搜索结果：Measurement

Interferometry techniques are essential for extracting phase information from optical systems enabling precise measurements of dispersion and highly sensitive detection of perturbations. While phase sensing offers enhanced sensitivity compared to conventional spectroscopy methods, this sensitivity often makes systems more vulnerable to external factors such as vibrations, introducing instability and noise. In this work, we demonstrate a broadband and AI-enhanced interferometry method, denoted general polarization common-path interferometry (GPCPI) that relaxes the polarization constraints of traditional common-path interferometry. The polarization decoupling feature enables simultaneous amplitude and phase measurements supplemented with deep neural autoencoders to detect phase anomalies in the spectrum through the analysis of second order derivative mapping of the phase profile, enhancing the accuracy of broadband phase measurements. The approach enables an order of magnitude improvement in phase stability compared to state-of-the-art interferometry techniques, leading to higher accuracy in phase sensing. Plasmonic metasurface phase sensing and hyperspectral single-cell dispersion imaging demonstrate the capability and sensitivity of the method over conventional spectroscopy. Our adopted version of deep learning model, ConvNeXt V2, enables real-time tracking of phase variation with minimized noise. Interference fringes affected by the cell-cultured samples reveal the fingerprints of the normal (CCD-32Sk) vs cancerous (COLO-829) skin cells, enabling cell classification and disease diagnosis at single-cell level through hyperspectral dispersion imaging. The proposed technique offers a reliable, compact, and stable solution for broadband phase measurements and single-cell dispersion imaging for applications in metrology, molecular diagnostics, drug discovery, and quantum sensing.

Spatial proteomics provides single-cell protein measurements under highly constrained and heterogeneous protein panels across datasets, resulting in limited and partially overlapping measurement spaces for cellular characterization. Existing analyses predominantly rely on statistical or task-specific modeling, while learning scalable representations of spatial protein data remain underexplored. This gap motivates the need for models that can learn stable representations of cellular identity from constrained protein measurements. Here we introduce Spatium, a protein language foundation model trained on over 51 million cells across multiple spatial proteomics platforms. Spatium learns intrinsic co-expression hierarchies that capture cell identity in a manner robust to panel composition and measurement scale. Spatium builds a generalizable representation of cell states grounded in biologically interpretable protein expression patterns. We demonstrate that Spatium learns biologically meaningful cell representations across multiple downstream tasks. Spatium recovers accurate cell identities with marker expression patterns consistent with known biology and reveals functionally distinct spatial microenvironments characterized by coherent marker enrichment signatures. It further enables reconstruction of missing protein measurements while preserving biologically meaningful expression patterns. Across these analyses, Spatium demonstrates stable and interpretable performance with lightweight task-specific adaptation, highlighting the robustness of the learned representations across diverse biological and experimental contexts.

Heterogeneity in host-pathogen interactions arises from variation in both host cell state and pathogen state, yet most single-cell methods capture these features separately. Joint profiling is limited by fundamental technical mismatches between host- and pathogen-derived material, particularly differences in cell wall structure, lysis requirements, and molecular abundance. Here we introduce a lysis-independent strategy that enables unified measurement of intracellular bacterial presence and state alongside host single-cell profiles. We repurpose bacterial surface display to encode promoter activity and bacterial identity as antibody-detectable signals, rendering bacterial features compatible with existing antibody-based single-cell assays. This approach is modular across promoters, epitope tags, display scaffolds, and bacterial species, including Escherichia coli and Mycobacterium tuberculosis . Surface-displayed reporters are detectable during intracellular infection and can be read out by flow cytometry and droplet-based single-cell RNA sequencing without pathogen-specific lysis optimization or pre-sorting on pathogen signal. Applying this method to infected macrophages, we link heterogeneous bacterial uptake to heterogeneous expression of phagocytosis-associated host programs. This strategy enables scalable, joint host-pathogen single-cell measurements and expands the range of pathogens and states accessible to high-throughput single-cell analysis.

