Puberty Timing and Adiposity Change Across Childhood and Adolescence

Disentangling Cause and Consequence

Linda M. O'Keeffe; Monika Frysz; Joshua A. Bell; Laura D. Howe; Abigail Fraser

Disclosures

Hum Reprod. 2020;35(12):2784-2792. 

In This Article

Discussion

This study aimed to better understand the nature of puberty timing and adiposity change by examining associations of an objective height-based measure of puberty timing (aPHV) with change in DXA-measured total body fat mass repeatedly measured throughout childhood and adolescence. In females, our findings suggest that earlier puberty timing is more likely to be the result of adiposity gain in childhood than a cause of adiposity gain in adulthood. In males, findings suggest that childhood adiposity may also contribute to early puberty timing and that differences in fat mass after puberty are driven partially by tracking of adiposity from early childhood but also greater gains in post-pubertal adiposity in males early to puberty. Taken together, the findings suggest that reducing levels of childhood adiposity may help to prevent earlier puberty, adult adiposity and its adverse health and social outcomes.

Comparison With Other Studies

Our findings in females are consistent with several previous studies showing inverse associations of pre-pubertal BMI and puberty timing (Wang, 2002; Buyken et al., 2008; Kaplowitz, 2008; Silventoinen et al., 2008). For example, in a Swedish study (N = 3650) which used growth data from age 7 to 18 years to estimate aPHV, faster rates of gain in BMI from 2 to 8 years were associated with earlier puberty in females (He and Karlberg, 2001). In a Danish study (N = 156 835), BMI at 7 years was associated with earlier puberty in females, based on onset of growth spurt and aPHV (Aksglaede et al., 2009). These findings are also comparable to a study from the Cardiovascular Risk in Young Finns study (N = 794) which concluded that greater childhood BMI contributed to earlier age at menarche and because of tracking, to greater adult BMI (Kivimèki et al., 2008). Our results are also similar to findings from a recent MR in our cohort which showed that associations of age at menarche with adulthood BMI result from tracking of childhood BMI (Bell et al., 2018). Our findings showed greater gains in fat mass after puberty among females with an older age at puberty which to our knowledge has not been demonstrated previously due to a lack of studies with repeated measures of pre- and post-pubertal fat mass. Though it is difficult to understand the precise reason for this, one possibility includes catch up in fat mass accrual after puberty among females with an older age at puberty due to slower rates of gain in fat mass prior to puberty.

Our results in males are consistent with most (He and Karlberg, 2001; Sandhu et al., 2006; Buyken et al., 2008; Silventoinen et al., 2008; Aksglaede et al., 2009) but not all (Wang, 2002; Lee et al., 2010) previous studies that found that greater adiposity in early childhood is associated with early puberty. The Christ's Hospital Cohort (N = 1520) showed that males with higher childhood BMI before puberty had earlier aPHV (Sandhu et al., 2006) while in the aforementioned Swedish study, change in BMI from 2 to 8 years was also associated with earlier puberty (He and Karlberg, 2001). Similarly, a Swedish study of 99 monozygotic and 76 dizygotic twins found that early childhood BMI was associated with earlier puberty in males (Silventoinen et al., 2008). One known exception to this is a US study of 401 males which showed that faster gains in BMI from 2 to 11.5 years were associated with later puberty onset in males, based on Tanner staging as assessed by paediatric endocrinologists (Lee et al., 2010). Our findings of greater gains in fat mass up to 3 years before puberty followed by slower gains in fat mass in the period directly before puberty among males early to puberty build on and partially consolidate these inconsistent findings to date in males. Increasing body fat is thought to play a critical role in switching on adrenal androgen secretion leading to the initiation of puberty; this may explain steeper rises in fat mass in males earlier to puberty, in the period up to 3 years before puberty (Kaplowitz, 2008). Once the underlying process of puberty is initiated in males, fat mass decreases and this decrease is steeper in males with a younger age at puberty. These decreases in fat mass in males prior to puberty may be linked to rising testosterone levels in males (Kaplowitz, 2008). aPHV has also been shown to be a marker of more advanced puberty stages in males than in females (Tanner genetalia stages 4 and 5 in males compared with Tanner breast stages 2 and 3 in females) (Granados et al., 2015). These differences may contribute to the different associations between fat mass change and aPHV in females and males as well as to the contrasting associations of aPHV with fat mass change up to 3 years before puberty and then between 3 years before puberty and aPHV.

Strengths and Limitations

The main strengths of our study include the use of an objective measure of puberty timing (aPHV) based on prospective, repeated measures of height from age 5 to 20 years which is a more accurate marker than measures that have been frequently used in previous studies such as age of voice breaking or Tanner staging. We also used repeated measures of adiposity from before to after puberty onset which were directly measured using DXA scans and have not been available in previous studies. Limitations include the lack of measures of fat mass before 9 years and the availability of few measures around puberty, which limit our ability to detect subtle and/or acute changes in fat mass around puberty. We aimed to minimise potential selection bias by including all participants with at least one measure of height from 5 to 20 years to estimate aPHV and all participants with at least one measure of fat mass from age 9 to 18 years. In addition, results from analyses with and without selection on complete confounder data were highly similar, indicating a low likelihood of selection driven by missing confounder data. However, while these approaches have minimised selection bias here, selection bias cannot be entirely ruled out. Furthermore, the vast majority of our cohort were of White ethnicity and were socially advantaged. Thus, while we were able to perform a sensitivity analysis restricted to White-only participants, a key limitation of our study is the generalisability of the findings to less advantaged populations and non-White ethnicities.

processing....