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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Am J Clin Nutr. Author manuscript; available in PMC 2009 March 1.
Published in final edited form as:
PMCID: PMC2646803

Red blood cell δ15N: a novel biomarker of EPA and DHA dietary intake



The long chain omega-3 fatty acids (n-3 fatty acids) that derive from fish (eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)) are associated with a reduced risk of cardiovascular and other chronic disease. However, studying associations between EPA and DHA intake and disease rigorously requires a valid biomarker of dietary intake, and measuring tissue fatty acid levels directly is expensive and time consuming.


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


We examined the relationship 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 EPA and DHA dietary intake, based on a 24-hour dietary recall and a 3-day food record completed by a subset of 221 participants.


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.


These results strongly support the validity of RBC δ15N as a biomarker of EPA and DHA intake. Because analysis of RBC δ15N is rapid and inexpensive, it could facilitate wide scale assessment of EPA and DHA intake for clinical and epidemiological studies.


The omega-3 fatty acids that derive from fish (eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)) are associated with a reduced risk of cardiovascular and other chronic disease (1-3). EPA and DHA promote an anti-inflammatory state (4), and regulate the expression of genes involved in fatty acid metabolism (5-7). 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 intakes (8-13), but their measurement requires technically challenging, expensive and time consuming assays that are impractical for 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 (14-18). This approach is useful for foods that are enriched with the heavier isotopes of carbon, nitrogen, oxygen or hydrogen (14, 18-23). For example, fish has a uniquely high 15N/14N (expressed as δ15N as defined in the Methods), for two reasons: first, marine environments tend to be enriched in 15N relative to terrestrial environments, particularly those that are fertilized, and second, 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 consumer tissues with only minor, predictable changes (25-27), and anthropologists have long used δ15N as a marker for consumption of marine foods in human populations (28-31). 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 hypothesize that there will be a strong relationship between these n-3 fatty acids and δ15N in human tissues, driven by differences in dietary intake of fish. If so, δ15N could serve as an alternative biomarker for EPA and DHA intake that is accurate, relatively inexpensive, and highly robust.

Here we examine the relationship 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 relationship because they have widely varying levels of fish intake, depending on the degree to which individuals adhere to a traditional, marine-based diet (33, 34). We also investigate the relationships among dietary intake of EPA and DHA, RBC EPA and DHA, and δ15N in a subset of 221 participants.

Participants and Methods

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 to 2005, 1003 men and women, ages 14 and older, were recruited from 10 communities in Southwest Alaska, as described in detail elsewhere (32, 35). At entry into the study, participants completed extensive interviewer-administered interviews covering demographic characteristics, economic status, ethnicity, and medical history. All participants completed an interviewer-administered 24-hr dietary recall (24hr), and were requested, but not required, to complete an additional 3-day food record (3DFR). Blood was collected into EDTA tubes and processed locally; serum, lymphocytes and the remaining RBC clot were aliquotted and stored at −20°C. Within six days, 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 examining RBC fatty acids were based on a subset of 496 of the 1003 CANHR participants. These were selected after recruitment was completed from seven of the 10 participating communities, with an effort to balance the sample across age and community. Three communities were very small and all participants were selected (n=164). For the remaining communities, we defined three age groups (14−19, 20−49, 50+), and selected a random sample from each to obtain approximately 28 from each age stratum. If 28 participants were not available, we selected all participants in that stratum and adjusted selection in the remaining age strata to yield approximately 84 per community. Of the 496 participants selected, 221 had completed both 24hr and a 3DFR, and this subsample was used in analyses examining dietary intake.

Stable isotope analyses

RBC aliquots were autoclaved for 20 minutes at 121° C to destroy blood-borne pathogens, and samples were weighed into 3.5 × 3.75 mm tin capsules and freeze dried to a final mass of 0.2 − 0.4 mg. Samples were analyzed at the Alaska Stable Isotope Facility by continuous-flow isotope ratio mass spectrometry, using a Costech ECS4010 Elemental Analyzer (Costech Scientific Inc.) interfaced to a Finnigan Delta Plus XP isotope ratio mass spectrometer via the Conflo III interface (Thermo-Finnigan Inc.). 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 analytical accuracy and precision; these were analyzed after every fifth sample and gave values of δ15N = 7.01 ± 0.22‰ (SD).

Red Blood Cell Fatty Acid Measurements

The red blood cell (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 of red cells were mixed with an equivalent volume of distilled water and lipids were extracted with 2-propanol and chloroform according to Rose and Oklander (36). Five mg BHT per 100 ml 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 (FAMEs) were recovered in hexane, dried under nitrogen (40° C) and re-dissolved in 100 μl hexane for gas chromatography.

