Association and Interaction Effects of Interleukin-12 Related Genes and Physical Activity on Cognitive Aging in Old Adults in the Taiwanese Population

Causal association of circulating cytokines with sarcopenia-related traits: A Mendelian randomization study

BACKGROUND

Observational studies have reported that circulating cytokines are associated with sarcopenia. However, the causal relationship between circulating cytokines and sarcopenia has not been elucidated.

OBJECTIVES

This study aimed to investigate the causal relationship between circulating cytokines and sarcopenia with genetic data using Mendelian randomization (MR).

METHODS

Two-sample bidirectional MR analysis was performed to investigate the causal relationship in individuals of European ancestry. The publicly available genome-wide association study statistics were used to select the key eligible single nucleotide polymorphisms significantly associated with circulating cytokines. Multiple MR analysis approaches, including inverse variance weighted (IVW), MR-Egger, weighted median method (WMM), and MR-Pleiotropy residual Sum and Outlier (MR-PRESSO) methods, were used for the analysis. Sarcopenia-related traits were appendicular lean mass (ALM) and grip strength.

RESULTS

This study demonstrated the causal effect of genetically predicted circulating interleukin interleukin-16 (IL16) levels on both ALM [odds ratio (OR) = 0.990, 95% confidence interval (CI): 0.980-1.000, P = 0.049] and grip strength (OR = 0.971, 95% CI: 0.948-0.995, P = 0.020]. Additionally, C-X-C motif chemokine ligand 10 (CXCL10), interleukin-1beta (IL1B), and hepatocyte growth factor (HGF) were correlated with ALM, while vascular endothelial growth factor (VEGF), interleukin-12 (IL12), and interleukin-15 (IL15) were correlated with grip strength. The results of MR-Egger, weighted median, weighted mode, and simple mode methods were consistent with the IVW estimates. Sensitivity analysis revealed that horizontal pleiotropy did not bias the causal estimates.

CONCLUSION

These findings indicate that inflammatory cytokines exert a significant causal effect on sarcopenia and provide promising leads for the development of novel therapeutic targets for the disease. By evaluating the role of circulating cytokines in the pathologic condition via a genetic epidemiological approach, our study made contributions to a further investigation of underlying mechanisms of sarcopenia.

Exploring the causal link between circulating cytokines and sarcopenia traits: A Mendelian randomization analysis

BACKGROUND

Previous observational studies have linked circulating cytokines to sarcopenia, but their causal relationship remains unclear. This study employed Mendelian Randomization (MR) to investigate the causal links between circulating cytokines and sarcopenia-related traits using genetic data.

METHODS

A two-sample bidirectional MR analysis was conducted using data from individuals of European ancestry, utilizing genome-wide association studies (GWAS) statistics. The study selected instrumental single nucleotide polymorphisms (SNPs) significantly associated with circulating cytokines and applied multiple MR methods, including inverse variance weighted (IVW), Weighted Median, MR-Egger, Weighted Mode, Simple Mode, and MR-PRESSO. The traits analyzed were appendicular lean mass (ALM) and grip strength. Heterogeneity, robustness, and consistency of results were assessed using Cochran's Q statistic, MR-Egger regression, and "leave-one-out" sensitivity analyses.

RESULTS

The IVM-MR analysis showed a casual association between genetically predicted circulating levels of interleukin-16 and both ALM and grip strength (ALM: OR = 0.990, 95% CI: 0.980-1.000, p = .049; grip strength: OR = 0.971, 95% CI: 0.948-0.995, p = .020). Additionally, interferon-gamma-induced protein 10 (IP-10), interleukin-1-beta (IL-1β), and hepatocyte growth factor (HGF) were correlated with ALM and vascular endothelial growth factor (VEGF), interleukin-12 (IL-12), and interleukin-5 (IL-5) with grip strength. Comparable results were confirmed via the MR-Egger, Weighted Median, Weighted Mode, and Simple Mode methods. Sensitivity analysis showed no horizontal pleiotropy to bias the causal estimates.

CONCLUSION

The results suggest a significant causal effect of inflammatory cytokines on sarcopenia, offering new avenues for therapeutic target development. However, the study's focus on a European ancestry cohort limits its generalizability to other populations. Future research should aim to include diverse ethnic groups to validate and broaden these findings, thereby enhancing our understanding of sarcopenia's mechanisms in a global context.

