Spatiotemporal and kinematic gait characteristics in older patients with type 2 diabetes mellitus with and without sarcopenia

The global prevalence of type 2 diabetes is predicted to reach 780 million by 2045¹. The development of type 2 diabetes is linked to increased mortality, complications such as sarcopenia, and an increased risk of falls^2,3,4,5,6. Early detection of functional abnormalities, particularly gait issues, is crucial for improving diagnostic and therapeutic approaches. The prevalence of sarcopenia among older individuals in Asia is 7–12%⁷, whereas that among their counterparts with type 2 diabetes is 12–20%². Glycemic control is influenced by a multitude of factors, including insulin secretion from pancreatic beta-cells, insulin sensitivity in peripheral tissues, such as the liver and skeletal muscles, cognitive function, as well as lifestyle factors such as diet and physical activity. Moreover, increasing attention focuses on the impact of conditions, such as sarcopenia and frailty, which are states of physical weakness, on worsening glycemic control. By the age of 80 years, humans lose approximately 40% of their skeletal muscle mass compared with when they were in their 20s⁸. In addition to age-related changes, poor nutrition, decreased physical activity, and chronic conditions, including diabetes, contribute to the development of sarcopenia^9,10.

Skeletal muscle facilitates over 80% of glucose uptake in the periphery in response to insulin. This glucose is utilized as energy or stored as glycogen. Consequently, the decrease in skeletal muscle mass due to sarcopenia leads to reduced capacity for glucose metabolism in response to insulin. Additionally, the concentration of glucose transporter type 4, which is regulated by insulin and responsible for cellular glucose transport, decreases within skeletal muscle cells with age¹¹. Therefore, insulin resistance worsens in patients with sarcopenia and type 2 diabetes and increases the risk of deteriorating glycemic control. The diagnosis of sarcopenia differs across regions and ethnicities, with various criteria being used worldwide. In Asia, the criteria proposed by the Asian Working Group for Sarcopenia (AWGS) are commonly used⁷. However, these diagnostic criteria for sarcopenia typically require specialized equipment and facilities for estimating skeletal muscle mass through techniques, such as bioelectrical impedance analysis (BIA) and grip strength and walking speed measurements. Meeting these requirements can be challenging in clinical settings, hindering the timely diagnosis of sarcopenia and the initiation of appropriate treatment. Consequently, a growing need exists to develop simpler evaluation methods.

Sarcopenia is a condition characterized by an age-related decline in skeletal muscle mass combined with decreased muscle function (strength or physical performance)^7,9. Current diagnostic criteria primarily evaluate physical function through walking speed and grip strength, neglecting other body movement assessments. In recent years, measuring joint angles while walking from footage captured using a monocular camera has become possible. Understanding the gait characteristics of patients with type 2 diabetes and sarcopenia could underpin the use of artificial intelligence (AI) to detect the presence of sarcopenia in these patients from easily measurable joint angle data during walking. However, to our knowledge, no reports have addressed the differences in gait characteristics in patients with type 2 diabetes with or without sarcopenia. Mori et al.¹² reported that older adult Japanese women with sarcopenia exhibited significant decreases in walking speed and stride length compared with healthy controls. However, studies evaluating the spatiotemporal parameters used to assess gait characteristics of individuals with sarcopenia are limited. Walking can be expressed using various parameters beyond walking speed, including cadence, step length, step width, stance time, swing time, and double support time. Analyzing kinematic features of walking, such as joint movements and postural changes, alongside spatiotemporal gait parameters, may provide fundamental insights for early detection of sarcopenia. Understanding differences in gait characteristics associated with sarcopenia is essential for establishing a simple measurement method for the early detection of the condition. Three-dimensional (3D) motion analysis systems involve attaching reflective markers to landmarks on the body and analyzing the raw data captured by multiple synchronized cameras to illustrate joint movements during activities, such as walking.

