Development and validation of a nomogram clinical prediction model for osteoporosis in elderly malnourished patients

doi:10.21203/rs.3.rs-4952794/v1

Download PDF

Article

Development and validation of a nomogram clinical prediction model for osteoporosis in elderly malnourished patients

https://doi.org/10.21203/rs.3.rs-4952794/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

The aim of this study was to establish a nomogram model for predicting the incidence of osteoporosis (OP) in elderly malnourished patients and to verify its predictive effect. We conducted a retrospective analysis of elderly malnourished patients hospitalized at the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine between December 2023 and June 2024. The cohort was randomly divided into a training set and a validation set in a 7:3 ratio. Optimal factors were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) regression, which were then incorporated into a multifactorial logistic regression model to ascertain independent predictors. The Hosmer-Lemeshow test, area under the curve (AUC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to assess the model's goodness of fit, discrimination, calibration, and clinical impact, respectively. A total of 381 patients were included in the analysis. Independent predictors of OP in this population included: Geriatric Nutritional Risk Index (GNRI)(OR=0.520,95%CI 0.282-0.958),activity situation(OR=0.590,95%CI 0.353 0.987),hypertension(OR=2.833,95%CI 1.384-5.798), type 2 diabetes mellitus(T2DM)(OR=4.314,95%CI 1.971-9.439),serum calcium (Ca)(OR=0.012,95%CI 0.001-0.180), total cholesterol(TC)(OR=4.185,95%CI 2.571-6.809), triglycerides (TG)(OR=2.003,95%CI 1.217-3.297),albumin (ALB) (OR=0.804,95%CI 0.683-0.946),overall hip joint bone mineral density (BMD)(OR=0.015,95%CI 0.001-0.225),overall lumbar spine BMD(OR=0.029, 95%CI 0.005-0.188),and alkaline phosphatase (ALP)(OR=1.022,95%CI 1.011-1.034). The AUC for the training and validation sets were 0.946(95%CI 0.920-0.972) and 0.963(95%CI 0.936-0.990), respectively, indicating great discriminatory ability. The nomogram model developed in this study exhibits good discrimination and accuracy, facilitating the identification of OP risk in elderly malnourished patients in a simple and efficient manner. This model supports early clinical decision-making and intervention, serving as a vital tool for improving patient prognosis. It is anticipated that larger, multicenter studies will be conducted to further validate, enhance, and update the model.

Health sciences/Risk factors

Health sciences/Diseases/Endocrine system and metabolic diseases/Metabolic bone disease/Osteoporosis

Malnutrition

Osteoporosis

Risk factors

Nomogram

Malnutrition is a health problem that poses a serious risk, adversely affecting the quality of life, survival rates, and overall development of patients. It is associated with increased morbidity and mortality, prolonged hospitalization, and heightened healthcare costs¹. Currently, malnutrition is recognized as a global health issue impacting over 1 billion individuals worldwide². Among the elderly population, the prevalence of malnutrition can surge to between 29% and 61%³, with individuals aged over 60 being more affected than those younger than this age threshold⁴.Osteoporosis (OP) is a condition marked by compromised bone structure, strength, and mass, leading to increased fragility and susceptibility to fractures^5–7. The global prevalence of OP is approximately 18.3%, with rates of 23.1% in women and 11.7% in men⁸. In the EU alone, it is estimated that nearly 5.5 million men and 22 million women suffer from OP, resulting in nearly 3.5 million fractures annually^9–11.Projections suggest that in China, osteoporosis-related fractures could reach 4.83 million cases per year by 2035, incurring costs of approximately $19.92 billion annually^11,12.

Research indicates that malnutrition adversely affects bone mass, disrupts muscle strength, and leads to tissue loss, all of which can contribute to the development of OP^13,14.Furthermore, nutritional deficiencies have been shown to increase the incidence of complications following fractures¹⁵. As global aging continues to intensify¹⁶, the number of elderly individuals has multiplied and the aging process is rapid¹⁷. The prevalence of malnutrition also escalates with age¹⁴, estimates indicate that 4%-10% of older adults living at home are malnourished, while the figures rise to 15%-38% in institutional settings and 30%-70% in hospitals¹⁸. Additionally, the prevalence of osteoporotic fractures can be as high as 50% in women and 20% in men over the age of 50¹⁹. The Geriatric Nutritional Risk Index(GNRI) serves as a valuable tool for assessing the nutritional status of elderly patients²⁰ and has been strongly associated with various health conditions, including diabetes and OP^21–23. Notably, research has demonstrated a significant correlation between GNRI and bone mineral density (BMD), with higher GNRI values correlating with increased BMD levels²³. Moreover, GNRI exhibits a negative correlation with the incidence of OP among patients with type 2 diabetes mellitus (T2DM)²². A study utilizing the NHANES database found that²¹GNRI was positively and linearly correlated with femoral neck BMD T-scores in older adults (P<0.001), revealing that the risk of OP decreases progressively with rising GNRI values, particularly when GNRI exceeds 100. Malnutrition can be caused by activating related inflammatory proteins and disorganization of related hormones, etc. ^24,25impairing the immune system and leading to bone pathology. Without timely intervention, a detrimental cycle may develop, wherein immunodeficiency exacerbates acute pathological events, further promoting malnutrition¹⁴. Consequently, early and standardized nutritional support can significantly enhance the nutritional status, immune function, and clinical outcomes for elderly individuals suffering from OP^26–28.In addition, such interventions can improve prognosis and reduce the likelihood of OP recurrence²⁹. However, much of the current literature has focused on correlation analyses and database evaluations of single or combined clinical data, revealing a lack of rapid and straightforward methods for predicting and identifying risk factors. Thus, there is a pressing need for quick and accurate assessments of OP risk in elderly malnourished patients.

In recent years, the establishment and validation of clinical prediction models based on nomograms have advanced our understanding of the critical factors influencing disease risk, enabling more precise evaluations of condition and prognosis³⁰. In this study, we retrospectively analyzed the clinical data of 381 elderly malnourished patients, identified the influencing factors affecting OP in this population. We constructed a clinical prediction model aimed at forecasting the risk of osteoporosis in elderly malnourished patients. This model is intended to serve as a vital reference for early intervention, treatment optimization, decision-making support, and improvement of prognosis³¹.

Study subjects and inclusion criteria

This retrospective study evaluated elderly patients with malnutrition who presented at the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine between December 2023 and June 2024. Participants were stratified into a training set and a validation set in a 7:3 ratio, and further categorized into "Osteoporosis" and "Non-osteoporosis" groups based on the presence or absence of OP. The study flow is illustrated in Fig. 1. This study was conducted at the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine and was approved by the Ethics Committee of the hospital in accordance with the STROBE criteria and the Declaration of Helsinki. Patient informed consent was not required because all included data were retrospective and anonymized, and confidential patient data were removed before analysis.

