To evaluate choroidal thickness (CT) in healthy and glaucomatous eyes using Swept Source Optical Coherence Tomography (SS-OCT).A cross-sectional observational study of 216 eyes of 140 subjects with glaucoma and 106 eyes of 67 healthy subjects enrolled in the Diagnostic Innovations in Glaucoma Study. CT was assessed from wide-field (12×9 mm) SS-OCT scans. The association between CT and potential confounding variables including age, gender, axial length, intraocular pressure, central corneal thickness and ocular perfusion pressure was examined using univariable and multivariable regression analyses.Overall CT was thinner in glaucomatous eyes with a mean (± standard deviation) of 157.7±48.5 µm in glaucoma compared to 179.9±36.1 µm in healthy eyes (P<0.001). The choroid was thinner in both the peripapillary and macular regions in glaucoma compared to controls. Mean peripapillary CT was 154.1±44.1 µm and 134.0±56.9 µm (P<0.001) and macular CT 199.3±46.1 µm and 176.2±57.5 µm (P<0.001) for healthy and glaucomatous eyes respectively. However, older age (P<0.001) and longer axial length (P<0.001) were also associated with thinner choroid and when differences in age and axial length between glaucomatous and healthy subjects were accounted for, glaucoma was not significantly associated with CT. There was also no association between glaucoma severity and CT.Glaucoma was not associated with CT measured using SS-OCT; however, older age and longer axial length were associated with thinner choroid so should be considered when interpreting CT measurements. Glaucoma is characterized by progressive damage to retinal ganglion cells leading to optic nerve head (ONH) morphological changes, thinning of the retinal nerve fiber layer and loss of visual field Initial histological studies supported an association between choroidal morphology and glaucoma, with several reports of thinner choroid in glaucomatous compared to non-glaucomatous eyes It is now possible to obtain in vivo images of the choroid using enhanced depth imaging optical coherence tomography (EDI-OCT), a modified version of spectral domain OCT (SD-OCT) The aim of the present study was to use SS-OCT to assess CT in healthy and glaucomatous eyes. We also assessed the effect of factors including age, gender, race, axial length, retinal nerve fiber layer (RNFL) thickness, mean deviation (MD), intraocular pressure (IOP), blood pressure (BP) and ocular perfusion pressure (OPP) on choroidal thickness.This was a cross-sectional observational study of participants from the Diagnostic Innovations in Glaucoma Study (DIGS) at the University of California San Diego (UCSD). The DIGS is a prospective longitudinal study designed to evaluate optic nerve structure and visual function in glaucoma. Informed consent was obtained from all participants, and the institutional review board and human subjects committee at UCSD prospectively approved all methods. All study methods adhered to the tenets of the Declaration of Helsinki for research involving human subjects and the study was conducted in accordance with the regulations of the Health Insurance Portability and Accountability Act.Methodological details have been described in detail previously The study included 322 eyes of 207 participants, including 106 healthy and 216 glaucomatous eyes. Eyes were classified as glaucoma if they had repeatable (≥2 consecutive) abnormal SAP test results or progressive glaucomatous changes on masked grading of optic disc stereophotographs. Healthy subjects were recruited from the general population through advertisements and from the staff and employees of the University of California San Diego. Healthy eyes had IOP≤21 mmHg, with no history of increased IOP and a normal SAP result.SAP was performed using the Humphrey Field Analyzer II  and the 24-2 Swedish interactive threshold algorithm . All visual fields were evaluated by the UCSD Visual Field Assessment Center (VisFACT) Simultaneous stereoscopic optic disc photography  was performed for all subjects and digital stereoscopic images were reviewed with a stereoscopic viewer  by two or more experienced graders. Each grader was masked to the subject's identity and to the other test results. Details of the methodology employed to grade optic disc photographs at the UCSD Optic Disc Reading Center have been provided elsewhere CT was assessed from images acquired using DRI-OCT, which is a new Swept Source OCT, currently not available commercially in the United States. The DRI-OCT acquires 100,000 A-scans per second and provides an axial resolution in tissue of 8 mm. DRI-OCT has a center wavelength of 1,050 nm and a sweeping range of approximately 100 nm, compared to the 850 nm wavelength of SDOCT DRI-OCT segmentation software  was used to identify the limits of the choroid and determine CT throughout the 12×9 mm scan. Data was exported using the manufacturer's OCT-Batch (version 9.1.10) utility. The quality of each scan and accuracy of the segmentation algorithm were reviewed by an experienced examiner and poor quality images  were excluded.Scans with segmentation failures and motion artifacts were also excluded.2 grid square of the 12×9 mm scan and allows this date to be displayed and exported. Therefore for each eye the average CT in a total of 108 locations was calculated  and 58 eyes with scans with segmentation failures and motion artifacts were excluded. This left a total of 216 glaucomatous eyes from 140 patients and 106 healthy eyes from 67 subjects.The demographic and clinical characteristics of those included in the study are summarized in The relationship between total CT and variables including age, gender, ancestry, axial length, systolic and diastolic OPP, and CCT was examined in healthy subjects. Scatter plots suggested a tendency for total CT to be lower in older subjects and those with longer axial length . UnivariA similar analysis was performed using glaucomatous and healthy eyes and for macular and peripapillary CT. Age and axial length were associated with thinner total, peripapillary and macular CT . In univIn the present study SS-OCT was used to measure CT in healthy and glaucomatous eyes. The wide-field SS-OCT scan allowed imaging of the macular and peripapillary regions in a single scan, and over a wider area than previously studied. Although we found an association between glaucoma and thinner choroid in the univariable analysis, any association disappeared after adjusting for differences in age and axial length between groups. Older age and longer axial length were both associated with thinner choroid, suggesting that the apparent association between glaucoma and thinner choroid was in fact due to the confounding effect of age and axial length. Accounting for age and axial length, there was also no significant relationship between glaucoma and thickness of the macular or peripapillary choroid.We also examined the relationship between CT and markers of glaucoma severity, however there was no significant association between SAP MD and total, macular or papillary CT. Thinner RNFL was associated with thinner choroid in univariable analyses , howeverThese findings are in agreement with previous studies using EDI-OCT suggesting that in vivo measurements of CT are not significantly different in patients with open angle glaucoma compared to controls Due to the proximity of the peripapillary choroid to the ONH, one might suppose that peripapillary CT would be a better measure of the blood supply to the ONH and therefore of more relevance to glaucoma. There is some evidence that peripapillary CT might be important in specific subtypes of glaucoma The current study has provided useful information about the normal choroid. The results indicate that CT is largest at the macula and decreases towards the peripapillary region. The macular choroid was typically 29% thicker than the peripapillary choroid in healthy subjects. The finding that CT is greatest near the macula is consistent with previous studies, however, overall the CT measurements in this study were slightly thinner than those previously reported using both SS-OCT and EDI-OCT Previous studies have shown an association between thinner choroid and older age, longer axial length, thicker CCT, and lower ocular perfusion pressure Interestingly, we found the regression coefficient between age and choroidal thickness was smaller in healthy subjects compared to those with glaucoma (−0.72 (95% CI = −1.41 to 0.04) versus −1.03 (95% CI = −1.65 to −0.40). This indicates that the relationship between older age and thinner choroid may be steeper in glaucomatous eyes than controls, or in other words, there may be a greater decrease in CT with aging in those with glaucoma compared to controls. Although the confidence intervals overlap, indicating the differences in slopes of change in CT did not reach statistical significance; it raises the possibility that there may be an interaction between glaucoma, aging and changes in choroidal thickness over time. It would be interesting to evaluate this possible interaction in a longitudinal study, using age-matched glaucomatous and healthy subjects.There were limitations to the present study. 68 of 390 eyes (17.4%) were excluded due to poor scan quality or segmentation failures, which potentially could have introduced bias. For example, it is possible that eyes with thicker choroid may have been more difficult to image due to deeper penetration needed. However, it was important to have a rigorous quality control to ensure included scans were accurate and significant bias seems unlikely as the proportion of glaucomatous and healthy eyes excluded was similar (49 of 265 (18.5%) versus 19 of 125 (15.2%) respectively). A further potential limitation of this study was that patients with glaucoma were already receiving treatment, For this reason, the study had limited ability to evaluate the association between IOP and CT and IOPs as shown from the observation that IOPs were not significantly different between glaucomatous and healthy eyes . It is pIn conclusion, this study has shown that SS-OCT is a useful tool for evaluation of CT. Using cross-sectional data we have demonstrated a relationship between increasing age, longer axial length and thinner choroid. However, when differences in age and axial length between glaucomatous and healthy subjects were accounted for there was no association between glaucoma and CT. Although we found no relationship between glaucoma and CT, prior studies have indicated an association between glaucoma and both impaired choroidal circulation and decreased blood flow to the ONH Table S1Choroidal thickness dataset. The dataset contains data for all 216 glaucomatous eyes and 106 healthy eyes included in the study.(CSV)Click here for additional data file.


