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Lu Q, Chen B, Li A, Liang Q, Yao J, Tao Y, Dai F, Hu X, Lu J, Liu Y, Liu Y, Wang Y, Long J, Zhang R, Liu Z. The correlation between HOMA-IR and cardiometabolic risk index among different metabolic adults: a cross-sectional study. Acta Diabetol 2024:10.1007/s00592-024-02332-y. [PMID: 39122878 DOI: 10.1007/s00592-024-02332-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 06/19/2024] [Indexed: 08/12/2024]
Abstract
AIMS This study aimed to explore the correlation between homeostasis model assessment of insulin resistance(HOMA-IR)and cardiometabolic risk index(CMRI) among different metabolic adults to evaluate the value of HOMA-IR in predicting cardiometabolic risk. METHODS This cross-sectional study was conducted over 18 months (from August 1, 2020 to February 18, 2022) and included 1550 participants divided into non-metabolic syndrome (non-MetS) group (n = 628) and metabolic syndrome (MetS) group (n = 922) in three centers of China. Logistic regression analysis was employed to investigate the correlation between HOMA-IR, body fat percentage, BMI (body mass index), visceral fat index, waist-to-hip ratio, vitamin D, and CMRI. Further analysis was conducted to evaluate the ability of HOMA-IR in diagnosing high CMRI within different metabolic, gender, and age groups to predict the risk of cardiovascular disease (CVD). RESULTS HOMA-IR was significantly higher in the MetS group compared with the non-MetS group (P < 0.05). CMRI was significantly higher in the MetS group compared to the non-MetS group (P < 0.05). According to ROC curve analysis, HOMA-IR can predict cardiovascular risk (CVR) in the general population, non-MetS individuals, and MetS people. Logistic regression analysis revealed that BMI, visceral fat index, waist-to-hip ratio, and HOMA-IR are independent risk indicators of high CVR, whereas vitamin D may exert a protective role. CONCLUSIONS HOMA-IR was an independent risk factor for increased CVR in MetS patients. Moreover, HOMA-IR elevates the risk of CVD regardless of MetS and thus can be used for screening the general population. TRIAL REGISTRATION The study was registered at the Chinese Clinical Trial Registry (Registration Number: ChiCTR2100054654).
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Affiliation(s)
- Qiyun Lu
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
- The Second Affiliated Hospital of Guangzhou, University of Chinese Medicine, Guangzhou, China
| | - Benjian Chen
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
- The Second Affiliated Hospital of Guangzhou, University of Chinese Medicine, Guangzhou, China
| | - Anxiang Li
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
- The Second Affiliated Hospital of Guangzhou, University of Chinese Medicine, Guangzhou, China
| | - Qingshun Liang
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
- The Second Affiliated Hospital of Guangzhou, University of Chinese Medicine, Guangzhou, China
| | - Jia Yao
- Guangzhou University of Traditional Chinese Medicine, Guangzhou, China
| | - Yiming Tao
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
- The Second Affiliated Hospital of Guangzhou, University of Chinese Medicine, Guangzhou, China
| | - Fangfang Dai
- The Affiliated Hospital of Nanjing University of Traditional Chinese Medicine, Nanjing, China
| | - Xiaoling Hu
- Xinjiang Uygur Autonomous Region Traditional Chinese Medicine Hospital, Urumqi, China
| | - Jiayan Lu
- Guangzhou University of Traditional Chinese Medicine, Guangzhou, China
| | - Yunwei Liu
- Guangzhou University of Traditional Chinese Medicine, Guangzhou, China
| | - Yunyi Liu
- Guangzhou Medical University, Guangzhou, China
| | - Yingxi Wang
- Guangzhou University of Traditional Chinese Medicine, Guangzhou, China
| | - Jieer Long
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
- The Second Affiliated Hospital of Guangzhou, University of Chinese Medicine, Guangzhou, China
| | | | - Zhenjie Liu
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China.
- The Second Affiliated Hospital of Guangzhou, University of Chinese Medicine, Guangzhou, China.
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Güil-Oumrait N, Stratakis N, Maitre L, Anguita-Ruiz A, Urquiza J, Fabbri L, Basagaña X, Heude B, Haug LS, Sakhi AK, Iszatt N, Keun HC, Wright J, Chatzi L, Vafeiadi M, Bustamante M, Grazuleviciene R, Andrušaitytė S, Slama R, McEachan R, Casas M, Vrijheid M. Prenatal Exposure to Chemical Mixtures and Metabolic Syndrome Risk in Children. JAMA Netw Open 2024; 7:e2412040. [PMID: 38780942 PMCID: PMC11117089 DOI: 10.1001/jamanetworkopen.2024.12040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 02/21/2024] [Indexed: 05/25/2024] Open
Abstract
Importance Prenatal exposure to ubiquitous endocrine-disrupting chemicals (EDCs) may increase the risk of metabolic syndrome (MetS) in children, but few studies have studied chemical mixtures or explored underlying protein and metabolic signatures. Objective To investigate associations of prenatal exposure to EDC mixtures with MetS risk score in children and identify associated proteins and metabolites. Design, Setting, and Participants This population-based, birth cohort study used data collected between April 1, 2003, and February 26, 2016, from the Human Early Life Exposome cohort based in France, Greece, Lithuania, Norway, Spain, and the UK. Eligible participants included mother-child pairs with measured prenatal EDC exposures and complete data on childhood MetS risk factors, proteins, and metabolites. Data were analyzed between October 2022 and July 2023. Exposures Nine metals, 3 organochlorine pesticides, 5 polychlorinated biphenyls, 2 polybrominated diphenyl ethers (PBDEs), 5 perfluoroalkyl substances (PFAS), 10 phthalate metabolites, 3 phenols, 4 parabens, and 4 organophosphate pesticide metabolites measured in urine and blood samples collected during pregnancy. Main Outcomes and Measures At 6 to 11 years of age, a composite MetS risk score was constructed using z scores of waist circumference, systolic and diastolic blood pressures, triglycerides, high-density lipoprotein cholesterol, and insulin levels. Childhood levels of 44 urinary metabolites, 177 serum metabolites, and 35 plasma proteins were quantified using targeted methods. Associations were assessed using bayesian weighted quantile sum regressions applied to mixtures for each chemical group. Results The study included 1134 mothers (mean [SD] age at birth, 30.7 [4.9] years) and their children (mean [SD] age, 7.8 [1.5] years; 617 male children [54.4%] and 517 female children [45.6%]; mean [SD] MetS risk score, -0.1 [2.3]). MetS score increased per 1-quartile increase of the mixture for metals (β = 0.44; 95% credible interval [CrI], 0.30 to 0.59), organochlorine pesticides (β = 0.22; 95% CrI, 0.15 to 0.29), PBDEs (β = 0.17; 95% CrI, 0.06 to 0.27), and PFAS (β = 0.19; 95% CrI, 0.14 to 0.24). High-molecular weight phthalate mixtures (β = -0.07; 95% CrI, -0.10 to -0.04) and low-molecular weight phthalate mixtures (β = -0.13; 95% CrI, -0.18 to -0.08) were associated with a decreased MetS score. Most EDC mixtures were associated with elevated proinflammatory proteins, amino acids, and altered glycerophospholipids, which in turn were associated with increased MetS score. Conclusions and Relevance This cohort study suggests that prenatal exposure to EDC mixtures may be associated with adverse metabolic health in children. Given the pervasive nature of EDCs and the increase in MetS, these findings hold substantial public health implications.
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Affiliation(s)
- Nuria Güil-Oumrait
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Nikos Stratakis
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Léa Maitre
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Augusto Anguita-Ruiz
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Jose Urquiza
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Lorenzo Fabbri
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Xavier Basagaña
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Barbara Heude
- Université Paris Cité and Université Sorbonne Paris Nord, National Institute of Health and Medical Research (INSERM), National Institute for Agriculture, Food and the Environment (INRAE), Center for Research in Epidemiology and StatisticS (CRESS), Paris, France
| | - Line Småstuen Haug
- Department of Food Safety, Norwegian Institute of Public Health, Oslo, Norway
| | - Amrit Kaur Sakhi
- Department of Food Safety, Norwegian Institute of Public Health, Oslo, Norway
| | - Nina Iszatt
- Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Hector C. Keun
- Cancer Metabolism & Systems Toxicology Group, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals National Health Service Foundation Trust, Bradford, United Kingdom
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles
| | - Marina Vafeiadi
- Department of Social Medicine, University of Crete, Heraklion, Crete, Greece
| | - Mariona Bustamante
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | | | - Sandra Andrušaitytė
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Rémy Slama
- Department of Prevention and Treatment of Chronic Diseases, Institute for Advanced Biosciences (IAB; INSERM U1209, CNRS UMR 5309), Université Grenoble Alpes, Grenoble, France
| | - Rosemary McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals National Health Service Foundation Trust, Bradford, United Kingdom
| | - Maribel Casas
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Martine Vrijheid
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
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Tagder P, Alfonso-Mora ML, Díaz-Vidal D, Quino-Ávila AC, Méndez JL, Sandoval-Cuellar C, Monsalve-Jaramillo E, Giné-Garriga M. Semiparametric modeling for the cardiometabolic risk index and individual risk factors in the older adult population: A novel proposal. PLoS One 2024; 19:e0299032. [PMID: 38635675 PMCID: PMC11025852 DOI: 10.1371/journal.pone.0299032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 02/02/2024] [Indexed: 04/20/2024] Open
Abstract
The accurate monitoring of metabolic syndrome in older adults is relevant in terms of its early detection, and its management. This study aimed at proposing a novel semiparametric modeling for a cardiometabolic risk index (CMRI) and individual risk factors in older adults. METHODS Multivariate semiparametric regression models were used to study the association between the CMRI with the individual risk factors, which was achieved using secondary analysis the data from the SABE study (Survey on Health, Well-Being, and Aging in Colombia, 2015). RESULTS The risk factors were selected through a stepwise procedure. The covariates included showed evidence of non-linear relationships with the CMRI, revealing non-linear interactions between: BMI and age (p< 0.00); arm and calf circumferences (p<0.00); age and females (p<0.00); walking speed and joint pain (p<0.02); and arm circumference and joint pain (p<0.00). CONCLUSIONS Semiparametric modeling explained 24.5% of the observed deviance, which was higher than the 18.2% explained by the linear model.
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Affiliation(s)
- Philippe Tagder
- Fisioterapia, Universidad de Boyacá Sede Tunja, Colombia
- Real World Evidence, IQVIA, Belgium
| | | | - Diana Díaz-Vidal
- Fisioterapia, Facultad Ciencias de la Salud- Grupo GIMHUS, Universidad de San Buenaventura-Cartagena, Colombia
| | | | - Juliana Lever Méndez
- Fisioterapia, Universidad de La Sabana, Campus del Puente del Común, Cundinamarca, Colombia
| | | | | | - María Giné-Garriga
- Department of Sport Sciences, Faculty of Psychology, Education and Sport Sciences Blanquerna, Universitat Ramon Llull, Barcelona, Spain
- Department of Physical Therapy, Faculty of Health Sciences Blanquerna, Universitat Ramon Llull, Barcelona, Spain
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Zhao X, Lu C, Song B, Chen D, Teng D, Shan Z, Teng W. The prevalence and clustering of metabolic syndrome risk components in Chinese population: a cross-sectional study. Front Endocrinol (Lausanne) 2023; 14:1290855. [PMID: 38152127 PMCID: PMC10751355 DOI: 10.3389/fendo.2023.1290855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 11/30/2023] [Indexed: 12/29/2023] Open
Abstract
Objective The metabolic syndrome (MetS) is diagnosed upon the manifestation of ≥ 3 out of 5 specific components. The present study evaluated the epidemiological characteristics of the MetS components and their clustering condition among Chinese adults. Methods 68383 participants aged 18-80 years from TIDE were scored on a six-point (0-5) MetS severity score (MSSS), which quantified their cumulative amount of MetS risk components. We evaluated the epidemiological characteristics of these components and their clustering conditions. Additionally, we examined the relation of age with the prevalence of different MSSSs or specific MetS components using restricted cubic splines. Results Among 68383 participants, 26113 men and 24582 women had abnormal MetS components. There were significant differences in most epidemiological characteristics between the 6 MSSS groups. The top three prevalence of abnormal metabolic components were high systolic blood pressure (SBP) (9.41%, n=6568), high waist circumference (WC) (8.13%, n=6120), and the cooccurrence of high SBP and high WC (6.33%, n=4622). Participants were more likely to have all five MetS components when HDL-C was low. Restricted cubic splines showed that when the MSSS ≥3, the MetS prevalence of male peaked and that of the female population increased most rapidly at 40-60 age group. Conclusion The 40-60 age group can be regarded as the high-risk period of MetS, and elderly women have a higher risk of multiple metabolic disorders than men. The top three clustering of abnormal metabolic components were high SBP, high WC, and their combination. Multiple components aggregation was more likely to occur when HDL-C decreased.
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Affiliation(s)
| | | | | | | | - Di Teng
- The Department of Endocrinology and Metabolism, Institute of Endocrinology, National Health Commission Key Laboratory of Diagnosis and Treatment of Thyroid Diseases, The First Hospital of China Medical University, Shenyang, Liaoning, China
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Wolters M, Marron M, Foraita R, Hadjigeorgiou C, De Henauw S, Eiben G, Lauria F, Iglesia I, Moreno LA, Molnár D, Veidebaum T, Ahrens W, Nagrani R. Longitudinal Associations Between Vitamin D Status and Cardiometabolic Risk Markers Among Children and Adolescents. J Clin Endocrinol Metab 2023; 108:e1731-e1742. [PMID: 37261399 DOI: 10.1210/clinem/dgad310] [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: 12/19/2022] [Revised: 05/24/2023] [Accepted: 05/25/2023] [Indexed: 06/02/2023]
Abstract
CONTEXT Vitamin D status has previously been associated with cardiometabolic risk markers in children and adolescents. In particular, it has been suggested that children with obesity are more prone to vitamin D deficiency and unfavorable metabolic outcomes compared with healthy-weight children. OBJECTIVE To conduct a longitudinal study assessing this association in children and stratify by body mass index (BMI) category. METHODS Children from the pan-European IDEFICS/I.Family cohort with at least one measurement of serum 25-hydroxyvitamin D [25(OH)D] at cohort entry or follow-up (n = 2171) were included in this study. Linear mixed-effect models were used to assess the association between serum 25(OH)D as an independent variable and z-scores of cardiometabolic risk markers (waist circumference, systolic [SBP] and diastolic blood pressure [DBP], high- [HDL] and low-density lipoprotein, non-HDL, triglycerides [TRG], apolipoprotein A1 [ApoA1] and ApoB, fasting glucose [FG], homeostatic model assessment for insulin resistance [HOMA-IR], and metabolic syndrome score) as dependent variables. RESULTS After adjustment for age, sex, study region, smoking and alcohol status, sports club membership, screen time, BMI, parental education, and month of blood collection, 25(OH)D levels were inversely associated with SBP, DBP, FG, HOMA-IR, and TRG. The HOMA-IR z-score decreased by 0.07 units per 5 ng/mL increase in 25(OH)D. The 25(OH)D level was consistently associated with HOMA-IR irrespective of sex or BMI category. CONCLUSION Low serum 25(OH)D concentrations are associated with unfavorable levels of cardiometabolic markers in children and adolescents. Interventions to improve vitamin D levels in children with a poor status early in life may help to reduce cardiometabolic risk.
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Affiliation(s)
- Maike Wolters
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, D-28359 Bremen, Germany
| | - Manuela Marron
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, D-28359 Bremen, Germany
| | - Ronja Foraita
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, D-28359 Bremen, Germany
| | | | - Stefaan De Henauw
- Department of Public Health and Primary Care, Ghent University, 9000 Ghent, Belgium
| | - Gabriele Eiben
- Department of Public Health and Community Medicine, University of Gothenburg, 40530 Gothenburg, Sweden
- Department of Public Health, School of Health Sciences, University of Skövde, 541 28 Skövde, Sweden
| | - Fabio Lauria
- Institute of Food Sciences, National Research Council, 83100 Avellino, Italy
| | - Iris Iglesia
- GENUD (Growth, Exercise, Nutrition and Development) Research Group, University of Zaragoza, Instituto Agroalimentario de Aragón (IA2), Instituto de Investigación Sanitaria de Aragón (IIS Aragón), 50009 Zaragoza, Spain
- Primary Care Interventions to Prevent Maternal and Child Chronic Diseases of Perinatal and Developmental Origin Network (RICORS), RD21/0012/0012, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Luis A Moreno
- GENUD (Growth, Exercise, Nutrition and Development) Research Group, University of Zaragoza, Instituto Agroalimentario de Aragón (IA2), Instituto de Investigación Sanitaria de Aragón (IIS Aragón), 50009 Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Dénes Molnár
- Department of Paediatrics, Medical School, University of Pécs, 7623 Pécs, Hungary
| | - Toomas Veidebaum
- National Institute for Health Development, 11619 Tallinn, Estonia
| | - Wolfgang Ahrens
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, D-28359 Bremen, Germany
- Institute of Statistics, Faculty of Mathematics and Computer Science, Bremen University, 28359 Bremen, Germany
| | - Rajini Nagrani
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, D-28359 Bremen, Germany
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Amouzegar A, Honarvar M, Masoumi S, Khalili D, Azizi F, Mehran L. Trajectory patterns of metabolic syndrome severity score and risk of type 2 diabetes. J Transl Med 2023; 21:750. [PMID: 37880756 PMCID: PMC10598905 DOI: 10.1186/s12967-023-04639-w] [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: 06/03/2023] [Accepted: 10/17/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND The available evidence indicates that the severity of metabolic syndrome tends to worsen progressively over time. We assessed the trajectory of age and sex-specific continuous MetS severity score (cMetS-S) and its association with the development of diabetes during an 18-year follow-up. METHODS In a prospective population-based Tehran Lipid and Glucose Study, 3931 eligible participants free of diabetes, aged 20-60 years, were followed at three-year intervals. We examined the trajectories of cMetS-S over nine years using latent growth mixture modeling (LGMM) and subsequent risks of incident diabetes eight years later. The prospective association of identified trajectories with diabetes was examined using the Cox proportional hazard model adjusting for age, sex, education, and family history of diabetes, physical activity, obesity (BMI ≥ 30 kg/m2), antihypertensive and lipid-lowering medication, and baseline fasting plasma glucose in a stepwise manner. RESULTS Among 3931 participants, three cMetS-S trajectory groups of low (24.1%), medium (46.8%), and high (29.1%) were identified during the exposure period. Participants in the medium and high cMetS-S trajectory classes had HRs of 2.44 (95% CI: 1.56-3.81) and 6.81 (95% CI: 4.07-10.01) for future diabetes in fully adjusted models, respectively. Normoglycemic individuals within the high cMetS-S class had an over seven-fold increased risk of diabetes (HR: 7.12; 95% CI: 6.05-12.52). CONCLUSION Although most adults exhibit an unhealthy metabolic score, its severity usually remains stable throughout adulthood over ten years of follow-up. The severity score of metabolic syndrome has the potential to be utilized as a comprehensive and easily measurable indicator of cardiometabolic dysfunction. It can be employed in clinical settings to detect and track individuals at a heightened risk of developing T2DM, even if their glucose levels are normal.
