The research, published in eClinicalMedicine, builds on the concept of allostatic load, the idea that repeated or chronic stressors progressively over-solicit the body’s regulatory systems, producing a kind of physiological wear-and-tear. Previous work by the same team developed a Biological Health Score, or BHS, that extends traditional allostatic load measures by incorporating physiological systems not directly tied to the stress response, such as liver and kidney function. In earlier analyses, the score showed strong social gradients and predicted mortality and disease incidence. But nearly all such studies relied on a single snapshot in time, leaving open whether the trajectory of biological dysregulation, rather than its level at any one moment, carries independent prognostic information.

To address that gap, the researchers turned to the UK Biobank’s repeat assessment visit. Of the 502,536 volunteers enrolled between 2006 and 2010, some 20,344 returned for a second assessment between December 2012 and June 2013, donating blood that was re-analysed for the same panel of biomarkers. After exclusions, 20,048 participants, 10,266 women and 9,782 men with a mean age of 57.1 years at the first assessment, formed the analytical sample. The team selected 15 biomarkers spanning five physiological systems: metabolic markers including HbA1c, HDL and LDL cholesterol and triglycerides; cardiovascular measures of blood pressure and pulse rate; inflammatory markers including C-reactive protein, insulin-like growth factor 1 and glycoprotein acetyls; liver enzymes ALT, AST and GGT; and kidney markers creatinine and cystatin C.

Each biomarker was log-transformed and standardised, and for protective markers such as HDL and IGF-1 the sign was reversed so that any increase in the score corresponded to greater biological risk. System-specific sub-scores were computed as the mean of their constituent standardised biomarkers, and the overall BHS was the mean of the five sub-scores, ensuring that each physiological system contributed equally regardless of how many biomarkers it contained. Crucially, the same methodology was applied to the standardised differences between the second and first measurements, yielding a change score that captured the direction and magnitude of biological drift over the roughly 4.3 years between visits.

The results confirmed that the score behaves as a cumulative measure should. The total BHS increased between the two time points, more strongly in women than in men, and every sub-score rose except the cardiovascular one. The largest biomarker increases were seen for cystatin C, creatinine and HbA1c, while HDL cholesterol, diastolic blood pressure and pulse rate declined. Notably, change was consistently smaller among participants with higher baseline scores, a statistical ceiling effect that the authors say underscores the value of modelling trajectories rather than static levels. The flat cardiovascular sub-score, they suggest, may partly reflect the widespread use of antihypertensive medication, which pharmacologically controls blood pressure and thereby blunts the sensitivity of those markers to underlying deterioration.

The social patterning of these changes was striking. Using a stability selection procedure based on LASSO regression across 100 random subsamples, the team identified confounders and mediators from a pool of 270 candidate variables, then modelled BHS change against six social exposures: age at completion of education, qualification level, household income, financial difficulties, ability to confide in someone, and loneliness. Educational and economic disadvantage emerged as the dominant predictors. Each additional year of education was associated with a smaller increase in the score in both sexes, while low qualifications and reported financial difficulties were linked to larger increases in both women and men. Low income predicted greater biological deterioration in men but not in women. By contrast, socio-affective factors, loneliness and rarely having someone to confide in, showed little association with BHS change once confounders were accounted for.

Did these biological trajectories matter for health? Follow-up through national registry linkage extended to December 2022, giving nine to ten years of outcome data after the second assessment. In age-scaled Cox models adjusted for baseline BHS and the interval between measurements, a one-unit increase in the BHS change score was associated with substantially higher risks. For all-cause mortality, the hazard ratio was 1.46 in men and 1.26 in women; for atherosclerotic cardiovascular disease, 1.33 in men and 1.24 in women; and for cancer incidence, 1.29 in women and 1.16 in men. Because these associations were independent of the baseline score, they suggest that two people with identical biological profiles at one point in time may nonetheless diverge in risk depending on the direction of their subsequent physiological drift.

The associations proved robust to adjustment. When the models were sequentially adjusted for history, social and environmental factors, behaviours, diet, anthropometrics and baseline health, the effects attenuated somewhat, most visibly in women, where fully adjusted estimates were no longer statistically significant. In men, however, the associations with mortality and cardiovascular disease persisted even after full adjustment, with hazard ratios of 1.38 and 1.25 respectively. Sub-score analyses hinted at system-specific pathways: mortality links appeared driven by cardiovascular and inflammatory markers in women and by inflammatory, liver and renal markers in men, while cancer associations centred on inflammatory and renal systems.

The most conceptually ambitious part of the study was its mediation analysis. Using the parametric g-formula implemented through Monte Carlo simulation, a counterfactual framework that can handle exposure-induced mediator-outcome confounding, the researchers decomposed the total effect of each social exposure on each outcome into direct effects and effects routed through BHS change. The total effects themselves were sobering: low income was associated with a 51 percent higher mortality hazard in men, financial difficulties with a 45 percent higher hazard, and each year less of education with a 4 to 5 percent higher hazard of mortality or cardiovascular disease. Yet the proportion of these effects explained by short-term BHS change was modest. Statistically significant mediated proportions appeared only for economic disadvantage and mortality in men, roughly 4.5 percent for low income and 6.9 percent for financial difficulties. No significant mediation was observed in women.

