P‐value < 0.05). None of the CTAs correlated with clinical outcomes. The current study provides a comprehensive evaluation of CTAs and suggests that their association with immune cells may indicate in situ immunogenic effects. The findings support the rationale to harness CTAs as targets for immunotherapy.The antigenic repertoire of tumors is critical for successful anti‐cancer immune response and the efficacy of immunotherapy. Cancer–testis antigens (CTAs) are targets of humoral and cellular immune reactions. We aimed to characterize CTA expression in non‐small cell lung cancer (NSCLC) in the context of the immune microenvironment. Of 90 CTAs validated by RNA sequencing, eight CTAs  were selected for immunohistochemical profiling in cancer tissues from 328 NSCLC patients. CTA expression was compared with immune cell densities in the tumor environment and with genomic, transcriptomic, and clinical data. Most NSCLC cases (79%) expressed at least one of the analyzed CTAs, and CTA protein expression correlated generally with RNA expression. CTA profiles were associated with immune profiles: high MAGEA4 expression was related to M2 macrophages (CD163) and regulatory T cells (FOXP3), low MAGEA4 was associated with T cells (CD3), and high EZHIP was associated with plasma cell infiltration  in non‐small cell lung cancer in the context of immune cells. We show that CTAs are associated with a specific pattern of immune cell infiltration in the tumor microenvironment. This systematic evaluation represents a framework for identifying CTAs with suggested  ACadenocarcinomaCDcluster of differentiationCTAcancer–testis antigenDPEP3dipeptidase 3EGFRepidermal growth factor receptoreTMBestimated tumor mutational burdenEZHIPenhancer of zeste inhibitory proteinFDRfalse discovery rateFOXP3forkhead box protein 3HPAHuman Protein AtlasKRASKirsten rat sarcoma viral oncogeneLUADlung adenocarcinomaLUSClung squamous cell carcinomaMAGEmelanoma‐associated antigenNKp46natural killer protein 46NSCLCnon‐small cell lung cancerNY‐ESO‐1New York esophageal squamous cell carcinoma‐1PAGE1prostate‐associated antigen 1PD1programmed death 1PD‐L1programmed death ligand 1PRAMEpreferentially expressed in melanoma antigenSCCsquamous cell carcinomaSTK11serine/threonine kinase 11TIMExtumor‐immune microenvironment deconvolution methodTKTL1transketolase ligand 1TMAtissue microarrayTP53tumor protein 531The introduction of immune checkpoint inhibitors, as single agents or in combination with chemotherapy, provides for the first time a chance of long‐term survival in advanced non‐small cell lung cancer (NSCLC) patients without driver alterations such as epidermal growth factor receptor or ALK , 2, 3. TCellular and humoral immune responses are intrinsically connected to cancer development, and most cancers can be regarded as immunogenic , 13. Tumin situ immune reaction? and (d) Is the expression of CTAs as immune targets associated—in analogy to certain immune cell infiltrates—with a favorable prognosis, which supports the concept of the functional relevance of antigen presentation?In a previous study , 90 CTAsin situ immune cell infiltrates and immune profiles provides information on the impact of CTAs on cancer immunity in NSCLC.The present study aimed to address these questions through an in‐depth evaluation of the CTA protein landscape in an extensive NSCLC tissue microarray (TMA) patient cohort with detailed clinical and molecular characterization. For protein profiling, we utilized a stringent validation pipeline taking advantage of the Human Protein Atlas (HPA) workflow , 19. The22.1The patient cohort used in the present study has been described previously  and is b2.2https://www.proteinatlas.org/. Mutational data from targeted deep‐sequencing have been described previously [Tissues were obtained from the Clinical Pathology Department, Uppsala University Hospital, Sweden, and collected within the Uppsala Biobank organization and handled following Swedish laws and regulations. For NSCLC tissue, RNA was extracted from fresh frozen tissue corresponding gene expression data for 197 patients  obtained from RNA‐Seq, which has previously been described . The RNAeviously .2.3https://www.ebi.ac.uk/biostudies) repository (accession S‐BIAD453). Immune annotation was done as described previously [Digitized immunohistochemical images were manually annotated by FH (CTA) and quality‐controlled by a second observer. The CTA staining was annotated by scoring the staining highest intensity as negative (0), weak (1), moderate (2), or strong (3). The percentage of positive tumor cells in both TMA cores was also scored by no staining (0), 0–1% (1), 2–10% (2), 11–25% (3), 26–50% (4), 51–75% (5), and > 75% (6). Intensity and quantity scores were multiplied to generate protein scores between 0 and 18. A protein score of 0–2 was considered a low expression, and a protein score of 3–18 was considered a high expression except when otherwise stated. For DPEP3 and PRAME, cytoplasmic staining was scored; for EZHIP, MAGEB2, MAGEC2, and PAGE1, nuclear staining was scored; and for MAGEA4 and TKTL1, nuclear and cytoplasmic staining was scored. For MAGEA4 and TKTL1 staining, with both cytoplasmic and nuclear staining, the maximum protein score value of either was used in the analysis. All immunohistochemical images from the CTA analysis can be retrieved from the BioStudies  and python  with the modules Pandas 1.1.5, NumPy 1.19.5, and SciPy 1.4.1. A P‐value of < 0.05  was defined as significant and used for all statistical analyses if not stated otherwise. For some analyses, the Benjamini–Hochberg procedure for false discovery rate (FDR) was applied to adjust P‐values. For clinical outcomes , data were initially analyzed with Fisher's exact test, followed by FDR, with different protein expression statuses (low vs. high). The treatment variable was not included in the analysis. The r package ‘survminer’ was used for Kaplan–Meier survival models with log‐rank analysis between CTA high and CTA low. This allowed studying the association between high and low CTA protein expressions and overall survival. We further proceeded with a multivariate  approach using a Cox regression analysis with the previously mentioned clinical criteria. To evaluate the immune cell infiltration score for each CTA protein, we used a Wilcoxon rank‐sum test due to skewed distribution, with confidence intervals and P‐values. Immune marker association to either CTA high or CTA low expression was plotted by forest plots with the r packages ‘forestmodel’ and ‘forestplot’. Heatmaps were done with the r package ‘Complexheatmap’ [t‐test for independent samples and FDR. TIMEx data was used to visualize the correlation of CTAs with deconvoluted tumor‐immune microenvironment data from the TCGA. Precomputed z‐scores from the TIMEx resource (http://timex.moffitt.org) were used to determine the relation of CTA expression against the different immune signatures developed by TIMEx. The data were generated on TCGA bulk transcriptomics and visualized as a heatmap to indicate correlation value (from −1 to 1) [r package ‘corrplot’, and correlations that were significant (P adj. < 0.05) were labeled with an asterisk.The statistical analysis was performed in heatmap’ , and imm−1 to 1) . Only ca3The study was based on a TMA cohort consisting of 328 NSCLC patients with extensive histopathological and clinical annotations Table . The coh3.1https://v19.proteinatlas.org/). The HPA project generates a map of all human proteins based on antibody‐based proteomics, comprehensively presented in the open‐access database. By validating the CTA expression across 44 different normal tissue types and 20 types of cancer, with a focus on distinct and clear staining in testis and no expression in other normal tissues (except placenta), we selected 37 CTA based on antibody data with the highest specificity. These 37 genes were subjected to re‐titration efforts and further optimized for immunohistochemistry to improve the signal‐to‐noise ratio, after which 19 genes were excluded. Out of the remaining 18 genes, we selected 10 genes with antibodies that showed the clearest and most distinct staining pattern in the testis in the HPA image resource. These 10 genes were further screened on a cohort of 60 NSCLC patients with antibodies targeting their corresponding protein, out of which two proteins were excluded due to all cases being negative (data not shown). The remaining eight proteins showed distinct expression in testis and a subset of NSCLC patients, characterized with stringently validated antibodies. These eight proteins were selected for further analysis, thereby constituting the top candidates for in‐depth profiling. These eight genes were dipeptidase 3 (DPEP3), enhancer of zest homologs inhibitory protein (EZHIP), three melanoma‐associated antigens , prostate‐associated antigen 1 (PAGE1), preferentially expressed antigen in melanoma (PRAME), and transketolase‐like 1 (TKTL1), corresponding to both highly characterized and poorly characterized proteins with regard to their function , mutation status, and overall survival. To obtain an integrated overview of CTA expression in the clinicopathological environment of NSCLC, we performed an unsupervised cluster analysis based on CTA expression Fig. . A small3.4To evaluate whether CTA expression is connected to specific genomic molecular subtypes of NSCLC, we used Fisher's exact test to test whether CTA expression is associated with mutation data procured from the targeted analysis of 82 lung cancer‐related genes  Table S. The exp3.5P adj. < 0.05). For MAGEA4 high‐expression cases, the infiltration of CD163‐positive macrophages showed a significant association (P adj. < 0.05). High MAGEA4 was also associated with FOXP3 infiltration (P adj. < 0.05). Interestingly, a low MAGEA4 signal was highly significant with the pan T‐cell marker CD3 (P adj. < 0.05) but not for the other T‐cell markers. High MAGEC2 was associated with CD163‐positive macrophages and PD1‐positive T‐helper cells (P < 0.05 for both), but was not significant after adjustment for multiple testing (P adj. = 0.20 for both). High PRAME expression was associated with CD4‐positive T‐helper cells (P < 0.05), but significance was not reached after adjusting for multiple testing (P = 0.53). DPEP3, MAGEB2, and PAGE1 did not show any significant association with any immune marker when considering all cases   and low TKTL1 was linked to the T‐cell markers CD3 and CD45RO (P < 0.05). Also, low TKTL1 expression showed a trend toward association with CD4 and CD20 expression but was deemed nonsignificant. In SCC cases, only high EZHIP was linked to CD138 (P < 0.05). Note that none of the CTA and immune associations were significant after adjusting for multiple testing when analyzing the data separately by histology.  [P adj. < 0.05) with a low inverse correlation. When histological subtypes were analyzed separately (AC or SCC), more cases demonstrated significant CTA‐immune signature correlations. However, the coefficients were in general low and did not confirm our results based on IHC‐based in situ CTA and immune cell counts.The immune cell microenvironment of the cancer tissue was characterized previously with immunohistochemical markers for CD3 (T lymphocytes), CD4 (T‐helper cells), CD8 (cytotoxic T cells), CD20 (B cells), CD45RO (memory T cells), CD138 (plasma cells), CD163 (M2‐like macrophages), FOXP3 (regulatory T cells), and NKp46 (NK cells) and quantified as the percentage of viable cells in the stroma and tumor compartment separately [05) Fig. . Furthergy. Fig. . To valiitt.org) . Preproc4The current study presents a detailed and comprehensive characterization of CTAs in their molecular and clinical context and for the first time also in their local tumor microenvironment. The studied CTAs were DPEP3, a membrane‐bound dipeptidase with an unclear function during meiosis ; EZHIP , by the detection of autoantibodies in the serum of patients with a variety of cancer types , 31. Likpatients  was the patients . These rpatients , 35, 36.patients . Also, tpatients . Alternapatients . Currentpatients , 41. IndThe accessibility of high‐throughput techniques such as RNA‐Seq makes it attractive to effectively characterize multiple CTAs in minute amounts of tissue . HoweverIn our study, we found that patients with MAGEA4‐positive tumors were harboring TP53 mutations. While this association may be purely correlated with the histological characteristics of cancer subtype , it is sin situ are likely to be immunogenic and could be candidates for focused studies. This is primarily true for MAGEA4, which is associated with decreased numbers of T lymphocytes and increased numbers of M2‐like macrophages. Notably, an association of CTA expression with lower T‐cell counts was not only detected for MAGEA4 but, although not significant, a similar pattern was also observed for DPEP3, EZHIP, PAGE1, and TKTL1. This finding is in contrast to neoantigens, which are formed when nonsynonymous mutations occur. There is evidence that cancers with a high degree of neoantigens expression attract T cells and patients with high neoantigen‐load respond better to immunotherapy [We anticipated that if the CTAs were found to be immunogenic, we may detect a specific immune reaction in the tissue. Indeed, we found that some CTAs are associated with the density of immune cells, supporting our assumption. However, these associations were heterogeneous, including M2‐like (CD163+) macrophages and plasma (CD138+) cells. Furthermore, several associations were lost when adjusting for multiple testing. The statistical analysis was also limited by the often very low number of positive cases for some immune and CTA markers, which could lead to hampered statistical associations. Keeping this weakness of our analyses in mind, the identified CTAs that show an immunological signature otherapy . It is yin situ environment of lung cancer tissue. The immune cells were counted based on morphology and marker expression in tissue sections. Similarly, the CTAs were quantified at the protein level under microscopic control. We and others have shown that gene and protein expression correlate only moderately [In an attempt to validate the CTA and immune associations, we utilized TIMEx for exploring the CTA gene expression and how it correlated with deconvoluted tumor‐immune microenvironment data. Unfortunately, this analysis did not confirm our findings. Our study intentionally used a direct quantification in the derately , 47. FurOur study showed that almost 40% of NSCLC patients showed an expression of MAGEA4, preferentially expressed in cases of SCC histology. Several reports confirmed our findings and showed broad RNA or protein expression of MAGEA4 not only in lung cancer, but also in other cancer types , 50, 51.We found that many CTAs are predominantly expressed in one of the main histological lung cancer types, indicating histology‐specific CTA immunogenicity. Consequently, we also included histology‐specific analyses. However, due to the small sample sizes, for these patient subgroups and smaller CTA‐positive cases, the statistical power was limited. Therefore, our histology‐related results should be interpreted with caution, with a risk of over‐ and underreporting of associations. In a previous study by Backman and co‐authors, a generally higher T‐cell infiltration was observed in AC compared with SCC. This also supports the notion that immune features are cancer‐type specific .The recently discovered CTA EZHIP demonstrated an association with a higher infiltration of local plasma cells. EZHIP was also identified in our previous CTA discovery study based on a comparative RNA‐Seq approach to a variety of normal and cancerous tissues. EZHIP was previously not regarded as a CTA, and, as with most other CTAs, the EZHIP gene is located on the X chromosome (Xp11.22). EZHIP was biologically mainly characterized by its mutation and overexpression in posterior fossa ependymoma . The funin situ immunohistochemistry‐based analysis might better reflect the CTA representation in the tumor environment rather than bulk RNA‐based correlation analysis. Here, newer multiplexing technologies might address some of these limitations by visualizing CTA markers indirectly in the context of relevant cancer and immune markers [The present study is the most comprehensive regarding the number of CTAs analyzed and the depth of the characterization based on several molecular, immune, and clinical properties. However, some limitations should be taken into consideration when interpreting its results. We analyzed only eight CTAs, which is less than 10% of the previously identified CTAs in NSCLC . The lim markers . Finally5in situ immune reaction. The described CTAs represent promising immune candidates, and the corresponding stringently validated antibodies may serve as valuable tools in companion diagnostics. Further studies using relevant methods are warranted, not only to validate the results of this study but to also define the immunogenic properties of these CTAs in order to harness them for immunotherapeutic strategies such as vaccination or T‐cell engineering [We provide a careful characterization of CTA expression in NSCLC and show that CTA expression is common, coordinated, and histology dependent. Some CTAs are connected with the infiltration of specific immune cell subsets, suggesting an ineering .The authors declare no conflict of interest.python. MB, FH, and LM generated figures and conducted the statistics in the r statistical environment. JSMM, DD, and HB complied and generated the lung cancer cohort. PM, JB, and HB carried out the histological review of the cases. All authors have read and accepted the final manuscript.PM and CL outlined and designed the study with assistance from FH and MR. FH prepared the figures and wrote the first drafts of the manuscript. Revisions were made by all authors but mainly by FH, PM, and CL. FH optimized and annotated the CTA stainings, and MB, JSMM, and DD optimized and annotated the immune stainings. MR generated figures and conducted the statistics in https://www.webofscience.com/api/gateway/wos/peer-review/10.1002/1878-0261.13474.The peer review history for this article is available at Fig. S1. CTA candidate antibodies screening and selection. The initial 90 CTAs were used as a starting point to screen for available antibodies in the HPA portal. Nonprotein‐coding genes, replaced genes, proteins with no antibody, and multitargeting antibodies were automatically filtered and omitted. The manual assessment was applied to remove nonspecific antibodies and lastly chose the most distinct antibodies concerning staining intensity to simplify annotation. The number on the left indicates the number of proteins left after each filtering process. The proteins highlighted at the bottom were included in the present study. Note that the gene symbol for CXORF67 has been replaced with EZHIP in newer versions of Ensembl.Click here for additional data file.Fig. S2. CTA staining distribution and correlation. (A) Distribution of CTA protein expression (protein score ≥ 1) per histological subtype. Percentages indicate total frequencies regardless of histology. (B) Cross‐correlation plot between the antibodies targeting the eight CTAs. Spearman correlation of CTA protein scores highlights positive (blue) or negative correlation (red) between CTAs. Significant (p < 0.05) correlates are shown with bold and black text, while nonsignificant are in gray. (C) The number and percentage distribution of stages represented in the lung cancer cohort (upper multicolored bar). Stacked percentage bar charts with positive and negative cases in each bar, grouped by stage for each CTA. Y‐axis represents percentages and the blue‐filled bars represent positive cases defined as protein score ≥ 2 (lower eight plots).Click here for additional data file.Fig. S3. Correlation between CTA protein score and RNA‐Seq levels. Spearman's rank correlations between RNA‐Seq and CTA protein scores. The RNA‐Seq data for each patient of the corresponding boxplots show the distribution of RNA‐Seq for the protein correlation divided by low CTA (red box) and high CTA (green box) protein expression. Circles indicate raw data points and diamonds indicate outliers. The respective p‐values are indicated for each correlate.Click here for additional data file.Fig. S4. Unsupervised hierarchical cluster analysis of the immune marker and CTA protein expression profile in NSCLC patients. Immune annotation scores and CTA protein scores were linearly transformed for each marker and plotted as heatmaps to visualize the global immune and CTA phenotypes in NSCLC patients. The immune data (red matrix) were stacked on top to visualize the immune distribution dictated by the unsupervised hierarchical cluster analysis of the CTA protein score matrix (blue matrix). The upper rows (green matrix) show stage, performance status, histological subtype, gender, age, and mutation status (yellow matrix) for four commonly analyzed genes in lung cancer—EGFR, KRAS, STK11, and TP53. Each column indicates one patient and the total number of patients used is defined at the bottom.Click here for additional data file.Fig. S5. Unsupervised hierarchical cluster analysis of immune and CTA gene expression profiles in NSCLC patients. FPKM gene expression levels of the immune and CTA markers were log2‐transformed and plotted as heatmaps to visualize the CTA‐to‐immune relationship in NSCLC patients. The immune gene expression data (red matrix) were stacked on top to visualize the immune distribution dictated by the unsupervised hierarchical cluster analysis of the CTA genes (blue matrix). The upper rows (green matrix) show stage, performance status, histological subtype, gender, age, and mutation status (yellow matrix) for four commonly analyzed genes in lung cancer—EGFR, KRAS, STK11, and TP53. Each column indicates one patient and the total number of patients used is defined