The inclusion criteria were as follows: (1) age ≥ 65 years; (2) malnutrition diagnosed adheres to the diagnostic criteria for malnutrition as defined by the American Society of Clinical Nutrition³², utilizing the GNRI:GNRI =$\:1.489\text{*}\text{A}\text{L}\text{B}(\text{g}/\text{d}\text{L})+41.7\text{*}(\text{r}\text{e}\text{a}\text{l}\text{i}\text{s}\text{t}\text{i}\text{c}\:\text{w}\text{e}\text{i}\text{g}\text{h}\text{t}/\text{i}\text{d}\text{e}\text{a}\text{l}\:\text{w}\text{e}\text{i}\text{g}\text{h}\text{t})$, ideal weight = $\:22*\text{h}\text{e}\text{i}\text{g}\text{h}\text{t}\left(\text{m}\right)\text{*}\:\text{h}\text{e}\text{i}\text{g}\text{h}\text{t}\left(\text{m}\right)$. GNRI thresholds were used for classification: 92 ≤ GNRI < 98 indicated mild malnutrition, 82 ≤ GNRI < 92 indicated moderate malnutrition, and GNRI < 82 indicated severe malnutrition; (3) BMD was assessed using dual-energy X-ray absorptiometry (DXA). The exclusion criteria included: (1) patients who had taken drugs affecting bone metabolism, such as serum calcium (Ca), vitamin D, glucocorticoids, etc., within the past 6 months; (2) patients with incomplete critical data or missing indicators for the target variables; (3) those with severe mental illness or significant difficulty cooperating with data collection.

Collection of information

Following a comprehensive literature review and an analysis of the interaction between malnutrition and OP^21,33,34, we identified 41 potential risk factors associated with the development of OP in malnourished patients. These factors include: (1) general information: age, gender, body mass index (BMI), history of smoking, history of drinking, hypertension, T2DM, coronary heart disease, cerebral infarction, chronic obstructive pulmonary disease, renal failure, and tumor; (2) the occurrence of malnutrition: GNRI, mid-arm circumference, gastrocnemius circumference, activity situation, neuropsychological issues, skin ulcers, diet status in recent 3 months, intake of vegetables and fruits, protein intake;(3) laboratory parameters: white blood cells (WBC), C-reactive protein (CRP), platelets (PLT), hemoglobin (Hb), Ca, serum phosphorus (P), uric acid (UA), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL-C), low-density lipoprotein (LDL-C), albumin (ALB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood creatinine (SCr), glucose randomization(GLU), D-dimer, Overall hip joint BMD, Overall lumbar spine BMD, alkaline phosphatase (ALP).

Data analysis

Data analysis was conducted using R version 4.4.1 and SPSS version 26.0. Normally distributed variables were expressed as mean ± standard deviation ($\:\stackrel{-}{\text{X}}\pm\:\text{S})$ and were analyzed using the independent samples t-test, while non-normally distributed variables were expressed as median (interquartile range) (M(Qn)) and were analyzed using the Mann-Whitney U-test. Categorical data were expressed as cases (percentage) (n (%)) and analyzed using the χ² test. The least absolute shrinkage and selection operator (LASSO) regression and multifactorial logistic regression were employed to identify relevant risk factors and to develop a nomogram. The goodness-of-fit of the model was evaluated using the Hosmer-Lemeshow test, and a calibration curve was constructed. The area under the curve (AUC) of the subjects was used to assess the model's discrimination capability, while decision curve analysis (DCA) and clinical impact curve (CIC) analysis were utilized to evaluate the model's validity and clinical impact. P<0.05 was considered a statistically significant difference.

Comparison of general data

A total of 381 elderly patients with malnutrition were included in this study, with 267 cases assigned to the training set and 114 cases to the validation set, following a 7:3 ratio. There were no statistically significant differences between the training and validation sets across the three major categories: general information, the occurrence of malnutrition, and laboratory parameters (P >0.05), as shown in Table 1.

