Physical frailty and oral health as risk factors for low bone density in community-dwelling residents

ABSTRACT This study aimed to explore the association between bone mineral density and physical frailty including nutrition, muscle mass, and oral function. We included participants aged

35–80 years and examined their bone mineral density, serum albumin level, body composition, and variance of hue (VOH) of two-colored gum. We also used the geriatric oral health assessment

index (GOHAI). These data were used to calculate the geriatric nutritional risk index (GNRI) and skeletal muscle index (SMI). Multinomial logistic regression was performed to assess the

relationship between GNRI, SMI, VOH, GOHAI, and bone mineral density after adjusting for comorbidities, including hypertension, diabetes mellitus, and previous bone fracture. We included 228

participants and classified their bone mineral density as normal, osteopenic, or osteoporotic. Older age (odds ratio (OR) 1.15, 95% confidence interval (CI) [1.08, 1.23]), low GNRI (OR

0.90, 95% CI [0.83, 0.98]), low SMI (OR 0.43, 95% CI [0.27, 0.68]), and high VOH (OR 1.08, 95% CI [1.01, 1.17]) were significantly associated with osteoporosis. Older age (OR 1.08, 95% CI

[1.04, 1.11]) and low GNRI (OR 0.93, 95% CI [0.87, 0.99]) were significantly associated with osteopenia. GNRI, SMI, and VOH were significantly associated with osteoporosis among male

participants. Although the multinomial logistic regression analysis indicated that GNRI, SMI, VOH, and GOHAI were not significantly associated with osteoporosis or osteopenia among female

participants, the demographic distribution showed that older age, low GNRI, and low SMI were significantly associated with bone mineral density decline. Physical frailty, including

nutritional decline, muscle mass loss, and poor oral status, is associated with low bone density. This easy-to-use tool can be used to detect osteoporosis early and to prevent osteoporosis

and osteoporosis-related fractures. SIMILAR CONTENT BEING VIEWED BY OTHERS ASSOCIATIONS BETWEEN ORAL HEALTH STATUS AND RISK OF FRACTURES IN ELDER ADULTS Article Open access 24 January 2023

THE ASSOCIATION BETWEEN TEN ANTHROPOMETRIC MEASURES AND OSTEOPOROSIS AND OSTEOPENIA AMONG POSTMENOPAUSAL WOMEN Article Open access 31 March 2025 LINKING METABOLIC SYNDROME WITH LOW BONE MASS

THROUGH INSIGHTS FROM BMI AND HEALTH BEHAVIORS Article Open access 01 September 2023 INTRODUCTION Aging-associated decline occurs in biological, physiological, psychological, behavioral,

and social processes as individuals age. Osteoporosis is an age-related systemic skeletal disease characterized by bone mass loss and bone microarchitecture deterioration. An imbalance in

the rates of bone resorption and formation causes osteoporosis. Furthermore, low bone mineral density can cause osteoporotic fractures, leading to socioeconomic burden, reduced quality of

life, post-fracture complications, and a high mortality risk. Notably, it has been positively proven that aging, menopause, comorbidities such as hypertension (HTN) and diabetes mellitus

(DM), and health behaviors, such as low physical activity, cigarette smoking, and alcohol consumption, stress, medication use, and air pollution contribute to the risk of

osteoporosis1,2,3,4. Poor oral status and low mandibular cortical width are also risk factors for osteoporosis5. Aging increases the risk of poor health outcomes and can lead to frailty.

Frailty is a multidimensional geriatric syndrome that occurs with age and ultimately causes morbidity, hospitalization, disability, and mortality. It represents the loss of harmonic

interaction among multiple domains, including the physical, psychological, and social components6. A recent study used frailty as a screening tool to predict osteoporotic fractures7. Among

the frailty domains, we focused on physical frailty, including nutritional status and muscle mass. The geriatric nutritional risk index (GNRI), used as an assessment, is a nutrition-related

risk index that identifies malnutrition and evaluates suitable timing for nutritional support8. The skeletal muscle index (SMI) assesses skeletal muscle mass. Masticatory dysfunction results

