The common carp, Cyprinus carpio, is one of the most important cyprinid species and globally accounts for 10% of freshwater aquaculture production. Here we present a draft genome of domesticated C. carpio (strain Songpu), whose current assembly contains 52,610 protein-coding genes and approximately 92.3% coverage of its paleotetraploidized genome (2n = 100). The latest round of whole-genome duplication has been estimated to have occurred approximately 8.2 million years ago. Genome resequencing of 33 representative individuals from worldwide populations demonstrates a single origin for C. carpio in 2 subspecies (C. carpio Haematopterus and C. carpio carpio). Integrative genomic and transcriptomic analyses were used to identify loci potentially associated with traits including scaling patterns and skin color. In combination with the high-resolution genetic map, the draft genome paves the way for better molecular studies and improved genome-assisted breeding of C. carpio and other closely related species.

Background: Concurrent atherogenic dyslipidemia and elevated inflammation are commonly observed in overt hyperglycemia and have long been proposed to contribute to diabetogenesis. However, the temporal relationship between them and the effect of their cumulative co-exposure on future incident type 2 diabetes (T2D) remains unclear. Methods: Longitudinal analysis of data on 52,224 participants from a real-world, prospective cohort study (Kailuan Study) was performed to address the temporal relationship between high-sensitivity C-reactive protein (hsCRP) and the atherogenic index of plasma (AIP, calculated as triglyceride/high-density lipoprotein) in an approximately 4-year exposure period (2006/2007 to 2010/2011). After excluding 8824 participants with known diabetes, 43,360 nondiabetic participants were included for further analysis of the T2D outcome. Cox regression models were used to examine the adjusted hazard ratios (aHRs) upon the cumulative hsCRP (CumCRP) and AIP (CumAIP) in the exposure period. Results: In temporal analysis, the adjusted standardized correlation coefficient (β1) of hsCRP_2006/2007 and AIP_2010/2011 was 0.0740 (95% CI, 0.0659 to 0.0820; P < 0.001), whereas the standardized correlation coefficient (β2) of AIP_2006/2007 and hsCRP_2010/2011 was − 0.0293 (95% CI, − 0.0385 to − 0.0201; P < 0.001), which was significantly less than β1 (P < 0.001). During a median follow-up of 7.9 years, 5,118 T2D cases occurred. Isolated exposure to CumAIP or CumCRP was dose-dependently associated with T2D risks, independent of traditional risk factors. Significant interactions were observed between the median CumAIP (− 0.0701) and CumCRP thresholds (1, 3 mg/L) (P = 0.0308). Compared to CumAIP < − 0.0701 and CumCRP < 1 mg/L, those in the same CumAIP stratum but with increasing CumCRP levels had an approximately 1.5-fold higher T2D risk; those in higher CumAIP stratum had significantly higher aHRs (95% CIs): 1.64 (1.45–1.86), 1.87 (1.68–2.09), and 2.04 (1.81–2.30), respectively, in the CumCRP < 1, 1 ≤ CumCRP < 3, CumCRP ≥ 3 mg/L strata. Additionally, the T2D risks in the co-exposure were more prominent in nonhypertensive, nondyslipidemic, nonprediabetic, or female participants. Conclusions: These findings suggest a stronger association between elevated hsCRP and future AIP changes than vice versa and highlight the urgent need for combined assessment and management of chronic inflammation and atherogenic dyslipidemia in primary prevention, particularly for those with subclinical risks of T2D.

Increased fasting plasma glucose (FPG) is an independent risk factor for type 2 diabetes mellitus (T2DM). The development of systems biology technologies for integration of multiomics data is crucial for predicting increased FPG levels. In this case–control study, immunoglobulin (Ig) G glycosylation profiling and genome-wide association analyses were performed on 511 participants, and among them 76 had increased FPG (aged 47.6 ± 6.14 years), and 435 had decreased or fluctuant FPG (aged 47.9 ± 6.08 years). We identified nine single nucleotide polymorphisms (SNPs) in five genes (RPL7AP27, SNX30, SLC39A12, BACE2, and IGFL2) that were significantly associated with increased FPG (odds ratios 1.937–2.393). Moreover, of the 24 glycan peaks (GPs), GPs 3, 8, and 11 presented positive trends with increased FPG levels, whereas GPs 4 and 14 presented negative trends. A significant improvement of predictive power was observed when adding 24 IgG GPs to 9 SNPs with the area under the curve increased from 0.75 to 0.81. This report shows that the combination of candidate SNPs with IgG glycomics offers biomarker potentials for T2DM. The substantial predictive power obtained from integrating genomics and glycomics biomarkers suggests the feasibility of applying such multiomics strategies to enable predictive, preventive, and personalized medicine for T2DM.

