Biomonitoring Of Mycotoxins in Plasma Of Patients With Alzheimer’s And Parkinson’s DiseaseⅢ
Apr 12, 2023
3. Conclusions
We present the results obtained in a first HBM study on the analysis of 19 mycotoxins and metabolites in plasma samples from healthy and patient (AD and PD) volunteers in La Rioja (Spain). The results of the study suggest some differences between both control and patients in OTA and STER levels. The reason behind this difference is unknown. Several factors might be influencing the outcome observed such as differences in diets, altered metabolism due to the disease, or the fact that gender and age might have an important role in the levels of mycotoxins detected in plasma.
Click to cistanche tincture for Alzheimer's disease and Parkinson's disease
All these confounding factors should be carefully evaluated with studies including a higher number of individuals. In the present study, differences in OTA between healthy companions and patients have been observed but the differences seem to be more related to gender than the disease itself. Moreover, OTA tended to decrease with age, especially in the PD group. One of the main findings is that STER appeared only after β-glucuronidase/arylsulfatase treatment, supporting the hypothesis that STER-glucuronides are formed during human metabolism. Moreover, STER plasmatic levels were found to be higher in the patients, with no differences between pathologies. Moreover, STER levels correlated positively with age in women.
When interpreting the data, it is important to point out that the present study was not designed to explain the pathobiology of PD or AD, but rather to explore if the presence of a possible environmental risk factor may act as perturbative agents involved in the gene–environment interaction. Samples from the control group match previous data obtained in a similar population in a neighboring region of Spain and use the same LC/MS-MS analysis [35].
However, when comparing healthy individuals between both studies it should be noted that: (i) the age range is different, as the majority of the samples in the present study were obtained from people older than 60 years, especially in the AD and PD groups, as expected for age-related diseases; and (ii) the number of control samples which is low in the present study. Moreover, other limitations should be also taken into account when interpreting data from the present study; many of the data were very close to the LOD and the number of samples was limited (especially in AD men). Overall, our results show some statistically significant differences between the control group and patients of neurodegenerative diseases but also point to some age- and sex-related responses that could be in turn influencing the metabolism.
4. Materials and Methods
4.1. Reagents
LC-MS grade methanol (Honeywell Riedel-de-Haën, Seelze, Germany), LC grade acetonitrile (ACN) (Merck, Darmstadt, Germany), MS grade formic acid (purity > 98%), ammonium formate (both from Fluka Sigma-Aldrich, Mannheim, Germany), and deionized water (>18 MΩ cm−1 resistivity) purified in an Ultramatic Type I system (Wasserlab, Navarra, Spain) were used for sample preparation and LC-MS/MS analysis. Captiva EMR-lipid (3 mL) cartridges were obtained from Agilent Technologies (Santa Clara, CA, USA).
All mycotoxins (reference material, purity ≥ 98%) were purchased from Sigma-Aldrich (St. Louis, MO, USA) as ACN solutions at the following concentrations AFM1, AFG2, and AFB2: 0.5 µg/mL; AFG1 and AFB1: 2 µg/mL; OTA-d5 and OTB: 10 µg/mL; STER and DOM-1: 50 µg/mL; NIV, DON, 3-ADON, 15-ADON, NEO, DAS, FUS-X, ZEA and T-2 and HT-2 toxins: 100 µg/mL. The reference materials were stored at −20 ◦C. Standard stock solutions were prepared in ACN and stored at −20 ◦C. The working solution, which includes all the mycotoxins, was prepared by diluting the corresponding individual stock solutions in ACN. The stability of the standard and working stock solutions in these storage conditions were previously assessed [36].
4.2. Subjects
Informed written consent was obtained from all participants before study inclusion. A total of 94 subjects, including 44 patients with mild PD, 25 patients with dementia not related to PD, and 25 healthy controls, were recruited from the neurological service at the San Pedro Hospital in La Rioja (Spain) with ethical approval (“Study of lipidic profiles in serum/plasma samples from Parkinson´s disease patients for the obtention a differential clinical pattern for diagnosis purposes” Ref: CEICLAR PI- 212, approved 4 April 2016) from the local ethical committee on human experimentation (CEImLar, Comité Ética de Investigación con medicamentos de La Rioja). Patients were diagnosed by experienced neurologists. PD patients were assessed by the modified HY scale (Table S3) while patients with dementia not related to PD were assessed by the GDS (Table S4) to determine their disease stage. Healthy controls included spouses or unrelated companions of patients with no apparent or known neurological disease or comorbidities.
