Influence of Acute Nut Intake on the Effects of High Saturated Fat Meals in Obese Diabetic Women
Phase: NA
Conditions: Type 2 Diabetes, Obesity
Sponsor: Raquel Ballarin

 
 
Influence of Walnut (Juglans regia) Intake on 
Acute Adverse Effects Induced by High 
Saturated Fat Meals in Obese and Diabetic 
Women 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
20 August, 2024 
 
 
 
 

RESEARCH PROJECT 
 
 
Influence of Walnut (Juglans regia) Intake on 
Acute Adverse Effects Induced by High 
Saturated Fat Meals in Obese and Diabetic 
Women 
 
 
 
Researchers: Prof. Dr. Marcos Ferreira Minicucci 
                    Prof. Dra. Barbara Rita Cardoso 
 
 
 
 
 
 
 
 
Botucatu, São Paulo, Brazil 
2024 
 
 
 
 
 
 

 
Initial Considerations 
 
The consumption of high-fat meals, particularly those rich in saturated fats, is 
increasing in Brazil and contributing to the rise of chronic non-communicable diseases, 
including obesity and diabetes. The known chronic effects of these diets arise from 
acute adverse effects, such as increased inflammation and oxidative stress, making it 
crucial to investigate the potential of certain foods to counteract the harmful effects of 
an unhealthy diet. In this context, chronic nut consumption is associated with reduced 
cardiovascular outcomes. Although some evidence also suggests benefits in the short 
term, studies of short duration are very limited. However, the mechanisms involved in 
the beneficial effects of nut intake remain to be elucidated. In our study, we aim to 
investigate whether circulating microRNA profiles and metabolomic profiles can 
contribute to pathways related to the beneficial effects of nut intake. These pathways are 
identified through bioinformatics analysis and may inform the mechanisms of action of 
nuts, as well as aid in obesity prevention and treatment. Additionally, bioinformatics 
analysis and pathway identification may contribute to developing diagnostic and 
prognostic algorithms in artificial intelligence. 
This study employs advanced methodologies and may contribute to the 
treatment of obesity and type II diabetes, conditions affecting more than 20% of the 
Brazilian population. The clinical trial will be conducted at the Botucatu Medical 
School, and biological material will be collected for further analysis at Monash 
University, Australia. Both institutions have a strong research tradition and laboratory 
facilities to support the project. Professor Marcos Minicucci, the project coordinator, 
has expertise in nutrition and studies on acute supplementation of bioactive compounds. 
He is an Associate Professor in the Department of Clinical Medicine at Botucatu 

Medical School – Unesp, with 191 full articles published in journals and 29 book 
chapters. He has an H-index of 33 with over 4,209 citations (Google Scholar), has 
received six regular grants from FAPESP and three from CNPq, holds a level 1D 
research productivity scholarship, served as Head of the Clinical Medicine Department 
from 2017 to 2021, and is currently Editor-in-Chief of the journal Nutrire of the 
Brazilian Society of Food and Nutrition. Professor Bárbara R. Cardoso, the international 
collaborator on this project, is an internationally recognized researcher in nutrition 
biochemistry and holds a Senior Lecturer position in the Department of Nutrition, 
Dietetics, and Food at Monash University, ranked among the top 50 universities 
worldwide. She is recognized as one of the most significant researchers in the field of 
nuts, ranking in the top 1% globally (http://expertscape.com). She has expertise in 
clinical trials, epidemiological studies, genomics, and proteomics, particularly related to 
nuts. She has published 48 full journal articles and 15 book chapters, with an H-index of 
25 and over 2,400 citations (Google Scholar). She has received funding from the 
Australian National Health and Medical Research Council (NHMRC) and philanthropic 
entities. 
The combination of these expertises will produce a unique, globally relevant 
work with the potential to inform international guidelines on healthy eating and diabetes 
treatment. Furthermore, the proposed partnership will allow for a doctoral student to be 
supervised by Professor Marcos with co-supervision by Professor Bárbara, access to 
advanced technologies, and postgraduate student mobility, strengthening the exchange 
between Monash University and Unesp. This will expand opportunities for the training 
and development of Brazilian researchers and promote academic and scientific 
excellence. 
 

