Sarcopenia Risk,Bone Turnover and Metabolic Health Indices Among Community-Dwelling Older Adults in Ghana: An Observational Study

Abstract

This study examined the risk of sarcopenia and its association with bone turnover markers, insulin resistance, and skeletal muscle mass among community-dwelling older adults in Ghana. In this cross-sectional study, 140 adults aged ≥60 years were assessed using the SARC-F questionnaire to estimate the risk of sarcopenia. Skeletal muscle mass was estimated using bioelectrical impedance analysis (Omron HBF-514), and metabolic and bone turnover markers were measured in fasting blood samples. The prevalence of sarcopenia risk was 44.3%, with women facing more difficulties climbing stairs. The risk of sarcopenia was more common in older age groups. Sarcopenia risk was associated with higher insulin resistance (τ = .475), C-telopeptide (τ = .540), urine calcium/creatinine ratio (τ = .620), and osteocalcin (τ = .651), but with lower skeletal muscle mass (τ = −.624; all p < .01). Nearly half of the older adults screened were at risk of sarcopenia, highlighting the need for early identification and preventive strategies. These findings underscore the importance of age and metabolic factors in sarcopenia risk, as well as the need for routine monitoring of bone turnover biomarkers in aging populations.

Keywords

C-terminal telopeptide sarcopenia bone fracture aging insulin resistance muscle mass

What This Paper Adds

The findings indicated that 44.3% of the participants were at risk of sarcopenia based on SARC-F screening.

Sarcopenia risk was associated with higher insulin resistance and altered bone turnover markers but with reduced skeletal muscle mass.

Sex-specific patterns were observed in bone health, underscoring the need for standardized protocols, and gender-specific interventions in Africa.

Application of the Study Findings to Gerontological Practice

The high proportion of older adults at risk of sarcopenia in this community underscores the urgent need for routine screening in primary care and public-health settings.

Simple tools, such as SARC-F, can be integrated into geriatric assessments to identify individuals who may benefit from early interventions.

In resource-limited contexts, strengthening awareness and capacity for sarcopenia risk screening and management may help reduce disability, improve the quality of life, and lower the healthcare burdens associated with aging populations.

Introduction

Aging is associated with a reduction in both the size and number of skeletal muscle fibers, particularly fast-twitch type II fibers, along with infiltration of fibrous and adipose tissue into the muscles (Larsson et al., 2019; Tieland et al., 2018). This decline can begin as early as the fourth decade of life (Tournadre et al., 2019), and by the eighth decade, up to half of one’s muscle mass may be lost (Metter et al., 1997). Since muscle mass accounts for up to 60% of body weight, changes to this metabolically active tissue can affect overall health (Landi et al., 2013; Tarantino et al., 2016). Sarcopenia, characterized by a progressive loss of muscle mass and strength, is widely recognized as a contributor to an increased risk of falls, fractures, frailty, reduced quality of life, increased mortality risk, and physical disability (Landi et al., 2018; Marzetti et al., 2017; Rodrigues et al., 2022). Sarcopenia is also considered a risk factor for several chronic diseases with poor outcomes, including cirrhosis, type 2 diabetes, and malignancies (Angulo et al., 2016; Yuan & Larsson, 2023). Reports suggest that insulin resistance (IR) in skeletal muscle is associated with obesity and type 2 diabetes and aging (Cleasby et al., 2016; Shou et al., 2020).

Diagnosing sarcopenia requires functional assessments of strength, muscle mass, and physical performance, as well as the use of frequently inaccessible technologies, including dual-energy X-ray absorptiometry, bioimpedance analysis, and computed tomography (Cruz-Jentoft et al., 2018). Individuals in sub-Saharan Africa face difficulties in accessing these instruments. Previous studies have emphasized the efficacy of the SARC-F questionnaire as a screening tool for the risk of sarcopenia to address these challenges (Nguyen et al., 2020; Noda et al., 2024). This self-assessment instrument evaluates five domains: strength, need for assistance with ambulation, chair rise, stair ascent, and falls in the preceding year. Generally, alterations in body composition can be assessed using anthropometric techniques, such as body mass index (BMI), arm muscle area, and waist and calf circumferences (Pinheiro et al., 2020; Santos et al., 2019). These metrics can also be used to classify individuals into various sarcopenia risk categories. For example, diminished muscle mass, decreased grip strength, and compromised physical function may indicate an increased risk of sarcopenia (Larsson et al., 2019; Lee et al., 2021).

Microstructural changes affecting bone quality can be measured using bone turnover markers, such as C-terminal telopeptide (CTX), alkaline phosphatase, osteocalcin, calcium, and phosphate. CTX, or carboxy-terminal collagen crosslinks, is a constituent of fibrillar collagens, such as types I and II, and is used as a blood biomarker for evaluating bone turnover rates. This peptide has also been implicated in metabolic syndrome, inflammation, osteoporosis, and hypertension (Allah et al., 2017; El-Tallawy et al., 2021; Fonseca et al., 2014). This has increased the clinical utility of CTX, as it may readily exhibit hormone-like properties and show promise as a diagnostic marker not only for bone resorption but also for aging and other pathologies, such as acute coronary syndromes (Kirk et al., 2022; McGavigan et al., 2008). Osteocalcin is one of the most abundant non-collagenous proteins in the bone. This small peptide, comprising 49 amino acids in humans, is predominantly synthesized by osteoblasts during osteogenesis, making its serum concentration a biomarker for bone formation (Razzaque, 2011). Calcium and phosphate, which are essential for hydroxyapatite synthesis in bones, are present in elevated urine concentrations during accelerated or excessive bone turnover.

Bone remodeling biomarkers provide valuable insights into sex-specific bone turnover dynamics during aging and in the context of related metabolic disorders. In older adults, bone and muscle functions are closely interconnected, with both tissues sharing embryological origins, responding to common mechanical stimuli, and being regulated by overlapping endocrine pathways. Bone turnover markers reflect the dynamic balance between bone resorption and formation and are sensitive to systemic metabolic and hormonal changes that influence muscle protein synthesis and degradation. Bone turnover markers in aging have been associated with poorer physical performance, accelerated loss of lean mass, and increased fall risk, making them relevant biomarkers for sarcopenia risk. Moreover, bone turnover markers provide a real-time, dynamic assessment of musculoskeletal remodeling, which is more physiologically relevant than static measures such as bone mineral density alone, thereby justifying their correlation with sarcopenic parameters in this cohort. Despite this biological rationale, the role of bone turnover biomarkers in musculoskeletal function remains poorly understood in sub-Saharan Africa. Therefore, the present study assessed markers of bone turnover in blood and urine and examined their relationship with sarcopenia risk and metabolic health in community-dwelling older adults.