Emerging Vistas for the Nutraceutical Withania somnifera in Inflammaging

Inflammaging, a coexistence of inflammation and aging, is a persistent, systemic, low-grade inflammation seen in the geriatric population. Various natural compounds have been greatly explored for their potential role in preventing and treating inflammaging. Withania somnifera has been used for thousands of years in traditional medicine as a nutraceutical for its numerous health benefits including regenerative and adaptogenic effects. Recent preclinical and clinical studies on the role of Withania somnifera and its active compounds in treating aging, inflammation, and oxidative stress have shown promise for its use in healthy aging. We discuss the chemistry of Withania somnifera, the etiology of inflammaging and the protective role(s) of Withania somnifera in inflammaging in key organ systems including brain, lung, kidney, and liver as well as the mechanistic underpinning of these effects. Furthermore, we elucidate the beneficial effects of Withania somnifera in oxidative stress/DNA damage, immunomodulation, COVID-19, and the microbiome. We also delineate a putative protein-protein interaction network of key biomarkers modulated by Withania somnifera in inflammaging. In addition, we review the safety/potential toxicity of Withania somnifera as well as global clinical trials on Withania somnifera. Taken together, this is a synthetic review on the beneficial effects of Withania somnifera in inflammaging and highlights the potential of Withania somnifera in improving the health-related quality of life (HRQoL) in the aging population worldwide.

Myeloid cell deficiency of the inflammatory transcription factor Stat4 protects long-term synaptic plasticity from the effects of a high-fat, high-cholesterol diet

Neuroinflammation is associated with neurodegenerative diseases, including Alzheimer's and Parkinson's. The cytokine interleukin-12 activates signal transducer and activator of transcription 4 (Stat4), and consumption of a high-fat, high-cholesterol diet (HFD-C) and Stat4 activity are associated with inflammation, atherosclerosis, and a diabetic metabolic phenotype. In studies of in vitro hippocampal slices from control Stat4fl/flLdlr-/- mice fed a HFD-C diabetogenic diet, we show that Schaffer collateral-CA1 synapses exhibited larger reductions in activity-dependent, long-term potentiation (LTP) of synaptic transmission, compared to mice fed a standard diet. Glucose tolerance and insulin sensitivity shifts produced by HFD-C diet were reduced in Stat4ΔLysMLdlr-/- mice compared to Stat4fl/flLdlr-/- controls. Stat4ΔLysMLdlr-/- mice, which lack Stat4 under control of the LysMCre promoter, were resistant to HFD-C induced impairments in LTP. In contrast, Schaffer collateral-CA1 synapses in Stat4ΔLysMLdlr-/- mice fed the HFD-C diet showed larger LTP than control Stat4fl/flLdlr-/- mice. Expression of a number of neuroinflammatory and synaptic plasticity genes was reduced by HFD-C diet in control mice, and less affected by HFD-C diet in Stat4ΔLysMLdlr-/- mice. These data suggest that suppression of Stat4 activation may protect against effects of Western diet on cognition, type 2 diabetes, and reduce risk of Alzheimer's disease and other neurodegenerative disorders associated with neuroinflammation.

Butyl benzyl phthalate as a key component of phthalate ester in relation to cognitive impairment in NHANES elderly individuals and experimental mice

