Linear hair growth rates in preschool children.
Journal
Pediatric research
ISSN: 1530-0447
Titre abrégé: Pediatr Res
Pays: United States
ID NLM: 0100714
Informations de publication
Date de publication:
04 Sep 2023
04 Sep 2023
Historique:
received:
13
09
2022
accepted:
15
08
2023
revised:
13
07
2023
medline:
5
9
2023
pubmed:
5
9
2023
entrez:
4
9
2023
Statut:
aheadofprint
Résumé
Human scalp hair is a validated bio-substrate for monitoring various exposures in childhood including contextual stressors, environmental toxins, prescription or non-prescription drugs. Linear hair growth rates (HGR) are required to accurately interpret hair biomarker concentrations. We measured HGR in a prospective cohort of preschool children (N = 266) aged 9-72 months and assessed demographic factors, anthropometrics, and hair protein content (HPC). We examined HGR differences by age, sex, race, height, hair pigment, and season, and used univariable and multivariable linear regression models to identify HGR-related factors. Infants below 1 year (288 ± 61 μm/day) had slower HGR than children aged 2-5 years (p = 0.0073). Dark-haired children (352 ± 52 μm/day) had higher HGR than light-haired children (325 ± 50 μm/day; p = 0.0019). Asian subjects had the highest HGR overall (p = 0.016). Younger children had higher HPC (p = 0.0014) and their HPC-adjusted HGRs were slower than older children (p = 0.0073). Age, height, hair pigmentation, and HPC were related to HGR in multivariable regression models. We identified age, height, hair pigment, and hair protein concentration as significant determinants of linear HGRs. These findings help explain the known hair biomarker differences between children and adults and aid accurate interpretation of hair biomarker results in preschool children. Discovery of hair biomarkers in the past few decades has transformed scientific disciplines like toxicology, pharmacology, epidemiology, forensics, healthcare, and developmental psychology. Identifying determinants of hair growth in children is essential for accurate interpretation of hair biomarker results in pediatric clinical studies. Childhood hair growth rates define the time-periods of biomarker incorporation into growing hair, essential for interpreting the biomarkers associated with environmental exposures and the mind-brain-body connectome. Our study describes age-, sex-, and height-based distributions of linear hair growth rates and provides determinants of linear hair growth rates in a large population of children. Age, height, hair pigmentation, and hair protein content are determinants of hair growth rates and should be accounted for in child hair biomarkers studies. Our findings on hair protein content and linear hair growth rates may provide physiological explanations for differences in hair growth rates and biomarkers in preschool children as compared to adults.
Sections du résumé
BACKGROUND
BACKGROUND
Human scalp hair is a validated bio-substrate for monitoring various exposures in childhood including contextual stressors, environmental toxins, prescription or non-prescription drugs. Linear hair growth rates (HGR) are required to accurately interpret hair biomarker concentrations.
METHODS
METHODS
We measured HGR in a prospective cohort of preschool children (N = 266) aged 9-72 months and assessed demographic factors, anthropometrics, and hair protein content (HPC). We examined HGR differences by age, sex, race, height, hair pigment, and season, and used univariable and multivariable linear regression models to identify HGR-related factors.
RESULTS
RESULTS
Infants below 1 year (288 ± 61 μm/day) had slower HGR than children aged 2-5 years (p = 0.0073). Dark-haired children (352 ± 52 μm/day) had higher HGR than light-haired children (325 ± 50 μm/day; p = 0.0019). Asian subjects had the highest HGR overall (p = 0.016). Younger children had higher HPC (p = 0.0014) and their HPC-adjusted HGRs were slower than older children (p = 0.0073). Age, height, hair pigmentation, and HPC were related to HGR in multivariable regression models.
CONCLUSIONS
CONCLUSIONS
We identified age, height, hair pigment, and hair protein concentration as significant determinants of linear HGRs. These findings help explain the known hair biomarker differences between children and adults and aid accurate interpretation of hair biomarker results in preschool children.
