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Estimation of the proportion of metabolic syndrome-free subjects on high cardiometabolic risk using two continuous cardiometabolic risk scores: a cross-sectional study in 16- to 20-year-old individuals

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Abstract

In contrast to the dichotomous classification of metabolic syndrome, continuous metabolic syndrome scores enable to assess cardiometabolic burden in metabolic syndrome-free individuals. Using receiver operating characteristics analysis, discrimination power of continuous metabolic syndrome score calculated from population-based Z-scores or individual measures corrected to the accepted international standards for presence/absence of metabolic syndrome was assessed. Calculated cutoff values were used to estimate the proportions of metabolic syndrome-free subjects presenting high cardiometabolic risk. Clinical data were collected from 2331 (52% females) 16- to 20-year-old subjects. Receiver operating characteristics analyses showed an acceptable performance of both scores to classify metabolic syndrome presence: area under the curve (97–98%), sensitivity (95–100%), and specificity (86–96%). Compared with the prevalence of metabolic syndrome, proportions of metabolic syndrome-free subjects on high cardiometabolic risk, e.g., presenting continuous scores ≥ cutoff points, were about 3-fold higher in males, and 4-fold higher in females. Both scores correlated significantly with markers of cardiometabolic risk.

Conclusion: Continuous cardiometabolic syndrome scores are practical tools to evaluate cardiometabolic risk in subjects not presenting metabolic syndrome. Accuracy, simplicity, and ability to classify metabolic syndrome-free subjects on high cardiometabolic risk make continuous metabolic syndrome score derived from international standards convenient for use in research and clinical practice.

What is Known:

• Dichotomous classification of metabolic syndrome is simple but not suitable for assessment of cardiometabolic burden in metabolic syndrome-free subjects. This prompted implementation of continuous scores, which are generally sample-specific. Score based on internationally accepted standards allows for comparison between populations and studies.

• The performance of different continuous metabolic syndrome scores to assess the prevalence of metabolic syndrome-free subjects presenting high cardiometabolic burden has not been compared yet.

What is New:

• We compared the discrimination power of sample-specific Z-score-derived continuous metabolic syndrome score and that calculated based on internationally accepted standards for presence or absence of metabolic syndrome in young subjects.

• The prevalence of metabolic syndrome-free subjects presenting high cardiometabolic risk was estimated using the cutoff points of continuous metabolic syndrome scores derived from the analyses of receiver operating characteristic curves.

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Abbreviations

AIP:

Atherogenic index of plasma

ANOVA:

Analysis of variance

AUC:

Area under the curve

BMI:

Body mass index

BP:

Blood pressure

CI:

Confidence interval

DBP:

Diastolic blood pressure

eGFR:

Estimated glomerular filtration rate

FPG:

Fasting plasma glucose

FPI:

Fasting plasma insulin

GLM:

General linear model

HDL-C:

High-density lipoprotein cholesterol

hsCRP:

High-sensitivity C-reactive protein

LDL-C:

Low-density lipoprotein cholesterol

MS:

Metabolic syndrome

OR:

Odds ratio

QUICKI:

Quantitative insulin sensitivity check index

RF:

Risk factor

ROC:

Receiver operating characteristic

SBP:

Systolic blood pressure

SD:

Standard deviation

siMSS:

Continuous metabolic syndrome score calculated using individual measures corrected to the accepted international standards

TAG:

Triacylglycerols

WHtR:

Waist-to-height ratio

Z-MSS:

Population-derived continuous metabolic syndrome score calculated using Z-scores

References

  1. Papakonstantinou E, Lambadiari V, Dimitriadis G, Zampelas A (2013) Metabolic syndrome and cardiometabolic risk factors. Curr Vasc Pharmacol 11:858–879. https://doi.org/10.2174/15701611113116660176

    Article  CAS  PubMed  Google Scholar 

  2. Ford ES (2005) Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome. Diabetes Care 28:1769–1778. https://doi.org/10.2337/diacare.28.7.1769

    Article  PubMed  Google Scholar 

  3. Zimmet P, George K, Alberti MM, Kaufman F, Tajima N, Silink M, Arslanian S, Wong G, Bennett P, Shaw J, Caprio S, Grp IDFC (2007) The metabolic syndrome in children and adolescents - an IDF consensus report. Pediatr Diabetes 8:299–306. https://doi.org/10.1111/j.1399-5448.2007.00271.x

