A novel computed tomography-based algorithm for the quantitative identification of lumbar spine-localized trabecular bone loss in patients with type 2 diabetes mellitus

Introduction

Type 2 diabetes mellitus (T2DM) is the most common type of diabetes, accounting for approximately 96% of diabetes cases (1). It has been reported that the number of individuals with diabetes has increased significantly over the past three decades worldwide, with Asia being a major region for T2DM prevalence and China being one of the most affected areas (2). T2DM is associated with numerous complications, among which fragility fractures are gaining recognition as a serious musculoskeletal consequence, leading to increased morbidity, mortality, and healthcare burden (3).

Bone mineral density (BMD) is currently the focus of bone assessment (4). However, many studies have found that compared to individuals without T2DM, those with T2DM have a BMD that remains unchanged or even increases (5-7), with these patients facing a higher risk of fractures (8,9). Other research has reported that BMD can only account for 60–70% of the variability in bone health (10). This highlights the limitations of relying solely on BMD for bone strength assessment in diabetic populations. Emerging evidence suggests that T2DM can impair bone quality through reduced bone turnover, alterations in bone material properties, and disruption of bone microarchitecture, contributing to diabetic osteoporosis and fracture occurrence (5,11). Therefore, in addition to assessing BMD, evaluating changes in the other bone structural components, such as trabecular bone and cortex of bone, has gained increased research attention (12).

Trabecular bone, due it to being highly metabolically active, plays a critical role in skeletal integrity and fracture resistance and is biomechanically distinct from cortical bone (13). Its complex microarchitecture makes it difficult to assess with conventional imaging, with resolution limitations (14) and partial volume effects (10) often being encountered. The common clinical methods used for quantifying bone changes are based on dual-energy X-ray (DXA) and quantitative computed tomography (QCT), but these two cannot adequately reflect its microstructure or local defects (15,16). Although advanced techniques such as high-resolution peripheral QCT (HR-pQCT) and micro computed tomography (CT) methods have been used to evaluate trabecular bone, there remain problems such as limited location (17), small imaging field of view, and long imaging time (18). Recently, an algorithm based on HR-pQCT to better quantify local trabecular bone loss (LTBL) within the trabecular network was proposed (19). However, this technique is achieved through HR-pQCT and is unsuitable for evaluating axial skeletal sites such as the vertebrae, limiting its clinical applicability. To address this limitation, Lin et al. developed a phantom-less local BMD detection system that does not require the standard phantom calibration necessary in QCT. Based on conventional CT, this system can achieve qualitative and quantitative evaluation of local BMD reduction areas in any area of the vertebral body (20). Therefore, we conducted this study to investigate whether there are differences in lumbar spine LTBL between individuals with T2DM and those without it who had BMD values within the same reference range. In addition, we aimed to clarify the relationship between LTBL, BMD, and age, with the goal of confirming LTBL as a potential imaging biomarker for the early detection and monitoring of vertebral microstructural changes in patients with T2DM. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1312/rc).

Methods

Study participants

This retrospective, cross-sectional study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by the Research Ethics Committee of the First Affiliated Hospital of Kunming Medical University (No. 2024-L-263). The requirement for informed consent was waived due to the retrospective nature of the analysis.

Individuals diagnosed with T2DM at the First Affiliated Hospital of Kunming Medical University between June 2022 and April 2024 were retrospectively enrolled. Nondiabetic individuals from the same period were included as controls. Clinical data including sex, age, height, and weight were collected, and body mass index (BMI) was calculated as the weight (kg) divided by the height² (m²). The inclusion criteria were as follows: (I) T2DM; (II) age older than 30 years and younger than 70 years; (III) availability of high-quality lumbar QCT images (no artifacts, clear images, sufficient range, correct posture, etc.); (IV) a BMD (21) >120 mg/cm³; and (V) complete clinical information. Meanwhile, the exclusion criteria were as follows: (I) other types of diabetes; (II) an age younger than 30 years or older than 70 years; (III) thyroid disease and autoimmune diseases, including hyperthyroidism, hypothyroidism, hyperparathyroidism, systemic lupus erythematosus, rheumatoid arthritis, etc.; (IV) long-term hormone, phosphate, or other drug treatments; (V) malignant tumors; (VI) severe heart, liver, or kidney dysfunction; and (VII) lumbar abnormalities, including lumbar surgery, fracture, infection, vertebral hemangioma, etc. The flowchart for inclusion and exclusion is shown in Figure 1. To calculate the sample size, G*Power software was used. With a Cohen’s d medium effect size of 0.5, a significance level set at α=0.05, a power (1−β) of 0.85, and an intergroup ratio of 1:1.3, we determined that the non-T2DM group required at least 65 people and that the T2DM group required at least 85 people. Ultimately, 72 individuals without T2DM and 94 with T2DM were enrolled.

