As one common feature of cerebral small vascular disease (cSVD), white matter lesions (WMLs) could lead to reduction in brain function. Using a convenient, cheap, and non-intrusive method to detect WMLs could substantially benefit to patient management in the community screening, especially in the settings of availability or contraindication of magnetic resonance imaging (MRI). Therefore, this study aimed to develop a useful model to incorporate clinical laboratory data and retinal images using deep learning models to predict the severity of WMLs.Two hundred fifty-nine patients with any kind of neurological diseases were enrolled in our study. Demographic data, retinal images, MRI, and laboratory data were collected for the patients. The patients were assigned to the absent/mild and moderate–severe WMLs groups according to Fazekas scoring system. Retinal images were acquired by fundus photography. A ResNet deep learning framework was used to analyze the retinal images. A clinical-laboratory signature was generated from laboratory data. Two prediction models, a combined model including demographic data, the clinical-laboratory signature, and the retinal images and a clinical model including only demographic data and the clinical-laboratory signature, were developed to predict the severity of WMLs.Approximately one-quarter of the patients (25.6%) had moderate–severe WMLs. The left and right retinal images predicted moderate–severe WMLs with area under the curves (AUCs) of 0.73 and 0.94. The clinical-laboratory signature predicted moderate–severe WMLs with an AUC of 0.73. The combined model showed good performance in predicting moderate–severe WMLs with an AUC of 0.95, while the clinical model predicted moderate–severe WMLs with an AUC of 0.78.Combined with retinal images from conventional fundus photography and clinical laboratory data are reliable and convenient approach to predict the severity of WMLs and are helpful for the management and follow-up of WMLs patients. Stroke is a leading cause of mortality and long-term disability, especially in low- and middle-income countries . In Chinin vivo  patients who underwent nonmydriatic fundus photography; (2) patients who underwent brain MRI; and (3) over 18 years old. The exclusion criteria were as follows: (1) poor quality nonmydriatic fundus photography or MRI images; (2) missing clinical data; (3) WMLs caused by hereditary diseases, immune disorder, or infection ; (4) intracranial hypertension or hydrocephalus; (5) major brain edema or intracerebral hemorrhage; (6) primary ophthalmological diseases impairing the retina or hindering observation of the retina by fundus photography; (7) recent (<6 months) ophthalmological surgery; and (8) concomitant significant systemic infection. Demographic-clinical data, including age, sex, systolic blood pressure (SBP) and diastolic blood pressure (DBP) on admission, were collected. Comorbidities, including hypertension, diabetes mellitus, statin usage, atrial fibrillation, coronary heart disease, previous stroke or transient ischemic attack (TIA), were documented. Hypertension was defined as systolic blood pressure of ≥140 mmHg or diastolic blood pressure of ≥90 mmHg or current treatment with anti-hypertensive medications. Diabetes mellitus was defined as fasting plasma glucose ≥7.0 mmol/L or hemoglobin A1c (HbA1c) ≥ 6.5% or current treatment with blood glucose lowering medication. This study met the criteria of the Declaration of Helsinki, and it was approved by the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University, which waived the requirement for written informed consent due to this study was retrospectively designed [No. (2022) 202].All patients underwent brain MRI at 3 Tesla scanners (Siemens Trio Tim or Siemens Verio). In the present study, T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), T2-fluid attenuation inversion recovery (T2-FLAIR), and diffusion-weighted imaging (DWI) were collected for all patients. The parameters were as follows: (1) T1WI, repetition time (TR)/echo time (TE) 500/8.9 ms, and slice thickness (SL) 6.0 mm; (2) T2WI, TR/TE = 4000/100 ms, and SL 6.0 mm; (3) T2-FLAIR, TR/TE = 9000/111 ms, inversion recovery 2,500 ms, and SL 6.0 mm; and (4) TR/TE = 5800/100 ms, b = 0/1000 s/mm2, and SL 5.0 mm.n = 81), final Fazekas score 1 (n = 62), final Fazekas score 2 (n = 51), final Fazekas score 3 (n = 29), final Fazekas score 4 (n = 16), final Fazekas score 5 (n = 8), and final Fazekas score 6 (n = 12). To minimize the redundancy and maximize the clinical practicability of our model, we assigned the patients with final Fazekas score 0 to 2 in the absent/mild WMLs group and the patients with final Fazekas score 3 to 6 in the moderate–severe WMLs group.WMLs were analyzed on T2WI and T2-FLAIR images and semi-quantitatively graded based on the Fazekas grading system. Periventricular hyperintensity (PVH) was scored as 0 for the absence of any lesions, 1 for cap or pencil-like lesions, 2 for halo-like lesions with diameters of 6 mm ~ 10 mm, and 3 for irregular lesions with diameters of more than 10 mm. Deep white matter hyperintensity (DWMH) was scored as 0 for the absence of any lesion, 1 for punctate-like lesions, 2 for small confluent lesions, and 3 for large confluent lesions . For patFundus photography was performed on both eyes for all patients without pupil dilatation . In brief, patients sat in front of the fundus photography system and were instructed to focus on the center of the viewing system while the fundus was examined. The field of view was set to 45° to enable visualization of the posterior portion of the examined eye. The position of the camera and focal length were finely adjusted so that the optic disk and macula were clearly viewed. The images of both eyes were stored in a workstation for further analysis .We used ResNet-18 model as the training model to predict the present moderate–severe WMLs in brain MRI scan by using retinal images. ResNet-18 is a residual network pretrained using the ImageNet dataset, but the associated fully connected softmax layer has been replaced by a supervised classifier. The pre-trained model used in our study is available on the code repository.Clinical-laboratory data were examined for all patients. The clinical-laboratory data can be concisely divided into four regiments, including lipid-glucose metabolism, systemic inflammation, and liver and renal function. In detail, the lipid-glucose metabolism regimen included total cholesterol (CHOL), total triglyceride (TG), low density lipoprotein cholesterol (LDL-c), high density lipoprotein cholesterol (HDL-c), apolipoprotein-E (Apo-E), apolipoprotein-A1 (Apo-A1), apolipoprotein-B (Apo-B), Apo-A1/Apo-B ratio, lipoprotein-α (Lp-α), fasting blood glucose (FBG), HbA1c, free fat acid (FFA), fructosamine, and homocysteine. The systemic inflammation regimen included C-reactive protein (CRP), high-sensitivity C-reactive protein (Hs-CRP), and the Hs-CRP/CRP ratio. The liver function regimen included aspartate aminotransferase (AST), alanine aminotransferase (ALT), total protein (TP), total bilirubin (TBIL), albumin, globulin, and albumin/globulin ratio (A/G ratio). The renal function regimen, including creatine and uric acid (UA).We developed a clinical-laboratory signature representing the metabolism, systemic inflammation, liver and renal function. A least absolute shrinkage and selection operator (LASSO) logistic regression model was implemented to eliminate collinearity, reduce overfitting, filter and select the clinical laboratory data that correlate with moderate–severe WMLs by tuning the hyperparameters that minimize Akaike information criteria (AIC).We developed a combined model including demographic-clinical characteristics, the clinical-laboratory signature, and the outputs of the retina deep learning neural network to predict WMLs severity. The demographic-clinical characteristics used in the predictive model were age, sex, SBP and DBP on admission. Age was included in the model as a continuous variable. Male was coded as 1 and female was coded as 0. SBP was further transformed into a hierarchical variable by defining values <140 mmHg as 0, values of 140 ~ 159 mmHg as 1, values of 160 ~ 179 mmHg as 2, and values of ≥180 mmHg as 3. Similarly, DBP was transformed by defining values <90 mmHg as 0, values of 90 ~ 99 mmHg as 1, values of 100 ~ 109 mmHg as 2, and values ≥110 mmHg as 3. The combined model was formulated based on the results of multivariate logistic regression that minimized the penalty parameter conducted by 10-fold cross-validation. Furthermore, a clinical model including demographic-clinical characteristics and clinical-laboratory signatures was generated based on the results of multivariate logistic regression that minimized the penalty parameter conducted by 10-fold cross-validation without the outputs of retina deep learning neural network.R2) and recall rate were further utilized to predict the performance of the clinical-laboratory signature, and the clinical mode.Data for continuous variables are summarized as the mean and standard deviation. Categorical variables were summarized by number and percentage. Univariate logistic regression was carried out in R (v4.0.5) to explore the relationship among demographic-clinical characteristics, individual clinical laboratory data and WMLs severity. The continuous variables were compared by independent-sample t test between patients in the absent/mild WMLs group and patients in the moderate–severe WMLs group. The categorical variables were compared by the chi-square test or Fisher’s exact test. Univariate logistic regression was used to explore the association between clinical-laboratory indices and dichotomized WMLs severity. Odds ratios (ORs) and 95% confident intervals (95% CIs) were used to predict the association between clinical-laboratory data and WMLs severity. The predictive model development and compilation of the clinical-laboratory signature were conducted in Python (v 3.7) and the scikit-learn package.p < 0.001). The SBP and DBP on admission were higher in the patients with moderate–severe WMLs than in those with absent/mild WMLs (p < 0.001). The proportions of hypertension, previous ischemic stroke/TIA, and atrial fibrillation were higher in the patients with moderate–severe WMLs than in those with absent/mild WMLs, while the frequency of diabetes mellitus was higher in the patients with absent/mild WMLs. The clinical characteristics of all patients are summarized in p = 0.127; p values of these diagnoses.Two hundred fifty-nine patients were enrolled in our study. The mean age of all patients was 52.3 ± 13.5 years. More than half of the included patients were male. The mean SBP of all patients was 140.4 ± 24.8 mmHg. The mean DBP of all patients was 87.7 ± 15.4 mmHg. The most common comorbidity was hypertension, which was present in 56.8% of all patients. Current smoking (35.1%), statin usage (21.6%), diabetes mellitus (17.4%), alcohol abuse (14.7%) and previous ischemic stroke/TIA (18.5%) were also common among all patients. A small proportion of the patients had atrial fibrillation (2.3%) and coronary heart disease (3.5%). Furthermore, the clinical characteristics of the patients with absent/mild and moderate–severe WMLs were compared. The patients with moderate–severe WMLs were significantly older than those with absent/mild WMLs , creatine , ApoA1 , and TG  were included in the regression model to fuse a clinical-laboratory signature for each patient.The association between