IMPACT
CONCLUSIONS
Discovery of hair biomarkers in the past few decades has transformed scientific disciplines like toxicology, pharmacology, epidemiology, forensics, healthcare, and developmental psychology. Identifying determinants of hair growth in children is essential for accurate interpretation of hair biomarker results in pediatric clinical studies. Childhood hair growth rates define the time-periods of biomarker incorporation into growing hair, essential for interpreting the biomarkers associated with environmental exposures and the mind-brain-body connectome. Our study describes age-, sex-, and height-based distributions of linear hair growth rates and provides determinants of linear hair growth rates in a large population of children. Age, height, hair pigmentation, and hair protein content are determinants of hair growth rates and should be accounted for in child hair biomarkers studies. Our findings on hair protein content and linear hair growth rates may provide physiological explanations for differences in hair growth rates and biomarkers in preschool children as compared to adults.
Identifiants
pubmed: 37667034
doi: 10.1038/s41390-023-02791-z
pii: 10.1038/s41390-023-02791-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.
Références
Niebel, A., Pragst, F., Krumbiegel, F. & Hartwig, S. Prevalence of cathinones and other new psychoactive substances in hair of parents and children of families with known or suspected parental abuse of conventional illegal drugs. Forensic Sci. Int. 331, 111148 (2022).
pubmed: 34923263
doi: 10.1016/j.forsciint.2021.111148
Bhattarai, D. et al. Hair microscopy: an easy adjunct to diagnosis of systemic diseases in children. Appl. Microsc. 51, 18 (2021).
pubmed: 34843009
pmcid: 8630179
doi: 10.1186/s42649-021-00067-6
Plott, T. J. et al. Age-related changes in hair shaft protein profiling and genetically variant peptides. Forensic Sci. Int. Genet. 47, 102309 (2020).
pubmed: 32485593
pmcid: 7388652
doi: 10.1016/j.fsigen.2020.102309
Leonhard, M. J., Chang, E. T., Loccisano, A. E. & Garry, M. R. A systematic literature review of epidemiologic studies of developmental manganese exposure and neurodevelopmental outcomes. Toxicology 420, 46–65 (2019).
pubmed: 30928475
doi: 10.1016/j.tox.2019.03.004
Fabelova, L. et al. Hair concentration of trace elements and growth in homeless children aged <6years: results from the enfams study. Environ. Int. 114, 318–325 (2018).
pubmed: 29150339
doi: 10.1016/j.envint.2017.10.012
Pineiro, X. F. et al. Heavy metal contamination in Peru: implications on children’s health. Sci. Rep. 11, 22729 (2021).
pubmed: 34815466
pmcid: 8611049
doi: 10.1038/s41598-021-02163-9
Takeuchi, H. et al. Lead exposure is associated with functional and microstructural changes in the healthy human brain. Commun. Biol. 4, 912 (2021).
pubmed: 34312468
pmcid: 8313694
doi: 10.1038/s42003-021-02435-0
Lyon-Caen, S. et al. Deciphering the impact of early-life exposures to highly variable environmental factors on foetal and child health: design of sepages couple-child cohort. Int. J. Environ. Res. Public Health 16, 1–29 (2019).
Perng, W. et al. Early life exposure in Mexico to environmental toxicants (element) project. BMJ Open 9, e030427 (2019).
pubmed: 31455712
pmcid: 6720157
doi: 10.1136/bmjopen-2019-030427
DePasquale, C. E. et al. Lifetime stressors, hair cortisol, and executive function: age-related associations in childhood. Dev. Psychobiol. 63, 1043–1052 (2021).
pubmed: 33350468
doi: 10.1002/dev.22076
Gray, N. A. et al. Determinants of hair cortisol concentration in children: a systematic review. Psychoneuroendocrinology 87, 204–214 (2018).
pubmed: 29112905
doi: 10.1016/j.psyneuen.2017.10.022
Slominski, R., Rovnaghi, C. R. & Anand, K. J. S. Methodological considerations for hair cortisol measurements in children. Ther. Drug Monit. 37, 812–820 (2015).
