Search Results (502)

Objectives: Individuals with type 2 diabetes (T2D) remain at high cardiovascular disease (CVD) risk. The pan-immune-inflammation value (PIV) integrates circulating neutrophils, monocytes, platelets, and lymphocytes but does not capture albumin-related nutritional and inflammatory reserve. We investigated associations between the PIV-to-albumin ratio (PIVA) and incident CVD, cardiovascular mortality, and disease progression in individuals with T2D. Methods: This prospective study included 15,355 UK Biobank participants with T2D and no baseline CVD. PIVA was calculated from peripheral blood cell counts and serum albumin and was natural log-transformed. Fine–Gray competing-risk and cause-specific Cox models were used to evaluate incident CVD and cardiovascular mortality. Multi-state models characterized transitions from T2D to CVD and death. We also examined nonlinear associations, renal biomarker mediation, joint associations with the CVD polygenic risk score and triglyceride–glucose index, sensitivity analyses, and external validation of cardiovascular mortality in the National Health and Nutrition Examination Survey. Results: During median follow-ups of 12.94 years for incident CVD and 14.49 years for cardiovascular mortality, 4829 incident CVD events and 514 cardiovascular deaths occurred. Each 1-unit increase in lnPIVA was associated with higher risks of incident CVD (subdistribution hazard ratio [sHR], 1.140; 95% confidence interval [CI], 1.088–1.194) and cardiovascular mortality (sHR, 1.449; 95% CI, 1.247–1.684). Associations were nonlinear. Higher lnPIVA was also associated with transitions from T2D to CVD, from T2D directly to cardiovascular death, and from incident CVD to cardiovascular death. Cystatin C explained a larger proportion of these associations than creatinine. Elevated lnPIVA identified excess cardiovascular risk across strata of genetic susceptibility and insulin resistance, and the findings were generally supported by sensitivity and external validation analyses. Conclusions: lnPIVA was associated with incident CVD, cardiovascular mortality, and adverse cardiovascular transitions in individuals with T2D. As an immune-inflammatory and albumin-based index, PIVA may help identify high-risk individuals beyond conventional cardiometabolic and genetic risk profiles. Full article

Background: Research in behavioral genetics has demonstrated that genetic factors significantly contribute to individual differences in behavior, while environmental exposures shape gene expression through epigenetic mechanisms. This interaction is also relevant to the development of antisocial behavior and psychopathic traits. One of the first evidence of gene–environment interaction was the association between low-activity variants of the MAOA gene, childhood maltreatment, and increased risk of antisocial behavior [¹]. Similarly, our research in incarcerated populations showed that adverse paternal parenting is associated with higher levels of psychopathy and the HTR1B rs13212041 TT genotype appears to modulate the individual susceptibility to negative experiences [²]. Single genetic variants, however, exert only modest effects and current evidence supports a polygenic model in which multiple genetic factors interact with environmental adversity to influence neurodevelopment and behavioral outcomes. Using a genome-wide/endophenotype informed analysis, for example, we identified novel gene–environment interactions as risk factors for psychopathy, involving three independent genetic loci in interaction with paternal maltreatment, which were previously associated with disruptive behavior, temperament, and neuroticism [³]. More recently, we also evaluated whether machine-learning models, integrating behavioral, environmental, and genetic variables, could be helpful to predict psychopathic traits. Methods: We compared logistic regression, random forest, support vector machine, XGBoost, and multilayer perceptron. Results: Support vector machine showed the highest accuracy for predicting Psychopathy Check List-Revised (PCL-R) Factor 2 (antisocial lifestyle). Feature-importance analyses identified impulsivity (BIS-11), empathy (IRI), childhood maltreatment (MOPS), and 12 SNPs as the most informative predictors. Notably, removing genetic variables or MOPS scores substantially reduced the model accuracy, indicating that both genetic and environmental information meaningfully contributed to prediction of antisocial behavior. Conclusions: These findings confirm that genetic influences are neither deterministic nor sufficient to explain criminal behavior but may contribute to interindividual differences in vulnerability, particularly through their interaction with environmental and psychosocial factors. In forensic psychiatry, the integration of genetic and environmental information into behavioral assessment may provide additional objective correlates that complement, rather than replace, traditional clinical and psychosocial evaluations. Such an integrated approach could potentially contribute to a more comprehensive understanding of individual vulnerability and behavioral trajectories. However, the use of genetic information in assessments of criminal responsibility should be approached with caution and proven expertise, given the complex, multifactorial nature of antisocial and criminal behavior. Full article

