Periodic Reporting for period 1 - MENTORING (Addressing malnutrition and metabolic health in non-communicable diseases through precision Nutrition: impact in quality of life and prognosis of lung cancer patients.)
Reporting period: 2024-09-01 to 2025-08-31
Lung cancer patients experience extremely high rates of malnutrition, sarcopenia, micronutrient deficiencies, systemic inflammation, and metabolic dysfunction, all of which worsen treatment tolerance, clinical benefit, and quality of life. Current nutritional support is generic and lacks the capacity to integrate patient-specific determinants such as metabolic phenotype, immune–inflammatory profile, glycomic signatures, genetic variation, and gut and lung microbiome composition. No existing model provides personalised dietary interventions based on a deep multi-omics characterisation of this highly vulnerable population.
MENTORING directly targets this gap by generating the most comprehensive precision-nutrition dataset ever collected in lung cancer: 150–180 patients, two cohorts (NSCLC and SCLC), full anthropometry, micronutrient biomarkers, clinical and haematological parameters, dietary records, lifestyle questionnaires, and extensive omics profiling (glycomics, metabolomics, proteomics, gut and lung microbiome, SNPs).
These data will feed predictive mechanistic and machine-learning models able to estimate how nutritional status—and its drivers—affect treatment tolerance, metabolic health, quality of life, and prognosis. The project will also test mechanistic hypotheses in vitro to clarify causal pathways between diet-derived bioactives, metabolic regulation, and tumour-related processes.
The pathway to impact proceeds through: (1) large-scale patient phenotyping; (2) computational integration into individual-level predictive models; (3) validation of nutritional effects on treatment outcomes; and (4) deployment of a clinician-ready digital decision-support tool for personalised dietary planning. Expected impacts include measurable improvements in nutritional biomarkers (KPI1), identification of multi-omics determinants of response (KPI2), and a ≥10% reduction in treatment-related adverse events through precision nutrition (KPI3).
MENTORING directly targets this gap by generating the most comprehensive precision-nutrition dataset ever collected in lung cancer: 150–180 patients, two cohorts (NSCLC and SCLC), full anthropometry, micronutrient biomarkers, clinical and haematological parameters, dietary records, lifestyle questionnaires, and extensive omics profiling (glycomics, metabolomics, proteomics, gut and lung microbiome, SNPs).
These data will feed predictive mechanistic and machine-learning models able to estimate how nutritional status—and its drivers—affect treatment tolerance, metabolic health, quality of life, and prognosis. The project will also test mechanistic hypotheses in vitro to clarify causal pathways between diet-derived bioactives, metabolic regulation, and tumour-related processes.
The pathway to impact proceeds through: (1) large-scale patient phenotyping; (2) computational integration into individual-level predictive models; (3) validation of nutritional effects on treatment outcomes; and (4) deployment of a clinician-ready digital decision-support tool for personalised dietary planning. Expected impacts include measurable improvements in nutritional biomarkers (KPI1), identification of multi-omics determinants of response (KPI2), and a ≥10% reduction in treatment-related adverse events through precision nutrition (KPI3).
The MENTORING Project starts with a longitudinal observational study of cancer patients, where patients are recruited in the Oncology Departments of two hospitals (Madrid and Parma) and followed up for a period of six months. This study has been called “LISTENING” and can be considered the main task developed during the reporting period. Major milestones accomplished during this period include the drafting of the Project Handbook (month 5), which was uploaded as a Deliverable; the approval of the Listening Protocol by the respective Ethical Committees of Madrid and Parma; and the start of the recruitment process in April (SERMAS, Madrid, Spain) and July (Parma, Italy). By the end of the reporting period, some patients had reached the second (3-month) scheduled visit. The withdrawal rate has remained very low so far.
Regarding the recruitment and follow-up task, a number of activities have been successfully performed, including:
Creation of a Database (LIMS: Laboratory Integrated Management System) to manage information from patients and samples.
Detailed definition of lifestyle data to be collected from patients, including nutritional questionnaires, quality of life (QoL)-related questionnaires, welfare questionnaires, and so on, which were discussed and agreed upon by the two institutions recruiting patients.
Detailed definition of biomarkers to be analysed from patients, including genomic, proteomic, glycomic, glycoproteomic, metabolomic, and metagenomic, as well as biochemical and inflammatory/immunological markers.
Definition of types and numbers of samples to be collected from patients, considering the high number of biomarkers needed for analysis but also respecting the principle of minimal harm to patients.