in poor chewing efficiency, difficulty in chewing, inadequate nutrient intake, and poor food digestion and absorption. The two-colored gum test for assessing masticatory performance has

previously been validated9,10. Masticatory performance can also be evaluated by the questionnaire. The self-reported geriatric oral health assessment index (GOHAI) focuses on subjective oral

health and can be used to evaluate oral health-related quality of life, including physical and psychosocial functions and pain or discomfort11. As the aging process gradually decreases

human physical and mental capacity, early detection of aberrations can help prevent subsequent disease burdens such as osteoporosis and bone fractures. In the present study, we used physical

frailty and oral health as indicators to explore their correlation with bone mineral density. METHODS PARTICIPANTS Adult participants from communities aged between 35 and 80 years were

recruited from Puzi Hospital (Chiayi County, southern Taiwan) between November 01, 2021, and December 31, 2022. Participants with a history of pharyngeal surgery, laryngeal surgery,

moderate-to-severe dementia, stroke, cerebral palsy, myasthenia gravis, oral cancer, aspiration pneumonia, moderate-to-severe Parkinson's disease, or Alzheimer's disease were

excluded. All participants agreed to participate in the study and signed informed consent forms. This study was approved by the Institutional Review Board of Biomedical Science Research,

Academia Sinica (AS-IRB-BM-21047). DATA COLLECTION The research team included a clinical dentist and a trained research nurse. The data included demographic characteristics (age, sex, and

self-reported height), electronic medical records, bone mineral density of femoral neck, serum albumin levels, body composition, masticatory ability, and GOHAI scores. Electronic medical

records collected from the Taiwan National Health Insurance (NHI) PharmaCloud System included comorbidities such as HTN, DM, and a history of bone fracture. The NHI PharmaCloud System

contains real-time inpatient and outpatient medication records for the previous 6 months. A workflow detailing the data collection procedure is provided in the supplement (Fig. S1). BONE

MINERAL DENSITY Bone mineral density of the femoral neck was evaluated using dual-energy X-ray absorptiometry (DXA; Hologic Discovery Wi, Hologic Inc., Bedford, MA, USA). T scores ≤ − 2.5,

between − 2.5 and − 1, and − 1 standard deviation (SD) were classified as osteoporosis, osteopenia, and normal, respectively. GERIATRIC NUTRITIONAL RISK INDEX (GNRI) The GNRI served as an

objective indicator to evaluate participants’ nutritional status. This was calculated as follows: $$ {\text{GNRI}} = \left[ {1.489 \times {\text{albumin}}\left( {\text{g/L}} \right)} \right]

+ \left[ {41.7 \times \left( {\text{weight/WLo}} \right)} \right] $$ where WLo represents the ideal weight determined by the Lorentz equation: $$ \begin{aligned} & {\text{For men: WLo}}

= {\text{height}} - 100 - \left[ {\left( {{\text{height}} - 150} \right)/4} \right] \\ & {\text{For women: WLo}} = {\text{height}} - 100 - \left[ {\left( {{\text{height}} - 150}

\right)/2.5} \right] \\ \end{aligned} $$ The albumin value used in the calculation was obtained from the blood test. SKELETAL MUSCLE INDEX (SMI) The SMI was calculated by dividing the limb

skeletal muscle mass (kg) by the square of the height (m2). Limb skeletal muscle mass was measured using a bioelectrical impedance analyzer (Tanita MC-780MA; Tanita Corporation, Japan). The

participants held the two handles with their arms separated from the trunk for 1 min to perform the body composition analysis. MASTICATORY ABILITY Masticatory ability was tested using

two-colored gum comprising green and dark violet layers produced by Vivident Fruit Swing Karpuz Üzüm Sakız 26 gr (Turkey). We excluded participants aged > 80 years for fear of accidental

swallowing while chewing gum. To determine the number of chewing cycles, participants aged 35–49 years chewed a series of two-colored gum samples in five subsequent tests, starting with five

cycles followed by 10, 20, 30, and 50 cycles12. The participants rested between tests. The increased difference in variance of hue (VOH) with increasing chewing cycles and 30 chewing cycles

