Skip to main content
Download PDF

Associations between body mass index, body composition and bone density in young adults: findings from a southern Brazilian cohort

BMC Musculoskeletal Disorders volume 20, Article number: 322 (2019) Cite this article

Abstract

Background

This study aimed to evaluate the association of body composition components and obesity with bone density.

Methods

Prospective study with data on 2968 members of the 1993 Pelotas Birth Cohort from follow-ups at 18 and 22 years of age. Areal bone mineral density (aBMD, g/cm2) was evaluated for whole body, lumbar spine, and femoral neck at 22 years using dual-energy X-ray absorptiometry. Simple and multiple linear regression, stratified by sex, were used to assess the effect of BMI, fat mass (FMI) and lean mass index (LMI), evaluated at 18 and 22 years, and obesity trajectories classified by FMI and categorized as “never”, “only at 18 years”, “only at 22 years” or “always” on aBMD.

Results

Among men, the largest coefficients were observed for BMI, followed by lean mass and fat mass. Compared to fat mass, lean mass presented the largest coefficients for all sites, with the strongest associations observed for the femoral neck (β: 0.035 g/cm2; 95% CI: 0.031; 0.039 for both follow-ups), while the largest effect for FMI was observed for whole-body aBMD at 18 years (β: 0.019 g/cm2; 95% CI: 0.014; 0.024). Among women, the strongest associations were observed for LMI. The largest coefficients for LMI and FMI were observed for femoral neck at age 18, presented β: 0.030 g/cm2, 95% CI: 0.026, 0.034 for LMI and β: 0.012 g/cm2; 95% CI: 0.009; 0.015) for FMI. Men who were “always obese” according to FMI had smallest aBMD for spine (β: -0.014; 95%CI: − 0.029; − 0.001). Women who were obese “only at 18 years” had smallest aBMD for the whole-body (β: -0.013; 95%CI: − 0.023; − 0.002), whereas those who were obese “only at 22 years” had larger whole-body and femoral neck aBMD (β: 0.013; 95%CI: 0.009; 0.017 and β: 0.027; 95%CI: 0.016; 0.038, respectively) and those “always obese” for whole-body aBMD (β: 0.005; 95%CI: 0.001; 0.011) compared to the reference category.

Conclusions

The indexes were positively associated with aBMD in this sample. Fat mass had smaller positive influence on these outcomes than lean mass, suggesting the most important body composition component for bone density is the lean mass.

Peer Review reports

Background

Peak bone mass is reached at the start of adulthood, determines fracture risk in adults [1], and has the potential to delay the onset of advanced age osteoporosis by 13 years [2]. Factors that affect it negatively, particularly during adolescence, can result in an increased risk of fracture and osteoporosis later in life [3].

The interaction between obesity and bone metabolism is complex and has not been entirely elucidated [4]. By 2030, obesity will affect more than one billion people [5,6,7], and total attributed healthcare costs may reach US$ 957 billion [8]. It had been thought that obesity, when defined as a high body mass index (BMI), had a protective effect on the skeleton [9], since it is related to increased bone mineral content and bone mineral density (BMD) [10,11,12,13] and exerts a greater mechanical load on the bones [14]. However, the influence of the two principal components of body weight – fat mass (FM) and lean mass (LM) – on BMD is still a subject of debate [15,16,17,18]. While the literature consistently shows that LM has a positive association with bone health [15, 16, 19, 20], the National Osteoporosis Foundation recently concluded that the effect of FM on the accumulation of bone mass in young populations is still open for debate [19].

A wide selection of investigations has observed that adiposity has a negative effect on bone mass [11, 20,21,22,23,24]. In a recent meta-analysis, Dolan et al. [24] stratified samples by age and found that adiposity had a negative effect on the bone mass of people under the age of 25 years, suggesting that the negative influence of increasing adiposity is more striking when bone metabolism is in a state of flux, as is the case during the growth period [24].

The objective of this study was to evaluate the effect of body composition components (FM and LM, evaluated as an index) and BMI at 18 and 22 years and trajectory of obesity among the follow-ups on bone density at 22 years, using data from a population cohort of young adults born in the Southern Brazil and followed since birth.

Methods

The 1993 Pelotas birth cohort

In 1993, all maternity units in the city of Pelotas were visited daily, and 5265 births to women residing in the urban area of Pelotas between January 1 and December 31 were identified [25]. A total of 5249 mothers agreed to enroll in the study, and their newborn infants were examined. After the perinatal interviews, subsets were assessed at the ages of 1, 3, and 6 months and at 1, 4, 6 and 9 years. At the ages of 11, 15, 18, and 22 years, all members of the original cohort were invited to further assessments. More detailed information on the methodology employed at follow-up assessments is available elsewhere [25,26,27].

This study uses data from the follow-ups conducted at 18 and 22 years of age on all cohort members for whom information on body composition and BMI was available from both follow-ups and bone mass from the 22-year follow-up. For the latest follow-up, a digital questionnaire was constructed on the REDCap (Research Electronic Data Capture) [28] platform to enable electronic data collection and subsequent construction of a database.