Genetic background strongly influences the penetrance and phenotypic expression of SMAD4-associated intestinal tumorigenesis, yet the underlying modifier loci remain poorly defined. To investigate the genetic architecture of tumor susceptibility and systemic physiology, we analyzed 260 Smad4⁺/⁻ × Collaborative Cross (CC)-F1 mice derived from 14 CC lines using 11 quantitative traits, including longitudinal body weight, adjusted organ weights, and intestinal polyp counts across anatomical regions. Principal component analysis reduced these traits to seven components explaining more than 85% of total phenotypic variance. PC1 represented a tumor burden–metabolic axis, whereas PC2 captured systemic organ-physiology variation. Genome-wide QTL mapping of principal component scores identified significant loci for PC1 on chromosomes 1 and 4 and a female-specific locus for PC5 on chromosome 10, with additional suggestive loci supporting a polygenic architecture. Founder-effect analysis revealed strong contributions from CAST/EiJ, 129S1/SvImJ, and WSB/EiJ haplotypes. Candidate gene annotation identified biologically relevant coding and noncoding loci, including Galnt7 and Galntl6, as well as regulatory regions with potential enhancer activity. Together, these findings indicate that intestinal tumor susceptibility in Smad4⁺/⁻ × CC-F1 mice is influenced by multiple coding and regulatory genetic modifiers with sex-dependent effects. This study demonstrates that integrating multivariate phenotyping with systems genetics analyses provides an effective framework for identifying the complex genetic networks underlying intestinal tumorigenesis and associated systemic physiological variation in genetically diverse mouse populations. Full article

Conventional milling separates the wheat germ and bran from the endosperm to obtain white semolina; although these fractions retain recognized nutritional value, a substantial share is still diverted to feed and other comparatively low-value uses rather than into food for direct human consumption. In this Perspective we ask whether recovering a larger share of these fractions—through a controlled industrial sequence of selection, de-oiling and physical fractionation—can be turned into a functional ingredient, commercialized as “altograno”, that improves the health profile of an ordinary pasta without compromising palatability or shelf stability. To address this objective we proceed in a fixed analytical sequence: what the product is and what a habitual portion actually delivers; a nutrient-by-nutrient appraisal graded by level of evidence; the biological mechanisms from the intestinal lumen to the hepatocyte; and only then the clinical endpoint. The evidence base integrates two complementary studies of one product line from the Casillo Next Gen Food chain: a full nutritional, microbiological and gastrointestinal characterization in healthy volunteers, and a double-blind randomized controlled trial with in vitro hepatocyte evidence in patients with metabolic syndrome (MetS), with and without major psychiatric disorders (MPDs). Because the clinically tested product (67% semolina, 27% de-oiled wheat germ, 6% microencapsulated wheat-germ oil) shares the same nominal formulation as the germ-plus-oil pasta (EP3) of the characterization study—although the measured nutritional values reported in the two papers differ, so the two products cannot be regarded as analytically identical—the two datasets can be read together, with due caution, across kernel processing, composition, hepatocellular mechanism and gut-microbiota switching to a clinical endpoint: a ~13.5% fall in non-HDL cholesterol, roughly 2.5-fold greater than with conventional pasta, with no statistically significant interaction with the polygenic risk score detected, and paralleled by normalized hepatocyte lipid loading in vitro. We argue that de-oiling is best understood as a deliberate technological optimization—trading a portion of labile lipids for stability and standardization while recovering the bioactive oil for protected re-addition—and that this evidence, while still preliminary on several fronts, supports dedicated, mechanistically instrumented clinical development of upcycled-kernel staples rather than establishing these products as proven therapeutic tools. Full article

