Association between body mass index and lumbar spine volumetric bone mineral density in diabetic and non-diabetic patients

Abstract

BACKGROUND

The association between body mass index (BMI) and bone mineral density (BMD) has shown inconsistent results, varying by sex and skeletal site. Despite normal or elevated bone mass, individuals with type 2 diabetes have an increased risk of hip and vertebral fractures.

AIM

To assess lumbar spine trabecular volumetric BMD (vBMD) across different BMI categories in individuals with and without diabetes.

METHODS

This cross-sectional study included 966 men over 50 years old and 1001 postmenopausal women from the Pinggu Metabolic Disease Study. The vBMD of lumbar vertebrae 2 through 4 was measured using quantitative computed tomography. Total adipose tissue, subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and lumbar skeletal muscle area were also quantified.

RESULTS

In men with obesity (P = 0.038) and overweight (P = 0.032), vBMD was significantly higher in the diabetes group compared to non-diabetic men. After adjusting for age and sex, no significant saturation effect between BMI and BMD was found in participants with diabetes or in women without diabetes. However, a BMI threshold of 22.33 kg/m² indicated a saturation point for vBMD in non-diabetic men. Independent predictors of vBMD in men included age (r = -0.387, P < 0.001), BMI (r = 0.130, P = 0.004), and VAT (r = -0.145, P = 0.001). For women, significant predictors were age (r = -0.594, P < 0.001), BMI (r = 0.157, P = 0.004), VAT (r = -0.112, P = 0.001), and SAT (r = -0.068, P = 0.035).

CONCLUSION

The relationship between BMI and trabecular vBMD differs in individuals with and without diabetes. Overweight and obese men with diabetes exhibit higher vBMD.

Key Words: Obesity and overweight; Body mass index; Diabetes; Lumbar spine volumetric bone mineral density; Quantitative computed tomography

Core Tip: The association between body mass index (BMI) and bone mineral density (BMD) has shown contradictory findings, which depends on sex and skeletal sites. By using data from Pinggu Metabolic Disease Study, we examined the association between BMI, body composition, and lumbar spine trabecular volumetric BMD (vBMD) in patients with and without diabetes. We found that men with obesity and overweight aged over 50 years with diabetes had favorable trabecular vBMD than participants without diabetes. The trabecular vBMD had different trend patterns with the increase of BMI in participants with and without diabetes. There was a saturation effect value between BMI (22.33 kg/m²) and vBMD in men without diabetes.

INTRODUCTION

Osteoporosis, a systemic skeletal disorder, is characterized by low bone mass and deteriorated bone microarchitecture, resulting in increased fragility and fracture risk. In China, the condition affects 32.1% of postmenopausal women and 6.9% of men over 50 years old, according to the China Osteoporosis Prevalence Study[1]. It significantly burdens healthcare systems and diminishes quality of life.

Obesity has traditionally been seen as protective against fractures[2]. However, studies on the relationship between body mass index (BMI) and bone mineral density (BMD) have produced inconsistent findings, influenced by factors such as sex and skeletal site[3-5]. Moreover, fat distribution may differentially impact bone health. Higher trunk fat mass assessed by dual-energy X-ray absorptiometry (DXA) and visceral adipose tissue (VAT) assessed by quantitative computed tomography (QCT) correlated with lower trabecular bone volume, reduced stiffness, decreased bone formation, and increased cortical porosity, as revealed by micro-computed tomography (CT) of transiliac biopsies[6]. Type 2 diabetes (T2D) is a growing global health concern. T2D patients are at increased risk for hip and vertebral fractures despite having normal or higher bone mass[7]. However, there is limited research on how BMI and body composition affect bone mass and microstructure in people with and without diabetes. Understanding the complex relationships among obesity, diabetes, and osteoporosis is vital for developing effective healthcare strategies[8].

DXA remains the gold standard for diagnosing osteoporosis and predicting fracture risk[9]. Most studies examining the impact of BMI on bone focus on lumbar spine and hip areal BMD[8]. However, DXA results can be skewed by factors such as osteophytes, aortic calcifications, and surrounding soft tissues like fat[10]. To address these limitations, QCT offers an alternative, measuring trabecular volumetric BMD (vBMD) alongside abdominal fat, providing a more accurate assessment of fracture risk[11].