Peripheral artery disease (PAD) is an occlusive arterial disease primarily affecting the lower extremities. It impacts over 230 million people worldwide and is associated with significant morbidity and mortality. The ankle brachial index (ABI) test is a non-invasive method to detect PAD that compares the blood pressure in the ankle and arm to evaluate lower extremity blood flow. An estimated 20-50% of individuals with detectable PAD are asymptomatic and remain undiagnosed; however, ABI screening in high-risk, asymptomatic populations is not currently guideline-recommended. Few studies have evaluated change in ABI over time in asymptomatic populations. Therefore, we aimed to identify distinct trajectories of ABI values from mid-to late-life. We utilized data from the Atherosclerosis Risk in Communities (ARIC) study; a longitudinal cohort study initiated in 1987 that enrolled 15,792 participants aged 45-64. ABI measurements were collected at five visits over a 30-year period. We used group-based trajectory modeling to identify trajectories of ABI from mid-to late-life. Final model selection was based on visual fit, statistical criteria, group sizes, and substantive knowledge. Lastly, we compared baseline demographics, social determinants of health, and overall cardiovascular (CV) health, assessed using the American Heart Association's Life's Essential 8 (LE8) framework, across trajectory groups. We identified 4,121 participants with ≥3 ABI measurements over the study period in at least one limb. At baseline, participants had an average age of 51.4 ± 4.9 years, were 57.3% female, 22.2% Black, and had an average overall LE8 score of 68.0 ± 13.9 points. Our final model identified three linear trajectories: low-normal, high-normal, and declining. Overall LE8 scores varied significantly across trajectory groups: 67.3 ± 10.7 (high-normal), 61.9 ± 13.3 (low-normal), and 50.1 ± 15.8 points (declining). Women had lower average ABI values, were more likely to experience a declining ABI trajectory, and had a delayed onset of decline compared to men. A greater proportion of Black participants experienced declining ABIs, with earlier, faster, and more severe declines than White participants. Poor overall CV health and common CV risk factors are associated with ABI decline. Targeted ABI screening in middle age may help detect PAD in its beginning stages and support early intervention.

NASA's ICESat-2 mission was launched in 2018, carrying a photon-counting laser altimeter, with a primary objective of measuring height changes across Earth's surface. ICESat-2 has provided measurements of ice surface height between 88º N and S, repeated four times per year, with high vertical accuracy and along-track spatial resolution. Its accuracy and coverage has enabled near-complete recovery of height changes across the ice sheets, capturing subtle changes in the interior, and rapid changes along the dynamic margins with steep slopes and the floating peripheral ice shelves. The ICESat-2 Science Team has developed a suite of algorithms that produce along-track and gridded land ice height products at various levels of processing, all freely available at the National Snow and Ice Data Center. Here, we describe three higher-level land-ice data products derived from ATL06 and their underlying algorithms: along-track height change (ATL11), digital elevation model (ATL14) and gridded surface height change (ATL15). We demonstrate the suitability of each data product for studying different ice sheet regions. We then show height changes for Greenland and Antarctica from ATL15 during the first 6 years of the ICESat-2 mission (October 2018-December 2024), illustrating how ICESat-2 measurements can distinguish the multi-year trends from seasonal fluctuations.

Multimodal single-cell assays profile complementary layers of cell state, but integration is complicated by modality mismatch, sparsity, and uneven cohort coverage. We present UniVI ( Uni fied V ariational I nference), a scalable mixture-of-experts β -variational autoencoder that learns a shared latent space while preserving modality-specific structure. UniVI couples modality-specific encoders/de-coders with a shared latent prior and a symmetric cross-modal alignment objective, enabling consistent integration of paired measurements without curated feature-link graphs or pre-annotated reference atlases; optional supervised heads can be added when labels are available. Across paired RNA-protein (CITE-seq) and RNA-chromatin (10x Multiome, SHARE-seq) data spanning human PBMCs and mouse back skin-a non-hematopoietic tissue with continuous differentiation hierarchies-UniVI produces coherent embeddings, improves label transfer, and enables cross-modal reconstruction and denoising. Extending to tri-modal measurements, UniVI maintains robust three-way alignment among RNA, chromatin accessibility, and surface proteins (TEA-seq), and accommodates DNA methylation in a paired scNMT-seq mouse gastrulation proof-of-concept under beta-binomial likelihoods. Performance degrades gracefully under severe cell-type imbalance and in the presence of modality-exclusive populations. In an acute myeloid leukemia mosaic design, a paired RNA-protein bridge anchors independent RNA-only and protein+genotype cohorts, revealing genotype-associated neighborhoods that sharpen with mutation-aware fine-tuning. UniVI thus provides a flexible, interpretable framework for multimodal integration across paired, tri-modal, and mosaic study designs and supports practical reference-to-query projection in partially observed studies.

