This study aims to describe the mortality risk of children in the community who had severe acute malnutrition (SAM) defined by either a mid-upper arm circumference (MUAC) <115mm, a low weight-for-height Z-score (WHZ) <-3 or both criteria.We pooled individual-level data from children aged 6–59 months enrolled in 3 community-based studies in the Democratic Republic of the Congo (DRC), Senegal and Nepal. We estimate the mortality hazard using Cox proportional hazard models in groups defined by either anthropometric indicator.In total, we had 49,001 time points provided by 15,060 children available for analysis, summing to a total of 143,512 person-months. We found an increasing death rate with a deteriorating nutritional status for all anthropometrical indicators. Children identified as SAM only by a low MUAC (<115mm) and those identified only by a low WHZ (Z-score <-3) had a similar mortality hazard which was about 4 times higher than those without an anthropometric deficit. Having both a low MUAC and a low WHZ was associated with an 8 times higher hazard of dying compared to children within the normal range. The 2 indicators identified a different set of children; the proportion of children identified by both indicators independently ranged from 7% in the DRC cohort, to 35% and 37% in the Senegal and the Nepal cohort respectively.In the light of an increasing popularity of using MUAC as the sole indicator to identify SAM children, we show that children who have a low WHZ, but a MUAC above the cut-off would be omitted from diagnosis and treatment despite having a similar risk of death. The term severe acute malnutrition (SAM) commonly refers to severely wasted children who require urgent admission to a therapeutic feeding program. Since 2009, WHO and UNICEF have recommended a mid-upper arm circumference (MUAC) <115 mm or a weight-for-height/length Z-score (WHZ) <-3, as well as nutritional edema, as independent criteria to define SAM in children over 6 months of age . RecentlWhile the recommendation to use MUAC <115mm and WHZ <-3 independently was reiterated by WHO in 2013 –15. In sThe objective of the present study was to describe the all-cause mortality hazard by categories of deficits in MUAC and/or WHZ. To inform the existing debate surrounding MUAC-only programming, we aimed to describe the relative mortality hazard of children in the community who would be identified under a MUAC-only or WHZ-only program (SAM by MUAC <115mm or WHZ <-3). In addition, we aimed to describe the mortality hazard of those children who would be excluded from treatment under those programs (i.e. children with single deficits of either only MUAC <115mm or only WHZ <-3 without concurrently having the other deficit), as well as those satisfying both criteria. We obtained rare longitudinal cohort datasets of children in the community not enrolled into nutritional programs, which had all the required anthropometric measurements and regular follow-up to determine survival. The effect of age, sex, and concomitant stunting on these relative mortality hazards were also examined, as well as the effect of anthropometric deficits on death due to specific causes.We identified previously completed large community-based prospective studies, which measured weight, length/height, MUAC and the vital status of participating children during follow-up, and contacted principal investigators to ask for their individual-level data. We obtained anonymous data for three studies; two prospective cohort studies that initially aimed to describe the links between anthropometric deficits and child mortality in Senegal  and DemoAs we used anonymous published secondary data only, formal ethical clearance was not required.For each original study, the only original eligibility criterion for study enrollment was an age between 0 and 5 years and residence within the catchment area of the study. For the DRC study, it is not known how many children in the study area declined participation. For the Senegal study and the Nepal study, 88.5% and 96% of eligible children in the community were enrolled respectively. In the Nepal study, height was only measured in a sub-set of the children, which was used for this analysis. It is not clear how the sub-set was selected. Children were included in the current analysis, if they had at least one visit during which their weight, length/height and MUAC were taken in the period where they were between 6 and 60 months of age, and a subsequent visit with their outcome recorded. Each eligible child contributed time to the study until s/he reached 60 months of age, died, was lost-to follow up, or was present at the administrative end of follow-up. For none of the studies were data available on whether the children did or did not have edema during their assessment.For this study, the main determinant variables were absolute mid-upper-arm circumference , and weight-for-height/length Z-scores (WHZ). The anthropometric indicators were each classified into 4 categories: (1) reference, (2) mild, (3) moderate and (4) severe deficits in accordance with WHO guidelines , 25. TheThe main outcome variable was mortality regardless of cause. The individual studies ascertained the vital status of participants at regular study visits and assigned causes of death using the verbal autopsy methods current at