    Article  Google Scholar 

  4. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart JC, James WPT, Loria CM, Smith SC (2009) Harmonizing the metabolic syndrome a joint interim statement of the international diabetes federation task force on epidemiology and prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 120:1640–1645. https://doi.org/10.1161/circulationaha.109.192644

    Article  CAS  Google Scholar 

  5. Vanlancker T, Schaubroeck E, Vyncke K, Cadenas-Sanchez C, Breidenassel C, Gonzalez-Gross M, Gottrand F, Moreno LA, Beghin L, Molnar D, Manios Y, Gunter MJ, Widhalm K, Leclercq C, Dallongeville J, Ascension M, Kafatos A, Castillo MJ, De Henauw S, Ortega FB, Huybrechts I, Grp HP (2017) Comparison of definitions for the metabolic syndrome in adolescents. The HELENA study. Eur J Pediatr 176:241–252. https://doi.org/10.1007/s00431-016-2831-6

    Article  Google Scholar 

  6. Reuter CP, Burgos M, Barbian C, Renner J, Franke S, de Mello ED (2018) Comparison between different criteria for metabolic syndrome in schoolchildren from southern Brazil. Eur J Pediatr 177:1471–1477. https://doi.org/10.1007/s00431-018-3202-2

    Article  PubMed  Google Scholar 

  7. Wijndaele K, Beunen G, Duvigneaud N, Matton L, Duquet W, Thomis M, Lefevre J, Philippaerts RM (2006) A continuous metabolic syndrome risk score - utility for epidemiological analyses. Diabetes Care 29:2329–2329. https://doi.org/10.2337/dc06-1341

    Article  PubMed  Google Scholar 

  8. Eisenmann JC (2008) On the use of a continuous metabolic syndrome score in pediatric research. Cardiovasc Diabetol 7:17. https://doi.org/10.1186/1475-2840-7-17

    Article  PubMed  PubMed Central  Google Scholar 

  9. DeBoer MD, Gurka MJ (2017) Clinical utility of metabolic syndrome severity scores: considerations for practitioners. Diabetes Metab Syndr Obes 10:65–72. https://doi.org/10.2147/dmso.s101624

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Heshmat R, Heidari M, Ejtahed HS, Motlagh ME, Mahdavi-Gorab A, Ziaodini H, Taheri M, Shafiee G, Beshtar S, Qorbani M, Kelishadi R (2017) Validity of a continuous metabolic syndrome score as an index for modeling metabolic syndrome in children and adolescents: the CASPIAN-V study. Diabetol Metab Syndr 9:89. https://doi.org/10.1186/s13098-017-0291-4

    Article  PubMed  PubMed Central  Google Scholar 

  11. Andersen LB, Lauersen JB, Brond JC, Anderssen SA, Sardinha LB, Steene-Johannessen J, McMurray RG, Barros MVG, Kriemler S, Moller NC, Bugge A, Kristensen PL, Ried-Larsen M, Grontved A, Ekelund U (2015) A new approach to define and diagnose cardiometabolic disorder in children. J Diabetes Res 2015:539835. https://doi.org/10.1155/2015/539835

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Pandit D, Chiplonkar S, Khadilkar A, Kinare A, Khadilkar V (2011) Efficacy of a continuous metabolic syndrome score in Indian children for detecting subclinical atherosclerotic risk. Int J Obes 35:1318–1324. https://doi.org/10.1038/ijo.2011.138

    Article  CAS  Google Scholar 

  13. Olza J, Aguilera CM, Gil-Campos M, Leis R, Bueno G, Valle M, Canete R, Tojo R, Moreno LA, Gil A (2015) A continuous metabolic syndrome score is associated with specific biomarkers of inflammation and CVD risk in prepubertal children. Ann Nutr Metab 66:72–79. https://doi.org/10.1159/000369981

    Article  CAS  PubMed  Google Scholar 

  14. Hesse MB, Young G, Murray RD (2016) Evaluating health risk using a continuous metabolic syndrome score in obese children. J Pediatr Endocrinol Metab 29:451–458. https://doi.org/10.1515/jpem-2015-0271

