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Cited by 13Diabetes, Cardiovascular Risks, and LipoproteinsMenopause: Health Impacts and TreatmentsAdipokines, Inflammation, and Metabolic Diseases

The Age-Dependent Increase of Metabolic Syndrome Requires More Extensive and Aggressive Non-Pharmacological and Pharmacological Interventions: A Cross-Sectional Study in an Italian Cohort of Obese Women

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Rigamonti, Antonello E., Cicolini, Sabrina, Tamini, Sofia, Caroli, Diana, Cella, Silvano G., Sartorio, Alessandro·2021·International Journal of Endocrinology
DOI 10.1155/2021/5576286PMC PMC8088356
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Abstract

Background. Metabolic syndrome is a combination of cardiovascular risk factors (i.e., visceral obesity, dyslipidaemia, glucose intolerance, and hypertension), which entails critical issues in terms of medical management and public health. Methods. The aim of the present cross-sectional study was to investigate the age-related changes of the single IDF (International Diabetes Federation) diagnostic criteria for metabolic syndrome (waist circumference, WC; high-density lipoprotein cholesterol, HDL-C; triglycerides; glucose; systolic and diastolic blood pressure, SBP and DBP) in a large population of (Italian) obese women (n = 1.000; body mass index, BMI >30 kg/m2; age: 18–83 yrs), subdivided into two subgroups depending on the presence (n = 630) or absence (n = 370) of metabolic syndrome. Parallelly, the percentages of treatment with hypolipidaemic drugs, hypoglycaemics, and antihypertensives and, among the treated subjects, of control of the underlying condition in accordance with the cut-offs of IDF criteria for dyslipidaemia, hyperglycaemia, and hypertension were determined over six age ranges (i.e., 18–30, 31–40, 41–50, 51–60, 61–70, and > 70 yrs). Results. The prevalence of metabolic syndrome increased with advancing age. In the subgroup with metabolic syndrome, an age-dependent increase in HDL-C, glycaemia, and SBP occurred, while the visceral adiposity was stable. In the same subgroup, triglycerides and DBP decreased age-dependently. In the subgroup without metabolic syndrome, an age-dependent increase in WC, HDL-C, glycaemia, SBP, and DBP was observed. A progressive age-dependent increase in the percentage of patients pharmacologically treated for the cardiometabolic abnormalities was detected in patients with metabolic syndrome, a similar trend being also observed in patients without metabolic syndrome only for the antihypertensives. A clear-cut disproportion between treated versus adequately controlled women (with pharmacotherapy) was detected in the whole population. Conclusions. At least in an Italian context of obese females, the age-dependent worsening of glycaemia and BP exerts a fundamental pathophysiological role in the progressive increase of metabolic syndrome with advancing age, which appears to be not adequately treated in a large part of obese subjects. The results of the present study might be useful for public health decision-makers for programming future more extensive and aggressive non-pharmacological and pharmacological interventions in the obese population.

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  • The risk or prevalence of metabolic syndrome increases with age.

    “Data from the National Institute of Health (NIH) indicate that the risk of MetS also increases with age [4].”

    From Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome (2025) · cites this paper as [19]

    The Results (Section 3; page unavailable) report that metabolic syndrome prevalence rose from 25.5% among women aged 18–30 to 71.0% among those over 70. This supports the age-related prevalence claim in the Italian cohort of obese women, but the paper does not present the finding as NIH data. (LLM, conf=0.85)

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Research works printed but not linked: 31