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Affiliation(s)
- Atieh Amouzegar
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, No. 23, Parvaneh Street, Velenjak, Tehran, P.O. Box: 19395-4763, IR, Iran
| | - Mohammadjavad Honarvar
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, No. 23, Parvaneh Street, Velenjak, Tehran, P.O. Box: 19395-4763, IR, Iran
| | - Safdar Masoumi
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, No. 23, Parvaneh Street, Velenjak, Tehran, P.O. Box: 19395-4763, IR, Iran
| | - Davood Khalili
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IR, Iran
- Department of Biostatistics and Epidemiology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IR, Iran
| | - Fereidoun Azizi
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, No. 23, Parvaneh Street, Velenjak, Tehran, P.O. Box: 19395-4763, IR, Iran
| | - Ladan Mehran
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, No. 23, Parvaneh Street, Velenjak, Tehran, P.O. Box: 19395-4763, IR, Iran.
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de Almeida Melo D, Dos Santos AM, da Cruz Silveira VN, Silva MB, da Silva Diniz A. Prevalence of metabolic syndrome in adolescents based on three diagnostic definitions: a cross-sectional study. ARCHIVES OF ENDOCRINOLOGY AND METABOLISM 2023; 67:e000634. [PMID: 37249462 PMCID: PMC10665060 DOI: 10.20945/2359-3997000000634] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 12/17/2022] [Indexed: 05/31/2023]
Abstract
Objective There is no consensus as to the best criterion for the evaluation of metabolic syndrome (MS), impairing the estimation of its prevalence. This study aims to compare MS estimates using three recommended definitions for adolescents based on a cross-sectional study nested in the Consortium of Brazilian Birth Cohorts in São Luís, Maranhão. Subjects and methods A total of 2,515 adolescents aged between 18 and 19 years were evaluated. The criteria of International Diabetes Federation (IDF) and National Cholesterol Education Program Panel III (NCEP-ATP) modified by Cook and cols. (2003) and De Ferranti and cols. (2004) defined SM. To compare the estimates of MS prevalence, the chi-square, Fisher´s exact and Cohen´s Kappa index tests were used. Results Among the 2,064 participants evaluated in the final sample. The prevalence of MS ranged from 4.2% (95% CI: 3.3-5.1) to 10.2% (95% CI: 8.8-11.4). When comparing the estimates of MS prevalence in the total sample and by sex, a statistically significant difference was observed. The agreement between the criteria ranged from 0.42 (CI 95%: 0.35-0.49) to 0.55 (CI 95%: 0.48-0.62) in the total sample, 0.33 (CI 95%: 0.24-0.42) to 0.59 (95%CI: 0.47-0.71) among boys and 0.39 (95% CI: 0.26-0.52) to 0.54 (95% CI: 0.44-0.64) among girls. Conclusion Different criteria provide different estimates for the prevalence of MS in adolescents, reflecting the importance of establishing a consensus.
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Affiliation(s)
- Dejane de Almeida Melo
- Programa de Pós-graduação em Nutrição, Universidade Federal de Pernambuco, Recife, PE, Brasil,
| | | | | | - Michele Bezerra Silva
- Programa de Pós-graduação em Saúde Coletiva, Universidade Federal do Maranhão, São Luís, MA, Brasil
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Hong X, Zhang X, You L, Li F, Lian H, Wang J, Mao N, Ren M, Li Y, Wang C, Sun K. Association between adiponectin and newly diagnosed type 2 diabetes in population with the clustering of obesity, dyslipidaemia and hypertension: a cross-sectional study. BMJ Open 2023; 13:e060377. [PMID: 36828662 PMCID: PMC9972409 DOI: 10.1136/bmjopen-2021-060377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/05/2023] [Indexed: 02/26/2023] Open
Abstract
OBJECTIVES Adiponectin is closely related to glucose metabolism and traditional diabetes risk factors (obesity, hypertension and dyslipidaemia). We aimed to explore the association between adiponectin levels and newly diagnosed type 2 diabetes mellitus (T2DM) and pre-diabetes in subgroups classified according to T2DM risk factors. SETTING Sun Yat-sen Memorial Hospital of Sun Yat-sen University. PARTICIPANTS 3680 individuals (1753 men and 1927 women) aged 18-70 years from Guangzhou and Dongguan, China, were enrolled from December 2018 to October 2019. PRIMARY AND SECONDARY OUTCOME MEASURES T2DM was defined as fasting plasma glucose (FPG)≥7.0 mmol/L or HbA1c≥6.5%, and pre-diabetes was defined as 6.1 mmol/L≤FPG<7.0 mmol/L or 5.7≤HbA1c<6.5%. RESULTS With the increasing number of T2DM risk factors, the proportion of the population with high-quartile adiponectin levels gradually decreased (p<0.001). A low level of adiponectin was significantly associated with diabetes and pre-diabetes in a population with ≥1 T2DM risk factor, whereas its association was not consistently significant in the population with all three T2DM risk factors. For instance, participants were more likely to have diabetes or prediabetes with low levels of adiponectin when they had ≥ one T2DM risk factor (quartile 2 vs. 1: OR 0.71 [95%CI: 0.56-0.89]; P=0.003; quartile 3 vs. 1: OR 0.57 [95%CIs: 0.44-0.72]; P<0.001; and quartile 4 vs. 1: OR 0.52 [95%CIs: 0.40-0.67]; P<0.001). CONCLUSION Adiponectin was negatively associated with diabetes and pre-diabetes in a population with few T2DM risk factors, while their relationship gradually attenuated with the accumulation of T2DM risk factors, especially in a population with coexisting diseases such as obesity, hypertension and dyslipidaemia.
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Affiliation(s)
- Xiaosi Hong
- Department of Endocrinology, Sun Yat-Sen Memorial Hospital, Guangzhou, China
- Guang Dong Clinical Research Center for Metabolic Diseases, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Xiaoyun Zhang
- Department of Endocrinology, Sun Yat-Sen Memorial Hospital, Guangzhou, China
- Guang Dong Clinical Research Center for Metabolic Diseases, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Lili You
- Department of Endocrinology, Sun Yat-Sen Memorial Hospital, Guangzhou, China
- Guang Dong Clinical Research Center for Metabolic Diseases, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Feng Li
- Department of Endocrinology, Sun Yat-Sen Memorial Hospital, Guangzhou, China
- Guang Dong Clinical Research Center for Metabolic Diseases, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Hong Lian
- Department of Endocrinology, Sun Yat-Sen Memorial Hospital, Guangzhou, China
- Guang Dong Clinical Research Center for Metabolic Diseases, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Jiahuan Wang
- Department of Endocrinology, Sun Yat-Sen Memorial Hospital, Guangzhou, China
- Guang Dong Clinical Research Center for Metabolic Diseases, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Na Mao
- Department of Endocrinology, Sun Yat-Sen Memorial Hospital, Guangzhou, China
- Guang Dong Clinical Research Center for Metabolic Diseases, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Meng Ren
- Department of Endocrinology, Sun Yat-Sen Memorial Hospital, Guangzhou, China
- Guang Dong Clinical Research Center for Metabolic Diseases, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Yan Li
- Department of Endocrinology, Sun Yat-Sen Memorial Hospital, Guangzhou, China
- Guang Dong Clinical Research Center for Metabolic Diseases, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Chuan Wang
- Department of Endocrinology, Sun Yat-Sen Memorial Hospital, Guangzhou, China
- Guang Dong Clinical Research Center for Metabolic Diseases, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Kan Sun
- Department of Endocrinology, Sun Yat-Sen Memorial Hospital, Guangzhou, China
- Guang Dong Clinical Research Center for Metabolic Diseases, Sun Yat-sen Memorial Hospital, Guangzhou, China
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Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Beaton AZ, Boehme AK, Buxton AE, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Fugar S, Generoso G, Heard DG, Hiremath S, Ho JE, Kalani R, Kazi DS, Ko D, Levine DA, Liu J, Ma J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Virani SS, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association. Circulation 2023; 147:e93-e621. [PMID: 36695182 DOI: 10.1161/cir.0000000000001123] [Citation(s) in RCA: 1239] [Impact Index Per Article: 1239.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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10
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Ong YY, Pang WW, Michael N, Aris IM, Sadananthan SA, Tint MT, Liang Choo JT, Ling LH, Karnani N, Velan SS, Fortier MV, Tan KH, Gluckman PD, Yap F, Chong YS, Godfrey KM, Chan SY, Eriksson JG, Chong MFF, Wlodek ME, Lee YS. Timing of introduction of complementary foods, breastfeeding, and child cardiometabolic risk: a prospective multiethnic Asian cohort study. Am J Clin Nutr 2023; 117:83-92. [PMID: 36789947 DOI: 10.1016/j.ajcnut.2022.10.021] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/22/2022] [Accepted: 10/28/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The timing of introduction of complementary foods and the duration of breastfeeding (BF) have been independently associated with child overweight and obesity; however, their combined influence on body fat partitioning and cardiometabolic risk is unclear. OBJECTIVE We investigated the associations of the timing of introduction of complementary foods, the duration of BF, and their interaction with child adiposity and cardiometabolic risk markers. METHODS We analyzed data from 839 children in the prospective Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort. Mothers reported the age at which infants were first fed complementary foods and BF duration, classified as early (≤4 mo) versus typical (>4 mo) complementary feeding (CF) and short (≤4 mo) versus long (>4 mo) duration of any BF, respectively. We measured adiposity and cardiometabolic risk markers at the age of 6 y and examined their associations with infant feeding patterns using multiple regression, adjusting for sociodemographics, parents' body mass index (BMI), maternal factors, birth weight for gestational age, and infant weight gain. RESULTS Of 839 children, 18% experienced early CF, whereas 54% experienced short BF. Short (vs. long) BF and early (vs. typical) CF were independently associated with higher z-scores of BMI [β (95% confidence interval), short BF, 0.18 standard deviation score (SDS) (-0.01, 0.38); early CF, 0.34 SDS (0.11, 0.57)] and sum of skinfolds [short BF, 1.83 mm (0.05, 3.61); early CF, 2.73 mm (0.55, 4.91)]. Children who experienced both early CF and short BF (vs. typical CF-long BF) had synergistically higher diastolic blood pressure [1.41 mmHg (-0.15, 2.97), P-interaction = 0.023] and metabolic syndrome score [0.81 (0.16, 1.47), P-interaction = 0.081]. Early CF-long BF (vs. early CF-short BF) was associated with a lower systolic blood pressure [-3.74 mmHg (-7.01, -0.48)], diastolic blood pressure [-2.29 mmHg (-4.47, -0.11)], and metabolic syndrome score [-0.90 (-1.80, 0.00)]. CONCLUSIONS A combination of early CF and short BF was associated with elevated child adiposity and cardiometabolic markers. Longer BF duration may protect against cardiometabolic risk associated with early CF. This trial was registered at clinicaltrials.gov as NCT01174875.
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Affiliation(s)
- Yi Ying Ong
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore; Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Wei Wei Pang
- Department of Obstetrics and Gynaecology and Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
| | - Navin Michael
- Singapore Institute for Clinical Science, Agency for Science, Technology, and Research, Singapore, Republic of Singapore
| | - Izzuddin M Aris
- Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Suresh Anand Sadananthan
- Singapore Institute for Clinical Science, Agency for Science, Technology, and Research, Singapore, Republic of Singapore
| | - Mya-Thway Tint
- Singapore Institute for Clinical Science, Agency for Science, Technology, and Research, Singapore, Republic of Singapore
| | | | - Lieng Hsi Ling
- Department of Cardiology, National University Heart Centre, National University Hospital, Singapore, Republic of Singapore
| | - Neerja Karnani
- Singapore Institute for Clinical Science, Agency for Science, Technology, and Research, Singapore, Republic of Singapore
| | - S Sendhil Velan
- Singapore Institute for Clinical Science, Agency for Science, Technology, and Research, Singapore, Republic of Singapore
| | - Marielle V Fortier
- Department of Diagnostic and Interventional Imaging, KK Women's and Children's Hospital, Singapore, Republic of Singapore
| | - Kok Hian Tan
- Duke-NUS Medical School, Singapore, Republic of Singapore; Department of Maternal Fetal Medicine, KK Women's and Children's Hospital, Singapore, Republic of Singapore
| | - Peter D Gluckman
- Singapore Institute for Clinical Science, Agency for Science, Technology, and Research, Singapore, Republic of Singapore; Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Fabian Yap
- KK Women's and Children's Hospital, Singapore, Republic of Singapore; Duke-NUS Medical School, Singapore, Republic of Singapore
| | - Yap-Seng Chong
- Department of Obstetrics and Gynaecology and Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore; Singapore Institute for Clinical Science, Agency for Science, Technology, and Research, Singapore, Republic of Singapore
| | - Keith M Godfrey
- MRC Lifecourse Epidemiology Centre and NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Shiao-Yng Chan
- Department of Obstetrics and Gynaecology and Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore; Singapore Institute for Clinical Science, Agency for Science, Technology, and Research, Singapore, Republic of Singapore
| | - Johan G Eriksson
- Department of Obstetrics and Gynaecology and Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore; Singapore Institute for Clinical Science, Agency for Science, Technology, and Research, Singapore, Republic of Singapore; Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland; Folkhälsan Research Center, Helsinki, Finland
| | - Mary F-F Chong
- Singapore Institute for Clinical Science, Agency for Science, Technology, and Research, Singapore, Republic of Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Republic of Singapore
| | - Mary E Wlodek
- Department of Obstetrics and Gynaecology and Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore; Singapore Institute for Clinical Science, Agency for Science, Technology, and Research, Singapore, Republic of Singapore; Department of Obstetrics and Gynaecology, University of Melbourne, Melbourne, Australia
| | - Yung Seng Lee
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore; Singapore Institute for Clinical Science, Agency for Science, Technology, and Research, Singapore, Republic of Singapore; Division of Paediatric Endocrinology, Department of Paediatrics, Khoo Teck Puat-National University Children's Medical Institute, National University Hospital, National University Health System, Singapore, Republic of Singapore.
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11
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Nur Zati Iwani AK, Jalaludin MY, Roslan FA, Mansor F, Md Zain F, Hong JYH, Zin RMWM, Yahya A, Ishak Z, Selamat R, Mokhtar AH. Cardiometabolic risk factors among children who are affected by overweight, obesity and severe obesity. Front Public Health 2023; 11:1097675. [PMID: 37181686 PMCID: PMC10173091 DOI: 10.3389/fpubh.2023.1097675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 03/14/2023] [Indexed: 05/16/2023] Open
Abstract
Background The increasing severity of obesity is expected to lead to more serious health effects. However, there is limited information on the prevalence and clinical characteristics of cardiometabolic risk factors in severely children affected by obesity in Malaysia. This baseline study aimed to investigate the prevalence of these factors and their association with obesity status among young children. Methods In this study, a cross-sectional design was employed using the baseline data obtained from the My Body Is Fit and Fabulous at school (MyBFF@school) intervention program involving obese school children. Obesity status was defined using the body mass index (BMI) z-score from the World Health Organization (WHO) growth chart. Cardiometabolic risk factors presented in this study included fasting plasma glucose (FPG), triglycerides (TGs), total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), blood pressure, acanthosis nigricans, insulin resistance (IR), and MetS. MetS was defined using the International Diabetes Federation (IDF) 2007 criteria. Descriptive data were presented accordingly. The association between cardiometabolic risk factors, such as obesity status, and acanthosis nigricans with MetS was measured using multivariate logistic regression, which was adjusted for gender, ethnicity, and strata. Results Out of 924 children, 38.4% (n = 355) were overweight, 43.6% (n = 403) were obese, and 18% (n = 166) were severely obese. The overall mean age was 9.9 ± 0.8 years. The prevalence of hypertension, high FPG, hypertriglyceridemia, low HDL-C, and the presence of acanthosis nigricans among severely children affected by obesity was 1.8%, 5.4%, 10.2%, 42.8%, and 83.7%, respectively. The prevalence of children affected by obesity who were at risk of MetS in <10-year-old and MetS >10-year-old was observed to be similar at 4.8%. Severely children affected by obesity had higher odds of high FPG [odds ratio (OR) = 3.27; 95% confdence interval (CI) 1.12, 9.55], hypertriglyceridemia (OR = 3.50; 95%CI 1.61, 7.64), low HDL-C (OR = 2.65; 95%CI 1.77, 3.98), acanthosis nigricans (OR = 13.49; 95%CI 8.26, 22.04), IR (OR = 14.35; 95%CI 8.84, 23.30), and MetS (OR = 14.03; 95%CI 3.97, 49.54) compared to overweight and children affected by obesity. The BMI z-score, waist circumference (WC), and percentage body fat showed a significant correlation with triglycerides, HDL-C, the TG: HDL-C ratio, and the homeostatic model assessment for IR (HOMA-IR) index. Conclusions Severely children affected by obesity exhibit a higher prevalence of and are more likely to develop cardiometabolic risk factors compared to overweight and children affected by obesity. This group of children should be monitored closely and screened periodically for obesity-related health problems to institute early and comprehensive intervention.
