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Abstract

Introduction

Osteoporosis increases fracture risk and mortality, and cancer treatments worsen bone loss. Although mGPS is a common inflammatory-nutritional marker in oncology, its role in predicting osteoporosis is unknown.

Methods

This cross-sectional retrospective study analyzed 93 cancer patients aged ≥50 who underwent dual-energy X-ray absorptiometry (DXA) scans within a year of the first chemotherapy allocation. The results were categorized into groups regarding T-score as normal (T ≥ −1.0), osteopenia (−2.5 < T < −1.0), and osteoporosis (T ≤ −2). Patients were categorized based on mGPS and body mass index (BMI), and regression analysis was performed to identify predictors of osteoporosis in the lumbar spine, femur neck, and total femur.

Results

Among the patients, 61.3% were female, the median age was 61 years, 41.9% had osteoporosis in the lumbar spine, and 49.5% had osteopenia in the femoral neck. A significant association was observed between BMI and osteoporosis, with higher BMI linked to lower osteoporosis prevalence, particularly in the femur regions (P < .03). There were no significant associations between bone density in the lumbar spine/femoral neck/total femur and age, gender, disease stage, type of chemotherapy, or BMI (all P values >.05). A significant association between mGPS and bone density was observed in the lumbar spine (P = .001) and femur total (P < .001). In the lumbar spine, patients with an mGPS score of 0 had the highest proportion of normal bone density (71.4%), while those with an mGPS score of 2 had a higher prevalence of osteoporosis (55.6%) (P = .001). In the femur total, 46.7% of patients with an mGPS score of 2 were classified with osteoporosis, compared to only 8.5% of those with an mGPS score of 0 (P < 001). Patients with an mGPS score of 2 were over six times more likely to have osteoporosis in the lumbar spine (OR = 6.25,P = 0.027). In the femur total, an mGPS score of 2 also significantly predicted osteoporosis (OR = 5.472, P = .013).

Conclusion

mGPS is a cost-effective and reliable tool for predicting osteoporosis in elderly cancer patients, enabling early interventions. Integrating it into routine assessments could enhance patient outcomes by addressing osteoporosis risk.

Keywords

osteoporosis cancer modified glasgow prognostic score bone density chemotherapy

Introduction

Cancer, a recent health concern in the recent century, causes almost one in six deaths worldwide. Lung cancer is the leading cause of cancer mortality worldwide, followed by colorectal, liver, breast, and stomach cancers.¹

Osteoporosis is another growing public health issue characterized by low bone mass and the deterioration of bone structure. This condition results in decreased bone strength and an increased risk of fractures, which are associated with a 15–20% rise in mortality within one year following the fracture. In the United States, osteoporosis affects 16.2% of adults aged 65 and older, while 48.3% have osteopenia.²

Advances in cancer treatment have improved survival but also increased long-term adverse effects, such as osteoporosis, impacting quality of life and creating social and economic burdens. Cancer treatment-induced bone loss is common, highlighting the need for osteoporosis assessment in cancer patients.³ Hence, many studies conclude that osteoporosis needs to be assessed in cancer patients.^4,5

Several chemotherapeutic agents indirectly harm bone health through renal toxicity, ovarian failure, and oxidative stress.³ Cisplatin causes electrolyte imbalances like hypomagnesemia, increasing bone fragility.⁶ Nephrotoxic agents such as ifosfamide can result in Fanconi syndrome, affecting bone development, especially in children.⁷ Cyclophosphamide induces premature ovarian failure and suppresses bone turnover by inhibiting bone cell precursors.⁷ Doxorubicin also impairs bone health by promoting osteoclast differentiation and reducing osteoblast formation, with bone loss observed in models even without tumors.⁸

The importance of nutritional status in cancer and osteoporosis has been shown in many studies.^9-11 A cost-effective and widely applicable tool for assessing nutritional status is the Modified Glasgow Prognostic Score (mGPS), which is based on C-reactive protein (CRP) and albumin levels.¹² mGPS impacted survival and treatment response in prostate cancer,¹³ predicted prognosis in lung cancer,¹⁴ and was significantly linked with survival in metastatic colorectal cancer as well.¹⁵ mGPS has not been evaluated in osteoporosis. However, low serum albumin levels have been shown to be independently associated with increased osteoporosis risk, highlighting the importance of albumin as a predictive marker for bone health.¹⁶ Additionally, higher CRP values served as a predictor for osteoporosis in another study.¹⁷

This study aimed to investigate whether mGPS, known for predicting outcomes in various cancers, could also indicate osteoporosis risk in the elderly cancer population. By investigating mGPS, we aimed to highlight its potential as a cost-effective tool for identifying patients at higher risk, enabling early interventions to enhance quality of life and reduce long-term health impacts.

