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Results for “Demographic modelling”

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At least 19 recordsLinked to original sources

PubMed2026

Faster inference of complex demographic models from large allele frequency spectra.

MOTIVATION: Demographic inference from the joint site frequency spectrum is limited by computation when many populations or many samples are analyzed. RESULTS: We present momi3, a JAX-based method for inferring complex demographic models from large allele frequency spectra. It supports continuous migration, GPU execution, automatic differentiation, standardized demographic model input, and genealogical pruning. These changes yield speedups up to 1000× over existing methods and enable analysis of archaic admixture models using hundreds of human genomes. AVAILABILITY AND IMPLEMENTATION: momi3 is implemented in Python/JAX as part of demestats. Source code is available at https://github.com/jthlab/demestats; documentation is available at https://demestats.readthedocs.org.

Humans↗

PubMed2024

Next-generation phylogeography reveals unanticipated population history and climate and human impacts on the endangered floodplain bitterling (Acheilognathus longipinnis).

BACKGROUND: Floodplains harbor highly biodiverse ecosystems, which have been strongly affected by both past climate change and by recent human activities, resulting in a high prevalence of many endangered species in these habitats. Understanding the history of floodplain species over a wide range of timescales can contribute to effective conservation planning. We reconstructed the population formation history of the Itasenpara bitterling Acheilognathus longipinnis, an endangered floodplain fish species in Japan, over a broad timescale based on phylogenetic analysis, demographic modeling, and historical demographic analysis using mitogenome and whole-genome sequences. A genome sequence was newly assembled as a reference for the resequencing analysis. This bitterling is distributed in three plains separated by high mountain ranges and exhibits ecological characteristics well adapted to floodplain environments. RESULTS: Our analyses revealed an unexpected population branching pattern, gene flow, and timing of the differentiation that occurred within a few hundred thousand years, i.e., long after the mountain uplift that was assumed to be the primary geological cause of the population differentiation. The analyses also showed that all local populations experienced a severe decline during the last glacial and post-glacial periods. CONCLUSIONS: Our results suggest that the floodplain bitterling was able to disperse through unknown routes after mountain uplift and that its populations were strongly influenced by climatic and geographic changes in glacial-interglacial cycles and subsequent human activities, probably related to its floodplain-dependent ecology. The genomic data highlight the unanticipated distribution process of this species and the magnitude of the impact of human activities, with important implications for its conservation.

Endangered Species↗

PubMed2026

Genome-wide insights into the evolutionary and demographic history of the red alga Mazzaella laminarioides: Evidence for speciation with ancient migration along the southeast Pacific coast.

The mechanisms driving lineage divergence in red algae remain unexplored, despite the group's remarkable diversity and ancient evolutionary history. The red alga Mazzaella laminarioides, a Chilean intertidal species complex composed of three parapatric cryptic lineages (North, Center, South), offers a valuable system to evaluate these processes, as its life history combines severe dispersal limitation with a haploid-diploid cycle that may influence the emergence of reproductive barriers. We reconstructed its evolutionary history using whole-genome sequencing and nuclear genome assembly of representative individuals from each lineage. Phylogenomic analyses based on 1,507 single-copy orthologs recovered three deeply divergent lineages with limited nuclear discordance consistent with incomplete lineage sorting. For both splits, demographic modelling was most consistent with an Ancient Migration scenario, although support over strict isolation was moderate, suggesting that divergence may have begun with low asymmetric ancestral gene flow followed by subsequent loss of connectivity, demographic bottlenecks, and later population expansion. Coding sequence analyses revealed lineage-specific dN/dS heterogeneity; only one South-lineage locus passed FDR correction (metaxin-1, mitochondrial protein import), with two further South-lineage candidates in chlorophyll and heme biosynthesis falling below the FDR threshold. Together, these signals suggest that divergent selective pressures on energy acquisition may have contributed to divergence at the southern end of the distribution. These results add to the small but growing body of whole-genome data for red algae and, alongside recent macroalgal studies, suggest that ancestral connectivity could be a recurrent feature of lineage divergence even in marine organisms with extremely restricted dispersal.

Rhodophyta↗

PubMed2025

Pleistocene island connectivity did not enhance dispersal or impact population size change in Galápagos geckos.

