Despite substantial evidence for sex differences in addiction epidemiology, addiction‐relevant behaviors and associated neurobiological phenomena, the mechanisms and implications of these differences remain unknown. Genetic analysis in model organism is a potentially powerful and effective means of discovering the mechanisms that underlie sex differences in addiction. Human genetic studies are beginning to show precise risk variants that influence the mechanisms of addiction but typically lack sufficient power or neurobiological mechanistic access, particularly for the discovery of the mechanisms that underlie sex differences. Our thesis in this review is that genetic variation in model organisms are a promising approach that can complement these investigations to show the biological mechanisms that underlie sex differences in addiction. Sex differences in addiction are well documented in prevalence, trajectory and treatment outcomes. Human genetic studies require tremendous sample sizes to identify the biological basis of these effects. Control over environmental influences and drug exposure combined with the ease of neuro chemical and neuro anatomical access make model organisms especially suited to reveal the mechanisms underlying sex differences in addiction. Although human genetic studies remain the standard for establishing genetic etiology for a complex neuropsychiatric condition like addiction, studies attempting to investigate sex‐specific differences in processes of addiction largely lack the phenotypic breadth, power and neural tissue access needed to discover the underlying molecular mechanisms. In contrast, genetic studies in model organisms benefit from lower sample size requirements, and a wealth of in vivo technologies for research into mechanisms. Model organism studies have showed or corroborated observations of sex and gender differences in addiction. Therefore, there is potential for genetic mapping of these phenomena to identify mechanisms of sex differences in addiction.In this review, we highlight the epidemiological evidence for sex differences in SUDs, the influence of the environment on such sex differences and the promise and challenge of genetic analysis for discovering the biological mechanisms that underlie sex differences in addiction through the use of human and model organism discovery genetics. We highlight the major gender differences in phenomena related to SUDs, and the progress made toward studying these phenomena in human genetic and conventional model organisms studies. The literature on these phenomena is quite uneven with respect to the classes of drugs investigated across species and genetic backgrounds but show some general phenomena across classes of drugs. In other cases, the magnitude and direction of effects vary across strains and species. We have chosen examples from the literature that reflect this diversity.Overall, few human genetic studies have succeeded in identifying sex‐specific loci for addiction because of inadequate statistical power. Although human genetic studies have been attempted for each major class of drugs, these studies are few in number and sex differences have been studied in even fewer. We have summarized these studies to call attention to this dearth of results. We argue that model organism studies show many sex differences in addiction‐related phenomena, and that with recent improvements to genetic analysis methods in model organisms, these are now amenable to genetic investigation. Model organisms have benefits of (a) increased minor allele frequency, (b) controlled genetic background variation, (c) controlled environments, (d) fully ascertained drug exposure histories and (e) access to neurobiological intermediate phenotypes that serve to increase the observed effect size of genetic loci, revealing previously undiscovered causal mechanisms underlying sex differences that can be further interrogated.2There are many documented differences in drug use and addiction between men and women. The Treatment Episode Data Set provided by the Substance Abuse and Mental Health Services Administration indicates sex differences in prevalence of substance use, the particular substances involved, the age at which drug use is initiated and drug use patterns.3Males and females exhibit differences in the substances they use and in patterns of drug use. For example, men are more likely to smoke marijuana, while women are more likely to use alcohol and prescription drugs, including benzodiazepines and sedatives.Subjective effects of drugs in women are affected by the stage of the menstrual cycle.Treatment outcomes also differ between the genders. Men and women face unique challenges in cessation of drug use. Women tend to enter treatment sooner after becoming substance dependentIn summary, gender differences in human addiction are likely the result of sex‐specific biological mechanisms that interact with sociocultural influences and life stressors that affect individuals of different genders differently. The complex biopsychosocial interactions underlying addiction make discovery of the biological basis of sex differences in humans challenging, yet identifying these biological mechanisms is critical for the development of more precisely tailored preventative and therapeutic interventions.44.1It has long been appreciated that a family history of SUD is one of the strongest risk factors for the development of drug addiction.A small number of these studies have assessed the heritability of substance use by gender of the subjectsGenome wide linkage studies, genome‐wide association studies (GWAS), whole genome sequencing and exome sequencing have begun to identify specific genes and genetic variants that explain the heritability of SUDs, although few such studies have reported gender specific genetic effects Table .4.2Early genome‐wide linkage studies4.3For highly complex diseases like addiction that are influenced by vast numbers of genetic variants, population‐based genome‐wide association approaches are better suited to identify risk loci with relatively small effects compared with family‐based genetic studies,A recent GWAS5Some GWAS studies have incorporated the stress‐related risk factors that contribute to the development of drug addiction and in addiction relapse susceptibility in GWAS. As noted above, the psychosocial and cultural factors that influence addiction epidemiology are largely attributable to stress. Stress increases vulnerability to drug addiction,6Genomic studies examine the abundance of all expressed genes under various conditions in the postmortem human brain. The transition from recreational substance use to addiction is accompanied by drug‐induced neuroadaptations brought about by long lasting expression changes in neural genes.Integrating genome‐wide genetic findings with tissue‐specific gene expression genetics could reveal additional biological mechanisms underlying substance dependence. Using this approach, Huggett and Stallings have identified a SNP associated with cocaine dependence and detected three genes (two loci) underlying this predisposition that displayed robust enrichment in numerous brain regions, including the hippocampus.7In summary, human GWAS have been making inroads into the discovery of genetic influences on addiction. However, identifying the genetic components that influence sex‐specific vulnerability to addiction using GWAS has been particularly challenging because of the high heterogeneity of the population in terms of the environmental factors that influence addiction, from drug exposure and use trajectories to the involvement of interacting histories of stressful life events and other confounding influences of social, economic and cultural factors. As a result of this heterogeneity, the common variants identified for SUD in humans by GWAS have modest effect sizes. The sample size requirements necessary to achieve adequate statistical power to detect the numerous polygenic and small effect sizes associated with complex and highly heterogeneous phenotypes8Despite the inherent challenges of modeling psychiatric disorders like addiction in animals,Model organisms, particularly rodents, have many conserved neurobiological and physiological features of humans and display specific facets of addiction‐related behavior and neurobiological endophenotypes. As such, they can be used to identify causal mechanisms in brain‐behavior relationships, including neurobiological and behavioral consequences of chronic substance exposure.Animal studies allow greater choice over the population's extent of genetic heterogeneity and each population has specific advantages in noise reduction for detection of small effect alleles or a broad survey of very high genetic heterogeneity with variation in nearly every gene in the genome. Minor allele frequencies in even the most complex mouse populations such as the Diversity Outbred (DO) are theoretically 12.5%, providing greater power for detection of variants that influence complex traits in substantially smaller sample sizes than required in human populations.9https://phenome.jax.org) in 2017, 47.5% of all behavioral measures are on males only, 5% on females only and 47.5% on both sexes. Strain by sex differences were observed in 42% of behavioral measures where males and females were included. Of these behavioral measures annotated to the Vertebrate Trait term “response to addictive substance trait,” 39% exhibited strain by sex differences. As this number grows, and as genetic mapping studies in these populations are performed, discovery of mechanisms of addiction‐related sex differences will be increasingly possible.Females have been systematically understudied in neuroscience and biomedical research.Reservations about including females have been based on the assumption that female hormonal cycles introduce substantial “noise” and complicate experimental studies relative to male‐only studies,In recent years, the number of studies that have included female subjects has increased. However, studies that have explicitly investigated sex differences10There are several reviews addressing sex differences in SUD and addiction.Sex differences in behavioral effects of addictive drugs are widely observed, but the extent and magnitude of these differences vary across species, strains and even vendors Table . For exaAlthough the magnitude and direction of the differences may vary in different species and strains, this is a “feature,” not a “bug.” It is precisely this variation that we are harnessing in the use of genetic variation to discover the biological mechanisms of sex differences. The genes and variants detected are likely to interact with stress, as employed in the laboratory and as perceived by the subject. Stress effects vary in their direction with variation in the magnitude of the stressor. Therefore, stress‐related genes may have similar roles across species but the magnitude and direction of the effects may vary. Such was the case observed in studies of melanocortin 1 variation in mice and humans, originally detected in rodent genetic studies of stress‐induced analgesia.11Gene expression analyses in reward‐related regions over time following drug exposure show mechanisms that underlie drug response, drug‐seeking and drug‐taking.12Despite the general consensus that females are more sensitive to many drug effects, some studies report either no difference13Rodent studies allow the evaluation of the nature of sex differences and to what extent they are attributable to chromosomal or hormonal influences. Sex differences are influenced by multiple separable and/or interacting sex‐biasing factors.Quantitative trait locus (QTL) mapping is one of the primary genetic strategies used to show mechanisms underlying sex differences. Modern behavioral QTL studies of sex‐specific loci are readily performed in recombinant inbred mouse strains C57BL/6JxDBA/2J (BXD), experimental crosses of closely related strains (C57BL/6J (B6) and C57L/J (C57)),Minor allele frequencies are typically higher in mouse populations than human populations, rendering possible the detection of small effect alleles. Selective breeding for behavioral phenotypes that correlate with drug‐seeking behaviors enriches and genetically fixes risk alleles.Gene expression is also influenced by sex by genotype interactions, and expression QTL that represent genomic loci responsible for differential transcriptional regulation have been identified. Through correlational analysis, we have been able to move beyond sex‐specific QTL and identify sex‐specific gene expression networks.14Model organisms allow the study of mechanisms of the interplay among, environmental and genetic interactions that contribute to sex differences in addiction vulnerabilities. Modulation of the interactions among stress‐ and drug‐related traits by sex has been investigated in rodents for alcohol use15In conclusion, genetic variation in humans and model organisms can be exploited in complementary ways to reveal the biological mechanisms that underlie sex differences in addiction. The genetic influence of sex differences in addiction‐related behavior can be detected but not readily identified in human genetic studies because of lack of statistical power at current sample sizes, and perhaps more importantly, the tremendous variability in drug exposure, lifetime history of stress and other environmental influences that contribute to human heterogeneity. However, there is now ample evidence for the existence of sex differences, and abundant evidence for genetic differences in stress‐related effects, known to often mediate or modulate sex differences in addiction‐related behaviors. Rodents exhibit many addiction‐related behaviors and sex and strain x sex differences are present in drug‐related phenotypes and predisposing traits such as vulnerability to stress effects on these behaviors. Sophisticated genetic mapping populations, neurobiological and molecular analysis tools are more readily deployed in rodent populations and sex by genotype analyses are more adequately powered as a result of the higher minor allele frequencies present in these populations. Therefore, the biological mechanisms of sex differences in many different processes of addiction are more readily discoverable using model organism genetics. The challenge remains in clearly establishing the meaning of the model—what human traits, including endophenotypes, are conserved? What elements of the biological mechanisms are conserved and which are not? Clearly, the precise genetic variants harbored by one human population or another are not readily found in a rodent population, but many elements of the molecular pathways are. These serve as valuable pointers to the mechanistic basis of sex differences in addiction and their implications for clinical applications in the prevention and treatment of SUDs.


