Alzheimer’s disease (AD) is a neurodegenerative disease representing the most common type of dementia worldwide. The early diagnosis of AD is very difficult to achieve due to its complexity and the practically unknown etiology. Therefore, this is one of the greatest challenges in the field in order to develop an accurate therapy. Within the different etiological hypotheses proposed for AD, we will focus on the two-hit vascular hypothesis and vascular alterations occurring in the disease. According to this hypothesis, the accumulation of β-amyloid protein in the brain starts as a consequence of damage in the cerebral vasculature. Given that there are several vascular and angiogenic alterations in AD, and that endothelial progenitor cells (EPCs) play a key role in endothelial repair processes, the study of EPCs in AD may be relevant to the disease etiology and perhaps a biomarker and/or therapeutic target. This review focuses on the involvement of endothelial dysfunction in the onset and progression of AD with special emphasis on EPCs as a biomarker and potential therapeutic target. World Alzheimer Report 2019: Attitudes to dementia there are 50 million people with dementia (two-thirds with AD), with an expected increase of more than 152 million patients by the year 2050 (Alzheimer’s disease (AD) is the main neurodegenerative disease leading to dementia and cognitive impairment. According to ear 2050 .APOE (APOE ε4), phatidylinositol binding clathrin assembly protein (PICALM), clusterin (CLU) or sortilin related receptor-1 (SORL1) genes have been identified in AD   precursor protein (APP), presenilin 1 (PS1), and presenilin 2 (PS2) genes. However, those mutations collectively represent less than 1% of total cases. Late-onset AD is diagnosed from the age of 65 onward, and it has a multifactorial cause in which both environmental and genetic risk factors are involved . Among, ed in AD . From aler (BBB) .premortem diagnosis of AD is particularly challenging. Regrettably, the definitive diagnosis is made by postmortem brain tissue histological tests. Currently, techniques such as magnetic resonance imaging (MRI)  , positrong (MRI) , and detng (MRI) , are onls values  and the s values . HoweverCurrently, there are different hypotheses about the AD onset:(a)β-amyloid deposit and hyperphosphorylated tau protein hypothesis: AD is originated from the presence of extraneuronal amyloid plaques formed by amyloid fibers composed of Aβ protein, and intraneuronal neurofibrillary tangles (NFTs), that are mainly formed by paired helical filaments (PHF) of the hyperphosphorylated tau protein .(b)Cholinergic hypothesis: the cause of AD is due to alterations of the cholinergic system. In AD there are modifications in cholinergic transport, acetylcholine release, expression of cholinergic receptors, reduction of acetylcholine transferase activity, and loss of cholinergic neurons. These events are relevant for AD since the cholinergic system is closely related to memory .(c)Two-hit vascular hypothesis: a damage in cerebral vasculature (hit one) induces the accumulation of Aβ in the brain (hit two) . See be. In thisFor all the aforementioned, this mini-review is focused on vascular and angiogenic alterations in AD, besides the potential key role of EPCs on the AD’s etiology, and their potential as a therapeutic biomarker.postmortem brain samples, and CSF biomarkers detection support a vascular dysfunction in AD -PET linked alterations in glucose transport to MCI and early AD. This was determined through a reduction in glucose transporter-1 (GLUT-1) in the BBB . MEOX-2 acts as a regulator for the proliferation of vascular cells. Low levels of the mentioned gene in AD generate aberrant angiogenesis that results in a decrease in cerebral microcirculation. In animal models, MEOX-2 deletion reduces LRP-1 levels, leading to decreased Aβ efflux from the brain parenchyma to the blood, among other vascular alterations  can be caused by either several vascular risk factors , or by genetic risk factors like e injury . Accordie injury , as welle injury . There ie injury . Moreovee injury .Finally, the cerebrovascular dysfunction triggers inflammation, defective Aβ clearance in the brain, and increased influx of peripheral Aβ through BBB . LikewisOverall, these events promote synaptic dysfunction, neuronal damage, neurodegenerative change, and finally neuronal death, thus generating dementia .Since angiogenesis and the integrity of the BBB are both crucial for the development of AD, and EPCs are essentials in endothelial repair processes, it is tempting to propose that EPCs may have a key role in this disease. In recent years, our group has described that EPCs are involved in other neurovascular diseases such as stroke and migraine .in vitro. Whereas e-EPCs appear after a few days in culture and form colonies with spindle-shaped cells around them, l-EPCs appear after 2–3 weeks in culture and present a cobblestone shape  gene had higher levels of circulating EPCs, EPCs-mobilizing cytokines, and, eventually, better functional outcome  are cells involved in tissue maintenance and repair . Differesubjects  and CD34early AD . In conto in CSF . Moreove and age . Contrar and age . Regardior cells . Concernerfusion . Howeverin vivo. Although, in other neurological pathologies, such as traumatic brain injury, the intraventricular administration of l-EPCs resulted in greater integrity of the BBB and increased angiogenesis in a mouse model (Additionally to the analysis of the number of EPCs in AD, the therapeutic potential of these cells has also been suggested in different animal models . For insse model . In ischse model . TherefoAlthough AD is the main type of dementia worldwide, its etiology remains unclear. The Zlokovic two-hit vascular hypothesis proposes that AD starts from initial damage in the cerebral vasculature. Different studies in AD have demonstrated that there is a dysfunction of the BBB leading to hypoperfusion and hypoxia, accumulation of Aβ and hyperphosphorylation of tau, accumulation of neurotoxic molecules, and inflammation, among others in cerebral parenchyma. Altogether, these mechanisms cause neurodegeneration, neuronal dysfunction, an increase of pro-angiogenic molecules, and aberrant angiogenesis.MEOX-2. However, the main cells responsible for carrying out angiogenesis in hypoxic sites are EPCs. Therefore, these cells could be involved in aberrant angiogenesis.The basis of the decreased angiogenesis seen in AD remains unclear, although it has been proposed that it is due to either the accumulation of Aβ or the low expression of In the last years, clinical studies have analyzed the relationship between the number of EPCs and AD patients. The obtained data were contradictory since some studies did not observe significant differences in the number of EPCs, while others did. As discussed above, age and inclusion criteria may lead to observed differences. Interestingly, studies using animal models of AD showed that exogenous administration of EPCs improved learning, memory, and angiogenesis, attenuated Aβ deposition, reduced p-tau levels, and up-regulated the number of tight junctions, among others.in vivo studies using EPCs as a therapeutic approach open a possible new path for the treatment of AD. However, further studies are necessary to confirm the potential key role of EPCs as an early diagnostic and therapeutic biomarker in AD, and to elucidate the underlying mechanisms associated with the EPC’s therapeutic properties.Taking all these data into account, we can conclude that there are several vascular and angiogenic alterations in AD and that EPCs may play a key role in endothelial and BBB dysfunction associated with AD. Moreover, JC and TS: conceptualization. AC, AO, DR-S, and JP-P: bibliographic study. JC, HV, and TS: supervision. AC, AO, and DR-S: writing – original draft. All authors contributed to writing – review and editing.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors.We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement.p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 , and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry.We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.The online version contains supplementary material available at 10.1186/s12885-021-09143-2. Colorectal cancer (CRC) is currently the third most commonly diagnosed type of cancer and the second cause of cancer death worldwide, with an estimated 1.8 million new cases and 880 thousands deaths in 2018, with a greater burden among males respect to females [Over the last decade, the majority of the countries in Europe, Oceania and North America witnessed a decrease in CRC mortality . Likely,However, the methods currently used by researchers to incorporate genetic factors into prediction models for CRC and the characteristics of the latter are highly heterogeneous . In addiFor these reasons, the primary aim of the present study is to perform a systematic review regarding polygenic risk prediction models for CRC in order to identify which prediction models including genetic risk variants for CRC have been reported in the Scientific Literature.The secondary aim is to assess the impact, in terms of improvement in discriminatory accuracy, of the addition of SNPs into prediction models with only traditional risk factors, and to test whether there is any relation between the number of SNPs included in the models and the improvement of their discriminatory accuracy. In addition, we aimed to evaluate which factors, besides the number of SNPs, influence the improvement of discriminatory accuracy.We registered a protocol for this review on PROSPERO (Record ID: CRD42019135304), the international prospective register of systematic reviews. We uploaded on the PROSPERO register, prior to completing data extraction, the review title, timescale, team details, methods, and general information.We queried Pubmed, Web of Knowledge, Embase and CINAHL Complete electronic databases up to February 2020 using the elements of the Population, Intervention, Comparator, Outcome (PICO) model  . In detaThe manuscript was written following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses  statemenWe systematically searched databases to retrieve all eligible scientific studies that developed, compared or validated a prediction model  using multiple (at least two) SNPs to predict the risk of CRC.Two independent investigators (M.M. and M.S.) screened titles and abstracts of all potentially pertinent articles to identify eligible studies. We obtained, read and included, if relevant, full papers following the same procedures. At all levels, any discrepancies and disagreement were solved by consensus or by involving a third investigator (R.P.).We included English-written peer-reviewed papers focusing on sporadic CRC reporting primary data and that evaluated the combined effect of two or more genes on CRC risk (e.g. GRS or PRS) or that reported a formal prediction model using genetic factors.We excluded all studies that tested a model on simulated populations, pediatric populations, or dealing with inherited forms of colorectal cancer (e.g. Lynch syndrome). Furthermore, we did not include in this review commentaries, editorials, review papers, case reports, case series, book chapters, and articles with no primary data. Lastly, as for articles updating previous ones, we included only the last updated study.Data extraction was conducted independently by two researchers (M.M. and M.S.), for articles deemed relevant, using an in-depth piloted data extraction form and following an