The effect of weight loss following 18 months of lifestyle intervention on brain age assessed with resting-state functional connectivity.

MRI Mediterranean diet brain age epidemiology functional connectivity global health human lifestyle intervention obesity

Journal

eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614

Informations de publication

Date de publication:
06 04 2023
Historique:
received: 21 09 2022
accepted: 31 03 2023
medline: 15 5 2023
pubmed: 7 4 2023
entrez: 6 4 2023
Statut: epublish

Résumé

Obesity negatively impacts multiple bodily systems, including the central nervous system. Retrospective studies that estimated chronological age from neuroimaging have found accelerated brain aging in obesity, but it is unclear how this estimation would be affected by weight loss following a lifestyle intervention. In a sub-study of 102 participants of the Dietary Intervention Randomized Controlled Trial Polyphenols Unprocessed Study (DIRECT-PLUS) trial, we tested the effect of weight loss following 18 months of lifestyle intervention on predicted brain age based on magnetic resonance imaging (MRI)-assessed resting-state functional connectivity (RSFC). We further examined how dynamics in multiple health factors, including anthropometric measurements, blood biomarkers, and fat deposition, can account for changes in brain age. To establish our method, we first demonstrated that our model could successfully predict chronological age from RSFC in three cohorts (n=291;358;102). We then found that among the DIRECT-PLUS participants, 1% of body weight loss resulted in an 8.9 months' attenuation of brain age. Attenuation of brain age was significantly associated with improved liver biomarkers, decreased liver fat, and visceral and deep subcutaneous adipose tissues after 18 months of intervention. Finally, we showed that lower consumption of processed food, sweets and beverages were associated with attenuated brain age. Successful weight loss following lifestyle intervention might have a beneficial effect on the trajectory of brain aging. The German Research Foundation (DFG), German Research Foundation - project number 209933838 - SFB 1052; B11, Israel Ministry of Health grant 87472511 (to I Shai); Israel Ministry of Science and Technology grant 3-13604 (to I Shai); and the California Walnuts Commission 09933838 SFB 105 (to I Shai). Obesity is linked with the brain aging faster than would normally be expected. Researchers are able to capture this process by calculating a person’s ‘brain age’ – how old their brain appears on detailed scans, regardless of chronological age. This approach also helps to monitor how certain factors, such as lifestyle, can influence brain aging over relatively short time scales. It is not clear whether lifestyle interventions that promote weight loss can help to slow obesity-driven brain aging. To answer this question, Levakov et al. studied 102 individuals who met the criteria for obesity and took part in a lifestyle intervention aimed to improve diet and physical activity levels over 18 months. The participants received a brain scan at the beginning and the end of the program; additional tests and measurements were also conducted at these times to capture other biological processes impacted by obesity, such as liver health. Levakov et al. used the brain scans taken at the start and end of the study to examine the impact of the lifestyle intervention on the aging trajectory. The results revealed that a reduction in body weight of 1% led to the participants’ brain age being nearly 9 months younger than the expected brain age after 18 months. This attenuated aging was associated with changes in other biological measures, such as decreased liver fat and liver enzymes. Increases in liver fat and production of specific liver enzymes were previously shown to negatively impact brain health in Alzheimer’s disease. Finally, examining more closely the food consumption reports completed by participants showed that reduced consumption of processed food, sweets and beverages were linked to attenuated brain aging. The findings show that lifestyle interventions which promote weight loss can have a beneficial impact on the aging trajectory of the brain observed with obesity. The next steps will include determining whether slowing down obesity-driven brain aging results in better clinical outcomes for patients. In addition, the work by Levakov et al. demonstrates a potential strategy to evaluate the success of lifestyle changes on brain health. With global rates of obesity rising, identifying interventions that have a positive impact on brain health could have important clinical, educational and social impacts.

