Ox) concentration is associated with cardiovascular mortality in dialysis patients. We assessed the associations between SOx and CAC or CVD events in Japanese hemodialysis patients. This cross-sectional and retrospective cohort study was done in 2011. Seventy-seven hemodialysis patients’ Agatston CAC score was measured, and serum samples were collected. SOx concentrations were measured in 2021 by using frozen samples. Also, new-onset CVD events in 2011–2021 were retrospectively recorded. The association between SOx concentration and CAC score ≥ 1000, and new-onset CVD events were examined. Median SOx concentration and CAC score were 266.9 (229.5–318.5) µmol/L and 912.5 (123.7–2944), respectively. CAC score ≥ 1000 was associated with SOx . The number of new-onset CVD events was significantly higher in patients with SOx ≥ median value . By Cox proportional hazard models, new-onset CVD events was associated with SOx ≥ median value . SOx was associated with CAC score ≥ 1000 and new-onset CVD events in Japanese hemodialysis patients.Coronary artery calcification (CAC) is associated with cardiovascular disease (CVD). CAC might contain calcium oxalate, and a high serum oxalate (S Vascular calcification, especially coronary artery calcification (CAC), is associated with CVD in ESRD patients3. Especially, “very high” CAC individuals, defined as Agatston CAC score4 ≥ 1000, are increasingly recognized as higher risk for CVD events and mortality7. A large body of evidence suggests that dysregulation of calcium and phosphate homeostasis, which are factors related to mineral and bone disorder in chronic kidney disease patients, has direct effects on vascular smooth muscle cells and promotes vascular calcification8. Indeed, vascular calcification in ESRD patients is formed from hydroxyapatite and calcium phosphate, both of which contain phosphate10. Phosphate binders and other treatments are available for the control of mineral and bone disorders11; however, even after these treatments, the prevalence of vascular calcification is still higher in ESRD patients than in the population with normal kidney function.Cardiovascular disease (CVD) is a major complication of end-stage renal disease (ESRD)13 and ESRD patients14 indicate that CAC contains calcium oxalate crystals. Oxalate is an organic acid abundant in plants such as spinach, where it acts as a controller of calcium concentration15. However, in humans, it is a waste product16. Urine is the major excretion pathway of oxalate17, meaning that patients with decreased kidney function also show elevated serum oxalate (SOx) concentrations, which can be 20–100 times normal in ESRD patients18. SOx is a small molecule, and its serum concentration can be reduced by approximately 90% per hemodialysis session, although it can easily rebound to its pre-dialysis level; for example, at only 2 h after hemodialysis treatment, SOx can already be back at its mid-dialysis level19. Excess SOx combines with calcium to form calcium oxalate crystals that are deposited in various tissues; for example, myocardium, renal tubules, and interstitium21. A recent study has reported a relationship between high SOx concentration and CVD mortality, especially in dialysis patients with high SOx concentrations22. This suggests that controlling SOx could be a novel strategy to reduce the risk of CVD in ESRD patients. However, the mechanism of how oxalate affects CVD is currently unknown, limiting the rational development of an effective therapeutic approach.Studies in non-ESRD patientsOx and CAC and the association between SOx and CVD events in Japanese hemodialysis patients. First, we conducted a cross-sectional analysis to understand more about the relationship between high SOx concentration and CAC ≥ 10007. Then, we conducted a retrospective cohort study to understand more about the relationship between SOx and CVD events.Here, we conducted analyses to examine the association between SOx concentrations and were excluded from the study. There were no missing values except for ankle-brachial index of two cases. Of the remaining 60 patients, 41 (68.3%) were male, mean age was 63.1 ± 11.9 years, and median dialysis duration was 87.6 (44.1–152.2) months. Twenty-one patients (35%) had diabetes mellitus and 10 (16.7%) had CVD. Median SOx, Agatston CAC score, and major artery calcification volume were 266.9 (229.5–318.5) µmol/L, 912.5 (123.7–2943.8), and 7.0 (3.0–18.4) cm3, respectively; these data were right skewed. When the subjects were divided into two groups according to CAC score ≥ 1000, baseline CVD, serum phosphate, low-density lipoprotein (LDL) cholesterol, and major artery calcification volume were significantly higher in the CAC ≥ 1000 group. Table Of the 77 patients that underwent the voluntary atherosclerosis checkup, 17 had normal SOx and vascular calcification, we performed a logistic regression analysis. The patients were stratified into those with CAC score < 1000 and those with CAC score ≥ 1000. All of the patients with CVD (n = 10) were in the CAC score ≥ 1000 group; those patients were excluded from this analysis, leaving 50 patients in the analysis group.To examine the association between S P value < 0.2 were SOx concentration, male, body mass index (BMI), serum phosphate, intact parathyroid hormone, triglyceride, LDL cholesterol, high-density lipoprotein (HDL) cholesterol, uric acid, and blood urea nitrogen (Table Ox , male , serum phosphate , intact parathyroid hormone , and LDL cholesterol  were selected as factors associated with CAC score ≥ 1000 ; the area under the receiver operating characteristic (ROC) curve was 0.87 (95% CI 0.76–0.98), and sensitivity and specificity were 81.8% and 76.5%, respectively , age , and beta-2 microglobulin  were selected as factors associated with major artery calcification volume  patients with SOx greater than or equal to the median value, whereas new-onset CVD events occurred in only 10/30 (33.3%) patients with SOx lower than the median value; the risk of new-onset CVD events was greater in the patients with SOx greater than or equal to the median value than in those with SOx lower than the median value [hazard ratio (HR) 2.71, 95% CI 1.26–6.16, P = 0.008; Fig. Ox greater than or equal to the median value and in 7/20 (35.0%) patients with SOx lower than the median value, respectively. The number of events was significantly higher in the patients with SOx greater than or equal to the median value than in the patients with lower than the median value  were censored due to transfer to another dialysis center, three patients (5.0%) were censored due to kidney transplantation, and nine patients (15.0%) died before experiencing a CVD event. A total of 29 (48.3%) patients experienced new-onset CVD events. We divided the subjects into two groups using the median SOx and new-onset CVD events. In a univariate analysis, variables with a P value < 0.2 were SOx greater than or equal to the median value, age, male, history of CVD, alkaline phosphatase, HDL cholesterol, and hemoglobin , history of CVD , and HDL cholesterol  were selected as factors associated with new-onset CVD events , whereas that of the whole Japanese dialysis population was 60.0%31. It should be noted that the prognosis of even health-conscious ESRD patients is poorer than that of the general population, meaning that strategies to lower SOx concentrations may bring about desirable outcomes even in well-managed ESRD patients.There are several limitations to the present study. The first is that we measured SOx concentration was associated with Agatston CAC score ≥ 1000, major artery calcification volume, and new-onset CVD events in Japanese ESRD patients. A novel strategy for SOx control may bring a better prognosis to ESRD patients.High S2. Blood and dialysate flows were 180–250 mL/min and 500 mL/min, respectively, with a constant ultrafiltration rate. The dialysate bath comprised 140 mmol/L sodium, 2.0 mmol/L potassium, 2.5 mmol/L calcium, 1.0 mmol/L magnesium, 8.0 mmol/L acetate, 25.0 mmol/L bicarbonate, and 150 mg/dL glucose . From April 2011 to March 2012, 77 subjects voluntarily received an atherosclerosis checkup in which the patients were examined by multidetector spiral computed tomography (MDCT), and Agatston CAC score4 and major artery calcification volume were determined by a skilled radiologist; further details are provided in the section titled “MDCT and Measurement of CAC and Major Artery Calcification Volume”. We did not calculate the sample size. We included all the participants who voluntarily received an atherosclerosis checkup from April 2011 to March 2012 with consent (N = 77) to this study. Patients without consent were excluded. All of the study participants provided written informed consent at the time of the atherosclerosis checkup in 2011, and we afforded them the opportunity to opt-out from this secondary analysis in 2021. The data were collected by using the hospital chart from January 2021 to May 2021. The study complies with the Declaration of Helsinki, and the study was approved by the Ethics Committee on Human Research at Heisei Hidaka Clinic  and the Ethics Committee on Clinical Research at Teikyo University . Confirms that all experiments were performed in accordance with relevant named guidelines and regulations.A total of 540 ESRD patients undergoing maintenance hemodialysis at a single hemodialysis center  were enrolled in the study Fig. . All subOx concentration was measured retrospectively by a laboratorian not directly involved in the planning of the study by using frozen serum collected around the atherosclerosis checkup date and stored at − 80 °C. Because our study targeted subjects with higher SOx concentrations, we excluded patients with SOx concentrations in the normal range (< 181 µmol/L) at the time of analysis. Detailed methods of SOx measurement were provided in the “Sample handling and measurement of SOx” section.In January 2021, SOx and vascular calcification, we conducted a cross-sectional analysis using data collected at the atherosclerosis checkup . Laboratory data were also collected from medical records at the start of the dialysis session with the longest interdialytic interval around the atherosclerosis checkup date. We divided subjects into two groups: those with CAC score < 1000 and those with CAC score ≥ 1000 to define “very high” CAC7 group. The predictor was SOx, and outcome was Agatston CAC score ≥ 1000. A logistic regression analysis was performed for the association between SOx and CAC score ≥ 1000. We further analyzed the relationship between SOx and major artery calcification volume in addition to the relationship between SOx and CAC. Potential confounders36 were age, gender, hemodialysis duration, history of CVD, diabetes mellitus, BMI, ankle-brachial index, albumin-adjusted calcium, serum phosphate, intact parathyroid hormone, alkaline phosphatase, serum magnesium, triglyceride, LDL cholesterol, HDL cholesterol, uric acid, beta-2 microglobulin, blood urea nitrogen, hemoglobin, serum albumin, vitamin D medication, calcium carbonate intake, and statin intake.To examine the association between SOx and new-onset CVD events, we conducted a retrospective cohort analysis. New-onset CVD events during the 10-year period from serum sample collection date