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Affiliation(s)
- Ahmad Kamil Nur Zati Iwani
- Endocrine and Metabolic Unit, Institute for Medical Research, Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Muhammad Yazid Jalaludin
- Department of Paediatrics, Faculty of Medicine, University Malaya, Kuala Lumpur, Malaysia
- *Correspondence: Muhammad Yazid Jalaludin
| | - Farah Aqilah Roslan
- Endocrine and Metabolic Unit, Institute for Medical Research, Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Fazliana Mansor
- Endocrine and Metabolic Unit, Institute for Medical Research, Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Fuziah Md Zain
- Department of Paediatrics, Hospital Putrajaya, Ministry of Health Malaysia, Putrajaya, Malaysia
| | - Janet Yeow Hua Hong
- Department of Paediatrics, Hospital Putrajaya, Ministry of Health Malaysia, Putrajaya, Malaysia
| | - Ruziana Mona Wan Mohd Zin
- Endocrine and Metabolic Unit, Institute for Medical Research, Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Abqariyah Yahya
- Department of Social and Preventive Medicine, Faculty of Medicine, University Malaya, Kuala Lumpur, Malaysia
| | - Zahari Ishak
- Department of Educational Psychology and Counselling, Faculty of Education, University Malaya, Kuala Lumpur, Malaysia
| | - Rusidah Selamat
- Nutrition Division, Ministry of Health Malaysia, Putrajaya, Malaysia
| | - Abdul Halim Mokhtar
- Department of Sports Medicine, Faculty of Medicine, University Malaya, Kuala Lumpur, Malaysia
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12
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Salus M, Tillmann V, Remmel L, Unt E, Mäestu E, Parm Ü, Mägi A, Tali M, Jürimäe J. Effect of Sprint Interval Training on Cardiometabolic Biomarkers and Adipokine Levels in Adolescent Boys with Obesity. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191912672. [PMID: 36231972 PMCID: PMC9564781 DOI: 10.3390/ijerph191912672] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/26/2022] [Accepted: 09/30/2022] [Indexed: 06/01/2023]
Abstract
This study investigated the effect of supervised sprint interval training (SIT) on different cardiometabolic risk factors and adipokines in adolescent boys with obesity. Thirty-seven boys were allocated to either a SIT group (13.1 ± 0.3 years; body mass index [BMI]: 30.3 ± 0.9 kg·m-2) or a control group (CONT) (13.7 ± 0.4 years; BMI: 32.6 ± 1.6 kg·m-2). The SIT group performed 4-6 × 30 s all-out cycling sprints, interspersed with 4 min rest, for 3 sessions/week, during a 12-week period, while the non-exercising CONT group maintained a habitual lifestyle. Anthropometric measurements, triglycerides, fasting insulin and glucose, total cholesterol (TC), high- (HDLc) and low-density (LDLc) cholesterol, leptin and adiponectin in blood, cardiorespiratory fitness (CRF), and a metabolic syndrome severity risk score (MSSS) were calculated before and after the 12-week period. Compared to baseline values, a significant reduction in MSSS was seen in the SIT group after intervention. LDLc showed favorable changes in SIT compared to CONT (-0.06 ± 0.1 vs. 0.19 ± 0.01 mmol·L-1; p = 0.025). Additionally, CRF increased in the SIT group compared to the CONT group (5.2 ± 1.1 vs. -2.1 ± 1.1 mL·min-1·kg-1, p < 0.001). Moreover, a 12-week all-out SIT training effectively improves cardiometabolic health in adolescent boys with obesity.
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Affiliation(s)
- Marit Salus
- Institute of Sports Sciences and Physiotherapy, Faculty of Medicine, University of Tartu, Ujula 4, 51008 Tartu, Estonia
- Department of Physiotherapy and Environmental Health, Tartu Health Care College, Nooruse 5, 50411 Tartu, Estonia
| | - Vallo Tillmann
- Department of Pediatrics, Institute of Clinical Medicine, Faculty of Medicine, University of Tartu, Lunini 6, 50406 Tartu, Estonia
- Children’s Clinic of Tartu University Hospital, Lunini 6, 50406 Tartu, Estonia
| | - Liina Remmel
- Institute of Sports Sciences and Physiotherapy, Faculty of Medicine, University of Tartu, Ujula 4, 51008 Tartu, Estonia
| | - Eve Unt
- Department of Sports Medicine and Rehabilitation, Institute of Clinical Medicine, Faculty of Medicine, University of Tartu, Puusepa 8, 50406 Tartu, Estonia
- Sports Medicine and Rehabilitation Clinic, Tartu University Hospital, Puusepa 8, 50406 Tartu, Estonia
| | - Evelin Mäestu
- Institute of Sports Sciences and Physiotherapy, Faculty of Medicine, University of Tartu, Ujula 4, 51008 Tartu, Estonia
| | - Ülle Parm
- Department of Physiotherapy and Environmental Health, Tartu Health Care College, Nooruse 5, 50411 Tartu, Estonia
| | - Agnes Mägi
- Sports Medicine and Rehabilitation Clinic, Tartu University Hospital, Puusepa 8, 50406 Tartu, Estonia
| | - Maie Tali
- Department of Sports Medicine and Rehabilitation, Institute of Clinical Medicine, Faculty of Medicine, University of Tartu, Puusepa 8, 50406 Tartu, Estonia
- Sports Medicine and Rehabilitation Clinic, Tartu University Hospital, Puusepa 8, 50406 Tartu, Estonia
| | - Jaak Jürimäe
- Institute of Sports Sciences and Physiotherapy, Faculty of Medicine, University of Tartu, Ujula 4, 51008 Tartu, Estonia
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Merry TL, Metcalf P, Scragg R, Gearry R, Foster M, Krebs JD. Metabolic syndrome severity score (MetSSS) associates with metabolic health status in multi-ethnic Aotearoa New Zealand cohorts. Diabetes Res Clin Pract 2022; 192:110088. [PMID: 36154929 DOI: 10.1016/j.diabres.2022.110088] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 08/24/2022] [Accepted: 09/12/2022] [Indexed: 11/20/2022]
Abstract
AIM To investigate the relationship of metabolic syndrome severity score (MetSSS) with glucose regulatory and cardiovascular disease (CVD) status in Aotearoa New Zealand. METHODS MetSSS and MetSSS component coefficients were calculated for participants from the cross-sectional Workforce Diabetes Study (WDS) (n = 5,806) and Diabetes, Heart and Health Survey (DHAH) (n = 4,010) and compared by ethnicity (European, Māori, Pacific and Asian), glucose regulatory status [impaired fasting glucose, impaired glucose tolerance and type 2 diabetes) and history of cardiovascular disease. RESULTS MetSSS positively associated with impaired glucose regulatory status and history of cardiovascular disease for all ethnic groups. Ethnicity significantly affected different coefficients of the MetSSS components, however all ethnicities had an approximately normal MetSSS distribution, with Māori and Pacific curves being right-shifted compared to European. While the MetSSS thresholds that capture 80% of participant with type 2 diabetes (T2D) were higher for Māori and Pacific, the difference in MetSSS between those participants with and without type 2 diabetes within an ethnicity group was similar across ethnicities. CONCLUSION MetSSS may have utility as a tool to quantify an individual's cardiometabolic disease risk within the multi-ethnic population of Aotearoa New Zealand, however ethnic-specific categories for disease risk are likely to be required.
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Affiliation(s)
- Troy L Merry
- Discipline of Nutrition, School of Medical Sciences, The University of Auckland, Auckland, New Zealand; Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand.
| | - Patricia Metcalf
- Department of Statistics, The University of Auckland, Auckland, New Zealand
| | - Robert Scragg
- School of Population Health, The University of Auckland, Auckland, New Zealand
| | - Richard Gearry
- Department of Medicine, University of Otago Christchurch, Christchurch, New Zealand
| | | | - Jeremy D Krebs
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, New Zealand; Department of Medicine, University of Otago Wellington, Wellington, New Zealand
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14
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Tang X, Wu M, Wu S, Tian Y. Continuous metabolic syndrome severity score and the risk of CVD and all-cause mortality. Eur J Clin Invest 2022; 52:e13817. [PMID: 35598176 DOI: 10.1111/eci.13817] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 04/26/2022] [Accepted: 05/19/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND The dualistic diagnostic criteria for metabolic syndrome overlooked the severity of metabolic syndrome, and the relationships between the severity of metabolic syndrome and adverse health conditions are poorly characterized. We therefore aimed to investigate the associations of metabolic syndrome severity with incident cardiovascular disease (CVD)/all-cause mortality. METHODS A total of 116,772 participants from the Kailuan study were followed up biennially between 2006 and 2018. The severity of metabolic syndrome was evaluated using a continuous metabolic syndrome severity score (MetS score). Cox proportional hazards model was used to examine the association between MetS score and the risk of CVD and all-cause mortality. Restricted cubic spline analyses were performed to explore the dose-response associations. RESULTS We found that the risk of CVD and all-cause mortality increased consistently with the MetS score. In the multivariable-adjusted model, the hazard ratios of CVD and all-cause mortality were 2.05 (95% CI 1.86-2.25) and 1.45 (95% CI 1.35-1.56), respectively, in those subjects>75th percentile compared with those <25th percentile. Additionally, a J-shaped dose-response relationship was found between MetS score and the risk of all-cause mortality (pnonlinearity <.001), while a linear relationship between MetS score and the risk of CVD was observed in this study (pnonlinearity = .737). CONCLUSIONS This study suggests significant dose-response relationships between MetS score and the risk of CVD/mortality. Subjects without metabolic syndrome but with a relatively high MetS score should raise their awareness and pay more attention to the possible increased risk of CVD events.
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Affiliation(s)
- Xiaoya Tang
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mingyang Wu
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shouling Wu
- Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, Tangshan City, China
| | - Yaohua Tian
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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15
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Booker R, Beech BM, Bruce MA, Thorpe RJ, Norris KC, Heitman E, Newton RL, Holmes ME. The Association of Sedentary Behavior and Physical Activity with Different Measurements of Metabolic Syndrome: The Jackson Heart Study. Am J Lifestyle Med 2022. [DOI: 10.1177/15598276221118044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Purpose: Cross-sectional investigation of the association of sedentary behavior and physical activity with metabolic syndrome (MetS) among the African American participants in the Jackson Heart Study (JHS). Methods: Prevalence, number of individual components, and MetS severity z-score (MetS-Z) were examined. MetS was classified using ATP-III thresholds. MetS-Z was calculated using sex-, race-, and ethnicity-specific formulas. Sedentary behavior and physical activity were calculated from the JHS Physical Activity Cohort survey (JPAC). Associations between sedentary behavior and physical activity with MetS were assessed by logistic, negative binomial, and ordinary least squares regressions. Results: The mean participant age ( N = 3370) was 61.7 ± 11.9 years and most were female (63.9%). Among all participants, 60.5% were classified with MetS. Overall MetS-Z was moderately high (.31 ± 1.07). Most waking hours were sedentary, with just under 40 daily minutes of self-reported physical activity. Physical activity was associated with lower prevalence of MetS, the number of individual components, and MetS-Z score ( p < .05). Sedentary behavior was not associated with MetS in any fully adjusted models ( p > .05). Conclusions: Physical activity was associated with lower cardiometabolic risk, irrespective of sedentary behavior. Further studies are needed to better understand why no relation was found between sedentary behavior and cardiometabolic risk in this cohort of African American adults.
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Affiliation(s)
- Robert Booker
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA (RB); University of Houston Population Health, University of Houston, Houston, TX, USA (BMB); Department of Health Systems and Population Health Sciences, University of Houston Tilman J. Fertitta Family College of Medicine, Houston, TX, USA (BMB, MAB); Program for Research on Men’s Health, Hopkins Center for Health Disparities Solutions, John Hopkins Bloomberg School of Public Health, Baltimore, MD,
| | - Bettina M. Beech
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA (RB); University of Houston Population Health, University of Houston, Houston, TX, USA (BMB); Department of Health Systems and Population Health Sciences, University of Houston Tilman J. Fertitta Family College of Medicine, Houston, TX, USA (BMB, MAB); Program for Research on Men’s Health, Hopkins Center for Health Disparities Solutions, John Hopkins Bloomberg School of Public Health, Baltimore, MD,
| | - Marino A. Bruce
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA (RB); University of Houston Population Health, University of Houston, Houston, TX, USA (BMB); Department of Health Systems and Population Health Sciences, University of Houston Tilman J. Fertitta Family College of Medicine, Houston, TX, USA (BMB, MAB); Program for Research on Men’s Health, Hopkins Center for Health Disparities Solutions, John Hopkins Bloomberg School of Public Health, Baltimore, MD,
| | - Roland J. Thorpe
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA (RB); University of Houston Population Health, University of Houston, Houston, TX, USA (BMB); Department of Health Systems and Population Health Sciences, University of Houston Tilman J. Fertitta Family College of Medicine, Houston, TX, USA (BMB, MAB); Program for Research on Men’s Health, Hopkins Center for Health Disparities Solutions, John Hopkins Bloomberg School of Public Health, Baltimore, MD,
| | - Keith C. Norris
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA (RB); University of Houston Population Health, University of Houston, Houston, TX, USA (BMB); Department of Health Systems and Population Health Sciences, University of Houston Tilman J. Fertitta Family College of Medicine, Houston, TX, USA (BMB, MAB); Program for Research on Men’s Health, Hopkins Center for Health Disparities Solutions, John Hopkins Bloomberg School of Public Health, Baltimore, MD,
| | - Elizabeth Heitman
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA (RB); University of Houston Population Health, University of Houston, Houston, TX, USA (BMB); Department of Health Systems and Population Health Sciences, University of Houston Tilman J. Fertitta Family College of Medicine, Houston, TX, USA (BMB, MAB); Program for Research on Men’s Health, Hopkins Center for Health Disparities Solutions, John Hopkins Bloomberg School of Public Health, Baltimore, MD,
| | - Robert L. Newton
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA (RB); University of Houston Population Health, University of Houston, Houston, TX, USA (BMB); Department of Health Systems and Population Health Sciences, University of Houston Tilman J. Fertitta Family College of Medicine, Houston, TX, USA (BMB, MAB); Program for Research on Men’s Health, Hopkins Center for Health Disparities Solutions, John Hopkins Bloomberg School of Public Health, Baltimore, MD,
| | - Megan E. Holmes
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA (RB); University of Houston Population Health, University of Houston, Houston, TX, USA (BMB); Department of Health Systems and Population Health Sciences, University of Houston Tilman J. Fertitta Family College of Medicine, Houston, TX, USA (BMB, MAB); Program for Research on Men’s Health, Hopkins Center for Health Disparities Solutions, John Hopkins Bloomberg School of Public Health, Baltimore, MD,
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Metabolic risk assessment in children and adolescents using the tri-ponderal mass index. Sci Rep 2022; 12:10094. [PMID: 35710910 PMCID: PMC9203500 DOI: 10.1038/s41598-022-13342-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 05/23/2022] [Indexed: 11/08/2022] Open
Abstract
We assessed the risk of metabolic syndrome in children and adolescents who were classified using the tri-ponderal mass index (TMI) with data from the Korea National Health and Nutrition Examination Survey (KNHANES). Data from 10 to 18-year-old subjects that were overweight or obese (n = 1362) were extracted from the KNHANES 2007-2018. Weight classifications were determined by TMI and included overweight and Class I, Class II, and Class III obesity. The standard deviation scores (SDS) of weight, waist circumference, and body mass index (BMI) as well as cardiometabolic risk factors, including blood pressure, serum glucose levels, total cholesterol (T-C), triglycerides, HDL-c, and low-density lipoprotein cholesterol (LDL-c), worsened with the severity of obesity. Most risk factors showed a linear association with the severity increase, except for fasting glucose levels, T-C, and LDL-c. The prevalence of cardiometabolic risks also increased with the severity of obesity, which developed earlier in boys than in girls. The risk of metabolic syndrome significantly increased with the severity of obesity in both unadjusted and adjusted analyses. TMI reflected the severity of obesity and predicted the risk of metabolic syndrome and its components. Therefore, clinical applications of TMI could be a useful to identify the incidence of childhood obesity and metabolic syndromes.
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17
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Leister KR, Cilhoroz BT, Rosenberg J, Brown EC, Kim JY. Metabolic syndrome: Operational definitions and aerobic and resistance training benefits on physical and metabolic health in children and adolescents. Diabetes Metab Syndr 2022; 16:102530. [PMID: 35709585 DOI: 10.1016/j.dsx.2022.102530] [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: 02/13/2022] [Revised: 05/25/2022] [Accepted: 05/27/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND The variation in parameters for childhood metabolic syndrome (MetS) has hindered the development of a consensus for the diagnostic criteria in this group. Despite these inconsistencies, it is accepted that exercise can ameliorate the deleterious effects of MetS. However, direct comparison between aerobic versus resistance exercise on MetS symptomology in adolescents is lacking. AIM Aim of this review was to discuss controversies associated with current MetS operation definitions in adolescents and present a review summarizing longitudinal studies relevant to the influence of aerobic and resistance training on children with MetS. METHODS Reviews of PubMed and Web of Science were conducted to identify literature focusing on the influence of aerobic and resistance training on children with MetS. Selected manuscripts featured longitudinal research only. RESULTS A universally accepted definition of MetS for the pediatric population has yet to be established. As such, consensus regarding diagnostic criteria for MetS among children is lacking despite the presence of various descriptions in the literature. Though studies support the importance of aerobic and resistance exercise to combat comorbidities associated with MetS, longitudinal studies investigating the benefits of each exercise type among adolescents are limited and inconsistent. CONCLUSION An improved understanding of the impact of aerobic and resistance training on children with MetS is clinically relevant because it may facilitate more appropriate exercise recommendations for children with MetS. Additional large cohort studies are warranted to determine optimal exercise type.
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Affiliation(s)
- Kyle R Leister
- Department of Exercise Science, David B. Falk College of Sport and Human Dynamics, Syracuse University, Syracuse, NY, USA
| | - Burak T Cilhoroz
- Department of Exercise Science, David B. Falk College of Sport and Human Dynamics, Syracuse University, Syracuse, NY, USA
| | - Jared Rosenberg
- Department of Exercise Science, David B. Falk College of Sport and Human Dynamics, Syracuse University, Syracuse, NY, USA
| | | | - Joon Young Kim
- Department of Exercise Science, David B. Falk College of Sport and Human Dynamics, Syracuse University, Syracuse, NY, USA.