Materials and Methods

A total of 260 cancer patients were initially screened through electronic medical records. The patient evaluation flowchart is shown in Supplemental Figure 1. This retrospective observational study included 93 cancer patients over 50 who attended medical oncology clinics between January 2023 and January 2024.

Inclusion criteria were: (1) age ≥50 years, (2) histologically confirmed diagnosis of cancer, (3) at least one dual-energy X-ray absorptiometry (DXA) scan performed within 12 months following the initiation of chemotherapy, and (4) availability of serum albumin and CRP values measured at the time of the DXA scan. Exclusions from the study criteria incorporated patients with bone metastases, recent or consistent alcohol consumption (defined as a daily intake of up to 14 grams within the past three months), significant liver (ALT or AST >2× ULN) or kidney impairments (eGFR <30 mL/min/1.73 m²), parathyroid or thyroid gland disorders, those undergoing or having undergone treatments involving estrogen, progesterone, glucocorticoids, biphosphate or calcium supplementation, individuals with surgically induced menopause or premature menopause (menopause before age 40) , and those with a family history of fragility fractures.

Demographic data, cancer type, stage, and treatment modality (neoadjuvant, adjuvant, palliative) were extracted from institutional medical records. The Modified Glasgow Prognostic Score (mGPS) was calculated based on CRP and albumin levels measured within 2 weeks of the DXA scan. The mGPS was defined as follows: an average Alb level ≧ of 3.5 g/dl and a CRP ≦ of 10 mg/dl was scored as 0, a low Alb and CRP ≥10 mg/dl was scored as 2, and only a low Alb or CRP ≥10 mg/dl was scored as 1. Body mass index (BMI) was calculated as weight in kilograms divided by the square of the height in meters.

DXA, performed using a GE Lunar Prodigy Advance device following standard manufacturer protocols, was conducted within a year of the first chemotherapy allocation. Measurements were taken at the lumbar spine (L1–L4), femoral neck, and total femur. The results were categorized into groups with respect to T-score as normal (T ≥ −1.0), osteopenia (−2.5 < T < −1.0), and osteoporosis ( T ≤ −2.5).

The power analysis was conducted based on the observed effect size (Cohen’s h = 0.78) between the mGPS 0 and mGPS 2 groups regarding lumbar spine osteoporosis prevalence. The analysis showed that with a sample size of 70 (50 in mGPS 0 and 20 in mGPS 2), the study achieved a power of 99.99% to detect this effect (α = 0.05, two-sided).

This study was conducted following the ethical principles of the Declaration of Helsinki. Its reporting conforms to STROBE guidelines.¹⁸ The patients’ details have been de-identified. Patients gave written informed consent, and the Local Ethics Committee of Istanbul Medipol University approved the study in February 2025, with Decision number E−10840098-202.3.02-1246.

Statistical Analysis

All statistical analyses were conducted using SPSS 24.0 (SPSS Inc., Chicago, IL, USA). Baseline characteristics were depicted as mean ± standard deviation (SD) for variables with a normal distribution, median (interquartile range) for those not adhering to a normal distribution, and frequencies with percentages for categorical data. Categorical variables underwent comparison through the chi-square test, while associations between continuous and categorical variables were evaluated using the Mann–Whitney U test and Kruskal-Wallis test. The binary logistic regression was utilized to identify independent markers predictive of osteoporosis. The 95% confidence interval [CI] was used to quantify the relationship between survival time and each independent factor. All P values were two-sided in tests, and P values less than or equal to .05 were considered statistically significant.