Table 1

Comparison of clinical information between training set and validation set.
Variables	Training Set (n = 267)	Validation Set (n = 114)	t/ Z/χ² value	P value
LDL-C (mmol/L)	3.04 ± 0.95	3.08 ± 0.73	t=-0.485	0.628
Age (years)	78.00 (73.00, 83.00)	76.00(72.00, 81.00)	Z=-1.323	0.186
BMI (kg/m²)	19.950 (18.85, 21.42)	20.14 (18.69, 21.51)	Z =-0.087	0.931
WBC (10^⁹/L)	5.840 (4.23, 7.52)	5.38(3.69, 7.64)	Z =-1.007	0.314
PLT (10^⁹/L)	220.00(169.50, 275.50)	209.00 (162.25,270.00)	Z =-0.874	0.382
Ca (mmol/L)	2.290 (2.17, 2.42)	2.33 (2.19, 2.43)	Z =-0.993	0.321
P (mmol/L)	1.150 (0.94, 1.34)	1.07 (0.92, 1.30)	Z =-1.360	0.174
UA (µmol/L)	333.000 (271.00, 386.50)	338.50 (281.00,394.00)	Z =-0.897	0.370
GLU (mmol/L)	5.040 (4.46, 5.85)	5.09 (4.29, 5.83)	Z =-0.349	0.727
TC (mmol/L)	4.600 (3.97, 5.26)	4.51 (4.11, 5.20)	Z =-0.602	0.547
TG (mmol/L)	1.630 (1.30, 2.10)	1.66 (1.37, 2.10)	Z =-0.842	0.400
HDL-C (mmol/L)	1.150 (1.02, 1.34)	1.16 (1.08, 1.45)	Z =-1.601	0.109
CRP (mg/L)	4.190 (1.24, 13.25)	4.72 (2.19, 13.82)	Z =-1.220	0.222
Hb(g/L)	117.00(102.00, 124.00)	119.50(109.25,124.75)	Z =-1.319	0.187
D-dimer (ug/L)	0.64 (0.32, 1.41)	0.81(0.39, 1.22)	Z =-0.863	0.388
ALB (g/L)	34.50 (32.20, 35.90)	34.20(33.03, 35.85)	Z =-0.388	0.698
SCr (µmol/L)	76.10 (60.45, 90.95)	76.74(67.20, 88.74)	Z =-1.152	0.250
Overall hip joint BMD (g/cm²)	0.74(0.64, 0.82)	0.75(0.58, 0.85)	Z =-0.394	0.694
Overall lumbar spine BMD (g/cm²)	0.69(0.61, 0.91)	0.73(0.59, 0.89)	Z =-0.001	1.000
ALP(U/L)	99.00 (73.50, 133.50)	99.00(78.00, 124.50)	Z =-0.142	0.887
Gender (%)			χ²=0.253	0.615
Male	126 (47.19)	57 (50.00)
Female	141 (52.81)	57 (50.00)
GNRI (%)			χ²=0.154	0.926
92 ≤ GNRI < 98	61 (22.85)	24 (21.05)
82 ≤ GNRI < 92	104 (38.95)	45 (39.47)
< 82	102 (38.20)	45 (39.47)
Mid-arm circumference (%)			χ²=1.104	0.576
< 21	55 (20.60)	27 (23.68)
21 ≤ Mid-arm circumference < 22	123 (46.07)	46 (40.35)
≥ 22	89 (33.33)	41 (35.97)
Gastrocnemius circumference (%)			χ²=0.021	0.884
< 31	131 (49.06)	55 (48.25)
≥ 31	136 (50.94)	59 (51.75)
History of smoking (%)			χ²=0.299	0.584
No	123 (46.07)	56 (49.12)
Yes	144 (53.93)	58 (50.88)
History of drinking (%)			χ²=0.069	0.793
No	146 (54.68)	64 (56.14)
Yes	121 (45.32)	50 (43.86)
Activity situation (%)			χ²=0.920	0.631
Bed-ridden or long-term seated person	66 (24.72)	24 (21.05)
Can leave the bed or table and chair, but cannot go out	101 (37.83)	42 (36.84)
Can go out on your own	100 (37.45)	48 (42.11)
Nervous and psychological problems (%)			χ²=1.459	0.482
No psychological problems	101 (37.83)	50 (43.86)
Mild dementia	102 (38.20)	37 (32.46)
Severe dementia or depression	64 (23.97)	27 (23.68)
Skin ulcer (%)			χ²=2.298	0.130
No	194 (72.66)	74 (64.91)
Yes	73 (27.34)	40 (35.09)
Diet status in recent 3 months (%)			χ²=5.566	0.135
No	78 (29.21)	36 (31.58)
Mild loss of appetite	85 (31.84)	24 (21.05)
Moderate loss of appetite	62 (23.22)	36 (31.58)
Severe loss of appetite	42 (15.73)	18 (15.79)
Intake of vegetables and fruits (%)			χ²=1.494	0.222
0	134 (50.19)	65 (57.02)
1	133 (49.81)	49 (42.98)
Protein intake (%)			χ²=1.716	0.424
Less	82 (30.71)	33 (28.95)
Commonly	100 (37.45)	37 (32.46)
More	85 (31.84)	44 (38.60)
Hypertension (%)			χ²=0.861	0.353
No	145 (54.31)	56 (49.12)
Yes	122 (45.69)	58 (50.88)
Type 2 diabetes (%)			χ²=1.631	0.202
No	182 (68.17)	70 (61.40)
Yes	85 (31.84)	44 (38.60)
Coronary Heart Disease (%)			χ²=1.778	0.182
No	151 (56.55)	56 (49.12)
Yes	116 (43.45)	58 (50.88)
Cerebral infarction (%)			χ²=2.998	0.083
No	175 (65.54)	85 (74.56)
Yes	92 (34.46)	29 (25.44)
Chronic obstructive pulmonary disease (%)			χ²=0.837	0.360
No	195 (73.03)	78 (68.42)
Yes	72 (26.97)	36 (31.58)
Renal failure (%)			χ²=0.325	0.569
No	195 (73.03)	80 (70.18)
Yes	72 (26.97)	34 (29.83)
Tumor (%)			χ²=1.367	0.242
No	207 (77.53)	82 (71.93)
Yes	60 (22.47)	32 (28.07)
ALT (%)			χ²=1.525	0.676
≤ 40	161 (60.30)	65 (57.02)
40 < AST ≤ 120	83 (31.09)	35 (30.70)
120 < AST ≤ 200	15 (5.62)	8 (7.02)
> 200	8 (3.00)	6 (5.26)
AST (%)			χ²=4.345	0.227
≤ 35	110 (41.20)	54 (47.37)
35 < AST ≤ 105	140 (52.43)	48 (42.11)
105 < AST ≤ 175	9 (3.37)	7 (6.14)
> 175	8 (3.00)	5 (4.39)

LASSO regression for screening predictors of OP in elderly malnourished patients

LASSO regression with 10-fold cross-validation was used to downscale the included variables and analyze their correlations, resulting in the LASSO coefficient curves of the included variables, as depicted Fig. 2. The optimal model was identified when the mean squared error was twice the standard error of the mean squared error (λ_1se), λ_1se = 0.046, log(λ) = -3.077. A total of 12 predictor variables were identified as influencing factors for OP, as shown in Fig. 3. These included: BMI, history of hypertension, history of T2DM, GNRI, activity situation, ALB, ALP, Ca, TC, TG, overall hip joint BMD, overall lumbar spine BMD.

Logistic regression analysis of predictors of OP in elderly malnourished patients

The 12 variables identified by LASSO regression were used as independent variables, with the presence of OP as a dependent variable. Multifactorial logistic regression analyses were then performed to calculate odds ratios (OR) and 95% confidence intervals (CI)³⁵. Related studies indicate that the width of the CI is influenced by sample size and the standard deviation within the study group³⁶. A wide CI may suggest a small sample size or high variability, leading to less precise conclusions³⁷. A positive correlation between exposure and outcome is indicated by an OR>1.0,while a negative correlation is indicated by an OR<1.0³⁸. In this study, the multifactorial logistic regression results demonstrated that GNRI(OR = 0.520,95%CI 0.282–0.958), Activity situation(OR = 0.590,95%CI 0.353–0.987), Hypertension(OR = 2.833,95%CI 1.384–5.798),T2DM(OR = 4.314,95%CI 1.971–9.439), Ca(OR = 0.012,95%CI 0.001–0.180),TC(OR = 4.185,95%CI 2.571–6.809),TG(OR = 2.003,95%CI 1.217–3.297),ALB (OR = 0.804,95%CI 0.683–0.946),overall hip joint BMD(OR = 0.015,95%CI 0.001–0.225), overall lumbar spine BMD(OR = 0.029,95%CI 0.005–0.188), and ALP(OR = 1.022,95%CI 1.011–1.034) were significant factors. The relatively wide CI for T2DM, hypertension, and TC might be due to the limited sample size and uncorrected bias and confounding factors. The Hosmer-Lemeshow test yielded χ² = 9.857, P = 0.275 (P>0.05), indicating a good model fit, as shown in Table 2.

