Hoefsloot, H.

Smilde, A.

van Velzen, E.

Westerhuis, J.

English

In the expanding field of nutritional metabolomics the use of optimized and robust strategies are essential to find meaningful and significant results. Data from nutritional studies can be extremely large, multidimensional and complex. Moreover the data may hide just a subtle biological variation due to the nutritional treatment, and the effect may be highly variable across individuals. Usually the treatment effects are much smaller than the biological variations between individuals. A properly chosen experimental design and a well-adapted data analysis strategy are therefore essential to ensure that the intended information in the data can be assessed and that the biological question of interest can be answered

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UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)UvA-DARE (Digital Academic Repository)Data analysis strategies in nutritional metabolomicsWesterhuis, J.; van Velzen, E.; Hoefsloot, H.; Smilde, A.Publication date2009Document VersionFinal published versionPublished inGIT Laboratory Journal EuropeLink to publicationCitation for published version (APA):Westerhuis, J., van Velzen, E., Hoefsloot, H., & Smilde, A. (2009). Data analysis strategies innutritional metabolomics. GIT Laboratory Journal Europe, 13(1-2), 17-19.http://www.laboratory-journal.com/science/lebensmittel/data-analysis-strategies-nutritional-metabolomicsGeneral rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s)and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an opencontent license (like Creative Commons).Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, pleaselet the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the materialinaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letterto: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. Youwill be contacted as soon as possible.Download date:09 Mar 2023Data Analysis Strategies in Nutritional Metabolomics In tile t'xp(!Inding field of nutriliol'l'" metabolomics the use of optimiled a nd robust strategies (Ire essential to find meaningful and significant results. Data from nutritional studies can be extremely large, multidimensional and complex, Moreovl!i" the dllta may hide just a subtle biological variation due to the nutritional treatment. and the effect may be highly variable across in-dividuals. Usually the treatment effect5 are mud! smaller than the biological variations between indr..iduals. A properly chosen experimental design and a well-adapted data analysis strategy are therefore essential to ensure that the intended information in the dat;ll can be assessed and that the biological question of interest can be answered. Nutritional in!e~!ions studies, in ......, Iet. the bioavai labil it; ardIOI the bioac!i"';ty 01 dietary in· SJed ients are investigated. are usually designed as CfOS5OWr stl.'Clies.A major benefit of the aoss-tMr design is that each individual acts as his own <ootroI. This feature allows for the direct ~son of l1eatmenl5, and is pankularty etf~ dent in the presen<:~ oflarge between-iodvidual ...,;I!tion [t I. A SJlKific method whidl is capable (0 ~ with the crossover strllCt\l"e in metabolo-me data is MultillM!l Data Analysis [MLDA) [1). This recently introduced method permits a sepa. J31e analysis of the between·individual variation and the within-individual ...,;ation in the data (Iij. 1).11>1' basi< l"iKipe 01 MlDA lI:'ies III