Diabetes Care

Aims The oral glucose tolerance test (OGTT) is effective for detecting post-load dysglycemia, but it is burdensome and therefore not routinely used. Continuous glucose monitoring (CGM) offers a convenient way to capture real-world glucose patterns, yet it remains unclear whether CGM-derived metrics reflect OGTT-defined dysglycemia. We therefore aimed to evaluate CGM-derived and clinical metrics for predicting OGTT 2-hour glucose, classifying OGTT-defined dysglycemia, and assessing day-to-day repeatability. Methods We analyzed a cohort with paired free-living CGM and OGTT. Multiple CGM-derived metrics and clinical measures were compared for prediction of OGTT 2-hour glucose, classification of OGTT-defined dysglycemia, and day-to-day stability. Predictive performance was assessed primarily by leave-one-out (LOO) R^2, and day-to-day repeatability by intraclass correlation coefficients (ICC). Results The glycemic persistence index (GPI), a metric integrating the magnitude and duration of glycemic elevation, was the strongest single predictor of OGTT 2-hour glucose (LOO R^2 = 0.439). GPI also showed strong day-to-day repeatability (ICC = 0.665) and ranked first on a combined prediction-stability score. For classification of OGTT-defined dysglycemia, HbA1c had a slightly higher AUC than GPI, but GPI plus HbA1c performed best overall, indicating complementary information. Conclusions GPI was a strong predictor of OGTT 2-hour glucose and showed a favorable balance between predictive performance and day-to-day stability, supporting its potential utility as a CGM-derived marker of dysglycemia.

This updated systematic review and network meta-analysis evaluated the efficacy and safety of obesity management medications (OMMs) in terms of reducing body weight and obesity related complications. Medline and Embase were searched up to 21 November 2025 for randomized controlled trials comparing OMMs versus placebo or active comparators in adults. The primary endpoint was percentage total body weight loss (TBWL%) at the end of the study. Secondary endpoints were TBWL% at 1, 2 and 3 years, anthropometric, metabolic, mental health and quality of life outcomes, cardiovascular morbidity and mortality, remission of obesity related complications, serious adverse events and all cause mortality. Sixty six RCTs (66 comparisons) were identified: orlistat (22), semaglutide (18), liraglutide (11), tirzepatide (8), naltrexone/bupropion (5) and phentermine/topiramate (2), enrolling 63,909 patients (34,861 and 29,048 with active compound and placebo, respectively). All OMMs showed significantly greater TBWL% versus placebo; tirzepatide and semaglutide exceeded 10% TBWL and showed the most favourable glycaemic effects. Semaglutide reduced major adverse cardiovascular events and all cause mortality. In dedicated complication specific trials, semaglutide and tirzepatide showed benefit on heart failure related outcomes; tirzepatide was associated with improved obstructive sleep apnoea syndrome and semaglutide with knee osteoarthritis pain remission. Tirzepatide and semaglutide were associated with improvements in metabolic dysfunction-associated steatohepatitis remission, and semaglutide with improvement in liver fibrosis. No OMMs were associated with an increased risk of serious adverse events. These updated results reinforce the need to individualize OMMs selection according to weight loss efficacy, complication profile and safety.

Non-invasive glucose monitoring (NIGM) has been pursued for decades, yet no device has achieved regulatory approval despite numerous studies reporting high accuracy. This systematic review and meta-analysis of 32 studies (38 cohorts: 20 NIGM, 18 iCGM; N = 1,693) investigated methodological factors underlying this accuracy-regulatory gap. The pooled Mean Absolute Relative Difference (MARD) for NIGM (10.21%; 95% CI: 8.73-11.69%) showed no significant difference from iCGM (11.82%; 95% CI: 10.36-13.29%; p = 0.13), with extreme heterogeneity (I^2 = 95.2%). Meta-regression revealed that study duration was the strongest predictor of NIGM accuracy ({beta} = 3.94, p < 0.001), with MARD degrading from 8.7% in short-term to 15.2% in long-term studies, while iCGM accuracy remained stable. Only 15% of NIGM cohorts validated in the hypoglycemia range, compared to 89% of iCGM studies (p < 0.001). These findings suggest that reported NIGM accuracy is substantially influenced by methodological asymmetries.

