Calcif Tissue Int (2015) 97:353–363 DOI 10.1007/s00223-015-0018-1
ORIGINAL RESEARCH
Fat Infiltration in the Leg is Associated with Bone Geometry and Physical Function in Healthy Older Women Amanda L. Lorbergs1,2 • Michael D. Noseworthy3,4 • Jonathan D. Adachi5 Paul W. Stratford2 • Norma J. MacIntyre2
•
Received: 20 March 2015 / Accepted: 1 June 2015 / Published online: 13 June 2015 Springer Science+Business Media New York 2015
Abstract The objective of this study was to estimate the associations between muscular fat infiltration, tibia bone mineral quantity and distribution, and physical function in healthy older women. Thirty-five women (aged 60–75 years, mean 70 years) were recruited from the community. Percent intramuscular fat (%IntraMF) within the right leg tibialis anterior, soleus, and gastrocnemius muscles and total intermuscular fat (IMF) were segmented from magnetic resonance imaging scans at the mid-calf. Intramyocellular lipid (IMCL) content in the right tibialis anterior was measured with proton magnetic resonance spectroscopy. Right tibia & Norma J. MacIntyre [email protected] Amanda L. Lorbergs [email protected] Michael D. Noseworthy [email protected]
Paul W. Stratford [email protected]
Keywords Aged Adipose tissue Magnetic resonance imaging (MRI) Proton magnetic resonance spectroscopy (1H MRS) X-ray computed tomography Mobility limitation
Institute for Aging Research, Hebrew SeniorLife and Harvard Medical School, 1200 Centre St., Rm. 620, Boston, MA 02131, USA
Introduction
Jonathan D. Adachi [email protected]
1
2
bone content, area, and strength were measured at the 4, 14, and 66 % sites using peripheral quantitative computed tomography. Physical function was assessed by gait speed on the 20 m walking test. After adjusting for age, body size, and activity level, %IntraMF had a negative association with bone content and area at all tibia sites (r = -0.31 to -0.03). Conversely, greater IMF was associated with increased bone content and area (r = 0.04–0.32). Correlation coefficients for the association between IMCL and bone were negative (r = -0.44 to -0.03). All measures of fat infiltration had a negative association with observed physical function (r = -0.42 to -0.04). Our findings suggest that muscular fat infiltration in the leg of healthy postmenopausal women has a compartment-specific relationship with bone status and physical function. Minimizing fat accumulation within and between muscle compartments may prevent bone fragility and functional decline in women.
School of Rehabilitation Science, McMaster University, 1400 Main Street West, IAHS 403, Hamilton, ON L8S 1C7, Canada
3
School of Biomedical Engineering, McMaster University, 1280 Main Street West, ETB 406, Hamilton, ON L8S 4K1, Canada
4
Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada
5
Department of Medicine, McMaster University, 501-25 Charlton Ave East, Hamilton, ON L8N 1Y2, Canada
Recent interest in age-related conditions such as sarcopenia and frailty highlights the important link between muscular fat infiltration, bone fragility, and mobility. Impaired mobility and low bone density are significant risk factors for falls and fractures, respectively [1, 2]. With aging, muscle atrophies and becomes weaker; these age-related changes are associated with declines in physical functioning and bone loss [3, 4]. However, muscle changes do not fully explain the high prevalence of bone fragility and
123
354
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
mobility impairments in older adults [5]. The accumulation of fat within muscle compartments is observed with advancing age, inactivity, and chronic diseases, and is proposed to modify the structure and function of muscle [6–8]. Greater muscular fat infiltration in the lower extremity is associated with muscle weakness [9], impaired mobility [10], increased falls risk [11], and fractures [12]. Consequences of muscular fat infiltration in the lower extremity, including increased risk of hospitalization [13], are concerning given the aging population. Compared to older men, aging women are especially at risk for bone fragility and mobility impairments because of the significant loss of bone mass and increase in fat within skeletal muscle that is observed with the menopausal transition [14]. Moreover, women have higher rates of nonfatal conditions that contribute to physical disability compared to men [15]. The negative effects of fat mass on mobility impairments were initially evaluated using dual energy X-ray absorptiometry (DXA). Since inherent limitations of DXA imaging preclude investigation of fat within and between muscle compartments, more recent studies utilize computed tomography (CT) and magnetic resonance imaging (MRI) to quantify muscular fat infiltration. Findings from CT-based studies show that lower muscle density, a proxy measurement reflecting greater fat infiltration, is associated with increased risk of hip fracture [16] and poorer physical functioning in older adults [5]. However, muscle density acquired by CT is a crude estimate of fat infiltration in muscles because the measurement includes blood vessels, nerves, and connective tissue within the imaged lean tissue. Further, muscle density derived by CT does not include measurement of the fat deposited around muscle groups and involves exposure to ionizing radiation. MRI is advantageous because it provides a high contrast image that distinguishes intramuscular fat (IntraMF) from intermuscular fat (IMF). IntraMF, located within muscle groups and external to the muscle cells, is known to increase with advancing age and inactivity [6, 8]. IntraMF accumulation increases the proportion of non-contractile tissue within muscle groups and reduces the force generation capacity of the muscle [17, 18]. Less is known about IMF, which is situated within the fascial envelope surrounding muscles and muscle compartments. Few studies distinguish IMF from IntraMF; studies describing IMF and IntraMF as separate compartments are limited to adults with diabetes [19]. Similarly, studies measuring muscle composition in the leg using proton magnetic resonance spectroscopy (1H MRS) have focused on aging [20] and chronic metabolic conditions, such as diabetes [7]. On a microscopic scale, 1H MRS enables the non-invasive quantification of several metabolites, including intramyocellular lipid (IMCL), or fat droplets stored within muscle cells. Greater IMCL
123
content in leg muscles is associated with obesity and insulin resistance [21] and may also influence tibia bone structure, since there is a high incidence of ankle fractures within obese [22] and diabetic populations [23]. Despite the known physiologic dysfunctions associated with muscular fat infiltration and the significant health care burden related to its sequelae, there is a paucity of research assessing the influence of muscular fat deposition on bone health and physical functioning in healthy older adults. Adipose tissue is recognized as an endocrine organ that communicates with bone cells through molecular and cellular signaling pathways [24]. Although adipose tissue infiltration in lower extremity muscles is associated with increased fracture risk [12, 16], older women with osteoporosis or elevated fracture risk are usually excluded from studies evaluating musculoskeletal tissue relationships because clinical practice guidelines indicate pharmacologic treatment for fracture risk reduction [25]. As such, there is a significant proportion of older women who are not studied and the relationship of muscular fat infiltration on bone mineral quantity and distribution remains unknown. To address this gap within the literature, we were interested in measuring musculoskeletal variables in otherwise healthy and physically active women aged 60–75 years. Given the importance of leg muscles for balance, mobility, and activities of daily living, we sought to improve our understanding of the relationship between muscular fat accumulation, bone fragility, and physical functioning. Therefore, the objective of our study was to estimate the associations between muscular fat infiltration, tibia bone mineral quantity and distribution, and physical function in healthy older women. We speculated that fat infiltration within leg muscles has an inverse association with bone mineral quantity and distribution and physical function.
Methods Study Design and Sample For this cross-sectional study, we recruited healthy women aged 60–75 years. Multiple community-based recruitment strategies were used: presentations to community groups, posters, newspaper and community newsletter advertisements, and patients attending a local osteoporosis clinic. Past study participants who consented to be contacted for future research were mailed invitations to participate in the current study. Prior to enrolment, all interested volunteers were screened by telephone. Exclusion criteria were diabetes, pulmonary disorders, stroke, cancer in the past 5 years, smoking cigarettes in the past 2 years, surgery in the past
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
6 weeks, or neurologic or musculoskeletal condition affecting the back or lower limbs (e.g., knee and/or hip osteoarthritis, rheumatoid arthritis). Volunteers were also excluded if they reported contraindications to MRI, such as claustrophobia. Interview Information on current health status, years since menopause, and current medications (including supplements) were collected in a short interview. Self-reported fracture history was provided by all participants. Fragility fractures were defined as occurring spontaneously or following a minor trauma, such as a fall from standing height or less, after the age of 40 years [26]. Fractures of the skull, fingers, and toes were excluded because they are not considered osteoporotic fractures [26]. Anthropometry Height (to the nearest 1 mm) and weight (to the nearest 0.1 kg) were measured using standard procedures. Height and weight were used to calculate body mass index (BMI, kg/m2). The length of the right tibia was measured to the nearest 1 mm from the base of the medial malleolus to the superior margin of the medial epicondyle, while the participant was seated with feet flat on the floor and knees bent at a 90-degree angle. The 66 % site of the tibia was calculated and marked with a fiducial marker for MRI scanning and with ink for peripheral quantitative computed tomography (pQCT) scanning. MRI Fat Infiltration Image Acquisition The right leg was imaged with a 3.0T MRI scanner (General Electric Healthcare Discovery MR 750, Milwaukee, WI) and an eight-channel phased array RF knee coil. Standardized positioning ensured each participant was resting supine with knees extended and feet immobilized in a custom positioning rig [27]. One of three accredited MRI technologists performed all scanning. The scan acquisition protocol is described elsewhere [28]. In brief, 30 contiguous axial proton density (PD)-weighted images (slice thickness = 4 mm; TE/TR = 30/2344 ms, field-of view (FOV) = 16 cm, matrix = 320 mm 9 320 mm, total scan time = 6 min 30 s) were acquired. The phased array uniformity enhancement post-processing filter was applied to correct for image intensity non-uniformity and reduce edge blurring. A 1H MRS point-resolved spectroscopy pulse sequence was used to obtain the IMCL resonance area of a single
355
voxel within the tibialis anterior muscle. This site was chosen because the muscle fiber orientation in this muscle permits the greatest separation of lipid compartments. The following parameters were used: TE/TR = 30/1500 ms, averages = 128, and total scan duration = 3 min 12 s. The 40 mm 9 20 mm 9 20 mm (16 mL) volume of interest was placed within the tibialis anterior muscle to exclude subcutaneous fat, blood vessels, and bone in an image-guided manner. Two pairs of saturation bands were manually placed to reduce the strong subcutaneous and bone marrow lipid signals. Automated shimming improved the field homogeneity. This procedure always resulted in a water peak full width half maximum of less than 0.326 ppm. Image Processing and Analyses MRI scans were visually inspected by an accredited MRI technologist to screen for potential subclinical pathology. One trained operator used SliceOmatic 4.3 image analysis software (TomoVision, Montreal, Canada) to segment 10 contiguous PD-weighted images corresponding to images 11 thru 20 out of the 30 images acquired. All image segmentations were performed on the same computer monitor; gamma settings were fixed prior to each analysis session. Volumes (cm3) of tibialis anterior, soleus, and gastrocnemius muscles and IntraMF, and IMF were segmented as illustrated in Fig. 1. Using the histogram of pixel intensity distribution, IntraMF and IMF separation thresholds for each participant were determined by placing the lower limit of the region-growing threshold at the base of the muscle peak. Small adjustments to the signal intensity histogram were made as needed to account for signal variability between individuals and along the imaged leg segment, as described previously [12]. In our laboratory, IntraMF is defined as bright pixels located beneath the muscle fascial envelope and within the epimysium and perimysium. IMF is defined as bright pixels located within the deep fascial envelopes surrounding the muscle bellies and muscle compartments. Our main variables of interest for this study were percent IntraMF (%IntraMF), calculated as a percentage of its respective muscle volume, and total IMF volume (cm3). Reliability of manual leg muscle segmentation from MRI scans to estimate muscle volume is excellent (ICC = 0.99) if eight to ten contiguous slices are analyzed [29]. LCModel software (v6.2) was used to analyze the spectra from the tibialis anterior muscle [30]. The LCModel-derived lipid content was determined using a two-step process. The signal from a metabolite resonance area in a water-suppressed spectrum was divided by the signal from the water peak in an unsuppressed water reference signal acquired from the same voxel. An automated spectra-fitting routine determined the IMCL methylene
123
356
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
Fig. 1 Representative proton density-weighted images of the leg in a postmenopausal woman (age = 65 years, BMI = 29 kg/m2) before a and after b segmentation. The tibialis anterior, soleus, and gastrocnemius muscles are orange, blue, and red, respectively. The intramuscular fat for each muscle is segmented in yellow, pink, and
gray for the tibialis anterior, soleus, and gastrocnemius, respectively. Intermuscular fat (green) is located beneath the muscle fascial plane and surrounding muscle bellies or compartments. The arrow points to the fiducial marker used to visualize the 66 % site
resonance area (institutional units) at around 1.3 ppm based on algorithms that adjust the phase and ppm shift of the spectra, estimate the baseline, and perform eddy current correction.
