Science Translational Medicine

People with HIV exhibit elevated inflammation and cardiovascular risk despite antiretroviral therapy. To define the genetic architecture of inflammasome-associated inflammation, we performed whole-genome sequencing and quantified plasma IL-6, IL-1{beta}, and IL-18 in 1,000 ART-suppressed PWH from the U.S. Military HIV Natural History Study. Genome-wide analyses identified 14 loci implicating antiviral defense (DDX17, DDX41, EEA1, BCL11A), lipid metabolism (ABCA1, ABCA12, ABCC1, AGMO), and vascular remodeling (KLHL29, RNF213, ETV1). Transcriptome-wide analyses across cardiovascular and immune tissues identified regulatory programs linking interferon signaling, immune activation, and vascular biology to circulating cytokine levels. Mendelian randomization analyses supported causal relationships between inflammasome-associated cytokines and vascular events. Functional integration with genome-wide CRISPR perturbation datasets in primary CD4 T cells linked cytokine-associated loci to HIV antiviral pathways and cytokine regulatory networks. External validation in cohorts without HIV demonstrated pathway-level convergence despite limited variant-level overlap. These findings define genetic mechanisms linking inflammasome signaling, antiviral defense, and cardiovascular risk.

Vaccines to prevent infant group B streptococcus (GBS) disease are advancing, with licensure likely based on safety and immunologic endpoints rather than clinical efficacy data. This approach requires robust, generalisable serological thresholds of risk reduction (SToRRs). We combined data from six case-control studies in Europe and Africa to define SToRRs for early-onset (EOD) and late-onset (LOD) GBS disease. Across diverse epidemiological and healthcare settings, anti-capsular polysaccharide IgG concentrations were consistently higher in infants who remained disease free than in those who developed disease. Higher antibody concentrations were required to reduce the risk of EOD than LOD, and higher concentrations were required for serotype Ia than for serotype III. This study provides a quantitative framework to support correlates-based evaluation and potential licensure of maternal GBS vaccines.

Gene therapy is rapidly emerging as a transformative treatment for monogenic neurological disorders, including pediatric movement disorders such as aromatic L-amino acid decarboxylase (AADC) deficiency. However, its success critically depends on defining target cells and windows for therapeutic intervention. Here, we present an open-access single-nucleus transcriptomic atlas of the human basal ganglia spanning a therapy-relevant window from second/third trimester to the perinatal period and adulthood. Across 35,755 nuclei, we identify major (non-)neuronal cell types, retrace developmental trajectories, and characterize gene-regulatory networks. We identify so far unrecognized human-specific expression of key neuronal signaling genes, including GNAO1 and ADCY5, and discuss the implications for targeted gene replacement therapies. Unexpectedly, we found that the Huntingtin gene (HTT) is already expressed during prenatal stages of human brain development, supporting a previously proposed neurodevelopmental component of Huntington's disease, which should be considered in diagnostic and therapeutic strategies. Moreover, FOXG1 expression and regulon activity are predominantly located in a prenatal time window, suggesting constraints on the effectiveness of postnatal interventions. Our findings highlight the importance of datasets capturing human brain development in real time and provide a publicly available resource to guide precision gene therapy strategies in the future.

Whole-genome sequencing comprehensively captures coding, non-coding and structural variation in families with suspected inherited disorders, yet its clinical utility remains constrained by an interpretation bottleneck: selecting a handful of relevant variants from millions of candidates. Current rule-based pipelines, anchored in ACMG/AMP criteria, excel at identifying highly penetrant Mendelian alleles but frequently miss variants of low-to-moderate penetrance, non-coding alterations and germline-somatic interactions. Here we introduce PolyCLIP-T, a topology-guided multimodal framework that transforms variant selection from a classification problem into a geometric discovery task. By contrastively aligning DNA-sequence embeddings with functional annotations, PolyCLIP-T constructs a unified latent space in which the displacement between reference and alternate embeddings quantifies the molecular perturbation induced by each variant. Persistent homology then identifies stable topological components - coherent variant groups shared among affected relatives - that transcend single-variant scoring logic. Applied to six families with multi-morbid cancer, autoimmune and cardiovascular disease, PolyCLIP-T recovered non-coding and structural candidates overlooked by conventional pipelines and revealed pleiotropic networks spanning disease categories. This approach provides an interpretable, scalable solution for genome-first investigations of disorders driven by polygenic architectures that evade single-variant analysis. The framework was developed and benchmarked on deeply characterised familial cohorts selected for transgenerational multimorbidity; validation in larger, independent populations will be essential to establish its generalisability. An interactive web tool is freely available at https://www.polyclip-t.uma.es/.