at the bottom.Click here for additional data file.Fig. S6. Kaplan–Meier survival analysis in NSCLC cases with high and low CTA protein scores. Nonparametric log‐rank Kaplan–Meier 5‐year survival analysis was performed to compare survival between high (red) and low (turquoise) CTA protein expression scores. The ‘Number at risk’ table shows the number of alive or noncensored patients at a specific time point for the CTA high or low group. The upper and lower 95% confidence interval is shown as dotted lines.Click here for additional data file.Fig. S7. Multivariate Cox regression analysis. The association of CTAs with multiple variables was analyzed in a multivariate Cox regression analysis. Hazard ratios are presented as log values with 95% confidence intervals.Click here for additional data file.Fig. S8. CTA protein score association to immune cell infiltrates by histology. Wilcoxon's rank‐sum test for each CTA was tested against the immune cell markers, T cells (red), B cells (green), NK cells (orange), macrophages (blue), and immune checkpoint inhibitor markers (black). Confidence levels for each immune marker are shown where the median of the difference between CTA high  and CTA low  is specified. For adenocarcinoma, 207 patients were analyzed, and for squamous cell carcinoma, 97 patients were analyzed. The level of significance by Wilcoxon's rank‐sum test was set at p < 0.05 and indicated in bold.Click here for additional data file.Fig. S9. CTA gene expression correlation against deconvoluted tumor‐immune microenvironment data. The heatmap shows the relation of CTA gene expression against different immune signatures, all retrieved from the TIMEx web portal. Pearson's correlation coefficient matrix was calculated and indicated as values 1 to −1 (blue to red). A red asterisk indicates a significant correlation (p. adj <0.05). Only cases that express the specific CTA were included in the analysis. The number of patients analyzed per CTA in total was as follows: EZHIP; 117, MAGEB2; 121, DPEP3: 229, TKTL1: 265, MAGEC2:162, PRAME: 446, MAGEA4: 141, and PAGE1: 60.Click here for additional data file.Table S1. Antibody list for immune and CTA profiling by immunohistochemistry. All antibodies used for tissue profiling and research resource identifier (RRID), catalog information, and dilution are listed.Table S2. Number of positive cases per CTA and histological subtype. The number of positive cases (protein score ≥ 1) by histological subtype per CTA is listed. Please note: individual cases express several CTAs. Number of annotated cases from TMA for each CTA, n = 310.Table S3. CTA protein expression and mutational status in all NSCLC patients. For each CTA, the protein expression status (protein score ≥ 2 = high vs. protein score 0–1 = low) was tested against mutational status in all NSCLC patients by Fisher's exact test. Significant p‐values and FDR‐adjusted p‐values cases are highlighted in green.Table S4. CTA protein expression and mutational status in adenocarcinoma patients. For each CTA, the protein expression status (high vs. low) was tested against mutational status in adenocarcinoma patients by Fisher's exact test. Significant p‐values and FDR‐adjusted p‐values cases are highlighted in green.Table S5. CTA protein expression and mutational status in squamous cell carcinoma patients. For each CTA, the protein expression status (high vs. low) was tested against mutational status in squamous cell carcinoma patients by Fisher's exact test. Significant p‐values and FDR‐adjusted p‐values cases are highlighted in green.Table S6. CTA protein expression and estimated tumor mutational burden. An estimated tumor mutational burden was calculated, and the average mutational burden between patients with high or low CTA expression was evaluated with a t‐test. Significant p‐values and FDR‐adjusted p‐values cases are highlighted in green. NA = not applicable.Table S7. CTA protein expression and clinical data. For each CTA, the protein expression status (high vs. low) was tested against clinical criteria  by Fisher's exact test. Significant p‐values and FDR‐adjusted p‐values cases are highlighted in green.Table S8. Cox regression analysis. Detailed information for Cox regression analysis in Fig S7. N = 308 patients for all analyses. Significant p‐values are highlighted in green.Click here for additional data file.


The aim of this study was to examine the effect of an online multidisciplinary weight loss management programme.Between July 2016 and July 2017 this randomized controlled trial recruited patients in Nanjing, China who were living with type 2 diabetes mellitus and who were obese or overweight and randomized them to online versus conventional groups. All participants were managed by a multidisciplinary team. The experimental group was managed using the Why Wait WeChat Platform for Weight Reduction Management.There were 55 and 52 participants in the online and conventional groups, respectively. The decreases in fasting blood glucose (−4.26 vs. −2.99 mmol/L), 2‐h postprandial blood glucose (−4.48 vs. −2.68 mmol/L) and glycated haemoglobin (−22.11 vs. −6.21 mmol/mol) were more pronounced in the online compared to conventional group . After the intervention, self‐management ability parameters, including diet control, foot care and total score, were improved in the online group compared with the conventional group, as well as all indexes of quality of life .The online multidisciplinary weight loss management programme improved blood glucose in obese or overweight patients living with type 2 diabetes mellitus. Self‐management ability parameters  and quality of life were improved in the online group compared with the conventional group. Multidisciplinary approaches have been introduced into weight loss management models for patients with type 2 diabetes mellitus and obese or overweight in outpatient clinics of third‐level hospitals, but their effects have not been clearly described.The online multidisciplinary weight loss management programme improved blood glucose in obese or overweight patients living with type 2 diabetes mellitus.Self‐management ability parameters  and quality of life were improved in the online group compared with the conventional group.This is the first trial of an online programme in the management of type 2 diabetes mellitus in China.Trial findings might have international applicability in the management of type 2 diabetes mellitus and could lead to improvement of prognosis. Therefore, the current study aimed to examine the effect of an online multidisciplinary weight loss management programme based on the Why Wait? programme  no cognitive disorders, able to read in Chinese and good communication with the investigators; (5) ability to use smartphones; (6) blood pressure <160/100 mmHg; (7) triglyceride <6.77 mmol/L; and (8) tolerance of extreme exercise.The inclusion criteria were (1) T2DM based on the 1999 WHO diagnosis and classification of diabetes; (2) age between 18 and 60 years; (3) body mass index (BMI) > 24 kg/mThe exclusion criteria were (1) severe heart, brain, kidney , eye , diabetic foot, severe cardiovascular and cerebrovascular diseases and abnormal cardiopulmonary exercise test; (2) unstable angina or co‐infection; (3) mental disorders; (4) cancer, with radiotherapy or chemotherapy in the past 6 months; (5) participation in other clinical trials; or (6) any condition requiring hypoglycemic drug discontinuation.2.2http://www.chictr.org.cn/showproj.aspx?proj=44368 (No. ChiCTR2000029321) and was approved by the Ethics Committee . All the patients enrolled signed informed consent forms. There were no important changes in the methods after trial commencement.This study was registered at 2.3n1=n2=2Zα/2+Zβσδ2. Zα/2 referred to the bilateral Z value under the test level alpha. The following was assumed: α = 0.05, Zα/2 = Z0.05/2 = 1.96, and test efficiency = 0.8, and with β = 0.2 (Zβ0.842). According to preliminary experiments, the mean HbA1c levels were 55.3 and 62.1 mmol/mol in the experimental and control groups, respectively. Therefore, the formula revealed a group sample size of N = 49.6 (50). Considering a 20% sample loss rate, the final sample size was 60 cases per group.Sample size was calculated according to the formula for sample size calculation Li, , compari2.4A third‐party statistician used SPSS 18.0  to generate a random number table, which was used by the specialist nurses to assign the participants to the online (multidisciplinary collaborative management) and conventional  groups. Each group was further divided into subgroups of 10 participants, according to the order of enrolment.Although the participants were told they were divided into two groups when signing the informed consent form, the investigators did not tell the grouping of the participants, and the participants were not informed of the exact content of the management plan. They were simply told what they needed to know. This blinding method was approved by the ethics committee. All investigators and personnel involved in data collection and evaluation were blind to grouping. All personnel involved in data entry, monitoring and analysis were blind to grouping until data lock.2.5The online group was managed for weight loss through the Why Wait WeChat Platform, which includes two sections and nine modules. The two sections were the informative section  and the functional section . The videos presented weight‐loss lessons, sports courses, action essentials, matters needing attention and practice time and frequency. The compulsory course presented knowledge about weight loss. A recent article about weight loss was uploaded every Monday. The participants had to take the courses at least once, but they could watch them, entirely or in parts, as many times as they wished. The video teaching and required reading took about 15 min for each course, and there were 56 courses in total. After learning, there was an assessment, and those who passed the assessment received points, and participants could redeem gifts according to the number of points.The video teaching used in this study was divided into two parts. One part was the correct training video from the coach, and the other part was the video to instruct the participants on the spot. For example, any possible misunderstandings and incorrect postures of the participants were corrected on the spot and uploaded by video.Original contributors shared their feelings during weight loss with others for encouragement. The participants in the same group could communicate with others online and on‐site, thus achieving the purpose of a virtuous circle.The participants were required to take photos and record their meals three times a day on diet forms each day. For resistance exercise, the participants were required to record the type of exercise, completion time, intensity and post‐exercise response. Aerobic exercises were recorded in the same manner. The total recording time was about 15 min. For body measurement, the