Materials and Methods

Study Design, Site and Participants

This observational study was conducted among community-dwelling older adults aged ≥ 60 years. The study recruited individuals who had lived in La Dade Kotopon Municipality, Greater Accra Region, Ghana, for at least 10 years. A random sampling method was used, with an initial volunteer chosen from each stratum, and every alternate volunteer thereafter was included until the target sample size was reached. A total of 140 eligible male and female volunteers were included in the study. These volunteers were ambulatory and not confined to bed, as immobility may reduce the bone mineral density. Before recruitment, participants completed an informed consent form indicating their voluntary acceptance to participate in the study. Participants were informed about the study’s purpose and duration, benefits of participation, items used for sample collection, and potential risks associated with blood sampling. Individuals with reservations or those who opted out of the study were not denied standard hospital treatment. Individuals with tuberculosis, immune-mediated disorders, immunocompromised status, or those diagnosed with viral hepatitis, liver injury, or any kidney or bone condition were excluded from the study. Patients who required urgent medical intervention, including those with intense chest pain, altered mental status, respiratory distress, and continuous hemorrhage, were excluded. Participants receiving insulin and/or corticosteroids and those receiving antidiabetic medications were excluded. This was done to avoid the potential confounding effects of these pharmacological agents on metabolic markers and insulin resistance, thereby enabling a more homogeneous sample for assessing the relationships between metabolic and musculoskeletal parameters. The minimum sample size was calculated using a sarcopenia prevalence rate of 15.7% ( L. Smith et al., 2020), with a 95% confidence interval and an anticipated margin of error of 5%.

Sociodemographic and Clinical Assessment

Demographic and clinical data, including age, sex, ethnicity, marital status, level of education, employment, household characteristics, dietary habits, substance use (alcohol and tobacco), physical activity, and the presence of any chronic disease, were collected using questionnaires administered by the interviewers. The SARC-F questionnaire was used to estimate the risk and distribution of sarcopenia components. This five-item assessment evaluates strength, specifically the difficulty experienced in lifting or carrying 10 pounds. It also evaluates the difficulty experienced by an individual when walking across a room. Additionally, it considers the difficulties in transitioning from a chair or bed, ascending a flight of 10 stairs, and the frequency of falls in the past year. Each item is scored as zero for “none,” one for “some,” two for “a lot,” or “unable.” In the case of difficulty climbing stairs, “a lot of difficulty” and “unable” responses were combined.. Higher scores indicate a greater likelihood of sarcopenia risk.

Participant heights were measured (in meters) using a wall-mounted stadiometer. Weight was assessed using an electronic digital scale ( SECA, Hamburg, Germany). Other body composition parameters, including skeletal muscle mass, body fat percentage, and visceral fat, were analyzed using an Omron HBF-514 body composition monitor (OMRON HEALTHCARE Company Limited, Kyoto, Japan), which requires the input of height, weight, age, and sex. The participants stood barefoot on the monitor while holding the electrodes, with a 5 mA electric current passing through their feet and palms to enable bioelectric impedance analysis. The validation of the Omron HBF-514 for body composition assessment in adults, including those over 50 years of age, has been reported in multiple studies (Dos Santos et al., 2026; Ferreira et al., 2023). However, we did not apply diagnostic cut-offs for low muscle mass, and skeletal muscle mass was analyzed as a continuous variable. BMI was calculated as weight (kg) divided by height squared (m²). After a 15-minute rest, blood pressure was measured using an OMRON digital sphygmomanometer (OMRON HEALTHCARE Company Limited, Kyoto, Japan). The average of the two readings was taken.

Laboratory Procedure and Measurement

Biochemical measurements were performed by drawing 4 mL of venous blood and collecting spot urine samples from each participant. Blood samples were drawn into a serum separator tube (SST), allowed to clot, and centrifuged at 3,500 rpm for 5 min to obtain the serum. The serum was aliquoted into 0.5 mL Eppendorf tubes and stored at −20 °C until analysis. Serum C-terminal telopeptide (CTX), osteocalcin, and insulin levels were measured using an enzyme-linked immunosorbent assay (ELISA; Sunlong Biotech Co., Ltd., China). Optical densities (ODs) at specific wavelengths were measured using a microtiter plate reader (Varioskan Lux; Thermo Fisher Scientific, USA), with the reagent blank OD value and reference point set to zero. An automated chemistry analyzer (Mindray BS-120, China) was used to determine the serum calcium and phosphate levels and to measure kidney (sodium, potassium, chloride, urea, creatinine) and liver function tests (aspartate aminotransferase [AST], alanine aminotransferase [ALT], alkaline phosphatase [ALP], gamma-glutamyl transferase [GGT], total bilirubin [TBIL], direct bilirubin [DBIL], total protein, and albumin). The Roche Cobas U601 Urine Analyzer (Roche Diagnostics, Basel, Switzerland) was used to measure the urine calcium, phosphate, and creatinine levels. The urine calcium/creatinine and urine phosphate/creatinine ratios were computed. Homeostatic assessment of insulin resistance (HOMA-IR) was calculated using a previously published formula (Wallace et al., 2004).

Statistical Analysis

Data were entered into Microsoft 365 and then transferred to Stata 15 for statistical analyses. The normality of all continuous data was evaluated using the Shapiro–Wilk normality test, with nonparametric data reported as medians [95% confidence intervals]. Gaussian-distributed data are reported as mean ± standard deviation. Categorical data are shown as frequencies and percentages in parentheses, with corresponding 95% confidence intervals using Wilson’s method where appropriate. To evaluate sex-based differences in continuous variables, including anthropometric, hemodynamic, and biochemical indices, unpaired parametric t-tests and unpaired nonparametric Mann–Whitney tests were used for Gaussian- and non-Gaussian-distributed data, respectively. A one-way analysis of variance with Tukey’s post-hoc test was used to assess variations in the parameters of interest across age groups.

Pearson’s chi-square test or Fisher’s exact test (with Yates’ continuity correction for cells with expected counts < 5) was used for categorical data. Poisson regression with robust standard errors was used to estimate prevalence ratios for bone turnover markers associated with the risk of sarcopenia. Kendall’s tau-b correlation was used to assess the correlation between insulin resistance and bone turnover markers. The Bonferroni correction was applied within families of related bivariate analyses (e.g., correlations among skeletal muscle mass, bone turnover markers, insulin resistance, and sarcopenia risk) to control for type I error due to multiple comparisons.

Sampling adequacy was evaluated using a Kaiser-Meyer-Olkin (KMO) coefficient of 0.87 (KMO > 0.6), while the appropriateness of the correlation matrix of the data was determined using a significant Bartlett’s test of sphericity. Statistical significance was set at p < .05.

Results

Demographic, Anthropometric, Clinical and Biochemical Characteristics of Participants

The study included 140 participants, equally distributed between men and women (70 each). The mean age was 72 ± 8 years for the total sample, with males averaging 73 ± 1 years. The age differences between the sexes were not significant (p = .456). The distribution of the participants’ anthropometric, clinical, and biochemical data is presented in Table 1. The median BMI was significantly higher in females (32.5 kg/m² [95% CI: 31.5, 34.6]) than in males (26.2 kg/m² [25.4, 26.7], p < .0001). Females had a higher median body fat percentage (43.35% [42.1, 43.8] vs. 24.6% [23.5, 25.3], p < .0001) and visceral fat levels (12% [12, 13] vs. 10% [10, 11], p < .0001). Males had higher median systolic (114 mmHg [108, 119] vs. 107 mmHg [105, 110], p = .0038) and diastolic blood pressure (87 mmHg [86, 93] vs. 85 mmHg [84, 86], p = .0001).

Table 1.

Anthropometry, Clinical and Biochemical Characteristics of Study Participants.

