The analysis, published in BMC Cancer , included 68,367,056 participants. Researchers searched Google Scholar, PubMed and Science Direct for studies available through June 6, 2024, selected cohort studies using predefined criteria and assessed risk of bias with the Newcastle-Ottawa Scale. Each study’s hazard ratio was standardized to a 10 parts per billion, or ppb, increase in nitrogen dioxide exposure, providing a common scale for interpreting the association across the included studies.

The review examined evidence from non-European countries, where the available research was described as limited compared with European evidence. Associations differed between lung cancer incidence and mortality. For incidence, the pooled hazard ratio was 1.06 per 10 ppb increase in nitrogen dioxide, with a 95% confidence interval of 1.01–1.12, corresponding to an estimated 6% higher risk.

The mortality estimate was 1.13, with a 95% confidence interval of 1.01–1.26, corresponding to a 13% higher risk. These are pooled observational estimates rather than direct measurements of nitrogen dioxide’s causal effect. The wider confidence interval for mortality also indicates greater uncertainty around that estimate than around the incidence result.

Subgroup analyses did not find statistically significant associations when participants were divided by smoking status. Among smokers, the hazard ratio was 1.06, but the 95% confidence interval of 0.98–1.15 included no change in risk. Among non-smokers, the estimate was 0.96, with a 95% confidence interval of 0.86–1.08.

These results do not demonstrate that the association differs between smokers and non-smokers, nor do they establish the absence of an association in either group. The confidence intervals indicate substantial uncertainty in both subgroup estimates. Regional subgroup results also varied.

Studies from the Americas showed a statistically significant association, with a hazard ratio of 1.06 and a 95% confidence interval of 1.02–1.11 per 10 ppb increase in nitrogen dioxide. The estimate for Asia was 1.22, but its 95% confidence interval of 0.96–1.55 included no change in risk, making the regional result statistically inconclusive. The authors identified evidence gaps in low-income countries and noted regional differences as limitations of the evidence base.

Subject of Research: Association between nitrogen dioxide exposure and lung cancer risk in cohort studies outside Europe

Article Title: Nitrogen dioxide exposure and lung cancer risk in non-European countries: a systematic review and meta-analysis of the decades 2014-2024

Article References: Tang, H.-W., Voon, F.-L., & Sim, E. U.-H. (2026). Nitrogen dioxide exposure and lung cancer risk in non-European countries: a systematic review and meta-analysis of the decades 2014-2024. BMC Cancer. https://doi.org/10.1186/s12885-026-17098-5

Image Credits: AI Generated

DOI: 10.1186/s12885-026-17098-5

Keywords: nitrogen dioxide, lung cancer, air pollution, cohort studies, meta-analysis, cancer incidence, cancer mortality, smoking status, regional differences, non-European populations, Nitrogen, dioxide

Researchers searched MEDLINE, CENTRAL, Scopus and Embase from database inception through October 2025. The primary outcome was death-censored graft loss, and random-effects meta-analyses used a three-level model to account for dependence among multiple effect sizes reported within studies. Based on the supplied abstract, the pooled association between high tacrolimus intrapatient variability and death-censored graft loss was a hazard ratio of 2.13 (95% confidence interval, 1.37–3.32), with substantial heterogeneity across studies indicated by an I² value of 69.3%.

High variability was also associated with rejection, with a pooled hazard ratio of 2.05 (95% confidence interval, 1.24–3.41) and no detected heterogeneity for that outcome (I² = 0.0%). Point estimates were similar across intrapatient-variability calculation methods, geographic regions and assessment timeframes. However, the subgroup analyses were exploratory and imprecise, so those comparisons should not be treated as definitive evidence of equivalence.

The reported prevalence of high intrapatient variability ranged from 11% to 69%. The abstract attributes this variation to differences in the variability metric, threshold and measurement window, illustrating how the studies defined and measured the exposure. The analysis is limited to observational cohort evidence, and the observed associations do not establish that variability causes graft loss or rejection. The authors stated that whether intrapatient variability can serve as a clinical monitoring tool will require standardized measurement, validated thresholds and prospective evidence.

