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Accurate ultrasound estimated fetal weight (EFW) supports key perinatal decisions. This study aimed to compare the accuracy of a population-specific formula with that of a widely used global model in pregnancies examined near delivery. This was a secondary analysis of a prospective Japanese cohort study to develop a new fetal ultrasound biometry reference chart. Fetal ultrasound measurements were obtained by Japan Society of Ultrasonics in Medicine-certified sonographers or under their supervision and submitted to the coordinating center, where EFW was calculated using the Shinozuka and Hadlock-3 formulas. Analyses were restricted to cases with EFW available from both formulas. The primary analysis included examinations within 7 days before delivery; a sensitivity analysis restricted the interval to ≤3 days. A subgroup analysis was performed in fetuses classified as small for gestational age (SGA). In the primary analysis (n = 310), the median signed error was -75.8 g for Shinozuka and -223.3 g for Hadlock-3 (Wilcoxon p < 0.001). The median absolute percentage signed error was 4.81% for Shinozuka and 7.93% for Hadlock-3 (Wilcoxon p < 0.001). The proportion within ±10% of BW was 83.9% with Shinozuka versus 61.6% with Hadlock-3 (McNemar p < 0.001). In the sensitivity analysis (n = 176), similar findings were observed. In the SGA subgroup (n = 40), the signed error and absolute percentage error were significantly lower with the Shinozuka formula than with Hadlock-3. In this multicenter cohort examined near delivery, the population-specific Shinozuka formula produced EFW closer to BW and a higher proportion within ±10% than Hadlock-3, which showed greater systematic underestimation. These findings support the use of locally validated, population-specific EFW formulas when available, particularly for clinical decision-making near delivery.

In his 1972 paper 'The apportionment of human diversity', Lewontin showed that, when averaged over loci, genetic diversity is predominantly attributable to differences among individuals within populations. However, selection can alter the apportionment of diversity of specific genes or genomic regions. We examine genetic diversity at the human leucocyte antigen (HLA) loci, located within the major histocompatibility complex (MHC) region. HLA genes code for proteins that are critical to adaptive immunity and are well-documented targets of balancing selection. The single-nucleotide polymorphisms (SNPs) within HLA genes show strong signatures of balancing selection on large timescales and are broadly shared among populations, displaying low 𝐹𝑆𝑇 values. However, when we analyse haplotypes defined by these SNPs (which define 'HLA alleles'), we find marked differences in frequencies between geographic regions. These differences are not reflected in the 𝐹𝑆𝑇 values because of the extreme polymorphism at HLA loci, illustrating challenges in interpreting 𝐹𝑆𝑇. Differences in the frequency of HLA alleles among geographic regions are relevant to bone-marrow transplantation, which requires genetic identity at HLA loci between patient and donor. We discuss the case of Brazil's bone marrow registry, where a deficit of enrolled volunteers with African ancestry reduces the chance of finding donors for individuals with an MHC region of African ancestry. This article is part of the theme issue 'Celebrating 50 years since Lewontin's apportionment of human diversity'.

