The American Heart Association and the U.S. 2010 Dietary Guidelines provide recommendations for added sugar intake, as it is theorized that added sugars in the diet, particularly sugar-sweetened beverages (SSB), have contributed to the rise in obesity prevalence due to research suggesting that they contribute to excessive energy intake 23 24 25 . Yet, significant evidence is needed to directly link SSB and added sugar intake to obesity and other co-morbidities such as hypertension, diabetes and cardiovascular disease 26 27 28 . Valid and reliable biomarkers of sugar intake are needed to support existing dietary recommendations. Additionally, the U.S. 2010 Dietary Guidelines suggest one half of grains consumed should be whole grains 29 , due to associations with heart health 30 31 . However, the general population may have difficulty distinguishing whole grains from refined grain products. Thus, a valid biomarker of whole grain intake would provide greater insight into the influence of diet on health and disease outcomes 31 .

Carbon stable isotope abundance of ¹³C is a novel biomarker for cane sugar and high fructose corn syrup (HFCS). Cane sugar and HFCS are derived from C₄ plants (includes molasses, brown and powdered cane sugar), making their intake measureable through ¹³C isotope measures 32 . Cook et al. 33 used ¹³C from blood glucose to determine its potential as a biomarker for cane sugar/HFCS; unfortunately, fasting glucose ¹³C levels were inadequate indicators of intake as gluconeogenesis caused ¹³C dilution. However, random plasma ¹³C measurements showed high correlations with consumption of cane sugar/HFCS from the previous meal (R²=0.90) 33 and serum ¹³C levels were shown to be correlated with SSB intake (r=0.18) in older adults 34 . Davy et al. 22 used fingerstick blood samples to measure the ¹³C isotope content, and reported higher correlations between SSB intake and ¹³C levels when compared to ¹³C venipuncture samples 34 . Correlations with added sugars (calories [kcals] and grams [g], r=0.37) and total SSB (kcal and g, r=0.35, 0.28) were noted 22 . Additionally, the reproducibility of ¹³C at 2 time points was found to be significant (r=0.87) 22 .

Although moderate correlations were found for serum ¹³C to added sugars and SSB, there are limitations that require further research before the ¹³C isotope is considered a valid biomarker of cane sugar and HFCS. While high correlations were found for random plasma glucose ¹³C measures to cane sugar and HFCS, this only reflects extremely recent intake (i.e., the previous meal). For this reason as well as less invasiveness, fingerstick serum ¹³C measures may be a better choice (possibly reflects a longer intake period and is less invasive) for a cane sugar/HFCS intake biomarker; however, further research is warranted to determine the intake period reflected in the measurement.

Beet sugar and maple syrup, which only account for a small fraction of added sugars in the diet, are not captured by ¹³C measures as they are C₃ plants, as well, honey is not included 32 . Thus, biomarkers for sugar intake that rely on ¹³C isotopes are only able to capture part of the general US population’s intake; even so, this does reflect a large portion of the consumed added sugars. Another limitation of using ¹³C isotopes is that corn is also a C₄ plant; thus corn, corn derivatives and meat from animals that consumed corn are reflected in the measurement. ¹³C was shown to be correlated with whole corn intake and animal protein intake (r=0.15, 0.28, respectively) 34 . A second isotope, ¹⁵N, may be able to account and correct for animal protein intake 35 . Overall, ¹³C measures have shown promise as they can distinguish low from high sugar consumers 22 , and have demonstrated significant correlations between SSB, added sugars and cane sugar/HFCS. Further research is needed to refine this added sugar biomarker and establish the intake period reflected by the measurement.

Sugar. Urinary sucrose and fructose have been investigated as possible biomarkers of sugar intake. Urinary sucrose, fructose and combined sucrose/fructose are associated with sugar intake (R²=0.86, 0.80, 0.89, respectively), and are reproducible (ICC=0.44, 0.81, 0.67, respectively) (examined using thirty 24-hour urine samples) 36 . Urinary sucrose and fructose concentrations did not significantly differ between normal and obese individuals when using a sugar controlled diet 37 . Kuhnle et al. 38 examined two analytical methods of determining urinary sucrose, gas chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS). GC-MS is able to identify more compounds than LC-MS, but the sample preparation for GC-MS is more labor-intensive and the analysis takes longer to conduct as it is examining more compounds than LC-MS.

