The obesity paradox in bone health: high prevalence of osteoporosis and its non-linear association with BMI among urban elderly in China

Abstract

Osteoporosis is a major public health concern in aging populations. Although data from rural Chinese populations are available, large-scale epidemiological studies on osteoporosis prevalence and its risk factors, particularly the complex relationship with body mass index (BMI), remain limited among urban community-dwelling elderly in China. A community-based cross-sectional study was conducted from April to August 2025 among elderly residents (aged ≥ 60 years) in Jiangling Sub-district, Suzhou. Participants were derived from a government-sponsored, non-mandatory health examination program through a two-stage voluntary process. Bone mineral density (BMD) was measured at the non-dominant distal radius using portable dual-energy X-ray absorptiometry (pDXA). Osteoporosis was defined as a T-score ≤ -2.5. Multivariate logistic regression was used to identify factors associated with osteoporosis. Among 6,496 participants (mean age 74.2 years, 55.2% female), the overall prevalence of osteoporosis was 46.7%. Prevalence was significantly higher in women (65.8%) than in men (23.2%) and increased markedly with age (from 32.6% in 60–69 years to 58.7% in ≥ 80 years). A non-linear relationship with BMI was observed: the highest prevalence was in the obese group (63.3%), followed by normal weight (50.1%), underweight (45.7%), and overweight (37.5%) groups. After adjusting for age and gender, female sex (OR = 6.28, 95% CI: 5.50–7.18) and obesity (OR = 2.18, 95% CI: 1.84–2.59) were independent risk factors, while overweight was protective (OR = 0.69, 95% CI: 0.60–0.80). This novel U-shaped relationship challenges conventional wisdom regarding the protective effect of higher body weight on bone health and suggests the existence of an optimal weight range for bone health in elderly populations. This study reveals a high prevalence of osteoporosis in an urban Chinese elderly population. The finding that obesity is an independent risk factor while overweight is protective challenges conventional wisdom and suggests that maintaining an optimal weight range (overweight by Chinese standards) may be crucial for bone health. Public health strategies should integrate targeted osteoporosis screening and promote evidence-based optimal weight management for bone health in urban elderly communities.

Introduction

Osteoporosis represents a major public health challenge in aging societies worldwide, characterized by low bone mass and microarchitectural deterioration of bone tissue, leading to increased bone fragility and susceptibility to fractures^1,2. An estimated 200 million people worldwide are affected by osteoporosis, with the elderly population being disproportionately impacted³. In China, the aging population is growing rapidly, with the number of people aged 60 years and above expected to reach 300 million by 2025⁴, accounting for approximately 21% of the total national population, underscoring the urgency of addressing age-related diseases such as osteoporosis.

While previous studies have reported varying prevalence rates of osteoporosis among Chinese elderly populations, ranging from 20% to 50%^2–6, a government-implemented universal screening program in rural China using portable DXA (pDXA) reported a prevalence of 38.2% among elderly residents⁷. However, limited data are available from urban community settings, where lifestyle factors, environmental conditions, and healthcare access may differ significantly from rural areas. Obtaining accurate epidemiological data from urban communities is crucial for developing targeted public health strategies.

Portable dual-energy X-ray absorptiometry (pDXA) has emerged as a valuable tool for osteoporosis screening, offering advantages of portability, lower cost, and reduced radiation exposure compared to conventional DXA⁸. The non-dominant distal radius is an ideal site for measurement as it is highly sensitive to age-related bone loss and is a common site for osteoporotic fractures^9,10. Studies have shown that distal radius BMD can effectively predict fracture risk at other sites, including the hip¹⁰.

The relationship between body mass index (BMI) and osteoporosis is complex. While low BMI is a well-established risk factor, the impact of high BMI, particularly obesity, is less clear. Traditional views suggest higher mechanical loading from increased weight is protective^3,11, but emerging evidence indicates that obesity-related factors may adversely affect bone metabolism. This relationship remains inadequately characterized in urban Chinese elderly populations.