bone mineral density (ToD, mg/cm3) and trabecular volumetric bone mineral density (TrD, mg/cm3), total cross-sectional area (ToA, mm2) and trabecular cross-sectional area (TrA, mm2), total bone mineral content (ToC, mg/mm) and trabecular mineral content (TrC, mg/mm), and resistance to compression loading represented by the bone strength index (BSI, mg2/mm4) [31]. At the 14 and 66 % tibia, we used Contour mode 3 and Peel mode 2 with thresholds set at 711 mg/cm3 to separate cortical bone from soft tissue. Variables measured were cortical volumetric bone mineral density (CoD, mg/cm3), cortical cross-sectional area (CoA, mm2), and cortical mineral content (CoC, mg/mm). At the 66 % site, we measured the polar strength strain index (SSI, mm3), a validated estimate of bone’s ability to resist bending forces along the neutral axis [32].
pQCT Bone Mineral Quantity and Distribution Image Acquisition The right leg was imaged using the Stratec XCT2000 pQCT (Pforzheim, Germany) according to the manufacturer’s recommended standardized protocol performed by one trained operator. Briefly, the anatomical reference line was placed at the medial tip of the distal tibia endplate using a 30-mm coronal scout scan. Measuring proximally from the reference line, the scanner calculated and acquired axial images at the 4 and 14 % scan sites with an in-plane resolution of 0.4 mm and 20 mm/s scan speed. The scanner was manually positioned at the 66 % scan site and images were acquired with an in-plane resolution of 0.5 mm scan speed of 15 mm/s. All pQCT images had a slice thickness of 2.4 mm. Total scan time was about 6 min. Image Analysis Using manufacturer’s software (Stratec, v5.4), one operator analyzed all images. The parameters applied to the 4 % site were Contour mode 2, Peel mode 1, and a threshold of 280 mg/cm3 to separate bone from surrounding soft tissue. Bone variables measured at the 4 % site were total volumetric
123
Observed Physical Function Using a standardized protocol [33], all participants performed the 30-s chair stand test (30-CST) twice during the study visit. The number of sit-stand-sit repetitions observed in 30 s was recorded. The average of the two trials was used for analysis. Preferred gait speed (m/s) was measured on a 20 m course in a straight uncarpeted hallway. Participants began the course 2 m in front of the start line and ended 2 m behind the line to account for initial acceleration and finish-line deceleration. Time (s) to walk the 20 m course was recorded using a stopwatch and the time divided by 20 m (m/s) was used for analysis.
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
Self-Reported Physical Activity Two self-reported physical activity questionnaires were administered at the interview. The rapid assessment of physical activity (RAPA) is a nine-item questionnaire that captures the frequency and intensity of non-sports related activity with ‘‘yes’’ or ‘‘no’’ responses. RAPA scores between six and ten represent an active lifestyle, whereas scores between two and five are considered under-active [34]. The human activity profile (HAP) is a 94-item questionnaire consisting of activities listed in ascending order of metabolic energy required to complete them [35]. Response options are ‘‘still doing this activity,’’ ‘‘stopped doing this activity,’’ and ‘‘never did this activity.’’ Each participant’s adjusted activity score (AAS) was reported and calculated as the difference between the Maximal Activity Score (i.e., highest numeral assigned to the activity that the individual is still doing) and the adjusted score (i.e., number of activities an individual has stopped doing) to represent the activities the participant can perform. Higher AAS scores (maximum score is 94) reflect better physical functioning [35].
Table 1 Descriptive statistics for the sample of women Variable
69.5 (4.3)
Height (cm)
161.4 (5.6)
Body weight (kg)
Results Participants Thirty-five community-dwelling women participated in the study. Demographic and clinical characteristics for the sample are shown in Table 1. Fragility fractures were reported by 11 women; fracture sites included the wrist (n = 5), vertebra (n = 3), ankle (n = 2), and rib and sternum (n = 1). Eighteen women reported current or recent (within past 2 years) use of bone-sparing medication; most
66.4 (10.6)
Body mass index (kg/m2)
25.5 (4.3)
years since menopause (years)
20.2 (5.6)
Tibia length (mm) Bone-sparing medication use (n, %) Fracture (n, %)
369.7 (20.1) 18 (51 %) 11 (31 %)
Table 2 Mean and standard deviation (SD) for fat infiltration in leg muscles measured by magnetic resonance imaging and adjusted for tibia length Variable
Total (N = 35) Mean (SD)
Intramuscular fat (%) Tibialis anterior
8.9 (3.0)
Soleus
7.9 (3.5)
Total intermuscular fat volume (cm3) Intramyocellular lipida (institutional units)
Data were inspected for normality visually by plotting histograms and statistically using the Shapiro–Wilk test. Levene’s test was used to test variance equality. Results were described using means and standard deviations. Partial Pearson correlation coefficients (r) and 95 % confidence intervals (95 % CI) adjusted for age, height, weight, and activity level were used to estimate the association between fat and bone. Associations between fat and physical function and physical activity variables were adjusted for age, height, and weight. All data management and statistical analyses were performed using PSAW software v20 (SPSS, Chicago, IL, USA). Statistical significance for all analyses was set at p less than 0.05.
Total (N = 35) Mean (SD)
Age (years)
Gastrocnemius
Statistical Analyses
357
a
9.0 (3.0) 14.6 (45.0) 0.017 (0.01)
Unadjusted, n = 29
used bisphosphonates (n = 13) and others were taking denosumab (n = 4) or parathyroid hormone (n = 1). Most (91 %) women responded ‘‘yes’’ to current daily vitamin D supplementation, whereas 43 % women said ‘‘yes’’ to current calcium supplementation. Fat Infiltration All PD scans were successfully acquired and analyzed. Table 2 shows the distribution of muscle-specific %IntraMF and total IMF. Six IMCL data points were excluded from the analysis due to improper data storage prior to a hardware system malfunction (n = 3), failed analyses (n = 2), and a statistical outlier (n = 1) that was greater than three SD units from the mean. Therefore, Table 2 presents values for tibialis anterior IMCL content obtained in 29 participants. Bone Mineral Quantity and Distribution All pQCT scans were analyzed; there were no protocol deviations. Table 3 presents the sample characteristics with respect to tibia bone variables.
123
358
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
Table 3 Mean and standard deviation (SD) for tibia bone geometry measured by peripheral quantitative computed tomography Variable
Total (N = 35) Mean (SD)
4 % site Total density (mg/cm3) Total area (mm2) Total content (mg)
249 (36) 1081 (185) 265 (40)
Trabecular density (mg/cm3)
204 (35)
Trabecular area (mm2)
487 (83)
Trabecular content (mg)
97 (15)
Bone strength index (mg2/mm4)
67 (16)
14 % site Cortical density (mg/cm3)
1054 (71)
Cortical area (mm2)
143 (34)
Cortical content (mg)
152 (40)
66 % site Cortical density (mg/cm3) 2
1080 (40)
Cortical area (mm )
254 (53)
Cortical content (mg)
275 (61)
Stress strain index (mm3)
1869 (315)
Bone area and content variables are adjusted for tibia length
Observed Physical Function and Self-Reported Physical Activity Performance on the 20 m walk test resulted in a mean (standard deviation) gait speed of 1.4(0.1) m/s and the women completed 14(4) chair stand repetitions. The median RAPA score was 9, with minimum and maximum scores of 3 and 10, respectively. The HAP AAS had a median score of 77, with a minimum score of 55 and a maximum score of 94. Associations Between Fat and Bone All associations between MRI-derived fat infiltration and pQCT bone mineral quantity and distribution are presented in Table 4. Women who reported fragility fractures or current use of bone-sparing medications were similar to women without fracture and not taking these medications and associations with bone variables did not differ (data not shown). With the exception of cortical density, associations between tibialis anterior %IntraMF and bone variables were in the negative direction; partial correlation coefficients were between -0. 31 and -0.12. Partial correlation coefficients for soleus %IntraMF and bone area and mineral content were negative (r = -0.25 and r = -0.03). Similarly, associations between gastrocnemius %IntraMF and bone area and mineral content were negative; partial correlation coefficients varied from -0.30 (95 % CI -0.59,
123
0.05) for ToA and TrA, to -0.03 (95 % CI -0.37, 0.32) for CoC at the 14 % site. In contrast to %IntraMF, associations between total IMF and bone area and mineral content variables were positive (r = 0.04–0.32). Associations between IMCL and bone variables were negative, with the exception of ToA and TrA at the 4 % site (Table 4). The unadjusted relationship between IMCL and BSI (r = -0.50, 95 % CI -0.93, -0.16) is presented in Fig. 2. Associations Between Fat and Physical Activity and Observed Physical Function Overall, partial correlations were negative for the association between fat infiltration and self-reported physical activity and observed physical function (Table 4). Figure 2 shows the unadjusted association between gait speed and %IntraMF in the tibialis anterior (r = -0.43, 95 % CI -0.81, 0.11) and gastrocnemius (r = -0.41, 95 % CI -0.78, -0.09). Adjusted associations between physical function and fat infiltration variables were negative (Table 4). Likewise, all fat compartments had negative associations with self-reported activity, with the exception of gastrocnemius %IntraMF and HAP (r = 0.11, 95 %CI -0.25, 0.44).