the time of the study. On average, children were visited every 3 months in the DRC study, every 6 months in the Senegal study and every 4 months in the Nepal study. In each study, the exact date of death was documented. In the cause-specific analyses, we classified deaths as those due to diarrheal diseases, acute respiratory infections, measles, malaria, trauma and unknown cause.Data were analyzed with Stata . We pooled the 3 datasets, and calculated all Z-scores according to WHO 2006 Child Growth Standards . BecauseWe report case-fatality rates (CFR) defined as numbers of death per 100 child-months. We also report deaths per 10,000 children per day, which is the unit that is usually used when assessing death rates in humanitarian situations.We estimated mortality hazard ratios for children in each exposure category defined at the start of each observation interval relative to the specified reference category. We used Cox proportional hazards regression models with robust sandwich covariance matrix estimates to account for repeated measurements for each child. Due to left truncation, we used child’s age (in months) as the time scale. Both the log-log plots and the Schoenfeld residuals indicated a violation of the assumption of the proportional hazards for the variable “cohort”, but not for either anthropometric variable or age. We therefore specified the cohort variable as a stratum in the main model (Stata command “strata”) to allow for separate baseline hazards . Sex wasIn order to examine the potential for measurement error to affect the results, an additional analysis was undertaken. We defined a measurement as implausible based on our long-standing clinical experience and deleted the raw values and associated Z-scores in the two adjacent measurements, if a child lost height (964 (2%) occurrences) or gained >4 cm within 3 months  occurrences), lost or gained >20% of its’ weight (409 (0.8%) and 36 (0.07%) occurrences) or lost or gained more than 2 cm in MUAC within 3 months (153 and 168 occurrences (both 0.3%)). Analyses with these additional cleaning criteria resulted in very similar regression coefficients; the results using these latter criteria are given in the supporting information There were 17,520 children in the three original datasets. Of the 68,888 measurements, 6,151 measurements were done when the child was younger than 6 months, 7,995 when the child was older than 59 months, and at 10 time points the age of the child was missing; thus, these data were excluded from this analysis. At 5,731 time points either WHZ and/or MUAC were not recorded, leaving 49,001 time points provided by 15,060 children for analysis. This resulted in a total of 143,512 person-months of observation available for analysis. Of the eligible children, 49% were female. There were 749 deaths recorded (5.0% of the study population) for which children had records of their anthropometric indicators at the start of the measurement period. Mortality was higher in the Senegal dataset (10.4%) than in the two other datasets. Of the children that died, 48% were female and the median age at death was 26 months (interquartile range 18–35 months). The mean WHZ (SD) was -0.22 (1.03) in the DRC cohort, -0.86 (1.03) in the Senegal cohort, and -0.41 (1.15) in the Nepal cohort. The children in Nepal had the highest prevalence of stunting and wasting based on HAZ and WHZ; those in DRC had a higher percent with a low absolute MUAC and MUAC-for-age Z-score. Of all measurements points with data available for MUAC and WHZ, 2,317 (4.7%) were <115mm for MUAC, 865 (1.8%) were <-3 Z-scores for WHZ, and 527 (1.1%) were under the cut-off points for both MUAC and WHZ. Of all measurements categorized as SAM, 338 measurements (13%) were under the cut-off for WHZ only and thus would be excluded from SAM diagnosis using MUAC as sole indicator . SimilarWe found a significant interaction of the anthropometric indices with age. A stratified analysis by age group is shown in There was no significant interaction of anthropometric indices with stunting status . The morThe increasing mortality hazard in children with an increasing anthropometric deficit was also seen when the data were analyzed by cause of death for children that died primarily due to diarrheal diseases, respiratory diseases, or measles . The hazWe have described the mortality hazard associated with SAM by MUAC, WHZ and both criteria using WHO recommended standards for the same groups of children living in the community where therapeutic treatment was not available. In our analysis pooling data of over 15,000 children aged 6–59 months from three community-based cohorts, we find that the mortality risk increases exponentially with decreasing anthropometric status measured by both WHZ and by MUAC. Children identified as having SAM only by MUAC and only by WHZ had a similar mortality hazard (HR 4.06 and 3.69 respectively) and this hazard was even higher when both MUAC and WHZ were below the SAM cut-off (HR 8.32) compared to children within the normal range.That low WHZ and low MUAC identify different children has been shown repeatedly –8. In coAs nearly 84% of severely malnourished children had SAM by only one or the other deficit in the >1800 surveys analyzed by Grellety and Golden , failingvery severe acute malnutrition. Alternatively, it may indicate