Body composition

Body composition variables (FM, LM, and bone mass) were measured using dual-energy X-ray absorptiometry (DXA) (Lunar Prodigy Advance – GE®). These examinations were not conducted with pregnant participants or participants in whom there was a suspicion of pregnancy, wheelchair users, people with bone and joint deformities, or those with weight exceeding 120 kg or height exceeding 192 cm, in accordance with the manufacturer’s instructions. To standardize examinations, participants were given appropriate clothing to wear and did not wear anything made of metal.

Both FM and LM at 18 and 22 years of age were expressed in kilos (kg), using whole body scans, and the respective indices were calculated from the ratio of each variable with the square of weight (kg)/[height(m)]2, representing lean mass index (LMI) and fat mass index (FMI), respectively.

Areal bone mineral density (aBMD) (g/cm2) was evaluated at 22 years of age for the whole body, lumbar spine (L1-L4), and femoral neck.

BMI assessment

Weight was measured using a balance connected to an air plethysmography displacement unit (BOD POD® Gold Standard - Body Composition Tracking System) with 10 g precision. Height was measured using a wooden stadiometer with 0.1 cm precision and a maximum amplitude of 2 m. These measurements were taken by examiners who had been trained and standardized using techniques proposed by Habicht [29]. These variables, in both follow-ups (18 and 22 years), were used to calculate BMI from the ratio of body mass to the square of weight (kg)/[height(m)]2.

Obesity classification

Obesity was assessed using FMI classification. In both ages, obesity was classified using cutoffs of 9 and 13 kg/m2 for men and women, respectively [30]. Combination of obesity status at both follow-ups was used to classify individuals’ trajectories as “never obese”, “obese only at 18 years”, “obese only at 22 years”, or “always obese”.

Covariates

The following perinatal variables were investigated as potential confounders: mother’s educational level (0–4, 5–8, 9–11, ≥12 years of study), family income (≤1; 1.1–3; 3.1–6; > 6 times the minimum wage), gestational age (< 34; 34–36; 37–40; > 40 weeks), mother’s pregestational nutritional status (underweight, healthy weight, overweight, or obese), birth weight (< 2500; 2500–2999; 3000–3999; ≥4000 g), and birth length (centimeters). Potential confounders collected at 15 years were self-reported skin color (white; black, brown, or other). Confounders at 18 years were smoking habit (at least one cigarette per day during the month prior to the interview), total physical activity (minutes per week), and daily calcium intake (mg, obtained from a food frequency questionnaire).

Statistical analysis

All statistical analyses were conducted using Stata 12.1® statistical software (Stata Corp., College Station, Texas, United States) and stratified by sex, since evidence shows that there are sex-linked differences in bone mass [31, 32], and tested for significant interactions (p < 0.1). The descriptive analysis used absolute and relative frequencies for categorical variables and means and standard deviations (SDs) or medians and interquartile ranges (p25-p75) for numerical variables. Participants included and excluded were compared using the chi-square test (categorical variables), t-test, or Wilcoxon Rank Sum Test (numerical variables), depending on normal or nonnormal distribution of data.

Simple and multiple linear regressions were applied to investigate associations between FMI, LMI and BMI (continuous variables, in kg/m2) at each follow-up (at 18 and 22 years of age) and aBMD at 22 years of age. To evaluate the effect according to obesity status at 18 and 22 years on bone mass, simple and multiple linear regressions were also performed, considering “never obese” as a reference category. The association with obesity by FMI was analyzed with an adjustment for LMI. In analyses using continuous exposures, after a test significant (p < 0.001) for deviation from linearity between aBMD and BMI for both sexes and for FMI among the men, a quadratic term was included in the respective adjusted regressions.

Beta coefficients, 95% confidence intervals (95% CIs), and p values from the Wald test of heterogeneity were calculated to a statistical significance level of 5%. When adjusting for possible confounding factors, variables were included in the regressions according to a complete adjustment model irrespective of the level of significance of the association with the outcome in bivariate analysis.

Ethics approval

All 1993 Pelotas birth cohort follow-ups were approved by the Research Ethics Committee at the Medical Faculty of the Universidade Federal de Pelotas, and the most recent ethics approval protocol is number 1.250.366. At all stages, participants (or their legal guardians) signed free and informed consent forms.

Results

Participants studied

At 18 years of age, 4106 participants were assessed (follow-up rate: 81.3%), while at 22 years, 3810 individuals were interviewed (follow-up rate: 76.3%). Body composition data were available for 2968 of the participants assessed at both follow-ups, of whom 1560 (52.6%) were female. Table 1 shows the differences between the participants included in this study and the remainder of the cohort. For both sexes, the proportion of participants born with weights in the range 3000–3999 g was greater among those included in the study, and so was the proportion of smokers. In contrast, FMI at 18 years and BMI at both follow-ups were both greater among those excluded.