Urinary sucrose and fructose are able to detect changes in sugar intake 36 , classify an individual as a high or low sugar consumer and are suitable for those of normal and obese weight status 37 . As well, both the LC-MS and GC-MS analytic methods predicted urinary sucrose as a suitable biomarker of sugar intake 38 . However, a major limitation of urinary sucrose and fructose is the capability to only reflect short term intake. Further research is needed to develop a biomarker of total sugar intake that is reflective over a longer period of time (i.e., habitual intake).

Several studies have examined plasma alkylresorcinol (AR) concentrations as a possible whole grain wheat/rye biomarker. Total plasma AR increases with whole grain intake and decreases with refined bread intake after one week 39 . Fasting plasma AR demonstrated high to moderate reproducibility, ICC>0.88 (8 time points) 40 , r=0.38 (2 time points) 41 and was correlated with whole grain intake (r=0.58) 31 , (R²=0.94) 39 , and rye/wheat intake (r=0.53) 41 . Non-fasting plasma AR concentrations were found to be significantly higher compared to fasting concentrations, however a moderate significant correlation of r=0.50 (p<0.05) between fasting and non-fasting was determined 41 . Red blood cell (RBC) AR increases and decreases with whole grain ingestion, and also correlates with plasma AR (R²=0.85) 39 . However, AR may be retained in RBC membranes during low AR intake 39 . Investigation of enterolactone (ENL), the main end-product of whole grains, revealed its poor function as a biomarker of whole grain intake, as it is a non-specific biomarker that has many dietary sources and varies greatly between genders 39 . AR homolog C17:0/C21:0 ratios have the potential to differentiate between types of whole grain intake, specifically wheat and rye 31 39 40 .

3,5-dihydroxybenzoic acid (DHBA) and 3-(3,5-dihydroxyphenyl)-propanoic acid (DHPPA) are two metabolites of AR that are excreted through urine. Urinary DHBA and DHPPA were both found to be significantly correlated with total plasma AR (r=0.481, 0.450, respectively, no p values given) 42 . Additionally, cereal fiber intake was significantly correlated with DHBA and DHPPA, as well as plasma AR 42 . Recovery was shown to decrease with high AR doses; it could be that a 24-hour urine collection was not enough time to recover the high dose. DHBA and DHPPA were able to demonstrate a higher dose-response effect than plasma AR at low intake levels 43 .

Total plasma AR appears to be a possible short term (half-life approximately four hours) biomarker of whole grain intake when assessing dose-response 43 ; conversely, reproducibility has been shown over a 2-3 month period, which indicates a medium term marker. However, AR may accumulate over periods of high intake, thus over-estimating intake at high levels and under-estimating at low levels 31 40 43 . RBC AR may be a longer term indicator of whole grain intake than plasma AR, as they retain AR. Urinary DHBA and DHPPA may provide a comparable indicator of whole grain intake as plasma AR, while being minimally invasive 42 . Further research is needed to assess effects of various other whole grains on the AR homolog C17:0/C21:0 ratio, in addition to determining the time period being reflected.

The current lack of a valid total fat dietary biomarker has hindered research targeting direct relationships of fat intake with cardiovascular disease, as dietary fat intake assessment has largely relied on subjective data 4 44 . The composition of fatty acid intake can be reflected in the measurements of blood cholesterol (e.g., LDL, HDL); however, the role of genetics must be acknowledged as having an effect on blood levels as well 14 . Nonetheless, actual intake of specific fatty acids (mono-unsaturated [MUFA], poly-unsaturated [PUFA] and saturated fatty acids [SFA]), which may be indicators of disease risk, is difficult to capture 4 45 . Additionally, intake of omega-3 fatty acids, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), have been linked with reduced cardiovascular disease risk; however, current methods of determining actual intake have proven to be costly and time-consuming 46 . Thus, research is needed to develop biomarkers that are cost-effective and able to detect dietary fat/fatty acid intake.