can effectively distinguish the mixing abilities. The research nurse instructed the participants to perform 30 chewing cycles. After chewing, the gum samples were returned, placed in

transparent plastic bags, and flattened to 1 mm using a plastic plate (Fig. 1A). Both sides of the flattened samples were photographed using a smartphone (HTC Desire 20+). The freely

available software ViewGum (Fig. 1B, http://www.dhal.com/viewgum.htm) transformed the images into a hue, saturation, and intensity (HSI) color space. The VOH is considered an indicator of

the degree of mixing, with a larger VOH signifying inadequate mixing and thus reduced masticatory ability. For the analysis, VOH was expressed as a percentage. GERIATRIC ORAL HEALTH

ASSESSMENT INDEX (GOHAI) The GOHAI measures self-perceived oral health in the last 3 months. The GOHAI comprises 12 items grouped into three dimensions: pain or discomfort, physical

function, and psychosocial function. Total scores were calculated by summing each item with the responses using a 5-point Likert scale. Higher scores indicated good oral health. This study

used a validated GOHAI questionnaire (Chinese version)13. STATISTICAL ANALYSIS AND DATA MINING Participants were divided into normal, osteopenic, and osteoporotic groups. Age, sex,

electronic medical records, GNRI, SMI, masticatory ability, and GOHAI scores were examined and calculated. Kruskal–Wallis and Fisher’s exact tests were used to compare the characteristics of

the three different bone mineral density groups. Multinomial logistic regression was used to explore the association between the characteristics and different bone mineral density groups.

The regression analyses included all variables of interest. Statistical significance was set at _P_ < 0.05. All analyses were performed using R software (version 4.2.2)14. ETHICS APPROVAL

AND CONSENT TO PARTICIPATE The study was approved by the Institutional Review Board (of Biomedical Science Research, Academia Sinica (AS-IRB-BM-21047). Written informed consent was obtained

from all participants. This study was performed in accordance with the Declaration of Helsinki and followed the approved protocol. RESULTS We included 228 participants aged 35–80 years.

According to the bone mineral density T score, the number of participants in the normal, osteopenic, and osteoporotic groups was 77, 103, and 48, respectively (Table 1). The average age in

the osteoporotic group was the oldest (mean = 71.5 years old, SD = 4.9) and that in the normal group was the youngest (mean = 58.8, SD = 12.6). The proportion of females was high, relatively

high, and relatively low in the osteoporotic (81.3%), osteopenic (57.3%), and normal (45.5%) groups, respectively. The proportion of participants with comorbidities, including HTN, DM, and

previous bone fractures, was relatively low, ranged from 11.7 to 18.8%, 16.7 to 31.2%, and 0 to 4.9%, respectively. The GNRI and SMI decreased with a reduction of bone mineral density,

indicating that poor nutrition and low muscle mass were associated with a reduction of bone mineral density. The VOH value increased with a reduction in bone mineral density, indicating poor

masticatory ability associated with a reduction in bone mineral density. Severe osteoporosis is associated with malnutrition, skeletal muscle mass loss, and low masticatory ability. The

GOHAI score was not associated with a reduction of bone mineral density. Age, sex, GNRI, and SMI were significantly different among the three groups; however, VOH, GOHAI, and comorbidities,

including HTN, DM, and previous bone fractures, did not exhibit significant differences. The trends in age, comorbidities, GNRI, SMI, and VOH were consistent across the total, female, and

male participants, indicating a reduction in bone mineral density (Table 2). In particular, the GOHAI score was significantly different among the three groups of bone mineral density among

female participants. Table 3 shows the results of the multinomial logistic regression analysis for GNRI, SMI, VOH, and bone mineral density after adjusting for comorbidities, including HTN,

DM, and previous bone fractures. Older age (odds ratio (OR) 1.15, 95% confidence interval (CI) [1.08, 1.23]), low GNRI (OR 0.90, 95% CI [0.83, 0.98]), low SMI (OR 0.43, 95% CI [0.27, 0.68]),

and high VOH (OR 1.08, 95% CI [1.01, 1.17]) are significantly associated with osteoporosis (Nagelkerke pseudo R-squared = 0.45). Older age (OR 1.08, 95% CI [1.04, 1.11]) and low GNRI (OR

