The impact of obesity on static and proactive balance and gait patterns in sarcopenic older adults: an analytical cross-sectional investigation

Participants

The estimation of our study’s sample size was rigorously determined using G*Power (version 3.1.9.4), with parameters set to control Type I error at alpha = 0.05 and Type II error at beta = 0.60. With an anticipated moderate effect size of r = 0.35, this power analysis indicated a necessity for at least 40 participants. Embarking on recruitment between January and April 2022, 72 volunteers were initially garnered from various regional obesity care centers. Strict adherence to our pre-established inclusion and exclusion criteria meant that of these, only 45 candidates qualified. However, due to challenges related to adherence to our study’s regimen, we witnessed a drop out of three individuals, culminating in a final sample size of 42 participants. Based on their body mass index (BMI, kg/m²), they were subsequently allocated into two distinct groups: the control group (CG; n = 22; M/F = 12/10; age = 81.1 ± 4.0 years; BMI = 24.9 ± 0.6 kg/m²) and the sarcopenic obese group (SOG; n = 20; M/F = 12/8; age = 77.7 ± 2.9 years; BMI = 34.5 ± 3.2 kg/m²).

To qualify, participants had to demonstrate a handgrip force less than 17 N, a gait speed under 1.0 m/s, be aged over 65 years, maintain the capacity for verbal communication with our team, and sustain physical independence. Individuals presenting with conditions such as severe neurological or cognitive impairments, significant cardiovascular diseases, major musculoskeletal deformities or injuries, or other chronic diseases, as well as those on medications potentially affecting our assessments or having a Montreal Cognitive Assessment (MoCA) score below 26, were systematically excluded. As a preliminary step, we had each potential participant fill out a detailed questionnaire, shedding light on aspects like the history of falls, medication usage, physical independence, and frailty levels. Their responses were meticulously examined by the medical professionals stationed at the recruitment centers. Questionnaires like the Ricci and Gagnon scales (Zulfiqar et al., 2022) and the MoCA test (Smith, Gildeh & Holmes, 2007) were employed to evaluate physical activity and cognitive statuses, respectively. The onset of the study was marked by each participant signing an informed consent form, reflective of their complete understanding and voluntary participation. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of South Ethics Committee for the Protection Persons (C.P.P. SOUTH /No. 0477/2022, 22 February 2022). Throughout the research, a steadfast commitment was maintained towards ensuring the confidentiality of participant data, with all analyses being conducted in an anonymous and aggregated manner, upholding the highest standards of research ethics.

Evaluation protocol

To ensure uniformity and eliminate variance, all these tests were administered in a specialized clinical examination room by a singular assessor. Additionally, to maintain clarity and coherence, participants were given standardized verbal directives concerning the procedural aspects of the tests.

Statistical analysis

The statistical analysis was carried out using Jamovi software (Version 2.3, Sydney, Australia). Prior to analysis, normality and homogeneity of variance assumptions were checked using the Shapiro-Wilk and Levene tests, respectively. All parameters met the assumptions of normality and homogeneity of variance. To compare differences between groups, independent t-tests were performed for all parameters, with obesity as the grouping variable. Additionally, Spearman correlation analysis was utilized to examine associations between anthropometric measures and gait/balance parameters. Mean values with their respective standard deviations were reported, and the significance level was set at p < 0.05.

Anthropometric measurements

Table 1 presents the anthropometric measurements of the CG and the SOG groups. The statistical analysis revealed that FBM, body fat, body mass, and BMI were higher in SOG compared to CG (p < 0.001). However, percentage of LBM was lower in SOG than CG (p < 0.001). No significant effect was revealed on age, height, LBM, and handgrip force.

Steady-state and proactive balance

Table 2 presents the results of steady-state and proactive balance evaluations for both the CG and SOG groups. Statistical analysis indicated that the distance achieved in the FRT was significantly shorter in the SOG group compared to the CG (p = 0.036, d = 0.67). Additionally, the time taken to complete the TUG test was significantly shorter in the CG compared to the SOG group (p = 0.006, d = 0.9). While the standing time in the ROM test was significantly longer in the CG compared to the SOG group (p = 0.002, d = −1.01).

Gait parameters

Table 3 presents the gait parameters for both the CG and SOG groups. The statistical analysis revealed significant differences in gait parameters between the two groups. Specifically, the gait speed was lower in the SOG compared to CG (p < 0.001, d = −1.63). The stride length and step length were all significantly lower in the SOG group compared to the CG (p < 0.001, d = −6.67; p < 0.001, d = −6.67, respectively). In terms of gait cycle phases, the support phase was significantly longer, while the swing phase was significantly shorter in the SOG group compared to the CG (p < 0.001, d = 5.26; p < 0.001, d = −5.26, respectively).

