The study of aging and thermoregulation has taken center stage in health sciences and physiological performance. Over time, heat dissipation mechanisms and peripheral vascular responses undergo alterations that modify skin temperature distribution. In this context, understanding which individual factors influence cutaneous thermal response is essential for accurately interpreting infrared images in older adults.

A new scientific study published in the journal Healthcare by Núñez-Rodríguez et al. (2026) investigates the relationship between body composition parameters (body fat percentage and skeletal muscle mass) and bilateral thermal asymmetry patterns in older adults.

Below, we analyze the methodology, key findings of this research, and their practical implications for the use of infrared thermography.

The Challenge of Thermoregulation in Aging

Biological aging is accompanied by vascular, autonomic, and endocrine modifications that affect cutaneous blood flow and the body's ability to adapt to thermal stress. Subcutaneous adipose tissue acts as a thermal insulator with lower conductivity than lean mass, affecting heat transfer from deep tissues to the skin surface.

Although it is common to assume that bodily changes occur homogeneously, the distribution of subcutaneous fat and microvascularization can exhibit regional variations. This raises a key question for clinical and healthcare settings: Do body fat percentage or muscle mass influence the levels of thermal asymmetry recorded at rest?

To address this question, researchers examined the association between metrics obtained via bioelectrical impedance and thermal indices processed using automated thermographic technology.

Experimental Design and Methodology

The study employed a cross-sectional design involving 127 community-dwelling older adults (85 women and 42 men) with a mean age of 81.0±10.7 years.

The evaluations were divided into two standardized blocks:

• Body Composition Assessment: Performed using direct segmental multi-frequency bioelectrical impedance analysis (InBody S10). Variables recorded included body fat percentage (PBF), appendicular skeletal muscle mass index (ASMI), phase angle (PhA), and the extracellular water/total body water ratio (ECW/TBW).
• Standardized Thermographic Assessment: A FLIR E6390 thermal camera was used under strictly controlled environmental conditions (22 ± 1 °C and 15 minutes of prior acclimation). Captures were taken at a distance of 1.5 meters in the anatomical position.

ThermoHuman® automated analysis software (version 3.0) was used for image processing and quantitative data extraction, with files subsequently reviewed using FLIR Tools.

Technical Clarification on ThermoHuman® Metrics and Tools

When interpreting the thermographic platform's workflow, it is important to distinguish between its analytical tools:

• Global Asymmetry Indices: Advanced metrics such as the Thermal Risk Index (TRI) and Session Asymmetry Percentage (SAP) provide a synthetic assessment of thermal homeostasis and global asymmetry percentages between homologous regions.
• Isotherm Map vs. Asymmetry Avatar: The platform's isotherm map highlights the 5th percentile of the hottest and coldest pixels in the image. Meanwhile, segmentation on the anatomical avatar represents regional asymmetry using a standardized color code: for instance, yellow tones identify asymmetries between 0.3 °C and 0.6 °C, while orange tones indicate thermal differences between 0.6 °C and 0.9 °C.

Analytical processing workflow using ThermoHuman®: Original thermograms (A, D), isotherm maps (B, E), and region of interest segmentation on the anatomical avatar (C, F).

Main Results: Body Fat Conditioning Thermal Asymmetry

The study findings showed a modest yet statistically significant relationship between adiposity and several thermographic asymmetry parameters, whereas muscle mass showed no relevant influence.

1. Positive Correlation Between Adiposity and Asymmetry

Body fat percentage (PBF) was the body composition variable that demonstrated the most consistent associations with thermal asymmetry:

• Upper Body Asymmetry: Significant positive correlation (r=0.229, p=0.015).
• Absolute Mean Thermal Asymmetry: Positive correlation with the overall magnitude of thermal imbalance (r=0.280, p=0.003).
• Synthetic Indices (TRI and SAP): PBF was positively associated with both the Thermal Risk Index (TRI) (r=0.250, p=0.008) and the Session Asymmetry Percentage (SAP) (r=0.276, p=0.003).

Linear relationship between Body Fat Percentage (X-axis) and Absolute Mean Thermal Asymmetry (Y-axis) in the evaluated sample.

2. Low Influence of Skeletal Muscle Mass

In contrast to fat mass, the appendicular skeletal muscle mass index (ASMI), phase angle, and body water ratios showed no significant associations with global thermographic asymmetry indices. Only total muscle mass demonstrated a weak inverse correlation with trunk asymmetry (r=−0.199, p=0.035).

3. Sex Differences and Multivariate Model

In the initial univariate analyses, women presented a higher fat percentage (40.4±11.8% vs. 34.3±9.2%, p<0.001) and significantly higher thermal asymmetry values than men.

However, when variables were introduced into a multiple linear regression model adjusted for age and sex, the difference between men and women ceased to be statistically independent. Body fat percentage maintained a significant coefficient (B=0.005, p=0.026), but the overall model explained only 5% of the total variance (R2=0.050, p=0.144).

Practical Applications and Clinical Relevance

These results provide relevant insights for healthcare professionals, researchers, and clinical thermography practitioners:

• Adiposity as a Confounding Factor in Screening: Since a higher body fat percentage is associated with higher baseline skin asymmetry values, adiposity must be considered as an individual reference variable when interpreting thermograms. Failing to account for this factor could lead to overestimating thermal asymmetries in individuals with higher body fat.
• Importance of Physiological Screening and Baseline Reference Lines: As advocated in elite sports and sports medicine, establishing an individualized thermal baseline profile is essential. Distinguishing what portion of asymmetry stems from body composition versus a reactive or inflammatory process is key to precise management.
• Exploratory and Complementary Nature: In line with ThermoHuman®’s commitment to scientific rigor and transparency, thermography should be highlighted as a non-invasive tool for biological monitoring and physiological assessment, rather than a standalone diagnostic instrument for systemic pathologies. Because body composition accounts for only a small fraction of thermal variability (5%), other critical factors—such as microvascular regulation, vasoactive medications, or autonomic nervous system activity in older adults—play a major role.

Conclusions

The study published in Healthcare by Núñez-Rodríguez et al. (2026) demonstrates that body fat percentage has a modest association with skin thermal asymmetry in older adults, whereas muscle mass exerts a marginal impact.

These findings support the need to integrate body composition assessment into thermal analysis protocols and reinforce the value of scientific validation studies by ThermoHuman® to standardize data collection and optimize clinical and physiological decision-making.

References

• Balmain, B. N., Sabapathy, S., Louis, M., & Morris, N. R. (2018). Aging and Thermoregulatory Control: The Clinical Implications of Exercising under Heat Stress in Older Individuals[cite: 1]. BioMed Research International, 2018, 8306154.
• Fernández-Cuevas, I., Marins, J. C. B., Lastras, J. A., Carmona, P. M. G., Cano, S. P., García-Concepción, M. Á., & Sillero-Quintana, M. (2015). Classification of factors influencing the use of infrared thermography in humans: A review[cite: 1]. Infrared Physics & Technology, 71, 28-55.
• Januário, W. M., Prímola-Gomes, T. N., Rovira-Esteve, M., Wanner, S. P., Sendra-Pérez, C., & Priego-Quesada, J. I. (2026). Agreement between automated ThermoHuman® and manual thermographic analyses for assessing lower-limb skin temperatures before and after running[cite: 3]. Journal of Thermal Biology, 140, 104533.
• Núñez-Rodríguez, S., Ortega-Santamaría, M., Menéndez-Vega, F., Collazo-Riobó, C., Alvarez-Pardo, S., Berardi, A., & González-Santos, J. (2026). Body Composition and Thermographic Asymmetry in Older Adults: A Cross-Sectional Study[cite: 1]. Healthcare, 14(15), 2295.
• Salamunes, A. C. C., Stadnik, A. M. W., & Neves, E. B. (2017). The effect of body fat percentage and body fat distribution on skin surface temperature with infrared thermography[cite: 1]. Journal of Thermal Biology, 66, 1-9.