Summary of Study ST004275

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR002701. The data can be accessed directly via it's Project DOI: 10.21228/M8HR9J This work is supported by NIH grant, U2C- DK119886. See: https://www.metabolomicsworkbench.org/about/howtocite.php

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Study IDST004275
Study TitleCardiometabolic and molecular adaptations to 6-month intermittent fasting in middle-aged men and women with overweight: secondary outcomes of a randomized controlled trial
Study SummaryIntermittent fasting (IF) has gained attention as a potential intervention for cardiometabolic health, though its long-term effects remain unclear. In this randomized clinical trial (ClinicalTrials.gov NCT01964118), 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 secondary 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.
Institute
Washington University in St. Louis
Last NameBarve
First NameRuteja
AddressDepartment of Genetics, Washington University, St. Louis, MO, USA
Emailrbarve@wustl.edu
Phone31428623811
Submit Date2025-10-02
Num Groups2
Total Subjects60
Num Males35
Num Females25
Publicationsin review
Analysis Type DetailGC-MS
Release Date2025-10-28
Release Version1
Ruteja Barve Ruteja Barve
https://dx.doi.org/10.21228/M8HR9J
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

Select appropriate tab below to view additional metadata details:

Project:

Project ID:PR002701
Project DOI:doi: 10.21228/M8HR9J
Project Title:Cardiometabolic and molecular adaptations to 6-month intermittent fasting in middle-aged men and women with overweight: secondary outcomes of a randomized controlled trial
Project Summary: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 (ClinicalTrials.gov NCT01964118), 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 secondary 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.
Institute:Washington University
Last Name:Barve
First Name:Ruteja
Address:4515 McKinley Ave, St.Louis, MO, 63110, USA
Email:rbarve@wustl.edu
Phone:3142862381
Contributors:Ruteja A Barve, Nicola Veronese, Beatrice Bertozzi, Valeria Tosti, Maria Lastra Cagigas, Francesco Spelta, Edda Cava, Laura Piccio, Dayna S Early, Richard D Head, Luigi Fontana

Subject:

Subject ID:SU004428
Subject Type:Human
Subject Species:Homo sapiens
Taxonomy ID:9606
Genotype Strain:NA
Age Or Age Range:30-65 year age range
Weight Or Weight Range:24 to 35 kg/m2
Height Or Height Range:1.74+/-1.23 meters
Gender:Male and female
Human Race:NA
Human Ethnicity:NA
Human Trial Type:randomized clinical trial
Human Lifestyle Factors:participants who are eating usual western diets and are sedentary to moderately active (not exercise trained)
Human Smoking Status:NO
Human Alcohol Drug Use:NO
Human Nutrition:participants who are eating usual western diets and are sedentary to moderately active (not exercise trained)
Human Inclusion Criteria:participants who are eating usual western diets and are sedentary to moderately active (not exercise trained)
Human Exclusion Criteria:history of any chronic disease, smoking, pregnancy, alcoholism, psychiatric problems, lifestyle situations that would interfere with study compliance
Species Group:Humans

Factors:

Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id local_sample_id Sample source Status Time
SA497878IF138 M6_6monthsPlasma C 6 months
SA497879IF163 M6_6monthsPlasma C 6 months
SA497880IF158 M6_6monthsPlasma C 6 months
SA497881IF157 M6_6monthsPlasma C 6 months
SA497882IF153 M6_6monthsPlasma C 6 months
SA497883IF152 M6_6monthsPlasma C 6 months
SA497884IF145 M6_6monthsPlasma C 6 months
SA497885IF141 M6_6monthsPlasma C 6 months
SA497886IF133 M6_6monthsPlasma C 6 months
SA497887IF169 M6_6monthsPlasma C 6 months
SA497888IF128 M6_6monthsPlasma C 6 months
SA497889IF116 M6_6monthsPlasma C 6 months
SA497890IF115 M6_6monthsPlasma C 6 months
SA497891IF111 M6_6monthsPlasma C 6 months
SA497892IF109 M6_6monthsPlasma C 6 months
SA497893IF108 M6_6monthsPlasma C 6 months
SA497894IF107 M6_6monthsPlasma C 6 months
SA497895IF105 M6_6monthsPlasma C 6 months
SA497896IF168 M6_6monthsPlasma C 6 months
SA497897IF101 M6_6monthsPlasma C 6 months
SA497898IF101 BL_BLPlasma C Baseline
SA497899IF141 BL_BLPlasma C Baseline
SA497900IF169 BL_BLPlasma C Baseline
SA497901IF168 BL_BLPlasma C Baseline
SA497902IF163 BL_BLPlasma C Baseline
SA497903IF158 BL_BLPlasma C Baseline
SA497904IF157 BL_BLPlasma C Baseline
SA497905IF153 BL_BLPlasma C Baseline
SA497906IF152 BL_BLPlasma C Baseline
SA497907IF145 BL_BLPlasma C Baseline
SA497908IF138 BL_BLPlasma C Baseline
SA497909IF105 BL_BLPlasma C Baseline
SA497910IF133 BL_BLPlasma C Baseline
SA497911IF128 BL_BLPlasma C Baseline
SA497912IF116 BL_BLPlasma C Baseline
SA497913IF115 BL_BLPlasma C Baseline
SA497914IF111 BL_BLPlasma C Baseline
SA497915IF109 BL_BLPlasma C Baseline
SA497916IF108 BL_BLPlasma C Baseline
SA497917IF107 BL_BLPlasma C Baseline
SA497918IF105 M12_6monthsPlasma IF 6months
SA497919IF101 M12_6monthsPlasma IF 6months
SA497920IF172 M6_6monthsPlasma IF 6months
SA497921IF135 M6_6monthsPlasma IF 6months
SA497922IF118 M6_6monthsPlasma IF 6months
SA497923IF121 M6_6monthsPlasma IF 6months
SA497924IF122 M6_6monthsPlasma IF 6months
SA497925IF127 M6_6monthsPlasma IF 6months
SA497926IF129 M6_6monthsPlasma IF 6months
SA497927IF130 M6_6monthsPlasma IF 6months
SA497928IF136 M6_6monthsPlasma IF 6months
SA497929IF112 M6_6monthsPlasma IF 6months
SA497930IF139 M6_6monthsPlasma IF 6months
SA497931IF149 M6_6monthsPlasma IF 6months
SA497932IF156 M6_6monthsPlasma IF 6months
SA497933IF160 M6_6monthsPlasma IF 6months
SA497934IF161 M6_6monthsPlasma IF 6months
SA497935IF164 M6_6monthsPlasma IF 6months
SA497936IF165 M6_6monthsPlasma IF 6months
SA497937IF113 M6_6monthsPlasma IF 6months
SA497938IF104 M6_6monthsPlasma IF 6months
SA497939IF170 M6_6monthsPlasma IF 6months
SA497940IF133 M12_6monthsPlasma IF 6months
SA497941IF107 M12_6monthsPlasma IF 6months
SA497942IF108 M12_6monthsPlasma IF 6months
SA497943IF109 M12_6monthsPlasma IF 6months
SA497944IF111 M12_6monthsPlasma IF 6months
SA497945IF115 M12_6monthsPlasma IF 6months
SA497946IF116 M12_6monthsPlasma IF 6months
SA497947IF128 M12_6monthsPlasma IF 6months
SA497948IF138 M12_6monthsPlasma IF 6months
SA497949IF169 M12_6monthsPlasma IF 6months
SA497950IF141 M12_6monthsPlasma IF 6months
SA497951IF145 M12_6monthsPlasma IF 6months
SA497952IF152 M12_6monthsPlasma IF 6months
SA497953IF153 M12_6monthsPlasma IF 6months
SA497954IF157 M12_6monthsPlasma IF 6months
SA497955IF158 M12_6monthsPlasma IF 6months
SA497956IF168 M12_6monthsPlasma IF 6months
SA497957IF167 M6_6monthsPlasma IF 6months
SA497958IF108 M6_BLPlasma IF Baseline
SA497959IF109 M6_BLPlasma IF Baseline
SA497960IF152 M6_BLPlasma IF Baseline
SA497961IF107 M6_BLPlasma IF Baseline
SA497962IF104 BL_BLPlasma IF Baseline
SA497963IF169 M6_BLPlasma IF Baseline
SA497964IF168 M6_BLPlasma IF Baseline
SA497965IF158 M6_BLPlasma IF Baseline
SA497966IF157 M6_BLPlasma IF Baseline
SA497967IF153 M6_BLPlasma IF Baseline
SA497968IF145 M6_BLPlasma IF Baseline
SA497969IF118 BL_BLPlasma IF Baseline
SA497970IF141 M6_BLPlasma IF Baseline
SA497971IF138 M6_BLPlasma IF Baseline
SA497972IF133 M6_BLPlasma IF Baseline
SA497973IF128 M6_BLPlasma IF Baseline
SA497974IF116 M6_BLPlasma IF Baseline
SA497975IF115 M6_BLPlasma IF Baseline
SA497976IF111 M6_BLPlasma IF Baseline
SA497977IF113 BL_BLPlasma IF Baseline
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Collection:

Collection ID:CO004421
Collection Summary:Venous blood samples were collected after an overnight fast to measure lipid, metabolite, and hormone concentrations. To minimize potential confounding effects from prolonged overnight fasting , blood samples were consistently collected after an overnight fast, but three days following the end of the fasting period. Plasma lipid and lipoprotein-cholesterol concentrations were measured in the Core Laboratory for Clinical Studies at the Washington University by technicians blinded to treatment conditions and sample identities. A 30 µL sample of EDTA-Plasma was analyzed for primary metabolism profiling at the University of California Davis
Sample Type:Blood (plasma)

Treatment:

Treatment ID:TR004437
Treatment Summary:Participants were randomly assigned to IF or control groups. Participants underwent 6 months of intermittent fasting (IF) or usual diet for control group . After this initial phase, 18 participants from the IF group continued the intervention for an additional 6 months (group C), while 19 participants initially randomized to the control group crossed over to IF for 6 months (group D).
Treatment:Intermittent Fasting
Treatment Doseduration:6 months
Human Fasting:This weight loss trial aimed to reduce weekly energy intake through IF while maintaining regular food intake on non-fasting days. Participants with a BMI of 24 to 27.9 kg/m2 fasted for 2 non-consecutive days per week, while those with a BMI between 28 and 35 kg/m2 fasted for 3 non-consecutive days per week. Once BMI dropped below 28, fasting frequency was reduced to 2 days per week. On fasting days, research volunteers consumed only non-starchy vegetables (raw or cooked) with up to 2 tablespoons of olive oil, vinegar or lemon dressing (less than 500 kcal/day), facilitating compliance without the need for calorie counting. Non-caloric beverages such as black coffee, unsweetened tea or zero-calorie soda, were permitted. IF participants met monthly with dietitians for weight tracking and dietary guidance, with compliance monitored through weekly phone calls and recorded body weights. 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 continued their regular diet without dietary intervention or counseling. Both groups were instructed to maintain their usual physical activity levels throughout the study.

Sample Preparation:

Sampleprep ID:SP004434
Sampleprep Summary:Venous blood samples were collected after a overnight fast to measure lipid, metabolite, and hormone concentrations. To minimize potential confounding effects from prolonged overnight fasting, blood samples were consistently collected after an overnight fast, but three days following the end of the fasting period. Plasma lipid and lipoprotein-cholesterol concentrations were measured in the Core Laboratory for Clinical Studies at the Washington University by technicians blinded to treatment conditions and sample identities.