Patterns of biodiversity on remote archipelagos are largely shaped by intra-archipelago colonization followed by in situ diversification. Pleistocene sea-level fluctuations purportedly enhanced gene flow among terrestrial organisms by increasing connectivity during periods of lower sea level. Furthermore, changes in sea-level are hypothesized to impact population sizes as a result of fluctuations in island sizes. Here, we used genomic data to test the role of Pleistocene island connectivity on the diversification and demographics of leaf-toed geckos (Phyllodactylus) endemic to the Galápagos. Consistent with previous studies, we found that present diversity of Galápagos Phyllodactylus stems from three independent dispersal events. Contrary to the hypothesis of Pleistocene-driven diversification, we found no correspondence between lineage divergence and island connectivity. Furthermore, we found no evidence of introgression; demographic modelling indicated that all species increased rapidly in effective population size in the period 20-150 ka, and these inferred demographic expansions were largely asynchronous and apparently unassociated with species or island age. Collectively, these results indicate that more complex abiotic and/or biotic factors may better explain the recent demographic history of Phyllodactylus and underscore the need for additional population genomic studies of terrestrial taxa to understand the impact of past climate cycles on Galápagos island communities.

Animals↗

PubMed2025

SimHumanity: Using SLiM 5.0 to run whole-genome simulations of human evolution.

The reconstruction of human evolutionary history has undergone repeated advances, each made possible by methodological innovations. In recent decades, genetic and genomic data played a central role in the reconstruction of major evolutionary events such as the out-of-Africa migration, and genetic simulations of human evolutionary history have come to play a major role in testing more specific hypotheses including proposed patterns of migration and admixture with archaic hominins. Increasing computational power has allowed human evolutionary history to be modeled at ever-larger scales, but simulations that encompass the complete human genome, including sex chromosomes and mitochondrial DNA, have been difficult due to the lack of support for whole-genome models in commonly used evolutionary simulation frameworks. With the recent introduction of SLiM 5 such simulations are now straightforward to construct, allowing the easy simulation of humans at whole-genome scale under different demographic models and evolutionary dynamics. We here present three versions of a reusable, customizable, open-source SLiM 5 model for simulating the molecular evolution of the full human genome. We also show some simple analyses of results from the model, to illustrate its utility. We hope this model, which we have nicknamed "SimHumanity" in jest, will facilitate further progress in the field of human evolutionary simulations.

SLiM↗

PubMed2024

Rapid vertebrate speciation via isolation, bottlenecks, and drift.

Speciation is often driven by selective processes like those associated with viability, mate choice, or local adaptation, and "speciation genes" have been identified in many eukaryotic lineages. In contrast, neutral processes are rarely considered as the primary drivers of speciation, especially over short evolutionary timeframes. Here, we describe a rapid vertebrate speciation event driven primarily by genetic drift. The White Sands pupfish (Cyprinodon tularosa) is endemic to New Mexico's Tularosa Basin where the species is currently managed as two Evolutionarily significant units (ESUs) and is of international conservation concern (Endangered). Whole-genome resequencing data from each ESU showed remarkably high and uniform levels of differentiation across the entire genome (global FST ≈ 0.40). Despite inhabiting ecologically dissimilar springs and streams, our whole-genome analysis revealed no discrete islands of divergence indicative of strong selection, even when we focused on an array of candidate genes. Demographic modeling of the joint allele frequency spectrum indicates the two ESUs split only ~4 to 5 kya and that both ESUs have undergone major bottlenecks within the last 2.5 millennia. Our results indicate the genome-wide disparities between the two ESUs are not driven by divergent selection but by neutral drift due to small population sizes, geographic isolation, and repeated bottlenecks. While rapid speciation is often driven by natural or sexual selection, here we show that isolation and drift have led to speciation within a few thousand generations. We discuss these evolutionary insights in light of the conservation management challenges they pose.

Animals↗

PubMed2026

Targeted population genomics uncovers demographic history and genetic divergence in north American wild cranberry.

