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Association between fat mass by bioelectrical impedance analysis and bone mass by quantitative ultrasound in relation to grip strength and serum 25-hydroxyvitamin D in postmenopausal Japanese women: the Unzen study

Journal of Physiological Anthropology volume 41, Article number: 7 (2022) Cite this article

Abstract

Background

Whether fat mass or lean mass affects bone mass in postmenopausal women is controversial. This study aimed to explore the association between body composition measured by bioelectrical impedance analysis (BIA) and bone mass measured by quantitative ultrasound (QUS) in postmenopausal women in Japan.

Methods

We conducted a cross-sectional study, The Unzen Study, on 382 community-dwelling postmenopausal Japanese women (mean (standard deviation) age: 68.2 (7.2) years) who participated in periodic health examinations. The stiffness index (SI) was measured using QUS, and body composition (e.g., fat mass and muscle mass) was measured using BIA. Grip strength was measured. Fasting blood samples were collected, and 25-hydroxyvitamin D (25(OH)D), tartrate-resistant acid phosphatase-5b (TRACP-5b), and parathyroid hormone (PTH) levels were measured. Data on current smoking, alcohol consumption, exercise, and any comorbidities (heart disease, lung disease, stroke, or diabetes mellitus) were collected.

Results

The SI increased with increasing quartiles of fat mass and muscle mass (both p for trend <  0.001), respectively. There were positive correlations between SI and log (25(OH)D) or grip strength. Fat mass significantly correlated with grip strength. Multiple linear regression analysis showed that higher fat mass was independently and significantly associated with higher SI after adjusting for age, height, comorbidity, current smoking, alcohol consumption, exercise, log (25(OH)D), log (TRACP-5b), log (PTH), and grip strength (p = 0.001). In contrast, no association was observed between muscle mass and SI.

Conclusions

Fat mass, but not muscle mass, was a significant determinant of SI in community-dwelling postmenopausal Japanese women.

Background

Osteoporotic patients aged 40 years and over in Japan were estimated to be approximately 12,800,000 (3,000,000 men and 9,800,000 women) based on a survey of osteoporosis in the general population [1]. Osteoporosis with bone loss is a major public health problem [2]. Age-related bone loss is associated with a fracture risk [3], high medical costs [4], and increased mortality risk [5].

Taaffe et al. [6] reported that lean mass determined using dual X-ray absorptiometry (DXA) was a significant independent contributor to bone mineral density (BMD) across race and sex. Some studies have shown that lean mass has a greater effect than fat mass on BMD in postmenopausal women after adjusting for potential confounders [7,8,9]. In contrast, other studies showed that BMD was related to fat mass but not to lean mass in postmenopausal women [10,11,12]. These results suggested biological interactions between bone mass, lean mass, and fat mass; thus, whether fat mass or lean mass affects BMD is controversial.

The gold standard for BMD measurement is DXA. However, DXA involves radiation exposure, is expensive, and has limited availability. Trimpou et al. [13] showed that the stiffness index (SI) by quantitative ultrasound (QUS) was positively correlated with BMD by DXA in each region (total, femoral neck, and lumbar spine). QUS has been shown to be a useful tool for osteoporosis screening [14,15,16].

Bioelectrical impedance analysis (BIA) is a method used to analyze body composition. Thomson et al. [17] reported a significant correlation between DXA and BIA for estimating fat mass and fat-free mass. Since BIA can examine a large number of individuals in a shorter period of time at a low cost, it has been used in large cohort studies, such as the National Health and Nutrition Examination Survey, USA [18].

Previous studies showed that BMD was associated with several factors, such as grip strength [19], serum 25-hydroxyvitamin D (25(OH)D) [20], serum tartrate-resistant acid phosphatase-5b (TRACP-5b) [21], and serum parathyroid hormone (PTH) [22]. Several studies reported the association between body composition and grip strength. Lean mass was significantly associated with grip strength [23]. Fat mass was positively associated [24], negatively associated [25], or not associated [26].