The objectives of this study were to: (1) determine the prevalence of osteoporosis among elderly residents in an urban community of Suzhou, China, using pDXA at the non-dominant distal radius; (2) examine the distribution characteristics of osteoporosis by age and gender; and (3) identify potential risk factors associated with osteoporosis, with special focus on elucidating the relationship with BMI categories given the increasing obesity rates in urban China.

This study aims to address this knowledge gap by investigating the complex relationship between BMI and osteoporosis in a large urban Chinese elderly population. We hypothesize that the relationship between BMI and osteoporosis is non-linear, with obesity potentially being a risk factor despite the mechanical loading benefits of higher body weight.

Methods

Study setting and population

This community-based cross-sectional study was conducted from April to August 2025 in Jiangling Sub-district, an urban community in Suzhou, China. The study population was derived from residents aged 60 years and above who were eligible for the government-implemented, non-mandatory, free annual health examination program offered by the Jiangling Community Health Service Center.

The participant screening and recruitment process is detailed in Fig. 1. Briefly, the community had 24,832 residents aged ≥ 60 years. Among them, 11,778 voluntarily participated in the general health examination (Stage 1). From this pool of health examination participants, all were invited to undergo an additional pDXA scan for osteoporosis screening. A total of 6,496 individuals voluntarily completed the pDXA measurement (Stage 2) and constituted the final analytical sample for this study. This two-stage voluntary process ensured that recruitment was standardized and free from systematic selection bias by healthcare providers, though it may introduce self-selection bias.

Fig. 1.

Flowchart of study participant selection. The diagram illustrates the two-stage voluntary recruitment process from the total eligible elderly population in the community to the final analytical sample included in the study. DXA: dual-energy X-ray absorptiometry.

Sample size was calculated using the formula for prevalence studies: n = Z²P(1-P)/d², where Z = 1.96 (for 95% confidence level), P = 0.382 (estimated prevalence from rural data⁷, and d = 0.05 (margin of error). This yielded a minimum sample size of 350 participants. We enrolled 6,496 participants to ensure sufficient statistical power for subgroup analyses and to account for potential missing data.

This study used a convenience sample derived from a voluntary community health examination program. Participants were recruited individually rather than by clusters (e.g., villages or communities), thus no cluster effect was anticipated in the analysis. The two-stage recruitment process ensured standardization and minimized selection bias by healthcare providers, though potential self-selection bias due to voluntary participation was acknowledged and addressed through sensitivity analyses.

Ethical considerations

This study was approved by the Ethics Committee of Suzhou Ninth People’s Hospital (approval number: KYLW2025-075-01). All participants provided written informed consent before participation. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki.

Data collection

Demographic and clinical data

Participants completed a standardized questionnaire to collect demographic information including age and gender. Height and weight were measured by trained healthcare professionals using standard protocols. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m²). Participants were categorized according to the Chinese BMI classification standards³. BMI was categorized according to Chinese guidelines: underweight (< 18.5 kg/m²), normal weight (18.5–23.9 kg/m²), overweight (24.0–27.9 kg/m²), and obese (≥ 28.0 kg/m²)³.

Bone mineral density measurement

The pDXA device used (Model: PY-DXA-B, Xuzhou Pinyuan Medical Technology Co., Ltd., China) has been validated against central DXA in previous studies^7,8, showing high correlation for radius BMD measurements (r > 0.85). Portable bone imaging devices such as pDXA have been confirmed as reliable tools for population-level osteoporosis screening¹². The non-dominant distal radius is an ideal site for measurement as it is highly sensitive to age-related bone loss and is a common site for osteoporotic fractures^9,10. Studies have shown that distal radius BMD can effectively predict fracture risk at other sites, including the hip¹⁰. Daily calibration was performed using a standard phantom to ensure measurement accuracy. All measurements were conducted by certified technicians in accordance with international standards for peripheral DXA assessment¹³.

The T-score was calculated as the number of standard deviations from the mean BMD of a young adult Chinese reference population. Osteoporosis was defined as a T-score ≤ -2.5, osteopenia as a T-score between − 1.0 and − 2.5, and normal bone density as a T-score ≥ -1.0, according to WHO criteria¹.

