1
|
Melo van Lent D, Mesa HG, Short MI, Gonzales MM, Aparicio HJ, Salinas J, Yuan C, Jacques PF, Beiser A, Seshadri S, Jacob ME, Himali JJ. Association between dietary inflammatory index score and incident dementia: results from the Framingham heart Study offspring cohort. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.21.23294374. [PMID: 37662354 PMCID: PMC10473791 DOI: 10.1101/2023.08.21.23294374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Background The Dietary Inflammatory Index (DII), has been specifically designed to capture the inflammatory content of diet and has shown association with neurodegenerative disease related outcomes. But literature is limited on the role of diet-driven inflammation measured by the DII on incident all-cause dementia and Alzheimer's disease dementia (AD). Objective We evaluated whether higher DII scores were associated with increased incidence of all-cause dementia and AD over 22.3 years of follow-up in the community-based Framingham Heart Study (FHS) Offspring cohort. Design Setting and Participants Observational longitudinal study in the FHS Offspring cohort. Dementia surveillance for present study: until 2020. Data were analyzed from December 2020 to June 2022. Participants completed a validated 126-item food frequency questionnaires (FFQ), administered at FHS examination cycle 7 (1998-2001) and examination cycle 5 (1991-1995), and/or 6 (1995-1998). Individuals aged <60 years, with prevalent dementia, no dementia follow-up, other relevant neurological diseases, and/or no FFQ data were excluded. Exposure A DII score (based on the published method by Shivappa et al. 2014) was created based on previous studies linking individual dietary factors to six inflammatory markers (i.e. C-reactive protein, interleukin (IL)-1β, IL-4, IL-6, IL-10, and tumor necrosis factor-alpha), consisting of 36 components. A cumulative DII score was calculated by averaging across a maximum of three FFQs. Main outcomes and measures Incident all-cause dementia and AD. Results We included 1487 participants (mean±SD, age in years 69 ± 6; 53·2% women; 31·6% college graduates]). 246 participants developed all-cause dementia (including AD n=187) over a median follow up time of 13·1 years. Higher DII scores were associated with an increased incidence of all-cause dementia and AD following adjustment for age and sex (Hazard ratio (HR) 1·16, 95% confidence interval (CI) 1·07 to 1·25, p<.001; HR 1·16, 95% CI 1·06 to 1·26, p=.001). The relationships remained after additional adjustment for demographic, lifestyle, and clinical covariates (HR 1·21, 95% CI 1·10 to 1·33, p<0.001; HR1·20, 95% CI1·07 to 1·35, p=.001). Conclusion and relevance Higher DII scores were associated with a higher risk of incident all-cause dementia and AD. Although these promising findings need to be replicated and further validated, our results suggest that diets which correlate with low DII scores may prevent late-life dementia.
Collapse
|
2
|
Shi Y, Lin F, Li Y, Wang Y, Chen X, Meng F, Ye Q, Cai G. Association of pro-inflammatory diet with increased risk of all-cause dementia and Alzheimer's dementia: a prospective study of 166,377 UK Biobank participants. BMC Med 2023; 21:266. [PMID: 37480061 PMCID: PMC10362711 DOI: 10.1186/s12916-023-02940-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 06/13/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND Increasing evidence suggests an association between pro-inflammatory diets and cognitive function. However, only a few studies based on small sample sizes have explored the association between pro-inflammatory diets and dementia using the dietary inflammatory index (DII). Additionally, the relationship between DII and different subtypes of dementia, such as Alzheimer's dementia and vascular dementia, remains largely unexplored. Given the changes in brain structure already observed in patients with dementia, we also investigated the association between DII and magnetic resonance imaging (MRI) measures of brain structure to provide some hints to elucidate the potential mechanisms between pro-inflammatory diet and cognitive decline. METHODS A total of 166,377 UK Biobank participants without dementia at baseline were analyzed. DII calculations were based on the information collected by the 24-h recall questionnaire. Brain structural anatomy and tissue-specific volumes were measured using brain MRI. Cox proportional hazards models, competing risk models, and restricted cubic spline were applied to assess the longitudinal associations. The generalized linear model was used to assess the association between DII and MRI measurements. RESULTS During a median follow-up time of 9.46 years, a total of 1372 participants developed dementia. The incidence of all-cause dementia increased by 4.6% for each additional unit of DII [hazard ratio (HR): 1.046]. Besides, DII displayed a "J-shaped" non-linear association with Alzheimer's dementia (Pnonlinear = 0.003). When DII was above 1.30, an increase in DII was significantly associated with an increased risk of Alzheimer's dementia (HR: 1.391, 95%CI: 1.085-1.784, P = 0.009). For brain MRI, the total volume of white matter hyperintensities increased with an increase in DII, whereas the volume of gray matter in the hippocampus decreased. CONCLUSIONS In this cohort study, higher DII was associated with a higher risk of all-cause dementia and Alzheimer's dementia. However, our findings suggested that the association with DII and vascular and frontotemporal dementia was not significant.
