Cardiometabolic and molecular adaptations to 6-month intermittent fasting in middle-aged men and women with overweight: secondary outcomes of a randomized controlled trial

Domain: biology
Published: 2025-12-13
Editorial outcome: major_revision

Abstract
Intermittent fasting (IF) has gained attention as a potential intervention for cardiometabolic health, though its long-term effects remain unclear. In this randomized clinical trial, we assessed the impact of 6 months of IF on body composition, cardiovascular risk factors, and related molecular pathways in middle-aged (30-65 years) men and women with overweight (BMI 24.8–35 kg/m²). In this trial, 41 participants were randomized to either an intermittent fasting (IF) intervention or to maintain their habitual diet. The primary outcome (circulating CRP concentration) was previously reported; here, we present exploratory analyses focusing on metabolomic and transcriptomic responses. IF led to an 8% reduction in body weight, a 16% decrease in body fat, and significant improvements in lipid profile, including substantial reductions in plasma LDL-cholesterol, non-HDL-cholesterol, and triglycerides (p = 0.001). However, no significant changes were observed in other cardiometabolic risk factors. To investigate the underlying molecular mechanisms, we performed untargeted plasma metabolomics and transcriptomic analysis of colon mucosa biopsies. Significant multi-omic changes were identified, particularly in lipid metabolism, bile acid signaling, and enteroendocrine regulation. Notably, there was a downregulation of transcripts related to glucagon-like peptide 1 (GLP-1) and related enteroendocrine hormones. Correlation analysis highlighted key molecular pathways, with PPAR-α and B-cell-mediated immune processes significantly associated with changes in non-HDL cholesterol. Our findings extend the understanding of IF in humans beyond weight loss, providing key mechanistic insights to inform targeted therapies for improving cardiometabolic health. ClinicalTrials.gov NCT01964118

Introduction
Cardiovascular disease remains a global health concern, contributing to substantial morbidity, disability, mortality, and escalating healthcare costs. Men and women with optimal cardiometabolic health at age 50 have a 13-fold lower risk of developing cardiovascular disease compared to those with two or more abnormal risk factors, even accounting for longer life expectancy. Previous research, both observational and randomized trials, has demonstrated that daily calorie restriction with a balanced intake of nutrient-rich foods, particularly those high in vegetable fiber and vitamins, causes substantial improvements in multiple major cardiometabolic risk factors. These effects have been observed even in lean and slightly overweight young adults. However, adhering to daily calorie restriction can be challenging, highlighting the need for easier-to-implement strategies. Intermittent fasting (IF) has gained popularity for weight loss, allowing individuals to follow their regular diet on non-fasting days. However, despite promising results in rodents, clinical trials on IF in adults with overweight and obesity have shown mixed effects on cardiometabolic indicators, with one trial even reporting increased plasma LDL-cholesterol levels.

The present randomized clinical trial was designed to assess whether sustained weight loss achieved through long-term IF can replicate the cardiometabolic benefits observed with chronic calorie restriction with optimal nutrient intake in men and women with overweight and obesity, while also exploring the underlying molecular mechanisms. The primary outcome (circulating CRP concentration) was previously reported, with no significant changes in CRP. Here, we present secondary analyses focusing on metabolomic and transcriptomic responses. A key aspect of this trial is the multi-omic analysis of colon mucosa biopsies, aimed at understanding how IF impacts lipid metabolism in the colon and cardiometabolic health. The colon plays a critical role in nutrient absorption, cholesterol metabolism, and bile acid recycling – key processes in maintaining metabolic homeostasis. Molecular analysis of colon tissue can elucidate how IF modulates lipid absorption, bile acid signaling, and inflammatory responses, all of which are essential for regulating cholesterol, triglycerides, and overall cardiovascular health. Additionally, the colon’s interaction with the gut microbiota and its involvement in systemic inflammation and immune modulation make it a critical site for understanding the broader metabolic effects of IF. By uncovering these mechanisms, this study aims to provide insights into how IF influences cardiometabolic outcomes, potentially informing targeted dietary, pharmacological, and personalized metabolic strategies.

In this study, we show that six months of IF significantly reduces body weight and adiposity, leading to favourable changes in lipid profiles, but without affecting other major cardiovascular risk factors. Notably, baseline levels of PPAR-α–related transcripts predicted the magnitude of lipid improvements. Integrated multi-omic profiling of plasma and colonic mucosa revealed downregulation of GLP-1–associated enteroendocrine signalling, including reduced glucagon (GCG) and peptide YY (PYY) expression, alongside modulation of B-cell pathways. These findings extend current understanding of IF beyond weight loss, providing mechanistic insights into its cardiometabolic benefits.

Results
Studydesign
ThestudydesignisoutlinedintheMethods.Outofthe66participantsinitiallyassessedforeligibility,50wererandomized,and49begantheintervention(SupplementaryFig.1).Intheinitial6-monthphase,75%(n=21)ofparticipantsintheIFgroup(GroupA)and91%(n=20)inthecontrolgroup(GroupB)completedthestudy.Followingthisphase,18ofthe21participantswhocompletedPhase1inGroupAcontinuedtheIFinterventionforanadditional6months(GroupC),while19ofthe20participantsfromGroupBwhocompletedPhase1crossedovertoIFforthesecond6-monthperiod(GroupD).Retentionratesduringthissecondphasewerestrong,withdropoutratesof16.6%(n=3)inGroupC(participantswhocontinuedfastingforanadditional6months)and0%(n=0)inGroupD(participantswhotransitionedfromthecontrolgrouptoIFinthesecondphase).AsillustratedinSupplementaryFig.2,principalcomponentanalysis(PCA)showsanear-completeoverlapofallmeasurementslistedinTable1forGroupsAandD.Consequently,these two independent 6-month fasting groups were combined (referred to as IF-combined or Group A+D) in Tables 1–3.

Intermittentfastingleadstoasignificantreductioninbodyweightandadiposity
Participants,whoonaveragepresentedoverweightatbaseline(meanBMI 29.6±2.7kg/m2), experienced significant weight loss after 6 months of IF (p<0.0001). As shown in Table 1, the IF Group A experiencedareductionof−5.9±3.6kg,whiletheIF-combinedGroupA+D saw a loss of −7.0±3.5kg. Conversely, the control Group B exhibitedaslightweightgainof+1.1±3.2kg(p=0.17).Thesechangescorrespondedto−7%,−8%,and+1%ofbaselinebodyweights,respectively.SignificantdecreasesinwaistcircumferencewereobservedintheIFGroupA(−8.4±7.8cm;p<0.0001),theIF-combinedGroupA+D(−7.7±6.6cm;p<0.0001),whilethecontrolGroupBexperiencedaslightnon-significantwaistlinegainof+1.1±6.9cm(Table1).Additionally,allIFgroupsshowedreductionsinwhole-bodyfat,trunkfat,andleanmasscomparedtocontrols(p<0.0001).Self-reportedenergyintakesignificantlydecreasedinallIFgroups,withanaverageweeklycalorierestrictionof22.8%achievedoverthe6months.Thesefindingshighlightthesignificantimprovementsinweight,bodycomposition,andcalorieintakeachievedthroughIF.