Wild populations of North American cranberry (Vaccinium macrocarpon Aiton) are reservoirs of genetic variation that may contribute to the improvement of breeding-relevant traits. However, the extent to which wild genetic variation is geographically structured and represented in elite germplasm remains unclear. We analysed 179 wild cranberry accessions from the upper Midwest and Eastern North America to estimate nucleotide diversity (π), population structure, and loci associated with genetic differentiation and environmental variables using a genome-informed targeted genotyping panel. Additionally, 14 demographic scenarios were evaluated using site-frequency-spectrum-based inference to identify historical events that could explain current genetic diversity. We observed extremely low nucleotide diversity within the targeted panel (π = 5 × 10-6). Rare allele distributions strongly influenced π and Tajima's D values, suggesting constrained diversity in the genomic regions assayed that is not captured by heterozygosity-based estimates alone. However, we interpreted these results as conservative lower bounds on genome-wide neutral diversity because the targeted panel is enriched for genic and conserved regions. A clear separation between the Midwest and East populations was observed, with inbreeding coefficients ranging from -0.13 to 0.15. Furthermore, site frequency spectrum inference from the targeted panel supported a demographic scenario consistent with a significant population reduction ≈15-14 thousand years ago (kya), followed by a divergence between the two regions ≈12 kya, and an asymmetric gene flow ≈1.3 kya. We detected 254 candidate loci showing regional allele-frequency differentiation. Several of these loci colocalized with candidate genes linked to stress response, development, and metabolic processes. To evaluate the representation of geographically differentiated wild alleles in a breeding context, we analysed Rutgers breeding materials (n = 484) and found that this panel is enriched for common alleles in Eastern wild populations. These findings indicate regionally structured allele-frequency variation in wild cranberry, with potential relevance to environmental response and breeding. This study extends prior wild cranberry population-genetic research by providing targeted-panel estimates of diversity, comparisons of demographic models, and breeding insights on geographically differentiated alleles, while highlighting the importance of conserving wild cranberry germplasm for use in modern breeding programs.

Journal Article↗

PubMed2026

Past genomes guide future conservation: insights from extinct populations of the endangered Pacific pocket mouse.

Efforts to recover endangered species often rely on restoring populations to their historical range, yet reestablishing lost genetic variation is challenging when the ancestral genetic landscape is poorly understood. The Pacific pocket mouse (Perognathus longimembris pacificus), a federally endangered heteromyid rodent, has been extirpated from most of its range in coastal southern California. Recovery efforts call for establishing new populations in their historic range through translocation, but the extent to which historical patterns of genetic variation can be recapitulated is unknown. To inform conservation planning, we sequenced whole genomes of historical samples, including individuals from populations that went extinct in the mid-1900s. Phylogenetic analyses revealed that mice from the southernmost extirpated population form a clade with a different subspecies, while populations to the north form a sister clade. These findings support morphological evidence calling for a taxonomic revision, which would modify the definition of the historic range and complicate the interpretation of suitable reintroduction sites. Despite this divergence, D-statistics and demographic models indicate historical gene flow among coastal populations, suggesting that alleles reintroduced to the southern coast may echo ancestral connectivity. Thus, management efforts should consider potential receiver sites that contain suitable habitat within this range as viable for population creation. These results highlight the value of historical genomics in guiding conservation decisions, particularly when taxonomic uncertainty, extirpation, and limited genetic diversity constrain modern management. Although historical baselines often cannot be restored, conservation strategies can leverage genomic insights to enhance future adaptive potential and long-term resilience of threatened species.

Endangered Species↗

PubMed2025

Host-Associated Genetic Differentiation in the Face of Ongoing Gene Flow: Ecological Speciation in a Pathogenic Parasite of Freshwater Fish.

Adaptive evolution in response to varying environments, leading to population divergence, is among the most intriguing processes of speciation. However, the extent to which these adaptive processes effectively drive population divergence amidst ongoing gene flow remains controversial. Our study addresses this by analyzing population genetic structure, gene flow, and genomic divergence between lineages of a tapeworm parasite (Ligula intestinalis) isolated from sympatric fish hosts. This parasite, which must overcome host immunological defenses for successful infection, significantly impacts host health. Utilizing genome-wide Single Nucleotide Polymorphisms (SNPs) and transcriptome data, we investigated whether host species impose distinct selection pressures on parasite populations. Genetic clustering analyses revealed clear divergence, with parasites from bream (Abramis brama) forming a distinct genetic cluster separate from those infecting roach (Rutilus rutilus), rudd (Scardinius erythrophthalmus), and bleak (Alburnus alburnus). Demographic modeling indicated isolation with continuous gene flow as the most plausible scenario for this divergence. Selection analyses identified 896 SNPs under selection, displaying low to moderate nucleotide diversity and genetic divergence compared with neutral loci. Transcriptome profiling supported these findings, revealing distinct gene expression profiles between parasite populations. Examination of selected SNPs and differentially expressed genes identified candidate genes linked to immune evasion mechanisms, potentially driving ecological speciation. This research highlights the interplay of host specificity, population demography, and disruptive selection in ecological speciation. By dissecting genomic factors, our study improves the understanding of mechanisms facilitating population divergence despite ongoing gene flow.