Results

A total of 93 patients were enrolled: 57 (61.3%) female and 36 (38.7%) male. All 57 female patients were postmenopausal at the time of enrollment. The median age was 61 years (range: 50-85). The number of patients with stage I, II, III, and IV diseases was 3 (3.2%), 16 (17.2%), 46 (49.4%), and 27 (29%), respectively. Forty-three of the patients had breast cancer, while 40 had gastrointestinal system cancer, and 10 had gynecological cancer. Regarding treatment modalities, 32 patients received neoadjuvant chemotherapy, 34 adjuvant chemotherapy, and 27 palliative chemotherapy.

Patients were classified into mGPS categories: 50 (53.8%) had a score of 0, 23 (24.7%) had a score of 1, and 20 (21.5%) had a score of 2. Based on BMI, three patients were underweight (BMI <18.5), 32 had normal weight (BMI 18.5-24.9), 30 were overweight (BMI 25-29.9), and 24 were obese (BMI >30).

The DXA examination was performed at a median of 3.1 months (range: 1.4-11.2 months). DXA measurements were available for 92 patients at the lumbar spine and 93 at the femoral neck and total femur regions. In the lumbar spine, 39 patients (42.4%) were classified as having osteoporosis, 39 (42.4%) had osteopenia, and 14 (15.2%) had normal bone density. In the femur neck region, 38 patients (42.7%) had normal bone mineral density (BMD), 46 patients (51.7%) were classified as having osteopenia, and 9 patients (10.1%) were diagnosed with osteoporosis. In the total femur region, 59 patients (63.7%) had normal BMD, 30 patients (32.9%) had osteopenia, and 4 patients (4.4%) had osteoporosis. Slight variations in patient numbers across anatomical regions reflect incomplete or technically limited DXA data in a small subset of patients.

There was no significant relationship between the lumbar spine DXA T-scores and age (P =.5), gender (P = .3), stage of disease (P = .6), and type of chemotherapy (P = .7). Similar results were observed with femoral neck and total femur T-scores (all P values >.05).

In the lumbar spine, among underweight patients (BMI <18.5), one patient (3.2%) had normal bone density, while two patients (5.1%) were classified as having osteopenia. In the normal weight group (BMI 18.5-24.9), seven patients (22.6%) had normal bone density, 13 patients (33.3%) had osteopenia, and 12 patients (66.7%) were diagnosed with osteoporosis. Among overweight patients (BMI 25-29.9), 12 patients (38.7%) had normal bone density, 11 patients (28.2%) had osteopenia, and six patients (33.3%) had osteoporosis. For obese patients (BMI 30-34.9), 11 patients (35.5%) had normal bone density, 13 patients (33.3%) had osteopenia, and no patients were classified as having osteoporosis (P = .03). A significant association was also observed between BMI and osteoporosis in the femur neck and femur total, suggesting that higher BMI is linked to a lower prevalence of osteoporosis across these regions.

Table 1 summarizes the characteristics of patients according to DXA T-score results.

Table 1.

Patient Characteristics with Regard to Bone Mineral Density.