Discussion This cross-sectional study showed that, in general, greater IMF is associated with increased bone mineral content and cortical bone area, whereas higher amounts of %IntraMF and IMCL are associated with lower bone quantity and poorer mineral distribution in the tibia. Gait speed was slower and the number of sit-to-stand repetitions was lower as %IntraMF, IMF, and IMCL in the leg increased. This is the first study to evaluate the relationships between MRIderived fat infiltration, bone mineral quantity and distribution, and observed physical function in the same individual. Our investigation of three depots of muscular fat infiltration suggests a potential for distinct relationships between specific fat compartments with bone structure and strength, and physical function in healthy postmenopausal women. Our main finding was that muscular fat deposition in the leg is related to the tibia in a compartment-specific manner. Greater IMF, but not %IntraMF, appears to confer skeletal benefits to cortical area and content at the tibia diaphysis, whereas IMCL content is associated with poorer bone strength at the distal tibia. In other words, our results suggest that fat stored outside the muscle groups affects bone differently than fat stored within muscle cells. IMCL has not been previously measured with bone outcomes. Our results showing greater IMCL in those women with lower
-0.25 (-0.55, 0.11)
Stress strain index (mm3)
-0.35 (-0.62, 20.01) -0.07 (-0.41, 0.28)
Rapid assessment of physical activity (points)
Human activity profile (Adjusted Activity Score)
b
a
Partial correlation adjusted for age, weight, and height
Partial correlation adjusted for age, weight, and activity level (HAP)
Bolded values represent associations with confidence intervals that exclude zero
-0.07 (-0.41, 0.28)
-0.13 (-0.46, 0.23)
30 s chair stand test (repetitions)
-0.04 (-0.38, 0.31)
-0.04 (-0.38, 0.31)
-0.33 (-0.61, 0.02)
-0.07 (-0.41, 0.28)
-0.03 (-0.37, 0.32)
-0.10 (-0.43, 0.26)
-0.14 (-0.47, 0.22)
0.16 (-0.20, 0.48)
0.02 (-0.33, 0.37)
-0.09 (-0.43, 0.27)
0.28 (-0.08, 0.57)
0.19 (-0.17, 0.51)
-0.14 (-0.47, 0.22)
-0.24 (-0.54, 0.12)
Gait speed (m/s)
Physical performance and physical activityb
-0.29 (-0.58, 0.06) -0.26 (0.56, 0.10)
Cortical area (mm )
Cortical content (mg)
2
Cortical density (mg/cm3) 0 (-0.35, 0.35)
-0.26 (-0.56, 0.10)
66 %
Cortical content (mg)
0.02 (-0.33, 0.37) -0.31 (0.59, 0.04)
Cortical area (mm2)
Cortical density (mg/cm3)
-0.12 (-0.45, 0.24)
-0.29 (-0.58, 0.06)
Trabecular content (mg)
Bone strength index (mg2/mm4) 14%
-0.17 (-0.49, 0.19)
Trabecular area (mm2)
0.18 (-0.18, 0.50)
-0.11 (-0.44, 0.25)
-0.31 (-0.59, 0.04) -0.12 (0.48, 0.24)
Total content (mg)
-0.17 (-0.49, 0.19)
Trabecular density (mg/cm3)
-0.25 (-0.55, 0.11)
-0.13 (-0.46, 0.23)
Total area (mm2)
0.21 (-0.15, 0.52)
Soleus
Total density (mg/cm3)
4%
Tibia bone variablesa
Tibialis anterior
Intramuscular fat, %
0.11 (-0.25, 0.44)
-0.19 (-0.51, 0.17)
-0.08 (-0.42, 0.28)
-0.17 (-0.49, 0.19)
-0.22 (-0.53, 0.14)
-0.13 (-0.46, 0.23)
-0.17 (-0.49, 0.19)
0.13 (-0.23, 0.46)
-0.03 (-0.37, 0.32)
-0.14 (-0.47, 0.22)
0.27 (-0.08, 0.57)
0 (-0.35, 0.35)
-0.30 (-0.59, 0.05)
-0.30 (-0.59, 0.05)
0.06 (-0.29, 0.40)
-0.28 (0.57, 0.08)
-0.30 (-0.59, 0.05)
0.07 (-0.28, 0.41)
Gastrocnemius
Partial correlation coefficient (95 % confidence interval)
-0.17 (-0.49, 0.19)
-0.24 (-0.54, 0.12)
-0.25 (-0.55, 0.11)
-0.14 (-0.47, 0.22)
-0.10 (-0.43, 0.26)
0.13 (-0.23, 0.46)
0.18 (-0.18, 0.50)
-0.17 (-0.49, 0.19)
0.32 (-0.03, 0.60)
0.29 (-0.06, 0.58)
0.16 (-0.20, 0.48)
-0.12 (-0.45, 0.24)
0.04 (-0.31, 0.38)
0.19 (-0.17, 0.51)
-0.17 (-0.49, 0.19)
0.18 (-0.18, 0.50)
0.20 (-0.16, 0.51)
-0.09 (-0.43, 0.27)
Intermuscular fat volume, cm3
-0.12 (-0.45, 0.24)
-0.35 (-0.62, -0.01)
-0.34 (-0.62, 0.01)
-0.42 (-0.67, 20.08)
-0.16 (-0.48, 0.20)
-0.08 (-0.42, 0.28)
-0.04 (-0.38, 0.31)
-0.18 (-0.55, 0.18)
-0.08 (-0.42, 0.28)
-0.03 (-0.37, 0.32)
-0.26 (-0.56, 0.10)
-0.44 (-0.68, 20.11)
-0.13 (-0.46, 0.23)
0.11 (-0.25, 0.44)
-0.27 (-0.57, 0.09)
-0.22 (-0.53, 0.14)
0.12 (-0.24, 0.45)
-0.36 (-0.63, 20.01)
Intramyocellular lipid content, institutional units
Table 4 Partial correlations for compartment-specific muscular fat infiltration, bone mineral quantity and distribution, and measures of physical function for postmenopausal women aged 60–75 years (N = 35)
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and… 359
123
360
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
Fig. 2 Unadjusted relationships between compartment-specific fat infiltration in the leg and bone geometry a and strength b, and physical performance c and d in healthy women aged 60–75 years
bone strength appear to be in agreement with studies that found deficits in tibia microstructure and increased risk of ankle fractures among postmenopausal women with diabetes [23, 36]. A report by Wong et al. [12] found that older women in the Canadian Multicentre Osteoporosis Study with greater MRI-derived measures of IMF in the calf muscle were more likely to sustain a fragility fracture over 14 years (OR 2.82; 95 % CI 1.19, 6.70). However, IMF did not remain a significant risk factor for fracture after adjusting the model for age [12]. The observed relationships between fat and bone may also be understood by considering networks of molecular crosstalk. The influence of body composition on skeletal strength and function is incompletely interpreted by biomechanics, but is increasingly understood by basic and translational research domains focused on identifying pathways that explain interrelationships between adipose tissue and bone [24]. Adipose cells actively release adipokines and inflammatory markers that disrupt bone cell function and survival. Our finding that IMF appears to be beneficial to bone may be related to the overall fatness of an individual that would result in greater habitual loads on the skeleton. This is
123
consistent with DXA studies that have found a positive relationship between areal bone mineral density and fat mass in older women [37]. However, as reported among persons with diabetes, higher bone density as measured by DXA does not equate to an optimal bone geometry or strength [23, 36]. The relationships between compartments of fat deposition and bone mineral quantity and distribution provide motivation for continued assessment of muscular fat infiltration in populations where bone fragility is prevalent. Gait speed is considered a robust predictor of physical functioning and survival in older community-dwelling adults [38]. The women in our sample demonstrated a high level of physical functioning, according to normative reference values for preferred walking speed and number of chair stand repetitions [39]. Despite the participants’ selfreported moderate levels of physical activity, there was a negative association between %IntraMF and gait speed. This finding is in agreement with previous studies that examined fat infiltration in the leg among communitydwelling adults with diabetes and peripheral neuropathy [10, 40]. Buford et al. [8] compared MRI-derived fat
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
infiltration in the leg and thigh of young adults, high functioning older adults, and low functioning older adults and did not observe a significant association between fat content and physical function measured using the short physical performance battery (SPPB) [8]. The discrepancy between our study and the earlier report [8] may be attributed to the differences in the sample, the physical function testing, or the definitions of muscular fat infiltration. Our study only enrolled older women, whereas Buford et al. [8] included men and women. Since risk factors for disability, rates of muscle atrophy, and muscle strength differ between men and women [15, 41], the exclusion of men in our sample is a plausible reason for divergent observations. The SPPB is a composite performance score that includes assessment of gait speed, standing balance, and a chair stand test. Thus, the SPPB integrates functioning of several body systems and predicts physical disability in older adults [42]. We also found a negative association between chair stand test performance and fat infiltration of the musculature of the leg. Although knee extensor forces generate large forces during rapid sit-tostand repetitions, our findings suggest that the degree of fat infiltration in the leg is associated with various functional tests. Further, it is possible that fatty infiltration of knee extensor muscles is similar to that of leg muscles. Our study supports the notion that physical function may be negatively influenced by fatty infiltration within and between the ankle plantar flexor and dorsiflexor muscles. The second source of disagreement between studies may be related to the discordant definitions of intermuscular fat infiltration; namely, Buford [8] did not distinguish IntraMF from IMF. While the segmentation method to separately quantify %IntraMF and IMF is labor intensive, it should be considered feasible for analyzing data from small study samples or subsets of larger samples. In fact, understanding how various depots of muscular fat infiltration increase and decrease in response to aging, exercise, and various diseases may lead to more comprehensive interventions that aim to improve physical functioning. Despite the increased use of pharmacotherapy in the prevention and management of osteoporosis, women taking drug therapies continue to represent an understudied population. Few studies have compared differences in pQCTderived measures of apparent bone structure and strength in older women with and without osteoporosis because evaluation of a true treatment-naı¨ve individual with osteoporosis would be upon clinical diagnosis prior to the initiation of pharmacologic therapy. In our sample, we observed variation in bone variables; however, women reporting current or recent pharmacologic osteoporosis treatment were similar in height, weight, age, and bone variables, compared to women without osteoporosis. Our findings are in line with a randomized trial that documented
361
no effect of pharmacologic therapy on pQCT-derived measures of tibia apparent structure and strength in women with osteoporosis [43]. In addition to similarities in bone variables, compartment-specific fat distribution was not different between women using and never having used osteoporosis pharmacotherapy. A retrospective study reported data from initial patient visits to an osteoporosis clinic whereby women (aged 60–80 years) who may have osteoporosis and other indication for pharmacologic treatment were undiagnosed and untreated at the time of measurements [44]. Compared to Stathopoulous et al. (2011), our sample had greater TrC, TrD, and TrA at the 4 % site and greater CoC, CoD, and CoA at the 14 % site, despite similarities in body size between study participants. However, ToD at the 4 % tibia in our sample was similar to the postmenopausal women in Canadian and European population-based studies using the same pQCT scanner model [12, 45]. Compared to Edwards et al. [45], our sample had greater CoA and CoC at the 14 % site. Since diaphyseal cortical structure adapts to support compression during mechanical loading, the higher values may be attributed to the moderate levels of physical activity reported by our sample. Overall, comparisons with previous study samples are obstructed by the absence of physical activity level reports and the exclusion of women taking pharmacotherapy. Our study suggests that there is no association between pharmacologic osteoporosis treatment and muscular fat content. Our findings must be interpreted in context of the study limitations. First, the study was cross-sectional in design and therefore causal associations and temporality cannot be determined. The study design also precludes assessment of the effect of bone-sparing therapies on bone geometry. Second, we studied a small sample in order to apply novel non-invasive musculoskeletal outcomes in postmenopausal women who are at increased risk for bone fragility and physical disability. We report several associations between MRI-based measures and numerous bone variables despite our small sample size. Notwithstanding that limitation, these novel results facilitate pragmatic design of future studies aiming to quantify fat infiltration using MRI. Our findings are generalizable to healthy, high functioning older women; however, the application of MRI to distinguish between muscular fat depots may be of considerable interest to rehabilitation strategies for several conditions that lead to functional decline and elevated fracture risk. In summary, we have found associations between compartment-specific fat infiltration, bone mineral quantity and distribution, and physical function in healthy older women aged 60–75 years. Our findings support a role for MRI-based measures in evaluating muscular fat infiltration in a compartment-specific manner to improve our understanding of musculoskeletal relationships that may
123
362
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
contribute to bone fragility and functional decline. Further prospective and intervention studies that evaluate muscular fat compartments are needed to confirm the associations and to determine whether reducing fat accumulation can prevent bone fragility and declines in physical function in the aged. Acknowledgments The study was funded in part by a grant from The Arthritis Society. The authors thank Dr. Jean Wessel for her contribution to study design. The authors appreciate the assistance and guidance from Norm Konyer and the MRI technologists (Janet Burr, Cheryl Contant, Julie Lecomte). Thanks to Dr. Christopher Gordon and Lesley Beaumont for their assistance with pQCT measurements. AL held doctoral scholarships from the Ontario Women’s Health Scholar Award and the Joint Motion Program, a Canadian Institutes of Health Research Training Program in Musculoskeletal Health Research and Leadership.
9.
10.
11.
12.
Conflict of interest Amanda L. Lorbergs, Michael D. Noseworthy, Jonathan D. Adachi, Paul W. Stratford, and Norma J. MacIntyre declare that they have no conflict of interest. Human and animal rights and informed consent Our institutional Research Ethics Review Board approved the study protocol, and all participants provided written informed consent prior to enrolling in the study.
13.
References
14.