Therefore, in this study, we aimed to investigate the differences in gait characteristics between older patients with type 2 diabetes, with and without sarcopenia, using a 3D motion analysis system.

Research design

This was a cross-sectional study (no. UMIN 000054792, www.umin.ac.jp/ctr/). Patients with type 2 diabetes attending the Kagawa University Hospital who were aged ≥ 65 years and capable of independently performing activities of daily living were recruited for this study. Participants were excluded if they: (1) experienced frequent hypoglycemia; (2) had preexisting gait dysfunction due to cerebrovascular or neurological diseases; (3) participated in other clinical trials; (4) had severe renal dysfunction (serum creatinine level of ≥ 1.5 mg/dL); (5) had severe diabetic neuropathy; (6) had other serious complications; and/or (7) regularly engaged in competitive sports on a professional level. All participants were capable of walking independently and had normal or corrected-to-normal vision. The sample size was determined using G*Power software (version 3.1.9.6, Dusseldorf, Germany) to obtain sarcopenic and non-sarcopenic data based on a previous study¹³ and detect walking function. The effect size was 1.06, with an α level set at 0.05, power set at 0.80, and allocation rate of non-sarcopenia/sarcopenia set at 2.0. The Mann–Whitney U-test was used to identify significant differences between two independent means. The estimated minimum number of participants was 34 (23 non-sarcopenia and 11 sarcopenia). Considering the potential for dropout, 38 participants (23 non-sarcopenia and 15 sarcopenia) were recruited. As a result, a total of 38 participants were enrolled. Participants were asked to maintain their normal dietary habits and refrain from vigorous physical activity the day before and immediately before the experiment. The participants’ demographic data included age, sex, height, body mass, body mass index (BMI), HbA1c level, duration of type 2 diabetes, grip strength, and skeletal muscle mass index (SMI).

The experimental protocol was approved by the Ethical Review Board at the Ethics Committee of the Kagawa University Faculty of Medicine (approval number: 2021-155). All participants provided written informed consent. The study complied with the principles of the Declaration of Helsinki.

Definition of sarcopenia

Although several criteria have been proposed to define sarcopenia, only the recently reported AWGS 2019 criteria were used to classify participants into different groups in this study. The SMI was measured using BIA with a bioelectrical impedance data acquisition system (Inbody 770; Biospace Co., Ltd., Seoul, Korea). Low muscle mass was defined as an SMI value of < 7.0 kg/m² in males and < 5.7 kg/m² in females. Low muscle strength was defined as a handgrip strength of < 28 kg in males and < 18 kg in females. The criterion for low physical performance was a 6-m walking speed of < 1.0 m/s. Sarcopenia was defined as “low muscle mass + low muscle strength or low physical performance,” and severe sarcopenia was defined as “low muscle mass + low muscle strength and low physical performance.”

Gait measurement

Gait measurements were obtained in a room with a straight 10-m path on which the participants could walk. During the experiment, all participants wore standardized clothing (sleeveless shirt and pants) provided by the experimenters, with clothing size selected by the participants. During gait measurement, participants were asked to walk barefoot at a comfortable, self-selected speed, and 3D positional data were obtained using reflective markers, a 10-camera, and a 3D motion capture system (MAC3D, Motion Analysis Corporation, Rohnert Park, California, USA) with a 200 Hz sampling frequency. A total of 57 infrared reflective markers were attached according to Visual 3D software (HAS-Motion Inc., Kingston, ON, Canada) guidelines. Simultaneously, ground reaction forces (GRFs) were obtained using four force plates (BP400600-2000, AMTI, Watertown, MA, USA) sampled at 2000 Hz. Marker positions were recorded with the participants standing stationary prior to the walking trials. Participants practiced walking to ensure a natural gait, followed by recordings of five successful trials per leg, in which each participant stepped correctly on a force plate. Based on the data obtained for each participant, the median, first, and third quartiles of spatiotemporal gait parameters and pelvis, hip, knee, and ankle joint angles were calculated at each time point.