to observation end date of May 15th, 2021 were recorded by medical staff blinded to the SOx results to avoid information bias. “New-onset CVD event” was defined as any admission due to non-fatal myocardial infarction, coronary artery disease, or heart failure during the observation period. We divided subjects into two groups by SOx median and compared new-onset CVD events between two groups. The explanatory variable was SOx lower or grater than median, and outcome was new-onset CVD event. Potential confounders were the same as listed on the “Cross-sectional Analysis of the Association Between SOx and Vascular Calcification” section.To examine the relationship between SOx concentration was measured in January 2021 by using an Oxalate Assay Kit (Colorimetric) . With this assay kit, the normal range of human SOx is < 181 µmol/L. All serum samples were analyzed in duplicate.Blood samples were collected at the start of the dialysis session with the longest interdialytic interval around the atherosclerosis checkup date. The collected samples were mixed immediately with 5 mg edetic acid, centrifuged at 3000 rpm for 5 min to separate the serum, and stored immediately at − 80 °C with strict thermal management until analysis. S4 , along with the volume (area × slice increment), mass (area × slice increment × mean CT density/250), and density (mass/volume) of the CAC were determined by a single radiologist. Using the electrocardiogram tracing, the workstation software automatically selected a reduced set of diastolic images from each cardiac cycle. All pixels with density > 130H were automatically highlighted on the images. The radiologist assigned one of four locations to each calcified plaque: left main, left anterior descending, circumflex, or right coronary artery. The score for each plaque equaled the plaque area × weighting factor × increment/slice width. The score for the entire specimen equaled the sum of the scores for each plaque. The mean intra-reader variability for CAC was 1.8%.Multi-slice computed tomography (CT) scans were performed with an Aquilion TSX-101A . Slices of 1.25-mm thickness were acquired eight at a time under the following conditions: 120–140 kVp, 85–150 mA, 500 ms exposure, and 0.5 s gantry rotation time. The entire heart was covered in a single breath-hold (20–30 s). The CT images were transferred to a Ziosoft M900 QUADRA workstation  and CAC score according to the algorithm suggested by Agatston et al.Quantification of major artery calcification volume was performed by three-dimensional calcified lesion reconstitution from multiple slices of the aorta from the top of the arch to the abdominal artery just before the bifurcation of the iliac artery along the longitudinal axis by using the Ziosoft M900 QUADRA workstation.t-test for normally distributed data, the Wilcoxon rank sum test for non-normally distributed data, and Fisher’s exact test for discrete variables. A two-tailed P value of < 0.05 was considered to indicate statistical significance. In univariate analyses, we did available-case analyses, whereas in multivariable analyses, we did complete-case analyses. No imputation methods for sensitivity analysis were used in our study.Continuous variables are reported as mean ± standard deviation for normally distributed data, or median (inter-quartile range) for non-normally distributed data. Discrete variables are expressed as numeral (percentage). Differences between subjects with CAC score ≥ 1000 and those with CAC score < 1000 were tested for statistical significance by using Welch’s Ox concentration and CAC score, we divided subjects into two groups: those with CAC score < 1000 and those with CAC score ≥ 1000. This cutoff value was selected from previous studies indicating that CAC score ≥ 1000 is associated with cumulative incidence of cardiovascular events7. A logistic regression analysis was performed for the association between SOx and CAC score ≥ 1000, and a linear regression analysis was performed for the association between SOx and major artery calcification volume. Moreover, we performed risk prediction modeling to predict CAC score ≥ 1000 for validation. We present a ROC curve for predicting CAC score ≥ 1000 by using variables selected from multivariable logistic regression with variable selection.In the analysis of an association between SOx and new-onset CVD events, we divided subjects into two groups by SOx median and performed a Kaplan–Meier analysis and log-rank test to compare the survival functions of the two groups and to determine whether SOx is associated with CVD events. We repeated the analysis, restricting it to patients with CAC score < 1000 and without history of CVD. We also conducted a Cox proportional hazard analysis using the SOx cut-off value to determine which factors were associated with CVD events.In the analysis of an association between SOx and the covariates were factors that are reported to be associated with vascular calcification33 or CVD36; specifically, age, gender, hemodialysis duration, history of CVD, diabetes mellitus, BMI, ankle-brachial index, albumin-adjusted calcium, serum phosphate, intact parathyroid hormone, alkaline phosphatase, serum magnesium, triglyceride, LDL cholesterol, HDL cholesterol, uric acid, beta-2 microglobulin, blood urea nitrogen, hemoglobin, and serum albumin, vitamin D medication, calcium carbonate intake, and statin intake were used as covariates. A univariable analysis was performed first, and factors with a two-tailed P value < 0.2 were used for a multivariable analysis. For variable selection, we used Akaike’s information criteria with stepwise backward elimination. All statistical analyses were performed using R software, version 4.1.3.In each analysis, the explanatory variable was S


Applying the same name to a set of different objects from the same category supports object categorization, with infants encoding commonalities among objects at the expense of individuating details. In contrast, applying a distinct name to each object supports individuation, with infants encoding distinct features at the expense of categorical information. Here, we consider the development of this nuanced link between naming and representation in infants’ first year. Infants at 12 months  and 7 months  participated in an online recognition memory task. All infants saw the same objects, but their recognition of these objects at test varied as a function of how they had been named. At both ages, infants successfully recognized objects that had been named with distinct labels but failed to recognize these objects when they had all been named with the same, consistent label. This new evidence demonstrates that a principled link between object naming and representation is available by 7 months, early enough to support infants as they begin mapping words to meaning.By their first birthdays, infants represent objects flexibly as a function of not only  This, in turn, has powerful downstream consequences for learning and reasoning about that object7.Human cognition is remarkable for its representational flexibility. We readily represent any given object  as either a member of an object category  or as a unique individual , and we switch effortlessly among these representations. This representational flexibility is supported by language: how an object is 9. If infants view a series of individuals from the same object category  and each is introduced in conjunction with language , then infants successfully identify the overarching category at test, looking longer to an individual from a novel category  than to a new member of the now-familiar category . In contrast, if the very same individual category members are paired consistently with other acoustic signals , infants fail to form the overarching category, looking equally to the novel-category and familiar-category exemplars. Moreover, by 12 months, infants are sensitive not only to whether, but how objects are named. Providing the same, consistently applied name to a set of individual objects facilitates categorization, but providing a distinct name for each (“Look at the dax! Look at the blicket!”) does not12: instead, it promotes object individuation16.This representational flexibility is supported by language even in infancy, well before infants produce their first words. The most robust evidence for this effect comes from infants’ performance in object categorization tasks. For infants as young as 4 months of age, listening to language facilitates object categorization in a way that other, carefully matched acoustic signals do not17 proposed that naming affects infants’ representation and encoding of objects: while consistent names highlight commonalities among objects, distinct names highlight differences. To test this, they developed a new recognition memory task , exhibiting a classic recency effect. Performance on Test trials 3 and 4, which featured the exemplars seen on the first two Learning trials, did not differ across conditions. This is consistent with the proposal that over the course of Learning, as the same, consistent name was applied to all individuals, infants increasingly focused on category-wide commonalities at the cost of encoding individual-level detail23. On this view, a link between how objects are named and how they are represented may be available to infants as they first build their lexicons.But how early is this principled link between object naming and object representation available? Perhaps this link must be built upon the bedrock of a rudimentary lexicon. On this view, infants must first acquire a lexicon that includes different words that can be applied to the same individual ; then, they use this lexicon as a foundation for linking the way an object is named to the way they represent it. Alternatively, perhaps infants begin the task of word learning with an expectation that lexical distinctions map onto conceptual distinctions 24. Certainly, infants’ attentional and memory resources are more limited at 7 than 12 months29. Nevertheless, 7-month-olds have also mastered several of the requisite skills for the establishment of a link between object naming and object representation. For instance, 7-month-olds successfully segment words from the continuous speech stream31, remember objects they have recently seen26, and are able to represent objects both as distinct individuals33 and as category members38. In addition, longitudinal training designs suggest that 7-month-old infants may be sensitive to the distinct representational consequences of naming objects with the same versus different names. In multiple studies, Scott and her colleagues40 recruited 6-month-old infants to participate in a 3-month training regimen. All infants viewed the same set of images  presented in a picture book. What varied was whether, over the training period, each image was paired with a unique name or a consistently applied name. At 9 months, the effect of this naming manipulation was apparent. Infants who had heard a distinct name for each training object successfully distinguished these from new members of the same category. In contrast, infants who had heard the same name applied consistently to all training objects failed to distinguish them from new members40.To adjudicate between