Obesity has been confirmed to be associated with infertility. However, the association between metabolically healthy obesity (MHO), a subset of obesity with no metabolic abnormalities, and female infertility has not yet been investigated. This study aimed to examine the association between MHO and the risk of female infertility among United States.This study utilized a cross-sectional design and included 3542 women aged 20–45 years who were selected from the National Health and Nutrition Examination Survey (NHANES) 2013–2020 database. The association between MHO and the risk of infertility was evaluated using risk factor–adjusted logistic regression models.P = 0.001; OR (95% CI): 1.02 , P < 0.001; respectively). After cross-classifying by metabolic health and obesity according to BMI and WC categories, individuals with MHO had a higher risk of infertility than those with MHN (OR (95% CI): 1.75 for BMI criteria; OR (95% CI): 2.01 for WC criteria). A positive linear relationship was observed between BMI/WC and infertility risk among metabolically healthy women .Higher BMI and WC were associated with increased infertility risk after adjusting for potential confounding factors (OR (95% CI): 1.04, MHO was associated with an increased risk of infertility among reproductive-aged women in the US. Obesity itself, regardless of metabolic health status, was associated with a higher infertility risk. Our results support implementing lifestyle changes aimed at achieving and maintaining a healthy body weight in all individuals, even those who are metabolically healthy.The online version contains supplementary material available at 10.1186/s12889-023-16397-x. Infertility is the failure to achieve a pregnancy after 12 months or more of regular unprotected sexual intercourse defined by the International Committee for Monitoring Assisted Reproductive Technology . Inferti2) has emerged as a severe global health issue, particularly in the United States, with an age-adjusted prevalence of 42.4% [Obesity (defined as body mass index (BMI) ≥ 30 kg/mof 42.4% . Among wof 42.4% . Variousof 42.4% .Obesity often coexists with metabolic abnormalities, and obesity-related metabolic disorders can mediate obesity-related morbidity . HoweverObesity can be defined in multiple dimensions. The body mass index (BMI) remains the most commonly used, widely accepted, relatively simple, and inexpensive measure of overweight and obesity. However, individuals with the same BMI can have markedly different fat distributions . CentralTherefore, the primary aim of this study was to investigate the association between MHO and female infertility risk utilizing multi-ethnic data from a nationally representative sample of US women. Two measures were used to define obesity: BMI as a measure of overall obesity and WC as a measure of central obesity.The original data were obtained from the National Health and Nutrition Examination Survey (NHANES). NHANES collects information on the health and nutrition status of a representative sample of the noninstitutionalized civilian population every two years using a complex, multistage, and stratified sampling design. National Health and Nutrition Examination Survey was approved by the National Center for Health Statistics Institutional Review Board and all participants signed an informed consent.2. After inclusion and exclusion criteria, we enrolled a total of 3542 participants  of NHANES because only these cycles included a reproductive health questionnaire with questions on infertility. The inclusion criteria were (1) Female participants aged 20–45. (2) Female participants with complete fertility and infertility information. The exclusion criteria were (1) Female participants with a history of hysterectomy, or bilateral oophorectomy. (2) Female participants with missing values for BMI, WC, and information on metabolic disorders. (3) Female participants with BMI < 18.5 Kg/m3–2020 ofOur primary outcome was self-reported infertility from the Reproductive Health Questionnaire (question RHQ074): “Have you ever attempted to become pregnant over a period of at least a year without becoming pregnant?“ Those who answered “yes” were labelled “ever infertile,“ whereas women who answered “no” were labelled “fertile.“2), overweight (BMI: 25.0–29.9 kg/m2), and obese (BMI: ≥30 kg/m2) were categorized according to WHO guidelines [Obesity was defined using BMI and WC. BMI was obtained by the body mass in kilograms divided by height in meters squared. Weight, height and WC were measured by the professional personnel. For BMI criteria, normal weight , metabolically healthy overweight (MHOW), metabolically healthy obese (MHO), metabolically unhealthy normal weight (MUN), metabolically unhealthy overweight (MUOW), and metabolically unhealthy obese (MUO). Similarly, according to WC criteria, participants were also categorized into the same six phenotypes: MHN, MHOW, MHO, MUN, MUOW, and MUO .Potential confounders and effect modifiers were identified from previous literature and incorporated into a directed acyclic graph,which guided our modeling strategy. , Wilcoxon Rank Sum Test  and T-test  were conducted to test for differences between fertility and infertility groups. Continuous variables were described using the mean and standard error, while categorical variables were described as frequency and percentages. Multiple imputation was conducted to reduce the sample size reduction caused by missing covariates. The principal analysis included five steps. First, we used three logistic regression models: 1) an unadjusted model, (2) an adjusted model containing age, and (3) an adjusted model including all covariables in Table  an unadjP < 0.001; 103.17 vs. 95.01, P < 0.001, respectively). And the proportions of individuals who were normal weight and overweight according to the BMI criteria were significantly lower among the infertility participants in both metabolically healthy and unhealthy groups  while the proportions of those who were obese were significantly higher among the infertility participants . In addition, participants with infertility were older , richer , more likely to married or living with a partner  and tended to have been pregnant . The infertile and fertile groups did not differ significantly in terms of race, smoking status, drinking status, education level, menstrual irregularities and physical activity.The inclusion and exclusion criteria are shown in Fig. P < 0.001; OR (95% CI): 1.02 , P < 0.001; respectively). Each unit increase in BMI or WC remained associated with an increased risk of infertility in models 2 (OR (95% CI): 1.04; P < 0.001; OR (95% CI): 1.02 ; P < 0.001; respectively) and 3(OR (95% CI): 1.04; P = 0.001; OR (95% CI): 1.02 ; P < 0.001; respectively). We transformed BMI and WC into categorical variables . After adjusting for all covariables, obesity according to the BMI criteria was associated with an increased risk of infertility (OR (95% CI): 1.83 , P = 0.006), and central obesity according to the WC criteria was associated with an increased risk of infertility also (OR (95% CI): 2.18, P < 0.001). Significant trend associations were observed for BMI/WC and infertility .Three binary logistic regression models were constructed to investigate the potential effect of BMI and WC on infertility. Table P = 0.109; WC criteria: OR (95% CI): 2.01, P = 0.042). Additionally, the risk of infertility in MUO participants was significantly higher than that in MUN individuals (BMI criteria: OR (95% CI): 1.85, P = 0.012; WC criteria: OR (95% CI): 2.24, P = 0.002). Each unit increase in BMI or WC was associated with an increased risk of infertility among both metabolic healthy and unhealthy participants (OR (95% CI): 1.06; P = 0.004; OR (95% CI): 1.04 ; P = 0.002; OR (95% CI): 1.03; P = 0.007; OR (95% CI): 1.02 ; P < 0.001; respectively). Significant trend associations were observed for BMI/WC and infertility in both metabolically healthy and unhealthy participants  except for BMI in the metabolically healthy group (P = 0.053).The risks for infertility cross-classified by metabolic health and obesity (BMI and WC categories) are presented in Table P for non-linearity > 0.05) throughout the range of their levels in whether metabolically healthy or metabolically unhealthy participants, indicating a positive association between BMI/WC and infertility.We utilized RCS to simulate and model the relation of BMI and WC with infertility among metabolically healthy and unhealthy participants Fig. . The dosMH vs. MUH = 0.865; WC criteria: PMH vs. MUH = 0.640).The area under the ROC curve (AUC) of the BMI among metabolically healthy participants for predicting the occurrence of infertility was 0.576 ), whereas the AUC of BMI among metabolically unhealthy participants was 0.581 ). In addition, the AUC of WC among metabolically healthy participants was 0.583 ), while the AUC of WC among metabolically unhealthy participants was 0.599 ). No significant differences was observed between the predicated ability of WC/BMI in metabolically healthy participants or metabolically unhealthy participants (BMI criteria: PSensitivity analyses were presented in Supplemental Table In this nationally representative cross-sectional study, we examined the associations between obesity and infertility among metabolically healthy and unhealthy women aged 20 to 45. According to the weighted analysis, the prevalence of infertility among 20-45-year-old women was 12.11%, matching the predicted national prevalence of 12–18% .We present numerous significant findings in this study. First, a high BMI and WC were related to an increased risk of infertility after controlling for conventional risk factors. Second, after dividing the population into metabolic healthy and unhealthy, the increased risk of infertility remained in both MHO and MUO phenotypes. Third, BMI and WC were positively associated with the risk of infertility in a linear dose–response manner among both metabolic healthy and unhealthy women, which further confirmed that MHO is not a benign condition and is associated with an increased risk of infertility. Lastly, the ROC curve showed that BMI and WC predicted infertility with no difference between metabolic healthy and unhealthy women.The association between obesity and infertility has been well-established in many studies , 27. HowThe connection between obesity and infertility has biological and social-psychological bases. Biologically, the effects of excess body fat on sex hormone secretion and metabolism are profound . AdiposiOur investigation also confirms that BMI or WC predicted infertility with no difference among metabolic healthy and unhealthy women, which suggests that obesity plays a similar role in both groups, indicating that MHO is also a risk factor for infertility once again. It advises that obesity should be avoided regardless of metabolic disorders.Also, as expected, obesity associated with higher infertility risk in metabolically unhealthy women. This reaffirms that obesity should be avoided, whether metabolically healthy or unhealthy.To our knowledge, this study first examined the association between MHO and female infertility using participants from a large, nationally representative population. We accounted for various covariates that might modulate infertility and obesity. Our resreach included women aged 20–45 years, though we lacked information about the causes of infertility. Therefore, it is possible to apply the findings of this study to a comparable population of 20-45-year-old American women. Finally, we conducted two sensitivity analyses to confirm the robustness of the conclusions.The conclusions of this study must be interpreted in light of several limitations. First, since this is a cross-sectional study, causal judgments regarding fat and infertility cannot be made. Second, we lack information regarding the duration of infertility, which could modulate BMI during this period. Third, infertility was self-reported; the underlying causes are unknown. We cannot ascribe obesity to infertility in all cases. Fourth, our study could have missed women who might be infertile but have not tried to conceive yet.MHO was associated with high infertility risk among reproductive-aged women in the US. Obesity relates to greater infertility risk, regardless of metabolic health. Our findings support lifestyle changes to achieve and maintain a healthy weight for all, even those metabolically healthy. To confirm our results, further prospective epidemiological studies are needed.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3Supplementary Material 4


