Asthma in the elderly needs more attention in an aging society. However, it is likely to remain underdiagnosed and undertreated. This study aimed to clarify clinical characteristics of new-onset ...asthma in the elderly, describing the prevalence, predictive factors, and comorbidities after asthma diagnosis of new-onset asthma in the elderly in the general population.
This community-based prospective cohort study enrolled 9804 generally healthy participants (30–74 years old) in Nagahama City, and conducted a follow-up assessment after 5 years. Elderly participants were those aged ≥65 years at baseline. Patients with new-onset asthma were defined as participants without asthma at baseline assessment and with asthma at the follow-up assessment.
Among the 7948 participants analyzed in this study, 28 (1.4%) elderly and 130 (2.2%) non-elderly had new-onset asthma. Multiple logistic regression analysis revealed low forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) and high blood eosinophil counts at baseline as predicting factors for new-onset asthma in the elderly. Additionally, subsequent incidence of new-onset asthma was higher in elderly participants with both predictors (high blood eosinophil counts and low FEV1/FVC at baseline) than those with none or one of the predictors before asthma diagnosis. Lastly, elderly patients with new-onset asthma had more frequent comorbidity of moderate to severe sleep disordered breathing than those non-elderly.
Eosinophilic inflammation and airflow obstruction may predict subsequent new-onset asthma after the age of 65 years. Revealing the characteristics of new-onset asthma in the elderly can aid in the prevention of underdiagnosed asthma.
Abstract
Recent reports have identified differences in the mutational spectra across human populations. Although some of these reports have been replicated in other cohorts, most have been reported ...only in the 1000 Genomes Project (1kGP) data. While investigating an intriguing putative population stratification within the Japanese population, we identified a previously unreported batch effect leading to spurious mutation calls in the 1kGP data and to the apparent population stratification. Because the 1kGP data are used extensively, we find that the batch effects also lead to incorrect imputation by leading imputation servers and a small number of suspicious GWAS associations. Lower quality data from the early phases of the 1kGP thus continue to contaminate modern studies in hidden ways. It may be time to retire or upgrade such legacy sequencing data.
•Prevalence of carotid and intracranial stenoses were 1.7 % and 3.8 %.•Alopecia areata was a common risk factor for carotid and intracranial stenosis.•RNF213 p.R4810K was associated with ...intracranial, but not with carotid stenosis.•Diabetes and older age were associated only with carotid stenosis.
Atherosclerotic burden increases the risk of both extracranial internal carotid artery stenosis (ICS) and intracranial large artery disease (ICAD). However, the differences in risk profiles have not been thoroughly investigated.
Participants were recruited from the Nagahama study cohort in Japan. Individuals over 60 years old who underwent 1.5-T head and neck magnetic resonance angiography (MRA) between July 2013 and February 2017 were included. ICAD was defined as WASID ≥ 50 %, and ICS was defined as NSCET ≥ 30 %. The prevalence and association of risk factors, including proatherogenic and proinflammatory factors, and the p.R4810K variant in the RNF213 gene, were investigated. Multivariable logistic regression analyses were performed.
A total of 3089 individuals participated in the study, with a mean age of 68.1 ± 5.3 years, and 36.0 % were males. Among them, 52 (1.7 %) had ICS, 119 (3.8 %) had ICAD, and 15 (0.49 %) had both conditions. Alopecia areata was an independent predictor for both ICS (Odds ratio OR 3.5; 95 % CI 1.3-8.3) and ICAD (OR 2.1; 95 % CI 1.0-3.9). Diabetes (OR 3.7; 95 % CI 2.0-7.0) and older age (OR 2.4; 95 % CI 1.2-4.5) were associated only with ICS, while the RNF213 variant was associated with only ICAD (OR 5.7; 95 % CI 1.6-16.0). ICS and ICAD were also independently associated with each other.