PURPOSE: As the armamentarium of BPH therapies continues to expand, it remains imperative to maximize patient satisfaction and minimize decisional regret. We sought to determine the impact of time from BPH diagnosis to index treatment on symptom improvement and subsequent procedural events. MATERIALS AND METHODS: We queried the American Urological Association Quality Registry for men [&ge;] 40 years old with BPH, available IPSS data, and no receipt of prior BPH treatment. Index treatment included medication, surgery, or minimally invasive surgical therapy (MIST). Outcomes included IPSS over 3 years of follow-up, change in percentage of mild lower urinary tract symptoms (LUTS) by 3 months, and time to procedural event. Patients were stratified by time from index diagnosis to treatment by <12 months, 1-3 years, and >3 years. Outcomes were compared across time-to-treatment cohorts with appropriate statistical tests with p < 0.05 as significant. RESULTS: 43,919 patients met criteria with 19,642 pursuing treatments. Patients pursued treatment at comparably lower baseline IPSS compared to prior prospective series. Patients undergoing surgery and MIST had significantly higher baseline IPSS, while medical comorbidities were significantly more common among men initiating pharmacotherapy. Early surgery and MIST were associated with significant improvement in IPSS within 6-12 months and an increase in mild LUTS by 3 months. All forms of early treatment were associated with delayed time to procedural events, including catheterization and fulguration. CONCLUSIONS: Early procedural intervention for BPH is associated with early symptom improvement and delayed time to procedural events among real-world, contemporary practice.

Background. Blood-based biomarkers are increasingly proposed for identifying high-risk individuals before clinical disease and for making prevention-oriented trials more efficient. Prognostic enrichment can increase event rates, but trial efficiency also depends on whether the intervention effect is preserved in the enriched population. Methods. Using the UK Biobank Pharma Proteomics Project, we trained disease-specific proteomic risk scores (ProRS) from 2,916 plasma proteins with elastic-net Cox models. We compared ProRS, polygenic risk scores (PRS), and combined PRS--ProRS scores across ten incident diseases. We estimated cumulative incidence and theoretical two-arm time-to-event trial sample sizes across risk strata. To evaluate effect preservation, we examined six intervention-analogue exposure--outcome pairs spanning genetic (PCSK9/coronary artery disease, APOE/Alzheimer's disease, PPARG/type 2 diabetes, IL23R/Crohn's disease), behavioural (physical activity/all-cause mortality), and pharmacological (RAAS inhibitors versus calcium channel blockers/coronary artery disease) examples. Results. ProRS outperformed PRS for 9 of 10 diseases (median C-index 0.75 versus 0.61). ProRS and PRS were weakly correlated (median Pearson |r| = 0.04), and joint PRS--ProRS stratification identified groups with higher observed incidence than either score alone for several endpoints. In the top risk quartile, combined-score enrichment reduced theoretical required sample sizes by 32--74\% under a fixed 20\% relative hazard reduction. These gains were not always preserved when stratum-specific intervention-analogue effects were used. Effects were broadly preserved for APOE/Alzheimer's disease and physical activity/mortality. The PPARG/type 2 diabetes effect attenuated toward the null under all three score types, showing that event-rate enrichment does not guarantee effect preservation. For IL23R/Crohn's disease and the antihypertensive comparison, point estimates differed across score types -- preserved under polygenic but attenuated under proteomic enrichment -- but confidence intervals were wide and overlapping. Conclusions. Proteomic risk scores can identify high-event-rate populations for prevention-oriented trials, but event-rate enrichment alone is insufficient for trial design. Biomarker-guided enrichment should evaluate mechanism-specific effect preservation and may be preferable as a stratification or adaptive-design variable rather than as a restrictive eligibility criterion.