The goal of this study was to comprehensively investigate the characteristics of gut microbiota dysbiosis and metabolites levels in very low or extremely low birth weight (VLBW/ELBW) infants with white matter injury (WMI).In this prospective cohort study, preterm infants with gestational age < 32 weeks and weight < 1.5 kg were investigated. Additionally, fecal samples were collected on days zero, 14d and 28d after admission to the intensive care unit. All subjects underwent brain scan via MRI and DTI at a corrected gestational age of 37 ~ 40 weeks. Based on the results of MRI examination, the VLBW/ELBW infants were divided into two groups: WMI and non-WMI. Finally, based on a multi-omics approach, we performed 16S rRNA gene sequencing, LC-MS/MS, and diffusion tension imaging to identify quantifiable and informative biomarkers for WMI.Staphylococcus and Acinetobacter species in the fecal samples of infants with WMI, as well as increasing levels of S. caprae and A._johnsonii. LEfSe analysis (LDA ≥ 4) showed that the WMI group carried an abundance of Staphylococcus species including S. caprae, members of the phyla Bacteroidota and Actinobacteriota, and Acinetobacter species. A total of 139 metabolic markers were significantly and differentially expressed between WMI and nWMI. KEGG pathway enrichment analysis revealed that the WMI group showed significant downregulation of 17 metabolic pathways including biosynthesis of arginine and primary bile acids. The WMI group showed delayed brain myelination, especially in the paraventricular white matter and splenium of corpus callosum. Staphylococcus species may affect WMI by downregulating metabolites such as cholic acid, allocholic acid, and 1,3-butadiene. Gut microbiota such as Acinetobacter and Bacteroidetes may alter white matter structurally by upregulating metabolites such as cinobufagin.We enrolled 23 patients with and 48 patients without WMI. The results of 16S RNA sequencing revealed an increase in the number of Based on 16S RNA sequencing results, severe gut microbiota dysbiosis was observed in the WMI group. The results might reveal damage to potential signaling pathways of microbiota-gut-brain axis in gut microbiota. The mechanism was mediated via downregulation of the bile acid biosynthetic pathway.The online version contains supplementary material available at 10.1186/s12866-023-03103-5. White matter injury (WMI) is characterized by the loss of oligodendrocyte precursor cells (OPCs), which is the main manifestation of brain injury in preterm infants, occurring in more than 50% of very low birthweight (VLBW) infants , 2. The Gut microbiota comprises trillions of microorganisms. It is the largest ecosystem and the “second genome” of humans, and plays a vital role in maintaining the immune system and health . HoweverKlebsiella was found to be highly predictive of brain injury [In the past decade, the importance of gut microbiota in brain function including its role in neurological diseases has been increasingly recognized as a biological marker of brain development , 12. Then injury . ImpaireDiffusion tensor imaging (DTI) is the only noninvasive technique currently available to study white matter fibers, and assess the degree of myelination of white matter fibers. Conventional T1- and T2-weighted MR imaging techniques only reveal macroscopic damage and cannot quantify the extent of white matter damage , 17. FraFew studies have established a direct link between gut microbiota and brain function in preterm infants in vivo. The composition of the gut microbiota associated with WMI, as well as underlying microbiological markers, have yet to be determined. The relationship between gut microbiota and brain structure and function in preterm infants is of great significance. Gut microbiota play a key role in brain development, although the specific mechanism is still unclear. Neuroimaging is a useful, non-invasive technique to elucidate the mechanisms of the microbiota-gut-brain axis underlying the relationship between gut microbiota and neuronal health. Based on the results of MRI examination, the patients were divided into WMI and non-white matter injury (nWMI) groups.In this study, we used 16S rRNA gene sequencing, LC-MS/MS metabolite analysis and DTI to compare the DTI parameters and gut microbiota and metabolite levels between the two groups of WMI and nWMI to elucidate the microbiota-gut-brain axis.This prospective cohort study was conducted from April 2022 to December 2022. Written informed consent of each infant’s guardian was obtained before samples were acquired for analysis. The inclusion criteria were: (1) GA at birth < 32 weeks; (2) birth weight < 1.5 kg; (3) age at admission ≤ 24 h. The exclusion criteria were: (1) death; (2) failure to complete head MRI and DTI scan during hospitalization; (3) patients with congenital chromosomal abnormalities or genetic metabolic diseases; (4) congenital brain abnormalities; (5) images that cannot be analyzed; and (6) use of microecological preparations .The meconium (within 24 h after birth) and the stool samples on days 14 and 28 after birth were collected, respectively. Sterile cotton swabs were used to collect fecal samples from three different locations, and each tube was about 0.2 g (the size of a soybean). The samples were immediately sealed, labeled and transferred to the refrigerator at -80℃ and frozen until genetic analysis. The meconium, day 14 and day 28 stool samples obtained from preterm infants in the WMI group were designated as WMI1, WMI14, and WMI28, respectively. The stool samples in the nWMI1 group were named nWMI1, nWMI14, and nWMI28, respectively. The specific details are presented in Table Microbial community genomic DNA was extracted from samples using the E.Z.N.A.® soil DNA Kit  according to manufacturer’s instructions. The hypervariable region V3-V4 of the bacterial 16S rRNA gene were amplified with primer pairs 338 F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R(5’-GGACTACHVGGGTWTCTAAT-3’) by an ABI GeneAmp® 9700 PCR thermocycler . The PCR amplification of 16S rRNA gene was performed as follows: initial denaturation at 95 ℃ for 3 min, followed by 27 cycles of denaturing at 95 ℃ for 30 s, annealing at 55 ℃ for 30 s and extension at 72 ℃for 45 s, and single extension at 72 ℃ for 10 min, and end at 4 ℃.Purified amplicons were pooled in equimolar and paired-end sequenced on an Illumina MiSeq PE300 platform  according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. . The raw reads were deposited into the NCBI Sequence Read Archive (SRA) database (BioProject: PRJNA1013131).https://cloud.majorbio.com). Based on the OTUs information, rarefaction curves and alpha diversity indices including observed OTUs, Chao1 richness and Shannon index were calculated with Mothur v1.30.1 [The taxonomy of each Operational Taxonomic Unit (OTU) representative sequence was analyzed by RDP Classifier version 2.2 against the 16S rRNA gene database (e.g. Silva v138) using confidence threshold of 0.7. Bioinformatic analysis of gut microbiota was carried out using the Majorbio Cloud platform ( v1.30.1 . The sim50 mg sample was added to a 2mL centrifuge tube and a 6 mm diameter grinding bead was added. 400 µL of extraction solution (methanol: water = 4:1 (v:v)) containing 0.02 mg/mL of internal standard  was used for metabolite extraction. The samples were left at -20℃ for 30 min, centrifuged for 15 min , and the supernatant was transferred to the injection vial for LC-MS/MS analysis.http://www.hmdb.ca/), Metlin (https://metlin.scripps.edu/) and Majorbio Database. Then, the R package “ropls”(Version 1.6.2) was used to perform principal component analysis (PCA) and orthogonal least partial squares discriminant analysis (OPLS-DA), and 7-cycle interactive validation evaluating the stability of the model. The metabolites with Variable importance in the projeciton (VIP) > 1, p < 0.05 were determined as significantly different metabolites. Differential metabolites among two groups were mapped into their biochemical pathways through metabolic enrichment and pathway analysis based on KEGG database (http://www.genome.jp/kegg/). Python packages “scipy.stats” (https://docs.scipy.org/doc/scipy/) was used to perform enrichment analysis to obtain the most relevant biological pathways for experimental treatments.The LC-MS/MS analysis of sample was conducted on a Thermo UHPLC-Q Exactive HF-X system equipped with an ACQUITY HSS T3 column  at Majorbio Bio-Pharm Technology Co. Ltd. . The pretreatment of LC/MS raw data was performed by Progenesis QI software, and a three-dimensional data matrix in CSV format was exported. Internal standard peaks, as well as any known false positive peaks , were removed from the data matrix, deredundant and peak pooled. At the same time, the metabolites were identified by searching database, and the main databases were the HMDB  and sagittal (TR: 3266ms/TE: 38ms), T2WI (TR: 5454ms/TE: 92ms) and T2WI FLAIR (TR: 8500ms/ TE: 90ms) axial imaging. The axial scanning sequence was based on the auditory canthus line, the slice thickness was 5 mm, the slice spacing was 1 mm, and the FOV = 20 × 20. The slice thickness of the sagittal scan is 5 mm, the slice distance is 0.5 mm, and the FOV = 20 × 20; the DWI and DTI scans adopt a single-shot echo planar imaging (EPI) sequence, and the b value of the DWI scan is 0,1000 s/mm2, and the FOV = 20 × 20; DTI scanning b value is 1000s/mm2, TR: 3000ms, TE: minimum, slice thickness 5 mm, slice distance 1 mm, FOV = 20 × 20, diffusion gradient direction adopts 25 directions not on the same straight line.The degree of WMI was classified as follows: (1) mild: abnormal T1 signaling involving ≤ 3 areas, each < 2 mm damage; (2) moderate: abnormal T1 signals in > 3 areas, each > 2 mm in size, but involving < 5% of the hemisphere; and (3) severe, involving > 5% of the hemisphere . AccordiThe color FA map was obtained by post-processing the original DTI diffusion image with FLnctool functional software developed by GE Company in the United States. Regions of interest (ROIs) were selected on T2WI, and 8 ROI regions were measured in the frontal white matter, parietal white matter, occipital white matter, periventricular white matter, anterior and posterior limb of internal capsule, genu and splenium of corpus callosum. All ROIs were measured bilaterally symmetrically, and the size of the ROI was 15–25 mm. In order to reduce the error, each ROI was measured 3 times by two experienced radiology technicians independently.P < 0.05 were considered significant. *P < 0.05, **P < 0.01, ***P < 0.001.All statistical analyses were performed using IBM SPSS Statistics 26 and R software (Version 2.15.3). Normally distributed data were analyzed using the Student’s t test. Data that did not conform to normal distribution  were analyzed using non-parametric test (Mann-Whitney U test or Wilcoxon rank sum test). Chi-squared or Fisher’s exact test was used to identify differences between categorical variables. Linear discriminant analysis effect size (LEfSE) with an LDA cutoff of 4 was also used to identify the unique taxa or function that differentiated the groups, based on statistically significant biomarkers. Spearman correlation analysis was used to analyze the relationship