BACKGROUND:Noninvasive detection of glomerular hyperfiltration, a key driver of diabetic nephropathy (DN), is needed for timely intervention (e.g., dapagliflozin), as is early DN identification to guide therapy. PURPOSE:To evaluate MR elastography (MRE) for detecting glomerular hyperfiltration and early DN, using glomerular filtration rate (GFR) and pathology as reference standards. STUDY TYPE:Animal proof of concept. ANIMAL MODEL:Ninety-five male Sprague-Dawley rats (80 with high-fat diet and low-dose streptozotocin-induced type 2 diabetes and 15 normal controls). FIELD STRENGTH/SEQUENCE:Briefly, 3.0 T; a multifrequency (100, 150, and 200 Hz) three-dimensional MRE sequence and a multi-b-value (0-800 s/mm2) intravoxel incoherent motion (IVIM) sequence. ASSESSMENT:MRE-derived shear stiffness (SS) and loss modulus (LM) and IVIM-derived true diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were measured. Glomerular hyperfiltration and DN classes were diagnosed by direct GFR measurement and renal pathology. Ex vivo rheometry validated MRE-derived viscoelastic parameters. STATISTICAL TESTS:Linear mixed-effects models; one-way analysis of variance (ANOVA) or Welch's ANOVA; Pearson correlation; intraclass correlation coefficient (ICC); area under the receiver operating characteristic curve (AUC) and DeLong's test. p < 0.05 was considered significant. RESULTS:Renal SS was significantly higher in hyperfiltration than in controls and early DN (class I-II), distinguishing hyperfiltration from controls (AUC = 0.94) and from early DN (AUC = 0.90). LM was elevated in hyperfiltration but did not decrease significantly in early DN (p > 0.99 vs. hyperfiltration). IVIM parameters showed limited diagnostic utility (AUCs: 0.53-0.72). Dapagliflozin treatment normalized the elevated SS and LM in diabetic rats. Ex vivo rheometry showed significant positive correlations with MRE-derived SS and LM. DATA CONCLUSION:Three-dimensional MRE-derived renal SS is a potential noninvasive biomarker to detect diabetic glomerular hyperfiltration, monitor response to dapagliflozin therapy, and enable early identification of DN. EVIDENCE LEVEL:1. TECHNICAL EFFICACY:Stage 2.

Supplementary Table S12: Sensitivity analysis for association between selected metabolic biomarkers and gastrointestinal cancers by LASSO in the training dataset

Supplementary Table S14: Sensitivity analysis for the associations between metabolite risk score and the risk of gastrointestinal cancers

BACKGROUND:Although research has focused on metabolomic profiles linked to individual cardiometabolic diseases, there is a lack of studies on metabolomic signatures associated with cardiometabolic multimorbidity. METHODS:We included 79,712 participants without cardiometabolic disease from the UK Biobank, randomly divided at a 70:30 training:testing ratio. Cox proportional hazards regression models were used to identify 249 metabolic biomarkers associated with cardiometabolic multimorbidity. Two-sample Mendelian randomization analyses were applied to explore the causal relationships between the identified metabolites and cardiometabolic outcomes. The association between the metabolic risk score and the risk of transitioning from disease-free status to cardiometabolic multimorbidity was evaluated using multi-state models. RESULTS:Of the 249 metabolites, 183 were associated with cardiometabolic multimorbidity. Very low-density lipoprotein cholesterol levels were positively associated, whereas high-density lipoprotein cholesterol levels were inversely associated. Mendelian randomization identified the apolipoprotein B to A1 ratio as causally associated with ischemic heart disease, cardiometabolic multimorbidity, and stroke. Metabolite risk scores demonstrated a positive association with the risk of cardiometabolic multimorbidity (hazard ratio 2.67; 95% confidence interval 2.05-3.49) in the testing set. In multi-state models, those with a higher metabolite risk score had an increased risk of progressing from disease-free status to first cardiometabolic disease (hazard ratio 1.71, 95% confidence interval 1.51-1.93) and from first cardiometabolic disease to cardiometabolic multimorbidity (hazard ratio 1.89, 95% confidence interval 1.45-2.47) in the testing set. CONCLUSIONS:Our findings indicate that metabolomic signatures can significantly aid risk stratification, underscoring the need to explore metabolic biomarkers in cardiometabolic multimorbidity to inform preventive strategies.

