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. 2020 Dec 17;15(12):e0244089. doi: 10.1371/journal.pone.0244089

Marine n-3 fatty acid consumption in a Norwegian renal transplant cohort: Comparison of a food frequency questionnaire with plasma phospholipid marine n-3 levels

Joe Chan 1,2,*, My Svensson 1,2, Trond Jenssen 2,3, Erik B Schmidt 4, Ivar A Eide 1,3
Editor: Stefano Turolo5
PMCID: PMC7746258  PMID: 33332416

Abstract

Background

High levels of plasma marine n-3 fatty acids (n-3FAs) are associated with improved patient and graft survival in renal transplant recipients (RTRs). The aim of this study was to evaluate the utility of a new food frequency questionnaire (FFQ) to estimate marine n-3FA consumption in future epidemiological research.

Methods

We developed an FFQ with a simple design of 10 questions to assess intake of marine sources of n-3FAs. RTRs included in the recent ORENTRA trial (n = 132) completed the study FFQ at the baseline visit eight weeks after engraftment and at the end of study visit one year post-transplant. We measured the reference biomarker plasma phospholipid (PL) marine n-3FA levels by gas chromatography at the same time points to evaluate association and degree of agreement between FFQ based marine n-3FA consumption estimates and the biomarker.

Results

The median plasma PL marine n-3FA level was 6.0 weight percentage (wt)% (interquartile range [IQR] 4.7 to 7.3) at baseline and 6.3 wt% (IQR 4.8 to 7.4) at end of study. Median FFQ based marine n-3FA consumption estimates were 22.8 g/month (IQR 13.0 to 34.0) at baseline and 20.3 g/month (IQR 14.5 to 32.3) at end of study. FFQ based marine n-3FA consumption estimates showed a moderate correlation with plasma PL marine n-3FA levels at baseline (Spearman’s correlation coefficient rs = 0.43, p<0.001) and a stronger correlation at end of study (rs = 0.62, p<0.001). Bland Altman plots showed a reasonable degree of agreement between the two methods at both time points.

Conclusions

Marine n-3FA consumption estimates based on the FFQ showed a moderate correlation with the reference biomarker plasma PL marine n-3FA levels. The FFQ might be useful in epidemiological studies where resources are limited.

Introduction

Marine n-3 fatty acid (n-3FA) consumption may benefit cardiovascular health and renal function following renal transplantation [1, 2]. Previous clinical trials in renal transplant recipients (RTRs) report lower triglyceride levels, higher high-density lipoprotein cholesterol levels and lower diastolic blood pressure after marine n-3FA supplementation [1]. A large cohort study in Norwegian RTRs showed that high plasma phospholipid (PL) n-3FA levels were associated with improved patient and graft survival [3, 4]. Antifibrotic and renoprotective effects of long-term high-dose marine n-3FA supplementation have also been shown for other cardiovascular high-risk populations like myocardial infarction survivors [5, 6]. The recent “Omega-3 fatty acids in Renal Transplantation (ORENTRA)” trial performed in Norwegian RTRs found lower levels of inflammatory biomarkers, less development of renal graft fibrosis and improvement of endothelial function, as well as reduced triglyceride levels after 44 weeks of high-dose n-3FA supplementation [2].

Observational studies and randomized clinical trials (RCTs) studying the influence of marine n-3FA intake on cardiovascular health report conflicting results [7–14]. But a recent meta-analysis, which included three recent large RCTs [15–17], concluded that marine n-3FA supplementation was associated with a lower risk of cardiovascular events and death [18]. In renal transplantation, further studies are warranted to evaluate to what extent marine n-3FA consumption may improve patient and graft survival.

The major marine n-3FAs eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are found in fish and other seafood. Plasma PL levels of EPA and DHA can be measured by fatty acid analysis and are considered valid and reliable measures of marine n-3FA consumption [19]. However, fatty acid analysis is more expensive and time-consuming to apply than a food frequency questionnaire (FFQ). Hence, replacing fatty acid analyses with an FFQ focused on marine n-3FA consumption seems attractive in epidemiological research, provided that the FFQ values show a high degree of agreement and association with the reference biomarker.

The main objective of this study was therefore to evaluate the utility of a new FFQ focused on marine n-3FA consumption, using plasma PL marine n-3FA level as the reference biomarker.

Materials and methods

Study participants and design

The study cohort consisted of 132 adult Norwegian RTRs included in the ORENTRA trial [2], who were randomized to receive daily supplementation of either 2.6 g of marine n-3FAs (EPA plus DHA) or 3 g of extra virgin olive oil (control oil) for 44 weeks. All patients gave written informed consent for participation in the trial, which also comprised the study FFQ and fatty acid analysis. The study was approved by the Regional Committees for Medical and Health Research Ethics in Norway and was performed in accordance with the Declaration of Helsinki (Clinical.Trials.gov identifier NCT01744067). FFQ and fatty acid analysis were performed eight weeks post-engraftment (baseline visit) and one year after transplantation (end of study visit). Patients were treated with standard triple maintenance immunosuppressive regimen consisting of prednisolone, mycophenolate and tacrolimus. Blood samples were drawn in a fasting state in the morning at the baseline and end of study visits. Gas chromatography was used to determine individual fatty acid levels in plasma PLs, quantified as weight percentage (wt%) of total plasma PL fatty acids. We defined marine n-3FA level as the sum of EPA and DHA. The study was performed at Oslo University Hospital during 2012–2015. Details regarding recruitment of patients, fatty acid analysis and the study FFQ are provided in the S1 File.