Previous studies have reported a relationship between body composition and BMD using DXA [6,7,8,9,10,11,12]. However, to the best of our knowledge, no study has examined the relationship between body composition measured by BIA and bone mass measured by QUS in postmenopausal women. DXA differentiates three components, such as fat mass, lean mass, and bone mass. On the other hand, BIA divides body composition into fat mass and fat-free mass. Muscle mass is calculated by subtracting the estimated bone mass from the fat-free mass using a BIA device (TANITA DC-320) and is similar to the lean mass measured by DXA. This study aimed to explore the association between body composition measured by BIA and bone mass measured by QUS in relation to grip strength, serum 25(OH)D, serum TRACP-5b, and serum PTH in postmenopausal women in Japan.

Methods

Participants

The participants were community-dwelling postmenopausal women in Unzen City, Nagasaki Prefecture, Japan, who were invited to participate in periodic health examinations between 2011 and 2013 (the Unzen study). Postmenopausal status was defined as menopause at least 1 year after the last menstruation. A total of 382 women (mean (standard deviation) age: 68.2 (7.2) years) participated in the study. All participants provided written informed consent before the examination. This study was approved by the Ethics Committee of the Nagasaki University Graduate School of Biomedical Sciences (project registration number 11072739-2). All procedures were performed in accordance with the ethical standards of the institutional and/or national research committee and in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

QUS measurement

The SI determined by QUS was measured on the right heel using a Lunar Achilles device (Achilles InSight GE Lunar Corp., Madison, WI, USA). It was calculated from broadband ultrasound attenuation (BUA) and speed of sound (SOS); the formula is SI = (0.67 × BUA) + (0.28 × SOS) – 420 [27].

Body composition

Body composition (fat mass, percent fat, fat-free mass, and muscle mass) was measured using a TANITA DC-320 foot-to-foot body composition analyzer (TANITA Cooperation, Tokyo, Japan). A BIA system was used in this device. The participants were barefoot, wore minimal clothing, and were instructed to stand still with their feet touching all four metal plates. The percent fat was estimated using the built-in TANITA equations. Fat mass was calculated as percent fat divided by 100 and then multiplied by body weight, and fat-free mass was subsequently calculated as the difference between body weight and fat mass. The muscle mass was calculated by subtracting the estimated bone mass from the fat-free mass.

Physical examination

Height (m) and weight (kg) were measured while wearing light clothing and without shoes, and body mass index (BMI) was calculated as weight/height squared (kg/m2). Grip strength was evaluated as an index of muscle strength in the upper limbs and was measured using a hydraulic hand dynamometer (Jamar hydraulic hand dynamometer; Lafayette Instrument Company, Inc., Lafayette, IN, US). Grip strength was measured twice and their excellent values were analyzed. Trained interviewers obtained information regarding the clinical characteristics of the participants. Comorbidity data, including heart disease, lung disease, stroke, and diabetes mellitus, were also collected. Data on current smoking (yes/no), alcohol consumption (≥ 20 g/day), and exercise (at least 30 min twice per week) were collected through interviews.

Blood data

Fasting blood samples were collected. Serum 25(OH)D was measured by chemiluminescence enzyme immunoassay (CLEIA), serum TRACP-5b by enzyme immunoassay, and PTH by electrochemiluminescence immunoassay (ECLIA).

Statistical analyses

Since 25(OH)D, TRACP-5b, and PTH were not normally distributed, these values were treated as log (25(OH)D), log (TRACP-5b), and log (PTH). Participants were classified into quartiles of fat mass and muscle mass, ranging from Q1 to Q4. Trends in SI among fat mass groups (FMQ1, FMQ2, FMQ3, and FMQ4) and muscle mass groups (MMQ1, MMQ2, MMQ3, and MMQ4) were evaluated using linear regression analysis. We applied Pearson’s product-moment correlations to assess for correlation among SI, fat mass, muscle mass, log (25(OH)D), log (TRACP-5b), log (PTH), and grip strength. Multiple linear regression analysis was used to explore the effects of fat mass and muscle mass on SI, with adjustment for age, height, comorbidity, current smoking, alcohol consumption, and exercise (Model 1) [11, 28, 29]. We added log (25(OH)D), log (TRACP-5b), log (PTH), and grip strength to model 1 (model 2) [30,31,32]. Statistical significance was set at p  < 0 .05. All statistical analyses were performed using SPSS software version 27 (SPSS Inc., Chicago, IL, USA).