Table 1 Characteristics of participants with complete data at both 18th and 22th-year follow-ups compared with those participants with missing data, loss of follow-up or death, stratified by sex
Full size table

Among men, there was a higher proportion of excluded individuals with family income at birth ≤1 minimum wage, and whole-body bone mass was greater among those included. Among the women, there was a smaller proportion among those included whose mothers had an educational level of 0–4 years at the time of their birth and a higher proportion of those born at > 40 weeks than among those excluded. Mean birth length was greater among participants included in the study, whereas mean LMI at 18 and 22 years of age and mean FMI at 22 were greater among those excluded.

Associations between FMI, LMI, BMI and bone mass

Figure 1 illustrates the associations between FMI, LMI and BMI at 18 and 22 years and bone mass at 22 years of age. Positive effects of all three indices on bone outcomes were observed and were usually largest for the follow-up at 18 years.

Fig. 1
figure 1

Association between body mass index, fat mass index and lean mass index (kg/m2) at 18 and 22 years and bone mineral density (g/cm2) at 22 years of age. The 1993 Birth Cohort, Pelotas, Brazil. (N = 2968). ♦Black symbols show crude and adjusted coefficients for the follow-up of 18 years and Grey symbols show crude and adjusted coefficients for the follow-up of 22 years of age. Adjusted for perinatal variables (maternal nutritional status, family income, maternal education, gestational age, birth weight, length at birth), 15 years (skin color) and 18 years (smoking habit, total physical activity score, calcium intake)

Full size image

For men, the largest coefficients were observed for BMI, followed by lean mass and fat mass. Compared to fat mass, the lean mass presented the largest coefficients for all sites, with the strongest associations observed for the femoral neck (β: 0.035 g/cm2; 95% CI: 0.031; 0.039 for both follow-ups) and whole-body aBMD (β: 0.026 g/cm2; 95% CI: 0.021; 0.031 at 18 years and β: 0.024 g/cm2; 95% CI: 0.019; 0.029 at 22 years). The largest effect for FMI was observed for whole-body aBMD at 18 years (β: 0.019 g/cm2; 95% CI: 0.014; 0.024) and lumbar spine, with the same coefficients for both follow-ups (β: 0.018 g/cm2; 95% CI: 0.013; 0.023).

Among women, lean mass presented the largest coefficients of aBMD gain, with the strongest associations for whole-body (β: 0.022 g/cm2; 95% CI: 0.017; 0.027 for the 18 years and β: 0.019 g/cm2; 95% CI: 0.014; 0.024 for the 22 years) and femoral neck sites (β: 0.030 g/cm2, 95% CI: 0.026, 0.034 for age 18 and β: 0.026 g/cm2, 95% CI: 0.022, 0.030 for age 22). For FMI, the largest effect was observed at 18 years for all sites, with strongest associations for femoral neck (β: 0.012 g/cm2; 95% CI: 0.009; 0.015) followed by lumbar spine (β: 0.011 g/cm2; 95% CI: 0.008; 0.014) and whole-body aBMD (β: 0.010 g/cm2; 95% CI: 0.007; 0.013).

Association between obesity and bone mass

Table 2 describes the relationship between obesity, classified by FMI from 18 to 22 years and bone mass at 22 years of age, showing that among men who were “obese” at both follow-ups, there was a reduction in lumbar spine aBMD compared to the reference category (β: − 0.014 g/cm2; 95% CI: − 0.029; − 0.001). Among women, those who were obese “only at 18 years” of age presented a reduction in whole-body aBMD (β: − 0.013 g/cm2; 95% CI: − 0.023; − 0.002), whereas those who were obese “only at 22 years” and “always obese” presented an increase in whole-body aBMD (β: 0.013 g/cm2; 95% CI: 0.009; 0.017 and β: 0.005 g/cm2; 95% CI: 0.001; 0.011, respectively). For femoral neck aBMD, women obese “only at 22 years” had a mean increase of 0.027 g/cm2 (95% CI: 0.016; 0.038), compared to those “never obese”.

Table 2 Association between obesity according to Fat Mass Index (FMI) from 18 to 22 years on bone mineral density (g/cm2) at 22 years of age
Full size table

Discussion

This study investigated the effect of body composition components (FMI and LMI) and BMI at 18 and 22 years and trajectory of obesity on bone density at 22 years. Our results suggest that despite the effect of BMI on bone mass, the impact of lean mass and fat mass differed, with a largest effect observed for lean mass. For both body composition components, the strongest associations were observed at 18 years. According to obesity classification, there was a negative effect in the lumbar spine among men who were “always obese”. For women, the negative effect was observed in the whole body between those who was obese “only at 18 years”. Between those who was obese “only at 22 years” and “always obese” presented a density increase in whole-body and femoral neck.

