Gibson RS, Principles of Nutritional
Assessment: Biomarkers
3rd Edition
September, 2021
Abstract
Nutritional biomarkers are defined as biological characteristics that can be objectively measured and evaluated as indicators of normal biological or pathogenic processes, or as responses to nutrition interventions. They can be classified as: (i) biomarkers of exposure; (ii) biomarkers of status; and (iii) biomarkers of function. Biomarkers of exposure are intended to measure intakes of foods or nutrients using traditional dietary assessment methods or objective dietary biomarkers. Status biomarkers measure a nutrient in biological fluids or tissues, or in the urinary excretion of a nutrient or its metabolites; these ideally reflect total body nutrient content or the status of the tissue store most sensitive to nutrient depletion. Functional biomarkers, subdivided into biochemical and physiological or behavioral biomarkers, assess the functional consequences of a nutrient deficiency or excess. They may measure the activity of a nutrient-dependent enzyme or the presence of abnormal metabolic products in urine or blood arising from reduced activity of the enzyme; these serve as early biomarkers of subclinical deficiencies. Alterations in DNA damage, in gene expression and in immune function are also emerging as promising functional biochemical biomarkers. Disturbances in functional physiological and behavioral biomarkers can occur with more severe nutrient deficiencies, often involving impairments in growth, vision, motor development, cognition, in response to vaccination, and the onset of, or an increase in, depression. Such functional biomarkers, however, lack both sensitivity and specificity as they are often also affected by social and environmental factors. Outlined here are the principles and procedures that influence the choice of the three classes of biomarkers, as well as confounding factors that may affect their interpretation. A brief review of biomarkers based on new technologies such as metabolomics, etc., is also provided. Methods for evaluating biomarkers at the population and individual level are also presented.
CITE AS:
Gibson RS. Principles of Nutritional Assessment.
Biomarkers. https://nutritionalassessment.org/biomarkers/
Email: Rosalind.Gibson@Otago.AC.NZ
Licensed under CC-BY-SA-4.0
15.1 Biomarkers to assess nutritional status
Nutritional biomarkers are increasingly important with the growing efforts to provide evidence-based clinical guidance, and advice on the role of food and nutrition in supporting health and preventing disease. A nutritional biomarker has been defined by the Biomarkers of Nutrition and Development (BOND) program as 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 (Raiten and Combs, 2015). Thus 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. Most useful are nutritional biomarkers that distinguish deficiency, adequacy and toxicity, and which assess aspects of physiological function and/or current or future health. Increasingly, understanding the effect of diet on health requires the study of mechanisms, not only of nutrients but also of other bioactive food constituents at the molecular level. Hence, there is also a need for molecular biomarkers that allow the detection of the onset of disease, in, ideally, the pre-disease state. Unfortunately, nutritional biomarkers are often affected by technical and biological factors other than changes in nutritional status, which can confound the interpretation of the results.
Nutritional biomarkers are used to support a range of applications at both the population and individual level; these applications are listed below.
At the population level
- National Nutrition surveys: assess overall nutritional status of populations
- Nutrition Screening: identify persons “at risk” in the population via cut-offs
- Surveillance: continuous monitoring of nutritional status of selected population groups over time (e.g., U.S. NHANES and U.K. Diet and Nutrition Survey Rolling Program)
- Monitoring and Evaluation: monitor coverage of / compliance with nutrition policies; evaluate the efficacy and/or effectiveness of public health programs and interventions over time; substantiate health claims.
- In apparently healthy patients: assess reserves, pool size, tissue amounts of the nutrient; determine response to clinical treatment of a nutrient deficiency or disease state
- In “sick” patients: determine status for a specific clinical problem; reflect current status of deficiency or clinical disease; predict future risk of disease or long-term functional outcome if abnormal values persist.
15.1.1 Classification of biomarkers
BOND has classified nutritional biomarkers into three groups, shown in Box 15.1, based on the assumption that an intake-response relationship exists between the biomarker of exposure (i.e., nutrient intake) and the biomarkers of status and function. Nevertheless, it is recognized that a single biomarker may not reflect exclusively the nutritional status of that single nutrient, but instead be reflective of several nutrients, their interactions, and metabolism. In addition, a nutritional biomarker may not be equally useful across different applications or life-stage groups where the critical function of the nutrient or the risk of disease may be different.Biomarkers of exposure are intended to assess what has been consumed, and, where possible, take into account bioavailability, defined as the proportion of the ingested nutrient that is absorbed and utilized through normal metabolic pathways (Hurrell et al., 2004). Biomarkers of exposure can be based on measurements of nutrient intake obtained using traditional dietary assessment methods. Alternatively, depending on the nutrient, nutrient exposure can be measured indirectly, based on surrogate indicators termed “dietary biomarkers”. These are intended to provide a more objective measure of dietary exposure that is independent of the measurement of food intake.
Box 15.1. Classification of nutritional biomarkers
-
Biomarkers of “exposure”: food or nutrient intakes;
dietary patterns; supplement usage. Assessed by:
- Traditional dietary assessment methods
- Dietary biomarkers: indirect measures of nutrient exposure
- Biomarkers of “status”: body fluids (serum, erythrocytes, leucocytes, urine, breast milk); tissues (hair, nails)
-
Biomarkers of “function":
measure the extent of the functional consequences of a
nutrient deficiency: serve as early biomarkers of subclinical deficiencies.
- Functional biochemical: enzyme stimulation assays; abnormal metabolites; DNA damage
- Functional physiological/behavioral”: more directly related to health status or disease such as vision, growth, immune function, taste acuity, cognition, depression. These biomarkers impact on clinical and health outcomes.
Biomarkers of function are intended to measure the extent of the functional consequences of a specific nutrient deficiency or excess, and hence have greater biological significance than the static biomarkers. Increasingly, functional biomarkers are also being used as substitutes for chronic disease outcomes in studies of associations between diet and chronic disease. When used in this way, they are termed “surrogate biomarkers”; see Yetley et al. (2017) for more details. Functional biomarkers can be subdivided into two groups: functional biochemical, and functional physiological or behavioral, biomarkers. In some cases functional biochemical biomarkers may serve as early biomarkers of subclinical deficiencies by measuring changes associated with the first limiting biochemical system, which in turn affects health and well-being. They may involve the measurement of an abnormal metabolic product in urine or blood or the activity of a nutrient-dependent enzyme. Alterations in DNA damage, in gene expression and in immune function are also emerging as promising functional biochemical biomarkers, some of which may become accepted as surrogate biomarkers for chronic disease.
Functional physiological and behavioral biomarkers are more directly related to health status and disease than are the functional biochemical biomarkers. Disturbances in these biomarkers are generally associated with more prolonged and severe nutrient deficiency states, or risk of chronic diseases. Examples include measurements of impairment in growth, of response to vaccination (as a biomarker of immune function), of vision, of motor development, cognition, depression, and high blood pressure, all of which are less invasive and easier to perform than many biochemical tests. However, these functional physiological and behavioral biomarkers often measure the net effects of contextual factors that may include social and environmental factors as well as nutrition, and hence lack sensitivity and specificity as nutrient biomarkers (Raiten and Combs, 2015), or as surrogate biomarkers substituting for clinical endpoints (Yetley et al., 2017).
15.1.2 Factors that may confound the interpretation of nutritional biomarkers
Unfortunately, nutritional biomarkers are affected by several factors, other than the effects of a change in nutritional status, which may confound their interpretation. These factors may include technical issues related to the quality of the specimens and their analysis, participant and health-related characteristics, and biological factors. These factors are listed in Box 15.2. Knowledge of their effects on the biomarkers for specific nutrients is discussed more fully in the nutrient-specific chapters.- Analytical quality performance factors: accuracy, precision, sensitivity, specificity, validity, predictive value
- Specimen-related limitations: half-life (days, weeks, months, years); stability (quality of the sample); biological variation; storage; contamination; hemolysis
- Participant-related factors: age, sex, ethnicity, race, genetic predisposition to disease, physiological state, hormonal status, supplement use, physical activity level, lifestyle, environment, recent dietary intake
- Biological factors: homeostatic regulation, diurnal and/or circadian variation, fasting status; nutrient interactions
- Health-related factors: medication use, inherited or acquired diseases, inflammation, stress; environmental enteropathy; obesity; unusual weight loss
The influence of these factors (if any) on each biomarker should be established before carrying out the tests, because these confounding effects can often be minimized or eliminated (Box 15.3). For example, in nutrition surveys the effects of diurnal variation on the concentration of nutrients such as zinc and iron in plasma can be eliminated by collecting the blood samples from all participants at a standardized time of the day. When factors such as age, sex, race, and physiological state influence the biomarker, the observations can be classified according to these variables. The influence of drugs, hormonal status, physical activity, weight loss, and the presence of disease conditions on the biomarker, can also be considered if the appropriate questions are included in a questionnaire.
- Use standardized methods to collect, process, and analyze
- Classify observations by life-stage/sex/ethnicity
- Record medications, supplements; hormonal status; physical activity; obesity; health status, disease
- Avoid using cutoffs mismatched for assay
- Assess Hb variants and malaria, where appropriate
- Adjust for intra-individual variation with replicate measures
- Measure CRP & AGP; apply BRINDA correction to adjust for inflammation where necessary
- Measure multi-micronutrient biomarkers where co-existing deficiencies exist
- Combine biomarkers instead of using only one to enhance specificity
During an infectious illness, after physical trauma, with inflammatory disorders, and with obesity and diabetes, certain systemic changes occur, referred to as the “acute-phase response”, to prevent damage to the tissues by removing harmful molecules and pathogens. The local reaction is inflammation. During this reaction, circulating levels for certain micronutrient biomarkers — for example, zinc, iron, copper, and vitamin A — are altered, often due to a redistribution in body compartments, but these changes do not correspond to changes in micronutrient status. Hence, systemic changes due to the acute phase response must be assessed together with micronutrient biomarkers to ensure a more reliable and valid interpretation of the micronutrient status assessment at both the individual and population levels. Such systemic changes can be detected by measurement of elevated concentrations of several plasma proteins, of which C‑reactive protein (CRP) and α‑1‑acid glycoprotein are recommended (Raiten et al., 2015).
15.2 Biomarkers of exposure
Biomarkers of exposure can be based on direct measurements of nutrient intake using traditional dietary assessment methods, or indirect measurements using surrogate indicators termed “dietary biomarkers”.Traditional dietary assessment methods include 24h recalls, food records and food frequency questionnaires, the choice depending primarily on the study objectives, the characteristics of the respondents, the respondent burden, and the available resources. Each method has its own strengths and limitations; see Chapter 3 for more details. For all dietary methods, care must be taken to ensure that information on any use of dietary supplements and/or fortified foods is also collected. Seasonality must also be taken into account where necessary (e.g., for vitamin A intakes). In the absence of appropriate food composition data for the nutrient of interest, duplicate diet composites can be collected for chemical analysis.
Nutrient intakes calculated from food composition data or determined from chemical analysis of duplicate diet composites represent the maximum amount of nutrients available and do not take into account bioavailability. The bioavailability of nutrients can be influenced by several dietary and host-related factors; see Gibson (2007) for a detailed discussion of these factors. Unfortunately, factors affecting the bioavailability of many nutrients are not well understood, with the exception of iron and zinc. Algorithms have been developed to estimate iron and zinc bioavailability from whole diets and are described in Lynch et al. (2018) and the International Zinc Nutrition consultative Group (IZiNCG) Technical Brief No. 03 (2019). Alternatively, qualitative systems that classify diets into broad categories of iron (FAO/WHO, 2002) and zinc (FAO/WHO, 2004) bioavailability based on various dietary patterns can be used.
Given the challenges with the traditional dietary methods, there is increasing interest in the use of dietary biomarkers as objective indicators of dietary exposure. Dietary biomarkers can be classified into three groups: recovery, concentration, and predictive — each has distinctive properties, as shown in Box 15.4. Several criteria must be considered when selecting a dietary biomarker. These include the half-life of the biomarker, day-to-day intra- and inter-individual variability, the requirements for sample collection, transport, storage and analysis, and the impact of potential biological confounders that may cause variation in biomarker concentrations, unrelated to the level of the dietary component of interest.
Examples for each of the three groups of dietary biomarkers are shown in Box 15.4. In general, nutrient levels in fluids such as urine and serum tend to reflect short-term (i.e., recent) dietary exposure, those in erythrocytes are medium-term (e.g., for fatty acids; folate), whereas examples of long-term biomarkers are nutrient levels in adipose tissue (for fatty acids), toenails or fingernails (for selenium), and scalp hair samples (for chromium). In some circumstances, the time integration of exposure of the urinary dietary biomarkers can be enhanced by obtaining urine samples at several points in time. For more specific details of nutrient levels in urine as dietary biomarkers, see Section 15.3.12.
-
Recovery biomarkers
- Measure total excretion of marker over a defined time period
- Excretion is a fixed proportion of intake with only negligible inter-individual variation.
- Best suited to measure absolute intake
- Examples include: urinary N2 for protein, K and Na in 24hr urines; doubly-labeled water for short-term energy expenditure
- Concentration biomarkers
- Based solely on the concentration of the biomarker
- Provide no information on physiological balance and excretion
- Cannot be translated into absolute levels of intake
- Positively correlated with intake, so can be used for ranking
- Examples include: total carotenoids (fruit + vegetable intake): plasma vitamin C; phenols
- Predictive biomarkers
- Incomplete recovery
- Stable and time-dependent, and a high correlation with intake
- Used in medicine to predict who is likely to respond to therapy
- Rank between concentration and recovery biomarkers in terms of ability to estimate absolute intake
- Examples include: urinary sucrose and fructose for sugar intake
Modified from Kuhnle (2012).
15.3 Biomarkers of status
Biomarkers based on nutrients in biological fluids and tissues are frequently used as biomarkers of status, and in some cases, of exposure. Measurements of (a) concentrations of a nutrient in biological fluids or tissues, or (b) the urinary excretion rate of a nutrient or its metabolite can be used. The biopsy material most frequently used for these biomarkers is whole blood or some fraction of blood. Other body fluids and tissues, less widely used, include urine, saliva, adipose tissue, breast milk, semen, amniotic fluid, hair, toenails, skin, and buccal mucosa. Four stages are involved in the analysis of these biopsy materials: sampling, storage, preparation, and analysis. Care must be taken to ensure that the appropriate safety precautions are taken at each stage. Contamination is a major problem for trace elements, and must be controlled at each stage of their analyses, especially when the expected analyte levels are at or below concentrations of 1×10−9g.Ideally, as discussed above, the nutrient content of the biopsy material should reflect the level of the nutrient in the tissue most sensitive to a deficiency, and any reduction in nutrient content should reflect the presence of a metabolic lesion. In some cases, however, the level of the nutrient in the biological fluid or tissue may appear adequate, but a deficiency state still arises: homeostatic mechanisms maintain concentrations within the biological specimen, even when intakes are marginal or inadequate (e.g., serum calcium, retinol or serum zinc). Alternatively, a metabolic defect may prevent the utilization of the nutrient.
15.3.1 Blood
Samples of blood are readily accessible, relatively noninvasive, and generally easily analyzed. They must be collected and handled under controlled, standardized conditions to ensure accurate and precise analytical results. Factors such as fasting, fluctuations resulting from diurnal variation and meal consumption, hydration status, use of oral contraceptive agents or hormone replacement therapy, medications, infection, inflammation, stress, body weight and genotype are among the many factors that may confound interpretation of the results (Hambidge, 2003; Potischman, 2003; Bresnahan and Tanumihardjo, 2014).Serum / plasma carries newly absorbed nutrients and those being transported to the tissues and thus tends to reflect recent dietary intake. Therefore, serum / plasma nutrient levels provide an acute, rather than long-term, biomarker of nutrient exposure and/or status. The magnitude of the effect of recent dietary intake on serum / plasma nutrient concentrations is dependent on the nutrient, and where necessary, can be reduced by collecting fasting blood samples. Alternatively, if this is not possible, the time interval since the preceding meal can be recorded, and incorporated into the statistical analysis and interpretation of the results (Arsenault et al., 2011).
For those nutrients for which concentrations in serum / plasma are strongly homeostatically regulated, concentrations in serum / plasma may be near-normal (e.g., calcium, zinc, vitamin A, Figure 15.1), even when there is evidence of functional impairment (Hambidge, 2003). In such cases, alternative biomarkers may be needed.The risk of contamination during sample collection, storage, preparation, and analysis is a particular problem in trace element analysis of blood. Trace elements are present in low concentrations in blood but are ubiquitous in the environment. Details of strategies to reduce the risk of adventitious sources of trace-element contamination are available in the International Zinc Nutrition Consultative Group (IZiNCG) Technical Briefs (2007, 2012). In addition, for certain vitamins such as retinol and folate, exposure to bright light and high temperature should be avoided, and for serum folate, suitable antioxidants (e.g., ascorbic acid, 0.5% w/v) are added to samples to stabilize the vitamin during collection and storage (Bailey et al., 2015; Tanumihardjo et al., 2016).
Additional confounding factors in the collection and analysis of micronutrients in blood are venous occlusion, hemolysis (IZiNCG Technical Brief No.6, 2018), use of an inappropriate anticoagulant, collection-separation time, leaching of divalent cations from rubber stoppers in the blood collection tubes, and element losses produced by adsorption on the container surfaces or by volatilization during storage (Tamura et al., 1994; Bowen and Remaley, 2013). For trace element analysis, trace-element-free evacuated tubes with siliconized rather than rubber stoppers must be used.