4.3. Plasma
Collection Blood samples were obtained at a medical visit. Blood was collected into 4 mL EDTA-coated tubes (Vacutainer, ref # 368171) and the plasma was separated within 2 h by centrifugation at 2200× g for 15 min at room temperature. Plasma was removed immediately and transferred into coded vials in 0.2 mL aliquots to avoid repeated freeze/thaw cycles and then stored at −80 ◦C. Appropriate care was taken to avoid contamination of the plasma samples with cells or components of the pellet obtained from the centrifugation.
4.4. Sample Preparation
Plasma treatment before and after enzymatic hydrolysis was as described in ArceLópez et al. (2020) [35,36]. Concisely, the plasma samples (400 µL) were added to a Captiva EMR-lipid cartridge, which contained 1200 µL of acidified ACN (1% formic acid). The vacuum was applied, and after 5 min, two 400 µL aliquots (one for each of the mycotoxin groups that will be investigated) from the effluent were separated and then evaporated to dryness (60 ◦C).
This methodology was used for the detection of 19 compounds (mycotoxins and metabolites), classified into two groups (according to the different elution programs needed for the chromatographic separation): DOM-1, AFG2, AFM1, AFG1, AFB2, AFB1, OTB, ZEA, STER, OTA, T-2, HT-2 (group I) and NIV, DON, FUS-X, NEO, 3-ADON, 15-ADON and DAS (group II). The residue was reconstituted with 200 µL of mobile phase in the proportion corresponding to the intended group of mycotoxins to be analyzed (40% B for group I and 5% B for group II).
Before chromatographic analysis, the solution was vortexed (5 min) and filtered (PVDF, 0.45 µm, Merck Millipore, Ireland). The procedure for enzymatic hydrolysis was: 400 µL of plasma was treated with 50 µL of a mixture of glucuronidase/sulfatase enzymes (250 U/mL, 0.2 U/mL in PBS; from Helix Pomatia (Sigma Aldrich, Mannheim, Germany). After agitation, samples were incubated overnight (37 ◦C) in a water bath. Then, plasma sample clean-up, using Captiva-EMR cartridges, was carried out similarly to that described above.
4.5. LC/MS-MS Analysis
LC-MS/MS analysis was performed in an LC system 1200 series coupled to a 6410 Triple Quadrupole (QqQ) in ESI(+) mode, both from Agilent Technologies (Mannheim, Germany). Separation was carried out at 45 ◦C on an Ascentis Express C18, 2.7 µm particle size 150 × 2.1 mm column (Supelco Analytical, St. Louis, MO, USA) with a mobile phase composed of 5 mM ammonium formate and 0.1% formic acid in water (A) and 5 mM ammonium formate and 0.1% formic acid in a 95:5 methanol/water (B) in gradient conditions.
The injection volume was 20 µL and the gradient elution was carried out at the flow rate of 0.4 mL/min. Data acquisition parameters are described in Arce-López et al. (2020) [36]. Samples were analyzed and grouped in analytical sequences. Each sequence included, along with the samples, eight matrix-matched calibrators.
These calibrators were employed in the preparation of calibration curves, which served for the quantification of mycotoxins in the samples analyzed in the same sequence. The acceptance criteria for the calibration curves were: a minimum of six points, a determination coefficient (R2 ) > 0.99, and back-calculated concentration for each calibrator not differing (expressed as RE) from the nominal value by more than 15% (20% for LOQ level) [32]. In addition, retention times should not differ by more than 2.5% between samples and calibrators [33]. Depending on the volume of plasma available, not all the samples could be analyzed after enzymatic treatment.
4.6. Analytical Method Validation
Validation of both procedures (before and after enzymatic hydrolysis) following FDA and EU guidelines as described in Arce-Lopez et al. (2020) [35,36]. LOD values were: 1.35 ng/mL for DOM-1; 0.35 ng/mL for AFG2, 0.18 ng/mL for AFM1; 0.07 ng/mL for AFG1 and AFB2; 0.04 ng/mL for AFB1; 2.70 ng/mL for HT-2; 0.40 ng/mL for OTA and OTB; 0.20 ng/mL for T-2 and STER; 1.80 ng/mL for ZEA; 9.10 ng/mL for NIV; 1.94 ng/mL for DON; 1.95 ng/mL for FUS-X; 0.18 ng/mL for NEO; 0.70 ng/mL for 3-ADON; 1.20 ng/mL for 15-ADON and 0.15 ng/mL for DAS.