According to the literature, at age 18, approximately 90% of the bone mass will have been accumulated [33]. The remainder of BMD accumulation occurs during late adolescence, up to the age of 21–25 years. The exact age at which bone accumulation reaches a plateau varies with sex and the region of the skeleton [34]. The peak bone mass of the proximal femur sites occurs around the age of 20 years, while the total body mass reaches its peak between 6 and 10 years later [35]. Many studies have estimated peak bone mass from cross-sectional data [36,37,38], and others have assessed the longitudinal change [39,40,41,42], but only a few have used longitudinal assessment in a population-based sample including teens and young adults [34, 43].

Berger et al. (2010) found that most bone accumulation, especially of the spine and hip, occurs before age 16 in men and women, with more than 94% of peak bone mass already reached by that age [34]. Lu et al. (2016), however, observed that total accumulation ranged from early to late 20s for both sexes, with women reaching their peaks significantly earlier [43]. Additionally, weight, height and BMI had a significant effect on bone tracking [43]. These results indicate that early intervention before and during puberty is necessary to achieve optimal peak bone mass.

The present study confirms that the body composition components affect bone mass with unequal magnitude in an important period of bone accumulation before reaching peak bone mass. This is important because attaining a high peak bone mass in early life predicts a higher bone mass and a reduced risk of osteopenia or osteoporosis later in life [1]. The effects are probably due to different causes, through mechanisms that go beyond the effect of the direct load on the skeleton [15]; genetic, environmental, and hormonal factors are also involved [44,45,46].

The literature shows that obesity in adulthood can be protective against osteoporotic fractures [9, 10, 18, 47], whereas at younger ages, obesity can have negative effects that are specific to bone [11, 18, 22,23,24]. Differences in age, severity, and duration of obesity, particularly among longitudinal studies of the subject [48,49,50], may explain these conflicting results [49,50,51]. In the current study, the obesity classification revealed a negative association with aBMD in men. Among women, although most of the observed effects were positive, a negative effect was observed among those obese “only at 18 years”. It should be highlighted that this analysis was adjusted for LMI when obesity was classified by FMI, thereby removing the effect of this component. Besides, in both sexes, most of the effects were largest when evaluated at 18 years, showing a lag time between these measures of body composition and bone mass. To confirm this, we performed a transversal analysis to assess the effect of LMI, FMI and BMI at age 18, on bone mass also at 18 years. We can observe that the magnitude of this transversal association was lower, mainly for FMI and BMI exposures (data not shown), reinforcing the existence of this latency period. We also performed analyses on the effect of 18-year exposures on change in bone mass between 18 and 22 years, and we found a positive effect for the whole body, but negative effects could be observed for the sites of the spine and femoral neck.

In addition to mechanical loading, adipose tissue can have an indirect positive effect on bone metabolism via adipokine, cytokines and hormones and can stimulate bone formation by producing estrogens from steroid precursors, increasing the levels of leptin and insulin in the circulation [52,53,54,55]. However, adipose tissue also produces adiponectin and cytokines related to inflammation, such as tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6), which can have harmful effects on the bones [52,53,54, 56]. In the present study, we observed a positive effect of FMI, although it was visibly inferior to that observed for LMI, which leads us to suppose that the small duration of time elapsed between collection of exposure and outcome data may have prevented the manifestation of the negative effect of body fat.

Evidence points to the existence of an FM threshold that, if exceeded during critical periods of skeletal development — particularly in adolescence — may result in skeletal fragility and ultimately a greater risk of fracture [3, 20, 57, 58]. Measures of bone content, density, and strength improve to the extent that LM and FM increase until a “fat threshold” is reached, beyond which additional fat has harmful effects on the growing skeleton [58, 59]. According to a recent meta-analysis, a greater negative correlation between relative adiposity (in percentages) and bone density was observed in obese people (r = − 0.20) than in those who were overweight (r = − 0.08), indicating that the negative impact of adiposity on BMD increases to the extent that adiposity progresses from the overweight category to obese levels, which was particularly evident among men and among those under the age of 25 years [24].

The present study has important strengths, such as aBMD measurements obtained using DXA, the gold standard for bone mass evaluation; a high follow-up rate; the possibility of assessing the association between obesity and bone mass adjusted for potential confounding factors assessed prospectively over the life course, e.g., maternal characteristics at birth and maternal nutritional status; and measurement of exposure at two points in time.

The main limitation of the present study is the short time period investigated. However, in the 1993 cohort, body composition was first evaluated at 18 years of age. We encourage studies of younger cohorts to include the assessment of body composition at early stages to better explore the longer effects of body composition on bone health, including subsequent follow-ups of the 1993 cohort. This recommendation is further justified by the fact that the literature on this topic generally evaluates the relationship between body composition and bone mass in older populations [60,61,62] and in premenopausal and postmenopausal women [15, 63, 64]. Another limitation is the lack of data on peak bone growth in our population.