Krishnan et al. use FISH to show that specific forms of RB induce changes in the organization of euchromatin and heterochromatin domains. These changes are visible under the microscope, occur after cell cycle arrest, are separable from senescence, and represent an E2F-independent activity of RB. RB restricts G1/S progression by inhibiting E2F. Here, we show that sustained expression of active RB, and prolonged G1 arrest, causes visible changes in chromosome architecture that are not directly associated with E2F inhibition. Using FISH probes against two euchromatin RB-associated regions, two heterochromatin domains that lack RB-bound loci, and two whole-chromosome probes, we found that constitutively active RB (ΔCDK-RB) promoted a more diffuse, dispersed, and scattered chromatin organization. These changes were RB dependent, were driven by specific isoforms of monophosphorylated RB, and required known RB-associated activities. ΔCDK-RB altered physical interactions between RB-bound genomic loci, but the RB-induced changes in chromosome architecture were unaffected by dominant-negative DP1. The RB-induced changes appeared to be widespread and influenced chromosome localization within nuclei. Gene expression profiles revealed that the dispersion phenotype was associated with an increased autophagy response. We infer that, after cell cycle arrest, RB acts through noncanonical mechanisms to significantly change nuclear organization, and this reorganization correlates with transitions in cellular state. The best-known molecular function of RB (the protein product of the retinoblastoma tumor susceptibility gene) is the regulation of E2F-dependent transcription . E2F conPerhaps less well known is that ChIP-seq experiments show an extensive distribution of RB-binding sites that extends far behind the conventional set of E2F-regulated genes . These sTo answer these questions, we used FISH probes and took advantage of oligopaint technology  to look To assess chromatin organization, we took a visual approach and performed FISH experiments to detect large chromosomal regions. We used probes against two euchromatin regions on chromosome 19  and two heterochromatin regions . These gave two clearly separated signals in nuclei of WT RPE1 cells , but eacTo examine the hypothesis that active RB influences the organization of large chromosomal regions, FISH was performed on cells induced to enter senescence. We chose this cellular context because previous studies have shown that oncogene-induced senescence is RB dependent and have also described reorganization of large chromosomal regions to form senescence-associated heterochromatic foci (SAHF) in some cell types . IR was To ask whether similar changes in organization occur in other forms of RB-mediated arrest, we examined RPE1 cells treated with the CDK4/6 inhibitor palbociclib . PalbociTo test whether active RB is not just necessary but sufficient to cause dispersion, we used a recently described set of RPE1-derived cell lines engineered such that addition of doxycycline (DOX) induces the knockdown of endogenous RB and its replacement by FLAG-tagged versions of RB . With thTo ask whether similar effects of RB activation are visible at other genomic loci, we used two euchromatic probes (19q13.42 probe and 19q13.2) and a second heterochromatin probe that detects the α-satellite regions on chromosome 6. Measurement of skeleton dot lengths revealed that ΔCDK-RB had a consistent effect in all four regions examined . ΔCDK-RBExamination of RB ChIP-seq data revealed an important difference between the euchromatic and heterochromatic regions probed here. While the euchromatic probes 19q13.2 and 19q13.42 contain 119 and 54 RB peaks, respectively (2.52 and 1.99 RB bound loci per 100 kb), the regions targeted by the chromosome 6 and 7 α-satellite probes contain 0 RB peaks and 0 E2F1 peaks . This suSince IR, palbociclib, and ΔCDK-RB expression all cause G1 arrest, it was plausible that chromatin organization might fluctuate during the cell cycle and that the measured effects might reflect a state that is prevalent during G1. To explore this, FUCCI-RPE1 cells  were useDuring the time course of IR-induced senescence in IMR-90 fibroblasts, we noted that dispersion of the FISH signal was not an immediate event. Western blots demonstrate that the kinetics and extent of RB dephosphorylation varies between sites . Since RAlthough, expression of all mP-RBs increased G1 , the mP-We infer that all of the regions examined can be organized into states that have different degrees of compaction/dispersion/scatter. In asynchronously dividing cells, these regions typically form compact foci during S/G2. A minor fraction of cells have a more diffuse organization during G1 or in G1-arrested cells. However, the expression of unphosphorylated RB (ΔCDK-RB), or specific mP-RBs, greatly increased the dispersion/scattering of these regions, effects that were more extensive and more prevalent than the changes seen in other G1 cells. Hence, the diffuse, dispersed, and scattered organization of these chromosomal regions induced by ΔCDK-RB is not a universal feature of G1 cells but increases under specific conditions and is driven by specific forms of RB.ΔCDK-RB increases the skeleton dot lengths of both heterochromatin and euchromatin regions, and visually it enhances the punctation, branching, and length of both kinds of chromatin. Previous studies showed that euchromatin regions tend to contract during replicative senescence, whereas heterochromatin regions expand . Thus, tThe four regions examined by FISH lack any well-studied E2F-regulated cell cycle genes or components of the RB loss gene signature , but theIn addition to E2F, RB interacts with many transcription factors and recruits various activities to chromatin . We usedRB has also been found to associate with type II topoisomerases (TOPIIs) and to facilitate the processing and repair of TOPII-induced double-strand breaks during alterations of DNA topologic states . Interesd-ribofuranosylbenzimidazole (DRB), a general inhibitor of transcription  and used chromatin conformation capture (3C), a proximity-based ligation assay, to look for effects of ΔCDK-RB on intrachromosomal interactions. An interaction map of the region  was usedCollectively, these molecular studies show that the activation of RB changes the patterns of interactions between some RB-bound loci. 3C assays are focused in nature, and a more complete assessment of chromatin interactions will be required to identify the global effects of RB on TAD boundaries and between loci bound (or unbound) by RB. Nevertheless, these results confirm that ΔCDK-RB causes changes in chromatin organization. ΔCDK-RB–induced dispersion does not appear to be driven by classic E2F targets, but it requires HDACs and topoisomerases, two chromatin regulatory activities previously shown to associate with RB.To ask whether the nuclear changes extend beyond the four regions examined by FISH and 3C, the effects of ΔCDK-RB were assessed using whole-chromosome probes. As expected, probes for chromosomes 17 and 19 gave FISH signals that were more extensive than the regional probes, yet they clearly scattered further following ΔCDK-RB expression . QuantifA second feature of the chromosome 19  and 17  FISH sigThese results show that RB does not simply cause reorganization of a few small regions. Instead RB has extensive effects on nuclear organization that are evident across whole chromosomes and even impact chromosome positioning within the nucleus. Using a complementary biochemical method to look for global changes, we treated chromatin with micrococcal nuclease (MNase) and found that ΔCDK-RB expression increased the resistance of chromatin to digestion . Genomewr > 0.6). In contrast, only three transcripts showed a negative correlation , including genes that are required for autophagosome formation and maturation  and the increase in their levels was not due just to defects in autophagosome clearance, we costained for LAMP2  as well. We compared ΔCDK-RB and mP-RB 356, two forms of RB that strongly induce dispersion with mP-RB 780 (largely defective for dispersion) and mP-RB 252 (an intermediate euchromatin dispersion phenotype). Cells were stained after treatment with or without bafilomycin, a vacuolar H+/ATPase inhibitor that prevents lysosomal acidification and allows autophagosomes to accumulate, making it possible to quantify autophagy levels. Time course samples  showed a progressive increase in LAMP2-colocalized LC3B-II puncta in cells expressing ΔCDK-RB and mP-RB 356  increased expression specifically in cells presenting high to medium chromatin dispersion , in a time-dependent manner, upon RB activation  to suppress dispersion. The number of LC3B-II puncta was reduced significantly by etoposide treatment . ConversIn summary, these results show that chromatin dispersion induced by active RB was associated with increased autophagy flux and increased expression of a panel of autophagy-related genes. RB is typically linked to the regulation of cell cycle genes, and this association is notable because it is an unusual signature for an RB-induced phenotype.In this study, we have visualized changes in chromosome and nuclear organization driven by the activation of RB. For years, we and others have speculated that RB might control aspects of chromosome structure, but to date, it had not been possible to observe RB performing this role. Here, using FISH probes, we show that expression of unphosphorylated RB (and specific mP-RBs) causes changes in the organization of euchromatin and heterochromatin domains that are visible under the microscope.Between 48 and 72 h after the induction of active RB, regions of heterochromatin changed in appearance, moving from a compact