A Method for Calculating BMI Z-scores and Percentiles Above the 95 Percentile of the CDC Growth Charts

Overview

Journal Ann Hum Biol

Specialty Biology

Date 2020 Sep 9

PMID 32901504

Citations 22

Authors

Rong Wei

Cynthia L Ogden

Van L Parsons

David S Freedman

Craig M Hales

Affiliations

Soon will be listed here.

Abstract

Background: The 2000 CDC growth charts are based on national data collected between 1963 and 1994 and include a set of selected percentiles between the 3 and 97 and LMS parameters that can be used to obtain other percentiles and associated z-scores. Obesity is defined as a sex- and age-specific body mass index (BMI) at or above the 95 percentile. Extrapolating beyond the 97 percentile is not recommended and leads to compressed z-score values.

Aim: This study attempts to overcome this limitation by constructing a new method for calculating BMI distributions above the 95 percentile using an extended reference population.

Subjects And Methods: Data from youth at or above the 95 percentile of BMI-for-age in national surveys between 1963 and 2016 were modelled as half-normal distributions. Scale parameters for these distributions were estimated at each sex-specific 6-month age-interval, from 24 to 239 months, and then smoothed as a function of age using regression procedures.

Results: The modelled distributions above the 95 percentile can be used to calculate percentiles and non-compressed z-scores for extreme BMI values among youth.

Conclusion: This method can be used, in conjunction with the current CDC BMI-for-age growth charts, to track extreme values of BMI among youth.

Citing Articles

Early Reinitiation of Obesity Pharmacotherapy Post Laparoscopic Sleeve Gastrectomy in Youth: A Retrospective Cohort Study.

Vidmar A, Vu M, Martin M, Kim A, Abel S, Weitzner M Obes Surg. 2025; 35(2):406-418.

PMID: 39798049 PMC: 11835899. DOI: 10.1007/s11695-024-07658-8.

Postoperative complications in the pediatric population. Malnutrition or phase angle? Which one do we use?.

Diaz-Amaya M, Rosales-Arreola L, Hernandez-Licona J, Perez-Guille B, Ignorosa-Arellano K, Carmona-Librado S Front Nutr. 2024; 11:1474616.

PMID: 39439520 PMC: 11493692. DOI: 10.3389/fnut.2024.1474616.

Genetic associations with neural reward responsivity to food cues in children.

Yeum D, Renier T, Carlson D, Ballarino G, Lansigan R, Meyer M Front Nutr. 2024; 11:1387514.

PMID: 39385774 PMC: 11461328. DOI: 10.3389/fnut.2024.1387514.

Association between endogenous sex hormones and adiposity in youth across a weight status spectrum.

Jang S, Ryder J, Kelly A, Bomberg E Pediatr Res. 2024; .

PMID: 39294241 DOI: 10.1038/s41390-024-03578-6.

The relationship between sphingomyelin and ceramide levels and soft neurological signs in ADHD.

Gulec A, Turkoglu S, Kocabas R J Neural Transm (Vienna). 2024; 132(1):157-168.

PMID: 39249516 DOI: 10.1007/s00702-024-02831-w.

References

Cole T, Green P . Smoothing reference centile curves: the LMS method and penalized likelihood. Stat Med. 1992; 11(10):1305-19. DOI: 10.1002/sim.4780111005. View

Cole T . Extreme percentiles of the 2000 Centers for Disease Control and Prevention BMI chart and the LMS method. Am J Clin Nutr. 2010; 91(3):814. DOI: 10.3945/ajcn.2009.28928. View

Ogden C, Flegal K . Changes in terminology for childhood overweight and obesity. Natl Health Stat Report. 2010; (25):1-5. View

Kuczmarski R, Ogden C, Guo S, Grummer-Strawn L, Flegal K, Mei Z . 2000 CDC Growth Charts for the United States: methods and development. Vital Health Stat 11. 2002; (246):1-190. View

Cole T . The LMS method for constructing normalized growth standards. Eur J Clin Nutr. 1990; 44(1):45-60. View

Freedman D, Butte N, Taveras E, Goodman A, Blanck H . Longitudinal changes in BMI z-scores among 45 414 2-4-year olds with severe obesity. Ann Hum Biol. 2017; 44(8):687-692. PMC: 5765752. DOI: 10.1080/03014460.2017.1388845. View

Flegal K, Cole T . Construction of LMS parameters for the Centers for Disease Control and Prevention 2000 growth charts. Natl Health Stat Report. 2014; (63):1-3. View

Freedman D, Butte N, Taveras E, Lundeen E, Blanck H, Goodman A . BMI z-Scores are a poor indicator of adiposity among 2- to 19-year-olds with very high BMIs, NHANES 1999-2000 to 2013-2014. Obesity (Silver Spring). 2017; 25(4):739-746. PMC: 5373980. DOI: 10.1002/oby.21782. View

Woo J . Using body mass index Z-score among severely obese adolescents: a cautionary note. Int J Pediatr Obes. 2009; 4(4):405-10. DOI: 10.3109/17477160902957133. View

10.

Berkey C, Colditz G . Adiposity in adolescents: change in actual BMI works better than change in BMI z score for longitudinal studies. Ann Epidemiol. 2006; 17(1):44-50. DOI: 10.1016/j.annepidem.2006.07.014. View

11.

C Grossman D, Bibbins-Domingo K, Curry S, Barry M, Davidson K, Doubeni C . Screening for Obesity in Children and Adolescents: US Preventive Services Task Force Recommendation Statement. JAMA. 2017; 317(23):2417-2426. DOI: 10.1001/jama.2017.6803. View

12.

Cole T, Faith M, Pietrobelli A, Heo M . What is the best measure of adiposity change in growing children: BMI, BMI %, BMI z-score or BMI centile?. Eur J Clin Nutr. 2005; 59(3):419-25. DOI: 10.1038/sj.ejcn.1602090. View

13.

Flegal K, Wei R, Ogden C, Freedman D, Johnson C, Curtin L . Characterizing extreme values of body mass index-for-age by using the 2000 Centers for Disease Control and Prevention growth charts. Am J Clin Nutr. 2009; 90(5):1314-20. DOI: 10.3945/ajcn.2009.28335. View

14.

Freedman D, Butte N, Taveras E, Goodman A, Ogden C, Blanck H . The Limitations of Transforming Very High Body Mass Indexes into z-Scores among 8.7 Million 2- to 4-Year-Old Children. J Pediatr. 2017; 188:50-56.e1. PMC: 5572545. DOI: 10.1016/j.jpeds.2017.03.039. View

15.

Freedman D, Woo J, Ogden C, Xu J, Cole T . Distance and percentage distance from median BMI as alternatives to BMI score. Br J Nutr. 2019; 124(5):493-500. PMC: 7035962. DOI: 10.1017/S0007114519002046. View

16.

Freedman D, Berenson G . Tracking of BMI Scores for Severe Obesity. Pediatrics. 2017; 140(3). PMC: 5574726. DOI: 10.1542/peds.2017-1072. View