participants were required to carry the Mi Smart Band bracelets daily, automatically recording daily steps, weight, body fat, muscle, protein, BMI, bone mass, water, BMR, skeletal muscle, visceral fat, sleep time and sleep quality. For operational feedback, the participants submitted homework and physical measurements daily, and weight loss managers replied within 12 h.2.62.6.1The online group was managed by a multidisciplinary team (MDT). Specialized nurses designed a nursing plan accordingly and instructed the participants on how to use the WeChat platform. With the use of the functional section function of the WeChat platform, the participants uploaded blood sugar, blood pressure, weight, diet and exercise‐related data. With the platform's information section function, the participants grasped the common sense of weight loss. The key members of the project team monthly discussed the bottlenecks and solutions encountered during the implementation of the project.Led by a diabetes specialist nurse, a weight‐loss management team typically comprised a specialist nurse, a dietitian, a sports coach, a psychological consultant and a full‐time physician. For the weekly conventional course, the specialist nurse and the experts of the project team determined the training time and place in advance and then informed each participant. Medical experts, psychological experts, nutrition experts and weight loss coaches gave lectures and explanations, breaking down the movements step by step and highlighting the essential points of the movements in a simple language. After class, the participants could have face‐to‐face communication with each expert to answer questions. This course had a total of 56 sessions, with reference to Joslin Diabetes Centre Why Wait Weight Loss Course  and performed informative lectures. The lectures included an overview of diabetes and obesity, diet therapy, exercise therapy, drug therapy, psychological adjustment and so on and occurred every 2 months for 30 min . Each class was followed by a group discussion for 30 min.2.7Diabetes specialist nurses entered health records, and telephone calls were made at the first, second, fifth, seventh, eighth, ninth, tenth, and eleventh months after admission. Outpatient follow‐up was conducted in the outpatient clinic by the weight‐loss management team during the third, sixth and twelfth months after admission.2.81c) levels 1 year after intervention initiation.The primary endpoint was glycosylated haemoglobin (HbASecondary endpoints included fasting blood glucose (FPG), 2‐h postprandial blood glucose (PPG), BMI, waist circumference, hip circumference, systolic pressure (SBP) and diastolic pressure (DBP).1c, BMI, waist circumference, hip circumference, SBP, DBP, total cholesterol (TC), triglycerides (TGs), high‐density lipoprotein (HDL) and low‐density lipoprotein (LDL). The body mass of the participants was measured using a smart Bluetooth body fat scale, and BMI was calculated. FBG and 2HBG were detected during an oral glucose tolerance test (OGTT). Venous blood was taken to measure the biochemical indexes. A mercury sphygmomanometer was used to measure blood pressure. The waist‐hip ratio, body fat percentage, target weight and health were assessed by the Inbody 720 human body composition analyzer before the intervention and at 6 months and 1 year after intervention initiation. There were no changes to the endpoints after trial commencement.The basic data of the two groups were collected before the intervention and at 3, 6 and 12 months after the intervention, including general demographic data, daily lifestyle, FPG, 2PPG, HbATwo questionnaires were completed at baseline, 3, 6 and 12 months. One was the Diabetes Self‐Management Behaviour Scale, and the other was the Diabetes Quality of Life Scale. Two investigators  used unified instructions to introduce the purpose and methods to fill out the survey to participants. After obtaining informed consent, the participants filled in the information by themselves. If the participants could not fill in the information by themselves, the investigators filled in the information on behalf of the participants according to their answers.α of self‐behaviour management scale was 0.84, including 11 items of the four subscales of dietary control (four items), exercise compliance (two items), monitoring compliance (two items) and foot care (two items); the Cronbach's α values range from 0.71 to 0.93, all greater than 0.70, with good homogeneity.The Diabetes Self‐Management Behaviour Scale revised by Toobert et al.  and tranα of the Diabetes QOL Scale is 0.76, and the split half reliability coefficient is 0.87.The Diabetes Quality of Life Scale was developed by Liao et al.  and incl2.9t test or non‐parametric tests, as appropriate. Categorical variables were reported as frequency or percentage and compared using the chi‐square test. Comparisons of variables at different time points between groups were performed by two‐way repeated‐measures analysis of variance (ANOVA). With P < 0.05 in the spherical symmetry test, the Greenhouse–Geisser correction or multivariable analysis of variance was performed to assess the degree of freedom. Two‐side P < 0.05 was considered statistically significant.All data were double entered by two specialist nurses. A third‐party statistician conducted all analyses. Per‐protocol (PP) analyses were performed by excluding the participants who dropped out of the study or failed to comply with the study protocol. The ITT analyses were performed using all participants according to their original grouping, regardless of whether they complied with the intervention protocol or not . For the participants missing follow‐up assessments or dropped out, their last measurement during follow‐up was recorded as the data at the endpoint. SPSS 18.0 was used for data analysis. Continuous variables were presented as mean ± standard deviation (SD) or median and range, according to distribution, as determined by the Kolmogorov–Smirnov test. Group comparisons were performed by Student's 33.1Of the 212 patients who made contact with the investigators from July 2016 to July 2017, 124 met the inclusion criteria. Four patients were excluded according to the exclusion criteria: Two patients were also enrolled in a clinical trial of drug weight loss that was not yet completed; one patient had diabetic high‐risk foot; one patient recently developed angina pectoris symptoms and was hospitalized in the Department of Cardiology. Therefore, 120 participants were enrolled in this study, including 60 each in the online and conventional groups. The participant flowchart is shown in Figure 3.21c were significantly different at various times after intervention between the online and conventional groups . Meanwhile, BMI, TC, SBP, DBP, TG, HDL and LDL were similar in both groups after intervention (P > 0.05). As shown in Table 1c, BMI, waist circumference, hip circumference, TC, SBP, DBP, TG, HDL and LDL were significantly different at various times after intervention between the experimental and control groups .FPG, 2‐h PPG, HbA1c, BMI, waist circumference, hip circumference, SBP, DBP, TC, TG, HDL and LDL met the normality and spherical symmetry (P > 0.05) criteria. As shown in Table 3.3The waist‐hip ratio, body fat percentage, target body weight and health assessment in both groups met the normality and spherical symmetry (P > 0.05) criteria. The results indicated significant differences in target body weight and health assessment between the two groups at various times (P < 0.05) Table .3.4All dimensions of the Diabetes Self‐Management Behaviour Scale  as well as total scores in both groups met the normality and spherical symmetry  criteria. Both groups were tested by repeated‐measures analysis of variance at each time. The results showed that diet control, foot care and total scores were significantly higher in the online group compared with controls (P < 0.05) Table .Before the intervention, all dimensions and total scores of Diabetes QOL Scale in both groups met the normality and spherical symmetry criteria . Repeated measures ANOVA showed that all four QOL dimensions and total scores significantly decreased over the 12 months . The intervention group had lower scores for all four dimensions and total scores than the conventional group at 3, 6 and 12 months . The time × grouping interaction was significant for the physiological dimension (P < 0.001), psychological dimension (P < 0.001), therapeutic dimension (P = 0.009) and total score (P < 0.001) but not for the social relation dimension (P = 0.051)  the medical staff should provide a detailed maintenance plan for weight loss, (2) lifestyle and behavioural interventions should be combined with drug therapy, (3) psychological counselling and (4) network intervention for maintaining the weight‐loss effect within 2 years. Studies have shown that appropriate interventions could prolong the maintenance time of body weight after weight loss to some extent , corroborating two large sample randomized controlled trials. The first study showed a weight loss of 8.5  kg over 6 months , including two in the online group (one moved and one dropped out due to disease) and five in the conventional group (three were lost to follow‐up and two withdrew their consent). For these patients, no data could be collected besides those at baseline, so only the data of those 113 patients were enrolled in ITT analysis. Considering the small sample size, this difference in the number of patients may cause an important bias in the results, and the true effect of the online intervention cannot be reflected. Still, the two analyses reported similar outcomes for glucose control.5The online multidisciplinary weight loss management programme improves blood glucose in obese or overweight patients living with T2DM. Self‐management ability parameters  and QOL were improved in the online group compared with the conventional group. Given the substantial resource required by this programme, one of the implications of the findings is the importance of a future health economic analysis. Such a trial might have international applicability in the management of T2DM and improvement of prognosis.The authors declare no conflicts of interest.This study was approved by the Ethics Committee of Changzhou Second People's Hospital . All the patients enrolled signed informed consent forms.Confirming that all listed authors meet the authorship criteria and that all authors are in agreement with the content of the manuscript.Figure S1. Line charts of changes by time. (A) HbA1c, PTime < 0.001, PGroup < 0.001, PInteraction = 0.006; (B) Fasting blood glucose (FBG), PTime < 0.001, PGroup < 0.001, PInteraction = 0.079; (C) 2‐hour postprandial blood glucose (2hBG), PTime < 0.001, PGroup < 0.001, PInteraction = 0.108.Click here for additional data file.Table S1. Baseline patient featuresClick here for additional data file.Data S1. Supporting informationClick here for additional data file.