Statistical analysis

The sample size was calculated based on an estimated osteoporosis prevalence of 38.2% from a previous government-led screening program in rural China⁷. Using standard formula for cross-sectional studies, with 95% confidence level, 80% power, and 5% margin of error, the minimum required sample size was 350 participants. We enrolled 6,496 participants to ensure sufficient statistical power for subgroup analyses (by age, gender, and BMI categories) and to account for potential missing data. This final sample size provides over 99% power to detect 5% differences in prevalence with alpha = 0.05.​​.

Notably, this study represents the largest community-based osteoporosis prevalence investigation conducted in an urban Chinese setting to date. Our sample size (n = 6,496) is nearly double that of the referenced rural study (n = 3,530) that utilized the same pDXA methodology, thereby enhancing the statistical robustness and generalizability of our findings.

Missing data were minimal (< 0.5%) for all key variables. Complete case analysis was employed as recommended for studies with missing data < 5%^14,15. Sensitivity analyses using multiple imputation confirmed the robustness of our findings (data available upon request).

Sensitivity analyses were performed to assess the robustness of our findings: (1) excluding participants with extreme BMI values (> 40 kg/m²); (2) using WHO BMI classification standards instead of Chinese standards; and (3) adjusting for potential confounding factors including age, gender, and comorbidities. The U-shaped relationship between BMI and osteoporosis remained consistent across all sensitivity analyses, confirming the reliability of our findings.

Since participation was voluntary, we assessed potential selection bias by comparing baseline characteristics between participants and non-participants where possible. Sensitivity analyses were performed to evaluate the robustness of findings to missing data and self-selection bias.

Data were analyzed using SPSS version 26.0 and R version 4.0.3. Continuous variables were expressed as mean ± standard deviation, and categorical variables as frequencies and percentages. The prevalence of osteoporosis was calculated with 95% confidence intervals (CI). Group comparisons for categorical variables were performed using Chi-square tests. Trends across ordered groups (e.g., age groups) were assessed using the Cochran-Armitage trend test. Multivariate logistic regression analysis was employed to identify factors independently associated with osteoporosis, adjusting for age and gender. A two-sided P value < 0.05 was considered statistically significant.

Results

Baseline characteristics of study participants

A total of 6,496 participants were included in the final analysis. The baseline characteristics of the study population are summarized in Table 1. The mean age of the participants was 74.2 ± 5.8 years, with no significant difference between women and men (P = 0.182). The majority of participants (69.5%) were aged 70–79 years. Women constituted 55.2% (n = 3,583) of the cohort. The mean BMI for the entire population was 24.3 ± 3.9 kg/m², with men having a slightly higher mean BMI than women (24.5 vs. 24.1 kg/m², P < 0.001). The distribution across BMI categories also differed significantly by gender (P < 0.001). As expected, women had significantly lower mean T-scores and BMD values than men (both P < 0.001).

Table 1.

Baseline characteristics of the study population (n = 6,496).

Characteristic	Total (n = 6,496)	Women (n = 3,583)	Men (n = 2,913)	P value
Age (years), mean ± SD	74.2 ± 5.8	74.1 ± 5.9	74.4 ± 5.6	0.182
Age group, n (%)				< 0.001
60–69 years	997 (15.3)	584 (16.3)	413 (14.2)
70–79 years	4,516 (69.5)	2,471 (69.0)	2,045 (70.2)
≥ 80 years	983 (15.1)	528 (14.7)	455 (15.6)
BMI (kg/m²), mean ± SD	24.3 ± 3.9	24.1 ± 3.8	24.5 ± 4.0	< 0.001
BMI category, n (%)				< 0.001
Underweight (< 18.5)	221 (3.4)	148 (4.1)	73 (2.5)
Normal weight (18.5–23.9)	2,908 (44.8)	1,944 (54.3)	964 (33.1)
Overweight (24.0-27.9)	2,554 (39.3)	1,239 (34.6)	1,315 (45.2)
Obese (≥ 28.0)	813 (12.5)	252 (7.0)	561 (19.3)
T-score, mean ± SD	-2.25 ± 1.83	-2.87 ± 1.86	-1.49 ± 1.45	< 0.001
BMD (g/cm²), mean ± SD	0.58 ± 0.21	0.51 ± 0.19	0.67 ± 0.21	< 0.001

SD = standard deviation; BMI = body mass index; BMD = bone mineral density. P values were calculated using independent t-tests for continuous variables and chi-square tests for categorical variables.

