Phenotypic Associations Research Articles

Association of ABO phenotype, rhesus factor, platelet count and hemoglobin level with oral hygiene status and severity of chronic periodontitis

BackgroundThe development of periodontal diseases has multifactorial causes including genetic factors. Limited investigations have been conducted to explore the association between ABO blood groups and the development and progression of periodontal diseases. AimTo evaluate and assess the association of ABO Phenotype and Rhesus factor with oral hygiene status, severity of chronic periodontitis and blood parameters like hemoglobin level and Platelet count in localized and generalized chronic periodontitis. Material and methodsStudy was carried out on 100 patients, out of which 80 patients of Generalized Chronic Periodontitis and 20 patients of Localized Chronic Periodontitis. Patients were categorized into Mild, Moderate and Severe Periodontitis. ResultA highly significant association was found between severity of periodontitis and blood groups with blood group B and O were found to be at a greater risk to develop moderate to severe form of chronic periodontitis. Also subjects with blood group B and O showed worst oral hygiene among all the blood groups. Also patients suffering from chronic periodontitis showed a general trend towards lower limit of both hemoglobin level and platelet. ConclusionGenetic factors such as ABO blood group antigens may act as a risk influencer that plays a role in progression and severity of the chronic periodontitis, with blood group B and O being worst affected. Another observation was that a long standing case of chronic periodontitis can lead to anemia thus having systemic implications.

Unsupervised electrocardiogram feature extraction using deep learning empowers discovery of genetic and phenotypic determinants of cardiac electrophysiology

Abstract Background Conventional analysis of the electrocardiogram (ECG) is based on the extraction of human-defined, visually recognisable features. However, these are not optimised to capture all the information content in the ECG. The variational autoencoder (VAE), a form of unsupervised machine learning, can address this shortcoming by computationally extracting comprehensive and interpretable new ECG features, called latent factors. These latent factors provide a low-dimensional representation of the ECG that maximises capture of data content. This approach could expand our understanding of electrophysiology by identifying novel genetic and phenotypic traits associations with the ECG. Purpose This study aims to uncover genetic determinants of VAE latent factors, comparing them to determinants of conventional, human-derived ECG parameters to gain novel insights into cardiac electrophysiology and related diseases. Methods Over one million median beat ECGs derived from a United States (US) based secondary care centre were used to train a machine learning VAE model, with external validation in the UK Biobank (UKB). We performed common and rare variant association studies for VAE latent factors and conventional ECG traits on quality-controlled UKB data. Associated genetic variants were compared to loci for conventional ECG parameters available in the UKB and literature. Novel GWAS associations were validated in a withheld subset of the UKB cohort. Additionally, we compared the associations of the VAE latent factors and conventional ECG traits with phenotypic traits, disease codes and echocardiographic traits. Results Utilising median beat ECGs, the VAE model identified 20 features, with partial correlations to traditional ECG traits. A genome-wide association study (GWAS) identified 105 associated genomic regions, compared with 62 regions identified with conventional ECG traits on the same dataset. Most discovered loci have been previously associated with ECG features in larger GWAS, providing a positive control validation. We also discovered and validated five genomic regions not previously linked to the ECG (mapped to TRIOBP, EFEMP1, NEDD9, ATP10A and P2RX1). We found rare variant associations for seven genes (NEK6, IL17RA, NME7, MYBPC3, CCT8, ADAMTS6 and SCN5A). The latent factors uncovered associations with 158 phenotypic traits (2093 phenotypes tested) and 147 disease codes (2074 disease phecodes tested) which were not identified by ECG traits. Out of 38 echocardiographic traits, 35 showed higher correlations with the latent factors, while six exhibited significant associations exclusively with the latent factors. Conclusion Our study demonstrates that VAE-derived latent factors outperform conventional ECG parameters in uncovering novel genetic and phenotypic associations. Our findings underscore the opportunity for optimised data-driven feature extraction to enhance our understanding of cardiac electrophysiology.