1. Rubenstein LZ (2006) Falls in older people: epidemiology, risk factors and strategies for prevention. Age Ageing 35(2):37–41. doi:10.1093/ageing/afl084 2. Kanis JA, Borgstrom F, De Laet C, Johansson H, Johnell O, Jonsson B, Oden A, Zethraeus N, Pfleger B, Khaltaev N (2005) Assessment of fracture risk. Osteoporos Int 16:581–589. doi:10. 1007/s00198-004-1780-5 3. Lauretani F, Russo CR, Bandinelli S, Bartali B, Cavazzini C, Di Iorio A, Corsi AM, Rantanen T, Guralnik JM, Ferrucci L (2003) Age-associated changes in skeletal muscles and their effect on mobility: An operational diagnosis of sarcopenia. J Appl Physiol 95:1851–1860. doi:10.1152/japplphysiol.00246.2003 4. Szulc P, Beck TJ, Marchand F, Delmas PD (2005) Low skeletal muscle mass is associated with poor structural parameters of bone and impaired balance in elderly men–the MINOS study. J Bone Min Res 20:721–729 5. Visser M, Kritchevsky SB, Goodpaster BH, Newman AB, Nevitt M, Stamm E, Harris TB (2002) Leg muscle mass and composition in relation to lower extremity performance in men and women aged 70 to 79: the Health, Aging and Body Composition study. J Am Geriatr Soc 50:897–904 6. Schwenzer NF, Martirosian P, Machann J, Schraml C, Steidle G, Claussen CD, Schick F (2009) Aging effects on human calf muscle properties assessed by MRI at 3 Tesla. J Magn Reson Imaging 29:1346–1354. doi:10.1002/jmri.21789 7. Boettcher M, Machann J, Stefan N, Thamer C, Haring HU, Claussen CD, Fritsche A, Schick F (2009) Intermuscular adipose tissue (IMAT): association with other adipose tissue compartments and insulin sensitivity. J Magn Reson Imaging 29:1340–1345. doi:10.1002/jmri.21754 8. Buford TW, Lott DJ, Marzetti E, Wohlgemuth SE, Vandenborne K, Pahor M, Leeuwenburgh C, Manini TM (2012) Age-related differences in lower extremity tissue compartments and associations
123
15.
16.
17.
18.
19.
20.
21.
with physical function in older adults. Exp Gerontol 47:38–44. doi:10.1016/j.exger.2011.10.001 Lott DJ, Forbes SC, Mathur S, Germain SA, Senesac CR, Lee Sweeney H, Walter GA, Vandenborne K (2014) Assessment of intramuscular lipid and metabolites of the lower leg using magnetic resonance spectroscopy in boys with Duchenne muscular dystrophy. Neuromuscul Disord 24:574–582. doi:10.1016/j.nmd. 2014.03.013 Tuttle LJ, Sinacore DR, Mueller MJ (2012) Intermuscular adipose tissue is muscle specific and associated with poor functional performance. J Aging Res 2012:172957. doi:10.1155/2012/ 172957 Frank AW, Farthing JP, Chilibeck PD, Arnold CM, Olszynski WP, Kontulainen SA (2015) Community-dwelling female fallers have lower muscle density in their lower legs than non-fallers: evidence from the Saskatoon Canadian Multicentre Osteoporosis study (CaMos) cohort. J Nutr Health Aging 19:113–120. doi:10. 1007/s12603-014-0476-6 Wong AK, Beattie KA, Min KK, Gordon C, Pickard L, Papaioannou A, Adachi JD, Canadian Multicentre Osteoporosis Study (CaMos) Research Group (2014) Peripheral quantitative computed tomography-derived muscle density and peripheral magnetic resonance imaging-derived muscle adiposity: precision and associations with fragility fractures in women. J Musculoskelet Neuronal Interact 14:401–410 Cawthon PM, Fox KM, Gandra SR, Delmonico MJ, Chiou CF, Anthony MS, Sewall A, Goodpaster B, Satterfield S, Cummings SR, Harris TB, Aging and Body Composition Study (2009) Do muscle mass, muscle density, strength, and physical function similarly influence risk of hospitalization in older adults? J Am Geriatr Soc 57:1411–1419. doi:10.1111/j.1532-5415.2009.02366.x Douchi T, Kosha S, Uto H, Oki T, Nakae M, Yoshimitsu N, Nagata Y (2003) Precedence of bone loss over changes in body composition and body fat distribution within a few years after menopause. Maturitas 46:133–138 Murtagh KN, Hubert HB (2004) Gender differences in physical disability among an elderly cohort. Am J Public Health 94:1406–1411 Lang T, Cauley JA, Tylavsky F, Bauer D, Cummings S, Harris TB, Health ABC Study (2010) Computed tomographic measurements of thigh muscle cross-sectional area and attenuation coefficient predict hip fracture: the health, aging, and body composition study. J Bone Miner Res 25:513–519. doi:10.1359/ jbmr.090807 Delmonico MJ, Harris TB, Visser M, Park SW, Conroy MB, Velasquez-Mieyer P, Boudreau R, Manini TM, Nevitt M, Newman AB, Goodpaster BH, Health, Aging and Body Composition Study (2009) Longitudinal study of muscle strength, quality, and adipose tissue infiltration. Am J Clin Nutr 90:1579–1585. doi:10. 3945/ajcn.2009.28047 Marcus RL, Addison O, Dibble LE, Foreman KB, Morrell G, Lastayo P (2012) Intramuscular adipose tissue, sarcopenia, and mobility function in older individuals. J Aging Res 2012:629637. doi:10.1155/2012/629637 Karampinos DC, Baum T, Nardo L, Alizai H, Yu H, CarballidoGamio J, Yap SP, Shimakawa A, Link TM, Majumdar S (2012) Characterization of the regional distribution of skeletal muscle adipose tissue in type 2 diabetes using chemical shift-based water/fat separation. J Magn Reson Imaging 35:899–907. doi:10. 1002/jmri.23512 Nakagawa Y, Hattori M, Harada K, Shirase R, Bando M, Okano G (2007) Age-related changes in intramyocellular lipid in humans by in vivo H-MR spectroscopy. Gerontology 53:218–223. doi:10. 1159/000100869 Sinha R, Dufour S, Petersen KF, LeBon V, Enoksson S, Ma YZ, Savoye M, Rothman DL, Shulman GI, Caprio S (2002)
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
Assessment of skeletal muscle triglyceride content by (1)H nuclear magnetic resonance spectroscopy in lean and obese adolescents: relationships to insulin sensitivity, total body fat, and central adiposity. Diabetes 51:1022–1027 Watts NB, GLOW investigators (2014) Insights from the Global Longitudinal Study of Osteoporosis in Women (GLOW). Nat Rev Endocrinol 10:412–422. doi:10.1038/nrendo.2014.55 Schwartz AV, Sellmeyer DE, Ensrud KE, Cauley JA, Tabor HK, Schreiner PJ, Jamal SA, Black DM, Cummings SR, Study of Osteoporotic Features Research Group (2001) Older women with diabetes have an increased risk of fracture: a prospective study. J Clin Endocrinol Metab 86:32–38. doi:10.1210/jcem.86.1.7139 Bermeo S, Gunaratnam K, Duque G (2014) Fat and bone interactions. Curr Osteoporos Rep 12:235–242. doi:10.1007/s11914014-0199-y Papaioannou A, Morin S, Cheung AM, Atkinson S, Brown JP, Feldman S, Hanley DA, Hodsman A, Jamal SA, Kaiser SM, Kvern B, Siminoski K, Leslie WD, Scientific Advisory Council of Osteoporosis Canada (2010) 2010 Clinical practice guidelines for the diagnosis and management of osteoporosis in Canada: summary. CMAJ 182:1864–1873. doi:10.1503/cmaj.100771 Kanis JA, Oden A, Johnell O, Jonsson B, de Laet C, Dawson A (2001) The burden of osteoporotic fractures: a method for setting intervention thresholds. Osteoporos Int 12:417–427. doi:10.1007/ s001980170112 Elzibak AH, Noseworthy MD (2013) Assessment of diffusion tensor imaging indices in calf muscles following postural change from standing to supine position. Magn Reson Mater Phys. doi:10.1007/s10334-013-0424-1 Lorbergs AL, Noseworthy MD, MacIntyre NJ (2014) Age-related differences in the response of leg muscle cross-sectional area and water diffusivity measures to a period of supine rest. Magn Reson Mater Phys. doi:10.1007/s10334-014-0464-1 Lund H, Christensen L, Savnik A, Boesen J, Danneskiold-Samsoe B, Bliddal H (2002) Volume estimation of extensor muscles of the lower leg based on MR imaging. Eur Radiol 12:2982–2987. doi:10.1007/s00330-002-1334-1 Provencher SW (1993) Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med 30:672–679. doi:10.1002/mrm.1910300604 Kontulainen SA, Johnston JD, Liu D, Leung C, Oxland TR, McKay HA (2008) Strength indices from pQCT imaging predict up to 85% of variance in bone failure properties at tibial epiphysis and diaphysis. J Musculoskelet Neuronal Interact 8:401–409 Lyritis G (ed) (1999) Biomechanical examinations for validation of the bone strength strain index SSI, calculated by peripheral quantitative computed tomography, vol 2. Holonome editions, Athens Jones CJ, Rikli RE, Beam WC (1999) A 30-s chair-stand test as a measure of lower body strength in community-residing older adults. Res Q Exerc Sport 70:113–119 Topolski TD, LoGerfo J, Patrick DL, Williams B, Walwick J, Patrick MB (2006) The rapid assessment of physical activity (RAPA) among older adults. Prev Chronic Dis 3:A118
363
35. Daughton DM, Fix AJ, Kass I, Bell CW, Patil KD (1982) Maximum oxygen consumption and the ADAPT quality-of-life scale. Arch Phys Med Rehabil 63:620–622 36. Patsch JM, Burghardt AJ, Yap SP, Baum T, Schwartz AV, Joseph GB, Link TM (2013) Increased cortical porosity in type 2 diabetic postmenopausal women with fragility fractures. J Bone Miner Res 28:313–324. doi:10.1002/jbmr.1763 37. Gnudi S, Sitta E, Fiumi N (2007) Relationship between body composition and bone mineral density in women with and without osteoporosis: relative contribution of lean and fat mass. J Bone Miner Metab 25:326–332. doi:10.1007/s00774-007-0758-8 38. Studenski S, Perera S, Patel K, Rosano C, Faulkner K, Inzitari M, Brach J, Chandler J, Cawthon P, Connor EB, Nevitt M, Visser M, Kritchevsky S, Badinelli S, Harris T, Newman AB, Cauley J, Ferrucci L, Guralnik J (2011) Gait speed and survival in older adults. JAMA 305:50–58. doi:10.1001/jama.2010.1923 39. Bennell K, Dobson F, Hinman R (2011) Measures of physical performance assessments: self-Paced Walk Test (SPWT), Stair Climb Test (SCT), Six-Minute Walk Test (6MWT), Chair Stand Test (CST), Timed Up & Go (TUG), Sock Test, Lift and Carry Test (LCT), and Car Task. Arthritis Care Res (Hoboken) 63(11):S350–S370. doi:10.1002/acr.20538 40. Volpato S, Bianchi L, Lauretani F, Lauretani F, Bandinelli S, Guralnik JM, Zuliani G, Ferrucci L (2012) Role of muscle mass and muscle quality in the association between diabetes and gait speed. Diab Care 35:1672–1679. doi:10.2337/dc11-2202 41. Ferrucci L, Penninx BW, Leveille SG, Corti MC, Pahor M, Wallace R, Harris TB, Havlik RJ, Guralnik JM (2000) Characteristics of nondisabled older persons who perform poorly in objective tests of lower extremity function. J Am Geriatr Soc 48:1102–1110 42. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, Scherr PA, Wallace RB (1994) A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol 49:M85–M94 43. Uusi-Rasi K, Kannus P, Cheng S, Sievanen H, Pasanen M, Heinonen A, Nenonen A, Halleen J, Fuerst T, Genant H, Vuori I (2003) Effect of alendronate and exercise on bone and physical performance of postmenopausal women: a randomized controlled trial. Bone 33:132–143 44. Stathopoulos KD, Katsimbri P, Atsali E, Metania E, Zoubos AB, Skarantavos G (2011) Age-related differences of bone mass, geometry, and strength in treatment-naive postmenopausal women. A tibia pQCT study. J Clin Densitom 14:33–40. doi:10. 1016/j.jocd.2010.11.004 45. Edwards MH, Gregson CL, Patel HP, Jameson KA, Harvey NC, Sayer AA, Dennison EM, Cooper C (2013) Muscle size, strength, and physical performance and their associations with bone structure in the Hertfordshire Cohort Study. J Bone Miner Res 28:2295–2304. doi:10.1002/jbmr.1972
123
ORIGINAL RESEARCH
Fat Infiltration in the Leg is Associated with Bone Geometry and Physical Function in Healthy Older Women Amanda L. Lorbergs1,2 • Michael D. Noseworthy3,4 • Jonathan D. Adachi5 Paul W. Stratford2 • Norma J. MacIntyre2
•
Received: 20 March 2015 / Accepted: 1 June 2015 / Published online: 13 June 2015 Springer Science+Business Media New York 2015
Abstract The objective of this study was to estimate the associations between muscular fat infiltration, tibia bone mineral quantity and distribution, and physical function in healthy older women. Thirty-five women (aged 60–75 years, mean 70 years) were recruited from the community. Percent intramuscular fat (%IntraMF) within the right leg tibialis anterior, soleus, and gastrocnemius muscles and total intermuscular fat (IMF) were segmented from magnetic resonance imaging scans at the mid-calf. Intramyocellular lipid (IMCL) content in the right tibialis anterior was measured with proton magnetic resonance spectroscopy. Right tibia & Norma J. MacIntyre [email protected] Amanda L. Lorbergs [email protected] Michael D. Noseworthy [email protected]
Paul W. Stratford [email protected]
Keywords Aged Adipose tissue Magnetic resonance imaging (MRI) Proton magnetic resonance spectroscopy (1H MRS) X-ray computed tomography Mobility limitation
Institute for Aging Research, Hebrew SeniorLife and Harvard Medical School, 1200 Centre St., Rm. 620, Boston, MA 02131, USA
Introduction
Jonathan D. Adachi [email protected]
1
2
bone content, area, and strength were measured at the 4, 14, and 66 % sites using peripheral quantitative computed tomography. Physical function was assessed by gait speed on the 20 m walking test. After adjusting for age, body size, and activity level, %IntraMF had a negative association with bone content and area at all tibia sites (r = -0.31 to -0.03). Conversely, greater IMF was associated with increased bone content and area (r = 0.04–0.32). Correlation coefficients for the association between IMCL and bone were negative (r = -0.44 to -0.03). All measures of fat infiltration had a negative association with observed physical function (r = -0.42 to -0.04). Our findings suggest that muscular fat infiltration in the leg of healthy postmenopausal women has a compartment-specific relationship with bone status and physical function. Minimizing fat accumulation within and between muscle compartments may prevent bone fragility and functional decline in women.
