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Husejko J, Gackowski M, Wojtasik J, Strzała D, Pesta M, Mądra-Gackowska K, Nuszkiewicz J, Woźniak A, Kozakiewicz M, Kędziora-Kornatowska K. Preliminary Report on the Influence of Acute Inflammation on Adiponectin Levels in Older Inpatients with Different Nutritional Status. Int J Mol Sci 2024; 25:2016. [PMID: 38396693 PMCID: PMC10889142 DOI: 10.3390/ijms25042016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Inflammation can be triggered by a variety of factors, including pathogens, damaged cells, and toxic compounds. It is a biological response of the immune system, which can be successfully assessed in clinical practice using some molecular substances. Because adiponectin, a hormone released by adipose tissue, influences the development of inflammation, its evaluation as a potential measure of inflammation in clinical practice is justified. In the present contribution, statistical comparison of adiponectin concentration and selected molecular substances recognized in clinical practice as measures of inflammation were utilized to demonstrate whether adipose tissue hormones, as exemplified by adiponectin, have the potential to act as a measure of rapidly changing inflammation when monitoring older hospitalized patients in the course of bacterial infection. The study showed no statistically significant differences in adiponectin levels depending on the rapidly changing inflammatory response in its early stage. Interestingly, the concentration of adiponectin is statistically significantly higher in malnourished patients than in people with normal nutritional levels, assessed based on the MNA. According to the results obtained, adiponectin is not an effective measure of acute inflammation in clinical practice. However, it may serve as a biomarker of malnutrition in senile individuals.
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Affiliation(s)
- Jakub Husejko
- Department of Geriatrics, Faculty of Health Science, L. Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Skłodowska-Curie 9 Street, 85-094 Bydgoszcz, Poland; (J.H.); (D.S.); (M.P.); (K.M.-G.); (M.K.); (K.K.-K.)
| | - Marcin Gackowski
- Department of Toxicology and Bromatology, Faculty of Pharmacy, L. Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, A. Jurasza 2 Street, 85-089 Bydgoszcz, Poland;
| | - Jakub Wojtasik
- Centre for Statistical Analysis, Nicolaus Copernicus University in Toruń, Chopina 12/18 Street, 87-100 Toruń, Poland;
| | - Dominika Strzała
- Department of Geriatrics, Faculty of Health Science, L. Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Skłodowska-Curie 9 Street, 85-094 Bydgoszcz, Poland; (J.H.); (D.S.); (M.P.); (K.M.-G.); (M.K.); (K.K.-K.)
| | - Maciej Pesta
- Department of Geriatrics, Faculty of Health Science, L. Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Skłodowska-Curie 9 Street, 85-094 Bydgoszcz, Poland; (J.H.); (D.S.); (M.P.); (K.M.-G.); (M.K.); (K.K.-K.)
| | - Katarzyna Mądra-Gackowska
- Department of Geriatrics, Faculty of Health Science, L. Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Skłodowska-Curie 9 Street, 85-094 Bydgoszcz, Poland; (J.H.); (D.S.); (M.P.); (K.M.-G.); (M.K.); (K.K.-K.)
| | - Jarosław Nuszkiewicz
- Department of Medical Biology and Biochemistry, Faculty of Medicine, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Karłowicza 24 Street, 85-092 Bydgoszcz, Poland;
| | - Alina Woźniak
- Department of Medical Biology and Biochemistry, Faculty of Medicine, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Karłowicza 24 Street, 85-092 Bydgoszcz, Poland;
| | - Mariusz Kozakiewicz
- Department of Geriatrics, Faculty of Health Science, L. Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Skłodowska-Curie 9 Street, 85-094 Bydgoszcz, Poland; (J.H.); (D.S.); (M.P.); (K.M.-G.); (M.K.); (K.K.-K.)
| | - Kornelia Kędziora-Kornatowska
- Department of Geriatrics, Faculty of Health Science, L. Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Skłodowska-Curie 9 Street, 85-094 Bydgoszcz, Poland; (J.H.); (D.S.); (M.P.); (K.M.-G.); (M.K.); (K.K.-K.)
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Cai X, Xue Z, Zeng FF, Tang J, Yue L, Wang B, Ge W, Xie Y, Miao Z, Gou W, Fu Y, Li S, Gao J, Shuai M, Zhang K, Xu F, Tian Y, Xiang N, Zhou Y, Shan PF, Zhu Y, Chen YM, Zheng JS, Guo T. Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome. Cell Rep Med 2023; 4:101172. [PMID: 37652016 PMCID: PMC10518601 DOI: 10.1016/j.xcrm.2023.101172] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 09/02/2023]
Abstract
Metabolic syndrome (MetS) is a complex metabolic disorder with a global prevalence of 20%-25%. Early identification and intervention would help minimize the global burden on healthcare systems. Here, we measured over 400 proteins from ∼20,000 proteomes using data-independent acquisition mass spectrometry for 7,890 serum samples from a longitudinal cohort of 3,840 participants with two follow-up time points over 10 years. We then built a machine-learning model for predicting the risk of developing MetS within 10 years. Our model, composed of 11 proteins and the age of the individuals, achieved an area under the curve of 0.774 in the validation cohort (n = 242). Using linear mixed models, we found that apolipoproteins, immune-related proteins, and coagulation-related proteins best correlated with MetS development. This population-scale proteomics study broadens our understanding of MetS and may guide the development of prevention and targeted therapies for MetS.
