1.Spicy food consumption and risk of vascular disease: Evidence from a large-scale Chinese prospective cohort of 0.5 million people.
Dongfang YOU ; Dianjianyi SUN ; Ziyu ZHAO ; Mingyu SONG ; Lulu PAN ; Yaqian WU ; Yingdan TANG ; Mengyi LU ; Fang SHAO ; Sipeng SHEN ; Jianling BAI ; Honggang YI ; Ruyang ZHANG ; Yongyue WEI ; Hongxia MA ; Hongyang XU ; Canqing YU ; Jun LV ; Pei PEI ; Ling YANG ; Yiping CHEN ; Zhengming CHEN ; Hongbing SHEN ; Feng CHEN ; Yang ZHAO ; Liming LI
Chinese Medical Journal 2025;138(14):1696-1704
BACKGROUND:
Spicy food consumption has been reported to be inversely associated with mortality from multiple diseases. However, the effect of spicy food intake on the incidence of vascular diseases in the Chinese population remains unclear. This study was conducted to explore this association.
METHODS:
This study was performed using the large-scale China Kadoorie Biobank (CKB) prospective cohort of 486,335 participants. The primary outcomes were vascular disease, ischemic heart disease (IHD), major coronary events (MCEs), cerebrovascular disease, stroke, and non-stroke cerebrovascular disease. A Cox proportional hazards regression model was used to assess the association between spicy food consumption and incident vascular diseases. Subgroup analysis was also performed to evaluate the heterogeneity of the association between spicy food consumption and the risk of vascular disease stratified by several basic characteristics. In addition, the joint effects of spicy food consumption and the healthy lifestyle score on the risk of vascular disease were also evaluated, and sensitivity analyses were performed to assess the reliability of the association results.
RESULTS:
During a median follow-up time of 12.1 years, a total of 136,125 patients with vascular disease, 46,689 patients with IHD, 10,097 patients with MCEs, 80,114 patients with cerebrovascular disease, 56,726 patients with stroke, and 40,098 patients with non-stroke cerebrovascular disease were identified. Participants who consumed spicy food 1-2 days/week (hazard ratio [HR] = 0.95, 95% confidence interval [95% CI] = [0.93, 0.97], P <0.001), 3-5 days/week (HR = 0.96, 95% CI = [0.94, 0.99], P = 0.003), and 6-7 days/week (HR = 0.97, 95% CI = [0.95, 0.99], P = 0.002) had a significantly lower risk of vascular disease than those who consumed spicy food less than once a week ( Ptrend <0.001), especially in those who were younger and living in rural areas. Notably, the disease-based subgroup analysis indicated that the inverse associations remained in IHD ( Ptrend = 0.011) and MCEs ( Ptrend = 0.002) risk. Intriguingly, there was an interaction effect between spicy food consumption and the healthy lifestyle score on the risk of IHD ( Pinteraction = 0.037).
CONCLUSIONS
Our findings support an inverse association between spicy food consumption and vascular disease in the Chinese population, which may provide additional dietary guidance for the prevention of vascular diseases.
Humans
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Male
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Female
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Prospective Studies
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Middle Aged
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Aged
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Vascular Diseases/etiology*
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Risk Factors
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China/epidemiology*
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Adult
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Proportional Hazards Models
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Cerebrovascular Disorders/epidemiology*
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East Asian People
2.Development and multicenter validation of machine learning models for predicting postoperative pulmonary complications after neurosurgery.
Ming XU ; Wenhao ZHU ; Siyu HOU ; Hongzhi XU ; Jingwen XIA ; Liyu LIN ; Hao FU ; Mingyu YOU ; Jiafeng WANG ; Zhi XIE ; Xiaohong WEN ; Yingwei WANG
Chinese Medical Journal 2025;138(17):2170-2179
BACKGROUND:
Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery.
METHODS:
PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed.
RESULTS:
PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model.
CONCLUSIONS:
The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes.
TRIAL REGISTRATION
ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult
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Aged
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Female
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Humans
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Male
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Middle Aged
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Algorithms
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Lung Diseases/etiology*
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Machine Learning
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Neurosurgical Procedures/adverse effects*
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Postoperative Complications/diagnosis*
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Risk Factors
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ROC Curve
3.Analysis of the application mechanism of clinical biobank
Haiyan LI ; Mingyu NI ; Jinxi LI ; Yun ZHANG ; Yonghong ZHANG ; Yanning CAI ; Hong YOU ; Xiaomin WANG
Chinese Journal of Medical Science Research Management 2019;32(5):397-400
Objective Analyze the application status and existed problems of clinical biobank in China,propose possible application mechanisms for clinical biobank.Methods Through questionnaire survey and case analysis combined with relevant literature reports from home and abroad,conduct qualitative analysis to understand the application status and problems of clinical biobank.Results With the rapid development of biobank,its application rate was far from expected.The construction of clinical database lags behind in China will affect the application of samples.The lag of application mechanism will affect the opening and application of the biobank.Conclusions At the beginning of construction,the clinical biobank should take full account of what and how resources can be used,and establish tailored application mechanism.More attention should be paid to the possible benefit of biobank construction.

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