April 27, 2022
Written by

Luke Marshall, PhD

Spirometry Predictive Equations: NHANES III vs. GLI 12

Author's Note: The following is an excerpt from the "Spirometry Predictive Equations: NHANES III vs. GLI 12 Report" compiled by myself, Dr. Wes Sublett, and Dr. Todd Rowland. To access the complete guide to understanding these standards and how to decide which is most appropriate for your patients, please follow this link.

The National Health and Nutrition Examination Survey (NHANES) III and the ERS Global Lung Initiative 2012 (GLI 12) predictive equations for spirometry are both based on regression equations that use demographic and physiological factors (such as height, age, and sex) to predict forced expiratory volume in one second (FEV1). For most patients, there is general agreement between NHANES III and GLI 12 spirometry prediction equations [1].


Based on data obtained using equipment and techniques that met or exceeded the ATS guidelines, NHANES III included 7429 subjects aged 8-80 years old [3]. Notably, the NHANES III polynomial model development used age and height as predictors for three specific US-based populations: Caucasian, African-American, and Mexican-American [3]. While the ATS/ERS task force does recommend extrapolating the NHANES III prediction equations for patients older than 80 years, it is common in clinical practice [2,4].

GLI 12

The GLI task force developed spirometric reference equations for ages 3–95 years using the generalized additive models for location, scale, and shape approach, resulting in the GLI 12 prediction equations [5-8]. The supporting data were collected in a number of international studies (73 centers globally), including NHANES III data, and are intended to be applied globally [5]. There were 74,187 subjects aged 2.5-95 years included in the GLI 12 spirometric prediction equations, and they were developed for four specific populations: Caucasian, Black, North-East Asian and South-East Asian [4]. The GLI 12 predictive equations are endorsed by a number of respiratory societies around the world.


  1. Linares-Perdomo, et al. Comparison of NHANES III and ERS/GLI 12 for airway obstruction classification and severity. Eur Respir J 2016; 48: 133–141.
  2. Pellegrino R, Viegi G, Brusasco V, et al. Interpretative strategies for lung function tests. Eur Respir J 2005; 26: 948–968.
  3. Hankinson JL, Odencrantz JR, Fedan KB. Spirometric reference values from a sample of the general U.S. population. Am J Respir Crit Care Med 1999; 159: 179–187.
  4. Miller MR, Thinggaard M, Christensen K, et al. Best lung function equations for the very elderly selected by survival analysis. Eur Respir J 2014; 43: 1338–1346.
  5. Quanjer PH, Stanojevic S, Cole TJ, et al. Multi-ethnic reference values for spirometry for the 3–95-yr age range: the global lung function 2012 equations. Eur Respir J 2012; 40: 1324–1343.
  6. Quanjer PH, Hall GL, Stanojevic S, et al. Age- and height-based prediction bias in spirometry reference equations. Eur Respir J 2012; 40: 190–197.
  7. Stanojevic S, Wade A, Stocks J, et al. Reference ranges for spirometry across all ages: a new approach. Am J Respir Crit Care Med 2008; 177: 253–260.
  8. Rigby RA, Stasinopoulos DM. Generalized additive models for location, scale and shape. Appl Statist 2005; 54: 507–554.
  9. Braun L, Wolfgang M, Dickersin K. Defining race/ethnicity and explaining difference in research studies on lung function. Eur Respir J. 2013;41(6):1362–1370.
  10. Robey, N. Race in Spirometry (Part II) The Predictive Formulas. VitalFlo Health 2020. URL: https://www.vitalflohealth.com/post/race-in-spirometry-part-ii. Last accessed April 8, 2022.
Written by

Luke Marshall, PhD