The role that environmental factors, such as neighborhood socioeconomics, food, and physical environment, play in the risk of obesity and chronic diseases is not well quantified. Understanding how spatial distribution of disease risk factors overlap with that of environmental  characteristics may inform health interventions and policies aimed at reducing the environment risk factors. We evaluated the extent to which spatial clustering of extreme body mass index (BMI) values among a large sample of adults with diabetes was explained by individual characteristics and contextual factors.We quantified spatial clustering of BMI among 15,854 adults with diabetes from the Diabetes Study of Northern California (DISTANCE) cohort using the Global and Local Moran’s I spatial statistic. As a null model, we assessed the amount of clustering when BMI values were randomly assigned. To evaluate predictors of spatial clustering, we estimated two linear models to estimate BMI residuals. First we included individual factors (demographic and socioeconomic characteristics). Then we added contextual factors  that may be associated with BMI. We assessed the amount of clustering that remained using BMI residuals.Global Moran’s I indicated significant clustering of extreme BMI values; however, after accounting for individual socioeconomic and demographic characteristics, there was no longer significant clustering. Twelve percent of the sample clustered in extreme high or low BMI clusters, whereas, only 2.67% of the sample was clustered when BMI values were randomly assigned. After accounting for individual characteristics, we found clustering of 3.8% while accounting for neighborhood characteristics resulted in 6.0% clustering of BMI. After additional adjustment of neighborhood characteristics, clustering was reduced to 3.4%, effectively accounting for spatial clustering of BMI.We found substantial clustering of extreme high and low BMI values in Northern California among adults with diabetes. Individual characteristics explained somewhat more of clustering of the BMI values than did neighborhood characteristics. These findings, although cross-sectional, may suggest that selection into neighborhoods as the primary explanation of why individuals with extreme BMI values live close to one another. Further studies are needed to assess causes of extreme BMI clustering, and to identify any community level role to influence behavior change.The online version of this article (doi:10.1186/1476-072X-13-48) contains supplementary material, which is available to authorized users. Area level socioeconomic and food environment factors have been associated in cross-sectional analysis with body mass index (BMI) –3, insulAmong patients diagnosed with diabetes, weight is viewed as a modifiable risk factor, and patients who are obese are encouraged to decrease weight to better manage and limit disease progression . Weight Identifying and assessing spatial clustering of extreme health values has been considered a hallmark indication that contextual (area level) exposures have an impact on health outcomes. Alternatively, spatial clustering of health outcomes may be the result of personal choices, conditions and preferences which result in residential selection with those with a given health condition living in the same area. For example, poverty may relegate some people to one neighborhood while preference for parks or schools might influence others to live near these resources. Poverty and personal preference may therefore be causally linked with an outcome and not neighborhood attributes. Spatial analysis of health has increased over the past decade, but most public health and epidemiology research is still “aspatial” despite having a focus on place-based influences on health determinants and outcomes . In thisSpatial hot spots of adults with diabetes in clusters of high or low BMI values might be interpreted as sentinel communities. Such communities could help us understand what environmental cues promote high and low BMI clusters—either by drawing individuals into the community or by assisting individuals to maintain extreme high or low BMI status. Identifying neighborhoods with high BMI clusters could help direct the distribution of resources for obesity prevention and treatment programs among adults with diabetes, and may “shape an appropriate intervention program tailored for the residents in a particular geographic region” .We hypothesized that high or low BMI values in a cohort of adults with diabetes would cluster geographically. More specifically, individuals with high (or low) BMI values would live close to others with a high (or low) BMI, respectively. We conducted a study among adults with diabetes from the Diabetes Study of Northern California (DISTANCE) to evaluate these hypotheses. Our aims were to: (1) examine the extent to which extreme BMI values were spatially clustered, and (2) identify significant associations between area level factors and spatial clustering in a large cohort of adults with diabetes.2 (0.38 mi2) where 78% of those participant census blocks are under 0.1 km2 (0.038 mi2). The study area covers all 19 counties in the Northern California Kaiser Permanente service area totaling 70,585 km2 .The Kaiser Permanente Northern California Diabetes Registry was established in 1993 within Kaiser Permanente Northern California, a large, integrated health care delivery system serving more than 3 million members. Kaiser Permanente of Northern California (KPNC) members are 30% of the population of Northern California  and haveOur initial goal was to examine the extent to which extreme BMI values displayed positive spatial autocorrelation. There are a variety of methods to measure spatial autocorrelation which are generally grouped into two categories: 1) global indicators which measure the overall global or population level spatial autocorrelation in a dataset, and 2) local indicators, also known as Local Indicators of Spatial Association (LISA), which measure the spatial autocorrelation of each feature in relation to each neighboring feature in a dataset. Global Moran’s I was selected for this study because it measures overall spatial autocorrelation based on feature attribute values and is intended for datasets where both high and low value clusters are assumed to exist. Additionally, Global Moran’s I is more sensitive to extreme values than similar indicators such as Geary’s C, while Kulldorff spatial scan, Ripley’s K, and Cuzick-Edwards k-Nearest Neighbor indicators do not consider the attribute values of the features under analysis, only their spatial location. Global Moran’s I calculates each feature’s measured index, expected index, variance, z-score, and p-value and indicates if the outcome is overall clustered (positive), dispersed or regular pattern (negative), or randomly distributed over space (zero) . For a d global i local inLocal Moran’s I was also selected for this study because it allows for the identification of spatial hotspots of local areas that represent clusters of unexpectedly high or low values compared to the global mean , 21 ThisWe assessed body mass index (BMI = kilograms/meters squared) and BMI residuals as continuous measures. BMI was calculated from electronic records using the first clinical measurement of height and weight recorded in an outpatient visit within one year before or after the survey date. For individuals with no measured weight and height within two years after the survey, self- reported weight and height from the survey was used . To produce BMI residuals, multivariate linear regression (ordinary least squares) was performed and BMI was regressed on a set of hypothesized confounders of the relation between BMI and place of residence. Model 1 included individual demographic and socioeconomic factors and Model 2 included neighborhood level factors. BMI residuals represent the portion of BMI not explained by covariates in a model, and the spatial analysis of the BMI residuals can be interpreted as clustering of unexplained low/low or high/high BMI variation .Model 1 regressed BMI on individual level characteristics that included race/ethnicity , marital status , sex, age , education , nativity (number of years in the US and US born), and income to poverty ratio, defined as self-reported family income for a given age and household size divided by the 2005 poverty level income for the same age and household size; this variable was categorized as >600% , 301-600%, 101-300%, 0-100% of poverty level, and “don’t know/refused/missing” [missing” . AdditioModel 2 regressed BMI on a set of neighborhood level characteristics that included neighborhood deprivation index (NDI) as a continuous and categorical quartile variable, healthful and unhealthful food environment retail density measures, 2000 census tract population density per square mile and percent of population white, distance to nearest Kaiser Permanente healthcare facility, and 2006 municipal level property and violent crime per 100,000 population rate from the US Federal Bureau of Investigation Uniform Crime Reports. The neighborhood deprivation index (NDI) [ex (NDI)  was creaex (NDI)  to createx (NDI)  as impleex (NDI) , 27, 28.Model 3 regressed BMI on the above set of individual and neighborhood level characteristics. Additional models were tested that included health indicators of Charlson-Deyo comorbidity score [ty score , smokingty score , 31. TheThe degree of clustering of BMI and BMI residual values from each regression model for the population  was conducted using Global and local Moran’s I. Global Moran’s I was calculated using a Euclidian neighborhood search radius of 1.6 km (1 mi) where the target feature (e.g. geocoded address) was weighted at one and the weight of all neighboring features (e.g. neighboring geocoded addresses) within this radius decreased by distance until the 1.6 km threshold was reached. Neighboring features outside the radius are weighted at 0. The 1.6 km radius approximates a typical neighborhood size in our study area and has been found to be associated with health outcomes . See ESRWe conducted two sensitivity analyses. First, we repeated the calculation of Local Moran’s I using a 1.6 km radius on 100 sets of randomly assigned cohort BMI values. The results were summarized to establish the magnitude and locations of clustering that could exist at random within our study area and to generate bootstrap confidence intervals to aid in interpreting the robustness of the non-randomized Local Moran’s I results. Second, we conducted a sensitivity analysis using a 3.2 km (2 mi) radius to test the sensitivity of clustering to our selected neighborhood radius. Spatial clustering analyses were conducted with ArcGIS v.10.1 .The point feature low/low and high/high cluster spatial results of the BMI, BMI residuals, and one randomly distributed BMI run example were transformed into raster density surfaces for display. The density surfaces perform two functions: first to mask individual locations by smoothing distinct point locations over a larger area and second, to facilitate the identification of spatial patterns within the study area with large concentrations of low/low and high/high clusters. The outcome density surfaces were created using a neighborhood radius of 3.2 km and a cell size of 500 m in units of square kilometers. The resulting density surface depicts a magnitude of the number of points (e.g. geocoded addresses) per unit area that are within the neighborhood radius.The spatial point results of the cluster analysis for BMI and BMI residuals were examined for patterns in the geographic distribution of any remaining clusters. A visual inspection for geographic locations that had large numbers of BMI residual clusters (≥50) within a municipality and whose location and cluster type persisted throughout each Local Moran’s I BMI and BMI residual model spatial result were noted.The race-stratified sample was comprised of 27.1% Asians, 23.4% white, 19.1% Hispanics, 17.8% African-Americans, and 12.7% of other or mixed race. BMI for our sample population ranged from 18.0 to 68.7 with a mean of 31.1 (SD 6.5)  radius, indicated 11.9% of cohort patients are significantly clustered in either a low/low (6.7%) or a high/high (5.2%) BMI cluster Table . PatientAfter controlling for possible confounders using regression Models 1, 2, and 3, the BMI residuals were predicted and again subjected to the Local Moran’s I analysis. The results of Model 1, controlling for individual