Functional cure for chronic hepatitis B (CHB) remains challenging due to the lack of direct intervention methods for hepatic inflammation. Multi‐omics research offers a promising approach to understand hepatic inflammation mechanisms in CHB. A Bayesian linear model linked gene expression with clinical parameters, and population‐specific expression analysis (PSEA) refined bulk gene expression into specific cell types across different clinical phases. These models were integrated into our analysis of key factors like inflammatory cells, immune activation, T cell exhaustion, chemokines, receptors, and interferon‐stimulated genes (ISGs). Validation through multi‐immune staining in liver specimens from CHB patients bolstered our findings. In CHB patients, increased gene expression related to immune cell activation and migration was noted. Marker genes of macrophages, T cells, immune‐negative regulators, chemokines, and ISGs showed a positive correlation with serum alanine aminotransferase (ALT) levels but not hepatitis B virus DNA levels. The PSEA model confirmed T cells as the source of exhausted regulators, while macrophages primarily contributed to chemokine expression. Upregulated ISGs (ISG20, IFI16, TAP2, GBP1, PSMB9) in the hepatitis phase were associated with T cell and macrophage infiltration and positively correlated with ALT levels. Conversely, another set of ISGs (IFI44, ISG15, IFI44L, IFI6, MX1) mainly expressed by hepatocytes and B cells showed no correlation with ALT levels. Our study presents a multi‐omics analysis integrating bulk transcriptomic, single‐cell sequencing data, and clinical data from CHB patients to decipher the cause of intrahepatic inflammation in CHB. The findings confirm that macrophages secrete chemokines like CCL20, recruiting exhausted T cells into liver tissue; concurrently, hepatocyte innate immunity is suppressed, hindering the antiviral effects of ISGs. This study integrates liver bulk transcriptomic data, single‐cell sequencing data, and clinical data to analyze the factors that induce hepatic inflammation in chronic hepatitis B from a multi‐omics perspective by Bayesian regression. Macrophages secrete chemokines like CCL20 and CXCL8 to recruit immune‐exhausted T lymphocytes (CTLA4, TIGIT) into liver tissue. Innate immunity within hepatocytes is suppressed, impeding interferon‐stimulated genes from initiating antiviral effects. Activation of innate immune pathways in infiltrating T cells and macrophages further exacerbates inflammation formation. Highlights This study integrates liver bulk transcriptomic data, single‐cell sequencing data, and clinical data to analyze the factors inducing hepatic inflammation in chronic hepatitis B from a multi‐omics perspective by Bayesian regression. Macrophages secrete chemokines like CCL20 and CXCL8 to recruit immune‐exhausted T lymphocytes (CTLA4, TIGIT) into liver tissue. Innate immunity within hepatocytes is suppressed, impeding interferon‐stimulated genes from initiating antiviral effects. Activation of innate immune pathways in infiltrating T cells and macrophages further exacerbates inflammation formation.

We report a GWAS of alcohol dependence (AD) in European-American (EA) and African-American (AA) populations, with replication in independent samples of EAs, AAs and Germans. Our sample for discovery and replication was 16 087 subjects, the largest sample for AD GWAS to date. Numerous genome-wide significant (GWS) associations were identified, many novel. Most associations were population specific, but in several cases were GWS in EAs and AAs for different SNPs at the same locus,showing biological convergence across populations. We confirmed well-known risk loci mapped to alcohol-metabolizing enzyme genes, notably ADH1B (EAs: Arg48His, P =1.17 × 10 −31 ; AAs: Arg369Cys, P =6.33 × 10 −17 ) and ADH1C in AAs (Thr151Thr, P =4.94 × 10 −10 ), and identified novel risk loci mapping to the ADH gene cluster on chromosome 4 and extending centromerically beyond it to include GWS associations at LOC100507053 in AAs ( P =2.63 × 10 −11 ), PDLIM5 in EAs ( P =2.01 × 10 −8 ), and METAP in AAs ( P =3.35 × 10 −8 ). We also identified a novel GWS association (1.17 × 10 −10 ) mapped to chromosome 2 at rs1437396, between MTIF2 and CCDC88A, across all of the EA and AA cohorts, with supportive gene expression evidence, and population-specific GWS for markers on chromosomes 5, 9 and 19. Several of the novel associations implicate direct involvement of, or interaction with, genes previously identified as schizophrenia risk loci. Confirmation of known AD risk loci supports the overall validity of the study; the novel loci are worthy of genetic and biological follow-up. The findings support a convergence of risk genes (but not necessarily risk alleles) between populations, and, to a lesser extent, between psychiatric traits.

Background Limited ancestral diversity has impaired our ability to detect risk variants more prevalent in ancestry groups of predominantly non-European ancestral background in genome-wide association studies (GWAS). We construct and analyze a multi-ancestry GWAS dataset in the Alzheimer’s Disease Genetics Consortium (ADGC) to test for novel shared and population-specific late-onset Alzheimer’s disease (LOAD) susceptibility loci and evaluate underlying genetic architecture in 37,382 non-Hispanic White (NHW), 6728 African American, 8899 Hispanic (HIS), and 3232 East Asian individuals, performing within ancestry fixed-effects meta-analysis followed by a cross-ancestry random-effects meta-analysis. Results We identify 13 loci with cross-population associations including known loci at/near CR1 , BIN1 , TREM2 , CD2AP , PTK2B , CLU , SHARPIN , MS4A6A , PICALM , ABCA7 , APOE , and two novel loci not previously reported at 11p12 ( LRRC4C ) and 12q24.13 ( LHX5-AS1 ). We additionally identify three population-specific loci with genome-wide significance at/near PTPRK and GRB14 in HIS and KIAA0825 in NHW. Pathway analysis implicates multiple amyloid regulation pathways and the classical complement pathway. Genes at/near our novel loci have known roles in neuronal development ( LRRC4C , LHX5-AS1 , and PTPRK ) and insulin receptor activity regulation ( GRB14 ). Conclusions Using cross-population GWAS meta-analyses, we identify novel LOAD susceptibility loci in/near LRRC4C and LHX5-AS1 , both with known roles in neuronal development, as well as several novel population-unique loci. Reflecting the power of diverse ancestry in GWAS, we detect the SHARPIN locus with only 13.7% of the sample size of the NHW GWAS study ( n  = 409,589) in which this locus was first observed. Continued expansion into larger multi-ancestry studies will provide even more power for further elucidating the genomics of late-onset Alzheimer’s disease.

