The sympathetic nervous system plays a vital role in various regulatory mechanisms. These include the well-known fight-or-flight response but also, for example, the processing of external stressors. In addition to many other tissues, the sympathetic nervous system influences bone metabolism. This effect could be highly relevant concerning osseointegration, which is responsible for the long-term success of dental implants. Accordingly, this review aims to summarize the current literature on this topic and to reveal future research perspectives. One in vitro study showed differences in mRNA expression of adrenoceptors cultured on implant surfaces. In vivo, sympathectomy impaired osseointegration in mice, while electrical stimulation of the sympathetic nerves promoted it. As expected, the beta-blocker propranolol improves histological implant parameters and micro-CT measurements. Overall, the present data are considered heterogeneous. However, the available publications reveal the potential for future research and development in dental implantology, which helps to introduce new therapeutic strategies and identify risk factors for dental implant failure. Improvement of osseointegration, biological compatibility and reduction of morbidity are intense research topics in implant dentistry. New technologies, materials and insights into the mechanisms underlying failure and success should help to address these challenges ,2.There was an enormous increase in the prevalence of dental implants between 1999 and 2016 in the US in individuals aged between 55 and 64 years by ~1000% . The tecThe mechanism responsible for the stability of the construction and its outstanding biomechanical properties is the so-called osseointegration . It has Implant placement is foremost an injury to the bone, and the reaction to this process is similar to that of a fracture . First, The initially formed disorganized woven bone is now further remodeled to distribute occlusal loads . At the The view on osseointegration as fracture healing alone explains some physiological processes, but research shows that there are other aspects to consider that add to the complexity. For example, titanium or titanium oxide are not bioinert . They inStress and mental strain are a severe burden of today’s society , which aPhases of healing and remodeling prolong the treatment time until the patient receives the finished prosthesis. Efforts to shorten it, for example, by immediate implant placement and loading, are not always practical and sometimes show reduced success rates . FurtherThus, this review aims to summarize the current state of basic research in dental implantology on the sympathetic nervous system and to link it with findings from other fields. It will identify future research needs and open up new perspectives. It serves as a basis for a central aspect of scientific and clinical research in dental implantology.−What influence does the SNS have on bone metabolism?−How does the SNS influence the osseointegration of dental implants?−What are the underlying mechanisms?−What is the role of drugs whose target is the SNS?This review was guided by the PRISMA checklist and statement and was registered in the International Prospective Register of Ongoing Systematic Reviews (PROSPERO) (ID: 389222). We wanted to clarify the following questions:Suitable PICO criteria were defined for this purpose:-P: Animals with dental implant surgery-I: Influencing the sympathetic nervous system through an applied intervention or medication-C: Implant placement without any additional treatment regarding the SNS-O: Histological, biomechanical, and radiographic measurementsA systematic electronic literature search was performed in the databases Medline (PubMed), Cochrane and Web of Science. The reference list and citations were also searched for relevant studies. The last check was performed on 17 August 2022. The search was documented using commercially available spreadsheet software (Microsoft Excel). Using the citation software Endnote 20, the results were collected, and duplicates and triplicates were excluded.J.K. performed the initial search independently. E.S. subsequently controlled it. After duplicate exclusion, J.K. determined the study eligibility based on the title and abstract. If there was disagreement with the other authors, consensus was reached in a joint discussion. One of the aims of this review was to provide a comprehensive overview of the topic in basic research. For this reason, a wide range of study types was accepted only in that field. Clinical studies, such as observational studies, randomized controlled trials, controlled and uncontrolled trials, systematic reviews with and without meta-analyses, longitudinal studies, case studies and series, were excluded. On the other hand, all non-clinical studies, such as laboratory research, animal studies, in vitro or ex vivo studies, or post-mortem studies, were suitable. The animal studies were rated using the SYRCLE’s tool for assessing the risk of bias . There wOnly studies that concerned dental implantology and the influence of the sympathetic nervous system through an applied intervention or medication or investigated relevant structures of the SNS were included. The broad field of search terms explains the extent of results that could be excluded with ease because they did not thematically correspond in any way to the aim of this review.The first search yielded 1244 results. These could be reduced to 1078 after the exclusion of duplicates. Since the search terms were very general, many publications were thematically far from the desired results . In the Five studies were excluded because they were related to stress and anxiety during surgery ,44,45,46Eight studies remained that fulfilled the inclusion criteria ,73,74,75The studies by Tekin et al. and Zhou et al. showed a high risk of bias in four out of ten categories, while Al-Subaie et al. had a low risk of bias in seven categories (three were unclear) ,73,74. TThe results of the eight included studies are now briefly presented see also .Morinaga et al. investigated the influence of the Neuronal PAS domain protein 2 on osseointegration . Therefop = 0.002), but this was not present after 9 weeks .The most recent study by Tavakoli et al. investigated the effect of propranolol on dental implants in four street dogs with three implants each . Two dogThree publications were prepared by a research group at Firat University ,71,72. TTekin et al. did not find a significant difference in biomechanical testing with the reverse torque test . HoweverGunes et al. additionally used a bovine bone graft. However, no significant differences were found in the histological parameter of newly formed bone tissue to the grafted area and the blood values .Yildirim et al. found no differences in blood values, but a significantly higher BIC ratio was noted by histologic analysis .3 vs. 2.04 ± 0.29 mm3, p < 0.001). The ratio of bone volume to trabecular volume also increased. Histological analysis showed fewer osteoclasts on the surface of the bone defects. The amount of mineralized tissue and collagen increased. As in the study of Yildirim et al., BIC was assessed and consistently increased in the test group.The fifth study on the effect of propranolol on dental implants in rats by Al-Subaie et al. did not use realistic dental implants, but simulated them using titanium cylinders . With a Zhou et al. did not want to block the SNS, but to promote its effect . For thiThe electrical stimulation showed only slight effects on the cardiovascular system. However, it resulted in greater new bone formation: after 4 weeks, a gap between bone and implant was still detectable in the control group; in the test group, it had largely healed. Bone mineral density increased significantly in the stimulated group after 4 weeks (0.62 ± 0.05 vs. 0.47 ± 0.07). Periodontal and peri-implant bone indices showed no differences after electrical stimulation. Thus, pre-existing bone was not affected.Harvested primary osteoblasts and sympathetic neurons were isolated from the rats and cultured separately or together. The survival rate of osteoblasts was higher in coculture with sympathetic neurons. Microscopy showed direct communication of neurons with osteoblasts.Yao et al. compared n = 20 chemically sympathectomized mice with n = 20 others as a control group . In all Micro-CT examinations showed significant differences for all measured values after 4 weeks. The quotient of bone volume and total volume, the proportion of osseointegrated implant surface, and the average thickness and number of trabeculae were reduced in the sympathectomy group. The quotient of bone surface area and bone volume, as well as the average distance of the trabeculae, increased.Histological examination showed no significant difference in bone implant contact ratio at week 2, whereas, after 4 weeks, BIC increased by only 43.6% in test animals and 71% in the control group. The measured mineral apposition rate and the bone formation rate per bone surface indicated an impairment of osseointegration by sympathectomy.A push-in test was performed to test the biomechanical properties. The bone implant