Abstract Background: Cardiometabolic diseases arise from metabolic dysfunction that develops decades before clinical onset. Conventional genetic risk models are typically derived in middle-aged or older populations, where genetic effects are confounded by cumulative environmental exposures, chronic comorbidities, and clinical interventions. Whether the life stage at which genetic liability is modelled influences the biological signal captured by polygenic scores remains unclear, particularly in underrepresented populations. We therefore tested whether genetic liability modelled in early adulthood, a period of relative physiological stability, is associated with cardiometabolic risk across the life course in Asian populations. Methods: We developed a polygenic score for metabolic syndrome, GenMetS, using data from 1,368 Singaporean women aged 18-45 years. The model integrates 15 established polygenic scores for metabolic traits and applies elastic-net penalized regression to optimize variant weights. GenMetS was evaluated in five cohorts comprising 670,952 individuals aged 0-94 years across population-based and disease-enriched settings, including Asian and European ancestry groups. Associations with metabolic traits, cardiometabolic diseases, multimorbidity, and early-life growth patterns were assessed. Results: In Asian populations, GenMetS explained 5.0-12.4% of the variance in metabolic syndrome in adults and 10.3% in children, with negligible performance in European populations (R squared < 0.001). Higher GenMetS was associated with increased odds of cardiometabolic diseases, including type 2 diabetes, heart failure, and stroke (odds ratios 1.32-1.52 per standard deviation). In UK Biobank participants of Asian ancestry, GenMetS improved discrimination of cardiometabolic multimorbidity beyond age alone. Associations were consistent across sexes. In children, higher GenMetS was associated with obesogenic growth trajectories and increased abdominal adiposity. Conclusions: Genetic liability to metabolic dysfunction modelled in early adulthood captures a stable biological signal associated with metabolic traits, disease risk, and multimorbidity from childhood to adulthood in Asian populations. These findings indicate that the life stage of model derivation shapes the biological signal captured by polygenic scores and support the development of life-stage and ancestry-informed approaches for cardiometabolic risk assessment and prevention.

Excess body weight has been associated with increased cancer risk, but the role of weight change across adulthood remains unclear. We examined body weight trajectories from ages 17 to 60 and their associations with site-specific cancer incidence. Data were based on the ODDS study, a pooled, nationwide cohort study in Sweden, with data on weight spanning 1911 to 2020, and cancer follow-up through 2023. Weight trajectories were estimated with linear mixed effects models in individuals with at least three weight measurements. Cox regressions estimated hazard ratios for associations between weight trajectories and established and potentially obesity-related cancers. Fifth versus first quintile of weight change was associated with many cancers, most strongly with esophageal adenocarcinoma in men (HR 2.25; 95% CI 1.66-3.04), liver cancer in men (HR 2.67; 95% CI 2.15-3.33), endometrial cancer in women (HR 3.78; 95% CI 3.09-4.61), and pituitary tumors in both sexes (men: HR 3.13 [95% CI 2.13-4.61]; women: HR 2.13 [95% CI 1.41-3.22]). Associations varied by sex and age. Heavier weight at age 17 years and earlier obesity onset were also associated with higher cancer incidence. These findings highlight the importance of a life-course approach to weight management and support sex- and age-targeted cancer prevention strategies.

Cardiovascular diseases (CVDs) frequently co-occur, yet the shared genetic basis of cardiovascular multimorbidity remains unclear. We analysed common- and rare-variant genetic overlap across eight major CVDs using genome-wide and exome-wide association data from ~1.7 million individuals in European and East Asian biobanks. Fifteen CVD pairs showed significant genetic correlations, with shared common-variant covariance explaining a modest proportion of phenotypic comorbidity. Genomic structural equation modelling identified three latent genetic clusters, while pleiotropic loci and genes frequently spanned cluster boundaries. Prioritised genes converged on atherosclerosis-related processes, myocardial structural and electrical programmes, and vascular-wall biology. In conditional analyses, body composition and metabolic traits consistently attenuated shared genetic liability, whereas circulating biomarkers showed smaller effects. For adequately powered traits, common-variant architecture was broadly similar between European and East Asian ancestries. These results define a shared genetic framework for cardiovascular multimorbidity centred on systemic risk factors and vascular biology.