Chronic kidney disease is a growing burden, yet the genetic architecture of kidney function requires further investigation. We performed a genome-wide association study of estimated glomerular filtration rate in 72,298 Korean individuals using a population-specific Korean Biobank Array and genetic imputation with a population matching imputation panel enabling high-resolution variant detection. We identified 30 independent signals including an East Asian-specific rare variant rs535291258. Through fine-mapping and functional annotation using epigenomic data, rs9895661 was predicted to modulate the binding affinity of the transcription factor TBX5 , thereby influencing TBX2 expression. We also experimentally validated the allele-specific enhancer activity of this variant. Our results reveal both common and rare variants underlying kidney function in an East Asian population, highlight the value of population-specific approaches and illustrate how integrating epigenomic profiling and experimental approaches with GWAS results can connect genetic associations with molecular mechanisms of kidney function.

Wearable sensors provide objective, continuous, and non-invasive quantification of physical activity, with step count serving as one of the most intuitive measures. However, significant gait alterations in individuals with neurological conditions limit the accuracy of step-counting algorithms trained on able-bodied individuals. Therefore, this study investigates the accuracy of step counting during activities of daily living (ADL) in a neurological population. Seven individuals with neurological conditions wore seven accelerometers while performing ADL for 30 min. Step events manually annotated from video served as ground truth. An optimal sensing and analysis configuration for machine learning algorithm development (sensor location, filter range, window length, and regressor type) was identified and compared to existing algorithms developed for able-bodied individuals. The most accurate configuration includes a waist-worn sensor, a 0.5–3 Hz bandpass filter, a 5 s window, and gradient boosting regression. The corresponding algorithm showed a significantly lower error rate compared to existing algorithms trained on able-bodied data. Notably, all algorithms undercounted steps. This study identified an optimal sensing and analysis configuration for machine learning-based step counting in a neurological population and highlights the limitations of applying able-bodied-trained algorithms. Future research should focus on developing accurate and robust step-counting algorithms tailored to individuals with neurological conditions.

Social media, encompassing online mental health communities (OMHCs), has revolutionized mental health discourse by fostering open discussions that drive population-specific empowerment. These discussions generate diverse empowerment processes, such as informational, family awareness, and social awareness support, that can empower different health-consumer populations based on their support needs. However, how OMHC discourse themes drive these empowerment processes remains unexplored. This study sought to understand how discourse themes in Twitter/X OMHCs drive three categories of population-specific empowerment processes aligned with all populations in Strategy 1 of the World Health Organization's (WHO's) Integrated People-Centred Health Services (IPCHS) framework: (1) individual-level processes for individuals with mental health conditions, including underserved and marginalized individuals (informational, self-expression, network, emotional, esteem, and tangible support); (2) informal carer processes for families and friends (family and friend awareness support); and (3) society-level processes for the broader public (main process: social awareness support). Specifically, it identified the discourse themes, their resonance with OMHC members, and the population-specific empowerment processes they drive. We analyzed discussions in the Twitter OMHC #bipolarclub. We collected 2068 tweets using its #bipolarclub hashtag between December 2022 and January 2023, of which 547 were eligible for analysis. We identified the discourse themes using qualitative thematic analysis and defined their resonance with OMHC members based on their prevalence in the tweets. We determined the population-specific empowerment processes driven by these themes by examining the processes embedded within them. We identified six overarching discourse themes, ranked by prevalence: (1) symptom, medication, treatment, and health care system experiences (187/547, 34.2%); (2) daily life challenges, coping experiences, and recommendations (94/547, 17.2%); (3) socializing and connecting (87/547, 15.9%); (4) mental health awareness and stigma prevention initiatives (69/547, 12.6%); (5) behavioral coaching and motivational dialogue (63/547, 11.5%); and (6) personal feelings, thoughts, experiences, and reflections (47/547, 8.6%). Theme 4's discussions extended beyond the OMHC's online environment to involve real-world settings. While all themes generated empowerment processes to different extents across the three population-specific categories, only themes 4 and 5 drove all processes within these categories. Informational support (the most common individual-level process in the discussions) and social awareness support were strongly driven by theme 4, whereas family awareness support and friend awareness support were primarily driven by themes 6 and 2, respectively. Our analysis highlights the multifaceted roles and ability of Twitter OMHC discourse themes in generating varied population-specific empowerment processes supporting all populations in Strategy 1 (WHO's IPCHS framework). This demonstrates Twitter-based OMHCs' potential to foster a comprehensive empowering environment aligned with global standards. This study provides significant insights that can help health care stakeholders develop more tailored OMHC empowering services for diverse populations. Additionally, we present a conceptual framework linking the discourse themes to the three categories of population-specific empowerment processes.