Variables		Sex
	Total	Male	Female	p-Value
	Median [95% CI], n (%)	Median [95% CI], n (%)	Median [95% CI], n (%)
Overall	140 (100)	70 (50)	70 (50)
Anthropometric
Body Mass Index (kg/m²)	28.3 [27.3, 29.4]	26.2 [25.4, 26.7]	32.5 [31.5, 34.6]	<.0001
Normal weight	25 (17.86)	22 (88)	3 (12)	<.0001
Overweight	65 (46.43)	48 (73.85)	17 (26.16)
Obese class 1	26 (18.58)	0 (0)	26 (100)
Obese class 2	24 (17.15)	0 (0)	24 (100)
Body Fat (BF%)	30.4 [26.4, 40.5]	24.6 [23.5, 25.3]	43.35 [42.1, 43.8]	<.0001
Normal	18 (12.86)	0 (0)	18 (100)	<.0001
Low	71 (50.72)	70 (98.6)	1 (1.41)
High	51 (36.43)	0 (0)	51 (100)
Visceral Fat (BIA)	11 [10, 12]	10 [10, 11]	12 [12, 13]	<.0001
Normal	93 (66.43)	55 (59.14)	38 (40.87)	.0020
High	47 (33.58)	15 (31.92)	32 (68.09)
Skeletal Muscle Mass (%)	20.45 [19.5, 21.3]	21 [20.3, 23.7]	19 [18.5, 20.7]	.0002
Normal	61 (43.58)	26 (42.63)	35 (57.38)	<.0001
Low	53 (37.86)	18 (33.97)	35 (66.04)
High	26 (18.58)	26 (100)	0 (0)
Hemodynamic
Systolic Blood Pressure (mmHg)	108 [106, 113]	114 [108, 119]	107 [105, 110]	.0038
Normal	101 (72.15)	46 (45.55)	55 (54.46)	.0900
High	39 (27.86)	24 (61.54)	15 (38.47)
Diastolic Blood Pressure (mmHg)	86 [85, 87]	87 [86, 93]	85 [84, 86]	.0001
Normal	17 (12.15)	0 (0)	17 (100)	<.0001
High	123 (87.86)	70 (56.92)	53 (43.09)
Not hypertensive	17 (12.15)	0 (0)	17 (100)	<.0001
Hypertensive	123 (87.86)	70 (56.92)	53 (43.09)
Biochemical
C-Telopeptide (ng/mL)	1.61 [1.38, 1.86]	1.37 [1.18, 1.68]	2.08 [1.54, 2.37]	<.0001
Normal	7 (5)	7 (100)	0 (0)	.0200
High	133 (95)	63 (47.37)	70 (52.64)
Urine Calcium/Creatinine Ratio	0.25 [0.21, 0.27]	0.235 [0.19, 0.28]	0.26 [0.21, 0.27]	.6795
Normal	54 (38.58)	29 (53.71)	25 (46.3)	.4870
High	86 (61.43)	41 (47.68)	45 (52.33)
Osteocalcin (ng/mL)	51.3 [49.4, 53.6]	51.15 [49.2, 53.6]	52.25 [48.1, 55.4]	.9809
Normal	34 (24.29)	11 (32.36)	23 (67.65)	.0180
High	106 (75.72)	59 (55.67)	47 (44.34)
Fasting blood sugar (mmol/L)	6.5 ± 0.8	6.6 ± 0.9	6.4 ± 0.6	.2739
Normal	29 (20.72)	18 (62.07)	11 (37.94)	.1440
High	111 (79.29)	52 (46.85)	59 (53.16)
ALT (IU/L)	31.7 [31.4, 32.8]	31.5 [28.6, 32.8]	32.6 [31.6, 34.2]	.2084
AST (IU/L)	25.8 [25.3, 27.1]	26.3 [25.3, 27.3]	25.8 [25.1, 27.2]	.9743
GGT (IU/L)	27.45 [26.3, 28.6]	27.55 [25.3, 30.5]	27.35 [26.3, 27.9]	.8427
ALP (IU/L)	159.6 [149.4, 167.3]	165.6 [153.2, 179.5]	154.8 [131.6, 165.7]	.0081
Normal	51 (36.43)	20 (39.22)	31 (60.79)	.0530
High	89 (63.58)	50 (56.18)	39 (43.83)
Total protein (g/L)	72.6 [72.1, 73.4]	72.2 [71.5, 72.8]	73.4 [72.5, 74.2]	.0857
Albumin (g/L)	42.5 [42.1, 43.1]	42.45 [41.5, 43.2]	42.7 [42.1, 43.2]	.4584
Creatinine (µmol/L)	84.5 [82.3, 89.4]	91.4 [88.4, 94.7]	76.1 [72.1, 81.4]	<.000
Normal	126 (90)	68 (53.97)	58 (46.04)	.0050
High	14 (10.00)	2 (14.29)	12 (85.72)
Urea (mmol/L)	4.3 [4.2, 4.5]	4.4 [4.2, 4.8]	4.2 [4.1, 4.5]	.0156
Normal	133 (95)	63 (47.37)	70 (52.64)	.0200
High	7 (5)	7 (100)	0 (0)
Potassium (mmol/L)	4.3 [4.3, 4.5]	4.3 [4.3, 4.6]	4.3 [4.2, 4.6]	.4527
Normal	138 (98.58)	68 (49.28)	70 (50.73)	.4760
High	2 (1.43)	2 (100)	0 (0)
Sodium (mmol/L)	142.8 [142.7, 143.7]	142.8 [141.8, 143.7]	143.4 [142.8, 143.8]	.0526
Normal	102 (72.86)	53 (51.97)	49 (48.04)	.4470
High	38 (27.15)	17 (44.74)	21 (55.27)
Chloride (mmol/L)	103.5 [103.2, 104.3]	103.2 [102.8, 103.8]	104 [103.2, 104.6]	.5183
Normal	114 (81.43)	55 (48.25)	59 (51.76)	.3850
High	26 (18.58)	15 (57.7)	11 (42.31)
Insulin (µIU/mL)	26.7 [26.1, 27.3]	26.7 [26.2, 27.5]	26.55 [24.2, 27.3]	.6558
Normal	51 (36.43)	21 (41.18)	30 (58.83)	.1140
High	89 (63.58)	49 (55.06)	40 (44.95)
Insulin Resistance (HOMA-IR)	7.6 [7.3, 8.1]	7.95 [7.3, 8.7]	7.5 [6.5, 8.1]	.7592

Note. Age [Overall $\bar{(X} \pm S D)$ = 72 ± 8 years; Males $\bar{(X} \pm S D)$ = 73 ± 1 year]; Females $\bar{(X} \pm S D)$ = 72 ± 1 year; ALT = Alanine Transaminase; AST = Aspartate Transferase; GGT = Gamma-glutamyl Transferase; ALP = Alkaline Phosphatase; HOMA-IR = Homeostatic Model Assessment for Insulin Resistance; CI = Confidence Interval.

Females had significantly higher median CTX levels (2.08 ng/mL [95% CI: 1.54, 2.37] vs. 1.37 ng/mL [95% CI: 1.18, 1.68]; p < .0001) and lower median creatinine levels (76.1 µmol/L [72.1, 81.4] vs. 91.4 µmol/L [88.4, 94.7]; p < .0001). The median urine calcium/creatinine ratio was similar between females (0.26 [0.21, 0.27] vs. 0.235 [0.19, 0.28] in males; p = .6795). Osteocalcin levels were also comparable between the sexes, with a median of 52.25 ng/mL [48.1, 55.4] in females and 51.15 ng/mL [49.2, 53.6] in males (p = .9809).