Figure 1 Flow diagram of patient selection. BMD, bone mineral density; QCT, quantitative computed tomography; T2DM, type 2 diabetes mellitus.

BMD measurement

QTC images were mainly scanned with the IQon Spectral CT system (Philips, Amsterdam, the Netherlands). Patients were placed head first in the supine position for scanning, and the scanning range was from the T12 to S1 vertebrae. Tube voltage was set at 120 kV, tube current was automatically selected (200–300 mAs), the matrix size was 512×512, the field of view was 500 mm, the slice thickness was 1 mm, and the interval was 1 mm. The iDose⁴ algorithm (Philips) was used to reconstruct the image. After scanning, thin-section soft tissue images were automatically reconstructed and transmitted to the QCT postprocessing workstation for analysis.

QCT postprocessing was performed with QCT Pro BMD analysis software (Mindways Software, Austin, TX, USA). Vertebral measurements were taken at the L1–L3 vertebrae, with the region of interest placed in the center of the vertebrae (Figure 2). The average value of the three vertebrae was used as the final BMD. All measurements were performed during QCT postprocessing by a skilled technician who was blinded to the patient’s condition, and 1 month later, consistency was verified by a random selection of 17 participants for repeat measurements.

Figure 2 Bone mineral density measurement. (A1-A3) Axial images of the L1–L3 vertebrae. (B1-B3) Sagittal images of the L1–L3 vertebrae. ROI, region of interest.

Localized trabecular bone loss (LTBL) measurement

LTBL quantification was performed with SurgiSpace-Island-V2.0 software (Bone’s Technology Limited, Shenzhen, China). Thin-section soft tissue images of the lumbar spine in Digital Imaging and Communications in Medicine (DICOM) format were imported into the software, and the lumbar vertebrae (L1–5) were then manually marked and confirmed. After confirmation, the LTBL threshold was set, and then the algorithm automatically identified and calculated the number and volume of LTBL areas in each vertebra. The threshold of LTBL areas was set to regions with BMD <40 mg/cm³ and a volume ≥16.5 mm³ (Figure 3); the specific principles and processes of this algorithm have been outlined in previous research (20). After completion of this process, the number and volume of LTBL areas were obtained for each vertebra. These measurements were then used to calculate each participant’s total number and volume of LTBL areas (calculated as the sum of the LTBL areas across all lumbar vertebral bodies), the mean volume of the LTBL areas (total volume of LTBL areas/total number of LTBL areas), and the number of vertebrae with LTBL.

Figure 3 Lumbar spine-localized trabecular bone loss measurement. (A) Manual marking of lumbar vertebrae. (B) Results of identified LTBL (color-marked regions). BMD, bone mineral density; LTBL, localized trabecular bone loss.

Statistical analysis

All data analyses were performed with SPSS 26.0 (IBM Corp., Armonk, NY, USA) and R v. 4.5.1 (The R Foundation for Statistical Computing, Vienna, Austria). Data distribution was assessed with the Shapiro-Wilk test. Normally distributed data are expressed as the mean ± standard deviation, nonnormally distributed data as the median with interquartile range, and categorical variables as percentages. Intra-observer reliability for BMD measurements was assessed with the intraclass correlation coefficient (ICC), with an ICC >0.75 indicating excellent consistency. The Mann-Whitney test was used to compare the clinical characteristics and LTBL parameters between the groups, and the Benjamini-Hochberg P value correction was used to reduce the risk of type I error. Spearman correlation analysis was conducted to determine the relationships between age, BMD, and LTBL parameters. An r value <0.4 was considered a low correlation, r≥0.4 but <0.7 was considered moderate correlation, and r≥0.7 was considered high correlation. To eliminate the potential influence of covariates, we conducted univariate and multivariate regression analyses with the number and volume of LTBL areas as dependent variables and with age, gender, BMI, BMD, and as independent variables. A P value <0.05 was considered statistically significant.

