Sleep dysfunction mediates the relationship between hypothalamic-insula connectivity and anxiety-depression symptom severity bidirectionally in young adults.

BACKGROUND: The hypothalamus plays a crucial role in regulating sleep-wake cycle and motivated behavior. Sleep disturbance is associated with impairment in cognitive and affective functions. However, how hypothalamic dysfunction may contribute to inter-related sleep, cognitive, and emotional deficits remain unclear. METHODS: We curated the Human Connectome Project dataset and investigated how hypothalamic resting state functional connectivities (rsFC) were associated with sleep dysfunction, as evaluated by the Pittsburgh Sleep Quality Index (PSQI), cognitive performance, and subjective mood states in 687 young adults (342 women). Imaging data were processed with published routines and evaluated with a corrected threshold. We examined the inter-relationship amongst hypothalamic rsFC, PSQI score, and clinical measures with mediation analyses. RESULTS: In whole-brain regressions with age and drinking severity as covariates, men showed higher hypothalamic rsFC with the right insula in correlation with PSQI score. No clusters were identified in women at the same threshold. Both hypothalamic-insula rsFC and PSQI score were significantly correlated with anxiety and depression scores in men. Further, mediation analyses showed that PSQI score mediated the relationship between hypothalamic-insula rsFC and anxiety/depression symptom severity bidirectionally in men. CONCLUSIONS: Sleep dysfunction is associated with negative emotions and hypothalamic rsFC with the right insula, a core structure of the interoceptive circuits. Notably, anxiety-depression symptom severity and altered hypothalamic-insula rsFC are related bidirectionally by poor sleep quality. These findings are specific to men, suggesting potential sex differences in the neural circuits regulating sleep and emotional states that need to be further investigated.

Non-Contact Monitoring of Fetal Movement Using Abdominal Video Recording.

Fetal movement (FM) is an important indicator of fetal health. However, the current methods of FM detection are unsuitable for ambulatory or long-term observation. This paper proposes a non-contact method for monitoring FM. We recorded abdominal videos from pregnant women and then detected the maternal abdominal region within each frame. FM signals were acquired by optical flow color-coding, ensemble empirical mode decomposition, energy ratio, and correlation analysis. FM spikes, indicating the occurrence of FMs, were recognized using the differential threshold method. FM parameters including number, interval, duration, and percentage were calculated, and good agreement was found with the manual labeling performed by the professionals, achieving true detection rate, positive predictive value, sensitivity, accuracy, and F1_score of 95.75%, 95.26%, 95.75%, 91.40%, and 95.50%, respectively. The changes in FM parameters with gestational week were consistent with pregnancy progress. In general, this study provides a novel contactless FM monitoring technology for use at home.

Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors.

Electrohysterogram (EHG) is a promising method for noninvasive monitoring of uterine electrical activity. The main purpose of this study was to characterize the multichannel EHG signals to distinguish between term delivery and preterm birth, as well as deliveries within and beyond 24 h. A total of 219 pregnant women were grouped in two ways: (1) term delivery (TD), threatened preterm labor (TPL) with the outcome of preterm birth (TPL_PB), and TPL with the outcome of term delivery (TPL_TD); (2) EHG recording time to delivery (TTD) ≤ 24 h and TTD > 24 h. Three bipolar EHG signals were analyzed for the 30 min recording. Six EHG features between multiple channels, including multivariate sample entropy, mutual information, correlation coefficient, coherence, direct partial Granger causality, and direct transfer entropy, were extracted to characterize the coupling and information flow between channels. Significant differences were found for these six features between TPL and TD, and between TTD ≤ 24 h and TTD > 24 h. No significant difference was found between TPL_PB and TPL_TD. The results indicated that EHG signals of TD were more regular and synchronized than TPL, and stronger coupling between multichannel EHG signals was exhibited as delivery approaches. In addition, EHG signals propagate downward for the majority of pregnant women regardless of different labors. In conclusion, the coupling and propagation features extracted from multichannel EHG signals could be used to differentiate term delivery and preterm birth and may predict delivery within and beyond 24 h.

Evaluation of VDT-Induced Visual Fatigue by Automatic Detection of Blink Features.

