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Erythrocyte fatty acid profiles in children are not predictive of autism spectrum disorder status: a case control study
© The Author(s) 2018
- Received: 11 December 2017
- Accepted: 27 February 2018
- Published: 14 March 2018
Biomarkers promise biomolecular explanations as well as reliable diagnostics, stratification, and treatment strategies that have the potential to help mitigate the effects of disorders. While no reliable biomarker has yet been found for autism spectrum disorder (ASD), fatty acids have been investigated as potential biomarkers because of their association with brain development and neural functions. However, the ability of fatty acids to classify individuals with ASD from age/gender-matched neurotypical (NEU) peers has largely been ignored in favor of investigating population-level differences. Contrary to existing work, this classification task between ASD and NEU cohorts is the main focus of this work. The data presented herein suggest that fatty acids do not allow for classification at the individual level.
- Autism spectrum disorder
- Fatty acids
- Diagnostic biomarkers
- Multivariate statistical analysis
Autism spectrum disorder (ASD) comprises a broad class of psychological disorders characterized by compromised social communication/interaction and the presence of restricted, repetitive patterns of behavior . The prevalence of ASD has increased markedly from 0.64% in 2002 to 1.14% in 2008 , a rate which exceeds that of other developmental disabilities . Despite the high prevalence rates, the impaired quality of life associated with ASD , and substantial health care costs to families , the biochemical basis for ASD is largely unknown and therefore still an active area of research. Currently, ASD is only diagnosed and assessed through a variety of psychometric tools. However, numerous research efforts investigating potential biomarkers of and therapeutic strategies for ASD are ongoing.
Post-mortem brain analysis has revealed several structural and functional abnormalities associated with ASD, including altered synapse connectivity/plasticity , decreased neuron size and increased neuron density in the amygdala and hippocampus , decreased Purkinje cell size and number in the cerebellum , neuroinflammation , and aberrant activity-dependent transcription/translation . On the molecular scale, alterations in Wnt/ β-catenin signaling (corroborated by putative mechanisms for valproate-inducing and folate-protective contributions to ASD), Ca2+ signaling, and glutamatergic/GABAergic signaling have been implicated in ASD. It is this role in neuroplasticity, neurogenesis, and synaptogenesis  that have led to investigations of polyunsaturated fatty acids (PUFAs) as potential targets for biomarker development and therapeutic intervention in ASD. PUFAs are essential fatty acids: precursors α-linolenic acid (ALA, 18:3n-3) and linoleic acid (LA, 18:2n-6) must be obtained from the diet. The downstream products docosahexaenoic acid (DHA; 22:6n-3) and arachidonic acid (AA; 20:4n-6) are the most abundant PUFAs in the brain and are vital components of neuronal phospholipids.
Fatty acids from erythrocyte membranes measured in this work
Number of subjects
Abs./Rel. FA Conc.
Differences reported (ASD versus control)
Vancassel et al. 2001 
ASD: 3-17 DD: 1-19
Meguid et al. 2008 
↓ linolenic and linoleic acids
Pastural et al. 2009
ASD: 8.7 ±3.9 DD: 7.9 ±2.9
Bell et al. 2010 
ASD: 7.5 ±3.5 DD: 7.5 ±3.6
Bell et al. 2010 
ASD: 7.5 ±3.5 DD: 6.0 ±3.3
El-Ansary et al. 2011 
ASD: 4-12 DD: 4-11
Brigandi et al. 2015 
Yui et al. 2016 
ASD: 13.6 ±4.3 DD: 13.2 ±5.4
Jory et al. 2016 
ASD: 3.1 ±0.8 DD: 3.9 ±1.1
↓ n-3/n-6 and linoleic acid
Parletta et al. 2016 
ASD: 5.3 ±2.1 DD: 8.3 ±2.5
A successful biomarker or therapeutic target for ASD requires the metabolite or metabolite panel to separate individuals with ASD from NEU controls and/or strongly correlate with ASD severity. Therefore, this ability to separate individuals with ASD and NEU participants is not appropriately assessed with hypothesis testing on population means. More appropriate metrics are given in terms of classification performance on individuals (e.g. sensitivity/specificity, C-statistic, etc.). El-Ansary et al.  reported their results in terms of sensitivity/specificity and ROC curves; however, they had limited sample sizes of 26 ASD and 26 NEU participants and they assessed participants on the basis of absolute erythrocyte concentrations. Furthermore, their observed near-perfect separation in multiple fatty acid measurements (e.g., C-statistic of 1.00 for AA) has not been observed in larger cohorts (e.g., AA from ).
