Predictive Factors for the Post Embolization Fever after TACE for Hepatocellular Carcinoma Patients: A Single-Center Study in China.

Abstract Background Fever is one of the main symptoms for post-embolism syndrome (PES). This study aimed to determine and validate a model to predict fever after transcatheter arterial chemoembolization (TACE) in patients receiving platinum as the main regimen. Materials and Methods Clinical data of HCC patients who underwent TACE with platinum was retrospectively collected in the Fudan University Zhongshan Hospital during January 2016 to January 2018. According to post-TACE medical records, patients were divided into fever group and non-fever group. Predictive factors were selected by multivariate logistic regression. The receiver operating characteristic (ROC) curve were then performed to detect accuracy and discriminative ability of these factors using the derivation cohort and an independent validation cohort.Results Fevers were detected in 44 of 252 patients. Demographics, laboratory data were statistically similar within fever group and non-fever group. Strongest predictors identified in multivariate logistic regression included Iopiodol emulsion dose (OR, 1.081; 95%CI, 1.006-1.162), number of hepatoprotectants (OR, 0.619; 95%CI, 0.419-0.914), K+ (OR, 2.992; 95%CI, 1.225-7.308), and albumin-bilirubin (ALBI) grade (OR, 2.249; 95%CI, 1.040-4.862). Furthermore, the area under the ROC curve of derivation cohort and validation cohort were 0.798 and 0.874 respectively, which indicated comparative stability and discriminative ability of this model. Conclusions Iopiodol emulsion dose, number of hepatoprotectants, K+, and ALBI grade are strong predictors for PEF. The multivariate logistic model of these factors shows a discriminative ability to predict PEF in the validation cohort.

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A Nomogram for Predicting the Risk of Common Bile Duct Stones Based on Preoperative Laboratory Tests and Ultrasonography

10.21203/rs.3.rs-265606/v1 ◽

2021 ◽

Author(s):

Kai Wu ◽

Dong Wang ◽

Jiegao Zhu ◽

Kun Liu ◽

Hongwei Wu ◽

...

Keyword(s):

Logistic Regression ◽

Bile Duct ◽

Common Bile Duct ◽

Predictive Factors ◽

Roc Curve ◽

Laboratory Tests ◽

Multivariate Logistic Regression ◽

Training Set ◽

Validation Set ◽

Cbd Stone

Abstract Objective: The objective of this study was to determine the predictive factors for common bile duct (CBD) stone and establish a nomogram model based on the preoperative laboratory tests and imaging findings.Methods: A total of 1701 patients who underwent laparoscopic cholecystectomy (LC) combined with common bile duct exploration (CBDE) for suspected choledochlithiasis from November 2014 to October 2020 were eligible for this analysis. All patients were divided into the training set (from November 2014 to November 2019, n=1,453) and validation set (from November 2019 to October 2020, n=248) based on the admission time. The predictive factors for CBD stone were determined by the univariate and multivariate logistic regression model. A nomogram model for predicting the presence of CBD stone was developed based on significant variables, and receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA) were used to assess the predictive performance of the nomogram. Results: The results of multivariate logistic regression analysis demonstrated that multiple gallbladder stones (OR: 7.463, 95%CI: 5.437-10.243, P<0.001), the maximal diameter of CBD stone measured by preoperative ultrasonography (OR for 0.8-1.5 cm: 4.756, 95%CI: 3.513-6.438, P<0.001; OR for 1.5-2.0 cm: 9.597, 95%CI: 4.621-19.931, P<0.001; OR for >2.0 cm: 24.473, 95%CI: 2.809-213.207, P<0.001), preoperative GGT level (OR for 90-225 U/L: 2.828, 95%CI: 1.898-4.214, P<0.001; OR for 225-450 U/L: 9.994, 95%CI: 4.668-21.403, P<0.001; OR for >450 U/L: 12.535, 95%CI: 4.452-35.292, P<0.001) and DB/TB ratio (OR: 394.329, 95%CI: 79.575-1954.064, P<0.001) were independent predictive factors for CBD stone. The nomogram model for predicting the presence of CBD stone was developed based on the above-mentioned variables. ROC curve showed that the C-index of the nomogram model for the training set and validation set was 0.875 (95% CI: 0.857-0.893) and 0.834 (95% CI: 0.784-0.883), which were better than that of MRCP for preoperative diagnosis of CBD stone. The calibration curve and DCA curve demonstrated that the nomogram model had a good clinical utility for predicting the presence of CBD stone .Conclusion: The nomogram based on preoperative laboratory tests and ultrasonography had an excellent predictive power for CBD stone, and it might provide useful information for making treatment strategies.