Animals↗

PubMed2026

Deep learning reveals genomic regions introgressed between two recurrently hybridizing lynx species.

Recently, diverged species with overlapping distributional ranges have high chances of hybridizing and if hybrids are viable, genomic material can be transferred between species in a process called introgression. To characterize the patterns and consequences of introgression in species with historically low population sizes and recent steep declines resulting in genetic erosion, we analyze the Iberian and Eurasian lynx (EL) as an illustrative and relevant case study. While genome-wide introgression was already detected, here we apply a method using a deep convolutional neural network to detect specific regions of the genome with signals of introgression in three populations of these two species. Over 6% of the genome of both Iberian lynx and ELw shows introgression from the other species, compared with only 2% in the ELs. This observation, along with the results from demographic modeling, suggests that the ELw population is genetically closest to the source of EL introgression, a probably now extinct group that coexisted with the Iberian lynx in Southern Europe and Northern Iberia until recently. As predicted by theory, introgression was generally higher in populations with smaller effective sizes and in genomic regions of high recombination. However, the Iberian lynx did not show higher overall introgression than the more abundant ELw, and coding regions introgressed as frequently as intergenic regions. Local genetic diversity is boosted approximately 3-fold in genomic windows where introgression occurs, potentially including the adaptively relevant and highly diverse MHC region of the Iberian lynx.

Animals↗

PubMed2025

Homoploid Hybrid Speciation in a Marine Pelagic Fish.

Homoploid hybrid speciation (HHS) is an enigmatic evolutionary process where new species arise through hybridisation of divergent lineages without changes in chromosome number. Although increasingly documented in various taxa and ecosystems, convincing cases of HHS in marine fishes have been lacking. This study presents a possible case of HHS in a pelagic marine fish based on comprehensive genomic, morphological, and ecological analyses. Population genomics, species tree estimation, and tests of introgression and admixture identified three sympatric clusters in Megalaspis cordyla in the western Pacific and the admixed nature of one cluster between the others. Moreover, model-based demographic inference favoured a hybrid speciation scenario over introgression for the origin of the admixed cluster. While contemporary gene flow suggested partial reproductive isolation, examination of occurrence data and ecologically relevant morphological characters suggested ecological differences between the clusters, potentially contributing to the reproductive isolation and niche partitioning in sympatry. The clusters are also morphologically distinguishable and thus can be taxonomically recognised as separate species. The hybrid cluster is restricted to the coasts of Taiwan and Japan, where all three clusters coexist. The parental clusters are additionally found in lower latitudes, where they display non-overlapping distributions. Given the geographical distributions, estimated times of species formation, and patterns of historical demographic changes, we propose that the Pleistocene glacial cycles were the primary driver of HHS in this system. We also develop an ecogeographic model of HHS in marine coastal ecosystems, including a novel hypothesis to explain the initial stages of HHS.

Animals↗

PubMed2026

Antidepressant use among American adults in a 50-state survey.

