Pulse pressure is associated with decline in physical function in older adults.

OBJECTIVES: This study examined the associations between pulse pressure, hypertension, and the decline in physical function in a prospective framework. STUDY DESIGN: The Healthy Aging Longitudinal Study tracked a group of Taiwanese adults aged 55 or more over an average of 6.19 years to assess pulse pressure and decline in physical function, including in handgrip strength, gait speed, and 6-min walking distance, at baseline (2009-2013) and in the second phase of assessments (2013-2020). MAIN OUTCOME MEASURES: Pulse pressure was calculated as the difference between systolic and diastolic blood pressure values. Weakness, slowness, and low endurance were defined as decreases of ≥0.23 m/s (one standard deviation) in gait speed, ≥5.08 kg in handgrip strength, and ≥ 57.73 m in a 6-min walk, as determined from baseline to the second phase of assessment. Linear and logistic regressions were employed to evaluate the associations between pulse pressure, hypertension, and decline in physical function. RESULTS: Baseline pulse pressure was associated with future handgrip strength (beta = -0.017, p = 0.0362), gait speed (beta = -0.001, p < 0.0001), and 6-min walking distance (beta = -0.470, p < 0001). In multivariable models, only handgrip strength (beta = -0.016, p = 0.0135) and walking speed (beta = -0.001, p = 0.0042) remained significantly associated with future pulse pressure. Older adults with high systolic blood pressure (≥140 mmHg) and elevated pulse pressure (≥60 mmHg) exhibited a significantly increased risk of weakness (odds ratio: 1.30, 95 % confidence interval: 1.08-1.58), slowness (1.29, 1.04-1.59), and diminished endurance (1.25, 1.04-1.50) compared with the reference group, who exhibited systolic blood pressure of <140 mmHg and pulse pressure of <60 mmHg. CONCLUSIONS: Among older adults, pulse pressure is associated with a decline in physical function, especially in terms of strength and locomotion.

Changes of neurofilament light chain in patients with alcohol dependence following withdrawal and the genetic effect from ALDH2 Polymorphism.

Neurofilament light chain (NFL), as a measure of neuroaxonal injury, has recently gained attention in alcohol dependence (AD). Aldehyde dehydrogenase 2 (ALDH2) is the major enzyme which metabolizes the alcohol breakdown product acetaldehyde. An ALDH2 single nucleotide polymorphism (rs671) is associated with less ALDH2 enzyme activity and increased neurotoxicity. We examined the blood NFL levels in 147 patients with AD and 114 healthy controls using enzyme-linked immunosorbent assay and genotyped rs671. We also followed NFL level, alcohol craving and psychological symptoms in patients with AD after 1 and 2 weeks of detoxification. We found the baseline NFL level was significantly higher in patients with AD than in controls (mean ± SD: 264.2 ± 261.8 vs. 72.1 ± 35.6 pg/mL, p < 0.001). The receiver operating characteristic curve revealed that NFL concentration could discriminate patients with AD from controls (area under the curve: 0.85; p < 0.001). The NFL levels were significantly reduced following 1 and 2 weeks of detoxification, with the extent of reduction correlated with the improvement of craving, depression, and anxiety (p < 0.001). Carriers with the rs671 GA genotype, which is associated with less ALDH2 activity, had higher NLF levels either at baseline or after detoxification compared with GG carriers. In conclusion, plasma NFL level was increased in patients with AD and reduced after early abstinence. Reduction in NFL level corroborated well with the improvement of clinical symptoms. The ALDH2 rs671 polymorphism may play a role in modulating the extent of neuroaxonal injury and its recovery.

Evaluating polygenic risk scores for predicting cardiometabolic traits and disease risks in the Taiwan Biobank.