Prevalence of osteoporosis

The overall prevalence of osteoporosis among study participants was 46.7% (95% confidence interval [CI]: 45.5–47.9%), corresponding to 3,032 individuals. Additionally, 2,010 participants (31.0%) were classified as having osteopenia, and 1,454 (22.4%) had normal bone density.

Gender and age differences in osteoporosis prevalence

A significant gender disparity in osteoporosis prevalence was observed. As detailed in Table 2 and illustrated in Fig. 2, the prevalence was significantly higher in women (65.8%, 95% CI: 64.2% to 67.4%) than in men (23.2%, 95% CI: 21.7% to 24.7%) (P < 0.001), a difference consistent across all age groups and in line with reports from other Chinese female elderly populations¹⁶.

Table 2.

Osteoporosis prevalence by age group and gender.

Age group	Women			Men			Total
	n	Osteoporosis, n (%)	95% CI	n	Osteoporosis, n (%)	95% CI	n	Osteoporosis, n (%)	95% CI
60–69 years	584	298 (51.0)	47.0–55.0	413	27 (6.5)	4.4–9.4	997	325 (32.6)	29.7–35.5
70–79 years	2,471	1,698 (68.7)	66.9–70.5	2,045	432 (21.1)	19.4–22.9	4,516	2,130 (47.2)	45.8–48.6
≥ 80 years	528	360 (68.2)	64.2–72.0	455	217 (47.7)	43.1–52.3	983	577 (58.7)	55.6–61.8
Total	3,583	2,356 (65.8)	64.2–67.4	2,913	676 (23.2)	21.7–24.7	6,496	3,032 (46.7)	45.5–47.9

CI = confidence interval. P values for gender differences within each age group were all < 0.001. P value for trend across age groups was < 0.001.

Fig. 2.

Prevalence of osteoporosis stratified by age group and gender. The chart clearly shows the significantly higher prevalence in women across all age groups and the increasing trend with age.

Osteoporosis prevalence also increased significantly with advancing age (P for trend < 0.001). The prevalence was 32.6% in the 60–69 years age group, rising to 47.2% in the 70–79 years group, and reaching 58.7% among those aged 80 years and above. This age-related increase was more pronounced in women.

Association between BMI and osteoporosis prevalence

The relationship between BMI and osteoporosis demonstrated a distinct and unexpected pattern, as presented in Table 3 and visualized in Fig. 3. Contrary to the conventional linear protective effect, the highest prevalence was observed in the obese group (63.3%), followed by the normal weight (50.1%), underweight (45.7%), and overweight (37.5%) groups. The differences in prevalence across BMI categories were statistically significant (P for trend < 0.001).

Table 3.

Osteoporosis prevalence by BMI category.

BMI category	n	Osteoporosis, n (%)	95% CI
Underweight (< 18.5)	221	101 (45.7)	39.0-52.4
Normal weight (18.5–23.9)	2,908	1,458 (50.1)	48.2–52.0
Overweight (24.0-27.9)	2,554	958 (37.5)	35.4–39.6
Obese (≥ 28.0)	813	515 (63.3)	59.8–66.7
P value for trend		< 0.001

BMI = body mass index; CI = confidence interval. P value for trend was calculated using Cochran-Armitage trend test. P values for pairwise comparisons: obese vs. overweight, P < 0.001; obese vs. normal, P < 0.001; overweight vs. normal, P < 0.001.

Fig. 3.

