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Blinding | BLIND | It was impossible to blind the operator and the operator was not involved in either the distribution or evaluation processes. Furthermore, all patients were unaware of which group they were in. Throughout the follow-up times, the assessor carried out each evaluation step while being entirely unaware of the treatment protocol. Likewise, statisticians were unaware of treatments and groups. | PMC10704731 | |
Criteria for patient selection | PMC10704731 | |||
Preoperative measures | For all patients, panoramic radiographs were taken to assess the mesiodistal width, the amount of bone above the apex and the root angulation PRF Group PRF preparation | PMC10704731 | ||
Surgical procedures | STERILE, CLOT | Following administration of local anesthesia (Mepivacaine HCL 2% with Levonordefrin 1:20,000. Alexandria Co. for Pharmaceuiticals and Chemical Ind., Alexandria, Egypt.), a three-line incision was made, and the mucoperiosteal flap was reflected. Atraumatic extraction of the tooth/root was then initiated by using a periotome (Helmut Zeph, Medizintechnik GMBH, Seitingen-Oberflacht, Germany) to sever the periodontal ligament attachments and preserve the socket walls followed by using suitable extraction forceps (Figs. The final decision regarding the size of the implant was made after assessing the dimensions of the socket. Drilling was done in the right direction at 600 to 800 rpm. Depending on the implant size, sequential drilling with abundant irrigation was done until the ideal dimensions were achieved. The sterile implant package was opened, and with gentle, steady finger pressure, the implant was placed in its proper location with a manual ratchet (40 Ncm of torque) (Figs. PRF Group. Xenograft Group The implants used in this study were double-threaded, two-piece, tapered body titanium dental implants with SLA surface. (Dentium® System, Superline, Seoul, Korea.)After implant placement, the buccal jumping gap was measured using periodontal probe to make sure that the distance from the implant surface and the buccal plat was more than 2 mm.RFA was used to test implant stability with an Osstell Mentor device. (Osstell, Integration Diagnostics, Savadaled, Sweden). The smart peg (type 7) was attached to the dental implant. The outcomes were presented as the implant stability quotient (ISQ).The buccal jumping gap in Group 1 was packed using PRF. Platelet-rich fibrin preparation, around 5–10 ml of whole venous blood was collected in each of the two sterile vacutainer tubes without anticoagulant. The vacutainer tubes were then placed in a centrifugal machine (Laboratory Centrifuge, Jiangsu, China) at 3000 rpm (800 gm) for 10 min, after which it settled into the following layers: red lower fraction containing red blood cells, upper straw coloured cellular plasma and the middle fraction containing the fibrin clot. The upper straw coloured layer was then removed and middle fraction was collected, 2 mm below lower dividing line, which was the PRF [Alloplast Group. The next step was to open the sealed package of the collagen membrane (Dentium® System, Resorbable membrane, Korea). The membrane was then trimmed to the size needed by the case. Care was taken to apply the membrane without wrinkling or buckling (Figs. | PMC10704731 | |
Postoperative care | For seven days, 500 mg of Amoxicillin (Emox, Egyption Int. Pharmaceutical Industries Co., E.I.P.I.C.O., A.R.E.) was used as an oral antibiotic every six hours. A non-steriodal analgesic and anti-inflammatory medication called Diclofenac Potassium 50 mg tablets (Oflam, Mepha Pharma Egypt S.A.E.) was prescribed. Patients were advised to avoid chewing solid food, and to maintain good oral hygiene with Chlorohexidine HCl (0.12%) (Hexitol, the Arab Drug Company, Cairo, A.R.E.). Then, after one week, the sutures were removed. | PMC10704731 | ||
Second stage surgery | Six months later, a second stage surgery was carried out. The surgical cover screw was exposed and replaced by a healing abutment for 15 days. | PMC10704731 | ||
Prosthetic rehabilitation | To create a working cast, an impression was made using an impression post and a laboratory analogue. Then the functional abutment replaced the healing abutment. Final restoration was made from porcelain fused to metal and cemented to the functional abutment. | PMC10704731 | ||
Evaluation | Every patient was seen on a regular basis for evaluation immediate, 6 and 18 months postoperative. | PMC10704731 | ||
A. Clinical evaluation | PMC10704731 | |||
1. Implant stability | At the time of implant placement, 6 months and 18 months postoperative, implant stability was measured. RFA was used to measure implant stability with an Osstell Mentor device. The outcomes were presented as ISQ. | PMC10704731 | ||
2. Peri-implant pocket depth | A graduated probe was used to measure the distance between the base of the pocket and the gingival margin. The probe was introduced until its blunt edge made contact with the base of the pocket in a straight line with the implant's vertical axis. Around each implant, the pocket depth was measured at 4 different sites (mesial, buccal, distal and palatal). Measurements were taken and recorded to the nearest 0.5 mm. | PMC10704731 | ||
B. Radiographic evaluation | CBCT was used to provide radiographic evaluation immediately, 6, and 18 months postoperative. All CBCT scans were performed in the same radiology centre (Planmeca, ProMax® 3D Max, Helsinki, Finland) using the same parameters (89 kVp, 24 s, 10 mA and field of view 6 cm × 8 cm). For image processing and reconstruction, OnDemand3D was used. | PMC10704731 | ||
1. Radiographic assessment of marginal bone loss | bone loss | CREST, BONE LOSS | The implant was utilized as a reference for the measurement of marginal bone loss (MBL) from the cross-sectional view by adjusting panoramic long axis in its center and bisecting it (showing the buccolingual dimensions).At the crest of the buccal plate of bone and ending at the apical level of the implant, a line was drawn directly parallel to the implant, and its height was measured in millimeters immediately, 6 months and 18 months postoperative. The measurement of the bone level at implant placement was considered as baseline. Radiographic MBL was calculated as the difference between the reading at 6 and 18 months postoperative and the baseline value. | PMC10704731 |
2. Radiographic assessment of changes in buccal bone thickness | bone loss | CREST, BONE LOSS | A perpendicular horizontal measurement was taken from the implant crest to the buccal bone plate immediately postoperative. This measurement acts as a baseline. A similar measurement was taken 18 months postoperative and subtracted from baseline value to determine horizontal bone loss. | PMC10704731 |