		NormalN (%)	OsteopeniaN (%)	OsteoporosisN (%)	P
LUMBAR VERTEBRAE BMD	Gender				0.3
	Female	18 (51.4%)	25 (64.1%)	13 (72.2%)
	Male	17 (48.6%)	14 (35.9%)	5 (27.8%)
	Stage				.6
	1	0 (0%)	3 (7.7%)	0 (0%)
	2	6 (17.1%)	7 (17.9%)	3 (16.7%)
	3	19 (54.3%)	16 (41%)	11 (61.1%)
	4	10 (28.6%)	13 (33.3%)	4 (22.2%)
	Treatment modality				.7
	Neoadjuvant	14 (40%)	11 (28.2%)	7 (38.9%)
	Adjuvant	13 (37.1%)	14 (35.9%)	7 (38.9%)
	Palliative	8 (22.9%)	14 (35.9%)	4 (22.2%)
	BMI				.03
	<18.5	1 (3.2%)	2 (5.1%)	0 (0%)
	18.5-24.9	7 (22.6%)	13 (33.3%)	12 (6.7%)
	25-2.9	12 (38,7%)	11 (28.2%)	6 (33.3%)
	30-34.9	11 (35.5%)	13 (33.3%)	0 (0%)
	mGPS				.001
	0	25 (71.4%)	20 (51.3%)	4 (22.2%)
	1	8 (22.8%)	11 (28.2%)	4 (22.2%)
	2	2 (5.7%)	8 (20.5%)	10 (55.6%)
FEMUR NECK BMD	Gender				.9
	Female	23 (60.5%)	29 (63.0%)	5 (55.6%)
	Male	15 (39.5%)	17 (37.0%)	4 (44.4%)
	Stage				.2
	1	0 (0%)	2 (4.3%)	1 (11.1%)
	2	6 (15.8%)	8 (17.4%)	2 (22.2%)
	3	18 (47.4%)	25 (54.3%)	4 (44.4%)
	4	14 (36.8%)	11 (23.9%)	2 (22.2%)
	Treatment modality				.3
	Neoadjuvant	13 (34.2%)	17 (37%)	2 (22.2%)
	Adjuvant	12 (31.6%)	19 (41.3%)	4 (44.4%)
	Palliative	13 (34.2%)	10 (21.7%)	3 (33.3%)
	BMI				.02
	<18.5	0 (0%)	1 (2.2%)	2 (22.2%)
	18.5-24.9	12 (34.3%)	17 (37.8%)	3 (33.3%)
	25-2.9	12 (34.3%)	14 (31.1%)	4 (44.4%)
	30-34.9	11 (31.4%)	13 (28.9%)	0 (0%)
	mGPS				.08
	0	24 (63.2%)	24 (52.2%)	2 (22.2%)
	1	8 (21.1%)	13 (28.3%)	2 (22.2%)
	2	6 (15.8%)	9 (19.6%)	5 (55.6%)
Femur total BMD	Gender				.4
	Female	40 (67.8%)	16 (53.3%)	1 (25%)
	Male	19 (32.2%)	14 (46.7%)	3 (75%)
	Stage				.7
	1	1 (1.7%)	2 (6.7%)	0 (0%)
	2	11 (18.6%)	5 (16.7%)	0 (0%)
	3	30 (50.8%)	13 (43.3%)	4 (100%)
	4	17 (28.8%)	10 (33.3%)	0 (0%)
	Treatment modality				>.05
	Neoadjuvant	21 (35.6%)	10 (33.3%)	1 (25%)
	Adjuvant	23 (39%)	10 (33.3%)	2 (50%)
	Palliative	15 (25.4%)	10 (33.3%)	1 (25%)
	BMI				<.001
	<18.5	0 (0%)	0 (0%)	3 (75%)
	18.5-24.9	14 (25.5%)	17 (56.7%)	1 (25%)
	25-2.9	17 (37.9%)	13 (43.3%)	0 (0%)
	30-34.9	24 (43.6%)	0 (0%)	0 (0%)
	mGPS				<.001
	0	41 (69.5%)	9 (30%)	0 (0%)
	1	13 (22%)	7 (23.3%)	3 (75%)
	2	5 (8.5%)	14 (46.7%)	1 (25%)

Abbreviations: mGPS, modified Glasgow prognostic index; BMI, body mass index.

*PS: Slight variations in patient counts across subgroups reflect missing or incomplete data for specific clinical variables, although DXA scans were performed for all patients at baseline.

In the lumbar spine, a significant association was found between mGPS and bone density (P = .001). Patients with an mGPS score of 0 had the highest proportion of normal bone density (71.4%), while those with higher mGPS scores, particularly mGPS 2, showed a higher prevalence of osteoporosis (55.6%). In the femur neck, although the association was not statistically significant (P = .08), patients with mGPS 2 still had a higher prevalence of osteoporosis (55.6%) compared to those with lower mGPS scores. In the femur total, a strong significant association was observed (P < .001), where 46.7% of patients with mGPS 2 were classified with osteoporosis, compared to 8.5% of those with mGPS 0 (Figures 1 –3) (Table 2).

Figure 1.

The Number of Patients With Osteoporosis in Lumbar Vertebrae With Respect to mGPS.

Figure 2.

The Number of Patients With Osteoporosis in the Femur Neck With Respect to mGPS.

Figure 3.

The Number of Patients With Osteoporosis in Femur Total With Respect to mGPS.

Table 2.

Patient Characteristics with Regard to mGPS.