Prevalence of osteoporosis across different body mass index (BMI) categories. The graph reveals a U-shaped or non-linear relationship, with the highest prevalence in the obese category.

Bone density parameters across subgroups

Figure 4 The distribution of bone density parameters across demographic subgroups is detailed in Table 4. Women had significantly lower T-scores and BMD values than men across all age groups. A significant negative correlation between age and T-score was confirmed in both genders, as shown in the scatter plot of Fig. 5.

Fig. 4.

Distribution of T-scores across different body mass index (BMI) categories. The box plots illustrate that the obese group has the lowest median T-score, aligning with the high prevalence of osteoporosis in this group.

Table 4.

Bone density parameters across different subgroups.

Subgroup	T-score, mean ± SD	BMD (g/cm²), mean ± SD	Area (cm²), mean ± SD	BMC (g), mean ± SD
Total population	-2.25 ± 1.83	0.58 ± 0.21	3.12 ± 0.45	1.82 ± 0.68
Gender
Women	-2.87 ± 1.86	0.51 ± 0.19	2.98 ± 0.42	1.52 ± 0.58
Men	-1.49 ± 1.45	0.67 ± 0.21	3.29 ± 0.46	2.19 ± 0.72
P value	< 0.001	< 0.001	< 0.001	< 0.001
Age group
60–69 years	-1.86 ± 1.72	0.63 ± 0.20	3.18 ± 0.44	1.99 ± 0.66
70–79 years	-2.29 ± 1.84	0.57 ± 0.21	3.11 ± 0.45	1.78 ± 0.68
≥ 80 years	-2.68 ± 1.85	0.53 ± 0.21	3.08 ± 0.46	1.64 ± 0.68
P value for trend	< 0.001	< 0.001	0.015	< 0.001
BMI category
Underweight	-2.18 ± 1.89	0.57 ± 0.22	2.89 ± 0.41	1.65 ± 0.67
Normal weight	-2.35 ± 1.85	0.57 ± 0.21	3.06 ± 0.43	1.75 ± 0.67
Overweight	-2.09 ± 1.78	0.60 ± 0.20	3.20 ± 0.45	1.92 ± 0.67
Obese	-2.58 ± 1.87	0.56 ± 0.21	3.35 ± 0.46	1.89 ± 0.70
P value	< 0.001	0.002	< 0.001	< 0.001
Bone status
Normal	-0.48 ± 0.41	0.79 ± 0.15	3.15 ± 0.44	2.49 ± 0.56
Osteopenia	-1.72 ± 0.43	0.63 ± 0.16	3.13 ± 0.45	1.97 ± 0.59
Osteoporosis	-3.68 ± 1.32	0.43 ± 0.18	3.09 ± 0.45	1.34 ± 0.59
P value	< 0.001	< 0.001	0.234	< 0.001

SD = standard deviation; BMD = bone mineral density; Area = bone area; BMC = bone mineral content. P values were calculated using analysis of variance for continuous variables across multiple groups. P values for trend were calculated using linear regression.

Fig. 5.

Scatter plot showing the relationship between age and T-score, stratified by gender and osteoporosis status. A significant negative correlation is observed in both genders, with a steeper decline in women.

Furthermore, T-scores varied significantly across BMI categories (P < 0.001). This pattern is visually reinforced by the box plots in Fig. 5, which clearly show that the obese group had the lowest median T-score, while the overweight group had the highest.

Multivariate analysis of risk factors

Multivariate logistic regression analysis, adjusting for age and gender, identified key factors independently associated with osteoporosis, with results presented in Table 5. Each one-year increase in age was associated with a 5% higher risk (OR = 1.05, 95% CI: 1.04–1.06, P < 0.001). Female gender was a strong risk factor (OR = 6.28, 95% CI: 5.50–7.18, P < 0.001).

Table 5.

Multivariate logistic regression analysis of factors associated with osteoporosis.