Table 2

Multifactorial logistic regression of predictive factors for OP in elderly malnourished patients.
Subjects	B value	SE value	Wald χ2 values	p value	OR value	95% CI
BMI (kg/m²)	-0.181	0.122	2.188	0.139	0.835	0.657–1.060
GNRI (%)	-0.654	0.312	4.399	0.036	0.520	0.282–0.958
Activity situation (%)	-0.527	0.262	4.044	0.044	0.590	0.353–0.987
Hypertension (%)	1.041	0.365	8.123	0.004	2.833	1.384–5.798
Type 2 diabetes (%)	1.462	0.400	13.388	0.000	4.314	1.971–9.439
Ca (mmol/L)	-4.384	1.362	10.353	0.001	0.012	0.001–0.180
TC (mmol/L)	1.431	0.248	33.197	0.000	4.185	2.571–6.809
TG (mmol/L)	0.695	0.254	7.471	0.006	2.003	1.217–3.297
ALB (g/L)	-0.218	0.083	6.871	0.009	0.804	0.683–0.946
Overall hip joint BMD(g/cm²)	-4.208	1.387	9.205	0.002	0.015	0.001–0.225
Overall lumbar spine BMD(g/cm²)	-3.526	0.947	13.872	0.000	0.029	0.005–0.188
ALP (U/L)	0.022	0.006	14.140	0.000	1.022	1.011–1.034
Constant	17.472	4.997	12.227	0.000	38714259.516	-

Development and validation of a clinical prediction model for OP in elderly malnourished patients

Establishment of a clinical prediction model

Based on the results of multifactorial logistic regression, we developed a nomogram to predict the likelihood of developing OP in elderly malnourished patients. The length of the line segments for each variable in the nomogram was positively correlated with its influence on clinical outcomes. The total score was calculated by summing the scores of each variable, and the scale value corresponding to the total score represented the probability of OP risk in elderly malnourished patients, as shown in Fig. 4.

Validation of the clinical prediction model

The model was validated using the data from the 114 cases in the validation set. The predictive efficacy of the model was evaluated in terms of discrimination, calibration, and clinical impact. The AUC of the training set was 0.946 (95%CI 0.920–0.972), and the AUC for the validation set was 0.963 (95%CI 0.936–0.990), indicating that the predictive model had a high level of discrimination, as shown in Fig. 5. Calibration curves for the training and validation sets were constructed using the Bootstrap sampling method with 1000 repetitions. The calibration curve suggests that the model prediction curve is closer to the actual observed curve when the prediction probability is 40%-90%, indicating that the calibration ability of the prediction model is good, as shown in Fig. 6. The DCA curve was used to evaluate the clinical applicability of the model. The results indicated that when the threshold probability was > 3%, using the model to predict the risk of OP in elderly malnourished patients was more beneficial than applying an intervention to all patients. The model’s net benefit was significantly higher than that of either all or no interventions, as shown in Fig. 7. The CIC, plotted based on the DCA, assessed the clinical impact of the model. The results suggested that when the risk threshold was > 80%, the predicted number of individuals with or without OP and the positive estimate of the actual number of OP cases at each risk threshold were closer to the actual number of cases, as shown in Fig. 8.

OP is a prevalent condition among elderly patients, often leading to pain, swelling, joint deformity, limited mobility, and even fractures in advanced stages, severely impacting the quality of life^39,40. Malnutrition, particularly protein-energy malnutrition, exacerbates the risk of OP and osteoporotic fractures in the elderly due to decreased bone mass and muscle strength⁴¹. Given the critical role of nutritional support in preventing and mitigating OP, understanding how to predict the progression and outcomes of malnutrition in elderly patients is of paramount importance. Common risk factors for OP include smoking, excessive alcohol consumption, and family history, with laboratory tests typically focusing on Ca, P, and SCr ^42,43. In this study, we retrospectively analyzed clinical and laboratory data from 381 malnourished elderly patients and identified independent predictors of OP through statistical methods. The significant predictors included a history of hypertension, a history of T2DM, a low GNRI, low activity situation, high ALP, low Ca, high TC, high TG, low ALB, low overall hip joint BMD, and low overall lumbar spine BMD. Based on these findings, we developed a convenient and efficient nomogram, which was internally validated, demonstrating good predictive accuracy and clinical applicability. Research has established a link between malnutrition and poor bone health, with malnutrition-related OP significantly increasing the risk of fractures, worsening prognosis, and elevating mortality rates. BMD, measured by DXA, is the gold standard for OP diagnosis. However, BMD measurements can be influenced by equipment quality, measurement location, and patient morphology ⁴⁴. Additionally, existing OP screening tools are limited and lack comprehensive validity and practicality. For instance, the OP Self-Assessment Tool for Asians (OSTA) is mainly applicable to postmenopausal women⁴⁵ and has shown poor sensitivity and predictive power, the sensitivity was only 32.3% in subjects, and the AUR was only 61.8%⁴⁶. Furthermore, studies exploring the relationship between malnutrition and OP in the elderly have predominantly focused on isolated clinical indicators or database-driven analyses^21,47,48. The development of simple and practical early prediction tools is crucial for accurately identifying high-risk populations.