Caenorhabditis elegans; and (c) enhances cognitive function in a mouse model of tauopathy. We also provide insight on potential underlying mechanisms, as well as evidence of translational relevance for these observations by showing that TE transcripts accumulate with brain aging in humans, and that these age‐related increases intersect with those observed in Alzheimer's disease. Collectively, our results suggest that TE transcript accumulation during aging may contribute to cognitive decline and neurodegeneration, and that targeting these events with reverse transcriptase inhibitors like 3TC could be a viable therapeutic strategy.Aging is the primary risk factor for most neurodegenerative diseases, including Alzheimer's disease. Major hallmarks of brain aging include neuroinflammation/immune activation and reduced neuronal health/function. These processes contribute to cognitive dysfunction (a key risk factor for Alzheimer's disease), but their upstream causes are incompletely understood. Age‐related increases in transposable element (TE) transcripts might contribute to reduced cognitive function with brain aging, as the reverse transcriptase inhibitor 3TC reduces inflammation in peripheral tissues and TE transcripts have been linked with tau pathology in Alzheimer's disease. However, the effects of 3TC on cognitive function with aging have not been investigated. Here, in support of a role for TE transcripts in brain aging/cognitive decline, we show that 3TC: (a) improves cognitive function and reduces neuroinflammation in old wild‐type mice; (b) preserves neuronal health with aging in mice and  Age‐related increases in transposable element transcripts may contribute to brain aging and neurodegeneration by stimulating neuroinflammation. Targeting these transcripts with the reverse transcriptase inhibitor 3TC has the potential to improve cognitive function by reducing immune activation/neuroinflammation. These repetitive DNA sequences account for >50% of the human genome, and some have the ability to propagate and/or change locations in the genome  on hippocampus samples from these mice, and we analyzed TE transcripts as described above. Although no individual TEs were significantly increased , we observed significant age‐related increases by group in all major types of TE transcripts, an effect that was not due to overall transcriptome differences, as gene counts/fold‐changes were evenly distributed Figure . In trea2.2TLR7; increased with aging, decreased with 3TC), which is involved in innate immune responses , a regulator of synaptic plasticity involved in learning and memory , which is centrally involved in preventing oxidative stress and neuroinflammation , which plays an important role in synaptic plasticity, cognitive function, and AD risk . Therefore, we probed for cytoplasmic DNA sensors like cGAS and other proteins involved in immune activation and cellular responses to episomal DNA by immunoblotting . Similar but non‐significant trends were present for phosphorylated Interferon Response Factor 3 (IRF3), a transcriptional regulator of cGAS/STING responses that stimulates the production of type‐1 interferons and other inflammatory mediators   and Interferon Gamma (IFN‐ɣ), pro‐inflammatory cytokines centrally involved in host defense and immune responses, including to retrotransposons  , suggesting the drug may have inhibited neurodegeneration in mice. Neurodegeneration is characterized by a progressive loss of neurons and their axons/dendrites, which is associated with neuronal and cognitive dysfunction  after correcting for sex and post‐mortem interval. However, to examine age‐related TE transcript increases more carefully, we performed a matched comparison of 10 old and 10 young subjects   and Religious Order Study/Memory Aging Project (ROSMAP) datasets  Figure . This an) Figure . Interes) Figure , which w) Figure . Importa) Figure . These f) Figure , and wit) Figure . Next, u) Figure . Increas) Figure . These r) Figure , includi) Figure . Alzheim) Figure  includin) Figure .not pathology; most of these were retrotransposons, especially LTRs, and they included the HERVK‐related sequences LTR5_HS and LTR13  on hippocampus tissue from mice subjected to long‐term 3TC treatment, which allowed us to examine both TE and gene expression. In these data, we found that TE transcript levels by major type were increased with aging, and this was independent of changes in overall gene/transcript expression—similar to our findings in human brain aging and Alzheimer's disease. No individual TEs were particularly increased in our data, which could be due to the ages of mice studied (less age difference between old and young mice in our study compared to others) and/or a result of the global TE dysregulation observed in aging and the underlying biology. For example, as reported by others, the most likely mechanism underlying TE transcript increases with aging and disease is epigenetic dysregulation   STING, a central mediator of cDNA sensing and inflammation    and older (19 months) male and female C57/B6 mice were purchased from the National Institute on Aging aged rodent colony. Male and Female rTg4510 mice and littermate controls (2 months of age) were purchased from the Jackson Laboratory. Mice were housed four per cage on a 12‐h light/dark cycle at 22–24°C at the laboratory animal facility at Colorado State University. All animals were given free access to water and food  and acclimatized to the facility before being randomly assigned to experimental groups. Body weights, food consumption, and water intake were measured every week, and all animals were routinely monitored by veterinary staff. All animal procedures were approved by Colorado State University, IACUC protocol #1441.4.23TC  was purchased from Health Warehouse and added directly to drinking water (2 mg/mL). Experimental cohorts were treated for 6 weeks with 3TC, and controls were provided with the same water but without the drug. Fresh water was routinely provided to all cages every 3–4 days.4.3behaviorcloud.com) to track and analyze mouse behavior and time spent in specific regions of each maze.Animals were handled extensively before testing to familiarize them with the experimenter and minimize anxiety. The same experimenter performed all behavioral tests. Mice received a minimum of 2 h room habituation before the start of each experiment and 2 days rest between different tests. Equipment was cleaned with 80% ethanol between tests to minimize scents. The Barnes Maze short‐term spatial memory test consisted of a white opaque circular table (90 cm in diameter) with 20 equally spaced holes around the perimeter. Spatial orientation cues  were placed on walls around the room and remained in the same position for the testing period. Animals underwent 4 trials per day, and if an animal was not able to find the escape hole after 1 min, it was gently guided to the hole and covered for 1 min. Novel Object Recognition tests were carried out as previously described  with phosphatase and protease inhibitors (Thermo Fisher). Protein concentration was determined using a BCA kit (Thermo Fisher/Pierce). 20 μg of protein per sample was separated by electrophoresis (Bio‐Rad Criterion system) and transferred to nitrocellulose membranes  then blocked. Primary antibodies  included p‐IRF3 (1:1000 dilution), IRF3 (1:1000), p‐STING (1:1000), STING (1:1000), p‐cGAS (1:1000), cGAS (1:1000), IBA1 (1:750), pTBK1 (1:1000), TBK1 (1:1000), and GFAP (1:1000). GAPDH  was used for normalizing protein expression on all blots. Membranes were incubated in primary antibody overnight at 4°C followed by HRP‐conjugated secondary antibodies  for 1 h at room temperature and then 1 min in ECL reagent (ThermoFisher/Pierce) for imaging on a ProteinSimple Alpha Imager. Cytoplasmic DNA was measured by lysing and fractionating remaining samples according to standardized protocols  in ice‐cold RIPA buffer containing 100 mM KCl, 40 mM Tris HCl, 10 mM Tris base, 5 mM MgCl4.7Frozen cortex samples were homogenized in RIPA buffer (ThermoFisher) with fresh protease and phosphatase inhibitors . Following centrifugation for 20 min at 14,000 RPM, supernatant was transferred to a fresh tube and protein concentrations were measured using a BCA assay . Cytokines were assessed in aliquots of lysate containing 40 μg of protein using commercially available kits as previously reported  was purchased from the Caenorhabditis Genetics Center at the University of Minnesota. Animals were maintained on standard nematode growth medium plates containing 1 M CaCl2, 1 M MgS04, 1 M KP04, cholesterol, and spotted with standard Escherichia coli OP50 or E. coli OP50 with 10 μM 3TC. Live worms were immobilized in 1% 1‐phenoxy‐2‐propanol and then imaged using an EVOS M7000 fluorescence microscope (ThermoFisher) with a 40× objective. Approximately 16‐plane Z‐stacks were captured per animal, and images were deconvolved before analyzing dendrite degeneration, characterized by bead‐like structures that were counted using Celleste (ThermoFisher) imaging software. Lifespan experiments were performed using a Nemalife Infinity apparatus (NemaLife Inc.), a microfluidic device that allows for semi‐automated lifespan determination without the need for simultaneous pharmacological inhibition of progeny, as recently reported  with or without 10 μM 3TC. Worms were video imaged daily, and live worms were counted by two blinded experimenters.The PVD::GFP 4.10Human TE/transcriptome data were generated from existing RNA‐seq datasets (North American Brain Expression Consortium [NABEC] and Religious Orders Study/Memory Aging Project [ROSMAP]). Data from subjects ≥25 years of age (when brain development is reported to end) in the NABEC dataset and all available raw fastqs on from ROSMAP subjects on the Synapse server were used for all analyses. TE transcripts and gene expression were analyzed following the same procedures described above for mouse RNA‐seq data but with the hg38 genome build, annotation and repeatmasker files, and differential expression analyses were performed using DESeq2 and all available samples in binary group comparisons  as described in results sections and figure legends. Gene Ontology and KEGG pathway analyses were performed using g:Profiler as described above  statistics test.Exact numbers of animals and subjects are reported in the figure legends, along with the relevant statistical analyses. Outlier analyses were performed using Grubbs' test, but no individual samples were identified as consistent outliers , so all data were used in analyses. As described above, differential expression of TE transcripts and genes was assessed using DESeq2, and significant differences were determined using the default Wald test. Correlations among TEs and age in human subjects and heatmaps of increased/decreased genes were analyzed using GraphPad Prism software . TE comparisons by type were performed using Shapiro–Wilk normality testing followed by Mann–Whitney test versus the mean Log2 fold‐change for all transcripts with >10 mean counts . For overlap comparisons of TEs in the NABEC and ROSMAP datasets, the most highly expressed TEs in subjects with higher versus lower cognitive function , higher versus lower pathology (Braak staging) and Alzheimer's disease versus cognitively normal controls were identified as those with Log2 fold‐change greater than the mean Log2 fold‐change for all TEs. All human and mouse Gene Ontology/KEGG pathway analyses were performed using g:Profiler C. elegans experiments. M.E.S., C.M.M., and A.N.C. analyzed and generated human transcriptome data. S.C.O. performed immunoblots. S.D.B. and R.A.G. generated/analyzed C. elegans data. D.N. generated/analyzed metabolomics data. D.S.L. assisted with mouse experiments and metabolomics, wrote/edited corresponding parts of the paper, and provided conceptual insight. C.D.L. provided conceptual insight, data analysis and editing suggestions. T.J.L. designed the study, wrote/edited the paper, assisted with human and mouse transcriptome analyses, provided conceptual insight, and provided funding.D.W. designed the study, wrote the paper, generated/analyzed human and mouse data, conducted the mouse study/behavioral experiments, and performed The authors declare no conflicts of interests.Supplementary Figure 1 Supporting data/information for Figures 1 and 3 . (a) Food and water consumption (g/mouse/day) in control and 3TC treated mice. (b) MA plot showing all changes in genes and TE transcripts in older versus younger mice.Supplementary Figure 2 Supporting data/information for Figure 2 . (a–c) KEGG pathway analyses of increased/decreased gene expression signatures in older, younger and older 3TC‐treated mice.Supplementary Figure 3 Supporting data/information for Figure 3 . (a) Western blot analyses of phosphorylated cGAS and TBK1, as well as Iba1 in younger, older and older 3TC‐treated mice. (b) ELISA analyses of IFN‐β in cortex and plasma concentrations of TNF‐α, IL‐1β, IL‐6, and IFN‐ɣ in younger, older and older 3TC‐treated mice. (c) Pathway analysis  of metabolic pathways most impacted by aging and 3TC treatment in older mice.Supplementary Figure 4 Supporting data/information for Figure 6 . (a) MA plot all age‐related changes in genes and TE transcripts in 10 older versus 10 younger (age/sex‐matched) subjects in NABEC dataset.  KEGG pathway analyses of gene expression signatures in the same subjects. (d) MA plot showing all changes in genes and TE transcripts in age/sex‐matched Alzheimer’s disease versus cognitively normal subjects in the ROSMAP dataset.  KEGG pathway analyses of increased/decreased gene expression signatures in the same subjects.Click here for additional data file.Appendix S1: Supporting InformationClick here for additional data file.