adapted version of the “CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies” (CHARMS) checklist . DisagreExtracted data include information regarding: author details; year of publication; study design; study population; sample size; genetic factors analyzed; GRS and related methods used to calculate it; factors other than genetic included in the model; internal and external validation; Area Under Curve (AUC) of non-SNP-enhanced models; AUC of SNP-enhanced models; Integrated discrimination improvement (IDI); and net reclassification improvement (NRI). In particular, NRI and IDI are measures used to compare the performances of two models, specifically an old model and a new model resulting from the addition of one or more predictors to the old one. The AUC is a measure of discriminatory accuracy and quantifies the ability of the model to discriminate between individuals with and without the outcome of interest , while NFor studies including both individuals with adenomas and CRC, we only extracted information about results related to CRC.The risk of bias of included studies was assessed by two investigators (M.M. and M.S.) using the Prediction model Risk Of Bias ASsessment Tool (PROBAST) . PROBASTStatistical analysis was carried out including only studies that reported both a model with only traditional risk factors and one incorporating also genetic factors. For studies that calculated the AUCs of the same model constructed in different ways (e.g. counted GRS and weighted GRS), only the model showing the best performance or, for those showing the same values of AUC, the simplest one was included in the analysis. Stratification according to the number of SNPs was conducted using tertiles based on the distribution of the number of SNPs included in the models across included studies, with lowest, mid, and highest tertile being represented by ≤22, 23–47, and ≥ 48 SNPs, respectively. We calculated standard errors of AUCs using the Hanley and McNeil method .I2 statistic. Moreover, we assessed whether specific factors  were significantly associated with AUC improvement and explained statistical heterogeneity by conducting meta-regression, with p-values adjusted for multiple testing computed using 1000 Monte-Carlo permutations.First, we tested whether a significant trend in the increase of the AUC of the SNP-enhanced models according to the number of SNPs included in the models could be observed. Secondly, we estimated the Pearson’s correlation coefficient between AUC improvement and number of SNPs. Eventually, we investigated whether the increasing number of SNPs added to the baseline models determined an observable trend in the improvement of the AUC by drawing a forest plot. In order to calculate a pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models, we conducted a meta-analysis using the random effects model, based on the assumption that clinical and methodological heterogeneity was very likely to occur and to have an effect on the results. We quantified statistical inconsistency using the All statistical analyses were conducted using the Stata software version 13.0 .The results of abstract and full-text screening with reasons for exclusion are shown in the PRISMA flow diagram  in Fig. The main characteristics of the articles included in the systematic review are summarized in Table The number of genetic variants evaluated in the risk prediction model ranged from 4  to 696 SIn order to include genetic factors into prediction models, different methodologies were investigated across the included studies. In particular, 26 (78.79%) studies used a GRS, 11 42.31%) of which used a weighted GRS % of whic, 52, othOf the remaining 7 studies that did not use GRS (21.21%), one  derived Using the Swets classification , i.e. loA total of 4 articles , 49, 51 p for trend = 0.774). Pearson’s correlation coefficient between AUC improvement and number of SNPs was also estimated, r = − 0.0993 . No correlation could be found between the number of SNPs and AUC increase.A total of 14 risk prediction models, from 10 studies were included in the AUC analysis , 49, 51.p < 0.001).The meta-analysis resulted in a pooled estimate of AUC improvement for SNP-enhanced prediction models compared with non-SNP-enhanced models of 0.040  for all 14 models Fig. . High heA stratified analysis by number of SNPs included across models was performed Fig. . The AUCp < 0.001). Furthermore, an inverse significant association was found also between the number of cases included in the study and AUC improvement (p = 0.002). Eventually, ethnicity was associated with AUC improvement too (p = 0.023), with better AUC improvements achieved by models constructed among Asians compared with individuals with European ancestry. No significant associations were found for other investigated factors. Overall, the factors included in the meta-regression explained almost half statistical heterogeneity, with a residual I2 equal to 54.18%.The results of the meta-regression Table  showed tResults of the overall risk of bias and applicability assessment can be found in Table The majority of the studies (93.94%) were scored as having high risk of bias –54, 57, A total of 22 (66.67%) studies were assessed only for the development of the model, 8 (24.24%) studies were assessed for both model development and validation, 3 (9.09%) only for model validation.As to the model development, 66.67, 36.67, 20.00 and 70.00% of the studies were assessed as having high risk of bias respect to participants, predictors, outcome and statistical analysis, respectively; 33.33, 20.00, 63.33, 3.33% were deemed as having a low risk of bias, while 0.00, 43.33, 16.67, 26.67% were assessed as having unclear risk of bias respectively for participants, predictors, outcome and statistical analysis assessment.As to validation models, 27.27, 36.36, 45.45, 9.09% of the included studies were assessed as having low risk of bias for participants, predictors, outcome and statistical analysis, respectively; while 72.73, 63.64, 54.55 and 90.91% were rated as high or unclear risk of bias.Regarding the applicability of prediction models, in development model studies 30.00, 3.33, and 0.00% were at high or unclear risk; in validation studies 18.18, 0.00, 9.09% were at high or unclear risk as to, respectively, participants, predictors and outcome.Overall, from the 35 studies that we included in our systematic review we identified prediction models for CRC incorporating genetic factors, with extreme heterogeneity regarding the number of genetic factors included. Instead, as for the methods to include genetic factors in the prediction model, most studies used a weighted GRS, with a minority of them using either the count model or both the weighted and count methods.As for studies reporting the AUC value of the model, most of them could not find a satisfactory discriminatory accuracy (e.g. AUC > 0.7 ) for the2 obtained. For this reason, the results of our study should be interpreted cautiously and cannot be considered conclusive.Regarding the pooled improvement in AUC, a clear trend in the improvement of AUC related to the number of SNPs could not be found. The best results were achieved in the lowest (≤22 SNPs) and highest (≥48 SNPs) tertiles of SNPs incorporated into the models, which led to a larger improvement in AUC compared with the mid tertile (23–47 SNPs). As expected, due to the extremely high heterogeneity among variables, regarding various SNPs and several environmental factors included in the retrieved prediction models and among statistical methods used to incorporate such variables in the models, our meta-analysis results show significant statistical heterogeneity, witnessed by the high values of the ISimilarly to our results, Fung et al. reported that the addition of genetic information improved discriminatory accuracy of the identified prediction models for breast cancer, even though AUC improvement was found to be not correlated or associated with the number of SNPs that were included in the model .It should be noted that the improvement of AUC values with the addition of biomarkers, such as SNPs, to a model depends on the starting AUC value, which means the higher the AUC value of the model including only traditional risk factors, the smaller the improvement in AUC after adding genetic information into the model , 60, 61.Furthermore, the ethnicity of study participants was found to significantly affect AUC improvement, suggesting possible differences in the role of genetic factors between different populations, and witnessing the need to foster research in the field of genetic prediction models for all ethnicities . The disEventually, results of the meta-regression showed that the number of SNPs, publication year, the number of traditional risk factors in the model, and inclusion of gender in the model were not associated with AUC improvement. However, they largely explained statistical heterogeneity between included studies.As far as we know, previous systematic reviews on prediction models for CRC including genetic factors were limited to a qualitative synthesis . Hence, However, our study has some limitations. As previously mentioned, we identified extremely different prediction models, both in terms of genetic factors included in the models and in the methods used to include them -which range from weighted and unweighted GRS, to machine learning methods. The accuracy of a model, in terms of AUC values, depends not only on predictors that were used, but also on the method used for its construction.  Hence, aMoreover, we found very limited high-quality evidence, with only one study having an overall low risk of bias , while mFurthermore, while recent research efforts in the field of PRS modelling are going towards the inclusion of thousand or even million SNPs into prediction models through the use of sophisticated methods,  such as To further implement and advance knowledge in the field, in near the future, the adequate application of existing guidelines to improve the quality of prediction model studies, especially regarding study design and/or standardization of methodology to conduct these types of study, will be essential . We showThe addition of genetic information may offer greater benefit when the models are used for risk prediction among specific subgroups of the population , 58. ThiEventually, as recently reported by Naber et al. , if a prThe integration of genetic information into traditional prediction risk models improves the discrimination accuracy respect to CRC. However, we could not find any association or correlation respect to the number of SNPs added to the model and an AUC improvement. High heterogeneity in the choice of baseline model, method of incorporating genetic information, and studied population suggest that standardization in the conduction of this kind of studies be needed. Further steps in research are surely needed in order to improve knowledge, increase comprehension and target people who would benefit more from this intervention. It is also crucial to consider how to apply the studied models into clinical and real-life settings, in fact, the implementation of prediction models into practice will require a better comprehension of potential economic benefits and organizational effects, as well as patient safety, ethical, social, and legal implications, which will make the impact of polygenic prediction models on Health Systems clearer.Additional file 1.Additional file 2: Table S1. Details of single nucleotide polymorphisms investigated by the studies included in the systematic review.


