Evaluation of thermal sensitivity is of potential clinical utility for the predictive, preventive, and personalized approach advancing metabolic syndrome management

Resting energy expenditure differs among individuals with different levels of perceived thermal sensitivity: A cross-sectional study

Metabolic rate has been used in thermophysiological models for predicting the thermal response of humans. However, only a few studies have investigated the association between an individual's trait-like thermal sensitivity and resting energy expenditure (REE), which resulted in inconsistent results. This study aimed to explore the association between REE and perceived thermal sensitivity. The REE of healthy adults was measured using an indirect calorimeter, and perceived thermal intolerance and sensation in the body were evaluated using a self-administered questionnaire. In total, 1567 individuals were included in the analysis (women = 68.9%, age = 41.1 ± 13.2 years, body mass index = 23.3 ± 3.3 kg/m2, REE = 1532.1 ± 362.4 kcal/d). More women had high cold intolerance (31.8%) than men (12.7%), and more men had high heat intolerance (23.6%) than women (16.1%). In contrast, more women experienced both cold (53.8%) and heat (40.6%) sensations in the body than men (cold, 29.1%; heat, 27.9%). After adjusting for age, fat-free mass, and fat mass, lower cold intolerance, higher heat intolerance, and heat sensation were associated with increased REE only in men (cold intolerance, P for trend = .001; heat intolerance, P for trend = .037; heat sensation, P = .046), whereas cold sensation was associated with decreased REE only in women (P = .023). These findings suggest a link between the perceived thermal sensitivity and REE levels in healthy individuals.

Mitochondrial Dysfunction Was Involved in Decabromodiphenyl Ethane-Induced Glucolipid Metabolism Disorders and Neurotoxicity in Zebrafish Larvae

Decabromodiphenyl ethane (DBDPE), a novel brominated flame retardant, is becoming increasingly prevalent in environmental and biota samples. While DBDPE has been shown to cause various biological adverse effects, the molecular mechanism behind these effects is still unclear. In this research, zebrafish embryos were exposed to DBDPE (50-400 μg/L) until 120 h post fertilization (hpf). The results confirmed the neurotoxicity by increased average swimming speed, interfered neurotransmitter contents, and transcription of neurodevelopment-related genes in zebrafish larvae. Metabolomics analysis revealed changes of metabolites primarily involved in glycolipid metabolism, oxidative phosphorylation, and oxidative stress, which were validated through the alterations of multiple biomarkers at various levels. We further evaluated the mitochondrial performance upon DBDPE exposure and found inhibited mitochondrial oxidative respiration accompanied by decreased mitochondrial respiratory chain complex activities, mitochondrial membrane potential, and ATP contents. However, addition of nicotinamide riboside could effectively restore DBDPE-induced mitochondrial impairments and resultant neurotoxicity, oxidative stress as well as glycolipid metabolism in zebrafish larvae. Taken together, our data suggest that mitochondrial dysfunction was involved in DBDPE-induced toxicity, providing novel insight into the toxic mechanisms of DBDPE as well as other emerging pollutants.

Oculomics for sarcopenia prediction: a machine learning approach toward predictive, preventive, and personalized medicine

Aims

Sarcopenia is characterized by a gradual loss of skeletal muscle mass and strength with increased adverse outcomes. Recently, large-scale epidemiological studies have demonstrated a relationship between several chronic disorders and ocular pathological conditions using an oculomics approach. We hypothesized that sarcopenia can be predicted through eye examinations, without invasive tests or radiologic evaluations in the context of predictive, preventive, and personalized medicine (PPPM/3PM).

Methods

We analyzed data from the Korean National Health and Nutrition Examination Survey (KNHANES). The training set (80%, randomly selected from 2008 to 2010) data were used to construct the machine learning models. Internal (20%, randomly selected from 2008 to 2010) and external (from the KNHANES 2011) validation sets were used to assess the ability to predict sarcopenia. We included 8092 participants in the final dataset. Machine learning models (XGBoost) were trained on ophthalmological examinations and demographic factors to detect sarcopenia.

Results

In the exploratory analysis, decreased levator function (odds ratio [OR], 1.41; P value <0.001), cataracts (OR, 1.31; P value = 0.013), and age-related macular degeneration (OR, 1.38; P value = 0.026) were associated with an increased risk of sarcopenia in men. In women, an increased risk of sarcopenia was associated with blepharoptosis (OR, 1.23; P value = 0.038) and cataracts (OR, 1.29; P value = 0.010). The XGBoost technique showed areas under the receiver operating characteristic curves (AUCs) of 0.746 and 0.762 in men and women, respectively. The external validation achieved AUCs of 0.751 and 0.785 for men and women, respectively. For practical and fast hands-on experience with the predictive model for practitioners who may be willing to test the whole idea of sarcopenia prediction based on oculomics data, we developed a simple web-based calculator application (https://knhanesoculomics.github.io/sarcopenia) to predict the risk of sarcopenia and facilitate screening, based on the model established in this study.

Conclusion

Sarcopenia is treatable before the vicious cycle of sarcopenia-related deterioration begins. Therefore, early identification of individuals at a high risk of sarcopenia is essential in the context of PPPM. Our oculomics-based approach provides an effective strategy for sarcopenia prediction. The proposed method shows promise in significantly increasing the number of patients diagnosed with sarcopenia, potentially facilitating earlier intervention. Through patient oculometric monitoring, various pathological factors related to sarcopenia can be simultaneously analyzed, and doctors can provide personalized medical services according to each cause. Further studies are needed to confirm whether such a prediction algorithm can be used in real-world clinical settings to improve the diagnosis of sarcopenia.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00292-3.