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A novel procalcitonin-based score for detecting sepsis among critically ill patients

PLoS ONE ◽

10.1371/journal.pone.0245748 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0245748

Author(s):

Tung-Lin Tsui ◽

Ya-Ting Huang ◽

Wei-Chih Kan ◽

Mao-Sheng Huang ◽

Min-Yu Lai ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Critically Ill ◽

Critically Ill Patients ◽

Validation Cohort ◽

Multivariate Logistic Regression Analysis ◽

Sepsis Group ◽

C Reactive Protein ◽

Multivariate Logistic Regression ◽

Reactive Protein

Background Procalcitonin (PCT) has been widely investigated as an infection biomarker. The study aimed to prove that serum PCT, combining with other relevant variables, has an even better sepsis-detecting ability in critically ill patients. Methods We conducted a retrospective cohort study in a regional teaching hospital enrolling eligible patients admitted to intensive care units (ICU) between July 1, 2016, and December 31, 2016, and followed them until March 31, 2017. The primary outcome measurement was the occurrence of sepsis. We used multivariate logistic regression analysis to determine the independent factors for sepsis and constructed a novel PCT-based score containing these factors. The area under the receiver operating characteristics curve (AUROC) was applied to evaluate sepsis-detecting abilities. Finally, we validated the score using a validation cohort. Results A total of 258 critically ill patients (70.9±16.3 years; 55.4% man) were enrolled in the derivation cohort and further subgrouped into the sepsis group (n = 115) and the non-sepsis group (n = 143). By using the multivariate logistic regression analysis, we disclosed five independent factors for detecting sepsis, namely, “serum PCT level,” “albumin level” and “neutrophil-lymphocyte ratio” at ICU admission, along with “diabetes mellitus,” and “with vasopressor.” We subsequently constructed a PCT-based score containing the five weighted factors. The PCT-based score performed well in detecting sepsis with the cut-points of 8 points (AUROC 0.80; 95% confidence interval (CI) 0.74–0.85; sensitivity 0.70; specificity 0.76), which was better than PCT alone, C-reactive protein and infection probability score. The findings were confirmed using an independent validation cohort (n = 72, 69.2±16.7 years, 62.5% men) (cut-point: 8 points; AUROC, 0.79; 95% CI 0.69–0.90; sensitivity 0.64; specificity 0.87). Conclusions We proposed a novel PCT-based score that performs better in detecting sepsis than serum PCT levels alone, C-reactive protein, and infection probability score.

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Ectopic eruption of the maxillary second molar: Predictive factors

The Angle Orthodontist ◽

10.2319/020917-95 ◽

2017 ◽

Vol 87 (4) ◽

pp. 583-589 ◽

Cited By ~ 1

Author(s):

