Search Results (156)

Background: The contribution of oral inflammatory conditions to systemic disease burden remains underexplored within multimorbidity frameworks. Emerging evidence suggests that periodontal inflammation may play a role in the clustering of chronic diseases, yet few studies have evaluated this at a population level using robust datasets. The aims of this study were to investigate whether periodontal diseases are associated with Multiple long-term conditions (MLTCs) burden and severity in two population-based cohorts and to examine whether systemic inflammatory biomarkers mediate these associations. Materials and Methods: We analyzed two population-based cohorts: the UK Biobank (UKB; n = 500,612) and the US National Health and Nutrition Examination Survey (NHANES; n = 10,714). MLTCs were defined as the coexistence of ≥2 chronic diseases. Associations between periodontal diseases and MLTCs were assessed using multivariable logistic and multinomial logistic regression. Causal mediation analyses examined the contribution of systemic inflammatory markers. Results: Approximately half of all participants had MLTCs. The prevalence of periodontal diseases was 17.8% in UKB (self-reported symptoms), and 42.3% in NHANES (clinically assessed). Periodontal diseases were independently associated with greater odds of MLTCs in both UKB (OR 1.12; 95% CI 1.10–1.14) and NHANES (OR 1.22; 95% CI 1.09–1.37). Associations were stronger among adults aged ≤ 60 years. A consistent dose-response relationship was observed between periodontal status and the number and severity of chronic conditions, as well as inflammatory-related MLTCs. Mediation analyses suggested partial effects through white blood cell count, neutrophils, and C-reactive protein. Conclusions: Periodontal inflammation is independently associated with greater multimorbidity burden, particularly in younger adults. Systemic inflammation may offer a plausible biological link, and these findings position oral health as an underrecognized and modifiable target in multimorbidity prevention and management frameworks, warranting prospective investigation. Full article

Background/Objectives: Red meat is often treated as a single dietary category in nutritional epidemiology, despite substantial heterogeneity in fat content, quality parameters, and preparation methods. This may obscure meaningful associations with brain aging. We tested whether global brain structural associations differed across lean red meat, fatty red meat, pork, processed pork, and organ meat in a large community-based neuroimaging cohort. Methods: Participants were 45,811 UK Biobank adults aged 50 to 80 years with structural MRI, dietary recall, and covariate data. Dietary intake was assessed using up to five administrations of the Oxford WebQ 24 h recall and averaged across available timepoints. Global cortical thickness, total gray matter volume, and total white matter volume were derived from T1-weighted MRI. Continuous predictors were screened for linear quadratic, or spline form prior to grouped penalized variable selection. Final multivariable models incorporated sensitivity analyses stratified by socioeconomic status (SES) and sex. Results: Associations with global brain structure differed by meat type and fat content. Lean red meat showed the most favorable overall pattern, including modest nonlinear favorable association with global cortical thickness and a positive association with white matter volume among higher-SES participants. Fatty red meat showed unfavorable associations with cortical thickness and gray matter volume. Pork showed an unfavorable association with cortical thickness. Organ meat showed an unfavorable association with gray matter volume and with white matter volume among lower-SES participants. Overall, findings suggested that lean red meat tracked with neutral-to-favorable brain structural correlates, whereas fattier red meat and organ meat generally tracked with less favorable structural outcomes. Conclusions: Meat did not relate to global brain structure as a single uniform exposure. Instead, associations differed meaningfully by meat type, fat content, and socioeconomic context. Treating red meat as a single undifferentiated exposure may flatten biologically relevant heterogeneity and contribute to mixed prior findings. These results support more precise dietary phenotyping in brain-health research and suggest that distinctions in meat quality may matter when evaluating long-term brain aging. Findings should not be interpreted to suggest that unlimited meat intake is broadly health-promoting, even if lean, given the established cardiometabolic and vascular risks associated with excess intake of high-fat or processed meats. Full article

