Proteogenomic Analysis Identifies a Causal Association between Plasma Apolipoprotein B Levels and Liver Cancer Risk.

Liver oncogenesis is accompanied by discernible protein changes in the bloodstream. By employing plasma proteomic profiling, we can delve into the molecular mechanisms of liver cancer and pinpoint potential biomarkers. In this nested case-control study, we applied liquid chromatography-tandem mass spectrometry for proteome profiling in baseline plasma samples. Differential protein expression was determined and was subjected to functional enrichment, network, and Mendelian randomization (MR) analyses. We identified 193 proteins with notable differential levels between the groups. Of these proteins, MR analysis offered a compelling negative association between apolipoprotein B (APOB) and liver cancer. This association was further corroborated in the UK Biobank cohort: genetically predicted APOB levels were associated with a 31% (95% CI 19-42%) decreased risk of liver cancer; and phenotypic analysis indicated an 11% (95% CI 8-14%) decreased liver cancer risk for every 0.1 g/L increase of circulating APOB levels. Multivariable MR analysis suggested that the hepatic fat content might fully mediate the APOB-liver cancer connection. In summary, we identified some plasma proteins, particularly APOB, as potential biomarkers of liver cancer. Our findings underscore the intricate link between lipid metabolism and liver cancer, offering hints for targeted prophylactic strategies and early detection.

GWAS-based polygenic risk scoring for predicting cerebral artery dissection in the Chinese population.

OBJECTIVE: Cerebral artery dissection (CeAD) is a rare but serious disease. Genetic risk assessment for CeAD is lacking in Chinese population. We performed genome-wide association study (GWAS) and computed polygenic risk score (PRS) to explore genetic susceptibility factors and prediction model of CeAD based on patients in Huashan Hospital. METHODS: A total of 210 CeAD patients and 280 controls were enrolled from June 2017 to September 2022 in Department of Neurology, Huashan Hospital, Fudan University. We performed GWAS to identify genetic variants associated with CeAD in 140 CeAD patients and 210 control individuals according to a case and control 1:1.5 design rule in the training dataset, while the other 70 patients with CeAD and 70 controls were used as validation. Then Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were utilized to identify the significant pathways. We constructed a PRS by capturing all independent GWAS SNPs in the analysis and explored the predictivity of PRS, age, and sex for CeAD. RESULTS: Through GWAS analysis of the 140 cases and 210 controls in the training dataset, we identified 13 leading SNPs associated with CeAD at a genome-wide significance level of P < 5 × 10- 8. Among them, 10 SNPs were annotated in or near (in the upstream and downstream regions of ± 500Kb) 10 functional genes. rs34508376 (OR2L13) played a suggestive role in CeAD pathophysiology which was in line with previous observations in aortic aneurysms. The other nine genes were first-time associations in CeAD cases. GO enrichment analyses showed that these 10 genes have known roles in 20 important GO terms clustered into two groups: (1) cellular biological processes (BP); (2) molecular function (MF). We used genome-wide association data to compute PRS including 32 independent SNPs and constructed predictive model for CeAD by using age, sex and PRS as predictors both in training and validation test. The area under curve (AUC) of PRS predictive model for CeAD reached 99% and 95% in the training test and validation test respectively, which were significantly larger than the age and sex models of 83% and 86%. CONCLUSIONS: Our study showed that ten risk loci were associated with CeAD susceptibility, and annotated functional genes had roles in 20 important GO terms clustered into biological process and molecular function. The PRS derived from risk variants was associated with CeAD incidence after adjusting for age and sex both in training test and validation.

Characteristics of auto-quantified tumor-infiltrating lymphocytes and the prognostic value in adenocarcinoma of the esophagogastric junction, gastric adenocarcinoma, and esophageal squamous cell carcinoma.

