The research team drew on the UK Biobank Pharma Proteomics Project, which measured 2,916 plasma proteins in more than 44,000 participants using the Olink Explore 3072 platform. From these protein profiles, the scientists built organ-specific aging clocks using elastic net regression, training models to predict chronological age from protein abundance within each organ domain. The difference between predicted biological age and actual chronological age—known as the age gap—served as the key outcome. A positive residual signals accelerated aging in that organ; a negative residual indicates the tissue is biologically younger than expected. In total, 18 organ-specific indices plus one composite index were generated, covering systems from the brain and heart to the thyroid, adrenal glands, and salivary glands.

After linking these aging indices to self-reported birth weight and breastfeeding status, the researchers analyzed fully adjusted models accounting for age, sex, ethnicity, childhood body size, adult body mass index, socioeconomic deprivation, education, smoking, alcohol use, physical activity, and major cardiometabolic diseases. The final analytic samples comprised 21,446 participants for the birth weight analysis and 29,713 for the breastfeeding analysis. The results were striking in their breadth: higher birth weight was associated with slower biological aging across 11 of the organ systems examined, pointing to a systemic rather than organ-isolated effect of fetal growth conditions.

The strongest inverse associations appeared in the digestive tract. Each additional kilogram of birth weight corresponded to lower intestinal aging scores, with similar patterns in the esophagus and stomach. Beyond the gut, higher birth weight was linked to decelerated aging of the immune system, kidneys, liver, salivary glands, thyroid, adrenal glands, skin, and muscle. These findings align with a well-established body of developmental evidence. Low birth weight has previously been tied to impaired intestinal vascular development, reduced nephron endowment in the kidneys, hepatic fat accumulation, and diminished muscle mass—structural constraints established in utero that may quietly accelerate organ decline across the entire lifespan.

Breastfeeding told a more selective story. Being breastfed was associated with slower aging of the intestine and adrenal glands, but—somewhat unexpectedly—with accelerated aging of the thyroid. The intestinal finding is biologically plausible: breast milk is rich in human milk oligosaccharides and immune factors such as secretory IgA, which promote beneficial microbial colonization, strengthen the gut barrier, and suppress low-grade inflammation, all processes that counteract hallmark features of gut aging. The adrenal association may reflect long-term modulation of the hypothalamic–pituitary–adrenal axis, the body’s central stress-regulation system. The thyroid result, the authors caution, remains speculative; variability in iodine and thyroid hormone content of breast milk could theoretically impose metabolic load on the developing gland, but this interpretation requires further study.

Perhaps the most compelling finding emerged when the two exposures were combined. Participants who both weighed more at birth and had been breastfed showed the most favorable aging profiles of any group, with significantly reduced aging indices in the intestine, kidney, liver, adrenal gland, lung, and stomach. The magnitude of the intestinal effect was roughly double that of either exposure alone, hinting at an additive or synergistic relationship between prenatal and postnatal nutritional advantages. Conversely, low-birth-weight infants who were breastfed gained only modest protection, suggesting that breastfeeding can partially mitigate—but cannot fully compensate for—the developmental constraints of restricted fetal growth.

To probe the mechanisms behind these associations, the team conducted causal mediation analyses testing two candidate pathways: systemic inflammation, measured by C-reactive protein, and allostatic load, a composite index of cumulative physiological wear across ten metabolic, cardiovascular, and inflammatory biomarkers. Allostatic load emerged as the dominant mediator. For skin aging, 35.3 percent of the birth weight benefit was attributable to lower cumulative stress burden, with substantial mediation also seen for the adrenal glands (30.5 percent) and thyroid (19.8 percent). Moderate contributions appeared for the salivary glands, kidneys, and intestines, while C-reactive protein mediated a smaller 4.8 percent of the liver effect. Notably, neither mediator explained the breastfeeding associations, suggesting that infant feeding may act through earlier developmental or localized biological pathways rather than chronic stress accumulation.