Phthalates are a group of neurotoxicants with cognitive-disrupting potentials. Given the structural diversity of phthalates, the corresponding neurotoxicity is dramatically altered. To identify the potential contributions of different phthalates on the process of cognitive impairment, data of 836 elders from the NHANES 2011-2014 cycles were used. Survey-weighted logistic regression and principal component analysis-weighted quantile sum regression (PCA-WQSR) models were applied to estimate the independent and combined associations of 11 urinary phthalate metabolites with cognitive deficit (assessed by 4 tests: Immediate Recall (IR), Delayed Recall (DR), Animal Fluency (AF), and Digit Symbol Substitution Test (DSST)) and to identify the potential phthalate with high weight. Laboratory mice were further used to examine the effect of phthalates on cognitive function and to explore the potential mechanisms. In logistic regression models, MBzP was the only metabolite positively correlated with four tests, with ORs of 2.53 (quartile 3 (Q3)), 2.26 (Q3), 2.89 (Q4) and 2.45 (Q2), 2.82 (Q4) for IR, DR, AF, and DSST respectively. In PCA-WQSR co-exposure models, low-molecular-weight (LMW) phthalates were the only PC positively linked to DSST deficit (OR: 1.93), which was further validated in WQSR analysis (WQS OR7-phthalates: 1.56 and WQS OR8-phthalates: 1.55); consistent with the results of logistic regression, MBzP was the dominant phthalate. In mice, butyl benzyl phthalate (BBP), the parent phthalate of MBzP, dose-dependently reduced cognitive function and disrupted hippocampal neurons. Additionally, the hippocampal transcriptome analysis identified 431 differential expression genes, among which most were involved in inhibiting the neuroactive ligand-receptor interaction pathway and activating the cytokine-cytokine receptor interaction pathway. Our study indicates the critical role of BBP in the association of phthalates and cognitive deficits among elderly individuals, which might be speculated that BBP could disrupt hippocampal neurons, activate neuroinflammation, and inhibit neuroactive receptors. Our findings provide new insight into the cognitive-disrupting potential of BBP.

Plasma extracellular vesicles reveal early molecular differences in amyloid positive patients with early-onset mild cognitive impairment

In the clinical course of Alzheimer's disease (AD) development, the dementia phase is commonly preceded by a prodromal AD phase, which is mainly characterized by reaching the highest levels of Aβ and p-tau-mediated neuronal injury and a mild cognitive impairment (MCI) clinical status. Because of that, most AD cases are diagnosed when neuronal damage is already established and irreversible. Therefore, a differential diagnosis of MCI causes in these prodromal stages is one of the greatest challenges for clinicians. Blood biomarkers are emerging as desirable tools for pre-screening purposes, but the current results are still being analyzed and much more data is needed to be implemented in clinical practice. Because of that, plasma extracellular vesicles (pEVs) are gaining popularity as a new source of biomarkers for the early stages of AD development. To identify an exosome proteomics signature linked to prodromal AD, we performed a cross-sectional study in a cohort of early-onset MCI (EOMCI) patients in which 184 biomarkers were measured in pEVs, cerebrospinal fluid (CSF), and plasma samples using multiplex PEA technology of Olink^© proteomics. The obtained results showed that proteins measured in pEVs from EOMCI patients with established amyloidosis correlated with CSF p-tau¹⁸¹ levels, brain ventricle volume changes, brain hyperintensities, and MMSE scores. In addition, the correlations of pEVs proteins with different parameters distinguished between EOMCI Aβ( +) and Aβ(-) patients, whereas the CSF or plasma proteome did not. In conclusion, our findings suggest that pEVs may be able to provide information regarding the initial amyloidotic changes of AD. Circulating exosomes may acquire a pathological protein signature of AD before raw plasma, becoming potential biomarkers for identifying subjects at the earliest stages of AD development.

Relationships between Inflammation and Age-Related Neurocognitive Changes

The relationship between inflammation and age-related neurocognitive changes is significant, which may relate to the age-related immune dysfunctions characterized by the senescence of immune cells and elevated inflammatory markers in the peripheral circulation and the central nervous system. In this review, we discuss the potential mechanisms, including the development of vascular inflammation, neuroinflammation, organelle dysfunctions, abnormal cholesterol metabolism, and glymphatic dysfunctions as well as the role that the key molecules play in the immune-cognition interplay. We propose potential therapeutic pharmacological and behavioral strategies for ameliorating age-related neurocognitive changes associated with inflammation. Further research to decipher the multidimensional roles of chronic inflammation in normal and pathological aging processes will help unfold the pathophysiological mechanisms underpinning neurocognitive disorders. The insight gained will lay the path for developing cost-effective preventative measures and the buffering or delaying of age-related neurocognitive decline.

