Objective This study aimed to investigate the association between fruit and vegetable intake and arterial stiffness.Methods We conducted a cohort-based study comprising 6,628 participants with arterial stiffness information in the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) project. A semi-quantitative food-frequency questionnaire was used to assess baseline (2007–2008) and recent (2018–2021) fruit and vegetable intake. We assessed changes in fruit and vegetable intake from 2007–2008 to 2018–2021 in 6,481 participants. Arterial stiffness was measured using the arterial velocity–pulse index (AVI) and arterial pressure-volume index (API). Elevated AVI and API values were defined according to diverse age reference ranges.Results Multivariable-adjusted linear regression models revealed that every 100 g/d increment in fruit and vegetable intake was associated with a 0.11 decrease in AVI (B = −0.11; 95% confidence interval [CI]: −0.20, −0.02) on average, rather than API (B = 0.02; 95% CI: −0.09, 0.13). The risk of elevated AVI (odds ratio [OR] = 0.82; 95% CI: 0.70, 0.97) is 18% lower in individuals with high intake (≥ 500 g/d) than in those with low intake (< 500 g/d). Furthermore, maintaining a high intake in the past median of 11.5 years of follow-up was associated with an even lower risk of elevated AVI compared with a low intake at both baseline and follow-up (OR = 0.64; 95% CI: 0.49, 0.83).Conclusion Fruit and vegetable intake was negatively associated with arterial stiffness, emphasizing recommendations for adherence to fruit and vegetable intake for the prevention of arterial stiffness.
Objective This study aimed to develop an artificial neural network (ANN) model combined with dietary retinol intake from different sources to predict the risk of non-alcoholic fatty liver disease (NAFLD) in American adults.Methods Data from the 2007 to 2014 National Health and Nutrition Examination Survey (NHANES) 2007–2014 were analyzed. Eligible subjects (n = 6,613) were randomly divided into a training set (n1 = 4,609) and a validation set (n2 = 2,004) at a ratio of 7:3. The training set was used to identify predictors of NAFLD risk using logistic regression analysis. An ANN was established to predict the NAFLD risk using a training set. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the accuracy of the model using the training and validation sets.Results Our study found that the odds ratios (ORs) and 95% confidence intervals (CIs) of NAFLD for the highest quartile of plant-derived dietary retinol intake (i.e., provitamin A carotenoids, such as β-carotene) (OR = 0.75, 95% CI: 0.57 to 0.99) were inversely associated with NAFLD risk, compared to the lowest quartile of intake, after adjusting for potential confounders. The areas under the ROC curves were 0.874 and 0.883 for the training and validation sets, respectively. NAFLD occurs when its incidence probability is greater than 0.388.Conclusion The ANN model combined with plant-derived dietary retinol intake showed a significant effect on NAFLD. This could be applied to predict NAFLD risk in the American adult population when government departments formulate future health plans.
Objective Exosomal long noncoding RNAs (lncRNAs) are the key to diagnosing and treating various diseases. This study aimed to investigate the diagnostic value of plasma exosomal lncRNAs in white matter hyperintensities (WMH).Methods We used high-throughput sequencing to determine the differential expression (DE) profiles of lncRNAs in plasma exosomes from WMH patients and controls. The sequencing results were verified in a validation cohort using qRT-PCR. The diagnostic potential of candidate exosomal lncRNAs was proven by binary logistic analysis and receiver operating characteristic (ROC) curves. The diagnostic value of DE exo-lncRNAs was determined by the area under the curve (AUC). The WMH group was then divided into subgroups according to the Fazekas scale and white matter lesion site, and the correlation of DE exo-lncRNAs in the subgroup was evaluated.Results In our results, four DE exo-lncRNAs were identified, and ROC curve analysis revealed that exo-lnc_011797 and exo-lnc_004326 exhibited diagnostic efficacy for WMH. Furthermore, WMH subgroup analysis showed exo-lnc_011797 expression was significantly increased in Fazekas 3 patients and was significantly elevated in patients with paraventricular matter hyperintensities.Conclusion Plasma exosomal lncRNAs have potential diagnostic value in WMH. Moreover, exo-lnc_011797 is considered to be a predictor of the severity and location of WMH.
Objective To investigate the effects of the pre-shock state on the mortality of patients with sepsis.Methods We enrolled patients with sepsis admitted to the medical intensive care unit of a tertiary care university hospital. These patients were then classified into three groups: sepsis, pre-shock state, and septic shock. The primary outcome was the 28-day mortality rate. The secondary outcomes were the 90-day, 180-day, and 1-year mortality rates.Results A total of 303 patients (groups: sepsis 135 [44.6%]), pre-shock state (93 [30.7%]), and septic shock (75 [24.8%]) completed the 1-year follow-up. The mortality rates at 28 days, 90 days, and 180 days and 1 year were significantly higher in the pre-shock state group than those of the sepsis group, but significantly lower than those in the septic shock group, especially among older patients. When compared with the pre-shock state group, the sepsis group had significantly lower mortality risks at 28 days, 90 days, and 180 days and 1 year, whereas the sepsis shock group had higher mortality risks at these time points.Conclusion The mortality rates of patients in the pre-shock state were notably different from those of patients with sepsis or septic shock. The introduction of a modified sepsis severity classification, which includes sepsis, pre-shock state, and septic shock, could offer valuable additional prognostic information.
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