This exploratory study evaluated whether a polygenic risk score (PRS) derived from a European genome-wide association study is transferable to a Japanese cohort, for both susceptibility to and severity of adult idiopathic hydrocele testis. Surgically confirmed hydrocele cases were compared with two independently ascertained control groups: prostate cancer patients with intraoperatively confirmed absence of hydrocele fluid (Control 1), and urolithiasis patients without malignancy (Stone Control). Multivariable logistic regression evaluated disease occurrence; multiple linear regression restricted to hydrocele cases evaluated the association between PRS and fluid volume. A higher PRS was associated with increased susceptibility to hydrocele when cases were compared with Stone Controls (strictest threshold: odds ratio 1.98, 95% CI 1.01–3.88, p = 0.048; AUC 0.733), though this was not observed using Control 1. Among cases, PRS showed a consistent inverse association with fluid volume across all three thresholds tested (standardized β = −0.54, p = 0.010 at ≥10 mL). Given the modest sample size and limited statistical power, these findings should be regarded as preliminary and hypothesis-generating. They nonetheless suggest that genetic susceptibility to hydrocele and its severity may show different associations with polygenic risk, a pattern requiring confirmation in larger, independent, ideally East Asian-derived cohorts. Full article

Background/Objectives: The physiological adaptations required to sustain elite football performance are influenced by both genetic background and dynamic biochemical responses, although their interaction across a full competitive season remains insufficiently characterized. This study aimed to examine the association between polygenic profiles and longitudinal biochemical adaptations in professional football players. Methods: Forty male professional football players competing in the Spanish league were monitored across two consecutive seasons. Blood samples were collected at six time points representing different phases of the competitive cycle. Biomarkers related to muscle metabolism, iron status, and hepatic function were analyzed. Polygenic profiles were calculated using Total Genotype Scores (TGS) for muscle performance, hepatic resilience, and metabolic efficiency. Associations were initially explored using Pearson correlations and subsequently evaluated using linear mixed-effects models accounting for repeated measurements within subjects. Results: Exploratory correlation analyses identified several associations between polygenic profiles and biochemical markers. Muscle performance TGS was inversely associated with serum iron (r = −0.36, p = 0.017) and positively associated with CK (r = 0.32, p = 0.041), Hb (r = 0.29, p = 0.046), and Hct (r = 0.33, p = 0.024). Hepatic resilience TGS showed inverse associations with ALT (r = −0.39, p = 0.012), urea (r = −0.51, p = 0.011), and BUN (r = −0.51, p = 0.011). Metabolic efficiency TGS was negatively associated with AST (r = −0.43, p = 0.044), ALT (r = −0.33, p = 0.025), and GGT across multiple time points (p = 0.001–0.013). However, although several nominal associations emerged in linear mixed-effects models accounting for repeated measurements, none remained statistically significant after false discovery rate correction. These findings should therefore be interpreted as exploratory and hypothesis-generating. Conclusions: Polygenic profiles may be associated with inter-individual variability in biochemical adaptations throughout a competitive season. These findings suggest the integration of genomic and biochemical data in precision athlete monitoring, while highlighting causal relationships and predictive applications require further investigation. Full article