Recovery values (in intermediate precision conditions) before enzymatic treatment were from 68.8% for STER to 97.6% for DAS (RDS ≤ 15% for all the mycotoxins) and no differences were found after enzymatic treatment. Matrix effects were also evaluated before and after enzymatic treatment, and there were no differences in most of the mycotoxins, obtaining RDS values ≤ 15% for all of them. Stability after two freeze–thaw cycles was assessed at two concentration levels (6 and 30xLOQ) (three replicates per level). All mycotoxins were stable (RSD < 15%) (Table S7).
4.7. Statistical Analysis and Data Handling
When treating analytical data with statistics, it is important to elucidate the kind of distribution that characterized the dataset. The descriptors and package tests to be used are different if we have normal-distributed or non-normal-distributed data. Shapiro– Wilk test was used to verifying the normal distribution of the mycotoxin concentration levels investigated.
As the hypothesis of normality was refused, a non-parametrical (which does not imply any distribution assumption) set of tests was used for the statistical treatment. To assess the possible differences between concentration levels of OTA and STER in groups and sub-groups, the Wilcoxon rank-sum test (Mann–Whitney two-sample statistic) was used [49]. In statistics, the Wilcoxon rank-sum test is a non-parametric test used to test whether two samples are likely to derive from the same population verifying that for two randomly selected values X and Y from the two populations, the probability of X being greater than Y is equal to the probability of Y being greater than X.
For the same purpose, a Kruskal–Wallis test was used where the collation of variables implied more than one distribution [50]. To assess the correlation between mycotoxin levels and age (quantitative variable) the Spearman’s rank correlation coefficient (or Spearman’s rho) was used. All tests were conducted with a level of significance of 5%. The outputs are reported together with the level of statistical significance and p-value. All the analyses were conducted using STATA14 software (Stata/IC 14.0, Copyright 1985–2015 StataCorp LP). As regards the data handling for the setting of the data set, quantitative and qualitative variables were treated as follows: OTA and STER level. Quantitative variable, non-negative variable expressed as values in ng/mL.
Values were assigned as follows:
Value = 0 ng/mL; when analytical result derived from the analysis was found
A quantitative variable was used to scale the diagnosis of PD patients (Table S3). In addition, a binary variable (HY_d = 0 and HY_d = 1) was defined: HY_d = 0 for PD patients who scored HY scale in the range 1–2 (not impaired postural reflexes); HY_d = 1 for PD patients who scored HY scale in the range 2.5–3 (impaired postural reflexes). GDS scale. A quantitative variable was used to scale the diagnosis of patients with AD (Table S4).
The mechanism of Cistanche's treat Alzheimer's disease and Parkinson's disease
Cistanche is a traditional Chinese herb that has been used for many years for its potential health benefits. In recent studies, it has been found that Cistanche may have neuroprotective effects and may be effective in treating Alzheimer's disease (AD) and Parkinson's disease (PD).
The mechanism of Cistanche in treating AD and PD effectively is attributed to its active components, such as echinacoside, acteoside, and cistanosides. These compounds are believed to have antioxidant and anti-inflammatory properties that can reduce oxidative stress and inflammation in the brain, which are associated with the development and progression of neurodegenerative diseases.
Cistanche can also promote the growth of nerve cells and improve cognitive function by increasing the levels of brain-derived neurotrophic factor (BDNF), a protein that plays a crucial role in the growth and maintenance of neurons. In addition, Cistanche has been shown to reduce β-amyloid plaques, which are hallmark features of Alzheimer's disease, and decrease the accumulation of α-synuclein in the brain, which is associated with Parkinson's disease.
Overall, the potential therapeutic benefits of Cistanche in treating AD and PD are promising, but further studies are needed to elucidate its exact mechanisms of action and confirm its efficacy and safety in clinical settings.