Intermittentfastinginducesfavorablechangesinlipidprofile
Table 2highlights significantchangesinducedbyIFintotalcholesterol,LDL-C,andnon-HDL-C.Notably,non-HDL-Cexperiencedthemostsignificantreduction,withan11%decreasefrombaselineintheIF-combinedgroup(P<0.001),contrastingwithasignificantincreaseobservedinthecontrolGroupB(p=0.03).AlthoughHDL-ClevelsincreasedinbothIFGroupAandIF-CombinedGroupA+D,theseincreasesdidnotreachstatisticalsignificance.However,asignificantdecreaseinthetotalcholesteroltoHDL-CratiowasobservedinbothIFgroups(p<0.001).Remarkably,plasmaTGconcentrationsmarkedlydecreasedinbothIFGroupAandIF-CombinedGroupA+Dby19.4%and23.4%,respectively(p<0.007).Nonetheless,thetriglyceridetoHDL-Cratio,aproxymeasureofinsulinresistance,wassignificantlyreducedonlyinIF-CombinedGroupA+D(p=0.001).

Intermittentfastingdoesnotreduceothermajorcardiovascularriskfactors
Despitenotablereductionsinbodyweight,waistcircumference,andtrunkfat,IF did not affect fasting glucose, insulin, HOMA-IR, and hsCRP levels (Table 3). Similarly, no changes were observed in systolic anddiastolicbloodpressures.Additionally,bothmeanandmaximumintima-mediathicknessofthecommoncarotidarteriesremainedunchangedinbothIFGroupAandIF-CombinedGroupA+D(Table3).

Multi-omicchangesinresponsetoIF
ToinvestigatethemolecularimpactofIFoncardiometabolichealth,weconductedgeneexpressionprofiling(mRNAsequencing)ofcolorectalmucosabiopsiesanduntargetedplasmametabolomicsin27participants(14GroupA+13GroupD)whounderwent6monthsofIF.Metabolomicsanalysiswasexploratoryandthereforenotaprotocolpre-specifiedoutcome.Integratedmulti-omicdatawerecomparedbetweenbaselineand6months(SupplementaryFig.3),withsignificantchangesidentifiedusinganon-parametricWilcoxonpairedtest(p<0.05)anda≥1.1-foldchangeforgenes.After6monthsofIF,369entitieswereupregulated,including359genesand9metabolites(benzylalcohol,ornithine,7unannotatedmetabolites,andtheproteinTNF-alpha). In contrast, 393 entities were downregulated, including 387 genes and 6 metabolites (lactulose, 2-hydroxybutanoic acid, 3 unannotated metabolites, and the protein IL-6). The biological knowledge analysis of significantly changed entities was conducted using CompBio v2.6, resulting in two distinct maps representing biological processes – one for upregulated processes and another for downregulated processes. No biological themes met the significance threshold for upregulated entities (Source Data). In contrast, more than 30 interconnected biological themes were significant for downregulated entities. Full maps, along with a comprehensive list of themes, associated genes, and concepts, are available in the supplementary material (Supplementary Fig. 4). Figure 1 highlights the key findings related to the downregulated entities associated with 6-month IF. Notably, enteroendocrine signaling emerged as a central theme, connecting various sub-themes such as the synthesis and secretion of glucagon-like peptide 1 (GLP-1), a hormone crucial for appetite regulation and insulin secretion. Genes most strongly enriched in this theme, such as glucagon (GCG) and peptide YY (PYY), are highlighted in the red panel (Fig. 1). Additionally, the metabolite lactulose was mapped to this theme, showing a modest enrichment score of 7.11.

Another significantly downregulated theme, Pituitary Adenylate Cyclase-Activating Polypeptide (PACAP) receptor activity, shown in blue (Fig. 1), was strongly interconnected with the GLP-1 theme. Key genes within this theme, such as vasoactive intestinal peptide (VIP) and adenylate cyclase activating polypeptide 1 (ADCYAP1), exhibited the greatest enrichment scores. The two themes shared 20 genes, many of which are involved in the synthesis, secretion, and proteolytic cleavage of endocrine peptides. These genes include not only PYY and GCG, but also pancreatic polypeptide (PPY), somatostatin (SST), and others like ISL LIM homeobox 1 (ISL1), carboxypeptidase E (CPE), and secretagogin (SCGN). Shared genes with the greatest contributions are visualized in the map. Figure 1 also presents a concept map for the GLP-1 theme, illustrating the interconnected terms and their relationships. On one side, terms related to appetite, hunger, and satiety are linked to GLP-1, while insulin and islet function are positioned on the opposite side, with GLP-1 at the center. Figure 1b (panel below the map) provides a schematic diagram that depicts the relationship between IF and enteroendocrine function, as revealed by the multi-omics analysis.

Correlationanalysisbetweenmulti-omicdataandlipid/lipoproteinchanges
Statisticallysignificantchangesinmultiplelipidandlipoproteinparameterswereobservedbetweenbaselineand6months,asdetailedinTable2.Toexplorethemolecularpathwayspotentiallylinkedtothese lipid-related changes, we performed correlation analysis between changes in entity expression/abundance and non-HDL cholesterol (non-HDL-C) levels over the 6-month IF period. Using non-parametric Spearman correlation analysis (r≥0.4 with p value<1.0E-9), we identified 280 entities positively correlated and 329 entities negatively correlated with plasma non-HDL-C levels. The CompBio knowledge map for entities positively correlated with non-HDL-C (Fig. 2) revealed a broad range of interconnected processes, including lipid metabolism regulation by PPAR alpha (PPAR-α), protein lipidation in autophagosome assembly, brush border-enterocyte transports, and unmethylated CpG binding. Genes showing the greatest enrichment scores within these themes included peroxisome proliferator-activated receptor PPAR-α (r=0.55, p=2.22E-14), sequestome 1 SQSTM1 (r=0.41, p=3.16E-10), myosin 7b MYO7B (r=0.46, p=1.08E-11), and methyl CpG binding domain 1 MBD1 (r=0.41, p=2.89E-10).

In contrast, the CompBio knowledge map for entities negatively correlated with non-HDL-C (Fig. 3) unveiled a significant cluster of seventeen B-cell and immune-based themes, including isotype switching, dendritic cells in Th1 and Th2 regulation, and CCR5 chemokine receptor binding. Highly enriched genes within these themes included CD79a (r=−0.51 p=3.05E-13), Bruton tyrosine kinase BTK (r=−0.52, p1.35E-13), complement receptor CR1 (r=−0.4, p=4.55E-10), Fcepsilon receptor 1 FCER2 (r=−0.42, p=9.89E-11), immunoglobulin heavy constant alpha 1 IGHA1 (r=−0.43, p=6.37E-11) and immunoglobulin heavy constant gamma 1 IGHG1 (r=−0.41, p=2.02E-10). Full maps, along with a complete list of the identified biological themes and associated genes, are provided (Source Data, Supplementary Figs. 5 and 6). Further analysis of the relationship between B-cell gene expression and changes in non-HDL-C revealed interesting dynamics. A median B-cell profile, constructed from seven marker genes (i.e., CD23, CD79A, CD79B, CD19, NUGGC, BTK, and CD27), showed a negative correlation with the non-HDL-C ratio (r=−0.56, p=0.0021). This suggests that as the non-HDL-C ratio decreases during IF, the B-cell profile tends to increase for most participants (Fig. 3). However, a small subset of individuals exhibited an inverse correlation, suggesting differential immune responses to lipid changes induced by IF. Interestingly, maps generated for triglyceride changes exhibited similar themes to those observed for non-HDL-C, though these did not reach statistical significance. As a result, all comparisons were made using non-HDL-C as the primary lipid parameter of interest.