Soonshin Hwang ◽

Yoon Jeong Choi ◽

Ji Yeon Lee ◽

Chooryung Chung ◽

Kyung-Ho Kim

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Predictive Factors ◽

Multivariate Logistic Regression Analysis ◽

Key Factors ◽

Multivariate Logistic Regression ◽

Factors Associated ◽

Ectopic Eruption ◽

Maxillary Tuberosity

ABSTRACT Objective: The purpose of this study was to investigate the diagnostic aspects, contributing conditions, and predictive key factors associated with ectopic eruption of maxillary second molars. Material and Methods: This retrospective study evaluated the study models, lateral cephalographs, and panoramic radiographs of 40 adult subjects (20 men, 20 women) with bilateral ectopic eruption and 40 subjects (20 men, 20 women) with normal eruption of the maxillary second molars. Studied variables were analyzed statistically by independent t-tests, univariate and multivariate logistic regression analysis, followed by receiver-operating characteristic analysis. Results: Tooth widths of bilateral lateral incisors, canines, and premolars were wider in the ectopic group, which resulted in greater arch lengths. The ANB angle and maxillary tuberosity distance (PTV-M1, PTV-M2) were smaller in the ectopic group. The long axes of the maxillary molars showed significant distal inclination in the ectopic group. The multivariate logistic regression analysis showed that three key factors—arch length, ANB angle, and PTV-M1 distance—were significantly associated with ectopic eruption of the second molars. The area under the curve (AUC) was the largest for the combination of the three key factors with an AUC greater than 0.75. PTV-M1 alone was the single factor that showed the strongest association with ectopic eruption (AUC = 0.7363). Conclusions: An increase in arch length, decrease in ANB angle, and decrease in maxillary tuberosity distance to the distal aspect of the maxillary first molar (PTV-M1) were the most predictive factors associated with ectopic eruption of maxillary second molars.

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Predictive value of serum procalcitonin for both initial and repeated immunoglobulin resistance in Kawasaki disease: a prospective cohort study

Pediatric Rheumatology ◽

10.1186/s12969-019-0379-5 ◽

2019 ◽

Vol 17 (1) ◽

Cited By ~ 2

Author(s):

Shuran Shao ◽

Chunyan Luo ◽

Kaiyu Zhou ◽

Yimin Hua ◽

Mei Wu ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Kawasaki Disease ◽

Logistic Regression Analysis ◽

Predictive Value ◽

Laboratory Data ◽

Multivariate Logistic Regression Analysis ◽

Multivariate Logistic Regression ◽

Ivig Resistance ◽

Resistance Prediction

Abstract Background Intravenous immunoglobulin (IVIG) resistance prediction is one pivotal topic of interests in Kawasaki disease (KD) since those patients with KD resistant to IVIG might improve of an early-intensified therapy. Data regarding predictive value of procalcitonin (PCT) for IVIG resistance, particularly for repeated IVIG resistance in KD was limited. This study aimed to testify the predictive validity of PCT for both initial and repeated IVIG resistance in KD. Methods A total of 530 KD patients were prospectively recruited between January 2015 and March 2019. The clinical and laboratory data were compared between IVIG-responsive and IVIG-resistant groups. Multivariate logistic regression analysis was applied to determine the association between PCT and IVIG resistance. Receiver operating characteristic (ROC) curves analysis was further performed to assess the validity of PCT in predicting both initial and repeated IVIG resistance. Results The serum PCT level was significantly higher in initial IVIG-resistance group compared with IVIG-response group (p = 0.009), as well as between repeated IVIG responders and nonresponders (p = 0.017). The best PCT cutoff value for initial and repeated IVIG resistance prediction was 1.48 ng/ml and 2.88 ng/ml, respectively. The corresponding sensitivity was 53.9 and 51.4%, while the specificity were 71.8 and 73.2%, respectively. Multivariate logistic regression analysis failed to identify serum PCT level as an independent predictive factor for both initial and repeated IVIG resistance in KD. Conclusions Serum PCT levels were significantly higher in IVIG nonresponders, but PCT may not be suitable as a single marker to accurately predict both initial and repeated IVIG resistance in KD.