Relationship between Nutrition, Lifestyle, and Neurodegenerative Disease: Lessons from ADH1B, CYP1A2 and MTHFR

In the present review, the main features involved in the susceptibility and progression of neurodegenerative disorders (NDDs) have been discussed, with the purpose of highlighting their potential application for promoting the management and treatment of patients with NDDs. In particular, the impact of genetic and epigenetic factors, nutrients, and lifestyle will be presented, with particular emphasis on Alzheimer’s disease (AD) and Parkinson’s disease (PD). Metabolism, dietary habits, physical exercise and microbiota are part of a complex network that is crucial for brain function and preservation. This complex equilibrium can be disrupted by genetic, epigenetic, and environmental factors causing perturbations in central nervous system homeostasis, contributing thereby to neuroinflammation and neurodegeneration. Diet and physical activity can directly act on epigenetic modifications, which, in turn, alter the expression of specific genes involved in NDDs onset and progression. On this subject, the introduction of nutrigenomics shed light on the main molecular players involved in the modulation of health and disease status. In particular, the review presents data concerning the impact of ADH1B, CYP1A2, and MTHFR on the susceptibility and progression of NDDs (especially AD and PD) and how they may be exploited for developing precision medicine strategies for the disease treatment and management.

A bagging ensemble machine learning framework to predict overall cognitive function of schizophrenia patients with cognitive domains and tests

BACKGROUND

It has been indicated that the interplay between functional outcomes and cognitive functions in schizophrenia is arbitrated by clinical symptoms, where cognitive functions are evaluated by cognitive domains and cognitive tests.

METHODS

To determine which single cognitive domain or test can best predict the overall cognitive function of schizophrenia, we established a bagging ensemble framework resulting from the analysis of factors such as 7 cognitive domain scores and 11 cognitive test scores of 302 schizophrenia patients in the Taiwanese population. We compared our bagging ensemble framework with other state-of-the-art algorithms such as multilayer feedforward neural networks, linear regression, support vector machine, and random forests.

RESULTS

The analysis revealed that among the 7 cognitive domains, the speed of processing domain can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework. In addition, among the 11 cognitive tests, the visual learning and memory test can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework. Finally, among the 7 cognitive domains and 11 cognitive tests, the speed of processing domain can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework.

CONCLUSION

The study implicates that the bagging ensemble framework may provide an applicable approach to develop tools for forecasting overall cognitive function in schizophrenia using cognitive domains and/or cognitive tests.

Logistic ridge regression to predict bipolar disorder using mRNA expression levels in the N-methyl-D-aspartate receptor genes

BACKGROUND

It is hypothesized that demographic variables and mRNA expression levels in the N-methyl-D-aspartate receptor (NMDAR) genes can be employed as potential biomarkers to predict bipolar disorder using artificial intelligence and machine learning approaches.

METHODS

To determine bipolar status, we established a logistic ridge regression model resulting from the analysis of age, gender, and mRNA expression levels in 7 NMDAR genes in the blood of 51 bipolar patients and 139 unrelated healthy individuals in the Taiwanese population. The NMDAR genes encompasses COMT, GCAT, NRG1, PSAT1, SHMT2, SLC1A4, and SRR. We also compared our approach with various state-of-the-art algorithms such as support vector machine and C4.5 decision tree.

RESULTS

The analysis revealed that the mRNA expression levels of COMT, GCAT, NRG1, PSAT1, SHMT2, SLC1A4, and SRR were associated with bipolar disorder. Moreover, the logistic ridge regression model (area under the receiver operating characteristic curve = 0.922) performed maximally among predictive models to infer the complicated relationship between bipolar disorder and biomarkers. Additionally, the results for the age- and gender-matched cohort were similar to those of the unmatched cohort.

LIMITATIONS

The cross-sectional study design limited the predictive value.

CONCLUSION

This is the first study demonstrating that the mRNA expression levels in the NMDAR genes may be altered in patients with bipolar disorder, thereby supporting the NMDAR hypothesis of bipolar disorder. The study also indicates that the mRNA expression levels in the NMDAR genes could serve as potential biomarkers to distinguish bipolar patients from healthy controls using artificial intelligence and machine learning approaches.

Neurological Symptoms and Their Associations With Inflammatory Biomarkers in the Chronic Phase Following Traumatic Brain Injuries

BACKGROUND

The underlying biological mechanisms for neurological symptoms following a traumatic brain injury (TBI) remain poorly understood. This study investigated the associations between serum inflammatory biomarkers and neurological symptoms in the chronic phase following moderate to severe TBI.

METHODS

The serum interleukin [IL]-1β, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12p70, and the tumor necrosis factor [TNF]-α in 72 TBI patients 6 months to 2 years post injury were measured. Neurological symptoms including depression, chronic headache, sleep disturbance, irritability, anxiety, and global neurological disability was assessed. The associations between the biomarkers and the neurological symptoms were assessed using correlation and regression analysis.