Results

Table 1 shows the characteristics of the participants. The mean (standard deviation) of age, BMI, SI, fat mass, muscle mass, and grip strength were 68.2 (7.2) years, 22.1 (3.1) kg/m2, 67.9 (12.7), 15.8 (5.3) kg, 33.0 (3.2) kg, and 24.2 (5.2) kg, respectively. The median of serum 25(OH)D, TRACP-5b, and PTH were 19.6ng/mL, 480.0 mU/dL, and 38.0 pg/mL, respectively. Fifteen percent of the participants had at least one comorbidity, 1.6% were alcohol drinkers, and 1.3% were current smokers. Approximately one third of the patients exercised.

Table 1 Characteristics of participants (n = 382)
Full size table

Table 2 shows the trends of SI among fat mass and muscle mass groups classified by quartile. The SI increased with increasing fat mass and muscle mass quartiles (both p for trend < 0 .001), respectively.

Table 2 Mean (SD) of stiffness index according to fat mass and muscle mass quartile
Full size table

There were positive correlations between SI and fat mass, muscle mass, log (25(OH)D), or grip strength, but not log (TRACP-5b) and log (PTH) (Fig. 1). Fat mass significantly correlated with muscle mass or grip strength, but not log (25(OH)D), log (TRACP-5b), or log (PTH) (Fig. 2).

Fig. 1
figure 1

Correlations between stiffness index and fat mass (A), muscle mass (B), grip strength (C), log (25(OH)D) (D), log (TRACP-5b) (E), or log (PTH) (F)

Full size image
Fig. 2
figure 2

Correlations between fat mass and muscle mass (A), grip strength (B), log (25(OH)D) (C), log (TRACP-5b) (D), or log (PTH) (E)

Full size image

Table 3 shows the results of the multiple linear regression analysis assessing the factors associated with SI in postmenopausal women. Higher fat mass was independently and significantly associated with higher SI after adjusting for age, height, comorbidities, alcohol consumption, current smoking, and exercise (p < 0.001) (Model 1). After adding log (25(OH)D), log (TRACP-5b), log (PTH), and grip strength to model 1, the results did not alter (Model 2). Younger age, alcohol consumption, exercise, higher log (25(OH)D), and stronger grip strength were also associated with a higher SI. In contrast, no association was observed between muscle mass and SI.

Table 3 Association between body composition (fat mass and muscle mass) and stiffness index
Full size table

Discussion

We examined the association of body composition (fat mass and muscle mass) measured by BIA with SI measured by QUS in Japanese postmenopausal women. Fat mass was significantly associated with SI after adjusting for covariates, whereas muscle mass was not. Several studies have shown that fat mass is the most significant predictor of BMD in postmenopausal women, compared with lean mass [6, 11, 12, 33, 34], which was consistent with our results. These findings suggest that fat mass may have a positive effect on bone formation in postmenopausal women.

Besides energy storage, fat tissues produce estrogen [35, 36]. Estrogen maintains bone mass by decreasing the lifespan of osteoclasts and increasing osteoblast and osteocyte survival [37]. The conversion of androstenedione to estrone occurs predominantly in fat tissue [38]. Fat tissues are considered the major source of circulating estrogen, and the contribution of fat tissues to the total circulating estrogens increases with advancing age [39]. Estrogen secreted by the fat mass may have a positive and significant effect on bone metabolism in postmenopausal women.

Thomas et al. [40] demonstrated the relationship of fat mass and leptin to BMD, both of which were positively correlated with BMD, and the strength of the association between fat mass and BMD decreased after adjusted serum leptin. Leptin significantly increased osteoprotegerin mRNA steady-state levels and protein secretion [41], suggesting an effect of leptin on inhibiting bone resorption. Leptin may mediate at least part of the protective effect of fat mass on bone.

Increased weight has a positive effect on BMD [42, 43] and reduces fracture risk in the elderly [10]. Osteoblasts produce new bone tissue when the daily mechanical load exceeds normal physiological levels, and osteoclasts remove existing bone tissue when the daily mechanical load falls below normal levels [44]. Previous reports suggested that microgravity contributes to bone loss [45, 46]. Goodship et al. [47] found that BMD was reduced by up to 7% in os clacis, which received no mechanical stimulus during long-term space flight. This means that bone mass and bone strength are maintained by appropriate weight-bearing. Therefore, higher weight, including higher fat mass, may contribute to good bone health.