This study examined the relationship between BMI, body composition, and lumbar spine trabecular vBMD in individuals with and without diabetes, using data from the Pinggu Metabolic Disease Study (PMDS). Additionally, it explored whether a saturation effect exists between BMI and vBMD in postmenopausal women and men over 50 years old.

MATERIALS AND METHODS

Data sources

This was a subgroup study of the PMDS, which was carried out in the Pinggu district of Beijing between September 2013 and July 2014. The study involved 4002 participants aged 26 to 76 years. Comprehensive details regarding participant enrollment and study methodology have been previously published[12]. For this analysis, 951 men under 50 years, 1002 premenopausal women, and 57 subjects lacking vBMD data were excluded. Participants identified as underweight (BMI < 18.5 kg/m²) were also omitted due to the known negative correlation between underweight status and BMD. This underweight subgroup was minimal, comprising only 25 individuals. Consequently, data from 1967 participants were included for further analysis (Figure 1).

Figure 1 Flowchart of participant selection. BMI: Body mass index; PMDS: The Pinggu Metabolic Disease Study; vBMD: Volumetric bone mineral density.

Ethical approval for the PMDS was received from the ethics committees of Peking University Medical Center and the University of Michigan. Additionally, the specific study received ethics approval from the Peking University People’s Hospital ethics committee (Approval No. 2021PHB441-001).

Anthropometric measurements

All participants engaged in face-to-face interviews and completed standardized questionnaires, as described previously[12]. Heights were measured without shoes to the nearest 0.1 cm, and weights were recorded in light clothing to the nearest 0.1 kg. The BMI was calculated using the formula: Weight (kg)/[height (m)]². Waist circumference (WC) was measured at the midpoint between the lower rib cage and the iliac crest with a flexible non-elastic tape, while hip circumference (HC) was measured at the midpoint between the iliac crest and the most lateral points of the greater trochanter, also to the nearest 0.1 cm. The waist-to-hip ratio (WHR) was calculated by dividing WC by HC. Participants were categorized into BMI groups as defined by the Working Group on Obesity in China[13], including obesity (≥ 28.0 kg/m²), overweight (24.0-27.9 kg/m²), and normal weight (18.5-23.9 kg/m²).

Biochemical assessments

Fasting blood samples were obtained following an 8- to 12-hour interval. Renal and liver functions were evaluated by measuring serum creatinine, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and lipid profiles, including total cholesterol, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol using a Coulter UniCel DxC 800 automated biochemical analyzer (Beckman, United States).

Participants without a diabetes diagnosis underwent an oral glucose tolerance test (OGTT) to evaluate their glucose tolerance. For those diagnosed with diabetes, fasting plasma glucose (FPG) was determined by the hexokinase method, and serum insulin levels were measured using radioimmunoassay. Hemoglobin A1c (HbA1c) was quantified through cation-exchange high-pressure liquid chromatography, with serum insulin also assessed by radioimmunoassay (China Institute of Atomic Energy, Beijing, China). The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated as follows: HOMA-IR = fasting insulin (FINS) (mU/mL) × fasting glucose (mmol/L)/22.5.

Participants were classified into glucose tolerance categories based on OGTT results in accordance with the 1999 World Health Organization criteria[14]. Participants with diagnosed diabetes were based on self-reported history and/or current use of hypoglycemic medications.

vBMD and body composition analysis

Participants underwent abdominal CT scans with a GE 64-slice CT scanner (LightSpeed VCT, GE, United States). Lumbar spine trabecular vBMD for the L2-L4 region was assessed via asynchronous calibration using QCT Pro analysis software (Mindways, Austin, TX, United States). According to the American College of Radiology criteria, participants were classified into osteoporosis (vBMD < 80 mg/cm³) or low bone mass (vBMD 80 to 120 mg/cm³)[15].

Abdominal fat was measured at the L4-L5 intervertebral disc space, while skeletal muscle area (SMA) was assessed at the L3 intervertebral disc space. Total adipose tissue (TAT), VAT, and SMA (cm²) were quantified semi-automatically using the Tissue Composition Module of the analysis software (Mindways, Austin, TX, United States). Subcutaneous adipose tissue (SAT) was determined by subtracting VAT from TAT.