Collapse
Affiliation(s)
- Yisen Shi
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou, 350001, China
| | - Fabin Lin
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou, 350001, China
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Yueping Li
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou, 350001, China
| | - Yingqing Wang
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou, 350001, China
| | - Xiaochun Chen
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou, 350001, China
| | - Fangang Meng
- Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China.
| | - Qinyong Ye
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China.
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China.
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou, 350001, China.
| | - Guoen Cai
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China.
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China.
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou, 350001, China.
| |
Collapse
|
3
|
Pan Y, Shen J, Cai X, Chen H, Zong G, Zhu W, Jing J, Liu T, Jin A, Wang Y, Meng X, Yuan C, Wang Y. Adherence to a healthy lifestyle and brain structural imaging markers. Eur J Epidemiol 2023:10.1007/s10654-023-00992-8. [PMID: 37060500 DOI: 10.1007/s10654-023-00992-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/13/2023] [Indexed: 04/16/2023]
Abstract
Previous research has linked specific modifiable lifestyle factors to age-related cognitive decline in adults. Little is known about the potential role of an overall healthy lifestyle in brain structure. We examined the association of adherence to a healthy lifestyle with a panel of brain structural markers among 2,413 participants in PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study in China and 19,822 participants in UK Biobank (UKB). A healthy lifestyle score (0-5) was constructed based on five modifiable lifestyle factors: diet, physical activity, smoking, alcohol consumption, and body mass index. Validated multimodal neuroimaging markers were derived from brain magnetic resonance imaging. In the cross-sectional analysis of PRECISE, participants who adopted four or five low-risk lifestyle factors had larger total brain volume (TBV; β = 0.12, 95% CI: - 0.02, 0.26; p-trend = 0.05) and gray matter volume (GMV; β = 0.16, 95% CI: 0.01, 0.30; p-trend = 0.05), smaller white matter hyperintensity volume (WMHV; β = - 0.35, 95% CI: - 0.50, - 0.20; p-trend < 0.001) and lower odds of lacune (Odds Ratio [OR] = 0.48, 95% CI: 0.22, 1.08; p-trend = 0.03), compared to those with zero or one low-risk factors. Meanwhile, in the prospective analysis in UKB (with a median of 7.7 years' follow-up), similar associations were observed between the number of low-risk lifestyle factors (4-5 vs. 0-1) and TBV (β = 0.22, 95% CI: 0.16, 0.28; p-trend < 0.001), GMV (β = 0.26, 95% CI: 0.21, 0.32; p-trend < 0.001), white matter volume (WMV; β = 0.08, 95% CI: 0.01, 0.15; p-trend = 0.001), hippocampus volume (β = 0.15, 95% CI: 0.08, 0.22; p-trend < 0.001), and WMHV burden (β = - 0.23, 95% CI: - 0.29, - 0.17; p-trend < 0.001). Those with four or five low-risk lifestyle factors showed approximately 2.0-5.8 years of delay in aging of brain structure. Adherence to a healthier lifestyle was associated with a lower degree of neurodegeneration-related brain structural markers in middle-aged and older adults.
Collapse
Affiliation(s)
- Yuesong Pan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jie Shen
- School of Public Health, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueli Cai
- Department of Neurology, Lishui Hospital, Zhejiang University School of Medicine, Lishui, China
| | - Hui Chen
- School of Public Health, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Geng Zong
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Wanlin Zhu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jing Jing
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beijing, China
| | - Aoming Jin
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Xia Meng
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Changzheng Yuan
- School of Public Health, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, USA.
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- China National Clinical Research Center for Neurological Diseases, Beijing, China.
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
| |
Collapse
|
4
|
Sun Y, Liang Z, Xia X, Wang MH, Zhu C, Pan Y, Sun R. Extra cup of tea intake associated with increased risk of Alzheimer's disease: Genetic insights from Mendelian randomization. Front Nutr 2023; 10:1052281. [PMID: 36761219 PMCID: PMC9905237 DOI: 10.3389/fnut.2023.1052281] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 01/09/2023] [Indexed: 01/27/2023] Open
Abstract
Background Observational studies report inconclusive effects of tea consumption on the risk of Alzheimer's disease (AD), and the mechanisms are unclear. This study aims to investigate the effects of genetically predicted tea intake (cups of tea consumed per day) on AD, brain volume, and cerebral small vessel disease (CSVD) using the two-sample Mendelian randomization (MR) method. Methods Summary statistics of tea intake were obtained from UK Biobank (N = 447,485), and AD was from the International Genomics of Alzheimer's Project (N = 54,162). Genetic instruments were retrieved from UK Biobank using brain imaging-derived phenotypes for brain volume outcomes (N > 33,224) and genome-wide association studies for CSVD (N: 17,663-48,454). Results In the primary MR analysis, tea intake significantly increased the risk of AD using two different methods (ORIVW = 1.48, 95% CI: [1.14, 1.93]; ORWM = 2.00, 95% CI: [1.26, 3.18]) and reached a weak significant level using MR-Egger regression (p < 0.1). The result passed all the sensitivity analyses, including heterogeneity, pleiotropy, and outlier tests. In the secondary MR analysis, per extra cup of tea significantly decreased gray matter (βWM = -1.63, 95% CI: [-2.41, -0.85]) and right hippocampus volume (βWM = -1.78, 95% CI: [-2.76, -0.79]). We found a nonlinear association between tea intake and AD in association analysis, which suggested that over-drinking with more than 13 cups per day might be a risk factor for AD. Association analysis results were consistent with MR results. Conclusion This study revealed a potential causal association between per extra cup of tea and an increased risk of AD. Genetically predicted tea intake was associated with a decreased brain volume of gray matter and the right hippocampus, which indicates that over-drinking tea might lead to a decline in language and memory functions. Our results shed light on a novel possible mechanism of tea intake to increase the risk of AD by reducing brain volume.