Subject of Research: Association of tacrolimus intrapatient variability with graft loss and rejection in adult kidney transplant recipients

Article Title: High tacrolimus intrapatient variability is associated with graft loss after kidney transplantation: a systematic review and meta-analysis

Article References: Chienwichai, K., Mongkolrattanakul, P., & Chang, A. (2026). High tacrolimus intrapatient variability is associated with graft loss after kidney transplantation: a systematic review and meta-analysis. Scientific Reports. https://doi.org/10.1038/s41598-026-75230-8

Image Credits: AI Generated

DOI: 10.1038/s41598-026-75230-8

Keywords: tacrolimus, kidney transplantation, intrapatient variability, graft loss, rejection, systematic review, meta-analysis, cohort studies, immunosuppression, High, intrapatient, variability

The research team categorized study participants into six distinct phenotypes by crossing three body mass index categories with two metabolic states. People could be metabolically healthy or unhealthy at normal weight, overweight, or obese. Metabolic health in these studies was typically defined by the presence or absence of clustered abnormalities such as elevated blood pressure, dyslipidemia, high fasting glucose, and related markers of the metabolic syndrome. The reference group for all comparisons was the metabolically healthy normal-weight individual, the profile conventionally assumed to carry the lowest cardiovascular risk. Using random-effects models to pool hazard ratios and their 95 percent confidence intervals, the investigators quantified how each phenotype fared across five outcomes: overall cardiovascular disease, coronary heart disease, myocardial infarction, stroke, and heart failure.

The headline numbers are striking. Compared with metabolically healthy people of normal weight, those with metabolically unhealthy obesity faced roughly double the risk across nearly every cardiovascular endpoint examined. Their hazard ratios reached 2.10 for overall cardiovascular disease, 1.97 for coronary heart disease, 2.20 for myocardial infarction, and 2.05 for heart failure, with stroke at 1.55. Every confidence interval sat comfortably above unity, signaling that these elevations are unlikely to be statistical noise. On its own, that finding would surprise no cardiologist. The twist lies in the comparisons that follow.

When the researchers arranged the phenotypes along a gradient of body size and metabolic status, a clear dose-response pattern emerged for cardiovascular disease, coronary heart disease, myocardial infarction, and heart failure: risk climbed steadily with both higher body mass index and deteriorating metabolic health, and the combination of the two conferred the greatest danger of all. Yet the analysis also revealed that people who were metabolically unhealthy but normal weight frequently faced risks that exceeded those of individuals who were obese but metabolically healthy. In other words, a lean person with high blood pressure, adverse blood lipids, or elevated glucose may be in more immediate cardiovascular danger than a heavier person whose metabolic panel remains clean. Body size, in this framing, is not the primary engine of risk; the metabolic machinery underneath it is.

Stroke emerged as the most dramatic illustration of this principle. For this outcome, the data pointed to poor metabolic health as the dominant determinant, with body mass index contributing comparatively little once metabolic status was accounted for. The finding carries mechanistic plausibility. Stroke risk is tightly linked to hypertension, atrial fibrillation, and diabetes-related vascular damage, all of which are metabolic disturbances rather than consequences of body mass per se. Elevated blood pressure damages cerebral arteries over decades, and impaired glucose metabolism accelerates atherosclerosis in the vessels supplying the brain. If excess weight raises stroke risk mainly by degrading metabolic health, then a normal-weight individual with undiagnosed hypertension or insulin resistance would be expected to lose the protective advantage that leanness is supposed to confer, and that is precisely what the pooled data show.

The study’s methodology reflects the current standards for evidence synthesis in observational epidemiology. The authors searched PubMed and Scopus for literature published through January 2025, followed PRISMA reporting guidelines, and assessed the methodological quality of included cohorts using the ROBINS-I tool, which is designed to evaluate bias in non-randomized studies. Statistical pooling employed random-effects models, an appropriate choice given the inevitable heterogeneity among cohorts that differ in geography, age structure, follow-up duration, and the specific criteria used to define metabolic health. The authors also applied techniques for deriving comparable effect estimates across studies that reported their results in different formats, and they examined publication bias using the classic funnel-plot asymmetry test developed by Egger and colleagues.