FAMEs were injected in a split mode (1:50) and were separated 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 chromatographic system was monitored using commercial standards (GLC-87, NIH-D, and NIH-F, NU-CHEK, Elysian, MN). The precision of the RBC fatty acids was monitored with repeat analysis of an in-house RBC quality control pool that was included in each batch of 23 study samples. The coefficient of variation (CV) for EPA (20:5n-3) was 2.7% and for DHA (22:6n-3) was 2%. Fatty acid composition is reported as a weight percent of the total RBC fatty acids.

Dietary Assessment

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

Due to 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 3DFR's 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 24HR and 3DFR were performed 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 examine data on dietary intake of EPA and DHA.

Statistical analyses

All statistical analyses were performed using JMP IN, version 5.1.2 (SAS Institute). We evaluated differences in the sex, age and BMI distribution between the complete sample of participants and the subset of participants with dietary intake data using the Chi-square test. We assessed mean differences in biomarkers between sex, age and datasets using two-tailed t-tests. Associations of δ15N with RBC EPA and DHA were assessed using both Pearson's product moment and Spearman rank correlation coefficients; however, Spearman rank correlation coefficients were only reported where these differed. We described the relationship between RBC δ15N and EPA and DHA using linear regression or nonlinear fitting, depending on the shape of the relationship. Exponential relationships were described by nonlinear fitting and parameter estimation, 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 non-linear relationships, normality of residuals was tested using the Shapiro-Wilks test. We tested whether mean EPA, DHA and δ15N differed between males and females using two-tailed t-tests. We tested the effects of age and sex on the associations between fatty acids and δ15N with multiple regression, where associations were linear and met the assumptions for parametric statistical tests. For regression analysis, age was treated as a dichotomous variable (< 40 yrs and ≥ 40 yrs). Dietary intake data were log transformed as follows: 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 following Wolfe (38).


Table 1 gives the age, sex and BMI distribution of the study participants. 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 years old, with a mean age of 39. Male participants ranged in age from 14 to 83 years old, with a mean age of 41 (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) were more likely to elect to complete a 3DFR, and thus were overrepresented in the dietary intake sample, whereas elder participants (ages 55+) were underrepresented (X2 = 12.6, P < 0.01) (32). The distribution of BMI was 37% normal weight, 31% overweight and 31% obese, and did not differ significantly in the subset of participants with dietary intake data.

Table 1
Age, sex and body weight distribution of the study population, both of the complete sample (n = 496), and the subset of participants with dietary intake data (n = 221).

Table 2 gives the means and distribution characteristics for δ15N, EPA, and DHA in red blood cells. 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 three markers were similar but significantly lower than in the complete sample (Table 2).

Table 2
Means and distributions of biomarker variables for the complete sample of participants (n = 496), and the subset of participants with dietary intake data (n = 221).

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 relationship was positive and linear across the range of δ15N (Figure 1A with R2 =0.70). The relationship between RBC δ15N and DHA was exponential, and approached an asymptote at DHA = 9.2 % of RBC membrane FA (upper and lower 95% CI = 8.8, 9.6, Fig 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 relationship was non-linear, the correlation between δ15N and DHA improved to ρ = 0.82 when assessed using Spearman rank correlation (Table 3). The association of δ15N with EPA differed significantly by sex (slope = 1.15 (M) vs. 0.95 (F); Pinteraction = 0.0014) and age (slope = 0.97 (< 40 yrs) vs. 0.82 (> 40 yrs); Pinteraction < 0.04); the three-way 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).

Figure 1
The relationship between RBC δ15N and RBC eicosapentanoic acid (EPA) and docosahexanoic acid (DHA) (n = 496). The relationship between δ15N and EPA was linear and highly significant (EPA = 1.04 (δ15N) – 6.7; R2 = 0.70, ...
Table 3
Pearson Correlations between 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).