The authors interpret this cautiously. Short-term changes in multisystem dysregulation, they conclude, capture only part of the longer-term biological embodiment of social experience, and multiple other pathways, from health behaviours to psychosocial stress to access to care, clearly contribute. Limitations also temper the findings: the UK Biobank’s well-documented healthy-volunteer bias is amplified among participants who return for repeat assessments, the broad cancer definition may have diluted site-specific signals given long latency periods, and residual confounding and reverse causality cannot be excluded. Still, the central message is compelling. Repeated multisystem biological measurements appear to capture an evolving physiological vulnerability that is socially patterned and predictive of death and disease beyond what any single snapshot reveals. As the researchers argue, extending such analyses to multiple measurements across the life course could open a genuine window onto the dynamics of how social disadvantage becomes biology, and potentially identify the points at which intervention might still rewrite that trajectory.

Subject of Research: Longitudinal changes in a multisystem Biological Health Score as a mediator between social disadvantage and mortality and disease in the UK Biobank cohort

Article Title: Contribution of short-term changes in a composite health score on pathways linking social disadvantage and health: evidence from the UK biobank prospective cohort study

Article References: Contribution of short-term changes in a composite health score on pathways linking social disadvantage and health: evidence from the UK biobank prospective cohort study. (n.d.). https://doi.org/10.1016/j.eclinm.2026.104221

Image Credits: AI Generated

DOI: 10.1016/j.eclinm.2026.104221

Keywords: UK Biobank, allostatic load, Biological Health Score, social disadvantage, health inequalities, biomarkers, mortality, cardiovascular disease, cancer incidence, mediation analysis, physiological dysregulation, prospective cohort study

The analytical strategy was deliberately layered. First, the researchers used multivariable logistic regression to ask which of 14 symptom domains independently predicted the presence of muscle problems. In the full model, three symptoms emerged as significant independent predictors: breathing problems carried an odds ratio of 2.49 (95 percent confidence interval 1.42 to 4.38, p = 0.001), flu-like symptoms an odds ratio of 1.97 (1.14 to 3.42, p = 0.015), and temperature-regulation disorders an odds ratio of 1.97 (1.10 to 3.51, p = 0.021). When the model was restricted to a reduced set of physiological symptoms, cardiovascular symptoms additionally reached significance with an odds ratio of 1.87 (1.09 to 3.24, p = 0.024). These odds ratios indicate that patients reporting each of these symptoms had roughly double the odds of also reporting muscle problems compared with patients who did not, after accounting for the other symptoms in the model.

Correlation analysis reinforced the impression that muscle symptoms travel with a distinct physiological cluster. Tetrachoric correlations, which estimate the latent association between binary symptom variables, showed substantial relationships between muscle problems and breathing difficulties (rho = 0.52), cardiovascular symptoms (rho = 0.49), and temperature-regulation disorders (rho = 0.49), with somewhat weaker but still notable links to visual disturbances (rho = 0.39) and flu-like symptoms (rho = 0.39). Values in this range suggest that the co-occurrence of these symptoms is far from random and points toward a common underlying driver rather than a coincidental clustering of unrelated complaints.

The centerpiece of the study, however, was its latent-variable modeling. Using exploratory factor analysis followed by structural equation modeling (SEM), the authors tested whether the physiological symptoms could be explained by a single latent factor, an unobserved variable hypothesized to generate the observed symptom pattern. The model fit was strong by conventional standards: the root mean square error of approximation (RMSEA) was 0.046, well below the 0.06 threshold typically considered acceptable; the comparative fit index (CFI) was 0.971 and the Tucker-Lewis index (TLI) was 0.952, both close to the 0.95 benchmark; and the standardized root mean square residual (SRMR) was 0.026, far under the 0.08 cutoff. Together these indices indicate that a single latent physiological dysregulation factor provides a parsimonious and statistically credible account of how these symptoms co-occur in the sample.

Not every symptom behaved identically within this structure. Gastrointestinal complaints loaded substantially on the latent factor even though they showed no independent association with muscle problems in the multivariable regression, suggesting they belong to the broader physiological pattern but are not tightly coupled to the muscle-specific manifestation. Urogenital symptoms also displayed a smaller but significant loading on the factor alongside substantial item-specific variance, indicating that they are partly woven into the shared dysregulation and partly driven by their own distinct processes. This kind of decomposition, separating shared from symptom-specific variance, is precisely what latent-variable approaches are designed to deliver, and it hints that ME/CFS pathology may involve both a common multisystem thread and parallel, partially independent symptom channels.

