Primary insomnia can severely impair daytime function by disrupting attention and working memory and imposes a danger to self and others by increasing the risk of accidents. We speculated that the neurobiological changes impeding working memory in primary insomnia patients would be revealed by resting-state functional MRI (R-fMRI), which estimates the strength of cortical pathways by measuring local and regional correlations in blood oxygen level dependent (BOLD) signs independent of specific task demands. We compared the R-fMRI activity patterns of 15 healthy controls to 15 primary insomnia patients (all 30 participants were right-handed) using a 3.0 T MRI scanner. The SPM8 and REST1.7 software packages were used for preprocessing and analysis. Activity was expressed relative to the superior parietal lobe (SPL, the seed region) to reveal differences in functional connectivity to other cortical regions implicated in spatial working memory. In healthy controls, bilateral SPL activity was associated with activity in the posterior cingulate gyrus, precuneus, ventromedial prefrontal cortex, and superior frontal gyrus, indicating functional connectivity between these regions. Strong functional connectivity between the SPL and bilateral pre-motor cortex, bilateral supplementary motor cortex, and left dorsolateral prefrontal cortex was observed in both the control group and the primary insomnia group. However, the strength of several other functional connectivity pathways to the SPL exhibited significant group differences. Compared to healthy controls, connectivity in the primary insomnia group was stronger between the bilateral SPL and the right ventral anterior cingulate cortex, left ventral posterior cingulate cortex, right splenium of the corpus callosum, right pars triangularis (right inferior frontal gyrus/Broca’s area), and right insular lobe, while connectivity was weaker between the SPL and right superior frontal gyrus (dorsolateral prefrontal cortex). Primary insomnia appears to alter the functional connectivity between the parietal and frontal lobes, cortical structures critical for spatial and verbal working memory.

This study aimed to investigate insomnia-related alterations in gut microbiota and their association with serum metabolites. A total of 24 patients with insomnia disorder and 22 healthy controls were recruited. The fecal and serum samples were collected. The 16s rRNA sequencing and bioinformatics analysis were conducted to explore insomnia-related changes in the diversity, structure, and composition of the gut microbiota. UPLC-MS was performed to identify insomnia-related serum metabolites. Spearman correlation analysis was used to investigate the correlations between insomnia-related gut bacteria and the serum metabolites. Despite the nonsignificant changes in the diversity and structure of gut microbiota, insomnia disorder patients had significantly decreased family Bacteroidaceae, family Ruminococcaceae, and genus Bacteroides, along with significantly increased family Prevotellaceae and genus Prevotella, compared with healthy controls. Genus Gemmiger and genus Fusicatenibacter were dominant in patients with insomnia disorder, whereas genus Coprococcus, genus Oscillibacter, genus Clostridium XI, and family Peptostreptococcaceae were dominant in healthy controls. The UPLC-MS analysis identified 97 significantly decreased metabolites and 74 significantly increased metabolites in the serum samples of patients with insomnia disorder, compared with those of healthy controls. KEGG enrichment analysis revealed 1 significantly upregulated metabolic pathway and 16 downregulated metabolic pathways in patients with insomnia disorder. Furthermore, Spearman correlation analysis unveiled significant correlations among the altered bacteria genus and serum metabolites. Patients with insomnia disorder have differential gut microbiota and serum metabolic profiles compared with healthy controls. The alterations in gut microbiota were correlated with specific serum metabolites, suggesting that some serum metabolites might mediate gut microbiota-brain communication in the pathogenesis of insomnia disorder.

The etiology and maintenance of insomnia are proposed to be associated with increased cognitive and physiological arousal caused by acute stressors and associated cognitive rumination. A core feature of such hyperarousal theory of insomnia involves increased sensory processing that interferes with the onset and maintenance of sleep. In this work, we collected structural magnetic resonance imaging data from 35 patients with primary insomnia and 35 normal sleepers and applied structural covariance analysis to investigate whether insomnia is associated with disruptions in structural brain networks centered at the sensory regions (primary visual, primary auditory, and olfactory cortex). As expected, insomnia patients showed increased structural covariance in cortical thickness between sensory and motor regions. We also observed trends of increased covariance between sensory regions and the default-mode network, and the salience network regions, and trends of decreased covariance between sensory regions and the frontoparietal working memory network regions, in insomnia patients. The observed changes in structural covariance tended to correlated with poor sleep quality. Our findings support previous functional neuroimaging studies and provide novel insights into variations in brain network configuration that may be involved in the pathophysiology of insomnia.