clinical-laboratory Data and WMLs severity was summarized by univariate logistic regression . HomocysIn this study, 204 images of retinas from the left eyes and 240 images from right eyes from total 259 participants were supplied to two ResNet-18 deep learning neural network frameworks to predict the dichotomized WML severity. The class activation maps and architecture was presented in the R2 of 0.14. This signature showed an AUC of 0.73, accuracy of 0.77, precision of 0.85, recall rate of 0.18, F1 score of 0.29, sensitivity of 0.18, specificity of 0.95  are the most common in clinical practice . We exclThe WMLs significantly impair cognitive function  and are ResNet has been applied to predict Alzheimer’s disease , diagnosTraditionally, the retinal vascular lesion was manually evaluated according the presentations of exudates, vessel tortuosity, arteriovenous nicking and so on. However, this evaluation method was affected by raters’ bias and not quantitative. In our study, we focused on the entire retina image instead of the traditional evaluation method. We developed the deep learning model to analyze the entire image of the retina and show good AUC to predict WMLs. Our result further confirmed the findings of previous studies that the retinal lesions were associated with WMLs. In addition, the regression coefficient of retina deep learning model was higher than the traditional risk factors such as age and blood pressure in the combined model. This result indicated that the retinal images can predict the severity of WMLs.Several limitations of this study should be mentioned. First, the WMLs severity was dichotomized as absent/mild or moderate–severe according to the Fazekas score in our study. Consequently, the models developed in our study could not quantitatively predict WMLs severity. Recent studies showed that severe WMLs were association with multiple neurological diseases such as cognitive deterioration , 52, 53 In conclusion, we integrated demographic-clinical characteristics, clinical laboratory data and deep learning on retinal images to develop a comprehensive model. This comprehensive model accurately predicted the severity of WMLs and offered non-invasive, high-throughput and low-cost screening tool for early detection of moderate–severe WMLs in the population in community, resource-poor area or with contraindication of MRI.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.LS and KZ carried out the study and wrote the manuscript with support from HH, WS, and JL. NC and WG develop the code for ResNet and statistics. HH conceived the original idea. HH and JR supervised the project. All authors contributed to the article and approved the submitted version.This work was supported by the Guangdong Key Laboratory of Non-human Primate Research (grant number 202OB121201006) and the Liuzhou Science and Technology Plan Project (grant number 2021YB0104B056).The 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.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.https://www.frontiersin.org/articles/10.3389/fneur.2023.1168836/full#supplementary-materialThe Supplementary material for this article can be found online at: Click here for additional data file.


Cholangiocarcinoma (CCA) is a highly aggressive malignant tumor, and its diagnosis is still a challenge. This study aimed to identify a novel bile marker for CCA diagnosis based on proteomics and establish a diagnostic model with deep learning.A total of 644 subjects (236 CCA and 408 non-CCA) from two independent centers were divided into discovery, cross-validation, and external validation sets for the study. Candidate bile markers were identified by three proteomics data and validated on 635 clinical humoral specimens and 121 tissue specimens. A diagnostic multi-analyte model containing bile and serum biomarkers was established in cross-validation set by deep learning and validated in an independent external cohort.n = 259) resulted in a similar accuracy for the detection of CCA. Finally, for the convenience of operation, a user-friendly prediction platform was built online for CCA.The results of proteomics analysis and clinical specimen verification showed that bile clusterin (CLU) was significantly higher in CCA body fluids. Based on 376 subjects in the cross-validation set, ROC analysis indicated that bile CLU had a satisfactory diagnostic power . Building on bile CLU and 63 serum markers, deep learning established a diagnostic model incorporating seven factors , which showed a high diagnostic utility . External validation in an independent cohort (This is the largest and most comprehensive study combining bile and serum biomarkers to differentiate CCA. This diagnostic model may potentially be used to detect CCA.The online version contains supplementary material available at 10.1186/s12916-023-02990-9. Cholangiocarcinoma (CCA) is known as a highly aggressive malignancy. According to the anatomical site of the lesion, CCA can be divided into intrahepatic cholangiocarcinoma (iCCA), perihilar cholangiocarcinoma (pCCA), and distal cholangiocarcinoma (dCCA). As the second most common malignant tumor in the hepatobiliary system, CCA accounts for about ~ 3% of all gastrointestinal tumors  and has Diagnosing CCA is difficult because of its silent clinical character and anatomic location. Currently, CCA is mainly detected by imaging methods, such as computed tomography (CT), magnetic resonance imaging (MRI), and endoscopy, but their diagnostic powers are disappointing with a modest accuracy and an estimated sensitivity of only 6 to 71.9% , 5. SeruBile is the direct microenvironment for the growth of bile duct tumor cells, and cancer-related proteins in CCA can be secreted into the bile and may potentially be used as biomarkers for diagnosis , 11. In In this study, we identified and evaluated the power of a novel bile biomarker for the diagnosis of CCA. Based on this, a deep learning model was established by combining other serum markers. Finally, the diagnostic performance of the model was validated by another independent group.This study was approved by the Human Research Ethics Committee of the First Hospital of Lanzhou University (LDYYLL2022-381) with an exemption of informed consent and was conducted in accordance with the Declaration of Helsinki principles. Clinical specimens came from two centers.A total of 644 patients were divided into a discovery set, cross-validation set, and external validation set for the study . There was no statistical difference in age between the CCA group and the non-CCA group Table . Data ofg for 15min at 4°C, and the supernatants were harvested and stored at − 80°C until the test.The bile collected from the bile duct was mainly obtained during ERCP, PTC, or surgery. None of the cancer patients received chemotherapy prior to cholangiography intervention or surgical treatment. Approximately 1 to 6 mL of bile (average 3 mL) was collected and transferred to a sterile tube each time. Bile and serum samples were shipped on ice immediately after being obtained, followed by centrifugation at 3000 × Briefly, proteins in the bile and cell supernatant were extracted and quantified with the Brandford test, followed by alkylation and enzymatic digestion. The tryptic-digested peptides of the bile were then labeled by the iTRAQ reagent kit according to the manufacturer’s protocol but not with tryptic-digested peptides of cell supernatant. Then, the processed peptides of the bile and cell supernatant were performed by off-gel separation and nano-LC–MS/MS analysis. An EASY-Nlc 1000 nanoflow LC instrument coupled to a high-resolution mass spectrometer  was used for LC–MS/MS analysis, the sample was injected into a tunnel-frit trap column and the trapped analytes were then separated by an analytical column, and the separated peptides were then identified and selected for data-dependent acquisition due to their electrical charge. After getting these experiment data, the raw files were analyzed by MaxQuant (version 1.6) and then were identified as corresponding proteins compared to those in the Swiss-Prot human protein sequence database . The false discovery rate of proteins was less than 1% (FDR < 1%) at both protein and peptide levels, and at least two peptides were identified for further data processing.−ΔΔCt value. Each experiment was repeated three times.The proteins in the bile were extracted by acetone precipitation. The protein solution was then separated by SDS/PAGE, transferred onto a PVDF membrane, and incubated overnight at 4°C with rabbit anti-CLU antibodies  or mouse anti-GAPDH . The membrane was later incubated with a secondary antibody of goat anti-mouse or anti-rabbit IgG  and then visualized with chemiluminescence detection. RT-PCR was performed according to the instructions provided by the manufacturer using the qRT-PCR Kit . The forward and reverse PCR primers for CLU were 5′-GAGCAGCTGAACGAGCAGTTT-3′ and 5′-CTTCGCCTTGCGTGAGGT-3′ respectively; whereas for GAPDH, the forward primer was 5′-CCATCACCAT CTTCCAGG-3′, and reverse was 5′-ATGAGTCCTTCCACGATAC-3′. The relative expression levels of CLU mRNA were compared with GAPDH using the 2A cholangiocarcinoma TMA slide (containing 90 CCA tissues and 31 interlobular bile duct tissues) was purchased from Shanghai Outdo Biotech Co. Ltd. . Immunochemical Staining Kit  and rabbit anti-CLU antibodies  were used for IHC staining. The images were analyzed by the Image pro plus 6.0 software. The expression intensity of CLU was judged by two senior pathologists independently without knowing any clinical and pathological data. Its staining intensity was divided into 4 grades: 0 represented negative expression (negative), 1 represented weak expression (weak), 2 was moderate expression (moderate), and 3 was strong expression (strong). Finally, negative, moderate, and weak expressions (0–2 points) were defined as low expression, and strong expression (3 points) was defined as high expression.The ELISA kits were used to detect the level of CLU  and CA19-9  in the bile or serum. First, the Reference Standard working solution and the samples are added to the Micro ELISA Plate, and then, the Biotinylated Detection Ab working solution is added immediately and incubated at 37°C for 90 min. Then aspirate or decant the solution from each well, add wash buffer to each well, and add HRP conjugate working solution to each well, then incubate for 30 min at 37°C. After washing again, add substrate reagent to each well and incubate for about 15–20 min at 37°C. Finally, add a Stop Solution to each well and determine the optical density  of each well at once with a micro-plate reader set to 450 nm. The bile and serum needed to be diluted before the test, and to measure the level of CLU, the bile and serum were diluted 100- and 5000-fold, respectively; to measure the level of CA199, the bile and serum were diluted 10,000- and fivefold, respectively.Our deep learning method was mainly divided into three steps: feature selection, training for establishing the best diagnostic panel, and external validation. Firstly, a classification prediction model was built using the random forest (RF) method in the cross-validation set. Based on the tenfold cross-validation classification method, the data of 64 features from 376 patients were divided into ten parts, two of them were used for the testing cohort and eight of them were used for the training cohort. Then, the Least Absolute Shrinkage and Selection Operator (LASSO) method was used to select the best diagnostic panel based on the criteria that the prediction value of minimum top-ranking features number was the same with the entire features, and the prediction value was mainly evaluated by some popular metrics, such as AUC value, accuracy (ACC), specificity, and sensitivity. Finally, the deep learning panel was evaluated by an external validation set to obtain a robust classification performance. Random forest and LASSO were carried out in glmnet version 4.1–3.U test or Student’s t-test. Categorical variables were expressed as rate and compared with each other by the chi-square test. The expression of the same protein in different body fluids was compared by party rank sum test. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of markers or panels and to establish cutoff levels, using the Youden index. The area under the curve (AUC) was calculated by the trapezoidal method, sensitivity, specificity, and accuracy (ACC) were calculated by standard 2 × 2 contingency tables, and a larger value represented better diagnostic performance. Decision curve analysis (DCA) was used to compare the diagnostic value of different clinical diagnostic models or markers. t-distributed stochastic neighbor embedding (tSNE) algorithm was used to visually evaluate the effects of the diagnostic model. p-values less than 0.05 were considered statistically significant. All analyses were performed with SPSS Statistics 20, GraphPad Prism version 7.0, and R version 4.1.0 .Continuous variables were expressed as median (interquartile range) or mean ± SD (standard deviation) and compared using the Mann–Whitney Bile and cell supernatant proteomics were used to screen CCA candidate biomarkers Fig. A. In theCell supernatant collected from four CCA cell lines  and one human intrahepatic biliary epithelial cell line (HIBEpiC) were used for label-free quantitative analysis, and a total of 932 proteins differently expressed were found, including 273 upregulated proteins and 659 downregulated proteins   and shorter relapse-free survival (RFS) time (p < 0.001) . Taken together, high expression of CLU could promote the progression of CCA.The level of CLU in CCA was verified in clinical specimens and cells. Sixteen bile samples (8 from CCA and 8 from benign biliary diseases) were collected for verifying the protein level of CLU. As shown in Fig. 01) Fig. B. The Ka01) Fig. C, D. Howp < 0.05). We have successfully extracted five primary CCA cells from postoperative tissues, and CLU was also overexpressed in them . High-abundant proteins are not suitable as diagnostic markers due to their low sensitivity [To verify potential markers between bile CLU and serum CLU, 40 cases of bile and blood from patients with CCA or benign biliary diseases and 40 cases of blood from healthy donors were collected for pilot studies Fig. A. As shop < 0.001). In the same way, CA19-9 in the serum was more suitable as a diagnostic biomarker for CCA.CA19-9 is found in both the serum and bile Fig. D. As shop = 0.23)  or tumor stages in patients with CCA   and single CCA patients (p = 0.26), but a significant difference was found between lithiasis-associated CCA patients and cholangiolithiasis patients (p < 0.001)  , which could be explained by their clinical relationship, similar to the same principle applied to the high correlation between GGT and ALP  .The diagnostic performance of a biomarker can be improved by combining it with different types of circulating biomarkers. Data of each patient used for machine learning contained bile CLU and 63 serum features. In the cross-validation set, the top 30 features were screened out according to their accuracy (the left) and Gini index (the right) by random forest (RF) Fig. A, and CL01) Fig. B. The otr < 0.5) between the seven biomarkers , lymph node metastasis (p = 0.537), and distant metastasis (p = 0.537).An optimal selection about the number of features is critical in establishing a classification model. Based on the top 10 selected markers, the LASSO method was applied to establish an optimal classification model . Users need only input the specific values of the seven biomarkers, and then click the “Submit” button   is a stress-activated, ATP-independent molecular chaperone, normally secreted from cells, and can promote cancer development by regulating cell proliferation, apoptosis, and cycle. It is highly expressed in several cancers, including esophagus, prostate, and breast , 20. Altp < 0.05). CLU had a good specificity but lacked sensitivity, while CA19-9 was just the opposite due to its high expression in some benign biliary diseases. But combining them into a panel improved the diagnostic value for CCA, and there were 33 CCA patients with Lewis-antigen negative (CA19-9 < 40 IU/mL), of which 81.8% (27 patients) had elevated bile fluid CLU. In conclusion, the diagnostic value of two mutually compensating indicators can be significantly improved by combining them.Previous studies have pointed out that serum CLU can be used for cancer diagnosis , 24. BefDuring the development of CCA, various indicators in the blood changed, such as transaminases and bilirubin, but they lack CCA specificity and are also similarly changed in other biliary diseases . Each bip < 0.05), and when compared to the combination of CLU and CA19-9, the seven-panel had a better prediction power with AUC increasing from 0.891 to 0.947 , gamma-glutamyl transferase (GGT), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), and total bile acid (TBA). Serum bilirubin mainly includes direct bilirubin (DBIL), indirect bilirubin (IBIL), and total bilirubin (TBIL), and they are often elevated due to liver function impairment or biliary obstruction . Gamma-gThere are many methods to distinguish CCA from benign biliary strictures, but their results are frustrating , Cellular Component (CC) and Molecular Function (MF) of clustering of the differentially expressed proteins in bile (A) and supernatant (B) by GO analysis. Fig. S3. The immunohistochemistry images of tissue microarray (TMA). Fig. S4. (A) The expression level of bile CLU in CCA, non-CCA cancers and benign biliary diseases. (B) The expression level of bile CLU at different TNM stages in CCA patients. (C) The expression level of bile CLU in each benign biliary disease. (D) The expression level of bile CLU in lithiasis-associated CCA group, single CCA group and lithiasis group. Fig. S5. (A) Lasso Cox regression analysis of 10 candidate markers. (B) and (C). tSNE and DCA analysis in external validation set.Additional file 2: Table S1. The detailed information of the 635 patients in cross-validation set and external validation set. Table S2. 1585 proteins were identified in the bile proteomics. Table S3. 932 proteins differently expressed were found in cell supernatant proteomics. Table S4. The abundance of CLU and CA19-9 in serum or bile in pilot studies.Additional file 3:Table S5. The diagnostic values of biomarkers and panels. Table S6. The AUC values of the top 30 features. Table S7. Other methods for CCA diagnosis.


LTA  gene polymorphism to HCC susceptibility. The main aim of this study is to investigate the association of LTA  variant with the HCC risk in the Egyptian population.Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide in terms of mortality, and susceptibility is attributed to genetic, lifestyle, and environmental factors. Lymphotoxin alpha (LTA) has a crucial role in communicating the lymphocytes with stromal cells and provoking cytotoxic effects on the cancer cells. There are no reports on the contribution of the LTA  polymorphism was assessed by tetra-primer amplification refractory mutation system polymerase chain reaction (T-ARMS-PCR) technique.This case-control study included 317 participants . The LTA  variant were statistically significant among HCC patients in comparison to controls . The A-allele of LTA  variant was statistically significant in HCC patients in comparison to controls (p ˂ 0.001).The frequencies of the dominant and recessive models  of the LTA  polymorphism was independently associated with an increased risk for hepatocellular carcinoma in the Egyptian population.The The online version contains supplementary material available at 10.1007/s11033-023-08281-z. Hepatocellular carcinoma (HCC) is the most common primary liver malignancy being the sixth most common type of tumor in the world and it represents the fourth most common cancer in Egypt . HepatitLymphotoxin alpha (LTA), previously known as tumor necrosis factor-beta (TNF–β), is a cytokine that belongs to the TNF superfamily of proteins . LTA is LTA gene  which is located on the short arm of chromosome 6 (6p21.33) and is nearby of the gene encoding major histocompatibility complex (MHC) . Thermo-cycling steps included 10 min of initial denaturation at 95 °C, followed by 30 cycles of denaturation at 95 °C for 1 min, an annealing step at 64 °C for 1 min, and extension at 72 °C for 1 min. A final extension was performed for five minutes at 72 °C. PCR products were electrophoresed on 2% agarose gel and visualized using ethidium bromide under ultraviolet illumination. The LTA C-allele was detected at 218 bp, while LTA A-allele was found at 279 bp, and a 460 bp is a common band. Using a digital camera, these products were photographed. For repeated genotyping, we randomly selected more than 10% of the samples. The results were completely consistent.The genotyping of the a et al. . The ampLTA gene polymorphism with any type of cancer; and (3) studies should provide enough data about alleles and genotypes frequencies to calculate the odds ratios (ORs) and the corresponding 95% confdence intervals (CIs). We excluded the studies that included abstract only or review studies.The following inclusion criteria should be met when the studies were included in the meta-analysis table: (1) studies should be case-control designed or cohort designed; (2) studies should examine the association of LTA  variant was tabulated, arranged, and processed using the Statistical Package for Social Science . The study power calculation was calculated using the G*power software version 3.1.9.4 (http://www.gpower.hhu.de/). With an alpha error probability of 0.05 and a total sample size of 317 participants for two groups, this case-control study could have a study power of 99.4% and a medium effect size of 0.30. The categorical variables were evaluated using the fisher’s exact method and expressed as numbers and percentages. While the non-parametric data was analysed using the Mann-Whitney test and presented as the median and interquartile range (IQR) after testing normality using the Kolmogorov-Smirnov test. In addition, the genotypic and allelic frequencies for LTA  variant were calculated and manipulated using the online SNPstats tool (www.SNPstats.org). Consistency and/or deviation from Hardy Weinberg equilibrium (HWE) were evaluated in HCC patients and cancer-free controls using the chi-square method. Several genetic models were applied including dominant, recessive, heterozygote, homozygote, and allelic comparisons to calculate the odds ratio (OR) with their 95% confidence intervals (CI) using the logistic regression method. R programming language software version 4.1.3 and R studio version 1.4.1103 were used to run the multivariate analysis. The STATA programme version 17.0 was used to carry out the meta-analysis approach. The significance level was set at p < 0.05.Data of the p = 0.47, p = 0.09; respectively). While it was shown that there was a significant difference in smoking