Serum is often preferred for trace element analysis because, unlike plasma, risk of adventitious contamination from anticoagulants is avoided, as is the tendency to form an insoluble protein precipitate during freezing. Nevertheless, serum is more prone than plasma to both contamination from platelets and to hemolysis. For capillary blood samples, the use of polyethylene serum separators with polyethylene stoppers are recommended for analysis of trace-elements (King et al., 2015).
15.3.2 Erythrocytes
The nutrient content of erythrocytes reflects chronic nutrient status because the lifespan of these cells is quite long (≈ 120d). An additional advantage is that nutrient concentrations in erythrocytes are not subject to the transient variations that can affect plasma. The anticoagulant used for the collection of erythrocytes must be chosen with care to ensure that it does not induce any leakage of ions from the red blood cells. At present, the best choice for trace element analysis is heparin (Vitoux et al., 1999).
The separation, washing and analysis of erythrocytes is technically difficult, and must be carried out with care. For example, the centrifugation speed must be high enough to remove the extracellular water but low enough to avoid hemolysis. Care must be taken to carefully discard the buffy coat containing the leukocytes and platelets, because these cells may contain higher concentrations of the nutrient than the erythrocytes. After separation, the packed erythrocytes must be washed three times with isotonic saline to remove the trapped plasma, and then homogenized. The latter step is critical because during centrifugation the erythrocytes become density stratified, with younger lighter cells at the top and older denser cells at the bottom.
There is no standard method for expressing the nutrient content of erythrocytes, and each has limitations. The methods used include nutrient per liter of packed cells, per number of cells, per g of hemoglobin (Hb), or per g of dry material (Vitoux et al., 1999). As an example, erythrocyte folate is expressed as µg/L or nmol/L, whereas erythrocyte zinc is often expressed as µg/g Hb. Concentrations of folate in erythrocytes reflect folate stores (Bailey et al., 2015), whereas results for zinc concentrations in erythrocytes are inconsistent. As a consequence, zinc in erythrocytes is presently not recommended as a biomarker of zinc status by the BOND Expert Panel (King et al., 2015), despite their use in several studies (Lowe et al., 2009).
Erythrocytes can also be used for the assay of a variety of functional biochemical biomarkers based on enzyme systems, especially those depending on B‑vitamin-derived cofactors; for more details, see Section 15.4.2. In such cases, the total concentration of vitamin-derived cofactors in the erythrocytes, or the extent of stimulation of specific enzymes by their vitamin-containing coenzymes, is determined. Some of these biomarkers are sensitive to marginal deficiency states and accurately reflect body stores of the vitamin.
15.3.3 Leukocytes
Leukocytes, and some specific cell types such as lymphocytes, monocytes and neutrophils, have been used to monitor medium- to long-term changes in nutritional status because they have a lifespan which is slightly shorter than that of erythrocytes. Therefore, at least in theory, nutrient concentrations in these cell types should reflect the onset of a nutrient deficiency state more quickly than do erythrocytes.However, several technical factors have limited their use as biomarkers of nutritional status. They include the relatively large volumes of blood required for their analysis, the necessity to process the cells as soon as possible after the specimen is obtained, the difficulties of separating specific leukocytic components from other white blood cell types, and unwanted contaminants in the final cell preparation. Additional technical difficulties may arise if the nutrient content of the cell types varies with the age and size of the cells. In some circumstances, for example during surgery or acute infection, there is a temporary influx of new granulocytes, which alters the normal balance between the cell types in the blood and thus may confound the results. Certain illnesses may also alter the size and protein content of some cell types, and this may also lead to difficulties in the interpretation of their nutrient content (Martin et al., 1993). Hence it is not surprising that results of studies on the usefulness of nutrient concentrations such as zinc in leukocytes or specific cell types as a biomarker of zinc exposure or status have been inconsistent. As a result, zinc concentrations in leukocytes or specific cell types were classified as “not useful” by the Zinc Expert Panel (King et al., 2015).
Detailed protocols for the collection, storage, preparation, and separation of human blood cells are available in Dagur and McCoy (2016). Several methods are used to separate leukocytes from whole blood. They include lysis of erythrocytes, isolating mononuclear cells by density gradient separation, and various non-flow sorting methods. Of the latter, magnetic bead separation can be used to enrich specific cell populations prior to flow cytometric analysis. Lysis of erythrocytes is much quicker than density gradient separation, and results in higher yields of leukocytes with good viability. Nevertheless, density gradient separation methods should be used when purification of cell populations is required rather than simple removal of erythroid contaminants. When flow cytometry is used, cells do not necessarily need to be purified or separated for the study of a particular subpopulation of cells. However, their separation or enrichment prior to flow cytometry does enhance the throughput and ultimately the yield of a desired population of cells.
Again, as noted for erythrocytes, no standard method exists for expressing the content or concentration of nutrients in cells such as leukocytes. Methods that are used include nutrient per unit mass of protein, nutrient concentration per cell, nutrient concentration per dry weight of cells, and nutrient per unit of DNA.
15.3.4 Breast milk
Concentrations of certain nutrients secreted in breast milk — notably vitamins A, D, B6, B12, thiamin and riboflavin, as well as iodine and selenium — can reflect levels in the maternal diet and body stores (Dror and Allen, 2018). Studies have shown that in regions where deficiencies of vitamin A (Tanumihardjo et al., 2016), vitamin B12 (Dror and Allen, 2018), selenium (Valent et al., 2011), and iodine (Dror and Allen, 2018) are endemic, concentrations of these micronutrients in breast milk are low. In some settings, it is more feasible to collect breast milk samples than blood samples. Nevertheless, sampling, extraction, handling and storage of the breast milk samples must be carried out carefully to obtain accurate information on their nutrient concentrations. To avoid sampling colostrum and transitional milk, which often have very high nutrient concentrations, mature breast milk samples should be taken at least 21d postpartum, when the concentration of most nutrients (except zinc) has stabilized. Ideally, complete 24h breast milk samples from both breasts should be collected, because the concentration of some nutrients (e.g., retinol) varies during a feed. In community-based studies, however, this is often not feasible. As a result, alternative breast milk sampling protocols have been developed, the choice depending on the study objectives and the nutrient of interest.To date, only breast milk concentrations of vitamin A have been extensively used to provide information about the vitamin A status of the mother and the breastfed infant (Dror and Allen, 2018a; Dror and Allen, 2018b; Figure 15.2). For the assessment of breast milk vitamin A at the individual level, the recommended practice is to collect the entire milk content of one breast that has not been used to feed an infant for at least 2h, into a dark glass bottle on ice. This procedure is necessary because the fat content of breast milk, and thus the content of fat-soluble vitamin A, increases from the beginning to the end of a single feed (Dror and Allen, 2018). If a full-breast milk sample cannot be obtained, then an aliquot (8–10mL) can be collected before the infant starts suckling, by using either a breast pump or manual self-expression (Rice et al., 2000).
For population-based studies, WHO (1996) suggests collecting random samples of breast milk throughout the day and at varying times following the last feed (i.e., casual samples) in an effort to ensure that the variation in milk fat is randomly sampled. When random sampling is not achievable, the fat-soluble nutrients should be expressed relative to fat concentrations as described in Dror and Allen (2018). The fat content of breast milk can be determined in the field by using the creamatocrit method; details are available in Meier et al. (2006).
Before shipping to the laboratory, the complete breast milk sample from each participant should be warmed to room temperature and homogenized by swirling gently, from which an aliquot of the precise volume needed for analysis can be withdrawn. This aliquot is then frozen at −20°C in an amber or yellow polypropylene tube with an airtight cap, preferably in a freezer without a frost/freeze cycle, until it is analyzed. This strategy of prehomogenization reduces subsequent problems such as attaining uniform mixing after prolonged storage in a freezer.
Table 15.1Indicator (month post-partum) | Vitamin A group [n] | Placebo group [n] | Standardized difference |
---|---|---|---|
Breast milk vit.A (µg/g fat) in casual samples (3 mo) | 2.05±0.44 [36] | 1.70±0.47 [37] | 0.76 |
Breast milk vit.A (µmol/L) in casual samples (3 mo) | 0.12±0.70 [36] | –0.18±0.48 [37] | 0.50 |
Maternal serum retinol (µmol/L) (3 mo) | 1.45±0.47 [34] | 1.33±0.42 [35] | 0.27 |
Breast milk vit.A (µmol/L) in full samples (3 mo) | –0.33±0.74 [33] | –0.45±0.53 [35] | 0.19 |
Breast milk vit.A (µg/g fat) in full samples (3 mo) | 1.87±0.51 [33] | 1.82±0.45 [35] | 0.10 |
The analytical methods selected for breast milk should be determined by the chemo-physical properties of the nutrients, their form in breast milk, and their concentrations. Reagents used must be free of adventitious sources of contamination; bound forms of some of the vitamins (e.g., folate, pantothenic acid, vitamins D and B12) must be released prior to extraction and analysis. Increasingly, multi-element mineral analysis is performed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS), whereas for the vitamins, a combination of High-Performance Liquid Chromatography (HPLC) (for thiamin, vitamin A, and vitamin E), ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) (for riboflavin, nicotinamide, pantothenic acid, vitamin B6, and biotin), and a competitive chemiluminescent enzyme immunoassay (IMMULITE 1000; Siemens) for vitamin B12 (cobalamin) are being used (Hampel et al., 2014).
15.3.5 Saliva
Several studies have investigated the use of saliva as a biopsy fluid for the assessment of nutritional status. It is readily available across all ages (newborn to elderly) and collection procedures are noninvasive (unlike blood) so that multiple collections can be performed in the field or in the home.Steroid and other nonpeptide hormones (e.g., thyroxine, testosterone), some therapeutic and other drugs, and antibodies to various bacterial and viral diseases, can be measured in saliva. The effect of physiological measures of stress such as cortisol and α‑amylase on inflammatory biomarkers and immunoglobulin A (IgA) can also be investigated in saliva specimens (Engeland et al., 2019). Studies on the utility of saliva as a biopsy material for metabolomic research are limited. Walsh et al. (2006) reported a high level of both inter‑ and intra-individual variation in salivary metabolic profiles which was not reduced by standardizing dietary intake on the day before sample collection.
Increasingly, energy expenditure, determined by the doubly labeled water (DLW) method, has been used to assess the validity of reported energy intakes measured using a variety of dietary assessment methods (Burrows et al., 2019). In the DLW method, at least two independent saliva samples, collected at the start and end of the observation interval, are required to measure body water enrichment for 18O and 2H; for more details, see Westerterp (2017).
Some micronutrient concentrations in saliva have also been investigated as a measure of exposure and/or status (e.g., zinc). However, interpreting the results is difficult — results do not relate consistently to zinc intake or status, and suitable certified reference materials and interpretive values for normal individuals are not available. Consequently, the BOND Zinc Expert Panel did not recommend salivary zinc as a biomarker of zinc exposure or status (King et al., 2015).
Saliva is a safer diagnostic specimen than blood; infections from HIV and hepatitis are less of a danger because of the low concentrations of antigens in saliva ( (Hofman, 2001). Some saliva specimens, depending on the assay, can be collected and stored at room temperature, and then mailed to the laboratory without refrigeration. However, before collecting saliva samples, several factors must be considered; these are summarized in Box 15.5.
- Is resting or stimulated saliva required? (Stimulated saliva can be collected using sugar-free gum)
- What volume of saliva is required for the assay?
- Is special pretreatment and storage of the saliva required?
- What is the health status of the participants in relation to medications and/or diseases causing a dry mouth?
- Will a quantitative or qualitative assay be performed?
Collection of saliva can be accomplished by expectorating saliva directly into tubes or small paper cups, with or without any additional stimulation. Participants may be requested to rinse their mouth with distilled water prior to the collection. In some cases (e.g., for the DLW method), cotton balls or absorbent pads are used to collect saliva. These can be immersed in a preservative which stabilizes the specimen for several weeks. A disadvantage of this method is that it may contribute interfering substances to the extract and is therefore not suitable for certain analytes.
Alternatively, devices can be placed in the mouth to collect a filtered saliva specimen. These include a small membrane sack that filters out bacteria and enzymes (Saliva Sac; Pacific Biometrics, Seattle, Washington) (Schramm and Smith, 1991), or a tiny plastic tube that contains cyclodextrin to bind the analyte. The latter device, termed the “Oral Diffusion Sink” (ODS), is available from the Saliva Testing and Reference Laboratory, Seattle, Washington (Wade and Haegle, 1991). The ODS device can be suspended in the mouth using dental floss, while the subject is sleeping or performing most of their normal activities with the exception of eating and drinking. In this way, the content of the analyte in the saliva represents an average for the entire collection period.
15.3.6 Sweat
Collection of sweat, like saliva, is noninvasive and can be performed in the field or in the home. Several collection methods for sweat have been used: some are designed to collect whole body sweat, whereas others collect sweat from a specific region of the body, often using some form of enclosing bag or capsule.Shirreffs and Maughan (1997) have developed a method for collecting whole body sweat involving the person exercising in a plastic-lined enclosure. The method does not interfere with the normal sweating process and overcomes difficulties caused by variations in the composition of sweat from different parts of the body. The method cannot be used for treadmill exercise but can be used for subjects exercising on a cycle ergometer.
A method designed to collect sweat from a specific region of the body involves using a nonocclusive skin patch known as an Osteo-patch. It consists of a transparent, hypo-allergenic, gas-permeable membrane with a cellulose fiber absorbent pad. The patch can be applied to the abdomen or lower back for five days. During the collection period, the nonvolatile components of sweat are deposited on the absorbent pad, whereas the volatile components evaporate through the semipermeable membrane. This method has been used to study collagen cross-link molecules such as deoxypyridinoline in sweat as biomarkers of bone resorption (Sarno et al., 1999).
Potassium levels in sweat are used to normalize the deoxypyridinoline values for variations in sweat volume, as these are highly correlated with sweat output and readily measured by flame atomic emission or ion-selective electrode techniques. Sweat sodium losses can also be measured using an Osteo-patch (Figure 15.3; Dziedzic et al., 2013). A more recent method, known as the Megaduct sweat collector, has been designed for the collection of sweat for mineral analyses (Ely et al., 2012). It appears to avoid skin encapsulation and hidromeiosis (excessive sweating) which may alter sweat mineral concentrations, and captures sweat with mineral concentrations similar to those reported for localized patches.Differences in the composition of human sweat have been linked, in part, to discrepancies in collection methods. Errors may be caused by contamination, incomplete collection, or real differences induced by the collection procedure.
15.3.7 Adipose tissue
Adipose tissue is a biopsy material that is used in both clinical (Cuerq et al., 2016) and population studies (Dinesen at al., 2018). It can be used as a measure of long-term dietary intake of fat-soluble nutrients, reflecting intakes of certain fatty acids, vitamin E, and carotenoids, all of which accumulate in adipose tissue.Only fatty acids that are absorbed and stored in adipose tissue without modification, and that are not synthesized endogenously, can be used as biomarkers. Examples of fatty acids that have been used include some specific n‑3 and n‑6 polyunsaturated fatty acids, trans unsaturated fatty acids, and some odd-numbered and branched-chain saturated fatty acids (e.g., pentadecanoic acid (15:0) and heptadecanoic acid (17:0)). Several other factors that influence the measurement of fatty acid profiles in adipose tissue must also be taken into account; these are summarized in Box 15.6.
- Dietary intake of the respondent
- Relative amounts of other fatty acids in the adipose tissue samples
- supplement use (such as fish‑oil capsules) by the respondent
- Genetic polymorphisms of elongase and desaturase enzymes
- Tissue-sampling site
- Tissue-sampling procedures and subsequent sample handling and storage
- Amount sampled in relation to the analytical method and detection limit
- Lipolysis (the breakdown of fat stored in fat cells)
- Nutritional status (Fe, Zn, Cu and Mg sufficiency)
- Lipogenesis (the production of fat from the metabolism of protein and carbohydrate)
- Diseases: cystic fibrosis, malabsorption, liver cirrhosis, diabetes, Zellweger syndrome
- Fat oxidation abnormalities
Several health outcomes associated with dairy fat consumption have been investigated based on fatty acid concentrations in adipose tissue. As an example, Mozaffarian (2019), in a large pooled analysis of 16 prospective cohort studies in the U.S., Europe, and Australia, showed that higher levels of pentadecanoic acid (15:0), heptadecanoic (17:0), and trans-palmitoleic acid (t16:1n‑7) in adipose tissue were associated with a lower risk of type 2 diabetes ( Figure 15.4; Imamura et al., 2018).
Biomarkers of fatty acids in adipose tissue have also been used to validate the classification of individuals as vegetarian and non-vegetarian in the Adventist Health Study‑2, based on the individuals self-reported patterns of consumption of animal and plant-based products (Miles et al., 2019). Results confirmed that the self-reported vegans had a lower proportion of the saturated fatty acids investigated (especially pentadecanoic acid) in adipose tissue, but higher levels of n‑6 polyunsaturated fatty acid linoleic (18:2ω‑6) and a higher proportion of total ω‑3 fatty acids compared to the self-reported non-vegetarians. These trends are consistent with a vegan dietary pattern.
Relationships between long-term dietary intakes of the antioxidant nutrients — α‑tocopherol and carotenoids — and their corresponding concentrations in adipose tissue have also been documented in healthy adults. In general, such correlations exceed those reported between plasma concentrations and diet (Kardinaal et al., 1995; Su et al., 1998). In a large epidemiologic study in which both plasma and adipose tissue carotenoid concentrations were measured, lycopene in adipose tissue (Kohlmeier et al., 1997) but not in plasma (Su et al., 1998) was found to be inversely associated with risk for myocardial infarction.