focus into an undefined shape with multiple domains, fingers, and clusters of foci. In a similar time frame, regions of euchromatin that already had a visibly noncompact organization became more diffuse, with increased numbers of foci that became spaced farther apart. These changes, which we call RB-induced dispersion, were apparent with multiple probes, were seen across whole chromosomes, and when quantified, caused a two- to threefold increase in the mean skeleton dot length of FISH signals. Accompanying these changes, chromosome 19 appeared to alter position so that it localized near to nucleolar structures. This was not a random change, since it was not seen with chromosome 17 probes. Together, these results show that RB-induced arrest involves extensive changes in nuclear organization. Such effects are consistent with the idea that RB is a master regulator of cell proliferation , and theStrikingly, RB-induced dispersion was evident in chromosome domains that are rich in RB-bound loci, as well as those that lack RB-binding sites. RB ChIP-seq data showed that the two euchromatin regions examined here are highly enriched in RB peaks, whereas the two tested heterochromatin regions lacked any mapped RB-binding sites. The fact that all of these regions showed RB-induced dispersion can be interpreted in three different ways. One interpretation is that the reorganization of chromatin domains following the activation of RB is not a direct effect of RB action but an indirect consequence that is triggered by active RB. Thus, dispersion occurs in chromosome domains regardless of the number of RB-binding sites that they contain. An alternative explanation suggested by the work of Dick and colleagues  is that Regardless of the model, the bottom line is that the RB-induced changes in organization are a noncanonical activity of RB . The chaThe finding that some mP-RBs strongly promote dispersion, while others are almost completely deficient for this activity, adds to an emerging view that there is not a single form of functional RB, but many variations of RB that have distinct properties . These fr > 0.6) have RB-binding sites in their promoter or enhancer sequences , and antibiotics . IMR-90 , a human diploid lung fibroblast cell line, was cultured in modified Eagle media supplemented with 10% tetracycline-free FBS and antibiotics (100 units/ml penicillin and 100 µg/ml streptomycin). To serum-starve RPE1 cells, we cultured them for 3 d in DMEM supplemented with 0.1% tetracycline-free FBS, 2 mM L-glutamine, and antibiotics (100 units/ml penicillin and streptomycin).Telomerase-expressing nontransformed human retina epithelial cells (RPE1) were kindly provided by Dr. David Pellman’s laboratory . RPE1 cells were cultured in DMEM  supplemented with 5% tetracycline-free FBS , 2 mM RB1 knockdown or replacement of endogenous by exogenous RB protein in RPE1 cells transduced with pINDUCER11-shRB1 and/or pINDUCER20-FLAG-RB constructs was induced by 2 µg/ml DOX  for 72 h. The above constructs and cell lines were described before by For autophagy time course experiments, ΔCDK RB, WT, and mP-RB cells were induced with DOX for 24, 48, 72, and 96 h. To accurately determine autophagy flux, 100 nM bafilomycin was added to the medium 3, 24, 24, and 24 h, respectively, before fixing for immunofluorescence (IF) experiments. For autophagy epistasis experiments, 100 nM bafilomycin was added 3 h before fixing for IF experiment.Cells were seeded 16 h before siRNA transfection. Cells were transfected with siRNAs against TOPIIβ, TOPIIα, CAPD3, WAPL . The sequences of siRNA pool used for knockdown were TOPIIβ, 5′-GAAGUUGUCUGUUGAGAGA-3′, 5′-CGAAAGACCUAAAUACACA-3′, 5′-GAUCAUAUGGGAUGUCUGA-3′, and 5′-GGUGUAUGAUGAAGAUGUA-3′; TOPIIα, 5′-CGAAAGGAAUGGUUAACUA-3′, 5′-GAUGAACUCUGCAGGCUAA-3′, 5′-GGAGAAGAUUAUACAUGUA-3′, and 5′-GGUAACUCCUUGAAAGUAA-3′; CAPD3, 5′-GAGAUAAGGUCAUCAGUUG-3′, 5′-GAGUCACCCUGGUACCUUA-3′, 5′-UAUGUUUGAUCGUUGCUUA-3′, and 5′-GUACACUGGUGGCAUUUU-3′; and WAPL, 5′-GGAGUAUAGUGCUCGGAAU-3′, 5′-GAGAGAUGUUUACGAGUUU-3′, 5′-CAAACAGUGAAUCGAGUAA-3′, and 5′-CCAAAGAUACACGGGAUUA-3′). After 24 h, transfection medium was removed and replaced with DOX-containing medium to induce either WT RB or ΔCDK-RB expression. After another 24 h, cells were split into new wells. Cells were transfected once more with siRNAs after 60 h of DOX addition. Cells were harvested or fixed after 72-h DOX induction was completed. The final concentration of siRNAs used was 20 nM for all the above gene targets.RPE1 cells were spotted onto slides using cytospin  and fixed with methanol/acetic acid (3:1) solution for 15 min, followed by two washes with 1× PBS for 5 min each. Slides were dried and then washed in 50% formamide (Sigma-Aldrich)/2× saline-sodium citrate buffer (SSCT) at 78°C for 2 min, followed by 50% formamide/2× SSCT at 60°C for 20 min. Probe mix , 2× SSCT, 10% dextran sulfate, and 1 µg RNaseA  was added to the samples, and coverslips were affixed and sealed using rubber cement. Slides were denatured at 78°C for 2 min and incubated at 37°C for 42–46 h. Coverslips were removed, and slides were washed in 2× SSCT at 60°C for 15 min, followed by 2× SSCT and 0.2× SSC washes for 2 min each. Slides were mounted then with Vectashield  and sealed.Coverslips with adhered RPE1 cells were washed with PBS and fixed by sequentially incubating in 70, 85, and 100% ethanol for 2 min each. Coverslips were dried, and probe mix was added for chromosome 7 α-satellite  or chromosome 6 α-satellite . Coverslips were affixed to slides and sealed using rubber cement. Slides were denatured at 75°C and incubated at 37°C for 16 h. Coverslips were removed and washed in 0.25× SSC at 72°C and 2× SSC (with 0.05% Tween-20) for 2 min each. Coverslips were mounted using Vectashield (with DAPI) and sealed.Coverslips with adhered RPE1 cells were washed with PBS and fixed by sequentially incubating in 70, 85,and 100% ethanol for 2 min each. Coverslips were dried, probe mix for chromosome 19 or 17 was added , and coverslips were affixed onto slides and sealed using rubber cement. Slides were denatured at 75°C and incubated at 37°C for 16 h. Coverslips were removed and washed in 0.4× SSC at 72°C and 2× SSC (with 0.05% Tween-20) for 2 min each. Coverslips were mounted using Vectashield (with DAPI) and sealed.Coverslips with adhered RPE1 cells were washed with PBS and fixed with methanol for 10 min at −20°C. Coverslips were washed with PBS two times for 2 min. Cells were permeabilized using permeabilizing buffer (PBS and 0.5% Triton X-100) for 3 min on ice. Coverslips were washed twice with PBS for 2 min. Blocking buffer (1% BSA and 0.05% Tween-20) was added for 1 h. Primary antibodies  were diluted in blocking buffer,added on coverslips, and incubated overnight. Coverslips were washed three times with PBS for 5 min each. Secondary antibodies  were diluted 1:500 in blocking buffer and incubated for 1 h at RT. Coverslips were washed three times with PBS for 5 min and stained with DAPI. Coverslips were mounted using Vectashield  and sealed.A Zeiss LSM 710 Laser Scanning Confocal Microscope and Zeiss Zen software were used to acquire FISH images. Images were captured using parameters for each probe that remained consistent across samples and experiments. Images were acquired at RT, and a Plan-Apochromat 63×/1.40 Oil DIC M27 was used to capture Z-stacks (6–12 sections of 0.5-µM thickness). FITC/Alexa Fluor 488 filter was used to acquire images for chromosome 7 α-satellite, 19q13.42, and whole-chromosome 19 and 17 probes. Alexa Fluor 568/Texas Red filter was used to acquire images for chromosome 6 α-satellite and 19q13.2 probes. Maximum projections of Z-stacks were used for image analysis. No other image processing was performed. Images were analyzed using Fiji (ImageJ) software. To effectively quantify the dispersion phenotype, we used a skeleton dot length plugin, and macros were made to execute the steps outlined in For IF images, a Zeiss LSM 780 Laser Scanning Confocal Microscope and Zeiss Zen software were used to acquire images. A Plan-Apochromat 63×/1.40 Oil DIC M27 was used to capture snaps. Images were quantified using Fiji (ImageJ). Cytoplasmic LC3B-II puncta were counted using Fiji (ImageJ). Four steps were performed to quantify the puncta: (1) individual cells were cropped/segmented out, (2) images were converted into binary scale using the default or moments dark threshold function, (3) background outliers (punta <1 µM) were removed (if any), and (4) puncta were counted by analyze particles function. Macros were made to execute these steps.4, 50 mM ascorbic acid, and 2.5 µM Alexa Fluor 647-azide ) for 30 min and 3 µM DAPI  in staining buffer  for 15 min. FACS analysis was performed using a LSR II flow cytometer (BD Biosciences), and data were analyzed using FlowJo software.After 3 d of DOX induction, RPE1 cells were incubated with 20 µM 5′-ethynyl-2’- deoxyuridine (EdU) for 2 h, fixed with 3.7% formaldehyde in PBS for 15 min, blocked with 3% BSA in PBS for 1 min, permeabilized with 0.5% Triton X-100 in PBS for 30 min, and stained with ClickIT reaction . Lysates were boiled for 10 min and vigorously vortexed in between every 3 min. Lysates were analyzed using Bio-Rad gel and transferred to PVDF membranes . Primary antibodies were diluted in 5% BSA  in TBS containing 0.1% Tween 20 (Bio-Rad). The following primary antibodies were used: WAPL (D9J1U) rabbit monoclonal antibody , TOPIIβ rabbit polyclonal antibody , TOPIIα rabbit monoclonal antibody , CAPD3 rabbit polyclonal antibody , RB1 (4H1) mouse monoclonal antibody , tri-methyl-histone H3 (Lys27) rabbit monoclonal antibody , anti-histone H3 rabbit polyclonal antibody , phospho-histone H2A.X , and anti-FLAG . Secondary antibodies were obtained from Cell Signaling Technologies  diluted in 5% milk