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Texture Analysis for Postmenopausal Osteoporosis

Changes in Bone Density, Radiographic Texture Analysis and Bone Turnover During Two Years of Antiresorptive Therapy for Postmenopausal Osteoporosis

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT00145977
Enrollment
36
Registered
2005-09-05
Start date
2001-07-31
Completion date
2009-12-31
Last updated
2018-09-11

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Osteoporosis, Osteopenia

Keywords

osteoporosis, bone density, women, endocrine, musculoskeletal, metabolic

Brief summary

The purpose of this study is to determine if a new test for osteoporosis can be useful in monitoring treatment. We are studying a new method for examining the quality of bone by an experimental method of computerized analysis of radiographic images (x-ray pictures) of the heel.

Detailed description

The study proposed in this application is a part of a larger project entitled Clinical utility of radiographic texture analysis in diagnosing and treating osteoporosis. The overall goal of the larger project is to determine whether computerized texture analysis of digitized high-resolution images of trabecular bone (texture analysis) improves our ability to diagnose bone fragility and follow natural history and/or response to pharmacological therapy of osteoporosis. In the study proposed here we plan to examine changes in the results of texture analysis during two years of pharmacological therapy for osteoporosis. Role of densitometry in osteoporosis: Measurement of bone mineral density is the principal diagnostic method used in clinical practice and in research studies, both to identify patients who have the disease and to follow their response to therapeutic agents. The technique used most widely is dual-energy X-ray absorptiometry (DXA), which has advantages of low cost and radiation exposure, and high precision and accuracy of 1-2% and 4-8%, respectively \[Garner, 1996 and Melton, 1990\]. Based on the association between the low BMD and increased risk of fracture, BMD-based treatment guidelines have been developed \[Melton, 1993 and National Osteoporosis Foundation, 1999\]. There is, however, a considerable overlap between BMD of patients who sustain fragility fractures and those who do not \[Cummings, 1993; Marshall, 1996; Melton, 1989; Ross, 1990 and Wasnich, 1990\]. The problem arises because the fragility is determined not only by the quantity of the bone (measured as bone density), but also by its quality which is believed to be related to the preservation of the normal trabecular pattern \[Parfitt, 1987\]. Bone quality is not specifically assessed using current diagnostic methods. Information about bone quality, however, would be of substantial clinical and scientific value, as it would complement the BMD measurement when selecting patients for therapy and when studying bone loss or assessing effects of therapeutic agents. Texture analysis: A novel approach to noninvasive and practical assessment of bone structure is to analyze the texture of high resolution radiographs of trabecular bone \[Link, 1999\]. Dr. Giger has developed a method for characterizing bone structure by computerized texture analysis of digitized high-resolution radiographs \[Jiang, 1999; Caligiuri, 1993; Caligiuri, 1994; Chinander, 1999 and Chinander, 2000\]. In this approach, the texture is analyzed in several ways, including Fourier based analysis, which yields root mean square (RMS) as a measure of magnitude of trabecular bone texture pattern, and the first moment of power spectrum (FMP) which characterizes the texture pattern's frequency; and Minkowski dimension fractal analysis \[Caligiuri, 1993; Chinander, 1999; Chinander, 2000; Benhamou, 1994; Jiang, 1999; Majumdar, 1993 and Maragos, 1994\]. Radiographic texture analysis has been studied in vivo, on lumbar spine radiographs and found to predict presence of vertebral fractures elsewhere in the spine more reliably than did the BMD of the spine \[Caligiuri, 1993 and Caligiuri, 1994;\]. In addition, in an in vitro study texture features as well as BMD were analyzed in femoral neck specimens obtained during surgical hip replacement. Mechanical loading (crush test) was then performed on cubes of trabecular bone machined from these specimens to determine their bone strength. It was found that the combination of BMD and texture analysis predicted bone strength better than BMD alone \[Jiang, 1999; Chinander, 1999 and Chinander, 2000\]. Biochemical markers of bone turnover: In studies of osteoporosis, the bone mass is assessed by measuring BMD while the metabolic activity of the bone is assessed by measuring the biochemical markers of bone turnover \[Looker, 2000\]. These markers have limited utility in individual patients because they have high within-person variability (low precision), and because it is not clear which markers are useful in which clinical situation \[Looker, 2000 and Bauer, 1999\]. In contrast, comparing biochemical markers between groups of patients in clinical studies has been found to be useful in two settings. Firstly, it has been found that high levels of biochemical markers of bone resorption predict fractures independent of BMD \[Garnero, 1996 and van Daele, 1996\]. Secondly, early