Dementia with Lewy Bodies (DLB) is the most common neurodegenerative dementia after Alzheimer’s disease (AD), but it remains challenging to diagnose due to overlapping symptoms and mixed pathologies. This pilot study tested whether DLB has different metabolic blood proteomic profiles compared to AD and controls. Serum was analysed from people with DLB (n = 20), a group with AD (either with Alzheimer’s disease dementia or Mild Cognitive Impairment with a positive amyloid positron emission tomography scan (MCI+/AD) (n = 15), and similarly aged controls (n = 15) using the Olink Metabolism panel encompassing 92 proteins. Six proteins (PILRB, LRIG1, NECTIN2, TINAGL1, SSC4D, and FKBP4) were significantly different in DLB compared with the controls, and one protein (SERPINB8) was differentially expressed when compared with MCI+/AD. Receiver operating characteristic curves for biologically relevant proteins that have previously established roles in neurodegenerative and cognitive properties showed that RNASE3 levels could differentiate DLB from MCI+/AD (area under the curve (AUC) = 0.717, sensitivity = 0.850, and specificity = 0.600). A multimarker model incorporating RNASE3 with phosphorylated tau 217 (pTau217) and polygenic risk scores for LBD achieved improved accuracy in discriminating DLB from MCI+/AD (AUC = 0.963, sensitivity = 1.000, and specificity = 0.900). Pathway analyses revealed dysregulation in cortisol signalling, inflammation resolution, and ErbB4-mediated neuroplasticity, which point towards peripheral protein alterations related to the adaptation to stress, immune regulation, and synaptic integrity in DLB. The present exploratory study highlights potential pathophysiological mechanisms implicated in DLB, suggesting that a multimodal biomarker panel may perform better compared to single proteins. Considering the small sample sizes, the findings will need to be replicated in larger cohorts. Full article

It is well known that genetic variants contribute to cellular sensitivity to chemotherapeutic agents and ionizing radiation (IR). The aim of this study was to identify single nucleotide polymorphisms (SNPs) and genes associated with the spectrum of normal cellular sensitivity of lymphoblastoid cell lines (LCLs) towards ionizing radiation and mitomycin C (MMC). In the first step, we determined the viability of LCLs established from male participants of the Berlin Aging Study II (BASE-II) aged ≥62 years following treatments with increasing doses of IR (n = 137 cell lines) or MMC (n = 140 cell lines) using the alamarBlue assay. Results from intra-experimental triplicates and three independent experiments for each cell line and treatment were used to calculate the area under the curves (AUCs) representing the specific sensitivity to IR and MMC of each LCL. The data from these experiments were subsequently used as outcomes in genome-wide association studies (GWASs). In addition, we calculated polygenic risk scores (PGS) from UK Biobank GWAS results for four cancer-related phenotypes and assessed the extent to which the variance in the IR and MMC sensitivity is explained by these PGS. The GWAS analyses revealed one variant, rs74728080, located in CDH13 on chromosome 16, to show genome-wide significant (p < 5 × 10⁻⁸, ß = 2.81) association with cellular viability after treatment with IR. In the GWAS on MMC sensitivity the most interesting signal was elicited by SNP rs113978558 in an intron of the PLD5 gene on chromosome 1 (p = 9.232 × 10⁻⁸; ß = 1.44). Several other SNPs with statistically suggestive (i.e., p < 1 × 10⁻⁵) evidence of association with IR or MMC sensitivity were identified. PGS calculations from GWAS of four cancer-related traits in UKB explained ~5% and ~3% of phenotypic variance in IR- and MMC-induced cell viability, respectively. The genome-wide significant association of rs74728080 with IR sensitivity and the location of this variant in CDH13 is interesting and functionally highly plausible given its known involvement in oxidative stress response and function as a tumor suppressor. Taken together, our novel data suggest that CDH13 may be genuinely involved in regulating cellular IR sensitivity. Full article