Statistical analysis | SPSS software, version 25 was used to analyze the data (SPSS Inc., PASW statistics for windows version 25. Chicago: SPSS Inc.). Quantitative data were described using mean ± standard deviation for normally distributed data after testing normality using Shapiro Wilk test. To compare more than two independent groups, the One Way ANOVA test was performed, and the Post Hoc Tukey test was utilized to identify pairwise comparisons. Significance of the obtained results was judged at the (≤ 0.05) level. | PMC10704731 | ||
Results | PMC10704731 | |||
Demographic data | This study involved 19 female patients and 17 male patients who received 36 dental implants to replace non-restorable maxillary anterior and premolar teeth (esthetic zone) by immediate implant. The average age was 33 years (range from 19 to 47 years). The distribution of replaced teeth was 20 maxillary central incisor, 8 maxillary lateral incisor, 2 maxillary canine, and 6 maxillary 1Patient’s demographic data | PMC10704731 | ||
Comparison of implant stability between the study groups | Implant stability was evaluated using ISQ at surgery, 6 months and 18 months postoperative without statistical difference at surgery (ISQ at different time intervalsThe *statistically significant | PMC10704731 | ||
Evaluation of the peri-implant pocket depth | The peri-implant pocket depth's mean values were all within acceptable ranges (2.5-4 mm). Between the study groups, there was no statistically significant difference at 6 months and at 18 months (Peri-implant pocket depth at different time intervalsThe | PMC10704731 | ||
Assessment of changes in buccal bone thickness | BONE LOSS | Bone loss changes was evaluated according to each material. Regarding buccal bone thickness changes, there was a significant difference between PRF Group (Group 1) and the other Groups (Group 2 and Group 3) after 18 months postoperative (Changes of buccal bone thickness after 18 monthsThe Similar superscripted letters denote significant difference between groups within the same row*statistically significant | PMC10704731 | |
Discussion | tooth replacement, infection, periodontium, bone loss | BONE LOSS, EPITHELIAL DOWNGROWTH, INFECTION, SCARRING, CREST | Immediate implant placement has highly predictable means of tooth replacement and shows high success rate [The area from the upper 1Before implant placement, the dimensions of the socket must be evaluated to establish the length and diameter of the implant. To obtain primary stability, the drilling extended 3–4 mm apically to engage the bone beyond the apex of the extraction socket [An essential step in preventing infection and epithelial downgrowth at the implant site is primary flap closure [The implant diameter should be smaller than the socket width, and the implant should be positioned palatally to ensure a minimum horizontal distance of 2 mm between the implant crest and the buccal bone to prevent buccal bone resorption [Concentrated platelets have been utilized in wound healing in recent years due to their high growth factor concentration [Furthermore, PRF can be prepared with antibiotic loading, and the drug is subsequently released from PRF with an antimicrobial effect over four days which can be used after surgical procedures. More in vitro and in vivo studies are needed to prove that PRF loaded with antibiotics represents a topical antibiotic delivery tool for oral surgical procedures that promotes tissue healing and prevents local infection [Implant stability assessment was done using ISQ values of RFA, and there was no significant difference between the three groups at the time of implant placement (Po-Sung Fu et.al [The measurement of the peri-implant pocket depth is (PPD) essential for diagnosing the periodontium. During assessment of PPD, there was no statistically significant difference (Our results were in line with that of Viswambaran et al. [In our study, an increase in PPD values can be attributable to reflection of a full thickness mucoperiosteal flap, which results in a junctional epithelium that is more apically positioned. In addition, open wounds heal slowly and with noticeable scarring because the peri-implant mucosa's vascular structure is impaired [A successful implant should have average bone loss of less than 1.5 mm over the first year after loading and less than 0.2 mm annually when measuring MBL [Our results were in line with that of Elbrashy et al. [Regarding buccal bone thickness changes, more reduction in buccal bone thickness in PRF group in comparison to other groups.These findings are in line with those of Nevins and colleagues [This might be explained by the biology of various grafting materials and their rates of resorption. According to various studies, PRF can continue to release growth factors for up to 10 days [The limitations of this study are a small sample size, a relatively short follow up period, and absence of a control group. In addition, soft tissue parameters such as keratinized mucosa width, gingival biotype, and gingival zenith position were not assessed. | PMC10704731 |
Conclusion | bone loss | BONE LOSS | This study demonstrated that the use of Xenograft and Alloplastic β-tricalcium phosphate as filling materials in conjunction with immediate implant have superior results regarding buccal bone loss and buccal bone thickness over use of PRF as a filling material. | PMC10704731 |
Authors’ contributions | All authors substantially contributed to the study design, drafting and revising the research, approving the submitted manuscript’s final version, and accuracy of work. | PMC10704731 | ||
Funding | Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). The authors received no funding for this research. | PMC10704731 | ||
Availability of data and materials | The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request. | PMC10704731 | ||
Declarations | PMC10704731 | |||
Ethics approval and consent to participate | The Institutional Review Board (IRB) of the Faculty of Dentistry, Mansoura University, Mansoura, Egypt, approved the current study in compliance with the seventh revision of the Helsinki Declaration in 2013 (A0103023OS). All of the participants gave their written informed consent. | PMC10704731 | ||
Consent for publication | Not applicable. | PMC10704731 | ||
Competing interests | The authors declare no competing interests. | PMC10704731 | ||
References | PMC10704731 | |||
