Unraveling the metabolic underpinnings of frailty using multicohort observational and Mendelian randomization analyses

Jonathan K.L. Mak; Laura Kananen; Chenxi Qin; Ralf Kuja-Halkola; Bowen Tang; Jake Lin; Yunzhang Wang; Tuija Jääskeläinen; Seppo Koskinen; Yi Lu; Patrik K.E. Magnusson; Sara Hägg; Juulia Jylhävä

doi:10.1111/acel.13868

Unraveling the metabolic underpinnings of frailty using multicohort observational and Mendelian randomization analyses

Jonathan K.L. Mak, Laura Kananen, Chenxi Qin, Ralf Kuja-Halkola, Bowen Tang, Jake Lin, Yunzhang Wang, Tuija Jääskeläinen, Seppo Koskinen, Yi Lu, Patrik K.E. Magnusson, Sara Hägg, Juulia Jylhävä

Health Sciences

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

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Abstract

Identifying metabolic biomarkers of frailty, an age-related state of physiological decline, is important for understanding its metabolic underpinnings and developing preventive strategies. Here, we systematically examined 168 nuclear magnetic resonance-based metabolomic biomarkers and 32 clinical biomarkers for their associations with frailty. In up to 90,573 UK Biobank participants, we identified 59 biomarkers robustly and independently associated with the frailty index (FI). Of these, 34 associations were replicated in the Swedish TwinGene study (n = 11,025) and the Finnish Health 2000 Survey (n = 6073). Using two-sample Mendelian randomization, we showed that the genetically predicted level of glycoprotein acetyls, an inflammatory marker, was statistically significantly associated with an increased FI (β per SD increase = 0.37%, 95% confidence interval: 0.12–0.61). Creatinine and several lipoprotein lipids were also associated with increased FI, yet their effects were mostly driven by kidney and cardiometabolic diseases, respectively. Our findings provide new insights into the causal effects of metabolites on frailty and highlight the role of chronic inflammation underlying frailty development.

Original language	English
Article number	e13868
Number of pages	17
Journal	AGING CELL
Volume	22
Issue number	8
DOIs	https://doi.org/10.1111/acel.13868
Publication status	Published - Aug 2023
Publication type	A1 Journal article-refereed

Funding

This work was supported by the Swedish Research Council (2018–02077, 2019–01272, 2020–06101, 2022–01608), the Academy of Finland (grant no. 349335), the Sigrid Jusélius Foundation, the Loo & Hans Osterman Foundation, the Strategic Research Program in Epidemiology at Karolinska Institutet, the Karolinska Institutet Foundations, the King Gustaf V and Queen Victoria's Foundation of Freemasons, the Yrjö Jahnsson Foundation (grant no 20217416), the Juho Vainio Foundation (grant number 202100335), and the Päivikki and Sakari Sohlberg Foundation (grant number 220032). This research was conducted using the UK Biobank resource, as part of the registered project 22224. The analyses of UK Biobank genotypes were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Uppmax, partially funded by the Swedish Research Council through grant agreement number 2018–05973. We acknowledge the Swedish Twin Registry for access to data. The Swedish Twin Registry is managed by Karolinska Institutet and receives funding through the Swedish Research Council under the grant number 2021-00180. The Health 2000 Survey was funded by the Finnish Institute for Health and Welfare (THL), the Finnish Centre for Pensions (ETK), the Social Insurance Institution of Finland (KELA), the Local Government Pensions Institution (KEVA), and other organizations listed on the survey website (https://thl.fi/en/web/thl-biobank/for-researchers/sample-collections/health-2000-and-2011-surveys). We also thank all participants and staff who contributed to this study. This study was accomplished within the context of the Swedish National Graduate School on Ageing and Health (SWEAH). This research was conducted using the UK Biobank resource, as part of the registered project 22224. The analyses of UK Biobank genotypes were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Uppmax, partially funded by the Swedish Research Council through grant agreement number 2018–05973. We acknowledge the Swedish Twin Registry for access to data. The Swedish Twin Registry is managed by Karolinska Institutet and receives funding through the Swedish Research Council under the grant number 2021‐00180. The Health 2000 Survey was funded by the Finnish Institute for Health and Welfare (THL), the Finnish Centre for Pensions (ETK), the Social Insurance Institution of Finland (KELA), the Local Government Pensions Institution (KEVA), and other organizations listed on the survey website ( https://thl.fi/en/web/thl‐biobank/for‐researchers/sample‐collections/health‐2000‐and‐2011‐surveys ). We also thank all participants and staff who contributed to this study. This study was accomplished within the context of the Swedish National Graduate School on Ageing and Health (SWEAH).