cigarettes between the HCC patients and controls (p < 0.001). It was noticed that 78 cases (70.3%) had positive ascites, twenty-five (32.1%) HCC patients had mild ascites, twenty-five (32.1%) had moderate ascites, and twenty-eight (35.8%) had large ascites. However, HCC patients had statistically significant elevated levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin, creatinine, alpha-fetoprotein (AFP), and white blood cells (WBCs), and significantly decreased albumin level, red blood cells (RBCs), hemoglobin, and platelets compared to cancer-free controls group. Nearly, most HCC patients (85.6%) had positive antibodies against the hepatitis C virus (anti-HCV). Conversely, limited HCC patients (9.9%) had positive antigens of the hepatitis B virus (HBs Ag).The main demographic, clinical, biochemical, and hematological measurements of the studied groups are summarized in Table p-value > 0.05). Testing for the dominant model (CA + AA vs. CC) of LTA  variant, HCC patients revealed significantly higher frequencies of (CA + AA) genotypes compared to controls . Testing for the recessive model (AA vs. CC + CA) of LTA  variant, HCC patients revealed significantly higher frequencies of AA homozygous genotype compared to controls . Similarly, testing for the homozygous genotype (AA vs. CC) of LTA  variant, HCC patients showed significantly higher frequencies of AA homozygous genotype compared to controls . Regarding the allelic frequencies, the HCC cases revealed a significantly higher frequency of the minor allele  of the LTA  variant compared to healthy controls   of LTA  variant showed that the HCC patients had no significant difference compared to controls  (Table 2). The allelic frequencies of the LTA  variant were equivalent with the America, Europe, and South Asia populations  was compatible with the predicted results among the HCC patients and healthy controls  variant with all studied clinical and laboratory parameters (p > 0.05) (Table ).However, the HCC patients failed to observe significant associations of the 5) Table .LTA  variant was associated with an increased risk of hepatocellular carcinoma  gene are presented in Fig. LTA gene [ENSG00000226979] has some synonyms, including LT, TNFB, and TNFSF1. LTA gene is located on the short arm of chromosome number 6 (6p21.33) and transverses 13,775 bases  along the forward strand. It is composed of four splice variants, including LTA-213, LTA-214, LTA-215, and LTA-216. The main transcript (LTA-213) within the LTA gene contains four exons and three introns. The LTA  variant is positioned at the third exon of chromosome 6:31573007 with the highest population MAF equals to 0.50 . The LTA gene encodes a signaling protein nominated lymphotoxin alpha (LTA) that has a major function in preventing tumor growth, and destroying cancerous cells, and comprises 205 amino acids with a molecular mass of 22,297 Daltons [Data source: Uniprot database (P01374)]. Protein interaction networks recommended that the LTA has a vital role in the apoptotic process, regulation of gene expression, positive regulation of the cellular process, tumor necrosis factor receptor binding, and cytokine receptor binding [Data source: STRING]. The cellular LTA is located only within the extracellular space and plasma membrane [Data source: Cellular compartment database]. Upon applying the Kaplan–Meier plotter, the liver cancer patients represented a non-significant association with the LTA expression over time  [Data source: Kaplan–Meier Plotter database].The bioinformatics outlines of the Understanding the underlying causes of hepatocellular carcinoma (HCC), which is now the main cause of cancer-related death is urgent priority . GrowingLTA  variant and different cancer types, no data on the association of the LTA  polymorphism and the HCC susceptibility has yet to be reported. So, this is the first study that investigated the association of LTA (rs1041981) variant with the HCC in Egyptian patients.LTA is the most common member of the TNF ligand family, which responds to immunological and inflammatory responses and is involved in cancer etiology . It was LTA  variant among the Egyptian HCC patients compared to controls . The relatively long arrow of LTA variant in the direction of patients confirms the association of LTA  gene polymorphism with HCC susceptibility, within the PCA figure Fig. LTA  gene polymorphism and the different types of malignancies from 2004 to 2022 (Table 4) and Fig. LTA  variant among cases with cervical cancer [LTA  variant in Swedish patients with cervical cancer [LTA  gene polymorphism in Korean cases with gastric cancer, German cases with colorectal cancer, and American women with breast cancer compared to controls [The current study revealed a significant prevalence of (CA + AA), AA genotypes, and A-allele of the l cancer , endometl cancer  comparedl cancer . Contrarcontrols , 35, 36.Hepatocarcinogenesis is widely recognized to be a complex biological process that occurs during the malignant transformation of normal hepatocytes and involves several factors, including genetic and epigenetic alterations . LTA canLTA gene is located on the 6p21.3 chromosome, in the MHC class III region. Many additional genes are found in this region, including TNF, a proinflammatory cytokine. Genetic polymorphisms of inflammation-related genes are shown to modify the inflammatory response regulation [The gulation . It is wgulation . Additiogulation , 18.LTA  variant is neither associated with the HCV nor HBV infection in Egyptian HCC patients. Even though HCC patients had a higher prevalence of the CA and AA genotypes and A-allele of the LTA  variant, these genotypes were not associated with HCC clinical symptoms or biochemical presentation.LTA has been related to the development of both acute and chronic HCV infections , 48. LTALTA gene expression in the studied cases. To generalize our findings, we recommend larger collaborative multi-center investigations.The limitations of the study originated due to this single-center study with a relatively small sample size and the lack of data concerning the LTA  variant may be associated with an elevated risk of the HCC in Egyptian patients but not affect the clinical and biochemical presentation of the disease.In conclusion, our findings show that the Below is the link to the electronic supplementary material.Supplementary Material 1


Pain is often neglected in disabled older population, especially in Taiwan where the population of institutional residents is rapidly growing. Our study aimed to investigate pain prevalence and associated factors among institutional residents to improve pain assessment and management.This nationwide study recruited 5,746 institutional residents in Taiwan between July 2019 and February 2020. Patient self-report was considered the most valid and reliable indicator of pain. A 5-point verbal rating scale was used to measure pain intensity, with a score ranging from 2 to 5 indicating the presence of pain. Associated factors with pain, including comorbidities, functional dependence, and quality of life, were also assessed.p < .001). Interestingly, pain was only related to instrumental activities of daily living (IADL) and not activities of daily living (ADL). On the other hand, dementia was significantly negatively associated with pain (p < .001), with an estimated odds of 0.63 times (95% CI: 0.53–0.75) for the presence of pain when compared to residents who did not have dementia.The mean age of the residents was 77.1 ± 13.4 years, with 63.1% of them aged over 75 years. Overall, 40.3% of the residents reported pain, of whom 51.2% had moderate to severe pain. Pain was more common in residents with comorbidities and significantly impacted emotions and behavior problems, and the mean EQ5D score, which is a measure of health-related quality of life (Unmanaged pain is common among institutional residents and is associated with comorbidities, IADL, emotional/behavioral problems, and health-related quality of life. Older residents may have lower odds of reporting pain due to difficulty communicating their pain, even through the use of a simple 5-point verbal rating scale. Therefore, more attention and effort should be directed towards improving pain evaluation in this vulnerable population .The online version contains supplementary material available at 10.1186/s12877-023-04337-8. Pain is a subjective experience that involves an unpleasant sensory and emotional response, usually associated with actual or potential tissue damage . As peopIn recent years, there has been an increasing number of adults over 65 years living in institutions . In addiPrevious research has revealed that estimated pain experiences in older adults within institutions ranged from 45 to 80% . InstituHoffmann et al.found more than 50% of the 4,584 nursing home residents in Germany had dementia based on the health insurance claims data . SimilarAdditionally, pain in institutional residents may be associated with a decline in their daily function and subsequent disability. Chronic pain has been shown to reduce activities of daily living (ADL) and physical activity in community-dwelling older people . A natioPain is a significant problem among institutional residents globally, and more research is needed to improve their care, particularly in countries like Taiwan where the population of older adults is rapidly growing. Therefore, our study aims to investigate the prevalence of pain and its associated factors among institutional older adults in Taiwan, with a larger sample size, providing further insights into the relationship between pain and comorbidities, cognition, daily functional dependence, and quality of life. This research is essential to improve care for disabled older adults in Taiwan and other countries facing a similar demographic shift.This cross-sectional study utilized data from the Taiwan National Health Research Institutes and was conducted between July 2019 and February 2020. The study population included 5,746 institutional residents from 22 counties and cities in Taiwan. A total of 299 long-term care institutions were recorded, including 164 welfare institutions for elderly people, 125 general nursing homes, and 10 veteran homes. The study was approved by the Institutional Review Board of the Research Ethics Committee of National Health Research Institutes (EC1080502), and written informed consent was obtained from the residents before their participation. If self-determination was not possible, proxy consent was requested from their families. All methods were conducted according to the Helsinki guidelines and regulations. The response rate was 87.7%, and socio-demographic parameters, pain experiences, current comorbidities (within 6 months), emotional status, and physical functional disabilities were obtained from residents through questionnaires (self- or other-report). In addition, all interviewers or specialists who participated in the study underwent a training course to reduce inter-interviewers’ bias in the cognitive dysfunction and dementia assessment.The primary outcome of this study was pain score, which was measured by assessing the intensity of pain using a verbal rating scale. The scale consisted of five-points of assessment that corresponded to the following level of pain: 1 (no pain), 2 (slight pain), 3 (moderate pain), 4 (severe pain), and 5 (unbearable pain). This 5-point rating scale is a reliable and valid tool for assessing pain in older adults, both with and without