Association of metabolic phenotypes with cardiovascular events in primary and secondary prevention

Abstract Background Metabolic disorders are established risk factors for the development of coronary artery disease (CAD) and major adverse cardiovascular events (MACE). Although obesity is strongly related with the metabolic health status, its role as a cardiovascular risk modifier in the absence of metabolic disorders remains controversial. Recent implementation of nutrient-stimulated hormone-based therapies (NUSH) including glucagon-like peptide-1 (GLP-1) receptor agonists in the treatment of obesity even in metabolically healthy individuals requires further understanding to ensure optimal patient management. Purpose The aim of this study is to investigate the association of metabolic phenotypes with cardiovascular events in primary and secondary prevention of CAD. Methods We included patients over 18 years electively evaluated for CAD by invasive coronary angiography (ICA) between 2010 and 2021 in our tertiary referral centre in one of Europe’s largest university hospitals. Metabolic disorder was considered as the presence of diabetes, irrespective of additional risk factors, or hypertension and hyperlipidaemia, and obesity as a BMI of ≥30 kg/m², distinguishing four metabolic phenotypes: metabolically healthy/unhealthy nonobese/obese (MHN, MHO, MUN, MUO). The primary study endpoint MACE was defined as a composite of cardiovascular death, non-fatal myocardial infarction, ischemic stroke, and hospitalization for heart failure. Results The total study population included 12 760 patients (68 [58-76] years; 57.3% male), of whom 56.5% presented metabolically healthy (43.3% MHN; 13.1% MHO) and 43.5% unhealthy (28.3% MUN; 15.2% MUO). During a median follow up time of 3.91 (1.75-7.09) years, 2 592 (20.3%) MACE and 3 351 (26.3%) all-cause deaths occurred. Cox regression analysis adjusted for age, sex, and chronic kidney disease (CKD) showed different risk patterns for distinct metabolic phenotypes (Table 1). While we observed higher event rates in metabolically unhealthy compared to healthy individuals, obesity alone was not associated with increased events (Figure 1). However, metabolically healthy individuals experienced lower event rates with increasing BMI. Among patients without or with nonobstructive CAD, metabolic disease was strongly associated with increased subsequent coronary revascularization regardless of BMI. These patterns were similar across all endpoints irrespective of presence of obstructive or nonobstructive CAD. Conclusions Whereas metabolic disorders are strongly associated with adverse clinical events, obesity alone does not reflect the cardiovascular risk profile in patients with or without obstructive CAD. Thus, focusing on obesity alone for primary or secondary prevention appears unjustified. Particularly after the recent implementation of GLP-1 receptor agonists for the treatment of obesity, precise and individual risk stratification is essential to allow for an optimal personalized patient management.Figure 1Table 1

Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease

Abstract Background Obesity, a key risk factor for cardiometabolic diseases, is poorly evaluated by body mass index (BMI), which doesn't account for visceral fat and fat distribution. Given that obesity-related cardiac changes can manifest in surface ECG changes, we hypothesised that an AI-ECG model could predict BMI, and the discrepancy between AI-predicted and actual BMI (delta-BMI) could indicate cardiometabolic health. We also sought to understand the biological mechanisms contributing to the AI-ECG-derived delta-BMI. Methods The AI-ECG model was developed using a data split of 60/10/30% in a USA secondary care dataset of 512,950 ECGs and externally validated in the UK Biobank (N = 42,386), employing a residual neural network architecture. Model performance was evaluated using Mean Absolute Error (MAE), Pearson correlation coefficient (r), and coefficient of determination (R2). The study aimed at identifying incident cardiometabolic diseases, using Cox regression analyses adjusted for BMI, age, and sex. To assess the underlying biological associations of delta-BMI, phenome-wide, genome-wide, metabolome-wide, and proteome-wide association studies were performed. Results In the test set, the model achieved an MAE of 3.95 (3.93–3.97), r of 0.65 (0.65-0.66), and R2 of 0.43 (0.42-0.43). In the UKB, the model achieved an MAE of 2.94 (2.91–2.96), r of 0.62 (0.62–0.63) and R2 of 0.39 (0.38-0.40). The top tertile of delta-BMI showed an increased risk of future cardiometabolic disease (test set: HR 1.15, p<0.001; UKB: HR 1.58, p<0.001) and diabetes mellitus (test set: HR 1.25, p<0.001; UKB: HR 2.28, p<0.001) after adjusting for covariates including measured BMI. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits. These results highlight delta-BMI's potential as a non-invasive, ECG-based biomarker for cardiometabolic risk stratification. Conclusion Our study demonstrates the effectiveness of an AI-enhanced ECG model in accurately predicting BMI, validated across diverse populations. The introduction of delta-BMI as a predictor of cardiometabolic disease offers valuable additional prognostic insights beyond traditional BMI assessments. Our exploration of biological pathways, spanning phenotypic, genotypic, metabolomic, and proteomic associations, contributes to a comprehensive understanding of the association between delta-BMI and cardiometabolic disease. The clinical implications could extend to enhanced screening strategies, personalised risk assessment, and motivation for lifestyle interventions.