Aims | hypoglycemia, hyper-insulinemia | HYPOGLYCEMIA, TYPE 1 DIABETES, EVENT | Edited by: Yanshan Dai, Bristol Myers Squibb, United StatesReviewed by: Shihao Hu, Janssen Pharmaceuticals, Inc., United States; Yue Chen, Cedars Sinai Medical Center, United States; Siyan Chen, Sanofi U.S., United StatesNon-severe hypoglycemia (NS-H) is challenging for people living with type 1 diabetes (PWT1D) and often results from relative iatrogenic hyper-insulinemia. Current guidelines recommend a one-size-fits-all approach of 15–20 g of simple carbohydrates (CHO) every 15 min regardless of the triggering conditions of the NS-H event. We aimed to test different amounts of CHO to treat insulin-induced NS-H at various glucose ranges. | PMC10272543 |
Methods | This is a randomized, four-way, crossover study involving PWT1D, testing NS-H treatment outcomes with 16 g vs. 32 g CHO at two plasma glucose (PG) ranges: A: 3.0–3.5 mmol/L and B: <3.0 mmol/L. Across all study arms, participants consumed an additional 16 g of CHO if PG was still <3.0 mmol/L at 15 min and <4.0 mmol/L at 45 min post-initial treatment. Subcutaneous insulin was used in a fasting state to induce NS-H. Participants had frequent venous sampling of PG, insulin, and glucagon levels. | PMC10272543 | ||
Results | Participants ( | PMC10272543 | ||
Conclusions | hyper-insulinemia | NS-H, in the context of hyper-insulinemia, is difficult to treat in PWT1D. Initial consumption of 32 g of CHO revealed some advantages at the 3.0–3.5 mmol/L range. This was not reproduced at lower PG ranges since participants needed additional CHO regardless of the amount of initial consumption. | PMC10272543 | |
Clinical trial registration | PMC10272543 | |||
Introduction | hypoglycemia | HYPOGLYCEMIA, TYPE 1 DIABETES | People living with type 1 diabetes (PWT1D) are faced with increased risks of hypoglycemia and its negative impacts when trying to achieve optimal glucose management (Recent studies and review reports have questioned the 15 g/15 min rule and have required extensive research studies to investigate the most efficient treatment strategies according to different NS-H settings and conditions (Thus, we hypothesized that a higher initial CHO consumption than the recommended 15–20 g might be more effective in correcting NS-H and reduce the need to repeated treatments. We aimed to test this approach at two hypoglycemia ranges. | PMC10272543 |
Methodology | PMC10272543 | |||
Study design | hypoglycemia | HYPOGLYCEMIA | This is an open-label, randomized, four-way, crossover study investigating 16 g vs. 32 g of CHO to correct insulin-induced NS-H at two hypoglycemia ranges, 3.0–3.5 mmol/L and <3.0 mmol/L, in PWT1D. | PMC10272543 |
Study subjects | abnormal blood panel, hypoglycemic episodes, anemia, T1D, cardiac rhythm abnormality | EVENT, ANEMIA, COMPLICATIONS | Eligible subjects were adults (≥18 years of old) with a clinical diagnosis of T1D for at least 1 year, treated with either multiple daily insulin injections (MDI) or continuous subcutaneous insulin injection (CSII) and a glycated hemoglobin HbA1c ≤ 89 mmol/mol (≤10%). Exclusion criteria included clinically significant microvascular complications, recent (<3 months) acute macrovascular event, significant cardiac rhythm abnormality, abnormal blood panel and/or anemia (hemoglobin < 100 g/L), ongoing pregnancy or breastfeeding, severe hypoglycemic episodes within 1 month of screening, or additional health problems that could affect the participation to the study as per medical assessment of the recruiting physician.The ethics committee at Montreal Clinical Research Institute approved the study that was conducted according to the Declaration of Helsinki. All participants provided written informed consent. Subjects were recruited at the Montreal Clinical Research Institute (IRCM) through the T1D clinic and from a pool of participants who had participated in previous study protocols. The study followed a block balanced randomization and envelopes were not opened until a participant had met the inclusion criteria and signed the informed consent. | PMC10272543 |
Study procedures | hypoglycemia | HYPOGLYCEMIA, BLOOD, NOCTURNAL HYPOGLYCEMIA | Participants installed a glucose sensor (Dexcom G4 or G5 Platinum, Dexcom, USA) 24–48 h before study visits and installation training was offered for those unfamiliar with the technology. At least 6-day intervals separated study visits. The day before each study visit, participants had to refrain from exercise and alcohol consumption and reduced their nighttime basal insulin by 10% to minimize nocturnal hypoglycemia risks. Upon arrival to the testing centre, Dexcom data were uploaded and verified. A test was reported in the case of nocturnal hypoglycemia (two separate periods of 20 min or one single period exceeding 40 min with glucose levels < 3.5 mmol/L). At 7:30 a.m., participants had subcutaneous rapid insulin analog injection according to body weight and starting glucose level to induce hypoglycemia. Insulin doses were 0.13 U/kg for plasma glucose (PG) 10.0–15.0 mmol/L, 0.10 U/kg for PG 7.0–9.9 mmol/L, and 0.08 U/kg for PG 4.0–6.9 mmol/L. Intravenous catheters were installed to collect venous blood samples every 15 min for PG above 5 mmol/L, every 10 min for PG above 4 mmol/L, and then every 5 min until hypoglycemia threshold. Then, ® glucose tablets for 16 g or 32 g were consumed and PG-T0 was defined as time 0 post-initial CHO consumption. Blood samples were later collected at 5, 10, 15, 20, 30, 45, and 60 min. Participants consumed a second treatment with 16 g of CHO at 15 min if PG-T15 was < 3.0 mmol/L and at 45 min if PG-T45 was < 4.0 mmol/L.All blood samples were centrifuged immediately, and PG was measured using YSI 2300 STAT Plus analyzer (Yellow Springs, OH, USA). Plasma samples were stored at −80°C until subsequent measurement of insulin and glucagon using respective immunological assays (Millipore, Billerica, MA, USA). | PMC10272543 |
Statistical analysis | hypoglycemia, hypoglycemic | HYPOGLYCEMIA, EVENT | We conducted analyses separately for each hypoglycemia range to compare the effect of initial treatment of NS-H with 16 g vs. 32 g CHO. The primary outcome was the change in PG at 15 min (Delta-PG-15) after CHO consumption. Secondary outcomes are listed in the Finally, all hypoglycemic event data ( | PMC10272543 |