The Caffeinated Coli Educational Module brings inquiry-driven learning to high schools, introducing students to scientific research, synthetic biology, and genetic engineering. The module focuses on the exploration of a genetically engineered strain of E. coli modified to grow exclusively on caffeine and, as such, can be used as a measurement device to determine the amount of caffeine in a liquid or beverage. Students conduct two bioassay experiments using these bacteria. During this process, they learn to create cultures and then measure bacterial growth followed by calculating the caffeine concentrations of unknown samples using their own data. This flexible module contains five weeks of original lectures and student learning materials that follow Next Generation Science Standards (NGSS), allowing the content to be adapted for basic, intermediate, or advanced biology courses. Upon completion of the module, students and teachers expressed that the most memorable aspects of the module include collaboration with peers, hands-on learning of content, and the opportunity to interact with the professor/mentors through office hours. Already implemented in 8 Texas high schools over the past two academic years, our module inspires STEM learning while bringing 21 st century biology research to new audiences.

Females of reproductive age with concussion often report greater symptom severity and duration than age-matched males; the mechanisms underlying female symptomology remain unclear. This study investigated the association between hormone profiles and time to return to learn/work (RTL/W) following concussion. A secondary aim was to explore differences in symptom severity and salivary miR-27a-5p/miR-30a-3p expression between hormone profile groups. Based on an a priori power calculation, 36 females aged 28.8 ± 7.5 (17-44 years) presenting to an emergency department within 72 h of a confirmed concussion were recruited. Participants were classified into three hormone profile groups: n = 20 natural menstrual cycle (NMC); n = 8 progestin-only hormonal contraception (PROG); and n = 8 oral contraception (Oral Contraceptive Pill; OCP). Saliva samples were collected for measurement of miR-27a-5p/miR-30a-3p, and participants completed weekly online surveys reporting symptom scores until achieving RTL/W. The mean initial symptom score was 47.0 ± 23.7 (8-100), and mean time to RTL/W was 27.3 ± 33.1 (2-179 days). Cox hazard regression revealed a statistically significant association of hormone profile with time to RTL/W. PROG (hazard ratio [HR]: 2.5, 95% confidence interval [CI]: 1.0-6.1, p = 0.048) and OCP (HR: 2.7, 95% CI: 1.1-6.4, p = 0.027) were significantly associated with increased likelihood of RTL/W compared with NMC. Initial symptom score was not significantly associated with time to RTL/W (p = 0.628). Exploratory analysis showed no statistically significant mean differences between groups for initial symptom score [F(2, 33) = 1.755, p = 0.189]. Fourteen saliva samples provided complete miR-27a-5p/miR-30a-3p data; mean miR-27a-5p/miR-30a-3p was 0.84 ± 0.06 (0.75-0.92). No statistically significant mean differences in miR-27/miR-30 expression were observed between hormone profile groups (F 2, 11 = 0.519, p = 0.609). Females using PROG or OCP were between 1.0 and 6.7 times more likely to achieve RTL/W than NMC at any given time. Hormone profile, but not initial symptom score, was predictive of time to RTL/W. Salivary miR-27a-5p/miR-30a-3p may be a useful biomarker in females with concussion and warrants further research.

Humans often communicate and learn in noisy, complex listening environments. Here, we investigated the effects of spatial hearing and semantic context cues on speech intelligibility and listening effort in young adults with typical hearing. The listening task included conditions in which target speech and speech maskers were either spatially co-located or separated. Target sentences were either semantically coherent or anomalous, while the masker comprised a mixture of two coherent sentences. Results showed higher speech intelligibility in spatially separated than co-located conditions, demonstrating a robust spatial release from masking (SRM), which is consistent with prior findings. SRM did not differ between semantically coherent and anomalous sentences, indicating comparable benefits of spatial cues across semantic contexts. However, within each spatial configuration, intelligibility was higher for coherent than anomalous sentences. Listening effort, indexed by peak pupil dilation in pupillometry measurement, was reduced in spatially separated conditions, suggesting a trend toward a release from listening effort. Analysis of the timing of peak pupil dilation revealed a significantly delayed peak dilation for anomalous sentences in the co-located condition compared with coherent sentences in the separated condition, indicating increased processing demands in the absence of spatial and semantic cues. Finally, SRM was correlated with the magnitude of release from listening effort for coherent sentences, but not for anomalous sentences, suggesting that intelligibility and listening effort benefits might co-occur when contextual cues are available.