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18
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Tan KML, Tint MT, Kothandaraman N, Yap F, Godfrey KM, Lee YS, Tan KH, Gluckman PD, Chong YS, Chong MFF, Eriksson JG, Cameron-Smith D. Association of plasma kynurenine pathway metabolite concentrations with metabolic health risk in prepubertal Asian children. Int J Obes (Lond) 2022; 46:1128-1137. [PMID: 35173282 PMCID: PMC7612806 DOI: 10.1038/s41366-022-01085-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 01/26/2022] [Accepted: 01/28/2022] [Indexed: 11/08/2022]
Abstract
BACKGROUND The tryptophan-kynurenine (KYN) pathway is linked to obesity-related systemic inflammation and metabolic health. The pathway generates multiple metabolites, with little available data on their relationships to early markers of increased metabolic disease risk in children. The aim of this study was to examine the association of multiple KYN pathway metabolites with metabolic risk markers in prepubertal Asian children. METHODS Fasting plasma concentrations of KYN pathway metabolites were measured using liquid chromatography-tandem mass spectrometry in 8-year-old children (n = 552) from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) prospective mother-offspring cohort study. The child's weight and height were used to ascertain overweight and obesity using local body mass index (BMI)-for-age percentile charts. Body fat percentage was measured by quantitative magnetic resonance. Abdominal circumference, systolic and diastolic blood pressure, homeostatic model assessment for insulin resistance (HOMA-IR), triglyceride, and HDL-cholesterol were used for the calculation of Metabolic syndrome scores (MetS). Serum triglyceride, BMI, gamma-glutamyl transferase (GGT), and abdominal circumference were used in the calculation of the Fatty liver index (FLI). Associations were examined using multivariable regression analyses. RESULTS In overweight or obese children (n = 93; 16.9% of the cohort), all KYN pathway metabolites were significantly increased, relative to normal weight children. KYN, kynurenic acid (KA), xanthurenic acid (XA), hydroxyanthranilic acid (HAA) and quinolinic acid (QA) all showed significant positive associations with body fat percentage (B(95% CI) = 0.32 (0.22,0.42) for QA), HOMA-IR (B(95% CI) = 0.25 (0.16,0.34) for QA), and systolic blood pressure (B(95% CI) = 0.14(0.06,0.22) for QA). All KYN metabolites except 3-hydroxykynurenine (HK) significantly correlated with MetS (B (95% CI) = 0.29 (0.21,0.37) for QA), and FLI (B (95% CI) = 0.30 (0.21,0.39) for QA). CONCLUSIONS Higher plasma concentrations of KYN pathway metabolites are associated with obesity and with increased risk for metabolic syndrome and fatty liver in prepubertal Asian children.
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Affiliation(s)
- Karen Mei-Ling Tan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Department of Laboratory Medicine, National University Hospital, Singapore, Singapore
| | - Mya-Thway Tint
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Human Potential Translational Research Programme, Yong Loo Lin School of Medicine (YLLSOM), National University of Singapore, Singapore, Singapore
| | - Narasimhan Kothandaraman
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Fabian Yap
- Duke-National University of Singapore (NUS) Medical School, Singapore, Singapore
- Department of Pediatric Endocrinology, KK Women's and Children's Hospital, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Keith M Godfrey
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University of Southampton Hospital, Southampton, UK
| | - Yung Seng Lee
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Khoo Teck Puat - National University Children's Medical Institute (KTPCMI), National University Health System, Singapore, Singapore
| | - Kok Hian Tan
- Duke-National University of Singapore (NUS) Medical School, Singapore, Singapore
- Perinatal Audit and Epidemiology, Department of Maternal Fetal Medicine, KK Women's and Children's Hospital, Singapore, Singapore
| | - Peter D Gluckman
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Yap-Seng Chong
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine (YLLSOM), National University of Singapore, Singapore, Singapore
| | - Mary F F Chong
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Johan G Eriksson
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Human Potential Translational Research Programme, Yong Loo Lin School of Medicine (YLLSOM), National University of Singapore, Singapore, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine (YLLSOM), National University of Singapore, Singapore, Singapore
- Folkhälsan Research Center, Helsinki, Finland
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
| | - David Cameron-Smith
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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19
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Zhou F, Sun X, Liu J, Li L, Li L, Li P. Triglyceride to high-density lipoprotein cholesterol ratio in adolescence as a predictive marker of metabolic syndrome and obesity in early adulthood in China. Endocrine 2022; 76:331-340. [PMID: 35254638 DOI: 10.1007/s12020-022-03014-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 02/06/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE To explore serum triglyceride (TG) to high-density cholesterol (HDL-C) ratio as a diagnostic marker of metabolic syndrome (MetS) in adolescents and its efficacy in predicting MetS and obesity in the early adulthood. METHODS A stratified cluster random sampling method was used to select a total of 935 subjects from senior and junior high schools in Liaoyang, northeast China. The subjects were physically examined and laboratory evaluation was performed. A follow-up examination was performed after 5 years on some (n = 93) of the subjects who had reached adulthood. RESULTS TG/HDL-C had significantly high diagnostic accuracy for MetS than HOMA-IR, TG or HDL-C. Subjects with the highest TG/HDL-C at baseline had higher risk of MetS (odds ratio [OR] = 11.65) and obesity (OR = 4.32) in early adulthood. CONCLUSION TG/HDL-C ratio has a strong and independent ability in diagnosing MetS in adolescents and predicting the occurrence of MetS and obesity in their early adulthood.
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Affiliation(s)
- Fang Zhou
- Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, 110022, People's Republic of China
| | - Xiaoshi Sun
- Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, 110022, People's Republic of China
| | - Juan Liu
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Xi 'an Jiaotong University, Xi 'an, Shanxi province, People's Republic of China
| | - Linlin Li
- Department of Academic affairs, Shenyang Open University, Shenyang, China
| | - Ling Li
- Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, 110022, People's Republic of China
| | - Ping Li
- Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, 110022, People's Republic of China.
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20
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Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, Boehme AK, Buxton AE, Carson AP, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Ferguson JF, Generoso G, Ho JE, Kalani R, Khan SS, Kissela BM, Knutson KL, Levine DA, Lewis TT, Liu J, Loop MS, Ma J, Mussolino ME, Navaneethan SD, Perak AM, Poudel R, Rezk-Hanna M, Roth GA, Schroeder EB, Shah SH, Thacker EL, VanWagner LB, Virani SS, Voecks JH, Wang NY, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association. Circulation 2022; 145:e153-e639. [PMID: 35078371 DOI: 10.1161/cir.0000000000001052] [Citation(s) in RCA: 2459] [Impact Index Per Article: 1229.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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21
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Stability and Transformation of Metabolic Syndrome in Adolescents: A Prospective Assessment in Relation to the Change of Cardiometabolic Risk Factors. Nutrients 2022; 14:nu14040744. [PMID: 35215393 PMCID: PMC8875515 DOI: 10.3390/nu14040744] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/04/2022] [Accepted: 02/07/2022] [Indexed: 12/27/2022] Open
Abstract
Underlying pathophysiological mechanisms drive excessive clustering of cardiometabolic risk factors, causing metabolic syndrome (MetS). MetS status may transform as adolescents transition to young adulthood. This study investigated the latent clustering structure and its stability for MetS during adolescence, and assessed the anthropometric and clinical metabolic determinants for MetS transformation. A community-based representative adolescent cohort (n = 1516) was evaluated for MetS using four diagnostic criteria, and was followed for 2.2 years to identify new-onset MetS. The clustering structure underlying cardiometabolic parameters was stable across adolescence; both comprised a fat—blood pressure (BP)—glucose three-factor structure (total variance explained: 68.8% and 69.7% at baseline and follow-up, respectively). Among adolescents with MetS-negative at baseline, 3.2–4.4% had incident MetS after 2.2 years. Among adolescents with MetS-positive at baseline, 52.0–61.9% experienced MetS remission, and 38.1–48.0% experienced MetS persistence. Increased systolic BP (SBP) was associated with a high MetS incidence risk, while decreased levels of SBP and glucose were associated with MetS remission. Compared with adolescents with a normal metabolic status at baseline, those with an initial abdominal obesity and increased triglycerides level had a 15.0- and 5.7-fold greater risk for persistent abnormality, respectively. Abdominal obesity and low high-density lipoprotein cholesterol are two abnormal MetS components that highly persist during adolescence, and are the intervention targets for reducing the future risk of cardiometabolic disorders.
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22
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Booker R, Chander H, Norris KC, Thorpe RJ, Vickers B, Holmes ME. Comparison of Leisure Time Physical Activities by Metabolic Syndrome Status among Adolescents. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031415. [PMID: 35162437 PMCID: PMC8834730 DOI: 10.3390/ijerph19031415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 01/10/2022] [Accepted: 01/20/2022] [Indexed: 12/04/2022]
Abstract
Background: Metabolic syndrome (MetS) increases the risk of premature morbidity and mortality. Physical activity (PA) beneficially affects MetS; however, it is unclear if PA types differ among adolescents 12-15 years old, according to their MetS status. This study compared self-reported PA types by MetS status. Methods: Using the 2015-2016 National Health and Nutritional Examination Survey (NHANES) data, 664 adolescents self-reported PA in the past seven days. MetS status was assessed using Ford's pediatric adaptation of the ATP-III adult criteria. Pearson chi-square and t-tests were conducted to determine self-reported PA differences. Results: The adolescents' mean age was 13.47 years (95% CIs; 13.04, 14.38) and 52.69% were male (352). Twenty-seven (4.07%) adolescents were MetS positive. The prevalence of PA engagement in the past seven days was similar for MetS-positive and -negative adolescents (77.67% and 70.51%, respectively; p > 0.05). No significant differences were observed for PA type by MetS status. MetS-positive adolescents reported higher sedentary time (565.77 [438.99, 692.56] vs. 490.59 [377.86, 603.33] minutes per day, respectively; p = 0239). Conclusions: Engagement in specific PA types does not appear to differ by MetS status, but MetS-positive adolescents have significantly higher sedentary time. PA promotion should target a variety of activities to maximize the effectiveness of public health programs and interventions should target reducing sedentary time.
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Affiliation(s)
- Robert Booker
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Correspondence:
| | - Harish Chander
- Department of Kinesiology, Mississippi State University, Starkville, MS 39762, USA; (H.C.); (B.V.); (M.E.H.)
| | - Keith C. Norris
- Program for Research on Men’s Health, John Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; (K.C.N.); (R.J.T.J.)
- Division of General Internal Medicine and Health Services Research, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Roland J. Thorpe
- Program for Research on Men’s Health, John Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; (K.C.N.); (R.J.T.J.)
- Hopkins Center for Health Disparities Solutions, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Brad Vickers
- Department of Kinesiology, Mississippi State University, Starkville, MS 39762, USA; (H.C.); (B.V.); (M.E.H.)
| | - Megan E. Holmes
- Department of Kinesiology, Mississippi State University, Starkville, MS 39762, USA; (H.C.); (B.V.); (M.E.H.)
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23
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Wu M, Shu Y, Wang L, Song L, Chen S, Liu Y, Bi J, Li D, Yang Y, Hu Y, Wang Y, Wu S, Tian Y. Metabolic syndrome severity score and the progression of CKD. Eur J Clin Invest 2022; 52:e13646. [PMID: 34197633 DOI: 10.1111/eci.13646] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 06/28/2021] [Accepted: 06/30/2021] [Indexed: 01/10/2023]
Abstract
BACKGROUND Metabolic syndrome severity, expressed by the continuous metabolic syndrome risk score (MetS score), has been demonstrated to be able to predict future health conditions. However, little is known about the association between MetS score and renal function. METHODS A total of 22,719 participants with normal renal function abstracted from the Kailuan Study were followed from 2006 to 2016. The new onset of chronic kidney disease (CKD) was defined as eGFR <60 ml/min per 1.73 m2 and/or proteinuria >300 mg/dl. Progressive decline in renal function was defined as an annual change rate of eGFR below the 10th percentile of the whole population. RESULTS In the multivariate-adjusted model, we found that the risk of progressive decline in renal function increased consistently with the MetS score, with an odds ratio of 1.49 (95% CI, 1.28, 1.73) for those subjects>75th percentile compared with those <25th percentile. Additionally, a high MetS score was found to be associated with an increased risk of CKD, with a hazard ratio of 1.53 (95% CI, 1.33, 1.78) for subjects >75th percentile compared with those <25th percentile. CONCLUSIONS Our findings suggested that the MetS score was associated with an increased risk of a progressive decline in renal function and was also a strong and independent risk factor for the development of CKD. These findings provide evidence of the potential clinical utility of the MetS score for assessing metabolic syndrome severity to detect the risk of decreased renal function and CKD.
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Affiliation(s)
- Mingyang Wu
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yanling Shu
- Department of Laboratory Medicine, Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
| | - Lulin Wang
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lulu Song
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuohua Chen
- Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, Tangshan City, China
| | - Yunyun Liu
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jianing Bi
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dankang Li
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yingping Yang
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yonghua Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Youjie Wang
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shouling Wu
- Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, Tangshan City, China
| | - Yaohua Tian
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Wu L, Lu XJ, Lin DJ, Chen WJ, Xue XY, Liu T, Xu JT, Xie YT, Li MQ, Lin WY, Zhang Q, Wu QP, He XX. Washed microbiota transplantation improves patients with metabolic syndrome in South China. Front Cell Infect Microbiol 2022; 12:1044957. [PMID: 36457852 PMCID: PMC9705737 DOI: 10.3389/fcimb.2022.1044957] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 10/27/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Metabolic syndrome (MS) is a growing public health problem worldwide. The clinical impact of fecal microbiota transplantation (FMT) from healthy donors in MS patients is unclear, especially in southern Chinese populations. This study aimed to investigate the effect of washed microbiota transplantation (WMT) in MS patients in southern China. METHODS The clinical data of patients with different indications receiving 1-3 courses of WMT were retrospectively collected. The changes of BMI, blood glucose, blood lipids, blood pressure and other indicators before and after WMT were compared, such as fasting blood glucose (FBG), glycated hemoglobin (HbA1c), total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-c)), high-density lipoprotein cholesterol (HDL-c), non-high-density lipoprotein (non-HDL-c), systolic blood pressure (SBP), diastolic blood pressure (DBP), etc. At the same time, comprehensive efficacy evaluation and atherosclerotic cardiovascular disease (ASCVD) grade assessment were performed on MS patients. Finally, 16S rRNA gene amplicon sequencing was performed on fecal samples of MS patients before and after transplantation. RESULTS A total of 237 patients were included, including 42 in the MS group and 195 in the non-MS group. For MS patients, WMT significantly improved the comprehensive efficacy of MS in short term 40.48% (p<0.001), medium term 36.00% (p=0.003), and long term 46.15% (p=0.020). Short-term significantly reduced FBG (p=0.023), TG (p=0.030), SBP (p=0.026) and BMI (p=0.031), and increased HDL-c (p=0.036). The medium term had a significant reduction in FBG (p=0.048), TC (p=0.022), LDL-c (p=0.043), non-HDL-c (p=0.024) and BMI (p=0.048). WMT had a significant short term (p=0.029) and medium term (p=0.011) ASCVD downgrading effect in the high-risk group of MS patients. WMT improved gut microbiota in MS patients. CONCLUSION WMT had a significant improvement effect on MS patients and a significant downgrade effect on ASCVD risk in the high-risk group of patients with MS. WMT could restore gut microbiota homeostasis in MS patients. Therefore, the regulation of gut microbiota by WMT may provide a new clinical approach for the treatment of MS.
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Affiliation(s)
- Lei Wu
- Department of Gastroenterology, Research Center for Engineering Techniques of Microbiota-Targeted Therapies of Guangdong Province, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Microbial Safety and Health, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Xin-Jian Lu
- Department of Gastroenterology, Research Center for Engineering Techniques of Microbiota-Targeted Therapies of Guangdong Province, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - De-Jiang Lin
- Department of Gastroenterology, Research Center for Engineering Techniques of Microbiota-Targeted Therapies of Guangdong Province, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - Wen-Jia Chen
- Department of Gastroenterology, Research Center for Engineering Techniques of Microbiota-Targeted Therapies of Guangdong Province, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - Xing-Ying Xue
- Xiamen Treatgut Biotechnology Co., Ltd., Xiamen, China
| | - Tao Liu
- Department of Gastroenterology, Research Center for Engineering Techniques of Microbiota-Targeted Therapies of Guangdong Province, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - Jia-Ting Xu
- Department of Gastroenterology, Research Center for Engineering Techniques of Microbiota-Targeted Therapies of Guangdong Province, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - Ya-Ting Xie
- Department of Gastroenterology, Research Center for Engineering Techniques of Microbiota-Targeted Therapies of Guangdong Province, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - Man-Qing Li
- Department of Gastroenterology, Research Center for Engineering Techniques of Microbiota-Targeted Therapies of Guangdong Province, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - Wen-Ying Lin
- Department of Gastroenterology, Research Center for Engineering Techniques of Microbiota-Targeted Therapies of Guangdong Province, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - Qing Zhang
- Department of Gastroenterology, Research Center for Engineering Techniques of Microbiota-Targeted Therapies of Guangdong Province, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - Qing-Ping Wu
- Guangdong Provincial Key Laboratory of Microbial Safety and Health, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
- *Correspondence: Xing-Xiang He, ; Qing-Ping Wu,
| | - Xing-Xiang He
- Department of Gastroenterology, Research Center for Engineering Techniques of Microbiota-Targeted Therapies of Guangdong Province, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- *Correspondence: Xing-Xiang He, ; Qing-Ping Wu,
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25
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Metabolic Syndrome Severity Score, Comparable to Serum Creatinine, Could Predict the Occurrence of End-Stage Kidney Disease in Patients with Antineutrophil Cytoplasmic Antibody-Associated Vasculitis. J Clin Med 2021; 10:jcm10245744. [PMID: 34945043 PMCID: PMC8708376 DOI: 10.3390/jcm10245744] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 12/07/2021] [Indexed: 12/11/2022] Open
Abstract
This study investigated whether the metabolic syndrome (MetS) severity (MSSS) at diagnosis could predict poor outcomes during follow-up in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) patients with MetS. The equation for the MSSS at diagnosis used in this study was developed and validated in Korean adults aged 20–59 years. The medical records of 261 patients with AAV were retrospectively reviewed, and finally, 36 AAV patients with MetS aged 20–59 years fulfilling the inclusion criteria were included in this study. All-cause mortality, relapse, end-stage kidney disease (ESKD), cerebrovascular accident, and cardiovascular disease were assessed as the poor outcomes of AAV. Their median age was 51.2 years and 36.1% were male. The MSSS was significantly correlated with age and serum albumin but not AAV-specific indices. Among the five poor outcomes, only ESKD showed a relatively significant area under the curve (area 0.696) in receiver operating characteristic curve analysis. In the multivariable Cox hazards model analysis, both serum creatinine (HR 3.033) and MSSS (HR = 2.221) were significantly associated with ESKD occurrence. When the cut-off of the MSSS for ESKD was set at 1.72, ESKD occurred more frequently in patients with MSSS ≥ 1.72 than in those with MSSS < 1.72 (75.0% versus 14.3%, p = 0.002). Furthermore, patients with MSSS ≥ 1.72 exhibited a significantly lower cumulative ESKD-free survival rate than those with MSSS < 1.72 (p = 0.001). MSSS at the time of AAV diagnosis independently predicted the occurrence of ESKD during follow-up in patients with AAV and MetS.