That heterogeneity deserves attention, because it cuts to the heart of a long-running controversy in this field. The definition of metabolically healthy obesity has never been standardized, and studies have used anywhere from one to five metabolic criteria, different thresholds for each, and varying follow-up windows. Prior work has shown that shifting the definition can materially change the estimated risk, and the new analysis inherits that ambiguity. There is also a deeper biological complication: metabolic health is not a fixed trait. Longitudinal studies, including large analyses of the Nurses’ Health Study and the UK Biobank, have demonstrated that a substantial share of people classified as metabolically healthy at baseline convert to metabolically unhealthy over follow-up, and that this transition sharply raises cardiovascular risk. A single baseline snapshot may therefore understate the true hazard carried by people who appear healthy on the day they enroll.

Even with those caveats, the consistency of the signal across 44 independent cohorts, multiple continents, and five distinct cardiovascular endpoints lends the conclusion considerable weight. The findings align with a growing body of evidence that has steadily eroded the notion of benign obesity. Earlier meta-analyses found that even metabolically healthy overweight and obesity were associated with elevated cardiovascular risk, and large single-cohort studies of millions of participants reached similar conclusions. What the new analysis adds is a head-to-head quantification of the two risk dimensions, showing that the metabolic axis generally dominates the adiposity axis, and that the two interact multiplicatively rather than redundantly. Carrying excess weight and having poor metabolic health together is worse than either alone.

The clinical and public health implications are substantial. First, the results argue against using body mass index as a stand-alone triage tool. A normal-weight patient with elevated blood pressure, triglycerides, or fasting glucose should not be waved through on the strength of a favorable BMI, particularly given the doubled stroke risk observed in metabolically unhealthy normal-weight individuals. Second, the findings do not give people with obesity a free pass. Metabolically healthy obesity still carried elevated risk in this analysis, and the transition studies suggest that this state is often a way station rather than a stable destination. Weight management remains relevant, but the data suggest that interventions targeting blood pressure, lipid profiles, and glucose metabolism, through lifestyle change or pharmacotherapy, may deliver cardiovascular benefits that are at least as large as those from weight loss alone.

The authors conclude that worsening metabolic health and higher body mass index each independently increase the incidence of cardiovascular disease, coronary heart disease, myocardial infarction, and heart failure, with their combination posing the greatest threat, while stroke risk is driven mainly by metabolic status. As obesity rates continue to climb globally and health systems grapple with where to direct finite screening and prevention resources, this analysis offers a reframing that is both humbling and actionable: the scale tells only part of the story, and the blood panel may tell the more important half. For clinicians, the message is to look past body size and interrogate metabolic function in every patient; for researchers, it is to refine the definitions and track the transitions that determine who converts from apparent health to genuine risk.

Subject of Research: The relationship between body mass index, metabolic health phenotypes, and cardiovascular disease incidence

Article Title: Metabolic health is a stronger determinant than obesity for cardiovascular disease incidence: a meta-analysis of longitudinal cohort studies

Article References: Ghorbani, Z., Soltani, S., Aune, D., Khandouzi, N., Noormohammadi, M., & Kazemi, A. (2026). Metabolic health is a stronger determinant than obesity for cardiovascular disease incidence: a meta-analysis of longitudinal cohort studies. International Journal of Obesity. https://doi.org/10.1038/s41366-026-02237-6

Image Credits: AI Generated

DOI: 10.1038/s41366-026-02237-6

Keywords: metabolic health, obesity, cardiovascular disease, meta-analysis, body mass index, stroke, coronary heart disease, heart failure, myocardial infarction, metabolic syndrome, cohort studies, epidemiology

Diabetes-related foot ulcers, often abbreviated as DFUs, are among the most feared complications of diabetes mellitus. They arise when prolonged high blood sugar damages nerves and blood vessels in the lower limbs, leaving the foot insensitive, poorly perfused, and vulnerable to wounds that refuse to heal. Globally, they affect a substantial share of the diabetic population, and their consequences are grim: infection, hospitalisation, amputation, and elevated mortality. Yet in recent years, clinicians have noticed something else. Patients with DFUs appear to perform worse on cognitive tests than their peers with diabetes but no ulcers, and the review’s authors cite emerging evidence of a potential association between these lower-extremity complications and declining brain function.

