Risk factors for osteoporosis in the elderly and predictive value of age, body mass index, and visceral fat area.

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INTRODUCTION

Osteoporosis (OS) is a systemic bone disease characterized by decreased bone mass, impaired bone quality, and decreased bone strength, leading to increased bone fragility and susceptibility to fractures. The inducement of OS is complex, including genetics, sex, age, and hormone changes. When OS occurs, the risk of fractures in patients increases significantly because of damaged bone strength [ 1 ]. With the development of an aging population, OS is becoming an increasing healthcare burden worldwide. OS, together with cardiovascular disease and diabetes, have become the 3 common diseases threatening the health of middle-aged and elderly people [ 2 ]. More than 200 million people worldwide are affected by OS, with one-third of women and one-fifth of men aged 50 and above facing a risk of fractures [ 3 ]. Elderly patients often have multiple underlying diseases and weakened immune function, which increases the risk of unexpected events such as falls [ 4 ].

Reduced secretion of human hormones in middle-aged and elderly individuals stimulates osteoclasts while inhibiting osteoblasts, decreasing bone mass. Second, during aging, decreased nutrient absorption capacity and organ dysfunction also lead to vitamin D deficiency and chronic negative calcium balance, decreasing bone mass [ 5 ]. Therefore, exploring the risk factors for OS in the elderly population and strengthening bone health management plays a vital role in improving the quality of life of patients. Previous studies confirmed that obesity may affect the occurrence of OS [ 6 ]. The metabolic dysfunction of visceral adipose tissue driven by obesity is considered a potential mechanism. Some studies suggest that fat accumulation promotes adipose tissue to secrete many factors, which can lead to sparse bone and, eventually, OS [ 7 ]. Obesity increases the levels of pro-inflammatory cytokines, further increasing bone resorption. In addition, obesity can increase visceral and bone marrow fat, and excessive fat mass may not protect bones, leading to fractures. Wang et al . [ 8 ] also believed that a low body mass index (BMI) is also a risk factor for OS, increasing the risk of fractures. Some studies suggested that increasing weight helps strengthen bone strength, delaying the occurrence of OS and reducing the degree of OS [ 9 ]. Human body fat contains visceral fat and subcutaneous fat. Visceral fat has a higher correlation with metabolic diseases because of its richer anatomical distribution of blood vessels and nerves, as well as more active cellular metabolic activity [ 10 ]. Currently, few reports exist on the relationship between visceral fat and OS.

This study examined the clinical data of elderly subjects in the authors’ hospital to explore the risk factors for OS in the elderly and the predictive value of age, BMI, and visceral fat area (VFA) on disease occurrence aiming to provide a stronger basis for the occurrence and prevention of OS.

SUBJECTS AND METHODS

Clinical data

Two hundred patients over 55 yrs old who underwent examinations in the authors’ hospital from July 2021 to October 2023 were selected randomly for retrospective analysis. The clinical data of the subjects were studied. This study was ratified by the Ethics Committee of the Changzhou Traditional Chinese Medicine Hospital (2023-LL-01[L]). Informed consent was obtained from participants for participation in the study, and all methods were carried out in accordance with relevant guidelines and regulations.

OS was defined as a T-value less than −2.5 SD based on dual-energy X-ray absorptiometry (DEXA) measurements of bone mineral density (BMD). Subjects who met the inclusion criteria were graded as the OS group (S group, 73 cases with T value ≤ −2.5 SD) and non-OS group (NS group, 127 cases with T value > −2.5 SD) based on the results of DEXA. Fig. 1 shows the inclusion process. The inclusion criteria were as follows: 1) all complied with the diagnosis and treatment guidelines for OS [ 11 ]; 2) all research subjects had complete clinical data; 3) the subjects were over 55 yrs old [ 12 ]; 4) The subjects had clear cognitive functions and could cooperate with medical staff. The exclusion criteria were as follows: 1) family members of subjects refused to continue the treatment; 2) the subject had a dysfunction in the liver, kidney, or heart function; 3) those with poor compliance; 4) those with a history of malignant tumors; 5) those with fractures caused by violence (such as car accidents, falls from heights, and high-energy impact injuries); 6) those accompanied by diseases, such as parathyroid disease, severe liver and kidney dysfunction, acute coronary syndrome, stroke, Cushing’s syndrome, and osteoarthritis, affecting bone metabolism or calcium metabolism abnormalities; 7) suffering from diseases, such as rheumatoid arthritis and multiple myeloma that affect the bone density measurement; 8) subjects who were currently receiving or had taken medications such as glucocorticoids, estrogen, calcitonin, and growth hormone in the past 6 mon; 9) patients with dementia, limited mobility, long-term bed rest, mental disorders, and cognitive impairment.