The aim of this study was to compare the level of erythrocyte-membrane fatty acids in a large cohort of ASD and NEU participants, and assess the ability of multivariate classification to separate ASD and NEU participants. The results presented herein contrast many of the conclusions about fatty acid biomarkers for ASD in the scientific literature, even though (as it will be shown) some other reports can be interpreted differently if these biomarkers are assessed on the individual level rather than comparing population means/medians. While no conclusions about the effectiveness of treatments that seek to raise fatty acid concentrations can be drawn from this work, the results indicate that fatty acid measurements are not a viable biomarker for ASD classification.
This paper analyzes baseline (prior to treatment) data from a 12-month nutrition/dietary treatment study known as the ASU Comprehensive Nutrition/Diet Treatment Study. Erythrocyte fatty acid measurements were available for 63 ASD and 49 NEU participants with a median (IQR) age of 9.7 (6.7) years and 10.0 (6.3) years, respectively. The average effect size d (i.e., Cohen’s d) for the fatty acid measurements under investigation was estimated a priori to be between 0.18 and 2.4 using data from the three largest studies in Table 1 [14–16]. With a d,α, and β of 0.5, 0.1, and 0.8, respectively, the minimum sample size is calculated to be 49 samples per group. The sample size used in this work is also greater than 8 of 10 studies reported in Table 1 that found statistically significant differences between ASD and NEU populations. This study was approved by the Institutional Review Board of Arizona State University. Eligibility and exclusion criteria, characteristics of the study population (including comorbidities), and descriptions of autism severity and overall functioning assessments are presented in . It is important to note that both ASD and NEU participants were not allowed to have taken nutritional supplements or restricted to abnormal diets in the previous two months to be eligible for this study. Furthermore, since seafood consumption is the largest contributor of n-3 fatty acids in the Western diet, parents/caregivers were required to report the number of seafood servings eaten by the participant per month. All data used in this study are provided in Additional file 1.
Fatty acid measurements
Fatty acids from erythrocyte membranes measured in this work
Dihomo- γ-linoleic acid
Individual measurements for each cohort were first assessed for normality with the Anderson-Darling test  at a significant level of 0.05. If distributions from both cohorts failed to reject the null hypothesis of the Anderson-Darling test, the F-test for equal variances at a 0.05 significance level was performed to determine whether a Student’s t or Welch’s test  should be performed to determine the significance of differences in mean values between cohorts. If distributions from one or more cohorts rejected the null hypothesis of the Anderson-Darling test, the two-sample Kolmogorov-Smirnov test  was used to test whether or not samples came from distributions of the same shape. If the distributions failed to reject the null hypothesis of the Kolmogorov-Smirnov test, the Mann-Whitney U test  was used to test for significant differences in the median values between cohorts; else, Welch’s test was used to test for significant differences in the mean values between cohorts. All statistical tests were performed in MATLAB. All probability distribution functions (PDFs) are visualized using kernel density estimation (KDE) .
Univariate classification for each measurement was assessed with receiver-operating characteristic (ROC) curve analysis of the PDFs of each cohort. The C-statistic is the area under the ROC curve and a C-statistic of 0.5 indicates a random separation, whereas a C-statistic of 1 indicates a perfect separation. Multivariate classification was assessed with Fisher Discriminant Analysis (FDA)  and PDFs were calculated on the resulting FDA scores in a similar manner as for the PDFs of the individual measurements.
No published study on fatty acid profiles in ASD discloses raw, individual-level data. Therefore, comparison data were extracted from reported figures in [14, 24]. Briefly, images of each figure were saved and masks of individual markers were manually selected. The center of each marker was identified by cross-correlation and the resulting data points were extracted for further analysis.