BACKGROUND: Antidepressants are among the most prescribed medications in the USA, yet challenges in access to mental health treatment persist. OBJECTIVE: To assess current and lifetime antidepressant and psychotherapy use among American adults, and examine attitudes towards potential federal restrictions on antidepressant prescribing. METHODS: We conducted a cross-sectional survey study using data from a national non-probability internet-based panel weighted to approximate national demographics (age, gender, race and ethnicity, education, US census region, and urbanicity) based on 2020 US Census data. Data were collected between 10 April and 27 May 2025 from 30 810 adults residing in the USA. The primary outcomes were self-reported current and past antidepressant and psychotherapy use, and support for or opposition to potential federal restrictions on antidepressant prescribing. Logistic regression models estimated demographic and treatment-related features associated with these outcomes. FINDINGS: Among 30 115 respondents with complete antidepressant data, 16.6% reported current antidepressant use, and of 30 098 respondents with psychotherapy data, 10.4% reported current psychotherapy. Use of both treatments was significantly greater among White respondents compared with all other racial groups. When asked about potential federal restrictions on doctors prescribing antidepressants, 16.4% of respondents supported and 48.0% opposed such regulation, with lesser opposition among those of male gender (OR 0.69, 95% CI 0.65 to 0.73), and greater opposition among those with lifetime antidepressant treatment (OR 2.37, 95% CI 2.21 to 2.54). CONCLUSIONS: Antidepressant and psychotherapy use remains unevenly distributed across demographic groups. A significant proportion of adults in every US state oppose efforts to restrict access to antidepressant prescribing, reflecting broad public support for maintaining access to treatment. CLINICAL IMPLICATIONS: Findings from this study suggest that restrictive policies on antidepressant prescribing are unlikely to align with public sentiment and may risk exacerbating existing inequities in care.

Humans↗

PubMed2026

Identification Matters: How Data Sharing Affects Pupil Honesty and Engagement in Universal School Well-Being Assessments.

PURPOSE: Universal well-being assessments in schools may support early identification of pupils needing mental health support. However, little is known about how privacy and confidentiality concerns influence pupils' acceptability of assessments and willingness to engage authentically. This study examined how hypothetical identification, where responses are linked to pupils and shared with key stakeholders, affects pupils' anticipated honesty and engagement, and whether known help-seeking barriers predict negative responses. METHODS: Cross-sectional data were collected from 12,377 primary (ages 8-10) and secondary pupils (ages 11-17) across 55 schools in England. Pupils reported whether their responses would change if identifiable and shared with school staff, parents/guardians, or external professionals. Responses indicating reduced honesty or likelihood of disengagement were coded as negative. Predictors were examined using mixed-effects logistic regression models, including demographics, school connectedness, and mental well-being. RESULTS: Identification and data sharing influenced pupils' anticipated engagement, particularly in secondary schools. Identification by school staff elicited the highest proportion of negative responses in both phases, whereas external professionals elicited the fewest. Most primary pupils reported they would respond authentically, while a larger proportion of secondary pupils indicated they would respond less honestly or disengage when responses were identifiable and shared. Across primary and secondary samples, low well-being, low school connectedness, and being female were associated with greater likelihood of negative response. DISCUSSION: Pupils' anticipated engagement with well-being assessments is shaped by who accesses their data, with marked developmental differences. Strengthening trust, privacy, and connectedness, and supporting pupils' autonomy, may improve the acceptability and response accuracy.

Humans↗

PubMed2026

Patient expectations assessed before randomisation and after the first treatment session, and their associations with pain outcome at 3 months in patients with tennis elbow: a secondary analysis of a randomised controlled feasibility trial in Norwegian secondary care.

OBJECTIVE: To evaluate patients' expectations of pain improvement before randomisation (T1) and after the first treatment (T2), and to examine how expectations at these two time points were associated with pain outcome measured at 3 months (T3). DESIGN: Exploratory secondary analyses of a three-arm, randomised controlled feasibility trial in patients with tennis elbow comparing heavy slow resistance training, shock wave therapy and advice (1:1:1). SETTING: Outpatient clinic at Oslo University Hospital. PARTICIPANTS: Adults with lateral epicondylalgia, commonly known as tennis elbow. MAIN OUTCOME MEASURES: Expected pain was rated on a Numeric Rating Scale (NRS, 0-10) at T1 and T2. Present pain (NRS, 0-10) was reported at 3 months (T3). Changes in expectations from T1 to T2 were summarised descriptively. Univariable linear regressions assessed associations between T3 pain and expectations at T1 and T2, treatment group and baseline factors. Explained variance was quantified by R2. Multivariable models including demographics and baseline pain were evaluated via adjusted R2. RESULTS: Fifty-four participants were included. In the shock wave group, nine (47%) came to expect greater improvement from T1 to T2; by contrast, in the advice group, seven (41%) expected less improvement. Expectations at T1 were not associated with T3 pain, whereas expectations at T2 were positively associated with T3 pain (b=0.61, 95% CI 0.33 to 0.89, p<0.01, R2=0.27), suggesting that higher expected pain at T2 was associated with higher reported pain at T3. Adding education and baseline pain increased explained variance modestly (R2 from 0.27 to 0.32). CONCLUSION: In this study, expectations measured after randomisation and one treatment session were associated with pain at 3 months for patients with tennis elbow. Larger, prospectively designed studies should investigate how postrandomisation expectations relate to clinical outcomes in non-blinded musculoskeletal trials. TRIAL REGISTRATION NUMBER: NCT04803825.

