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PubMed2026

Methods for modeling gene-environment interplay using polygenic risk scores.

Polygenic risk scores (PRS) are increasingly recognized as pivotal tools for quantifying disease risk through the aggregation of multiple genetic variants. As sample sizes in genome-wide association studies (GWAS) continue to expand and PRS become more powerful, they are set to play a key role in translational research and personalized medicine. Understanding the interplay of PRS with environmental factors is critical for interpreting and applying PRS in diverse contexts. This interplay manifests in two forms: PRS-by-environment interaction (PRS × E) and gene-environment correlation (rGE). However, despite the growing application and importance of PRS, there are limited guidelines for performing PRS × E interaction analyses while controlling for rGE, which can lead to inconsistencies across studies and misinterpretation of results. Here we provide a review of different methods for performing PRSxE interaction in various epidemiological study designs, propose recommendations for best-practice, and discuss future challenges.

Gene-Environment Interaction↗

PubMed2025

Associations Between Polygenic Risk Score for Blood Pressure and Risk of Hypertension in Northeast Asian Individuals.

BACKGROUND: Data on associations between genetic predisposition to high blood pressure (BP) and hypertension and its complications in non-European populations are limited. The current study investigated associations between polygenic risk scores (PRSs) for BP and risks of hypertension, cardiovascular disease, and chronic kidney disease in Northeast Asian populations. METHODS: A genome-wide association study of systolic BP (SBP) and diastolic BP (DBP) was conducted using data from the KoGES (Korean Genome and Epidemiology Study). Results were meta-analyzed using summary statistics from Biobank Japan to construct PRSs. RESULTS: Compared with a PRS in the lowest 5 percentiles, a PRS in the highest 5 percentiles was associated with an increased risk of hypertension (hazard ratio [HR], 2.44 [95% CI, 1.67-3.56] for PRS for SBP; and HR, 1.77 [95% CI, 1.20-2.62] for PRS for DBP) and earlier onset of hypertension (by a median of 8.5 years for PRS for SBP and 8.0 years for PRS for DBP). These associations remained significant when continuous PRS was analyzed. The genetic risk of hypertension incidence was attenuated by moderate to vigorous physical activity. Adding the PRS for BP to the clinical risk factors improved the predictive value for hypertension (both area under the curve values, 0.787 [95% CI, 0.771-0.803]; P=0.063 for PRS for SBP and [95% CI, 0.771-0.804]; P=0.031 for PRS for DBP). However, neither PRS for SBP nor PRS for DBP was associated with the incidence of cardiovascular or chronic kidney disease. CONCLUSIONS: The PRS for BP was associated with a higher risk of incident hypertension and earlier-onset hypertension in a Northeast Asian population. PRS may facilitate early identification and targeted management of individuals at high risk of developing hypertension.

Adult↗

PubMed2026

Association between oxidative balance score and cardiovascular risk factors, aging, and incidence of dementia risk score: A prospective cohort study.

BackgroundOxidative stress is a key contributor to the pathogenesis of Alzheimer's disease and other dementias. The oxidative balance score (OBS), which reflects combined dietary and lifestyle exposure to pro-oxidant and antioxidant factors, serves as an integrated measure of oxidative stress burden.ObjectiveTo investigate the association between OBS and predicted late-life dementia risk using the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) score.MethodsWe analyzed data from 5088 participants aged 40-69 years without dementia at baseline from the Korean Genome and Epidemiology Study. Participants were categorized by OBS tertiles. Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for developing a high risk of late-life dementia, defined by a CAIDE score ≥8. Longitudinal changes in CAIDE scores were assessed using linear mixed-effects models.ResultsDuring a mean follow-up of 12.8 years, 1468 participants (28.9%) progressed to CAIDE-predicted high risk for late-life dementia. Compared with the lowest OBS tertile (T1), participants in the highest tertile (T3) had a significantly lower risk for developing high-risk late-life dementia (HR 0.74, 95% CI 0.65-0.84) and exhibited the lowest CAIDE scores (p < 0.001). Each one-point increase in the OBS was associated with a 3% reduction in CAIDE-predicted dementia risk.ConclusionsA higher OBS was significantly associated with a lower predicted risk of late-life dementia. These findings suggest that maintaining an antioxidant-rich diet and a healthy lifestyle during midlife may be effective strategies for dementia prevention.