at 1:5,000. Molecular weight in the figures was estimated by comparing to molecular weight markers.The assay was performed using Cellular Senescence Assay Kit . Culture plate wells with adhered RPE1 cells were washed with 1× PBS and fixed with 1× fixing solution for 15 min. Cells were washed twice with 1× PBS. 1× SA-β-galactosidase detection solution was added to the cells and incubated overnight in dark at 37°C. Cells were washed with 1× PBS, and the stained cells were imaged under bright-field light microscopy using an ECHO Revolve microscope.RNA was extracted using RNeasy kit  and DNase treatment of RNA was performed before any downstream applications . cDNA was prepared using Taqman Reverse Transcription Reagents . qPCR was performed using Lightcycler 480 SYBR Green I Master  and was run using a Roche LightCycler 480 system. β-Actin qPCR was run as internal control for each sample/time point. qPCR primers used for IMR-90 senescence time course were β-actin_F, 5′-CACCATGTACCCTGGCATTG-3′; β-actin_R, 5′-GTACTTGCGCTCAGGAGGAG-3′; LMNB1_F, 5′-AAGCATGAAACGCGCTTGG-3′; LMNB1_R, 5′-AGTTTGGCATGGTAAGTCTGC-3′; ICAM1_F, 5′-ATGCCCAGACATCTGTGTCC-3′; ICAM1_R, 5′-GGGGTCTCTATGCCCAACAA-3′; HDDC2_F, 5′-GGGCAGCTCAAGAGAGTCC-3′; HDDC2_R, 5′-GGCGTACACATCGGTCTTTGT-3′; CCND1_F, 5′-CAATGACCCCGCACGATTTC-3′; and CCND_R, 5′-CATGGAGGGCGGATTGGAA-3′. qPCR primers used to measure expression of autophagy genes were β-actin_F, 5′-CACCATGTACCCTGGCATTG-3′; β-actin_R: 5′-GTACTTGCGCTCAGGAGGAG-3′; LC3B_F, 5′-ACGCATTTGCCATCACAGTTG-3′; LC3B_R, 5′-TCTCTTAGGAGTCAGGGACCTTCAG-3′; p62_F, 5′-GACTACGACTTGTGTAGCGTC-3′; p62_R, 5′-AGTGTCCGTGTTTCACCTTCC-3′; ATG14_F, 5′-GCGCCAAATGCGTTCAGAG-3′; ATG14_R: 5′-AGTCGGCTTAACCTTTCCTTCT-3′; WIPI1_F, 5′-AACAGGTCTATGTGCTCTCTCT-3′; WIPI_R, 5′-CTCATGGGCAGCAATAGTGC-3′; ATG4B_F, 5′-ATGGACGCAGCTACTCTGAC-3′; ATG4_R: 5′-TTTTCTACCCAGTATCCAAACGG-3′; VCP_F: 5′-CAAACAGAAGAACCGTCCCAA-3′; VCP_R, 5′-TCACCTCGGAACAACTGCAAT-3′; PSAP_F, 5′-ATGCAAAGACGTTGTCACCG-3′; PSAP_R, 5′-GGGAGGTAGGAGTCCACTATCT-3′; SEC23B_F, 5′-GCTGGAGGCTACAAGAATGGT-3′; and SEC23B_R, 5′-AACCTGACAAAGTGGGTTGAG-3′.PLA assay was performed using a Duolink In Situ PLA GREEN kit from Sigma-Aldrich and following manufacturer instructions. WT and ΔCDK-RB RPE cells were incubated at 37°C for 24 and 48 h in the presence of 2 µg/ml DOX on glass coverslips (EdU incubation was performed at 37°C for 30 min in the presence of 10 µM). Cells were then washed twice with cold PBS and fixed with 3.7% formaldehyde in PBS for 15 min at RT and permeabilized for 10 min at RT with 0.5% Triton X-100 in PBS. Blocking was performed with 2.5% BSA in PBS. EdU was detected using Click-iT EdU Cell Proliferation Kit for Imaging, Alexa Fluor 647 . Primary antibody incubation was carried at RT for 2 h using mouse-α-RB  and rabbit-α-TOPIIβ . Cells were then incubated with α-rabbit PLA plus probe and α-mouse PLA minus probe for 1 h at 37°C. Ligation reaction was then performed for 30 min at 37°C followed by amplification reaction for 100 min at 37°C. Finally, coverslips were mounted using SlowFade Diamond Antifade Mountant with DAPI . Images were taken with an ECHO Revolve microscope; images were analyzed and fluorescence was quantified with Matlab software. For analysis, in brief, cell nuclei were segmented using a custom-made image-processing pipeline that can distinguish them from the background. The pipeline identifies the nuclei based on DAPI staining, size, and circularity. Nuclei that were close to image borders or too close to each other and could not be individually segmented were automatically removed by the software. PLA foci numbers and PLA foci and EdU fluorescence intensities were quantified by the software only within the segmented nuclei. Edu-positive cells were classified as S phase cells, and Edu-negative cells were classified as either G1 or G2 cells .p) values to the average Cp values obtained across all tested loci for a particular sample. Fold-changes were calculated using origin ligation product  as the control. Fold-change was calculated using the formula 2(Cp − CpORIGIN), where pC is the normalized qPCR signal obtained for a specific locus, and pORIGINC is the qPCR signal obtained for the origin self-ligation product. Three biological replicates were performed for each sample, and mean fold-change was calculated and plotted. P values were calculated using multiple t test and Holm–Sidak multiple comparison with GraphPad Prism 6.0 software.5 million to 10 million cells were used per experiment, and 3C was done according to the protocol in To determine interactions between TAD boundaries, qPCR primers were designed around HindIII sites located at the edge of TADs. All TAD boundary interactions were measured with respect to the origin, which was TAD4-reg1. The following primers were used: TAD4-reg1 (origin primer), 5′-CATCAGACAAGCCCTCCCTC-3′; TAD4-reg1, 5′-TTGGGCTTTCCTGTGCTTCT-3′; TAD4-reg2, 5′-TTGTCTAAGTCGCTGCTGGG-3′; TAD4-reg3, 5′-ACAGGTGCTTATGTTTGCATTCT-3′; TAD3-reg1, 5′-GTGCAGGGGTACAGAACAGA-3′; TAD3-reg2, 5′-CCAGCCACCGACAATTCCTT-3′; TAD2-reg1, 5′-TATTGGATGCCAGCAGAGGC-3′; TAD2-reg2, 5′-ACGGTATCCCTCTTCCCCTT-3′; and TAD2-reg3, 5′-GGAGAGACAGGAGATGGGGA-3′. For interactions between RB bound sites located in TAD loops, qPCR primers were designed around HindIII sites located within RB binding sites or closest to binding site. All loop interactions were measured with respect to the origin, which was KMT5C. The following primers were used: KMT5C (origin primer), 5′-TGGGACAGCTCCTCTTTCCA-3′; KMT5C, 5′-GGCTTTCTCCCTCCTGTGG-3′; UBE2S, 5′-CCGTCTGCTCACAGAGATCC-3′; U2AF2, 5′-CACAGGCCACAGAATGGTCT-3′; ZNF524, 5′-ACGTACTTGCCCAGACATGC-3′; SSC5D, 5′-TGAAATTGCAACTGAGGGGG-3′; HSPBP1, 5′-GGACCCTATGACGAGCACAG-3′; PPP6R1, 5′-GGGCAGAGTTAGGGTTACAGTG-3′; PTPRH, 5′-TGGGGGAATTTCTAGGGGCT-3′; and PRPF31, 5′-TTTTAAGCCCCTCTCCCCAG-3′.To determine the effect of DNA damage on dispersion, damage was induced in WT RB and DCDK RB–expressing cells using 10Gy ionizing radiation and camptothecin. We chose these damaging agents because they have the ability to generate damage in cells arrested in G1 as well as later stages. Cells were treated with IR after 72-h DOX induction of ΔCDK-RB or WT RB (control). Cells were fixed/harvested 2 h after IR treatment. 1 µM camptothecin was added to ΔCDK-RB or WT RB (control) cells between 48 and 72 h of DOX induction. Cells were fixed/harvested after the completion of 72-h DOX induction. Lysates for Western blot were prepared as noted above, and FISH for the chromosome 7 α-satellite was performed as stated above.2, 0.5 mM dithiothreitol, 1 mM PMSF, and protease inhibitor cocktail ). Cells were allowed to swell by keeping them on ice for 10 min. 400 µl of ice-cold Buffer A + 0.4% NP-40 was added and incubated on ice for 10 min. Cells were centrifuged at 300 g for 5 min at 4°C to pellet nuclei and other fragments. The isolated nuclei were washed with 1 ml PBS without disturbing the pellet and centrifuged at 2,800 g for 5 min at 4°C. The pellet was resuspended in 300 µl of 1× Micrococcal Nuclease Buffer (supplemented with 100 µg/ml BSA) and either 1,500 or 40 units of Micrococcal Nuclease  and incubated at 37°C on a shaker (300 rpm) for various time intervals. 45 µl of reaction was taken out at every time interval, and digestion was stopped by adding 5 µl of 0.5 M EGTA and 200 ng RNase A. Reactions were incubated at 37°C for 1 h and supplemented with 1% SDS and 10 µg of proteinase K  at 55°C for ≥2 h. To extract DNA, an equal volume of phenol/chloroform/isoamyl alcohol  was added to the samples and vortexed vigorously. Samples were centrifuged for 5 min at 16,000 g, and the upper aqueous layer was transferred to a new tube. To precipitate the DNA, glycogen (20 µg), NH4OAc , and 100% (2.5 volume) ethanol was added to the aqueous phase. Precipitated DNA was washed twice with 70% ethanol and resuspended in H2O. DNA was quantified using Nanodrop, and 1 µg of DNA per lane was loaded onto an 0.8% agarose gel.The MNase assay was performed using 2 million RPE1 cells per sample. Cells were trypsinized and resuspended in 400 µl of ice-cold Buffer A   for 30 min at RT and 1% formaldehyde  for 10 min at 37°C. Chromatin was fragmented to a size range of 200–700 bases with a Branson 250 sonicator. Solubilized chromatin was immunoprecipitated with FLAG rabbit monoclonal antibody  or E2F1 rabbit polyclonal antibody  overnight at 4°C. Antibody–chromatin complexes were purified with Dynabeads Protein G . After cross-link reversal and RNase A and proteinase K treatment, immunoprecipitated DNA was extracted with AMP Pure XP beads  and analyzed by SYBR-Green real-time qPCR, along with the input DNA using the Roche LightCycler 480 system . ChIP reU test, and exact P values were reported. For multiple comparisons, datasets were analyzed by nonparametric Kruskal–Wallis–Dunn’s multiple comparison test, one-way ANOVA–Dunnett’s multiple comparison test, and multiple t tests. P values are displayed as ns, P > 0.05; *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001; and ****, P ≤ 0.0001.Figures of quantifications were assembled, and statistics were run in GraphPad Prism 6. For comparisons between sample pairs, datasets were analyzed by the nonparametric two-tailed Mann–Whitney r > 0.6) with chromatin dispersion and RB binding status.Table S1shows the results of GO analysis of transcripts positively correlating with dispersion.Click here for additional data file.Table S2r > 0.6) with chromatin dispersion and RB binding status.lists genes positively correlating (Click here for additional data file.SourceData FS1contains original blots for Fig. S1.Click here for additional data file.SourceData FS3contains original blots for Fig. S3.Click here for additional data file.SourceData FS4contains original blots for Fig. S4.Click here for additional data file.