Simple, rapid sampling methods have been devised for collecting subcutaneous adipose-tissue biopsies, generally from the upper buttock (El-Sohemy et al., 2002), although other sites have also been investigated (Chung et al., 2009). For more discussion on the use of adipose tissue for the assessment of long-term fatty acid and vitamin E status, see Chapters 7 and 18.
15.3.8 Liver and bone
Iron and vitamin A are stored primarily in the body in the liver, and calcium in the bones. Sampling these sites is too invasive for population studies: they are sampled only in research or clinical settings. Dual photon absorptiometry (DXA) is now used to determine total bone mineral content, and is described in detail in Chapter 23.15.3.9 Hair
Scalp hair has been used as a biopsy material for screening populations at risk for certain trace element deficiencies (e.g., zinc, selenium) and to assess excessive exposure to heavy metals (e.g., lead, mercury, arsenic). Detailed reviews are available from the IAEA (1993; 1994). Caution must be used when interpreting results for hair mineral analysis from commercial laboratories because results can be unreliable (Hambidge, 1982; Seidel et al., 2001; Mikulewicz et al., 2013).Hair incorporates trace elements and heavy metals into the matrix when exposed to the blood supply during synthesis within the dermal papilla. When the growing hair approaches the skin surface, it undergoes keratinization and the trace elements accumulated during its formation become sealed into the keratin protein structures and isolated from metabolic processes. Hence, the trace element content of the hair shaft reflects the quantity of the trace elements available in the blood supply at the time of its synthesis, not at the time of sampling (Kempson et al., 2007).
Analysis of trace element levels in hair has several advantages compared to that of blood or urine; these are summarized in Box 15.7.
- Higher concentrations of trace elements are found in hair, relative to blood or urine, making analysis easier; results for the ultra-trace elements such as chromium and manganese are more consistent.
- Concentrations are more stable and hair trace element levels are not subject to the rapid fluctuations associated with diet, diurnal variation, and so on.
- No trauma is involved in the collection of hair samples.
- No special preservatives are needed, and samples can be stored in plastic bags at room temperature without deterioration.
The currently recommended hair sampling method is to use the proximal 10–20mm of hair, cut at skin level from the occipital portion of the scalp (i.e., across the back of the head in a line between the top of the ears) with stainless steel scissors. This procedure, involving the sampling of recently grown hair, minimizes the effects of abrasion of the hair shaft and exogenous contamination. In addition, the specimens collected in this way will reflect the uptake of trace elements or heavy metals by the follicles 4–8 weeks prior to sample collection provided that the rate of hair growth has been normal. Before washing the hair specimens to remove exogenous contaminants such as atmospheric pollutants, water and sweat, any nits and lice should be removed under a microscope or magnifying glass where necessary, using Teflon-coated tweezers. For each sample details of the ethnicity, age, sex, hair-color, height, weight, season of collection, smoking, presence of disease states including malnutrition, and use of antidandruff shampoos or cosmetic treatments, should always be recorded to aid in the interpretation of the data.
Some investigators suggest that the rate of hair growth influences hair trace element concentrations. Scalp hair grows at about 1cm/mo, but in some cases of severe protein-energy malnutrition (Erten et al., 1978) and the zinc deficiency state acrodermatitis enteropathica (Hambidge et al., 1977), growth of the hair is impaired. In such cases, hair zinc concentrations may be normal or even high. No significant differences, however, were observed in the trace element concentrations of scalp and pubic hair samples (DeAntonio et al., 1982), despite marked differences in the rate of hair growth at the two anatomical sites. These results suggest that the relative rate of hair growth is not a significant factor in controlling hair trace element levels.
Several different washing procedures have been investigated, including the use of nonionic or ionic detergents, followed by rinsing in distilled or deionized water to remove absorbed detergent. Various organic solvents such as hexane-methanol, acetone, and ether, have also been recommended, either alone or in combination with a detergent (Salmela et al., 1981). Washing with nonionic detergents (e.g., Triton X‑100) (with or without acetone) is preferred as nonionic detergents are less likely to leach bound trace minerals from the hair and yet are effective in removing superficial adsorbed trace elements. Washing with chelating agents such as EDTA should be avoided because of the risk of removing endogenous trace minerals from the hair shaft (Shapcott, 1978).
After washing and rinsing, the hair samples must be vacuum- or oven-dried depending on the chosen analytical method, and stored in a desiccator prior to laboratory analysis. When the traditional analytical methods such as flame Atomic Absorption Spectrophotometry (AAS) or multi-element Inductively- Coupled Plasma Mass Spectrometry (ICP-MS) are used, washed hair specimens must be prepared for analysis using microwave digestion, or wet or dry ashing. In the future, tetramethylammonium hydroxide (TMAH) to solubilize hair at room temperature may be used, eliminating time-consuming ashing or wet digestion (Batista et al., 2018). Non-destructive instrumental neutron activation analysis (INAA) can also be used, when the washed hair specimens are placed in small, weighed, TE-free, polyethylene bags or tubes, and oven dried for 24h at 55°C. After cooling in a desiccator, the packaged specimens are sealed and weighed, prior to irradiation in a nuclear reactor.
A Certified Reference Material (CRM) for human hair is available (e.g., Community Bureau of Reference, Certified Reference Material no. 397) from the Institute for Reference Materials and Measurements, Retieseweg, B-2440 Geel, Belgium. Currently, interpretation of hair trace element concentrations for screening populations at risk of deficiency is limited by the absence of universally accepted reference values. For a detailed step-by-step guide to measuring hair zinc concentrations, the reader is advised to consult IZiNCG (2018).
In summary, more data on other tissues from the same individuals are urgently required to interpret the significance of hair trace element concentrations. Hair is certainly a very useful indicator of the body burden of heavy metals such as lead, mercury, cadmium and arsenic. It is also valuable in the case of selenium and chromium, and possibly zinc. Data for other elements such as iron, calcium, magnesium, and copper should be interpreted with caution (Seidel et al., 2001).
15.3.10 Fingernails and toenails
Nails have been investigated as biopsy materials for trace element analysis (Bank et al., 1981; van Noord et al., 1987). Nails, like hair, also incorporate trace elements into the nail matrix when it is exposed to the blood supply within the nail matrix germinal layer, and thus reflect the quantity of trace elements available in the blood supply at the time of nail synthesis (He, 2011). During the growth of the nail, the proliferating cells in the nail germinal layer are converted into horny lamellae. Nails grow more slowly than hair at rates ranging from 1.6mm/month for toenails to 3.5mm/month for fingernails, and, like hair, are easy to sample and store. In cases where nail growth is arrested, as may occur in onychophagia (compulsive nail biting), nails should not be used (He, 2011).
The elemental composition of toenails has been used as a long-term biomarker of nutritional status for some elements, notably selenium. Selenium concentrations in toenails correlate with geographic differences in selenium exposure ( Figure 15.5), (Morris et al., 1983; Hunter et al., 1990). At the individual level, concentrations of selenium in toenails correlate with those in habitual diets, serum, and whole blood (Swanson et al., 1990).In a recent study of young children in Laos, nail zinc concentrations were higher at endline in those children receiving a daily preventive zinc supplement (7–10mg Zn/d) for 32–40 weeks compared to those given a therapeutic zinc dose (20g) for only 10d (geometric mean, 95% CI) (115.8, 111.6–119.9 vs. 110.4, 106.0–114.8µg/g; p=0.055) (Wessells et al., 2020). Nail zinc concentrations have also been used as a longer-term retrospective measure of zinc exposure in case-control studies. For example, in a prospective study of U.S. urban adults (n=3,960), toenail zinc was assessed in relation to the incidence of diabetes, although no significant longitudinal association was found (Park et al., 2016).
The elemental composition of nails is influenced by age, possibly sex, rate of growth, onychophagia (compulsive nail biting), geographical location, and possibly by disease states (e.g., cystic fibrosis, Wilson's disease, Alzheimer's disease, and arthritis) (Takagi et al., 1988; Vance et al., 1988). Environmental contamination and chemicals introduced by nail polish could be a potential problem, unless they are removed by washing (He, 2011). Bank et al. (1981) recommend cleaning fingernails with a scrubbing brush and a mild detergent, followed by mechanical scraping to remove any remaining soft tissue before clipping. Nail samples should then be washed in aqueous non-ionic detergents rather than organic solvents, and dried under vacuum prior to preparation and analysis by the same traditional analytical techniques as are used for hair specimens. Tetramethylammonium hydroxide (TMAH) can also be used to solubilize nails at room temperature, eliminating time-consuming ashing or wet digestion, thus enhancing sample throughput (Batista et al., 2018).
For non-destructive analytical methods such as instrumental neutron activation analysis (INAA) and the newer technique involving laser-induced breakdown spectroscopy (LIBS), cleaning fingernail clippings with acetone (analytical grade) in an ultrasonic bath for 10min followed by drying in air for 20–30min is recommended (Riberdy et al., 2017). Preliminary results suggest that in situ measurement of fingernail zinc by LIBS has potential as a non-invasive, convenient screening tool for identifying zinc deficiency in populations, but may lack the precision required to generate absolute concentrations for individuals (Riberdy et al., 2017). A non-destructive portable X-ray fluorescence system has also been used to explore the measurement of zinc in a single nail clipping; more studies are needed to establish its usefulness (Fleming et al., 2020).
Unlike hair, no Standard Reference Materials presently exist for nail trace element analysis. Instead, in-house controls prepared from homogenous pooled samples of powdered fingernails and toenails can be prepared and spiked with several different known quantities of the trace element of interest and the recoveries measured. Alternatively, an aliquot of the in-house control can be sent to a reputable laboratory and the results compared. Likewise, there are no universally accepted reference values for nail trace element concentrations, limiting their use for assessing risk of trace element deficiencies in populations. More studies comparing the trace element composition of fingernails and toenails with corresponding concentrations in other biomarkers of body tissues and fluids, as well as habitual dietary intakes, are needed before any definite recommendations on the use of fingernails or toenails as a biomarker of exposure or status can be made.
15.3.11 Buccal mucosal cells
Buccal mucosal cells have been investigated as a biopsy sample for assessing α‑tocopherol status (Kaempf et al., 1994; Chapter 18) and dietary lipid status (McMurchie et al., 1984; Chapter 7), but interpretive criteria to assess these results are not available. These cells have also been explored as a biomarker of folate status (Johnson et al., 1997), although smoking is a major confounder as a localized folate deficiency is generated in tissues exposed to cigarette smoke (Piyathilake et al., 1992). Buccal mucosal cells are also increasingly used in epidemiological studies that involve DNA (Potischman, 2003).Buccal mucosal cells can be sampled easily and noninvasively by gentle scraping with a spatula. Cells must be washed with isotonic saline prior to sonication and analysis. Contamination of buccal cells with food is a major problem, however, and has prompted research into new methods for the collection of buccal mucosal cells.
15.3.12 Urine
If renal function is normal, biomarkers based on urine or the urinary excretion rate of a nutrient or its metabolite can be used to assess exposure or status for some trace elements (e.g., chromium, iodine, selenium), the water-soluble B‑complex vitamins, and vitamin C. The method depends on the existence of a renal conservation mechanism that reduces the urinary excretion of the nutrient or metabolite when body stores are depleted. Urine cannot be used to assess the status of the fat-soluble vitamins A, D, E, and K, as metabolites are not excreted in proportion to the amount of these vitamins consumed, absorbed, and metabolized.Urinary excretion can also be used to measure exposure to certain nutrients, as well as some food components and food groups. Isaksson (1980) was one of the first investigators to use urinary nitrogen excretion levels in single 24h urine samples to estimate exposure to protein intakes from a 24h food record. Since that time, several urinary biomarkers for other nutrients, and for certain food components and food groups, have been investigated, in some cases as biomarkers of exposure or status, as noted in Section 15.2.
Urinary excretion assessment methods almost always reflect recent dietary intake or acute status, rather than chronic nutritional status. If information on long-term exposure is required, multiple 24h urine samples collected over a period of weeks should be used. For example, to obtain a stable measurement of long-term exposure to sodium, potassium, calcium, phosphate and magnesium, three 24h urine samples from healthy adults spaced over a predefined time period are required (Sun et al., 2017).
For some of the water-soluble vitamins (e.g., thiamin, riboflavin and vitamin C), the amount excreted depends on both the nutrient saturation of tissues and on the dietary intake. Furthermore, urinary excretion tends to reflect intake when intakes of the vitamins are moderate to high relative to the requirements, but less so when intakes are habitually low. In other circumstances such as infections, trauma, the use of antibiotics or medications, and conditions that produce negative balance, increases in urinary excretion may occur despite depletion of body nutrient stores. For example, drugs with chelating abilities, alcoholism, and liver disease can increase urinary zinc excretion, even in the presence of zinc deficiency.
For measurement of a nutrient or a corresponding metabolite in urine, it is essential to collect a clean, properly preserved urine sample, preferably over a complete 24h period. Thymol crystals dissolved in isopropanol are often used as a preservative (Mente et al., 2009). For nutrients that are unstable in urine (e.g., vitamin C), acidification and cold storage are required to prevent degradation.
To monitor the completeness of any 24h urine collection, urinary creatinine excretion is often measured (Chapter 16). This approach assumes that daily urinary creatinine excretion is constant for a given individual, the amount being related to muscle mass. In fact, this excretion can be highly variable within an individual (Webster and Garrow, 1985), and varies with age (Yuno et al., 2011).- Failure to take all three PABA tablets
- Taking tablets late in the evening with a large meal that reduces gastric emptying time and uptake in the intestine
- Impaired renal function
- Errors in preparation of urine aliquots
- Analytical errors
British investigators have used an alternative marker, paraaminobenzoic acid (PABA), to assess the completeness of urine collections (Bingham and Cummings, 1985). Para-aminobenzoic acid is taken in tablet form with meals — one tablet of 80mg PABA three times per day. It is harmless, easy to measure, and rapidly and completely excreted in urine.
Possible explanations for low PABA recovery values besides the under-collection of urine samples are summarized in Box 15.8. Studies have shown that any urine collection containing less than 85% of the administered dose is probably incomplete (Bingham and Cummings, 1985), suggesting that PABA is a useful marker for monitoring the completeness of urine collection. The incomplete nature of urine collections with a mean PABA recovery of < 79% is emphasized in Figure 15.6.
A method has been devised for adjusting urinary concentrations of nitrogen, sodium and potassium in cases where the recovery of PABA is between 50% and 80%. It is based on the linear relationship between the PABA recovery and the amount of analytes in the urine, as shown in Figure 15.7, and allows the use of incomplete 24h urine collections. However, this adjustment method is not recommended in cases where PABA recovery is below 50% Figure 15.7.Several investigators have measured urinary biomarker concentrations of nitrogen, sodium and potassium to validate dietary intakes in population studies, some of which assessed the completeness of 24h urine collection by analysis of PABA concentration in the urine. For example, Wark et al. (2018) assessed the validity of intakes in adults (n=212) of protein, sodium and potassium estimated from 3 × 24h recalls taken 2 weeks apart using an online 24h recall tool (myfood24) by comparison with urinary biomarkers. Participants were instructed to take one 80mg PABA tablet with each of three meals during the 24h urine collection period, and urinary concentrations for nitrogen, sodium and potassium were then adjusted for completeness of urine samples when PABA recovery was 50–85%. The investigators calculated that 93% of PABA, 81% of nitrogen, 86% of sodium and 80% of potassium were excreted within 24h. Table 15.2.
myfood24 | Biomarker/reference tool | |||
---|---|---|---|---|
n | Geometric mean (95% CI) | n | Geometric mean (95% CI) | |
Nutrient intake: | ||||
Protein (g) | 208 | 70.5 (66.1, 75.2) | 192 | 68.4 (64.1, 72.8) |
Potassium (g) | 208 | 2.7 (2.5, 2.9) | 192 | 2.1 (1.9, 2.3) |
Sodium (g) | 208 | 2.3 (2.1, 2.5) | 192 | 1.8 (1.7, 2.0) |
Nutrient density: | ||||
Protein (g/MJ) | 208 | 9.5 (9.0, 9.9) | 180 | 6.2 (5.8, 6.7) |
Potassium (g/MJ | 208 | 0.36 (0.35, 0.38) | 180 | 0.19 (0.18, 0.21) |
Sodium (g/MJ) | 208 | 0.31 (0.29, 0.33) | 180 | 0.16 (0.15, 0.18) |
Twenty-four-hour urine samples can be difficult to collect in non-institutionalized population groups. Instead, first-voided fasting morning urine specimens are often used, as they are less affected by recent dietary intake. Such specimens were used in the U.K. National Diet and Nutrition Survey of young people 4–18y (Gregory et al., 2000). Special Bori-Vial vials containing a small amount of boric acid as a preservative can be used for the collection of first-voided fasting samples. Sometimes, only nonfasting casual urine samples can be collected. Such casual urine samples are not recommended for studies at the individual level, because concentrations of nutrients and metabolites in such samples are affected by liquid consumption, recent dietary intake, body weight, physical activity and other factors.
When first-voided fasting or casual urine specimens are collected, urinary excretion is sometimes expressed as a ratio of the nutrient to urinary creatinine in an effort to correct for both diurnal variation and fluctuations in urine volume. For some urinary biomarkers, specific gravity has been used to correct for urine volume in casual urine samples rather than urinary creatinine (Newman et al., 2000).
As a biomarker of recent exposure to iodine at the population level, WHO / UNICEF / ICCIDD, 2007 recommend collecting casual urine samples and expressing the results in terms of the population median urinary iodine concentration (µg/L). A median urinary iodine concentration of 100–199µg/L in school-age children, for example, indicates adequate iodine nutrition. However, this does not quantify the percentage of individuals with habitually deficient or excessive intakes of iodine.