Methods

The clinical data of subjects were collected using a medical record management information system, including age (≤ 65 yrs old or > 65 yrs old), sex (female or male), BMI (< 18.5 kg/m 2 , 18.5–23.9 kg/m 2 , or ≥ 24 kg/m 2 ), VFA, sleep time (≤ 8 h or > 8 h), smoking history (with or without), waist circumference, hip circumference, family history (with or without), history of alcohol consumption (with or without), physical activity (frequent or occasional, rare), dietary habits (low-fat, high-fat, or variable) and calcium intake (low: daily calcium intake < 1,000 mg for more than 20 days per mon or high: daily calcium intake ≥ 1,000 mg for more than 20 days per mon). According to the recommendations [ 13 ], the recommended daily calcium intake for people 50 yrs and older is 1,000 mg.

Photoshop software was used to calculate the VFA as follows: The cross-sectional computed tomography (CT) image of the subjects’ umbilicus was selected and opened using the Philips Autorun (model 160E1SB/93; Philips Medical Devices Co., Ltd., Amsterdam, The Netherlands) interface. The measurement area was determined using Photoshop’s “pencil” and “paint bucket,” and the subcutaneous fat and VFAs were marked with different colors. The number of pixels (x) in a region was automatically measured using the Photoshop histogram, and the corresponding body surface area (y) was calculated for each pixel based on the field of view (FOV) parameter (mm), total number of pixels, and image size (number of pixels) of the CT image. The product of x and y is the area of the measurement area, which is the VFA or subcutaneous fat.

The following outlines the laboratory indicator collection. Elbow vein blood (4.5 mL) was collected on an empty stomach for more than 8 h after admission, and the supernatant was collected after centrifugation. The contents of the indicators, such as total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), osteocalcin (OCN), and parathyroid hormone (PTH), were examined using a fully automated biochemical analyzer. The BMD was determined by DEXA. Briefly, mainly check the lower back or both hips were checked. The subjects were asked to remove metal items that may affect X-ray penetration, such as jewelry and glasses. An auxiliary positioner was used to move the subject’s knee medially so that both lower limbs were rotated inward 30° to fix the position. X-ray scanning using DEXA equipment was used to measure the BMD values by high and low-energy X-rays (40 keV and 70–80 keV). The BMD values were recorded based on the scan results, and the necessary quality control was carried out to ensure the accuracy of the measurement results.

Statistical analysis

SPSS 21.0 statistical software (IBM Corp., Armonk, NY, USA) was used for data analysis. An independent sample t -test was conducted to compare the measurement data between the 2 groups, represented by ± SD, with indicators, including TC, HDL, LDL, OCN, and PTH. The enumeration data of the study groups was compared using χ 2 tests, expressed as cases with percentage, including age, sex, BMI, sleep time, smoking history, family history, and calcium intake. A logistic regression model was built to analyze the factors influencing OS. A logistic regression equation with P = 1/{1+exp (−X)} was established to evaluate the sensitivity, specificity, and accuracy of the model in predicting the occurrence of OS. Receiver operating characteristic (ROC) curves were depicted to analyze the predictive value of age, BMI, and VFA on the occurrence of OS. The differences in statistical results of P < 0.05 were considered significant.

RESULTS

Demographic data

The clinical data, in terms of sleep time, smoking history, waist circumference, hip circumference, family history, history of alcohol use, physical activity, and dietary habits, were similar in the S and NS groups ( P > 0.05). Compared to the NS group, the proportion of those aged > 65 yrs old, female, and low calcium intake in the S group was increased significantly ( P < 0.05), the proportion of BMI 18.5–23.9 kg/m 2 was decreased significantly ( P < 0.001), and the level of VFA was increased significantly ( P < 0.001; Table 1 ).

Analysis of clinical indicators of subjects

The TC, HDL, LDL, and OCN contents were similar in the NS and S groups ( P > 0.05). Compared to the NS group, the subjects in the S group showed significantly lower levels of BMD ( P < 0.001) and higher levels of PTH ( P < 0.001; Table 2 ).

Logistic multivariate analysis of OS

Indicators with statistical differences in clinical data and laboratory-related indicators were selected as independent variables, and whether elderly people had OS was taken as the dependent variable. Table 3 lists the assignment of independent variables. Continuous independent variables were classified according to the median. The Logistic regression analysis showed that age (> 65 yrs), sex (female), VFA (> 86 cm 2 ), calcium intake (low), BMD (≤ 81 mg/cm 2 ), and PTH (> 67 pg/mL) were risk factors affecting the development of OS, and BMI (18.5–23.9 kg/m 2 ) was a protective factor ( P < 0.05; Table 4 ).