Severe preeclampsia (sPE) is a major cause of maternal and fetal morbidity worldwide, yet its placental molecular heterogeneity remains poorly defined by current clinical diagnosis. To resolve the molecular architecture of sPE, here we integrated DNA methylation and proteomic profiling from a multi-ethnical cohort of 444 placentas from the Hawaiian Biorepository (HiBR), including 169 sPE cases, matched preterm controls and full-term controls. To address cellular heterogeneity in bulk placental tissue, we developed HOMED (Hierarchically Optimized Methylation Deconvolution), a single-cell-guided hierarchical framework for inferring placental cell-type composition from DNA methylation data. HOMED-adjusted integrative analyses identified extensive subtype-specific alterations involving hypoxia, angiogenesis, immune activation, trophoblast differentiation and metabolic remodeling. Molecular stratification revealed two reproducible sPE subtypes with divergent placental aging trajectories. One subtype exhibited a pre-mature placental state marked by accelerated placental aging, whereas the other displayed slower accelerated placental aging but a substantially increased risk of small-for-gestational-age birth (P = 0.028). These subtypes were independently replicated across six external cohorts and further supported by proteomic signatures achieving a classification accuracy of 0.88. Integrative epigenomic and proteomic analyses linked the growth-restricted subtype to hypoxia-associated glycolytic remodeling, suggesting distinct pathogenic mechanisms underlying clinically diagnosed sPE. Together, our findings redefine severe preeclampsia as a biologically heterogeneous placental disorder composed of molecularly distinct subtypes with divergent aging trajectories and fetal growth outcomes, providing a framework for mechanism-based stratification and precision obstetric medicine.

Accurate assessment of body composition is important to risk stratification and management of metabolic, musculoskeletal, and aging-related diseases, yet reference modalities such as Dual-energy X-ray absorptiometry (DXA) are costly and impractical for frequent monitoring. Commodity 3D body scans offer a low-cost, radiation-free alternative, but extracting meaningful and predictive shape features from scans remains challenging due to nonuniform point density, variable body size and cross-device differences. We introduce BodyMAE, a self-supervised, surface-area aware masked autoencoder for metric-scale 3D body scans. The pipeline integrates area-adjusted sampling, a long-range focused encoder, and a lightweight decoder regularized to promote locally uniform reconstructions. Trained and evaluated on 917 paired 3D body scans paired with clinical DXA reports, BodyMAE achieves strong accuracy on fat percentage (root-mean-square error (RMSE) 3.825 percentage points, R^2 0.908), fat mass (RMSE 3.694 kg, R^2 0.968), and lean mass (RMSE 3.608 kg, R^2 0.901), with competitive performance on bone mineral content (RMSE 0.284 kg, R^2 0.754).We also assess feature stability across pretrained baselines, finding higher retrieval accuracy for our representations (Top-1 90.131%). These results indicate that combining metric-aware sampling, long-range relational encoding, and local geometric regularization enables accurate body composition estimation from 3D body scans, as validated by comparisons to DXA-derived measurements.

