Background/Objectives: Cancer and their treatments could impact physical, nutritional, and psychological health, negatively influencing overall well-being. Accordingly, Health-Related Quality of Life (HRQoL) could be influenced by lifestyle habits, such as physical activity. This study aimed to assess physical activity levels in patients with a primary cancer diagnosis and their association with HRQoL at the first nutritional assessment. Methods: Data from the NUTRISCREEN project, part of the ONCOCAMP study (ClinicalTrials.gov ID: NCT06270602), were analyzed. Nutritional and sarcopenia risk, anthropometry and body composition parameters were collected. HRQoL and physical activity (as MET levels) were assessed through validated questionnaires. Descriptive statistics summarized categorical and continuous variables, and multivariable ordinal logistic regression models were performed. Results: Nutritional and sarcopenia risk decreased progressively with higher MET levels (p = 0.005 and p < 0.001, respectively). Adjusted multivariable models showed that HRQoL functional scores improved with increasing MET levels, with significant positive trends for physical (p < 0.001), role (p < 0.001), emotional (p = 0.003), and social functioning (p = 0.001), and global health status (p < 0.001). Conversely, symptom burden, including fatigue, nausea and vomiting, pain, dyspnea, insomnia, appetite loss, and constipation, decreased across MET quartiles (all p < 0.05). Conclusions: Overall, our findings suggest that physical activity may positively influence HRQoL among cancer patients. Early assessment helps to identify patients at risk of inactivity and support tailored rehabilitation programs to promote active lifestyles, preserve muscle mass, improve outcomes and overall health status.

Hormone receptor-positive (HR+)/HER2-negative (HER2-) early-stage breast cancers (EBC) are treated with adjuvant endocrine therapy (ET), with chemotherapy (CT) reserved for high-risk cases. Obesity is linked to increased recurrence risk. The Oncotype DX® assay predicts prognosis and CT benefit. The PRO BONO study evaluated Oncotype DX test's impact on treatment decisions and explored associations between genomic risk, tumor features, and patient metabolic profiles. Patients with HR+ /HER2-EBC undergoing Oncotype DX testing were enrolled. Body mass index (BMI), tumor characteristics (ER, PR, Ki67, grading, size, nodal status), a large panel of metabolic analytes, and Oncotype DX Recurrence Score® (RS) results were collected. Treatment recommendations (ET vs CT-ET) were recorded pre- and post-Oncotype DX, and concordance was determined using Cohen's Kappa. Associations were tested using Chi-Square test and Spearman Correlation. Of the 248 EBC patients (2019-2021), Oncotype DX testing reduced CT use by 47.7 %. Higher RS positively correlated with serum triglycerides and inversely with GIP (all p < 0.05). No significant association was found between patient BMI and RS result. Conversely, tumor size positively correlated with BMI (p = 0.0286) and with serum levels of leptin (p = 0.0079), PAI-1 (p = 0.0083), C-peptide (p = 0.0124), GIP (p = 0.0036), GLP-1 (p = 0.0476), glucagon (p = 0.0224), and insulin (p = 0.0327). A BMI≥ 30 and higher GLP-1 levels (>148.85pg/ml) were independently associated with increased odds of having larger tumor size (>2 cm). Recurrence Score result significantly impacts treatment decisions in HR+ /HER2-EBC. RS result was not associated with BMI, although unfavorable metabolic profiles and obesity-related markers correlated with larger tumors. These findings highlight the need to further investigate the link between metabolic profiles and breast cancer biology.

Background: Anthropometric measures, including body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR), are predictors of breast cancer (BC) risk. A Body Shape Index (ABSI) has been proposed as a novel independent metric to capture central obesity. In breast cancer survivors, we investigated the prognostic relevance of combining traditional anthropometric measures with ABSI to define distinct body shape phenotypes (BSP).Methods: Data from a cohort of 955 early-stage BC survivors for BMI, WC, WHR, ABSI, metabolic syndrome (MetS) were analysed. BSP were obtained from principal component analysis to combine general and central obesity metrics. The associations of BSP with all-cause and BC-specific mortality were evaluated with multivariable Weibull regression.Findings: Four distinct BSP were identified: “Young and normal weight”, “Young and overweight” (pear), “Short and overweight” (Little square) and “General obesity” (apple). The “General obesity” cluster was associated with >2-fold increased all-cause mortality compared to “Young and normal weight” (HR = 2·02, 95% CI = 1·23–3·29). The inclusion of metabolic factors (blood pressure, HDL-C, triglycerides, glycemia, and MetS) strengthened the risk gradient. MetS itself was independently associated with increased all-cause mortality (HR = 1·92, 95% CI = 1·32–2·80; p < 0·001). MetS remained a strong independent predictor of BC death, with an HR of 2·07 (95% CI: 1·29–3·32; p < 0·002).Interpretation: Our findings highlight the importance of assessing BSP and metabolic health beyond BMI in BC survivors. MetS strongly mediates mortality risk, suggesting that targeting dyslipidemia and hyperglycaemia may be more effective than weight-focused strategies alone.

