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Cefalo CMA, Riccio A, Succurro E, Marini MA, Fiorentino TV, Rubino M, Perticone M, Sciacqua A, Andreozzi F, Sesti G. Frequency of prediabetes in individuals with increased adiposity and metabolically healthy or unhealthy phenotypes. Diabetes Obes Metab 2024; 26:3191-3199. [PMID: 38720197 DOI: 10.1111/dom.15646] [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: 03/01/2024] [Revised: 04/21/2024] [Accepted: 04/22/2024] [Indexed: 07/10/2024]
Abstract
AIMS To utilize the estimated glucose disposal rate (eGDR) index of insulin sensitivity, which is based on readily available clinical variables, namely, waist circumference, hypertension and glycated haemoglobin, to discriminate between metabolically healthy and unhealthy phenotypes, and to determine the prevalence of prediabetic conditions. METHODS Non-diabetic individuals (n = 2201) were stratified into quartiles of insulin sensitivity based on eGDR index. Individuals in the upper quartiles of eGDR were defined as having metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW) or metabolically healthy obesity (MHO) according to their body mass index, while those in the lower quartiles were classified as having metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW) and metabolically unhealthy obesity (MUO), respectively. RESULTS The frequency of impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and IFG + IGT status was comparable among the MHNW, MHOW and MHO groups, while it increased from those with MUNW status towards those with MUOW and MUO status. As compared with participants with MHNW, the odds ratio of having IFG, IGT, or IFG + IGT was significantly higher in participants with MUOW and MUO but not in those with MUNW, MHOW and MHO, respectively. CONCLUSIONS A metabolically healthy phenotype is associated with lower frequency of IFG, IGT, and IFG + IGT status across all body weight categories.
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Affiliation(s)
- Chiara M A Cefalo
- Department of Clinical and Molecular Medicine, University of Rome-Sapienza, Rome, Italy
| | - Alessia Riccio
- Department of Clinical and Molecular Medicine, University of Rome-Sapienza, Rome, Italy
| | - Elena Succurro
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | | | - Teresa Vanessa Fiorentino
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Mariangela Rubino
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Maria Perticone
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Angela Sciacqua
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Francesco Andreozzi
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Giorgio Sesti
- Department of Clinical and Molecular Medicine, University of Rome-Sapienza, Rome, Italy
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Chiriacò M, Nesti L, Flyvbjerg A, Golay A, Nazare JA, Anderwald CH, Mitrakou A, Bizzotto R, Mari A, Natali A. At any Level of Adiposity, Relatively Elevated Leptin Concentrations Are Associated With Decreased Insulin Sensitivity. J Clin Endocrinol Metab 2024; 109:461-470. [PMID: 37650623 DOI: 10.1210/clinem/dgad505] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 06/29/2023] [Accepted: 08/30/2023] [Indexed: 09/01/2023]
Abstract
CONTEXT The impact of obesity on glucose homeostasis has high interindividual variability, which may be partially explained by different adipokine concentrations. Leptin regulates energy balance and metabolism, and although its plasma levels are proportional to fat mass, they vary significantly across individuals with the same level of adiposity. OBJECTIVE We tested whether glucose homeostasis differs in subjects with similar degrees of adiposity but different leptin levels. METHODS We analyzed 1290 healthy adults from the Relationship Between Insulin Sensitivity and Cardiovascular Disease study cohort (30-60 years; male/female, 577/713; body mass index [BMI], 25 ± 3 kg/m2) characterized for body composition and metabolic variables with a 75-g oral glucose tolerance test, euglycemic-hyperinsulinemic clamp, β-cell function, and lipidomics. RESULTS Individuals were divided into relatively high and low leptin (RHL and RLL) if they were above or below the sex-specific leptin-fat mass (%) regression. Despite similar glucose tolerance, RHL showed markedly higher fasting and oral glucose tolerance test insulin concentration (+30% and +29%, respectively; P < .0001) and secretion (+17% and +11%, respectively; P < .0001). Regardless of BMI, RHL individuals had lower whole-body (-17-23%, P < .0001) and adipose tissue insulin sensitivity (-24%, P < .0001) compared with RLL. Notably, lean RHL individuals showed similar insulin sensitivity and β-cell function to RLL individuals with overweight/obesity. CONCLUSION Subjects with leptin levels that are inappropriately elevated for their fat mass show whole-body/adipose tissue insulin resistance and hyperinsulinemia, regardless of BMI.