The use of specialized perturbation systems has become an increasingly popular approach for investigating walking stability. By accelerating or decelerating one belt, researchers can induce slip- and trip-like perturbations in a controlled laboratory setting. However, many existing studies rely on specialized perturbation systems that require expertise in device-specific software and handling of the equipment, limiting the accessibility of perturbation-based gait research to laboratories with access to such equipment. To address this limitation, we developed an open-source method capable of inducing slip- and trip-like perturbations using a standard split-belt treadmill. Here, we 1) describe the hardware and software components of the system, 2) validate the application's accuracy and precision, and 3) characterize the stability demands imparted by the perturbations with spatial stability measurements. Measured perturbation onset delay and duration were compared to the desired onset timing and programmed duration in addition to step length, step width, minimum mediolateral margin of stability, and sagittal-plane whole-body angular momentum range during the perturbed and recovery steps. Five participants with traumatic unilateral transtibial limb loss experienced perturbations consisting of brief, rapid increases or decreases in unilateral treadmill velocity, eliciting a "slip" or "trip". The mean (standard deviation) onset delay was 183.3 (9.7) ms, or 24.18% (1.91%) of stance duration. Mean perturbation duration was 239.90 (7.5) ms, 18.14% longer than the intended duration. The perturbations produced measurable changes in gait stability, such as increased step length during the perturbed step and step width during the subsequent recovery step in addition to increased minimum mediolateral margin of stability and sagittal whole body angular momentum. This open-source method successfully induced instability in individuals with impaired balance, demonstrating its feasibility as an accessible alternative to specialized perturbation systems. Future work will focus on refining both the software and hardware components to further improve timing, accuracy, and consistency.

Water molecules exchange incessantly across cell membranes and between intracellular compartments, but the dominant steady-state transport pathways, and whether they are active or passive, remain unclear. Low-field, high-gradient diffusion exchange spectroscopy (DEXSY) nuclear magnetic resonance (NMR) measurements on viable ex vivo neonatal mouse spinal cords show that water exchange is primarily passive. The apparent exchange rate constant (AXR) depends on osmotic conditions because it reflects multiple exchange pathways, each weighted by the exchanging compartments' volume fractions. A faster transmembrane path that becomes more visible with increasing extracellular space (ECS) fraction has a high activation energy but is ion-independent, suggesting passive transport through the phospholipid bilayer but not active or passive transport through co-transporter or channel proteins. A slower pathway which dominates when the extracelluar space shrinks has a low activation energy, consistent with geometric exchange between intracellular environments. Moreover, we show how DEXSY can be used to non-invasively measure tonicity in tissue, and inform us about the status of the tissue milieu. These findings may inform future translation to clinical MRI. We use advanced nuclear magnetic resonance methods to address two unanswered questions in cellular biology: How does water exchange between tissue microenvironments under steady-state conditions, and do these processes involve active water cycling?

Hyperglycemia is a recognized prognostic marker in neurocritical care, but condition-specific data and glycemic targets for traumatic spinal cord injury (TSCI) remain undefined. We conducted a scoping review to map how early hyperglycemia has been defined and measured in adults with acute TSCI and how it relates to neurological and functional outcomes. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses for scoping reviews guidance, PubMed, Embase, and Scopus were searched from inception to November 2025 for studies enrolling adults (≥18 years) within 7 days of TSCI, defining hyperglycemia at ≥126 mg/dL, and reporting neurological, functional, or mortality outcomes. Eligible designs included prospective and retrospective cohorts and registry-based analyses; pediatric, diabetic-only, nontraumatic, animal, and chronic-phase studies were excluded. Of 2323 records, 6 observational studies (n = 2586) met the inclusion criteria. Most cohorts involved predominantly cervical injuries and used admission or 24-h glucose as the primary exposure. Across studies, more severe neurological impairment at presentation was associated with higher admission glucose, and admission hyperglycemia was generally linked to poorer motor recovery, lower Spinal Cord Independence Measurement scores, and unfavorable AIS profiles at discharge, although these associations frequently attenuated after multivariable adjustment. Overall, evidence on hyperglycemia in acute TSCI is limited, heterogeneous, and observational. Early hyperglycemia shows signals of association with adverse neurological and functional outcomes, but effect estimates are inconsistent and do not support specific glycemic targets. Glucose and composite indices remain candidate prognostic biomarkers that require validation in standardized, prospective multicenter cohorts before interventional trials can be justified.

