Category: 8. Health

Published on
12/08/2025 – 16:57 GMT+2

A simple voice recording could one day help doctors spot early signs of throat cancer, according to new research.

In a study published in Frontiers in Digital Health, scientists found that artificial intelligence (AI) could potentially detect abnormal growths on the vocal cords, from benign nodules to early-stage laryngeal cancer, by analysing short voice recordings.

The findings could support efforts to find an easier, faster way to diagnose cancerous lesions on the vocal cords, also known as folds.

“With this dataset we could use vocal biomarkers to distinguish voices from patients with vocal fold lesions from those without such lesions,” said Phillip Jenkins, the study’s lead author and a postdoctoral researcher in clinical informatics at Oregon Health and Science University in the United States.

Why early detection of throat cancer matters

Cancer of the voice box, or larynx, affects more than a million people worldwide and kills roughly 100,000 every year. It is the 20th most common cancer in the world.

Smoking, alcohol use, and certain strains of HPV (human papillomavirus) are key risk factors, and survival rates vary from around 35 per cent to 90 per cent depending on how early the disease is diagnosed, according to Cancer Research UK.

One of the most common warning signs for laryngeal cancer is hoarseness or changes in the voice that last more than three weeks. Other symptoms include a persistent sore throat or cough, difficulty or pain when swallowing, a lump in the neck or throat, and ear pain.

Early detection of laryngeal cancer is crucial because it significantly improves survival rates and treatment outcomes.

Yet current diagnostic methods, including nasal endoscopies and biopsies, are invasive, uncomfortable, and often slow, requiring specialist equipment and expertise that many patients struggle to access quickly.

Developing a simple tool to flag early signs of vocal fold abnormalities through a quick voice recording could transform how throat cancer is detected – making it faster, more affordable and accessible to a wider population.

The next steps for AI-driven diagnosis

The research team examined about 12,500 voice recordings from 306 people across North America. They looked for subtle acoustic patterns, such as changes in pitch, loudness, and harmonic clarity.

The team identified clear differences for men in the harmonic-to-noise ratio and pitch between those with healthy voices, benign lesions, and cancer. No significant patterns were found in women, but the researchers say this may be due to the smaller dataset.

Jenkins said that the results indicate large datasets “could soon help make our voice a practical biomarker for cancer risk in clinical care”.

The next step is to train AI models on larger, professionally labelled datasets and test them in clinical settings. The team would also need to test the system to make sure it works well for both men and women, he said.

“Voice-based health tools are already being piloted,” Jenkins said.

“Building on our findings, I estimate that with larger datasets and clinical validation, similar tools to detect vocal fold lesions might enter pilot testing in the next couple of years”.

Background

Chronic obstructive pulmonary disease (COPD) is a multifactorial lung condition driven by various pathogenic mechanisms, characterized by persistent respiratory symptoms and airflow obstruction.^1,2 The clinical diagnostic criterion for COPD is a forced expiratory volume in 1 second (FEV1) to forced vital capacity (FVC) ratio of less than 0.7, reflecting the proportion of air forcefully exhaled in one second relative to the total volume exhaled after maximal inhalation. The key pathological features of COPD include chronic bronchitis and emphysema. Globally, COPD ranks among the top three causes of death. In 2012, it was responsible for over 3 million deaths, accounting for 6% of all global fatalities, imposing a substantial burden on clinical care and healthcare resources.^3–5 Although COPD remains a major public health challenge, it is both preventable and manageable with appropriate interventions.

The onset of COPD is often considered to be related to multiple factors. Studies have shown that abnormal inflammatory responses are strongly associated with the development of COPD, and these responses are often chronic and destructive. Over the past decade, research has increasingly highlighted the close link between COPD and inflammation.^6,7 Several factors associated with COPD, including dietary patterns, exposure to cigarette smoke, and infections, have been shown to significantly influence inflammation levels.^8,9 Chronic systemic inflammation is a nonspecific defense mechanism of the body in response to external stimuli or internal injury, involving the activation of various cellular and molecular processes. Prolonged inflammation can accelerate the progression of COPD.¹⁰ The systemic inflammation response index (SIRI) is an emerging and promising inflammatory biomarker, calculated based on neutrophil, monocyte, and lymphocyte counts. It serves as an indicator of the body’s systemic inflammatory status and offers a novel perspective for evaluating inflammatory responses. Increasing evidence has demonstrated associations between elevated SIRI levels and various inflammation-related diseases, including osteoarthritis, pancreatic cancer, esophageal cancer, and heart failure. Notably, higher SIRI levels have been linked to increased mortality in patients with heart failure and elevated all-cause mortality among individuals with diabetes. However, the relationship between SIRI and the development of COPD has not yet been fully elucidated.^11–15

The National Health and Nutrition Examination Survey (NHANES) is a comprehensive survey conducted in the United States, utilizing complex, multi-stage, and probability sampling methods to collect nutritional and health information about the population. Through the NHANES database, an increasing number of factors related to human health and diseases have been identified. This cross-sectional study aims to investigate the potential association between COPD and the inflammatory marker SIRI in individuals aged over 40 within the US population. All participants were registered through the NHANES between 2013 and 2018.

Materials and Methods

Data Sources

This is a cross-sectional analysis utilizing data from the National Health and Nutrition Examination Survey (NHANES) (https://www.cdc.gov/nchs/nhanes). NHANES is a national survey conducted by the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC). It combines interviews and physical examinations. NHANES uses a complex, multi-stage design to collect and analyze data that reflects the non-institutionalized US population. The execution of the study involves two key components: a comprehensive interview conducted at participants’ homes and detailed health examinations carried out at mobile examination centers. Both components of the study are carefully conducted by skilled and certified researchers.

Study Design and Participants

The detailed participant selection process is illustrated in Figure 1. This study analyzed publicly available NHANES data from the 2013–2018 cycles. Detailed information regarding the NHANES sampling design, study procedures, and survey protocols can be accessed on the official website of the National Center for Health Statistics (NCHS). The inclusion criteria for this study were as follows: 1) Age > 40 years; 2) Clear diagnosis of COPD; 3) Complete data on neutrophils, monocytes, and lymphocytes; 4) Exclusion of incomplete information on other essential data.

Figure 1 Flowchart of Patient Inclusion and Exclusion in NHANES 2013–2018.

Calculation of SIRI

Since inflammation levels have a significant impact on COPD, we used SIRI to comprehensively assess its effect on COPD. The calculation of SIRI is as follows: SIRI = N * M / L, where N, M, and L represent the counts of neutrophils, monocytes, and lymphocytes in the peripheral blood after preprocessing, respectively. Based on SIRI levels, participants were divided into three groups according to the 3rd quartiles: Quartile 1 (Q1), Quartile 2 (Q2), and Quartile 3 (Q3).

Inclusion of Covariates

Based on standardized questionnaire surveys, we extracted participants’ sociodemographic characteristics, including age, gender, race, education level, poverty-to-income ratio (PIR), smoking status, obesity, use of diabetic medications or insulin, and hypertension. Additionally, laboratory measurements such as monocyte count, neutrophil count, and lymphocyte count were collected. Missing data for these covariates were excluded from this study. Race/ethnicity was categorized as non-Hispanic White, non-Hispanic Black, Mexican American, and other races. Marital status was divided into married and unmarried. Education level was classified as less than high school and high school or higher. Household income was categorized based on the family PIR into two levels: poor and non-poor. Smoking status was classified as current smoker and non-smoker. Obesity status was categorized as obese and non-obese. Diabetes was categorized as diabetic and non-diabetic.

We obtained personal interview data on the history of COPD from participants aged over 40 in the NHANES, covering the period from 2013 to 2018. The questionnaire item used to determine the history of COPD was: “Has a doctor or other health professional ever told {you/SP} that {you/s/he} had COPD?” (MCQ160o).

Statistical Analysis

In descriptive statistics, continuous variables are expressed as means (± SD), while categorical variables are presented as frequencies or percentages (n, %). When analyzing baseline characteristics, continuous variables with a normal distribution were analyzed using independent sample t-tests, non-normally distributed continuous variables were analyzed using the Wilcoxon rank-sum test, and categorical variables were analyzed using the chi-square test. Additionally, appropriate weighted data were used in the analysis based on the specific situation.

We analyzed the association between SIRI and COPD using multivariable logistic regression. To control for confounding factors, we applied three multivariable logistic regression models: Model 1: Unadjusted; Model 2: Adjusted for age, sex, and race; Model 3: adjusted for all covariates. To investigate the potential non-linear relationship between SIRI and COPD, we performed restricted cubic spline (RCS) analysis. The reference point was set at 1.08, and adjustments were made for age, gender, race, education level, marital status, smoking status, body mass index, hypertension, diabetes, blood cell counts, and other factors. In addition, we conducted subgroup analyses by stratifying participants based on age, gender, race, marital status, smoking status, obesity, and history of hypertension. We also evaluated potential interactions across these subgroups. All analyses were conducted using the statistical software package R 4.4.2, and p < 0.05 was considered statistically significant.

Results

Baseline Analysis of Study Participants Based on COPD Status

The results show that patients with COPD have distinct clinical characteristics, including higher age, smoking rates, metabolic comorbidity burden, and systemic inflammation levels. These factors may collectively contribute to the progression and poor prognosis of COPD. A total of 10,273 participants were included in this study, 595 participants were diagnosed with COPD, and 9678 were diagnosed with non-COPD. In Table 1, there were 4940 males (47.0%) and 5333 females (53.0%). Among the participants, 6743 (70.4%) were aged 41–65 years, and 3530 (29.6%) were over 65 years old. The majority of participants were non-Hispanic White (4004 participants, 69.6%). Significant differences were observed in all baseline variables except for gender, lipids, and BMI (p < 0.05). Among participants diagnosed with COPD, the proportion of smokers was higher. Additionally, the prevalence of metabolic disorders such as hyperlipidemia and hypertension was also higher in this group. Furthermore, the proportion of participants with SIRI in the third quartile was significantly higher compared to those in the first quartile among those diagnosed with COPD.

Table 1 Baseline Characteristics of Study Population

Correlation Between Serum SIRI Levels and COPD

Multivariate logistic regression analysis, adjusted for various confounding variables, showed a positive correlation between SIRI and the prevalence of COPD in all three models. Notably, participants in the highest third (Q3) of SIRI levels had a significantly higher risk of COPD compared to those in the lowest third (Q1). The trends observed in all three models were statistically significant (p < 0.05) (Table 2).

Table 2 Association Between the SIRI and COPD

RCS Analysis

We used RCS curves to investigate whether there is a linear relationship between SIRI and the prevalence of COPD. The results (Figure 2) showed a non-linear correlation between the two variables (P < 0.05). As SIRI levels increased, the risk of COPD gradually rose, suggesting a positive association between systemic inflammation and COPD risk. However, this risk did not continue to rise indefinitely at very high levels of SIRI.

Figure 2 The restricted cubic spline model revealed a significant dose–response relationship between SIRI and COPD (P < 0.05).

Subgroup Analysis

The results of the subgroup analysis (Figure 3) showed that when stratified by age, gender, race, marital status, smoking, obesity, and hypertension, SIRI remained positively correlated with COPD (OR > 1). The results indicated that the association between SIRI and COPD was more pronounced among individuals aged over 65 years. This association also appeared stronger in males compared to females, and was notably enhanced in individuals with elevated blood pressure. Furthermore, interaction tests revealed no significant interactions between SIRI and COPD across subgroups (P > 0.05), suggesting that the positive association remained consistent and did not differ significantly among the various subpopulations.

Figure 3 Subgroup analysis for the association between SIRI and COPD.

Discussion

Until now, there has been a lack of research on the correlation between serum inflammatory marker SIRI and the incidence of COPD. In our current study, we observed a positive correlation between serum SIRI levels and the prevalence of COPD. Higher serum SIRI levels were associated with an increased risk of COPD, and this relationship remained consistent even after adjusting for covariates.

The pathogenesis of COPD remains unclear. Previous studies suggest that its pathogenesis primarily involves inflammation, oxidative stress, and an imbalance between proteases and antiproteases.¹⁶ When the immune system is compromised and the body is unable to compensate, it eventually leads to small airway damage and emphysema. Inflammation is a key factor in the onset and progression of COPD. Airway inflammation in COPD is primarily characterized by the infiltration and proliferation of inflammatory cells, such as neutrophils, lymphocytes, and monocytes/macrophages, which migrate into the airways and lung tissues. These cells can be detected in sputum and bronchoalveolar lavage fluid. The increase in inflammatory cells is often triggered by the activation of cytokines and mediators released by airway epithelial cells in response to inhaled cigarette smoke and particulate matter. Numerous inflammatory mediators, including free radicals, chemokines, cytokines, and growth factors, contribute to the development and progression of COPD.^17–19 A study found²⁰ that the levels of the Neutrophil-to-Lymphocyte Ratio (NLR) in patients with acute exacerbation of COPD were positively correlated with poor outcomes, including mortality, the need for mechanical ventilation, and transfer to the intensive care unit. Another cross-sectional study involving 10,364 participants found²¹ that Systemic Immune-Inflammation Index (SII) was positively correlated with the risk of COPD and showed a non-linear relationship. Compared to composite inflammatory markers like NLR and SII, SIRI may offer distinct advantages in assessing COPD. By incorporating monocytic parameters, SIRI provides a more comprehensive reflection of the chronic inflammatory response and tissue remodeling processes characteristic of COPD. Unlike acute-phase inflammatory proteins such as C-reactive protein (CRP), SIRI, based on the ratio of neutrophils, monocytes, and lymphocytes, is less affected by confounding factors like acute infections, offering better stability and reproducibility in the results. These features make SIRI a promising biomarker for evaluating the inflammatory status of COPD.

The SIRI is calculated based on the number of neutrophils, monocytes, and lymphocytes, reflecting the relative proportion of these three cell types. Neutrophils can be excessively activated in the airways of COPD patients, releasing inflammatory mediators such as interleukin-8, which attract more neutrophils to the affected areas and initiate oxidative stress by releasing oxygen free radicals.²² Therefore, neutrophils are considered key cells in the pathogenesis of COPD. A reduction in lymphocytes is a marker of stress, which can lead to the destruction of alveoli in COPD patients. CD8⁺ cells produce pro-inflammatory cytokines, including interleukin-2(IL-2), interferon-gamma, and TNF-alpha (TNF-α). These cytokines are increased in COPD patients and recruit other inflammatory cells.²³ When the counts of neutrophils and monocytes increase, or when the lymphocyte count decreases, SIRI typically rises. A 20-year comprehensive follow-up study involving 2656 rheumatoid arthritis (RA) patients found²⁴ that serum SIRI was non-linearly positively correlated with all-cause mortality and cardiovascular mortality in RA patients. This correlation was especially prominent in female patients and those with a high BMI. Another study on the relationship between SIRI and the risk of kidney stones found²⁵ a significant positive correlation between SIRI and kidney stones. As SIRI levels increased, the risk of kidney stones gradually rose. Based on the existing literature evidence, we can tentatively infer that SIRI may be an effective biomarker for predicting the development of inflammation-related diseases. It is worth noting that although SIRI has been the subject of research in many different fields, there is still limited research exploring its relationship with COPD.

This study found a significant positive correlation between serum SIRI levels and the prevalence of COPD. In this study, SIRI was divided into three groups: Q1, Q2, and Q3. In all three models adjusted for potential confounding factors, the Q3 group showed a higher prevalence compared to the Q1 group. Subgroup analysis results indicated that this association was consistent across different populations. The study also found a non-linear positive correlation between SIRI and COPD, suggesting that high SIRI levels may be an independent risk factor for COPD. This prospective cohort study involving 10,273 participants found that an increase in SIRI was significantly associated with a higher prevalence of COPD. Based on previous research, the potential mechanism behind this phenomenon may be that an increase in neutrophils in the blood triggers a higher probability of chronic inflammation in the lungs, while the decrease in lymphocytes leads to reduced lung immune function, ultimately resulting in a higher prevalence of COPD.

The findings of this study may provide some insights into the screening and management of COPD. First, SIRI can be derived from routine blood count data, eliminating the need for specialized equipment or additional costs, making it well-suited for primary healthcare settings. Second, for high-risk populations (smokers + age > 40), SIRI could be considered as part of routine screening, serving as an inflammatory monitoring tool for the comprehensive management of COPD. However, several limitations should be acknowledged. First, the NHANES population represents only the US population and may not be applicable to other populations. Second, our study focused on individuals above 40 years old, excluding children and adolescents. Third, due to the cross-sectional design of NHANES, inferring a causal relationship between SIRI and COPD is limited. Fourth, COPD diagnosis relies on self-report, and some important variables, such as lung function data (FEV1%, FVC%, FEV1/FVC), were not included in the analysis, which may introduce bias into the results. Fifth, due to the limitations of the dataset, we were unable to conduct parallel analyses involving NLR and SII. This represents an important area for future research.

Conclusion

In this cross-sectional study based on the NHANES database, we observed that elevated serum SIRI levels were significantly associated with an increased risk of COPD, indicating that SIRI may serve as a potential novel inflammatory marker for predicting the development of COPD. However, due to the study’s observational design, the causal relationship between SIRI and the onset or progression of COPD remains to be confirmed in future prospective studies.

Abbreviations

COPD, Chronic obstructive pulmonary disease; SIRI, Systemic inflammation response index; NHANES, National Health and Nutrition Examination Survey; RCS, restricted cubic spline; FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; PIR, poverty-to-income ratio; NCHS, National Center for Health Statistics; CDC, Centers for Disease Control and Prevention; 95% CI, 95% confidence interval; OR, Odds ratio.

Data Sharing Statement

The data supporting the findings of this study were obtained from the National Health and Nutrition Examination Survey (NHANES), which is publicly available at https://www.cdc.gov/nchs/nhanes. In accordance with transparency standards, the corresponding author affirms their commitment to making all data supporting this study’s conclusions available upon reasonable request.

Ethics Approval and Consent to Participate

This study was based on publicly available data from the National Health and Nutrition Examination Survey (NHANES), which is conducted by the National Center for Health Statistics (NCHS). All NHANES participants provided written informed consent, and the survey protocol was approved by the NCHS Research Ethics Review Board. As this study involved only secondary analysis of de-identified data, it was exempt from further review by the Institutional Review Board of The Second Affiliated Hospital of Jiaxing University.

Acknowledgments

The authors would like to express their sincere gratitude to all the participants and staff involved in the NHANES for their invaluable contributions and dedication.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

The study was supported by Zhejiang Provincial Medical and Health Technology Plan (Grant No. 2021PY029).

Disclosure

The authors declare no competing interests.

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Maggie’s Centres

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Members of the Prevent Blindness Diabetes + the Eyes Advisory Committee include:

•     Karen Allison, MD, MBA, FACS – Flaum Eye Institute, University of Rochester Medical Center, Prevent Blindness Scientific Committee Chair and Prevent Blindness Board of Directors member

Patient advocates include Tamara Joseph, Prevent Blindness ASPECT Patient Engagement Program graduate, and Jennifer Kim. Representatives from Prevent Blindness affiliates are also part of the Committee, including Prevent Blindness Georgia, Prevent Blindness North Carolina, Prevent Blindness Ohio, Prevent Blindness Texas, and Prevent Blindness Wisconsin.

Guided in part by its new Diabetes + the Eyes Advisory Committee, Prevent Blindness is implementing new initiatives to reduce diabetes-related eye disease:

•     Piloting refined systems of care in North Carolina Community Health Centers in partnership with Prevent Blindness North Carolina. The project will test new educational materials for parents of children with diabetes and young adults; expand tele-retinal screenings for individuals under 40 in community health centers, and improve care coordination. This effort is made possible with support from UnitedHealthcare.

These new efforts will be added to existing resources, including the Diabetes + the Eyes Educational Toolkit created in 2019 to help educate the public on diabetes and its effects on vision, along with providing resources to assist in access to eyecare. This program, with materials available in English and Spanish, is supported by funding from Regeneron and VSP Vision.

“We are thrilled to welcome this impressive group of leaders to our new Diabetes + the Eyes Advisory Committee,” said Jeff Todd, Prevent Blindness president and CEO. “By bringing together different perspectives, expertise, and lived experiences, we are continuing our mission to bring an end to vision loss from diabetes.”

For general diabetes-related eye disease information, please visit PreventBlindness.org/diabetes. For more information on the “Diabetes + the Eyes” program, please visit PreventBlindness.org/diabetes-and-eyes-educational-toolkit.
For a free listing of organizations and services that provide financial assistance for vision care in English or Spanish, please visit https://preventblindness.org/vision-care-financial-assistance-information.

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