Summary 
 
Obesity is a multifactorial disease in which excess body fat accumulation 
increases the risk of other chronic diseases such as type 2 diabetes. The primary cause 
of obesity is the energy imbalance between calories consumed and expended. A 
balanced diet is essential for weight control and maintaining a healthy body weight. In 
this context, the consumption of high saturated fat meals (HSFM) plays a detrimental 
role, contributing to the development of obesity, type 2 diabetes, cancer, cardiovascular 
diseases, and exacerbating chronic inflammation. Additionally, it may induce metabolic 
endotoxemia and modulate circulating microRNA profiles. Thus, due to the harmful 
health impacts of HSFM, studies on metabolomics and circulating microRNA levels 
could enhance understanding of the mechanisms by which walnuts (Juglans regia) 
prevent and treat diseases.  
The objective of this study is to evaluate whether acute walnut supplementation 
attenuates the inflammatory response, oxidative stress, glycemic response, and regulates 
the metabolomic and circulating microRNA responses induced by HSFM in women with 
obesity and type 2 diabetes. The study will be randomized and cross-over, including obese 
and non-insulin-dependent diabetic women over 18 years of age without other 
comorbidities. In the first session, participants will arrive at the clinic after a 10-hour fast 
and will be offered HSFM with or without 30g of walnuts. In the second session, those 
who did not receive walnuts will now receive them with HSFM, and vice versa. Blood 
samples will be collected, and serum insulin, glucose, inflammatory biomarkers, and 
oxidative stress will be assessed. TNF-alpha, IL-6, IL-1β, IL-1ra, IL-8, and IL-10 will be 
measured by ELISA, and MDA by HPLC. Additionally, metabolomic and microRNA 

analyses will be conducted. Data analysis will be performed using JAMOVI v 2.3.21, 
with a significance level of 5%. 
 
Introduction 
 
Obesity is a multifactorial disease in which excess body fat accumulation leads to 
negative health effects [1]. It is considered a global public health problem, mainly due to 
its association with an increased risk of developing other non-communicable chronic 
diseases (NCDs) [2]. According to the World Obesity Atlas 2023 report, 38% of the 
global population is currently overweight or obese. Additionally, it is projected that 78% 
of adults in the U.S. will be overweight/obese [1,3]. In Brazil, it is estimated that one-
quarter of the population is obese, making this a significant risk factor for pathological 
aging [4]. 
The primary cause of obesity is the energy imbalance between calories consumed 
and expended, and its high prevalence is associated with physical inactivity and sedentary 
behavior [5]. A balanced diet, with the consumption of whole and minimally processed 
foods, is known to be essential for weight control. However, in the current scenario, there 
has been a significant increase in the consumption of ultra-processed foods and a 
reduction in the intake of whole and minimally processed foods, resulting in a substantial 
impact on the prevalence of obesity [6, 7].  
Obesity is one of the most relevant risk factors for type 2 diabetes, a disease that 
is increasingly prevalent worldwide and is considered one of the health emergencies of 
the 21st century. Diabetes impacts morbidity and mortality and is also a major risk factor 
for stroke, renal dysfunction, cardiovascular diseases, vision loss, and neuropathy [8]. 
Non-pharmacological strategies for controlling diabetes typically include nutritional 

intervention, regular physical activity, smoking cessation, and maintaining a healthy body 
weight [10, 11]. 
Acute intake of macronutrients can induce adverse responses even in healthy 
individuals [9–19], which can lead to chronic effects over time. In this context, the 
consumption of high saturated fat meals (HSFM) plays an important role, as chronic 
consumption of an HSFM diet is known to contribute to the development of obesity, type 
2 diabetes, cancer, and cardiovascular diseases, in addition to exacerbating chronic 
inflammation induced by insulin resistance and excess weight [9]. Furthermore, studies 
show that an HSFM can also induce metabolic endotoxemia and modulate circulating 
microRNA profiles [20]. MicroRNAs are non-coding RNA molecules approximately 22 
nucleotides in length that regulate gene expression at the post-transcriptional level by 
degrading mRNA or inhibiting the translation of target genes [21]. Thus, microRNAs 
may contribute to the adverse effects of an HSFM. 
Given the detrimental health impacts of HSFM, studies have been conducted with 
acute supplementation of foods with anti-inflammatory and antioxidant properties, such 
as orange juice and green tea [17-22], in order to minimize the negative effects of an 
unhealthy diet. A study conducted by our research group showed that acute 
supplementation of green tea inhibited the expression of 62 microRNAs induced by 
HSFM intake [22]. Bioinformatics analysis indicated that these microRNAs regulated 
genes associated with TGF-beta, CARM1, RSK, and BMP pathways [22]. The 
identification of these miRNAs and their molecular pathways can contribute to 
understanding the benefits of dietary compounds for health and disease. 
Regular walnut (Juglans regia) consumption is associated with better 
cardiovascular health [23] and insulin response, possibly due to the regulation of insulin 
response by reducing the activity of carbohydrate-digesting enzymes [24]. However, the 

