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  ["project_title"]=>
  string(133) "Association Between Body Mass Index (BMI) and Prognosis in Cancer Patients: Analysis of Baseline, Trajectories, and Mediating Effects"
  ["project_narrative_summary"]=>
  string(792) "Obesity, measured by Body Mass Index (BMI), is a major global health issue linked to increased risk of many cancers. Surprisingly, some studies show that obese cancer patients may live longer—a phenomenon known as the “obesity paradox.” The reasons for this are unclear and may be due to research limitations such as reverse causation, confounding factors, or selection bias. This study will use detailed data from multiple clinical trials to examine how BMI—both at the start of treatment and over time—affects survival, treatment response, and side effects in patients with various solid tumors. We will group patients by similar BMI patterns and analyze whether these patterns influence how long patients live, how well treatments work, and whether they experience side effects. "
  ["project_learn_source"]=>
  string(4) "conf"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(3) "Bin"
    ["last_name"]=>
    string(4) "Peng"
    ["degree"]=>
    string(6) "Doctor"
    ["primary_affiliation"]=>
    string(28) "Chongqing Medical University"
    ["email"]=>
    string(19) "pengbin@cqmu.edu.cn"
    ["state_or_province"]=>
    string(9) "Chongqing"
    ["country"]=>
    string(5) "China"
  }
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      ["p_pers_f_name"]=>
      string(7) "JiaWei "
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      string(4) "Zhou"
      ["p_pers_degree"]=>
      string(6) "Doctor"
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      string(5) "Baolu"
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      string(3) "Yue"
      ["p_pers_degree"]=>
      string(23) "Master Degree Candidate"
      ["p_pers_pr_affil"]=>
      string(28) "Chongqing Medical University"
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      string(2) "Ni"
      ["p_pers_degree"]=>
      string(23) "Doctor Degree Candidate"
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      string(28) "Chongqing Medical University"
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      ["p_pers_l_name"]=>
      string(5) "Cheng"
      ["p_pers_degree"]=>
      string(23) "Master Degree Candidate"
      ["p_pers_pr_affil"]=>
      string(28) "Chongqing Medical University"
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      ["p_pers_l_name"]=>
      string(4) "Wang"
      ["p_pers_degree"]=>
      string(23) "Master Degree Candidate"
      ["p_pers_pr_affil"]=>
      string(28) "Chongqing Medical University"
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      ["requires_data_access"]=>
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  ["project_ext_grants"]=>
  array(2) {
    ["value"]=>
    string(2) "no"
    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
  }
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1202) "Background: The “obesity paradox” describes the counterintuitive association between higher BMI and improved survival in certain cancers. The mechanisms remain poorly understood, and methodological issues may contribute to conflicting findings.



Objective: To evaluate the association between BMI and prognosis (overall survival, progression-free survival) and safety (adverse events) in patients with solid tumors receiving targeted therapy, immunotherapy, or chemotherapy, and to explore mediating effects of adverse events.



Study Design: Individual participant data meta-analysis of multiple clinical trials.



Participants: Patients with non-small cell lung, breast, prostate, gastric, or colon cancer from selected trials.



Primary and Secondary Outcome Measures: Primary: overall survival (OS) and progression-free survival (PFS). Secondary: incidence of adverse events (AEs), and early mortality (within 60 days).



Statistical Analysis: Cox models for survival outcomes, mediation analysis for adverse events, random-effects meta-analysis to pool estimates across trials. Subgroup analyses by PD-L1 expression and gender."
  ["project_brief_bg"]=>
  string(2920) "Obesity means having too much body fat, which is now a major health concern worldwide. A common way to measure obesity is the Body Mass Index (BMI), which is calculated from a person’s height and weight. BMI is often used in medical research as a rough measure of body fat. In the past 30 years, obesity rates have increased by more than a quarter in adults and nearly half in children. Obesity can cause serious health problems and often requires lifestyle changes and medication to manage.

Cancer is the second leading cause of death worldwide, with nearly 20 million new cases and 9.7 million deaths reported in 2022. Lung cancer is the most common and deadliest form, followed by cancers of the breast, bowel, prostate, liver, and stomach. The number of new cancer cases is expected to rise to 35 million by 2050.



Obesity has been linked to a higher risk of developing many cancers, such as non-small cell lung, breast, prostate, bowel, and stomach cancer. However, studies have found a surprising pattern known as the “obesity paradox.” In some cases—such as bladder cancer, non-small cell lung cancer, and advanced skin cancer—people with obesity appear to live longer than those with lower body weight. The reasons for this are unclear, and associations with tumor type and cancer therapy need to be further validated. It may be linked to research design problems, such as:

•	Reverse causation – where weight loss is caused by illness rather than the other way around.