between gut microbiota and metabolites and DTI parameters (FA and ADC) at different sites. Differences with P > 0.05), as shown in Table From April 2022 to December 2022, the Affiliated Hospital of Guangdong Medical University enrolled a total of 83 subjects who met the inclusion criteria. The study finally included 71 cases after excluding 4 deaths, 6 patients who failed to complete MRI + DTI testing, 1 case lost to treatment, and 1 case of inherited metabolic disease. Our sample finally included 23 cases of WMI and 48 cases of nWMI. The incidence of WMI in preterm infants was 32.3% (23/71). The WMI group included 12 cases (16.9%) of mild WMI, 7 cases (9.8%) of moderate WMI, and 4 cases (5.6%) of severe WMI. The incidence of moderate and severe WMI was 15%. The GA of the WMI group was 30.0 ± 1.8 weeks, while that of the nWMI group was 29.9 ± 1.7 weeks; the difference was not statistically significant. No significant differences in birth weight, sex, prenatal hormone levels, prenatal antibiotic use, APGAR score and postnatal antibiotic use were found between the two groups , while the Simpson index was significantly lower than that of the nWMI1 group (P = 0.002)  obtained from the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model and the P value of the t test. Metabolites with VIP > 1 and P value < 0.05). Compared with nWMI group, taurine and hypotaurine metabolism, arginine biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis, cyanoamino acid metabolism, and primary metabolic pathways such as primary bile acid biosynthesis were downregulated in the WMI group  of differential metabolites annotated by KEGG, FA01 Fatty Acids and Conjugates, ST04 Bile acids and derivatives, and ST02 steroids in WMI and nWMI groups were the most Fig. A. Among oup Fig. C, D. TheP < 0.0001). Actinobacteria was positively correlated with cyclocalamin and crocin 4  and other toxic proteolytic peptides, thereby damaging the nervous system . ZhanfenStaphylococcus, Bifidobacterium, Acinetobacter and lactobacillus in the WMI group was higher than in the nWMI group. You, W et al. (2022). found that traumatic brain injury induced gut microbiota dysbiosis and changed the distribution of bile acids, suggesting the role of Staphylococcus in altered bile acid metabolism [Staphylococcus was significantly increased in the WMI14 group. The WMI group was colonized with abnormal Staphylococcus within 14 days of life. In the nWMI group, the abundance of Staphylococcus decreased by week 2 after birth; while the abundance of Staphylococcus in the WMI group increased significantly at week 2 after birth, persisting until 28 days after birth. The WMI group showed an abnormal increase in the abundance of Staphylococcus within 1 month after birth. Our study also found that the abundance of Acinetobacter was significantly and abnormally increased in the WMI group on day 14 after birth, while the abundance of Acinetobacter in the nWMI group did not appear to be abnormally increased. Cekanaviciute, E et al. (2017). reported that the abundance of Acinetobacter was significantly increased in patients with multiple sclerosis, which can induce pro-inflammatory response [Staphylococcus and Acinetobacter in preterm infants may facilitate the prevention and treatment of WMI. Our study also found that the WMI group was enriched in Lactobacillus and Bifidobacterium. Although Lactobacillus and Bifidobacterium represent probiotics, many studies have shown that the relative abundance of Lactobacillus increased in the gut microbiota of patients with high-fat diet and diabetes, and Bifidobacterium was detected in different infections [Klebsiella and Parabacteroides in the meconium of WMI group was significantly higher than in the nWMI group. Patients with extrauterine growth retardation carry an abundance of Parabacteroides, which can affect the storage of sugar in muscle, liver and fat [Klebsiella within 2 weeks of life, whereas full-term infants showed an increase in Klebsiella after 6 months of life [Klebsiella is associated with pro-inflammatory response, and overgrowth of Klebsiella in the gastrointestinal tract is highly predictive of brain injury [Klebsiella in the meconium of the WMI group was significantly higher than in the nWMI group, suggesting possible brain damage early in life. Based on LefSe analysis, the WMI group was enriched in a large number of Staphylococcus species, suggesting a potential role in the early diagnosis of WMI.This study found that the relative abundance of tabolism . Compareresponse . Inhibit and fat . Fecal s of life . Notablyn injury . The relS. caprae was significantly higher in the WMI group, while the abundance of B. longum was significantly decreased. S. caprae is closely related to S. epidermidis and Streptococcus capitis at the species level, especially in the ability to form biofilms, which may increase the virulence of S._caprae [B. longum and B. longum subspecies were found to exhibit protective effects. Studies have found that the probiotic B. longum NCC3001 reduced the excitability of enteric neurons and normalized the levels of hippocampal brain-derived neurotrophic factor, resulting in anti-anxiety effects [B. longum R0175, Lactobacillus helveticus R0052 and L. plantarum R1012 regulated brain activity and functional connectivity in emotional and stress response areas [B. longum in the nWMI group increased significantly, but whether B. longum plays a protective role in the pathogenesis of WMI requires further investigation. Compared with nWMI1, the relative abundance of K. pneumoniae was significantly increased in the WMI1 group. The increased abundance of K. pneumoniae was associated with the risk of NEC in preterm infants [At the species level, compared with the nWMI group, the abundance of ._caprae . B. long effects . Intervese areas . During se areas . In this infants .P < 0.05). Compared with the nWMI group, the WMI group showed significant downregulation in metabolite pathways of taurine and hypotaurine, arginine biosynthesis, phenylalanine and primary bile acid biosynthesis. Metabolites and related functional pathways were significantly altered in patients with WMI compared with those of nWMI.Gut microbiota and metabolites not only play a role in maintaining the homeostasis of the gastrointestinal tract, but also regulate remote organs such as the brain . ClinicaAcinetobacter species of WMI group were positively correlated with metabolites such as cinobufagin, didesethylflurazepam, N-acetylneuraminic acid and adenosine 3’-monophosphate, whereas they were negatively correlated with metabolites such as cholic acid and allocholic acid. The characteristic Staphylococcus species of the WMI group were negatively correlated with metabolites such as cholic acid and allocholic acid. Didesethylflurazepam is a benzodiazepine. Cinobufagin is a steroid lactone. N-Acetylneuraminic acid participates in amino sugar and nucleotide sugar metabolism. Adenosine 3’-monophosphate participates in purine metabolism. Cholic acid is a bile acid and participates in primary bile acid biosynthesis. Allocholic acid is also a bile acid and participates in the synthesis of secondary bile acids. Metabolites such as didesethylflurazepam, cinobufagin, N-acetylneuraminic acid, and adenosine 3’-monophosphate were significantly upregulated in the WMI group, while cholic acid and allocholic acid were significantly downregulated. KEGG pathway analysis also showed that biosynthesis of primary and secondary bile acids in the WMI group was significantly downregulated. The main function of bile acids is to promote intestinal absorption of lipids and fat-soluble vitamins in the diet. SCFAs and bile acids also affect host energy production, and secondary bile acids help regulate innate immunity, insulin sensitivity and host metabolic pathways (including carbohydrate and lipid metabolism). In addition, secondary bile acids maintain gut microbiota balance by modulating the innate immune response of the gut [Acinetobacter significantly increased metabolites related to purine metabolism, amino sugar and nucleotide sugar metabolism, and significantly inhibited metabolites of bile acid biosynthesis. Staphylococcus may alter bile acid biosynthesis, and thereby affect WMI by downregulating metabolites such as cholic acid and allocholic acid. Further, Bacteroidota and Acinetobacter play a role in WMI by upregulating the levels of didesethylflurazepam, cinobufagin, N-acetylneuraminic acid, and adenosine 3’-monophosphate, and downregulating cholic acid and allocholic acid levels. These results suggested that Bacteroides, Acinetobacter and Staphylococcus and related fecal microbial metabolites may regulate the microbe-gut-brain axis during the pathogenesis of WMI.This study is the first to identify several differential metabolites that may be associated with WMI. The characteristic Bacteroidetes of WMI were generally positively correlated with metabolites such as didesethylflurazepam, cinobufagin, N-acetylneuraminic acid, and adenosine 3’-monophosphate, but they were negatively correlated with metabolites such as cholic Acid and allocholic acid. The characteristic  the gut . Bile ac the gut . Notably the gut . Shansha the gut  reported the gut . WMI-chaB. longum was negatively correlated with occipital white matter ADC, suggesting a protective effect. This finding may provide a new therapeutic target for probiotics in the treatment of WMI. Our study found that cinobufagin, fumagillin, cyclocalamin, isoaustin, crocin 4 and other metabolites were positively correlated with ADC values in different regions of white matter of the brain, and negatively correlated with FA values, which may lead to brain damage. However, N-docosahexaenoyl cysteine was negatively correlated with ADC values in different regions of white matter in the brain, and the positive correlation with FA value may have a protective effect on the brain. These results suggest the need for further investigations. Ze-Jun Wang [Acinetobacter and Bacteroidetes may affect the structure of white matter by altering metabolites such as cinobufagin.Increased ADC and decreased FA values in the periventricular white matter and splenium of corpus callosum may represent early markers of WMI in preterm infants. This study also showed that the incidence of WMI in VLBW/ELBW infants was 32.3%, and the incidence of moderate-to-severe WMI was 15.4%. Although the incidence of moderate-to-severe WMI was consistent with the study of Dawn Gano , birth wJun Wang  reportedThe current study has several limitations. First, the sample size was relatively small, suggesting the need for studies with larger sample size. Based on current evidence, it has not possible to establish a causal relationship between alterations in the gut microbiota and metabolome and the pathogenesis of WMI, a limitation shared by observational studies. Although the precise mechanisms of gut microbiota affecting the brain are unknown, our findings suggest key signaling pathways and molecules involved in gut-brain interactions in WMI. Second, we only collected fecal samples three times, which limited the investigation of the bacterial colonization time in preterm infants. Due to the impact of the COVID-19 pandemic, the study subjects could not successfully complete the follow-up. Finally, our study lacks basic data and follow-up studies involving animal models are needed to elucidate the mechanisms outlined in this study to screen specific metabolites and pathways.Staphylococcus and Acinetobacter are strongly associated with altered fecal microbial metabolites, possibly damaging the white matter in the brain by dow-regulating the bile acid biosynthesis pathway.In summary, these results suggest severe gut microbiota dysbiosis in patients with WMI. A few WMI-characteristic groups, such as Bacteroidetes, Additional file 1. Additional file 2. Additional file 3. Additional file 4. Additional file 5.