integration strength was 37% higher in the control group.Bone remodeling is a central part of osseointegration. Some researchers investigated the influence of the sympathetic nervous system on bone. The autonomic nervous system is roughly divided into the sympathetic and parasympathetic nervous system . The SNSThe fibers of the SNS innervate various tissues ,82. The In bone, sympathetic nerve fibers could also be detected immunohistochemically via TH, dopamine beta-hydroxylase (another enzyme in NE synthesis), and neuropeptide Y . They arFinally, α- and β-adrenoceptors (ARs) mediate the effect of NE at the target tissue. These heptahelical G-protein-coupled receptors trigger different signaling cascades intracellularly .q coupled, so via activation of phospholipase C, Phosphatidylinositol 4,5-bisphosphate is cleaved into inositol trisphosphate and diacylglycerol. An increase in intracellular calcium concentration occurs and mediates, for example, the contraction of smooth muscle cells [α1-ARs can be further divided into three subgroups A, B, and D. All are Gle cells . The α1-le cells . α1B- anle cells ,99 and αle cells . Howeverle cells .i/o coupled. All three subgroups  relay the signal via inhibition of adenylate cyclase [α2-ARs, in contrast, are presynaptic receptors that are G cyclase . In part cyclase ,103. Oth cyclase . Again, s-coupled and increase the cyclic adenosine monophosphate (cAMP) concentration in the cell via the activation of the adenylate cyclase. cAMP binds the regulatory subunit of protein kinase A (PKA), which can then phosphorylate and regulate enzymes [It is believed that β-receptors play the most significant role . There a enzymes . β1- and enzymes ,109,110. enzymes . RANKL b enzymes . These r enzymes .The comparison of β2-, β1- and β1/2-deficient mice suggests that β1 and β2-ARs cause different effects on bone turnover: as expected, β2-deficient mice exhibited a high bone mass phenotype, whereas mice missing β1-AR, as well as β1/2, showed a lower bone mass .Overall, the dominant effect of the SNS on bone via α- and β-adrenoceptors is likely to be catabolic. Further studies will be necessary to investigate the interplay of different cell types and receptors, particularly in humans.Preclinical research offers the opportunity for extensive standardization, even of the test subjects, which is not achievable in clinical studies . Since tAlthough Morinaga et al. did not primarily focus on adrenoceptors, it is a study with some relevance to this review . They liIndeed, that BMSCs express ARs is widely known and proven. Hedderich et al. showed, in a high-quality study, that the proliferation of BMSCs is affected by NE through the PKA and ERK1/2 signaling pathway . HoweverThe following five studies focused on the effect of propranolol and provided a first answer to the fourth question mentioned in the methods section.The results of Tavakoli et al. suggest that propranolol has a favorable effect on bone metabolism, especially in the first phase . The strRegarding the publications from Firat University, the first item to discuss is the choice of the experimental animal ,71,72. RThe difference found in alkaline phosphatase concentration, a classic parameter of bone metabolism, must be critically questioned because it has a lower specificity compared to modern bone remodeling values . CalciumApart from propranolol, further work is necessary using specific agonists or antagonists at the corresponding receptors. Overall, these three studies are still insufficient due to their weaknesses and the inhomogeneity of the results. A comprehensive study that does not focus solely on one BB with a nonspecific effect would be helpful. In addition, more parameters should be measured in one experimental set.The study by Al-Subaie presents the results in a more multi-layered way by looking at both micro-CT and histology . A disadThe effects presented are comparable to results found for BMP-2, which showed an improvement in osseointegration of almost 50% ,125. TheThe change in osteoclast numbers due to the suppression of RANKL activation is consistent with other studies ,134. In As in the study by Tekin et al., the rats were still growing at the age of about 2.5 months. Al-Subaie concluded, based on a publication by Aguirre, that they respond less to drugs affecting the bone .At first glance, one would like to think that Zhou’s study contradicts the work on propranolol . HoweverIt would be reasonable to assume that the electrical stimulation could lead directly to altered bone conditions. On the contrary, the electrodes were located far from the actual implantation site. On the other hand, studies by Dergin showed that electrical stimulation alone had no effect. Thus, it becomes more likely that the SNS is especially relevant ,141.The cell culture results should be interpreted with caution. It is rather unphysiological that the cell bodies grow directly adjacent, although direct contact of all bone cells to sympathetic fibers could be demonstrated ,89. StimYao et al. used a chemical sympathectomy to eliminate the sympathetic nervous system . This meThe present review aimed to shed light on the effect of the sympathetic nervous system in dental implantology and to discuss its relevance for future research. Despite the partially heterogeneous results, this topic holds a non-negligible potential. The goal for the future is to bridge the gap between laboratory and clinical applications.Clinical studies showed that antidepressants that increase norepinephrine concentrations reduce implant success, while beta-blocker therapy failed to show any differences in implant stability ,67. ThesShould an effect be confirmed, imaginable applications would be systemic therapy and a local application of sympathetically active substances. Thus, oriented to work on BMP, the drugs identified as most effective could be applied to implant surfaces , furtherSong et al. compared the fracture risk of elderly patients under antihypertensive therapy in a study with over 500,000 patients and found no protective effect of unselective BB. For β1-selective BB users, however, the fracture risk was about 35% lower . PossiblBut not only the therapeutic application could be deduced: an overactive sympathetic nervous system could be a risk factor. The study by Hakam et al. already showed that antidepressants acting on NE release might be of relevance for implant survival . A recenThe so-called human factors are gaining importance throughout medicine ,158,159.As a final point, the focus of this review was mainly on bone and osseointegration due to the results in the literature. The soft tissue, which is essential for successful implant therapy ,165 and Overall, the sympathetic nervous system offers potential for dental implantology, especially since most current studies lack sufficient quality. Therefore, the first two questions of this review can be discussed, but not finally answered right now. Publications from other specialties mention the influence on bone metabolism, and there seem to be complex interactions between NE, its cotransmitters and the systemic effect of the SNS. NE might have a catabolic effect on bone cells, while the findings on sympathectomy and SNS activation are not consistent ,116,145.Medication and its influence on dental implants are highly relevant, but the data are insufficient to provide clinical recommendations for sympathomimetic and sympatholytic drugs. Nevertheless, basic research provides the first approach to answering the fourth question.The fascination with influencing the remodeling of an amazingly dynamic tissue such as bone via the nervous system  will hop


Little is known about the recent status of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) in the U.S. emergency department (ED). This study aimed to describe the disease burden  of AECOPD in the ED and to investigate factors associated with the disease burden of AECOPD.Data were obtained from the National Hospital Ambulatory Medical Care Survey (NHAMCS), 2010–2018. Adult ED visits (aged 40 years or above) with AECOPD were identified using International Classification of Diseases codes. Analysis used descriptive statistics and multivariable logistic regression accounting for NHAMCS’s complex survey design.There were 1,366 adult AECOPD ED visits in the unweighted sample. Over the 9-year study period, there were an estimated 7,508,000 ED visits for AECOPD, and the proportion of AECOPD visits in the entire ED population remained stable at approximately 14 per 1,000 visits. The mean age of these AECOPD visits was 66 years, and 42% were men. Medicare or Medicaid insurance, presentation in non-summer seasons, the Midwest and South regions (vs. Northeast), and arrival by ambulance were independently associated with a higher visit rate of AECOPD, whereas non-Hispanic black or Hispanic race/ethnicity (vs. non-Hispanic white) was associated with a lower visit rate of AECOPD. The proportion of AECOPD visits that were hospitalized decreased from 51% to 2010 to 31% in 2018 (p = 0.002). Arrival by ambulance was independently associated with a higher hospitalization rate, whereas the South and West regions (vs. Northeast) were independently