Could the TyG index be a screening tool for postmenopausal osteoporosis?

doi:10.21203/rs.3.rs-4941509/v1

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Could the TyG index be a screening tool for postmenopausal osteoporosis?

https://doi.org/10.21203/rs.3.rs-4941509/v1

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Purposes: This study aims to explore the sensitivity and efficacy of the TyG index in the screening of postmenopausal osteoporosis, and to provide an objective new method for the prevention and early screening of postmenopausal osteoporosis.

Methods: This retrospective study selected 1032 subjects who completed bone mineral density examination in the Department of Nuclear Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine from January 2021 to December 2023 according to the inclusion and exclusion criteria. The baseline data include age, weight, height, BMI, lumbar spine T-value (LS T-value), total hip T-value (TH T-value), femoral neck T-value (FN T-value), fasting blood glucose (FBG), triglyceride (TG), the TyG index and OSTA. After grouping, the differences in postmenopausal osteoporosis were compared. The correlation of the TyG index and OSTA with baseline data was analyzed. The ROC curve results of the TyG index in the total population, 60-year-old stratification, FBG and TG stratification were analyzed, and the sensitivity and efficacy of the TyG index in the screening of postmenopausal osteoporosis were obtained.

Results: In 1032 postmenopausal women, there were significant differences (P< 0.001) in age, weight, height, BMI, and T-values of three different sites, the TyG index and OSTA. The results of correlation analysis showed that the TyG index and OSTA were positively correlated with weight, BMI, and T-values of three different sites in 1032 postmenopausal women and after 60-year-old stratification (P<0.001). In the total population and after stratification by 60 years old, the TyG index was positively correlated with FBG and TG (P<0.001), but not correlated with age and height. Meanwhile, OSTA was negatively correlated with age (P<0.001) and positively correlated with height (P<0.001). OSTA was not correlated with FBG and TG in the total population and in postmenopausal women aged <60, but was positively correlated with TG in postmenopausal women aged≥60 (P<0.001). ROC curve analysis showed that the area under the curve of the TyG index and OSTA was close in postmenopausal women aged≥60 with abnormal FBG and/or TG. The cut-off value of the TyG index in postmenopausal women aged≥60 was higher than that in postmenopausal women aged<60, indicating that the risk of osteoporosis increased in postmenopausal women aged≥60 with increased TyG index.

Conclusion: The TyG index has the potential to objectively screen osteoporosis in postmenopausal women aged≥60 and postmenopausal women aged≥60 with abnormal FBG and/or TG.

Health sciences/Biomarkers/Predictive markers

Health sciences/Health care/Disease prevention

the TyG index

OSTA

Osteoporosis

Postmenopausal women

According to the newly released "World Population Prospects 2024" ^[1], the global aging trend is accelerating, and it is expected that by 2100, the elderly population will reach one-third of the global population. China's 7th national population census pointed out that the country has the largest elderly population in the world, with 264 million persons aged 60 to 264 million (accounts for about 18.7% of the total population), an increase of 5.4% over the previous census ^[2]. Osteoporosis (OP) is a systemic degenerative disease defined by a loss in bone mass, the breakdown of bone microstructure, and an increased risk of fracture ^[3]. The prevalence of osteoporosis worldwide was 18.3%, with women having almost twice as high prevalence (23.1%) than men (11.7%) ^[4]. The most hazardous side effect of osteoporosis is osteoporotic fractures, which are most common in the distal portions of the spine, hip, and forearm ^[5] and affect 18.9% of older persons in China. About 50% of postmenopausal women get osteoporotic fractures, and hip fractures double the risk of death within a year ^[6]. Therefore, osteoporosis is called the "silent killer", and screening is the premise of prevention and treatment of osteoporosis and prevention of fragility fractures. Osteoporosis Self-Assessment Tool for Asians (OSTA) is a tool recommended by guidelines ^[7] for early identification and screening of postmenopausal osteoporosis, and only weight and age are used as calculation variables. Weight is easily affected by diet, exercise, and diseases, and could have a large range of short-term fluctuations, which reduces the screening efficiency of OSTA. The data of OSTA calculation model come from postmenopausal women in eight Asian countries, and there are limitations in the use of gender and ethnicity ^[8]. There is an urgent need for an objective and widely applicable osteoporosis screening model to accurately and efficiently identify high-risk populations.

