The new system, called MetsObesity, was developed and validated by Junaid Iqbal, Hou-De Zhou, and colleagues using data from the UK Biobank, a prospective cohort of roughly half a million adults in the United Kingdom. After applying careful exclusion criteria designed to remove conditions that distort body weight or confound cardiovascular outcomes, including pregnancy, cancer, thyroid disease, dementia, and pre-existing cardiovascular disease, the study settled on 346,001 participants with a median age of 57 years at baseline. These individuals were followed for a median of 15 years, during which the researchers tracked the first occurrence of a composite endpoint known as major adverse cardiovascular events, or MACE, comprising acute myocardial infarction, heart failure, stroke, unstable angina, and death from cardio-cerebrovascular causes.

What sets MetsObesity apart from existing frameworks is the breadth of information it folds into a single classification. Rather than relying on body mass index or waist circumference alone, the researchers considered three categories of variables. The first was anthropometric: body mass index, waist circumference, waist-to-hip ratio, and waist-to-height ratio. The second comprised the classic metabolic abnormalities that define metabolic syndrome, including systolic and diastolic blood pressure, fasting blood glucose, high-density lipoprotein cholesterol, triglycerides, along with low-density lipoprotein cholesterol and total cholesterol. The third category, often neglected in conventional risk assessment, captured additional abnormalities: C-reactive protein, a marker of systemic inflammation; serum urate; and the liver enzymes alanine aminotransferase, aspartate aminotransferase, and gamma-glutamyl transferase, which reflect fatty liver disease and hepatic dysfunction.

To decide which of these variables actually mattered, the team turned to machine learning. Recursive feature elimination was used to identify the single best anthropometric predictor, and waist-to-hip ratio emerged at the top of the ranking, outperforming body mass index, waist circumference, and waist-to-height ratio. Random forest models, an ensemble learning method well suited to large, high-dimensional datasets, generated mean decreased accuracy scores to rank the influence of every candidate variable on cardiovascular outcomes. Variables with negative importance scores, including alkaline phosphatase, cystatin C, and serum creatinine, were discarded. Fasting blood glucose beat glycated hemoglobin as the preferred glycemic marker. The final feature set contained twelve variables: waist-to-hip ratio, low-density lipoprotein cholesterol, total cholesterol, systolic blood pressure, diastolic blood pressure, urate, high-density lipoprotein cholesterol, alanine aminotransferase, C-reactive protein, aspartate aminotransferase, gamma-glutamyl transferase, and fasting glucose. SHAP analysis, which quantifies each feature’s contribution to model predictions, confirmed the same hierarchy.

From these building blocks, the researchers constructed eight preliminary classes representing every meaningful combination of anthropometric, classic metabolic, and additional abnormalities, then consolidated them into five final MetsObesity classes. Class 1 describes people with a normal waist-to-hip ratio and no abnormalities at all, the metabolically healthy reference group. Class 2 covers those with normal waist-to-hip ratio but one or more classic metabolic abnormalities such as hypertension or hyperglycemia. Class 3 includes people with normal waist-to-hip ratio who carry one or more additional abnormalities, such as elevated C-reactive protein or liver enzymes, with or without classic metabolic problems. Class 4 captures a high waist-to-hip ratio without additional metabolic abnormalities, while Class 5, the highest-risk tier, combines a high waist-to-hip ratio with additional abnormalities, with or without classic ones. The design deliberately replaces the binary language of metabolically healthy versus unhealthy obesity with a graded spectrum.

The results were striking. In the validation cohort, the proportion of participants who developed major cardiovascular events rose steadily across the classes: 1.4 percent in Class 1, 3.3 percent in Class 2, 5.0 percent in Class 3, 6.0 percent in Class 4, and 8.5 percent in Class 5. After adjusting for medications, age, sex, smoking, alcohol consumption, and socioeconomic deprivation, the hazard ratios for cardiovascular events climbed from Class 1 through Class 5. In the two highest classes, the hazard ratios for major adverse cardiovascular events reached 4.75 and 6.79, several times higher than the hazard ratios produced by body mass index categories, waist circumference, body fat percentage, or the metabolic syndrome definitions recommended by the National Cholesterol Education Program Adult Treatment Panel and the International Diabetes Federation, which yielded hazard ratios between roughly 1.1 and 2.0.

Performance testing reinforced the picture. MetsObesity achieved a Harrell C-statistic of 0.62, modest in absolute terms but clearly superior to body mass index at 0.57, waist circumference and body fat percentage at 0.55, trunk fat percentage at 0.51, and the IDF and NCEP metabolic syndrome definitions at 0.57 and 0.56. Calibration plots showed good agreement between predicted and observed event probabilities, verified with bootstrap-corrected estimates. Decision curve analysis, which quantifies the clinical net benefit of a prediction model across a range of risk thresholds, demonstrated that MetsObesity delivered superior value across the clinically relevant 2 to 8 percent threshold range, peaking at a standardized net benefit of 0.638 at the 2 percent threshold. Net reclassification improvement analysis showed that MetsObesity meaningfully reclassified risk beyond standard markers, with an NRI of 0.204 at five years and 0.160 at ten years.

Sensitivity analyses tested the system’s robustness from multiple angles. The findings held when missing data were excluded rather than imputed, when the cohort was randomly split in different proportions, and when participants with weight-affecting comorbidities were retained. A subgroup analysis of Asian participants, using population-specific body mass index and body fat thresholds, showed the same pattern of superior prediction compared with conventional measures. Notably, when compared with established cardiovascular risk calculators such as SCORE2 and the Pooled Cohort Equations, MetsObesity showed somewhat lower discrimination, but the authors point out a crucial distinction: those models lean heavily on age and sex, which are non-modifiable, whereas MetsObesity is built exclusively on changeable metabolic and anthropometric factors, making it potentially more actionable for prevention, particularly in younger people in whom age-driven models perform poorly.

The clinical appeal of MetsObesity lies partly in its practicality. Every variable it requires, from waist-to-hip ratio to C-reactive protein and liver enzymes, is already measured in routine health examinations and standard metabolic panels in many healthcare settings. No advanced imaging, genetic testing, or complex continuous risk functions are needed; the categorical thresholds align with established clinical cut-points, allowing bedside application and straightforward communication of risk to patients. The authors suggest that embedding the system into electronic medical records or mobile risk calculators could automate the calculation and ease the workload in busy clinics, and they emphasize that MetsObesity is intended to complement rather than replace established scores like SCORE2 and the Pooled Cohort Equations, especially in overweight and obese populations where traditional tools perform least well.

The study is not without limitations. It was conducted in a predominantly European-ancestry UK population, and external validation in independent, ethnically diverse cohorts remains an essential next step, particularly given that adiposity patterns and anthropometric cut-offs differ across South Asian, East Asian, and Middle Eastern populations. Data on physical activity and diet were unavailable, leaving residual confounding possible, and the system currently addresses cardiovascular outcomes only, not the kidney, cancer, reproductive, and perinatal consequences of obesity. Still, as obesity rates climb worldwide and cardiovascular disease follows in its wake, MetsObesity offers what its authors describe as a paradigm shift from one-size-fits-all assessment to phenotype-specific prevention, a five-step ladder that identifies high-risk individuals years before their first heart attack, stroke, or diagnosis of heart failure, using nothing more exotic than a tape measure and a standard blood panel.

Subject of Research: A novel obesity and metabolic classification system for predicting long-term cardiovascular risk

Article Title: MetsObesity: a novel classification system for predicting 15-year cardiovascular risk in the UK Biobank population

Article References: Iqbal, J., Wu, H.-X., Wu, Y.-X., Jiang, H.-L., Li, L., Zhou, X.-Y., Bu, Y.-H., & Zhou, H.-D. (2026). MetsObesity: a novel classification system for predicting 15-year cardiovascular risk in the UK Biobank population. Journal of Advanced Research, 88, 947-957. https://doi.org/10.1016/j.jare.2026.01.050

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DOI: 10.1016/j.jare.2026.01.050

Keywords: obesity, cardiovascular disease, UK Biobank, MetsObesity, risk prediction, metabolic syndrome, waist-to-hip ratio, C-reactive protein, liver enzymes, BMI, precision medicine, cohort study

Schizophrenia, a complex and debilitating mental disorder characterized by psychosis, hallucinations, and cognitive disruption, has long challenged scientists due to its multifactorial etiology involving both genetic and environmental components. Though GWAS have previously identified numerous genetic variants associated with schizophrenia, the clinical interpretation of these findings remains obscure without mechanistic links to biological intermediates. The current study pioneers this integration by exploring how aggregated genetic risk translates to quantifiable changes in circulating proteins, offering unprecedented insight into the molecular consequences of genetic liability for psychosis.

The research team utilized polygenic scores, which aggregate the small effects of thousands of genetic variants across the genome into a single predictive metric of schizophrenia risk. This score was calculated for tens of thousands of participants within the UK Biobank, a massive repository of genetic, proteomic, and health data from over half a million individuals. By correlating PRS with levels of myriad blood-based proteins measured via high-throughput multiplex assays, the investigators aimed to identify protein signatures that mediate the relationship between genetic risk and the eventual diagnosis of psychotic disorders.

Crucially, this approach transcends traditional case-control studies by leveraging continuous measures of genetic risk and intermediate protein traits, affording greater statistical power and revealing subtle biomolecular cascades that characterize schizophrenia pathogenesis. The integration of proteomics acts as a bridge, connecting genomic susceptibility loci to downstream biological pathways implicated in neuronal function, inflammation, and immune regulation—domains increasingly recognized as central to schizophrenia’s etiology.

Among the most striking findings was the identification of several proteins whose concentrations in the blood correlated both with heightened schizophrenia polygenic scores and with clinically confirmed psychosis diagnoses. These proteins implicate diverse biological systems, including synaptic remodeling, neuroinflammation, and myelination processes, which may underlie the neurodevelopmental disruptions observed in schizophrenia patients. Such biomarkers not only enhance our understanding of disease mechanisms but suggest novel targets for therapeutic intervention.

The study employed rigorous statistical models designed to adjust for confounding factors such as age, sex, ancestry, and medication status, ensuring that detected associations reflect genuine biological links rather than spurious correlations. By harnessing the depth and breadth of the UK Biobank dataset, the researchers achieved a level of robustness rarely attainable in psychiatric genetics, where heterogeneity and phenotypic complexity often impede conclusive insights.

Importantly, the findings hint at the potential future utility of combined polygenic and proteomic profiling as a predictive tool for stratifying individuals at high risk of developing psychosis before symptom onset. Early identification could pave the way for preemptive clinical interventions, tailoring treatments to an individual’s molecular risk profile and perhaps ameliorating disease severity or even preventing progression altogether.

Furthermore, the results challenge the classical view of schizophrenia purely as a brain disorder by demonstrating that peripheral blood proteins reflect central nervous system pathological processes. This peripheral signature opens up more accessible avenues for monitoring disease state and therapeutic efficacy through minimally invasive blood tests, facilitating longitudinal studies and precision psychiatry.

The intersection of genetics and proteomics also fosters the identification of biological pathways shared across psychiatric disorders, shedding light on why schizophrenia frequently co-occurs with mood disorders and other neuropsychiatric conditions. By mapping protein networks impacted by genetic risk variants, the study provides a scaffold upon which future research can build to unravel the complex biological web that shapes mental health.

This comprehensive analysis exemplifies the power of combining large-scale biobanks with cutting-edge omics technologies, marking a critical step toward decoding the biological underpinnings of psychiatric illness. Through this integrative lens, schizophrenia emerges not as a monolithic disease entity but as a constellation of molecular dysfunctions orchestrated by a polygenic genetic background and manifesting through measurable protein perturbations.

Looking ahead, expanding such integrative analyses to include longitudinal proteomic measurements, neuroimaging data, and environmental exposures will further refine our understanding of causality and trajectory in psychosis. As multi-omics datasets grow increasingly available, machine learning and systems biology approaches will be instrumental in extracting actionable insights from this complex data landscape.

In summary, the research advances a paradigm shift in psychiatric genomics: moving beyond static genetic associations towards dynamic biomolecular networks that mediate disease risk. By pinpointing specific proteins linked to schizophrenia polygenic scores and psychosis diagnosis, the study sets the stage for biomarker-guided clinical care, improved risk assessment, and targeted drug development in a field desperately in need of transformative breakthroughs.

The confluence of large-scale genetic data and proteomics analytics presented here exemplifies an era of precision psychiatry that harnesses the molecular heterogeneity of schizophrenia to tailor individualized interventions. This investigative framework not only enriches our fundamental biology knowledge but holds promise to alleviate the considerable human and societal burden posed by psychotic disorders.

Such pioneering work underscores the imperative for continued investment in genetic epidemiology and biomarker discovery initiatives. By forging these multi-disciplinary alliances, we edge closer to demystifying schizophrenia’s complexity, improving lives through earlier diagnosis, personalized treatment modalities, and ultimately, prevention strategies informed by robust molecular evidence.

This landmark study signals a future where psychiatric diagnosis and management are increasingly defined by biological metrics rather than solely clinical observations, heralding a new era in mental health care with improved outcomes borne from integrative science and technological innovation.

Subject of Research: Genetics and proteomics of schizophrenia and psychosis diagnosis

Article Title: The relationship between schizophrenia polygenic scores, blood-based proteins and psychosis diagnosis in the UK Biobank

Article References:
Kendall, K.M., Legge, S.E., Fenner, E. et al. The relationship between schizophrenia polygenic scores, blood-based proteins and psychosis diagnosis in the UK Biobank. Schizophr (2026). https://doi.org/10.1038/s41537-025-00725-8

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Human metabolism, the complex network of biochemical reactions that sustain life, exhibits remarkable variability among individuals. However, parsing out the precise genetic contributions to this variability has remained a formidable challenge for scientists. This new study harnessed extensive genetic and metabolomic datasets from the UK Biobank, examining over half a million participants. By analyzing the blood levels of 250 distinct metabolites—including vital lipid molecules and amino acids—the researchers have systematically delineated the genetic factors that shape metabolic diversity.

The scale and scope of this analysis are unparalleled. The consortium, spearheaded by experts at the Berlin Institute of Health @ Charité (BIH) and Queen Mary University of London, integrated large-scale genetic data encompassing individuals of European, African, and Asian descent residing in the UK. This multi-ancestry approach allowed the scientists to ascertain that genetic control of metabolites is remarkably conserved across diverse populations and between sexes, suggesting that the findings have broad biological relevance and are applicable to populations worldwide.

One of the study’s remarkable achievements is the identification of genes previously unassociated with metabolic pathways. These discoveries deepen our understanding of human metabolism, highlighting intricate genetic networks and interactions previously hidden in genetic studies limited by smaller sample sizes or single ancestral backgrounds. Such insights not only enrich fundamental biological knowledge but also open new avenues for clinical and therapeutic research.

Beyond mapping genetic determinants of typical metabolic variation, the study elucidated how certain genes regulating blood metabolite levels also predispose individuals to complex diseases. This overlap between metabolic regulation and disease susceptibility underscores the clinical importance of metabolic traits as biomarkers or targets in disease prevention and treatment strategies. For instance, the researchers identified the gene VEGFA as a novel regulator of high-density lipoprotein (HDL) cholesterol, often dubbed the “good cholesterol.” As HDL has a protective role against cardiovascular disease, VEGFA emerges as a promising target for drug development aimed at cardiovascular risk reduction.

The remarkable depth of this analysis was enabled by biobanks—massive repositories of genetic, phenotypic, and health-related data. The UK Biobank stands as a paragon in this field, having recruited a diverse cohort of 500,000 people and combining their genetic profiles with extensive health and lifestyle information. Utilizing this unprecedented dataset, the researchers conducted a highly powered and systematic investigation, maximizing statistical robustness and ensuring broad generalizability of the findings.

While genetics clearly play a pivotal role in metabolic individuality, the authors emphasize the indispensable influence of modifiable environmental factors such as diet, physical activity, and lifestyle choices. These elements dynamically interact with genetic predispositions to shape metabolism, underscoring the holistic complexity of metabolic health. This nuanced understanding reinforces the need for integrative approaches in managing health and disease, balancing genetic insights with personalized lifestyle interventions.

Lead author Dr. Martijn Zoodsma, a postdoctoral researcher at the BIH in Berlin, articulated the transformative potential of this work: “Mapping the genetic regulation of hundreds of blood metabolites at this scale is a landmark achievement that provides a comprehensive reference framework. It equips the scientific community with powerful tools to unravel disease risk mechanisms and decode the genetic architecture underlying metabolic diversity.” His assertion highlights the study’s foundational role in driving forward the field of metabolic genetics.

Senior author Professor Maik Pietzner, an expert in Health Data Modelling affiliated with both BIH and Queen Mary University’s Precision Health University Research Institute (PHURI), contextualized the findings within public health priorities. “Despite advances in lipid-lowering therapies, such as statins, heart disease persists as a leading cause of mortality. Our genetic mapping identifies novel molecular pathways that could inspire innovative therapeutics, aiming to reduce cardiovascular fatalities further by targeting metabolic regulation more precisely.”

The study also exemplifies the power of collaborative academic and industrial partnerships. Senior author Professor Claudia Langenberg, director of PHURI and head of Computational Medicine at the BIH, lauded the integration of Nightingale Health’s metabolomic technology, which enabled precise quantification of lipid and metabolite profiles in the entire UK Biobank cohort. Professor Langenberg remarked, “Such large-scale technology deployment is essential to capture rare genetic variations and to reveal the fundamental commonalities in metabolic control across ancestries and sexes—reminding us that despite our diversity, we share core biological processes.”

This research propels the field towards a future where tailored metabolic profiling and genetic risk assessment converge to foster personalized prevention and treatment strategies. By illuminating the genetic landscape governing metabolites tied to health and disease, this study not only broadens scientific horizons but also holds promise for transformative impacts on global health.

As metabolomics and genomics continue to intertwine, the vast treasure trove of data offered by biobanks worldwide will fuel deeper explorations into how our genetic code influences metabolism. This work sets a precedent and benchmark for such investigations, pushing the boundaries of what we understand about human biology and offering hope for more effective interventions tailored to individual metabolic profiles.

The implications of this study extend beyond heart disease. Metabolic dysregulation underpins a wide array of conditions—from diabetes and neurodegenerative disorders to cancer and aging. Decoding the genetic architecture of metabolism thus promises ripple effects across medicine, enabling insights into disease mechanisms and the development of novel biomarkers and therapeutics.

In closing, this landmark genetic map of human metabolism exemplifies the confluence of big data science, cutting-edge technology, and international collaboration. It demonstrates how leveraging vast, diverse datasets can unravel the intricate genetic influences shaping our biological individuality, setting the stage for a new era in precision health and personalized medicine.

Subject of Research: People
Article Title: A genetic map of human metabolism across the allele frequency spectrum
News Publication Date: 3-Oct-2025
Web References: DOI link
Keywords: DNA, Genetic testing, Genetic methods

Environmental epidemiology traditionally grapples with the formidable challenge of quantifying the exposure that each individual accrues in dynamic real-world settings. Traditional approaches often rely on coarse geographic proxies or aggregate measures that blur the nuances of personal exposure variations. The approach elaborated upon by Vanoli et al. redefines this paradigm by deploying advanced exposure reconstruction algorithms that integrate diverse environmental data streams—including air pollution metrics, land-use characteristics, and personal activity patterns—to simulate individualized exposure profiles over time. This granularity facilitates unprecedented fidelity in exposure assessment, which is instrumental for unraveling dose-response relationships that underpin epidemiologic inferences.

At the core of this refined methodology lies the synthesis of high-resolution spatial environmental data with rich individual-level information available in the UK Biobank. The UK Biobank’s expansive repository, encompassing over half a million participants with detailed phenotypic, genotypic, and residential histories, provides an ideal substrate for testing robust exposure reconstruction frameworks. By coupling geospatial environmental datasets with participant-specific temporal activity patterns, including residential mobility and commuting behaviors, the authors reconstruct individualized exposure trajectories that reflect the dynamic environmental contexts individuals inhabit throughout their lives.

Such sophisticated modeling necessitates overcoming formidable computational and statistical hurdles. The correction addressed by Vanoli et al. involves crucial recalibrations in exposure assignment algorithms, harmonizing temporal alignment between environmental data sampling intervals and participant time-stamped location records. These recalibrations enhance temporal resolution, reducing latency-related exposure misclassification that could otherwise bias risk estimates. Furthermore, the correction clarifies computational workflows involving spatial interpolation techniques and multi-source data fusion, which collectively amplify the precision and validity of estimated exposures.

The implications of accurate individual-level exposure reconstructions extend far beyond methodological refinements; they enable a deeper understanding of environmental contributions to complex diseases. Chronic conditions such as cardiovascular diseases, respiratory illnesses, and neurodegenerative disorders frequently exhibit multifactorial etiologies where environmental exposures serve as modifiable risk factors. By harnessing meticulously reconstructed exposure data, epidemiologists can delineate dose-response relationships with greater precision, identify vulnerable subpopulations, and establish exposure thresholds critical for informing public health policies and interventions.

Within the broader context of exposome research—the systematic cataloguing of environmental exposures across the lifespan—this methodological advancement exemplifies the transition from coarse population-level proxies to highly resolved, personalized exposure metrics. It aligns with a paradigm shift towards precision epidemiology, where environmental risk assessments are tailored to individual exposure histories and susceptibility profiles. Such granularity affords nuanced insights into gene-environment interactions, epigenetic modifications, and mechanistic pathways underpinning environmental health outcomes.

The correction featured in the Journal of Exposure Science and Environmental Epidemiology is not merely a technical footnote but a testament to the iterative nature of scientific inquiry—where data precision and methodological transparency are paramount. The authors’ openness in addressing and correcting computational nuances fortifies confidence in subsequent analyses derived from these reconstructed exposures. It also serves as a model for best practices, encouraging reproducibility and robustness in environmental epidemiologic research.

On a technical level, the exposure reconstruction framework leverages advanced geostatistical techniques, including kriging and land-use regression models, to estimate environmental pollutant concentrations at unmonitored locations based on sparse measurement networks. By integrating satellite remote sensing data, atmospheric chemistry transport models, and ground-based sensor networks, the method achieves high spatial and temporal resolution. Participants’ geocoded residential addresses are linked with these environmental grids, adjusted for temporal changes, to produce personalized exposure histories that reflect both chronic and episodic exposure events.

Moreover, accounting for individual activity patterns is pivotal in this framework. The modeling incorporates self-reported and device-measured mobility data to refine exposure estimates beyond stationary residential proxies. For instance, time spent commuting through high-traffic corridors or working in industrial zones can significantly modify one’s exposure profile compared to residential location alone. By reconstructing these nuanced movement patterns, the framework captures exposure heterogeneity more faithfully, mitigating classical exposure misclassification that can dilute epidemiologic associations.

One of the most formidable challenges addressed in this correction relates to temporal synchronization across diverse datasets with varying measurement frequencies and latencies. Environmental pollutant data often derive from monitoring networks with daily or hourly granularity, while cohort participants’ data on location or activity may rely on periodic updates or retrospective questionnaires. The corrected framework incorporates sophisticated temporal alignment algorithms that interpolate or extrapolate missing data points, ensuring coherent temporal overlays that preserve causal integrity in exposure-risk modeling.

In addition to computational aspects, the authors emphasize rigorous validation procedures. Exposure estimates are cross-validated against independent measurement campaigns and biomarker proxies when available, enhancing confidence in reconstructed exposure metrics. Such validation is essential for translating modeled exposures into actionable insights for risk assessment, policy formulation, and targeted interventions aimed at reducing the burden of environmentally linked diseases.

The methodological refinement and transparency demonstrated in this correction spotlight the critical importance of reproducibility in exposure science. As large cohort studies increasingly harness big data and complex analytical pipelines, the potential for latent errors or oversights grows. The proactive rectification and detailed exposition by Vanoli et al. epitomize scientific diligence, ensuring that subsequent epidemiologic findings rest on a solid exposure assessment foundation.

Looking forward, these methodological advances herald new frontiers in environmental health research. With ongoing enhancements in sensor technology, data integration platforms, and machine learning algorithms, exposure reconstruction is poised to become even more precise, dynamic, and user-centric. Personalized environmental risk profiles derived from such frameworks may soon inform individualized preventive strategies, clinical decision-making, and population-level health surveillance in unprecedented ways.

In conclusion, the correction by Vanoli and colleagues refracts light on the intricate, often underappreciated technical scaffolding underpinning environmental epidemiology’s quest to elucidate causal links between exposures and health outcomes. Through meticulous recalibration of exposure reconstruction algorithms within the UK Biobank context, the authors contribute a critical piece to the puzzle of how we measure and interpret the environmental determinants of disease. Their work not only advances methodological rigor but also accelerates the translation of environmental data into meaningful health insights, promising a future where the invisible hazards of our environment are more precisely captured and mitigated.

This groundbreaking correction invites the scientific community to reassess and refine their exposure assessment strategies, promoting a culture of transparency and continuous methodological improvement. It reaffirms that in the pursuit of public health, precision in measurement is paramount, and that the evolving landscape of environmental exposure science is both a technical challenge and a profound opportunity to safeguard human health on a global scale.

Subject of Research: Reconstructing individual-level environmental exposures in large cohort studies for improved epidemiologic analyses.

Article Title: Correction: Reconstructing individual-level exposures in cohort analyses of environmental risks: an example with the UK Biobank.

Article References: Vanoli, J., Mistry, M.N., De La Cruz Libardi, A. et al. Correction: Reconstructing individual-level exposures in cohort analyses of environmental risks: an example with the UK Biobank. J Expo Sci Environ Epidemiol (2025). https://doi.org/10.1038/s41370-025-00771-5

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