Results | hypoglycemia | HYPOGLYCEMIA, REGRESSION | Participants included in the analysis (Baseline characteristics of the participants.Trial flowchart.Plasma glucose profiling for each of the four trial arms is shown in Plasma glucose profiles starting at 60 min before hypoglycemia treatment level was reached until 60 min after CHO consumption for the four trial arms.We report the comparison of 16 g vs. 32 g of CHO for the PG range 3.0–3.5 mmol/L in 32 participants. Delta-PG-15-min reached 0.1 (0.8) mmol/L vs. 0.6 (0.9) mmol/L, Outcomes of hypoglycemia treatment with 16 vs. 32 g oral CHO.Data presented as mean (SD) or n (%). *Primary endpoint. PG-T0 is plasma glucose at time 0, which is at the end of glucose tablets consumption. 2nd CHO treatment given at 15 min if PG still < 3 mmol/L and at 45 min if PG < 4 mmol/L. Rate of change of PG, insulin, and glucagon are calculated from hypoglycemia induction until first CHO consumption.Statistically significant p values were put in bold.We compare the consumption of 16 g vs. 32 g CHO intake for PG range < 3 mmol/L in 29 participants. Delta-PG-15-min reached 0.8 (0.9) mmol/L vs. 0.8 (1.0) mmol/L, At both hypoglycemia ranges, insulin boluses per kg of body weight used to induce hypoglycemia and plasma insulin and glucagon levels upon reaching hypoglycemia thresholds were comparable between arms (According to the best-fit regression model built for pooled data, significant predictors ( | PMC10272543 |
Discussion | hypoglycemia | HYPOGLYCEMIA, SECONDARY, SPONTANEOUS HYPOGLYCEMIA | For insulin-induced NS-H, an initial CHO intake of 32 g compared to 16 g of CHO at the 3.0–3.5 mmol/L hypoglycemic range showed some benefits. This resonates with real-life practices of PWT1D who consumed averages of 32 g of CHO (In our study, an increase in PG at 15 min post-treatment with 16 g of CHO was similar to that reported by a recent study (0.85 mmol/L) (As previously mentioned, we chose 32 g of CHO for initial consumption based on averages reported in real-life patients’ practices (This potential advantage of an initial 32 g of CHO at a PG range of 3.0-3.5 mmol/L was not seen at lower hypoglycemia at which more intense and unpleasant symptoms were reported (data not shown) (The pooled data were limited by its secondary analysis nature but it leads to interesting observations. It demonstrated that both absolute glucose levels and its rates of change (data that can be obtained with glucose sensors) and possibly rates of change of plasma insulin levels (research in progress designing insulin sensors) were positively associated with change of PG at 15 min (Mini-doses of glucagon could be an alternative to oral CHO in NS-H, especially to avoid additional caloric intake with the increased prevalence of excess weight in PWT1D (Our study had some limitations. Subcutaneous insulin could result in inter-patient variability due to differences in absorption, but it is more representative of real-life conditions than the historical use of the intravenous route. These results need replication during spontaneous hypoglycemia episodes in diverse free-living conditions using glucose sensors or capillary values and in pediatric populations. Alternate ways to treat NS-H should be investigated. We strongly believe that CHO intake at glucose thresholds higher than 4 mmol/L to prevent reaching serious hypoglycemia levels needs to be investigated.In conclusion, this study has confirmed treatment difficulties of insulin-induced NS-H with the current guidelines. While it did not aim to change those guidelines, it added one important piece to the puzzle. Advantages of higher initial CHO consumption in some circumstances and treatment at higher glucose thresholds or ranges should be further reproduced in other trials and settings. This study adds to the scarce literature needed to fine-tune the treatment approach for this daily struggle for PWT1D and asserts the need to conduct other trials to cover various clinical scenarios and conditions of NS-H ( | PMC10272543 |
Data availability statement | The data analyzed in this study is subject to the following licenses/restrictions: Granular data can be made available upon direct request to the senior author. Requests to access these datasets should be directed to | PMC10272543 | ||
Ethics statement | The studies involving human participants were reviewed and approved by the Ethics committee at Montreal Clinical Research Institute. The patients/participants provided their written informed consent to participate in this study. | PMC10272543 | ||
Author contributions | NT, VG, VM, and RR-L designed the study. NT, VP, and DB conducted data collection. NT and AS conducted data analysis. NT, A-SB, VG, RC, and RR-L helped in data interpretation. NT drafted the manuscript, which was critically revised by VP, VG, A-SB, AS, DB, RC, and RR-L. All authors revised the final draft and approved its contents. RR-L is the principal guarantor of this work. | PMC10272543 | ||
Acknowledgments | hypoglycemic | We thank all the participants in our clinical trials for their valuable time and acceptance for repeated induced hypoglycemic episodes, and the nurses and clinical research personnel at the Montreal Clinical Research Institute for their invaluable work and efforts. | PMC10272543 | |
Conflict of interest | Nordisk, and Sanofi. | TYPE 2 DIABETES | RR-L has received research grants from Astra-Zeneca, Eli Lilly, Merck, Novo-Nordisk, and Sanofi-Aventis. He has been a consultant or member of advisory panels of Abbott, Amgen, Astra-Zeneca, Boehringer, Carlina Technology, Eli Lilly, Janssen, Medtronic, Merck, Neomed, Novo-Nordisk, Roche, Sanofi-Aventis, and Takeda. He has received honoraria for conferences by Abbott, Astra-Zeneca, Eli Lilly, Janssen, Medtronic, Merck, Novo-Nordisk, and Sanofi-Aventis. He has received in kind contributions related to closed-loop technology from Animas, Medtronic, and Roche. He also benefits from unrestricted grants for clinical and educational activities from Eli Lilly, Lifescan, Medtronic, Merck, Novo Nordisk, and Sanofi. He holds intellectual property in the field of type 2 diabetes risk biomarkers, catheter life, and the closed-loop system. RR-L and VM received purchase fees from Eli Lilly in relation with closed-loop technology. NT has received consultant fees from Dexcom, Eli Lilly, and Viatris.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. | PMC10272543 |
Publisher’s note | All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. | PMC10272543 | ||
References | PMC10272543 | |||
Abstract | PMC10067030 | |||
Purpose | This study aimed to compare the prognostic value of multiple lymph node metastasis (LNM) indicators and to develop optimal prognostic nomograms for bladder cancer (BC) patients. | PMC10067030 | ||