Alzheimer's disease↗

PubMed2026

Harnessing Polygenic Risk Scores to Refine Venous Thromboembolism Risk Stratification.

BACKGROUND: Venous thromboembolism (VTE) is a major cause of morbidity in patients of all ages. Despite growing interest in polygenic risk scores (PRS) for VTE, their utility remains understudied. Our objective was to evaluate the independent impact of a PRS on VTE susceptibility in adults and children. METHODS: We completed a retrospective, case-control study of two separate cohorts with evaluation of three VTE PRS models, with the primary analysis focused on a 293 single nucleotide polymorphism (SNP) PRS. The adult cohort included 597 VTE cases and 31 998 controls, and the pediatric cohort included 109 cases and 448 controls, both obtained from a de-identified databank with linked genetic data. Separate adult and pediatric multivariable logistic regressions were performed to measure the association of risk factors with VTE. RESULTS: Higher PRS in adults was significantly associated with increased odds of VTE, with each 1-standard deviation increase in PRS conferring an adjusted odds ratio of 1.25 (OR = 1.25, 95% CI 1.15-1.36, p < 0.001). Leading risk factors for adults were cancer (OR = 2.43, 95% CI: 2.04-2.89, p < 0.001) and recent surgery (OR = 2.16, 95% CI: 1.83-2.54, p < 0.001). The standardized PRS also exhibited increased risk for VTE in children (OR = 1.38, 95% CI 1.10-1.74, p = 0.003). Central venous catheterization (OR = 5.65, 95% CI 3.40-9.50, p < 0.001) was the foremost risk factor for pediatric VTE. CONCLUSION: VTE in adults and children is multifactorial, with clinical and genome-wide risk factors contributing. PRS may serve as a valuable adjunct to clinical risk factors for VTE risk stratification.

Humans↗

PubMed2025

Integrating Imaging-Derived Clinical Endotypes with Plasma Proteomics and External Polygenic Risk Scores Enhances Coronary Microvascular Disease Risk Prediction.

Coronary microvascular disease (CMVD) is an underdiagnosed but significant contributor to the burden of ischemic heart disease, characterized by angina and myocardial infarction. The development of risk prediction models such as polygenic risk scores (PRS) for CMVD has been limited by a lack of large-scale genome-wide association studies (GWAS). However, there is significant overlap between CMVD and enrollment criteria for coronary artery disease (CAD) GWAS. In this study, we developed CMVD PRS models by selecting variants identified in a CMVD GWAS and applying weights from an external CAD GWAS, using CMVD-associated loci as proxies for the genetic risk. We integrated plasma proteomics, clinical measures from perfusion PET imaging, and PRS to evaluate their contributions to CMVD risk prediction in comprehensive machine and deep learning models. We then developed a novel unsupervised endotyping framework for CMVD from perfusion PET-derived myocardial blood flow data, revealing distinct patient subgroups beyond traditional case-control definitions. This imaging-based stratification substantially improved classification performance alongside plasma proteomics and PRS, achieving AUROCs between 0.65 and 0.73 per class, significantly outperforming binary classifiers and existing clinical models, highlighting the potential of this stratification approach to enable more precise and personalized diagnosis by capturing the underlying heterogeneity of CMVD. This work represents the first application of imaging-based endotyping and the integration of genetic and proteomic data for CMVD risk prediction, establishing a framework for multimodal modeling in complex diseases.

Cardiovascular Disease↗

PubMed2026

Genome-wide association, polygenic risk scores, and machine learning for chronic post-surgical pain risk stratification: A UK biobank study.