BACKGROUND:Emerging evidence indicates that lipid metabolism plays a crucial role in gastrointestinal (GI) cancers. METHODS:This prospective cohort study included 112,655 cancer-free adults from the UK Biobank with metabolomics data at baseline. Multivariate Cox proportional hazards regression models were used to evaluate the associations between metabolites and GI cancers. LASSO regression was used to create metabolite risk scores, and their predictive performance for GI cancers was assessed using the C-statistic derived from the time-dependent ROC curves. RESULTS:A total of 93, 95, 33, 97, 129, and 29 metabolites were associated with colorectal, pancreatic, esophageal, gastric, hepatocellular, and gallbladder/biliary tract cancers, respectively. Higher levels of phospholipids to total lipids in large high-density lipoprotein percentage and monounsaturated fatty acids to total fatty acids percentage were associated with increased risk of all GI cancers (HRs from 1.09 to 1.34). Conversely, higher levels of unsaturated fatty acids, polyunsaturated fatty acids, ratio of polyunsaturated to monounsaturated fatty acids, and docosahexaenoic acid were associated with lower risk of six GI cancers (HRs from 0.69 to 0.93). The highest tertile of metabolic risk scores were positively associated with all GI cancers (HRs from 1.86 to 6.42), and new prediction models showed moderate accuracy for five-year cancer incidence (C-statistics from 0.713 to 0.793). CONCLUSIONS:Our findings highlight the significant role of lipid metabolism in GI cancers and provide potential noninvasive biomarkers for enhancing precise prevention. IMPACT:Lipid metabolism holds clinical potential for precise prevention and early intervention of GI cancers.

Supplementary Figure S3: Sensitivity analysis for receiver operating characteristics (ROC) curves of selected metabolites at 5-year follow-up for five gastrointestinal cancers prediction models in the training set. C-statistics are shown for CRC (A), PC (B), EC (C), GC (D), and HCC (E). Abbreviations: CRC, colorectal cancer; EC, esophageal cancer; GC, gastric cancer; HCC, hepatocellular carcinoma; PC, pancreatic cancer.

Supplementary Table S16: Sensitivity analysis for the association between clinical factors and the risk of gastrointestinal cancers in the training set

Supplementary Figure S1: Metabolite screening in LASSO model. The upper panels show the ten-fold cross-validation used to determine the optimal penalty parameter (λ). The x-axis represents Log(λ), and the y-axis shows the partial likelihood deviance with error bars indicating standard errors. The lower panels display the coefficient paths illustrating variable shrinkage, allowing the identification of key metabolites associated with each cancer type. Abbreviations: CRC, colorectal cancer; EC, esophageal cancer; GC, gastric cancer; GCBTC, gallbladder and biliary tract cancer; HCC, hepatocellular carcinoma; PC, pancreatic cancer.

Supplementary Table S13: The associations between metabolite risk score and the risk of gastrointestinal cancers

Supplementary Table S3: HR (95% CI) of the association between plasma metabolites and incident gastrointestinal cancer events in model 2