that these independent criteria not only identify different children, but also are indicative of different underlying metabolic changes. This is supported by studies in children showing different associations of MUAC or WHZ with body composition [Those children fulfilling the criteria for SAM by both MUAC and WHZ had more than twice the mortality hazard of children with single deficits. This may be because these children are far below the SAM cut-off point for at least one of the criteria so that they then also become SAM by the alternative criterion; in effect, they have position  and clinposition . Thus, cposition , and eacIn our study, hazard ratios for child mortality were higher for children older than two years for both MUAC and WHZ, and a combination of the two indices. Normal children’s MUAC increases steadily with age/height. As low MUAC is an absolute cut-off rather than a relative measurement adjusted for age or height, as a child gets older/taller the nutritional deficit with a MUAC <115mmm becomes steadily greater. Although a low MUAC is much more common in younger children, it carries less risk of death for these individuals than for older children; because with a progressive increase in the MUAC deficit with age, the risk of death increases exponentially. With the Cox analysis the reference mortality hazard is greater for the younger than the older children, so that the change in the denominator  largely removes the effect of the inherently higher risk of death in younger children. However, these considerations do not account for the increased mortality risk in older children with a low WHZ. As well-nourished older children have a lower mortality rate than the younger children, the greatly increased hazard ratio, being a ratio to non-malnourished children, is in part due to the reduction in the denominator with age. High mortality in older compared to younger children has been noted elsewhere , but hasChildren with acute malnutrition who are also stunted are reported to have an increased mortality over those who are not stunted . BecauseIn our cause-specific analysis, deaths with a primary cause of diarrhea, respiratory diseases and measles were associated with anthropometric status with the largest effect seen with diarrheal disease. Children with respiratory disease had a marginally increased hazard of mortality, but this did not reach significance in any group; this finding was unexpected , 38–40, Deaths that could not be assigned to a specific cause were related to anthropometric status indicating potential misclassification and the limitation of verbal autopsy to ascertain the cause of death. This might have influenced the estimates for the cause-specific analysis.A main strength of our study is that we were able to gather rare community-based cohort studies that included all required measures and assess them using the WHO recommended standards. These studies are more likely to have minimized the risk of a selection bias compared to health facility based studies. However, selection bias in the original studies cannot be excluded. Publications reporting the original methods state that in Senegal 88.5% and in Nepal 96% of all the children of eligible ages were enrolled into the studies. However, in the Nepal study, we received a sub-set of the original study with 6,112 of the 28,630 children selected. We do not have any information on what criteria the selection of this sub-set was based. In the DRC dataset, the proportion of eligible children included is unknown although it is reported that 16 out of 52 villages in the study area were randomly selected and all mothers and children in these villages were enrolled into the study .The studies investigated are quite old and there may have been changes in public health provision and general nutritional status since they were conducted. In order to investigate whether this could be a problem with interpretation of the data we have compared the ratios of SAM and GAM  obtained from surveys of randomly selected children in the three countries with the data from the historical cohorts used in this analysis. The data are shown in Despite pooling individual-level data for analysis, the sample size in some categories for sub-analysis was low, reducing the power to draw clear conclusions on some of the (secondary) objectives. In the included cohorts, the death rate was lower than expected. Even in the absence of nutritional programs, children found severely ill were referred to the next health station  or strongly advised to visit the collaborating hospital . This could have biased our estimates of death rates.Another limitation refers to the question of whether the datasets we received were thoroughly cleaned. When we checked for consistency at subsequent time points, we found that many anthropometric measurements were not plausible based on criteria defined prior to our analysis see . The surIn the light of the ongoing debate about the increase in use of MUAC as the sole indicator for defining severe acute malnutrition, we contribute a comprehensive analysis of the mortality hazard for the different anthropometric indicators in children in the community rather than in facilities or programmatic settings. We found that children identified as having SAM by only MUAC <115mm had a similar hazard of death as those identified with only a WHZ <-3. If MUAC was used as the sole indicator, wasted children by WHZ with a similarly increased risk of death would be excluded from diagnosis. We would expect this proportion to be higher in contexts where there is a high SAM caseload by WHZ criteria, and probably in severe crisis situations, where WHZ-only diagnosis tends to increase in prevalence more than MUAC-only . While iS1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