Type 2 diabetes (T2D) and hypertension are common comorbidities and, along with hyperlipidemia, serve as risk factors for cardiovascular diseases. This study aimed to evaluate the predictive value of polygenic risk scores (PRSs) on cardiometabolic traits related to T2D, hypertension, and hyperlipidemia and the incidence of these three diseases in Taiwan Biobank samples. Using publicly available, large-scale genome-wide association studies summary statistics, we constructed cross-ethnic PRSs for T2D, hypertension, body mass index, and nine quantitative traits typically used to define the three diseases. A composite PRS (cPRS) for each of the nine traits was constructed by aggregating the significant PRSs of its genetically correlated traits. The associations of each of the nine traits at baseline as well as the change of trait values during a 3- to 6-year follow-up period with its cPRS were evaluated. The predictive performances of cPRSs in predicting future incidences of T2D, hypertension, and hyperlipidemia were assessed. The cPRSs had significant associations with baseline and changes of trait values in 3-6 years and explained a higher proportion of variance for all traits than individual PRSs. Furthermore, models incorporating disease-related cPRSs, along with clinical features and relevant trait measurements achieved area under the curve values of 87.8%, 83.7%, and 75.9% for predicting future T2D, hypertension, and hyperlipidemia in 3-6 years, respectively.

Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features.

BACKGROUND: Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated. METHODS: A total of 184 probable AD patients and 3773 healthy individuals aged 65 and over were enrolled. AD-related genes (51 SNPs) and 8 environmental factors were selected as features for multilayer ANN modeling. Random Forest (RF) and Support Vector Machine with RBF kernel (SVM) were also employed for comparison. Model results were verified using traditional statistics. RESULTS: The ANN achieved high accuracy (0.98), sensitivity (0.95), and specificity (0.96) in the intrinsic test for AD classification. Excluding age and genetic data still yielded favorable results (accuracy: 0.97, sensitivity: 0.94, specificity: 0.96). The assigned weights to ANN features highlighted the importance of mental evaluation, years of education, and specific genetic variations (CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650) for AD classification. Receiver operating characteristic analysis revealed AUC values of 0.99 (intrinsic test), 0.60 (TWB-GWA), and 0.72 (CG-WGS), with slightly lower AUC values (0.96, 0.80, 0.52) when excluding age in ANN. The performance of the ANN model in AD classification was comparable to RF, SVM (linear kernel), and SVM (RBF kernel). CONCLUSIONS: The ANN model demonstrated good sensitivity, specificity, and accuracy in AD classification. The top-weighted SNPs for AD prediction were CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650. The ANN model performed similarly to RF and SVM, indicating its capability to handle the complexity of AD as a disease entity.

Exploring concepts and trends in informal caregiver burden: systematic review using citation network and content analysis.

BACKGROUND: With the increase in the aging population, informal caregivers have become an essential pillar for the long-term care of older individuals. However, providing care can have a negative impact and increase the burden on caregivers, which is a cause for concern. OBJECTIVE: This study aimed to comprehensively depict the concept of "informal caregiver burden" through bibliometric and content analyses. METHODS: We searched the Web of Science (WoS) database to obtain bibliometric data and included only papers published between 2013 and 2022. We used content analysis to extract and identify the core concepts within the text systematically. RESULTS: Altogether, 934 papers were included in the bibliometric analysis, from which we selected 19 highly impactful papers for content analysis. The results indicate that researchers have focused on exploring the factors that impact informal caregiver burden. Meanwhile, there has been a widespread discussion regarding the caregiver burden among those caring for recipients with specific illnesses, such as dementia, Alzheimer's disease, and cancer, as these illnesses can contribute to varying levels of burden on informal caregivers. In addition, questionnaires and interviews emerged as the predominant methods for data collection in the realm of informal caregiver research. Furthermore, we identified 26 distinct assessment tools specifically tailored for evaluating burden, such as caregiver strain index (CSI). CONCLUSION: For future studies, we suggest considering the intersectionality of factors contributing to the burden on informal caregivers. This approach could enhance the well-being of both caregivers and older care recipients.

Predicting Long-Term Care Service Demands for Cancer Patients: A Machine Learning Approach.