EffectofbaselineprofileonIF-inducedmulti-omicchangesinnon-HDLcholesterol
Variabilityinnon-HDL-Clevelchangeswasobservedamongparticipants overthe6-monthIFintervention(SupplementaryFig.7).Toexplorethemolecularbasisofthisvariability,wecorrelatedbaselinegeneexpressionwithchangesinnon-HDL-Clevels(6-monthtobaselineratio).Usingnon-parametricSpearmancorrelationanalysis(r≥0.4withpvalue<1.0E-9), we identified 347 genes positively correlated and 535 negatively correlated with non-HDL-C. These genes underwent analysis using CompBio v2.6 for biological knowledge mapping, revealing significant insights, particularly in the negatively correlated map. Three main clusters emerged, linked to adherens junctions and signaling, PKA signaling and cargo transport, and epigenetic modulation (Fig. 4). Full maps, along with a comprehensive list of identified biological themes and associated genes, are available (Source Data, Supplementary Fig. 8). At the core of these clusters is the regulation of lipid metabolism by the PPAR-α theme, comprising 42 entities and 32 concepts. Within each cluster, graph panels highlight genes with the strongest enrichment for representative themes. As expected, the PPAR-α theme demonstrated strong enrichment for PPAR-α (r=−0.56, p=1.13E-14) and UCP3 (r=−0.41, p=2.76E-10). Given the robust correlation between baseline PPAR-α expression and non-HDL cholesterol changes, we performed a more detailed investigation into individual participant-level alterations. Figure 4 shows the relationship between each participant’s baseline PPAR-α expression (log2 CPM) and their corresponding non-HDL-C ratio (6-month to baseline). Remarkably, as baseline PPAR-α expression increased during IF, the non-HDL cholesterol ratio decreased, indicating a reduction in non-HDL-C levels over the 6 months compared to baseline. The summary of findings is presented in Fig. 5.

Methods
Participants
ThestudywasapprovedbytheHumanStudiesCommitteeandtheGeneral Clinical Research Center Scientific Advisory Committee at Washington University School of Medicine (IRB#201303081), conductedinaccordancewiththeDeclarationofHelsinki.Allparticipantsprovidedwritteninformedconsent.ThetrialisregisteredatClinicalTrials.gov(NCT01964118).Fiftymen(n=20)andwomen(n=30)aged30–65years,withaBMIof24.8–35kg/m2,wererecruitedfromtheSt.Louismetropolitanarea.Participantswereweight-stable(<2kgchangeover ≥3 months),consumed a Western diet, and were nonsmokers. Individuals exercising more than twice a week or with conditions affecting study interpretation were excluded. None had conditions that could affect study interpretation. Baseline characteristics are detailed in Supplementary Table 1. Adherence, primary outcomes (hsCRP), and findings on glucose tolerance and insulin sensitivity have been previously published.

Studydesign
At baseline, all participants underwent a comprehensive medical examination,routinebloodtests,andanelectrocardiogram.Fiftyeligible participants (30 women, 20 men) were randomly assigned to eithertheIFgroup(A)orthenon-interventioncontrolgroup(B)for6months.AsshowninSupplementaryFig.1(CONSORTdiagram),49participants began the intervention. After completing the 6-month study,18participantsfromtheIFgroupcontinuedtheinterventionforanadditional6months(groupC),while19participantsinitiallyrandomizedtothecontrolgroupcrossedovertoIFfor6months(groupD).Allparticipantswereincludedinallexperimentsunlessotherspecified.

Intervention
ThisweightlosstrialaimedtoreduceweeklyenergyintakethroughIF whilemaintainingregularfoodintakeonnon-fastingdays.ParticipantswithaBMIof24to27.9kg/m2fastedfor2non-consecutivedaysperweek,whilethosewithaBMIbetween28and35kg/m2fastedfor3non-consecutivedaysperweek.OnceBMIdroppedbelow28,fastingfrequency was reduced to 2 days per week. On fasting days, research volunteers consumed only non-starchy vegetables (raw or cooked) withupto2tablespoonsofoliveoil,vinegar,orlemondressing(lessthan500kcal/day),facilitatingcompliancewithouttheneedforcalorie counting. Non-caloric beverages such as black coffee, unsweetened tea, or zero-calorie soda were permitted. IF participants met monthlywithdietitiansforweighttrackinganddietaryguidance,withcompliancemonitoredthroughweeklyphonecallsandrecordedbodyweights. Self-reported food intake was assessed using a 4-day food diary, analyzed via the Nutrition Data System for Research (NDS-R, versions 2013–2015, University of Minnesota). The control group continuedtheirregulardietwithoutdietaryinterventionorcounseling. Both groups were instructed to maintain their usual physical activitylevelsthroughoutthestudy.

Anthropometricsandbodycomposition
Heightandwaistcircumferenceweremeasuredtothenearest0.1cm,whilebodyweightwasrecordedinduplicateusingacalibratedscale(Scale-Tronix,WelchAllyn,Inc.)afteranovernightfast,withparticipantswearingahospitalgownandnoshoes.IFparticipantsreceiveddigitalscales(HD-357,TanitaCorporationofAmericaInc.,Illinois)forweeklyat-homeweighttracking,withmeasurementsregularlyreportedtostudydietitians.Bodycompositionwasassessedviadual-energyX-ray absorptiometry (DXA, Lunar iDXA, software version 13.31, GE Healthcare,Madison,WI).

Bloodanalysis
Venousbloodsampleswerecollectedafteranovernightfasttomeasure lipid, metabolite, and hormone concentrations. To minimize potential confounding effects from prolonged overnight fasting, bloodsampleswereconsistentlycollectedafteranovernightfast,butthreedaysfollowingtheendofthefastingperiod.Plasmalipidandlipoprotein-cholesterol concentrations were measured in the Core Laboratory for Clinical Studies at Washington University by techniciansblindedtotreatmentconditionsandsampleidentities.Cholesterol and glycerol-blanked triglycerides (TG) were measured by automated enzymatic commercialkits (Miles-Technicon, Tarrytown, NY). High-density lipoprotein cholesterol (HDL-C) was assessed in plasmaafterprecipitationofapolipoproteinB-containinglipoproteinsbydextransulfate(50,000MW)andmagnesium.Thesemethodsarecontinuouslystandardized by the Lipid Standardization Program of theCentersforDiseaseControlandPrevention.Plasmaglucosewasmeasuredviatheglucoseoxidasemethod(YSIInstruments,Fullerton,CA), and insulin was quantified by radioimmunoassay. Insulin resistancewascalculatedusinghomoeostasismodelassessment(fasting glucose [mmol/L]×fasting insulin [mIU/L]/22∙5). High sensitive C-reactiveprotein (hsCRP)was measured using aparticle enhanced immunoturbidimetric essay with Roche reagents on the Roche cobasc501.