In Muscle and fat cells, insulin-stimulated glucose uptake was flawed, and impaired insulin suppression of hepatic glucose output, which is known as insulin resistance (IR) ^[9]. The triglyceride-glucose (TyG) index is a reliable and emerging indicator for evaluating Insulin resistance (IR). A cross-sectional study by Zhuo M. et al ^[10]. verified that IR causes low bone mass and the risk of osteoporosis. The risk of osteoporosis increases with the increased level of IR in female patients ^[11]. The triglyceride-glucose (TyG) index is a reliable and emerging indicator for evaluating Insulin resistance (IR) ^{[12, 13]}. Recent studies have shown that ^{[14, 15]} the TyG index obtain the ability to predict osteoporosis, offering an objective and applicable approach to osteoporosis prevention and early screening.

Data collection and study population

The retrospective analysis of the screening was conducted between January 2021 and December 2023 at, Affiliated Hospital of Nanjing University of Chinese Medicine, which involved nuclear medicine bone mineral density assessment of clinical data pertaining to 20568 patients. Exclusion criteria for this study are: (1) lack comprehensive blood index information; (2) a history of kidney disease and malignant tumors; (3) Male; and (4) Females who have not reached menopausal status. The ultimate analysis included 1032 participants in total (Fig. 1). This study has been registered in the Chinese Clinical Trial Registry (Registration number: ChiCTR2400085209), and approved by the Ethics Committee of the Affiliated Hospital of Nanjing University of Chinese Medicine (Ethics number: 2024NL-085-02). Since this study employed anonymized patient records, the need for informed consent to participate was waived by an Institutional Review Board (IRB). We confirm that all methods were performed in accordance with the relevant guidelines and regulations. The study population's medical history was retrieved from the clinical information system, and our hospital's laboratory department provided the FBG and TG blood test indices. Using dual-energy X-ray absorptiometry (Hologic Discovery ASY-03954), the density of bone was determined.

Laboratory analysis

Blood triglyceride (TG) and fasting blood glucose (FBG) levels were measured quantitatively through an automatic biochemical analyzer (Beckman Coulter AU5800, USA). Chromatography and the GPO-PAP method are employed by TG and FBG, respectively, for assessment.

Definition of index

BMI = Weight (kg) / Height (m)²

TyG = Ln [ TG (mg/dL) × FBG (mg/dL) / 2] ^[12]

OSTA = [ Weight (kg) - Age (years) ] × 0.2 ^[7]

Data analysis

SPSS 29.0 software was used for statistical analysis. The normal distribution was expressed as mean ± standard deviation, and the non-normal distribution was expressed as median and interquartile range M(P25, P75). The non-normal distribution of the Mann-Whitney U test, P < 0.05 is a significant difference. The Spearman correlation analysis, the analysis of the TyG index and OSTA, and the correlation of the indexes. Graphpad Prism10 software was used for correlation heat mapping. receiver operating characteristic (ROC) curves of the TyG index, OSTA, age, and FBG and TG stratification were drawn and the area under the curve (AUC) were analyzed. Sensitivity, specificity, and the AUC was compared by z-test. With P < 0.05, the difference was statistically significant.

Baseline Characteristics

This study enrolled 1032 postmenopausal women in total, including 492 with osteoporosis, 540 without osteoporosis, 315 with abnormal FBG and/or TG, and 717 with normal FBG and TG. Grouping by T-value ≤-2.5, there were significant differences in the enrolled population's age, weight, height, BMI, T-values of three sites, the TyG index, and OSTA (P < 0.001). Blood TG and FBG did not differ statistically (Table 1).