Dietary biomarkers that represent total fat intake have demonstrated conflicting results. Fatty acid RBC concentrations of MUFA, PUFA and SFA do not appear to be adequate biomarkers of total fat intake, especially SFA. Also, EPA, DHA and oleic acid may provide short term biomarkers of relative intake but not total fat intake 47 . King et al. investigated the possibility of using a combination of fatty acids to create a biomarker of total fat intake. Using three different biological samples (i.e., RBC, plasma phospholipids (PL) and cholesterol esters (CE)), to measure fatty acid status, three prediction models were produced that had high sensitivity and specificity (all >90%) in discerning between low fat/high fat intakes 44 . Trans-fats were a common indicator of total fat intake for all models, but it may be less useful as a biomarker as trans-fats are being removed from many foods. RBC markers may be a useful long term marker, as the RBC turnover is approximately 120 days; RBC also showed smaller changes in fatty acid composition compared to PL and CE measures 44 . Thus, utilizing a combination of various fatty acids may prove to be a biomarker of total fat intake.

Several studies examining biomarkers of relative fatty acid intakes have produced favorable outcomes. PUFA measured in adipose tissue showed strong correlations with dietary intake (r=0.15-0.58), specifically linoleic and alpha-linolenic acids 45 . Others have suggested that n-6 and n-3 PUFA in PL is a long-term biomarkers of relative intake (r=0.16, 0.29, respectively) 48 . A study comparing pre- to post-menopausal women reported a significant correlation between RBC and PUFA in post-menopausal women (r=0.39), but not pre-menopausal (r=0.17) 49 , and correlations between RBC MUFA and relative intake (r=0.40-0.48) 49 . Also, plasma MUFA cis18:1n-9 was reported to be a long-term biomarker for total MUFA (r=0.22) 48 . RBC Oleic acid was found to be a valid biomarker of intake (r=0.45-0.47) 49 , but RBC SFA does not appear to be a valid intake biomarker 49 . However, serum SFA 15:0 correlated with total SFA dietary intake (r=0.19) 48 . Adipose trans-fatty acids were also shown to correlate significantly with dietary intake (r=0.43) 45 .

According to existing literature, adipose and plasma PUFA levels appear to be the best indicators of relative intake; RBC PUFA levels warrant additional research as correlations differed between population groups. RBC and plasma MUFA appear to be valid measures of MUFA intake, while RBC SFA does not appear to be a valid indicator of intake. Serum SFA measures show potential as biomarkers, but trans-fatty acid biomarkers may not be as useful due to reductions in the food supply in recent years.

Alpha-linolenic and linoleic acid are two essential fatty acids (EFA). Significant tissue-dietary correlations of alpha-linolenic and linoleic acid, respectively, in adipose tissue (r=0.51, 0.52), fasting blood (r=0.38, 0.43) and fasting plasma levels (r=0.39, 0.41) have been reported 50 . Others 49 have noted a significant correlation between RBC linoleic acid and relative dietary intake (r=0.23 pre-menopausal, 0.39 post-menopausal), but not for RBC alpha-linolenic acid. Fasting blood is comparable in results to plasma and adipose tissue, less expensive and less invasive than adipose tissue sampling. Thus, whole blood measures appear to be the ideal indicator of long-term linoleic acid intake, and possibly alpha-linolenic acid 50 .

EPA and DHA are omega-3 fatty acids primarily obtained from fish consumption 46 . Levels of plasma EPA and DHA, when compared to their relative dietary intake, produce significant correlations (EPA, r=0.57 males, 0.60 females; DHA, r=0.57 males, 0.30 females) 51 . The stable isotope ¹⁵N is associated with fish intake; thus, levels of EPA and DHA ¹⁵N were assessed in blood and hair samples. Dietary EPA and DHA were correlated with blood ¹⁵N levels (r=0.47, 0.46, respectively) 46 . Hair ¹⁵N was correlated with dietary EPA and DHA (r=0.83, 0.84, respectively) 52 . However, because ¹⁵N can be influenced by intake of other sources of animal protein (beef, etc.) feeding studies should be completed to further evaluate this biomarker’s specificity. As the availability of EPA and DHA fortified food items (bread, cereal, etc.) and supplements has increased over the past several years, and due to the amount of EPA and DHA varying within types of fish, this biomarker must be acknowledged to be a biomarker of EPA and DHA intake and not fish consumption.