BACKGROUND: Long-term care (LTC) service demands among cancer patients are significantly understudied, leading to gaps in healthcare resource allocation and policymaking. OBJECTIVE: This study aimed to predict LTC service demands for cancer patients and identify the crucial factors. METHODS: 3333 cases of cancers were included. We further developed two specialized prediction models: a Unified Prediction Model (UPM) and a Category-Specific Prediction Model (CSPM). The UPM offered generalized forecasts by treating all services as identical, while the CSPM built individual predictive models for each specific service type. Sensitivity analysis was also conducted to find optimal usage cutoff points for determining the usage and non-usage cases. RESULTS: Service usage differences in lung, liver, brain, and pancreatic cancers were significant. For the UPM, the top 20 performance model cutoff points were adopted, such as through Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), and XGBoost (XGB), achieving an AUROC range of 0.707 to 0.728. The CSPM demonstrated performance with an AUROC ranging from 0.777 to 0.837 for the top five most frequently used services. The most critical predictive factors were the types of cancer, patients' age and female caregivers, and specific health needs. CONCLUSION: The results of our study provide valuable information for healthcare decisions, resource allocation optimization, and personalized long-term care usage for cancer patients.

Prevalence and predictive modeling of undiagnosed diabetes and impaired fasting glucose in Taiwan: a Taiwan Biobank study.

INTRODUCTION: We investigated the prevalence of undiagnosed diabetes and impaired fasting glucose (IFG) in individuals without known diabetes in Taiwan and developed a risk prediction model for identifying undiagnosed diabetes and IFG. RESEARCH DESIGN AND METHODS: Using data from a large population-based Taiwan Biobank study linked with the National Health Insurance Research Database, we estimated the standardized prevalence of undiagnosed diabetes and IFG between 2012 and 2020. We used the forward continuation ratio model with the Lasso penalty, modeling undiagnosed diabetes, IFG, and healthy reference group (individuals without diabetes or IFG) as three ordinal outcomes, to identify the risk factors and construct the prediction model. Two models were created: Model 1 predicts undiagnosed diabetes, IFG_110 (ie, fasting glucose between 110 mg/dL and 125 mg/dL), and the healthy reference group, while Model 2 predicts undiagnosed diabetes, IFG_100 (ie, fasting glucose between 100 mg/dL and 125 mg/dL), and the healthy reference group. RESULTS: The standardized prevalence of undiagnosed diabetes for 2012-2014, 2015-2016, 2017-2018, and 2019-2020 was 1.11%, 0.99%, 1.16%, and 0.99%, respectively. For these periods, the standardized prevalence of IFG_110 and IFG_100 was 4.49%, 3.73%, 4.30%, and 4.66% and 21.0%, 18.26%, 20.16%, and 21.08%, respectively. Significant risk prediction factors were age, body mass index, waist to hip ratio, education level, personal monthly income, betel nut chewing, self-reported hypertension, and family history of diabetes. The area under the curve (AUC) for predicting undiagnosed diabetes in Models 1 and 2 was 80.39% and 77.87%, respectively. The AUC for predicting undiagnosed diabetes or IFG in Models 1 and 2 was 78.25% and 74.39%, respectively. CONCLUSIONS: Our results showed the changes in the prevalence of undiagnosed diabetes and IFG. The identified risk factors and the prediction models could be helpful in identifying individuals with undiagnosed diabetes or individuals with a high risk of developing diabetes in Taiwan.

Genome-wide association study and functional characterization identifies candidate genes for insulin-stimulated glucose uptake.

Distinct tissue-specific mechanisms mediate insulin action in fasting and postprandial states. Previous genetic studies have largely focused on insulin resistance in the fasting state, where hepatic insulin action dominates. Here we studied genetic variants influencing insulin levels measured 2 h after a glucose challenge in >55,000 participants from three ancestry groups. We identified ten new loci (P < 5 × 10-8) not previously associated with postchallenge insulin resistance, eight of which were shown to share their genetic architecture with type 2 diabetes in colocalization analyses. We investigated candidate genes at a subset of associated loci in cultured cells and identified nine candidate genes newly implicated in the expression or trafficking of GLUT4, the key glucose transporter in postprandial glucose uptake in muscle and fat. By focusing on postprandial insulin resistance, we highlighted the mechanisms of action at type 2 diabetes loci that are not adequately captured by studies of fasting glycemic traits.

Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1% to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8% to 5.1% (SBP) and 4.7% to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs.

Whole genome sequence analysis of blood lipid levels in >66,000 individuals.

Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

Insights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension.

BACKGROUND: The availability of whole-genome sequencing data in large studies has enabled the assessment of coding and noncoding variants across the allele frequency spectrum for their associations with blood pressure. METHODS: We conducted a multiancestry whole-genome sequencing analysis of blood pressure among 51 456 Trans-Omics for Precision Medicine and Centers for Common Disease Genomics program participants (stage-1). Stage-2 analyses leveraged array data from UK Biobank (N=383 145), Million Veteran Program (N=318 891), and Reasons for Geographic and Racial Differences in Stroke (N=10 643) participants, along with whole-exome sequencing data from UK Biobank (N=199 631) participants. RESULTS: Two blood pressure signals achieved genome-wide significance in meta-analyses of stage-1 and stage-2 single variant findings (P<5×10-8). Among them, a rare intergenic variant at novel locus, LOC100506274, was associated with lower systolic blood pressure in stage-1 (beta [SE]=-32.6 [6.0]; P=4.99×10-8) but not stage-2 analysis (P=0.11). Furthermore, a novel common variant at the known INSR locus was suggestively associated with diastolic blood pressure in stage-1 (beta [SE]=-0.36 [0.07]; P=4.18×10-7) and attained genome-wide significance in stage-2 (beta [SE]=-0.29 [0.03]; P=7.28×10-23). Nineteen additional signals suggestively associated with blood pressure in meta-analysis of single and aggregate rare variant findings (P<1×10-6 and P<1×10-4, respectively). DISCUSSION: We report one promising but unconfirmed rare variant for blood pressure and, more importantly, contribute insights for future blood pressure sequencing studies. Our findings suggest promise of aggregate analyses to complement single variant analysis strategies and the need for larger, diverse samples, and family studies to enable robust rare variant identification.

Association of the D-amino acid oxidase gene with methadone dose in heroin dependent patients under methadone maintenance treatment.

Methadone is a synthetic opioid used for the maintenance treatment (MMT) of heroin dependence. It primarily binds to the µ-opioid receptor (MOR; with its gene, namely OPRM1). Methadone is also an N-methyl-D-aspartate (NMDA) receptor antagonist. The role of NMDA receptor in the regulatory mechanisms of methadone dosage in heroin dependent patients is so far not clear. D-amino acid oxidase (DAO) is an important enzyme that indirectly activates the NMDA receptor through its effect on the D-serine level. To test the hypothesis that genetic polymorphisms in the DAO gene are associated with methadone treatment dose and responses, we selected four single nucleotide polymorphisms (SNPs) in DAO from the literature reports of the Taiwanese population. SNPs were genotyped in 344 MMT patients. In this study, we identified a functional SNP rs55944529 in the DAO gene that reveals a modest but significant association with the methadone dosage in the recessive model of analysis (P = 0.003) and plasma concentrations (P = 0.003) in MMT patients. However, it did not show association with plasma methadone concentration in multiple linear regression analysis. It is also associated with the methadone adverse reactions of dry mouth (P = 0.002), difficulty with urination (P = 0.0003) in the dominant model, and the withdrawal symptoms of yawning (P = 0.005) and gooseflesh skin (P = 0.004) in the recessive model. Our results suggest a role of the indirect regulatory mechanisms of the NMDA reporter, possibly via the DAO genetic variants, in the methadone dose and some adverse reactions in MMT patients.

Increase in plasma CCL11 (Eotaxin-1) in patients with alcohol dependence and changes during detoxification.

