Conventional linear ultrasonic phased-array imaging is often limited by crack closure and propagation direction, making stable, high-contrast imaging of closed fatigue cracks difficult. To address residual linear artifacts in odd-even fundamental wave amplitude difference (OE-FAD) imaging associated with system nonlinearity in some conventional phased-array instruments, this study investigates a nonlinear ultrasonic phased-array imaging method based on complex-valued correction-factor-enhanced OE-FAD. The method is combined with conjugate delay-multiply-and-sum (conj-DMAS) post-processing and a conventional matrix-scanning physical focusing strategy to visualize randomly oriented closed fatigue cracks and evaluate crack-tip localization. Experiments were conducted on five 6061 aluminum alloy specimens containing closed fatigue cracks with different propagation directions. The results show that the proposed method effectively compensates for amplitude and phase mismatch between different imaging responses and markedly suppresses residual linear artifacts induced by system nonlinearity. After conj-DMAS post-processing, the nonlinear responses in the crack region become more concentrated, leading to improved crack-tip localization and characterization of crack propagation direction. The proposed method provides a practical approach for nonlinear detection of closed fatigue cracks using conventional phased-array equipment.
Congestive heart failure (CHF) among critically ill patients is a significant cardiovascular disorder that is linked to elevated mortality rates. The lactate/albumin ratio (LAR) is a readily available clinical metric that may provide valuable information regarding the metabolic and nutritional conditions. However, its association with mortality in patients with CHF remains unexplored. This study aimed to investigate the association between LAR and 28-day intensive care unit (ICU) mortality among critically ill patients with CHF. Using the eICU Collaborative Research Database (eICU-CRD), this study was retrospective and observational. The study included those admitted to the ICU with a preliminary CHF diagnosis. The nonlinear relationship between LAR and 28-day mortality was assessed using multivariate Cox regression and generalized additive models (GAMs), with confounders adjusted, and the crucial LAR value was identified through threshold effect analysis. Among 2117 patients diagnosed with CHF, 291 (13.7%) died within 28 days of ICU admission. A nonlinear relationship was observed between the LAR and mortality. For LAR values under 1.65, each unit increment was associated with a 2.41-fold increase in the likelihood of mortality (p < 0.001). Nonetheless, this link was not significant for LARs ≥ 1.65 (HR = 1.14, p = 0.111). Subgroup analyses validated the robustness of this nonlinear relationship, except for a significant interaction with the respiratory rate. Mediation analysis revealed that white blood cell (WBC) counts partially mediated the link between LAR and mortality, suggesting potential connections between metabolic disturbances, inflammatory pathways, and adverse outcomes in this population. LAR was nonlinearly related to 28-day ICU mortality, identifying a particular turning point in critically ill patients with CHF. These findings suggest that the LAR, a readily available clinical metric, may help identify high-risk patients with CHF and inform clinical management strategies.
The proliferation of deep learning applications has intensified the demand for electronic hardware with low energy consumption and fast computing speed. Neuromorphic photonics have emerged as a viable alternative to process high-throughput information at the physical space. However, the simultaneous attainment of high linear and nonlinear expressivity poses a considerable challenge due to the power efficiency and impaired manipulability in conventional nonlinear materials and optoelectronic conversion. Here, we introduce a parallel nonlinear neuromorphic processor that enables arbitrary superposition of information states in multidimensional channels, only by leveraging the temporal encoding of spatiotemporal metasurfaces. We experimentally demonstrated the concept based on distributed spatiotemporal metasurfaces, showcasing robust performance in multilabel recognition and multitask parallelism with asynchronous modulation. Our nonlinear processor demonstrates dynamic memory capability in real-time responsiveness to canonical maze-solving problem. Our work opens up a flexible avenue for a variety of temporally modulated neuromorphic processors tailored for complex scenarios.