Table 1

Population grouping of study participants
	Overall (n−1032)	PMOP (n = 492)	Non-PMOP (n = 540)	Z	P
Age (years)	61.00(54.00,69.00)	66(59,72)	57(51,66)	−11.166	< .001
Weight (kg)	57.00(51.50,63.00)	55(50,60)	60(55,65)	−9.84	< .001
Height (cm)	159.00(155.00,163.00)	158(155,161)	160(156,163)	−5.642	< .001
BMI(kg/m²)	22.70(20.70,24.80)	22.04(20.03,24.03)	23.44(21.48,25.39)	−8.109	< .001
LS T-value	−2.10(−2.80,−1.10)	−2.9(−3.4,−2.5)	−1.2(−1.8,−0.4)	−24.68	< .001
TH T-value	−1.30(−2.00,−0.50)	−1.9(−2.5,−1.4)	−0.7(−1.2,−0.1)	−21.07	< .001
FN T-value	−1.70(−2.40,−0.90)	−2.5(−2.9,−1.8)	−1.1(−1.7,−0.5)	−20.634	< .001
FBG(mmol/L)	5.41(5.05,6.04)	5.41(5.08,6.06)	5.40(5.03,6.00)	−0.75	0.453
TG(mmol/L)	1.21(0.89,1.62)	1.21(0.89,1.57)	1.22(0.89,1.63)	−0.699	0.485
TyG	1.45(1.09,1.91)	1.34(0.99,1.80)	1.55(1.16,1.99)	−4.665	< .001
OSTA	−0.60(−2.80,1.20)	−2.2(−3.8,−0.4)	0.45(−1.2, 2.2)	−14.138	< .001

医学

TyG index and OSTA with T-values of three different sites and age distribution trends

In Fig. 2 (a), (b), and (c) diagrams represent a decrease in T-values of three sites and an increase in the TyG index with aging in the 1032 postmenopausal women. The T-values of three sites and OSTA decreased with aging in the 1032 postmenopausal women, as shown in Fig. 2 (d), (e), (f).

Correlation analysis

The results of the 60-year-old stratification of 1032 postmenopausal women showed the TyG index and OSTA were positively correlated with body weight, BMI, LS T-value, TH T-value and FN T-value (P < 0.001)(Fig. 3). The TyG index's correlation coefficient was lower than OSTA's. The TyG index showed a positive correlation with TG and FBG but not with age or height (P < 0.001). Age showed a negative correlation with OSTA (P < 0.001), while height showed a positive correlation with OSTA (P < 0.001). OSTA had a negative correlation with age and FBG (P < 0.001) and a positive correlation with height and TG (P < 0.001, P = 0.013). In postmenopausal women age < 60, there was no correlation found between OSTA and FBG and TG; however, in women over 60, there was a positive correlation between OSTA and FBG and TG.

Analysis of Receiver Operating Characteristic (ROC) curves

Taking DXA T-value≤-2.5 as the diagnostic criteria for osteoporosis, the AUC of OSTA was better than that of TyG index in 1032 postmenopausal women. The cut-off value of the TyG index was 1.41, and the cut-off value of OSTA was − 0.25. (Table 2 and Fig. 4a)

The results of the 60-year-old stratification showed that the AUC and sensitivity of the TyG index were inferior to that of OSTA, but the specificity was superior. The sensitivity and specificity of the TyG index and OSTA were comparable in postmenopausal women aged ≥ 60 and their area under the curves was similar. The TyG index’s cut-off value for postmenopausal women aged ≥ 60 was higher than for postmenopausal women aged < 60, suggesting that an increase in TyG index in postmenopausal women aged ≥ 60 increases their risk of osteoporosis. The cut-off value of OSTA in postmenopausal women aged ≥ 60 years (-2.1) was lower than that in postmenopausal women aged < 60 years (1.35), indicating that the reduction of OSTA in postmenopausal women aged ≥ 60 years increased the risk of osteoporosis. (Table 2 and Fig. 4b)