	Modified Glasgow Prognostic ScoreN (%)			P
	0	1	2	P
Gender				.009
Female	36 (72%)	8 (34.8%)	13 (65%)
Male	14 (28%)	15 (65.2%)	7 (35%)
Stage				.05
1	2 (4%)	1 (4.3%)	0 (0%)
2	13 (26%)	1 (4.3%)	2 (10%)
3	27 (54%)	11 (47.8%)	9 (45%)
4	8 (16%)	10 (43.5%)	9 (45%)
Treatment modality				.06
Neoadjuvant	18 (36%)	7 (30.4%)	6 (30%)
Adjuvant	24 (48%)	6 (26.1%)	5 (25%)
Palliative	8 (16%)	10 (43.5%)	9 (45%)
BMI				.001
<18.5	0 (0%)	3 (13.6%)	0 (0%)
18.5-24.9	12 (25.5%)	7 (31.8%)	13 (65%)
25-2.9	18 (38.3%)	5 (22.7%)	7 (35%)
30-34.9	17 (36.2%)	7 (31.8%)	0 (0%)

Abbreviations: BMI,body mass index.

*Slight variations in patient counts across subgroups reflect missing or incomplete data for specific clinical variables, although DXA scans were performed for all patients at baseline.

In the lumbar spine, regression analysis was used to identify independent predictive markers for osteoporosis. Age and BMI were not found to be significant predictors of osteoporosis (P = .538 and P = .188, respectively). However, the modified Glasgow mGPS showed a notable impact. Patients with an mGPS of 2 had a significantly higher likelihood of osteoporosis, with an odds ratio of 6.25 (P = .027), indicating that these patients were over six times more likely to have osteoporosis than those with an mGPS of 0. None of the categorical BMI groups showed a significant association with osteopenia or osteoporosis in the femur neck. For the femur total, the second category of mGPS (mGPS 2) was a significant predictor of osteopenia/osteoporosis compared to normal DXA results (OR = 5.472, 95% CI: 1.423-21.035, P = .013). Age was also a significant predictor (OR = 1.074, 95% CI: 1.001-1.151, P = 0.045). However, BMI categories remained non-significant across the board (Table 3).

Table 3.

The Logistic Regression Analysis of Predictive Markers in Elderly Cancer Patients.

	Factors	Coefficient β	Wald X ²	P	OR	95% CI
Lumbar vertebrae	BMI	−20.5	-	0.9	-	-
	Mgps
	0 vs1	1,1	2.4	0.1	3.2	0.7-13.8
	0 vs 2	1.7	6.1	0.01	5.4	1.4-21
	Age	0.07	4	0.04	1	1-1.1
Femur total	BMI	−20	-	0.9	-	-
	mGPS
	0 vs1	0.2	0.2	0.6	1.3	0.4-4.1
	0 vs 2	0.4	0.5	0.4	1.6	0.4-5.5
	Age	0.03	1.6	0.1	1	0.9-1
Femur neck	BMI	0.04	1.7	0.1	0.9	0.9-1
	mGPS
	0 vs1	0.6	1.3	0.2	1.9	0.6-6
	0 vs 2	1.8	4.9	0.02	6.2	1.2-31.6
	Age	0.01	0.3	0.5	1.01	0.9-1.07

Abbreviations: BMI, body mass index; mGPS, modified Glasgow prognostic score.

Discussion

The life expectancy of cancer patients has significantly increased with advancements in cancer diagnosis and treatment. However, prolonged therapeutic interventions are associated with a high incidence of persistent side effects, which can substantially impair quality of life. One of these side effects is bone loss. Cancer treatment-induced bone loss is often linked to antitumor therapies.¹⁹

A recent meta-analysis reported that the overall global prevalence of osteoporosis was 18.3%, with 23.1% in women and 11.7% in men.²⁰ FRACTURK conducted a study in Turkey demonstrating the prevalence of osteoporosis at 24.8% in the overall population,22.2% in men, and 27.2 % in women over 50 years.²¹ On the other hand, the prevalence of osteoporosis has been evaluated in specific cancer types in various studies.^22,23 Barzi et al. showed that the prevalence of osteoporosis in colorectal cancer patients was 40% higher than in the non-cancer population.²² Go et al compared breast cancer survivors to non-cancer patients regarding lumbar spine bone density. They found that the osteopenia rates were 50.7% vs 35.8%, and the osteoporosis rates were 15.1% vs 10.8%, respectively.²³ In our study, the overall rates of osteoporosis in the lumbar vertebrae and femoral neck were 19.4% and 9.7%, respectively. The osteopenia rates were 41.9 and 49.5%, respectively. Despite the overall osteoporosis rates being lower than those in the literature, the osteopenia rates were similar. Similarly to the literature, 72.2% of women had osteoporosis, while 27.8% of men had osteoporosis in our study. However, the difference between genders was not significant (P = .3). The discrepancy might be explained by the small sample size and including various types of cancer instead of a specific one. Another reason could be that after 2012, no published study assessing osteoporosis in Turkey was published.