Our study identified history of hypertension, history of T2DM, low GNRI, low activity status, high ALP, low Ca, high TC, high TG, low ALB, low overall hip joint BMD, and low overall lumbar spine BMD as independent risk factors for OP in malnourished elderly patients. Hypertension and T2DM were found to be significant risk factors, with ALP, TC, TG, and Ca also contributing to OP risk, while GNRI, ALB, physical activity, overall hip joint BMD, and lumbar spine BMD were protective factors. Previous research suggests common risk factors and pathophysiological mechanisms between hypertension and OP, including secondary hyperparathyroidism, increased sympathetic activity, oxidative stress, and inhibition of matrix proteins dependent on vitamin K^49–51. The renin-angiotensin system (RAS) plays a pivotal role in OP, with RAS activation promoting bone resorption and OP progression, whereas RAS inhibitors have been shown to reduce the incidence and complications of OP ^52,53. T2DM and OP, both metabolic disorders, are often associated with systemic metabolic bone disease, increasing disability and mortality. Chen et al. found a significantly higher prevalence of low BMD in the T2DM group compared to nondiabetic controls, with increased risks of low BMD and fractures⁵⁴. However, some studies report no association or even a protective effect of T2DM on OP. A meta-analysis of 33437 cases found that BMD in patients with T2DM was overall 25%-50% higher than in non-T2DM controls, regardless of the site of skeletal measurements, sex, or age⁵⁵. Several Mendelian randomization studies have also identified a causal relationship between T2DM and OP, suggesting that T2DM may have a protective effect on OP. Therefore, there is a need for large, multicenter, randomized controlled trials to further clarify this relationship. Elevated levels of total alkaline phosphatase (T-ALP) typically indicate increased osteoblast activity and bone turnover. A cross-sectional study based on NHANES data found a negative correlation between T-ALP levels and lumbar spine BMD scores in young adults, suggesting that T-ALP may serve as a valid biomarker for the diagnosis and treatment of OP⁵⁶. Another study demonstrated that T-ALP activity above 129 U/L could be used as an indicator of OP in men⁵⁷. Additionally, treatment with alendronate has been shown to reduce T-ALP levels from 79.7 U/L to 64.8 U/L⁵⁸. Therefore, ALP can be considered an important marker in the diagnosis and management of OP. Lipids as important metabolic indicators play a key role in assessing metabolic diseases. A study conducted in Saudi Arabia found a significant increase in lipid levels and ALP in the OP group compared to the normal group⁵⁹. Several studies have also demonstrated that higher levels of TC and TG are associated with a greater risk of OP^60,61. The underlying mechanism may involve lipid metabolites activating the nuclear hormone receptor peroxisome proliferator-activated receptor γ (PPAR γ), leading to increased PPAR γ activity⁶²,which inhibits osteogenesis and accelerates bone loss. Furthermore, elevated oxidative stress levels can inhibit osteoblast differentiation while promoting adipocyte differentiation⁶³. Ca, an essential nutrient stored in bones, is crucial for human growth and development⁶⁴. The results of the present study found that the occurrence of OP was inversely related to Ca levels, consistent with clinical studies showing that Ca and vitamin D supplementation reduces bone loss and fracture risk in OP treatment regimens^65,66. The negative correlation between Ca and OP is closely related to aging, which increases serum parathyroid hormone and ALP levels while decreasing Ca, P, and vitamin D metabolites⁶⁷. Elderly patients are characterized by a complex and diverse disease profile, facing higher nutritional risks. The overall prevalence of malnutrition in the elderly ranges from 1–24.6%⁶⁸. In this study, the GNRI, an indicator reflecting the nutritional status of the elderly, was included. The prevalence of OP was higher in the low GNRI group compared to the high GNRI group, which aligns with the findings of Huang-Wei et al²¹. High GNRI has also been identified as an independent protective factor against OP in elderly patients with T2DM, and it is a better predictor of OP in these patients compared to other nutritional scores such as COUNT and PNI25⁶⁹. The GNRI assessment incorporates ALB levels and body weight, consistent with our findings that ALB is an independent protective factor in predicting OP. This may be related to hypoproteinemia, which activates osteoclasts and inhibits osteoblasts through inflammatory factors such as NF-κB⁷⁰. A retrospective study also found that low ALB concentration was significantly and independently associated with the prevalence of OP⁷¹. A review also found that low ALB concentrations were significantly and independently associated with the prevalence of OP. Physical activity has been shown to have a positive effect on bone health in several studies^72–74. A meta-analysis found that higher net training frequency in older adults had a better effect on BMD⁷⁵. This aligns with our findings that increased activity levels help reduce OP risk in older adults. The mechanism may involve microRNAs (miRNAs) that regulate bone marrow stromal cell (BMSC) differentiation and physical activity-induced bone remodeling through different pathways⁷⁶. BMD remains one of the most important diagnostic criteria for OP⁷⁷. Studies have shown that, excluding physiological and mechanical factors, BMD decreases with age^78,79. BMD is also associated with OP risk by gender, with gender differences leading to varying correlations between BMD and age. For example, lumbar spine and femoral neck BMD show significant negative correlations with age in males, while clavicle and femoral diaphysis BMD show significant negative correlations with age in females. Additionally, significant positive correlations were found between BMD loci in various combinations, such as between lumbar spine and femoral neck in males, and between clavicle and femoral stem in females⁸⁰.These findings are consistent with our observation that BMD decreases with the onset of OP.

The aim of this study was to investigate the risk factors for OP in the elderly and to establish a nomogram for predicting its risk. 12 predictors were identified for inclusion in the nomogram, with a predictive power of 0.946 in the training group and 0.963 in the validation group. This study has three main strengths: First, the clinical data used to construct the prediction model are simple and objective; Second, the variables used are easy to obtain, enhancing the model's generalizability and facilitating its application in clinical practice; Third, we may be the first to develop a model for predicting OP based on elderly malnourished patients, which is crucial for early diagnosis and prevention. However, our study has limitations. First, it was a cross-sectional study with a limited sample size, which may introduce selection bias; Second, there are some limitations in the factors considered, as serum hormones, bone metabolism indices, and other relevant indicators were not included in this analysis; Moreover, this study only focused on elderly patients with OP, and further research is needed to determine whether the findings are applicable to other populations. Future studies should involve large-sample, multicenter, prospective studies to validate and refine the model.

In this study, a clinical prediction model for OP in elderly malnourished patients was developed, demonstrating good predictive efficacy. This model can assist clinicians in quickly and accurately assessing patient prognosis. Additionally, it suggests that controlling underlying diseases, increasing physical activity, and improving nutrition can significantly reduce the risk of OP, providing a solid basis for clinical decision-making and improving patient outcomes. Accurate OP prevention not only benefits patient health but also offers economic advantages. However, as lifestyle changes and medical technologies advance, the pathogenic factors, diagnostic methods, and therapeutic measures for OP are dynamically evolving, potentially reducing the model’s standardization and performance³¹. Therefore, future research should focus on collecting more relevant factors, expanding sample sizes, and conducting multicenter, multi-instrument, and multi-dimensional studies to minimize information bias and continuously update and improve the model.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgements

The authors wish to express gratitude to all respondents and their families for their enthusiastic participation in this study.

Author contributions statement

J.C and YH.P formulated the research questions and designed the study; J.C, XP.L,YH.P, and YC.Z conducted the research study; J.C and YC.Z analyzed the data; J.C and CX.S wrote the paper; and J.C, CX.S, and XP.L co-edited, revised, and gave final review to the manuscript for important ideological content. All authors read and approved the final version of the manuscript.

Ethics statement

This study was conducted at the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine and was approved by the Ethics Committee of the hospital in accordance with the STROBE criteria and the Declaration of Helsinki. The Ethics Committee of Chengdu University of Traditional Chinese Medicine Affiliated Hospital waived the need to informed consent for this study due its retrospective nature.

