Muscle-to-bone and soft tissue-to-bone ratios in track and field athletes

Abstract

The purpose of this study was to compare the muscle-to-bone (MBR) and soft tissue-to-bone ratios (SBR) of 459 track and field athletes across event groups to identify differences in MBR and SBR. Dual X-ray absorptiometry provided total and regional (i.e., arm, leg, trunk) lean mass (LM), fat mass (FM), and bone mineral content (BMC). MBR was calculated by dividing LM by BMC. The SBR was calculated by dividing LM+FM by BMC. Kruskal-Wallis tests were used to compare ratios across event groups. Dunn’s post-hoc tests were utilized to adjust for multiple comparisons. Total MBR for females was higher in the throwers compared to the multievent athletes (p=0.02). For the males, total MBR was lower in jumpers compared to all events except pole vaulters (PV) (p<0.05). Trunk MBR was higher in the long-distance runners (LD) compared to jumpers, PV, and throwers (p<0.05). The throwers had higher total, arm, and leg SBRs compared to the jumpers, LD, middle distance, PV, and sprint groups (p<0.05). Significant differences in total and regional MBR and SBR were identified across event groups for both sexes, and may indicate event-specific adaptations impacting the balance between soft tissue and bone.

Keywords

bone health - body composition - athletics - injury risk

Introduction

Track and field is a unique sport that includes events with differing physiological and mechanical demands [1] [2]. As a result of these demands, body composition of muscle, fat, and bone masses among male and female track and field athletes can significantly vary between event groups [3] [4] [5]. To understand further how varied sport-specific training affects athlete body composition, ratios between the amount of muscle to bone [muscle-to-bone ratio (MBR)] and soft tissue to bone [soft tissue-to-bone ratio (SBR)] have been utilized [6] [7] [8] [9] [10] [11]. The MBR and SBR, which examine the amount of soft tissue (lean and fat mass) in relation to bone, provide unique insight into the possible stress tissue (i. e. muscle or muscle and fat) places on bone during sports-specific training and competition [12]. This approach of examining MBR and SBR to understand the potential bone strain and possible adaptations resulting from an athlete’s body composition and sport-specific training demands would, for example, in track and field athletes, provide new insight into how different events possibly influence tissue-to-bone ratios. Description of these ratios among male and female athletes participating in different track and field events could help future research determine what ratios are ideal for each event, thereby providing coaches, performance staff, and athletes with a way to monitor training-induced changes in muscle, fat, and bone. Tissue-to-bone ratios could also be used to assess the success or lack of success of strength programs and/or nutrition plans in track and field athletes. This could be extremely important for distance runners who often have low BMD and are often at an increased risk for bone stress injuries.

The use of dual X-ray absorptiometry (DXA) allows one to determine both total as well as regional measures of lean, fat, and bone masses, permitting the assessment of MBR and SBR. Total and regional MBR has previously been determined in professional American football athletes [8], collegiate football athletes [7], and collegiate rowing athletes [9] as a means to monitor changes in the muscle and bone in response to training or nutritional plans. However, no studies have examined the MBR and SBR of collegiate track and field athletes. Addressing this gap would contribute to the development of normative data in the sport of track and field and aid future research in promoting or identifying desirable ratio ranges to support bone health during event-specific participation. Thus, the purpose of this study is to compare the DXA-derived MBR and SBR of National Collegiate Athletic Association (NCAA) Division I (DI) collegiate track and field athletes across event groups.

Materials and Methods

Study Design and Subjects

This cross-sectional secondary analysis used data from a previous study that examined body composition in NCAA DI track and field athletes (Dengel et al., 2020). A total of 459 (n=253 females, n=206 males) athletes across multiple universities were included in this analysis to assess DXA-derived total and regional MBR and SBR across event groups. Subjects were categorized by their primary event group into one of seven categories: sprints (100 m, 200 m, 400 m, 100 m hurdles, 110 m hurdles, 400 m hurdles; n=79 females, 44 males), middle-distance (800 m, 1500 m; n=18 females, 23 males), long distance (3000 m, 5000 m, 10000 m; n=82 females, 70 males), jumps (long jump, high jump, triple jump; n=22 females, 23 males), throws (shot put, discus, javelin; n=17 females, 17 males), multi-event (decathletes or heptathletes; n=9 females, 11 males), and pole vault (n=26 females, 18 males). This study was approved by our University’s Institutional Review Board.