Chronic post-surgical pain is a prevalent and debilitating complication following surgery, representing a clinical challenge. Despite the established heritability of pain phenotypes, large-scale genetic studies remain limited. This study aimed to identify genetic variants associated with chronic post-surgical pain, develop polygenic risk scores, and integrate these with clinical features for risk prediction. UK Biobank data from 47,836 participants (2490 cases and 45,346 controls) were split into training (80%; n = 38,268) and validation (20%; n = 9568) sets prior to analysis. A genome-wide association study was conducted on the training set only, across 19 million variants, and polygenic risk scores were constructed and integrated with clinical features in a logistic regression framework. Two close, rare, imputed signals crossed the genome-wide significance threshold but lacked local linkage-disequilibrium support, while 220 variants crossed the suggestive threshold. In the held-out validation set, cases had higher mean polygenic risk scores than controls (0.138 vs. -0.021; Cohen's d = 0.16, p < 0.001). A logistic regression model integrating clinical features and polygenic risk scores achieved an area under the curve of 0.639 (95% CI: 0.583-0.693), higher than models using either feature set alone. The polygenic risk score for chronic post-surgical pain was among the most important predictors. Risk stratification revealed the top quartile had 3.84-fold higher odds of chronic post-surgical pain than the bottom quartile (95% CI: 2.00-7.37). These findings suggest a possible modest genetic contribution to chronic post-surgical pain. Polygenic risk scores may complement clinical factors in surgical risk stratification. PERSPECTIVE: Chronic post-surgical pain may have a modest genetic contribution. This UK Biobank study identified over 220 variants at suggestive significance and constructed a polygenic risk score that was significantly elevated in cases. A combined clinical-genomic model achieved a 3.84-fold difference in odds across predicted-risk quartiles.

Chronic post-surgical pain↗

PubMed1977

Clinical application of high-risk scoring on an obstetric service.

Obstetric risk scoring is a formalized way of recognizing, documenting, and cumulating antepartum and intrapartum factors to predict later complications for mother, fetus, and infant. If simple, practical, and reliable, risk scoring can be clinically useful in determining appropriate levels of care. In this prospective study, antepartum and intrapartum risk scales were integrated into the clinical record, and the relationship of risk scores to outcome was evaluated for 1,275 consecutively delivered gravid women. The forms could be simply and quickly filled out by the staff. Increased risk on both scales was significantly related to lowered one- and five-minute Apgar scores. The perinatal mortality rate increased from 0 to 93.4 per thousand from the lowest to the highest risk group. More than 80% of all perinatal deaths occurred in the one quarter of patients in the highest risk group. These results suggest that this risk scoring system can be used effectively in a clinical setting to identify patients at increased risk for neonatal depression and perinatal death.

Apgar Score↗

PubMed2024

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.

BACKGROUND: Incorporating genomic data into risk prediction has become an increasingly useful approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. METHODS: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. RESULTS: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p-0.003), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. CONCLUSION: Results, especially those from the eMRS, reinforce earlier findings that methylation and trauma are interconnected and can be leveraged to increase the correct classification of those with vs. without PTSD. Moreover, our models can potentially be a valuable tool in predicting the future risk of developing PTSD. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting the condition and, relatedly, improve their performance in independent cohorts.

DNA methylation↗

PubMed2024

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.

BACKGROUND: Incorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. METHODS: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. RESULTS: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p=0.006), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. CONCLUSION: The inclusion of exposure variables adds to the predictive power of MRS. Classification-based MRS may be useful in predicting risk of future PTSD in populations with anticipated trauma exposure. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting PTSD and, relatedly, improve their performance in independent cohorts.

Humans↗

PubMed2026

Nonuniform Association of Genetic Risk Scores for Intraocular Pressure.