Several risk factors for osteoporosis have been well-documented, including age, sex, ethnicity, family history, body weight, nutritional deficiencies, calcium and vitamin D levels, medication use, lifestyle factors, smoking, alcohol consumption, and comorbid conditions.²⁴ In cancer patients, in addition to these factors, the risk of osteoporosis is further increased due to cancer treatments,¹⁹ chemotherapy, hormonal therapies,^25,26 as well as radiotherapy, glucocorticoids, and tyrosine kinase inhibitors, which also contribute to an increased risk of osteoporosis.²⁷

Papaleontiou et al. found that the risk of osteoporosis increased with age in thyroid cancer.²⁸ Christensen et al. found that adjuvant chemotherapy, combined with anthracyclines and taxanes, followed by endocrine therapy, resulted in bone loss. This effect was especially pronounced in younger patients as opposed to older patients with early-stage breast cancer.²⁹ Our study showed no significant correlation between age and osteoporosis (P =0.5).

Lee et al. compared cervical and endometrial cancer in terms of osteoporosis and showed no significant difference among cancer types and cancer stages.³⁰ Our study contributes to the literature by showing neither cancer stage (P = .6) nor cancer type (0.9) was linked to osteoporosis or osteopenia.

It is well known that lower body mass index correlates with bone mineral density loss.^31,32 However, studies on cancer patients have conflicting results. A recent meta-analysis³³ proved that higher adult BMI was associated with a decreased risk of osteoporosis in breast, prostate, and non-melanoma cancer. However, Zhou³⁴ and Al Janabi³⁵ et al did not find significant associations between lung and breast cancer. These findings indicated the need for further studies to elucidate the relationship between BMI and the risk of osteoporosis. Our analysis revealed that the prevalence of osteoporosis in the lumbar vertebra, femur neck, and femur total was significantly associated with BMI (P = .03, P = .02, and P < .001, respectively), being highest in individuals with a BMI <18.5 (3 cases, 75%) and absent in those with a BMI of 25-34.9 in femur total. Despite this, binary logistic regression analysis showed that BMI was not an independent factor for osteoporosis.

Nutritional status has a significant impact on bone health.^10,11,36 One helpful tool for assessing nutritional status is mGPS, which is based on CRP and albumin levels.¹² Higher CRP values³⁶ and lower albumin levels^37,38 were reported to be associated with higher osteoporosis rates in various health circumstances. mGPS is also a marker that predicts prognosis in cancer patients.^13-15 Takeda et al. showed that osteo-sarcopenia was associated with poor survival and also higher mGPS values in unresectable or recurrent biliary tract cancer.³⁹ Mun et al conducted a meta-analysis that concluded that a high level of CRP was associated with a significantly increased risk of osteoporotic fracture [40].

Based on this discussion, our findings align with previous studies linking inflammation and nutritional status to bone health in cancer patients over 50 years. In the lumbar spine, patients with mGPS 0 had the highest proportion of normal bone density (71.4%), while mGPS 2 showed the highest prevalence of osteoporosis (55.6%, P = .001).In the femur neck, although the association was not statistically significant (P = .08), a trend was observed where mGPS 2 patients had the highest rate of osteoporosis (55.6%). For the femur total, there was a strong significant association (P < .001). Patients with mGPS 0 had the highest percentage of normal bone density (69.5%), while those with mGPS 2 showed the highest osteoporosis prevalence (46.7%). In other words, this study demonstrated that mGPS was a strong predictor of osteoporosis, particularly in the lumbar spine (OR = 6.25, P = .027) and femur total (OR = 5.472, P = .013).