Univariate statistics and classification
Univariate tests for group mean/median differences between ASD and NEU cohorts
Mean/Median values [95% CI]
20.05 [17.04, 22.76]
20.16 [18.12, 22.03]
2.045 [1.39, 2.80]
2.226 [1.548, 3.110]
Mann-Whitney U test
3.707 [2.05, 7.11]
3.816 [2.721, 5.347]
Mann-Whitney U test
0.349 [0.158, 0.828]
0.344 [0.225, 0.569]
Mann-Whitney U test
0.177 [0.136, 0.274]
0.176 [0.120, 0.253]
14.18 [11.17, 17.26]
14.00 [12.43. 15.61]
Mann-Whitney U test
13.90 [12.36, 16.31]
13.94 [11.86, 15.55]
0.0198 [0.0110, 0.0286]
0.0201 [0.0105, 0.0282]
Mann-Whitney U test
25.50 [24.14, 26.87]
25.44 [24.04, 27.15]
Mann-Whitney U test
0.229 [0.124, 0.389]
0.205 [0.0147, 0.306]
19.27 [18.23, 20.41]
19.51 [18.24, 20.49]
Mann-Whitney U test
0.190 [0.116, 0.350]
0.182 [0.140, 0.280]
Mann-Whitney U test
0.0165 [0.0073, 0.0462]
0.0168 [0.0110, 0.0292]
Mann-Whitney U test
0.111 [0.0707, 0.224]
0.113 [0.0850, 0.163]
26.54 [23.35, 29.90]
26.65 [24.34, 29.11]
For the sake of completeness, regression analysis of 12 measures of ASD severity  against combinations of fatty acids has been performed using partial least squares and its nonlinear extension kernel partial least squares. The prediction accuracy was generally poor with low R2 values even for the best combinations of fatty acids.
Regression with seafood intake
Regression of red blood cell fatty acids onto seafood consumption per month for each listed group
Red blood cell
ASD + NEU
There are other methods beyond calculation of the C-statistic that can provide similar insights into the biological relevance of the hypothesis under investigation. In particular calculation of effect sizes and CIs usually provide more insight into the underlying biological hypothesis than null hypothesis significance testing and many fields, including clinical trial research, are beginning to move toward these approaches for reporting research findings [28, 29]. In particular, these approaches can include information on the spread of the distributions under investigation, which is usually as, if not more, important than the sample means in biological classification problems. The null hypothesis significance testing conducted in this work is used mainly to illustrate that these methods can lead to inappropriate conclusions that can be rectified by using CIs or the C-statistic to quantify differences between two populations.
Biomarkers represent the “holy grail” of precision medicine  in that they quantify changes in single molecules or even entire molecular pathways and quantitatively link clinical outcomes with physiology in health and disease . Despite their promise, translating biomarker research into clinical practice is poor with a less than one percent success rate [32, 33]. Many of these failed biomarkers persist in the literature due to a lack of access to raw data (hence, the reliance on data extraction from published figures in this paper) and a culture that does not credit negative results . Biomarker research in ASD would benefit from improving data access, embracing negative results, and focusing on individual-level classification (with a validation strategy such as cross-validation) [31, 33] to more quickly reach diagnostics and treatments that positively impact those with ASD.
It is important to note that this study did not investigate the therapeutic effects of fatty acid supplementation. Recent meta-analyses reach conflicting conclusions [11, 34, 35], some of which may be attributed to small sample sizes, low doses, and inadequate lengths of supplementation and observational time frames. However, this study indicates that there are no differences between fatty acid levels in ASD and NEU cohorts; therefore, fatty acid therapeutics would need to achieve a different fatty acid profile than the average NEU profile for possible therapeutic benefit.
The results of this study suggest that fatty acid profiles are similar between individuals with ASD and NEU controls; therefore, fatty acid profiles are not promising biomarkers for classifying ASD and NEU children. A repository of individual-level measurements in biomarker studies for ASD, including those reporting negative results, would greatly help the field iterate toward more promising biomarkers for classifying ASD.
The authors thank Devon Coleman and Valeria Fimbres for work on data entry and analysis.
The ASU portion of this study was funded by a gift from the Autism Research Institute (ARI), www.autism.com. The RPI portion of the study acknowledges partial financial support from the National Institutes of Health (https://www.nih.gov/, Grant 1R01AI110642). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Availability of data and materials
The dataset supporting the conclusions of this article is included within the article (and its additional file).