Humans↗

PubMed2026

Population genomics of Plasmodium malariae from 4 African countries.

BACKGROUNDMalaria caused by Plasmodium malariae is geographically widespread and sometimes associated with prolonged infection, yet little is known about its genomic epidemiology.METHODSWe performed hybrid capture and whole-genome sequencing of 77 isolates collected from Cameroon (n = 7), the Democratic Republic of the Congo (n = 16), Nigeria (n = 4), and Tanzania (n = 50) between 2015 and 2021, analyzing parasite genetic population structure and demography.RESULTSThere is no evidence of geographic population structure. Nucleotide diversity was significantly lower than in colocalized P. falciparum isolates, while linkage disequilibrium was significantly higher. Genome-wide selection scans identified no erythrocyte invasion ligands or antimalarial resistance orthologs as top hits; however, targeted analyses of these loci revealed evidence of selective sweeps around 4 erythrocyte invasion ligands and 6 antimalarial resistance orthologs. Demographic inference modeling suggests that African P. malariae is recovering from a bottleneck.CONCLUSIONP. malariae is genomically atypical among human Plasmodium spp. and lacks strong population structure in Africa. The low diversity has potential impacts on understanding persistent versus new infection through genomic epidemiology.FUNDINGBill & Melinda Gates Foundation (grant 002202), USAID/PMI through Jhpiego and CDC, NIH (T32AI007151, T32AI070114, R01AI107949, R01AI129812, R21 AI148579, R01AI137395, R21AI152260, R01AI132547, and K24AI134990), and the DELTAS Africa initiative (DELGEME grant 107740/Z/15/Z).

Plasmodium malariae↗

PubMed2026

Construction of precision clinical-proteomics risk model based on machine learning for predicting heart failure in type II diabetes mellitus.

BACKGROUND AND AIMS: Heart failure (HF) is a severe complication in type 2 diabetes mellitus (T2DM), but current risk stratification scores have limited predictive accuracy. We aimed to develop novel prediction tools integrating clinical variables with proteomics to improve risk stratification of hospitalization for HF in T2DM. METHODS AND RESULTS: In this study, we included 2111 UK Biobank participants with T2DM but no prior HF, and profiled 2920 proteins to predict 10-year incident HF hospitalization. Participants were randomly divided into training (70%), tuning (10%), and validation (20%) sets.Three prediction models were developed: a Clinical model based on demographic characteristics, comorbidities, medication use, and laboratory indices; a Protein model based on 40 proteins selected by the Light Gradient Boosting Machine (LGBM); and the Clinical OMics and Protein ASSessment for Heart Failure (COMPASS-HF) model, which integrated both clinical variables and the LGBM-selected proteins. Models were evaluated for area under the curve (AUC), sensitivity, and specificity. During follow-up, 168 participants (7.96%) developed incident HF. The COMPASS-HF model showed better discrimination than the Clinical model, with an AUC of 0.897 (95% CI: 0.850-0.945) versus 0.790 (95% CI: 0.723-0.856). It also demonstrated higher sensitivity (0.882; 95% CI: 0.725-0.967) and consistent performance in subgroups. COMPASS-HF effectively stratified risk of hospitalization for HF, with cumulative incidence rates of 31.9% in the high-risk group and 1.2% in the low-risk group. CONCLUSIONS: By combining clinical and proteomic variables, we developed a high-performance HF prediction model for T2DM, enabling precise risk stratification and informing early intervention strategies.