Type 2 diabetes (T2D) is a heterogeneous disease shaped by genetic pathways related to insulin resistance and beta cell dysfunction, but how this heterogeneity is reflected molecularly remains unclear. We integrated partitioned polygenic scores (pPS) with proteomic and metabolomic profiling to define molecular signatures of T2D and their clinical relevance. We analyzed UK Biobank participants with genomic, proteomic, and metabolomic data. In a disease-free training subset, we used LASSO regression to identify multi-omic signatures associated with each pPS by jointly modeling proteins and metabolites. In an independent testing set, we constructed multi-omic scores and examined their associations with clinical traits and diabetes-related outcomes. Mediation analyses were used to investigate putative causal pathways. Key findings were evaluated in the Multi-Ethnic Study of Atherosclerosis (MESA). We identified distinct multi-omic signatures that capture the molecular architecture of T2D genetic risk across physiological subtypes. Compared with genetic scores alone, multi-omic pPS showed larger effect sizes and better disease discrimination. These scores recapitulated subtype-specific physiology and were associated with T2D risk. The Beta-Cell 2 multi-omic score showed marked stratification for insulin use, which was replicated in MESA, where it also predicted future insulin use. Mediation analyses implicated lipoprotein remodeling and fatty acid metabolism in the Lipodystrophy 1 cluster, accounting for up to 45% of the total effect of pPS on T2D risk. Integrating process-specific genetic risk with circulating multi-omic profiles reveals biologically distinct endotypes of T2D and supports a framework for improved patient stratification and risk assessment.

GLP-1 receptor agonists have reshaped obesity therapeutics, but their impact on neuropsychiatric outcomes remains poorly characterized. From 29 million patients in a large federated data platform across the USA, including 489,785 semaglutide treated patients, we conducted an observational study integrating longitudinal neuropsychiatric outcomes. From this population, we assembled a cohort of 63,215 patients with baseline neuropsychiatric conditions before treatment initiation and evaluated 24 incident neuropsychiatric outcomes. In propensity-matched comparator analyses, during the 2 year time-period from treatment initiation, semaglutide was associated with broadly lower neuropsychiatric event risk than metformin, SGLT2 inhibitors, and DPP-4 inhibitors. Within the semaglutide-treated cohort, higher attained dose during the first two years after the first prescription ("pre-landmark period") was associated with significantly lower incidence during the following two years ("post-landmark period") of diagnostic codes associated with substance-related disorders (P<0.001), mood disorders (P<0.001), anxiety- and stress-related disorders (P<0.001), CNS atrophies (P<0.001), neuromuscular disorders (P=0.013), eating/sleep/behavioral disorders (P=0.022), and personality/impulse-control disorders (P=0.028). Consistent with previous clinical trials, the post-landmark incidence of dementia or CNS degenerative diseases was similar between the high-dose and low-dose semaglutide cohorts (P=0.15). For most neuropsychiatric diagnoses, post-landmark incidence was strongly associated with the maximum attained semaglutide dose during the pre-landmark period, but incident cognitive symptoms and speech/language symptoms were more closely linked to the pre-landmark weight-loss magnitude (p<0.001 and p<0.003, respectively). Bulk and single-cell transcriptomic analyses demonstrated GLP1R expression in CNS tissues (hypothalamus, caudate, putamen, nucleus accumbens, cerebellum) and peripheral nerves. Age-associated heterogeneity in GLP1R expression was evident in several of these compartments including the caudate nucleus, suggesting dynamic changes in the availability of the neurobiological substrate for semaglutide response. Together, these data support a model in which semaglutide confers a sustained, dose-dependent, weight loss-independent benefit across multiple neuropsychiatric conditions via direct CNS target engagement. This observational study motivates prospective clinical studies and mechanistic analyses to clarify the impact of GLP-1 receptor agonists on human neuropsychiatric pathways and disease processes.