BACKGROUND: Adenocarcinoma of the esophagogastric junction (AEGJ) with a specific pathological profile and poor prognosis has limited therapeutic options. Previous studies have found that TILs exhibit distinct characteristics in different tumors and are correlated with tumor prognosis. We established cellular training sets to obtain auto-quantified TILs in pathological images. And we compared the characteristics of TILs in AEGJ with those in esophageal squamous cell carcinoma (ESCC) and gastric adenocarcinoma (GAC) to gain insight into the unique immune environments of these three tumors and investigate the prognostic value of TILs in these three tumors. METHODS: Utilizing a case-control study design, we analyzed 214 AEGJ, 256 GAC, and 752 ESCC cases. The TCGA dataset was used to validate prognostic value of auto-quantified TILs. The specific cellular training sets were established by experienced pathologists to determine TILs counts. Kruskal-Wallis test and multi-variable linear regression were conducted to explore TILs characteristics. Survival analyses were performed with Kaplan-Meier method and Cox proportional hazards model. RESULTS: The three cellular training sets of these cancers achieved a classification accuracy of 87.55 at least. The median auto-quantified TILs of AEGJ, GAC, and ESCC cases were 4.82%, 1.92%, and 0.12%, respectively. The TILs demonstrated varied characteristics under distinctive clinicopathological traits. The higher TILs proportion was associated with better prognosis in esophagogastric cancers (all P < 0.05) and was an independent prognostic biomarker on AEGJ in both datasets (Taixing: HR = 0.965, 95% CI = 0.938-0.994; TCGA: HR = 0.811, 95% CI = 0.712-0.925). CONCLUSIONS: We found variations in TILs across ESCC, GAC, and AEGJ, as assessed by image processing algorithms. Additionally, TILs in these three cancers are an independent prognostic factor. This enhances our understanding of the unique immune characteristics of the three tumors, improving patient outcomes.

Associations of sleep behaviors and genetic risk with risk of incident osteoporosis: A prospective cohort study of 293,164 participants.

BACKGROUND: Unhealthy sleep behaviors are associated with higher risks of osteoporosis (OP), while prospective evidence is limited. This study aimed to prospectively investigate this association, quantify the attributable burden of OP incidence reduction due to unhealthy sleep behaviors, and explore potential modifications by genetic risk factors. METHODS: This longitudinal cohort study was conducted utilizing data from the UK Biobank, comprising 293,164 participants initially free of OP and with requisite sleep behaviors data at baseline. We followed the participants after recruitment until November 30, 2022, to ascertain incident OP. We assessed the associations of five sleep behaviors including sleep duration, chronotype, insomnia, daytime napping, and morning wake-up difficulties, as well as sleep behavior patterns identified based on the above sleep behaviors, with the risk of OP, using Cox models adjusted for multiple confounders. The analyses were then performed separately among individuals with different OP susceptibility, indexed by standard polygenetic risk scores(PRS) for OP. Our secondary outcome was OP with pathologic fracture. Subgroup and sensitivity analyses were performed. Additionally, attributable risk percent in the exposed population (AR%) and population attributable fraction (PAF) of sleep behaviors were calculated. RESULTS: Over a median follow-up of 13.7 years, 8253 new-onset OP cases were documented. Unhealthy sleep behaviors, such as long or short sleep duration, insomnia, daytime napping, morning wake-up difficulties, and unhealthy sleep patterns, were associated with elevated risks of OP (HRs ranging from 1.14 to 1.46, all P-value <0.001) compared to healthy sleep behaviors. Similar associations were observed for OP with pathologic fractures. Insomnia exhibited the largest AR% of 39.98 % (95%CI: 36.46, 43.31) and PAF of 33.25 % (95%CI: 30.00, 36.34) among healthy sleep patterns and components. A statistically significant multiplicative interaction was noted between sleep behaviors and OP PRS on OP risk (all P-interaction <0.001). CONCLUSIONS: Four unhealthy sleep behaviors and sleep behavior patterns were associated to increased OP risk, with insomnia contributing the most to OP incidence, while genetic risk for OP modified this association. These findings underscore the crucial role of adhering to healthy sleep behaviors for effective OP prevention.

Protein truncating variants in mitochondrial-related nuclear genes and the risk of chronic liver disease.