characteristics, reduced the percentage of the sample population that was spatially clustered by 68%; from 11.9% to 3.8%  and clusters were concentrated within 4.8 km (3 mi) of each other. The number of high/high clusters from Model 3 in the three highlighted areas numbered 76 in area 1, 57 in area 2 and 51 in area 3. These three areas represented roughly 34% (184/547) of the residual group and indicated that high/high BMI residuals among clustered cohort members in these locations were not explained well by our model. The residual clustering may be due to some factor not captured in our models.This study assessed the presence and extent of clustered low and high BMI values among adult Kaiser Permanente members with diabetes in Northern California. Our findings show a moderately low level of global autocorrelation, but a substantial percent of local clustering of low/low and high/high BMI values. In our case, we applied a 1.6 km (1 mi) radius to determine if local areas have BMI values that are higher or lower than would be expected based on the global average or a random expectation for the entire study area . To direWe found that after adjusting for individual demographic and socioeconomic characteristics the Global Moran’s I was reduced to near zero, suggesting that individual factors accounted for most of the spatial autocorrelation in the BMI values and individual factors explained roughly 68% of the local BMI clustering. Adjusting for only neighborhood factors reduced the Global Moran’s I by half but it remained significant. The pattern of spatial clustering was similar between Models 1 and 2. Although the neighborhood characteristics did not explain as much of the spatial clustering as did the individual factors, 50% and 68% reduction might not represent a substantial difference in regards to spatial clustering of extreme BMI values. Model 3, which accounted for both individual and neighborhood characteristics, reduced clustering the greatest amount, suggesting that the individual and neighborhood factors address spatial autocorrelation and explained almost all of the extreme BMI clustering. The residual amount of spatial autocorrelation is similar to what we found when randomizing the BMI values or what we would expect by chance.Although the regression models were able to account for nearly all of the clustering of extreme BMI residual values, we mapped the remaining observations (<3.45%) that demonstrated persistent clustering. The remaining observations with low/low and high/high BMI residual values had a similar geographic pattern compared to the spatial pattern for the unadjusted BMI values. Throughout all the models, three locations within a 4.8 km (3 mi) radius persisted with 50 or more individuals who had very high BMI values and no locations had 50 or more individuals with low BMI values.Our analysis is cross-sectional and therefore cannot infer causality. While most neighborhood studies employ cross-sectional designs, longitudinal studies may better capture the fluidity of the neighborhood environment and how changes over time affect health outcomes. Many neighborhoods are fairly stable within the time frame of a few years, although changes in residence could also influence neighborhood characteristics. Our findings that individual characteristics explained much of the variation in BMI, rendered Global Moran’s I to be non-significant and reduced the amount of clustering of the BMI residuals, suggest that individual factors may be the cause of the clustering. Neighborhood selection, whether voluntary or involuntary, is believed to be driven by individual choices, conditions, and preferences in response to life events such as illness, change in job, retirement, or other changes in family composition or social status. These findings support the idea that additional research may be needed to understand individual selection factors that may be correlated with health. Compared with other studies, we had a very large sample size; however, our cohort of adult Kaiser Permanente Northern California (KPNC) patients with diabetes may not be generalizable to the larger population of adults with diabetes, although it may be generalizable to the larger Kaiser population of adults with diabetes in Northern California and also to adults with diabetes who have health insurance. KPNC members make up 30% of the population of Northern California and have similar demographic and socioeconomic distributions to the population from the surrounding geographical region except for the very extremes of the income distribution; fewer very rich and fewer very poor . We did Our findings indicate a significant level of clustering of extreme high and low BMI values among adults with diabetes across Northern California. Individual demographic and socioeconomic factors accounted for somewhat more (68% vs 50%) than neighborhood or contextual factors, this finding suggests that individual choices, conditions, and preferences may play a strong role in how individuals select the neighborhoods in which they live. Although individual demographic and socioeconomic factors explain much of the BMI clustering, these methods may help identify where people at greatest risk live. While recent studies have found spatial clustering of cardiometabolic risk factors –38, confBarbara A Laraia and Samuel D Blanchard shared first author.Literature review of spatial autocorrelation methods in the public health literature.(DOCX 31 KB)Additional file 1:Additional file 2: Table S1: Examples of spatial cluster methods reported in the public health literature. Literature review table detailing examples of spatial cluster methods reported in the public health literature which includes focal target , cluster algorithm(s), target (attribute), location, and reference. (XLSX 15 KB)


The upper respiratory tract (URT) hosts a complex microbial community of commensal microorganisms and potential pathogens. Analyzing the composition and nature of the healthy URT microbiota and how it changes over time will contribute to a better understanding of the pathogenesis of pneumonia and otitis. A longitudinal study was conducted including 174 Holstein calves that were divided in four groups: healthy calves, calves diagnosed with pneumonia, otitis or both diseases. Deep pharyngeal swabs were collected on days 3, 14, 28, and 35 of life, and next-generation sequencing of the 16S rRNA gene as well as quantitative PCR was performed. The URT of Holstein dairy calves aged 3 to 35 days revealed to host a highly diverse bacterial community. The relative abundances of the bacterial genera Mannheimia, Moraxella, and Mycoplasma were significantly higher in diseased versus healthy animals, and the total bacterial load of newborn calves at day 3 was higher for animals that developed pneumonia than for healthy animals. Our results corroborate the existing knowledge that species of Mannheimia and Mycoplasma are important pathogens in pneumonia and otitis. Furthermore, they suggest that species of Moraxella can potentially cause the same disorders (pneumonia and otitis), and that high neonatal bacterial load is a key contributor to the development of pneumonia. Bovine respiratory disease (BRD) is a complex, multifactorial disorder caused by a combination of microbial pathogens1245565912Pasteurella multocida18Mannheimia haemolytica18Histophilus somniMycoplasma bovis20Mycoplasma spp.Several viral and bacterial etiological agents have been associated with bovine respiratory tract (RT) disease. Bovine viral diarrhea virus (BVDV), bovine respiratory syncytial virus (BRSV), and parainfluenza type 3 virus (PI-3) have all been described as important causative agents of the BRD complex131516Mannheimia haemolyticaHistophilus somniMycoplasma spp.Mycoplasma bovis30Pasteurella multocida22Staphylococcus spp.23Streptococcus spp.Another relatively common disease affecting dairy calves is otitis media. This infection of the middle ear manifests as head tilt and sometimes facial paralysis due to involvement of cranial nerves VII and VIII and peripheral vestibular structures222326et al.et al.38Currently, intensive efforts have focused on understanding the composition and nature of bodily microbial populations in a balanced microbiome state, and how shifts in such microbial community structures impact the health of both humans135Therefore, the goal of this study was to characterize longitudinally the URT microbiome of Holstein dairy calves by using high-throughput sequencing of the 16S rRNA gene. We aimed to compare the URT microbial communities of healthy and unhealthy subjects at each time point of our data collection.In total, 174 calves were enrolled in this study. Of these, 37 (21.3%) were diagnosed with pneumonia, 62 (35.6%) with otitis, 11 (6.3%) with pneumonia-otitis combined, and 64 (36.8%) were healthy . The aveWe collected 696 deep nasal swab samples from 174 Holstein calves. Samples were collected at days 3, 14, 28, and 35 of life. All samples collected were used individually to assess the microbiome by amplification and next-generation sequencing of the V4 region of the 16s rRNA gene. A total of 5 sequencing runs were performed using the Miseq sequencer  and theV2 chemistry kits (300-cycles); approximately 140 barcoded samples were sequenced on each run.Sequences were filtered for size, quality, and for the presence of chimeras and the total post-quality control number of sequences used in the study were 63,638,904. The average coverage was 91,567, the SD was 58,425, and the range was 1,423 to 657, 375 numbers of reads per sample.The mean number of reads for each health status  was not significantly different within each time point . RegardiP-value < 0.0001)  (data not shown).A negative correlation was detected between the total bacterial load, as assessed by the number of 16S rRNA gene copies, and the Shannon diversity index  . Similar 0.0001) . No corr10 (SE = 0.10) for animals that were later diagnosed with pneumonia and 3.55 log10 (SE = 0.06) for healthy animals  using a calibration curve is provided. At day 3 of life, healthy calves had significantly lower total bacterial loads, as defined by the log10 copy numbers of the 16S rRNA gene, than calves diagnosed with pneumonia. The average counts of the 16s rRNA gene copies at day 3 was 3.80 log animals .The relative abundances of the eight most common phyla of the URT regardless of age and health conditions  are depicted in Mannheimia, Mycoplasma, Moraxella, Psychrobacter, and Pseudomonas were the top 5 genera regardless of health status  of the 30 most abundant bacterial genera identified in the calf URT at days 3, 14, 24 and 35 of life is depicted in h status . Notablyver time ,B.Mannheimia and Moraxella at day 14 in calves diagnosed with pneumonia was significantly higher when compared to healthy calves  was not predictive of pneumonia, but the total URT bacterial load was. Therefore, pneumonia and otitis pathogens are already present in the URT of newborn calves, but those that will eventually develop respiratory disease simply have a higher load of total bacteria at 3 days of life.To our knowledge, this is the first report to have longitudinally evaluated the URT microbiota of Holstein dairy calves from birth (day 3 of life) until 35 days of life. The URT is a critical point of entry for pathogens, and thus a potential route for infection of the lower respiratory tractet al.4647The incidence of pneumonia and otitis reported in the present study was higher than the rates reported by the NAHMS 2002In this present study, all calves that were diagnosed with pneumonia received antibiotic therapy. Systemic antibiotic therapy in calves has been reported to alter the fecal microbiomeMoraxella and, disease statuses. Calves that were diagnosed with pneumonia, otitis, or pneumonia-otitis combined had a significantly higher abundance of Moraxella at day 14 when compared to healthy calves. Although members of the genus Moraxella are often isolated from cases of keratoconjunctivitis, an important ocular illness in bovines5051Moraxella with pneumonia and/or otitis media. Catry et al.Moraxella ovis from the upper and lower respiratory tract of calves affected with acute and chronic respiratory diseaseet al.Moraxella spp. enhance the growth of RT bacterial pathogens such as Mannheimia haemolytica, Pasteurella multocida, and Haemophilus somnusMoraxella and the incidence of pneumonia and otitis in pre-weaned dairy cattle described in the present study is novel and of potential significance.An important