Asthma is a heritable complex disease that disproportionately burdens minority and admixed populations in the US. However, the causal genes and regulatory mechanisms governing inherited risk remain largely unresolved. We performed a European-ancestry meta-analysis of 141,894 cases and 1,361,846 controls drawn from the Trans-national Asthma Genetic Consortium (TAGC) and Global Biobank Meta-analysis Initiative (GBMI), yielding an estimated h 2 SNP of 0.056 (SE = 0.0038) and 275 independently associated loci. To enhance mechanistic inference beyond variant-level associations, we developed a multimodal framework to predict asthma risk integrating GWAS summary statistics, bulk tissue expression quantitative trait loci (eQTL) data from the Genotype-Tissue Expression (GTEx) project, and single-cell gene eQTL data from the OneK1K Project. We performed transcriptome-wide association studies (TWAS) and subsequently applied probabilistic fine-mapping with FOCUS to prioritize putative causal genes expressed in bulk tissues and higher resolution immune cell populations. Fine-mapping asthma-associated genes implicated barrier-immune and metabolic-endocrine tissues alongside adaptive T-cell subsets as the primary mediators of asthma genetic risk, resolving canonical CD4+ Th2 effector genes including IL1RL1 , TSLP , STAT6 , and GATA3 . Using these prioritized genes, we constructed a polygenic transcriptome risk score (PTRS) using random forest to integrate gene-level effects across critical tissues and cell types. Evaluated in two ancestrally distinct pediatric asthma cohorts, the Childhood Asthma Management Program (CAMP) and the Genetics of Asthma in Costa Rica Study (GACRS), our PTRS demonstrated improved transferability over the standard variant-level and gene-level baseline models. While modest common variant heritability limits the discriminative power of our models, we estimated a theoretical maximum achievable area under the receiver operating characteristic (AUROC) curve of 0.64. Our integrative nonlinear model of PRS-CSx and cross-modal (bulk tissue and single cell) FOCUS PTRS resulted in the best cross-cohort performance (CAMP AUC = 0.632, sd = 0.04, 3.55 case/control odds ratio in top vs. bottom quartiles), representing an increase of +0.118 AUC over PRS-CSx, +0.067 AUC over tissue-specific TWAS pruning and thresholding, and +0.041 AUC over cell-type-specific FOCUS PTRS. Our results demonstrate that modeling nonlinear interactions between variant- and gene-level effects across both bulk tissue and single cell eQTL data improves our ability to determine high-risk individuals and to explain the likely mechanisms driving genetic susceptibility of childhood-onset asthma. Multimodal putative causal gene prioritization integrates GWAS summary statistics, bulk tissue, and single-cell expression data to resolve effector genes underlying childhood-onset asthma.Gene-level fine-mapping of underlying asthma GWAS loci reveals CD4+ Th2 effector genes, which are known drivers of eosinophilic airway inflammation, across multiple tissue and cell types including CD4+ cytotoxic T cells, naïve CD4+ T cells, and natural killer cells.We constructed and evaluated variant-level multi-ancestry asthma PRS and gene-level PTRS for genes in fine-mapped credible sets.We identified tissues and cell types that consistently helped discriminate asthma cases from controls, namely esophagus mucosa and CD4+ naïve T cells.We found that nonlinear modeling of both variant-level and gene-level effects across top tissues and cell types, tested across various integration strategies, substantially outperformed linear models in the distinction of asthma cases from controls.The model that most effectively distinguished cases from controls was a cross-modal model of PRS-CSx combined with the esophagus mucosa and CD4+ naïve T cell PTRS models, resulting in an AUC = 0.632 ± 0.040 and case/control enrichment odds ratio of 3.55 in top/bottom quartiles.Overall modest discrimination between cases and controls with genetic predictors supports asthma as a complex disease with a substantial non-genetic component.
A fluorination modulation theory embeds nonmetalfluorine covalent bonds into tetrahedral units, breaking local symmetry and stabilizing electronic structures to enable rational design of high-performance vacuum ultraviolet nonlinear optical crystals.