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26
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Booker R, Jones R, Galloway R, Holmes ME. Differences of Sedentary Behavior, Physical Activity, and Metabolic Syndrome Severity Among Metabolic Syndrome Clusters. Am J Lifestyle Med 2021. [DOI: 10.1177/15598276211056874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background: Sedentary behavior (SB), physical activity (PA), and metabolic syndrome (MetS) are risk indicators for cardiometabolic diseases. Novel methods for researching MetS by the 16 unique clusters (i.e., WC+FBG+BP) and as a continuous severity z-score (MetS-Z) have emerged. This study examined how SB, PA, and MetS-Z differed among MetS clusters and SB and PA differences by MetS-Z tertiles. Methods: Using 2015-2016 National Health and Nutritional Examination Survey (NHANES) data, participants with MetS (N = 792) were identified. Subsequently, sex-, race-, and ethnicity-specific MetS-Z were calculated. SB and PA differences were compared between MetS clusters and MetS-Z tertiles. Additionally, MetS-Z was compared between MetS clusters. Results: The WC+FBG+BP MetS cluster was prevalent among 23.80% of participants (95% CIs, 18.41-30.18) and the overall mean MetS-Z was 1.16 (1.08-1.24). Participants reported over 6 daily hours of SB (393.41 minutes⋅day-1 [370.07-416.75]). The TRI+FBG+BP+HDL MetS cluster had less SB than the WC+TRI+FBG+HDL, WC+TRI+HDL, and WC+FBG+BP MetS clusters. PA did not differ between MetS clusters and no differences in SB or PA between MetS-Z tertiles. Conclusions: Limited differences in SB and PA were observed. Participants with 4 or more MetS criteria had worse MetS-Z. Efforts should support replacing SB with PA to improve cardiometabolic health.
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Affiliation(s)
- Robert Booker
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Raymond Jones
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Riley Galloway
- School of Kinesiology and Nutrition, University of Southern Mississippi, Hattiesburg, MS, USA
| | - Megan E. Holmes
- Department of Kinesiology, Mississippi State University, Mississippi State, MS, USA
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27
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Gallardo-Alfaro L, Bibiloni MDM, Bouzas C, Mascaró CM, Martínez-González MÁ, Salas-Salvadó J, Corella D, Schröder H, Martínez JA, Alonso-Gómez ÁM, Wärnberg J, Vioque J, Romaguera D, Lopez-Miranda J, Estruch R, Tinahones FJ, Lapetra J, Serra-Majem L, Bueno-Cavanillas A, Micó RM, Pintó X, Gaforio JJ, Ortíz-Ramos M, Altés-Boronat A, Luca BL, Daimiel L, Ros E, Sayon-Orea C, Becerra-Tomás N, Gimenez-Alba IM, Castañer O, Abete I, Tojal-Sierra L, Pérez-López J, Torres-Collado L, Colom A, Garcia-Rios A, Castro-Barquero S, Bernal R, Santos-Lozano JM, Fernandez-Lazaro CI, Hernández-Alonso P, Saiz C, Zomeño MD, Zulet MA, Belló-Mora MC, Basterra-Gortari FJ, Canudas S, Goday A, Tur JA. Physical activity and metabolic syndrome severity among older adults at cardiovascular risk: 1-Year trends. Nutr Metab Cardiovasc Dis 2021; 31:2870-2886. [PMID: 34366176 DOI: 10.1016/j.numecd.2021.06.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/08/2021] [Accepted: 06/21/2021] [Indexed: 12/26/2022]
Abstract
BACKGROUND AND AIMS Modifiable lifestyle factors, such as physical activity (PA) and Mediterranean diet (MD), decrease metabolic syndrome (MetS). The aim was to assess 1-year changes of leisure-time physical activity (LTPA), sedentary behavior, and diet quality according to MetS severity in older population at high cardiovascular risk. METHODS AND RESULTS Prospective analysis of 55-75-year-old 4359 overweight/obese participants with MetS (PREDIMED-Plus trial) categorized in tertiles according to 1-year changes of a validated MetS severity score (MetSSS). Anthropometrics, visceral adiposity index, triglycerides and glucose index, dietary nutrient intake, biochemical marker levels, dietary inflammatory index, and depression symptoms were measured. Diet quality was assessed by 17-item MD questionnaire. PAs were self-reported using the Minnesota-REGICOR Short Physical Activity Questionnaire and 30-s chair stand test. Sedentary behaviors were measured using the Spanish version of the Nurses' Health Study questionnaire. After 1-year follow-up, decreasing MetSSS was associated with an anti-inflammatory dietary pattern, high intake of vegetables, fruits, legumes, nuts, whole grain cereals, white fish, and bluefish and low intake of refined cereals, red and processed meat, cookies/sweets, and snacks/ready-to-eat-meals. It resulted in high intake of polyunsaturated fatty acids, omega-3 fatty acids, protein, fiber, vitamins B1, B6, B9, C, D, potassium, magnesium, and phosphorus and low glycemic index and saturated fatty acid, trans fatty acid, and carbohydrates intake. Regarding PA and sedentary behavior, decreasing MetSSS was associated with increased moderate-to-vigorous LTPA, chair stand test, and decreased sedentary and TV-viewing time. CONCLUSION Decreasing MetSSS was associated with an anti-inflammatory dietary pattern, high LTPA, high MD adherence, low sedentary time, and low depression risk.
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Affiliation(s)
- Laura Gallardo-Alfaro
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands, 07122 Palma de Mallorca, Spain; Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma de Mallorca, Spain
| | - Maria Del Mar Bibiloni
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands, 07122 Palma de Mallorca, Spain; Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma de Mallorca, Spain
| | - Cristina Bouzas
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands, 07122 Palma de Mallorca, Spain; Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma de Mallorca, Spain
| | - Catalina M Mascaró
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands, 07122 Palma de Mallorca, Spain; Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma de Mallorca, Spain
| | - Miguel Ángel Martínez-González
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; University of Navarra, Department of Preventive Medicine and Public Health, IDISNA, 31008 Pamplona, Spain; Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, USA
| | - Jordi Salas-Salvadó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Human Nutrition Unit, Biochemistry and Biotechnology Department, IISPV, Hospital Universitari de Sant Joan. Universitat Rovira I Virgili, 43201 Reus, Spain
| | - Dolores Corella
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Department of Preventive Medicine, University of Valencia, 46100 Valencia, Spain
| | - Helmut Schröder
- Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d'Investigació Mèdica (IMIM), 08003 Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain
| | - J Alfredo Martínez
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Cardiometabolics Nutrition Program, IMDEA Food, CEI UAM + CSIC, 28049 Madrid, Spain; Department of Nutrition, Food Sciences, and Physiology, Center for Nutrition Research, University of Navarra, 31008 Pamplona, Spain
| | - Ángel M Alonso-Gómez
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Bioaraba Health Research Institute, Osakidetza Basque Health Service, Araba University Hospital, University of the Basque Country UPV/EHU, 48013 Vitoria-Gasteiz, Spain
| | - Julia Wärnberg
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Department of Nursing, School of Health Sciences, University of Málaga-IBIMA, 29071 Málaga, Spain
| | - Jesús Vioque
- CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Miguel Hernández University, Instituto de Investigación Sanitaria y Biomédica de Alicante, ISABIAL-UMH, 46020 Alicante, Spain
| | - Dora Romaguera
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma de Mallorca, Spain
| | - José Lopez-Miranda
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Lipids and Atherosclerosis Unit, Department of Internal Medicine, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, 14004 Córdoba, Spain
| | - Ramon Estruch
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Department of Internal Medicine, IDIBAPS, Hospital Clinic, University of Barcelona, 08036 Barcelona, Spain
| | - Francisco J Tinahones
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Virgen de la Victoria Hospital, Department of Endocrinology, University of Málaga, 29010 Málaga, Spain
| | - José Lapetra
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, 41013 Sevilla, Spain
| | - Luís Serra-Majem
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Institute for Biomedical Research, University of Las Palmas de Gran Canaria, 35016 Las Palmas, Spain
| | - Aurora Bueno-Cavanillas
- CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Department of Preventive Medicine, University of Granada, 18071 Granada, Spain
| | - Rafael M Micó
- CIBER Diabetes y enfermedades metabólicas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Institute of Biomedicine (IBIOMED), University of León, 24071 Leon, Spain
| | - Xavier Pintó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Lipids and Vascular Risk Unit, Internal Medicine, Hospital Universitario de Bellvitge, Hospitalet de Llobregat, 08907 Barcelona, Spain
| | - José J Gaforio
- CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Department of Health Sciences, Center for Advanced Studies in Olive Grove and Olive Oils, University of Jaen, 23071 Jaen, Spain
| | - María Ortíz-Ramos
- Department of Endocrinology and Nutrition, Instituto de Investigación Sanitaria San Carlos (IdISSC), 28040 Madrid, Spain
| | - Andreu Altés-Boronat
- Department of Endocrinology, IDIBAPS, Hospital Clinic, University of Barcelona, 08036 Barcelona, Spain
| | - Bogdana L Luca
- Department of Endocrinology, Fundación Jiménez-Díaz, 28040 Madrid, Spain
| | - Lidia Daimiel
- Nutritional Control of the Epigenome Group, Precision Nutrition and Obesity Program.IMDEA Food, CEI UAM + CSIC, 28049 Madrid, Spain
| | - Emilio Ros
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Lipid Clinic, Department of Endocrinology and Nutrition, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, 08036 Barcelona, Spain
| | - Carmen Sayon-Orea
- University of Navarra, Department of Preventive Medicine and Public Health, IDISNA, 31008 Pamplona, Spain; Servicio Navarro de Salud, Osasunbidea, 31003, Pamplona, Spain
| | - Nerea Becerra-Tomás
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Human Nutrition Unit, Biochemistry and Biotechnology Department, IISPV, Hospital Universitari de Sant Joan. Universitat Rovira I Virgili, 43201 Reus, Spain
| | - Ignacio Manuel Gimenez-Alba
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Department of Preventive Medicine, University of Valencia, 46100 Valencia, Spain
| | - Olga Castañer
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d'Investigació Mèdica (IMIM), 08003 Barcelona, Spain
| | - Itziar Abete
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Department of Nutrition, Food Sciences, and Physiology, Center for Nutrition Research, University of Navarra, 31008 Pamplona, Spain
| | - Lucas Tojal-Sierra
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Bioaraba Health Research Institute, Osakidetza Basque Health Service, Araba University Hospital, University of the Basque Country UPV/EHU, 48013 Vitoria-Gasteiz, Spain
| | - Jéssica Pérez-López
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Department of Nursing, School of Health Sciences, University of Málaga-IBIMA, 29071 Málaga, Spain
| | - Laura Torres-Collado
- Miguel Hernández University, Instituto de Investigación Sanitaria y Biomédica de Alicante, ISABIAL-UMH, 46020 Alicante, Spain
| | - Antoni Colom
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma de Mallorca, Spain
| | - Antonio Garcia-Rios
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Lipids and Atherosclerosis Unit, Department of Internal Medicine, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, 14004 Córdoba, Spain
| | - Sara Castro-Barquero
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Department of Internal Medicine, IDIBAPS, Hospital Clinic, University of Barcelona, 08036 Barcelona, Spain
| | - Rosa Bernal
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Virgen de la Victoria Hospital, Department of Endocrinology, University of Málaga, 29010 Málaga, Spain
| | - José Manuel Santos-Lozano
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, 41013 Sevilla, Spain
| | - Cesar I Fernandez-Lazaro
- University of Navarra, Department of Preventive Medicine and Public Health, IDISNA, 31008 Pamplona, Spain
| | - Pablo Hernández-Alonso
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Human Nutrition Unit, Biochemistry and Biotechnology Department, IISPV, Hospital Universitari de Sant Joan. Universitat Rovira I Virgili, 43201 Reus, Spain
| | - Carmen Saiz
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Department of Preventive Medicine, University of Valencia, 46100 Valencia, Spain
| | - Maria Dolors Zomeño
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d'Investigació Mèdica (IMIM), 08003 Barcelona, Spain
| | - Maria Angeles Zulet
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Department of Nutrition, Food Sciences, and Physiology, Center for Nutrition Research, University of Navarra, 31008 Pamplona, Spain
| | - Maria C Belló-Mora
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Bioaraba Health Research Institute, Osakidetza Basque Health Service, Araba University Hospital, University of the Basque Country UPV/EHU, 48013 Vitoria-Gasteiz, Spain
| | - F Javier Basterra-Gortari
- University of Navarra, Department of Preventive Medicine and Public Health, IDISNA, 31008 Pamplona, Spain; Servicio Navarro de Salud, Osasunbidea, 31003, Pamplona, Spain
| | - Silvia Canudas
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Human Nutrition Unit, Biochemistry and Biotechnology Department, IISPV, Hospital Universitari de Sant Joan. Universitat Rovira I Virgili, 43201 Reus, Spain
| | - Albert Goday
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d'Investigació Mèdica (IMIM), 08003 Barcelona, Spain
| | - Josep A Tur
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands, 07122 Palma de Mallorca, Spain; Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma de Mallorca, Spain.
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Osei E, Zandbergen A, Brouwers PJAM, Mulder LJMM, Koudstaal P, Lingsma H, Dippel DWJ, den Hertog H. Safety, feasibility and efficacy of metformin and sitagliptin in patients with a TIA or minor ischaemic stroke and impaired glucose tolerance. BMJ Open 2021; 11:e046113. [PMID: 34531203 PMCID: PMC8449977 DOI: 10.1136/bmjopen-2020-046113] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
INTRODUCTION Impaired glucose tolerance (IGT) is highly prevalent after stroke and is associated with recurrent stroke and unfavourable outcome. OBJECTIVES We aimed to assess the feasibility, safety and effects on glucose metabolism of metformin or sitagliptin in patients with transient ischaemic attack (TIA) or minor ischaemic stroke and IGT. DESIGN We performed a multicentre, randomised, controlled, open-label phase II trial with blinded outcome assessment. INTERVENTIONS Patients were randomised in a 2:1:1 ratio to 'no medication', sitagliptin or metformin. PRIMARY AND SECONDARY OUTCOME MEASURES Primary outcome measures were baseline adjusted differences of 2-hour postload glucose; secondary outcome measures fasting glucose, glycosylated haemoglobin 1c (HbA1c) levels, tolerability and safety of metformin and sitagliptin at 6 months. Patients on metformin or sitagliptin were contacted by telephone for recording of possible adverse events and to support continuation of treatment at 2 weeks, 6 weeks and 3 months after inclusion. These events were not analysed as outcome measures. RESULTS Fifty-three patients were randomised to control group, 26 to metformin and 22 to sitagliptin. We found no significant differences in 2-hour postload glucose between patients on antidiabetic drugs and controls ((-0.04 mmol/L (95% CI -0.53 to 0.45)). Patients in the treatment arms had reduced fasting glucose: ((-0.21 mmol/L (95% CI -0.36 to -0.06)) and HbA1c levels ((-1.16 mmol/mol (95% CI -1.84 to -0.49)). Thirteen patients (50%) on metformin and 7 (32%) on sitagliptin experienced side effects. Sixteen patients (61%) in the metformin and 13 (59%) in the sitagliptin group were still on treatment after 6 months. CONCLUSIONS Metformin and sitagliptin were both effective in reducing fasting glucose and HbA1c levels in patients with recent TIA or minor ischaemic stroke and IGT. However, the reduction of glucose levels and sample size was relatively small. The clinical relevance, therefore, needs to be tempered. A phase III trial is needed to investigate whether medical treatment, compared with lifestyle intervention or a combination of both, not only improves glucose metabolism in IGT, but also leads to reduction of recurrent TIA or ischaemic stroke in these patients. TRIAL REGISTRATION NUMBER NL3048.
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Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Cheng S, Delling FN, Elkind MSV, Evenson KR, Ferguson JF, Gupta DK, Khan SS, Kissela BM, Knutson KL, Lee CD, Lewis TT, Liu J, Loop MS, Lutsey PL, Ma J, Mackey J, Martin SS, Matchar DB, Mussolino ME, Navaneethan SD, Perak AM, Roth GA, Samad Z, Satou GM, Schroeder EB, Shah SH, Shay CM, Stokes A, VanWagner LB, Wang NY, Tsao CW. Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association. Circulation 2021; 143:e254-e743. [PMID: 33501848 DOI: 10.1161/cir.0000000000000950] [Citation(s) in RCA: 3076] [Impact Index Per Article: 1025.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2021 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease. RESULTS Each of the 27 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Höchsmann C, Dorling JL, Martin CK, Newton RL, Apolzan JW, Myers CA, Denstel KD, Mire EF, Johnson WD, Zhang D, Arnold CL, Davis TC, Fonseca V, Lavie CJ, Price-Haywood EG, Katzmarzyk PT. Effects of a 2-Year Primary Care Lifestyle Intervention on Cardiometabolic Risk Factors: A Cluster-Randomized Trial. Circulation 2021; 143:1202-1214. [PMID: 33557578 DOI: 10.1161/circulationaha.120.051328] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Intensive lifestyle interventions (ILIs) are the first-line approach to effectively treat obesity and manage associated cardiometabolic risk factors. Because few people have access to ILIs in academic health centers, primary care must implement similar approaches for a meaningful effect on obesity and cardiometabolic disease prevalence. To date, however, effective lifestyle-based obesity treatment in primary care is limited. We examined the effectiveness of a pragmatic ILI for weight loss delivered in primary care among a racially diverse, low-income population with obesity for improving cardiometabolic risk factors over 24 months. METHODS The PROPEL trial (Promoting Successful Weight Loss in Primary Care in Louisiana) randomly allocated 18 clinics equally to usual care or an ILI and subsequently enrolled 803 (351 usual care, 452 ILI) adults (67% Black, 84% female) with obesity from participating clinics. The usual care group continued to receive their normal primary care. The ILI group received a 24-month high-intensity lifestyle-based obesity treatment program, embedded in the clinic setting and delivered by health coaches in weekly sessions initially and monthly sessions in months 7 through 24. RESULTS As recently demonstrated, participants receiving the PROPEL ILI lost significantly more weight over 24 months than those receiving usual care (mean difference, -4.51% [95% CI, -5.93 to -3.10]; P<0.01). Fasting glucose decreased more in the ILI group compared with the usual care group at 12 months (mean difference, -7.1 mg/dL [95% CI, -12.0 to -2.1]; P<0.01) but not 24 months (mean difference, -0.8 mg/dL [95% CI, -6.2 to 4.6]; P=0.76). Increases in high-density lipoprotein cholesterol were greater in the ILI than in the usual care group at both time points (mean difference at 24 months, 4.6 mg/dL [95% CI, 2.9-6.3]; P<0.01). Total:high-density lipoprotein cholesterol ratio and metabolic syndrome severity (z score) decreased more in the ILI group than in the usual care group at both time points, with significant mean differences of the change of -0.31 (95% CI, -0.47 to -0.14; P<0.01) and -0.21 (95% CI, -0.36 to -0.06; P=0.01) at 24 months, respectively. Changes in total cholesterol, low-density lipoprotein cholesterol, triglycerides, and blood pressure did not differ significantly between groups at any time point. CONCLUSIONS A pragmatic ILI consistent with national guidelines and delivered by trained health coaches in primary care produced clinically relevant improvements in cardiometabolic health in an underserved population over 24 months. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02561221.
