# Metabolic Risk Factors at the Time of Cancer Diagnosis in Primary Care: A Cross-Sectional Study

## TL;DR

This study explores how metabolic risk factors like high glucose and BMI in primary care patients may be linked to cancer types, suggesting potential for early risk identification.

## Contribution

The study identifies associations between metabolic markers and cancer types in primary care, proposing their use for opportunistic cancer risk stratification.

## Key findings

- Elevated fasting glucose was significantly associated with cancer type (p = 0.0238).
- 89.1% of patients were overweight or obese, and 97.7% had at least one metabolic abnormality.
- Only 2.3% of patients had no metabolic risk factors measured.

## Abstract

Background

Cancer is a growing public health burden and a leading cause of death and disability globally. An estimated 30-50% of cancer deaths are preventable through the modification of behavioral and metabolic risk factors. Primary care offers an essential platform for population-level cancer prevention and early detection, especially by leveraging existing screening and chronic disease monitoring infrastructure. This study, conducted in a single primary care clinic in Poland, evaluates metabolic markers routinely collected in this setting to assess their potential role in identifying cancer risk.

Methods

We conducted a cross-sectional study analyzing 347 adult patients diagnosed with cancer (ICD-10 codes: C00-C14, C15-C26, C30-C39, C43-C44, C50, C51-C58, C60-C63, C64-C68, C69-C72, C73-C75, C76-C80, C81-C96, D00-D48) between 2017 and 2022 in a single primary care clinic in Poland. Fasting glucose, total cholesterol, triglycerides, and body mass index (BMI) were selected because they are routinely measured in primary care settings and frequently associated with metabolic risk and cancer development. These parameters were analyzed in relation to cancer type using descriptive statistics, chi-square tests, and Kruskal-Wallis non-parametric analyses.

Results

Elevated fasting glucose (≥100 mg/dL) was significantly associated with cancer type (p = 0.0238). This abnormality was observed in 83.6% (N = 290) of all patients. Although BMI, cholesterol, and triglyceride levels were not significantly associated with cancer type, metabolic abnormalities were highly prevalent in the entire sample. A total of 89.1% (N = 309) of patients were overweight or obese, and nearly one-third (N = 110) presented with all four metabolic abnormalities concurrently. Only 2.3% (N = 8) of the patients had none of the measured metabolic risk factors. The vast majority, 97.7% (N = 339), had at least one metabolic abnormality. Given the high prevalence of metabolic disturbances, the possibility of selection bias should be considered, as individuals visiting primary care may be more likely to have pre-existing chronic conditions or risk factors under surveillance.

Conclusions

Routine, low-cost metabolic tests performed in primary care may offer useful insights into cancer risk patterns and could inform opportunistic risk stratification efforts. However, given the cross-sectional design of this study, the findings do not support causal inference. Integrating routinely collected metabolic data into public health frameworks may enhance early detection strategies and support timely diagnostic referrals. These results highlight the potential for scalable, population-based interventions within primary care to contribute to reducing the cancer burden, morbidity, and disability-adjusted life years.