Plasma EPA and DHA may be useful dietary biomarkers of their respective intake; further research is required to determine the time-period of intake reflected. Blood and hair ¹⁵N both provide accurate biomarkers of EPA and DHA intake. The turnover of EPA and DHA differ, thus RBC ¹⁵N levels may be providing indicators for two different time periods 46 . Hair ¹⁵N is able to reflect the previous two months of intake 52 . Plasma EPA and DHA, RBC and hair ¹⁵N all show potential as biomarkers of EPA and DHA intake; yet, further research is needed to determine dose-response as well as intake periods being measured.

An additional biomarker of omega-3 fatty acid intake, known as the Omega-3 Index, is the sum of EPA and DHA in RBC membranes, expressed as a percent of total fatty acids 53 . The validity and dose-response of the index was assessed by having 57 subjects randomized into 4 varying dose supplementation groups and was then compared to plasma phospholipid and whole blood EPA and DHA. Correlations between the Omega-3 Index and plasma phospholipid and whole blood EPA and DHA were both found to be significantly correlated, r=0.91, p<0.001, p<0.0001, respectively. Additionally, significant dose-responses were demonstrated between the varying intake levels. Significant changes from baseline levels were also found in all intake groups, with the exception of the 2 highest intake levels, which may indicate a ceiling effect 53 . Additional testing is required to assess the reliability of the Omega-3 Index, as well as the time period being assessed.

Lower incidences of cardiovascular disease have been associated with diets where olive oil is a major contributor to fat intake 54 . Tyrosol and hydroxytyrosol are two phenolic compounds derived from olive oil intake. Tyrosol shows a strong dose-response effect in 24-hour urine samples, as well as similar recovery for a single dose and a week of sustained doses (16.9%, 19.4%, respectively). Hydroxytyrosol had a recovery of 78.5% after a single dose and 121.5% recovery after a week of sustained intake. This reveals that hydroxytyrosol probably accumulates as the recovery was higher than the intake of olive oil; additionally, hydroxytyrosol can also be derived from other sources, including endogenous sources 54 . Although further research is needed, tyrosol shows promise as a biomarker of olive oil intake.

A dietary biomarker of protein intake may be useful for determining nutritional status (over/under nourished). In addition, animal protein intake has been linked to increased risk of cancer, obesity, diabetes and the metabolic syndrome 55 . However, research determining the long-term effects of dietary protein intake is lacking due to the absence of a valid biomarker.

Urinary nitrogen is a valid method of assessing total protein intake, though several limitations exist. A comparison of a 28-day feeding study with multiple 24-hour urine nitrogen outputs produced a correlation of 0.99. When the time period is reduced to a single observation, the correlation is reduced to 0.50, but improves to a correlation of 0.95 with 18 days (p values not reported) 56 . To obtain the most accurate measurements, individuals should maintain a constant daily intake and be in nitrogen balance. Urinary nitrogen may underestimate high protein intake levels and overestimate at low intake levels, yet it is considered an adequate biomarker of protein intake. It is suggested that multiple 24-hour urine samples are needed to fully establish protein status 56 .