changes in bone markers (at 3-6 months) during anti-resorptive therapy predict later changes in BMD and fracture rates \[Ravin, 1999; Greenspan, 1998; Chesnut, 1997 and Bjarnason, 1997\]. The mechanisms underlying these observations have not been elucidated to date. It is speculated that increased bone resorption, which is reflected in elevation of biochemical markers of bone turnover, increases fragility by weakening trabecular structure prior to or independent of measurable BMD changes. Similarly, decreased bone resorption during pharmacological therapy is likely to improve the trabecular structure before or independent of its effects on BMD. Since the aim of our research is to (indirectly) examine the trabecular structure by performing the radiographic texture analysis, we plan to determine whether the changes in biochemical markers of bone turnover during antiresorptive therapy will correlate with changes in the results of texture analysis. Rationale for the study: Anti-resorptive therapy reduces bone fragility and increases bone density. It is likely that the trabecular structure of the bone also changes during treatment. Peripheral densitometry has not been used so far to monitor response to therapy. If the combination of texture analysis and peripheral BMD change reproducibly during treatment it may be possible to employ this combination to monitor therapeutic response. In so doing, one could avoid the need to use the central densitometry and biochemical markers of bone turnover since the former is cumbersome while the latter suffers from low precision. Potential advantages of using a portable peripheral densitometer: The texture analyses described above were developed for high-resolution radiographs, which were digitized and subjected to computer analysis. The new DXA imaging systems such as GE/Lunar PIXI which will be used in our research, provide digital images with resolution sufficient for computerized texture analysis (200 micron pixels). Furthermore, PIXI can generate the image in a shorter time (seconds vs. minutes) and at a fraction of radiation dose of conventional radiographs. Finally, since this is a portable densitometer, the methodology developed in this proposal has the potential to be widely applicable to large segments of the population, including frail elderly who have limited mobility and high prevalence of osteoporosis. STUDY PROCEDURES The studies will be performed in the outpatient facility of the University of Chicago. Every 3 months for the first 6 months and every 6 months for the remainder of 2 years, the subjects will come in the morning in the fasting state, provide a urine sample (second morning void) and blood sample for measurement of biochemical markers of bone turnover. Height and weight will be recorded at each visit, and any change in health status, including fractures ascertained. We will also assess other factors known to influence bone turnover, such as diet and physical activity. Every 12 months, the subjects will fill out Block food frequency questionnaire from Berkley Nutrition Services. In addition, every 6 months they will fill out a calcium intake questionnaire, which will be analyzed by the nutritionist and a short physical activity questionnaire, which was used in PEPI trial for assessment of physical activity. Medication compliance will be assessed by questioning the patients and counting the number of calcium and alendronate tablets remaining from the previous visit. After these tests are completed, the subjects will go to the densitometry suite of the Endocrinology clinic where BMD will be measured and heel images obtained for texture analysis. The left heel will be scanned twice using the PIXI densitometer (GE/Lunar corporation) for measurement of BMD of the heel and texture analysis. (If there is a deformity of the left heel, right heel will be used for all examinations.) In addition, every 6 months, BMD of the lumbar spine and proximal femur will be measured using the central densitometer Prodigy (GE/Lunar corporation). The same instrument will be used for lateral vertebral assessment (a method used for detecting vertebral deformities on images of the lateral spine from the densitometer), which will be performed every 12 months.

Interventions

DIETARY_SUPPLEMENTCalcium Citrate

600 mg of calcium citrate

DIETARY_SUPPLEMENTVitamin D

500 mg of Vitamin D consumed with the evening meal.

DRUGAlendronate

alendronate 70 mg once weekly

Sponsors

National Institutes of Health (NIH)
CollaboratorNIH
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
CollaboratorNIH
University of Chicago
Lead SponsorOTHER

Study design

Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
NONE

Eligibility

Sex/Gender
FEMALE
Age
59 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* The study will enroll 40 postmenopausal women with a T score \< -2 either at the lumbar spine or the femoral neck: 20 who decide to begin anti-resorptive therapy (treated group), and 20 women who decline such therapy (control group). We will attempt to match the patients and the controls for T score (within 0.3) and age (within 5 years). * All study participants will be: * at least 3 years past the last menstrual period, * not on HRT, Raloxifene or calcitonin for at least 6 months.