Sections du résumé

Background
Obesity negatively impacts multiple bodily systems, including the central nervous system. Retrospective studies that estimated chronological age from neuroimaging have found accelerated brain aging in obesity, but it is unclear how this estimation would be affected by weight loss following a lifestyle intervention.
Methods
In a sub-study of 102 participants of the Dietary Intervention Randomized Controlled Trial Polyphenols Unprocessed Study (DIRECT-PLUS) trial, we tested the effect of weight loss following 18 months of lifestyle intervention on predicted brain age based on magnetic resonance imaging (MRI)-assessed resting-state functional connectivity (RSFC). We further examined how dynamics in multiple health factors, including anthropometric measurements, blood biomarkers, and fat deposition, can account for changes in brain age.
Results
To establish our method, we first demonstrated that our model could successfully predict chronological age from RSFC in three cohorts (n=291;358;102). We then found that among the DIRECT-PLUS participants, 1% of body weight loss resulted in an 8.9 months' attenuation of brain age. Attenuation of brain age was significantly associated with improved liver biomarkers, decreased liver fat, and visceral and deep subcutaneous adipose tissues after 18 months of intervention. Finally, we showed that lower consumption of processed food, sweets and beverages were associated with attenuated brain age.
Conclusions
Successful weight loss following lifestyle intervention might have a beneficial effect on the trajectory of brain aging.
Funding
The German Research Foundation (DFG), German Research Foundation - project number 209933838 - SFB 1052; B11, Israel Ministry of Health grant 87472511 (to I Shai); Israel Ministry of Science and Technology grant 3-13604 (to I Shai); and the California Walnuts Commission 09933838 SFB 105 (to I Shai).
Obesity is linked with the brain aging faster than would normally be expected. Researchers are able to capture this process by calculating a person’s ‘brain age’ – how old their brain appears on detailed scans, regardless of chronological age. This approach also helps to monitor how certain factors, such as lifestyle, can influence brain aging over relatively short time scales. It is not clear whether lifestyle interventions that promote weight loss can help to slow obesity-driven brain aging. To answer this question, Levakov et al. studied 102 individuals who met the criteria for obesity and took part in a lifestyle intervention aimed to improve diet and physical activity levels over 18 months. The participants received a brain scan at the beginning and the end of the program; additional tests and measurements were also conducted at these times to capture other biological processes impacted by obesity, such as liver health. Levakov et al. used the brain scans taken at the start and end of the study to examine the impact of the lifestyle intervention on the aging trajectory. The results revealed that a reduction in body weight of 1% led to the participants’ brain age being nearly 9 months younger than the expected brain age after 18 months. This attenuated aging was associated with changes in other biological measures, such as decreased liver fat and liver enzymes. Increases in liver fat and production of specific liver enzymes were previously shown to negatively impact brain health in Alzheimer’s disease. Finally, examining more closely the food consumption reports completed by participants showed that reduced consumption of processed food, sweets and beverages were linked to attenuated brain aging. The findings show that lifestyle interventions which promote weight loss can have a beneficial impact on the aging trajectory of the brain observed with obesity. The next steps will include determining whether slowing down obesity-driven brain aging results in better clinical outcomes for patients. In addition, the work by Levakov et al. demonstrates a potential strategy to evaluate the success of lifestyle changes on brain health. With global rates of obesity rising, identifying interventions that have a positive impact on brain health could have important clinical, educational and social impacts.

