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Logo of ajcnThe American Journal of Clinical NutritionSearchSubmit ManuscriptSubscribeAll ArticlesPublished Version
Am J Clin Nutr. Mar 2009; 89(3): 913–919.
Published online Jan 28, 2009. doi:  10.3945/ajcn.2008.27054
PMCID: PMC2646803

Red blood cell δ15N: a novel biomarker of dietary eicosapentaenoic acid and docosahexaenoic acid intake1,2,3,4


Background: The long-chain omega-3 (n–3) fatty acids derived from fish, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are associated with a reduced risk of cardiovascular disease and other chronic diseases. Study of the associations between EPA and DHA intake and disease requires a valid biomarker of dietary intake; however, the direct measurement of tissue fatty acid concentrations is expensive and time consuming.

Objective: Because the nitrogen stable isotope ratio (15N/14N, expressed as δ15N) is elevated in fish, we investigated whether δ15N is a valid alternative biomarker of EPA and DHA intake.

Design: We examined the relation between red blood cell (RBC) δ15N and RBC EPA and DHA in a community-based sample of 496 Yup'ik Eskimos with widely varying intake of n–3 fatty acids. We also assessed the correlation between δ15N and dietary EPA and DHA intake based on 24-h dietary recalls and 3-d food records completed by a subset of 221 participants.

Results: RBC δ15N was strongly correlated with RBC EPA and DHA (r = 0.83 and 0.75, respectively). These correlations differed only modestly by sex and age class. RBC δ15N also correlated with dietary EPA and DHA intake (r = 0.47 and 0.46, respectively) and did not differ by sex and age.

Conclusions: The results strongly support the validity of RBC δ15N as a biomarker of EPA and DHA intake. Because the analysis of RBC δ15N is rapid and inexpensive, this method could facilitate wide-scale assessment of EPA and DHA intake in clinical and epidemiologic studies.


The omega-3 (n–3) fatty acids derived from fish, eicosapentaenoic acid (EPA; 20:5n–3) and docosahexaenoic acid (DHA; 22:6n–3), are associated with a reduced risk of cardiovascular disease and other chronic diseases (13). EPA and DHA promote an antiinflammatory state (4) and regulate the expression of genes involved in fatty acid metabolism (57). However, our ability to detect associations between EPA and DHA intake, gene variants, and disease is limited by the validity and feasibility of dietary assessment. Both plasma and red blood cell (RBC) fatty acid composition are valid biomarkers of EPA and DHA intake (813), but their measurement requires technically challenging, expensive, and time-consuming assays that are impractical in large-scale studies. Simpler and less expensive biomarkers of EPA and DHA intake are clearly needed.

Ratios of naturally occurring stable isotopes have recently gained attention for their potential as accurate, inexpensive dietary biomarkers in nutritional studies (1418). This approach is useful for foods that are enriched with the heavier isotopes of carbon, nitrogen, oxygen, or hydrogen (14, 1823). For example, fish has a uniquely high 15N/14N (expressed as δ15N as defined in Subjects and Methods), for 2 reasons: 1) marine environments tend to be enriched in 15N relative to terrestrial environments, particularly those that are fertilized, and 2) fish are typically predatory and δ15N reflects the length of an animal's food chain (24). These isotopic differences in diet are passed on to the tissues with only minor, predictable changes (2527), and anthropologists have long used δ15N as a marker of the consumption of marine foods in human populations (2831). Recently, δ15N was shown to be highly elevated in a Greenland Inuit population with a very high dietary intake of marine foods (17). Because fish is also the predominant source of EPA and DHA in human diets, we hypothesized that there would be a strong relation between these n–3 fatty acids and δ15N in human tissues, driven by differences in the dietary intake of fish. If true, δ15N could serve as an alternative biomarker of EPA and DHA intake that is accurate, relatively inexpensive, and highly robust.

We examined the relation between RBC EPA and DHA and RBC δ15N in a community-based sample of 496 Yup'ik Eskimos (32). This population is ideal for testing this relation because they have widely varying fish intake, depending on the degree to which they adhere to a traditional, marine-based diet (33, 34). We also investigate the relations between δ15N and dietary intake of EPA and DHA and RBC EPA and DHA in a subset of 221 participants.