Activity-Based (Component) Estimation of Sarcopenia Risk Among Participants

Table 2 presents the components of sarcopenia risk stratified by sex of the participants. Using the SARC-F questionnaire, 76.43% of the participants reported little or no difficulty in lifting and carrying a 10-pound weight, with a nearly equal distribution between men (49.54%) and women (50.47%; p = .8422). Similarly, 27.86% reported difficulty walking across a room [men (53.85%); women (46.16%; p = .5717)]. Difficulty rising from a chair or bed without help was reported by 45% of participants, with no significant sex difference (men: 50.8%; women: 49.21%, p = .8651). However, a significant difference was observed in the difficulty of climbing 10 stairs between males and females (p = .0123). Falls in the past year were experienced by 36.43% of participants, with no significant difference between men (54.91%) and women (45.1%, p = .3799).

Table 2.

Components of Sarcopenia Risk Stratified by Sex.

Components	Variable	Response		Sex
Components	Variable	Response	Overall	Male	Female	p-Value
Strength	Difficulty in lifting and carrying 10 pounds weight	Little, None	107 (76.43)	53 (49.54)	54 (50.47)	.8422
Strength	Difficulty in lifting and carrying 10 pounds weight	A lot or unable	33 (23.58)	17 (51.52)	16 (48.49)
Assist in walking	Difficulty in walking across a room	None	101 (72.15)	49 (48.52)	52 (51.49)	.5717
Assist in walking	Difficulty in walking across a room	A lot, use aids or unable	39 (27.86)	21 (53.85)	18 (46.16)
Rise from a chair	Difficulty in transferring from a chair or bed	None	77 (55)	38 (49.36)	39 (50.65)	.8651
Rise from a chair	Difficulty in transferring from a chair or bed	A lot or unable without help	63 (45)	32 (50.8)	31 (49.21)
Climb stairs	Difficulty in climbing a flight of 10 stairs	None	6 (4.29)	6 (100)	0 (0)	.0123
Climb stairs	Difficulty in climbing a flight of 10 stairs	A lot or unable	134 (95.72)	64 (47.77)	70 (52.24)
Falls	Number of falls in the past year	None	89 (63.58)	42 (47.2)	47 (52.81)	.3799
Falls	Number of falls in the past year	1 to 3 falls	51 (36.43)	28 (54.91)	23 (45.1)

Table S1 shows the distribution of sarcopenia risk components across age groups. Among individuals aged 61 to 70 years, the majority reported some difficulty in lifting and carrying 10 pounds (57.94%), whereas a large proportion of those aged 81 to 90 years were unable to do so or had significant difficulty (29.91%; p < .001). Difficulty walking across a room was absent in the younger group (61–70 years) but present in 30.84% of the 81 to 90 year group (p < .001). Similarly, while 80.52% of those aged 61 to 70 years reported no difficulty rising from a chair, 30.84% of those in the oldest group required help (p < .001). Climbing stairs was particularly challenging for older individuals, with 30.84% of the 81 to 90 year group unable to do so (p = .019). Additionally, falls were significantly more frequent in the older groups, with 30.84% of the 81 to 90 year group reporting 1 to 3 falls in the past year (p < .001).

Prevalence of Sarcopenia Risk Among Participants Stratified by Sex and Age

This study reported an overall sarcopenia risk prevalence of 44.3% (95% CI: [36.3, 52.6]). When stratified by sex, men had a slightly higher prevalence risk of 51.6% [39.4, 63.6] than women, at 48.4% [36.4, 60.6] (Figure 1).

Figure 1.

Prevalence of sarcopenia risk stratified by sex.

This study demonstrated an increase in the prevalence of sarcopenia risk from as low as 0% to as high as 97.0% among those aged 61 to 70 and 81 to 90 years, respectively. Thus, the proportion of participants at risk of sarcopenia increased progressively with age (Figure S1).

Relationship Between Markers of Bone Turnover and Insulin Resistance

The correlation analysis of the relationship between bone turnover markers and insulin resistance (IR) is presented in Table 3. Skeletal muscle mass was strongly negatively correlated with CTX (τ = −.669, p < .01), urine calcium/creatinine ratio (τ = −.685, p < .01), and osteocalcin (τ = −.683, p < .01) in the general population. This pattern was consistent across sex stratifications, with males and females showing strong negative correlations between skeletal muscle mass and these biomarkers. In men, skeletal muscle mass was even more strongly negatively correlated with osteocalcin (τ = −.786, p < .01) and urine calcium/creatinine ratio (τ = −.716, p < .01), whereas in women, the correlations were strongest with urine calcium/creatinine ratio (τ = −.790, p < .01) and osteocalcin (τ = −.788, p < .01). Additionally, IR and sarcopenia risk were significantly negatively correlated with skeletal muscle mass in both sexes. In women, IR showed a notably stronger negative correlation (τ = −.765, p < .01) than in men (τ = −.432, p < .01). Conversely, positive correlations were observed between CTX, urine calcium/creatinine ratio, osteocalcin, ALP, IR, and the risk of sarcopenia, with the strongest associations observed between the urine calcium/creatinine ratio and osteocalcin (τ = .702, p < .01 in the general population; τ = .812, p < .01 in women; Table 3).

Table 3.

Kendall’s tau-b (τ) Correlation Analysis Between Markers of Bone Turnover, Sarcopenia Risk and Insulin Resistance.

Variable	A	B	C	D	E	F	G
General Population
Skeletal Muscle Mass (%) [A]	1
C-Telopeptide (ng/mL) [B]	−.669**	1
Urine calcium/Creatinine Ratio [C]	−.685**	.638**	1
Osteocalcin (ng/mL) [D]	−.683**	.597**	.702**	1
ALP (IU/L) [E]	−.508**	.454**	.641**	.643**	1
Insulin Resistance (HOMA-IR) [F]	−.513**	.472**	.556**	.575**	.515**	1
Sarcopenia risk [G]	−.624**	.540**	.620**	.651**	.580**	.475**	1
Males
Skeletal Muscle Mass (%) [A]	1
C-Telopeptide (ng/mL) [B]	−.574**	1
Urine calcium/Creatinine Ratio [C]	−.716**	.656**	1
Osteocalcin (ng/mL) [D]	−.786**	.576**	.704**	1
ALP (IU/L) [E]	−.682**	.620**	.697**	.660**	1
Insulin Resistance (HOMA-IR) [F]	−.432**	.318**	.425**	.385**	.416**	1
Sarcopenia risk [G]	−.675**	.539**	.619**	.673**	.606**	.327**	1
Females
Skeletal Muscle Mass (%) [A]	1
C-Telopeptide (ng/mL) [B]	−.721**	1
Urine calcium/Creatinine Ratio [C]	−.790**	.759**	1
Osteocalcin (ng/mL) [D]	−.788**	.749**	.812**	1
ALP (IU/L) [E]	−.653**	.635**	.674**	.708**	1
Insulin Resistance (HOMA-IR) [F]	−.765**	.757**	.739**	.757**	.619**	1
Sarcopenia risk [G]	−.648**	.636**	.643**	.637**	.572**	.639**	1

Note. Kendall’s tau-b (τ). ALP is alkaline phosphatase.

p < .01.