Bloodpressuremeasurements
Blood pressure was assessed in the morning after a 12-h fast with participantsinaseatedpositionusinganoscillometricbloodpressure monitor (SureSigns VM4, Philips Medical System, Andover, MA). Standardizedprotocolswerestrictlyfollowed,withregularmonitoringto ensure adherence. Briefly, after a 5-min rest, two consecutive readings were taken from each arm, with a 2-min interval between measurements. The final blood pressure value was recorded as the averageofallfourreadings.

Ultrasonographicmeasurementofcarotidintima–mediathickness
High-resolutionB-modeultrasonographywithan11-MHztransducer wasused to assesscarotidintima–media thickness(IMT).A trained sonographer, blinded to participant data, performed all scans and analyses.Inbrief,boththerightandleftcommoncarotidarterieswereimagedlongitudinallyovera30mmsegmentofthedistalartery.IMT was measured in the anterior wall (from the trailing edge of the adventitiatotheleadingedgeoftheintima–media)andtheposteriorwall(fromtheleadingedgeoftheintima–mediatothetrailingedgeofthe adventitia). The mean and maximal IMT values were calculated fromanaverageof16measurementsforeachparticipant.

Colonmucosabiopsyandgeneexpressionanalyses
Endoscopic cold biopsy specimens were obtained from macroscopically normal sigmoid mucosa using a flexible video sigmoidoscope (Olympus OSF V60) after an overnight fast and enema preparation. The biopsies were performed on a subgroup of 14 IF participants(8womenand6men)and14controls(9womenand5men)atbaseline,6,and12months,withonecontrolparticipantnotcompletingthefollow-upsigmoidoscopy(13controlsincludedintheanalysis).Ten tissue samples were collected using single-usebiopsy forceps(RadialJaw4,BostonScientific),washedinPBS(LifeTechnologies,Carlsbad,CA,USA),flash-frozeninliquidnitrogen,andstoredat−80°C until analysis. RNA was extracted using Trizol reagent (Invitrogen,Carlsbad,CA,USA)bytechniciansattheTissueProcurementCoreofWashingtonUniversity.RNAsequencingandbioinformatics wereconductedattheGenomicTechnologyAccessCenter(GTAC)ofWashingtonUniversity. Library preparation followed the manufacturer’sprotocol,andsamplesweresequencedonanIlluminaHiSeq. Base calling and demultiplexing were performed using Illumina’s bcl2fastqsoftwareandacustomPythondemultiplexingprogramwithamaximumofonemismatchintheindexingread.RNA-seqreadswerealignedtotheEnsemblrelease76top-levelassemblyusingSTARversion2.0.4b.GenecountswereobtainedfromuniquelyalignedreadsusingSubread:featureCountversion1.4.5,andisoformexpressionwasestimatedwithSailfishversion0.6.13.Sequencingperformance wasassessedbythenumberofalignedreads,uniquelyalignedreads,andfeaturesdetected,withqualitymetricssuchasribosomalfractionandreaddistributionovergenemodelsquantifiedusingRSeQCversion 2.3. All gene counts were imported into the R/Bioconductor packageEdgeR andTMMnormalizationtoadjustfordifferencesinlibrary size. Genes or transcripts notexpressed in any sample were excludedfromfurtheranalysis.TheTMMsizefactorsandthecountmatrixwerethenimportedintotheR/BioconductorpackageLimma. Weightedlikelihoodsbasedontheobservedmean-varianceshipwerethenderivedforeachgeneusingVoom.Theperformance ofallgeneswasassessedbyplottingtheresidualstandarddeviationofeverygeneagainsttheaveragelog-countwitharobustlyfittedtrendlineoftheresiduals.Differentialexpression(geneswithmedianCPM of0.1),proteins,andmetaboliteswascalculatedbetweenthebaseline and6monthstimepointsusingapairedWilcoxontest.Entitieswithp<0.05andaratio>1.1or<0.9wereselectedforfurtheranalysis.FDR/multipletestingcorrectionwasnotapplied,astheCompBioplatform, designed to eliminate biological noise, uses nominal p-values for generatingspecificmolecularentitylists.GeneCPMratios(6monthstobaseline)werecalculatedforeachsubjectandusedforSpearman correlationanalysiswithnon-HDLC(R≥0.4;p<8.0E-10).

Plasmametabolomics
Approximately30µLofEDTAplasmawasanalyzedattheUniversityofCalifornia,Davis,forprimarymetaboliteprofilingusinggaschromatography/time-of-flight (GC-TOF) mass spectrometry. Analyses were performedonaGerstelCIS4systemwithdualMPSinjectorcoupledto an Agilent 6890 GC and Pegasus III TOF-MS, as previously described. ThemetabolomicsworkflowfollowedestablishedprotocolsfromMetaboliteProfilinginBloodPlasma(Fiehn,2006)andMetabolomics:Methods and Protocols (Fiehn, 2016). Metabolomics data have been depositedandarepubliclyaccessibleintheMetabolomicsWorkbench(MetabolomicsWorkbench:Home)undertheprojecthttps://doi.org/10.21228/M8HR9J.

Statisticalanalysis
Allparticipantswhoprovidedbothbaselineand6-monthdatawereincluded in the analyses. Baseline characteristics were compared betweengroupsusingtestsorFisher’sexacttestforcategoricalvariables, and analysis of variance for continuous variables. Analysis of covariancewasusedforbetween-groupcomparisonsofdatacollectedatbaselineandat6months,withthe6-monthvalueasthedependentvariable and the baseline value as the covariate. When the overall model was significant, Tukey’s adjusted pairwise comparisons of groups were performed. Paired t-tests were used for within-group comparisons.Pairwisewithin-group comparisons withineachgroup andcomparisonsacrossgroupswithclassicalcontrolswereperformed using the Wilcoxon test and Mann–Whitney U test, respectively. Spearman’scorrelationwasusedtoexaminerelationshipsintheIFandcontrolgroupsamongchangesinphenotypicvariablesandchangesinmolecularmarkers. Allstatisticaltestsweretwo-tailed,andsignificancewasaccepted atp<0.05.Dataareexpressedasmean±SD,unlessindicatedotherwise.AllanalyseswereperformedusingRversion4.1.1(2021-08010) andGraphPadPrism9(version9.2.0(283)).