•	Confounding factors – not accounting for other variables that may influence the results.

•	Selection bias – when the study group is not representative of the wider population.

•	Ignoring mediators – not accounting for factors that explain how or why BMI affects cancer outcomes.



Studies may only record BMI before treatment begins (baseline BMI). This may not show the full picture because weight can change during treatment and illness. We do not yet know if BMI directly affects cancer survival or if it influences how patients respond to treatment.



In this study, we will use both baseline BMI and repeated BMI measurements during treatment. This will allow us to track changes over time (BMI trajectories) and see how they relate to cancer outcomes. We will group patients with similar BMI patterns and study whether these patterns affect how long they live, how well treatments work, and whether they experience treatment side effects (adverse events). We will also explore whether side effects play a role in the link between BMI and cancer outcomes.



Our goal is to provide clearer evidence on the role of BMI in cancer treatment and survival. This could help doctors tailor treatment plans, improve patient care, and highlight the importance of good nutrition and healthy weight management during cancer treatment."
  ["project_specific_aims"]=>
  string(1254) "In our study, we define three hypotheses

Hypothesis one: Obese patients of solid tumors may have a better prognosis with targeted therapy, immunotherapy, or chemotherapy. This hypothesis is based on the ‘obesity paradox’, in which a higher BMI is associated with better survival in cancer patients under certain circumstances. 

Hypothesis two: Obese patients might face shorter survival and a higher incidence of adverse events (AEs) when treated with targeted therapy, immunotherapy, or chemotherapy. This hypothesis considers the potential for individuals with higher BMI to exhibit poor therapeutic efficacy or different immunomodulatory responses, thereby possibly leading to poorer survival or increased risk of adverse events.

Hypothesis three: There is a mediating effect between BMI and prognosis or safety, which may reveal complex relationships and causal pathways between body mass index, treatment response (AEs, etc.), and clinical outcomes.

Aims

The objective of this study is to evaluate the association of body mass index (BMI) with prognosis and safety profile in multiple solid tumors and to explore whether BMI should be considered as a stratification factor for future treatment of solid tumors."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(14) "indiv_trial_an"
    ["label"]=>
    string(25) "Individual trial analysis"
  }
  ["project_purposes"]=>
  array(8) {
    [0]=>
    array(2) {
      ["value"]=>
      string(56) "new_research_question_to_examine_treatment_effectiveness"
      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(49) "new_research_question_to_examine_treatment_safety"
      ["label"]=>
      string(49) "New research question to examine treatment safety"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(76) "confirm_or_validate previously_conducted_research_on_treatment_effectiveness"
      ["label"]=>
      string(76) "Confirm or validate previously conducted research on treatment effectiveness"
    }
    [3]=>
    array(2) {
      ["value"]=>
      string(69) "confirm_or_validate previously_conducted_research_on_treatment_safety"
      ["label"]=>
      string(69) "Confirm or validate previously conducted research on treatment safety"
    }
    [4]=>
    array(2) {
      ["value"]=>
      string(37) "develop_or_refine_statistical_methods"
      ["label"]=>
      string(37) "Develop or refine statistical methods"
    }
    [5]=>
    array(2) {
      ["value"]=>
      string(34) "research_on_clinical_trial_methods"
      ["label"]=>
      string(34) "Research on clinical trial methods"
    }
    [6]=>
    array(2) {
      ["value"]=>
      string(28) "research_on_comparison_group"
      ["label"]=>
      string(28) "Research on comparison group"
    }
    [7]=>
    array(2) {
      ["value"]=>
      string(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
    }
  }
  ["project_research_methods"]=>
  string(779) "We plan to conduct a pooled analysis combining YODA project data with data from the following additional studies:

Additional studies: NCT03088540, NCT02474355, NCT01085136, NCT00805194, NCT01953913, NCT00078260, NCT00789373, NCT00411229, NCT00275210, NCT01254279

Data Source: Obtained from the Vivli Data Sharing Platform.

Analysis Location: All individual patient data (IPD) analysis will be conducted at Chongqing Medical University via this vivli platform. We maintain a secure data analysis environment and robust confidentiality measures.

Inclusion: patients with solid tumors (non-small cell lung, breast, prostate, gastric, colon) who received targeted, immunotherapy, or chemotherapy. 

Exclusion: missing baseline height or weight data."
  ["project_main_outcome_measure"]=>
  string(333) "Primary:



OS: time from randomization to death from any cause.



PFS: time from randomization to disease progression or death.