Prevalence Ratio (PR) of Participants’ Markers of Bone Turnover

The prevalence ratios of bone turnover markers among participants with and without sarcopenia risk are shown in Table 4. Markers, such as C-telopeptide (PR: 1.80 [95% CI: 1.60, 2.02], urine calcium/creatinine ratio (PR: 1.73 [1.58, 1.89], osteocalcin (PR: 1.33 [1.27, 1.39], ALP (PR: 1.35 [1.28, 1.42]), and insulin resistance (PR: 1.39 [1.23, 1.56]) were significantly elevated in participants with sarcopenia risk, whereas skeletal muscle mass was significantly lower (PR: 0.79 [0.74, 0.85]; all p < .001). When stratified by sex, men showed elevated PR for C-telopeptide (1.77 [1.50, 2.10], urine calcium/creatinine ratio (1.83 [1.59, 2.10], osteocalcin (1.24 [1.19, 1.30], ALP (1.32 [1.24, 1.40]), and insulin resistance (1.24 [1.04, 1.47]), whereas skeletal muscle mass was lower (0.76 [0.68, 0.84]; all p < .001, except for insulin resistance, p = .015).

Table 4.

Prevalence Ratio of Bone Turnover, Muscle Mass and Insulin Resistance Among Participants With and Without Sarcopenia Risk.

Dependent variable	Reference	PR [95% CI]
Dependent variable	No sarcopenia	Sarcopenia risk	p-Value
General Population
C-Telopeptide (ng/mL)	1	1.80 [1.60, 2.02]	<.001
Urine Calcium/Creatinine Ratio	1	1.73 [1.58, 1.89]	<.001
Osteocalcin (ng/mL)	1	1.33 [1.27, 1.39]	<.001
ALP (IU/L)	1	1.35 [1.28, 1.42]	<.001
Skeletal Muscle Mass (%)	1	0.79 [0.74, 0.85]	<.001
Insulin Resistance (HOMA-IR)	1	1.39 [1.23, 1.56]	<.001
Males
C-Telopeptide (ng/mL)	1	1.77 [1.50, 2.10]	<.001
Urine Calcium/Creatinine Ratio	1	1.83 [1.59, 2.10]	<.001
Osteocalcin (ng/mL)	1	1.24 [1.19, 1.30]	<.001
ALP (IU/L)	1	1.32 [1.24, 1.40]	<.001
Skeletal Muscle Mass (%)	1	0.76 [0.68, 0.84]	<.001
Insulin Resistance (HOMA-IR)		1.24 [1.04, 1.47]	.015
Females
C-Telopeptide (ng/mL)	1	1.85 [1.63, 2.10]	<.001
Urine Calcium/Creatinine Ratio	1	1.64 [1.47, 1.82]	<.001
Osteocalcin (ng/mL)	1	1.42 [1.32, 1.53]	<.001
ALP (IU/L)	1	1.37 [1.37, 1.47]	<.001
Skeletal Muscle Mass (%)	1	0.83 [0.74, 0.93]	<.001
Insulin Resistance (HOMA-IR)	1	1.55 [1.31, 1.84]	<.001

Note. ALP = Alkaline Phosphatase; PR = Prevalence Ratio; CI = Confidence Interval.

Discussion

This study evaluated markers of bone turnover in blood and urine and examined their associations with musculoskeletal decline, metabolic health, and sarcopenia risk among older adults in Ghana. The prevalence of sarcopenia risk among participants was 44.3%. Stratified by age, the prevalence of sarcopenia risk increased, underscoring the profound impact of aging on muscle and bone health. With the exception of one variable, the ability to climb stairs, the distribution of sarcopenia risk components from the SARC-F questionnaire did not differ significantly between the sexes. Previous reports from Italy involving nursing home residents aged ≥ 70 years showed a slightly lower incidence of sarcopenia risk (32.8%), with men having a higher incidence rate (67.7% vs. 20.8%, p < .001; Landi et al., 2012).

Another study in community-dwelling Chinese older adults reported an overall sarcopenia risk prevalence of 37.7% (Chen et al., 2022). However, stratified by sex, Chen et al.’s study found that women had a higher incidence of sarcopenia risk than men (38.7% compared to 36.9%; Chen et al., 2022). Cultural norms and lifestyle disparities may lead to reduced muscle mass and strength in women, thereby increasing the risk of sarcopenia. The challenges women face when ascending a flight of 10 stairs may be attributed to a more pronounced decline in muscle mass and strength. This finding can also be attributed to hormonal fluctuations, especially post-menopause, when estrogen levels diminish, resulting in muscle atrophy. Estrogen has protective effects on muscle metabolism, and its decline is associated with reduced muscle strength, particularly in the lower limbs. Another study that used “hand-grip strength cut points to screen older persons at risk for mobility limitation” showed that older women experienced greater difficulties with physical tasks, such as stair climbing, than men (Sallinen et al., 2010).

The SARC-F questionnaire showed that most participants, regardless of sex, had difficulty lifting and carrying a 10-pound weight. This may suggest that the upper-limb strength required for daily tasks may be affected in both older adult males and females. No difference was observed between the sexes in terms of difficulty walking across a room or getting up from a chair. These findings align with global evidence showing that functional decline, particularly in activities requiring lower limb strength and balance, affects older adults regardless of sex (Milanović et al., 2013; Woo et al., 2014). However, it is worth noting that while SARC-F provides useful insight into sarcopenia risk at the population level, the findings should be considered preliminary until corroborated by objective diagnostic assessments. Falls, a notable outcome of sarcopenia risk, occurred in one-third of patients but showed no significant difference between sexes. Falls are a primary cause of morbidity and mortality among older adults. A study by Burns et al. in 2016 identified falls as a significant source of both fatal and non-fatal injuries in older adults (Burns et al., 2016). The risk of falls and sustaining severe injuries also increases with age, particularly in individuals aged > 65 years, making falls a critical factor affecting the geriatric population. Injuries from falls often lead to long-term incapacity and an increased need for medical care.

This study found significantly higher median CTX levels in women than in men. CTX is a biomarker of bone resorption that indicates the rate at which bones are broken down. A previous study reported higher CTX levels in postmenopausal women than in men of the same age group (Eastell & Szulc, 2017). Higher CTX levels in women, particularly postmenopausal women, may be linked with estrogen deficiency, compounded by reduced mechanical loading on the bone due to reduced muscle mass and strength in this age group, thereby accelerating bone resorption (Hsu et al., 2024). The current study revealed that men exhibited elevated ALP levels compared with women. This corroborates a recent study that reported that men have higher ALP levels than women (Tariq et al., 2019). This observation may be attributed to the increased bone mass in men, which necessitates heightened osteoblast activity to maintain bone growth balance.