CompBioanalysis
Lists of molecular entities(genes/transcripts, proteins, metabolites) identifiedasdifferentiallyexpressedacrossdietaryconditionsorcorrelatedwithlipidparameters,fromthedescribedstatisticalanalysis, werefurtheranalyzedusingtheCompBioplatformV2.6(GTAC@MGI, WashU School of Medicine, https://gtac-compbio-ex.wustl.edu—Academic/Non-Profit). CompBio is an artificial intelligence platform designedtointerpretmulti-omicsdatathroughasystematicanalysis ofliteraturerelatedtoagivenlistofmolecularentities(genes,proteins,miRNAs,ormetabolites).CompBioinitiallycreatesabiologicalknowledgebasefromtheinformationcontainedwithinPubMed (>33millionabstracts,>3millionfull-textarticles)usingnaturallanguageprocessingandconditionalprobability.Theplatformevaluatestheprovidedmolecularentitiesagainstthisknowledgebasetoidentify statisticallyenrichedbiologicalconceptsassociatedwiththem.These conceptsarefurtheranalyzedforcontextuallyrelevantassociationsto form biological themes, representing pathways and processes. For interpretability, these themes and their interrelationships are displayed asan interactive 3-dimensionalknowledge map. Themes are depicted as spheres, with size and rank determined by the overall absolute enrichment observed between the concepts and entities contained within them. Closely related themes are generally positioned proximally and may share edges, with edge thickness indicatingthenumberofsharedgenesbetweentheadjoinedthemes. Normalized enrichment scores (NES) and empirical p values are derived by comparing the absolute enrichment score of a ranked themetothemesofthesamerankfromthousandsofrandomizeddatasets ofsimilarsizetotheusersinputlist.Giventheholisticnatureof CompBio analysis, stringent fold change or p-value cutoffs are not requiredtoeliminaterandomnoisewithintheinputlist.Significant themes(NormalizedEnrichmentScore>1.2andpvalue<0.1)canbe reviewed for biological interpretation. The annotation of themes is also a fully contextualized process. In short, CompBio assesses the most relevant annotations for each theme based not only on the conceptsandtheirassociatedenrichmentswithinthattheme,butalso withinthecontextofotherrelatedconceptsinadjoiningthemes.This ensuresafullexperimentawarenessduringtheautomatedannotation process.Furthermore,sincetheassemblyofthethemesmayidentify multiplespecificcomponentsofcloselyrelatedbiologicalprocesses, eachthemecanbeidentifiedbyuptothreebiologicallabelstocapture theserelatedcomponentscomprehensively.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study by Barve highlights body weight and adiposity, metabolomic, and molecular adaptations from IF despite lack of physiological changes in cardiometabolic function. The study is well written and rich in novel data.\nThe major limitation is that age and sex are not taken into consideration in the analysis. I would like to know if either is a significant variable. Moreover, it is unclear if and what % of women are pre or post-menopause. IF in women may lead to a substantially different responses compared to men due to changes in cycling hormones.\nIs the untargetted metabolomics data deposited?", "reviewer_2": "The study concept was novel and manuscript was well written. However, there are several concerns regarding statistics analysis:\n1.Based upon the study protocol, the sample size calculation was based on inflammatory marker (C-reactive protein): the sample size of N=25 for each group seems calculated using two-tailed t-test (power=0.8,alpha error rate 0.05). However, the final sample size that can be used for outcomes changes (6 month-baseline) was only 21 for A group (IF) and 20 for B group (control). It means the final analysis was underpowered.\n2.As in the protocol, the primary objective of this study is to determine whether or not IF reduces the level of chronic inflammation as evidenced by a decrease in high sensitive C-reactive protein (hsCRP), as the main outcome measure. In addition, the sample size calculation was based upon hsCRP as well. But the manuscript seemed to have little discussion about hsCRP results (although result not statistically significant).\n3.In table 1, the third column is the group A+D (sample size was noted as 41). Based on line 90-91, 20 out of 22 participants from control group B crossed over to IF for the second 6 months (group D). Then 5% dropout from group D and n=15 was analyzed at month 12. Since both baseline and follow-up were needed to calculate the delta change, therefore, the actually sample size would be only 15 in D group?", "reviewer_3": "Intermittent fasting (IF) exerts beneficial effects in experimental models, whose application for clinical therapy is one of the emerging interests. In this manuscript, the authors designed RCT trial to evaluate the physiological impact of 6-month IF. Participants with overweight (BMI 24.8–35 kg/m²) at middle-aged (30-65 years) were randomly assigned to IF or usual diet. The populations in IF displayed reduction in body weight and body fat, 8% and 16% respectively, accompanied with improvements in lipid profile, including substantial reductions in plasma LDL, non-HDL-cholesterol, and triglycerides. By multi-omics approach, including untargeted plasma metabolomics and transcriptomic analysis of colon mucosa biopsies, the authors identified the significant changed profiles in lipid metabolism, bile acid signaling, glucagon-like peptide 1 (GLP-1), and enteroendocrine hormones. Moreover, non-HDL cholesterol significantly correlated with PPAR-α and B-cell-mediated immune processes. These finding disclosed the novel aspects of human IF, beyond weight loss. However, several flaws in the study make the reviewer difficult to interpret the data properly. The reviewer shows several concerns, which should be resolved. Otherwise, the manuscript is not acceptable for the journal.\nMajor points\n# The manuscript lacks the conclusive interpretations based on their findings. For example, in the abstract (line 46-47), the authors described that “Notably, upregulation of glucagon-like peptide 1 (GLP-1) and associated enteroendocrine hormones was observed.”\nHowever, in line 264-267 of the discussion, the authors concluded oppositely as follows; “In our study, analysis of multi-omic changes in human colon biopsies before and after IF revealed a notable downregulation of themes associated with enteroendocrine function and signaling. Particularly intriguing was the downregulation of the theme related to GLP-1 synthesis and secretion.”\nThus, the manuscript is not readable for the reviewer and general readership. This is partly due to the lack of proper information in figures. The reviewer cannot understand whether the face values of the data in Figures are increased or decreased. Figures should present the data properly.\n# The beneficial effects of the short-term fasting have been controversial. The previous study by Kim K B Clemmensen et al suggested that the longer duration of overnight fasting was associated with a worsening of glucose tolerance and increased incretin response to oral glucose, based on data from the ADDITION-PRO study, where 2082 individuals attended a health examination including an OGTT (Endocr Connect. 2020 Mar 11;9(4):279–288. doi: 10.1530/EC-20-0009). In contrast, here, the authors identified the beneficial effects of the long-term IF; reduction in body weight, BMI, and LDL levels. However, in the manuscript, no scientific data is described or presented about the comparison of short-term and long-term IF. Suck lack of essential information makes the readers very difficult to interpret the manuscript.\n# It is well known that the increased fasting plasma total GLP-1 concentrations is predictive of higher CMR (Cardiometabolic Risk) factors in children and adolescents with overweight/obesity, as the study by Sara E Stinson et al reported (J Clin Endocrinol Metab. 2021 ;106(6):1718-1727. doi: 10.1210/clinem/dgab098). Thus, one of the most concerns is whether serum GLP-1 protein levels were downregulated or upregulated after 6-month IF, which is not described in the manuscript. This also makes the readers difficult to interpret the manuscript.\n# Interestingly, it has been reported that the modestly positive effect of medium-term IF could not persist for longer period. Ramadan intermittent fasting for about one month induced the downregulation of GLP-1 levels, although its levels returned to the basal level of pre-study period at 21 days after Ramadan (Int J Environ Res Public Health. 2020 ;17(15):5600. doi: 10.3390/ijerph17155600). On the contrary, the authors observed the beneficial effect of the long-term IF, which might be induced and maintained by epigenetic modulation, altered immune response, or tissue reorganization. The findings in Fig2-3 might support such possible notion. The comparison of omics study between short-term and long-term IF would give a clue to understand how the beneficial effect of long-term IF is mechanistically established.