Clinical biomarkers and associations with healthspan and lifespan: Evidence from observational and genetic data.


Journal

EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039

Informations de publication

Date de publication:
Apr 2021
Historique:
received: 25 01 2021
revised: 15 03 2021
accepted: 16 03 2021
pubmed: 5 4 2021
medline: 30 11 2021
entrez: 4 4 2021
Statut: ppublish

Résumé

Biomarker-disease relationships are extensively investigated. However, associations between common clinical biomarkers and healthspan, the disease-free lifespan, are largely unknown. We aimed to explore the predictive values of ten biomarkers on healthspan and lifespan, and to identify putative causal mechanisms. Using data from 12,098 Swedish individuals aged 47-94 years, we examined both serum concentrations and genetically predicted levels of ten glycemic, lipid-, inflammatory, and hematological biomarkers. During a follow-up period of up to 16 years, 3681 incident cases of any chronic disease (i.e., end of healthspan) and 2674 deaths (i.e., end of lifespan) were documented. Cox regression models were applied to estimate the associations of a one standard deviation increase in biomarkers with healthspan and lifespan. Seven out of ten serum biomarkers were significantly associated with risks of any chronic disease and death; elevated glycemic biomarkers and high-density lipoprotein-related biomarkers showed the strongest detrimental (hazard ratio [HR] 1·29 [95% CI 1·24-1·34]) and protective effects (HR 0·92 [95% CI 0·89-0·96]), respectively. Genetic predisposition to elevated fasting blood glucose (FBG) was associated with increased risks of any chronic disease (HR 1·05 [95% CI 1·02-1·09]); genetically determined higher C-reactive protein correlated with lower death risks (HR 0·91 [95% CI 0·87-0·95]). Notably, the genetically proxied FBG-healthspan association was largely explained by serum FBG concentration. Circulating concentrations of glycemic, lipid-, and inflammatory biomarkers are predictive of healthspan and lifespan. Glucose control is a putative causal mechanism and a potential intervention target for healthspan maintenance. This study was supported by the Swedish Research Council (2015-03,255, 2018-02,077), FORTE (2013-2292), the Loo & Hans Osterman Foundation, the Foundation for Geriatric Diseases, the Magnus Bergwall Foundation, the Strategic Research Program in Epidemiology at Karolinska Institutet (SH, JJ), the China Scholarship Council, and the Swedish National Graduate School for Competitive Science on Ageing and Health. The Swedish Twin Registry is managed by Karolinska Institutet and receives funding as an infrastructure through the Swedish Research Council, 2017-00,641.

Sections du résumé

BACKGROUND BACKGROUND
Biomarker-disease relationships are extensively investigated. However, associations between common clinical biomarkers and healthspan, the disease-free lifespan, are largely unknown. We aimed to explore the predictive values of ten biomarkers on healthspan and lifespan, and to identify putative causal mechanisms.
METHODS METHODS
Using data from 12,098 Swedish individuals aged 47-94 years, we examined both serum concentrations and genetically predicted levels of ten glycemic, lipid-, inflammatory, and hematological biomarkers. During a follow-up period of up to 16 years, 3681 incident cases of any chronic disease (i.e., end of healthspan) and 2674 deaths (i.e., end of lifespan) were documented. Cox regression models were applied to estimate the associations of a one standard deviation increase in biomarkers with healthspan and lifespan.
FINDINGS RESULTS
Seven out of ten serum biomarkers were significantly associated with risks of any chronic disease and death; elevated glycemic biomarkers and high-density lipoprotein-related biomarkers showed the strongest detrimental (hazard ratio [HR] 1·29 [95% CI 1·24-1·34]) and protective effects (HR 0·92 [95% CI 0·89-0·96]), respectively. Genetic predisposition to elevated fasting blood glucose (FBG) was associated with increased risks of any chronic disease (HR 1·05 [95% CI 1·02-1·09]); genetically determined higher C-reactive protein correlated with lower death risks (HR 0·91 [95% CI 0·87-0·95]). Notably, the genetically proxied FBG-healthspan association was largely explained by serum FBG concentration.
INTERPRETATION CONCLUSIONS
Circulating concentrations of glycemic, lipid-, and inflammatory biomarkers are predictive of healthspan and lifespan. Glucose control is a putative causal mechanism and a potential intervention target for healthspan maintenance.
FUNDING BACKGROUND
This study was supported by the Swedish Research Council (2015-03,255, 2018-02,077), FORTE (2013-2292), the Loo & Hans Osterman Foundation, the Foundation for Geriatric Diseases, the Magnus Bergwall Foundation, the Strategic Research Program in Epidemiology at Karolinska Institutet (SH, JJ), the China Scholarship Council, and the Swedish National Graduate School for Competitive Science on Ageing and Health. The Swedish Twin Registry is managed by Karolinska Institutet and receives funding as an infrastructure through the Swedish Research Council, 2017-00,641.

Identifiants

pubmed: 33813140
pii: S2352-3964(21)00111-0
doi: 10.1016/j.ebiom.2021.103318
pmc: PMC8047464
pii:
doi:

Substances chimiques

Biomarkers 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

103318

Commentaires et corrections

Type : CommentIn

Informations de copyright

Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest All authors declare no competing interest.

Auteurs

Xia Li (X)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Alexander Ploner (A)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Yunzhang Wang (Y)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Yiqiang Zhan (Y)

Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.

Nancy L Pedersen (NL)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Patrik Ke Magnusson (PK)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Juulia Jylhävä (J)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Sara Hägg (S)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Electronic address: sara.hagg@ki.se.

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Classifications MeSH