D in synthetic antibodies was lower than wild antibody. In terms of δH°, TδS°, and δG°, the results were consistent with binding parameters. Here, the lowest value of thermodynamic parameters was obtained for antibody 62. These data show that the synthetic antibodies, especially antibody 62, had a higher affinity than the wild‐type antibody.As a result of smallpox immunization programs that ended more than 40 years ago, a significant portion of the world's population is not immune. Moreover, due to the lack of anti‐monkeypox drugs and vaccines against monkeypox, the spread of this virus may be the beginning of another challenge. In this study, novel antibodies against monkeypox virus were modeled based on a heavy chain of human antibody and a small peptide fragment. Docking of modeled antibodies with C19L protein showed the range of docking energy, and root‐mean‐square deviation (RMSD) was from −124 to −154 kcal/mL and 4–6 angstrom, respectively. Also, docking of modeled antibodies‐C19L complex with gamma Fc receptor type I illustrated the range of docking energy, and RMSD was from −132 to −155 kcal/ml and 5–7 angstrom, respectively. Moreover, molecular dynamics simulation showed that antibody 62 had the highest stability with the lowest energy level and RMSD. Interestingly, no modeled antibodies had immunogenicity, allergenicity, and toxicity. Although all of them had good stability, only antibodies 25, 28, 54, and 62 had a half‐life of >10 h. Moreover, the interaction between C19L protein and anti‐C19L antibodies (wild‐type and synthetic) was evaluated by the SPR method. We found that K The three‐dimensional structure model of antibody 25 (A), antibody 28 (B), antibody 32 (C), antibody 37 (D), antibody 54 (E), antibody 62 (F), C19L (G), and gamma Fc receptor type I (H), prepared by SWISS‐model server. It can infect a wide range of birds, reptiles, insects, and mammals.Orthopoxviruses (mpox) are large viruses with a size of 140–450 nm and a brick‐like structure. They have a genome consisting of 200–500 kbp, which encodes more than 200 genes.Recently, the spread of monkeypox in endemic and even non‐endemic areas has attracted international attention.The specific diagnostic test for monkeypox is polymerase chain reaction and it should be said that limited laboratories can perform this test. Virus propagation using cell culture allows further identification but is limited to reference laboratories with biosafety level 3.Smallpox is estimated to have killed millions worldwide22.1https://www.ncbi.nlm.nih.gov/protein/QGA67041.1), and then 10 amino acids were added to the beginning of this sequence. In this study, about 100 modeled antibodies with different sequences were designed and evaluated. It should be noted that the initial sequence had only 2 types of amino acids, including Lysine (K) and Glutamic acid (E). SWISS‐model server (https://swissmodel.expasy.org/) was used to prepare the three‐dimensional (3D) structure of the modeled antibodies. SWISS‐model, SAVES v6.0 (https://saves.mbi.ucla.edu/), and ProSA (https://prosa.services.came.sbg.ac.at/prosa.php) servers were also used to validate all antibody models.To design modeled antibodies, the sequence of a heavy chain of human antibody was first extracted from the NCBI (2.2https://www.ncbi.nlm.nih.gov/protein/17529825), and then its 3D structure was prepared with SWISS‐model server and validated by SWISS‐model, SAVES v6.0, and ProSA servers. Then, docking was done with each modeled antibody and C19L protein by HDOCK online server (http://hdock.phys.hust.edu.cn/). Finally, six of them with the best docking energy and RMSD were selected.C19L is the most important surface protein in the monkeypox virus2.3https://www.ncbi.nlm.nih.gov/protein/NP_001365733.1) by HDOCK online server.In this part of the work, the interaction of modeled antibodies‐C19L complex with gamma Fc receptor type I was evaluated. In this regard, 6 of the best‐modeled antibodies that had the maximum ability to bind to C19L protein were selected and docked separately with gamma Fc receptor type I , AllerTOP (https://www.ddg-pharmfac.net/AllerTOP), and ToxinPred (https://webs.iiitd.edu.in/raghava/toxinpred/design.php) servers, respectively. Also, Protparam server (https://web.expasy.org/protparam/) was used to calculate number of amino acids, molecular weight, theoretical pI, total number of negatively/positively charged residues, estimated half‐life, instability index, aliphatic index, and grand average of hydropathicity of modeled antibodies.Immunogenicity, allergenicity, and toxicity of modeled antibodies were obtained from Vaxijen (2.6N‐hydroxysuccinimide/N‐ethyl‐N′‐(3‐dimethylaminopropyl) carbodiimide hydrochloride and then immobilized by C19L. Here, the contact and dissociation time was 5 min and 20 min, respectively. Association rate (kon), dissociation rate (koff), and dissociation constant (KD) were calculated by a global fitting analysis assuming a Langmuir binding model.The interaction between C19L protein and anti‐C19L antibodies (wild‐type and synthetic) was evaluated by the SPR method. First, SPR surface was activated by H°) and entropy (ΔS°) were calculated from the slope and intercept, respectively.R is the gas constant, and T is the absolute temperature.Also, changes in enthalpy  are used in the treatment of many diseases. IgG molecules can bind to specific antigens, and then the antibody‐antigen complex can interact with Fc receptors, located on innate and acquired immune cells.The ab initio methods can be used for antibody modeling.D in synthetic antibodies was lower than wild antibody. In terms of δH°, TδS°, and δG°, the results were consistent with binding parameters. These data show that the synthetic antibodies, especially antibody 62, had a higher affinity than the wild‐type antibody.As a result of smallpox immunization programs that ended more than 40 years ago, a significant portion of the world's population is not immune to smallpox and the zoonotic mpox.One of the important challenges in the design of antibodies is the docking of antibodies and antigens. Since epitopes and paratopes are usually planar, conventional docking methods may not be suitable. SnugDock or RosettaDock are better choices because they rely on ensemble stochastic perturbation and generate a large number (~105) of models to capture the minimum energy.As a last word, we must say that this dangerous virus will come to human society, and ways to deal with it should be prepared from now. The mechanisms related to this virus should be known and drugs or vaccines should be designed for it.5D in synthetic antibodies was lower than wild antibody. In terms of δH°, TδS°, and δG°, the results were consistent with binding parameters. These data show that the synthetic antibodies, especially antibody 62, had a higher affinity than the wild‐type antibody.Due to the lack of anti‐monkeypox drugs and a specialized vaccine against this virus, the outbreak of monkeypox may be the beginning of another pandemic. In this study, novel antibodies against monkeypox virus were modeled based on a heavy chain of human antibody and a small peptide sequence at its beginning. Docking of modeled antibodies with C19L protein showed the range of docking energy, and RMSD was from −124 to −154 kcal/mL and 4–6 angstrom, respectively. Also, docking of modeled antibodies‐C19L complex with gamma Fc receptor type I confirmed the results and showed the best result was for antibody 62. Molecular dynamics simulation also showed that antibody 62 had the highest stability with the lowest energy level and RMSD. No modeled antibodies had immunogenicity, allergenicity, and toxicity. Although all the modeled antibodies had good stability, only antibodies 25, 28, 54, and 62 had a half‐life of >10 h. It was found that KSadeq Shabani: Visualization; writing—original draft; writing—review & editing. Mohsen Rashidi: Conceptualization; validation. Shakila Radgoudarzi: Supervision; validation; visualization.The authors declare no conflicts of interest.Not applicable. This study was an in silico study.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.