Conclusions

This study observed positive effects of FMI and LMI on bone density at 22 years, with a largest effect observed for lean mass. For both body composition components, the strongest associations were observed at 18 years. According to obesity classification, some negative effects were found at 22 years. These findings emphasize that the body composition components have different effects on bone mass and raise questions about the effects of fat mass at young ages, especially whether the longer time of adiposity exposure may have harmful consequences for bone health.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

95% CI:

95% confidence intervals

aBMD:

Areal bone mineral density

BMD:

Bone mineral density

BMI:

Body mass index

DXA:

Dual-energy X-ray absorptiometry

FM:

Fat mass

FMI:

Fat mass index

IL-6:

interleukin 6

LM:

Lean mass

LMI:

Lean mass index

MMW:

monthly minimum wages

REDCap:

Research Electronic Data Capture

SD:

standard deviations

TNF-α:

tumor necrosis factor α

WHO:

World Health Organization

References

  1. Heaney RP, Abrams S, Dawson-Hughes B, Looker A, Marcus R, Matkovic V, et al. Peak bone mass. Osteoporos Int. 2000;11(12):985–1009.

    Article  CAS  Google Scholar 

  2. Hernandez CJ, Beaupré GS, Carter DR. A theoretical analysis of the relative influences of peak BMD, agerelated bone loss and menopause on the development of osteoporosis. Osteoporos Int. 2003;14(10):843–7.

    Article  CAS  Google Scholar 

  3. Dimitri P. Fat and bone in children - where are we now? Ann Pediatr Endocrinol Metab. 2018;23:262–9.

    Article  Google Scholar 

  4. Savvidis C, Tournis S, Dede AD. Obesity and bone metabolism. Hormones. 2018;17(2):205–17.

    Article  Google Scholar 

  5. Hwang LC, Bai CH, Sun CA, Chen CJ. Prevalence of metabolically healthy obesity and its impacts on incidences of hypertension, diabetes and the metabolic syndrome in Taiwan. Asia Pac J Clin Nutr. 2012;21(2):227–33.

    CAS  PubMed  Google Scholar 

  6. Phillips CM, Dillon C, Harrington JM, McCarthy VJ, Kearney PM, Fitzgerald AP, et al. Defining metabolically healthy obesity: role of dietary and lifestyle factors. PLoS One. 2013;8(10):e76188.

    Article  CAS  Google Scholar 

  7. Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, Moodie ML, et al. The global obesity pandemic: shaped by global drivers and local environments. Lancet. 2011;378(9793):804–14.

    Article  Google Scholar 

  8. Wang CY, McPherson K, Marsh T, Gortmaker SL, Brown M. Health and economic burden of the projected obesity trends in the USA and the UK. Lancet. 2011;378(9793):815–25.

    Article  Google Scholar 

  9. De Laet C, Kanis JA, Oden A, Johanson H, Johnell O, Delmas P, et al. Body mass index as a predictor of fracture risk: a meta-analysis. Osteoporos Int. 2005;16(11):1330–8.

    Article  Google Scholar 

  10. Lloyd JT, Alley DE, Hawkes WG, Hochberg MC, Waldstein SR, Orwig DL. Body mass index is positively associated with bone mineral density in US older adults. Arch Osteoporos. 2014;9:175.

    Article  Google Scholar 

  11. Mosca LN, Goldberg TB, Da Silva VN, Da Silva CC, Kurokawa CS, Bisi Rizzo AC, et al. Excess body fat negatively affects bone mass in adolescents. Nutrition. 2014;30(7–8):847–52.

    Article  Google Scholar 

  12. Winther A, Dennison E, Ahmed LA, Furberg AS, Grimnes G, Jorde R, et al. The Tromso study: fit futures: a study of Norwegian adolescents' lifestyle and bone health. Arch Osteoporos. 2014;9(1):185.

    Article  Google Scholar 

  13. Leonard MB, Shults J, Wilson BA, Tershakovec AM, Zemel BS. Obesity during childhood and adolescence augments bone mass and bone dimensions. Am J Clin Nutr. 2004;80(2):514–23.

    Article  CAS  Google Scholar 

  14. Mosca LN, Da Silva VN, Goldberg TB. Does excess weight interfere with bone mass accumulation during adolescence? Nutrients. 2013;5(6):2047–61.

    Article  Google Scholar 

  15. Kim J, Kwon H, Heo BK, Joh HK, Lee CM, Hwang SS, et al. The association between fat mass, lean mass and bone mineral density in premenopausal women in Korea: a cross-sectional study. Korean J Fam Med. 2018;39(2):74–84.

    Article  Google Scholar 

  16. Ho-Pham LT, Nguyen UD, Nguyen TV. Association between lean mass, fat mass, and bone mineral density: a meta-analysis. J Clin Endocrinol Metab. 2014;99(1):30–8.

    Article  CAS  Google Scholar 

  17. Sioen I, Lust E, De Henauw S, Moreno LA, Jiménez-Pavón D. Associations between body composition and bone health in children and adolescents: a systematic review. Calcif Tissue Int. 2016;99(6):557–77.