Osteocalcin is an additional biomarker of bone formation, synthesized by osteoblasts, and contributes to bone mineralization. No notable disparities in the median osteocalcin levels were detected between males and females. Interestingly, a higher proportion of females (67.65%) than males (32.36 %) had normal osteocalcin levels, suggesting potential sex-specific differences in osteoblast activity and bone formation. The comparable osteocalcin levels between the sexes did not align with the findings of a previous study (Li et al., 2023). However, that study included younger women with adequate estrogen levels. Age-related variations in circulating osteocalcin levels have been reported, with females generally exhibiting more stable osteocalcin levels than males (Jung et al., 2016). We found significant differences between males and females in the urine calcium/creatinine ratio, which is used as an index of calcium excretion and, by extension, of bone resorption. This aligns with the findings of a previous study (Fenton et al., 2009) and may suggest that calcium excretion is relatively stable across sexes when normalized to creatinine. Stratified by age, elevated CTX, osteocalcin, and ALP levels were most prevalent in the 71 to 80 and 81 to 90 age groups, respectively. The increase in these biomarkers may suggest an adaptive response to heightened bone remodeling in older individuals, potentially linked to the body’s attempt to maintain bone density despite age-related declines. These findings are consistent with those of earlier studies (Razi et al., 2015; Shieh et al., 2016), which indicated that bone resorption and formation rates increase with age. Hormonal changes and reduced mechanical loading on bones increase bone turnover and, together, may heighten the risk of bone loss and fractures in older adults.

This study also revealed significant correlations among skeletal muscle mass (sMM), bone turnover markers (CTX and osteocalcin), and metabolic indicators, including sarcopenia risk and IR. Indeed, these associations are contextualized within screening-based risk classification (SARC-F questionnaire) and estimated muscle mass (bioelectrical impedance device).

The urine calcium-to-creatinine ratio, CTX, and osteocalcin levels were negatively correlated with sMM. This may be attributed to diminished muscle mass, which is frequently associated with reduced mechanical strain on bones, thereby increasing bone resorption and lowering the bone mineral density. Reduced muscle mass may intensify bone loss and increase the risk of fractures. Furthermore, sMM was inversely associated with sarcopenia risk and IR, suggesting a potential relationship, and underscoring the detrimental effects of reduced muscle mass on metabolic health and insulin sensitivity (DeFronzo & Tripathy, 2009). These findings emphasize the importance of preserving muscle mass and mitigating the risk of metabolic diseases and sarcopenia, especially in the aging population. A consumer-grade BIA device (Omron HBF-514) was used to estimate skeletal muscle mass. Although not diagnostic, BIA provides useful population-level estimates in low-resource settings. While convenient for fieldwork, it is worth noting that diagnostic cut-offs for older Ghanaian adults have not been validated. Furthermore, BIA accuracy decreases with hydration and age-related changes; therefore, sMM estimates should be interpreted cautiously.

Increased circulating osteocalcin and ALP levels were positively associated with sarcopenia risk and IR. The role of osteocalcin in regulating glucose metabolism may explain these positive correlations with IR and sarcopenia risk. Osteocalcin can function as a hormone or ligand that influences energy metabolism and muscle function (Mizokami et al., 2017; C. Smith et al., 2024). The positive correlation between ALP levels and both sarcopenia risk and IR further links bone turnover to metabolic dysfunction and muscle loss. These findings align with those of a previous study, in which individuals with sarcopenic osteoarthritis had a higher odds ratio for the highest HOMA-IR quartile than the other groups (Chung et al., 2016).

This study has several strengths that enhance its contribution to existing literature. First, it is one of the few studies to examine sarcopenia risk, metabolic health, and bone turnover markers among community-dwelling older adults in Ghana, providing novel data from an under-represented population in geriatric research. Second, the use of the SARC-F questionnaire (albeit limited) enabled standardized screening for sarcopenia risk in a resource-limited setting, facilitating comparisons with other studies. Third, the integration of biochemical markers of bone turnover and insulin resistance with functional measures (e.g., stair-climbing ability) provides a multidimensional perspective on musculoskeletal aging. Fourth, the inclusion of both men and women, considering menopausal status, strengthens the generalizability of the findings across sexes. Finally, this study highlights the practical challenges of geriatric assessment in low-resource contexts, offering insights that may inform future methodological adaptations and public health strategies.

This study had several limitations that warrant consideration. First, sarcopenia risk was assessed using the SARC-F questionnaire, which is a screening tool and does not provide a definitive diagnosis. Therefore, our findings reflect the risk of sarcopenia rather than confirmed prevalence. Future studies in this region should consider combining the SARC-F questionnaire with low-cost objective measures, such as handgrip strength, to enhance the accuracy and comprehensiveness of sarcopenia risk screening in resource-limited settings. This combined approach balances feasibility with improved screening performance, particularly in areas where access to advanced imaging or dual-energy X-ray absorptiometry may be limited.

Second, the skeletal muscle mass was estimated using a consumer-grade bioelectrical impedance device (Omron HBF-514). Although references supporting its use in older adults have been cited, the results should be interpreted cautiously, given the absence of gold-standard validation. Third, the cross-sectional design precludes causal inference, and the use of Poisson regression to estimate prevalence ratios represents an approximation rather than incidence modeling. Fourth, given that multivariable adjustment was not performed for key confounders (age, BMI, hypertension, physical activity), residual confounding cannot be excluded, and the data should be interpreted as unadjusted and underscores that the results are hypothesis-generating rather than confirmatory. Fifth, the exclusion of participants receiving insulin or antidiabetic medications may have introduced selection bias, although this was intended to minimize pharmacological confounding. Notably, although the sample size was adequate for the primary correlational analyses, it is relatively modest for estimating population-level prevalence risk, and we caution against overgeneralizing the findings beyond the study population. Finally, the large number of bivariate comparisons increases the risk of type I error. Although multiple-testing corrections (Bonferroni) were applied, exploratory analyses should be interpreted with caution. Future studies with larger sample sizes should consider alternative multiplicity adjustment techniques, aimed at controlling Type I error while preserving sensitivity to detect genuine effects. Despite these limitations, this study is among the few to evaluate bone turnover markers and their association with sarcopenia risk and metabolic health among sub-Saharan Africans, and its findings address important epidemiological gaps.

Conclusion

In this community-based study of older adults in Ghana, nearly half (44.3%) of the participants screened were at risk of sarcopenia, as assessed by the SARC-F questionnaire. Functional limitations, particularly difficulty in climbing stairs, were highly prevalent and highlighted the real-world impact of sarcopenia risk on daily living. These findings underscore the urgent need for early screening and preventive strategies in resource-limited settings such as ours. Clinicians should incorporate simple tools, such as SARC-F, into routine geriatric assessments, while policymakers should prioritize interventions that promote physical activity, adequate nutrition, and bone health. Future research should employ validated diagnostic criteria, longitudinal designs, and larger sample sizes to establish causal pathways and guide targeted interventions.

Supplemental Material

sj-docx-1-ggm-10.1177_30495334261456207 – Supplemental material for Sarcopenia Risk, Bone Turnover and Metabolic Health Indices Among Community-Dwelling Older Adults in Ghana: An Observational Study

Supplemental material, sj-docx-1-ggm-10.1177_30495334261456207 for Sarcopenia Risk, Bone Turnover and Metabolic Health Indices Among Community-Dwelling Older Adults in Ghana: An Observational Study by Isaac Lawer Kpabitey, Emmanuel Kwaku Ofori, Eric NanaYaw Nyarko, Richmond Owusu Ateko, Seth Kwabena Amponsah, Aliu Issahaku, Nafisa Akua Assan, Clement Nii Amugi, Henry Asare-Anane and Alfred Edwin Yawson in Sage Open Aging

Supplemental Material

sj-docx-2-ggm-10.1177_30495334261456207 – Supplemental material for Sarcopenia Risk, Bone Turnover and Metabolic Health Indices Among Community-Dwelling Older Adults in Ghana: An Observational Study

Supplemental material, sj-docx-2-ggm-10.1177_30495334261456207 for Sarcopenia Risk, Bone Turnover and Metabolic Health Indices Among Community-Dwelling Older Adults in Ghana: An Observational Study by Isaac Lawer Kpabitey, Emmanuel Kwaku Ofori, Eric NanaYaw Nyarko, Richmond Owusu Ateko, Seth Kwabena Amponsah, Aliu Issahaku, Nafisa Akua Assan, Clement Nii Amugi, Henry Asare-Anane and Alfred Edwin Yawson in Sage Open Aging

Footnotes

Acknowledgements

The authors thank the management and staff of Solis Hospital for their support and the Department of Chemical Pathology at the University of Ghana for their institutional support.

