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Yu J, Pu F, Yang G, Hao M, Zhang H, Zhang J, Cao X, Zhu L, Wan Y, Wang X, Liu Z. Sex-Specific Association Between Childhood Adversity and Accelerated Biological Aging. Adv Sci (Weinh) 2024:e2309346. [PMID: 38704685 DOI: 10.1002/advs.202309346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/19/2024] [Indexed: 05/07/2024]
Abstract
Is childhood adversity associated with biological aging, and if so, does sex modify the association, and do lifestyle and mental health mediate the association? A lifespan analysis is conducted using data on 142 872 participants from the UK Biobank to address these questions. Childhood adversity is assessed through the online mental health questionnaire (2016), including physical neglect, physical abuse, emotional neglect, emotional abuse, sexual abuse, and a cumulative score. Biological aging is indicated by telomere length (TL) measured from leukocyte DNA using qPCR, and the shorter TL indicates accelerated biological aging; a lifestyle score is constructed using body mass index, physical activity, drinking, smoking, and diet; mental disorder is assessed using depression, anxiety, and insomnia at the baseline survey. The results reveal a sex-specific association such that childhood adversity is associated with shorter TL in women after adjusting for covariates including polygenic risk score for TL, but not in men. Unhealthy lifestyle and mental disorder partially mediate the association in women. The proportions of indirect effects are largest for sexual and physical abuse. These findings highlight the importance of behavioral and psychological interventions in promoting healthy aging among women who experienced childhood adversity, particularly sexual and physical abuse.
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Affiliation(s)
- Jie Yu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Fan Pu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Gan Yang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Meng Hao
- Human Phenome Institute and State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Fudan University, Shanghai, 200433, China
| | - Hui Zhang
- Human Phenome Institute and State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Fudan University, Shanghai, 200433, China
- National Clinical Research Center for Ageing and Medicine, Huashan Hospital, Fudan University, Shanghai, 200433, China
| | - Jingyun Zhang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Xingqi Cao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Lijun Zhu
- Zhejiang Provincial Key Laboratory for Diagnosis and Treatment of Aging and Physic-chemical Injury Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Yuhui Wan
- MOE Key Laboratory of Population Health across Life Cycle/Anhui Provincial Key Laboratory of Population Health and Aristogenics, and Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei, 230032, China
| | - Xiaofeng Wang
- Human Phenome Institute and State Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Fudan University, Shanghai, 200433, China
- National Clinical Research Center for Ageing and Medicine, Huashan Hospital, Fudan University, Shanghai, 200433, China
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, China
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Ling B, Chen L, Zhang J, Cao X, Ye W, Ouyang Y, Chi F, Ding Z. [Dosimetric analysis of different optimization algorithms for three-dimensional brachytherapy for gynecologic tumors]. Nan Fang Yi Ke Da Xue Xue Bao 2024; 44:773-779. [PMID: 38708512 DOI: 10.12122/j.issn.1673-4254.2024.04.20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
OBJECTIVE To investigate the dosimetric difference between manual and inverse optimization in 3-dimensional (3D) brachytherapy for gynecologic tumors. METHODS This retrospective study was conducted among a total of 110 patients with gynecologic tumors undergoing intracavitary combined with interstitial brachytherapy or interstitial brachytherapy. Based on the original images, the brachytherapy plans were optimized for each patient using Gro, IPSA1, IPSA2 (with increased volumetric dose limits on the basis of IPSA1) and HIPO algorithms. The dose-volume histogram (DVH) parameters of the clinical target volume (CTV) including V200, V150, V100, D90, D98 and CI, and the dosimetric parameters D2cc, D1cc, and D0.1cc for the bladder, rectum, and sigmoid colon were compared among the 4 plans. RESULTS Among the 4 plans, Gro optimization took the longest time, followed by HIPO, IPSA2 and IPSA1 optimization. The mean D90, D98, and V100 of HIPO plans were significantly higher than those of Gro and IPSA plans, and D90 and V100 of IPSA1, IPSA2 and HIPO plans were higher than those of Gro plans (P < 0.05), but the CI of the 4 plans were similar (P > 0.05). For the organs at risk (OARs), the HIPO plan had the lowest D2cc of the bladder and rectum; the bladder absorbed dose of Gro plans were significantly greater than those of IPSA1 and HIPO (P < 0.05). The D2cc and D1cc of the rectum in IPSA1, IPSA2 and HIPO plans were better than Gro (P < 0.05). The D2cc and D1cc of the sigmoid colon did not differ significantly among the 4 plans. CONCLUSION Among the 4 algorithms, the HIPO algorithm can better improve dose coverage of the target and lower the radiation dose of the OARs, and is thus recommended for the initial plan optimization. Clinically, the combination of manual optimization can achieve more individualized dose distribution of the plan.
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Affiliation(s)
- B Ling
- Department of Radiation Medicine, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - L Chen
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - J Zhang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - X Cao
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - W Ye
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Y Ouyang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - F Chi
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Z Ding
- Department of Radiation Medicine, School of Public Health, Southern Medical University, Guangzhou 510515, China
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Han Y, Chen H, Cao X, Yin X, Zhang J. A novel perspective for exploring the relationship between cerebral small vessel disease and deep medullary veins with automatic segmentation. Clin Radiol 2024:S0009-9260(24)00188-0. [PMID: 38670919 DOI: 10.1016/j.crad.2024.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 03/26/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND This study aimed to establish an intelligent segmentation algorithm to count the number of deep medullary veins (DMVs) and analyze the relationship between DMVs and imaging markers of cerebral small vessel disease (CSVD). METHODS DMVs on magnetic resonance imaging (MRI) of patients with CSVD were counted by intelligent segmentation and manual counting. The dice coefficient and intraclass correlation coefficient (ICC) were used to evaluate their consistency and correlation. Structural MR images were used to assess imaging markers and total burden of CSVD. A multivariate linear regression model was used to evaluate the correlation between the number of DMVs counted by intelligent segmentation and imaging markers of CSVD, including white matter hyperintensities of the presumed vascular origin, lacune, perivascular spaces, cerebral microbleeds, and total CSVD burden. RESULTS A total of 305 patients with CSVD were enrolled. An intelligent segmentation algorithm was established to calculate the number of DMVs, and it was validated and tested. The number of DMVs counted intelligently significantly correlated with the manual counting method (r = 0.761, P< 0.001). The number of smart-counted DMVs negatively correlated with the imaging markers and total burden of CSVD (P< 0.001), and the correlation remained after adjusting for age and hypertension (P< 0.05). CONCLUSIONS The proposed intelligent segmentation algorithm, which was established to count DMVs, can provide objective and quantitative imaging information for the follow-up of patients with CSVD. DMVs are involved in CSVD pathogenesis and a likely new imaging marker for CSVD.
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Affiliation(s)
- Y Han
- Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China
| | - H Chen
- Academy for Engineering and Technology, Fudan University, Shanghai 200040, China
| | - X Cao
- Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China; National Center for Neurological Disorders, 12 Wulumuqi Middle Road, Shanghai 200040, China
| | - X Yin
- Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China
| | - J Zhang
- Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Fudan University, 12 Wulumuqi Middle Road, Shanghai 200040, China; National Center for Neurological Disorders, 12 Wulumuqi Middle Road, Shanghai 200040, China.
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Lua CZB, Gao Y, Li J, Cao X, Lyu X, Tu Y, Jin S, Liu Z. Influencing Factors of Healthy Aging Risk Assessed Using Biomarkers: A Life Course Perspective. China CDC Wkly 2024; 6:219-224. [PMID: 38532748 PMCID: PMC10961214 DOI: 10.46234/ccdcw2024.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 01/23/2024] [Indexed: 03/28/2024] Open
Abstract
Assessing individual risks of healthy aging using biomarkers and identifying associated factors have become important areas of research. In this study, we conducted a literature review of relevant publications between 2018 and 2023 in both Chinese and English databases. Previous studies have predominantly used single biomarkers, such as C-reactive protein, or focused on specific life course stages and factors such as socioeconomic status, mental health, educational levels, and unhealthy lifestyles. By summarizing the progress in this field, our study provides valuable insights and future directions for promoting healthy aging from a life course perspective.
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Affiliation(s)
- Cedric Zhang Bo Lua
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou City, Zhejiang Province, China
| | - Yajie Gao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou City, Zhejiang Province, China
| | - Jinming Li
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou City, Zhejiang Province, China
| | - Xingqi Cao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou City, Zhejiang Province, China
| | - Xinwei Lyu
- Institute of Epidemiology and Health Care, University College London, London, UK
| | - Yinuo Tu
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou City, Zhejiang Province, China
| | - Shuyi Jin
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou City, Zhejiang Province, China
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou City, Zhejiang Province, China
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Wang X, Cao X, Yu J, Jin S, Li S, Chen L, Liu Z, Ge X, Lu Y. Associations of perceived stress with loneliness and depressive symptoms: the mediating role of sleep quality. BMC Psychiatry 2024; 24:172. [PMID: 38429635 PMCID: PMC10905934 DOI: 10.1186/s12888-024-05609-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 02/14/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Whether perceived stress is associated with loneliness and depressive symptoms in general adults, and to what extent sleep quality mediates the associations, remains unknown. The aim of this study was to estimate the associations of perceived stress with loneliness and depressive symptoms, and the mediating role of sleep quality in these associations. METHODS Cross-sectional data on 734 participants (aged 18-87 years) were analyzed. Perceived stress was assessed using the 10-item Perceived Stress Scale (PSS-10; range 0-40). Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI; range 0-21). Loneliness was assessed using the three-item short form of the Revised University of California, Los Angeles (UCLA) loneliness scale (range 3-9). Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression (CESD-10) Scale (range 0-30). General linear regression models, multivariable logistic regression models, and formal mediation analysis were performed. RESULTS After adjustment for age and sex, we found that with each 1-point increment in the perceived stress score, both the loneliness score (β = 0.07; 95% confidence interval [CI]: 0.06, 0.08) and depression score (β = 0.45; 95% CI: 0.40, 0.49) increased significantly. Robust results were observed when adjusting for more confounders. Furthermore, sleep quality mediated 5.3% (95% CI: 1.3%, 10.0%; P = 0.014) and 9.7% (95% CI: 6.2%, 14.0%; P < 0.001) of the associations of perceived stress score with loneliness score and depression score, respectively. CONCLUSIONS In general Chinese adults, perceived stress was positively associated with loneliness and depressive symptoms, and sleep quality partially mediated these associations. The findings reveal a potential pathway from perceived stress to mental health through sleep behaviors, and highlight the importance of implementing sleep intervention programs for promoting mental health among those who feel highly stressed.
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Affiliation(s)
- Xiao Wang
- Department of General Practice, Dongyang People's Hospital, 322100, Jinhua, Zhejiang, China
| | - Xingqi Cao
- Center for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 310058, Hangzhou, Zhejiang, China
| | - Jiening Yu
- Center for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 310058, Hangzhou, Zhejiang, China
| | - Shuyi Jin
- Center for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 310058, Hangzhou, Zhejiang, China
| | - Shengyi Li
- School of Mathematical Sciences, University of Nottingham, University Park, NG7 2RD, Nottingham, UK
| | - Liying Chen
- Department of General Practice, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, 310016, Hangzhou, Zhejiang, China
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 310058, Hangzhou, Zhejiang, China.
| | - Xuan Ge
- Health Management Center, Dongyang People's Hospital, 322100, Jinhua, Zhejiang, China.
| | - Yangzhen Lu
- Department of General Practice, Dongyang People's Hospital, 322100, Jinhua, Zhejiang, China.
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Zhang L, Chen S, Cao X, Yu J, Yang Z, Abdelrahman Z, Yang G, Wang L, Zhang X, Zhu Y, Wu S, Liu Z. Trajectories of Body Mass Index and Waist Circumference in Relation to the Risk of Cardiac Arrhythmia: A Prospective Cohort Study. Nutrients 2024; 16:704. [PMID: 38474832 DOI: 10.3390/nu16050704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 02/14/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND The aim of the current study was to explore the trajectories, variabilities, and cumulative exposures of body mass index (BMI) and waist circumference (WC) with cardiac arrhythmia (CA) risks. METHODS In total, 35,739 adults from the Kailuan study were included. BMI and WC were measured repeatedly during the 2006-2010 waves. CA was identified via electrocardiogram diagnosis. BMI and WC trajectories were fitted using a group-based trajectory model. The associations were estimated using Cox proportional hazards models. RESULTS We identified four stable trajectories for BMI and WC, respectively. Neither the BMI trajectories nor the baseline BMI values were associated with the risk of CA. Compared to the low-stable WC group, participants in the high-stable WC group had a higher risk of CA (hazard ratio (HR) = 1.40, 95% confidence interval (CI): 1.06, 1.86). Interestingly, the cumulative exposures of BMI and WC instead of their variabilities were associated with the risk of CA. In the stratified analyses, the positive associations of the high-stable WC group with the risk of CA were found in females only (HR = 1.98, 95% CI: 1.02, 3.83). CONCLUSIONS A high-stable WC trajectory is associated with a higher risk of CA among Chinese female adults, underscoring the potential of WC rather than BMI to identify adults who are at risk.
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Affiliation(s)
- Liming Zhang
- Second Affiliated Hospital, and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Shuohua Chen
- Department of Cardiology, Kailuan General Hospital, Hebei United University, Tangshan 063000, China
| | - Xingqi Cao
- Second Affiliated Hospital, and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Jiening Yu
- Second Affiliated Hospital, and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Zhenqing Yang
- Second Affiliated Hospital, and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Zeinab Abdelrahman
- Centre for Public Health, Queen's University of Belfast, Belfast BT12 6BA, UK
| | - Gan Yang
- Second Affiliated Hospital, and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Liang Wang
- Department of Public Health, Robbins College of Human Health and Sciences, Baylor University, Waco, TX 76711, USA
| | - Xuehong Zhang
- Department of Nutrition, Harvard T.H. Chan School of Public Health; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Yimin Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University, Hangzhou 310058, China
| | - Shouling Wu
- Department of Cardiology, Kailuan General Hospital, Hebei United University, Tangshan 063000, China
| | - Zuyun Liu
- Second Affiliated Hospital, and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
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Yang G, Cao X, Yu J, Li X, Zhang L, Zhang J, Ma C, Zhang N, Lu Q, Wu C, Chen X, Hoogendijk EO, Gill TM, Liu Z. Association of Childhood Adversity With Frailty and the Mediating Role of Unhealthy Lifestyle: A Lifespan Analysis. Am J Geriatr Psychiatry 2024; 32:71-82. [PMID: 37770350 PMCID: PMC11078585 DOI: 10.1016/j.jagp.2023.08.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/10/2023] [Accepted: 08/23/2023] [Indexed: 09/30/2023]
Abstract
OBJECTIVES Childhood adversity and lifestyle have been associated with frailty in later life, but not much is known about factors that may explain these associations. Therefore, this study aims to investigate the association of childhood adversity with frailty, and the mediating role of unhealthy lifestyle in the association. METHODS This lifespan analysis included 152,914 adults aged 40-69 years old from the UK Biobank. We measured childhood adversity with five items: physical neglect, emotional neglect, sexual abuse, physical abuse, and emotional abuse through online mental health survey. Frailty was measured by the frailty index; an unhealthy lifestyle score (range: 0-5) was calculated based on unhealthy body mass index, smoking, alcohol consumption, physical inactivity, and unhealthy diet at the baseline survey. Multiple logistic regression and mediation analysis were performed. RESULTS A total of 10,078 participants (6.6%) were defined as having frailty. Participants with any childhood adversity had higher odds of frailty. For example, in the fully adjusted model, with a one-point increase in cumulative score of childhood adversity, the odds of frailty increased by 38% (odds ratio: 1.38; 95% Confidence Interval: 1.36, 1.40). Unhealthy lifestyle partially mediated the associations of childhood adversity with frailty (mediation proportion: 4.4%-7.0%). The mediation proportions were largest for physical (8.2%) and sexual (8.1%) abuse. CONCLUSIONS Childhood adversity was positively associated with frailty, and unhealthy lifestyle partially mediated the association. This newly identified pathway highlights the potential of lifestyle intervention strategies among those who experienced childhood adversity (in particular, physical, and sexual abuse) to promote healthy aging.