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Affiliation(s)
- Xue Cai
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Zhangzhi Xue
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Fang-Fang Zeng
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510080, China
| | - Jun Tang
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Liang Yue
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Bo Wang
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1 Yunmeng Road, Cloud Town, Xihu District, Hangzhou, Zhejiang 310024, China
| | - Weigang Ge
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1 Yunmeng Road, Cloud Town, Xihu District, Hangzhou, Zhejiang 310024, China
| | - Yuting Xie
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Zelei Miao
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Wanglong Gou
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Yuanqing Fu
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Sainan Li
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Jinlong Gao
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Menglei Shuai
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Ke Zhang
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Fengzhe Xu
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Yunyi Tian
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Nan Xiang
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1 Yunmeng Road, Cloud Town, Xihu District, Hangzhou, Zhejiang 310024, China
| | - Yan Zhou
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Peng-Fei Shan
- Department of Endocrinology, the Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, Zhejiang 310009, China
| | - Yi Zhu
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China.
| | - Yu-Ming Chen
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Ju-Sheng Zheng
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China.
| | - Tiannan Guo
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China.
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4
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Kim D, Memili A, Chen HH, Highland HM, Polikowsky HG, Anwar MY, Laing ST, Lee M, McCormick JB, Fisher-Hoch SP, Below JE, North KE, Gutierrez AD. Sex-specific associations between adipokine profiles and carotid-intima media thickness in the Cameron County Hispanic Cohort (CCHC). Cardiovasc Diabetol 2023; 22:231. [PMID: 37653519 PMCID: PMC10472619 DOI: 10.1186/s12933-023-01968-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 08/16/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND Adipokines are hormones secreted from adipose tissue and are associated with cardiometabolic diseases (CMD). Functional differences between adipokines (leptin, adiponectin, and resistin) are known, but inconsistently reported associations with CMD and lack of studies in Hispanic populations are research gaps. We investigated the relationship between subclinical atherosclerosis and multiple adipokine measures. METHODS Cross-sectional data from the Cameron County Hispanic Cohort (N = 624; mean age = 50; Female = 70.8%) were utilized to assess associations between adipokines [continuous measures of adiponectin, leptin, resistin, leptin-to-adiponectin ratio (LAR), and adiponectin-resistin index (ARI)] and early atherosclerosis [carotid-intima media thickness (cIMT)]. We adjusted for sex, age, body mass index (BMI), smoking status, cytokines, fasting blood glucose levels, blood pressure, lipid levels, and medication usage in the fully adjusted linear regression model. We conducted sexes-combined and sex-stratified analyses to account for sex-specificity and additionally tested whether stratification of participants by their metabolic status (metabolically elevated risk for CMD as defined by having two or more of the following conditions: hypertension, dyslipidemia, insulin resistance, and inflammation vs. not) influenced the relationship between adipokines and cIMT. RESULTS In the fully adjusted analyses, adiponectin, leptin, and LAR displayed significant interaction by sex (p < 0.1). Male-specific associations were between cIMT and LAR [β(SE) = 0.060 (0.016), p = 2.52 × 10-4], and female-specific associations were between cIMT and adiponectin [β(SE) = 0.010 (0.005), p = 0.043] and ARI [β(SE) = - 0.011 (0.005), p = 0.036]. When stratified by metabolic health status, the male-specific positive association between LAR and cIMT was more evident among the metabolically healthy group [β(SE) = 0.127 (0.015), p = 4.70 × 10-10] (p for interaction by metabolic health < 0.1). However, the female-specific associations between adiponectin and cIMT and ARI and cIMT were observed only among the metabolically elevated risk group [β(SE) = 0.014 (0.005), p = 0.012 for adiponectin; β(SE) = - 0.015 (0.006), p = 0.013 for ARI; p for interaction by metabolic health < 0.1]. CONCLUSION Associations between adipokines and cIMT were sex-specific, and metabolic health status influenced the relationships between adipokines and cIMT. These heterogeneities by sex and metabolic health affirm the complex relationships between adipokines and atherosclerosis.
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Affiliation(s)
- Daeeun Kim
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Aylin Memili
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Hung-Hsin Chen
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Heather M Highland
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hannah G Polikowsky
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mohammad Yaser Anwar
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Susan T Laing
- Department of Internal Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Miryoung Lee
- Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health, Brownsville Regional Campus, Brownsville, TX, USA
| | - Joseph B McCormick
- Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health, Brownsville Regional Campus, Brownsville, TX, USA
| | - Susan P Fisher-Hoch
- Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health, Brownsville Regional Campus, Brownsville, TX, USA
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Absalon D Gutierrez
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX, USA.
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