    Article  CAS  Google Scholar 

  18. Dimitri P, Bishop N, Walsh JS, Eastell R. Obesity is a risk factor for fracture in children but is protective against fracture in adults: a paradox. Bone. 2012;50(2):457–66.

    Article  CAS  Google Scholar 

  19. Weaver CM, Gordon CM, Janz KF, Kalkwarf HJ, Lappe JM, Lewis R, et al. The National Osteoporosis Foundation’s position statement on peak bone mass development and lifestyle factors: a systematic review and implementation recommendations. Osteoporos Int. 2016;27(4):1281–386.

    Article  CAS  Google Scholar 

  20. Wey HE, Binkley TL, Beare TM, Wey CL, Specker BL. Cross-sectional versus longitudinal associations of lean and fat mass with pQCT bone outcomes in children. J Clin Endocrinol Metab. 2011;96(1):106–14.

    Article  CAS  Google Scholar 

  21. Compston JE, Watts NB, Chapurlat R, Cooper C, Boonen S, Greenspan S, et al. Obesity is not protective against fracture in postmenopausal women: GLOW. Am J Med United States. 2011;124(11):1043–50.

    Google Scholar 

  22. Janicka A, Wren TA, Sanchez MM, Dorey F, Kim PS, Mittelman SD, et al. Fat mass is not beneficial to bone in adolescents and young adults. J Clin Endocrinol Metab. 2007;92(1):143–7.

    Article  CAS  Google Scholar 

  23. Russell M, Mendes N, Miller KK, Rosen CJ, Lee H, Klibanski A, et al. Visceral fat is a negative predictor of bone density measures in obese adolescent girls. J Clin Endocrinol Metab. 2010;95(2):1247–55.

    Article  CAS  Google Scholar 

  24. Dolan E, Swinton PA, Sale C, Healy A, O’Reilly J. Influence of adipose tissue mass on bone mass in an overweight or obese population: systematic review and meta-analysis. Nutr Rev. 2017;75(10):858–70.

    Article  Google Scholar 

  25. Victora CG, Hallal PC, Araújo CLP, Menezes AMB, Wells JCK, Barros FC. Cohort profile: the 1993 Pelotas (Brazil) birth cohort study. Int J Epidemiol. 2008;37:704–9.

    Article  Google Scholar 

  26. Gonçalves H, Assunção MCF, Wehrmeister FC, Oliveira IO, Barros FC, Victora CG, et al. Cohort profile update: the 1993 Pelotas (Brazil) birth cohort follow-up visits in adolescence. Int J Epidemiol. 2014;43:1–7.

    Article  Google Scholar 

  27. Gonçalves H, Wehrmeister FC, Assunção MCF, Tovo-Rodrigues L, Oliveira IO, Murray J, et al. Cohort profile update: the 1993 Pelotas (Brazil) birth cohort follow-up at 22 years. Int J Epidemiol. 2017:1–7.

  28. Harris PA, Taylor R, Thielke R, Payne J, Gonzales N, Conde JG. Research electronic data capture (REDCap) - a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–81.

    Article  Google Scholar 

  29. Habicht JP. Estandartización de métodos epidemiológicos quantitativos sobre el terreno. Bol Oficina Sanit Panam. 1974;76:375–84.

    CAS  PubMed  Google Scholar 

  30. Kelly TL, Wilson KE, Heymsfield SB. Dual energy X-ray absorptiometry body composition reference values from NHANES. PLoS One. 2009;4(9):e7038.

    Article  Google Scholar 

  31. Leonard MB, Elmi A, Mostoufi-Moab S, Shults J, Burnham JM, Thayu M, et al. Effects of sex, race, and puberty on cortical bone and the functional muscle bone unit in children, adolescents, and young adults. J Clin Endocrinol Metab. 2010;95(4):1681–8.

    Article  CAS  Google Scholar 

  32. Alswat KA. Gender Disparities in Osteoporosis. J Clin Med Res. 2017;9(5):382–7.

    Article  Google Scholar 

  33. Bachrach LK. Acquisition of optimal bone mass in childhood and adolescence. Trends Endocrinol Metab. 2001;12(1):22–8.

    Article  CAS  Google Scholar 

  34. Berger C, Goltzman D, Langsetmo L, Joseph L, Jackson S, Kreiger N, et al. Peak bone mass from longitudinal data: implications for the prevalence, pathophysiology, and diagnosis of osteoporosis. J Bone Miner Res. 2010;25(9):1948–57.

    Article  Google Scholar 

  35. Matkovic V, Jelic T, Wardlaw GM, Ilich JZ, Goel PK, Wright JK, et al. Timing of peak bone mass in Caucasian females and its implication for the prevention of osteoporosis. Inference from a cross-sectional model. J Clin Invest. 1994;93(2):799–808.