Combined analysis:

Analysis ID AN007114
Chromatography ID CH005405
MS ID MS006811
Analysis type MS
Chromatography type GC
Chromatography system Agilent 6890N
Column Restek Rtx-5Sil MS (30m x 0.25mm, 0.25um)
MS Type EI
MS instrument type TOF
MS instrument name Leco Pegasus IV TOF
Ion Mode POSITIVE
Units normalized peak heights

Chromatography:

Chromatography ID:CH005405
Chromatography Summary:A 30 µL sample of EDTA-Plasma was analyzed for primary metabolism profiling at the University of California Davis. The analysis was performed by gas chromatography/time-of-flight mass spectrometry (GCTOF) using Gerstel CIS4 –with dual MPS Injector/ Agilent 6890 GC-Pegasus III TOF MS. See See Fiehn O, K.T. Metabolite profiling in blood plasma. in Metabolomics: Methods and Protocols (ed. Weckwerth W (ed.)) (Humana Press, Totowa NJ, 2006).
Methods Filename:Data_Dictionary_Fiehn_laboratory_GCTOF_MS_primary_metabolism_10-15-2013_general.pdf
Chromatography Comments:Fiehn, O. Metabolomics by Gas Chromatography-Mass Spectrometry: Combined Targeted and Untargeted Profiling. Curr Protoc Mol Biol 114, 30 34 31-30 34 32 (2016)
Instrument Name:Agilent 6890N
Column Name:Restek Rtx-5Sil MS (30m x 0.25mm, 0.25um)
Column Temperature:50-330
Flow Gradient:-
Flow Rate:1mL/min
Injection Temperature:50°C ramped to 250°C by 12°C s-1
Retention Index:Fiehn retention indices used based on FAME istd
Sample Injection:Injection volume: 0.5 µL Injection: 25 splitless time into a multi-baffled glass liner
Solvent A:-
Solvent B:-
Oven Temperature:50°C for 1 min, then ramped at 20°C min-1 to 330°C, held constant for 5 min
Chromatography Type:GC

MS:

MS ID:MS006811
Analysis ID:AN007114
Instrument Name:Leco Pegasus IV TOF
Instrument Type:TOF
MS Type:EI
MS Comments:Mass spectrometry parameters are used as follows: a Leco Pegasus IV mass spectrometer is used with unit mass resolution at 17 spectra s-1 from 80-500 Da at -70 eV ionization energy and 1800 V detector voltage with a 230°C transfer line and a 250°C ion source.Raw data files are preprocessed directly after data acquisition and stored as ChromaTOF-specific *.peg files, as generic *.txt result files and additionally as generic ANDI MS *.cdf files. ChromaTOF vs. 2.32 is used for data preprocessing without smoothing, 3 s peak width, baseline subtraction just above the noise level, and automatic mass spectral deconvolution and peak detection at signal/noise levels of 5:1 throughout the chromatogram. Apex masses are reported for use in the BinBase algorithm. Result *.txt files are exported to a data server with absolute spectra intensities and further processed by a filtering algorithm implemented in the metabolomics BinBase database.The BinBase algorithm (rtx5) used the settings: validity of chromatogram (<10 peaks with intensity >10^7 counts s-1), unbiased retention index marker detection (MS similarity>800, validity of intensity range for high m/z marker ions), retention index calculation by 5th order polynomial regression. Spectra are cut to 5% base peak abundance and matched to database entries from most to least abundant spectra using the following matching filters: retention index window ±2,000 units (equivalent to about ±2 s retention time), validation of unique ions and apex masses (unique ion must be included in apexing masses and present at >3% of base peak abundance), mass spectrum similarity must fit criteria dependent on peak purity and signal/noise ratios and a final isomer filter. Failed spectra are automatically entered as new database entries if s/n >25, purity <1.0 and presence in the biological study design class was >80%. All thresholds reflect settings for ChromaTOF v. 2.32. Quantification is reported as peak height using the unique ion as default, unless a different quantification ion is manually set in the BinBase administration software BinView. A quantification report table is produced for all database entries that are positively detected in more than 10% of the samples of a study design class (as defined in the miniX database) for unidentified metabolites. A subsequent post-processing module is employed to automatically replace missing values from the *.cdf files. Replaced values are labeled as ‘low confidence’ by color coding, and for each metabolite, the number of high-confidence peak detections is recorded as well as the ratio of the average height of replaced values to high-confidence peak detections. These ratios and numbers are used for manual curation of automatic report data sets to data sets released for submission.
Ion Mode:POSITIVE
Analysis Protocol File:Data_Dictionary_Fiehn_laboratory_GCTOF_MS_primary_metabolism_10-15-2013_general.pdf
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