References
1Serrano-Pozo, A.; Frosch, M.P.; Masliah, E.; Hyman, B.T. Neuropathological alterations in Alzheimer's disease. Cold Spring Harb. Perspect. Med. 2011, 1, a006189. [CrossRef]
2. Fearnley, J.M.; Lees, A.J. Ageing and Parkinson’s disease: Substantia nigra regional selectivity. Brain 1991, 114, 2283–2301. [CrossRef]
3. Barber, R.C. The genetics of Alzheimer’s disease. Scientifica (Cairo) 2012, 2012, 1–14. [CrossRef]
4. Izco, M.; Carlos, E.; Alvarez-Erviti, L. The two faces of Exosomes in Parkinson’s disease: From pathology to therapy. Neuroscientist 2021, 107385842199000. [CrossRef] [PubMed]
5. Hou, Y.; Dan, X.; Babbar, M.; Wei, Y.; Hasselbalch, S.G.; Croteau, D.L.; Bohr, V.A. Ageing as a risk factor for neurodegenerative disease. Nat. Rev. Neurol. 2019, 15, 565–581. [CrossRef] [PubMed]
6. Johnson, M.E.; Stecher, B.; Labrie, V.; Brundin, L.; Brundin, P. Triggers, Facilitators, and Aggravators: Redefining Parkinson’s Disease Pathogenesis. Trends Neurosci. 2019, 42, 4–13. [CrossRef]
7. Wainaina, M.N.; Chen, Z.; Zhong, C. Environmental factors in the development and progression of late-onset Alzheimer’s disease. Neurosci. Bull. 2014, 30, 253–270. [CrossRef]
8. Rahman, M.A.; Rahman, M.S.; Uddin, M.J.; Mamum-Or-Rashid, A.N.M.; Pang, M.-G.; Rhim, H. Emerging risk of environmental factors: Insight mechanisms of Alzheimer’s diseases. Environ. Sci. Pollut. Res. 2020, 27, 44659–44672. [CrossRef] [PubMed]
9. Bush, A.I. The Metal Theory of Alzheimer’s Disease. J. Alzheimer’s Dis. 2012, 33, S277–S281. [CrossRef] 10. Moulton, P.V.; Yang, W. Air pollution, oxidative stress, and Alzheimer’s disease. J. Environ. Public Health 2012, 2012, 472751. [CrossRef]
11. Li, Y.; Fang, R.; Liu, Z.; Jiang, L.; Zhang, J.; Li, H.; Liu, C.; Li, F. The association between toxic pesticide environmental exposure and Alzheimer’s disease: A scientometric and visualization analysis. Chemosphere 2021, 263, 128238. [CrossRef]
12. Fulop, T.; Witkowski, J.M.; Bourgade, K.; Khalil, A.; Zerif, E.; Larbi, A.; Hirokawa, K.; Pawelec, G.; Bocti, C.; Lacombe, G.; et al. Can an infection hypothesis explain the beta-amyloid hypothesis of Alzheimer’s disease? Front. Aging Neurosci. 2018, 10, 224. [CrossRef]
13. Vasefi, M.; Ghaboolian-Zare, E.; Abedelwahab, H.; Osu, A. Environmental toxins and Alzheimer’s disease progression. Neurochem. Int. 2020, 141, 104852. [CrossRef]
14. Goldman, S.M. Environmental toxins and Parkinson’s disease. Annu. Rev. Pharmacol. Toxicol. 2014, 54, 141–164. [CrossRef] [PubMed]
15. Marras, C.; Canning, C.G.; Goldman, S.M. Environment, lifestyle, and Parkinson’s disease: Implications for prevention in the next decade. Mov. Disord. 2019, 34, 801–811. [CrossRef] [PubMed]
16. Van der Mark, M.; Brouwer, M.; Kromhout, H.; Nijssen, P.; Huss, A.; Vermeulen, R. Is pesticide use related to Parkinson's disease? Some clues to heterogeneity in study results. Environ. Health Perspect. 2012, 120, 340–347. [CrossRef] [PubMed]
17. Gorell, J.M.; Johnson, C.C.; Rybicki, B.A.; Peterson, E.L.; Richardson, R.J. The risk of Parkinson’s disease with exposure to pesticides, farming, well water, and rural living. Neurology 1998, 50, 1346–1350. [CrossRef]
18. Priyadarshi, A.; Khuder, S.A.; Schaub, E.A.; Priyadarshi, S.S. Environmental risk factors and Parkinson’s disease: A meta-analysis. Environ. Res. 2001, 86, 122–127. [CrossRef]