Results

Clinical characteristics

A total of 166 participants were included in this study, with 94 individuals with T2DM and 72 without T2DM. Age was not significantly different between the two groups (P>0.05). In addition, the differences between the groups were not significant after adjustment for BMI and gender (P>0.05). Although BMD was slightly higher in the T2DM group, the difference was not statistically significant (P>0.05), as shown in Table 1.

Seventeen participants were randomly selected for repeated BMD measurements after 1 month. The intra-observer ICC for BMD was 0.859, with P<0.001 indicating excellent consistency in BMD measurements.

LTBL analysis

The study identified a total 2,759 areas of LTBL, with 1,838 found in the T2DM group and 921 in the non-T2DM group. Overall, there was a pattern of increasing LTBL from L1 to L5 vertebrae. The L4 vertebra had the highest number of LTBL areas, accounting for 25.2% of the total. Among the T2DM group, the L4 vertebra had the highest proportion of LTBL areas at 26.7%, while in the non-T2DM group, the L5 vertebra had the highest prevalence of LTBL areas at 26.7% (Figure 4).

Figure 4 Distribution of LTBL areas among participants. LTBL, localized trabecular bone loss; T2DM, type 2 diabetes mellitus.

Compared with the non-T2DM group, the T2DM group had a significantly higher total number of LTBL areas and number of vertebrae with LTBL (P<0.05). Regarding the size of the LTBL, the total volume of LTBL was significantly greater in the T2DM group than in the non-T2DM group (P<0.05), and the mean volume was also higher but not significantly so (P>0.05) (Table 2 and Figures 5-7). In the subgroup analysis, there was no statistically significant difference in the number and volume of LTBL areas or BMD between males and females (P>0.05), as shown in Table 3.

Figure 5 Comparison of the number of LTBL areas between the T2DM and non-T2DM groups. (A) Total number of LTBL areas. (B) Number of vertebral bodies with LTBL. *, P<0.05. LTBL, localized trabecular bone loss; T2DM, type 2 diabetes mellitus.

Figure 6 Comparison of LTBL volume between the T2DM and non-T2DM groups. (A) Total volume of LTBL areas; (B) mean volume of LTBL areas. *, P<0.05. LTBL, localized trabecular bone loss; T2DM, type 2 diabetes mellitus.

Figure 7 LTBL in the T2DM and non-T2DM groups. (A) A 48-year-old female with T2DM, with BMD =130.17 mg/cm³. (A1-A5) The L1–L5 vertebrae, respectively, with a total of 24 LTBL areas and a total volume of 1,463 mm³. (B) A 48-year-old female without T2DM, with BMD =126.87 mg/cm³. (B1-B5) The L1–L5 vertebrae, respectively, with a total of 16 LTBL areas and a total volume of 856 mm³. BMD, bone mineral density; LTBL, localized trabecular bone loss; T2DM, type 2 diabetes mellitus.

Correlation analysis

Spearman correlation analysis indicated that in the T2DM group, age had a slight negative correlation with BMD (r=−0.39; P<0.05), a slight positive correlation with the total number of LTBL areas (r=0.35; P<0.05), and a slight positive correlation with the total volume of LTBL areas (r=0.33; P<0.05); meanwhile, BMD had a moderate negative correlation with both the total number of LTBL areas (r=−0.47; P<0.05) and the total volume of LTBL areas (r=−0.47; P<0.05). There was a strong positive correlation between the total number and the total volume of LTBL areas (r=0.91; P<0.05), as shown in Figure 8A.