Associations between RBC Biomarkers vs. Dietary Intake

Dietary intake of EPA, calculated from a combined 24hr 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). For both EPA and DHA, the correlations of dietary intake of DHA and EPA with δ15N were significantly weaker than those between dietary intake 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 for EPA and DHA intake (9-13). These relationships differed little by sex and age. Dietary EPA and DHA intake, as measured by a combined 24HR and 3DFR, were also strongly correlated with red blood cell 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 coefficients of variation of RBC fatty acids (72% and 29% for EPA and DHA, respectively), compared to 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 weeks of the blood sample). While turnover of RBC nitrogen matches that of the cells, which live approximately 120 days, plasma fatty acids can be incorporated into RBC membranes on a shorter timeframe (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 RBC, 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 relationship between δ15N and RBC DHA was nonlinear, with RBC DHA composition leveling off at ~ 9% of total membrane fatty acids. Other studies have documented this effect (9, 42, 43), suggesting either that EPA displaces DHA, or DHA is converted to EPA at high levels of dietary intake. Our dataset allows the form of the relationship and the value of the asymptote to be clearly established because dietary intakes are so high.

From a practical standpoint, measurement of δ15N has many advantages over measuring EPA and DHA in red blood cell membranes. Analysis of δ15N is inexpensive, high-throughput, 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 could provide a tool for dietary assessment that provides the advantages of a biomarker (measurement accuracy, lack of bias, low participant burden), but that is also feasible to 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 people. Like many other populations that have undergone a shift toward “westernized” diets, Alaska Natives are experiencing a rapid increase in the rates of diabetes and other chronic diseases (44-46). Many researchers have suspected that high PUFA intake characteristic of the indigenous diet may be protective against chronic disease in these and other arctic populations (47-51). The ability to accurately measure EPA and DHA intake without relying 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 the 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 (28-31) and modern ecosystem studies (e.g., 52, 53), relatively little is known about the relationship between δ15N and fish intake in modern human populations. Hair δ15N was positively correlated (r = 0.39) with the reported frequency of eating fish among 110 community-dwelling older people (mean age = 74) in Oxford, UK (16). This relationship is suggestive, but not definitive, and further study is required in order 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 and 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 (hereafter, δ13C). δ15N and δ13C were positively correlated with each other and with animal protein intake among German participants in the VERA nutrition study (15). In our study, however, there was no relationship 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 relationships reflect a tradeoff between consumption of traditional subsistence (high δ15N) vs. 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 to the rest of the diet (17). These conflicting results demonstrate how differing 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 has several limitations. It is 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 relationships can be generalized to the rest of the US population remains to be investigated. Physiological influences on δ15N are not fully understood in humans, although nitrogen status and severe liver damage both are known to have effects (56-58). 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, gender and other individual characteristics; therefore, the magnitudes of observed associations between dietary intake and biomarkers are underestimated. This study also has significant and unique strengths. It is based on a large sample of participants with a very large variability in dietary EPA and DHA intake, which makes the sample ideal for testing the performance of alternative biomarkers of EPA and DHA. It is 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 find 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 0.49 to 0.65. Thus, we propose that the RBC nitrogen stable isotope ratio provides an accurate, inexpensive biomarker for 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 for this population, we recommend that future studies include measurement of RBC δ15N as a proxy for RBC membrane EPA and DHA. We also suggest that δ15N would likely be an effective and accurate biomarker of EPA and DHA intake in other populations with varying levels of fish consumption.


We gratefully acknowledge our participants in the Y-K Delta and the CANHR field team, especially Scarlett Hopkins. We thank Tim Howe and Norma Haubenstock at the Alaska Stable Isotope Facility for assistance with stable isotope analyses, and Dr. 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.

Sources of Support: This research was supported by a Centers of Biomedical Research Excellence grant from the NIH National Center for Research Resources (NCRR) (P20 RR16430), as well as by undergraduate research awards to MJW and MAJ from Alaska EPSCOR (NSF 0346770), Alaska INBRE (NIH NCRR RR016466), and the UAF Center for Research Services. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NCRR, NIH, or NSF.


Publisher's Disclaimer: “This is an un-copyedited author manuscript that has been accepted for publication in The American Journal of Clinical Nutrition, copyright American Society for Nutrition (ASN). This manuscript may not be duplicated or reproduced, other than for personal use or within the rule of ‘Fair Use of Copyrighted Materials’ (section 107, Title 17, US Code) without permission of the copyright owner, the ASN. The final copyedited article, which is the version of record, can be found at The ASN disclaims any responsibility or liability for errors or omissions in this version of the manuscript or in any version derived from it by the National Institutes of Health or other parties.”

The authors’ responsibilities were as follows - DOB designed the study, analyzed the data and wrote the first draft of the manuscript. MJW and MAJ conducted the isotope analyses. BL and AB were responsible for collecting and compiling the nutritional data. ARK was involved in data analysis, interpretation and writing. All authors contributed to the final draft of the manuscript. None of the authors had any conflicts of interest.


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