Methods | tumor, TCGA, Cancer | REGRESSION, TUMOR, CANCER | BC patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015, and randomly partitioned into training and internal validation cohorts. Genomic and clinical data were collected from The Cancer Genome Atlas (TCGA) as external validation cohort. The predictive efficiency of LNM indicators was compared by constructing multivariate Cox regression models. We constructed nomograms on basis of the optimal models selected for overall survival (OS) and cause‐specific survival (CSS). The performance of nomograms was evaluated with calibration plot, time‐dependent area under the curve (AUC) and decision curve analysis (DCA) in three cohorts. We subsequently estimated the difference of biological function and tumor immunity between two risk groups stratified by nomograms in TCGA cohort. | PMC10067030 |
Results | tumor, TCGA | TUMOR | Totally, 10,093 and 107 BC patients were screened from the SEER and TCGA databases. N classification, positive lymph nodes (PLNs), lymph node ratio (LNR) and log odds of positive lymph nodes (LODDS) were all independent predictors for OS and CSS. The filtered models containing LODDS had minimal Akaike Information Criterion, maximal concordance indexes and AUCs. Age, LODDS, T and M classification were integrated into nomogram for OS, while nomogram for CSS included gender, tumor grade, LODDS, T and M classification. The nomograms were successfully validated in predictive accuracy and clinical utility in three cohorts. Additionally, the tumor microenvironment was different between two risk groups. | PMC10067030 |
Conclusions | tumor | TUMOR, METASTASIS | LODDS demonstrated superior prognostic performance over N classification, PLN and LNR for OS and CSS of BC patients. The nomograms incorporating LODDS provided appropriate prediction of BC, which could contribute to the tumor assessment and clinical decision‐making.LODDS had better predictive accuracy than other lymph nodes metastasis indicators for BC patients. Nomograms including LODDS could improve the prediction of OS and CSS for BC patients.
| PMC10067030 |
INTRODUCTION | Bladder cancer | BLADDER CANCER, MALIGNANT TUMOR OF URINARY TRACT | Bladder cancer (BC) is the most common malignant tumor of urinary tract.In recent years, several LN prognostic factors were proposed to estimate the prognosis of BC patients, including the positive lymph nodes (PLNs) and the lymph node ratio (LNR).The present study aimed to compare the prognostic values among different LN status factors of BC patients by analyzing data from the Surveillance, Epidemiology, and End Results (SEER) database. Subsequently, we established novel nomograms incorporating LODDS for predicting OS and CSS, and successfully validated them in internal and external validation cohorts. | PMC10067030 |
METHODS | PMC10067030 | |||
Data source | tumor, Cancer | TUMOR, CANCER | We collected patients from the SEER database (SEER*Stat version 8.3.9.2) of the National Cancer Institute (NCI) program, which is one of the most representative tumor databases and covers approximately 28% of the US population. | PMC10067030 |
Study population | Cancer, tumor, primary bladder cancer | CANCER, TUMOR, BLADDER | Patients diagnosed with primary bladder cancer (ICD‐O‐3/WHO 2008: “Urinary Bladder”) between 2004 and 2015 were enrolled into the study. The exclusion criteria for data extraction were (1) patients aged <20 years or ≥ 80 years at diagnosis; (2) patients with no surgery or undergoing local resection; (3) patients with no LN removed or with unclear examined LNs (ELNs) and PLNs; (4) patients with T0/Ta/Tis classification or with unclear AJCC TNM stage and tumor grade; (5) patients with unclear survival data or survival time less than 1 month. Finally, we included 10,093 BC patients after cystectomy or radical cystectomy from SEER and partitioned them into training cohort (Flowchart illustrating patient selection of this study. ELN, examined lymph node; PLN, positive lymph node; SEER, Surveillance, Epidemiology, and End Results; TCGA, The Cancer Genome Atlas. | PMC10067030 |
Measurement of variables | tumor, death, Low‐, TCGA, tumors | TUMOR, TUMORS, SECONDARY | We collected variables of patients including age at diagnosis, gender, T/N/M classification, tumor grade, the amount of regional ELNs and PLNs, survival time and status. The tumor stage was based on the sixth edition of AJCC staging system, which was adapted to SEER‐derived patients diagnosed from 2004 to 2015. Low‐ and high‐grade tumors in TCGA were considered as grade I‐II and III‐IV in SEER database, respectively. The LNR was defined as the ratio of the amount of PLNs and ELNs. The LODDS was formulated by log [(PLNs +0.5) / (ELNs – PLNs +0.5)]. To avoid division by zero error, we added 0.5 to both numerator and denominator.The primary and secondary endpoints were OS and CSS, which were shown as “COD to site recode” and “SEER cause‐specific death classification” in SEER database, respectively. Of note, the data from the SEER and TCGA were downloaded on December 9, 2021. | PMC10067030 |
Independence of | REGRESSION | We selected the clinicopathological predictors with univariable Cox regression analyses through “survival” R package for OS and CSS in training cohort. To further assess the predictive values, each LN status factor (including N classification, PLN, LNR and LODDS) was integrated into multivariate regression models together with other risk variables with | PMC10067030 | |
Comparison of predictive performance among | Backward stepwise selection (via “MASS” R package) was utilized into the above models using Akaike Information Criterion (AIC) as the stopping rule, respectively. | PMC10067030 | ||
Construction and validation of nomograms | TCGA | Variables included in the filtered models with the highest accuracy were integrated to develop nomograms for predicting OS and CSS in training cohort (via “rms” R package), respectively. The efficiency of nomograms was evaluated by bootstrapped C‐indexes, time‐dependent AUCs and calibration plots in training, internal validation and TCGA cohorts. Additionally, we preformed decision curve analysis (DCA) to assess the net benefit and clinical performance of the nomograms using the “ggDCA” R package. | PMC10067030 | |
Survival risk classifiers established by nomograms | REGRESSION | The multivariate Cox regression formulas of the nomograms for OS and CSS formed in training cohort were applied into patients in three cohorts using “nomogramFormula” R package. All patients were divided into high‐ and low‐risk groups according to the total points calculated via “survminer” R package. The Kaplan–Meier (K‐M) method was used to assess the survival difference of OS and CSS between two risk groups. | PMC10067030 | |
Functional enrichment | TCGA | Differential expression genes (DEGs) from TCGA were searched for by comparing high‐ and low‐risk groups with the threshold of |log fold change| >1 and | PMC10067030 | |