Statistical analysis

Baseline characteristics are expressed as the mean ± SD or as frequencies and percentages where appropriate. Group differences were evaluated using Student’s t-test, Mann-Whitney U test, or Kruskal-Wallis H test for continuous variables, while categorical variables were analyzed by the χ² test or Fisher’s exact test. Pearson correlation coefficients were calculated to assess the relationships between vBMD and variables including BMI, WC, VAT, SAT, and SMA, with stratification by glucose status and sex. Multivariate linear regression models were applied to examine the relationships between BMI, WC, VAT, SAT, SMA, and vBMD, adjusting for confounding factors. Nonlinear associations between BMI and vBMD were explored through fitting smooth curves and threshold effect analyses. Two piecewise linear regression models were implemented to identify the optimal inflection point on the curve.

Statistical analyses were performed using SPSS software (version 23.0, SPSS Inc., Chicago, IL, United States) and EmpowerStats (version 4.1, X&Y Solutions, Inc., Boston, MA, United States), with a significance threshold set at P ≤ 0.05.

RESULTS

Clinical characteristics and osteoporosis prevalence among participants

This study comprised 1967 participants, including 966 men over 50 years and 1001 postmenopausal women, with an average age of 59.4 ± 6.7 years. Of these, 427 had diabetes, categorized as follows: 162 were obese, 184 were overweight, and 81 were of normal weight. Table 1 details the characteristics of participants stratified by BMI and glucose status. In the obesity group, diabetic participants had significantly higher WC and WHR, and elevated levels of ALT, FPG, FINS, and HOMA-IR compared to non-diabetics (P < 0.05). In the overweight group, those with diabetes also presented greater WC, HC, WHR, ALT, AST, FPG, FINS, HbA1C, HOMA-IR, and TG, along with lower levels of HDL-C compared to their non-diabetic counterparts (P < 0.05). In the normal weight group, diabetic participants exhibited higher WHR, FPG, HbA1C, HOMA-IR, and TG, as well as lower diastolic blood pressure than non-diabetics (P < 0.05).

Table 1 Demographic and clinical characteristics of study population.