Collapse
Affiliation(s)
- Yuxuan Sun
- Scientific Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
- Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Zixin Liang
- Scientific Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Xiaoxuan Xia
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Maggie Haitian Wang
- The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Chengming Zhu
- Scientific Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Yihang Pan
- Scientific Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
- Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Rui Sun
- Scientific Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
- Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| |
Collapse
|
5
|
Beversdorf DQ, Crosby HW, Shenker JI. Complementary and Alternative Medicine Approaches in Alzheimer Disease and Other Neurocognitive Disorders. MISSOURI MEDICINE 2023; 120:70-78. [PMID: 36860601 PMCID: PMC9970340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
As our population ages, there is interest in delaying or intervening in cognitive decline. While newer agents are under development, agents in mainstream use do not impact the course of diseases that cause cognitive decline. This increases interest in alternative strategies. Even as we welcome possible new disease-modifying agents, they are likely to remain costly. Herein, we review the evidence behind other complementary and alternative strategies for cognitive enhancement and prevention of cognitive decline.
Collapse
Affiliation(s)
- David Q Beversdorf
- Departments of Neurology, Radiology, and Psychological Sciences, and is the William and Nancy Thompson Endowed Chair in Radiology, , University of Missouri-Columbia School of Medicine, Columbia, Missouri
| | - Haley W Crosby
- Fourth-year medical student at the School of Medicine, , University of Missouri-Columbia School of Medicine, Columbia, Missouri
| | - Joel I Shenker
- Department of Neurology, University of Missouri-Columbia School of Medicine, Columbia, Missouri
| |
Collapse
|
6
|
Zhang X, Guo Y, Yao N, Wang L, Sun M, Xu X, Yang H, Sun Y, Li B. Association between dietary inflammatory index and metabolic syndrome: Analysis of the NHANES 2005-2016. Front Nutr 2022; 9:991907. [PMID: 36276824 PMCID: PMC9582939 DOI: 10.3389/fnut.2022.991907] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 08/29/2022] [Indexed: 11/19/2022] Open
Abstract
Objective Metabolic syndrome (MetS) is a global problem that increasingly violates human health and quality of life. We explored the relationship between dietary inflammatory potential represented by dietary inflammatory index (DII) and the occurrence of MetS to provid data support for the prevention of it through dietary structure intervention. Methods The data was come from National Health and Nutrition Examination Survey 2007-2018, including demographic, dietary, questionnaire variables and laboratory indicators. MetS was defined according to the criteria proposed by the American Endocrine Association (ACE) and the American Society of Clinical Endocrinology (ACCE). DII was calculated using the scoring method established by Shivappa. We divided DII scores into 4 quartiles, the chi-square test was used to compare the variable difference between DII quartiles groups. A logistic regression model was used to analyze the association between DII and MetS. We also performed subgroup analysis. A generalized linear regression model was used to explore the association of DII level and the levels of seven MetS related biochemical indicators. Results The final sample size was 8,180, and the DII scores of the subjects were -5.50 to 5.22. The proportions of men, young people, non-Hispanic blacks, poor people, smokers, and MetS patients in the Q1-Q4 DII quantiles groups were gradually increased. The risk of MetS in the Q4 group which had highest dietary inflammatory degree was 1.592 (1.248, 2.030) times higher than that in the Q1 group, respectively (P < 0.001). After subgroup analysis, women, youth, non-smokers and alcohol drinkers were found to be more sensitive to the dietary inflammation. Then we found that the level of DII was significantly positively correlated with waist circumference (WC), body mass index (BMI), triglyceride (TG), systolic blood pressure (SBP) and diastolic blood pressure (DBP), but negatively correlated with high density lipoprotein cholesterol (HDL-C). Conclusions In the research subjects, the degree of dietary inflammation was associated with the occurrence of MetS and significantly affected WC, BMI, blood pressure, and blood lipid levels. It is necessary to conduct investigations and early dietary interventions for women and young people to prevent the occurrence of chronic metabolic diseases.
Collapse
Affiliation(s)
- Xiaochen Zhang
- School of Public Health, Jilin University, Changchun, China
| | - Yinpei Guo
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Nan Yao
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Ling Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Mengzi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Xiaomeng Xu
- School of Public Health, Jilin University, Changchun, China
| | - Huanshuai Yang
- School of Public Health, Jilin University, Changchun, China
| | - Yang Sun
- School of Public Health, Jilin University, Changchun, China
| | - Bo Li
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China,*Correspondence: Bo Li
| |
Collapse
|