Estimation of tumor immune infiltration | tumor, TCGA | TUMOR | To estimate the tumor microenvironment (TME), the stromal and immune scores of each sample in TCGA cohort were analyzed through the “estimate” R package. | PMC10067030 |
Statistical analysis | Continuous variables with non‐normal distribution were reported as median with interquartile range (IQR) and categorical variables were presented as frequencies with percentages. Statistical significance was achieved with a two‐sided | PMC10067030 | ||
RESULTS | PMC10067030 | |||
Patient characteristics and survival | TCGA | BLADDER CANCER | The patients' characteristics of training, internal validation and TCGA cohorts are shown in Table Clinical and pathologic characteristics of patients with BC in three cohortsAbbreviations: BC, bladder cancer; CI, confidence interval; IQR, interquartile range; LNR, lymph node ratio; LODDS, log odds of positive lymph node; PLN, positive lymph node. | PMC10067030 |
Prognostic analyses for | REGRESSION | The detailed results of the univariate Cox regression analyses in training cohort are demonstrated in Table Univariate Cox regression analyses for predicting OS and CSS in training cohortAbbreviations: CI, confidence interval; CSS, cause‐specific survival; HR, hazard ratio; LNR, lymph node ratio; LODDS, log odds of positive lymph node; OS, overall survival; PLN, positive lymph node.Represented We further performed multivariate analyses and generated prognostic models including different LN indicators, respectively. Briefly, N classification, PLN, LNR and LODDS were all independent risk factors for OS (Table Multivariate Cox regression analyses for predicting OS in training cohortAbbreviations: CI, confidence interval; HR, hazard ratio; LNR, lymph node ratio; LODDS, log odds of positive lymph node; OS, overall survival; PLN, positive lymph node.Represented Multivariate Cox regression analyses for predicting CSS in training cohortAbbreviations: CI, confidence interval; CSS, cause‐specific survival; HR, hazard ratio; LNR, lymph node ratio; LODDS, log odds of positive lymph node; PLN, positive lymph node.Represented | PMC10067030 | |
Comparison of N classification, | The comparison of LN status indicators in training cohort is shown in Table Prognostic efficiency of different lymph node status indicators in training cohortAbbreviations: AIC, Akaike information criterion; AUC, area under the curve; C‐index, concordance index; LNR, lymph node ratio; LODDS, log odds of positive lymph node; PLN, positive lymph node. | PMC10067030 | ||
Construction and validation of nomograms | TCGA, Cancer | BLADDER CANCER, CANCER | We developed nomograms based on the selected models containing LODDS in training cohort. As results, age, LODDS, T and M classification were incorporated into final nomogram for predicting OS (Figure Nomogram for OS of BC patients. (A) Prediction for 1‐, 3‐ and 5‐year OS of nomogram. Calibration plots for 1‐, 3‐ and 5‐year in training (B), internal validation (C) and TCGA cohorts (D). BC, bladder cancer; LODDS, log odds of positive lymph node;OS, overall survival; TCGA, The Cancer Genome Atlas.Nomogram for CSS of BC patients. (A) Prediction for 1‐, 3‐ and 5‐year CSS of nomogram. Calibration plots for 1‐, 3‐ and 5‐year in training (B), internal validation (C) and TCGA cohorts (D). BC, bladder cancer; CSS: cause‐specific survival; LODDS, log odds of positive lymph node; TCGA, The Cancer Genome Atlas.Evaluation of the nomograms for BC patients with AUC and DCA. The time‐dependent AUC for OS (A) and CSS (E) in three cohorts. Decision curves for predicting 1‐, 3‐ and 5‐year OS (B‐D) and CSS (F‐H) in training, internal validation and TCGA cohorts. Solid black line: assume no patients need clinical intervention, net benefit is zero. Solid color lines: assume all patients need receive clinical intervention. Dotted color lines: net benefits of the nomogram for predicting 1‐, 3‐ and 5‐year survival when patients receive intervention if predictions exceed the threshold. AUC, area under the curve; BC, bladder cancer; CSS, cause‐specific survival; DCA, decision curve analysis; OS, overall survival; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas. | PMC10067030 |
Survival risk classifiers based on nomograms | TCGA, Cancer | BLADDER CANCER, CANCER | To further verify the performance of nomograms, we divided patients into high‐ and low‐risk groups based on the total points calculated by nomograms for OS and CSS. The cutoff values of the total points were 100.57 for OS and 82.94 for CSS, respectively (Figure Kaplan–Meier (K‐M) analyses for BC patients classified by nomograms. (A, B) K‐M curves for OS and CSS in training cohort. (C, D) K‐M curves for OS and CSS in internal validation cohort. (E, F) K‐M curves for OS and CSS in TCGA cohort. BC, bladder cancer; CSS, cause‐specific survival; OS, overall survival; TCGA, The Cancer Genome Atlas. | PMC10067030 |
Pathway enrichment analyses | tumor, TCGA | TUMOR | After standardization among the RNA‐seq in TCGA, we extracted 78 DEGs based on the risk classifier for OS. The representative statistically enriched pathways (both GO and KEGG) were clustered together and shown with a network plot (Figure Analyses of pathway enrichment and tumor immunity of the risk classifier stratified by OS. (A) Network plot of the enriched terms clustered by Metascape. (B) The significantly enriched Hallmark gene sets performed by Gene Set Enrichment Analysis. (C) The stromal and immune scores between two groups. (D) Different expression of ICIs‐related genes among risk groups. (E) The different proportion of tumor‐infiltrating cells between two groups. * represented | PMC10067030 |
The relationship between risk classifiers and tumor immunity | We investigated the TME and calculated stromal and immune scores for each BC sample based on the risk classifier for OS. The higher stromal score was observed in samples of high‐risk group (Figure | PMC10067030 | ||
