Ming-Jie Zhang’s research while affiliated with First Affiliated Hospital of China Medical University and other places

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Publications (2)


Flowchart of the study participants.
Frequency of occurrence of EE in the different cohorts of metabolic obesity according to the sex. Frequency of occurrence of EE in the (A) different phenotypes of metabolic obesity, and (B) according to the proportion of metabolic risk factors.
Association between the metabolic obesity phenotypes at baseline and risks of developing EE based on the sex.
Association between the metabolic obesity phenotypes at baseline and risks of developing EE based on the age.
Association Between Different Metabolic Obesity Phenotypes and Erosive Esophagitis: A Retrospective Study
  • Article
  • Full-text available

August 2024

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19 Reads

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1 Citation

Tao He

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Meng-Han Tong

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Zhi-Jun Duan

Background and Aim Obesity is association with elevated risks of erosive esophagitis (EE), and metabolic abnormalities play crucial roles in its development. The aim of the study was to assess the association between metabolic obesity phenotypes and the risk of EE. Methods This retrospective study enrolled 11,599 subjects who had undergone upper gastrointestinal endoscopy at the First Affiliated Hospital of Dalian Medical University from January 1, 2008, to December 31, 2023. The enrolled individuals were grouped into four cohorts based on their metabolic health and obesity profiles, namely, metabolically healthy non-obesity (MHNO; n=2134, 18.4%), metabolically healthy obesity (MHO; n=1736, 15.0%), metabolically unhealthy non-obesity (MUNO; n=4290, 37.0%), and metabolically unhealthy obesity (MUO; n=3439, 29.6%). The relationships of the different phenotypes of metabolic obesity with the risks of developing EE in the different sexes and age groups were investigated by multivariate logistic regression analysis. Results The MUNO, MHO, and MUO cohorts exhibited elevated risks of developing EE than the MHNO cohort. The confounding factors were adjusted for, and the findings revealed that the MUO cohort exhibited the greatest risk of EE, with odds ratios (ORs) of 5.473 (95% CI: 4.181–7.165) and 7.566 (95% CI: 5.718–10.010) for males and females, respectively. The frequency of occurrence of EE increased following an increase in proportion of metabolic risk factors. Subgroup analyses showed that the individuals under and over 60 years of age in the MHO, MUNO, and MUO cohorts exhibited elevated risks of developing EE. Further analysis suggested that obesity has a stronger influence on the risks of developing EE compared to metabolic disorders. Conclusion Metabolic disorders and obesity are both related with an elevated risk of EE, in which obesity has a potentially stronger influence. Clinical interventions should target both obesity and metabolic disorders to reduce EE risk.