    Article  CAS  Google Scholar 

  36. Høiberg M, Nielsen TL, Wraae K, Abrahamsen B, Hagen C, Andersen M, et al. Population-based reference values for bone mineral density in young men. Osteoporos Int. 2007;18(11):1507–14.

    Article  Google Scholar 

  37. Henry MJ, Pasco JA, Korn S, Gibson JE, Kotowicz MA, Nicholson GC. Bone mineral density reference ranges for Australian men: Geelong osteoporosis study. Osteoporos Int. 2010;21:909–17.

    Article  CAS  Google Scholar 

  38. Ribom EL, Ljunggren O, Mallmin H. Use of a Swedish T-score reference population for women causes a two-fold increase in the amount of postmenopausal Swedish patients that fulfill the WHO criteria for osteoporosis. J Clin Densitom. 20008;11(3):404–11.

    Article  Google Scholar 

  39. Bachrach LK, Hastie T, Wang MC, Narasimhan B, Marcus R. Bone mineral acquisition in healthy Asian, Hispanic, black, and Caucasian youth: a longitudinal study. J Clin Endocrinol Metab. 1999;84(12):4702–12.

    CAS  PubMed  Google Scholar 

  40. Mein AL, Briffa NK, Dhaliwal SS, Price RI. Lifestyle influences on 9-year changes in BMD in young women. J Bone Miner Res. 2004;19(7):1092–8.

    Article  Google Scholar 

  41. Lloyd T, Petit MA, Lin HM, Beck TJ. Lifestyle factors and the development of bone mass and bone strength in young women. J Pediatr. 2004;144(6):776–82.

    PubMed  Google Scholar 

  42. Walsh JS, Henry YM, Fatayerji D, Eastell R. Lumbar spine peak bone mass and bone turnover in men and women: a longitudinal study. Osteoporos Int. 2009;20(3):355–62.

    Article  CAS  Google Scholar 

  43. Lu J, Shin Y, Yen MS, Sun SS. Peak bone mass and patterns of change in Total bone mineral density and bone mineral contents from childhood into Young adulthood. J Clin Densitom. 2016;19(2):180–91.

    Article  Google Scholar 

  44. Seeman E, Hopper JL, Young NR, Formica C, Goss P, Tsalamandris C. Do genetic factors explain associations between muscle strength, lean mass, and bone density? A twin study. Am J Phys. 1996;270(2 Pt 1):E320–7.

    CAS  Google Scholar 

  45. Nguyen TV, Howard GM, Kelly PJ, Eisman JA. Bone mass, lean mass, and fat mass: same genes or same environments? Am J Epidemiol. 1998;147(1):3–16.

    Article  CAS  Google Scholar 

  46. Lang TF. The bone-muscle relationship in men and women. J Osteoporos. 2011;2011:702735.

    Article  Google Scholar 

  47. Johansson H, Kanis JA, Odén A, McCloskey E, Chapurlat RD, Christiansen C, et al. A meta-analysis of the Association of Fracture Risk and Body Mass Index in women. J Bone Miner Res. 2013;29(1):223–33.

    Article  Google Scholar 

  48. Viljakainen HT, Valta H, Lipsanen-Nyman M, Saukkonen T, Kajantie E, Andersson S, et al. Bone characteristics and their determinants in adolescents and Young adults with early-onset severe obesity. Calcif Tissue Int. 2015;97(4):364–75.

    Article  CAS  Google Scholar 

  49. Petit MA, Beck TJ, Hughes JM, Lin HM, Bentley C, Lloyd T. Proximal femur mechanical adaptation to weight gain in late adolescence: a six-year longitudinal study. J Bone Miner Res. 2008;23(2):180–8.

    Article  Google Scholar 

  50. Sayers A, Tobias JH. Fat mass exerts a greater effect on cortical bone mass in girls than boys. J Clin Endocrinol Metab. 2010;95(2):699–706.

    Article  CAS  Google Scholar 

  51. Vandewalle S, Taes Y, Van Helvoirt M, Debode P, Herregods N, Ernst C, et al. Bone size and bone strength are increased in obese male adolescents. J Clin Endocrinol Metab. 2013;98(7):3019–28.

    Article  CAS  Google Scholar 

  52. Dimitri P, Wales JK, Bishop N. Adipokines, bone-derived factors and bone turnover in obese children; evidence for altered fat-bone signalling resulting in reduced bone mass. Bone. 2011;48(2):189–96.

    Article  CAS  Google Scholar 

  53. Kawai M, De Paula FJ, Rosen CJ. New insights into osteoporosis: the bone-fat connection. J Intern Med. 2012;272(4):317–29.

    Article  CAS  Google Scholar 

  54. Reid IR. Fat and bone. Arch Biochem Biophys. 2010;503(1):20–7.

    Article  CAS  Google Scholar 

  55. Hamrick MW, Ferrari SL. Leptin and the sympathetic connection of fat to bone. Osteoporos Int. 2008;19(7):905–12.

    Article  CAS  Google Scholar 

  56. Braun T, Schett G. Pathways for bone loss in inflammatory disease. Curr Osteoporos Rep. 2012;10(2):101–8.

    Article  Google Scholar 

  57. Farr JN, Dimitri P. The impact of fat and obesity on bone microarchitecture and strength in children. Calcif Tissue Int. 2017;100(5):500–13.