As discussed previously, isotopes ¹³C and ¹⁵N are potential dietary biomarkers for added sugars and fatty acids 22 32 33 34 35 46 52 . These isotopes have also been evaluated for their potential to measure animal protein intake via ¹⁵N and ¹³C hair, yet baseline measurements showed low correlations with dietary intake (R²=0.17, 0.44, respectively). A decrease in both isotopes with decreased protein intake has been reported, but not a significant increase with increased protein intake after four weeks. Thus, hair ¹⁵N and ¹³C do not appear to be valid short term dietary biomarkers of protein intake, but further research is needed to determine if they could be valid longer-term biomarkers 55 . Several potential biomarkers of red meat intake have been identified, creatinine, taurine, 1-methylhistidine, and 3-methylhistidine. These components are specific to meat intake and are excreted in the urine 57 . Two randomized crossover studies examined the mean levels of each component with four dietary conditions, three varying levels of red meat intake and one vegetarian diet. All components demonstrated a significant dose-response to the increase of red meat intake (P_trend<0.0001). Furthermore, 1-methylhistidine and 3- methylhistidine demonstrated significant differences in the means across the four dietary conditions (all p<0.03). Taurine and creatinine did not appear to be as sensitive to intake and were not able to distinguish between the low red meat and the vegetarian diets. (p=0.95, 0.88, respectively) 57 . 3- methylhistidine and creatinine can be formed during muscle catabolism, thus, as markers of red meat intake they could potentially be falsely elevated. Furthermore, 3- methylhistidine was shown to have greater variance among participants on the same diet; the same was not demonstrated with 1- methylhistidine, which may be the most promising biomarker of the four components. This study had participants consume the respective diet for 15 days, with three 24-hour urine collections occurring on the final three days. Because no washout period was used, it is difficult to determine the time period measured. The half-lives of 1- and 3- methylhistidine are reported to be approximately 12 hours; thus, they are both considered short term biomarkers of red meat intake 57 . Further research reporting the reproducibility and the intake period being measured is needed.

Table 2 presents a summary of the various food/dietary component biomarker studies that could not be categorized within a macronutrient category, as follows: caffeine, citrus, cocoa, garlic and wine.

^aHPLC, High-performance liquid chromatography; 1H NMR, 1H Nuclear magnetic resonance spectroscopy; FT-ICR-MS, Linear trap quadrupole-Fourier-transform ion cyclotron resonance mass spectroscopy ultra; HPLC-q-TOF, High-performance liquid chromatography with time of flight mass spectrometry; GC-MS, Gas chromatography-mass spectrometry; LC-MS/MS, Liquid chromatography-tandem mass spectrometry; LC-ESI-MS/MS Liquid chromatography-electrospray ionization/multi-stage mass spectrometry.

^bShort term: hours/days; Medium term: weeks/months; Long term: months/years.

^cResults of comparsion of biomarkers to an appropriate dietary assessment method.

^dRepresentative values from the literature.

^eBiomarker is able to detect changes in intake over time or distinguish high from low consumers; + = sensitivity has been demonstrated.

*Significance not reported.

Caffeine intake is difficult to assess via questionnaires and dietary recalls, as caffeine concentrations can vary greatly among different foods/beverages and may not be present in many nutritional software databases. However, due to the potentially harmful side effects of high caffeine intake, it may be important to develop acceptable intake levels and a biomarker that reflects consumption 58 . Caffeine (137X) is broken down into four known metabolites in the urine, 17X, 17U, 1X, AFMU. Caffeine, in its un-metabolized form, and AFMU are greatly influenced by inter-individual differences (e.g., genetic variability) and are not acceptable indicators of caffeine intake. Although 17X is minimally influenced by genetic variability and shows significant correlation with caffeine intake (R²=0.58), it requires more research before it is considered a valid biomarker of intake. 17U and 1X are both minimally influenced by inter-individual differences, show high correlations with intake (R²=0.87, 0.78, respectively) and may be acceptable biomarkers of caffeine consumption 58 .

Total fruit and vegetable intake is difficult to objectively quantify due to most biomarkers measuring the effect of fruit and vegetables on health outcomes (e.g., reduction of oxidative biomarkers 59 ) or intake of non-specific nutrients, such as Vitamin C (which is found/added in many dietary items) 4 . However, proline betaine was identified through nutrimetabolomic metabolic profiling as a possible marker of citrus consumption, which may be able to identify true intake of citrus fruits. Proline betaine was shown to be sensitive (86.3%), specific (90.6%) and significantly correlated with citrus consumption (R²=0.40). A limitation of proline betaine is its rapid urinary excretion, (i.e., 24 hours after intake) 21 . Lloyd et al. 60 demonstrated the ability of urinary proline betaine levels to differ among low, medium and high citrus consumers after an overnight fast. Levels of proline betaine were assessed throughout the study; however, oranges were the only citrus source used. Additionally, two metabolites, specific to orange intake, were identified: hesperidin and nariruin. However, they were found to be insufficient indicators of citrus intake. The study concluded that additional sensitivity and specificity of proline betaine, with additional intake of varying citrus foods, is necessary before it can be considered a useful dietary biomarker 60 .