Background: The atherogenic index of plasma (AIP) is closely associated with the onset of diabetes, with obesity being a significant risk factor for type 2 diabetes mellitus (T2DM). However, the association between the AIP and T2DM in overweight and obese populations has been infrequently studied. Therefore, this study aimed to explore this association in overweight and obese individuals with T2DM. Methods: This cross-sectional analysis utilized data from 40,633 participants with a body mass index (BMI) ≥ 24 kg/m² who were screened from January 2018 to December 2023 at Henan Provincial People’s Hospital. Participants were categorized into groups of overweight and obese individuals with and without diabetes according to the T2DM criteria. The AIP, our dependent variable, was calculated using the formula log10 [(TG mol/L)/HDL-C (mol/L)]. We investigated the association between the AIP and T2DM in overweight and obese individuals using multivariate logistic regression, subgroup analysis, generalized additive models, smoothed curve fitting, and threshold effect analysis. Additionally, mediation analysis evaluated the role of inflammatory cells in AIP-related T2DM. Results: Overweight and obese patients with T2DM exhibited higher AIP levels than those without diabetes. After adjusting for confounders, our results indicated a significant association between the AIP and the risk of T2DM in overweight and obese individuals (odds ratio (OR) = 5.17, 95% confidence interval (CI) 4.69–5.69). Notably, participants with a high baseline AIP (Q4 group) had a significantly greater risk of T2DM than those in the Q1 group, with an OR of 3.18 (95% CI 2.94–3.45). Subgroup analysis revealed that the association between the AIP and T2DM decreased with increasing age (interaction P < 0.001). In overweight and obese populations, the association between AIP and T2DM risk displayed a J-shaped nonlinear pattern, with AIP > – 0.07 indicating a significant increase in T2DM risk. Various inflammatory cells, including neutrophils, leukocytes, and monocytes, mediated 4.66%, 4.16%, and 1.93% of the associations, respectively. Conclusion: In overweight and obese individuals, the AIP was independently associated with T2DM, exhibiting a nonlinear association. Additionally, the association between the AIP and T2DM decreased with advancing age. Multiple types of inflammatory cells mediate this association.

Background: The Atherogenic Index of Plasma (AIP) is a novel biomarker for assessing the severity of atherosclerosis and has been shown to be closely associated with the risk of Type 2 Diabetes Mellitus (T2DM). However, no prospective cohort study has comprehensively evaluated both the immediate risk stratification through baseline AIP and the long-term risk assessment through multi-time point AIP trajectories in health screened adults in relation to T2DM risk. Methods: This longitudinal study included data from 42,850 participants who underwent health check-ups at Henan Provincial People’s Hospital between January 2018 and August 2024. AIP was calculated as the logarithm of the ratio of triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C). The Kaplan-Meier method was employed to analyze the incidence of T2DM across different AIP groups. A Cox model with restricted cubic splines assessed the dose-response relationship between AIP and T2DM risk, while latent class trajectory models (LCTM) evaluated the trends of AIP over multiple time points. Cox proportional hazards models were used to examine the relationship between baseline AIP quartiles, AIP trajectories, and T2DM risk. Results: During an average follow-up of 47.95 months, 3,228 participants developed T2DM. Stratifying by baseline AIP quartiles revealed that higher AIP levels were associated with an increased risk of T2DM. Compared to the lowest quartile, the highest quartile had a hazard ratio (HR) of 2.10 (95% CI: 1.74, 2.53). The LCTM identified three trajectory patterns for AIP: with the low-stable group as the reference, the medium-stable and high-stable groups had HRs of 1.72 (95% CI: 1.50, 1.96) and 2.50 (95% CI: 2.06, 3.03), respectively, indicating a significantly elevated risk of T2DM (P < 0.05). Conclusion: Elevated baseline AIP levels, medium stable trajectories and high stable trajectories are associated with an increased risk of T2DM in health screened adults.

Background: Metabolic syndrome (MetS) is increasingly diagnosed in individuals with normal body weight, and visceral fat emerges as a significant risk factor. However, the relationship between visceral fat area (VFA) and MetS within this population remains inadequately explored, and the diagnostic threshold for MetS in normal-weight individuals is yet to be established. Methods: This study used a cross-sectional design combined with longitudinal cohort analysis. Data were collected from 5,944 normal-weight participants who underwent health screenings at Henan Provincial People’s Hospital of China between October 2018 and October 2024. VFA was measured via multislice computed tomography scanning, and VFA-based tertile categorization was applied among the participants. The relationship between VFA and MetS was examined using univariate and multivariate logistic regression analyses. Nonlinear relationship was investigated by restricted cubic spline (RCS) modeling, and diagnostic accuracy was determined by receiver operating characteristic (ROC) curve analysis. Furthermore, data from individuals who completed three or more screenings were used to construct Kaplan–Meier survival curves for MetS events, with significance tested using the log-rank method. Results: Among the individuals with a normal BMI, elevated VFA was associated with a high incidence of MetS. After the adjustment for confounders, VFA was significantly associated with MetS risk [odds ratio (OR) = 1.13, 95% confidence interval (CI): 1.12–1.25]. The subjects in the highest VFA tertile showed significantly elevated MetS risk (OR = 30.33; 95% CI: 19.00–48.43, P < 0.001) versus those in the lowest tertile. The RCS model demonstrated a nonlinear, positive association between VFA and MetS risk (P for nonlinearity < 0.001), with risk escalation slowing down when the VFA exceeded 100 cm². ROC analysis showed that VFA had the highest diagnostic accuracy for MetS compared with other abdominal fat measures (AUC = 0.844, sensitivity = 0.839, specificity = 0.793, and accuracy = 0.785). In a longitudinal subset of 398 normal-weight participants followed for 6 years, 106 MetS cases occurred, with cumulative incidence rising as VFA increased (log-rank test, P < 0.001). Conclusion: VFA shows an independent, nonlinear, positive association with MetS risk among normal-weight individuals, with a threshold effect at 100 cm². VFA = 162.85 cm² may serve as an accurate and effective predictor for MetS in this population.