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Affiliation(s)
- Martina Chiriacò
- Metabolism, Nutrition, and Atherosclerosis Laboratory, Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy
| | - Lorenzo Nesti
- Metabolism, Nutrition, and Atherosclerosis Laboratory, Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy
| | - Allan Flyvbjerg
- Steno Diabetes Center Copenhagen, Capital Region of Denmark, 2730 Copenhagen, Denmark
| | - Alain Golay
- Department of Endocrinology, Diabetology, Nutrition and Therapeutic Education of the Patient, Geneva University Hospital, 1206 Geneva, Switzerland
| | - Julie-Anne Nazare
- Department of Human Nutrition Research Center Rhône-Alpes, CarMeN Laboratory, Université Claude Bernard Lyon 1, 69100 Villeurbanne, France
| | - Christian-Heinz Anderwald
- Obesity Research Unit, University Hospital Salzburg, Paracelsus Medical University, 5020 Salzburg, Austria
- Division of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of Vienna, 1090 Vienna, Austria
| | - Asimina Mitrakou
- Department of Clinical Therapeutics, Alexandra Hospital, School of Medicine, National and Kapodistrian University of Athens, 115 27 Athens, Greece
| | - Roberto Bizzotto
- Institute of Neuroscience, National Research Council, 35127 Padova, Italy
| | - Andrea Mari
- Institute of Neuroscience, National Research Council, 35127 Padova, Italy
| | - Andrea Natali
- Metabolism, Nutrition, and Atherosclerosis Laboratory, Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy
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Wei Y, Wang R, Wang J, Han X, Wang F, Zhang Z, Xu Y, Zhang X, Guo H, Yang H, Li X, He M. Transitions in Metabolic Health Status and Obesity Over Time and Risk of Diabetes: The Dongfeng-Tongji Cohort Study. J Clin Endocrinol Metab 2023; 108:2024-2032. [PMID: 36718514 DOI: 10.1210/clinem/dgad047] [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: 11/07/2022] [Revised: 12/26/2022] [Accepted: 01/24/2023] [Indexed: 02/01/2023]
Abstract
CONTEXT Evidence regarding the association between metabolically healthy overweight or obesity (MHOO) and diabetes is controversial, and mostly ignores the dynamic change of metabolic health status and obesity. OBJECTIVE To explore the association between transitions of metabolic health status and obesity over 5 years and diabetes incidence. METHODS We examined 17 309 participants derived from the Dongfeng-Tongji cohort and followed from 2008 to 2018 (median follow-up 9.9 years). All participants were categorized into 4 phenotypes based on body mass index (BMI) and metabolic health status: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), MHOO, and metabolically unhealthy overweight or obesity (MUOO). The associations of changes in BMI-metabolic health status (2008-2013) with diabetes incidence (2018) were performed among 12 206 individuals with 2 follow-up examinations. RESULTS Compared with stable MHNW, stable MHOO (hazard ratio [HR] 1.76; 95% CI 1.26, 2.45) and transition from MHOO to metabolically unhealthy phenotypes were associated with higher risk for diabetes (HR 2.97; 95% CI 1.79, 4.93 in MHOO to MUNW group and HR 3.38; 95% CI 2.54, 4.49 in MHOO to MUOO group). Instead, improvements to metabolic healthy phenotypes or weight loss occurring in MUOO reduced the risk of diabetes compared with stable MUOO, changing from MUOO to MHNW, MUNW, and MHOO resulted in HRs of 0.57 (95% CI 0.37, 0.87), 0.68 (95% CI 0.50, 0.93), and 0.45 (95% CI 0.34, 0.60), respectively. CONCLUSION People with MHOO, even stable MHOO, or its transition to metabolically unhealthy phenotypes were at increased risk of diabetes. Metabolic improvements and weight control may reduce the risk of diabetes.