BACKGROUND: Mitochondrial (MT) dysfunction is a hallmark of liver diseases. However, the effects of functional variants such as protein truncating variants (PTVs) in MT-related genes on the risk of liver diseases have not been extensively explored. METHODS: We extracted 60,928 PTVs across 2466 MT-related nucleus genes using whole-exome sequencing data obtained from 442,603 participants in the UK Biobank. We examined their associations with liver dysfunction that represented by the liver-related biomarkers and the risks of chronic liver diseases and liver-related mortality. RESULTS: 96.10% of the total participants carried at least one PTV. We identified 866 PTVs that were positively associated with liver dysfunction at the threshold of P value < 8.21e - 07. The coding genes of these PTVs were mainly enriched in pathways related to lipid, fatty acid, amino acid, and carbohydrate metabolisms. The 866 PTVs were presented in 1.07% (4721) of participants. Compared with participants who did not carry any of the PTVs, the carriers had a 5.33-fold (95% CI 4.15-6.85), 2.82-fold (1.69-4.72), and 4.41-fold (3.04-6.41) increased risk for fibrosis and cirrhosis of liver, liver cancer, and liver disease-related mortality, respectively. These adverse effects were consistent across subgroups based on age, sex, body mass index, smoking status, and presence of hypertension, diabetes, dyslipidemia, and metabolic syndrome. CONCLUSIONS: Our findings revealed a significant impact of PTVs in MT-related genes on liver disease risk, highlighting the importance of these variants in identifying populations at risk of liver diseases and facilitating early clinical interventions.

Healthy lifestyles are associated with alleviating the single-nucleotide polymorphism-based genetic risks of ischaemic stroke, intracerebral haemorrhage and myocardial infarction.

BACKGROUND: Both genetic and lifestyle factors contribute to myocardial infarction (MI) and stroke, including ischaemic stroke (IS) and intracerebral haemorrhage (ICH). We explored how and the extent to which a healthy lifestyle, by considering a comprehensive list, could counteract the genetic risk of those diseases, respectively. METHODS: 315 044 participants free of stroke and MI at baseline were identified from the UK Biobank. Genetic risk scores (GRS) for those diseases were constructed separately and categorised as low, intermediate and high by tertile. Lifestyle risk scores (LRS) were constructed separately using smoking, alcohol intake, physical activity, dietary patterns and sleep patterns. Similarly, participants were categorised into low, intermediate and high LRS. The data were analysed using Cox proportional hazard models. RESULTS: Over a median follow-up of 12.8 years, 4642, 1046 and 9485 participants developed IS, ICH and MI, respectively. Compared with participants with low levels of GRS and LRS, the HRs of those with high levels of GRS and LRS were 3.45 (95% CI 2.71 to 4.41), 2.32 (95% CI 1.40 to 3.85) and 4.89 (95% CI 4.16 to 5.75) for IS, ICH and MI, respectively. Moreover, among participants with high GRS, the standardised 14-year rates of IS events were 4.40% (95% CI 3.45% to 5.36%) among those with high LRS. In contrast, it is only 1.78% (95% CI 1.63% to 1.94%) among those with low LRS. Similarly for MI, the high LRS group had standardised rates of 8.60% (95% CI 7.38% to 9.81%), compared with 3.34% (95% CI 3.12% to 3.56%) in low LRS. Among the high genetic risk group of ICH, the rate is reduced by about half compared low LRS to high LRS, although the rate was low for both (0.36% (95% CI 0.31% to 0.42%) and 0.71% (95% CI 0.36% to 1.05%), respectively). CONCLUSION: Healthy lifestyles were substantially associated with a reduction in the risk of IS, ICH and MI and attenuated the genetic risk of IS, ICH and MI by at least half, respectively.

Sleep Pattern, Genetic Susceptibility, and Abdominal Aortic Aneurysm in UK Biobank Participants: Large-Scale Cohort Study.

Background: Abdominal aortic aneurysm (AAA) is an important cause of cardiovascular mortality. Objectives: The authors aimed to explore the associations between sleep patterns and genetic susceptibility to AAA. Methods: We included 344,855 UK Biobank study participants free of AAA at baseline. A sleep pattern was defined by chronotype, sleep duration, insomnia, snoring, and daytime sleepiness, and an overall sleep score was constructed with a range from 0 to 5, where a high score denotes a healthy sleep pattern. Polygenic risk score based on 22 single nucleotide polymorphisms was categorized into tertiles and used to evaluate the genetic risk for AAA. Cox proportional hazards regression models were used to assess the association between sleep, genetic factors, and the incidence of AAA. Results: During a median of 12.59 years of follow-up, 1,622 incident AAA cases were identified. The HR per 1-point increase in the sleep score was 0.91 (95% CI: 0.86-0.96) for AAA. Unhealthy sleep patterns, defined as a sleep score ranging from 0 to 3, were found to be associated with a higher risk of AAA for the intermediate (HR: 1.18, 95% CI: 1.06-1.31) and poor sleep patterns (HR: 1.40, 95% CI: 1.13-1.73), respectively, compared to the healthy pattern. Participants with poor sleep patterns and high genetic risks had a 2.5-fold higher risk of AAA than those with healthy sleep patterns and low genetic risk. Conclusions: In this large prospective study, healthy sleep patterns were associated with a lower risk of AAA among participants with low, intermediate, or high genetic risk.