Daily iodine intake can be calculated from urinary iodine based on the following assumptions: over 90% of iodine is excreted in the urine in the subsequent 24–48h; median 24h urine volume is about 0.0009L/h/kg; average bioavailability of iodine in the diets is 92%. Therefore:
\[\small \mbox {Iodine intake = 0.0009 × 24/0.92 × Wt × Ui }\] \[\small \mbox{ = 0.0235 × Wt × Ui }\] where Wt is the body weight (kg) and Ui is the urinary iodine (µg/L).
This equation has been applied to calculate daily iodine intakes of children based on casual urinary iodine concentrations collected during national surveys in Kuwait, Oman, Thailand, and Qatar, and during a regional study in China. In these surveys, a second repeat casual urine sample was collected in a random subsample of the children on a nonconsecutive day (Figure 15.8). This permits an adjustment to be made to the observed distribution of iodine intakes to remove the variability introduced by day-to-day variation in iodine intakes within an individual (i.e., to remove the within-subject variation) using specialized software, in this case the Iowa State University method (Carriquiry, 1999). For more details of this adjustment method, see Chapter 3. Age group of children |
Unadjusted prevalence below the EAR |
True prevalence below the EAR, adjusted with internal variance |
Unadjusted prevalence above the UL |
True prevalence above the UL, adjusted with internal variance |
---|---|---|---|---|
4–8y | ||||
Kuwait | 35.3 ± 1.7 | 19.4 ± 5.7 | 2.4 ± 0.5 | 0.2 ± 0.4 |
Oman | 24.3 ± 1.8 | 7.5 ± 4.7 | 2.7 ± 0.7 | 0.2 ± 0.5 |
China | 20.5 ± 2.5 | 10.1 ± 4.4 | 10.2 ± 1.9 | 8.2 ± 4.0 |
9–13y | ||||
Kuwait | 30.9 ± 1.4 | 17.4 ± 3.6 | 0.7 ± 0.2 | 0.1 ± 0.1 |
Oman | 18.6 ± 1.1 | 10.5 ± 2.1 | 0.4 ± 0.2 | 0.2 ± 0.2 |
China | 24.0 ± 3.9 | 3.5 ± 7.3 | 1.7 ± 1.2 | 0.0 ± ND |
15.4 Biomarkers of function
Functional biomarkers can be subdivided into two groups: functional biochemical, and functional physiological or behavioral, biomarkers. They measure the extent of the functional consequences of a specific nutrient deficiency and hence have greater biological significance than the static biomarkers, as noted earlier some functional biomarkers are also being used as substitutes for chronic disease outcomes, when they are termed “surrogate biomarkers” (Yetley et al., 2017).Functional biochemical biomarkers serve as early biomarkers of subclinical deficiencies. They may involve the measurement of an abnormal metabolic product in blood or urine samples arising from a deficiency of a nutrient- dependent enzyme. Alternatively, for some nutrients, reduction in the activity of enzymes that require a nutrient as a coenzyme or prosthetic group can be measured. For example, the activity of erythrocyte glutamic oxaloacetic transaminase has been reported to better reflect the intake of vitamin B6 than the plasma concentrations of pyridoxal phosphate, especially in adults < 65y (Elmadfa and Meyer, 2014).
Changes in blood components related to intake of a nutrient can also be determined, and load or tolerance tests conducted on individuals in vivo. Sometimes, tissues or cells are isolated and maintained under physiological conditions for biomarkers of in vivo functions. Biomarkers related to host defense and immunocompetence are the most widely used of this type. For some of the nutrients (e.g., niacin), functional biochemical biomarkers may not be available.
In research settings, stable isotope techniques are used to measure the size of the body pool(s) of a nutrient (e.g., the vitamin A content of the liver; see Chapter 18), and for kinetic modeling to assess the integrated whole-body response to changes in nutrient status (e.g., protein, copper, zinc). The latter approach is especially useful for detecting subtle changes that may not be responsive to static indices (King et al., 2000). Figure 15.9 shows a marked reduction in the endogenous fecal excretion of zinc over a 6mo period on a low-zinc diet. Such a reduction can be quantified only with isotopic techniques.New molecular techniques are now used in research to measure, for example, mRNA for proteins (e.g., metallothionein), the expression of which is regulated by metal ions such as zinc (Hirschi et al., 2001). Correlations between biomarkers of DNA damage and micronutrient status are also being investigated in view of the growing knowledge of their roles as cofactors or as components of DNA repair enzymes. For example, marginal zinc depletion impairs DNA repair and increases the number of DNA strand breaks. However, these breaks are not specific markers for zinc depletion as insufficient intakes of choline, folate, and niacin also cause an increase in DNA strand breaks (Zyba et al., 2017). Genetic variation can now be identified through DNA testing, and when used in combination with nutritional biomarkers, can assist in understanding variations in metabolism and in identifying subpopulations at risk of disease; see Section 15.7.
Most functional physiological and behavioral biomarkers are less invasive, often easier to perform, and more directly related to disease mechanisms or health status than are functional biochemical biomarkers. In general, however, functional physiological or behavioral biomarkers are not very sensitive or specific and must be interpreted in conjunction with more specific nutrient biomarkers. As noted earlier, these functional physiological and behavioral biomarkers often measure the net effects of contextual factors that often include social and environmental factors as well as nutrition.
Disturbances in these biomarkers are generally associated with more prolonged and severe nutrient deficiency states or in some circumstances, risk of chronic diseases (Yetley et al., 2017). Examples include measurements of impairments in growth, of response to vaccination (as a biomarker of immune function), and of vision, motor development, cognition, depression and high blood pressure, all of which are less invasive and easier to perform. Some important examples of functional biomarkers include the following:
Functional biochemical biomarkers
- Abnormal concentrations of metabolic products in blood or urine arising from reduced activity of a nutrient-dependent enzyme (e.g., urinary excretion of xanthurenic acid, formiminoglutamic acid (FIGLU), and methylmalonic acid as a test of vitamin B6 and vitamin B12 deficiency.
- Changes in enzyme activities that depend on a given nutrient (e.g., erythrocyte glutathione reductase activity for riboflavin; erythrocyte transketolase activity for thiamin; erythrocyte glutamic oxaloacetic transaminase for vitamin B6).
- Changes in blood components (e.g., whole blood hemoglobin for iron assessment; thyroglobulin for iodine status; retinol-binding protein for vitamin A; holotranscobalamin for vitamin B12).
Functional physiological and behavioral biomarkers
- In vitro tests of in vivo functions (e.g., lymphocyte proliferation for protein-energy, zinc, and iron).
- Load and tolerance tests and induced responses in vivo (e.g., Relative Dose Response: load test for vitamin A; CobaSorb test: load test to assess vitamin B12 absorption).
- Induced responses in vivo (e.g., delayed-type hypersensitivity, often used to identify protein-energy malnutrition).
- Spontaneous in vivo responses (e.g., dark adaptation / vision at low intensity for vitamin A; taste acuity for zinc; handgrip strength for lower-body strength).
- Growth or developmental responses (e.g., growth velocity for protein-energy, zinc, etc.; cognitive performance for iron, iodine, vitamin D, folate, and vitamin B12; motor development for micronutrients; depression for folate and zinc).
15.4.1 Abnormal metabolic products in blood or urine
Many of the vitamins and minerals act as coenzymes or as prosthetic groups for enzyme systems. During deficiency, the activities of these enzymes may be reduced, resulting in the accumulation of abnormal metabolic products in the blood or urine.Xanthurenic acid excretion in urine, together with other tryptophan metabolites, is elevated in vitamin B6 deficiency because the activity of kynureninase in the tryptophan-niacin pathway is reduced. This leads to the increased formation and excretion in the urine of xanthurenic acid and other tryptophan metabolites, including both kynurenic acid and 3-hydroxyl-kynurenine. Determination of urinary xanthurenic acid is usual because it is easily measured.
Plasma homocysteine concentrations are elevated in both vitamin B12 and folate deficiency. In vitamin B12 deficiency, when levels fall below 300pmol/L, the activity of methionine synthase, an enzyme that requires vitamin B12, is reduced. This enzyme catalyzes the remethylation of homocysteine to methionine. Hence, reduction in the activity of methionine synthase leads to increases in plasma homocysteine concentrations (Allen et al., 2018). The remethylation pathway of homocysteine to methionine is also dependent on folate, so when folate status is low or deficient, then plasma homocysteine is generally elevated (Bailey et al., 2015). Therefore, in folate or vitamin B12 deficiency, homocysteine accumulates and concentrations in plasma increase. Measurement of plasma homocysteine as a sensitive functional biomarker of low folate status has been recommended by the BOND Folate Expert Panel. However, they highlight its poor specificity because it is elevated with other B‑vitamin deficiencies besides folate and vitamin B12(including vitamin B6, and riboflavin), with lifestyle factors, with renal insufficiency, and with drug treatments (Bailey et al., 2015).
Elevated circulating homocysteine concentrations have been associated with an increased risk of hypertension, cardiovascular disease, and cerebrovascular disease based on observational studies. Several mechanisms have been proposed whereby hyperhomocysteinemia may mediate risk of these diseases. Details of the collection and analyses of plasma samples for homocysteine are available in Bailey et al. (2015).
Methyl malonic acid (MMA) concentrations in plasma or urine are elevated in vitamin B12 deficiency but unaffected by folate or other B vitamins. Vitamin B12 serves as a cofactor for the enzyme methylmalonic-CoA mutase. This enzyme is required for the conversion of methylmalonyl-CoA to succinyl-CoA. Methylmalonic acid (MMA) is a side reaction product of methylmalonyl-CoA metabolism, and increases with vitamin B12 depletion. Concentrations of MMA reflect B12 stores rather than recent B12 intake and are considered a relatively specific and sensitive biomarker of vitamin B12 status by the BOND Vitamin B12 Expert Panel (Allen et al., 2018).
In serum or urine, MMA concentrations reflect the adequacy of B12 status for the biochemical function of the enzyme methylmalonic-CoA mutase, which is required for the conversion of methylmalonyl-CoA to succinyl-CoA. MMA, usually a side reaction product of methylmalonyl CoA metabolism, increases with B12 depletion (Allen et al., 2018). If urinary MMA is to be measured and the collection of 24h urine samples is not feasible, then urinary creatinine should also be assayed to correct for variability in urine concentration and results expressed as per mg or mmol creatinine.
For more information on elevated levels of homocysteine and MMA, readers are advised to consult the two BOND reports: Bailey et al. (2015) and Allen et al. (2018).
15.4.2 Reduction in activity of enzymes
Methods that involve measuring a change in the activity of enzymes which require a specific nutrient as a coenzyme or prosthetic group are generally the most sensitive and specific. Often the enzyme is associated with a specific metabolic defect and associated nutrient deficiency (e.g., lysyl oxidase for copper, aspartate aminotransferase for vitamin B6, glutathione reductase for riboflavin, transketolase for thiamin).The activity of the enzyme is sometimes measured both with and without the addition of saturating amounts of the coenzyme added in vitro. The in vitro stimulation of the enzyme by the coenzyme indicates the degree of unsaturation of the enzyme, and therefore a measure of deficiency. When nutritional status is adequate, the added coenzyme has little effect on the overall enzyme activity, so the ratio of the two measurements is very close to unity. However, when a deficiency exists, the added coenzyme increases enzyme activity to a variable extent, depending on the degree of deficiency. Such tests, often termed “enzyme stimulation tests”, may be used for vitamin B6, riboflavin and thiamin, and employ the activities of aminotransferases, glutathione reductase and transketolase respectively. Erythrocytes are used for these enzyme stimulation tests because erythrocytes are particularly sensitive to marginal deficiencies and provide an accurate reflection of body stores for vitamin B6, riboflavin, and thiamin. Indeed, such vitamin-deficient erythrocytes may respond to supplements of B6, riboflavin, and thiamin within 24 hours.
The test measures the extent to which the erythrocyte enzyme has been depleted of coenzyme, and the results are expressed either as the Activation coefficient or as the Percentage Stimulation: \[\small \mbox{Activation coefficient = } \frac {\mbox {activity of the coenzyme − stimulated enzyme}}{\mbox {activity of unstimulated enzyme}}\] \[\small \mbox{Percentage Stimulation = }\frac{\mbox {stimulated activity – basic activity}}{\mbox{basic activity}}× \mbox{100%}\] Table 15.4 Vitamin Thiamin | Enzyme Transketolase | Coenzyme and comments thiamine pyrophosphate |
---|---|---|
Status: AC: 1.00-1.25 | Normal or marginal status except when basic transketolase activity is low, then probably chronic deficiency. |
Unstable enzyme. Store at −70°C or measure fresh |
Status: AC: > 1.25 | Biochemical deficiency, high
values likely to be acute deficiency | 1.15
–1.25 may
be at intermediate risk |
Riboflavin | Glutathione reductase | adenine dinucleotide |
Status: AC: 1.00-1.30 | Normal status | Very stable enzyme |
Status: AC: 1.30-1.80 | Marginal/deficient status | Measure of tissue status. |
Status: AC: > 1.80 | Deficient, intake <0.5mg riboflavin/d | Unreliable in -ve N2 balance |
Pyrodoxine | Aspartate aminotransferase | pyridoxal phosphate |
Status: AC: 1.00-1.50 | Normal status | No agreed standard method |
Status: AC: 1.50-2.00 | Marginal status | No agreement on thresholds |
Status: /AC: > 2.00 | Deficient status | Uncertain stability at −20°C |
Many nutrients have more than one functional role and thus the activities of several enzymes may be affected during the development of a deficiency, thereby providing additional information on the severity of the deficiency state. For example, in the case of copper, platelet cytolysyl oxidasechrome c oxidase (Chapter 24) is more sensitive to deficiency than plasma erythrocyte superoxide dismutase, the activity of which is reduced only in more severe deficiency states (Milne and Nielsen, 1996).
15.4.3 Changes in blood components
Instead of measuring the activity of an enzyme, changes in blood components that are related to the intake of a nutrient can be measured. A well-known example is the measurement of hemoglobin concentrations in whole blood for iron deficiency anemia; iron is an essential component of the hemoglobin molecule (Chapter 17). Other examples include the determination of the two transport proteins — transferrin and retinol-binding protein (RBP) — as indicators of iron and vitamin A status, respectively, serum holotranscobalamin, a functional biomarker of vitamin B12 deficiency (Allen et al., 2018), and serum thyroglobulin, a thyroid-specific protein and a storage and synthesis site for thyroid hormones (Rohner et al., 2014;Serum RBP is used increasingly as a proxy for serum retinol to assess vitamin A status at the population level, correlating closely with serum retinol concentrations, at least in individuals with normal kidney function who are not obese (Tanumihardjo et al., 2016). RBP in serum is also more stable, and easier and cheaper to analyze than retinol, although as with retinol, levels are reduced during inflammation. RBP is synthesized primarily in hepatocytes as the apo-form and secreted bound to retinol as the holo-RBP complex to provide vitamin A to peripheral tissues; one molecule of holo-RBP binds to one molecule of retinol. However, RBP is not secreted when stores of vitamin A are low and retinol limited. Because holo‑RBP is complexed with transthyretin, loss of holo‑RBP to glomerular filtration in the kidney is prevented.
Serum holotranscobalamin (holoTC), the component that delivers vitamin B12 to the tissues, has become increasingly used as a functional biomarker of B12, with a specificity and sensitivity slightly higher than that of serum methylmalonic acid (MMA). Serum holoTC is most sensitive to recent intake, when concentrations can be increased even if stores are low. Concentrations of holoTC, like serum MMA, are elevated in persons with impaired renal function, but are unaffected by pregnancy. Currently, there is no consensus on the cutoff to use and the assay of serum holoTC is expensive and not widely available (Allen et al., 2018). For more details see Chapter 22.
Serum thyroglobulin is recommended by WHO (2007) for monitoring the iodine status of school-aged children and a reference range for this age group has been established. Thyroglobulin concentrations in dried blood spots are also under investigation as a sensitive biomarker of iodine status in pregnant women (Stinca et al., 2017).
15.4.4 In vitro tests of in vivo functions
Tissue samples or cells can be removed from test subjects and isolated and maintained under physiological conditions. Attempts can then be made to replicate in vivo functions under in vitro conditions. Tests related to host-defense and immunocompetence are probably the most widely used assays of this type. They appear to provide a useful, functional, and quantitative measure of nutritional status.Thymus‑dependent lymphocytes originate in the thymus and are the main effectors of cell‑mediated immunity. During protein-energy malnutrition, both the proportion and the absolute number of T‑cells in the peripheral blood may be reduced.Peripheral T‑lymphocytes are isolated from heparinized blood, then stained with fluorescent-labeled monoclonal antibodies (mABs), prior to analysis on a flow cytometer. The flow cytometer measures the properties of light scattering by the cells and the emission of light from fluorescent-labeled mAbs bound to the surface of the cell; details are given in Field (1996).
Lymphocyte proliferation assays are also examples of tests of this type. They are functional measures of cell-mediated immunity, assessed by the in vitro responses of lymphocytes to selected mitogens. Again, peripheral T‑lymphocytes are isolated from blood and incubated in vitro with selected mitogens (Field, 1996). Details are summarized in Chapter 16.
Other in vitro tests include the erythrocyte hemolysis test and the dU suppression test, although the latter is no longer used. In the former, the rate of hemolysis of erythrocytes is measured; the rate correlates inversely with serum tocopherol levels (Chapter 18). Unfortunately, this test is not very specific, as other nutrients (e.g., selenium) influence the rate of erythrocyte hemolysis.