The sex-stratified analyses produced a striking descriptive asymmetry. In women, all five physiological symptoms, breathing, cardiovascular, thermoregulatory, visual, and flu-like, independently predicted muscle problems. In men, only breathing problems and temperature-regulation disorders remained significant. Yet when the authors formally tested the overall symptom-by-sex interaction, the result did not reach statistical significance, meaning the data do not establish that the symptom associations genuinely differ between the sexes. One exception appeared at the individual symptom level: the association between breathing-related symptoms and muscle problems showed a statistically significant interaction with sex, hinting that this particular link may be stronger in one sex than the other. The authors urge caution here, noting that the overall interaction test was non-significant and the confidence interval for the interaction estimate was wide, leaving the finding exploratory at best.

Menopausal status, approximated by age-defined proxy groups, added another descriptive layer. Women classified as premenopausal by this proxy showed significant associations between muscle problems and cardiovascular symptoms, visual disturbances, and temperature-regulation disorders, whereas women classified as postmenopausal showed significant associations with breathing difficulties and flu-like symptoms. Flu-like symptoms were independently associated with muscle problems in the postmenopausal subgroup but not in the premenopausal one. Once again, however, the formal test told a more conservative story: the overall menopausal-by-symptom interaction was not statistically significant (Wald chi-square of 5.96 on 5 degrees of freedom, p = 0.310). The authors therefore frame these subgroup contrasts as exploratory differences in association rather than evidence of a menopause-specific effect, a distinction that matters enormously for how the findings should be cited and built upon.

Stability across disease duration offered further reassurance about the latent factor’s measurement properties. The specified physiological factor showed statistical comparability across cross-sectional disease-duration groups, meaning there was no overall evidence that the factor means something different in patients early versus late in their illness. This kind of measurement invariance is a prerequisite for meaningful comparison across patient subgroups and lends weight to the idea that the dysregulation factor is a stable feature of the disease rather than an artifact of illness stage, survivorship bias, or shifting symptom reporting over time.

What could the latent factor represent biologically? The authors are careful not to overclaim, but they note that the findings are compatible with the hypothesis that the symptom-dysregulation factor reflects a chronic post-exertional malaise-associated multisystem response. The symptom cluster it captures, spanning respiratory, cardiovascular, thermoregulatory, visual, and flu-like domains, overlaps substantially with the territory of autonomic dysfunction, vascular dysregulation, immune activation, and metabolic disturbance, all of which have been implicated in ME/CFS by prior physiological studies. Whether the latent factor corresponds to genuinely interacting mechanisms across these systems is a question the present cross-sectional design cannot answer, and the authors explicitly call for longitudinal and biomarker-based studies to test it.

The practical implications could be considerable. If muscle symptoms indeed serve as a candidate integrative symptom within a measurable latent dysregulation factor, then symptom-based phenotyping may help stratify patients for clinical trials, a persistent challenge in a disease as heterogeneous as ME/CFS. Better stratification could, in turn, accelerate the development of mechanism-based therapeutic approaches by ensuring that patients sharing a common physiological signature are studied together. The study was conducted in 2022 under Declaration of Helsinki protocols approved by the Ethics Committee of Furtwangen University, Germany, and was partially funded by the Ministry of Social Affairs, Health and Integration using state funds approved by the Baden-Württemberg State Parliament, with the subsequent analyses unfunded. As an exploratory, cross-sectional analysis of registry data, it cannot establish causality or direction, and its subgroup findings demand replication. But by quantifying how tightly muscle symptoms are woven into a broader physiological web, and by providing a statistically well-fitting latent structure that future biomarker studies can interrogate, the work offers ME/CFS researchers a concrete, testable framework for one of medicine’s most perplexing chronic illnesses.

Subject of Research: Latent physiological symptom-dysregulation and muscle symptoms in ME/CFS analyzed by sex and menopausal-status proxy group

Article Title: Muscle symptoms and a latent physiological symptom-dysregulation factor in ME/CFS: exploratory analyses by sex and age-defined menopausal-status proxy group

Article References: Habermann-Horstmeier, L., & Horstmeier, L. M. (2026). Muscle symptoms and a latent physiological symptom-dysregulation factor in ME/CFS: exploratory analyses by sex and age-defined menopausal-status proxy group. Journal of Translational Medicine. https://doi.org/10.1186/s12967-026-09018-9

Image Credits: AI Generated

DOI: 10.1186/s12967-026-09018-9

Keywords: ME/CFS, chronic fatigue syndrome, muscle symptoms, physiological dysregulation, post-exertional malaise, structural equation modeling, factor analysis, sex differences, menopausal status, autonomic dysfunction, symptom phenotyping, Journal of Translational Medicine

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: A systematic review and empiric examination of the conceptual and analytic overlap between allostatic load and multimorbidity. (n.d.). https://doi.org/10.1016/j.ssmph.2026.101962

Image Credits: AI Generated

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