literature remains limited and inconclusive regarding the effects of walnuts on the 
inflammatory and antioxidant response [25]. Additionally, there is only a limited number 
of studies investigating the acute effects of walnuts, with indications of positive response. 
In this regard, postprandial attenuation of insulin resistance, improved endothelial 
function, oxidative stress, and satiety have been demonstrated in some but not all studies 
[26-29]. Interestingly, Torabian et al. [27], in an intervention study, observed that acute 
walnut consumption increased plasma concentrations of polyphenols, as well as 
enhancing total antioxidant capacity and reducing plasma lipid peroxidation. 
In addition to the limited evidence regarding the acute effects of walnuts, their 
effects alongside HSFM on metabolomic and microRNA profiles have not yet been 
studied. Metabolomics was initially defined as the quantitative measurement of the 
dynamic multiparametric metabolic response of living systems to physiopathological 
stimuli or genetic modification. Thus, it directly reflects the phenotype of a particular 
biological system at the molecular metabolic level, also allowing for the detection of 
changes in previously unknown, uncharacterized, or rarely reported metabolites [29]. In 
this way, metabolomics can provide important insights into the mechanisms of walnuts 
in the prevention and treatment of diseases. The same applies to the study of microRNAs, 
which can aid in identifying the intracellular pathways related to the beneficial effects of 
walnut consumption. 
Therefore, this study will generate new evidence regarding the acute effects of 
walnuts in attenuating the adverse effects of HSFM. Furthermore, mitigating the adverse 
effects of HSFM could yield significant benefits, particularly for patients with insulin 
resistance, such as the obese and diabetics. 
 
Objective   

To evaluate whether acute supplementation with walnuts (Juglans regia) 
attenuates inflammatory markers, oxidative stress, metabolomic profiles, and circulating 
microRNAs induced by high saturated fat meals (HSFM) in obese and diabetic women. 
 
Methods 
The project has been approved by the Ethics Committee of the Faculty of 
Medicine of Botucatu. The project is currently being registered in Clinical Trials. This 
study will be randomized and crossover, including women with obesity (body mass index 
[BMI] ≥ 30) and non-insulin-dependent diabetes aged over 18, recruited during outpatient 
consultations at the Faculty of Medicine of Botucatu. The exclusion criteria will include: 
use of dietary supplements, use of corticosteroids in the last 3 months, cancer, heart 
failure, kidney diseases, liver diseases, lung diseases, neurological diseases, allergy to 
any component of HSFM or walnuts, and habitual walnut consumption (≤3 times per 
week) in the last month. 
The sample size was estimated based on a previous study that showed a 60% 
reduction in IL-6 levels after meals with HSFM associated with orange juice compared 
to the control group (without orange juice) [12]. Considering this reduction, a type I error 
of 5%, and a power of 90%, this study will include 15 individuals. 
Patients who sign the Informed Consent Form (ICF) will be evaluated at two time 
points, separated by a minimum washout of one week. In the first meeting, after fasting 
for 10 hours, HSFM will be offered with or without 30g of walnuts. In the second meeting, 
the condition will be switched. This dosage of walnuts refers to the daily recommended 
intake of nuts proposed by different dietary guidelines [30, 31]. Simple randomization 
will be performed by a nurse who will not participate in the research. HSFM will be 
offered in the outpatient clinic of the hospital, and patients will be required to consume 

the entire meal and all the walnuts. During the study protocol, patients will be instructed 
to avoid walnut consumption for 2 weeks before the first study time point. 
Blood samples will be collected at time points 0 (immediately before the meal), 
15 minutes, 30 minutes, 60 minutes, and 150 minutes after the interventions. From time 
points 0 to 60 minutes, serum insulin and glucose will be measured, while at time points 
0 and 150 minutes, serum biomarkers of inflammation, oxidative stress, circulating 
microRNAs, and metabolomic profiles will be assessed. Additionally, we will evaluate 
satiety using a visual analog scale (VAS) at all time points after the meal. Figure 1 
illustrates the study design. 
 
Figure 1. Randomized crossover study. HSFM, high saturated fat meal. 
 