these alternatives, we focus on 7-month-old infants because this is the developmental period at which infants are just beginning to learn the names of a few, highly frequent words 17 paradigm. However, restrictions imposed by the COVID-19 pandemic required that we use an online platform (Lookit)41. Because results obtained in laboratories do not always converge with those obtained from online platforms44, our first goal was to perform a conceptual replication of LaTourrette and Waxman’s17 in-lab finding in an online platform (Study 1). As in the original study, 12-month-old infants viewed four different objects, paired with either a consistent name for all objects or a distinct name for each object. Infants were then tested on their ability to discriminate these objects from new category members. This replication provided the foundation for assessing 7-month-old infants in the same paradigm (Study 2). We predicted that despite 7-month-old infants’ more limited attentional and memory resources, their representation and memory for objects would nevertheless be shaped by how the objects were named. Seven-month-old infants’ recognition of individual objects should be stronger if each object had previously been named with a distinct label than with the same, consistently applied label.This outcome is impressive but leaves open a key developmental question: is young infants’ sensitivity to how objects are named available only after months of prolonged training, or is this sensitivity present from the moment of labeling? To address this question, we adopted LaTourrette and Waxman’sIn both studies, we adopted a conservative analytic approach in order to align the current results with those reported in the prior in-lab study. Based on that prior study, we predicted that the effects of naming would be evident only within the first two Test trials . Nevertheless, we assessed infants’ performance for each object seen during Learning, first reporting the results combined across all four Test trials, and next reporting trial-by-trial analyses to assess the key trial-based predictions outlined below.17 but implemented online with the testing platform Lookit41 , MConsistent = 55.28 s (SD = 10.40 s); t(53) = 0.30, p = 0.8). Nor was there any difference in looking time during Learning in the present study compared to LaTourrette and Waxman’s17 in-lab investigation, t(102) = 1.21, p = 0.23. Together, these analyses provide assurances that any condition differences at Test, should they emerge, cannot be attributed to differences in infants’ visual attention to the individuals during Learning.There was no difference between conditions in the time that infants spent looking to the objects during Learning, 46. In contrast, if infants in the Consistent Name condition encoded the commonalities among the Learning objects rather than their unique features, then they should fail to discriminate these previously seen objects from new category members, resulting in them looking equally to the two test objects.We predicted that infants in the Distinct Names condition would have encoded the unique features of the objects seen during Learning and should therefore recognize these objects at Test, resulting in a preference for looking to the novel object, particularly on the first two Test trialsp = 0.076), Test Trial , and the Condition × Test Trial interaction . Notably, the difference between conditions was in the same direction as that reported in LaTourrette and Waxman17, with infants in the Distinct Names condition showing stronger novelty preferences those in the Consistent Name condition. However, in the online platform, this difference fell short of statistical significance. This is consistent with previous reports of greater variability and smaller effect sizes in results from online platforms than in the laboratory 56.We first constructed a linear mixed effects model predicting infants’ preference for the novel object with sum-coded fixed effects for Condition, Test Trial, and their interaction, a random effect of participant, and trial-by-participant random slopes. This model yielded non-significant effects of Condition  = 3.3, p = 0.003, d = 0.63) and Test Trial 2  = 3.7, p = 0.001, d = 0.72). They also favored the novel object on Test Trial 3  = 2.8, p = 0.009, d = 0.54); on Test Trial 4, infants showed no preference  = 0.44, p = 0.67). In contrast, infants in the Consistent Name condition did not show a preference for the novel object in any of the four Test Trials, MTrial1 = 0.55 (SD = 0.13), MTrial2 = 0.52 (0.14), MTrial3 = 0.51 (0.19), MTrial4 = 0.53 (0.20), ts < 1.8, ps > 0.05  = 1.24, p = 0.22, Trial 2: t(50) = 1.54, p = 0.13, Trial 3: t(50) = 1.41, p = 0.16, Trial 4: t(50) = 0.34, p = 0.74.Next, to test the trial-based predictions outlined above, we conducted a series of planned comparisons, comparing infants’ performance on each test trial in each condition against a 50% preference , as in the original study. As predicted, infants in the Distinct Names condition favored the novel object on Test Trial 1 .t = 1.04, p = 0.30 and no Platform x Condition interaction, β = 0.02, SE = 0.027, p = 0.45. We also observed no main effect of Condition, β = 0.018, SE = 0.019, p = 0.34, or Test Trial, β = 0.004, SE = 0.01, p = 0.72, but the analysis did reveal a significant Condition x Trial interaction, β = 0.028, SE = 0.014, p = 0.042. To characterize this interaction, we compared performance across conditions on each trial using Welch t-tests. This revealed the predicted pattern of results: better memory for objects in the Distinct Names condition over the Consistent Name condition on both Test Trial 1, t(103) = 2.28, p = 0.025, and Test Trial 2, t(94) = 2.23, p = 0.028, but not Test Trial 3, t(94) = 1.45, p = 0.15, or Test Trial 4, t(94) = 0.92, p = 0.36. As a final measure of alignment across platforms, we examined infants’ mean looking preference on each platform for each Test trial in each condition . Together, these analyses provide a strong foundation for assessing the effect of naming on 7-month-old infants in Study 2.Thus, the effect sizes in Study 1 were more modest than in the in-lab sampleSeven-month-old infants participated in the same recognition memory task as in Study 1. Here, we essentially doubled the sample size from Study 1 (to n = 48 per condition) because (a) younger infants’ performance tends to be more variable than older infants’, and (b) effect sizes in Study 1 were more modest than in the in-laboratory sample.51. As in Study 1, we still tested infants’ memory for all exemplars shown during learning.We expected that 7-month-olds, like 12-month-olds, would show better recognition of objects labeled with distinct names rather than consistent names, but that they would reveal this effect only on the first Test trial. This prediction was motivated by evidence that 7-month-olds recall only one object in detail while 12-month-olds successfully recall multipleDistinct = 54.29 s, SD = 12.8 s; MConsistent = 52.08 s, SD = 12.35 s; t(94) = 0.86, p = 0.39). In addition, their total looking time in each condition was comparable to 12-month-old infants’ total looking in each condition in Study 1, ts < 1.5, ps > 0.2.Seven-month-olds’ total time looking to the exemplars during Learning was comparable across conditions (Mp = 0.04), but no significant effects of Condition  or the Condition x Test Trial interaction .We began by constructing a linear mixed effects model predicting preference for the novel object with Condition, Test Trial, and their interaction as sum-coded fixed effects and a random effect of participant and Trial-by-participant random slopes. This model yielded a significant effect of Test Trial  = 3.2, p = 0.003, d = 0.47), but those in the Consistent Name condition showed no preference  = -0.019, p = 1). Moreover, performance on Test Trial 1 differed reliably as a function of Condition, t(90) = -2.58, p = 0.01, d = 0.54. This suggests that 7-month-olds, like 12-month-olds, are sensitive to how objects are named, and this has consequences for their recognition memory. While distinct naming helped infants to retain the individuating features of the most recently seen object, consistent naming likely focused infants on category-wide commonalities instead. Test Trial 2, though, yielded an unanticipated difference between conditions, t(88) = 2, p = 0.05: infants in the Consistent Name condition  = 3.4, p = 0.002, d = 0.51), but not the Distinct Names condition  = 0.32, p = 0.7), favored the novel object. Infants showed no effect of condition on either Test Trial 3  = 0.30, p = 0.76), or Test Trial 4  = 0.36, p = 0.72)  and Test Trial, as well as random effects of participant and trial-by-participant slope, for each condition. In the Distinct Names condition, we found a significant effect of Age Group, β = 0.046, SE = 0.018, A role for visual attention Finally, we conducted an exploratory analysis, motivated by prior findings that among infants 7 months and younger, greater visual attention during learning was associated with greater success on categorization and recognition tasks50. To assess whether the same was true for the current task, we identified two groups of 24 infants in each condition – “High Lookers” and “Low Lookers” – determined by whether each infant’s accumulated looking time during Learning was above or below the median. As predicted, the effect of labeling was stronger among the High Lookers. On Test Trial 1, High Lookers in the Distinct Names condition showed a significant preference for the novel object  = 3.6, p = 0.002, d = 0.75). This preference was significantly greater than that of High Lookers in the Consistent Name condition , t(45) = 2.65, p = 0.011. In contrast, among the Low Lookers, there was no difference between conditions on Trial 1, t(43) = 1.10, p = 0.28. On Test Trials 2–4, t-tests comparing performance in the two conditions revealed no significant differences for either High or Low Lookers, ps > 0.05  and individuals .These results shed new light on how, and how early, infants become sensitive to the tight coupling between object naming and object representation. Within the first year of life, how an object is named—as either a unique individual or a member of a category—influences how infants encode and remember it. In Study 1, when a series of individual objects all received the same name, 12-month-olds failed to recognize even the most recently seen object from Learning. Yet when each individual object received its own distinct name, infants successfully distinguished them from other, within-category objects, showing recognition for the last three objects seen in Learning. In this online implementation, the difference between conditions at 12 months did not reach significance. Nevertheless, the direction of the results is consistent with prior evidence from more tightly controlled laboratory settings indicating that when the same name is applied consistently to a set of objects, infants increasingly focus on commonalities among the objects, but at the cost of encoding individuating details51 and suggests that infants’ increasing attentional and memory capacities can amplify the downstream cognitive consequences of object naming on object encoding and memory.Our findings reveal both developmental