The results of FBG and TG stratification of 1032 postmenopausal women showed that the AUC and specificity of the TyG index were inferior to those of TyG index. The sensitivity of FBG was similar to that of TG, and the specificity of FBG and/or TG was higher than that of OSTA. The cut-off value of the TyG index with abnormal FBG and/or TG (1.66) is higher than that with normal FBG and TG (1.38), and the risk of osteoporosis was increased. The cutoff value of OSTA with abnormal FBG and/or TG (-0.35) were larger than those with normal FBG and TG (-1.1). This indicated that postmenopausal women with abnormal FBG and/or TG have a higher risk of osteoporosis than those with normal FBG and TG. (Table 2 and Fig. 4c)

The stratification results of FBG and TG values in 451 postmenopausal women aged < 60 showed that the AUC and sensitivity of the TyG index with normal FBG and TG were inferior to those of OSTA, but the specificity was superior to that of OSTA. The AUC of the TyG index with abnormal FBG and/or TG was superior to that of OSTA, but the sensitivity and specificity were inferior to those of OSTA, and the P value of the TyG index was 0.430, which was not statistically significant. In postmenopausal women aged < 60, the OSTA cut-off values with normal FBG and TG (1.35) were higher than those with abnormal FBG and/or TG (0.35). The results indicated that The risk of osteoporosis decreases with the increase of OSTA in postmenopausal women aged < 60. (Table 2 and Fig. 4d)

The results of 581 postmenopausal women aged ≥ 60 stratified by FBG and TG showed that the AUC and specificity of the TyG index with normal FBG and TG values were inferior to those of OSTA, but the sensitivity was similar to that of OSTA. The AUC of the TyG index with abnormal FBG and/or TG values was similar to that of OSTA, but the sensitivity was inferior and the specificity was superior to that of OSTA. The similar TyG index cut-off values for FBG and TG value stratification indicated that the TyG index was not affected by FBG and TG in postmenopausal women aged ≥ 60. The OSTA cut-off value with normal FBG and TG (-1.95) was higher than that with abnormal FBG and/or TG (-2.10), indicating that abnormal FBG and/or TG may increase the risk of osteoporosis in postmenopausal women aged ≥ 60. (Table 2 and Fig. 4e)

Table 2

ROC curve analysis of the TyG index and OSTA
		AUC (95%CI)	Cut-off value	Sensitivity(%)	Specificity(%)	Youden index	P
The TyG index	overall	0.584(0.549,0.619)	1.41	54.1	61.9	0.159	0.000
Age	Age<60	0.564(0.506,0.621)	1.27	49.7	65.4	0.15	0.029
Age	Age ≥ 60	0.629(0.583,0.676)	1.53	62.0	62.4	0.244	0.000
FBG and TG	Normal FBG and TG	0.580(0.538,0.621)	1.38	64.8	50.1	0.15	0.000
FBG and TG	Abnormal FBG and/or TG	0.601(0.538,0.664)	1.66	40.5	79.0	0.196	0.002
Age < 60	Normal FBG and TG	0.580(0.520,0.643)	1.00	40.7	78.5	0.192	0.004
Age < 60	Abnormal FBG and/or TG	0.623(0.550,0.697)	1.88	51.4	66.7	0.18	0.430
Age ≥ 60	Normal FBG and TG	0.588(0.530,0.648)	1.48	72.5	44.9	0.173	0.002
Age ≥ 60	Abnormal FBG and/or TG	0.640(0.560,0.719)	1.49	34.2	86.9	0.212	0.001
OSTA	overall	0.754(0.725,0.784)	−0.25	75.8	62.6	0.384	0.000
Age	Age<60	0.749(0.700,0.798)	1.35	79.3	60.5	0.398	0.000
Age	Age ≥ 60	0.689(0.645,0.733)	−2.10	68.9	62.8	0.317	0.000
FBG and TG	Normal FBG and TG	0.767(0.733,0.802)	−1.10	63.7	78.0	0.417	0.000
FBG and TG	Abnormal FBG and/or TG	0.728(0.674,0.783)	−0.35	79.7	54.5	0.342	0.000
Age < 60	Normal FBG and TG	0.719(0.660,0.777)	1.35	75.0	61.8	0.368	0.000
Age < 60	Abnormal FBG and/or TG	0.593(0.530,0.656)	0.35	81.1	81.7	0.627	0.000
Age ≥ 60	Normal FBG and TG	0.675(0.620,0.734)	−1.95	73.3	57.5	0.308	0.000
Age ≥ 60	Abnormal FBG and/or TG	0.700(0.630,0.769)	−2.10	65.8	66.4	0.321	0.000