INTRODUCTIONCancer pain is a complex, multidimensional syndrome affecting approximately 55% of patients with cancer, depending on tumor stage and treatment, and is associated with poorer clinical outcomes and reduced quality of life. Pain assessment and management become particularly challenging when patients experience transient exacerbations of pain despite otherwise controlled baseline pain with opioid therapy, a condition known as breakthrough cancer pain (BTcP), which affects up to 70% of patients with chronic cancer pain. Artificial intelligence (AI) and advanced statistical approaches offer new opportunities to investigate the complex relationships among clinical variables contributing to BTcP. The Ruggi Study (ClinicalTrials.gov: NCT07038434) is a single-center, non-profit, observational-interventional study designed to characterize chronic pain through multimodal AI-based assessments in adult oncologic and non-oncologic patients.OBJECTIVESThis preliminary analysis aimed to investigate the causal relationship between opioid therapy and BTcP, evaluating the mediating role of chronic pain phenotype in a subcohort of 99 cancer patients assessed at their first follow-up visit.METHODSA Bayesian network was constructed including age, smoking status, body surface area (BSA), comorbidities, cardiovascular therapy, cancer severity (presence of metastases or bone metastases), ongoing anticancer treatment at enrollment, time from diagnosis, treatment duration, opioid therapy, chronic pain phenotype, BTcP, and quality of life assessed by the Brief Pain Inventory (BPI, item 9g). The network structure was learned using the Bayesian Information Criterion, allowing the identification of statistical dependencies among variables. Missing data were preliminarily assessed using Little’s test and subsequently imputed through a model-based approach.RESULTSOverall, 22% of the dataset contained missing values. Little’s test excluded a missing-not-at-random mechanism (p = 0.14), supporting the imputation strategy, which mainly affected variables related to cancer therapies. The median patient age was 65 years, with a median BSA of 1.4 m²; approximately 98% had a sex-adjusted normal BSA. Most patients had comorbidities, 66% had a time from diagnosis exceeding 12 months, 17% presented with bone metastases, and 39% were receiving opioid therapy. Neuropathic pain was the most frequent chronic pain phenotype (>60%), while eight patients experienced BTcP. Twenty-six patients reported impaired quality of life (BPI item 9g >4). The Bayesian network did not identify a direct causal relationship between opioid therapy and BTcP. Instead, chronic pain phenotype emerged as a mediator: patients with nociceptive pain receiving opioids showed a higher probability of developing BTcP than those with neuropathic or mixed pain (25% vs 19%).CONCLUSIONSThese preliminary findings suggest that the association between opioid therapy and BTcP is influenced by the underlying chronic pain phenotype rather than representing a direct causal relationship. Bayesian network analysis provides a valuable framework for disentangling complex clinical interactions and identifying patient subgroups at increased risk of BTcP, potentially supporting more personalized pain management strategies. Larger prospective studies are warranted to validate these findings and assess their clinical applicability.

Body composition (BC) analysis may integrate assessment of nutritional risk in patients with gastrointestinal cancers. Applying unsupervised machine learning techniques, we derived BC profiles (BCPs) and investigated their association with malnutrition risk and quality of life. This cross-sectional analysis includes gastrointestinal cancer patients enrolled in the NUTRItional and sarcopenia SCREENing project, part of the ONCOCAMP study (ClinicalTrials.gov ID: NCT06270602). Malnutrition assessment was performed using the NRS-2002, anthropometry and BC analysis; HRQoL was evaluated with the EORTC QLQ-C30 questionnaire. Principal component analysis and k-means clustering identified BCPs. Data from 189 patients (mean age: 66.3 ± 11.2 years) with gastric (37%), pancreatic (26.5%), rectal (24.3%) and hepatocellular carcinoma (12.2%) were analyzed. Overall, 36.5% of patients were at nutritional risk (NRS-2002 ≥ 3). Two BC clusters, High Muscle Profile (HMP) and Low Muscle Profile (LMP), were derived. Although no statistically significant association was observed in the overall cohort, multivariable analysis stratified by gender identified an association between LMP and nutritional risk among male patients (OR = 3.77, 95% CI: 1.08–14.3, p = 0.042); however, this finding should be considered exploratory. Nutritional risk, but not BC profiles, was associated with poorer Summary Score (β = 8.0, 95% CI: −14 to −2.3, p = 0.007) and higher Symptom Score (β = 9.8, 95% CI:4.5 to 15, p < 0.001). These preliminary results warrant further confirmation in larger, adequately powered longitudinal cohorts. Future studies should determine whether BCPs provide additional value beyond established nutritional screening and individual BC parameters in clinical practice.