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Title: Preoperative albumin-to-fibrinogen ratio predicts postoperative pulmonary complications in patients after minimally invasive esophagectomy

10.21203/rs.3.rs-604731/v1 ◽

2021 ◽

Author(s):

Zhang Peng ◽

Zhao Song

Keyword(s):

Risk Factors ◽

Esophageal Cancer ◽

Logistic Regression ◽

Roc Curve ◽

Training Group ◽

Pulmonary Complications ◽

Curve Analysis ◽

Postoperative Pulmonary Complications ◽

Multivariate Logistic Regression ◽

Roc Curve Analysis

Abstract Background Postoperative pulmonary complications (PPCs) are the most common postoperative complications in patients with esophageal cancer. Prediction of PPCs by establishing a preoperative physiological function parameter model can help patients make adequate preoperative preparation, reduce treatment costs, and improve prognosis and quality of life. The purpose of this study was to investigate the relationship between albumin-to-fibrinogen ratio (AFR), prognostic nutritional index (PNI), albumin-to-globulin ratio (AGR), neutrophils-to-lymphocyte ratio (NLR), platelet-to-lymphocyte (PLR), and monocyte-to -lymphocyte ratio (MLR) and other preoperative laboratory tests and PPCs in patients after esophagectomy. Methods Retrospective analysis was performed on total 712 consecutive patients who underwent esophagectomy in the Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University from July 2018 to December 2020. Patients were divided into training (535 patients) and validation (177) groups for comparison of baseline data, perioperative indicators, and laboratory examination data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the efficacy, sensitivity and specificity of AFR, and Youden’s index was used to calculate the cut-off values of AFR. Univariate and multivariate logistic regression analyses were used to assess the risk factors for PPCs in training group. Results 112 (20.9%) in training group and 36 (20.3%) in validation group developed PPCs. The AUC value predicted by AFR using ROC curve analysis was 0.817, sensitivity 76.2% and specificity 78.7% in training group while AUC 0.803, sensitivity 69.4% and specificity 85.8%. Multivariate logistic regression analysis showed that smoking index, American Society of Anesthesiologists (ASA), AFR, and recurrent laryngeal nerve palsy were independent risk factors for PPCs. Conclusion Preoperative AFR can effectively predict the occurrence of PPCs in patients with esophageal cancer

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Predictive Factors of Chronic Post-Surgical Pain at 6 Months Following Knee Replacement: Influence of Postoperative Pain Trajectory and Genetics

January 2018 - Pain Physician ◽

10.36076/ppj/2019.19.e729 ◽

2016 ◽

Vol 5;19 (5;19) ◽

pp. E729-E741

Author(s):

Dr Célia Lloret-Linares

Keyword(s):

