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Although previous studies have explored the associations of white matter hyperintensity with psychiatric disorders, the sample size is small and the conclusions are inconsistent. The present study aimed to further systematically explore the association in a larger sample. All data were extracted from the UK Biobank. First, general linear regression models and logistic regression models were used to assess the association between white matter hyperintensity volume and anxiety/depression. White matter hyperintensity has been classified into periventricular white matter hyperintensity and deep white matter hyperintensity. Anxiety was determined by General Anxiety Disorder-7 score (n = 17,221) and self-reported anxiety (n = 15,333), depression was determined by Patient Health Questionnaire-9 score (n = 17,175), and self-reported depression (n = 14,519). Moreover, we employed Cox proportional hazard models to explore the association between white matter hyperintensity volume and anxiety/depression. The covariates included in fully adjusted model are age, gender, body mass index, Townsend deprivation index, healthy physical activity, cigarette consumption, alcohol consumption, educational attainment, diabetes, hypertension, and coronary heart disease. The results of the fully adjusted model showed that white matter hyperintensity volume was significantly associated with General Anxiety Disorder-7 score (periventricular white matter hyperintensity: ß = 0.152, deep white matter hyperintensity: ß = 0.094) and Patient Health Questionnaire-9 score (periventricular white matter hyperintensity: ß = 0.168). Logistic regression analysis results indicated that periventricular white matter hyperintensity volume (odds ratio = 1.153) was significantly associated with self-reported anxiety. After applying the Cox proportional hazard models, we found that larger white matter hyperintensity volume was associated with increased risk of depression (periventricular white matter hyperintensity: hazard ratio = 1.589, deep white matter hyperintensity: hazard ratio = 1.200), but not anxiety. In summary, our findings support a positive association between white matter hyperintensity volume and depression.
Subject(s)
Depression , White Matter , Humans , White Matter/diagnostic imaging , Anxiety Disorders/diagnostic imaging , Anxiety Disorders/epidemiology , AnxietyABSTRACT
OBJECTIVES: To assess whether there is a bidirectional causal relationship between the composition of gut microbiota and rheumatoid arthritis (RA), and to identify specific pathogenic bacterial taxa via the Mendelian randomisation (MR) analysis. METHODS: We acquired single nucleotide polymorphisms (SNPs) associated with the composition of gut microbiota (n=18,340) and with RA (n=331,313) from publicly available genome-wide association studies (GWAS). The genome-wide threshold was 1 × 10-5 in the forward MR analysis and was 5 × 10-8 in the reverse MR analysis. Inverse variance weighted (IVW) was the main method to analyse causality, and MR results were verified by several sensitivity analyses including weighted median, MR Egger, and MR Pleiotropy Residual Sum and Outlier (PRESSO). RESULTS: The IVW method suggested that eight taxa were positively correlated with RA, including: MollicutesRF9 (pIVW <0.01), Alphaproteobacteria (pIVW <0.01), Betaproteobacteria (p IVW =0.04), Bacteroidaceae (pIVW <0.01), Adlercreutzia (pIVW <0.01), Bacteroides (pIVW <0.01), Butyricimonas (p IVW =0.03) and Holdemanella (pIVW =0.03). Six bacterial taxa were negatively correlated with RA, including Desulfovibrionales (pIVW = 0.01), Methanobacteriales (pIVW <0.01), Methanobacteria (PIVW <0.01), Desulfovibrionaceae (pIVW <0.01), Methanobacteriaceae (pIVW <0.01) and Butyrivibrio (pIVW =0.02). Heterogeneity (p>0.05) and pleiotropy (p>0.05) analysis confirmed the robustness of the MR results. CONCLUSIONS: We identified some specific bacterial taxa that were causally associated with the risk of RA, providing new insights into prevention and diagnosis of RA.
