Endocrinology

Introduction: Pesticide exposure has been associated with poorer mental health, yet little is known about whether seasonal fluctuations in agricultural pesticide use are associated with cyclical changes in mood among people living in agricultural communities. We evaluated associations between 10 peak and off-peak pesticide spray seasons and ecological momentary assessment (EMA)-derived mood in a floricultural region in Ecuador. Methods: 496 participants completed repeated EMA surveys across 10 assessment administrations over 2.5 years (2022-2024), including one single-assessment administration and nine 10-day bursts (Nobservations=14,332). Mood outcomes included negative affect (sadness, anxiety, stress, anger, tiredness, difficulty concentrating; overall mean=2.79, Standard Deviation [SD]=1.32), positive affect (happiness, motivation; overall mean=4.21, SD=1.60), and behavioral dysregulation (aggression and impulsivity, overall mean=1.86, SD=1.29). Generalized linear mixed models estimated pooled, between-person and within-person associations between peak pesticide spray seasons and mood, adjusting for demographic and longitudinal factors Effect modification by sex, floricultural occupation, and cohabitation with a floricultural worker was assessed. Results: Peak spray seasons were associated with higher negative affect ({beta}=0.11 [0.06, 0.16]) and behavioral dysregulation ({beta}=0.10 [0.05, 0.15]) but not positive affect ({beta}=0.01 [-0.05, 0.07]). Associations were driven by within-person changes. Sex modified associations with negative affect, which were stronger in males than females. Floricultural occupation modified associations, with poorer mood, greater behavioral dysregulation and lower positive affect among floricultural workers than non-workers. Discussion: Peak pesticide spray seasons were associated with worsening of negative affect and behavioral dysregulation. EMA may provide a sensitive approach for detecting short-term neurobehavioral changes associated with seasonal pesticide exposure.

Polyendocrine Metabolic Ovarian Syndrome (PMOS, formerly PCOS) is associated with chronic low-grade inflammation. This study aimed to characterize the peripheral cytokine landscape in women with PMOS and identify cytokines associated with disease pathogenesis and phenotypic variation. Plasma concentrations of 48 cytokines were quantified using a multiplex bead-based assay in 102 women with PMOS and 114 healthy controls. We performed receiver operating characteristic (ROC) analysis, odds ratio estimation, correlation analysis, and network analyses to characterise cytokine alterations and identify phenotype-associated inflammatory signatures. Fourteen cytokines were significantly elevated in PMOS compared with healthy controls: IL-18, IL-1Ra, IL-12p70, IL-13, IL-16, MCP-1, MCP-3, CTACK, Eotaxin, RANTES, IP-10, M-CSF, HGF, and {beta}-NGF. ROC analysis identified IL-16, CTACK, and IP-10 as potential discriminators of phenotype A, whereas IL-18, M-CSF, and HGF demonstrated promising discriminatory performance for phenotype B. Odds ratio analysis identified 11 cytokines with odds ratios greater than 1, with IL-16 showing the strongest association with PMOS. Correlation analysis revealed 10 strongly correlated cytokine pairs (r [≥] 0.7) in the PMOS group, suggesting coordinated regulation of inflammatory mediators. Gene ontology and network analysis highlighted alterations in chemotaxis and immune cell migration pathways, implicating these processes in PMOS-associated immune dysregulation. These findings reveal a distinct peripheral inflammatory cytokine signature and phenotype-associated differences in PMOS. IL-16 emerged as a prominent candidate for further investigation, given its strong association with PMOS. Peripheral cytokine profiling may provide a minimally invasive approach to characterising immune alterations and identifying candidate biomarkers for disease characterisation and phenotype stratification.

