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Xian G, Chai Y, Gong Y, He W, Ma C, Zhang X, Zhang J, Ma Y. The relationship between healthy lifestyles and cognitive function in Chinese older adults: the mediating effect of depressive symptoms. BMC Geriatr 2024; 24:299. [PMID: 38549104 PMCID: PMC10979595 DOI: 10.1186/s12877-024-04922-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 03/26/2024] [Indexed: 04/01/2024] Open
Abstract
BACKGROUND Previous studies have proven the positive relationship between healthy lifestyles and cognitive function in older adults. However, the specific impacts and mechanisms require further investigation. Therefore, this study aimed to investigate whether healthy lifestyles and cognitive function were associated with Chinese older adults and whether depressive symptoms mediated their association. METHODS 8272 valid samples were included using the latest data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Pearson's test was applied to investigate the relationship between the key variables. Regression models were employed to examine the mediating effects of healthy lifestyles, using Sobel's test and the bootstrap method to confirm path effects. RESULTS There was a significant correlation between healthy lifestyles, depressive symptoms, and cognitive function (p < 0.01). Healthy lifestyles directly impact cognitive function (β = 0.162, p < 0.01). Healthy lifestyles had a significant effect on depressive symptoms (β=-0.301, p < 0.01), while depressive symptoms have a significant impact on cognitive function (β=-0.108, p < 0.01). Depressive symptoms partially mediated the effect of healthy lifestyles on cognitive function (β = 0.032, p < 0.01). The Sobel and bootstrap tests confirmed the robustness of the regression analysis results. CONCLUSION Depressive symptoms mediate the relationship between healthy lifestyles and cognitive function. Our findings suggest that prevention strategies for cognitive impairment in older adults should focus on healthy lifestyles and mental health.
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Affiliation(s)
- Guowei Xian
- School of Management, Shandong Second Medical University, 261053, Weifang, Shandong, China
| | - Yulin Chai
- School of Management, Shandong Second Medical University, 261053, Weifang, Shandong, China
| | - Yunna Gong
- School of Management, Shandong Second Medical University, 261053, Weifang, Shandong, China
| | - Wenfeng He
- School of Management, Shandong Second Medical University, 261053, Weifang, Shandong, China
| | - Chunxiao Ma
- School of Management, Shandong Second Medical University, 261053, Weifang, Shandong, China
| | - Xiaolin Zhang
- School of Management, Shandong Second Medical University, 261053, Weifang, Shandong, China
| | - Jing Zhang
- School of Management, Shandong Second Medical University, 261053, Weifang, Shandong, China
| | - Yong Ma
- School of Management, Shandong Second Medical University, 261053, Weifang, Shandong, China.
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Wang Q, Zhou S, Zhang J, Wang Q, Hou F, Han X, Shen G, Zhang Y. Risk assessment and stratification of mild cognitive impairment among the Chinese elderly: attention to modifiable risk factors. J Epidemiol Community Health 2023:jech-2022-219952. [PMID: 37321832 DOI: 10.1136/jech-2022-219952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 05/28/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND The early identification of individuals at risk of mild cognitive impairment (MCI) has major public health implications for Alzheimer's disease prevention. OBJECTIVE This study aims to develop and validate a risk assessment tool for MCI with a focus on modifiable factors and a suggested risk stratification strategy. METHODS Modifiable risk factors were selected from recent reviews, and risk scores were obtained from the literature or calculated based on the Rothman-Keller model. Simulated data of 10 000 subjects with the exposure rates of the selected factors were generated, and the risk stratifications were determined by the theoretical incidences of MCI. The performance of the tool was verified using cross-sectional and longitudinal datasets from a population-based Chinese elderly cohort. RESULTS Nine modifiable risk factors (social isolation, less education, hypertension, hyperlipidaemia, diabetes, smoking, drinking, physical inactivity and depression) were selected for the predictive model. The area under the curve (AUC) was 0.71 in the training set and 0.72 in the validation set for the cross-sectional dataset. The AUCs were 0.70 and 0.64 in the training and validation sets, respectively, for the longitudinal dataset. A combined risk score of 0.95 and 1.86 was used as the threshold to categorise MCI risk as 'low', 'moderate' and 'high'. CONCLUSION A risk assessment tool for MCI with appropriate accuracy was developed in this study, and risk stratification thresholds were also suggested. The tool might have significant public health implications for the primary prevention of MCI in elderly individuals in China.
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Affiliation(s)
- Qiong Wang
- School of Health Service Management, Anhui Medical University, Hefei, Anhui, China
| | - Shuai Zhou
- School of Health Service Management, Anhui Medical University, Hefei, Anhui, China
| | - Jingya Zhang
- School of Health Service Management, Anhui Medical University, Hefei, Anhui, China
| | - Qing Wang
- School of Health Service Management, Anhui Medical University, Hefei, Anhui, China
| | - Fangfang Hou
- School of Health Service Management, Anhui Medical University, Hefei, Anhui, China
| | - Xiao Han
- School of Health Service Management, Anhui Medical University, Hefei, Anhui, China
| | - Guodong Shen
- Department of Geriatrics, University of Science and Technology of China, Hefei, Anhui, China
| | - Yan Zhang
- School of Health Service Management, Anhui Medical University, Hefei, Anhui, China
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Ge S, Ma W, Qu Z, Zhu X, Chen Z, Lin X, Fu Z. Urinary tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) and cognitive functioning in older adults: The National Health and Nutrition Examination Survey 2013-2014. Tob Induc Dis 2023; 21:68. [PMID: 37252031 PMCID: PMC10210584 DOI: 10.18332/tid/162368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 02/10/2023] [Accepted: 03/18/2023] [Indexed: 05/31/2023] Open
Abstract
INTRODUCTION Tobacco contains carcinogens called tobacco-specific nitrosamines. Among the tobacco-specific nitrosamines, is nicotine-derived nitrosamine ketone (NNK) which produces the metabolite 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL). We aimed to examine the association between urinary tobacco-specific NNAL and cognitive functioning among older adults. METHODS A total of 1673 older adults aged ≥60 years from the National Health and Nutrition Examination Survey 2013-2014 were included. Urinary tobacco-specific NNAL was analyzed in the laboratory. Cognitive functioning was measured using the Consortium to Establish a Registry for Alzheimer's Disease Word Learning subtest (CERAD-WL) immediate and delayed memory tests, the Animal Fluency test (AFT), and the Digit Symbol Substitution Test (DSST). Test-specific and global cognition z-scores were calculated based on means and standard deviations of the cognitive test scores. Multivariable linear regression models were constructed to examine the independent association between quartiles of urinary tobacco-specific NNAL and cognitive test-specific and global cognition z-scores controlling for age, sex, race/ethnicity, education level, depressive symptoms, body mass index, systolic blood pressure, urinary creatinine, hypertension, diabetes, alcohol use, and smoking status. RESULTS About half of the participants (mean age 69.8 years) were female (52.1%), non-Hispanic White (48.3%), and completed some college and above (49.7%). Multivariable linear regression results showed that participants in the 4th quartile (highest quartile) of urinary NNAL, compared with those in the 1st quartile (lowest quartile), had lower DSST z-scores (β= -0.19; 95% CI: -0.34 - -0.04). CONCLUSIONS Tobacco-specific NNAL was negatively associated with processing speed, sustained attention, and working memory in older adults.
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Affiliation(s)
- Song Ge
- Department of Natural Sciences, College of Sciences and Technology, University of Houston-Downtown, Houston, United States
| | - Weixia Ma
- Department of Pulmonary and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Zhe Qu
- School of Nursing, Xuzhou Medical University, Xuzhou, China
| | - Xingmei Zhu
- Yaxin School of Nursing, Wuhan Institute of Design and Science, Wuhan, China
| | | | - Xuechun Lin
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhenmei Fu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
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Zhu Y, Guan Y, Xiao X, Jiao B, Liao X, Zhou H, Liu X, Qi F, Peng Q, Zhou L, Xu T, Yang Q, Zhang S, Li M, Zhu Z, Lu S, Li J, Tang B, Shen L, Yao J, Zhou Y. Mendelian randomization analyses of smoking and Alzheimer's disease in Chinese and Japanese populations. Front Aging Neurosci 2023; 15:1157051. [PMID: 37251809 PMCID: PMC10213305 DOI: 10.3389/fnagi.2023.1157051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 04/28/2023] [Indexed: 05/31/2023] Open
Abstract
Background Previous epidemiological studies have reported controversial results on the relationship between smoking and Alzheimer's disease (AD). Therefore, we sought to assess the association using Mendelian randomization (MR) analysis. Methods We used single nucleotide polymorphisms (SNPs) associated with smoking quantity (cigarettes per day, CPD) from genome-wide association studies (GWAS) of Japanese population as instrumental variables, then we performed two-sample MR analysis to investigate the association between smoking and AD in a Chinese cohort (1,000 AD cases and 500 controls) and a Japanese cohort (3,962 AD cases and 4,074 controls), respectively. Results Genetically higher smoking quantity showed no statistical causal association with AD risk (the inverse variance weighted (IVW) estimate in the Chinese cohort: odds ratio (OR) = 0.510, 95% confidence interval (CI) = 0.149-1.744, p = 0.284; IVW estimate in the Japanese cohort: OR = 1.170, 95% confidence interval CI = 0.790-1.734, p = 0.434). Conclusion This MR study, for the first time in Chinese and Japanese populations, found no significant association between smoking and AD.
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Affiliation(s)
- Yuan Zhu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Ying Guan
- Joint Institute of Tobacco and Health, Kunming, Yunnan, China
| | - Xuewen Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Bin Jiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Xinxin Liao
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Hui Zhou
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Xixi Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Feiyan Qi
- Joint Institute of Tobacco and Health, Kunming, Yunnan, China
| | - Qiyuan Peng
- Joint Institute of Tobacco and Health, Kunming, Yunnan, China
| | - Lu Zhou
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Tianyan Xu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Qijie Yang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Sizhe Zhang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Meng Li
- Joint Institute of Tobacco and Health, Kunming, Yunnan, China
| | - Zhouhai Zhu
- Joint Institute of Tobacco and Health, Kunming, Yunnan, China
| | - Sheming Lu
- Joint Institute of Tobacco and Health, Kunming, Yunnan, China
| | - Jinchen Li
- Bioinformatics Center & National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Beisha Tang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Lu Shen
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jianhua Yao
- Joint Institute of Tobacco and Health, Kunming, Yunnan, China
| | - Yafang Zhou
- Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
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Li C, Ge S, Yin Y, Tian C, Mei Y, Han P. Frailty is associated with worse cognitive functioning in older adults. Front Psychiatry 2023; 14:1108902. [PMID: 36816402 PMCID: PMC9928943 DOI: 10.3389/fpsyt.2023.1108902] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 01/12/2023] [Indexed: 02/04/2023] Open
Abstract
INTRODUCTION Frailty and impaired cognitive functioning often co-occur in older adults and are associated with adverse health outcomes. However, their relationship is unclear. This study sought to examine the association of frailty status with cognitive functioning in older adults. METHOD The study population consisted of 2,296 older adults aged ≥60 from the National Health and Nutrition Examination Survey 2011-2014. Frailty status was measured based on the Fried Phenotype and the participants were categorized into three groups- robust, pre-frailty, and frailty. Cognitive functioning was measured using the Consortium to Establish a Registry for Alzheimer's Disease Word Learning subtest (CERAD-WL) immediate and delayed recall tests, the Animal Fluency test (AFT), and the Digit Symbol Substitution Test (DSST). Test-specific and global cognition z-scores were calculated. Multinomial linear regression models were constructed to examine the association between frailty status (reference: robust) and test-specific and global cognition z-scores. Multiple linear regression models were used to examine the relationship between the number of frailty dimensions and test-specific and global cognition z-scores. All models controlled for age, race/ethnicity, education, total cholesterol level, and systolic blood pressure. RESULTS About half of the participants (median age 68 years) were female (49.9%) and non-Hispanic White (48.7%). A quarter (23.3%) of the participants completed some college and above. Multinominal linear regression showed that compared with participants who were robust, those with frailty had worse DSST [β = -0.234, 95% confidence interval (CI): -0.391, -0.078, P = 0.003] and global cognition z scores (β = -0.129, 95% CI -0.233, -0.025, P = 0.02). Multiple linear regression model showed that the number of frailty dimensions was significantly associated with decreased the DSST (β = -0.065, 95% CI -0.103, -0.026, P = 0.001) and global cognition z-scores (β= -0.034, 95% CI -0.06, -0.009, P = 0.009). CONCLUSION Frailty is associated with worse processing speed, sustained attention, working memory, and global cognition in older adults. Prevention and treatment of frailty in older adults may help protect their cognitive functioning. Further, clinicians should consider assessing cognitive functioning, especially processing speed, sustained attention, and working memory, among frail older patients, which may allow early identification and interventions of cognitive impairment.
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Affiliation(s)
- Chunmei Li
- Infection Clinic, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Song Ge
- Department of Natural Sciences, University of Houston-Downtown, Houston, TX, United States
| | - Yueheng Yin
- School of Nursing, Nanjing Medical University, Nanjing, China
| | - Chong Tian
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yongxia Mei
- School of Nursing and Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Peijin Han
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, United States
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Deng Y, Lin X, Zhou J, Li M, Fu Z, Song D. Concurrent serum lead levels and cognitive function in older adults. Front Neurosci 2023; 17:1180782. [PMID: 37139533 PMCID: PMC10149719 DOI: 10.3389/fnins.2023.1180782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 03/15/2023] [Indexed: 05/05/2023] Open
Abstract
Introduction In this study, we investigated the relationship between serum lead levels and cognitive functioning in a sample of older adults in the US. Method Using the National Health and Nutrition Examination Survey (NHANES) 2011-2013, a total of 768 older adults aged ≥60 years were included in the analysis. Lead concentrations in the whole blood samples were assessed using mass spectrometry. We used the immediate and delayed memory portions of the Consortium to Establish a Registry for Alzheimer's Disease Word Learning Subtest (CERAD-WL), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST) to assess the participants' cognitive performance. Using sample averages and standard deviations (SDs), we computed test-specific and global cognition z-scores. To assess the relationships between the quartiles of serum lead levels and cognitive performance, we built multiple linear regression models and adjusted for covariates, including age, sex, race/ethnicity, education, depressive symptoms, alcohol usage, and body mass index. Results The average age of the participants was 69.6 (SD 6.6) years. Approximately half of the participants were women (52.6%), non-Hispanic white (52.0%), and had completed at least some college education (51.8%). The average serum lead concentration was 1.8 g/dL (SD 1.6) for these participants. The results of multiple linear regression using individuals in the lowest serum lead quantile as a reference group revealed that the serum lead level was not associated with test-specific (CERAD-WL, AFT, and DSST) or global cognitive z-scores. Conclusions In older adults, concurrent serum lead concentration is not related to cognitive performance. Early or continuous lead exposure may exert a greater effect on the etiology of accelerated cognitive decline with old age.
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Affiliation(s)
- Yu Deng
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Xuechun Lin
- Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health, Department of Nutrition and Food Hygiene, Tongji Medical College, School of Public Health, Huazhong University of Science and Technology, Wuhan, China
| | - Jie Zhou
- Department of Nursing, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, Guangdong Province, China
| | - Mengchi Li
- School of Nursing, John Hopkins University, Baltimore, MD, United States
| | - Zhenmei Fu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- *Correspondence: Zhenmei Fu
| | - Dan Song
- Department of Nursing, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, Guangdong Province, China
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Hou J, Huang C, Zhu B, Liu W, Zhu QQ, Wang L, Li T, Yuan CJ, Lai SY, Wu DS, Zhu FQ, Zhang JF, Huang J, Gao EW, Huang YD, Nie LL, Lu SY, Yang XF, Zhou L, Ye F, Yuan J, Liu JJ. Effect modification by aging on the associations of nicotine exposure with cognitive impairment among Chinese elderly. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:9530-9542. [PMID: 36057059 DOI: 10.1007/s11356-022-22392-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 08/01/2022] [Indexed: 06/15/2023]
Abstract
Active and passive exposure to tobacco smoke may increase risk of cognitive decline. However, effects of enhanced the aging process on the association of urinary nicotine metabolites with cognitive impairment remain unclear. In this study, 6657 Chinese older adults completed the physical examinations and cognitive tests. We measured urinary nicotine metabolite levels, mitochondrial DNA copy number (mtDNA-CN), and relative telomere length (RTL) and analyzed effects of urinary nicotine metabolites and their interaction with mtDNA-CN or RTL on cognitive impairment by generalized linear models and qg-computation, respectively. Each 1-unit increase in urinary 3-OHCot, 3-OHCotGluc, CotGluc, or NicGluc levels corresponded to a 1.05-, 1.09-, 1.04-, and 0.90-fold increased risk of cognitive impairment. Each 1-quantile increment in the mixture level of 8 nicotine metabolites corresponded to an increment of 1.40- and 1.34-fold risk of cognitive impairment in individuals with longer RTL or low mtDNA-CN. Urinary 3-OHCotGluc and RTL or mtDNA-CN exhibited an additive effect on cognitive impairment in addition to the mixture of 8 nicotine metabolites and mtDNA-CN. The findings suggested that aging process may increase the risk of tobacco-related cognitive impairment.
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Affiliation(s)
- Jian Hou
- Key Laboratory of Environment & Health (Huazhong University of Science and Technology), Ministry of Education, State Environmental Protection Key Laboratory of Environment and Health (Wuhan) and State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Chao Huang
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Bo Zhu
- Shenzhen Luohu District Center for Disease Control and Prevention, Shenzhen, 518020, Guangdong, China
| | - Wei Liu
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Qing-Qing Zhu
- Key Laboratory of Environment & Health (Huazhong University of Science and Technology), Ministry of Education, State Environmental Protection Key Laboratory of Environment and Health (Wuhan) and State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Lu Wang
- Key Laboratory of Environment & Health (Huazhong University of Science and Technology), Ministry of Education, State Environmental Protection Key Laboratory of Environment and Health (Wuhan) and State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Tian Li
- Key Laboratory of Environment & Health (Huazhong University of Science and Technology), Ministry of Education, State Environmental Protection Key Laboratory of Environment and Health (Wuhan) and State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Chun-Jie Yuan
- Key Laboratory of Environment & Health (Huazhong University of Science and Technology), Ministry of Education, State Environmental Protection Key Laboratory of Environment and Health (Wuhan) and State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Shao-Yang Lai
- Shenzhen Luohu District Center for Disease Control and Prevention, Shenzhen, 518020, Guangdong, China
| | - De-Sheng Wu
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Fei-Qi Zhu
- Cognitive Impairment Ward of Neurology Department, the Third Affiliated Hospital of Shenzhen University Medical College, Shenzhen, 518020, Guangdong, China
| | - Jia-Fei Zhang
- Key Laboratory of Environment & Health (Huazhong University of Science and Technology), Ministry of Education, State Environmental Protection Key Laboratory of Environment and Health (Wuhan) and State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Jia Huang
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Er-Wei Gao
- Key Laboratory of Environment & Health (Huazhong University of Science and Technology), Ministry of Education, State Environmental Protection Key Laboratory of Environment and Health (Wuhan) and State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Yi-Dan Huang
- Key Laboratory of Environment & Health (Huazhong University of Science and Technology), Ministry of Education, State Environmental Protection Key Laboratory of Environment and Health (Wuhan) and State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Lu-Lin Nie
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Shao-You Lu
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Xi-Fei Yang
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Li Zhou
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Fang Ye
- Key Laboratory of Environment & Health (Huazhong University of Science and Technology), Ministry of Education, State Environmental Protection Key Laboratory of Environment and Health (Wuhan) and State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
| | - Jing Yuan
- Key Laboratory of Environment & Health (Huazhong University of Science and Technology), Ministry of Education, State Environmental Protection Key Laboratory of Environment and Health (Wuhan) and State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China.
| | - Jian-Jun Liu
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China.
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Fu Z, Qi G, Qu Z, Lin X, Xu L, Shen B, Dong F, Ge S. Higher blood cotinine level is associated with worse cognitive functioning in non-smoking older adults. Front Neurosci 2022; 16:1080066. [PMID: 36507320 PMCID: PMC9730528 DOI: 10.3389/fnins.2022.1080066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 11/08/2022] [Indexed: 11/25/2022] Open
Abstract
Introduction Secondhand smoke (SHS) is common in older adults; however, its cognitive effect is unclear. We aimed to examine the association between serum cotinine level and cognitive functioning among non-smoking older adults. Materials and methods A total of 2,703 older adults aged 60 and above from the National Health and Nutrition Examination (NHANES) Survey 2011-2014 were included. Serum cotinine level was analyzed in the laboratory. A level ≤10 ng/ml and a response of "no" to the question "Do you currently smoke?" were used to select non-smokers. Cognitive functioning was measured using the Consortium to Establish a Registry for Alzheimer's disease Word Learning subtest (CERAD-WL) immediate and delayed recall tests, the Animal Fluency test (AFT), and the Digit Symbol Substitution test (DSST). Multivariable linear regression models were constructed to examine the association between serum cotinine level quartile and test-specific and global cognition z scores adjusting for age, race/ethnicity, education, depressive symptoms, body mass index, alcohol use, smoking history, prevalent coronary heart disease (CHD), stroke, and systolic blood pressure. Results About half of the participants (mean age 70.5 years) were female (53.6%), non-Hispanic White (48.3%), and completed some college and above (50.2%). Multivariate linear regressions with a reference group being those in the 1st quantile (lowest) showed that participants in the 4th quartile (highest) of serum cotinine level had lower immediate recall [β = -0.16, 95% confidence interval (CI) = -0.29, -0.03], AFT (β = -0.19, 95% CI = -0.33, -0.05), DSST (β = -0.27, 95% CI = -0.39, -0.15), and global cognition (β = -0.26, 95% CI = -0.39, -0.14) z scores. Participants in the 3rd quartile had lower immediate recall (β = -0.16, 95% CI = -0.30, -0.02) and global cognition (β = -0.16, 95% CI = -0.29, -0.02) z scores. Participants in the 2nd quartile had lower delayed recall z scores (β = -0.16, 95% CI = -0.29, -0.02). Conclusion Higher serum cotinine level was associated with worse cognitive functioning in non-smoking older adults. Prevention and reduction of SHS in older adults may help protect their cognitive functioning.
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Affiliation(s)
- Zhenmei Fu
- 1Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Guiye Qi
- 2Department of Medical Engineering, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Zhe Qu
- 3School of Nursing, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Xuechun Lin
- 4Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Li Xu
- 5Department of Pulmonary and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China,*Correspondence: Li Xu,
| | - Biyu Shen
- 6Department of Nursing, Shanghai Children’s Medical Center Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fanghong Dong
- 7School of Nursing, Hebei University, Baoding, China
| | - Song Ge
- 8Department of Natural Sciences, University of Houston-Downtown, Houston, TX, United States
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Fu Z, Zhou T, Dong F, Li M, Lin X, Ma W, Song Y, Ge S. Secondhand smoke is positively associated with pre-frailty and frailty in non-smoking older adults. Front Psychiatry 2022; 13:1095254. [PMID: 36590628 PMCID: PMC9800009 DOI: 10.3389/fpsyt.2022.1095254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION Either exposure to secondhand smoke (SHS) or frailty has been linked to adverse health outcomes in nonsmoking adults. However, their relationship is rarely studied. The purpose of this study is to examine the association between serum cotinine level and frailty status among non-smoking older adults. METHOD The study population consisted of 2,703 older adults aged ≥60 from the National Health and Nutrition Examination Survey 2011-2014. Non-smokers were included based on (1) a serum cotinine level ≤ 10 ng/mL and 2) a response of "no" to the question, "Do you currently smoke?" Frailty status was measured based on the Fried Phenotype and had three groups- robust, pre-frailty, and frailty. Multinomial logistic regression models were constructed to examine the association between serum cotinine level quartile and frailty status, controlling for age, sex, race/ethnicity, education, depressive symptoms, alcohol use, and systolic blood pressure. RESULTS About half of the participants (median age 70.0 years, range 64-78) were female (53.6%), non-Hispanic White (48.3%), and completed some college and above (50.1%). Multinomial logistic regression with a reference group being those in the 1st quantile (the lowest) of serum cotinine level showed that participants in the 4th quartile (the highest) of serum cotinine level had increased odds of pre-frailty vs. robust (OR 1.522, 95% confidence interval [CI] 1.060, 2.185, P = 0.023) as well as increased odds of frailty vs. robust (OR 2.349, 95% CI 1.081, 5.107, P = 0.031). CONCLUSIONS Higher serum cotinine level is associated with increased risk of pre-frailty and frailty versus robust in non-smoking older adults. Prevention and reduction of SHS in older adults may help protect them from developing pre-frailty or frailty.
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Affiliation(s)
- Zhenmei Fu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Tian Zhou
- School of Nursing, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | | | - Mengchi Li
- Johns Hopkins University School of Nursing, Baltimore, MD, United States
| | - Xuechun Lin
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weixia Ma
- Department of Pulmonary and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Yuting Song
- Qingdao University School of Nursing, Qingdao, Shandong, China
| | - Song Ge
- Department of Natural Sciences, University of Houston-Downtown, Houston, TX, United States
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Li CC, Chen YF, Liang J, Matthews AK, Barnes LL. Trajectories of Multiple Behavioral Risk Factors and Their Associations With Cognitive Function Trajectories Among Older African Americans and White Americans. J Aging Health 2021; 33:674-684. [PMID: 33788658 DOI: 10.1177/08982643211005905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Objectives: This study examined the joint trajectories of behavioral risk factors (smoking, alcohol drinking, and body mass index) and their associations with cognitive function trajectories among older African Americans and white Americans. Methods: Data from the Health and Retirement Study (1998-2014) were used. Group-based mixture modeling and multinomial logistic regression analysis were performed. Results: Three joint trajectories of behavioral risk factors (overweight, smoking and drinking, and drinking and overweight) and three cognitive function trajectories (low, moderate, and high) were identified. A significantly higher percentage of African Americans were in the "overweight," "smoking and drinking," and "low" cognitive functioning groups as measured by the total cognition composite score compared to white Americans. After accounting for covariates, the "drinking and overweight" group was associated with the "moderate" or "high" cognitive functioning group. Discussion: Future interventions targeting the combinations of behavioral risk factors are needed to promote healthy aging among high-risk populations.
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Affiliation(s)
- Chien-Ching Li
- Department of Health Systems Management, 2461Rush University, Chicago, IL, USA
| | - Yi-Fan Chen
- Center for Clinical Translational Science, 14681University of Illinois at Chicago, Chicago, IL, USA
| | - Jersey Liang
- Department of Health Management and Policy, 1259University of Michigan, Ann Arbor, MI, USA
| | - Alicia K Matthews
- Department of Population Health Nursing Science, 14681University of Illinois at Chicago, Chicago, IL, USA
| | - Lisa L Barnes
- Alzheimer's Disease Center, 2461Rush University Medical Center, Chicago, IL, USA
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