The study also revealed that early-life advantages do not operate in a social vacuum. Interaction analyses showed that breastfeeding’s protective effects were stronger in women, particularly for brain, pituitary, arterial, and adipose aging, hinting at sex-specific modulation involving lifelong hormonal milieu. The benefits of both exposures were most pronounced among individuals with higher body mass index and, paradoxically, among those living in more favorable socioeconomic circumstances as measured by the Townsend Deprivation Index. This pattern resonates with the cumulative advantage framework in life-course epidemiology: early biological endowment appears to compound over time when paired with supportive later-life environments, while adverse social and behavioral contexts may erode the protective legacy of good early nutrition.

Sensitivity analyses bolstered the robustness of the findings. Adding medication use to the models left the results virtually unchanged, and mutually adjusting for both perinatal exposures preserved the significance of nine of the eleven birth weight associations and the intestinal benefit of breastfeeding. Restricted cubic spline analyses confirmed that the birth weight relationships were approximately linear across the observed range. Still, the authors acknowledge important limitations. Both exposures were self-reported decades after the fact, introducing potential recall bias that would likely bias results toward the null. Breastfeeding was captured as a simple yes-or-no question, precluding analysis of duration or exclusivity, and proteomic aging indices, while scalable, may not fully capture the structural and functional deterioration visible on imaging or histopathology.

Even with these caveats, the study delivers a provocative message: the trajectory of biological aging is shaped, at least in part, by nutritional conditions in the earliest days of life. By integrating developmental origins theory, population-scale proteomics, and mediation analysis within a single framework, the research reframes organ aging not as a process that begins in middle age, but as the culmination of exposures accumulated across the entire life course. If confirmed in prospective cohorts with granular perinatal data, these findings could elevate early-life nutrition—from maternal-fetal health through infant feeding—into a central pillar of healthy-aging policy, alongside the familiar levers of diet, exercise, and smoking cessation pursued in adulthood.

Subject of Research: Associations of birthweight and breastfeeding with proteome-based organ-specific biological aging in the UK Biobank

Article Title: Birthweight and Breastfeeding Shape Patterns of Biological Aging Across Adult Organs

Article References: Wei, W., Qi, X., Cheng, B., Zhao, B., He, D., Hui, J., Feng, J., Cheng, S., Yang, X., Pan, C., Gou, Y., Wen, Y., Liu, H., Jia, Y., Liu, L., & Zhang, F. (2026). Birthweight and Breastfeeding Shape Patterns of Biological Aging Across Adult Organs. Aging Cell, 25(10), Article e70762. https://doi.org/10.1111/acel.70762

Image Credits: AI Generated

DOI: 10.1111/acel.70762

Keywords: biological aging, birth weight, breastfeeding, UK Biobank, proteomics, organ aging, Developmental Origins of Health and Disease, allostatic load, inflammation, life-course epidemiology, Aging Cell, early-life nutrition

The Lancet Diabetes and Endocrinology Commission, in its 2025 report, proposed a fundamental reframing of obesity. Rather than treating a high BMI as a diagnosis in itself, the Commission distinguished between clinical obesity, in which excess body fat is accompanied by measurable organ dysfunction or limitations in daily activities, and preclinical obesity, in which excess fat has not yet caused observable harm. The distinction matters because BMI is an imperfect proxy: it overestimates adiposity in muscular individuals and underestimates it in people with central fat accumulation but normal weight. By requiring evidence of organ dysfunction, the new framework aims to identify people whose excess fat is actively damaging their health.

Applying this framework at population scale required an unprecedented collaborative effort. No single cohort had collected all the anthropometric measures, body composition data, and clinical and laboratory markers the Commission’s criteria demand. So the research consortium, known as ECOS, integrated cross-sectional data from 11 population-based cohorts in Australia, Canada, Estonia, Finland, the Netherlands, the United Kingdom, and the United States, covering 44 assessment waves collected between 1996 and 2023. In total, 786,557 participants aged four and above were classified, making this the largest clinical obesity prevalence study conducted to date.

The results reveal a striking age-dependent divergence between BMI-defined and clinical obesity. In early childhood, clinical obesity affected just 3.7 percent of children aged four to five, well below the 8.8 percent prevalence of BMI-defined obesity in the same group. Throughout childhood, adolescence, and early and mid-adulthood, BMI-defined obesity consistently overestimated the burden of clinically significant disease. At ages 35 to 44, for example, 26.3 percent of participants met BMI criteria for obesity, but only 15.1 percent met the Commission’s criteria for clinical obesity. Then the pattern inverts: from age 55 onward, clinical obesity becomes more common than BMI-defined obesity, climbing to 52.0 percent among adults aged 65 to 74.

The organ dysfunction criteria driving these classifications varied systematically with age. Among children with clinical obesity, elevated systolic blood pressure was overwhelmingly the most common finding, present in 87.3 percent of affected four- to nine-year-olds. Renal dysfunction, detected through microalbuminuria, was also most prevalent in childhood, affecting 44.2 percent of young children with clinical obesity. In early and mid-adulthood, liver dysfunction took center stage, with elevated alanine aminotransferase found in 56.3 percent of affected adults aged 20 to 34 and elevated gamma-glutamyl transferase in 48.8 percent of those aged 35 to 54. In later life, musculoskeletal dysfunction dominated, affecting 55.1 percent of people aged 75 and older with clinical obesity.

Sex emerged as a powerful modifier of these patterns. From adolescence through mid-adulthood, males carried a higher prevalence of clinical obesity than females; at ages 35 to 44, 18.6 percent of men versus 13.9 percent of women met the criteria. But from age 55 onward the relationship reversed, with women showing markedly higher prevalence. At ages 65 to 74, 57.1 percent of women had clinical obesity compared with 44.1 percent of men, and female prevalence peaked at 58.5 percent at ages 75 to 84. Preclinical obesity followed a different trajectory: in women it rose through early adulthood, peaked in the mid-thirties to mid-forties, and then declined, whereas in men it remained relatively constant across the life course. The authors suggest these divergences may partly reflect survival bias and differences in how excess adiposity manifests between the sexes, though the underlying biology remains to be fully explored.

Socioeconomic position, measured through education level, showed a more modest influence. Across all age groups, clinical obesity prevalence was slightly lower among people with high education compared with those in the middle and low education categories; at ages 65 to 74, prevalence was 46.8 percent in the high education group versus 53.9 and 55.0 percent in the middle and low groups respectively. The age-dependent patterns of both clinical and preclinical obesity were broadly similar across all three education strata, suggesting that while education shapes obesity risk, the relationship between excess fat and organ dysfunction follows a comparable life course architecture regardless of socioeconomic background.

The study also exposed how sensitive prevalence estimates are to the specific measures chosen to define excess adiposity. In a subgroup of cohorts with complete anthropometric and body composition data, waist-to-height ratio was met by virtually all participants classified with clinical or preclinical obesity, between 95.4 and 100 percent depending on the cohort, while body fat percentage was the least commonly satisfied criterion, met by as few as 9.6 percent of UK Biobank participants with clinical obesity. When excess adiposity was defined by any two anthropometric criteria, clinical obesity prevalence in adults ranged from 5.2 percent at ages 20 to 24 to 51.2 percent at ages 65 to 74, substantially higher than estimates based on BMI plus one anthropometric measure or body fat percentage alone. These differences carry real consequences for diagnosis, risk prediction, and treatment eligibility.

The authors are candid about the limitations inherent in retrofitting a new diagnostic framework onto data collected before it existed. No cohort captured all 13 diagnostic domains specified by the Commission, meaning clinical obesity prevalence was likely underestimated in some groups. Data on central nervous system, respiratory, lymphatic, and reproductive criteria were sparse or absent, and the researchers could not always verify that observed organ dysfunction was genuinely attributable to excess adiposity rather than to other common conditions of later life. Laboratory thresholds varied across jurisdictions, and the treatment of medicated conditions, such as hypertension controlled by antihypertensive drugs, required pragmatic decisions that the Commission’s guidelines do not address. The cohorts were also predominantly from high-income, majority-White populations, limiting generalizability to more diverse settings where the burden of obesity is growing fastest.

Despite these caveats, the consistency of the life course pattern across cohorts from different countries, recruitment epochs, and measurement protocols lends weight to the central conclusion: clinical obesity is rarer than BMI-defined obesity in childhood and early adulthood, but substantially more common in later life. The findings underscore an urgent need for prospective longitudinal studies with standardized collection of all diagnostic criteria, which would allow researchers to track how individuals transition from preclinical to clinical obesity and to determine whether early identification and intervention in childhood, a critical window for prevention, can alter that trajectory. For now, the study provides the most comprehensive picture yet of what the new definition of obesity means for real populations, and it suggests that the number on the scale, and even the ratio of weight to height, may be far less informative than the quiet damage that excess fat does to organs over decades of exposure.

Subject of Research: Life course prevalence of clinical and preclinical obesity using the Lancet Commission diagnostic criteria

Article Title: The lancet commission definition of clinical obesity: a multi-cohort cross-sectional analysis from early childhood to older age in seven high-income countries

Article References: Longmore, D. K., MacKechnie, G. P., Huntington, P. A., Chen, Z. H., de Groot, J., Mikkonen, S., Mykkänen, J., Pätsi, S., Puusepp, T., Fischer, K., Jaddoe, V., Kerr, J. A., Lakka, T. A., Lehtimäki, T., Miliku, K., Nedelec, R., Ponsonby, A.-L., Sebert, S., Vuillermin, P., … Viikari, J. (2026). The lancet commission definition of clinical obesity: a multi-cohort cross-sectional analysis from early childhood to older age in seven high-income countries. eClinicalMedicine, Article 104231. https://doi.org/10.1016/j.eclinm.2026.104231

Image Credits: AI Generated

DOI: 10.1016/j.eclinm.2026.104231

Keywords: clinical obesity, preclinical obesity, Lancet Commission, body mass index, organ dysfunction, life course epidemiology, adiposity, pediatric obesity, cohort study, prevalence, metabolic health, public health

The investigation was built on the Danish Life Course (DANLIFE) cohort, a register-based resource encompassing all children born in Denmark since 1980. Because every Danish resident carries a unique personal identification number, researchers at the University of Copenhagen and collaborators could link national registries covering births, hospital contacts, psychiatric diagnoses, social welfare, and residential addresses into a single longitudinal record. The analytical sample comprised 1,235,519 individuals who had complete adversity information and survived to age 16. The team, led by Naja Hulvej Rod, conceptualised adversity along two dimensions: time, measured annually from birth to age 16, and layers, spanning the individual, the family, and the neighbourhood.

At the individual layer, the researchers captured three indicators. Perinatal adversity was defined as preterm birth before 37 weeks of gestation or being small for gestational age, below the tenth percentile on standardised growth curves. Physical-health adversity was operationalised as membership in the top 20 percent of cumulative emergency and inpatient hospital contacts between ages 0 and 15, drawn from the National Patient Registry. Mental-health adversity was any psychiatric diagnosis recorded before age 16 in the Danish Psychiatric Central Research Register. These markers reflect biological susceptibility and early disease burden that may compound later social exposures.

The family layer used group-based multi-trajectory modelling of annual adversity counts across three expert-identified dimensions: material deprivation, encompassing family poverty and parental long-term unemployment; loss or threat of loss, covering serious illness or death of a parent or sibling; and family dynamics, including maternal separation, foster-care placement, and parental or sibling psychiatric illness or substance abuse. Five distinct trajectory groups emerged: low adversity, early material deprivation, persistent material deprivation, loss or threat of loss, and high adversity, the last characterised by escalating hardship across all three dimensions simultaneously. Each individual was assigned to the trajectory with the highest probability of membership.

The neighbourhood layer broke new ground for cohort research of this scale. Denmark was divided into 1,885 small-area data zones of roughly 2,500 inhabitants each, nested within 98 municipalities, using residential coordinates and a clustering algorithm. Neighbourhood material deprivation was assessed annually through four indicators: the proportion of residents with low income, basic education only, unemployment, and overcrowded housing with more than one person per room. High neighbourhood deprivation was defined as falling in the top 20 percent for at least two of these indicators averaged across childhood, providing a granular portrait of the structural conditions surrounding each child as they grew up.