Background: This study compared functional and cognitive trajectories in older women treated for early breast cancer with matched women without cancer. Methods: Data were derived from two Dutch cohorts: the CLIMB study (women aged 75 years and older with operable breast cancer) and the ISCOPE study (community-dwelling women aged 75 years and older without cancer). Breast cancer patients were matched 1:2 to controls on age and baseline functional or cognitive status. Functional status was assessed using the Groningen Activity Restriction Scale (GARS), and cognitive function using the Mini-Mental State Examination (MMSE). Longitudinal changes over 12 months were analyzed using both unadjusted and confounder-adjusted linear mixed models. Results: 168 breast cancer patients and 336 matched breast cancer-free controls were included with a mean age of 80 years. Most breast cancer patients had stage 0-I or II disease and received surgery and/or radiotherapy; 48% received adjuvant endocrine therapy, chemotherapy was rare (<1%). At baseline, both cohorts were comparable in age and functional or cognitive status, although there was a higher comorbidity burden in the control cohort. No significant differences in functional or cognitive decline were observed between the cohorts over 12 months. Conclusions: In older women, early breast cancer and its treatment did not lead to accelerated decline in functional status or cognitive function within the first year compared to matched older women without cancer. These findings suggest that, for relatively fit older patients, breast cancer treatment may not accelerate functional decline in the short term.

Purpose: Biochemical recurrence (BCR) after radical prostatectomy (RP) presents heterogeneous recurrence patterns and variable outcomes following salvage radiotherapy (sRT). We evaluated clinicopathologic determinants of PSMA-PET/CT detection, anatomical recurrence distribution, and biochemical disease-free survival (bDFS) following PSMA-PET-guided sRT. Methods: This retrospective single-institution study included 140 patients with BCR after RP who underwent Ga-PSMA-PET/CT prior to sRT with or without androgen deprivation therapy. PSMA-PET-positive findings were categorized as prostate bed only (PBO) or pelvic nodal involvement with or without the prostate bed (PLNI). Receiver operating characteristic (ROC) analysis, logistic regression, and Cox proportional hazards models were employed. Results: PSMA-PET detected locoregional recurrence in 77/140 patients (55%). On multivariable analysis, PSA at recurrence (OR=2.07; p=0.003) and age (OR=1.07; p=0.016) were the sole independent predictors of PSMA-PET positivity. ROC analysis yielded AUC=0.74 (optimal threshold of 0.58 ng/mL). Among PSMA-positive patients, PBO was associated with a longer time to BCR (tBCR) (37.5 versus 15 months; p=0.006). Seminal vesicle invasion (SVI) and pT-stage predicted PLNI on univariable analysis. Among 124 of 140 evaluable patients (median follow-up 31 months), estimated bDFS at 24, 36, and 60 months was 89.7%, 80.2%, and 54.1%, respectively. SVI (HR=2.39; p=0.031), EPE (HR=2.82; p=0.012), pT-stage (HR=1.64; p=0.021), and pN-stage (HR=1.83; p=0.045) were associated with increased failure on univariable Cox regression, though none retained independent significance. Conclusion: PSA burden and age independently predicted PSMA-PET detection. Recurrence patterns reflected disease behavior, as PBO was associated with longer tBCR. Adverse pathologic features were associated with pelvic nodal disease and reduced biochemical control; none remained independently predictive.

New prognostic biomarkers for oesophageal adenocarcinoma (OAC) may help treatment planning as current staging does not accurately predict survival for many patients. We used a 451 cancer-associated gene panel to assess genomic alterations associated with survival for a cohort of 65 OAC patients who underwent surgical resection without perioperative treatment, reflecting institutional practice at the time. Median age at operation was 72 years, 88% were male, median 32 lymph nodes were removed per patient, and minimum follow-up exceeded 11 years. TNM stages (pTNM, 8th ed.) were 14% stage 1, 14% stage 2, 45% stage 3, and 6% stage 4 (non-locoregional nodal metastases). Copy number changes were common, and somatic mutations were similar to those reported previously. Genetic associations with worse survival included amplification of CCND1 (HR=3.08, p=0.02), EGFR (HR=2.98, p=0.01), loss of ARID2 (HR=2.12, p=0.05), KMT2D (HR=1.99, p=0.07) and missense mutation within RYR1 (HR=2.68, p=0.01). This study identifies candidate genomic prognostic biomarkers in OAC using a cohort treated by surgery alone, enabling assessment of tumour biology independent of the confounding effects of peri-operative systemic therapy and radiotherapy. Several of the identified alterations are potentially targetable, supporting further investigation of genomic profiling to refine prognostication and guide treatment selection.