Caveolinopathies caused by CAV3 mutations present with heterogeneous clinical phenotypes ranging from asymptomatic hyperCKemia to limb-girdle-type muscular dystrophy. Although prior imaging studies have described commonly affected muscles, structured modeling of muscle involvement patterns in caveolinopathy has not been established. We analyzed whole-body skeletal muscle computed tomography imaging in eight patients with pathogenic or likely pathogenic CAV3 variants, comprising 14 imaging study samples. Fat infiltration across 43 muscles was graded using modified Mercuri scores. Computational multivariate analysis,including principal component analysis, clustering, and pseudotime modeling,was applied to characterize severity staging and distribution patterns. A statistically supported, stage-dependent continuum of muscle involvement was identified. Most samples demonstrated a distributed limb-girdle-predominant pattern with coordinated progression across muscle clusters. In contrast, one patient (three samples in longitudinal series) exhibited a compartment-restricted thigh-dominant pattern characterized by early posterior and medial thigh involvement. Rectus femoris showed consistent stage-dependent progression, while greater medial gastrocnemius involvement was associated with advanced severity. None of the patients exhibited clinical evidence of rippling muscle disease. These findings suggest that integrating semi-quantitative imaging with computational modeling may provide an objective framework for characterizing muscle involvement patterns in CAV3-related myopathy.

Cardiovascular diseases (CVDs) remain the primary global health burden, motivating the search for robust, non-invasive risk biomarkers. We harness a foundation model pretrained on over 10 million recordings, to evaluate ECG-derived age deviation as a cross-cohort biomarker of CVD burden. A predictive model, trained exclusively on healthy subjects, achieved accurate age prediction. Diseased subjects exhibited significant positive age acceleration across multiple categories, with structural and ischemic heart diseases showing the largest effects. External validation in a hospital-based cohort (n=160,493) confirmed that age acceleration independently predicts all-cause mortality, with the strongest prognostic value in patients under 65 years. Furthermore, we demonstrated that disease discrimination and mortality prediction are preserved across 6-lead and single-lead configurations, supporting potential deployment in wearable or mobile devices. Our analysis also revealed a striking morphological confound from the complete left bundle branch block, leading us to propose absolute age deviation as a more robust, universal risk marker. These findings establish ECG-derived biological age deviation as a highly generalizable and clinically actionable biomarker for assessing cardiovascular risk. We have also developed a web application at https://bioinformatics.mdc-berlin.de/ECGage that allows users to easily test our framework.

Biological systems evolve as continuous dynamical processes, but at organ-scale and across human lifespans they are rarely observed longitudinally--population data typically exist instead as sparse, cross-sectional snapshots. Inferring lifespan dynamics from such data requires methods distinct from those used at cellular and tissue scales where dense observations are accessible. We address this problem in the thoracic aorta, where surgical decisions currently rest on static, age- and sex-agnostic diameter thresholds that reduce three-dimensional morphology to a single scalar. Treating normal aortic morphology as a stochastic dynamical system, we pose a continuous-time drift-diffusion process in a two-coordinate state space of normalized surface area (A) and normalized fluctuation in integrated Gaussian curvature ({delta} K), and fit closed-form solutions of the Fokker-Planck equation by maximum likelihood to a sex-balanced, age-uniform cohort spanning infancy to age 99. Inter-individual variability is treated as a fitted diffusion parameter rather than as residual scatter, which is distinct from prior normative studies that report variability as scatter around a regression line. The framework identifies two growth regimes for aortic size (childhood expansion followed by persistent adult growth, with adult males growing approximately 70% faster than adult females) and a single dynamical regime for aortic shape, with heteroscedastic variability accumulating at a rate comparable to the mean drift over the lifespan. Applied to independent cohorts of acute and chronic thoracic aortic dissections, the multivariate model identifies over 95% as statistical outliers via Mahalanobis distance, consistently outperforming either coordinate alone. The same probabilistic envelope that describes normal aging thus defines a baseline against which disease can be detected, supporting a shift toward dynamic, age- and sex-aware assessment of thoracic aortic pathology.