(1) Background: Polyphenols are believed to prevent the development of various diseases by counteracting inflammation and oxidative stress. However, polyphenols are difficult to quantify in foods and show substantial variability in intake and epidemiological evidence on their relationship with lung cancer remains limited. This study explored the association between dietary polyphenols and lung cancer in two prospective cohorts. (2) Methods: Data from the UK Biobank and the Japan Collaborative Cohort Study for Evaluation of Cancer Risk (JACC) Study were used. The Phenol-Explorer^® database was used to assess the total and subtypes of dietary polyphenols. Lung cancer cases were identified using The International Classification of Diseases, 10th revision. Cox proportional hazards models were employed to estimate the association by hazard ratios (HRs) and 95% confidence intervals (CIs) among overall participants and in stratified analysis by smoking status and sex, which were further meta-analyzed. The associations of polyphenol compounds and food sources with lung cancer were also examined. (3) Results: A total of 177,971 and 57,971 participants from the UK Biobank and the JACC Study, respectively, were included. Wines and fruits were identified as the primary dietary sources of stilbenes. Dietary stilbenes were associated with a lower risk of lung cancer in fully adjusted models for both UK Biobank (HR: 0.849, 95% CI: 0.723–0.996) and JACC Study (HR: 0.803, 95% CI: 0.648–0.996), as well as in the meta-analysis (HR: 0.832, 95% CI: 0.732–0.946). (4) Conclusions: Considering pharmacokinetic properties and biological plausibility, our findings suggest that stilbenes may serve as markers of polyphenol-rich foods lowering lung cancer risk. Further biomarker-based and interventional studies are warranted to clarify causality and elucidate the underlying mechanisms. Full article

Background: Although air pollution is increasingly considered an environmental hazard for inflammatory bowel disease (IBD), existing evidence predominantly relies on single-pollutant models that fail to capture mixed exposures, with modifying effects of individual lifestyle and residential environments remaining largely unexplored. Methods: We conducted a prospective cohort study using UK Biobank data, including 323,608 participants followed for incident IBD. Annual mean concentrations of five air pollutants [nitrogen dioxide (NO₂), nitrogen oxides (NO_x), and PM with aerodynamic diameters of ≤2.5, 2.5–10, and ≤10 μm (PM_2.5, PM_2.5–10, PM₁₀)] and greenspace percentage within 300 m and 1000 m buffers were assigned to each participant’s residential address. A healthy lifestyle score was defined by five factors: smoking status, alcohol consumption, physical activity, sleep patterns, and dietary quality. Cox proportional hazards models with quantile g-computation (QGC) were employed to examine associations between single- and mixed-air-pollutant exposures and IBD risk. Stratified analyses were performed by healthy lifestyle, lifestyle score, and greenspace percentage. Results: During the follow-up period, 1649 and 805 participants developed ulcerative colitis (UC) and Crohn’s disease (CD), respectively. Single-pollutant models suggested that exposures to most air pollutants were substantially associated with increased risk of IBD, and the association strengths were more pronounced for UC than for CD. QGC analyses indicated that the hazard ratios (HR) of IBD risk were 1.068 (95%CI: 1.018–1.121) for each one-quantile increase in the air pollutant mixture, with NO₂ weighted as the largest contributor. High physical activity was significantly linked to an attenuated UC-pollutant mixture association. Conclusions: This study found that exposure to an air pollutant mixture was associated with increased risk of IBD, especially for UC, with NO₂ contributing the largest effect size. The certain attenuated air pollution effects of healthy lifestyles and residential greenspaces underscore the need for integrated public health strategies with environmental management. Full article

