level of risk of developing a disease or level of risk of developing complications of a disease. As an example, metabolomics is being used to investigate potential risk biomarkers of pre-diabetes that are distinct from the known diabetes risk indicators (glycosylated hemoglobin levels, fasting glucose, and insulin) (Wang-Sattler et al., 2012). BOND classified nutritional biomarkers into three groups shown in Box 1.5,
Nutritional indicator | Application |
---|---|
Dietary indicators | |
Prevalence of the population with zinc intakes below the estimated average requirement (EAR) | Risk of zinc deficiency in a population |
Proportion of
children 6–23mos of age who receive foods from 4 or more food groups | Prevalence of minimum dietary diversity |
Anthropometric indicators | |
Proportion of children
age 6–60mos in the population with mid-upper arm circumference < 115mm | Risk of severe acute malnutrition in the population |
Percentage of children < 5y with length- or height-for-age less than −2.0 SD below the age-specific median of the reference population | Risk of zinc deficiency in the population |
Lab. indicators based on micronutrient biomarkers | |
Percentage of population with serum Zn concentrations below the age/sex/time of day-specific lower cutoff | Risk of zinc deficiency in the population |
Percentage of children
age 6–71mos in the population with a serum retinol < 0.70µmol/L | Risk of vitamin A deficiency in the population |
Median urinary iodine <20µg/L based
on > 300 casual urine samples |
Risk of severe IDD in the population |
Proportion of children (of defined age and sex) with two or more abnormal iron indices (serum ferritin, erythrocyte protoporphyrin, transferrin receptor) plus an abnormal hemoglobin |
Risk of iron deficiency anemia in the population |
Clinical indicators | |
Prevalence of goiter in school-age children ≥ 30% |
Severe risk of IDD among the children in the population |
Prevalence of maternal night blindness ≥ 5% |
Vitamin A deficiency is a severe public health problem |
Coefficient of variation (%) | ||
---|---|---|
Measurement | Within-person | Analytical |
Serum retinol | ||
Daily | 11.3 | 2.3 |
Weekly | 22.9 | 2.9 |
Monthly | 25.7 | 2.8 |
Serum ascorbic acid | ||
Daily | 15.4 | 0.0 |
Weekly | 29.1 | 1.9 |
Monthly | 25.8 | 5.4 |
Serum albumin | ||
Daily | 6.5 | 3.7 |
Weekly | 11.0 | 1.9 |
Monthly | 6.9 | 8.0 |
Precision or reproducibility | Accuracy | |
---|---|---|
Definition |
The degree to which repeated measurements of the same variable give the same value |
The degree to which a measurement is close to the true value |
Assess by | Comparison among repeated measures | Comparison with certified reference materials, criterion method, or criterion anthropometrist |
Value to study | Increases power to detect effects | Increases validity of conclusions |
Adversely affected by |
Random error contributed by the measurer, the respondent, or the instrument | Systematic error (bias) contributed by: the measurer, the respondent, or the instrument |
Test result | The true situation: Malnutrition present | The true situation: No malnutrition |
---|---|---|
Positive | True positive (TP) | False positive (FP) |
Negative | False negative (FN) | True negative (TN) |
Arm circum- ference (mm) | Sensitivity (%) | Specificity (%) |
Relative Risk of death |
---|---|---|---|
≤ 100 | 42 | 99 | 48 |
100–110 | 56 | 94 | 20 |
110–120 | 77 | 77 | 11 |
120–130 | 90 | 40 | 6 |
Biomarker in serum | Geometric mean (95% CI) | Proportion at risk (%) |
---|---|---|
Ferritin*: No adjustment | 14.5µg/L (13.6–17.5) | 44.9 |
Ferritin: Brinda adjustment | 8.8µg/L (8.0–9.8) | 64.9 |
Retinol binding protein**: No adjustment | 0.98 (µmol/L) (0.94–1.01) | 24.3 |
Retinol binding protein: Brinda adjustment | 1.07µmol/L (1.04–1.10) | 12.4 |
Zinc***: No adjustment | 11.5µmol/L (11.2–11.7) | 13.0 |
Zinc: Brinda adjustment | 11.7µmol/L (11.4–12.0) | 10.4 |
Predictive Value | Prevalence 0.1% 1% 10% 20% 30% 40% |
---|---|
Positive | 0.02 0.16 0.68 0.83 0.89 0.93 |
Negative | 1.00 1.00 0.99 0.99 0.98 0.97 |
Wasting | overweight | Stunting | ||||||
---|---|---|---|---|---|---|---|---|
Prevalence thresholds (%) | Labels | (n) | Prevalence thresholds (%) | Labels | (n) | Prevalence thresholds (%) | Labels | (n) |
< 2·5 | Very low | 36 | < 2·5 | Very low | 18 | < 2·5 | Very low | 4 |
2·5 – < 5 | Low | 33 | 2·5 – < 5 | Low | 33 | 2·5 – < 10 | Low | 26 |
5 – < 10 | Medium | 39 | 5 – < 10 | Medium | 50 | 10 – < 20 | Medium | 30 |
10 – < 15 | High | 14 | 10 – < 15 | High | 18 | 20 – < 30 | High | 30 |
≥ 15 | Very high | 10 | ≥ 15 | Very high | 9 | ≥ 30 | Very high | 44 |