Xiao Chen

Objectives: While the health risks associated with intrapancreatic fat deposition (IPFD) have attracted considerable attention, its contributing or risk factors remain poorly understood. Elevated uric acid (UA) levels are associated with multiple metabolic diseases. This study aimed to reveal the association between UA and IPFD. Methods: A total of 9200 subjects who underwent computed tomography (CT) examinations for lung cancer screening were included. Data, including basic information (age, sex, BMI), laboratory tests, and medical history, were collected from the healthcare system. IPFD was defined based on three markers: pancreatic fat CT attenuation (PanCT) P /S) P /S, OR=1.002, 95%CI:1.001-1.003; P-S, OR=1.002, 95%CI:1.001-1.003). A clear dose-response relationship was observed when UA was analyzed in quartiles. Participants in the highest UA quartile (Q4) had significantly elevated risks (PanCT, OR=1.534, 95%CI:1.220-1.930; P /S, OR=1.756, 95%CI:1.385-2.227; P-S, OR=1.747, 95%CI:1.482-2.059). Subgroup analysis revealed that this positive association was more pronounced in females. Conclusion: UA levels are positively associated with IPFD and is a valuable marker for IPFD management.

Introduction: Visceral fat is associated with cancer risk. Intrapancreatic fat deposition (IPFD) has been associated with pancreatic diseases, but its role in extra-pancreatic malignancies remains unclear. This case-control study aimed to investigate the association between IPFD and prostate cancer risk. Methods: A total of 143 patients with newly diagnosed, treatment-naïve prostate cancer and 286 age-matched controls without malignancy were enrolled in this retrospective study. IPFD was assessed using unenhanced computed tomography (CT) by two markers: pancreatic CT attenuation (PanCT) and pancreatic-to-spleen attenuation ratio (P/S). Multivariable logistic regression was performed to evaluate the association between IPFD and prostate cancer risk, with sequential adjustment for demographic, metabolic, and hepatic parameters. Results: The risk of prostate cancer decreases as PanCT (p < 0.01) and P/S increases (p = 0.05). Participants with prostate cancer had lower pancreatic CT attenuation compared to controls. In fully adjusted models, higher PanCT quartiles were associated with progressively lower odds of prostate cancer (Q2: OR 0.32, 95% CI 0.15–0.69; Q3: OR 0.21, 95% CI 0.08–0.52; Q4: OR 0.11, 95% CI 0.04–0.29; all p < 0.01). Similar inverse associations were observed for P/S ratio quartiles (Q3: OR 0.30, 95% CI 0.13–0.68; Q4: OR 0.31, 95% CI 0.13–0.75). Conclusion: Intrapancreatic fat deposition is independently associated with an increased risk of prostate cancer, regardless of hepatic fat and body mass index.

Serum cholesterol level plays a vital role in cancer development. Several studies have suggested a potential relationship between non-high-density lipoprotein cholesterol (non-HDL-c) levels and cancer risk. However, little is known about the association between non-HDL-c levels and high-risk pancreatic intraductal papillary mucinous neoplasms (IPMNs). This study aimed to explore the relationship between non-HDL-c levels and high-risk IPMNs. This retrospective study included 185 patients with IPMNs. Participants were divided into high and low non-HDL-c groups according to the third quartile of non-HDL-c levels. The associations between total cholesterol, non-HDL-c, and malignant IPMN were evaluated with univariate and multivariate logistic regression. In addition, the relationship between non-HDL-c levels and invasive carcinoma was examined. Finally, we explored the associations of the non-HDL-C/total cholesterol and non-HDL-c/HDL-c ratios with high-risk IPMNs. In addition to HDL-c levels, the average levels of triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), and carcinoembryonic antigen (CEA) in patients with high non-HDL-c levels were higher than those in the low non-HDL-c group (P < 0.05). In multivariate logistic regression analysis, non-HDL-c levels were significantly associated with the high-risk IPMNs (P = 0.043), but such an association was not observed between non-HDL-c levels and invasive carcinoma. No such relationship was observed between the non-HDL-c/TC ratio, non-HDL-c/HDL ratio, and high-risk IPMNs (P = 0.43; P = 0.16). High non-HDL-c levels are associated with high-risk IPMNs. Serum non-HDL-c levels may be a potential associated factor for high-risk IPMNs.