19. Mitchell, N.J.; Bowers, E.; Hurburgh, C.; Wu, F. Potential economic losses to the US corn industry from aflatoxin contamination. Food Addit. Contam. Part A 2016, 33, 540–550. [CrossRef] [PubMed]
20. Marin, S.; Ramos, A.J.; Cano-Sancho, G.; Sanchis, V. Mycotoxins: Occurrence, toxicology, and exposure assessment. Food Chem. Toxicol. 2013, 60, 218–237. [CrossRef]
21. Janik, E.; Niemcewicz, M.; Ceremuga, M.; Stela, M.; Saluk-Bijak, J.; Siadkowski, A.; Bijak, M. Molecular aspects of mycotoxins—A serious problem for human health. Int. J. Mol. Sci. 2020, 21, 8187. [CrossRef]
22. Logrieco, A.; Miller, J.; Eskola, M.; Krska, R.; Ayalew, A.; Bandyopadhyay, R.; Battilani, P.; Bhatnagar, D.; Chulze, S.; De Saeger, S.; et al. The mycotoxin charter: Increasing awareness of, and concerted action for, minimizing mycotoxin exposure worldwide. Toxins (Basel) 2018, 10, 149. [CrossRef] 23. European Commission. Commission Regulation (EC) No 1881/2006 of 19 December 2006 setting maximum levels for certain contaminants in foodstuffs. Off. J. Eur. Union 2006, 364, 5–24.
24. European Parliament. European Parliament and the Council of the EU Directive of The European Parliament and of the Council of 7 May 2002 on undesirable substances in animal feed 2002/32. Off. J. Eur. Communities 2002, L140, 10–22.
25. European Commission. Commission Recommendation of 17 August 2006 on the presence of deoxynivalenol, zearalenone, ochratoxin A, T-2, and HT-2 and fumonisins in products intended for animal feeding. Off. J. Eur. Union 2006, L299, 7–9.
26. Eskola, M.; Kos, G.; Elliott, C.T.; Hajšlová, J.; Mayar, S.; Krska, R. Worldwide contamination of food-crops with mycotoxins: Validity of the widely cited ‘FAO estimate’ of 25%. Crit. Rev. Food Sci. Nutr. 2020, 60, 2773–2789. [CrossRef] [PubMed]
27. Escrivá, L.; Font, G.; Manyes, L.; Berrada, H. Studies on the presence of mycotoxins in biological samples: An overview. Toxins (Basel) 2017, 9, 251. [CrossRef]
28. Gurusankar, R.; Yenugadhati, N.; Krishnan, K.; Hays, S.; Haines, D.; Zidek, A.; Kuchta, S.; Kinniburgh, D.; Gabos, S.; Mattison, D.; et al. The role of human biological monitoring in health risk assessment. Int. J. Risk Assess. Manag. 2017, 20, 136–197. [CrossRef]
29. Bredesen, D.E. Inhalational Alzheimer’s disease: An unrecognized—And treatable—Epidemic. Aging (Albany NY) 2016, 8, 304–313. [CrossRef]
30. Martins, I. Overnutrition determines LPS regulation of mycotoxin-induced neurotoxicity in neurodegenerative diseases. Int. J. Mol. Sci. 2015, 16, 29554–29573. [CrossRef] [PubMed]
31. Izco, M.; Vettorazzi, A.; Forcen, R.; Blesa, J.; de Toro, M.; Alvarez-Herrera, N.; Cooper, J.M.; Gonzalez-Peñas, E.; Lopez de Cerain, A.; Alvarez-Erviti, L. Oral subchronic exposure to the mycotoxin ochratoxin A induces key pathological features of Parkinson’s disease in mice six months after the end of the treatment. Food Chem. Toxicol. 2021, 152, 112164. [CrossRef]
32. Center for Drug Evaluation and Research (FDA). Bioanalytical Method Validation Guidance for Industry. 2018. Available online: https://www.fda.gov/files/drugs/published/Bioanalytical-Method-Validation-Guidance-for-Industry.pdf (accessed on 20 November 2019).
33. European Commission. Commission Decision of 12 August 2002 implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results (2002/657/EC). Off. J. Eur. Communities 2002, 221, 8–36.
