In this cross-sectional study, we clearly demonstrated that dietary fish intakes measured from the interviewer-administered FFQ were correlated for plasma DHA levels among ethnic Chinese adults in Taiwan; however, the association for plasma EPA was not significant. In addition, a dose-response relationship between the FFQ and plasma DHA levels was shown.
The use of FFQs is feasible in epidemiological studies for the association between diet and disease. In a study by Arsenault et al., the correlation coefficients were 0.37 for EPA and 0.48 for DHA among 327 older adults (> = 65 yrs), and the magnitude was stable irrespective of the status of cognitive impairment . Sullivan and colleagues conducted a validation study based on 53 healthy Australian adults to collect FFQs and 3-day weighed food records to estimate the long-chain n-3 polyunsaturated fatty acids and they found that the correlations were 0.62 for EPA and 0.72 for DHA . In addition, this FFQ has been validated from plasma biomarker validation: the correlations were 0.54 for EPA and 0.48 for DHA . Another validation study based on electronic FFQ, plasma biomarkers and weighted food records among 41 healthy adults showed high correlations for EPA and DHA .
Various resources have been used to provide the biochemical measurements, including adipose tissue, red blood cells, platelet membranes and subfractions of phospholipids (Additional file 2: Table S1). The correlation coefficients for EPA and DHA derived from adipose tissues have been found to be smaller than those for n-6 fatty acids and trans fats, and the correlation coefficients of n-3 fatty acids have been shown to range from 0.3 to 0.6. Only oil fish and EPA association was found among Australian population . However, our study indicated that the coefficient was significant only for DHA, but not for EPA. From an European study, fish intake showed a statistically significant relationship with n-3 PUFA, EPA and DHA in serum . These findings are consistent with previous literature based on middle American adults  and African Americans with prostate cancer . Two possible explanations for the discrepancy between DHA and EPA coefficients are that firstly the proportion of DHA was much higher than that of EPA for the total fat contribution; and secondly that DHA was more biologically active than EPA due to its longer-chain characteristics . Indeed, it was not clear why the proportion of DHA being higher would matter: the lower variability of EPA may better explain of a lack of association. And other sources of EPA that the questionnaire may have missed: Our data showed that dietary EPA values were less than other Asian populations.
The coefficients in our study were somewhat smaller than previous studies, especially for EPA. The validity of biochemical indicators is vulnerable to the problems of nutrient homeostatic mechanisms, bioavailability, time integration, medical condition, genetic backgrounds, and types of analytic procedures . Admittedly, only a few biochemical indicators provide a sensitive and time-integrated reflection of nutrient intake. Our study indicated that plasma DHA, but not EPA, was related to dietary fish and marine n-3 fatty acid measurements. In addition, we did not consider the cod liver oil and n-3 fatty acid supplements because scanty data were available.
The association between DHA and EPA concentrations and lipid profiles in the general population is inconsistent. A population study based on Japanese and Americans showed that EPA was associated with HDL cholesterol only in Caucasians, but not in Japanese . In addition, DHA was inversely associated with triglycerides in Caucasians, but not in Japanese. Our findings provided further evidence about the correlation of plasma fatty acid biomarkers as the surrogate indicators of dietary intakes , and contributed to the studies with the population with an Asian dietary habit. With regards to the reproducibility of the FFQ, our previous study  has shown that the FFQ is reproducible for Chinese-speaking people in Taiwan, and the correlation coefficients for n-3 fatty acids were similar to Sullivan and colleagues’ study .
This study has two strengths. First, we collected a well-established sample with archived clinical samples, adequate sample size, and extensive measures of various nutrient intakes and clinical information. Second, the participants were recruited from community and hospital settings, and the results can be applied to general practice. However, some limitations of this study should be mentioned. First, no other information, such as dietary record and recall, was available and the 32 items of questionnaire was relatively short form, so that the correlations were modest in strength although they were statistically significant. Second, only 3 questions for fish/seafood consumptions in the questionnaire may decrease the power of detecting dietary intake. Third, our study lacked the gold standard of the weighed food records data for intake of fatty acids. Finally, we did not measure the total energy expenditure, basal metabolic rate and energy intake among participants. Instead, we used the cutoff of convenient criteria from the Taiwanese community survey . Goldberg and colleagues developed a feasible tool to assess the energy balance in populations  and evidence showed that the Goldberg cut-off for energy intake: basal metabolic rate information was a good indicator to define the under-, acceptable- and over-reporters for diet intake .