Objective: To investigate the factors influencing intraoperative blood transfusion in patients with hip fractures and to develop a machine learning (ML) model for predicting this risk. Methods: A total of 424 patients with hip fractures who underwent surgical treatment between November 2022 and March 2025 in our hospital were selected. Key feature variables of intraoperative blood transfusion risk were identified using the Boruta algorithm. Four different ML algorithms—support vector machine (SVM), linear discriminant analysis (LDA), mixed discriminant analysis (MDA), and extreme gradient boosting (XGBoost)—were used to develop predictive models for intraoperative blood transfusion risk. The predictive performance of the four ML models were evaluated using accuracy, precision, receiver operating characteristic (ROC) curves, precision-recall curves (PRC), precision-recall gain curves (PRGC), and F1 scores. Shapley additive interpretation (SHAP) was used to interpret the final model. Results: Among the 424 patients, 77(18.2%) received intraoperative blood transfusion. The Boruta algorithm identified albumin (ALB), activated partial thromboplastin time (APTT), types of anesthesia, types of fracture, and hemoglobin (Hb) as key feature variables for predicting intraoperative blood transfusion risk. In model evaluation, the SVM model outperforms the other three models across multiple metrics, including the area under the receiver operating characteristic curve (AUC), recall, recall gain, accuracy, precision, F1 score, and the area under the precision-recall curve (PRC-AUC). The SVM model, interpreted and visualized based on SHAP values, effectively predicted intraoperative blood transfusion risk in patients with hip fracture. A visual online application was developed based on the SVM model (https://pbo-nomogram.shinyapps.io/blood/). Conclusion: Preoperative low ALB and Hb levels, prolonged APTT, general anesthesia, and intertrochanteric fractures are risk factors for intraoperative blood transfusion in hip fracture patients. The risk prediction model for intraoperative blood transfusion constructed based on the SVM algorithm has optimal performance, which provides new ideas and methods for the clinical early identification of hip fracture patients with high transfusion risk and the implementation of targeted interventions.

Objective: To report the antibody identification, blood management during pregnancy and the monitoring process of fetal hemolytic disease of fetus and newborn (HDFN) in a pregnant woman with a history of blood transfusion and pregnancy who developed anti-Jr . Methods: Saline tube technique and anti-human globulin technique were used for maternal blood typing, unexpected antibody screening and identification, as well as for determining antibody titer and IgG subclasses. PCR-SSP was employed for genotyping of 18 blood group systems. Next-generation sequencing (NGS) was utilized for gene sequencing of 38 blood group systems. Sanger sequencing was applied to verify rare blood group mutations detected by NGS and to investigate the corresponding rare blood group genes in family members. Blood preparation was achieved through anemia management in prenatal clinics and autologous blood collection during pregnancy. The newborn underwent the three primary tests for HDFN and plasma IgG subclass testing. Results: The pregnant woman's blood type was B, RhD positive, with a positive unexpected antibody screen, and the antibody identification pattern was consistent with a high-frequency antigen antibody. Gene sequencing revealed a homozygous ABCG2 c.376C>T mutation in the woman, resulting in the Jr(a-) phenotype, and anti-Jr antibody was present in her plasma. No compatible Jr(a-) blood was found among family members. The maternal anti-Jr IgG titer remained stable at 256 during pregnancy, with no detectable IgG1 or IgG3 subclasses against the Jr antigen. A total of 800 mL of autologous blood was collected in two stages during pregnancy. The newborn was B, RhD positive, Jr(a+), with a positive unexpected antibody screen (anti-Jr ). IgG subclass typing detected no IgG1 or IgG3. The direct antiglobulin test was positive, while the acid elution test was negative. Conclusion: The combination of serology and blood group genetic analysis provides a diagnostic basis for identifying antibodies to high-frequency antigens. Managing perinatal anemia and implementing staged autologous blood storage can secure blood supply for the perioperative period. IgG antibody subclass typing offers a reference for clinical assessment and prevention of HDFN.

Driven by the growing practical need to accelerate drug development and the continuous innovation of trial design in recent years， the number of protocol amendments during clinical trials have gradually increased， and the changed contents have become more flexible and complex， which significantly heightens the difficulty of ethical review on amendments. Against this backdrop， it is of great importance to fully leverage the role and responsibilities of ethics committees， effectively control clinical trial risks， and ensure subject safety. This paper analyzed development trends of protocol amendments in recent years， sorted out requirements for protocol amendments in Chinese regulations and guiding principles， and examined difficulties of amendment ethical review in practical work. Based on these， targeted strategies and recommendations were proposed， namely， strengthening the integration with scientific review， enhancing the formal review， adjusting the scope of review according to approval notifications， and adopting appropriate review methods， with a view to providing insights and references for the management of the amendment ethical review in drug clinical trials.

