Correlation between glucose metabolism and body mass index in tumor lesions of patients with lung cancer

Abstract

Objective

Lung cancer, along with various other cancers, is characterized by increased glucose metabolism. The maximum standardized uptake value (SUVmax), derived from positron emission tomography-computed tomography (PET-CT), serves as an indicator of glucose metabolic activity in tumor lesions. This study aimed to evaluate the correlation between body mass index (BMI) and SUVmax in individuals with lung cancer.

Methods

This study included 41 patients with lung cancer, who were divided into two groups: Group 1 (n = 21), with a BMI greater than 22.4, and Group 2 (n = 20), with a BMI less than 22.4. All participants underwent 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET-CT) imaging. The SUVmax was calculated by manually delineating the regions of interest. A t-test was performed to assess whether the differences in SUVmax between the two groups were statistically significant.

Results

The mean SUVmax for Group 1 was 11.20 ± 5.45, while for Group 2 it was 10.65 ± 5.96. Although the mean SUVmax was higher in Group 1 compared to Group 2, the difference between the groups was not statistically significant (P = 0.757).

Conclusion

The findings indicate a non-significant difference in glucose metabolism in lung cancer lesions between patients with different BMI levels. These results offer valuable insights into the metabolic characteristics of lung cancer and contribute to a deeper understanding of its pathophysiology.

Introduction

Cancer is the second most prevalent cause of mortality globally. Among the various forms of cancer, including lung cancer, risk factors such as smoking and body mass index (BMI) significantly contribute to disease progression [1–4]. Understanding the effect of these cancer risk factors is crucial for both global and localized cancer prevention strategies.

The demand for imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and nuclear medicine, has risen sharply in cancer care [5]. With advancements in nuclear medicine imaging technology, positron emission tomography-computed tomography (PET-CT) is being increasingly utilized in cancer management, including lung cancer. PET-CT, for example, has demonstrated prognostic value in stage I-II non-small cell lung cancer (NSCLC), particularly in predicting disease-specific survival [6]. Among the molecular imaging techniques, 18F-fluorodeoxyglucose positron emission tomography-computed tomography ([18F]FDG PET-CT) is notable for revealing critical glucose metabolic activity in lung cancer. This imaging modality can improve local control in patients undergoing radiotherapy for locally advanced NSCLC [7]. [18F]FDG PET/CT is recommended in some clinical guidelines for the management of lung cancer [8]. Patients with lung cancer who undergo PET staging and image-guided therapy tend to experience better survival outcomes compared to those in similar studies without PET imaging [9, 10].

Globally, BMI is recognized as the third most significant risk factor for cancer mortality and disability-adjusted life years (DALYs), following smoking and alcohol consumption [11]. Efforts to reduce exposure to these risk factors could potentially lower cancer-related mortality and DALYs. Moreover, BMI is suggested to be a possible risk factor for lung cancer. Despite this, the specific role of BMI in cancer progression remains unclear. Gaining clarity on this relationship is critical for understanding lung cancer pathogenesis and for the development of cost-effective primary prevention strategies [12]. This study aims to investigate the correlation between glucose metabolism and BMI in tumor lesions of patients with lung cancer.

Methods

Study population

This retrospective study was conducted at the PET/CT Center of Guangdong Medical University. Participants were selected according to specific inclusion and exclusion criteria, with each patient having been diagnosed with lung cancer. Patients were divided into two groups based on their body mass index (BMI): Group 1 included patients with a BMI > 22.4, while Group 2 included those with a BMI < 22.4. All patients underwent routine PET-CT scans at the Nuclear Medicine PET-CT Center. For research purposes, the maximum standardized uptake value (SUVmax) was manually measured from the scans. Informed consent was obtained from all participants prior to undergoing the examinations.

The diagnostic criteria for lung cancer used in this study was based on the International Classification of Diseases, 10th revision [13]. Patients were diagnosed using imaging or pathological examinations. A total of 41 patients were selected for inclusion in this study based on these diagnostic results.

Inclusion criteria required that participants have a confirmed diagnosis of lung cancer and be at least 18 years of age. Patients with any other malignancies were excluded from the study.

Between November 2018 and February 2021, 41 patients with lung cancer were selected for the study. BMI was calculated for each patient by measuring height and weight. All participants underwent PET-CT examinations, and informed consent for the procedure was obtained from each individual, rendering them eligible for participation in this research.