IMPA1, coding for the enzyme inositol monophosphatase 1 (IMPase), has recently been associated with severe intellectual disability (ID) in a geographically isolated consanguineous family in Northeastern Brazil . However, the neurophysiologic mechanisms that mediate the IMPA1 mutation and associated ID phenotype have not been characterized. To this end, resting EEG (eyes-open and eyes-closed) was collected from the Figueredo et al. pedigree. Quantitative EEG measures, including mean power, dominant frequency and dominant frequency variability, were investigated for allelic associations using multivariate family-based association test using generalized estimating equations.Dysregulation of the inositol cycle is implicated in a wide variety of human diseases, including developmental defects and neurological diseases. A homozygous frameshift mutation in IMPA1 mutation was associated with relative decreases in frontal theta band power as well as altered alpha-band variability with no regional specificity during the eyes-open condition. For the eyes-closed condition, there was altered dominant theta frequency variability in the central and parietal regions.We found that the IMPA1 mutation, and thus an important first step towards an understanding the pathophysiologic mechanisms of intellectual disability associated with the mutation that affects this critical metabolic pathway.These findings represent the first human in vivo phenotypic assessment of brain function disturbances associated with a loss-of-function  IMPA1) has recently been associated with severe intellectual disability (ID) in a geographically isolated consanguineous family in Northeastern Brazil  werResting EEG data were collected in an air-conditioned room under eyes open and eyes closed conditions (2 min each). EEG data were collected from a 32 channel ActiCAP with active Ag/AgCl electrodes using a BrainAmpMR amplifier . Scalp locations were based on the International 10/20 system. Data were digitized at 5000 Hz with a 0.1 to 1000 Hz hardware band-pass filter. Ground and reference electrodes were placed at AFz and FCz respectively. Electrode impedances were maintained at or below 20 kΩ. For the final 11 participants , the ActiCAP electrode impedance measurement failed, preventing accurate recording of impedances. Therefore, online EEG data were evaluated by two experts for overall quality. As an additional index of data integrity, an on-line running average of visual evoked potentials from a separate EEG task were reviewed during the recordings to determine that the EEG data collected were valid.Data preprocessing was done offline using custom Matlab scripts . First, Continuous EEG data were notch filtered from 59 to 61 Hz followed by a 0.2 to 150 Hz band-pass filter. Data were then segmented into 2500 ms epochs for artifact identification. Individual trials and channels were rejected based on statistical distance from channel and trial means and variances. Data which were deemed to be contaminated by artifact were removed from the data, the remaining clean data were submitted to independent components analysis (ICA) . BrieflyIMPA1 mutation on the spectral profile of resting EEG, two quantitative EEG (qEEG) measures were derived from the power spectrum densities (PSD) of clean EEG data. First, mean band power (MBP) was calculated using Welch’s method of windowed averaging over a time series [http://github.com/cholab/IMPA1-EEG.To evaluate the influence of the e series . Window IMPA1 genotype and the observed qEEG phenotypes, we employed a multivariate family-based associate test using generalized estimating equations  [To test for associations between the eb-page) . Brieflyi for the ith individual. The value of xi codes for the hypothesized phenotype expression based on allele frequency for the marker of interest . Both E(S) and Vs set the normalization parameters to be conditional on the genotype expression in the parents.where m is number of phenotypes being tested, and the variance is given by the m x m matrix defined ast signifies a vector transpose. The final FBAT-GEE statistic is thus given byThe multivariate extension replaces S and E(S) with the m-dimensional vector χ2-distributed with degrees of freedom equal to IMPA1 mutation has previously demonstrated a strongly recessive pattern, we elected to use a recessive FBAT model where aa carriers are coded as 1, and AA and Aa carriers are coded as 0.which is asymptotically a priori assumption that the inheritance pattern would follow that of the more global phenotype of intellectual disability. However, since the narrower neurophysiologic phenotypes investigated here may show expression even with partial allelic loading, as an additional exploratory step, we repeated our FBAT-GEE analyses with the additive model  to identify potential phenotypes that are more sensitive to such partial