Polyendocrine metabolic ovarian syndrome (PMOS) and early-life irregular cycles (ELIC) carry higher metabolic risks, but real-world glycemic patterns remain inadequately characterized. We analyzed 247,316 continuous glucose monitoring (CGM) days from 716 consented and enrolled participants (11/2019-08/2026) in the Apple Women's Health Study, using cluster-weighted generalized estimating equations to evaluate associations between PMOS, ELIC, and glycemic metrics. Same-day wearable-assessed physical activity, digital dietary logs, and medication health records were assessed as effect modifiers. Compared to participants without PMOS and with regular cycles, those with PMOS had higher mean glucose (6.81 mg/dL; 95% CI: -0.70, 14.72) and %observations above target range (3.77%; 95% CI: 0.04, 8.14). Estimates were smaller-in-magnitude for participants without PMOS but with ELIC, and larger for PMOS with androgen excess. Meeting activity goals, consuming protein-/fiber-rich diets, or using metformin/GLP-1 receptor agonists attenuated these associations. PMOS/ELIC were associated with unfavorable glycemic profiles, suggesting opportunities for risk identification and modification.

Background: Neoadjuvant chemotherapy (NACT) facilitates de-escalation of breast and axillary treatment in breast cancer, but pathological responses may differ between the primary tumour and axillary lymph nodes. Understanding differential response is crucial for de-escalation strategies. Methods: This single university hospital retrospective study analysed operable breast cancers (cT1-cT3, cN0-cN3) treated with NACT (January 2013-November 2020). Two-tailed Fishers exact test or the Chi-squared test (for 2x2 comparisons) analysed concordance between breast and axillary pathological responses; Cox proportional hazards regression ascertained predictors of distant recurrence and survival; and logistic regression ascertained predictors of complete axillary response. Results: Among 413 patients (cN0=229; cN+=184) with a median age of 51, pathological response concordance between the breast and axilla was 81% (both pCR, 22.2%; concurrent residual disease, 58.8%). The cN0 group had a significantly higher ypN0 rate in breast pCR versus non-pCR (97.1% vs 80.8%, p<0.001). cN+ had axillary pCR concordant with breast pCR, compared to only 16.5% with residual breast disease. Breast pCR was the most significant predictor of axillary pCR (OR=11.6, p<0.001). Axillary pCR was highest in ER-/HER2+(66.7%) and triple-negative(46.9%) subtypes. Residual axillary disease was independently associated with the worst breast cancer-specific (HR=7.13, p=0.006) and overall Survival (HR=3.32, p=0.029). Kaplan-Meier analyses showed a trend towards worse survival outcomes with axillary residual disease, even with a breast pCR. Conclusions: In this limited series, breast pCR predicts axillary pCR in cN0 and cN+, particularly in HER2-positives and triple-negatives, supporting axillary de-escalation strategies in selected patients stratified by careful assessment of disease at the end of NACT.

Src family kinases (SFKs) constitute a major non-receptor tyrosine kinase class that regulate processes important for pregnancy. We profiled placental SFKs to assess cell-specific expression, tissue localization, and clinical associations in early-onset preeclampsia (PE). Placental SFK transcripts were analyzed using bulk RNA sequencing discovery (GSE203507; n=15 PE, n=15 controls) and external validation (GSE114691; n=40 PE, n=21 controls) cohorts. Single-cell and single-nucleus RNA sequencing atlases mapped cell-specific expression. Computational deconvolution and causal mediation analyses were applied to bulk datasets. Findings were cross-validated by real-time quantitative PCR (qPCR; n=9 early-onset PE, n=7 controls) and immunohistochemistry (IHC). Transcriptomic data showed that LYN was the sole SFK member consistently elevated in early-onset PE placentas across discovery (1.8-fold, FDR<0.01) and validation cohorts (2.2-fold, FDR<0.001). Elevation was verified by qPCR (p<0.0001) and remained significant after adjusting for clinical covariates. Placental LYN abundance correlated positively with maximum antepartum maternal systolic blood pressure (r=0.63, p<0.01). Single-cell transcriptome atlases demonstrated LYN enrichment in myeloid lineages, extravillous trophoblasts, and syncytiotrophoblast (SCT). Deconvolution and mediation analyses indicated that altered tissue composition and increased per-cell expression each accounted for approximately half of the PE-associated LYN increase. IHC localized Lyn protein to Hofbauer macrophages and the apical microvillar SCT membrane in PE placentas. We conclude that placental LYN expression is selectively elevated in early-onset PE and correlates with maternal blood pressure severity. Lyn localization to trophoblasts and placental immune cells suggests potential roles in mediating inflammation and hypoxia-associated signaling in PE pathogenesis.