Background: Associations of cholesterol, high-density lipoprotein, and glucose (CHG) index and triglyceride-glucose (TYG)-related indices are biomarkers of insulin resistance. However, the relationships between carotid atherosclerosis and the CHG index and TYG-related indices in young and middle-aged people are unclear. This cross-sectional study reported such associations in a general population. Methods: A total of 9,110 participants aged between 18 and 60 years were included. TYG index (ln(triglyceride (mg/dL) × blood glucose (mg/dL)/2), TYG-BMI index (TYG×BMI), TYG/HDL index (TYG/HDL cholesterol), and CHG index (ln[cholesterol (mg/dL) × blood glucose (mg/dL)/2 × HDL (mg/dL)) were calculated. The associations between carotid atherosclerosis and CHG- and TYG-related indices were analyzed via logistic regression analyses. Results: Carotid atherosclerosis was observed in 3,089 (33.9%) participants. CHG index was significantly associated with carotid atherosclerosis (odds ratio (OR): 2.47, 95% confidence interval (CI): 1.87–3.26; Q4 vs. Q1, OR: 1.79, 95% CI: 1.42–2.26). Similar associations were observed for the TYG index (OR: 1.26, 95% CI: CI: 1.08–1.48; Q4 vs. Q1, OR: OR: 1.32, 95% CI: 1.08–1.62), TY-BMI (OR: 1.01, 95% CI: 1.00–1.02; Q4 vs. Q1, OR: 1.55, 95% CI: 1.15–2.11) and TYG/HDL (OR: 1.10, 95% CI: 1.06–1.13; Q4 vs. Q1, OR: 1.60, 95% CI: 1.32–1.94). CHG had better performance than the TYG, TYGBMI, and TYGHDL in identifying carotid atherosclerosis (the area under the curve was 0.642 vs 0.599, 0.620, and 0.612, respectively). Conclusion: CHG index and TYG-related indices were associated with carotid atherosclerosis in young and middle-aged individuals. CHG had better performance than did TYG-related indices in identifying carotid atherosclerosis.

Study design/setting: Retrospective longitudinal study. Purpose: Osteoporotic vertebral fractures (OVF) are common in middle-aged and elderly populations. However, few studies have predicted the risk of OVF from the perspective of the bone heterogeneity. This study conducted a longitudinal study to predict the risk of OVF in individuals over 50 years old based on habitat radiomics which can quantify heterogeneity of vertebral trabecular bone. Methods: Individuals aged over 50 years who had not experienced OVF and underwent CT scans between 2016 and 2023 were enrolled and followed up until 2024. During the follow-up period, 107 cases developed new OVF, and 270 individuals without fractures were selected as the control group. Radiomic features of each pixel within the vertebra were extracted, and the optimal segmentation of vertebral sub-regions was determined using the K-means unsupervised clustering method. Results: The habitat radiomics model significantly outperformed the CT value model (AUC = 0.702, DeLong test P -value = 0.001) and also surpassed the traditional radiomics model. The Cox proportional hazards analysis showed that the habitat radiomics risk score could serve as an independent predictor of vertebral fractures (hazards ratio = 1.092, 95% confidence interval (CI): 1.074 - 1.111, P < 0.001). The C-index of the habitat radiomics nomogram model was 0.803 in the training set (95% CI: 0.752 - 0.854) and 0.748 in the validation set (95% CI: 0.667 - 0.829). Conclusion: The habitat radiomics model can predict vertebral fractures based on vertebral heterogeneity, with better performance than traditional bone density prediction methods.

