Dataset Information

Association between metabolic overweight/obesity phenotypes and readmission risk in patients with lung cancer: A retrospective cohort study.

ABSTRACT:

Background

Increased body mass index (BMI) and metabolic abnormalities are controversial prognostic factors of lung cancer. However, the relationship between metabolic overweight/obesity phenotypes and hospital readmission in patients with lung cancer is rarely reported.

Methods

We established a retrospective cohort using the United States (US) Nationwide Readmissions Database (NRD). We included adult patients diagnosed with lung cancer from January 1, 2018 to November 30, 2018 and excluded patients combined with other cancers, pregnancy, died during hospitalization, low body weight, and those with missing data. The cohort was observed for hospital readmission until December 31, 2018. We defined and distinguished four metabolic overweight/obesity phenotypes: metabolically healthy with normal weight (MHNW), metabolically unhealthy with normal weight (MUNW), metabolically healthy with overweight or obesity (MHO), and metabolically unhealthy with overweight or obesity (MUO). The relationship between metabolic overweight/obesity phenotypes and 30-day readmission risk was assessed by multivariable Cox regression analysis.

Findings

Of the 115,393 patients included from the NRD 2018 (MHNW [58214, 50.4%], MUNW [44980, 39.0%], MHO [5044, 4.4%], and MUO [7155, 6.2%]), patients with the phenotype MUNW (6531, 14.5%), MHO (771, 15.3%), and MUO (1155, 16.1%) had a higher readmission rate compared to those with MHNW (7901, 13.6%). Compared with patients with the MHNW phenotype, those with the MUNW (hazard ratio [HR], 1.10; 95% CI, 1.06-1.14), MHO (HR, 1.15; 95% CI, 1.07-1.24), and MUO (HR, 1.28; 95% CI, 1.20-1.36) phenotypes had a higher risk of readmission, especially in men, those without surgical intervention, or those aged >60 years. In women, similar results with respect to readmission were observed in people aged >60 years (MUNW [HR, 1.07; 95% CI, 1.01-1.13], MHO [HR, 1.19; 95% CI, 1.06-1.35], and MUO [HR, 1.28; 95% CI, 1.16-1.41]). We also found increased costs for 30-day readmission in patients with MHO (OR, 1.18; 95% CI, 1.07-1.29) and MUO (OR, 1.11; 95% CI, 1.02-1.20).

Interpretation

Increased BMI and metabolic abnormalities are independently associated with higher readmission risks in patients with lung cancer, whereas increased BMI also increases the readmission costs. Follow-up and intervention method targeting increased BMI and metabolic abnormalities should be considered for patients with lung cancer.

Funding

The National Key Research and Development Program of China (2017YFC1309800).

SUBMITTER: Yuan Z

PROVIDER: S-EPMC9310118 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectivesTo explore the effects of weight change on the risk of metabolic abnormalities in the Chinese population.MethodsA total of 1895 metabolically healthy adults aged 21-78 years completed anthropometric and biological measurements at baseline (2012) and at an eight year follow-up (2020). Based on absolute weight change and relative weight change, the participants were split into five classes. A Cox proportional hazards regression model was used to estimate the relative risk (RR) and 95% confidence intervals (95% CI) for the risk of metabolic abnormalities using stable weight as the reference group. Stratified analysis was used to explore this relationship in participants with different baseline body mass index (BMI) levels.ResultsDuring the follow-up period, 35.41% of the participants retained a stable weight, and 10.71% had metabolic abnormalities. After covariate adjustment, for every kilogram gained over eight years, the risk of developing metabolic abnormalities increased by 22% (RR: 1.094; 95% CI: 1.063-1.127). Compared with stable weight participants, weight gain of 2-4 Kg and weight gain ≥ 4 Kg exhibited significantly higher risks of metabolic abnormalities, with RR of 1.700 (95% CI 1.150-2.513) and 1.981 (95% CI 1.372-2.859), respectively. A weight gain of ≥ 4 Kg had an opposite effect on the overweight/obesity and non-overweight/obesity groups, with an increased risk of metabolic abnormalities only in the non-overweight/obesity group (RR, 2.291; 95% CI, 1.331-3.942). Moreover, weight loss ≥ 4 Kg significantly reduced the risk of metabolic abnormalities only among overweight/obese adults (RR 0.373; 95% CI 0.154-0.906). Similar results were observed in relative body weight change analyses.ConclusionsLong-term excessive body weight gain is positively associated with an increased risk of metabolic abnormalities among adults with non-overweight/obesity, whereas long-term body weight loss is a protective factor for metabolic health among adults with overweight/obesity.