Participant recruitment and procedures

Data are from the Center for Alaska Native Health Research I (CANHR I) Study, a cross-sectional, community-based participatory research study of biological, genetic, nutritional, and psychosocial risk factors for obesity and related disease in Yup'ik Eskimos. The CANHR study was approved by the University of Alaska Institutional Review Board, the National and Area Indian Health Service Institutional Review Board, and the Yukon-Kuskokwim Health Corporation Human Subjects Committee. Between 2003 and 2005, 1003 men and women aged ≥14 y were recruited from 10 communities in southwest Alaska, as described in detail elsewhere (32, 35). At entry into the study, the participants completed extensive interviewer-administered interviews covering demographic characteristics, economic status, ethnicity, and medical history. All participants completed an interviewer-administered 24-h dietary recall and were requested, but not required, to complete an additional 3-d food record (3DFR). Blood was collected into EDTA-containing tubes and processed locally; serum, lymphocytes, and the remaining RBC clot were separated into aliquots and stored at −20°C. Within 6 d, samples were shipped to the University of Alaska Fairbanks and stored at −80°C. Aliquots of RBCs were removed for fatty acid and stable isotope analyses, as described below.

Study sample

Analyses of RBC fatty acids were based on a subset of 496 of the 1003 CANHR participants, who were selected after recruitment was completed from 7 of the 10 participating communities, with an effort to balance the sample across age and community. Three communities were very small; therefore, all participants were selected (n = 164). For the remaining communities, we defined 3 age groups (14–19, 20–49, and ≥50 y) and selected a random sample from each to obtain ≈28 from each age stratum. If 28 participants were not available, we selected all participants in that stratum and adjusted the selection in the remaining age strata to yield ≈84 per community. Of the 496 participants selected, 221 had completed both a 24-h dietary recall and a 3DFR, and this subsample was used in analyses of dietary intake.

Stable isotope analyses

RBC aliquots were autoclaved for 20 min at 121°C to destroy blood-borne pathogens, and the samples were weighed into tin capsules (3.5 × 3.75 mm) and freeze-dried to a final mass of 0.2–0.4 mg. Neither autoclaving nor the use of EDTA-treated tubes affects RBC nitrogen isotope ratios (22). Samples were analyzed at the Alaska Stable Isotope Facility by continuous-flow isotope ratio mass spectrometry with a Costech ECS4010 Elemental Analyzer (Costech Scientific Inc, Valencia, CA) interfaced to a Finnigan Delta Plus XP isotope ratio mass spectrometer via the Conflo III interface (Thermo-Finnigan Inc, Bremen, Germany). Data are presented in standard delta values as δX = (Rsample − Rstandard)/(Rstandard) · 1000‰, where R is the ratio of heavy to light isotope (15N/14N) and the standard is atmospheric nitrogen. δ15N values are hereafter abbreviated as δ15N. We concurrently prepared and ran multiple peptone standards (δ15N = 7.00) to assess analytic accuracy and precision, which were analyzed after every fifth sample and gave values of δ15N = 7.01 ± 0.22‰ (SD).

RBC fatty acid measurements

The RBC fatty acids were analyzed at the Fred Hutchinson Cancer Research Center in Seattle, WA. Fatty acids were extracted from washed RBCs in a total lipid fraction with a combination of organic solvents. Briefly, 250 μL RBCs were mixed with an equivalent volume of distilled water, and lipids were extracted with 2-propanol and chloroform according to Rose and Oklander (36); 5 mg BHT/100 mL of 2-propanol was added as an antioxidant. The lipid extract was transesterified in 5 mL acetyl chloride reagent and processed according to Lepage and Roy (37). After transesterification, fatty acid methyl esters were recovered in hexane, dried under nitrogen (40°C), and redissolved in 100 μL hexane for gas chromatography.

Fatty acid methyl esters were injected in a split mode (1:50) and were separated by using an SP-2560 (Supelco, Bellefonte, PA) capillary column (100 m × 0.25 mm × 0.2 μm) on a Hewlett-Packard (model 5890B) gas chromatograph (GC) (now Agilent, Santa Clara, CA). The GC system was equipped with a flame ionization detector, electronic pressure control, Chemstation software (Hewlett-Packard), and automatic sampler (HP7673). This method allows the resolution of 46 different membrane fatty acids. The accuracy of the GC system was monitored by using commercial standards (GLC-87, NIH-D, and NIH-F; Nu-Chek, Elysian, MN). The precision of the RBC fatty acid measurements was monitored with repeat analysis of an in-house RBC quality-control pool that was included in each batch of 23 study samples. The CV for EPA was 2.7% and for DHA was 2%. Fatty acid composition is reported as the percentage by weight of total RBC fatty acids.