This study evaluates the progression of visual fatigue induced by visual display terminal (VDT) using automatically detected blink features. A total of 23 subjects were recruited to participate in a VDT task, during which they were required to watch a 120-min video on a laptop and answer a questionnaire every 30 min. Face video recordings were captured by a camera. The blinking and incomplete blinking images were recognized by automatic detection of the parameters of the eyes. Then, the blink features were extracted including blink number (BN), mean blink interval (Mean_BI), mean blink duration (Mean_BD), group blink number (GBN), mean group blink interval (Mean_GBI), incomplete blink number (IBN), and mean incomplete blink interval (Mean_IBI). The results showed that BN and GBN increased significantly, and that Mean_BI and Mean_GBI decreased significantly over time. Mean_BD and Mean_IBI increased and IBN decreased significantly only in the last 30 min. The blink features automatically detected in this study can be used to evaluate the progression of visual fatigue.

Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data.

Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give rise to relatively low performance. Numerous studies obtained promising preterm labor prediction results using the synthetic minority oversampling technique. However, these studies generally overestimate mathematical models' real generalization capacity by generating synthetic data before splitting the dataset, leaking information between the training and testing partitions and thus reducing the complexity of the classification task. In this work, we analyzed the effect of combining feature selection and resampling methods to overcome the class imbalance problem for predicting preterm labor by EHG. We assessed undersampling, oversampling, and hybrid methods applied to the training and validation dataset during feature selection by genetic algorithm, and analyzed the resampling effect on training data after obtaining the optimized feature subset. The best strategy consisted of undersampling the majority class of the validation dataset to 1:1 during feature selection, without subsequent resampling of the training data, achieving an AUC of 94.5 ± 4.6%, average precision of 84.5 ± 11.7%, maximum F1-score of 79.6 ± 13.8%, and recall of 89.8 ± 12.1%. Our results outperformed the techniques currently used in clinical practice, suggesting the EHG could be used to predict preterm labor in clinics.

Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals.

One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.

[Internal quality control products for computer-assisted sperm analysis: Research and application].

OBJECTIVE: To investigate the application of the self-made semen quality control (QC) product in internal QC of computer-assisted sperm analysis (CASA). METHODS: CASA was calibrated with high- and low-concentration commercially available semen QC product and meanwhile 15 samples of self-made mixed semen QC product were placed in 75 cryotubes containing liquid nitrogen, followed by CASA of the concentration, motility, curvilinear velocity (VCL), straight line velocity (VSL), average path velocity (VAP), linearity (LIN), wobble (WOB) and straightness (STR) of the sperm using standard procedures and 50 days of continuous monitoring. The Makler counting plate was used to measure the concentration and motility of the self-made sperm. RESULTS: The coefficients of variation (CV) of the commercially available semen QC product at high and low concentrations were 6.18% and 7.85%, respectively. CASA showed that the concentration of the self-made QC product was (25.97 ± 1.41) ×106/ml, with a CV of 5.42%, and the sperm motility, VCL, VSL, VAP, LIN, WOB and STR were (22.15 ± 1.75)% (CV = 7.9%), (59.18 ± 2.05) µm/s (CV = 3.46%), (26.79 ± 1.2) µm/s (CV = 4.48%), (34.98 ± 1.4) µm/s (CV = 4.01%), 46.81 ± 1.55 (CV = 3.3%), 60.52 ± 1.3 (CV = 2.15%) and 76.46 ± 1.98 (CV = 2.59%), respectively. The concentration and motility of the self-made sperm detected with the Makler counting plate were (34.39 ± 2.37) ×106/ml (CV = 6.89%) and (38.04 ± 1.69)% (CV = 4.44%), respectively. Levey-Jennings QC charts were plotted for the indicators using the means and standard deviation. CONCLUSIONS: The self-made internal QC product by liquid nitrogen cryopreservation is feasible and effective for monitoring the accuracy and precision of CASA-derived sperm concentration and motion parameters, and it has a smaller CV than the commercially available QC product in measuring sperm concentration.

[Recognition of water-injected meat based on visible/near-infrared spectrum and sparse representation].