The connection is biologically plausible. Diabetes is already a recognised risk factor for vascular dementia and Alzheimer-type pathology, operating through chronic hyperglycaemia, inflammation, endothelial dysfunction, and recurrent hypoglycaemic episodes. A foot ulcer may act as both a marker and an amplifier of this systemic damage. It signals long-standing, poorly controlled disease, and it can itself trigger inflammatory cascades, pain, sleep disruption, depression, and social isolation, all of which have been linked to cognitive decline. Understanding which factors drive cognitive impairment in this population could therefore reshape how clinicians screen, monitor, and support patients whose wounds and minds deteriorate together.

To find those factors, the team followed the PRISMA guidelines, the international standard for conducting and reporting systematic reviews. They searched five major databases, MEDLINE via EBSCOhost, PubMed, CINAHL, EMBASE, and PsycINFO, from their inception to June 2025, looking for observational studies that assessed cognition in people with DFUs. Two reviewers independently screened the results for eligibility, a safeguard against individual bias. Methodological quality was appraised with the Mixed Methods Appraisal Tool, a validated instrument for judging the rigour of diverse study designs, and data were extracted using a customised template by two independent reviewers. The search initially identified 460 records, of which 317 were screened, and just four studies survived the process.

That number is striking. Four studies, all conducted at single centres, represent the entire quantitative evidence base on factors associated with cognitive impairment in a complication that affects tens of millions of people worldwide. Three of the four were case-control studies, which compare people with and without a condition retrospectively, and the remaining two were cross-sectional studies, which capture a snapshot of a population at a single point in time. No cohort studies, which follow patients forward in time and can establish temporal sequences, were available. Crucially, none of the included studies was designed to identify risk factors for cognitive impairment, meaning the review could describe associations but not causation.

Despite these constraints, the review identified fourteen factors that remained statistically significant in multivariate analyses, the statistical technique that adjusts for the influence of other variables, in at least one of the included studies. The factors clustered into five domains. Sociodemographic characteristics included older age, male sex, and lower educational attainment, all well-established correlates of cognitive performance in the general population. Lifestyle factors encompassed smoking, body mass index, and depression, the latter being both a common companion of chronic wounds and a recognised contributor to cognitive difficulties. Diabetes-specific factors included glycated haemoglobin, or HbA1c, the standard measure of long-term blood sugar control, and the duration of diabetes itself, both proxies for cumulative metabolic injury.

The remaining two domains point directly to the burden of advanced disease. Comorbidities associated with cognitive impairment included cardiovascular disease, cerebrovascular disease, nephropathy, and retinopathy, conditions that share vascular pathology with both diabetes and dementia. Foot-related complications, meanwhile, included peripheral neuropathy, the nerve damage that often precedes ulceration, and amputation, the most drastic outcome of a non-healing wound. The pattern is coherent: the more organ systems damaged by diabetes, and the more severe the lower-limb involvement, the more likely a patient is to show measurable cognitive deficits. This supports the hypothesis that DFUs and cognitive impairment are parallel manifestations of systemic vascular and metabolic deterioration rather than coincidental neighbours.

The methodological details of the underlying studies matter for interpreting these findings. Cognitive assessment in the included research relied on standard screening instruments such as the Mini-Mental State Examination and the Montreal Cognitive Assessment, alongside tests like the Trail Making Test, which probes executive function and processing speed. Such tools are sensitive to education and language, which complicates comparisons across populations. The authors judged the studies with the Mixed Methods Appraisal Tool but found the evidence too heterogeneous, in design, measures, and populations, to pool statistically. No meta-analysis was possible, and the single-centre designs limit generalisability. Bivariate associations that failed to hold up under multivariate adjustment were excluded, which strengthens confidence in the fourteen surviving factors but also underscores how much depends on small samples.