result of the present study was the association between the genus Mannheimia and Mycoplasma increased substantially over time. At days 14, Mannheimia were significantly more abundant in disease statuses, and the same trend was observed for Mannheimia and Mycoplasma at day 28 of life. It is known that Mannheimia haemolytica is a commensal organism that inhabits the nasopharynx and can lead to disease when calves are exposed to stress factors such as weaning, comingling, and coinfection with others microorganismsMycoplasma bovis13Pasteurella, another important bacterium for the BRD complex in calves1819et al., who detected a lower occurrence of Pasteurella multocida in a BRD outbreak in North American feedlotsThe abundance of Mannheimia, Mycoplasma, Moraxella, Psychrobacter, and Pseudomonas. Recently, Holman et al.Staphylococcus, Mycoplasma, Mannheimia, and Moraxella were the dominant genera identified in the URT samples collected at 60 days after feedlot arrival, comparable to what we observed in young calves with bacterial infections. The Holman results at 60 days may in part be a consequence of the high levels of stress In our study the most abundant genera detected in the URT of dairy calves were Mycoplasma, Mannheimia, Pasteurella, Staphylococcus, and Streptococcus were detected in all animals at four time points examined, regardless of health conditions. These pathogens have been repeatedly described as the primary cause of otitis media301326The genera et al.et al.et al.The microbial diversity is understood as being a function of the number of different categories (richness) and the relative distribution of individual elements among these categories (evenness)Mannheimia, Mycoplasma, Moraxella were the three most common bacterial genera detected in the calves URT. Our data supports the concept that Mannheimia and Mycoplasma are two dominant bacteria associated with pneumonia and otitis. Additionally, the genus Moraxella could play an important role in the pathogenesis of pneumonia and otitis, and the high neonatal bacterial load is a significant contributor to the development of pneumonia. These results provide an unprecedented understanding of the evolution of the bovine URT microbiome in pre-weaning calves and its association with RT diseases.In summary, this study demonstrated that the URT of Holstein dairy calves from 3 days to 35 days of life encompasses a highly rich and diverse bacterial community. Thirty genera were shared between all ages and health statuses and o 518)This study was conducted on a commercial dairy farm located near Ithaca, New York, from November 2013 until February of 2014. The collection of nasal swabs samples from calves at days 3, 14, 28 and 35 of life was authorized by the farm owner. “The Animal Care and Use Procedures are produced and enforced to ensure the welfare of animals used in research and teaching at Cornell University. They ensure that high quality care is provided for all research animals, and that they are used in compliance with federal, state, and local regulations and guidelines”. Animal Care and Use Procedures were cared for according to the guidelines set by Dairy Cattle Husbandry (n2 deep-bedded pens). After parturition, calves were removed from the maternity pen and placed into a newborn pen bedded with dry sawdust and heated with heating lamps during the winter months. Colostrum from multiparous and primiparous cows was pooled and used in the study. All calves were fed approximately 4 L of raw colostrum at once by an esophageal feeder  within 4 hours of birth.Pregnant cows at stage 1 or 2 of parturition were transferred from the close-up free-stall barn into two maternity pens . All calves remained in the same pen from day 1 of life until fully weaned (approximately 65 days). Birth weight and weight at weaning of all heifer calves were measured by farm employees using a Waypig 15, 62-inch digital scale .Twice daily, newborn calves were allocated from the newborn pen to the calf barn. The calf barn was a greenhouse type of barn with positive ventilation and divided into 18 identical group-pens. Group-pens had a total area of 70 mCalves were fed ad-libitum acidified non-saleable milk. The feeding system was fully automated. Briefly, the acidification was performed inside a sealed stainless-steel tank where the non-saleable cold (5 °C) milk was mixed continuously with organic acid until pH 4.5 was reached. The acidified milk was kept for 72 hours inside the stainless-steel tank after the acidification process was finished. Then, the milk was directed to a smaller stainless-steel tank, which maintained the milk at a warm temperature and distributed it to the pens. To support the ad-libitum system, 6 nipples per pen were connected to the smaller tank and the acidified non-saleable milk was available from day 1 to day 55 of life, when a reduction of milk availability was initiated. All calves in this study were weaned by reducing the milk availability starting on day 55 until complete absence of acidified non-saleable milk at 65 days of life.o rotation was performed to better standardize sample collection. The swab was then retracted back into the plastic sheath and removed. The tip of the swab was placed inside a sterile plastic tube and labeled. Samples were kept on ice until they were transferred to the laboratory at Cornell Veterinary School and stored at −20 °C until further processing.A cohort of 174 Holstein heifer calves was selected randomly for this study. Deep pharyngeal swabs were performed on days 3, 14, 28, and 35 of life using a 20-cm DNA-free sterile swab  covered by a thin sterile plastic sheath. Prior to sampling, the selected calves were appropriately restrained and the nostril was cleaned using a paper towel. The plastic-covered sterile swab was inserted into the right nasal cavity at a depth of approximately 15 cm, the plastic sheet was then broken, exposing the swab to the URT mucosa, and a 360Pneumonia was defined when two or more of the following clinical signs were detected in a calf: cough, rectal temperature >39.5 °C, respiratory rate >40 breaths/min, increased cranioventral lung sounds or wheezes. Otitis was defined by observation of ear pain evidenced by head shaking, scratching or rubbing the ears, epiphora, ear droop, signs of facial nerve paralysis, with or without fever .Two dedicated farm employees with over 10 years of experience and trained by Cornell University veterinarians (Ambulatory and Production Medicine Department), were responsible for overseeing the calf facility and making the initial detection of pneumonia and otitis. When farm employees detected animals that were displaying signs of disease such as depression, inappetence, dehydration, increased respiratory rate, or a head tilt (otitis) a full physical examination was carried out to determine the diagnosis of the disease. Once the farm employees examined the affected calves an experienced veterinarian member of the research team performed a second confirmatory physical examination. Calves diagnosed with pneumonia and/or otitis were treated according to standard farm protocol .”Isolation of DNA from swabs of the URT was performed by adding 1.5 ml of DNA-free water into a 2-ml microcentrifuge tube containing a nasal swab sample, which was disrupted using a Mini-Beadbeater-8 . Swabs were removed from the microcentrifuge tubes and the remaining liquid was centrifuged for 10 minutes at 13,000 rpm. The supernatant was discarded and the DNA was extracted from the pellet using the PowerSoil DNA Isolation Kit . DNA concentration and purity were evaluated using a NanoDrop ND-1000 spectrophotometer  at wavelengths of 230, 260, and 280 nm.® TOPO® PCR cloning kit . Plasmid was purified with a QIAprep Spin Miniprep Kit  and quantified using Quant-iT™ PicoGreen® and a dsDNA Broad Range Assay Kit . Insertion was confirmed by agarose gel electrophoresis, and by sequencing the plasmid at the Cornell University Life Science Core Laboratories Center.In order to determine the total bacterial load of the URT samples, we cloned a plasmid containing the amplified V6 hypervariable region into TOP10 cells by using a Zero BluntTMSybr Green Supermix , 300 nM of each primer and 50 ng–5 pg of genomic DNA (or plasmid DNA standards). The thermal cycler conditions were as follows: denaturation at 95 °C for 3 min, 40 amplification cycles  and two final steps at 95 °C for 1 min and 55 °C for 1 min followed by melting curve determination. All reactions were performed in duplicate using a MyiQTM Real-Time PCR Detection System . Quantification of 16S rRNA target DNA was achieved by 10-fold serial dilutions ranging from 100 to 107 plasmid copies of the previously quantified plasmid standards. Plasmid standards and URT samples were run in duplicates. The average of the cycle threshold value was used for calculation of the total bacterial load.16S rRNA gene copy numbers were measured by quantitative PCR (qPCR) using Unibac primer 65http://www.earthmicrobiome.org/)® Plus Green Master Mix  and 10 μM of each primer. The PCR conditions for amplification of the 16S rRNA gene included an initial denaturing step of 94 °C for 3 min followed by 35 cycles of 94 °C for 45 s, 50 °C for 1 min and 72 °C for 90 s and a final elongation step of 72 °C for 10 min. Before sequencing, replicate amplicons were pooled and purified with a QIAquick PCR Purification Kit  and visualized by electrophoresis using 1.2% (wt/vol) agarose gels stained with 0.5 mg/ml ethidium bromide. Blank controls in which no DNA was added to the reaction were performed. Purified amplicon DNA was quantified using Quant-iT™ PicoGreen® and a dsDNA Broad Range Assay Kit .The 16S rRNA gene was amplified by PCR from individual metagenomic DNA samples from the URT using barcoded primers. For amplification of the V4 hypervariable region of the bacterial/archaeal 16S rRNA gene, primers 515F and 806R were used according to previously described methods and optimized for the Illumina MiSeq platformhttp://greengenes.lbl.gov/) and the output of this workflow is a classification of reads at multiple taxonomic levels: kingdom, phylum, class, order, family, genus, and species.Aliquots of URT amplicon samples were standardized to the same concentration and pooled into 5 different sequencing runs according to individual barcode primers for the 16S rRNA gene. Final equimolar libraries were sequenced using the MiSeq reagent kit v2 (300 cycles) on the MiSeq platform . The generated 16S rRNA gene sequences were processed through the open source software pipeline Quantitative Insights Into Microbial Ecology (QIIME) version 1.7.0-devShannon and Chao1 indexes output were generated by QIIME pipeline. Before estimating the Shannon and Chao1 indexes, all sample libraries were rarefied to an equal depth of 10,000 sequences using QIIME. Chao1 and Shannon indexes, total number of reads, and the log of the 16S rDNA copy number  were analyzed using repeated measures ANOVA by general linear models fitted in JMP Pro 11 . Dunnett’s multiple comparisons procedure was performed to compare the mean number of reads, Shannon index and Chao 1 index of each disease status  against the healthy samples within each day of data collection .Correlations between total bacterial load and alpha-diversity indexes (Shannon and Chao 1 indexes) were assessed using simple linear regression in JMP Pro 11 software (SAS Institute Inc.).P ≤ 0.05 were considered significant and those with a value of 0.05 < P ≤ 0.10 were considered tendencies.The relative abundances of microbial phyla and genera types in URT samples of calves at ages 3, 14, 28, and 35 within each health status were compared using general linear models fitted in JPM Pro 11 (SAS Institute Inc.). Dunnett’s multiple comparisons procedure was used to compare the mean relative abundance of the most abundant bacterial phyla and the genera of each disease status  against the healthy samples within each day of data collection . Differences with a value of Descriptive statistics for birth weight (BW) and average daily gain (ADG) were determined according to health status by using a general linear model (ANOVA) with JMP Pro 11 (SAS Institute Inc.). In total, 174 calves were enrolled in this study. Number of calves, disease incidence, mortality, BW and ADG  during the pre-weaning period are presented in How to cite this article: Lima, S. F. et al. The upper respiratory tract microbiome and its potential role in bovine respiratory disease and otitis media. Sci. Rep.6, 29050; doi: 10.1038/srep29050 (2016).