Subject(s)
Arthritis, Rheumatoid , Gastrointestinal Microbiome , Humans , Gastrointestinal Microbiome/genetics , Genome-Wide Association Study , Arthritis, Rheumatoid/diagnosis , Arthritis, Rheumatoid/epidemiology , Arthritis, Rheumatoid/genetics , Polymorphism, Single NucleotideABSTRACT
Subjective well-being (SWB) is an important measure for mental health status. Previous research has shown that physical activity can affect an individual's well-being, yet the underlying molecular mechanism remains to be clarified. In this study, we aim to evaluate the potential interactions between mitochondrial genes and physical activity (PA) as well as their combined effects on individual well-being. SWB phenotype data in UK Biobank were enrolled for this study including nine aspects such as work/job satisfaction, health satisfaction, family relationship satisfaction, friendships satisfaction, financial situation satisfaction, ever depressed for a whole week, general happiness, general happiness with own health and belief that own life is meaningful. We made analysis for each aspects separately. Firstly, mitochondria-wide association studies (MiWAS) was conducted to assess the association of mitochondrial Single Nucleotide Polymorphisms SNP with each aspect of SWB. Then an interaction analysis of mitochondrial DNA (mtDNA) mutation and PA was performed to evaluate their joint effect on SWB status. Meanwhile, these two analysis were made for female and male group separately as well as the total samples, all under the control of possible confounding factors including gender, age, Townsend Deprivation Index (TDI), education, alcohol consumption, smoking habits, and 10 principal components. MiWAS analysis identified 45 mtSNPs associated with 9 phenotypes of SWB. For example, m.15218A > G on MT-CYB in the health satisfaction phenotype of the total subjects. Gender-specific analyses found 30 mtSNPs in females and 58 in males, involving 13 mtGenes. In mtDNA-PA interaction analysis, we also identified 10 significant mtDNA-PA interaction sets for SWB. For instance, m.13020 T > C (MT-ND5) was associated with the SWB financial situation satisfaction phenotype in all subjects (P = 0.00577). In addition, MiWAS analysis identified 12 mtGene variants associated with SWB, as MT-ND1 and MT-ND2. However, in mtDNA-PA interactions we detected 7 mtDNA affecting psychiatric disorders occurring, as in the friendships satisfaction phenotype (m.3394 T > C on MT-ND1). Our study results suggest an implication of the interaction between mitochondrial function and physical activity in the risk of psychiatric disorder development.
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OBJECTIVE: This study aims to investigate the impact of first pass effect (FPE) on outcomes in the posterior circulation acute ischemic stroke (PC-AIS) and the independent predictors of FPE. METHODS: This was a multicenter, retrospective study. PC-AIS patients who underwent endovascular treatment were reviewed. The cohort achieving complete or nearly complete reperfusion (defined as expanded treatment in cerebralischemia [eTICI] ≥ 2c) was categorized into the FPE and multiple pass effect (MPE) groups. FPE was defined as achieving eTICI ≥ 2c with a single pass and without the use of rescue therapy. Modified FPE (mFPE) was defined as meeting the criteria for FPE but with eTICI ≥ 2b. The association of FPE with 90-day clinical outcomes and predictors for FPE were both investigated. RESULTS: The study included a total of 328 patients, with 69 patients (21 %) in the FPE group. For primary outcome, FPE had a significant higher favorable outcome (mRS ≤ 3) rate than MPE (65.2 % vs. 44.8 %, p = 0.003). Similar outcomes were observed in the mFPE. Furthermore, FPE was significantly associated with favorable outcome (adjusted OR 2.23, 95 % CI 1.06-4.73, p = 0.036). Positive predictors for FPE included occlusion in the distal basilar artery, the first-line aspiration or combination, and cardioembolic etiology. Negative predictors for FPE included hypertension and general anesthesia. CONCLUSION: For PC-AIS patients due to large or medium vessel occlusion, FPE is associated with favorable clinical outcomes. The first-line techniques of aspiration or combination, as well as avoiding general anesthesia, contribute to a better realization of FPE.
Subject(s)
Endovascular Procedures , Ischemic Stroke , Humans , Endovascular Procedures/methods , Ischemic Stroke/diagnostic imaging , Ischemic Stroke/therapy , Retrospective Studies , Treatment OutcomeABSTRACT
BACKGROUND: Multiple epidemiological studies have observed the connection between aging and brain volumes. The concept of accelerated biological aging (BA) is more powerful for observing the degree of aging of an individual than chronologic age (CA). The objective of this study is to explore the relationship between BA and brain volumes. METHODS: BA was measured from clinical traits using two blood-chemistry algorithms, the Klemera-Doubal method (KDM) and the PhenoAge. The two age acceleration biomarkers were calculated by the residuals from regressing CA, termed "KDM-acceleration" and "PhenoAge-acceleration". Brain volumes were from brain magnetic resonance imaging (MRI) data. After adjustment for confounding factors, general linear regression models were used to examine associations between KDM-acceleration and PhenoAge-acceleration and brain volumes, respectively. Additionally, we stratified participants by sex, age, and the four quartiles of the Townsend Deprivation Index (TDI) for extra subgroup analysis. RESULTS: 14,725 participants with available information were enrolled. After full adjustment, we observed negative associations between KDM-acceleration and brain volumes, such as gray matter (ß = -0.029), white matter (ß = -0.021), gray and white matter (ß = -0.026), and hippocampus (ß = -0.011 for left and ß = -0.014 for right). There were also negative associations between PhenoAge-acceleration and brain volumes, such as white matter (ß = -0.008), gray and white matter (ß = -0.010), thalamus (ß = -0.012 for left and ß = -0.012 for right). In the subgroup analysis stratified by sex, age, and the four quartiles of TDI, the association between KDM-acceleration and PhenoAge-acceleration and brain volumes still existed. In subgroup analyses, the variation in associations suggests that socioeconomic and biological factors may differentially influence brain aging. CONCLUSIONS: Our research indicated that more advanced BA was associated with less brain tissue.