Type 2 diabetes mellitus (T2DM) is a progressive metabolic disorder characterized by persistent hyperglycemia, insulin resistance, and chronic low-grade inflammation. Despite the widespread use of established therapies such as metformin, long-term glycemic control remains suboptimal, and disease progression is often not adequately prevented. This highlights the need for novel therapeutic strategies that address both metabolic dysfunction and the underlying immunometabolic components of the disease. In this study, GLX10 (GLXM100) was evaluated as a novel immune modulator in a high-fat diet (HFD) and low-dose streptozotocin (STZ)-induced rat model of T2DM over a 91-day period. Glycemic outcomes were assessed using terminal random blood glucose and oral glucose tolerance testing (OGTT), with glucose exposure quantified by area under the curve (AUC 0-120). Complementary in vitro investigations were performed in hepatic and macrophage cell models to assess cytocompatibility, nitric oxide production, and modulation of pro-inflammatory cytokines, including IL-6 and TNF-. GLX10 treatment resulted in a significant reduction in random blood glucose levels and a marked improvement in glucose tolerance compared to diabetic control animals. Importantly, GLX10 demonstrated greater improvement in OGTT AUC compared to metformin under the same experimental conditions, indicating enhanced dynamic glucose regulation. In vitro, GLX10 maintained viability in normal hepatic cells while significantly suppressing nitric oxide production and inflammatory cytokine outputs in macrophages, supporting a favorable safety and immune profile. Collectively, these findings demonstrate that GLX10 exerts robust antidiabetic activity through a dual mechanism involving metabolic regulation and suppression of inflammatory signaling. The integration of in vivo efficacy with supportive in vitro safety and mechanistic data provides a strong preclinical foundation and supports the further development of GLX10 as a promising therapeutic candidate for T2DM.

Background Group A streptococcus (GAS) infections have been associated with neuropsychiatric disorders in epidemiologic studies and animal models, but data in US health care populations are limited. GAS is also associated with autoimmune sequelae, including acute rheumatic fever (ARF)/Sydenham chorea (SC), poststreptococcal reactive arthritis (PSRA), poststreptococcal glomerulonephritis (PSGN), and guttate psoriasis (GP). Epstein-Barr virus (EBV) has been linked to systemic lupus erythematosus (SLE) and multiple sclerosis (MS) and the complexity of these associations parallels that of GAS-associated conditions, providing a useful comparison. Objectives 1) Assess the association between a positive GAS test and incident neuropsychiatric diagnoses within 1 year in a large US health care database. 2) Assess the validity of the same database in detecting well-established disease associations while avoiding false associations. Design, Setting, Participants Retrospective cohort study using TriNetX data from US health care organizations. Patients with positive or negative tests were propensity score-matched (GAS cohort n=178,301; EBV cohort n=64,854). Patients with documented neuropsychiatric diagnoses prior to testing were excluded. To approximate a primary care population, inclusion required at least one well-visit. Exposures Positive vs negative GAS test; positive vs negative EBV test (separate cohorts). Main Outcomes and Validations Main outcome: incident neuropsychiatric diagnoses within 1 year of GAS testing. Positive control outcomes: ARF/SC, PSRA, PSGN, and GP (for GAS cohort); SLE and MS (for EBV cohort). Negative control outcomes: conditions without known association with GAS. Results After matching, a positive GAS test was associated with attention-deficit/hyperactivity disorder (ADHD) (RR: 1.09; 95% CI: 1.03-1.15). Among established poststreptococcal conditions, only GP was associated with prior GAS (RR: 1.75; 95% CI: 1.06-2.89). Case counts were insufficient to evaluate ARF/SC, PSRA, and PSGN. Negative control outcomes showed no association. In the EBV cohort, no association was observed with SLE, and MS showed a decreased risk. Conclusions and Relevance A positive GAS test was associated with ADHD but not with other neuropsychiatric disorders. The database detected poststreptococcal GP but did not identify most established postinfectious autoimmune associations, likely reflecting rarity, heterogeneity, and diagnostic complexity. These findings begin to describe the range of real-world health care databases to evaluate postinfectious neuropsychiatric risk.