In this MRA-based large scale study, alopecia areata, known as a systemic inflammatory disease, was shown to be a common risk factor for ICS and ICAD. While conventional atherosclerotic factors were associated with ICS, non-atherosclerotic factors appear to contribute to ICAD in Japan.
Abstract Gut-microbiota derived metabolites are important regulators of host biology and metabolism. To understand the impacts of the microbial metabolite 4-cresol sulfate (4-CS) on four chronic ...diseases type 2 diabetes mellitus, metabolic syndrome (MetS), non-alcoholic fatty liver disease, and chronic kidney disease (CKD), we conducted association analyses of plasma 4-CS quantified by liquid chromatography coupled to mass spectrometry (LC–MS) in 3641 participants of the Nagahama study. Our results validated the elevation of 4-CS in CKD and identified a reducing trend in MetS. To delineate the holistic effects of 4-CS, we performed a phenome-wide association analysis (PheWAS) with 937 intermediate biological and behavioral traits. We detected associations between 4-CS and 39 phenotypes related to blood pressure regulation, hepatic and renal functions, hematology, sleep quality, intraocular pressure, ion regulation, ketone and fatty acid metabolisms, disease history and dietary habits. Among them, 19 PheWAS significant traits, including fatty acids and 14 blood pressure indices, were correlated with MetS, suggesting that 4-CS is a potential biomarker for MetS. Consistent associations of this gut microbial-derived metabolite on multiple endophenotypes underlying distinct etiopathogenesis support its role in the overall host health, with prospects of probiotic-based therapeutic solutions in chronic diseases.
Objective
Takayasu arteritis (TAK) is a systemic vasculitis affecting large arteries and large branches of the aorta. Ulcerative colitis (UC) is a prevalent autoimmune colitis. Since TAK and UC share ...HLA–B*52:01 and IL12B as genetic determinants, and since there are case reports of the co‐occurrence of these diseases, we hypothesized that UC is a common complication of TAK. We undertook this study to perform a large‐scale analysis of TAK, both to evaluate the prevalence of concurrent cases of TAK and UC and to identify and estimate susceptibility genes shared between the 2 diseases.
Methods
We analyzed a total of 470 consecutive patients with TAK from 14 institutions. We characterized patients with TAK and UC by analyzing clinical manifestations and genetic components. Genetic overlapping of TAK and UC was evaluated with the use of UC susceptibility single‐nucleotide polymorphisms by comparing risk directions and effect sizes between susceptibility to the 2 diseases.
Results
Thirty of 470 patients with TAK had UC (6.4% 95% confidence interval 4.3–9.0). This percentage was strikingly higher than that expected from the prevalence of UC in Japan. Patients with TAK complicated with UC developed TAK at an earlier stage of life (P = 0.0070) and showed significant enrichment of HLA–B*52:01 compared to TAK patients without UC (P = 1.0 × 10−5) (odds ratio 12.14 95% confidence interval 2.96–107.23). The 110 non‐HLA markers of susceptibility to UC significantly displayed common risk directions with susceptibility to TAK (P = 0.0054) and showed significant departure of permutation P values from expected P values (P < 1.0 × 10−10).
Conclusion
UC is a major complication of TAK. These 2 diseases share a significant proportion of their genetic background, and HLA–B*52:01 may play a central role in their co‐occurrence.
Background
Cultural and ethnic differences are present both in subjective and objective measures of patient health, but scoring systems do not always reflect these differences, and so validation of ...outcomes tools in different cultural settings is important. Recently, a revised version of The Knee Society Score
®
(KSS 2011) was developed, but to our knowledge, the degree that this tool evaluates clinical symptoms, physical activities, and radiographic grades in the general Japanese population is not known.
Questions/purposes
We therefore asked: (1) how KSS 2011 reflects knee conditions and function in the general Japanese population, in particular evaluating changes with increasing patient age; (2) can objective measures of physical function be correlated with KSS 2011; and (3) does radiographic osteoarthritis (OA) grade correlate with KSS 2011?