Association of Sleep Pattern and Genetic Susceptibility with Obstructive Sleep Apnea: A Prospective Analysis of the UK Biobank.

Purpose: The prevalence of obstructive sleep apnea (OSA) is high worldwide. This study aimed to quantify the relationship between the incidence of OSA and sleep patterns and genetic susceptibility. Methods: A total of 355,133 white British participants enrolled in the UK Biobank between 2006 and 2010 with follow-up data until September 2021 were recruited. We evaluated sleep patterns using a customized sleep scoring method based on the low-risk sleep phenotype, defined as follows: morning chronotype, 7-8 hours of sleep per day, never/rarely experience insomnia, no snoring, no frequent daytime sleepiness, never/rarely nap, and easily getting up early. The polygenic risk score was calculated to assess genetic susceptibility to OSA. Cox proportional hazard models were used to evaluate the associations between OSA and sleep patterns and genetic susceptibility. Results: During a mean follow-up of 12.57 years, 4618 participants were diagnosed with OSA (age: 56.83 ± 7.69 years, women: 31.3%). Compared with those with a poor sleep pattern, participants with a normal (HR: 0.42, 95% CI: 0.38-0.46), ideal (HR: 0.21, 95% CI: 0.19-0.24), or optimal (HR: 0.15, 95% CI: 0.12-0.18) sleep pattern were significantly more likely to have OSA. The genetic susceptibility of 173,239 participants was calculated, and the results showed that poor (HR: 3.67, 95% CI: 2.95-4.57) and normal (HR: 1.89, 95% CI: 1.66-2.16) sleep patterns with high genetic susceptibility can increase the risk for OSA. Conclusion: This large-scale prospective study provides evidence suggesting that sleep patterns across seven low-risk sleep phenotypes may protect against OSA in individuals with varying degrees of genetic susceptibility.

Lipid metabolism mediates the association between body mass index change and bone mineral density: The Taizhou imaging study.

BACKGROUND: Limited research explores the impact of body mass index (BMI) change on osteoporosis, regarding the role of lipid metabolism. We aimed to cross-sectionally investigate these relationships in 820 Chinese participants aged 55-65 from the Taizhou Imaging Study. METHODS: We used the baseline data collected between 2013 and 2018. T-score was calculated by standardizing bone mineral density and was used for osteoporosis and osteopenia diagnosis. Multinomial logistic regression was used to examine the effect of BMI change on bone health status. Multivariable linear regression was employed to identify the metabolites corrected with BMI change and T-score. Exploratory factor analysis (EFA) and mediation analysis were conducted to ascertain the involvement of the metabolites. RESULTS: BMI increase served as a protective factor against osteoporosis (OR = 0.79[0.71-0.88], P-value<0.001) and osteopenia (OR = 0.88[0.82-0.95], P-value<0.001). Eighteen serum metabolites were associated with both BMI change and T-score. Specifically, high-density lipoprotein (HDL) substructures demonstrated negative correlations (ß = -0.08 to -0.06 and - 0.12 to -0.08, respectively), while very low-density lipoprotein (VLDL) substructions showed positive correlations (ß = 0.09 to 0.10 and 0.10 to 0.11, respectively). The two lipid factors (HDL and VLDL) extracted by EFA acted as mediators between BMI change and T-score (Prop. Mediated = 8.16% and 10.51%, all P-value<0.01). CONCLUSION: BMI gain among Chinese aged 55-65 is beneficial for reducing the risk of osteoporosis. The metabolism of HDL and VLDL partially mediates the effect of BMI change on bone loss. Our research offers novel insights into the prevention of osteoporosis, approached from the perspective of weight management and lipid metabolomics.