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Affiliation(s)
- Christoph Höchsmann
- Pennington Biomedical Research Center, Baton Rouge, LA (C.H., J.L.D., C.K.M., R.L.N., J.W.A., C.A.M., K.D.D., E.F.M., W.D.J., D.Z., P.T.K.)
| | - James L Dorling
- Pennington Biomedical Research Center, Baton Rouge, LA (C.H., J.L.D., C.K.M., R.L.N., J.W.A., C.A.M., K.D.D., E.F.M., W.D.J., D.Z., P.T.K.)
| | - Corby K Martin
- Pennington Biomedical Research Center, Baton Rouge, LA (C.H., J.L.D., C.K.M., R.L.N., J.W.A., C.A.M., K.D.D., E.F.M., W.D.J., D.Z., P.T.K.)
| | - Robert L Newton
- Pennington Biomedical Research Center, Baton Rouge, LA (C.H., J.L.D., C.K.M., R.L.N., J.W.A., C.A.M., K.D.D., E.F.M., W.D.J., D.Z., P.T.K.)
| | - John W Apolzan
- Pennington Biomedical Research Center, Baton Rouge, LA (C.H., J.L.D., C.K.M., R.L.N., J.W.A., C.A.M., K.D.D., E.F.M., W.D.J., D.Z., P.T.K.)
| | - Candice A Myers
- Pennington Biomedical Research Center, Baton Rouge, LA (C.H., J.L.D., C.K.M., R.L.N., J.W.A., C.A.M., K.D.D., E.F.M., W.D.J., D.Z., P.T.K.)
| | - Kara D Denstel
- Pennington Biomedical Research Center, Baton Rouge, LA (C.H., J.L.D., C.K.M., R.L.N., J.W.A., C.A.M., K.D.D., E.F.M., W.D.J., D.Z., P.T.K.)
| | - Emily F Mire
- Pennington Biomedical Research Center, Baton Rouge, LA (C.H., J.L.D., C.K.M., R.L.N., J.W.A., C.A.M., K.D.D., E.F.M., W.D.J., D.Z., P.T.K.)
| | - William D Johnson
- Pennington Biomedical Research Center, Baton Rouge, LA (C.H., J.L.D., C.K.M., R.L.N., J.W.A., C.A.M., K.D.D., E.F.M., W.D.J., D.Z., P.T.K.)
| | - Dachuan Zhang
- Pennington Biomedical Research Center, Baton Rouge, LA (C.H., J.L.D., C.K.M., R.L.N., J.W.A., C.A.M., K.D.D., E.F.M., W.D.J., D.Z., P.T.K.)
| | - Connie L Arnold
- Department of Medicine and Feist-Weiller Cancer Center, Louisiana State University Health Sciences Center, Shreveport (C.L.A., T.C.D.)
| | - Terry C Davis
- Department of Medicine and Feist-Weiller Cancer Center, Louisiana State University Health Sciences Center, Shreveport (C.L.A., T.C.D.)
| | - Vivian Fonseca
- Department of Medicine, Section of Endocrinology, Tulane University Health Sciences Center, New Orleans, LA (V.F.)
| | - Carl J Lavie
- Department of Cardiovascular Diseases, John Ochsner Heart and Vascular Institute, New Orleans, LA (C.J.L.)
| | - Eboni G Price-Haywood
- Ochsner Clinic Foundation, Center for Outcomes and Health Services Research, New Orleans, LA (E.G.P.-H.)
| | - Peter T Katzmarzyk
- Pennington Biomedical Research Center, Baton Rouge, LA (C.H., J.L.D., C.K.M., R.L.N., J.W.A., C.A.M., K.D.D., E.F.M., W.D.J., D.Z., P.T.K.)
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Esquivel Zuniga R, DeBoer MD. Prediabetes in Adolescents: Prevalence, Management and Diabetes Prevention Strategies. Diabetes Metab Syndr Obes 2021; 14:4609-4619. [PMID: 34858039 PMCID: PMC8629936 DOI: 10.2147/dmso.s284401] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 11/03/2021] [Indexed: 12/16/2022] Open
Abstract
The ongoing obesity epidemic in children and adolescents has greatly increased the prevalence of related comorbidities. Prediabetes is defined based on levels of fasting glucose, oral glucose tolerance tests or hemoglobin A1c, that are intermediate between normal levels and thresholds that define type 2 diabetes mellitus (T2DM). As such, prediabetes represents a sign of early pathophysiology preceding T2DM development. Recent analyses of data from US adolescents estimate prediabetes to be present in 4-23% of adolescents, depending on criteria used, with other studies finding an 8% risk of progression from prediabetes to T2DM over a 3-year period. These data support the importance of intervention to avoid long-term sequelae, focusing on reducing degree of obesity and insulin resistance. Lifestyle modification, with increases in physical activity and dietary improvements, remains the first-line approach. Other interventions are based on additional long-term risks and range from metformin treatment for more moderate cases of prediabetes to bariatric surgery for adolescents with severe obesity and comorbidities. As data accumulate regarding sequelae of T2DM in adolescents, there remains a critical need for prevention of obesity and T2DM throughout childhood, and prediabetes should be a trigger for improving this risk profile.
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Affiliation(s)
- Rebeca Esquivel Zuniga
- Division of Pediatric Endocrinology, Department of Pediatrics, University of Virginia, Charlottesville, VA, USA
| | - Mark D DeBoer
- Division of Pediatric Endocrinology, Department of Pediatrics, University of Virginia, Charlottesville, VA, USA
- Correspondence: Mark D DeBoer Division of Pediatric Endocrinology, University of Virginia, PO Box 800386, Charlottesville, VA, 22903, USATel +1 434-924-5956Fax +1 434-924-9181 Email
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Gurka MJ, Mack JA, Chi X, DeBoer MD. Use of metabolic syndrome severity to assess treatment with vitamin E and pioglitazone for non-alcoholic steatohepatitis. J Gastroenterol Hepatol 2021; 36:249-256. [PMID: 32506513 PMCID: PMC7719569 DOI: 10.1111/jgh.15131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/13/2020] [Accepted: 05/31/2020] [Indexed: 12/22/2022]
Abstract
BACKGROUND AND AIM Non-alcoholic steatohepatitis (NASH), which can lead to liver failure, requires liver biopsies to follow and is difficult to treat. Our goal was to assess metabolic syndrome (MetS) severity as a predictor of treatment success and a marker of response. METHODS We assessed data from the Pioglitazone, Vitamin E, or Placebo for NASH Study, in which individuals with biopsy-confirmed NASH were randomized to receive pioglitazone, vitamin E, or placebo for 96 weeks. We assessed associations of a sex-specific and race/ethnicity-specific MetS severity Z-score (MetS-Z) at baseline and 48 weeks with biopsy-determined endpoint of NASH resolution at 96 weeks. RESULTS Baseline MetS-Z was inversely associated with odds of NASH resolution (odds ratio [OR] per 1 SD of MetS-Z: 0.47, 95% confidence interval [CI] 0.28, 0.79). Decrease in MetS-Z during initial 48-week intervention was greatest for pioglitazone treatment (effect size: -0.31, 95% CI -0.15, -0.48) and for vitamin E tended toward being greater for those with versus without NASH resolution (-0.18 vs -0.05). Overall, 48-week change in MetS-Z was associated with NASH resolution (OR per 1-SD change: 0.53, 95% CI 0.33, 0.85), although this was attenuated in models that included transaminases, which remained linked to treatment success (OR by change-in-aspartate aminotransferase Z-score: 0.38, 95% CI 0.19, 0.76). CONCLUSIONS Individuals with more severe metabolic derangement at baseline were less likely to exhibit NASH resolution, suggesting that individuals may have a threshold of MetS severity beyond which successful treatment is unlikely. As an integrated marker of metabolic abnormalities, MetS-Z was correlated with successful treatment, although transaminases were a more consistent marker of NASH resolution.
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Affiliation(s)
- Matthew J. Gurka
- Professor, Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States, 32608
| | - Jasmine A. Mack
- Data Management Analyst, Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States, 32608
| | - Xiaofei Chi
- Data Management Analyst, Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States, 32608
| | - Mark D. DeBoer
- Professor, Department of Pediatrics, Division of Pediatric Endocrinology, PO Box 800386, University of Virginia, Charlottesville, Virginia, United States, 22908;,Address correspondence to: Mark D. DeBoer, MD, MSc., MCR, 409 Lane Rd., Room 2017, P.O. Box 800386, Charlottesville, VA 22908, Phone: 434-924-5956, Fax: 434-924-9181,
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The prevalence of pediatric metabolic syndrome-a critical look on the discrepancies between definitions and its clinical importance. Int J Obes (Lond) 2020; 45:12-24. [PMID: 33208861 PMCID: PMC7752760 DOI: 10.1038/s41366-020-00713-1] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 10/16/2020] [Accepted: 11/02/2020] [Indexed: 02/06/2023]
Abstract
Introduction The Metabolic Syndrome (MetS) describes the clustering of cardio-metabolic risk factors—including abdominal obesity, insulin resistance, elevated blood pressure, high levels of triglycerides, and low levels of high-density lipoproteins—that increase the risk for developing cardiovascular diseases and type 2 diabetes mellitus. However, a generally accepted definition of MetS in pediatric patients is still lacking. Objectives The aim was to summarize current prevalence data of childhood MetS as well as to discuss the continuing disagreement between different pediatric definitions and the clinical importance of such diagnosis. Methodology A systematic literature search on the prevalence of pediatric MetS was conducted. Articles that were published during the past 5 years (2014–2019), using at least one of four predetermined classifications (International Diabetes Federation, Cook et al., Ford et al., and de Ferranti et al.), were included. Results The search resulted in 1167 articles, of which 31 publications met all inclusion criteria. Discussion The prevalence of MetS ranged between 0.3 and 26.4%, whereby the rising number of children and adolescents with MetS partly depended on the definition used. The IDF definition generally provided the lowest prevalences (0.3–9.5%), whereas the classification of de Ferranti et al. yielded the highest (4.0–26.4%). In order to develop a more valid definition, further research on long-term consequences of childhood risk factors such as abdominal obesity, insulin resistance, hypertension, and dyslipidemia is needed. There is also a temptation to suggest one valid, globally accepted definition of metabolic syndrome for pediatric populations but we believe that it is more appropriate to suggest definitions of MetS that are specific to males vs. females, as well as being specific to race/ethnicity or geographic region. Finally, while this notion of definitions of MetS specific to certain subgroups is important, it still needs to be tested in future research.
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DeBoer MD, Filipp SL, Sims M, Musani SK, Gurka MJ. Risk of Ischemic Stroke Increases Over the Spectrum of Metabolic Syndrome Severity. Stroke 2020; 51:2548-2552. [PMID: 32552367 DOI: 10.1161/strokeaha.120.028944] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE Ischemic stroke is associated with the metabolic syndrome (MetS) as diagnosed using dichotomous criteria; however, these criteria exhibit racial/ethnic discrepancies. Our goal was to assess whether ischemic stroke risk extended over the spectrum of worsening MetS severity using a sex- and race/ethnicity-specific MetS-severity Z score. METHODS We used Cox-proportional hazards models to assess the relationship between baseline MetS-Z score and incident ischemic stroke among participants of the Atherosclerosis Risk in Communities study and Jackson Heart Study who were free from diabetes, coronary heart disease or stroke at baseline, evaluating 13 141 white and black individuals with mean follow-up of 18.6 years. RESULTS We found that risk of ischemic stroke increased consistently with MetS severity, with a hazard ratio of 1.75 (95% CI, 1.35-2.27) for those >75th percentile compared to those <25th percentile. This risk was highest for white females (hazard ratio, 2.63 [CI, 1.70-4.07]) though without significant interaction by sex and race. Relationships between stroke and all the individual components of MetS were only noted for white females, though again without sex-race interactions. Hazard ratio's for systolic blood pressure and stroke were significant among all sex/racial subgroups. CONCLUSIONS Ischemic stroke risk increased over the spectrum of MetS severity in the absence of baseline diabetes mellitus, further implicating potential etiologic risks from processes underlying MetS. Individuals with elevated MetS severity should be counselled toward lifestyle modification to lower ischemic stroke risk.
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Affiliation(s)
- Mark D DeBoer
- Division of Pediatric Endocrinology, University of Virginia, Charlottesville (M.D.D.)
| | - Stephanie L Filipp
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville (S.L.F., M.L.G.)
| | - Mario Sims
- University of Mississippi Medical Center, Jackson (M.S., S.K.M.)
| | - Solomon K Musani
- University of Mississippi Medical Center, Jackson (M.S., S.K.M.)
| | - Matthew J Gurka
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville (S.L.F., M.L.G.)
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Meamar R, Amini M, Aminorroaya A, Nasri M, Abyar M, Feizi A. Severity of the metabolic syndrome as a predictor of prediabetes and type 2 diabetes in first degree relatives of type 2 diabetic patients: A 15-year prospective cohort study. World J Diabetes 2020; 11:202-212. [PMID: 32477456 PMCID: PMC7243485 DOI: 10.4239/wjd.v11.i5.202] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 03/16/2020] [Accepted: 03/24/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) has high morbidity and mortality worldwide, therefore there is of paramount importance to identify the risk factors in the populations at risk early in the course of illness. A strong correlation between severity of metabolic syndrome (MetS) and HbA1c, fasting insulin and insulin resistance has been reported. Accordingly, the MetS severity score (or MestS Z-score) can potentially be used to predict the risk of T2DM progression over time.
AIM To evaluate the association the of MestS Z-score in first degree relatives (FDRs) of T2DM with the risk of prediabetes and type 2 diabetes in future.
METHODS A prospective open cohort study was conducted between 2003-2018. At baseline, the sample comprised of 1766 FDRs of patients with T2DM who had a normal glucose tolerance test. Relative risk (RR) and 95% confidence interval were calculated based on logistic regression. The receiver-operator characteristic analysis and area under the curve based on MetS Z-score were used to evaluate the risk of prediabetes and diabetes among the FDR population.
RESULTS Baseline MetS Z-scores were associated with the its latest values (P < 0.0001). Compared with individuals who were T2DM free at the end of follow up, those who developed T2DM had higher MetS Z-score at baseline (P < 0.001). In multivariable logistic regression analyses for every unit elevation in MetS Z-score at the baseline, the RR for developing future T2DM and prediabetes was (RR = 1.94, RR = 3.84), (RR = 1.5, RR = 2.17) in total population and female group, respectively (P < 0.05). The associations remained significant after adjusting the potential confounding variables. A cut off value of 0.97 and 0.94 was defined in the receiver-operator characteristic curve based on the MetS Z-score for differentiating female patients with diabetes and prediabetes from the normal population, respectively.
CONCLUSION The MetS Z-score was associated with an increased risk of future T2DM. Appropriate interventions at earlier stages for preventing and attenuating MetS effects may be considered as an effective strategy for FDR as at-risk population.
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Affiliation(s)
- Rokhsareh Meamar
- Isfahan Endocrine and Metabolism Research Center, Isfahan Clinical Toxicology Research Center, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Masoud Amini
- Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Ashraf Aminorroaya
- Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Maryam Nasri
- Grovemead Health Center, London NW4-3EB, United Kingdom
| | - Majid Abyar
- Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Awat Feizi
- Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
- Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
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Gallardo-Alfaro L, Bibiloni MDM, Mascaró CM, Montemayor S, Ruiz-Canela M, Salas-Salvadó J, Corella D, Fitó M, Romaguera D, Vioque J, Alonso-Gómez ÁM, Wärnberg J, Martínez JA, Serra-Majem L, Estruch R, Fernández-García JC, Lapetra J, Pintó X, García Ríos A, Bueno-Cavanillas A, Gaforio JJ, Matía-Martín P, Daimiel L, Micó-Pérez RM, Vidal J, Vázquez C, Ros E, Fernandez-Lázaro CI, Becerra-Tomás N, Gimenez-Alba IM, Zomeño MD, Konieczna J, Compañ-Gabucio L, Tojal-Sierra L, Pérez-López J, Zulet MÁ, Casañas-Quintana T, Castro-Barquero S, Gómez-Pérez AM, Santos-Lozano JM, Galera A, Basterra-Gortari FJ, Basora J, Saiz C, Pérez-Vega KA, Galmés-Panadés AM, Tercero-Maciá C, Sorto-Sánchez C, Sayón-Orea C, García-Gavilán J, Muñoz-Martínez J, Tur JA. Leisure-Time Physical Activity, Sedentary Behaviour and Diet Quality are Associated with Metabolic Syndrome Severity: The PREDIMED-Plus Study. Nutrients 2020; 12:nu12041013. [PMID: 32272653 PMCID: PMC7230557 DOI: 10.3390/nu12041013] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 03/31/2020] [Accepted: 04/01/2020] [Indexed: 12/13/2022] Open
Abstract
Healthy lifestyle factors, such as physical activity (PA) and Mediterranean diet (MD), decrease the likelihood of developing metabolic syndrome (MetS). The aim of this study was to report main lifestyle components and related factors according to the MetS severity. Cross-sectional analysis was done of baseline lifestyle factors from 5739 participants with overweight/obesity and MetS features (aged 55–75 years) included in the PREDIMED-PLUS primary cardiovascular prevention randomized trial. Participants were categorized in tertiles according to a validated MetS severity score (MetSSS). Anthropometrics, visceral adiposity index, dietary nutrient intake, biochemical marker levels, as well as a Dietary Inflammatory Index and depression symptoms (Beck Depression Inventory-II) were measured. Diet quality was assessed using a 17-item energy-restricted MD questionnaire. Duration and intensity of PA was self-reported using the Minnesota-REGICOR Short Physical Activity Questionnaire. Sedentary behaviours were measured using the Spanish version of the Nurses’ Health Study questionnaire. The 30 s chair stand test was also assessed. Participants with highest MetSSS showed higher values of cardiovascular risk factors (except for total cholesterol and LDL cholesterol), depression risk, sedentary and TV viewing time, and lower moderate and vigorous leisure-time physical activity (LTPA). Highest MetSSS participants tended to a pro-inflammatory dietary pattern and tended to lower MD adherence. In addition, they showed lower carbohydrate and nut intake and higher intake of protein, saturated and trans fatty acids, cholesterol, iodine, sodium, red and processed meat products, other oils different from olive oil and spirit alcoholic drinks. The highest MetS severity score was associated with lower moderate and vigorous LTPA and higher sedentary time and depression risk, as they tended to a pro-inflammatory dietary pattern and lower MD adherence.
