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Abstract Background Clonal hematopoiesis of indeterminate potential (CHIP) was associated with diabetes and cardiovascular diseases (CVD). However, the effect of CHIP on CVD have not been evaluated among patients with diabetes, and whether maintaining the healthy indictors could mitigate the adverse influence was also unclear. Methods A total of 22,239 adults from the UK Biobank with diabetes and available whole-exome sequence data, and free of CVD were included. Multivariable-adjusted Cox regressions were used to explore the associations of any CHIP (variant allele fraction ≥ 2%), large CHIP (variant allele fraction ≥ 10%), and the top 10 commonly mutated driver genes for CHIP and with risk of CVD. The joint associations between health indicators (body mass index [BMI], HbA1c, blood pressure [BP], and low-density lipoprotein cholesterol [LDL]) and CHIP were further investigated. Results Over a median follow-up of 13.2 years, 5366 participants with diabetes developed CVD events. The hazard ratios (HRs) (95% confidence intervals [CIs]) of any CHIP and large CHIP were (1.21, 1.08–1.36) and (1.25, 1.09–1.43) for incident CVD, respectively. Significant associations between any CHIP and coronary heart disease (HR, 95%CI: 1.18, 1.03–1.36) and heart failure (1.73, 1.46–2.06) were observed, but not for stroke (1.14, 0.89–1.48). Gene-specific analyses suggested that the greatest association were for SF3B1 (HR, 95%CI: 2.50, 1.25–5.01) and TET2 (HR, 95%CI: 1.36, 1.07–1.77) with risk of CVD. There was no significant interaction between the four health indicators and CHIP in relation to incident CVD. Compared to patients without CHIP, those with any CHIP and ideal health indicators still exhibited significantly or nonsignificantly higher HRs (BMI: 1.18, 0.82–1.68; HbA1c: 1.12, 0.96–1.30; BP: 1.24, 1.03–1.49; LDL: 1.29, 1.09–1.53). Similar results were demonstrated using large CHIP. Conclusions CHIP is independently associated with an increased risk of CVD in patients with diabetes, regardless of health indicator levels. Diabetic patients with CHIP but ideal health indicators still exhibited higher CVD risk compared with diabetic patients without CHIP. Graphic abstract

Objectives: Physical frailty has been found to increase the risk of multiple adverse outcomes including cardiovascular disease (CVD) in diabetic patients, but whether this could be modified by traditional risk factor control remains unknown. We aimed to explore the joint and interaction effects of frailty and traditional risk factor control on the risk of CVD. Design: A population-based cohort study. Setting and participants: We included 15,753 participants with type 2 diabetes at baseline from UK Biobank. Measurements: Physical frailty was assessed by Fried criteria’s frailty phenotype. The degree of risk factor control was determined by the numbers of the following factors controlled within the target range, including glycated hemoglobin, blood pressure, low-density lipoprotein cholesterol, smoking, and kidney condition. Incident CVD included coronary heart disease, stroke, or heart failure. Cox proportional hazard models were used to assess the individual and joint effects of frailty and risk factor control on the risk of CVD. Results: After a median follow-up of 13.5 years, 1129 incident CVD events were observed. Compared with non-frailty, both prefrailty and frailty were significantly associated with increased risk of CVD (HR 1.22, 95% CI [1.13, 1.31] for pre-frailty and 1.70 [1.53, 1.90] for frailty). For the joint effects, participants with frailty and a low degree of risk factor control (control of 0−1 risk factors) had the highest risk of CVD (2.92 [2.04, 4.17]) compared to those with non-frailty and optimal risk factor control (control of 4−5 risk factors). Moreover, a significant additive interaction between frailty and risk factor control was observed, with around 3.8% of CVD risk attributed to the interactive effects. Conclusions: Both prefrailty and frailty were associated with a higher risk of CVD in participants with type 2 diabetes. Moreover, physical frailty could interact with the degree of risk factor control in an additive manner to increase the CVD risk.

Background: Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes. Methods: This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites. Results: During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes. Conclusions: Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions. Funding: Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).