RESULTS

It was found that the most common post-injury symptom was sleep disturbance (84.7%), followed by chronic headaches (59.7%), irritability (55.6%), and depression (54.2%). TNF-α was a protective factor for chronic headache (OR = 0.473, 95% CI = 0.235-0.952). IL-6 was positively associated with sleep disturbance (r = 0.274, p = 0.021), while IL-5 and IL-12p70 were negatively associated with the degree of global neurological disability (r = -0.325, p = 0.006; r = -0.319, p = 0.007).

CONCLUSION

This study provides preliminary evidence for the association between chronic inflammation with neurological symptoms following a TBI, which suggests that anti-inflammatory could be a potential target for post-TBI neurological rehabilitation. Further research with larger sample sizes and more related biomarkers are still needed, however, to elucidate the inflammatory mechanisms for this association.

An association study in the Taiwan Biobank elicits the GABAA receptor genes GABRB3, GABRA5, and GABRG3 as candidate loci for sleep duration in the Taiwanese population

BACKGROUND

Gamma-aminobutyric acid type A (GABAA) receptors mainly mediate the effects of gamma-aminobutyric acid, which is the primary inhibitory neurotransmitter in the central nervous system. Abundant evidence suggests that GABAA receptors play a key role in sleep-regulating processes. No genetic association study has explored the relationships between GABAA receptor genes and sleep duration, sleep quality, and sleep timing in humans.

METHODS

We determined the association between single-nucleotide polymorphisms (SNPs) in the GABAA receptor genes GABRA1, GABRA2, GABRB3, GABRA5, and GABRG3 and sleep duration, sleep quality, and sleep timing in the Taiwan Biobank with a sample of 10,127 Taiwanese subjects. There were 10,142 subjects in the original study cohort. We excluded 15 subjects with a medication history of sedative-hypnotics.

RESULTS

Our data revealed an association of the GABRB3-GABRA5-GABRG3 gene cluster with sleep duration, which has not been previously identified: rs79333046 (beta = - 0.07; P = 1.21 × 10-3) in GABRB3, rs189790076 (beta = 0.92; P = 1.04 × 10-3) in GABRA5, and rs147619342 (beta = - 0.72; P = 3.97 × 10-3) in GABRG3. The association between rs189790076 in GABRA5 and sleep duration remained significant after Bonferroni correction. A variant (rs12438141) in GABRB3 was also found to act as a potential expression quantitative trait locus. Additionally, we discovered interactions between variants in the GABRB3-GABRA5-GABRG3 gene cluster and lifestyle factors, such as tea and coffee consumption, smoking, and physical activity, that influenced sleep duration, although some interactions became nonsignificant after Bonferroni correction. We also found interactions among GABRB3, GABRA5, and GABRG3 that affected sleep duration. Furthermore, we identified an association of rs7165524 (beta = - 0.06; P = 2.20 × 10-3) in GABRA5 with sleep quality and an association of rs79465949 (beta = - 0.12; P = 3.95 × 10-3) in GABRB3 with sleep timing, although these associations became nonsignificant after Bonferroni correction. However, we detected no evidence of an association of individual SNPs in GABRA1 and GABRA2.

CONCLUSIONS

Our results indicate that rs189790076 in GABRA5 and gene-gene interactions among GABRB3, GABRA5, and GABRG3 may contribute to sleep duration in the Taiwanese population.

An association study in the Taiwan Biobank elicits three novel candidates for cognitive aging in old adults: NCAM1, TTC12 and ZBTB20