Experimental Procedures

Height and weight were collected via stadiometer and digital scale, respectively. Body composition was measured using DXA standard full-body imaging in the supine position. All scans were performed using GE Healthcare Lunar systems (iDXA/Prodigy, GE Healthcare Lunar, Madison, WI). Scans were analyzed by a single technician using EnCore software v16.2. DXA provided the MBR values of LM and bone mineral content (BMC), as well as FM, bone mineral density (BMD), total Z-score for total-body BMD, and body composition measures. MBR was obtained by dividing LM by BMC. SBR was obtained by dividing (LM+FM) by BMC. Total and regional (i. e. arms, legs, trunk) MBR and SBR were compared between event groups for each sex.

Statistical Analysis

Descriptive statistics included median and interquartile range for continuous variables. The Shapiro-Wilk and Bartlett’s tests were used to test the assumption of normality and homogeneity of variance, respectively. The Kruskal-Wallis test was utilized due to the unequal variances across groups and non-normal distribution of data to compare total and regional body composition, as well as MBR and SBR, across event categories for each sex. Dunn’s post-hoc test was used to adjust for multiple comparisons. Significance was set at p<0.05. All analyses and visualization were done using GraphPad PRISM version 10.1.2.

Results

Descriptive physical characteristics of participants are presented in [Table 1]. There were no age differences across event groups for the males or females. Females were 19.9±1.5 years old on average, and the males were 20.2±1.5 years old. For the females, the athletes in the LD, MD, PV, and sprint groups were significantly shorter than those in the jump, ME, and throw groups. Additionally, the throwers were significantly heavier and had a markedly higher BMI than all other event groups while the LD were significantly lighter than other event groups. For the males, similarly, the LD runners were significantly shorter than jumpers, ME athletes, and throwers. The throwers were also significantly heavier than other event groups and had a significantly higher BMI while the LD had a significantly lower BMI compared to the other event groups. For both sexes, total Z-scores were lower in the LD groups compared to the jumpers, ME athletes, PVs, sprinters, and throwers. For the males, total Z-scores were also higher for throwers than sprinters and MD runners, but this was not observed in the females.

Table 1 Physical characteristics of NCAA DI track and field athletes by event category presented as median and interquartile range.