The objective of the study was to validate a new scale for assessing habitual behavior—the Daily Habit Scale in patients with Parkinson's disease.Parkinson's disease patients are impaired in habit learning and skill acquisition. Despite repeated practice, they have difficulty developing habitual responses.One hundred seventy‐nine patients (Median (Mdn) = 69 [64–76], 65 females) participated in the study. Corrected item‐to‐total correlations were calculated to assess the item‐convergent and item discriminant validity. Confirmatory factor analysis and assessment of internal consistency were also carried out. Concurrent validity in respect to measures of anxiety and depression, apathy, impulsivity, personality, multidimensional health locus of control, and health‐related quality of life was also calculated. To determine the test–retest reliability of the scale, 30 patients  completed a second copy of the scale 6 months after the first.Twenty‐nine items (76%) and 9 items (24%) of the 38‐item scale, respectively, showed a very good and good convergent validity. All the items discriminated between their own factor and the other factors. The comparative fit index of 0.932 indicated an acceptable model fit of the data, whereas the root mean square error of approximation of 0.06 moderate model fit. The scale had a good internal consistency (Cronbach α = 0.792), and a moderate test–retest reliability (0.57). Females had higher scores on two factors compared to men (Factor 3: household activities and Factor 8: sleep‐related activities).The Daily Habit Scale is a reliable and valid tool to measure daily habits in Parkinson's disease. Parkinson's disease (PD) is a chronic, neurodegenerative disorder characterized by a progressive loss of dopamine‐producing cells in the substantia nigra pars compacta and a consequent dopamine deficiency in the nigrostriatal dopamine system.In confirmation, there is empirical evidence that PD patients are impaired in skill and habit learning tasks, such as, the serial reaction time or weather prediction task, which are acquired through repetition and practice.PD is a progressive, multisystemic disorder. Motor, as well as non‐motor symptoms including cognitive decline,Our daily lives are full of naturalistic habits related to self‐care , eating, smoking, drinking, and using the internet and sending/receiving emails. Recently, a new 38‐item Daily Habit Scale (DHS) was developed and validated in young healthy controls (HC).PD patients attending a movement disorders outpatient clinic (PL) at the National Hospital for Neurology and Neurosurgery, London. Inclusion criteria for the patients who participated in the study were those with a clinical diagnosis of PD according to United Kingdom (UK) Brain Bank Criteria,The questionnaires were sent out to 220 PD patients, of whom 179 responded (81% response rate) and were included in the study. A second copy of the DHS was mailed to 50 respondents (~28% of the test sample) 6 months after the initial completion to assess of the test–retest reliability of the new scale. This subgroup of patients was chosen randomly from the test sample using random number generator .The ethics committee of University College London approved the study. All participants were mailed a booklet of questionnaires including the 38‐item DHS  was conducted to assess the construct validity, that is, to determine whether the factor structure of DHS published beforeU test was used to compare DHS results between male and female patients and between the test and retest samples. The χ2‐test was used to compare gender and handedness distribution between the test and retest samples. Except for the CFA, which was conducted by the use of the software Ωnyx,Internal consistency was assessed with Cronbach α,P > 0.107), except for the PDQ‐8 score, which was significantly higher in the test sample than in the retest sample (P = 0.028).The demographic information and scores on the various measures are presented in Table The results of the analysis are presented in Table The CFI index was 0.934 indicating acceptable model fit, and the RMSEA was 0.06, indicating a moderate model fit of the data to DHS.P < 0.001, testing for the test–retest reliability, was moderately high. In addition, the correlations between the factors in the test and retest samples were all significant , ρ = −0.26, P = 0.001, and Factor 7 (technology and internet use), ρ = −0.16, P = 0.034 and positively with factor Factor 5  ρ = 0.21, P = 0.007. Anxiety did not correlate significantly with any of the factors . Apathy correlated positively with Factor 7 (technology and internet use), ρ = 0.19, P = 0.011. Health‐related quality of life correlated negatively with DHS Factor 3 (household activities) ρ = −0.21, P = 0.008.A correlation analysis was performed between the factor scores of the DHS and measures of anxiety, depression, apathy, trait impulsivity, and other personality dimensions, and health‐related quality of life for the PD patients. For PD patients, depression correlated negatively with Factor 3 (household activities), ρ = −0.22, P = 0.015. Although the BIS‐11 attentional subscale and BIS‐11 motor subscale did not correlate with any of the factors, the BIS‐11 non‐planning scale correlated with Factor 1 (hygiene and self‐care activities) ρ = −0.18, P = 0.036 and Factor 7 (technology and internet use) ρ = −0.19, P = 0.028. The QUIP did not correlate significantly with any of the DHS factors . The total MHCL score correlated negatively with Factor 7 (technology and internet use), ρ = −0.23, P = 0.005. Although the IHLC subscale correlated positively with Factor 1 (hygiene and self‐care activities), ρ = 0.19, P = 0.016, Factor 3 (household activities), ρ = 0.23, P = 0.003 and Factor 6 (sport‐related activities), ρ = 0.24, P = 0.002, the PHLC subscale negatively correlated with Factor 5 , ρ = −0.21, P = 0.013 and Factor 7 (technology and internet use), ρ = −0.36, P < 0.001. The CHLC subscale did not correlate significantly with any of the factors . There was no significant correlation between the measures of personality as assessed by the total TPQ and the TPQ subscales with any of the DHS factors . Schwab and England ADL scale positively correlated with Factor 2 (leisure activities) ρ = 0.15, P = 0.045, Factor 3 (household activities) ρ = 0.35, P < 0.001, Factor 5  ρ = 0.29, P < 0.001, Factor 6 (sport‐related activities) ρ = 0.25, P < 0.001 and Factor 7 (technology and internet use) ρ = 0.26, P = 0.001.Factor 1 (hygiene and self‐care activities) correlated negatively with the total trait impulsivity score as measured by BIS‐11, ρ = −0.23, Z = −2.41, P = 0.016, and Factor 8 (sleep‐related activities), Mdn = 3 (2.5–4) and Mdn = 2.5 (2.0–3.5) for females and males, respectively, Z = −3.17, P = 0.001. There were no significant differences in the other factors .The differences between male and female patients are presented in Figure The aim of this study was to validate the DHS in PD. The results of the study showed that the DHS is a valid and reliable scale for measuring daily life habits in PD patients.PD has a major impact on patients’ daily activities. The motor and non‐motor impairments lead to disability in performing and fulfilling daily tasks.CFA is considered a good way of assessing construct validity of scales. It necessitates a strong prior theory underlying the model before analyzing the data. The eight factor structure of DHS has already been established in a large sample of healthy young adults.There were significant correlations between some of the mood, behavior, and personality measures and the DHS. Because depression is associated with impaired reward processing and social functioning, this could lead to impaired habit formation.As for the correlations between impulsivity and DHS in PD, there was no significant correlation between attentional and motor impulsivity to any of the factors of the DHS. The negative correlation between non‐planning impulsivity and hygiene and self‐care activities, and technology and internet use that initially appears surprising, in fact seems plausible because it could lead to less attention to hygiene and other self‐care activities, and less engagement in activities, social networking, and checking emails. In our study, the QUIP did not correlate with any of the factors of the DHS. The reason for this is that the QUIP assesses specific impulsive and compulsive behaviors usually related to dopaminergic medication use, such as hypersexuality, gambling, or compulsive use of dopaminergic medication,Regarding health locus of control,We found significant differences between male and female patients on two factors of the DHS. Namely, females scored higher on household activities and sleep‐related activities. Regarding household activities, it seems that women still predominate when it comes to performing activities related to home maintenance, such as cooking and cleaning. The sleep‐related activities factor of the DHS consists of two elements: sleeping late and waking up early. Studies have shown that women are at increased risk of insomnia,There are several limitations to the study. The CFA based on CFI and RMSEA showed only an acceptable/moderate fit for the model.The items of the DHS do not include important activities such as administrative work, paying bills, making appointments, and activities related to gainful or ungainful employment. Although these activities are important, some such as employment‐related activities or making appointment or paying bills are more goal‐oriented than habitual. The DHS with its 38 items is comprehensive and captures most daily habitual activities. In addition, some of the items, such as spending money, may include paying bills.The retest was administered 6 months after the first test. Although the most commonly recommended time interval between test and retest is 2 weeks, the optimal time interval varies depending on the construct being measured and the stability of the construct over time.In summary, we validated the DHS consisting of 38 items distributed across eight factors, for testing the basic, daily habits in PD. The usefulness of this questionnaire remains to be further assessed in future clinical studies testing habitual performance in PD. By providing an opportunity to identify abnormalities in habitual behavior in these eight domains, this scale can potentially help to better understand the mechanisms of habit formation and reliance on daily habits in PD.(A) Design, (B) Execution, (C) Analysis, (D) Writing, (E) Editing of final version of the manuscript.D.G.: A, B, C, D, EA.T.: A, ER.S.: A, B, C, EP.L.: B, EM.J.: A, B, C, D, EEthical Compliance Statement: The ethics committee of University College London approved the study. All subjects signed written consent before entering the study. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines.Funding Sources and Conflicts of Interest: No specific funding was received for this work. The authors declare that there are no conflicts of interest relevant to this work.Financial Disclosures for the Previous 12 months: The authors declare that there are no additional disclosures to report.Data S1. Supplementary Methods. Detailed description of the scales.Click here for additional data file.Table S1.(A) Frequency (%) of all responses, minimal (min.) and maximal (max.) values on strength (S = (F + A)/2), frequency of minimal and maximal responses on strength, and skewness and kurtosis of the responses on strength for the PD test sample (N = 179). (B). Frequency (%) of all responses, minimal (min.) and maximal (max.) values on strength (S = (F + A)/2), frequency of minimal and maximal responses on strength, and skewness and kurtosis of the responses on strength for the PD retest sample (N = 30).Table S2. (A) Factor by factor Spearman ρ correlation coefficients and the respective p‐values based on the PD test sample (N = 179). (B) Factor by factor Spearman ρ correlation coefficients and the respective p‐values based on the PD retest sample (N = 30).Click here for additional data file.