Background: The directional relationship between epigenetic age acceleration (EAA) and musculoskeletal disease remains unresolved. This study integrated bidirectional Mendelian randomization (MR) with multi-layer genomic evidence to evaluate directionality, shared genetic architecture, and robustness to instrument definition. Methods: Four EAA clocks (IEAA, PhenoAA, HannumAA, and GrimAA) and ten musculoskeletal phenotypes were analyzed in a 10 × 4 bidirectional two-sample MR design. EAA instruments underwent GRCh37 functional annotation, genome-wide-significant external-association screening for the index variants and European linkage-disequilibrium proxies, pair-specific Steiger filtering, and conservative Set A/B/C sensitivity analyses. The juvenile-arthritis reverse models underwent instrument-flow reconstruction, strength assessment, liability-scale directionality testing, and minimum-detectable-effect analysis. Additional analyses comprised LD score regression (LDSC), PLACO+ cross-trait locus mapping, Bayesian colocalization, multivariable MR (MVMR) with exact-SNP matched univariable comparators, and integrated evidence synthesis. Results: Forward MR yielded two nominal HannumAA associations. The inverse HannumAA–spondyloarthritis estimate remained directionally consistent across the original, Steiger-filtered, and conservative external-association-filtered sets, whereas the HannumAA–pain-in-thoracic-spine estimate lost nominal significance in the conservative set; no forward result survived correction across 40 tests. GrimAA forward estimates were sensitive to use of the fallback instrument threshold. Reverse MR identified ten nominal associations. For juvenile arthritis, three harmonized instruments had F statistics of 51.25–102.35; liability-scale Steiger comparisons supported the tested direction under all 16 outcome-by-prevalence combinations, although the 788-case discovery GWAS and possible winner’s curse remained important limitations. LDSC identified FDR-significant positive genetic correlations of GrimAA with hip osteoarthritis (r_g = 0.267, p = 8.49 × 10⁻⁵, q = 0.0019) and knee osteoarthritis (r_g = 0.269, p = 9.52 × 10⁻⁵, q = 0.0019). PLACO+ identified 738 genome-wide-significant cross-trait variants and 65 independent loci; six of 37 evaluable loci showed strong colocalization. Of 96 MVMR models, 43 had primary-exposure conditional F ≥ 10, and 32 also had candidate-trait conditional F ≥ 10. After exact-SNP matching, the 43 primary-strength models were operationally classified as 35 partially attenuated and eight independent-signal models, with no fully attenuated model; no adjusted association survived multiplicity correction. Conclusions: The results support a prioritized genomic map with substantial instrument- and model-specific uncertainty. Disease-to-clock signals were richer than clock-to-disease signals, GrimAA shared polygenic architecture with osteoarthritis, and selected loci showed strong shared-variant evidence, while the MR and MVMR findings remained unsuitable for definitive causal or mediation claims. Full article

Background/Objectives: Substance use behaviors share a complex, overlapping polygenic architecture, yet translating genome-wide association study (GWAS) findings into actionable biological mechanisms remains challenging. This study aimed to characterize the genetic architecture of five substance use traits (alcohol consumption, alcohol dependence, nicotine use, illicit drug use, and behavioral disinhibition) and identify shared and distinct gene expression signatures within the neural circuits governing addiction. Methods: We reanalyzed 7188 individuals from the Minnesota Center for Twin and Family Research (MCTFR) cohort utilizing longitudinal composite phenotypes spanning five substance-use domains and general behavioral disinhibition. Post-QC, 6874 individuals were retained for downstream analysis. Following genomic imputation and linear mixed model GWAS (GEMMA), we utilized the SNipar framework to partition polygenic risk scores (PRS) into direct and indirect genetic effects, investigating intergenerational shifts in genetic penetrance and effects of assortative mating. Finally, we integrated our summary statistics with brain tissue reference panels to perform a transcriptome-wide association study (TWAS) modeling genetically regulated gene expression within neural circuits relevant to addiction. Results: Partitioning of polygenic risk revealed that while surface-level parental DNA correlations were modest (r = 0.08), underlying latent genetic correlations approached unity (R_δ ≈ 0.99), indicating that addiction risk clustering in families is driven by intense assortive mating and concentrated biological inheritance. Multi-phenotype TWAS identified several significant gene–phenotype associations—notably ADAM32 and SLC9A3, which demonstrated pleiotropic effects across multiple substance use categories. Crucially, these significant TWAS signals were enriched in striatal structures (caudate, putamen, substantia nigra) and frontal cortical regions. Conclusions: Our findings support a model of shared genetic liability across diverse substance use behaviors, mediated by specific gene expression patterns in the mesolimbic dopamine system and frontal cortex. By integrating multi-phenotype GWAS and TWAS, this study highlights pleiotropic candidate genes and provides critical insights into the tissue-specific neurobiological pathways underlying addiction vulnerability. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) has traditionally been conceptualized as a predominantly metabolic disorder driven by obesity and insulin resistance. However, recent advances in human genetics have revealed a more complex picture that encompasses germline susceptibility variants, protective loss-of-function alleles, polygenic risk models, and somatic clonal evolution. Since the discovery of PNPLA3 (patatin-like phospholipase domain-containing 3) I148M, multiple loci—including TM6SF2, MBOAT7, GCKR, HSD17B13, MTARC1, GPAM, and CIDEB—have substantially expanded the mechanistic understanding of disease heterogeneity and hepatocellular vulnerability. Recent studies integrating partitioned polygenic risk scores and unsupervised phenotypic clustering suggest that MASLD may be organized into at least two predominant subtypes: a liver-specific subtype characterized by intrinsic hepatocellular susceptibility, and a cardiometabolic subtype associated with systemic metabolic dysfunction and increased cardiovascular risk. Analyses of cirrhotic liver tissue have, in turn, demonstrated somatic clonal expansion of hepatocytes harboring adaptive metabolic mutations, adding an evolutionary dimension to advanced disease. On this basis, we propose an integrated LS/CM/C framework encompassing liver-specific (LS), cardiometabolic (CM), and clonal (C) components. This model offers a conceptual structure that links germline genetics, metabolic heterogeneity, somatic adaptation, and emerging pharmacogenomic strategies. The recent development of genotype-directed therapies targeting PNPLA3 and HSD17B13, together with the approval of resmetirom and semaglutide, further supports the transition toward biologically stratified hepatology. Although prospective validation remains necessary, the convergence of genetics, clonal biology, and targeted therapeutics suggests that MASLD is moving toward an era of precision medicine. Full article