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Affiliation(s)
- Laura Gallardo-Alfaro
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands, 07122 Palma de Mallorca, Spain
- Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma de Mallorca, Spain
| | - Maria del Mar Bibiloni
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands, 07122 Palma de Mallorca, Spain
- Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma de Mallorca, Spain
| | - Catalina M. Mascaró
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands, 07122 Palma de Mallorca, Spain
- Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma de Mallorca, Spain
| | - Sofía Montemayor
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands, 07122 Palma de Mallorca, Spain
- Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma de Mallorca, Spain
| | - Miguel Ruiz-Canela
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Department of Preventive Medicine and Public Health, IdISNA, University of Navarra, 31008 Pamplona, Spain
| | - Jordi Salas-Salvadó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Human Nutrition Unit, Biochemistry and Biotechnology Department, IISPV, Universitat Rovira i Virgili, 43201 Reus, Spain
| | - Dolores Corella
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Department of Preventive Medicine, University of Valencia, 46100 Valencia, Spain
| | - Montserrat Fitó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d’Investigació Mèdica (IMIM), 08003 Barcelona, Spain
| | - Dora Romaguera
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma de Mallorca, Spain
| | - Jesús Vioque
- Nutritional Epidemiology Unit, Miguel Hernández University, Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), 46020 Alicante, Spain; (J.V.); (L.C.-G.)
- CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (A.B.-C.); (J.J.G.)
| | - Ángel M. Alonso-Gómez
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Bioaraba Health Research Institute, Osakidetza Basque Health Service, Araba University Hospital, University of the Basque Country UPV/EHU, 48013 Vitoria-Gasteiz, Spain
| | - Julia Wärnberg
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Department of Nursing, School of Health Sciences, University of Málaga-IBIMA, 29071 Málaga, Spain
| | - J. Alfredo Martínez
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Precision Nutrition Program, IMDEA Food, CEI UAM + CSIC, 28049 Madrid, Spain
- Department of Nutrition, Food Sciences, and Physiology, Center for Nutrition Research, University of Navarra, 31008 Pamplona, Spain
| | - Lluís Serra-Majem
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Institute for Biomedical Research, University of Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain
| | - Ramon Estruch
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Department of Internal Medicine, IDIBAPS, Hospital Clinic, University of Barcelona, 08036 Barcelona, Spain
| | - José Carlos Fernández-García
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Virgen de la Victoria Hospital, Department of Endocrinology, University of Málaga, 29010 Málaga, Spain
| | - José Lapetra
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, 41013 Sevilla, Spain
| | - Xavier Pintó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Lipids and Vascular Risk Unit, Internal Medicine, Hospital Universitario de Bellvitge, Hospitalet de Llobregat, 08907 Barcelona, Spain
| | - Antonio García Ríos
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Lipids and Atherosclerosis Unit, Department of Internal Medicine, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, 14004 Cordoba, Spain
| | - Aurora Bueno-Cavanillas
- CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (A.B.-C.); (J.J.G.)
- Department of Preventive Medicine, University of Granada, 18071 Granada, Spain
| | - José J. Gaforio
- CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (A.B.-C.); (J.J.G.)
- Department of Health Sciences, Centro de Estudios Avanzados en Olivar y Aceites de Oliva, University of Jaen, 23071 Jaen, Spain
| | - Pilar Matía-Martín
- Department of Endocrinology and Nutrition, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdISSC), 28040 Madrid, Spain;
| | - Lidia Daimiel
- Nutritional Genomics and Epigenomics Group, IMDEA Food, CEI UAM + CSIC, 28049 Madrid, Spain;
| | - Rafael M. Micó-Pérez
- CIBER Diabetes y Enfermedades Metabólicas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain;
- Network of Researchers REDI Fundación SEMERGEN, 28009 Madrid, Spain
| | - Josep Vidal
- Department of Endocrinology, IDIBAPS, Hospital Clinic, University of Barcelona, 08036 Barcelona, Spain;
| | - Clotilde Vázquez
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Department of Endocrinology, Fundación Jiménez-Díaz, 28040 Madrid, Spain
| | - Emilio Ros
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Lipid Clinic, Department of Endocrinology and Nutrition, Institut d’Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, 08036 Barcelona, Spain
| | - Cesar Ignacio Fernandez-Lázaro
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Department of Preventive Medicine and Public Health, IdISNA, University of Navarra, 31008 Pamplona, Spain
| | - Nerea Becerra-Tomás
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Human Nutrition Unit, Biochemistry and Biotechnology Department, IISPV, Universitat Rovira i Virgili, 43201 Reus, Spain
| | - Ignacio Manuel Gimenez-Alba
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Department of Preventive Medicine, University of Valencia, 46100 Valencia, Spain
| | - María Dolors Zomeño
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d’Investigació Mèdica (IMIM), 08003 Barcelona, Spain
| | - Jadwiga Konieczna
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma de Mallorca, Spain
| | - Laura Compañ-Gabucio
- Nutritional Epidemiology Unit, Miguel Hernández University, Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), 46020 Alicante, Spain; (J.V.); (L.C.-G.)
- CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (A.B.-C.); (J.J.G.)
| | - Lucas Tojal-Sierra
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Bioaraba Health Research Institute, Osakidetza Basque Health Service, Araba University Hospital, University of the Basque Country UPV/EHU, 48013 Vitoria-Gasteiz, Spain
| | - Jéssica Pérez-López
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Department of Nursing, School of Health Sciences, University of Málaga-IBIMA, 29071 Málaga, Spain
| | - M. Ángeles Zulet
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Precision Nutrition Program, IMDEA Food, CEI UAM + CSIC, 28049 Madrid, Spain
| | - Tamara Casañas-Quintana
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Institute for Biomedical Research, University of Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain
| | - Sara Castro-Barquero
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Department of Medicine, Faculty of Medicine and Health Sciences, University of Barcelona, 08036 Barcelona, Spain
| | - Ana María Gómez-Pérez
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Virgen de la Victoria Hospital, Department of Endocrinology, University of Málaga, 29010 Málaga, Spain
| | - José Manuel Santos-Lozano
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, 41013 Sevilla, Spain
| | - Ana Galera
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Lipids and Vascular Risk Unit, Internal Medicine, Hospital Universitario de Bellvitge, Hospitalet de Llobregat, 08907 Barcelona, Spain
| | - F. Javier Basterra-Gortari
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Department of Preventive Medicine and Public Health, IdISNA, University of Navarra, 31008 Pamplona, Spain
- Servicio Navarro de Salud, Osasunbidea. 31071 Pamplona, Spain
| | - Josep Basora
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Human Nutrition Unit, Biochemistry and Biotechnology Department, IISPV, Universitat Rovira i Virgili, 43201 Reus, Spain
| | - Carmen Saiz
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Department of Preventive Medicine, University of Valencia, 46100 Valencia, Spain
| | - Karla Alejandra Pérez-Vega
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d’Investigació Mèdica (IMIM), 08003 Barcelona, Spain
| | - Aina M. Galmés-Panadés
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma de Mallorca, Spain
| | | | - Carolina Sorto-Sánchez
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Bioaraba Health Research Institute, Osakidetza Basque Health Service, Araba University Hospital, University of the Basque Country UPV/EHU, 48013 Vitoria-Gasteiz, Spain
| | - Carmen Sayón-Orea
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Department of Preventive Medicine and Public Health, IdISNA, University of Navarra, 31008 Pamplona, Spain
- Servicio Navarro de Salud, Osasunbidea. 31071 Pamplona, Spain
| | - Jesús García-Gavilán
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Human Nutrition Unit, Biochemistry and Biotechnology Department, IISPV, Universitat Rovira i Virgili, 43201 Reus, Spain
| | - Júlia Muñoz-Martínez
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Nutritional Epidemiology Unit, Miguel Hernández University, Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), 46020 Alicante, Spain; (J.V.); (L.C.-G.)
| | - Josep A. Tur
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; (L.G.-A.); (M.d.M.B.); (C.M.M.); (S.M.); (M.R.-C.); (J.S.-S.); (D.C.); (M.F.); (D.R.); (Á.M.A.-G.); (J.W.); (J.A.M.); (L.S.-M.); (R.E.); (J.C.F.-G.); (J.L.); (X.P.); (A.G.R.); (C.V.); (E.R.); (C.I.F.-L.); (N.B.-T.); (I.M.G.-A.); (M.D.Z.); (J.K.); (L.T.-S.); (J.P.-L.); (M.Á.Z.); (T.C.-Q.); (S.C.-B.); (A.M.G.-P.); (J.M.S.-L.); (A.G.); (F.J.B.-G.); (J.B.); (C.S.); (K.A.P.-V.); (A.M.G.-P.); (C.S.-S.); (C.S.-O.); (J.G.-G.); (J.M.-M.)
- Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands, 07122 Palma de Mallorca, Spain
- Health Research Institute of the Balearic Islands (IdISBa), 07120 Palma de Mallorca, Spain
- Correspondence: ; Tel.: +34-971-1731; Fax: +34-971-173184
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Matilla-Santander N, Espinola M, Cartanyà-Hueso À, Lidón-Moyano C, González-Marrón A, Martín-Sánchez JC, Cainzos-Achirica M, Martínez Sánchez JM. Prevalence and determinants of metabolic syndrome in Spanish salaried workers: evidence from 15 614 men and women. J Public Health (Oxf) 2020; 42:141-148. [PMID: 30715426 DOI: 10.1093/pubmed/fdz003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 12/17/2018] [Accepted: 01/07/2019] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVE To describe the prevalence of Spanish workers with Metabolic Syndrome (MetS) and those at risk of developing MetS in 2015. METHODS Cross-sectional study of workers (n = 15 614). We used a modified definition of the NCEP:ATPIII criteria for MetS (we used body mass index (BMI) above 28.8 kg/m2 instead of the waist circumference criterion). We calculated the prevalence of MetS (having at least three components) and of being at risk of MetS (having one or two components). We calculated adjusted odds ratios (aOR) of MetS according to socio-economic and workplace characteristics. RESULTS The proportions of workers with and at risk of MetS were 7.1 and 31.9%, respectively. The most prevalent criterion was having a BMI > 28.8 kg/m2 (24.1%) in men and cHDL < 40 mg/dl in women (12.9%). There were significant associations between MetS and men (aOR compared to women = 3.73, CI 95%: 3.19; 4.36); age (higher among oldest, aOR = 5.75, CI 95%: 4.37;7.56); and social class (higher among lower social class, aOR = 2.03, CI 95%: 1.65;2.48). CONCLUSION Reducing any of the five MetS components, while taking into account the differences found by socio-economic and workplace characteristics, should be one priority for reducing MetS prevalence.
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Affiliation(s)
- Nuria Matilla-Santander
- Group of Evaluation of Health Determinants and Health Policies, Universitat Internacional de Catalunya, Sant Cugat del Vallès, Spain
| | - Marina Espinola
- Group of Evaluation of Health Determinants and Health Policies, Universitat Internacional de Catalunya, Sant Cugat del Vallès, Spain
| | - Àurea Cartanyà-Hueso
- Group of Evaluation of Health Determinants and Health Policies, Universitat Internacional de Catalunya, Sant Cugat del Vallès, Spain
| | - Cristina Lidón-Moyano
- Group of Evaluation of Health Determinants and Health Policies, Universitat Internacional de Catalunya, Sant Cugat del Vallès, Spain.,Health Sciences Research Institute, University of California Merced (UC Merced), Merced, CA, USA
| | - Adrián González-Marrón
- Group of Evaluation of Health Determinants and Health Policies, Universitat Internacional de Catalunya, Sant Cugat del Vallès, Spain
| | - Juan Carlos Martín-Sánchez
- Group of Evaluation of Health Determinants and Health Policies, Universitat Internacional de Catalunya, Sant Cugat del Vallès, Spain
| | - Miguel Cainzos-Achirica
- Group of Evaluation of Health Determinants and Health Policies, Universitat Internacional de Catalunya, Sant Cugat del Vallès, Spain.,Hospital Universitari de Bellvitge and Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain.,Johns Hopkins Ciccarone Center for the Prevention of Heart Disease, The Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Jose M Martínez Sánchez
- Group of Evaluation of Health Determinants and Health Policies, Universitat Internacional de Catalunya, Sant Cugat del Vallès, Spain.,Tobacco Control Unit, Cancer Prevention and Control Program, Institut Català d'Oncologia, L'Hospitalet de Llobregat, Barcelona, Spain.,Cancer Prevention and Control Group, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
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38
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Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Delling FN, Djousse L, Elkind MSV, Ferguson JF, Fornage M, Khan SS, Kissela BM, Knutson KL, Kwan TW, Lackland DT, Lewis TT, Lichtman JH, Longenecker CT, Loop MS, Lutsey PL, Martin SS, Matsushita K, Moran AE, Mussolino ME, Perak AM, Rosamond WD, Roth GA, Sampson UKA, Satou GM, Schroeder EB, Shah SH, Shay CM, Spartano NL, Stokes A, Tirschwell DL, VanWagner LB, Tsao CW. Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association. Circulation 2020; 141:e139-e596. [PMID: 31992061 DOI: 10.1161/cir.0000000000000757] [Citation(s) in RCA: 4819] [Impact Index Per Article: 1204.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2020 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association's 2020 Impact Goals. RESULTS Each of the 26 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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DeBoer MD, Filipp SL, Gurka MJ. Associations of a metabolic syndrome severity score with coronary heart disease and diabetes in fasting vs. non-fasting individuals. Nutr Metab Cardiovasc Dis 2020; 30:92-98. [PMID: 31662283 PMCID: PMC7393664 DOI: 10.1016/j.numecd.2019.08.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 08/14/2019] [Accepted: 08/15/2019] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Many traditional assessments of risk for coronary heart disease (CHD) and diabetes require laboratory studies performed after an 8-h fast. We assessed whether metabolic-syndrome (MetS) severity would remain linked to future CHD and diabetes even when assessed from non-fasting samples. METHODS AND RESULTS Participants in the Atherosclerosis Risk in Communities study were assessed at 4 visits and followed for 20-years of adjudicated CHD outcomes. We used Cox proportional-hazard models (for 20-year CHD outcomes) and logistic regression (for 9-year diabetes outcomes) to compare incident disease risk associated with a race/ethnicity-specific MetS-severity Z-score (MetS-Z) calculated in participants who were fasting (≥8 h) or non-fasting. All analyses were adjusted for sex, race, education, income and smoking. MetS Z-scores were overall similar between participants who were always fasting vs. those non-fasting at Visits 1-3 (all values -0.1 to 0.4), while MetS-Z for participants who were non-fasting at Visit-4 were higher at each visit. Baseline MetS-Z was linked to future CHD when calculated from both fasting and non-fasting measurements, with hazard ratio (HR) for fasting MetS-Z 1.53 (95% confidence interval [CI] 1.42, 1.66) and for non-fasting 1.28 (CI 1.08, 1.51). MetS-Z at Visit-1 also remained linked to future diabetes when measured from non-fasting samples, with odds ratio for fasting MetS-Z 3.10 (CI 2.88, 3.35) and for non-fasting 1.92 (CI 1.05, 3.51). CONCLUSIONS MetS-Z remained linked to future CHD and diabetes when assessed from non-fasting samples. A score such as this may allow for identification of at-risk individuals and serve as a motivation toward interventions to reduce risk.
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Affiliation(s)
- Mark D DeBoer
- Department of Pediatrics, Division of Pediatric Endocrinology, University of Virginia, PO Box 800386 Charlottesville, VA, 22908, United States.
| | - Stephanie L Filipp
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, 32608, United States.
| | - Matthew J Gurka
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, 32608, United States.
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40
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Liu Y, Téllez-Rojo M, Sánchez BN, Ettinger AS, Osorio-Yáñez C, Solano M, Hu H, Peterson KE. Association between fluoride exposure and cardiometabolic risk in peripubertal Mexican children. ENVIRONMENT INTERNATIONAL 2020; 134:105302. [PMID: 31726363 PMCID: PMC6904509 DOI: 10.1016/j.envint.2019.105302] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Revised: 10/30/2019] [Accepted: 11/01/2019] [Indexed: 05/06/2023]
Abstract
BACKGROUND Several animal studies have suggested that fluoride exposure may increase the levels of cardiometabolic risk factors, but little is known about whether fluoride exposure is associated with such risk in humans. OBJECTIVES We examined the cross-sectional association between peripubertal exposure to fluoride and markers of cardiometabolic risk in 280 girls and 256 boys at age 10-18 years living in Mexico City. METHODS We measured plasma fluoride concentration using a microdiffusion method. We collected data on anthropometry including BMI, waist circumference (WC) and trunk fat percentage. We measured serum markers of cardiometabolic risk, including fasting glucose, insulin and lipids. All the indicators of outcome were converted to age- and sex-specific z-scores. We also calculated a summary cardiometabolic risk score for each participant. Multivariable linear regression models were used to examine these associations. RESULTS The geometric mean (95% confidence interval (CI)) of plasma fluoride was 0.21 μmol/L (0.20, 0.23 μmol/L) in the total sample. In girls, plasma fluoride concentrations were associated with higher z-scores for all the individual markers (except for lipids) and for the combined cardiometabolic risk score (risk score: β = 1.28, 95% CI: 0.57-2.00, p-sex interaction = 0.02)), adjusting for covariates. No associations were found in boys. CONCLUSIONS We found that higher peripubertal fluoride exposure at the levels observed in this study population was significantly associated with increased levels of cardiometabolic risk factors in Mexican girls but not boys. Future studies with a longitudinal design are needed to confirm our findings and further elucidate the role of fluoride in cardiometabolic risk.