Subject(s)
Aging , Brain , Magnetic Resonance Imaging , Humans , Male , Female , Brain/diagnostic imaging , Brain/pathology , Cross-Sectional Studies , Middle Aged , Aging/pathology , Aging/physiology , Aged , Adult , Organ Size , Gray Matter/diagnostic imaging , Gray Matter/pathology , White Matter/diagnostic imaging , White Matter/pathology , Aged, 80 and overABSTRACT
OBJECTIVES: This study aimed to investigate the interplay between genetic susceptibility and socioeconomic disparities on psychiatric disorders. METHODS: In this study, we utilized data from the UK Biobank to analyze the Generalized Anxiety Disorder (GAD)-7 scale (N = 74,425) and the Patient Health Questionnaire (PHQ)-9 (N = 74,101), along with the Index of Multiple Deprivation (IMD). The polygenic risk score (PRS) was calculated to assess the genetic risk associated with GAD-7/PHQ-9 scores, and the individuals were classified into low, medium, and high genetic risk groups according to tertiles of PRSs related to the GAD-7/PHQ-9. Linear regression models were used to explore the relationships between GAD-7/PHQ-9 scores and IMD scores in patients with different genetic susceptibilities. RESULTS: Disadvantaged socioeconomic status was associated with the risk of anxiety and depression across all strata of genetic risk, and stronger associations were shown for individuals with greater genetic susceptibility. From low to high genetic risk, the risk of psychiatric disorders increased for the GAD-7 (ß = 0.002 to 0.032) and PHQ-9 (ß = 0.003 to 0.045) scores. In addition, strong associations of high genetic risk with anxiety (ß = 0.875) and depression (ß = 1.152) were detected in the IMD quartile 4 group compared with the least deprivation quartile group. Furthermore, income and employment were estimated to contribute strongly to anxiety (ßemployment = 7.331, ßincome = 4.492) and depression (ßemployment = 9.951, ßincome = 6.453) in the high genetic risk group. CONCLUSION: The results suggest that we should pay more attention to psychiatric disorders with high genetic susceptibility and try to improve their socioeconomic status to prevent the development of psychiatric disorders.
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PURPOSE: Previous studies have demonstrated the link between micronutrients and mental health. However, it remains uncertain whether this connection is causal. We aim to investigate the potential causal effects of micronutrients on mental health based on linkage disequilibrium score (LDSC) regression and Mendelian randomization (MR) analysis. METHODS: Utilizing publicly available genome-wide association study (GWAS) summary datasets, we performed LDSC and MR analysis to identify candidate micronutrients with potential causal effects on mental health. Single nucleotide polymorphisms (SNPs) significantly linked with candidate micronutrients with a genome-wide significance level (p < 5 × 10-8) were selected as instrumental variables (IVs). To estimate the causal effect of candidate micronutrients on mental health, we employed inverse variance weighted (IVW) regression. Additionally, two sensitivity analyses, MR-Egger and weighted median, were performed to validate our results. RESULTS: We found evidence supporting significant causal associations between micronutrients and mental health. LDSC detected several candidate micronutrients, including serum iron (genetic correlation = -0.134, p = 0.032) and vitamin C (genetic correlation = -0.335, p < 0.001) for attention-deficit/hyperactivity disorder (ADHD), iron-binding capacity (genetic correlation = 0.210, p = 0.037) for Alzheimer's disease (AD), and vitamin B12 (genetic correlation = -0.178, p = 0.044) for major depressive disorder (MDD). Further MR analysis suggested a potential causal relationship between vitamin B12 and MDD (b = -0.139, p = 0.009). There was no significant heterogeneity or pleiotropy, indicating the validity of the findings. CONCLUSION: In this study, we identified underlying causal relationships between micronutrients and mental health. Notably, more research is necessary to clarify the underlying biological mechanisms by which micronutrients affect mental health.