Autophagy-mitophagy pathways are essential for regulating immune homeostasis. However, their contribution to population-level chronic low-grade systemic inflammation (SI) remains unclear. The objective was to investigate the association between variation in the genes related to the autophagy-mitophagy pathways and SI, and to examine whether lifestyle factors modify this relationship. We conducted genome-wide association studies and gene-set enrichment analyses using data from the Korean Genome and Epidemiology Study (KoGES, n = 28,102) and UK Biobank (UKBB, n = 343,892). SI was defined as an elevated white blood cell count or high-sensitivity C-reactive protein. Using Core Longevity State Vectors (CLSVs)—gene sets representing immune-longevity pathways derived from comparative transcriptomic analysis—we tested six pathways and constructed a weighted genetic risk score (GRS) from significant variants. Gene–lifestyle interactions were examined with respect to major dietary and lifestyle factors. Among six CLSVs, only CLSV-2 (mitophagy and autophagy) showed a significant association with SI (β = 0.425, p = 0.008). Six single nucleotide polymorphisms (SNPs) in autophagy-mitophagy genes (INPP5D, ATG16L1, ATG7, AP3S1, OPTN, and VPS33A) were associated with SI in KoGES (p < 5 × 10⁻⁵), and ten SNPs (genes selected in KoGES plus RAB7A, ATG12, VPS33A, BECN1) reached genome-wide significance in UKBB (p < 5 × 10⁻⁸). A higher GRS was associated with increased SI in both cohorts and was strongly associated with metabolic syndrome (MetS, OR = 1.91 in KoGES; OR = 1.62 in UKBB). SI was characterized by neutrophilia with relative lymphopenia. In UKBB, significant gene–lifestyle interactions were observed for diet, physical activity, smoking, and alcohol (p < 0.01). Favorable lifestyle factors reduced SI most effectively in individuals with protective genotypes. Among individuals with a high vegetable/fruit intake, SI prevalence was 35%, 36%, and 38% in the negative-, zero-, and positive-GRS groups, respectively, compared with 36%, 45%, and 48% in the low-intake groups. In conclusion, genetic variations in autophagy-mitophagy pathways specifically influence SI. Genetic predisposition substantially modifies the benefits of lifestyle, underscoring the importance of integrating genetic and lifestyle factors in understanding SI susceptibility. Full article

Type 1 diabetes (T1D) is an autoimmune disease with a strong genetic component (~70% heritability). Early identification of individuals at risk is crucial for early intervention or risk assessment. Although polygenic risk scores (PRS) have shown promise in risk assessment, most current approaches remain constrained by linear assumptions and limited generalizability. We aimed to develop a neural network-driven classifier using T1D-associated single nucleotide polymorphisms (SNPs). In addition, we explored the inclusion of an entropy-derived feature as a complementary variable, representing the degree of genetic variability within an individual’s genotype profile across the 67 T1D-associated SNPs, to evaluate its potential additive contribution to the model performance. We analyzed genotype data from 11,909 individuals in the UK BioBank (546 T1D cases and 11,363 controls). Sixty-seven well-known SNPs associated with T1D were utilized as inputs to the model, using two distinct allele-encoding strategies. A feed-forward neural network was evaluated under varying case–control ratios through five-fold cross-validation. Performance was assessed using the area under the receiver operating characteristic curve (AUC) on a held-out test set and on an external European cohort as a validation cohort. Across five-fold cross-validation, the best configuration achieved a median AUC of 0.903. On the held-out UK Biobank test set, the model generalized well, with an AUC of 0.8889 (95% CI: 0.8516–0.9262). A probability-based risk framework, constructed using five risk groups (“very low”, “low”, “intermediate”, “high”, and “very high” risk), yielded a negative predictive value (NPV) of 98.9% for the “very low” risk group and a Positive Predicted Value (PPV) of 61.9% with a specificity of 97.3% for the “very high” risk group, assuming a 10% T1D prevalence. External validation in the German Diabetes Study reproduced clear case–control separation; for individuals with recent onset diabetes and glutamic acid decarboxylase antibodies (GADA+) vs. controls, specificity reached 91.9% in the “high” risk group (PPV of 94.3%) and 97.6% in the “very high” risk group (PPV of 95.7%). The proposed neural network reliably predicts T1D genetic risk using a compact SNP panel of 67 SNPs and maintains accuracy in both internal and external European cohorts. Its probabilistic output enables clinically interpretable risk thresholds, while entropy features contributed modestly to performance. These results demonstrate that a neural network-based approach achieves discriminative performance that is comparable to established T1D genetic risk models, while offering flexible probability-based risk stratification and architectural extensibility for future integration of additional features. Full article