Data analysis

Raw motion and GRF data were digitally filtered using a zero-lag, fourth-order, low-pass Butterworth filter with cut-off frequencies of 10 Hz for positional data and 56 Hz for GRF data, based on a previous study¹⁴. The angles of the pelvis, hip, knee, and ankle joints during one gait cycle were calculated for the x axis (flexion–extension), y axis (abduction–adduction), and z axis (internal–external rotation) using a Cardan sequence of rotations (X-Y-Z) from the trajectories measured in each trial. Based on a previous study¹⁵, the angles were time-normalized using the gait cycle duration determined from the force plate data and divided into 101 data points ranging from 0 to 100%. Central tendencies (median and first and third quartiles) of walking speed, stride length, step width, stride time, stance time, swing time, double support time, and cadence were also determined to help understand gait characteristics. Low-pass filtering, variable calculation (i.e., spatiotemporal gait parameters and joint angles), and time normalization were performed using Visual 3D software. Walking speed, stride length, and step width were normalized and compared with reference to the non-dimensional normalization method proposed by Pinzone et al.¹⁶, as expressed in the following formula:

Statistical analyses were performed using Statistical Package for the Social Sciences version 28.0 (SPSS Inc., Chicago, IL, USA). The Chi-square test was used to compare sex in non-sarcopenic and sarcopenic participants. The Mann–Whitney U tests were used to analyze the differences in other demographic data and spatiotemporal gait parameters of the participants. Effect sizes (ES_r) for the Mann-Whitney U test were calculated using the following formula:

Differences in range of motion (ROM; maximum angle minus minimum angle) of the pelvis, hip, knee, and ankle joint angles on the sagittal, frontal, and horizontal planes in the gait cycle between participants with and without sarcopenia were evaluated using ANCOVA, with gait speed included as a covariate. For comparisons where significant differences were observed (p < 0.05), effect sizes were evaluated using partial eta squared ((:{eta:}_{p}^{2})) and Cohen’s f. Cohen’s f was calculated from (:{eta:}_{p}^{2})​ using in the following formula:

The correlations between HbA1c levels, duration of type 2 diabetes, demographic data, and spatiotemporal gait parameters were analyzed using Spearman’s rank correlation coefficient. Differences in HbA1c levels and duration of type 2 diabetes by sex were analyzed using the Mann–Whitney U test. Statistical significance was set at p < 0.05.

The participants’ demographic data and spatiotemporal gait parameters are presented in Table 1. According to the criteria established by the AWGS 2019, among the 38 participants with type 2 diabetes, 15 were categorized as having sarcopenia (five of whom had severe sarcopenia) and 23 were classified as non-sarcopenic. Comparison of participants with and without sarcopenia showed significant differences in grip strength (p = 0.007, ES_r = − 0.429) and SMI (p = 0.006, ES_r = − 0.442); however, no significant differences were observed in age, sex, height, body mass, BMI, HbA1c level, or duration of type 2 diabetes mellitus. Regarding spatiotemporal gait parameters, participants with sarcopenia demonstrated significantly slower walking speed (p < 0.001, ES_r = − 0.632), shorter stride length (p = 0.003, ES_r = − 0.477), longer stride time (p = 0.004, ES_r = 0.461), longer stance time (p = 0.011, ES_r = 0.408), longer double support time (p = 0.048, ES_r = − 0.322), and lower cadence (p < 0.001, ES_r = − 0.540) than non-sarcopenic participants. However, no significant differences were observed in step width or swing time. In addition, no correlations exist between HbA1c levels or the duration of type 2 diabetes with demographic data or spatiotemporal gait parameters (Table 2).