The clinical implications are nonetheless significant. If cognitive impairment is common among DFU patients, it has direct consequences for wound care. Self-management of a foot ulcer demands vigilance: daily inspection of the feet, dressing changes, offloading pressure, glycaemic control, and prompt reporting of deterioration. A patient with impaired memory, attention, or executive function may struggle with every one of these tasks, creating a vicious cycle in which cognitive decline worsens the ulcer and the ulcer’s systemic effects worsen cognition. The review’s findings suggest that clinicians should consider cognitive screening when assessing DFU patients, particularly those who are older, less educated, depressed, smokers, or carrying multiple diabetic complications, and that caregivers may need to be involved in treatment plans.

The authors are careful about what their review cannot claim. Because no study was designed to identify risk factors, the temporal relationship between ulcer onset and cognitive decline remains unresolved: poor cognition may contribute to ulcer development through neglected self-care, or the ulcer’s inflammatory and psychological burden may accelerate cognitive deterioration, or both. The review concludes that these associated factors, spanning sociodemographic, lifestyle, diabetes-related, comorbid, and foot-related domains, must be explicitly considered in future research to establish a robust relationship between cognition and DFUs. More rigorous cohort studies, the authors argue, are warranted to identify true risk factors for cognitive impairment in this population. Until such studies exist, the review stands as both a synthesis of what is known and a pointed indictment of how little has been studied at the intersection of a wound and a mind.

Subject of Research: Cognitive impairment and its associated factors in people with diabetes-related foot ulcers

Article Title: Factors associated with cognitive impairment in people with diabetes-related foot ulcers: a systematic review

Article References: Karunathilaka, N., Lokeesan, V., & Rathnayake, S. (2026). Factors associated with cognitive impairment in people with diabetes-related foot ulcers: a systematic review. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02561-y

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02561-y

Keywords: diabetes-related foot ulcers, cognitive impairment, systematic review, diabetes complications, peripheral neuropathy, HbA1c, depression, vascular disease, amputation, PRISMA, MMSE, cohort studies

The research, led by Cara L. Booker and colleagues at the University of Essex and published in SSM – Population Health, combines a systematic review of 428 population-based studies with an empirical analysis of more than 17,000 adults drawn from four major cohort studies: the English Longitudinal Study of Ageing, the Health and Retirement Study in the United States, the Midlife in the United States study, and Understanding Society, the UK Household Longitudinal Study. The team’s central question was deceptively simple: how much does allostatic load, as currently measured, actually overlap with multimorbidity, the co-occurrence of two or more long-term health conditions in the same individual? The answer matters because allostatic load is supposed to represent early, subclinical physiological dysregulation, while multimorbidity is a clinical endpoint. If the two constructs are largely the same thing in practice, thousands of studies interpreting allostatic load as a distinct marker of chronic stress may be measuring something rather different.

The systematic review, which screened 9,892 records and followed PRISMA 2020 guidelines, revealed striking inconsistency in how the construct is operationalised. Across the 428 included papers, the number of biomarkers used ranged from two to 28, spanning one to seven physiological systems. Cardiovascular and metabolic markers dominated: 410 of 428 studies included both metabolic and cardiovascular systems, while far fewer incorporated the primary neuroendocrine mediators that sit at the heart of allostatic theory. Systolic blood pressure appeared in 396 studies, diastolic blood pressure in 362, and C-reactive protein in 317. Despite this heavy reliance on a small core of markers, no consensus set of biomarkers has emerged, and the most common scoring approach remained a simple count of high-risk values.

The review’s most revealing findings concerned thresholds. When researchers use sample-based cut-points, typically the highest-risk quartile of their own study population, those thresholds can drift relative to established clinical criteria. The review found that quantile-based cut-offs for diastolic blood pressure averaged 83 mmHg, above the clinical threshold of 80 mmHg, and fasting glucose cut-offs averaged 7.4 mmol/L, exceeding the clinical 7 mmol/L. For body mass index, the typical quantile cut-off of 28.5 kg/m² sat well above the overweight threshold of 25. By contrast, sample-based thresholds for HbA1c, LDL cholesterol and total cholesterol fell below their clinical values. In other words, depending on the biomarker, a ‘high-risk’ allostatic load score may either exceed diagnostic criteria or remain comfortably within the normal range, positioning the index at very different points along the continuum from subclinical dysregulation to overt disease.

Medication use compounds the problem. Older adults frequently take antihypertensives, statins and glucose-lowering drugs that pharmacologically normalise the very biomarkers allostatic load indices count. A recent multi-cohort consensus statement warned that blood pressure and cholesterol, the ‘mainstays’ of allostatic load, may be inappropriate components in studies of older adults unless medication is explicitly considered. Yet the new review found that only about 36 percent of studies reported how medications were handled. Among those that did, most simply assigned participants taking relevant medication to the highest-risk category for that biomarker, a reasonable correction on its face, but one that injects diagnostic information directly into the exposure.

To test these concerns empirically, the team constructed eight different versions of allostatic load within each cohort: counts of high-risk biomarkers using sample quartiles or clinical cut-points, with and without medication adjustment, a pooled common-biomarker index, and a brief five-item score based on C-reactive protein, resting heart rate, HDL cholesterol, waist-to-height ratio and HbA1c, as recommended by the recent consensus statement. They then quantified overlap with three outcomes, general multimorbidity, cardiometabolic multimorbidity and immune multimorbidity, using C-statistics from logistic regression models adjusted for age, sex, ethnicity and survey year. C-statistics measure how well the allostatic load score discriminates between people with and without multimorbidity, with values above 0.8 indicating high overlap and values near 0.5 indicating none.

The results showed moderate-to-high overlap across the board. C-statistics ranged from roughly 0.68 to 0.82 for general multimorbidity, 0.69 to 0.86 for cardiometabolic multimorbidity, and 0.64 to 0.77 for immune multimorbidity. Strikingly, the different operationalisation strategies, sample quartiles, clinical thresholds, pooled biomarkers and the simplified five-item index, performed nearly identically, echoing earlier evidence that scoring algorithms differ little in predictive power. This convergence suggests that all current approaches draw on essentially the same metabolic and cardiovascular information, and that the brief five-item score captures little that longer biomarker batteries do not. The authors argue this reflects a broader feature of allostatic load research: indices are driven by downstream metabolic and inflammatory alterations rather than by the dynamic regulatory processes the theory describes.

Medication adjustment made things worse rather than better from a construct-validity standpoint. Incorporating medication information raised C-statistics in all four cohorts, with increases of up to +0.15 in the ageing studies ELSA and HRS, and the effect was largest for cardiometabolic multimorbidity. Because cardiometabolic conditions, including diabetes, hypertension, coronary heart disease and stroke, are defined by exactly the biomarker abnormalities that populate allostatic load indices, overlap with cardiometabolic multimorbidity exceeded that with general or immune multimorbidity in nearly every specification. Overlap with immune multimorbidity, covering conditions such as asthma, arthritis, chronic lung disease and thyroid disease, was consistently lower, likely reflecting the scarcity of inflammatory biomarkers and the absence of established cut-points for many of them. The pattern implies that what allostatic load most strongly detects is cardiometabolic disease itself, not a distinct stress-driven process preceding it.

The authors are careful to note that the overlap does not invalidate allostatic load as a concept. Multimorbidity may represent the clinical endpoint of allostatic processes, with chronic stress shifting physiological set points over time until dysregulation crosses into diagnosable disease. Cross-sectional measurement, however, cannot disentangle whether elevated allostatic load scores reflect the physiological embodiment of existing conditions or a shared pathophysiology, and the study’s reliance on data collected between 2004 and 2012 in predominantly White UK and US samples limits generalisability. The authors also acknowledge that C-statistics capture only one dimension of construct alignment and that complete-case analyses were used rather than multiple imputation.

The practical implications are nonetheless substantial. The authors recommend that researchers explicitly state where their chosen operationalisation sits on the subclinical-to-clinical continuum, consider sociodemographic influences on biomarker levels, including sex- and ethnicity-specific thresholds such as those proposed for BMI, and align medication-handling decisions with the analytical goal: identifying disease states or characterising latent physiological dysregulation. More fundamentally, they call for longitudinal designs, life-course biomarker collection and greater transparency in threshold definition. Until then, the study suggests, an index hailed as the biology of chronic stress may, in many published analyses, be functioning in practice as a partial census of diagnosed disease, a distinction that could reshape how hundreds of findings linking stress to health are interpreted.

Subject of Research: The conceptual and analytic overlap between allostatic load and multimorbidity

Article Title: A systematic review and empiric examination of the conceptual and analytic overlap between allostatic load and multimorbidity

Article References: Booker, C. L., Liang, Y., Meadows, A., Cranshaw, O., Dearman, A., Dilkes, E., Hignell, B., Malih, A. J., Navyte, G., & Kumari, M. (2026). A systematic review and empiric examination of the conceptual and analytic overlap between allostatic load and multimorbidity. SSM – Population Health, 35, Article 101962. https://doi.org/10.1016/j.ssmph.2026.101962

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DOI: 10.1016/j.ssmph.2026.101962

Keywords: allostatic load, multimorbidity, chronic stress, biomarkers, cardiometabolic disease, physiological dysregulation, medication adjustment, clinical cut-points, population health, systematic review, ageing, cohort studies

The significance of this work lies in its comparative design. Most studies of metal exposure draw on a single population and a single set of biospecimens, which makes it difficult to know whether observed associations between metals and disease are robust or are artifacts of how exposure was measured. By aligning biomarker data across three cohorts that differ sharply in ancestry, geography, diet, and lifestyle, the researchers were able to probe how consistently metal concentrations appear in blood and urine, how the metals correlate with one another within individuals, and how demographic and behavioral characteristics shape the exposure profiles that epidemiologists rely on.

MASALA focuses on South Asian immigrants in the United States, a population that experiences elevated cardiovascular risk at lower body weights and through pathways that remain incompletely understood. Environmental exposures, including metals accumulated through diet, water, and occupational contact, have been proposed as one contributing factor. MESA-LA, part of the larger Multi-Ethnic Study of Atherosclerosis, brings together participants from multiple racial and ethnic groups in Los Angeles, offering a densely urban exposure environment shaped by traffic, industry, and aging infrastructure. The Strong Heart Family Study, meanwhile, is anchored in American Indian communities and benefits from family-based sampling, which allows investigators to account for shared genetic and household influences on measured biomarkers.

Metal biomarkers in epidemiology typically come from two matrices: whole blood and urine. Blood lead and blood cadmium reflect a combination of recent exposure and, in the case of lead, mobilization from long-term skeletal stores, making them useful integrative markers of cumulative internal dose. Urinary arsenic, cadmium, and other metals capture renal excretion of absorbed doses over recent days to years, depending on the element and its chemical form. The choice of matrix matters enormously. A metal that is well measured in urine may be poorly captured in blood, and vice versa, and the interpretation of any given concentration depends on speciation, timing of sample collection, and the physiological behavior of the element in question.

A central theme of the new analysis is the metal mixture itself. Environmental exposures rarely arrive one at a time. People are simultaneously exposed to dozens of metals through drinking water, rice and other grains, seafood, tobacco smoke, dust, and occupational settings, and these exposures can interact. Arsenic, cadmium, and lead, for example, have each been individually linked to cardiovascular disease, diabetes, and kidney dysfunction, but growing evidence suggests that their combined presence may produce risks that differ from the sum of their parts. Statistical approaches to mixtures, including methods that model correlated exposures jointly rather than one metal at a time, have therefore become a priority in environmental epidemiology, and their validity depends on having well-characterized, comparable biomarker data.

The three cohorts offer a natural laboratory for testing that comparability. Because MASALA, MESA-LA, and the Strong Heart Family Study each collected biospecimens under their own protocols, harmonization required careful attention to collection tubes, storage conditions, assay platforms, and quality control procedures. Differences in laboratory methods can introduce systematic bias that masquerades as true population differences, so cross-cohort analyses must document and, where possible, correct for such variation. The study’s comparative framework provides a template for how multi-cohort environmental research can be conducted rigorously, and its findings speak to both the promise and the practical challenges of pooling biomarker data across studies.

Population differences in metal biomarkers reflect more than differences in exposure. Diet composition plays a major role: rice consumption, which is relatively high among many South Asian communities, is a recognized pathway for inorganic arsenic intake, while seafood consumption drives methylmercury and contributes organic arsenic species that can confound urinary arsenic measurements if not separated analytically. Smoking is a dominant source of cadmium, so tobacco use patterns strongly influence cadmium distributions. Housing age and water systems affect lead exposure, and regional geology shapes background arsenic and uranium in drinking water. Sex, age, kidney function, and iron status further modify how metals are absorbed, distributed, and excreted, meaning that identical external exposures can yield different biomarker readings in different people.

These considerations matter because metal exposure is increasingly recognized as a modifiable cardiovascular risk factor. Large pooled analyses have associated low-level arsenic, cadmium, and lead exposure with hypertension, atherosclerosis, coronary heart disease, and cardiovascular mortality at concentrations once considered inconsequential. If biomarker measurements can be harmonized across diverse cohorts, investigators can test whether these associations replicate across ancestries and environments, estimate exposure–response relationships with greater precision, and identify subgroups bearing disproportionate burdens. That is precisely the kind of evidence needed to inform regulatory standards for drinking water, food, and consumer products, and to target screening or interventions toward the communities at highest risk.

The Strong Heart Family Study adds a further dimension: the ability to examine familial aggregation of metal biomarkers. Family-based designs can help distinguish shared household and environmental sources from genetic contributions to biomarker variation, and they permit exploration of how exposures in one generation may relate to health outcomes in the next. Metals cross the placenta, and early-life exposure has been linked to developmental and cardiometabolic outcomes, making intergenerational considerations central to the public health significance of metal mixtures. Including a family-based American Indian cohort alongside two urban cohorts therefore broadens the inferential reach of the analysis considerably.

For the broader environmental health community, the study underscores a practical message: biomarker-based exposure assessment is feasible and informative at scale, but it demands transparency about methods and humility about interpretation. Cross-cohort variation in metal concentrations should not be over-read as pure exposure difference when analytical and physiological factors are in play. At the same time, the consistency of measurable metal burdens across three demographically distinct American populations is itself a striking finding, a reminder that industrial-era contaminants have become a routine feature of human internal chemistry. As mixture methods mature and cohorts continue to accrue health outcomes, harmonized metal biomarker data of this kind will underpin the next generation of research linking environmental exposures to chronic disease, and could ultimately help shift prevention efforts upstream, toward the sources of exposure themselves.

Subject of Research: Comparative assessment of metal and metal mixture biomarkers across three U.S. population cohorts

Article Title: Metal and metal mixture biomarkers across three U.S. cohorts: MASALA, MESA-LA, and Strong Heart Family Study

Article References: Schilling, K., Martinez-Morata, I., Anderson, W. A., Basu, A., Izuchukwu, C., Collado, W., Navas-Acien, A., & Kanaya, A. M. (2026). Metal and metal mixture biomarkers across three U.S. cohorts: MASALA, MESA-LA, and Strong Heart Family Study. Journal of Exposure Science & Environmental Epidemiology. https://doi.org/10.1038/s41370-026-00954-8

Image Credits: AI Generated

DOI: 10.1038/s41370-026-00954-8

Keywords: metal biomarkers, metal mixtures, MASALA, MESA-LA, Strong Heart Family Study, environmental epidemiology, arsenic, cadmium, lead exposure, cardiovascular risk, exposure science, cohort studies