Correlation analysis

The results from the data highlight significant correlations between various anthropometric measures and different balance and gait parameters in SOG (Table 4). Body weight exhibited a significant negative correlation with the ROM and the FRT with correlation values of −0.51 (p = 0.02) and −0.51 (p = 0.022), respectively. Interestingly, it showed a positive correlation with the TUG test (r = 0.61, p = 0.004) but a negative correlation with gait speed (r = −0.62, p = 0.004). BMI showed a significant negative correlation with the ROM (r = −0.54, p = 0.014) and FRT (r = −0.51, p = 0.022) while being positively correlated with the TUG test (r = 0.64, p = 0.002) and negatively with gait speed (r = −0.64, p = 0.002). The percentage of LBM revealed a notable positive correlation with the ROM (r = 0.77, p < 0.001) and the FRT (r = 0.74, p < 0.001). Additionally, it exhibited a negative correlation with the TUG test (r = 0.80, p < 0.001) but was positively correlated with gait speed (r = 0.85, p < 0.001) (Fig. 2). Finally, no significant correlations were observed between anthropometric parameters and balance and gait measures (Fig. 2).

Effect of obesity on the steady-state and proactive balance

Our analysis reveals a definitive influence of obesity on the balance of older adults with sarcopenia. When considering the steady-state balance, as evaluated through the ROM test, there was a noticeable 20% lower performance among the older SO group. In the context of proactive balance evaluations, our data from both FRT and TUG tests revealed a 14% lower FRT score and a 12.5% higher TUG timing. Such results resonate with existing research, particularly those focused on older adults not classified as sarcopenic (Carneiro et al., 2012; Dutil et al., 2013; Maktouf et al., 2018, 2019, 2020; Kong, Won & Kim, 2020; Liao et al., 2022). Our findings emphasize the compounded adversities posed by obesity in conjunction with the inherent challenges of aging, accentuating their combined toll on functional capabilities (Dutil et al., 2013; Handrigan et al., 2017; Maktouf et al., 2018, 2020). The amalgamation of obesity and aging not only escalates the scope of functional deficits but may also increase the predisposition towards accidents like falls and related injuries (Baumgartner et al., 2004; Handrigan et al., 2017; Li et al., 2022).

To comprehend the amplified risk factors for falls among the obese populace, scholarly investigations put forth three primary conjectures. Initially, the incessant strain from bearing excess weight often results in decreased sensitivity in the foot soles, attributable to overstimulation of the plantar mechanoreceptors (Handrigan et al., 2012b; Wu & Madigan, 2014). The next rationale hinges on the biomechanical challenges of sustaining augmented body mass, especially when a sizable chunk of this mass is positioned away from the pivotal axis (using the ankle joint’s inverted pendulum paradigm). This configuration necessitates an increased muscular torque to counterbalance the heightened gravitational pull and maintain verticality (Corbeil et al., 2001; Simoneau & Teasdale, 2015). A third proposition emphasizes cognitive demands; these might impose additional challenges in balance management for obese subjects (Mignardot et al., 2010). The implications suggested by these conjectures seem to be magnified in the presence of sarcopenia, a muscle degeneration condition. This condition results in sequential changes in the neuromuscular, proprioceptive, and visual apparatus, culminating in a compromise in balance and posture. The intertwined nature of obesity and sarcopenia in the elderly thus significantly impinges upon their balance and postural stability.

Effect of obesity on gait

In pioneering the evaluation of gait’s spatiotemporal parameters in elderly sarcopenic individuals, our study ventured beyond the often-singular focus on gait speed, a characteristic approach in preceding research. However, existing literature on SO and gait exhibits mixed findings. While Meng et al. (2014) and Sallinen et al. (2011) found minimal effects of SO on gait speed and mobility for those over 80, Kong, Won & Kim (2020) and Stenholm et al. (2009) associated SO with substantial functional declines.

Corroborating with the latter group, our findings illustrated a decline in gait speed in older adults with SO, characterized by shortened step length. These individuals also exhibited a high support phase duration during walking, characterized by a prolonged double support phase. Interestingly, research on older obese individuals without sarcopenia reveals similar gait dynamics. The lengthened double support phases might be a strategic adaptation to limit postural instability. This is buttressed by postural control theories which posit enhancing dynamic joint stability to counter obesity-induced imbalances (McGraw et al., 2000; Malatesta et al., 2009).

Evidence suggests obese individuals experience broader foot contact areas and heightened pressure during postural tasks, potentially impacting the feedback from plantar mechanoreceptors (Hue et al., 2007; Wu & Madigan, 2014). Moreover, obesity might lead to extended double support phases to absorb and propel excessive body mass (Ko, Stenholm & Ferrucci, 2010). Maktouf et al. (2020) even suggested that body mass plays a substantial role in influencing muscle activity during walking, hinting at the amplified energy expenditure in obese older adults.