Autres résumés

Type: plain-language-summary (eng)
Obesity is linked with the brain aging faster than would normally be expected. Researchers are able to capture this process by calculating a person’s ‘brain age’ – how old their brain appears on detailed scans, regardless of chronological age. This approach also helps to monitor how certain factors, such as lifestyle, can influence brain aging over relatively short time scales. It is not clear whether lifestyle interventions that promote weight loss can help to slow obesity-driven brain aging. To answer this question, Levakov et al. studied 102 individuals who met the criteria for obesity and took part in a lifestyle intervention aimed to improve diet and physical activity levels over 18 months. The participants received a brain scan at the beginning and the end of the program; additional tests and measurements were also conducted at these times to capture other biological processes impacted by obesity, such as liver health. Levakov et al. used the brain scans taken at the start and end of the study to examine the impact of the lifestyle intervention on the aging trajectory. The results revealed that a reduction in body weight of 1% led to the participants’ brain age being nearly 9 months younger than the expected brain age after 18 months. This attenuated aging was associated with changes in other biological measures, such as decreased liver fat and liver enzymes. Increases in liver fat and production of specific liver enzymes were previously shown to negatively impact brain health in Alzheimer’s disease. Finally, examining more closely the food consumption reports completed by participants showed that reduced consumption of processed food, sweets and beverages were linked to attenuated brain aging. The findings show that lifestyle interventions which promote weight loss can have a beneficial impact on the aging trajectory of the brain observed with obesity. The next steps will include determining whether slowing down obesity-driven brain aging results in better clinical outcomes for patients. In addition, the work by Levakov et al. demonstrates a potential strategy to evaluate the success of lifestyle changes on brain health. With global rates of obesity rising, identifying interventions that have a positive impact on brain health could have important clinical, educational and social impacts.

Identifiants

pubmed: 37022140
doi: 10.7554/eLife.83604
pii: 83604
pmc: PMC10174688
doi:
pii:

Types de publication

Randomized Controlled Trial Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2023, Levakov, Kaplan et al.

Déclaration de conflit d'intérêts

GL, AK, AY, ER, GT, HZ, UC, MS, IS, GA, IS No competing interests declared, MB has received consulting fees from Amgen, Astra Zeneca, Boehringer-Ingelheim, Bayer, Lilly, Novo Nordisk, Novartis, Sanofi and Pfizer; and fees for lectures/ presentations from Amgen, Astra Zeneca, Boehringer-Ingelheim, Bayer, Daiichi-Sankyo, Lilly, Novo Nordisk, Novartis, Sanofi and Pfizer. The author is also on the advisory board for Boehringer-Ingelheim. The author has no other competing interests to declare