continuity and change. At 7 and 12 months, even a single naming episode affected infants’ encoding of the named object. But infants’ expression of this sensitivity also became more robust from 7 months (when they recognized a single object) to 12 months (when they could recognize three). This is congruent with prior work on the development of infants’ memory for objects54. Previous work suggests bilingual infants may not share their monolingual counterparts’ assumption that each distinct word maps to a distinct object kind 55, so it remains an open question whether and when distinct (or consistent) labels influence their encoding of objects. Finally, future work might consider how naming objects with distinct proper nouns, as compared to distinct count nouns, influences objects’ representation. Recent evidence reveals that infants as young as 6 months comprehend the more restricted scope of at least a few proper names21. This raises an interesting possibility: as infants become sensitive to surface-level syntactic cues that distinguish proper names from count nouns, then labeling individuals in the Distinct Names condition with proper nouns might yield even more striking effects on infants’ encoding and representation of individual objects.The current findings also open new avenues for investigation. For example, it will be important in future work to more comprehensively examine the processes underlying infants’ identification of commonalities or distinctions among exemplars, including the extent to which the present results are driven by infants’ attention to the exemplars’ commonalities or to their differences. Another avenue for future investigation will be to clarify the mechanism by which labeling affects 7-month-olds’ encoding of objects, including evaluating 7-month-olds’ unexpectedly above-chance performance on Test Trial 2 in the Consistent Name condition. For instance, if 7-month-olds are slower than 12-month-olds to identify category-wide commonalities among exemplars, then they may still be encoding individuating details on the third learning exposure. Future work might also investigate naming effects with infants acquiring more than one languageAltogether, our results show that by 7 months, infants’ representations and memory of individual objects are shaped by how the objects were named: distinct, but not consistent, names facilitated infants’ encoding of each object’s distinguishing features. This suggests that even at the onset of word learning, infants are equipped with an early and powerful link between language and conceptual representations, underpinning their online encoding and memory for objects.Participants, recruited from either Lookit or Northwestern University’s Infant and Child Development Center database, received a $5 gift card upon study completion. All caregivers gave informed consent for their child’s participation. All infants were full term (> 37 weeks) and acquiring English . In Study 1, 64% of families self-identified as White, 4% as Hispanic, 4% as Asian, and 29% as multiracial. In Study 2, 76% of families self-identified as White, 1% as Black, 4% as Hispanic, 6% as Asian, and 13% as multiracial. Studies 1 and 2 included a total of 151 infants.age = 11;30, range: 11;16–12;19, 12 female, 16 male) and 27 in the Consistent Name condition . This sample size  provided approximately 85% power, based on the effect size of d = 0.88 reported for the first test trial in LaTourrette and Waxman17. We focused on this first trial effect, rather than an effect across all trials, as we expected the effect of condition to be present on each of the first two test trials but not subsequent trials, as in the original study.Study 1 (12-month-olds) included 28 infants in the Distinct Names condition  and 48 in the Consistent Name condition . A sample size of 48 infants per condition (Study 2) provided 99% power, based on LaTourrette and Waxman’s17 effect size of d = 0.88 and alpha = 0.05.Study 2 (7-month-olds) included 48 infants in the Distinct Names condition , because of platform-specific problems with stimulus presentation , or because the quality of infants’ video recordings was too poor for coding (Study 2: 4).All methods conform to the standards outlined in the Declaration of Helsinki and were approved by the Northwestern University Institutional Review Board.17. Visual materials were images of eight stuffed animals .All stimuli were identical to LaTourrette and Waxman41. After consenting, caregivers were instructed to hold their infants over their shoulders, turning their own backs to the screen so that they could not influence their infant’s attention to the images. Otherwise, the procedure was identical to LaTourrette and Waxman17. Infants participated in two phases: Learning and Test  appeared alternately on each side of the screen to attract infants’ attention to both sides. Next, infants viewed images of four different animals, presented one at a time for 20 s each, on alternating sides of the screen. In the Distinct Names condition, a different novel name was paired with each object. In the Consistent Name condition, the same novel name was paired with all four objects. The side on which the first image appeared and the label(s) paired with the objects were randomly determined.Test phase To begin each trial, the attention-grabber appeared at the center of the screen (5 s). Next, a pair of objects appeared side-by-side in silence for 20 s. Each pair included one animal that infants had seen previously during Learning (familiar object) and one they had not seen (novel object). As in LaTourrette and Waxman17, the familiar objects were presented in the reverse order from the Learning phase. That is, the object presented on Learning Trial 4 was presented on Test Trial 1.57. Infant gaze was coded as center, right, left, or off screen. Intercoder reliability, computed by a second trained coder for 21 videos in Study 1 and 17 videos in Study 2, was excellent .Infants’ visual attention throughout was coded manually by a trained research assistant offline 36. These preference scores, expressed as proportions, were arcsine-square-root transformed for linear mixed effects models in lme459, with fixed effects for Condition (sum-coded), Test Trial , and the Condition x Test Trial interaction, as well as a random effect of Participant. To assess significance, we used the Satterthwaite approximation for degrees of freedom to minimize type I error60, as implemented in the lmerTest package61 in R62.For Learning trials, we calculated infants’ total looking time to the objects on all four trials. For Test trials, we used the first 10 s of accumulated looking in each test trial to calculate a novelty preference score, defined as time looking at the novel test object/time looking at the novel and familiar test objects combinedWe included in analysis only those test trials in which the infant had looked at that object for at least 5 s during Learning, looked to both objects at Test, and devoted at least 5 s of looking  to the objects during Test.Supplementary Tables.


Compared with recommended 30 mg, the “overdosed” group had lower IS  and all-cause death  without higher MB . In conclusion: non-recommended dosing was infrequent, but more common near dose-reduction thresholds. “Underdosing” was not associated with better clinical outcomes. The “overdosed” group had lower IS and all-cause death without higher MB.Non-recommended dosing occurs in ~25–50% of non-vitamin K antagonist oral anticoagulant prescriptions, with limited data for edoxaban. We analyzed edoxaban dosing patterns in atrial fibrillation patients from the Global ETNA-AF program, relating patterns to baseline characteristics and 1-year clinical outcomes. The following dosing groups were compared: non-recommended 60 mg (“overdosed”) vs. recommended 30 mg; non-recommended 30 mg (“underdosed”) vs. recommended 60 mg. Most  patients received recommended doses. Non-recommended dosing was more frequent near label-specified dose-reduction thresholds. Ischemic stroke (IS; HR 0.85, 95% CI 0.50–1.47;  Direct oral anticoagulants (DOACs) have replaced vitamin K antagonists (VKAs) as the standard of care for stroke prevention in patients with atrial fibrillation (AF) in most countries . Label rDosing criteria for the DOACs were established based on data derived from pivotal phase III prospective, randomized, controlled clinical trials. Despite all reassuring data from these trials, prescriptions of non-recommended doses have been reported in a considerable percentage of patients in daily practice. Indeed, 25–50% of DOAC prescriptions were off-label according to a recent meta-analysis of 75 studies . Among tEdoxaban 60 mg once daily (OD) is approved for prevention of stroke and SEE in patients with AF, with dose reduction to 30 mg OD recommended for patients who meet the following criteria: creatinine clearance (CrCl) 15–50 mL/min, low body weight (≤60 kg), and/or concomitant use of P-gp inhibitors . Data onThe design of the Global ETNA-AF non-interventional program has been described previously . The stuEligible patients participating in Global ETNA-AF were those treated before enrolment with edoxaban for stroke prevention in AF according to the local labels. At the time of this study, there were no differences in dose reduction criteria between labels for the regions included in the analysis. In Japan, patients were only eligible for the study if they had received edoxaban for the first time to prevent ischemic stroke and SEE. Patients could not be included in the Global ETNA-AF program if they were participating simultaneously in any interventional study .Patients’ medical and treatment history were collected at baseline. Clinical events captured at 12 months after enrolment included ischemic stroke, transient ischemic attack, myocardial infarction, SEE, bleeding, and all-cause and cardiovascular (CV)-related mortality; these events were reported based on physicians’ diagnoses and assessments per available guideline definitions. Events considered to be of special importance  were adjudicated by an independent clinical event adjudication committee . No missThose who received the non-recommended 60 mg  dose vs. those receiving the recommended 30 mg (QD) dose, thus, assessing the effects in the “overdosed” group;And those who received the recommended edoxaban 60 mg (QD) dose vs. those receiving the non-recommended 30 mg (QD) dose, thus, assessing the effects in the “underdosed” group.In the present analysis, comparisons were made between:The term “recommended” dosing here used refers to the receipt of an edoxaban dose that is in accordance with the local edoxaban label (60 mg QD in patients who do not meet any of the aforementioned dose reduction criteria and 30 mg QD in patients who meet at least one of the criteria for reduction); conversely, the term “non-recommended” dosing refers to the receipt of an edoxaban dose that is not aligned with the local edoxaban label. The terms “underdosing/underdosed” and “overdosing/overdosed” are utilized herein for simplification and to align with terminology from other publications and, for this reason, are put in quotation marks; these terms do not imply that patients received an insufficient dose or a drug