Design of sample flow between the laboratories where samples are initially collected and processed (SERMAS/IMDEA and University of Parma) and the recipient laboratories (all groups, each specialising in different sets of biomarkers).
Preparation of SOPs (Standard Operating Procedures) to harmonise sample collection and guarantee the highest sample quality throughout the study.
Design of informatics tools to upload, download, and manage comprehensive data coming from very different sources (questionnaires, biochemical analyses, omic studies, clinical data, etc.).
In parallel with the establishment of all infrastructure needed for sample collection, processing, and delivery, two pilot samples were sent to the analytical laboratories to ensure that the system had been correctly designed and to provide some initial data, helping to prevent possible future issues.
In addition, a first version of the Data Management Plan was discussed and prepared, and uploaded as a Deliverable, alongside the Communication and Dissemination Plan, both in month 6 of the project timeline.
Regarding the recruitment and follow-up task, a number of activities have been successfully performed, including:
Creation of a Database (LIMS: Laboratory Integrated Management System) to manage information from patients and samples.
Detailed definition of lifestyle data to be collected from patients, including nutritional questionnaires, quality of life (QoL)-related questionnaires, welfare questionnaires, and so on, which were discussed and agreed upon by the two institutions recruiting patients.
Detailed definition of biomarkers to be analysed from patients, including genomic, proteomic, glycomic, glycoproteomic, metabolomic, and metagenomic, as well as biochemical and inflammatory/immunological markers.
Definition of types and numbers of samples to be collected from patients, considering the high number of biomarkers needed for analysis but also respecting the principle of minimal harm to patients.
Design of sample flow between the laboratories where samples are initially collected and processed (SERMAS/IMDEA and University of Parma) and the recipient laboratories (all groups, each specialising in different sets of biomarkers).
Preparation of SOPs (Standard Operating Procedures) to harmonise sample collection and guarantee the highest sample quality throughout the study.
Design of informatics tools to upload, download, and manage comprehensive data coming from very different sources (questionnaires, biochemical analyses, omic studies, clinical data, etc.).
In parallel with the establishment of all infrastructure needed for sample collection, processing, and delivery, two pilot samples were sent to the analytical laboratories to ensure that the system had been correctly designed and to provide some initial data, helping to prevent possible future issues.
In addition, a first version of the Data Management Plan was discussed and prepared, and uploaded as a Deliverable, alongside the Communication and Dissemination Plan, both in month 6 of the project timeline.
Nutritional care of lung cancer patients has so far not been considered a useful tool to improve treatment effectiveness and patient well-being. Precision Nutrition strategies will go beyond general health recommendations by projecting directly into clinical practice, supporting oncological treatments, and ultimately offering better chances of survival and health improvement.
The number and variety of biomarkers now being analysed is truly comprehensive, making the effort to integrate data and develop multi-omic models highly challenging. This task has begun by employing a table-based model where personal data from RedCap registries and data derived from the various omics approaches—each summarised after normalisation and harmonisation procedures—are compiled in CSV (table) format.
Additionally, perhaps the most distinctive feature of the first phase (“LISTENING”) of the study protocol is its longitudinal design. Patients’ health status is likely to evolve over the six-month period, reflecting either cancer progression or disease evolution, or conversely, improvements due to immune or chemotherapy treatments and/or lifestyle changes. This six-month follow-up will enable measurement of multiple health biomarkers while minimising individual variability that often obscures conclusions in many cohort studies.
Furthermore, because data will be analysed on an individual basis, the study can consider less prevalent subgroups within lung cancer, such as Small Cell Lung Cancer patients—a less well-understood disease that may benefit greatly from the deep biomarker analyses initiated for these patients.
The number and variety of biomarkers now being analysed is truly comprehensive, making the effort to integrate data and develop multi-omic models highly challenging. This task has begun by employing a table-based model where personal data from RedCap registries and data derived from the various omics approaches—each summarised after normalisation and harmonisation procedures—are compiled in CSV (table) format.
Additionally, perhaps the most distinctive feature of the first phase (“LISTENING”) of the study protocol is its longitudinal design. Patients’ health status is likely to evolve over the six-month period, reflecting either cancer progression or disease evolution, or conversely, improvements due to immune or chemotherapy treatments and/or lifestyle changes. This six-month follow-up will enable measurement of multiple health biomarkers while minimising individual variability that often obscures conclusions in many cohort studies.
Furthermore, because data will be analysed on an individual basis, the study can consider less prevalent subgroups within lung cancer, such as Small Cell Lung Cancer patients—a less well-understood disease that may benefit greatly from the deep biomarker analyses initiated for these patients.