People experiencing homelessness (PEH) have substantially lower quality-adjusted life expectancy and substantially higher healthcare spending compared to housed populations. California has the largest population of PEH in the United States, and an estimated ten percent of PEH have co-occurring opioid use disorder (OUD). We developed a lifetime, individual-level microsimulation of the 18,928 PEH with OUD in California to evaluate the health and economic effects of expanding access to housing through a Housing First approach, which does not require OUD treatment or abstinence. Increases in permanent housing and shelter access were based on Los Angeles County's Measure A goals, and we accounted for both housing and healthcare costs. Expanding housing access increased mean age at death by 0.34 years, reduced fatal overdoses for the cohort by 39, and produced an incremental cost-effectiveness ratio of $31,083 per quality-adjusted life-year gained, well below conventional willingness-to-pay thresholds. We provide evidence that housing is a high-value health intervention for PEH with OUD in California. Policymakers should support Housing First programs as part of integrated responses to the intertwined crises of homelessness and opioid use.

Background: The relationship between recent viral infection and subsequent susceptibility to, or protection against, other viruses remains unclear, particularly across ages and symptom status. Methods: A prospective cohort study enrolled participants aged 6 months to 49 years in Washington and Oregon, USA, 2022-2024. Weekly nasal swabs were tested for SARS-CoV-2, respiratory syncytial virus (RSV), and influenza A/B; additional swabs were collected during symptomatic illness and were also tested for parainfluenza (PIV) 1-4, endemic human coronaviruses (hCOV), adenovirus, human metapneumovirus, human rhinovirus (hRV), and enteroviruses. Proportional hazards regression models were used to characterize associations between incident symptomatic infection (illness) and history of recent illness due to other viruses (within 1-7, 8-14, 15-30, 31-60, 61-90, 91-180 days), adjusted for geographic region, age, sex, daycare/school/work attendance, household size, and number of children in the household. For SARS-CoV-2, influenza, and RSV, these analyses were repeated to study incidence of infection (including asymptomatic cases). Findings: Of 3,560 participants, 2,969 (83%) had [&ge;]1 illness over 4,299 person-years. Adjusted hazard ratios (aHRs) in the short term ([&le;]2 weeks) after an illness were predominantly below 1, indicating that recent viral illness was often associated with lower risk of subsequent illness with other viruses. Across age groups, recent hRV illness was associated with lower risk of hCOV, PIV, and RSV illness (aHRs 0.07-0.27). SARS-CoV-2 illness was associated with a higher short-term risk of adenovirus and PIV illness (aHRs 3.20-3.33). In children aged 6 months-4 years, recent influenza illness was associated with higher short-term risk of SARS-CoV-2 illness. Including asymptomatic infections generally attenuated these associations. Interpretation: Some viral illnesses may alter short-term susceptibility to subsequent viral illnesses. Interactions may differ by age and symptom status. Funding: US Centers for Disease Control and Prevention; Washington State Department of Health.