overexposure, respectively, which might translate to an unfavorable outcome. Patients receiving edoxaban 15 mg were excluded from this analysis.p-values. Stratification by dose in the Cox model and Kaplan–Meier curves is intended as a comparison of dosing subgroups and not as a measure of dose effect, as dosing decisions were at the clinician’s discretion.Baseline data are presented descriptively as the mean with standard deviation (SD) for normal distributions, frequencies, and/or as summary statistics. The Wilcoxon two-sample test and chi-square test were applied for subgroup comparisons. Outcomes are reported with annual event rates and hazard ratios (HRs), both with 95% confidence intervals (CIs). HRs were calculated by using a Cox proportional hazard model and are reported along with their The main goal of this study was to report the clinical outcomes related to “prescription patterns” of edoxaban dosing in daily practice rather than investigating the real impacts of dosing on risks of clinical events. Therefore, we did not aim to adjust for baseline characteristics between different dosing groups when comparing the risks of clinical events since the adjusted results would fail to demonstrate the real picture of clinical risks under different prescription patterns.Overall, 22, 166/26, and 823 (82.6%) of patients received a recommended dose  . In Europ < 0.0001), had a lower CrCl (mean (SD) CrCl: 72.2 (20.7) vs. 85.8 (26.8) mL/min, p < 0.0001), and a greater proportion had long-standing persistent AF (15.0% vs. 6.8%) (p < 0.0001), myocardial infarction , heart failure  and ischemic stroke , or history of major bleeding  than those who received the recommended 60 mg dose (2DS2-VASc (3.3 (1.5) vs. 2.8 (1.4), p < 0.0001) and HAS-BLED (2.5 (1.1) vs. 2.3 (1.1), p < 0.0001) scores were observed in the “underdosed” group compared to that of the recommended 60 mg group.The group of patients that received the non-recommended 30 mg dose (“underdosed”) vs. the group that received the recommended 60 mg dose was older (mean (SD) age: 74 (9.0) vs. 70 (9.3) years, s. 6.8%) . Patient mg dose . Thus, tp < 0.0001), had a higher CrCl (54.8 (20.5) vs. 49.6 (18.1) mL/min, p < 0.0001), and, in a smaller proportion, had long-standing persistent AF (6.7% vs. 16.7%) than the recommended 30 mg dose group. The mean (SD) CHA2DS2-VASc score at baseline was significantly lower in the “overdosed” group than the recommended 30 mg dose group (3.5 (1.4) vs. 3.9 (1.5), p < 0.0001; Patients who received the non-recommended 60 mg dose (“overdosed”) were younger (mean (SD) age: 75 [9.1] vs. 78 (8.5) years, p = 0.0007), ischemic stroke , intracranial hemorrhage  or comorbidities such as diabetes mellitus , heart failure  and myocardial infarction  than the recommended 30 mg dose group    . Heparin = 0.01) . The useIn Europe, CrCl ≤ 50 mL/min was the most commonly met dose reduction criterion (59.8%) , whereasp = 0.6) and major bleeding  did not differ significantly between “underdosed” and recommended 60 mg dosing groups, whereas rates of major gastrointestinal (GI) bleeding , all-cause death , and CV death  were significantly higher in the “underdosed” group  was lower and the rate of major bleeding  was not significantly different from the recommended edoxaban 30 mg dose group (p = 0.03) was significantly lower in the “overdosed” group compared with the recommended edoxaban 30 mg group, whereas there was no statistical difference in the rate of CV deaths . The annualized ICH rates were 0.46%/year vs. 0.07%/year  in the recommended 30 mg dose group vs. the non-recommended 60 mg dose group (“overdosed”). If none of the criteria for edoxaban dose adjustment were fulfilled, then ICH annual rates did not differ (recommended 60 mg dose group vs. non-recommended 30 mg (“underdosed”) group, 0.26%/year vs. 0.18%/year; HR 0.70, 95% CI 0.27–1.79; p = 0.5).In the “overdosed” group, the rate of ischemic stroke  non-recommended edoxaban use (either “under-” or “overdosing”) occurred in 17% of patients with AF, and was more frequent when patients were close to the threshold of dose reduction for CrCl (50 mL/min) or body weight (60 kg); (ii) compared with the recommended 60 mg dosing group, patients in the “underdosing” group were older, and with higher percentages of prior major bleeding or ischemic stroke; (iii) compared with the recommended 30 mg dosing group, patients in the “overdosing” group were younger, with lower percentages of prior major bleeding, including intracranial hemorrhage or ischemic stroke; (iv) compared with the recommended 60 mg dosing group, patients in the “underdosing” group had a higher rates of major GI bleeding and all-cause and CV death without higher ischemic stroke rates; and (v) compared with recommended 30 mg dosing group, patients in the “overdosing” group had lower rates of ischemic stroke without higher major bleeding rates. Non-recommended dosing was applied most often near the threshold for dose change, with apparent consequences in death outcomes in the “underdosed” group but no notable adverse consequences in either effectiveness or safety in the “overdosed” group.In Global ETNA-AF, approximately 83% of patients were dosed according to label recommendations globally, with some regional differences . Of the overall population, 11.2% were “underdosed” and 6.1% were “overdosed”. Despite the small percentages of the overall 26,823 patients in this registry, both groups of “underdosed” and “overdosed” patients are relatively sizeable, consisting of 3016 and 1640 patients, respectively, allowing for meaningful interpretation. The recommended dosing rate in Global ETNA-AF (83%) was higher than that of previous reports  , ~77% in2DS2-VASc scores compared with those who received a recommended dose [2) and bleeding (HAS-BLED) scores, suggesting that patients perceived as high-risk for both stroke and bleeding were more likely to receive off-label DOAC doses [Clinical factors besides the dosage reduction criteria may have influenced physicians’ clinical decisions on edoxaban dosing. Compared with the group of patients who received the recommended dosing, patients in the “underdosed” group in Global ETNA-AF were older and presented with more CV comorbidities. They also were less frequently receiving antiplatelet agents and parenteral anticoagulants such as heparins or fondaparinux , suggestded dose . LikewisAC doses .Non-recommended edoxaban use was more frequent when patients were near the threshold of dose reduction for CrCl or body weight. Such finding suggests that, in patients close to the threshold for a dose change, physicians may replace the binary dose reduction criteria with other patient characteristics, attaining dosing decisions based on composite clinical evaluations of the overall patient characteristics.n = 286). Another analysis assessed DOAC dosing patterns and associated outcomes in a US claims database  in patients who met the dose-reduction criteria (n = 1473) based on renal dysfunction: in this analysis, compared with standard doses of apixaban, those who were underdosed had a significantly higher risk of stroke  without the benefit of fewer major bleeding events [p = 0.484). However, that study was limited by its retrospective design and the relatively low number of edoxaban patients. Data now reported in our Global ETNA-AF program, with a significantly greater number of patients , a prosCompared with patients who received the recommended 60 mg dose, the “underdosed” group did not have a higher rate of stroke/SEE events but did have a higher rate of major GI bleeding and all-cause and CV deaths. These clinical events might have been affected by factors other than stroke, such as older age, major bleeding history, and a higher prevalence of CV comorbidities. It is unlikely that concomitant medications such as NSAIDs, antiplatelets, and heparin contributed to the higher rate of major GI bleeding events in the “underdosed” group, as the baseline NSAIDs used in this subgroup were infrequent (0.3% of patients), and the baseline use of antiplatelets and heparin was actually significantly lower than use by the recommended 60 mg dose group. Patients in the “overdosed” group had a major bleeding event rate similar to that of patients who received the recommended edoxaban 30 mg dose, with lower rates of ischemic stroke and all-cause death in the “overdosed” group. In aggregate, these data suggest that, in real-world practice, clinicians’ dosing judgement/decisions and the subsequent clinical outcomes are often driven by patient characteristics that are not reflected in the label, especially for patients who are near the dose-reduction threshold. Further analyses of ETNA-AF with a longer (4-year) follow-up period will be performed to investigate the real impacts of edoxaban dosing on clinical outcomes after statistical adjustments for the differences in clinical characteristics between dosing groups out of the aims of the present report.One critical strength of the Global ETNA-AF analysis is the large sample size of 26,823 patients: the present analysis is indeed the largest prospective source of routine-care data for a single DOAC in patients with AF. Global ETNA-AF also included a very diverse patient population, with more than 13,000 patients from Asia. In addition, data quality was enhanced by adjudicating efficacy and safety outcomes and calculating CrCl, a dose reduction criterion, with very few missing data points (8.5%). In contrast, nearly 35% of patients had missing CrCl values in the XANTUS registry , and CrCNotable limitations of this study include its one-armed observational design. Another limitation is that patients were grouped by edoxaban dose at baseline. As per the nature of the registry, it was not possible to control for changes in dosing and for adherence to the prescribed dose. In addition, we had no information on the duration of exposure to drugs throughout the study, which would have been useful to better assess the comparability of study groups. Ongoing analyses will allow adjustments for physicians’ characteristics, such as medical specialty and the site of prescription , among others, with a propensity score factoring all characteristics associated with inappropriate dosing. A small difference in “overdosed”/”underdosed” patient proportions is here reported within the three macro-regions where the study was run. Therefore, it is plausible that different reasons  may have driven different dosage choices.Finally, the eligibility criteria for the Global ETNA-AF program should be taken into consideration before extrapolating the present findings to DOAC- or edoxaban-naïve patients, as only patients with edoxaban experience were included to avoid any potential influence on physicians’ or patients’ decision making.The present Global ETNA-AF study analysis suggests that physicians’ decisions for non-recommended dosing of edoxaban—while less frequent—are common near the dose reduction thresholds, with “underdosing” occurring more often than “overdosing”. When compared with patients who received the recommended edoxaban dosing, patients who were “underdosed” did not have better clinical outcomes, whereas “overdosed” patients did not have higher major bleeding rates and had lower rates of ischemic stroke and all-cause death.