Project description:BackgroundSex disparity between metabolic-obesity (defined by body mass index, BMI) phenotypes and obesity-related cancer (ORC) remains unknown. Considering BMI reflecting overall obesity but not fat distribution, we aimed to systematically assess the association of our newly proposed metabolic-anthropometric phenotypes with risk of overall and site-specific ORC by sex.MethodsA total of 141,579 men (mean age: 56.37 years, mean follow-up time: 12.04 years) and 131,047 women (mean age: 56.22 years, mean follow up time: 11.82 years) from the UK Biobank was included, and designated as metabolic-anthropometric phenotypes based on metabolic status (metabolically healthy/unhealthy), BMI (non-obesity/obesity) and body shape (pear/slim/apple/wide). The sex-specific association of different phenotypes with overall and site-specific ORC was assessed by hazard ratios (HRs) and 95% confidence intervals (CIs) using Cox proportional hazards regression models.ResultsWe found metabolically unhealthy and/or obesity phenotypes conveyed a higher risk in men than in women for overall ORC and colorectal cancer compared with metabolically healthy non-obesity phenotype (Pinteraction < 0.05). Of note, metabolically healthy obesity phenotype contributed to increased risks of most ORC in men (HRs: 1.58 ~ 2.91), but only correlated with higher risks of endometrial (HR = 1.89, 95% CI: 1.54-2.32) and postmenopausal breast cancers (HR = 1.17, 95% CI: 1.05-1.31) in women. Similarly, even under metabolically healthy, men carrying apple and wide shapes phenotypes (metabolically healthy apple/wide and metabolically healthy non-obesity apple/wide) suffered an increased risk of ORC (mainly colorectal, liver, gastric cardia, and renal cancers, HRs: 1.20 ~ 3.81) in comparison with pear shape or non-obesity pear shape.ConclusionsThere was a significant sex disparity between metabolic-anthropometric phenotypes and ORC risk. We advised future ORC prevention and control worth taking body shape and sex disparity into account.

Project description:Background and aims: Youth populations with overweight/obesity (OW/OB) exhibit heterogeneity in cardiometabolic health phenotypes (e.g. fasting glucose, serum triglycerides, etc.). The underling mechanisms for those differences are still unclear. This study aimed to analyze the whole blood transcriptome profile (RNA-seq) of children with metabolic healthy overweight/obesity (MHO) and metabolic unhealthy overweight/obesity (MUO) phenotypes. Methods: 27 children with OW/OB (10.14 ± 1.3 years, 59% boys) from the ActiveBrains project were included. MHO was defined as having none of the following criteria for metabolic syndrome: elevated fasting glucose, high serum triglycerides, low high-density lipoprotein cholesterol, and high systolic or diastolic blood pressure, while MUO was defined as presenting one or more of these criteria. Inflammatory markers interleukin-1β (IL-1β), IL-6, tumor necrosis factor-α (TNF-α), epidermal growth factor (EGF) and, vascular endothelial growth factor A (VEGF) were additionally determined. Total-blood RNA was analyzed by 5’-end RNA sequencing. Two different bioinformatics approaches were performed: 1) Differential gene expression analyses using Limma R/Bioconductor software package (analyses adjusted by sex and maturational status) and 2) weighted gene coexpression network analyses (WGCNA). Results: Children with MHO had lower values of pro-inflammatory cytokines TNF-α and IL-6 compared to MUO (p < 0.05). 40 genes were differentially expressed (FDR < 0.05) in children with MHO compared to MUO. WGCNA analysis identified 23 gene coexpression modules (i.e. clusters of genes) with low preservation (Zsummary < 2) in children with MHO compared to MUO. Differential and WGCNA gene expression patterns identified 32 genes linked to metabolism, mitochondrial and immune functions. Conclusions: Whole blood transcriptome analysis revealed a distinct pattern of gene expression in children with MHO compared to MUO children. The identified gene expression patterns related to metabolism, mitochondrial and immune functions can promote a better understanding of cardiovascular disease prognosis in individuals with MHO.