Group division

Patients were divided into two groups based on their BMI, which ranged from 16.29 to 28.87, with a mean BMI of 22.36. Baseline characteristics of the two groups are presented in Table 1. Apart from weight and BMI, no statistically significant differences were observed in age, gender, height, or blood glucose levels between the groups, indicating comparability of baseline characteristics across the study population.

Table 1.

Patient characteristics

Characteristic	Group 1 (n = 21)	Group 2 (n = 20)	P
Age (years)	62.71 ± 11.40	57.50 ± 12.16	0.164
Gender (male/female)	14/7	16/4	0.093
Weight (kg)	66.61 ± 8.62	55.58 ± 6.30	< 0.001
Height (m)	1.64 ± 0.76	1.67 ± 0.08	0.222
Body mass index (kg/m2)	24.69 ± 1.53	19.91 ± 1.33	< 0.001
Blood glucose (mmol/L)	6.05 ± 1.32	5.55 ± 0.98	0.176
Injected dose (mCi)	9.42 ± 2.13	7.90 ± 1.75	0.017

Experiment steps

A total of 41 patients with lung cancer were included in this study. BMI was calculated for each patient by measuring both height and weight. All participants underwent PET-CT imaging after a 6-h fasting period, ensuring blood glucose levels remained below 11.1 mmol/L. The SUVmax was measured for each patient. Imaging was conducted using a GE Healthcare Discovery 690 Elite PET-CT scanner. Images were acquired at varying doses, employing both conventional filtered back-projection (FBP) and adaptive statistical iterative reconstruction (ASIR) algorithms. SUVmax measurements were performed independently by at least two physicians, with a high degree of inter-observer agreement. In cases where discrepancies arose, a senior physician was consulted for a re-evaluation. The region of interest (ROI) for SUVmax measurements was a circular area with an approximate diameter of 0.5 cm.

Measurement indexes

The primary measurement index was the BMI of the patients with lung cancer, while the secondary measurement index was the SUVmax for each patient.

Data analysis

The t-test was used to evaluate differences between the two groups. For both BMI and SUVmax, differences were considered statistically significant when P < 0.05.

Results

SUVmax levels in different groups

The mean SUVmax in Group 1 was 11.20 ± 5.45, while in Group 2, it was 10.65 ± 5.96. The maximum SUVmax observed was 24.6 in Group 1 and 25.4 in Group 2, with the lowest values recorded at 4.9 and 1.4, respectively. Although the mean SUVmax was higher in Group 1 compared to Group 2, the difference was not statistically significant (P = 0.757). A detailed comparison of SUVmax between the two groups is provided in Table 2.

Table 2.

SUVmax between groups

Characteristic	Group 1 (n = 21)	Group 2 (n = 20)	P
Median SUVmax	11.20 ± 5.45	10.65 ± 5.96	P = 0.757
Highest SUVmax	24.6	25.4
Lowest SUVmax	4.9	1.4

Discussion

To the best of our knowledge, this is the first retrospective study examining the correlation between glucose metabolism in tumor lesions and BMI in patients with lung cancer.

BMI is recognized as a risk factor for cancer development. However, studies examining the direct association between BMI and lung cancer have produced inconsistent findings [14, 15], warranting further investigation to clarify whether BMI is indeed a risk factor for lung cancer. BMI, calculated by dividing body weight by the square of height (kg/m²), is a simple yet widely used clinical tool for assessing nutritional status and obesity. A BMI exceeding certain thresholds indicates overweight or obesity. Excessive intake of carbohydrates and high-fat foods can elevate BMI.

When carbohydrates are consumed, they are metabolized into glucose. A portion of this glucose provides energy through metabolic pathways such as the tricarboxylic acid cycle and glycolysis. Excess glucose can be converted by the liver into glycogen, which is subsequently stored as fat. Glucose metabolism is a crucial biochemical process, and its dysregulation is commonly observed in malignant tumors.

Fluorodeoxyglucose (FDG), a glucose analog, allows for the visualization of glucose metabolism in tissues, including tumors, using 18F-FDG PET-CT. This imaging modality is effective in assessing glucose metabolism in various tissues, offering valuable insights into tumor biology.

The Global Burden of Disease (GBD) 2019 study categorizes risk factors into three broad groups: environmental, occupational, and metabolic. Among these, metabolic risk factors have shown the largest percentage increase in age-standardized mortality and DALY rates for cancers attributed to global risks. According to the Global Cancer Observatory, 3.6% of new cancer cases in 2012 were attributable to high BMI. Furthermore, the GBD study reported that high BMI accounted for 4.6% of cancer-related deaths in 2019 [16, 17]. In certain countries, the rate of age-standardized cancer incidence and DALYs linked to all risk factors has increased, largely driven by metabolic risks such as elevated BMI. Despite this, few studies have specifically examined the impact of BMI on glucose metabolism in lung cancer lesions. The findings of this study may offer valuable insights into this area.