Diagnostic and prognostic evaluation of an echocardiography-based artificial intelligence algorithm for detecting HFpEF: a case-control analysis

Abstract Background Heart failure (HF) with preserved Ejection Fraction (HFpEF) is common among older adults and is associated with a high burden of morbidity and mortality. Despite its increasing prevalence, the diagnosis of HFpEF remains challenging. Recently, the FDA cleared an echocardiography-based AI HFpEF model that utilizes a 3-dimensional convolutional neural network to detect HFpEF using a single 4-chamber clip from a resting echocardiogram. However, the external validation of this algorithm against clinically adjudicated and confirmed HFpEF is limited. Purpose To evaluate the diagnostic and prognostic performance of the echocardiography-based AI HFpEF model in a cohort of HFpEF patients and matched controls. Methods The study included patients with clinically adjudicated HFpEF based on clinical history, normal ejection fraction (>45%), and evidence of elevated filling pressure by resting (PCWP > 15 mm Hg) or exercise invasive hemodynamics (PCWP > 25 mm Hg) or echocardiogram (E/e’ >14). The controls were age, sex, and BMI-matched patients without HF and a normal echocardiogram. The performance of the AI HFpEF model was evaluated using receiver operator curves. In patients with HFpEF, the association of the AI-HFpEF phenotype with elevated resting/exercise PCWP and peak exercise oxygen uptake (VO2peak) was assessed using multivariable logistic and linear regression models adjusting for age, sex, race, BMI, and comorbidities (diabetes, hypertension, kidney disease, atrial fibrillation). Results Of the 166 patients referred for evaluation of HFpEF, 82% had clinically adjudicated HFpEF, and 69.8% had elevated LV filling pressure at rest or exercise. In the matched cohorts of patients with clinically adjudicated HFpEF and matched control individuals (N = 122 each), the AI algorithm-based probability of HFpEF demonstrated good performance in identifying clinically adjudicated and hemodynamically confirmed HFpEF (AUROC: 0.75 for each) that was greater than the widely used H2FpEF score (0.69 and 0.70, Figure). In the HFpEF referral cohort, a higher probability of HFpEF based on the AI-algorithm phenotype was significantly associated with lower VO2peak (b [95% CI] per 5% higher probability: -0.11 [-0.21 to -0.01, P-value: 0.03] and greater odds of elevated PCWP (Odds ratio [95% CI] per 5% higher probability: 1.07 [1.01 – 1.15, P-value: 0.04] at rest or exercise after accounting for other confounders. Based on Youden’s index, the AI algorithm-based probability threshold of >0.75 was identified as the optimal cutoff for detecting HFpEF by the AI algorithm, with high sensitivity (0.85) and accuracy (0.74) and, adequate specificity (0.66). Conclusion The echocardiography-based AI HFpEF model demonstrated excellent sensitivity and discrimination in identifying patients with vs. without clinical HFpEF. Furthermore, the AI HFpEF model also had prognostic utility such that individuals with a higher probability of AI-HFpEF phenotype had more severe HFpEF.

Association of MRI-based knee osteoarthritis structural phenotypes with short-term structural progression and subsequent total knee replacement

BackgroundThe failure of disease-modifying osteoarthritis drugs (DMOADs) trials lies mainly in the heterogeneity of the disease, which calls for a more precise population with specific progression and outcomes. This study aimed to determine whether and which MRI-based structural phenotype of knee osteoarthritis (KOA) is associated with short-term structural progression and subsequent total knee replacement (TKR).MethodsA longitudinal study was conducted among participants with baseline Kellgren-Lawrence grade (KLG) ≥ 2 from the Osteoarthritis Initiative (OAI). The structural phenotypes at baseline were defined as subchondral bone, meniscus/cartilage and inflammatory phenotypes according to the MRI Osteoarthritis Knee Score (MOAKS). The primary outcome was the progression of structural abnormalities within 24 months and multivariable logistic regressions were applied to evaluate the associations. The secondary outcome was the incidence of TKR during 108 months. Cox regressions and Kaplan–Meier survival curves were used for the analysis.ResultsA total of 733 participants with KOA were finally included in our study, with 493 (67.3%) having the three main structural phenotypes. For the primary outcome, the subchondral bone phenotype (OR [95% CI]:1.71 [1.02, 2.83], 1.52 [1.06, 2.18], 1.65 [1.11, 2.42], respectively) and the inflammatory phenotype (OR [95% CI]: 1.69 [1.05, 2.74], 1.82 [1.31, 2.52], 2.15 [1.48, 3.14], respectively) were both associated with the short-term progression of joint space narrowing, osteophytes and sclerosis in 24 months, whereas the meniscus/cartilage phenotype was only associated with the progression of osteophytes and sclerosis. For the secondary outcome, the subchondral bone phenotype (HR [95% CI]: 1.71 [1.06–2.78]) and inflammatory phenotype (HR [95%CI]: 2.00 [1.02–2.67]) were associated with shorter time to subsequent TKR, but not the meniscus/cartilage phenotype. Besides, the cumulative effect when the structural phenotype overlapped was confirmed in both outcomes.ConclusionsThe subchondral bone phenotype and inflammatory phenotype were associated with the progression of joint space narrowing, osteophytes and sclerosis in 24 months, along with subsequent TKR in 108 months. Besides, additive effects of overlapped phenotypes were further determined. These phenotypes could serve as valuable screening tools for future clinical trials and provide guidance for risk evaluation.

