With the increasing frequency of human heat exposure events driven by global climate warming, this study employed the health ecological model to systematically investigate the influencing factors of heat tolerance and their interaction pathways and mechanisms across multiple dimensions including personal traits, behavioral characteristics, environmental adaptation behaviors, and interpersonal networks, aiming to address the risk of heat injury in high-temperature environments.

A cross-sectional survey was conducted on 2596 university faculty and students in Chongqing from August 21 to September 13, 2024, via the Wenjuanxing and WeChat platforms. Based on the survey data, heat tolerance was classified into 3 levels (low, moderate, strong). Chi-square test, multinomial logistic regression, and structural equation modeling were applied to analyze group differences, independent effects, and path relationships of demographic characteristics and factors at each level of the health ecological model on heat tolerance.

Heat tolerance exhibited group differences with sociodemographic characteristics (sex, age, heatwave experience), personal traits, behavioral characteristics, environmental adaptation behaviors (shower frequency, wearing hats outdoors), and interpersonal networks (weather monitoring, timely seeking help for heatstroke) (P<0. 05). Specifically, females demonstrated significantly lower probabilities of achieving moderate heat tolerance (OR=0. 496, 95%CI: 0. 390 to 0. 631) and strong heat tolerance (OR=0. 250, 95%CI: 0. 178 to 0. 349) compared to males (P<0. 001). The participants aged 16 to 18 years (OR=3. 778, 95%CI: 2. 028 to 7. 039) and 19 to 21 years (OR=1. 913, 95%CI: 1. 021 to 3. 586) were more likely to attain strong heat tolerance than those aged ≥26 years (P<0. 05), and those without heatwave experience showed significantly reduced probability of achieving strong heat tolerance (OR=0. 475, 95%CI: 0. 257 to 0. 878, P<0. 05). Structural equation modeling further revealed the multipath interactions among these factors. The male advantage in heat tolerance was manifested not only through direct physiological effects (standardized path coefficient=0. 261) but also indirectly via enhancement of personal traits (standardized path coefficient=0. 185). Although increasing age exerted a direct negative effect on heat tolerance (standardized path coefficient=-0. 067), its promotion of personal traits (standardized path coefficient=0. 038) indirectly offset this adverse effect. Personal traits emerged as the core determinant of heat tolerance, with a standardized path coefficient as high as 0. 696, substantially exceeding all other factors, and serving as a critical mediating variable regulating the overall effects of sex, age, and behavioral characteristics on heat tolerance. Furthermore, although behavioral characteristics showed no significant direct effect on heat tolerance (P=0. 871), they generated significant indirect effects through strengthening personal traits (standardized path coefficient=0. 304), whereas environmental adaptation behaviors demonstrated a negative impact (standardized path coefficient=-0. 143), suggesting that over-reliance on environmental adaptation may suppress the expression of heat tolerance.

Heat tolerance is influenced by multiple integrated factors, among which personal traits constitute the core determinant exerting significant direct and indirect effects on heat tolerance. Enhancing personal traits represents the key strategy for improving heat tolerance and reducing the risk of heat injury.

To investigate the differences in plasma metabolites between patients with altitude-related hypertension (ARH) and healthy individuals, and analyze the potential pathogenesis of ARH.

Convenient sampling was conducted on a unit of male healthy officers and soldiers who resident at altitude of < 500 m and migrated to an altitude of 4 200 m in July 2020. Twenty of them diagnosed with ARH were assigned into the ARH group, and another 30 non-ARH individuals served as the control group. Their blood pressure, body mass index (BMI), blood oxygen saturation, and heart rate were measured and recorded, and fasting venous blood samples were harvested to screen and identify plasma metabolites with ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Metabolite fingerprinting was performed using unsupervised Principal Component Analysis (PCA) and supervised Orthogonal Partial Least Squares Discrimination Analysis (OPLS-DA) models in order to assist in biomarker screening. The quality of the OPLS-DA model was assessed and validated to guarantee the stability and reliability of the model. Differential plasma metabolites were screened using independent sample t test and fold change (FC) analysis, and volcano plots were drawn. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis was used to perform functional pathway enrichment and topological analysis on the screened differential metabolites.

Compared to the control group, the ARH group showed significantly higher systolic and diastolic blood pressure and heart rate, and lower arterial oxygen saturation (P < 0.05). PCA analysis showed that 81.96% of the variance was explained in the positive ion mode and 79.25% in the negative ion mode, indicating significant metabolic differences between the 2 groups. OPLS-DA model analysis indicated that in the positive ion mode, PC1 explained 77.36% of the variance and PC2 explained 12.25% of the variance, with R²Y=0.96 and Q²Y=0.91; in the negative ion mode, PC1 explained 84.15% of the variance and PC2 explained 17.24% of the variance, with R²Y=0.99 and Q²Y=0.86. Inter-group difference exceeded 75%, and intra-group difference was less than 20%. The 7-fold cross-validation and 200 permutation test confirmed that the model was stable and reliable. In the positive ion mode, the Y-axis intercepts of the R² and Q² fitted lines were 0.58 and -0.48, respectively; in the negative ion mode, the Y-axis intercepts were 0.93 and -0.41, respectively. A total of 32 significantly different metabolites were screened out, including amino acids, nucleosides, fatty acids, and organic alkaloids. KEGG analysis revealed that among the 10 metabolic pathways, 4 were amino acid metabolic pathways, with the aminoacyl-tRNA biosynthesis pathway having the most enriched metabolites.

Based on UHPLC-MS/MS technology, untargeted metabolomics analysis identifies 32 significantly different metabolites, which may serve as characteristic biomarkers for ARH, and the aminoacyl-tRNA biosynthesis pathway may be associated with the pathogenesis of ARH.

To investigate the effect of lifestyle on high-altitude de-adaptation (HADA) through questionnaire during the whole process of entering and existing the plateau in order to provide scientific basis for prevention and treatment of the disease.

A case-control trial was conducted on 1 751 participants from a certain unit who entered and existed the plateau together during 2021 and 2022. In 1 to 2 weeks after they returning from the plateau, they were surveyed, and finally, 1 544 valid questionnaires were obtained. According to the score of the plateau deacclimation scale ≥6 or not, the subjects were divided into plateau deacclimation group (n=192) and control group (n=1 352). They were further surveyed for their lifestyles and general conditions. Rank sum test was used to analyze the differences of lifestyles between the 2 groups, and unconditional logistic regression analysis was employed to identify the independent risk factors of HADA.

Hair loss (19.95%), drowsiness (16.58%) and tiredness (12.31%) were the most common symptoms of HADA. High salt diet before entering the plateau, smoking at the plateau, altitude sickness (OR=1.893, 95%CI: 1.142~3.137, P=0.013), leaving the plateau by plane (OR=1.688, 95%CI: 1.082~2.634, P=0.021), and drinking much tea, excessive exercise intensity and insufficient sleep after leaving the plateau were independent risk factors for HADA.

Low salt diet before entering the plateau, active prevention for altitude sickness, smoking cessation at the plateau, taking a slower means of transportation to enter and leave the plateau, drinking less tea, moderate exercise intensity and keeping enough sleep after leaving the plateau can effectively reduce the risk of HADA.