0.93, 95% CI [0.87, 0.99]) were significantly associated with osteopenia. Among male participants, low GNRI (OR 0.81, 95% CI [0.68, 0.96]), low SMI (OR 0.31, 95% CI [0.10, 0.996]), and high

VOH (OR 1.19, 95% CI [1.05, 1.35]) were significantly associated with osteoporosis (Nagelkerke pseudo R-squared = 0.52). Older age (OR 1.07, 95% CI [1.01, 1.13]), low GNRI (OR 0.89, 95% CI

[0.80, 0.99]), and high VOH levels (OR 1.10, 95% CI [1.00, 1.21]) were significantly associated with osteopenia. Although the results of the multinomial logistic regression indicated that

GNRI, SMI, VOH, and GOHAI were not significantly associated with osteoporosis or osteopenia among female participants, the demographic distribution showed that older age, low GNRI, and low

SMI were significantly associated with a decline in bone mineral density. Furthermore, we found that high VOH (OR 0.88, 95% CI [0.80, 0.97]) was negatively associated with increased GNRI

among all participants (Table S1), indicating that low masticatory ability is related to poor nutritional status. DISCUSSION To our knowledge, this is the first study to combine physical

frailty and oral health to predict low bone mineral density, including osteopenia and osteoporosis, allowing for the development of an indicator for the early identification of osteoporosis

and further behavioral changes in the early stages. In our study, age, physical frailty, and oral health were significantly associated with osteoporosis, particularly in males, after

adjusting for comorbidities. The related biological mechanisms are discussed below: The GNRI was calculated using the albumin level, weight, and height. Hypoalbuminemia has been reported to

be directly and indirectly linked to nuclear factor-κB (NF-κB). NF-κB decreases osteoblastic activity and increases osteoclast activity15. In addition, a low GNRI is significantly associated

with low dietary protein intake16. An animal study reported that protein restriction reduced plasma concentrations of insulin-like growth factor-I (IGF-I)17. IGF-I regulates bone metabolism

and adapts bone structure to mechanical loads during growth and development18. In the biological domain of physical frailty, loss of muscle mass influences physical function and energy

metabolism19. Notably, muscle mass increases the mechanical load on bones and directly loads the bone through muscle contractions during physical activity20. A high SMI stimulates and

maintains bone mineral density. Similar biological pathways and common risk factors for frailty and osteoporosis were identified7, including age-related factors, low physical activity,

weight loss, cognitive decline, sarcopenia, depression, and hormonal imbalances such as testosterone, estrogen, insulin-like growth factor-I (IGF-I), growth hormone (GH), vitamin D, and

pro-inflammatory cytokines21,22. The VOH of the bicolor chewing gum represents the degree of mixing and masticatory ability. Studies have shown that 30 chewing cycles can effectively

discriminate masticatory ability9. This test can be performed on individuals with natural dentition as well as on those with complete dentures10. Decline in oral function restricts food

intake and the absorption of nutrients, including calcium, vitamin D, magnesium, potassium, vitamin C, vitamin K, several B vitamins, and omega-3 polyunsaturated fatty acids, all related to

bone mineral density23. A previous study on 114 patients diagnosed with gastric cancer found that the Eichner index, classified based on occlusal contacts, had a significant positive

correlation with the GNRI24. We used VOH to represent the masticatory ability and observed the same trend. Another cross-sectional study involving 100 participants aged > 60 years

investigated the relationship between osteoporosis in the mandibular bones and oral health-related quality of life25. In this study, mandibular osteoporosis and GOHAI scores were not

significantly correlated. The GOHAI is self-reported, and the total score for each of the 12 items ranges from 0 to 60. The mean GOHAI score in our study was approximately 54 and 55,

representing our participants’ self-perception of oral health as relatively healthy. Therefore, no association between the GOHAI and boned mineral density was found. A systematic review

highlighted the relationships between oral health and bone mineral density disorders in older adults, reporting that masticatory function is associated with osteoporosis and occlusal force

