ปัจจัยส่วนบุคคลที่สัมพันธ์กับความหนาแน่นของกระดูกสะโพกและการทำนายภาวะกระดูกพรุน

Artist Suebpanich

Authors

Artist Suebpanich Chum Phae Hospital, Khon Kaen Province

Keywords:

bone mineral density (BMD), osteoporosis, personal factor, prediction

Abstract

Osteoporosis represents a significant health concern among the elderly population globally, including Thailand, particularly among high-risk groups with personal and behavioral factors influencing bone mineral density (BMD). This investigation aimed to examine the associations between personal factors, such as gender, age, body mass index (BMI), and chronic diseases, and hip bone density (BMD), as well as to develop a predictive model for osteoporosis in individuals aged 50 years and older. This cross-sectional descriptive study utilized medical record data from 611 patients who underwent BMD testing (dual-energy X-ray absorptiometry: DXA) between 2021 and 2023. Statistical analyses, including Pearson correlation, chi-square, multiple regression analysis, and multiple logistic regression, were employed to examine associations. Logistic regression was utilized to predict osteoporosis risk. Significant factors associated with osteoporosis were identified, including female gender (OR = 5.605, 95% CI: 3.22–9.75, p < 0.001), advanced age (71–80 years: OR = 7.867, 95% CI: 3.95–15.67, p < 0.001; > 80 years: OR = 8.604, 95% CI: 3.69–20.08, p < 0.001), lower BMI (OR = 4.680, 95% CI: 1.86–11.77, p = 0.001), smoking (OR = 4.202, 95% CI: 1.17–15.13, p = 0.028), alcohol drinking (OR = 3.593, 95% CI: 1.48–8.70, p = 0.005), and chronic kidney disease (CKD) (OR = 7.046, 95% CI: 1.72–28.87, p = 0.007). Multiple logistic regression suggested the optimal model fit for osteoporosis and osteopenia: OST = -2.989 + 1.688 (Gender) + 1.920 (CKD) + 1.515 (Smoking) + 1.331 (Drinking) - 0.192 (BMI). Individuals exhibiting these risk factors demonstrated a 28.9% probability of developing osteoporosis. The prevention of osteoporosis remains a challenge within Thailand’s healthcare system, particularly among the expanding elderly population. This study elucidates the significant associations of personal factors–such as gender, age, BMI, CKD, smoking, and alcohol consumption–with osteoporosis. The developed predictive equation can inform risk screening tools to support clinical decision-making and early prevention strategies. The integration of BMD screening into primary healthcare systems could effectively reduce osteoporosis incidence among high-risk groups.

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