Hypertensive disorders of pregnancy, including preeclampsia, are major contributors to maternal morbidity. The goal of this study was to evaluate the potential of metabolomics to predict preeclampsia and gestational hypertension from urine and serum samples in early pregnancy, and elucidate the metabolic changes related to the diseases. Metabolic profiles were obtained by nuclear magnetic resonance spectroscopy of serum and urine samples from 599 women at medium to high risk of preeclampsia . Preeclampsia developed in 26 (4.3%) and gestational hypertension in 21 (3.5%) women. Multivariate analyses of the metabolic profiles were performed to establish prediction models for the hypertensive disorders individually and combined. Urinary metabolomic profiles predicted preeclampsia and gestational hypertension at 51.3% and 40% sensitivity, respectively, at 10% false positive rate, with hippurate as the most important metabolite for the prediction. Serum metabolomic profiles predicted preeclampsia and gestational hypertension at 15% and 33% sensitivity, respectively, with increased lipid levels and an atherogenic lipid profile as most important for the prediction. Combining maternal characteristics with the urinary hippurate/creatinine level improved the prediction rates of preeclampsia in a logistic regression model. The study indicates a potential future role of clinical importance for metabolomic analysis of urine in prediction of preeclampsia. New and improved predictive biomarkers are warranted. Gestational hypertension is often included in the disorder spectrum of preeclampsia, particularly if other symptoms are present. Clinical findings in gestational hypertension are often intermediate between normal pregnancy and preeclampsia [Hypertensive disorders of pregnancy, including preeclampsia and gestational hypertension, are major causes of maternal morbidity and mortality, and affect up to 10% of pregnant women ,3. Earlysitivity ,6,7,8. S [et al.  have pre1H NMR) spectroscopy or Mass Spectrometry (MS) [+0–13+6 of pregnancy in combination with maternal markers [1H NMR analysis of urine and serum from early pregnancy in a complete prospective cohort of women.Metabolomics represents a “top-down” view of the metabolism, which more closely characterises the phenotype of the organism than genomic and proteomic applications. Metabolomics is the detection and semi-quantitation of low molecular weight metabolites present in cells, tissues or body fluids, using high throughput analysis platforms such as proton nuclear magnetic resonance (try (MS) ,11. Rece markers ,13, and  markers ,15,16,17+0–13+6 could predict preeclampsia and/or gestational hypertension. Secondly, we aimed to elucidate the metabolic changes that may accompany the early stages of these hypertensive disorders of pregnancy.The aim of this study was to evaluate whether metabolic profiles of urine and serum collected from a cohort of women at gestational week 11+0and 13+6 weeks gestation. A flow chart describing the women included in the analysis is shown in A total of 640 women (585 nulliparous and 55 parous women) attended the study visit between 11Characteristics of the study participants for each pregnancy outcome group are shown in 1H NMR spectra showed a characteristic clustering of urine samples from women who later developed preeclampsia or gestational hypertension, indicating a difference in urinary metabolic profiles between healthy and later hypertensive pregnancies. No apparent clustering was seen in serum samples.Identification of 54 urine metabolites and 30 serum metabolites was achieved . The expPreeclampsia, gestational hypertension and both combined were predicted with urine metabolic profiles using partial least squares discriminant analysis (PLS-DA). The prediction parameters are given in Preeclampsia, gestational hypertension and both combined were predicted with serum metabolic profiles using PLS-DA. The prediction results are given in Metabolites were combined with maternal characteristics to predict preeclampsia. The best logistic regression models for prediction of preeclampsia are shown in +0–13+6 weeks gestation were significantly different between women who developed preeclampsia or gestational hypertension, and women with normotensive pregnancies. Both urine and serum metabolic profiles could predict preeclampsia and gestational hypertension, with urine profiles giving the best prediction. Decreased urinary hippurate, increased urinary creatinine, and increased levels of serum lipids were the most important metabolic differences identified in women who later developed preeclampsia or gestational hypertension.Metabolic profiles in urine and serum samples from pregnant women at 11This is the first metabolomics study performed on first trimester urine samples for prediction of preeclampsia and gestational hypertension. Changes in hippurate excretion preceding preeclampsia are novel to this study. Decreased urinary hippurate has been shown to correlate with increased blood pressure , and mayFirst-trimester maternal plasma or serum is more commonly used for prediction of preeclampsia by metabolomics methods ,13,16,29Combining urinary hippurate to creatinine ratios with maternal MAP and age increased the prediction rates of preeclampsia compared to using the uterine artery Doppler measurement with MAP and age. Replacing the UtAPI measurement, which requires skilled ultrasound technicians and time, with an easily accessible urinary marker, would be an advantage especially in low resource areas. However, clinical application of the metabolic profiling prediction method will require confirmation in cohorts from other populations, where the metabolites identified in this study form the basis of prediction. Currently, early identification of women at risk for developing hypertensive pregnancy disorders would enable a closer follow-up of these women . Extensi1H NMR spectroscopy to detect small molecular weight metabolites in serum, due to viscosity, lipid signal overlap and lower concentrations of 1H NMR-visible metabolites. However, the large contribution to the model from the lipid regions of the spectra indicate that a lipidomic approach could give additional information about the first-trimester maternal serum changes related to preeclampsia.The prediction of preeclampsia and gestational hypertension using metabolic profiling of urine performed with similar sensitivity as previous approaches using maternal biophysical and biochemical markers on the same cohort ,41, and The major strength of our study was the prospective design with complete follow-up of almost 600 women with medium to high risk of preeclampsia. The multivariate statistical analyses were performed up to standards of the field, with rigid cross validation . WeaknesThe sensitivity of prediction of preeclampsia using metabolic biomarkers in this study (51% at 10% FPR) was markedly higher than previously published prediction of preeclampsia based on clinical markers such as MAP, maternal age and UtAPI in the same cohort (38.5%)  and in a+0–13+6 weeks of gestation (crown-rump length 45–84 mm). At the study visit participants were interviewed about their health and pregnancy. All participants were weighed, and BMI was calculated in kg/m2. The women were asked to fast for one hour before their visit to avoid immediate effects of meals on the metabolic profiles. Venous blood was drawn into non-heparinised tubes, left to clot for 30 min at room temperature, and centrifuged at 1800× g for 10 min. A serum sample (0.8 mL) was separated and stored at −80 °C, thawed once and an aliquot of 120 μL was stored at −80 °C. Spot urine samples were collected at the study visit and aliquots (1.8 mL) were stored directly at −80 °C. Blood pressure was measured with a CAS 740 MAX NIBP automated device  [http://www.fetalmedicine.com). Data on pregnancy outcomes were collected from hospital records. Preeclampsia was defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg in combination with proteinuria ≥0.3 g per 24 h measured twice within 4–6 h by dipstick ≥+1, occurring after gestational week 20 [et al. were applied [The study population has previously been described in detail ,42. BrieCT, USA) . The UtA week 20 . Gestati applied . The defg  for five minutes. The supernatant (540 μL) was mixed with a bacteriostatic buffer (60 μL) , 2 mM NaN3)  and transferred to 5 mm NMR tubes . Serum samples were thawed on ice. Serum (100 μL) was mixed with a bacteriostatic buffer (100 μL)  (Bruker Biospin) and transferred to 3 mm NMR tubes.Laboratory analyses were done after all women had delivered their babies. Urine samples were thawed on ice and centrifuged at 5600× 1H, 2H and 13C. Experiments were fully automated using the SampleJet™ in combination with Icon-NMR on TopSpin 3.1 software (Bruker Biospin). The 1H NMR analyses were performed blinded to pregnancy outcomes. 1H NMR spectroscopy acquisition parameters are described in NMR analysis was performed at the MR Core Facility at the Norwegian University of Science and Technology (NTNU), Trondheim, Norway using a Bruker Avance III Ultrashielded Plus 600 MHz spectrometer  equipped with a 5 mm QCI Cryoprobe with integrated, cooled preamplifiers for 2O and urea signals removed. Peaks in the urine spectra were aligned by the iCoshift algorithm [Spectra were automatically Fourier transformed, phased and baseline corrected in TopSpin with a line broadening of 0.3Hz. Spectra were imported to Matlab r2013b . Urine spectral regions containing metabolites of interest (δ 0.5 to 9.0 ppm) were extracted and the residual Hlgorithm  with 245lgorithm . The urilgorithm . Finallylgorithm . Serum s1H NMR spectra were evaluated by sensitivity at 10% false positive rate and at the best cutoff on the Receiver Operator Characteristic (ROC) curve.Statistical analyses were done in Matlab and in Statistical Package for the Social Sciences (SPSS) . Baseline characteristics of the study population were tested for normality using the Kolmogorov-Smirnov test . Non-norPCA is a powerful method of data extraction which finds combinations of variables, called principal components (PCs), describing the main variation in large data. These are visualised in scores and loading plots, making it possible to visualise high dimensional data using only a few dimensions. The score plots show each spectrum as an object in the PC space, and are useful for identifying clusters, trends and outliers in the dataset. The loading plots show the variable’s contributions for defining each PC. PLS-DA models the relationship between the spectra and class information using multivariate regression methods, and is used to establish prediction models. The metabolites responsible for separation between classes are given by the latent variables (LVs) . Similarp-values ≤0.05 considered significant. Permutation testing performs classification on reshuffled classes, and tests whether those prediction results are significantly different from prediction of randomised classes [Multivariate models were constructed using PLS Toolbox 7.3.1 . Preeclampsia, gestational hypertension and both disorders combined were predicted within the entire cohort; e.g., for prediction of preeclampsia the gestational hypertension remained in the control group. The input to the classification models was either full, preprocessed spectra, or sets of variables selected by different variable selection algorithms (VIP or CARS). VIP selection was performed by creating a five-fold cross validated model, then selecting variables with VIP >1 to use in a further model . CARS va classes . One hun1H NMR spectra. The metabolites were input to logistic regression models alone and in combination with maternal characteristics. The maternal variables found to give the best prediction of preeclampsia in the same cohort by Skråstad et al. [Finally, urine metabolites found to be important to the multivariate predictions based on high VIP scores and/or CARS selection  were quantified by measuring the area under the curve in the d et al.  (UtAPI, d et al. . The resMetabolic profiling of urine and serum in early pregnancy revealed specific, significant changes in the metabolism of women who later developed preeclampsia or gestational hypertension. Preeclampsia and gestational hypertension could be successfully predicted in early pregnancy using urine and serum metabolic profiles. The consistent changes in the urinary metabolome represent an attractive avenue for clinical prediction, since spot urine is easily accessible. Combining a panel of urine metabolites with maternal characteristics may improve the accuracy of prediction of preeclampsia.