The kinematic waveforms of the pelvis, hip, knee, and ankle joint angles in the sagittal, frontal, and horizontal planes are illustrated in Fig. 1. Each waveform was divided into 101 time frames for one gait cycle, starting from the moment of heel contact. To account for variations in the ratio of stance time to swing time among participants, a gray bar indicated the estimated toe-off phase, delimiting the stance phase before it and the swing phase after it. Comparisons of ROM for each joint from these joint kinematic waveforms during the gait cycle revealed that participants with sarcopenia had a significantly smaller ROM for ankle joint plantar dorsiflexion (p = 0.012, (:{eta:}_{p}^{2}) = 0.166, f = 0.446) (Fig. 2).

Waveforms of central tendency. Data are expressed as means ± standard deviation (SD). The gray highlighted area indicates the instance of the toe off (the transition from the stance phase to the swing phase). This area has a certain width because the stance phase was not separated from the swing phase in the time-normalization procedure. The joint motion waveforms of the non-sarcopenic and sarcopenic participants are shown in gray and orange, respectively. Post. posterior tilt, Ant. anterior tilt, Hike. pelvic hike, Drop. pelvic drop, I.R. internal rotation, E.R. external rotation, Flex. flexion, Ext. extension, Add. adduction, Abd. abduction, D.F. dorsi-flexion, P.F. plantar flexion, Ever. eversion, Inv. inversion, deg. degree.

Full size image

Comparison of joint ranges of motion during the gait cycle. Data are presented as medians and interquartile ranges. The whiskers indicate the minimum and maximum values. The ranges of motion for the participants with and without sarcopenia are shown using orange and gray box plots, respectively. The asterisks indicate significant differences between the participants with and without sarcopenia (*p < 0.05). deg. degree.

Full size image

This study aimed to clarify the differences in gait characteristics between older patients with type 2 diabetes, with and without sarcopenia. The findings revealed that patients with type 2 diabetes and sarcopenia had lower grip strengths, decreased SMIs, and slower walking speeds than those with their counterparts without sarcopenia. Furthermore, comparison of spatiotemporal parameters of gait revealed that these patients had shorter stride lengths, longer stride times, stance times and double support times, and lower cadence. Decreased range of ankle joint plantar dorsiflexion angles during the gait cycle were also observed in patients with sarcopenia and type 2 diabetes. Previous studies have analyzed spatiotemporal gait parameters in individuals with sarcopenia^12,13,17,18 and joint ROM during walking in older adults¹⁹. However, no studies have compared joint ROM during walking between individuals with and without sarcopenia or specifically examined joint ROM during walking in patients with type 2 diabetes. Therefore, this study is the first to reveal the effect of sarcopenia on spatiotemporal and kinematic parameters of gait in patients with type 2 diabetes.

The development of sarcopenia is influenced by age-related physiological changes and the pathophysiology of type 2 diabetes. Older patients with type 2 diabetes reportedly experience muscle mass reduction²⁰ and decreased muscle strength²¹ compared with their healthy counterparts. The mechanisms by which type 2 diabetes induce sarcopenia include insulin deficiency, hyperglycemia, and vascular and neuropathic complications. Protein synthesis may be one of the factors explaining muscle atrophy observed in patients with type 2 diabetes. A cross-sectional study of 13,644 participants using data from the National Health and Nutrition Examination Survey showed a negative correlation between insulin resistance, assessed by the homeostasis model assessment for insulin resistance, and muscle mass²². Furthermore, a cross-sectional study conducted in Taiwan that analyzed 844 older individuals found that, alongside reduced physical activity, insulin resistance was associated with a decrease in skeletal muscle mass²³. Barzilay et al.²⁵. Furthermore, longitudinal studies have revealed significant declines in muscle strength and physical activity capacity in individuals stratified by HbA1c levels, particularly in the hyperglycemic group²⁶. However, when comparing between the non-sarcopenic and sarcopenic participants in this study, no significant differences were observed in HbA1c levels or duration of type 2 diabetes. This discrepancy may be attributed to the fact that the HbA1c value during the gait test may not fully reflect glycemic control throughout the disease period, as this study was not a time-limited clinical investigation. This study aimed to analyze the gait function in older patients with sarcopenia and type 2 diabetes. Several time-limited interventions have indicated that glycemic control is crucial to the development and progression of sarcopenia. Additionally, vascular impairment^27,28 and neuropathy²⁹ associated with type 2 diabetes may contribute to the development of sarcopenia. However, individuals with severe vascular or neuropathic impairments were excluded from this study, making it unlikely that these factors significantly affected the results. Although the findings of this study did not reveal the specific impact of type 2 diabetes on gait characteristics, some gait characteristics specific to patients with type 2 diabetes are potentially present in the results.