Background: Recognising the early signs of ischemic stroke (IS) in emergency settings has been challenging. Machine learning (ML), a robust tool for predictive, preventive and personalised medicine (PPPM/3PM), presents a possible solution for this issue and produces accurate predictions for real-time data processing. Methods: This investigation evaluated 4999 IS patients among a total of 10,476 adults included in the initial dataset, and 1076 IS subjects among 3935 participants in the external validation dataset. Six ML-based models for the prediction of IS were trained on the initial dataset of 10,476 participants (split participants into a training set [80%] and an internal validation set [20%]). Selected clinical laboratory features routinely assessed at admission were used to inform the models. Model performance was mainly evaluated by the area under the receiver operating characteristic (AUC) curve. Additional techniques—permutation feature importance (PFI), local interpretable model-agnostic explanations (LIME), and SHapley Additive exPlanations (SHAP)—were applied for explaining the black-box ML models. Results: Fifteen routine haematological and biochemical features were selected to establish ML-based models for the prediction of IS. The XGBoost-based model achieved the highest predictive performance, reaching AUCs of 0.91 (0.90–0.92) and 0.92 (0.91–0.93) in the internal and external datasets respectively. PFI globally revealed that demographic feature age, routine haematological parameters, haemoglobin and neutrophil count, and biochemical analytes total protein and high-density lipoprotein cholesterol were more influential on the model’s prediction. LIME and SHAP showed similar local feature attribution explanations. Conclusion: In the context of PPPM/3PM, we used the selected predictors obtained from the results of common blood tests to develop and validate ML-based models for the diagnosis of IS. The XGBoost-based model offers the most accurate prediction. By incorporating the individualised patient profile, this prediction tool is simple and quick to administer. This is promising to support subjective decision making in resource-limited settings or primary care, thereby shortening the time window for the treatment, and improving outcomes after IS.

Type 2 diabetes mellitus (T2DM) is a common complex trait arising from interactions among multiple environmental, genomic, and postgenomic factors. We report here the first attempt to investigate the association between immunoglobulin G (IgG) N-glycan patterns, T2DM, and their clinical risk factors in an Australian population. N-glycosylation of proteins is one of the most frequently observed co- and post-translational modifications, reflecting, importantly, the real-time status of the interplay between the genomic and postgenomic factors. In a community-based case–control study, 849 participants (217 cases and 632 controls) were recruited from an urban community in Busselton, Western Australia. We applied the ultraperformance liquid chromatography method to analyze the composition of IgG N-glycans. We then conducted Spearman's correlation analyses to explore the association between glycan biomarker candidates and clinical risk factors. We performed area under the curve (AUC) analysis of the receiver operating characteristic curves by fivefold cross-validation for clinical risk factors, IgG glycans, and their combination. Two directly measured and four derived glycan peaks were significantly associated with T2DM, after correction for extensive clinical confounders and false discovery rate, thus suggesting that IgG N-glycan traits are highly correlated with T2DM clinical risk factors. Moreover, adding the IgG glycan profiles to fasting blood glucose in the logistic regression model increased the AUC from 0.799 to 0.859. The AUC for IgG glycans alone was 0.623 with a 95% confidence interval 0.580–0.666. In addition, our study provided new evidence of diversity in T2DM complex trait by IgG N-glycan stratification. Six IgG glycan traits were firmly associated with T2DM, which reflects an increased proinflammatory and biological aging status. In summary, our study reports novel associations between the IgG N-glycome and T2DM in an Australian population and the putative role of proinflammatory mechanisms. Furthermore, IgG N-glycomic alterations offer future prospects as inflammatory biomarker candidates for T2DM diagnosis, and monitoring of T2DM progression to cardiovascular disease or renal failure.

Background: Both elevated inflammation and atherogenic dyslipidemia are prominent in young-onset diabetes and are increasingly identified as biologically intertwined processes that contribute to diabetogenesis. We aimed to investigate the age-specific risks of type 2 diabetes (T2D) upon concomitant chronic inflammation and atherogenic dyslipidemia. Methods: Age-stratified Cox regression analysis of the risk of incident diabetes upon co-exposure to time-averaged cumulative high-sensitivity C-reactive protein (CumCRP) and atherogenic index of plasma (CumAIP) among 42,925 nondiabetic participants from a real-world, prospective cohort (Kailuan Study). Results: During a median 6.41 years of follow-up, 3987 T2D developed. Isolated CumAIP and CumCRP were significantly associated with incident T2D in the entire cohort and across all age subgroups. Both CumAIP and CumCRP were jointly associated with an increased risk of diabetes (P-interaction = 0.0126). Compared to CumAIP < -0.0699 and CumCRP < 1 mg/L, co-exposure to CumAIP ≥ − 0.0699 and CumCRP ≥ 3 mg/L had a significant hazard ratio (HR) [2.55 (2.23–2.92)] after adjusting for socio-demographic, life-style factors, family history of diabetes, blood pressure, renal function and medication use. The co-exposure-associated risks varied greatly by age distribution (P-interaction = 0.0193): < 40 years, 6.26 (3.47–11.28); 40–49 years, 2.26 (1.77–2.89); 50–59 years, 2.51 (2.00–3.16); 60–69 years, 2.48 (1.86–3.30); ≥ 70 years, 2.10 (1.29–3.40). In young adults (< 45 years), both exposures had a significant supra-additive effect on diabetogenesis (relative excess risk due to interaction: 0.80, 95% CI 0.10–1.50). Conclusions: These findings highlight the need for age-specific combined assessment and management of chronic inflammation and dyslipidemia in primary prevention against T2D, particularly for young adults. The clinical benefit derived from dual-target intervention against dyslipidemia and inflammation will exceed the sum of each part alone in young adults.