Secondary:



AEs: incidence of any grade adverse events (NCI CTCAE v4.0/4.03).



Early mortality: death within 60 days post-randomization."
  ["project_main_predictor_indep"]=>
  string(705) "The main predictor will be BMI trajectories identified through Latent Class Growth Mixed Models (LCGMM) analysis. These trajectories represent distinct patterns of BMI change over time during treatment. Additionally, baseline BMI (kg/m²) will be categorized per WHO: underweight (<18.5), normal (18.5–24.9), overweight (25–29.9), obese (≥30).The main predictor will be BMI trajectories identified through Latent Class Growth Mixed Models (LCGMM) analysis. These trajectories represent distinct patterns of BMI change over time during treatment. Additionally, baseline BMI (kg/m²) will be categorized per WHO: underweight (<18.5), normal (18.5–24.9), overweight (25–29.9), obese (≥30)."
  ["project_other_variables_interest"]=>
  string(1880) "Besides treatment arm, survival endpoints, AEs and BMI assessments, we need some covariates, such as demographics, clinical confounders, concomitant medication, and medication history. For demographics, age, gender, geographic region and race will be included in our analysis. As for clinically relevant confounding factors, we need smoker status, alcohol consumption, malnutrition, physical activity, Eastern Cooperative Oncology Group performance status, tumor histologic type, number of tumor sites, number of prior treatments in the advanced setting, epidermal growth factor receptor mutation, anaplastic lymphoma kinase mutation, Programmed Death-Ligand 1 (PD-L1) expression, serum lactate dehydrogenase level, estrogen receptor status, lymph vascular invasion, lymph node status, blood C-reactive protein level, blood neutrophil to lymphocyte ratio, the scales of health-related quality of life, (patient-reported outcomes of health-related quality of life, cancer−related symptoms, physical functioning, and health status as assessed by the EORTC QLQ-C30, QLQ-LC13 and QLQHCC18), (EORTC: European Organization for Research and Treatment of Cancer; QLQ-C30: Quality-of-Life Questionnaire Core 30; EORTC QLQ-LC13: Quality-of-Life Questionnaire Lung Cancer module; EORTC QLQ-HCC18: Hepatocellular carcinoma (HCC) disease-specific module), utility scores of the EQ-5D-5L (Euro-QoL 5 Dimensions 5-Level Version), and utility scores of the EQ-5D-3L. For  concomitant medication and medication history, medications that may be associated with weight loss, such as metformin, glucagon-like peptide-1 (GLP-1) receptor agonists, chemotherapy-induced anorexia medications, and corticosteroids) will be considered, and for adverse events (AE), we will focus on weight/body mass index related adverse events (including anorexia, nausea, vomiting, diarrhea) and metabolic disturbances."
  ["project_stat_analysis_plan"]=>
  string(3830) "Patients with missing baseline height and/or weight will be excluded. Body mass index (BMI) will be categorized according to standard clinical definitions (normal weight, overweight, and obese). Primary comparisons will contrast overweight/obese groups with normal weight, and secondarily BMI ≥ 25 kg/m² versus < 25 kg/m². Baseline characteristics will be summarized using descriptive statistics, including means with standard deviations, medians with interquartile ranges, and frequencies with percentages. Between-group differences will be assessed using t-tests or Wilcoxon tests for continuous variables and chi-square tests for categorical variables.

Each clinical trial will first be analyzed independently to preserve study integrity. Overall survival (OS) and progression-free survival (PFS) will be estimated using Kaplan–Meier methods, with comparisons conducted by log-rank tests. Associations between baseline BMI and time-to-event outcomes will be evaluated using Cox proportional hazards regression models, reporting hazard ratios (HRs) and 95% confidence intervals (CIs). Multivariable models will adjust for prespecified confounders. Proportional hazards assumptions will be examined using Schoenfeld residuals.

To evaluate dynamic changes in body composition during treatment, longitudinal BMI measurements will be analyzed using latent class growth mixture models (LCGMM) to identify distinct BMI trajectory patterns. Patients will be assigned to trajectory classes based on posterior probabilities. Trajectory membership will then serve as the primary exposure in multivariable Cox models to estimate adjusted HRs for OS and PFS. Kaplan–Meier curves will be generated for each trajectory group, with intergroup differences assessed using log-rank tests.

Treatment-related adverse events will be summarized by BMI categories and compared using logistic or Poisson regression models as appropriate. Mediation analyses will explore whether adverse events mediate the association between BMI and survival outcomes using the product-of-coefficients approach to estimate indirect effects. Subgroup analyses will be conducted by sex, PD-L1 expression, cancer type, and treatment modality to assess effect modification through interaction terms.