cognitive impairment –29. PainWe conducted further analyses to examine the presence of cognitive dysfunction and the prevalence of diagnosed dementia among institutional residents. The Mini-Mental State Examination (MMSE) was commonly used as a brief screening tool for cognitive disorders , with loA painful condition often impairs the moods, functional dependence, and health-related quality of life of individuals. The presence of pain was investigated in relation to several factors including activities of daily living (ADL), instrumental activities of daily living (IADL), emotional or behavioral problems, and the EQ-5D-5L questionnaire as measured by the EQ-5D-5L questionnaire. ADL was scored from 0 to 100, with higher scores indicating better functioning in basic self-care tasks . IADL wat-test, while categorical data were expressed as number (%) and analyzed using the chi-square test. Statistical analyses were performed using SAS 9.4 . A total of 30 comparisons were made in this study, and to account for multiple testing, the significance level was adjusted to 0.0017 (0.05/30) using the Bonferroni correction. Therefore, a p-value less than 0.0017 was considered statistically significant. The odds ratio (OR) and 95% confidence intervals (CI) were calculated using logistic regression analysis after adjusting for age, sex, and educational level.Institutional residents in the study were divided into two groups based on their experience of pain, and various covariates, such as demographics, comorbidities, cognitive functions, and quality of life  were men, 81.6%  were aged > 65 years, and 26.8%  had completed at least junior high school education. The mean MMSE score was 17.0 ± 6.66. Demographic characteristics, including age, sex, educational level, and mean MMSE score, showed no association with the presence of pain. Furthermore, up to 40.3%  of the institutional residents reported different levels of pain, with 1,131 residents reporting slight pain, 553 (9.6%) reporting moderate pain, 282 (4.9%) reporting severe pain, and 352 (6.1%) reporting unbearable pain (data not shown). These findings suggest that undertreated pain is not uncommon among institutional residents.Table p < 0.0001), urogenital disorders , and spinal cord injury  were significantly associated with pain in institutional residents after adjusting for potential confounders.Table p = 0.89 and OR: 0.98, 95% of CI: 0.72–1.33). After adjusting for educational level (literacy or illiteracy), cognitive impairment defined as MMSE < 25 for literacy and MMSE < 14 for illiteracy was not associated with pain. However, our study found a high prevalence of dementia in older institutional residents, and a significant negatively association was observed between dementia and pain  compared to residents without dementia. The distribution of dementia was determined using four criteria: 1. confirmation from hospital, 2. CDR ≥ 0.5 and MMSE < cutoff point (MMSE < 25 for literacy and MMSE < 14), 3. CDR ≥ 1 but MMSE ≥ cutoff point, 4. CDR ≥ 1. Institutional residents with a hospital certificate of dementia diagnosis and those with CDR scores of 1 or above were less likely to report pain, with a p-value less than 0.001, indicating a statistically significant difference. Additionally, a post-hoc analysis of the data based on CDR scores was also conducted, and the results showed no significant difference in the likelihood of reporting pain across different CDR scores.Table p < 0.001), and the moderate dependent group had an estimated 2.08 times the odds of experiencing pain when compared to residents who had no dependence in IADL. These findings suggest that residents with higher IADL scores reported more pain experience. Additionally, we found a strong association between pain and emotional/behavioral problems , and pain had a significantly negative effect on quality of life, using the ED5Q assessment tool (p < 0.001).Table This study is the first large-scale and nationwide study in Taiwan, to our knowledge, that aim to examine pain prevalence and its effects on institutional older residents. The study enrolled 5,746 residents from various institutions in Taiwan, with over 60% aged over 75 years. The results showed that pain is prevalent among institutional residents, with 40.3% experienced pain, and more than half of them (51.2%) having moderate or more pain. These finding suggest that undermanaged pain is a common problem among institutional residents, which is consistent with the prevalence of pain reported in nursing home in other countries such as the US, Canada and Sweden (ranging from 40 to 80%) . The stuAtee et al  conductep < 0.01). However, a systematic review of 11 studies revealed controversial results in eight of the studies and showed no or weak association between pain and functional impairment. Therefore, we investigated the relationship between pain report and functional deficits in institutional older residents. We observed that the presence of pain showed a significant association with IADL, but not ADL. Other contributing factors may have affected the association between pain and functioning in our study population, in which most residents had cognitive impairment. Basic ADLs comprise daily fundamental skills, whereas IADLs require a more complex capacity to live independently in a community. Declined performance in IADLs is observed in the early stages of dementia, whereas impairments in ADL are often noted in later stages of dementia[Functional deficits are common among institutional residents, and the decline in physical function may increase the risk of mortality and adverse health outcomes , 49. In  dementia. In our Pain assessment in institutional residents is a complex task that requires more comprehensive and reliable tools to accurately detect pain in. Previous systemic review studies have highlighted the importance of identifying of barriers and facilitators to pain assessment in improving the quality of care provided for nursing home residents , 52. WhiThe strength of our study lies in its large sample size and highly representative nature. However, this study still has some limitations. A previous study reported that the EQ-5D as a self-report tool is suitable for use in older nursing home residents with cognitive impairment, whereas the validity of the EQ VAS was lower . AlthougUnmanaged pain is common among the institutional residents in Taiwan. Painful conditions impact on functional dependence (IADL), emotional/behavioral problems and quality of life. Patients with a certain degree of cognitive impairment often underestimate their painful conditions, which may subsequently lead to devastating health consequences. Thus, more attention and focus must be given to the evaluation of the pain conditions in this vulnerable population.Additional file 1: Supplemental Table 1. Association between comorbidities and pain for those with dementia. Supplemental Table 2. Association between ADL, IADL, Mood, Quality of Life and pain for those with dementia. Supplemental Table 3. Association between comorbidities and pain for those without dementia. Supplemental Table 4. Association between ADL, IADL, Mood, Quality of Life and pain for those without dementia


FR) with five standardization sample age groups  using EGA. The primary research questions include (a) how many WISC–VFR dimensions are identified in each age subgroup? (b) how are subtest scores associated within the dimensions? Because the number and the content of the dimensions identified by EGA could vary with samples, the secondary research questions include (c) is there evidence of reproducibility and generalizability of the dimensions identified by EGA? We used another procedure called bootstrap exploratory graph analysis (bootEGA). EGA and bootEGA suggested only three dimensions, which are consistent with processing speed, verbal comprehension, and the “old” perceptual reasoning factor. Results did not support the distinction between visual–spatial and fluid reasoning dimensions. EGA and bootEGA represent new tools to assess the construct validity of psychological instruments, such as the WISC–VFR.Since the seminal work of Spearman, exploratory and confirmatory factor analysis represents the standard method of examining the dimensionality of psychological instruments. Recently, within the network psychometrics approach, a new procedure was proposed to estimate the dimensionality of psychological instruments: exploratory graph analysis (EGA). This study investigated the structure of the French Wechsler Intelligence Scale for Children–Fifth Edition (WISC–V It means that variables are locally independent from each other because covariance among variables is only and fully explained by the latent variable, the g-factor. However, in 1916, this causal psychological interpretation of the g-factor was challenged by Thomson. He hypothesized that the g-factor does not necessarily reflect a psychological attribute, but that the pattern of overlapping psychological attributes causes the positive manifold , the main purpose of differential psychology is to describe and explain [inter]-individual differences, and, to a lesser extent, intra-individual variability with tests and theories. The main issue regarding test development and psychological [intelligence] theory is to estimate the correct number of dimensions/factors studies . Spearmamanifold . Thomsonmanifold  that “…aExploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are commonly used to investigate the internal structure of psychological instruments . Hence, exploratory graph analysis (EGA) (nodes (vertices), and statistical relations between pairs of nodes are called edges . Based on strategies used in the network science to analyze network structure, local network properties can be quantified with centrality, the clustering of nodes, and global network properties, such as the small-worldness of a network.To overcome some limitations inherent in EFA, is (EGA) ; 2022. Eis (EGA) . With neis (EGA) . It meanis (EGA) . Within s links; . The staWithin psychology, the development of network psychometric approaches is indebted to Peter Molenaar, who understood the similarity between the Ising model introduced in statistical physics for studying magnetic interactions and item response theory within psychometrics models . MolenaaWhile NP was mainly applied to the field of psychopathology , it has Like EFA, the main goal of NP is to describe and to explain interindividual differences in psychological instruments, by modeling the variance–covariance structure of the variables, that is, between nodes. However, there are several substantial differences between EFA and NP . The mosA second difference between EFA and NP concerns the measure of associations used to estimate parameters. While EFA uses zero-order correlations, some network models, as it is the case for EGA, are based on partial correlations . PartialBy using EGA, the main objective is to determine which edges must be included in the network, and to select the best model. Model selection is the most fundamental issue because it is necessary to remove “non-significant” edges and to keep only “true” edges.bootstrap exploratory graph analysis , visual–spatial (VS), fluid reasoning (FR), working memory (WM), and processing speed (PS). The WISC–VFR publisher indicates that this factor structure is appropriate for the total sample (N = 1049) and for the five age groups. However, exploratory factor analyses (EFA) conducted by FR total standardization