Therapy for osteoarthritis ideally aims at preserving structure before radiographic change occurs. This study tests: a) whether longitudinal deterioration in cartilage thickness and composition (transverse relaxation-time T2) are greater in radiographically normal knees “at risk” of incident osteoarthritis than in those without risk factors; and b) which risk factors may be associated with these deteriorations.755 knees from the Osteoarthritis Initiative were studied; all were bilaterally Kellgren Lawrence grade [KLG] 0 initially, and had magnetic resonance images available at 12- and 48-month follow-up. 678 knees were “at risk”, whereas 77 were not . Cartilage thickness and composition change was determined in 16 femorotibial subregions, with deep and superficial T2 being analyzed in a subset (n = 59/52). Subregion values were used to compute location-independent change scores.2<1%), with only knee pain reaching statistical significance.In KLG0 knees “at risk”, the femorotibial cartilage thinning score (−634 ± 516 μm) over 3 years exceeded the thickening score by approximately 20%, and was 27% greater  than the thinning score in “non-exposed” knees (−501 ± 319 μm). Superficial and deep cartilage T2 change, however, did not differ significantly between both groups (p ≥ 0.38). Age, sex, body mass index, knee trauma/surgery history, family history of joint replacement, presence of Heberden's nodes, repetitive knee bending were not significantly associated with cartilage thinning (rKnees “at risk” of incident knee OA displayed greater cartilage thinning scores than those “non-exposed”. Except for knee pain, the greater cartilage loss was not significantly associated with demographic or clinical risk factors. In this context, articular cartilage represents a primary treatment target, as it ensures proper joint function by providing an almost frictionless gliding surface that, by virtue of hydrostatic pressurization , distribIt is assumed that loss in cartilage thickness, and longitudinal change in cartilage composition, commence before signs of ROA become apparent. As it would be unreasonable, and potentially harmful, to treat everyone with radiographically normal knees preventively, it is important to validate risk factors that put people at risk of cartilage loss and/or composition change, in order to apply targeted preventive therapy that slows down or even stops OA development or progression. MRI spin–spin or transverse relaxation time T2) has been used as a marker of cartilage composition, and has specifically been proposed to detect structural cartilage pathology before the onset of ROA and cartilage matrix loss . Cartila has beenThere are methodological challenges in studying the effect of risk factors of knee OA progression, once incident ROA has occurred . ObservaThe current study was designed to test a) whether longitudinal change in cartilage thickness or composition (and that of radiographic JSW) is greater in radiographically normal knees “at risk” of incident ROA than in those without such risk factors, and b) which specific risk factors may be associated with such a difference. Given relatively small rates of cartilage change over time in KLG0 knees, a relatively long (3 year) longitudinal observation period was chosen.22.1http://www.oai.ucsf.edu/, clinicaltrials.gov identifier: NCT00080171). The OAI enrolled 4796 participants aged 45–79 years, in order to collect clinical data, 3 T magnetic resonance images (MRIs) and fixed-flexion radiographs from both knees at four clinical sites  0) at 12-month follow-up (the beginning of the observation period), and had MRIs available at 12- and 48-month follow-up. 678 of these subjects were from the so-called incidence cohort who were bilaterally KLG0 at baseline and Y1 follow up  and had 12- and 48-month follow-up MRI data available for longitudinal analysis. The selection was not based on specific criteria, but by omitting cases at the end of the full reading list due to budget restrictions [The current study did not rely on the initial selection of “non-exposed” participants made by the sites (n = 122), but on those in whom bilateral KLG0 status was confirmed by central radiographic readings from a team of three expert radiologists/rheumatologists at Boston University. Of 112 of the initial “non-exposed” subjects with at least one follow-up MRI , 89 had rictions , with kn2.2Femorotibial cartilage thickness measurement in one knee of each participant  was based on segmentation of sagittal DESS MRIs, and computation using Chondrometrics software , as described previously . The meaRadiographic JSW was measured quantitatively from weight-bearing fixed flexion radiographs in those in which radiographic measures were available, as described previously . Specifi2.3Femorotibial cartilage T2 measurement was based on segmentation of sagittal MESE MRIs ,18 acqui2.4Given that, in KLG0 knees, no compartment or location can be assumed to be particularly “vulnerable”, the primary analytic focus of this study was the comparison of the location-independent cartilage “thinning score” between the 678 “at risk” knees and the 77 “non-exposed” knees; the co-primary focus was the cartilage “thickening score”. The secondary analytic focus was the comparison of the T2 “lengthening score”, and the co-secondary focus the T2 “shortening score”. Between-group comparisons were performed using independent sample t-tests. Comparisons for all other measures of cartilage thickness and composition change  were considered exploratory. As outlined above, the T2 analysis was performed in a subsample, and a sensitivity analysis of cartilage thickness measures was performed in the same  sample so that the findings could be compared directly.Then, differences in age, sex, body mass index (BMI), NRS and WOMAC knee pain between the “non-exposed” and the “at risk” groups were tested for statistical significance using independent sample t-tests, non-parametric Mann–Whitney U tests and chi  tests as33.1In the 678 “at risk” participants, 677 right and 1 left knee were studied; 384 (57%) were from women and 294 from men . In the “non-exposed” reference cohort, 77 right knees were analyzed; 46 were from women (60%) and 31 from men. The ratio between men and women did not differ between both groups (p = 0.60). In the “at risk” cohort, 171 (25%) reported a relevant past injury of the knee studied, 37 (5.5%) previous surgery of the knee studied, 113 (17%) a family history TKR replacement, 169 (25%) presence of Heberden's nodes, and 493 (73%) frequent repetitive knee bending activity; in 139 (21%) the knee studied was frequently painful, 302 (45%) infrequently painful, and 234 (34%) did not have knee pain at 12-month follow-up. Differences in age, BMI, NRS pain, and WOMAC pain between the “at risk” and the non-exposed cohort were statistically significant , 5 KLG2, and 4 KLG3 or 4, whereas 621 (92%) remained KLG0; 37 did not have a radiographic classification at this time point. Of the 77 knees of the “non-exposed” cohort, 3 were classified as KLG1 at 48-month follow-up, 2 KLG2, and none KLG3 or 4, whereas 68 (88%) remained KLG0; 4 had no classification. At the last time point at which knee were radiographically evaluated in the OAI (96-month follow-up), 49 were classified as KLG1 15 KLG2, and 13 KLG3 or 4 in the “at risk” cohort, whereas 476 (70%) remained KLG0; 125 did not have a radiographic classification at this time point. In the “non-exposed” cohort, 4 were classified as KLG 1 at 96-month follow-up, 2 KLG2, and none KLG3 or 4, whereas 68 (75%) remained KLG0; 13 had no classification.3.2The MRI-derived location-independent cartilage thinning score (ThC_TnS) was statistically significantly greater than that in “non-exposed” cohort , whereas the thickening score (ThC_TkS) was similar between the two . In the 3.3In the subset of the “at risk” cohort (n = 59), the deep and superficial cartilage T2 lengthening scores (dT2_LeS) exceeded the shortening scores in the same layer, but the same was observed in the “non-exposed” cohort , and only NRS pain reached borderline statistical significance (p = 0.06). When including both the “non-exposed” and the “at risk” cohort in the analysis, the results were similar, with NRS pain becoming statistically significant  in the “at risk” cohort, and in the “non-exposed cohort” and “at risk” cohort taken together.There was no statistically significant association between the single risk factor and the cartilage thinning score in the “at risk” cohort, with all beta values falling below ±0.04 and r4This study explored, for the first time, whether clinical risk factors are associated with cartilage thickness and composition (T2) change in radiographically normal (KLG0) knees, prior to the onset of ROA. The location-independent cartilage thinning score was found to be significantly greater in a cohort “at risk” of incident ROA than in “non-exposed” reference cohort that did not express these risk factors. However, this difference could not be statistically related to any of the specific demographic or clinical risk factors, based on which the selection of the “at risk” cohort was made, except for NRS pain. In contrast to the cartilage thinning score, no differences in the longitudinal change of compartment-specific cartilage thickness, radiographic JSW, or superficial or deep cartilage T2 were noted between the “at risk” and the “non-exposed” reference cohort.A limitation of the current analysis is some restriction with regard to generalizability: The OAI is not a community-based study that is representative of the general population, and there is some likelihood of ascertainment bias, in particular with regard to the “non-exposed” reference group that may actually represent “super-controls” given the general absence not only of radiographic OA and pain, but also of important risk factors of incident knee OA. On the other hand, these “super-controls” provide a unique opportunity to study the impact of general clinical risk factors before the actual onset of disease. Another limitation is that, due to budget restrictions, only 91% of the knees of the “at risk” cohort were studied. However, this was by a random, not systematic, criterion, and the main findings of the study (differentiation of “at risk” and “non-exposed” knees by the thinning score) were reproduced in the much smaller subcohort in which T2 measurements were available; therefore it is highly unlikely that this finding was impacted by selection bias.A specific limitation of the T2 analysis component of the study is that only a subset of the “at risk” cohort was studied. However, the difference in cartilage thinning observed in the full cohort was also observed in the smaller subset of knees for which cartilage T2-measurements were available, so that the results appear to be robust, and the lack of detecting a difference in longitudinal T2 change is unlikely