R2 > 0.69). Unit recruitment occurred over a wide force range (0.2–103% of 50 Hz force). Significant unit rate modulation occurred during spasms . Mean recruitment frequency  was significantly higher than derecruitment frequency . Coactive unit pairs that fired for more than 4 s showed high  or low  rate-rate correlations, and derecruitment reversals . Later recruited units had higher or lower maximal firing rates than lower threshold units. These discrepant data show that coactive motoneurons are drive both by common inputs and by synaptic inputs from different sources during muscle spasms. Further, thenar motoneurons can still fire at high rates in response to various peripheral inputs after SCI, supporting the idea that low maximal voluntary firing rates and forces in thenar muscles result from reduced descending drive.Involuntary contractions of paralyzed muscles (spasms) commonly disrupt daily activities and rehabilitation after human spinal cord injury (SCI). Our aim was to examine the recruitment, firing rate modulation, and derecruitment of motor units that underlie spasms of thenar muscles after cervical SCI. Intramuscular electromyographic activity (EMG), surface EMG, and force were recorded during thenar muscle spasms that occurred spontaneously or that were triggered by movement of a shoulder or leg. Most spasms were submaximal  with strong relationships between EMG and force ( Force is graded during voluntary contractions by the recruitment of motor units and by changes in motor unit firing rate  or a motor vehicle (n = 2), bicycle (n = 1) or diving accident (n = 3) that occurred from 1–26 years ago. Injury level was at C4 (n = 1), C5 (n = 2), C6 (n = 2) or C7 (n = 2) and was classified as A (n = 5) or B (n = 2) using the American Spinal Injury Association impairment scale. The thenar muscles of the hand more likely to contract involuntarily were studied once in each subject . Two subjects had some voluntary control of their thenar muscles. The University of Miami Institutional Review Board approved all of the experiments and each subject gave written informed consent to participate.Data were recorded from seven individuals with chronic (>1 year) cervical SCI  due to a fall , the intramuscular electrode was moved in order to sample different motor units. Maximal thenar muscle force was evoked by stimulating the median nerve just proximal to the wrist for 1 s at 50 Hz using supramaximal pulses . The force generated during spasms was normalized to the respective 50 Hz force for a given muscle to enable comparison of data across spasms and subjects.Intramuscular EMG, surface EMG, and force were sampled online at 12,800 Hz, 3,200 Hz, and 400 Hz, respectively, using a SC/Zoom system . Potentials belonging to the same motor unit were identified by amplitude, duration and shape, and verified by overlaying all of the marked potentials for a given spasm. The time between the potentials of a single motor unit was converted to instantaneous frequency by using the reciprocal of the interspike intervals (ISI). For each spasm, firing frequency was determined: (1) when the motor unit was recruited  above 0.7 were used.When pairs of units were coactive during spasms, estimates were made of the amplitude of persistent inward currents boosting the effective synaptic drive by calculating the change in firing frequency of the first recruited unit  when the second unit  was recruited vs. derecruited  data are given. Relationships between surface EMG and force, recruitment and derecruitment frequency, maximal frequency and maximal force, and maximal frequency of the first and second recruited units used for paired analysis were all analyzed using least squares linear regression. Differences in rate of force development and decline during the upward and downward phase of a spasm were tested with a multilevel analysis  with force rate as the first level and the muscle status  as the second level variable. In the analysis the intercept of the relation between the rate of force change and muscle status was first modeled as a fixed variable. Including a random intercept improved our model significantly, but including a random slope made no further improvement (−2 log likelihood did not change significantly).Motor unit firing rates were compared at recruitment, derecruitment and at different force levels. Differences in motor unit firing rates were analyzed using a multi-level analysis so all data were included in the analysis . The data were modeled in a hierarchical multi-level model with unit firing rate as the lowest level variable. To identify that the data came from a specific motor unit and subject, higher level group-identifiers were included. We first assessed possible differences in firing rates between subjects with or without residual voluntary force (muscle status). No significant differences were observed in recruitment, maximal or derecruitment frequencies. Therefore frequency data were pooled for all subjects. Differences in firing rate were examined at specified force levels. In the analysis the intercept of the relation between firing frequency and force level was first modeled as a fixed variable. Including a random intercept improved our model significantly. Including a random slope, however, did not improve our model any further (−2 log likelihood did not change significantly). Therefore, only the statistical analysis including the random intercept was presented in the Results.n = 37 spasms). Two other spasms lasted up to 24 s because they had double peaks. Possibly two spasms had occurred in close succession. The rate of change for EMG and force was similar during the upward phase of a spasm  and during the downward phase . Some motor units were not derecruited following spasms but rather continued to fire for several minutes at low firing rates  while the force decrease lasted 6.50 s  or 39.5 % (33.2 SD) of the 50 Hz force. We could track 72 motor units throughout or during part of a spasm Figures , 3. The  SD or 39n = 66 units) and increased to a mean maximal firing frequency of 11.6 Hz  was significantly lower than both the recruitment and maximal firing rates (both P < 0.001). Of these units, 41% (25/61) had higher firing rates at derecruitment than at recruitment in at least one spasm , at 50% was 10.0 Hz , at 75% was 12.2 Hz  and at 100% was 16.1 Hz  showed clear rate modulation Figure . The aveMotor units did not necessarily behave similarly in repeat spasms. Figure During voluntary contractions, coactive units can display similar rate modulation , but only 8 pairs showed a high rate-rate correlation (R2 > 0.7). Only 24 of the 92 pairs fired simultaneously for more than 4 s. Of the 24 pairs, 12 pairs (50%) showed a moderate rate-rate correlation, but only 4 pairs showed a high correlation  were suitable for the conventional paired unit analysis to estimate the amplitude of the persistent inward current  had a moderate rate-rate correlation  showed reversals of derecruitment. That is, the unit that was recruited first was also derecruited first Figure . Eleven The main finding of the present study was distinct rate modulation of motor units during thenar muscle spasms in individuals with SCI. These data confirm in a larger group of motor units that thenar motoneurons impacted by SCI are still capable of firing at higher frequencies in response to various peripheral inputs, as are motoneurons innervating limb muscles  were observed in 29% of the motor unit pairs during spasms, which is comparable to the percentage found in units left under voluntary control after injury (25%; Zijdewind and Thomas, In able-bodied subjects, motor units often show an association between recruitment threshold and mean motor unit firing rates but these relationships can differ. In some studies, motor units recruited at higher forces fire at a lower rate than units activated earlier at weaker forces; i.e., an onion skin profile (e.g., De Luca and Hostage, R2 = 0.78; Mottram et al., During spasms there was no systematic relationship between the maximal firing rate of earlier and later recruited units Figure , even foper se. Chronic baclofen, however, weakens motor units (Thomas et al., The present data show the importance of various afferent inputs for motor unit rate modulation after SCI. However, excitatory input from the periphery seems less focused and distributed to a wider range of motoneuron pools after SCI (Hyngstrom et al., Zijdewind: Project conception and design, data acquisition, analysis and interpretation, manuscript writing, final approval of the manuscript.Rob Bakels: Data analysis and interpretation, manuscript writing, final approval of the manuscript.Christine K. Thomas: Project conception and design, data acquisition, analysis and interpretation, manuscript writing, final approval of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Aberrant neurodevelopment may lead to inappropriate neural circuit formation and cerebral dysconnectivity in 22q11DS, which may contribute to symptom development. Here we examined: (1) differences between 22q11DS participants and typically developing controls in diffusion tensor imaging (DTI) measures within white matter tracts; (2) whether there is an altered age-related trajectory of white matter pathways in 22q11DS; and (3) relationships between DTI measures, social cognition task performance, and positive symptoms of psychosis in 22q11DS and typically developing controls. Sixty-four direction diffusion weighted imaging data were acquired on 65 participants . We examined differences between 22q11DS vs. controls in measures of fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD), using both a voxel-based and region of interest approach. Social cognition domains assessed were: Theory of Mind and emotion recognition. Positive symptoms were assessed using the Structured Interview for Prodromal Syndromes. Compared to typically developing controls, 22q11DS participants showed significantly lower AD and RD in multiple white matter tracts, with effects of greatest magnitude for AD in the superior longitudinal fasciculus. Additionally, 22q11DS participants failed to show typical age-associated changes in FA and RD in the left inferior longitudinal fasciculus. Higher AD in the left inferior fronto-occipital fasciculus (IFO) and left uncinate fasciculus was associated with better social cognition in 22q11DS and controls. In contrast, greater severity of positive symptoms was associated with lower AD in bilateral regions of the IFO in 22q11DS. White matter microstructure in tracts relevant to social cognition is disrupted in 22q11DS, and may contribute to psychosis risk. One preDiffusion weighted imaging (DWI), which measures the diffusion patterns of water molecules in brain tissue, offers a method to examine structural connectivity between brain regions . SpecifiSince DTI was introduced in 1994, there have been over 200 articles published on DTI and idiopathic schizophrenia. These studies have revealed disrupted white matter integrity  in multiple white matter