Eighty-five percent of multiple sclerosis cases begin with a discrete attack termed clinically isolated syndrome, but 37% of clinically isolated syndrome patients do not experience a relapse within 20 years of onset. Thus, the identification of biomarkers able to differentiate between individuals who are most likely to have a second clinical attack from those who remain in the clinically isolated syndrome stage is essential to apply a personalized medicine approach. We sought to identify biomarkers from biochemical, metabolic and proteomic screens that predict clinically defined conversion from clinically isolated syndrome to multiple sclerosis and generate a multi-omics-based algorithm with higher prognostic accuracy than any currently available test. An integrative multi-variate approach was applied to the analysis of cerebrospinal fluid samples taken from 54 individuals at the point of clinically isolated syndrome with 2–10 years of subsequent follow-up enabling stratification into clinical converters and non-converters. Leukocyte counts were significantly elevated at onset in the clinical converters and predict the occurrence of a second attack with 70% accuracy. Myo-inositol levels were significantly increased in clinical converters while glucose levels were decreased, predicting transition to multiple sclerosis with accuracies of 72% and 63%, respectively. Proteomics analysis identified 89 novel gene products related to conversion. The identified biochemical and protein biomarkers were combined to produce an algorithm with predictive accuracy of 83% for the transition to clinically defined multiple sclerosis, outperforming any individual biomarker in isolation including oligoclonal bands. The identified protein biomarkers are consistent with an exaggerated immune response, perturbed energy metabolism and multiple sclerosis pathology in the clinical converter group. The new biomarkers presented provide novel insight into the molecular pathways promoting disease while the multi-omics algorithm provides a means to more accurately predict whether an individual is likely to convert to clinically defined multiple sclerosis. Probert et al. use an integrative analysis approach to identify novel CSF biomarkers from biochemical, proteomics, and metabolomics assays for the prediction of clinically conversion to multiple sclerosis in patients recruited at clinically isolated syndrome onset. The proposed multivariate algorithm significantly outperforms oligoclonal band status for prediction of clinical conversion.  Clinically isolated syndrome (CIS) is the first manifestation of multiple sclerosis (MS) in 85% of patients.,Currently, MS diagnosis relies upon exclusion of other possible diagnoses followed the interpretation of a combination of detailed clinical evaluation, magnetic resonance imaging (MRI), and CSF analysis according to the latest McDonald Diagnostic criteria.,2 weighted MRI lesions,,,,Recognised risk factors of clinically defined conversion from CIS to MS have been identified including younger age of disease onset, male gender,To identify novel predictive biomarkers for the transition to MS we collected baseline CSF samples from 54 patients with CIS with 2–10 years of follow-up to determine clinical conversion. This study represents the first comprehensive multi-omics investigation of CIS to include simultaneously clinical chemistry, metabolomics and proteomics analysis of baseline CSF samples coupled with extensive clinical follow-up and to investigate the power of such biomarkers at predicting clinically defined conversion in an individualized manner. Here we report the capacity of novel biochemical and protein markers to predict the transition from CIS to clinically defined MS. The future use of simple multivariant biomarker algorithms in the clinical care pathway has the evident potential to facilitate the personalization and optimization of therapy for individuals with CIS.CSF samples were collected in the Department of Neurology of the University Hospital Basel, during routine diagnostic measures, as indicated by the treating physicians. Inclusion criteria were as follows: (i) the presence of a monophasic clinical episode suggestive of MS (CIS), not attributable to other diseases Supplementary Fig.Written informed consent was obtained from all patients according to the Declaration of Helsinki. Ethical approval was obtained by the local ethics committee.n = 22 in the converter group would be sufficient.Prior to analysis, a power calculation (PPCA model using the R package MetSizeR) was carried out. This confirmed that a sample size of 40 (20 non-converters and 20 converters) would be sufficient to achieve an FDR cut-off of 0.05 assuming the significance of 10% of variables. These assumptions are in line with our previous omics analysis of MS patient cohorts and indicated that g for 10 min at room temperature, and the cell-free supernatant stored at −80°C within 2 h of collection.3), and total protein concentration (mg/dl). Serum samples were collected at the same visit to calculate the CSF/serum albumin ratio . The integrity of the blood–CSF barrier was determined by calculating the CSF/serum ratio for albumin .CSF samples were centrifuged at 400 2O (pH 7.4) containing 1 mM maleic acid as an internal reference standard. Samples were briefly centrifuged at 3000 × g for 5 min before transferring to a 5-mm NMR tube.On the day of metabolomics analysis, CSF samples were thawed at room temperature and 100 µl was then diluted with 450 μl of 75 mM sodium phosphate buffer prepared in D1H [13C/15N] TCI cryoprobe . The noesygppr1d  pulse sequence was used to acquire 1H NMR spectra with a 2 s presaturation, 32 data collections, a spectral width of 16 ppm, and an acquisition time of 1.46 s. All spectra were preprocessed in Topspin 2.1 ; zero filled by a factor of 2 and multiplied by a 1D exponential corresponding to a 0.3 Hz line broadening. All spectra were baseline corrected with a fifth-degree polynomial and referenced to the lactate doublet at 1.33 ppm. Following visual inspection for errors in baseline correction, referencing, spectral distortion or contamination, the processed spectra were exported to ACD/Labs Spectrus Processor Academic Edition 12.01 , whereby regions of the spectra between 0.83 and 8.47 were split into 0.02-ppm-wide bins. The residual water resonance region (4.13–5.22 ppm) was removed from the analysis. The integral of each spectral bin was calculated and exported as a .csv file for statistical analysis. Metabolite assignment was performed by referencing to literature values,,All nuclear magnetic resonance (NMR) spectra were acquired at 310 K using a 700-MHz Bruker AVIII spectrometer operating at 16.4 T equipped with a ® 8000 modular analyzer  and the Gluc3 and LAC2 assays, respectively. There was a significant correlation between the NMR-determined concentration and the laboratory chemistry determined concentration for both glucose  and lactate  . In order to validate the quantification of the metabolites by NMR, the glucose and lactate levels in all CSF samples were measured using a Cobas< 0.001) and B and< 0.001) and D.® platform from SomaLogic Somalogic Inc, Boulder, Co).Protein biomarker profiling in CSF samples was performed using the SomaScan® is multiplexed proteomic tool that measures more than 5000 protein analytes including 4783 SOMAmers (slow off-rate modified aptamers) that recognize 4137 distinct human gene targets. The SOMAmers are constructed with chemically modified nucleotides that expand the physicochemical diversity of the large randomized nucleic acid libraries from which the SOMAmer reagents are selected. The SomaScan® assay measures native proteins in complex matrices by transforming each individual protein concentration into a corresponding SOMAmer reagent concentration, which is then quantified in customized DNA microarrays. The assay takes advantage of SOMAmer reagents’ dual nature as both protein affinity-binding reagents with defined three-dimensional structures, and unique nucleotide sequences recognizable by specific DNA hybridization probes.,SomaScan® analysis.The CSF samples were stored at −80°C and shipped to SomaLogic  on dry ice for SomaScan‘omics datasets (as used in other similar aforementioned studies), such as those studied here, because it helps reduce the dimension of such datasets by extracting a subset of most salient biomarkers (described as a linear equation) that can subsequently be used to predict the class of interest—in this instance clinical converters and non-converters. OPLS-DA models were validated using an external 10-fold cross-validation strategy with repetition coupled with permutation testing as previously described.R2 and Q2 values are then used to assess the model performance  on the training data. The model generated is then applied to the test set (to which the OPLS-DA model is blinded) to determine the predictive accuracy, sensitivity, and specificity of the model . This process of model training and testing is repeated for a total of 1000 times, thereby creating an ensemble of models. It has been shown that in cases where the sample sizes are small, one can achieve a prediction accuracy of up to 70% or higher by chance alone in differentiating two-classes.P-value 0.05 or less), then the discriminatory variables responsible for the observed class separation are extracted by inspection of the average variable importance (VIP) scores. The VIP score of a given variable represents the mean decrease in accuracy which occurs when that variable is removed from the model. Thus, a variable which is highly significant and plays a large role in the diagnostic accuracy of the model will result in a large decrease in accuracy when removed from the model resulting a large VIP score. Conversely, variables which do not play a role in discriminating between groups have very little effect on model accuracy when removed and have a low VIP score.Multivariate orthogonal partial least squares discriminant analysis (OPLS-DA) was performed in R software ,Elastic Net feature selection was applied to the proteomics data using the glmnet packageIn order to identify the combination of clinical chemistry, metabolomics and proteomics variables with the highest predictive accuracy a combined multi-omics strategy was applied to the selected discriminatory variables  by applying the OPLS-DA cross-validation strategy (described above) to every combination of one to six variables and performing a ROC analysis on each model to assess the performance. The linear combination of metabolites which resulted in the highest performing model  are then reported. There was no significant increase in AUC or accuracy between five and six variables and so linear combinations of variables containing greater than 6 were not pursued.t-tests were used for continuous variables while Chi-square tests were used for categorical variables as appropriate. A Bonferroni correction, to account for multiple comparisons, was applied throughout. Two-tailed P-values < 0.05 were considered statistically significant. Receiver operator curves (ROC), area under the curve (AUC), 95% confidence intervals, optimal thresholds for diagnosis and P-values (relative to a null distribution ROC curve with AUC = 0.5) were calculated for each discriminatory variable using the pROC package.All analysis was performed in R software . Two-sample http://metascape.org].P-value < 0.01, a minimum count of 3 and an enrichment factor (the ratio of observed counts to the counts expected by chance) >1.5 were collected and grouped into clusters based on their membership similarities. Kappa scoresPathway enrichment analysis was performed on the discriminatory proteins identified by the multivariate analysis (described above) using Metascape  and followed up for up to 10 years. Twenty-two patients