Comprehensive Analysis of Phenotypic Traits in Chinese Cabbage Using 3D Point Cloud Technology

Studies on the phenotypic traits and their associations in Chinese cabbage lack precise and objective digital evaluation metrics. Traditional assessment methods often rely on subjective evaluations and experience, compromising accuracy and reliability. This study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology, with the aim of enhancing the precision, reliability, and standardization of the comprehensive phenotypic traits of Chinese cabbage. By using multi-view image sequences and structure-from-motion algorithms, 3D point clouds of 50 plants from each of the 17 Chinese cabbage varieties were reconstructed. Color-based region growing and 3D convex hull techniques were employed to measure 30 agronomic traits. Comparisons between 3D point cloud-based measurements of the plant spread, plant height, leaf area, and leaf ball volume and traditional methods yielded R2 values greater than 0.97, with root mean square errors of 1.27 cm, 1.16 cm, 839.77 cm3, and 59.15 cm2, respectively. Based on the plant spread and plant height, a linear regression prediction of Chinese cabbage weights was conducted, yielding an R2 value of 0.76. Integrated optimization algorithms were used to test the parameters, reducing the measurement time from 55 min when using traditional methods to 3.2 min. Furthermore, in-depth analyses including variation, correlation, principal component analysis, and clustering analyses were conducted. Variation analysis revealed significant trait variability, with correlation analysis indicating 21 pairs of traits with highly significant positive correlations and 2 pairs with highly significant negative correlations. The top six principal components accounted for 90% of the total variance. Using the elbow method, k-means clustering determined that the optimal number of clusters was four, thus classifying the 17 cabbage varieties into four distinct groups. This study provides new theoretical and methodological insights for exploring phenotypic trait associations in Chinese cabbage and facilitates the breeding and identification of high-quality varieties. Compared with traditional methods, this system provides significant advantages in terms of accuracy, speed, and comprehensiveness, with its low cost and ease of use making it an ideal replacement for manual methods, being particularly suited for large-scale monitoring and high-throughput phenotyping.

The Spondylocarpotarsal Synostosis Syndrome Case with Ventricular Septal Defect Caused by a Homozygous Frameshift Variation in FLNB

AbstractSpondylocarpotarsal synostosis syndrome (SCT) (OMIM: 272460), an autosomal recessive disorder, is characterized by block vertebrae, carpal–tarsal synostosis, and disproportionate short stature. The clinical features of SCT include dysmorphic facial traits, cleft palate, hypoplastic tooth enamel, brachydactyly, clinodactyly, limited joint movement, and mixed hearing loss. SCT is associated with biallelic nonsense and frameshift pathogenic variants in the FLNB gene. An 8-year-old patient was referred with suspected skeletal dysplasia. The patient presented with short stature, scoliosis, carpal–tarsal synostosis, and ventricular septal defect (VSD). Clinical exome sequence analysis revealed a homozygous frameshift variation in the FLNB gene (c.219_222dup p.Met75Profs*17). No other pathogenic variations in genes related to VSD were found. This study aims to discuss novel clinical findings related to the genotype–phenotype association. To the best of our knowledge, this is the first reported case of SCT with VSD.

Clinical Phenotypes Associated With Cerebral Small Vessel Disease: A Study of 45,013 UK Biobank Participants.

Cerebral small vessel disease (cSVD) is the most common pathology underlying vascular cognitive impairment. Although other clinical features of cSVD are increasingly recognized, it is likely that certain symptoms are being overlooked. A comprehensive description of cSVD associations with clinical phenotypes at scale is lacking. The objective of this study was to conduct a large-scale, hypothesis-free study of associations between cSVD and clinical phenotypes in UK Biobank (UKB). We included participants from the UKB imaging study who had available information on total volume of white matter hyperintensities (WMHs), the most common cSVD neuroimaging feature. We included various UKB variables describing clinical phenotypes, defined as observable signs and symptoms of individuals with concurrent neuroimaging evidence of cSVD. We conducted a phenome scan using the open-source PHESANT software package. Total volume of WMHs was introduced as the independent variable and clinical phenotypes as the dependent variables in the regression model. The association of each phenotype with total volume of WMHs was tested using one of several regression analyses (all age at recruitment and sex-adjusted). All associations were corrected for multiple comparisons using the false discovery rate (FDR) correction method. We included 45,013 participants in the analysis (mean age = 54.97 years, SD = 7.55). We confirm previously reported associations with depression (odds ratio [OR] = 1.07 [95% CI 1.05-1.10]), apathy (OR = 1.11 [95% CI 1.08-1.14]), falls (OR = 1.11 [95% CI 1.09-1.13]), respiratory problems (OR = 1.14 [95% CI 1.04-1.25]), and sleep disturbance (OR = 1.07 [95% CI 1.04-1.09], all FDR-adjusted p < 0.001). We further identified associations with all-cause dental issues (OR = 0.94 [95% CI 0.96-0.92]), hearing problems (OR = 1.06 [95% CI 1.03-1.08]), and eye problems (OR = 0.93 [95% CI 0.91-0.95], all FDR-adjusted p < 0.001). Our findings suggest that presence of cSVD associates with concurrent clinical phenotypes across several body systems. We have corroborated established associations of cSVD and present novel ones. While our results do not provide causality or direction of association because of the cross-sectional nature of our study, they support the need for a more holistic view of cSVD in research, practice, and policy.

PWAS Hub for exploring gene-based associations of common complex diseases.

PWAS (proteome-wide association study) is an innovative genetic association approach that complements widely used methods like GWAS (genome-wide association study). The PWAS approach involves consecutive phases. Initially, machine learning modeling and probabilistic considerations quantify the impact of genetic variants on protein-coding genes' biochemical functions. Secondly, for each individual, aggregating the variants per gene determines a gene-damaging score. Finally, standard statistical tests are activated in the case-control setting to yield statistically significant genes per phenotype. The PWAS Hub offers a user-friendly interface for an in-depth exploration of gene-disease associations from the UK Biobank (UKB). Results from PWAS cover 99 common diseases and conditions, each with over 10,000 diagnosed individuals per phenotype. Users can explore genes associated with these diseases, with separate analyses conducted for males and females. For each phenotype, the analyses account for sex-based genetic effects, inheritance modes (dominant and recessive), and the pleiotropic nature of associated genes. The PWAS Hub showcases its usefulness for asthma by navigating through proteomic-genetic analyses. Inspecting PWAS asthma-listed genes (a total of 27) provide insights into the underlying cellular and molecular mechanisms. Comparison of PWAS-statistically significant genes for common diseases to the Open Targets benchmark shows partial but significant overlap in gene associations for most phenotypes. Graphical tools facilitate comparing genetic effects between PWAS and coding GWAS results, aiding in understanding the sex-specific genetic impact on common diseases. This adaptable platform is attractive to clinicians, researchers, and individuals interested in delving into gene-disease associations and sex-specific genetic effects.

Exploring the variation association between four head traits

This research work presents a comprehensive overview of four traits related to the head, with the aim of assessing the statistical phenotypic association among them. The traits examined in this study encompass earlobe type, tongue rugosity, cleft chin and tongue rolling. The primary objective was to investigate the potential associations between these traits and understand their interrelationships. The study focused on examining specific traits in a diverse group of 7431 unrelated individuals, where the genders were almost evenly distributed. To facilitate a comprehensive analysis, three distinct groups were created for each characteristic, comprising the total population, as well as male and female subsets. The selection of subjects was carefully done to ensure a fair representation across different geographical regions within Bosnia and Herzegovina, thereby accurately reflecting the nation's national and ethnic diversity. The association among these traits was assessed for statistical significance using the Chi-squared test, with Fisher's exact test used as a supplementary method to examine the connection between each pair of observed traits. Additionally, the Chi-squared test was applied to examine gender-based differences in the frequencies of the phenotypic characteristics of the head. Following traits were shown to have a statistically significant association: tongue rugosity - tongue rolling, tongue rugosity – earlobe type, cleft chin – earlobe type, cleft chin – tongue rolling and earlobe type – tongue rolling. Investigation into the variations in the frequencies of observed phenotypic traits of the head, with respect to gender, revealed statistically significant results for every trait examined.

Associations of metabolic heterogeneity of obesity with the risk of dementia in middle-aged adults: three prospective studies

BackgroundThe associations of different obesity and metabolic phenotypes during midlife with the risk of incident dementia remain unclear. This study aimed to investigate the associations between metabolic heterogeneity of obesity and long-term risk of dementia.MethodsWe conducted prospective analyses from three cohorts, including the UK Biobank (UKB), Atherosclerosis Risk in Communities (ARIC) study, and Framingham Offspring Study (FOS). Eligible participants were those aged 45–65 years with valid assessments of body mass index (BMI) and metabolic status at the study baseline. Obesity was defined as a BMI of ≥ 30.0 kg/m2, while metabolic abnormality was defined as meeting ≥ 2 of the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) criteria. Metabolic heterogeneity of obesity was evaluated based on obesity and metabolic phenotypes and grouped as metabolically normal non-obesity (MNNO), metabolically abnormal non-obesity (MANO), metabolically normal obesity (MNO), and metabolically abnormal obesity (MAO).ResultsIncluded in this study were 295,823 participants aged 56.3 ± 5.9 years from the UKB, 12,547 participants aged 54.0 ± 5.7 years from the ARIC, and 2,004 participants aged 53.9 ± 5.9 years from the FOS. Over 4,348,208 person-years, a total of 6,190 participants (3,601 in the UKB, 2,405 in the ARIC, and 184 in the FOS) developed incident dementia. In the pooled analysis of three cohorts, metabolic abnormality was associated with a hazard ratio (HR) of 1.41 (95% confidence interval [CI]: 1.10–1.80) for dementia, while obesity was associated with an HR of 1.20 (1.03–1.41). Compared with MNNO, individuals with MANO and MAO had increased risks of dementia (pooled HR: 1.33, 95% CI: 1.04–1.71 for MANO and 1.48, 1.16–1.89 for MAO). However, there was no significant difference in the risk of dementia among MNO (pooled HR: 1.10, 95% CI: 0.98–1.24). In addition, participants who recovered from MANO to MNNO had a lower risk of dementia (pooled HR: 0.79, 95% CI: 0.64–0.97), as compared with stable MANO.ConclusionsMetabolic abnormality has a stronger association with dementia than obesity. Metabolically abnormal non-obesity and obesity, but not metabolically normal obesity, are associated with higher risks of incident dementia as compared with metabolically normal non-obesity. Recovering from an abnormal metabolic status to normal reduces the risk of dementia in populations without obesity. Our findings highlight the important role of metabolic status in the development of dementia and recommend the stratified management of obesity based on metabolic status.

Association of serum interferon alpha-2a levels with disease severity and prognosis in systemic sclerosis.

To determine serum type I interferon IFN-α2a concentrations in systemic sclerosis (SSc) patients, explore its association with cytokine/chemokine expressions, and evaluate correlation with the phenotype including the predictive value for interstitial lung disease (ILD) progression. Serum samples were obtained from 200 SSc patients and 29 healthy controls. IFN-α2a levels were measured by ultrasensitive electrochemiluminescence assay. Pro-inflammatory and chemokine panels were determined by Luminex® Discovery Assay multiplex kit. Baseline SSc disease characteristics were recorded together with longitudinal data for determining ILD progression after 2 years. IFN-α2a concentrations were higher in SSc patients compared with controls, although not reaching significance (means ± SD of 49.20 ± 156.8 pg/ml vs 9.606 ± 4.399 pg/ml, respectively; (p = 0.158)). Using the cut-off of 15.9 pg/ml, we identified 62 patients as having a type 1 (T1) IFN signature in their circulation. Patients with an IFN signature had significantly higher levels of chemokines (CCL8, CCL19, CXCL10, CXCL11), and the cytokine IL-1α compared with those without an IFN signature. IFN-α2a concentrations strongly correlated with a T1 IFN related chemokine score supporting activation of this pathway. Phenotyping association queries revealed association between IFN values and both skin and ILD involvements at baseline. Longitudinal data did not identify IFN as a predictive marker for ILD progression. Using serum determinations, the activation of the T1 IFN pathway showed strong correlations with inflammatory mediators and associations with clinical manifestations, especially skin fibrosis and ILD in SSc patients. However, activated IFN pathway was not predictive of ILD progression.

Association of metabolically healthy obesity with risk of heart failure and left ventricular dysfunction among older adults.

Obesity is major cause of heart failure (HF), but it is related with a better prognosis among the elderly. Therefore, we aimed to examine whether metabolically healthy obesity (MHO) in late life increases HF risk and is reflected in impaired left ventricular (LV) function. The participants were grouped into four metabolic phenotypes based on obesity and metabolic status: metabolically healthy non-obesity (MHN), MHO, metabolically unhealthy non-obesity (MUN), metabolically unhealthy obesity (MUO). Association of metabolic phenotypes with LV function was evaluated using multiple linear regression models. And association between metabolic phenotypes and risk of HF was assessed using multivariable logistic regression models. In addition, we validated the association of metabolic phenotypes and HF risk in a separate longitudinal cohort. In the primary cohort of 6335 participant, there were 434 participants diagnosed with HF. Compared to MHN participants, the risk of HF was higher among older individuals with MUN (OR = 1.51 [95% CI: 1.14-1.99]) and MUO (OR = 2.01 [95% CI: 1.39-2.91]), but not older individuals with MHO (OR = 0.86 [95% CI: 0.30-2.43). Regarding to LV function, worse LV diastolic function was noted among MUN and MUO individuals rather than MHO individuals. Older adults with MHO were also not associated with risk of HF in the validation cohort. Among older individuals, the metabolic health status might modify the association of obesity with risk of HF and LV diastolic dysfunction. Worse LV diastolic function and higher risk of HF were just noted in individuals with MUO, but not in those with MHO.

Genetic Associations of Persistent Opioid Use After Surgery Point to OPRM1 but Not Other Opioid-Related Loci as the Main Driver of Opioid Use Disorder.

Persistent opioid use after surgery is a common morbidity outcome associated with subsequent opioid use disorder, overdose, and death. While phenotypic associations have been described, genetic associations remain unidentified. Here, we conducted the largest genetic study of persistent opioid use after surgery, comprising ~40,000 non-Hispanic, European-ancestry Michigan Genomics Initiative participants (3198 cases and 36,321 surgically exposed controls). Our study primarily focused on the reproducibility and reliability of 72 genetic studies of opioid use disorder phenotypes. Nominal associations (p < 0.05) occurred at 12 of 80 unique (r2 < 0.8) signals from these studies. Six occurred in OPRM1 (most significant: rs79704991-T, OR = 1.17, p = 8.7 × 10-5), with two surviving multiple testing correction. Other associations were rs640561-LRRIQ3 (p = 0.015), rs4680-COMT (p = 0.016), rs9478495 (p = 0.017, intergenic), rs10886472-GRK5 (p = 0.028), rs9291211-SLC30A9/BEND4 (p = 0.043), and rs112068658-KCNN1 (p = 0.048). Two highly referenced genes, OPRD1 and DRD2/ANKK1, had no signals in MGI. Associations at previously identified OPRM1 variants suggest common biology between persistent opioid use and opioid use disorder, further demonstrating connections between opioid dependence and addiction phenotypes. Lack of significant associations at other variants challenges previous studies' reliability.

A comprehensive analysis of the effects of DGAT1 K232A polymorphism on milk production and fertility traits in Holstein Friesian and Jersey cows reared in Türkiye

Abstract. Research on the diacylglycerol acyltransferase 1 (DGAT1) K232A marker in cattle shows inconsistent results across regions, largely due to small sample sizes, limited genetic variation, and data restricted to few lactations, which complicates establishing a reliable genotype–phenotype correlation. This research aimed to determine the effect of the K232A polymorphism of the bovine DGAT1 gene on milk production and quality traits in dairy cattle. We used 1104 cattle, including 828 Holstein Friesian and 276 Jersey cows. The analysis utilized extensive data from six lactations of cows raised on four commercial dairy farms. We genotyped the population using the polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) technique and Sanger sequencing for verification. We then evaluated the 305 d and test-day milk yields as well as fat and protein yields and percentages. The number of inseminations per conception and calving ease were also assessed as reproduction indices. Genotype–phenotype associations were quantified using linear mixed models. The AA genotype was absent in Jersey cows, and the heterozygous genotype was predominant in both breeds. The K232A marker was significantly associated with test-day milk yield, fat, and protein content in Jersey cows. Further, it substantially affected the fat percentage of milk in Holstein Friesian cows (p&lt;0.001). We found that the KK genotype is highly desirable for milk quality and especially fat content. This comprehensive assessment demonstrated that the KK genotype of the DGAT1 K232A polymorphism significantly influenced fat and protein contents in dairy cattle.

A meta-analysis on polymorphic trait of taste perception mediated by TAS2R38 genotype.