Exclusion criteria

* All study participants will not be on bisphosphonates during the previous 12 months. * Women with secondary causes of osteoporosis will be excluded.

Design outcomes

Primary

MeasureTime frameDescription
Changes in Lumbar Spine BMD +/- Treatment With AlendronateBaseline to Month 24Percent Change in lumbar spine BMD from Baseline to Month 24

Secondary

MeasureTime frameDescription
Changes in Peripheral Heel BMD +/- Treatment With AlendronateBaseline to Month 24Percent Change in peripheral heel BMD from Baseline to Month 24
Changes in Femoral Neck BMD +/- Treatment With AlendronateBaseline to Month 24Percent Change in femoral neck BMD from Baseline to Month 24
Changes in Total Hip BMD +/- Treatment With AlendronateBaseline to Month 24Percent Change in total hip BMD from Baseline to Month 24
Changes in Radiographic Texture Analysis (RTA) Integrated Root Mean Square (iRMS) From Baseline to Month 24Baseline to Month 24Root Mean Square (RMS) is a measure of the variability in the radiographic texture pattern, the relative difference in the contrast between light and dark areas is expressed in a grayscale level. In practical terms, a bone image with a washed-out appearance due to loss of trabecular structure such as that seen in osteoporosis, will have a low value for RMS because there will be relatively little contrast between lighter and darker areas of the image. An image of a bone with strong trabecular structure will have a high RMS value because the contrast between the lighter and darker areas of the image will be greater. To derive a measure of variability in the RMS in the region of interest in the bone image, the power spectrum is divided into 24 angular sectors at 15 degree intervals, and RMS is calculated for each segment. The iRMS (integrated RMS) roughly corresponds to RMS averaged across all 24 angular sectors
Changes in Radiographic Texture Analysis (RTA) Feature Standard Deviation of Root Mean Square (sdRMS) From Baseline to Month 24Baseline to Month 24Root Mean Square (RMS) is a measure of the variability in the radiographic texture pattern, the relative difference in the contrast between light and dark areas is expressed in a grayscale level. In practical terms, a bone image with a washed-out appearance due to loss of trabecular structure such as that seen in osteoporosis, will have a low value for RMS because there will be relatively little contrast between lighter and darker areas of the image. An image of a bone with strong trabecular structure will have a high RMS value because the contrast between the lighter and darker areas of the image will be greater. To derive a measure of variability in the RMS in the region of interest of the bone image, the power spectrum is divided into 24 angular sectors at 15 degree intervals, and RMS is calculated for each segment. We use sdRMS (standard deviation of the RMS across the segments) as a measure of the direction dependence (anisotropy) of the trabeculae in the bone image.
Changes in Radiographic Texture Analysis (RTA) Feature Integrated First Moment of the Power Spectrum (iFMP) From Baseline to Month 24Baseline to Month 24To derive a measure of variability and directionality in the first moment of the power spectrum (FMP) in the region of interest of the bone image, the power spectrum is divided into 24 angular sectors at 15 degree intervals, and FMP is calculated for each segment. We use iFMP (integrated FMP) as a measure of overall special frequency of the radiographic pattern. FMP characterizes spatial frequency in the radiographic pattern and the underlying trabecular structure. This corresponds to the coarseness or fineness of the radiographic texture pattern. A high level of FMP indicates thin and closely spaced trabecular structure. Low FMP indicates widely spaced dark areas usually corresponding to a strong, thick trabecular structure.
Changes in Radiographic Texture Analysis (RTA) Minimum First Moment of the Power Spectrum (minFMP) From Baseline to Month 24Baseline to Month 24To derive a measure of variability and directionality in the first moment of the power spectrum (FMP) in the region of interest of the bone image, the power spectrum is divided into 24 angular sectors at 15 degree intervals and FMP is calculated for each segment. We use minFMP (minimum FMP) to represent the lowest value of FMP across the 24 angular sectors corresponding to the special frequency in the most washed-out direction. FMP characterizes spatial frequency in the radiographic pattern and the underlying trabecular structure. This corresponds to the coarseness or fineness of the radiographic texture pattern. A high level of FMP indicates thin and closely spaced trabecular structure. Low FMP indicates widely spaced dark areas usually corresponding to a strong, thick trabecular structure.
Changes in Radiographic Texture Analysis (RTA) Minkowski Fractal Dimension (MINK) From Baseline to Month 24Baseline to Month 24The Percent Change in Radiographic Texture Analysis (RTA) Minkowski Fractal Dimension (MINK) from Baseline to Month 24 is a description of the similarity of texture of the images at different magnifications. The Minkowski fractal dimension is calculated from the slope of the least -square fitted line relating log volume and log magnification.
Changes in Radiographic Texture Analysis (RTA) Spectral Density Coefficient Beta (BETA) From Baseline to Month 24Baseline to Month 24The Percent Change in Radiographic Texture Analysis (RTA) spectral density coefficient beta (BETA) from Baseline to Month 24 is an analysis of spectral density vs. the spacial frequency on a log-log plot. BETA is the coefficient (slope) of this plot. Higher values of beta correspond to rougher (strong bone) and lower values to smoother, higher-frequency texture pattern (washed out bone).