Early screening and risk assessment of osteoporosis are essential. Decreased secretion of estrogen in postmenopausal women leads to increased secretion of Receptor Activator of Nuclear Kappa-B Ligand (RANKL). The competitive binding of Osteoprotegerin (OPG) secreted by osteoblasts to RANKL is inhibited, and the formation and bone resorption of osteoclasts are enhanced, resulting in the decrease of bone mineral density and bone strength ^[16]. The proliferation of osteoblasts induced by the Wnt/β-catenin signaling pathway and the differentiation of pre-osteoblasts into osteoblasts promoted by the BMP signaling pathway are inhibited by estrogen deficiency. Estrogen deficiency can also increase the secretion of proinflammatory factors such as IL-1, IL-6 and tumor necrosis factor α (TNFα), and promote osteoclast formation ^[17]. Both osteocytes and osteoclasts contain insulin and IGF-1 receptors. Deficiency in bone development and maturation produces systemic IR and bone-specific IR, which in turn regulates glucose homeostasis and energy metabolism through Osteocalcin (OC) ^[18]. Insulin can stimulate bone formation and resorption through mitogen-activated protein kinase (MAPK) and Phosphatidylinositol 3-Kinase (PI3K) signaling pathways. Thus, it increases the growth, proliferation and survival of osteoblasts, which in turn increases bone mass. IR inhibits OC production by increasing insulin secretion and hyperinsulinism, which in turn affects BMD ^[19]. The TyG index, as an emerging assessment method of IR, is negatively correlated with BMD ^{[10, 20]}. A cross-sectional study in the United States showed that TyG index had a nonlinear relationship with bone mineral density, and the risk of osteoporosis increased with the increase of TyG index, and was not affected by gender and race [14]. A large Chinese population cross-sectional study showed that TyG index can effectively and objectively predict the risk of osteoporosis in women and people aged ≥ 60 and < 60 ^[21]. The results of our team further confirmed that TyG index had a nonlinear relationship with T-values of three different sites, and the TyG index had a better predictive effect on osteoporosis in people aged ≥ 60 and those aged ≥ 60 with abnormal FBG and/or TG. A 6-year follow-up, which used the TyG index as the best predictor of fragility fracture endpoint events in postmenopausal patients with type 2 diabetes and postmenopausal osteoporosis, found that type 2 diabetes patients with normal bone mineral density had a higher risk of fracture ^[22]. This prospective study lays the foundation for the TyG index to be used as an independent or auxiliary predictor in clinical research and opens up a new perspective for predicting osteoporotic fractures.