Pathogenic variants in ATP13A2, which encodes an endolysosomal polyamine exporter, cause Kufor-Rakeb syndrome and are associated with early-onset parkinsonism and related neurodegenerative disorders, however, the mechanisms by which ATP13A2 dysfunction drives disease remain incompletely defined. In Atp13a2 knockout mice, we identified an early, transient reduction in brain polyamines that precedes overt gliosis and behavioural abnormalities. Pharmacological polyamine depletion exacerbates phenotypes, whereas oral supplementation of spermidine, but not spermine, rescues parkinsonian symptoms establishing metabolic polyamine deficiency as a pathogenic driver. Mechanistically, spermidine counteracts microglia lysosomal dysfunction in the brain and exerts mitochondrial antioxidant and anti-inflammatory effects in primary mouse microglia, thereby improving neuronal integrity. In the absence of Atp13a2, microglial spermidine import relies on the related polyamine transporter Atp13a3. Importantly, these findings translate to human systems, whereby spermidine attenuates inflammation in ATP13A2-deficient human differentiated microglia, while postmortem ATP13A2-deficient brain analysis confirms increased microglia reactivity. Spermidine also rescues motor deficits and dopaminergic neuron loss in ATP13A2-deficient Drosophila and other fly parkinsonism models. Together, these findings identify early polyamine dysregulation as a mechanistic contributor to ATP13A2-associated parkinsonism and nominate spermidine supplementation as a potential therapeutic strategy for ATP13A2-driven pathology and possibly a broader range of parkinsonian sub-types.

Background: Chronic low-grade inflammation drives cardiovascular-kidney-metabolic (CKM) syndrome. Clonal hematopoiesis of indeterminate potential (CHIP), an age-related driver of systemic inflammation, is linked to several cardiometabolic disorders. However, whether CHIP modifies CKM progression and contributes to heterogeneity in cardiovascular disease (CVD) risk within the CKM framework remains uninvestigated. Methods: This cohort study included 307,025 UK Biobank participants at CKM stages 0-3 free of baseline CVD. CHIP status was identified via whole-exome sequencing (WES). The association between CHIP and baseline CKM severity was examined, along with the independent and joint effects of CHIP and CKM stages on incident CVD risk. The joint effects of CHIP and polygenic risk scores (PRS) were further assessed, and the incremental predictive value of incorporating CHIP into the AHA PREVENT equations was evaluated. Results: CHIP carriers were more likely to present with advanced CKM stages [OR 1.14 (1.09-1.20), P < 0.001] and exhibited higher incident CVD risk during follow-up [HR 1.13 (1.08-1.18), P < 0.001]. Significant joint effects between CHIP and CKM stages were observed, with the highest risk among CHIP carriers at CKM stage 3 [HR 1.63 (1.50-1.78), P < 0.001]. Large or multiple CHIP mutations conferred greater hazards, with distinct gene-specific effects observed. Moreover, CHIP and high genetic risk also jointly amplified CVD susceptibility. Most importantly, incorporating CHIP into AHA PREVENT significantly improved risk discrimination. Conclusions: CHIP is a significant risk factor associated with more advanced CKM stages and amplifies incident CVD risk. Integrating CHIP into existing prevention strategies may refine CVD risk stratification.

Background. Tethered cord syndrome (TCS) is classically associated with a low-lying conus medullaris, yet many surgically treated children have a normally positioned conus (occult TCS). Large-scale normative data on conus position in children, and the diagnostic value of quantitative conus assessment, are limited. Purpose. To establish a large-cohort reference distribution for conus medullaris termination level in children, to quantify conus position in children surgically treated for presumed (occult) TCS, and to test whether automated conus segmentation and radiomics can distinguish TCS from normal. Materials and Methods. In this retrospective single-center study, conus termination level was extracted from structured radiology reports of consecutive pediatric lumbosacral MRI examinations and encoded numerically (L1 = 1, L2 = 2, etc.). Children surgically treated for tethered cord were identified by linkage to an operative registry (name and date of birth) and restricted to preoperative examinations. A deep-learning model (nnU-Net) was trained for conus segmentation on axial T2-weighted images. IBSI-compliant radiomic features were extracted; reproducibility was assessed by intra- and inter-observer intraclass correlation (ICC). A case-control radiomics analysis used batch-only ComBat harmonization and cross-validated L1-penalized logistic regression; discrimination was compared with conus level by paired bootstrap. Results. Among 9,808 examinations with a parseable conus level (98.5% of reports; parser validated against dual blinded annotation, 99.4% agreement, weighted kappa 0.946), the conus terminated in the L1 region in 85.7% and the L2 region in 14.3% of the reference cohort (postoperative examinations excluded, n = 9,655); a low-lying conus (>=L3) occurred in only 0.05% (5/9,655), and remained rare (0.14%, 14/9,808) including operated examinations (median L1; mean 1.13 +/- 0.33). A slightly more cephalad position was seen with increasing age (negligible correlation). Among 475 preoperative children surgically treated for tethered cord, 99.6% had a normally positioned conus (<=L2) and only 0.4% were low-lying. Automated conus segmentation achieved a held-out Dice of 0.85. Conus radiomics likewise did not distinguish TCS from controls (equivalence-tested null; full segmentation/radiomics pipeline reported in the companion methodological paper). Conclusion. In children, the conus medullaris terminates at L1-L2 in more than 99% of cases and is normally positioned in virtually all children surgically treated for TCS. Within the conus, neither position nor texture (radiomics) identifies tethered cord; whether the filum terminale carries a diagnostic signal was not tested here.