Type 2 diabetes (T2D) is a growing global health crisis, affecting over 537 million people as of 2021. Early prediction remains particularly challenging in low- and middle-income countries due to missing data, class imbalance, and population-specific risk factors. This study presents a four-stage predictive framework— Feature-Weighted Class-Adaptive Generative Imputation Network-Weighted Classifier Aggregation Ensemble (FW-CAGIN-WCAE)—designed to address these limitations. First, Zero-Threshold Feature Removal (ZTFR) is applied to eliminate low-quality variables. Second, missing values are imputed FW-CAGIN, a novel class-aware and feature-weighted GAN model that accounts for both class and feature importance. Third, a performance-weighted ensemble of 15 machine and deep learning algorithms is constructed. Finally, SHAP analysis is used to uncover population-specific risk indicators. The proposed method was evaluated on three benchmark datasets—PIDD, FHGDD, and BDD—and their combinations, using nested five-fold cross-validation. The model achieved a peak AUC of 0.936 ± 0.018 in PIDD-BDD combination and reduced the imputation mean absolute error (MAE) from 0.8028 to 0.0033. It also lowered AUC variability by 36.3% and improved the diagnostic odds ratio (DOR) to 68.4 ± 20.5. SHAP analysis identified as a key predictive feature across both Asian and European populations. These findings demonstrate that the proposed framework offers an accurate, interpretable, and population-sensitive solution for early T2D detection, especially in resource-limited healthcare settings.

Background/Objectives: Traditional predictive equations derived from regression analyses exhibit varying degrees of accuracy in estimating resting energy expenditure (REE). AI models can increase the predictability of such equations, even for population-specific ones. This work aimed to improve the prediction of REE in a dataset of Saudi population-specific equations using suitable AI tools. Methods: The dataset from the previously published Saudi population-specific equation by Almajwal and Abulmeaty (AA) in 2019 was used to develop an artificial neural network (ANN)-based version to better predict REE in the adult population. Anthropometric and body composition parameters were used as proposed features. The proposed hybrid prediction model underwent an extensive two-stage, iterative training process. First, the Extreme Gradient Boosting (XGBoost) model is used to compute feature importance scores. Then, the most prominent features were identified and incorporated into the ANN model. These significant features were used to train the ANN model to capture nonlinear correlations among them and make accurate predictions. Subsequently, XGBoost and Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) are used for their ability to provide a multi-layer abstraction of complex input data. Results: A total of 423 participants (208 male, 215 female) were divided into three non-overlapping sets: training (295, 70%), validation (64, 15%), and testing (64, 15%). The ANN model, combined with XGBoost, helped us to develop two equations: AA_ANN1= 2.47 × BMI + 11.9 × AdjBW + 962.5 and AA_ANN2 = 4.29 × age + 9.4 × fat mass + 15.71 × FFMI + 1289.3, where BMI is Body Mass Index (kg/m2), AdjBW is Adjusted Body Weight (kg), and FFMI is Fat Free Mass Index (kg/m2). The AA_ANN1 presented a Root Mean Square Error (RMSE) of 215 and an accuracy of 66.2%, whereas AA_ANN2 presented a lower RMSE of 193 and a higher accuracy of 71.4%. The ANN model was trained on the top 10 features ranked by XGBoost, achieving an average accuracy of 90.2%. Conclusions: The two new predictive equations, developed using an ANN combined with XGBoost, significantly improved REE prediction accuracy to 90.2%, achieved only with the full ANN model. Future external validation in an independent cohort is essential before clinical application of these equations.