We did not observe an association between muscle mass (corresponding to lean mass measured using DXA) and SI. Reid et al. [33] showed that lean mass was not an independent correlation of BMD at any site once fat mass was considered, which was consistent with our results. In contrast, some studies reported that lean mass is a significant determinant of BMD [6, 48]. Marin et al. [9] showed that lean mass was significantly related to all BMD sites (e.g., lumbar spine, femoral neck, and total body), whereas fat mass was only weakly related to the BMD of the femoral neck. Further research is needed to clarify whether fat and muscle mass are independent predictors of bone mass.

Log (25(OH)D) positively correlated with SI in this study. Previous studies suggested that higher serum 25(OH)D concentrations are associated with increased BMD among postmenopausal women [20, 49]. Vitamin D might be important for good bone health in postmenopausal women.

The present study showed that grip strength was positively associated with SI. Previous study demonstrated a relationship between grip strength and a higher SI [28]. These results suggest that muscle strength, but not muscle mass, is important for the maintenance of bone mass in postmenopausal women.

In this study, fat mass positively correlated with grip strength. Sherk et al. [24] reported that fat mass was significantly correlated with upper muscle strength in postmenopausal women. On the other hand, a previous study reported that fat mass of the upper limbs was negatively associated with grip strength in the elderly [25]. Moreover, Ibeneme et al. [26] demonstrated that there was no significant correlation between fat mass and grip strength in postmenopausal women. Thus, there is a controversial relationship between fat mass and muscle strength (grip strength). Further studies are needed to elucidate the association between fat mass and muscle strength (grip strength).

This study had several limitations. First, because body composition was measured using the BIA method, this study cannot be compared directly with a previous study that measured body composition using DXA. Second, because this study was conducted in a cross-sectional setting, the results do not show a causal relationship. Longitudinal studies are required to establish the causal relationships between body composition and bone mass. Third, the participants in this study were recruited from community-dwelling residents who voluntarily attended a health examination, contributing to selection bias. Fourth, the present results were obtained only from Japanese postmenopausal women; therefore, it is not possible to extrapolate the results to other ethnicities, premenopausal women, or men. Fifth, potential confounders that could affect body composition and decrease bone mass, such as calcium intake and renal insufficiency, were not available in this study.

Conclusion

In conclusion, fat mass, but not muscle mass, is a significant determinant of SI in community-dwelling postmenopausal Japanese women. This study suggests that body fat may be more important than muscle in maintaining good bone health in postmenopausal women.

Availability of data and materials

The datasets are available from the corresponding author for reasonable request. The datasets of the Unzen study analyzed in the current study are not publicly available because they include in-depth information, and we are planning to report other association studies using the same dataset.

Abbreviations

DXA:

Dual x-ray absorptiometry

BMD:

Bone mineral density

SI:

Stiffness index

QUS:

Quantitative ultrasound

BIA:

Bioelectrical impedance analysis

25(OH)D:

25-Hydroxyvitamin D

TRACP-5b:

Tartrate-resistant acid phosphatase-5b

PTH:

Parathyroid hormone

BUA:

Broadband ultrasound attenuation

SOS:

Speed of sound

BMI:

Body mass index

CLEIA:

Chemiluminescence enzyme immunoassay

ECLIA:

Electrochemiluminescence immunoassay

SD:

Standard deviation

SE:

Standard error

References

  1. Orimo H, Nakamura T, Hosoi T, Iki M, Uenishi K, Endo N, et al. Japanese 2011 guidelines for prevention and treatment of osteoporosis--executive summary. Arch Osteoporos. 2012;7(1-2):3–20.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Wright NC, Looker AC, Saag KG, Curtis JR, Delzell ES, Randall S, et al. The recent prevalence of osteoporosis and low bone mass in the United States based on bone mineral density at the femoral neck or lumbar spine. J Bone Miner Res. 2014;29(11):2520–6.