School of Rehabilitation Science, McMaster University, 1400 Main Street West, IAHS 403, Hamilton, ON L8S 1C7, Canada
3
School of Biomedical Engineering, McMaster University, 1280 Main Street West, ETB 406, Hamilton, ON L8S 4K1, Canada
4
Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada
5
Department of Medicine, McMaster University, 501-25 Charlton Ave East, Hamilton, ON L8N 1Y2, Canada
Recent interest in age-related conditions such as sarcopenia and frailty highlights the important link between muscular fat infiltration, bone fragility, and mobility. Impaired mobility and low bone density are significant risk factors for falls and fractures, respectively [1, 2]. With aging, muscle atrophies and becomes weaker; these age-related changes are associated with declines in physical functioning and bone loss [3, 4]. However, muscle changes do not fully explain the high prevalence of bone fragility and
123
354
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
mobility impairments in older adults [5]. The accumulation of fat within muscle compartments is observed with advancing age, inactivity, and chronic diseases, and is proposed to modify the structure and function of muscle [6–8]. Greater muscular fat infiltration in the lower extremity is associated with muscle weakness [9], impaired mobility [10], increased falls risk [11], and fractures [12]. Consequences of muscular fat infiltration in the lower extremity, including increased risk of hospitalization [13], are concerning given the aging population. Compared to older men, aging women are especially at risk for bone fragility and mobility impairments because of the significant loss of bone mass and increase in fat within skeletal muscle that is observed with the menopausal transition [14]. Moreover, women have higher rates of nonfatal conditions that contribute to physical disability compared to men [15]. The negative effects of fat mass on mobility impairments were initially evaluated using dual energy X-ray absorptiometry (DXA). Since inherent limitations of DXA imaging preclude investigation of fat within and between muscle compartments, more recent studies utilize computed tomography (CT) and magnetic resonance imaging (MRI) to quantify muscular fat infiltration. Findings from CT-based studies show that lower muscle density, a proxy measurement reflecting greater fat infiltration, is associated with increased risk of hip fracture [16] and poorer physical functioning in older adults [5]. However, muscle density acquired by CT is a crude estimate of fat infiltration in muscles because the measurement includes blood vessels, nerves, and connective tissue within the imaged lean tissue. Further, muscle density derived by CT does not include measurement of the fat deposited around muscle groups and involves exposure to ionizing radiation. MRI is advantageous because it provides a high contrast image that distinguishes intramuscular fat (IntraMF) from intermuscular fat (IMF). IntraMF, located within muscle groups and external to the muscle cells, is known to increase with advancing age and inactivity [6, 8]. IntraMF accumulation increases the proportion of non-contractile tissue within muscle groups and reduces the force generation capacity of the muscle [17, 18]. Less is known about IMF, which is situated within the fascial envelope surrounding muscles and muscle compartments. Few studies distinguish IMF from IntraMF; studies describing IMF and IntraMF as separate compartments are limited to adults with diabetes [19]. Similarly, studies measuring muscle composition in the leg using proton magnetic resonance spectroscopy (1H MRS) have focused on aging [20] and chronic metabolic conditions, such as diabetes [7]. On a microscopic scale, 1H MRS enables the non-invasive quantification of several metabolites, including intramyocellular lipid (IMCL), or fat droplets stored within muscle cells. Greater IMCL
123
content in leg muscles is associated with obesity and insulin resistance [21] and may also influence tibia bone structure, since there is a high incidence of ankle fractures within obese [22] and diabetic populations [23]. Despite the known physiologic dysfunctions associated with muscular fat infiltration and the significant health care burden related to its sequelae, there is a paucity of research assessing the influence of muscular fat deposition on bone health and physical functioning in healthy older adults. Adipose tissue is recognized as an endocrine organ that communicates with bone cells through molecular and cellular signaling pathways [24]. Although adipose tissue infiltration in lower extremity muscles is associated with increased fracture risk [12, 16], older women with osteoporosis or elevated fracture risk are usually excluded from studies evaluating musculoskeletal tissue relationships because clinical practice guidelines indicate pharmacologic treatment for fracture risk reduction [25]. As such, there is a significant proportion of older women who are not studied and the relationship of muscular fat infiltration on bone mineral quantity and distribution remains unknown. To address this gap within the literature, we were interested in measuring musculoskeletal variables in otherwise healthy and physically active women aged 60–75 years. Given the importance of leg muscles for balance, mobility, and activities of daily living, we sought to improve our understanding of the relationship between muscular fat accumulation, bone fragility, and physical functioning. Therefore, the objective of our study was to estimate the associations between muscular fat infiltration, tibia bone mineral quantity and distribution, and physical function in healthy older women. We speculated that fat infiltration within leg muscles has an inverse association with bone mineral quantity and distribution and physical function.
Methods Study Design and Sample For this cross-sectional study, we recruited healthy women aged 60–75 years. Multiple community-based recruitment strategies were used: presentations to community groups, posters, newspaper and community newsletter advertisements, and patients attending a local osteoporosis clinic. Past study participants who consented to be contacted for future research were mailed invitations to participate in the current study. Prior to enrolment, all interested volunteers were screened by telephone. Exclusion criteria were diabetes, pulmonary disorders, stroke, cancer in the past 5 years, smoking cigarettes in the past 2 years, surgery in the past
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
6 weeks, or neurologic or musculoskeletal condition affecting the back or lower limbs (e.g., knee and/or hip osteoarthritis, rheumatoid arthritis). Volunteers were also excluded if they reported contraindications to MRI, such as claustrophobia. Interview Information on current health status, years since menopause, and current medications (including supplements) were collected in a short interview. Self-reported fracture history was provided by all participants. Fragility fractures were defined as occurring spontaneously or following a minor trauma, such as a fall from standing height or less, after the age of 40 years [26]. Fractures of the skull, fingers, and toes were excluded because they are not considered osteoporotic fractures [26]. Anthropometry Height (to the nearest 1 mm) and weight (to the nearest 0.1 kg) were measured using standard procedures. Height and weight were used to calculate body mass index (BMI, kg/m2). The length of the right tibia was measured to the nearest 1 mm from the base of the medial malleolus to the superior margin of the medial epicondyle, while the participant was seated with feet flat on the floor and knees bent at a 90-degree angle. The 66 % site of the tibia was calculated and marked with a fiducial marker for MRI scanning and with ink for peripheral quantitative computed tomography (pQCT) scanning. MRI Fat Infiltration Image Acquisition The right leg was imaged with a 3.0T MRI scanner (General Electric Healthcare Discovery MR 750, Milwaukee, WI) and an eight-channel phased array RF knee coil. Standardized positioning ensured each participant was resting supine with knees extended and feet immobilized in a custom positioning rig [27]. One of three accredited MRI technologists performed all scanning. The scan acquisition protocol is described elsewhere [28]. In brief, 30 contiguous axial proton density (PD)-weighted images (slice thickness = 4 mm; TE/TR = 30/2344 ms, field-of view (FOV) = 16 cm, matrix = 320 mm 9 320 mm, total scan time = 6 min 30 s) were acquired. The phased array uniformity enhancement post-processing filter was applied to correct for image intensity non-uniformity and reduce edge blurring. A 1H MRS point-resolved spectroscopy pulse sequence was used to obtain the IMCL resonance area of a single
355
voxel within the tibialis anterior muscle. This site was chosen because the muscle fiber orientation in this muscle permits the greatest separation of lipid compartments. The following parameters were used: TE/TR = 30/1500 ms, averages = 128, and total scan duration = 3 min 12 s. The 40 mm 9 20 mm 9 20 mm (16 mL) volume of interest was placed within the tibialis anterior muscle to exclude subcutaneous fat, blood vessels, and bone in an image-guided manner. Two pairs of saturation bands were manually placed to reduce the strong subcutaneous and bone marrow lipid signals. Automated shimming improved the field homogeneity. This procedure always resulted in a water peak full width half maximum of less than 0.326 ppm. Image Processing and Analyses MRI scans were visually inspected by an accredited MRI technologist to screen for potential subclinical pathology. One trained operator used SliceOmatic 4.3 image analysis software (TomoVision, Montreal, Canada) to segment 10 contiguous PD-weighted images corresponding to images 11 thru 20 out of the 30 images acquired. All image segmentations were performed on the same computer monitor; gamma settings were fixed prior to each analysis session. Volumes (cm3) of tibialis anterior, soleus, and gastrocnemius muscles and IntraMF, and IMF were segmented as illustrated in Fig. 1. Using the histogram of pixel intensity distribution, IntraMF and IMF separation thresholds for each participant were determined by placing the lower limit of the region-growing threshold at the base of the muscle peak. Small adjustments to the signal intensity histogram were made as needed to account for signal variability between individuals and along the imaged leg segment, as described previously [12]. In our laboratory, IntraMF is defined as bright pixels located beneath the muscle fascial envelope and within the epimysium and perimysium. IMF is defined as bright pixels located within the deep fascial envelopes surrounding the muscle bellies and muscle compartments. Our main variables of interest for this study were percent IntraMF (%IntraMF), calculated as a percentage of its respective muscle volume, and total IMF volume (cm3). Reliability of manual leg muscle segmentation from MRI scans to estimate muscle volume is excellent (ICC = 0.99) if eight to ten contiguous slices are analyzed [29]. LCModel software (v6.2) was used to analyze the spectra from the tibialis anterior muscle [30]. The LCModel-derived lipid content was determined using a two-step process. The signal from a metabolite resonance area in a water-suppressed spectrum was divided by the signal from the water peak in an unsuppressed water reference signal acquired from the same voxel. An automated spectra-fitting routine determined the IMCL methylene
123
356
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
Fig. 1 Representative proton density-weighted images of the leg in a postmenopausal woman (age = 65 years, BMI = 29 kg/m2) before a and after b segmentation. The tibialis anterior, soleus, and gastrocnemius muscles are orange, blue, and red, respectively. The intramuscular fat for each muscle is segmented in yellow, pink, and
gray for the tibialis anterior, soleus, and gastrocnemius, respectively. Intermuscular fat (green) is located beneath the muscle fascial plane and surrounding muscle bellies or compartments. The arrow points to the fiducial marker used to visualize the 66 % site
resonance area (institutional units) at around 1.3 ppm based on algorithms that adjust the phase and ppm shift of the spectra, estimate the baseline, and perform eddy current correction.