Insect-borne diseases, such as malaria, and plant pathogens, like the tobacco mosaic virus, are responsible for human deaths and poor crop yields in communities around the world. The use of insecticides has been one of the major tools in pest control. However, the development of insecticide resistance has been a major problem in the control of insect pest populations that threaten the health of both humans and plants. The overexpression of detoxification genes is thought to be one of the major mechanisms through which pests develop resistance to insecticides. Hundreds of research papers have explored how overexpressed detoxification genes increase the resistance status of insects to an insecticide in recent years. This study is, for the first time, a synthesis of these resistance and gene expression data aimed at (1) setting up an example for the application of meta-analysis in the investigation of the mechanisms of insecticide resistance and (2) seeking to determine if the overexpression detoxification genes are responsible for insecticide resistance in insect pests in general. A strong correlation of increased levels of insecticide resistance has been observed in tested insects with cytochrome P450 (CYP), glutathione-S-transferase (GST), and esterase gene superfamilies, confirming that the overexpression of detoxification genes is indeed involved in the insecticide resistance through the increased metabolism of insecticides of insects, including medically  and agriculturally  important insects. There are thought to be approximately five million insect species worldwide with ∼80% remaining to be discovered . Among tOf these various pest management strategies, the use of insecticides is still the most widely-used approach  becauseNumerous papers have been published that address the underlying mechanisms that govern the development of insecticide resistance. Some have shown evidence to suggest that gene mutations can decrease the sensitivity of the target site and thus be responsible for increasing insecticide resistance , while oCulex” OR “Aedes” OR “Anopheles” OR “Aphid” OR “Helicoverpa” OR “Spodoptera” OR “*idae”  using the following search terms: “gene overexpression” AND “insecticide resistance” OR “pesticide resistance.” In addition, “ “*idae”  were use “*idae” .To be considered eligible for inclusion, a study had to meet the following criteria:(1)Gene expression must have been measured through relative mRNA abundance by applying qPCR or real-time PCR. Protein-based or enzyme activity-based measurements were excluded because real-time PCR is use for measuring gene expression as well as validating Microarray and RNAseq data, thus, we only use RT-PCR data in this analysis. In addition, meta-analysis requires the similar measurement methods for the data examination and the mix of different gene expression testing methodology, such as mix of microarray and RNAseq and RT-PCR, may generate unexpected variation. Thus, we only chose RT-PCR as our methodology in the study. Moreover, all the genes should be annotated, the genes with Gene Bank ID or vector base ID or other type ID will be excluded. The gene expression should be measured on the level of the whole body.(2)A study must have contained a susceptible strain (or susceptible field strain) as the control and a resistant strain (or resistance field strain) as the measurement of comparison. Thus, studies measuring the gene expression level of insects collected from different locations that did not consider significant differences in the resistance levels of insects from different populations were excluded. The studies that measure the gene expression level before and after insecticides exposure were excluded.(3)A study must have reported either the mean value of the gene expression level and some estimate of error  around the mean estimates for the susceptible and resistant strains or the ratio of these gene expression levels.(4)A study must have reported no gene modification of the strains. Thus, studies testing gene overexpression after editing the genomes of the strains using RNAi, CRISPR-Cas9, or other gene-editing methods were excluded.(5)Drosophila, or some other materials were excluded.A study must have reported no transformation of the gene to other materials; studies testing gene overexpression after the gene had been transferred to sf9 cell, metaDigitise package (1.0.0)  were collected using this method.To gather data from each study, a standardized form, including author(s), publication year(s), insecticide(s), and species, was utilized that facilitated the extraction of the important study characteristics. To perform moderator analysis, the class of insecticides, insect genus, and gene name were collected from each study. The sample size, mean value, and variance components  of gene expression were also collected. Where necessary, standard errors or 95% confidence intervals were converted to standard deviation using sample sizes and t-statistics. Data from the figures were extracted using R with the  (1.0.0) . For figFor this study, the effect size reflected the strength of the relationship between gene expression and insecticide resistance . This ef0.41 = 1.5, 1.5/1 = 1.5) compared to the susceptible strain.For the studies that only provided the relative expression ratio  but did not provide the exact gene expression data for both strains , LnRR wa2) test, in which values above 30 and 50% were considered indicative of moderate and high heterogeneity, respectively. To improve the quality of our results, we conducted sensitivity analyses by omitting each study in turn and re-calculating the effect size to check the resulting difference in the effect size.Since our dataset included some studies where error estimates were not provided with mean gene expression, two separate analyses were conducted to evaluate the robustness of our findings. The first analysis was an unweighted analysis that included mean gene expression for all of the studies. The second analysis was a weighted analysis, which only included studies that provided the mean gene expression data and the associated error for both resistant and susceptible strains. Both of the two analyses were conducted using a random-effects model for the gene expression of the susceptible and resistant strain in R using both the “metafor”  and “metAedes transmit the dengue virus to humans. Agricultural pests include genera that threaten plant health such as Helicoverpa, which can damage cotton and tomato crops. This made it possible to compare the effect on insecticide resistance of overexpressed genes in different gene families and different P450 gene clades, as well as different insecticide classes, different genera, and different vector types. Finally, moderator analyses were conducted to compare the effect on insecticide resistance of overexpressed genes in different gene families within specific medical or agricultural pests. All moderator analyses were conducted utilizing unweighted meta-analyses for all available effect sizes.We classified genes into different gene families and clades and insecticides into different classes according to the modes of action recommended by the Insecticide Resistance Action Committee (IRAC) . Insect metaviz R package), Egger’s tests (dmetar R package), and regression tests and p-curves (metafor R package)  . The sen package . GOSH  and 1.50 , respectively. Overall, the expression level of detoxification genes tested was over 1.5 that seen in the resistant strains compared to the susceptible strains, suggesting that the overexpression of detoxifying genes is indeed associated with increased insecticide resistance  of the unweighted and weighted analyses (including studies both with and without susceptible strain) were 1.82  higher in resistant strain compared to the susceptible strain; the expression level of the GST gene was 1.97 fold  higher in resistant strain compared to susceptible strain; and the expression level of the P450 gene was 1.73 fold  higher in resistant strain compared to susceptible strain. All three gene families were clearly involved in insecticide resistance through upregulated gene expression , 2.17 fold , and 4.67 fold , higher compared to the susceptible strains, respectively (P < 0.01) (2 > 94%), suggesting that a single summary effect size may not be as informative. To further explore this heterogeneity, these data were further partitioned into various groups using specific moderators.To further investigate overexpression of which gene family or clade is responsible for increased insecticide resistance, the overexpression of cytochrome P450, esterase, and GST (glutathione S-transferase) was analyzed. Given the small number of studies on the overexpression of esterase genes, all esterase-related genes, including carboxylesterase (COE) and acetylcholinesterase (AchE), were classified simply as the “esterase” gene. None of the effect sizes for these three gene families overlapped with the null group: the expression level of the esterase gene was 3.46 fold (pression . This abectively . In cont < 0.01) . There wP-value < 0.01), 4.11 fold , 1.60 fold , 2.68 fold  and 3.35 fold  higher compared to the susceptible strain, respectively. In contrast, the overexpression of detoxification genes was not significantly influenced by the insect’s resistance to other classes of insecticides, such as Avermectins, DDT, Diamides, Organochlorines, Phenlypyrazoles, Spinosyns, Sulfoxaflor, and Tetronic , Anopheles , Apolygus , Ceratitis , Culex , Drosophila , Diaphorine , Helicoverpa , Laodelphax , Leptinotarsa , Musca , Plutella , Sogatella , and Spodoptera  all showed significant differences between gene expression in susceptible and resistant strains, while other genera, such