to be attributed to selection bias or a smaller statistical power. The same location-independent analysis methodology was used here to report lengthening of cartilage T2 , which iA strength of the current study was the use of a location-independent analysis method, which was previously shown to be more sensitive to differences in rates of change between different risk strata of knee OA than region-specific analysis . InteresIt appears puzzling that, despite the significant difference in cartilage thinning rates between knees “at risk” versus those “non-exposed”, the difference could not be attributed to commonly accepted risk factors that were used to select the “at risk” cohort, except for NRS pain. A potential explanation is that most of these demographic and clinical risk factors are not “strong” enough to explain the observed difference, and that there are other un-measured risk factors in play that determine the greater cartilage thickness change in KLG0 knees in the “at risk” cohort. This is supported by the observation that incident radiographic knee OA was only marginally greater over 48- (and even 96-month) follow-up in the “at risk” than in the “non-exposed” cohort. Another explanation may be that, despite the relatively long observation period of 3 years, this period may be too short in view of the relatively small rates of worsening observed in knees without radiographic OA at the beginning of the observation interval; identifying risk factors in KLG0 knees may hence require even longer observation intervals. In patients with established ROA of the knee, thinning scores were reported to amount to >1100 μm over only 2 years, and to >2000 μm when followed by total joint replacement . This maSelecting KLG0 knees at risk of OA for disease-modifying osteoarthritis drug (DMOAD) therapy  remains In conclusion, in radiographically normal knees “at risk” of incident radiographic knee OA, the cartilage thinning score was observed to be significantly greater than that in a “non-exposed” reference cohort without risk factors. Superficial or deep cartilage T2 change scores, in contrast, did not differ significantly between both cohorts. Age, sex, body mass index, previous knee trauma and/or surgery, family history of joint replacement, presence of Heberden's nodes, repetitive knee bending were each not significantly associated with cartilage thinning, and only NRS pain reached statistical significance. This lack of association with a large set of common risk factors renders it challenging to select participants for testing a “preventive” DMOAD in a clinical trial based on demographic and clinical criteria alone. Further, the analysis of the effect of demographic and clinical risk factors on structural progression starting at the pre-radiographic phase, during which collider bias is irrelevant, requires long observation periods, despite the presence of sensitive measurement technology of structural progression.Study conception and design: FE, WW, AC, LS.Acquisition of data: SM, WW, LS.Analysis & interpretation of data: All authors.Writing of first manuscript draft: FE.Critical manuscript revision and approval of final manuscript: All authors.WW had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.10.13039/100008250Feinberg School of Medicine, Northwestern University (R01 AR52918). The cartilage T2 analysis was funded by the Bundesministerium für Bildung und Forschung (BMBF – 01EC1408D (OVERLOAD-PREVOP)). The statistical analysis and writing of this article were independent from and not contingent upon approval from the study sponsors.The cartilage thickness analysis was funded by an ancillary study to the OAI held by the Division of Rheumatology, Dr. Maschek and Dr. Wirth are part time employees and co-owners of Chondrometrics GmbH. Dr. Roemer was a part time employee of Chondrometrics during the funding period of the grant by the Bundesministerium für Bildung und Forschung (BMBF – 01EC1408D OVERLOAD-PREVOP) from 10/2015 until 02/2019 and is shareholder, CMO and Director of Research of Boston Imaging Core Lab (BICL), LLC. Dr. Duda and Dr. Sharma have no conflicts to declare. Dr. Eckstein is CEO/CMO and co-owner of Chondrometrics GmbH, and he has provided consulting services to Merck KGaA, Samumed, Tissuegene, Servier, Galapagos and Roche. He also has received speaker honoraria from Medtronic.


This cross-sectional study evaluates disordered eating attitudes and food choice motives of individuals who follow a vegan diet in Brazil. Is there an association between disordered eating attitudes and food choice motives among vegan dieters?In this cross-sectional study including 971 individuals who follow a vegan diet, 94% were categorized with the lowest level of disordered eating attitudes, with only 0.6% of participants being associated with disordered eating attitudes. Some food choice motives were associated with disordered eating attitudes in this sample.Understanding the motivations of adhering to diets that may impose restrictions, which include vegan diets, may help tailor interventions focused on promoting healthy eating and preventing or treating disordered eating. It remains controversial whether adhering to a vegan diet may be associated with a higher prevalence of disordered eating. Also, main food choice motives and their association with disordered eating in this population are still unknown.To determine the association between disordered eating attitudes and food choice motives of individuals who follow a vegan diet.This is a cross-sectional online survey conducted between September 2021 and January 2023. Individuals of both sexes, aged 18 years or older, following a vegan diet for at least 6 months, currently living in Brazil were recruited through advertisements on social media.Adherence to a vegan diet and food choice motives.Disordered eating attitudes and food choice motives.Nine hundred seventy-one participants completed the online survey. Median (IQR) age and BMI of participants was 29 (24-36) years and 22.6 (20.3-24.9), respectively, and 800 participants (82.4%) were female. Most participants (908 respondents [94%]) were categorized with the lowest level of disturbed eating attitudes. “Need and hunger,” “liking,” “health,” “habits,” and “natural concerns” were the most important food choice motives in this population, while “affect regulation,” “social norms,” and “social image” were the less important ones. Adjusted models showed that “liking,” “need and hunger,” and “health” were associated with lower levels of disordered eating attitudes, while “price,” “pleasure,” “sociability,” “traditional eating,” “visual appeal,” “social norms,” “social image,” “weight control,” and “affect regulation” were associated with higher levels of disordered eating attitudes.In this cross-sectional study, as opposed to previous suggestions, levels of disordered eating were very low among vegans, although certain food choice motives were associated with disordered eating attitudes. Understanding the motivations of adhering to diets that may impose restrictions, which include vegan diets, may help tailor interventions focused on promoting healthy eating and preventing or treating disordered eating. This may be the case when the switch to a vegan diet is motivated by the pursuit of weight loss and the exclusion of foods from the diet.7 In fact, a recent systematic review showed that vegetarianism (including veganism) appears to be associated with eating disorders.4 Also, some food choice motives  may be associated with disordered eating attitudes in this population.10 Nonetheless, the association between motivations to follow a vegan diet and eating disorder symptoms remains controversial in the literature.12An important concern regarding vegan diets is that they may be associated with a higher prevalence of eating disorder symptoms, as they impose dietary restrictions.13 Gathering knowledge on eating motivation is key to understanding human behavior.14 Multiple reasons may explain eating choices, which are part of a complex interaction involving food characteristics, as well as biological, anthropological, psychological, cultural, and socioeconomic factors.16 Moreover, food choice motives have been shown to vary according to sex, body mass index (BMI), and socioeconomic status.19 However, it is still unknown how these motives are presented in individuals who decide to follow a vegan diet. Shedding light on this topic could allow us to better understand the motivations behind adhering to a vegan diet, as well as whether and how it is associated with disordered eating in this population. Therefore, the aims of the present study are to identify and associate disordered eating attitudes and food choice motives of individuals who follow a vegan diet.The reasons for shifting and adhering to a vegan diet are various, such as religious and ethical beliefs, environmental concerns, cultural and social values, as well as health-related aspects.The Vegan Eating Habits and Nutritional Evaluation Survey (VEGAN-EatS) was a cross-sectional survey conducted between September 2021 and January 2023, aimed at gathering knowledge on multiple aspects of dietary behaviors in vegans. Participants were recruited through advertisements on social media sites . Inclusion criteria were as follows: individuals of both sexes, aged 18 years or older, following a vegan diet for at least 6 months, currently living in Brazil, with the ability to read, and with internet access.20. An approved informed consent form was signed digitally by all participants before initiating the survey. This report follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies.All participants completed an online survey on the Google Forms platform (Google LLC), which enquired about their demographic, socioeconomic, and anthropometric characteristics, lifestyle, disordered eating attitudes, and food choice motives. This study was approved by the local ethical committee  and was conducted in accordance with the Helsinki declaration21The online survey included questions categorized into the following sections: (1) participant characteristics and lifestyle ; (2) anthropometric data (self-reported weight and height); and diet-related questions (duration of adherence to a vegan diet and motivations to shift toward a vegan diet). Income was categorized according to the Brazilian Institute of Geography and Statistics in income levels of A (≥US $4200), B (US $1350 to $4200), C (US $550 to $1350) and D/E (≤US $550).22 It contains 17 questions and the final score ranges from 17 to 75 points. Individuals are categorized according to their score in the following categories: below 44.6; 44.6 to 49.9; 49.9 to 55.3; 55.3 to 60.7; 60.7 to 66.1; 66.1 to 71.4; and 71.4 to 76.8, with each successive category representing more dysfunctional eating attitudes. Scores of 66.1 or higher identified individuals with disordered eating