bone mineral density (ToD, mg/cm3) and trabecular volumetric bone mineral density (TrD, mg/cm3), total cross-sectional area (ToA, mm2) and trabecular cross-sectional area (TrA, mm2), total bone mineral content (ToC, mg/mm) and trabecular mineral content (TrC, mg/mm), and resistance to compression loading represented by the bone strength index (BSI, mg2/mm4) [31]. At the 14 and 66 % tibia, we used Contour mode 3 and Peel mode 2 with thresholds set at 711 mg/cm3 to separate cortical bone from soft tissue. Variables measured were cortical volumetric bone mineral density (CoD, mg/cm3), cortical cross-sectional area (CoA, mm2), and cortical mineral content (CoC, mg/mm). At the 66 % site, we measured the polar strength strain index (SSI, mm3), a validated estimate of bone’s ability to resist bending forces along the neutral axis [32].
pQCT Bone Mineral Quantity and Distribution Image Acquisition The right leg was imaged using the Stratec XCT2000 pQCT (Pforzheim, Germany) according to the manufacturer’s recommended standardized protocol performed by one trained operator. Briefly, the anatomical reference line was placed at the medial tip of the distal tibia endplate using a 30-mm coronal scout scan. Measuring proximally from the reference line, the scanner calculated and acquired axial images at the 4 and 14 % scan sites with an in-plane resolution of 0.4 mm and 20 mm/s scan speed. The scanner was manually positioned at the 66 % scan site and images were acquired with an in-plane resolution of 0.5 mm scan speed of 15 mm/s. All pQCT images had a slice thickness of 2.4 mm. Total scan time was about 6 min. Image Analysis Using manufacturer’s software (Stratec, v5.4), one operator analyzed all images. The parameters applied to the 4 % site were Contour mode 2, Peel mode 1, and a threshold of 280 mg/cm3 to separate bone from surrounding soft tissue. Bone variables measured at the 4 % site were total volumetric
123
Observed Physical Function Using a standardized protocol [33], all participants performed the 30-s chair stand test (30-CST) twice during the study visit. The number of sit-stand-sit repetitions observed in 30 s was recorded. The average of the two trials was used for analysis. Preferred gait speed (m/s) was measured on a 20 m course in a straight uncarpeted hallway. Participants began the course 2 m in front of the start line and ended 2 m behind the line to account for initial acceleration and finish-line deceleration. Time (s) to walk the 20 m course was recorded using a stopwatch and the time divided by 20 m (m/s) was used for analysis.
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
Self-Reported Physical Activity Two self-reported physical activity questionnaires were administered at the interview. The rapid assessment of physical activity (RAPA) is a nine-item questionnaire that captures the frequency and intensity of non-sports related activity with ‘‘yes’’ or ‘‘no’’ responses. RAPA scores between six and ten represent an active lifestyle, whereas scores between two and five are considered under-active [34]. The human activity profile (HAP) is a 94-item questionnaire consisting of activities listed in ascending order of metabolic energy required to complete them [35]. Response options are ‘‘still doing this activity,’’ ‘‘stopped doing this activity,’’ and ‘‘never did this activity.’’ Each participant’s adjusted activity score (AAS) was reported and calculated as the difference between the Maximal Activity Score (i.e., highest numeral assigned to the activity that the individual is still doing) and the adjusted score (i.e., number of activities an individual has stopped doing) to represent the activities the participant can perform. Higher AAS scores (maximum score is 94) reflect better physical functioning [35].
Table 1 Descriptive statistics for the sample of women Variable
69.5 (4.3)
Height (cm)
161.4 (5.6)
Body weight (kg)
Results Participants Thirty-five community-dwelling women participated in the study. Demographic and clinical characteristics for the sample are shown in Table 1. Fragility fractures were reported by 11 women; fracture sites included the wrist (n = 5), vertebra (n = 3), ankle (n = 2), and rib and sternum (n = 1). Eighteen women reported current or recent (within past 2 years) use of bone-sparing medication; most
66.4 (10.6)
Body mass index (kg/m2)
25.5 (4.3)
years since menopause (years)
20.2 (5.6)
Tibia length (mm) Bone-sparing medication use (n, %) Fracture (n, %)
369.7 (20.1) 18 (51 %) 11 (31 %)
Table 2 Mean and standard deviation (SD) for fat infiltration in leg muscles measured by magnetic resonance imaging and adjusted for tibia length Variable
Total (N = 35) Mean (SD)
Intramuscular fat (%) Tibialis anterior
8.9 (3.0)
Soleus
7.9 (3.5)
Total intermuscular fat volume (cm3) Intramyocellular lipida (institutional units)
Data were inspected for normality visually by plotting histograms and statistically using the Shapiro–Wilk test. Levene’s test was used to test variance equality. Results were described using means and standard deviations. Partial Pearson correlation coefficients (r) and 95 % confidence intervals (95 % CI) adjusted for age, height, weight, and activity level were used to estimate the association between fat and bone. Associations between fat and physical function and physical activity variables were adjusted for age, height, and weight. All data management and statistical analyses were performed using PSAW software v20 (SPSS, Chicago, IL, USA). Statistical significance for all analyses was set at p less than 0.05.
Total (N = 35) Mean (SD)
Age (years)
Gastrocnemius
Statistical Analyses
357
a
9.0 (3.0) 14.6 (45.0) 0.017 (0.01)
Unadjusted, n = 29
used bisphosphonates (n = 13) and others were taking denosumab (n = 4) or parathyroid hormone (n = 1). Most (91 %) women responded ‘‘yes’’ to current daily vitamin D supplementation, whereas 43 % women said ‘‘yes’’ to current calcium supplementation. Fat Infiltration All PD scans were successfully acquired and analyzed. Table 2 shows the distribution of muscle-specific %IntraMF and total IMF. Six IMCL data points were excluded from the analysis due to improper data storage prior to a hardware system malfunction (n = 3), failed analyses (n = 2), and a statistical outlier (n = 1) that was greater than three SD units from the mean. Therefore, Table 2 presents values for tibialis anterior IMCL content obtained in 29 participants. Bone Mineral Quantity and Distribution All pQCT scans were analyzed; there were no protocol deviations. Table 3 presents the sample characteristics with respect to tibia bone variables.
123
358
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
Table 3 Mean and standard deviation (SD) for tibia bone geometry measured by peripheral quantitative computed tomography Variable
Total (N = 35) Mean (SD)
4 % site Total density (mg/cm3) Total area (mm2) Total content (mg)
249 (36) 1081 (185) 265 (40)
Trabecular density (mg/cm3)
204 (35)
Trabecular area (mm2)
487 (83)
Trabecular content (mg)
97 (15)
Bone strength index (mg2/mm4)
67 (16)
14 % site Cortical density (mg/cm3)
1054 (71)
Cortical area (mm2)
143 (34)
Cortical content (mg)
152 (40)
66 % site Cortical density (mg/cm3) 2
1080 (40)
Cortical area (mm )
254 (53)
Cortical content (mg)
275 (61)
Stress strain index (mm3)
1869 (315)
Bone area and content variables are adjusted for tibia length
Observed Physical Function and Self-Reported Physical Activity Performance on the 20 m walk test resulted in a mean (standard deviation) gait speed of 1.4(0.1) m/s and the women completed 14(4) chair stand repetitions. The median RAPA score was 9, with minimum and maximum scores of 3 and 10, respectively. The HAP AAS had a median score of 77, with a minimum score of 55 and a maximum score of 94. Associations Between Fat and Bone All associations between MRI-derived fat infiltration and pQCT bone mineral quantity and distribution are presented in Table 4. Women who reported fragility fractures or current use of bone-sparing medications were similar to women without fracture and not taking these medications and associations with bone variables did not differ (data not shown). With the exception of cortical density, associations between tibialis anterior %IntraMF and bone variables were in the negative direction; partial correlation coefficients were between -0. 31 and -0.12. Partial correlation coefficients for soleus %IntraMF and bone area and mineral content were negative (r = -0.25 and r = -0.03). Similarly, associations between gastrocnemius %IntraMF and bone area and mineral content were negative; partial correlation coefficients varied from -0.30 (95 % CI -0.59,
123
0.05) for ToA and TrA, to -0.03 (95 % CI -0.37, 0.32) for CoC at the 14 % site. In contrast to %IntraMF, associations between total IMF and bone area and mineral content variables were positive (r = 0.04–0.32). Associations between IMCL and bone variables were negative, with the exception of ToA and TrA at the 4 % site (Table 4). The unadjusted relationship between IMCL and BSI (r = -0.50, 95 % CI -0.93, -0.16) is presented in Fig. 2. Associations Between Fat and Physical Activity and Observed Physical Function Overall, partial correlations were negative for the association between fat infiltration and self-reported physical activity and observed physical function (Table 4). Figure 2 shows the unadjusted association between gait speed and %IntraMF in the tibialis anterior (r = -0.43, 95 % CI -0.81, 0.11) and gastrocnemius (r = -0.41, 95 % CI -0.78, -0.09). Adjusted associations between physical function and fat infiltration variables were negative (Table 4). Likewise, all fat compartments had negative associations with self-reported activity, with the exception of gastrocnemius %IntraMF and HAP (r = 0.11, 95 %CI -0.25, 0.44).