The purpose of this study is to explore the association between D-dimer levels and the likelihood of preoperative deep vein thrombosis (DVT) in patients with ankle fractures. This retrospective study included ankle fracture patients admitted to Xi'an Honghui Hospital's Foot and Ankle Surgery Center from January 2024 to November 2025. Preoperative DVT was identified using Doppler ultrasound, and the relationship between D-dimer levels and DVT was analyzed using multivariate logistic regression and generalized additive models. Among 818 patients, 13.45% developed preoperative DVT. D-dimer was an independent risk factor of preoperative DVT (OR = 1.14, 95% CI: 1.03-1.26, P = 0.010). There was a nonlinear relationship between D-dimer and DVT risk, After adjusting for confounding factors (age, sex, CCI, diabetes and preoperative waiting time),exploratory analysis identified an inflection point at 3.92 mg/L. Below this threshold, each 1 mg/L increase in D-dimer was associated with a significantly higher odds of DVT (OR = 1.60, 95% CI: 1.24-2.06, P < 0.001). D-dimer is independently associated with preoperative DVT in ankle fracture patients, and the relationship appears to be non-linear.
Uneven distribution of high-quality nephrology care in China has driven rising intercity patient mobility for chronic kidney disease (CKD). This study examined the spatial correlates of this mobility using over 4 million cross-city hospitalization records from 2014 to 2018. First, the Geodetector model was used to identify the key factors and their complex interactions driving patient inflows and outflows, including socioeconomic status, healthcare resource availability, and transportation accessibility. Then, multiscale geographically weighted regression (MGWR) was applied to explore geographical heterogeneity in the influence of these factors on intercity patient mobility. According to the Geodetector model, the leading correlates of patient outflows included hospital bed density, doctor density, and population growth rate, with evident nonlinear and synergistic associations. Patient inflows were mainly influenced by nephrology workforce availability and population structure. MGWR analysis revealed substantial spatial variation in the associations of general and nephrology-specific healthcare resources on intercity patient mobility, underscoring the complex interaction between healthcare capacity and geographic context. This study proposes a novel framework for understanding the spatial correlates of intercity CKD patient mobility in China and highlights the geographic heterogeneity of their associations. The findings support policies aimed at improving the equity and efficiency of CKD care across regions.
Barycentric rational interpolation is characterized by excellent numerical stability, high approximation accuracy, and strong adaptability to node distribution. In this paper, we propose a high-precision barycentric rational interpolation collocation method to provide approximate solutions for both linear and nonlinear two-dimensional integro-differential equations (2D-IDEs) subject to specified boundary conditions. Firstly, the discrete scheme for 2D-IDEs is derived by employing the two-dimensional barycentric rational interpolation formula and the two-dimensional Gauss-Legendre numerical integration formula. Then, the error estimation of the approximate solution and the convergence of the method are analyzed. Finally, numerical examples are provided to validate the effectiveness and accuracy of the proposed method.
To systematically evaluate the effects of exercise interventions on post-stroke depression (PSD) and to clarify the dose-response relationship between physical activity dosage and depressive symptoms in individuals with PSD. A structured and comprehensive search was conducted in PubMed, Web of Science, Embase, Scopus, and the Cochrane Library. Restricted cubic spline models were applied to examine the dose-response association between physical activity dosage and depressive symptoms in PSD. A total of 27 publications comprising 31 randomized controlled trials were included, involving 2,247 participants. The meta-analysis indicated that exercise intervention was associated with a modest improvement in depressive symptoms among patients with PSD [SMD = -0.15, 95% CI (-0.23, -0.07), p < 0.01], with moderate heterogeneity (I 2 = 41.9%). Dose-response analysis revealed a non-linear association between exercise dosage and symptom improvement, with the greatest apparent benefit observed at approximately 801 MET-min/week. Subgroup analyses suggested that more favorable improvements were more commonly observed in interventions characterized by resistance training [SMD = -0.55, 95% CI (-0.85, -0.25)], a frequency of 1-2 sessions per week [SMD = -0.41, 95% CI (-0.67, -0.15)], a session duration of ≤30 min [SMD = -0.44, 95% CI (-0.70, -0.19)], and an intervention duration of 9-12 weeks [SMD = -0.16, 95% CI (-0.27, -0.05)]. Moderate-dose exercise intervention, approximately 801 MET-min/week, was associated with a modest improvement in post-stroke depressive symptoms. These findings provide a reference for optimizing exercise dosage and informing individualized prescription strategies for patients with PSD. https://www.crd.york.ac.uk/PROSPERO/view/CRD420251167322, identifier (CRD420251167322).