Event Category	Jump	LD	MD	ME	PV	Sprint	Throw	p
MALES	N=23	N=70	N=23	N=11	N=18	N=44	N=17
Age (years)	20.0 (19.0–21.0)a	20.0 (19.0–21.3)a	21.0 (19.0–22.0)a	21.0 (20.0–22.0)a	20.5 (19.0–22.0)a	20.0 (19.0–21.0)a	20.0 (19.0–21.0)a	0.33
Height (cm)	184.9 (178.6–186.7)a	177.8 (172.7–182.9)b	181.6 (175.3–184.2)ab	184.4 (180.3–186.9)a	181.6 (177.8–185.6)ab	180.2 (175.3–186.9)ab	185.4 (182.2–190.5)a	0.0001
Weight (kg)	78.2 (74.3–83.8)a	65.4 (61.5–69.3)b	70.1 (67.0–78.4)ab	81.4 (74.6–83.4)a	79.2 (73.2–81.7)a	76.1 (70.9–81.6)a	106.0 (85.3–119.0)c	<0.0001
BMI (kg/m2)	23.4 (22.3–24.5)a	20.7 (19.9–21.8)b	22.2 (21.4–22.5)a	23.6 (22.4–25.2)a	23.7 (22.8–24.4)a	23.1 (22.1–24.5)a	29.8 (26.5–32.0)c	<0.0001
Total Z-score	1.8 (1.4–2.2)ac	0.7 (0.2–1.2)b	1.1 (0.4–2.0)ab	1.9 (1.5–2.8)ac	1.7 (1.2–2.6)ac	1.3 (0.8–2.2)a	2.6 (2.0–3.8)c	<0.0001
FEMALES	N=22	N=82	N=18	N=9	N=26	N=79	N=17
Age (years)	19.5 (18.0–21.0)a	20.0 (19.0–21.0)a	21.0 (19.0–21.3)a	20.0 (18.0–21.0)a	19.0 (19.0–21.0)a	20.0 (18.0–21.0)a	21.0 (19.0–22.0)a	0.39
Height (cm)	173.4 (167.6–176.8)a	165.1 (161.3–169.2)b	167.6 (163.5–170.2)b	175.8 (171.7–179.7)a	166.0 (162.6–170.2)b	166.9 (164.3–171.5)b	174.8 (169.0–177.5)a	<0.0001
Weight (kg)	61.1 (59.4–65.0)a	54.2 (50.7–59.3)b	58.6 (57.2–62.9)a	65.3 (60.3–78.4)a	60.8 (58.1–64.1)a	60.5 (57.4–64.6)a	86.8 (72.1–102.3)c	<0.0001
BMI (kg/m2)	20.5 (19.6–22.3)ab	19.9 (18.9–21.1)b	21.5 (20.3–22.4)ab	21.1 (20.4–23.4)ab	21.9 (20.6–22.9)a	21.8 (20.4–23.0)a	29.2 (23.7–34.6)c	<0.0001
Total Z-score	2.0 (1.1–2.4)a	1.2 (0.5–1.5)b	1.6 (1.1–2.1)ab	2.3 (1.8–3.5)a	2.1 (1.5–2.6)a	2.0 (1.0–2.4) a	2.1 (1.3–2.3)a	<0.0001

If event categories share a letter within each row, they are not significantly different at p<0.05. BMI=body mass index (weight in kg/height in m²). LD=long distance; MD=mid-distance; ME=multi-event; PV=pole vault.

Total and regional MBRs and SBRs are provided as medians with interquartile ranges in [Table 2]. To visualize the kernel density estimate of the data, MBR distributions are presented as violin plots in [Fig. 1] [3], while [Fig. 2] [4] present SBR distributions. The only difference in total MBR for the females was that thrower values were greater than values for ME athletes. For the males, total MBR was significantly lower in jumpers compared to all other event groups except PVs. Arm MBR was lower in LD groups compared to sprinters and throwers for both sexes. Additionally, for the males and females, leg MBR was higher in throwers compared to jumpers and LD runners. For females, the leg MBR was also higher for throwers than sprinters, PVs, and ME athletes, and for males it was also higher than MD. Lastly, trunk MBR was higher in the male and female LD groups compared to jumpers, PVs, and throwers. For males, MD trunk MBR was also higher than these same groups, and for female LDs, it was also higher than ME and sprint athletes.

Fig. 1 Total, arm, leg, and trunk MBR between event groups for females. If event groups share a letter within a region (i. e. total, arm, or leg), they are not significantly different at p<0.05.

Fig. 2 Total, arm, leg, and trunk SBR between event groups for females. If event groups share a letter within a region (i. e. total, arm, or leg), they are not significantly different at p<0.05.

Fig. 3 Total, arm, leg, and trunk MBR between event groups for males. If event groups share a letter within a region (i. e. total, arm, or leg), they are not significantly different at p<0.05.

Fig. 4 Total, arm, leg, and trunk SBR between event groups for males. If event groups share a letter within a region (i. e. total, arm, or leg), they are not significantly different at p<0.05.

Table 2 Total and regional muscle-to-bone ratio and soft tissue-to-bone ratios of NCAA DI track and field athletes presented as median (interquartile range).