Countries

United States

Participant flow

Participants by arm

ArmCount
Alendronate
All subjects will receive 600 mg of elemental calcium (as calcium citrate) and 500 mg of Vitamin D with their evening meal. This group will also receive alendronate 70 mg once weekly, according to standard recommendations. Alendronate: Subjects will receive 600 mg of elemental calcium (as calcium citrate) and 500 mg of Vitamin D with their evening meal. This group will also receive alendronate 70 mg once weekly, according to standard recommendations.
19
Control
All subjects will receive 600 mg of elemental calcium (as calcium citrate) and 500 mg of Vitamin D with their evening meal. Control: All subjects will receive 600 mg of elemental calcium (as calcium citrate) and 500 mg of Vitamin D with their evening meal.
17
Total36

Withdrawals & dropouts

PeriodReasonFG000FG001
Overall StudyLack of Efficacy04
Overall StudyLost to Follow-up10
Overall StudyWithdrawal by Subject10

Baseline characteristics

CharacteristicAlendronateControlTotal
Age, Continuous67.4 years
STANDARD_DEVIATION 10.1
61.1 years
STANDARD_DEVIATION 7.7
64.4 years
STANDARD_DEVIATION 9.4
Sex: Female, Male
Female
19 Participants17 Participants36 Participants
Sex: Female, Male
Male
0 Participants0 Participants0 Participants

Adverse events

Event typeEG000
affected / at risk
EG001
affected / at risk
deaths
Total, all-cause mortality
— / —— / —
other
Total, other adverse events
0 / 190 / 17
serious
Total, serious adverse events
0 / 190 / 17

Outcome results

Primary

Changes in Lumbar Spine BMD +/- Treatment With Alendronate

Percent Change in lumbar spine BMD from Baseline to Month 24

Time frame: Baseline to Month 24

Population: Three missing values in Alendronate group and five missing values in control group were not imputed.

ArmMeasureValue (MEAN)Dispersion
AlendronateChanges in Lumbar Spine BMD +/- Treatment With Alendronate5.28 Percent ChangeStandard Deviation 10
ControlChanges in Lumbar Spine BMD +/- Treatment With Alendronate-1.48 Percent ChangeStandard Deviation 4.56
Secondary

Changes in Femoral Neck BMD +/- Treatment With Alendronate

Percent Change in femoral neck BMD from Baseline to Month 24

Time frame: Baseline to Month 24

Population: Three missing values in Alendronate group and five missing values in control group were not imputed.

ArmMeasureValue (MEAN)Dispersion
AlendronateChanges in Femoral Neck BMD +/- Treatment With Alendronate-4.21 Percent ChangeStandard Deviation 10.24
ControlChanges in Femoral Neck BMD +/- Treatment With Alendronate0.04 Percent ChangeStandard Deviation 5.01
Secondary

Changes in Peripheral Heel BMD +/- Treatment With Alendronate

Percent Change in peripheral heel BMD from Baseline to Month 24

Time frame: Baseline to Month 24

Population: Three missing values in Alendronate group and five missing values in control group were not imputed.