Frailty and depressive symptoms are common in later life and may be related to cerebrovascular risk. Evidence remains limited on whether frailty burden and frailty change are associated with incident stroke across diverse aging cohorts. We analyzed harmonized longitudinal data from five population-based aging cohorts: the Health and Retirement Study (HRS), China Health and Retirement Longitudinal Study (CHARLS), Survey of Health, Ageing and Retirement in Europe (SHARE), English Longitudinal Study of Ageing (ELSA), and Mexican Health and Aging Study (MHAS). Frailty was measured using a harmonized 24-item deficit-accumulation frailty index (FI). The primary analysis used cohort-specific Cox proportional hazards models to estimate associations between baseline FI and first observed incident stroke during follow-up. Secondary exploratory analyses evaluated nonlinearity, FI change, competing mortality, depressive symptoms as a pathway marker, and two-wave cross-lagged associations. The analytic sample included 81482 participants and 5,089 incident stroke events. In fully adjusted cohort-specific Cox models, each 0.1-unit increase in FI was associated with higher stroke risk in HRS, CHARLS, SHARE, and MHAS, but not in ELSA. Substantial between-cohort heterogeneity was observed; therefore, cohort-specific estimates were interpreted as the primary results and the random-effects pooled estimate was treated as descriptive. Fine-Gray sensitivity analyses treating death as a competing event supported positive frailty-stroke associations across all five cohorts. Restricted cubic spline (RCS) analyses suggested nonlinear associations for baseline FI and FI change. Exploratory pathway analyses indicated that depressive symptoms statistically accounted for part of selected frailty-stroke associations, although patterns varied by cohort and exposure definition. Two-wave cross-lagged panel models (CLPMs) suggested small, cohort-specific prospective associations between elevated frailty vulnerability and later depressive symptoms or stroke; these findings were interpreted as exploratory temporal associations rather than causal within-person effects. Higher frailty burden was associated with incident stroke in most, but not all, harmonized aging cohorts, with substantial heterogeneity across populations. The findings support repeated frailty assessment and integrated mood evaluation in older adults while emphasizing the need for cohort-specific interpretation and confirmatory studies with adjudicated stroke outcomes.
Accurate and timely flood forecasting is essential for issuing effective early warnings and reducing casualties as well as economic losses. However, urban flood forecasting models often struggle to balance computational efficiency with nonlinear representation capability. Forecast errors are rapidly amplified during highly dynamic flooding processes. Assimilating observational data into the model can correct forecast trajectories and reduce overall uncertainties. Existing mainstream data assimilation techniques, such as the ensemble Kalman filter and particle filter, can hardly balance the requirements of nonlinearity and timeliness in urban flood forecasting. Furthermore, effectively utilizing limited observed data to balance the assimilation update frequency and forecast accuracy is critical. To address these challenges, a dynamic urban flood forecasting model coupling the ensemble particle filter with the gradient boosting decision tree was proposed in this study. The performance of state-only and joint state-hyperparameter assimilation for flood prediction at typical ponding points in the central urban area of Zhengzhou was compared. The results indicate that data assimilation improves flood forecast accuracy. State-only assimilation reduces the root mean square error from 0.051-0.084 m to 0.009-0.031 m, while increasing the mean Kling-Gupta efficiency from 0.794 to 0.961. The underlying mechanism whereby incorporating hyperparameters into state variables fails to achieve further performance improvement is analyzed. When the assimilation update frequency is 50 min, an optimal balance between forecast accuracy and observational cost is achieved. Using the sliding window mechanism to identify effective forecast windows for different ponding points provides important support for urban flood emergency decision-making.