    Article  PubMed  Google Scholar 

  3. Leboime A, Confavreux CB, Mehsen N, Paccou J, David C, Roux C. Osteoporosis and mortality. Joint Bone Spine. 2010;77(Suppl 2):S107–12.

    Article  PubMed  Google Scholar 

  4. Burge R, Dawson-Hughes B, Solomon DH, Wong JB, King A, Tosteson A. Incidence and economic burden of osteoporosis-related fractures in the United States, 2005-2025. J Bone Miner Res. 2007;22(3):465–75.

    Article  PubMed  Google Scholar 

  5. Bliuc D, Tran T, van Geel T, Adachi JD, Berger C, van den Bergh J, et al. Reduced bone loss is associated with reduced mortality risk in subjects exposed to nitrogen bisphosphonates: a mediation analysis. J Bone Miner Res. 2019;34(11):2001–11.

    Article  CAS  PubMed  Google Scholar 

  6. Taaffe DR, Cauley JA, Danielson M, Nevitt MC, Lang TF, Bauer DC, et al. Race and sex effects on the association between muscle strength, soft tissue, and bone mineral density in healthy elders: the Health, Aging, and Body Composition Study. J Bone Miner Res. 2001;16(7):1343–52.

    Article  CAS  PubMed  Google Scholar 

  7. Namwongprom S, Rojanasthien S, Mangklabruks A, Soontrapa S, Wongboontan C, Ongphiphadhanakul B. Effect of fat mass and lean mass on bone mineral density in postmenopausal and perimenopausal Thai women. Int J Womens Health. 2013;5:87–92.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Sotunde OF, Kruger HS, Wright HH, Havemann-Nel L, Kruger IM, Wentzel-Viljoen E, et al. Lean mass appears to be more strongly associated with bone health than fat mass in urban black South African women. J Nutr Health Aging. 2015;19(6):628–36.

    Article  CAS  PubMed  Google Scholar 

  9. Marin RV, Pedrosa MAC, Moreira-Pfrimer LDF, Matsudo SMM, Lazaretti-Castro M. Association between lean mass and handgrip strength with bone mineral density in physically active postmenopausal women. J Clin Densitom. 2010;13(1):96–101.

    Article  PubMed  Google Scholar 

  10. Yang S, Nguyen ND, Center JR, Eisman JA, Nguyen TV. Association between abdominal obesity and fracture risk: a prospective study. J Clin Endocrinol Metab. 2013;98(6):2478–83.

    Article  CAS  PubMed  Google Scholar 

  11. Reid IR, Plank LD, Evans MC. Fat mass is an important determinant of whole body bone density in premenopausal women but not in men. J Clin Endocrinol Metab. 1992;75(3):779–82.

    CAS  PubMed  Google Scholar 

  12. Reid IR, Evans MC, Ames RW. Volumetric bone density of the lumbar spine is related to fat mass but not lean mass in normal postmenopausal women. Osteoporos Int. 1994;4(6):362–7.

    Article  CAS  PubMed  Google Scholar 

  13. Trimpou P, Bosaeus I, Bengtsson BA, Landin-Wilhelmsen K. High correlation between quantitative ultrasound and DXA during 7 years of follow-up. Eur J Radiol. 2010;73(2):360–4.

    Article  PubMed  Google Scholar 

  14. Steiner B, Dimai HP, Steiner H, Cirar S, Fahrleitner-Pammer A. Prescreening for osteoporosis with quantitative ultrasound in postmenopausal white women. J Ultrasound Med. 2019;38(6):1553–9.

    Article  PubMed  Google Scholar 

  15. Thomsen K, Jepsen DB, Matzen L, Hermann AP, Masud T, Ryg J. Is calcaneal quantitative ultrasound useful as a prescreen stratification tool for osteoporosis? Osteoporos Int. 2015;26(5):1459–75.

    Article  CAS  PubMed  Google Scholar 

  16. Soen S, Fukunaga M, Sugimoto T, Sone T, Fujiwara S, Endo N, et al. Diagnostic criteria for primary osteoporosis: year 2012 revision. J Bone Miner Metab. 2013;31(3):247–57.

    Article  PubMed  Google Scholar 

  17. Thomson R, Brinkworth GD, Buckley JD, Noakes M, Clifton PM. Good agreement between bioelectrical impedance and dual-energy X-ray absorptiometry for estimating changes in body composition during weight loss in overweight young women. Clin Nutr. 2007;26(6):771–7.