Figure 8 Heatmap of the correlation analysis. (A) T2DM group. (B) Non-T2DM group. *, P<0.05. 1NA, not applicable; BMD, bone mineral density; LTBL, localized trabecular bone loss; T2DM, type 2 diabetes mellitus.

In the non-T2DM patients group, age had a slight negative correlation with BMD (r=−0.32, P<0.05), a slight positive correlation with the total number of LTBL areas (r=0.29; P<0.05), and a slight positive correlation with the total volume of LTBL areas (r=0.35; P<0.05). BMD had strong negative correlation with both the total number of LTBL areas (r=−0.77; P<0.05) and the total volume of LTBL areas (r=−0.74; P<0.05). A strong positive correlation was observed between the total number and total volume of LTBL areas (r=0.93; P<0.05), as shown in Figure 8B.

Additionally, scatter plots were drawn to visually demonstrate the relationships between age, BMD, and the LTBL parameters in the T2DM (Figure 9A) and non-T2DM (Figure 9B) groups.

Figure 9 Scatter plot for the correlation analysis. (A1-A4) T2DM group. (B1-B4) Non-T2DM group. BMD, bone mineral density; LTBL, localized trabecular bone loss; T2DM, type 2 diabetes mellitus.

Regression analysis

In the univariate regression analysis, age, BMD, and T2DM were associated with total number of LTBL areas and number of vertebrae with LTBL (all P values <0.05), while gender and BMI were not (all P values >0.05) (Table 4). In the multivariable analysis, the independent factors for the total number of LTBL areas were age (β=0.336; P= 0.030), BMD (β=−0.252; P<0.001), and T2DM (β=6.222; P=0.009); meanwhile, the independent factors for the number of vertebrae with LTBL were age (β=0.019; P=0.049), BMD (β=−0.014; P<0.001), and T2DM (β=0.533; P<0.001). Univariate regression analysis showed that age, BMD, and T2DM were associated with total volume of LTBL areas (all P values <0.05) and that gender and BMI were not (all P values >0.05). Meanwhile, only age was associated with the mean volume of LTBL areas (P<0.05) (Table 5). In the multivariable analysis, the independent factors for the total volume of LTBL areas were age (β=34.778; P=0.006), BMD (β=−14.778; P<0.001), and T2DM (β=497.558; P=0.010) (Table 5).

Discussion

Conventional approaches to assessing bone health in individuals with T2DM typically focus on measuring BMD via QCT or DXA, classifying bone status as normal, osteopenia, or osteoporosis. However, BMD reflects only the average mineral content across a region and often fails to capture localized deficits in bone quality. This phenomenon of localized trabecular loss has long been noted, yet there is a lack of precise, uniform terminology, with “trabecular network heterogeneity” and “trabecular deterioration”, among other terms, being used (22,23). Earlier studies attempting to quantify such localized changes required HR-pQCT or complex processing techniques (24,25), while other clinical CT-based studies have focused primarily on the spatial distribution of local bone changes, with limited assessment of the actual extent of local structural alterations (26,27). Therefore, in this study, we applied a recently developed algorithm integrated with a phantom-less QCT system to analyze conventional CT images, enabling high-precision detection and quantification of LTBL without the need for specialized equipment or site-specific limitations.

LTBL reflects the localized bone loss within the trabecular network, although they do not indicate a complete absence of tissue, as these areas may be filled with bone marrow and/or unmineralized bone (19). To better quantify LTBL, we used a new algorithm, and LTBL in this algorithm was defined as a focal region with BMD <40 mg/cm³ and volume ≥16.5 mm³, in line with previously validated thresholds (20). This algorithm has high repeatability and accuracy in detecting localized BMD. In our study, the T2DM group had a significantly higher number and greater total volume of LTBL areas compared with the non-T2DM group even when BMD was within the same reference range. This suggests that T2DM is associated with more severe LTBL. It has been suggested that bone fragility stems not only from reduced mineral density but also from microstructural changes, ultimately altering the material properties of the bone itself (5). Thus, the changes in LTBL quantity and quality in the lumbar vertebrae of those with T2DM may be among the factors contributing to their increased risk of fragility fractures. However, this local bone loss is not captured by BMD, as the individuals with T2DM in this study exhibited slightly higher BMD than did those without T2DM, consistent with other findings (6). Moreover, even among nondiabetic individuals, localized bone loss has been associated with increased vertebral fracture risk independent of BMD (28). Bone metabolism is tightly regulated by a balance between osteoclast-driven resorption and osteoblast-driven formation; when this balance is disrupted, microstructural defects can occur, which is potentially associated with fractures but not to baseline BMD (29). Studies indicate that individuals with T2DM have a reduced number of osteoblasts (30), along with impaired proliferation and differentiation function (31,32). Bone biopsies also indicate a decrease in trabecular bone formation rates among the T2DM population (33). This may explain why individuals with T2DM exhibit a greater number and larger size of LTBL areas than do those without T2DM.