Keywords

biomarkers
frailty
Mendelian randomization
metabolomics
twins

Publication forum classification

Publication forum level 2

ASJC Scopus subject areas

Ageing
Cell Biology

UN SDGs

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

Access to Document

10.1111/acel.13868Licence: CC BY

Aging Cell - 2023 - MakFinal published version, 1.54 MBLicence: CC BY

https://urn.fi/URN:NBN:fi:tuni-202309088044Licence: CC BY

Cite this

Mak, J. K. L., Kananen, L., Qin, C., Kuja-Halkola, R., Tang, B., Lin, J., Wang, Y., Jääskeläinen, T., Koskinen, S., Lu, Y., Magnusson, P. K. E., Hägg, S., & Jylhävä, J. (2023). Unraveling the metabolic underpinnings of frailty using multicohort observational and Mendelian randomization analyses. AGING CELL, 22(8), Article e13868. https://doi.org/10.1111/acel.13868

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title = "Unraveling the metabolic underpinnings of frailty using multicohort observational and Mendelian randomization analyses",

abstract = "Identifying metabolic biomarkers of frailty, an age-related state of physiological decline, is important for understanding its metabolic underpinnings and developing preventive strategies. Here, we systematically examined 168 nuclear magnetic resonance-based metabolomic biomarkers and 32 clinical biomarkers for their associations with frailty. In up to 90,573 UK Biobank participants, we identified 59 biomarkers robustly and independently associated with the frailty index (FI). Of these, 34 associations were replicated in the Swedish TwinGene study (n = 11,025) and the Finnish Health 2000 Survey (n = 6073). Using two-sample Mendelian randomization, we showed that the genetically predicted level of glycoprotein acetyls, an inflammatory marker, was statistically significantly associated with an increased FI (β per SD increase = 0.37%, 95% confidence interval: 0.12–0.61). Creatinine and several lipoprotein lipids were also associated with increased FI, yet their effects were mostly driven by kidney and cardiometabolic diseases, respectively. Our findings provide new insights into the causal effects of metabolites on frailty and highlight the role of chronic inflammation underlying frailty development.",

keywords = "biomarkers, frailty, Mendelian randomization, metabolomics, twins",

author = "Mak, {Jonathan K.L.} and Laura Kananen and Chenxi Qin and Ralf Kuja-Halkola and Bowen Tang and Jake Lin and Yunzhang Wang and Tuija J{\"a}{\"a}skel{\"a}inen and Seppo Koskinen and Yi Lu and Magnusson, {Patrik K.E.} and Sara H{\"a}gg and Juulia Jylh{\"a}v{\"a}",

note = "Funding Information: This work was supported by the Swedish Research Council (2018–02077, 2019–01272, 2020–06101, 2022–01608), the Academy of Finland (grant no. 349335), the Sigrid Jus{\'e}lius Foundation, the Loo & Hans Osterman Foundation, the Strategic Research Program in Epidemiology at Karolinska Institutet, the Karolinska Institutet Foundations, the King Gustaf V and Queen Victoria's Foundation of Freemasons, the Yrj{\"o} Jahnsson Foundation (grant no 20217416), the Juho Vainio Foundation (grant number 202100335), and the P{\"a}ivikki and Sakari Sohlberg Foundation (grant number 220032). Funding Information: This research was conducted using the UK Biobank resource, as part of the registered project 22224. The analyses of UK Biobank genotypes were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Uppmax, partially funded by the Swedish Research Council through grant agreement number 2018–05973. We acknowledge the Swedish Twin Registry for access to data. The Swedish Twin Registry is managed by Karolinska Institutet and receives funding through the Swedish Research Council under the grant number 2021-00180. The Health 2000 Survey was funded by the Finnish Institute for Health and Welfare (THL), the Finnish Centre for Pensions (ETK), the Social Insurance Institution of Finland (KELA), the Local Government Pensions Institution (KEVA), and other organizations listed on the survey website (https://thl.fi/en/web/thl-biobank/for-researchers/sample-collections/health-2000-and-2011-surveys). We also thank all participants and staff who contributed to this study. This study was accomplished within the context of the Swedish National Graduate School on Ageing and Health (SWEAH). Funding Information: This research was conducted using the UK Biobank resource, as part of the registered project 22224. The analyses of UK Biobank genotypes were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Uppmax, partially funded by the Swedish Research Council through grant agreement number 2018–05973. We acknowledge the Swedish Twin Registry for access to data. The Swedish Twin Registry is managed by Karolinska Institutet and receives funding through the Swedish Research Council under the grant number 2021‐00180. The Health 2000 Survey was funded by the Finnish Institute for Health and Welfare (THL), the Finnish Centre for Pensions (ETK), the Social Insurance Institution of Finland (KELA), the Local Government Pensions Institution (KEVA), and other organizations listed on the survey website ( https://thl.fi/en/web/thl‐biobank/for‐researchers/sample‐collections/health‐2000‐and‐2011‐surveys ). We also thank all participants and staff who contributed to this study. This study was accomplished within the context of the Swedish National Graduate School on Ageing and Health (SWEAH). Publisher Copyright: {\textcopyright} 2023 The Authors. Aging Cell published by Anatomical Society and John Wiley & Sons Ltd.",