Humans↗

PubMed2026

Discovery and validation of a multi-protein panel for predicting non-fatal major adverse cardiovascular events in diabetic kidney disease.

OBJECTIVE: To identify plasma protein biomarkers associated with incident non-fatal major adverse cardiovascular events (MACE) in diabetic kidney disease (DKD) patients. RESEARCH DESIGN AND METHODS: We analyzed 317 DKD patients from the UK Biobank. Plasma proteomics and clinical data (demographics, metabolism, renal function) were integrated. In an exploratory discovery phase, three sequential Cox regression models (crude, socio-demographic-adjusted, socio-demographic-metabolic adjusted) screened non-fatal MACE-associated proteins. To prevent information leakage, the cohort was then randomly split into training (70%) and testing (30%) sets; machine-learning feature selection, hyperparameter optimization, and final model development were performed exclusively within the training set. The associated proteins were input into the four-step machine-learning pipeline (LASSO-Cox, random survival forest, Boruta, XGBoost-Cox). Predictive performance was validated using Kaplan-Meier survival analyses, longitudinal trajectory modeling, and ROC benchmarking. An interactive web application was deployed for clinical implementation. RESULTS: Of 1,463 plasma proteins, 561 were associated with non-fatal MACE across Cox models, with 14 overlapping proteins. Nine core proteins (ANG, IL1R1, CXCL14, ESAM, PTGDS, HAVCR1, FGFR2, IGSF8, CCL3) were validated: ANG showed the strongest non-fatal MACE association (HR = 3.88, 95%CI 2.33-6.48, p<0.001), and all high-expression groups had elevated non-fatal MACE risk. GO/KEGG enrichment highlighted inflammatory-immune pathways like positive regulation of MAPK cascade, Cytokine-cytokine receptor interaction and PI3K-Akt signaling pathway as key mechanisms. The model integrating proteins, demographic factors, and clinical variables achieved the highest predictive performance across non-fatal MACE (AUC = 0.768), myocardial infarction (MI) (0.808), and stroke (0.816) outcomes, with superior stability in cross-validation. CoxBoost + Elastic Net framework was selected as the optimal framework via benchmarking of 101 algorithms. The model demonstrated favorable calibration in high-risk patients and yielded positive net clinical benefit across decision thresholds of 5% to 45%. The web tool (https://jiangli2941.github.io/MACE-prediction-v2/) enables input of 28 variables, outputs non-fatal MACE risk status, risk probability, and highlights abnormal indicators. CONCLUSION: Plasma proteomics combined with machine learning identifies robust non-fatal MACE predictors in DKD.

Humans↗

PubMed2026

Association between cumulative social disadvantage, as measured by the social determinants of health score, and epilepsy: a cross-sectional study.

BACKGROUND: Social determinants of health (SDoH) shape access to care, health behaviors, and long-term outcomes, yet their cumulative relationship with epilepsy has not been well quantified. This study examined whether a composite SDoH score was associated with epilepsy in adults. METHODS: This cross-sectional study used data from the National Health and Nutrition Examination Survey 2013-2018. The SDoH score ranged from 0 to 8 and summarized eight unfavorable social conditions. Epilepsy was identified using medication-based ascertainment. Survey-weighted logistic regression models were applied to evaluate the association between SDoH score and epilepsy. Restricted cubic spline, subgroup, sensitivity, and receiver operating characteristic analyses were also performed. RESULTS: A total of 13,119 participants were included, of whom 114 had epilepsy. Participants with epilepsy had a higher mean SDoH score than those without epilepsy (3.41 ± 0.24 vs. 2.35 ± 0.06, P < 0.001). In the fully adjusted model, each 1-point increase in SDoH score was associated with 31% higher odds of epilepsy (OR 1.31, 95% CI 1.16-1.48). Compared with the low-score group (0-2), the adjusted odds ratios were 2.09 (95% CI 1.06-4.15) for scores of 3-5 and 2.67 (95% CI 1.34-5.33) for scores of 6-8. Spline analysis showed a significant overall association without evidence of nonlinearity. Adding SDoH components to demographic variables improved model discrimination (AUC 0.731 vs. 0.589, P for difference <0.001). CONCLUSION: Greater cumulative social disadvantage, as reflected by the SDoH score, was associated with higher odds of epilepsy.

Humans↗