Références

Annu Rev Biochem. 2018 Jun 20;87:295-322
pubmed: 29925262
Neuroimage. 2021 Nov 15;242:118469
pubmed: 34390875
Metab Brain Dis. 2011 Dec;26(4):253-67
pubmed: 21881966
N Engl J Med. 2018 Jun 21;378(25):2441-2442
pubmed: 29897867
Neuropsychopharmacology. 2021 Apr;46(5):911-919
pubmed: 33495545
Am J Clin Nutr. 2022 May 1;115(5):1270-1281
pubmed: 35021194
Dialogues Clin Neurosci. 2013 Mar;15(1):45-52
pubmed: 23576888
Neuroimage. 2022 Oct 1;259:119415
pubmed: 35760293
Gut. 2021 Nov;70(11):2085-2095
pubmed: 33461965
Schizophr Bull. 2014 Sep;40(5):1140-53
pubmed: 24126515
Neurobiol Aging. 2016 Nov;47:63-70
pubmed: 27562529
Front Immunol. 2017 Dec 07;8:1745
pubmed: 29270179
JAMA Intern Med. 2015 Jul;175(7):1094-1103
pubmed: 25961184
Nat Commun. 2021 Sep 9;12(1):5346
pubmed: 34504080
J Nutr. 2005 Mar;135(3):573-9
pubmed: 15735096
Cereb Cortex. 2018 Mar 1;28(3):988-997
pubmed: 28119342
Nutrients. 2017 Jul 01;9(7):
pubmed: 28671558
Obes Rev. 2020 Apr;21(4):e12991
pubmed: 32020741
J Am Coll Cardiol. 2020 Mar 3;75(8):919-930
pubmed: 32130928
Proc Natl Acad Sci U S A. 2011 May 3;108(18):7641-6
pubmed: 21502525
J Psychiatr Res. 2018 Apr;99:151-158
pubmed: 29454222
Brain Struct Funct. 2020 Sep;225(7):2111-2129
pubmed: 32696074
Neurobiol Dis. 2014 Dec;72 Pt A:22-36
pubmed: 25131449
Hum Brain Mapp. 2022 Jul;43(10):3113-3129
pubmed: 35312210
Neurosci Biobehav Rev. 2018 Jan;84:225-244
pubmed: 29203421
Gastroenterology. 2021 Jan;160(1):158-173.e10
pubmed: 32860791
Int J Obes (Lond). 2019 May;43(5):943-951
pubmed: 30022057
Nat Med. 2009 Sep;15(9):996-7
pubmed: 19734871
Neuroimage. 2007 Aug 1;37(1):90-101
pubmed: 17560126
Science. 2010 Sep 10;329(5997):1358-61
pubmed: 20829489
Nature. 2022 Apr;604(7906):525-533
pubmed: 35388223
Front Aging Neurosci. 2014 May 23;6:94
pubmed: 24904408
Curr Opin Neurobiol. 2016 Oct;40:1-7
pubmed: 27209150
Nat Methods. 2019 Jan;16(1):111-116
pubmed: 30532080
Neuroreport. 2013 Oct 23;24(15):866-71
pubmed: 24022176
Am J Hypertens. 2020 Nov 3;33(11):975-986
pubmed: 32453820
Age Ageing. 2016 Jan;45(1):14-21
pubmed: 26764391
Neurosci Biobehav Rev. 2021 Oct;129:133-141
pubmed: 34284063
Alzheimers Dement. 2017 Feb;13(2):168-177
pubmed: 27461490
Front Aging Neurosci. 2010 Aug 26;2:
pubmed: 20890449
Metabolism. 2014 Jun;63(6):760-6
pubmed: 24684821
Cereb Cortex. 2018 Sep 1;28(9):3095-3114
pubmed: 28981612
Front Neurol. 2019 Aug 14;10:789
pubmed: 31474922
J Nutr. 2019 Jun 1;149(6):1004-1011
pubmed: 30915471
Neurobiol Aging. 2022 Jul;115:60-69
pubmed: 35472831
Neuroimage. 2014 Jan 1;84:320-41
pubmed: 23994314
Nat Rev Gastroenterol Hepatol. 2020 Nov;17(11):655-672
pubmed: 32855515
Biochim Biophys Acta Mol Basis Dis. 2020 Jun 1;1866(6):165767
pubmed: 32171891
Clin Epigenetics. 2021 Mar 4;13(1):48
pubmed: 33663610
Rev Endocr Metab Disord. 2022 Aug;23(4):861-879
pubmed: 34159504
Neuroimage. 2010 Sep;52(3):1059-69
pubmed: 19819337
Brain. 2020 Jul 1;143(7):2312-2324
pubmed: 32591831
Heart. 2020 Nov 23;:
pubmed: 33234670
Proc Natl Acad Sci U S A. 2007 Jun 12;104(24):10240-5
pubmed: 17548818
Diabetes Care. 2012 Feb;35(2):342-9
pubmed: 22190676
Neurology. 2011 Oct 25;77(17):1619-28
pubmed: 21998317
Obes Rev. 2015 Apr;16(4):273-81
pubmed: 25676886
Cereb Cortex. 2017 Mar 1;27(3):2303-2317
pubmed: 27073220
Hypertension. 2010 Jun;55(6):1331-8
pubmed: 20305128
Diabetes Care. 2016 May;39(5):764-71
pubmed: 27208378
Trends Endocrinol Metab. 2010 Nov;21(11):660-7
pubmed: 20817486
J Endocrinol. 2018 Aug;238(2):R79-R94
pubmed: 29848608
Physiol Behav. 2019 Sep 1;208:112578