The dopamine receptor-related loci have been suggested to be associated with cognitive functions and neurodegenerative diseases. It is unknown whether genetic variants such as single nucleotide polymorphisms (SNPs) in the dopamine receptor-related loci could contribute to cognitive aging independently as well as by virtue of complicated interplays in the elder population. To assess whether SNPs in the dopamine receptor-related loci are associated with cognitive aging in the elder population, we evaluated SNPs in the DRD1, NCAM1-TTC12-ANKK1-DRD2, DRD3-LOC107986115-ZNF80-TIGIT-MIR568-ZBTB20, DRD4, and DRD5-SLC2A9 loci from 25,195 older Taiwanese individuals from the Taiwan Biobank. Mini-Mental State Examination (MMSE) was scrutinized for all participants, where MMSE scores were employed to evaluate cognitive functions. From our analysis, we identified three novel genes for cognitive aging that have not previously been reported: ZBTB20 on chromosome 3 and NCAM1 and TTC12 on chromosome 11. NCAM1 and ZBTB20 are strong candidates for having a role in cognitive aging with mutations in ZBTB20 resulting in intellectual disability, and NCAM1 previously found to be associated with associative memory in humans. Additionally, we found the effects of interplays between physical activity and these three novel genes. Our study suggests that genetic variants in the dopamine receptor-related loci may influence cognitive aging individually and by means of gene-physical activity interactions.

Genome-wide association study in the Taiwan biobank identifies four novel genes for human height: NABP2, RASA2, RNF41 and SLC39A5

Numerous genome-wide association studies (GWASs) have been conducted for the identification of genetic variants involved with human height. The vast majority of these studies, however, have been conducted in populations of European ancestry. Here, we report the first GWAS of adult height in the Taiwan Biobank using a discovery sample of 14 571 individuals and an independent replication sample of 20 506 individuals. From our analysis we generalize to the Taiwanese population genome-wide significant associations with height and 18 previously identified genes in European and non-Taiwanese East Asian populations. We also identify and replicate, at the genome-wide significance level, associated variants for height in four novel genes at two loci that have not previously been reported: RASA2 on chromosome 3 and NABP2, RNF41, and SLC39A5 at 12q13.3 on chromosome 12. RASA2 and RNF41 are strong candidates for having a role in height with copy number and loss of function variants in RASA2 previously found to be associated with short stature disorders, and decreased expression of the RNF41 gene resulting in insulin resistance in skeletal muscle. The results from our analysis of the Taiwan Biobank underscore the potential for the identification of novel genetic discoveries in underrepresented worldwide populations, even for traits, such as height, that have been extensively investigated in large-scale studies of European ancestry populations.

Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach

In light of recent advancements in machine learning, personalized medicine using predictive algorithms serves as an essential paradigmatic methodology. Our goal was to explore an integrated machine learning and genome-wide analysis approach which targets the prediction of probable major depressive disorder (MDD) using 9828 individuals in the Taiwan Biobank. In our analysis, we reported a genome-wide significant association with probable MDD that has not been previously identified: FBN1 on chromosome 15. Furthermore, we pinpointed 17 single nucleotide polymorphisms (SNPs) which show evidence of both associations with probable MDD and potential roles as expression quantitative trait loci (eQTLs). To predict the status of probable MDD, we established prediction models with random undersampling and synthetic minority oversampling using 17 eQTL SNPs and eight clinical variables. We utilized five state-of-the-art models: logistic ridge regression, support vector machine, C4.5 decision tree, LogitBoost, and random forests. Our data revealed that random forests had the highest performance (area under curve = 0.8905 ± 0.0088; repeated 10-fold cross-validation) among the predictive algorithms to infer complex correlations between biomarkers and probable MDD. Our study suggests that an integrated machine learning and genome-wide analysis approach may offer an advantageous method to establish bioinformatics tools for discriminating MDD patients from healthy controls.

Association Between Interleukin-12 and Sarcopenia

Background

Emerging studies have proposed that cytokines secreted following macrophage polarization may contribute to skeletal muscle aging. The current study primarily aimed to determine whether these cytokines have an impact on the progression of sarcopenia in an elderly population.

Methods

In total, 120 elderly adults aged 65 years and older who underwent health examinations from 2015 to 2019 were included in this retrospective study. Sarcopenia was based on the criteria proposed by the European Working Group on Sarcopenia in Older People in 2019. Macrophages and cytokines datasets were obtained from Gene Expression Omnibus (GEO) database. Comprehensive assessments were performed for muscle strength, muscle mass, gait speed, tumor growth factor-β (TGF-β), and interleukin-12 (IL-12). Thereafter, the association between sarcopenia and cytokines was analyzed using regression models.