DISCUSSION | heterogeneous malignancy, bladder cancer | SOLID TUMORS, BLADDER CANCER | Despite improvement in diagnosis and surgery, bladder cancer remains a heterogeneous malignancy with poor prognosis.Considering this situation, several modified LN status factors has been proposed to predict the survival of BC, including PLN, LNR and LODDS.The N classification of AJCC staging system evaluates LN status mainly by detecting in certain regional areas, which was unsatisfactory due to the unclear number of negative and removed LNs.The current study suggested that the prognostic models with LODDS had best predictive capability for OS and CSS, which was similar to Jin et al.'s study for OS of BC patients. However, we paid more attention to clinical utilization and constructed two novel nomograms incorporating LODDS for predicting OS and CSS of BC patients for the first time. Then, the nomograms were further validated in internal and external validation cohorts. The calibration curves showed stable linearity and appropriate efficacy of the nomograms, and the calculated C‐indexes and time‐dependent AUCs were most above 0.70 and 0.75 in three cohorts, respectively. In terms of clinical utility, the DCA curves demonstrated that the nomogram showed consistent larger net benefit across a broad range of threshold probabilities. We believed that the nomograms had satisfactory applicability in predicting survival for BC patients. Taken together, better predictive accuracy and clinical validity were verified in our nomograms compared with AJCC and other LN status systems.Furthermore, risk classifiers for OS and CSS were established on basis of the total scores from nomograms, stratifying BC patients into different risk groups. Patients in two high‐risk groups had worse survival in each cohort. We subsequently explored the biological function between the two groups. The results showed that there were major differences in immunoglobulin production and secretion, ECM organization, epithelial mesenchymal transition, adipogenesis and DNA reparation. It has been reported that the immunoglobulin, produced by B lymphocytes and plasma cells in response to immunogen, was associated with the prognosis in solid tumors.Indeed, as a retrospective study, we need prospective and multicenter study to verify the prognostic value of LODDS. Secondly, the details of surgical approach, such as the extent of LN dissection at specific nodal level are not recorded in detail, which is worth further investigation. Despite these limitations, our study proves better predictive value of LODDS and firstly incorporates it into prognostic nomograms for both OS and CSS in BC patients. | PMC10067030 |
CONCLUSIONS | We confirmed that LODDS had better predictive accuracy compared with other LNM indicators for BC patients after surgery. Novel nomograms containing LODDS for predicting OS and CSS were established based on SEER database and successfully validated in external dataset, which could assist urologists with more accurate therapeutic decision and personalized follow‐up management for BC patients. | PMC10067030 | ||
AUTHOR CONTRIBUTIONS | PMC10067030 | |||
CONFLICT OF INTEREST | The authors declared no conflicts of interest. | PMC10067030 | ||
ETHICAL APPROVAL STATEMENT | Ethical approval was not needed in this retrospective study, as all data abstracted from the public‐used databases was anonymous. | PMC10067030 | ||
Supporting information |
Figure S1.
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Figure S2.
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Figure S3.
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Table S1.
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Table S2.
Click here for additional data file. | PMC10067030 | ||
ACKNOWLEDGMENTS | TCGA | The authors appreciate the contributors and handlers of the SEER and TCGA databases for making these datasets publicly available. | PMC10067030 | |
DATA AVAILABILITY STATEMENT | This article is based on available data from the SEER ( | PMC10067030 | ||
REFERENCES | PMC10067030 | |||
Aims | T2DM, FM | TYPE 2 DIABETES MELLITUS | Managed by Antonio Secchi.This investigation aimed to determine the effect of different intensities of training on non-exercise physical activity (NEPA) and estimated thermogenesis (NEAT) from a 1-year exercise randomized controlled trial (RCT) in individuals with type 2 diabetes mellitus (T2DM) on non-training days. Additionally, changes in NEPA and estimated NEAT in those who failed (low-responders) or succeeded (high-responders) in attaining exercise-derived clinically meaningful reductions in body weight (BW) and fat mass (FM) (i.e., 6% for FM and 3% for BW) was assessed. | PMC10063485 |
Methods | T2DM | Individuals with T2DM ( | PMC10063485 | |
Results | After adjustments, no time*group interactions were found for estimated NEAT in the MICT (β = − 5.33, | PMC10063485 | ||
Conclusions | FM | Both MICT and HIIT did not result in any compensatory changes in estimated NEAT and NEPA with the intervention on non-training days. Moreover, no changes in estimated NEAT and NEPA were found when categorizing our participants as low-responders and high-responders to FM and BW when compared to controls.Trial registration clinicaltrials.gov ID.NCT03144505. | PMC10063485 | |
Keywords | Open access funding provided by FCT|FCCN (b-on). | PMC10063485 | ||
Introduction | Obesity, obesity, T2DM | OBESITY, OBESITY, TYPE 2 DIABETES | Obesity is an underlying risk factor for type 2 diabetes (T2DM), in which exercise alongside with medication and nutrition are the most used strategies to prevent, control, and treat this condition [An important aspect of energy balance to consider for obesity management [Characteristics of the exercise dose may influence the degree and amount of change observed in NEPA and NEAT following an exercise intervention, with most of the information on this topic deriving from either healthy adults or overweight/obese individuals [ | PMC10063485 |
Methods | PMC10063485 | |||
Subjects and study design | T2DM | SECONDARY | This investigation is a secondary analysis of a 1-year randomized crossover trial conducted in individuals with T2DM that aimed to compare the effect of different exercise intensities on glycated hemoglobin as the main outcome. A total of 80 participants with T2DM completed baseline assessments and were allocated to one of three arms: (1) high-intensity interval cycling combined with resistance training (HIIT); (2) moderate-intensity cycling combined with resistance training (MICT); (3) control group. Sample size calculation procedures were based on a predicted glycated hemoglobin difference of 0.66 units, with a standard deviation of 1.2 units (α, 0.05; 1 − β, 0.80) and an expected dropout rate of 10% (Fig. Study flowchart | PMC10063485 |
Intervention protocol | The detailed protocol is described elsewhere [ | PMC10063485 | ||
Anthropometry and body composition | Participants’ weight and height were measured to the nearest 0.01 kg and 0.1 cm, respectively, on an electronic scale with stadiometer (Seca, Hamburg, Germany) according to the standardized procedures [ | PMC10063485 | ||