as Amsacta, Aphis, Bemisia, Blattella, Frankliniella, Nilaparvata, Rhopalosiphum, and Triatoma, did not . To investigate which gene families are involved in insecticide resistance in both the agricultural pests group and medical vectors group, each group was further classified into three groups based on their gene families. In the medical vector group, the expression of detoxification genes in all three gene families was higher in the resistant strains than in the susceptible strains by 4.28 fold in the GST genes , 5.35 fold in the esterase genes , and 3.29 fold in the P450 genes , respectively , 2.30 fold , and 1.50 fold  higher. higher..P value < 0.05, while the regression test of the funnel plot of the weighted meta-analysis showed symmetry (P = 0.23),  was assessed using multiple approaches, including funnel plot evaluations, regression and Egger’s tests . The Egg= 0.23), , indicat= 0.23), ]. The re dataset . A leave dataset , 4. Cons dataset .Aedes, Anopheles, Plutella, Helicoverpa, etc.), and different type of insecticides . Plenty of studies have proven the importance of the overexpression of detoxified genes in insecticide resistance , various species  exhibited significant differences, while some insecticides  showed little difference between resistant and susceptible strains. As we have known, the long-term and overuse of an insecticide can lead to resistance due to diverse mechanisms . Thus, tThis paper demonstrates a new method in the investigation of the mechanism of insecticide resistance. Based on this example, we conclude that the overexpression of detoxified gene is involved in insecticide resistance. The following researchers could focus on other different questions through meta-analysis, for example, what kind of fitness cost happened in resistance strain compared to the susceptible strain? Although there is no experiment be done in the meta-analysis, the result of meta-analysis is based on numerous experimental papers with the real experimental data. Otherwise, one experimental paper can only focus on one insect species to one or several insecticides with the expression of several detoxified genes. In the meta-analysis, we can combine and analyze the data from 22 insect genera to 14 types of insecticides with the expression of over 200 genes in a total of 68 papers.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/NL, YW, and AW conceived and designed the study, wrote the manuscript, and reviewed the manuscript. YW searched the manuscript and analyzed the data. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Patients with cardiovascular disease (CVD) tend to have higher mortality rates and reduced physical activity (PA). We aimed to evaluate the effect of PA on mortality in older adults with specific CVD.n = 23,871 with CVD, n = 44,352 without CVD) aged ≥65 years with available physical activity data between 2005 and 2012 from the Korean National Health Insurance Service of Korea-Senior database. CVD was defined as a history of ischemic stroke, transient ischemic attack, heart failure, myocardial infarction, and peripheral artery disease.We enrolled 68,223 participants (int = 0.006) or heart failure Patients with CVD were older than those without CVD. Compared with the sedentary group, the physically active groups with and without CVD had a lower incidence and risk of all-cause death during a median follow up period of 42 (interquartile range 30–51) months. A 500 metabolic equivalent task-min/week increase in PA resulted in an 11% and 16% reduction in the risk of mortality in the non-CVD and CVD groups, respectively. With regard to specific CVDs, the risk of mortality progressively reduced with increasing PA in patients with heart failure or myocardial infarction. However, the reduction reached a plateau in patients with stroke or peripheral artery disease, but was significantly greater in patients with stroke (20% vs. without stoke, 11%, PPA was associated with a reduced risk of all-cause mortality in older adults with and without CVD. The benefits of PA in patients with CVD, especially patients with stroke or heart failure, were greater than those without. The inverse relationship between physical activity and mortality has been proven in previous studies , 2. CurrP < 0.001). However, the benefits of physical activity have not been precisely elucidated, especially in elderly patients with CVD  to identify the relationship between physical activity and mortality among elderly patients with and without CVD and (ii) to compare the associations between physical activity and mortality according to specific CVD.Data were collected from the National Health Insurance Service of Korea (NHIS)-Senior database, which includes data for 5,58,147 individuals recruited by 10% simple random sampling from a total of 5.5 million populations aged ≥60 years in the National Health Information Database , 16. IndFrom the Korean NHIS-Senior database, 68,223 participants aged ≥65 years with available physical activity data between 2005 and 2012 were enrolled in this study and followed up until December 2014. Each individual's claims records were reviewed for a history of CVD from 2002 until the date of the health check-up. Participants with prior myocardial infarction, peripheral artery disease, other vascular diseases, ischemic/hemorrhagic stroke, transient ischemic attack, and heart failure were considered to have CVD. Patients with one or more of the CVD were based on the first diagnosed CVD. A flow chart of the study population enrollment ant analysis is presented in Information on comorbid conditions was identified from the International Classification of Disease-10 codes, and prescription medications before the index date . In ordeThe leisure-time physical activity level was assessed using self-reported intensity and frequency of exercise via structured questionnaires using a 7-day recall method . The surRatings of 3.3, 4.0, and 8.0 METs were assigned for light, moderate, and vigorous physical activity, respectively . The phyThe endpoint was all-cause mortality. Using unique personal identification numbers, information on death (date and cause of death) was confirmed from the National Population Registry of the Korea National Statistical Office, in which deaths are centrally registered on the basis of death certificates –24. The t-test.Descriptive statistics were used to characterize baseline characteristics and comorbidities. Categorical variables are reported as frequencies (percentages). Continuous variables are expressed as medians with interquartile ranges. Categorical variables were compared using Fisher's exact test or Pearson's chi-square test, and continuous variables were compared using Student's Incidence rates of mortality were calculated by dividing the number of events by person-time at risk and presented as the rate per 1,000 person-years. We analyzed the hazard ratios and 95% confidence intervals or mortality according to the physical activity level. Competing risk regression was performed using the Fine-Gray sub distribution hazard model, with mortality as a competing risk for mortality events. Multivariable regression models were constructed by adjusting for age, sex, body mass index, smoking, alcohol consumption, hypertension, diabetes mellitus, dyslipidemia, chronic kidney disease, chronic obstructive pulmonary disease, osteoporosis, malignancy, hospital frailty risk score, and Charlson comorbidity index score. We used cubic spline curves to examine the effects of continuous values of physical activity (0 MET-min/week as reference) on all-cause mortality. We conducted subgroup analyses for the primary outcome stratified by age, sex, body mass index, and other baseline comorbidities.p < 0.05. Statistical analyses were conducted using R programming version 4.0.3 .All tests were two-tailed, and statistical significance was set at n = 23,871) and the remaining 65.0%  had no history of CVD. In the CVD group, participants were older (74.7 vs. 73.0 years) and the proportion of men was lower (34.8 vs. 41.0%) than that in the non-CVD group (P < 0.001).The baseline characteristics of the 68,223 study subjects are shown in P < 0.001). The proportion of sedentary participants was higher in the CVD group than in the non-CVD group   . SupplemBaseline characteristics of the whole study population, CVD group, and non-CVD group according to leisure-time physical activity are shown in The median follow-up duration was 42 months . In participants with CVD, the unadjusted risk of mortality was reduced with a higher level of physical activity. After adjusting for age, sex, body mass index, and other baseline comorbidities, mortality risk was the highest in participants with a sedentary lifestyle and the lowest in participants with a physical activity level of >1,500 MET-min/week . While iP = 0.247). As physical activity increased, the difference in the adjusted hazard ratios between the two groups increased.The cubic spline curve is shown in The risks of cardiovascular and non-cardiovascular deaths according to the level of physical activity were similar to those of all-cause mortality . In the The incidence rates and adjusted hazard ratios according to the physical activity level for each CVD are presented in P = 0.006), and 13 and 11% (interaction P = 0.045) reduction in the risk of mortality in participants with and without stroke and with and without heart failure, respectively.The present study investigated the association between physical activity and mortality according to the presence of specific CVDs in a nationwide elderly population. Our principal finding was that physical activity was associated with a reduced risk of all-cause mortality in older adults with or without CVD, and the benefits of physical