sample, and by FR five age standardization samples  do not support a five-factor model. Whether for the total sample or the five age groups, results are more consistent with a four first-order factors model. Their results do not support the distinction between visual–spatial and fluid reasoning. These findings are consistent with those obtained for the US WISC–V . As far as we know, EGA was never applied to the five WISC–VFR standardization sample age groups. By using EGA, the main objective is to determine which edges must be included in the network and to select the best model. Our second goal is to use bootEGA to estimate the stability of the dimensions identified by EGA and to estimate the stability of each subtest score within a dimension ; exploratory analyses were not conducted. However, the WISC–VFR deviates from the WISC–IVFR: three subtests were added in the new WISC–VFR , and two subtests were removed . Substantive structural and methodological changes were introduced in the WISC–VFR. Therefore, and as recommended by some methodologists, the assessment of the structural validity of a new test or a new version of tests, should be conducted with exploratory procedures in complement with confirmatory analyses; a revised test should be treated as a new one  reported in the WISC–Vanalysis . The totFR is a standardized, individual test of intelligence for children and adolescents (6 to 16:11 years old). It consists of 15 subtests. The Full-Scale IQ [FSIQ] estimates general intelligence and is based on the sum of 7 primary subtests: Similarities (SI), Vocabulary (VO), Matrix Reasoning (MR), Figure Weights (FW), Block Design (BD), Digit Span (DS), and Coding (CD). To estimate the five primary indexes, three subtests are necessary in addition to the 7 subtests mentioned above: Visual Puzzles (VP), Picture Span (PS), and Symbol Search (SS). Based on the CHC compendium of cognitive abilities , Comprehension (CO), Letter-Number Sequencing (LN), Cancellation (CA), and Arithmetic (AR). The FSIQ and the five indexes are standard scores . The subtest scores are also standardized scores . In addition, five ancillary index scores are also available: Quantitative Reasoning [QR], Auditory Working Memory [AWM], Nonverbal [NV], General Ability [GA], and Cognitive Proficiency [CP].The WISC–Vbilities , the fivFR, internal consistency of subtest scores ranged from 0.77 (CO) to 0.94 (FW). Only two of the fifteen internal consistency coefficients were lower than 0.80 (CO and SS). The internal consistency of indices ranged from 0.87 (PSI) to 0.95 (FSIQ). Three internal consistency coefficients were lower than 0.90 .As reported in the technical manual of the WISC–VThe correlation coefficients ranged from 0.04 (FW-CA) to 0.66 (SI-VO) for 6–7 years, from 0.05 (SI-CA) to 0.63 (SI-VO) for 8–9 years; from 0.10 (FW-CA) to 0.70 (SI-VO) for 10–11 years; from 0.00 (VO-CA) to 0.65 (SI-IN) for 12–13 years; and ranged from 0.10 (VO-CA) to 0.58 (SI-VO) for 14–16 years.FR interpretive manual. Because we used the data reported in the WISC–VFR interpretive manual, we know that the quality of the data is relatively appropriate for network analysis . Furthermore, the positive manifold is strong enough in the WISC–VFR to use EGA and bootEGA. All network analyses were completed using R . EBIC relies on a hyperparameter (γ) that controls for the simplicity of the network. Higher γ values lead to sparser models. EGA starts with γ = 0.50. Finally, the Walktrap algorithm  is used to identify the number and the content of dense subgraphs of the sparse inverse correlation matrix (communities), and to examine the number of dimensions  which is based on partial correlations. According to mensions . In sum,mensions  showed, structural consistency, that is, the proportion of time that each dimension of EGA was exactly retrieved across the replicate bootstrap samples. Structural consistency corresponds to the estimation of internal consistency within EFA, and hence, can be interpreted as an indicator of the homogeneity of the dimension , while nine subtest scores were stable . Most of the unstable subtest scores belonged to dimension 1 [FR-VS-WM].Next, the structural consistency of each dimension was estimated. Structural consistency allows the estimation of how often each dimension is the same across the replicate sample . Results indicate that all dimensions were very unstable . This instability is consistent with the Regarding these unstable subtest scores, the analyses of the item stability values across each dimension in the bootstrap samples indicate that FW was sometimes replicated in dimension 1 ([FR-VS-WM]: 65%) and dimension 2 ([VC]: 21%)item stability analysis. Item stability values across each dimension in the bootstrap samples indicate that all subtest scores values were higher than 0.70; hence, all subtest scores were stable. Removing these six subtest scores improved the stability of the dimensions.As suggested by EGA with the GLASSO and Walktrap community detection algorithm estimated four dimensions. A first dimension represents a mixture of visual–spatial , and fluid reasoning , and corresponds to the perceptual reasoning factor. The second dimension represents working memory  with arithmetic (AR), while the third dimension is consistent with verbal comprehension . Finally, the fourth dimension represents processing speed .Parametric bootEGA for the age group 8–9 estimated a typical (median) structure with the same four dimensions as EGA. Then, we evaluated the stability, by looking at the frequency of each dimension solution. Frequencies indicate that four dimensions were found 54.2% of the time of the 500 bootstrap replicates, three dimensions were found 41.2% of the time, and five dimensions were found 2.8% of the time. These results suggest that the four dimensions solution is relatively unstable.Next, the structural consistency was estimated. Results indicate that dimensions 3 and 4 were stable , while dimensions 1 and 2 were unstable . This finding is relatively consistent with item stability analysis, which indicates that four subtest scores values were below 0.70, and hence unstable . The other eleven subtest scores were stable .Regarding these four unstable subtest scores, the analyses of item stability values across each dimension in the bootstrap samples indicate that LN was sometimes replicated in dimension 2 ([WM]: 65%) and dimension 1 ([VS-FR]: 25.4%); DS was replicated in dimension 2 ([WM]: 64.4%) and dimension 1 ([VS-FR]: 26.4%); Picture Span (PS) was replicated in dimension 2 ([WM]: 61.1%) and dimension 1 ([VS-FR]: 25.8%); AR was replicated in dimension 1 ([VS-FR]: 69.6%) and dimension 2 ([WM]: 24%). These four subtest scores are multidimensional, and hence have cross-loadings.These four unstable subtest scores were removed, and EGA and bootEGA were re-estimated. EGA and bootEGA revealed three clusters see : DimensiEGA with the GLASSO and Walktrap community detection algorithm estimated three clusters of nodes, three dimensions. A first dimension represents a mixture of visual–spatial , fluid reasoning , and two working memory subtest scores . Dimension 2 represents verbal comprehension  with Picture Span (PS). Dimension 3 is consistent with processing speed .Parametric bootEGA for the age group 10–11 estimated a typical (median) structure with three dimensions, which is relatively like the one estimated by EGA. A first dimension represents verbal comprehension  with Picture Span (PS), while the second dimension represents processing speed . The third dimension represents a mixture of [VS-FR-WM] .Then, we estimated the stability by looking at the frequency of each dimension solution. Frequencies indicate that three dimensions were found 62% of the time, four dimensions were found 32% of the time, and five dimensions were found 2.6% of the time. These results suggest that the three-dimensions solution was relatively unstable.Next, the structural consistency was estimated. Results indicate that only dimension 3 [PS] was stable (81.8%). Dimension 1 and dimension 2 [VC-PS] were unstable . This finding is partially consistent with item stability analysis. Results indicate that all subtest scores were higher than 0.70, and hence stable, except Picture Span (PS). Regarding Picture Span’s score, item stability values across each dimension in the bootstrap samples indicate that it was sometimes replicated in dimension 1 (33%), in dimension 4 (27.6%), and in dimension 2 (25.4%). Thus, Picture Span’s score is multidimensional, and hence has multiple cross-loadings.Finally, Picture Span’s (PS) score was removed, and EGA and bootEGA were re-estimated. EGA and bootEGA disclosed three clusters see : the firEGA with the GLASSO and Walktrap community detection algorithm estimated three dimensions . A firstSimilarly, parametric bootEGA for the age group 12–13 reported a network with the same three dimensions: verbal comprehension  with two working memory subtests ; a second dimension with fluid reasoning , visual–spatial , and Picture Span (PS). The third dimension is consistent with processing speed . Frequencies indicate that three dimensions were found 81.8% of the time, four dimensions were found 12.8% of the time, and five dimensions were found 1.6% of the time. These results suggest that the three clusters solution was relatively stable.Regarding the structural consistency, results indicate that only dimension 3 [PS] was very stable (99.4%). Dimensions 1 and 2 were unstable . Item stability analysis indicates that three subtest scores values were below 0.70, and hence unstable . The other twelve subtest scores were stable .Concerning the three unstable subtest scores, item stability values across each dimension in the bootstrap samples indicate that AR was replicated in dimension 1 ([VS-FR]: 68%) and dimension 2 ([VC-WM]: 22.4%); FW was replicated in dimension 1 ([VS-FR]: 68%) and dimension 2 ([VC-WM]: 23.6%); LN was sometimes replicated in dimension 2 ([VC-WM]: 52.8%) and dimension 1 ([VS-FR]: 36.4%). These three subtest scores were multidimensional, and hence had cross-loadings.Finally, the three unstable subtest scores were removed, and EGA and bootEGA were re-estimated. EGA and bootEGA showed three dimensions see . DimensiEGA with the GLASSO estimation and Walktrap community detection algorithm estimated three dimensions . A firstParametric bootEGA with 500 iterations for the age group 14–16 estimated a typical structure (median structure) with four dimensions. A first dimension represents a mixture of visual–spatial , fluid reasoning , and Picture Span (PS). The second dimension is consistent with working memory . The third dimension represents processing speed , while the fourth dimension is consistent with verbal comprehension . Frequencies indicate that four dimensions were found 49.4% of the time of bootstrap replicates, and three dimensions were found 47.6% of the time. These results suggest that the three to four dimensions solutions were relatively unstable.Then, structural consistency indicates that dimensions 2 [VC] and 3 [PS] were stable . Dimension 1 [VS-FR-WM] was unstable (31.4%). This finding is partially consistent with item stability analysis. Results indicate that three subtest scores values were below 0.70 and hence unstable . Most of the unstable subtest scores belonged to dimension 1.Regarding these unstable subtest scores, item stability values across each dimension in the bootstrap samples indicate that DS was replicated in dimension 1 ([VS-FR]: 