Variable	Odds Ratio (OR)	95% Confidence Interval	P value
Age (per 1-year increase)	1.05	1.04–1.06	< 0.001
Gender (Women vs. Men)	6.28	5.50–7.18	< 0.001
BMI category (vs. Normal weight)
Underweight	0.93	0.70–1.23	0.612
Overweight	0.69	0.60–0.80	< 0.001
Obese	2.18	1.84–2.59	< 0.001
Model fit statistics
Nagelkerke R²	0.284
Hosmer-Lemeshow test	χ² = 12.34, P = 0.136
Area under ROC curve	0.786 (95% CI: 0.775–0.797)

BMI = body mass index. The model was adjusted for all variables shown in the table. ROC = receiver operating characteristic.

Regarding BMI categories (with normal weight as the reference):

• Obesity was identified as a significant independent risk factor (OR = 2.18, 95% CI: 1.84–2.59, P < 0.001).

• Overweight was associated with a protective effect (OR = 0.69, 95% CI: 0.60–0.80, P < 0.001).

• Underweight showed no significant association (OR = 0.93, 95% CI: 0.70–1.23, P = 0.612).

The model showed good fit and discrimination. Hosmer-Lemeshow goodness-of-fit test confirmed adequate model fit (χ² = 12.34, P = 0.136), with an area under the ROC curve of 0.786 (95% CI: 0.775–0.797) and Nagelkerke R² of 0.284.

Comparison with previous studies

To contextualize our findings, we compared the osteoporosis prevalence observed in our urban cohort with that reported in a large-scale, government-led screening program conducted in rural China using the same pDXA methodology⁷(Table 6). The overall prevalence of osteoporosis in our urban population (46.7%) was significantly higher than the 38.2% reported in the rural population (P < 0.001). This disparity was consistent in both women (65.8% vs. 58.1%) and men (23.2% vs. 18.5%). Our study, with a sample size of 6,496, nearly doubles that of the referenced rural study (n = 3,530), enhancing the statistical robustness and representativeness of urban data.

Table 6.

Comparison with previously published osteoporosis prevalence studies in China.

Study characteristic	Current study (Urban)	Rural China study [7]	P value
Sample size	6,496	3,530	-
Overall prevalence	46.7%	38.2%	< 0.001
Female prevalence	65.8%	58.1%	< 0.001
Male prevalence	23.2%	18.5%	< 0.001
Mean age (years)	74.2 ± 5.8	72.1 ± 6.2	< 0.001
Female proportion	55.2%	52.3%	0.012

Data are presented as prevalence (%) or mean ± standard deviation. P values are for comparisons between the current urban study and the referenced rural study⁴. This study represents the largest community-based osteoporosis prevalence investigation conducted in an urban Chinese setting to date.

Absolute risk estimates

The absolute risk difference for osteoporosis was highest in the obese category (25.8% higher than overweight group). The number needed to harm (NNH) for obesity was 4.3, meaning for every 4–5 obese individuals, one additional case of osteoporosis occurred compared to the overweight reference group. These absolute risk estimates provide important clinical context for interpreting the relative risk findings.

Sensitivity analyses

All sensitivity analyses confirmed the robustness of our main findings. The U-shaped relationship between BMI and osteoporosis remained consistent regardless of analytical approach. Excluding participants with extreme BMI values (> 40 kg/m²), using WHO BMI classification standards instead of Chinese standards, and adjusting for additional potential confounding factors all yielded similar results. These consistent findings across different analytical approaches strengthen the reliability of our observations regarding the complex relationship between BMI and osteoporosis.

Discussion

This large-scale, community-based study provides important new insights into the epidemiology of osteoporosis among urban-dwelling elderly in China. The principal findings include a high overall prevalence of osteoporosis (46.7%), significant disparities associated with female gender and advanced age, and most notably, a non-linear relationship with BMI wherein obesity was an independent risk factor while overweight appeared protective.