The present paper proposed a new nondestructive method based on visible/near infrared spectrum (Vis/NIRS) and sparse representation to rapidly and accurately discriminate between raw meat and water-injected meat. Water-injected meat model was built by injecting water into non-destructed meat samples comprising pigskin, fat layer and muscle layer. Vis/NIRS data were collected from raw meat and six scales of water-injected meat with spectrometers. To reduce the redundant information in the spectrum and improve the difference between the samples,. some preprocessing steps were performed for the spectral data, including light modulation and normalization. Effective spectral bands were extracted from the preprocessed spectral data. The meat samples were classified as raw meat and water-injected meat, and further, water-injected meat with different water injection rates. All the training samples were used to compose an atom dictionary, and test samples were represented by the sparsest linear combinations of these atoms via l1-minimization. Projection errors of test samples with respect to each category were calculated. A test sample was classified to the category with the minimum projection error, and leave-one-out cross-validation was conducted. The recognition performance from sparse representation was compared with that from support vector machine (SVM).. Experimental results showed that the overall recognition accuracy of sparse representation for raw meat and water-injected meat was more than 90%, which was higher than that of SVM. For water-injected meat samples with different water injection rates, the recognition accuracy presented a positive correlation with the water injection rate difference. Spare representation-based classifier eliminates the need for the training and feature extraction steps required by conventional pattern recognition models, and is suitable for processing data of high dimensionality and small sample size. Furthermore, it has a low computational cost. In this paper, spare representation is employed for the first time to identify water-injected meat based on Vis/NIRS, with a promising recognition accuracy. The experimental results demonstrate that the proposed method can be effectively used for discriminating water-injected meat from raw meat.

Applying support vector regression analysis on grip force level-related corticomuscular coherence.

Voluntary motor performance is the result of cortical commands driving muscle actions. Corticomuscular coherence can be used to examine the functional coupling or communication between human brain and muscles. To investigate the effects of grip force level on corticomuscular coherence in an accessory muscle, this study proposed an expanded support vector regression (ESVR) algorithm to quantify the coherence between electroencephalogram (EEG) from sensorimotor cortex and surface electromyogram (EMG) from brachioradialis in upper limb. A measure called coherence proportion was introduced to compare the corticomuscular coherence in the alpha (7-15Hz), beta (15-30Hz) and gamma (30-45Hz) band at 25 % maximum grip force (MGF) and 75 % MGF. Results show that ESVR could reduce the influence of deflected signals and summarize the overall behavior of multiple coherence curves. Coherence proportion is more sensitive to grip force level than coherence area. The significantly higher corticomuscular coherence occurred in the alpha (p < 0.01) and beta band (p < 0.01) during 75 % MGF, but in the gamma band (p < 0.01) during 25 % MGF. The results suggest that sensorimotor cortex might control the activity of an accessory muscle for hand grip with increased grip intensity by changing functional corticomuscular coupling at certain frequency bands (alpha, beta and gamma bands).

Functional Networks of Reward and Punishment Processing and Their Molecular Profiles Predicting the Severity of Young Adult Drinking.

Alcohol misuse is associated with altered punishment and reward processing. Here, we investigated neural network responses to reward and punishment and the molecular profiles of the connectivity features predicting alcohol use severity in young adults. We curated the Human Connectome Project data and employed connectome-based predictive modeling (CPM) to examine how functional connectivity (FC) features during wins and losses are associated with alcohol use severity, quantified by Semi-Structured Assessment for the Genetics of Alcoholism, in 981 young adults. We combined the CPM findings and the JuSpace toolbox to characterize the molecular profiles of the network connectivity features of alcohol use severity. The connectomics predicting alcohol use severity appeared specific, comprising less than 0.12% of all features, including medial frontal, motor/sensory, and cerebellum/brainstem networks during punishment processing and medial frontal, fronto-parietal, and motor/sensory networks during reward processing. Spatial correlation analyses showed that these networks were associated predominantly with serotonergic and GABAa signaling. To conclude, a distinct pattern of network connectivity predicted alcohol use severity in young adult drinkers. These "neural fingerprints" elucidate how alcohol misuse impacts the brain and provide evidence of new targets for future intervention.

Effects of long-term electromagnetic field exposure on spatial learning and memory in rats.