BACKGROUND: The chromosome 9p21.3 locus is among the strongest genetic risk loci for cardiovascular disease (CVD), yet the regulatory mechanisms underlying this association remain poorly understood. Previous vascular disease studies focused predominantly on the role of this locus in smooth muscle cell (SMC) dysfunction, whereas its potential role in endothelial cells (ECs) remains less well characterized. Here, we sought to identify candidate regulatory variants at 9p21.3, their distal target genes, and effector transcripts that contribute to CVD-related phenotypes in ECs. METHODS: Promoter capture Hi-C was used to map chromatin interactions at 9p21.3 in primary human ascending aortic SMCs (n=6), supplementing the previously obtained ECs (n=16) dataset. To support the regulatory relevance of these interactions, expression quantitative trait locus analysis was performed in 136 vascular biopsies. Variant effects were prioritized in silico using AlphaGenome. The regulatory activity of the prioritized region was validated using CRISPR activation. RESULTS: Chromatin interaction mapping identified a complex regulatory architecture at the 9p21.3 locus involving 19 putative regulatory elements and 15 genes across ECs and SMCs. MIR31HG showed the most recurrent distal promoter connectivity with the 9p21.3 region. Consistent with the chromatin interaction map, expression of MIR31HG transcripts was associated with genetic variation at the 9p21.3 locus. In ECs, the lncRNA MIR31HG, but not miR-31, was responsive to hemodynamic flow. Depletion of MIR31HG in ECs was associated with endothelial activation. Epigenetic tracks and deep learning predictions highlighted an active enhancer element at 9p21.3, which was subsequently validated by CRISPR activation and increased MIR31HG expression, confirming its capacity to regulate MIR31HG over a large genomic distance of approximately 538 kb. CONCLUSIONS: Altogether, these data implicate lncRNA MIR31HG as a likely effector of the 9p21.3 CVD locus, linking the cis-regulatory element and heritable genetic variation in the respective region to endothelial activation through long-range enhancer regulation.

Background: Artificial intelligence (AI) may improve electrocardiographic differentiation of Takotsubo syndrome from acute anterior ST-segment elevation myocardial infarction (STEMI), but its impact on human diagnostic performance, the added value of visual explanations, and differences by reader background and experience remain uncertain. Methods: Using 12-lead electrocardiogram (ECG) images from two hospitals, we developed and externally validated an ensemble of three deep learning models after self-supervised pretraining and fine-tuning. The diagnostic task used ECGs recorded within 14 days after symptom onset, regardless of reperfusion status. We conducted a four-phase reader study. Phases 1-3 compared unaided interpretation, a reference sheet, and additional AI assistance. In Phase 4, readers evaluated a new ECG set after randomization to reference-sheet support alone, with AI predictions, or with AI predictions plus gradient-weighted class activation mapping (Grad-CAM) heatmaps. Diagnostic correctness was analyzed using generalized linear mixed-effects models. Exploratory analyses examined differences by cardiologist status and experience. Results: Development and external-validation cohorts, including healthy controls, comprised 832 and 95 participants, respectively. For Takotsubo-STEMI discrimination, external-validation accuracy was 0.914, with areas under the receiver operating characteristic and precision-recall curves of 0.970 and 0.984. Seventy healthcare providers completed the reader study. The estimated probability of correct diagnosis increased from 0.632 without assistance to 0.864 with AI assistance (odds ratio [OR], 3.71; P<0.001). In randomized Phase 4, AI assistance improved accuracy vs reference-sheet support alone (OR, 4.28; 95% CI, 3.08-5.95; P<0.001), and Grad-CAM provided additional benefit (OR, 1.53; 95% CI, 1.04-2.25; P=0.033). Exploratory analyses showed greater improvement among less experienced readers; with AI assistance, non-cardiologists outperformed cardiologists. AI-assisted readers remained less accurate overall than the model alone. Conclusions: An ECG image-based ensemble differentiated Takotsubo syndrome from anterior STEMI and improved reader accuracy. Visual explanations provided additional benefit, while experience-related differences highlight the importance of effective human-AI interaction.