ArmMeasureValue (MEAN)Dispersion
AlendronateChanges in Peripheral Heel BMD +/- Treatment With Alendronate1.02 Percent ChangeStandard Deviation 3.89
ControlChanges in Peripheral Heel BMD +/- Treatment With Alendronate-1.99 Percent ChangeStandard Deviation 6.73
Secondary

Changes in Radiographic Texture Analysis (RTA) Feature Integrated First Moment of the Power Spectrum (iFMP) From Baseline to Month 24

To derive a measure of variability and directionality in the first moment of the power spectrum (FMP) in the region of interest of the bone image, the power spectrum is divided into 24 angular sectors at 15 degree intervals, and FMP is calculated for each segment. We use iFMP (integrated FMP) as a measure of overall special frequency of the radiographic pattern. FMP characterizes spatial frequency in the radiographic pattern and the underlying trabecular structure. This corresponds to the coarseness or fineness of the radiographic texture pattern. A high level of FMP indicates thin and closely spaced trabecular structure. Low FMP indicates widely spaced dark areas usually corresponding to a strong, thick trabecular structure.

Time frame: Baseline to Month 24

Population: Three missing values in Alendronate group and five missing values in control group were not imputed.

ArmMeasureValue (MEAN)Dispersion
AlendronateChanges in Radiographic Texture Analysis (RTA) Feature Integrated First Moment of the Power Spectrum (iFMP) From Baseline to Month 240.09 Percent ChangeStandard Deviation 5.1
ControlChanges in Radiographic Texture Analysis (RTA) Feature Integrated First Moment of the Power Spectrum (iFMP) From Baseline to Month 241.04 Percent ChangeStandard Deviation 2.54
Secondary

Changes in Radiographic Texture Analysis (RTA) Feature Standard Deviation of Root Mean Square (sdRMS) From Baseline to Month 24

Root Mean Square (RMS) is a measure of the variability in the radiographic texture pattern, the relative difference in the contrast between light and dark areas is expressed in a grayscale level. In practical terms, a bone image with a washed-out appearance due to loss of trabecular structure such as that seen in osteoporosis, will have a low value for RMS because there will be relatively little contrast between lighter and darker areas of the image. An image of a bone with strong trabecular structure will have a high RMS value because the contrast between the lighter and darker areas of the image will be greater. To derive a measure of variability in the RMS in the region of interest of the bone image, the power spectrum is divided into 24 angular sectors at 15 degree intervals, and RMS is calculated for each segment. We use sdRMS (standard deviation of the RMS across the segments) as a measure of the direction dependence (anisotropy) of the trabeculae in the bone image.

Time frame: Baseline to Month 24

Population: Three missing values in Alendronate group and five missing values in control group were not imputed.

ArmMeasureValue (MEAN)Dispersion
AlendronateChanges in Radiographic Texture Analysis (RTA) Feature Standard Deviation of Root Mean Square (sdRMS) From Baseline to Month 246.85 Percent ChangeStandard Deviation 39.3
ControlChanges in Radiographic Texture Analysis (RTA) Feature Standard Deviation of Root Mean Square (sdRMS) From Baseline to Month 241.08 Percent ChangeStandard Deviation 18.85
Secondary

Changes in Radiographic Texture Analysis (RTA) Integrated Root Mean Square (iRMS) From Baseline to Month 24

Root Mean Square (RMS) is a measure of the variability in the radiographic texture pattern, the relative difference in the contrast between light and dark areas is expressed in a grayscale level. In practical terms, a bone image with a washed-out appearance due to loss of trabecular structure such as that seen in osteoporosis, will have a low value for RMS because there will be relatively little contrast between lighter and darker areas of the image. An image of a bone with strong trabecular structure will have a high RMS value because the contrast between the lighter and darker areas of the image will be greater. To derive a measure of variability in the RMS in the region of interest in the bone image, the power spectrum is divided into 24 angular sectors at 15 degree intervals, and RMS is calculated for each segment. The iRMS (integrated RMS) roughly corresponds to RMS averaged across all 24 angular sectors

Time frame: Baseline to Month 24

Population: Three missing values in Alendronate group and five missing values in control group were not imputed.