JBA conceptualized the study. E Gehn coordinated the study and recruited patients. E Geis was the lead nurse, obtaining blood samples for the study. DPH, JBA, UK, and JH conceptualized the statistical analysis. DPH performed the statistical analysis, which was subsequently verified by UK and JH. DPH drafted the manuscript, with revisions and edits provided by JBA and JH. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The participants reported in this study were part of a 12-month nutrition/dietary treatment study known as the ASU Nutrition/Diet Treatment Study. All measurements reported in this paper are taken at baseline, prior to the initiation of treatment. Participants and/or their parents/guardians provided written informed consent and written assent was performed when applicable. This study was approved by the Institutional Review Board of Arizona State University.
Consent for publication
JBA is on the Scientific Advisory Board for the Autism Research Institute (ARI). All other authors declare that they have no conflict of interest.
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- American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th edn. Washington: American Psychiatric Association; 2013.View ArticleGoogle Scholar
- Centers for Disease Control. Prevalence of Autism Spectrum Disorders – Autism and Developmental Disabilities Monitoring Network, 14 Sites, United States, 2008. 2012. http://www.cdc.gov/mmwr/preview/mmwrhtml/ss6103a1.htm. Accessed 17 Mar 2016.
- Boyle CA, Boulet S, Schieve LA, Cohen RA, Blumberg SJ, Yeargin-Allsopp M, Visser S, Kogan MD. Trends in the prevalence of developmental disabilities in US children, 1997–2008. Pediatrics. 2011; 127(6):1034–42. https://doi.org/10.1542/peds.2010-2989.View ArticlePubMedGoogle Scholar
- van Heijst BF, Geurts HM. Quality of life in autism across the lifespan: A meta-analysis. Autism. 2015; 19(2):158–67. https://doi.org/10.1177/1362361313517053.View ArticlePubMedGoogle Scholar
- Buescher A, Cidav Z, Knapp M, Mandell D. Costs of autism spectrum disorders in the United Kingdom and the United States. JAMA Pediatr. 2014; 168(8):721–8. https://doi.org/10.1001/jamapediatrics.2014.210.View ArticlePubMedGoogle Scholar
- Di Martino A, Yan CG, Li Q, Denio E, Castellanos FX, Alaerts K, Anderson JS, Assaf M, Bookheimer SY, Dapretto M, et al. The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry. 2014; 19(6):659–67. https://doi.org/10.1038/mp.2013.78.View ArticlePubMedGoogle Scholar
- Chen JA, Peñagarikano O, Belgard TG, Swarup V, Geschwind DH. The emerging picture of autism spectrum disorder: Genetics and pathology. Annu Rev Pathol Mech Dis. 2015; 10(1):111–44. https://doi.org/10.1146/annurev-pathol-012414-040405.View ArticleGoogle Scholar
- de la Torre-Ubieta L, Won H, Stein JL, Geschwind DH. Advancing the understanding of autism disease mechanisms through genetics. Nat Med. 2016; 22(4):345–61. https://doi.org/10.1038/nm.4071.View ArticlePubMedPubMed CentralGoogle Scholar
- Dinel AL, Rey C, Bonhomme C, Le Ruyet P, Joffre C, Layé S. Dairy fat blend improves brain DHA and neuroplasticity and regulates corticosterone in mice. Prostaglandins Leukot Essent Fat Acids (PLEFA). 2016; 109:29–38. https://doi.org/10.1016/j.plefa.2016.03.013.View ArticleGoogle Scholar
- Kuratko CN, Salem N. Biomarkers of DHA status. Prostaglandins Leukot Essent Fat Acids. 2009; 81(2):111–8. https://doi.org/10.1016/j.plefa.2009.05.007.View ArticleGoogle Scholar