Previous studies showed that there is a positive association between mothers’ and children’s autistic traits. We also tested if this association is more pronounced in mothers with a higher pre-pregnancy body mass index (BMI).M = 13.5 years) and with the Quantitative Checklist for Autism in Toddlers (Q-CHAT) in the CUSP cohort (M = 1.6 years).The study was embedded in two cohorts with information available for 4,659 participants from the Generation R and for 179 participants from the Cambridge Ultrasound Siblings and Parents Project (CUSP) cohort. In both cohorts, maternal autistic traits were assessed using the short form of the Autism Spectrum Quotient, and information about maternal height and weight before pregnancy was obtained by questionnaire. Child autistic traits were assessed with the short form of Social Responsiveness Scale in Generation R , in early childhood , and in early adolescence . Furthermore, a higher maternal pre-pregnancy BMI was associated with higher child autistic traits, but only in Generation R . There was no significant moderating effect of maternal pre-pregnancy BMI on the association between autistic traits of mothers and children, neither in Generation R nor in CUSP. In addition, child autistic traits scores were significantly higher in mothers who were underweight and in mothers who were overweight compared to mothers with a healthy weight.Higher maternal autistic traits were associated with higher autistic traits in toddlerhood (CUSP cohort; We confirm the association between maternal and child autistic traits in toddlerhood, early childhood, and early adolescence. Potential interacting neurobiological processes remain to be confirmed.The online version contains supplementary material available at 10.1186/s13229-023-00578-x. Autism is a neurodevelopmental condition defined by difficulties in social communication and interaction, and the presence of stereotyped behaviours and interests . The aet2 increment in BMI, there is a 16% increase in autism likelihood. However, the opposite has also been found, as mothers deemed underweight also have an elevated likelihood for autism among offspring, particularly in older mothers  and clinically diagnosed autism in the offspring –14. Thes mothers . In summMaternal pre-pregnancy BMI, as an indicator of maternal health and nutritional status , could iFor this, we use data from the Generation R and the Cambridge Ultrasound Siblings and Parents (CUSP) cohorts. Considering our sample comprises children from both the UK and the Netherlands, we acknowledge that there are different preferences regarding terminology within the autism community in these two countries , 24. To N = 2,047) were excluded, leaving 4,659 participants for the analyses.The study included two different cohorts: the Generation R and the Cambridge Ultrasound and Pregnancy (CUSP) cohort. Generation R is a large population-based cohort of mothers and children in Rotterdam, the Netherlands . PregnanN = 40) were excluded, resulting in 179 participants for analyses. Table The Cambridge Ultrasound and Pregnancy (CUSP) cohort recruited mothers before or during their routine 20-week ultrasound scan, between 2016 and 2018 at the Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust. Participating mothers gave informed consent for access to all their pregnancy-related clinical records, test results and the biological samples obtained during routine clinical care. A favourable ethical opinion was given by the East of England Cambridge Central Research Ethics Committee (REC Ref 16/EE/0004) and the Research and Development Department of Cambridge University Hospitals. Eligibility inclusion criteria for the CUSP cohort were as follows: (1) little/no consumption of alcohol during pregnancy, (2) no smoking or recreational drug use during pregnancy, (3) a singleton pregnancy, and (4) measurements indicated no intrauterine growth restrictions or large-for-gestational age foetuses , 27. ConMaternal autistic traits To obtain self-reports of autistic traits in adults, the Generation R and the CUSP cohort used the Autism Spectrum Quotient (AQ)—Adult version , to which participants responded on a 4-point Likert scale from ‘1 = definitely agree’ to ‘4 = definitely disagree’. In the CUSP cohort, the Pearson’s correlation between the abbreviated version and full version sum scores was r = 0.93 (p < 0.001). version . In the  version . In ordeChild autistic traits In the Generation R cohort, when children were 5–6 and 13–15 years of age, the 18-item abbreviated form of the Social Responsiveness Scale (SRS) was administered to obtain a quantitative measure of autistic traits , to which parents responded on a 4-point Likert scale, with possible choices from ‘1 = not true’ to ‘4 = almost always true’. The questionnaire was mostly completed by the mothers (participation rate: 52.5% at age 5–6 years and 47.6% at age 13–15 years). The SRS short form, as used in the Generation R sample, is highly correlated (r = 0.95) with the full 65 item version ’ with possible answering options: ‘A = many times a day; ‘B = a few times a day; ‘C = a few times a week; ‘D = less than once a week’ and ‘E = never’.In the CUSP cohort, questionnaires were sent to the mothers, as fathers and other primary caregivers were not formally enrolled in the study. Mothers were invited via email to complete the 25-item online version of the Quantitative Checklist for Autism in Toddlers (Q-CHAT)  after thMaternal Pre-pregnancy BMI In the Generation R cohort, information about maternal height and weight before pregnancy was obtained by questionnaire. While maternal height and weight during the first trimester of pregnancy were objectively measured at the research centre visit, we used the self-reported measure given our focus on pre-pregnancy BMI. The correlation between self-reported pre-pregnancy weight and weight measured at the enrolment visit was 0.95 (p < 0.001). In the CUSP cohort, information about maternal weight and height before pregnancy was obtained by questionnaire based on memory recall of their weight and height at 18 years of age. In addition, CUSP also measured maternal weight and height during first weeks of pregnancy in the first ultrasound appointment. The correlation of self-reported pre-pregnancy weight and height at age 18 years and weight and height measured at the first ultrasound appointment was 0.67 (p < 0.001). BMI was calculated as weight in kilograms divided by the square of height in metres (kg/m2) for both cohorts.Several variables  were considered as possible covariates based on previous studies. Covariates were only added into the model if they changed the main effect estimates by more than 5%. Consequently, only maternal age, paternal age, child sex, birthweight, gestational age and Apgar score at 5 min were included in the main analyses.Information on the age of the parents in both cohorts was obtained at the time of recruitment into the study. Information on sex, birth weight, gestational age, and Apgar score of the child in both cohorts was obtained from medical records completed by community midwives and obstetricians at birth.linearitycheck argument in R to see whether estimated lines fit with the predicted lines. All variables were standardized into z-scores for ease of comparison across various time points, resulting in standardized regression coefficients. We also calculated the Cohen’s ƒ2 using R2 from the multiple regression output.In this study, we employed multiple linear regression analyses. First, we examined the association between maternal autistic traits and child autistic traits, using two models. The first model was unadjusted. In the second model, covariates were added. Second, we examined the association between maternal pre-pregnancy BMI and child autistic traits, applying the same two models. For each analysis, we checked for potential nonlinearity of associations by introducing quadratic terms into the model. Next, we examined the interaction between maternal autistic traits and maternal pre-pregnancy BMI in relation with child autistic traits. In addition, we plotted the interaction by using the 2 (153) = 686.2, p = 0.07). In the CUSP cohort, the variables with missing values were maternal autistic traits (9.5%), pre-pregnancy BMI (14.0%), paternal age (6.7%), Apgar score (14.5%), gestational age (12.8%) and birth weight (12.3%). These data were also missing completely at random as tested by Little’s MCAR test (χ2 (71) = 90.1, p = 0.06). Additionally, we calculated a priori how many samples were needed with seven variables in the regression model and assuming a moderate effect size of Cohen’s d = 0.50 by using G*Power , pre-pregnancy BMI (23.7%), paternal age (12.3%), Apgar score (7.4%), gestational age (0.6%) and birth weight (0.2%). These data were missing completely at random as tested by Little’s MCAR test (χ G*Power . The ana2) with underweight (≤ 18.5 kg/m2), overweight (25.0–29.9 kg/m2) and obesity (≥ 30.0 kg/m2) by using ANCOVA.In CUSP cohort, we re-ran the analysis by using AQ scores calculated based on all items of the questionnaire, rather than the validated short version. We also re-ran the analysis by using maternal BMI measured at the first ultrasound, rather than the maternal BMI at 18 years, to determine the role of the potential large time gap between BMI at 18 years and the pregnancy. For the Generation R, we re-ran the analysis by using the child autistic traits score at 5–6 years of age to check whether findings were consistent across ages. In addition, we re-ran the analyses in the complete cases only. Further, in the Generation R cohort, maternal glycaemic index measured at intake during early pregnancy was examined as a possible moderator. Previous studies have shown a positive association between maternal glycaemic index and maternal BMI –39, withM = 53.3, SD = 11.6) than in the Generation R cohort ; however, the effect size for the mean difference was relatively small (d = 0.03). Of the children, 49.4% were boys in the Generation R cohort, and 48.6% in the CUSP cohort. Maternal and paternal age correlated strongly (r = 0.61 in both cohorts). The correlations between all study variables are presented in Table Child and maternal characteristics are presented in Table M = 29.1 years, SD = 5.4, F  = 322.9, p =  < 0.01), had a higher pre-pregnancy BMI  = 24.4, p =  < 0.01), and higher maternal autistic traits  = 18.3, p =  < 0.01), see details in Additional file We conducted a non-response analysis to examine any potential patterns or differences between participants who responded to the survey and those who did not. In the CUSP cohort, there were 40 participants excluded and no significant differences were found on several characteristics compared to those who were included in the study, see details in Additional file βmodel1 = 0.17, p < 0.01, ƒ2 = 0.04) and at 18 months . This association remained statistically significant in the fully adjusted model both in the Generation R  and the CUSP cohort .).The results of the regression analyses on the association of maternal autistic traits and child autistic traits are shown in Table βadjusted =—0.02, p = 0.655). Higher maternal pre-pregnancy BMI was associated with higher child autistic traits at 13 years , see details in Table p = 0.18), see details in Additional file Table βadjusted =—0.01, p = 0.734) or the CUSP cohort .Table N = 2202) showed a similar association between maternal autistic traits and child autistic traits , see Additional file βadjusted = -0.01, p = 0.334), see Additional file βadjusted = 0.19, p < 0.01, ƒ2 = 0.04, Additional file βadjusted = 0.04, p < 0.01, Additional file Sensitivity analysis in Generation R on cases with complete information on all variables  (see Table p < 0.01) and in mothers who were overweight  compared to the mothers with a healthy weight. There was no statistically significant difference in child autistic traits scores between the healthy weight and obesity groups (p = 0.09). In the CUSP cohort, we did not find any statistically significant differences between the maternal pre-pregnancy BMI groups in child autistic traits scores.In our categorical approach, we found a statistically significant mean difference between the maternal pre-pregnancy BMI groups in the Generation R cohort as determined by a one-way ANCOVA,  to Kröger et al. [2 = 0.04). In addition, our finding in the Generation R cohort that maternal pre-pregnancy BMI was positively associated with child autistic traits both in middle childhood and adolescence has only been shown a few times before . However, a sensitivity analysis with maternal pre-pregnancy BMI data from the initial ultrasound in CUSP resulted in similar results.Second, it is important to highlight that there was a significant amount of missing data in the Generation R cohort and the non-response analysis revealed that those with a higher BMI and more autistic traits were more likely to be non-responders. This may have led to an underestimation of the associations. Another concern in this study is the presence of unmeasured confounding, primarily attributed to genetics. Notably, a study  employinFinally, sensitivity analysis using a categorical approach of maternal pre-pregnancy BMI showed that in the Generation R cohort, not only a higher maternal weight (overweight), but also a lower maternal weight (underweight) was associated with higher autistic traits scores in childhood and adolescence. However, in the CUSP cohort, findings for the underweight and higher maternal weight groups were in a similar direction, though not statistically significant. Potentially, the analyses with weight groups might have had limited power to detect differences, particularly due to low numbers in the underweight and obesity categories.2 = 0.04). Results from the analyses with weight categories are indicative of a nonlinear link with the more extreme ends of the maternal pre-pregnancy BMI spectrum being associated with more child autistic traits. In CUSP cohort, maternal autistic traits, but not maternal BMI, were positively associated with child autistic traits in toddlerhood, with a small effect size (ƒ2 = 0.05). We did not find an interaction of maternal pre-pregnancy BMI on the association between autistic traits of mothers and their children in both Generation R and CUSP cohort.Using two different cohort datasets from the UK and the Netherlands enabled us to uncover whether maternal autistic traits and weight are related with child autistic traits in different developmental periods . In conclusion, we showed that in the Generation R cohort, both maternal autistic traits and maternal pre-pregnancy BMI were positively associated with child autistic traits in early childhood and early adolescence, yet with small effect sizes  may be needed to further understand the mechanisms underlying the role of maternal weight on the development of child autistic traits. In particular, the scope of this study may be extended to examining additional independent variables, such as prenatal inflammation, placental function and maternal mental health during and after pregnancy.Additional file 1. Supplementary figure and tables.