In essence, while the observed gait alterations among SO individuals seem adaptive, optimizing their mobility within their strength constraints, they are not without risks. Given the inherent muscular vulnerabilities in SO (Tomlinson et al., 2016), an over-reliance on these adaptations may escalate fall risks (Handrigan et al., 2017). This juxtaposition underscores the delicate balance that these individuals navigate daily, balancing between mobility optimization and potential hazards.

The interrelation of body metrics with gait and balance parameters

The novelty of our study lies in its nuanced exploration of anthropometric parameters’ influence, especially body weight, BMI and LBM (%) on gait and balance among older adults with SO. Our analysis underscored LBM (%) as a paramount factor, establishing a significant correlation between muscle mass and both postural control and walking competence in older adults with SO. Whereas studies like that by Hue et al. (2007) emphasized the role of overall body mass in gait and balance among obese adults, our investigation extends this understanding specifically to the SO subset. The intricate correlations discovered between body metrics and gait and balance assessments suggest that while factors such as body weight and BMI play roles, it is undeniably LBM (%) that exerts the most substantial influence, especially for those with SO. This denotes the distinct difference in body composition and its functional implications between the general obese population and older adults with SO.

Literature reveals that obesity can modify plantar feedback due to enlarged foot contact regions and heightened pressure, potentially undermining balance via decreased signals from plantar mechanoreceptors (Birtane & Tuna, 2004; Bensmaïa, Leung & Johnson, 2005). Additionally, excessive abdominal mass in the obese might demand increased ankle torque for balance, introducing more motor function variability and potentially affecting stability (Handrigan et al., 2012a). However, when delving deeper, the salient influence of LBM (%) is perhaps attributed to the muscle decline characteristic of sarcopenia (Tomlinson et al., 2016). At their core, muscles exert force fundamental for myriad gait and balance functions (Cattagni et al., 2014). In the context of walking, muscles attenuate shock during the first double support phase and later furnish propulsion in the second. Beyond their biomechanical significance, muscle power directly correlates with and predicts balance (Gimmon et al., 2015; Maslivec et al., 2018). A diminished LBM (%), indicating a decline in muscle mass and consequent force production capability, inherently hampers an individual’s gait and balance. As indicated previously, results indicated strong correlations strong correlations between gait and balance parameters with LBM (%) more than BMI and BM. Notably, these correlations were absent in older adults without SO. Moreover, our findings indicate the presence of a threshold effect, where correlations become notably stronger when LBM (%) drops below 75% or when FBM (%) surpasses 25%. From a clinical perspective, it is conceivable to suggest that these values could be considered as an alert threshold, indicating a high risk of balance and walking alterations and consequently an increased risk of falls. Although these results need further confirmation through prospective studies, it is worth noting that it may be relevant to recommend strength training for individuals identified below this threshold to modify their body composition, favoring an increase in muscle mass and strength.

Previous studies have demonstrated that handgrip force serves as a reliable predictor of various negative outcomes, including falls, postsurgical complications, and future disability (Rijk et al., 2016; Benton, Spicher & Silva-Smith, 2022). However, in our study involving older adults with SO, we did not observe any significant correlation between handgrip force and balance and gait parameters. One possibility is that force production capacities in the upper limbs of older adults with SO may not necessarily reflect force production in the lower limbs, as observed in older adults without obesity (Benton, Spicher & Silva-Smith, 2022). Additionally, it is possible that, below a certain threshold, as seen in our study group where LBM (%) dropped below 75%, there is no correlation between muscle mass and force production. This may be due to neuromuscular system alterations exacerbated by obesity in this particular population (Erskine et al., 2017). These intriguing possibilities warrant further investigation to better understand the complex relationship between handgrip force, muscle mass, and neuromuscular function in older adults with SO and how it differs from their counterparts without obesity.

Limitations and perspectives

Our study’s primary strength lies in its unique focus on older adults diagnosed with SO, offering a fresh perspective on the relationship between body mass and lean body mass on gait and balance. However, the study’s relatively small cohort of 42 subjects limits the broad generalizability of our findings, especially considering the inherent variability and heterogeneity commonly found in older populations. The use of impedance-meters for deducing LBM and FBM might introduce some imprecision, with tools like dual-energy X-ray absorptiometry offering potentially more accurate insights. Our results highlight the importance of interventions for individuals with SO to focus on enhancing both the quality and quantity of lean muscle mass, while also addressing overall body mass reduction. Programs that solely emphasize weight loss might inadvertently diminish lean muscle mass in tandem with fat mass, potentially exacerbating conditions in sarcopenic subjects due to muscle’s pivotal role in ensuring stability and functional mobility. Further research employing more sophisticated tools and larger samples is vital to derive comprehensive intervention strategies for this demographic.