For the ORENTRA trial, we developed a specific FFQ with a simple design of 10 multiple-choice questions (Fig 1), focusing on food items containing marine sources of n-3FA that are typically found in a Nordic diet [20].

Fig 1. Study food frequency questionnaire focusing on food items containing marine n-3 polyunsaturated fatty acids (English version).

Fig 1

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The study subjects responded to the question “During a typical month, how often do you eat these food items?” using one of six response alternatives for each of the ten food item categories.

We used three different approaches to estimate marine n-3FA consumption at baseline and end of study based on the FFQ recordings:

  1. Marine n-3FA consumption estimates, calculated by combining data from the FFQ with known content of EPA and DHA in fish and other seafoods [21], assuming a standard portion size for a Norwegian population (S1 File, S2 Fig).

  2. Marine n-3FA consumption estimates calculated as in approach 1 using only data on fatty fish intake for lunch and dinner (S3 Fig).

  3. Number of fish servings per month (S4 Fig).

Statistical analysis

We used correlation analysis (Spearman’s correlation coefficient [rs]) and multivariate regression analysis (data presented as standardized regression coefficients [Std. β-coeff.]) to study associations between FFQ based marine n-3FA consumption estimates and plasma PL marine n-3FA levels. Data obtained by the reference biomarker and the study FFQ were standardized using z-statistics to produce data for both methods on the same scale. This allowed for a more meaningful visual presentation (scatter plots) and made it possible to analyze degree of agreement using Bland Altman plots and one-sample t-test. Since the study drug used in the ORENTRA trial was high-dose marine n-3FA supplementation, we excluded patients in the intervention group when performing statistical analysis of data from the end of study visit. Two patients belonging to the control group did not meet at the end of study visit (n = 66 at baseline, n = 64 at end of study visit). Patient characteristics at baseline grouped according to plasma PL marine n-3FA tertiles were evaluated with analysis of variance for continuous data and Mantel-Haenszel linear-by-linear-trend for categorical data. A two-sided p-value of < 0.05 was considered statistically significant. We used SPSS® version 25.0 (IBM, New York, NY, US) for statistical analyses.

Results

Patient characteristics for the study cohort have previously been published in detail [2]. Selected variables, grouped according to plasma PL n-3FA tertiles at baseline eight weeks post-transplant, are presented in Table 1. Patients in the upper tertile were older and less often current smokers. Supplementation with cod liver oil was used by 28% of patients in the upper tertile compared with 9% in the lower. Median plasma PL n-3FA levels were 6.0 wt% (interquartile range [IQR] 4.7 to 7.3, n = 132) at the baseline visit and 6.3 wt% (IQR 4.8 to 7.4, n = 64) at the end of study visit. Median FFQ based marine n-3FA consumption estimates were 22.8 g/month (IQR 13.0 to 34.0, n = 132) at baseline and 20.3 g/month (IQR 14.5 to 32.3, n = 64) at end of study. Marine n-3FA consumption remained stable during follow-up in the control group with a median increase of plasma PL marine n-3FA level of 0.1 wt% (IQR -0.8 to 1.0) and change in FFQ based marine n-3FA consumption estimates of -1.0 g/month (IQR -9.4 to 6.3).

Table 1. Patient characteristics at baseline eight weeks after renal transplantation according to plasma phospholipid marine n-3 fatty acid tertiles.

Variables All patients Plasma PL marine n-3FA level, wt% p (trend)
≤5.1 5.2–6.9 ≥7.0
Number of patients 132 44 44 44
FFQ based marine fatty acid consumption estimate, g/month 26.0 (16.6) 19.8 (15.0) 25.4 (17.5) 33.1 (14.7) 0.001
Number of servings of fish / month 19.9 (15.6) 13.3 (13.8) 16.2 (13.4) 26.9 (16.8) 0.12
Marine n-3FA supplements, % 14.5 9.1 6.8 27.9 0.01
Recipient age, years 53.4 (13.8) 45.7 (12.6) 55.6 (13.6) 59.1 (12.0) <0.001
Recipient gender (Female), % 25.8 27.3 25.0 25.6 0.86
Ethnicity, White, % 92.4 93.2 86.0 97.7 0.39
Body mass index, kg/m2 26.0 (3.9) 25.2 (4.0) 25.9 (3.8) 26.8 (3.7) 0.16
Educational level, %
>3 years at University 29.8 27.3 27.3 34.9
1–3 years at University 7.6 6.8 6.8 9.3
Secondary school 35.1 40.9 36.4 27.9
Primary school 27.5 25.0 29.5 27.9 0.56
Physical exercise, %
High intensity ≥ twice per week 42.1 51.2 36.4 39.0
High intensity once per week 9.5 2.4 11.4 14.6
Low intensity ≥ twice per week 34.9 36.6 36.4 31.7
Low intensity once per week 7.1 9.8 9.1 2.4
None 6.3 0.0 6.8 12.2 0.29
Smoking habits, %
Daily smoker 12.7 19.5 11.4 7.3
Non-daily smoker 3.2 7.3 0.0 2.4
Former heavy smoker 7.1 7.3 4.5 9.8
Former light smoker 35.7 34.1 43.2 29.3
Life-long non-smoker 41.3 31.7 40.9 51.2 0.03
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Patient characteristics are presented as percentage for categorical data and mean value (standard deviation) for continuous variables. Differences between groups were analyzed by analysis of variance and Mantel Haenszel linear-by-linear trend as appropriate.