- Mazahery H, Stonehouse W, Delshad M, Kruger MC, Conlon CA, Beck KL, von Hurst PR. Relationship between long chain n-3 polyunsaturated fatty acids and autism spectrum disorder: Systematic review and meta-analysis of case-control and randomised controlled trials. Nutrients. 2017; 9(2):155. https://doi.org/10.3390/nu9020155.View ArticlePubMed CentralGoogle Scholar
- Sergeant S, Ruczinski I, Ivester P, Lee TC, Morgan TM, Nicklas BJ, Mathias RA, Chilton FH. Impact of methods used to express levels of circulating fatty acids on the degree and direction of associations with blood lipids in humans. Br J Nutr. 2016; 115(2):251–61. https://doi.org/10.1017/S0007114515004341.View ArticlePubMedPubMed CentralGoogle Scholar
- El-Ansary AK, Bacha AGB, Al- Ayahdi LY. Plasma fatty acids as diagnostic markers in autistic patients from Saudi Arabia. Lipids Health Dis. 2011; 10(1):62–9. https://doi.org/10.1186/1476-511X-10-62.View ArticlePubMedPubMed CentralGoogle Scholar
- Brigandi SA, Shao H, Qian SY, Shen Y, Wu BL, Kang JX. Autistic children exhibit decreased levels of essential fatty acids in red blood cells. Int J Mol Sci. 2015; 16(5):10061–76. https://doi.org/10.3390/ijms160510061.View ArticlePubMedPubMed CentralGoogle Scholar
- Bell JG, Miller D, MacDonald DJ, MacKinlay EE, Dick JR, Cheseldine S, Boyle RM, Graham C, O’Hare AE. The fatty acid compositions of erythrocyte and plasma polar lipids in children with autism, developmental delay or typically developing controls and the effect of fish oil intake. Br J Nutr. 2010; 103(8):1160–7. https://doi.org/10.1017/S0007114509992881.PubMedGoogle Scholar
- Parletta N, Niyonsenga T, Duff J. Omega-3 and omega-6 polyunsaturated fatty acid levels and correlations with symptoms in children with attention deficit hyperactivity disorder, autistic spectrum disorder and typically developing controls. PLoS ONE. 2016; 11(5):0156432. https://doi.org/10.1371/journal.pone.0156432.View ArticleGoogle Scholar
- Adams J, Howsmon DP, Kruger U, Geis E, Gehn E, Fimbres V, Pollard E, Mitchell J, Ingram J, Hellmers R, Quig D, Hahn J. Significant association of urinary toxic metals and autism-related symptoms – A nonlinear statistical analysis with cross validation. PLoS ONE. 2017; 12(1):0169526. https://doi.org/10.1371/journal.pone.0169526.Google Scholar
- Anderson TW, Darling DA. A test of goodness of fit. J Am Stat Assoc. 1954; 49(268):765. https://doi.org/10.2307/2281537.View ArticleGoogle Scholar
- Welch BL. The generalization of ‘Student’s’ problem when several different population variances are involved. Biometrika. 1947; 34(1/2):28–35. https://doi.org/10.2307/2332510.View ArticlePubMedGoogle Scholar
- Massey, Jr. FJ. The Kolmogorov-Smirnov test for goodness of fit. J Am Stat Assoc. 1951; 46(253):68. https://doi.org/10.2307/2280095.View ArticleGoogle Scholar
- Mann HB, Whitney DR. On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat. 1947; 18(1):50–60. https://doi.org/10.1214/aoms/1177730491.View ArticleGoogle Scholar
- Silverman BW. Density Estimation for Statistics and Data Analysis. Boca Raton: CRC Press; 1986.View ArticleGoogle Scholar
- Fisher R. The use of multiple measurements in taxonomic problems. Ann Eugenics. 1936; 7(2):179–88. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x.View ArticleGoogle Scholar
- Yui K, Imataka G, Kawasaki Y, Yamada H. Down-regulation of a signaling mediator in association with lowered plasma arachidonic acid levels in individuals with autism spectrum disorders. Neurosci Lett. 2016; 610:223–8. https://doi.org/10.1016/j.neulet.2015.11.006.View ArticlePubMedGoogle Scholar