with bone mineral density26. Similarly, we found poor masticatory ability was significantly associated with osteoporosis. No association between nutrition, SMI, and bone mineral density was

observed in the female participants. This may be because our study did not comprehensively consider potential confounders, including estrogen and glucocorticoid levels, which are known to

influence bone mineral density. Osteoporosis can arise from either estrogen deficiency or excess glucocorticoids. Estrogen deficiency can lead to an imbalance in bone remodeling, induce

osteoclast apoptosis, alter the osteocyte microenvironment, affect the immune system, and cause hypermineralization27. Similarly, excess glucocorticoids can lead to microscopic heterogeneity

in bone structure, creating mechanically inhomogeneous regions and potentially weak sites28. Additionally, excess glucocorticoid are associated with osteoblast and osteocyte apoptosis,

resulting in decreased bone formation29. Our results showed that physical frailty, including nutritional decline and muscle mass loss, can be used to detect different levels of bone mineral

density, including normal, osteopenic, and osteoporotic. By increasing the awareness of health status and encouraging regular health examinations, medical personnel can use the results of

blood tests, body composition analysis, and oral function performance as references to assess the risk of osteoporosis and help prevent osteoporosis-related fractures in the future. We

proposed an integrated approach for screening low bone mineral density; however, the present study had some limitations. First, evaluating the causal relationships in this cross-sectional

study was difficult. We need to ensure continuous follow-up of these participants for mechanism-related explanations. Second, serum albumin data and bone mineral density of the femoral neck

were collected only once from the participants, leading to deviations in the GNRI and T scores. These values vary over time, and a single time point cannot accurately represent long-term

status. Third, some parameters related to bone mineral levels, such as menopausal status and hormone levels, may have been overlooked. Considering menopausal status or hormone levels, the

factors we explored, such as GNRI and SMI, may demonstrate the significance of bone mineral density among female participants. CONCLUSION We identified the multidimensional aspects of bone

mineral density, including physical frailty and oral health. Using this approach, we can detect low mineral density early in older adults, which can be expanded to prevent osteoporosis and

osteoporosis-related fractures. DATA AVAILABILITY The datasets used during the current study are available from the corresponding author on reasonable request. REFERENCES * Lane, N. E.

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PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS We are grateful to Miss Yu-Ping Wang and Mr. Chun-Wei Su for collecting and preprocessing the data. FUNDING This research

was supported by a grant from Academia Sinica, Taiwan (Grant Number AS-HLGC-110-01). The funder had no role in the study design, data collection and analysis, decision to publish, or

manuscript preparation. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Dentistry, Puzi Hospital, Ministry of Health and Welfare, Chiayi, Taiwan Ping-Chen Chung * Research Center

for Humanities and Social Sciences, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan Ta-Chien Chan * Institute of Public Health, School of Medicine, National Yang

Ming Chiao Tung University, Taipei, Taiwan Ta-Chien Chan * Department of Public Health, College of Public Health, China Medical University, Taichung Campus, Taichung City, Taiwan Ta-Chien

Chan * School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan Ta-Chien Chan Authors * Ping-Chen Chung View author publications You can also search for

this author inPubMed Google Scholar * Ta-Chien Chan View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS P.C.C. contributed to conception,

design, data collection, data analysis, interpretation and drafted the manuscript. T.C.C. contributed to conception, design, and interpretation, acquired to research resources, and

critically revised the manuscript. All the authors have read and approved the final manuscript. CORRESPONDING AUTHOR Correspondence to Ta-Chien Chan. ETHICS DECLARATIONS COMPETING INTERESTS

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low bone density in community-dwelling residents. _Sci Rep_ 14, 18131 (2024). https://doi.org/10.1038/s41598-024-68958-8 Download citation * Received: 27 April 2024 * Accepted: 30 July 2024

* Published: 05 August 2024 * DOI: https://doi.org/10.1038/s41598-024-68958-8 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get shareable

link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative KEYWORDS * Bone mineral

density * Osteoporosis * Frailty * GNRI * SMI * Two-colored gum