BackgroundDiabetic kidney disease (DKD) is a leading cause of kidney failure in individuals with type 2 diabetes (T2D), yet risk identification in routine clinical practice remains incomplete. A critical and often overlooked barrier is risk observability: how much of a patients underlying risk is actually captured in their clinical record at the time of screening. Existing prediction models evaluate performance using model-specific thresholds, making it difficult to understand how additional data sources alter real-world screening behavior or which individuals benefit when models are expanded. MethodsWe developed a series of five nested machine learning models evaluated at a one-year landmark following T2D diagnosis using data from the All of Us Research Program (N = 39,431; cases = 16,193). Each successive model added a distinct information layer -- intrinsic risk, laboratory snapshots, medication exposure, longitudinal care trajectories, and social determinants of health (SDOH) -- while retaining all prior features. All models were evaluated under a fixed screening policy targeting 90% specificity, so that the false positive rate remained constant as the information available to the model grew. External validation was conducted in the BioMe Biobank (N = 9,818) without retraining. ResultsDiscrimination improved consistently across layers, from AUROC 0.673 (M1) to 0.797 (M5). Under the fixed screening policy, sensitivity nearly doubled from 0.27 to 0.49, with a cumulative recovery of 30.4% of cases missed by the base model. Gains were driven by distinct subgroups at each transition: laboratory features identified biologically high-risk individuals; medication features captured those with high treatment intensity reflecting advanced cardiometabolic burden; longitudinal care trajectory features rescued cases with biological instability observable only through repeated measurements; and SDOH features recovered individuals with limited clinical observability, with rescue probability highest among those with the fewest recorded monitoring domains. Sparse data in the clinical record indicated low observability, not low risk. Social and genetic features each contributed most when downstream physiologic signal was limited, supporting a contextual rather than universal role for each. In BioMe, discrimination was attenuated (M4 AUROC 0.659), but the relative ordering of information layers was fully preserved, and a systematic upward shift in predicted probability distributions underscored the need for recalibration before deployment in a new setting. ConclusionsDKD risk detection in T2D is substantially improved by integrating complementary information layers under a fixed clinical screening policy, with gains arising from distinct domains that identify at-risk individuals in different clinical contexts. The layered landmark framework introduced here reveals how risk observability -- shaped by monitoring intensity, healthcare engagement, and access -- determines what a screening model can detect, and provides a foundation for context-aware EHR-based screening that accounts for data availability at the time of risk assessment. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/26351384v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1cc7f4borg.highwire.dtl.DTLVardef@b92956org.highwire.dtl.DTLVardef@48ffbcorg.highwire.dtl.DTLVardef@8dc627_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract.C_FLOATNO Study design and layered DKD screening framework The top row defines the cohort timeline, in which predictors are derived from clinical data collected between T2D diagnosis and the 1-year landmark, and incident DKD is ascertained after the landmark. The second row depicts the nested model architecture, in which five successive models sequentially incorporate intrinsic risk, laboratory snapshot features, medication exposure, longitudinal care trajectories, and social determinants of health, while retaining all features from prior layers. The third row summarizes model development in the All of Us Research Program (N = 39,431) and external validation in the BioMe Biobank (N = 9,818), where the same trained models and risk thresholds were applied without retraining. The bottom row highlights the three evaluation domains: predictive performance, fixed-policy screening, and missed-case recovery context. DKD, diabetic kidney disease; T2D, type 2 diabetes; PRS, polygenic risk scores; AUROC, area under the receiver operating characteristic curve; AUPRC, area under the precision-recall curve; PPV, positive predictive value; SHAP, SHapley Additive exPlanations. C_FIG

