Highly elevated systemic inflammation is a strong independent predictor of early mortality in advanced non-small cell lung cancer

Joint analysis of diet quality, inflammatory potential of diet and ultra-processed food exposure in relation to chronic respiratory diseases and lung cancer mortality

OBJECTIVE

Examined the combined effects of ultra-processed food (UPF), Dietary Inflammatory Index (DII), Healthy Eating Index-2015 (HEI-2015), and Dietary Antioxidant Index (DAI) on mortality from chronic respiratory diseases (CRDs), chronic obstructive pulmonary disease (COPD), and lung cancer.

METHODS

A prospective analysis included 96,607 participants (53 % women). Diet intake was measured using food frequency questionnaire. Associations of dietary exposures with CRD, COPD, and lung cancer mortality were examined using Cox regression.

RESULTS

During 1,459,299 person-years of follow-up, there were 30,623 all-cause deaths, including 5218 from CRDs, 1613 from COPD, and 2127 from lung cancer. A 10 % increase in UPF intake (% grams/day) showed a non-linear association with higher CRD and COPD mortality but not lung cancer. Stronger curvature was observed between DII and mortality from all three conditions. However, HEI-2015 was inversely associated with CRD, COPD, and lung cancer mortality, while DAI showed an inverse relationship with CRD and COPD mortality but not lung cancer. Adjusting for DII attenuated UPF-related mortality risks by 39 % (CRD), 11 % (COPD), and 18 % (lung cancer), while HEI-2015 adjustment showed less attenuation. Additionally, the DII-mortality associations were less attenuated after adjusting for UPF intake but were offset after adjusting for HEI-2015. However, the HEI-2015-mortality associations remained unaffected when adjusted for UPF, DII, or DAI.

CONCLUSIONS

The findings highlight that the UPF-mortality relationship is potentially explained by DII and, to a lesser extent, by HEI-2015. Adhering to HEI-2015 guidelines can counterbalance the effects of DII on respiratory health but may not offset the effects associated with UPF.

Exploration of oxidized phosphocholine profile in non-small-cell lung cancer

Introduction: Lung cancer is one of the most frequently studied types of cancer and represents the most common and lethal neoplasm. Our previous research on non-small cell lung cancer (NSCLC) has revealed deep lipid profile reprogramming and redox status disruption in cancer patients. Lung cell membranes are rich in phospholipids that are susceptible to oxidation, leading to the formation of bioactive oxidized phosphatidylcholines (oxPCs). Persistent and elevated levels of oxPCs have been shown to induce chronic inflammation, leading to detrimental effects. However, recent reports suggest that certain oxPCs possess anti-inflammatory, pro-survival, and endothelial barrier-protective properties. Thus, we aimed to measure the levels of oxPCs in NSCLC patients and investigate their potential role in lung cancer. Methods: To explore the oxPCs profiles in lung cancer, we performed in-depth, multi-level metabolomic analyses of nearly 350 plasma and lung tissue samples from 200 patients with NSCLC, including adenocarcinoma (ADC) and squamous cell carcinoma (SCC), the two most prevalent NSCLC subtypes and COPD patients as a control group. First, we performed oxPC profiling of plasma samples. Second, we analyzed tumor and non-cancerous lung tissues collected during the surgical removal of NSCLC tumors. Because of tumor tissue heterogeneity, subsequent analyses covered the surrounding healthy tissue and peripheral and central tumors. To assess whether the observed phenotypic changes in the patients were associated with measured oxPC levels, metabolomics data were augmented with data from medical records. Results: We observed a predominance of long-chain oxPCs in plasma samples and of short-chain oxPCs in tissue samples from patients with NSCLC. The highest concentration of oxPCs was observed in the central tumor region. ADC patients showed higher levels of oxPCs compared to the control group, than patients with SCC. Conclusion: The detrimental effects associated with the accumulation of short-chain oxPCs suggest that these molecules may have greater therapeutic utility than diagnostic value, especially given that elevated oxPC levels are a hallmark of multiple types of cancer.

Nomogram combining clinical and radiological characteristics for predicting the malignant probability of solitary pulmonary nodules measuring ≤ 2 cm

Background

At present, how to identify the benign or malignant nature of small (≤ 2 cm) solitary pulmonary nodules (SPN) are an urgent clinical challenge. This retrospective study aimed to develop a clinical prediction model combining clinical and radiological characteristics for assessing the probability of malignancy in SPNs measuring ≤ 2 cm.

Method

In this study, we included patients with SPNs measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to December 2021. Clinical features, preoperative biomarker results, and computed tomography characteristics were collected. The enrolled patients were randomized at a ratio of 7:3 into a training cohort of 775 and a validation cohort of 331. The training cohort was used to construct the predictive model, while the validation cohort was used to test the model independently. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. The prediction model and nomogram were established based on the independent risk factors. The receiver operating characteristic (ROC) curve was used to evaluate the identification ability of the model. The calibration power was evaluated using the Hosmer-Lemeshow test and calibration curve. The clinical utility of the nomogram was also assessed by decision curve analysis (DCA).

Result

A total of 1,106 patients were included in this study. Among them, the malignancy rate of SPNs was 85.08% (941/1,106). We finally identified the following six independent risk factors by logistic regression: age, carcinoembryonic antigen, nodule shape, calcification, maximum diameter, and consolidation-to-tumor ratio. The area under the ROC curve (AUC) for the training cohort was 0.764 (95% confidence interval [CI]: 0.714-0.814), and the AUC for the validation cohort was 0.729 (95% CI: 0.647-0.811), indicating that the prediction accuracy of nomogram was relatively good. The calibration curve of the predictive model also demonstrated a good calibration in both cohorts. DCA proved that the clinical prediction model was useful in clinical practice.

Conclusion

We developed and validated a predictive model and nomogram for estimating the probability of malignancy in SPNs measuring ≤ 2 cm. With the application of predictive models, thoracic surgeons can make more rational clinical decisions while avoiding overtreatment and wasting medical resources.