    Article  PubMed  Google Scholar 

  18. Xiao J, Purcell SA, Prado CM, Gonzalez MC. Fat mass to fat-free mass ratio reference values from NHANES III using bioelectrical impedance analysis. Clin Nutr. 2018;37:6 Pt A:2284-7.

    Article  Google Scholar 

  19. Li YZ, Zhuang HF, Cai SQ, Lin CK, Wang PW, Yan LS, et al. Low grip strength is a strong risk factor of osteoporosis in postmenopausal women. Orthop Surg. 2018;10(1):17–22.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Nakamura K, Tsugawa N, Saito T, Ishikawa M, Tsuchiya Y, Hyodo K, et al. Vitamin D status, bone mass, and bone metabolism in home-dwelling postmenopausal Japanese women: Yokogoshi Study. Bone. 2008;42(2):271–7.

    Article  CAS  PubMed  Google Scholar 

  21. Indridason OS, Franzson L, Sigurdsson G. Serum osteoprotegerin and its relationship with bone mineral density and markers of bone turnover. Osteoporos Int. 2005;16(4):417–23.

    Article  CAS  PubMed  Google Scholar 

  22. Choi SW, Kweon SS, Choi JS, Rhee JA, Lee YH, Nam HS, et al. The association between vitamin D and parathyroid hormone and bone mineral density: the Dong-gu Study. J Bone Miner Metab. 2016;34(5):555–63.

    Article  CAS  PubMed  Google Scholar 

  23. Hiol AN, von Hurst PR, Conlon CA, Mugridge O, Beck KL. Body composition associations with muscle strength in older adults living in Auckland, New Zealand. PLoS One. 2021;16(5):e0250439.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sherk VD, Palmer IJ, Bemben MG, Bemben DA. Relationships between body composition, muscular strength, and bone mineral density in estrogen-deficient postmenopausal women. J Clin Densitom. 2009;12(3):292–8.

    Article  PubMed  Google Scholar 

  25. Nonaka K, Murata S, Shiraiwa K, Abiko T, Nakano H, Iwase H, et al. Effect of skeletal muscle and fat mass on muscle strength in the elderly. Healthcare (Basel). 2018;6(3):72.

    Article  PubMed Central  Google Scholar 

  26. Ibeneme S, Ezeigwe C, Ibeneme GC, Ezuma A, Okoye I, Nwankwo JM. Response of gait output and handgrip strength to changes in body fat mass in pre- and postmenopausal women. Curr Ther Res Clin Exp. 2019;90:92–8.

    Article  PubMed  Google Scholar 

  27. Economos CD, Sacheck JM, Wacker W, Shea K, Naumova EN. Precision of Lunar Achilles+ bone quality measurements: time dependency and multiple machine use in field studies. Br J Radiol. 2007;80(959):919–25.

    Article  CAS  PubMed  Google Scholar 

  28. Tomita Y, Arima K, Mizukami S, Tsujimoto R, Kawashiri SY, Nishimura T, et al. Association between self-reported walking speed and calcaneal stiffness index in postmenopausal Japanese women. BMC Geriatr. 2020;20(1):466.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Tanaka N, Arima K, Nishimura T, Tomita Y, Mizukami S, Okabe T, et al. Vitamin K deficiency, evaluated with higher serum ucOC, was correlated with poor bone status in women. J Physiol Anthropol. 2020;39(1):9.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Grigoriou EV, Trovas G, Papaioannou N, Makras P, Kokkoris P, Dontas I, et al. Serum 25-hydroxyvitamin D status, quantitative ultrasound parameters, and their determinants in Greek population. Arch Osteoporos. 2018;13(1):111.

    Article  PubMed  Google Scholar 

  31. Nishimura T, Arima K, Abe Y, Kanagae M, Mizukami S, Okabe T, et al. Relationship between bone turnover markers and the heel stiffness index measured by quantitative ultrasound in post-menopausal Japanese women. Ann Hum Biol. 2019;46(4):330–4.