Biochemical, Inflammation, and Oxidative Stress Analysis 
The cytokines TNF-alpha, IL-6, IL-1β, IL-1ra, IL-8, and IL-10 will be measured 
using ELISA following the manufacturer's instructions for the kits. Serum concentrations 
of malondialdehyde (MDA) will be measured by high-performance liquid 
chromatography (HPLC) based on the reaction with thiobarbituric acid [32]. Insulin will 

be measured using a chemiluminescent immunoassay, and glucose will be assessed using 
the dry chemistry method. 
 
Metabolomic Analysis 
Analyses will be conducted using liquid chromatography on a Thermo Dionex 
Ultimate 3000 system coupled to a high-resolution mass spectrometer, Thermo 
QExactive Plus, with electrospray ionization operating in both positive and negative 
modes. Separation will be achieved using a Waters® Acquity BEH C18 column (100 x 
2.1 mm x 1.7 μm particle diameter) maintained at 40 °C with a flow rate of 0.350 mL/min 
for the mobile phase and a sample injection of 5 μL. The mobile phases (MP) will be: 
(MP A) ultrapure water with 0.1% formic acid and 5 mM ammonium formate, and (MP 
B) methanol with 0.1% formic acid. The chromatographic separation will be performed 
using a gradient elution mode: 0-1.0 min 5% B; 1.0-3.0 min 35% B; 3.0-13.0 min 95% 
B; 13.0-17.0 min 95% B; 17.0-20.0 min 5% B. General source conditions will include: 
sheath gas and auxiliary gas: 50 and 15 arbitrary units, respectively; spray voltage: ± 3600 
V; S-lens voltage: 50 V; capillary temperature: 320 °C; source temperature: 400 °C. Data 
will be acquired in full ion scan mode over the range of m/z 100-1000 using a resolution 
of 35,000 FWHM (Full Width at Half Maximum), combined with data-dependent 
acquisition experiments (DDA) using a resolution of 17,500 FWHM, AGC 1e5, IT 50 
ms; NCE 15-35%, and an isolation window of 1.2 Da. 
The processing of the raw files obtained from high-resolution liquid 
chromatography-mass spectrometry (LC-MS) analyses will be conducted using the MS-
DIAL software (RIKEN, version 4.9) for the extraction of detected signals, spectral 
deconvolution, and peak alignment [33]. Parameters for processing will include: MS1 
tolerance of 0.005 Da and MS2 tolerance of 0.05 Da; minimum peak height of 1.0E5; 

mass width of 0.05 Da; sigma value for deconvolution window of 0.5; and alignment 
tolerance of 0.3 min and 0.005 Da. A sample blank (extraction solvent) will be used to 
subtract interfering signals. 
For the putative annotation of key metabolites, experimental MS/MS spectra will 
be compared to data from a spectral library from the laboratory that combined the public 
library Mass Bank of North America and the commercial NIST MSMS 2020, considering 
a similarity level of over 80% between spectra. An error of less than 8 ppm for similarity 
between experimental and theoretical m/z values will also be considered. Detected ions 
will be exported to Excel (Microsoft Office 2016) for generating a data matrix table. 
Additionally, molecular formulas will be determined using MS-FINDER (RIKEN, 
version 3.44) [34]. Analytical standards will be used for compound identification based 
on comparisons of m/z of precursor ions, fragmentation spectrum, and retention time. 
Analyses will be performed at the Monash Proteomics & Metabolomics Platform 
(MPMP). 
 
Circulating MicroRNA Analysis  
Following RNA extraction, we will utilize the human miRNA array 4.0 
Affymetrix GeneChip® (ThermoFisher Scientific), according to the manufacturer's 
instructions. This array contains 2,578 sets of probes for mature human miRNAs and 
2,025 sets of probes for human pre-miRNAs (stem-loop). Probe sets (a total of 1,996) for 
determining the presence of human snoRNA and scaRNA are also included in this array. 
GeneChips will be scanned using the Affymetrix GeneChip G3000 7G scanner with 
standard settings to capture signal intensities for the miRNAs. Raw intensity data will be 
imported into the Affymetrix Expression Console software (v1.4.1.46) for signal 
preprocessing, including background correction using the robust multi-array average 

(RMA) algorithm, median polish summarization of probe signals at the probe set level, 
and quantile normalization across multiple arrays. A detection call for the miRNA signal 
strength will be performed using the Affymetrix "Detection Above Background" (DABG) 
algorithm, which generates a p-value for each signal above the background probability. 
The method will follow the protocol recommended by the manufacturer. Analyses will 
be conducted at Monash in Hudson Genomics. 
 