15.4.5 Load tests and induced responses in vivo
In the past, functional biomarkers conducted on the individual in vivo included load and tolerance tests (Solomons and Allen, 1983). Today, many of these tests are no longer used and have largely been superseded by other methods.Load tests were used to assess deficiencies of water-soluble vitamins (e.g., tryptophan load test for pyridoxine, histidine load test for folic acid, vitamin C load test), and certain minerals (e.g., magnesium, zinc and selenium). In a load test, the baseline urinary excretion of the nutrient or metabolite is first determined on a timed preload urine collection (Robberecht and Deelstra, 1984). Then a loading dose of the nutrient or an associated compound is administered orally, intramuscularly, or intravenously. After the load, a timed sample of the urine is collected and the excretion level of the nutrient or a metabolite determined. The net retention of the nutrient is calculated by comparing the basal excretion data with net excretion after the load. In a deficiency state, when tissues are not saturated with the nutrient, excretion of the nutrient or a metabolite will be low because net retention is high.
The relative dose response (RDR) test is the most well known functional in vivo load test in use today. This test is accepted as a functional reference method to assess the presence or absence of low vitamin A stores in the liver (Chapter 18). However, in the RDR test, unlike the conventional loading tests described above, the response is greatest in deficient individuals. The principal of RDR is based on the observation that in vitamin A inadequacy, retinol binding protein (RBP) that has not bound to retinol (apo‑RBP) accumulates in the liver. Following the administration of a test dose of vitamin A (commonly in the form of retinyl palmitate), some of the retinol binds to the accumulated apo‑RBP in the liver and the resulting holo‑RBP (i.e., RBP bound to retinol) is rapidly mobilized from the liver into the circulation. In individuals with vitamin A deficiency, a small dose of retinyl palmitate leads to a rapid sustained increase in serum retinol, whereas in vitamin A replete individuals, there is very little increase. An RDR value > 20% is considered to reflect vitamin A stores of < 0.7µmol/g liver (WHO, 1996). Investigations are underway to explore the assessment of the RDR test based on serum RBP to determine low hepatic vitamin A stores instead of serum retinol in an effort to eliminate the need to use HPLC for the serum retinol assay (Fujita et al., 2009).
The modified relative dose response (MRDR) has been developed as an alternative because the RDR test requires two blood samples per individual. The MRDR uses 3,4‑didehydroretinyl acetate (DRA), or vitamin A2 instead of retinyl palmitate as the challenge dose, and requires only a single blood sample, taken between 4 and 7h after dosing. Serum is analyzed for both 3,4,‑didehydroretinol (DR) and retinol in the same sample, and the ratio of DR to retinol in serum is called the MRDR value and used to indicate liver reserves. Values ≥ 0.060 at the individual level usually indicate insufficient liver reserves (≤ 0.1µmol retinol/g), whereas values < 0.06 are indicative of sufficient liver reserves (≥ 0.1µmol retinol/g). Group mean ratios of < 0.030 appear to correlate with adequate status. The MRDR test has been used in numerous population groups, in both children and adults worldwide. The values obtained, however, are not useful for defining vitamin A status above adequacy. For more details of the use of RDR and MRDR as functional biomarkers of vitamin A status, see Tanumihardjo et al. (2016).
The qualitative CobaSorb test is another example of an in vivo load test. It is used to detect malabsorption of vitamin B12 and has replaced the earlier Schilling test and its food-based version (using cobalamin-labeled egg yolk), which have been discontinued. For the CobaSorb test, a dose of 9mg of crystalline B12 in water is administered orally at 6h intervals over a 24h period, and the increase in serum holo-transcobalamin measured on the following day. The test is a qualitative assay and is used to determine if patients will respond to low-dose B12 supplements or will require treatment with pharmacological doses. The test does not provide a quantitative estimate of bioavailability of vitamin B12 — for more details of the test, see Brito et al. (2018).
Delayed-type hypersensitivity is a well known example of a biomarker based on an induced response in vivo. This is a direct functional measure of cell-mediated immunity used in both hospital and community settings. Suppression of cell-mediated immunity signals a failure of multiple components of the host-defense system. The test involves injecting a battery of specific antigens intradermally into the forearm; those commonly used are purified protein derivative (PPD), mumps, Tricophyton, Candida albicans, and dinitrochlorobenzene (DNCB). In healthy persons re-exposed to recall antigens intradermally, the recall antigens induce the T‑cells to respond first by proliferation and then by the release of soluble mediators of inflammation, producing an induration (hardening) and erythema (redness). This induced response is noted at selected time intervals, and is often reduced in persons with protein-energy malnutrition and micronutrient deficiencies such as vitamin A, zinc, iron and pyridoxine. However, the test is not specific enough to detect individual micronutrient deficiencies (Raiten et al., 2015). For details of the technique, interpretation, and some of the limitations of DTH skin testing, see Ahmed and Blose (1983).
15.4.6 Spontaneous in vivo responses
Functional tests based on spontaneous in vivo responses often measure the net effects of contextural factors that may include social and environmental factors as well as nutrition. Hence, they are less sensitive and specific than biomarkers that assess nutrient exposure, status, or biochemical function (Raiten and Combs, 2015). As a consequence, they should be assessed alongside more specific biomarkers so that the functional impact of the status of a specific nutrient can be identified.Formal dark adaptometry is one of several functional biomarkers based on spontaneous physiological in vivo responses that exist for vitamin A. It was the classical method for assessing night blindness (i.e., poor vision in low-intensity light) associated with vitamin A deficiency. This condition arises when the ability of the rod cells in the retina to adapt in the dark, and the ability of the pupils to properly meter light in and out of the eye, are impaired. However, the equipment used for formal dark adaptometry was cumbersome, and the method very time-consuming, so it is no longer used.
Rapid dark adaptation test (RDAT) has superseded formal dark adaptometry, with results that correlate with those of the classical method. The test is based on the measurements of the timing of the Purkinje shift, in which the peak wavelength sensitivity of the retina shifts from the red toward the blue end of the visual spectrum during the transition from photopic or cone-mediated day vision to scotopic or rod-mediated night vision. This shift causes the sensitivity of blue light to appear brighter than that of red light under scotopic lighting conditions. The test requires a light-proof room, a light source, a dark, non-reflective work surface, a standard X-ray view box, and sets of red, blue, and white discs; details are given in Vinton and Russell (1981). The RDAT, however, is not appropriate for young children.
Pupillary threshold test can be used for children from age 3y, for adults, and under field conditions (Tanumihardjo et al., 2016). The test measures the threshold of light at which pupillary contraction occurs under dark adapted conditions. Minimal cooperation from the subjects is required for the test which is performed in a darkened facility, often a portable tent, and takes about 20min per subject. Special pairs of goggles have been invented to measure the pupillary response to light stimuli — details are given in Chapter 18.
Capillary fragility has been used as a functional biomarker of vitamin C deficiency since 1913 because frank petechial hemorrhages occur in overt vitamin C deficiency. The test, however, is not very specific to vitamin C deficiency states (see Chapter 19); static biochemical tests are preferred to assess the status of vitamin C.
Taste acuity can be associated with suboptimal zinc status in children and adults, (Gibson et al., 1989), as well as in some disease states in which secondary zinc deficiency may occur (e.g., cystic fibrosis, Crohn's disease, celiac sprue and chronic renal disease (Desor and Maller, 1975; Kim et al., 2016). Positive associations between taste acuity for salt and biomarkers of zinc status (i.e., erythrocyte zinc) have been reported in the elderly (Stewart-Knox et al., 2005). Moreover, an increase in taste acuity for salt was reported in older adults in response to 30mg zinc/day compared to a placebo during a six-month double-blind randomized controlled trial (Stewart-Knox et al., 2008).
Taste acuity can be assessed by using the forced drop method that measures both the detection and recognition thresholds (Buzina et al., 1980), or the recognition thresholds only (Desor and Maller, 1975). An electrogustometer, which measures taste threshold by applying a weak electric current to the tongue, has been used in some studies (Prosser et al., 2010). Many other factors affect taste function, and taste acuity alone should not be used to measure zinc status.
Handgrip strength measured using a dynamometer, is a well-validated proxy measurement for lower-body strength (Abizanda et al., 2012). It has been used in several intervention studies designed to improve muscle strength and function among non-malnourished sarcopenic older adults at high risk for disability (Bauer et al., 2015; Tieland et al., 2015).
15.4.7 Growth responses
Responses in growth have limited sensitivity and are not specific for any particular nutrient, and hence are preferably measured in association with other more specific nutrient biomarkers.Linear growth is considered the best functional biomarker associated with the risk of zinc deficiency in populations. It is is usually measured alongside serum zinc, a biomarker of zinc exposure and status at the population level. The International Zinc Consultative Group (IZiNCG) recommend using the percentage of children under five years of age with length-for-age (LAZ) or height-for-age z‑score (HAZ) < −2 as a functional biomarker to estimate the risk of zinc deficiency in a population (Technical Brief No.01, 2007; de Benoist et al., 2007).
The WHO Global Database on Child Growth and Malnutrition also use LAZ or HAZ < −2 to define children as “stunted”, and include stunting as one of the six global nutrition targets for 2030. In a healthy population, about 2.5% of all children have a LAZ or HAZ < −2. In communities where short stature is the norm, stunting often goes unrecognized.
Figure 15.10 presents the distribution of length/height-for-age Z‑scores of children from the India National Family Health Survey 2005–2006, and shows the entire distribution shifted to the left compared with the WHO Child Growth Standards (WHO, 2006). These results highlight the fact that those children who are stunted are only a subset of those with linear growth retardation. Here all the children were affected by some degree of linear growth retardation (de Onis and Branca, 2016).Height-for-age difference (HAD) defined as a child's height minus the median reference value of height-for-age of WHO Child Growth Standard, expressed in centimeters, is recommended to describe and compare changes in height as children age (Leroy et al., 2014). Leroy and colleagues argue that HAZ is inappropriate to evaluate changes in height as children age because HAZ scores are constructed using standard deviations from cross-sectional data that change with age. Leroy et al. (2015) compared changes in growth in populations of children 2–5y using HAD vs. HAZ from cross-sectional data based on six Demographic and Health Surveys (DHS). There was no evidence of population-level catch-up in linear growth in children aged 2–5y when using HAD, but instead a continued deterioration reflected in a decrease in mean HAD between 2 and 5y. In contrast, based on HAZ, there was no change in mean HAZ (Leroy et al., 2015); see Chapter 13 for more details.
Linear growth velocity is also used as a functional biomarker of malnutrition in infants and young children. It can be assessed via measurements of changes in recumbent length for children < 2y and changes in height for older children. A high degree of precision is required for these measurements because two measurements are needed. During infancy, length increments can be assessed at 1mo intervals for the first 6mos and at 2mos intervals from 6 to 12mos. Increments measured over 6mos are the minimum interval that can be used to provide reliable data during adolescence (WHO, 1995). Seasonal variation in growth may occur. In high-income countries, height velocity, for example, may be faster in the spring than in the fall and winter.
The following formula is used to calculate velocity: Velocity = (x2 − x1) / (t2 − t1) where x2 and x1 are values of the measurement on two occasions t2 and t1. Length or height velocity is normally expressed as cm/y. Currently, no uniform criteria exist for defining growth faltering based on growth velocity data, although in practice zero growth in two consecutive periods is sometimes used (WHO, 1995). WHO has developed a set of growth velocity charts which are recommended for international use (de Onis et al., 2011).
Knee height measurements using a portable knemomter can be used to provide a more sensitive short-term measure of growth velocity in children > 3y based on lower leg length ( Davies et al., 1996). This equipment was used to obtain accurate measurements of the lower leg length of indigenous Shuar children aged 5–12y from Ecuador; the technical error of the measurement (TEM) was low — 0.18mm (Urlacher et al., 2016). For children < 3y, a mini-knemometer can be used to measure lower leg length (Kaempf et al., 1998).
15.4.8 Developmental responses
The assessment of cognitive function requires rigorous methodology. Even with careful methodology, a relationship between cognitive function and nutrient deficiency can be established only by: (a) documenting clinically important differences in cognitive function between deficient subjects and healthy placebo controls, and (b) demonstrating improvement in cognitive function after an intervention. The individuals should be matched, and the design should preferably be a double-blind, placebo-controlled, randomized intervention (Lozoff and Brittenham, 1986).To date, for example, results of meta-analyses have concluded that there is no clear evidence of the benefits of iron supplementation on visual, cognitive, or psychomotor development in preschool children (Larson and Yousafzai., 2017). In contrast, evidence for the benefits of such supplementation on cognitive performance for school-aged children who are anemic at baseline is strong (Low et al., 2013). Larson et al. (2017) have emphasized the need to conduct high-quality placebo-controlled, adequately powered trials of iron interventions on cognitive performance in young children to resolve the current uncertainties.
Several measurement scales of cognitive function are available, some of which are summarized briefly below.
Bayley Scales of Infant and Toddler Development are the most widely used method worldwide for assessing various domains of cognitive function in infants and toddlers (Albers and Grieve, 2007). The third edition of the scales (Bayley‑III) measures child development across five domains: cognition, receptive and expressive language, motor, adaptive, and social-emotional skills. They were constructed in the U.S. and have norms based on an American sample, so cultural adaptions are often needed when using them elsewhere. Appropriate training and standardization are a prerequisite to obtain reliable assessment across testers.
Ages and Stages questionnaire (ASQ‑3), a parental screening tool, is frequently used in large-scale research studies, as it is a cheaper, and a less time-consuming measure of early childhood development. The ASQ‑3 consists of 30 simple, straightforward questions covering five skillsets of childhood development: problem solving, communication, fine motor skills, gross motor skills, and personal social behavior. The ASQ‑3 was also developed in the U.S. to identify infants and toddlers 1–66mos at risk of a developmental delay (Steenis et al., 2015).
Several studies have compared the ASQ‑3 with the Bayley scales to assess developmental level of infants and toddlers. Substantial variations in the sensitivity and specificity of the ASQ‑3 across studies have been reported. Such variations may be due in part due to differences in study design, study samples (high or low risk), versions of the Bayley scales used, as well as the countries in which the studies were conducted. In several of these comparative studies, the ASQ‑3 has been found to have only low to moderate sensitivity (Steenis et al., 2015; Yue et al., 2019).
Fagan Test of Infant Intelligence is also used to assess cognitive function at four ages: 27,29,39, and 52 weeks postnatal age corrected for prematurity. The test is made up of 10 novelty problems, which comprise one familiar and one novel stimulus presented simultaneously. All the stimuli are pictures of faces of infants, women, and men. A novelty preference score for each age is calculated as the average percent of time spent fixating the novel picture across the 10 problems (Andersson, 1996). This test was used in a large trial in which Chilean infants 6–12mos (n=1123) were supplemented with iron and compared to a no-added-iron group (n=534) (Lozoff et al., 2003).
Wechsler Preschool and Primary Scale of Intelligence (WISC) is designed for children age 6y through 16y 11mos. WISC‑IV contains 10 core subsets and five supplementary subtests. The core subsets consist of Block Design, Similarities, Digit Span, Reasoning, Coding, Vocabulary, Letter-Number Sequencing, Symbol Search, Comprehension, and Picture Concepts. The five supplementary subtests comprise information, Word Reasoning, Picture Completion, Arithmetic, and Cancellation. The ten core subsets combine to form four composite index scores: Verbal Comprehension Index (VCI), Perceptual Reasoning Index (PRI), Working Memory Index (WMI), and Processing Speed Index (PSI). The Full Scale Intelligence Quotient (FSIQ) is derived from the sum of the 10 core subtest scores (Watkins and Smith, 2013). The WISC‑IV has now been modified as the WISC‑V, which can be administered more quickly than previously, and with more accurate scoring, electronically (Na and Burns, 2016). The WISC‑III was used in a follow-up study to investigate the effect of folic acid supplementation during trimesters 2 and 3 of pregnancy on cognitive performance in the child at 7y (McNulty et al., 2019).
Raven's Progressive Matrices (RPM) and Raven's Mill Hill Vocabulary Scales (MHV) have been used in school children and adults to assess basic cognitive functioning. The RPM test is made up of a series of diagrams or designs with a part missing. Respondents are asked to select the correct part to complete the designs from a number of options printed beneath. The MHV scale consists of 88 words, arranged in order of ascending difficulty which respondents are asked to define. Both the RPM and MHV have been used for different cultural, ethnic, and socioeconomic groups worldwide (Raven, 2000). Raven's Progressive Matrices were used in a study of Kenyan school children designed to test whether animal source foods have a key role in the optimal cognitive development of children. Results are shown in Figure 15.11. Post-hoc analyses showed that children who received a supplement with meat had significantly greater gains on the Raven's Progressive Matrices than any other group (Whaley et al., 2003).Mini-Mental State Examination (MMSE) is the most studied instrument for use as a screening measure of cognitive impairment in the elderly (Lin et al., 2013). The MMSE is divided into two parts and is not timed. The first part requires vocal responses only and covers orientation, memory, and attention, with a maximum score of 21. The second part tests the ability to name, follow verbal and written commands, write a senetence spontaneously, and copy a complex polygon. This part has a maximum score of 9, giving a maximum total score of 30; for more details see Folstein and Folstein (1975).
The MMSE was used to assess cognitive decline at six monthly intervals in a 3y double-blind, placebo controlled randomized controlled trial of healthy postmenopausal African American women aged 65y and older (n=260). Women were randomized to receive vitamin D (adjusted to achieve a serum level > 30ng/mL) with calcium (diet and supplement total of 1,200mg/d) or placebo (with calcium supplement of 1,200mg/d). Over three years there was no difference in cognition between the two groups, providing no support in this trial for providing a vitamin D intake greater than the recommended daily allowance for the prevention of cognitive decline (Owusu et al., 2019).