BACKGROUND: Alcohol is known to modulate the immune system. Neuroinflammatory cytokine dysregulation plays an essential role in the pathophysiology of alcohol dependence (AD). Preclinical studies have indicated that alcohol consumption upregulates the pro-inflammatory cytokine CC motif ligand 11 (CCL11, also known as eotaxin-1). We examined CCL11 levels in patients with AD and in mice administered alcohol. METHODS: The plasma CCL11 levels of 151 patients with AD and 116 healthy controls were measured. In addition, we followed the CCL11 levels, alcohol cravings and psychological symptoms in patients with AD after 1 and 2 weeks of detoxification. Furthermore, we examined CCL11 changes in mice administered alcohol for 5 days. RESULTS: CCL11 levels were higher in patients with AD than in controls and declined during detoxification. CCL11 levels were positively correlated with AD severity (p < 0.001). Furthermore, mice exposed to alcohol exhibited a higher CCL11 level. The receiver operating characteristic curve revealed that a CCL11 level of 72.5 pg/mL could significantly differentiate patients with AD from controls (area under the curve: 0.77; p < 0.001). Reductions in CCL11 levels during detoxification were correlated with reductions in alcohol craving, depression, and anxiety. CONCLUSIONS: Our data from humans and mice suggest that chronic alcohol consumption is associated with an increase in CCL11 levels. CCL11 levels are correlated with AD severity and may be a potential indicator of AD. The CCL11 reduction after alcohol discontinuation is associated with alleviation of clinical symptoms. Collectively, our findings suggest that CCL11 is involved in the neurobiological mechanisms underlying AD.

Splice-Site Variants in the Gene Encoding GABA-A Receptor Delta Subunit Are Associated with Amphetamine Use in Patients under Methadone Maintenance Treatment.

Chronic opioid use disorder patients often also use other substances such as amphetamines. The gene-based analysis method was applied in the genomic database obtained from our previous study with 343 methadone maintenance treatment (MMT) patients. We found that the gene encoding gamma-aminobutyric acid type A receptors (GABA-A receptor) delta subunit isoforms (GABRD) was associated with amphetamine use in heroin dependent patients under MMT in Taiwan. A total of 15% of the 343 MMT patients tested positive for amphetamine in the urine toxicology test. Two genetic variants in the GABRD, rs2889475 and rs2376805, were found to be associated with the positive urine amphetamine test. They are located in the exon 1 of the splice variant and altered amino acid compositions (T126I, C/T, for rs2889475, and R252Q, G/A, for rs2376805). The CC genotype carriers of rs2889475 showed a four times higher risk of amphetamine use than those with TT genotype. The GG genotype carriers of rs2376805 showed a three times higher risk of amphetamine use than the AA genotype carriers. To our knowledge, this is the first report that demonstrated an association of the delta splice variant isoform in the GABA-A receptor with an increased risk of amphetamine use in MMT patients. Our results suggest that rs2889475 and rs2376805 may be indicators for the functional role and risk of amphetamine use in MMT patients.

Multi-omics analysis identifies CpGs near G6PC2 mediating the effects of genetic variants on fasting glucose.

AIMS/HYPOTHESIS: An elevated fasting glucose level in non-diabetic individuals is a key predictor of type 2 diabetes. Genome-wide association studies (GWAS) have identified hundreds of SNPs for fasting glucose but most of their functional roles in influencing the trait are unclear. This study aimed to identify the mediation effects of DNA methylation between SNPs identified as significant from GWAS and fasting glucose using Mendelian randomisation (MR) analyses. METHODS: We first performed GWAS analyses for three cohorts (Taiwan Biobank with 18,122 individuals, the Healthy Aging Longitudinal Study in Taiwan with 1989 individuals and the Stanford Asia-Pacific Program for Hypertension and Insulin Resistance with 416 individuals) with individuals of Han Chinese ancestry in Taiwan, followed by a meta-analysis for combining the three GWAS analysis results to identify significant and independent SNPs for fasting glucose. We determined whether these SNPs were methylation quantitative trait loci (meQTLs) by testing their associations with DNA methylation levels at nearby CpG sites using a subsample of 1775 individuals from the Taiwan Biobank. The MR analysis was performed to identify DNA methylation with causal effects on fasting glucose using meQTLs as instrumental variables based on the 1775 individuals. We also used a two-sample MR strategy to perform replication analysis for CpG sites with significant MR effects based on literature data. RESULTS: Our meta-analysis identified 18 significant (p < 5 × 10-8) and independent SNPs for fasting glucose. Interestingly, all 18 SNPs were meQTLs. The MR analysis identified seven CpGs near the G6PC2 gene that mediated the effects of a significant SNP (rs2232326) in the gene on fasting glucose. The MR effects for two CpGs were replicated using summary data based on the European population, using an exonic SNP rs2232328 in G6PC2 as the instrument. CONCLUSIONS/INTERPRETATION: Our analysis results suggest that rs2232326 and rs2232328 in G6PC2 may affect DNA methylation at CpGs near the gene and that the methylation may have downstream effects on fasting glucose. Therefore, SNPs in G6PC2 and CpGs near G6PC2 may reside along the pathway that influences fasting glucose levels. This is the first study to report CpGs near G6PC2, an important gene for regulating insulin secretion, mediating the effects of GWAS-significant SNPs on fasting glucose.

CUX2, BRAP and ALDH2 are associated with metabolic traits in people with excessive alcohol consumption.

Molecular mechanisms that prompt or mitigate excessive alcohol consumption could be partly explained by metabolic shifts. This genome-wide association study aims to identify the susceptibility gene loci for excessive alcohol consumption by jointly measuring weekly alcohol consumption and γ-GT levels. We analysed the Taiwan Biobank data of 18,363 Taiwanese people, including 1945 with excessive alcohol use. We found that one or two copies of the G allele in rs671 (ALDH2) increased the risk of excessive alcohol consumption, while one or two copies of the C allele in rs3782886 (BRAP) reduced the risk of excessive alcohol consumption. To minimize the influence of extensive regional linkage disequilibrium, we used the ridge regression. The ridge coefficients of rs7398833, rs671 and rs3782886 were unchanged across different values of the shrinkage parameter. The three variants corresponded to posttranscriptional activity, including cut-like homeobox 2 (a protein coded by CUX2), Glu504Lys of acetaldehyde dehydrogenase 2 (a protein encoded by ALDH2) and Glu4Gly of BRCA1-associated protein (a protein encoded by BRAP). We found that Glu504Lys of ALDH2 and Glu4Gly of BRAP are involved in the negative regulation of excessive alcohol consumption. The mechanism underlying the γ-GT-catalytic metabolic reaction in excessive alcohol consumption is associated with ALDH2, BRAP and CUX2. Further study is needed to clarify the roles of ALDH2, BRAP and CUX2 in the liver-brain endocrine axis connecting metabolic shifts with excessive alcohol consumption.

Genetic variants in NECTIN4 encoding an adhesion molecule are associated with continued opioid use.

Methadone is a synthetic opioid used as maintenance treatment for patients addicted to heroin. Skin irritation is one of the adverse events caused by opioid use. 344 methadone maintenance treatment (MMT) patients were recruited with records and measurements on methadone dose, plasma methadone concentrations, and treatment emergent symptom scales (TESS). 15 patients reported with skin irritation. Five SNPs located within the NECTIN4 genetic region were genotyped. The NECTIN4 gene within the adherens junction interaction pathway was associated with methadone dose in pathway-based genome wide association analyses (P = 0.0008). Three highly-linked SNPs, rs11265549, rs3820097, and rs4656978, were significantly associated with methadone dose (P = 0.0003), plasma concentrations of R,S-methadone (P = 0.0004) and TNF-α (P = 0.010) in all 344 MMT patients, and with self-report skin irritation symptom scores (P = 0.010) in the 15 MMT patients who reported with skin irritation. To identify the possible roles of plasma level of Nectin-4 in the responses to MMT and opioid use, additional age- and gender-matched 51 controls and 83 methadone-free abstinent former heroin users were recruited. Plasma level of Nectin-4 was the highest in MMT patients among the three groups. The results suggest involvement of genetic variants on NECTIN4 in methadone dose. Plasma Nectin-4 level is likely an indicator for continued use of opioids.

Genetic polymorphisms in the opioid receptor delta 1 (OPRD1) gene are associated with methadone dose in methadone maintenance treatment for heroin dependence.