activity were greater in patients with CVD than in those without CVD. Second, physical activity was associated with a reduced risk of all-cause mortality in older adults irrespective of the specific CVD. Third, the risk of mortality progressively reduced with increasing physical activity in patients with heart failure or myocardial infarction, but reached a plateau in patients with stroke or peripheral artery disease. Finally, the benefits of physical activity were greater, in patients with stroke or heart failure.Contemporary guidelines emphasize that physical activity can make people feel better, function better, sleep better, and reduce the risk of many chronic diseases. The suggested target range of physical activity is 500–1,000 MET-min/week of aerobic physical activity, which is equivalent to 150–300 min of moderate-intensity or 75–150 min of vigorous physical activity per week. This recommendation is based on observations that the greatest survival benefit is provided by achieving a physical activity level of 500–1,000 MET-min/week . The finPrevious studies that showed the survival benefits of physical activity primarily focused on healthy individuals and did not consider the presence of CVD (or specific CVD) –9. The pStrong evidence supports exercise-based cardiac rehabilitation in patients with coronary heart disease and exercise training in patients with chronic heart failure –28. In pIn this study, physical activity was associated with a reduced risk of all-cause mortality in older adults irrespective of the specific CVD, but the effect was greater in patients with stroke or heart failure. Notably, the risk of mortality was progressively reduced by increasing physical activity in patients with heart failure or myocardial infarction. The benefits were not significantly different between patients with and without peripheral artery disease. In the case of peripheral artery disease, there is a possibility that the effect of physical activity was not fully elucidated, as peripheral artery disease may have restricted the amount of exercise. However, all data suggest that a physical activity level of 500 MET-min/week should be considered as the minimum requirement for patients with CVD.This study had several limitations. First, this retrospective and non-randomized cohort study cannot prove or disprove causal relationships. Second, we relied on self-reported questionnaires of physical activity collected at specific time points. The questionnaires surveyed lifestyle behaviors during the preceding 1 week. Information obtained from questionnaires may not represent the level of actual physical activity, and behavioral changes that occur during follow-up could be assessed in our study. However, the large sample size of this study reduced the level of potential uncertainty and provided a reliable approximation of the level of physical activity at the population level. Third, only physical activity during leisure time was analyzed, but various types of physical activity, such as household, occupation, and transportation, that can occur throughout the day were not included or analyzed. Firth, the existence of unadjusted confounders could not be excluded despite strict statistical adjustments. Confounders such as diet habits, changes in medication use, and compliance were not adjusted for in the analysis. Finally, the existence of CVD was determined using claims data, so there is a possibility of errors due to incorrect coding. To minimize this problem, definitions that were verified in previous studies using sample cohorts of the Korean NHIS were applied, and the diagnostic reliability of the cohort data was high –24.Physical activity is important for improved outcomes in elderly patients with CVD and should be routinely recommended. Physical activity was associated with a reduced risk of all-cause mortality in older adults with or without CVD. The benefits of physical activity were greater in patients with CVD, especially patients with stroke of heart failure, than those in patients without CVD.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the Institutional Review Board of the Yonsei University Health System (4-2021-0894). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.M-HK and J-HS contributed to the conception and design of the work, interpretation of data, and drafting of the manuscript. P-SY and BJ are joint senior authors and contributed to the conception and design of the work and critical revision of the manuscript. EJ and P-SY contributed to the acquisition and analysis of data. M-NJ, HY, T-HK, H-NP, M-HL, and GL contributed to the conception and design of the work and revision of the manuscript. All authors read and approved the manuscript before its submission.This research was supported by a grant of Patient-Centered Clinical Research Coordinating Center (PACEN) funded by the Ministry of Health and Welfare, Republic of Korea .GL has served as a consultant and speaker for BMS/Pfizer, Boehringer Ingelheim, and Daiichi-Sankyo. No fees were received either directly or personally. BJ has served as a speaker for Bayer, BMS/Pfizer, Medtronic, and Daiichi-Sankyo and received research funds from Medtronic and Abbott. No fees were received either directly or personally. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Objectives: To map the alcohol hot spots and understand the Sociodemographic Indices (SDI) affecting alcohol consumption in Indian men and women.Methods: Data from National Family Health Survey-4 carried out from 2015 to 2016 with a sample size of 103,411 men and 699,686 women were used for Geographic Information System mapping, and hot spot identification by spatial statistics (Getis-Ord Gi*). Bivariate analyses and multiple logistic regressions were used to analyze SDI.Results: India has three major alcohol hot spots: (1) North-East (NE) states, (2) Eastern Peninsular states formed by Chhattisgarh, Odisha, Jharkhand, and Telangana, and (3) Southern states of Tamil Nadu and Kerala. Hot spot analysis strongly correlated with region-wise analysis of SDI. Respondents who consumed tobacco have higher odds  of consuming alcohol. Except for religion and social category, other socioeconomic factors have a low to moderate effect on alcohol consumption.Conclusions: Hot spots and high-risk districts of alcohol consumption identified in this study can guide public health policies for targeted intervention. Alcohol use is at the discretion of individual states and union territories, and stringent anti-alcohol policies strictly enforced across India are the keys to control alcohol use. As per WHO, worldwide, an estimated annual three million deaths and 5.1% of Disability-Adjusted Life Years (DALYs) of the world are attributed to alcohol consumption . AlcoholIn India, alcohol consumption is widespread across all the states and the union territories (UT), and an estimated 160 million consume alcohol . AccordiThe datasets of NFHS-4 carried out by the International Institute of Population Sciences from January 20, 2015, to December 4, 2016, were used for analysis . The datThe outcome of interest in this study is the use of alcohol by the respondents. This was based on questions: 1. Do you drink alcohol? (Response was categorized as “yes” or “no”) and 2. What type of alcohol do you usually drink? The options given were tadi madi, country liquor, beer, wine, hard liquor, and others.The SDI used for this analysis are the place of residence , religion , age-group , states are grouped into region , social group , other backward classes, and others), wealth quintile , marital status , educational level , and occupation . The independent variables were selected after a prior literature search . In the final step, any variable  not selected for the original multivariate model was added back one at a time, with significant covariates and confounders retained earlier. Variables that are significant at the 0.10 level were analyzed in the model. Survey weights computed in the NFHS were used in the analysis. STATA version 16.0 (StataCorp LLC) was used for analyses, and the results are considered significant at alpha level 5%, and 95% confidence intervals are reported for the estimates.Descriptive and bivariate statistical analyses were carried out to estimate the prevalence of alcohol consumption under the different socioeconomic and demographic variables. The missing values (<1%) were not included in the analysis. The variables for the multivariate regression analysis were based on the purposeful selection of variables . At firs*, Geary's C, spline-based models, simultaneous autoregressive models, and Gaussian Markov random field are among the many spatial autocorrelation approaches used for describing how a variable is autocorrelated through space  and p-values (probability) for each district within the context of neighboring districts , beer, wine, or other alcoholic beverages, and the state-wise distribution is given in * statistic to identify statistically significant spatial clusters of alcohol prevalence among men and women. To form a statistically significant hotspot, the district with high prevalence should be surrounded by districts with high prevalence. Based on z-scores and p-values, hot spots of alcohol consumption for men and women are shown with 99, 95, and 90% confidence intervals  and men density (per sq.km) . The anaThe sociodemographic profile of the respondents aged 15–49 years is given in The outcome variable was alcohol consumption . Ten covariates selected through literature are listed in Men (aOR: 5.42) and women (aOR: 4.30) who use tobacco have higher odds of alcohol consumption than those who do not consume. South Indian men (aOR: 1) and NE men (aOR: 0.61) and women (aOR: 2.06) consume more