Periodontitis is a chronic infectious disease characterized by the destruction of the tooth-supporting tissues, including the gingiva, periodontal ligament, and alveolar bone, which may ultimately lead to tooth loss. However, blood-based biomarkers reflecting systemic inflammation in periodontitis remain poorly defined. We analyzed plasma proteomic data from the UK Biobank using Olink Explore proteomics to identify systemic protein signatures distinguishing chronic periodontitis patients (n = 90) from healthy controls (n = 2234). Among 2151 proteins passing quality control, 29 proteins showed significant differential expression (FDR < 1.0 × 10⁻⁵). Growth differentiation factor 15 (GDF15) exhibited the strongest upregulation (mean NPX: −0.183 to 0.157, effect size = 0.337, FDR = 2.82 × 10⁻¹²), followed by N-terminal pro-B-type natriuretic peptide (NT-proBNP) (effect size = 0.594), Interleukin-6 (IL-6) (effect size = 0.450), and Insulin-like growth factor binding protein-(4IGFBP4) (effect size = 0.269). Multiple TNF receptor superfamily members (TNFRSF1A/1B, TNFRSF10A/10B) and proteins involved in extracellular matrix remodeling (COL6A3, ADAM12) and vascular stress (ADM) were significantly elevated. In contrast, EGFR and DNER showed decreased expression. Protein–protein interaction network analysis revealed IL-6 as a central hub protein forming a tightly interconnected cluster with TNF receptor family members. These findings indicate systemic plasma protein profiles associated with chronic periodontitis within this population-based cohort. The identified proteins may provide a basis for future evaluation of blood-based biomarkers for chronic periodontitis, pending further validation. Full article

Background: Cholesterol imbalances and elevated blood pressure (BP) are closely interrelated risk factors for cardiovascular disease (CVD) and are subject to genetic influences. We sought to identify novel associations between candidate genetic coding variants and CVD traits in our isolated study cohort and validate them in a general population cohort. Methods: We leveraged the population genetic features of the Norfolk Island Health Study (NIHS, n = 601), to identify candidate functional variants which were analysed for association with CVD and metabolic syndrome traits. We followed up suggestive variant-trait associations in the 2022 release of UK Biobank whole exome data (n = 200,625). Results: We identified a novel ten-base-pair in-frame missense multi-nucleotide variant (MNV), tagged by rs35724886, in the lipid metabolism gene ACOT4, which was associated with cholesterol levels and blood pressure. The MNV was associated with a lower incidence of ‘elevated BP’—systolic BP ≥ 130 mmHg or diastolic BP ≥ 80 mmHg—(OR: 0.70; 95% CI: 0.51, 0.97; p = 0.03), and higher total cholesterol levels (β = 0.08; p = 0.04) in the NIHS. Validation in the UK Biobank revealed consistent associations between the MNV (proxied by rs35725886) and lower incidence of ‘elevated BP’ (p = 0.0001), higher total cholesterol (p = 0.01), and reduced use of medication for managing blood pressure (p = 1.8 × 10⁻⁶) and cholesterol (p = 0.002). Structural modelling and in-silico predictions suggested that the MNV introduced destabilising changes in the ACOT4 protein, likely influencing peroxisomal lipid metabolism pathways critical to CVD risk. Conclusions: This study identified a coding MNV with potential implications for understanding the genetic regulation of lipid metabolism and its impact on cardiovascular health. Full article

Background/Objectives: Pancreatic cancer is one of the most lethal malignancies, with poor survival and few established modifiable risk factors. While Helicobacter pylori (H. pylori) infection is a known cause of gastric cancer, its role in pancreatic cancer remains unclear, with inconsistent observational evidence. Methods: We applied two-sample Mendelian randomisation (2SMR) to assess the causal effect of H. pylori infection on pancreatic cancer risk. Genetic instruments were derived from GWAS data on anti-H. pylori IgG levels in the ALSPAC cohort (n = 4638). Outcomes were pancreatic cancer cases from UK Biobank (936 cases, 400,294 controls) and a combined dataset including UK Biobank, FinnGen, and MVP (5979 cases, 1,234,860 controls). Inverse-variance weighted (IVW) MR was the primary method, supported by MR-Egger, weighted median/mode and MR-PRESSO, with sensitivity analyses for pleiotropy. Results: No significant causal association was observed. IVW ORs were 1.039 (95% CI: 0.846–1.440, p = 0.466) for UK Biobank and 1.077 (95% CI: 0.962–1.206, p = 0.197) for the combined dataset. All complementary methods yielded null results, with no strong evidence of pleiotropy. Conclusions: This 2SMR study found no evidence that H. pylori infection causally increases pancreatic cancer risk. Larger studies with refined exposure measures are warranted. Full article

Neuropsychiatric disorders constitute an escalating public health challenge worldwide, with growing evidence suggesting that environmental factors like air pollution may contribute substantially. This prospective cohort study investigated the associations between long-term exposure to fine particulate matter (PM_2.5) and nitrogen oxides (NO_x) and the progression of eight neuropsychiatric disorders among 502,356 UK Biobank participants. Using multi-state models, we analyzed three distinct trajectory stages: stage 1 (transition from baseline healthy status to PHQ-4-positive mood disorders), stage 2 (transition from baseline to ICD-10-diagnosed disorders), and stage 3 (progression from PHQ-4-positive status to clinical diagnosis). Nonlinear exposure–response relationships were subsequently characterized using restricted cubic spline (RCS) regression models. The findings indicated that exposure to both PM_2.5 and NO_x per IQR increase was strongly associated with stage 1, with a corresponding hazard ratio of 1.28 (95% CI: 1.27–1.30) and 1.10 (95% CI: 1.09–1.11). Across the three stages, the risk pattern evolved from being broadly significant to one characterized by disease-specific significance. Alzheimer’s disease was consistently identified as the condition with the strongest association and highest risk linked to air pollution. Specifically, hazard ratios across stages were as follows: 1.08–1.13 in stage 2 and 1.14–1.20 in stage 3 for PM_2.5; and 1.04–1.05 in stage 2 and 1.05–1.10 in stage 3 for NO_x. Subgroup analyses identified heightened vulnerability in females (particularly subjects with depression, Parkinson’s disease, and sleep disorders), younger individuals, and socioeconomically deprived populations. These findings underscore the importance of considering air pollution as a modifiable risk factor in the prevention of neuropsychiatric disorders. Full article

Background: Obesity is a risk factor for breast cancer mortality in postmenopausal women. However, it remains unclear which specific components of adipose tissue and skeletal muscle are associated with risk. This study assessed the associations between MRI-assessed adiposity, skeletal mass, and breast cancer risk in a population-based cohort. Methods: We analyzed data from 15,669 postmenopausal women in the UK Biobank who underwent MRI for body composition assessment. Age- and multivariable-adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated using Cox proportional-hazards regression to evaluate the associations between body composition and breast cancer risk, adjusting for relevant confounders. Sensitivity analyses were conducted by excluding breast cancer cases diagnosed within 2 years of the MRI scan. To explore nonlinear relationships, we applied restricted cubic splines with three knots to model associations between visceral adipose tissue (VAT), muscle-fat infiltration (MFI), and breast cancer risk. Results: The mean age of participants was 58.6 years (SD = 5.2; range = 40–69). Higher VAT was significantly associated with increased breast cancer risk (3rd vs. 1st tertile aHR = 1.24, 95% CI: 1.10–1.45). Elevated MFI was also linked with greater risk (3rd vs. 1st tertile aHR = 1.53, 95% CI: 1.25–1.87). These associations persisted after excluding early cancer cases. We observed a J-shaped relationship between VAT, MFI, and breast cancer risk. Conclusions: Higher levels of VAT and MFI are associated with elevated breast cancer risk in postmenopausal women, suggesting that imaging-derived body composition measures may enhance risk prediction and inform prevention strategies. Full article

Background/Objectives: Evidence of associations between serum calcium, phosphate, and vitamin D concentrations and development of arrhythmias is limited and inconsistent. This study aimed to investigate the quantification and characterization of the dose–response relationship between serum mineral levels and arrhythmia subtypes in a general population cohort. Methods: We included 348,094 UK Biobank participants without prevalent cardiovascular disease or arrhythmias in whom serum calcium, phosphate, and vitamin D concentrations were available. Serum calcium and phosphate levels were multiplied to derive calcium–phosphate product. Incident outcomes were all arrhythmias and subtypes: AF, other (non-AF) arrhythmias, bradyarrhythmia, and ventricular arrhythmias. Cox proportional hazards regression models were conducted. Results: Compared with the lowest quartile, participants in the highest quartile of serum calcium had a significantly lower risk of all arrhythmias (hazard ratio [HR] 0.92, 95% confidence interval [CI], 0.88–0.95), particularly AF (0.89, 0.85–0.93). Negative associations were found between serum vitamin D and arrhythmias, especially ventricular arrhythmias (HR 0.68, 95% CI 0.57–0.81). Higher serum phosphate and calcium–phosphate product were associated with higher risk of all outcomes. Restricted cubic splines revealed nonlinear associations for calcium and vitamin D but linear associations for phosphate and calcium–phosphate product. The associations were not modified by kidney function. Conclusions: Lower calcium and vitamin D concentrations and higher serum phosphate and calcium–phosphate product were associated with increased risk of arrhythmias and presented a dose–response manner. These findings may indicate that maintaining optimal serum calcium, phosphate, and vitamin D may be important for reducing arrhythmic risk, emphasizing the need for targeted monitoring and management, particularly in high-risk populations. Full article

Background: Heritable breast cancer (BC) predisposition is strongly influenced by high-penetrance genes such as BRCA1 and BRCA2, but many moderate- and low-penetrance genes remain poorly characterized. Although over 100 loci have been reported, the causal genes often include false positives or uncertain associations. Methods: We applied a gene-centric, integrative approach to multi-ethnic genomic datasets, including the UK Biobank (UKB) and FinnGen (FG). We assessed consistency across multiple GWAS in Open Targets (OT) and additional complementary genetic association approaches, including ExPheWAS, TWAS, and PWAS. Collapsing variant-level effects to a gene-level view enhanced confidence and reaffirmed contributions from genes such as BRCA1, BRCA2, PALB2, CHEK2, and other DNA repair genes. Results: Using this integrative framework, we identified 38 high-confidence BC predisposition genes, including 8 previously reported drivers, 13 supported by multiple lines of evidence, and additional candidates (e.g., APOBEC3A, TNS1, PEX14) with emerging evidence. PWAS revealed several genes with potential recessive effects often missed by standard GWAS. Multi-cohort replication showed robust findings in European ancestry populations, while transferability to other populations was more limited. Conclusions: This work demonstrates the value of a gene-centric, integrative framework for prioritizing high-confidence BC predisposition genes, highlighting associated cellular pathways, and uncovering new candidates for further functional study, providing a reliable foundation for future research. Full article

Background: The current definition of metabolic dysfunction-associated steatotic liver disease (MASLD) relies on a classical assessment of steatosis via liver biopsy, with grades S0–S3 (5–100% fat) potentially underestimating low-grade steatosis. We propose a new, more sensitive classification based on magnetic resonance imaging–proton-density fat fraction (MRI-PDFF), splitting the existing S0 and S1 grades into three classes: new-S0, very early S1 (S1A), and later S1 (S1B). We aimed to determine whether these early S1A/S1B phenotypes differed clinically or biologically from the new-S0 grade using large population cohorts. Methods: We assessed the prevalence of the new MRI-PDFF—based grades in 29,252 healthy participants from the UK Biobank discovery cohort, 286 outpatients with type 2 diabetes, and in six previously published databases (N = 149,212) using SteatoTest-2 or a proxy. We performed a multimodal assessment of steatosis using longitudinal MRI-PDFF and liver biopsy data (N = 286). Models were used to adjust for phenotypes and overall mortality, controlling for age, sex, and cardiometabolic factors. Results: In the UK Biobank cohort, the prevalences of the new-S0, S1A, and S1B grades were 54%, 26%, and 17%, respectively. Grades S1A and new-S0 were most discriminated by triglycerides (odds ratio [OR]: 2.40, 95% confidence interval [CI]: 2.07–2.77, p < 0.00001) and body mass index (BMI; OR: 1.30, 95% CI: 1.27–1.33, p < 0.00001), and grades S1A and S1B were most discriminated by triglycerides, BMI, systolic blood pressure (SBP), and glycated hemoglobin (HbA1c). Adjusting for age, sex, SBP, BMI, HbA1c, triglycerides, and high-density lipoprotein–cholesterol) revealed significantly lower 15-year survival in the high-risk group (97.2%, 95% CI: 96.9–97.7) versus the low-risk (99.4%, 95% CI: 99.2–99.6) group (p < 0.00001). Conclusions: The early trajectory of liver steatosis is undetectable in 26% of middle-aged adults. This early steatosis phenotype differs clinically and biologically from the new-S0 grade in large population cohorts. Full article

Background: Colorectal cancer (CRC) is a leading cause of cancer death, and the incidence and mortality rates among young adults are rising. Although a subset of CRC cases presents with a family history, suggesting a hereditary component, the specific genetic underpinnings remain incompletely understood, particularly in early-onset CRC (EOCRC). This study aimed to discover novel risk genes for EOCRC using exome sequencing and gene-based rare variant burden testing. Methods: Our cohort consisted of 212 European-ancestry cases (174 diagnosed with CRC and 38 with significant polyps) from the South Australian Young Onset Colorectal Polyp and Cancer Study (SAYO) and 31,699 unaffected controls from the Simons Foundation Powering Autism Research for Knowledge (SPARK) cohort. After filtering for ancestry, relatedness, variant quality, and population allele frequency, we performed gene-set and individual-gene burden tests using predicted deleterious missense and loss-of-function variants. Statistical significance was assessed using permutation-corrected binomial testing. An independent validation was conducted in the UK Biobank. Results: Loss-of-function variants in known CRC tumor suppressor genes were significantly enriched in SAYO cases. Gene-level analyses identified MEIKIN as a novel EOCRC susceptibility candidate (p value = 1.0 × 10⁻⁷), with supporting enrichment of deleterious missense and loss-of-function variants in distal colon cancer cases from the UK Biobank. Additional genes (STK25, PGBD4, DIRAS3, ATG3, RPS6KA4, and DDX42) demonstrated borderline significance, implicating pathways related to kinetochore assembly, autophagy regulation, and immune signaling. Both predicted gain-of-function and loss-of-function variants contributed to the EOCRC risk, supporting heterogeneous mechanisms of CRC pathogenesis. Conclusions: This study identified novel candidate risk genes for EOCRC, underscoring the role of rare variants and expanding our understanding of the genetic architecture of CRC. Future studies should include functional validation and replication studies on other ancestries to confirm and extend these results. Full article