tracts across the phases of illness, including individuals at clinical high-risk (CHR) for psychosis , first-eA recent review of the existing literature on anomalous white matter development associated with schizophrenia proposes a model that combines the influence of both neurodevelopmental and neuroprogressive influences on white matter in schizophrenia . In thisWhile many studies have shown that decreased FA is associated with cognitive functioning in patients with idiopathic schizophrenia , to our As in idiopathic schizophrenia and those at clinical high risk, white matter integrity may also be disrupted in multiple brain regions in 22q11DS, but findings are mixed. Three cross-sectional studies of children with 22q11DS found lower FA in the SLF , complemincreased FA in the splenium and genu of the corpus callosum  to examine group differences between 22q11DS participants and controls on multiple DTI measures  via a whole-brain and ROI-based approach; (2) to explore where there are age-associated disruptions in these DTI metrics in 22q11DS patients versus typically developing controls, and (3) to relate positive symptoms and social cognition performance to measures of white matter microstructure in 22q11DS and controls. First, based on prior work , we expeTable 1).The initial sample consisted of 76 participants . DTI data from 11 participants  were excluded due to poor image quality or severe motion/scanning artifacts. Thus, the final sample consisted of 65 participants . Typically developing healthy controls were also recruited from this study. Exclusion criteria for all study participants were: neurological or medical condition disorder that might affect performance, insufficient fluency in English, and/or if they endorsed substance or alcohol abuse and/or dependence within the past six months. Controls also must not meet criteria for any major mental disorder, with the exception of attention deficit-hyperactivity disorder (ADHD) or past episode of depression, based on information gathered during the Structured Clinical Interview for DSM-IV Axis I Disorders . Three cAll participants gave verbal and written informed consent. Participants under the age of 18 years provided written assent, while their parent or guardian completed written consent. The UCLA Institutional Review Board (IRB) approved all study procedures and informed consent documents.A master’s level trained clinician assessed all participants on the positive, negative, disorganized, and general symptom scales from the Structured Interview for Prodromal Syndromes SIPS, . SymptomStudy participants received the Penn Emotion Recognition Test (ER40), a computerized emotion identification task in which 40 color photographs of adult faces, varying in race and gender, are randomly presented . ParticiAll participants were administered Part 3 of The Awareness of Social Inference Test TASIT; . The TASp = 0.009); however, the gender distribution was similar for all other comparisons , which we will call the b0 image.All scanning was carried out on an identical Siemens 3 Tesla Tim Trio MRI scanner at either the UCLA Brain Mapping Center  or at the Center for Cognitive Neuroscience . The age distribution for both 22q11DS patient and control subjects did not differ across scanner locations. There were, however, significantly more female 22q11DS participants compared to controls scanned at the CCN (b0 image with FSL’s brain extraction tool (BET) and the resulting mask was then applied to the remaining 64 volumes. We then corrected the images for eddy current distortion by using FSL’s eddy correct tool. The original gradient vectors were rotated using the linear rotation and translation matrix. Then, each individual’s eddy corrected DWI volumes were linearly aligned (9 degrees of freedom) to the corresponding skullstripped T1 image in the MNI-ICBM 152 standard space. The gradient vectors were once more adjusted by using the resulting linear rotation and translation matrix of this transformation. To compensate for EPI-induced susceptibility artifacts, the b0 image was non-linearly registered in three directions to the T1 structural scan in the MNI-ICBM 152 space (The T1-weighted images were skull-stripped using Brainsuite’s surface extraction tool (BSE), and then manually edited to remove any remaining non-brain tissue. The skull-stripped T1 images were then linearly aligned using FSL’s “FLIRT” (with 6 degrees of freedom) to a common space (MNI-ICBM 152 non-linear sixth generation) with 1mm isotropic voxels and a 182 × 182 × 182 voxel matrix. For the DWI scans, we removed non-brain regions from the T2-weighted weighted 52 space , by usin52 space .Group maps were then created using FSL’s Tract-Based Spatial Statistics (TBSS). TBSS is a rigorous registration approach, which is imperative for comparisons in which tract shape or volume is likely to differ between groups. Because our sample included children, we aligned each FA map to each other and identified the most representative scan from our sample, which was then used as the target image. All images were then aligned to the target FA image through non-linear registration. All images are merged into a single 4D image and non-linear registration is used to create an FA “skeleton” based on the center of all of the tracts common to the entire group. Data are then projected from the center of each subject’s tracts onto the skeleton for group comparison. This method ensures that statistics are only applied in regions where data exist for all subjects, and maximizes the likelihood that the pooled data originate from the center of a tract in every subject.To characterize group differences between 22q11DS and controls at the voxel-wise level, we first used a whole-brain based approach. In this approach, we were not restricted to specific areas of interest and could identify nuanced abnormalities that may be obscured by the examining only regions of interest (ROI). Given the variability in 22q11DS findings, we decided to first take an unbiased approach. We also implemented a ROI analysis to confirm our whole-brain analysis, identify age-associated changes in known white matter tracts between 22q11DS and controls, and determine whether DTI indices in specific tracts are related to psychotic symptoms and measures of social cognition in 22q11DS participants and controls.Figure 1; For the ROI-based approach, 20 regions per hemisphere were identified and determined based on the John Hopkins University probabilistic tractography atlas  for each identified region in each hemisphere, with scanner location as the between-group factor and group as a covariate.Statistical analyses were performed using SPSS software v. 21 . Correction for multiple comparisons was conducted using publicly available R software , which is a rigorous method that identifies “clusters” in the data without having to predefine the clusters . Demeaneq-values were estimated using R software.All neuroanatomic measures were first examined for normality using the Kolmogorov–Smirnov and Shapiro–Wilk tests; none were found to violate the assumptions of normality. To compare putative indices of white matter integrity (FA), and its sub-components, AD and RD, respectively, in 22q11DS vs. controls, we conducted an ANCOVA for each identified region in each hemisphere, with diagnosis (22q11DS vs. control) as the between subjects factor and sex, age, and scanner location as covariates. For these analyses, due to the large number of comparisons (60), false discovery rate (FDR) ∗group interaction we then added the age∗group interaction term to the original ANCOVA models . This resulted in a total of six additional analyses: FA in bilateral regions of the ILF, and SLF, and RD in bilateral regions of the ILF. For any significant age∗group interaction, we followed up by examining Pearson correlations between age and the DTI measure within groups. Following a visual inspection of the age∗group interactions, we confirmed that the other regions did not show any significant interactions . Additionally, we ran a Pearson correlation to examine the relationship between age and psychotic symptoms in the 22q11DS sample.To address whether the relationship between age and DTI measures differed between groups, we first visually examined the scatterplots of age vs. all DTI measures. Being mindful of multiple comparisons, we only wished to conduct statistical tests where the data suggested a possible interaction effect. For any DTI measure for which visual inspection suggested an ageq-values were estimated using R software. In regions that showed significant relationships between DTI indices and social cognition variables, we conducted a secondary analysis in which we also regressed out the effects of global cognitive abilities (WASI IQ score) and re-ran the Pearson correlations.To explore the relationships of DTI measures with positive symptoms and social cognition tasks, we conducted correlational analyses for regions that showed significant group differences between 22q11DS participants and controls. Residuals were calculated from each DTI variable examined, after regressing out the effects of age, sex, and scanner. We also calculated residuals for the clinical and social cognition variables, regressing out the effects of age, sex, and scanner location. Next, we conducted Pearson correlations  between each neuroanatomic brain region and residualized total positive symptoms. Due to the restricted range of positive symptoms in controls, we conducted this analysis only in 22q11DS patients. These analyses were then repeated within each group (22q11DS participants and controls), to investigate the relationship between DTI indices and social cognition performance. FDR In regions that showed a significant relationship with overall ER40 performance, we conducted secondary analyses, focusing on Pearson correlations between the DTI measure and performance on each individual emotion , particularly since a previous publication from our laboratory showed differential impairment in emotion recognition performance in 22q11DS .q-values were estimated using R software.It is known that RD and AD make up the measure of FA; however, it is not clear if these two measures are related to each other in white matter tracts of interest. To evaluate the relationship between measures of AD and RD in each group, we correlated the residuals  for each DTI AD variable with the residuals for the corresponding DT RD variable in 22q11DS patients and controls separately. To directly compare the strength of correlations between the two groups, a Fisher r-to-z transformation was conducted. FDR Table 1, 22q11DS patient and control groups did not significantly differ in any of the demographic factors .As shown in q-values ranging from 0.04 to 0.000001 . Most of these significant differences were in the right hemisphere, and included the posterior limb of the internal capsule and the superior and posterior corona radiata. FA was higher in 22q11DS subjects in the body of the corpus callosum and in a small region of