Lipidomic signatures of changes in adiposity: A large prospective study of 5849 adults from the australian diabetes, obesity and lifestyle study

Habtamu B. Beyene; Gavriel Olshansky; Corey Giles; Kevin Huynh; Michelle Cinel; Natalie A. Mellett; Adam Alexander T. Smith; Jonathan E. Shaw; Dianna J. Magliano; Peter J. Meikle

doi:10.3390/metabo11090646

Lipidomic signatures of changes in adiposity: A large prospective study of 5849 adults from the australian diabetes, obesity and lifestyle study

Habtamu B. Beyene
, Gavriel Olshansky
, Corey Giles
, Kevin Huynh
, Michelle Cinel
, Natalie A. Mellett
, Adam Alexander T. Smith
, Jonathan E. Shaw
, Dianna J. Magliano
, Peter J. Meikle

Research output: Contribution to journal › Article › Research › peer-review

22 Citations (Scopus)

Abstract

Lipid metabolism is tightly linked to adiposity. Comprehensive lipidomic profiling offers new insights into the dysregulation of lipid metabolism in relation to weight gain. Here, we inves-tigated the relationship of the human plasma lipidome and changes in waist circumference (WC) and body mass index (BMI). Adults (2653 men and 3196 women), 25–95 years old who attended the baseline survey of the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) and the 5-year follow-up were enrolled. A targeted lipidomic approach was used to quantify 706 distinct molecular lipid species in the plasma samples. Multiple linear regression models were used to examine the relationship between the baseline lipidomic profile and changes in WC and BMI. Metabolic scores for change in WC were generated using a ridge regression model. Alkyl-diacylglycerol such as TG(O-50:2) [NL-18:1] displayed the strongest association with change in WC (β-coefficient = 0.125 cm increment per SD increment in baseline lipid level, p = 2.78 × 10^-11. Many lipid species containing linoleate (18:2) fatty acids were negatively associated with both WC and BMI gain. Compared to traditional models, multivariate models containing lipid species identify individuals at a greater risk of gaining WC: top quintile relative to bottom quintile (odds ratio, 95% CI = 5.4, 3.8–6.6 for women and 2.3, 1.7–3.0 for men). Our findings define metabolic profiles that characterize individuals at risk of weight gain or WC increase and provide important insight into the biological role of lipids in obesity.

Original language	English
Article number	646
Number of pages	21
Journal	Metabolites
Volume	11
Issue number	9
DOIs	https://doi.org/10.3390/metabo11090646
Publication status	Published - Sept 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Change in BMI
Change in WC
Metabolic scores10.3390/metabo11090646
Plasma lipidomics

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10.3390/metabo11090646Licence: CC BY

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title = "Lipidomic signatures of changes in adiposity: A large prospective study of 5849 adults from the australian diabetes, obesity and lifestyle study",

abstract = "Lipid metabolism is tightly linked to adiposity. Comprehensive lipidomic profiling offers new insights into the dysregulation of lipid metabolism in relation to weight gain. Here, we inves-tigated the relationship of the human plasma lipidome and changes in waist circumference (WC) and body mass index (BMI). Adults (2653 men and 3196 women), 25–95 years old who attended the baseline survey of the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) and the 5-year follow-up were enrolled. A targeted lipidomic approach was used to quantify 706 distinct molecular lipid species in the plasma samples. Multiple linear regression models were used to examine the relationship between the baseline lipidomic profile and changes in WC and BMI. Metabolic scores for change in WC were generated using a ridge regression model. Alkyl-diacylglycerol such as TG(O-50:2) [NL-18:1] displayed the strongest association with change in WC (β-coefficient = 0.125 cm increment per SD increment in baseline lipid level, p = 2.78 × 10-11. Many lipid species containing linoleate (18:2) fatty acids were negatively associated with both WC and BMI gain. Compared to traditional models, multivariate models containing lipid species identify individuals at a greater risk of gaining WC: top quintile relative to bottom quintile (odds ratio, 95\% CI = 5.4, 3.8–6.6 for women and 2.3, 1.7–3.0 for men). Our findings define metabolic profiles that characterize individuals at risk of weight gain or WC increase and provide important insight into the biological role of lipids in obesity.",

keywords = "Change in BMI, Change in WC, Metabolic scores10.3390/metabo11090646, Plasma lipidomics",