Although sex hormones influence bone metabolism and structure (34), our subgroup analysis revealed no significant sex-based differences in LTBL metrics in either group. This may be attributed to the limited sample size and the inclusion of only individuals within one BMD reference range. However, another study reported that this bone loss is more common in females (19). Our correlation analysis indicated a slight positive relationship between age and both the number and size of LTBL areas in both individuals with and without T2DM, consistent with other studies (19,20). There was a moderate-to-strong negative correlation between BMD and both the number and volume of LTBL areas, suggesting that these parameters can reflect changes in bone strength to some extent. Interestingly, this correlation was weaker in the T2DM group than in the non-T2DM group, which is perhaps because the formation of LTBL in T2DM is also affected by other factors, thus weakening the correlation. Additionally, we observed that LTBL was primarily distributed in the L3–L5 vertebrae in both groups, and this region may be more susceptible to biomechanical loading and early structural deterioration.

We further employed regression analysis to evaluate the influencing factors of LTBL. The analysis indicated that age, BMD, and T2DM were independently associated with both the number and total volume of LTBL areas, whereas gender and BMI were not significant predictors. These results were in line with correlation analysis described above, corroborating the influence of age and BMD on LTBL. Importantly, T2DM emerged as an independent risk factor for both number and total volume of LTBL areas even after adjustments were made for age and BMD. This also suggests that in addition to BMD, it is also necessary to consider local changes in trabecular bone in individuals with T2DM. However, based on the results, only age is an influencing factor for the average volume of LTBL. This is in line with findings indicating no significant difference between the T2DM and non-T2DM groups. This might be related to the calculation method adopted in this study. After averaging the data, the differences within the data itself were weakened.

This study involved several limitations that should be addressed. First, we employed a single-center, retrospective design with a relatively small sample size, which might have introduced selection bias and limits the generalizability of the findings. Second, we only included patients with BMD >120 mg/cm³ and excluded a large number of patients with abnormalities, resulting in a certain selection bias; in addition, some studies have shown that the existing BMD thresholds may not be applicable to the Chinese population and that different genders should have different separate ranges (35,36). Third, metabolic parameters such as hemoglobin A1c levels, duration of diabetes, and medication use that could affect bone metabolism were not analyzed in relation to LTBL. Finally, as this was a cross-sectional study, we could only demonstrate that individuals with T2DM exhibited a greater severity of LTBL, while the lack of longitudinal data limited further assessment of its association with fracture risk.

Conclusions

In this study, we used a novel algorithm based on conventional CT and found that individuals with T2DM had a greater severity of lumbar spine LTBL than did those without T2DM within the same BMD range. LTBL is affected by age, BMD, and T2DM status and may be a valuable imaging marker for detecting early microstructural deterioration in diabetic bone; however, its clinical predictive value for fractures requires further validation in prospective studies. This new algorithm-based quantification of LTBL from conventional CT scans provides a novel and practical approach to improving bone risk assessment in those with T2DM.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1312/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1312/dss

Funding: This work was supported by the National Natural Science Foundation of China (No. 82302131), First-Class Discipline Team of Kunming Medical University (No. 2024XKTDTS03), and the Yunnan Fundamental Research Project (No. 202301AY070001-114).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1312/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by Research Ethics Committee of the First Affiliated Hospital of Kunming Medical University (No. 2024-L-263) and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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