To examine the association between the serum uric acid-to-high-density lipoprotein cholesterol ratio (UHR) and peripheral arterial disease (PAD) among U.S. adults. This cross-sectional study used data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES). PAD was defined as ABI <0.90 in either leg. Weighted multivariable logistic regression was used to estimate odds ratios (ORs) for PAD according to UHR modeled continuously and by quartiles. Restricted cubic spline (RCS) regression, subgroup analyses, interaction tests, sensitivity analyses, stratified receiver operating characteristic (ROC) analyses and propensity score matching (PSM) were performed. A total of 6149 participants were included, among whom 459 had PAD (7.5%).Each one-unit increase in UHR was associated with higher odds of PAD (OR 1.04, P = 0.004). The highest UHR quartile (≥14.47) was associated with 52% higher odds of PAD compared with the lowest quartile (<7.78) (P = 0.036). RCS analysis showed an overall association (P for overall = 0.006) without evidence of a statistically significant nonlinear pattern (P for nonlinearity = 0.375). Stratified ROC analysis identified optimal cut-offs of 10.69 for the overall population (AUC = 0.577), 12.55 for statin users (AUC = 0.602), and 9.82 for non-statin users (AUC = 0.554). PSM confirmed the overall association (P = 0.027) , with significance retained only among statin users (P = 0.002).Subgroup and sensitivity analyses generally supported the robustness of the findings. Higher UHR is independently associated with PAD. UHR may serve as an adjunctive screening marker, particularly for statin users (cut-off ≥12.55).
Chimeric Antigen Receptor (CAR) T-cell therapy has transformed cancer immunotherapy by genetically engineering T-cells to target tumor antigens. Acute myeloid leukemia (AML) presents unique challenges due to resistance mechanisms, especially in patients with TP53 loss mutations. The complex dynamics of CAR T-cell expansion remain poorly understood. The field lacks validated quantitative frameworks to systematically evaluate different CAR T-cell target constructs, such as CD33, CD123, and CD371, against resistant AML variants. We address this gap by combining mathematical modeling with in vitro assay data and Bayesian inference. We select, train, and validate a two-compartment deterministic mathematical model that describes the nonlinear dynamics of target AML and CAR T cells, accounting for expansion, killing, and exhaustion. Using Bayesian inference, we train and select the best-performing functional form for CAR T expansion and then validate it on unseen data. Our framework selects a CAR T-cell expansion model that accounts for handling time and T-cell self-interference, highlighting that expansion is a dynamic process in which target-cell handling time and T-cell crowding negatively affect T-cell expansion. Analysis of posterior parameter distributions reveals target-antigen-specific responses against TP53-deficient AML. For instance, CD33-targeting CARs have reduced attack rates against TP53-deficient cells, while CD123- and CD371-targeting CARs show moderately increased attack rates; however, the former exhibit higher death rates, and the latter have increased handling times, impeding efficacy. This target-dependent form of resistance challenges the assumption of uniform performance and reveals a unifying nonlinear expansion model for integrated, yet antigen-specific, preclinical predictions of efficacy.
Patient-reported outcomes (PROs) are now considered essential components of clinical development and regulatory assessment. Despite their widespread collection, PRO data remain under-utilized, with analyses often limited to descriptive summaries or single time-point comparisons that fail to capture the richness, longitudinal structure, and patient-centric meaning rooted within these measures. In this tutorial, we have outlined key considerations for aligning fit-for-purpose PRO measures with clearly articulated research questions and estimands, and applying analytically robust methods that respect the bounded, noisy, and often incomplete nature of PRO data. Through working examples, we demonstrated how pharmacometric approaches, such as bounded outcome score modeling and item response theory-nonlinear mixed effects modeling, can enable more efficient use of all available data, improve sensitivity to treatment effects, and provide interpretable longitudinal trajectories that are directly relevant to clinical decision-making. Importantly, pharmacometric approaches allow PROs to move beyond exploratory endpoints to becoming integral tools for understanding treatment tolerability, differentiating therapies with similar efficacy, and predicting patient-centric treatment outcomes. Looking forward, realizing the full potential of PROs will require early integration of patient-reported outcome measures (PROMs) into clinical development programs, careful consideration of assessment timing and frequency, and continued collaboration among clinicians, statisticians, pharmacometricians, and regulators. Ultimately, integrating PROs in drug development as one of the clinical outcome assessments is a necessary step toward ensuring that drug development and clinical decision-making more accurately reflect what matters most to patients: how they feel, how they function, and how treatments affect their quality of life.