Statistical Analysis 
 
Data will be presented as mean and standard deviation when normally distributed, 
median and 25-75% percentiles when non-normally distributed, or percentage. For 
comparisons between the two groups (with and without walnuts), we will use the 
Student's t-test when the distribution is normal, the Mann-Whitney test when the 
distribution is non-normal, and the Chi-Square test for categorical variable comparisons. 
Additionally, generalized estimating equations (GEE) will be used to evaluate repeated 
measures. Data analyses will be performed using JAMOVI v 2.3.21. The significance 
level will be set at 5%. 
 
References 
1. Lin X, Li H. Obesity: epidemiology, pathophysiology, and therapeutics. Front 
Endocrinol 2021;12:706978. 
2. Boutari, CS, Mantzoros, A. A 2022 update on the epidemiology of obesity and a call to 
action: as its twin COVID-19 pandemic appears to be receding, the obesity and 
dysmetabolism pandemic continues to rage on. Metabolism 2022; 133: 155217. 
3. Koliaki C, Dalamaga M, Liatis S. Update on the Obesity Epidemic: After the Sudden 
Rise, Is the Upward Trajectory Beginning to Flatten? Curr Obes Rep 2023; 12: 514–527. 

4. Watanabe LM, Navarro AM, Seale LA Intersection between Obesity, Dietary Selenium, 
and Statin Therapy in Brazil. Nutrients 2021; Jun 12;13:2027. doi: 10.3390/nu13062027. 
5. Silveira EA, Mendonça CR, Delpino FM, Souza GVE, Rosa LPS, Oliveira C, Noll M. 
Sedentary behavior, physical inactivity, abdominal obesity and obesity in adults and older 
adults: A systematic review and meta-analysis 2022. Clin Nutr ESPEN 2022; 50:63-73. 
doi: 10.1016/j.clnesp.2022.06.001. 
6. Vandevijvere S, De Pauw R, Djojosoeparto S, Gorasso V, Guariguata L, Lovhaug AL, 
et al. Upstream determinants of overweight and obesity in Europe. Curr Obes Rep 2023; 
12:417–28. doi: 10.1007/s13679-023-00524-1. 
7. Popkin BM, Ng SW. The nutrition transition to a stage of high obesity and 
noncommunicable disease prevalence dominated by ultra-processed foods is not 
inevitable. Obes Rev 2022; 23:e13366. 
8.  
9. Ojo, O. Recent Advances in Nutrition and Diabetes. Nutrients 2021; 13: 1573. 
10. Forouhi, NG. Embracing complexity: making sense of diet, nutrition, obesity and type 
2 diabetes. Diabetologia 2023; 66:786-799. 
11. Hotamisligil GS. Inflammation and metabolic disorders. Nature 2006; 444: 860–867. 
12. Chaves DFS, Carvalho PC, Brasili E, Rogero MM, Hassimotto NA, Diedrich JK, et 
al. Proteomic analysis of peripheral blood mononuclear cells after a high-fat, high-
carbohydrate meal with orange juice. J Proteome Res 2017; 16:4086–92. 
13. Ghanim H, Sia CL, Upadhyay M, Korzeniewski K, Viswanathan P, Abuayshesh S, et 
al. Orange juice neutralizes the proinflammatory effect of a high-fat, high- carbohydrate 
meal and prevents endotoxin increase and Toll-like receptor expression. Am J Clin Nutr 
2021; 91:940–9. 