The objective of this study is to review the association of TAS2R38 polymorphisms and taste phenotypes to bitter compounds (phenylthiocarbamide [PTC]/propylthiouracil [PROP]), and its association among persons who drink alcohol and individuals with smoking behavior. A literature search was carried out in PubMed, ScienceDirect, Cochrane, and Wiley online library databases using the keyword "(Bitter taste receptor genes OR TAS2R38) AND (PROP OR propylthiouracil) AND (PTC OR phenylthiocarbamide)," "(Bitter taste receptor genes OR TAS2R38) AND (alcohol)," "(Bitter taste receptor genes OR TAS2R38) AND (tobacco OR smoker)" to find articles evaluating the association of taste phenotypes and TAS2R38 polymorphisms, and its association among persons who drink alcohol and individuals with smoking behavior. The analysis show that TAS2R38 taster genotype (proline-alanine-valine [PAV] allele) was significantly (OR, 5.88; CI [3.87, 8.95], p < .001) associated with taster phenotype for bitter compounds (PTC/PROP), and TAS2R38 nontaster genotype (alanine-valine-isoleucine allele) was significantly (OR, 6.73; CI [4.57, 9.90], p < .001) associated with nontaster phenotype for bitter compounds. Further, TAS2R38 taster genotypes (PAV homozygotes and heterozygotes) were significantly associated with higher alcohol intake (OR, 5.15; 95% CI [2.66, 9.98]; p < .001) and among individuals with smoking behavior (OR, 1.73; 95% CI [1.24, 2.42]; p = .001). This suggests that TAS2R38 single nucleotide polymorphisms can be identified by clinically assessing taste phenotype status for bitter compounds and can be used as a potential therapeutic target in the prevention and treatment of harmful higher alcohol intake and smoking behavior. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Prevalence of malnutrition based on different combinations of Global Leadership Initiative on Malnutrition (GLIM) phenotypic and etiologic criteria and association with the course of Crohn's Disease (CD)

Prevalence of malnutrition based on different combinations of Global Leadership Initiative on Malnutrition (GLIM) phenotypic and etiologic criteria and association with the course of Crohn's Disease (CD)

Talking about diseases; developing a model of patient and public-prioritised disease phenotypes.

Deep phenotyping describes the use of standardised terminologies to create comprehensive phenotypic descriptions of biomedical phenomena. These characterisations facilitate secondary analysis, evidence synthesis, and practitioner awareness, thereby guiding patient care. The vast majority of this knowledge is derived from sources that describe an academic understanding of disease, including academic literature and experimental databases. Previous work indicates a gulf between the priorities, perspectives, and perceptions held by different healthcare stakeholders. Using social media data, we develop a phenotype model that represents a public perspective on disease and compare this with a model derived from a combination of existing academic phenotype databases. We identified 52,198 positive disease-phenotype associations from social media across 311 diseases. We further identified 24,618 novel phenotype associations not shared by the biomedical and literature-derived phenotype model across 304 diseases, of which we considered 14,531 significant. Manifestations of disease affecting quality of life, and concerning endocrine, digestive, and reproductive diseases were over-represented in the social media phenotype model. An expert clinical review found that social media-derived associations were considered similarly well-established to those derived from literature, and were seen significantly more in patient clinical encounters. The phenotype model recovered from social media presents a significantly different perspective than existing resources derived from biomedical databases and literature, providing a large number of associations novel to the latter dataset. We propose that the integration and interrogation of these public perspectives on the disease can inform clinical awareness, improve secondary analysis, and bridge understanding and priorities across healthcare stakeholders.

Delineation of Selection Criterion Using Correlation and Path Analysis in Forage Pearl Millet (Pennisetum glaucum (L.) R. Br.) in North Gujarat Condition

Knowledge of genetic and phenotypic association among economic traits helps plant breeders in outlying efficient breeding strategies for development of high yielding forage pearl millet variety. An experiment was conducted at the research farm of Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar, Gujarat to evaluate plant characteristics associated with green forage yield and its attributes in forage pearl millet [Pennisetum glaucum (L.) R. Br.] genotypes in north Gujarat condition through correlation and path analyses during 2023. Twelve independent and 1 dependent variables were evaluated for the character association analysis of the 30 forage pearl millet genotypes. The evaluation was performed in a Randomized Complete Block Design with three replications. Data collection was done from 5 randomly selected plants and this data was used for analysis using R Studio. The green forage yield per plant had positive and significant correlation with plant height, number of tiller per plant, stem thickness, leaf length, leaf width, and dry fodder yield per plant at both genotypic and phenotypic level. Path analysis revealed the importance of leaf width and dry fodder yield per plant by showing high and positive direct effects towards green forage yield per plant. These characters also exhibited significant and positive association with green forage yield per plant at the genotypic and phenotypic. Hence, these traits could be considered as the vital component characters for development of high yielding genotypes in forage pearl millet.