Dietary assessment

Diet was assessed with an interviewer-administered 24-h dietary recall and a 3DFR. Data from these instruments were combined to achieve a stable estimate of dietary nutrient intake. Data from the 24-h dietary recall were collected from each participant by certified interviewers using a computer-assisted recall [Nutrition Data System for Research (NDS-R) software version 4.06; University of Minnesota, Minneapolis, MN]. Participants were asked to recall all food and beverages consumed over a 24-h period using a multiple pass approach. Although most of the participants were bilingual, a native Yup'ik speaker who was trained in the use of NDS-R software assisted the non-English speakers.

Because of an already high participant burden, the 3DFR was not mandatory, although it was offered to all participants. Participants were instructed to maintain their usual eating habits. A research team member reviewed all 3DFRs for completeness, which were then entered into the NDS-R software package by certified coders. A second researcher reviewed all entries for accuracy.

Nutrient calculations for both the 24-h dietary recall and 3DFR were performed by using the NDS-R Food and Nutrient Database 33, released July 2003. A few Alaska Native foods were missing from the database; these were either substituted for similar food items when appropriate or the food was added to the database. Here we only examined data on dietary intake of EPA and DHA.

Statistical analyses

All statistical analyses were performed by using JMP IN software (version 5.1.2; SAS Institute, Cary, NC). We used a chi-square test to evaluate differences in the sex, age, and BMI distribution between the complete sample of participants and the subset of participants with dietary intake data. We assessed mean differences in biomarkers between sex, age, and data sets using 2-tailed t tests. Associations of δ15N with RBC EPA and DHA were assessed by using both Pearson's product moment and Spearman rank correlation coefficients; however, Spearman rank correlation coefficients were only reported when these differed. We described the relation between RBC δ15N and EPA and DHA using linear regression or nonlinear fitting, depending on the shape of the relation. Exponential relations were described by nonlinear fitting and parameter estimation by using the general model y = a (1 − e r x). This procedure generates a best-fit model value for each y, and R2 is calculated as 1 − (RSS/TSS), where RSS and TSS are the residual sum of squares and the total sum of squares, respectively. For parametric analyses of both linear and nonlinear relations, the normality of residuals was tested by using the Shapiro-Wilks test. We used 2-tailed t tests to determine whether mean EPA, DHA, and δ15N differed between males and females. We used multiple regression analysis to test the effects of age and sex on the associations between fatty acids and δ15N, where associations were linear and met the assumptions for parametric statistical tests. For regression analysis, age was treated as a dichotomous variable (<40 y and ≥40 y). Dietary intake data were log transformed—ln (1 + daily FA intake)—and averaged across all days of intake. We tested whether the correlation between RBC FA and dietary FA intake differed from that between RBC δ15N and dietary FA intake according to Wolfe (38).


The age, sex, and BMI distribution of the study participants are shown in Table 1. Of the 496 participants selected for this study, 58% of the participants were female and 42% were male. Female participants ranged in age from 14 to 94 y and had a mean age of 39 y. Male participants ranged in age from 14 to 83 y and had a mean age of 41 y (Table 1). Forty-three percent of females and 47% of males completed a 3DFR, and the proportion of males and females did not differ between the complete sample and the diet data subset. Younger participants (age: 14–24 y) were more likely to elect to complete a 3DFR and were overrepresented in the dietary intake sample, whereas elder participants (age: ≥55 y) were underrepresented (χ2 = 12.6, P < 0.01) (32). The distribution of BMI was 37% for normal-weight, 31% for overweight, and 31% for obese participants and did not differ significantly in the subset of participants with dietary intake data.

Age, sex, and body weight distribution for the complete study sample (n = 496) and the subset of participants with dietary intake data (n = 221)

The means and distribution characteristics for δ15N, EPA, and DHA in RBCs are shown in Table 2. In the complete sample of 496 participants, the mean δ15N and EPA did not differ between sexes, and the mean DHA was 14% higher in females (P < 0.0001). In the subset of 221 participants with dietary intake data, means of all 3 markers were similar but significantly lower than in the complete sample (Table 2).