IMPORTANCE: Elevated intraocular pressure (IOP) is a risk factor for primary open-angle glaucoma, and genetic risk scores hold promise as a tool for screening for ocular hypertension. However, genetic risk scores for IOP have a nonuniform association across the range of IOP, which reduces their accuracy. OBJECTIVE: To test the hypothesis that nonuniform behavior of genetic risk scores for IOP is associated with a specific type of genetic interaction. DESIGN, SETTING, AND PARTICIPANTS: Cross-sectional, post hoc genetic association studies were performed using linear and quantile regression in a sample of UK Biobank participants. Data were analyzed from January to September 2025. EXPOSURES: Ninety-eight genetic variants associated with IOP. MAIN OUTCOMES AND MEASURES: Tests were carried out for 98 genetic variants associated with IOP (P < 5.0 ×10-8) to examine (1) dominant or recessive genetic effects, (2) genotype × genotype interactions, (3) genotype × age interactions, and (4) genotype × sex interactions. RESULTS: A total of 98 235 participants (mean [SD] age, 58.1 [7.9] years; 52 168 female [53.1%]) were included in this analysis. More variants exhibited genotype × age interactions than expected by chance (14 of the 98 variants associated with IOP had at least nominal evidence of an interaction with age; P = 3.76 ×10-4). For 12 of these 14 variants, age increased rather than decreased the magnitude of the IOP vs genotype association. However, integrating age interactions into the genetic risk score construction process did not yield improved accuracy (incremental noninteraction model, R2 = 4.05; 95% CI, 3.82-4.31 and interaction model, R2 = 4.04; 95% CI, 3.80-4.27). There was little support for other types of genetic interaction. CONCLUSIONS AND RELEVANCE: In the current work, findings show minimal evidence that nonadditive allelic effects, genotype × genotype interactions, and genotype × sex interactions contributed to the nonuniform association of genetic variants with IOP across quantiles of IOP. Although a genetic risk score for IOP was more accurate in older vs younger individuals, efforts to account for genotype × age interactions in genetic risk score construction did not improve accuracy. These findings suggest other factors, such as gene-environment interactions, contribute to the nonuniform relationship of genetic variants with IOP.

Humans↗

PubMed2025

XPRS: a tool for interpretable and explainable polygenic risk score.

SUMMARY: The polygenic risk score (PRS) is an important method for assessing genetic susceptibility to diseases; however, its clinical utility is limited by a lack of interpretability tools. To address this problem, we introduce eXplainable PRS (XPRS), an interpretation and visualization tool that decomposes PRSs into genes/regions and single nucleotide polymorphism (SNP) contribution scores via Shapley additive explanations (SHAPs), which provide insights into specific genes and SNPs that significantly contribute to the PRS of an individual. This software features a multilevel visualization approach, including Manhattan plots, LocusZoom-like plots, and tables at the population and individual levels, to highlight important genes and SNPs. By implementing with a user-friendly web interface, XPRS allows for straightforward data input and interpretation. By bridging the gap between complex genetic data and actionable clinical insights, XPRS can improve communication between clinicians and patients. AVAILABILITY AND IMPLEMENTATION: The XPRS software is publicly available on GitHub at https://github.com/nayeonkim93/XPRS and can see the demo through our cloud-based web service at https://xprs.leelabsg.org/.

Software↗

PubMed2025

Cross-omics risk scores of inflammation markers are associated with all-cause mortality: The Canadian Longitudinal Study on Aging.

Inflammation is a critical component of chronic diseases, aging progression, and lifespan. Omics signatures may characterize inflammation status beyond blood biomarkers. We leveraged genetics (polygenic risk score [PRS]), metabolomics (metabolomic risk score [MRS]), and epigenetics (epigenetic risk score [ERS]) to build multi-omics-multi-marker risk scores for inflammation status represented by the level of circulating C-reactive protein (CRP), interleukin 6 (IL-6), and tumor necrosis factor alpha (TNF-α). We found that multi-omics risk scores generally outperformed single-omics risk scores in predicting all-cause mortality in the Canadian Longitudinal Study on Aging. Compared with circulating inflammation biomarkers, some multi-omics risk scores had a higher hazard ratio (HR) for all-cause mortality when including both score and circulating IL-6 in the same model (1-SD IL-6 MRS-ERS: HR = 2.20 [1.55-3.13] vs. 1-SD circulating IL-6 HR = 0.94 [0.67,1.32]. 1-SD IL-6 PRS-MRS: HR = 1.47 [1.35,1.59] vs. 1-SD circulating IL-6 HR = 1.33 [1.18, 1.51]. 1-SD PRS-MRS-ERS: HR = 1.95 [1.40, 2.70] vs. 1-SD circulating IL-6: HR = 0.99 [0.71, 1.39]). In the Nurses' Health Study (NHS), NHS II, and Health Professional Follow-up Study with available omics, 1 SD of IL-6 PRS and 1-SD IL-6 PRS-MRS had HR = 1.12 [1.00,1.26] and HR = 1.13 [1.01,1.26] among individuals >65 years old without mutual adjustment of the score and circulating IL-6. Our study demonstrates that some multi-omics scores for inflammation markers may characterize important inflammation burden for an individual beyond those represented by blood biomarkers and improve our prediction capability for the aging process and lifespan.