pubmed: 31194997
Hum Brain Mapp. 2020 Aug 15;41(12):3235-3252
pubmed: 32320123
Lancet Public Health. 2019 Mar;4(3):e159-e167
pubmed: 30851869
Arch Physiol Biochem. 2023 Oct;129(5):1028-1037
pubmed: 33651961
PLoS One. 2013 Jul 24;8(7):e70684
pubmed: 23894679
Trends Cogn Sci. 2019 Apr;23(4):349-361
pubmed: 30824229
BMC Neurol. 2014 Oct 14;14:204
pubmed: 25412575
Curr Opin Immunol. 2009 Aug;21(4):446-53
pubmed: 19500963
Hum Brain Mapp. 2017 Jul;38(7):3502-3515
pubmed: 28397392
Dig Dis Sci. 2021 Sep;66(9):3179-3185
pubmed: 33037968
Neurology. 2021 May 5;:
pubmed: 33952652
Front Psychol. 2015 May 21;6:663
pubmed: 26052298
Front Psychiatry. 2020 Feb 28;11:100
pubmed: 32180739
Neuroimage. 2017 Mar 1;148:179-188
pubmed: 27890805
Obesity (Silver Spring). 2010 Apr;18(4):841-7
pubmed: 19834463
Prog Nucl Magn Reson Spectrosc. 2013 Aug;73:56-80
pubmed: 23962884
Proc Natl Acad Sci U S A. 2011 Feb 15;108(7):3017-22
pubmed: 21282661
Alzheimers Dement. 2017 Mar;13(3):205-216
pubmed: 27697430
Eur Radiol. 2015 Oct;25(10):2869-79
pubmed: 25903702
Curr Nutr Rep. 2018 Sep;7(3):139-149
pubmed: 29974344
Neuroimage Clin. 2022;33:102949
pubmed: 35114636
Biochim Biophys Acta Mol Basis Dis. 2017 May;1863(5):1037-1045
pubmed: 27156888
JAMA Netw Open. 2019 Jul 3;2(7):e197978
pubmed: 31365104
Front Endocrinol (Lausanne). 2019 May 03;10:266
pubmed: 31130916
Circulation. 2005 Oct 25;112(17):2735-52
pubmed: 16157765
Neuroimage. 2019 Oct 15;200:528-539
pubmed: 31201988
Elife. 2023 Apr 06;12:
pubmed: 37022140
Front Neurosci. 2012 Oct 16;6:152
pubmed: 23087608
Neurobiol Aging. 2022 Jan;109:204-215
pubmed: 34775211
Neuroimage. 2017 Jan;144(Pt B):262-269
pubmed: 26375206
Lancet. 2017 Dec 16;390(10113):2627-2642
pubmed: 29029897
Circulation. 2018 Mar 13;137(11):1143-1157
pubmed: 29142011
Diabetes Care. 2019 Jul;42(7):1162-1169
pubmed: 31076421
Neuroimage. 1999 Feb;9(2):195-207
pubmed: 9931269
Trends Neurosci. 2017 Dec;40(12):681-690
pubmed: 29074032
Cell Metab. 2014 Dec 2;20(6):967-77
pubmed: 25456739
Cereb Cortex. 2020 Apr 14;30(4):2489-2505
pubmed: 31808790
Hum Brain Mapp. 2023 Feb 15;44(3):1118-1128
pubmed: 36346213
BMC Med. 2022 Sep 30;20(1):327
pubmed: 36175997
Mol Psychiatry. 2018 May;23(5):1385-1392
pubmed: 28439103

Auteurs

Gidon Levakov (G)

Department of Brain and Cognitive Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.

Alon Kaplan (A)

The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.
Department of Internal Medicine D, Chaim Sheba Medical Center, Ramat-Gan, Israel.

Anat Yaskolka Meir (A)

The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.

Ehud Rinott (E)

The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.

Gal Tsaban (G)

The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.

Hila Zelicha (H)

The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.

Matthias Blüher (M)

Department of Medicine, University of Leipzig, Leipzig, Germany.

Uta Ceglarek (U)

Department of Medicine, University of Leipzig, Leipzig, Germany.

Michael Stumvoll (M)

Department of Medicine, University of Leipzig, Leipzig, Germany.

Ilan Shelef (I)

Department of Diagnostic Imaging, Soroka Medical Center, Beer Sheva, Israel.

Galia Avidan (G)

Department of Psychology, Ben-Gurion University of the Negev, Beer Sheva, Israel.

Iris Shai (I)

The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.
Department of Medicine, University of Leipzig, Leipzig, Germany.
Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, United States.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH