Low-grade inflammation (LGI) mainly acted as the mediator of the association of obesity and inflammatory diet with numerous chronic diseases, including neuropsychiatric diseases. However, the evidence about the effect of LGI on brain structure is limited but important, especially in the context of accelerating aging. This study was then designed to close the gap, and we leveraged a total of 37,699 participants from the UK Biobank and utilized inflammation score (INFLA-score) to measure LGI. We built the longitudinal relationships of INFLA-score with brain imaging phenotypes using multiple linear regression models. We further analyzed the interactive effects of specific covariates. The results showed high level inflammation reduced the volumes of the subcortex and cortex, especially the globus pallidus (β [95% confidence interval] = −0.062 [−0.083, −0.041]), thalamus (−0.053 [−0.073, −0.033]), insula (−0.052 [−0.072, −0.032]), superior temporal gyrus (−0.049 [−0.069, −0.028]), lateral orbitofrontal cortex (−0.047 [−0.068, −0.027]), and others. Most significant effects were observed among urban residents. Furthermore, males and individuals with physical frailty were susceptive to the associations. The study provided potential insights into pathological changes during disease progression and might aid in the development of preventive and control targets in an age-friendly city to promote great health and well-being for sustainable development goals.

Background: The global rise in ultra-processed food (UPF) consumption and the persistent burden of mental disorders have raised growing public health concerns. Emerging evidence suggests that unfavorable dietary patterns, particularly with high UPF intake, contribute to the development of mental disorders. Objective: To assess the associations of UPF-related metabolic signatures and mental disorders. Methods: In this population-based cohort study of 30,059 participants from the UK Biobank, we first identified a plasma metabolic signature associated with UPF intake leveraging nuclear magnetic resonance metabolomics. We then applied Cox and logistic regression models to investigate the associations of both UPF consumption and its metabolic signature with incident mental disorders and specific psychological symptoms, respectively. Results: Higher UPF intake was significantly associated with increased risks of overall mental disorder (hazard ratio per 10% increment [95% confidence interval]: 1.04 [1.02, 1.06]), depressive disorder (1.14 [1.08, 1.20]), anxiety disorder (1.12 [1.06, 1.18]), and substance use disorder (1.06 [1.01, 1.11]), as well as several psychological symptoms including suicidal ideation (odds ratios [95% confidence interval]: 1.12 [1.03, 1.16]) and anxiety feeling (1.05 [1.01, 1.09]). Similarly, the UPF-related metabolic signature was independently associated with elevated risks of these mental health outcomes and partially mediated the associations between UPF intake and mental disorders. Conclusions: These findings highlighted the potential metabolic pathways underlying the neuropsychiatric risks of UPF consumption and underscored the importance of dietary quality in mental health.

Despite accumulating evidence linking air pollution to type 2 diabetes (T2D), the underlying mechanisms remain largely unexplored. We aimed to profile proteomic signatures associated with air pollution and examine their relationship to T2D. We conducted proteome-wide association studies on 2911 plasma proteins among 49,134 UK Biobank participants. Exposures to fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and benzene were estimated based on residential addresses. Proteomic signatures and their corresponding scores for each air pollutant were identified using linear and elastic net regression models, comprising 368 proteins for PM_2.5, 207 for NO₂, 206 for SO₂, and 236 for benzene. Cox proportional hazards regression models were subsequently used to examine the effect of air pollution and proteomic signature scores on the risk of incident T2D. In both the time-independent and time-dependent Cox models, all four air pollutants were significantly associated with higher T2D risk. In the time-dependent Cox models, the hazard ratios (HRs) and 95% confidence intervals (CIs) were 1.02 (1.00, 1.05) for PM_2.5, 1.02 (1.01, 1.02) for NO₂, 1.12 (1.06, 1.18) for SO₂, and 1.88 (1.38, 2.57) for benzene, respectively. Higher proteomic signature scores of PM_2.5, NO₂, SO₂, and benzene were also associated with an elevated risk of T2D, with HRs (95% CIs) of 1.05 (1.00, 1.09), 1.17 (1.12, 1.23), 1.11 (1.06, 1.16), and 1.11 (1.06, 1.16) for a per-standard-deviation increase, respectively. Moderate mediation effects of the proteomic signature scores were observed. Pathway analyses further implicated systemic inflammation as a potential underlying mechanism. Our findings suggested that air pollution might contribute to T2D risk through inflammation-related proteins, highlighting the potential of proteomics as a tool for precision public health. Building on this, our study also offered a framework to explore molecular pathways connecting modifiable risk factors to diseases.