associated with a lower hospitalization rate. The use of antibiotics appeared to be stable over time, but the use of systemic corticosteroids appeared to increase with near statistical significance (p = 0.07).The number of ED visits for AECOPD remained high; however, hospitalizations for AECOPD appeared to decrease over time. Some patients were disproportionately affected by AECOPD, and certain patient and ED factors were associated with hospitalizations. The reasons for decreased ED admissions for AECOPD deserve further investigation. Chronic obstructive pulmonary disease (COPD) remains a leading cause of mortality and morbidity in the U.S. and worldwide . Acute eSome earlier research has reported the epidemiology of ED visits for AECOPD. For example, Tsai et al. found a persistently high burden of ED visits for AECOPD during 1993–2005 . In addiTo address these knowledge gaps, we analyzed recent data from the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2010 to 2018. The objectives of this study were (1) to describe the incidence of and hospitalization for AECOPD in the ED, and (2) to investigate factors associated with the disease burden of AECOPD.The NHAMCS is a cross-sectional, multistage probability sample of visits to non-institutional general and short-stay hospitals, excluding federal, military, and Veterans Administration hospitals, located in the 50 states and the District of Columbia. The NHAMCS is conducted annually by the National Center for Health Statistics (NCHS). It covers geographic primary sampling units, hospitals within primary sampling units, EDs within hospitals, and patients within EDs. The number of EDs sampled is approximately 300–400 per year. Trained ED staff collected clinical information during a randomly assigned four-week period for each of the sampled EDs using a structured Patient Record Form (PRF). Data included patient demographics, reasons for the visit, diagnoses, procedures, medications given at the visit, and the basic characteristics of the treating physician and hospital. Quality control was performed using a two-way independent verification procedure with a 10% sample of the records. The non-response rate for most items was < 5%. The coding error rates were < 2% . BecauseNHAMCS data from 2010 to 2018 were used in this analysis. We included ED visits for patients 40 years of age or older for this analysis because individuals younger than 40 years are less likely to have COPD. This also decreased the likelihood of including visits for asthma that were misclassified as COPD. For this study, adults were defined as persons aged 40 or older throughout the text for brevity and consistency. Up to five diagnosis fields in the NHAMCS were coded according to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), or ICD-10. For the current analysis, we used ICD-9-CM codes , and ICD-10 codes (J41-J44) to define COPD. These codes have been validated in administrative data . We idenTo preserve consistency across years, race/ethnicity was recoded as non-Hispanic white, non-Hispanic black, Hispanic, and other. Insurance was recoded as private, Medicare, Medicaid or other state-based programs, self-pay, and other. The US regions represented standardized geographical divisions, as defined by the US Census Bureau  . Up to fThe primary outcome measure was the AECOPD incidence (visit) rate in the ED. We calculated this as the number of ED visits for AECOPD divided by the total number of adult ED visits. The co-primary outcome measure was the admission rate for ED AECOPD visits.Stata 16.0  was used to adjust the variances for the NHAMCS estimates, accounting for the complex design of the survey. Statistics from the NHAMCS were derived by a multistage estimation procedure, including (1) inflation by reciprocals of the sampling selection probabilities; (2) adjustment for nonresponse; and (3) a population weighting ratio adjustment. Standard errors (SEs) were calculated for the NHAMCS estimates. All statistical tests were based on reliable estimates that had at least 30 cases and a < 30% relative SE in the sample data, according to the NCHS recommendations. Descriptive data were presented as proportions , means (with SEs), or medians (with interquartile ranges [IQRs]). We used the weighted chi-square test to analyze the differences between proportions. Logistic regression models were used to test for significant changes in the primary outcomes (ED visit and admission rates for AECOPD) during the study period, in which the calendar year was a linear independent variable. As a sensitivity analysis, we repeated the trend analyses using the more restrictive exacerbation codes (ICD-9-CM code 491.21 and ICD-10 code J44.1). Regression discontinuity analysis was used to test the change in slope and intercept of hospitalization rates before and after 2014, when the HRRP was implemented. Multivariable logistic regression modeling was performed to evaluate independent factors associated with the two primary outcomes. The factors included in the model were age, sex, race/ethnicity, insurance, season, weekend, time of presentation, geographic region, and arrival mode. Odds ratios (ORs) are presented with 95% CI. All P values are two-sided, with a P value < 0.05 considered statistically significant.From 2010 to 2018, 221,622 ED visits were recorded in the NHAMCS. After excluding 123,835 visits from patients aged less than 40 years, a total of 97,787 adult ED visits were included in the analysis. Of these adult ED visits, 1,366 visits were labeled as AECOPD, and 96,421 were non-AECOPD. The flowchart is presented in Fig. After weighting, there were an estimated 7,508,000 adult ED visits for AECOPD from 2010 to 2018 in the United States. The clinical characteristics of these ED visits are summarized in Table Figure Table Figure Figure In this national sample representative of 7,508,000 AECOPD visits, the overall ED visit rate for AECOPD remained persistently high during the 9-year study period. Certain patient and ED factors were associated with higher ED visits for AECOPD. Hospitalizations through the ED decreased over time, and there was a near statistically significant increase in the use of systemic corticosteroids in the ED during the same time period.Compared to studies using the NHAMCS data from earlier years , 6, the With regard to regional variations in AECOPD rates, the ED visit rates were higher in the Midwest and in the South. Similar findings were reported in an earlier study using the National Emergency Department Sample from 2009 to 2012 . This reThis study showed that the hospitalization rate decreased significantly from 2010 to 2018. This result differed from an earlier study showing the total hospitalizations for AECOPD have increased from 2001 to 2012 , suggestInterestingly, despite the fact that the ED visit rate was higher in the South, we found that the hospitalization rate in the South was actually lower. This may be due to regional differences in hospital admission practices or avoiding re-admitting AECOPD patients considering the associated financial penalties. For example, hospitals may avoid hospitalization through the ED in the first place, resulting in decreased hospitalizations. Although not statistically significant, the admissions from the ED appeared to decline before the formal inclusion of COPD as a target diagnosis in the HRRP in 2014, a finding that was consistent with a previous study using national inpatient databases . This stAs with all retrospective studies, this study has several limitations. First, many sources of variation can affect national estimates of ED visit rates for AECOPD, including case definitions and ICD codes. We have attempted to define the more precise patient population by using the primary diagnosis of AECOPD. Second, although the NHAMCS collected a wealth of information, some clinical granularity was lacking. Specifically, the lack of lung function test results and previous exacerbations history precluded us from detailed assessments of the stages and severity of COPD. In addition, NHAMCS data did not include the information on pre-hospital use of medications, i.e., self-medication at home or medications administered en route to the hospital . This may explain the lower use of bronchodilators in our study.In summary, in this large study using 2010–2018 NHAMCS data, we provide updated information on the epidemiology of ED visits for AECOPD. The number of ED visits for AECOPD remained high; however, hospitalization for AECOPD appeared to decrease over time. Some patients were disproportionately affected by AECOPD, and certain patient and ED factors were associated with hospitalizations. Given the significant burden of disease, much work remains to be done to reduce the AECOPD ED visit rates, both at the individual level  and at the system level .Within the ED, the increasing use of systemic corticosteroids may partly explain decreased hospitalizations. Continuous quality improvement efforts in the implementation of clinical practice guidelines may further improve ED care for AECOPD. Whether decreased hospitalizations from the ED were related to the avoidance of readmissions of COPD deserves further investigation.