14. Quintanilha BJ, Ferreira LRP, Ferreira FM, Neto EC, Sampaio GR, Rogero MM. 
Circulating plasma microRNAs dysregulation and metabolic endotoxemia induced by a 
high-fat high-saturated diet. Clin Nutr 2020; 39:554–62. 
15. Winter J, Jung S, Keller S, Gregory RI, Diederichs S. Many roads to maturity: 
microRNA biogenesis pathways and their regulation. Nat Cell Biol 2009; 11:228–34. 
16. Quintanilha BJ, Chaves DFS, Brasili E, Correa TAF, Capetini VC, Ferreira FM, et al. 
Ingestion of Orange juice prevents hyperglycemia and increases plasma miR-375 
expression, Clin Nutr ESPEN 2022; 47:240-245. 
17. Dorna MS, Barbosa EMS, Callegari MA, Tanni SE, Chiuso-Minicucci F, Felix TF, et 
al. Orange juice attenuates circulating miR-150-5p, miR-25-3p, and miR-451a in healthy 
smokers: a randomized crossover study. Front Nutr 2021; 8:775515. 
18. Rodrigues LL, Cooper JA, Paton CM. Acute consumption of black walnuts increases 
fullness and decreases lipid peroxidation in humans. Nutr Res 2019; 71:56-64. 
19. Bastos RVS, Dorna MS, Chiuso-Minicucci F, Felix TF, Fernandes AAH, Azevedo PS, 
et al. Acute green tea intake attenuates circulating microRNA expression. Induced by a 
high-fat, high-saturated meal in obese women: A randomized crossover study. J Nutr 
Biochem 2023; 112:109203. 
20. Davis L, Stonehouse W, du Loots T, Mukkuddem-Petersen J, van der Westhuizen F, 
Hanekom S, et al. The effects of high walnut and cashew nut diets on the antioxidant 
status of subjects with metabolic syndrome. Eur J Nutr 2007; 46:155–64. 
21. Brennan AM, Sweeney LL, Liu X, Mantzoros CS. Walnut consumption increases 
satiation but has no effect on insulin resistance or the metabolic profile over a 4-day 
period. Obesity 2010; 18:1176–82. 

22. Rock CL, Flatt SW, Barkai HS, Pakiz B, Heath DD. A walnut-containing meal had 
similar effects on early satiety, CCK, and PYY, but attenuated the postprandial GLP-1 
and insulin response compared to a nut-free control meal. Appetite 2017; 117:51–7 
23. Chou-Yi Hsu et al. Effect of walnut consumption on markers of endothelial function 
in adults: A systematic review and meta-analysis of randomized controlled trials. 
Phytother Res. 2024; 38:1358-1366. 
24. Farazi et al. Inhibition of human starch digesting enzymes and intestinal glucose 
transport by walnut polyphenols. Food Res Int. 2024; 189:114572.  
25. Anne E de la Hunty et al. Walnut consumption and health outcomes with public health 
relevance - a systematic review of cohort studies and randomized controlled trials 
published from 2017 to present. Nutr Rev. 2022;81:26-54 
26. Cortés B, Núñez I, Cofán M, Gilabert R, Pérez-Heras A, Casals E, et al. Acute effects 
of high-fat meals enriched with walnuts or olive oil on postprandial endothelial function. 
J Am Coll Cardiol 2006; 48:1666–71. 
27. Torabian S, Haddad E, Rajaram S, Banta J, Sabaté J. Acute effect of nut consumption 
on plasma total polyphenols, antioxidant capacity and lipid peroxidation. J Hum Nutr Diet 
2009; 22:64–71. 
28. Berryman C, Grieger J, West S, Chen C, Blumberg J, Rothblat G, et al. Acute 
consumption of walnut and walnut components differentially affect postprandial lipemia, 
endothelial function, oxidative stress, and cholesterol efflux in humans with mild 
hypercholesterolemia. J Nutr 2013; 146:788–94. 
29. Haddad EH, Gaban-Chong N, Oda K, Sabaté J. Effect of a walnut meal on 
postprandial oxidative stress and antioxidants in healthy individuals. Nutr J 2014; 13:4. 
30. Dietary Guidelines for Americans 2020–2025 [https://www.dietaryguidelines. 
gov/sites/default/files/2020-12/Dietary_Guidelines_for_Americans_2020-2025.pdf] 

31. National Health and Medical Research Council: Australian Dietary Guidelines 
Summary. In. Edited by Department of Health and Ageing: National Health and Medical 
Research Council; 2013. 
32. Nielsen F, Mikkelsen BB, Nielsen JB, Andersen HR, Grandjean P. Plasma 
malondialdehyde as biomarker for oxidative stress: reference interval and effects of life 
style factors. Clin Chem 1997;43:1209–1214. 
33. Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B, Ikeda K, et al. MS-DIAL: Data-
Independent MS/MS Deconvolution for Comprehensive Metabolome Analysis. Nat 
Methods 2015; 12: 523-526. 
34.  Tsugawa H, Kind T, Nakabayashi R, Yukihira D, Tanaka W, Cajka T, et al. Hydrogen 
Rearrangement Rules: Computational MS/MS Fragmentation and Structure Elucidation 
Using MS-FINDER Software. Anal Chem 2016; 88: 7946–7958.