Data availability statement

The data analyzed in this study have been uploaded publicly as Supplementary Material and are also available upon request from the corresponding author.

Additional information

Supplementary Information see attached .XLSX form for details.

Correspondence and requests for materials should be addressed to XP.L.

Serón-Arbeloa, C. et al. Malnutrition Screening and Assessment. Nutrients 14, doi:10.3390/nu14122392 (2022).
Schuetz, P. et al. Management of disease-related malnutrition for patients being treated in hospital. Lancet 398, 1927-1938, doi:10.1016/s0140-6736(21)01451-3 (2021).
Hickson, M. Malnutrition and ageing: This article is part of a series on ageing edited by Professor Chris Bulpitt. Postgraduate Medical Journal 82, 2-8, doi:10.1136/pgmj.2005.037564 (2006).
Kyle, U. G., Unger, P., Mensi, N., Genton, L. & Pichard, C. Nutrition status in patients younger and older than 60 y at hospital admission: a controlled population study in 995 subjects. Nutrition 18, 463-469, doi:https://doi.org/10.1016/S0899-9007(01)00804-8 (2002).
Foessl, I., Dimai, H. P. & Obermayer-Pietsch, B. Long-term and sequential treatment for osteoporosis. Nat Rev Endocrinol 19, 520-533, doi:10.1038/s41574-023-00866-9 (2023).
Consensus development conference: diagnosis, prophylaxis, and treatment of osteoporosis. Am J Med 94, 646-650, doi:10.1016/0002-9343(93)90218-e (1993).
Clynes, M. A. et al. The epidemiology of osteoporosis. Br Med Bull 133, 105-117, doi:10.1093/bmb/ldaa005 (2020).
Salari, N. et al. The global prevalence of osteoporosis in the world: a comprehensive systematic review and meta-analysis. J Orthop Surg Res 16, 609, doi:10.1186/s13018-021-02772-0 (2021).
Johnell, O. & Kanis, J. A. An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporosis International 17, 1726-1733, doi:10.1007/s00198-006-0172-4 (2006).
Hernlund, E. et al. Osteoporosis in the European Union: medical management, epidemiology and economic burden. Archives of Osteoporosis 8, 136, doi:10.1007/s11657-013-0136-1 (2013).
Xiao, P. L. et al. Global, regional prevalence, and risk factors of osteoporosis according to the World Health Organization diagnostic criteria: a systematic review and meta-analysis. Osteoporosis International 33, 2137-2153, doi:10.1007/s00198-022-06454-3 (2022).
Si, L., Winzenberg, T. M., Jiang, Q., Chen, M. & Palmer, A. J. Projection of osteoporosis-related fractures and costs in China: 2010–2050. Osteoporosis International 26, 1929-1937, doi:10.1007/s00198-015-3093-2 (2015).
Qing, B. et al. Association between geriatric nutrition risk index and bone mineral density in elderly Chinese people. Archives of Osteoporosis 16, 55, doi:10.1007/s11657-020-00862-w (2021).
Riaudel, T. et al. [Nutrition and osteoporosis in elderly]. Geriatr Psychol Neuropsychiatr Vieil 9, 399-408, doi:10.1684/pnv.2011.0310 (2011).
Camacho, P. M. et al. American Association of Clinical Endocrinologists/American College of Endocrinology Clinical Practice Guidelines for the Diagnosis and Treatment of Postmenopausal Osteoporosis—2020 Update. Endocrine Practice 26, 1-46, doi:https://doi.org/10.4158/GL-2020-0524SUPPL (2020).
Beard, J. R. et al. The World report on ageing and health: a policy framework for healthy ageing. Lancet 387, 2145-2154, doi:10.1016/s0140-6736(15)00516-4 (2016).
Beard, J. R. & Bloom, D. E. Towards a comprehensive public health response to population ageing. Lancet 385, 658-661, doi:10.1016/s0140-6736(14)61461-6 (2015).
Aubert, L. [Nutritional requirements of the elderly]. Soins Gerontol 23, 18-21, doi:10.1016/j.sger.2018.06.004 (2018).
Coughlan, T. & Dockery, F. Osteoporosis and fracture risk in older people. Clin Med (Lond) 14, 187-191, doi:10.7861/clinmedicine.14-2-187 (2014).
Bouillanne, O. et al. Geriatric Nutritional Risk Index: a new index for evaluating at-risk elderly medical patients. Am J Clin Nutr 82, 777-783, doi:10.1093/ajcn/82.4.777 (2005).
Huang, W., Xiao, Y., Wang, H. & Li, K. Association of geriatric nutritional risk index with the risk of osteoporosis in the elderly population in the NHANES. Front Endocrinol (Lausanne) 13, 965487, doi:10.3389/fendo.2022.965487 (2022).
Wang, L., Zhang, D. & Xu, J. Association between the Geriatric Nutritional Risk Index, bone mineral density and osteoporosis in type 2 diabetes patients. J Diabetes Investig 11, 956-963, doi:10.1111/jdi.13196 (2020).
Qing, B. et al. Association between geriatric nutrition risk index and bone mineral density in elderly Chinese people. Arch Osteoporos 16, 55, doi:10.1007/s11657-020-00862-w (2021).
Lang, T. et al. Sarcopenia: etiology, clinical consequences, intervention, and assessment. Osteoporos Int 21, 543-559, doi:10.1007/s00198-009-1059-y (2010).
Morley, J. E., Baumgartner, R. N., Roubenoff, R., Mayer, J. & Nair, K. S. Sarcopenia. J Lab Clin Med 137, 231-243, doi:10.1067/mlc.2001.113504 (2001).
Mizuiri, S. et al. Association and predictive value of geriatric nutritional risk index, body composition, or bone mineral density in haemodialysis patients. Nephrology (Carlton) 26, 341-349, doi:10.1111/nep.13826 (2021).
Tokumoto, H. et al. Association between Bone Mineral Density of Femoral Neck and Geriatric Nutritional Risk Index in Rheumatoid Arthritis Patients Treated with Biological Disease-Modifying Anti-Rheumatic Drugs. Nutrients 10, doi:10.3390/nu10020234 (2018).
Nagai, T., Uei, H. & Nakanishi, K. Association Among Geriatric Nutritional Risk Index and Functional Prognosis in Elderly Patients with Osteoporotic Vertebral Compression Fractures. Indian J Orthop 56, 338-344, doi:10.1007/s43465-021-00478-3 (2022).