- Howsmon DP, Kruger U, Melnyk S, James SJ, Hahn J. Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylation. PLoS Comput Biol. 2017; 13(3):1005385. https://doi.org/10.1371/journal.pcbi.1005385.View ArticleGoogle Scholar
- Katan MB, Deslypere JP, Birgelen APv, Penders M, Zegwaard M. Kinetics of the incorporation of dietary fatty acids into serum cholesteryl esters, erythrocyte membranes, and adipose tissue: An 18-month controlled study. J Lipid Res. 1997; 38(10):2012–22.PubMedGoogle Scholar
- Harris WS, Pottala JV, Sands SA, Jones PG. Comparison of the effects of fish and fish-oil capsules on the n-3 fatty acid content of blood cells and plasma phospholipids. Am J Clin Nutr. 2007; 86(6):1621–5.View ArticlePubMedGoogle Scholar
- Nakagawa S, Cuthill IC. Effect size, confidence interval and statistical significance: A practical guide for biologists. Biol Rev. 2007; 82(4):591–605. https://doi.org/10.1111/j.1469-185X.2007.00027.x.View ArticlePubMedGoogle Scholar
- Cumming G. The new statistics: Why and how. Psychol Sci. 2014; 25(1):7–29. https://doi.org/10.1177/0956797613504966.View ArticlePubMedGoogle Scholar
- Barker AD, Compton CC, Poste G. The National Biomarker Development Alliance: Accelerating the translation of biomarkers to the clinic. Biomark Med. 2014; 8(6):873–6. https://doi.org/10.2217/bmm.14.52.View ArticlePubMedGoogle Scholar
- Poste G, Compton CC, Barker AD. The national biomarker development alliance: Confronting the poor productivity of biomarker research and development. Expert Rev Mol Diagn. 2015; 15(2):211–8. https://doi.org/10.1586/14737159.2015.974561.View ArticlePubMedGoogle Scholar
- Poste G. Bring on the biomarkers. Nature. 2011; 469(7329):156–7. https://doi.org/10.1038/469156a.View ArticlePubMedGoogle Scholar
- McPartland JC. Considerations in biomarker development for neurodevelopmental disorders. Curr Opin Neurol. 2016; 29(2):118–22. https://doi.org/10.1097/WCO.0000000000000300.View ArticlePubMedPubMed CentralGoogle Scholar
- Horvath A, Łukasik J, Szajewska H. ω-3 fatty acid supplementation does not affect autism spectrum disorder in children: A systematic review and meta-analysis. J Nutr. 2017:242354. https://doi.org/10.3945/jn.116.242354.
- Cheng YS, Tseng PT, Chen YW, Stubbs B, Yang WC, Chen TY, Wu CK, Lin PY. Supplementation of omega 3 fatty acids may improve hyperactivity, lethargy, and stereotypy in children with autism spectrum disorders: A meta-analysis of randomized controlled trials. Neuropsychiatr Dis Treat. 2017; 13:2531–43. https://doi.org/10.2147/NDT.S147305.View ArticlePubMedPubMed CentralGoogle Scholar
- Vancassel S, Durand G, Barthélémy C, Lejeune B, Martineau J, Guilloteau D, Andrès C, Chalon S. Plasma fatty acid levels in autistic children. Prostaglandins Leukot Essent Fat Acids (PLEFA). 2001; 65(1):1–7. https://doi.org/10.1054/plef.2001.0281.View ArticleGoogle Scholar
- Meguid NA, Atta HM, Gouda AS, Khalil RO. Role of polyunsaturated fatty acids in the management of Egyptian children with autism. Clin Biochem. 2008; 41(13):1044–8. https://doi.org/10.1016/j.clinbiochem.2008.05.013.View ArticlePubMedGoogle Scholar
- Pastural l, Ritchie S, Lu Y, Jin W, Kavianpour A, Khine Su-Myat K, Heath D, Wood PL, Fisk M, Goodenowe DB. Novel plasma phospholipid biomarkers of autism: Mitochondrial dysfunction as a putative causative mechanism. Prostaglandins Leukot Essent Fat Acids. 2009; 81(4):253–64. https://doi.org/10.1016/j.plefa.2009.06.003.View ArticleGoogle Scholar
- Jory J. Abnormal fatty acids in Canadian children with autism. Nutrition. 2016; 32(4):474–7. https://doi.org/10.1016/j.nut.2015.10.019.View ArticlePubMedGoogle Scholar