ObjectiveTo observe the effects of Dihuang Yinzi （DY） on motor dysfunction in rats with advanced Parkinson's disease （PD） and to investigate the mechanisms by which DY improves advanced PD symptoms through the "gut microbiota-short-chain fatty acids （SCFAs）-inflammation-neuroprotection pathway". MethodsAn advanced PD rat model was induced by rotenone. Rats were divided into a normal group， model group， positive drug group （levodopa， 50 mg·kg^-1）， and DY low-， medium-， and high-dose groups （5.2， 10.4， 20.8 g·kg^-1）. After 7 days of administration， motor function was evaluated using the open-field， pole-climbing， and inclined plate tests. Hematoxylin-eosin （HE） staining was used to observe pathological changes in the substantia nigra and colon， and immunohistochemistry was performed to detect α-Synuclein （α-Syn） and tyrosine hydroxylase （TH） expression in the substantia nigra. Enzyme-linked immunosorbent assay （ELISA） was used to measure levels of dopamine （DA）， 5-hydroxytryptamine （5-HT）， 3，4-dihydroxyphenylacetic acid （DOPAC）， Levodopa， homovanillic acid （HVA）， tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， and interleukin-1β （IL-1β）. Western blot analysis was used to detect the expression of zonula occludens-1 （ZO-1） and occludin. Gut microbiota diversity was analyzed by 16S rRNA sequencing， and gas chromatography （GC） was used to determine the content of SCFAs in colonic contents. ResultsCompared with the normal group， the model group showed significantly decreased movement speed and distance in the open-field test， prolonged pole-climbing time， and reduced retention angle on the inclined plate （P<0.01）， accompanied by increased α-Syn expression （P<0.01） and decreased TH expression （P<0.01） in the brain. Compared with the model group， all DY dose groups improved motor dysfunction in advanced PD rats to varying degrees （P<0.05， P<0.01） and alleviated pathological damage in the brain and colon. High-dose DY significantly reduced α-Syn aggregation in the substantia nigra （P<0.01） and increased TH expression （P<0.01）. ELISA and Western blot results showed that， compared with the normal group， the model group exhibited decreased levels of DA， 5-HT， DOPAC， Levodopa， and HVA in the striatum （P<0.01）， increased levels of TNF-α， IL-6， and IL-1β in the colon and striatum （P<0.01）， and significantly reduced expression of ZO-1 （P<0.05） and occludin in the colon （P<0.01）. Compared with the model group， all DY dose groups increased the levels of DA， 5-HT， DOPAC， Levodopa， and HVA in the striatum to varying degrees （P<0.05， P<0.01）. In the high-dose DY group， the levels of TNF-α， IL-6， and IL-1β in the colon and striatum were reduced （P<0.01）， while the expression of ZO-1 （P<0.05） and occludin in the intestine was increased. The 16S rRNA sequencing results indicated that the relative abundances of Actinobacteriota， Enterobacteriaceae， and Erysipelotrichaceae were increased in the model group， whereas the relative abundances of Bacteroidota， class Clostridia， Lachnospiraceae， and Akkermansia muciniphila were decreased. These changes were effectively reversed after high-dose DY intervention. GC analysis showed that the content of SCFAs in the colonic contents of rats in the model group was decreased （P<0.05， P<0.01）， while after high-dose DY intervention， the levels of acetate， propionate， isobutyrate， and butyrate were significantly increased （P<0.05， P<0.01）. ConclusionDY may exert therapeutic effects in advanced PD by regulating the gut microbiota-SCFAs-inflammation pathway.