Results

Low muscle strength and low-speed gait were negatively associated with IL-12 [β: −8.96 (95% CI: −14.12, −3.79) and −7.16 (95% CI: −12.54, −1.78), respectively]. Participants with more sarcopenia components and more severe sarcopenia had lower IL-12 (P for trend < 0.001). Conversely, more amount of sarcopenia components were associated with increased TGF-β (P for trend < 0.05). A definite diagnosis of sarcopenia was associated with decreased IL-12 and increased TGF-β with β of −8.96 (95% CI: −14.12, −3.79) and 147.75 (95% CI: 36.27, 259.23). Furthermore, increased IL-12 levels were significantly associated with reduced occurrence of sarcopenia with and odd ratio (OR) of 0.36 (95% CI: 0.15–0.834).

Conclusion

Our findings on the relationship between cytokines and age-related muscle loss showed that IL-12 may be an early diagnosis indicator for sarcopenia in the elderly population.

Prediction of functional outcomes of schizophrenia with genetic biomarkers using a bagging ensemble machine learning method with feature selection

Genetic variants such as single nucleotide polymorphisms (SNPs) have been suggested as potential molecular biomarkers to predict the functional outcome of psychiatric disorders. To assess the schizophrenia’ functional outcomes such as Quality of Life Scale (QLS) and the Global Assessment of Functioning (GAF), we leveraged a bagging ensemble machine learning method with a feature selection algorithm resulting from the analysis of 11 SNPs (AKT1 rs1130233, COMT rs4680, DISC1 rs821616, DRD3 rs6280, G72 rs1421292, G72 rs2391191, 5-HT2A rs6311, MET rs2237717, MET rs41735, MET rs42336, and TPH2 rs4570625) of 302 schizophrenia patients in the Taiwanese population. We compared our bagging ensemble machine learning algorithm with other state-of-the-art models such as linear regression, support vector machine, multilayer feedforward neural networks, and random forests. The analysis reported that the bagging ensemble algorithm with feature selection outperformed other predictive algorithms to forecast the QLS functional outcome of schizophrenia by using the G72 rs2391191 and MET rs2237717 SNPs. Furthermore, the bagging ensemble algorithm with feature selection surpassed other predictive algorithms to forecast the GAF functional outcome of schizophrenia by using the AKT1 rs1130233 SNP. The study suggests that the bagging ensemble machine learning algorithm with feature selection might present an applicable approach to provide software tools for forecasting the functional outcomes of schizophrenia using molecular biomarkers.

Applying a bagging ensemble machine learning approach to predict functional outcome of schizophrenia with clinical symptoms and cognitive functions

It has been suggested that the relationship between cognitive function and functional outcome in schizophrenia is mediated by clinical symptoms, while functional outcome is assessed by the Quality of Life Scale (QLS) and the Global Assessment of Functioning (GAF) Scale. To determine the outcome assessed by QLS and GAF, we established a bagging ensemble framework with a feature selection algorithm resulting from the analysis of factors such as 3 clinical symptom scales and 11 cognitive function scores of 302 patients with schizophrenia in the Taiwanese population. We compared our bagging ensemble framework with other state-of-the-art algorithms such as multilayer feedforward neural networks, support vector machine, linear regression, and random forests. The analysis revealed that the bagging ensemble model with feature selection performed best among predictive models in predicting the QLS functional outcome by using 20-item Scale for the Assessment of Negative Symptoms (SANS20) and 17-item Hamilton Depression Rating Scale (HAMD17). Moreover, to predict the GAF outcome, the bagging ensemble model with feature selection performed best among predictive models by using SANS20 and the Positive and Negative Syndrome Scale-Positive (PANSS-Positive) subscale. The study indicates that there are synergistic effects between negative (SANS20) and depressive (HAMD17) symptoms as well as between negative and positive (PANSS-Positive) symptoms in influencing functional outcome of schizophrenia using the bagging ensemble framework with feature selection.