Risk factors for progression to severe infection and prolonged viral clearance time in hospitalized elderly patients infected with the Omicron variant of SARS-CoV-2: a retrospective study at Shanghai Fourth People's Hospital, School of Medicine, Tongji University.

Introduction: In elderly patients infected with the Omicron variant, disease progression to severe infection can result in poor outcomes. This study aimed to identify risk and protective factors associated with disease progression to severe infection and viral clearance time in elderly Omicron-infected patients. Methods: Shanghai Fourth People's Hospital, School of Medicine, Tongji University, was officially designated to provide treatment to patients with COVID-19. This study was conducted on confirmed Omicron cases admitted to the hospital between 10 April 2022 and 21 June 2022. In total, 1,568 patients aged 65 years or older were included. We conducted a retrospective, observational study using logistic regression to analyze risk and protective factors for the development of severe disease and Cox proportional hazards regression models to analyze factors influencing viral clearance time. Results: Aged over 80 years, having 2 or more comorbidities, combined cerebrovascular disease, chronic neurological disease, and mental disorders were associated with the development of severe disease, and full vaccination was a protective factor. Furthermore, aged over 80 years, combined chronic respiratory disease, chronic renal disease, cerebrovascular disease, mental disorders, and high viral load were associated with prolonged viral clearance time, and full vaccination was a protective factor. Discussion: This study analyzed risk factors for progression to severe infection and prolonged viral clearance time in hospitalized elderly Omicron-infected patients. Aged patients with comorbidities had a higher risk of developing severe infection and had longer viral clearance, while vaccination protected them against the Omicron infection.

Analysis of gait pattern related to high cerebral small vessel disease burden using quantitative gait data from wearable sensors.

BACKGROUND AND OBJECTIVES: Sensor-based wearable devices help to obtain a wide range of quantitative gait parameters, which provides sufficient data to investigate disease-specific gait patterns. Although cerebral small vessel disease (CSVD) plays a significant role in gait impairment, the specific gait pattern associated with a high burden of CSVD remains to be explored. METHODS: We analyzed the gait pattern related to high CSVD burden from 720 participants (aged 55-65 years, 42.5 % male) free of neurological disease in the Taizhou Imaging Study. All participants underwent detailed quantitative gait assessments (obtained from an insole-like wearable gait tracking device) and brain magnetic resonance imaging examinations. Thirty-three gait parameters were summarized into five gait domains. Sparse sliced inverse regression was developed to extract the gait pattern related to high CSVD burden. RESULTS: The specific gait pattern derived from several gait domains (i.e., angles, phases, variability, and spatio-temporal) was significantly associated with the CSVD burden (OR=1.250, 95 % CI: 1.011-1.546). The gait pattern indicates that people with a high CSVD burden were prone to have smaller gait angles, more stance time, more double support time, larger gait variability, and slower gait velocity. Furthermore, people with this gait pattern had a 25 % higher risk of a high CSVD burden. CONCLUSIONS: We established a more stable and disease-specific quantitative gait pattern related to high CSVD burden, which is prone to facilitate the identification of individuals with high CSVD burden among the community residents or the general population.

Bidirectional mediation of bone mineral density and brain atrophy on their associations with gait variability.

This mediation analysis aimed to investigate the associations among areal bone mineral density, mobility-related brain atrophy, and specific gait patterns. A total of 595 participants from the Taizhou Imaging Study, who underwent both gait and bone mineral density measurements, were included in this cross-sectional analysis. We used a wearable gait tracking device to collect quantitative gait parameters and then summarized them into independent gait domains with factor analysis. Bone mineral density was measured in the lumbar spine, femoral neck, and total hip using dual-energy X-ray absorptiometry. Magnetic resonance images were obtained on a 3.0-Tesla scanner, and the volumes of brain regions related to mobility were computed using FreeSurfer. Lower bone mineral density was found to be associated with higher gait variability, especially at the site of the lumbar spine (ß = 0.174, FDR = 0.001). Besides, higher gait variability was correlated with mobility-related brain atrophy, like the primary motor cortex (ß = 0.147, FDR = 0.006), sensorimotor cortex (ß = 0.153, FDR = 0.006), and entorhinal cortex (ß = 0.106, FDR = 0.043). Bidirectional mediation analysis revealed that regional brain atrophy contributed to higher gait variability through the low lumbar spine bone mineral density (for the primary motor cortex, P = 0.018; for the sensorimotor cortex, P = 0.010) and the low lumbar spine bone mineral density contributed to higher gait variability through the primary motor and sensorimotor cortices (P = 0.026 and 0.010, respectively).