At baseline, moderate correlations were found between FFQ based marine n-3FA consumption estimates and the reference biomarker plasma PL marine n-3FA levels (approach 1, rs = 0.43, p<0.001, n = 132, Fig 2). A reasonable degree of agreement between the study FFQ estimates and the reference biomarker was shown in a Bland Altman plot (Fig 3) and significant bias was ruled out by a one-sample t-test (t = 0.04, p = 0.96). Two groups of outlier observations were identified. One group consisted of patients reporting high intake of marine n-3FAs but had average plasma PL marine n-3 FA levels. Another group with high or very high plasma PL marine n-3FA levels had average marine n-3FA consumption according to the study FFQ.

Fig 2. Scatterplots of standardized plasma PL marine n-3FA levels and standardized FFQ based marine n-3FA consumption estimates with regression line at eight weeks post-transplant (n = 132).

Fig 2

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Fig 3. Bland-Altman plot assessing degree of agreement between standardized plasma PL marine n-3FA levels and standardized FFQ based marine n-3FA consumption estimates at baseline eight weeks post-transplant.

Fig 3

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We used standardization of data obtained by the study FFQ and reference biomarker, hence the mean value was set at 0. The upper and lower limits of agreement were set at 2 standard deviations from the mean. The Bland Altman plot includes all patients enrolled in the ORENTRA trial (n = 132) at the baseline time-point.

Correlations between FFQ based marine n-3FA consumption estimates and plasma PL marine n-3FA levels were stronger at the end of study (rs = 0.60, p<0.001, n = 64, Fig 4) than at baseline. One-sample t-test (t = 0.06, p = 0.95) and a Bland Altman plot confirmed an acceptable degree of agreement between the two methods at this time point (Fig 5).

Fig 4. Scatterplots of standardized plasma PL marine n-3FA levels and standardized FFQ based marine n-3FA consumption estimates with regression line at one year post-transplant for patients belonging to the control group of the ORENTRA trial (n = 64).

Fig 4

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Fig 5. Bland-Altman plot assessing degree of agreement between standardized plasma PL marine n-3FA levels and standardized FFQ based marine n-3FA consumption estimates at one year post-transplant.

Fig 5

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We used standardization of data obtained by the study FFQ and reference biomarker, hence the mean value was set at 0. The upper and lower limits of agreement were set at 2 standard deviations from the mean. The Bland Altman plot includes only patients belonging to the control group of the ORENTRA trial (n = 64) at the end of study time-point.

Baseline correlation analysis was repeated for patients belonging to the ORENTRA trial control group (rs = 0.45, p<0.001, n = 66, S5 Fig) and we found a high degree of agreement between the methods (S6 Fig), similar to what was shown for the whole study cohort at baseline.

We performed a multivariate stepwise forward regression analysis, adjusting for the potential confounding factors recipient age, gender, height, weight, body mass index, renal function, physical activity, educational level and smoking habits (p<0.10 for inclusion of variables in the final regression model) at baseline and end of study. The reference biomarker plasma PL marine n-3FA level was associated with FFQ based marine n-3FA consumption estimates (Std. β-coeff. 0.24, p = 0.01), as well as recipient age (Std. β-coeff. 0.25, p = 0.01) and smoking habits (Std. β-coeff. 0.15, p = 0.06) at baseline (n = 132). Together the three variables included in final regression model explained 23% of the variance in plasma PL marine n-3FA levels. At the end of study, only FFQ based marine n-3FA consumption estimates (Std. β-coeff. 0.54, p<0.001) was included in the final regression model, and it explained 29% of the variance in the reference biomarker.

Correlations between FFQ based marine n-3FA consumption estimates and the reference biomarker were slightly weaker for fatty fish intake (approach 2, baseline rs = 0.35 and end of study rs = 0.46) and number of fish servings per month (approach 3, baseline rs = 0.38 and end of study rs = 0.43) than for total marine n-3FA consumption estimates (approach 1). Correlations with the reference biomarker for individual food items included in the study FFQ are shown in S7 Fig. The food item cod liver oil showed a low correlation with the reference biomarker at baseline (rs = 0.21). Marine n-3FA supplementation, including cod liver oil, was discontinued after enrollment in the ORENTRA trial and consequently intake of cod liver oil did not influence results at one year post-transplant.

Discussion

The main finding of the present study was that marine n-3FA consumption estimates based on a new FFQ focused on fish consumption showed a moderate correlation with the reference biomarker plasma PL marine n-3FA levels at eight weeks post-transplant and a slightly stronger correlation at one year post-transplant. The correlations found in the present study are in the range of what is regarded as acceptable in FFQ validation studies [22].

Previous FFQs have mainly focused on fatty fish intake, assumed to reflect marine n-3FA consumption better than total fish intake [23–35]. We hypothesized that a more meticulous approach using weighted response scales based on EPA and DHA content in fatty and lean fish, other seafoods and marine n-3FA supplements would provide a more precise estimation of marine n-3FA consumption. In our cohort, approach 1, which estimated total marine n-3FA consumption from all the data obtained by the study FFQ, showed a stronger correlation with the reference biomarker than approach 2 (which only focused on fatty fish intake) and 3 (which used the number of fish servings), suggesting that our hypothesis was correct.

However, the study FFQ only provided slightly stronger correlations than most recent FFQs focused on fish consumption (Table 2) and the utility of the study FFQ will have to be confirmed by other studies before it can be used in epidemiological research.

Table 2. Summary of selected food frequency questionnaire validation studies published during the last six years, focusing on fish and/or marine fatty acid consumption, using circulating phospholipids or erytrocytes as the reference biomarker.