Arterial hypertension (AH) is a risk factor for cardiovascular diseases (CVD). We sought to evaluate the association between two adiposity indices  with traditional markers of cardiometabolic risk in hypertensive patients.This is a cross-sectional study with 1,273 subjects with hypertension treated as outpatients at a university hospital. The VAI and LAP were calculated using formulas stratified by sex. Cardiometabolic risk variables were considered: overweight, risk for waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHA), and altered biochemical test values. The predictive effect of independent variables on outcomes was assessed by multivariate linear regression analysis. There was statistical significance when p ≤ 0.05.Higher cardiometabolic risk  was associated with higher values of VAI and LAP with statistical significance (p ≤ 0.05). The regression models used explained 30.7% and 10.5% of the changes in LAP and VAI, respectively.LAP and VAI are associated with cardiometabolic risk parameters in the individuals evaluated, suggesting that these indices can be used to screen for CVD risk in individuals with AH. Arterial hypertension (AH) is a multifactorial chronic non-communicable disease (NCD) characterized by persistent elevation in blood pressure levels: systolic blood pressure ≥ 140 and/or diastolic blood pressure ≥ 90 mmHg  in 2017It is known that AH is the main risk factor for cardiovascular diseases (CVD). It holds a significant contribution to the increase in morbidity and mortality . TherefIn this sense, some parameters have been proposed to estimate the amount of visceral adipose tissue related to adverse outcomes, such as the lipid accumulation product (LAP), derived from the product between waist circumference (WC) and fasting triglyceride concentration (TG), and visceral adiposity index (VAI), an empirical mathematical model based on the association of anthropometric measurements (WC and body mass index (BMI)) with laboratory parameters (TG and high-density lipoprotein cholesterol [HDL-c]) .Previous studies have argued for LAP  and VAIHospital das Clínicasof the Federal University of Pernambuco (HC/UFPE) from January 1996 to July 2011. Data collection was carried out in four periods. In this study, the collected and transcribed data referring to the baseline were used.This is a cross-sectional study conducting the analysis of a research database. The study is entitled ‘Kidney disease in hypertensive patients with metabolic syndrome’ and was carried out with patients treated at the Hypertension Clinic of the Cardiology Service of theThe population consisted of patients with AH, adults and elderly, of both sexes, followed up at the clinic at the first moment of collection.diabetes mellitus(n = 203), and patients with incomplete data in the medical records (n = 553). The final sample of the present study included 1,273 individuals.To eliminate possible biases, we excluded from the baseline survey patients with secondary AH (n = 5), cardiac arrhythmias/electrical conduction disturbances on the electrocardiogram (n = 26), heart failure (n = 7), previous myocardial infarction (n = 9), dyslipidemia with the use of lipid-lowering medication (n = 46), andThe VAI was calculated by the equation proposed by Amato and cols.  stratifThe LAP was obtained using a specific formula for each sex. For women: (WC [cm]-58) × (TG [mmol/L]); for men: (WC [cm]-65) × (TG [mmol/L]) .Due to the lack of consensus regarding cut-off points for both the LAP and the IAV that would allow their categorization into normal and high values, they were analyzed continuously.The following cardiometabolic risk variables were considered: overweight assessed by BMI, risk by WC, waist-to-hip ratio (WHR) and waist-to-height ratio (WHA), serum levels of low-density lipoprotein cholesterol (LDL-c), total cholesterol (TC), high fasting and postprandial glycemia, and glycated hemoglobin (HbA1c) and low HDL-c.Weight and height were measured using standardized techniques . BMI waWC was measured and classified according to WHO criteria  consideThe waist-to-hip ratio (WHR) was obtained from the ratio between WC and HC and the cutoff points ≥ 0.90 for men and ≥ 0.85 for women were used as a risk indicator .Furthermore, the waist-to-height ratio (WHA) was obtained by dividing WC by height, adopting the cutoff point ≥ 0.5 for cardiometabolic risk .Blood collection was performed after fasting for 10 to 12 hours, at the Laboratory Unit of the HC/UFPE (ULAB-HC-UFPE). For biochemical tests, the Analytical Standard Operating Procedure of the Biochemistry Sector of the Laboratory Unit (ULAB) of the hospital was followed, which uses the automated Dimension AR-Dade Behring equipment and a Centrifuge to separate serum and red blood cells. The cut-off points of the Brazilian societies of diabetes  and carAge (complete years), sex , and self-reported color (white and non-white) were analyzed. The practice of regular physical exercise reported and guided by a professional was considered when there was practice at least three times a week, for about 60 minutes. In terms of smoking, the habit of smoking at least one cigarette per day was considered, and individuals were stratified as smokers or non-smokers.Regarding self-reported diet, participants were stratified into those who reported following or not following some dietary advice given by a nutritionist. As for alcohol consumption, self-reported intake was considered regardless of quality and quantity.This research was approved by the Ethics Committee for Studies on Humans, HC/UFPE (CNPJ: 15.126.437/0016-20), according to the Resolution nº 466/2012 of the National Health Council/Ministry of Health under CAAE: 34950620.7.0000.8807. All study participants signed the informed consent.Data processing was performed by the software Statistical Package for the Social Sciences (SPSS), version 15.0, for Windows . Exposure variables were treated as categorical and the outcome variables as continuous.The Mann Whitney test was used to verify the median differences of LAP and VAI between the groups and, in this analysis, the variables WC and TG were excluded; BMI, WC, TG and HDL-c are part of the LAP and VAI calculation, respectively. After analyzing the difference of medians, the variables were transformed into logarithmic functions to conduct a multivariate linear regression. A level of statistical significance of p ≤ 0.05 was considered. The absence of collinearity between the variables was confirmed by Pearson’s correlation test.The predictive effect of independent variables on outcomes was assessed by multivariate linear regression analysis using a hierarchic block of variable input. The first block was formed by sociodemographic variables, the second by anthropometric data, the third by the lipid profile, the fourth by variables of the glucose profile and fifth by lifestyle data. All the variables that in the bivariate analysis that presented a p < 0.20 were selected using the enter method.The block modeling process was used, and the variables that presented p < 0.10 in each block were kept. With LAP as the outcome, the following blocks were formed: 1st Block – sex, self-reported color; 2nd Block – BMI, WHR, WHA; 3rd Block – TC, LDL-c, HDL-c; 4th Block – fasting and postprandial glycemia and HbA1c; and 5th Block – diet and physical exercise.With VAI, the Blocks were 1st Block – Sex, age, self-reported color; 2nd Block – WHR, WHA; 3rd Block – CT, LDL-c; 4th Block – fasting glucose and HbA1c; and 5th Block – alcohol consumption.The population of the present study comprised 1,273 individuals aged between 44 and 92 years, mainly women and old people.Women had higher medians of VAI than men . With rIn the linear regression model , being The present analysis found, in agreement with previous studies carried out with men and women aged between 25 and 65 years from Indonesia  and indIt is known that the change in the lifestyle of populations changed the body composition of individuals, resulting mainly in an increase in body fat, especially in the abdominal region. Also, visceral adipose tissue, as it is considered a metabolically active component, secretes pro-inflammatory adipokines that increase the risk for cardiovascular and metabolic disorders ,25.LAP is proposed as an index that reflects the physiological and anatomical changes that are associated with visceral fat deposition , with aThe VAI had a significant correlation with visceral adiposity, showing superiority in relation to the components that enter its equation in terms of discrimination of cardiovascular and cerebrovascular events in a previous study . FurtheUnlike the indices discussed here, excess weight assessed by BMI is not a good indicator of body adiposity distribution. However, it may indicate a higher risk for CVD as it is associated with cardiometabolic alterations . As 66.It is known that excess adiposity is involved with changes in biochemical markers, exacerbating the risk of atherosclerosis . The reWhen evaluating the difference in indices by sex, women had a higher median VAI. It is known that sex steroids play a role in both the distribution and function of adipose tissue . FurtheThe present study identified that individuals that self-declared as non-white had higher means of adiposity indices. A strong relationship between the worst socioeconomic level and the black or mixed race  was preAnother finding was that individuals who reported following dietary guidelines had increased LAP values and that it was associated with an increase in the index in the regression model, while individuals self-reported as non-alcoholic had a higher median value of VAI. It is important to highlight the fragility of these data, since these are information referred to in an outpatient care context to which individuals may have distorted the response because they were being approached by a health professional. In addition, the cross-sectional design of the study precludes causal associations. Furthermore, an association between excessive consumption of alcoholic beverages and the risk of CVD was previously established in the literature .This study has as a limitation the fact that VAI and LAP were not developed for the Brazilian population. However, it is expected that the results found may stimulate the development of clinical trials and prospective cohorts to support the definition of cutoff points for adiposity indices to identify cardiometabolic risk in Brazilians. Also, as the study was conducted only with individuals diagnosed with hypertension, caution is suggested in extrapolating the results to the population without hypertension. Despite this, the present analysis encompassed a large number of individuals and assessed cardiometabolic risk using simple indicators that are applicable in clinical care.The results of this study show that LAP and VAI are associated with anthropometric and biochemical markers of cardiometabolic risk and that they increased both visceral adiposity indices, indicating that individuals predisposed to greater risk for adverse outcomes can be identified by them.