Event Category	Jump	LD	MD	ME	PV	Sprint	Throw	p
MALES	N=23	N=70	N=23	N=11	N=18	N=44	N=17
Total MBR	17.3 (16.3–18.1)a	18.5 (17.4–19.5)b	18.6 (17.9–19.3)b	18.7 (18.2–19.7)b	18.3 (17.0–19.0)ab	18.6 (17.8–19.5)b	18.5 (18.1–19.4)b	<0.01
Arm MBR	15.6 (15.2–17.0)ab	15.6 (14.7–16.6)a	16.6 (15.5–17.4)ab	16.6 (15.4–17.4)ab	15.7 (14.9–16.3)ab	16.5 (15.6–18.0)b	16.7 (16.0–17.6)b	<0.001
Leg MBR	14.8 (14.0–15.2)a	15.0 (14.4–15.9)ab	15.2 (14.6–16.3)ab	16.2 (14.7–17.0)abc	15.9 (15.1–16.6)abc	15.8 (15.2–17.0)bc	16.9 (15.9–17.9)c	<0.0001
Trunk MBR	27.1 (25.0–29.4)a	32.0 (30.0–34.5)b	31.6 (29.9–35.2)b	30.8 (27.8–32.5)ab	28.7 (26.4–30.8)ac	30.3 (28.3–32.5)bc	27.2 (24.7–30.3)ac	<0.0001
Total SBR	19.9 (18.3–20.9)a	21.2 (19.9–22.5)b	21.0 (19.4–22.1)ab	21.5 (20.6–21.8)abc	21.1 (19.0–22.3)ab	20.9 (19.9–21.9)ab	22.9 (22.2–24.9)c	<0.0001
Arm SBR	17.7 (17.0–19.1)a	17.6 (16.9–18.8)a	18.5 (17.2–19.7)a	17.9 (17.5–19.1)ab	17.5 (16.7–18.4)a	18.3 (17.4–19.7)a	20.3 (19.2–21.5)b	<0.001
Leg SBR	17.2 (16.0–17.8)a	17.5 (16.6–18.5)a	17.4 (16.5–18.6)a	17.9 (17.5–18.7)ab	18.2 (17.3–19.4)a	17.9 (17.2–19.1)a	20.4 (19.6–22.4)b	<0.0001
Trunk SBR	31.0 (28.3–32.6)a	35.6 (34.0–38.6)b	34.3 (32.3–38.7)bc	33.8 (30.6–36.2)abc	32.5 (29.3–35.4)ac	33.4 (31.0–36.0)c	35.4 (33.1–36.4)bc	<0.0001
FEMALES	N=22	N=82	N=18	N=9	N=26	N=79	N=17
Total MBR	16.6 (16.3–17.5)ab	17.4 (16.6–18.4)ab	17.5 (16.4–17.9)ab	16.3 (15.9–16.7)a	17.3 (16.5–18.1)ab	17.2 (16.4–18.2)ab	18.4 (16.8–19.1)b	0.01
Arm MBR	14.3 (13.6–15.8)ab	13.8 (13.0–14.8)a	14.2 (13.7–15.0)ab	13.8 (13.1–14.7)ab	14.6 (13.3–14.9)ab	14.7 (13.8–15.5)b	16.0 (14.6–16.7)b	<0.001
Leg MBR	15.3 (14.6–16.2)a	15.2 (14.4–16.0)a	15.7 (14.5–16.6)abc	14.7 (14.2–15.0)a	15.6 (14.9–16.0)ab	16.0 (14.9–16.9)b	17.4 (16.0–18.7)c	<0.0001
Trunk MBR	26.7 (24.3–29.2)a	30.9 (28.6–33.0)b	28.6 (26.3–30.3)ab	26.4 (25.0–28.3)a	28.4 (25.6–29.9)a	28.0 (25.7–29.5)a	26.7 (24.6–28.2)a	<0.0001
Total SBR	20.5 (19.6–22.3)ab	22.4 (20.8–23.6)b	22.2 (20.4–23.2)ab	21.1 (20.5–21.5)ab	21.9 (21.2–23.2)ab	20.9 (20.0–21.9)a	25.6 (23.6–28.0)c	<0.0001
Arm SBR	17.8 (16.7–20.1)a	18.4 (17.1–19.8)a	18.8 (16.9–19.4)a	18.2 (17.3–19.0)a	18.4 (17.7–19.3)a	18.0 (17.1–19.1)a	22.8 (19.8–24.8)b	<0.001
Leg SBR	19.2 (18.5–21.0)a	20.6 (19.3–21.4)a	20.7 (19.1–22.1)a	19.5 (18.8–20.3)a	21.0 (19.5–21.9)a	20.2 (19.0–21.3)a	24.8 (23.1–27.2)b	<0.0001
Trunk SBR	31.7 (29.4–34.3)a	37.1 (34.6–40.1)b	34.9 (33.0–37.4)ab	33.2 (32.1–34.2)ab	35.2 (32.7–37.6)ab	33.2 (31.3–34.8)a	37.0 (33.9–43.1)b	<0.0001