Background: The World Health Organization has expanded its recommendations for prophylactic Ebola vaccination for at-risk populations. Durable vaccine-induced immunity is important for sustaining outbreak preparedness in regions with recurrent Ebola virus disease (EVD). We assessed five-year persistence of vaccine-induced immune responses in adults and children from the PREVAC trial. Methods: Two large randomised phase 2 trials (NCT02876328), in adults and children aged [&ge;]1 year, were conducted in four west African countries. Participants were randomly assigned to placebo or to one of three Ebola vaccine strategies: Ad26.ZEBOV followed by MVA-BN-Filo at 56 days; rVSV{Delta}G-ZEBOV-GP followed by placebo; or rVSV{Delta}G-ZEBOV-GP followed by a homologous booster dose at 56 days. After 12 months of follow-up, the primary results were published, participants unblinded to their vaccine assignment, and follow-up continued for 60 months. After Month 24, placebo group recipients were offered active vaccination. Anti Ebola virus glycoprotein Immunoglobulin G (IgG) concentrations were measured for 5 years. Findings: 1401 adults and 1401 children were initially randomized, and 1315 (93.9%) adults and 1322 (94.4%) children attended at least one long-term visit. Retention was high, with 95% followed beyond 1 year and 83% completion at 5-year follow-up. For the three vaccine strategies, antibody geometric mean concentrations (GMC) declined modestly between Months 12 and 24, followed by a stable plateau from Months 24 to 60. At Month 60, antibody GMC were higher in the rVSV-based groups (1099 and 1216 EU/ml for adults; 1982 and 2347 EU/ml for children) than in the Ad26.ZEBOV, MVA-BN-Filo group (252 adults and 645 EU/ml children). Antibody persistence at Month 60 was heterogeneous, varying by age, sex, country, and baseline IgG concentration. Interpretation: Licensed Ebola vaccines induced sustained antibody responses in adults and children for up to 5 years. While the protective antibody level is unknown, these data demonstrate long-lasting immune responses from currently employed vaccine strategies.

Postoperative resilience varies widely among older adults, yet the biological drivers of recovery remain unclear. We evaluated whether preoperative immune profiles, measured in plasma and through ex vivo whole blood stimulation, predict resilience to the acute stress of total knee arthroplasty. A total of 152 adults (greater or equal to 60 years) in the PRIME KNEE cohort underwent elective total knee arthroplasty and had available blood samples for measurement of 45 immune biomarkers, quantified in plasma and in whole blood stimulated ex vivo for 24 hours with lipopolysaccharide (LPS) or influenza antigen (FLU). Resilience was assessed using Expected Recovery Differential (ERD) and Resilience Trajectory (RT) across pain severity, pain interference, lower extremity physical activities of daily living (LE PADLs), and step counts. An exploratory stability selection framework using LASSO identified biomarker predictors of postoperative outcomes. Plasma and stimulated biomarkers showed broadly similar predictive performance. A shared set of biomarkers, including LBP, leptin, TNFR1, CD30, and LIF, was consistently selected across models. Immune predictors explained ~12-24% of the variance in resilience outcomes. Distinct immune signatures emerged for pain versus functional recovery: pain related predictors mapped to local inflammatory and neuroimmune pathways, whereas function related predictors reflected systemic inflammatory load and cytokine signaling. Preoperative immune biomarkers, whether measured in plasma or after ex vivo stimulation, capture meaningful variance in postoperative resilience. The divergence between pain related and function related immune signatures highlights biologically distinct pathways underlying different dimensions of recovery and supports further development of immune based perioperative risk assessment.