    Article  PubMed  Google Scholar 

  32. Uchiyama M, Mizukami S, Arima K, Nishimura T, Tomita Y, Abe Y, et al. Association between serum 25-hydroxyvitamin D and physical performance measures in middle-aged and old Japanese men and women: the Unzen study. PLoS One. 2021;16(12):e0261639.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Reid IR, Ames R, Evans MC, Sharpe S, Gamble G, France JT, et al. Determinants of total body and regional bone mineral density in normal postmenopausal women--a key role for fat mass. J Clin Endocrinol Metab. 1992;75(1):45–51.

    CAS  PubMed  Google Scholar 

  34. Nur H, Toraman NF, Arica Z, Sarier N, Samur A. The relationship between body composition and bone mineral density in postmenopausal Turkish women. Rheumatol Int. 2013;33(3):607–12.

    Article  CAS  PubMed  Google Scholar 

  35. Kaye SA, Folsom AR, Soler JT, Prineas RJ, Potter JD. Associations of body mass and fat distribution with sex hormone concentrations in postmenopausal women. Int J Epidemiol. 1991;20(1):151–6.

    Article  CAS  PubMed  Google Scholar 

  36. Simpson ER, Bulun SE, Nichols JE, Zhao Y. Estrogen biosynthesis in adipose tissue: regulation by paracrine and autocrine mechanisms. J Endocrinol. 1996;150(Suppl):S51–7.

    CAS  PubMed  Google Scholar 

  37. Almeida M, Laurent MR, Dubois V, Claessens F, O'Brien CA, Bouillon R, et al. Estrogens and androgens in skeletal physiology and pathophysiology. Physiol Rev. 2017;97(1):135–87.

    Article  PubMed  Google Scholar 

  38. Schindler AE, Ebert A, Friedrich E. Conversion of androstenedione to estrone by human tissue. J Clin Endocrinol Metab. 1972;35(4):627–30.

    Article  CAS  PubMed  Google Scholar 

  39. Hemsell DL, Grodin JM, Brenner PF, Siiteri PK, MacDonald PC. Plasma precursors of estrogen. II. Correlation of the extent of conversion of plasma androstenedione to estrone with age. J Clin Endocrinol Metab. 1974;38(3):476–9.

    Article  CAS  PubMed  Google Scholar 

  40. Thomas T, Burguera B, Melton LJ 3rd, Atkinson EJ, O'Fallon WM, Riggs BL, et al. Role of serum leptin, insulin, and estrogen levels as potential mediators of the relationship between fat mass and bone mineral density in men versus women. Bone. 2001;29(2):114–20.

    Article  CAS  PubMed  Google Scholar 

  41. Burguera B, Hofbauer LC, Thomas T, Gori F, Evans GL, Khosla S, et al. Leptin reduces ovariectomy-induced bone loss in rats. Endocrinology. 2001;142(8):3546–53.

    Article  CAS  PubMed  Google Scholar 

  42. Nguyen TV, Kelly PJ, Sambrook PN, Gilbert C, Pocock NA, Eisman JA. Lifestyle factors and bone density in the elderly: implications for osteoporosis prevention. J Bone Miner Res. 1994;9(9):1339–46.

    Article  CAS  PubMed  Google Scholar 

  43. Gourlay ML, Hammett-Stabler CA, Renner JB, Rubin JE. Associations between body composition, hormonal and lifestyle factors, bone turnover, and BMD. J Bone Metab. 2014;21(1):61–8.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Manske SL, Boyd SK, Zernicke RF. Muscle and bone follow similar temporal patterns of recovery from muscle-induced disuse due to botulinum toxin injection. Bone. 2010;46(1):24–31.

    Article  CAS  PubMed  Google Scholar 

  45. Saxena R, Pan G, Dohm ED, McDonald JM. Modeled microgravity and hindlimb unloading sensitize osteoclast precursors to RANKL-mediated osteoclastogenesis. J Bone Miner Metab. 2011;29(1):111–22.

    Article  CAS  PubMed  Google Scholar 

  46. Rucci N, Rufo A, Alamanou M, Teti A. Modeled microgravity stimulates osteoclastogenesis and bone resorption by increasing osteoblast RANKL/OPG ratio. J Cell Biochem. 2007;100(2):464–73.