Chen, S. C. et al. Associations among Geriatric Nutrition Risk Index, bone mineral density, body composition and handgrip strength in patients receiving hemodialysis. Nutrition 65, 6-12, doi:10.1016/j.nut.2019.02.013 (2019).
Abedi, V. et al. Prediction of Long-Term Stroke Recurrence Using Machine Learning Models. Journal of Clinical Medicine 10, 1286 (2021).
Chen, J. et al. Predicting COVID-19 Re-Positive Cases in Malnourished Older Adults: A Clinical Model Development and Validation. Clin Interv Aging 19, 421-437, doi:10.2147/cia.S449338 (2024).
Bouillanne, O. et al. Geriatric Nutritional Risk Index: a new index for evaluating at-risk elderly medical patients. The American Journal of Clinical Nutrition: Official Journal of the American Society for Clinical Nutrition, 82 (2005).
Rachner, T. D., Khosla, S. & Hofbauer, L. C. Osteoporosis: now and the future. Lancet 377, 1276-1287, doi:10.1016/s0140-6736(10)62349-5 (2011).
Shangguan, X. et al. Impact of the Malnutrition on Mortality in Patients With Osteoporosis: A Cohort Study From NHANES 2005-2010. Front Nutr 9, 868166, doi:10.3389/fnut.2022.868166 (2022).
van Beek, S., Nieboer, D., Klimek, M., Stolker, R. J. & Mijderwijk, H. J. Development and external validation of a clinical prediction model for predicting quality of recovery up to 1 week after surgery. Sci Rep 14, 387, doi:10.1038/s41598-023-50518-1 (2024).
Bender, R. & Lange, S. [What is a confidence interval?]. Dtsch Med Wochenschr 132 Suppl 1, e17-18, doi:10.1055/s-2007-959031 (2007).
du Prel, J. B., Hommel, G., Röhrig, B. & Blettner, M. Confidence interval or p-value?: part 4 of a series on evaluation of scientific publications. Dtsch Arztebl Int 106, 335-339, doi:10.3238/arztebl.2009.0335 (2009).
Furcada, J. M., Patino, C. M. & Ferreira, J. C. Estimating risk in clinical studies: odds ratio and risk ratio. J Bras Pneumol 46, e20200137, doi:10.36416/1806-3756/e20200137 (2020).
Compston, J. et al. UK clinical guideline for the prevention and treatment of osteoporosis. Arch Osteoporos 12, 43, doi:10.1007/s11657-017-0324-5 (2017).
Harvey, N. C., McCloskey, E., Kanis, J. A., Compston, J. & Cooper, C. Cost-effective but clinically inappropriate: new NICE intervention thresholds in osteoporosis (Technology Appraisal 464). Osteoporos Int 29, 1511-1513, doi:10.1007/s00198-018-4505-x (2018).
Rizzoli, R. & Bonjour, J. P. [Malnutrition and osteoporosis]. Z Gerontol Geriatr 32 Suppl 1, I31-37, doi:10.1007/s003910050178 (1999).
Chandran, M. et al. Development of the Asia Pacific Consortium on Osteoporosis (APCO) Framework: clinical standards of care for the screening, diagnosis, and management of osteoporosis in the Asia-Pacific region. Osteoporos Int 32, 1249-1275, doi:10.1007/s00198-020-05742-0 (2021).
Gregson, C. L. et al. UK clinical guideline for the prevention and treatment of osteoporosis. Arch Osteoporos 17, 58, doi:10.1007/s11657-022-01061-5 (2022).
Cerdas Pérez, S., Herrera, L. E. & González, E. Clinical impact of misinterpretation of dual-energy X-ray absorptiometry during the evaluation of osteoporotic patients. Climacteric 24, 577-586, doi:10.1080/13697137.2021.1918079 (2021).
Koh, L. K. et al. A simple tool to identify asian women at increased risk of osteoporosis. Osteoporos Int 12, 699-705, doi:10.1007/s001980170070 (2001).
Subramaniam, S. et al. The performance of osteoporosis self-assessment tool for Asians (OSTA) in identifying the risk of osteoporosis among Malaysian population aged 40 years and above. Arch Osteoporos 14, 117, doi:10.1007/s11657-019-0666-2 (2019).
Groenendijk, I. et al. A Combined Nutrition and Exercise Intervention Influences Serum Vitamin B-12 and 25-Hydroxyvitamin D and Bone Turnover of Healthy Chinese Middle-Aged and Older Adults. J Nutr 150, 2112-2119, doi:10.1093/jn/nxaa149 (2020).
Larsson, S. C., Melhus, H. & Michaëlsson, K. Circulating Serum 25-Hydroxyvitamin D Levels and Bone Mineral Density: Mendelian Randomization Study. J Bone Miner Res 33, 840-844, doi:10.1002/jbmr.3389 (2018).
Sun, P., Huang, T., Huang, C., Wang, Y. & Tang, D. Role of histone modification in the occurrence and development of osteoporosis. Front Endocrinol (Lausanne) 13, 964103, doi:10.3389/fendo.2022.964103 (2022).
Hu, R. et al. Aloperine improves osteoporosis in ovariectomized mice by inhibiting RANKL-induced NF-κB, ERK and JNK approaches. Int Immunopharmacol 97, 107720, doi:10.1016/j.intimp.2021.107720 (2021).
Stoll, S., Wang, C. & Qiu, H. DNA Methylation and Histone Modification in Hypertension. Int J Mol Sci 19, doi:10.3390/ijms19041174 (2018).
Kim, K. M., Hwang, E. J., Lee, S. & Yoon, J. H. The impact of Renin-Angiotensin System Inhibitors on bone fracture risk: a nationwide nested case-control study. BMC Musculoskelet Disord 25, 3, doi:10.1186/s12891-023-07102-5 (2024).
Zhao, J., Yang, H., Chen, B. & Zhang, R. The skeletal renin-angiotensin system: A potential therapeutic target for the treatment of osteoarticular diseases. Int Immunopharmacol 72, 258-263, doi:10.1016/j.intimp.2019.04.023 (2019).
Chen, H. L., Deng, L. L. & Li, J. F. Prevalence of Osteoporosis and Its Associated Factors among Older Men with Type 2 Diabetes. Int J Endocrinol 2013, 285729, doi:10.1155/2013/285729 (2013).
Ma, L. et al. Association between bone mineral density and type 2 diabetes mellitus: a meta-analysis of observational studies. Eur J Epidemiol 27, 319-332, doi:10.1007/s10654-012-9674-x (2012).
Shu, J., Tan, A., Li, Y., Huang, H. & Yang, J. The correlation between serum total alkaline phosphatase and bone mineral density in young adults. BMC Musculoskelet Disord 23, 467, doi:10.1186/s12891-022-05438-y (2022).