ORCID iD

Emmanuel Kwaku Ofori

Ethical Considerations

The College of Health Sciences (CHS) at the University of Ghana, through its Ethical and Protocol Review Committee (EPRC) (ID: CHS-Et/M.10-P5.11/03-2024), granted ethical approval for this study. The management of Solis Hospital also granted permission to recruit the volunteers.

Consent to Participate

All participants signed a written consent form after receiving the necessary information about the study. The consent process provided participants with comprehensive information about the study’s purpose, duration, potential benefits, materials used for sample collection, and any associated risks of the sampling procedure.

Author Contributions

Isaac Kpabitey: Conceptualization, Methodology, Investigation, Writing- original draft and final approval. Emmanuel Kwaku Ofori: Conceptualization, Supervision, Writing- original draft, Writing- review & editing and final approval. Eric Nana Yaw Nyarko: Resources, Writing- review & editing and final approval. Richmond Owusu Ateko: Resources, Writing- review & editing and final approval. Seth Kwabena Amponsah: Software, Writing- review & editing and final approval. Nafisa Akua Assan: Methodology, Investigation and final approval. Aliu Issahaku: Methodology, Investigation and final approval. Clement Amugi: Methodology, Investigation and final approval. Henry Asare-Anane: Resources, Writing- review & editing and final approval. Alfred Edwin Yawson: Supervision, Writing- review & editing and final approval.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Emmanuel Kwaku Ofori and Isaac Kpabitey Lawer are willing to share the datasets used in this study with any interested party who makes a reasonable request.

Supplemental Material

Supplemental material for this article is available online.

References

Allah

A. M. A.

El Tarhouny

S. A.

Khodeer

S. A.

Taha

I. M.

(2017). Evaluation of osteopontn, c-telopeptide of type-I collagen and matrix metalloprotein in post-menopausal Egyptian women with osteoporosis. Biomedical Research, 28(8), 3645–3650.

Angulo

El Assar

Rodriguez-Manas

(2016). Frailty and sarcopenia as the basis for the phenotypic manifestation of chronic diseases in older adults. Molecular Aspects of Medicine, 50, 1–32.

Burns

E. R.

Stevens

J. A.

Lee

(2016). The direct costs of fatal and non-fatal falls among older adults — United States. Journal of Safety Research, 58, 99–103. https://doi.org/10.1016/j.jsr.2016.05.001

Chen

Chau

P. H.

(2022). Prevalence, incidence, and associated factors of possible sarcopenia in community-dwelling Chinese older adults: A population-based longitudinal study. Frontiers in Medicine, 8, Article 769708.

Chung

Hyun

Lee

Seo

(2016). Novel effects of sarcopenic osteoarthritis on metabolic syndrome, insulin resistance, osteoporosis, and bone fracture: The national survey. Osteoporosis International, 27, 2447–2457.

Cleasby

M. E.

Jamieson

Atherton

P. J.

(2016). Insulin resistance and sarcopenia: Mechanistic links between common co-morbidities. Journal of Endocrinology, 229(2), R67–R81.

Cruz-Jentoft

A. J.

Bahat

Bauer

Boirie

Bruyère

Cederholm

Cooper

Landi

Rolland

Sayer

A. A.

Schneider

S. M.

Sieber

C. C.

Topinkova

Vandewoude

Visser

Zamboni

, Writing Group for the European Working Group on Sarcopenia in Older People 2, & and the Extended Group for EWGSOP2. (2018). Sarcopenia: Revised European consensus on definition and diagnosis. Age and Ageing, 48(1), 16–31. https://doi.org/10.1093/ageing/afy169

DeFronzo

R. A.

Tripathy

(2009). Skeletal muscle insulin resistance is the primary defect in type 2 diabetes. Diabetes Care, 32(Suppl 2), Article S157.

Dos Santos

T. B. d. L.

Maciel

G. R.

Bandeira

. (2026). Can a consumer-grade bioimpedance device accurately estimate sarcopenia in women with type 2 diabetes? A cross-sectional agreement study with dual-energy X-ray absorptiometry. Diabetes Research and Clinical Practice, 231, Article 113069.

10.

Eastell

Szulc

(2017). Use of bone turnover markers in postmenopausal osteoporosis. The Lancet Diabetes & Endocrinology, 5(11), 908–923. https://doi.org/10.1016/S2213-8587(17)30184-5

11.

El-Tallawy

S. N.

Nalamasu

Salem

G. I.

LeQuang

J. A. K.

Pergolizzi

J. V.

Christo

P. J.

(2021). Management of musculoskeletal pain: An update with emphasis on chronic musculoskeletal pain. Pain and Therapy, 10, 181–209.

12.

Fenton

T. R.

Lyon

A. W.

Eliasziw

Tough

S. C.

Hanley

D. A.

(2009). Meta-analysis of the effect of the acid-ash hypothesis of osteoporosis on calcium balance. Journal of Bone and Mineral Research, 24(11), 1835–1840.

13.

Ferreira

T. J.

Pessanha

C. R.

da Costa Salvador

de Souza

M. L. R.

Rangel

L. F. P.

da Silva

R. R. M.

Pierucci

A. P. T. R

. (2023). Agreement between bioelectrical impedance analysis, 2D photo and dual energy X-Ray absorptiometry in adults: 1142. Medicine & Science in Sports & Exercise, 55(9S), 365–366.

14.

Fonseca

Moreira-Gonçalves

Coriolano

H.-J. A.

Duarte

J. A.

(2014). Bone quality: The determinants of bone strength and fragility. Sports Medicine, 44, 37–53.

15.

Hsu

S.-H.

Chen

L.-R.

Chen

K.-H.

(2024). Primary Osteoporosis induced by androgen and estrogen deficiency: The molecular and cellular perspective on pathophysiological mechanisms and treatments. International Journal of Molecular Sciences, 25(22), Article 12139. https://www.mdpi.com/1422-0067/25/22/12139

16.

Jung

K. Y.

Kim

K. M.

E. J.

Kim

Y. J.

Lee

D. H.

Choi

S. H.

Jang

H. C.

Shin

C. S.

Park

K. S.

Lim

(2016). Age- and sex-specific association of circulating osteocalcin with dynamic measures of glucose homeostasis. Osteoporosis International, 27(3), 1021–1029. https://doi.org/10.1007/s00198-015-3315-7

17.

Kirk

Lieu

Vogrin

Sales

Pasco

J. A.

Duque

(2022). Serum levels of C-terminal telopeptide (CTX) are associated with muscle function in community-dwelling older adults. The Journals of Gerontology: Series A, 77(10), 2085–2092.

18.