Logistic Regression ◽

Postoperative Pain ◽

Knee Replacement ◽

Predictive Factors ◽

High Intensity ◽

Walking Ability ◽

Single Center ◽

Multivariate Logistic Regression ◽

Postsurgical Pain ◽

Chronic Postsurgical Pain

Background: The frequency of chronic postsurgical pain (CPSP) after knee replacement remains high, but might be decreased by improvements to prevention. Objectives: To identify pre- and postsurgical factors predictive of CPSP 6 months after knee replacement. Study Design: Single-center prospective observational study. Setting: An orthopedic unit in a French hospital. Methods: Consecutive patients referred for total or unicompartmental knee arthroplasty from March to July 2013 were prospectively invited to participate in this study. For each patient, we recorded preoperative pain intensity, anxiety and depression levels, and sensitivity and pain thresholds in response to an electrical stimulus. We analyzed OPRM1 and COMT single-nucleotide polymorphisms. Acute postoperative pain (APOP) in the first 5 days after surgery was modeled by a pain trajectory. Changes in the characteristics and consequences of the pain were monitored 3 and 6 months after surgery. Bivariate analysis and multivariate logistic regression were conducted to identify predictors of CPSP. Results: We prospectively evaluated 104 patients in this study, 74 (28.8%) of whom reported CPSP at 6 months. Three preoperative factors were found to be associated with the presence of CPSP in multivariate logistic regression analysis: high school diploma level (OR = 3.83 [1.20 – 12.20]), consequences of pain in terms of walking ability, as assessed with the Brief Pain Inventory short form “walk” item (OR = 4.06 [1.18 – 13.94]), and a lack of physical activity in adulthood (OR = 4.01 [1.33 – 12.10]). One postoperative factor was associated with the presence of CPSP: a high-intensity APOP trajectory. An association of borderline statistical significance was found with the A allele of the COMT gene (OR = 3.4 [0.93 – 12.51]). Two groups of patients were identified on the basis of their APOP trajectory: high (n = 28, 26%) or low (n = 80, 74%) intensity. Patients with high-intensity APOP trajectory had higher anxiety levels and were less able to walk before surgery (P < 0.05). Limitations: This was a single-center study and the sample may have been too small for the detection of some factors predictive of CPSP or to highlight the role of genetic factors. Conclusion: Our findings suggest that several preoperative and postoperative characteristics could be used to facilitate the identification of patients at high risk of CPSP after knee surgery. All therapeutic strategies decreasing APOP, such as anxiety management or performing knee replacement before the pain has a serious effect on ability to walk, may help to decrease the risk of CPSP. Further prospective studies testing specific management practices, including a training program before surgery, are required. Key words: Chronic postsurgical pain, opioids, arthroplasty, pain trajectory, genetics, COMT, predictive factors

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A high triglyceride glucose index is more closely associated with hypertension than lipid or glycemic parameters in elderly individuals: a cross-sectional survey from the Reaction Study.

10.21203/rs.3.rs-20132/v2 ◽

2020 ◽

Author(s):

Binruo Zhu ◽

Jie Wang ◽

Kang Chen ◽

Wenhua Yan ◽

Anping Wang ◽

...

Keyword(s):

Logistic Regression ◽

Multivariate Logistic Regression Analysis ◽

Multivariate Logistic Regression ◽

Cross Sectional Survey ◽

Discriminative Ability ◽

Cross Sectional ◽

Elderly Individuals ◽

Reaction Study ◽

Tyg Index ◽

Risk Of Cancer

Abstract Background: Both lipid and glucose abnormalities are associated with hypertension (HTN). However, it is unclear whether the triglyceride-glucose (TyG) index is associated with HTN. Therefore the aim of this study is to investigate the association of the TyG index and HTN and to compare the discriminative power of the TyG index, lipid, glycemic parameters for the risk of HTN in elderly individuals.Methods: The present study was nested in a longitudinal (REACTION) study from May 2011 to December 2011, which was designed to demonstrate the association of abnormal glucose metabolism with the risk of cancer in the Chinese population. In total, 47808 participants were recruited in this cross-sectional study. The TyG index was divided into five groups: the <20% group, the 20-39% group, the 40-59% group, the 60-79% group and the ≥80% group, according to quintile division of the subjects. Three multivariate logistic regression models were used to evaluate the association between the TyG vs. lipid parameters, glycemic parameters and HTN.Results: Multivariate logistic regression analysis shows that compared with lipid and glycemic parameters, the TyG index remains significantly associated with HTN in either total subjects or subjects separated into men and women (odds ratio (OR) 1.33, 95% confidence interval (CI) 1.18-1.51, p <0.0001 in total subjects; OR 1.39, 95% CI 1.11-1.74, p=0.0042 in men; OR 1.28, 95% CI 1.11-1.49, p=0.0010 in women). In a stratified analysis, an elevated TyG index is significantly associated with HTN in the subgroup of the oldest age (≥65) (OR 1.67, 95% CI 1.30-2.14, p <0.0001), as well as with obesity (Body mass index (BMI) ≥28 kg/m2) (OR 1.85, 95% CI 1.29-2.66, p=0.0009) or lower estimated glomerular filtration rate (eGFR) (<90 mL/ (min·1.73 m2)) (OR 1.72, 95% CI 1.33-2.21, p <0.0001).Conclusion: The TyG index is significantly associated with HTN and shows the superior discriminative ability for HTN compared with lipid and glycemic parameters in the Chinese elderly population.