ArmMeasureValue (MEAN)Dispersion
AlendronateChanges in Radiographic Texture Analysis (RTA) Integrated Root Mean Square (iRMS) From Baseline to Month 24-3.70 Percent ChangeStandard Deviation 18.31
ControlChanges in Radiographic Texture Analysis (RTA) Integrated Root Mean Square (iRMS) From Baseline to Month 24-0.53 Percent ChangeStandard Deviation 3.51
Secondary

Changes in Radiographic Texture Analysis (RTA) Minimum First Moment of the Power Spectrum (minFMP) From Baseline to Month 24

To derive a measure of variability and directionality in the first moment of the power spectrum (FMP) in the region of interest of the bone image, the power spectrum is divided into 24 angular sectors at 15 degree intervals and FMP is calculated for each segment. We use minFMP (minimum FMP) to represent the lowest value of FMP across the 24 angular sectors corresponding to the special frequency in the most washed-out direction. FMP characterizes spatial frequency in the radiographic pattern and the underlying trabecular structure. This corresponds to the coarseness or fineness of the radiographic texture pattern. A high level of FMP indicates thin and closely spaced trabecular structure. Low FMP indicates widely spaced dark areas usually corresponding to a strong, thick trabecular structure.

Time frame: Baseline to Month 24

Population: Three missing values in Alendronate group and five missing values in control group were not imputed.

ArmMeasureValue (MEAN)Dispersion
AlendronateChanges in Radiographic Texture Analysis (RTA) Minimum First Moment of the Power Spectrum (minFMP) From Baseline to Month 24-3.61 Percent ChangeStandard Deviation 11.31
ControlChanges in Radiographic Texture Analysis (RTA) Minimum First Moment of the Power Spectrum (minFMP) From Baseline to Month 24-0.18 Percent ChangeStandard Deviation 6.89
Secondary

Changes in Radiographic Texture Analysis (RTA) Minkowski Fractal Dimension (MINK) From Baseline to Month 24

The Percent Change in Radiographic Texture Analysis (RTA) Minkowski Fractal Dimension (MINK) from Baseline to Month 24 is a description of the similarity of texture of the images at different magnifications. The Minkowski fractal dimension is calculated from the slope of the least -square fitted line relating log volume and log magnification.

Time frame: Baseline to Month 24

Population: Three missing values in Alendronate group and five missing values in control group were not imputed.

ArmMeasureValue (MEAN)Dispersion
AlendronateChanges in Radiographic Texture Analysis (RTA) Minkowski Fractal Dimension (MINK) From Baseline to Month 24-0.06 Percent ChangeStandard Deviation 0.48
ControlChanges in Radiographic Texture Analysis (RTA) Minkowski Fractal Dimension (MINK) From Baseline to Month 240.12 Percent ChangeStandard Deviation 0.42
Secondary

Changes in Radiographic Texture Analysis (RTA) Spectral Density Coefficient Beta (BETA) From Baseline to Month 24

The Percent Change in Radiographic Texture Analysis (RTA) spectral density coefficient beta (BETA) from Baseline to Month 24 is an analysis of spectral density vs. the spacial frequency on a log-log plot. BETA is the coefficient (slope) of this plot. Higher values of beta correspond to rougher (strong bone) and lower values to smoother, higher-frequency texture pattern (washed out bone).

Time frame: Baseline to Month 24

Population: Three missing values in Alendronate group and five missing values in control group were not imputed.

ArmMeasureValue (MEAN)Dispersion
AlendronateChanges in Radiographic Texture Analysis (RTA) Spectral Density Coefficient Beta (BETA) From Baseline to Month 243.09 Percent ChangeStandard Deviation 21.3
ControlChanges in Radiographic Texture Analysis (RTA) Spectral Density Coefficient Beta (BETA) From Baseline to Month 246.53 Percent ChangeStandard Deviation 11.02
Secondary

Changes in Total Hip BMD +/- Treatment With Alendronate

Percent Change in total hip BMD from Baseline to Month 24

Time frame: Baseline to Month 24

Population: Three missing values in Alendronate group and five missing values in control group were not imputed.

ArmMeasureValue (MEAN)Dispersion
AlendronateChanges in Total Hip BMD +/- Treatment With Alendronate-3.68 Percent ChangeStandard Deviation 9.3
ControlChanges in Total Hip BMD +/- Treatment With Alendronate-0.77 Percent ChangeStandard Deviation 2.7

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026