With the development of communications industry, mobile phone plays an important role in daily life. Whether or not the electromagnetic radiation emitted by mobile phone causes any adverse effects on brain function has become of a great concern. This paper investigated the effect of electromagnetic field on spatial learning and memory in rats. 32 trained Wistar rats were divided into two groups: exposure group and control group. The exposure group was exposed to 916 MHz, 10w/m2 mobile phone electromagnetic field (EMF) 6 h a day, 5 days a week, 10 weeks. The completion time, number of total errors and the neuron discharge signals were recorded while the rats were searching for food in an eight-arm radial maze at every weekend. The neuron signals of one exposed rat and one control rat in the maze were obtained by the implanted microelectrode arrays in their hippocampal regions. It can be seen that during the weeks 4-5 of the experiment, the average completion time and error rate of the exposure group were longer and larger than that of control group (p < 0.05). During the weeks 1-3 and 6-9, they were close to each other. The hippocampal neurons showed irregular firing patterns and more spikes with shorter interspike interval during the whole experiment period. It indicates that the 916 MHz EMF influence learning and memory in rats to some extent in a period during exposure, and the rats can adapt to long-term EMF exposure.

[Research progress of techniques of 7 T MRI system in brain imaging].

7 T high field magnetic resonance imaging (MRI) provides a useful tool for microscopic spatial resolution visualizing anatomy. In addition, it enables the observation and analysis of tissue metabolism and function. 7 T MRI is now developing fast both in its technology and in its potential prospective medical applications. This review introduces current applications and possible future developments of the 7 T MRI in the field of human brain imaging for clinical studies and practices.

Sex differences in externalizing and internalizing traits and ventral striatal responses to monetary loss.

Ventral striatum (VS) processes rewarding and punishing stimuli. Women and men vary in externalizing and internalizing traits, which may influence neural responses to reward and punishment. To investigate sex differences in how individual traits influence VS responses to reward and punishment, we curated the data of the Human Connectome Project and identified 981 (473 men) subjects evaluated by the Achenbach Adult Self-Report Syndrome Scales. We processed the imaging data with published routines and extracted VS response (ß) to win and to loss vs. baseline in a gambling task for correlation with externalizing and internalizing symptom severity. Men vs. women showed more severe externalizing symptoms and higher VS response to monetary losses (VS-loss ß) but not to wins. Men but not women showed a significant, positive correlation between VS-loss ß and externalizing traits, and the sex difference was confirmed by a slope test. The correlations of VS-loss vs. externalizing and of VS-win vs. externalizing and those of VS-loss vs. externalizing and of VS-loss vs. internalizing traits both differed significantly in slope, confirming its specificity, in men. Further, the sex-specific relationship between VS-loss ß and externalizing trait did not extend to activities during exposure to negative emotion in the face matching task. To conclude, VS responses to loss but not to win and their correlation with externalizing rather than internalizing symptom severity showed sex differences in young adults. The findings highlight the relationship of externalizing traits and VS response to monetary loss and may have implications for psychological models of externalizing behaviors in men.

A novel four-dimensional prediction model of soil heavy metal pollution: Geographical explanations beyond artificial intelligence "black box".

The current artificial intelligence (AI)-based prediction approaches of soil pollutants are inadequate in estimating the geospatial source-sink processes and striking a balance between the interpretability and accuracy, resulting in poor spatial extrapolation and generalization. In this study, we developed and tested a geographically interpretable four-dimensional AI prediction model for soil heavy metal (Cd) contents (4DGISHM) in Shaoguan city of China from 2016 to 2030. The 4DGISHM approach characterized spatio-temporal changes in source-sink processes of soil Cd by estimating spatio-temporal patterns and the effects of drivers and their interactions of soil Cd at local to regional scales using TreeExplainer-based SHAP and parallel ensemble AI algorithms. The results demonstrate that the prediction model achieved MSE and R2 values of 0.012 and 0.938, respectively, at a spatial resolution of 1 km. The predicted areas exceeding the risk control values for soil Cd across Shaoguan from 2022 to 2030 increased by 22.92% at the baseline scenario. By 2030, enterprise and transportation emissions (SHAP values 0.23 and 0.12 mg/kg, respectively) were the major drivers. The influence of driver interactions on soil Cd was marginal. Our approach surpasses the limitations of the AI "black box" by integrating spatio-temporal source-sink explanation and accuracy. This advancement enables geographically precise prediction and control of soil pollutants.

Cellular neoplastic transformation induced by 916 MHz microwave radiation.

There has been growing concern about the possibility of adverse health effects resulting from exposure to microwave radiations, such as those emitted by mobile phones. The purpose of this study was to investigate the cellular neoplastic transformation effects of electromagnetic fields. 916 MHz continuous microwave was employed in our study to simulate the electromagnetic radiation of mobile phone. NIH/3T3 cells were adopted in our experiment due to their sensitivity to carcinogen or cancer promoter in environment. They were divided randomly into one control group and three microwave groups. The three microwave groups were exposed to 916 MHz EMF for 2 h per day with power density of 10, 50, and 90 w/m(2), respectively, in which 10 w/m(2) was close to intensity near the antenna of mobile phone. The morphology and proliferation of NIH/3T3 cells were examined and furthermore soft agar culture and animal carcinogenesis assay were carried out to determine the neoplastic promotion. Our experiments showed NIH/3T3 cells changed in morphology and proliferation after 5-8 weeks exposure and formed clone in soft agar culture after another 3-4 weeks depending on the exposure intensity. In the animal carcinogenesis study, lumps developed on the back of SCID mice after being inoculated into exposed NIH/3T3 cells for more than 4 weeks. The results indicate that microwave radiation can promote neoplastic transformation of NIH/3T3cells.

Analysis of 2,297 expressed sequence tags (ESTs) from a cDNA library of flax (Linum ustitatissimum L.) bark tissue.

Bast fibre crops are the second most important natural fibre crops following cotton. Of these, flax (Linum ustitatissimum L.) is the most widely planted in the world, with its fibre used for high quality linen textile. A cDNA library of flax bark tissues was constructed with the purpose of identifying genes involved in the Bast fibre development. A total of 2,297 unigene sequences were obtained from 3,200 randomly selected clones of the cDNA library. These sequences were grouped into 155 clusters and 2,142 singletons, which have been submitted to the GenBank databases. By putative functional annotation, 23.3% of these sequences were similar to known proteins in GenBank, 44.0% of these sequences were similar to unknown proteins, and 32.7% of these sequences showed no significant similarity to any other protein sequences in existing databases. Classified by the Gene Ontology, 24.8, 23.1 and 14.3% were assigned to molecular function, biological process, and cellular component GO terms, respectively. By further bioinformatics approaches, about 110 ESTs matched cell wall related genes in the MAIZEWALL database, representing 16 functional categories of all 19 categories, of which, the most abundant category was protein synthesis. Based on the PlantTFDB database, 39 of the 64 transcription factor families in the Arabidopsis thaliana genome were identified as being involved in flax cell wall formation. The sequences and bioinformatics analysis data generated in this paper will be useful for gene expression, cloning and genetic engineering studies to characterize bast fibre development and improve the properties of the bast fibres.

Regional identification of information flow termination of electrohysterographic signals: Towards understanding human uterine electrical propagation.

BACKGROUND AND OBJECTIVE: The uterine electrohysterogram (EHG) contains important information about electrical signal propagation which may be useful to monitor and predict the progress of pregnancy towards parturition. Directed information processing has the potential to be of use in studying EHG recordings. However, so far, there is no directed information-based estimation scheme that has been applied to investigating the propagation of human EHG recordings. To realize this, the approach of directed information and its reliability and adaptability should be scientifically studied. METHODS: We demonstrated an estimation scheme of directed information to identify the spatiotemporal relationship between the recording channels of EHG signal and assess the algorithm reliability initially using simulated data. Further, a regional identification of information flow termination (RIIFT) approach was developed and applied for the first time to extant multichannel EHG signals to reveal the terminal zone of propagation of the electrical activity associated with uterine contraction. RIIFT operates by estimating the pairwise directed information between neighboring EHG channels and identifying the location where there is the strongest inward flow of information. The method was then applied to publicly-available experimental data obtained from pregnant women with the use of electrodes arranged in a 4-by-4 grid. RESULTS: Our results are consistent with the suggestions from the previous studies with the added identification of preferential sites of excitation termination - within the estimated area, the direction of surface action potential propagation towards the medial axis of uterus during contraction was discovered for 72.15% of the total cases, demonstrating that our RIIFT method is a potential tool to investigate EHG propagation for advancing our understanding human uterine excitability. CONCLUSIONS: We developed a new approach and applied it to multichannel human EHG recordings to investigate the electrical signal propagation involved in uterine contraction. This provides an important platform for future studies to fill knowledge gaps in the spatiotemporal patterns of electrical excitation of the human uterus.