ObjectiveThis paper aims to study the potential active compound components and action mechanism of Shenqi Dihuangtang in the treatment of diabetic kidney disease （DKD） through network pharmacology and in vivo experimental verification. MethodsUltra-high-performance liquid chromatography-Q-exactive orbitrap mass spectrometry （UPLC-Q-Exactive Orbitrap MS） technology was used to clarify the main active chemical components of Shenqi Dihuangtang， and it was combined with network pharmacology methods such as gene ontology （GO） functional annotations and Kyoto encyclopedia of genes and genome （KEGG） to predict the potential action mechanism of Shenqi Dihuangtang in treating DKD. Subsequently， the DKD model of db/db male mice was established， and the mice were randomly divided into model group， low-dose Shenqi Dihuangtang group （6.10 g·kg^-1）， medium-dose Shenqi Dihuangtang group （12.19 g·kg^-1）， high-dose Shenqi Dihuangtang group （24.38 g·kg^-1）， and daplizin group （1.25 mg·kg^-1）. During the same period， C57BL/6J male mice were selected into normal group and received drug intervention for 8 weeks， respectively. During this period， the body weight and fasting blood glucose （FBG） of the mice were dynamically monitored， and oral glucose tolerance test （OGTT） and insulin tolerance test （ITT） were performed at the end of dosing. The levels of serum creatinine （SCr）， blood urea nitrogen （BUN）， uric acid （UA）， albumin （ALB）， and total protein （TP） were measured by an automatic biochemical analyzer， and 24-hour urine protein was measured by a urine protein quantitative kit. Hematoxylin-eosin （HE）， periodic-acid Schiff （PAS）， and Masson staining were employed to observe the renal histopathology. The expression of nephrotic protein Nephrin was observed by immunohistochemistry. Western blot was used to detect the expression of epithelial-mesenchymal transition （EMT）-related proteins such as TGF-β₁， Smad2/3， α-smooth muscle actin （α-SMA）， neural-cadherin （N-Cadherin）， and snail protein. ResultsUPLC-Q-Exactive Orbitrap MS identified 384 active compounds in the aqueous extract of Shenqi Dihuangtang. According to oral bioavailability≥30% and the five drug-like principles， 44 key active ingredients were screened out， and 169 intersection targets highly correlated with DKD were matched. Among them， there was a significant interaction relationship between tumor necrosis factor（TNF）， interleukin（IL）-6， protein kinase B（Akt）1， Caspase-3， Jun proto-oncogene （JUN）， hypoxia inducible factor-1α（HIF-1α）， B cell lymphoma-2（Bcl-2）， matrix metallopeptidase-9（MMP-9）， IL-1β， and TGF-β₁. GO functional annotations were significantly enriched in cellular components such as membrane rafts， membrane microdomains， and collagen-containing extracellular matrix， molecular functions such as DNA-binding transcription factor binding， R-Smad binding， and Smad protein binding， as well as biological processes such as reactions to lipopolysaccharides（LPS）， reactions to bacteria-derived molecules， and wound healing. The KEGG pathway was significantly enriched in lipids and atherosclerosis， TGF-β signaling pathway， phosphatidylinositol 3 kinase （PI3K）/Akt signaling pathway， etc. In vivo experimental results showed that the high-dose Shenqi Dihuangtang group could significantly reduce FBG levels in db/db mice （P<0.01）， improve OGTT （P<0.01） and ITT （P<0.01） levels， reduce SCr （P<0.01）， BUN （P<0.01）， UA （P<0.01） and 24-hour BUN （P<0.01）， and increase ALB （P<0.01） and TP （P<0.01） levels. Pathological staining confirmed that the high-dose Shenqi Dihuangtang group could significantly reduce the glomerular mesangial matrix area and collagen deposition （P<0.01） and upregulate the positive expression rate of Nephrin （P<0.01）. Western blot results showed that the high-dose Shenqi Dihuangtang group significantly downregulated the expression of TGF-β₁ （P<0.01） and Smad2/3 （P<0.01） signal molecules and inhibited the protein levels of α-SMA （P<0.01）， N-Cadherin （P<0.01）， and Snail （P<0.01）. ConclusionShenqi Dihuangtang can inhibit the TGF-β₁/Smad signaling axis and block the renal EMT process， thereby improving DKD renal fibrosis damage. Further analysis of its key active components and clinical transformation pathways is needed in the future.