Morphological changes in prostate glands, assessed by Gleason grading, remain the gold standard for diagnosing prostate cancer, yet molecular biomarkers associated with gland shape are not well understood. Here, we introduce CurvSeq, a mechanomorphology-informed framework for spatial sequencing data, and CurvSee, its complementary version for proteomic and imaging datasets. These methods integrate gland boundary curvature, pocket architecture, microenvironmental composition, and molecular profiles to study morphomechanical relationships in prostate adenocarcinoma. Using five independent spatial transcriptomic and multiplexed imaging datasets, we segmented individual prostate glands, extracted gland contours, quantified local curvature and pocket-like concavities, and projected these features onto spatially resolved gene and protein measurements. In Xenium data, CurvSeq distinguished benign and GG1 glands, identifying cancer-associated genes such as PCA3 and AMACR in GG1 glands and basal, basement membrane, and mechanotransduction-associated programs in benign glands. In Visium data, a diffusion-based morphomechanical score ordered benign glands by area, circularity, pocket number, smooth muscle abundance, immune-cell proximity, and remodeling-associated genes including MMP7. In GG4 glands, CurvSeq identified neuroendocrine-like boundary regions associated with MMP7 expression, COL1A1-rich adjacent stroma, and immune-cell accumulation. Finally, CurvSee extended this framework to multiplexed protein imaging, where combined morphology and protein-expression features distinguished Gleason-associated gland states. Together, CurvSeq and CurvSee provide a quantitative framework for linking gland architecture, local microenvironment, and molecular state, showing that prostate gland morphology can be integrated with spatial omics to identify morphomechanical niches associated with cancer progression.

Despite the widespread use of nonsteroidal anti-inflammatory drugs and oral contraceptives (OC), population-level analyses on how menstrual disorder diagnoses vary across ibuprofen and OC use remain limited. Prior studies emphasize treatment efficacy for individual conditions rather than cross-sectional diagnosis co-occurrence across these treatment categories. The NIH All of Us Research Program enables age-stratified analyses of menstrual disorder diagnoses across ibuprofen and OC use. We conducted a retrospective cross-sectional analysis of female participants 18-35 years old using All of Us data from electronic health records, survey responses, and physical measurements. Participants were stratified into ages 18-24 and 25-35 years and grouped by recorded ibuprofen only, OC only, or ibuprofen + OC exposure. Standardized Observational Medical Outcomes Partnership concept sets identified dysmenorrhea, irregular menstruation, excessive/heavy bleeding, and amenorrhea. Pairwise chi-square tests compared diagnosis presence across treatment groups. The Holm-Bonferroni (HB) adjustment was applied. Treatment duration, dose, adherence, and temporal sequencing relative to diagnosis could not be established. In women aged 18-24 years, no significant differences in diagnosis prevalence were observed between treatment groups (ibuprofen: n = 2832; OC: n = 4256; and combination: n = 2315). Between-group prevalence differences were modest (0.9 to 2.1 percentage points) and did not remain significant after HB correction (all HB adjusted p > 0.05). Women aged 25-35 years (ibuprofen: n = 9533; OC: n = 9517; and combination: n = 9617) had OC use associated with lower dysmenorrhea prevalence versus combination use (prevalence difference: 1.4%; HB-adjusted p < 0.05). Irregular menstruation was more frequent in the OC group than in the ibuprofen group (2.1%; HB-adjusted p < 0.05). Excessive/heavy bleeding was more prevalent with ibuprofen use than with OC use and combination therapy (2.2% and 1.6%, respectively; HB-adjusted p < 0.05). Amenorrhea prevalence did not differ significantly (&#x2264;0.9%; HB-adjusted p > 0.05). Menstrual disorder diagnosis frequencies differed across ibuprofen and OC exposure groups, primarily among women 25-35 years old. Longitudinal studies with untreated comparators and better characterizations of treatment timing, dose, and duration are needed.

We sought to analyze the effect of a palliative care intervention on quality of life (QoL) in patients with fibrotic interstitial lung disease (fILD). This was a prospective observational study including 14 patients with fILD treated with a bundle of care provided by multidisciplinary specialists in pain and palliative care, a psychologist, physical therapists, and a nutritionist, with all patients initiating 10 mg of morphine sulfate. Measurements at baseline, 30 days, and 90 days included cough, dyspnea, pain, tiredness, nausea, depression, anxiety, sleepiness, appetite, and difficulty sleeping. QoL was recorded using the modified St. George's Respiratory Questionnaire (SGRQ-1). Change over time in each endpoint was analyzed. Baseline assessment reflected an impaired QoL (median SGRQ-1, 91 points). All symptom scores improved at 90 days, with a statistically significant and clinically meaningful 20-point decrease in the SGRQ-1 (p = 0.001). Palliative care intervention improves symptom and QoL in fILD.