    Article  CAS  PubMed  Google Scholar 

  15. Prochotska K, Kovacs L, Vitariusova E, Feber J (2016) Is arterial stiffness predicted by continuous metabolic syndrome score in obese children? J Am Soc Hypertens 10:47–54. https://doi.org/10.1016/j.jash.2015.10.011

    Article  PubMed  Google Scholar 

  16. Soldatovic I, Vukovic R, Culafic D, Gajic M, Dimitrijevic-Sreckovic V (2016) siMS score: simple method for quantifying metabolic syndrome. PLoS One 11:e0146143. https://doi.org/10.1371/journal.pone.0146143

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Ashwell M, Gibson S (2014) A proposal for a primary screening tool: ‘Keep your waist circumference to less than half your height’. BMC Med 12:207. https://doi.org/10.1186/s12916-014-0207-1

    Article  PubMed  PubMed Central  Google Scholar 

  18. Rochlani Y, Pothineni NV, Mehta JL (2015) Metabolic syndrome: does it differ between women and men? Cardiovasc Drugs Ther 29:329–338. https://doi.org/10.1007/s10557-015-6593-6

    Article  CAS  PubMed  Google Scholar 

  19. Pradhan AD (2014) Sex differences in the metabolic syndrome: implications for cardiovascular health in women. Clin Chem 60:44–52. https://doi.org/10.1373/clinchem.2013.202549

    Article  CAS  PubMed  Google Scholar 

  20. Gurecka R, Koborova I, Sebek J, Sebekova K (2015) Presence of cardiometabolic risk factors is not associated with microalbuminuria in 14-to-20-years old Slovak adolescents: a cross-sectional, population study. PLoS One 10:e0129311. https://doi.org/10.1371/journal.pone.0129311

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Friedewald WT, Levy RI, Fredrickson DS (1972) Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 18:499–502

    CAS  Google Scholar 

  22. Dobiasova M, Frohlich J (2001) The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER (HDL)). Clin Biochem 34:583–588. https://doi.org/10.1016/S0009-9120(01)00263-6

    Article  CAS  PubMed  Google Scholar 

  23. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, Quon MJ (2000) Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 85:2402–2410. https://doi.org/10.1210/jcem.85.7.6661

    Article  CAS  Google Scholar 

  24. Schwartz GJ, Munoz A, Schneider MF, Mak RH, Kaskel F, Warady BA, Furth SL (2009) New equations to estimate GFR in children with CKD. J Am Soc Nephrol 20:629–637. https://doi.org/10.1681/asn.2008030287

    Article  PubMed  PubMed Central  Google Scholar 

  25. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D, Modification Diet Renal Dis Study G (1999) A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 130:461–470. https://doi.org/10.7326/0003-4819-130-6-199903160-00002

    Article  Google Scholar 

  26. Okosun IS, Lyn R, Davis-Smith M, Eriksen M, Seale P (2010) Validity of a continuous metabolic risk score as an index for modeling metabolic syndrome in adolescents. Ann Epidemiol 20:843–851. https://doi.org/10.1016/j.annepidem.2010.08.001

    Article  PubMed  Google Scholar 

  27. Eisenmann JC, Laurson KR, DuBose KD, Smith BK, Donnelly JE (2010) Construct validity of a continuous metabolic syndrome score in children. Diabetol Metab Syndr 2:8. https://doi.org/10.1186/1758-5996-2-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Vukovic R, Milenkovic T, Stojan G, Vukovic A, Mitrovic K, Todorovic S, Soldatovic I (2017) Pediatric siMS score: a new, simple and accurate continuous metabolic syndrome score for everyday use in pediatrics. PLoS One 12:e0189232. https://doi.org/10.1371/journal.pone.0189232

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kelly AS, Steinberger J, Jacobs DR, Hong CP, Moran A, Sinaiko AR (2011) Predicting cardiovascular risk in young adulthood from the metabolic syndrome, its component risk factors, and a cluster score in childhood. Int J Pediatr Obes 6:e283–e289. https://doi.org/10.3109/17477166.2010.528765

    Article  PubMed  Google Scholar 

  30. Viitasalo A, Lakka TA, Laaksonen DE, Savonen K, Lakka H-M, Hassinen M, Komulainen P, Tompuri T, Kurl S, Laukkanen JA, Rauramaa R (2014) Validation of metabolic syndrome score by confirmatory factor analysis in children and adults and prediction of cardiometabolic outcomes in adults. Diabetologia 57:940–949. https://doi.org/10.1007/s00125-014-3172-5