the "",iation splitting property 01 AN(JIA, and un be oonsidered as the ~ate eotension of a pailed t·test.'" ClOSSOYeI designed metaboIorric • fOOO TECHNOLOGY From loft: M""b H""hIoot. EwOlld ... an 'hl .. n, JoIwon Wesrem"", A~ Smilde: Biosyslf1ns Dota An~1y>i>, Unive"iIy of Am"'rdam experiments the use of MlDA is prefe"red OIler das$icaI megavariate data analysis methods such as Prilcipal (~t Analysis (PC.A) or Partial lI'ast Squares Q;saiminant Analysis (PIS· M). An important ,mitation of using these ~"";ate methods is m.t the pai"'" structure in the data is not taken into ac:oount. ~tial ly, the in-dJced variation due ro the nutritional treatment is often 1ar~1y a<eowhelmed by the biological FOOD TECHNOLOGY. --- tIioIogIcIl .. ---------- I ~ .. , -, • .~ -.. " '. " , . H .. " '. " .. " " " --- -,--r . ····1. -, '- ""---' --).'...-.I~'" ---- - , ~ '. " '. " '. H '. " '. " .. " " " ---- __ 1001 __ r' . ····1 -, -" ~- .. ,. " ,. " ,. H .. , .. ,. " " " " " ---- _100' __ , C;I _. -.JI1L..,' '. ,. " ,. " , . H .. " '. " .. " " " ..... _-- _1<01 __ --.---- ,-Fi9- I: U.iMIy 600 ~H. 'H N~~ specI" obt ....... lrom lour iodMdual, afl .. placebo int ....... t .... (black) ond g"pe! w .... ' '''rart in_lion (.ri). In 110, lp«lral r~ ~ 1.0$-1.50 wm. lIoe biologiul .. ri.l .... belwffn the four IIoto·~ ... is ~Iu<lfated (Nole tIoallM v •• ialion due 10 tfI.lme-nI is much smaller Ih.n the V.rial .... _ .... indi· vidu.,.). In !he re-g .... S 1.45-1.90 wm. Iho effKt of the grapo/Wino \Ie;Otmonl within !he inclilriclual. is .hown by the syswn.lic ina_ 01 hippuric Kid. .. a •.• '\l ••. •• •• \ .' .. .. • . , ...... ,l'f' ...... ~ .... , .. .... ..:. :" ... . . . .......... ... .. .. ..-.. -.. , ~. --A m cifi'" '. ! •• • .. b .. ···· .... .. .. i ': ... , .. • \" .. ...•. _, , " , " ....... . . -. " . " . '. .. : .. .. • " • ;;10:"" guul'" ~--. : " .. . , , .. ' , " I[d! I ; O. H , .. " 18 • G.I.lllbor'looy JooornfII-212009 varialion that e.iltlo between ind~ ,;dual~ We have demonst.ated the MlDA metlood for the U!oe of ~ piatebo-tontrolled crosloover de-signed human nutritional inleNe'" lion study in whidlthe metabolic impact of glapelwine e.tract con· sumption 00 the urinary 'H NMR profiles was evalu.ated. To investi· gate the ooderIying v.riOOoo in the between· individual d;!la. multilevei PCA (or MlCA (3]) wa!o used_ In the mu lti level PCA two major sou'tes of villiation coo ld be identified. i.e. biological variation between indi· viduals (gendef) and experimental variation (measuremenl batdles). A!o Iollown in figure 2a the P(1·P(4 KOIe\. of the male\. ,oold be "'Ioti ... !J.Ii!ohed lrom the lemale\. in the Fig. 2: PCA btlw ..... individual score. of (1) tho I" Ind tho.- P< ond (b) the 2"" ond tho 4110 P(. The score plots rofIW the biologic.ol (gtnd>r) Ind "lpfrimtntol (batdl-IO-I>ot<ll) va.iation in 1110 .Iudy gr ...... lO$pKIivtly. The Iq NMR $i. gnal. tha1 ina .. sed signffi<.ontiy with the l,oUnom dlKt Id) <.In be . .. ignod I ..... Iive ..... 11 phenolic <OmpOUnd$ ihot derive from gut ...ocrobiol polyphenol _bolism. study group, whereas the PCl·P(4 score plol ;n f;gure 2b reveals a dislioction between two different NMR run~ To find systematic differences ..... ong the int.......,ti(ll1 9fOl4I$, lTO.I~ilevel PLS·DA 00 the within-irldividual d;!ta was used_ The re-sults from the muhilevel PLS·DA anaiylois (111 the within·irldividual differences Io!low thal the levels of a few phenoI i' metabo(ites we«' sign~ic.1ntly increased in the urine after the treatment (lOg . 