pubmed: 25811341
pmcid: 4581896
doi: 10.1097/FTD.0000000000000209
Anand, K. J. S. et al. Measuring socioeconomic adversity in early life. Acta Paediatr. 108, 1267–1277 (2019).
pubmed: 30614554
pmcid: 6584079
doi: 10.1111/apa.14715
Anand, K. J. S. et al. Demographic and psychosocial factors associated with hair cortisol concentrations in preschool children. Pediatr. Res. 87, 1119–1127 (2020).
pubmed: 31791042
doi: 10.1038/s41390-019-0691-2
Lakshmi Priya, M. D. & Geetha, A. A biochemical study on the level of proteins and their percentage of nitration in the hair and nail of autistic children. Clin. Chim. Acta 412, 1036–1042 (2011).
pubmed: 21338594
doi: 10.1016/j.cca.2011.02.021
Jursa, T., Stein, C. R. & Smith, D. R. Determinants of hair manganese, lead, cadmium and arsenic levels in environmentally exposed children. Toxics 6, 1–15 (2018).
Gareri, J. & Koren, G. Prenatal hair development: implications for drug exposure determination. Forensic Sci. Int. 196, 27–31 (2010).
pubmed: 20074880
doi: 10.1016/j.forsciint.2009.12.024
Courtois, M., Loussouarn, G., Hourseau, S. & Grollier, J. F. Periodicity in the growth and shedding of hair. Br. J. Dermatol. 134, 47–54 (1996).
pubmed: 8745886
doi: 10.1046/j.1365-2133.1996.d01-748.x
Grymowicz, M. et al. Hormonal effects on hair follicles. Int. J. Mol. Sci. 21, 5342 (2020).
pubmed: 32731328
pmcid: 7432488
doi: 10.3390/ijms21155342
Pierard-Franchimont, C., Petit, L., Loussouarn, G., Saint-Leger, D. & Pierard, G. E. The hair eclipse phenomenon: sharpening the focus on the hair cycle chronobiology. Int J. Cosmet. Sci. 25, 295–299 (2003).
pubmed: 18494912
doi: 10.1111/j.1467-2494.2003.00198.x
Van Neste, D. J., de Brouwer, B. & De Coster, W. The phototrichogram: analysis of some technical factors of variation. Ski. Pharm. 7, 67–72 (1994).
doi: 10.1159/000211276
Duggins, O. H. & Trotter, M. Age changes in head hair from birth to maturity. Ii. Medullation in hair of children. Am. J. Phys. Anthropol. 8, 399–415 (1950).
pubmed: 14777371
doi: 10.1002/ajpa.1330080317
Duggins, O. H. & Trotter, M. Changes in morphology of hair during childhood. Ann. N. Y Acad. Sci. 53, 569–575 (1951).
pubmed: 14819883
doi: 10.1111/j.1749-6632.1951.tb31958.x
Trotter, M. & Duggins, O. H. Age changes in head hair from birth to maturity; index and size of hair of children. Am. J. Phys. Anthropol. 6, 489–506 (1948).
pubmed: 18105839
doi: 10.1002/ajpa.1330060417
Trotter, M. & Duggins, O. H. Age changes in head hair from birth to maturity. Am. J. Phys. Anthropol. 8, 467–484 (1950).
pubmed: 14789983
doi: 10.1002/ajpa.1330080417
Liu, C. et al. Changes in Chinese hair growth along a full year. Int. J. Cosmet. Sci. 36, 531–536 (2014).
pubmed: 25065943
doi: 10.1111/ics.12151
Loussouarn, G. et al. Diversity in human hair growth, diameter, colour and shape. an in vivo study on young adults from 24 different ethnic groups observed in the five continents. Eur. J. Dermatol. 26, 144–154 (2016).
pubmed: 27019510
doi: 10.1684/ejd.2015.2726
Randall, V. A. & Ebling, F. J. Seasonal changes in human hair growth. Br. J. Dermatol. 124, 146–151 (1991).