Other instruments such as the Clock Drawing test (CDT), Mini-Cog, Memory Impairment Screen, Abbreviated Mental Test (AMT), and Short Portable Mental Status Questionnaire (SPMSQ) can be used to detect dementia, although with more limited evidence. Moreover, for the AMT and SPMSQ, evidence of their usefulness in English is limited (Lin et al., 2013).
Motor development is also an essential component of child development. In several randomized controlled trials in low income countries, positive effects on gross motor milestones, particularly attainment of walking unassisted have been reported in infants receiving iron and/or zinc supplements or micronutrient-fortified-complementary foods (Adu-afarwuah et al., 2007; Black et al., 2004; Bentley et al., 1997 ; Masude and Chitundu, 2019).
Readers are advised to consult the WHO website for details of the The Motor Development Study undertaken as a component of the WHO Multicenter Growth Reference Study. (MGS). During this longitudinal study, data were collected from five countries (Ghana, India, Norway, Oman, and the United States) on six gross motor milestones using standardized testing procedures. The gross motor milestones and their performance criteria are outlined in Box 15.9: see Wijnhoven et al. ( 2004) for further details on the methods and training and standardization of fieldworkers. Because achievements of the six milestones were assessed repeatedly between 4–24mos, the sequence and tempo of the milestones as well as the ages of their attainment can be documented (Wijnhoven et al., 2004). Use of these WHO data are recommended for future studies involving assessment of gross motor development.
- Sitting without support: Child sits up straight with the head erect for at least 10 seconds. Child does not use arms or hands to balance body or support position.
- Hands-and-knees crawling: Child alternately moves forward or backward on hands and knees. The stomach does not touch the supporting surface. There are continuous and consecutive movements, at least three in a row.
- Standing with assistance: Child stands in upright position on both feet, holding onto a stable object (e.g., furniture) with both hands without leaning on it. The body does not touch the stable object, and the legs support most of the body weight. Child thus stands with assistance for at least 10 seconds.
- Walking with assistance: Child is in upright position with the back straight. Child makes sideways or forward steps by holding onto a stable object (e.g., furniture) with one or both hands. One leg moves forward while the other supports part of the body weight. Child takes at least five steps in this manner.
- Standing alone: Child stands in upright position on both feet (not on the toes) with the back straight. The legs support 100% of the child’s weight. There is no contact with a person or object. Child stands alone for at least 10 seconds.
- Walking alone: Child takes at least five steps independently in upright position with the back straight. One leg moves forward while the other supports most of the body weight. There is no contact with a person or object.
15.4.9 Depression
Links between depression and micronutrient deficiencies have been reported for folate, vitamin B12, calcium, magnesium, iron, selenium, zinc, and n‑3 fatty acids. Several mechanisms have been proposed, including mitochondrial function (including inadequate energy), disturbances in normal metabolism, genetic polymorphisms requiring increased or atypical nutrient requirements, increased inflammation, oxidative stress, and alterations in the microbiome (Campisi et al., 2020). Of the micronutrients, those most frequently studied have been zinc, vitamin D, iron, folate, and vitamin B12 in investigations in children, adolescents and the elderly. There have been reports of improvements in patients with depression given supplemental zinc, especially when the supplemental zinc is used as an adjunct to conventional antidepressant drug therapy (Ranjbar et al., 2014). However, methodological limitations exist in some of the studies, especially those on children and adolescents, and more well-designed and adequately powered placebo-controlled randomized controlled trials are needed (Campisi et al., 2020).Beck Depression Inventory-II (BDI-II) is frequently used to measure depression (Richter et al., 1998; Levis et al., 2019). This instrument is said to be a cost-effective questionnaire with a high reliability and capacity to discriminate between depressed and non-depressed individuals and which is applicable to both research and clinical practice worldwide (Wang and Gorenstien, 2013).
Patient Health Questionnaire (PHQ) is a useful screening tool to detect major depression in population-based studies. Depression can be defined with PHQ‑9 by using a cutoff point of 10 or above regardless of age, although specificity of the PHQ‑9 may be less for younger than for older patients (Levis et al., 2019).
15.5 Factors affecting choice of nutritional biomarkers
Biomarkers should be selected with care, and their limitations under conditions of health, inflammation, genetic and disease states understood. Several biological factors must be taken into account when selecting nutritional biomarkers to assess nutritional status and these are discussed more fully in the following sections. They are also affected by non-biological sources of variation arising from specimen collection and storage, seasonality, time of day, contamination, stability, and laboratory quality assurance. Both the biological and non‑biological sources of variation will impact on the validity, precision, accuracy, specificity, sensitivity, and predictive value of the biomarker. Because almost all techniques are subject to both random and systematic measurement errors, personnel should use calibrated equipment and should be trained to use standardized and validated techniques which are monitored continuously by appropriate quality-control procedures.15.5.1 Study objectives
The choice of nutritional biomarkers is strongly influenced by the study objectives. Nutritional biomarkers can be used to determine the health impacts of nutritional status at the population and/or the individual level. At the population level, factors such as cost, technical and personnel requirements, feasibility, and respondent burden are important considerations when choosing biomarkers. Population-level assessment is used to develop programs such as surveillance, to identify populations or sub-groups at risk, to monitor and evaluate public health programs, and to develop evidence-based national or global policies related to food, nutrition, and health.Nutritional biomarkers are also used at the individual level by clinicians to assess the nutritional status of patients who are “apparently healthy” or “apparently sick”, or who have subclinical illnesses. They may also be used to predict the future risk of disease or long-term functional outcomes if abnormal values persist, and to generate data to support evidence-based clinical guidelines (Combs et al., 2013). The effects of genetic polymorphisms on the clinical usefulness of biomarkers are increasingly being recognized; see Section 15.7.1. for more details.
Whether the study is at the population or individual level can influence, for example, the choice of a biomarker of nutritional exposure. To determine the risk of nutrient inadequacy in a national survey, a single 24hr recall per person (with repeats on at least a subsample) is required so that the distribution of usual intakes can be adjusted statistically. To assess the usual dietary intakes of a patient for dietary counseling, a food frequency questionnaire or dietary history is often used. For more details of these dietary assessment methods, see Chapter 3.
15.5.2 Population and setting
Factors such as life-stage group and ethnicity must also be taken into account when selecting nutritional biomarkers. In studies during early infancy, local or national ethics committees may prohibit the collection of venipuncture blood samples, and instead suggest that less invasive biomarkers based on urine, saliva, hair, or fingernails are used. Hormonal changes during pregnancy, along with an increase in plasma volume during the second and third trimester affect concentrations of several micronutrient biomarkers (e.g., serum zinc and vitamin B12), making it essential to identify women who are pregnant in order to ensure the correct interpretation of biomarker values.Rural settings may present many challenges associated with the appropriate collection, transport, centrifugation and storage of biomarkers; for example, it may be difficult to ensure a temperature-controlled supply chain or “cold chain” for specimen collection. Increasingly, serum retinol binding protein (RBP) is being used as a surrogate for serum retinol in studies at the population level. As noted earlier, serum RBP is more stable, and the assay is easier, cheaper, and correlates closely with serum retinol, provided that the individuals tested are neither obese nor have abnormal kidney function (Tanumihardjo et al., 2016).
15.5.3 Validity
Validity refers to how well the biomarker correctly describes the nutritional parameter of interest. As an example, if the biomarker selected reflects recent dietary exposure, but the study objective is to assess the total body store of a nutrient, the biomarker is said to be invalid. In U.S. NHANES I, thiamin and riboflavin were analyzed on casual urine samples because it was not practical to collect 24h urine specimens. However, results were not indicative of body stores of thiamin or riboflavin, and hence were considered invalid and thus not included in U.S. NHANES II or U.S. NHANES III (Gunter and McQuillan, 1990). Valid biomarkers are ideally free from random and systematic errors and are both sensitive and specific. Unfortunately the action of inflammation, stress, or certain medications,on enzyme activity and nutrient metabolism may alter nutrient status and thus affect the validity of a nutritional biomarker. As an example, the acute-phase response observed during an infectious illness may cause changes in certain nutrient levels in the blood (e.g., plasma zinc may fall and retinol binding protein fall, whereas plasma ferritin increases), that do not reflect alterations in the nutrient status per se, but indicate instead a redistribution of the nutrient mediated by the release of cytokines (Raiten et al., 2015).In view of the possible effects of inflammation on biomarker levels, measures of infection status should be assessed concurrently. For example, to adjust for the presence of systemic inflammation, WHO has recommended the concurrent measurement of two inflammatory biomarkers — serum C‑reactive protein and α‑1‑acid glycoprotein — so that an adjustment using regression modeling can be applied (Suchdev et al., 2016).
Table 15.5 shows, for Indonesian infants at 12mos, the impact of inflammation on the geometric mean and prevalence estimates of iron, vitamin A, and zinc deficiency based on serum ferritin, RBP, and zinc. Note the decrease in geometric mean for serum ferritin but the corresponding increases for serum RBP and zinc after applying the recommended BRINDA adjustment for inflammation. As a consequence, there is a marked increase in the estimated proportion at risk to low serum ferritin (indicative of depleted iron stores), and a marked decrease in the estimate of prevalence of both vitamin A deficiency and zinc deficiency Table 15.5
Biomarker in serum | Geometric mean (95% CI) | Proportion at risk (%) |
---|---|---|
Ferritin*: No adjustment | 14.5 µg/L (13.6–17.5) | 44.9 |
Ferritin: Brinda adjustment | 8.8 µg/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 | 1.07 µmol/L (1.04–1.10) | 12.4 |
Zinc***: No adjustment | 11.5 µmol/L (11.2–11.7) | 13.0 |
Zinc: Brinda adjustment | 11.7 µmol/L (11.4–12.0) | 10.4 |
Other disease processes may alter the nutrient status as a result of impaired absorption, excretion, transport, or conversion to the active metabolite and thus confound the validity of the chosen biomarker. In some cases the cause of these disease processes is hereditary, but in other cases the cause is acquired. Some examples of disease processes that affect nutrient status and, in turn, nutritional biomarkers, are shown in Table 15.6.
Disease | Biomarkers of nutrient indices that may be altered (usually lowered) |
---|---|
Pernicious anemia | Vitamin B12 (secondary effect on folate) |
Vitamin-responsive metabolic errors | Usually B‑vitamins (e.g., vitamins B12, B6, riboflavin, biotin, folate) |
Tropical sprue | Vitamins B12 and folate (local deficiencies); protein |
Steatorrhea | Fat-soluble vitamins, lipid levels, energy |
Abetalipoproteinemia | Vitamin E |
Thyroid abnormality | Riboflavin, iodine, selenium, lipid levels, energy |
Diabetes | Possibly vitamin C, zinc, chromium, and several other nutrients; lipid levels |
Infections, inflammation, acute phase reaction | Zinc, copper, iron, vitamin C, vitamin A, lipids, protein, energy |
Upper respiratory tract infections, diarrheal disease, measles | Especially vitamin A, lipid levels, protein |
Renal disease | Increased retention or increased loss of many circulating nutrients, lipid levels, protein |
Cystic fibrosis | Especially vitamin A, lipid levels, protein |
Various cancers | Lowering of vitamin indices |
Acute myocardial infarction | Lipid levels affected for about 3 mo |
Malaria, hemolytic disease, hookworm, etc | Iron, vitamin A, lipid |
Huntington's chorea | Energy |
Acrodermatitis enteropathica; various bowel, pancreatic, and liver diseases | Zinc, lipid levels, protein |
Hormone imbalances | Minerals, corticoids, parathyroid hormone, thyrocalcitonin (effects on the alkali metals and calcium), lipid levels affected by oral contraceptive agents and estrogen therapy |
Depending on the biomarker, potential interactions with several physiological factors such as fasting status, diurnal variation, time of previous meal consumption and homeostatic regulation must also be considered. For instance, fluctuations in serum zinc in response to meal consumption can be as much as 20% (King et al., 2015).
15.5.4 Precision
Precision refers to the degree to which repeated measurements of the same biomarker give the same value. The precision of a nutritional biomarker is assessed by repeated measurements on a single specimen or individual. The coefficient of variation (CV), as determined by the ratio of the standard deviation to the mean of the replicates (SD/mean × 100%) is the best quantitative measure of the precision. Ideally, the CV should be calculated for specimens at the bottom, middle, and top of the reference concentration range for the biomarker, as determined on apparently healthy individuals. These same specimens then serve as quality controls.Typically, the quality-control specimens used to calculate the CV are pooled samples from donors similar to the study participants. It is important that these quality-control specimens should, to the analyst, appear identical to the specimens from the study participants. This means that the same volume, type of vial, label and so on should be used.
Quality-control specimens should be inserted blind into each batch of specimens from the study participants. Both the intra- and inter-run CVs should be calculated on these quality-control specimens. The former is calculated from the values for aliquots of the quality-control specimens analyzed within the same batch, and the latter normally calculated from the values for aliquots of the quality-control specimens analyzed on different days (Blanck et al., 2003).
The precision of the measurement of a biomarker is in part a function of the random measurement errors that occur during the actual analytical process, and in some cases also a function of the intra-individual biological variations that occur naturally over time. The relative importance of these two sources of uncertainly varies with the measurement. For some biochemical measurements (e.g., serum iron), the intra-individual biological variation is quite large: coefficients of variation may exceed 30%, and be greater than any analytical variation. Consideration of intra-individual variation is also important when assessing dietary exposure, because nutrient intakes of an individual always vary over time. However, in this case, the intra-individual variation is a measure of the “true day-to-day” variation in the dietary intake of an individual. Strategies exist to account for the impact of intra-individual variation on the measurement of true usual intake of an individual — see chapter 6 for more details.
The attainable level of precision for the measurement of any particular biomarker depends on the procedure, whereas the required precision is a function of the study objectives. Some investigators have stipulated that, ideally, the analytical CV for an assay used in epidemiological studies should not exceed 5%. In practice, this level of precision is difficult to achieve for many assays and less precise measurements in epidemiological studies may result in a failure to detect a real relationship of the nutritional biomarker and the outcome of interest (Blanck et al., 2003). Of note, as shown in Figure 15.12, even if the precision is acceptable, the analytical method may not be accurate.
15.5.5 Sensitivity and specificity
Sensitivity refers to the extent to which the biomarker identifies individuals who genuinely have the condition under investigation (e.g., a nutrient deficiency state). Sensitive biomarkers show large changes as a result of only small changes in nutritional status. A biomarker with 100% sensitivity correctly identifies all those individuals who are genuinely deficient; no individuals with the nutrient deficiency are classified as “well” (i.e., there are no false negatives). Numerically, sensitivity is the proportion of individuals with the condition who have positive tests (true positives) divided by the sum of true positive and false negatives. The sensitivity of a biomarker changes with the prevalence of the condition as well as with the cutoff point.Biomarkers that are strictly homeostatically controlled have very poor sensitivity. Figure 15.1 shows the relationship between mean plasma vitamin A and liver vitamin A concentrations. Note that plasma retinol concentrations reflect the vitamin A status only when liver vitamin A stores are severely depleted (< 0.07µmol/g liver) or excessively high (> 1.05µmol/g liver). When liver vitamin A concentrations are between these limits, plasma retinol concentrations are homeostatically controlled and levels remain relatively constant and do not reflect total body reserves of vitamin A. Hence, in populations from higher income countries where liver vitamin A concentrations are generally within these limits, the usefulness of plasma retinol as a sensitive biomarker of vitamin A exposure and status is limited (Tanumihardjo et al., 2016). Likewise, the use of serum zinc as a biomarker of exposure or status at the individual level is limited due to tight homeostatic control mechanisms. Based on a recent meta-analysis, doubling the intake of zinc was shown to increase plasma zinc concentrations by only 6% (King, 2018).
Specificity refers to the ability of a nutritional biomarker to identify and classify those persons who are genuinely well nourished. If the biomarker has 100% specificity, all genuinely well-nourished individuals will be correctly identified; no well-nourished individuals will be classified as under-nourished (i.e., there are no false positives). Numerically, specificity is the proportion of individuals without the condition who have negative tests (true negatives divided by the sum of true negatives and false positives).
Unfortunately, many of the health and biological factors noted in Box 15.2 and diseases summarized in Table 15.6 reduce the specificity of a biomarker. Inflammation, for example, reduces serum zinc (Table 15.5), yielding a concentration that does not reflect true zinc status, so misclassification occurs; individuals are designated “at risk” with low serum zinc concentrations when they are actually unaffected (false positives). In contrast, inflammation increases serum ferritin, so that in this case individuals may be designated “not at risk” when they are truly affected by the condition (false negatives).
The ideal biomarker has a low number of both false positives (high specificity) and false negatives (high sensitivity), and hence is able to completely separate those who genuinely have the condition from those individuals who are healthy. In practice, a balance has to be struck between specificity and sensitivity, depending on the consequences of identifying false negatives and false positives.
15.5.6 Analytical sensitivity and analytical specificity
Unfortunately, the term “sensitivity” is also used to describe the ability of an analytical method to detect the substance of interest. The more specific term “analytical sensitivity” should be used in this context.For any analytical method, the smallest concentration that can be distinguished from the blank is termed the “analytical sensitivity” or the “minimum detection limit.” The blank should have the same matrix as the test sample and, therefore, usually contains all the reagents but none of the added analyte. Recognition of the analytical sensitivity of a biochemical test is particularly important when the nutrient is present in low concentrations (e.g., the ultra-trace elements Cr, Mn, and Ni).
In practical terms, the minimum detection limit or the analytical sensitivity is best defined as three times the standard deviation (SD) of the measurement at the blank value. To calculate the SD of the blank value, 20 replicate measurements are generally recommended. Routine work should not include making measurements close to the detection limit and should normally involve analyzing the nutrient of interest at levels at least five times greater than the detection limit. Measured values at or below the detection limit should not be reported.