34. EFSA. Management of left-censored data in dietary exposure assessment of chemical substances. EFSA J. 2010, 8, 1–96.
35. Arce-López, B.; Lizarraga, E.; Irigoyen, Á.; González-Peñas, E. Presence of 19 mycotoxins in human plasma in a region of Northern Spain. Toxins (Basel) 2020, 12, 750. [CrossRef]
36. Arce-López, B.; Lizarraga, E.; Flores-Flores, M.; Irigoyen, Á.; González-Peñas, E. Development and validation of a methodology based on Captiva EMR-lipid clean-up and LC-MS/MS analysis for the simultaneous determination of mycotoxins in human plasma. Talanta 2020, 206, 120193. [CrossRef]
37. Remiro, R.; González-Peñas, E.; Lizarraga, E.; López de Cerain, A. Quantification of ochratoxin A and five analogs in Navarra red wines. Food Control 2012, 27, 139–145. [CrossRef]
38. Yang, S.; Zhang, H.; De Saeger, S.; De Boevre, M.; Sun, F.; Zhang, S.; Cao, X.; Wang, Z. In vitro and in vivo metabolism of ochratoxin A: A comparative study using ultra-performance liquid chromatography-quadrupole/time-of-flight hybrid mass spectrometry. Anal. Bioanal. Chem. 2015, 407, 3579–3589. [CrossRef] [PubMed]
39. Muñoz, K.; Cramer, B.; Dopstadt, J.; Humpf, H.U.; Degen, G.H. Evidence of ochratoxin A conjugates in urine samples from infants and adults. Mycotoxin Res. 2017, 33, 39–47. [CrossRef] [PubMed]
40. Vidal, A.; Mengelers, M.; Yang, S.; De Saeger, S.; De Boevre, M. Mycotoxin Biomarkers of Exposure: A Comprehensive Review. Compr. Rev. Food Sci. Food Saf. 2018, 17, 1127–1155. [CrossRef]
41. EFSA CONTAM Panel (EFSA Panel on Contaminants in the Food Chain); Schrenk, D.; Bodin, L.; Chipman, J.K.; del Mazo, J.; Grasl-Kraupp, B.; Hogstrand, C.; Hoogenboom, L.; Leblanc, J.; Nebbia, C.S.; et al. Risk assessment of ochratoxin A in food. EFSA J. 2020, 18, 06113.
42. Arce-López, B.; Lizarraga, E.; Vettorazzi, A.; González-Peñas, E. Human biomonitoring of mycotoxins in blood, plasma, and serum in recent years: A review. Toxins (Basel) 2020, 12, 147. [CrossRef]
Beatriz Arce-López 1 , Lydia Alvarez-Erviti 2 , Barbara De Santis 3 , María Izco 2 , Silvia López-Calvo 4 , Maria Eugenia Marzo-Sola 4 , Francesca Debegnach 3 , Elena Lizarraga 1 , Adela López de Cerain 5,6 , Elena González-Peñas 1,† and Ariane Vettorazzi 5,6,* ,†
1 Department of Pharmaceutical Technology and Chemistry, Research Group MITOX, School of Pharmacy and Nutrition, Universidad de Navarra, 31008 Pamplona, Spain; barce@alumni.unav.es (B.A.-L.); elizarraga@unav.es (E.L.); mgpenas@unav.es (E.G.-P.)
2 Laboratory of Molecular Neurobiology, Center for Biomedical Research of La Rioja (CIBIR), Piqueras 98, 3rd Floor, 26006 Logroño, Spain; laerviti@riojasalud.es (L.A.-E.); mizco@riojasalud.es (M.I.)
3 National Reference Laboratory for Mycotoxins and Plant Toxins, Istituto Superiore di Sanità, 00161 Roma, Italy; barbara.desantis@iss.it (B.D.S.); francesca.debegnach@iss.it (F.D.)
4 Servicio de Neurología, Hospital San Pedro, Piqueras 98, 26006 Logroño, Spain; slcalvo@riojasalud.es (S.L.-C.); memarzo@riojasalud.es (M.E.M.-S.)
5 Department of Pharmacology and Toxicology, Research Group MITOX, School of Pharmacy and Nutrition, Universidad de Navarra, 31008 Pamplona, Spain; acerain@unav.es 6 IdiSNA, Navarra Institute for Health Research, 31008 Pamplona, Spain
