Findings of the available studies regarding the roles of branched-chain amino acids (BCAAs) and aromatic amino acids (AAAs) in hypertension are inconsistent, conflicting and inconclusive. The purpose of this study was to explore and clarify the existence of any relationships of individual BCAAs and AAAs with hypertension with adjustments for potential relevant confounders. A total of 2805 healthy controls and 2736 hypertensive patients were included in the current analysis. The associations between individual amino acids and hypertension were explored by logistic regression analyses adjusted for potential confounding variables. Among the investigated amino acids, only the BCAAs showed consistently significant positive associations with hypertension in the adjusted models (p-trend < 0.05 to 0.001). However, compared with the corresponding lowest quartile of individual BCAAs, the positive association with hypertension remained significant only in the highest quartile (p < 0.01 to 0.001). We confirmed in a relatively large cohort of subjects that BCAAs, not AAAs, demonstrated consistent positive associations with hypertension. The results display the promising potential for the use of BCAAs as relevant and accessible biomarkers, and provide perspectives on interventions directed towards the reduction in plasma BCAA levels in the prevention and management of hypertension.

Background: Non-invasive application of whole-body vibration (WBV) has the potential for inducing improvements in impaired peripheral circulation, cutaneous sensation and balance among older adults. However, relevant studies have frequently applied high magnitudes of vibration and show conflicting and inconclusive results. Therefore, we attempted to ascertain the acute responses in those parameters from exposure of thirty older subjects to WBV of three different magnitudes, defined according to ISO 2631-1 (1997). Methods: Each subject randomly underwent four sessions of intervention (three bouts of 1 min exposure with 1 min between-bout rests): WBV at 15, 20, or 25 Hz with a peak-to-peak displacement of 4 mm, or control condition. Results: Both during and after intervention, dorsal foot skin blood flow increased significantly under 20 and 25 Hz exposure conditions with greater responses under the latter condition, the magnitude of which slightly exceeded the recommended value. Plantar vibrotactile perception showed significant increases after WBV exposure with overall greater responses under higher frequencies of vibration. In contrast, no WBV-induced change in balance was observed. Conclusions: WBV at 20 Hz with a magnitude within the recommended limit can be effective in inducing enhancements in peripheral blood flow; however, the same magnitude of vibration seems insufficient in improving balance among older adults.

The published literature on the association of circulatory branched-chain amino acids (BCAAs) and aromatic amino acids (AAAs) with reduced kidney function is inconsistent or conflicting. Clarification of it might help to better understand the underlying pathophysiology and to determine potential biomarkers for early detection and evaluation of kidney function decline. Our main purpose was to explore and clarify the potential relationships of individual BCAAs and AAAs with estimated glomerular filtration rate (eGFR) decline. We included the data from 2804 healthy subjects and categorized them into three groups based on eGFR tertiles. The associations between individual amino acids and eGFR were explored by covariate-adjusted logistic regression models. There was a progressive increase in the concentrations of BCAAs and AAAs from the upper to the lower tertiles. We revealed significant positive associations of isoleucine, leucine, and phenylalanine with lower tertiles of eGFR in the adjusted models (p < 0.01–0.001). The findings hold a promising potential of using plasma isoleucine, leucine, and phenylalanine levels for evaluation of kidney function decline. Future longitudinal studies should investigate the causal association between altered levels of these amino acids and impaired kidney function and also the utility of the former as potential biomarkers for evaluating the risk and early detection of the latter.

Innate immune factors exert widespread effects on cytokine secretion, cell survival, autophagy, and apoptosis. Nucleotide-binding and oligomerization domain-like receptors (NLRs) are members of the innate immune system in the cytosol that sense pathogens, endogenous danger molecules such as uric acid, and pollutants. Nucleotide-binding oligomerization domain-containing protein 1 and 2 (NOD1 and NOD2) are components of NLR family, and ligands of these factors are γ-d-glutamyl-meso-diaminopimelic acid (iE-DAP) and muramyl dipeptide (MDP), respectively. Upon recognition of ligands, NOD1 and NOD2 induce the production of inflammatory cytokines and transcription factors including interleukin-6 (IL-6) and nuclear factor-κB (NF-κB). We examined the function of NOD1 and NOD2 in innate immunity, with a focus on their differing roles in disease pathogenesis between Japanese and Caucasian populations. Susceptibility to several immune-related diseases, including Crohn’s disease, colorectal and breast cancers, and graft-versus-host-disease (GVHD) showed a correlation with genetic variants of NOD2 in Caucasian, but not in Japanese, populations. This difference may be primarily due to the fact that three major NOD2 SNPs (R702W, G908R, L1007insC) prevalent in Caucasians are rare or absent in Japanese populations. Because NLR has diverse effects on immune function, it is possible that many as yet uncharacterized immune-related diseases will also show different susceptibilities between races due to the different ratio of genetic variants in innate immune genes.