Primary hyperlipoproteinemias represent a heterogeneous group of inherited lipid metabolism disorders characterized by persistent abnormalities in plasma lipoproteins, a markedly increased risk of premature atherosclerotic cardiovascular disease (ASCVD) and, in selected phenotypes, acute pancreatitis. Traditionally classified according to the Fredrickson phenotypic system, these disorders are now increasingly understood through a multidimensional framework integrating molecular genetics, intracellular lipid trafficking, inflammatory signaling, and systemic metabolic regulation. Recent advances have identified both monogenic and polygenic determinants underlying disease expression, including pathogenic variants affecting LDLR, APOB, PCSK9, APOE, and lipoprotein lipase pathways, as well as the cumulative contribution of multiple common lipid-associated variants. Furthermore, emerging evidence highlights the role of endoplasmic reticulum stress, oxidative imbalance, adipose–hepatic crosstalk, intestinal lipid absorption, and inflammatory mediators in modulating lipoprotein metabolism and cardiovascular risk. Novel regulators such as angiopoietin-like proteins (ANGPTLs), microRNAs, and pathways involved in cholesterol efflux and remnant lipoprotein clearance have further refined our understanding of disease heterogeneity and therapeutic responsiveness. Familial hypercholesterolemia and familial combined hyperlipidemia exemplify the complex interplay between genetic susceptibility, metabolic dysfunction, and environmental influences that shape phenotype severity and long-term cardiovascular outcomes. Advances in diagnostic strategies, including genetic testing, polygenic risk scores, apolipoprotein profiling, and vascular imaging, have significantly improved risk stratification and personalized management. Simultaneously, innovative therapies—including PCSK9 inhibitors, ANGPTL3-targeted agents, antisense oligonucleotides, and RNA-silencing technologies—are reshaping treatment paradigms and expanding options for high-risk patients. This chapter synthesizes contemporary insights into the pathogenesis of primary hyperlipoproteinemias, emphasizing the transition from traditional lipid-based classification toward precision medicine approaches focused on lifetime cardiovascular risk, molecular characterization, and individualized therapeutic intervention. Full article