Subject(s)
Genome-Wide Association Study , Linkage Disequilibrium , Mendelian Randomization Analysis , Mental Health , Micronutrients , Polymorphism, Single Nucleotide , Humans , Attention Deficit Disorder with Hyperactivity/genetics , Alzheimer Disease/geneticsABSTRACT
OBJECTIVES: Our study aimed to investigate the association of dietary diversity score (DDS), as reflected by five dietary categories, with biological age acceleration. DESIGN: A cross-sectional study. SETTING AND PARTICIPANTS: This study included 88,039 individuals from the UK Biobank. METHODS: Biological age (BA) was assessed using Klemerae-Doubal (KDM) and PhenoAge methods. The difference between BA and chronological age represents the age acceleration (AgeAccel), termed as "KDMAccel" and "PhenoAgeAccel". AgeAccel > 0 indicates faster aging. Generalized linear regression models were performed to assess the associations of DDS with AgeAccel. Similar analyses were performed for the five dietary categories. RESULTS: After adjusting for multiple variables, DDS was inversely associated with KDMAccel (ßHigh vs Low= -0.403, 95%CI: -0.492 to -0.314, P < 0.001) and PhenoAgeAccel (ßHigh vs Low= -0.545, 95%CI: -0.641 to -0.450, P < 0.001). Each 1-point increment in the DDS was associated with a 4.4% lower risk of KDMAccel and a 5.6% lower risk of PhenoAgeAccel. The restricted cubic spline plots demonstrated a non-linear dose-response association between DDS and the risk of AgeAccel. The consumption of grains (ßKDMAccel = -0.252, ßPhenoAgeAccel = -0.197), vegetables (ßKDMAccel = -0.044, ßPhenoAgeAccel = -0.077) and fruits (ßKDMAccel = -0.179, ßPhenoAgeAccel = -0.219) was inversely associated with the two AgeAccel, while meat and protein alternatives (ßKDMAccel = 0.091, ßPhenoAgeAccel = 0.054) had a positive association (All P < 0.001). Stratified analysis revealed stronger accelerated aging effects in males, smokers, and drinkers. A strengthening trend in the association between DDS and AgeAccel as TDI quartiles increased was noted. CONCLUSIONS: This study suggested that food consumption plays a role in aging process, and adherence to a higher diversity dietary is associated with the slowing down of the aging process.
Subject(s)
Aging , Diet , Humans , Male , Cross-Sectional Studies , Female , Aging/physiology , Diet/statistics & numerical data , Middle Aged , Aged , United Kingdom , AdultABSTRACT
Observational studies have shown that alterations in gut microbiota composition are associated with low back pain. However, it remains unclear whether the association is causal. To reveal the causal association between gut microbiota and low back pain, a two-sample bidirectional Mendelian randomization (MR) analysis is performed. The inverse variance weighted regression (IVW) is performed as the principal MR analysis. MR-Egger and Weighted Median is further conducted as complementary analysis to validate the robustness of the results. Finally, a reverse MR analysis is performed to evaluate the possibility of reverse causation. The inverse variance weighted (IVW) method suggests that Peptostreptococcaceae (odds ratio [OR] 1.056, 95% confidence interval [CI] [1.015-1.098], P IVW = 0.010), and Lactobacillaceae (OR 1.070, 95% CI [1.026-1.115], P IVW = 0.003) are positively associated with back pain. The Ruminococcaceae (OR 0.923, 95% CI [0.849-0.997], P IVW = 0.033), Butyricicoccus (OR 0.920, 95% CI [0.868 - 0.972], P IVW = 0.002), and Lachnospiraceae (OR 0.948, 95% CI [0.903-0.994], P IVW = 0.022) are negatively associated with back pain. In this study, underlying causal relationships are identified among gut microbiota and low back pain. Notably, further research is needed on the biological mechanisms by which gut microbiota influences low back pain.
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Lentinus edodes, an important edible mushroom cultivated in East Asia for thousands of years, has been widely used as food and medicinal ingredient worldwide. Modern phytochemistry studies have demonstrated that L. edodes is very rich in bioactive polysaccharides, especially the ß-glucans. Over the past two decades, the isolation, chemical properties, and bioactivities of polysaccharides from fruiting bodies, mycelium and fermentation broth of L. edodes have been drawing much attention from scholars around the world. It has been demonstrated that L. edodes polysaccharides possess various remarkable biological activities, including anti-oxidant, anti-tumor, anti-aging, anti-inflammation, immunomodulatory, antiviral, and hepatoprotection effects. This review summarizes the recent development of polysaccharides from L. edodes including the isolation methods, structural features, bioactivities and mechanisms, and their structure-activity relationship, which can provide useful research underpinnings and update information for their further application as therapeutic agents and functional foods.