\n# As supplemental data (555939_0_data_set_9849389_smnjkq) file suggests, untargeted plasma metabolomics in this study disclosed significant changes in several metabolites, which are not explained in the text; 2-hydroxybutanoic acid, benzylalcohol, citrulline, glycine, serine, and tocopherol alpha- (vitamin E). Please explain why these specific compounds, including vitamins, were affected by long-term IF. Is this due to the change of food preference during the study, as vitamin is also reduced in IF group? Or IF groups were not sufficiently served with the vitamin-rich food? Why citrulline, glycine, or serine is affected in IF?\n＃The authors utilized CompBio v2.6 analysis to integrate the omics data, including metabolome. On page 5 line 140-141, the authors described that “These entities underwent biological knowledge analysis utilizing CompBio v2.6, generating two separate maps.”\nBut the reviewer can not fully understand how any relevant metabolite, e.g. citrulline, glycine, serine, or else, are integrated to the pathway analysis, as they are missing in Figures. Please explain and present the data in Figures, relevant to metabolome analysis.\nMinor points\n# Figure 4 is duplicated by unknown reason. Figure should be presented properly.\n# On page 5 line 137, “For genes, an additional criterion of a ratio ≥ 1.1-fold change was applied.” is not acceptable. Usually, over 2.0 folds change is acceptable as the significant values. Please explain properly the methods of the protocol.\n# On page line 138-139, the authors described that “we identified a total of 369 upregulated entities (359 genes, 9 metabolites, 1 protein) and 393 downregulated entities (387 genes, 6 metabolites, and 1 protein)”. Thus, the authors identified the positive results of 15 metabolites in total. However, in the file of the metabolome dataset (555939_0_data_set_9849389_smnjkq), parts of the metabolites are described as only numbers. The reviewer speculates that the peaks of these metabolites are still not assigned properly. Please describes the metabolites name properly as positive data. Otherwise, the total hit number should be described properly, by presenting which metabolites are assigned or not.\n# The reviewer cannot understand whether the face values of the data in Figures are increased or decreased. Figures should present the data properly."}, "author_response": "We thank the reviewers for their valuable comments and feedback. We believe that these suggestions will greatly improve the manuscript. We have provided a point-by-point response to each reviewer.\nReviewer #1\nThe study by Barve highlights body weight and adiposity, metabolomic, and molecular adaptations from IF despite lack of physiological changes in cardiometabolic function. The study is well written and rich in novel data.\nResponse: Thank you for your thoughtful review and for recognizing the strengths of our study. We truly appreciate your positive feedback on the quality of our work and the novelty of our findings.\n1. The major limitation is that age and sex are not taken into consideration in the analysis. I would like to know if either is a significant variable. Moreover, it is unclear if and what % of women are pre or post-menopause. IF in women may lead to a substantially different responses compared to men due to changes in cycling hormones.\nResponse: Thank you for your thoughtful comment. While age and sex are important considerations, our study was not designed or powered to assess their independent effects on responses to intermittent fasting. With a relatively small sample size, any analysis of these variables would lack the statistical strength needed for meaningful conclusions. Additionally, while we did not collect data on menopausal status, we acknowledge its potential relevance. We appreciate your suggestion and are currently designing a larger randomized controlled trial to specifically address these questions and better understand the influence of age, sex, and menopausal status on cardiometabolic responses to intermittent fasting.\n2. Is the untargetted metabolomics data deposited?\nResponse: Thank you for your question. The untargeted metabolomics data is provided as supplementary material for reference.\nReviewer #2\nThe study concept was novel and manuscript was well written. However, there are several concerns regarding statistics analysis:\n1. Based upon the study protocol, the sample size calculation was based on inflammatory marker (C-reactive protein): the sample size of N=25 for each group seems calculated using two-tailed t-test (power=0.8,alpha error rate 0.05). However, the final sample size that can be used for outcomes changes (6 month-baseline) was only 21 for A group (IF) and 20 for B group (control). It means the final analysis was underpowered.\nResponse: Thank you for your thoughtful feedback and for emphasizing this crucial aspect of our sample size calculation for C-reactive protein. To clarify, our analysis was conducted using two independent cohorts, which were later combined for the final assessment. This resulted in a total of 40 participants who initiated and completed the 6-month IF intervention (21 in Group A and 19 in Group D). Our total sample size remains well-aligned with the power calculation outlined in our study protocol, ensuring that the statistical power remains within an acceptable range for detecting meaningful differences.\n2. As in the protocol, the primary objective of this study is to determine whether or not IF reduces the level of chronic inflammation as evidenced by a decrease in high sensitive C-reactive protein (hsCRP), as the main outcome measure. In addition, the sample size calculation was based upon hsCRP as well. But the manuscript seemed to have little discussion about hsCRP results (although result not statistically significant).\nResponse: We apologize for any confusion regarding the discussion of hsCRP results. We would like to clarify that findings related to hsCRP and other cytokines (primary outcome) were previously published in our earlier paper, which we have now appropriately cited in the current manuscript (Previous Ref 33, now Ref 34): https://agingcelljournal.org/Archive/Volume3/when_a_calorie_is_not_a_calorie_metabolic/agingbio.20230013.pdf\nThe current paper presents secondary outcomes.\n3. In table 1, the third column is the group A+D (sample size was noted as 41). Based on line 90-91, 20 out of 22 participants from control group B crossed over to IF for the second 6 months (group D). Then 5% dropout from group D and n=15 was analyzed at month 12. Since both baseline and follow-up were needed to calculate the delta change, therefore, the actually sample size would be only 15 in D group?\nResponse: Thank you for your thoughtful feedback. To clarify, out of the 66 participants initially assessed for eligibility, 50 were randomized, and 49 began the intervention (Supplementary Figure 1). In the initial 6-month phase, 75% (n=21) of participants in the IF group (Group A) and 91% (n=20) in the control group (Group B) completed the study. Following this phase, 18 of the 21 participants who completed Phase 1 in Group A continued the IF intervention for an additional 6 months (Group C), while 19 of the 20 participants from Group B who completed Phase 1 crossed over to IF for the second 6-month period (Group D). Retention rates during this phase were strong, with dropout rates of 16.6% (n=3) in Group C (participants who continued fasting for an additional 6 months) and 0% (n=0) in Group D (participants who transitioned from the control group to IF in the second phase). As illustrated in Supplementary Figure 2, principal component analysis (PCA) shows a near-complete overlap of all measurements listed in Table 1 for Groups A and D. Consequently, these two independent 6-month fasting groups were combined (referred to as IF-combined or Group A+D) in Tables 1–3.\nHowever, we identified an error in Tables 1–3. A total of 21 participants completed the IF intervention during the first 6 months (Group A), and 19 participants crossed over from the control group to the IF group in the second phase (Group D), completing the study and being included in the statistical analysis. Therefore, the total number of participants who underwent 6 months of IF (Groups A+D) is 40. We have corrected this error and updated the result and method sections of the manuscript accordingly.