Potential confounding will be addressed through multivariable adjustment. Prespecified covariates include demographic and clinical factors commonly associated with cancer prognosis, such as age, sex, race, performance status, smoking history, alcohol use, histology, immune markers, number of prior therapies, tumor burden, and relevant molecular characteristics (e.g., EGFR mutation and PD-L1 expression in non-small cell lung cancer). Covariate selection will be based on clinical relevance and prior literature. Sensitivity analyses will evaluate robustness to alternative adjustment sets and missing data patterns.

For exploratory pooled analyses, patients with comparable cancer types or treatment mechanisms will be combined to increase statistical power. To account for between-study heterogeneity, effect estimates will be synthesized using meta-analytic approaches. Heterogeneity will be quantified using Cochran’s Q test and the I² statistic. Fixed-effects models will be applied when heterogeneity is minimal; otherwise, mixed- or random-effects models will be used. Study origin and/or cancer type will be incorporated as stratification factors or random effects. This framework allows both within-study and between-study variability to be appropriately modeled.

All analyses will follow a two-sided significance level of 0.05. Results will be presented as HRs, odds ratios, or risk ratios with 95% CIs. Statistical analyses will be conducted using R software within the Vivli secure research environment."
  ["project_software_used"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
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  }
  ["project_timeline"]=>
  string(234) "Start date: January 1, 2026



Analysis completion:January 1, 2027



Manuscript drafted and submitted: Within 6 months of analysis completion



Results reported to YODA: Concurrent with submission"
  ["project_dissemination_plan"]=>
  string(253) "Results will be submitted to high-impact oncology journals such as ESMO or HHS Public Access. Findings will be presented at international conferences and shared with the clinical research community to inform future trial design and treatment guidelines."
  ["project_bibliography"]=>
  string(2623) "
1. Rubino F, Cummings D E, Eckel R H, etal. Definition and diagnostic criteria of clinical obesity[J]. Lancet Diabetes and Endocrinology, 2025, 13(3): 221-262.

2. Global Burden of Disease 2019 Cancer Collaboration. Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019. JAMA Oncol. 2022;8(3):420–444. doi:10.1001/jamaoncol.2021.6987

3. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024 May-Jun;74(3):229-263. doi:10.3322/caac.21834. Epub 2024 Apr 4. PMID: 38572751.

4. Zhu Q, Yao Y, Chen R, Han B, Wang S, Li L, Sun K, Zheng R, Wei W. Lifetime probabilities of developing and dying from cancer in China: comparison with Japan and the United States in 2022. Sci China Life Sci. 2025 May;68(5):1478-1486. doi: 10.1007/s11427-024-2810-y. Epub 2025 Feb 26. PMID: 40029451.

5. Clinton SK, Giovannucci EL, Hursting SD. The World Cancer Research Fund/American Institute for Cancer Research Third Expert Report on Diet, Nutrition, Physical Activity, and Cancer: Impact and Future Directions. J Nutr. 2020 Apr 1;150(4):663-671. doi: 10.1093/jn/nxz268. PMID: 31758189; PMCID: PMC7317613.

6. Bray F, Parkin DM; African Cancer Registry Network. Cancer in sub-Saharan Africa in 2020: a review of current estimates of the national burden, data gaps, and future needs. Lancet Oncol. 2022 Jun;23(6):719–728. [DOI] [PubMed]

7. Petrelli F, Cortellini A, Indini A, et al. Association of Obesity With Survival Outcomes in Patients With Cancer: A Systematic Review and Meta-analysis. JAMA Netw Open. 2021 Mar 1;4(3):e213520. doi: 10.1001/jamanetworkopen.2021.3520. PMID: 33779745; PMCID: PMC8008284.

8. Ihara Y, Sawa K, Imai T, Bito T, Shimomura Y, Kawai R, Shintani A. Immunotherapy and Overall Survival Among Patients With Advanced Non-Small Cell Lung Cancer and Obesity. JAMA Netw Open. 2024 Aug 1;7(8):e2425363. doi: 10.1001/jamanetworkopen.2024.25363. PMID: 39093562; PMCID: PMC11297387.

9. Nie W, Lu J, Qian J, Wang SY, Cheng L, Zheng L, Tao GY, Zhang XY, Chu TQ, Han BH, Zhong H. Obesity and survival in advanced non-small cell lung cancer patients treated with chemotherapy, immunotherapy, or chemoimmunotherapy: a multicenter cohort study. BMC Med. 2024 Oct 14;22(1):463. doi: 10.1186/s12916-024-03688-2. PMID: 39402614; PMCID: PMC11475647.
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