Distribution of biomarker variables for the complete study sample (n = 496) and the subset of participants with dietary intake data (n = 221)1

Associations between RBC δ15N and RBC EPA and DHA

RBC δ15N was strongly correlated with the percentages of EPA (r = 0.84) and DHA (r = 0.75) in RBC membranes (Table 3, Figure 1). For EPA, the relation was positive and linear across the range of δ15N (Figure 1A; R2 = 0.70). The relation between RBC δ15N and DHA was exponential and approached an asymptote at DHA = 9.2% of RBC membrane fatty acids (upper and lower 95% CIs: 8.8, 9.6; Figure 1B). The predicted best-fit values of the exponential model are shown as a line in Figure 1B (R2 = 0.64). These associations were similarly strong when analyzed within the subset of participants for whom we also have dietary intake data (Table 3). Because the relation was nonlinear, the correlation between δ15N and DHA improved to ρ = 0.82 when assessed by using Spearman rank correlation (Table 3). The association of δ15N with EPA differed significantly by sex (slope = 1.15 for males and 0.95 for females; Pinteraction = 0.0014) and age (slope = 0.97 for those aged <40 y and 0.82 for those aged > 40 y; Pinteraction < 0.04); the 3-factor interaction (δ15N × sex × age) was not significant. Because of these differences, correlation coefficients are presented both for the entire sample and stratified by sex and age category (Table 3).

Pearson correlations between red blood cell (RBC) δ15N and RBC fatty acids for the complete sample of participants (n = 496) and the subset of participants with dietary intake data (n = 221)1
Relation between red blood cell (RBC) δ15N and RBC eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) concentrations (n = 496). The relation between δ15N and EPA was linear and highly significant: EPA = 1.04 (δ15N) − ...

Associations between RBC biomarkers and dietary intake

Dietary intake of EPA, calculated from a combined 24-h dietary recall and 3DFR, was strongly and significantly correlated with both RBC δ15N (r = 0.47) and RBC EPA (r = 0.64); dietary intake of DHA was also strongly and significantly correlated with both RBC δ15N (r = 0.46) and RBC DHA (r = 0.54) (all P < 0.001). The correlations of dietary intake of both DHA and EPA with δ15N were significantly weaker than those of dietary intake of both DHA and EPA and each RBC FA (P < 0.0001). The association between δ15N and dietary intake of EPA and DHA did not differ by sex or age.


In this population, RBC δ15N correlated very strongly (r ≈ 0.8) with RBC polyunsaturated fatty acids EPA and DHA, which are well-established and validated biomarkers of EPA and DHA intake (913). These relations differed little by sex and age. Dietary EPA and DHA intake, as measured by a combination of 24-h dietary recall and 3DFR, were also strongly correlated with RBC δ15N; however, the correlations between biomarkers were stronger.

The correlation between dietary fatty acids and RBC fatty acids was slightly but significantly stronger than the correlation between dietary fatty acids and RBC δ15N. The difference in correlation strength may be driven by the greater CV of RBC fatty acids (72% and 29% for EPA and DHA, respectively) than of RBC δ15N (19%). Alternatively, RBC fatty acids may better capture recent diet than RBC δ15N, thus more closely matching the diet records (collected within 1–2 wk of the blood sample). Whereas turnover of RBC nitrogen matches that of the cells, which live ≈120 d, plasma fatty acids can be incorporated into RBC membranes in a shorter time frame (39). EPA, which is preferentially distributed in the outer leaflet of the cell membrane, turns over more rapidly than DHA, which is distributed in the inner leaflet (40, 41). Thus, EPA, DHA, and δ15N in RBC may provide dietary information over different time frames. It is important to note that, although both δ15N and fatty acids were measured in RBCs, they are independent markers reflecting different cellular components. Thus, the strong correlation between these markers can only derive from their having the same dietary sources.

The relation between δ15N and RBC DHA was nonlinear; RBC DHA composition leveled off at ≈9% of total membrane fatty acids. Other authors have documented this effect (9, 42, 43) and have suggested that either that EPA displaces DHA or DHA is converted to EPA at high dietary intake. Our data set allows the form of the relation and the value of the asymptote to be clearly established because dietary intake is so high.

From a practical standpoint, measurement of δ15N has many advantages over measurement of EPA and DHA in RBC membranes. Analysis of δ15N is inexpensive, has a high throughput, is highly accurate, and requires no specialized sample handling. It continues to increase linearly as dietary intake increases and does not approach detection limits at either its highest or lowest values. Thus, δ15N may be a useful tool for dietary assessment that provides the advantages of a biomarker (measurement accuracy, lack of bias, and low participant burden) and that is feasible for use in large-scale studies.

Accurate information on polyunsaturated fatty acid intake is of great importance in studies of diet and health generally, but particularly in Alaska Natives and other indigenous Arctic populations. Similarly to many other populations that have undergone a shift toward a Westernized diet, Alaska Natives are experiencing a rapid increase in the rates of diabetes and other chronic diseases (4446). Many researchers have suspected that a diet high in polyunsaturated fatty acids, characteristic of the indigenous diet, may protect against chronic disease in these and other Arctic populations (4751). The ability to accurately measure EPA and DHA intake without reliance on self-reporting is critical to understanding the rising rates of obesity and chronic disease in the Yup'ik people and to developing effective interventions for this population.