    Article  CAS  PubMed  Google Scholar 

  47. Goodship AE, Cunningham JL, Oganov V, Darling J, Miles AW, Owen GW. Bone loss during long term space flight is prevented by the application of a short term impulsive mechanical stimulus. Acta astronaut. 1998;43(3-6):65–75.

    Article  CAS  PubMed  Google Scholar 

  48. Chen Y, Xiang J, Wang Z, Xiao Y, Zhang D, Chen X, et al. Associations of bone mineral density with lean mass, fat mass, and dietary patterns in postmenopausal Chinese women: a 2-year prospective study. PLoS One. 2015;10(9):e0137097.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Mezquita-Raya P, Muñoz-Torres M, Luna JD, Luna V, Lopez-Rodriguez F, Torres-Vela E, et al. Relation between vitamin D insufficiency, bone density, and bone metabolism in healthy postmenopausal women. J Bone Miner Res. 2001;16(8):1408–15.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

Not applicable.

Funding

This work was supported by JSPS KAKENHI (grant number JP23370105).

Author information

Authors and Affiliations

  1. Department of Public Health, Nagasaki University Graduate School of Biomedical Sciences, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan

    Satoshi Mizukami, Kazuhiko Arima, Yoshihito Tomita, Hiroki Nakashima, Yuzo Honda, Michiko Uchiyama, Tetsuji Ookawachi, Mitsuo Kanagae & Kiyoshi Aoyagi

  2. Leading Medical Research Core Unit, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan

    Satoshi Mizukami & Kiyoshi Aoyagi

  3. Department of Health and Nutrition Science, Nishikyusyu University, Kanzaki, Japan

    Yasuyo Abe

  4. School of Rehabilitation, Department of Physical Therapy, Tokyo Professional University of Health Science, Tokyo, Japan

    Yoshihito Tomita

  5. Ken-Hoku Health Care Office, Nagasaki, Japan

    Hisashi Goto

  6. Medical Policy Division, Nagasaki Prefectural Government, Nagasaki, Japan

    Maiko Hasegawa

  7. National Health Insurance & Health Improvement Division, Nagasaki Prefectural Government, Nagasaki, Japan

    Youko Sou

  8. Department of Rehabilitation, Nishi-Isahaya Hospital, Isahaya, Japan

    Mitsuo Kanagae

  9. Department of Orthopedic Surgery, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan

    Makoto Osaki

Authors
  1. Satoshi Mizukami
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  2. Kazuhiko Arima
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  3. Yasuyo Abe
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  4. Yoshihito Tomita
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  5. Hiroki Nakashima
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  6. Yuzo Honda
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  7. Michiko Uchiyama
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  8. Tetsuji Ookawachi
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  9. Hisashi Goto
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  10. Maiko Hasegawa
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  11. Youko Sou
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  12. Mitsuo Kanagae
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  13. Makoto Osaki
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  14. Kiyoshi Aoyagi
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Contributions

Study design was done by YA, MO, and KA. Study conduct was done by SM and KA. Data collection and analysis were done by SM, KA, YA, YT, HN, YH, MU, TO, HG, MH, YS, and MK. Statistical analysis was done by SM and KA. Drafting of the manuscript was done by SM, KA, YA, and KA. Revising of the manuscript content was done by SM, YT, HN, YH, MU, TO, HG, MH, YS, MK, MO, and KA. Approval of the final version of the manuscript was done by all authors. KA takes responsibility for the integrity of data analysis.

Corresponding author

Correspondence to Kiyoshi Aoyagi.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the Nagasaki University Graduate School of Biomedical Sciences (11072739-2). All procedures were performed in accordance with the ethical standards of the institutional and/or national research committee and in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Mizukami, S., Arima, K., Abe, Y. et al. Association between fat mass by bioelectrical impedance analysis and bone mass by quantitative ultrasound in relation to grip strength and serum 25-hydroxyvitamin D in postmenopausal Japanese women: the Unzen study. J Physiol Anthropol 41, 7 (2022). https://doi.org/10.1186/s40101-022-00281-5

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  • DOI: https://doi.org/10.1186/s40101-022-00281-5

Keywords

Journal of Physiological Anthropology

ISSN: 1880-6805

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