Circulating Lipoproteins Mediate the Association Between Cardiovascular Risk Factors and Cognitive Decline: A Community-Based Cohort Study.

Cardiovascular health metrics are now widely recognized as modifiable risk factors for cognitive decline and dementia. Metabolic perturbations might play roles in the linkage of cardiovascular diseases and dementia. Circulating metabolites profiling by metabolomics may improve understanding of the potential mechanism by which cardiovascular risk factors contribute to cognitive decline. In a prospective community-based cohort in China (n = 725), 312 serum metabolic phenotypes were quantified, and cardiovascular health score was calculated including smoking, exercise, sleep, diet, body mass index, blood pressure, and blood glucose. Cognitive function assessments were conducted in baseline and follow-up visits to identify longitudinal cognitive decline. A better cardiovascular health was significantly associated with lower risk of concentration decline and orientation decline (hazard ratio (HR): 0.84-0.90; p < 0.05). Apolipoprotein-A1, high-density lipoprotein (HDL) cholesterol, cholesterol ester, and phospholipid concentrations were significantly associated with a lower risk of longitudinal memory and orientation decline (p < 0.05 and adjusted-p < 0.20). Mediation analysis suggested that the negative association between health status and the risk of orientation decline was partly mediated by cholesterol ester and total lipids in HDL-2 and -3 (proportion of mediation: 7.68-8.21%, both p < 0.05). Cardiovascular risk factors were associated with greater risks of cognitive decline, which were found to be mediated by circulating lipoproteins, particularly the medium-size HDL components. These findings underscore the potential of utilizing lipoproteins as targets for early stage dementia screening and intervention. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-023-00120-2.

Mobility-related brain regions linking carotid intima-media thickness to specific gait performances in old age.

BACKGROUND: Gait disturbance is common in older adults with vascular diseases. However, how carotid atherosclerosis affects gait remains poorly understood. The objectives were to investigate the associations between carotid intima-media thickness and specific gait performances and explore the potential role of brain structure in mediating these associations. METHODS: A cross-sectional analysis of data from the Taizhou Imaging Study was conducted, including 707 individuals who underwent both gait and carotid ultrasound examinations. Gait assessments include the Timed-Up-and-Go test, the Tinetti test, and quantitative gait assessment using a wearable device. Quantitative parameters were summarized into independent gait domains with factor analysis. Magnetic resonance images were obtained on a 3.0-Tesla scanner, and the volumes of fifteen brain regions related to motor function (primary motor, sensorimotor), visuospatial attention (inferior posterior parietal lobules, superior posterior parietal lobules), executive control function (dorsolateral prefrontal cortex, anterior cingulate), memory (hippocampus, entorhinal cortex), motor imagery (precuneus, parahippocampus, posterior cingulated cortex), and balance (basal ganglia: pallidum, putamen, caudate, thalamus) were computed using FreeSurfer and the Desikan-Killiany atlas. Mediation analysis was conducted with carotid intima-media thickness as the predictor and mobility-related brain regions as mediators. RESULTS: Carotid intima-media thickness was found to be associated with the Timed-Up-and-Go performance (ß = 0.129, p = 0.010) as well as gait performances related to pace (ß=-0.213, p < 0.001) and symmetry (ß = 0.096, p = 0.045). Besides, gait performances were correlated with mobility-related brain regions responsible for motor, visuospatial attention, executive control, memory, and balance (all FDR < 0.05). Notably, significant regions differed depending on the gait outcomes measured. The primary motor (41.9%), sensorimotor (29.3%), visuospatial attention (inferior posterior parietal lobules, superior posterior parietal lobules) (13.8%), entorhinal cortex (36.4%), and motor imagery (precuneus, parahippocampus, posterior cingulated cortex) (27.3%) mediated the association between increased carotid intima-media thickness and poorer Timed-Up-and-Go performance. For the pace domain, the primary motor (37.5%), sensorimotor (25.8%), visuospatial attention (12.3%), entorhinal cortex (20.7%), motor imagery (24.9%), and balance (basal ganglia: pallidum, putamen, caudate, thalamus) (11.6%) acted as mediators. CONCLUSIONS: Carotid intima-media thickness is associated with gait performances, and mobility-related brain volume mediates these associations. Moreover, the distribution of brain regions regulating mobility varies in the different gait domains. Our study adds value in exploring the underlying mechanisms of gait disturbance in the aging population.