Many of the Yup'ik participants in the CANHR study consume a large fraction of their total energy from fish, which is a major source of EPA and DHA in their diet (33, 34). An obvious question for further study is whether δ15N will predict EPA and DHA intake in populations with lower levels of fish consumption. Although δ15N is widely used as a marker of marine inputs into ancient human diets (2831) and modern ecosystem studies (52, 53,), relatively little is known about the relation between δ15N and fish intake in modern human populations. Hair δ15N was positively correlated (r = 0.39) with the reported frequency of eating fish in 110 community-dwelling older people (mean age: 74 y) in Oxford, United Kingdom (16). This relation is suggestive, but not definitive, and further study is required to better understand the broader utility of δ15N as a marker of fish, EPA, and DHA intake.

Hair δ15N and δ13C have also been proposed as markers for dietary intake of animal protein (15). δ15N is typically ≈3‰ higher in animals relative to their diets; thus, vegetarians can be identified by low hair δ15N (18, 54, 55). Because corn is enriched in 13C relative to nearly all other food plants, livestock that are corn-fed also have high δ13C values. δ15N and δ13C were positively correlated with each other and with animal protein intake among German participants in the Nutrition Survey and Risk Factor Analysis (Verbundstudie Ernährungserhebung und Risikofaktoren-Analytik) study (15). In our study, however, there was no relation between either δ15N or δ13C and animal protein intake, and δ13C was negatively correlated with δ15N, EPA, and DHA (r = −0.16, −0.31, −0.36, respectively; all P < 0.0004). These negative relations reflect a tradeoff between consumption of traditional subsistence (high δ15N) and market (high δ13C) foods, where market foods are largely corn-based (34). In contrast, fingernail δ15N and δ13C were strongly correlated in Greenland Inuit, because available market foods were not corn-based, and marine subsistence foods had high values of both δ15N and δ13C compared with the rest of the diet (17). These conflicting results indicate how different complements of foods can drive different patterns of δ15N and δ13C and why caution is required when applying isotopic biomarkers to nutritional studies.

This study had several limitations. It was not based on a representative random sample of the population, and the population in which it has been tested is fairly unique. Thus, whether the relations can be generalized to the rest of the US population remains to be investigated. Physiologic influences on δ15N are not fully understood in humans, although nitrogen status and severe liver damage both are known to have effects (5658). The measure of dietary intake of EPA and DHA was based on self-report and is subject to errors in recall and potential biases associated with age, sex, and other individual characteristics; therefore, the magnitude of the observed associations between dietary intake and biomarkers were underestimated. This study also had important and unique strengths. It was based on a large sample of participants with a wide variability in dietary EPA and DHA intake, which made the sample ideal for testing the performance of alternative biomarkers of EPA and DHA. It was also the first study to compare the performance of natural abundance stable isotope values against validated nutritional biomarkers in a human population.

In summary, we found that RBC δ15N is highly correlated with RBC EPA and DHA and that both isotopic and fatty acid biomarkers show similar correlations with dietary intake, ranging from 0.46 to 0.65. Thus, we propose that the RBC nitrogen stable isotope ratio provides an accurate and inexpensive biomarker of dietary EPA and DHA intake that could make assessment feasible in large-scale studies. Because accurate assessment of individual dietary intake of these fatty acids is of particular interest in Yup'ik Eskimos, we recommend that future studies include measurement of RBC δ15N as a proxy for RBC membrane EPA and DHA. We also suggest that δ15N is likely an effective and accurate biomarker of EPA and DHA intake in other populations with various levels of fish consumption.


We gratefully acknowledge our participants in the Y-K Delta and the CANHR field teams, especially Scarlett Hopkins. We thank Tim Howe and Norma Haubenstock at the Alaska Stable Isotope Facility for assistance with the stable isotope analyses and Irena King and her staff at the Fred Hutchinson Cancer Research Center for the fatty acid analyses. This manuscript was improved by comments from Bruce Fowler, Sarah Nash, Kate West, and Jordan Metzgar, and we especially thank Mary Sexton for her thoughtful reviews and input.

The authors’ responsibilities were as follows—DMO: designed the study, analyzed the data, and wrote the manuscript; MJW and MAJ: conducted the isotope analyses; BL and AB: collected and compiled the nutritional data; and ARK: analyzed and interpreted the data and wrote the manuscript. All authors contributed to the final draft of the manuscript. None of the authors had any conflicts of interest.


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