pubmed: 2003996
doi: 10.1111/j.1365-2133.1991.tb00423.x
Eerkens, J. W., Ryder, A., Evoy, A. & Hull, B. Hydrogen isotopes in serial hair samples record season of death in a mummified child from 19th century San Francisco, Ca. Am. J. Phys. Anthropol. 173, 606–614 (2020).
pubmed: 32888333
doi: 10.1002/ajpa.24137
Commo, S. et al. Age-dependent changes in eumelanin composition in hairs of various ethnic origins. Int J. Cosmet. Sci. 34, 102–107 (2012).
pubmed: 22017184
doi: 10.1111/j.1468-2494.2011.00691.x
Sarifakioglu, E., Yilmaz, A. E., Gorpelioglu, C. & Orun, E. Prevalence of scalp disorders and hair loss in children. Cutis. 90, 225–229 (2012).
pubmed: 23270190
Khumalo, N. P. & Gumedze, F. African hair length in a school population: a clue to disease pathogenesis? J. Cosmet. Dermatol. 6, 144–151 (2007).
pubmed: 17760690
doi: 10.1111/j.1473-2165.2007.00326.x
Alves, R. & Grimalt, R. Hair loss in children. Curr. Probl. Dermatol. 47, 55–66 (2015).
pubmed: 26370644
doi: 10.1159/000369405
Dawber, R. P. The embryology and development of human scalp hair. Clin. Dermatol. 6, 1–6 (1988).
pubmed: 3063365
doi: 10.1016/0738-081X(88)90059-4
Das-Chaudhuri, A. B. & Chopra, V. P. Variation in hair histological variables: medulla and diameter. Hum. Hered. 34, 217–221 (1984).
pubmed: 6479986
doi: 10.1159/000153466
Wynkoop, E. M. A study of the age correlations of the cuticular scales, medullas, and shaft diameters of human head hair. Am. J. Phys. Anthropol. 13, 177–188 (1929).
doi: 10.1002/ajpa.1330130222
Smith, D. W. & Gong, B. T. Scalp hair patterning as a clue to early fetal brain development. J. Pediatr. 83, 374–380 (1973).
pubmed: 4269340
doi: 10.1016/S0022-3476(73)80258-6
Barth, J. H. Normal hair growth in children. Pediatr. Dermatol. 4, 173–184 (1987).
pubmed: 3321003
doi: 10.1111/j.1525-1470.1987.tb00775.x
Baque, C. S. et al. Relationships between hair growth rate and morphological parameters of human straight hair: a same law above ethnical origins? Int J. Cosmet. Sci. 34, 111–116 (2012).
pubmed: 21951315
doi: 10.1111/j.1468-2494.2011.00687.x
Van Neste, D. Thickness, medullation and growth rate of female scalp hair are subject to significant variation according to pigmentation and scalp location during ageing. Eur. J. Dermatol. 14, 28–32 (2004).
pubmed: 14965792
Kirschbaum, C., Tietze, A., Skoluda, N. & Dettenborn, L. Hair as a retrospective calendar of cortisol production-increased cortisol incorporation into hair in the third trimester of pregnancy. Psychoneuroendocrinology 34, 32–37 (2009).
pubmed: 18947933
doi: 10.1016/j.psyneuen.2008.08.024
de Kruijff, I. et al. Lc-Ms/Ms-based reference intervals for hair cortisol in healthy children. Psychoneuroendocrinology 112, 104539 (2020).
pubmed: 31841987
doi: 10.1016/j.psyneuen.2019.104539
Neumann, A. et al. Predicting hair cortisol levels with hair pigmentation genes: a possible hair pigmentation bias. Sci. Rep. 7, 8529 (2017).
pubmed: 28819144
pmcid: 5561185
doi: 10.1038/s41598-017-07034-w
Buffoli, B. et al. The human hair: from anatomy to physiology. Int J. Dermatol. 53, 331–341 (2014).