The ability of an analytical method to measure exclusively the substance of interest is a characteristic referred to as the “analytical specificity.” Methods that are nonspecific generate false-positive results because of interferences. For example, in U.S. NHANES II, the radioassay used gave falsely elevated results for vitamin B12. This arose because the porcine intrinsic factor (IF) antibody source initially used reacted both with vitamin B12 and with nonspecific cobalamins present in serum. As a result, erroneously high concentrations were reported and the samples had to be reanalyzed using a modified method based on purified human IF, specific for vitamin B12 (Gunter and McQuillan, 1990).
Strategies exist to enhance analytical specificity (and sensitivity). Examples include the use of dry ashing or wet digestion to remove organic material prior to the analysis of minerals and trace elements.
15.5.7 Analytical accuracy
The difference between the reported and the true amount of the nutrient/metabolite present in the sample is a measure of the analytical accuracy (“trueness”) of the laboratory test (Figure 15.12). Guidelines on choosing a laboratory for assessment of a nutritional biomarker are given in Blanck et al. (2003).Several strategies can be used to ensure that analytical methods are accurate. For methods involving direct analysis of nutrients in tissues or fluids, a recovery test is generally performed. This involves the addition of known amounts of nutrient to the sample. These spiked samples are then analyzed together with unspiked aliquots to assess whether the analytical value accounts for close to 100% of the added nutrient.
As an additional test for accuracy, aliquots of a reference material, similar to the sample and certified for the nutrient of interest, should be included routinely with each batch of specimens. If possible, several reference materials, with values spanning the range observed in the study samples, should be analyzed (Blanck et al., 2003). Such a practice will document the accuracy achieved.
Standard reference materials (SRMs) can be obtained from the U.S. National Institute of Standards and Technology (NIST) (for serum Zn, vitamins B6 and B12, folate, vitamin D, carotenoids), the U.S. Centers for Disease Control and Prevention (CDC) (for serum vitamins A and C), the International Atomic Energy Authority (IAEA) in Vienna, the Community Bureau of Reference of the Commission of the European Communities (BCR) in Belgium (serum proteins), and the U.K. National Institute of Biological Standards and Controls (serum ferritin, soluble transferrin receptor). A reference material for erythrocyte enzymes for vitamin B6, riboflavin, and thiamin is also available from the Wolfson Research Laboratory, Birmingham, England.
The importance of the use of SRMs is highlighted by the discrepancies in serum folate and red blood cell folate based on the radioprotein-binding assay (RPBA) and a microbiological assay. By using the newly available SRM for folate, U.S. NHANES established that values based on the RPBA assay were 25–40% lower for serum folate and 45% lower for red blood cell folate compared to both the microbiological method and that using liquid chromatography-tandem mass spectrometry. Because most of the cutoffs to assess the adequacy of folate status were established using the RPBA assay, applying such mismatched cutoffs for the microbiological assay resulted in risks of folate deficiency which were markedly higher (i.e., 16% vs 5.6% for serum folate and 28% vs. 7.4% for RBC folate) (Pfeiffer et al., 2016). These data emphasize the importance of using accurate analytical methods and applying method-specific cutoffs to avoid misinterpretation of the data (MacFarlane, 2016).
If suitable reference materials are not available, aliquots from a single homogeneous pooled test sample should be analyzed by several independent laboratories using different methods. Programs are available which compare the performance of different laboratories in relation to specific analytical methods. Some examples include the programs operated by IAEA, the Toxicology Centre in Québec, Canada, and the U.S. National Institute of Standards and Technology (NIST).
Important differences distinguish assays undertaken by a hospital clinical laboratory from those completed during a survey or research study. Clinical laboratories often focus on values for the assay that are outside the normal range, whereas in nutrition surveys such as U.S. NHANES III), and in research studies, the emphasis is often on concentrations that fall within the normal range. This latter emphasis requires an even more rigorous level of internal laboratory quality control (Potischman, 2003).
- Bench quality-control pools for each analyte, at multiple concentration levels
- Blind quality-control pools for each analyte, low-normal and high-normal levels
- Random re-analysis of 5% of specimens for each method
- Split-duplicates from one original specimen submitted from the mobile examination center
- Re-collection from sample participants to provide two observations for comparison of values
- External proficiency testing for many analytes, such as the College of American Pathologists, New York State, CDC‑Wisconsin programs.
Most clinical chemistry laboratories are required to belong to a certified quality assurance program. The U.S. CDC operates a National Public Health Performance Standards Program (NPHPSP), designed to improve the quality of public health practice and performance of public health systems, particularly statewide assessments.
15.5.8 Predictive value
The predictive value describes the ability of a nutritional biomarker, when used with an associated cutoff, to predict correctly the presence or absence of a nutrient deficiency or disease. Numerically, the predictive value of a biomarker is the proportion of all results of the biomarkers that are true (i.e., the sum of the true positives and true negatives divided by the total number of tests). Because it incorporates information on both the biomarker and the population being tested, predictive value is a good measure of overall clinical usefulness.The predictive value can be further subdivided into the positive predictive value and the negative predictive value. The positive predictive value of a biomarker is the proportion of positive biomarker results that are true (the true positives divided by the sum of the true positives and false positives). The negative predictive value of a biomarker is the proportion of negative biomarker results that are true (the true negatives divided by the sum of the true negatives and false negatives). In other words, the positive predictive value is the probability of a deficiency state in an individual with an abnormal result, whereas the negative predictive value is the probability of an individual not having the condition when the biomarker result is negative.
Sensitivity, specificity, and prevalence of the nutrient deficiency or disease affect the predictive value of a biomarker. Of the three, prevalence has the most influence on the predictive value of a biomarker. When the prevalence of the condition is low, even very sensitive and specific biomarker tests have a relatively low positive predictive value. In general, the highest predictive value is achieved when specificity is high, irrespective of sensitivity.
15.5.9 Scoring criteria to select biomarkers
European researchers have developed a set of criteria which can be used to select the appropriate biomarkers in nutrition research (Calder et al., 2017), and these criteria are shown in Box 15.11. Once the biomarker has been assessed by applying these criteria, then the information obtained can be used to score the biomarker to determine its usefulness. Details of the proposed scoring system are given in Calder et al. (2017).-
Methodological aspects, excluding study design
- Method should be validated according to recognized guidelines
- Appropriate sensitivity
- Appropriate specificity
- Reproducibility, accuracy, standardization, stability (quality of sample) and technical variation
- Biological variation
- Reflects/marks the biological purpose of the biomarker
- A change in the biomarker is linked with a change in the endpoint in one or more target populations
- Method should be validated according to recognized guidelines
- Relevance to nutrition research
- What is considered as a normal range for healthy people?
- What is a significant change (consider both biological and statistical)?
- Is there evidence that nutrition influences the marker? If so, what is the size of the effect reported?
- Which other factors also have an effect on the biomarker (if any)
- Are there experimental data where dietary intervention has not resulted in an anticipated change?
Modified from Calder et al. (2017).
Nut- rient | Biomarkers of Exposure | Biomarkers of status | Functional biomarkers* | Adverse Clinical outcomes |
---|---|---|---|---|
Folate | Dietary folate equivalents | Serum folate; RBC folate: MBA method | Plasma homocysteine | Megaloblastic anemia |
Iodine | Salt iodine | Urinary iodine | Thyroglobulin | Goitre |
Iron | Bioavailable iron intakes | Ferritin; RBC proto-porphyrin; transferrin receptor; body Iron index | Currently no biomarker of brain Fe deficiency | Microcytic, hypochromic anemia |
Vitamin A | Dietary vitamin A as retinol activity equivalents (RAE) | Retinol in plasma, DBS, & breast milk; Retinol binding protein in plasma or DBS | Modified relative dose response Dark adaptation Pupillary threshold test | Xeropthalmia Night blindness |
Vitamin B12 | Dietary B12 intake | Serum B12; Serum holoTC | Serum methyl- malonic acid. Plasma homo- cysteine | Megaloblastic anemia |
Zinc | Dietary Zn intakes; Absorbable Zn | Serum zinc | Impaired linear growth | Stunting |
15.6 Evaluation of the selected nutritional biomarker
At the population level, nutritional biomarkers are often used for surveys, screening, surveillance, monitoring, and evaluation (Box 15.1), when they are evaluated by comparison with a distribution of reference values from a reference sample group (if available) using percentiles or standard deviation scores.Alternatively, individuals in the population can be classified as “at risk” by comparing biomarker values with either statistically predetermined reference limits drawn from the reference distribution, or with clinically or functionally defined “cutoff points”. At the population level, the biomarkers do not necessarily provide certainty with regard to the status of every individual in the population. In contrast, when using biomarkers for the diagnosis, treatment, follow-up, or counseling of individual patients, their evaluation needs to be more precise, with cut-offs chosen accordingly (Raghaven et al., 2016). Note that statistically defined “reference limits” are technically not the same as clinically or functionally defined “cutoffs”, and the two terms should not be used interchangeably.
15.6.1 Reference distribution
Males | hemoglobin percentiles (g/dL) | ||||||
---|---|---|---|---|---|---|---|
Age (y) | 5 | 10 | 25 | 50 | 75 | 90 | 95 |
20–44 | 13.7 | 14.0 | 14.6 | 15.3 | 15.9 | 16.5 | 16.8 |
45–64 | 13.5 | 13.8 | 14.4 | 15.1 | 15.8 | 16.4 | 16.8 |
Males | Transferrin saturation percentiles (%) | ||||||
Age (y) | 5 | 10 | 25 | 50 | 75 | 90 | 95 |
20–44 | 16.6 | 18.4 | 23.3 | 29.1 | 35.9 | 43.7 | 48.5 |
45–64 | 15.2 | 17.6 | 21.8 | 27.8 | 34.2 | 39.7 | 44.4 |
As an example, if anemia is present in the study population, the hemoglobin distribution will be shifted to the left, as shown in the school-aged children from Zanzibar when compared to the optimal hemoglobin distribution for the U.S. NHANES II reference population of healthy African American children in Figure 15.13.
Distributions of serum zinc concentrations from the U.S. NHANES II survey based on a healthy reference sample (Pilch and Senti, 1984) were developed by Hotz et al. (2003). Data for individuals with conditions known to significantly affect serum zinc concentrations were excluded,i. e., those with low serum albumin (< 35g/L), those with an elevated white blood cell count (> 11.5×109/L), and those using oral contraceptive agents, hormones or steroids, or experiencing diarrhea. The International Zinc Consultative Group (IZiNCG) also took age, sex, fasting status (i.e., > 8h since last meal), and time of day of the blood sample collection into account, in the reanalysis. From these data, distributions of reference values for serum zinc (by age, sex, fasting status and time of sampling) were compiled.
Unfortunately, none of the other biochemical data generated from U.S. NHANES II or U.S. NHANES III have been treated in this way (Looker et al., 1997). As a consequence, and in practice, the reference sample group used to derive the values for the reference distribution is usually drawn from the “apparently healthy” general population sampled during nationally representative surveys and assumed to be disease-free. For example, Ganji and Kafai (2006) compiled population reference values in this way for plasma homocysteine concentrations for U.S. adults by sex and age in non-Hispanic whites, in non-Hispanic blacks, in Mexican Americans and in Hispanic subjects using data from U.S. NHANES 1999–2001 and 2001–2002.
15.6.2 Reference limits
The reference distribution can also be used to statistically derive reference limits and also to derive a reference interval. Two reference limits are often defined, and the interval between and including them is termed the “reference interval”. On average, 120 “healthy” individuals are needed to generate the reference limits for subgroups within strata such as age group, sex, and possibly race (Lahti et al., 2002). The reference interval usually includes the central 95% of reference values, and is often termed the “reference range” or “range of normal”, with the lower 2.5th percentile value often corresponding to the lower reference limit and the upper 2.5th percentile value to the upper reference limit. For example, the reference limit determined by IZiNCG was based on the 2.5th percentile of serum zinc concentrations for males and females aged < 10y and ≥ 10y, qualified by fasting status and time of blood collection (Hotz et al., 2003). Similarly, in the U.K. national surveys, the lower reference limit for hemoglobin was represented by the 2.5th percentile qualified by age and sex. The number and percentage of individuals with observed values falling below the 2.5th percentile value can then be calculated.Box 15.12 depicts the relationship between reference values, the reference distribution, and reference limits, and how reference samples are used to compile these values. Observed values for individuals in the survey are classified as “unusually low”, “usual”, or “unusually high”, according to whether they are situated below the lower reference limit, between or equal to either of the reference limits, or above the upper reference limit.
- REFERENCE INDIVIDUALS
↓ make up a - REFERENCE population
↓ from which is selected a - REFERENCE SAMPLE GROUP
↓ on which are determined - REFERENCE VALUES
↓ on which is observed a - REFERENCE DISTRIBUTION
↓ from which are calculated - REFERENCE LIMITS
↓ that may define - REFERENCE INTERVALS
Unfortunately, no data are available from national nutrition surveys for the distribution of reference values for most functional physiological biomarkers (e.g., relative dose-response for vitamin A) with the exception of child growth (de Onis et al, 2008), and six child gross motor milestones (Wijnhoven et al., 2004), and for behavioral biomarkers (e.g., cognition; depression). Use of such biomarkers (with the exception of growth) is generally not feasible in large-scale nutrition surveys. Consequently, these functional biomarkers are often evaluated by monitoring their improvement serially, during a nutrition intervention program. Alternatively, observational studies have examined correlations between a static or functional biomarker of a nutrient and a physiological or behavioral biomarker. These observational studies have comprised cross-sectional, case-control, and cohort studies.
The observed values may also be compared using cutoff points as described below.
15.6.3 Cutoff points
Cutoff points, unlike statistically defined reference limits, are based on the relationship between a nutritional biomarker and low body stores, functional impairment or clinical signs of deficiency or excess (Raghavan et al., 2016). The Institute of Medicine (IOM) defines a cutoff for a biomarker as a “specified quantitative measure used to demarcate the presence or absence of a health-related condition often used in interpreting measures obtained from analyses of blood” (IOM, 2010) .The use of cut-off points is less frequent than that of reference limits because information relating biomarkers and functional impairment or clinical signs of deficiency or excess is often not available. Cutoff points may vary with the local setting because relationships between the biomarkers and functional outcomes is unlikely to be the same from area to area.
Cutoff points, like reference limits, are often age-, race-, or sex-specific, depending on the biomarker. For biomarkers based on biochemical tests, cutoff points must also take into account the precision of the assay. Poor precision leads an overlap between those individuals classified as having low or deficient values with those having normal values and thus to misclassification of individuals. This affects the sensitivity and specificity of the test. The International Vitamin A Consultative Group (IVACG), for example, now recommends the use of HPLC for measuring serum retinol concentrations because this is the best method for detecting concentrations < 0.70µmol/L with adequate precision (Tanumihardjo et al., 2016).
The BOND Expert Panel has recommended cutoffs for the biomarkers of exposure, status, or function for six micronutrients — folate, iodine, iron, vitamin A, vitamin B12 and zinc, although for some (e.g., serum zinc), the so-called cutoffs are in fact statistically defined and hence are actually reference limits. Note that in some cases, the so-called cutoffs for status or functional biochemical biomarkers are assay-specific, as discussed earlier for folate. Assay-specific cutoffs are also available for soluble transferrin receptor, a useful biomarker for identifying iron deficiency because it is less strongly affected by inflammation. Assay-specific cutoffs arise when there is no CRM available for the biomarker, as has been the issue for folate and soluble transferrin receptor, until recently. Different cutoff units are sometimes used, presenting an additional challenge when interpreting data across laboratories (Raghavan et al., 2016).
Table 15.9Cutoffs. Serum vitamin B12 pmol/L | Prevalence estimates for for vitamin B12 deficiency, % |
---|---|
< 148 | 2.9 ± 0.2 |
< 200 | 10.6 ± 0.4 |
< 258 | 25.7 ± 0.6 |
The area under the ROC curve (AUC), also known as the cut-point “c” statistic or c-index, is a commonly used summary measure of the accuracy of the biomarker cutoff. AUCs can range from 0.5 (random chance, or no predictive ability — a 45° line on the ROC plot, Figure 15.4) to 0.75 (good), and to > 0.9 (excellent). The cutoff value that provides the highest sensitivity and specificity is calculated. On the rare occasions that the estimated AUC for the biomarker cutoff is < 0.5, then the biomarker cutoff is worse than chance! When multiple biomarkers are available for the same nutrient, the biomarker with the highest AUC is often selected.
Youden index (J) is another summary statistic of the ROC curve used in the interpretation and evaluation of biomarkers. It defines the maximum potential effectiveness of a biomarker. The statistic J can be defined as J = (maximum sensitivity (c) + specificity (c) − 1). The cut-off that achieves the maximum is referred to as the optimal cutoff (c*) because it is the cut-off that optimizes the biomarker’s differentiating ability when equal weight is given to sensitivity and specificity. The statistic J can range from 0 to 1, with 1 indicating a perfect diagnostic test, whereas values closer to 0 signify a limited effectiveness (Schisterman et al., 2005; Ruopp et al., 2008).