First author (reference) Published, year n Study population Reference marine fatty acid biomarker Correlation coefficient
Giovannelli J [23] 2014 2630 General population Plasma phospholipid r = 0.39–0.43
Lassale C [24] 2016 198 General population Plasma phospholipid rs = 0.51–0.54
Sluik D [25] 2016 383 General population Plasma phospholipid r = 0.43–0.47
Whitton C [26] 2017 161 General population Plasma phospholipid r = 0.36
Laursen UB [27] 2018 200 General population Plasma phospholipid rs = 0.45
Shen W [28] 2019 108 General population Whole blood phospholipid r = 0.67
Schumacher TL [29] 2016 39 Hyperlipidemia Erythrocyte rs = 0.53–0.62
Allaire J [30] 2015 60 Prostate cancer Erythrocyte rs = 0.59
Brunvoll SH [31] 2018 49 Breast cancer Serum phospholipid r = 0.36–0.53
Lepsch J [32] 2014 248 Pregnant women Serum phospholipid rs = 0.21–0.26
Zhou YB [33] 2017 804 Pregnant women Plasma phospholipid rs = 0.35
Erythrocyte rs = 0.33
Kobayashi M [34] 2017 188 Pregnant women Serum phospholipid rs = 0.33–0.45
Liu MJ [35] 2016 408 Lactating women Plasma phospholipid rs = 0.36
Erythrocyte rs = 0.24
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Plasma PL marine n-3FA levels did not differ between baseline and end of study visits for the majority of patients. This is consistent with previous reports from large Norwegian cohorts and supports the notion that a single fatty acid measurement may be acceptable for epidemiological studies [3, 19]. However, the association between FFQ based marine n-3FA consumption estimates and the reference biomarker was stronger at end of study than at baseline. There could be several explanations to this finding. Study participants might have become more aware of their eating habits due to participation in the ORENTRAL trial and reported fish consumption more accurately when they completed the FFQ the second time. We found a lower correlation with the reference biomarker for cod liver oil than for other food items in the study FFQ at baseline, which likely influenced the results. Some patients with high plasma PL marine n-3FA levels reported only average marine n-3FA intake according to the study FFQ, all of whom reported frequent use of cod liver oil. This signals that the study FFQ weighted response scale for cod liver oil likely underestimated marine n-3FA content, thus the study FFQ in its current form lacks precision for patients taking daily marine n-3FA supplements. Additionally, patients with average plasma PL marine n-3 FA levels who reported high levels of marine n-3FA intake in the FFQ, showed this pattern both at baseline and end of study. This might be due to social desirability bias and has likely influenced results at both time-points.

Fish intake in Norway is higher than in most other European countries, due to the rich fishing grounds along the Norwegian coastline with easy access to fresh cold-water fish [36]. Plasma PL marine n-3FA levels in the present cohort were relatively high, even for a Norwegian population, signaling a selected population that focuses on healthy eating habits. On the other hand, plasma PL marine n-3FA levels in the present study were comparable to a previous large cohort study in Norwegian RTRs, suggesting that the sample was representative of a Norwegian transplant cohort [3]. Confounding factors like socioeconomical class, educational level, smoking habits and physical activity may influence associations between fish intake and outcomes in epidemiological research [36]. In this cohort, FFQ based marine n-3FA consumption estimates and plasma PL marine n-3FA level were associated with smoking habits, but not other life-style factors.

Dietary habits are changing in the Nordic countries, with lower fish consumption in younger patients, including Norwegian RTRs [3], thus necessitating revision of questions and response categories for the present study FFQ in future studies. Cod liver oil intake is an old tradition in Norway [37] and was therefore included as one of the food times in the study FFQ. This may be omitted in areas where intake of cod liver oil or other marine n-3FA supplements are uncommon.

Strengths of the present study include a well-described cohort, plasma PL fatty acid analysis and a study FFQ performed at two time points, which might improve accuracy. The study FFQ has a simple design, is easy to read and understand and only takes a few minutes to answer, which is desirable in large epidemiological studies.

There were also several limitations, including limitations by design such as recall bias and social desirability bias and a relatively small sample size. The study FFQ marine n-3FA consumption estimates were based on the sum of weighted response scales for ten food items, containing questions on how frequent the food items were consumed, but not on portion size. Thus, the weighted responses used to calculate marine n-3FA intake were based on assumptions of standard portion size for each item, constituting a major limitation in the present study. Moreover, the study FFQ did not contain any questions regarding seasonal variations, which could be relevant for some of the included food items in a Norwegian cohort. The study FFQ contains rather detailed questions about fish and seafood intake and response categories with minor differences (Fig 1). This likely improved precision for patients who are well aware of their eating habits but could have been challenging for other patients, possibly leading to random responses. Broader response categories might have produced more reliable data [38]. For patients on marine n-3FA supplements, like cod liver oil, weighted responses for this food item in the study FFQ likely underestimated supplements as a source of marine n-3FAs.

The questionnaire was designed to estimate marine n-3FA consumption in a Norwegian transplant cohort. Due to dietary differences between regions and between patient populations, FFQ validation studies designed for one region or one particular target population may not apply to other regions or other patient cohorts [22]. In other regions, food items and weighted response scales should be revised to reflect fish consumption in that region. Moreover, adjustment for portion size and seasonal variations can be made to improve FFQ performance.

In conclusion, marine n-3FA consumption estimates based on our study FFQ showed a moderate correlation with the reference biomarker plasma PL marine n-3FA levels, with comparable performance to previous FFQs. We recommend using fatty acid analysis to ensure objective measurement of marine n-3FA consumption in clinical trials, but our FFQ might be useful in epidemiological studies where resources are limited.