converted to clinically defined MS (hence forth referred to as ‘converters’) while 32 patients had no signs of further relapses during follow-up (hence forth referred to as ‘non-converters’). The median time to sample collection in both the converter and non-converter groups was 3 weeks and there was no significant difference in the means  still the majority (69%) of the non-converters also tested positive at onset . As a realb. There was no significant difference in the Qalb or total CSF protein concentration between converters and non-converters at onset. Twenty-nine patients (54%) had ‘normal’ (< 4 cell/mm3) CSF leukocyte cell counts at baseline. The highest leukocyte cell count was 28.7 cell/mm3 and 13 (24%) patients (nine converters and four non-converters) exhibiting a cell count above 10 cell/mm3 at onset. The leukocyte cell count was increased (>4 cell/mm3) in a larger proportion of converter patients (72%) than non-converters (41%) . Interestingly, the mononuclear cell sub-population was elevated in the converter group relative to non-converters, while the PMN subset was not significantly altered  and weekly correlated with PMN although this correlation did not reach significance following correction for multiple comparisons . As expected, a strong correlation  between total protein levels and Qalb was observed  cell counts], total CSF protein levels and Q altered . Indeed, altered . Mononucobserved .P-value < 0.001) we then investigated how each of these metabolites would perform in isolation, to determine if measuring a single biomarker could produce sufficient predictive accuracy for use in a clinical setting. Both myo-inositol and glucose CSF levels showed greater specificity for predicting the occurrence of a second clinical attack resulting in improved overall accuracy and AUC compared to OCGB status alone . The multi-variate analysis uncovered a panel of 89 proteins driving the discrimination between converter and non-converter in CIS CSF samples, 72 of which predict occurrence of a second attack with greater AUC (>0.66) than OCGB alone  with an AUC of 0.94; the addition of further variables provided no significant increase in accuracy. The variables selected by the multivariate model with greatest accuracy included MUSK, Ribosomal protein S6 kinase alpha-5 (RPS6KA5), Dynein light chain Tctex-type 1 (DYNLT1), CSF myo-inositol and mononuclear cell levels. While inclusion of mononuclear cell counts gave the highest performance, replacing this measure with leukocyte cell counts resulted in a decrease in accuracy of only 2%. The predictive accuracy of the multivariate multi-omics model greatly outperforms the accuracy of each identified biomarker in isolation . InteresProtein pathway enrichment analysis revealed that the top 89 discriminatory variables are linked through several pathways and physiological functions. The discriminatory proteins upregulated in the converter group relative to the non-converter group are consistent with perturbations in cytokine, TNF, and interferon-gamma signalling pathways along with leukocyte proliferation, leukocyte mediated immune response and chemotaxis . In contThis study provides a detailed exploration of predictive biomarkers in a prospective cohort of 54 individuals with CIS. Due to differences in past McDonald diagnostic criteria and the fact that the 2017 McDonald criteria have low specificity, the occurrence of a second attack was used to define clinical conversion in this cohort. This not only ensures that the predictive biomarkers identified are applicable to the ‘gold standard’ definition of MS, but also ensures clinical utility as those individuals who have a second clinical attack are more likely to develop severe permanent disability than those with ‘silent’ demyelinating events and, thus, should be treated as early as possible.In total, only 41% of the patients recruited had a second attack and converted to clinically defined MS over the course of follow-up, which is somewhat lower than figures reported in a 20-year follow-up study where 63% of patients experienced a second clinical event.Previous reports suggest that between 61 and 68.9% of patients with CIS test positive for OCGB3) in up to 60% of MS cases3) are known to have increased annualized relapse rates.3 predicted transition to clinically defined MS with an accuracy of 65%, although ROC analysis revealed that a threshold of 3.5 cells/mm3 was optimal (CSF leukocyte cell count was moderately elevated (>4 but < 30 cells/µl) in 54% of the cohort at baseline. A larger proportion of the patients who went on to have a second clinical attack (72%) exhibited elevated leukocyte cell counts at baseline when compared to those who did not clinically convert over the follow up period (41%). As a result, a significant elevation in leukocyte cell counts was observed in converter CSF samples relative to non-converters, which is consistent with an increased immune response at baseline in these individuals. Interestingly, when the total leukocyte population was evaluated at the cell-type level, a significant elevation in the mononuclear, but not PMN cells, was observed in the converter group suggesting that the elevated leukocyte levels are dominated by changes in mononuclear cells. Leukocytes are known to be moderately elevated , dynein light chain Tctex-TYPE 1 (DYNLT1), and natural cytotoxicity triggering receptor 1 (NCR1) with AUC values of 0.84, 0.84 and 0.83, respectively. Partial loss of XRCC1 renders brain cells vulnerable to oxidative damageThe extensive biomarker discovery employed here successfully identified clinical chemistry, metabolite, and protein biomarkers of conversion to clinically defined MS, each of which perform well in isolation and provide novel insight into the different pathological mechanisms in converters and non-converters at baseline. Future work, on larger prospective cohorts, will investigate whether the biomarkers identified here could be included in the McDonald criteria to improve specificity when identifying patients at high risk of second clinical attack. In particular, the data presented suggest that a larger follow-on study investigating the impact of leukocyte and mononuclear cell counts on prediction of clinical conversion would be particularly attractive as these measures are routinely available. Ongoing work will validate the identified biomarkers in the further independent cohorts, develop a method to measure the identified biomarkers using a single, cost-effective, assay for use in a clinical setting, and compare this test with other recently identified predictive biomarkers including baseline MRI findings, clinical variables, NfL and IgG/M levels.Brain Communications online.F.P. received funding from the Multiple Sclerosis Society UK (grant 59) and the Medical Research Council (MC_PC_15029). T.Y. is supported by the Ministry of Health, Singapore through the National Medical Research Council Research Training Fellowship (NMRC/Fellowship/0038/2016).Y.Z., M.S., S.A., T.D.W.C., R.H., J.O., D.L. and D.C.A. declare no competing interests. S.A. declares no competing interests. J.K. received speaker fees, research support, travel support, and/or served on advisory boards by ECTRIMS, Swiss MS Society, Swiss National Research Foundation, (320030_160221), University of Basel, Bayer, Biogen, Celgene, Merck, Novartis, Roche, Sanofi. FP has received travel awards from ECTRIMS, Merck, and the Multiple Sclerosis Society UK. T.Y. has received travel grants from UCB, Merck and PACTRIMS, and travel awards from ECTRIMS, ACTRIMS and Orebro University. J.P. is partly funded by highly specialized services to run a national congenital myasthenia service and a neuromyelitis service. She has received support for scientific meetings and honorariums for advisory work from Merck Serono, Biogen Idec, Novartis, Teva, Chugai Pharma and Bayer Schering, Alexion, Roche, Genzyme, MedImmune, EuroImmun, MedDay, Abide ARGENX, UCB and Viela Bio and grants from Merck Serono, Novartis, Biogen Idec, Teva, Abide, MedImmune, Bayer Schering, Genzyme, Chugai and Alexion. She has received grants from the MS society, Guthrie Jackson Foundation, NIHR, Oxford Health Services Research Committee, EDEN, MRC, GMSI, John Fell and Myaware for research studies.fcab084_Supplementary_DataClick here for additional data file.


Two patients experienced post-operative complications. At the one-year follow-up, difference in skin pigmentation was reported in 10 cases, a depressed contour of the ear was found in 4 cases, and moderate ear asymmetry was found in 11 cases. No patient needed a secondary procedure. In conclusion, the proposed reconstructive algorithm represents a reconstructive indication that is simple and characterized by low complication rates and good outcomes for both the patient and the surgeon.Ear reconstructive surgery aims to solve the deformities caused by cancer excision. Despite the numerous surgical procedures described, recreating the complex anatomy of the ear still represents a challenge, particularly for young surgeons. The purpose of this exploratory pilot study is to review our experience with single stage reconstruction of the partial defects of the auricle, and propose an algorithm based on defect size, location, and characteristics. We retrospectively reviewed patients who underwent ear reconstruction after cancer excision at our institution between February 2018 and November 2020. The data collected included patients’ demographics, defect characteristics, reconstructive technique used, complications, and outcomes. The patients were evaluated at a minimum follow-up time of 12 months. Forty-six patients were included in the study. The most common cause for ear reconstruction was basal cell carcinoma. The mean area of defect was 4.3 cm The reconstruction of the auricle represents a complex challenge for the plastic surgeon. The different etiologies, the wide range of reconstructive options available, and the patient’s expectations require a global evaluation of the appropriate surgical technique . MoreoveEar reconstructive surgery aims to resolve the deformities generated by cancer excision, using various techniques capable of restoring the original shape of the auricle. However, some of the surgical procedures described in literature require multiple stages, and this might be an issue for elderly patients ,3. In the present work, we reviewed our experience with single-stage reconstruction of the partial defects of the auricle after cancer excision. The aim of this exploratory pilot study is to show how principles of single-stage ear reconstruction can be applied to most partial defects of the auricle with satisfying results for both the surgeon and the patient, as it allows for a quick recovery with acceptable outcomes. Moreover, we propose an algorithm based on defect size, location, and characteristics to guide the surgeon approaching this topic.In this study, we retrospectively reviewed patients treated with ear reconstruction at our institution between February 2018 and November 2020. The inclusion criteria were auricle reconstruction after cancer excision and a minimum follow-up time of one year. The exclusion criteria were smaller defects treated with primary closure, larger defects requiring major ear reconstruction and multiple-stage procedures, patients who did not attend the follow-up, and lack of documentation. The study was performed with respect to the ethical standards of the Declaration of Helsinki, and all patients signed an informed consent form comprising a photo release section. The data collected from patients included in the study were patients’ demographics , histological diagnosis and defect characteristics , reconstructive technique used, and cancer recurrence and complications. Photographic documentation was also collected. At a minimum follow-up time of