Background. In people with type 2 diabetes (T2D), postprandial glucose weighs on glycaemic control, and carbohydrate counting is difficult. Patients can describe their meal but rarely quantify it. Objective. To assess (1) whether a small open large language model (LLM), fine-tuned on public data, estimates the carbohydrate content of a meal described in words, and (2) what this carbohydrate adds to the prediction of the postprandial glucose rise in two public cohorts. Methods. Step 1: the Qwen3-4B LLM was fine-tuned with QLoRA on Nutrition5k dishes and compared with an automatically matched USDA food composition table and with Qwen3-4B and Qwen3-8B used without training, on test dishes (internal control) and on 100 real meals of ShanghaiT2DM patients, whose reference carbohydrate was computed from the recorded quantities. Step 2: the maximum glucose rise between 0 and 120 minutes ({Delta}Gmax) was predicted by personalised gradient boosting (M2), compared with a spline linear regression and with the Chronos-2 foundation model, using only information available at meal start and person-level cross-validation, in ShanghaiT2DM (people with T2D recruited in two Shanghai hospitals; carbohydrate estimated by the LLM) and CGMacros (free-living adults in Santa Barbara, California; carbohydrate entered in the dataset, no text). Step 3: the model was transferred between datasets. 95% confidence intervals (CI) were obtained by cluster BCa bootstrap (2,000 resamples). Results. On the 100 real meals, the carbohydrate mean absolute error (MAE) of the fine-tuned model was 12.2 [9.9, 15.2] g, almost half that of the table (22.6 g; paired difference -10.5 [-16.8, -6.0] g) and nearly a third of that of the same model without fine-tuning (34.3 g), with a bias of -10.3 g; on test dishes it was 2.2 [2.0, 2.5] g. Personalised gradient boosting (M2) reached a correlation between predicted and observed rises of r = 0.63 [0.55, 0.69] in CGMacros (1,286 meals, 45 adults) and 0.49 [0.42, 0.54] in ShanghaiT2DM (2,339 meals, 99 people with T2D), about twice the linear reference (r = 0.32 and 0.23); for a rise above 50 mg/dL, its area under the ROC curve (AUROC) was 0.80 and 0.74, versus 0.64 and 0.68 with pre-meal glucose alone. Adding carbohydrate to pre-meal glucose changed r by 0.095 [0.068, 0.128] in CGMacros and by 0.004 [-0.010, 0.017] in ShanghaiT2DM; difference between datasets: 0.091 [0.060, 0.126]. Applied from one dataset to the other, the model lost correlation ({Delta}r = -0.11 [-0.17, -0.05] towards ShanghaiT2DM and -0.12 [-0.19, -0.04] towards CGMacros). The full "text [-&gt;] LLM [-&gt;] glucose" chain was evaluated in ShanghaiT2DM only. Conclusion. An open 4-billion-parameter LLM, fine-tuned with QLoRA on public data and usable offline, almost halves the error of carbohydrate counting for real meals described in words compared with an automatically matched table, with an underestimation of large meals. Personalised gradient boosting, based on pre-meal glucose and the person's usual response, doubles the correlation of a linear reference for predicting the glucose rise in both cohorts. Carbohydrate improved this prediction in CGMacros but not in ShanghaiT2DM; the two datasets differ in many respects, and the cause of this gap is not established.

Background: Epidermal Growth Factor Receptor (EGFR) tyrosine kinase inhibitors (TKIs) are widely used for EGFR-mutated non-small cell lung cancer (NSCLC) but have distinct toxicity profiles across generations. Aims: To investigate relationships between EGFR-TKI physicochemical, pharmacokinetic and pharmacological properties and drug-specific adverse drug reactions (ADRs) using national and global pharmacovigilance data. Methods: ADRs for erlotinib, afatinib and osimertinib were extracted from the UK MHRA Yellow Card scheme and WHO VigiAccess. Disproportionality analysis used reporting odds ratios (RORs). Signals were defined as ROR >2, lower 95% confidence interval >1 and >3 cases. Drug properties were curated from ChEMBL, emc, SmPC and EMA sources. Results: VigiAccess contained 88,662 individual case safety reports (163,619 ADRs) and Yellow Card contained 752 reports (1,814 ADRs). Four System Organ Class (SOC) signals were identified in VigiAccess and three in Yellow Card, with five additional Yellow Card Preferred Term signals. Eye disorders showed the strongest consistent association with erlotinib (VigiAccess ROR 4.33; Yellow Card ROR 4.18). Osimertinib was associated with pneumonitis (ROR 7.67) and pulmonary embolism (ROR 3.94). Afatinib showed psychiatric disorder signals in both databases (ROR 3.2-3.3) and diarrhoea (ROR 2.79). Neoplasm-related signals likely reflected confounding by indication. Conclusions: EGFR-TKIs demonstrate generation-specific pharmacovigilance profiles. Integration with drug properties supports mechanistic plausibility for several signals, while the atypical afatinib psychiatric association warrants further investigation. Findings are hypothesis-generating and require confirmation in controlled observational or mechanistic studies.

