Integrative phenotyping of glycemic responders upon clinical weight loss using multi-omics

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Integrative phenotyping of glycemic responders upon clinical weight loss using multi-omics. / Valsesia, Armand; Chakrabarti, Anirikh; Hager, Jörg; Langin, Dominique; Saris, Wim H M; Astrup, Arne; Blaak, Ellen E; Viguerie, Nathalie; Masoodi, Mojgan.

In: Scientific Reports, Vol. 10, 9236, 2020.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Valsesia, A, Chakrabarti, A, Hager, J, Langin, D, Saris, WHM, Astrup, A, Blaak, EE, Viguerie, N & Masoodi, M 2020, 'Integrative phenotyping of glycemic responders upon clinical weight loss using multi-omics', Scientific Reports, vol. 10, 9236. https://doi.org/10.1038/s41598-020-65936-8

APA

Valsesia, A., Chakrabarti, A., Hager, J., Langin, D., Saris, W. H. M., Astrup, A., Blaak, E. E., Viguerie, N., & Masoodi, M. (2020). Integrative phenotyping of glycemic responders upon clinical weight loss using multi-omics. Scientific Reports, 10, [9236]. https://doi.org/10.1038/s41598-020-65936-8

Vancouver

Valsesia A, Chakrabarti A, Hager J, Langin D, Saris WHM, Astrup A et al. Integrative phenotyping of glycemic responders upon clinical weight loss using multi-omics. Scientific Reports. 2020;10. 9236. https://doi.org/10.1038/s41598-020-65936-8

Author

Valsesia, Armand ; Chakrabarti, Anirikh ; Hager, Jörg ; Langin, Dominique ; Saris, Wim H M ; Astrup, Arne ; Blaak, Ellen E ; Viguerie, Nathalie ; Masoodi, Mojgan. / Integrative phenotyping of glycemic responders upon clinical weight loss using multi-omics. In: Scientific Reports. 2020 ; Vol. 10.

Bibtex

@article{823ba30c67544aa8a52254f3f8fa086c,
title = "Integrative phenotyping of glycemic responders upon clinical weight loss using multi-omics",
abstract = "Weight loss aims to improve glycemic control in obese but strong variability is observed. Using a multi-omics approach, we investigated differences between 174 responders and 201 non-responders, that had lost >8% body weight following a low-caloric diet (LCD, 800 kcal/d for 8 weeks). The two groups were comparable at baseline for body composition, glycemic control, adipose tissue transcriptomics and plasma ketone bodies. But they differed significantly in their response to LCD, including improvements in visceral fat, overall insulin resistance (IR) and tissue-specific IR. Transcriptomics analyses found down-regulation in key lipogenic genes (e.g. SCD, ELOVL5) in responders relative to non-responders; metabolomics showed increase in ketone bodies; while proteomics revealed differences in lipoproteins. Findings were consistent between genders; with women displaying smaller improvements owing to a better baseline metabolic condition. Integrative analyses identified a plasma omics model that was able to predict non-responders with strong performance (on a testing dataset, the Receiving Operating Curve Area Under the Curve (ROC AUC) was 75% with 95% Confidence Intervals (CI) [67%, 83%]). This model was based on baseline parameters without the need for intrusive measurements and outperformed clinical models (p = 0.00075, with a +14% difference on the ROC AUCs). Our approach document differences between responders and non-responders, with strong contributions from liver and adipose tissues. Differences may be due to de novo lipogenesis, keto-metabolism and lipoprotein metabolism. These findings are useful for clinical practice to better characterize non-responders both prior and during weight loss.",
author = "Armand Valsesia and Anirikh Chakrabarti and J{\"o}rg Hager and Dominique Langin and Saris, {Wim H M} and Arne Astrup and Blaak, {Ellen E} and Nathalie Viguerie and Mojgan Masoodi",
note = "CURIS 2020 NEXS 185",
year = "2020",
doi = "10.1038/s41598-020-65936-8",
language = "English",
volume = "10",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "nature publishing group",

}

RIS

TY - JOUR

T1 - Integrative phenotyping of glycemic responders upon clinical weight loss using multi-omics

AU - Valsesia, Armand

AU - Chakrabarti, Anirikh

AU - Hager, Jörg

AU - Langin, Dominique

AU - Saris, Wim H M

AU - Astrup, Arne

AU - Blaak, Ellen E

AU - Viguerie, Nathalie

AU - Masoodi, Mojgan

N1 - CURIS 2020 NEXS 185

PY - 2020

Y1 - 2020

N2 - Weight loss aims to improve glycemic control in obese but strong variability is observed. Using a multi-omics approach, we investigated differences between 174 responders and 201 non-responders, that had lost >8% body weight following a low-caloric diet (LCD, 800 kcal/d for 8 weeks). The two groups were comparable at baseline for body composition, glycemic control, adipose tissue transcriptomics and plasma ketone bodies. But they differed significantly in their response to LCD, including improvements in visceral fat, overall insulin resistance (IR) and tissue-specific IR. Transcriptomics analyses found down-regulation in key lipogenic genes (e.g. SCD, ELOVL5) in responders relative to non-responders; metabolomics showed increase in ketone bodies; while proteomics revealed differences in lipoproteins. Findings were consistent between genders; with women displaying smaller improvements owing to a better baseline metabolic condition. Integrative analyses identified a plasma omics model that was able to predict non-responders with strong performance (on a testing dataset, the Receiving Operating Curve Area Under the Curve (ROC AUC) was 75% with 95% Confidence Intervals (CI) [67%, 83%]). This model was based on baseline parameters without the need for intrusive measurements and outperformed clinical models (p = 0.00075, with a +14% difference on the ROC AUCs). Our approach document differences between responders and non-responders, with strong contributions from liver and adipose tissues. Differences may be due to de novo lipogenesis, keto-metabolism and lipoprotein metabolism. These findings are useful for clinical practice to better characterize non-responders both prior and during weight loss.

AB - Weight loss aims to improve glycemic control in obese but strong variability is observed. Using a multi-omics approach, we investigated differences between 174 responders and 201 non-responders, that had lost >8% body weight following a low-caloric diet (LCD, 800 kcal/d for 8 weeks). The two groups were comparable at baseline for body composition, glycemic control, adipose tissue transcriptomics and plasma ketone bodies. But they differed significantly in their response to LCD, including improvements in visceral fat, overall insulin resistance (IR) and tissue-specific IR. Transcriptomics analyses found down-regulation in key lipogenic genes (e.g. SCD, ELOVL5) in responders relative to non-responders; metabolomics showed increase in ketone bodies; while proteomics revealed differences in lipoproteins. Findings were consistent between genders; with women displaying smaller improvements owing to a better baseline metabolic condition. Integrative analyses identified a plasma omics model that was able to predict non-responders with strong performance (on a testing dataset, the Receiving Operating Curve Area Under the Curve (ROC AUC) was 75% with 95% Confidence Intervals (CI) [67%, 83%]). This model was based on baseline parameters without the need for intrusive measurements and outperformed clinical models (p = 0.00075, with a +14% difference on the ROC AUCs). Our approach document differences between responders and non-responders, with strong contributions from liver and adipose tissues. Differences may be due to de novo lipogenesis, keto-metabolism and lipoprotein metabolism. These findings are useful for clinical practice to better characterize non-responders both prior and during weight loss.

U2 - 10.1038/s41598-020-65936-8

DO - 10.1038/s41598-020-65936-8

M3 - Journal article

C2 - 32514005

VL - 10

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

M1 - 9236

ER -

ID: 242711396