Chronic metabolic diseases like diabetes, hypertension, obesity, and PCOS are on the rise today. Health-conscious people want to know if they could detect these earlier. Early detection would be easier for the management. Well, here is a hopeful development.
Scientists have found that using facial temperature with the help of trained AI models can help us detect aging and metabolic diseases earlier.
Aging is an important risk factor for various diseases. However, many are unaware that the aging process that better predicts our disease probability is not how old we are, but how old our body cells are.
There are two types of aging, viz., chronological aging and biological aging. Chronological aging is how long we have lived until now. Biological aging refers to how old our body cells are. It is our biological age that determines our health span—the period of life during which we are free of age-related disease. [1]
Finding the biological age using our facial temperature and it’s relation to our metabolic state has been the objective of this study.
Our body temperature is associated with aging. Several species have shown increased lifespans with lower body temperatures. Studies have also shown that calorie restriction, which decreases body temperature, increases lifespan. [2] Interestingly, human body temperature decreases with age. However, it is known to increase in states of high metabolic rate, which are in turn brought on by both psychological and metabolic stress. Examples of this state include conditions like diabetes and cardiovascular diseases.
J. Han and her colleagues have already developed a model that uses a three-dimensional (3D) face image to assess the aging rate of an individual. The human face, which is enriched with blood vessels and fat, is closely correlated with body surface temperature. But using facial temperature for screening age and diseases hasn’t been explored much yet. Realizing this gap, the team designed a model to do it.
They collected the thermal facial images of 2811 Chinese individuals aged 20 to 90 years using a thermal image camera. With this, they developed a ThermoFace model that integrates face recognition with a temperature extraction system by matching infrared and visible face images. This model then creates a thermal-image-based aging clock (TF aging). It estimates the biological age and the difference from the actual age is calculated to determine the corrected thermal age.
They further identified several lifestyle factors that either speed up or slow down this thermal aging. They also confirmed the association of the body’s metabolic processes with facial temperature by studying gene expression. It was found that the energy-consuming heat-releasing processes indeed lead to an increase in face temperature, especially over the mid-face, eyes, and forehead.
Normally, the temperature around facial areas like the nose and cheeks decreases with age, starting at 50 years for women and 60 years for men.
The study analyzed parameters like BMI, blood pressure, blood glucose, and lipids, which are metabolic indicators. They can be abnormal in metabolic diseases. The thermal age difference was higher for people with higher levels of these parameters. With this information, they found that thermal facial patterns were better predictors of metabolic diseases than 3D facial patterns.
They also found that good-quality sleep and regular yogurt intake can lead to healthier facial temperature patterns.
Diseases like hypertension, diabetes, and liver cysts show accelerated thermal aging. It was also found that the reason for this aging pattern was inflammation.
They even found the pattern of temperature change in each area of the face for the diseases:
| Region | Temperature increase | Temperature decrease |
|---|---|---|
| Nose | Aging, hypertension, and smoking (weak association) | |
| Forehead center and chin | Aging | Smoking and other diseases |
| The rest of the forehead, temples, nasolabial folds, and mouth | Aging, hypertension, diabetes, and smoking. | |
| Eyeballs and undereye (The most consistent sign of aging and related changes) | Aging, hypertension, diabetes, fatty liver, and smoking | |
| Cheek | Hypertension, diabetes, fatty liver, and smoking | Aging |
6. Also, they tested if anti-aging measures like exercise could affect this thermal aging. They asked 23 participants to jump rope for at least 10 minutes, or at least 800 times, for two weeks and then rechecked their age. Their thermal age decreased by 5 years after only two weeks of jump training. This further confirms that exercise can treat metabolic diseases.
Aging is a natural process. But our tool has the potential to promote healthy aging and help people live disease-free.Dong Jackie Han, Peking University, Beijing, China
The team wants to extend this study to different ethnic populations. Also, they want to study other diseases, like sleeping disorders. The author, Han, hopes that this technique could be made applicable for clinical use.
The ThermoFace model can be used to analyze aging using facial temperatures. This aging difference can be used to predict metabolic diseases like diabetes and hypertension earlier. This model requires further development so that it can be implemented in clinical practice. This model has a high potential for earlier detection of metabolic diseases.
References:
https://www.nm.org/healthbeat/medical-advances/science-and-research/What-is-Your-Actual-Age#:~:text=Chronological%20age%20is%20how%20long,of%20any%20aging%2Drelated%20disease. (Cited on 17/07/2024)
Ravussin, Eric, Leanne M. Redman, James Rochon, Sai Krupa Das, Luigi Fontana, William E. Kraus, Sergei Romashkan et al. "A 2-year randomized controlled trial of human caloric restriction: feasibility and effects on predictors of health span and longevity." Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences 70, no. 9 (2015): 1097-1104. (Cited on 17/07/2024)
Cell Press. "Doctors could soon use facial temperature for early diagnosis of metabolic diseases." ScienceDaily. www.sciencedaily.com/releases/2024/07/240702135411.htm (accessed July 16, 2024). (accessed July 16, 2024). (Cited on 17/07/2024)
Yu, Zhengqing, Zhengqing Yu, Zhengqing Yu, Zhengqing Yu, Zhengqing Yu, Zhengqing Yu, Zhengqing Yu, et al. “Thermal facial image analyses reveal quantitative hallmarks of aging and metabolic diseases.” Cell Metabolism 36, no. 7 (July 1, 2024): 1482-1493.e7. https://doi.org/10.1016/j.cmet.2024.05.012. (The main article) (Cited on 17/07/2024)