Objective To examine the impact of acute illness on long-term health and describe any differences in these associations between socioeconomic and ethnic groups. Design Longitudinal population study. Setting Linked primary and secondary care data recorded in the Clinical Practice Research Datalink (CPRD). Participants Adults ([&ge;]18 years) residing in England registered with a primary care general practice (GP) between 1st January 2012 and 31st December 2022 who have not opted out of inclusion into CPRD and linked data sources. Socioeconomic deprivation was defined using the Index of Multiple Deprivation (IMD) and ethnicity by UK census 2011 definitions. Main outcome measures The primary outcome was new long-term disease and multimorbidity (two or more long-term diseases). We describe incidence of hospitalisation for acute illness as the exposure. Results We included 18,329,659 people, with 9,339,394 (51.0%) women, 7,430,555 (40.5%) people from the most deprived deciles (IMD 1-4) and 3,009,717 (16.4%) from a minority ethnic group. 6,038,272 (32.9%) people experienced hospitalisation for acute illness. Hospitalisation was associated with increased onset of long-term disease in those alive at the end of follow up (41.1% hospitalised vs 18.7% not hospitalised; adjusted HR 2.48 (2.47 to 2.48)). Compared to non-hospitalised, those who had been hospitalised were more likely to change from being disease free at baseline to having a new long-term disease (12.9% vs. 7.5%), develop multimorbidity (4.7% vs. 1.1%), or transition to multimorbidity if they had pre-existing disease (8.1% vs. 1.8%). Age-standardised hospitalisation rates were highest in the most deprived decile and in people with Black ethnicity. Comparative hospitalisation ratio for IMD 1 compared to IMD 10 ranging from 1.78 in 2018 to 1.96 in 2021 and for Black ethnicity compared to White ranging from 1.03 in 2017 to 1.08 in 2021. Conclusions Acute hospitalisation is a key stage in the development of long-term disease and may be an underutilised opportunity for intervention to change healthy life trajectory and reduce health inequality.

Pancreatic cancer disproportionately affects Black individuals in the United States, but they have limited representation in genetic studies of pancreatic ductal adenocarcinoma (PDAC). To address this gap, we performed admixture mapping and genome-wide association analysis (GWAS) in genetically inferred African ancestry individuals (1,030 cases and 889 controls). Admixture mapping identified three regions with a significantly higher proportion of African ancestry in cases compared to controls (5q33.3, 10p1, 22q12.3). GWAS identified a genome-wide significant association at 5p15.33 (CLPTM1L, rs383009:T>C, T Allele Frequency=0.51, OR:1.45, P value=1.24x10-8), a locus previously associated with PDAC. Known loci at 5p15.33, 7q32.3, 8q24.21 and 7q25.1 also replicated (P value <0.01). Multi-ancestral fine-mapping identified two potential causal SNPs (rs3830069 and rs2735940) at 5p15.33. Collectively these findings identified novel PDAC risk loci and expanded our understanding of this deadly cancer in underrepresented populations, emphasizing the multifactorial nature of PDAC risk including inherited genetic and non-genetic factors. Statement of SignificanceTo understand how genetic variation contributes to PDAC risk in Black people in North American, we studied individuals of genetically-inferred African ancestry. We identified novel risk loci and differences in the contribution of known loci. This demonstrates that ancestry-informed genetic analyses improve our understanding of PDAC risk and enhances discovery.

OBJECTIVE: To evaluate the return on investment (ROI) of a community based Diabetes Self Management Program (DSMP) enhanced with health related social needs (HRSN) screening and referrals, implemented by the New York City (NYC) Department of Health and Mental Hygiene with three community based organizations in highly impacted, under resourced neighborhoods. RESEARCH DESIGN AND METHODS: A retrospective cost benefit analysis from a public sector payer perspective was conducted among 171 adults with type 2 diabetes who completed a six week, peer led DSMP delivered by community health workers (CHWs) in English, Spanish, and Korean during 2018 2019. A time driven, activity based costing model captured direct implementation costs, CHW workforce turnover, and administrative overhead. Monetized benefits included avoided diabetes related complications, reductions in self reported emergency department (ED) visits and hospitalizations, and quality adjusted life year (QALY) gains from improved medication adherence. Univariate sensitivity analyses tested robustness under conservative assumptions. RESULTS: Total program costs were $179,224; monetized benefits totaled $1,824,213, yielding a net benefit of $1,644,989 and an ROI of 918%, approximately $10 returned per $1 invested. Excluding QALY gains, ROI remained 551%. Self reported ED visits declined from 149 to 82 and hospitalizations from 93 to 24 in the six months following intervention. Over 80% of participants reported housing instability; 72% were Medicaid covered and 16% uninsured. Sensitivity analyses confirmed a positive ROI under all conservative scenarios. CONCLUSIONS: A CHW led, community based DSMP integrated with HRSN screening and referrals delivered substantial economic and public health value among adults facing housing instability and structural barriers to care. Findings support inclusion of DSMP as a covered benefit in Medicaid managed care, value based payment arrangements, and housing access initiatives to advance equitable diabetes outcomes.