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The distribution of propensity scores before and after PSM. (A) The distribution of propensity scores between the GERD group and the Non-GERD group before PSM. (B) The distribution of propensity scores between the GERD group and the Non-GERD group after PSM. PSM propensity score matching.
Spearman correlation analysis of demographics and esophageal HRM metrics. BMI body mass index, AET acid exposure time, EGJ-P EGJ rest pressure, EGJ-L EGJ length, DCI distal contractile integral, IEM ineffective esophageal motility, IEP intra-esophageal pressure, IGP intra-gastric pressure, GEG gastric-esophageal gradient, EGJ-RP EGJ retention pressure, EGJ-RI EGJ-RP integral, EGJ-CIA EGJ contractile integral (EGJ-CI) by the reference to atmospheric pressure, EGJ-CIG EGJ-CI by the reference to intra-gastric pressure, adEGJ-CIA EGJ-CIA adjusted with BMI, adEGJ-CIG EGJ-CIG adjusted with BMI, adEGJ-RP EGJ-RP adjusted with BMI, adEGJ-RI EGJ-RI adjusted with BMI.
ROC of esophageal HRM metrics and MNBI in predicting GERD susceptibility. (A) ROC of EGJ-CIG, EGJ-CIA, adEGJ-CIG and adEGJ-CIA in predicting GERD susceptibility. AUC of EGJ-CIG: 0.61 (95% CI 0.54–0.68). AUC of EGJ-CIA: 0.54 (95% CI 0.47–0.61). AUC of adEGJ-CIG: 0.68 (95% CI 0.62–0.74). AUC of adEGJ-CIA: 0.65 (95% CI 0.59–0.72). (B) ROC of EGJ-RP, EGJ-RI, adEGJ-RP and adEGJ-RI in predicting GERD susceptibility. AUC of EGJ-RP: 0.58 (95% CI 0.51–0.65). AUC of EGJ-RI: 0.59 (95% CI 0.53–0.66). AUC of adEGJ-RP: 0.67 (95% CI 0.60–0.73). AUC of adEGJ-RI: 0.67 (95% CI 0.61–0.74). (C) ROC of MNBIZ5, MNBIZ6, adMNBIZ5 and adMNBIZ6 in predicting GERD susceptibility. AUC of MNBIZ5: 0.87 (95% CI 0.83–0.92). AUC of MNBIZ6: 0.90 (95% CI 0.86–0.94). AUC of adMNBIZ5: 0.92 (95% CI 0.88–0.95). AUC of adMNBIZ6: 0.93 (95% CI 0.90–0.96). EGJ-CIG EGJ contractile integral (EGJ-CI) by the reference to intra-gastric pressure, EGJ-CIA EGJ-CI by the reference to atmospheric pressure, adEGJ-CIG EGJ-CIG adjusted with BMI, adEGJ-CIA EGJ-CIA adjusted with BMI, MNBIZ5 mean nocturnal baseline impedance channel 5, MNBIZ6 mean nocturnal baseline impedance channel 6, adMNBIZ5 MNBIZ5 adjusted with BMI, adMNBIZ6 MNBIZ6 adjusted with BMI, AUC area under curve, 95% CI 95% confidence interval.
Decision curve of esophageal HRM metrics and MNBI in predicting GERD susceptibility. (A) Decision curve of EGJ-CIG and adEGJ-CIG in predicting GERD susceptibility. (B) Decision curve of EGJ-CIA and adEGJ-CIA in predicting GERD susceptibility. (C) Decision curve of EGJ-RP and adEGJ-RP in predicting GERD susceptibility. (D) Decision curve of EGJ-RI and adEGJ-RI in predicting GERD susceptibility. (E) Decision curve of EGJ-CIG, EGJ-CIA, EGJ-RP and EGJ-RI in predicting GERD susceptibility. (F) Decision curve of adEGJ-CIG, adEGJ-CIA, adEGJ-RP and adEGJ-RI in predicting GERD susceptibility. (G) Decision curve of MNBIZ6, MNBIZ5, adMNBIZ6 and adMNBIZ5 in predicting GERD susceptibility. EGJ-RP EGJ retention pressure, EGJ-RI EGJ-RP integral, EGJ-CIA EGJ contractile integral (EGJ-CI) by the reference to atmospheric pressure, EGJ-CIG EGJ-CI by the reference to intra-gastric pressure, adEGJ-CIA EGJ-CIA adjusted with BMI, adEGJ-CIG EGJ-CIG adjusted with BMI, adEGJ-RP EGJ-RP adjusted with BMI, adEGJ-RI EGJ-RI adjusted with BMI, MNBIZ5 mean nocturnal baseline impedance channel 5, MNBIZ6 mean nocturnal baseline impedance channel 6, adMNBIZ5 MNBIZ5 adjusted with BMI, adMNBIZ6 MNBIZ6 adjusted with BMI.
Incorporating body mass index into esophageal manometry metrics and mean nocturnal baseline impedance for the evaluation of gastro-esophageal reflux disease

August 2024

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13 Reads

This study aims to enhance the effectiveness of high resolution manometry (HRM) and pH-impedance monitoring metrics in distinguishing between gastro-esophageal reflux disease (GERD) and non-GERD. A retrospective propensity score matching (PSM) study was conducted on 643 patients with GERD symptoms. PSM matched 134 GERD patients with 134 non-GERD controls. Body mass index (BMI), intra-esophageal pressure (IEP) and intra-gastric pressure (IGP) were significantly higher in the GERD group compared to the non-GERD group. BMI was correlated with IEP and IGP positively. IGP was positively correlated with esophagogastric (EGJ) pressure (EGJ-P) in participants with EGJ type 1 and 2, but not in participants with EGJ type 3. BMI was correlated with distal MNBI negatively. Logistic regression showed BMI as an independent risk factor for GERD. Receiver operating characteristic curve (ROC) and decision curve analysis (DCA) showed that BMI adjusted EGJ contractile integral (EGJ-CI) and BMI adjusted MNBI were superior to the corresponding original ones in predicting GERD susceptibility. According to the findings, BMI and IGP are the main factors contributing to the development of GERD. BMI affects IEP through the adaptive response of EGJ-P to IGP. Incorporating BMI into the calculations of EGJ-CI and MNBI can improve their ability in predicting GERD susceptibility.

Citations (1)


... -ожиріння та значний відсоток вісцерального жиру [11,15,21,31]; ...

Reference:

Risk factors for the formation of gastroesophageal reflux disease and erosive lesions of the esophagus in adolescents
Association Between Different Metabolic Obesity Phenotypes and Erosive Esophagitis: A Retrospective Study