Variable	Total	Obesity		Overweight		Normal weight
	n = 1967	Diabetes, n = 162	Non-diabetes, n = 370	Diabetes, n = 184	Non-diabetes, n = 683	Diabetes, n = 81	Non-diabetes, n = 487
Age (years)	59.4 ± 6.7	59.5 ± 6.1	58.8 ± 6.2	59.8 ± 6.9	59.2 ± 6.8	61.1 ± 6.8	59.8 ± 7.20
Men (%)	49.1	35.8	37.3	53.8	50.7	54.3	57.7
Annual household income (%)
< ¥50000	60.3	53.7	57.0	48.9	60.8	65.4	67.8
¥50000-99000	30.1	32.1	33.6	36.9	30.0	22.2	25.9
≥ ¥100000	9.5	14.2	9.5	14.2	9.2	12.4	6.4
Education (%)
Elementary school or lower	33.6	39.5	33.2	29.3	29.9	45.7	36.8
Middle school	60.0	51.9	60.9	59.7	62.4	54.3	59.8
College or higher	6.4	8.7	5.7	10.8	7.7	0	3.5
BMI (kg/m2)	26.09 ± 3.47	30.53 ± 2.25	30.40 ± 2.22	25.97 ± 1.15	26.01 ± 1.14	22.35 ± 1.21	22.12 ± 1.34
WC (cm)	88.0 ± 9.6	99.0 ± 6.9	97.6 ± 6.9a	89.8 ± 6.1	87.5 ± 5.9b	80.5 ± 5.5	80.0 ± 5.7
HC (cm)	98.4 ± 6.9	104.9 ± 5.9	105.2 ± 6.2	97.3 ± 4.7	98.3 ± 4.7b	92.3 ± 5.04	92.7 ± 4.7
WHR	0.89 ± 0.07	0.94 ± 0.06	0.93 ± 0.06a	0.92 ± 0.06	0.89 ± 0.06b	0.87 ± 0.06	0.84 ± 0.06b
ALT (U/L)	22.5 ± 12.2	28.8 ± 20.6	23.9 ± 10.8b	24.1 ± 16.7	22.2 ± 11.0a	19.3 ± 8.9	19.6 ± 7.8
AST (U/L)	23.3 ± 8.9	28.5 ± 14.4	23.5 ± 8.7	23.2 ± 13.8	23.0 ± 7.2a	21.1 ± 8.0	23.2 ± 6.0b
Cr (μmol/L)	63.4 ± 33.6	61.6 ± 17.3	62.0 ± 14.2	68.4 ± 56.9	62.1 ± 14.5	76.6 ± 103.7	62.8 ± 31.5
FPG (mmol/L)	6.38 ± 1.81	8.59 ± 2.28	5.80 ± 0.54b	8.44 ± 2.23	5.76 ± 0.58b	9.54 ± 3.81	5.64 ± 0.55b
FINS (uIU/mL)	9.25 ± 6.88	15.55 ± 10.09	12.02 ± 6.19b	11.19 ± 10.16	8.57 ± 4.11b	7.42 ± 11.53	5.60 ± 3.00
HbA1c (%)	6.03 ± 1.04	7.31 ± 1.36	5.80 ± 0.47	7.12 ± 1.34	5.69 ± 0.40b	7.30 ± 1.80	5.62 ± 0.70b
HOMA-IR	2.74 ± 2.69	6.07 ± 4.63	3.14 ± 1.82b	4.22 ± 4.10	2.22 ± 1.16b	3.04 ± 4.94	1.42 ± 1.84b
SBP (mmHg)	133 ± 18	137 ± 17	136 ± 18	137 ± 19	132 ± 16	136 ± 21	130 ± 18
DBP (mmHg)	79 ± 11	81 ± 11	82 ± 12	79 ± 10	79 ± 10	75 ± 11	76 ± 11a
Cholesterol (mmol/L)	5.09 ± 1.41	5.27 ± 1.31	5.15 ± 1.09	5.15 ± 1.12	5.03 ± 0.94	5.14 ± 1.21	5.04 ± 0.92
Triglyceride (mmol/L)	1.59 ± 1.41	2.26 ± 1.49	1.87 ± 1.51b	2.00 ± 2.01	1.51 ± 1.13b	1.41 ± 1.24	1.13 ± 1.21a
LDL-C (mmol/L)	2.98 ± 0.84	3.06 ± 0.91	3.07 ± 0.91	2.97 ± 0.93	2.99 ± 0.78	2.89 ± 0.93	2.91 ± 0.77
HDL-C (mmol/L)	1.18 ± 0.31	1.06 ± 0.18	1.09 ± 0.24	1.11 ± 0.29	1.17 ± 0.27b	1.30 ± 0.44	1.33 ± 0.36
vBMD	103.0 ± 30.8	105.9 ± 33.2	102.1 ± 29.4	104.0 ± 32.9	103.9 ± 30.6	101.6 ± 30.4	100.8 ± 30.9

^aP < 0.05.

^bP < 0.01.

BMI: Body mass index; WC: Waist circumference; HC: Hip circumference; WHR: Waist hip rate; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; Cr: Creatinine; FPG: Fasting plasma glucose; FINS: Fasting insulin; HbA1c: Hemoglobin A1c; HOMA-IR: Homeostasis model assessment of insulin resistance; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; LDL-C: Low-density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol; vBMD: Volumetric bone mineral density.

Figure 2 shows the age-dependent mean vBMD stratified by sex and BMI. In the general population, mean vBMD decreased with age, with a more pronounced decline in women than in men. Among men, those with obesity experienced a slower decline in vBMD compared to men without obesity. In women, the prevalence of osteoporosis increased from 5.2% in those under 55 years to 77.9% in those aged 70 years and older. For men, the prevalence was 4.0% between ages 50 and 55, increasing to 36.6% in those aged 70 years and older.

Figure 2 Mean values of volumetric bone mineral density with age stratified by body mass index and sex. vBMD: Volumetric bone mineral density.

Impact of diabetes and obesity on vBMD

We evaluated the influence of BMI on vBMD by classifying participants into three categories: Normal weight, overweight, and obese. Analysis revealed no significant differences in vBMD among these groups when stratified by sex and glucose metabolism (Figure 3A).

Figure 3 Values of volumetric bone mineral density stratified by body mass index and glucose metabolism. A: Body mass index; B: Glucose metabolism. BMI: Body mass index; vBMD: Volumetric bone mineral density; NW: Normal weight; OW: Overweight; OB: Obesity; DM: Diabetes mellitus; NDM: Non-diabetes mellitus.