Another factor suggested for predicting osteoporosis is the level of CRP, with results indicating a negative and independent relationship between bone mineral density (BMD) and CRP levels [41-43]. While the independent relationship between CRP and albumin levels and osteoporosis is still under discussion, it can be hypothesized that treatment targeting systemic inflammation and hypoalbuminemia may reduce the risk of bone loss. The mGPS examined in our study is a score based on CRP and albumin levels.¹² In a 2023 study by Takeda et al, which investigated the impact of osteosarcopenia in patients with biliary tract cancer, it was found that the group with osteosarcopenia had a higher mGPS compared to those without.³⁹

In a study by Afshinnia et al, hypoalbuminemia was associated with higher odds of osteoporosis, with albumin ≤3 g/dL showing a 3.31-fold (95% CI 2.08-5.28, P < .001) increased risk at the femoral neck, 2.98-fold (95% CI 1.76-5.01, P < .001) at the total hip, and 2.18-fold (95% CI 1.43-3.31, P < .001) at the lumbar spine compared to albumin >4 g/dL.¹⁶ Recently, Yan et al⁴⁰ demonstrated a negative effect of higher CRP levels on bone health [40]. Dong et al showed that elevated CRP levels were significantly associated with osteoporosis in elderly men, with logistic regression analysis showing a decreased odds ratio of 0.311 (95% CI 0.132-.682, P = .011). CRP levels were higher in the osteoporosis group (6.87 ± 1.99 mg/L) compared to the control group (4.03 ± 1.5 mg/L, P < .001)¹⁷

Like many retrospective studies, our work has several limitations that should be acknowledged. Firstly, the sample size was relatively small and came from a single center, which could affect how broadly the results apply. We also couldn’t evaluate some other factors that may influence bone health, such as vitamin D levels, lifestyle habits, and dietary intake. In addition, mGPS was measured at one point in time, so we couldn’t explore how changes in inflammation or nutrition over time might impact bone density. Lastly, we did not assess pre-existing osteoporosis through prior DXA scans, as the main objective of our study was not to evaluate changes in bone mineral density over time but rather to explore the cross-sectional association between current mGPS scores and bone health status. even if some patients had underlying osteoporosis, the relationship between mGPS and BMD remains clinically meaningful.

Our study suggested that mGPS could be a cost-effective, widely applicable marker to select patients for DXA scans and to decide on osteoporosis screening in cancer patients. Therefore, this study not only confirms previously observed associations between inflammation, nutrition, and bone health but also introduces mGPS as a novel, practical marker for osteoporosis risk stratification in elderly oncology patients. Moreover, this is the first study in Turkish cancer patients addressing a previously unexplored area.

Conclusion

This study suggests that the modified Glasgow Prognostic Score may be independently associated with osteoporosis risk in elderly cancer patients. Moreover, our findings contribute to an understudied area of oncology, emphasizing the importance of monitoring bone health as part of comprehensive cancer care. Future research should validate these findings in larger, multi-center cohorts and explore the longitudinal impact of interventions targeting systemic inflammation and nutritional deficiencies on bone density outcomes.

Supplemental Material

Supplemental Material - Predicting Osteoporosis in Elderly Cancer Patients Using the Modified Glasgow Prognostic Index

Supplemental Material for Predicting Osteoporosis in Elderly Cancer Patients Using the Modified Glasgow Prognostic Index by Muge Ustuner, Sabin Goktas Aydin, Ahmet Aydin, Bahar Ozguze, Eda Nur Duran, Elif Kadioglu Yeniyurt, Elif Senocak Tasci, and Bahar Bayramova

Footnotes

ORCID iDs

Sabin Goktas Aydin

Ahmet Aydin

Author Contributions

M. Ustuner and S Goktas Aydin contributed substantially to the design of the manuscript. A. Aydin performed the statistical analysis. M. Ustuner, E.N. Duran, E. Kadioglu Yeniyurt, B. Bayramova, and B. Ozguzel participated in data curation and manuscript drafting, and S. Goktas Aydin revised it critically. All authors read and approved the final version of the manuscript.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data supporting this study’s findings are not openly available. Further inquiries can be directed to the corresponding author.*

Supplemental Material

Supplemental material for this article is available online.

Appendix

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