author = "Beyene, \{Habtamu B.\} and Gavriel Olshansky and Corey Giles and Kevin Huynh and Michelle Cinel and Mellett, \{Natalie A.\} and Smith, \{Adam Alexander T.\} and Shaw, \{Jonathan E.\} and Magliano, \{Dianna J.\} and Meikle, \{Peter J.\}",

note = "Funding Information: Funding: National Health and Medical Research Council of Australia (Project grant APP1101320) and the Victorian Government{\textquoteright}s Operational Infrastructure Support Program. JES and DJM are supported by an Investigator Grant and a Senior Research Fellowship, respectively, from the National Health and Medical Research Council of Australia. HBB was supported by the Baker institute and Monash University Scholarships. Funding Information: Acknowledgments: The AusDiab study, initiated and coordinated by the International Diabetes Institute, and subsequently coordinated by the Baker Heart and Diabetes Institute, gratefully acknowledges the support and assistance given by: A Allman, B Atkins, S Bennett, A Bonney, S Chadban, M de Courten, M Dalton, D Dunstan, T Dwyer, H Jahangir, D Jolley, D McCarty, A Meehan, N Meinig, S Murray, K O{\textquoteright}Dea, K Polkinghorne, P Phillips, C Reid, A Stewart, R Tapp, H Taylor, T Welborn, T Whalen, F Wilson, P Zimmet and all the study participants. For funding or logistical support, we are grateful to the National Health and Medical Research Council (NHMRC grant 233200), Australian Government Department of Health and Ageing. Abbott Australasia Pty Ltd., Alphapharm Pty Ltd., AstraZeneca, Bristol-Myers Squibb, City Health Centre-Diabetes Service-Canberra, Department of Health and Community Services—Northern Territory, Department of Health and Human Services—Tasmania, Department of Health—New South Wales, Department of Health—Western Australia, Department of Health—South Australia, Department of Human Services—Victoria, Diabetes Australia, Diabetes Australia Northern Territory, Eli Lilly Australia, Estate of the Late Edward Wilson, GlaxoSmithKline, Jack Brockhoff Foundation, Janssen-Cilag, Kidney Health Australia, Marian \& FH Flack Trust, Menzies Research Institute, Merck Sharp \& Dohme, Novartis Pharmaceuticals, Novo Nordisk Pharmaceuticals, Pfizer Pty Ltd., Pratt Foundation, Queensland Health, Roche Diagnostics Australia, Royal Prince Alfred Hospital, Sydney, Sanofi Aventis, sanofi-synthelabo and the Victorian Government{\textquoteright}s OIS Program. Publisher Copyright: {\textcopyright} 2021 by the authors. Li-censee MDPI, Basel, Switzerland.",

year = "2021",

month = sep,

doi = "10.3390/metabo11090646",

language = "English",

volume = "11",

journal = "Metabolites",

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publisher = "MDPI AG",

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T1 - Lipidomic signatures of changes in adiposity

T2 - A large prospective study of 5849 adults from the australian diabetes, obesity and lifestyle study

AU - Beyene, Habtamu B.

AU - Olshansky, Gavriel

AU - Giles, Corey

AU - Huynh, Kevin

AU - Cinel, Michelle

AU - Mellett, Natalie A.

AU - Smith, Adam Alexander T.

AU - Shaw, Jonathan E.

AU - Magliano, Dianna J.

AU - Meikle, Peter J.

N1 - Funding Information: Funding: National Health and Medical Research Council of Australia (Project grant APP1101320) and the Victorian Government’s Operational Infrastructure Support Program. JES and DJM are supported by an Investigator Grant and a Senior Research Fellowship, respectively, from the National Health and Medical Research Council of Australia. HBB was supported by the Baker institute and Monash University Scholarships. Funding Information: Acknowledgments: The AusDiab study, initiated and coordinated by the International Diabetes Institute, and subsequently coordinated by the Baker Heart and Diabetes Institute, gratefully acknowledges the support and assistance given by: A Allman, B Atkins, S Bennett, A Bonney, S Chadban, M de Courten, M Dalton, D Dunstan, T Dwyer, H Jahangir, D Jolley, D McCarty, A Meehan, N Meinig, S Murray, K O’Dea, K Polkinghorne, P Phillips, C Reid, A Stewart, R Tapp, H Taylor, T Welborn, T Whalen, F Wilson, P Zimmet and all the study participants. For funding or logistical support, we are grateful to the National Health and Medical Research Council (NHMRC grant 233200), Australian Government Department of Health and Ageing. Abbott Australasia Pty Ltd., Alphapharm Pty Ltd., AstraZeneca, Bristol-Myers Squibb, City Health Centre-Diabetes Service-Canberra, Department of Health and Community Services—Northern Territory, Department of Health and Human Services—Tasmania, Department of Health—New South Wales, Department of Health—Western Australia, Department of Health—South Australia, Department of Human Services—Victoria, Diabetes Australia, Diabetes Australia Northern Territory, Eli Lilly Australia, Estate of the Late Edward Wilson, GlaxoSmithKline, Jack Brockhoff Foundation, Janssen-Cilag, Kidney Health Australia, Marian & FH Flack Trust, Menzies Research Institute, Merck Sharp & Dohme, Novartis Pharmaceuticals, Novo Nordisk Pharmaceuticals, Pfizer Pty Ltd., Pratt Foundation, Queensland Health, Roche Diagnostics Australia, Royal Prince Alfred Hospital, Sydney, Sanofi Aventis, sanofi-synthelabo and the Victorian Government’s OIS Program. Publisher Copyright: © 2021 by the authors. Li-censee MDPI, Basel, Switzerland.