Unraveling the role of serum metabolites in the relationship between plant-based diets and bone health in community-dwelling older adults.

The potential adverse effects of the plant-based dietary pattern on bone health have received widespread attention. However, the biological mechanisms underlying the adverse effects of plant-based diets on bone health remain incompletely understood. The objective of this study was to identify potential biomarkers between plant-based diets and bone loss utilizing metabolomic techniques in the Taizhou Imaging Study (TIS) (N = 788). Plant-based diet indexes (overall plant-based diet index (PDI), healthy plant-based diet index (hPDI), and unhealthy plant-based diet index (uPDI)) were calculated using the food frequency questionnaire, and bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry. A multinomial logistic regression was used to explore the associations of plant-based diet indexes with bone loss. Furthermore, mediation analysis and exploratory factor analysis (EFA) were performed to explore the mediated effects of metabolites on the association of plant-based diets with BMD T-score. Our results showed that higher hPDI and uPDI were positively associated with bone loss. Moreover, nineteen metabolites were significantly associated with BMD T-score, among them, seven metabolites were associated with uPDI. Except for cholesterol esters in VLDL-1, the remaining six metabolites significantly mediated the negative association between uPDI and BMD T-score. Interestingly, we observed that the same six metabolites mediated the positive association between fresh fruit and BMD T-score. Collectively, our results support the deleterious effects of plant-based diets on bone health and discover the potential mediation effect of metabolites on the association of plant-based diets with bone loss. The findings offer valuable insights that could optimize dietary recommendations and interventions, contributing to alleviate the potential adverse effects associated with plant-based diets.

Low thyroid function is associated with metabolic dysfunction-associated steatotic liver disease.

Background and Aim: Metabolic dysfunction-associated steatotic liver disease (MASLD) is recently introduced to better highlight the pathogenic significance of cardiometabolic dysfunction, as compared with non-alcoholic fatty liver disease. This study aimed to investigate the association between low thyroid function and MASLD in the new context. Methods: We recruited 2901 participants for our retrospective cohort study from 2016 to 2021. Participants were divided into strict-normal thyroid function and low thyroid function groups (low-normal thyroid function, subclinical hypothyroidism) based on initial thyroid stimulating hormone (TSH) levels, respectively. Cox regression models were used to estimate the hazard ratios (HRs) and 95% CI. Results: During a median follow-up of 15.6 months, 165 (8.9%) strict-normal thyroid function subjects and 141 (13.4%) low thyroid function subjects developed MASLD; this result was statistically relevant (P < 0.05). Univariate regression analysis showed that low thyroid function and subclinical hypothyroidism were statistically significantly associated with MASLD (low thyroid function: HR1.53; 95% CI 1.22-1.92; subclinical hypothyroidism: HR1.95; 95% CI 1.47-2.60). Conclusions: MASLD is associated with low thyroid function and the relationship between MASLD and low thyroid function is independent.

Development and validation of a prognostic model based on clinical laboratory biomarkers to predict admission to ICU in Omicron variant-infected hospitalized patients complicated with myocardial injury.

Aims: The aim of this study was to develop and validate a prognostic model based on clinical laboratory biomarkers for the early identification of high-risk patients who require intensive care unit (ICU) admission among those hospitalized with the Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and complicated with myocardial injury (MI). Methods: This single-center study enrolled 263 hospitalized patients with confirmed Omicron variant infection and concurrent MI. The patients were randomly divided into training and validation cohorts. Relevant variables were collected upon admission, and the least absolute shrinkage and selection operator (LASSO) was used to select candidate variables for constructing a Cox regression prognostic model. The model's performance was evaluated in both training and validating cohorts based on discrimination, calibration, and net benefit. Results: Of the 263 eligible patients, 210 were non-ICU patients and 53 were ICU patients. The prognostic model was built using four selected predictors: white blood cell (WBC) count, procalcitonin (PCT) level, C-reactive protein (CRP) level, and blood urea nitrogen (BUN) level. The model showed good discriminative ability in both the training cohort (concordance index: 0.802, 95% CI: 0.716-0.888) and the validation cohort (concordance index: 0.799, 95% CI: 0.681-0.917). For calibration, the predicted probabilities and observed proportions were highly consistent, indicating the model's reliability in predicting outcomes. In the 21-day decision curve analysis, the model had a positive net benefit for threshold probability ranges of 0.2 to 0.8 in the training cohort and nearly 0.2 to 1 in the validation cohort. Conclusion: In this study, we developed a clinically practical model with high discrimination, calibration, and net benefit. It may help to early identify severe and critical cases among Omicron variant-infected hospitalized patients with MI.