2Q. Re· maltabl;\ these metabolile\. cou(d not be idenl~ied sign ifi'ar'l11y in an orig inal PLS-DA approarn. Among the ~ met<lbol ite!, ~ found hippuric acid as the Iotroo' gest biomarl:er lor the intake 01 the grapetwine eXlract. As shown in figure 2d. hippuric acid is '!"pIO' sente-d by three signals in the aro-matic region (/\ 1.8] ppm, d. ell,! CHo; /; 7.&4 ppm. ~ CH.; /; 1.;; wm. ~ CHICH,J. The pre!oefI(e of hippuric acid Is in agreement with ~0U!01y rej)OIted Iotudi6 whe<e the melabolic impact of polyphe. nol iC-fich diets was studied (4-7). Hippuric acid is thoughl 10 be the metabolic end·product 01 flavonoid !legrad;!1ion by the gul mkrOOiota. IIeside!o hippuric acid alw other phenolic compounds; we«' Ioign~i­cantly ell!llated in the urine. i.e . 4·llydroxyllippuic acid arid 4-hy. droqphenylacetic acid. Like hippu· ric acid these phenolic acids are !mown gut microbia( fermentatioo produtts of flav<tnOids. Recentfo/ ~ we«' atloo able to r.tegrate MLDA in a phalll"lato!<i-!IeIic expe-rirnenl. 10 a crossover de!oigned. placebo control(ed. ;nler· _tion study. a human study group was ioYl'Stiga\ed upon the te~ raf. IXinary exaelion 01 poIypheoo-lie metaboli\e\. after tIoe inlake 01 black lea solids. loYI'Stigation 01 these urinary metaboIite\. was per • formed usiog the ' If NMR and GeMS urinary profiles. Due 10 the integration of experimental design information in the data ana~, ~ was possible 10 uocover a consider· able (iSI of gut mediated met.Jbo.. iles. Among the metabolites ~ coUd identify phenol ic inll!fmedi· ate\. ,ud! as 1.2,]·trihyoo))ryben· Ie!\e, 1.3·dihyd'oxypherr;O--o~ul· phate. 4-o-metl1ylgallic acid, gallic aticl hippuric acid and 4-hydroxy. hippuric acid. The ."".Iion in the -I • , ,10 I I'· o , .' ':' . :.; ; ." . . :' :: '. , ~ . .. ., •. ' ··t, • 'I. ... , • • J~'" .. . .. . . ~ . ' , ;';.;0' <, . ' .. ~ . .. , !. ;; ~~:, ... , ... " ·f • ! • I 11 • ! 0 0 , i . __ • O.S I b • · '; - , , . • • • • 0.6 • , • • • • 0.' • •• •• • • • ••• .' . 0.2 r • • • • • • • 0 .. •• • • ............ ,. .... .... ,,, ...... IndIYllluaI ""_ • fOOO TECHNOLOGY [2 [ .on.eIlen tu 01 01" lProteome IIeo. 1, 4483--4191 (ZOO8) [ll f""","""",, M.E,' BrlMl1n,Stat. 1'sy<hoI, S9. 301~l20 (1006) 141 D¥in CA. " ai,' lAgo'oc.food ChItn, 53. 1428-14;14 (1005) [SI Mo.<lde< lP . • t ,.1.: Am J CIi-I Nutr 81. 2S65-2&OS (1005) [61 W"" U, 01 01.' F<ee R.><k.8io1, Mod, ll. W,· 21S (2002) • • • • • -- • Po<>< mo-tobc>llzooR ~_il .. o [11 .on Dcnlo'n fA Of 01.' lAgri<, food o..m, 54. sg1H9]8 (2006) [81 .on VeIlen EH Of .1" lProteome 1Ieo.. ~ fur publi(ation "00", [91 \>OIl CI<rwrIM B" Nutrition 20. H "~I Fig. ), The (a) romulal;';e IKinary .. ,,.lion ,utVeS of 1.J.dihy""''')'III>en)'l·2·0· .... phal. during 48 houn aft., bIad< IN iru_nlion (black) and pl<>cebo intervention ( ..... ) for an ind;';iduots (sf.s20)_ The (b) _1im:I1O",1 "'"PI'! Authors' 1 ... 1, for hippwi< acid, (.lrydrol')'llip.,..-i< acid and l,l .dilrydro""""MyI-l-O-iUlphat. 1ft., 48 bow, 'o. In twtnty individuak dislinguishing poet Ind strong metabolizers in the study group. Johan Westerlluis. £woud van Vell:en. Ikrub HoeIsloot. Age Smilde IKirle of 1.3-d i hy~xyphenyl-HJ­sulph.lte4 0VI't 48 hours ts sllown in figure la, The la'9" .",iation lhat <an be oIMrved in me excretion profil~ indica1l'S that poIyphenol metabo-lism is e>Cttemely liable 10 inter.in. di-.idual differencM. fi)r nutrition· ists thts obse!va\ion is esr.ential to iIISfSS the intended efficacy and activity 01 the nutritional dosf. ~, ts ~ pI~ ible th;!t a major oonsisten<:y in nUUikinetic rt'Sp:>ns-es also leads to a major consisten-t:; in physiological rt'SjXII'ISeS (e.g. blood pressure. cholesterol Iowef-ing etc.). Large inter· iooividual v",iabons in nUUikinetia rel-pOns-es on the other hand may be prog-mstic for l;vge .