    Article  CAS  Google Scholar 

  58. Laddu DR, Farr JN, Laudermilk MJ, Lee VR, Blew RM, Stump C, et al. Longitudinal relationships between whole body and central adiposity on weight-bearing bone geometry, density, and bone strength: a pQCT study in young girls. Arch Osteoporos. 2013;8:156.

    Article  Google Scholar 

  59. Burrows M, Baxter-Jones A, Mirwald R, Macdonald H, McKay H. Bone mineral accrual across growth in a mixedethnic group of children: are Asian children disadvantaged from an early age? Calcif Tissue Int. 2009;84(5):366–78.

    Article  CAS  Google Scholar 

  60. Kim KM, Lim S, Oh TJ, Moon JH, Choi SH, Lim JY, et al. Longitudinal changes in muscle mass and strength, and bone mass in older adults: gender-specific associations between muscle and bone losses. J Gerontol A Biol Sci Med Sci. 2018;73(8):1062–9.

    Article  Google Scholar 

  61. Zhu K, Hunter M, James A, Lim EM, Walsh JP. Associations between body mass index, lean and fat body mass and bone mineral density in middle-aged Australians: the Busselton healthy ageing study. Bone. 2015;74:146–52.

    Article  Google Scholar 

  62. Jiang Y, Zhang Y, Jin M, Gu Z, Pei Y, Meng P. Aged-related changes in body composition and association between body composition with bone mass density by body mass index in Chinese Han men over 50-year-old. PLoS One. 2015;10(6):e0130400.

    Article  Google Scholar 

  63. Lekamwasam S, Weerarathna T, Rodrigo M, Arachchi WK, Munidasa D. Association between bone mineral density, lean mass, and fat mass among healthy middle-aged premenopausal women: a cross-sectional study in southern Sri Lanka. J Bone Miner Metab. 2009;27(1):83–8.

    Article  Google Scholar 

  64. Alissa EM, Alnahdi WA, Alama N, Ferns GA. Relationship between nutritional profile, measures of adiposity, and bone mineral density in postmenopausal Saudi women. J Am Coll Nutr. 2014;33(3):206–14.

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This article is based on data from the study “Pelotas Birth Cohort, 1993” conducted by Postgraduate Program in Epidemiology at Universidade Federal de Pelotas with the collaboration of the Brazilian Public Health Association (ABRASCO). From 2004 to 2013, the Wellcome Trust supported the 1993 birth cohort study. The European Union, National Support Program for Centers of Excellence (PRONEX), the Brazilian National Research Council (CNPq), and the Brazilian Ministry of Health supported previous phases of the study. The 22-year follow-up was supported by the Science and Technology Department/Brazilian Ministry of Health, with resources transferred through the Brazilian National Council for Scientific and Technological Development (CNPq), grant 400943/2013–1.

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

Author information

Authors and Affiliations

  1. Postgraduate Program in Epidemiology, Federal University of Pelotas, Rua Marechal Deodoro, 1160 – 3o andar, Pelotas/RS, 96020-220, Brazil

    Isabel Oliveira Bierhals, Juliana dos Santos Vaz, Renata Moraes Bielemann, Christian Loret de Mola, Fernando Celso Barros, Helen Gonçalves, Fernando César Wehrmeister & Maria Cecília Formoso Assunção

Authors
  1. Isabel Oliveira Bierhals
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juliana dos Santos Vaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Renata Moraes Bielemann
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christian Loret de Mola
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fernando Celso Barros
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Helen Gonçalves
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Fernando César Wehrmeister
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Maria Cecília Formoso Assunção
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

IOB, JSV and MCFA designed research; IOB performed statistical analyses; IOB, JSV, RMB and MCFA wrote the paper. JSV, MCFA, RMB. CLM, FCB, HG and FCW reviewed all the drafts of the manuscript and contributed with suggestions to the work. IOB, JV and MCFA had primary responsibility for final content. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Isabel Oliveira Bierhals.

Ethics declarations

Ethics approval and consent to participate

All the follow-ups of the 1993 Pelotas Birth Cohort Study were approved by the Research Ethics Committee of the Federal University of Pelotas Medical School under permit number 1.250.366. At all stages, the participants (or their legal guardians up to 15 years old follow-up) signed an informed consent form.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bierhals, I.O., dos Santos Vaz, J., Bielemann, R.M. et al. Associations between body mass index, body composition and bone density in young adults: findings from a southern Brazilian cohort. BMC Musculoskelet Disord 20, 322 (2019). https://doi.org/10.1186/s12891-019-2656-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12891-019-2656-3

Keywords

BMC Musculoskeletal Disorders

ISSN: 1471-2474

Contact us