Delta opioid receptor (DOR) is well known to be involved in heroin dependence. This study tested the hypothesis that single nucleotide polymorphisms (SNPs) in the opioid receptor delta 1 (OPRD1) gene coding region are associated with treatment responses in a methadone maintenance therapy (MMT) cohort in Taiwan. Three hundred forty-four MMT patients were recruited. Diastolic/systolic blood pressure, heart rate, methadone dosage, and plasma concentrations of methadone were recorded. Twenty-five SNPs located within the OPRD1 genetic region were selected and genotyped from the genomic DNA of all 344 participants. After pairwise tagger analyses, tagger SNP rs204047 showed a significant association with methadone dosage (P = 0.0019), and tagger SNPs rs204047 and rs797397 were significantly associated with plasma R, S-methadone concentrations (P < 0.0006) in patients tested negative in the urine morphine test, which indicated patients with a better response to MMT. The major genotype carriers showed a higher methadone dosage and higher plasma concentrations of R, S-methadone than the minor genotype carriers. The results indicated that OPRD1 genetic variants were associated with methadone dosage and methadone plasma concentration in MMT patients with a negative morphine test result.

pWGBSSimla: a profile-based whole-genome bisulfite sequencing data simulator incorporating methylation QTLs, allele-specific methylations and differentially methylated regions.

MOTIVATION: DNA methylation plays an important role in regulating gene expression. DNA methylation is commonly analyzed using bisulfite sequencing (BS-seq)-based designs, such as whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS) and oxidative bisulfite sequencing (oxBS-seq). Furthermore, there has been growing interest in investigating the roles that genetic variants play in changing the methylation levels (i.e. methylation quantitative trait loci or meQTLs), how methylation regulates the imprinting of gene expression (i.e. allele-specific methylation or ASM) and the differentially methylated regions (DMRs) among different cell types. However, none of the current simulation tools can generate different BS-seq data types (e.g. WGBS, RRBS and oxBS-seq) while modeling meQTLs, ASM and DMRs. RESULTS: We developed profile-based whole-genome bisulfite sequencing data simulator (pWGBSSimla), a profile-based bisulfite sequencing data simulator, which simulates WGBS, RRBS and oxBS-seq data for different cell types based on real data. meQTLs and ASM are modeled based on the block structures of the methylation status at CpGs, whereas the simulation of DMRs is based on observations of methylation rates in real data. We demonstrated that pWGBSSimla adequately simulates data and allows performance comparisons among different methylation analysis methods. AVAILABILITY AND IMPLEMENTATION: pWGBSSimla is available at https://omicssimla.sourceforge.io. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting Lung Cancer Occurrence in Never-Smoking Females in Asia: TNSF-SQ, a Prediction Model.

BACKGROUND: High disease burden suggests the desirability to identify high-risk Asian never-smoking females (NSF) who may benefit from low-dose CT (LDCT) screening. In North America, one is eligible for LDCT screening if one satisfies the U.S. Preventive Services Task Force (USPSTF) criteria or has model-estimated 6-year risk greater than 0.0151. According to two U.S. reports, only 36.6% female patients with lung cancer met the USPSTF criteria, while 38% of the ever-smokers ages 55 to 74 years met the USPSTF criteria. METHODS: Using data on NSFs in the Taiwan Genetic Epidemiology Study of Lung Adenocarcinoma and the Taiwan Biobank before August 2016, we formed an age-matched case-control study consisting of 1,748 patients with lung cancer and 6,535 controls. Using these and an estimated age-specific lung cancer 6-year incidence rate among Taiwanese NSFs, we developed the Taiwanese NSF Lung Cancer Risk Models using genetic information and simplified questionnaire (TNSF-SQ). Performance evaluation was based on the newer independent datasets: Taiwan Lung Cancer Pharmacogenomics Study (LCPG) and Taiwan Biobank data after August 2016 (TWB2). RESULTS: The AUC based on the NSFs ages 55 to 70 years in LCPG and TWB2 was 0.714 [95% confidence intervals (CI), 0.660-0.768]. For women in TWB2 ages 55 to 70 years, 3.94% (95% CI, 2.95-5.13) had risk higher than 0.0151. For women in LCPG ages 55 to 74 years, 27.03% (95% CI, 19.04-36.28) had risk higher than 0.0151. CONCLUSIONS: TNSF-SQ demonstrated good discriminative power. The ability to identify 27.03% of high-risk Asian NSFs ages 55 to 74 years deserves attention. IMPACT: TNSF-SQ seems potentially useful in selecting Asian NSFs for LDCT screening.