65.8%) and dimension 4 ([WM]: 33.2%); LN was replicated in dimension 1 ([VS-FR]: 64.8%) and dimension 4 ([WM]: 33%); Picture Span (PS) was sometimes replicated in dimension 1 (57.8%), in dimension 3 ([PS]: 21.8%), and in dimension 4 (20%). These three subtest scores are multidimensional, and hence, have cross-loadings.Finally, these three unstable subtest scores were removed, and EGA and bootEGA were re-estimated. EGA and bootEGA suggested three dimensions see . DimensiBecause previous results could be due to the small sample size of each age subgroups , we run FR with five standardization sample age groups .Within the NP approach, FR between the visual–spatial and fluid reasoning index scores. This dimension is more aligned with the “old” perceptual reasoning factor [PR]. The lack of distinction between visual–spatial and fluid reasoning is also consistent with EFA and CFA conducted on the WISC–VFR by FR.For all age groups, EGA with the GLASSO network estimation method and the Walktrap community detection algorithm estimated three dimensions, except for 8–9 years for whom a four-dimension structure was identified see . First, item stability. Indeed, the variability can conduct to a dimensional structure that does not generalize to other samples. Therefore, the number and the content of the dimensions can be inconsistent across replicate samples. Thus, the second goal of the present paper was to examine the stability and the robustness of the number of dimensions identified by EGA in the WISC–VFR with five standardization sample age groups  by using bootEGA. We applied bootEGA for 500 iterations.However, because random sampling variability and/or parameter estimation variability influence the stability of the results, we examined the reproducibility of EGA results with bootstrap exploratory graph analysis (bootEGA) developed by FR.Parametric bootEGA results indicate that the median structure had three dimensions for ages 6–7, 10–11, and 12–13 years, and four dimensions for ages 8–9 and 14–16 years. bootEGA results are consistent with EGA’s results for ages 6–7, 8–9, 10–11, and 12–13 years. For ages 14–16 years, bootEGA suggested four dimensions while EGA identified three dimensions only. However, the confidence interval was relatively large for the 14–16 age group (2.85–5.15). These typical three-dimension to four-dimension solutions are different from the five-factor structure based on CFA reported by the publisher of the WISC–VFR.The next step concerned the frequency of each dimension solution across all bootstrap replicate samples, which shows important instability. These results suggest that the three-dimension solution might be stable only for the ages of 12–13 years (81.8%). In other words, three dimensions were found 81.8% of the time of 500 bootstrap replicates for this age sample. For ages 6–7 and 10–11 years, the three-dimension solution was more frequent but unstable . For ages 8–9 and 14–16 years, the four-dimension solution was more frequent but unstable . Overall, three-dimension to four-dimension solutions were not stable enough. Most importantly, the frequencies do not support the five-factor structure based on CFA proposed by the publisher of the WISC–VTherefore, we examined the structural consistency, which indicates how often the EGA dimension is exactly replicated across all bootstrap replicate samples. The structural consistency allows the determination of which dimensions are particularly unstable, in a multidimensional context. This indicator can be considered as an internal consistency measure and represents the proportion of time each dimension is exactly found. The structural consistency is an indicator of the construct validity. If we consider that acceptable structural consistency is ≥0.75 (75%), then results indicate that the processing speed [PS] dimension was stable for all age groups, except for 6–7 years. In addition, results indicate that the verbal comprehension [VC] dimension was stable for ages 8–9 and 14–16 years. It means that all other dimensions presented low structural consistency, and hence, are not reflecting homogenous dimensions.item stability for some age groups. These subtest scores are Picture Span , Digit Span , Arithmetic , Letter-Number , Figure Weights (FW: 6–7 and 12–13 years), and Cancellation (CA: 6–7 years). These subtest scores are multidimensional. Regarding Arithmetic, results support the hypothesis that Arithmetic is a mixture of perceptual reasoning , verbal comprehension (6–7 and 12–13 years) and working memory (6–7 and 8–9 years).Because most of the dimensions were not stable enough, we examined item stability. This statistic indicates how often a subtest replicates in the dimension identified by EGA, but also in the other dimensions in the replicate networks. This statistic is useful to determine which subtest score causes the instability of the dimension and lowers the structural consistency. We consider that subtest stability is appropriate if the subtest replicates at least 70% of the time in its EGA dimension (≥0.70). The subtest stability analysis allows the determination of which subtests to retain to increase the dimension’s structural consistency. Results indicate that most subtest scores replicated adequately in the dimensions proposed by EGA. However, six subtest scores presented low FR is to interpret homogenous scores, EGA and bootEGA were re-estimated after removing the subtests with the lowest item stability within each age group. For all age groups, the final EGA and bootEGA dimensionality structures had three dimensions only  for all age groups, except for 10–11 years (68.6%). All subtest scores had item stability >0.70, indicating that all subtest scores were replicating in their respective EGA dimension. A structure with four dimensions occurred from 0.2% (6–7) to 25.4% (10–11 years). A five-dimension solution was observed 2.4% in ages 10–11 years. The first dimension includes Coding, Symbol Search, and Cancellation in all age groups, except in ages 6–7 for whom Cancellation was removed. This first dimension represents processing speed [PS]. The second dimension corresponds to verbal comprehension and includes Similarities (SI), Vocabulary (VO), Information (IN), and Comprehension (CO). It should be noted that for ages 12–13, Digit Span (DS) was associated with the four verbal tasks. The third dimension represents the “old” perceptual reasoning factor [PR] and shows strong association between Block Design (BD), Visual Puzzles (VP), and Matrix Reasoning (MR) for all age groups. This dimension was associated with Figure Weights (FW) for ages 8–9, 10–11, and 14–16 years. Arithmetic (AR) was also associated with the PR dimension for ages 10–11 and 14–16 years. Finally, Digit Span (DS) and Letter-Number (LN) were associated with PR for ages 10–11, while Picture Span (PS) was associated with the PR dimensions for ages 12–13.Because the main purpose when administering the WISC–Vonly see . RemovinFR with the total sample (FR should be based on the FSIQ (as a proxy of the g-factor) and only three dimensions: verbal comprehension (SI-VO-IN-CO), processing speed  and a mixture of visuospatial , reasoning , and working memory . Only two broad CHC scores are supported when the large psychometric g variance is removed: VCI and PSI. bootEGA did not support the interpretation of the distinct broad abilities Gf (fluid reasoning), Gv  and Gwm (working memory). The mixture Gf–Gv–Gwm is consistent with many studies that have shown the overlap between Gf and working memory  conducted on the WISC–Vl sample  and the l sample . In bothl sample . Thus, ng memory . This miFR examined the factorial structure through confirmatory factor analysis only. However, we agree with FR and the WISC–VFR. Therefore, it seems necessary to examine the structural validity of the WISC–VFR using exploratory analyses instead of confirmatory ones  . Like thory ones , 2022. TFRinterpretive manual. Third, these correlation matrices are cross-sectional rather than longitudinal, which does not allow for really testing the mutualism model proposed by This study contains several limitations. First, the data are the correlation matrices reported in the French interpretive manual for the five age groups . Second,FR. EGA and bootEGA supported a three-dimension solution with any of the five WISC–VFR age groups. EGA and bootEGA results suggested that the five-factor higher-order model preferred by the WISC–VFR publisher might be overfactored. From a practical point of view, the present results have several very important implications for the use and the interpretation of the WISC–VFR subtest scores. The five-factor higher-order model preferred by the publisher is not adequate across the five age groups. After removing unstable subtest scores, the final dimensionality structure has three dimensions only: verbal comprehension [VC], processing speed [PS], and perceptual reasoning [PR]. EGA and bootEGA do not support the distinction between [VS] and [FR] in any of the five age groups. Furthermore, the present finding suggests that the WM index is not appropriate because DS, PS, and LN are mostly unstable, and/or associated with other dimensions. The present results suggest that interpretation of the WISC–VFR scores should focus on VC , PS , and PR , which are stable dimensions. To interpret these three broad abilities, practitioners must create three new extended composite scores. However, these three broad abilities might not be latent attributes, but emerge from the interactions between cognitive processes. The new composite scores will not estimate a single attribute but represent summary statistics.EGA and bootEGA represent new tools to assess the construct validity of psychological instruments, such as the WISC–V


Limited studies are available testing through multiple regression models the association between the two main oral diseases: dental caries and periodontitis. The aim of this cross-sectional population-based study was to verify whether dental caries and periodontitis co-occur in a representative sample of the South Korea population.A total of 23,405 subjects representative of 36.2 million of adults (KNHANES) were examined. Univariate and multiple regression analyses using 7 different models were applied, controlling for age, gender, smoking status, frequency of toothbrushing, use of interproximal toothbrushes and flossing, educational level, income, gum diseases treatment and tooth filling in the previous year, BMI, Vitamin D serum levels, alcoholism, diabetes status, stress and carbohydrates dietary intake.In the fully adjusted model, participants with periodontitis had, respectively, a mean of 0.82 (95% CI: 0.41–1.23) and of 0.36 (95% CI: 0.22–0.50) more untreated decayed surfaces and teeth than participants without periodontitis, with an OR to have at least one untreated decayed surface of 1.96 (95% CI: 1.66–2.32). However, cumulative caries experience (DF scores) and periodontitis were not associated.In this large nationally representative population, periodontitis and untreated dental caries co-occur. However, when considering cumulative caries experience (DF scores), the two diseases do not appear related.In light of their possible co-occurrence, clinicians should implement integrative diagnostic, preventive and treatment strategies for both diseases.The online version contains supplementary material available at 10.1007/s00784-023-05253-2. Taken together, periodontitis and dental caries are among the most common diseases