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Affiliation(s)
- Gan Yang
- Second Affiliated Hospital, and School of Public Health (GY, XC, JY, XL, LZ, JZ, ZL), The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xingqi Cao
- Second Affiliated Hospital, and School of Public Health (GY, XC, JY, XL, LZ, JZ, ZL), The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jie Yu
- Second Affiliated Hospital, and School of Public Health (GY, XC, JY, XL, LZ, JZ, ZL), The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xueqin Li
- Second Affiliated Hospital, and School of Public Health (GY, XC, JY, XL, LZ, JZ, ZL), The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Liming Zhang
- Second Affiliated Hospital, and School of Public Health (GY, XC, JY, XL, LZ, JZ, ZL), The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jingyun Zhang
- Second Affiliated Hospital, and School of Public Health (GY, XC, JY, XL, LZ, JZ, ZL), The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chao Ma
- School of Economics and Management (CM), Southeast University, Nanjing, Jiangsu, China
| | - Ning Zhang
- Department of Social Medicine School of Public Health and Center for Clinical Big Data and Analytics Second Affiliated Hospital (NZ), Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Qingyun Lu
- School of Public Health (QL), Nantong University, Nantong, JiangSu, China
| | - Chenkai Wu
- Global Health Research Center (CW), Duke Kunshan University, Kunshan, Jiangsu, China
| | - Xi Chen
- Department of Health Policy and Management (XC), Yale School of Public Health, New Haven, CT, USA; Department of Economics (XC), Yale University, New Haven, CT, USA
| | - Emiel O Hoogendijk
- Department of Epidemiology & Data Science (EOH), Amsterdam Public Health research institute, Amsterdam UMC-Location VU University Medical Center, Amsterdam, The Netherlands
| | - Thomas M Gill
- Department of Internal Medicine (TMG), Yale School of Medicine, New Haven, CT, USA
| | - Zuyun Liu
- Second Affiliated Hospital, and School of Public Health (GY, XC, JY, XL, LZ, JZ, ZL), The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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Li X, Cao X, Zhang J, Fu J, Mohedaner M, Zhuogadanzeng, Sun X, Yang G, Yang Z, Kuo CL, Chen X, Cohen AA, Liu Z. Accelerated aging mediates the associations of unhealthy lifestyles with cardiovascular disease, cancer, and mortality. J Am Geriatr Soc 2024; 72:181-193. [PMID: 37789775 PMCID: PMC11078652 DOI: 10.1111/jgs.18611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/16/2023] [Accepted: 08/24/2023] [Indexed: 10/05/2023]
Abstract
BACKGROUND With two well-validated aging measures capturing mortality and morbidity risk, this study examined whether and to what extent aging mediates the associations of unhealthy lifestyles with adverse health outcomes. METHODS Data were from 405,944 adults (40-69 years) from UK Biobank (UKB) and 9972 adults (20-84 years) from the US National Health and Nutrition Examination Survey (NHANES). An unhealthy lifestyles score (range: 0-5) was constructed based on five factors (smoking, drinking, physical inactivity, unhealthy body mass index, and unhealthy diet). Two aging measures, Phenotypic Age Acceleration (PhenoAgeAccel) and Biological Age Acceleration (BioAgeAccel) were calculated using nine and seven blood biomarkers, respectively, with a higher value indicating the acceleration of aging. The outcomes included incident cardiovascular disease (CVD), incident cancer, and all-cause mortality in UKB; CVD mortality, cancer mortality, and all-cause mortality in NHANES. A general linear regression model, Cox proportional hazards model, and formal mediation analysis were performed. RESULTS The unhealthy lifestyles score was positively associated with PhenoAgeAccel (UKB: β = 0.741; NHANES: β = 0.874, all p < 0.001). We further confirmed the respective associations of PhenoAgeAccel and unhealthy lifestyles with the outcomes in UKB and NHANES. The mediation proportion of PhenoAgeAccel in associations of unhealthy lifestyles with incident CVD, incident cancer, and all-cause mortality were 20.0%, 17.8%, and 26.6% (all p < 0.001) in UKB, respectively. Similar results were found in NHANES. The findings were robust when using another aging measure-BioAgeAccel. CONCLUSIONS Accelerated aging partially mediated the associations of lifestyles with CVD, cancer, and mortality in UK and US populations. The findings reveal a novel pathway and the potential of geroprotective programs in mitigating health inequality in late life beyond lifestyle interventions.
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Affiliation(s)
- Xueqin Li
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Xingqi Cao
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Jingyun Zhang
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Jinjing Fu
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Mayila Mohedaner
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Zhuogadanzeng
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Xiaoyi Sun
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Gan Yang
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Zhenqing Yang
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Chia-Ling Kuo
- Department of Community Medicine and Health Care, Connecticut Convergence Institute for Translation in Regenerative Engineering, Institute for Systems Genomics, University of Connecticut Health, Farmington, CT 06030, USA
| | - Xi Chen
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT 06520, USA
- Department of Economics, Yale University, New Haven, CT 06520, USA
| | - Alan A Cohen
- Department of Family Medicine, Research Centre on Aging, CHUS Research Centre, University of Sherbrooke, Sherbrooke, QC, Canada
- Butler Columbia Aging Center and Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
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Li X, Cao X, Ying Z, Yang G, Hoogendijk EO, Liu Z. Corrigendum to "Plasma superoxide dismutase activity in relation to disability in activities of daily living and objective physical functioning among Chinese older adults" [Maturitas, Volume 161, July 2022, Pages 12-17]. Maturitas 2023; 178:107850. [PMID: 37833184 DOI: 10.1016/j.maturitas.2023.107850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2023]
Affiliation(s)
- Xueqin Li
- The Second Affiliated Hospital, School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, Zhejiang, China
| | - Xingqi Cao
- The Second Affiliated Hospital, School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, Zhejiang, China
| | - Zhimin Ying
- Department of Orthopedic Surgery, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Guangtao Yang
- School of Laboratory Medicine and School of Life Sciences, Wenzhou Medical University, Wenzhou, China
| | - Emiel O Hoogendijk
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC-location VU University Medical Center, Amsterdam, the Netherlands
| | - Zuyun Liu
- The Second Affiliated Hospital, School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, Zhejiang, China.
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Jin S, Li C, Miao J, Sun J, Yang Z, Cao X, Sun K, Liu X, Ma L, Xu X, Liu Z. Sociodemographic Factors Predict Incident Mild Cognitive Impairment: A Brief Review and Empirical Study. J Am Med Dir Assoc 2023; 24:1959-1966.e7. [PMID: 37716705 DOI: 10.1016/j.jamda.2023.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 08/15/2023] [Accepted: 08/17/2023] [Indexed: 09/18/2023]
Abstract
OBJECTIVES Mild cognitive impairment (MCI) is a transitional stage between normal cognitive aging and dementia that increases the risk of progressive cognitive decline. Early prediction of MCI could be beneficial for identifying vulnerable individuals in the community and planning primary and secondary prevention to reduce the incidence of MCI. DESIGN A narrative review and cohort study. SETTING AND PARTICIPANTS We review the MCI prediction based on the assessment of sociodemographic factors. We included participants from 3 surveys: 8915 from wave 2011/2012 of the China Health and Retirement Longitudinal Study (CHARLS), 9765 from the 2011 Chinese Longitudinal Healthy Longevity Survey (CLHLS), and 1823 from the 2014 Rugao Longevity and Ageing Study (RuLAS). METHODS We searched in PubMed, Embase, and Web of Science Core Collection between January 1, 2019, and December 30, 2022. To construct the composite risk score, a multivariate Cox proportional hazards regression model was used. The performance of the score was assessed using receiver operating characteristic (ROC) curves. Furthermore, the composite risk score was validated in 2 longitudinal cohorts, CLHLS and RuLAS. RESULTS We concluded on 20 articles from 892 available. The results suggested that the previous models suffered from several defects, including overreliance on cross-sectional data, low predictive utility, inconvenient measurement, and inapplicability to developing countries. Our empirical work suggested that the area under the curve for a 5-year MCI prediction was 0.861 in CHARLS, 0.797 in CLHLS, and 0.823 in RuLAS. We designed a publicly available online tool for this composite risk score. CONCLUSIONS AND IMPLICATIONS Attention to these sociodemographic factors related to the incidence of MCI can be beneficially incorporated into the current work, which will set the stage for better early prediction of MCI before its incidence and for reducing the burden of the disease.
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Affiliation(s)
- Shuyi Jin
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Chenxi Li
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jiani Miao
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jingyi Sun
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhenqing Yang
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xingqi Cao
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Kaili Sun
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaoting Liu
- School of Public Affairs, Zhejiang University, Hangzhou, Zhejiang, China
| | - Lina Ma
- Department of Geriatrics, Xuanwu Hospital Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Xin Xu
- Department of Big Data in Health Science School of Public Health, and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, School of Medicine, Zhejiang University, China.
| | - Zuyun Liu
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
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Zhou YQ, Xu JK, Yin GP, Cao X, Li JJ, Zhang YH, Ye JY. [Characteristics of genioglossus neuromuscular activity in patients with obstructive sleep apnea during drug-induced sleep]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2023; 58:951-958. [PMID: 37840159 DOI: 10.3760/cma.j.cn115330-20221104-00661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Objective: To analyze genioglossus (GG) activation responses to the negative pressure of upper airway cavity during awake and different sleep stages in patients with different obstructive sleep apnea (OSA) graduation. Methods: This prospective cohort study started from August 2019 to January 2021, recruited 42 male OSA patients aged from 21 to 59 (38.77±8.42) years. After completing whole night polysomnography (PSG) and upper airway CT, each subject underwent drug-induced sleep with simultaneous monitoring of genioglossal electromyography (GGEMG) and pressure of epiglottis (Pepi). Subjects were divided into three groups of mild OSA(7 males), moderate OSA(12 males), and severe OSA(23 males). The differences in upper airway CT measurements, parameters of GGEMG and Pepi during awake and induced sleep were compared. Statistical analysis was conducted by SPSS 21.0. Results: There was no significant difference in the GGEMG parameters between the mild and moderate groups. In wakefulness, the peak phasic GGEMG of the severe group was higher than the mild group (t=1.249, P=0.025), with no statistically difference in the corresponding Pepi. In the sleep onset, the GGEMG parameters and Pepi in severe group were higher than the other two groups. Linear regression analysis of the maximum GGEMG and maximum Pepi at the end of obstructive apnea (OA) in all moderate plus severe patients (n=35) was shown nonlinear correlation (r=0.28, P=0.694). The airway length of the glossopharyngeal cavity was linearly correlated with the maximum Pepi of OA (r=0.468, R2=0.219, P=0.005). Conclusions: The individual difference of GG activation in OSA patients is related to the severity of the disease (frequency of respiratory events) and negative pressure stimulation. In moderate and severe OSA patients, GG activity is not in harmony with the corresponding negative pressure stimulation, which may be one of the mechanisms leading to the aggravation of OSA.
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Affiliation(s)
- Y Q Zhou
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
| | - J K Xu
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
| | - G P Yin
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
| | - X Cao
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
| | - J J Li
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
| | - Y H Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
| | - J Y Ye
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
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Cao X, Ye JY. [Interpret the indications of OSA surgery: case analysis of the TCM scoring system-Ⅲ]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2023; 58:1018-1023. [PMID: 37840169 DOI: 10.3760/cma.j.cn115330-20230116-00027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Affiliation(s)
- X Cao
- Department of Otorhinopharyngology Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 100218, China
| | - J Y Ye
- Department of Otorhinopharyngology Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 100218, China
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Chen EX, Tong JH, Che G, She ZF, Cao X. Comparison between oral and enteral tube refeeding in hyperlipidemic acute pancreatitis. Eur Rev Med Pharmacol Sci 2023; 27:9309-9314. [PMID: 37843344 DOI: 10.26355/eurrev_202310_33958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
OBJECTIVE Hyperlipidemic acute pancreatitis (HLAP) remains one of the major digestive emergencies with increasing health risks. Oral refeeding tolerant (ORT) and enteral tube feeding tolerant (ETFT) are commonly used for nutritional management in HLAP. However, the differences between ORT and ETFT are yet to be characterized. PATIENTS AND METHODS This study included consecutive patients admitted to the Ordos Central Hospital between January 2019 and April 2023, with predefined inclusion criteria. RESULTS A total of 335 HLAP patients were recruited according to the inclusion criteria. 268 patients were diagnosed with moderately severe acute pancreatitis (MSAP), of which 193 were in the OFT group and 75 in the ETFT group. In the ETFT group, abdominal pain and abdominal distension were significantly higher than that in the OFT group. No significant result was identified in the laboratory data. However, the OFT group showed a higher hospitalization and cost, as well as exocrine insufficiency and newly onset diabetes, than the ETFT group. CONCLUSIONS Based on the incidence of HLAP retrieved in this study, MSAP is the major type with increasing clinical value. From the nutritional management sense, patients who received OFT showed higher hospitalization and cost, as well as lower exocrine insufficiency and newly onset diabetes.
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Affiliation(s)
- E-X Chen
- Department of General Surgery, Ordos Central Hospital, Inner Mongolia, China.
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Edwards DM, Hopkins A, Scott A, Mannan R, Cao X, Zhang L, Andren A, Heth JA, Muraszko K, Sagher O, Orringer D, Hollon T, Hervey-Jumper S, Venneti S, Camelo-Piragua S, Al-Holou W, Chinnaiyan A, Lyssiotis CA, Wahl DR. Identification of Excellent Prognosis IDH Wildtype Glioblastomas Using Genomic and Metabolic Profiling. Int J Radiat Oncol Biol Phys 2023; 117:e101. [PMID: 37784627 DOI: 10.1016/j.ijrobp.2023.06.870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) High grade gliomas (HGGs) are aggressive brain tumors with altered cellular metabolism. HGGs can carry mutations in the tricarboxylic acid (TCA) cycle enzyme isocitrate dehydrogenase 1 (IDH1), conferring distinct biology and improved patient prognosis compared to IDH wildtype (wt) tumors. Using metabolomic analyses of tumor tissue, we previously showed that IDH wt and IDH mutant (IDH mut) tumors have unique metabolomic signatures that correlate with different survival outcomes. Among this cohort of 69 HGG samples, we identified two unique patient tumors that metabolically clustered with IDH mut tumors, but lacked both the IDH mutation and its product 2-hydroxyglutarate. We aimed to discover unique mutations in these two tumors that may impart an IDH mutant-like phenotype in the absence of an IDH1 or IDH2 mutation. MATERIALS/METHODS Whole exome sequencing (WES) was performed on frozen tumor samples from two patients diagnosed as glioblastoma (GBM), IDH wt via Agilent v5 + IncRNA platform. Alignment to the hg38 genome and variant calling were completed using an accelerated implementation of GATK's BWA and MuTect2 algorithms from Sentieon. Variants were filtered based on supporting reads and variant allele thresholds, with synonymous variants and common SNPs removed. High-confidence variants were further filtered by membership in the four KEGG pathways associated with IDH1 and IDH2. Identified variants were corroborated with metabolomics data from the two unique IDH wt tumors compared with classical GBM IDH wt, oligodendrogliomas IDH mut and astrocytomas IDH mut to identify putative drivers of an IDH mutant-like metabolomic phenotype in these unique IDH wt tumors. RESULTS Despite the lack of an IDH mutation, one patient survived 45.6 months and the other patient remains alive at last follow up 64 months post diagnosis, much longer than the 16-18-month median survival typical of patients with GBM IDH wt. WES of outlier IDH wt tumor samples revealed 65 unique mutations in the queried KEGG pathways, of which 34 had a variant allele frequency > = 0.15. These variants were processed in Gprofiler, confirming expected enrichment of the carboxylic acid metabolic biologic process, a functional gene set consisting of TCA genes, among these variants (p = 0.002, 3.6-fold enrichment). Accordingly, metabolite levels of intermediates of the TCA cycle, including malate and isocitrate were decreased in the outlier tumor samples compared to classic GBMs IDH wt (p<0.001). Presence of genetic alterations in key variants of the carboxylic acid metabolic biologic process (including ME1, GYP4F3, PTGIS, PFKL, PSPH, AKR1A1, HK2, NOS1) correlated with improved overall survival among GBM patients in the TCGA (p = 0.04). Laboratory validation of these findings in preclinical GBM models is ongoing. CONCLUSION Disruption of the TCA cycle independent of an IDH mutation is associated with favorable survival in GBM. Pharmacologic inhibition of these pathways may be a promising strategy to improve GBM outcomes.