If event categories share a letter within each row, they are not significantly different at p<0.05. MBR=muscle-to-bone ratio; SBR=soft tissue-to-bone ratio; LD=long distance; MD=mid-distance; ME=multi-event; PV=pole vault.

The female throwers had significantly higher total, arm, and leg SBRs compared to all other event groups. Trunk SBR was also higher in throwers and LDs compared to jumpers and sprinters. Among the males, throwers had higher total, arm, and leg SBRs compared to jumpers, LD and MD runners, PVs, and sprinters. Total and trunk SBR were also higher in LDs compared to jumpers, and trunk SBR was also higher in LDs compared to PVs.

Discussion

The current study is, to the best of our knowledge, the first to compare DXA-derived total and regional MBRs and SBRs of NCAA DI track and field athletes by event group. Differences identified may be a result of event-specific training impacting the balance of muscle/soft tissue and bone. Findings of this study are important as they may provide insight into how training-induced body composition impacts the balance between the tissues which could inform strategies to mitigate discrepancies and prevent injury and optimize performance.

For the females, our study found the only difference in total MBR was between the throwers and ME athletes, as the ME group had the lowest median value, while the throw group had the highest. This finding may be due to the nature of the two events. Throwers tend to maximize their LM, as increased total LM has been previously linked to throwing performance [13]. This was true of the throwers in the current study, as they had the highest LM compared to other groups. They also had more FM than other groups, which contributed to their SBRs being significantly higher than all other event groups. On the other hand, ME athletes require more well-rounded training to compete in various events. This includes training for and performing events involving not only powerful and explosive movements like throwing, but also movements involving high impact and repetitive, compressive forces such as those involved in the 800 m run, the 200 m sprint, or the high jump. As a result of these different movements involved in their training and competition, the culmination of muscle strengthening in conjunction with high-impact activities may have enhanced the bone mass of the females in the ME group in relation to their LM when compared to their thrower counterparts. However, it should be noted that the sample size of the ME group (females: n=9) was smaller than that of the rest of the event categories, which may have impacted findings.

In males, total MBR was lower in jumpers compared to all other events except PVs, indicating less LM per unit of bone mass. The decreased total MBR of the jumpers is interesting and reflects previous research, which found that high-impact forces involved in jumping are effective at eliciting bone adaptation [14]. Specifically in collegiate jumpers, a previous study identified enhanced tibial bone properties in the jump leg of long jump and high jump athletes compared to their lead non-jump leg [15]. In addition to the within-group differences, this study also identified enhanced tibial bone properties in the jumping athletes compared to a group of controls.