of humans and the leading cause of tooth loss –3. They Although they exhibit a distinct pathophysiology, the emergence of a bacterial dysbiosis in genetically or environmentally susceptible individuals is the main etiologic factor for both these chronic complex diseases. Other shared determinants of susceptibility have been identified, with oral hygiene, nutrition, social, educational and economic factors providing the highest level of evidence. A plausible link between caries and periodontitis could therefore be hypothesized.In 2017, the European Federation of Periodontology (EFP) and the European Organisation for Caries Research (ORCA) joined in the 1st European Workshop on Periodontal diseases and Dental Caries with the aim of assessing the interdependency between the two conditions and providing behavioural guidelines to clinicians, researchers and health policy-makers –19. In tHaving clear information on the epidemiological relationship between periodontitis and dental caries would result to uttermost importance in order to set individual and population-based prevention strategies, as well as to correctly plan comprehensive oral treatments. Therefore, this cross-sectional population-based study aimed to verify whether dental caries and periodontitis co-occur, and also, in case an association is found, if common risk factors can explain it.This nationally representative cross-sectional study was reported according to the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines , 27.Data for this study were obtained from the 2010 and 2012 sections of the Fifth Korea National Health And Nutrition Examination Survey (KNHANES V) and from the 2013–2015 sections of KNHANES VI. The 2011 section of KNHANES V was excluded as it didn’t pass the quality control for periodontal evaluation.KNHANES is a nationwide cross-sectional survey promoted by the Korea Centre for Disease Control and Prevention (KCDC) and conducted on an annually representative sample of the total non-institutionalized South Korea population . WrittenKNHANES applied a clustered stratified multistage sampling protocol with a rolling survey model.Each survey initially selected 192 Primary Sampling Units (PSUs) from around 200,000 geographically defined areas in the whole of South Korea. For each targeted PSU, 20 targeted households were chosen through systematic sampling, so that 3840 households were selected. A PSU consisted of approximately 50–60 households. The surveys included all subjects living in the enrolled households, who were at least 1 year old.Due to statistical purposes, sample weights were set for sample participants. This way the whole South Korea population was represented and the complex survey design, the null response and the post-stratification (performed either by sex and age) were considered.KNHANES survey consisted of three parts: the health interview, the health examination and the nutrition survey. Further information about the study design and methods are reported elsewhere .A total of 25–30 trained and calibrated public health dentists assessed each year the dental and periodontal status of the participants according to the WHO criteria for oral health surveys .Dental caries was identified using the WHO criteria as part of the dental health status examination, scoring each dental surface as sound, decayed, missing or filled . They weNumber/proportion of decayed surfaces/teeth (DS/DT);Presence of at least 1 decayed surface/tooth.Untreated dental caries (decayed):Number/proportion of filled surfaces/teeth (FS/FT);Presence of at least 1 filled surface/tooth.Treated dental caries (filled):Number/proportion of cumulative caries experience (DFS/DFT scores);Presence of cumulative caries experience (presence of a DFS/DFT score of at least 1).Cumulative caries experience (decayed and filled):For the present analyses, dental caries was expressed at participant-level with measures of extent and prevalence as:The periodontal status of the participants was assessed through the Community Periodontal Index (CPI) . The meaThe dentition was split into six sextants represented by the following tooth numbers (FDI system): 18–14, 13–23, 24–28, 38–34, 33–43 and 44–48. A sextant was examined just in case two or more unscheduled teeth for extraction were present. Ten index teeth were used for the examination: #17, #16, #11, #26, #27, #37, #36, #31, #46, #47 (FDI system). If a sextant missed one of the index teeth, an adjacent tooth was examined. If no adjacent tooth was even present, all remaining teeth of that sextant were considered for examination.The Community Periodontal Index was scored from 0 to 4 as follows: 0 , 1 , 2 , 3 (PPD between 3.5 and 5.5 mm) and 4 (PPD > 5.5 mm). The score of the sextant was calculated as its highest score.“No periodontitis” ;“For the current analysis, the periodontal status at participant-level was dichotomized into:“No severe periodontitis” ;“Severe periodontitis” (CPI = 4 in at least one sextant).As a sensitivity analysis, a different categorization was also employed, naming it ‘severe periodontitis’, as follows:The covariate assessment methods are reported in Appendix p-values are two-tailed, and the significance was in advance set at p < 0.05. All missing data with any assumption were handled with complete case analyses with covariates adjustment, as for current statistic literature orientation , using analysis for complex samples with a design-based approach . Furtheron Table  31]..p-valuesThe statistical plan has been elaborated similarly to previous reports –35. Briep-values deriving from an F-test.In order to evaluate the “crude” (unadjusted) association between periodontitis and caries measurements, the preliminary Odds Ratios (ORs) or Mean Differences (MDs) together with 95% confidence intervals (CIs) were obtained and reported together with the relative Model 1 (only age and gender);Model 2 (Model 1 + smoking status);Model 3 ;Model 4 ;Model 5 (Model 4 + gum diseases treatment previous year + tooth filling previous year);Model 6 (Model 5 + BMI + Vitamin D serum levels + AUDIT score + diabetes status + stress);Model 7 (Model 6 + carbohydrates dietary intake).Multiple linear/logistic regression analyses were then applied to examine the association between periodontitis and each caries measure, adjusting for the potential confounders  using the following 7 models:p-values derived from a F-test.The ORs/MDs (95% CIs) obtained from the multiple regression analyses were reported, as well as the The sampling strategy gave rise to the selection of 50,326 people: 39,964 (79.3%) of them accepted to participate. Of those participants, 37,842 took part to both the health interview and the health examination . As previously stated, the final sample weights considered this survey-non-response rate.The present analysis included only adults (≥ 19 years old) who received the periodontal and caries examination, for a total of 23,405 participants representative of 36.2 million adults Figure .Table In the unadjusted models, the presence of periodontitis (both case definitions) was directly associated with presence/number of decayed teeth/surfaces.After controlling for the selected confounders, a significant direct association continued to be present in almost all models. In model 7, participants with periodontitis CPI ≥ 3) had a mean of 0.82 (95% CI: 0.41–1.23) more decayed surfaces and of 0.36 (95% CI: 0.22–0.50) more decayed teeth than participants without periodontitis, with an OR to have at least one decayed surface/tooth of 1.96 (95% CI: 1.66–2.32)  was inversely associated with the presence/number of filled teeth/surfaces. In the multiple regression models the association — when present — was always still inverse. In particular, in Model 7 participants with periodontitis (CPI ≥ 3) had an OR of 0.73 (95% CI: 0.60–0.88) to have at least one filled surface/tooth, while the estimate was not significant for severe periodontitis .In the univariate models, cumulative caries experience was inversely associated with both periodontitis case definitions, with the only exception of percentage of DFS with severe periodontitis.After controlling for possible confounders, cumulative caries experience and periodontitis (both case definitions) were not associated in all the multiple regression models Tables  and 3.In this study, periodontitis has shown to be independently associated with an increase in the number of untreated decayed teeth and with a decrease in the number of filled teeth. In particular, the subjects with periodontitis had an increase of 96% in the estimated odds to have at least one untreated caries, while they had reduced estimated odds to have at least one filled tooth. Conversely, periodontitis was not associated with cumulative caries experience after adjusting for known common risk factors. Sensitivity analyses for severe periodontitis showed mostly consistent results, however the estimates were generally lower than for CPI ≥ 3 case definition.Untreated and treated dental caries demonstrated divergent associations with periodontitis in our cohort. This finding is likely to be explained by a possible residual confounding by access to dental care. Indeed, we defined periodontitis based on PPD (CPI index), which is modifiable in nature by periodontal treatment, as dental caries status can be modified by fillings. When looking however at cumulative caries experience, a higher prevalence of both decayed and filled teeth could have also been expected in individuals with periodontitis. Indeed, clinical attachment loss due to periodontal destruction may result in an increased tooth surface area exposed to the oral cavity , 37, whiResults of investigations on a simultaneous experience of caries and periodontitis have not been consistent so far. On the one hand, authors of the initial quantitative studies comparing the occurrence of cavities and calculus proposed the concept of an inverse relationship between caries and periodontitis . HoweverThe results from this study should be regarded with caution due the above-mentioned risk of residual confounding  and due to its cross-sectional design, which prevent from any causal inferences. Moreover, a risk of information bias may exist in relation to the assessment of both periodontal status and dental caries. Indeed, periodontitis in KNHANES was assessed by the use of CPI, which is a partial mouth examination protocol only based on PPD –46. SimiThe results presented here are advisable for further investigation. These findings need to be replicated in other populations, possibly employing a full-mouth periodontal examination protocol to allow a site-level analysis, and verifying if the results may change depending on whether clinical attachment loss affects the supra-(recession) or sub-(increased PPD) gingival environment. Moreover, the information on dental caries should be completed through radiographs, measures of severity and distinction among coronal and radicular caries , 50. ResIn this large nationally representative population, treated and untreated dental caries demonstrated divergent associations with periodontitis. However, when considering cumulative caries experience, the two diseases appeared not to be related.In light of their possible co-occurrence, clinicians should implement integrative diagnostic, preventive and treatment strategies for both diseases.Supplementary file1 (DOCX 17 KB)Supplementary file2 (TIFF 1299 KB)Supplementary file3 (DOCX 17 KB)Below is the link to the electronic supplementary material.