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Affiliation(s)
- D M Edwards
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - A Hopkins
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI
| | - A Scott
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - R Mannan
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI
| | - X Cao
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI
| | - L Zhang
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI
| | - A Andren
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI
| | - J A Heth
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI
| | - K Muraszko
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI
| | - O Sagher
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI
| | - D Orringer
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI
| | - T Hollon
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI
| | - S Hervey-Jumper
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI
| | - S Venneti
- Department of Pathology, University of Michigan, Ann Arbor, MI
| | | | - W Al-Holou
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI
| | - A Chinnaiyan
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI
| | - C A Lyssiotis
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI
| | - D R Wahl
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
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Qin LH, Chen L, Cao X, Huang TJ, Li ZY, Li S, Wang GZ. The identification of sex-specific biomarkers in peripheral blood mononuclear cells from elderly individuals with ischemic stroke. Eur Rev Med Pharmacol Sci 2023; 27:6496-6509. [PMID: 37522661 DOI: 10.26355/eurrev_202307_33120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
OBJECTIVE The aim of this study was to identify sex-specific biomarkers for ischemic stroke (IS) prophylaxis in elderly individuals. MATERIALS AND METHODS The GSE22255 dataset for elderly individuals with IS was retrieved from the gene expression omnibus database. Thereafter, gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, as well as gene set enrichment analysis (GSEA). Furthermore, protein-protein interactions (PPIs) were explored using the STRING database, and to screen central genes from the Cytoscape PPI network, corresponding to peripheral blood samples from elderly individuals, we used the molecular complex detection plug-in and cytoHubba. Moreover, a Venn diagram was used to visualize the key genes common among elderly women and men with IS. Statistical analysis was also performed, and receiver operating characteristic (ROC) curves were constructed to evaluate the specificity and sensitivity of the prediction of IS in the elderly. RESULTS Compared with the healthy controls, in elderly women with IS, 511 biological process (BP) terms, 16 molecular function (MF) terms, and 34 KEGG terms were significantly enriched, whereas in the elderly men with IS, 681 BP terms, 12 MF terms, and 44 KEGG terms were enriched. The GSEA revealed 99 and 140 significantly enriched gene sets in elderly women and men with IS, respectively. Furthermore, in the PPI network, 10 hub genes for each sex with high specificity and sensitivity were identified using ROC curves. CONCLUSIONS Ten genes for each sex with significant differential expression were also identified in individuals with IS. The novel sex-specific gene targets may be promising diagnostic or prognostic markers and potential therapeutic targets for IS in the elderly.
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Affiliation(s)
- L-H Qin
- School of Nursing, Hunan University of Chinese Medicine, Changsha, China.
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Li C, Guo J, Zhao Y, Sun K, Abdelrahman Z, Cao X, Zhang J, Zheng Z, Yuan C, Huang H, Chen Y, Liu Z, Chen Z. Visit-to-visit HbA1c variability, dementia, and hippocampal atrophy among adults without diabetes. Exp Gerontol 2023; 178:112225. [PMID: 37263368 DOI: 10.1016/j.exger.2023.112225] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/13/2023] [Accepted: 05/26/2023] [Indexed: 06/03/2023]
Abstract
OBJECTIVES Adults without diabetes are not completely healthy; they are probably heterogeneous with several potential health problems. The management of hemoglobin A1c (HbA1c) is crucial among patients with diabetes; but whether similar management strategy is needed for adults without diabetes is unclear. Thus, this study aimed to investigate the associations of visit-to-visit HbA1c variability with incident dementia and hippocampal volume among middle-aged and older adults without diabetes, providing potential insights into this question. METHODS We conducted a prospective analysis for incident dementia in 10,792 participants (mean age 58.9 years, 47.8 % men) from the UK Biobank. A subgroup of 3793 participants (mean age 57.8 years, 48.6 % men) was included in the analysis for hippocampal volume. We defined HbA1c variability as the difference in HbA1c divided by the mean HbA1c over the 2 sequential visits ([latter - former]/mean). Dementia was identified using hospital inpatient records with ICD-9 codes. T1-structural brain magnetic resonance imaging was conducted to derive hippocampal volume (normalized for head size). The nonlinear and linear associations were examined using restricted cubic spline (RCS) models, Cox regression models, and multiple linear regression models. RESULTS During a mean follow-up (since the second round) of 8.4 years, 90 (0.8 %) participants developed dementia. The RCS models suggested no significant nonlinear associations of HbA1c variability with incident dementia and hippocampal volume, respectively (All P > 0.05). Above an optimal cutoff of HbA1c variability at 0.08, high HbA1c variability (increment in HbA1c) was associated with an increased risk of dementia (Hazard Ratio, 1.88; 95 % Confidence Interval, 1.13 to 3.14, P = 0.015), and lower hippocampal volume (coefficient, -96.84 mm3, P = 0.037), respectively, in models with adjustment of covariates including age, sex, etc. Similar results were found for a different cut-off of 0. A series of sensitivity analyses verified the robustness of the findings. CONCLUSIONS Among middle-aged and older adults without diabetes, increasing visit-to-visit HbA1c variability was associated with an increased dementia risk and lower hippocampal volume. The findings highlight the importance of monitoring and controlling HbA1c fluctuation in apparently healthy adults without diabetes.
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Affiliation(s)
- Chenxi Li
- School of Public Health, The Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Junyan Guo
- School of Public Health, The Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Yining Zhao
- School of Public Health, The Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Kaili Sun
- School of Public Health, The Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Zeinab Abdelrahman
- Department of Neurobiology, Department of Orthopedics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China; NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China; Department of Rehabilitation Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang, China
| | - Xingqi Cao
- School of Public Health, The Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Jingyun Zhang
- School of Public Health, The Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Zhoutao Zheng
- School of Public Health, The Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Changzheng Yuan
- Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Huiqian Huang
- Clinical Research Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China
| | - Yaojing Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Zuyun Liu
- School of Public Health, The Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China.
| | - Zuobing Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang, China.
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Gu RQ, Qiu JY, Zheng CY, Wu JM, Nie ZJ, Zhang LF, Chen Z, Wang X, Hu Z, Song YX, Zhang DD, Shan WP, Cao X, Tian YX, Shao L, Tian Y, Pan XB, Wang ZW. [Long-term mortality risk of valvular heart disease adults over 35 years old in Chinese communities]. Zhonghua Yi Xue Za Zhi 2023; 103:1818-1823. [PMID: 37357186 DOI: 10.3760/cma.j.cn112137-20221118-02430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
Objective: To investigate the risk and influencing factors of long-term mortality of valvular heart disease (VHD) adults aged 35 years and over in Chinese communities. Methods: A cohort study was carried out. The data of the subjects who underwent echocardiography were collected from the Chinese Hypertension Survey between 2012 and 2015 and survival outcomes were followed up between 2018 and 2019. Kaplan-Meier survival curves were plotted and compared using log-rank test. Cox proportional hazards models were used to analyze the influence of VHD on mortality. Results: During an average follow-up time of (4.6±0.9) years, a total of 23 237 participants (10 881 males and 12 356 females) were pooled into the final analysis from 5 eastern, 5 central, and 4 western provinces, cities and autonomous regions in China, with a mean age of (56.9±13.2) years. Among the included participants, 1 004 had VHD (467 males and 537 females), with a mean age was of (68.1±12.6) years. In the Kaplan-Meier analysis, participants with VHD had a significantly increased risk of all-cause mortality (log-rank χ2=351.82, P<0.001) and cardiovascular mortality (log-rank χ2=284.14, P<0.001) compared with those without VHD. Multivariate Cox regression analysis showed that compared with those without VHD, the participants with rheumatic VHD had a 45% increased risk of all-cause mortality (HR=1.45, 95%CI: 1.12-1.89) and degenerative VHD increased the risk of cardiovascular mortality by 69% (HR=1.69, 95%CI: 1.19-2.38). The risk factors of cardiovascular mortality for VHD were age 55 years and over (55-<75 years: HR=4.93, 95%CI: 1.17-20.85;≥75 years: HR=11.92, 95%CI: 2.85-49.80) and diabetes mellitus (HR=1.71, 95%CI: 1.00-2.93). Conclusions: VHD is a risk factor of all-cause mortality and cardiovascular mortality among adults aged 35 years and over. Age 55 years and over and diabetes mellitus are adverse prognostic factors for patients with VHD.
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Affiliation(s)
- R Q Gu
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102308, China
| | - J Y Qiu
- School of Public Health, Medical College of Soochow University, Suzhou 215006, China
| | - C Y Zheng
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102308, China
| | - J M Wu
- School of Management, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Z J Nie
- School of Public Health, Medical College of Soochow University, Suzhou 215006, China
| | - L F Zhang
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102308, China
| | - Z Chen
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102308, China
| | - X Wang
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102308, China
| | - Z Hu
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102308, China
| | - Y X Song
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102308, China
| | - D D Zhang
- School of Public Health, Medical College of Soochow University, Suzhou 215006, China
| | - W P Shan
- School of Public Health, Medical College of Soochow University, Suzhou 215006, China
| | - X Cao
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102308, China
| | - Y X Tian
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102308, China
| | - L Shao
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102308, China
| | - Y Tian
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102308, China
| | - X B Pan
- Department of Structural Heart Disease, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Z W Wang
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102308, China
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Cao X, Li X, Zhang J, Sun X, Yang G, Zhao Y, Li S, Hoogendijk EO, Wang X, Zhu Y, Allore H, Gill TM, Liu Z. Associations Between Frailty and the Increased Risk of Adverse Outcomes Among 38,950 UK Biobank Participants With Prediabetes: Prospective Cohort Study. JMIR Public Health Surveill 2023; 9:e45502. [PMID: 37200070 PMCID: PMC10236284 DOI: 10.2196/45502] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/17/2023] [Accepted: 03/23/2023] [Indexed: 05/19/2023] Open
Abstract
BACKGROUND Compared with adults with normal glucose metabolism, those with prediabetes tend to be frail. However, it remains poorly understood whether frailty could identify adults who are most at risk of adverse outcomes related to prediabetes. OBJECTIVE We aimed to systematically evaluate the associations between frailty, a simple health indicator, and risks of multiple adverse outcomes including incident type 2 diabetes mellitus (T2DM), diabetes-related microvascular disease, cardiovascular disease (CVD), chronic kidney disease (CKD), eye disease, dementia, depression, and all-cause mortality in late life among middle-aged adults with prediabetes. METHODS We evaluated 38,950 adults aged 40 years to 64 years with prediabetes using the baseline survey from the UK Biobank. Frailty was assessed using the frailty phenotype (FP; range 0-5), and participants were grouped into nonfrail (FP=0), prefrail (1≤FP≤2), and frail (FP≥3). Multiple adverse outcomes (ie, T2DM, diabetes-related microvascular disease, CVD, CKD, eye disease, dementia, depression, and all-cause mortality) were ascertained during a median follow-up of 12 years. Cox proportional hazards regression models were used to estimate the associations. Several sensitivity analyses were performed to test the robustness of the results. RESULTS At baseline, 49.1% (19,122/38,950) and 5.9% (2289/38,950) of adults with prediabetes were identified as prefrail and frail, respectively. Both prefrailty and frailty were associated with higher risks of multiple adverse outcomes in adults with prediabetes (P for trend <.001). For instance, compared with their nonfrail counterparts, frail participants with prediabetes had a significantly higher risk (P<.001) of T2DM (hazard ratio [HR]=1.73, 95% CI 1.55-1.92), diabetes-related microvascular disease (HR=1.89, 95% CI 1.64-2.18), CVD (HR=1.66, 95% CI 1.44-1.91), CKD (HR=1.76, 95% CI 1.45-2.13), eye disease (HR=1.31, 95% CI 1.14-1.51), dementia (HR=2.03, 95% CI 1.33-3.09), depression (HR=3.01, 95% CI 2.47-3.67), and all-cause mortality (HR=1.81, 95% CI 1.51-2.16) in the multivariable-adjusted models. Furthermore, with each 1-point increase in FP score, the risk of these adverse outcomes increased by 10% to 42%. Robust results were generally observed in sensitivity analyses. CONCLUSIONS In UK Biobank participants with prediabetes, both prefrailty and frailty are significantly associated with higher risks of multiple adverse outcomes, including T2DM, diabetes-related diseases, and all-cause mortality. Our findings suggest that frailty assessment should be incorporated into routine care for middle-aged adults with prediabetes, to improve the allocation of health care resources and reduce diabetes-related burden.
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Affiliation(s)
- Xingqi Cao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueqin Li
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingyun Zhang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoyi Sun
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
| | - Gan Yang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
| | - Yining Zhao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
| | - Shujuan Li
- Department of Neurology, Fuwai Hospital, National Center for Cardiovascular Diseases, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Emiel O Hoogendijk
- Department of Epidemiology & Data Science, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Xiaofeng Wang
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Yimin Zhu
- Department of Epidemiology & Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Heather Allore
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States
| | - Thomas M Gill
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China
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Zhang J, Cao X, Li X, Li X, Hao M, Xia Y, Huang H, Jørgensen TSH, Agogo GO, Wang L, Zhang X, Gao X, Liu Z. Associations of Midlife Dietary Patterns with Incident Dementia and Brain Structure: Findings from the UK Biobank Study. Am J Clin Nutr 2023:S0002-9165(23)48900-9. [PMID: 37150507 DOI: 10.1016/j.ajcnut.2023.05.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/09/2023] Open
Abstract
BACKGROUND At present, the results on the associations between dietary patterns and the risk of dementia are inconsistent, and studies on the associations between dietary patterns and brain structures are limited. OBJECTIVE We aimed to investigate the associations of midlife dietary patterns with incident dementia and brain structures. METHODS Based on the UK Biobank Study, we investigated the 1) prospective associations of four healthy dietary pattern indices (healthy plant-based diet index [hPDI], Mediterranean diet score [MDS], Recommended food score [RFS], and Mediterranean-Dietary Approaches to Stop Hypertension Intervention [DASH] Intervention for Neurodegenerative Delay Diet [MIND]) with incident dementia (identified using linked hospital data; N = 114,684; mean age, 56.8 years; 55.5% females) using Cox proportional-hazards regressions and the 2) cross-sectional associations of these dietary pattern indices with brain structures (estimated using magnetic resonance imaging; N = 18,214; mean age, 55.9 years; 53.1% females) using linear regressions. A series of covariates were adjusted, and several sensitivity analyses were conducted. RESULTS A total of 481 (0.42%) participants developed dementia during the average 9.4-year follow-up. Although the associations were not statistically significant, all dietary patterns exerted protective effects against incident dementia (all hazard ratios < 1). Furthermore, higher dietary pattern indices were significantly associated with larger regional brain volumes, including volumes of gray matter in the parietal and temporal cortices and volumes of the hippocampus and thalamus. The main results were confirmed via sensitivity analyses. CONCLUSIONS Greater adherence to hPDI, MDS, RFS, and MIND was individually associated with larger brain volumes in specific regions. This study shows a comprehensive picture of the consistent associations of midlife dietary patterns with the risk of dementia and brain health, underscoring the potential benefits of a healthy diet in the prevention of dementia.
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Affiliation(s)
- Jingyun Zhang
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou 310058, Zhejiang, China
| | - Xingqi Cao
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou 310058, Zhejiang, China
| | - Xin Li
- Welch Center for Prevention, Epidemiology, and Clinical Research Department of Epidemiology, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Xueqin Li
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou 310058, Zhejiang, China
| | - Meng Hao
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai 200433, China
| | - Yang Xia
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Huiqian Huang
- Clinical Research Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Terese Sara Høj Jørgensen
- Section of Social Medicine, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Øster Farimagsgade 5, PO Box 2099, Copenhagen DK-1014, Denmark
| | - George O Agogo
- StatsDecide Analytics and Consulting Ltd, P.O.Box 17438-20100, Nakuru, Kenya
| | - Liang Wang
- Department of Public Health, Robbins College of Human Health and Sciences, Baylor University, Waco, TX 76711, USA
| | - Xuehong Zhang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Xiang Gao
- Department of Nutrition and Food Hygiene, School of Public Health, Fudan University, Shanghai 200032, China
| | - Zuyun Liu
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou 310058, Zhejiang, China.
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Chen C, Cao X, Xu J, Jiang Z, Liu Z, McGoogan J, Wu Z. Comparison of healthspan-related indicators between adults with and without HIV infection aged 18-59 in the United States: a secondary analysis of NAHNES 1999-March 2020. BMC Public Health 2023; 23:814. [PMID: 37142969 PMCID: PMC10157932 DOI: 10.1186/s12889-023-15538-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 03/27/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND As persons with HIV (PWH) live longer they may experience a heightened burden of poor health. However, few studies have characterized the multi-dimentional health of PWH. Thus, we aimed to identify the extent and pattern of health disparities, both within HIV infection status and across age (or sex) specific groups. METHODS We used cross-sectional data from the US National Health and Nutrition Examination Survey, 1999-March 2020. The adjusted prevalence of six healthspan-related indicators-physical frailty, activities of daily living (ADL) disability, mobility disability, depression, multimorbidity, and all-cause death-was evaluated. Logistic regression and Cox proportional hazards analyses were used to investigate associations between HIV status and healthspan-related indicators, with adjustment for individual-level demographic characteristics and risk behaviors. RESULTS The analytic sample consisted of 33 200 adults (170 (0.51%) were PWH) aged 18-59 years in the United States. The mean (interquartile range) age was 35.1 (25.0-44.0) years, and 49.4% were male. PWH had higher adjusted prevalences for all of the 6 healthspan-related indicators, as compared to those without HIV, ranged from 17.4% (95% CI: 17.4%, 17.5%) vs. 2.7% (95%CI: 2.7%, 2.7%) for all-cause mortality, to 84.3% (95% CI: 84.0%, 84.5%) vs. 69.8% (95%CI: 69.7%, 69.8%) for mobility disability. While the prevalence difference was largest in ADL disability (23.4% (95% CI: 23.2%, 23.7%); P < 0.001), and least in multimorbidity (6.9% (95% CI: 6.8%, 7.0%); P < 0.001). Generally, the differences in prevalence by HIV status were greater in 50-59 years group than those in 18-29 group. Males with HIV suffered higher prevalence of depression and multimorbidity, while females with HIV were more vulnerable to functional limitation and disabilities. HIV infection was associated with higher odds for 3 of the 6 healthspan-related indicators after fully adjusted, such as physical frailty and depression. Sensitivity analyses did not change the health differences between adults with and without HIV infection. CONCLUSIONS In a large sample of U.S. community-dwelling adults, by identifying the extent and pattern of health disparities, we characterized the multi-dimentional health of PWHs, providing important public health implications for public policy that aims to improve health of persons with HIV and further reduce these disparities.
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Affiliation(s)
- Chen Chen
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, 155 Changbai Road, Changping District, Beijing, 102206, China
- National Institute of Environmental and Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xingqi Cao
- Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jie Xu
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, 155 Changbai Road, Changping District, Beijing, 102206, China
| | - Zhen Jiang
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, 155 Changbai Road, Changping District, Beijing, 102206, China
| | - Zuyun Liu
- Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | | | - Zunyou Wu
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, 155 Changbai Road, Changping District, Beijing, 102206, China.
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Zhai Z, Fang Y, Cheng J, Tian Y, Liu L, Cao X. Intrinsic morphology and spatial distribution of non-structural carbohydrates contribute to drought resistance of two mulberry cultivars. Plant Biol (Stuttg) 2023. [PMID: 37099325 DOI: 10.1111/plb.13533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 04/23/2023] [Indexed: 05/11/2023]
Abstract
Drought is one of the most adverse environmental stresses limiting plant growth and productivity. However, the underlying mechanisms regarding metabolism of non-structural carbohydrates (NSC) in source and sink organs are still not fully elucidated in woody trees. Saplings of mulberry cv Zhongshen1 and Wubu were subjected to a 15-day progressive drought stress. NSC levels and gene expression involved in NSC metabolism were investigated in roots and leaves. Growth performance and photosynthesis, leaf stomatal morphology, and other physiological parameters were also analysed. Under well-watered conditions, Wubu had a higher R/S, with higher NSC in leaves than in roots; Zhongshen1 had a lower R/S with higher NSC in roots than leaves. Under drought stress, Zhongshen1 showed decreased productivity and increased proline, abscisic acid, ROS content and activity of antioxidant enzymes, while Wubu sustained comparable productivity and photosynthesis. Interestingly, drought resulted in decreased starch and slightly increased soluble sugars in leaves of Wubu, accompanied by notable downregulation of starch-synthesizing genes and upregulation of starch-degrading genes. Similar patterns in NSC levels and relevant gene expression were also observed in roots of Zhongshen1. Concurrently, soluble sugars decreased and starch was unchanged in roots of Wubu and leaves of Zhongshen1. However, gene expression of starch metabolism in roots of Wubu was unaltered, but in leaves of Zhongshen1 starch metabolism was more activated. These findings revealed that intrinsic R/S and spatial distribution of NSC in roots and leaves concomitantly contribute to drought resistance in mulberry.
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Affiliation(s)
- Z Zhai
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, College of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
- Key Laboratory of Silkworm and Mulberry Genetic Improvement, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Sericultural Research Institute, Zhenjiang, Jiangsu, China
| | - Y Fang
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, College of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
- Key Laboratory of Silkworm and Mulberry Genetic Improvement, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Sericultural Research Institute, Zhenjiang, Jiangsu, China
| | - J Cheng
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, College of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
- Key Laboratory of Silkworm and Mulberry Genetic Improvement, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Sericultural Research Institute, Zhenjiang, Jiangsu, China
| | - Y Tian
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, College of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
- Key Laboratory of Silkworm and Mulberry Genetic Improvement, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Sericultural Research Institute, Zhenjiang, Jiangsu, China
| | - L Liu
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, College of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
- Key Laboratory of Silkworm and Mulberry Genetic Improvement, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Sericultural Research Institute, Zhenjiang, Jiangsu, China
| | - X Cao
- Jiangsu Key Laboratory of Sericultural Biology and Biotechnology, College of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
- Key Laboratory of Silkworm and Mulberry Genetic Improvement, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Sericultural Research Institute, Zhenjiang, Jiangsu, China
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Pu F, Li C, Zhang X, Cao X, Yang Z, Hu Y, Xu X, Ma Y, Hu K, Liu Z. Transition of cooking fuel types and mortality risk in China, 1991-2015. Sci Total Environ 2023; 869:161654. [PMID: 36702279 DOI: 10.1016/j.scitotenv.2023.161654] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 01/12/2023] [Accepted: 01/12/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND The decision for household cooking fuel choice is a complex and multi-dimensional process. This study aims to: 1) examine the trend of cooking fuel types during past decades; and 2) examine the association between switching from polluting to clean fuels for cooking and mortality risk. METHODS This analysis included data on 39,359 participants from 9 waves of the China Health and Nutrition Survey (CHNS) (1991-2015). Participants with consistent polluting fuel use and with the polluting-to-clean transition were identified. Generalized estimating equations were used to examine the trend of clean fuel use from 1991 to 2015. Propensity score matching was used to address the data imbalance and confounding factors and Cox proportional hazards models were used to estimate the association. RESULTS We found an increasing trend of clean fuel use after adjusting for potential confounders in the full sample (OR = 56.89, 95 % CI: 48.17, 67.19), which appeared to be more pronounced for those in rural areas and with low socioeconomic status. Switching from polluting to clean fuels was associated with a 75 % lower risk of mortality (HR = 0.25, 95 % CI: 0.11, 0.54). These associations became more pronounced during the lag period from 9 to 15 years. CONCLUSIONS The transition from polluting to clean cooking fuels reduced excess deaths in China, particularly over a long period. Our findings support the increasing implementation of clean fuels and call for more efforts to improve its universal service, especially in rural and low socioeconomic status areas, to minimize health inequality.
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Affiliation(s)
- Fan Pu
- School of Public Health and the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chenxi Li
- School of Public Health and the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xinrui Zhang
- School of Public Health and the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xingqi Cao
- School of Public Health and the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zhenqing Yang
- School of Public Health and the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yingying Hu
- School of Public Affairs, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaolin Xu
- School of Public Health and the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yanan Ma
- Department of Biostatistics and Epidemiology, School of Public Health, China Medical University, Shenyang, Liaoning, China.
| | - Kejia Hu
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
| | - Zuyun Liu
- School of Public Health and the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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Abstract
Corona Virus Disease 2019 (COVID-19) has caused several pandemic peaks worldwide due to its high variability and infectiousness, and COVID-19 has become a long-standing global public health problem. There is growing evidence that severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) frequently causes multi-organ injuries and more severe neurological manifestations. Therefore, increased awareness of possible neurological complications is beneficial in preventing and mitigating the impact of long-term sequelae and improving the prognostic outcome of critically ill patients with COVID-19. Here, we review the main pathways of SARS-CoV-2 neuroinvasion and the potential mechanisms causing neurological damage. We also discuss in detail neurological complications, aiming to provide cutting-edge basis for subsequent related basic research and clinical studies of diagnosis and treatment.
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Affiliation(s)
- X Dai
- From the Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, P. R. China
| | - X Cao
- Department of Clinical Medicine, The First Clinical College of Anhui Medical University, Hefei 230032, P. R. China
| | - Q Jiang
- Department of Clinical Medicine, The First Clinical College of Anhui Medical University, Hefei 230032, P. R. China
| | - B Wu
- From the Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, P. R. China
| | - T Lou
- Department of Clinical Medicine, The First Clinical College of Anhui Medical University, Hefei 230032, P. R. China
| | - Y Shao
- Department of Clinical Medicine, The First Clinical College of Anhui Medical University, Hefei 230032, P. R. China
| | - Y Hu
- From the Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, P. R. China
| | - Q Lan
- Department of Neurosurgery, The Second Affiliated Hospital of Soochow University, Suzhou 215004, P. R. China
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He L, Zheng Z, Li X, Cao X, Zhang J, Chen C, Lv Y, Wu C, Barry LC, Ying Z, Jiang X, Shi X, Liu Z. Association of spouse's health status with the onset of depressive symptoms in partner: Evidence from the China Health and Retirement Longitudinal Study. J Affect Disord 2023; 325:177-184. [PMID: 36603600 DOI: 10.1016/j.jad.2022.12.118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 10/29/2022] [Accepted: 12/23/2022] [Indexed: 01/04/2023]
Abstract
BACKGROUND This study aimed to evaluate the associations between the multidimensional health status of one spouse and the onset of depressive symptoms in partner, and whether the associations differed by gender and residence. METHODS We analyzed data from 2401 females and their husbands (scenario 1), and 2830 males and their wives (scenario 2) who participated in the 2011/2012 and 2015 waves of China Health and Retirement Longitudinal Study. Depressive symptoms were assessed using the 10-item Centre for Epidemiological Studies Depression Scale. Multidimensional health indicators included mobility disability, activities of daily living disability, frailty, global cognition, depressive symptoms, comorbidity, and self-reported health. Principal component analysis was used to construct a composite health indicator reflecting overall health status that was then categorized into three groups (poor, moderate, and excellent). Logistic regression models were performed. RESULTS We observed strong associations of spouse's health status with the onset of depressive symptoms in partner. For instance, females whose husbands had poor overall health status reported more depressive symptoms than those having husbands with excellent overall health after four years (OR: 1.75; 95 % CI: 1.35, 2.26). These associations were statistically significant in rural females and urban males, but surprisingly disappeared in rural males and urban females. LIMITATIONS No exact timing of depressive symptoms onset. CONCLUSIONS In Chinese middle-aged and older adults, spouse's health status is associated with depressive symptoms in partner and the associations vary by gender and residence. The findings underscore the importance of considering partner's health status to manage one spouse's mental health.
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Affiliation(s)
- Liu He
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Zhoutao Zheng
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Xueqin Li
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Xingqi Cao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Jingyun Zhang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Chenkai Wu
- Global Health Research Center, Duke Kunshan University, Jiangsu 215316, China
| | - Lisa C Barry
- Department of Psychiatry, UCONN Health, CT 06030-1410, USA
| | - Zhimin Ying
- Department of Orthopedic Surgery, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China.
| | - Xiaoyan Jiang
- Key Laboratory of Arrhythmias, Ministry of Education, Department of Pathology and Pathophysiology, School of Medicine, Tongji University, Shanghai 200092, China.
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China.
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25
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Gu RQ, Zheng CY, Zhang LF, Chen Z, Wang X, Cao X, Tian YX, Chen L, Zhou HH, Chen C, Hu Z, Song YX, Shao L, Tian Y, Wang ZW. [Prevalence of albuminuria and its association with cardiovascular diseases in Chinese residents aged over 35 years]. Zhonghua Nei Ke Za Zhi 2023; 62:290-296. [PMID: 36822855 DOI: 10.3760/cma.j.cn112138-20220328-00214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Objective: To investigate the prevalence of albuminuria in Chinese residents aged >35 years and its potential association with cardiovascular disease (CVD). Methods: A total of 34 647 Chinese subjects aged ≥35 years were selected by stratified multi-stage random sampling from 2012 to 2015. Data were collected through questionnaires, physical examinations, and laboratory tests. Albuminuria was categorized into 3 types according to urinary albumin-to- creatinine ratio: normal (<30 mg/g), microalbuminuria (MAU, 30-300 mg/g), and macroalbuminuria (≥300 mg/g). Measurement data were expressed as x¯±s, and t-tests were used for comparisons between indicators. Qualitative data were expressed as rate or constituent ratio, and the χ2 test or Kruskal-Wallis test was used to examine differences. Logistic regression was used for multivariate analyses. SAS 9.4 software was used for statistical analyses, and P<0.05 was considered statistically significant. Results: The prevalence of abnormal albuminuria was 19.1%; the prevalence was 17.2% for MAU and lower in males (13.8%) than females (20.1%, P<0.01). The risk of CVD was higher among subjects with MAU (OR=1.23, 95%CI 1.12-1.35) and macroalbuminuria (OR=1.86, 95%CI 1.50-2.32). When MAU was complicated by hypertension and diabetes mellitus, the CVD risk was 1.76 times higher. Conclusions: The prevalence of MAU is high among Chinese subjects aged 35 years and over. Those with MAU have higher CVD risk, especially those with hypertension and diabetes mellitus.
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Affiliation(s)
- R Q Gu
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China Division of Prevention and Community Health, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College,National Center for Cardiovascular Diseases, Beijing 102308, China
| | - C Y Zheng
- Division of Prevention and Community Health, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College,National Center for Cardiovascular Diseases, Beijing 102308, China
| | - L F Zhang
- Division of Prevention and Community Health, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College,National Center for Cardiovascular Diseases, Beijing 102308, China
| | - Z Chen
- Division of Prevention and Community Health, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College,National Center for Cardiovascular Diseases, Beijing 102308, China
| | - X Wang
- Division of Prevention and Community Health, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College,National Center for Cardiovascular Diseases, Beijing 102308, China
| | - X Cao
- Division of Prevention and Community Health, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College,National Center for Cardiovascular Diseases, Beijing 102308, China
| | - Y X Tian
- Division of Prevention and Community Health, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College,National Center for Cardiovascular Diseases, Beijing 102308, China
| | - L Chen
- Division of Prevention and Community Health, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College,National Center for Cardiovascular Diseases, Beijing 102308, China
| | - H H Zhou
- Division of Prevention and Community Health, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College,National Center for Cardiovascular Diseases, Beijing 102308, China
| | - C Chen
- Division of Prevention and Community Health, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College,National Center for Cardiovascular Diseases, Beijing 102308, China
| | - Z Hu
- Division of Prevention and Community Health, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College,National Center for Cardiovascular Diseases, Beijing 102308, China
| | - Y X Song
- Division of Prevention and Community Health, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College,National Center for Cardiovascular Diseases, Beijing 102308, China
| | - L Shao
- Division of Prevention and Community Health, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College,National Center for Cardiovascular Diseases, Beijing 102308, China
| | - Y Tian
- Division of Prevention and Community Health, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College,National Center for Cardiovascular Diseases, Beijing 102308, China
| | - Z W Wang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China Division of Prevention and Community Health, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College,National Center for Cardiovascular Diseases, Beijing 102308, China
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Cao X, Yang Z, Li X, Chen C, Hoogendijk EO, Zhang J, Yao NA, Ma L, Zhang Y, Zhu Y, Zhang X, Du Y, Wang X, Wu X, Gill TM, Liu Z. Association of frailty with the incidence risk of cardiovascular disease and type 2 diabetes mellitus in long-term cancer survivors: a prospective cohort study. BMC Med 2023; 21:74. [PMID: 36829175 PMCID: PMC9951842 DOI: 10.1186/s12916-023-02774-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 02/09/2023] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND Comorbidities among cancer survivors remain a serious healthcare burden and require appropriate management. Using two widely used frailty indicators, this study aimed to evaluate whether frailty was associated with the incidence risk of cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) among long-term cancer survivors. METHODS We included 13,388 long-term cancer survivors (diagnosed with cancer over 5 years before enrolment) free of CVD and 6101 long-term cancer survivors free of T2DM, at the time of recruitment (aged 40-69 years), from the UK Biobank. Frailty was assessed by the frailty phenotype (FP_Frailty, range: 0-5) and the frailty index (FI_Frailty, range: 0-1) at baseline. The incident CVD and T2DM were ascertained through linked hospital data and primary care data, respectively. The associations were examined using Cox proportional hazards regression models. RESULTS Compared with non-frail participants, those with pre-frailty (FP_Frailty [met 1-2 of the components]: hazard ratio [HR]=1.18, 95% confidence interval [CI]: 1.05, 1.32; FI_Frailty [0.10< FI ≤0.21]: HR=1.51, 95% CI: 1.32, 1.74) and frailty (FP_Frailty [met ≥3 of the components]: HR=2.12, 95% CI: 1.73, 2.60; FI_Frailty [FI >0.21]: HR=2.19, 95% CI: 1.85, 2.59) had a significantly higher risk of CVD in the multivariable-adjusted model. A similar association of FI_Frailty with the risk of incident T2DM was observed. We failed to find such an association for FP_Frailty. Notably, the very early stage of frailty (1 for FP_Frailty and 0.1-0.2 for FI_Frailty) was also positively associated with the risk of CVD and T2DM (FI_Frailty only). A series of sensitivity analyses confirmed the robustness of the findings. CONCLUSIONS Frailty, even in the very early stage, was positively associated with the incidence risk of CVD and T2DM among long-term cancer survivors, although discrepancies existed between frailty indicators. While the validation of these findings is required, they suggest that routine monitoring, prevention, and interventive programs of frailty among cancer survivors may help to prevent late comorbidities and, eventually, improve their quality of life. Especially, interventions are recommended to target those at an early stage of frailty when healthcare resources are limited.
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Affiliation(s)
- Xingqi Cao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Zhejiang, 310058, Hangzhou, China
| | - Zhenqing Yang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Zhejiang, 310058, Hangzhou, China
| | - Xueqin Li
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Zhejiang, 310058, Hangzhou, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100000, China
| | - Emiel O Hoogendijk
- Department of Epidemiology & Data Science, Amsterdam Public Health research Institute, Amsterdam UMC - location VU University Medical Center, P.O. Box 7057, 1007MB, Amsterdam, the Netherlands
| | - Jingyun Zhang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Zhejiang, 310058, Hangzhou, China
| | - Nengliang Aaron Yao
- Home Centered Care Institute, Schaumburg, IL, USA
- Center For Health Management and Policy, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
- Section of Geriatrics, University of Virginia, Charlottesville, VA, USA
| | - Lina Ma
- Department of Geriatrics, Xuanwu Hospital Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
- Beijing Geriatric Healthcare Center, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Yawei Zhang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yong Zhu
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, 06510, USA
| | - Xuehong Zhang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Yuxian Du
- Bayer Healthcare Pharmaceuticals U.S. LLC, Whippany, NJ, 07981, USA
| | - Xiaofeng Wang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200433, China
- National Clinical Research Center for Ageing and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Xifeng Wu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Zhejiang, 310058, Hangzhou, China
| | - Thomas M Gill
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 06511, USA
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Zhejiang, 310058, Hangzhou, China.
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Yang G, Cao X, Yu J, Li X, Zhang L, Zhang J, Ma C, Zhang N, Lu Q, Wu C, Chen X, Hoogendijk EO, Gill TM, Liu Z. Association of childhood adversity with frailty and the mediating role of unhealthy lifestyle: Findings from the UK biobank. medRxiv 2023:2023.02.08.23285634. [PMID: 36798168 PMCID: PMC9934802 DOI: 10.1101/2023.02.08.23285634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Background Childhood adversity and lifestyle have been associated with frailty in later life, but not much is known about factors that may explain these associations. An unhealthy lifestyle may play an important role in the pathway from childhood adversity to frailty. Therefore, this study aims to investigate the association of childhood adversity with frailty, and the mediating role of unhealthy lifestyle in the association. Methods This lifespan analysis included 152914 adults aged 40-69 years old from the UK Biobank. We measured childhood adversity with five items: physical neglect, emotional neglect, sexual abuse, physical abuse, and emotional abuse through online mental health survey. Frailty was measured by the frailty index; an unhealthy lifestyle score (range: 0-5) was calculated based on unhealthy body mass index, smoking, drinking, physical inactivity, and unhealthy diet at the baseline survey. Multiple logistic regression and mediation analysis were performed. Results A total of 10078 participants (6.6%) were defined as having frailty. Participants with any childhood adversity had higher odds of frailty. For example, in the fully adjusted model, with a one-point increase in cumulative score of childhood adversity, the odds of frailty increased by 41% (Odds Ratio: 1.41; 95% Confidence Interval: 1.39, 1.44). Unhealthy lifestyle partially mediated the associations of childhood adversity with frailty (mediation proportion: 4.4%-7.0%). The mediation proportions were largest for physical (8.2%) and sexual (8.1%) abuse. Conclusions Among this large sample, childhood adversity was positively associated with frailty, and unhealthy lifestyle partially mediated the association. This newly identified pathway highlights the potential of lifestyle intervention strategies among those who experienced childhood adversity (in particular, physical and sexual abuse) to promote healthy aging.
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Affiliation(s)
- Gan Yang
- School of Public Health and Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Xingqi Cao
- School of Public Health and Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Jie Yu
- School of Public Health and Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Xueqin Li
- School of Public Health and Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Liming Zhang
- School of Public Health and Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Jingyun Zhang
- School of Public Health and Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Chao Ma
- School of Economics and Management, Southeast University, Nanjing 211189, Jiangsu, China
| | - Ning Zhang
- Department of Social Medicine School of Public Health and Center for Clinical Big Data and Analytics Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Qingyun Lu
- School of Public Health, Nantong University, Nantong 226007, JiangSu, China
| | - Chenkai Wu
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Xi Chen
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT 06520, United States of America
- Department of Economics, Yale University, New Haven, CT 06520, United States of America
| | - Emiel O. Hoogendijk
- Department of Epidemiology & Data Science, Amsterdam Public Health research institute, Amsterdam UMC – location VU University medical center, Amsterdam, the Netherlands
| | - Thomas M. Gill
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06520, United States of America
| | - Zuyun Liu
- School of Public Health and Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
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Lin A, Wang T, Li C, Pu F, Abdelrahman Z, Jin M, Yang Z, Zhang L, Cao X, Sun K, Hou T, Liu Z, Chen L, Chen Z. Association of Sarcopenia with Cognitive Function and Dementia Risk Score: A National Prospective Cohort Study. Metabolites 2023; 13:metabo13020245. [PMID: 36837864 PMCID: PMC9965467 DOI: 10.3390/metabo13020245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/01/2023] [Accepted: 02/07/2023] [Indexed: 02/11/2023] Open
Abstract
The relationship between skeletal muscle and cognitive disorders has drawn increasing attention. This study aims to examine the associations of sarcopenia with cognitive function and dementia risk score. Data on 1978 participants (aged 65 years and older) from the 2011 wave of the China Health and Retirement Longitudinal Study, with four follow-up waves to 2018, were used. Cognitive function was assessed by four dimensions, with a lower score indicating lower cognitive function. Dementia risk was assessed by a risk score using the Rotterdam Study Basic Dementia Risk Model (BDRM), with a higher score indicating a greater risk. Sarcopenia was defined when low muscle mass plus low muscle strength or low physical performance were met. We used generalized estimating equations to examine the associations of sarcopenia. In the fully adjusted models, sarcopenia was significantly associated with lower cognitive function (standardized, β = -0.15; 95% CIs: -0.26, -0.04) and a higher BDRM score (standardized, β = 0.42; 95% CIs: 0.29, 0.55). Our findings may provide a new avenue for alleviating the burden of cognitive disorders by preventing sarcopenia.
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Affiliation(s)
- Ailing Lin
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Ting Wang
- The Second Affiliated Hospital and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Chenxi Li
- The Second Affiliated Hospital and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Fan Pu
- The Second Affiliated Hospital and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Zeinab Abdelrahman
- Department of Neurobiology and Department of Orthopedics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain–Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310003, China
- Department of Rehabilitation Medicine, First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Rd., Hangzhou 310003, China
| | - Mengqi Jin
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Zhenqing Yang
- The Second Affiliated Hospital and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Liming Zhang
- The Second Affiliated Hospital and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Xingqi Cao
- The Second Affiliated Hospital and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Kaili Sun
- The Second Affiliated Hospital and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Tongyao Hou
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Zuyun Liu
- The Second Affiliated Hospital and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, China
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd., Hangzhou 310058, China
- Correspondence: or (Z.L.); (L.C.); (Z.C.); Tel.: +86-0571-87077127 (Z.L.); +86-0571-86002113 (L.C.); +86-13957116610 (Z.C.)
| | - Liying Chen
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
- Department of General Practice, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, 3 Qingchun East Rd., Hangzhou 310016, China
- Correspondence: or (Z.L.); (L.C.); (Z.C.); Tel.: +86-0571-87077127 (Z.L.); +86-0571-86002113 (L.C.); +86-13957116610 (Z.C.)
| | - Zuobing Chen
- Department of Rehabilitation Medicine, First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Rd., Hangzhou 310003, China
- Correspondence: or (Z.L.); (L.C.); (Z.C.); Tel.: +86-0571-87077127 (Z.L.); +86-0571-86002113 (L.C.); +86-13957116610 (Z.C.)
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Cao X, Wang C, Lin ZC, Lyu X. Radiation-induced cancer after treatment for nasopharyngeal carcinoma: a study from a high prevalence area. Rhinology 2023; 61:77-84. [PMID: 36527736 DOI: 10.4193/rhin22.281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND Radiation-induced cancer (RIC) is a late complication in patients who have been treated for nasopharyngeal carcinoma (NPC). The comparison of index anatomic location, index histological type, and survival of RIC in patients with NPC after different radiotherapy modalities (intensity-modulated radiotherapy [IMRT], 3-dimensional conformal radiotherapy [3D-CRT], and conventional 2D radiotherapy) is currently unavailable. METHODOLOGY A total of 38,565 patients with NPC who received curative-intent radiotherapy at Sun Yat-sen University Cancer Center between January 1986 and December 2017 were reviewed. A total of 141 patients who developed RIC and fulfilled the study criteria were included. Categorical variables were compared by the chi-square test or Fisher's exact test. Kaplan-Meier curves were used to evaluate overall survival. Cox proportional hazards models were used to examine the independent significance of RIC treatment. RESULTS Among IMRT, 3D-CRT, and conventional 2D radiotherapy, the incidence of mandible RIC was higher in patients who received 3D-CRT (0.07%) than in those who received IMRT (0%). The proportion of mandible RICs was higher in patients who received 3D-CRT (16.667%) than in those who received IMRT (0%) and conventional 2D radiotherapy (3.529%). Regarding the histological type, the incidence of squamous cell carcinoma (SCC) was higher in patients who received conventional 2D radiotherapy (0.266%) than in those who received 3D-CRT (0.175%); patients who received IMRT had a higher proportion of SCC than those who received 3D-CRT/conventional 2D radiotherapy (86.4% vs. 41.7% vs. 74.2%); the incidence of sarcoma was higher in patients who received 3D-CRT (0.175%) than in those who received IMRT (0.025%); and the proportion of sarcoma was higher in patients who received 3D-CRT (41.667%) than in those who received IMRT (6.818%) and conventional 2D radiotherapy (17.647%). Patients who received surgery for RICs had better survival than those who received no surgery (64.49 vs. 12.42 months). In the univariate and multivariate analyses, surgery was an independent prognostic factor for overall survival. CONCLUSIONS Our results have implications for long-term follow-up of RIC, multidisciplinary management, and patient counseling of RIC after nasopharyngeal carcinoma treatment by treating clinicians.
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Affiliation(s)
- X Cao
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, Guangdong, China and Department of Critical Care Medicine, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong
| | - C Wang
- Department of Surgical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong, China
| | - Z C Lin
- Department of Medical Records, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China
| | - X Lyu
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, Guangdong, China
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Pu F, Hu Y, Li C, Cao X, Yang Z, Liu Y, Zhang J, Li X, Yang Y, Wang W, Liu X, Hu K, Ma Y, Liu Z. Association of solid fuel use with a risk score capturing dementia risk among middle-aged and older adults: A prospective cohort study. Environ Res 2023; 218:115022. [PMID: 36502898 DOI: 10.1016/j.envres.2022.115022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/17/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
OBJECTIVES Whether household air pollution is associated with dementia risk remains unknown. This study examined the associations between solid fuel use for cooking and heating (the main source of household air pollution) and dementia risk. METHODS This analysis included data on 11,352 participants (aged 45+ years) from the 2011 wave of China Health and Retirement Longitudinal Study, with follow-up to 2018. Dementia risk was assessed by a risk score using the Rotterdam Study Basic Dementia Risk Model (BDRM), which was subsequently standardized for analysis. Household fuel types of cooking and heating were categorized as solid (e.g., coal and crop residue) and clean (e.g., central heating and solar). Multivariable analyses were performed using generalized estimating equations. Moreover, we examined the joint associations of solid fuel use for cooking and heating with the BDRM score. RESULTS After adjusting for potential confounders, we found an independent and significant association of solid (vs. clean) fuel use for cooking and heating with a higher BDRM score (e.g., β = 0.17 for solid fuel for cooking; 95% confidence interval [CI]: 0.15-0.19). Participants who used solid (vs. clean) fuel for both cooking and heating had the highest BDRM score (β = 0.32; 95% CI: 0.29-0.36). Subgroup analysis suggested stronger associations in participants living in rural areas. CONCLUSIONS Solid fuel use for cooking and heating was independently associated with increased dementia risk in Chinese middle-aged and older adults, particularly among those living in rural areas. Our findings call for more efforts to facilitate universal access to clean energy for dementia prevention.
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Affiliation(s)
- Fan Pu
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Yingying Hu
- School of Public Affairs, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Chenxi Li
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Xingqi Cao
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Zhenqing Yang
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Yi Liu
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Jingyun Zhang
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Xueqin Li
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Yongli Yang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Wei Wang
- Department of Occupational Health and Occupational Disease, College of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Xiaoting Liu
- School of Public Affairs, Zhejiang University, Hangzhou, 310058, Zhejiang, China; Institute of Wenzhou, Zhejiang University, Hangzhou, Zhejiang, China
| | - Kejia Hu
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Yanan Ma
- Department of Biostatistics and Epidemiology, School of Public Health, China Medical University, Shenyang, Liaoning, China
| | - Zuyun Liu
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
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Cao X, Yang G, Li X, Fu J, Mohedaner M, Danzengzhuoga, Høj Jørgensen TS, Agogo GO, Wang L, Zhang X, Zhang T, Han L, Gao X, Liu Z. Weight change across adulthood and accelerated biological aging in middle-aged and older adults. Am J Clin Nutr 2023; 117:1-11. [PMID: 36789928 DOI: 10.1016/j.ajcnut.2022.10.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 10/21/2022] [Accepted: 10/28/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Little is known regarding the association between weight change and accelerated aging. OBJECTIVES This study aimed to estimate the influence of weight change across adulthood on biological aging acceleration in middle-aged and older adults in the United States. METHODS We used data of 5553 adults (40-84 y) from the National Health and Nutrition Examination Survey 1999-2010. Weight change patterns (i.e., stable normal, maximal overweight, obese to nonobese, nonobese to obese, and stable obese) and absolute weight change groups across adulthood (i.e., from young to middle adulthood, young to late adulthood, and middle to late adulthood) were defined. A biological aging measure (i.e., phenotypic age acceleration [PhenoAgeAccel]) at late adulthood was calculated. Survey analysis procedures with the survey weights were performed. RESULTS Across adulthood, maximal overweight, nonobese to obese, and stable obesity were consistently associated with higher PhenoAgeAccel. For instance, from young to middle adulthood, compared with participants who had stable normal weight, participants experiencing maximal overweight, moving from the nonobese to obese, and maintaining obesity had 1.71 (standard error [SE], 0.21; P < 0.001), 3.62 (SE, 0.28; P < 0.001), and 6.61 (SE, 0.58; P < 0.001) higher PhenoAgeAccel values, respectively. From young to middle adulthood, relative to absolute weight loss or gain of <2.5 kg, weight loss of ≥2.5 kg was marginally associated with lower PhenoAgeAccel (P = 0.054), whereas an obese to nonobese pattern from middle to late adulthood was associated with higher PhenoAgeAccel (P < 0.001). CONCLUSIONS Maximal overweight, nonobese to obese, and stable obesity across adulthood, as well as an obese to nonobese pattern from middle to late adulthood, were associated with accelerated biological aging. In contrast, weight loss from young to middle adulthood was associated with decelerated biological aging. The findings highlight the potential role of weight management across adulthood for aging. Monitoring weight fluctuation may help identify the population at high risk of accelerated aging.
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Affiliation(s)
- Xingqi Cao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Zhejiang, China
| | - Gan Yang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Zhejiang, China
| | - Xueqin Li
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Zhejiang, China
| | - Jinjing Fu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Zhejiang, China
| | - Mayila Mohedaner
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Zhejiang, China
| | - Danzengzhuoga
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Zhejiang, China
| | - Terese Sara Høj Jørgensen
- Section of Social Medicine, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Liang Wang
- Department of Public Health, Robbins College of Human Health and Sciences, Baylor University, Waco, TX, USA
| | - Xuehong Zhang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Tao Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong, China
| | - Liyuan Han
- Department of Global Health, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Zhejiang, China; Hwa Mei Hospital, University of Chinese Academy of Sciences, Zhejiang, China
| | - Xiang Gao
- Department of Nutrition and Food Hygiene, School of Public Health, Fudan University, Shanghai, China
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Zhejiang, China.
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Cao X, Ye JY. [Interpreting the indications of OSA surgery: case analysis of the TCM scoring system-Ⅱ]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2022; 57:1511-1515. [PMID: 36707961 DOI: 10.3760/cma.j.cn115330-20220227-00087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- X Cao
- Department of Otorhinopharyngology Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 100218, China
| | - J Y Ye
- Department of Otorhinopharyngology Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 100218, China
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Cao X, Zhao Z, Kang Y, Tian Y, Song Y, Wang L, Zhang L, Wang X, Chen Z, Zheng C, Tian L, Yin P, Fang Y, Zhang M, He Y, Zhang Z, Weintraub WS, Zhou M, Wang Z, Cao X, Zhao Z, Kang Y, Tian Y, Song Y, Wang L, Zhang L, Wang X, Chen Z, Zheng C, Tian L, Chen L, Cai J, Hu Z, Zhou H, Gu R, Huang Y, Yin P, Fang Y, Zhang M, He Y, Zhang Z, Weintraub WS, Zhou M, Wang Z. The burden of cardiovascular disease attributable to high systolic blood pressure across China, 2005–18: a population-based study. The Lancet Public Health 2022; 7:e1027-e1040. [DOI: 10.1016/s2468-2667(22)00232-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/26/2022] [Accepted: 09/05/2022] [Indexed: 12/05/2022] Open
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Jian S, Ya M, Qian Z, Meihua Y, Cao X, Dela Rosa RD. Research progress on humanistic care ability and influencing factors of intern nursing students. Eur Rev Med Pharmacol Sci 2022; 26:8637-8643. [PMID: 36524483 DOI: 10.26355/eurrev_202212_30534] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
This paper is dedicated to reviewing relative research on humanistic caring ability of intern nursing students and proposing strategies to improve humanistic caring ability. Firstly, current paper collected information from both domestic and foreign literature, and then scientific analysis, summary and overview of research results were conducted with regards to humanistic caring ability of interns nursing students. By analyzing the current situation of intern nursing students' humanistic caring ability, and factors that have influence on humanistic caring ability of intern nursing students, the present paper is determined to come up with feasible change methods and form strategic paths. At present, the humanistic care ability of intern nursing students is relatively low. Students, schools, hospitals, and the society all exert influence on the humanistic care ability of intern nursing students. Although scholars' research is different in topics or focus, the conclusions drawn from this research are highly consistent. Nursing humanistic care is the internal quality of nursing staff concerning morality, humanity, and integration of knowledge, concepts, and attitudes. Nursing humanistic care ability includes caring experience ability and caring behavior ability. The necessary psychological characteristics of personality are regarded as a special ability. It is of great significance to promote the quality of nursing and humanistic care ability of intern nursing students who serve as the backup force of nursing talent team. Meanwhile, it is imperative to strengthen the construction of intern nursing students' humanistic care ability.
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Affiliation(s)
- S Jian
- Philippines Women's University, School of Nursing, Malate, Manila, Philippines.
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Rahman M, Ashraf R, Zhang R, Cao X, Gladstone D, Jarvis L, Hoopes P, Pogue B, Bruza P. In Vivo Cherenkov Imaging-Guided FLASH Radiotherapy. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Chen EX, Tu Ya SQ, She ZF, Wang HM, Yang PF, Wang YH, Xu ZH, Hao BJ, Cao X, Mao EQ. The clinical characteristic of alcohol-hyperlipidemia etiologically complex type of acute pancreatitis. Eur Rev Med Pharmacol Sci 2022; 26:7212-7218. [PMID: 36263531 DOI: 10.26355/eurrev_202210_29913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
OBJECTIVE The aim of our study was to elucidate the clinical characteristics of alcoholic-hyperlipidemic etiologically complex acute pancreatitis. PATIENTS AND METHODS We reviewed complete data from 233 patients with acute pancreatitis treated in our hospital during the period January 2017-January 2022. They were divided into three groups according to etiology: alcoholic acute pancreatitis (AAP), hyperlipidemic acute pancreatitis (HLAP), and alcoholic-hyperlipidemic acute pancreatitis (AHAP). General clinical data, co-morbidities, laboratory results, imaging data, and disease severity were analyzed and compared between groups. RESULTS The proportion of male individuals in the AHAP group was significantly higher than that in the HLAP group (p<0.001). Age of onset was lower and the number of cases with antibiotic use was higher in the AHAP group than in the AAP group (p<0.05). Additionally, the average alcohol intake each time and weekly alcohol intake were also higher in the AHAP group than in the AAP group (p<0.05). Comparison of disease severity (moderate and severe acute pancreatitis, severe acute pancreatitis, and modified computed tomography severity index score) revealed the disease condition to be more severe in the AHAP group than in the AAP and HLAP groups (p<0.05). Accordingly, patients in the AHAP group had longer hospital stays than those in the other two groups (p<0.05). There were no significant differences in alcohol consumption, severity, or length of hospital stay in the AHAP group (p>0.05). CONCLUSIONS The clinical characteristics of patients in the AHAP, AAP and HLAP groups were different, and the patients in the AHAP group were more likely to have a moderate to severe disease course, with longer hospital stay. As a new AP classification concept, AHAP would offer high significance for diagnosis, treatment, and prognosis.
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Affiliation(s)
- E-X Chen
- Department of General Surgery, Physical Examination Center, Ordos Central Hospital, Inner Mongolia, China.
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Cao X, Ma C, Zheng Z, He L, Hao M, Chen X, Crimmins EM, Gill TM, Levine ME, Liu Z. Contribution of life course circumstances to the acceleration of phenotypic and functional aging: A retrospective study. EClinicalMedicine 2022; 51:101548. [PMID: 35844770 PMCID: PMC9284373 DOI: 10.1016/j.eclinm.2022.101548] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/27/2022] [Accepted: 06/20/2022] [Indexed: 10/28/2022] Open
Abstract
BACKGROUND Accelerated aging leads to increasing burdens of chronic diseases in late life, posing a huge challenge to the society. With two well-developed aging measures (i.e., physiological dysregulation [PD] and frailty index [FI]), this study aimed to evaluate the relative contributions of life course circumstances (e.g., childhood and adulthood socioeconomic status) to variance in aging. METHODS We assembled data for 6224 middle-aged and older adults in China from the 2014 life course survey (June to December 2014), the 2015 biomarker collection (July 2015 to January 2016), and the 2015 main survey (July 2015 to January 2016) of the China Health and Retirement Longitudinal Study. Two aging measures (PD and FI) were calculated, with a higher value indicating more accelerated aging. Life course circumstances included childhood (i.e., socioeconomic status, war, health, trauma, relationship, and parents' health) and adulthood circumstances (i.e., socioeconomic status, adversity, and social support), demographics, and behaviours. The Shapley value decomposition, hierarchical clustering, and general linear regression models were performed. FINDINGS The Shapley value decomposition revealed that all included life course circumstances accounted for about 6·3% and 29·7% of variance in PD and FI, respectively. We identified six subpopulations who shared similar patterns in terms of childhood and adulthood circumstances. The most disadvantaged subpopulation (i.e., subpopulation 6 [more childhood trauma and adulthood adversity]) consistently exhibited accelerated aging indicated by the two aging measures. Relative to the most advantaged subpopulation (i.e., subpopulation 1 [less childhood trauma and adulthood adversity]), PD and FI in the most disadvantaged subpopulation were increased by an average of 0·14 (i.e., coefficient, by one-standard deviation, 95% confidence interval [CI] 0·06-0·21; p < 0·0001) and 0·10 (by one-point, 95% CI 0·09-0·11; p < 0·0001), respectively. INTERPRETATION Our findings highlight the different contributions of life course circumstances to phenotypic and functional aging. Special attention should be given to promoting health for the disadvantaged subpopulation and narrowing their health gap with advantaged counterparts. FUNDING National Natural Science Foundation of China, Milstein Medical Asian American Partnership Foundation, Natural Science Foundation of Zhejiang Province, Fundamental Research Funds for the Central Universities, National Institute on Aging, National Centre for Advancing Translational Sciences, and Yale Alzheimer's Disease Research Centre.
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Affiliation(s)
- Xingqi Cao
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou 310058, Zhejiang, China
| | - Chao Ma
- School of Economics and Management, Southeast University, Nanjing 211189, Jiangsu, China
| | - Zhoutao Zheng
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou 310058, Zhejiang, China
| | - Liu He
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou 310058, Zhejiang, China
| | - Meng Hao
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Xi Chen
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT 06520, USA
- Department of Economics, Yale University, New Haven, CT 06520, USA
| | - Eileen M. Crimmins
- Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
| | - Thomas M. Gill
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06520, USA
| | - Morgan E. Levine
- Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA
| | - Zuyun Liu
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou 310058, Zhejiang, China
- Corresponding author at: School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, 866 Yuhangtang Rd, Hangzhou, 310058, Zhejiang, China.
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Cao X, Zhang J, Ma C, Li X, Chia-Ling K, Levine ME, Hu G, Allore H, Chen X, Wu X, Liu Z. Life course traumas and cardiovascular disease-the mediating role of accelerated aging. Ann N Y Acad Sci 2022; 1515:208-218. [PMID: 35725988 PMCID: PMC10145586 DOI: 10.1111/nyas.14843] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The complex relationship between life course traumas and cardiovascular disease (CVD) and the underpinning pathways are poorly understood. We aimed to (1) examine the associations of three separate assessments including childhood, adulthood (after 16 years of age), and lifetime traumas (childhood or adulthood) with CVD; (2) examine the associations between diverse life course traumatic profiles and CVD; and (3) examine the extent to which PhenoAge, a well-developed phenotypic aging measure, mediated these associations. Using data from 104,939 participants from the UK Biobank, we demonstrate that subgroups of childhood, adulthood, and lifetime traumas were associated with CVD. Furthermore, life course traumatic profiles were significantly associated with CVD. For instance, compared with the subgroup experiencing nonsevere traumas across life course, those who experienced nonsevere childhood and severe adulthood traumas, severe childhood and nonsevere adulthood traumas, or severe traumas across life course had significantly higher odds of CVD (odds ratios: 1.07-1.33). Formal mediation analyses suggested that phenotypic aging partially mediated the above associations. These findings suggest a potential pathway from life course traumas to CVD through phenotypic aging, and underscore the importance of policy programs targeting traumas over the life course in ameliorating inequalities in cardiovascular health.
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Affiliation(s)
- Xingqi Cao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jingyun Zhang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Chao Ma
- School of Economics and Management, Southeast University, Nanjing, China
| | - Xueqin Li
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Kuo Chia-Ling
- Department of Public Health Sciences, Connecticut Convergence Institute for Translation in Regenerative Engineering, Institute for Systems Genomics, University of Connecticut Health, Farmington, Connecticut, USA
| | - Morgan E. Levine
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Guoqing Hu
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Heather Allore
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Xi Chen
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, USA
- Department of Economics, Yale University, New Haven, Connecticut, USA
| | - Xifeng Wu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
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Yang G, Cao X, Li X, Zhang J, Ma C, Zhang N, Lu Q, Crimmins EM, Gill TM, Chen X, Liu Z. Association of Unhealthy Lifestyle and Childhood Adversity With Acceleration of Aging Among UK Biobank Participants. JAMA Netw Open 2022; 5:e2230690. [PMID: 36066889 PMCID: PMC9449787 DOI: 10.1001/jamanetworkopen.2022.30690] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 07/23/2022] [Indexed: 11/14/2022] Open
Abstract
Importance Accelerated aging makes adults more vulnerable to chronic diseases and death. Whether childhood adversity is associated with accelerated aging processes, and to what extent lifestyle mediates the association, remain unknown. Objective To examine the associations of childhood adversity with a phenotypic aging measure and the role of unhealthy lifestyle in mediating these associations. Design, Setting, and Participants A retrospective cohort analysis was conducted using data from adult participants in the UK Biobank baseline survey (2006-2010) and online mental health survey (2016). Data analysis was performed from September 1, 2021, to February 28, 2022. Exposures Childhood adversity, including physical neglect, emotional neglect, sexual abuse, physical abuse, and emotional abuse, was assessed retrospectively through the online mental health survey (2016). Main Outcomes and Measures A phenotypic aging measure, phenotypic age acceleration, was calculated, with higher values indicating accelerated aging. Body mass index, smoking status, alcohol consumption, physical activity, and diet were combined to construct an unhealthy lifestyle score (range, 0-5, with higher scores denoting a more unhealthy lifestyle). Results A total of 127 495 participants aged 40 to 69 years (mean [SD] chronological age at baseline, 56.4 [7.7] years; 70 979 women [55.7%]; 123 987 White participants [97.2%]) were included. Each individual type of childhood adversity and cumulative childhood adversity score were associated with phenotypic age acceleration. For instance, compared with participants who did not experience childhood adversity, those who experienced 4 (β = 0.296, 95% CI, 0.130-0.462) or 5 (β = 0.833; 95% CI, 0.537-1.129) childhood adversities had higher phenotypic age acceleration in fully adjusted models. The formal mediation analysis revealed that unhealthy lifestyle partially mediated the associations of childhood adversity with phenotypic age acceleration by 11.8% to 42.1%. Conclusions and Relevance In this retrospective cohort study, childhood adversity was significantly associated with acceleration of aging and, more importantly, unhealthy lifestyle partially mediated these associations. These findings reveal a pathway from childhood adversity to health in middle and early older adulthood through lifestyle and underscore the potential of more psychological strategies beyond lifestyle interventions to promote healthy aging.
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Affiliation(s)
- Gan Yang
- School of Public Health and Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xingqi Cao
- School of Public Health and Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xueqin Li
- School of Public Health and Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jingyun Zhang
- School of Public Health and Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chao Ma
- School of Economics and Management, Southeast University, Nanjing, Jiangsu, China
| | - Ning Zhang
- Department of Social Medicine School of Public Health and Center for Clinical Big Data and Analytics Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Qingyun Lu
- School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Eileen M. Crimmins
- Davis School of Gerontology, University of Southern California, Los Angeles
| | - Thomas M. Gill
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Xi Chen
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut
- Department of Economics, Yale University, New Haven, Connecticut
| | - Zuyun Liu
- School of Public Health and Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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Ren Z, Cao X, Li C, Zhang J, Li X, Song P, Zhu Y, Liu Z. Ferritin, transferrin, and transferrin receptor in relation to metabolic obesity phenotypes: Findings from the China Health and Nutrition Survey. Front Public Health 2022; 10:922863. [PMID: 36091521 PMCID: PMC9459082 DOI: 10.3389/fpubh.2022.922863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 08/04/2022] [Indexed: 01/22/2023] Open
Abstract
Background This study aimed to explore the relationship between iron markers and metabolic obesity phenotypes and the role of age. Methods Data were from the China Health and Nutrition Survey 2009. Metabolic obesity phenotypes included metabolically healthy with normal weight (MHNW), metabolically unhealthy with normal weight (MUNW), metabolically healthy with overweight/obesity (MHO), and metabolically unhealthy with overweight/obesity (MUO). Iron markers including ferritin, transferrin, and soluble transferrin receptor were calculated as Log and quartered. The linear regression and multinomial logistic regression were used to explore the association of iron markers with age and metabolic obesity phenotypes, respectively. Results Ferritin was linearly related with age, with β (95% confidence interval, CI) of 0.029 (0.027 to 0.032) and -0.005 (-0.007 to -0.002) for women and men. Transferrin was negatively associated with age in both men and women (β < -0.011). Furthermore, compared with participants in the quartile 1 ferritin group, those in the quartile 4 had increased odds of MUNW, MHO, and MUO, with odds ratio and 95% confidence interval (OR, 95% CI) of 3.06 (2.20 to 4.25), 1.66 (1.35 to 2.05), and 5.27 (4.17 to 6.66). Transferrin showed similar relationships with MUNW, MUO, and MHO; whereas transferrin receptor showed no significance. We also found joint associations of ferritin and transferrin with MUNW, MUO, and MHO. The interactive effect of ferritin and transferrin on MUO was significant (P = 0.015). Conclusion Increased ferritin and transferrin were associated with MUNW, MHO, and MUO. Age should be considered when investigating iron.
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Affiliation(s)
- Ziyang Ren
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Xingqi Cao
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Chenxi Li
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingyun Zhang
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueqin Li
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Peige Song
- School of Public Health and Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,*Correspondence: Peige Song
| | - Yimin Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China,Yimin Zhu
| | - Zuyun Liu
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China,Zuyun Liu ;
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Li C, Jin S, Cao X, Han L, Sun N, Allore H, Hoogendijk EO, Xu X, Feng Q, Liu X, Liu Z. Catastrophic health expenditure among Chinese adults living alone with cognitive impairment: findings from the CHARLS. BMC Geriatr 2022; 22:640. [PMID: 35922775 PMCID: PMC9351200 DOI: 10.1186/s12877-022-03341-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 07/26/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND The catastrophic health expenditure of older adults results in serious consequences; however, the issue of whether cognitive status and living situations contribute to such financial burdens is uncertain. Our aim was to compare the differences in catastrophic health expenditure between adults living alone with cognitive impairment and those adults living with others and with normal cognition. METHODS We identified 909 observations of participants living alone with cognitive impairment (cases) and 37,432 observations of participants living with others and with normal cognition (comparators) from the 2011/2012, 2013, 2015 and 2018 waves of the China Health and Retirement Longitudinal Study (CHARLS). We used propensity score matching (1:2) to create matched cases and comparators in a covariate-adjusted logistic regression analysis. Catastrophic health expenditure was defined as an out-of-pocket cost for health care ≥40% of a household's capacity to pay. RESULTS In comparison with participants living with others and with normal cognition, those adults living alone with cognitive impairment reported a higher percentage of catastrophic health expenditure (19.5% vs. 11.8%, respectively, P < 0.001). When controlling for age, sex, education, marital status, residence areas, alcohol consumption, smoking status and disease counts, we found that this subpopulation had significantly higher odds of having catastrophic health expenditure (odds ratio [OR] = 1.89, 95% confidence interval [CI]: 1.40, 2.56). Additional analyses confirmed the robustness of the results. CONCLUSIONS This study demonstrated that adults living alone with cognitive impairment in the CHARLS experienced a high burden of catastrophic health expenditure. Health care policies on social health insurance and medical assistance should consider these vulnerable adults.
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Affiliation(s)
- Chenxi Li
- grid.13402.340000 0004 1759 700XDepartment of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Zhejiang, 310058 Hangzhou China
| | - Shuyi Jin
- grid.13402.340000 0004 1759 700XDepartment of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Zhejiang, 310058 Hangzhou China
| | - Xingqi Cao
- grid.13402.340000 0004 1759 700XDepartment of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Zhejiang, 310058 Hangzhou China
| | - Ling Han
- grid.47100.320000000419368710Department of Internal Medicine, Yale School of Medicine, New Haven, CT USA
| | - Ning Sun
- grid.496809.a0000 0004 1760 1080Ningbo College of Health Sciences, Ningbo, Zhejiang, China
| | - Heather Allore
- grid.47100.320000000419368710Department of Internal Medicine, Yale School of Medicine, New Haven, CT USA
| | - Emiel O. Hoogendijk
- grid.16872.3a0000 0004 0435 165XDepartment of Epidemiology & Data Science, Amsterdam Public Health research institute, Amsterdam UMC – location VU University medical center, Amsterdam, the Netherlands
| | - Xin Xu
- grid.13402.340000 0004 1759 700XDepartment of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Zhejiang, 310058 Hangzhou China
| | - Qiushi Feng
- grid.4280.e0000 0001 2180 6431Department of Sociology, National University of Singapore, Singapore, Singapore
| | - Xiaoting Liu
- School of Public Affairs, Zhejiang University, 866 Yuhangtang Rd, Zhejiang, 310058, Hangzhou, China.
| | - Zuyun Liu
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Zhejiang, 310058, Hangzhou, China.
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Li X, Cao X, Ying Z, Yang G, Hoogendijk EO, Liu Z. Plasma superoxide dismutase activity in relation to disability in activities of daily living and objective physical functioning among Chinese older adults. Maturitas 2022; 161:12-17. [PMID: 35688489 DOI: 10.1016/j.maturitas.2022.01.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 01/03/2022] [Accepted: 01/19/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND This study aimed to examine the associations of plasma superoxide dismutase (SOD) activity, an indicator of oxidative stress, with disability in activities of daily living (ADL) and objective physical functioning among Chinese older adults. METHODS We used cross-sectional data of 2223 older adults (≥65 years, including 1505 adults≥80 years) from the 2011/2012 main survey of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and the 2012 biomarker sub-study. Plasma SOD activity was assessed by the T-SOD assay kit based on the hydroxylamine method. Outcomes included ADL disability and disability in three objective physical tasks (standing up from a chair, picking up a book from the floor, and turning around 360°). Logistic regression models were used to examine the associations of plasma SOD activity with outcomes. RESULTS After controlling for age and sex, compared with participants in the lowest quartile group of SOD activity, those in the highest quartile group had 31% lower odds of ADL disability (odds ratio [OR]: 0.69; 95%CI: 0.48, 0.98); 60% lower odds of disability in standing up from a chair (OR: 0.40; 95%CI: 0.25, 0.63); and 57% lower odds of disability in picking up a book from a floor (OR: 0.43; 95%CI: 0.28, 0.65). The results did not change substantially after controlling for additional covariates. We did not observe statistically significant age and sex differences. CONCLUSIONS Overall, plasma SOD activity was associated with subjectively and objectively measured disability in Chinese older adults, highlighting the potential of SOD activity to serve as a biomarker of physical functioning.
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Affiliation(s)
- Xueqin Li
- Department of Big Data in Health Science and Center for Clinical Big Data and Analytics, Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xingqi Cao
- Department of Big Data in Health Science and Center for Clinical Big Data and Analytics, Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zhimin Ying
- Department of Orthopedic Surgery, the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Guangtao Yang
- School of Laboratory Medicine and School of Life Sciences, Wenzhou Medical University, Wenzhou, China
| | - Emiel O Hoogendijk
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC-location VU University Medical Center, Amsterdam, the Netherlands
| | - Zuyun Liu
- Department of Big Data in Health Science and Center for Clinical Big Data and Analytics, Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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Wei MZ, Luo QN, Li WJ, Yan HG, Cao X, Li X. [Reconstruction of facial skin defects by the V-Y subcutaneous pedicle flap]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2022; 57:718-723. [PMID: 35725315 DOI: 10.3760/cma.j.cn115330-20210728-00496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Objective: To investigate the method and efficacy of reconstruction of facial skin defects after removing the lesions by applying the V-Y subcutaneous pedicle flap. Methods: A retrospective analysis was performed on 23 patients with facial reconstruction by using V-Y subcutaneous pedicle flap in the Otolaryngology Department of Guangdong Integrated Traditional Chinese and Western Medicine Hospital from March 2012 to April 2021. Patient ages ranged from 45 to 85 years old, with a mean age of 66.5 years (14 males and 9 females). The facial lesion sites included cheek in 12 cases (nearly lower eyelid in 4 cases), nose in 4 cases, lips in 3 cases, temporal in 2 cases and mental region in 2 cases. The initial pathology included malignant tumors (7 cases of basal cell carcinoma (BCC), 2 cases of squamous cell carcinoma(SCC), and 1 case of malignant melanoma) and benign lesions (7 cases of keratoderma, 3 cases of intradermal nevus, 1 case of pilomatricoma, 1 case of cutaneous mixed tumor and 1 case of epidermal cyst). The V-Y subcutaneous facial pedicled flaps were designed reasonably after the facial lesions were excised. The advantages of blood supply, survival rate and adverse events of the flap were analyzed Chi-square test was used to compare the observation results of different types of patients. Results: The primary focus of 23 patients was excised surgically, and intraoperative frozen-section examinations were performed for obtaining margins negative as far as possible. One positive margin was still found in 1 patient after multiple resection in our group. The defect sizes were 14 mm×12 mm-59 mm×54 mm. All the flaps survived. The adverse events were slight necrosis of the epidermis at the junction or vicinity of the three arms of "Y" shaped in 4 cases, but the wounds finally recovered by wet compress and dressing change. There were no significant differences in the incidences of adverse events between double and single pedicle flaps (4/19 vs. 0/4), between benign and malignant lesions (4/13 vs. 0/10), and between patients with and without underlying diseases (1/6 vs. 3/17) (χ2 values were 0.98, 3.56, 0.01, respectively, all P>0.05). There were no other major complications such as dehiscence, hematoma, eyelid ectropion and lip deformation. The patients with benign lesions were followed-up at least for 3 months, while those with malignant tumors were followed-up for 6-36 months postoperatively, without recurrence. Conclusions: V-Y subcutaneous facial pedicled skin flap may be a "no-easy-necrotic" local flap in the repair of small and medium-sized facial defects.
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Affiliation(s)
- M Z Wei
- Department of Otolaryngology, Guangdong Province Hospital of Integrated Traditional Chinese and Western Medicine, Foshan 528200, China
| | - Q N Luo
- Department of Pathology, Guangdong Province Hospital of Integrated Traditional Chinese and Western Medicine, Foshan 528200, China
| | - W J Li
- Department of Otolaryngology, Guangdong Province Hospital of Integrated Traditional Chinese and Western Medicine, Foshan 528200, China
| | - H G Yan
- Department of Otolaryngology, Guangdong Province Hospital of Integrated Traditional Chinese and Western Medicine, Foshan 528200, China
| | - X Cao
- Department of Otolaryngology, Guangdong Province Hospital of Integrated Traditional Chinese and Western Medicine, Foshan 528200, China
| | - Xiang Li
- Department of Otolaryngology, Guangdong Province Hospital of Integrated Traditional Chinese and Western Medicine, Foshan 528200, China
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Muratovic D, Findlay DM, Quarrington RD, Cao X, Solomon LB, Atkins GJ, Kuliwaba JS. Elevated levels of active Transforming Growth Factor β1 in the subchondral bone relate spatially to cartilage loss and impaired bone quality in human knee osteoarthritis. Osteoarthritis Cartilage 2022; 30:896-907. [PMID: 35331858 DOI: 10.1016/j.joca.2022.03.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 03/03/2022] [Accepted: 03/09/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE The association between the spatially distributed level of active TGFβ1 in human subchondral bone, and the characteristic structural and cellular parameters of human knee OA, was assessed. DESIGN Paired subchondral bone samples from 35 OA arthroplasty patients, (15 men and 20 women, aged 69 ± 9 years) were obtained from beneath macroscopically present (CA+) or denuded cartilage (CA-) to determine the concentration of active TGFβ1 (ELISA) and its relationship to bone quality (synchrotron micro-CT), cellularity, and vascularization (histology). RESULTS Bone samples beneath (CA-) regions had significantly increased concentrations of active TGFβ1 protein (mean difference: 26.4; 95% CI: [3.2, 49.7]), when compared to bone in CA + regions. Trabecular Bone below (CA-) regions had increased bone volume (median difference: 4.3; 96.49% CI: [-1.7, 17.8]), increased trabecular number (1.5 [0.006, 2.6], decreased trabecular separation (-0.05 [-0.1,-0.005]), and increased bone mineral density (394.5 [65.7, 723.3]) comparing to (CA+) regions. Further, (CA-) bone regions showed increased osteocyte density (0.012 [0.006, 0.018]), with larger osteocyte lacunae (39.8 [7.8, 71.7]) that were less spherical (-0.02 [-0.04, -0.003]), and increased bone matrix vascularity (12.4 [0.3, 24.5]) compared to (CA+). In addition, increased levels of active TGFβ1 related to increased bone volume (0.04 [-0.11, 0.9]), while increased OARSI grade associated with lacunar volume (-44.1 [-71.1, -17.2]), and orientation (2.7 [0.8, 4.6]). CONCLUSION Increased concentration of active TGFβ1 in the subchondral bone of human knee OA associates spatially with impaired bone quality and disease severity, suggesting that TGFβ1 is a potential therapeutic target to prevent or reduce human OA disease progression.
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Affiliation(s)
- D Muratovic
- Centre for Orthopaedic & Trauma Research, The University of Adelaide, Adelaide, South Australia 5000, Australia.
| | - D M Findlay
- Centre for Orthopaedic & Trauma Research, The University of Adelaide, Adelaide, South Australia 5000, Australia.
| | - R D Quarrington
- Centre for Orthopaedic & Trauma Research, The University of Adelaide, Adelaide, South Australia 5000, Australia.
| | - X Cao
- Department of Orthopaedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - L B Solomon
- Centre for Orthopaedic & Trauma Research, The University of Adelaide, Adelaide, South Australia 5000, Australia; Orthopaedic and Trauma Service, The Royal Adelaide Hospital and the Central Adelaide Local Health Network, Adelaide, South Australia 5000, Australia.
| | - G J Atkins
- Centre for Orthopaedic & Trauma Research, The University of Adelaide, Adelaide, South Australia 5000, Australia.
| | - J S Kuliwaba
- Centre for Orthopaedic & Trauma Research, The University of Adelaide, Adelaide, South Australia 5000, Australia.
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Yang Z, Pu F, Cao X, Li X, Sun S, Zhang J, Chen C, Han L, Yang Y, Wang W, Zhang Y, Liu Z. Does healthy lifestyle attenuate the detrimental effects of urinary polycyclic aromatic hydrocarbons on phenotypic aging? An analysis from NHANES 2001-2010. Ecotoxicol Environ Saf 2022; 237:113542. [PMID: 35468442 DOI: 10.1016/j.ecoenv.2022.113542] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 04/08/2022] [Accepted: 04/17/2022] [Indexed: 06/14/2023]
Abstract
Existing evidence has showed that exposure to polycyclic aromatic hydrocarbons (PAHs) increases the risk of many chronic diseases. Given the close connection between aging (a major risk factor) and chronic diseases, however, very few studies have evaluated the association between PAHs and aging. Furthermore, whether modifiable healthy lifestyle could attenuate the detrimental effect of PAHs on aging remains unknown. Therefore, we conducted this study, aiming to: (1) examine the associations of urinary monohydroxy polycyclic aromatic hydrocarbons (OH-PAHs) and lifestyle with Phenotypic Age Acceleration (PhenoAge.Accel), a novel aging measure that captures morbidity and mortality risk; and (2) evaluate the potential interaction effects of OH-PAHs and lifestyle on PhenoAge.Accel. Cross-sectional data of 2,579 participants (aged 20-84 years, n = 1,292 females) from the National Health and Nutrition Examination Survey for years 2001-2010 were analyzed. A lifestyle index was constructed based on five components (drinking, smoking, body mass index, physical activity, and diet), ranging from 0 to 5. We calculated PhenoAge.Accel using algorithms developed previously. General linear regression models were used to examine the associations. We observed strong associations of OH-PAHs and lifestyle with PhenoAge.Accel. For instance, one unit increase in ∑NAP (sum of 1- and 2-hydroxynaphthalene) was associated with 0.37 year (95% confidence interval [CI]: 0.26, 0.48) increase in PhenoAge.Accel. We did not observe statistically significant interaction effects between OH-PAHs and lifestyle on PhenoAge.Accel. After stratified by sex, we observed strong associations as well as statistically significant interactions of OH-PAHs and lifestyle with PhenoAge.Accel among females. In conclusion, both OH-PAHs and lifestyle were independently associated with phenotypic aging and there were statistically significant interactions between OH-PAHs and lifestyle on phenotypic aging among females. The findings highlight the importance of adherence to a healthy lifestyle to attenuate the detrimental effects of exposures to PAHs on phenotypic aging among females.
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Affiliation(s)
- Zhenqing Yang
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Fan Pu
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Xingqi Cao
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Xueqin Li
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Sudan Sun
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Jingyun Zhang
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Chen Chen
- National Institute of Environmental and Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Liyuan Han
- Department of Global Health, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315200, Zhejiang, China; Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo 315200, Zhejiang, China
| | - Yongli Yang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Wei Wang
- Department of Occupational Health and Occupational Disease, College of Public Health, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Yawei Zhang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Zuyun Liu
- Department of Big Data in Health Science School of Public Health and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China.
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Ma MY, Chen XL, Chen Z, Wang X, Zhang LF, Li SN, Zheng CY, Kang YT, Zhou HH, Chen L, Cao X, Hu JH, Wang ZW. [Investigation on status of dyslipidemia in Chinese females aged 35 years or above]. Zhonghua Xin Xue Guan Bing Za Zhi 2022; 50:486-493. [PMID: 35589598 DOI: 10.3760/cma.j.cn112148-20211201-01035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Objective: To investigate the prevalence, awareness, treatment and control status of dyslipidemia among females aged ≥35 years old across China. Methods: Participants were selected by stratified multistage random sampling method in the "Twelfth Five-Year Plan" National Science and Technology Support Project "Survey on the Prevalence of Important Cardiovascular Diseases and Key Technology Research in China" project. This study is a retrospective, cross-sectional study. A total of 17 418 females aged 35 years and over were included in the current study. The basic information such as age, medical history and menopause was collected by questionnaire. The blood lipid parameters were derived from clinical laboratory examinations. The prevalence of dyslipidemia and the rate of awareness, treatment, and control of dyslipidemia were analyzed in females aged 35 years and over. Results: The age of participants was (56.2±13.0) years old, and the prevalence of dyslipidemia was 33.1% (5 765/17 418). The prevalence rates of high total cholesterol, hypertriglyceridemia, low HDL-C and high LDL-C were 9.7% (1 695/17 418), 11.1% (1 925/17 418), 10.9% (1 889/17 418) and 7.3% (1 262/17 418), respectively. The prevalence of dyslipidemia increased with age and the prevalence of dyslipidemia in women who were not married, Han, menarche age>16 years, obesity, central obesity, alcohol consumption, diabetes, hypertension and family history of cardiovascular disease were higher than those without such characteristics (P<0.05). There were 10 432 (59.9%) menopausal females in this cohort and prevalence of dyslipidemia of these participants was 38.8% (4 048/10 432), which was higher than that of non-postmenopausal females (24.6%, 1 717/6 986) (P<0.05). The awareness rates, treatment rates and control rates of dyslipidemia were 33.9% (1 953/5 765), 15.1% (870/5 765) and 2.5% (143/5 765) respectively among females aged 35 years and over in China. Conclusion: The prevalence of dyslipidemia in Chinese females aged 35 years and over is high, and its awareness, treatment, and control rates need to be optimized.
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Affiliation(s)
- M Y Ma
- School of Public Health, Gansu University of Traditional Chinese Medicine, Lanzhou 730000, China
| | - X L Chen
- School of Public Health, Gansu University of Traditional Chinese Medicine, Lanzhou 730000, China
| | - Z Chen
- Department of Community Prevention and Treatment, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 102308, China
| | - X Wang
- Department of Community Prevention and Treatment, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 102308, China
| | - L F Zhang
- Department of Community Prevention and Treatment, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 102308, China
| | - S N Li
- Department of Community Prevention and Treatment, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 102308, China
| | - C Y Zheng
- Department of Community Prevention and Treatment, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 102308, China
| | - Y T Kang
- Department of Community Prevention and Treatment, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 102308, China
| | - H H Zhou
- Department of Community Prevention and Treatment, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 102308, China
| | - L Chen
- Department of Community Prevention and Treatment, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 102308, China
| | - X Cao
- Department of Community Prevention and Treatment, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 102308, China
| | - J H Hu
- School of Public Health, Gansu University of Traditional Chinese Medicine, Lanzhou 730000, China
| | - Z W Wang
- Department of Community Prevention and Treatment, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 102308, China
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Wei LJ, Hou Q, Yao NN, Liang Y, Cao X, Sun BC, Li HW, Liu JT, Xu SM, Cao J. [Construction of a nomogram model for predicting 2-year survival rate of small cell lung cancer based on more comprehensive variables]. Zhonghua Yi Xue Za Zhi 2022; 102:1283-1289. [PMID: 35488697 DOI: 10.3760/cma.j.cn112137-20211106-02467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: To construct a novel prognostic nomogram model based on more comprehensive variables for patients with small-cell lung cancer (SCLC). Methods: The data of 722 patients with SCLC confirmed by pathology in Affiliated Cancer Hospital of Shanxi Medical University from January 2015 to December 2018 were retrospectively analyzed [including 592 males and 130 females, aged from 23 to 82(61±9) years]. A random seed count of 133 was used to divide those patients into training set (n=422) and validation set (n=300). Kaplan-Meier was used for survival curves analysis and univariate Log-rank test was used for evaluating the influence of clinical variables on the prognosis of sclc, variables with P<0.05 in univariate analysis were included in a multivariate Cox regression model. The nomogram was constructed based on the variables which P<0.05 in multivariate analysis. Receiver operating characteristic (ROC) curve, calibration by Integrated Brier score (IBS) and clinical net benefit by decision curve analysis (DCA) were used to evaluate model discriminative power, prediction error value, and clinical net benefit, and compared with the American Joint Committee on Cancer 8th TNM. Results: Male, abnormal monocyte (MON) counts, abnormal neuron specific enolase (NSE), abnormal cytokeratin 19 fragment (Cyfra211), M1a stage, M1b stage, M1c stage, radiotherapy (RT), chemotherapy ≥4 cycles and prophylactic cranial irradiation (PCI) were prognostic factors for SCLC[HR(95%CI)=1.39(1.00-1.92), 1.29(1.02-1.63), 1.41(1.11-1.80), 2.02(1.48-2.76), 1.09(0.77-1.55), 1.44(0.94-2.22), 2.01(1.49-2.71), 0.75(0.57-0.98), 0.40(0.31-0.51)and 0.42(0.26-0.68), respectively, all P<0.05]. The area under ROC curve (AUC) of the nomogram in training set and validation set were 0.814(95%CI: 0.765-0.862)and 0.787 (95%CI: 0.725-0.849), which were higher than TNM [0.616(95%CI: 0.558-0.674) and 0.648(95%CI: 0.581-0.715)].The calibration curve showed a good correlation between the nomogram prediction and actual observation for the 2-year overall survival (OS). IBS indicted a lower prediction error rate (training set: 0.132 vs 0.169; validation set: 0.138 vs 0.169). DCA showed a wider threshold range than TNM (training set: 0.01-0.96 vs 0.01-0.85, validation set: 0.01-0.94 vs 0.01-0.86) and a greater improvement of the clinical net benefit (in training set the nomogram had a greater clinical benefit than TNM in the range of 0.19-0.96, and remained in validation set in the range of 0.19-0.94). Conclusion: The established nomogram model for predicting 2-year OS in patients with SCLC based on 8 variables, including gender, MON, NSE, Cyfra211, M stage, RT, CT cycles and PCI can be used for an more accurately prognosis prediction and reference for therapeutic regimen selection.
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Affiliation(s)
- L J Wei
- Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030010, China
| | - Q Hou
- Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030010, China
| | - N N Yao
- Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030010, China
| | - Y Liang
- Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030010, China
| | - X Cao
- Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030010, China
| | - B C Sun
- Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030010, China
| | - H W Li
- Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030010, China
| | - J T Liu
- Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030010, China
| | - S M Xu
- Department of CT, the Shanxi Children's Hospital, Taiyuan 030013, China
| | - Jianzhong Cao
- Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030010, China
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Liu P, Cao X, Gao X, Shang S, Liu J, Wang Z, Ding X. PO-1505 Feasibility of acute hematologic toxicity model-based patient selection for proton beam therapy. Radiother Oncol 2022. [DOI: 10.1016/s0167-8140(22)03469-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Cao X, Chen C, Zhang J, Xue QL, Hoogendijk EO, Liu X, Li S, Wang X, Zhu Y, Liu Z. Aging metrics incorporating cognitive and physical function capture mortality risk: results from two prospective cohort studies. BMC Geriatr 2022; 22:378. [PMID: 35484496 PMCID: PMC9052591 DOI: 10.1186/s12877-022-02913-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 03/01/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Aging metrics incorporating cognitive and physical function are not fully understood, hampering their utility in research and clinical practice. This study aimed to determine the proportions of vulnerable persons identified by three existing aging metrics that incorporate cognitive and physical function and the associations of the three metrics with mortality. METHODS We considered three existing aging metrics including the combined presence of cognitive impairment and physical frailty (CI-PF), the frailty index (FI), and the motoric cognitive risk syndrome (MCR). We operationalized them using data from the China Health and Retirement Longitudinal Study (CHARLS) and the US National Health and Nutrition Examination Survey (NHANES). Logistic regression models or Cox proportional hazards regression models, and receiver operating characteristic curves were used to examine the associations of the three metrics with mortality. RESULTS In CHARLS, the proportions of vulnerable persons identified by CI-PF, FI, and MCR were 2.2, 16.6, and 19.6%, respectively. Each metric predicted mortality after adjustment for age and sex, with some variations in the strength of the associations (CI-PF, odds ratio (OR) (95% confidence interval (CI)) 2.87 (1.74-4.74); FI, OR (95% CI) 1.94 (1.50-2.50); MCR, OR (95% CI) 1.27 (1.00-1.62)). CI-PF and FI had additional predictive utility beyond age and sex, as demonstrated by integrated discrimination improvement and continuous net reclassification improvement (all P < 0.001). These results were replicated in NHANES. CONCLUSIONS Despite the inherent differences in the aging metrics incorporating cognitive and physical function, they consistently capture mortality risk. The findings support the incorporation of cognitive and physical function for risk stratification in both Chinese and US persons, but call for caution when applying them in specific study settings.
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Affiliation(s)
- Xingqi Cao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health; National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jingyun Zhang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China
| | - Qian-Li Xue
- Department of Medicine Division of Geriatric Medicine and Gerontology and Center on Aging and Health, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Emiel O Hoogendijk
- Department of Epidemiology and Data Science, Amsterdam Public Health research institute, Amsterdam UMC - location VU University Medical Center, Amsterdam, the Netherlands
| | - Xiaoting Liu
- School of Public Affairs, Zhejiang University, Zhejiang, Hangzhou, China
| | - Shujuan Li
- Department of Neurology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Xiaofeng Wang
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, and Human Phenome Institute, Fudan University, Shanghai, China.
| | - Yimin Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China.
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China.
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Zhang J, Cao X, Chen C, He L, Ren Z, Xiao J, Han L, Wu X, Liu Z. Predictive Utility of Mortality by Aging Measures at Different Hierarchical Levels and the Response to Modifiable Life Style Factors: Implications for Geroprotective Programs. Front Med (Lausanne) 2022; 9:831260. [PMID: 35530042 PMCID: PMC9072659 DOI: 10.3389/fmed.2022.831260] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 03/14/2022] [Indexed: 01/01/2023] Open
Abstract
Background Aging, as a multi-dimensional process, can be measured at different hierarchical levels including biological, phenotypic, and functional levels. The aims of this study were to: (1) compare the predictive utility of mortality by three aging measures at three hierarchical levels; (2) develop a composite aging measure that integrated aging measures at different hierarchical levels; and (3) evaluate the response of these aging measures to modifiable life style factors. Methods Data from National Health and Nutrition Examination Survey 1999–2002 were used. Three aging measures included telomere length (TL, biological level), Phenotypic Age (PA, phenotypic level), and frailty index (FI, functional level). Mortality information was collected until December 2015. Cox proportional hazards regression and multiple linear regression models were performed. Results A total of 3,249 participants (20–84 years) were included. Both accelerations (accounting for chronological age) of PA and FI were significantly associated with mortality, with HRs of 1.67 [95% confidence interval (CI) = 1.41–1.98] and 1.59 (95% CI = 1.35–1.87), respectively, while that of TL showed non-significant associations. We thus developed a new composite aging measure (named PC1) integrating the accelerations of PA and FI, and demonstrated its better predictive utility relative to each single aging measure. PC1, as well as the accelerations of PA and FI, were responsive to several life style factors including smoking status, body mass index, alcohol consumption, and leisure-time physical activity. Conclusion This study demonstrates that both phenotypic (i.e., PA) and functional (i.e., FI) aging measures can capture mortality risk and respond to modifiable life style factors, despite their inherent differences. Furthermore, the PC1 that integrated phenotypic and functional aging measures outperforms in predicting mortality risk in comparison with each single aging measure, and strongly responds to modifiable life style factors. The findings suggest the complementary of aging measures at different hierarchical levels and highlight the potential of life style-targeted interventions as geroprotective programs.
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Affiliation(s)
- Jingyun Zhang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Xingqi Cao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Chen Chen
- National Institute of Environmental and Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Liu He
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Ziyang Ren
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Junhua Xiao
- College of Chemistry, Chemical Engineering and Biotechnology, Donghua University, Shanghai, China
| | - Liyuan Han
- Department of Global Health, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China
- Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Xifeng Wu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- Xifeng Wu
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Zuyun Liu ;
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