\nReviewer #3\nIntermittent fasting (IF) exerts beneficial effects in experimental models, whose application for clinical therapy is one of the emerging interests. In this manuscript, the authors designed RCT trial to evaluate the physiological impact of 6-month IF. Participants with overweight (BMI 24.8–35 kg/m²) at middle-aged (30-65 years) were randomly assigned to IF or usual diet. The populations in IF displayed reduction in body weight and body fat, 8% and 16% respectively, accompanied with improvements in lipid profile, including substantial reductions in plasma LDL, non-HDL-cholesterol, and triglycerides. By multi-omics approach, including untargeted plasma metabolomics and transcriptomic analysis of colon mucosa biopsies, the authors identified the significant changed profiles in lipid metabolism, bile acid signaling, glucagon-like peptide 1 (GLP-1), and enteroendocrine hormones. Moreover, non-HDL cholesterol significantly correlated with PPAR-α and B-cell-mediated immune processes. These finding disclosed the novel aspects of human IF, beyond weight loss. However, several flaws in the study make the reviewer difficult to interpret the data properly. The reviewer shows several concerns, which should be resolved. Otherwise, the manuscript is not acceptable for the journal.\nMajor points:\n1- The manuscript lacks the conclusive interpretations based on their findings. For example, in the abstract (line 46-47), the authors described that “Notably, upregulation of glucagon-like peptide 1 (GLP-1) and associated enteroendocrine hormones was observed.” However, in line 264-267 of the discussion, the authors concluded oppositely as follows; “In our study, analysis of multi-omic changes in human colon biopsies before and after IF revealed a notable downregulation of themes associated with enteroendocrine function and signaling. Particularly intriguing was the downregulation of the theme related to GLP-1 synthesis and secretion.” Thus, the manuscript is not readable for the reviewer and general readership. This is partly due to the lack of proper information in figures. The reviewer cannot understand whether the face values of the data in Figures are increased or decreased. Figures should present the data properly.\nResponse: We sincerely apologize for the confusion and appreciate your careful review of our manuscript. Thank you so much for bringing this discrepancy to our attention. The mention of GLP-1 upregulation in the abstract was an oversight; it should have been described as downregulated that is consistent with our findings presented in the result and discussion sections. We have now corrected this in line 46 on page 2 to ensure clarity and coherence throughout the text. Additionally, we recognize the importance of clear data presentation. To improve readability and interpretation, we have updated the figure legends to explicitly clarify that the map is based on downregulated entities (page 19, line 608). We truly appreciate your valuable feedback and have taken these steps to enhance the manuscript’s clarity for both reviewers and the broader readership.\n2- The beneficial effects of the short-term fasting have been controversial. The previous study by Kim K B Clemmensen et al suggested that the longer duration of overnight fasting was associated with a worsening of glucose tolerance and increased incretin response to oral glucose, based on data from the ADDITION-PRO study, where 2082 individuals attended a health examination including an OGTT (Endocr Connect. 2020 Mar 11;9(4):279–288. doi: 10.1530/EC-20-0009). In contrast, here, the authors identified the beneficial effects of the long-term IF; reduction in body weight, BMI, and LDL levels. However, in the manuscript, no scientific data is described or presented about the comparison of short-term and long-term IF. Suck lack of essential information makes the readers very difficult to interpret the manuscript.\nResponse: We sincerely appreciate your thoughtful feedback and the opportunity to clarify this important point. In our previously published manuscript (https://doi.org/10.59368/agingbio.20230013), we have reported the effects of 6-months IF intervention. Our findings showed an 8% reduction in body weight and a 16% decrease in body fat (measured by DXA). However, we did not observe significant changes in hsCRP (the primary outcome) or other cytokines, and minor improvements in OGTT glucose tolerance or insulin sensitivity—results that were unexpected and contrary to our original hypothesis.\nIn contrast, the current study demonstrates that 6-months IF is associated with significant improvements in lipid profiles, including notable reductions in plasma LDL, non-HDL cholesterol, and triglycerides. To minimize the potential confounding effects of prolonged overnight fasting, blood samples were consistently collected after an overnight fast, but three days following the end of the fasting period. Unfortunately, blood and tissue samples were not collected at the 1-month mark, so we are unable to provide data on the effects of short-term IF.\nTo enhance clarity and provide readers with the necessary context, we will ensure that our previous findings and the work by Clemmensen et al. are appropriately referenced and discussed in the manuscript. We have incorporated these revisions accordingly (lines 283-285 and lines 347-349). Thank you again for your valuable insights.\n3- It is well known that the increased fasting plasma total GLP-1 concentrations is predictive of higher CMR (Cardiometabolic Risk) factors in children and adolescents with overweight/obesity, as the study by Sara E Stinson et al reported (J Clin Endocrinol Metab. 2021 ;106(6):1718-1727. doi: 10.1210/clinem/dgab098). Thus, one of the most concerns is whether serum GLP-1 protein levels were downregulated or upregulated after 6-month IF, which is not described in the manuscript. This also makes the readers difficult to interpret the manuscript.\nResponse: We appreciate your insightful comment and the opportunity to clarify this point. In this study, we did not measure serum GLP-1 concentrations. Instead, our analysis focused on gene expression changes within the colonic mucosa. While we recognize the importance of circulating GLP-1 levels in relation to cardiometabolic risk, our findings specifically pertain to tissue-level gene expression, which may reflect local regulatory mechanisms rather than systemic hormone levels. To enhance clarity and interpretation, we have now referenced the work of Stinson et al. and discussed its relevance in the manuscript (page 9, lines 268-270). Thank you for your valuable feedback.\n4- Interestingly, it has been reported that the modestly positive effect of medium-term IF could not persist for longer period. Ramadan intermittent fasting for about one month induced the downregulation of GLP-1 levels, although its levels returned to the basal level of pre-study period at 21 days after Ramadan (Int J Environ Res Public Health. 2020 ;17(15):5600. doi: 10.3390/ijerph17155600). On the contrary, the authors observed the beneficial effect of the long-term IF, which might be induced and maintained by epigenetic modulation, altered immune response, or tissue reorganization. The findings in Fig2-3 might support such possible notion. The comparison of omics study between short-term and long-term IF would give a clue to understand how the beneficial effect of long-term IF is mechanistically established.\nResponse: Thank you for your insightful observation and for highlighting this interesting perspective. Unfortunately, we do not have data from a 1-month time point to directly compare the short-term effects of intermittent fasting. Our measurements were conducted at 6 and 12 months, with a focus on the longer-term impacts of IF. We agree that comparing omics data between short-term and long-term IF could provide valuable insights into the mechanisms driving sustained metabolic benefits, including potential contributions from epigenetic modulation, immune responses, or tissue reorganization. This is an important avenue for future research, and we will acknowledge this limitation in the manuscript. We truly appreciate your thoughtful feedback and the opportunity to refine our discussion (page 9, lines 283-284).\n5- As supplemental data (555939_0_data_set_9849389_smnjkq) file suggests, untargeted plasma metabolomics in this study disclosed significant changes in several metabolites, which are not explained in the text; 2-hydroxybutanoic acid, benzylalcohol, citrulline, glycine, serine, and tocopherol alpha- (vitamin E). Please explain why these specific compounds, including vitamins, were affected by long-term IF. Is this due to the change of food preference during the study, as vitamin is also reduced in IF group? Or IF groups were not sufficiently served with the vitamin-rich food? Why citrulline, glycine, or serine is affected in IF?\nResponse: Thank you for reviewing our manuscript with such a high level of attention. We sincerely appreciate the reviewers' insightful comments and the opportunity to clarify these findings. As part of the study design, participants maintained their usual Western diet on feasting days while consuming only non-starchy raw or cooked vegetables dressed with olive oil, lemon, or vinegar on fasting days. This dietary pattern likely contributed to some of the observed metabolomic changes. Among the identified metabolites, four—glycine (p=0.0164), leucine (p=0.0106), serine (p=0.0270), and tocopherol alpha (p=0.0030)—showed statistically significant differences between the IF (combined Group A+D) and control groups.