Methods
Two hundred twenty-six people in the general Japanese population, aged 35 to 92 years, with and without knee arthritis, voluntarily participated in this cross-sectional study. Residents who had no serious disease or symptoms based on a self-assessment were recruited. This study consisted of a questionnaire including self-administered KSS 2011, physical examination, and weightbearing radiographs of the knee. Leg muscle strength, Timed Up and Go test, and body mass index (BMI) were examined in all the participants. Radiographs were graded according to the Kellgren and Lawrence scale (KL grade).
Results
Multivariable linear regression analysis showed that KSS 2011 correlated with age (coefficient: −0.30 ± 0.12, p = 0.011), BMI (coefficient: −1.47 ± 0.42, p < 0.001), leg muscle strength (coefficient: 0.41 ± 0.13, p = 0.002), and Timed Up and Go Test (coefficient: −1.96 ± 0.92, p = 0.034), but not sex, as independent variables by a stepwise method. KSS 2011 was also correlated with radiographic OA evaluated by KL grade (coefficient: −12.2 ± 2.9, p < 0.001).
Conclusions
KSS 2011 reflects symptoms, physical activities, and radiographic OA grades of the knee in an age-dependent manner in the general Japanese population.
Level of Evidence
Level IV, diagnostic study. See Guidelines for Authors for a complete description of levels of evidence.
The factors influencing long-term responses to a tumor necrosis factor inhibitor (TNFi) in rheumatoid arthritis (RA) patients currently remain unknown. Therefore, we herein conducted a multi-omics ...analysis of TNFi responses in a Japanese RA cohort. Blood samples were collected from 27 biological disease-modifying antirheumatic drug (DMARD)-naive RA patients at the initiation of and after three months of treatment with TNFi. Treatment responses were evaluated at one year. Differences in gene expression levels in peripheral blood mononuclear cells (PBMCs), plasma protein levels, drug concentrations, and the presence/absence of anti-drug antibodies were investigated, and a cell phenotypic analysis of PBMCs was performed using flow cytometry. After one year of treatment, thirteen patients achieved clinical remission (responders), while the others did not or switched to other biologics (non-responders). Differentially expressed genes related to treatment responses were enriched for the interferon (IFN) pathway. The expression of type I IFN signaling-related genes was higher in non-responders than in responders before and after treatment (
P
= 0.03, 0.005, respectively). The expression of type II IFN signaling-related genes did not significantly differ before treatment; however, it increased in non-responders and decreased in responders, with a significant difference being observed after three months of treatment (
P
= 1.2×10
-3
). The total number of lymphocytes and C-X-C Motif Chemokine Ligand 10 (CXCL10) protein levels were associated with the type I IFN signature (
P
= 6.7×10
-7
, 6.4×10
-3
, respectively). Hepatocyte growth factor (HGF) protein levels before treatment predicted fold increases in type II IFN (
P
= 0.03). These IFN signature-related indices (the number of lymphocytes, CXCL10, and HGF) significantly differed between responders and non-responders (
P
= 0.01, 0.01, and 0.04, respectively). A single-cell analysis revealed that the type I IFN signature was more highly enriched in monocytes than in other cell types. A deconvolution analysis of bulk-RNA sequence data identified CD4+ and CD8+ T cells as the main sources of the type II IFN signature in non-responders. Collectively, the present results demonstrated that the dynamics of the type I and II IFN pathways affected long-term responses to TNFi, providing information on its biological background and potential for clinical applications.