Expanding access to cellular immune analysis is essential for decentralized clinical care, clinical trials, and population-based research. However, current flow cytometry workflows require rapid processing of fresh blood, proximity to a centralized laboratory, and cold chain logistics. Although dried blood spots (DBS) have transformed decentralized molecular diagnostics, no comparable approach has enabled robust flow cytometric analysis of immune cells. Here, we present FlowSpot, a novel platform that enables recovery of leukocytes from DBS and preserves their immunophenotypic characteristics, allowing downstream flow cytometric analysis following ambient-temperature storage and shipment. FlowSpot recovers intact leukocytes while preserving immune cell subset frequencies with strong concordance to fresh whole blood. We demonstrate its clinical utility by enabling remote CD4&#x207a; T cell immunophenotyping in people living with HIV, showing high agreement with routine clinical measurements across a broad range of CD4&#x207a; T cell frequencies. Beyond cellular phenotyping, FlowSpot extends immune monitoring to functional profiling by enabling detection of intracellular cytokine responses, including IFN&#x3b3;, IL-2, and TNF&#x3b1; production by CD4&#x207a; and CD8&#x207a; T cells following ex vivo PMA/ionomycin stimulation. By overcoming a longstanding barrier to leukocyte recovery from DBS, FlowSpot extends flow cytometry beyond specialized laboratories, expanding access to cellular immune analysis for clinical care, decentralized clinical trials, and population-scale immunology.

Excess epicardial adipose tissue (EAT) is associated with cardiovascular-kidney-metabolic (CKM) dysfunction, but its assessment has traditionally required advanced imaging. We tested whether AI-enhanced echocardiography could enable scalable phenotyping of adverse epicardial adiposity and identify individuals at increased cardiometabolic risk. We developed PanAdipo , a video-based deep learning model in 1,114,441 videos from 28,797 studies across the Yale-New Haven Health System (YNHHS; 2016-2022), using expert reader annotations of prominent EAT (2.5% of studies). PanAdipo was evaluated in four cohorts: a temporally distinct YNHHS TTE set (n=4,588), an emergency-department point-of-care ultrasound cohort across YNHHS (n=10,957), the geographically distinct MIMIC-IV cohort (n=4,549), and the community-based Multi-Ethnic Study of Atherosclerosis (MESA Exam 6, n=2,740). Analyses examined (i) discrimination of prominent EAT; (ii) independence from conventional echocardiographic outputs; (iii) spatial explainability using gradient-weighted class activation mapping; (iv) correspondence with paired CT-derived body composition phenotypes (n=5,594); and (v) age-, sex-, and BMI-independent associations with cardiometabolic biomarkers and incident metabolic disease. In the held-out health system test set, PanAdipo discriminated prominent EAT with an AUROC of 0.91 (95% CI, 0.88-0.94), exceeding conventional measures of cardiac function and structure. In explainability analyses, the model's attention localized to the epicardial area across views and throughout the cardiac cycle. On paired cardiac CT imaging, the PanAdipo score correlated most strongly with epicardial adiposity (Spearman &#x3c1;=0.75; P<10 -300 ) with weaker correlations with other adipose and non-adipose compartments and only modest correlations with BMI across cohorts (&#x3c1;=0.19-0.40). In MESA, greater PanAdipo scores were independently associated with higher HOMA-IR and triglycerides, associations that persisted among normoglycemic participants. Higher PanAdipo scores were also associated with newly documented metabolic disease, including MASLD/MASH, after adjustment for BMI (HR pooled 1.25 [1.08-1.44] per 1-SD increment in log[PanAdipo]). AI-enabled echocardiography provides a scalable, view-agnostic biomarker that characterizes adverse epicardial adiposity and is associated with cardiometabolic dysfunction, highlighting a new role for echocardiography in CKM risk stratification. What Is New?: AI-enabled echocardiography derives a scalable, view-agnostic biomarker of adverse epicardial adiposity from standard transthoracic and point-of-care ultrasound acquisitions.The biomarker captures information not represented by conventional echocardiographic measurements and corresponds most strongly to CT-defined epicardial adiposity on paired cross-sectional imaging.Higher biomarker values are associated with insulin resistance, dyslipidemia, and subsequently documented metabolic disease beyond body mass index and waist-hip ratio.What Are the Clinical Implications?: Routine echocardiography may provide an opportunistic window into adverse adiposity and extend cardiac imaging to metabolic risk stratification.This approach provides a portable platform for prospective evaluation of point-of-care adiposity phenotyping, integration with complementary cardiometabolic data, and enrichment of prevention trials with participants at increased metabolic risk.