Of the predicted planted forest areas in East Asia , China contributed 87%, most of which is located in the lowland tropical/subtropical regions, and Sichuan Basin. With 95% accuracy and an F1 score of 0.77, our spatially-continuous maps of planted forests enable accurate quantification of the role of planted forests in climate change mitigation. Our findings inform effective decision-making in forest conservation, management, and global restoration projects.Planted forests are critical to climate change mitigation and constitute a major supplier of timber/non-timber products and other ecosystem services. Globally, approximately 36% of planted forest area is located in East Asia. However, reliable records of the geographic distribution and tree species composition of these planted forests remain very limited. Here, based on extensive  According to the Food and Agriculture Organization of the United Nations (FAO)2, planted forests globally increased by 41,000 km2 per year between 2000 and 2020 and currently amounted to approximately 2,930,000 km2. Today, FAO estimates that 36% of the world’s planted forests are distributed in East Asian countries, namely China, Japan, the Republic of Korea (ROK), and the Democratic People’s Republic of Korea (DPRK)2. In East Asia, a large proportion of forest area is planted forests , while other regions in the world remain well below 20% . Unlike Western countries, where planting was traditionally conducted for silvicultural practices, East Asian countries planted trees for varying purposes with local species and unique history11.Planted forests are forest ecosystems established by artificial tree planting or seeding for the provision of income and goods, as well as for climate change mitigation and the restoration of ecosystem services and processes2. Since the end of the 1970s, China has established several afforestation projects, including the Three-North Forest Shelterbelt Program4, the Natural Forest Conservation Program , and the Grain to Green Program 6. Currently, China has committed to preserving and expanding forest cover, aiming at mitigating soil erosion, air pollution, and climate change in the coming decades7. Although hundreds of tree species have been used for plantation establishment in China, a few species dominate the planted forests across the country, such as Chinese fir (Cunninghamia lanceolata) and eucalyptus 8. In Japan, most planted forests were established after World War II to meet the growing demand for timber and other wood products. Thus, fast-growing and highly productive species, such as Japanese cedar (Cryptomeria japonica) and Hinoki cypress (Chamaecyparis obtusa), were extensively planted9. ROK underwent severe deforestation and forest degradation during World War II and the Korean War (1950–1953), followed by active conversion of forests to agricultural lands due to post-war poverty10. In response, the government implemented five National Forest Development Plans from 1973 to 2017. A variety of fast-growing species were planted during this period, and the successful recovery of healthy forests and ROK’s sustainable management strategies are internationally recognized11.East Asian countries have implemented a variety of tree-planting policies at different spatial and temporal scales. China leads all countries worldwide with the largest estimated plantation area of about 840,000 km16 and which tree species are planted17. The geospatial distribution of planted forests in East Asia still remains unclear due to a scarcity of complete, transparent, and publicly accessible data records. National governments have published some planted forest maps based on site visits, forest inventory, and satellite data. Yet, the spatial coverage is incomplete for Japan12, and the map produced by the Chinese Forest Inventory remains unverified and largely inconsistent with independent studies14. The existing large-scale maps of planted forests are based on inconsistent data sources with varying reliability and scale13 or solely based on satellite images14. Because of these differences in spatial extent, underlying data sources, and methods in existing datasets, a database that provides complete, consistent, and ground-truth-based records of the geographic distribution of planted forests and associated dominant tree species for East Asia constitutes a consistent and harmonized product.With active tree planting being implemented throughout the world for climate change mitigation, forest restoration, and biological conservation, it has become urgent to establish cost-effective guidelines for all ongoing and upcoming tree-planting projects. Assessment of the costs and benefits of planted forests, the key to the development of such cost-effective guidelines, is contingent on knowing where the existing planted forests are distributed18 and Moderate Resolution Imaging Spectroradiometer (MODIS) data to account for potential differences in forest structure and vegetation characteristics between planted and natural forests. In addition to the main products, we also estimated the upper and lower bounds of potential planted forest extent to account for the uncertainty associated with the varied quality of multi-source training data. With previous records of planted forests being inconsistent in resolution, quality, and accessibility, our map provides a complete, consistent, and in situ data-based estimation of the extent and species distribution of planted forests in East Asia.Here, we produced the spatial database of planted forests in East Asia at a 1-km resolution and identify dominant tree species in these planted forests to the genus level. Our planted forest map encompasses forests of all ages planted for various purposes, including forest restoration, commercial plantation, and disaster prevention. These mapping products are based on ensemble machine learning models, data fusion, and multi-source data of planted forests. Our multi-source data comprised ~7,000 ground-truth inventory plots in China, five independent digitized maps across the study region, as well as 57 auxiliary datasets and layers, including satellite data such as the Global Ecosystem Dynamics Investigation (GEDI)in situ inventories and digitized data sources in a high-level data fusion algorithm  and eucalyptus , and forests planted for restoration purposes. We also obtained the national planted forest map of China  and Hinoki cypress (Chamaecyparis obtusa), and disaster prevention planting, such as Japanese black pine (Pinus thunbergii) from coastal erosion and tropical species  as windbreaks. The national vegetation map has been gradually developed and improved since 2005. Finally, data specific to ROK was a polygon map of planted and natural forests from the national forest cover map  for the study region in East Asia. National planted forest maps of China15, Japan12, and ROK16, as well as SDPT13 and the Global Extent of Planted Trees14 were extracted to the centroid of each grid cell using the “sf” or “raster” packages in R268. China’s in situ observations were associated with each grid cell by taking the majority vote of in situ points within each grid cell to determine whether that cell is a planted or natural forest. Grid cells with a 50/50 vote were removed from the training dataset. We then derived the response variable – a label of “planted” or “natural” forest – based on these underlying datasets following the Quality-Oriented Data Integration (QODI).To prepare a training dataset for machine learning classification models, we prepared a 0.009° by 0.009° grid  as planted if and only if the grid cell was identified as a planted forest by either of these in situ-based datasets or identified by at least three other datasets as a planted forest.The first level of integration took the most conservative approach in deriving the lower bound of our estimation. Since China’s 15, SDPT13, and Global Extent of Planted Trees14 datasets agreed so.The second level of integration took a midway approach in which, in addition to planted forests identified in the first level of integration, a given grid cell was also labeled as a planted forest if two out of the national planted forest maps of ChinaThe third level of integration took the most liberal approach in deriving the upper bound of our estimation, in which we assumed all underlying data sources were equally reliable and labeled a given grid cell as planted forest if it was identified as a planted forest by either of these datasets.270, seven MODIS-derived vegetation characteristics, 21 bioclimatic attributes274, 13 topographic attributes275, four anthropogenic attributes279, and seven soil attributes280. We obtained four forest structure attributes from the most recent Global Ecosystem Dynamics Investigation (GEDI) dataset, namely canopy height (rh100), plant area index (pai), foliage height diversity , and total canopy cover (cover) (see Supplementary Table 269. We downloaded the raw footprint-level GEDI data (L2B), among which only full-power lasers were used in this study to ensure the accuracy of the measurement. GEDI data was processed using the “rGEDI” package in R281. Another forest structure attribute, tree height270, represents the 90th or 95th percentile of energy return height relative to the ground.We also compiled 57 predictor variables for the supervised learning of the classification models Fig. . The pre268. GEDI footprint-level data was associated with each grid cell by taking the mean value of each attribute. We kept only grid cells with a minimum of 5 m tree height in accordance with FAO’s definition of “forest”270. Our final training dataset encompassed more than 1.5 million grid cells for the upper bound dataset, 1.0 million grid cells for the midpoint dataset, and 0.9 million grid cells for the lower bound dataset, consisting of one response variable labeled as either “planted” or “natural” and 57 predictor variables. Finally, to account for the differences in terrestrial ecoregions, we divided the overall training dataset into three biomes , covering forested area (≥5 m tree height)270 in the study region with all predictor variables . We calculated a 95% CI using the t0.975 value with 19 degrees of freedom.For mapping purposes, we prepared another 0.009° by 0.009° grid , and XGBoost. RF is a non-parametric ensemble learning approach0.975 value with 9 degrees of freedom. Classification accuracy shows the proportion of overall correct prediction. While accuracy is the most widely used and intuitive evaluation metric of a classification problem, it overestimates the performance of imbalanced data. F1 score is an equal measure of precision and recall and is more appropriate for imbalanced data292. Precision represents the correct prediction of the positive class  among all positive predictions, and recall represents the correct prediction of the positive class among all actual positive cases293. Since precision and recall are in an inverse relationship, the combined metric, F1 score, provides a better evaluation perspective of incorrectly predicted cases. Using both accuracy and F1 score, we present a suite of evaluation metrics of our candidate models for both correct and incorrect predictions of an imbalanced dataset. Other potential evaluation metrics include Cohen’s Kappa. However, we did not use it in our study due to the controversy of its use294. Compared with SVM and XGBoost, the RF model was 0.7–8.1% more accurate in terms of overall classification accuracy and 1.4–4.5% more reliable in terms of F1 score  for the upper bound and midpoint datasets and 80% of the sample points for the lower bound dataset for each of the ten repetitions to create the training set and the rest composed the testing set Table . DefaultTo improve the performance of the model while minimizing the time it takes to compute, we adjusted two hyperparameters of the RF algorithm: the number of decision trees and the number of predictor variables. Similar to the cross-validation described above, we randomly sampled 50,000 points  for the upper bound and midpoint models and 80% of the sample points for the lower bound model for each of the ten repetitions to assess RF performance using different hyperparameter values Table . Specifii.e., 50% planted forest and 50% natural forest) by randomly under-sampling the dominant class. The prediction of our classification model was