ObjectiveTo establish a mouse model of particulate matter 2.5 （PM_2.5）-induced dry eye and investigate whether Chufeng Yisuntang can ameliorate the PM_2.5-induced ocular surface damage by regulating the reactive oxygen species （ROS）/p38 mitogen-activated protein kinase （p38 MAPK） signaling pathway. MethodsSixty 8-week-old male C57BL/6J mice were used. Ten were randomly selected as the control group. The remaining 50 mice received topical instillation of 1 drop （0.1 mL） of 5 g·L^-1 PM_2.5 suspension in both eyes， four times daily. Successfully modeled mice were randomized into four groups （n=10）： Model， p38 MAPK inhibitor， Chufeng Yisuntang， and combination （Chufeng Yisuntang at 7.3 g·kg^-1 + p38 MAPK inhibitor SB203580 at 5 mg·kg^-1）. Chufeng Yisuntang was administered via gavage， and the inhibitor group via intraperitoneal injection. The control and model groups received equal volumes of distilled water by gavage. All treatments lasted for 4 weeks. General conditions were dynamically observed. Tear secretion， tear film break-up time， and corneal fluorescein staining were assessed. After intervention for 4 weeks， hematoxylin and eosin （HE） staining was used to examine the histopathological changes. Enzyme-linked immunosorbent assay （ELISA） was adopted to measure serum levels of ROS， malondialdehyde （MDA）， superoxide dismutase （SOD） 1， and SOD2. Western blot and Real-time PCR were employed to determine the protein and gene levels， respectively， of p38 MAPK， B-cell lymphoma-2 （Bcl-2）， Bcl-2-associated X protein （Bax）， and cysteinyl aspartate-specific proteinase-3 （Caspase-3） in the corneal tissue. ResultsCompared with the control group， the model group exhibited reduced tear secretion volume and tear film breakup time， along with increased corneal fluorescein staining scores （P<0.01）. Compared with the model group， the Chufeng Yisuntang group， p38 MAPK inhibitor group， and combination group demonstrated increased tear secretion volume and tear film breakup time， along with decreased corneal fluorescein staining scores （P<0.01）. HE staining revealed that compared with the control group， the model group exhibited marked increases in corneal epithelial cell layers and epithelial thickness， along with reduced meibomian gland acini and intensely stained， densely packed nuclei around the acini. Compared with the model group， the Chufeng Yisuntang group， p38 MAPK inhibitor group， and combination group showed intact corneal structure， improved cell morphology， and reduced damage severity. ELISA revealed elevated ROS and MDA levels （P<0.01） and decreased SOD1 and SOD2 levels （P<0.01） in the model group compared with the control group. Compared with the model group， Chufeng Yisuntang， p38 MAPK inhibitor， and the combination lowered ROS and MDA levels （P<0.01）， while raising SOD1 and SOD2 levels （P<0.05， P<0.01）. Western blot revealed that compared with the control group， the model group exhibited increased protein levels of p38 MAPK， Bax， and Caspase-3 （P<0.01） and reduced protein level of Bcl-2 （P<0.01）. Compared with the model group， Chufeng Yisuntang， p38 MAPK inhibitor， and the combination down-regulated the protein levels of p38 MAPK， Bax， and Caspase-3 （P<0.01）， while up-regulating the protein level of Bcl-2 （P<0.01）. Compared with the Chufeng Yisuntang group， the combination group exhibited decreased protein levels of p38 MAPK， Bax， and Caspase-3 （P<0.01） and increased protein level of Bcl-2 （P<0.01）. Real-time PCR revealed that compared with the control group， the model group exhibited upregulated mRNA levels of p38 MAPK， Bax， and Caspase-3 （P<0.01）， and downregulated mRNA level of Bcl-2 （P<0.01）. Compared with the model group， Chufeng Yisuntang， p38 MAPK inhibitor， and the combination down-regulated the mRNA levels of p38 MAPK， Bax， and Caspase-3 （P<0.01）， while up-regulating the mRNA level of Bcl-2 （P<0.05， P<0.01）. Compared with the Chufeng Yisuntang group， the combination group exhibited decreased mRNA levels of p38 MAPK， Bax， and Caspase-3 expression （P<0.05， P<0.01） and increased mRNA level of Bcl-2 （P<0.01）. ConclusionChufeng Yisuntang may partially protect against PM_2.5-induced corneal injury by inhibiting the ROS/p38 MAPK pathway， enhancing antioxidant defense， and reducing epithelial apoptosis.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