To investigate the impact of diabetes on vBMD, the participants were further divided into two categories: Those with diabetes and those without. In the obese and overweight groups, the mean vBMD was significantly higher in individuals with diabetes compared to their non-diabetic counterparts (obese: 117.47 ± 30.80 mg/cm³ vs 107.28 ± 28.50 mg/cm³, P = 0.038; overweight: 114.40 ± 29.97 mg/cm³ vs 108.23 ± 29.83 mg/cm³, P = 0.032) among men aged over 50 years. In contrast, no significant differences in vBMD were found between diabetic and non-diabetic participants among normal-weight men and postmenopausal women (Figure 3B).

Sex-based differences in associations between BMI, body composition, and vBMD

We conducted correlation analyses to explore variables associated with vBMD. In men with diabetes, age showed a negative correlation with vBMD (r = -0.380, P < 0.001). However, no significant associations were observed between vBMD and BMI, WC, WHR, VAT, SAT, SMA, FPG, FINS, or HbA1c. In contrast, in men without diabetes, vBMD was negatively correlated with age (r = -0.385, P < 0.001) and positively correlated with FPG (r = 0.091, P = 0.012), BMI (r = 0.093, P = 0.010), and SMA (r = 0.098, P = 0.009). In women with diabetes, vBMD was negatively associated with both age (r = -0.602, P < 0.001) and VAT (r = -0.138, P = 0.049). Among women without diabetes, vBMD had negative associations with age (r = -0.612, P < 0.001), HbA1c (r = -0.112, P = 0.002), and VAT (r = -0.144, P < 0.001), while it positively correlated with SMA (r = 0.108, P = 0.004).

Additionally, we conducted multiple regression analyses to determine the most significant independent predictors of vBMD. In men, the independent predictors identified were age (β = -0.387, P < 0.001), BMI (β = 0.130, P = 0.004), and VAT (β = -0.145, P = 0.001), accounting for an R² value of 17.9% (P < 0.001). In women, significant predictors included age (β = -0.594, P < 0.001), BMI (β = 0.157, P = 0.004), VAT (β = -0.112, P = 0.001), and SAT (β = -0.068, P = 0.035), resulting in an R² value of 39.4% (P < 0.001).

Saturation effect analysis of BMI and vBMD

Figure 4 presents smooth curve fits that illustrate the nonlinear relationship between BMI and vBMD. The saturation effect analysis indicated that this relationship was not significant in patients with diabetes or in women without diabetes, as determined by likelihood ratio tests greater than 0.05 after adjusting for age and sex. In men without diabetes, a saturation effect was observed at a BMI of 22.33 kg/m² (Figure 4 and Table 2). Beyond this point, further increases in BMI did not correspond to significant changes in vBMD.

Figure 4 Association between body mass index and volumetric bone mineral density in total population, men, and women. A: Total population; B: Men; C: Women. The solid red line represents the smooth curve fit between variables. Blue bands represent the 95%CI from the fit. BMI: Body mass index; vBMD: Volumetric bone mineral density.

Table 2 Saturation effect analysis of body mass index and volumetric bone mineral density.