Cocoa is a major source of phytochemicals (phenolic compounds), which have been shown to improve cardiovascular health and antioxidant status 61 . A study utilizing metabolomic metabolic profiling identified twenty-seven cocoa urinary metabolites that occurred over the 24-hour period following intake 61 . Additional research on the various identified cocoa metabolites should be conducted in order to develop a valid biomarker of cocoa intake.

It has been hypothesized that garlic may provide chemo-preventive effects; thus, the development of a biomarker of garlic intake may enhance research targeting cancer prevention, as well as prevention of other chronic disease 62 . S-allyl-mercapturic acid (ALMA) has been identified as a urinary metabolite of dietary garlic intake. In a research investigation, the presence of ALMA was detected in the majority of garlic consumers (fifteen out of sixteen), while only two control subjects out of fourteen had detectable levels of ALMA. Therefore, ALMA appears to differentiate garlic consumers from non-consumers. However, ALMA is a short term biomarker of garlic intake as the half-life is approximately six hours, and ALMA may increase with other sources; it is not specific to garlic intake 62 .

Resveratrol, a phenolic compound found in wine, has been shown to be negatively correlated with cardiovascular disease 63 . A biomarker for wine intake may prove to be useful, as individuals may not always accurately report alcoholic beverage consumption due to social undesirability (2). Metabolites of resveratrol have been discovered in urine and plasma, and total resveratrol metabolites (TRM) were analyzed to determine exposure and responsiveness of wine intake. Plasma TRM have an extremely short half-life of approximately two hours, and only reflect very recent intake. Urinary TRM, however, may differentiate between wine drinkers and non-drinkers with high sensitivity and specificity (73%, 93%, respectively). TRM also show a strong dose-response effect. A limitation of TRM is that it only reflects intake of regular consumers and may prove less useful in intermittent consumers of wine 63 .

A later study demonstrated the ability of urinary resveratrol metabolites to be significantly correlated with wine intake (r=0.895, p<0.001) and also established sensitivity (93.3%) and specificity (92.1%) between consumers and non-consumers 64 . Further research is needed to be able to objectively classify consumers into groups based on their wine consumption levels 65 . Certain limitations exist, such as resveratrol is not specific to wine intake and can be found in grapes, peanuts, and berries; furthermore, the amount of resveratrol can vary between types of wine 64 .

Rotches-Ribalta et al. 66 used mass spectrometry to identify resveratrol metabolites after ingestion of red wine and grape extract. Seventeen metabolites were identified, including trans- and cis-resveratrol and trans- and cis-piceid. Significant differences were found between red wine consumption and pharmaceutical ingestion of grape extract, which suggests further research is needed to assess resveratrol metabolism. Specifically, it has been shown that resveratrol has low bioavailability and a complex diet (especially fiber intake) may affect the concentrations of the metabolites. Colonic microflora also play a role in producing resveratrol’s metabolites, which is in need of further investigation as intra-individual microflora variability is high 66 67 .

Biomarkers are needed to provide objective measures of nutrient status, which is a commonly cited limitation of subjective dietary assessment methods. However, some dietary intake methods use biomarkers to validate the data being collected. As noted by The Institute of Medicine, the need to expand upon dietary assessment methods is critical 10 . Biomarkers that will allow for the assessment of specific consumption of items which could be deemed socially undesirable, such as sugar-sweetened beverages or high fat/saturated fat foods, without confounds of human subjective nature need to be developed 13 . Future research pertaining to biomarkers should emphasize the development of biomarkers for evaluating adherence to national recommendations for specific food groups such as the U.S. 2010 Dietary Guidelines (e.g., whole grains, fruits and vegetables, low fat/fat free dairy products, added sugar) 29 . Future research should be directed at refining existing biomarkers by accounting for confounding factors, establishing new indicators of specific food intake and developing techniques that are cost-effective, noninvasive, rapid and accurate measures of nutritional status. The emerging field of metabolomics in human nutrition, as well as the development of valid FFQ and the continued expansion of food metabolome databases will permit the identification of specific dietary components in food, produce more valid biomarkers of exposure to certain foods and possibly advance nutritional science research which aims to evaluate diet and disease relationships.