alcohol. The place of residence  influences the consumption pattern in men and the urban population (aOR: 1.18) tends to use alcohol more. Alcohol consumption among women increases with age, and it is higher in women older than 40 years . Alcohol use is higher in men aged 25–39 years (aOR: 1.70). Never-married men consume less alcohol compared to their other counterparts i.e., married (aOR: 1.09) and others (aOR: 1.23).Women from the poorest wealth index (aOR: 1.00) and uneducated background (aOR: 1.00) have a high prevalence of alcohol consumption compared to the richest and highly educated. When the occupation was analyzed, not working men (aOR: 1.00) and women (aOR: 1.00) consumed less alcohol than working counterparts.Among the SDI, religion and social categories had the greatest influence on alcohol use. Compared to other religions, Muslim men (aOR: 0.24) and women (aOR: 0.17) consume less alcohol. Among the different social categories in men, STs (aOR: 1.37) and scheduled castes (aOR: 1.00) have higher alcohol prevalence. Compared to the other castes, ST women (aOR: 5.83) have six times higher odds of consuming alcohol.Alcohol consumption is widespread across India, and an estimated 160 million consume alcohol . In addiNFHS-4 findings indicate that alcohol consumption is seen predominantly in men (29.2%). Peer pressure, habituation, stress, and tiredness from work are attributed as reasons for alcohol use in Indian men . Social The district-wise distribution and subsequent hot spot analysis show that the NE region forms the first hot spot. Almost all the districts of Chhattisgarh, Odisha, Telangana, and Northeastern Andhra Pradesh constitute the second hot spot, and the third hot spot is made up by all the districts of Tamil Nadu and Southern districts of Kerala. The NE region shares porous international borders with Myanmar and Bangladesh, and the easy availability of cheap tobacco and alcohol in NE smuggled through these countries could be a major reason for the high prevalence . Even thOur analysis shows that most of the SDI have a low to moderate association with alcohol use. Religion and social category had a major influence on alcohol consumption. Alcohol use is lower in Muslims , as Islam prohibits alcohol consumption . AlcoholAs the study is based on a cross-sectional survey, a major limitation is it is not possible to measure the direction of association between the sociodemographic characteristics and the outcome variables. Furthermore, alcohol use was self-reported by the survey participants, hence there is a chance of under- or overreporting. Even though the majority of alcohol users in India were men, the sample size of women in the survey was nearly seven times higher than men.Hot spots and vulnerable districts in the hot spots have to be given priority for targeted intervention. Health awareness programs, especially for ST women have to be implemented. Alcohol policy of individual states and UT appears to be the key determinant of alcohol use in India. Unfettered access to alcohol in most states has led to high prevalence. In a low middle-income country like India, the amount of money spent on tobacco and alcohol by millions of Indians on a daily basis would have a direct bearing on the quality of life. Strong anti-alcohol policies have to be implemented strictly across India to protect the financial, mental, and physical health of millions of Indian families.https://www.dhsprogram.com.Publicly available datasets were analyzed in this study. This data can be found at: https://www.dhsprogram.com. Authorization to use the NFHS-4 datasets for this manuscript was obtained from demographic and health survey (DHS) program. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.The studies involving human participants were reviewed and approved by the Institutional Review Board of International Institute for Population Sciences, Mumbai. Verbal, informed consent was obtained from respondents aged ≥18 years. The datasets of this study are available for public use at KB, WP, and PB contributed to the study design, data collection, data analysis, figures, data interpretation, literature search, and writing. RR, SC, and SB contributed to the literature search, data analysis, and interpretation. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


School climate is related to a wide variety of positive results at the school level; however, its relationship with the construct of values has received little attention, despite being a key variable in the development of personality. This study aimed to examine the direct and indirect relationships between school climate, attitudes towards authority, and values. The participants in this study were 2683 students (51.2% men and 48.8% women) from 32 schools aged between 12 and 20 years . Two models of structural equations were estimated, and the model that best fit the data confirmed that school climate was indirectly related to values through attitudes towards authority. The reciprocal and interactive relationships between school climate, attitudes towards authority, and values are also discussed. School climate is a highly relevant variable both to academic research and for society in general. There is a wide range of conceptualizations ,2 regardSchool climate refers to the quality of social relationships and the character of school life ,5,6, andA positive school climate is a very important trait in the promotion of academic success, favoring not only academic achievement ,13,14,15To understand the factors that configure school climate, various investigations have been based on Bronfenbrenner’s ecological systems theory . This thIn the school context, the individual interacts with various variables located at different levels of the ecological system. The microsystem and mesosystem would be located at the proximal level. The school is located within the microsystem, and this subsystem is made up of direct interactions between members of the educational community, which gives character and tone to the school climate . Sex is Thus, in schools with deteriorated relationships, various problems can arise both at the student level, through bullying , school Continuing at the microsystem level, formal authority figures, such as teachers and normative references of the establishment, are highly relevant to adolescent development, since they contribute to adolescents having a better perception of the school climate . The conThe mesosystem is a function of the interactions between members of the educational community and very relevant variables are located in this subsystem. For example, positive relationships between family and teachers promote a better school climate, which subsequently results in a positive impact on student engagement , and botContinuing at the mesosystemic level, a large proportion of the studies on school relationships in adolescence focus on multiple negative factors, for example, drug use, risky behavior, and bullying ,19,45. TIt is in this context where values, understood from a positive adolescent development approach, become relevant. The value construct refers to cognitive representations of socially desired goals, which vary in importance and serve as guiding life principles that identify desired and appropriate behaviors in social interactions . SpecifiValues and their formation are influenced by different social subsystems . At the Further from the individual is the exosystem, which is made up of a series of factors that must be considered as potential influences on school climate. For example, more distant environments such as the neighborhood or type of teaching significantly influence the individual behaviors of the students ,55. RegaConsidering the theoretical and conceptual background previously exposed, this study sought to fill the gaps in school research by examining the roles and relationships of the explanatory factors of the school climate, such as values and attitudes towards authority. While there are studies of great international relevance that highlight the relationship between values and school climate ,4, that N = 486,427). For the selection of the participants, a multistage, stratified probabilistic sampling procedure was used, with a reliability of 99.7%, a standard error of 2.48%, and a variance of p = q = 0.5 [The population was made up of adolescent students from public schools, subsidized private schools, and private schools. All students attended secondary education and lived in one of five macro-zones in Chile  2683 students from 32 schools  of both To answer the research objectives, four instruments were applied simultaneously. A sociodemographic questionnaire consisted of the following closed-ended questions: age, sex, grade, region, area, type of education , type of school .