Uterus Modeling From Cell to Organ Level: Towards Better Understanding of Physiological Basis of Uterine Activity.

The relatively limited understanding of the physiology of uterine activation prevents us from achieving optimal clinical outcomes for managing serious pregnancy disorders such as preterm birth or uterine dystocia. There is increasing awareness that multi-scale computational modeling of the uterus is a promising approach for providing a qualitative and quantitative description of uterine physiology. The overarching objective of such approach is to coalesce previously fragmentary information into a predictive and testable model of uterine activity that, in turn, informs the development of new diagnostic and therapeutic approaches to these pressing clinical problems. This article assesses current progress towards this goal. We summarize the electrophysiological basis of uterine activation as presently understood and review recent research approaches to uterine modeling at different scales from single cell to tissue, whole organ and organism with particular focus on transformative data in the last decade. We describe the positives and limitations of these approaches, thereby identifying key gaps in our knowledge on which to focus, in parallel, future computational and biological research efforts.

Recognition of uterine contractions with electrohysterogram and exploring the best electrode combination.

BACKGROUND: As an essential indicator of labour and delivery, uterine contraction (UC) can be detected by manual palpation, external tocodynamometry and internal uterine pressure catheter. However, these methods are not applicable for long-term monitoring. OBJECTIVE: This paper aims to recognize UCs with electrohysterogram (EHG) and find the best electrode combination with fewer electrodes. METHODS: 112 EHG recordings were collected by our bespoke device in our study. Thirteen features were extracted from EHG segments of UC and non-UC. Four classifiers of the decision tree, support vector machine (SVM), artificial neural network, and convolutional neural network were established to identify UCs. The optimal classifier among them was determined by comparing their classification results. The optimal classifier was applied to evaluate all the possible electrode combinations with one to eight electrodes. RESULTS: The results showed that SVM achieved the best classification capability. With SVM, the combination of electrodes on the right part of the uterine fundus and around the uterine body's median axis achieved the overall best performance. CONCLUSIONS: The optimal electrode combination with fewer electrodes was confirmed to improve the clinical application for long-term monitoring of UCs.

Risk prevention of different forms of gestational diabetes mellitus based on energy metabolism prior to diagnosis.

BACKGROUND: Gestational diabetes mellitus (GDM) is a metabolic disease that seriously endangers the health of mothers and children. It is important to monitor GDM in real-time before diagnosis and to prevent it effectively. OBJECTIVE: GDM was divided into the second trimester diagnosed diabetes mellitus (GDM_24) and the third trimester diagnosed diabetes mellitus (GDM_30). The risk prediction of two types of GDM was performed in normal pregnant women at 11-13 and 16-19 weeks of pregnancy, respectively. METHODS: By stages, the K-W test was used to analyze the differences between basic information and energy metabolism factors, and multiple logistic regression was used to analyze the risk of energy metabolism factors and to correct the confounders with significant differences. RESULTS: For the GDM_24 group, each additional unit of oxygen consumption (VO2), carbon dioxide production, and resting energy expenditure (REE) increased the risk by 2.4%, 3.5%, 0.4%, and 2.1%, 2.6%, and 0.3%, respectively, at 11-13 and 16-19 weeks of pregnancy. For the GDM_30 group, each additional unit of VO2 and REE was associated with an increased risk of 2.3% and 0.3%, respectively, at 16-19 weeks of pregnancy. CONCLUSION: The risk of GDM_30 only appeared in pregnant women during 16-19 weeks of pregnancy, which may indicate that GDM_24 and GDM_30 have different pathogenesis.