pubmed: 24372228
doi: 10.1111/ijd.12362
Pecoraro, V., Astore, I., Barman, J. & Ignacioaraujo, C. The normal trichogram in the child before the age of puberty. J. Invest Dermatol. 42, 427–430 (1964).
pubmed: 14172199
doi: 10.1038/jid.1964.92
Nicolaides, N. & Rothman, S. Studies on the chemical composition of human hair fat. I. The squalene-cholesterol relationship in children and adults. J. Invest Dermatol. 19, 389–391 (1952).
pubmed: 13023010
doi: 10.1038/jid.1952.113
Adav, S. S. et al. Studies on the proteome of human hair—identification of histones and deamidated keratins. Sci. Rep. 8, 1599 (2018).
pubmed: 29371649
pmcid: 5785504
doi: 10.1038/s41598-018-20041-9
Cruz, C. F. et al. Keratins and lipids in ethnic hair. Int J. Cosmet. Sci. 35, 244–249 (2013).
pubmed: 23301816
doi: 10.1111/ics.12035
Nicolaides, N. & Rothman, S. Studies on the chemical composition of human hair fat. II. The overall composition with regard to age, sex and race. J. Invest Dermatol. 21, 9–14 (1953).
pubmed: 13069836
doi: 10.1038/jid.1953.63
Robbins, C. R. Chemical and Physical Behavior of Human Hair: Chapter 2. Chemical Composition of Different Hair Types 4th edn, 105–176 (Springer, 2002).
Wang, B., Fang, Y. & Jin, M. Combining type-III analyses from multiple imputations. SAS J. 14, 1543 https://Support.Sas.Com/Resources/Papers/Proceedings14/1543-2014.Pdf (2015).
Mishra, P. et al. Descriptive statistics and normality tests for statistical data. Ann. Card. Anaesth. 22, 67–72 (2019).
pubmed: 30648682
pmcid: 6350423
doi: 10.4103/aca.ACA_157_18
Janniger, C. K. & Bryngil, J. M. Hair in infancy and childhood. Cutis 51, 336–338 (1993).
pubmed: 8513685
Furdon, S. A. & Clark, D. A. Scalp hair characteristics in the newborn infant. Adv. Neonatal Care 3, 286–296 (2003).
pubmed: 14695500
doi: 10.1016/j.adnc.2003.09.005
Courtois, M., Loussouarn, G., Hourseau, C. & Grollier, J. F. Ageing and hair cycles. Br. J. Dermatol. 132, 86–93 (1995).
pubmed: 7756156
doi: 10.1111/j.1365-2133.1995.tb08630.x
Halloy, J., Bernard, B. A., Loussouarn, G. & Goldbeter, A. Modeling the dynamics of human hair cycles by a follicular automaton. Proc. Natl Acad. Sci. USA 97, 8328–8333 (2000).
pubmed: 10899998
pmcid: 26947
doi: 10.1073/pnas.97.15.8328
Thom, E. Stress and the hair growth cycle: cortisol-induced hair growth disruption. J. Drugs Dermatol. 15, 1001–1004 (2016).
pubmed: 27538002
Neuberger, F. M., Jopp, E., Graw, M., Puschel, K. & Grupe, G. Signs of malnutrition and starvation–reconstruction of nutritional life histories by serial isotopic analyses of hair. Forensic Sci. Int. 226, 22–32 (2013).
pubmed: 23374882
doi: 10.1016/j.forsciint.2012.10.037
Morgan, M. D. et al. Genome-wide study of hair colour in Uk Biobank explains most of the SNP heritability. Nat. Commun. 9, 5271 (2018).
pubmed: 30531825
pmcid: 6288091
doi: 10.1038/s41467-018-07691-z
De la Mettrie, R. et al. Shape variability and classification of human hair: a worldwide approach. Hum. Biol. 79, 265–281 (2007).
pubmed: 18078200
doi: 10.1353/hub.2007.0045