Female fertility decreases with age. A decline in oocyte quality plays a key role in reproductive problems in older women. Whether advanced maternal age (AMA) is associated with a decline in endometrial receptivity (ER) remains controversial. A systematic review and meta-analysis were conducted to evaluate the relationship between AMA and ER. Eighteen eligible studies were included in this meta-analysis. Of the 18 studies, 17, 8, 14, and 9 studies reported the impact of AMA on clinical pregnancy rate (CPR), implantation rate (IR), miscarriage rate (MR), and live birth rate (LBR), respectively. The combined results showed a trend (without significance) toward lower CPR in women with AMA than in younger women. A similar trend of worse outcomes in terms of IR was observed in women with AMA. A significantly higher MR and lower LBR were observed in infertile women with AMA than in younger women. In conclusion, there was a slightly lower IR and CPR without significance; however, significantly increased MR and decreased LBR were observed in women with AMA than in younger women, indicating that AMA is related to the decline of ER. Further prospective cohort studies with a preimplantation genetic testing for aneuploidy model are needed to observe the relationship between AMA and ER and explore the possible mechanisms. With the social trend to delay childbearing because of careers, the treatment of older infertile female has become a major challenge for today’s fertility specialists. Female fertility decreases with age. A decline in oocyte quality plays a major role in reproductive problems in older women. Whether advanced age is related to the decline of endometrial receptivity (ER) remains controversial. One study reported that in women aged >40 years, both embryo viability and, to a lesser extent, ER were decreased .Oocyte donation (OD) affords a scientific model to study the impact of advanced age on ER. Animal experiments have shown age-related changes in the uterus, accompanied by a decrease in implantation and pregnancy rates. Despite transferring embryos from younger animals, older animals were ultimately unable to conceive . When a Many clinical trials have studied the impact of AMA on the ER and results after OD. There have been no systematic reviews and meta-analyses on this issue.Currently, there is a pressing need for a systematic review and meta-analysis to evaluate the clinical question of the relationship between AMA and the decline of ER with the OD model.In total, 352 articles were obtained using this research strategy. Of these, 317 articles were excluded because they were found irrelevant after reviewing their titles and abstracts. Of the remaining 35 studies, 17 were excluded for different reasons: 15 were reviews and 2 had incomplete data. Finally, 18 studies were included in the present study .th studies reported the impact of AMA on clinical pregnancy rate (CPR), IR, miscarriage rate (MR), and live birth rate (LBR)  .P=0.16). I2, which was used to describe the heterogeneity of the included studies, was 52%, indicating statistical heterogeneity in the results (P=0.007). Therefore, the random-effects model was used . I2 was 59%, indicating moderate heterogeneity (P=0.01), and a random-effects model was used . The fixed effects model was used and the pooled RR was 1.37   .2=22%, P=0.25). The fixed effect model combined RR was 0.77   .The results included in this meta-analysis scored medium to high on the Newcastle-Ottawa Scale (not shown). The funnel plots evaluating the impact of AMA on CPR, IR, MR, and LBR suggest a lack of publication bias due to their symmetrical shape –4.To the best of our knowledge, this study is the first systematic review and meta-analysis to assess the impact of AMA on ER using an OD model. The results showed a trend toward a lower IR and CPR without significance; however, significantly increased MR and decreased LBR were observed in women with AMA, indicating that ER was negatively affected by advanced maternal age.It is well known that fecundity declines in women with AMA are associated with decreased oocyte quality. However, there is no consistent conclusion regarding whether ER is also impaired in women with AMA. OD is considered a perfect model for ascertaining the extent of this relationship. Some studies have evaluated the impact of AMA on endometrial tissues using an OD model.Early in 1990, one study explored whether ER decreased in older women. In this study, women aged 40-44 years with ovarian failure were enrolled and underwent embryo transfer with OD. These results suggest that the endometrium retains the ability to respond to gonadal steroids and receptivity for embryo implantation, even in older women . Other sOther studies have compared pregnancy and implantation rates in oocyte recipients of different ages and showed significant differences in pregnancy and implantation rates according to the age of recipients, suggesting that the ER decreased with age , 10, 13.Although there were discrepancies among the studies, the pooled results suggest that AMA may have a negative effect on the ER. As donated oocytes are obtained from young women, the age-related decline in LBR and increased MR with OD cannot be attributed to oocyte quality. Possible explanations for our findings are as follows.in vitro experiment found that the expression of HOXA10, a marker of ER, was inversely correlated with uterine age [in vitro obtained from young and aged cows using next generation sequencing (NGS) and polymerase chain reaction (PCR), and found that endometrial cells of aged cows have higher levels of inflammatory, IFN-signaling, and cell division dysfunction than those of young cows [First, there is an age-related decline in ER. An rine age . An animung cows . In humaung cows . Older wung cows .Second, another possible explanation for our results may be embryo quality. The oocytes were donated by younger women, which did not contribute to poor embryo quality. However, advanced paternal age may be a reason for poor embryo quality. New dominant mutations, which may be embryologically fatal, are now known to be common in men of advanced age. Thus, it is reasonable to assume that the male partners of older recipients are more likely to be older, and such new dominant mutation may lead to decreased embryo quality .Third, the risk of adverse pregnancy outcomes, such as gestational diabetes, preeclampsia, stillbirth, intrauterine growth restriction, and placenta previa, markedly increased among women with AMA. The increased complications may be related to impaired placentation function and progressive uterine vascular endothelial damage with aging –28.The strength of this study is that it is the first systematic review and meta-analysis to describe the relationship between AMA and ER decline. The sample size was very large (7037 women), which provided an excellent precision for estimates with pooled RRs.This study had several limitations. First, there was significant between-study heterogeneity, such as different study designs (prospective or retrospective studies), varied definition for AMA , and different endometrium preparation protocol. In addition, most of the studies were retrospective in design, and there were residual confounding factors. Finally, some of the included studies had a small sample size. Despite these drawbacks, this systematic review and meta-analysis provide a valuable analysis and summary of the relevant literatures.In conclusion, this study found that AMA is related to a decline in ER. Because of the small sample size and the possibility of aneuploidy embryos, further prospective cohort studies using the preimplanation genetic testing-Aneuploid (PGT-A) model are needed to observe the impact of AMA and analyze the possible causes.This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. As the data were extracted from previously published studies, and our paper did not include animal and human studies, institutional review board approval was an exemption.A comprehensive search of PubMed, EMBASE, and Google Scholar was conducted from their inception dates until May 2022. The keywords used for the search were as follows: a term including advanced age  and a term that included the endometrial receptivity (uterine receptivity). These subsets were summarized with “AND” to obtain the most complete literatures related to our study object. Cohort, retrospective, and prospective studies published in English were included. Full-text review and data extraction were completed by two separate reviewers, and any disagreements were resolved by consensus or by a third reviewer.After reviewing the retrieved titles and abstracts, irrelevant studies were assessed using the Newcastle-Ottawa Quality Assessment Scale. Data were extracted by two authors independently using pre-defined criteria. Data extraction included the research features and results.2 was used to quantify the heterogeneity between studies. A fixed or random-effect model was used to calculate RR and its 95% confidence interval (CI). Because of the low power of the χ2 test for heterogeneity with a small sample size, a P value of 0.10 rather than 0.05 was considered to be statistically significant.Review Manager Version 5.3 was used for meta-analysis. Categorical variables were calculated using the Mantel-Haenszel statistical method and expressed as risk ratio (RR); Forest plots were used to assess the heterogeneity of the included studies, and ISupplementary Figures