Early identification of high-risk fracture populations is clinically vital for comprehensive diabetes management. This study aimed to investigate whether the vertebral bone composition is associated with vertebral fracture risk in patients with type 2 diabetes mellitus (T2DM). This retrospective longitudinal study included 405 T2DM patients (135 with new-onset vertebral fractures and 270 matched controls) from a cohort of 937 individuals with at least two spinal computed tomography (CT) scans between July 2019 and July 2022. Vertebral bodies (T12 level) were segmented on baseline CT images into three regions based on Hounsfield Unit (HU) thresholds: Area A (< 110 HU, low density), Area B (110–160 HU, median density), and Area C (> 160 HU, high density). The areas of these regions and the mean vertebral CT value were measured. Univariate and multivariate Cox regression analyses were performed to identify independent risk factors for vertebral fractures. Predictive models were developed and evaluated using time-dependent receiver operating characteristic (ROC) curves, C-index, calibration, and decision curve analysis. After adjusting for potential confounders, both a smaller Area of C (adjusted Hazard Ratio [aHR] = 0.99 per mm² increase; 95% confidence interval (CI): 0.99–0.99) and a lower vertebral CT value (aHR = 0.99 per HU increase; 95% CI: 0.99–0.99) were independent predictors of vertebral fractures. Patients with a C-area < 308.7 mm² had a 4.32-fold higher fracture risk (95% CI: 2.61–7.32), and those with a vertebral CT value ≤ 100.7 HU had a 1.58-fold higher risk (95% CI: 1.06–2.63). The combined model (C-area + CT value) demonstrated superior predictive performance, with area under the curve (AUC) values of 0.775, 0.796, and 0.822 at 2, 3, and 4 years, respectively, outperforming models based on either variable alone (C-index: 0.751 for combined vs. 0.713 for C-area and 0.659 for CT value). Smaller high-density bone area (Area C) is a significant and independent risk factor for vertebral fractures in T2DM patients. A model combining high-density area and vertebral CT value provides a more robust prediction of fracture risk than traditional bone mass metrics alone, suggesting the potential for improved risk stratification in clinical practice.

Aims: Cancer is a recognized risk factor for sarcopenia. While some studies suggest that androgen deprivation therapy (ADT) may contribute to muscle loss in patients with prostate cancer (PCa), it remains unclear whether alterations in muscle composition are already present at the initial diagnosis, before any treatment begins. This study aimed to evaluate computed tomography (CT)–based muscle size and muscle fat infiltration in patients with newly diagnosed prostate cancer. Methods: A total of 143 patients with newly diagnosed PCa (January 2023–June 2024) were enrolled and matched 1:2 with 286 controls. CT attenuation and cross-sectional area of the erector spinae (ES) and full-layer muscle at the T12 level were measured. The muscle-to-spleen attenuation ratio (M/S) was calculated. Muscle fat infiltration was assessed based on muscle CT attenuation. Multivariable logistic regression analyses were performed to examine the association between PCa and the risk of low muscle CT attenuation or low muscle area. Results: In PCa patients, muscle CT attenuation, M/S ratio, ES area, and full-layer muscle area decreased with advancing age. Overall, muscle CT attenuation and M/S ratio were significantly lower in the PCa group than in controls. Both ES area and full-layer muscle area were also significantly reduced in PCa patients younger than 70years. A similar difference in ES area was observed in the 70–80-year age group. After full adjustment, PCa was significantly associated with an increased risk of low ES CT attenuation (OR=2.59, 95%CI: 1.25–5.39), low full-layer muscle CT attenuation (OR=7.14, 95%CI: 3.30–15.45), low full-layer muscle M/S ratio (OR=4.29, 95%CI: 2.01–8.87), and low ES area (OR=2.94, 95%CI: 1.33–6.53). Similar associations were observed in the non-diabetic subgroup. Conclusion: Newly diagnosed prostate cancer is associated with an increased risk of CT-based skeletal muscle fat infiltration and reduced muscle area.