one year, post-operative pictures were taken, and patients were reassessed, evaluating aspects such as difference in skin pigmentation between the reconstructed site and adjacent areas, donor site morbidity, altered and depressed contour of the reconstructed site, constriction of the external auditory canal, and ear asymmetry.Intraoperative antibiotic prophylaxis with amoxicillin was administered in all cases. Most procedures were performed under local anesthesia by injecting carbocaine and ropivacaine with epinephrine in the subcutaneous plane. In the case of the local flap, the donor area was also injected. We applied the same protocol even in the case of patients undergoing general anesthesia to prevent massive bleeding and post-operative pain. After surgery, patients were evaluated for early complications.The surgical reconstructive procedures performed in this study included full thickness skin grafts and local flaps. The Antia-Buch flap was performed in the case of defects of the helix greater than 1.5 cm with cartilage involvement. This flap requires incisions to be placed along the helical sulcus inferiorly, superiorly, or in both directions. Incisions should avoid damaging the skin of the posterior aspect of the ear, as this ensures the vascular supply to the flaps. Further mobilization of the upper segment can be achieved through a V-Y advancement flap of the helix root, which allows the advancement of the upper flap to be extended, and, therefore, was used for larger defects. The preauricular flap is designed to exploit the skin laxity of the preauricular area and its design should not exceed a 4:1 length to width ratio. The flap can be harvested thinner in its distal aspect to match the thickness of the skin of the auricle, and thicker at the level of the pedicle to ensure blood supply to the tissues. It can reach the defect at the ear level directly or by creating a tunnel under healthy skin according to reconstructive requirements. In our study, it was used for antihelix, tragus, antitragus, and lobule defects. The retroauricular flap was used for the reconstruction of the marginal defects of the helix without cartilage involvement. The flap is designed on the posterior aspect of the ear and advanced to cover the defect. It provides an excellent skin match without the need for reduction in the size of the auricle. The “revolving door” flap was used to reconstruct the anterior auricular defects of the scapha and concha when the cartilage was removed. The surgical technique involves the rotation of the flap from the retroauricular to the preauricular surface on a vertical axis represented by the neurovascular subcutaneous peduncle at the level of the retroauricular sulcus. The lobular flaps were used for the reconstruction of the lower aspect of the ear  when an excess of skin was found at the level of the lobule. This is usually possible in elderly patients.p < 0.05.Statistical analysis was performed with SPSS software . The relationships between patients’ demographics and complication rates were analyzed by chi-square tests. The statistical significance was defined as From February 2018 to November 2020, a total of 49 patients underwent auricle reconstruction, but three of them were excluded from the study as they died during follow-up, due to circumstances not related to the cause of reconstruction. Among the 46 patients included in the study, 36 were males and 10 were females. The average patient age was 79.9 years (range: 61 to 94 years). Regarding additional patients’ demographics, 14 patients were smokers (30.4%), none of the patients were diabetic, and 16 patients were affected by cardiovascular disease and were on anticoagulants (34.8%). The most common cause for ear reconstruction was represented by basal cell carcinoma (20–43.5%), followed by squamous cell carcinoma (16–34.8%) and precancerous lesions (10–21.7%) .The standard excision limits for the primary lesions were ≥0.5 mm. Among patients affected by squamous cell carcinoma, 11 lesions were 2 cm or less in greatest dimension and 5 lesions were between 2 and 4 cm in greatest dimension. No patients showed lymph node involvement prior to tumor excision nor during follow-up.2. Regarding defect location, in 24 cases the helix was affected, in 14 cases the antihelix was involved, 4 cases required lobule reconstruction, in 2 cases the tumor affected the tragus and antitragus, and in 2 cases the concha was involved (The mean area of defect was 4.3 cminvolved .p > 0.05). The most commonly performed reconstructive procedures were Antia-Buch flap (30.4%), followed by wedge excision (26.1%), preauricular flap (13.0%), retroauricular flap (8.7%), lobular flap (8.7%), full thickness skin graft (8.7%), and “revolving door” flap (4.3%). In terms of post-operative complications, one patient (2.2%) developed a post-operative infection treated with antibiotics, and another patient (2.2%), who was treated with a skin graft and experienced partial graft loss, resolved with secondary healing not requiring additional procedures . The relAt a minimum follow-up time of 12 months , the characteristics related to the outcomes of the reconstruction were evaluated. In particular, a difference in skin pigmentation of the reconstructed site compared to the adjacent areas was reported in 10 cases (21.7%). For none of the cases in which a donor site was used for reconstruction, relevant issues were identified with the donor site itself. A depressed contour of the ear at the level of the reconstruction was highlighted in four (8.7%) cases. No cases of a constriction of the external auditory canal were recorded. In 11 cases (23.9%), however, a moderate ear asymmetry with the contralateral was found. Secondary surgeries were not performed in any cases, neither for enhancing cosmetic outcomes nor due to recurrence of disease .In this study, the defects were reconstructed according to location, size of defect, and tissue involved . In partIn the upper aspect of the ear, it is appropriate to consider cartilage involvement when considering defects affecting the helical region. If it was possible to spare the cartilage, we performed a retroauricular advancement flap by lifting the flap from the retroauricular region and advancing it horizontally until full coverage of the defect was achieved . On the other hand, if the cartilage was involved, the size of the defect was considered. For lesions smaller than 1.5 cm, we performed a wedge-shaped excision of the lesion or its variants (star resection) to reduce the tension resulting from juxtaposing the edges of the defect . For defects larger than 1.5 cm, but less than 40% or the whole length of the auricle, we performed the Antia-Buch flap . If needed, a V-Y advancement flap was performed at the helix root to obtain an additional advancement . Still considering the upper aspect of the ear, if the scapha and antihelix region was involved but not the helix, and the perichondrium was intact, a full thickness skin graft was used to cover the defect. On the other hand, if the cartilage was removed, we performed a ”revolving door” island flap .Regarding the middle aspect of the ear, the same considerations presented before were applied to the helix area. If the defect involved the antihelix but spared the ear margin, a local lobule flap was performed . If the perichondrium was intact, the defects of the concha were treated with a full thickness skin graft. Otherwise, the ”revolving door” island flap was used. Preauricular flaps were used to reconstruct the defects of the tragus, antitragus, and the lobule .Recreating the complex anatomy of the auricle after cancer excision still represents a challenge, particularly for young plastic surgeons. A wide range of procedures have been described to approach this topic, but it is not always easy to determine which is the most suitable for any specific situation. We retrospectively evaluated 46 patients treated at our institution for partial defects of the auricle. The most common cause for ear reconstruction was represented by basal cell carcinoma followed by squamous cell carcinoma. Patients in which the defect was caused by excision of the precancerous lesions were also included, as the reconstructive procedure followed the same principles. Even though conservative treatment of precancerous lesions is usually performed, in our cases the excision was suggested after thorough assessment by a dermatologist due to characteristics of the lesion which could not exclude its malignant nature . In thisAs ear reconstruction after cancer excision is often performed in elderly patients (mean age of 79.9 in our study), it is advisable to keep in mind that a simple yet effective reconstructive procedure, with low risks of complication, is usually the best choice. Interestingly, Sanniec et al. found no statistically significant difference in complication rate in elderly patients compared with younger patients ,9. At a long-term follow-up, we evaluated the quality of reconstruction by analyzing the different factors, such as difference in skin pigmentation between the reconstructed site and adjacent areas, donor site sequelae, altered and depressed contour of the reconstructed site, constriction of the external auditory canal, and ear asymmetry. In terms of the difference in skin pigmentation, this was noticed in 10 cases (21.7%) which were reconstructed with full thickness skin grafts and preauricular flaps. Skin grafts represent a simple and reliable procedure when only the skin is resected and the underlying perichondrium is intact, but they usually determine color mismatch and a longer healing time ,11. WhenIn four cases (8.7%), a depressed contour of the ear at the level of the reconstruction was noticed. It must be stated that these were cases reconstructed with the Antia-Buch flap for defects greater than 2.5 cm. According to a cadaveric study performed by Calhoun et al., the helical rim advancement flaps provide satisfactory closure of helical rim defects up to at least 2 cm, but also longer in some cases, preserving the normal anatomic appearance . This isIn our study, no case of constriction of the external auditory canal was recorded. This is a complication which may affect the reconstruction of the defects involving the concha . In our Regarding ear asymmetry, even though a mild asymmetry was noticed at the follow-up of 11 patients, this was not found to be an issue as the vertical height of the helix root was maintained, hence allowing the patients to wear glasses. As no secondary procedures were performed to enhance cosmetic outcomes, we believe that our tailored approach to partial defects of the ear was successful in addressing common issues in auricle reconstruction. The selection of a single stage procedure was enthusiastically accepted by the elderly patients and, in our opinion, represents a valuable solution, especially during the COVID-19 pandemic, as it reduces patient access to hospitals ,24,25,26The aim of this paper is to present the authors’ experience with the tailored reconstruction of the complex structures of the face, which carry both an aesthetic and functional burden, as previously discussed in studies and reviews on the topic ,29,30,31The high anatomical complexity of the skin and cartilage of the auricle is an issue when addressing ear reconstruction. Since these procedures are often required in elderly patients, a simple yet effective and reliable approach is advisable. The goal of this exploratory pilot study is to propose a single-stage reconstructive approach for the elderly patients which is easy to apply, with low complication rates and good outcomes for both the patient and the surgeon. For this reason, we propose an algorithm to choose the most suitable technique for each specific partial defect based on size, location, and tissue involvement.