Metabolism, particularly glucose metabolism, is not only a risk factor for lung cancer but also one of its defining characteristics. The SUVmax derived from 18F-FDG PET-CT reflects glucose metabolic activity. This study analyzed the relationship between BMI and glucose metabolism in lung cancer lesions by examining SUVmax values from 18F-FDG PET-CT scans.

There were no statistically significant differences in age, gender, height, or blood glucose levels between the two patient groups. However, significant differences were observed in weight, BMI, and the dose of injections. Lung cancer has a higher incidence among older individuals, and most patients in this study were older adults. The average age in Group 1 was 62.71 ± 11.40 years, compared to 57.50 ± 12.16 years in Group 2 (P = 0.164). Gender distribution also showed no significant difference between groups (P = 0.093), though both groups had more male than female participants, with male/female ratios of 14/7 and 16/4, respectively. The difference in height between the groups was also not statistically significant, and all patients had fasted before the PET-CT examinations, resulting in no significant differences in blood glucose levels.

The lack of a statistically significant difference in SUVmax between the two groups (P = 0.757) suggests that glucose metabolism in lung cancer lesions is not influenced by BMI. Whether BMI impacts glucose metabolism in tumor lesions, especially as the disease progresses, remains an important and unresolved question. However, due to the limitations of this study, further research is needed to more thoroughly investigate and clarify this relationship.

This study has several limitations. Firstly, relying solely on SUVmax may not fully capture the relationship between glucose metabolism and BMI in tumor lesions in patients with lung cancer. Other variables, such as blood glucose and insulin levels, are also critical in understanding this relationship. Future research should incorporate these parameters to help explain the absence of a statistically significant difference in this study. While many factors contribute to cancer prevention, BMI may still play a role [18, 19].

Additionally, the lack of prognostic data is a significant limitation, as prognosis is a crucial factor in lung cancer outcomes. Future studies should integrate prognostic information. The relatively small sample size may also have limited the accuracy of the findings, necessitating larger cohorts in future investigations. Furthermore, biochemical analyses and histopathological examinations, which could have offered more comprehensive insights, were not included in this study. Future research should incorporate these elements to allow for a more thorough investigation. It is also important to recognize that results may vary across different subtypes of lung cancer, and such variations should be accounted for in future studies.

Although few comparable trials have been conducted, making these findings intriguing, the limitations discussed underscore the need for further research. Future studies will implement methodological changes, which may yield additional significant results. In addition, thyroid hormones, which play a critical role in regulating metabolism and are closely linked to BMI, will be explored in relation to lung cancer cell growth in subsequent research.

Conclusion

The results of this study highlight the relationship between glucose metabolism and BMI in tumor lesions in patients with lung cancer. After lung cancer has developed, there appears to be a trend in which BMI no longer significantly influences glucose metabolism in tumor lesions. Further research is needed to elucidate the underlying mechanisms involved in lung cancer progression.

Abbreviations

BMI

Body mass index

PET

Positron emission tomography

CT

Computerized tomography

SUV

Standard uptake value

18F-FDG

18F-flurodeoxyglucose

MRI

Magnetic resonance image

DALY

Disability-adjusted life years

GBD

Global burden of disease

Author contributions

Zhao ZQ conceived the idea and conceptualised the study. Zhao ZQ, Zhang JH, Yang HJ and Wang D collected the data. Zhao ZQ, Wang XN, Zhang JH, Yang HJ and Wang D analysed and interpreted the data. Zhao ZQ and Wang XN statistically analyzed the data. Zhao ZQ drafted the manuscript. Zhao ZQ reviewed the manuscript. All authors read and approved the final draft.

Funding

This research was supported by Zhanjiang science and technology special project (No.2023B01181); The high-level talents scientific research start-up funds of the Affliated Hospital of Guangdong Medical University (No.GCC2021014).

Data availability

The data and materials used to support the findings of this study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was conducted with approval from the Ethics Committee of Affiliated Hospital of Guangdong Medical University. This study was conducted in accordance with the declaration of Helsinki. Written informed consent was obtained from all participants.

Competing interests

The authors declare no competing interests.