Cancer remains a formidable challenge in medicine due to its propensity for recurrence and metastasis, which can result in unfavorable treatment outcomes. This challenge is particularly acute for early-stage patients, who may experience recurrence and metastasis without timely detection. Here, we first analyzed the differences in clinical characteristics among the primary tumor, recurrent tumor, and metastatic tumor in different stages of cancer, which may be caused by the molecular level. Moreover, the importance of predicting early cancer recurrence and metastasis is emphasized by survival analyses. Next, we used a multi-omics approach to identify key molecular changes associated with early cancer recurrence and metastasis and discovered that early metastasis in cancer demonstrated a high degree of genomic and cellular heterogeneity. We performed statistical comparisons for each level of omics data including gene expression, mutation, copy number variation, immune cell infiltration, and cell status. Then, various analytical techniques, such as proportional hazard model and Fisher’s exact test, were used to identify specific genes or immune characteristics associated with early cancer recurrence and metastasis. For example, we observed that the overexpression of BPIFB1 and high initial B-cell infiltration levels are linked to early cancer recurrence, while the overexpression or amplification of ANKRD22 and LIPM, mutation of IGHA1 and MUC16, high fibroblast infiltration level, M1 polarization of macrophages, cellular status of DNA repair are all linked to early cancer metastasis. These findings have led us to construct classifiers, and the average area under the curve (AUC) of these classifiers was greater than 0.75 in The Cancer Genome Atlas (TCGA) cancer patients, confirming that the features we identified could be biomarkers for predicting recurrence and metastasis of early cancer. Finally, we identified specific early sensitive targets for targeted therapy and immune checkpoint inhibitor therapy. Once the biomarkers we identified changed, treatment-sensitive targets can be treated accordingly. Our study has comprehensively characterized the multi-omics characteristics and identified a panel of biomarkers of early cancer recurrence and metastasis. Overall, it provides a valuable resource for cancer recurrence and metastasis research and improves our understanding of the underlying mechanisms driving early cancer recurrence and metastasis. In recent years, cancer has become the third most fatal disease due to its high mortality rate of cancer recurrence and metastasis . As mediRecent studies have harnessed omics data to identify molecular features associated with cancer recurrence and metastasis in various types of cancer. For example, based on transcriptomics, 76 genes associated with breast cancer metastasis were identified, all of which had specific expression, and 11 gene markers were used to predict the risk of recurrence in colorectal cancer . A wholeHowever, few studies have explored the early detection of recurrence and metastasis that are often difficult to identify clinically, and the focus has primarily been on a single type of cancer or a specific omics data type. Pan-cancer analysis can exhibit common characteristics among cancers, while comparing early- and late-stage cancer can provide insights into the fundamental mechanisms of cancer development . FurtherHere, we investigated the clinical characteristics of patients with recurrent, metastatic, and primary tumors at different stages to identify specific biomarkers for early recurrence and early metastasis in pan-cancer using multi-omics data. Our results revealed that in genome, immune cell infiltration, and cell status, there were significant differences in the molecular characteristics of early metastasis compared to primary tumors, but no significant differences were observed between early recurrence and primary tumors. Our research also identified possible biomarkers for early recurrence and early metastasis, which could identify early-stage cancer patients with high risk of recurrence and metastasis, and we verified the predictive power of these biomarkers with the constructed classifier results. It was expected to help with clinical treatment and improve prognosis. Additionally, the flow of our entire study is shown in https://portal.gdc.cancer.gov/projects), which included information on 12,636 patients such as gender, age, tumor weight, tumor stage, and survival time (days). We eliminated the normal samples or the samples with no stage-related information and the samples with unknown stage-related information. Finally, we obtained the clinical data of 8,342 patients .We downloaded the clinical data from the GDC database (https://xenabrowser.net/datapages/). We selected the GDC Hub, and then, for each of these 33 cancers, we selected “HTSeq—Counts” for RNA-seq expression data and downloaded the processed data [log2 (x+1)].Gene-level RNA-seq expression datasets for 33 types of cancer, which included 11,057 samples and 60,483 genes, were downloaded from UCSC Xena  database, we downloaded gene expression and clinical data as external validation data, including GSE31210, GSE37745, GSE44295, and GSE20685. Among them, GSE31210 was the gene expression data of 226 patients, with lung adenocarcinoma at stage I–II, GSE37745 was the gene expression data of 196 non-small lung cancer (NSCLC) cases, with clinical information and long-term follow-up, GSE44295 was the gene expression in primary uveal melanoma cells and normal cell controls of 63 patients, and GSE20685 was the gene expression profiles of 327 breast cancer samples.From the GEO . This dataset involves the infiltrating scores of six immune cells in 11,509 patients with 33 types of cancer, including B cells, CD4 T cells, CD8 T cells, neutrophils, macrophages, and dendritic cells. The other two sets of data were calculated using the R package EPIC (https://github.com/GfellerLab/EPIC) and R package MCPcounter (https://github.com/ebecht/MCPcounter), respectively. The input of MCPcounter was the read count data of gene expression, so we directly used the previously downloaded read count data as input. Also, the input of EPIC was the FPKM data of gene expression, so we downloaded the FPKM data of gene expression (HTSeq-FPKM_UQ) from UCSC Xena. The MCPcounter exported the infiltration scores of 10 immune cells, such as T cells, natural killer cells, and fibroblasts, in 10,906 patients, while EPIC exported the infiltration scores of eight immune cells, such as macrophages, CD4 T cells, and CD8 T cells, in 10,906 patients.We obtained three sets of the immune cell infiltration data in TCGA cancer patients. The first set was downloaded from TIMER (2 (x + 1)] them separately. Next, we combined the three sets of normalized data, and we first intersected the samples to obtain three sets of data of the same samples and then combined the immune cells, in which the same immune cells were averaged, and finally obtained 13 kinds of immune cell infiltration scores of 10,906 patients, including B cells, fibroblasts, CD4 T cells, CD8 T cells, endothelial cells, macrophages, natural killer cells, T cells, monocytes, cytotoxic cells, dendritic cells, neutrophils, and other cells.To combine these three sets of data, we normalized [logWe obtained 26 genes related to T-cell exhaustion, 25 genes related to T-cell senescence, 292 genes related to T-cell elimination, and 90 genes related to T-cell dysfunction from the article . All theWe downloaded the immune characteristic score of 11,080 patients from literature attachments , includihttp://biocc.hrbmu.edu.cn/CancerSEA/home.jsp) database, which involved 14 functional cell statuses of 41,900 single cancer cells of 25 cancer types. Additionally, we also downloaded a total of 1,574 genes related to various functional cell states from the CancerSEA database.We downloaded cancer cell status data from the CancerSEA  database. In total, there were 3,398 ligand–receptor pairs, including 815 ligand genes and 780 receptor genes.We downloaded the data of ligand–receptor interaction pairs from the CellTalkDB , we obtained 158 genes and their information related to targeted therapy.From the Sino Biological database. The genes corresponding to these proteins and their information related to immune checkpoint therapy were obtained from the GeneCards database. Finally, we obtained 49 immune checkpoint-related genes, including 25 immune checkpoint-related genes in T cells and 24 immune checkpoint-related genes in tumor cells.Immune checkpoint proteins were downloaded from the p-value was less than 0.05. These survival analyses were conducted using the “survival” package in the R environment.We used the Kaplan–Meier curve and the log-rank test to estimate the survival probabilities and test the differences in the survival rate among patient subgroups. Differences were considered significant if the two-sided p-value for each gene in primary and recurrent tumors or primary and metastatic tumors of different cancer stages. Genes with a fold-change value greater than 2 or less than −2 and a p-value less than 0.05 were considered to be differentially expressed.We used the limma package in R to analyze the differential expression of genes in 33 cancer patients, and the input data were the read count data. We first de-log-processed the read count data to obtain the raw count data, which were then normalized using the voom method in the limma package. The voom method normalized the read counts and applied a variance stabilizing transformation to the data, which allowed for the use of linear modeling methods that assumed normally distributed data. Next, we used the limma package to calculate the fold-change and p-value less than 0.05 were considered to be significantly differentially expressed.We further performed multiple testing corrections using the Benjamini–Hochberg method to control the false discovery rate (FDR). Genes with an adjusted To investigate the biological pathways and functions associated with the genes identified in our analysis, we utilized the R package clusterProfiler and org.Hs.eg.db. Specifically, we used the enrichGO and enrichKEGG functions to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, respectively. This allowed us to identify the enriched GO terms and KEGG pathways associated with the identified genes.Additionally, we used the emapplot function from the clusterProfiler package to create an enrichment map that visually represents the relationship between enriched pathways. This provided a comprehensive view of different biological processes that may be involved in the development and progression of cancer. Finally, we analyzed and interpreted the results of the enrichment analysis to gain insights into the underlying mechanisms and potential therapeutic targets associated with the identified genes.p-value less than 0.05 were considered to be associated with poor prognosis. Each predictor variable was represented by a separate row in the plot, and HRs and 95% CIs were plotted as a horizontal line with a circular marker. The location of the circular marker on the horizontal line indicated the point estimate of the HR, while the length of the horizontal line represents the 95% CI.We performed a Cox proportional hazard regression analysis to assess the impact of several factors on survival and occurrences of new tumors. We calculated HRs and 95% confidence intervals (CIs) for each predictor variable and used forest plots to present the results. The HR represents the risk or hazard of an event occurring in the presence of the predictor variable as compared to its absence. Variables with an HR greater than 1 and a p-value for each gene. Similarly, for copy number variations, we used the same four values as input, but we use the number of samples with or without a copy number variation, instead of having or not having a gene mutation. A p-value less than 0.05 was considered statistically significant and indicated that mutations or copy number variations in that gene were associated with recurrence (or metastasis).To investigate the association between gene mutations or copy number variations and recurrence or metastasis, we employed Fisher’s exact test. For each gene, we calculated four values based on samples from different cancer stages: the number of samples with no recurrence (or no metastasis) and no gene mutations, the number of samples with no recurrence (or no metastasis) and gene mutations, the number of samples with recurrence (or metastasis) and no gene mutations, and the number of samples with recurrence (or metastasis) and gene mutations. We then used these four values as input for Fisher’s exact test, which calculated a To evaluate the performance of the identified biomarkers in predicting recurrence and metastasis, we conducted a machine learning analysis. For addressing the imbalance between the number of primary and recurrent/metastatic samples in stage I, we randomly selected the same number as recurrence/metastasis samples from primary tumor samples. We then combined these samples with all the samples from the recurrence/metastasis group and randomly split them into training and test sets, with 80% of the samples in the training set and 20% in the test set. We performed this procedure separately for recurrence and metastasis samples.Based on the identified biomarkers, we constructed classifiers using support vector machine (SVM), k-nearest neighbor, and random forest algorithms to predict the recurrence/metastasis status of the samples in the test dataset. We then calculated the specificity and sensitivity of each classifier using the actual recurrence/metastasis status of patients. Finally, we plotted receiver operating characteristic (ROC) curves to evaluate the performance of each classifier. The area under the curve (AUC) was calculated with a value closer to 1, indicating better classifier performance.To evaluate differences in the cell status between primary and recurrent or metastatic tumors at different cancer stages, we used the R package gene set variation analysis (GSVA). First, we obtained a set of cell state-related genes from the CancerSEA database. This gene set included genes that were known to be associated with various cell states, such as quiescent cells, activated fibroblasts, and immune cells. Next, we used the GSVA function in the GSVA package to calculate the score of the corresponding cell state for each sample with the gene expression data. This allowed us to obtain a quantitative measure of the degree to which each sample was associated with each of the 14 cell states included in the gene set. Finally, we obtained the enrichment scores of all 14 kinds of cell states for each sample. These scores provided a comprehensive view of the extent to which each cell state was represented in the gene expression profile of each tumor sample.https://github.com/bioWzz/Multi-omics-profiling-reveals-characterization-of-the-early- recurrence-and-early-metastasis-in-pan-c). All the codes were in the .txt file called multi-omics profiling reveals characterization of the early recurrence and early metastasis in pan-cancer. The rest was part of the input or output data.We uploaded all the code and some of the data for this study to GitHub , while metastasis was observed to be a relatively important factor affecting survival (p = 1.85*e−21) and stage III (p = 5.20*e−14) cancer, the Kaplan–Meier survival curves showed that both recurrence and metastasis impacted the prognosis (p = 0.133)  .To further study the impact of recurrence and metastasis on patient survival, we used the Cox proportional hazard regression to investigate the risk of recurrence and metastasis tumors for patient survival. Our findings, as shown in To further explore the differences in clinical characteristics among early recurrence, early metastasis, and other stages, we investigated their differences in a patient cohort of 8,324 individuals. The cohort was stratified into nine different groups based on the stage and state of cancer progression, including primary tumors, recurrence at stage I, metastasis at stage I, recurrence at stage II, metastasis at stage II, recurrence at stage III, metastasis at stage III, recurrence at stage IV and V, and metastasis at stage IV and V.p < 0.0001). We also observed significant differences in tumor weight between recurrent tumors in stage I and stage II (p = 0.002), compared to other stages, with recurrent tumors generally weighing more than metastatic tumors at each stage .Then, we examined the distribution of recurrence and metastasis across different cancer types to identify differences in the incidence of these events in various cancers. Our analysis of TCGA data revealed the difference of recurrence and metastasis in different cancer stages . For exach stage . Next, wch stage . Furtherch stage  and discOur findings suggest that several factors, including age, gender, and type of cancer, may influence the status of recurrence and metastasis of patients with different stages of cancer.2FC|>1, p < 0.05, p = 0.00021, p = 0.0025, and p = 0.0059), which are associated with poor prognosis in early recurrence  showed that the enrichment pathways were roughly divided into two parts, one was transport-related pathways, including BPIFB1 and SLC6A14, and the other was immune-related pathways only including BPIFB1. These associated pathways may contribute to the occurrence of recurrence.To investigate the potential biomarkers and molecular mechanisms of early recurrence in cancer, we conducted differential expression analysis of gene expression data from primary and recurrent tumors at different stages and identified 14 genes that were specifically differentially expressed in stage I    . Further: 0.628) .S100P, BPIFB1, and SLC6A14 may serve as reliable biomarkers for predicting the risk of recurrence in early-stage cancer. Their overexpression may increase the likelihood of early recurrence.These findings suggest that 2FC|>1,p < 0.05, Identifying differentially expressed genes associated with early-stage metastasis can provide valuable insights into the biological processes driving the metastasis. Therefore, we investigated the genes associated with stage I metastasis using a method similar to that used for studying early cancer recurrence and identified 18 genes that were specifically differentially expressed in stage I (|logp < 0.05), which are associated with poor prognosis in early metastasis. Gene enrichment results obtained by an emapplot function showed that the nine genes enriched pathways shared many of the same genes and most of them were associated with immune functions that may promote metastasis. Moreover, we identified an RAB27B-related pathway by an enrichKEGG function, which is involved in the transport of intracellular vesicles and proven to be associated with the metastasis    . In orde: 0.624) . The resTo gain deeper insights into the molecular mechanisms underlying early recurrence and early metastasis, we performed additional analyses to identify a specific signature from the angle of gene mutations that could potentially serve as biomarkers for early recurrence and early metastasis. First, we investigated the relationship between the number of gene mutations and the likelihood of early metastasis by studying the ratio of metastasis to primary in different mutation groups. The results revealed that tumors with a number of gene mutations within the range of 600–1,199 were most prone to metastasis, as demonstrated by the highest proportion of metastasis in this group . To furtp-value less than 0.05 only in stage I and greater than 0.05 in the