Association of accelerated biological aging with brain volumes: A cross-sectional study.

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.

Micronutrient-Associated Single Nucleotide Polymorphism and Mental Health: A Mendelian Randomization Study.

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.

Evaluating the associations between intelligence quotient and multi-tissue proteome from the brain, CSF and plasma.

Intelligence quotient is a vital index to evaluate the ability of an individual to think rationally, learn from experience and deal with the environment effectively. However, limited efforts have been paid to explore the potential associations of intelligence quotient traits with the tissue proteins from the brain, CSF and plasma. The information of protein quantitative trait loci was collected from a recently released genome-wide association study conducted on quantification data of proteins from the tissues including the brain, CSF and plasma. Using the individual-level genotypic data from the UK Biobank cohort, we calculated the polygenic risk scores for each protein based on the protein quantitative trait locus data sets above. Then, Pearson correlation analysis was applied to evaluate the relationships between intelligence quotient traits (including 120 330 subjects for 'fluid intelligence score' and 38 949 subjects for 'maximum digits remembered correctly') and polygenic risk scores of each protein in the brain (17 protein polygenic risk scores), CSF (116 protein polygenic risk scores) and plasma (59 protein polygenic risk scores). The Bonferroni corrected P-value threshold was P < 1.30 × 10-4 (0.05/384). Finally, Mendelian randomization analysis was conducted to test the causal relationships between 'fluid intelligence score' and pre-specific proteins from correlation analysis results. Pearson correlation analysis identified significant association signals between the protein of macrophage-stimulating protein and fluid intelligence in brain and CSF tissues (P brain = 1.21 × 10-8, P CSF = 1.10 × 10-7), as well as between B-cell lymphoma 6 protein and fluid intelligence in CSF (P CSF = 1.23 × 10-4). Other proteins showed close-to-significant associations with the trait of 'fluid intelligence score', such as plasma protease C1 inhibitor (P CSF = 4.19 × 10-4, P plasma = 6.97 × 10-4), and with the trait of 'maximum digits remembered correctly', such as tenascin (P plasma = 3.42 × 10-4). Additionally, Mendelian randomization analysis results suggested that macrophage-stimulating protein (Mendelian randomization-Egger: ß = 0.54, P = 1.64 × 10-61 in the brain; ß = 0.09, P = 1.60 × 10-12 in CSF) had causal effects on fluid intelligence score. We observed functional relevance of specific tissue proteins to intelligence quotient and identified several candidate proteins, such as macrophage-stimulating protein. This study provided a novel insight to the relationship between tissue proteins and intelligence quotient traits.

Assessing the joint effects of mitochondrial genes and physical activity on the psychiatric phenotype of subjective well-being based on the UK Biobank data.

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.

Dietary diversity score and the acceleration of biological aging: a population-based study of 88,039 participants.

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.

Association between white matter hyperintensity and anxiety/depression.

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.

Insight into the Causal Relationship between Gut Microbiota and Back Pain: A Two Sample Bidirectional Mendelian Randomization Study.

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.

A Metabolomics Study of Feces Revealed That a Disturbance of Selenium-Centered Metabolic Bioprocess Was Involved in Kashin-Beck Disease, an Osteoarthropathy Endemic to China.

Background: Kashin-Beck disease (KBD) is a distinct osteoarthropathy in China with an unclear pathogenesis. This study aims to explore whether perturbations in the intestine metabolome could be linked to KBD individuals. Methods: An investigation was conducted in KBD endemic villages and fecal samples were collected. After applying inclusion and exclusion criteria, a total of 75 subjects were enrolled for this study, including 46 KBD (including 19 Grade I KBD and 27 Grade II KBD) and 29 controls. Untargeted metabolomics analysis was performed on the platform of UHPLC-MS. PLS-DA and OPLS-DA were conducted to compare the groups and identify the differential metabolites (DMs). Pathway analysis was conducted on MPaLA platform to explore the functional implication of the DMs. Results: Metabolomics analysis showed that compared with the control group, KBD individuals have a total of 584 differential metabolites with dysregulated levels such as adrenic acid (log2FC = -1.87, VIP = 4.84, p = 7.63 × 10-7), hydrogen phosphate (log2FC = -2.57, VIP = 1.27, p = 1.02 × 10-3), taurochenodeoxycholic acid (VIP = 1.16, log2FC = -3.24, p = 0.03), prostaglandin E3 (VIP = 1.17, log2FC = 2.67, p = 5.61 × 10-4), etc. Pathway analysis revealed several significantly perturbed pathways associated with KBD such as selenium micronutrient network (Q value = 3.11 × 10-3, Wikipathways), metabolism of lipids (Q value = 8.43 × 10-4, Reactome), free fatty acid receptors (Q value = 3.99 × 10-3, Reactome), and recycling of bile acids and salts (Q value = 2.98 × 10-3, Reactome). Subgroup comparisons found a total of 267 differential metabolites were shared by KBD vs. control, KBD II vs. control, and KBD I vs. control, while little difference was found between KBD II and KBD I (only one differential metabolite detected). Conclusions: KBD individuals showed distinct metabolic features characterized by perturbations in lipid metabolism and selenium-related bioprocesses. Our findings suggest that the loss of nutrients metabolism balance in intestine was involved in KBD pathogenesis. Linking the nutrients metabolism (especially selenium and lipid) to KBD cartilage damage should be a future direction of KBD study.