Bone remodeling is a tightly regulated physiological process that maintains bone health through coordinated action of bone-resorbing osteoclasts and bone-forming osteoblasts. Disruption of this balance, such as the one induced by estrogen decline after menopause, results in bone loss and osteoporosis. Genetic factors play an important role in determining bone mineral density (BMD) loss over time. However, translating genetic associations into individualized risk prediction remains challenging due to small effect size of individuals variants and non-linear interactions within the bone remodeling unit. Here, we present a bone cell population dynamics model that includes major regulatory pathways, such as the RANK/RANKL/OPG axis, Wnt signaling, and hormonal regulation by estrogen, parathyroid hormone, and TGF-{beta}. We calibrate the model on clinical data from healthy postmenopausal women, and women with reduced BMD undergoing anti-osteoporotic therapy. The calibrated model captures healthy BMD decline in postmenopausal women and therapeutic response to anti-osteoporotic medications. We mechanistically incorporate the effect of 22 variants across 8 genes involved in bone remodeling and simulate BMD trajectories in 1,000 virtual subjects differing by ancestry and genetic makeup. The median predicted 5-year BMD loss was 3.57% (95% prediction interval: 1.31-5.24), consistent with the values reported in the literature. The virtual individuals with African ancestry were predicted to experience the highest average 5-year BMD loss. The strongest genetic risk factors for bone loss were predicted to be CYP19A1 rs727479 and OPG rs3102735, while LRP5 rs11228240 emerged as a protective factor that could partially counteract the detrimental effects of other variants. Several epistatic effects were observed in the genetic interaction analysis. Mechanistically, our model suggested that estrogen exerts its effect on bone remodeling primarily by modulating osteoclast apoptosis. Overall, this framework demonstrates a proof-of-concept for integration of genetic risk factors into mechanistic models of disease and can be extended to other conditions with polygenic inheritance.

Background Continuous health monitoring enables early detection of diseases and improves therapeutic outcomes. Non-intrusive biosignal sensors, such as capacitive ECG (cECG), offer a practical solution for daily monitoring in private environments, such as smart homes and vehicles. However, artifacts reduce signal quality and compromise reliability. Methods Following a registered report protocol (Warnecke JM et al. Plos One. 2021; 16(7):e0254780), we record data of 44 subjects and develop an artifact index for cECG. We use three signal quality indices (SQIs): the correlation of QRS complexes (corSQI), the R-peak detection consistency (bSQI) and the absolute amplitude ratio (aSQI). Our index classifies overlapping 10s segments with a step-width of 2s into clean or artifact segments. We label a 2s interval as artifacts if all five overlapping segments indicate artifacts. We record cECGs using an armchair with integrated electrodes in a single-arm study involving 44 subjects performing two activities -- reading and watching television (TV); for 11 minutes each. We record a time-synchronized reference ECG with skin electrodes on the chest. To evaluate the artifact index, we compare it with manually generated ground truth. Moreover, we evaluate the clothing materials cotton, linen, jeans, and polyester in 5 subjects. Results Watching TV results in longer, continuously clean signal durations than reading. On average, 88.3% of the signal has a minimum continuous clean duration of 10s, versus 79.8% during reading. All clothing configurations achieve a clean signal duration exceeding 10s. Among the SQI metrics, bSQI performs best, achieving an accuracy of 90.7% and an F1 score of 79.9%. Combining the three SQI metrics in a voting approach improves accuracy to 92.0% and F1 score to 82.1%. Discussion Our artifact index automatically distinguishes clean from artifact cECG segments, promoting health monitoring in unsupervised real-world settings, earlier disease detection, and preventive health management. A limitation is the investigation of only two scenarios (reading and watching TV).