other stages. We did not observe an overlap between genes with specific mutations and high-frequency mutation genes with a mutation rate greater than 5% in cases of early recurrence. However, in metastasis, we identified 27 genes with both specific mutations and high-frequency mutations in stage I (p = 2.2*e−08), which could cause worse prognosis (p < 0.0001) , and the proportion of early metastatic samples increased with the increase in the number of mutated genes. Moreover, patients with mutations of these 12 genes showed significant differences in survival from those without mutations (p < 0.0001), indicating that these genes can serve as biomarkers for early metastasis  . Next, tpathways , which apathways . Interestastasis . So, we  samples . Then, w samples  . As amplification typically leads to upregulation of gene expression, we further selected 29 genes from these 5,858 genes with upregulated expression in stage I (|log2FC| > 0.8) and downregulated expression in other stages    . Notably= 0.035) . This fi: 0.002) , suggestp-value and up–downregulation between patients with recurrence or metastasis, and primary patients at each cancer stage through difference analysis, we identified specific immune characteristics that were positively associated with the emergence of new tumors in stage I, as shown in p-value of the difference in survival between the predicted primary and predicted recurrence samples: 0.012)   . Similar: 0.012) . Also, 1: 0.012) . Then, t: 0.036) .In summary, we found that the high infiltration of B cells, T cells, and fibroblasts is positively correlated with early metastasis, while no immune cells may serve as a potential biomarker for early recurrence. Furthermore, we identified specific immune characteristics unique to stage I tumors, such as B-cell naive proliferation, which appeared to contribute to an increased risk of early recurrence, and macrophage M1 polarization, which appeared to contribute to an increased risk of early metastasis. Our findings present the potential of immune characteristics as useful biomarkers for predicting the likelihood of tumor recurrence and metastasis in stage I cancers.The cellular states of primary, recurrent, and metastatic tumors play a critical role in cancer progression and treatment and in order to gain insights into the cellular states that are involved in early recurrence and early metastasis of cancer; we identified different cell states in early recurrence and early metastasis. Using the expression of 14 cell state-related genes, we calculated the cell state scores for each patient using GSVA. We identified four cell states  that were different in stage I and other stages of recurrence than in the primary tumor, that is, there was no stage I recurrence-specific cell state. Meanwhile, six cell states  were significantly different in stage I metastasis . Notably2FC| > 0.8 and p < 0.05), with no expression differences in other stages. Then, for the networks comprising these ligand–receptor pairs, we further screened out hub nodes in networks, which had a degree no less than three   . These hnd CD36)  and metand CD36) .These results demonstrate that early metastasis exhibits greater divergence from the primary tumor in terms of changes in cellular states compared to early recurrence. Moreover, the analysis of ligand–receptor interactions between cells can help better understand the heterogeneity of the tumor microenvironment. Targeting these hub nodes could potentially inhibit early recurrence and early metastasis by affecting adjacent genes in the network.Targeted therapy is a kind of treatment at the molecular level and can be tailored to each patient’s specific genetic mutations, making it a personalized approach that can potentially improve patients’ quality of life and survival rates . To iden2FC| > 0.8 and p < 0.05) (Immune checkpoint inhibitor therapy has proven to be one of the most promising and effective immunotherapies in recent years by modulating immune cells to inhibit tumor . In orde < 0.05) , which a < 0.05) .Overall, when patients with high risk of metastasis in stage I tumors are identified, personalized targeted or immune checkpoint therapy with corresponding drugs for the aforementioned specific gene loci may be considered to delay or inhibit the occurrence of metastasis and improve prognosis. It is important to note that the effectiveness of immune checkpoint therapy and targeted therapy may vary depending on the individual patient’s cancer type and status. Therefore, careful consideration of each patient’s unique circumstances is necessary to determine the optimal treatment approach. By providing timely and effective treatment, we can more comprehensively and effectively control disease’s development and improve patient outcomes.Our study aimed to identify biomarkers related to early recurrence and early metastasis in pan-cancer patients by analyzing multi-omics data. These findings highlight the importance of identifying these biomarkers as they could aid in the detection of cancer patients with a high risk of early recurrence and metastasis. We observed significant differences in the distribution of various factors between patients with and without recurrence or metastasis in early cancer, indicating the importance of identifying high-risk patients for early intervention. We also identified several specific genes related to early recurrence and metastasis that, when overexpressed, could promote their occurrence. Additionally, mutations and copy number amplifications in certain genes were observed to increase the risk of early metastasis. Interestingly, by comparing the genes identified in the genome and the transcriptome, we discovered that there were intersections between these genes. The overexpression and copy number amplification of ANKRD22 and LIPM in early cancer, and overexpression, mutation, and copy number amplification of IGHA1 in early cancer may all promote early metastasis. The reasons for this intersection are worth investigating further. Furthermore, we identified immune characteristics related to early recurrence and early metastasis and also found treatment-sensitive genes that may help improve therapeutic outcomes for cancer patients.S100P was associated with an increased risk of early recurrence and poor prognosis in patients with hepatocellular carcinomas . Hendrix et al. demonstrated that the gene RAB27B was associated with metastasis of breast cancer (Our findings are consistent with previous studies that have identified specific genes and pathways associated with cancer recurrence and metastasis. For example, a study by t cancer . Other st cancer . In addiOur study adds to the growing body of the literature on the use of multi-omics data to identify biomarkers for early cancer recurrence and metastasis. By integrating transcriptomic, genomic, and immune cell data, we were able to identify specific genes and characteristics associated with early recurrence and early metastasis in pan-cancer. These findings may provide insights into the underlying mechanisms of early cancer recurrence and metastasis and may have implications for the development of new strategies for early cancer recurrence and metastasis identification.One of the strengths of our study is the integration of transcriptomic, genomic, and immune cell data to identify specific genes and characteristics associated with early recurrence and early metastasis in pan-cancer. However, there are some limitations that should be considered. One limitation of our study is the exclusion of several cancer types due to the lack of pathological stage information. Although our study includes most cancer types, it is possible that relevant biomarkers for early recurrence and early metastasis may have been missed. Therefore, future studies should consider obtaining more comprehensive pathological stage information to improve the accuracy of the results. Another limitation of our study is the lack of treatment response data in TCGA. While we identified treatment-sensitive genes, the lack of relevant data may have biased our results. Future studies should aim to incorporate more comprehensive treatment response data to better understand the relationship between treatment and early recurrence and early metastasis. Furthermore, our study only analyzed transcriptomic, genomic, and immune cell data. Additional omics data, such as epigenomics and proteomics, may provide additional insights into the mechanisms of early recurrence and early metastasis in cancer. Therefore, future studies should aim to incorporate other omics data to gain a more comprehensive understanding of these processes.Despite these limitations, our study adds to the growing body of literature on the use of multi-omics data to identify biomarkers for early cancer recurrence and metastasis. Our findings may have implications for the development of new therapies and strategies for the identification of early cancer recurrence and metastasis. Not only that, future studies should also aim to incorporate other omics data, such as epigenomics and proteomics, to further understand the mechanisms of early recurrence and early metastasis in cancer.In summary, our multi-omics analysis reveals that early recurrence and early metastasis in various cancer types have distinct molecular mechanisms and tumor microenvironments. The biomarkers identified through our multi-omics analysis have context-dependent prognostic implications and potential as targets for predicting the risk of early recurrence and early metastasis. These findings provide new insights into the underlying biology of cancer progression and may have implications for the development of personalized therapies for cancer patients.