Schizophrenia spectrum disorders (SSD) have been linked to immune dysregulation and altered intestinal barrier function, although findings across studies remain inconsistent. The present study investigated circulating markers of intestinal permeability and immune activation in individuals with early-stage SSD and healthy controls and examined their associations with clinical outcomes. Serum concentrations of intestinal fatty acid-binding protein (iFABP), zonulin, soluble CD14 (sCD14), bactericidal/permeability-increasing protein (BPI), and lipopolysaccharide-binding protein (LBP) were measured in 36 patients with SSD at baseline and one-year follow-up and in 36 matched healthy controls. Linear mixed-effects models adjusted for age, sex, and body mass index (BMI) were used to evaluate group differences and longitudinal changes. No biomarker differences between patients and controls, and no longitudinal changes within patients, remained statistically significant after correction for multiple testing. Similarly, biomarker concentrations were not associated with symptom severity, global functioning, or neutrophil-to-lymphocyte ratio (NLR). Exploratory analyses identified a robust positive association between BMI and zonulin concentrations and additional associations among iFABP, LBP, and sCD14. Exploratory mixed-effects models identified a negative non-significant association between iFABP and sCD14 after adjustment for age, sex, and BMI. Overall, the findings do not support a generalized alteration of gut permeability or immune activation markers in SSD. Instead, they suggest that variation in gut-related biomarkers may be more strongly influenced by metabolic and individual factors than by diagnosis or current symptom severity.

Objective To examine the association between patient-reported shared decision-making (SDM) and documented advance care planning (ACP) among adults with Alzheimer disease and related dementias (ADRD), and to assess whether this association differed between adults with and without ADRD. Design Retrospective observational study using linked survey and electronic health record (EHR) data. Setting and Participants Participants aged [&ge;]50 years in the All of Us Research Program who completed a patient-reported SDM measure and had linked EHR records. The primary analytic cohort included 3,635 adults with ADRD; 123,681 adults without ADRD comprised a secondary comparison cohort. Methods SDM was measured by how often clinicians asked participants about their opinions or preferences regarding medical care or treatment and was categorized as high (always/most of the time) or low (some of the time/ None of the time). Documented ACP was identified within 3 years before or after the SDM assessment. Modified Poisson regression with robust variance estimated prevalence ratios (PRs) and adjusted PRs (aPRs). Results Among adults with ADRD, 60.6% reported high SDM. Documented ACP prevalence was 5.1% among participants with high SDM and 3.6% among those with low SDM (P=0.027). High SDM was associated with greater ACP prevalence in the unadjusted model (PR, 1.44; 95% CI, 1.04-1.99); the association remained positive but was attenuated after full adjustment (aPR, 1.28; 95% CI, 0.93-1.77). Among adults without ADRD, high SDM was associated with documented ACP (aPR, 1.17; 95% CI, 1.03-1.33). Conclusions and Implications Among adults with ADRD, high SDM was associated with greater ACP prevalence in unadjusted analyses, although the fully adjusted association was not statistically significant. In contrast, higher SDM remained significantly associated with ACP among those without ADRD. The low prevalence of ACP highlights an important opportunity to expand discussions of current and future care preferences as a routine component of patient-centered care.

Exposure to armed conflict can leave persistent effects on mental health, yet its neurophysiological correlates remain poorly characterized, particularly in populations exposed to prolonged violence outside narrowly defined post traumatic stress disorder samples. Here we examined resting state electroencephalographic activity in 122 Colombian adults, including 74 participants exposed to armed conflict and 48 controls. Eyes closed EEG was analysed for 240 seconds per participant. We quantified absolute and relative spectral power across delta, theta, alpha and beta bands and regional functional connectivity using weighted phase lag index in theta, alpha and beta. Group comparisons were adjusted for age, education, sex and assessment year, with correction across 78 EEG contrasts. Median occipital and global alpha power was lower in exposed participants, although adjusted estimates were close to the null and confidence intervals were broad. Temporal theta relative power was lower in exposed participants at nominal significance, but this association did not survive Holm correction. No spectral power or regional connectivity contrast remained significant after multiple testing correction. Exploratory associations between EEG measures and MoCA performance were modest in participants with confirmed scores. These findings provide a systematic characterization of resting EEG after exposure to armed conflict in a population shaped by prolonged violence and displacement. They also indicate that large or widespread alterations in resting spectral power and connectivity were not detectable with the current sample and analytical framework, while defining candidate signals and methodological priorities for larger studies of conflict related brain health.

