Body temperature is affected by multiple influence factors. Fernandez-Cuevas et al. (2015) made an in-depth description of those individual factors, both intrinsic and extrinsic, that can affect skin temperature. Among them, the percentage of fat and the BMI stand out as one of the main factors that can differentiate the temperature between healthy individuals.
The description of thermal patterns in healthy adults in the scientific literature has attempted to find anomalies to discriminate those normative values from the unusual ones. As a general rule, the body tends to an equilibrium (thermal homeostasis) as shown by the investigations of Uematsu et al. (1988) and Vardasca et al. (2012) or the results we gathered from the users of the ThermoHuman database (Escamilla-Galindo et al. 2022). For this reason, the asymmetries methodology is the most robust and reliable for evaluating human beings with thermography.
As seen in the description of the influence factors, aspects such as gender, age or even BMI will influence skin temperature. In fact, normal thermal patterns differ between men and women, due to hormonal reasons during the menstruation period or biological reasons such as regions of fat accumulation (Marins et al. 2014).
On the other hand, the function of fat in the body is to isolate and protect from the environment, as can be verified with the works on this subject (Chudecka et al. 2014; Chudecka et al. 2016; Neves et al. 2017; Salamunes et al. 2017), where the conduction of the temperature towards the outside and the subsequent radiation have a lower impact in people with a higher percentage of fat, which directly affects the BMI.
The article by Reis et al. (2022) divides a sample of 100 healthy adolescents into three types of weight profile: low weight (n=33), normal weight (n=34) and overweight (n=33), according to the classification of the WHO (World Health Organization) for BMI. The objective was to know the normative values obtained through infrared thermography.
The images were taken with a FLIR T420 infrared thermal camera and analyzed with the ThermoHuman software, which automatically provides objective skin temperature values in 48 regions of the upper body and 36 regions of the lower limbs. In the study, all regions were grouped together into arms, trunk and legs.
The results show a significant difference between the three groups for the Tsk of all the regions analyzed, being the underweight subjects those with the highest skin temperatures and the overweight adolescents the ones with the lowest temperature (figure 1):
Figure 1. Tsk values observed in the regions of interest (ROI) considered according to the BMI groups: underweight (n=33), normal weight (n=34) and overweight/obese (n=33). Extracted from Reis et al. (2022).
In addition, an inverse correlation was observed between the temperature values and the BMI of the subjects. The most significant occurs with emphasis on the anterior and posterior part of the trunk, respectively (r= -0.68; r= -0.64; p<0.05). Figure 2 shows the thermal patterns of the most representative individuals of each group.
Figure 2. Thermograms of four representative study participants (n=100). Note: “A” = underweight (BMI: 16.8 kg/m²). “B” = normal weight (BMI: 21.40 kg/m²), “C” = overweight (BMI: 25.6 kg/m²) and “D” = obesity (BMI: 31.1 kg/m²). Extracted from Reis et al. (2022).
The authors establish some temperature percentiles (P5, P25, P50, P75 and P95) for each population group. This shows that there is a continuum with one extreme of hyporadiant individuals and another of hyperradiant individuals, being under the same BMI condition.
As a suggestion to the authors, it should be noted that BMI is a controversial metric in the scientific literature due to its risk of bias when classifying subjects with a physical condition with great muscle mass (Pribis et al. 2010). This could lead to an error in the interpretation of the data, to a greater extent in the application of the hypo and hyper radiant profiles.
Finally, understanding the influence factors of skin temperature obtained by thermography is crucial for the evaluation and interpretation of thermograms. This information can help support the diagnosis of various changes in the normal pattern of the individual.
Chudecka M, Lubkowska A, Kempińska-Podhorodecka A. Body surface temperature distribution in relation to body composition in obese women. J Therm Biol. 2014 Jul;43:1-6.
Chudecka M, Lubkowska A. Thermal Imaging of Body Surface Temperature Distribution in Women with Anorexia Nervosa. Eur Eat Disord Rev. 2016 Jan;24(1):57-61.
Escamilla-Galindo VL, Del Estal-Martínez A, Fernández-Cuevas I. (2022). Description of the thermal pattern of 950 athletes using thermography to measure skin temperature. 27th Annual Congress of the European College of Sport Sciences ECSS.
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. Infrared Physics & Technology, 71, 28-55.
Marins JCB, Fernandes AA, Cano SP, Moreira DG, da Silva FS, Costa CMA, Fernandez-Cuevas I, Sillero-Quintana M. (2014). Thermal body patterns for healthy Brazilian adults (male and female). Journal of Thermal Biology. 42; 1–8.
Neves EB, Salamunes ACC, de Oliveira RM, Stadnik AMW. Effect of body fat and gender on body temperature distribution. J Therm Biol. 2017 Dec;70(Pt B):1-8.
Pribis P, Burtnack CA, McKenzie SO, Thayer J. Trends in body fat, body mass index and physical fitness among male and female college students. Nutrients. 2010 Oct;2(10):1075-85
Teixeira Reis, H. H., Brito, C. J., Sillero-Quintana, M., Gomes da Silva, A., Fernández-Cuevas, I., Santos Cerqueira, M., . . . Bouzas Marins, J. C. (2023). Can the Body Mass Index influence the skin temperature of adolescents assessed by infrared thermography? Journal of Thermal Biology, 111, 103424. doi:https://doi.org/10.1016/j.jtherbio.2022.103424
Salamunes ACC, Stadnik AMW, Neves EB. The effect of body fat percentage and body fat distribution on skin surface temperature with infrared thermography. J Therm Biol. 2017 May;66:1-9.
Vardasca, R., Ring, E. F. J., Plassmann, P., & Jones, C. D. (2012). Thermal symmetry of the upper and lower extremities in healthy subjects. Thermology international, 22(2), 53-60.
Uematsu, S., Edwin, D. H., Jankel, W. R., Kozikowski, J., & Trattner, M. (1988). Quantification of thermal asymmetry: part 1: normal values and reproducibility. Journal of neurosurgery, 69(4), 552-555.
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ThermoHuman has had the support of the Funds of the European Union and the Community of Madrid through the Operational Programme on Youth Employment. Likewise, ThermoHuman within the framework of the Export Initiation Program of ICEX NEXT, had the support of ICEX and the co-financing of the European Regional Development Fund (ERDF).
Thermohuman has received funding from the Centre for the Development of Industrial Technology (CDTI), in participation with the European Regional Development Fund (ERDF), for the R+D activities involved in creating a new tool, based on thermography, for the prediction and prevention of rheumatoid arthritis. See project detail.