Supporting information

S1 Fig. Study food frequency questionnaire focusing on food items containing marine n-3 fatty acids (Norwegian version).

The study subjects responded to the question “During a typical month, how often do you eat these food items?” using one of six response alternatives for each of the ten food item categories.

(PDF)

Click here for additional data file. (35.7KB, pdf)
S2 Fig. Study food frequency questionnaire focusing on food items containing marine n-3 fatty acids (investigator’s scoring sheet version in English).

The study subjects responded to the question “During a typical month, how often do you eat these food items?” using one of six response alternatives for each of the ten food item categories. Based on EPA and DHA content in the meat of various fish and other seafoods presented in the US Department of Agriculture Food Composition Database and assuming a standard portion size for dinner and bread spread, every potential response was given a weight (shown inside boxes). Total intake of marine n-3 fatty acids per month was calculated as the sum of the ten weighted responses in grams.

(PDF)

Click here for additional data file. (49.1KB, pdf)
S3 Fig. Study food frequency questionnaire focusing on food items containing marine n-3 fatty acids (investigator’s scoring sheet version in English comprising fatty fish items only).

The study subjects responded to the question “During a typical month, how often do you eat these food items?” using one out of six response alternatives for each food item categories. Based on EPA and DHA content in the meat of various fish and other seafoods presented in the US Department of Agriculture Food Composition Database and assuming a standard portion size for dinner and bread spread, every potential response was given a weight (shown inside boxes). Total intake of marine n-3 fatty acids per month was calculated as the sum of the weighted responses in grams, which for fatty fish intake comprised the four items shown.

(PDF)

Click here for additional data file. (44.2KB, pdf)
S4 Fig. Study food frequency questionnaire focusing on food items containing marine n-3 fatty acids (investigator’s scoring sheet version in English comprising fish servings per month).

The study subjects responded to the question “During a typical month, how often do you eat these food items?” using one out of six response alternatives for each of the ten food item categories. Servings of fish per month was calculated as the sum of the ten responses, using the center value for each response category as shown.

(PDF)

Click here for additional data file. (46KB, pdf)
S5 Fig. Scatterplots of standardized plasma marine n-3FA levels and standardized FFQ based marine n-3FA consumption estimates with regression lines at baseline eight weeks post-transplant for patients belonging to the control group of the ORENTRA trial (n = 66).

(TIF)

Click here for additional data file. (258.5KB, tif)
S6 Fig. Bland-Altman plot assessing degree of agreement between standardized plasma marine n-3FA levels and standardized FFQ based marine n-3FA consumption estimates at baseline eight weeks post-transplant for patients belonging to the control group of the ORENTRA trial (n = 66).

(TIF)

Click here for additional data file. (272.1KB, tif)
S7 Fig. Correlation matrix presenting Spearman’s correlation coefficients at eight weeks (baseline visit) after renal transplantation for the whole study population (n = 132) and one year post-transplant (end of study visit) for patients belonging to the control group of the ORENTRA trial (n = 64).

(TIF)

Click here for additional data file. (1.2MB, tif)
S1 File. Supporting information.

Includes information regarding “Patient screening and recruitment in the ORENTRA trial”, “Fatty acid analysis”, “Sample Size and Power Calculation” and “Development of the study Food Frequency Questionnaire”.

(DOCX)

Click here for additional data file. (21.7KB, docx)

Acknowledgments

We thank coworkers Rikke Bülow Eschen, Annette Andreassen, Birthe H. Thomsen and Inge Aardestrup at The Lipid Research Laboratory, Aalborg University Hospital, Denmark for analyzing plasma phospholipid fatty acids. We thank statistician Owen Thomas and colleague dr. Anupam Chandra at Akershus University Hospital for their contribution to this manuscript. We thank the funding sources Gidske and Peter Jacob Sørensen Research Fund and the South-Eastern Norway Regional Health Authority. Finally, we thank the study participants in the ORENTRA trial.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

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Decision Letter 0

Carmen Melatti

8 Oct 2020

PONE-D-20-12551

Marine fatty acid consumption in a Norwegian renal transplant cohort: comparison of a food frequency questionnaire with plasma n-3 levels

PLOS ONE

Dear Dr. Chan,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The manuscript has been evaluated by three reviewers, and their comments are available below.

The reviewers have raised a number of concerns that need attention. They request additional information on methodological aspects of the study, and revisions to the statistical analyses.

Could you please revise the manuscript to carefully address the concerns raised?

  

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2. Please include additional information regarding the survey or questionnaire used in the study and ensure that you have provided sufficient details that others could replicate the analyses. For instance, if you developed a questionnaire as part of this study and it is not under a copyright more restrictive than CC-BY, please include a copy, in both the original language and English, as Supporting Information.

3. Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified (1) whether consent was informed and (2) what type you obtained (for instance, written or verbal). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.

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Reviewers' comments:

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Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Partly

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Reviewer #2: No

Reviewer #3: No

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #2: Yes

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**********

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Reviewer #1: The authors validate a new and simple food frequency questionnaire to determine total PUFAn3 level on patients, in their case with kidney transplant.

The text is interesting but there are some not clear points.

In the introduction, line 53-54, they define EPA and DHA as "essential marine n-3FAs", I understand what they mean, but I suggest to modify the sentence because the essential n3 FA is ALA.

On line 55, authors describe fatty acid analysis as "expensive". Fatty acid analysis with GC is not expensive, it is better if they can report data and cost about it.

Regarding the rs difference between first test and end of study, authors can exclude that the difference is due by the different sample size (132 vs 71), it could be good to show the rs at the start of study regarding the 71 patients present at the end of the study.