Humans↗

PubMed1979

Perinatal mortality and antepartum risk scoring.

A simplified, numerical form for antepartum risk scoring was introduced as a component of the prenatal record for use in all pregancies in a large geographic area under a variety of collection practices. In a population of approximately 1,000,000 with 16,733 deliveries, 19% of the pregnant population scored greater than or equal to 3 and were designated as high risk on the basis of previous pilot studies. This group with high-risk scores had a perinatal mortality rate of 69/1000 compared to the low-risk group with a perinatal mortality of 7/1000 (P = less than 0.0001). The high-risk group accounted for almost 70% of the total perinatal deaths. The implications of being able to predict the statistical likelihood of perinatal deaths are discussed. It is suggested that the risk scoring system has its greatest potential as a screening process and as a method of recording regional statistical trends rather than in dictating the final management of the pregnancy.

Birth Weight↗

PubMed1979

A Simplified antepartum risk-scoring system.

The effectiveness of a simple antepartum risk-scoring system was evaluated in 2085 consecutive deliveries. Neonatal morbidity was observed in 42.1% of infants of mothers classified as high risk (score greater than or equal to 7) compared to 12.5% of infants of mothers classified as low risk (score less than 7). No neonatal deaths were observed in the low-risk group, compared with 34 in the high-risk group (P less than 0.001). Of all perinatal deaths, 88.6% occurred in the high-risk group. The perinatal mortality rates for low- and high-risk pregnancies were 7.2 and 63.3, respectively, per 1000 live births.

False Negative Reactions↗

PubMed2025

Development and evaluation of patient-centred polygenic risk score reports for glaucoma screening.

BACKGROUND: Polygenic risk scores (PRS), which provide an individual probabilistic estimate of genetic susceptibility to develop a disease, have shown effective risk stratification for glaucoma onset. However, there is limited best practice evidence for reporting PRS and patient-friendly reports for communicating PRS effectively are lacking. Here we developed patient-centred PRS reports for glaucoma screening based on the literature, and evaluated them with participants using a qualitative research approach. METHODS: We first reviewed existing PRS reports and literature on probabilistic risk communication. This informed the development of a draft glaucoma screening PRS report for a hypothetical high risk individual from the general population. We designed three versions of the report to illustrate risk using a pictograph, a pie chart and a bell curve. We then conducted semi-structured interviews to assess preference of visual risk communication aids, understanding of risk, content, format and structure of the reports. Participants were invited from an existing study, which aims to evaluate the clinical validity of glaucoma PRS among individuals > 50 years from the general population. Numeracy and literacy levels were assessed. RESULTS: We interviewed 12 individuals. The cohort was highly educated (42% university education), all were European and 50% were female. Numeracy (mean 2.1 ± 0.9, range 0 to 3), graph literacy (mean 2.8 ± 0.8, range 0 to 4) and genetic literacy (mean 24.2 ± 6.2, range - 20 to + 46) showed a range of levels. We analysed the reports under three main themes: visual preferences, understanding risk and reports formatting. The visual component was deemed important to understanding risk, with the pictograph being the preferred visual risk representation, followed by the pie chart and the bell curve. Participants expressed preference for absolute risk in understanding risk, along with the written content explaining the results. The importance of follow-up recommendations and time to glaucoma onset were deemed important. Participants expressed varied opinions in the level of information and the colours used, which informed revisions of the report. CONCLUSIONS: Our study revealed preferences for reporting PRS information in the context of glaucoma screening, to support the development of clinical PRS reporting. Further research is needed to assess PRS communication in other groups representative of target populations and with other target audiences (e.g. referring clinicians), and its potential psychosocial impact in the wider community.