Landi

Calvani

Cesari

Tosato

Martone

A. M.

Ortolani

Savera

Salini

Sisto

Picca

(2018). Sarcopenia: An overview on current definitions, diagnosis and treatment. Current Protein and Peptide Science, 19(7), 633–638.

19.

Landi

Cruz-Jentoft

A. J.

Liperoti

Russo

Giovannini

Tosato

Capoluongo

Bernabei

Onder

(2013). Sarcopenia and mortality risk in frail older persons aged 80 years and older: Results from ilSIRENTE study. Age and Ageing, 42(2), 203–209.

20.

Landi

Liperoti

Fusco

Mastropaolo

Quattrociocchi

Proia

Russo

Bernabei

Onder

(2012). Prevalence and risk factors of sarcopenia among nursing home older residents. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences, 67(1), 48–55.

21.

Larsson

Degens

Salviati

Lee

Y. I.

Thompson

Kirkland

J. L.

Sandri

(2019). Sarcopenia: Aging-related loss of muscle mass and function. Physiological Reviews, 99(1), 427–511.

22.

Lee

J. Y.

Kim

Y. H.

(2021). Low grip strength and muscle mass increase the prevalence of osteopenia and osteoporosis in elderly women. Healthcare (Basel), 9(4), Article 476.

23.

Wang

Dong

Liu

(2023). Age- and sex-specific differences in the association of serum osteocalcin and cardiometabolic risk factors in type 2 diabetes. Diabetology & Metabolic Syndrome, 15(1), Article 48. https://doi.org/10.1186/s13098-023-01021-0

24.

Marzetti

Calvani

Tosato

Cesari

Di Bari

Cherubini

Collamati

D’Angelo

Pahor

Bernabei

(2017). Sarcopenia: An overview. Aging Clinical and Experimental Research, 29, 11–17.

25.

McGavigan

A. D.

Maxwell

P. R.

Dunn

F. G.

(2008). Serological evidence of early remodeling in high-risk non-ST elevation acute coronary syndromes. International Journal of Cardiology, 125(1), 22–27.

26.

Metter

E. J.

Conwit

Tobin

Fozard

J. L.

(1997). Age-associated loss of power and strength in the upper extremities in women and men. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 52(5), B267–B276.

27.

Milanović

Pantelić

Trajković

Sporiš

Kostić

James

(2013). Age-related decrease in physical activity and functional fitness among elderly men and women. Clinical Interventions in Aging, 8, 549–556. https://doi.org/10.2147/cia.S44112

28.

Mizokami

Kawakubo-Yasukochi

Hirata

(2017). Osteocalcin and its endocrine functions. Biochemical Pharmacology, 132, 1–8.

29.

Nguyen

T. N.

Nguyen

A. T.

Khuong

L. Q.

Nguyen

T. X.

Nguyen

H. T. T.

Nguyen

T. T. H.

Hoang

M. V.

Pham

Nguyen

T. N.

H. T. T.

(2020). Reliability and validity of SARC-F questionnaire to assess sarcopenia among Vietnamese geriatric patients. Clinical Interventions in Aging, 15, 879–886.

30.

Noda

Kamiya

Hamazaki

Yamashita

Miki

Nozaki

Uchida

Ueno

Maekawa

Terada

(2024). Screening for sarcopenia with SARC-F in older patients hospitalized with cardiovascular disease. European Journal of Cardiovascular Nursing, 23(6), 675–684.

31.

Pinheiro

P. A.

da Silva Coqueiro

Carneiro

J. A. O.

Correia

T. M. L.

Pereira

Fernandes

M. H.

(2020). Anthropometric indicators as screening tools for sarcopenia in older adult women. Enfermería Clínica (English Edition), 30(4), 269–274.

32.

Razi

Birkhold

A. I.

Weinkamer

Duda

G. N.

Willie

B. M.

Checa

(2015). Aging leads to a dysregulation in mechanically driven bone formation and resorption. Journal of Bone and Mineral Research, 30(10), 1864–1873.

33.

Razzaque

M. S.

(2011). Osteocalcin: A pivotal mediator or an innocent bystander in energy metabolism? Nephrology Dialysis Transplantation, 26(1), 42–45.

34.

Rodrigues

Domingos

Monteiro

Morouço

(2022). A review on aging, sarcopenia, falls, and resistance training in community-dwelling older adults. International Journal of Environmental Research and Public Health, 19(2), Article 874.

35.

Sallinen

Stenholm

Rantanen

Heliövaara

Sainio

Koskinen

(2010). Hand-grip strength cut points to screen older persons at risk for mobility limitation. Journal of the American Geriatrics Society, 58(9), 1721–1726.

36.

Santos

L. A.

Lima

T. B.

Ietsugu

M. d. V.

Nunes

H. R. d. C.

Romeiro

F. G.

(2019). Anthropometric measures associated with sarcopenia in outpatients with liver cirrhosis. Nutrition & Dietetics, 76(5), 613–619.

37.

Shieh

Han

Ishii

Greendale

G. A.

Crandall

C. J.

Karlamangla

A. S.

(2016). Quantifying the balance between total bone formation and total bone resorption: An index of net bone formation. The Journal of Clinical Endocrinology & Metabolism, 101(7), 2802–2809.

38.

Shou

Chen

P.-J.

Xiao

W.-H.

(2020). Mechanism of increased risk of insulin resistance in aging skeletal muscle. Diabetology & Metabolic Syndrome, 12, 1–10.

39.

Smith

Lin

Parker

Yeap

B. B.

Hayes

Levinger

(2024). The role of bone in energy metabolism: A focus on osteocalcin. Bone, 188, Article 117238.

40.

Smith

Tully

Jacob

Blackburn

Adlakha

Caserotti

Soysal

Veronese

López Sánchez

G. F.

Vancampfort

(2020). The association between sedentary behavior and sarcopenia among adults aged ≥65 years in low-and middle-income countries. International Journal of Environmental Research and Public Health, 17(5), Article 1708.

41.

Tarantino

Baldi

Scimeca

Piccirilli

Piccioli

Bonanno

Gasbarra

(2016). The role of sarcopenia with and without fracture. Injury, 47, S3–S10.

42.

Tariq

Lone

K. P.

Khaliq

(2019). Alkaline phosphatase is a predictor of Bone Mineral Density in postmenopausal females. Pakistan Journal of Medical Sciences, 35(3), 749–753. https://doi.org/10.12669/pjms.35.3.188

43.

Tieland

Trouwborst

Clark

B. C.

(2018). Skeletal muscle performance and ageing. Journal of Cachexia, Sarcopenia and Muscle, 9(1), 3–19.

44.

Tournadre

Vial

Capel

Soubrier

Boirie

(2019). Sarcopenia. Joint BoneSpine, 86(3), 309–314.

45.

Wallace

T. M.

Levy

J. C.

Matthews

D. R.

(2004). Use and abuse of HOMA modeling. Diabetes Care, 27(6), 1487–1495.

46.

Woo

Leung

Morley

J. E.

(2014). Validating the SARC-F: A suitable community screening tool for sarcopenia? Journal of the American Medical Directors Association, 15(9), 630–634. https://doi.org/10.1016/j.jamda.2014.04.021

47.

Yuan

Larsson

S. C.

(2023). Epidemiology of sarcopenia: Prevalence, risk factors, and consequences. Metabolism, 144, Article 155533.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.02 MB

0.00 MB