the percent planted forest based on how many decision trees returned the “planted” prediction. We built 20 models to derive the mean percentage for each biome and model  (Table Data fusion), we created a map covering the entire forested area in East Asia using model prediction.For the final RF model, we ensured that the training set had an equal number of points for each class (d) Table . Finallyd) Table . Grid ced) Table . Using tin situ records in China265 with the tree-level records of Japan295 and ROK296 National Forest Inventories (NFI). Specifically, we calculated importance value for each species for each NFI plot within the predicted planted forest expanse and identified the species with the highest importance value as the dominant species for the given plot. Importance value is the sum of the percent basal area and the percent number of individuals of each species and represents the overall dominance of the species298. After identifying the dominant species for each NFI plot, we aggregated the plots into the 0.009° by 0.009° grid cells by taking the majority vote of the dominant species. We retained the genus names of the dominant species, and only genera with 60 or more samples were included to ensure a sufficient size of training data.Over the planted forest expanse in East Asia identified by the final RF classification model, we predicted the dominant tree species (to the genus level) of the planted forest for each criterion Fig. . For thei.e., genus) by combining random under-sampling and oversampling using the “UBL” package in R299. To assess the performance of the RF model in mapping dominant genera across the planted forest expanse in East Asia, we performed a 90/10 cross-validation using bootstrapping. In each iteration, we used stratified sampling to split the entire training dataset into the training (90%) and testing (10%) sets using the “caret” package in R300 and conducted a combination of under-sampling and oversampling of the training set to address the class imbalance . We ensured that the training set had an equal number of points for each class (301. The database is in shapefiles where each polygon is 0.009° by 0.009° in size within the forested area of 2020 (≥5 m tree height) based on the FAO’s definition of “forest”270. Each polygon contains the following attributes:The spatial database of planted forests consists of maps of estimated planted forest distribution Figs. , 8 and dID: Polygon IDBiome: Biome classes used in the studyCountry: CountryPrc_Pln: Percent planted forest. The values represented the average of the three models . NA for ROK and a majority of areas in Japan, where national planted forest maps16 were used as the final planted/natural label . For our predicted percent planted forest, “Planted” if Prc_Pln ≥ 0.5 and “Natural” if Prc_Pln < 0.5. For Prc_Pln = NA, national planted forest maps16 were used to determine if the given polygon is a planted forest, and if not, “Natural.”Typ_Upp: “Planted” or “Natural” forests based on the upper-bound model.Typ_Lwr: “Planted” or “Natural” forests based on the lower-bound model.Genus: For Type = “Planted”, this attribute indicates the predicted dominant genus. NA for Type = “Natural”.Gns_Upp: For Typ_Upp = “Planted”, this attribute indicates the predicted dominant genus. NA for Typ_Upp = “Natural”.Gns_Lwr: For Typ_Lwr = “Planted”, this attribute indicates the predicted dominant genus. NA for Typ_Lwr = “Natural”.Besnard_Yr: Estimated planted year based on forest age302 (10.17871/ForestAgeBGI.2021). See Usage Notes.Du_Yr: Estimated planted year based on the map of planting year of plantations304 (10.6084/m9.figshare.19070084.v2). A value of 1981 indicates the planting year was before 1982, and values from 1982 to 2019 correspond to the planting years. See Usage Notes.Area_m2: Area of the planted forest polygons in square meters.301.Raster layers are also available for percent planted forest, type , and dominant genus, at 10.6084/m9.figshare.21774725.v32, ranging between 600,529 and 1,277,549 km2. China shared 87% of the planted forest area in East Asia, most of which is in the lowland subtropical and tropical regions, and Sichuan Basin , black locust (Robinia spp.), and pine (Pinus spp.) were widely observed in northern and central China, and eucalyptus dominated planted forests in tropical regions.Based on our prediction, the total area of planted forests in East Asia was 948,863 kmChamaecyparis- or Cryptomeria-dominant  covered northern planted forests  and chestnut (Castanea spp.). DPRK’s planted forests were mainly distributed in the south, largely composed of oak, larch, and pine.In Japan and ROK, planted forests were uniformly distributed across the country Fig. . More thnt Table , while osts Fig. . ROK’s p305. Underlying data included in situ and digitized planted-natural forest data:The input training data, including the response variable and predictor variables, used in this study are available at 10.6084/m9.figshare.21774812.v2Thein situobservational data of China265The Japan Vegetation Map12 (http://gis.biodic.go.jp/webgis/sc-025.html?kind=vg67)The national planted forest map of China15The national planted forest map of ROK16SDPT version 1.013 Global planted trees extent 201514 (10.5281/zenodo.3931930)Japan National Forest Inventory295 (http://forestbio.jp/datafile/datafile.html)ROK National Forest Inventory296The predictor variables used in this study are all available through open sources as follows:GEDI L2B269 (https://search.earthdata.nasa.gov/search)Tree height (https://glad.umd.edu/dataset/GLCLUC2020)MODIS (https://modis.gsfc.nasa.gov/)Corrected precipitation: PBCOR271 (http://www.gloh2o.org/pbcor/)Bioclimate data: CHELSA273 (https://chelsa-climate.org/bioclim/)Global aridity index and potential evapotranspiration: CGIAR-CSI v.2274 (10.6084/m9.figshare.7504448.v3)Topography: EarthEnv275 (http://www.earthenv.org/topography)Global cattle distribution276 (10.7910/DVN/GIVQ75)Roadless area277 (10.1126/science.aaf7166)Protected area: UNEP-WCMC278 (https://www.protectedplanet.net/en)Human footprint279 (10.5061/dryad.052q5)Soil characteristics: WISE30sec v1.0280 (https://www.isric.org/explore/wise-databases)Other data used in this study include:The Nature Conservancy (TNC) Terrestrial Ecoregions map19 the national planted forest map of China15, the national planted forest map of ROK16, and the ROK National Forest Inventory296. The sensitive information in these datasets will be available upon request via Science-i (https://science-i.org/) and approval from data contributors.All the data listed above are open access, except Quality-Oriented Data Integration (QODI) in Methods). R2 was within the range of 31% and 42% for all the GEDI attributes in Temperate Grassland and Temperate Forest  among all GEDI attributes in all groups . The number of samples for natural forests was almost 10 times greater than that of planted forests in our validation set Table . While w2 and the National Forest Inventory dataset from China309 . The predicted area of China’s planted forests was 825,751 km2 , while the FRA reports 846,960 km2 and the Ninth National Forest Inventory of China reports 795,428 km2. For Japan, the range of estimated areas of planted forests was between 103,447 and 105,633 km2, while the FRA reported value is 101,840 km2. Our estimated area of planted forests in DPRK was 7,986 km2 , while the FRA reported value is 9,870 km2. Overall, our estimate was consistent with those reported by the FRA and the National Forest Inventory of China.To further validate the quality of our prediction, we also compared our estimated total area of planted forests against the reported values from the FAO Global Forest Resources Assessment (FRA)09 Table . Our totCunninghamia and Eucalyptus had the highest F1 score , with high recall  and satisfactory precision  , which likely resulted from a small sample size compared to other genera. Acer, Alnus, Betula, Cryptomeria, Picea, Pinus, Quercus, and Tilia showed low recall compared to precision, indicating that true labels for these genera tended to be classified as other genera. Abies, Carpinus, Castanea, Castanopsis, Chamaecyparis, Cunninghamia, Eucalyptus, Fagus, Ilex, Larix, and Robinia had lower precision than recall due to the overprediction of these genera . Among all the planted tree species, in situ, digitized, and modeled datasets, uncertainties arose from two main sources. First, limited in situ data, especially from Japan, ROK, and DPRK constitute one of the largest sources of uncertainties. The limited in situ data from these countries could lead to lower accuracy in our planted forests prediction. Nevertheless, to mitigate this uncertainty, we integrated different data sources for modeling , and the final map product for these countries relied on external sources16.While this study advances the current understanding of planted forests in East Asia based on multi-source data consisting of 310. This uncertainty in tree species richness can be mitigated by integrating the mapping products presented here with recent global high-resolution maps of local tree species richness and co-limitation289. Furthermore, some genera predicted in our study had low F1 scores, which can be mitigated by increasing the sample size for these species. Nevertheless, it is not realistic to achieve perfectly balanced data, and differences in predictive performance among genera are inevitable.Secondly, our map of planted tree species depicts the spatial distribution of the dominant tree species to the genus level across the range of planted forests. However, it is beyond the scope of this study to identify the spatial distribution of monoculture planted forests versus mixed-species planted forests, the latter of which are common in certain regions12 and ROK16. Users of these particular maps should cite these sources accordingly.Our final maps of planted forest range Fig.  for JapaPlanted forests in this study include forests of all ages that have been planted for ecological restoration, commercial plantation, and other purposes, such as landscape and disaster prevention.302 and planted year304 values. Based on these two sources, some planted forests were planted more than 100 years ago, while other planted forests are less than five years in age (Fig. 302 presents consistency with planting history in each country; the majority of planted forests were established post-war in Japan, followed by efforts in the Korean peninsula, while planted forests in China come from more recent planting (Fig. Since the underlying training datasets differ by planting years, we were only able to quantify a roughly estimated range of underlying years. Specifically, we overlaid our final map with two existing map layers with estimated forest ageing Fig. . We inclSupplementary Table S1