  1. [1]Eckel R. H. Grundy S. M. Zimmet P. Z. The metabolic syndrome The Lancet 2005 365 9468 1415 1428 10.1016/s0140-6736(05)66378-7 2-s2.0-17144380822 15836891 · DOI
  2. [2]Blüher M. Obesity: global epidemiology and pathogenesis Nature Reviews Endocrinology 2019 15 5 288 298 10.1038/s41574-019-0176-8 2-s2.0-85062263392 30814686 · DOI · Paper page
  3. [3]Ripsin C. The metabolic syndrome: underdiagnosed and undertreated Southern Medical Journal 2009 102 12 1194 1195 10.1097/smj.0b013e3181c03173 2-s2.0-73949150471 20016419 · DOI
  4. [4]Haffner S. Taegtmeyer H. Epidemic obesity and the metabolic syndrome Circulation 2003 108 13 1541 1545 10.1161/01.cir.0000088845.17586.ec 2-s2.0-0141506880 14517149 · DOI
  5. [5]Tune J. D. Goodwill A. G. Sassoon D. J. Mather K. J. Cardiovascular consequences of metabolic syndrome Translational Research 2017 183 57 70 10.1016/j.trsl.2017.01.001 2-s2.0-85011269409 28130064 PMC5393930 · DOI
  6. [6]van Namen M. Prendergast L. Peiris C. Supervised lifestyle intervention for people with metabolic syndrome improves outcomes and reduces individual risk factors of metabolic syndrome: a systematic review and meta-analysis Metabolism 2019 101 153988 10.1016/j.metabol.2019.153988 2-s2.0-85074152358 31672441 · DOI
  7. [7]Rigamonti A. E. Cicolini S. Caroli D. Effects of a 3-week in-hospital body weight reduction program on cardiovascular risk factors, muscle performance, and fatigue: a retrospective study in a population of obese adults with or without metabolic syndrome Nutrients 2020 12 1495 PMC7284609 32455545
  8. [8]Rigamonti A. E. Tringali G. Micheli R. Impact of a three-week in-hospital multidisciplinary body weight reduction program on body composition, muscle performance and fatigue in a pediatric obese population with or without metabolic syndrome Nutrients 2020 12 1 208 PMC7019212 31941135
  9. [9]Rigamonti A. E. Agosti F. De Col A. Changes in plasma levels of ghrelin, leptin, and other hormonal and metabolic parameters following standardized breakfast, lunch, and physical exercise before and after a multidisciplinary weight-reduction intervention in obese adolescents Journal of Endocrinological Investigation 2010 33 9 633 639 10.1007/bf03346662 20339311 · DOI
  10. [10]Rask Larsen J. Dima L. Correll C. U. Manu P. The pharmacological management of metabolic syndrome Expert Review of Clinical Pharmacology 2018 11 4 397 410 10.1080/17512433.2018.1429910 2-s2.0-85044310304 29345505 · DOI
  11. [11]Achike F. I. To N.-H. P. Wang H. Kwan C.-Y. Obesity, metabolic syndrome, adipocytes and vascular function: a holistic viewpoint Clinical and Experimental Pharmacology and Physiology 2011 38 1 1 10 10.1111/j.1440-1681.2010.05460.x 2-s2.0-78650670158 21083697 · DOI
  12. [13]Devers M. C. Campbell S. Simmons D. Influence of age on the prevalence and components of the metabolic syndrome and the association with cardiovascular disease BMJ Open Diabetes Research and Care 2016 4 e000195 10.1136/bmjdrc-2016-000195 2-s2.0-85006186496 PMC4853802 27158519 · DOI
  13. [14]Leong K. S. W. Jayasinghe T. N. Wilson B. C. High prevalence of undiagnosed comorbidities among adolescents with obesity Scientific Reports 2020 10 1 20101 PMC7674474 33208826
  14. [15]Chan R. S. M. Woo J. Prevention of overweight and obesity: how effective is the current public health approach International Journal of Environmental Research and Public Health 2010 7 3 765 783 10.3390/ijerph7030765 2-s2.0-77950909739 20617002 PMC2872299 · DOI
  15. [16]Alberti K. G. M. M. Eckel R. H. Grundy S. M. Harmonizing the metabolic syndrome Circulation 2009 120 16 1640 1645 10.1161/circulationaha.109.192644 2-s2.0-70350245011 19805654 · DOI · Paper page
  16. [17]Deurenberg P. International consensus conference on impedance in body composition Age and Nutrition 1994 5 142 145
  17. [18]Radetti G. Fanolla A. Lupi F. Sartorio A. Grugni G. Accuracy of different Indexes of body composition and adiposity in identifying metabolic syndrome in adult subjects with Prader-Willi syndrome, Journal of Clinical Medicine 2020 9 1646 10.3390/jcm9061646 PMC7356766 32486250 · DOI
  18. [19]Rigamonti A. E. Resnik M. Compri E. The cholestyramine-induced decrease of PYY postprandial response is negatively correlated with fat mass in obese women Hormone and Metabolic Research 2011 43 8 569 573 10.1055/s-0031-1280783 2-s2.0-79960530024 21769759 · DOI
  19. [20]Hudish L. I. Reusch J. E. B. Sussel L. β Cell dysfunction during progression of metabolic syndrome to type 2 diabetes Journal of Clinical Investigation 2019 129 10 4001 4008 10.1172/jci129188 2-s2.0-85072791063 PMC6763241 31424428 · DOI
  20. [21]Chait A. den Hartigh L. J. Adipose tissue distribution, inflammation and its metabolic consequences, including diabetes and cardiovascular disease Frontiers in Cardiovascular Medicine 2020 7 22 PMC7052117 32158768
  21. [22]Derby C. A. Crawford S. L. Pasternak R. C. Sowers M. Sternfeld B. Matthews K. A. Lipid changes during the menopause transition in relation to age and weight: the study of women’s health across the nation American Journal of Epidemiology 2009 169 11 1352 1361 10.1093/aje/kwp043 2-s2.0-66249134613 19357323 PMC2727246 · DOI
  22. [23]Stevenson J. C. Tsiligiannis S. Panay N. Cardiovascular risk in perimenopausal women Current Vascular Pharmacology 2019 17 6 591 594 10.2174/1570161116666181002145340 2-s2.0-85073072353 30280667 · DOI
  23. [24]Fernandez M. L. Murillo A. G. Postmenopausal women have higher HDL and decreased incidence of low HDL than premenopausal women with metabolic syndrome Healthcare (Basel) 2016 4 1 20 PMC4934554 27417608
  24. [25]Mendoza M. F. Kachur S. M. Lavie C. J. Hypertension in obesity Current Opinion in Cardiology 2020 35 4 389 396 10.1097/hco.0000000000000749 32398606 · DOI
  25. [26]Athyros V. G. Ganotakis E. S. Bathianaki M. Awareness, treatment and control of the metabolic syndrome and its components: a multicentre Greek study Hellenic Journal of Cardiology 2005 46 380 386 16422124
  26. [27]Seidu S. Walker N. S. Bodicoat D. H. Davies M. J. Khunti K. A systematic review of interventions targeting primary care or community based professionals on cardio-metabolic risk factor control in people with diabetes Diabetes Research and Clinical Practice 2016 113 1 13 10.1016/j.diabres.2016.01.022 2-s2.0-84960099634 26972954 · DOI
  27. [28]Steinman M. A. Seth Landefeld C. Rosenthal G. E. Berthenthal D. Sen S. Kaboli P. J. Polypharmacy and prescribing quality in older people Journal of the American Geriatrics Society 2006 54 10 1516 1523 10.1111/j.1532-5415.2006.00889.x 2-s2.0-33749861027 17038068 · DOI
  28. [29]Kezerle L. Shalev L. Barski L. Treating the elderly diabetic patient: special considerations Diabetes, Metabolic Syndrome and Obesity 2014 7 391 400 PMC4154881 25210468
  29. [30]Bischoff S. C. Boirie Y. Cederholm T. Towards a multidisciplinary approach to understand and manage obesity and related diseases Clinical Nutrition 2017 36 4 917 938 10.1016/j.clnu.2016.11.007 2-s2.0-85007258148 27890486 · DOI
  30. [31]Lopes N. Zanini A. C. Casella-Filho A. Chagas A. C. Metabolic syndrome patient compliance with drug treatment Clinics (Sao Paulo) 2008 63 573 580 10.1590/s1807-59322008000500002 2-s2.0-54349108897 18925314 PMC2664712 · DOI
  31. [32]Farrer S. Beyond Statins: emerging evidence for HDL-increasing therapies and diet in treating cardiovascular disease Advances in Preventive Medicine 2018 2018 9 6024747 10.1155/2018/6024747 PMC6077683 30112217 · DOI