Discussion This cross-sectional study showed that, in general, greater IMF is associated with increased bone mineral content and cortical bone area, whereas higher amounts of %IntraMF and IMCL are associated with lower bone quantity and poorer mineral distribution in the tibia. Gait speed was slower and the number of sit-to-stand repetitions was lower as %IntraMF, IMF, and IMCL in the leg increased. This is the first study to evaluate the relationships between MRIderived fat infiltration, bone mineral quantity and distribution, and observed physical function in the same individual. Our investigation of three depots of muscular fat infiltration suggests a potential for distinct relationships between specific fat compartments with bone structure and strength, and physical function in healthy postmenopausal women. Our main finding was that muscular fat deposition in the leg is related to the tibia in a compartment-specific manner. Greater IMF, but not %IntraMF, appears to confer skeletal benefits to cortical area and content at the tibia diaphysis, whereas IMCL content is associated with poorer bone strength at the distal tibia. In other words, our results suggest that fat stored outside the muscle groups affects bone differently than fat stored within muscle cells. IMCL has not been previously measured with bone outcomes. Our results showing greater IMCL in those women with lower
-0.25 (-0.55, 0.11)
Stress strain index (mm3)
-0.35 (-0.62, 20.01) -0.07 (-0.41, 0.28)
Rapid assessment of physical activity (points)
Human activity profile (Adjusted Activity Score)
b
a
Partial correlation adjusted for age, weight, and height
Partial correlation adjusted for age, weight, and activity level (HAP)
Bolded values represent associations with confidence intervals that exclude zero
-0.07 (-0.41, 0.28)
-0.13 (-0.46, 0.23)
30 s chair stand test (repetitions)
-0.04 (-0.38, 0.31)
-0.04 (-0.38, 0.31)
-0.33 (-0.61, 0.02)
-0.07 (-0.41, 0.28)
-0.03 (-0.37, 0.32)
-0.10 (-0.43, 0.26)
-0.14 (-0.47, 0.22)
0.16 (-0.20, 0.48)
0.02 (-0.33, 0.37)
-0.09 (-0.43, 0.27)
0.28 (-0.08, 0.57)
0.19 (-0.17, 0.51)
-0.14 (-0.47, 0.22)
-0.24 (-0.54, 0.12)
Gait speed (m/s)
Physical performance and physical activityb
-0.29 (-0.58, 0.06) -0.26 (0.56, 0.10)
Cortical area (mm )
Cortical content (mg)
2
Cortical density (mg/cm3) 0 (-0.35, 0.35)
-0.26 (-0.56, 0.10)
66 %
Cortical content (mg)
0.02 (-0.33, 0.37) -0.31 (0.59, 0.04)
Cortical area (mm2)
Cortical density (mg/cm3)
-0.12 (-0.45, 0.24)
-0.29 (-0.58, 0.06)
Trabecular content (mg)
Bone strength index (mg2/mm4) 14%
-0.17 (-0.49, 0.19)
Trabecular area (mm2)
0.18 (-0.18, 0.50)
-0.11 (-0.44, 0.25)
-0.31 (-0.59, 0.04) -0.12 (0.48, 0.24)
Total content (mg)
-0.17 (-0.49, 0.19)
Trabecular density (mg/cm3)
-0.25 (-0.55, 0.11)
-0.13 (-0.46, 0.23)
Total area (mm2)
0.21 (-0.15, 0.52)
Soleus
Total density (mg/cm3)
4%
Tibia bone variablesa
Tibialis anterior
Intramuscular fat, %
0.11 (-0.25, 0.44)
-0.19 (-0.51, 0.17)
-0.08 (-0.42, 0.28)
-0.17 (-0.49, 0.19)
-0.22 (-0.53, 0.14)
-0.13 (-0.46, 0.23)
-0.17 (-0.49, 0.19)
0.13 (-0.23, 0.46)
-0.03 (-0.37, 0.32)
-0.14 (-0.47, 0.22)
0.27 (-0.08, 0.57)
0 (-0.35, 0.35)
-0.30 (-0.59, 0.05)
-0.30 (-0.59, 0.05)
0.06 (-0.29, 0.40)
-0.28 (0.57, 0.08)
-0.30 (-0.59, 0.05)
0.07 (-0.28, 0.41)
Gastrocnemius
Partial correlation coefficient (95 % confidence interval)
-0.17 (-0.49, 0.19)
-0.24 (-0.54, 0.12)
-0.25 (-0.55, 0.11)
-0.14 (-0.47, 0.22)
-0.10 (-0.43, 0.26)
0.13 (-0.23, 0.46)
0.18 (-0.18, 0.50)
-0.17 (-0.49, 0.19)
0.32 (-0.03, 0.60)
0.29 (-0.06, 0.58)
0.16 (-0.20, 0.48)
-0.12 (-0.45, 0.24)
0.04 (-0.31, 0.38)
0.19 (-0.17, 0.51)
-0.17 (-0.49, 0.19)
0.18 (-0.18, 0.50)
0.20 (-0.16, 0.51)
-0.09 (-0.43, 0.27)
Intermuscular fat volume, cm3
-0.12 (-0.45, 0.24)
-0.35 (-0.62, -0.01)
-0.34 (-0.62, 0.01)
-0.42 (-0.67, 20.08)
-0.16 (-0.48, 0.20)
-0.08 (-0.42, 0.28)
-0.04 (-0.38, 0.31)
-0.18 (-0.55, 0.18)
-0.08 (-0.42, 0.28)
-0.03 (-0.37, 0.32)
-0.26 (-0.56, 0.10)
-0.44 (-0.68, 20.11)
-0.13 (-0.46, 0.23)
0.11 (-0.25, 0.44)
-0.27 (-0.57, 0.09)
-0.22 (-0.53, 0.14)
0.12 (-0.24, 0.45)
-0.36 (-0.63, 20.01)
Intramyocellular lipid content, institutional units
Table 4 Partial correlations for compartment-specific muscular fat infiltration, bone mineral quantity and distribution, and measures of physical function for postmenopausal women aged 60–75 years (N = 35)
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and… 359
123
360
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
Fig. 2 Unadjusted relationships between compartment-specific fat infiltration in the leg and bone geometry a and strength b, and physical performance c and d in healthy women aged 60–75 years
bone strength appear to be in agreement with studies that found deficits in tibia microstructure and increased risk of ankle fractures among postmenopausal women with diabetes [23, 36]. A report by Wong et al. [12] found that older women in the Canadian Multicentre Osteoporosis Study with greater MRI-derived measures of IMF in the calf muscle were more likely to sustain a fragility fracture over 14 years (OR 2.82; 95 % CI 1.19, 6.70). However, IMF did not remain a significant risk factor for fracture after adjusting the model for age [12]. The observed relationships between fat and bone may also be understood by considering networks of molecular crosstalk. The influence of body composition on skeletal strength and function is incompletely interpreted by biomechanics, but is increasingly understood by basic and translational research domains focused on identifying pathways that explain interrelationships between adipose tissue and bone [24]. Adipose cells actively release adipokines and inflammatory markers that disrupt bone cell function and survival. Our finding that IMF appears to be beneficial to bone may be related to the overall fatness of an individual that would result in greater habitual loads on the skeleton. This is
123
consistent with DXA studies that have found a positive relationship between areal bone mineral density and fat mass in older women [37]. However, as reported among persons with diabetes, higher bone density as measured by DXA does not equate to an optimal bone geometry or strength [23, 36]. The relationships between compartments of fat deposition and bone mineral quantity and distribution provide motivation for continued assessment of muscular fat infiltration in populations where bone fragility is prevalent. Gait speed is considered a robust predictor of physical functioning and survival in older community-dwelling adults [38]. The women in our sample demonstrated a high level of physical functioning, according to normative reference values for preferred walking speed and number of chair stand repetitions [39]. Despite the participants’ selfreported moderate levels of physical activity, there was a negative association between %IntraMF and gait speed. This finding is in agreement with previous studies that examined fat infiltration in the leg among communitydwelling adults with diabetes and peripheral neuropathy [10, 40]. Buford et al. [8] compared MRI-derived fat
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
infiltration in the leg and thigh of young adults, high functioning older adults, and low functioning older adults and did not observe a significant association between fat content and physical function measured using the short physical performance battery (SPPB) [8]. The discrepancy between our study and the earlier report [8] may be attributed to the differences in the sample, the physical function testing, or the definitions of muscular fat infiltration. Our study only enrolled older women, whereas Buford et al. [8] included men and women. Since risk factors for disability, rates of muscle atrophy, and muscle strength differ between men and women [15, 41], the exclusion of men in our sample is a plausible reason for divergent observations. The SPPB is a composite performance score that includes assessment of gait speed, standing balance, and a chair stand test. Thus, the SPPB integrates functioning of several body systems and predicts physical disability in older adults [42]. We also found a negative association between chair stand test performance and fat infiltration of the musculature of the leg. Although knee extensor forces generate large forces during rapid sit-tostand repetitions, our findings suggest that the degree of fat infiltration in the leg is associated with various functional tests. Further, it is possible that fatty infiltration of knee extensor muscles is similar to that of leg muscles. Our study supports the notion that physical function may be negatively influenced by fatty infiltration within and between the ankle plantar flexor and dorsiflexor muscles. The second source of disagreement between studies may be related to the discordant definitions of intermuscular fat infiltration; namely, Buford [8] did not distinguish IntraMF from IMF. While the segmentation method to separately quantify %IntraMF and IMF is labor intensive, it should be considered feasible for analyzing data from small study samples or subsets of larger samples. In fact, understanding how various depots of muscular fat infiltration increase and decrease in response to aging, exercise, and various diseases may lead to more comprehensive interventions that aim to improve physical functioning. Despite the increased use of pharmacotherapy in the prevention and management of osteoporosis, women taking drug therapies continue to represent an understudied population. Few studies have compared differences in pQCTderived measures of apparent bone structure and strength in older women with and without osteoporosis because evaluation of a true treatment-naı¨ve individual with osteoporosis would be upon clinical diagnosis prior to the initiation of pharmacologic therapy. In our sample, we observed variation in bone variables; however, women reporting current or recent pharmacologic osteoporosis treatment were similar in height, weight, age, and bone variables, compared to women without osteoporosis. Our findings are in line with a randomized trial that documented
361
no effect of pharmacologic therapy on pQCT-derived measures of tibia apparent structure and strength in women with osteoporosis [43]. In addition to similarities in bone variables, compartment-specific fat distribution was not different between women using and never having used osteoporosis pharmacotherapy. A retrospective study reported data from initial patient visits to an osteoporosis clinic whereby women (aged 60–80 years) who may have osteoporosis and other indication for pharmacologic treatment were undiagnosed and untreated at the time of measurements [44]. Compared to Stathopoulous et al. (2011), our sample had greater TrC, TrD, and TrA at the 4 % site and greater CoC, CoD, and CoA at the 14 % site, despite similarities in body size between study participants. However, ToD at the 4 % tibia in our sample was similar to the postmenopausal women in Canadian and European population-based studies using the same pQCT scanner model [12, 45]. Compared to Edwards et al. [45], our sample had greater CoA and CoC at the 14 % site. Since diaphyseal cortical structure adapts to support compression during mechanical loading, the higher values may be attributed to the moderate levels of physical activity reported by our sample. Overall, comparisons with previous study samples are obstructed by the absence of physical activity level reports and the exclusion of women taking pharmacotherapy. Our study suggests that there is no association between pharmacologic osteoporosis treatment and muscular fat content. Our findings must be interpreted in context of the study limitations. First, the study was cross-sectional in design and therefore causal associations and temporality cannot be determined. The study design also precludes assessment of the effect of bone-sparing therapies on bone geometry. Second, we studied a small sample in order to apply novel non-invasive musculoskeletal outcomes in postmenopausal women who are at increased risk for bone fragility and physical disability. We report several associations between MRI-based measures and numerous bone variables despite our small sample size. Notwithstanding that limitation, these novel results facilitate pragmatic design of future studies aiming to quantify fat infiltration using MRI. Our findings are generalizable to healthy, high functioning older women; however, the application of MRI to distinguish between muscular fat depots may be of considerable interest to rehabilitation strategies for several conditions that lead to functional decline and elevated fracture risk. In summary, we have found associations between compartment-specific fat infiltration, bone mineral quantity and distribution, and physical function in healthy older women aged 60–75 years. Our findings support a role for MRI-based measures in evaluating muscular fat infiltration in a compartment-specific manner to improve our understanding of musculoskeletal relationships that may
123
362
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
contribute to bone fragility and functional decline. Further prospective and intervention studies that evaluate muscular fat compartments are needed to confirm the associations and to determine whether reducing fat accumulation can prevent bone fragility and declines in physical function in the aged. Acknowledgments The study was funded in part by a grant from The Arthritis Society. The authors thank Dr. Jean Wessel for her contribution to study design. The authors appreciate the assistance and guidance from Norm Konyer and the MRI technologists (Janet Burr, Cheryl Contant, Julie Lecomte). Thanks to Dr. Christopher Gordon and Lesley Beaumont for their assistance with pQCT measurements. AL held doctoral scholarships from the Ontario Women’s Health Scholar Award and the Joint Motion Program, a Canadian Institutes of Health Research Training Program in Musculoskeletal Health Research and Leadership.
9.
10.
11.
12.
Conflict of interest Amanda L. Lorbergs, Michael D. Noseworthy, Jonathan D. Adachi, Paul W. Stratford, and Norma J. MacIntyre declare that they have no conflict of interest. Human and animal rights and informed consent Our institutional Research Ethics Review Board approved the study protocol, and all participants provided written informed consent prior to enrolling in the study.
13.
References
14.