Intravoxel incoherent motion (IVIM) analysis in diffusion-weighted MRI (DWI-MRI) shows potential for characterizing pancreatic tissue, but its clinical application remains limited by sensitivity to fitting algorithms. This study assessed the repeatability of neural network (NN)-based IVIM fitting versus classical nonlinear least-squares in pancreatic DWI. The repeatability cohort included ten healthy volunteers and two type 1 diabetes (T1D) individuals, each scanned twice; the glucose-response cohort included three T1D participants and three healthy controls scanned pre- and post-oral glucose. Diffusion data were acquired at 13 b-values (0-1200 s/mm[Formula: see text]). Four NN-based methods (IVIM-NET, SUPER-IVIM-DC, U-Net, IVIM-MORPH) were compared with two classical approaches (SLS, SLS-TRF) using full (13-point) and reduced (7-point) protocols. Repeatability was quantified using within-subject coefficient of variation (wCV) and Bland-Altman analysis. All NN-based methods significantly improved test-retest repeatability of the perfusion fraction ƒ compared with classical approaches ([Formula: see text]), with SUPER-IVIM-DC showing the lowest wCV and IVIM-MORPH offering balanced performance across parameters. A reduced protocol shortened scan time through fewer acquisitions while maintaining or improving repeatability compared to the full protocol. Preliminary glucose-response results show both NN and classical IVIM analyses detect physiologically relevant changes. Parameter estimates varied across NN architectures, requiring further validation to establish accuracy.
Brazilian women's widespread mammography access is a challenge for public health. Social determinants of health have been analyzed to try to explain the channels responsible for differences in access to breast cancer screening. This study investigates the difference in mammography performance between white and yellow (WY) and black, brown and indigenous (BBI) women based on socioeconomic indicators available in the Longitudinal Study of Brazilian Elderly Health (ELSI-Brazil) and the use of the decomposition for nonlinear models. The results indicate that mammography probability is 10.24 percentage points higher among WY. The difference in explanatory variables composition contributes 57.8% to the total difference. Which means that if BBI had the same composition of explanatory variables (such as health insurance, education, marital status, number of children) as WY the probability of having a mammogram would be 5.92 percentage points higher. In addition to the characteristics related to the public health system, there is a need for public policies that address the physical, mental and emotional well-being of women, especially those who need them most, with a view to reducing inequalities in access to mammography.
This study explored risk factors associated with depressive symptoms in older patients with obstructive sleep apnea (OSA) and assessed correlations of mean pulse oxygen saturation (MSpO2) with depressive symptoms. In total, 1,085 older patients diagnosed with OSA via polysomnography (PSG) were included. Based on scores from the 12-item Geriatric Depression Scale (GDS-12), participants were classified into two subgroups to identify depressive symptom-related risk factors. Logistic regression analysis, restricted cubic splines, and subgroup analyses were performed to evaluate correlations of MSpO2 with depressive symptoms. Depressive symptoms were observed in 139 patients (12.8% of the sample). Logistic regression analysis indicated that age (per 1-year increase, odds ratio [OR] = 1.11, 95% confidence interval [CI]: 1.08-1.14; P < 0.001), smoking (OR = 1.66, 95% CI: 1.02-2.69; P = 0.041), MSpO₂ (per 1% increase, OR = 0.90, 95% CI: 0.85-0.94; P < 0.001), diabetes mellitus (OR = 1.61, 95% CI: 1.03-2.51; P = 0.038), and renal dysfunction (OR = 2.26, 95% CI: 1.10-4.66; P = 0.027) were significantly associated with depressive symptoms. Additionally, sleep parameters including AHI, ODI, and LSpO₂ were independently associated with depressive symptoms. Restricted cubic splines suggested a linear correlation between MSpO₂ and depressive symptoms (nonlinear P = 0.38). Compared with patients in the highest category (MSpO₂ ≥ 95.0%), those in the lowest category (MSpO₂ ≤ 91.7%) showed increased depressive symptom risk (OR = 2.25, 95% CI: 1.34-3.78, P = 0.002). Subgroup analyses confirmed this linear relationship. A linear correlation exists between MSpO2 and depressive symptoms in older patients with OSA. Additionally, age, smoking, diabetes mellitus, and renal dysfunction are strongly associated with depressive symptoms in this population.