    Article  CAS  PubMed  Google Scholar 

  31. DeBoer MD, Gurka MJ, Golden SH, Musani SK, Sims M, Vishnu A, Guo Y, Pearson TA (2017) Independent associations between metabolic syndrome severity & future coronary heart disease by sex and race. J Am Coll Cardiol 69:1204–1205. https://doi.org/10.1016/j.jacc.2016.10.088

    Article  PubMed  PubMed Central  Google Scholar 

  32. Gurka MJ, Golden SH, Musani SK, Sims M, Vishnu A, Guo Y, Cardel M, Pearson TA, DeBoer MD (2017) Independent associations between a metabolic syndrome severity score and future diabetes by sex and race: the Atherosclerosis Risk in Communities Study and Jackson Heart Study. Diabetologia 60:1261–1270. https://doi.org/10.1007/s00125-017-4267-6

    Article  PubMed  PubMed Central  Google Scholar 

  33. Jung KJ, Jee YH, Jee SH (2017) Metabolic risk score and vascular mortality among Korean adults. Asia Pac J Public Health 29:122–131. https://doi.org/10.1177/1010539516688082

    Article  PubMed  Google Scholar 

  34. Gurka MJ, Guo Y, Filipp SL, DeBoer MD (2018) Metabolic syndrome severity is significantly associated with future coronary heart disease in type 2 diabetes. Cardiovasc Diabetol 17:17. https://doi.org/10.1186/s12933-017-0647-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hillier TA, Rousseau A, Lange C, Lepinay P, Cailleau M, Novak M, Calliez E, Ducimetiere P, Balkau B (2006) Practical way to assess metabolic syndrome using a continuous score obtained from principal components analysis. Diabetologia 49:1528–1535. https://doi.org/10.1007/s00125-006-0266-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Okosun IS, Boltri JM, Lyn R, Davis-Smith M (2010) Continuous metabolic syndrome risk score, body mass index percentile, and leisure time physical activity in American children. J Clin Hypertens 12:636–644. https://doi.org/10.1111/j.1751-7176.2010.00338.x

    Article  Google Scholar 

  37. Hu W, Wu XJ, Ni YJ, Hao HR, Yu WN, Zhou HW (2017) Metabolic syndrome is independently associated with a mildly reduced estimated glomerular filtration rate: a cross-sectional study. BMC Nephrol 18:192. https://doi.org/10.1186/s12882-017-0597-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

The authors wish to thank all those who put their effort toward the accomplishment of the Respect for Health study.

Funding

This study was funded by The Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic and the Slovak Academy of Sciences (APVV), grant no. 1/0637/13; The Slovak Research and Development Agency (VEGA), grant no. 0447–12; and Bratislava Self-governing Region. The sponsors had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

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Authors and Affiliations

  1. Institute of Molecular BioMedicine, Faculty of Medicine, Comenius University, Sasinkova 4, 811 08, Bratislava, Slovakia

    Katarína Šebeková, Radana Gurecká, Melinda Csongová & Ivana Koborová

  2. Institute of Medical Physics, Biophysics, Informatics and Telemedicine, Faculty of Medicine, Comenius University, Bratislava, Slovakia

    Radana Gurecká

  3. Institute of Materials & Machine Mechanics, Slovak Academy of Sciences, Bratislava, Slovakia

    Jozef Šebek

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  1. Katarína Šebeková
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Contributions

Conceived and designed the study: KS. Performed the measurements: RG, IK, MSc. Analyzed the data: JS. Wrote the first draft: KS. All coauthors read and approved the manuscript.

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Correspondence to Katarína Šebeková.

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The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in this study were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and later amendments thereto or the comparable ethical standards.

Informed consent

A written informed consent was obtained from full-aged participants. In subjects under 18 years of age, their verbal assent and written consent from a parent or legal guardian were acquired.

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Communicated by Mario Bianchetti

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Šebeková, K., Gurecká, R., Csongová, M. et al. Estimation of the proportion of metabolic syndrome-free subjects on high cardiometabolic risk using two continuous cardiometabolic risk scores: a cross-sectional study in 16- to 20-year-old individuals. Eur J Pediatr 178, 1243–1253 (2019). https://doi.org/10.1007/s00431-019-03402-y

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