\nThe changes in amino acids, vitamins, and other metabolites may reflect a reduced overall intake of protein and specific micronutrients throughout the intervention, highlighting the potential need for vitamin and nutrient supplementation during prolonged intermittent fasting, as we have now acknowledged in the manuscript (page 10, lines 288-291). However, given the lack of significance in the crossover groups and the modest significance observed in the combined IF group, we included these metabolites in the computational pathway analysis maps but did not emphasize them in the main text.\n6- The authors utilized CompBio v2.6 analysis to integrate the omics data, including metabolome. On page 5 line 140-141, the authors described that “These entities underwent biological knowledge analysis utilizing CompBio v2.6, generating two separate maps.” But the reviewer can not fully understand how any relevant metabolite, e.g. citrulline, glycine, serine, or else, are integrated to the pathway analysis, as they are missing in Figures. Please explain and present the data in Figures, relevant to metabolome analysis.\nResponse: We apologize for any confusion regarding the integrated pathway analysis. The two separate maps referenced in the results section were generated to distinguish between upregulated and downregulated entities. Each map was created by integrating genes, metabolites, and proteins. However, only a limited number of metabolites were mapped to specific biological themes. For instance, lactulose was associated with the “Synthesis, Secretion, and Inactivation of GIP” theme in the downregulated map, with a modest enrichment score of 7.11. To improve clarity, we have now explicitly mentioned these details in the results section (page 5, line 135–139; page 5, lines 151-152). We appreciate the reviewers' feedback and have revised the manuscript accordingly.\nMinor points:\n1- Figure 4 is duplicated by unknown reason. Figure should be presented properly.\n# On page 5 line 137, “For genes, an additional criterion of a ratio ≥ 1.1-fold change was applied.” is not acceptable. Usually, over 2.0 folds change is acceptable as the significant values. Please explain properly the methods of the protocol.\nResponse: We apologize for the duplication of Figure 4. The issue has been corrected, and only one copy of Figure 4 is now presented. Regarding the fold change cutoff, a ≥1.1-fold change criterion was applied for gene analysis due to the inherent high variability of human tissue samples. Colonic biopsy samples, especially those from enteroendocrine cells, are expected to show smaller fold changes due to the relatively low abundance of these cells, despite their functional significance. To accurately capture cell-type-specific biology, a lower fold change cutoff was used. Additionally, the pathway analysis was performed using the Compbio V2.6 method, which is designed to effectively extract meaningful signals from noisy data. This approach allowed for the inclusion of data with smaller fold changes, which was essential for our analysis.\n2- On page line 138-139, the authors described that “we identified a total of 369 upregulated entities (359 genes, 9 metabolites, 1 protein) and 393 downregulated entities (387 genes, 6 metabolites, and 1 protein)”. Thus, the authors identified the positive results of 15 metabolites in total. However, in the file of the metabolome dataset (555939_0_data_set_9849389_smnjkq), parts of the metabolites are described as only numbers. The reviewer speculates that the peaks of these metabolites are still not assigned properly. Please describes the metabolites name properly as positive data. Otherwise, the total hit number should be described properly, by presenting which metabolites are assigned or not.\nResponse: We apologize for the lack of clarity in the metabolite descriptions. To address this, we have now updated the text (Page 5, lines 136-138) to include the proper names of the metabolites and proteins. The 10 upregulated metabolites include benzyl alcohol, ornithine, 7 unannotated metabolites, and the cytokine TNF-alpha. The 6 downregulated metabolites consist of lactulose, 2-hydroxybutanoic acid, 3 unannotated metabolites, and IL-6. While a few of these metabolites were associated with significant pathways, they did not reach statistical significance in the larger sample (IF A+D, n=40). As a result, we did not emphasize these findings in the figures. To clarify, we now present the assigned metabolites in detail, and the metabolites that were unannotated are noted accordingly.\n3- The reviewer cannot understand whether the face values of the data in Figures are increased or decreased. Figures should present the data properly.\nResponse: We apologize for the confusion. To improve clarity, we have revised the figure legends and descriptions to clearly indicate whether the values in the figures are increased or decreased. This should help in understanding the data presented."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have sufficiently responded to my comments and the comments raised by the other reviewers. This study maybe underpowered, but presents important novel data that will guide other studies and practices in the field.", "reviewer_2": "Thank you for your thorough explanations. They have been extremely helpful, and all of my questions have been addressed.", "reviewer_3": "Unofortunately, the revised maniscript does not properly respond to the majority of concerns raised by the reviewer.\n# No data is presented on the effects of short-term fasting.\n# Serum GLP-1 concentrations are still missing.\n# As other metabolites including vitamins and aminoacids are also affected in this study, it is very difficult to interpret the data in the context of long-tem fasting, or simply of malnutrition.\nThus, the revised manuscript is not acceptable for the journal, and should be submitted to the more specialized journal."}, "author_response": "Author’s response: Thank you for your comment.\nAuthor’s response: Thank you for your comment.\nAuthor’s response: Thank you for your comment. We have addressed your concerns below.\nComment 1: No data is presented on the effects of short-term fasting.\nAuthor’s response: We appreciate the reviewer’s interest in short-term fasting. Indeed, we have recently published findings from a 10-day fasting study examining its impact on inflammatory and cardiometabolic markers (Prolonged fasting promotes systemic inflammation and platelet activation in humans: A medically supervised, water-only fasting and refeeding study - ScienceDirect), and we are currently conducting a clinical trial assessing the effects of a 3-day fast in healthy volunteers (Investigating the Impact of Glycogen-Depleting Exercise Combined with Prolonged Fasting on Autophagy and Cellular Health in Humans: A Randomised Controlled Crossover Trial - PubMed). However, the present study was specifically designed to investigate the effects of intermittent fasting over a 6-month period. This longer intervention addresses a distinct research question, namely, the sustained impact of repeated fasting on cardiometabolic health, which is not captured by short-term fasting regimens. Thus, while short-term fasting is of great relevance, it falls outside the predefined scope and aims of the current trial.\nComment 2: Serum GLP-1 concentrations are still missing.\nAuthor’s response: We agree that GLP-1 is a metabolically relevant hormone, and its inclusion would have added valuable mechanistic insight. However, accurate quantification of active GLP-1 requires immediate stabilization using specialized DPP-4 inhibitor tubes, which were not part of our original sample collection protocol, as GLP-1 was not among the predefined primary or secondary outcomes. Retrospective measurement is therefore not technically feasible. We strongly support the inclusion of GLP-1 in future studies designed with this outcome in mind, but it falls outside the scope and methodological constraints of the present trial.\nComment 3: Interpretation of metabolite changes as malnutrition:\nAs other metabolites including vitamins and aminoacids are also affected in this study, it is very difficult to interpret the data in the context of long-tem fasting, or simply of malnutrition.\nAuthor’s response: We appreciate the reviewer’s concern regarding the interpretation of metabolite changes. However, none of the participants exhibited clinical or biochemical signs of nutrient deficiency throughout the study, as confirmed by routine monitoring conducted by an accredited physician. The observed shifts in amino acid and vitamin levels are consistent with known metabolic adaptations to prolonged intermittent fasting, rather than indicators of malnutrition. These changes likely reflect altered nutrient utilization, redistribution, or conservation mechanisms, which are well-documented physiological responses during fasting states."}]

Decision letter