Background: Few studies have examined racioethnic disparities in cardiovascular disease (CVD) in women after breast cancer treatment, who are at higher risk due to cardiotoxic cancer treatment. Methods: Based on the Pathways Heart Study of women with a history of breast cancer, this analysis examines the association between cardiometabolic risk factors (hypertension, diabetes, and dyslipidemia) and CVD events with self-reported race and ethnicity, as well as genetic similarity. Multivariable logistic and Cox proportional hazards regression models were used to test race and ethnicity and genetic similarity with prevalent and incident cardiometabolic risk factors and CVD events. Results: Of the 4,071 patients in this analysis, non-Hispanic Black (NHB), Asian, and Hispanic women were more likely to have prevalent and incident diabetes than non-Hispanic White (NHW) women. Analysis of genetic similarity revealed results consistent with self-reported race and ethnicity. For CVD risk, NHB women were more likely to develop heart failure and cardiomyopathy than NHW women. In contrast, Hispanic women were at lower risk of any incident CVD, serious CVD, arrhythmia, heart failure or cardiomyopathy, and ischemic heart disease, which was consistent with the associations found with Native American ancestry. Conclusions: This is the largest multi-ethnic study of disparities in CVD health in breast cancer survivors, demonstrating corroborating findings between self-reported race and ethnicity and genetic similarity. The results highlight disparities in cardiometabolic risk factors and CVD among breast cancer survivors that warrant more research and clinical attention in these distinct, high-risk populations.

Glycosylation is a key structural modification of immunoglobulin G (IgG) that modulates its effector functions and has multiple roles in balancing inflammation. Altered IgG glycosylation has been reported in many diseases, often years before clinical manifestation, suggesting its causal role and biomarker potential. Here, we analyzed IgG glycome composition in 20,405 individuals from 42 different studies processed at the Genos Glycoscience Research Laboratory between 2008 and 2025. Across nearly all diseases, specific IgG glycome profiles reflected accelerated biological aging. Accelerated glycan aging was strongly associated with increased risk of all-cause mortality, independent of established clinical risk factors and potential confounders. Moreover, interventions known to reduce mortality risk, including hormone replacement therapy, therapeutic plasma exchange and caloric restriction, were associated with reversal of glycan aging. Given their role in modulating low-grade systemic inflammation, IgG glycans may represent a functional link between chronic inflammation, aging, disease susceptibility and all-cause mortality.

Background Surgery is a widely used treatment option but the impact of surgery on long-term disease across socioeconomic groups is unknown. Methods Longitudinal population study using linked primary and secondary care data describing adults ([&ge;]18 years) in England recorded in the Clinical Practice Research Datalink (CPRD) between 1st January 2012 and 31st December 2021. Socioeconomic deprivation was defined using the Index of Multiple Deprivation (IMD). The exposure was surgery and primary outcome was long-term disease. Data are presented as n (%), median (IQR), and adjusted hazards ratios (HR) with 95% confidence intervals. Findings Of 18,329,659 people, 8,951,145 (48.8%) underwent surgery. 78.6% of index surgeries were elective (n=7,032,475), 21.4% were emergency (n=1,918,670). Amongst surgical patients, 4,741,188 (52.0%) were women, 3,540,136 (39.6%) from the most deprived deciles (IMD 1-4) and 994,595 (11.1%) from a minority ethnic group. Age-standardised rates of surgery were higher in deprived individuals (comparative rate ratio IMD 1 vs. IMD 10 elective: 1.11 (95% CI 1.11-1.11), emergency: 1.54 (1.54-1.54)). Age at first surgery was 42 (27-60) years for elective and 42 (25-65) years for emergency surgery overall, but lower for people from IMD 1-4 (elective: 39 (26-57) years, emergency: 38 (24-60) years). Rates of long-term disease increased following both elective (baseline 19.6%, three years 24.5%) and emergency surgery (baseline 10.3%, three years 12.3%). Risk of new long-term disease following surgery increased with increasing levels of deprivation (IMD 1 vs. IMD 10 elective: HR 1.46 (1.45-1.48), emergency: HR 1.46 (1.44-1.48)). Interpretation Surgical treatment is strongly associated with the onset of long-term disease and factors which limit healthy life expectancy. Surgery occurs at a younger age among socioeconomically deprived groups and may be linked to health inequalities. Similar but more complex patterns of inequality were seen in minority ethnic groups. Funding Barts Charity and UK Academy of Medical Sciences.