Background: Polygenic risk scores (PRS) have proven valuable for disease risk prediction, but their predictive utility often remains limited because human traits result from complex interactions between environmental and genetic factors. Blood lipid levels are heritable and clinically important risk factors for cardiovascular disease, yet it remains unclear whether multi-omics integration can enhance lipid trait prediction beyond PRS alone. Methods: We first constructed single-omics scores, where gene expression, plasma protein, and plasma/serum metabolite levels were genetically predicted and weighted by effect sizes estimated via LASSO regression. Subsequently, we implemented two integration strategies to develop composite multi-omics risk scores (MoRS): step-MoRS, which integrates single-omics scores using stepwise regression, and Lasso-MoRS, which directly models all predicted features across omics layers using LASSO regression. Both approaches were evaluated across European, South Asian, and African ancestries within the UK Biobank. Results: MoRS-based methods consistently demonstrated superior predictive accuracy compared to PRS alone for four lipid traits across diverse populations. Notably, Lasso-MoRS prioritized key biomarkers with predictive utility complementary to genomic data. Conclusions: These findings confirm that integrating multi-omics biomarkers with genomic data significantly enhances lipid trait prediction across diverse ancestries, offering biological insights into the molecular regulation of lipid metabolism. Full article

Genomic testing for rare, highly penetrant cardiovascular pathogenic mutations is well established, but its scarcity limits broader clinical utility. As cardiogenomics matures, common single nucleotide polymorphisms (SNPs) associated with cardiovascular disease (CVD) risk are increasingly accessible to clinicians and patients through both clinician-ordered and direct-to-consumer (DTC) platforms. This narrative review synthesizes evidence for six common loci—APOA1, APOE, LIPC, LPL, ANGPTL3, and FADS1/2, here termed “candidate-actionable” in the limited sense that genotype-by-diet associations have been described, but the full chain of analytical validity, clinical validity, clinical utility, and evidence-supported management has not been demonstrated—that modulate lipid metabolism and CVD risk and that show genotype-by-diet interactions in observational and small interventional studies. We frame these loci as complementary to validated polygenic risk scores (PRSs), discuss two illustrative epistatic axes (APOE ε4 × FADS major T-allele; ANGPTL3 × LPL), summarize emerging epigenetic modulators of the same loci, and propose an integrated framework combining PRS, locus-level SNPs, and epigenetic state. All locus-specific dietary considerations are explicitly framed as hypothesis-generating pending validation in adequately powered, genotype-stratified prospective trials. A comparison of consumer and clinical testing platforms—updated to reflect recent ownership and regulatory changes—is provided. Full article

Background/Objectives: Multiple sclerosis (MS) is a chronic immune-mediated neurological disorder for which few modifiable risk factors are established. Vitamin C, due to its antioxidant and neuroprotective properties, has been hypothesized to reduce MS risk, but epidemiological evidence remains inconsistent. Methods: We conducted a prospective cohort study using UK Biobank data, including 486,908 adults aged 37–70 years free of neurological disease. Regular vitamin C supplementation was self-reported at recruitment. Incident MS cases were identified during a median follow-up of 13.5 years. Propensity score weighting was used to balance a wide range of demographic, lifestyle, and health-related confounders. Weighted Cox proportional hazards models were used to estimate hazard ratios (HRs). Effect modification by polygenic risk score (PRS) for MS was assessed, and multiple sensitivity analyses were performed. Results: During follow-up, 452 participants were diagnosed with MS. Vitamin C supplementation was reported by 8.8% of participants (missingness = 1.5%) and was associated with a lower risk of incident MS (HR = 0.51, [95%CI: 0.32; 0.82], p = 0.004). The estimate was consistent across multiple sensitivity analyses. The association varied according to genetic susceptibility, with significant nonlinear multiplicative interaction (p = 0.004) and additive interaction (p < 0.001). Specifically, significant associations were observed only among those with average or high MS-PRS. Conclusions: Vitamin C supplementation was associated with a lower risk of incident MS, with the association varying across levels of genetic susceptibility. Although residual confounding cannot be excluded, sensitivity analyses did not identify similar associations for overall supplement use or multivitamin use, providing some reassurance against a generalized healthy-user effect. Replication in independent cohorts is warranted. Full article