χ2 (df = 76) = 669,238, p < 0.001; CFI = 0.963; TLI = 0.955; RMSEA = 0.055). All factor loadings were statistically significant (p < 0.001). Reliability indices were also satisfactory: SC was linked to a Cronbach’s alpha of 0.780 , and the TC factor was linked to a Cronbach’s alpha of 0.690 .In addition, the Questionnaire to Assess School Climate (CECSCE) was applied. The CECSCE is a scale that evaluates school climate from the students’ perspective and has 14 items that are answered using a 5-point ordinal scale (from 1 = strongly disagree to 5 = strongly agree). Psychometric studies carried out in Chile provide evidence of their psychometric quality ,59. The χ2 (df = 51) = 2351.090, p < 0.001; CFI = 0.955; TLI = 0.942; RMSEA = 0.078). All factor loadings were statistically significant (p < 0.001). Reliability indices were also satisfactory: PV had a Cronbach’s alpha of 0.882 , the SV factor was linked to a Cronbach’s alpha of 0.840 , and IV was linked to a Cronbach’s alpha of 0.873 .A reduced version of the Scale of Values for Adolescent Positive Development (EVDPA-R) was also applied. The EVDPA-R assesses the perceived importance of a set of values based on the adolescent positive development approach. This scale has 12 items that are answered using a 7-point ordinal scale (from 1 = not important to 7 = most important). This scale was used for a psychometric study in Chile  that endFinally, an adapted version of the Attitudes to Institutional Authority in Adolescence Scale (AIA-A) was applied. The AIA-A is a self-report scale that assesses adolescent attitudes towards authority figures. The AIA-A has 9 items that are answered through a 5-point ordinal scale . This scale was used in two psychometric studies in Chile ,61. The χ2 (df = 26) = 453.503, p < 0.001; CFI = 0.968; TLI = 0.955; RMSEA = 0.074). All factor loadings were statistically significant (p < 0.001). AIA-A factors presented adequate reliability indices: positive attitude to authority was linked to a Cronbach’s alpha of 0.716 , and positive attitude to transgression was linked to a Cronbach’s alpha of 0.775 .In this study, the AIA-A presented an adequate construct validity and the confirmatory factor analysis presented adequate goodness-of-fit indices . Results were statistically significant, so, given the absence of multivariate normality, a robust estimator was selected. All models used suitable methods for the analysis of categorical variables, such as a matrix of polychoric correlation matrix and the weighted least squares means and variance adjusted (WLSMV) estimation method. The following goodness-of-fit indexes were used to evaluate the CFA models: WLSMV-χ2, comparative fit index (CFI), Tucker–Lewis index (TLI), and root mean square error of approximation (RMSEA). For CFI and TLI, values equal to or greater than 90 were considered a reasonable fit [Medians, standard deviations, and polychoric correlations between the latent variables were estimated. Furthermore, a confirmatory factor analysis (CFA) and structural equation models (SEM) were evaluated using the MPLUS v.8.1 software  . Structuable fit . For RMSable fit . The varable fit ,67.χ2 (df = 602) = 5243.543, p < 0.001; CFI = 0.941; TLI = 0.935; RMSEA = 0.055 (CI 90% = 0.053–0.056)). As can be seen, the factors personal values and social values did not show direct relationships with school climate. The individualistic values factor presented significant, but very weak relationships with both factors of school climate . Subsequently, in order to improve the fit of the model, non-significant or low-association pathways were eliminated. Under these criteria, the direct relationships between values and school climate were eliminated.χ2 (df = 608) = 5186.944, p < 0.001; CFI = 0.942; TLI = 0.937; RMSEA = 0.054 (CI 90% = 0.053–0.056)). The structural model of mediated relationships presented satisfactory goodness of fit indices  and positively related to favorable attitude towards authority . Consistently, the individualistic values presented negative relationships with positive attitude towards authority  and positive relationships with favorable attitude towards transgression .The direct relationship showed that personal values and social values were negatively related to a positive attitude towards transgression . Finally, favorable attitude towards authority presented significant and positive relationships with teacher climate and school climate . Consequently, the model that best fits the data is the model of mediated relationships . In the case of the teaching type variable (1 = scientific-humanistic), it presented a statistically significant and positive relationship with school climate .As can be seen , the conThis study aimed to examine the direct and indirect relationships between values, attitudes towards authority, and school climate. The findings of this investigation supported three of the four hypotheses. In general terms, the model that best fits the data confirms that values are indirectly related to school climate through attitude towards authority.The first hypothesis posed the following: h1) values have direct and significant relationships with school climate. The findings of this study allowed us to reject h1, since the dimensions involving the construct “values”  did not present a statistically significant path with both factors of school climate. The rejection of the hypothesis of direct relationships between values and school climate can be explained in this way: values, despite being a construct that is formed from multiple interactions with the environment, is nevertheless an individual measure that evaluates attitudes developed at the personal level ,50. On t values hThe second hypothesis posed the following: (h2) values have direct and significant relationships with attitude towards authority. The results allowed us to maintain this hypothesis, as social values and personal values were found to have significant and negative relationships with attitudes towards transgression and positive relationships with favorable attitudes towards authority; in the same way, the individualistic values factor presented positive relationships with the factor attitudes towards transgression and negative relationships with the factor favorable attitude to authority. In this regard, personal values, which characterize adolescents with higher levels of responsibility, moral integrity, and honesty, are positively related to attitudes of respect for authority, which refers to school regulations, teachers, and the police . These rRegarding the third hypothesis—(h3) attitudes towards authority have direct and significant relationships with school climate—the results allowed us to maintain this hypothesis, given that the factor attitude towards transgression presented negative relationships with both factors of school climate. On the other hand, the factor favorable attitude towards authority presented significant and positive relationships with both factors of school climate. In this regard, research in the field suggests that students who show high levels of rule transgression also show high levels of violence towards their parents and low levels of school climate, an aspect that would directly impact their social relationships at school . The resConsidering these antecedents, a positive attitude towards the rules and institutions such as the school and police not only protects children against participation in violent behavior, but also favors academic success  and psycFinally, the fourth hypothesis posed the following: (h4) values have an indirect and significant relationship with school climate through attitudes towards authority. The results suggest maintaining this hypothesis, given that the model with the best fit was the model of relationships between school climate and values mediated by attitudes towards authority. This study highlights that school climate, which is located in the microsystem , is indiIn relation to the theoretical implications of the study, this research contributes to the theoretical development of educational psychology by examining factors that promote or hinder school climate. The interpretive framework that allowed these relationships to be understood is Bronfenbrenner’s theory of ecological systems . In thisThe formation of school climate is complex and can be the result of multiple influences at the proximal and distal levels of the educational system. The characteristics of students, teachers, and staff can be considered as factors related to the internal development of school climate ,69.Another relevant theoretical contribution of the present study has to do with the evaluation of values using the positive adolescent development approach . This apSince school climate is a factor that can be modified ,55, the Schools must assume the permanent role of teaching and cultivating values, an approach which has been called education in values . This apIt is important to note that the intervention work of schools must be rooted in the ecological model  and focuDespite the indirect influence of distal and structural factors located at the exosystemic and macrosystemic level , the schThe results of the present study should be interpreted with caution, because, like most studies of school climate, they follow a cross-sectional design. This is a problem since it is not possible to reflect the dynamic and changing nature of the school climate over time ,18,73. IThe present study has provided an explanatory model of the direct and indirect relationships between values, attitudes towards authority, and school climate. The structural relationship model that best fits the data established that values were indirectly related to school climate through attitudes towards authority, controlling for the effect of sex and type of teaching. This model was necessary to explain the importance of values in the social relations of adolescents, and it provides a relevant interpretive framework for the creation of future intervention programs in the field of educational psychology. When it comes to strengthening school climate in the adolescent stage, these findings generally show the importance of strengthening social values, that is, stimulating participation in social organizations, dedicating time to helping others, and personal values aspects such as honesty and responsibility. One aspect to pursue is decreasing the tendency towards individualistic values, which are those in which adolescents show a tendency towards hedonism, or a tendency towards transgressive negative relationships with their peers and teachers. These factors would be essential components of efforts to indirectly and significantly stimulate healthy relationships in schools.


