001     179134
005     20240229145534.0
024 7 _ |a 10.1186/s13073-022-01030-0
|2 doi
024 7 _ |a pmid:35287713
|2 pmid
024 7 _ |a altmetric:124652907
|2 altmetric
037 _ _ |a DKFZ-2022-00491
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Sowah, Solomon
|0 P:(DE-He78)b4004ee4b650b575447f59a4a0471312
|b 0
|e First author
|u dkfz
245 _ _ |a Calorie restriction improves metabolic state independently of gut microbiome composition: a randomized dietary intervention trial.
260 _ _ |a London
|c 2022
|b BioMed Central
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1647511529_5965
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a #EA:C020#LA:C020#
520 _ _ |a The gut microbiota has been suggested to play a significant role in the development of overweight and obesity. However, the effects of calorie restriction on gut microbiota of overweight and obese adults, especially over longer durations, are largely unexplored.Here, we longitudinally analyzed the effects of intermittent calorie restriction (ICR) operationalized as the 5:2 diet versus continuous calorie restriction (CCR) on fecal microbiota of 147 overweight or obese adults in a 50-week parallel-arm randomized controlled trial, the HELENA Trial. The primary outcome of the trial was the differential effects of ICR versus CCR on gene expression in subcutaneous adipose tissue. Changes in the gut microbiome, which are the focus of this publication, were defined as exploratory endpoint of the trial. The trial comprised a 12-week intervention period, a 12-week maintenance period, and a final follow-up period of 26 weeks.Both diets resulted in ~5% weight loss. However, except for Lactobacillales being enriched after ICR, post-intervention microbiome composition did not significantly differ between groups. Overall weight loss was associated with significant metabolic improvements, but not with changes in the gut microbiome. Nonetheless, the abundance of the Dorea genus at baseline was moderately predictive of subsequent weight loss (AUROC of 0.74 for distinguishing the highest versus lowest weight loss quartiles). Despite the lack of consistent intervention effects on microbiome composition, significant study group-independent co-variation between gut bacterial families and metabolic biomarkers, anthropometric measures, and dietary composition was detectable. Our analysis in particular revealed associations between insulin sensitivity (HOMA-IR) and Akkermansiaceae, Christensenellaceae, and Tanerellaceae. It also suggests the possibility of a beneficial modulation of the latter two intestinal taxa by a diet high in vegetables and fiber, and low in processed meat.Overall, our results suggest that the gut microbiome remains stable and highly individual-specific under dietary calorie restriction.The trial, including the present microbiome component, was prospectively registered at ClinicalTrials.gov NCT02449148 on May 20, 2015.
536 _ _ |a 313 - Krebsrisikofaktoren und Prävention (POF4-313)
|0 G:(DE-HGF)POF4-313
|c POF4-313
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
650 _ 7 |a Gut microbiome
|2 Other
650 _ 7 |a Intermittent calorie restriction
|2 Other
650 _ 7 |a Obesity
|2 Other
650 _ 7 |a Overweight
|2 Other
650 _ 7 |a Weight loss
|2 Other
700 1 _ |a Milanese, Alessio
|b 1
700 1 _ |a Schübel, Ruth
|0 P:(DE-He78)ceb74219d144ab5760a228e71440c5ca
|b 2
700 1 _ |a Wirbel, Jakob
|b 3
700 1 _ |a Kartal, Ece
|b 4
700 1 _ |a Johnson, Theron S
|0 P:(DE-He78)79ab945544e5bc017a2317b6146ed3aa
|b 5
|u dkfz
700 1 _ |a Hirche, Frank
|b 6
700 1 _ |a Grafetstätter, Mirja
|0 P:(DE-He78)9a183bfc8348c1db81a0ecf1458d0708
|b 7
700 1 _ |a Nonnenmacher, Tobias
|b 8
700 1 _ |a Kirsten, Romy
|b 9
700 1 _ |a López-Nogueroles, Marina
|b 10
700 1 _ |a Lahoz, Agustín
|b 11
700 1 _ |a Schwarz, Kathrin V
|b 12
700 1 _ |a Okun, Jürgen G
|b 13
700 1 _ |a Ulrich, Cornelia M
|b 14
700 1 _ |a Nattenmüller, Johanna
|b 15
700 1 _ |a von Eckardstein, Arnold
|b 16
700 1 _ |a Müller, Daniel
|b 17
700 1 _ |a Stangl, Gabriele I
|b 18
700 1 _ |a Kaaks, Rudolf
|0 P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a
|b 19
|u dkfz
700 1 _ |a Kühn, Tilman
|0 P:(DE-He78)0907a10ba1dc8f53f04907f54f6fdcfe
|b 20
|e Last author
700 1 _ |a Zeller, Georg
|0 0000-0003-1429-7485
|b 21
773 _ _ |a 10.1186/s13073-022-01030-0
|g Vol. 14, no. 1, p. 30
|0 PERI:(DE-600)2484394-5
|n 1
|p 30
|t Genome medicine
|v 14
|y 2022
|x 1756-994X
909 C O |o oai:inrepo02.dkfz.de:179134
|p VDB
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 0
|6 P:(DE-He78)b4004ee4b650b575447f59a4a0471312
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 2
|6 P:(DE-He78)ceb74219d144ab5760a228e71440c5ca
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 5
|6 P:(DE-He78)79ab945544e5bc017a2317b6146ed3aa
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 7
|6 P:(DE-He78)9a183bfc8348c1db81a0ecf1458d0708
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 19
|6 P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 20
|6 P:(DE-He78)0907a10ba1dc8f53f04907f54f6fdcfe
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-313
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Krebsrisikofaktoren und Prävention
|x 0
914 1 _ |y 2022
915 _ _ |a Creative Commons Attribution CC BY (No Version)
|0 LIC:(DE-HGF)CCBYNV
|2 V:(DE-HGF)
|b DOAJ
|d 2021-02-03
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-02-03
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2021-02-03
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-02-03
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2021-02-03
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2021-02-03
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b GENOME MED : 2021
|d 2022-11-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2022-11-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2022-11-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2021-02-14T16:20:36Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2021-02-14T16:20:36Z
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Blind peer review
|d 2021-02-14T16:20:36Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2022-11-22
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2022-11-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2022-11-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2022-11-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2022-11-22
915 _ _ |a IF >= 15
|0 StatID:(DE-HGF)9915
|2 StatID
|b GENOME MED : 2021
|d 2022-11-22
920 2 _ |0 I:(DE-He78)C020-20160331
|k C020
|l C020 Epidemiologie von Krebs
|x 0
920 1 _ |0 I:(DE-He78)C020-20160331
|k C020
|l C020 Epidemiologie von Krebs
|x 0
920 0 _ |0 I:(DE-He78)C020-20160331
|k C020
|l C020 Epidemiologie von Krebs
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)C020-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21