Hemodialysis (HD) patients are at risk for sarcopenia (SP) and bone loss, which may impact falls and bone fragility and lead to poor prognosis. Patients with HD and those with osteoporosis (OP) are still underdiagnosed and untreated. The aims of the present study were to evaluate the factors that affect bone mineral density (BMD) loss in HD patients, and explore traditional and novel approaches to manage chronic kidney disease–mineral-bone disorder (CKD-MBD).Patients who underwent regular HD at the First Affiliated Hospital of Soochow University were retrospectively evaluated. According to the WHO osteoporosis criteria, patients were categorized into three groups: normal BMD, osteopenia, and osteoporosis. Demographic and clinical data, skeletal muscle mass, and bone turnover markers(BTM) were compared between the three groups. The correlation between bone density and muscle mass was calculated, and related risk factors were analyzed.r = 0.23, p < 0.01) and femoral neck . In ordinal logistic regression analysis, the odds ratio (OR) for low BMD was high for patients with sarcopenia , older age , higher TRACP-5b levels , and lower 25-OH vitamin D levels .This study enrolled 130 HD patients, 36 patients were diagnosed with sarcopenia (27.7%), 44 patients were diagnosed with osteopenia (33.8%), 19 patients were diagnosed with osteoporosis (14.6%), and 23 patients were diagnosed with osteosarcopenia (17.7%). The SMI was positively correlated with the BMD of the lumbar spine (The preservation of skeletal muscle mass could be important to prevent a BMD decrease in HD patients. Adequate intake of vitamin D and control of TRACP-5b levels will help reduce the occurrence and progression of osteopenia/sarcopenia in HD patients. Chronic kidney disease–mineral and bone disorder (CKD-MBD) are common in hemodialysis patients, with not only disturbances of calcium, phosphate, parathyroid hormone (PTH) and vitamin D metabolism but also cardiovascular calcification and bone abnormalities, leading to an increased incidence of fracture and other poor outcomes . CKD staMeanwhile, as part of HD, patients often suffer from sarcopenia, a loss of strength and mass in their skeletal muscles . Osteopon = 75) samples were selected from the Center of Health Management in our hospital. This study was approved by the ethics committees of The First Affiliated Hospital of Soochow University (2022-160), and this study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. All participant’s data were kept confidential.330 patients who underwent regular hemodialysis were screened for this study in the First Affiliated Hospital of Soochow University from January 2022 to October 2022. Adult patients were eligible to participate in the study if they: (1) aged 18–90 years; (2) underwent regular maintenance hemodialysis for >3 month, 3 times a week with 4 h sessions; and (3) were able to complete the interview. Exclusion criteria: (1) acute infection; (2) history of parathyroidectomy; (3) other major diseases ; (4) taking steroids, anti-resorptive drugs (bisphosphonates), contraceptives, or calcitonin; (5) were bedridden or unable to undergo the examination; (6) declined to participate in this study. Healthy control (Among 330 HD patients from HD centers for this study (January 2022 to October 2022), a total of 130 subjects were finally recruited for analyses in the present study.2), comorbidities, etiology of kidney disease, dialysis vintage (months) and dialysis modality were collected. The presence of diabetes mellitus (DM), and vitamin D, calcium supplement, and calcimimetic use were noted.Demographic and clinical data, including age, sex, body mass index , which was collected from patients at the end of HD. Routine laboratory parameters for CKD patients , albumin, intact PTH (PTH), 25(OH) vitamin D (25(OH)D) and β 2 microglobulin (β 2-MG) levels were analyzed at the University Hospital Central Laboratory. Serum bone alkaline phosphatase (bAP) and tartrate-resistant acid phosphatase 5b (TRACP-5b) samples were taken before the dialysis session, kept at −20 °C, and sent to a clinical laboratory of KingMed Center for Clinical Laboratory Co., Ltd for the assays, bAP was using a chemiluminescent immunoassay, and TRACP-5b was using enzyme-linked immuno sorbent assay. Venous blood samples for BNP analysis were using i-STAT BNP POC device .Body composition characteristics which include fat-free mass (FFM), soft lean mass (SLM), and skeletal muscle mass (SMM) values were measured by multifrequency segmental bio-impedance (MFBIA) , which was regularly serviced and calibrated. We performed BIA in healthy controls and HD patients while they were fasting and after they urinated to determine normal hydration status, using a standardized protocol .2).HD patients were measured 30 min after dialysis treatment to allow for re-equilibration on a midweek session ,13. BaseHandgrip strength was measured using a myometer . Two separate trials were performed using the non-fistula arm of the subject with a rest period of at least 1 min between trials, and the maximum values were recorded. Healthy participants were measured using the same equipment and in the same manner as HD patients.The usual walking speed was measured as an index of physical performance. The time taken (s) to walk 6 m at normal walking speed was recorded, and the usual gait speed was calculated.2 for men and 5.7 kg/m2 for women by using bioimpedance analysis), handgrip strength (<28 kg for men and <18 kg for women), and usual gait speed (<1 m/s). Sarcopenia was diagnosed when a patient had a reduced SMI combined with lower hand grip strength and/or slower speed.Sarcopenia was defined as proposed by the Asian Working Group for Sarcopenia (AWGS) , with cu2, was measured at the lumbar spine, total hip, femoral neck.Lumbar spine and femoral neck bone mineral density (BMD) values were determined using dual-energy X-ray absorptiometry . The measurements were performed after dialysis, according to the manufacturer’s recommended standard analysis procedures for the lumbar spine (vertebrae L2–L4) and hip femoral neck. In the same way as HD patients, healthy controls were also measured. Instrument quality control was performed regularly. Bone mass density, expressed in gm/cmT-score no less than −1.0 SD, osteopenia with a T-score between −1.0 SD and −2.5 SD, and osteoporosis for patients with a T-score less than −2.5 SD in at least one of these sites: lumbar spine, femoral neck or total hip  combined with sarcopenia and (2) osteoporosis (BMD T score of −2.5 SD or less) combined with severe sarcopenia. Thus, osteosarcopenia is a syndrome defined by the combination of low bone density (osteopenia/osteoporosis) and sarcopenia ,17.We selected the study conducted by Montenegro et al.  evaluatip < 0.05. All analyses were performed using the SPSS 26 package.Mean ± standard deviation expressed continuous variables when normal distribution and the median (inter-quartile range) had expressed them when the distribution was not normal. Qualitative variables were expressed as frequencies. Comparisons of continuous variables were performed according to distribution pattern by ANOVA or Kruskal–Wallis tests and by independent samples T-tests or Mann–Whitney tests. Differences in proportions were compared using the chi-square test. Pearson’s or Spearman’s correlation coefficients (according to continuous variable distribution patterns) were calculated to analyze the degree of association between two variables. Ordinal logistic regression analysis was performed to explore possible risk factors  associated with BMD status  as a categorical dependent variable. The results are presented as odds ratios (ORs) with 95% confidence intervals (CIs). Unadjusted and adjusted analysis Statistical significance was considered when p > 0.05).From January 2022 to October 2022, a total of 330 patients who underwent regular hemodialysis were screened, and 130 of them had sufficient data in the final analysis . Additiop < 0.01). In our study, HD patients in the 18–59 years stratum had a 4.2-fold higher proportion of Osteopenia than control groups (30.1% vs 7.1%) and a 1.5-fold higher proportion of Osteoporosis than control groups (7.2% vs 4.8%). In the 60+ age group, HD patients had a 1.9-fold higher proportion of Osteopenia than control groups (40.4% vs 21.2%) and had a 3-fold higher proportion of Osteoporosis than control groups (27.2% vs 9.1%). Patients on hemodialysis are at increased risk of osteopenia/osteoporosis than the healthy controls. Meanwhile, HD patients had higher TRACP-5b in bone turnover markers than control groups regardless of the age subgroup(p < 0.001)  .2. The main underlying diseases were glomerulonephritis , diabetes mellitus , hypertension ), other diseases including vascular nephropathy  and polycystic kidney disease , tubulointerstitial nephropathy  and unknown etiologies .The baseline characteristics of the HD patient are summarized in p < 0.001) and have a higher incidence of DM (p < 0.001), while they showed higher TRACP-5b and lower 25 hydroxyvitamin D in bone turnover markers (p < 0.05)  .p > 0.05), except albumin (p < 0.05). Patients in the osteopenia and osteoporosis groups presented with lower albumin levels than patients in the normal BMD group (p < 0.05)  .The percentage of patients receiving calcium-containing or non-calcium-containing phosphate binders, the percentage of patients receiving VDRAs and the percentage of patients receiving CaSR agonists were no differences among the three categories in p > 0.05), suggesting similar hydration status. The normal BMD group presented with higher height and weight than the osteopenia and osteoporosis groups (p < 0.01). However, BMI was similar in each BMD subgroup (p > 0.05). Patients in the osteopenia and osteoporosis groups presented with lower fat-free mass (FFM), soft lean mass (SLM), skeletal muscle mass (SMM), appendicular skeletal muscle mass (ASM), ASMI and grip strength values than patients in the normal BMD group (p < 0.05)  and the ratio of ECW/TBW were similar between the three BMD subgroups ( < 0.05) .Among the 130 hemodialysis patients, 36 patients were diagnosed with sarcopenia (27.7%), 44 patients were diagnosed with osteopenia (33.8%), 19 patients were diagnosed with osteoporosis (14.6%). Over 50% of Patients  with OP had SP, and the majority of patients (63.9%) with SP had low BMD in p = 0.016). The distribution frequency of Sarcopenia among the three BMD subgroups  was significantly different(p = 0.027)   and D).As presented in r = 0.23, p < 0.01), left femoral neck , and right femoral neck . Low SMI was associated with a higher risk for low BMD in both the lumbar spine and femoral neck , older age , higher TRACP-5b levels , and lower 25-OH vitamin D levels  (< 0.001) .Patients with CKD have a higher risk of fractures due to abnormal bone turnover, which includes high turnover (hyperparathyroid bone disease), low bone turnover or adynamic bone disease (ABD), osteomalacia and mixed uremic osteodystrophy, and is associated with high morbidity, mortality  and econWe observed that patients in the osteopenia and osteoporosis groups had lower muscle mass parameters  than those in the normal BMD group because skeletal muscle supports the bones. Ito et al. find that SMI was the independent factor that affected lumbar spine and femoral neck BMD in 50 HD patients . Other sIn general, and among older adults, muscle health and BMD are positively correlated . This isNo differences in laboratory parameters were found among the groups with normal BMD, osteopenia, and osteoporosis, including those related to Hb, hsCRP, and bone mineral metabolism . We used various diagnostic and therapeutic strategies to appropriately prevent and manage the CKD-MBD status of patients undergoing HD, therefore the CKD-MBD-related data were well controlled. This might explain why these markers were not independent risk factors associated with BMD in the present study. Kazama JJ found that PTH level was negatively correlated with cortical bone thickness, but this relationship disappeared among those with intact PTH levels <1000 pg/mL . In our As both muscle and bone express vitamin D receptors, vitamin D plays a crucial role in muscle and bone development . Aside fTherefore, it is important that the assay be used for HD patients to monitor BTMs, especially TRACP-5b and vitamin D levels, which might help identify patients who might benefit the most from bone-preserving measures. Furthermore, appropriate medications can be used to achieve optimal 25OH-D and TRACP-5b levels and to control the incidence of sarcopenia and OP while on HD.However, there are limitations to this study. First, the sample size was relatively small. Second, this was a cross-sectional study, so causality cannot be inferred. A prospective, longitudinal study is needed to investigate the relationship between bone loss and its influencing factors in CKD-MBD. Larger multicenter cohort studies and corresponding basic research are needed to elucidate the pathogenesis and influencing factors of BMD loss, with long-term follow-up to explore its impact on fracture risk and mortality risk.The prevalence of SP, OP and osteosarcopenia was high in this single-center cross-sectional study of patients on maintenance dialysis in China. Individuals with advanced age, sarcopenia, lower 25-OH vitamin D levels and higher TRACP-5b levels tended to have a higher risk of BMD loss, suggesting that HD patients with bone loss should be assessed for concomitant SP and BTM. Adequate intake of vitamin D and control of TRACP-5b levels will help reduce the occurrence and progression of osteopenia/sarcopenia in HD patients. Therefore, it may be time to reevaluate the diagnostic and therapeutic protocols for bone loss prevention in CKD-MBD. Novel therapeutic approaches to treat musculoskeletal pathologies should be explored and novel approaches with integrated management need to supplement the traditional management of CKD-MBD.


To evaluate the predictive value of monocyte (M) to high-density lipoprotein cholesterol (HDL-C) ratio (MHR) and tumor markers in colorectal cancer (CRC) and their correlation with clinicopathological characteristics.Hematology test data and medical records of 202 CRC patients and 201 healthy subjects were collected retrospectively. The diagnostic efficacy of MHR was evaluated using receiver operating characteristic (ROC) curves and risk factors for CRC were analyzed by multivariate logistic regression.P < 0.05). Additionally, MHR was positively correlated with tumor differentiation in CRC patients (P = 0.049); CEA and CA199 levels in CRC patients increased with increased stage, lymph node metastasis and tumor size ≥ 5 cm . Furthermore, high levels of MHR, CA199 and CEA were independent risk factors for CRC. The area under ROC curve of MHR combined with CEA and CA199 was 0.882/0.869 for the diagnosis of CRC, respectively.CRC patients had significantly higher M, MHR, carcinoembryonic antigen (CEA), and carbohydrate antigen 199 (CA199) levels, but significantly lower HDL-C levels than healthy controls (all This is the first study to explore the predictive value of MHR in CRC, and its continuous increase is an independent risk factor for CRC. MHR is a promising predictor for CRC progression along with CA199 and CEA. Colorectal cancer (CRC) refers to cancers that arise from the colorectal epithelium, including colon cancer and rectal cancer. According to statistics, CRC mortality in the United States ranked second among all malignancies in 2020 , 2. SimiMonocyte count  to high-density lipoprotein cholesterol  ratio (MHR) is a new prognostic marker for inflammation associated with cardiovascular diseases. MHR has been used in studies of diabetic retinopathy, nonalcoholic hepatitis and atherosclerosis –9. CarciA total of 202 CRC patients (CRC group) diagnosed by histopathology who were hospitalized in our hospital from March 2018 to June 2022 were enrolled . 201 healthy individuals  who underwent physical examination at our hospital during the same period were also included. This study was approved by the Ethics Committee of The First Affiliated Hospital of Guangxi Medical University . All the participants were orally informed and agreed to participate. There was no significant difference in age between the two groups. Inclusion criteria for the CRC group: (1) CRC confirmed by histopathology; (2) Hematology, blood biochemistry, blood lipid and tumor marker tests before surgery. Exclusion criteria: (1) After undergoing surgery or treatment; (2) other tumors or malignancies; (3) serious infection and chronic disease.Fasting venous blood was collected from all patients, and monocytes were counted using the LH780 analyzer within 2 h. Aspartate aminotransferase, alanine aminotransferase (ALT), and other parameters were measured using a biochemical analyzer, and CEA, CA199 and carbohydrate antigen 125 (CA125) levels were measured using the Roche immunology analyzer. According to the clinicopathological data of CRC patients, the clinical stage of each patient (stage I to IV) was determined based on the 8th edition of the TNM Classification of Malignant Tumors published by the American Joint Commission on Cancer (AJCC).P < 0.05 was considered statistically significant.Data were analyzed using SPSS 20.0 . Count data were expressed as frequency and percentage, and measured data were expressed as mean ± standard (X ± SD) or mean (quartile). Data were compared between groups using the independent sample T-test or Mann–Whitney U-test, and correlation was analyzed using Spearman correlation analysis. Risk factors for CRC were identified by multivariate logistic regression. A As shown in Table P = 0.014), total protein (P < 0.001), total cholesterol (P < 0.001), Triglyceride (P < 0.001), M (P < 0.001), HDL-C (P < 0.001), MHR (P < 0.001), CEA (P < 0.001), CA199 (P < 0.001) levels were significantly different between CRC patients and healthy controls (Table The ALT (ls Table . HDL-C wP = 0.049). CEA was significantly different from tumor stage (P < 0.001), lymph node metastasis (P = 0.004), tumor size (P = 0.013) and tumor type (P = 0.017). Meanwhile, there were significant differences between CA199 and tumor stage (P = 0.001), lymph node metastasis (P < 0.001), and tumor size (P = 0.013). Of note, MHR of CRC patients increased with the low degree of tumor differentiation , lymph node metastasis (P/r =  < 0.001/0.251), tumor size (P/r = 0.012/0.176), and negatively correlated with body mass index (P/r = 0.041/-0.144). Similarly, CEA , lymph node metastasis (P/r = 0.004/0.203), tumor size (P/r = 0.012/0.176), and tumor type (P/r = 0.016/0.169) in CRC patients.As shown in Table P < 0.001) CA199 (P = 0.040) and CEA (P < 0.001) were independent risk factors for CRC. Especially, individuals with a high MHR level were 14.79 times more likely to suffer from CRC than those with a low MHR level.As shown in Table P < 0.05).As shown in Table The area under ROC curve (AUC) of MHR, CEA, CA199 for the diagnosis of CRC was 0.842, 0.723, 0.604, respectively. However, the AUC of MHR combined with CEA, and CA199 for the diagnosis of CRC was 0.882, and 0.869, respectively, which were higher than those of CEA, and CA199 alone  enters the intima, followed by oxidative modification, which damages the intima. Damaged vascular endothelial cells express adhesion molecules which allow monocytes to bind and migrate to the lower endothelium and mature into macrophages. Oxidized LDL-C promotes the release of pro-inflammatory cytokines and induces chronic inflammation, thereby facilitating the occurrence and development of CRC. HDL is involved in cholesterol reverse transport and has antioxidative and anti-inflammatory effects . Low ChoWhen the relationship between MHR level and the clinicopathological characteristics of CRC patients was analyzed, it was found that MHR level was positively correlated the degree of tumor differentiation in CRC patients. The degree of tumor differentiation is defined as the closeness between tumor cells and normal cells. The lower the degree of tumor differentiation, the higher the malignancy of the tumor. Tumor tissues are similar to the immature morphology of the source tissue, which are greatly different from the corresponding normal source tissue, and grow faster and metastasize more easily. In this study, the MHR increased as the degree of tumor differentiation decreased, indicating that the tumors in patients with high MHR tend to be poorly differentiated, and the degree of malignancy is higher in patients with high MHR than in those with low MHR. Therefore, a continuous increase of MHR serves as a promising marker for CRC progression.It is well known that serum tumor markers are closely associated with tumor occurrence and development, including CEA, CA199. CEA is a glycoprotein produced by CRC tissues which can be used to assess the existence, development and prognosis of various tumors clinically. CA199 has been used for the diagnosis of adenocarcinoma since its discovery in 1979, and is expressed in various malignant tumors. It is commonly used for disease detection and efficacy evaluation, and is particularly detected in CRC. Some studies have shown that simultaneous detection of various tumor markers can improve the detection rate of malignant tumors , 22. In TNM staging developed by the AJCC is the most widely used CRC staging system and has demonstrated remarkable value in early evaluation, surgical selection, treatment scheme selection and prognosis evaluation . TherefoThe AUC of the ROC curves indicated that MHR has the highest diagnostic efficacy for CRC compared with CEA and CA199. However, combination of MHR with CEA and CA199 showed better diagnostic efficacy and was closely associated with the clinical features of CRC.Several limitations of this study include the retrospective nature of the study, small sample size, and the same region of origin of the patients, which may introduce selection bias in the data. Therefore, a large-cohort multicenter study will be warranted to further confirm these findings.In summary, this is the first study to show that a continuous increase of MHR is an independent risk factor for CRC. MHR is a promising new indicator, and its combination with CEA and CA199 has great predictive value for CRC progression.