attitudes.22 The second was the Brazilian Portuguese version of The Eating Motivation Survey (TEMS)23 that evaluates food choice motives . This instrument comprises 45 questions preceded by the phrase “I eat what I eat…” and responses are given in a 5-point scale, from 1 (“never”) to 5 .Subsequently, participants filled out 2 inventories. The first was the short version of the Disordered Eating Attitude Scale (DEAS) that evaluates the presence of disordered eating attitudes.24 using the mice package in R,25 set to 5 iterations and the classification and regression trees method. A matrix of predictors was used to impute missing body mass data; the predictors selected were variables present in the main models, namely age, sex, income, exercise habits, motivation to shift to a vegan diet, and DEAS and TEMS scores. A comparison between original and imputed values is available in eFigure 1 in Due to important missingness (33%) in anthropometric data , individuals with and without these missing data were compared to visualize whether possible associations between missingness and important variables, such as DEAS score, were present (eTable in Descriptive data are presented as median and IQR for continuous variables and absolute and relative frequency (number and percentage) for categorical variables. Associations between food choice motives (independent variables) and disordered eating attitude scores (dependent variable) were individually (one model for each motive) tested using multivariable linear regression models. A directed acyclic graph was constructed to identify confounders of the possible outcomes of food choice motives on disordered eating attitudes, considering expert knowledge and current evidence from the literature . A complete case analysis  is also present in eFigure 3 in 28 and RStudio version 2022.12.0 , using the dplyr,29 ggplot2,30 and stats28 packages for data cleaning, visualizations, and linear models, respectively. The level of significance was set a priori at P ≤ .05.These models were carried in all 5 imputed data sets, and results from models were pooled using the methods proposed by Rubin.One thousand and fourteen participants completed the online survey; however, 43 were excluded for reportedly not following a vegan diet. Thus, data of 971 confirmed vegan dieters were used to describe sample characteristics and lifestyle, disordered eating attitudes and food choice motives . Linear The median (IQR) age of participants was 29 (24-36) years, and the median (IQR) BMI of participants was 22.6 (20.3-24.9) . The largest groups were female (800 respondents [82.4%]), had a postgraduate educational level (354 participants [36%]), belonged to socioeconomic class B (359 participants [37%]), reported no alcohol consumption (405 participants [42%]) or smoking habits (887 participants [91%]), exercised 3 to 4 hours per week (219 participants [23%]) and adhered to a vegan diet for 5 or more years (280 participants [29%]) . The mos22 Most importantly, only 0.6% of our sample (1 participant) was identified with disordered eating attitudes. “Need and hunger,” “liking,” “health,” “habits,” and “natural concerns” were the most important  food choice motives in this population, while “affect regulation,” “social norms,” and “social image” were the least important ones (Median (IQR) DEAS score was 20 (18-25), with most participants (908 respondents [94%]) being categorized with the lowest level of disturbed eating attitudes.ant ones .P < .001), “need and hunger”  and “health”  were associated with lower disordered eating attitudes scores. In contrast, “price” , “pleasure” , “sociability” , “traditional eating” , “visual appeal” , “social norms” , “social image” , “weight control” , and “affect regulation”  were associated with higher disordered eating attitude scores  the vast majority of participants (94%) were categorized into the lowest level of disordered eating attitudes, with only 0.6% of participants being identified with disordered eating attitudes; (2) the most important food choice motives were “need and hunger,” “liking,” “health,” “habits” and “natural concerns”; and (3) 9 food choice motives were associated with disordered eating attitudes.31 However, these associations are still controversial, with previous data showing no difference in disordered eating attitudes among vegetarians and omnivores.11 Interestingly, very low levels of disordered eating attitudes were found in our population. Namely, only 0.6% of our sample was identified with disordered eating attitudes. This is a significantly lower prevalence than that found in a similar Brazilian cohort within the general population (6.5%).22Some studies have found that following a vegetarian diet may be associated with disordered eating attitudes and eating disorders.32 Moreover, eating motives commonly related to disordered eating attitudes 10 were not reported as important eating motives in our population. In fact, vegetarians (including vegans) reported more eating motivations related to “health” and “natural content,” but less related to “weight control” when compared with omnivores.11 Conversely, another study exploring differences and commonalities in eating motives of vegetarians (including vegans) and omnivores found that motives were very similar across groups, with “liking” and “health” being among the top 4 motives, and “social norms,” “social image,” and “religion” being among the 4 least important ones.33 Likewise, findings of our study were that individuals who follow a vegan diet reported health-related motives as the most relevant, alongside with preference and physiological needs.These low rates could be partially explained by 62% of participants reporting “ethics and animal rights” and only 10% reporting “health reasons” as motivation to adhere to a vegan diet. Indeed, a previous small-scale study (62 participants) that comprised vegan dieters mainly due to ethical reasons did not find a higher risk of eating disorder symptoms.7 However, this was a smaller study, possibly precluding detection of the associations observed herein. In fact, it is known that some eating motives are related to disordered eating attitudes among the general population.10 Although we found that levels of disordered eating were low, we were able to identify certain food choice motives that were associated with increased DEAS scores in vegans: “sociability,” “traditional eating,” “visual appeal,” “pleasure,” “social norms,” “social image,” “weight control,” and “affect regulation.” This aligns with previous research that shows that individuals more concerned with their appearance and social image,36 as well as emotional eaters,37 exhibit more disordered eating attitudes, while individuals who respect internal cues to eat 40 and have a non–weight related health concern9 tend to have a better relationship with their own bodies and healthier eating attitudes. Although it is challenging to estimate the clinical significance of changes in DEAS score, the magnitude of association between some food choice motives found herein could be of potential clinical relevance, as an approximately 3 to 5 unit increase for some motives  would lead, on average, to a 5-point increase in DEAS score, shifting the respondent’s disordered eating attitudes category. As such, understanding food choice motives could be a clinically useful tool in identifying individuals at a higher risk of disordered eating behavior.Contrary to our findings, previous research found that diet motives were not associated with greater disordered eating in a sample of 110 vegans.5 the presence of disordered eating attitudes in individuals following this diet may be associated with restriction itself  and food choice motives , rather than the decision to adopt a vegan diet, specifically. In fact, a study that reported higher levels of disordered eating behaviors in vegans compared with omnivores showed that it was significantly predicted by cognitive restraint and eating concern.7 Additionally, any claims that a vegan diet serves as a socially acceptable way to restrict food intake and conceal disordered eating attitudes should consider that such a disordered behavior is more likely among individuals whose eating choices, regardless of their diet type, are motivated by self-image and social acceptance. Therefore, it is unlikely that the association between vegan diet and disordered eating attitudes could be solely due to the diet per se, but to common factors related to the adherence to restrictive diets.Our findings suggest that, although vegan diets could be associated with disordered eating attitudes,22 Nonetheless, although it is not possible to infer eating disorder prevalence from our data, assessment of disordered eating attitudes is of utmost value due to its prevalence and consequences.46Strengths of this study include the large sample size, specific dietary status  and the use of validated questionnaires. In addition, our data add to the existing literature by identifying food choice motives that are associated with disordered eating attitudes in this population. It is important to highlight that DEAS evaluates disordered eating attitudes  rather than eating disorder symptoms.48 Additionally, ours was a convenience sample, predominantly composed of female participants, with eutrophic BMI and high educational level (as compared with the average in the country), thus limiting our findings’ generalizability. Further studies with more heterogeneous, probabilistic samples are warranted, and so are studies including qualitative assessments, which could enable more in-depth understanding of disordered eating attitudes and food choice motives among individuals who follow a vegan diet.This study is not without limitations. First, the cross-sectional design does not allow causality to be inferred. Second, the use of self-reported weight and height may also be perceived as a limitation, although previous studies have shown that these self-reported measures are valid across different sexes and BMIs.In conclusion, in this large survey among vegan dieters, we observed low levels of disordered eating attitudes. Moreover, “need and hunger,” “liking,” “health,” “habits,” and “natural concerns” were the most important food choice motives in this population. We also identified that several food choice motives were associated with disordered eating attitudes. Our findings may help shift the current perspective away from vegan diet per se leading to disordered eating, while highlighting the importance of food choice motives in general as factors associated with risk for disordered eating. Understanding food choice motives for shifting and adhering to diets that may impose restriction, which include, but are not limited to, vegan diets, may help tailor better interventions focused on promoting healthy eating and preventing or treating disordered eating.