Sensor-based data | Participants were instructed to wear an accelerometer (ActiGraph, GT3X model, Fort Walton Beach, FL) on the right hip for 7 days at baseline, 6-, and 12-months (during the exercise intervention). Data were recorded at a 100 Hz frequency, and downloaded into 10 s epochs. Troiano et al. cut-points and validation criteria were used to analyze the data [ | PMC10063485 | ||
Non-exercise activity thermogenesis determination | The ActiLife software and the refined 2-regression model Crouter equation [ | PMC10063485 | ||
Non-exercise physical activity determination | All the activity counts were summed for each of the non-exercise days and then averaged to get the average NEPA per non-exercise day for each participant. | PMC10063485 | ||
Identifying individual exercise fat mass responders | FM loss | Currently, there are no accepted guidelines for the percent of FM loss considered to be clinically meaningful. Therefore, we considered someone who had a FM loss greater than the typical error (TE) as clinically meaningful. The TE was calculated from the standard deviation (SD) of the differences in FM over 1-year in the control group, an approach suggested by Bonafiglia et al. [ | PMC10063485 | |
Statistical analysis | FM | Descriptive statistics, including measures of central tendency (mean) and variability (standard deviation), were used to describe baseline characteristics of the control group, MICT, and HIIT. A one-way ANOVA with a Bonferroni adjustment for multiple comparisons was used to test differences in descriptive characteristics between the three groups and a chi-square test was used to assess differences in gender among the groups.Generalized estimating equations were used to assess group by time interactions in NEPA and estimated NEAT on non-exercise days between the controls and exercise groups at 6-months and 1-year while using sex, age, wear time, and total number of trainings as covariates. Baseline NEPA and estimated NEAT were also included in their respective models as covariates given the significant difference between the groups at baseline. A least significant difference post hoc test was used to estimate the between and within-group effects on NEPA and estimated NEAT. A linear distribution for the response was assumed and an autoregressive correlation matrix was set to the data. Similarly, generalized estimating equations were used to assess group by time interactions in NEPA and estimated NEAT between the controls, low responders, and high responders for both FM and BW loss at 6 months and 1-year, while adjusting for sex, age, total number of trainings, wear time, and baseline NEPA or estimated NEAT.A | PMC10063485 | |
Discussion | obese, FM, weight reduction, overweight, T2DM, weight loss | OBESE | To the best of our knowledge, this is the first experimental investigation that examined how estimated NEAT and NEPA are affected by a 1-year exercise intervention performed at different intensities in T2DM. Our results showed no compensatory decreases in NEPA and estimated NEAT on the non-exercise days following 1-year of exercise training, regardless of training protocol (HIIT/MICT). Moreover, whether an individual attained exercise-derived clinically meaningful reductions in BW or FM did not influence changes in NEPA and NEAT after 1-year of intervention.Although it can be speculated that higher exercise intensities would promote more noticeable behavioral and physiological changes toward reducing NEAT and NEPA, the available evidence regarding this issue is still scarce, with none of the literature addressing the effect of exercise intensity on these compensatory mechanisms in the T2DM population. Indeed, the intensity issue has been previously reviewed by Washburn et al. in a population of healthy/overweight/obese adults, where no evidence was found for the intensity of the exercise influencing NEPA and NEAT following short to medium-term exercise interventions [Another possible explanation for the absence of compensatory mechanisms in estimated NEAT and NEPA may be partially related with the inclusion of both aerobic and resistance training in our protocol. Research has suggested that the compensatory reduction in NEPA is lower after resistance training compared to aerobic exercise [During exercise programs, the achieved weight loss often does not match the expected weight reduction due to potential compensatory mechanisms toward energy saving, such as reductions in NEAT and NEPA that attenuate the overall impact of the EE derived from the exercise sessions. Indeed, these compensations may contribute to the inter-individual variability in weight loss achieved from an exercise intervention, where those having clinically meaningful weight losses (i.e., high responders), potentially having higher changes in NEAT and NEPA when compared to low responders in weight loss. A recent systematic review reported that the participants who lost the most weight were also the ones who compensated the most with decreases in NEAT [In our exercise-based intervention, we observed no compensatory reductions in NEPA and estimated NEAT, after 1-year of intervention, when categorizing participants with T2DM as low or high responders for FM or BW, when compared to non-exercising controls. Conversely, Herrmann et al., using a long-term exercise intervention in overweight and obese adults reported that men who were categorized as low weight loss responders (< 5% weight loss) decreased their NEPA and NEAT levels as well as increased their energy intake to a greater extent when compared with the high responder group (≥ 5%) [This investigation is not without limitations. Since this investigation aimed to assess NEPA and NEAT in free-living conditions, and given the known limitations of gold standard methods, such as indirect calorimetry or doubly labeled water to assess these constructs in the field, we opted to use motion sensors, while acknowledging some validation issues for estimating EE [As a major strength, it is important to highlight that this is the first investigation to explore the impact of different exercise regimes and the inter-individual variability in body composition changes resulting from a 1-year exercise intervention on NEPA and estimated NEAT levels in individuals with T2DM on non-exercise days. This is relevant considering that most studies have reported the combined changes on both exercise and non-exercise days, making it impossible to understand what is happening with NEPA and estimated NEAT on the non-training days following exercise interventions. This issue is even more relevant when longer-term interventions are planned, where exercise attendance rates tend to decrease over time. | PMC10063485 |
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