Interleukin-6 knockout reverses macrophage differentiation imbalance and alleviates cardiac dysfunction in aging mice

Several interleukins (ILs) have been shown to be involved in aging, but the effects of IL-6 on aging-related cardiac dysfunction remain unknown. In this study, the expression and sources of cardiac IL-6 in aging hearts were investigated for the first time. The results showed that cardiac IL-6 expression in mice gradually increased with age, and the expression at 16 months, 20 months and 25 months was higher than that at 3 months. In addition, cardiac macrophages (Møs) were shown to be the main sources of IL-6 in aging mice. IL-6 knockout (KO) significantly alleviated cardiac dysfunction, increased M2 macrophage (Mø2) differentiation, reduced M1 macrophage (Mø1) differentiation and protected against cardiomyocyte apoptosis in aging mice. IL-6 KO also reversed the stimulatory effect of doxorubicin (DOX) treatment on Mø1s and the inhibitory effect of DOX treatment on Mø2s in vitro. Furthermore, the mRNA expression of both aging markers and apoptosis-related markers was markedly inhibited by IL-6 KO. Our results suggest that aging can be significantly reversed by IL-6 KO and that the mechanisms of this effect are related to alleviation of Mø1/Mø2 imbalance and protection against apoptosis in cardiomyocytes.

An Ensemble Approach to Predict Schizophrenia Using Protein Data in the N-methyl-D-Aspartate Receptor (NMDAR) and Tryptophan Catabolic Pathways

In the wake of recent advances in artificial intelligence research, precision psychiatry using machine learning techniques represents a new paradigm. The D-amino acid oxidase (DAO) protein and its interaction partner, the D-amino acid oxidase activator (DAOA, also known as G72) protein, have been implicated as two key proteins in the N-methyl-D-aspartate receptor (NMDAR) pathway for schizophrenia. Another potential biomarker in regard to the etiology of schizophrenia is melatonin in the tryptophan catabolic pathway. To develop an ensemble boosting framework with random undersampling for determining disease status of schizophrenia, we established a prediction approach resulting from the analysis of genomic and demographic variables such as DAO levels, G72 levels, melatonin levels, age, and gender of 355 schizophrenia patients and 86 unrelated healthy individuals in the Taiwanese population. We compared our ensemble boosting framework with other state-of-the-art algorithms such as support vector machine, multilayer feedforward neural networks, logistic regression, random forests, naive Bayes, and C4.5 decision tree. The analysis revealed that the ensemble boosting model with random undersampling [area under the receiver operating characteristic curve (AUC) = 0.9242 ± 0.0652; sensitivity = 0.8580 ± 0.0770; specificity = 0.8594 ± 0.0760] performed maximally among predictive models to infer the complicated relationship between schizophrenia disease status and biomarkers. In addition, we identified a causal link between DAO and G72 protein levels in influencing schizophrenia disease status. The study indicates that the ensemble boosting framework with random undersampling may provide a suitable method to establish a tool for distinguishing schizophrenia patients from healthy controls using molecules in the NMDAR and tryptophan catabolic pathways.

An association study in the Taiwan Biobank reveals RORA as a novel locus for sleep duration in the Taiwanese Population

BACKGROUND

Sleep is a key factor for health-related quality of life since sleep disturbances are a significant and common problem for patients with various human diseases such as psychiatric disorders. While single nucleotide polymorphisms (SNPs) in circadian clock genes have been indicated to be associated with sleep duration, most of the association studies have been investigated in populations with European ancestry. It is believed that no studies have been conducted to investigate a link between sleep duration and the circadian clock genes RORA and RORB, which play a key role, with NR1D1, in an additional feedback loop for the circadian rhythm machinery.

METHODS

In this study, we assessed the relationships between sleep duration and SNPs in the circadian clock genes NR1D1, RORA, and RORB in the Taiwan Biobank with a sample of 10,112 Taiwanese subjects.

RESULTS

From our data, we revealed a novel significant association in sleep duration with the rs75981965 SNP (P = 9.93 × 10^-5) in the RORA gene that has not been previously identified. The association of sleep duration with this SNP remained significant after performing Bonferroni correction. RORA is a potential candidate for sleep duration as RORA has been suggested to play a key role in the regulation of sleep disorders. Additionally, we pinpointed the effects of interactions between RORA rs75981965 and environmental factors such as tea consumption (P = 0.0015), coffee consumption (P = 0.0029), physical activity (P = 0.011), alcohol consumption (P = 0.0146), and smoking (P = 0.0223) in influencing sleep duration. We also found interactions between RORA and NR1D1 (P = 0.0023) as well as between RORA and RORB (P = 0.0061) in affecting sleep duration.

CONCLUSIONS

Our results indicate that the circadian clock gene RORA may contribute to sleep duration independently as well as through gene-gene and gene-environment interactions in the Taiwanese population.