Misclassification arises when there is overlap between individuals who actually have the deficiency and those falsely identified (i.e., false positives). Neither reference limits nor cutoff values can separate the “deficient” and the “adequately nourished” without some misclassification occurring. This is shown in Figure 15.15 for the real-life situation (B). Note that the cut-offs finally selected can vary according to whether the consequences of a high number of individuals being falsely classified as positive is more or less important than the consequences of a large number of individuals being falsely classified as negatives. Minimizing either misclassification may be considered more important than minimizing the total number of individuals misclassified.Note that the sensitivity can be improved (i.e., reducing the false positives) by moving the cut-off to the right but this reduces the specificity (false negatives), whereas moving the cut-off to the left reduces the false negatives (higher specificity) at the cost of a reduction in sensitivity. The former scenario may be preferred for the clinical diagnosis of a fatal condition, whereas cut-offs with a high specificity may be preferred for diagnostic tests that are invasive or expensive.
Misclassification arises because there is always biological variation among individuals (and hence in the physiological normal levels defined by the biomarker), depending on their nutrient requirements. As well, for many biomarkers there is high within-individual variance, which influences both the sensitivity and specificity of the biomarker, as well as the population prevalence estimates. These estimates can be more accurately determined if the effect of within-individual variation is taken into account. This can only be done by obtaining repeated measurements of the biomarker for each individual on at least a sub-sample of the individuals. The number of repeated measurements required depends on the ratio of the within-individual to between-individual variation for the biomarker and population concerned (see analogous discussion of adjustments to prevalence estimates for inadequate dietary intakes in Chapter 3).
The specificity of the diagnosis can be enhanced by combining biomarkers. The presence of two or more abnormal values can be taken as indicative of deficiency, often improving the specificity of the diagnosis. This approach has been used in several national nutrition surveys for diagnosing iron deficiency, including the U.S. NHANES in 2003. Here a multivariable approach for estimating total-body iron stores was developed based on the ratio of soluble transferrin receptor to serum ferritin (Gupta et al., 2017). Increasingly, a combined indicator is being used for diagnosing B12 deficiency that is based initially on 4 status biomarkers (serum B12, methylmalonic acid (MMA), holotranscobalamin (holoTC), and total homocysteine (tHcy). However, the indicator can be adapted for use with three or two biomarkers; for more details see Allen et al. (2018).
In the future, a more flexible cutoff approach may be adopted in which two cutoffs are provided, separated by a gray zone. The first cutoff in this gray zone approach is selected to include deficiency with near certainty, while the second is chosen to exclude deficiency with near certainty. When a biomarker falls within the gray zone (suggesting subclinical deficiency), investigators are prompted to seek additional assessment tools in an effort to provide a more precise diagnosis. In this way, unwarranted clinical interventions are avoided (Raghaven et al., 2016).
15.6.4 Trigger levels for surveillance and public health decision making
In population studies, cutoff points may be combined with trigger levels to set the level of an indicator (or a combination of indicators) at which a public health problem exists of a specified level of concern. Trigger levels may highlight regions, populations or sub-groups where specific nutrient deficiencies are likely to occur, or may serve to monitor and evaluate intervention programs. They should, however, be interpreted with caution because they have not always been validated in population-based surveys. Box 15.13 presents examples of trigger levels for zinc biomarkers set by the International Zinc Nutrition Consultative Group (IZiNCG).
- Prevalence of serum zinc less than age/sex/time-of-day specific cutoffs is > 20%
- Prevalence of inadequate zinc intakes below the appropriate estimated average requirements is > 25%
- Prevalence of low height-for-age or length-for-age Z‑scores (i.e., < −2SD) is at least 20%.
Note: Ideally, all three indicators should be used together to obtain the best estimate of the risk of zinc deficiency in a population, and to identify specific sub-groups with elevated risk (de Benoist et al., 2007).
WHO (2011) have classified the public health significance of anemia at the population level based on the prevalence of low hemoglobin concentrations. Moreover, reductions in the prevalence of anemia are targets for public health efforts in many low-income countries. To be successful, however, such efforts must address the multifactorial etiology of anemia, and avoid the presumption that anemia is synonymous with nutritional iron deficiency (Raiten et al., 2012).
WHO (2011) defines vitamin A deficiency as a severe public health problem requiring intervention when 20% of children aged 6–71mo have a serum retinol concentration < 0.7µmol/L and another biological indicator of poor vitamin A status. These may include night blindness, breast milk retinol, relative dose response, or modified dose response; or when at least four demographic and ecological risk factors are met; see Tanumiharjo et al.(2016) and Chapter 18 for more details.
Trigger levels to define the severity of iodine deficiency in a population based on total goiter rate have also been defined by WHO (Rohner et al., 2015). The criteria used are < 5%, iodine sufficiency; 5.0–19.9%, mild deficiency; 20–29.9%, moderate deficiency; and > 30%, severe deficiency. Details of the classification system used to diagnose goiter are available in WHO (2007).
A generalized discussion of the specific procedures used for the evaluation of dietary, anthropometric, laboratory, and clinical methods of nutritional assessment are discussed more fully in Chapters 8b, 13, 25, and 26, respectively.
15.7 Application of new technologies
With the development of new technologies, the focus is changing from the use of biomarkers that are associated with specific biochemical pathways to methods that assess the activity of multiple macro- and micro-nutrients and their interactions within complex physiological systems. These new technologies apply “omics” techniques that allow the simultaneous large-scale measurements of multiple genes, proteins, or metabolites, coupled with statistical and bioinformatics tools. Such measurements offer the possibility of characterizing alterations associated with disease conditions, or exposure to food components. However, further work on the development and implementation of appropriate quality control systems for “omics” techniques is required. A brief description of these “omics” techniques and their application in nutritional assessment follows.15.7.1 Nutrigenetics
Nutrigenetics focuses on understanding how genomic variants interact with dietary factors and the implications of such interactions on health outcomes (Mathers, 2017). Nutrigenetics is being used increasingly to predict the risk of developing chronic diseases, explain their etiology, and personalize nutrition interventions to prevent and treat chronic diseases.Nutrigenetics uses a combination of recombinant DNA technology, DNA sequencing methods, and bioinformatics to sequence, assemble, and analyse the structure and function of genomes. Genetic variation among individuals is minimal. Nevertheless, there is approximately 1% genetic variation that can lead to a wide variability in health outcomes, depending on dietary intake and other environmental exposures. The most common type of genetic variability among individuals is the single nucleotide polymorphism (SNP), which is a base change in the DNA sequence. With the development of genetic SNP databases, individuals can be screened for genetic variations, some of which can have an effect on an individual's health.
One of the earliest examples is the effect of the common SNP‑C677T (A22V) associated with the MTHFR gene. This C677T polymorphism is responsible for a genetic defect in the enzyme methylenetetrahydrofolate reductase (MTHFR) that can cause a severe or a more moderate accumulation of homocysteine. Several studies in both younger and older subjects have shown that individuals homozygous for the MTHFR polymorphism C677T (A222V) have increased levels of plasma homocysteine concentrations, although only in the face of low folate status. No association has been found in homozygotes with adequate folate status. In view of the influence of MTHFR C677T (A222V) polymorphism on plasma homocysteine, this C677T polymorphism has been proposed as an independent risk factor for coronary heart disease (Gibney and Gibney, 2004). Since this early example, there have been several other reports in which polymorphisms have been associated with common chronic diseases through interactions with the intake of both micronutrients and macronutrients, as well as with the consumption of particular foods and dietary patterns.
Chronic diseases such as obesity, type 2 diabetes, and coronary heart disease are probably associated with multiple genetic variants that interact with diet and other environmental exposures. Therefore, predictive testing based on a single genetic marker for these chronic diseases is likely to be of limited value. As a result, increasingly, studies are combining genetic polymorphisms to yield genetic-predisposition scores, often termed genetic risk scores (GRS), in an effort to examine the cumulative effect of SNPs on diet interactions and susceptibility to diseases such as obesity and type 2 diabetes.
As an example, the use of a GRS has been applied in studies examining the interactions between genetic predisposition and consumption of certain foods in relation to body mass index and obesity. In several prospective cohort studies, an interaction between the consumption of sugar-sweetened beverages (Qi et al., 2012), and a GRS based on 32 BMI-associated variants on BMI, has been reported. (Qi et al., 2014). These findings have highlighted the importance of reducing consumption of these foods in individuals genetically predisposed to obesity.
Interactions between dietary patterns and GRS may also be associated with adiposity-related outcomes. In a large study based on 18 cohorts of European ancestry, nominally significant associations were observed between diet score and a GRS based on 14 variants commonly associated with BMI-adjusted waist-hip ratio. Moreover, stronger genetic effects were observed in those individuals with a higher diet score (i.e., those consuming healthier diets) (Nettleton et al., 2015). The clinical relevance of these findings, however, is uncertain, and further experimental and functional studies are required.
Several studies have also examined the effects of GRS on the differential responses to nutrition interventions. Huang et al. (2016), for example, showed that individuals with lower GRS for type 2 diabetes mellitus had greater improvements in insulin resistance and β‑cell function when consuming a low‑protein diet. In contrast, individuals with higher GRS for glucose disorders had greater increases in fasting glucose when consuming a high‑fat diet (Wang et al., 2016). For more examples of interactions between dietary intakes and genes involved in risk of disease, see Ramos-Lopez et al. (2017).
Clearly, advances in nutrigenetics have the potential to enhance the prediction for risk of developing chronic diseases, as well as personalizing their prevention and treatment. Indeed, increasingly, genetic tests are being used to customize diets based on the predisposition to weight gain by saturated fat intake and the increased risk of developing hypertension by high salt intake.
15.7.2 Proteomics
Proteomics refers to the systematic identification and quantification of the overall protein content of a cell, tissue, or an organism. The proteome is defined as a dynamic collection of proteins that demonstrate variation between individuals, between cell types, and between entities of the same type but under different pathological or physiological conditions (Huber, 2003).Comparison of proteome profiles between differing physiologic and disease states is used to identify potential biomarkers for the early diagnosis and prognosis of disease states, monitoring disease development, understanding pathogenic mechanisms, and for developing targets for treatment and therapeutic intervention.
Three major steps are involved in proteomics analysis: (i) sample preparation; (ii) separation and purification of complex proteins, and (iii) protein identification. Several methods can be used to separate and purify the samples, including chromatography-based techniques, enzyme-linked immunosorbent assays (ELISA) or Western blotting. More advanced techniques are also being used such as protein microarrays and two-dimensional difference in‑gel electrophoresis (DIGE). To identify proteins in great depth, mass-spectrometry-based proteomics is used to measure the highly accurate mass and fragmentation spectra of peptides derived from sequence-specific digestion of proteins. Finally, the raw data from mass spectrometry (MS) are searched using database search engines and software such as MASCOT or Protein-Pilot, etc. For more details of these techniques, see Aslam et al. (2017).
Further work is required to improve the reproducibility and performance of proteomics tools. Systematic errors can be introduced during each step that may artificially discriminate disease from non-disease. Sources of biological and analytical variation have not always been controlled and the sample size for testing a candidate biomarker has sometimes been inadequate. However, with improvements, proteomics has the potential to screen large cohorts for multiple biomarkers, and to identify protein patterns characteristic of particular health or disease states.
15.7.3 Metabolomics
Metabolomics characterizes the small molecular weight molecules, called metabolites, that are present in human biofluids, cells, and tissues at any given time (Brennan, 2013). The aim of metabolomics is to provide an overview of the metabolic status and global biochemical events associated with a cellular or biological system under different biological conditions. The metabolome is comprised of small intermediary molecules and products of metabolism, including those associated with energy storage and utilization, precursors to proteins and carbohydrates, regulators of gene expression, and signalling molecules.Five major steps are involved in metabolomics: (i) experimental design; (ii) sample preparation; (iii) data acquisition by nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry-based analysis; (iv) data processing; and (v) statistical analyses (O'Gorman and Brennan, 2017). Computational tools have been developed to relate the structure of the metabolites identified to biochemical pathways. This is a complex task as a metabolite may belong to more than one pathway; see Misra (2018).
The biofluids most widely used for metabolomics are blood, urine, and saliva. Several analytical techniques are used to analyze metabolites in these biofluids; each technique has advantages and disadvantages. The major analytical techniques are NMR spectroscopy or mass spectrometry-(MS)-based methods (e.g., gas chromatography (GC)-MS, liquid chromatography (LC)-MS, capillary electrophoresis (CE)-MS) and high performance liquid chromatography (HPLC). No single technique is capable of measuring the entire metabolome.
Both non-targeted and targeted metabolomics can be used, depending on the research question. The non-targeted approach aims to measure as many metabolites as possible in a biological sample simultaneously, thus providing a broad coverage of metabolites, and an opportunity for novel target discovery. In contrast, the targeted approach involves measuring one metabolite or a specific class of known metabolites with similar chemical structures. This requires the metabolites of interest to be known a priori and commercially available in a purified form for use as internal standards so that the amount of a targeted metabolite can be quantified (O'Gorman and Brennan, 2017). Currently, there are no standardized protocols for sample collection and storage for metabolomic studies.
There are three main applications of metabolomics in nutrition research: (i) dietary intervention studies; (ii) diet-related disease studies; and (iii) dietary biomarker studies designed to identify and validate novel biomarkers of nutrient exposure (Brennan, 2013).
Dietary intervention studies can be used to investigate the mechanistic effects of the intervention and to determine the impact of specific foods or diets on metabolic pathways. An example includes the application of metabolomics to investigate the impact of consuming either wholegrain rye bread or refined wheat bread. Metabolomics of serum samples from 33 postmenopausal women indicated that consumption of rye bread decreased the branched chain amino acids leucine and iso-leucine and increased NN-dimethylglycine. Such alterations suggest that wholegrain rye bread may confer beneficial health effects (Moazzami et al., 2012). Consumption of dark chocolate has also been investigated in dietary intervention studies involving metabolomics. In a study by Martin et al. (2009), 30 participants were classified into low and high anxiety traits using validated psychological questionnaires. Participants then received 40g dark chocolate daily for 14d during which urine and plasma were collected at baseline, mid-line, and endline. Consumption of dark chocolate for 14d was reported to reduce stress related molecules in the urine (i.e., cortisol and catecholamines) and partially normalized levels of glycine, citrate, trans-aconitate, proline, and β‑alanine in those participants with a high anxiety trait compared to those with a low anxiety trait. These findings indicated alterations in stress-related energy metabolism (Martin et al., 2009).
Diet-related diseases such as type 2 diabetes and cardiovascular disease have been investigated by metabolomics in an effort to understand their etiology and identify new biomarkers. There is now strong evidence that elevated plasma levels of branched chain amino acids (BCAAs) (i.e., leucine, isoleucine, and valine) and their derivatives are linked to the risk of developing insulin resistance and type 2 diabetes. Moreover, depending on the metabolite, changes may be apparent as long as 13 years ahead of clinical manifestations of type 2 diabetes. Several investigators have also shown that BCAAs and related metabolites are also associated with coronary heart disease, even when controlled for diabetes. See Newgard (2012), Klein and Shearer (2016), and Bhattacharya et al. (2014) for more details.
With the accumulating evidence of the importance of the gut microbiota in the development of certain diseases, metabolomics is also being used to identify metabolites that originate from gut microbial metabolism, and follow alterations that may occur (Brennan, 2013).
Nevertheless, some of the findings reported from metabolomics studies have been contradictory, highlighting the need for further research before metabolomic results can be translated into clinical applications.
Dietary biomarker studies are being explored to overcome some of the limitations of traditional dietary assessment methods, and thus improve the assessment of the relationship between diet and chronic disease. The food metabolome (i.e., metabolites derived from foods, and food constituents) is a promising resource to discover novel food biomarkers.
Several approaches are used to identify novel biomarkers of dietary intake. They may involve acute feeding studies and short- to medium-term dietary intervention studies in a controlled setting. These intervention studies focus only on one or a few specific type(s) of food(s), after which biofluids, most notably urine or serum, are collected postprandially or following the short-to medium term dietary intervention. This approach has been used to identify several putative biomarkers of specific foods and drinks such as citrus fruits, cruciferous vegetables, red meat, coffee, sugar-sweetened beverages, and wine (O'Gorman and Brennan, 2017). However, the biomarkers identified in this way reveal no information on other dietary origins of the identified biomarkers. In addition, for those biomarkers that are short-term, and excreted rapidly and almost completely over 24hr, their usefulness as biomarkers of habitual intake remains questionable.
Potential biomarkers for specific foods can also be identified using cohort studies. In this approach, metabolic profiles of high or low consumers of specific food(s), identified by a self-reported dietary questionnaire are examined. Studies of this type have identified proline betaine and flavanone glucuronides as potential biomarkers of citrus fruit intake (Pujos-Guillot et al., 2013). Biomarkers for fish, red meat, whole-grain bread, and walnuts have also been identified using this approach (O'Gorman and Brennan, 2017). Nevertheless, because these studies only generate associations, validation of the metabolite as a specific biomarker of intake should be confirmed through a controlled dietary intervention study.
Large cross-sectional or cohort studies have also used dietary patterns to identify multiple biomarkers of food intake. Dietary patterns can be identified by principal component analysis or k‑means cluster analysis. Once identified, the dietary patterns are linked to metabolomic profiles through regression (or other statistical methods) to identify dietary biomarkers. Using this approach, metabolites have been identified that can be used to predict compliance to complex diets and to study relationships between diet and disease (Bouchard-Mercier et al., 2013). In some of these studies, the predictive accuracy of the identified biomarkers has been evaluated through the use of receiver operating characteristic (ROC) analysis (Heinzmann et al., 2012; Wang et al., 2018).
Once identified, the performance of all biomarkers of dietary exposure must always be validated in an independent and diverse epidemiological study, and across different laboratories to establish whether they are generalizable to free-living populations. This approach was used by Heinzmann et al. (2012) to validate proline betaine as a biomarker of citrus intake. In addition, the suitability of the biomarker over a range of intakes should be confirmed through a dose-response relationship (O'Gorman and Brennan, 2017).