Hepatocellular carcinoma (HCC) is the most common type of cancer and causes a significant number of cancer-related deaths worldwide. The molecular mechanisms underlying the development of HCC are complex, and the heterogeneity of HCC has led to a lack of effective prognostic indicators and drug targets for clinical treatment of HCC. Previous studies have indicated that the unfolded protein response (UPR), a fundamental pathway for maintaining endoplasmic reticulum homeostasis, is involved in the formation of malignant characteristics such as tumor cell invasiveness and treatment resistance. The aims of our study are to identify new prognostic indicators and provide drug treatment targets for HCC in clinical treatment based on UPR-related genes (URGs).Gene expression profiles and clinical information were downloaded from the TCGA, ICGC and GEO databases. Consensus cluster analysis was performed to classify the molecular subtypes of URGs in HCC patients. Univariate Cox regression and machine learning LASSO algorithm were used to establish a risk prognosis model. Kaplan–Meier and ROC analyses were used to evaluate the clinical prognosis of URGs. TIMER and XCell algorithms were applied to analyze the relationships between URGs and immune cell infiltration. Real time-PCR was performed to analyze the effect of sorafenib on the expression levels of four URGs.Most URGs were upregulated in HCC samples. According to the expression pattern of URGs, HCC patients were divided into two independent clusters. Cluster 1 had a higher expression level, worse prognosis, and higher expression of immunosuppressive factors than cluster 2. Patients in cluster 1 were more prone to immune escape during immunotherapy, and were more sensitive to chemotherapeutic drugs. Four key UPR genes  were established in the prognostic model and HCC patients with high risk score had a worse clinical prognosis. Additionally, patients with high expression of four URGs are more sensitive to sorafenib. Moreover, ATF4 was upregulated, while GOSR2, PDIA6 and SRPRB were downregulated in sorafenib-treated HCC cells.The UPR-related prognostic signature containing four URGs exhibits high potential application value and performs well in the evaluation of effects of chemotherapy/immunotherapy and clinical prognosis. Liver cancer has become the main cause of cancer-associated deaths worldwide. It is predicted that 1.3 million people will die of liver cancer by 2040, an increase of 56.4% over 2020 .protein kinase R (PKR)-like ER kinase (PERK), inositol requiring enzyme 1 (IRE1) and activating transcription factor 6 (ATF6)  –11. The 6 (ATF6) , 14. UndAn increasing number of studies have indicated that UPR is significantly involved in many physiological processes, such as promoting cell survival, regulating angiogenesis and modulating the immune response during cancer progression . TherefoIn this study, we used UPR-related genes (URGs) to analyze the precise roles of UPR pathway in the progression of HCC, as well as its immune infiltration characteristics and correlation with drug sensitivity. According to the expression of URGs, we used consensus clustering to divide the patients into two subgroups. We analyzed the mutation maps, signaling pathways, and differences in immune infiltration characteristics in two distinct clusters. We built a risk prognostic model and predicted drug sensitivity. The high-risk group corresponded to a lower survival rate and higher chemotherapeutic responses, which suggested that URGs had potential application value and guided clinical therapies in the clinical treatment of HCC patients. Meanwhile, we identified four core URGs with high prognostic value in HCC and validated that sorafenib affected the expression of these four URGs. The flow chart of this study is shown , ICGC  database (https://dcc.icgc.org/releases/current/Projects) and GEO (Gene Expression Omnibus) database (https://www.ncbi.nlm.nih.gov/geo/). In the TCGA database, we collected 371 HCC samples and 50 normal samples. In the ICGC database, we collected 240 HCC samples and 202 normal samples. GSE14520 and GSE36376 datasets were used to analyze the expression changes of four key URGs in normal and HCC tissues. The TNMplot database  was used to compare the expression of UPR-related signature in HCC tissues and normal tissues.The mRNA expression data and clinical profiles were provided by the TCGA (The Cancer Genome Atlas) database (2(fold change)| > 0.58 and P < 0.05 were set as a threshold to screen the differentially expressed genes (DEGs) between HCC and normal samples. Then, we obtained genes associated with UPR in the MSigDB (http://software.broadinstitute.org/gsea/msigdb/index.jsp) and UALCAN  databases, and intersected them with DEGs to finally obtain 31 URGs for establishing a UPR-related gene set. The R language packages “ggplot2”, “ggunchained” and “pheatmap” were used. Signaling pathway enrichment analysis was completed by the Metascape database (https://metascape.org/gp/index.html#/main/step1).In the TCGA dataset, |LogThe R language package “ConsensusClusterPlus” was used to investigate the underlying molecular clusters based on the expression of 31 URGs. Principal component analysis (PCA) was used to evaluate the expression patterns of URGs. The R package “pheatmap” was used to draw a clustering heatmap. The R packages “survival” and “survminer” were used for Kaplan–Meier (KM) analysis.Univariate Cox analysis was utilized to screen prognosis-related UPRs based on the TCGA and ICGC databases. Least absolute shrinkage and selection operator (LASSO) regional analysis was further carried out to build a risk scoring model with the R language package “glmnet”. In the risk scoring model, risk score = sum of coefficients × prognostic URG expression. The HCC samples were separated into high-risk and low-risk groups by means of the median risk score as the cutoff point. KM analysis was performed to assess the differences in prognosis using the log-rank test. Receiver operating characteristic (ROC) curves were generated using the R package “timeROC” to evaluate the accuracy of the risk scoring model.P value, HR and 95% CI in regression analysis. The “rms” package was used to make nomogram diagrams based on URG expression, age tumor (T), node (N), metastasis (M) and grade. The predictive and discriminative ability of the nomogram for 1-, 3-, and 5-year OS was assessed using ROC and concordance index (C-index) using the R package “survival”. Calibration curves were used to evaluate the difference between predicted results and actual survival rate of patients.The R language package “forestplot” was used to construct the forest plot and display variables such as The R language package “maftools” was applied to visualize the mutation maps of two clusters and generate the waterfall plots.2(fold change)| > 0.58 and P <0.05 were used as thresholds. The “ClusterProfiler” package was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs between cluster 1 and cluster 2.The R language package “limma” was used to select DEGs between cluster 1 and cluster 2. |LogThe R language package “immunedecov” was utilized to calculate the infiltrated abundance of immune cells in two clusters according to the XCell and TIMER algorithms. The “pheatmap” package was used to display the abundance of immune cells in each HCC sample.https://tide.dfci.harvard.edu) was used to evaluate the difference in immunotherapy response between two clusters.We analyzed the difference in the expression of common immune checkpoint genes between cluster 1 and cluster 2, and used the “ggplot2” and “ggpubr” packages for visualization. The tumor immune dysfunction and exclusion (TIDE) algorithm (https://www.proteinatlas.org/). To verify the expression of core URG expression in different cells in HCC tissues, single-cell RNA-sequencing results were obtained from the Human Liver Browser (https://itzkovitzwebapps.weizmann.ac.il/webapps/home/session.html?app=HumanLiverBrowser).Immunohistochemical staining (IHC) results of normal liver tissues and HCC tissues were obtained from the Human Protein Atlas (HPA) database (https://www.cancerrxgene.org/) and R language package “pRRophetic” were used to evaluate the sensitivity of two clusters to chemotherapeutic drugs, and the half maximum inhibitory concentration (IC50) was calculated by ridge regression. The correlations between IC50 and the risk score and expression of core URGs were evaluated.The Genomics of Drug Sensitivity in Cancer (GDSC) database (2) as described previously  supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin in a humid atmosphere . The Wilcoxon test was used to compare the significant differences between two groups. The Kruskal–Wallis test was applied to estimate the differences among three groups. The log-rank test was used for KM analysis. Pearson correlation analysis was used to measure the correlation between the two groups. 2(fold change)| > 0.58 and P < 0.05 , we comprehensively considered the following three aspects. First, we collected mRNA expression data from 371 HCC samples and 50 normal samples in the TCGA database. A total of 7509 DEGs were selected according to |LogP < 0.05 S2A. SecWe investigated the expression of 31 URGs in HCC and normal samples using the TCGA database. Among 31 URGs, ARFGAP1, ATF4, ATF6, ATP6V0D1, BAG3, CALR, CXXC1, DCTN1, FKBP14, GOSR2, HSP90B1, HSPA5, HSPA9, HYOU1, KDELR3, NPM1, PDIA6, PREB, SHC1, SRPRB, SSR1, STC2, TATDN2, TSPYL2, WFS1 and WIPI1 were significantly upregulated in HCC samples  and cellular response to unfolded protein  from 2 to 6, and found that the well clustering effect was obtained when k = 2 Table 2.P < 0.05 | > 0.58 and 6A methylation play key roles in tumorigenesis, metastasis and immune escape in HCC. We then analyzed the expression of ferroptosis-related genes and m6A-related genes between C1 and C2. Most ferroptosis-related genes and m6A-related genes were upregulated in C1 compared with C2 (Ferroptosis is a new type of programmed cell death and N6-methyladenosine (m6A) methylation is a common modification in eukaryotic mRNA. Both ferroptosis and m with C2 S5B.Because identified 31 URGs and DEGs between C1 and C2 were involved in immunity-related pathways   and advanced grade (G3+G4), early stage (T1+T2) and advanced stage (T3+T4), M0 and N0, TNM stage I+II and TNM stage III+IV. In the above clinicopathological parameters, the OS of high-risk group was worse than that of low-risk group (To further verify the expression pattern of four URGs in liver cells, we collected single-cell RNA-sequencing data from the Human Life Browser database. Four URGs were expressed not only in HCC cells but also in immune cells S10B. WeSorafenib is the first-line drug for the treatment of advanced liver cancer. We then analyzed the relationship between sorafenib sensitivity and expression of 4 URGs. The IC50 of sorafenib was significantly negatively correlated with the expression of 4 URGs (To further confirm whether sorafenib could alter the expression level of four URGs, HepG2 and Huh7 cells were treated with different concentrations of sorafenib for 24 h. Consistent with previous results, cell viability was decreased in a dose-dependent manner (Liver cancer is still a serious public health problem that globally endangers people’s health. HCC is the most common pathological type. At present, the survival rate of HCC patients is still not optimistic, and there is a lack of appropriate clinical prognostic indicators in HCC treatment. It is critical to further understand the pathological molecular mechanisms of HCC and identify appropriate clinical prognostic indicators. UPR plays an important role in maintaining protein homeostasis in cells under numerous intracellular and extracellular stresses and closely associates with oncogenesis , 25. In We analyzed the role of UPR in the HCC from the expression level and immune cell infiltration, constructed a risk score model, and evaluated the potential value of URGs as clinical prognostic indicators of HCC. Thirty-one URGs were selected for further analyses Figure 1Over the last decade, emerging evidence has revealed that UPR is closely related to tumor initiation, progression, metastasis and chemoresistance. Previous studies have demonstrated that various UPR-related genes, including XBP1s, eIF-2α, CHOP, ATF4 and ATF6, are overexpressed or activated in many tumor cells –32. HighUPR not only affects the survival of cancer cells but also participates in tumor immunosuppression. Immunotherapy has become a major therapy for patients with advanced HCC. Cancer cells also recruit immune cells and cause immune cells to lose their original functions, such as destroying the antigen transmission function of dendritic cells . VariousA recent study has reported that high level of UPR score is related with worse prognosis in HCC patients . In the To improve the prognosis of HCC patients, a growing number of therapeutic strategies and chemotherapy regimens are gradually emerging. However, systemic chemotherapy for HCC has limited value in clinical practice at present, because only a small portion of patients obtain significant effects according to clinical trial results –47. HereIn summary, we identified two clusters according to UPR-related gene expression, and constructed and verified a novel UPR-related signature with excellent prognostic potential using different independent datasets. Additionally, this signature was significantly associated with the tumor immune microenvironment and immunotherapeutic/chemotherapeutic responses in HCC patients. In summary, this UPR-related signature serves as a clinical prognostic indicator of HCC, clarifies the molecular mechanism of prognosis, and provides guidance for the clinical management of HCC patients.The original contributions presented in the study are included in the article/Study concept and design: KT and HG. Acquisition of data: KT, HG, SZ, BZ and YS. Analysis and interpretation of data: KT, HG, SZ, BZ, YS, XL and MW. RT-PCR analysis: HG, MW and SZ. Statistical analysis: KT, HG, SZ and HW. Drafting of the article: KT. Critical revision and final approval of the article: HG, YF and KT. Obtained funding: KT. Study supervision: KT. All authors contributed to the article and approved the submitted version.