The prevention and management of sarcopenia could play a crucial role in enhancing glycemic control in patients with type 2 diabetes. However, research evaluating the differences in gait characteristics in patients with type 2 diabetes, with or without sarcopenia, is lacking. In this study, HbA1c levels and the duration of type 2 diabetes did not reveal any correlation with spatiotemporal gait parameters. Future studies should explore gait characteristics in relation to glycemic control over a longer period of time to determine the effects of glycemic control on gait characteristics. Fan et al.¹⁷ demonstrated that older adults with low skeletal muscle mass exhibit shorter step and stride lengths than those with normal skeletal muscle mass. The decreased walking speed among individuals with sarcopenia may be attributed to shortened stride length¹⁸. Additionally, gait analyses in older individuals have reported age-related decreases in walking speed, shortened stride length, widened step width, increased double support time, and decreased cadence (prolonged stride time)³⁰. The results of this study, which showed decreased walking speeds and cadence and shortened stride lengths and prolonged stride times, stance times and double support times among participants with sarcopenia, generally support the findings of previous studies.

Few previous studies have investigated joint ROM during the gait cycle in individuals with or without sarcopenia. In a systematic review of gait patterns in older adults, moderate evidence indicated that they exhibited reduced pelvic rotation range during comfortable walking³¹. Considering that older adults often experience significant reductions in muscle mass and strength compared with younger individuals³², as well as weakness in trunk muscle strength^32,33,34, the decreased pelvic rotation range observed among participants with sarcopenia (Supplemental data), may be attributed to the decline in muscle strength, particularly when walking speed is not accounted for. Judge et al.¹⁹ reported a significant decrease in the maximum ankle joint dorsiflexion angle during the terminal stance phase in older individuals compared with younger individuals, attributing this decrease to reduced eccentric contraction strength of the gastrocnemius and soleus muscles. The decrease in the maximum ankle dorsiflexion angle during the terminal stance phase, as demonstrated in our waveforms, suggests that similar ankle joint movement characteristics occur in patients with sarcopenia and type 2 diabetes. However, no previous study has investigated the decrease in ankle plantar dorsiflexion ROM during gait in individuals with sarcopenia. Therefore, future research should examine the relationship between these joint ROMs and muscle strength and include biomechanical analyses of gait characteristics.

The results of this study showed that the gait characteristics of older patients with type 2 diabetes were decreased walking speed and cadence, shorter stride lengths, longer stride, stance and double support times, and decreased ROM of ankle plantar dorsiflexion. By evaluating gait based on these endpoints, it may be possible to objectively evaluate the impact of sarcopenia on gait in patients with type 2 diabetes. Moreover, incorporating gait analysis tools, such as AI posture-estimation systems that utilize mobile or tablet devices, into clinical practice along with gait assessments conducted by physical therapists may prove beneficial.

This study had some limitations. First, the influence of mild vascular or neurological impairments could not be completely excluded. Second, we did not analyze participants separately based on sex. As the diagnostic criteria for sarcopenia vary according to sex, we were unable to assess the influence of sex on the impact of sarcopenia on physical function. Older men generally exhibit faster walking speeds, longer stride lengths, and lower cadence than women, suggesting potential differences in walking patterns between older men and women^35,36. Third, this study did not compare older individuals without diabetes to older patients with type 2 diabetes; thus, we could not examine the impact of type 2 diabetes on gait characteristics. Fourth, the relationship between glycemic control using factors such as HbA1c levels and gait parameters was not evaluated. Evaluation of the relationships between glycemic control and related parameters, such as insulin resistance, and gait parameters, including walking speed, is a subject for future studies. Finally, the small sample size in this study might limit the generalizability of the findings.