The weak point of this study is, as the authors themselves said, is that the presented FFQ is strongly linked to Norwegian diet.

Reviewer #2: Given the limited data analysis, the authors claim to have shown that n-3FA consumption estimates based on the FFQ demonstrated a moderate correlation with the reference biomarker plasma phospholipid n-3FA levels. The correlation and trend test were the primary analyses attempted. The trend test was certainly of interest. However, there are some concerns.

1. This is basically a convenience sample of 132 subjects. The investigators should provide a statistical rationale for this sample size and its adequacy from a power perspective.

2. Given the extensive patient characteristic data in Table 1, why wasn’t a multivariate analysis attempted to show the effect of adjustment of the variables in a reasonable format?

Reviewer #3: This manuscript reports on the evaluation of a food frequency questionnaire to assess n-3FA consumption. It is a manuscript of scientific relevance, but it has inadequate statistical analysis for the purpose of the study. The authors should use agreement methods to complementing statistical analyzes.

Moreover, “Methods and reagents” are not described in sufficient detail for another researcher to reproduce the experiments described.

Other specific comments:

1. Introduction: Renal function benefits are reported only by n-3 FA supplementation? And the objective of this study was to evaluate a new FFQ to estimates n-3 FA consumption. The arguments do not make sense due to inconsistency.

2. Line 63: “Methods and reagents” are not described in sufficient detail for another researcher to reproduce the experiments described.

3. Line 73: Please explain fatty acid extraction method.

4. Lines 92-93: Standard portion size for dinner and bread spread was estimated after the study subjects responded to the FFQ? Portions size are not in the questionnaire.

5. Line 98: Correlation analysis are not appropriate to asses the agreement of two methods. Use agreement methods to complementing statistical analyzes. Bland-Altman is a reliable approach for statistical analysis.

6. Lines 99-102: This is “study participants”. Remove from “statistical analysis”.

7. Line 108: Results and Discussion should be reviewed after further statistical analysis

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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PLoS One. 2020 Dec 17;15(12):e0244089. doi: 10.1371/journal.pone.0244089.r002

Author response to Decision Letter 0

23 Nov 2020

Thank you for the opportunity to submit a revised version of this manuscript. The Reviewers provided many insightful comments, for which we are very grateful. We have revised the manuscript and figures according to these comments and performed additional statistical analyses like multivariate regression and Bland Altman plots. We also include a Supporting Information File presenting patient recruitment, fatty acid analysis and the study FFQ in detail as requested.

The revised manuscript contains two tables and five figures. We have included Bland Altman plots in the revised manuscript, which also marks a shift in statistical approach, as suggested by Reviewer 2 and Reviewer 3. We feel that these changes, which are highlighted in the track changes version, have improved the quality of the manuscript, and that study limitations and key information have been made clearer and more available to the reader in the revised manuscript.

Point to point responses:

Editor:

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Reply: We have made our best effort to ensure that the manuscript complies with PLOS ONE’s style requirements. Formatting of the tables has been reworked. Please let us know if there are still requirements we have failed to notice.

2. Please include additional information regarding the survey or questionnaire used in the study and ensure that you have provided sufficient details that others could replicate the analyses. For instance, if you developed a questionnaire as part of this study and it is not under a copyright more restrictive than CC-BY, please include a copy, in both the original language and English, as Supporting Information.

Reply: Please find a copy of the questionnaire in English and Norwegian and a scoring sheet as supporting figures. The last Author developed the questionnaire in Norwegian and in English and tested it in a pilot study (n=10 Norwegian RTRs) before it was implemented in the ORENTRA trial. Together with a professional translator he performed translations back and forth to ensure adequacy of the English version, thus both the Norwegian and English versions should be considered the original questionnaire. There is no copyright.

In the revised manuscript we discuss potential limitations of the present study FFQ, including lack of questions on portion size and seasonal variation, and highlight that questions and response categories should be revised to reflect fish consumption in the region and/or the patient population at hand. This should enable other researchers not only to replicate, but also to adequately revise the FFQ to improve performance in future research.

3. Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified (1) whether consent was informed and (2) what type you obtained (for instance, written or verbal). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.

Reply: Please find a more detailed description of the written informed consent needed to participate in the study in the revised manuscript. Only adult RTRs participated in the ORENTRA trial, which is highlighted in the revised manuscript.

4. Please note that according to our submission guidelines, outmoded terms and potentially stigmatizing labels should be changed to more current, acceptable terminology. For example: “Caucasian” should be changed to “white” or “of [Western] European descent” (as appropriate).

Reply: We apologize for this. The word “Caucasian” is changed to “White”.

5. In your Methods section, please provide additional information about the related clinical trial (for example please describe how plasma n-3 levels were measured, and what was the intervention) and about the participant recruitment method and the demographic details of your participants. Please ensure you have provided sufficient details to replicate the analyses such as: a) the recruitment date range (month and year), b) a description of any inclusion/exclusion criteria that were applied to participant recruitment, c) a table of relevant demographic details, d) a statement as to whether your sample can be considered representative of a larger population, e) a description of how participants were recruited, and f) descriptions of where participants were recruited and where the research took place.

Reply: Certainly. The Method section has been revised according to comments put forward by Editor and Reviewers and we included a Supporting Information File as recommended by the Editor. We chose to put the majority of additional information on methods (a, b and e) in the Supporting Information File, as the Method section would otherwise be too long and hard to follow. However, we refer to what information can be found in the Supporting Information (including a, b and e) in the revised manuscript. The manuscript already includes Table 1 with demographic data (c). Description of where participants were recruited and where the research took place are now mentioned in the revised manuscript and further explored in the Supporting Information File (f). The revised manuscript now contains clear statements on whether the sample is representative of a larger population (d) and highlights the need for confirmatory studies.

Reviewer #1:

The authors validate a new and simple food frequency questionnaire to determine total PUFAn3 level on patients, in their case with kidney transplant. The text is interesting but there are some not clear points.

In the introduction, line 53-54, they define EPA and DHA as "essential marine n-3FAs", I understand what they mean, but I suggest to modify the sentence because the essential n3 FA is ALA.

Reply: Thank you for pointing this out. We have replaced the phrase “n-3FA” with “marine n-3FA” throughout the revised manuscript to avoid confusing the reader. This also includes the study title.

On line 55, authors describe fatty acid analysis as "expensive". Fatty acid analysis with GC is not expensive, it is better if they can report data and cost about it.

Reply: We apologize for this rather bombastic statement, which has been rephrased in the revised manuscript. GC was performed at a research laboratory in Aarhus, Denmark. We didn’t feel comfortable including data on GC costs in the manuscript. Laboratory costs partly depend on coworker salary and differ considerably between regions, thus adding this information could potentially be misleading.

Regarding the rs difference between first test and end of study, authors can exclude that the difference is due by the different sample size (132 vs 71), it could be good to show the rs at the start of study regarding the 71 patients present at the end of the study.

Reply: We agree. In the revised manuscript, we include data for patients in the control group of the ORENTRA trial at baseline 8 weeks (n=66) and end of study 1 year post-transplant (n=64, two patients did not meet at the end of study visit).

In the original manuscript, patients who withdrew early from the intervention group were added to the control group at the end of study visit, i.e., included in statistical analyses. This might confuse the reader, particularly when we include correlation analysis for patients belonging to the control group at baseline, as requested by Reviewer 1. Thus, we chose to exclude all patients randomized to the intervention group of the ORENTRA trial for statistical analysis both at the end of study and in the baseline subgroup analysis that Reviewer 1 requested.

At baseline, we found statistically significant and very similar correlations between the study FFQ estimates and the reference biomarker in the control group (n=66) and the whole study cohort (n=132), suggesting that sample size did not influence results to a major extent.

The weak point of this study is, as the authors themselves said, is that the presented FFQ is strongly linked to Norwegian diet.

Reply: You are quite right. That said, we speculate (not included in the manuscript) that with revision of questions and response categories to reflect regional fish intake, this approach may work better in populations with low intake of marine n-3FA supplements. We have included a paragraph in the revised manuscript discussing reasons for the lower correlation between the study FFQ estimates and the reference biomarker at baseline, where use of cod liver oil at the baseline time-point could be one reason, as illustrated under (not in manuscript): (Figure in the Word-document).

Reviewer #2:

Given the limited data analysis, the authors claim to have shown that n-3FA consumption estimates based on the FFQ demonstrated a moderate correlation with the reference biomarker plasma phospholipid n-3FA levels. The correlation and trend test were the primary analyses attempted. The trend test was certainly of interest. However, there are some concerns.

1. This is basically a convenience sample of 132 subjects. The investigators should provide a statistical rationale for this sample size and its adequacy from a power perspective.

Reply: Thank you for pointing this out. The present study cohort consisted of patients who were enrolled in the ORENTRA trial (n=132). Power calculation for this trial was based on the primary endpoint renal function, which for the study FFQ makes this is a convenience sample – you are absolutely right. That said, the clinical steering committee discussed power perspectives upfront also for secondary endpoints, including the study FFQ. Comparable studies used power estimations based on a correlation coefficient of at least 0.3 (95% CI and 20% drop-out rate was used for calculating sample size in these studies), necessitating a sample size of ≥ 84 patients. For r ≥ 0.4 we need ≥ 46 patients, for r ≥ 0.5 we need ≥ 29 patients and for r ≥ 0.6 we need ≥ 19 patients. Based on these reports it seemed obvious that the sample size in the present study was adequate from a power perspective. We have included this information in the Supporting Information file.

2. Given the extensive patient characteristic data in Table 1, why wasn’t a multivariate analysis attempted to show the effect of adjustment of the variables in a reasonable format?

Reply: We agree that a multivariate regression analysis in addition to the univariate (correlation) analysis was of interest, and it has been included in the revised manuscript.

Reviewer #3:

This manuscript reports on the evaluation of a food frequency questionnaire to assess n-3FA consumption. It is a manuscript of scientific relevance, but it has inadequate statistical analysis for the purpose of the study. The authors should use agreement methods to complementing statistical analyzes.

Reply: We agree. Please find Bland Altman plots where we use z-statistics to standardize both study FFQ estimates and the reference biomarker. This allows for a more meaningful comparison of methods.

Moreover, “Methods and reagents” are not described in sufficient detail for another researcher to reproduce the experiments described.

Reply: We apologize for this. Please find a detailed description of methods in the revised manuscript and particularly in the Supporting Information File.

Other specific comments:

1. Introduction: Renal function benefits are reported only by n-3 FA supplementation? And the objective of this study was to evaluate a new FFQ to estimates n-3 FA consumption. The arguments do not make sense due to inconsistency.

Reply: We have rephrased the Introduction to make the aim of this study clear to the reader.

2. Line 63: “Methods and reagents” are not described in sufficient detail for another researcher to reproduce the experiments described.

Reply: A detailed description of methods can be found in the Supporting Information.

3. Line 73: Please explain fatty acid extraction method.

Reply: A detailed description of the fatty acid analysis including FA extraction can be found in the Supporting Information.

