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Metabolomic phenotyping of obesity for profiling cardiovascular and ocular diseases

BACKGROUND: We aimed to evaluate the impacts of metabolomic body mass index (metBMI) phenotypes on the risks of cardiovascular and ocular diseases outcomes. METHODS: This study included cohorts in UK and Guangzhou, China. By leveraging the serum metabolome and BMI data from UK Biobank, this study de...

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Autores principales: Zhong, Pingting, Tan, Shaoying, Zhu, Zhuoting, Bulloch, Gabriella, Long, Erping, Huang, Wenyong, He, Mingguang, Wang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10262415/
https://www.ncbi.nlm.nih.gov/pubmed/37308902
http://dx.doi.org/10.1186/s12967-023-04244-x
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author Zhong, Pingting
Tan, Shaoying
Zhu, Zhuoting
Bulloch, Gabriella
Long, Erping
Huang, Wenyong
He, Mingguang
Wang, Wei
author_facet Zhong, Pingting
Tan, Shaoying
Zhu, Zhuoting
Bulloch, Gabriella
Long, Erping
Huang, Wenyong
He, Mingguang
Wang, Wei
author_sort Zhong, Pingting
collection PubMed
description BACKGROUND: We aimed to evaluate the impacts of metabolomic body mass index (metBMI) phenotypes on the risks of cardiovascular and ocular diseases outcomes. METHODS: This study included cohorts in UK and Guangzhou, China. By leveraging the serum metabolome and BMI data from UK Biobank, this study developed and validated a metBMI prediction model using a ridge regression model among 89,830 participants based on 249 metabolites. Five obesity phenotypes were obtained by metBMI and actual BMI (actBMI): normal weight (NW, metBMI of 18.5–24.9 kg/m(2)), overweight (OW, metBMI of 25–29.9 kg/m(2)), obesity (OB, metBMI ≥ 30 kg/m(2)), overestimated (OE, metBMI-actBMI > 5 kg/m(2)), and underestimated (UE, metBMI-actBMI < − 5 kg/m(2)). Additional participants from the Guangzhou Diabetes Eye Study (GDES) were included for validating the hypothesis. Outcomes included all-cause and cardiovascular (CVD)-cause mortality, as well as incident CVD (coronary heart disease, heart failure, myocardial infarction [MI], and stroke) and age-related eye diseases (age-related macular degeneration [AMD], cataracts, glaucoma, and diabetic retinopathy [DR]). RESULTS: In the UKB, although OE group had lower actBMI than NW group, the OE group had a significantly higher risk of all-cause mortality than those in NW prediction group (HR, 1.68; 95% CI 1.16–2.43). Similarly, the OE group had a 1.7–3.6-fold higher risk than their NW counterparts for cardiovascular mortality, heart failure, myocardial infarction, and coronary heart disease (all P < 0.05). In addition, risk of age-related macular denegation (HR, 1.96; 95% CI 1.02–3.77) was significantly higher in OE group. In the contrast, UE and OB groups showed similar risks of mortality and of cardiovascular and age-related eye diseases (all P > 0.05), though the UE group had significantly higher actBMI than OB group. In the GDES cohort, we further confirmed the potential of metabolic BMI (metBMI) fingerprints for risk stratification of cardiovascular diseases using a different metabolomic approach. CONCLUSIONS: Gaps of metBMI and actBMI identified novel metabolic subtypes, which exhibit distinctive cardiovascular and ocular risk profiles. The groups carrying obesity-related metabolites were at higher risk of mortality and morbidity than those with normal health metabolites. Metabolomics allowed for leveraging the future of diagnosis and management of ‘healthily obese’ and ‘unhealthily lean’ individuals. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-04244-x.
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spelling pubmed-102624152023-06-15 Metabolomic phenotyping of obesity for profiling cardiovascular and ocular diseases Zhong, Pingting Tan, Shaoying Zhu, Zhuoting Bulloch, Gabriella Long, Erping Huang, Wenyong He, Mingguang Wang, Wei J Transl Med Research BACKGROUND: We aimed to evaluate the impacts of metabolomic body mass index (metBMI) phenotypes on the risks of cardiovascular and ocular diseases outcomes. METHODS: This study included cohorts in UK and Guangzhou, China. By leveraging the serum metabolome and BMI data from UK Biobank, this study developed and validated a metBMI prediction model using a ridge regression model among 89,830 participants based on 249 metabolites. Five obesity phenotypes were obtained by metBMI and actual BMI (actBMI): normal weight (NW, metBMI of 18.5–24.9 kg/m(2)), overweight (OW, metBMI of 25–29.9 kg/m(2)), obesity (OB, metBMI ≥ 30 kg/m(2)), overestimated (OE, metBMI-actBMI > 5 kg/m(2)), and underestimated (UE, metBMI-actBMI < − 5 kg/m(2)). Additional participants from the Guangzhou Diabetes Eye Study (GDES) were included for validating the hypothesis. Outcomes included all-cause and cardiovascular (CVD)-cause mortality, as well as incident CVD (coronary heart disease, heart failure, myocardial infarction [MI], and stroke) and age-related eye diseases (age-related macular degeneration [AMD], cataracts, glaucoma, and diabetic retinopathy [DR]). RESULTS: In the UKB, although OE group had lower actBMI than NW group, the OE group had a significantly higher risk of all-cause mortality than those in NW prediction group (HR, 1.68; 95% CI 1.16–2.43). Similarly, the OE group had a 1.7–3.6-fold higher risk than their NW counterparts for cardiovascular mortality, heart failure, myocardial infarction, and coronary heart disease (all P < 0.05). In addition, risk of age-related macular denegation (HR, 1.96; 95% CI 1.02–3.77) was significantly higher in OE group. In the contrast, UE and OB groups showed similar risks of mortality and of cardiovascular and age-related eye diseases (all P > 0.05), though the UE group had significantly higher actBMI than OB group. In the GDES cohort, we further confirmed the potential of metabolic BMI (metBMI) fingerprints for risk stratification of cardiovascular diseases using a different metabolomic approach. CONCLUSIONS: Gaps of metBMI and actBMI identified novel metabolic subtypes, which exhibit distinctive cardiovascular and ocular risk profiles. The groups carrying obesity-related metabolites were at higher risk of mortality and morbidity than those with normal health metabolites. Metabolomics allowed for leveraging the future of diagnosis and management of ‘healthily obese’ and ‘unhealthily lean’ individuals. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-04244-x. BioMed Central 2023-06-12 /pmc/articles/PMC10262415/ /pubmed/37308902 http://dx.doi.org/10.1186/s12967-023-04244-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhong, Pingting
Tan, Shaoying
Zhu, Zhuoting
Bulloch, Gabriella
Long, Erping
Huang, Wenyong
He, Mingguang
Wang, Wei
Metabolomic phenotyping of obesity for profiling cardiovascular and ocular diseases
title Metabolomic phenotyping of obesity for profiling cardiovascular and ocular diseases
title_full Metabolomic phenotyping of obesity for profiling cardiovascular and ocular diseases
title_fullStr Metabolomic phenotyping of obesity for profiling cardiovascular and ocular diseases
title_full_unstemmed Metabolomic phenotyping of obesity for profiling cardiovascular and ocular diseases
title_short Metabolomic phenotyping of obesity for profiling cardiovascular and ocular diseases
title_sort metabolomic phenotyping of obesity for profiling cardiovascular and ocular diseases
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10262415/
https://www.ncbi.nlm.nih.gov/pubmed/37308902
http://dx.doi.org/10.1186/s12967-023-04244-x
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