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Affiliation(s)
- Yun Liu
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Martha Téllez-Rojo
- Nutrition and Health Research, National Institute of Public Health, Cuernavaca, Morelos, Mexico.
| | - Brisa N Sánchez
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Adrienne S Ettinger
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Citlalli Osorio-Yáñez
- Nutrition and Health Research, National Institute of Public Health, Cuernavaca, Morelos, Mexico; Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
| | - Maritsa Solano
- Nutrition and Health Research, National Institute of Public Health, Cuernavaca, Morelos, Mexico
| | - Howard Hu
- Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, USA; Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Karen E Peterson
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
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Ruíz-Fernández NA, Leal U, Espinoza M. Comparison of scores for the classification of cardiometabolic risk in adult patients enrolled in a Venezuelan program for chronic non-communicable diseases: a cross-sectional study. SAO PAULO MED J 2020; 138:69-78. [PMID: 32321108 PMCID: PMC9673846 DOI: 10.1590/1516-3180.2019.0236.r1.06112019] [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: 10/17/2019] [Accepted: 11/06/2019] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Several continuous measurements of cardiometabolic risk (CMR) have emerged as indexes or scores. To our knowledge, there are no published data on its application and validation in Latin America. OBJECTIVE To evaluate four continuous measurements of metabolic status and CMR. We established its predictive capacity for four conditions associated with CMR. DESIGN AND SETTING Cross-sectional study conducted at a healthcare center in the state of Carabobo, Venezuela. METHODS The sample comprised 176 Venezuelan adults enrolled in a chronic disease care program. Four CMR scores were calculated: metabolic syndrome (MetS) Z-score; cardiometabolic index (ICMet); simple method for quantifying MetS (siMS) score; and siMS risk score. CMR biomarkers, proinflammatory status and glomerular function were assessed. MetS was established in accordance with a harmonized definition. RESULTS Patients with MetS showed higher levels of all scores. All scores increased as the number of MetS components rose. The scores showed significant correlations with most CMR biomarkers, inflammation and glomerular function after adjusting for age and sex. In the entire sample, MetS Z-score, siMS score and siMS risk score showed the ability to detect MetS, reduced glycemic control, proinflammatory status and decreased estimated glomerular filtration rate. ICMet only discriminated MetS and proinflammatory state. There were some differences in the predictive capacity of the scores according to sex. CONCLUSIONS The findings support the use of the scores assessed here. Follow-up studies should evaluate the predictive capacity of scores for cardiovascular events and diabetes in the Venezuelan population.
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Affiliation(s)
- Nelina Alejandra Ruíz-Fernández
- PhD. Medical Laboratory Technician and Professor, Department of Morphophysiopathology, School of Bioanalysis, Faculty of Health Sciences, Universidad de Carabobo, Valencia, Carabobo, Venezuela; and Principal Researcher, Institute of Nutritional Research, Faculty of Health Sciences, Universidad de Carabobo, Valencia, Carabobo, Venezuela.
| | - Ulises Leal
- MD. Physician and Internal Medicine Specialist, Integral Medical Care Unit, University of Carabobo, Valencia, Carabobo, Venezuela; and Specialist Physician type II, Outpatient Clinic of the Municipality of San Diego, Carabobo, Venezuela.
| | - Milagros Espinoza
- PhD. Medical Laboratory Technician and Professor, Department of Research and Professional Development, School of Bioanalysis, Faculty of Health Sciences, Universidad de Carabobo, Valencia, Carabobo, Venezuela.
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Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Das SR, Delling FN, Djousse L, Elkind MSV, Ferguson JF, Fornage M, Jordan LC, Khan SS, Kissela BM, Knutson KL, Kwan TW, Lackland DT, Lewis TT, Lichtman JH, Longenecker CT, Loop MS, Lutsey PL, Martin SS, Matsushita K, Moran AE, Mussolino ME, O'Flaherty M, Pandey A, Perak AM, Rosamond WD, Roth GA, Sampson UKA, Satou GM, Schroeder EB, Shah SH, Spartano NL, Stokes A, Tirschwell DL, Tsao CW, Turakhia MP, VanWagner LB, Wilkins JT, Wong SS, Virani SS. Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association. Circulation 2019; 139:e56-e528. [PMID: 30700139 DOI: 10.1161/cir.0000000000000659] [Citation(s) in RCA: 5298] [Impact Index Per Article: 1059.6] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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King AK, McGill-Meeks K, Beller JP, Burt Solorzano CM. Go Girls!-Dance-Based Fitness to Increase Enjoyment of Exercise in Girls at Risk for PCOS. CHILDREN (BASEL, SWITZERLAND) 2019; 6:E99. [PMID: 31500180 PMCID: PMC6769571 DOI: 10.3390/children6090099] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 08/28/2019] [Indexed: 12/11/2022]
Abstract
Weight loss can reduce the hyperandrogenemia associated with polycystic ovary syndrome (PCOS) in peripubertal girls. Yet, adolescent girls have the lowest rates of physical activity and enjoyment of exercise. We created a dance-based support group (Go Girls!) to entice physical activity and improve enjoyment. Girls ages 7-21 over the 85th BMI percentile were recruited and attended once-weekly sessions for 3-6 months. We assessed changes in Physical Activity Enjoyment Scale (PACES), anthropometrics, laboratory data, and amounts of home exercise at 0, 3, and 6 months. Sixteen girls completed either 3 or 6 months. PACES scores were surprisingly high at baseline and remained high. Systolic blood pressure percentile decreased post-intervention. Although no group differences were observed, the majority of individual girls had decreased waist circumference, triglycerides, and metabolic syndrome severity score. Forty percent had decreased free testosterone levels. More girls enjoyed physical education class, got exercise outside of school, and made other lifestyle changes. This dance-based support group was enjoyed by girls and demonstrated health benefits. Continued efforts to engage girls in physical activity are necessary to protect girls from the consequences of obesity, including PCOS and metabolic syndrome. Dance exercise remains a promising tool to encourage physical activity in girls.
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Affiliation(s)
- Anna K King
- Department of Pediatrics, Children's Fitness Clinic, University of Virginia, Charlottesville, VA 22908, USA
| | - Kara McGill-Meeks
- Augusta Health, Outpatient Diabetes and Nutrition Education Program, Waynesboro, VA 22939, USA
| | - Jennifer P Beller
- Saratoga Hospital Medical Group, Endocrinology and Diabetes, Wilton, NY 12831, USA
| | - Christine M Burt Solorzano
- Department of Pediatrics, Children's Fitness Clinic, University of Virginia, Charlottesville, VA 22908, USA.
- Center for Research in Reproduction, University of Virginia, Charlottesville, VA 22908, USA.
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Assessing and Managing the Metabolic Syndrome in Children and Adolescents. Nutrients 2019; 11:nu11081788. [PMID: 31382417 PMCID: PMC6723651 DOI: 10.3390/nu11081788] [Citation(s) in RCA: 97] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Revised: 07/10/2019] [Accepted: 07/30/2019] [Indexed: 02/06/2023] Open
Abstract
The metabolic syndrome (MetS) is a group of cardiovascular risk factors that are associated with insulin resistance and are driven by underlying factors, including visceral obesity, systemic inflammation, and cellular dysfunction. These risks increasingly begin in childhood and adolescence and are associated with a high likelihood of future chronic disease in adulthood. Efforts should be made at both recognition of this metabolic risk, screening for potential associated Type 2 diabetes, and targeting affected individuals for appropriate treatment with an emphasis on lifestyle modification. Effective interventions have been linked to reductions in MetS-and in adults, reductions in the severity of MetS have been linked to reduced diabetes and cardiovascular disease.
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Ramírez-Vélez R, Pérez-Sousa MÁ, Izquierdo M, Cano-Gutierrez CA, González-Jiménez E, Schmidt-RioValle J, González-Ruíz K, Correa-Rodríguez M. Validation of Surrogate Anthropometric Indices in Older Adults: What Is the Best Indicator of High Cardiometabolic Risk Factor Clustering? Nutrients 2019; 11:nu11081701. [PMID: 31344803 PMCID: PMC6723899 DOI: 10.3390/nu11081701] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 07/19/2019] [Accepted: 07/22/2019] [Indexed: 12/17/2022] Open
Abstract
The present study evaluated the ability of five obesity-related parameters, including a body shape index (ABSI), conicity index (CI), body roundness index (BRI), body mass index (BMI), and waist-to-height ratio (WtHR) for predicting increased cardiometabolic risk in a population of elderly Colombians. A cross-sectional study was conducted on 1502 participants (60.3% women, mean age 70 ± 7.6 years) and subjects’ weight, height, waist circumference, serum lipid indices, blood pressure, and fasting plasma glucose were measured. A cardiometabolic risk index (CMRI) was calculated using the participants’ systolic and diastolic blood pressure, triglycerides, high-density lipoprotein and fasting glucose levels, and waist circumference. Following the International Diabetes Federation definition, metabolic syndrome was defined as having three or more metabolic abnormalities. All surrogate anthropometric indices correlated significantly with CMRI (p < 0.01). Receiver operating characteristic curve analysis of how well the anthropometric indices identified high cardiometabolic risk showed that WtHR and BRI were the most accurate indices. The best WtHR and BRI cut-off points in men were 0.56 (area under curve, AUC 0.77) and 4.71 (AUC 0.77), respectively. For women, the WtHR and BRI cut-off points were 0.63 (AUC 0.77) and 6.20 (AUC 0.77), respectively. In conclusion, BRI and WtHR have a moderate discriminating power for detecting high cardiometabolic risk in older Colombian adults, supporting the idea that both anthropometric indices are useful screening tools for use in the elderly.
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Affiliation(s)
- Robinson Ramírez-Vélez
- Department of Health Sciences, Public University of Navarra, Navarrabiomed-Biomedical Research Centre, IDISNA-Navarra's Health Research Institute, C/irunlarrea 3, Complejo Hospitalario de Navarra, 31008 Pamplona, Navarra, Spain.
| | - Miguel Ángel Pérez-Sousa
- Faculty of Sport Sciences, University of Huelva, Avenida de las Fuerzas Armadas s/n, 21007 Huelva, Spain
| | - Mikel Izquierdo
- Department of Health Sciences, Public University of Navarra, Navarrabiomed-Biomedical Research Centre, IDISNA-Navarra's Health Research Institute, C/irunlarrea 3, Complejo Hospitalario de Navarra, 31008 Pamplona, Navarra, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Carlos A Cano-Gutierrez
- Hospital Universitario San Ignacio - Aging Institute, Pontificia Universidad Javeriana, Bogotá 110111, Colombia
| | - Emilio González-Jiménez
- Department of Nursing, Faculty of Health Sciences, University of Granada, Av. Ilustración, 60, 18016 Granada, Spain
| | - Jacqueline Schmidt-RioValle
- Department of Nursing, Faculty of Health Sciences, University of Granada, Av. Ilustración, 60, 18016 Granada, Spain
| | - Katherine González-Ruíz
- Grupo de Ejercicio Físico y Deportes, Vicerrectoría de Investigaciones, Facultad de Salud, Universidad Manuela Beltrán, Bogotá 110231, DC, Colombia
| | - María Correa-Rodríguez
- Department of Nursing, Faculty of Health Sciences, University of Granada, Av. Ilustración, 60, 18016 Granada, Spain
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Low S, Khoo KCJ, Wang J, Irwan B, Sum CF, Subramaniam T, Lim SC, Wong TKM. Development of a metabolic syndrome severity score and its association with incident diabetes in an Asian population-results from a longitudinal cohort in Singapore. Endocrine 2019; 65:73-80. [PMID: 31161560 DOI: 10.1007/s12020-019-01970-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 05/24/2019] [Indexed: 11/25/2022]
Abstract
PURPOSE Metabolic syndrome (MetS) is a constellation of clinical factors that indicates elevated risk of diabetes. It is diagnosed based on three or more abnormalities in its components. This does not take into account that MetS can likely present as a continuum of risk. We aim to develop a MetS severity score and assess its association with incident diabetes. METHODS In total, 4149 subjects without baseline diabetes participated in a community screening programme in 2013-2017. MetS was defined according to International Diabetes Federation criteria. A MetS severity z-score was derived from standardised loading coefficients of a confirmatory factor analysis for waist circumference, triglycerides, HDL-cholesterol, blood pressure and fasting plasma glucose (FPG). Multivariable cox proportional hazards regression model was used to assess the risk of diabetes by the score with adjustment for demographics and MetS components. RESULTS Diabetes occurred in 130 subjects. Quintile 5 of the baseline MetS severity z-score was significantly associated with development of diabetes even in fully adjusted model with HR 2.63 (95% CI: 1.04-6.64; p = 0.040). The relationship between MetS and incident diabetes became attenuated and non-significant in fully adjusted model with HR 0.67 (95% CI: 0.34-1.29; p = 0.228). Mediation analysis showed that MetS severity z-score accounted 61.0% of the association between increasing body mass index and development of diabetes (p < 0.001). CONCLUSIONS The MetS severity z-score is an inexpensive and clinically-available continuous measure of MetS to identify individuals at high risk of diabetes.
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Affiliation(s)
- Serena Low
- Diabetes Centre, Admiralty Medical Centre, Singapore, Singapore
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Singapore
| | | | - Jiexun Wang
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Bastari Irwan
- Transformation Office, Hospital Administration, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Chee Fang Sum
- Diabetes Centre, Admiralty Medical Centre, Singapore, Singapore
| | | | - Su Chi Lim
- Diabetes Centre, Admiralty Medical Centre, Singapore, Singapore.
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Singapore.
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
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Hosseinpour-Niazi S, Bakhshi B, Betru E, Mirmiran P, Darand M, Azizi F. Prospective study of total and various types of vegetables and the risk of metabolic syndrome among children and adolescents. World J Diabetes 2019; 10:362-375. [PMID: 31231459 PMCID: PMC6571485 DOI: 10.4239/wjd.v10.i6.362] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 05/09/2019] [Accepted: 05/14/2019] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Data available on the association between consumption of various types of vegetables and metabolic syndrome (MetS) remain inconsistent.
AIM To investigate the association between the intake of various types of vegetables and MetS among children and adolescents and MetS.
METHODS The Tehran Lipid and Glucose Study cohort included 424 children and adolescents initially free of MetS. At the 3.6 year follow-up, 47 new cases of MetS were identified. A 168-item semi-quantitative food-frequency questionnaire was used to collect information about total and various types of vegetables consumed, including allium-, green leafy-, fruity-, root-, stalk-, starchy-, potatoes, and cabbage. MetS was defined according to the Cook et al[32] criteria.
RESULTS The median (interquartile range) of total vegetable consumption was 217 (146-344) g/d. After adjustment for demographic characteristics and dietary intake, higher total- (≥ 350 g/d) and higher allium vegetable consumption (≥ 30 g/d) in the fourth quartile were significantly and inversely associated with risk of MetS compared to the first quartile. Consumption of green leafy vegetables in the third (21.4-38.3 g/d) versus the first quartile (≤ 13.5 g/d) demonstrated a significant inverse association with lower risk of MetS in children and adolescents; associations for other types of vegetables consumed were not significant.
CONCLUSION Consumption of vegetables, especially allium and green leafy vegetables, in sufficient amounts may be beneficial in reducing the risk of MetS among children and adolescents.
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Affiliation(s)
- Somayeh Hosseinpour-Niazi
- Nutrition and Endocrine Research center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran 19395-4763, Iran
| | - Bahar Bakhshi
- Nutrition and Endocrine Research center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran 19395-4763, Iran
| | - Ekbal Betru
- Nutrition and Endocrine Research center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran 19395-4763, Iran
| | - Parvin Mirmiran
- Department of Clinical Nutrition and Dietetics, Faculty of Nutrition Sciences and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran 19395-4763, Iran
| | - Mina Darand
- Nutrition and Endocrine Research center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran 19395-4763, Iran
| | - Fereidoun Azizi
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran 19395-4763, Iran
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DeBoer MD, Gurka MJ, Golden SH, Musani SK, Sims M, Vishnu A, Guo Y, Pearson TA. Independent Associations Between Metabolic Syndrome Severity and Future Coronary Heart Disease by Sex and Race. J Am Coll Cardiol 2019; 69:1204-1205. [PMID: 28254184 DOI: 10.1016/j.jacc.2016.10.088] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Revised: 10/12/2016] [Accepted: 10/15/2016] [Indexed: 12/12/2022]
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Lee K. Comparison of Body Mass Index Percentiles to Detect Metabolic Syndrome Using the Korean, United States Centers for Disease Control and Prevention, and World Health Organization References in Korean Children Aged 10–16 Years. Metab Syndr Relat Disord 2019; 17:210-216. [DOI: 10.1089/met.2018.0126] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Affiliation(s)
- Kayoung Lee
- Department of Family Medicine, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
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50
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DeBoer MD, Filipp SL, Gurka MJ. Geographical variation in the prevalence of obesity and metabolic syndrome among US adolescents. Pediatr Obes 2019; 14:e12483. [PMID: 30515979 PMCID: PMC6513350 DOI: 10.1111/ijpo.12483] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 08/28/2018] [Accepted: 09/20/2018] [Indexed: 12/23/2022]
Abstract
BACKGROUND Among adolescents, obesity and the metabolic syndrome (MetS) contribute to adult cardiovascular disease risk. By parent report, obesity prevalence in the USA was highest in the South. OBJECTIVES The aim of this study was to determine the prevalence of obesity and MetS by US division and region. METHODS We used in-person assessment of 4600 US adolescents age 12-19 years participating in the National Health and Nutrition Examination Survey, 1999-2014. RESULTS Prevalence of obesity was highest in the East North Central division (21.3%) and the three census divisions in the South (all >20%), compared with lower prevalence in the Mountain and New England divisions (both <15%). MetS was most prevalent in the two divisions in the Midwest (both >10%) and lowest in the Mountain and New England divisions (both <6%). For the amount of obesity in each division, there was a higher prevalence of MetS in the West North Central division (obesity 17.1%, MetS 13.6%) and lower prevalence in the East South Central (obesity 23.5%, MetS 6.6%) and South Atlantic divisions (obesity 20.4%, MetS 6.7%). CONCLUSIONS The degree of obesity-related and MetS-related risk among adolescents in the Midwest is higher than suggested from previous parent-reported weight data. The Midwest and South may warrant particularly strong cardiovascular disease prevention efforts.
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Affiliation(s)
- Mark D. DeBoer
- Department of Pediatrics, Division of Pediatric Endocrinology, PO Box 800386, University of Virginia, Charlottesville, Virginia, United States, 22908,Address correspondence to: Mark D. DeBoer, MD, MSc., MCR, 409 Lane Rd., Room 2017, P.O. Box 800386, Charlottesville, VA 22908, Phone: 434-924-5956, Fax: 434-924-9181,
| | - Stephanie L. Filipp
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States, 32608
| | - Matthew J. Gurka
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States, 32608
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