Disease clusters subsequent to anxiety and stress-related disorders and their genetic determinants.

Anxiety/stress-related disorders have been associated with multiple diseases, whereas a comprehensive assessment of the structure and interplay of subsequent associated diseases and their genetic underpinnings is lacking. Here, we first identify 136, out of 454 tested, medical conditions associated with incident anxiety/stress-related disorders attended in specialized care using a population-based cohort from the nationwide Swedish Patient Register, comprising 70,026 patients with anxiety/stress-related disorders and 1:10 birth year- and sex-matched unaffected individuals. By combining findings from the comorbidity network and disease trajectory analyses, we identify five robust disease clusters to be associated with a prior diagnosis of anxiety/stress-related disorders, featured by predominance of psychiatric disorders, eye diseases, ear diseases, cardiovascular diseases, and skin and genitourinary diseases. These five clusters and their featured diseases are largely validated in the UK Biobank. GWAS analyses based on the UK Biobank identify 3, 33, 40, 4, and 16 significantly independent single nucleotide polymorphisms for the link to the five disease clusters, respectively, which are mapped to several distinct risk genes and biological pathways. These findings motivate further mechanistic explorations and aid early risk assessment for cluster-based disease prevention among patients with newly diagnosed anxiety/stress-related disorders in specialized care.

A study of clinical and serological correlation of early myocardial injury in elderly patients infected with the Omicron variant.

Introduction: Myocardial injury in elderly Omicron variant patients is a leading cause of severe disease and death. This study focuses on elucidating the clinical characteristics and potential risk factors associated with myocardial injury in elderly patients infected with the Omicron variant. Methods: Myocardial injury was defined based on elevated cardiac troponin concentrations exceeding the 99th percentile upper reference limit. Among 772 elderly Omicron-infected patients, categorized into myocardial injury (n = 263) and non-myocardial injury (n = 509) groups. The stratified log-rank statistic was used to compare the probability of patients developing intensive care. Receiver operating characteristic curves were used to determine the best cut-off values of clinical and laboratory data for predicting myocardial injury. Univariate and multivariate logistic regression was adopted to analyze the risk factors for myocardial injury. Results: The occurrence of myocardial injury in Omicron variant-infected geriatric patients was up to 34.07% and these patients may have a higher rate of requiring intensive care (P < 0.05). By comparing myocardial injury patients with non-myocardial injury patients, notable differences were observed in age, pre-existing medical conditions (e.g., hypertension, coronary heart disease, cerebrovascular disease, arrhythmia, chronic kidney disease, and heart failure), and various laboratory biomarkers, including cycle threshold-ORF1ab gene (Ct-ORF1ab), cycle threshold-N gene (Ct-N), white blood cell count, neutrophil (NEUT) count, NEUT%, lymphocyte (LYM) count, LYM%, and D-dimer, interleukin-6, procalcitonin, C-reactive protein, serum amyloid A, total protein, lactate dehydrogenase, aspartate aminotransferase, glomerular filtration rate, blood urea nitrogen, and serum creatinine (sCr) levels (P < 0.05). Furthermore, in the multivariable logistic regression, we identified potential risk factors for myocardial injury in Omicron variant-infected elderly patients, including advanced age, pre-existing coronary artery disease, interleukin-6 > 22.69 pg/ml, procalcitonin > 0.0435 ng/ml, D-dimer > 0.615 mg/L, and sCr > 81.30 µmol/L. Conclusion: This study revealed the clinical characteristics and potential risk factors associated with myocardial injury that enable early diagnosis of myocardial injury in Omicron variant-infected elderly patients, providing important reference indicators for early diagnosis and timely clinical intervention.