Literature DB >> 33431923

Biological age and lifestyle in the diagnosis of metabolic syndrome: the NHIS health screening data, 2014-2015.

Chul-Young Bae1, Meihua Piao2, Miyoung Kim1, Yoori Im1, Sungkweon Kim1, Donguk Kim3, Junho Choi4, Kyung Hee Cho5.   

Abstract

Metabolic syndrome (MS) is diagnosed using absolute criteria that do not consider age and sex, but most studies have shown that the prevalence of MS increases with age in both sexes. Thus, the evaluation of MS should consider sex and age. We aimed to develop a new index that considers the age and sex for evaluating an individual's relative overall MS status. Data of 16,518,532 subjects (8,671,838 males and 7,846,694 females) who completed a validated health survey of the National Health Insurance Service of the Republic of Korea (2014‒2015) were analyzed to develop an MS-biological age model. Principal component score analysis using waist circumference, pulse pressure, fasting blood sugar, triglyceride levels, and high-density lipoprotein level, but not age, as independent variables were performed to derive an index of health status and biological age. In both sexes, the age according to the MS-biological age model increased with rising smoking and alcohol consumption habits and decreased with rising physical activity. Particularly, smoking and drinking affected females, whereas physical activity affected males. The MS-biological age model can be a supplementary tool for evaluating and managing MS, quantitatively measuring the effect of lifestyle changes on MS, and motivating patients to maintain a healthy lifestyle.

Entities:  

Year:  2021        PMID: 33431923      PMCID: PMC7801435          DOI: 10.1038/s41598-020-79256-4

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  46 in total

Review 1.  A comprehensive definition for metabolic syndrome.

Authors:  Paul L Huang
Journal:  Dis Model Mech       Date:  2009 May-Jun       Impact factor: 5.758

2.  Biological age and its estimation. III. Introduction of a correction to the multiple regression model of biological age in cross-sectional and longitudinal studies.

Authors:  T L Dubina; E V Zhuk
Journal:  Exp Gerontol       Date:  1984       Impact factor: 4.032

Review 3.  Alcohol consumption and risk of metabolic syndrome: a meta-analysis of prospective studies.

Authors:  Kan Sun; Meng Ren; Dan Liu; Chuan Wang; Chuan Yang; Li Yan
Journal:  Clin Nutr       Date:  2013-10-14       Impact factor: 7.324

4.  Development of models for predicting biological age (BA) with physical, biochemical, and hormonal parameters.

Authors:  Chul-Young Bae; Young Gon Kang; Sehyun Kim; Chooyon Cho; Hee Cheol Kang; Byung Yeon Yu; Sang-Wha Lee; Kyung Hee Cho; Duk Chul Lee; Kyurae Lee; Jong Sung Kim; Kyung Kyun Shin
Journal:  Arch Gerontol Geriatr       Date:  2007-09-24       Impact factor: 3.250

Review 5.  Consequences of smoking for body weight, body fat distribution, and insulin resistance.

Authors:  Arnaud Chiolero; David Faeh; Fred Paccaud; Jacques Cornuz
Journal:  Am J Clin Nutr       Date:  2008-04       Impact factor: 7.045

6.  Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women.

Authors:  M C Pouliot; J P Després; S Lemieux; S Moorjani; C Bouchard; A Tremblay; A Nadeau; P J Lupien
Journal:  Am J Cardiol       Date:  1994-03-01       Impact factor: 2.778

7.  A model for estimating body shape biological age based on clinical parameters associated with body composition.

Authors:  Chul-Young Bae; Young Gon Kang; Young-Sung Suh; Jee Hye Han; Sung-Soo Kim; Kyung Won Shim
Journal:  Clin Interv Aging       Date:  2012-12-28       Impact factor: 4.458

8.  Are objective measures of physical capability related to accelerated epigenetic age? Findings from a British birth cohort.

Authors:  Andrew J Simpkin; Rachel Cooper; Laura D Howe; Caroline L Relton; George Davey Smith; Andrew Teschendorff; Martin Widschwendter; Andrew Wong; Diana Kuh; Rebecca Hardy
Journal:  BMJ Open       Date:  2017-11-01       Impact factor: 2.692

9.  Association of Concurrent Changes in Metabolic Health and Weight on Cardiovascular Disease Risk: A Nationally Representative Cohort Study.

Authors:  Ye Seul Bae; Seulggie Choi; Kiheon Lee; Joung Sik Son; Hyejin Lee; Mi Hee Cho; Hye-Yeon Koo; In Young Cho; Jooyoung Chang; Kyuwoong Kim; Sung Min Kim; Sang Min Park
Journal:  J Am Heart Assoc       Date:  2019-08-27       Impact factor: 5.501

10.  The Prevalence of Metabolic Syndrome and Its Related Risk Complications among Koreans.

Authors:  Seung-Hoo Lee; Shuting Tao; Hak-Seon Kim
Journal:  Nutrients       Date:  2019-07-30       Impact factor: 5.717

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  3 in total

1.  Association of Age and Sex with Metabolic Syndrome in Taiwanese Adults.

Authors:  Pang-Li Liu; Ming-Yi Hsu; Chao-Chin Hu; Disline Manli Tantoh; Wen-Yu Lu; Oswald Ndi Nfor; Yung-Po Liaw
Journal:  Int J Gen Med       Date:  2021-04-20

2.  TG: HDL-C Ratio as Insulin Resistance Marker for Metabolic Syndrome in Children With Obesity.

Authors:  Ahmad Kamil Nur Zati Iwani; Muhammad Yazid Jalaludin; Abqariyah Yahya; Fazliana Mansor; Fuziah Md Zain; Janet Yeow Hua Hong; Ruziana Mona Wan Mohd Zin; Abdul Halim Mokhtar
Journal:  Front Endocrinol (Lausanne)       Date:  2022-03-10       Impact factor: 5.555

Review 3.  Impact of Combined Antiretroviral Therapy on Metabolic Syndrome Components in Adult People Living with HIV: A Literature Review.

Authors:  Mariusz Sapuła; Magdalena Suchacz; Andrzej Załęski; Alicja Wiercińska-Drapało
Journal:  Viruses       Date:  2022-01-11       Impact factor: 5.048

  3 in total

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