	Diabetes		Non-diabetes		Total
	β (95%CI)	P value	β (95%CI)	P value	β (95%CI)	P value
vBMD
Model 1
One line effect	0.54 (-0.20, 1.29)	0.155	0.40 (0.01, 0.78)	0.043	0.43 (0.09, 0.77)	0.014
Model 2
BMI turning point (K), kg/m2	29.24		21.48		21.42
< K, effect 1	0.28 (-0.81, 1.38)	0.612	3.84 (0.41, 7.26)	0.028	3.70 (0.33, 7.08)	0.032
> K, effect 2	1.12 (-0.83, 3.06)	0.261	0.21 (-0.22, 0.64)	0.339	0.28 (-0.09, 0.66)	0.139
Effect 2 - 1	0.83 (-1.77, 3.43)	0.532	-3.63 (-7.22, -0.04)	0.048	-3.42 (-6.93, 0.09)	0.056
LRT test		0.529		0.047		0.056
Men
Model 1
One line effect	0.33 (-0.86, 1.52)	0.589	0.53 (-0.08, 1.15)	0.043	0.49 (-0.06, 1.04)	0.080
Model 2
BMI turning point (K), kg/m2	26.45		22.33		21.65
< K, effect 1	1.07 (-1.26, 3.39)	0.370	3.51 (0.56, 6.46)	0.020	3.93 (0.14, 7.72)	0.042
> K, effect 2	-0.33 (-2.48, 1.82)	0.763	0.05 (-0.73, 0.82)	0.907	0.19 (-0.45, 0.83)	0.567
Effect 2 - 1	-1.40 (-5.18, 2.38)	0.470	-3.46 (-6.82, -0.11)	0.043	-3.74 (-7.82, 0.34)	0.072
LRT test		0.465		0.043		0.072
Women
Model 1
One line effect	0.7 (-0.2, 1.7)	0.133	0.4 (-0.1, 0.8)	0.143	0.49 (-0.06, 1.04)	0.080
Model 2
BMI turning point (K), kg/m2	27.3		28.7		29
< K, effect 1	-0.2 (-2.4, 2.0)	0.844	-0.1 (-0.8, 0.6)	0.760	-0.1 (-0.7, 0.6)	0.879
> K, effect 2	1.4 (-0.3, 3.1)	0.108	1.3 (0.1, 2.5)	0.035	1.4 (0.4, 2.5)	0.009
Effect 2 - 1	1.6 (-1.8, 5.0)	0.347	1.4 (-0.2, 3.0)	0.096	1.5 (0, 3.0)	0.049
LRT test		0.342		0.095		0.049

BMI: Body mass index; vBMD: Volumetric bone mineral density; LRT: Likelihood ratio test.

DISCUSSION

In this cross-sectional study, we found that men over 50 years with obesity or overweight and diabetes had higher lumbar spine trabecular vBMD than their non-diabetic counterparts. The relationship between BMI and vBMD differed between participants with and without diabetes, with a BMI saturation point of 22.33 kg/m² observed in non-diabetic men.

The associations between BMI, bone mass, microarchitecture, and fracture risk are complex[16-20]. Some studies suggest that obesity is linked to favorable bone characteristics[16,17]. For instance, postmenopausal French women with obesity showed higher volumetric density, lower trabecular separation, more trabeculae, and a larger cortical area at the distal tibia and radius[16]. Similarly, postmenopausal Chinese women with obesity had higher BMD and improved cortical and trabecular parameters compared to normal-weight women[17]. However, results have been inconsistent. Shen et al[18] found that higher BMI was associated with greater trabecular and cortical vBMD, cross-sectional area, and integral volume at the proximal hip in men with obesity, though this relationship did not extend to other bone parameters. A Mendelian randomization study revealed a positive causal relationship between BMI and BMD at the lumbar spine and heel, but no significant effect on BMD at the femoral neck or forearm[5]. These variations may be due to differences in study designs, populations, measurement methods, or skeletal sites analyzed. Fracture risk also varies by skeletal site in individuals with obesity or overweight. The risk of fractures at non-vertebral sites, such as the proximal humerus, upper leg, and ankle, is higher compared to those with normal weight[19], while the risk at vertebral sites and the proximal femur is lower[20]. In this study, lumbar spine trabecular vBMD did not differ significantly across BMI categories. However, future research should assess BMD at additional skeletal sites and incorporate more detailed bone microarchitecture parameters to clarify these relationships.

Higher BMD in individuals with obesity is often attributed to increased mechanical loading on bones, but findings are inconsistent. Some studies report that higher BMI is linked to greater bone mineral content at the femoral neck and lumbar spine, but not at the radius[21,22]. Others have found increased bone mass at the lumbar spine, tibia, and radius in women with obesity, but not in men[23,24]. In addition to mechanical factors, obesity influences bone metabolism through hormones such as leptin and adiponectin[25,26]. Certain fat deposits, particularly visceral fat, have been associated with lower bone mass and detrimental bone microarchitecture, as shown in CT scans. Our study similarly found a negative association between VAT and vBMD in both men and women[6,27]. Obesity also leads to a disproportionate increase in fat mass relative to lean mass, which may explain why bone mass gains in individuals with obesity do not fully correspond to their excess body weight. Relatively lower lean mass in these individuals may partially account for these imbalanced bone mass changes.