Establishment and evaluation of predictive models

After adjusting for confounding factors such as age and sex, the variable coefficients in Table 4 were substituted into the regression formula. The logistic regression prediction model obtained was P = 1/1 + ExpΣ (6.012 − 0.759X 1 − 0.570X 2 + 0.693X 3 − 0.706X 4 − 0.643X 5 − 0.871X 6 − 0.937X 7 ), where the closer the P -value was to 1, the greater the likelihood of OS in older adults. The model in Table 5 , which included constant terms and 7 variables, used a probability value of 0.5 as the intersection point to obtain a comparison table between the predicted values and actual data. The probability model had an accuracy of 86.50% (173/200) in predicting the likelihood of OS in the elderly, with a sensitivity and specificity of 93.15% (68/73) and 82.68% (105/127), respectively.

Predictive value analysis

The ROC curves were established, and the areas under the curves (AUCs) for age, BMI, VFA, and combined detection were 0.733, 0.757, 0.811, and 0.902, respectively. The combined detection had a higher predictive value for OS ( Table 6 , Fig. 2 ).

DISCUSSION

Bones are also dynamic organs maintained using strict regulatory mechanisms. As the age increases, the complex bone homeostasis between bone formation and bone resorption can be disrupted, increasing the risk of OS [ 14 ]. OS and osteoporotic fractures often occur in more vulnerable elderly populations, which is often reflected in the adverse consequences of fractures, including mortality and decreased quality of life [ 15 ]. Therefore, analyzing the risk factors for OS in the elderly is significant for preventing or avoiding the occurrence of OS.

In this study, an analysis of the changes in the relevant indicators of different subjects showed that age (> 65 yrs old), sex (female), VFA (> 86 cm 2 ), low calcium intake, BMD (≤ 81 mg/cm 2 ), and PTH (> 67 pg/mL) were all risk factors affecting the occurrence of OS. Upon investigation, OS is an age-related disease. Age is one of the main factors determining the absolute risk of fractures and the type of osteoporotic fracture [ 16 , 17 ]. With age, the number of osteoprogenitor cells and osteoblasts gradually decreases, leading to a decrease in BMD, decreased bone mass, deterioration of microstructure, and loss of bone quality [ 18 ]. More than half of osteoporotic fractures occur in people over 50 yrs of age [ 19 ]. In addition, estrogen loss is one of the key driving factors for female bone loss. Research has found that women, particularly postmenopausal women aged 55–75 yrs and above, have a higher incidence of OS, increasing the risk of fractures and reducing the quality of life [ 20 ]. The demand for calcium varies throughout the entire lifecycle. As bone loss increases gradually with age, maximizing calcium intake and overcoming impaired intestinal calcium absorption play an essential role in reducing trabecular bone loss [ 21 ]. In addition, as a type of tissue, bones have their complexity and act as a storage repository for minerals, such as calcium, phosphorus, and magnesium. Combined with the strict regulation of hormones, such as vitamin D, PTH, and calcitonin, and supported by multiple organ systems, it can maintain balance in the body [ 22 ]. OS patients have much higher PTH levels than those without [ 23 ]. This might be because increased PTH levels can result in the following: promote bone turnover, bone breakdown, and bone resorption; reduce bone mass; and lead to OS. In the early days of the development of nutrition, researchers focused on the relationship between calcium intake and bone health, as calcium plays the most significant role in bones, and calcium deficiency causes diseases (e.g., rickets and OS) that are more serious [ 24 ]. In addition, to exclude the interference of other factors, researchers often choose a single variable for research, and calcium intake, as an easily measurable and controllable indicator, has become the first choice for research. In contrast, the intake of nutrients, such as phosphorus and magnesium, is affected by various factors, such as sunlight exposure and food sources, which are difficult to control accurately [ 25 ].