Fetal development is highly sensitive to environmental influences, including maternal smoking during pregnancy (MSDP) and air pollution. The placenta may play a central role in mediating these effects through epigenetic mechanisms, including small non-coding RNAs (sncRNAs). While most studies have focused on microRNAs (miRNAs), other sncRNA classes remain poorly characterized. Here, we characterized the placental sncRNA landscape and investigated its associations with prenatal exposure to MSDP and air pollution. The sncRNA expression profile of 390 chorionic villi samples from the Barcelona Life Study Cohort (BiSC) was measured using small RNA sequencing and the seqBuster/seqCluster pipeline. MSDP was assessed via questionnaire, and prenatal exposure to black carbon (BC), particulate matter [&le;]2.5 m (PM2.5), and nitrogen dioxide (NO2) was estimated using hybrid dispersion-land-use regression (LUR) spatiotemporal models. Associations were evaluated using linear regressions adjusted for relevant covariates. We identified 979 placental sncRNA clusters, assigned primarily to miRNAs (33.2%), piRNAs (26.4%), snoRNAs (12.2%), tRFs (7.8%), and others (20.4%). MSDP was associated with the downregulation of miR-421 and the upregulation of multiple snoRNAs from the imprinted DLK1-DIO3 locus (SNORD113-4, SNORD114-2, SNORD114-3, and SNORD114-17), as well as the piR-26394. No sncRNAs remained significantly associated with air pollution after multiple-testing correction, although several miRNAs showed suggestive associations. Overall, this study provides a comprehensive characterization of placental sncRNAs and identifies previously unreported smoking-associated sncRNAs within the imprinted DLK1-DIO3 locus, expanding our understanding of placental epigenetic responses to prenatal environmental exposures.

Estimating the correlation between subject-specific rates of change is a common objective in longitudinal studies. A natural approach is to fit ordinary least squares regressions per subject and correlate the resulting slope estimates directly. Under complete data, this slopes-first estimator is algebraically equivalent to a contrast-based plug-in estimator derived from the joint variance-covariance matrix of the two signals across time. Under independent dropout, however, the slopes-first approach implicitly applies heterogeneous weights across missing-data patterns, introducing bias whenever the cross-covariance between the two signals is not constant over time. We formalise this result and propose an unbiased alternative that estimates the joint covariance matrix before applying the contrast. We further extend our results to include covariates as well. Simulations confirm that the slopes-first estimator is biased under non-constant cross-covariance while the proposed alternative remains unbiased. Further, we show in a real-world example that the two estimators can lead to different scientific conclusions and recommend that researchers working with longitudinal data inspect the missing data pattern to make an informed decision about the estimator of choice.

Introduction: This study aimed to characterize the neurochemical profile of Alzheimer s disease (AD) continuum stages using proton magnetic resonance spectroscopy (1H-MRS) data for brain metabolites and macromolecules. We used Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) to classify diagnostic groups and assess feature importance. Methods: Our dataset consisted of 7 T short-echo time 1H-MRS spectra of the posterior cingulate cortex/precuneus of individuals diagnosed with AD (n = 27) or Mild Cognitive Impairment (MCI, n = 27) and Healthy Controls (HC, n = 31). Classification used 100-round Monte Carlo cross-validation based on metabolite concentrations and macromolecular peak intensities. Variable importance in projection (VIP) was assessed across classification models to identify the most relevant neurochemical features. The resulting metabolite rankings were compared with previous literature to evaluate their biological plausibility. Results: Classification was strongest for AD versus HC (AUC=0.88) and lower for comparisons involving MCI. Metabolites contributing most strongly to classification included myo-inositol, ascorbate, and choline-containing compounds, consistent with previous studies on AD-related neurochemical alterations. In addition, specific macromolecular components were identified as relevant features, suggesting a potential contribution of MM signals to the characterization of AD-related neurochemistry. Discussion: These findings support the use of short-echo-time 1H-MRS as a complementary tool for characterizing neurochemical changes in the AD continuum, with potential applications in disease characterization and monitoring, warranting validation in larger cohorts.