Fink, H. A. et al. Clinical utility of routine laboratory testing to identify possible secondary causes in older men with osteoporosis: the Osteoporotic Fractures in Men (MrOS) Study. Osteoporos Int 27, 331-338, doi:10.1007/s00198-015-3356-y (2016).
Kyd, P. A., Vooght, K. D., Kerkhoff, F., Thomas, E. & Fairney, A. Clinical usefulness of bone alkaline phosphatase in osteoporosis. Ann Clin Biochem 35 ( Pt 6), 717-725, doi:10.1177/000456329803500603 (1998).
Al-Hariri, M. & Aldhafery, B. Association of Hypertension and Lipid Profile with Osteoporosis. Scientifica (Cairo) 2020, 7075815, doi:10.1155/2020/7075815 (2020).
Kan, B. et al. Association between lipid biomarkers and osteoporosis: a cross-sectional study. BMC Musculoskelet Disord 22, 759, doi:10.1186/s12891-021-04643-5 (2021).
Chen, Y. Y., Wang, W. W., Yang, L., Chen, W. W. & Zhang, H. X. Association between lipid profiles and osteoporosis in postmenopausal women: a meta-analysis. Eur Rev Med Pharmacol Sci 22, 1-9, doi:10.26355/eurrev_201801_14093 (2018).
Nuttall, M. E. & Gimble, J. M. Controlling the balance between osteoblastogenesis and adipogenesis and the consequent therapeutic implications. Curr Opin Pharmacol 4, 290-294, doi:10.1016/j.coph.2004.03.002 (2004).
Yamaguchi, T. et al. Plasma lipids and osteoporosis in postmenopausal women. Endocr J 49, 211-217, doi:10.1507/endocrj.49.211 (2002).
Nieves, J. W. Osteoporosis: the role of micronutrients. Am J Clin Nutr 81, 1232s-1239s, doi:10.1093/ajcn/81.5.1232 (2005).
Henriksen, K. et al. A randomized, double-blind, multicenter, placebo-controlled study to evaluate the efficacy and safety of oral salmon calcitonin in the treatment of osteoporosis in postmenopausal women taking calcium and vitamin D. Bone 91, 122-129, doi:10.1016/j.bone.2016.07.019 (2016).
Reyes-Garcia, R. et al. Effects of Daily Intake of Calcium and Vitamin D-Enriched Milk in Healthy Postmenopausal Women: A Randomized, Controlled, Double-Blind Nutritional Study. J Womens Health (Larchmt) 27, 561-568, doi:10.1089/jwh.2017.6655 (2018).
López-Baena, M. T., Pérez-Roncero, G. R., Pérez-López, F. R., Mezones-Holguín, E. & Chedraui, P. Vitamin D, menopause, and aging: quo vadis? Climacteric 23, 123-129, doi:10.1080/13697137.2019.1682543 (2020).
Crichton, M. et al. A systematic review, meta-analysis and meta-regression of the prevalence of protein-energy malnutrition: associations with geographical region and sex. Age Ageing 48, 38-48, doi:10.1093/ageing/afy144 (2019).
Ji, Y. et al. Relationship between geriatric nutritional risk index and osteoporosis in type 2 diabetes in Northern China. BMC Endocrine Disorders 22, 308, doi:10.1186/s12902-022-01215-z (2022).
Zheng, C. M. et al. Hypoalbuminemia differently affects the serum bone turnover markers in hemodialysis patients. Int J Med Sci 16, 1583-1592, doi:10.7150/ijms.39158 (2019).
Nagayama, Y. et al. Low serum albumin concentration is associated with increased risk of osteoporosis in postmenopausal patients with rheumatoid arthritis. J Orthop Sci 27, 1283-1290, doi:10.1016/j.jos.2021.08.018 (2022).
Kelley, G. A., Kelley, K. S. & Kohrt, W. M. Exercise and bone mineral density in premenopausal women: a meta-analysis of randomized controlled trials. Int J Endocrinol 2013, 741639, doi:10.1155/2013/741639 (2013).
Troy, K. L., Mancuso, M. E., Butler, T. A. & Johnson, J. E. Exercise Early and Often: Effects of Physical Activity and Exercise on Women's Bone Health. Int J Environ Res Public Health 15, doi:10.3390/ijerph15050878 (2018).
Su, Y., Chen, Z. & Xie, W. Swimming as Treatment for Osteoporosis: A Systematic Review and Meta-analysis. Biomed Res Int 2020, 6210201, doi:10.1155/2020/6210201 (2020).
Zitzmann, A. L. et al. The effect of different training frequency on bone mineral density in older adults. A comparative systematic review and meta-analysis. Bone 154, 116230, doi:10.1016/j.bone.2021.116230 (2022).
Vita, F., Gangemi, S., Pioggia, G., Trimarchi, F. & Di Mauro, D. Physical Activity and Post-Transcriptional Regulation of Aging Decay: Modulation of Pathways in Postmenopausal Osteoporosis. Medicina (Kaunas) 58, doi:10.3390/medicina58060767 (2022).
Link, T. M. Radiology of Osteoporosis. Can Assoc Radiol J 67, 28-40, doi:10.1016/j.carj.2015.02.002 (2016).
Mazess, R. B. On aging bone loss. Clin Orthop Relat Res, 239-252 (1982).
Hemmatian, H., Bakker, A. D., Klein-Nulend, J. & van Lenthe, G. H. Aging, Osteocytes, and Mechanotransduction. Curr Osteoporos Rep 15, 401-411, doi:10.1007/s11914-017-0402-z (2017).
Nishi, K. et al. Similarities and Differences in Bone Mineral Density between Multiple Sites in the Same Individual: An Elderly Cadaveric Study. Biomed Res Int 2022, 6094663, doi:10.1155/2022/6094663 (2022).

No competing interests reported.

SupplementaryInformation.xlsx

Download PDF

Editor invited by journal
02 Sep, 2024
Submission checks completed at journal
02 Sep, 2024
First submitted to journal
21 Aug, 2024

You are reading this latest preprint version

Development and validation of a nomogram clinical prediction model for osteoporosis in elderly malnourished patients

Status:

Version 1

Abstract

Figures

Introduction

Methods

Study subjects and inclusion criteria

Collection of information

Data analysis

Results

Comparison of general data

LASSO regression for screening predictors of OP in elderly malnourished patients

Logistic regression analysis of predictors of OP in elderly malnourished patients

Development and validation of a clinical prediction model for OP in elderly malnourished patients

Establishment of a clinical prediction model

Validation of the clinical prediction model

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1