ROC analysis is a statistical method that shows the performance of classification models by drawing curves, which is widely used in clinical screening, diagnosis, and treatment ^[23]. The UK Health system ^[24] screened the prevalence of colorectal cancer (CRC) in the UK by drawing the ROC curve model of fecal hemoglobin concentration. ROC analysis used AUC as the main measure of accuracy, and sensitivity and specificity as auxiliary criteria ^[25]. AUC represents the area under the ROC curve, and the superiority and inferiority of the model are judged by comparing the size of the AUC. In this study, the maximum AUC of the TyG index screening was presented in postmenopausal women aged ≥ 60 with abnormal FBG and/or TG, indicating that such people have a higher risk of osteoporosis. Sensitivity represents the true positive rate, which is the rate at which actual patients are detected. The TyG index has the best sensitivity in postmenopausal women aged ≥ 60 with normal FBG and TG, and it is close to OSTA. The TyG index is similar to OSTA in the detection of osteoporosis in this population. Specificity, however, represents the false positive rate, which is the proportion of nonpatients in the negative population. The best specificity region of the TyG index was in postmenopausal women aged ≥ 60 with abnormal FBG and/or TG. In conclusion, the TyG index had the best performance in screening for osteoporosis in postmenopausal women aged ≥ 60. The maximum value of Youden's index, also known as the correct classification rate, corresponds to the best diagnostic critical value of the model, namely the Cut-off value. In this study, the TyG index cut-off value of postmenopausal women aged ≥ 60 was higher than that of postmenopausal women aged < 60, and the AUC, sensitivity, and specificity were better, indicating that the TyG index increased with age, and the risk of osteoporosis increased.

In this retrospective study, with OSTA as contrast, ROC analysis was used to comprehensively and objectively compare the efficacy of the TyG index in screening postmenopausal osteoporosis. Despite the innovative use of the TyG index for postmenopausal osteoporosis screening in this study, there are certain limitations. ① The participants in this study were all from Nanjing, Jiangsu Province, which could not represent the population characteristics of China and the world. ② To objectively compare the screening efficacy of the TyG index and OSTA, the population included in this study was postmenopausal osteoporosis. ③ Gender and finer age stratification were not included in the comparison of screening models. In the future, our research group will verify the efficacy of the TyG index in screening osteoporosis from GHD, NHANES, KNHANES and other public medical databases. At the same time, the objectivity of the TyG index will be further verified in the multi-center clinical study undertaken by our team.

In conclusion, TyG index is not affected by age and height, and postmenopausal women with abnormal FBG and/or TG have a greater risk of osteoporosis. The TyG index has a great prospect in screening osteoporosis in postmenopausal women aged ≥ 60 and aged ≥ 60 with abnormal FBG and/or TG.

Acknowledgment

The authors would love to show their appreciation to all the contributors.

Funding

Key project of TCM science and technology development plan of Jiangsu Province (ZD202313)
Natural Fund of Nanjing University of Chinese Medicine (XZR2021006)
Nanjing Traditional Chinese Medicine Science and Technology Project (ZYYB202220)
Jiangsu Provincial Administration of Traditional Chinese Medicine (MS2022021)
Graduate student scientific research innovation projects in Jiangsu province (SJCX24_0997)

Data availability

The datasets generated and analyzed during the current study can be obtained by contacting the corresponding author upon request.

Conflict of interest

All authors affirm that they possess no competing interests.

Author contributions

DMX and BYZ provide guidance for research ideas and quality control of the whole paper. LB, SW and XZ made substantial contributions to providing and collating data. ZW and DZ carried out a significant portion of data analysis. The preliminary version of this paper was authored by JYY and JL with subsequent contributors enhancing ideas, conducting additional analyses, and finally completing the final manuscript. All authors meticulously checked the paper.

Authors details:

Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China.
School of Acupuncture-Moxibustion and Tuina of Nanjing University of Chinese Medicine·School of Health Preservation and Rehabilitation of Nanjing University of Chinese Medicine, Nanjing 210023, China.

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Could the TyG index be a screening tool for postmenopausal osteoporosis?

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Abstract

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Background

Methods

Data collection and study population

Laboratory analysis

Definition of index

Data analysis

Results

Baseline Characteristics

医学

Correlation analysis

Analysis of Receiver Operating Characteristic (ROC) curves

Discussion

Conclusion

Declarations

References

Additional Declarations

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