PY - 2021/9

Y1 - 2021/9

N2 - Lipid metabolism is tightly linked to adiposity. Comprehensive lipidomic profiling offers new insights into the dysregulation of lipid metabolism in relation to weight gain. Here, we inves-tigated the relationship of the human plasma lipidome and changes in waist circumference (WC) and body mass index (BMI). Adults (2653 men and 3196 women), 25–95 years old who attended the baseline survey of the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) and the 5-year follow-up were enrolled. A targeted lipidomic approach was used to quantify 706 distinct molecular lipid species in the plasma samples. Multiple linear regression models were used to examine the relationship between the baseline lipidomic profile and changes in WC and BMI. Metabolic scores for change in WC were generated using a ridge regression model. Alkyl-diacylglycerol such as TG(O-50:2) [NL-18:1] displayed the strongest association with change in WC (β-coefficient = 0.125 cm increment per SD increment in baseline lipid level, p = 2.78 × 10-11. Many lipid species containing linoleate (18:2) fatty acids were negatively associated with both WC and BMI gain. Compared to traditional models, multivariate models containing lipid species identify individuals at a greater risk of gaining WC: top quintile relative to bottom quintile (odds ratio, 95% CI = 5.4, 3.8–6.6 for women and 2.3, 1.7–3.0 for men). Our findings define metabolic profiles that characterize individuals at risk of weight gain or WC increase and provide important insight into the biological role of lipids in obesity.

AB - Lipid metabolism is tightly linked to adiposity. Comprehensive lipidomic profiling offers new insights into the dysregulation of lipid metabolism in relation to weight gain. Here, we inves-tigated the relationship of the human plasma lipidome and changes in waist circumference (WC) and body mass index (BMI). Adults (2653 men and 3196 women), 25–95 years old who attended the baseline survey of the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) and the 5-year follow-up were enrolled. A targeted lipidomic approach was used to quantify 706 distinct molecular lipid species in the plasma samples. Multiple linear regression models were used to examine the relationship between the baseline lipidomic profile and changes in WC and BMI. Metabolic scores for change in WC were generated using a ridge regression model. Alkyl-diacylglycerol such as TG(O-50:2) [NL-18:1] displayed the strongest association with change in WC (β-coefficient = 0.125 cm increment per SD increment in baseline lipid level, p = 2.78 × 10-11. Many lipid species containing linoleate (18:2) fatty acids were negatively associated with both WC and BMI gain. Compared to traditional models, multivariate models containing lipid species identify individuals at a greater risk of gaining WC: top quintile relative to bottom quintile (odds ratio, 95% CI = 5.4, 3.8–6.6 for women and 2.3, 1.7–3.0 for men). Our findings define metabolic profiles that characterize individuals at risk of weight gain or WC increase and provide important insight into the biological role of lipids in obesity.

KW - Change in BMI

KW - Change in WC

KW - Metabolic scores10.3390/metabo11090646

KW - Plasma lipidomics

UR - https://www.scopus.com/pages/publications/85115780049

U2 - 10.3390/metabo11090646

DO - 10.3390/metabo11090646

M3 - Article

C2 - 34564462

AN - SCOPUS:85115780049

SN - 2218-1989

VL - 11

JO - Metabolites

JF - Metabolites

IS - 9

M1 - 646

ER -

Lipidomic signatures of changes in adiposity: A large prospective study of 5849 adults from the australian diabetes, obesity and lifestyle study

Abstract

UN SDGs

Keywords

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