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Authors
Rigamonti, A. E., Cicolini, S., Tamini, S., Caroli, D., Cella, S. G., & Sartorio, A.
Affiliations
  • University of Milan (IT)
  • IRCCS Istituto Auxologico Italiano (IT)
  • Instituto Auxologico Italiano, IRCCS, Experimental Laboratory for Auxo-endocrinological Research, Piancavallo (VB) 28824, Italy
Corresponding author
Rigamonti, Antonello E.
ORCID
Venue
International Journal of Endocrinology
Publisher
Wiley
Published
2021-04-23
Volume
2021
Pages
1-10
ISSN
  • 1687-8337 (ISSN-L)
  • 1687-8345
DOI
10.1155/2021/5576286
PMC
PMC8088356
OpenAlex
W3159386898
Cited by
13
References
2
Citations by year
2026: 2 · 2025: 6 · 2024: 1 · 2023: 1 · 2022: 2 · 2021: 1
FWCI
0.93
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Endocrinology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Istituto Auxologico Italiano
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Menopause: Health Impacts and Treatments
  • Health Sciences › Medicine › Epidemiology › Adipokines, Inflammation, and Metabolic Diseases
Keywords
metabolic syndrome, obesity, women, aging, age-related changes, IDF criteria, dyslipidemia, hyperglycemia, hypertension, visceral fat, waist circumference, HDL cholesterol, triglycerides, antihypertensive therapy, treatment control, cardiometabolic risk factors, non-pharmacological treatment

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