Background With China's socioeconomic development, significant lifestyle changes have occurred among occupational groups, leading to alterations in cardiovascular metabolic risk factors. However, few studies have examined the secular trends of these risk factors in China's working population. Objective To analyze the trends in cardiovascular metabolic risk factors among the occupational population in nine provinces of China from 2009 to 2018, and to explore the associations between different occupational types and these risk factors, along with their clustering patterns, thereby providing evidence for targeted interventions. Methods This study utilized data from the China Health and Nutrition Survey (CHNS) in 2009, 2015, and 2018. The dataset covered 13274 employed individuals aged 18-59 years. Only participants with complete blood test results and physical measurement data were included in the final analysis. The average annual percentage change (AAPC) in the positive rates of risk factors was calculated using Joinpoint regression to analyze temporal trends. Multinomial logistic regression and binary logistic regression models were employed to examine the cross-sectional associations between occupational types (categorized as white-collar workers, agricultural workers, blue-collar workers, service workers, and others) and nine cardiovascular metabolic risk factors and their clustering (defined as having ≥2 risk factors) in 2018. Results A total of 8451 participants were included. From 2009 to 2018, significant increasing trends (AAPC>0, P<0.05) were observed for overweight, obesity, central obesity, borderline elevated triglycerides (TG), borderline elevated to elevated total cholesterol (TC), borderline elevated low-density lipoprotein cholesterol (LDL-C), elevated blood pressure and hypertension, diabetes. The most rapid increases were found for obesity (AAPC=6.07%, 95%CI: 3.42%, 8.88%), hypertension (AAPC=4.10%, 95%CI: 0.62%, 7.77%), and central obesity (AAPC=3.27%, 95%CI: 1.24%, 5.36%). The clustering rate of risk factors increased from 62.3% in 2009 to 73.2% in 2018 (AAPC=1.87%, 95%CI: 1.02%, 2.73%). The cross-sectional analysis in 2018, after adjusting for confounders, showed that compared with white-collar workers, blue-collar workers had a significantly lower risk of overweight (OR=0.67, 95%CI: 0.48, 0.95), central obesity (OR=0.65, 95%CI: 0.45, 0.92), and borderline high TG (OR=0.54, 95%CI: 0.35, 0.82). In contrast, agricultural workers had a higher risk of hypertension (OR=1.76, 95%CI: 1.14, 2.72). Conclusion Between 2009 and 2015, the burden of most cardiometabolic risk factors increases significantly among the Chinese occupational population, with the rate of increase slowing by 2018. Risks vary across occupational types, among which blue- collar workers report lower target outcomes. Targeted interventions focusing on the rapidly increasing risk factors and high-risk occupational groups are recommended.

Background With rising obesity rates and earlier hypertension onset among occupational populations, there is an urgent need to elucidate the long-term cardiovascular impacts of dynamic body weight patterns. Current evidence lacks trajectory modeling studies examining occupation-specific prevention strategies. Objective To investigate the association between long-term body mass index (BMI) trajectories and incident hypertension risk in Chinese working adults, and to examine occupation-specific heterogeneity in this relationship. Methods A dynamic sub-cohort of 4 413 occupational participants was constructed from ten survey waves (1991–2018) of the China Health and Nutrition Survey (CHNS). Eligible individuals had valid key BMI records at three or more independent follow-ups before the outcome event; the individual baseline was set as the year of their first participation in the survey. Group-based trajectory modeling (GBTM) was used to identify BMI change patterns. Cox proportional hazards regression was used to calculate hazard ratios (HRs) and 95% confidence interval (CI) for hypertension incidence across trajectory groups, with stratified analysis by occupational categories. Results Among 4413 occupational participants, 1441 incident hypertension cases were reported during the follow-up window. Three long-term BMI trajectories were identified: low-stable, moderate-slowly rising, and high-rising. In Model 3 with all selected confounders adjusted, compared with the low-stable group, the moderate-slowly rising group showed a significantly higher risk of incident hypertension (HR=1.28, 95%CI: 1.12, 1.45; P < 0.001), as did the high-increasing group (HR=1.80, 95%CI: 1.45, 2.23; P < 0.001). The stratified analyses revealed occupational differences in the association. Among agricultural workers, the moderate-slowly rising group (HR=1.35, 95%CI: 1.15, 1.57) and the high-rising group (HR=2.00, 95%CI: 1.50, 2.66) both exhibited significantly increased risks of hypertension (P<0.001). Conversely, among white-collar workers, an increased risk of hypertension was observed only in the high-rising group (HR=1.97, 95%CI: 1.23, 3.17; P<0.05). Conclusion Rapid BMI escalation constitutes a significant risk factor for incident hypertension in occupational populations. Targeted long-term weight monitoring and tailored interventions are recommended for high-risk occupations.