The recent availability of at-home menstrual cycle tracking technology has created opportunities for personalized assessment of reproductive health, alongside improved characterization of hormone patterns in women with and without reproductive disorders such as polyendocrine metabolic ovarian syndrome (PMOS), which affects approximately 10% of reproductive-age women. In this study, we leverage self-tracked urinary hormone data to develop an autoregressive Hidden Markov model (arHMM) that maps cycle days to physiologically meaningful phases based on hormone trajectories. By modeling day-to-day hormonal dynamics rather than absolute hormone levels, and allowing variable phase durations, this approach accommodates substantial variability in menstrual cycles, thereby enabling meaningful comparisons within and between individuals. Across more than 3800 cycles from over 1100 individuals, we find that arHMM-derived phases reproduce expected hormonal patterns within follicular, periovulatory, and luteal phases, and that phase-based timing for hormone testing outperforms conventional cycle day-based testing in capturing the luteinizing hormone surge and post-ovulatory progesterone rise, highlighting limitations of fixed-day clinical protocols. We identify phase-specific differences between healthy controls and individuals with self-reported PMOS, including lower luteinizing hormone in the periovulatory phase, and reduced luteal-phase progesterone levels in PMOS. Furthermore, features derived from arHMM phase assignments enable classification of PMOS status with ~78% accuracy, demonstrating the potential of this approach for non-invasive PMOS screening.

Background: Low-intensity focused ultrasound (LIFU) is an emerging noninvasive neuromodulation technique capable of targeting deep cortical and subcortical structures with high spatial precision. In healthy human volunteers, LIFU has demonstrated a favorable safety and tolerability profile across multiple studies. However, its safety and tolerability in clinical populations remains poorly characterized, representing a critical barrier to clinical translation. Here, we prospectively evaluate the safety and tolerability of LIFU targeting the left dorsal anterior insula (dAI) in patients with fibromyalgia (FM). Methods: In a single-blind, sham-controlled, within-subjects crossover design, 13 individuals with FM (43.1 +/- 13.2 years; 12 female) received 10 minutes of active LIFU (500 kHz, 1 kHz PRF, 36% duty cycle, 4.2 W/cm2 Isppa; 100 x 1-second pulse trains with a 5-second inter-train interval) targeting the left dorsal anterior insula (dAI) or sham on separate visits. Safety was evaluated through neuroradiological review of post vs. pre LIFU FLAIR MRI, quantitative voxel-wise FLAIR analysis, and patient report of symptoms (ROS). Tolerability was assessed using an experience assessment. Efficacy of the LIFU intervention was assessed using quantitative sensory testing (QST) including temporal summation of pain (TSP) and conditioned pain modulation (CPM). Results: Neuroradiological review identified no new evidence of edema, microhemorrhage, acute ischemia, or white matter injury on post-LIFU structural imaging. Quantitative FLAIR analysis using contralateral-mirror-referenced relative FLAIR (rFLAIR) showed no significant within-subject change in the stimulated beam volume (delta rFLAIR = 0.002 +/- 0.025, t(12) = 0.30, P = 0.769, Cohen's dz = 0.08). No serious adverse events were documented and ROS indicated no change due to LIFU sonication. Participants rated the procedure as comfortable and could not distinguish active from sham LIFU. LIFU did not result in statistically significant changes for TSP (p = 0.797) or CPM (p = 0.465). Conclusions: Ten minutes of LIFU targeting the left dAI was safe and well tolerated in individuals with FM, with no neuroradiological or quantitative MRI evidence of tissue effects and no serious adverse events. Blinding was preserved, and participants rated the procedure as comfortable. Although no significant changes were observed in experimental pain measures, these findings support the feasibility of targeting deep salience and pain amplification circuitry with LIFU in patients with FM and provide a foundation for adequately powered efficacy trials.