Humans↗

PubMed2026

Genetic risk scores, perceived neighborhood disorder, and sleep duration.

STUDY OBJECTIVES: Most studies of neighborhood context and sleep health emphasize direct effects and fail to account for the role of genetics. In this paper, we draw on the socioecological model to examine the interplay of genetics, neighborhood context, and sleep health. We specifically examine the independent and joint effects of genetic risk scores (GRS) and perceived neighborhood disorder on sleep duration. METHODS: We combine genomic and cross-sectional survey data from the All of Us Research Program, a non-probability sample of 22 575 adults of European ancestry living in the United States. We use the sleep duration-increasing risk allele count for 78 genome-wide single nucleotide polymorphisms (SNPs) to construct weighted genetic risk scores. Our analyses include an index of perceived neighborhood disorder and an objective measure of sleep duration based on wrist actigraphy. RESULTS: Genetic risk scores are inversely associated with neighborhood disorder, positively associated with continuous sleep duration, and inversely associated with the odds of short sleep. Neighborhood disorder is inversely associated with continuous sleep duration and positively associated with the odds of short and long sleep. The association between genetic risk scores and sleep duration (continuous and categorical) is invariant across levels of neighborhood disorder. CONCLUSIONS: Our analyses confirm the independent direct effects of genetic risk scores and neighborhood disorder on sleep duration. Our findings extend the socioecological model by assessing the role of genetics in the study of neighborhood context and sleep health. Although we observed a gene-environment correlation between genetic risk scores and perceived neighborhood disorder, there was little indication of genetic confounding and no evidence of gene-environment interaction.

Humans↗

PubMed1977

A study of the relationship between Goodwin's high-risk score and fetal outcome.

Correlation between Goodwin's high-risk score and Apgar score was studied in 266 pregnancies managed with the use of the information from clinical monitoring. The correlation coefficients between Goodwin's score and Apgar scores were -0.3178 for one-minute Apgar scores and -0.2668 for five-minute Apgar scores. Both are significant at the level of p less than 0.001. When the patients were divided into two groups by Goodwin's score, fetuses of the group with the higher score (greater than or equal to 4) were significantly more acidotic than those of the group with the lower score. Therefore, Goodwin's high-risk scoring system is simple and useful in the selection of potential risk patients.

Adult↗

PubMed2026

Transcriptome-wide association analysis of Alzheimer's disease: construction and clinical validation of transcriptomic risk scores.

Early identification of individuals at high risk for Alzheimer's disease (AD) is crucial for disease prevention and intervention. This study aims to develop AD-specific transcriptomic risk scores (TRSs) through multi-tissue transcriptome-wide association study (TWAS) and to evaluate its clinical utility in AD diagnosis and risk prediction. Using GWAS summary statistics combined with expression quantitative trait loci (eQTL) data from 14 tissues, a multi-tissue TWAS approach was applied to identify AD-associated genes. Peripheral blood RNA expression data from the ADNI and GEO databases were used to construct the AD-specific TRSs. The associations of TRSs with AD pathological features and cognitive function were assessed in two independent cohorts. Furthermore, the diagnostic performance, differential diagnostic capability, and risk prediction efficiency of TRSs were evaluated. The TWAS identified 131 genes significantly associated with AD. The TRSs were significantly elevated in patients with AD and mild cognitive impairment (MCI) compared to cognitively normal (CN) individuals, and showed significant correlations with AD pathological markers and cognitive performance. When combined with APOE4 status, the TRSs demonstrated robust diagnostic ability for AD and MCI. When combined with age, the TRSs showed good diagnostic performance in distinguishing AD from frontotemporal dementia (FTD) (AUC = 0.86). Additionally, the TRSs effectively predicted the risk of progression to AD in non-AD individuals (HR = 1.74). The AD-specific TRSs developed in this study shows promising clinical utility in AD diagnosis, differential diagnosis, and risk prediction, providing valuable translational medical evidence for early screening and precision prevention of Alzheimer's disease.

Humans↗