Objective: Aging with cerebral palsy is accompanied by a declining health and function status across neurological and non-neurological systems. There is a need to understand the shared pathophysiology among comorbidities for adults with cerebral palsy, to inform clinical assessment and guidelines for interventions to improve healthful aging. To begin defining multimorbidity, this study identified the most common comorbidity combinations and their association with mortality among a representative sample of adults with cerebral palsy.Methods: Data from 2016 to 2018 were used from a random 20% sample from the fee-for-service Medicare database. Adults ≥18 years with cerebral palsy and 25 neurological and non-neurological comorbidities were obtained from 2016. Principal component (PC) analysis identified the most common comorbidity combinations, defined as individual PCs. Cox regression estimated the hazard ratio (HR) of 2-year mortality including all PCs and demographics in a single model. To facilitate comparisons, PC scores were transformed into quintiles (reference: lowest quintile).Results: Among the 16,728 adults with cerebral palsy, the most common comorbidity combinations (PCs) in order were: cardiorespiratory diseases, dysphagia, and fluid/electrolyte disorders; metabolic disorders ; neurologic-related disorders ; gastrointestinal issues; and orthopedic-related disorders. During the 2-year follow-up, 1,486 (8.9%) died. In the adjusted model, most PCs were associated with an elevated mortality rate, especially the first PC .Discussion: This study identified the most common comorbidity combinations for adults with cerebral palsy, many of them were deadly, which may inform on the underlying pathophysiology or shared characteristics of multimorbidity for this population. Neurological, functional, metabolic, and psychosocial aspects can manifest in multiple ways for children with cerebral palsy, and collectively increase risk for a variety of morbidities as these children become adults. The declining health and function with age can further complicate neurologic status in cerebral palsy –7, underMultimorbidity, defined as ≥2 morbidities, is a major burden for adults with cerebral palsy, and contributes to their greater healthcare utilization and costs . For exaImportantly, many morbidities can be prevented, delayed, or better clinically managed; yet, the field lacks evidence-based approaches to monitor morbidities for adults with cerebral palsy. To address this need, the Whitney Comorbidity Index (WCI) was recently developed  and valiAs the field progresses, there is a need to understand the shared etiologies and characteristics among groups of comorbidities for adults with cerebral palsy, to ultimately inform clinical assessment and guidelines for interventions. For example, a WCI of 3 identifies those at risk for premature mortality . HoweverPrincipal component analysis (PCA), a data reduction technique, is an excellent analytic approach to identify comorbidity combinations as it explains the variance within a dataset based on inter-correlations among variables. In the context of multimorbidity, PCA allows for meaningful analysis and clinically-relevant interpretation of the most common combinations of comorbidities. Accordingly, using a large, nationwide sample, the primary objective of this study was to perform PCA to identify the most common comorbidity combinations from the WCI present in adults with cerebral palsy. The secondary objective was to determine how each of these comorbidity combinations associated with mortality.Data for this retrospective cohort study came from a random 20% sample of the Medicare fee-for-service claims database. Demographics and the WCI comorbidities were obtained from the year 2016. Death date was obtained from the years 2017 and 2018. As described previously , MedicarThe start date of follow-up was January 1, 2017 for all participants, which allows for a 1-year baseline period . IndividAs epilepsy and intellectual disabilities often co-occur with cerebral palsy and can complicate the individual's medical needs , 19, a sAll-cause mortality up to December 31, 2018 (2-year period) was derived from the Medicare database. Medicare has >99% of deaths validated . Since the WCI includes epilepsy and intellectual disabilities as individual comorbidities, these were removed for the subgroup analysis, thereby totaling 23 comorbidities. All comorbidities were identified in the same manner as cerebral palsy.The WCI comorbidities were previously identified using an iterative process that harmonized bidities . For thePCA is a multivariable data reduction technique that operates in a highly correlated environment to identify inter-correlated variables. In the context of multimorbidity, PCA allows for meaningful analysis and interpretation of comorbidity data by reducing the number of comorbidities into a few inter-correlated combinations. Each comorbidity combination corresponds to a specific principal component (PC) , 22, whiThe resulting PCs for the group analysis provides information on the most common comorbidity combinations. This group-level analysis also provides information on how much of the total variance of the data is explained for each PC, and when more than 1 PC, the cumulative variance explained. In this context, the explained variance provides indirect evidence of the heterogeneity of comorbidity profiles for adults with cerebral palsy, where a low cumulative percent for all identified PCs is indicative of a greater number of ways in which comorbidities are inter-related. PCA also provides individual-level data in a standardized manner, where each person is assigned a PC score for each PC separately with a mean of 0 and standard deviation of 1. For example, if a PC is defined by 4 comorbidities, individuals with 3 or 4 of those comorbidities would have a higher PC score compared to those with 2 or fewer.Cox proportional hazards regression was used to determine the association between each of the identified PCs with mortality. Models were developed to estimate the hazard ratio [HR with 95% confidence intervals (CI)] of 2-year mortality after including all PCs in a single model, while controlling for age, sex, race, and U.S. region of residence. To facilitate comparisons across PCs and enhance clinical interpretation, individual-level PC scores were transformed into quintiles and the lowest quintile (lower likelihood of having the PC's comorbidities) was set as the reference. Interpretation of the HR is the risk of mortality per increase in a comorbidity profile that is more exemplary of that PC.P ≤ 0.05 was considered statistically significant.Analyses were performed using SAS version 9.4  and n = 16,728) and the subgroups, CP only , CP+EP , CP+ID , and CP+EP+ID , is presented in Baseline descriptive characteristics and prevalence of the WCI comorbidities for the entire group of adults with cerebral palsy  of 696 (126) days , 1,486 (8.9%) died from the entire group, 27 (0.1%) were right-censored due to loss of follow-up, and 15,215 (91.0%) were right-censored due to the end of the study period. The follow-up time was similar for the subgroups. In total, 602 (8.0%) died from CP only, 203 (7.8%) died from CP+EP, 260 (9.4%) died from CP+ID, and 421 (11.1%) died from CP+EP+ID.P < 0.05).The adjusted HR of 2-year mortality after including all PCs in the model as quintiles (reference: lowest quintile) is presented in PC1 was significantly associated with an elevated HR of mortality for each subgroup, which included similar comorbidities  across subgroups. Additionally, PC1 had the highest HR for the 5th quintile compared to other PCs for each subgroup. There were unique associations between PCs with mortality for each subgroup.This study identified the most common comorbidity combinations among adults with cerebral palsy with and without co-occurring epilepsy and/or intellectual disabilities, advancing the knowledge of what multimorbidity looks like for this population, and additionally, what it may mean for survival. These findings inform on the potential underlying shared set of etiologies or characteristics among these combinations of comorbidities unique to adults with cerebral palsy. Some comorbidity combinations were more obvious given what is known in the field, such as dysphagia and pneumonia in PC1. Some comorbidity combinations were less obvious, and may inform on new shared pathophysiologic pathways among comorbidities not previously regarded as established knowledge in the field, such as dysphagia and myocardial infarction, or the comorbidities present in PC3 for the entire sample.It is important to note that the most common comorbidity combinations in the entire sample explained ~40% of the total variance. The total variance explained for defining multimorbidity profiles in this study is between that previously reported from other studies among adults without cerebral palsy 15–65%) –26. The –65% –26.As expected, the PCs had general themes, with PC1 including several cardiorespiratory diseases along with dysphagia and fluid/electrolyte disorders. PC1 was also the deadliest for the entire sample and all subgroups. This finding may be in part driven by those with more severe forms of cerebral palsy, as severity of cerebral palsy is associated with a greater risk of respiratory diseases, dysphagia, and premature mortality, as well as mal-nourishment contributing to fluid/electrolyte disorders , 28. TheNotably, this study identified that in the entire sample and each subgroup, dementia and cerebrovascular disease were together in a PC. Further, for the entire group and subgroups with ID, depression was included in the PC with dementia and cerebrovascular disease. There is growing interest in the neurological field in examining dementia risk for adults with cerebral palsy , 8. HoweInterestingly, for the entire sample and for CP+EP+ID, but not the other subgroups, osteoarthritis loaded onto the factor containing cerebrovascular disease, dementia, and depression. While often regarded as an orthopedic or peripheral pain condition, osteoarthritis has been shown to modify brain activity over time, and increase risk of dementia, stroke, and mental health disorders through several pathways, and can also re-direct pain experience and symptoms among centralized brain networks –37. ThisThis study also identified that diabetes and hypertension were present in PC2 in all subgroups, where renal disease was additionally combined in CP only, CP+EP, and the entire sample (for PC3), indicating that these comorbidity combinations are relatively common among adults with cerebral palsy (based on the position of the PC). This is consistent with previous studies that have shown that these 3 morbidities often co-occur in the general population , 24 and For the entire sample, PC6, defined by bone fragility and rheumatoid arthritis, was not associated with mortality. This may shed some light on a newly forming concept in the field that bone fragility is implicated in the pathogenesis of unhealthful aging for adults with cerebral palsy. A recent epidemiologic study have found that sustaining a fragility fracture is a robust risk factor for premature mortality for adults with cerebral palsy, even after accounting for demographics, cardiorespiratory diseases, diabetes, cancer, and kidney disease . AdditioLimitations of this study based on the use of administrative claims data for adults with cerebral palsy has been reported previously , 45–47. This study provides empirical evidence of the most common comorbidity combinations among adults with cerebral palsy with and without co-occurring epilepsy and/or intellectual disabilities, thus enhancing interpretations for the broader adult population with cerebral palsy. This study also observed that many of the comorbidity combinations were associated with an elevated mortality rate, but to a differing degree. Taken together, study findings provide a comprehensive qualitative assessment of multimorbidity, informing on the potential underlying shared etiologies or characteristics of comorbidities unique to adults with cerebral palsy.https://www.cms.gov/Research-Statistics-Data-and-Systems/Research-Statistics-Data-and-Systems.Publicly available datasets were analyzed in this study. This data can be found here: the data analyzed in this study was obtained from the Centers for Medicare & Medicaid Services, the following licenses/restrictions apply: The dataset analyzed in this study may be accessed through a contractual agreement with the Centers for Medicare & Medicaid Services following the payment of an administrative fee. Information about these datasets and requests for dataset access can be found at: DW designed the study, analyzed the data, and wrote the first draft of the manuscript. MS and EH edited the manuscript. All authors conceptualized the study, approve the final version of this manuscript, and agree to be accountable for the content of the work.This work was supported by the University of Michigan Office of Health Equity and Inclusion Diversity Fund (DW). The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.