Bone mass and microarchitecture in patients with T2D remain complex, with studies reporting conflicting results. Some show no differences in bone structure, while others indicate either worsening or improvement in bone microarchitecture among diabetics[28-32]. The Framingham Study, for instance, found that diabetic patients had lower cortical vBMD, greater cortical porosity, and reduced cross-sectional area at the tibia, but no changes at the radius[29]. Interestingly, trabecular bone indices were similar or more favorable in individuals with T2D compared to non-diabetics, regardless of sex or obesity status[30]. Nilsson et al[28] reported that elderly diabetic women had higher trabecular bone volume fraction, greater distal cortical vBMD, and larger cortical area at the radius and ultradistal tibia compared to non-diabetics. They also noted lower cortical porosity in diabetics at the distal radius, but not at the tibia or ultradistal radius[28]. Similarly, Rasmussen et al[30] observed higher cortical and trabecular vBMD, increased cortical area, and reduced trabecular separation in diabetic patients at both the tibia and radius. In contrast, Vigevano et al[31] found that men with both diabetes and obesity had greater trabecular separation at the tibia and radius than their non-diabetic counterparts, but observed no differences in cortical bone microarchitecture[28]. Burghardt et al[32] further reported that postmenopausal women with T2D exhibited higher trabecular vBMD and thickness, alongside increased cortical porosity at the radius[32]. In our study, no significant differences in trabecular vBMD were observed between diabetic and non-diabetic participants. However, many of the diabetic patients had been newly diagnosed at the time of the QCT assessments, and the relatively short duration of diabetes may have obscured potential differences in trabecular vBMD between the groups.

Postmenopausal women and men over 50 years are at a heightened risk of fractures with increasing age[1]. Furthermore, the incidence of clinical fractures in the past five years is significantly higher among individuals with obesity and overweight[1]. Therefore, it is essential to balance BMI and bone mass in this population. However, previous studies have failed to determine the optimal BMI that enhances bone mass while reduces the risk of obesity-related complications. Recent findings suggested that overweight men show improved trabecular and cortical microarchitecture in the inferomedial and superolateral regions of the femoral neck compared to controls, indicating better resistance to femoral fractures[33]. An analysis of data from the 2017-2020 National Health and Nutrition Examination Survey revealed that adults over 50 years maintain an optimal balance between BMI and femoral neck BMD at approximately 24.3 kg/m²[34]. Another study identified BMI saturation values of 26.13 kg/m² for the total femur and 26.82 kg/m² for the total spine among United States adults over 50[35]. In contrast, some research has found no saturation effects between BMI and BMD[17]. In our study, we found a saturation effect between BMI and BMD in men without diabetes, but not in participants with diabetes or in women without diabetes. Notably, the BMI saturation value that we identified was lower than those reported in previous studies. Additional research is needed to investigate saturation effects across different skeletal sites and various bone microarchitecture parameters. Moreover, saturation values should be examined concerning sex, age, and race for a more comprehensive understanding.

Our study has several limitations. First, the cross-sectional design precludes the establishment of a causal relationship between obesity and bone mass. Second, although bone quality encompasses bone mass, microarchitecture, and tissue material properties, we focused solely on lumbar spine trabecular vBMD. This limitation restricts our ability to assess how BMI impacts other skeletal sites, highlighting the need for future studies to explore a wider array of bone microarchitecture parameters. Third, the varying stages of obesity (Class I, Class II, and Class III) may differently affect bone mass. However, since only 24 participants in our study had a BMI greater than 35 kg/m², we could not adequately evaluate the influence of these stages on bone mass. Lastly, factors such as metabolic-associated fatty liver disease, smoking, alcohol consumption, lifestyle choices, and hypoglycemic medications may also affect bone mass. Unfortunately, we did not consider these confounding variables in our analysis of vBMD.

CONCLUSION

Men over 50 years with obesity or overweight and diabetes demonstrated higher trabecular vBMD than those without diabetes. The association between trabecular vBMD and BMI revealed distinct patterns based on diabetes status. Understanding how obesity and diabetes affect bone microarchitecture is essential for identifying and managing patients at high risk for fractures.

ACKNOWLEDGEMENTS

We thank all staff involved in the Pinggu Metabolic Disease Study.