~ri~tions in physi· ological responses. In order to have a quantitative description 01 the in1er·indi-.idual differences. we es-timated the quantities of thrH pIlarrnacokinetic parameters for eadt 01 the identified biomarter~ and for each individual. ~ pa-ro>meters include (I) the Ulmulative IKinary oUtpUt a/tef 48 hours, {Ill the rate constant 01 the eocretion and (Ill) the lag time. A fjJ";t order kinetic model w'" fined to the ex· cretion Ulrvt'S to estimate the quan(ities of mese pharmacokirlet-H: palametefS [8]. Atso here. we used the crossover Sh"ucture in the data for optimal model fining through a simultaneous palarne-lerililtion of the ex~,,-," Ulrvt'S deri>'ed from the placl'bo pttiod and the treWllem period . The pIlarrnacokinetic quanti(ies derived hom the parame(erization allow distingutshing between different metabolic pIlenotypo>s. An example is given in figure 3b witere the tc>-tal output 01 hippuric add, 4-hy-droxyh ippuri<; add and 1.3-dihy-droxyphenyl-2-O-Sulpha(e ahe< 48 hours were used to distinguish be-tween poor and suong metaboliz· er .. The ability to subdivide sub--!JOUPS is the lundamental principle in personalized nutrition wh im aims for appropriate treatment and dosing to humans on the individual level (91 _ In condusion. in nUUitional in· (erVention studies whefe the treat· ment effects are typically sub~e and liable to large .anat ions be-tween ind ividuals. crossover de-signs are often used to improve the i nter~tabi l ity and informa-tion yield_ The paired sttUC1UJe in data obtained from sum designs is igoored by the default data analy· sis methods as PCA and PLSOA. Specifica lly developed mu~ilevel data analysis methods are able to separate the treatment effect ham the biologica l variation between the iooividuals and therefore can study the treatment effect in mum more deta il. Also in kinetic Slkldies a proper selection of the e>peri -mental design and the associated data analysis method may improve the esbmation 01 the ki netic pa. rametm. We demonstrated that a crosSOVer designed pharmaroki· rletic: study in combination with a Ulstomized data analysis approam is indispensable for the (un target-eel) selection 01 bioma~ as well as for the pair-wise parameter iza · lion 01 the pharmacokinetic pa-rameters. Re/ ... ne .. (I) 801ton S on<! 90n C ' Pborrno<oUli· uI Statistics ~ PrO<1iul.-od Cln-uI A;>pIiutions. 4 od.; M~ DeUe!.loc., NowYolt, 2004 I1'>srstom>o.t.~ ~ofAmlrHdim ~m.","~ rei., .3r 10 5156546 Fo>; . 31105156jn to. _r.,."i>OV>'O-nI ....... ,bdo9foup.nI Digital Imoging lor Mlcroscopy, Endoscopy (et cl.) In labs, QA cnd UfeSclence: • Pfole~onoI eQ..JIpmenl (DIgItal usa camera .. colit:<otton ~Ide) • Eosy-to-use sottwore for moge ocquIsItton. measuring cnd arc/"M'>Q (English/German. 1nCl, documentOOOO) • F~ TO stCJ'ldord C-MCU'l1 odopl= • Unbeo1ob1e poIcel . X ~ (J) X 0.. For morelnlormoHon and reltlll I50UtCes cUc k www.plJl:el·.ox.com 

Data analysis strategies in nutritional metabolomics

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Data analysis strategies in nutritional metabolomics

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