Unrelated bone marrow transplantation (uBMT) is performed to treat blood disorders, and it uses bone marrow from an unrelated donor as the transplant source. Although the importance of HLA matching in uBMT has been established, that of other genetic factors, such as single-nucleotide polymorphisms (SNPs), remains unclear. The application of immunoinhibitory receptors as anticancer drugs has recently been attracting attention. This prompted us to examine the importance of immunoinhibitory receptor SNPs in uBMT. We retrospectively genotyped five single-nucleotide polymorphisms (SNPs) in the immune checkpoint genes, BTLA, PD-1, LAG3, and CTLA4, and two SNPs in the methylase genes, DNMT1 and EZH2, in 999 uBMT donor–recipient pairs coordinated through the Japan Marrow Donor Program matched at least at HLA-A, -B, and -DRB1. No correlations were observed between these SNPs and post-uBMT outcomes (p > 0.005). This result questions the usefulness of these immune checkpoint gene polymorphisms for predicting post-BMT outcomes. However, the recipient EZH2 histone methyltransferase gene SNP, which encodes the D185H substitution, exhibited a low p-value in regression analysis of grade 2–4 acute graft-versus-host disease (p = 0.010). Due to a low minor allele frequency, this SNP warrants further investigation in a larger-scale study.

This study developed a two-stage, explainable machine learning framework to predict 18-month MMSE-based cognitive status from baseline multimodal data in community-dwelling older adults in Japan. A hierarchical design was used in which Stage 1 distinguished cognitively Normal participants from those with any abnormality (Possible Mild Cognitive Impairment (MCI) or Impaired), and Stage 2 further separated Possible MCI from Impaired within the abnormal subgroup. Both an Imbalanced-Learn Random Forest and a penalized logistic regression baseline were trained under Leave-One-Out Cross-Validation, yielding fair discrimination in Stage 1 (Random Forest AUC = 0.72, accuracy = 0.71; logistic regression AUC = 0.71, accuracy = 0.76) and apparently strong separability in Stage 2 (Random Forest AUC = 0.95, accuracy = 0.96; logistic regression AUC = 0.82, accuracy = 0.92) in a small sample size with high class imbalance. SHapley Additive exPlanations (SHAP) with TreeExplainer for Random Forest and LinearExplainer for logistic regression were used to identify interpretable biomarkers at each stage though feature attribution. In Stage 1, both models highlighted renal and systemic metabolic markers (e.g., creatinine, uric acid, blood urea nitrogen), amino acid and redox-related metabolites (including D-serine, D-amino acid proportions, L-asparagine, alanine, L-glutamic acid, cysteine, methionine sulfoxide), and wearable-derived activity variability (e.g., fluctuation coefficients and steps per minute), with the Simpson index of gut microbiome diversity also contributing in the logistic model. In Stage 2, the models converged on a distinct signature involving glucose and albumin, uric acid and uridine, choline and carnitine, multiple amino acids (such as phenylalanine, proline, ornithine, tryptophan, threonine, and short-chain amino acids), oxidative/energy markers (niacinamide, methionine, methionine sulfoxide, ergothioneine), hematologic indices, and high-MET activity fluctuation metrics. Collectively, these results support a stage-dependent, multisystem view of cognitive aging in which broad renal–metabolic, amino acid, and behavioral vulnerabilities characterize early abnormality, whereas more pronounced alterations in energy metabolism, nucleotide and choline pathways, oxidative stress, and activity irregularity accompany progression from Possible MCI to Impaired status. By combining routine clinical chemistry, targeted metabolomics, gut microbiome diversity, and wearable-derived behavioral measures within an explainable AI framework, this two-stage approach illustrates a scalable, biologically grounded strategy for stage-aware risk stratification and monitoring of cognitive decline in community settings.