4. Lines 92-93: Standard portion size for dinner and bread spread was estimated after the study subjects responded to the FFQ? Portions size are not in the questionnaire.

Reply: Correct. This is highlighted as a major limitation in the revised manuscript. We performed an additional correlation analysis where FFQ based marine n-3FA consumption estimates were adjusted for patient weight (g/month/kg) and correlations with the reference biomarker were nearly identical to unadjusted estimates, signaling that lack of portion size estimates likely did not strongly influence on results. Nonetheless, it is obvious that our results were hampered by lack of portion size estimates to some extent and should have been included in the FFQ. For the record, in the revised manuscript we also address that the study FFQ lacked questions on seasonal variation for the food items included. For most regions, seasonal variations would not constitute a major problem, but for some coastal regions in Norway this could influence results. Future versions of the FFQ should include portion size and seasonal variations, and this has been made clear to the reader in the revised manuscript.

5. Line 98: Correlation analysis are not appropriate to asses the agreement of two methods. Use agreement methods to complementing statistical analyzes. Bland-Altman is a reliable approach for statistical analysis.

Reply: Thank you for pointing this out. Please find Bland-Altman plots in Figure 3 and 5.

6. Lines 99-102: This is “study participants”. Remove from “statistical analysis”.

Reply: We have rephrased this sentence.

7. Line 108: Results and Discussion should be reviewed after further statistical analysis

Reply: Certainly. Revision of the statistical approach produced new data, which we present in the Result section and we address this data in the Discussion section.

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Decision Letter 1

Stefano Turolo

3 Dec 2020

Marine n-3 fatty acid consumption in a Norwegian renal transplant cohort: comparison of a food frequency questionnaire with plasma phospholipid marine n-3 levels

PONE-D-20-12551R1

Dear Dr. Chan,

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Acceptance letter

Stefano Turolo

9 Dec 2020

PONE-D-20-12551R1

Marine n-3 fatty acid consumption in a Norwegian renal transplant cohort: comparison of a food frequency questionnaire with plasma phospholipid marine n-3 levels

Dear Dr. Chan:

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Study food frequency questionnaire focusing on food items containing marine n-3 fatty acids (Norwegian version).

    The study subjects responded to the question “During a typical month, how often do you eat these food items?” using one of six response alternatives for each of the ten food item categories.

    (PDF)

    Click here for additional data file. (35.7KB, pdf)
    S2 Fig. Study food frequency questionnaire focusing on food items containing marine n-3 fatty acids (investigator’s scoring sheet version in English).

    The study subjects responded to the question “During a typical month, how often do you eat these food items?” using one of six response alternatives for each of the ten food item categories. Based on EPA and DHA content in the meat of various fish and other seafoods presented in the US Department of Agriculture Food Composition Database and assuming a standard portion size for dinner and bread spread, every potential response was given a weight (shown inside boxes). Total intake of marine n-3 fatty acids per month was calculated as the sum of the ten weighted responses in grams.

    (PDF)

    Click here for additional data file. (49.1KB, pdf)
    S3 Fig. Study food frequency questionnaire focusing on food items containing marine n-3 fatty acids (investigator’s scoring sheet version in English comprising fatty fish items only).

    The study subjects responded to the question “During a typical month, how often do you eat these food items?” using one out of six response alternatives for each food item categories. Based on EPA and DHA content in the meat of various fish and other seafoods presented in the US Department of Agriculture Food Composition Database and assuming a standard portion size for dinner and bread spread, every potential response was given a weight (shown inside boxes). Total intake of marine n-3 fatty acids per month was calculated as the sum of the weighted responses in grams, which for fatty fish intake comprised the four items shown.

    (PDF)

    Click here for additional data file. (44.2KB, pdf)
    S4 Fig. Study food frequency questionnaire focusing on food items containing marine n-3 fatty acids (investigator’s scoring sheet version in English comprising fish servings per month).

    The study subjects responded to the question “During a typical month, how often do you eat these food items?” using one out of six response alternatives for each of the ten food item categories. Servings of fish per month was calculated as the sum of the ten responses, using the center value for each response category as shown.

    (PDF)

    Click here for additional data file. (46KB, pdf)
    S5 Fig. Scatterplots of standardized plasma marine n-3FA levels and standardized FFQ based marine n-3FA consumption estimates with regression lines at baseline eight weeks post-transplant for patients belonging to the control group of the ORENTRA trial (n = 66).

    (TIF)

    Click here for additional data file. (258.5KB, tif)
    S6 Fig. Bland-Altman plot assessing degree of agreement between standardized plasma marine n-3FA levels and standardized FFQ based marine n-3FA consumption estimates at baseline eight weeks post-transplant for patients belonging to the control group of the ORENTRA trial (n = 66).

    (TIF)

    Click here for additional data file. (272.1KB, tif)
    S7 Fig. Correlation matrix presenting Spearman’s correlation coefficients at eight weeks (baseline visit) after renal transplantation for the whole study population (n = 132) and one year post-transplant (end of study visit) for patients belonging to the control group of the ORENTRA trial (n = 64).

    (TIF)

    Click here for additional data file. (1.2MB, tif)
    S1 File. Supporting information.

    Includes information regarding “Patient screening and recruitment in the ORENTRA trial”, “Fatty acid analysis”, “Sample Size and Power Calculation” and “Development of the study Food Frequency Questionnaire”.

    (DOCX)

    Click here for additional data file. (21.7KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (321.6KB, docx)

    Data Availability Statement

    All relevant data are within the paper and its Supporting Information files.

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