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Exploring the Value of Lung Texture Features in Distinguishing Usual and Non-specific Interstitial Pneumonia

10.21203/rs.3.rs-533242/v1 ◽

2021 ◽

Author(s):

Xinhui Chen ◽

Ge Cheng ◽

Xinguan Yang ◽

Yuting Liao ◽

Zhipeng Zhou

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Interstitial Pneumonia ◽

Roc Curve ◽

Logistic Regression Model ◽

Usual Interstitial Pneumonia ◽

Texture Features ◽

Feature Reduction ◽

Multivariate Logistic Regression ◽

Lung Segmentation

Abstract Backgground At present, the most common types of interstitial pneumonia are usual interstitial pneumonia (UIP) and non-specific interstitial pneumonia (NSIP), and different types have different prognosis. In addition, if there is a mixture of different classifications, it will be difficult for radiologists to diagnose, and it will make clinical treatment difficult. Therefore, clinicians urgently need new imaging methods to solve such problems. This article aims to explore the CT lung texture images of UIP and NSIP to provide evidence for the identification of UIP and NSIP. Methods A retrospective analysis of 96 cases of interstitial pneumonia diagnosed by the Department of Pathology and the Affiliated Hospital of Guilin Medical College. Among them, there are 40 cases of UIP and 56 cases of NSIP. All patients are scanned by CT. Lung Intelligence Kit was utilized to perform lung segmentation and texture feature extraction. Variance analysis, least absolute shrinkage and selection operator (Lasso) and multivariate logistic regression were used to select effective features. Finally, a multivariate logistic regression model was constructed to identify two kinds of interstitial pneumonia. Receiver operating characteristic (ROC) curve, area under the curve (AUC), sensitivity, specificity were used to evaluate performance of the constructed model. We used the LK software to segment the two sets of lungs. Feature calculation and selection were performed on the data of the two groups of interstitial pneumonia after lung segmentation, the logistic regression model was established for the selected features, and the ROC curve was drawn. Results A total of 100 texture features are extracted from the whole lung segmented by LK, and finally 8 features are left after feature reduction. The above-mentioned values of UIP and NSIP of the training group are greater than those of the test group. Conclusions It is possible to distinguish UIP and NSIP by using LK software to extract lung texture in CT images.

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Predicting COVID-19 malignant progression with AI techniques

10.1101/2020.03.20.20037325 ◽

2020 ◽

Cited By ~ 15

Author(s):

Xiang Bai ◽

Cong Fang ◽

Yu Zhou ◽

Song Bai ◽

Zaiyi Liu ◽

...

Keyword(s):