Late-night overeating (LNO) is associated with several cardiovascular disease (CVD) risk factors. Limited data exist regarding the association between late-night (LN) systematic food consumption, LNO, and LN poor food quality with subclinical vascular damage (SVD) which precedes the onset of CVD. This study aimed to investigate the above associations with SVD in a large sample of adults, free of established CVD, with one or more CVD risk factors. In total, 901 adults  underwent anthropometric, dietary  and vascular assessment. LN systematic eating was defined as consumption of food after 19:00 h in both dietary recalls and LNO was defined as systematic consumption of >40% of daily total energy intake (dTEI) after 19:00 h. Systematic LN food consumption was inversely associated with diastolic blood pressure (DBP) ) after adjusting for age, sex, hypertension, diabetes, dyslipidemia, smoking, BMI and dTEI. LNO was positively associated with existence of carotid plaques ), while LN increased consumption of red meat, refined grains and wine and low consumption of whole wheat grains was positively associated with Aix (Augmentation Index) ), after adjusting for all the mentioned confounders. Systematic LN eating is associated with lower DBP while systematic LNO and consumption of poor-quality food late at night, is associated with SVD. Further research is needed to define more accurately the impact of LN eating habits on vascular health. Late-night overeating is considered the consumption of a large amount of daily total energy intake (dTEI) late in the day and/or during the night. However, so far there is no standardized and widely acceptable definition for late-night overeating. Different approaches have been suggested in relevant studies, such as intake of >25% of dTEI between 19:00 to 4:59 h or ≥33% at night, an occasion of eating in between sleep, consumption of dinner within two hours before bedtime, and intake of >35% of dTEI after 20:00 h ,7,8.Subclinical vascular damage (SVD) precedes the onset of CVD; therefore, its detection can contribute to identifying potential risk factors and improving timely prevention strategies . VasculaLate-night overeating has been associated with several CVD risk factors, such as obesity, hypertension, glycemic and lipid profile disorders and increased CRP ,12,13,14The present study has a cross-sectional design, and it was conducted at the Laiko University Hospital, Athens, Greece. The study is in accordance with the Declaration of Helsinki and its later amendments, it was approved by the Bioethics Committee of the University Hospital and all participants signed a written consent form before entering the study.The study sample consisted of 901 adults  with one or more CVD risk factors  but free of established CVD. Individuals with cancer, liver disease, gastrointestinal health issues or eating disorders were excluded from the study. All patients abstained from food, drink, or any medication for 12 h prior to their visit in the laboratory for the conduction of anthropometric measurements and vascular assessment.2) (Kg/m2) and used as a marker of obesity. Family history of premature CVD was defined as the presence of coronary heart disease in a 1st degree relative under the age of 55 years for males and 65 years for females. Hypertension was defined by the use of antihypertensive drugs and/or office blood pressure measurement higher than 139/89 . Dyslipidemia was defined on the basis of treatment with lipid modifying drugs or low-density lipoprotein cholesterol level > 160 mg/dL. Current smoking was defined by the use of at least 1 cigarette per day each day of the week; ex smoking was defined as discontinuation for more than 6 months. Body mass index was calculated as weight/, height (by stadiometer SECA 213) and BMI were measured.The study’s participants underwent vascular assessment, performed by the same examiner. BP measurement was conducted three sequential times, after a 10-min rest of the volunteers in the supine position. The average of the three measurements was used as the final BP value. Central pressures, pressure wave reflections by AIx and carotid to femoral PWV were measured by tonometry methods in order to assess aortic stiffness, as described previously . SubclinFollowing vascular assessment, two 24 h dietary recalls were conducted (one weekday and one weekend day) with an interval of at least one week in between, by trained dieticians. Data were analyzed in food groups, where separation was based on food equivalents with further separation in subgroups where needed. Analysis of energy content, macronutrients and micronutrients was also conducted by using the Nutritionist pro Software . Regarding late-night overeating, food consumption data for the evening hours were recorded separately. Energy intake, macro- and micronutrients consumed after 19:00 h and their percentage in dTEI were calculated. Systematic late-night food consumption was defined as food intake after 19:00 h in both dietary recalls and non-systematic late-night food consumption was defined as food intake after 19:00 h in 0 or 1 dietary recall. Systematic late-night food consumption was further divided into systematic late-night eating (consumption of <40% of dTEI after 19:00 h) and systematic late-night overeating (consumption of >40% of dTEI after 19:00). Quality of dinner among systematic late-night food consumers was performed using 3 dietary patterns, which statistically emerged a posteriori (description below) based on the food consumed after 19:00 h.p ≤ 0.05 for all analyses.Statistical analyses were conducted using statistical package 21.0 . Level of statistical significance was set at t-test analysis was applied to address differences between non-systematic late-night food consumption and systematic late-night food consumption, and between systematic late-night eating and systematic late-night overeating. Multiple linear and logistic regression analysis was performed to determine the independent associations between vascular biomarkers and (i) systematic late-night food consumption (vs. non-systematic), and (ii) systematic late-night overeating (vs. systematic late-night eating). Results are presented as standardized beta coefficients and 95% confidence intervals (CI)  or as odds ratio (OR) expressed as ExpB and 95% CIs . Three different models were applied to assess the above associations: Model 1: adjusted for age and sex;Model 2: adjusted for age, sex, hypertension, diabetes mellitus, dyslipidemia, smoking and BMI;Model 3: adjusted for age, sex, hypertension, diabetes mellitus, dyslipidemia, smoking, BMI and dTEI.The normality of distribution of variables was checked using the Kolmogorov–Smirnov test. Categorical variables are presented as relative frequencies (%) and continuous as mean ± standard deviation (SD). Paired Principal Components Analysis (PSA) was applied to identify dietary patterns (DP) among study participants with systematic late-night food consumption. The Kaiser–Meyer–Olkin (KMO) criterion was applied, and it was equal to 0.592. The orthogonal rotation (varimax option) was used to derive optimal non-correlated components (DPs) and the Bartlett’s method was used to estimate factor scores. The selection of the optimal number of components was based on an eigen value > 1 (Kaizer criterion) and it was further corroborated by visual assessment of the scree plot, retaining only components on the steep slope. If one of the initial variables correlated with more than one component, it participated in the interpretation of the DP in which it displayed the highest coefficient value. Three DPs emerged after PCA. Multiple linear and logistic regression analysis were applied to determine possible associations between DPs and subclinical vascular biomarkers. Results are presented as standardized beta coefficients and 95% confidence intervals (CI)  or as odds ratio (OR) expressed as ExpB and 95% CIs . The aforementioned models that were applied in food quantity analyses, were also applied in this analysis, in order to assess the above associations.2 with at least one risk factor for CVD. One hundred and seventy-eight participants were non-systematic late-night food consumers and 723 were systematic late-night food consumers. Systematic late-night food consumers were divided into two subgroups: 572 systematic late-night eaters (<40% of dTEI) and 151 systematic late-night overeaters (>40% of dTEI). After an unadjusted T-test comparison, systematic late-night food consumers present with significantly better values of several vascular biomarkers  compared to non-systematic late-night food consumers. Systematic late-night food consumers of <40% of dTEI had significantly better values of HDL and LDL cholesterol, but higher values of PWV and AIx compared to late-night overeaters. Descriptive and clinical characteristics of study’s participants are presented in The study sample consisted of 901 CVD free adults , with mean age 52.4 ± 13.81 years, mean BMI 28.0 ± 5.53 Kg/mt-test comparisons. No statistically significant differences were observed between dTEI (p = 0.172) and macronutrients intake expressed as percentage of dTEI  between systematic vs. non-systematic late-night food consumers. However, in systematic late-night food consumers lower consumption of legumes (p = 0.006) and higher consumption of cheese (low (p = 0.012) and full fat (p = 0.027)) was observed. On the other hand, systematic late-night overeaters had significantly higher dTEI compared to systematic late-night food consumers of <40% of dTEI , but lower percentage of carbohydrates . Moreover, systematic late-night overeaters tended to have worst diet quality compared with systematic late-night food consumers of <40% of dTEI, consuming less low-fat dairy products , fruits and fresh juices and more refined grains , dressings and soft drinks with sugar .Dietary intake of the study participants is presented in The associations of subclinical vascular biomarkers with systematic food consumption late at night compared to non-systematic food consumption late at night, and with systematic late-night eating compared to systematic late-night overeating, are presented in p = 0.094) after adjusting for age, sex, hypertension, diabetes mellitus, dyslipidemia, smoking and BMI. However, after adjustment for dTEI, DP3 was positively associated with AIx , p = 0.028). In further analysis, after adjusting for age, sex, BMI, dyslipidemia, diabetes mellitus, hypertension, smoking and total energy intake after 19.00 (instead of dTEI), the positive association with Aix remained unaltered , p = 0.019) (data are not presented in tables).Finally, In the present study, we aimed to examine the potential associations of eating late at night with SVD, by taking into consideration two major parameters of eating, i.e., total energy intake (food quantity) and dietary patterns  in adults with CVD risk factors. Our findings suggest that systematic late-night overeating (consumption of >40% of dTEI late at night), non-systematic late-night food consumption and low-quality food choices late at night is associated with SVD. Regarding food quantity, a negative association between systematic late-night food consumption and DBP, and a positive association between late-night overeating with the existence of carotid plaques was observed. Furthermore, regarding food quality, there was a positive association between the dietary pattern characterized by high consumption of red meat, wine, refined grains and low consumption of whole wheat grains with AIx.To begin with, there was no difference in dTEI between systematic or non-systematic late-night food consumption. However, dTEI was significantly higher in systematic late-night overeaters compared to systematic late-night food consumers of <40% of dTEI. Our findings are in accordance with the results of another cross-sectional study, where it was also found that late-night overeating was associated with high total caloric intake . RegardiNon-systematic compared to systematic late-night food consumers had higher BP levels ; however, this might be attributed to the high percentage of hypertension in the study’s participants. In a retrospective cohort study, Kakamu et al. found that eating before bedtime could be a risk factor for hypertension in normotensive elderly , but theLate-night overeating was positively associated with the existence of carotid plaques. There is only one available study examining late-night overeating and its association with SVD, but their analysis showed a positive association only with PWV . There aRegarding the quality of food in systematic late-night food consumers, it was found that a DP characterized by increased consumption of red meat, wine, refined grains and decreased consumption of whole grains was positively associated with AIx, regardless of various confounders, such as age, BMI, dTEI, and energy intake of any meal after 19:00 h. Since the presented association is independent of dTEI and energy intake after 19:00 h, quality of food after 19:00 h, independently of quantity is associated with AIx. Although, there is no available study examining consumption of the above DP with AIx, components of the DP have been studied separately. Red meat consumption in young students was positively associated with AIx . ExcessiThe present study is one of the few available studies to examine the associations of (i) systematic vs. non-systematic late night food consumption, (ii) systematic late-night food consumption of <40% of dTEI vs. systematic late-night overeating and (iii) the quality of food consumed late at night among systematic late-night food consumers, with different SVD biomarkers. In terms of nutritional evaluation, the 24-h recalls which were applied in our study, are based on participants’ memory, thus they are prone to recall bias. However, in our study, we use the multiple pass method during the dietary assessment, in order to minimize the potential error. Furthermore, 24-h recalls remain a valuable tool for dietary evaluation in studies, as they not only allow the assessment of dietary intake of the population, but they also provide the advantage of additional information recording, regarding the overall quality of nutrition. It is also important to note, that dietary intake analysis was conducted by Nutritionist Pro, a program that mainly contains American foods, and there may be variation in food composition from country to country. However, a variety of common greek foods and their nutritional analysis were registered into the program, to minimize possible declinations. Furthermore, regarding vascular assessment, it was conducted by the same examiner with the same equipment, thus reducing any possible measurement error. Another possible limitation of the study is that the majority of the participants were late-night eaters. However, this reflects a very typical dietary behavior in Greece as late-night overeating is very common among Greeks. Lack of data on participants’ physical activity is also a limitation of the present study. Finally, although the cross-sectional design of this study does not allow to establish a causative relationship, we performed a statistical analysis with detailed adjustment for all potential confounders, in order to limit potential bias, as possible.In conclusion, both quantity and quality of food late at night are associated with SVD. Regarding the quantity of food, late-night overeating was positively associated with the existence of carotid plaques and non-systematic late-night food consumption was associated with higher DBP levels. In terms of food quality of systematic late-night food consumers, a dietary pattern consisting of increased red meat consumption, wine, refined grains and reduced consumption of whole grains was positively associated with AIx, therefore with arterial stiffening. As eating late at night concerns many people due to the modern way of life, further investigation is necessary in order to identify dietary behaviors and/or food choices late at night which will be possibly associated with improved vascular health and consequently will enable CVD prevention.