This study compared the spatiotemporal and kinematic parameters of gait between patients with type 2 diabetes with and without sarcopenia. Patients with type 2 diabetes and sarcopenia had slower walking speeds, shorter stride lengths, longer stride, stance and double support times, and lower cadence than their counterparts without sarcopenia. In addition, decreased range of ankle joint plantar dorsiflexion angles during the gait cycle were also observed in patients with type 2 diabetes and sarcopenia. Our findings indicate that patients with type 2 diabetes and sarcopenia exhibit not only decreased walking speed but also abnormalities in walking rhythm and reduced joint mobility in the gait cycle. Assessment of these gait characteristics may be an indicator of sarcopenia in older patients with type 2 diabetes. In addition to contributing new knowledge to the field, this may have practical implications for sarcopenia detection in clinical settings.

1. International Diabetes Federation. IDF Diabetes Atlas, 10th edn. https://diabetesatlas.org/idfawp/resource-files/2021/07/IDF_Atlas_10th_Edition_2021.pdf (2021).

2. Mori, H. et al. High prevalence and clinical impact of dynapenia and sarcopenia in Japanese patients with type 1 and type 2 diabetes: findings from the impact of diabetes mellitus on dynapenia study. J. Diabetes Investig. 12, 1050–1059 (2021).

   Article  CAS  PubMed  Google Scholar 

3. Morley, J. E. Falls–where do we stand? Mol. Med. 104, 63–67 (2007).

   ADS  Google Scholar 

4. Miller, D. K., Lui, L. Y., Perry, H. M., Kaiser, F. E., Morley, J. E. & rd, & Reported and measured physical functioning in older inner-city diabetic African Americans. J. Gerontol. Biol. Sci. Med. Sci. 54, M230–M236 (1999).

   Article  CAS  Google Scholar 

5. Wallace, C. et al. Incidence of falls, risk factors for falls, and fall-related fractures in individuals with diabetes and a prior foot ulcer. Diabetes Care. 25, 1983–1986 (2002).

   Article  PubMed  Google Scholar 

6. Cavanagh, P. R., Derr, J. A., Ulbrecht, J. S., Maser, R. E. & Orchard, T. J. Problems with gait and posture in neuropathic patients with insulin-dependent diabetes mellitus. Diabet. Med. 9, 469–474 (1992).

   Article  CAS  PubMed  Google Scholar 

7. Chen, L. K. et al. Asian working group for sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J. Am. Med. Dir. Assoc. 21, 300–307 (2020).

   Article  PubMed  Google Scholar 

8. Larsson, L., Grimby, G. & Karlsson, J. Muscle strength and speed of movement in relation to age and muscle morphology. J. Appl. Physiol. Respir Environ. Exerc. Physiol. 46, 451–456 (1979).

   CAS  PubMed  Google Scholar 

9. Cruz-Jentoft, A. J. et al. Sarcopenia: European consensus on definition and diagnosis: report of the European working group on sarcopenia in older people. Age Ageing. 39, 412–423 (2010).

   Article  PubMed  PubMed Central  Google Scholar 

10. Kim, T. N. et al. Prevalence and determinant factors of sarcopenia in patients with type 2 diabetes: the Korean sarcopenic obesity study (KSOS). Diabetes Care. 33, 1497–1499 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

11. Houmard, J. A. et al. Skeletal muscle GLUT4 protein concentration and aging in humans. Diabetes 44, 555–560 (1995).

    Article  CAS  PubMed  Google Scholar 

12. Mori, K. et al. Gait characteristics of dynapenia, sarcopenia, and presarcopenia in community-dwelling Japanese older women: A cross-sectional study. Healthc. (Basel). 10, 1905 (2022).