CD8+ T cells differentiate into diverse states that shape immune outcomes in cancer and chronic infection1-4. To define systematically the transcription factors (TFs) driving these states, we built a comprehensive atlas integrating transcriptional and epigenetic data across nine CD8+ T cell states and inferred TF activity profiles. Our analysis catalogued TF activity fingerprints, uncovering regulatory mechanisms governing selective cell state differentiation. Leveraging this platform, we focused on two transcriptionally similar but functionally opposing states that are critical in tumour and viral contexts: terminally exhausted T (TEXterm) cells, which are dysfunctional5-8, and tissue-resident memory T (TRM) cells, which are protective9-13. Global TF community analysis revealed distinct biological pathways and TF-driven networks underlying protective versus dysfunctional states. Through in vivo CRISPR screening integrated with single-cell RNA sequencing (in vivo Perturb-seq) we delineated several TFs that selectively govern TEXterm cell differentiation. We also identified HIC1 and GFI1 as shared regulators of TEXterm and TRM cell differentiation and KLF6 as a unique regulator of TRM cells. We discovered new TEXterm-selective TFs, including ZSCAN20 and JDP2, with no previous known function in T cells. Targeted deletion of these TFs enhanced tumour control and synergized with immune checkpoint blockade but did not interfere with TRM cell formation. Consistently, their depletion in human T cells reduces the expression of inhibitory receptors and improves effector function. By decoupling exhaustion TEX-selective from protective TRM cell programmes, our platform enables more precise engineering of T cell states, accelerating the rational design of more effective cellular immunotherapies.

Gastric cancer (GC) remains a lethal malignancy with limited therapeutic options and poor prognosis. In this study, we employed integrated RNA sequencing and ribosome nascent-chain complex sequencing analyses to identify a coding circular RNA (circRNA), circRAD23B, which is markedly up-regulated in GC tissues. Its expression correlates strongly with advanced tumor stage, lymph node metastasis, and reduced overall survival. We further demonstrate that the splicing factor U2AF65 facilitates circRAD23B biogenesis through direct binding to intron 1 of the RAD23B pre-mRNA. Functionally, circRAD23B encodes a novel 208-amino acid protein via an internal ribosome entry site-dependent mechanism. This protein promotes GC proliferation, invasion, and lung metastasis in vivo. Mechanistically, circRAD23B-208aa recruits the E2 ligase UBC9 to catalyze SUMOylation of PDIA5 at lysine 25, thereby attenuating its ubiquitination and enhancing protein stability. Stabilized PDIA5 facilitates ATF6 activation by promoting its proteolytic processing, nuclear translocation, and transcriptional induction of key unfolded protein response (UPR) effectors—TXNRD1 and HERPUD1—thereby alleviating endoplasmic reticulum stress and promoting tumor survival. Our findings reveal circRAD23B-208aa as the first circRNA-encoded activator of the UPR pathway via posttranslational regulation of PDIA5, highlighting the therapeutic potential of targeting the SUMOylation-dependent PDIA5/ATF6 axis in GC.

Supplementary Figure S2: Sensitivity analysis for cumulative probability of gastrointestinal cancers by tertiles of the metabolite risk scores among White participants. The model was adjusted for age, sex, ethnicity and assessment center. The x-axis represents follow-up time (years), and the y-axis indicates the cumulative probability of cancer incidence. A) CRC. B) PC. C) EC. D) GC. E) HCC. F) GCBTC. Abbreviations: CRC, colorectal cancer; EC, esophageal cancer; GC, gastric cancer; GCBTC, gallbladder and biliary tract cancer; HCC, hepatocellular carcinoma; HR, hazard ratio; PC, pancreatic cancer.

Supplementary Table S9: Metabolites associated with the risk of cancers in other organ systems, adjusted for age, sex, assessment center, and ethnicity

Supplementary Table S5: Sensitivity analysis for the association between plasma metabolites and incident gastrointestinal cancer events after additional adjustment for alternate Mediterranean Index

Supplementary Table S1: Information of metabolites in the UK Biobank

Supplementary Table S15: The association between clinical factors and the risk of gastrointestinal cancers

Supplementary Table S6: Sensitivity analysis for the association between plasma metabolites and the risk of gastrointestinal cancer when excluding the participants who had GI cancers within the first two years

Supplementary Table S10: Association between selected metabolic biomarkers and gastrointestinal cancers by LASSO