The prevalence of osteoporosis in this urban cohort (46.7%) was substantially higher than the 38.2% reported in a comparable government-led screening program in rural China using the same pDXA methodology [7]. This urban-rural discrepancy may be attributed to differences in lifestyle factors. Urban elderly may engage in lower levels of weight-bearing physical activity compared to their rural counterparts who often perform agricultural labor, which is beneficial for bone health [8,17]. Furthermore, urban environments are associated with distinct dietary patterns and potentially higher exposure to air pollution, which has been increasingly linked to adverse effects on bone metabolism [18,19]. These findings underscore the necessity of developing context-specific public health strategies for osteoporosis prevention.

The pronounced gender disparity and the strong correlation with advancing age align with the well-established global epidemiology of osteoporosis [1,5,6]. The markedly higher risk observed in women (OR = 6.28) is primarily attributable to postmenopausal estrogen decline, which accelerates bone loss [4]. The steady increase in prevalence with age, particularly the sharp rise in the ≥ 80 years age group, highlights the growing burden of osteoporosis and fragility fractures among the “oldest old” segment of the population, necessitating targeted screening and fall-prevention initiatives for this vulnerable demographic.

The most intriguing and novel finding of this study is the identification of a U-shaped relationship between BMI and osteoporosis, challenging the conventional paradigm that higher body weight universally protects against osteoporosis. Our results demonstrate for the first time in a large urban Chinese elderly population that obesity (BMI ≥ 28 kg/m²) is an independent risk factor for osteoporosis (OR = 2.18), while overweight (BMI 24.0–27.9 kg/m²) is associated with the lowest risk. This “obesity paradox” in bone health has important implications for public health guidelines and clinical practice. This observation is consistent with the “goldilocks hypothesis” [11,20], which posits that a moderate weight provides optimal mechanical loading for bone stimulation without the detrimental effects associated with excessive adiposity. Several potential pathophysiological mechanisms may explain the adverse impact of obesity. These include chronic, low-grade inflammation which alters the RANKL/RANK/OPG pathway and promotes osteoclast activity [21,22]; metabolic dysfunction such as insulin resistance; and lower bioavailability of vitamin D due to its sequestration in adipose tissue [19]. The protective effect of overweight may represent an optimal balance where mechanical loading is sufficient for bone formation [3], but pro-inflammatory adipokine levels are not yet detrimental.

The underlying mechanisms for this U-shaped relationship warrant further investigation. The protective effect of moderate overweight may be attributed to optimal mechanical loading that stimulates bone formation without excessive strain on bone microstructure. In contrast, obesity may exert detrimental effects through multiple pathways: (1) chronic inflammation with increased production of pro-inflammatory cytokines (TNF-α, IL-6) that disrupt the RANKL/RANK/OPG balance [21,22]; (2) adipokine dysregulation including increased leptin and decreased adiponectin levels that promote osteoclastogenesis [23]; (3) vitamin D sequestration in adipose tissue leading to functional vitamin D deficiency [19]; and (4) mechanical stress shielding of trabecular bone due to altered load distribution [24].

Strengths and limitations

The major strengths of this study include: (1) its large, community-based sample of 6,496 participants, which, to our knowledge, is the largest of its kind in an urban Chinese population and nearly double the size of comparable rural studies using the same methodology [7]; (2) the use of standardized pDXA measurements with daily calibration and certified technicians; (3) comprehensive sensitivity analyses confirming the robustness of our findings; and (4) detailed subgroup analyses that revealed important patterns in the relationship between BMI and osteoporosis.

However, several limitations should be acknowledged. First, the cross-sectional design precludes the establishment of causal relationships. Second, potential sources of bias were systematically addressed: selection bias was minimized through a standardized two-stage recruitment process; measurement bias was reduced through daily calibration of pDXA equipment and use of certified technicians; and confounding bias was addressed through multivariate logistic regression adjusting for age and gender. However, residual confounding from unmeasured variables (e.g., physical activity, dietary factors) remains possible. Third, the lack of data on potential confounders, including detailed dietary intake, physical activity levels, reproductive history in women, medication use, and serum phosphate levels (which have been associated with BMI, BMD and fracture risk, especially in men) restricts our ability to fully adjust for all relevant factors. Fourth, BMD was measured only at the radius using pDXA. While radius BMD is a recognized predictor of fracture risk [10,18], it may not fully correlate with BMD at the hip or spine [19,25]. However, pDXA offers advantages of portability, lower cost, and reduced radiation exposure, enabling large-scale community screening that would not be feasible with central DXA [8]. Future longitudinal studies incorporating central DXA measurements and comprehensive lifestyle data are warranted to confirm these associations and elucidate the underlying mechanisms.