Logistic Regression ◽

Deep Learning ◽

Treatment Strategy ◽

Protective Factor ◽

Prediction Models ◽

Laboratory Data ◽

Malignant Progression ◽

Medical Systems ◽

Multivariate Logistic Regression ◽

Critical Stage

AbstractBackground and purposeThe worldwide pandemic of coronavirus disease 2019 (COVID-19) greatly challenges public medical systems. With limited medical resources, the treatment priority is determined by the severity of patients. However, many mild outpatients quickly deteriorate into severe/critical stage. It is crucial to early identify them and give timely treatment for optimizing treatment strategy and reducing mortality. This study aims to establish an AI model to predict mild patients with potential malignant progression.MethodsA total of 133 consecutively mild COVID-19 patients at admission who was hospitalized in Wuhan Pulmonary Hospital from January 3 to February 13, 2020, were selected in this retrospective IRB-approved study. All mild patients were categorized into groups with or without malignant progression. The clinical and laboratory data at admission, the first CT, and the follow-up CT at the severe/critical stage of the two groups were compared. Both multivariate logistic regression and deep learning-based methods were used to build the prediction models, with their area under ROC curves (AUC) compared.ResultsMultivariate logistic regression depicted 6 risk factors for malignant progression: age >55years (OR 5.334, 95%CI 1.8-15.803), comorbid with hypertension (OR 5.093, 95%CI 1.236-20.986), a decrease of albumin (OR 4.01, 95%CI 1.216-13.223), a decrease of lymphocyte (OR 3.459, 95%CI 1.067-11.209), the progressive consolidation from CT1 to CTsevere (OR 1.235, 95%CI 1.018-1.498), and elevated HCRP (OR 1.015, 95%CI 1.002-1.029); and one protective factor: the presence of fibrosis at CT1 (OR 0.656, 95%CI 0.473-0.91). By combining the clinical data and the temporal information of the CT data, our deep learning-based models achieved the best AUC of 0.954, which outperformed logistic regression (AUC: 0.893),ConclusionsOur deep learning-based methods can identify the mild patients who are easy to deteriorate into severe/critical cases efficiently and accurately, which undoubtedly helps to optimize the treatment strategy, reduce mortality, and relieve the medical pressure.

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High triglyceride glucose index is more closely associated with hypertension than lipid or glycemic parameters in elderly individuals: a cross-sectional survey from the Reaction Study.

10.21203/rs.3.rs-20132/v1 ◽

2020 ◽

Author(s):

Binruo Zhu ◽

Jie Wang ◽

Kang Chen ◽

Wenhua Yan ◽

Anping Wang ◽

...

Keyword(s):

Logistic Regression ◽

Multivariate Logistic Regression Analysis ◽

Multivariate Logistic Regression ◽

Cross Sectional Survey ◽

Discriminative Ability ◽

Cross Sectional ◽

Elderly Individuals ◽

Reaction Study ◽

Tyg Index ◽

Risk Of Cancer

Abstract Background: Both lipid and glucose abnormalities are associated with hypertension (HTN). However, it is unclear whether triglyceride-glucose (TyG) index is associated with HTN. Therefore, the aim of this study is to investigate the association of TyG index and HTN and compare the discriminative power of TyG index, lipid, glycemic parameters for the risk of HTN in the elderly individuals.Methods: The present study was nested in a longitudinal (REACTION) study from May 2011 to December 2011, which was designed to demonstrate the association of abnormal glucose metabolism with the risk of cancer in the Chinese population. 43591 participants were recruited in this cross-sectional study. TyG index were divided into 5 groups: <20% group, the 20-39% group, the 40-59% group, the 60-79% group and the ≥80% group according quartile division of the subjects. Three multivariate logistic regression models were used to evaluate the association between TyG v.s lipid parameters, glycemic parameters and HTN.Results: Multivariate logistic regression analysis shows that compared with lipid and glycemic parameters, TyG index remains significantly associated with HTN in either total subjects or subjects separated into men and women (odds ratio (OR) 1.33, 95% confidence interval (CI) 1.18-1.51, p <0.0001 in total subjects; OR 1.39, 95% CI 1.11-1.74, p=0.0042 in men; OR 1.28, 95% CI 1.11-1.49, p=0.0010 in women). In stratified analysis, elevated TyG index is significantly associated with HTN in the subgroup of oldest age (≥65) (OR 1.67, 95% CI 1.30-2.14, p <0.0001), obesity (Body mass index (BMI) ≥28 kg/m2) (OR 1.85, 95% CI 1.29-2.66, p 0.0009) or lower estimated glomerular filtration rate (eGFR) (<90 mL/ (min·1.73 m2)) (OR 1.72, 95% CI 1.33-2.21, p <0.0001).Conclusion: TyG index is significantly associated with HTN and shows the superior discriminative ability for HTN compared with lipid and glycemic parameters in the Chinese elderly population.

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