The results revealed a striking pattern of co-occurrence. Children in the high family-adversity group were far more likely than their peers to have been born small for gestational age, to receive a childhood mental-health diagnosis, and to be high users of hospital services, while those in the persistent material-deprivation group most often lived in deprived neighbourhoods. Adversity, in other words, clusters. A child facing poverty or parental illness is also more likely to face perinatal complications, health difficulties, and neighbourhood disadvantage, producing cascading patterns in which individual health, family conditions, and place act as both causes and consequences of one another across development and generations.

Each layer was independently associated with mortality when analysed separately using Cox proportional hazards models, complemented by Aalen’s additive hazards models to quantify absolute effects. Perinatal adversity carried a hazard ratio of 1.36, corresponding to 14 excess deaths per 100,000 person-years. A childhood mental-health diagnosis tripled the risk, with a hazard ratio of 3.00 and 68 excess deaths per 100,000 person-years, while high physical-health service use yielded a hazard ratio of 2.36. High family adversity showed the strongest single-layer association at a hazard ratio of 3.95, or 95 excess deaths per 100,000 person-years, and neighbourhood deprivation contributed a hazard ratio of 1.20. The leading causes of death were suicide and assault, accidents, and cancer, all socially patterned outcomes.

Crucially, the additive hazards analysis uncovered cross-layer interactions, meaning more deaths occurred than the sum of each layer’s separate effects would predict. Children exposed to both high family adversity and a mental-health diagnosis experienced 181 excess deaths per 100,000 person-years, of which 64 were attributable to the interaction itself. High family adversity combined with high physical-health service use produced 194 excess deaths per 100,000 person-years, with an estimated 106 due to interaction. Perinatal adversity plus high family adversity yielded 116 excess deaths, 22 attributable to interaction. No interaction emerged between family and neighbourhood adversity, though their combined independent effects still produced 89 excess deaths per 100,000 person-years. The highest cumulative risk appeared among the 17,810 individuals in the high family-adversity group who also experienced individual-layer adversity: a hazard ratio of 7.16, with 434 deaths in this group representing just 1.4 percent of the population. Even among children with low family adversity, individual and neighbourhood adversity together raised the hazard ratio to 1.82, showing that no layer of adversity is harmless in isolation.

The authors emphasise that these findings, derived from nearly complete national lifecourse data over four decades, empirically validate long-standing theoretical frameworks, from Bronfenbrenner’s ecological model to the bio-exposome concept, that had rarely been operationalised at such scale. Sensitivity analyses adjusting for parental education, restricting to birth years with unextrapolated neighbourhood data, and weighting for missing data all confirmed robustness. Limitations remain: registry data could not capture unreported abuse, undiagnosed illness, air pollution, racism, or bullying, likely leading to underestimation of effects, and results are conditional on survival to age 16. Denmark’s universal health care and strong social-security system may also make these estimates a lower bound for less supportive settings. The public-health implications, however, are clear. Because adversity at one layer amplifies harm at another, policies must operate across levels simultaneously: reducing preterm births, supporting families in crisis, expanding mental-health services, and tackling poverty, education, housing, and neighbourhood deprivation. Such integrated, multi-layered intervention offers the best hope of identifying highly vulnerable children early and breaking intergenerational cycles of disadvantage before they culminate in premature death.

Subject of Research: Multilayered childhood adversity and its association with mortality in young adulthood

Article Title: Multilayered childhood adversity and mortality: a population-based cohort study of 1.2 million individuals

Article References: Multilayered childhood adversity and mortality: a population-based cohort study of 1.2 million individuals. (n.d.). https://doi.org/10.1016/j.lanepe.2026.101863

Image Credits: AI Generated

DOI: 10.1016/j.lanepe.2026.101863

Keywords: childhood adversity, mortality, DANLIFE cohort, health inequality, adverse childhood experiences, neighbourhood deprivation, perinatal adversity, mental health, family adversity, population-based cohort, lifecourse epidemiology, social determinants