Generalizability

While our study population represents urban community-dwelling elderly in Eastern China, the demographic characteristics and BMI distribution are comparable to other urban Chinese populations [4,7]. Our findings also complement our team’s previous population-based pDXA screening study in rural Tongcun, Wujiang [7,26], enabling direct urban-rural comparison of osteoporosis epidemiology in the same Suzhou region. The consistent findings across sensitivity analyses support the robustness of our results. However, caution should be exercised when generalizing to rural populations, institutionalized elderly, or other ethnic groups with different body composition characteristics. The findings may not be generalizable to elderly individuals living in nursing homes or other institutional settings, who may have different body composition and health profiles. Future studies in diverse populations are needed to confirm the generalizability of the observed U-shaped relationship between BMI and osteoporosis.

Implications for public health

These findings have important implications for public health policy in rapidly urbanizing China. The high osteoporosis prevalence (46.7%) in this urban population suggests an emerging public health crisis.

The U-shaped BMI relationship provides a more nuanced approach to weight management guidelines for bone health. Rather than simply advocating for weight gain in underweight individuals, our data support maintaining an optimal weight range (BMI 24.0–27.9 kg/m² by Chinese standards).

This has practical implications for clinical practice: (1) targeted screening programs should prioritize obese elderly individuals despite their higher weight; (2) weight management interventions should aim for gradual weight reduction in obese patients rather than aggressive weight loss; and (3) nutritional counseling should focus on optimal weight maintenance rather than extreme weight changes. Integrating osteoporosis screening into routine community-based health examinations for the elderly represents a feasible and high-impact strategy for early detection and intervention.

Conclusions

This large-scale study conducted in an urban Chinese community reveals a high prevalence of osteoporosis among the elderly, characterized by significant disparities related to female gender and advanced age. The most notable finding is the demonstration of a non-linear relationship between body mass index and osteoporosis, wherein obesity (BMI ≥ 28.0 kg/m²) was identified as an independent risk factor, while overweight (BMI 24.0–27.9 kg/m²) was associated with a protective effect.​.

These findings challenge the conventional paradigm that higher body weight solely protects against osteoporosis. They suggest that public health strategies for bone health in urban elderly populations should evolve from a singular focus on preventing low weight to a more nuanced approach that promotes maintenance of an optimal weight range (overweight by Chinese standards) and actively addresses the risks associated with obesity. Integrating systematic osteoporosis screening into existing community-based elderly health programs represents a pragmatic and potentially highly effective strategy for early detection and intervention.​.

Future longitudinal studies are needed to confirm the causal nature of the association between obesity and osteoporosis and to elucidate the underlying biological mechanisms.

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Author contributions

Conceptualization: Fangfang Li, Huan Du, Qin LiuData curation: Fangfang Li, Minzhi Yang, Xiaojun Fei, Wenting Zhao, Qiong Wu, Ying XiaoFormal analysis: Fangfang Li, Minzhi YangFunding acquisition: Huan DuInvestigation: Minzhi Yang, Fangfang Li, Xiaojun Fei, Wenting Zhao, Qiong Wu, Bin Huang, Ying XiaoMethodology: Fangfang Li, Qin LiuProject administration: Huan DuResources: Huan Du, Bin HuangSupervision: Huan Du, Qin LiuValidation: Qin Liu, Bin Huang, Xiaojun FeiVisualization: Minzhi Yang, Wenting ZhaoWriting – original draft: Fangfang Li, Minzhi YangWriting – review & editing: All authorsFangfang Li and Minzhi Yang contributed equally to this work.

Funding

This study received no specific funding.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.