The negative impact of obesity on health involves multiple organ systems and a large portion of the population. Recent research has found a significant relationship between body weight or BMI and bone mass. People with more fat have lower bone resorption rates than those with less fat [ 26 ]. In addition, research has confirmed the relationship between BMI and BMD. A lower BMI correlates with greater BMD loss and a higher risk of OS [ 27 ]. The relationship between fat and bone can be attributed to the pressure generated by mechanical loads and to the metabolic effects of bone regulatory hormones secreted or regulated by adipocytes [ 28 ]. Excessive or insufficient BMI can have adverse effects on the body. In addition, the report confirms that local fat deposition patterns in the subcutaneous and visceral regions are more predictive of the disease risk than the overall fat mass [ 29 ]. VFA showed a significant correlation with the BMD because it can inhibit bone formation and enhance bone resorption, causing an imbalance in the process of bone remodeling and increasing the risk of OS. In this study, the BMI (18.5–23.9 kg/m 2 ) was a protective factor, and VFA (> 86 cm 2 ) was a risk factor affecting the occurrence of OS. Combined detection plays an important role in predicting the occurrence of OS in patients. VFA has a richer blood flow supply and nerve distribution. Venous reflux reaches the liver through the portal vein, while subcutaneous adipose tissue flows back to the systemic vein, allowing the serum-free fatty acids and adipokines produced by VFA to enter the liver directly [ 30 ]. In addition, VFA has more active cellular metabolic activity that can secrete various cytokines. Adipose tissue can affect the bone density in obese individuals by increasing the bone mechanical load and altering the bone metabolism [ 31 ]. VFA has higher metabolic activity than human subcutaneous adipose tissue and can better affect bone density by altering the bone metabolism. In addition, the correlation between visceral fat-related parameters and bone density is theoretically different in OS patients compared to parameters such as BMI and weight [ 32 ]. The area of subcutaneous fat was not analyzed. The relationship between visceral fat and OS is reflected mainly in endocrine dysfunction, insulin resistance, and hormone imbalance, and visceral fat significantly impacts these factors more than subcutaneous fat. In addition, the inflammatory factors and hormones secreted by visceral fat can act directly on bone cells and affect bone metabolism. On the other hand, the effect of subcutaneous fat on the bone metabolism is relatively weak, and the action pathway is complex [ 30 ]. Studies have shown that lipid metabolism disorders can lead to an increase in the concentration of calcium ions in bone tissue, affecting the proliferation, differentiation, and migration of bone cells, in turn influencing the formation and regeneration of bone tissue [ 33 ]. In addition, lipid metabolism disorders can lead to elevated levels of fatty acids in bone tissue, which hinder bone formation through paracrine mechanisms and may increase bone mass and decrease bone formation-related factors in the long term [ 34 ]. This study found no relationship between lipid markers and OS, possibly related to the single population and small sample size.

The sensitivity and specificity on the ROC curve were calculated to evaluate more accurately the predictive ability of age, BMI, and VFA for the occurrence of OS in the elderly and find the optimal threshold, making the model highly accurate in predicting fracture risk. In this study, an AUC of 65 yrs old, BMI of 23 kg/m 2 , and VFA of 86 cm 2 were 0.733, 0.757, and 0.811, respectively. The AUC of the combined detection was 0.902, indicating a higher predictive value for the occurrence of OS. Age, BMI, and VFA have a certain correlation with the onset of OS, and all have a specific predictive effect on the risk of OS. Age and BMI are more convenient and sensitive, making them more suitable for clinical screening. On the other hand, in practical work, it is necessary to apply various indicators flexibly according to the specific situation of the research object to improve the evaluation effect.

In general, age, sex, BMI, VFA, calcium intake, BMD, and PTH were all risk factors for the occurrence of OS. The combined detection of age, BMI, and VFA could predict the occurrence of OS. The risk of OS can be assessed, and the prognosis can be improved by monitoring the changes in various indicators. Nevertheless, this study was retrospective and had limited study time. Therefore, a multi-center, randomized controlled study with a larger sample size and prolonged study time is still needed to confirm the research results. In addition, the proposal and validation of the OS risk prediction model need to be carried out separately in different populations, and the model in this study was predicted and validated in the same population. Hence, the model has limited applications. Thus, it is necessary to include different populations for analysis to verify the clinical value of the model.

Novelty: A comprehensive risk assessment model was established to assess the risk of OS in the elderly more accurately based on 3 key factors: age, BMI, and VFA, combined with clinical data. The model quantifies the impact of various factors on the risk of OS through logistic regression analysis to provide a solid basis for clinical diagnosis. In practice, doctors can use the model to calculate the risk of OS based on the patient’s age, BMI, and VFA to formulate a targeted prevention plan.

Footnotes

Funding: This project has received support from the Youth Talent Project of Changzhou Municipal Health Commission (QN201940).

Conflict of Interest: The authors declare no potential conflicts of interests.

Author Contributions: Conceptualization: Zhou Y, Yang Y. Data curation: Song Q, Zhou Y, Xu F. Formal analysis: Song Q. Investigation: Song Q. Methodology: Yang Y. Resources: Zhou Y. Software: Wei Y, Sheng L. Validation: Song Q, Yang Y, Wei Y, Sheng L. Writing - original draft: Yang Y. Writing - review & editing: Song Q, Yang Y.

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