ABSTRACT Aim This study aims to investigate the association of glycemia risk index (GRI), a novel composite metric derived from continuous glucose monitoring (CGM), with arterial stiffness in patients with type 2 diabetes. Materials and Methods A total of 342 adults with type 2 diabetes were enrolled between April and June 2023 from 11 communities in Shanghai, China. Medical examinations, including measurements of anthropometric parameters, blood pressure, and venous blood samples were conducted. Brachial‐ankle pulse wave velocity (baPWV) was examined to evaluate arterial stiffness. All the participants underwent a 14 day CGM recording and GRI was calculated from the CGM data. Results The mean age was 70.3 ± 6.8 years, and 162 (47.4%) were male. Participants with a higher baPWV had significantly higher levels of GRI and hyperglycemia component (both P for trend

Abstract Background The incidence of cervical spine fractures is increasing every day, causing a huge burden on society. This study aimed to develop and verify a nomogram to predict the in-hospital mortality of patients with cervical spine fractures without spinal cord injury. This could help clinicians understand the clinical outcome of such patients at an early stage and make appropriate decisions to improve their prognosis. Methods This study included 394 patients with cervical spine fractures from the Medical Information Mart for Intensive Care III database, and 40 clinical indicators of each patient on the first day of admission to the intensive care unit were collected. The independent risk factors were screened using the Least Absolute Shrinkage and Selection Operator regression analysis method, a multi-factor logistic regression model was established, nomograms were developed, and internal validation was performed. A receiver operating characteristic (ROC) curve was drawn, and the area under the ROC curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination of the model. Moreover, the consistency between the actual probability and predicted probability was reflected using the calibration curve and Hosmer–Lemeshow (HL) test. A decision curve analysis (DCA) was performed, and the nomogram was compared with the scoring system commonly used in clinical practice to evaluate the clinical net benefit. Results The nomogram indicators included the systolic blood pressure, oxygen saturation, respiratory rate, bicarbonate, and simplified acute physiology score (SAPS) II. The results showed that our model had satisfactory predictive ability, with an AUC of 0.907 (95% confidence interval [CI] = 0.853–0.961) and 0.856 (95% CI = 0.746–0.967) in the training set and validation set, respectively. Compared with the SAPS-II system, the NRI values of the training and validation sets of our model were 0.543 (95% CI = 0.147–0.940) and 0.784 (95% CI = 0.282–1.286), respectively. The IDI values of the training and validation sets were 0.064 (95% CI = 0.004–0.123; P = 0.037) and 0.103 (95% CI = 0.002–0.203; P = 0.046), respectively. The calibration plot and HL test results confirmed that our model prediction results showed good agreement with the actual results, where the HL test values of the training and validation sets were P = 0.8 and P = 0.95, respectively. The DCA curve revealed that our model had better clinical net benefit than the SAPS-II system. Conclusion We explored the in-hospital mortality of patients with cervical spine fractures without spinal cord injury and constructed a nomogram to predict their prognosis. This could help doctors assess the patient’s status and implement interventions to improve prognosis accordingly.

Abstract Background Low-carbohydrate diet (LCD) is effective for weight loss and glycaemic control in humans. Here, the study aimed to explore the effects of LCD/high-fat diet (HFD) in both humans and mice. Methods Twenty-two overweight or obese participants received LCD for 3 weeks. Based on carbohydrate intake > 10% or ≤ 10% of calories, the participants were divided into moderate LCD (MLCD) and very LCD (VLCD) groups. The participants completed a 10-question food preference survey. Meanwhile, C57BL/6J mice were assigned to five groups: chow diet (CD, 10% fat), HFD with 60%, 70%, and 75% fat from cocoa butter (HFD-C), and HFD with 60% fat from lard (HFD-L) and fed for 24 weeks. Eight mice were acclimatised for the food-choice test. Results LCD decreased the total energy intake in humans. The VLCD group showed greater weight loss and better glycaemic control than the MLCD group. A food preference survey showed that 65% of participants tended to choose high-carbohydrate foods. In mice, HFD resulted in energy overconsumption, obesity, and metabolic disorders. When CD and HFD-L were administered simultaneously, mice rarely consumed CD. In the HFD-C groups, the energy intake and body weight increased with increasing dietary fat content. Compared with the HFD-C group, the HFD-L group consumed more energy and had poorer metabolism. Conclusions Lower carbohydrate intake contributed to lower energy intake and improved metabolism in humans. In mice, diets with a higher proportion of fat become more attractive and obesogenic by fixing the fat sources. Since the mice preferred lard to cocoa butter, lard induced excess energy intake and poorer metabolism. Different food preferences may be the underlying mechanism behind the opposite effects of the LCD/HFD in humans and mice. Trial registration The clinical trial was registered with the Chinese Clinical Trial Registry ( www.chictr.org.cn ). The registration number is ChiCTR1800016786. All participants provided written informed consent prior to enrolment.