Human T-cell leukemia virus type 1 (HTLV-1) infects mainly CD4+CCR4+ effector/memory T cells in vivo. However, it remains unknown whether HTLV-1 preferentially infects these T cells or this virus ...converts infected precursor cells to specialized T cells. Expression of viral genes in vivo is critical to study viral replication and proliferation of infected cells. Therefore, we first analyzed viral gene expression in non-human primates naturally infected with simian T-cell leukemia virus type 1 (STLV-1), whose virological attributes closely resemble those of HTLV-1. Although the tax transcript was detected only in certain tissues, Tax expression was much higher in the bone marrow, indicating the possibility of de novo infection. Furthermore, Tax expression of non-T cells was suspected in bone marrow. These data suggest that HTLV-1 infects hematopoietic cells in the bone marrow. To explore the possibility that HTLV-1 infects hematopoietic stem cells (HSCs), we analyzed integration sites of HTLV-1 provirus in various lineages of hematopoietic cells in patients with HTLV-1 associated myelopathy/tropical spastic paraparesis (HAM/TSP) and a HTLV-1 carrier using the high-throughput sequencing method. Identical integration sites were detected in neutrophils, monocytes, B cells, CD8+ T cells and CD4+ T cells, indicating that HTLV-1 infects HSCs in vivo. We also detected Tax protein in myeloperoxidase positive neutrophils. Furthermore, dendritic cells differentiated from HTLV-1 infected monocytes caused de novo infection to T cells, indicating that infected monocytes are implicated in viral spreading in vivo. Certain integration sites were re-detected in neutrophils from HAM/TSP patients at different time points, indicating that infected HSCs persist and differentiate in vivo. This study demonstrates that HTLV-1 infects HSCs, and infected stem cells differentiate into diverse cell lineages. These data indicate that infection of HSCs can contribute to the persistence and spread of HTLV-1 in vivo.
Cross-sectional relationships between nocturia and sleep problems have been well evaluated but the risk association for each incidence is scarcely reported. This analysis included 8076 participants ...of the Nagahama study in Japan (median age 57, 31.0% male) and associations between nocturia and self-reported, sleep-related problems (poor sleep) were evaluated cross-sectionally. Causal effects on each new-onset case were analyzed longitudinally after 5 years. Three models were applied: univariable analysis, adjustment for basic variables (i.e., demographic and lifestyle variables) and full adjustment for basic and clinical variables. The overall prevalences of poor sleep and nocturia were 18.6% and 15.5%, while poor sleep was positively associated with nocturia (OR = 1.85, p < 0.001) and vice versa (OR = 1.90, p < 0.001). Among 6579 good sleep participants, 18.5% developed poor sleep. Baseline nocturia was positively associated with this incident poor sleep (OR = 1.49, p < 0.001, full adjustment). Among 6824 non-nocturia participants, the nocturia incidence was 11.3%. Baseline poor sleep was positively associated with this incident nocturia (OR = 1.26, p = 0.026); such associations were significant only in women (OR = 1.44, p = 0.004) and under-50-year-old groups (OR = 2.82, p < 0.001), after full adjustment. Nocturia and poor sleep associate with each other. Baseline nocturia can induce new-onset poor sleep while baseline poor sleep may induce new-onset nocturia only in women.
To assess the use of plasma free amino acids (PFAAs) as biomarkers for metabolic disorders, it is essential to identify genetic factors that influence PFAA concentrations. PFAA concentrations were ...absolutely quantified by liquid chromatography-mass spectrometry using plasma samples from 1338 Japanese individuals, and genome-wide quantitative trait locus (QTL) analysis was performed for the concentrations of 21 PFAAs. We next conducted a conditional QTL analysis using the concentration of each PFAA adjusted by the other 20 PFAAs as covariates to elucidate genetic determinants that influence PFAA concentrations. We identified eight genes that showed a significant association with PFAA concentrations, of which two, SLC7A2 and PKD1L2, were identified. SLC7A2 was associated with the plasma levels of arginine and ornithine, and PKD1L2 with the level of glycine. The significant associations of these two genes were revealed in the conditional QTL analysis, but a significant association between serine and the CPS1 gene disappeared when glycine was used as a covariate. We demonstrated that conditional QTL analysis is useful for determining the metabolic pathways predominantly used for PFAA metabolism. Our findings will help elucidate the physiological roles of genetic components that control the metabolism of amino acids.
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