1. Rubenstein LZ (2006) Falls in older people: epidemiology, risk factors and strategies for prevention. Age Ageing 35(2):37–41. doi:10.1093/ageing/afl084 2. Kanis JA, Borgstrom F, De Laet C, Johansson H, Johnell O, Jonsson B, Oden A, Zethraeus N, Pfleger B, Khaltaev N (2005) Assessment of fracture risk. Osteoporos Int 16:581–589. doi:10. 1007/s00198-004-1780-5 3. Lauretani F, Russo CR, Bandinelli S, Bartali B, Cavazzini C, Di Iorio A, Corsi AM, Rantanen T, Guralnik JM, Ferrucci L (2003) Age-associated changes in skeletal muscles and their effect on mobility: An operational diagnosis of sarcopenia. J Appl Physiol 95:1851–1860. doi:10.1152/japplphysiol.00246.2003 4. Szulc P, Beck TJ, Marchand F, Delmas PD (2005) Low skeletal muscle mass is associated with poor structural parameters of bone and impaired balance in elderly men–the MINOS study. J Bone Min Res 20:721–729 5. Visser M, Kritchevsky SB, Goodpaster BH, Newman AB, Nevitt M, Stamm E, Harris TB (2002) Leg muscle mass and composition in relation to lower extremity performance in men and women aged 70 to 79: the Health, Aging and Body Composition study. J Am Geriatr Soc 50:897–904 6. Schwenzer NF, Martirosian P, Machann J, Schraml C, Steidle G, Claussen CD, Schick F (2009) Aging effects on human calf muscle properties assessed by MRI at 3 Tesla. J Magn Reson Imaging 29:1346–1354. doi:10.1002/jmri.21789 7. Boettcher M, Machann J, Stefan N, Thamer C, Haring HU, Claussen CD, Fritsche A, Schick F (2009) Intermuscular adipose tissue (IMAT): association with other adipose tissue compartments and insulin sensitivity. J Magn Reson Imaging 29:1340–1345. doi:10.1002/jmri.21754 8. Buford TW, Lott DJ, Marzetti E, Wohlgemuth SE, Vandenborne K, Pahor M, Leeuwenburgh C, Manini TM (2012) Age-related differences in lower extremity tissue compartments and associations
123
15.
16.
17.
18.
19.
20.
21.
with physical function in older adults. Exp Gerontol 47:38–44. doi:10.1016/j.exger.2011.10.001 Lott DJ, Forbes SC, Mathur S, Germain SA, Senesac CR, Lee Sweeney H, Walter GA, Vandenborne K (2014) Assessment of intramuscular lipid and metabolites of the lower leg using magnetic resonance spectroscopy in boys with Duchenne muscular dystrophy. Neuromuscul Disord 24:574–582. doi:10.1016/j.nmd. 2014.03.013 Tuttle LJ, Sinacore DR, Mueller MJ (2012) Intermuscular adipose tissue is muscle specific and associated with poor functional performance. J Aging Res 2012:172957. doi:10.1155/2012/ 172957 Frank AW, Farthing JP, Chilibeck PD, Arnold CM, Olszynski WP, Kontulainen SA (2015) Community-dwelling female fallers have lower muscle density in their lower legs than non-fallers: evidence from the Saskatoon Canadian Multicentre Osteoporosis study (CaMos) cohort. J Nutr Health Aging 19:113–120. doi:10. 1007/s12603-014-0476-6 Wong AK, Beattie KA, Min KK, Gordon C, Pickard L, Papaioannou A, Adachi JD, Canadian Multicentre Osteoporosis Study (CaMos) Research Group (2014) Peripheral quantitative computed tomography-derived muscle density and peripheral magnetic resonance imaging-derived muscle adiposity: precision and associations with fragility fractures in women. J Musculoskelet Neuronal Interact 14:401–410 Cawthon PM, Fox KM, Gandra SR, Delmonico MJ, Chiou CF, Anthony MS, Sewall A, Goodpaster B, Satterfield S, Cummings SR, Harris TB, Aging and Body Composition Study (2009) Do muscle mass, muscle density, strength, and physical function similarly influence risk of hospitalization in older adults? J Am Geriatr Soc 57:1411–1419. doi:10.1111/j.1532-5415.2009.02366.x Douchi T, Kosha S, Uto H, Oki T, Nakae M, Yoshimitsu N, Nagata Y (2003) Precedence of bone loss over changes in body composition and body fat distribution within a few years after menopause. Maturitas 46:133–138 Murtagh KN, Hubert HB (2004) Gender differences in physical disability among an elderly cohort. Am J Public Health 94:1406–1411 Lang T, Cauley JA, Tylavsky F, Bauer D, Cummings S, Harris TB, Health ABC Study (2010) Computed tomographic measurements of thigh muscle cross-sectional area and attenuation coefficient predict hip fracture: the health, aging, and body composition study. J Bone Miner Res 25:513–519. doi:10.1359/ jbmr.090807 Delmonico MJ, Harris TB, Visser M, Park SW, Conroy MB, Velasquez-Mieyer P, Boudreau R, Manini TM, Nevitt M, Newman AB, Goodpaster BH, Health, Aging and Body Composition Study (2009) Longitudinal study of muscle strength, quality, and adipose tissue infiltration. Am J Clin Nutr 90:1579–1585. doi:10. 3945/ajcn.2009.28047 Marcus RL, Addison O, Dibble LE, Foreman KB, Morrell G, Lastayo P (2012) Intramuscular adipose tissue, sarcopenia, and mobility function in older individuals. J Aging Res 2012:629637. doi:10.1155/2012/629637 Karampinos DC, Baum T, Nardo L, Alizai H, Yu H, CarballidoGamio J, Yap SP, Shimakawa A, Link TM, Majumdar S (2012) Characterization of the regional distribution of skeletal muscle adipose tissue in type 2 diabetes using chemical shift-based water/fat separation. J Magn Reson Imaging 35:899–907. doi:10. 1002/jmri.23512 Nakagawa Y, Hattori M, Harada K, Shirase R, Bando M, Okano G (2007) Age-related changes in intramyocellular lipid in humans by in vivo H-MR spectroscopy. Gerontology 53:218–223. doi:10. 1159/000100869 Sinha R, Dufour S, Petersen KF, LeBon V, Enoksson S, Ma YZ, Savoye M, Rothman DL, Shulman GI, Caprio S (2002)
A. L. Lorbergs et al.: Fat Infiltration in the Leg is Associated with Bone Geometry and…
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
Assessment of skeletal muscle triglyceride content by (1)H nuclear magnetic resonance spectroscopy in lean and obese adolescents: relationships to insulin sensitivity, total body fat, and central adiposity. Diabetes 51:1022–1027 Watts NB, GLOW investigators (2014) Insights from the Global Longitudinal Study of Osteoporosis in Women (GLOW). Nat Rev Endocrinol 10:412–422. doi:10.1038/nrendo.2014.55 Schwartz AV, Sellmeyer DE, Ensrud KE, Cauley JA, Tabor HK, Schreiner PJ, Jamal SA, Black DM, Cummings SR, Study of Osteoporotic Features Research Group (2001) Older women with diabetes have an increased risk of fracture: a prospective study. J Clin Endocrinol Metab 86:32–38. doi:10.1210/jcem.86.1.7139 Bermeo S, Gunaratnam K, Duque G (2014) Fat and bone interactions. Curr Osteoporos Rep 12:235–242. doi:10.1007/s11914014-0199-y Papaioannou A, Morin S, Cheung AM, Atkinson S, Brown JP, Feldman S, Hanley DA, Hodsman A, Jamal SA, Kaiser SM, Kvern B, Siminoski K, Leslie WD, Scientific Advisory Council of Osteoporosis Canada (2010) 2010 Clinical practice guidelines for the diagnosis and management of osteoporosis in Canada: summary. CMAJ 182:1864–1873. doi:10.1503/cmaj.100771 Kanis JA, Oden A, Johnell O, Jonsson B, de Laet C, Dawson A (2001) The burden of osteoporotic fractures: a method for setting intervention thresholds. Osteoporos Int 12:417–427. doi:10.1007/ s001980170112 Elzibak AH, Noseworthy MD (2013) Assessment of diffusion tensor imaging indices in calf muscles following postural change from standing to supine position. Magn Reson Mater Phys. doi:10.1007/s10334-013-0424-1 Lorbergs AL, Noseworthy MD, MacIntyre NJ (2014) Age-related differences in the response of leg muscle cross-sectional area and water diffusivity measures to a period of supine rest. Magn Reson Mater Phys. doi:10.1007/s10334-014-0464-1 Lund H, Christensen L, Savnik A, Boesen J, Danneskiold-Samsoe B, Bliddal H (2002) Volume estimation of extensor muscles of the lower leg based on MR imaging. Eur Radiol 12:2982–2987. doi:10.1007/s00330-002-1334-1 Provencher SW (1993) Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med 30:672–679. doi:10.1002/mrm.1910300604 Kontulainen SA, Johnston JD, Liu D, Leung C, Oxland TR, McKay HA (2008) Strength indices from pQCT imaging predict up to 85% of variance in bone failure properties at tibial epiphysis and diaphysis. J Musculoskelet Neuronal Interact 8:401–409 Lyritis G (ed) (1999) Biomechanical examinations for validation of the bone strength strain index SSI, calculated by peripheral quantitative computed tomography, vol 2. Holonome editions, Athens Jones CJ, Rikli RE, Beam WC (1999) A 30-s chair-stand test as a measure of lower body strength in community-residing older adults. Res Q Exerc Sport 70:113–119 Topolski TD, LoGerfo J, Patrick DL, Williams B, Walwick J, Patrick MB (2006) The rapid assessment of physical activity (RAPA) among older adults. Prev Chronic Dis 3:A118
363
35. Daughton DM, Fix AJ, Kass I, Bell CW, Patil KD (1982) Maximum oxygen consumption and the ADAPT quality-of-life scale. Arch Phys Med Rehabil 63:620–622 36. Patsch JM, Burghardt AJ, Yap SP, Baum T, Schwartz AV, Joseph GB, Link TM (2013) Increased cortical porosity in type 2 diabetic postmenopausal women with fragility fractures. J Bone Miner Res 28:313–324. doi:10.1002/jbmr.1763 37. Gnudi S, Sitta E, Fiumi N (2007) Relationship between body composition and bone mineral density in women with and without osteoporosis: relative contribution of lean and fat mass. J Bone Miner Metab 25:326–332. doi:10.1007/s00774-007-0758-8 38. Studenski S, Perera S, Patel K, Rosano C, Faulkner K, Inzitari M, Brach J, Chandler J, Cawthon P, Connor EB, Nevitt M, Visser M, Kritchevsky S, Badinelli S, Harris T, Newman AB, Cauley J, Ferrucci L, Guralnik J (2011) Gait speed and survival in older adults. JAMA 305:50–58. doi:10.1001/jama.2010.1923 39. Bennell K, Dobson F, Hinman R (2011) Measures of physical performance assessments: self-Paced Walk Test (SPWT), Stair Climb Test (SCT), Six-Minute Walk Test (6MWT), Chair Stand Test (CST), Timed Up & Go (TUG), Sock Test, Lift and Carry Test (LCT), and Car Task. Arthritis Care Res (Hoboken) 63(11):S350–S370. doi:10.1002/acr.20538 40. Volpato S, Bianchi L, Lauretani F, Lauretani F, Bandinelli S, Guralnik JM, Zuliani G, Ferrucci L (2012) Role of muscle mass and muscle quality in the association between diabetes and gait speed. Diab Care 35:1672–1679. doi:10.2337/dc11-2202 41. Ferrucci L, Penninx BW, Leveille SG, Corti MC, Pahor M, Wallace R, Harris TB, Havlik RJ, Guralnik JM (2000) Characteristics of nondisabled older persons who perform poorly in objective tests of lower extremity function. J Am Geriatr Soc 48:1102–1110 42. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, Scherr PA, Wallace RB (1994) A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol 49:M85–M94 43. Uusi-Rasi K, Kannus P, Cheng S, Sievanen H, Pasanen M, Heinonen A, Nenonen A, Halleen J, Fuerst T, Genant H, Vuori I (2003) Effect of alendronate and exercise on bone and physical performance of postmenopausal women: a randomized controlled trial. Bone 33:132–143 44. Stathopoulos KD, Katsimbri P, Atsali E, Metania E, Zoubos AB, Skarantavos G (2011) Age-related differences of bone mass, geometry, and strength in treatment-naive postmenopausal women. A tibia pQCT study. J Clin Densitom 14:33–40. doi:10. 1016/j.jocd.2010.11.004 45. Edwards MH, Gregson CL, Patel HP, Jameson KA, Harvey NC, Sayer AA, Dennison EM, Cooper C (2013) Muscle size, strength, and physical performance and their associations with bone structure in the Hertfordshire Cohort Study. J Bone Miner Res 28:2295–2304. doi:10.1002/jbmr.1972
123