Social adaptation is critical for healthy aging, yet few longitudinal studies examine multidimensional social adaptation trajectories and its' predictors among Chinese older adults. Using four-wave panel data (2014-2020) of 1,990 older adults, we applied latent growth and group-based trajectory models to map social adaptation developmental patterns. Personal development adaptation exhibited a nonlinear declining trajectory, while ideological-cultural adaptation fell into four subgroups: gradual decline, stable low, initial decline with subsequent stabilization, and sustained moderate-to-high adaptation. Baseline education and self-rated health positively predicted initial personal development adaptation levels. Cognitive function showed consistent positive longitudinal effects on personal development adaptation and heterogeneous predictive links to ideological-cultural subgroup membership. Depressive symptoms negatively predicted personal development adaptation and only predicted membership in the sustained high-adaptation ideological-cultural subgroup. Friendship had no significant predictive effect on two dimensions. This study confirms domain-specific heterogeneity in older adults' social adaptation and offers empirical evidence for targeted active aging interventions.
Human complex diseases are affected by both genetic and environmental factors. When multiple environmental risk factors are present, the interaction effect between a gene and the environmental mixture can be larger than the addition of individual interactions, resulting in the so-called synergistic gene-environment (G×E) interactions. Existing literature has shown the power of synergistic gene-environment interaction analysis with cross-sectional traits. In this work, we propose a functional varying index coefficient model for longitudinal traits together with multiple longitudinal environmental risk factors and assess how the genetic effects on a longitudinal disease trait are nonlinearly modified by a mixture of environmental influences. We derive an estimation procedure for the nonparametric functional varying index coefficients under the quadratic inference function and penalized spline framework. We evaluate some theoretical properties such as estimation consistency and asymptotic normality of the estimates. We further propose a hypothesis testing procedure to assess the significance of the synergistic G×E effect. The performance of the estimation and testing procedure is evaluated through Monte Carlo simulation studies. Finally, the utility of the method is illustrated by a real dataset from a pain sensitivity study in which SNP effects are nonlinearly modulated by a mixture of drug dosages and other environmental variables to affect patients' blood pressure and heart rate.
Despite extensive documentation of the immunomodulatory effects of anthocyanidins, population-based epidemiological studies have yet to elucidate their association with thyroid autoimmunity (TAI). Using flavonoid intake data from the Flavonoid and Anthocyanidin Database (FNDDS) and 2007-2010 National Health and Nutrition Examination Survey (NHANES) datasets (n = 5,487), we constructed multivariable logistic regression and restricted cubic spline models to examine the dose-response relationship between the log10-transformed anthocyanidin intake values (LgAAs) and TAI incidence, adjusting for demographic, anthropometric, and metabolic covariates. The intake of anthocyanidins by TAI patients was significantly greater than that by controls [median (IQR): 2.20(0.00, 14.92) vs. 0.98 (0.00, 7.18) mg/d, p < 0.001]. Each unit increase in LgAA corresponded to a 16% increase in the risk of TAI (adjusted OR 1.16, 95% CI 1.04-1.29; p = 0.0088). Compared with the lowest quartile, the highest LgAA quartile was associated with a 47% increase in the incidence of TAI (OR 1.47, 95% CI 1.12-1.93; p = 0.0055; P trend = 0.014), with consistent associations across subgroups (P interaction = 0.528). Restricted cubic spline analysis confirmed a linear dose-response relationship (P nonlinearity = 0.217). This cross-sectional study identified an association between dietary anthocyanidin intake and TAI. Future prospective cohort studies and randomized trials are warranted to validate our observations and elucidate underlying mechanisms.