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T1 - Unraveling the metabolic underpinnings of frailty using multicohort observational and Mendelian randomization analyses

AU - Mak, Jonathan K.L.

AU - Kananen, Laura

AU - Qin, Chenxi

AU - Kuja-Halkola, Ralf

AU - Tang, Bowen

AU - Lin, Jake

AU - Wang, Yunzhang

AU - Jääskeläinen, Tuija

AU - Koskinen, Seppo

AU - Lu, Yi

AU - Magnusson, Patrik K.E.

AU - Hägg, Sara

AU - Jylhävä, Juulia

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N2 - Identifying metabolic biomarkers of frailty, an age-related state of physiological decline, is important for understanding its metabolic underpinnings and developing preventive strategies. Here, we systematically examined 168 nuclear magnetic resonance-based metabolomic biomarkers and 32 clinical biomarkers for their associations with frailty. In up to 90,573 UK Biobank participants, we identified 59 biomarkers robustly and independently associated with the frailty index (FI). Of these, 34 associations were replicated in the Swedish TwinGene study (n = 11,025) and the Finnish Health 2000 Survey (n = 6073). Using two-sample Mendelian randomization, we showed that the genetically predicted level of glycoprotein acetyls, an inflammatory marker, was statistically significantly associated with an increased FI (β per SD increase = 0.37%, 95% confidence interval: 0.12–0.61). Creatinine and several lipoprotein lipids were also associated with increased FI, yet their effects were mostly driven by kidney and cardiometabolic diseases, respectively. Our findings provide new insights into the causal effects of metabolites on frailty and highlight the role of chronic inflammation underlying frailty development.

AB - Identifying metabolic biomarkers of frailty, an age-related state of physiological decline, is important for understanding its metabolic underpinnings and developing preventive strategies. Here, we systematically examined 168 nuclear magnetic resonance-based metabolomic biomarkers and 32 clinical biomarkers for their associations with frailty. In up to 90,573 UK Biobank participants, we identified 59 biomarkers robustly and independently associated with the frailty index (FI). Of these, 34 associations were replicated in the Swedish TwinGene study (n = 11,025) and the Finnish Health 2000 Survey (n = 6073). Using two-sample Mendelian randomization, we showed that the genetically predicted level of glycoprotein acetyls, an inflammatory marker, was statistically significantly associated with an increased FI (β per SD increase = 0.37%, 95% confidence interval: 0.12–0.61). Creatinine and several lipoprotein lipids were also associated with increased FI, yet their effects were mostly driven by kidney and cardiometabolic diseases, respectively. Our findings provide new insights into the causal effects of metabolites on frailty and highlight the role of chronic inflammation underlying frailty development.

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KW - frailty

KW - Mendelian randomization

KW - metabolomics

KW - twins

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DO - 10.1111/acel.13868

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AN - SCOPUS:85159160359

SN - 1474-9718

VL - 22

JO - AGING CELL

JF - AGING CELL

IS - 8

M1 - e13868

ER -

Unraveling the metabolic underpinnings of frailty using multicohort observational and Mendelian randomization analyses

Abstract

Funding

Keywords

Publication forum classification

ASJC Scopus subject areas

UN SDGs

Access to Document

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