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Affiliation(s)
- Yue Wei
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Ruixin Wang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Jing Wang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Xu Han
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Fei Wang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Zefang Zhang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yali Xu
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Xiaomin Zhang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Huan Guo
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Handong Yang
- Dongfeng Central Hospital, Dongfeng Motor Corporation and Hubei University of Medicine, Shiyan, Hubei 442000, China
| | - Xiulou Li
- Dongfeng Central Hospital, Dongfeng Motor Corporation and Hubei University of Medicine, Shiyan, Hubei 442000, China
| | - Meian He
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
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Liu X, Abudukeremu A, Jiang Y, Cao Z, Wu M, Ma J, Sun R, He W, Chen Z, Chen Y, Yu P, Zhu W, Zhang Y, Wang J. U-shaped association between the triglyceride-glucose index and atrial fibrillation incidence in a general population without known cardiovascular disease. Cardiovasc Diabetol 2023; 22:118. [PMID: 37208737 DOI: 10.1186/s12933-023-01777-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 02/20/2023] [Indexed: 05/21/2023] Open
Abstract
OBJECTIVE The triglyceride-glucose (TyG) index has been shown to be a new alternative measure for insulin resistance. However, no study has attempted to investigate the association of the TyG index with incident atrial fibrillation (AF) in the general population without known cardiovascular diseases. METHODS Individuals without known cardiovascular diseases (heart failure, coronary heart disease, or stroke) from the Atherosclerosis Risk in Communities (ARIC) cohort were recruited. The baseline TyG index was calculated as the Ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. The association between the baseline TyG index and incident AF was examined using Cox regression. RESULTS Of 11,851 participants, the mean age was 54.0 years; 6586 (55.6%) were female. During a median follow-up of 24.26 years, 1925 incidents of AF cases (0.78/per 100 person-years) occurred. An increased AF incidence with a graded TyG index was found by Kaplan‒Meier curves (P < 0.001). In multivariable-adjusted analysis, both < 8.80 (adjusted hazard ratio [aHR] = 1.15, 95% confidence interval [CI] 1.02, 1.29) and > 9.20 levels (aHR 1.18, 95% CI 1.03, 1.37) of the TyG index were associated with an increased risk of AF compared with the middle TyG index category (8.80-9.20). The exposure-effect analysis confirmed the U-shaped association between the TyG index and AF incidence (P = 0.041). Further sex-specific analysis showed that a U-shaped association between the TyG index and incident AF still existed in females but not in males. CONCLUSIONS A U-shaped association between the TyG index and AF incidence is observed in Americans without known cardiovascular diseases. Female sex may be a modifier in the association between the TyG index and AF incidence.
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Affiliation(s)
- Xiao Liu
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, China
- Institute for the Study of Endocrinology and Metabolism in Jiangxi Province, The Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Ayiguli Abudukeremu
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Yuan Jiang
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, China
| | - Zhengyu Cao
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, China
| | - Maoxiong Wu
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, China
| | - Jianyong Ma
- Department of Pharmacology and Systems Physiology, University of Cincinnati College of Medicine, Cincinnati, USA
| | - Runlu Sun
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, China
| | - Wanbing He
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, China
| | - Zhiteng Chen
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, China
| | - Yangxin Chen
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, China
| | - Peng Yu
- Institute for the Study of Endocrinology and Metabolism in Jiangxi Province, The Second Affiliated Hospital of Nanchang University, Jiangxi, China.
- Department of Endocrine, The Second Affiliated Hospital of Nanchang University, Jiangxi, China.
| | - Wengen Zhu
- Department of Cardiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
| | - Yuling Zhang
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China.
- Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, China.
| | - Jingfeng Wang
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China.
- Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, China.