Urgent detection of calculous pyonephrosis is crucial for surgical planning and preventing severe outcomes. This study aims to evaluate the performance of computed tomography (CT)-based radiomics and a three-dimensional convolutional neural network (3D-CNN) model, integrated with independent clinical factors, to identify patients with calculous pyonephrosis. We recruited 182 patients receiving either percutaneous nephrostomy tube placement or percutaneous nephrolithotomy for calculous hydronephrosis or pyonephrosis. The regions of interest were manually delineated on plain CT images and the CT attenuation value (HU) was measured. Radiomics analysis was performed using least absolute shrinkage and selection operator (LASSO). A 3D-CNN model was also developed. The better-performing machine-learning model was combined with independent clinical factors to build a comprehensive clinical machine-learning model. The performance of these models was assessed using receiver operating characteristic analysis and decision curve analysis. Fever, blood neutrophils, and urine leukocytes were independent risk factors for pyonephrosis. The radiomics model showed higher area under the curve (AUC) than the 3D-CNN model and HU (0.876 vs. 0.599, 0.578; p = 0.003, 0.002) in the testing cohort. The clinical machine-learning model surpassed the clinical model in both the training (0.975 vs. 0.904, p = 0.019) and testing (0.967 vs. 0.889, p = 0.045) cohorts.

Objective: To evaluate the applicability of artificial intelligence-assisted compressed sensing (ACS) to anal fistula magnetic resonance imaging (MRI). Methods: 51 patients were included in this study and underwent T2-weighted sequence of MRI examinations both with ACS and without ACS technology in a 3.0 T MR scanner. Subjective image quality scores, and objective image quality-related metrics including scanning time, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), were evaluated and statistically compared between the images collected with and without ACS. Results: No significant difference in the subjective image quality of lesion conspicuity was observed between the two groups. However, ACS MRI decreased the acquisition time with regard to control group (74.00 s vs. 156.00 s). Besides, SNR of perianal and muscle in the ACS group was significantly higher than that of the control group (164.07 ± 33.35 vs 130.81 ± 29.10, p

Abstract Background Dietary management is the most important and effective treatment for citrin deficiency, as well as a decisive factor in the clinical outcome of patients. However, the dietary management ability of caregivers of children with citrin deficiency is generally poor, especially in East Asia where carbohydrate-based diets are predominant. The aim of this study was to identify the difficulties that caregivers encounter in the process of home-based dietary management, and the reasons responsible for these challenges. Results A total of 26 caregivers of children with citrin deficiency were recruited, including 24 mothers, one father, and one grandmother. Grounded theory was employed to identify three themes (covering 12 sub-themes) related to the dilemma of dietary management: dietary management that is difficult to implement; conflicts with traditional concepts; and the notion that children are only a part of family life. The first theme describes the objective difficulties that caregivers encounter in the process of dietary management; the second theme describes the underlying reasons responsible for the non-adherent behavior of caregivers; the third theme further reveals the self-compromise by caregivers in the face of multiple difficulties. Conclusions This study reflects the adverse effects of multi-dimensional contradictions on the adherence of caregivers to dietary management. These findings reveal that the dietary management of citrin deficiency is not only a rational process, rather it is deeply embedded in family, social, and dietary traditions.