the left SLF. In these analyses, compared to typically developing controls, 22q11DS did not show decreased FA in any regions. There were also widespread reductions in AD and RD in 22q11DS in comparison to typically developing controls . The body and splenium of the corpus callosum, bilateral regions of ATR, and bilateral portions of the SLF and ILF showed reductions in both AD and RD in 22q11DS. Reductions that were specific to AD (but not observed for measures of RD) in 22q11DS were found in the bilateral IFO, while reductions that were specific to RD in 22q11DS were in the left superior corona radiata and upper regions of the corticospinal tract. When we plotted the overall raw mean FA values for regions that showed significant differences between 22q11DS and controls in the whole-brain analysis , we saw that FA was consistently higher in 22q11DS participants than controls, regardless of age  or approached significance (p≤ 0.10) when the total sample was broken into subgroups according to scanner location . For measures of RD, differences in the primary tract of the right SLF remained statistically significant (p ≤ 0.05) and approached significance in the temporal portion of the SLF (p ≤ 0.07) when the sample was divided into sub-groups according to scanner. However, for all other measures of RD that were statistically significant when the whole group was examined  these results only remained statistically significant when comparing 22q11DS vs. controls in the BMC scanner subgroup. Results are presented in Supplementary Table All regions that showed significant FA or AD differences remained statistically significant  = 4.2, p = 0.04] and RD . In controls, ILF FA values increased with increasing age . However, this relationship was not present in 22q11DS participants . In the left ILF, controls showed decreasing RD with increasing age , but again, this pattern was not seen in 22q11DS . Of note, we did not find a significant relationship with age and psychotic symptoms in 22q11DS .There were significant ager = -0.53, q = 0.008, right: r = -0.49, q = 0.02, Figure 6). In both regions, decreased AD was associated with greater severity of positive symptoms. There were no relationships between FA and RD and positive symptoms in 22q11DS. When effects of IQ were regressed out, and the significant correlations were re-run, all correlations remained significant  and left uncinate fasciculus  was associated with better performance on the TASIT in both 22q11DS participants and controls. A similar pattern of results was observed for the relationship between ER40 performance and the left IFO  and left uncinate fasciculus  in 22q11DS, but not in controls . Controls also showed a significant associations between increased AD in the SLF bilaterally  and ILF bilaterally  with better social cognition performance on the TASIT. However, relationships between SLF and ILF AD and TASIT performance were not observed in 22q11DS participants . When effects of IQ were regressed out, and the significant correlations were re-run, all correlations remained significant for both 22q11DS participants and controls, aside from relationships between social cognition performance and the ILF regions for controls .We conducted secondary analyses of AD measures in the left IFO and left uncinate fasciculus  and task performance for specific emotions in 22q11DS. These results indicate that the only emotion that significantly correlated with these two measures was fear, with better recognition of fear associated with increased AD in both regions in 22q11DS , the genu , and the right uncinate fasciculus . 22q11DS showed significant positive relationships between RD and AD in the right ATR, bilateral regions of the IFO, the right SLF, left uncinate fasciculus, but controls did not show significant relationships in these regions. However, compared to controls, 22q11DS patients did not show a statistically stronger linear relationship in these regions. We found that both controls and patients showed significant positive linear relationships between RD and AD in the left ATR and left cingulum bundle in the hippocampal region. In all cases where there was a linear relationship between AD and RD, increased AD was associated with increased RD.Results of these analyses in 22q11DS patients and controls are presented in This study used a whole brain and region of interest (ROI) approach to examine multiple DTI indices in 22q11DS, a neurogenetic disorder that confers significantly increased risk for the development of psychosis. Several findings emerged – some novel, while others extend upon the small body of existing 22q11DS DTI literature: (1) in comparison to controls, 22q11DS participants had reduced AD, putatively indexing axonal damage , 2003, iIn line with prior, smaller studies we found decreased AD, a putative index of axonal disruption , in multafter axonal degeneration, and It is important to note that axonal damage does not occur in isolation. Surprisingly, in both our ROI and whole-brain analyses, we also found decreased RD in 22q11DS, which was not observed in two prior 22q11DS studies that examined specific sub-components of FA . HoweverIn our whole-brain analyses, those with 22q11DS had higher FA in several regions, including the posterior limb of the internal capsule, the corona radiata, the body of the corpus callosum, and a small region of the left SLF. These findings are unexpected, given that the majority of DTI studies to date have found decreased FA in 22q11DS patients e.g., . HoweverAlthough the cellular mechanisms underlying increased FA in 22q11DS in these regions are unknown, our findings were likely driven by a combination of decreased AD and RD. Regional increases in FA in children with other neurodevelopmental disorders has been observed, and the authors of these studies have offered a number of speculations as to causal mechanisms at the cellular level, such as: decreases in axonal branching , fewer oIn contrast, our ROI analyses did not indicate higher FA in 22q11DS participants relative to controls within the tracts investigated. The ROI approach, which averages all the voxels within a specific region, may have masked the increased FA identified in specific tracts  in the whole-brain approach. For example, the SLF is a large ROI and the small portion of increased FA that we identified in the whole-brain analysis could have been obscured when the average FA was calculated for that ROI. Additionally, the ROIs used in this study did not cover the entire mean skeleton obtained , and thus such regions were not included in the ROI analyses. However, despite the lack of significantly increased FA in the ROI analyses, we did find decreased AD and RD in regions that produced comparable results to the whole brain findings. These findings also highlight the importance of using separate measures of diffusivity to examine white matter abnormalities in patient groups, as in other brain disorders .b0 image to a T1-weighted structural scan to correct for EPI induced artifacts, which result in spatial distortions that could substantially change the results. Another reason for these discrepant findings may be due to difficulties encountered when registering DWI data , the findings have varied, due in part to the methodological differences and sample characteristics. For example, many of the studies did not align the DWI data , particuDWI data , differeDWI data .∗group interaction only reached significance within this tract, several other regions, including the SLF, appeared to follow a similar pattern, and these results approached significance. There may be an atypical neurodevelopmental trajectory of white matter microstructure in 22q11DS; one hypothesis is that those with 22q11DS undergo “precocious maturation,” with myelination occurring before the period of adolescence, closing the window for heightened brain plasticity during adolescence and young adulthood. This pattern of increased early myelination has been observed in mice who underwent early life stress  in the IFO and uncinate was associated with improved social cognition in both 22q11DS patients and healthy controls. Similar results have been reported in a combined analysis of 22q11DS participants and controls: increased AD in the right hemisphere of the SLF, the posterior corona radiata, and IFO was related to better social skills  to better social cognition in both controls and 22q11DS participants. The uncinate connects the amygdala to the anterior temporal lobe and orbitofrontal cortex  and is bTo our knowledge, this is the first study to relate positive symptoms and lower AD in 22q11DS. Importantly, decreased AD in bilateral IFO was related to increased positive symptom severity in 22q11DS, is consistent with the previously noted association between AD in the left IFO with improved social cognition performance in 22q11DS participants. In a prior behavioral study, the same social cognition task  was the most significantly related to psychotic symptomatology in 22q11DS, when compared to other measures of social and non-social cognition . DisruptMultiple studies in patients with idiopathic schizophrenia have consistently demonstrated that disruption in white matter microstructure in multiple regions is driven by increased radial, not axial, diffusivity, which authors interpret as indicating that white matter dysfunction in idiopathic schizophrenia is primarily driven by demyelination, rather than axonal damage . This hyThis work complements the existing structural and functional neuroimaging work in 22q11DS. For example, structural abnormalities of the corpus callosum in 22q11DS have been detected in multiple studies, and we The observed relationship between reduced AD and increased psychotic symptom severity in 22q11DS also extends upon the existing literature on relationships between neuroimaging measures and the psychosis phenotype in 22q11DS. Two recent studies have found white matter microstructure abnormalities in the cingulum bundle (increased FA and decreased RD), anterior limb of the internal capsule , and uncinate fasciculus  have been associated with increased psychotic symptoms in 22q11DS . To our Several limitations of this study should be noted. First, given our cross-sectional design we were unable to investigate changes in DTI measures over time as predictors of subsequent development of psychotic symptoms in 22q11DS. In the future, it will be critical to incorporate a longitudinal approach, as studies of structural neuroanatomic predictors of psychosis have found change over time to be a strong predictor of symptom development, in both 22q11DS  and idioThe current study sets a foundation to develop future multi-modal neuroimaging biomarker studies in 22q11DS. The white matter pathways that we found to be associated with social cognition and psychosis  connect to a gray matter region, the medial orbitofrontal cortex, variation in which we have also found to be associated with psychosis in 22q11DS . Thus, tThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.