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Gagnon E, Mitchell PL, Arsenault BJ. Body Fat Distribution, Fasting Insulin Levels, and Insulin Secretion: A Bidirectional Mendelian Randomization Study. J Clin Endocrinol Metab 2023; 108:1308-1317. [PMID: 36585897 DOI: 10.1210/clinem/dgac758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 12/07/2022] [Accepted: 12/28/2022] [Indexed: 01/01/2023]
Abstract
CONTEXT Hyperinsulinemia and adiposity are associated with one another, but the directionality of this relation is debated. OBJECTIVE Here, we tested the direction of the causal effects of fasting insulin (FI) levels and body fat accumulation/distribution using 2-sample bidirectional Mendelian randomization (MR). METHODS We included summary statistics from large-scale genome-wide association studies for body mass index (BMI, n = 806 834), waist to hip ratio adjusted for BMI (WHRadjBMI, n = 694 649), abdominal subcutaneous, visceral and gluteofemoral adipose tissue (n = 38 965), FI levels (n = 98 210), pancreatic islets gene expression (n = 420), and hypothalamus gene expression (n = 155). We used inverse variance-weighted and robust MR methods that relied on statistically and biologically driven genetic instruments. RESULTS Both BMI and WHRadjBMI were positively associated with FI. Results were consistent across all robust MR methods and when variants mapped to the hypothalamus (presumably associated with food behavior) were included. In multivariable MR analyses, when waist circumference and BMI were mutually adjusted, the direct effect of waist circumference on FI was 2.43 times larger than the effect of BMI on FI. FI was not associated with adiposity. By contrast, using genetic instruments mapped to gene expression in pancreatic islets (presumably more specific to insulin secretion), insulin was positively associated with BMI and abdominal subcutaneous and gluteofemoral adipose tissue, but not with visceral adipose tissue. CONCLUSION Although these results will need to be supported by experimental investigations, results of this MR study suggest that abdominal adiposity may be a key determinant of circulating insulin levels. Alternatively, insulin secretion may promote peripheral adipose tissue accumulation.
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Affiliation(s)
- Eloi Gagnon
- Quebec Heart and Lung Institute, Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC G1V 4G5, Canada
| | - Patricia L Mitchell
- Quebec Heart and Lung Institute, Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC G1V 4G5, Canada
| | - Benoit J Arsenault
- Quebec Heart and Lung Institute, Centre de recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Québec, QC G1V 4G5, Canada
- Department of Medicine, Faculty of Medicine, Université Laval, Québec, QC G1V 5C3, Canada
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Cadenas-Sanchez C, Medrano M, Villanueva A, Cabeza R, Idoate F, Osés M, Rodríguez-Vigil B, Álvarez de Eulate N, Alberdi Aldasoro N, Ortega FB, Labayen I. Differences in specific abdominal fat depots between metabolically healthy and unhealthy children with overweight/obesity: The role of cardiorespiratory fitness. Scand J Med Sci Sports 2023. [PMID: 37081735 DOI: 10.1111/sms.14372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/11/2023] [Accepted: 04/03/2023] [Indexed: 04/22/2023]
Abstract
OBJECTIVES Fat depots localization has a critical role in the metabolic health status of adults. Nevertheless, whether that is also the case in children remains under-studied. Therefore, the aims of this study were: (i) to examine the differences between metabolically healthy (MHO) and unhealthy (MUO) overweight/obesity phenotypes on specific abdominal fat depots, and (ii) to further explore whether cardiorespiratory fitness plays a major role in the differences between metabolic phenotypes among children with overweight/obesity. METHODS A total of 114 children with overweight/obesity (10.6 ± 1.1 years, 62 girls) were included. Children were classified as MHO (n = 68) or MUO. visceral (VAT), abdominal subcutaneous (ASAT), intermuscular abdominal (IMAAT), psoas, hepatic, pancreatic, and lumbar bone marrow adipose tissues were measured by magnetic resonance imaging. Cardiorespiratory fitness was assessed using the 20 m shuttle run test. RESULTS MHO children had lower VAT and ASAT contents and psoas fat fraction compared to MUO children (difference = 12.4%-25.8%, all p < 0.035). MUO-unfit had more VAT and ASAT content than those MUO-fit and MHO-fit (difference = 34.8%-45.3%, all p < 0.044). MUO-unfit shows also greater IMAAT fat fraction than those MUO-fit and MHO-fit peers (difference = 16.4%-13.9% respectively, all p ≤ 0.001). In addition, MHO-unfit presented higher IMAAT fat fraction than MHO-fit (difference = 13.4%, p < 0.001). MUO-unfit presented higher psoas fat fraction than MHO-fit (difference = 29.1%, p = 0.008). CONCLUSIONS VAT together with ASAT and psoas fat fraction, were lower in MHO than in MUO children. Further, we also observed that being fit, regardless of metabolic phenotype, has a protective role over the specific abdominal fat depots among children with overweight/obesity.