    Google Scholar 

13. Tanishima, S. et al. Association between sarcopenia and low back pain in local residents prospective cohort study from the GAINA study. BMC Musculoskelet. Disord. 18, 452 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

14. van den Bogert, A. J. & de Koning, J. J. On optimal filtering for inverse dynamics analysis. In Proceedings of the IXth Biennial Conference of the Canadian Society for Biomechanics 214–215 (1996).

15. Kobayashi, Y., Hobara, H., Heldoorn, T. A., Kouchi, M. & Mochimaru, M. Age-independent and age-dependent sex differences in gait pattern determined by principal component analysis. Gait Posture. 46, 11–17 (2016).

    Article  PubMed  Google Scholar 

16. Pinzone, O., Schwartz, M. H. & Baker, R. Comprehensive non-dimensional normalization of gait data. Gait Posture. 44, 68–73 (2016).

    Article  PubMed  Google Scholar 

17. Fan, Y. et al. Sarcopenia: body composition and gait analysis. Front. Aging Neurosci. 14, 909551 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

18. Kim, J. K., Bae, M. N., Lee, K. B. & Hong, S. G. Identification of patients with sarcopenia using gait parameters based on inertial sensors. Sens. (Basel). 21, 1786 (2021).

    Article  ADS  Google Scholar 

19. Judge, J. O., Davis, R. B., Ounpuu, S. & 3rd & Step length reductions in advanced age: the role of ankle and hip kinetics. J. Gerontol. Biol. Sci. Med. Sci. 51, M303–M312 (1996).

    Article  CAS  Google Scholar 

20. Park, S. W. et al. Excessive loss of skeletal muscle mass in older adults with type 2 diabetes. Diabetes Care. 32, 1993–1997 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

21. Park, S. W. et al. Accelerated loss of skeletal muscle strength in older adults with type 2 diabetes: the health, aging, and body composition study. Diabetes Care. 30, 1507–1512 (2007).

    Article  PubMed  Google Scholar 

22. Srikanthan, P. & Karlamangla, A. S. Relative muscle mass is inversely associated with insulin resistance and prediabetes. Findings from the third National health and nutrition examination survey. J. Clin. Endocrinol. Metab. 96, 2898–2903 (2011).

    Article  CAS  PubMed  Google Scholar 

23. Yang, C. W. et al. The joint association of insulin sensitivity and physical activity on the skeletal muscle mass and performance in community-dwelling older adults. Exp. Gerontol. 95, 34–38 (2017).

    Article  CAS  PubMed  Google Scholar 

24. Barzilay, J. I. et al. Insulin resistance is associated with decreased quadriceps muscle strength in nondiabetic adults aged > or = 70 years. Diabetes Care. 32, 736–738 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

25. Sugimoto, K. et al. Hyperglycemia in non-obese patients with type 2 diabetes is associated with low muscle mass: the multicenter study for clarifying evidence for sarcopenia in patients with diabetes mellitus. J. Diabetes Investig. 10, 1471–1479 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

26. Yoon, J. W. et al. Hyperglycemia is associated with impaired muscle quality in older men with diabetes: the Korean longitudinal study on health and aging. Diabetes Metab. J. 40, 140–146 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

27. Kim, T. N. et al. Skeletal muscle mass to visceral fat area ratio is associated with metabolic syndrome and arterial stiffness: the Korean sarcopenic obesity study (KSOS). Diabetes Res. Clin. Pract. 93, 285–291 (2011).

    Article  PubMed  Google Scholar 

28. Ko, B. J. et al. Relationship between low relative muscle mass and coronary artery calcification in healthy adults. Arterioscler. Thromb. Vasc Biol. 36, 1016–1021 (2016).