Childhood obesity remains a pressing global health challenge, yet the impact of dynamic adiposity changes during active developmental window retains poorly understood. Leveraging longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study (N=8519 at baseline; N=1873 at 4-year follow-up), our study reveals distinct neurodevelopmental implications of central fat dynamics during adolescence. At baseline, central fat indices (body roundness index, BRI / waist-to-height ratio, WHtR) outperformed BMI in predicting cognitive deficits, showing robust associations with impaired inhibitory control and episodic memory. The prediction effect was partially mediated by cortical changes in prefrontal and temporal regions. Longitudinally, the rate of fat accumulation ({Delta}) emerged as a critical predictor: faster adiposity accrual predicted attenuated cortical thinning (i.e., slower development) in parietal lobes and poorer executive function at follow-up, while baseline adiposity showed no significant effects on the follow-up brain morphology or cognitive development. Notably, subgroup analyses uncovered that obese adolescents with central fat reduction exhibited accelerated cortical thinning in posterior cingulate (change difference p=0.006-0.029) alongside rapid improvement in inhibitory control (Flanker slope difference p<0.05), whereas those with persistent adiposity showed delayed thinning in the postcentral gyrus. The study reveals that central fat (BRI/WHtR) is closely linked to neurocognitive risks, and longitudinal fat accumulation?rather than baseline adiposity?drives cortical alteration. Notably, fat reduction activated adaptive neural change in obese adolescents, underscoring the importance of weigh regulation during neurodevelopment.

The theory of island biogeography and its trophic extensions predict that both species richness and food-web complexity should increase with increasing ecosystem surface area. Accordingly, Species-Area Relationships (SARs) and Network-Area Relationships (NARs) are often observed to be positively-sloped, an observation that came to be considered as a law, and on which rest many area-based conservation plans for biodiversity. However, our mechanistic understanding of the driving mechanisms of SARs and NARs slopes remains limited, undermining our ability to predict how biodiversity will respond to habitat gain or loss. We show in 180 rural ponds sampled across five years that invasive alien predators reversed the SAR and NARs from positive in invader-free ponds, to negative in invaded ponds. Relationship reversal resulted from a higher prevalence of invasive alien predators driving magnified prey extinctions and simplified food webs in larger ponds. The ability of invasive alien predators to reverse SAR and NARs presumably reflected disproportionately high predation rates combined with a low sensitivity to prey extinction conferred by a wide trophic generalism. In a world where virtually all ecosystems face biological invasions, omnipresent invasive alien predators stress the pivotal role played by predation in shaping biocomplexity-area relationships, and highlight a growing need to preserve small ecosystems where invasive alien predators are less prevalent.

Toxoplasma gondii is a widespread human pathogen that has multiple, clinically relevant stages in its complex life cycle, including fast-replicating tachyzoites and latent bradyzoites. Bradyzoite differentiation is triggered by stress responses that lead to changes in transcription, translation, and metabolism. Two aspects of this process are addressed in this report: first, whether proteins that play roles in bradyzoite differentiation are specific to T. gondii and other bradyzoite-forming parasites of the Sarcocystidae family, and second, whether new bradyzoite differentiation proteins can be identified in T. gondii. To answer these questions, a phylogenetic approach was used, comparing proteomes of select members of the Sarcocystidae family that form morphologically different bradyzoite cysts and members of the Eimeriidae family that do not form cysts. This approach resulted in 8 distinct clusters of T. gondii proteins that reflected different conservation patterns; for example, one cluster showed conservation among all organisms, while another showed conservation in bradyzoite cyst-forming organisms. Known T. gondii proteins involved in bradyzoite differentiation were found in all clusters, indicating that this process uses both highly conserved pathways as well as bradyzoite-specific pathways. Importantly, the cluster containing proteins that are conserved in bradyzoite-forming organisms contained several known regulators of bradyzoites, and will be a source for identifying novel T. gondii proteins that are involved in bradyzoite differentiation.