Overall, the sex differences of the current study may indicate that combination training, which stresses multiple systems, may be an effective means to enhance bone adaptation in females, while in males, event-specific training alone is adequate for bone acquisition. Regarding regional differences, the male LD group had higher trunk MBR compared to the jumpers, PVs, and throwers. Among the females, it was higher in the LD group compared to jumpers, ME athletes, PVs, sprinters, and throwers. Similarly, trunk SBRs were higher in the LD males compared to jumpers, PVs, and sprinters, as well as in the LD females compared to jumpers and sprinters. As evidenced by their regional body composition, these increased trunk ratios were a result of low trunk BMC. The parent study of this secondary analysis also identified lower whole body lean mass, as well as decreased spine and pelvis BMD, in the male and female LD group compared to other events [4]. These findings align with previous research that identified a similar risk for low lumbar spine BMD in male and female LD runners [16]. However, that study also found that LD runners who included resistance training at least two times per week in their training had improved lumbar spine BMD compared to those who did not. In addition, another study identified decreased cortical bone strength, cortical area, and muscle cross sectional area in runners with a history of stress fracture [17]. The authors suggest that increasing muscle size and strength may reduce the risk of bone stress injury in competitive distance runners. Thus, the possibility for detrimental bone strain may be mitigated by increasing muscle mass to absorb the high-impact forces associated with distance running and protect the skeleton [18]. Because muscle strength is associated with bone health [19], examining bone health in the context of muscle is important. As such, the MBR and SBR may prove to be an effective means of monitoring the bone health of LD runners.

Ultimately, the ratios may be increased as a result of either increased LM or FM or via decreased BMC, which could result from excess exercise-related bone stress without adequate recovery [20] [21] [22]. Therefore, the MBR and SBR may be important metrics to examine in the context of track and field, as they may provide insight into stress being placed on the bone. This information may be especially useful for middle- and long-distance runners who are at higher risk for bone stress reactions and fractures due to their high volume, high impact training, and lower levels of muscle mass. Should a range of optimal ratios be determined, team nutritionists or strength and conditioning coaches may use them as a means to monitor how training-induced adaptations influence injury risk.

The current study has multiple strengths. A large total sample size and inclusion of data from a multitude of programs across the country provided a well-rounded and robust analysis. Additionally, the use of DXA to obtain values of LM, FM, and BMC provided accurate and reliable measures when compared to values obtained from anthropometry. One limitation to this study is the lack of availability of training data. While we can infer, based on common training principles, that athletes participating in certain events are undergoing certain types of training, this cannot be confirmed. Another limitation to this study is the small sample size of some event categories, particularly the ME groups. Additionally, the time of year that data collection took place may be considered another limitation. DXA scans were performed prior to the indoor track season in September-December, however, body composition may change from pre- to post- season as athletes reach their peak toward the end of the season [23]. Furthermore, it is likely that many of the LD and MD runners also compete in cross country running during the fall season. This would mean that scanning took place during or shortly after their competitive season, which may have impacted their results. Lastly, it would be ideal to monitor MBR and SBR in accordance with injury incidence in athletes to further elucidate the link between the ratios and risk of injury.

Conclusion

The MBR and SBR explore the balance between muscle, fat, and bone mass as it relates to mechanical-induced bone strain. The current study found, for the females, a lower total MBR in the ME athletes compared to throwers, while in the males, it was lowest among the jumpers. These results may speak to importance of incorporating concurrent training to enhance skeletal health in females, while event-specific training alone is sufficient for males. Trunk ratios were elevated in the LD groups due to their decreased BMC. As previous research has suggested, muscle mass has positive effects on bone health, and it is important to monitor bone health in the context of muscle. In this way, MBR and SBR may be important metrics to consider for LD runners who are at risk for bone injuries. Thus, future research should seek to identify associations between MBRs and SBRs with training and/or injuries to establish optimal ratio ranges for track and field athletes.

Conflict of Interest

The authors declare that they have no conflict of interest.

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Publication History

Received: 25 July 2024

Accepted: 07 October 2024

Article published online:
04 February 2025