    Article  CAS  PubMed  Google Scholar 

29. Andersen, H., Nielsen, S., Mogensen, C. E. & Jakobsen, J. Muscle strength in type 2 diabetes. Diabetes 53, 1543–1548 (2004).

    Article  CAS  PubMed  Google Scholar 

30. Hollman, J. H., McDade, E. M. & Petersen, R. C. Normative Spatiotemporal gait parameters in older adults. Gait Posture. 34, 111–118 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

31. Cury, A. C., Pinto, R. Z., Madaleno, F. O. & Resende, R. A. Do older adults present altered pelvic and trunk movement pattern during gait? A systematic review with meta-analysis and GRADE recommendations. Braz J. Phys. Ther. 25, 484–499 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

32. Moreland, J. D., Richardson, J. A., Goldsmith, C. H. & Clase, C. M. Muscle weakness and falls in older adults: A systematic review and meta-analysis. J. Am. Geriatr. Soc. 52, 1121–1129 (2004).

    Article  PubMed  Google Scholar 

33. Suri, P., Kiely, D. K., Leveille, S. G., Frontera, W. R. & Bean, J. F. Trunk muscle attributes are associated with balance and mobility in older adults: A pilot study. PM R. 1, 916–924 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

34. Granacher, U., Lacroix, A., Muehlbauer, T., Roettger, K. & Gollhofer, A. Effects of core instability strength training on trunk muscle strength, spinal mobility, dynamic balance and functional mobility in older adults. Gerontology 59, 105–113 (2013).

    Article  PubMed  Google Scholar 

35. Winter, D. A., Patla, A. E., Frank, J. S. & Walt, S. E. Biomechanical walking pattern changes in the fit and healthy elderly. Phys. Ther. 70, 340–347 (1990).

    Article  CAS  PubMed  Google Scholar 

36. Latorre Román, P. A., García-Pinillos, F., Herrador, H., Cózar, J. A. & Barba, M. Muñoz Jiménez, M. Relationship between sex, body composition, gait speed and body satisfaction in elderly people. Nutr. Hosp. 30, 851–857 (2014).

    PubMed  Google Scholar 

Download references

Authors and Affiliations

1. Department of Endocrinology and Metabolism, Faculty of Medicine, Kagawa University, 1750-1 Ikenobe, Miki-cho, Kita-gun, Kagawa, 761-0793, Japan

   Tomotaka Manabe, Toshihiro Kobayashi, Kensaku Fukunaga, Takanobu Saheki, Takafumi Yoshimura, Hitomi Imachi & Koji Murao

2. Division of Rehabilitation, Department of Medical Technology, Kagawa University Hospital, 1750-1 Ikenobe, Miki-cho, Kita-gun, Kagawa, 761-0793, Japan

   Tomotaka Manabe

3. Integrated Research Center for Self-Care Technology , National Institute of Advanced Industrial Science and Technology (AIST), 2217-14 Hayashi-cho, Takamatsu, Kagawa, 761-0395, Japan

   Wakako Tsuchida, Masahiro Fujimoto, Takuma Inai, Kohei Kido & Shoma Kudo

4. Health and Medical Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 2217-14 Hayashi-cho, Takamatsu, Kagawa, 761-0395, Japan

   Wakako Tsuchida, Masahiro Fujimoto, Takuma Inai, Kohei Kido & Shoma Kudo

Contributions

T.M. wrote the first draft of the manuscript. W.T., T.K., and K.M. analyzed the data and reviewed and edited the manuscript. K.F., T.S., T.Y., and H.I. contributed to the discussion. M.F., T.I., K.K., and S.K. contributed to the discussion and reviewed and edited the manuscript. All the authors approved and reviewed the manuscript.

Corresponding authors

Correspondence to Wakako Tsuchida or Toshihiro Kobayashi.

Competing interests

The authors declare no competing interests.

Publisher’s note

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

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions