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Howlader M, Selim A, Shohan MH, Shuvo SNA, Al-Humaidi JY, Islam MM, Shaibur MR, Althomali RH, Akter N, Afrin S, Sultana T, Singha SK, Betto ZS, Rahman MM. Exploring cigarette butts pollution in Saint Martin Island: A marine protected area. MARINE POLLUTION BULLETIN 2024; 203:116439. [PMID: 38718549 DOI: 10.1016/j.marpolbul.2024.116439] [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: 01/22/2024] [Revised: 04/15/2024] [Accepted: 04/28/2024] [Indexed: 06/06/2024]
Abstract
Saint Martin Island (SMI), the only coral island in Bangladesh, is located in the Bay of Bengal and has been identified as a marine protected area (MPA). Littering cigarette butts (CBs) waste in an ecologically sensitive environment can have numerous adverse effects. The purpose of this research is to investigate the abundance and density of CBs in SMI and to assess the pollution status using the Cigarette Butt Pollution Index (CBPI). This study is conducted based on the visual survey method in the three types of land use zones of SMI. During the peak season, the investigation was carried out from 9 a.m. to 5 p.m. in December 2023. A total of 4481 CBs item were counted, and the density ranged from 0.37 to 1.76 m-2 with an average value of 0.99 m-2 across 12 sampling campaigns. The highest density was observed at service zones, and the fishing zones had the lowest density. The calculated CBPI values revealed that 75 % of the sampling stations were in the "severe pollution" while 25 % were classified as "high pollution" status, underscoring the prevalence of hazardous CBs across most areas of SMI. To tackle these issues requires regulatory measures, public awareness initiatives, and community involvement. Effective waste management and eco-friendly product promotion can help reduce CBs pollution risks in marine protected islands.
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Affiliation(s)
- Masum Howlader
- Environment and Resource Analysis Center Ltd., Dhaka 1212, Bangladesh.
| | - Abu Selim
- International Centre for Integrated Mountain Development, Kathmandu 3226, Nepal
| | - Mobin Hossain Shohan
- Department of Aquaculture, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh
| | | | - Jehan Y Al-Humaidi
- Department of Chemistry, College of Science, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
| | | | - Molla Rahman Shaibur
- Department of Environmental Science and Technology, Jashore University of Science and Technology, Jashore 7408, Bangladesh
| | - Raed H Althomali
- Department of Chemistry, College of Art and Science, Prince Sattam bin Abdulaziz University, Wadi Al-Dawasir 11991, Saudi Arabia
| | - Nurunnahar Akter
- Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore 7408, Bangladesh
| | | | - Tania Sultana
- Indian Institute of Science Education and Research, Kolkata 411008, India
| | - Santush Kumar Singha
- Department of Electrical and Electronic Engineering, American International University Bangladesh, Dhaka 1229, Bangladesh
| | - Zaren Subah Betto
- Department of Agronomy, Faculty of Agriculture, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh
| | - Mohammed M Rahman
- Department of Chemistry, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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2
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Chen J, Li T, Zhao B, Chen H, Yuan C, Garden GA, Wu G, Zhu H. The interaction effects of age, APOE and common environmental risk factors on human brain structure. Cereb Cortex 2024; 34:bhad472. [PMID: 38112569 PMCID: PMC10793588 DOI: 10.1093/cercor/bhad472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 10/09/2023] [Accepted: 11/06/2023] [Indexed: 12/21/2023] Open
Abstract
Mounting evidence suggests considerable diversity in brain aging trajectories, primarily arising from the complex interplay between age, genetic, and environmental risk factors, leading to distinct patterns of micro- and macro-cerebral aging. The underlying mechanisms of such effects still remain unclear. We conducted a comprehensive association analysis between cerebral structural measures and prevalent risk factors, using data from 36,969 UK Biobank subjects aged 44-81. Participants were assessed for brain volume, white matter diffusivity, Apolipoprotein E (APOE) genotypes, polygenic risk scores, lifestyles, and socioeconomic status. We examined genetic and environmental effects and their interactions with age and sex, and identified 726 signals, with education, alcohol, and smoking affecting most brain regions. Our analysis revealed negative age-APOE-ε4 and positive age-APOE-ε2 interaction effects, respectively, especially in females on the volume of amygdala, positive age-sex-APOE-ε4 interaction on the cerebellar volume, positive age-excessive-alcohol interaction effect on the mean diffusivity of the splenium of the corpus callosum, positive age-healthy-diet interaction effect on the paracentral volume, and negative APOE-ε4-moderate-alcohol interaction effects on the axial diffusivity of the superior fronto-occipital fasciculus. These findings highlight the need of considering age, sex, genetic, and environmental joint effects in elucidating normal or abnormal brain aging.
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Affiliation(s)
- Jie Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill NC 27514, United States
| | - Tengfei Li
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, United States
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Chapel Hill, NC 27599, United States
| | - Bingxin Zhao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, 265 South 37th Street, 3rd & 4th Floors, Philadelphia, PA 19104-1686, United States
| | - Hui Chen
- School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, China
| | - Changzheng Yuan
- School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, China
- Department of Nutrition, Harvard T H Chan School of Public Health, 665 Huntington Avenue Boston, MA, 02115, United States
| | - Gwenn A Garden
- Department of Neurology, School of Medicine, University of North Carolina at Chapel Hill, 170 Manning Drive Chapel Hill, NC 27599-7025, United States
| | - Guorong Wu
- Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, United States
- Departments of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 E Cameron Ave #3260, Chapel Hill, NC 27599, United States
- Departments of Computer Science, University of North Carolina at Chapel Hill, 201 South Columbia Street, Chapel Hill, NC 27599, United States
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Dr, Chapel Hill, NC 27599, United States
- Carolina Institute for Developmental Disabilities, 101 Renee Lynne Ct, Carrboro, NC 27510, United States
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill NC 27514, United States
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Chapel Hill, NC 27599, United States
- Departments of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 E Cameron Ave #3260, Chapel Hill, NC 27599, United States
- Departments of Computer Science, University of North Carolina at Chapel Hill, 201 South Columbia Street, Chapel Hill, NC 27599, United States
- Departments of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27514, United States
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van de Weijer MP, Vermeulen J, Schrantee A, Munafò MR, Verweij KJH, Treur JL. The potential role of gray matter volume differences in the association between smoking and depression: A narrative review. Neurosci Biobehav Rev 2024; 156:105497. [PMID: 38100958 DOI: 10.1016/j.neubiorev.2023.105497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/14/2023] [Accepted: 11/28/2023] [Indexed: 12/17/2023]
Abstract
Tobacco use and major depression are both leading contributors to the global burden of disease and are also highly comorbid. Previous research indicates bi-directional causality between tobacco use and depression, but the mechanisms that underlie this causality are unclear, especially for the causality from tobacco use to depression. Here we narratively review the available evidence for a potential causal role of gray matter volume in the association. We summarize the findings of large existing neuroimaging meta-analyses, studies in UK Biobank, and the Enhancing NeuroImaging Genetics through MetaAnalysis (ENIGMA) consortium and assess the overlap in implicated brain areas. In addition, we review two types of methods that allow us more insight into the causal nature of associations between brain volume and depression/smoking: longitudinal studies and Mendelian Randomization studies. While the available evidence suggests overlap in the alterations in brain volumes implicated in tobacco use and depression, there is a lack of research examining the underlying pathophysiology. We conclude with recommendations on (genetically-informed) causal inference methods useful for studying these associations.
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Affiliation(s)
- Margot P van de Weijer
- Department of Psychiatry, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands.
| | - Jentien Vermeulen
- Department of Psychiatry, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Marcus R Munafò
- School of Psychological Science, University of Bristol, Bristol, the United Kingdom
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Jorien L Treur
- Department of Psychiatry, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
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4
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Chang Y, Thornton V, Chaloemtoem A, Anokhin AP, Bijsterbosch J, Bogdan R, Hancock DB, Johnson EO, Bierut LJ. Investigating the Relationship Between Smoking Behavior and Global Brain Volume. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:74-82. [PMID: 38130847 PMCID: PMC10733671 DOI: 10.1016/j.bpsgos.2023.09.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/21/2023] [Accepted: 09/26/2023] [Indexed: 12/23/2023] Open
Abstract
Background Previous studies have shown that brain volume is negatively associated with cigarette smoking, but there is an ongoing debate about whether smoking causes lowered brain volume or a lower brain volume is a risk factor for smoking. We address this debate through multiple methods that evaluate directionality: Bradford Hill's criteria, which are commonly used to understand a causal relationship in epidemiological studies, and mediation analysis. Methods In 32,094 participants of European descent from the UK Biobank dataset, we examined the relationship between a history of daily smoking and brain volumes, as well as an association of genetic risk score to ever smoking with brain volume. Results A history of daily smoking was strongly associated with decreased brain volume, and a history of heavier smoking was associated with a greater decrease in brain volume. The strongest association was between total gray matter volume and a history of daily smoking (effect size = -2964 mm3, p = 2.04 × 10-16), and there was a dose-response relationship with more pack years smoked associated with a greater decrease in brain volume. A polygenic risk score for smoking initiation was strongly associated with a history of daily smoking (effect size = 0.05, p = 4.20 × 10-84), but only modestly associated with total gray matter volume (effect size = -424 mm3, p = .01). Mediation analysis indicated that a history of daily smoking mediated the relationship between the smoking initiation polygenic risk score and total gray matter volume. Conclusions A history of daily smoking is strongly associated with a decreased total brain volume.
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Affiliation(s)
- Yoonhoo Chang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Vera Thornton
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Ariya Chaloemtoem
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Andrey P. Anokhin
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Janine Bijsterbosch
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Ryan Bogdan
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri
| | - Dana B. Hancock
- Social, Statistical and Environmental Sciences, Research Triangle Institute International, Research Triangle Park, North Carolina
| | - Eric Otto Johnson
- Fellow Program, Research Triangle Institute International, Research Triangle Park, North Carolina
| | - Laura J. Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
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5
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Yang A, Yang YT, Zhao XM. An augmented Mendelian randomization approach provides causality of brain imaging features on complex traits in a single biobank-scale dataset. PLoS Genet 2023; 19:e1011112. [PMID: 38150468 PMCID: PMC10775988 DOI: 10.1371/journal.pgen.1011112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 01/09/2024] [Accepted: 12/12/2023] [Indexed: 12/29/2023] Open
Abstract
Mendelian randomization (MR) is an effective approach for revealing causal risk factors that underpin complex traits and diseases. While MR has been more widely applied under two-sample settings, it is more promising to be used in one single large cohort given the rise of biobank-scale datasets that simultaneously contain genotype data, brain imaging data, and matched complex traits from the same individual. However, most existing multivariable MR methods have been developed for two-sample setting or a small number of exposures. In this study, we introduce a one-sample multivariable MR method based on partial least squares and Lasso regression (MR-PL). MR-PL is capable of considering the correlation among exposures (e.g., brain imaging features) when the number of exposures is extremely upscaled, while also correcting for winner's curse bias. We performed extensive and systematic simulations, and demonstrated the robustness and reliability of our method. Comprehensive simulations confirmed that MR-PL can generate more precise causal estimates with lower false positive rates than alternative approaches. Finally, we applied MR-PL to the datasets from UK Biobank to reveal the causal effects of 36 white matter tracts on 180 complex traits, and showed putative white matter tracts that are implicated in smoking, blood vascular function-related traits, and eating behaviors.
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Affiliation(s)
- Anyi Yang
- Department of Neurology, Zhongshan Hospital and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, People’s Republic of China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, People’s Republic of China
| | - Yucheng T. Yang
- Department of Neurology, Zhongshan Hospital and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, People’s Republic of China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, People’s Republic of China
| | - Xing-Ming Zhao
- Department of Neurology, Zhongshan Hospital and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, People’s Republic of China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, People’s Republic of China
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, People’s Republic of China
- International Human Phenome Institutes (Shanghai), Shanghai, People’s Republic of China
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6
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Lin W, Zhu L, Lu Y. Association of smoking with brain gray and white matter volume: a Mendelian randomization study. Neurol Sci 2023; 44:4049-4055. [PMID: 37289285 DOI: 10.1007/s10072-023-06854-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 05/12/2023] [Indexed: 06/09/2023]
Abstract
BACKGROUND Observational studies have found a significant association between smoking and smaller gray matter volume, but this finding was limited by the reverse causality bias and possible confounding factors. Therefore, we conducted a Mendelian randomization (MR) study to explore the causal association of smoking with brain gray and white matter volume from a genetic perspective, and to investigate the possible mediators influencing the association. METHODS Smoking initiation (ever being a regular smoker) was used as the primary exposure from the GWAS & Sequencing Consortium of Alcohol and Nicotine use in up to 1,232,091 individuals of European descent. Their associations with brain volume were acquired from a recent genome-wide association study of brain imaging phenotypes conducted among 34,298 individuals of the UK Biobank. The random-effects inverse-variance weighted method was applied as the main analysis. Multivariable MR analysis was performed to assess the potential interference of confounding factors on causal effect. RESULTS Genetic liability to smoking initiation was significantly associated with lower gray matter volume (beta, -0.100; 95% CI, -0.156 to -0.043; P=5.23×10-4) but not with white matter volume. Multivariable MR results suggested that the association with lower gray matter volume might be mediated by alcohol drinking. Regarding localized gray matter volume, genetic liability to smoking initiation was associated with lower gray matter volume in left superior temporal gyrus, anterior division and right superior temporal gyrus, posterior division. CONCLUSIONS This MR study supports the association between smoking and lower gray matter volume, and highlights the importance of never smoking.
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Affiliation(s)
- Wenjuan Lin
- Department of Cardiology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, China
| | - Lisheng Zhu
- Cardiovascular Key Lab of Zhejiang Province, Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Yunlong Lu
- Department of Cardiology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, China.
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7
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Demnitz N, Hulme OJ, Siebner HR, Kjaer M, Ebmeier KP, Boraxbekk CJ, Gillan CM. Characterising the covariance pattern between lifestyle factors and structural brain measures: a multivariable replication study of two independent ageing cohorts. Neurobiol Aging 2023; 131:115-123. [PMID: 37619515 DOI: 10.1016/j.neurobiolaging.2023.07.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 07/12/2023] [Accepted: 07/20/2023] [Indexed: 08/26/2023]
Abstract
Modifiable lifestyle factors have been shown to promote healthy brain ageing. However, studies have typically focused on a single factor at a time. Given that lifestyle factors do not occur in isolation, multivariable analyses provide a more realistic model of the lifestyle-brain relationship. Here, canonical correlation analyses (CCA) examined the relationship between nine lifestyle factors and seven MRI-derived indices of brain structure. The resulting covariance pattern was further explored with Bayesian regressions. CCA analyses were first conducted on a Danish cohort of older adults (n = 251) and then replicated in a British cohort (n = 668). In both cohorts, the latent factors of lifestyle and brain structure were positively correlated (UK: r = .37, p < 0.001; Denmark: r = .27, p < 0.001). In the cross-validation study, the correlation between lifestyle-brain latent factors was r = .10, p = 0.008. However, the pattern of associations differed between datasets. These findings suggest that baseline characterisation and tailoring towards the study sample may be beneficial for achieving targeted lifestyle interventions.
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Affiliation(s)
- Naiara Demnitz
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Hvidovre, Denmark.
| | - Oliver J Hulme
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Hvidovre, Denmark; London Mathematical Laboratory, London, UK; Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark; Institute for Clinical Medicine, Faculty of Medical and Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael Kjaer
- Institute of Sports Medicine Copenhagen (ISMC), Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark; Center for Healthy Aging, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Klaus P Ebmeier
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Carl-Johan Boraxbekk
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark; Institute for Clinical Medicine, Faculty of Medical and Health Sciences, University of Copenhagen, Copenhagen, Denmark; Institute of Sports Medicine Copenhagen (ISMC), Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark; Department of Radiation Sciences, Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden
| | - Claire M Gillan
- School of Psychology, Trinity College Dublin, Dublin, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
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8
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Johansson L, Guo X, Sacuiu S, Fässberg MM, Kern S, Zettergren A, Skoog I. Longstanding smoking associated with frontal brain lobe atrophy: a 32-year follow-up study in women. BMJ Open 2023; 13:e072803. [PMID: 37802622 PMCID: PMC10565256 DOI: 10.1136/bmjopen-2023-072803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 08/18/2023] [Indexed: 10/10/2023] Open
Abstract
OBJECTIVE To examine the association between midlife tobacco smoking and late-life brain atrophy and white matter lesions. METHODS The study includes 369 women from the Prospective Population Study of Women in Gothenburg, Sweden. Cigarette smoking was reported at baseline 1968 (mean age=44 years) and at follow-up in 1974-1975 and 1980-1981. CT of the brain was conducted 32 years after baseline examination (mean age=76 years) to evaluate cortical atrophy and white matter lesions. Multiple logistic regressions estimated associations between midlife smoking and late-life brain lesions. The final analyses were adjusted for alcohol consumption and several other covariates. RESULTS Smoking in 1968-1969 (adjusted OR 1.85; 95% CI 1.12 to 3.04), in 1974-1975 (OR 2.37; 95% CI 1.39 to 4.04) and in 1980-1981 (OR 2.47; 95% CI 1.41 to 4.33) were associated with late-life frontal lobe atrophy (2000-2001). The strongest association was observed in women who reported smoking at all three midlife examinations (OR 2.63; 95% CI 1.44 to 4.78) and in those with more frequent alcohol consumption (OR 6.02; 95% CI 1.74 to 20.84). Smoking in 1980-1981 was also associated with late-life parietal lobe atrophy (OR 1.99; 95% CI 1.10 to 3.58). There were no associations between smoking and atrophy in the temporal or occipital lobe, or with white matter lesions. CONCLUSION Longstanding tobacco smoking was mainly associated with atrophy in the frontal lobe cortex. A long-term stimulation of nicotine receptors in the frontal neural pathway might be harmful for targeted brain cell.
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Affiliation(s)
- Lena Johansson
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), Institute of Neuroscience and Physiology, University of Gothenburg, Goteborg, Sweden
- Department of Addiction and Dependency, Sahlgrenska University Hospital, Sahlgrenska universitetssjukhuset, Goteborg, Sweden
- Institute of Health and Care Sciences at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Xinxin Guo
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), Institute of Neuroscience and Physiology, University of Gothenburg, Goteborg, Sweden
| | - Simona Sacuiu
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), Institute of Neuroscience and Physiology, University of Gothenburg, Goteborg, Sweden
| | - Madeleine Mellqvist Fässberg
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), Institute of Neuroscience and Physiology, University of Gothenburg, Goteborg, Sweden
| | - Silke Kern
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), Institute of Neuroscience and Physiology, University of Gothenburg, Goteborg, Sweden
| | - Anna Zettergren
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), Institute of Neuroscience and Physiology, University of Gothenburg, Goteborg, Sweden
| | - Ingmar Skoog
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), Institute of Neuroscience and Physiology, University of Gothenburg, Goteborg, Sweden
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9
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Binnewies J, Nawijn L, Brandmaier AM, Baaré WFC, Boraxbekk CJ, Demnitz N, Drevon CA, Fjell AM, Lindenberger U, Madsen KS, Nyberg L, Topiwala A, Walhovd KB, Ebmeier KP, Penninx BWJH. Lifestyle-related risk factors and their cumulative associations with hippocampal and total grey matter volume across the adult lifespan: A pooled analysis in the European Lifebrain consortium. Brain Res Bull 2023; 200:110692. [PMID: 37336327 DOI: 10.1016/j.brainresbull.2023.110692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 06/16/2023] [Indexed: 06/21/2023]
Abstract
BACKGROUND Lifestyle-related risk factors, such as obesity, physical inactivity, short sleep, smoking and alcohol use, have been associated with low hippocampal and total grey matter volumes (GMV). However, these risk factors have mostly been assessed as separate factors, leaving it unknown if variance explained by these factors is overlapping or additive. We investigated associations of five lifestyle-related factors separately and cumulatively with hippocampal and total GMV, pooled across eight European cohorts. METHODS We included 3838 participants aged 18-90 years from eight cohorts of the European Lifebrain consortium. Using individual person data, we performed cross-sectional meta-analyses on associations of presence of lifestyle-related risk factors separately (overweight/obesity, physical inactivity, short sleep, smoking, high alcohol use) as well as a cumulative unhealthy lifestyle score (counting the number of present lifestyle-related risk factors) with FreeSurfer-derived hippocampal volume and total GMV. Lifestyle-related risk factors were defined according to public health guidelines. RESULTS High alcohol use was associated with lower hippocampal volume (r = -0.10, p = 0.021), and overweight/obesity with lower total GMV (r = -0.09, p = 0.001). Other lifestyle-related risk factors were not significantly associated with hippocampal volume or GMV. The cumulative unhealthy lifestyle score was negatively associated with total GMV (r = -0.08, p = 0.001), but not hippocampal volume (r = -0.01, p = 0.625). CONCLUSIONS This large pooled study confirmed the negative association of some lifestyle-related risk factors with hippocampal volume and GMV, although with small effect sizes. Lifestyle factors should not be seen in isolation as there is evidence that having multiple unhealthy lifestyle factors is associated with a linear reduction in overall brain volume.
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Affiliation(s)
- Julia Binnewies
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress program, Amsterdam, the Netherlands.
| | - Laura Nawijn
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress program, Amsterdam, the Netherlands
| | - Andreas M Brandmaier
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany; Department of Psychology, MSB Medical School Berlin, Berlin, Germany
| | - William F C Baaré
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Carl-Johan Boraxbekk
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark; Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden; Institute for Clinical Medicine, Faculty of Medical and Health Sciences, University of Copenhagen, Copenhagen, Denmark; Institute of Sports Medicine Copenhagen (ISMC) and Department of Neurology, Copenhagen University Hospital Bispebjerg, Copenhagen, Denmark
| | - Naiara Demnitz
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Christian A Drevon
- Vitas Ltd. Oslo Science Park & Department of Nutrition, IMB, University of Oslo, Norway
| | - Anders M Fjell
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Norway; Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Norway
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Kathrine Skak Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Lars Nyberg
- Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Anya Topiwala
- Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Kristine B Walhovd
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Norway; Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Norway
| | - Klaus P Ebmeier
- Department of Psychiatry, University of Oxford, United Kingdom
| | - Brenda W J H Penninx
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress program, Amsterdam, the Netherlands
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Luo J, Ma Y, Agboola FJ, Grant E, Morris JC, McDade E, Fagan AM, Benzinger TLS, Hassenstab J, Bateman RJ, Perrin RJ, Gordon BA, Goyal M, Strain JF, Yakushev I, Day GS, Xiong C. Longitudinal Relationships of White Matter Hyperintensities and Alzheimer Disease Biomarkers Across the Adult Life Span. Neurology 2023; 101:e164-e177. [PMID: 37202169 PMCID: PMC10351551 DOI: 10.1212/wnl.0000000000207378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 03/20/2023] [Indexed: 05/20/2023] Open
Abstract
BACKGROUND AND OBJECTIVES White matter hyperintensities (WMH) correlate with Alzheimer disease (AD) biomarkers cross-sectionally and modulate AD pathogenesis. Longitudinal changes have been reported for AD biomarkers, including concentrations of CSF β-amyloid (Aβ) 42, Aβ40, total tau and phosphorylated tau181, standardized uptake value ratio from the molecular imaging of cerebral fibrillar Aβ with PET using [11C] Pittsburgh Compound-B, MRI-based hippocampal volume, and cortical thickness. Correlations between established AD biomarkers and the longitudinal change for WMH have not been fully evaluated, especially among cognitively normal individuals across the adult life span. METHODS We jointly analyzed the longitudinal data of WMH volume and each of the established AD biomarkers and cognition from 371 cognitively normal individuals whose baseline age spanned from 19.6 to 88.20 years from 4 longitudinal studies of aging and AD. A 2-stage algorithm was applied to identify the inflection point of baseline age whereby older participants had an accelerated longitudinal change in WMH volume, in comparison with the younger participants. The longitudinal correlations between WMH volume and AD biomarkers were estimated from the bivariate linear mixed-effects models. RESULTS A longitudinal increase in WMH volume was associated with a longitudinal increase in PET amyloid uptake and a decrease in MRI hippocampal volume, cortical thickness, and cognition. The inflection point of baseline age in WMH volume was identified at 60.46 (95% CI 56.43-64.49) years, with the annual increase for the older participants (83.12 [SE = 10.19] mm3 per year) more than 13 times faster (p < 0.0001) than that for the younger participants (6.35 [SE = 5.63] mm3 per year). Accelerated rates of change among the older participants were similarly observed in almost all the AD biomarkers. Longitudinal correlations of WMH volume with MRI, PET amyloid biomarkers, and cognition seemed to be numerically stronger for the younger participants, but not significantly different from those for the older participants. Carrying APOE ε4 alleles did not alter the longitudinal correlations between WMH and AD biomarkers. DISCUSSION Longitudinal increases in WMH volume started to accelerate around a baseline age of 60.46 years and correlated with the longitudinal change in PET amyloid uptake, MRI structural outcomes, and cognition.
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Affiliation(s)
- Jingqin Luo
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL
| | - Yinjiao Ma
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL
| | - Folasade Jane Agboola
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL
| | - Elizabeth Grant
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL
| | - John C Morris
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL
| | - Eric McDade
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL
| | - Anne M Fagan
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL
| | - Tammie L S Benzinger
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL
| | - Jason Hassenstab
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL
| | - Randall J Bateman
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL
| | - Richard J Perrin
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL
| | - Brian A Gordon
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL
| | - Manu Goyal
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL
| | - Jeremy F Strain
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL
| | - Igor Yakushev
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL
| | - Gregory S Day
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL
| | - Chengjie Xiong
- From the Division of Public Health Sciences (J.L.), Department of Surgery, Siteman Cancer Center Biostatistics Core (J.L.), Division of Biostatistics (J.L., Y.M., F.J.A., E.G., C.X.), Knight Alzheimer Disease Research Center (Y.M., F.J.A., E.G., J.C.M., A.M.F., T.L.S.B., J.H., R.J.B., R.J.P., B.A.G., C.X.), Department of Neurology (J.C.M., E.M., A.M.F., J.H., R.J.B., R.J.P., M.G., J.F.S.), Department of Pathology and Immunology (J.C.M., R.J.P.), and Department of Radiology (T.L.S.B., B.A.G., M.G.), Washington University School of Medicine, St. Louis, MO; Department of Nuclear Medicine (I.Y.), and Klinikum rechts der Isar (I.Y.), School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Germany; and Department of Neurology (G.S.D.), Mayo Clinic, Jacksonville, FL.
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11
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Seyedsalehi A, Warrier V, Bethlehem RAI, Perry BI, Burgess S, Murray GK. Educational attainment, structural brain reserve and Alzheimer's disease: a Mendelian randomization analysis. Brain 2023; 146:2059-2074. [PMID: 36310536 PMCID: PMC10151197 DOI: 10.1093/brain/awac392] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 09/01/2022] [Accepted: 09/19/2022] [Indexed: 11/13/2022] Open
Abstract
Higher educational attainment is observationally associated with lower risk of Alzheimer's disease. However, the biological mechanisms underpinning this association remain unclear. The protective effect of education on Alzheimer's disease may be mediated via increased brain reserve. We used two-sample Mendelian randomization to explore putative causal relationships between educational attainment, structural brain reserve as proxied by MRI phenotypes and Alzheimer's disease. Summary statistics were obtained from genome-wide association studies of educational attainment (n = 1 131 881), late-onset Alzheimer's disease (35 274 cases, 59 163 controls) and 15 measures of grey or white matter macro- or micro-structure derived from structural or diffusion MRI (nmax = 33 211). We conducted univariable Mendelian randomization analyses to investigate bidirectional associations between (i) educational attainment and Alzheimer's disease; (ii) educational attainment and imaging-derived phenotypes; and (iii) imaging-derived phenotypes and Alzheimer's disease. Multivariable Mendelian randomization was used to assess whether brain structure phenotypes mediated the effect of education on Alzheimer's disease risk. Genetically proxied educational attainment was inversely associated with Alzheimer's disease (odds ratio per standard deviation increase in genetically predicted years of schooling = 0.70, 95% confidence interval 0.60, 0.80). There were positive associations between genetically predicted educational attainment and four cortical metrics (standard deviation units change in imaging phenotype per one standard deviation increase in genetically predicted years of schooling): surface area 0.30 (95% confidence interval 0.20, 0.40); volume 0.29 (95% confidence interval 0.20, 0.37); intrinsic curvature 0.18 (95% confidence interval 0.11, 0.25); local gyrification index 0.21 (95% confidence interval 0.11, 0.31)]; and inverse associations with cortical intracellular volume fraction [-0.09 (95% confidence interval -0.15, -0.03)] and white matter hyperintensities volume [-0.14 (95% confidence interval -0.23, -0.05)]. Genetically proxied levels of surface area, cortical volume and intrinsic curvature were positively associated with educational attainment [standard deviation units change in years of schooling per one standard deviation increase in respective genetically predicted imaging phenotype: 0.13 (95% confidence interval 0.10, 0.16); 0.15 (95% confidence interval 0.11, 0.19) and 0.12 (95% confidence interval 0.04, 0.19)]. We found no evidence of associations between genetically predicted imaging-derived phenotypes and Alzheimer's disease. The inverse association of genetically predicted educational attainment with Alzheimer's disease did not attenuate after adjusting for imaging-derived phenotypes in multivariable analyses. Our results provide support for a protective causal effect of educational attainment on Alzheimer's disease risk, as well as potential bidirectional causal relationships between education and brain macro- and micro-structure. However, we did not find evidence that these structural markers affect risk of Alzheimer's disease. The protective effect of education on Alzheimer's disease may be mediated via other measures of brain reserve not included in the present study, or by alternative mechanisms.
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Affiliation(s)
- Aida Seyedsalehi
- Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UK
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford OX3 7JX, UK
| | - Varun Warrier
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - Richard A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Benjamin I Perry
- Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- CAMEO, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge CB4 1PX, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0BB, UK
| | - Graham K Murray
- Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- CAMEO, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge CB4 1PX, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane 4072, Australia
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12
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Linli Z, Rolls ET, Zhao W, Kang J, Feng J, Guo S. Smoking is associated with lower brain volume and cognitive differences: A large population analysis based on the UK Biobank. Prog Neuropsychopharmacol Biol Psychiatry 2023; 123:110698. [PMID: 36528239 DOI: 10.1016/j.pnpbp.2022.110698] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 11/25/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022]
Abstract
The evidence about the association of smoking with both brain structure and cognitive functions remains inconsistent. Using structural magnetic resonance imaging from the UK Biobank (n = 33,293), we examined the relationships between smoking status, dosage, and abstinence with total and 166 regional brain gray matter volumes (GMV). The relationships between the smoking parameters with cognitive function, and whether this relationship was mediated by brain structure, were then investigated. Smoking was associated with lower total and regional GMV, with the extent depending on the frequency of smoking and on whether smoking had ceased: active regular smokers had the lowest GMV (Cohen's d = -0.362), and former light smokers had a slightly smaller GMV (Cohen's d = -0.060). The smaller GMV in smokers was most evident in the thalamus. Higher lifetime exposure (i.e., pack-years) was associated with lower total GMV (β = -311.84, p = 8.35 × 10-36). In those who ceased smoking, the duration of abstinence was associated with a larger total GMV (β = 139.57, p = 2.36 × 10-08). It was further found that reduced cognitive function was associated with smoker parameters and that the associations were partially mediated by brain structure. This is the largest scale investigation we know of smoking and brain structure, and these results are likely to be robust. The findings are of associations between brain structure and smoking, and in the future, it will be important to assess whether brain structure influences smoking status, or whether smoking influences brain structure, or both.
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Affiliation(s)
- Zeqiang Linli
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China; School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou, PR China.
| | - Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK; Department of Computer Science, University of Warwick, Coventry, UK
| | - Wei Zhao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China
| | - Jujiao Kang
- Centre for Computational Systems Biology, Fudan University, Shanghai, PR China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry, UK; Centre for Computational Systems Biology, Fudan University, Shanghai, PR China.
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China.
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13
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Shi Z, Li X, Byanyima J, O’Brien CP, Childress AR, Lynch KG, Loughead J, Wiers CE, Langleben DD. Effects of current smoking severity on brain gray matter volume in opioid use disorder - a voxel-based morphometry study. THE AMERICAN JOURNAL OF DRUG AND ALCOHOL ABUSE 2023; 49:180-189. [PMID: 36787540 PMCID: PMC10164057 DOI: 10.1080/00952990.2023.2169616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 01/06/2023] [Accepted: 01/13/2023] [Indexed: 02/16/2023]
Abstract
Background: Cigarette smoking (CS) and opioid use disorder (OUD) significantly alter brain structure. Although OUD and cigarette smoking are highly comorbid, most prior neuroimaging research in OUD did not control for smoking severity. Specifically, the combined effect of smoking and OUD on the brain gray matter volume (GMV) remains unknown.Objectives: We used structural magnetic resonance imaging (sMRI) to examine: (1) the GMV differences between OUD and non-OUD individuals with comparable smoking severity; and (2) the differential effect of smoking severity on the brain GMV between individuals with and without OUD.Methods: We performed a secondary analysis of existing sMRI datasets of 116 individuals who smoked cigarettes daily, among whom 60 had OUD (CS-OUD; 37 male, 23 female) and 56 did not (CS; 31 male, 25 female). Brain GMV was estimated by voxel-based morphometry analysis.Results: Compared to the CS group, the CS-OUD group had a higher GMV in the occipital cortex and lower GMV in the prefrontal and temporal cortex, striatum, and pre/postcentral gyrus (whole-brain corrected-p < .05). There was a significant interaction between group and smoking severity on GMV in the medial orbitofrontal cortex (whole-brain corrected-p < .05), such that heavier smoking was associated with lower medial orbitofrontal GMV in the CS-OUD but not CS participants (r=-0.32 vs. 0.12).Conclusions: Our findings suggest a combination of independent and interactive effects of cigarette smoking and OUD on the brain gray matter. Elucidating the neuroanatomical correlates of comorbid opioid and tobacco use may shed the light on the development of novel interventions for affected individuals.
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Affiliation(s)
- Zhenhao Shi
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Xinyi Li
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Juliana Byanyima
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Charles P. O’Brien
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Anna Rose Childress
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Kevin G. Lynch
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - James Loughead
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
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14
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Yang X, Cheng B, Yang J, Cheng S, Pan C, Zhao Y, Zhang H, Liu L, Meng P, Zhang J, Zhang Z, Li C, Chen Y, He D, Wen Y, Jia Y, Liu H, Zhang F. Assessing the interaction effects of brain structure longitudinal changes and life environmental factors on depression and anxiety. Hum Brain Mapp 2023; 44:1227-1238. [PMID: 36416531 PMCID: PMC9875931 DOI: 10.1002/hbm.26153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 10/16/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022] Open
Abstract
Disrupted brain structures and several life environmental factors have been shown to influence depression and anxiety, but their interactions with anxiety and depression remain elusive. Genome-wide association study datasets of 15 brain structure longitudinal changes (N = 15,640) were obtained from the published study. Genotype and phenotype-related data of depression, anxiety, and life environmental factors (including smoking, alcohol drinking, coffee intake, maternal smoking, physical activity, vitamin D, insomnia, sleep duration, and family satisfaction) were collected from UK Biobank. We calculated the polygenic risk scores (PRS) of 15 brain structure changes and then conducted linear regression analyses to explore the interactions of brain structure changes and life environmental factors on depression and anxiety using 15 brain structure change-related PRS, life environmental factors and interactions of them as instrumental variables, and depression score or anxiety score as outcomes. Sex stratification in all analyses was performed to reveal sex-specific differences in the interactions. We found 14 shared interactions related to both depression and anxiety in total sample, such as alcohol drinking × cerebellum white matter 3 (WM; beta = -.003, p = .018 for depression; beta = -003, p = .008 for anxiety) and maternal smoking × nucleus accumbens 2 (beta = .088, p = .002 for depression; beta = .070, p = .008 for anxiety). We also observed sex-specific differences in the interactions, for instance, alcohol drinking × cerebellum WM 3 was negatively associated with depression and anxiety in males (beta = -.004, p = .020 for depression; beta = -.005, p = .002 for anxiety). Our study results reveal the important interactions between brain structure changes and several life environmental factors on depression and anxiety, which may help to explore the pathogenesis of depression and anxiety.
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Affiliation(s)
- Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Jian Yang
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Yijing Zhao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Huijie Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Jingxi Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Zhen Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Chun'e Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Yujing Chen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Dan He
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Huan Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.,Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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15
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Knodt AR, Meier MH, Ambler A, Gehred MZ, Harrington H, Ireland D, Poulton R, Ramrakha S, Caspi A, Moffitt TE, Hariri AR. Diminished Structural Brain Integrity in Long-term Cannabis Users Reflects a History of Polysubstance Use. Biol Psychiatry 2022; 92:861-870. [PMID: 36008158 PMCID: PMC9637748 DOI: 10.1016/j.biopsych.2022.06.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 05/26/2022] [Accepted: 06/14/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Cannabis legalization and use are outpacing our understanding of its long-term effects on brain and behavior, which is fundamental for effective policy and health practices. Existing studies are limited by small samples, cross-sectional measures, failure to separate long-term from recreational use, and inadequate control for other substance use. Here, we address these limitations by determining the structural brain integrity of long-term cannabis users in the Dunedin Study, a longitudinal investigation of a population-representative birth cohort followed to midlife. METHODS We leveraged prospective measures of cannabis, alcohol, tobacco, and other illicit drug use in addition to structural neuroimaging in 875 study members at age 45 to test for differences in both global and regional gray and white matter integrity between long-term cannabis users and lifelong nonusers. We additionally tested for dose-response associations between continuous measures of cannabis use and brain structure, including careful adjustments for use of other substances. RESULTS Long-term cannabis users had a thinner cortex, smaller subcortical gray matter volumes, and higher machine learning-predicted brain age than nonusers. However, these differences in structural brain integrity were explained by the propensity of long-term cannabis users to engage in polysubstance use, especially with alcohol and tobacco. CONCLUSIONS These findings suggest that diminished midlife structural brain integrity in long-term cannabis users reflects a broader pattern of polysubstance use, underlining the importance of understanding comorbid substance use in efforts to curb the negative effects of cannabis on brain and behavior as well as establish more effective policy and health practices.
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Affiliation(s)
- Annchen R Knodt
- Department of Psychology and Neuroscience, Duke University, Durham, North Carolina
| | - Madeline H Meier
- Department of Psychology, Arizona State University, Tempe, Arizona
| | - Antony Ambler
- Institute of Psychiatry, Psychology, & Neuroscience, King's College London, London, United Kingdom; Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Maria Z Gehred
- Department of Psychology and Neuroscience, Duke University, Durham, North Carolina
| | - HonaLee Harrington
- Department of Psychology and Neuroscience, Duke University, Durham, North Carolina
| | - David Ireland
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Avshalom Caspi
- Department of Psychology and Neuroscience, Duke University, Durham, North Carolina; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina; Institute of Psychiatry, Psychology, & Neuroscience, King's College London, London, United Kingdom
| | - Terrie E Moffitt
- Department of Psychology and Neuroscience, Duke University, Durham, North Carolina; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina; Institute of Psychiatry, Psychology, & Neuroscience, King's College London, London, United Kingdom
| | - Ahmad R Hariri
- Department of Psychology and Neuroscience, Duke University, Durham, North Carolina.
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16
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Deng YT, Kuo K, Wu BS, Ou YN, Yang L, Zhang YR, Huang SY, Chen SD, Guo Y, Zhang RQ, Tan L, Dong Q, Feng JF, Cheng W, Yu JT. Associations of resting heart rate with incident dementia, cognition, and brain structure: a prospective cohort study of UK biobank. Alzheimers Res Ther 2022; 14:147. [PMID: 36199126 PMCID: PMC9535982 DOI: 10.1186/s13195-022-01088-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 09/22/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Resting heart rate (RHR) has been linked with an increased risk of dementia. However, evidence characterizing the associations of RHR with different dementia subtypes and their underlying mechanisms remains scarce. This study aims to investigate the relationships of RHR with different dementia types, cognitive function, and brain structural abnormalities. METHODS Three hundred thirty-nine thousand nine hundred one participants with no prior diagnosis of dementia from the UK biobank were analyzed. Cox regression and restricted cubic spline models examined the associations between RHR with all-cause dementia (ACD) and its major subtypes-Alzheimer's disease (AD) and vascular dementia (VaD). Logistic regression models assessed the associations of RHR with cognitive function, and linear regression models estimated the associations with hippocampal subfield volume and white matter tract integrity indexed by magnetic resonance imaging data. RESULTS During an average of 3148 (± 941.08) days of follow-up, 4177 individuals were diagnosed with dementia, including 2354 AD and 989 VaD cases. RHR ≥ 80bpm was associated with ACD (HR: 1.18, 95% CI: 1.08-1.28, P < 0.001) and VaD (HR: 1.29, 95% CI: 1.08-1.54, P = 0.005) but not AD in multi-adjusted models. A 10-bpm increment of RHR demonstrated non-linear effects in VaD, consisting of J-shape relationships. Several heterogeneities were indicated in stratified analysis, in which RHR measures only showed associations with dementia incidents in relatively younger populations (age ≤ 65) and females. Apart from dementia analysis, elevated RHR was associated with worsening performance in fluid intelligence and reaction time of cognitive tasks, decreased hippocampal subfields volume, and poor white matter tract integrity. CONCLUSIONS RHR is associated with increased risks of ACD and VaD but also presented with few heterogeneities across different sex and age groups. Elevated RHR also appears to have deleterious effects on cognitive function and is distinctively associated with volume reduction in hippocampal subfields and impaired white matter tract integrity.
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Affiliation(s)
- Yue-Ting Deng
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.,National Center for Neurological Disorders, Shanghai, China
| | - Kevin Kuo
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.,National Center for Neurological Disorders, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.,National Center for Neurological Disorders, Shanghai, China
| | - Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Liu Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.,National Center for Neurological Disorders, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.,National Center for Neurological Disorders, Shanghai, China
| | - Shu-Yi Huang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.,National Center for Neurological Disorders, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.,National Center for Neurological Disorders, Shanghai, China
| | - Yu Guo
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.,National Center for Neurological Disorders, Shanghai, China
| | - Rui-Qi Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.,National Center for Neurological Disorders, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Qiang Dong
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.,National Center for Neurological Disorders, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China. .,National Center for Neurological Disorders, Shanghai, China. .,Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China. .,Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China.
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17
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Mulugeta A, Navale SS, Lumsden AL, Llewellyn DJ, Hyppönen E. Healthy Lifestyle, Genetic Risk and Brain Health: A Gene-Environment Interaction Study in the UK Biobank. Nutrients 2022; 14:nu14193907. [PMID: 36235559 PMCID: PMC9570683 DOI: 10.3390/nu14193907] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 09/15/2022] [Accepted: 09/20/2022] [Indexed: 11/16/2022] Open
Abstract
Genetic susceptibility and lifestyle affect the risk of dementia but there is little direct evidence for their associations with preclinical changes in brain structure. We investigated the association of genetic dementia risk and healthy lifestyle with brain morphometry, and whether effects from elevated genetic risk are modified by lifestyle changes. We used prospective data from up to 25,894 UK Biobank participants (median follow-up of 8.8 years), and defined healthy lifestyle according to American Heart Association criteria as BMI < 30, no smoking, healthy diet and regular physical activity). Higher genetic risk was associated with lower hippocampal volume (beta −0.16 cm3, 95% CI −0.22, −0.11) and total brain volume (−4.34 cm3, 95% CI −7.68, −1.01) in participants aged ≥60 years but not <60 years. Healthy lifestyle was associated with higher total brain, grey matter and hippocampal volumes, and lower volume of white matter hyperintensities, with no effect modification by age or genetic risk. In conclusion, adverse effects of high genetic risk on brain health were only found in older participants, while adhering to healthy lifestyle recommendations is beneficial regardless of age or genetic risk.
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Affiliation(s)
- Anwar Mulugeta
- Australian Centre for Precision Health, Unit of Clinical and Health Sciences, University of South Australia, Adelaide, SA 5001, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA 5000, Australia
- Department of Pharmacology and Clinical Pharmacy, College of Health Science, Addis Ababa University, Addis Ababa P.O. Box 9086, Ethiopia
| | - Shreeya S. Navale
- Australian Centre for Precision Health, Unit of Clinical and Health Sciences, University of South Australia, Adelaide, SA 5001, Australia
| | - Amanda L. Lumsden
- Australian Centre for Precision Health, Unit of Clinical and Health Sciences, University of South Australia, Adelaide, SA 5001, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA 5000, Australia
| | - David J. Llewellyn
- College of Medicine and Health, University of Exeter, Devon EX1 2LU, UK
- Alan Turing Institute, London NW1 2DB, UK
| | - Elina Hyppönen
- Australian Centre for Precision Health, Unit of Clinical and Health Sciences, University of South Australia, Adelaide, SA 5001, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA 5000, Australia
- Correspondence: ; Tel.: +61-(08)-83022518
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18
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Jiang R, Westwater ML, Noble S, Rosenblatt M, Dai W, Qi S, Sui J, Calhoun VD, Scheinost D. Associations between grip strength, brain structure, and mental health in > 40,000 participants from the UK Biobank. BMC Med 2022; 20:286. [PMID: 36076200 PMCID: PMC9461129 DOI: 10.1186/s12916-022-02490-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 07/20/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Grip strength is a widely used and well-validated measure of overall health that is increasingly understood to index risk for psychiatric illness and neurodegeneration in older adults. However, existing work has not examined how grip strength relates to a comprehensive set of mental health outcomes, which can detect early signs of cognitive decline. Furthermore, whether brain structure mediates associations between grip strength and cognition remains unknown. METHODS Based on cross-sectional and longitudinal data from over 40,000 participants in the UK Biobank, this study investigated the behavioral and neural correlates of handgrip strength using a linear mixed effect model and mediation analysis. RESULTS In cross-sectional analysis, we found that greater grip strength was associated with better cognitive functioning, higher life satisfaction, greater subjective well-being, and reduced depression and anxiety symptoms while controlling for numerous demographic, anthropometric, and socioeconomic confounders. Further, grip strength of females showed stronger associations with most behavioral outcomes than males. In longitudinal analysis, baseline grip strength was related to cognitive performance at ~9 years follow-up, while the reverse effect was much weaker. Further, baseline neuroticism, health, and financial satisfaction were longitudinally associated with subsequent grip strength. The results revealed widespread associations between stronger grip strength and increased grey matter volume, especially in subcortical regions and temporal cortices. Moreover, grey matter volume of these regions also correlated with better mental health and considerably mediated their relationship with grip strength. CONCLUSIONS Overall, using the largest population-scale neuroimaging dataset currently available, our findings provide the most well-powered characterization of interplay between grip strength, mental health, and brain structure, which may facilitate the discovery of possible interventions to mitigate cognitive decline during aging.
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Affiliation(s)
- Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA.
| | - Margaret L Westwater
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Wei Dai
- Department of Biostatistics, Yale University, New Haven, CT, 06520, USA
| | - Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, 30303, USA
| | - Jing Sui
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, 30303, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, 30303, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA.
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06520, USA.
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA.
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA.
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19
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Qi S, Fu Z, Wu L, Calhoun VD, Zhang D, Daughters SB, Hsu PC, Jiang R, Vergara VM, Sui J, Addicott MA. Cognition, Aryl Hydrocarbon Receptor Repressor Methylation, and Abstinence Duration-Associated Multimodal Brain Networks in Smoking and Long-Term Smoking Cessation. Front Neurosci 2022; 16:923065. [PMID: 35968362 PMCID: PMC9363622 DOI: 10.3389/fnins.2022.923065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 06/20/2022] [Indexed: 02/04/2023] Open
Abstract
Cigarette smoking and smoking cessation are associated with changes in cognition and DNA methylation; however, the neurobiological correlates of these effects have not been fully elucidated, especially in long-term cessation. Cognitive performance, percent methylation of the aryl hydrocarbon receptor repressor (AHRR) gene, and abstinence duration were used as references to supervise a multimodal fusion analysis of functional, structural, and diffusion magnetic resonance imaging (MRI) data, in order to identify associated brain networks in smokers and ex-smokers. Correlations among these networks and with smoking-related measures were performed. Cognition-, methylation-, and abstinence duration-associated networks discriminated between smokers and ex-smokers and correlated with differences in fractional amplitude of low frequency fluctuations (fALFF) values, gray matter volume (GMV), and fractional anisotropy (FA) values. Long-term smoking cessation was associated with more accurate cognitive performance, as well as lower fALFF and more GMV in the hippocampus complex. The methylation- and abstinence duration-associated networks positively correlated with smoking-related measures of abstinence duration and percent methylation, respectively, suggesting they are complementary measures. This analysis revealed structural and functional co-alterations linked to smoking abstinence and cognitive performance in brain regions including the insula, frontal gyri, and lingual gyri. Furthermore, AHRR methylation, a promising epigenetic biomarker of smoking recency, may provide an important complement to self-reported abstinence duration.
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Affiliation(s)
- Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Lei Wu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Stacey B. Daughters
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Ping-Ching Hsu
- Department of Environmental and Occupational Health, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - Victor M. Vergara
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Merideth A. Addicott
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
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20
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Zhou Y, Hu Y, Wang Q, Yang Z, Li J, Ma Y, Wu Q, Chen S, Yang D, Hao Y, Wang Y, Li M, Peng P, Liu T, Yang WFZ. Association between white matter microstructure and cognitive function in patients with methamphetamine use disorder. Hum Brain Mapp 2022; 44:304-314. [PMID: 35838008 PMCID: PMC9842920 DOI: 10.1002/hbm.26020] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 06/15/2022] [Accepted: 06/28/2022] [Indexed: 01/25/2023] Open
Abstract
Methamphetamine use disorder (MUD) has been associated with broad neurocognitive impairments. While the cognitive impairments of MUD have been demonstrated, the neuropathological underpinnings remain inadequately understood. To date, the published human diffusion tensor imaging (DTI) studies involving the correlation between diffusion parameters and neurocognitive function in MUD are limited. Hence, the present study aimed to examine the association between cognitive performance and white matter microstructure in patients with MUD. Forty-five patients with MUD and 43 healthy controls (HCs) completed their demographic information collection, cognitive assessments, and DTI imaging. DTI images were preprocessed to extract fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) of various fiber tracts. Univariate tests were used to examine group differences in cognitive assessments and DTI metrics. Linear regression was used to examine the relationship between these two metrics. The results revealed that patients with MUD had lower subset scores of the MATRICS Consensus Cognitive Battery (MCCB), which reflects five cognitive domains: processing speed, attention, verbal learning, visual learning, problem-solving. Patients with MUD also had significantly higher AD, MD, and RD values of the left superior longitudinal fasciculus than HCs. Furthermore, the RD value of the left superior longitudinal fasciculus was a significant predictor of processing speed and problem-solving ability, as shown by the digit-symbol coding test and NAB-Mazes scores, respectively. Findings extended our understanding of white matter microstructure that is related to neurocognitive deficits in MUD and provided potential targets for the prevention and treatment of this chronic disorder.
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Affiliation(s)
- Yanan Zhou
- National Clinical Research Center for Mental Disorders, and Department of Psychiatrythe Second Xiangya Hospital of Central South UniversityChangshaHunanChina,Department of PsychiatryBrain Hospital of Hunan Province (The Second People's Hospital of Hunan Province)ChangshaChina
| | - Yang Hu
- Laboratory of Psychological Heath and Imaging, Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Qianjin Wang
- National Clinical Research Center for Mental Disorders, and Department of Psychiatrythe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Zhi Yang
- Laboratory of Psychological Heath and Imaging, Shanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Jinguang Li
- National Clinical Research Center for Mental Disorders, and Department of Psychiatrythe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Yuejiao Ma
- National Clinical Research Center for Mental Disorders, and Department of Psychiatrythe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Qiuxia Wu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatrythe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Shubao Chen
- National Clinical Research Center for Mental Disorders, and Department of Psychiatrythe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Dong Yang
- Department of PsychiatryBrain Hospital of Hunan Province (The Second People's Hospital of Hunan Province)ChangshaChina
| | - Yuzhu Hao
- National Clinical Research Center for Mental Disorders, and Department of Psychiatrythe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Yunfei Wang
- National Clinical Research Center for Mental Disorders, and Department of Psychiatrythe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Manyun Li
- National Clinical Research Center for Mental Disorders, and Department of Psychiatrythe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Pu Peng
- National Clinical Research Center for Mental Disorders, and Department of Psychiatrythe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Tieqiao Liu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatrythe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Winson Fu Zun Yang
- Department of Psychological Sciences, College of Arts & SciencesTexas Tech UniversityLubbockTexasUSA
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21
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Shen X, Raghavan S, Przybelski SA, Lesnick TG, Ma S, Reid RI, Graff-Radford J, Mielke MM, Knopman DS, Petersen RC, Jack CR, Simon GJ, Vemuri P. Causal structure discovery identifies risk factors and early brain markers related to evolution of white matter hyperintensities. Neuroimage Clin 2022; 35:103077. [PMID: 35696810 PMCID: PMC9194644 DOI: 10.1016/j.nicl.2022.103077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/25/2022] [Accepted: 06/03/2022] [Indexed: 11/25/2022]
Abstract
Our goal was to understand the complex relationship between age, sex, midlife risk factors, and early white matter changes measured by diffusion tensor imaging (DTI) and their role in the evolution of longitudinal white matter hyperintensities (WMH). We identified 1564 participants (1396 cognitively unimpaired, 151 mild cognitive impairment and 17 dementia participants) with age ranges of 30-90 years from the population-based sample of Mayo Clinic Study of Aging. We used computational causal structure discovery and regression analyses to evaluate the predictors of WMH and DTI, and to ascertain the mediating effect of DTI on WMH. We further derived causal graphs to understand the complex interrelationships between midlife protective factors, vascular risk factors, diffusion changes, and WMH. Older age, female sex, and hypertension were associated with higher baseline and progression of WMH as well as DTI measures (P ≤ 0.003). The effects of hypertension and sex on WMH were partially mediated by microstructural changes measured on DTI. Higher midlife physical activity was predictive of lower WMH through a direct impact on better white matter tract integrity as well as an indirect effect through reducing the risk of hypertension by lowering BMI. This study identified key risks factors, early brain changes, and pathways that may lead to the evolution of WMH.
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Affiliation(s)
- Xinpeng Shen
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA; Departments of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Sisi Ma
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Robert I Reid
- Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | - Michelle M Mielke
- Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA; Departments of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - György J Simon
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
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22
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Boban J, Thurnher MM, Boban N, Law M, Jahanshad N, Nir TM, Lendak DF, Kozic D. Gradient Patterns of Age-Related Diffusivity Changes in Cerebral White Matter. Front Neurol 2022; 13:870909. [PMID: 35720102 PMCID: PMC9201287 DOI: 10.3389/fneur.2022.870909] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 04/25/2022] [Indexed: 11/24/2022] Open
Abstract
The current concept of brain aging proposes three gradient patterns of changes in white matter that occur during healthy brain aging: antero-posterior, supero-inferior, and the myelodegeneration-retrogenesis (or the “last-in-first-out”) concept. The aim of this study was to correlate white matter diffusivity measures (fractional anisotropy-FA, mean diffusivity-MD, radial diffusivity-RD, and axial diffusivity-AD) in healthy volunteers with chronological age and education level, in order to potentially incorporate the findings with proposed patterns of physiological brain aging. The study was performed on 75 healthy participants of both sexes, with an average age of 37.32 ± 11.91 years underwent brain magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI). DTI was performed using tract-based spatial statistics (TBSS), with the analysis of four parameters: FA, MD, RD, and AD. Skeletonized measures were averaged in 29 regions of interest in white matter. Correlations between age and DTI measures and between education-level and DTI measures were performed using Pearson's correlation test. To correct for multiple comparisons, we applied a Bonferroni correction to the p-values. Significance was set at p ≤ 0.001. A significant negative correlation of FA with age was observed in posterior thalamic radiation (PTR) (p< 0.001). A significant positive correlation between age and MD was observed in sagittal stratum (SS) (p< 0.001), between age and RD in PTR, SS, and retrolenticular internal capsule (p< 0.001), and between age and AD in the body of the corpus callosum (p< 0.001). There were no significant correlations of DTI parameters with educational level. According to our study, RD showed the richest correlations with age, out of all DTI metrics. FA, MD, and RD showed significant changes in the diffusivity of projection fibers, while AD presented diffusivity changes in the commissural fibers. The observed heterogeneity in diffusivity changes across the brain cannot be explained by a single aging gradient pattern, since it seems that different patterns of degradation are true for different fiber tracts that no currently available theory can globally explain age-related changes in the brain. Additional factors, such as the effect of somatosensory decline, should be included as one of the important covariables to the existing patterns.
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Affiliation(s)
- Jasmina Boban
- Faculty of Medicine Novi Sad, Department of Radiology, University of Novi Sad, Novi Sad, Serbia
- Vojvodina Institute of Oncology, Center for Diagnostic Imaging, Sremska Kamenica, Serbia
- *Correspondence: Jasmina Boban
| | - Majda M. Thurnher
- Department for Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria
| | - Nikola Boban
- Clinical Center of Vojvodina, Center for Radiology, Novi Sad, Serbia
| | - Meng Law
- Department for Neuroscience, The Alfred Centre, Central Clinical School, Monash University, Melbourne, VIC, United States
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Talia M. Nir
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Dajana F. Lendak
- Faculty of Medicine Novi Sad, Department of Infectious Diseases, University of Novi Sad, Novi Sad, Serbia
- Clinical Center of Vojvodina, Clinic for Infectious Diseases, Novi Sad, Serbia
| | - Dusko Kozic
- Faculty of Medicine Novi Sad, Department of Radiology, University of Novi Sad, Novi Sad, Serbia
- Vojvodina Institute of Oncology, Center for Diagnostic Imaging, Sremska Kamenica, Serbia
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23
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Whitsel N, Reynolds CA, Buchholz EJ, Pahlen S, Pearce RC, Hatton SN, Elman JA, Gillespie NA, Gustavson DE, Puckett OK, Dale AM, Eyler LT, Fennema-Notestine C, Hagler DJ, Hauger RL, McEvoy LK, McKenzie R, Neale MC, Panizzon MS, Sanderson-Cimino M, Toomey R, Tu XM, Williams MKE, Bell T, Xian H, Lyons MJ, Kremen WS, Franz CE. Long-term associations of cigarette smoking in early mid-life with predicted brain aging from mid- to late life. Addiction 2022; 117:1049-1059. [PMID: 34605095 PMCID: PMC8904283 DOI: 10.1111/add.15710] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 09/03/2021] [Accepted: 09/15/2021] [Indexed: 11/27/2022]
Abstract
BACKGROUND AND AIMS Smoking is associated with increased risk for brain aging/atrophy and dementia. Few studies have examined early associations with brain aging. This study aimed to measure whether adult men with a history of heavier smoking in early mid-life would have older than predicted brain age 16-28 years later. DESIGN Prospective cohort observational study, utilizing smoking pack years data from average age 40 (early mid-life) predicting predicted brain age difference scores (PBAD) at average ages 56, 62 (later mid-life) and 68 years (early old age). Early mid-life alcohol use was also evaluated. SETTING Population-based United States sample. PARTICIPANTS/CASES Participants were male twins of predominantly European ancestry who served in the United States military between 1965 and 1975. Structural magnetic resonance imaging (MRI) began at average age 56. Subsequent study waves included most baseline participants; attrition replacement subjects were added at later waves. MEASUREMENTS Self-reported smoking information was used to calculate pack years smoked at ages 40, 56, 62, and 68. MRIs were processed with the Brain-Age Regression Analysis and Computation Utility software (BARACUS) program to create PBAD scores (chronological age-predicted brain age) acquired at average ages 56 (n = 493; 2002-08), 62 (n = 408; 2009-14) and 68 (n = 499; 2016-19). FINDINGS In structural equation modeling, age 40 pack years predicted more advanced age 56 PBAD [β = -0.144, P = 0.012, 95% confidence interval (CI) = -0.257, -0.032]. Age 40 pack years did not additionally predict PBAD at later ages. Age 40 alcohol consumption, but not a smoking × alcohol interaction, predicted more advanced PBAD at age 56 (β = -0.166, P = 0.001, 95% CI = -0.261, -0.070) with additional influences at age 62 (β = -0.115, P = 0.005, 95% CI = -0.195, -0.036). Age 40 alcohol did not predict age 68 PBAD. Within-twin-pair analyses suggested some genetic mechanism partially underlying effects of alcohol, but not smoking, on PBAD. CONCLUSIONS Heavier smoking and alcohol consumption by age 40 appears to predict advanced brain aging by age 56 in men.
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Affiliation(s)
- Nathan Whitsel
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Chandra A Reynolds
- Department of Psychology, University of California, Riverside, Riverside, CA, USA
| | - Erik J Buchholz
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Shandell Pahlen
- Department of Psychology, University of California, Riverside, Riverside, CA, USA
| | - Rahul C Pearce
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Sean N Hatton
- Department of Neuroscience, University of California San Diego, La Jolla, San Diego, CA, USA
| | - Jeremy A Elman
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
- QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Daniel E Gustavson
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Olivia K Puckett
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Anders M Dale
- Department of Neuroscience, University of California San Diego, La Jolla, San Diego, CA, USA
| | - Lisa T Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
- Department of Neuroscience, University of California San Diego, La Jolla, San Diego, CA, USA
| | - Donald J Hagler
- Department of Neuroscience, University of California San Diego, La Jolla, San Diego, CA, USA
| | - Richard L Hauger
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Linda K McEvoy
- Department of Neuroscience, University of California San Diego, La Jolla, San Diego, CA, USA
| | - Ruth McKenzie
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Mark Sanderson-Cimino
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
- San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, La Jolla, CA, USA
| | - Rosemary Toomey
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Xin M Tu
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla, San Diego, CA, USA
| | - Mc Kenna E Williams
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
- San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, La Jolla, CA, USA
| | - Tyler Bell
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Hong Xian
- Department of Epidemiology and Biostatistics, St Louis University, St Louis, MO, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
| | - Carol E Franz
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, USA
- Center for Behavior Genetics of Aging, University of California, La Jolla, San Diego, CA, USA
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24
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Berry SC, Lawrence AD, Lancaster TM, Casella C, Aggleton JP, Postans M. Subiculum - BNST Structural Connectivity in Humans and Macaques. Neuroimage 2022; 253:119096. [PMID: 35304264 PMCID: PMC9227740 DOI: 10.1016/j.neuroimage.2022.119096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 02/21/2022] [Accepted: 03/11/2022] [Indexed: 11/27/2022] Open
Abstract
Invasive tract-tracing studies in rodents implicate a direct connection between the subiculum and bed nucleus of the stria terminalis (BNST) as a key component of neural pathways mediating hippocampal regulation of the Hypothalamic-Pituitary-Adrenal (HPA) axis. A clear characterisation of the connections linking the subiculum and BNST in humans and non-human primates is lacking. To address this, we first delineated the projections from the subiculum to the BNST using anterograde tracers injected into macaque monkeys, revealing evidence for a monosynaptic subiculum-BNST projection involving the fornix. Second, we used in vivo diffusion MRI tractography in macaques and humans to demonstrate substantial subiculum complex connectivity to the BNST in both species. This connection was primarily carried by the fornix, with additional connectivity via the amygdala, consistent with rodent anatomy. Third, utilising the twin-based nature of our human sample, we found that microstructural properties of these tracts were moderately heritable (h2 ∼ 0.5). In a final analysis, we found no evidence of any significant association between subiculum complex-BNST tract microstructure and indices of perceived stress/dispositional negativity and alcohol use, derived from principal component analysis decomposition of self-report data. Our findings address a key translational gap in our knowledge of the neurocircuitry regulating stress.
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Affiliation(s)
- Samuel C Berry
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.
| | - Andrew D Lawrence
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | | | - Chiara Casella
- Department of Perinatal Imaging and Health, School of Biomedical Engineering & Imaging Sciences, Kings College London, London, UK
| | - John P Aggleton
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Mark Postans
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
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25
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Linli Z, Feng J, Zhao W, Guo S. Associations between smoking and accelerated brain ageing. Prog Neuropsychopharmacol Biol Psychiatry 2022; 113:110471. [PMID: 34740709 DOI: 10.1016/j.pnpbp.2021.110471] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/27/2021] [Accepted: 10/31/2021] [Indexed: 12/31/2022]
Abstract
Smoking accelerates the ageing of multiple organs. However, few studies have quantified the association between smoking, especially smoking cessation, and brain ageing. Using structural magnetic resonance imaging data from the UK Biobank (n = 33,293), a brain age predictor was trained using a machine learning technique in the non-smoker group (n = 14,667) and then tested in the smoker group (n = 18,626) to determine the relationships between BrainAge Gap (predicted age - true age) and smoking parameters. Further, we examined whether smoking was associated with poorer cognition and whether this relationship was mediated by brain age. The predictor achieved an appreciable performance in training data (r = 0.712, mean-absolute-error [MAE] = 4.220) and test data (r = 0.725, MAE = 4.160). On average, smokers showed a larger BrainAge Gap (+0.304 years, Cohens'd = 0.083) than controls, more explicitly, the extents vary depending on their smoking characteristic that active regular smokers had the largest BrainAge Gap (+1.190 years, Cohens'd = 0.321), and light smokers had a moderate BrainAge Gap (+0.478, Cohens'd = 0.129). The increased smoking amount was associated with a larger BrainAge Gap (β = 0.035, p = 1.72 × 10-20) while a longer duration of quitting smoking in ex-smokers was associated with a smaller BrainAge Gap (β = -0.015, p = 2.14 × 10-05). Furthermore, smoking was associated with poorer cognition, and this relationship was partially mediated by BrainAge Gap. The study provides insight into the association between smoking, brain ageing, and cognition, which provide more publicly acceptable propaganda against smoking.
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Affiliation(s)
- Zeqiang Linli
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, PR China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; Centre for Computational Systems Biology, Fudan University, Shanghai 200433, PR China
| | - Wei Zhao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, PR China.
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, PR China.
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26
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Precision Preventive Medicine of Relapse in Smoking Cessation: Can MRI Inform the Search of Intermediate Phenotypes? BIOLOGY 2021; 11:biology11010035. [PMID: 35053034 PMCID: PMC8773102 DOI: 10.3390/biology11010035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/17/2021] [Accepted: 12/20/2021] [Indexed: 11/16/2022]
Abstract
Simple Summary Addiction to tobacco is a serious health and economical problem because it is one of the most addictive and the most consumed substance in the world. Although well documented, and despite the desire of numerous smokers to quit, maintenance of abstinence is a daily challenge for most of them. The heterogeneity in achieving this maintenance raises the question of potential differences in brain reactivity. An emerging field of research has been interested in brain markers helping to identify individuals who are the most likely to relapse. Using brain imaging techniques such as Magnetic Resonance Imaging (MRI), one can hope it will be possible to offer tailored care for each patient. Abstract Chronic tobacco smoking remains a major health problem worldwide. Numerous smokers wish to quit but most fail, even if they are helped. The possibility of identifying neuro-biomarkers in smokers at high risk of relapse could be of incredible progress toward personalized prevention therapy. Our aim is to provide a scoping review of this research topic in the field of Magnetic Resonance Imaging (MRI) and to review the studies that investigated if MRI defined markers predicted smoking cessation treatment outcome (abstainers versus relapsers). Based on the available literature, a meta-analysis could not be conducted. We thus provide an overview of the results obtained and take stock of methodological issues that will need to be addressed to pave the way toward precision medicine. Based on the most consistent findings, we discuss the pivotal role of the insula in light of the most recent neurocognitive models of addiction.
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27
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Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants. Sci Rep 2021; 11:20563. [PMID: 34663856 PMCID: PMC8523533 DOI: 10.1038/s41598-021-99153-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 09/14/2021] [Indexed: 11/08/2022] Open
Abstract
Brain age can be estimated using different Magnetic Resonance Imaging (MRI) modalities including diffusion MRI. Recent studies demonstrated that white matter (WM) tracts that share the same function might experience similar alterations. Therefore, in this work, we sought to investigate such issue focusing on five WM bundles holding that feature that is Association, Brainstem, Commissural, Limbic and Projection fibers, respectively. For each tract group, we estimated brain age for 15,335 healthy participants from United Kingdom Biobank relying on diffusion MRI data derived endophenotypes, Bayesian ridge regression modeling and 10 fold-cross validation. Furthermore, we estimated brain age for an Ensemble model that gathers all the considered WM bundles. Association analysis was subsequently performed between the estimated brain age delta as resulting from the six models, that is for each tract group as well as for the Ensemble model, and 38 daily life style measures, 14 cardiac risk factors and cardiovascular magnetic resonance imaging features and genetic variants. The Ensemble model that used all tracts from all fiber groups (FG) performed better than other models to estimate brain age. Limbic tracts based model reached the highest accuracy with a Mean Absolute Error (MAE) of 5.08, followed by the Commissural ([Formula: see text]), Association ([Formula: see text]), and Projection ([Formula: see text]) ones. The Brainstem tracts based model was the less accurate achieving a MAE of 5.86. Accordingly, our study suggests that the Limbic tracts experience less brain aging or allows for more accurate estimates compared to other tract groups. Moreover, the results suggest that Limbic tract leads to the largest number of significant associations with daily lifestyle factors than the other tract groups. Lastly, two SNPs were significantly (p value [Formula: see text]) associated with brain age delta in the Projection fibers. Those SNPs are mapped to HIST1H1A and SLC17A3 genes.
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Ye Z, Mo C, Liu S, Hatch KS, Gao S, Ma Y, Hong LE, Thompson PM, Jahanshad N, Acheson A, Garavan H, Shen L, Nichols TE, Kochunov P, Chen S, Ma T. White Matter Integrity and Nicotine Dependence: Evaluating Vertical and Horizontal Pleiotropy. Front Neurosci 2021; 15:738037. [PMID: 34720862 PMCID: PMC8551454 DOI: 10.3389/fnins.2021.738037] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 09/01/2021] [Indexed: 01/26/2023] Open
Abstract
Tobacco smoking is an addictive behavior that supports nicotine dependence and is an independent risk factor for cancer and other illnesses. Its neurogenetic mechanisms are not fully understood but may act through alterations in the cerebral white matter (WM). We hypothesized that the vertical pleiotropic pathways, where genetic variants influence a trait that in turn influences another trait, link genetic factors, integrity of cerebral WM, and nicotine addiction. We tested this hypothesis using individual genetic factors, WM integrity measured by fractional anisotropy (FA), and nicotine dependence-related smoking phenotypes, including smoking status (SS) and cigarettes per day (CPDs), in a large epidemiological sample collected by the UK Biobank. We performed a genome-wide association study (GWAS) to identify previously reported loci associated with smoking behavior. Smoking was found to be associated with reduced WM integrity in multiple brain regions. We then evaluated two competing vertical pathways: Genes → WM integrity → Smoking versus Genes → Smoking → WM integrity and a horizontal pleiotropy pathway where genetic factors independently affect both smoking and WM integrity. The causal pathway analysis identified 272 pleiotropic single-nucleotide polymorphisms (SNPs) whose effects on SS were mediated by FA, as well as 22 pleiotropic SNPs whose effects on FA were mediated by CPD. These SNPs were mainly located in important susceptibility genes for smoking-induced diseases NCAM1 and IREB2. Our findings revealed the role of cerebral WM in the maintenance of the complex addiction and provided potential genetic targets for future research in examining how changes in WM integrity contribute to the nicotine effects on the brain.
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Affiliation(s)
- Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
| | - Chen Mo
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Kathryn S Hatch
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Ashley Acheson
- Department of Psychiatry and Behavioral Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Hugh Garavan
- Department of Psychiatry, The University of Vermont, Burlington, VT, United States
| | - Li Shen
- Department of Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Thomas E Nichols
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, College Park, MD, United States
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Valdés Hernández MDC, Grimsley-Moore T, Sakka E, Thrippleton MJ, Chappell FM, Armitage PA, Makin S, Wardlaw JM. Lacunar Stroke Lesion Extent and Location and White Matter Hyperintensities Evolution 1 Year Post-lacunar Stroke. Front Neurol 2021; 12:640498. [PMID: 33746892 PMCID: PMC7976454 DOI: 10.3389/fneur.2021.640498] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 02/08/2021] [Indexed: 11/13/2022] Open
Abstract
Lacunar strokes are a common type of ischemic stroke. They are associated with long-term disability, but the factors affecting the dynamic of the infarcted lesion and the brain imaging features associated with them, reflective of small vessel disease (SVD) severity, are still largely unknown. We investigated whether the distribution, volume and 1-year evolution of white matter hyperintensities (WMH), one of these SVD features, relate to the extent and location of these infarcts, accounting for vascular risk factors. We used imaging and clinical data from all patients [n = 118, mean age 64.9 (SD 11.75) years old] who presented to a regional hospital with a lacunar stroke syndrome within the years 2010 and 2013 and consented to participate in a study of stroke mechanisms. All patients had a brain MRI scan at presentation, and 88 had another scan 12 months after. Acute lesions (i.e., recent small subcortical infarcts, RSSI) were identified in 79 patients and lacunes in 77. Number of lacunes was associated with baseline WMH volume (B = 0.370, SE = 0.0939, P = 0.000174). RSSI volume was not associated with baseline WMH volume (B = 3.250, SE = 2.117, P = 0.129), but predicted WMH volume change (B = 2.944, SE = 0.913, P = 0.00184). RSSI location was associated with the spatial distribution of WMH and the pattern of 1-year WMH evolution. Patients with the RSSI in the centrum semiovale (n = 33) had significantly higher baseline volumes of WMH, recent and old infarcts, than patients with the RSSI located elsewhere [median 33.69, IQR (14.37 50.87) ml, 0.001 ≤ P ≤ 0.044]. But patients with the RSSI in the internal/external capsule/lentiform nucleus experienced higher increase of WMH volume after a year [n = 21, median (IQR) from 18 (11.70 31.54) ml to 27.41 (15.84 40.45) ml]. Voxel-wise analyses of WMH distribution in patients grouped per RSSI location revealed group differences increased in the presence of vascular risk factors, especially hypertension and recent or current smoking habit. In our sample of patients presenting to the clinic with lacunar strokes, lacunar strokes extent influenced WMH volume fate; and RSSI location and WMH spatial distribution and dynamics were intertwined, with differential patterns emerging in the presence of vascular risk factors. These results, if confirmed in wider samples, open potential avenues in stroke rehabilitation to be explored further.
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Affiliation(s)
| | - Tara Grimsley-Moore
- College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Eleni Sakka
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Francesca M. Chappell
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Paul A. Armitage
- Academic Unit of Radiology, University of Sheffield, Sheffield, United Kingdom
| | - Stephen Makin
- Centre for Rural Health, University of Aberdeen, Aberdeen, United Kingdom
| | - Joanna M. Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
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Heldt NA, Reichenbach N, McGary HM, Persidsky Y. Effects of Electronic Nicotine Delivery Systems and Cigarettes on Systemic Circulation and Blood-Brain Barrier: Implications for Cognitive Decline. THE AMERICAN JOURNAL OF PATHOLOGY 2020; 191:243-255. [PMID: 33285126 DOI: 10.1016/j.ajpath.2020.11.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 10/23/2020] [Accepted: 11/06/2020] [Indexed: 12/17/2022]
Abstract
Electronic nicotine delivery systems (often known as e-cigarettes) are a novel tobacco product with growing popularity, particularly among younger demographics. The implications for public health are twofold, as these products may represent a novel source of tobacco-associated disease but may also provide a harm reduction strategy for current tobacco users. There is increasing recognition that e-cigarettes impact vascular function across multiple organ systems. Herein, we provide a comparison of evidence regarding the role of e-cigarettes versus combustible tobacco in vascular disease and implications for blood-brain barrier dysfunction and cognitive decline. Multiple non-nicotinic components of tobacco smoke have been identified in e-cigarette aerosol, and their involvement in vascular disease is discussed. In addition, nicotine and nicotinic signaling may modulate peripheral immune and endothelial cell populations in a highly context-dependent manner. Direct preclinical evidence for electronic nicotine delivery system-associated neurovascular impairment is provided, and a model is proposed in which non-nicotinic elements exert a proinflammatory effect that is functionally antagonized by the presence of nicotine.
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Affiliation(s)
- Nathan A Heldt
- Department of Pathology and Laboratory Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania; Center for Substance Abuse Research, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania.
| | - Nancy Reichenbach
- Department of Pathology and Laboratory Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania
| | - Hannah M McGary
- Department of Pathology and Laboratory Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania
| | - Yuri Persidsky
- Department of Pathology and Laboratory Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania; Center for Substance Abuse Research, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania.
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