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Affiliation(s)
- Cristina Cadenas-Sanchez
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Research Institute for Innovation & Sustainable Food Chain Development (IS-FOOD), Public University of Navarre. Department of Health Sciences, Public University of Navarre, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
| | - María Medrano
- Research Institute for Innovation & Sustainable Food Chain Development (IS-FOOD), Public University of Navarre. Department of Health Sciences, Public University of Navarre, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
| | - Arantxa Villanueva
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
- Smart Cities Institute, Public University of Navarre, Pamplona, Spain
- Department of Electrical, Electronic and Communications Engineering, Public University of Navarre Pamplona, Pamplona, Spain
| | - Rafael Cabeza
- Department of Electrical, Electronic and Communications Engineering, Public University of Navarre Pamplona, Pamplona, Spain
| | - Fernando Idoate
- Radiology Department, Mutua Navarra, Pamplona, Spain
- Department of Health Sciences, Public University of Navarre, Pamplona, Spain
| | - Maddi Osés
- Research Institute for Innovation & Sustainable Food Chain Development (IS-FOOD), Public University of Navarre. Department of Health Sciences, Public University of Navarre, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Beatriz Rodríguez-Vigil
- Osakidetza Basque Health Service, Osatek, Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain
| | - Natalia Álvarez de Eulate
- Sección de Radiología Musculoesquelética, Servicio de Radiología, Hospital Universitario de Navarra, Pamplona, Spain
| | - Nerea Alberdi Aldasoro
- Sección de Radiología Musculoesquelética, Servicio de Radiología, Hospital Universitario de Navarra, Pamplona, Spain
| | - Francisco B Ortega
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Idoia Labayen
- Research Institute for Innovation & Sustainable Food Chain Development (IS-FOOD), Public University of Navarre. Department of Health Sciences, Public University of Navarre, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
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Ren Z, Cao X, Li C, Zhang J, Li X, Song P, Zhu Y, Liu Z. Ferritin, transferrin, and transferrin receptor in relation to metabolic obesity phenotypes: Findings from the China Health and Nutrition Survey. Front Public Health 2022; 10:922863. [PMID: 36091521 PMCID: PMC9459082 DOI: 10.3389/fpubh.2022.922863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 08/04/2022] [Indexed: 01/22/2023] Open
Abstract
Background This study aimed to explore the relationship between iron markers and metabolic obesity phenotypes and the role of age. Methods Data were from the China Health and Nutrition Survey 2009. Metabolic obesity phenotypes included metabolically healthy with normal weight (MHNW), metabolically unhealthy with normal weight (MUNW), metabolically healthy with overweight/obesity (MHO), and metabolically unhealthy with overweight/obesity (MUO). Iron markers including ferritin, transferrin, and soluble transferrin receptor were calculated as Log and quartered. The linear regression and multinomial logistic regression were used to explore the association of iron markers with age and metabolic obesity phenotypes, respectively. Results Ferritin was linearly related with age, with β (95% confidence interval, CI) of 0.029 (0.027 to 0.032) and -0.005 (-0.007 to -0.002) for women and men. Transferrin was negatively associated with age in both men and women (β < -0.011). Furthermore, compared with participants in the quartile 1 ferritin group, those in the quartile 4 had increased odds of MUNW, MHO, and MUO, with odds ratio and 95% confidence interval (OR, 95% CI) of 3.06 (2.20 to 4.25), 1.66 (1.35 to 2.05), and 5.27 (4.17 to 6.66). Transferrin showed similar relationships with MUNW, MUO, and MHO; whereas transferrin receptor showed no significance. We also found joint associations of ferritin and transferrin with MUNW, MUO, and MHO. The interactive effect of ferritin and transferrin on MUO was significant (P = 0.015). Conclusion Increased ferritin and transferrin were associated with MUNW, MHO, and MUO. Age should be considered when investigating iron.
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Affiliation(s)
- Ziyang Ren
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Xingqi Cao
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Chenxi Li
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingyun Zhang
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueqin Li
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Peige Song
- School of Public Health and Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,*Correspondence: Peige Song
| | - Yimin Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China,Yimin Zhu
| | - Zuyun Liu
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China,Zuyun Liu ;
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