loading.The recessive FBAT-GEE model was employed based on the Statistical analyses were conducted in the FBAT-GEE framework for each frequency band and qEEG measure with scalp region acting as the multivariate dimension. Univariate FBATs were run post-hoc within each region to determine if effects were global or regional. Owing to the small sample size, the uniqueness of the mutation identified in the study population, and the generally exploratory nature of our investigation, all tests were evaluated at α = 0.05, uncorrected. Therefore, we present the following analysis under the framework of hypothesis generation, and stress the need for confirmatory follow-up experiments.IMPA1 mutation and therefore chosen for our analysis.Quantitative EEG measures including mean power and dominant frequency variability were extracted from resting EEG data of 30 participants . From this sample, 27 subjects  were from the eight families informative for the IMPA1 mutation and our quantitative phenotypes . See Tables 2 = 18.451, p = .018) and dominant α-band variability  for the eyes-open condition, and dominant θ-band variability  for the eyes-closed condition. With these identified frequency bands, the 8 individual scalp regions were subsequently tested with univariate analysis. For the eyes-open condition, lower than expected θ power over the left frontal scalp region was significantly associated  with the mutated allele, while higher than expected θ power over the right frontal scalp region was significantly associated  with the wild-type allele  with the mutated allele, while dominant θ variability over the parietal scalp region was significantly associated  with the wild-type allele  between θ-band power and the mutated allele for the eyes-open condition. Univariate analysis showed lower than expected θ power over the right frontal scalp region was found to be significantly associated .As noted above, we conducted FBAT-GEE analyses employing the additive model as an exploratory analysis. This showed a significant association . Resting-state EEG placed minimal burden on participants and as such was an ideal approach for probing neural phenotypic expression of the IMPA1 mutation in this population. While no cognitive process is actively engaged through explicit task performance, such intrinsic resting state activity is nevertheless thought to reflect the activity and functional integrity of networks that support various cognitive and perceptual processes [The rocesses , 16 and rocesses .IMPA1 mutation. Theta oscillations have been associated with cognitive control functions such as error monitoring [IMPA1 mutation, it seems highly likely that the disturbances in theta were associated with severe impairments in both these cognitive processes. Interestingly, both these cognitive processes are highly correlated with IQ [In the current study, two prominent rhythms in resting EEG, θ-band and α-band activity, showed variation with the nitoring  and worknitoring , 20 inclnitoring  and medinitoring . In our  with IQ –26 and t with IQ , 28 and  with IQ , 30. TheIMPA1 mutation.Alpha-band oscillations are thought to underlie a different putative function. By and large, alpha oscillations are considered to reflect cortical inhibitory processes which can serve to suppress or gate information transfer in the brain  , 32. In IMPA1 mutation. The power of any oscillatory rhythm is dependent on a number of factors including the numbers of pyramidal neurons and synapses giving rise to the rhythm and how synchronous their activations are. It is not yet known the degree to which IMPA1 mutation may be associated with decreased neuronal number or disturbances in synaptic morphology or function. However, Impa1 knockout has been found to increase autophagy [IMPA1 mutation may affect the dominant frequency is also unclear, but potential mechanisms are suggested by studies of calbindin (CB), which can activate IMPase [IMPA1 mutation could lead to disturbances in the coordination of network activity that manifest as instabilities in the dominant frequency of cortical theta rhythms.The theta disturbances in the current study manifested in both the power and dominant frequency variability of theta in association with the utophagy  which, ie IMPase –47. CB se IMPase , 49. It e IMPase . Thus, iIMPA1 mutation. Administration of lithium leads to reductions in IMPA1 activity and enhancements in low-frequency EEG rhythms [Smit1 mRNA expression [IMPA1 mutation may be fundamentally different from lithium administration. In the case of an IMPA1 mutation, the protracted course of effects could start as early as in utero and impact a neurodevelopment course that may also involve compensatory mechanisms in inositol metabolism and associated pathways that together result in neurophysiologic outcomes altogether different from much shorter time scale lithium administration in adults.Our findings of impaired cortical theta rhythms run counter to the predictions that would be made taking lithium as a model of  rhythms . Howeverpression  and thusIMPA1 allele were too impaired to accurately assess IQ or perform cognitive behavioral tasks during EEG. As such, the data was collected under passive resting state conditions, avoiding reliance on subject motivation and attention. Thus, the present findings are not confounded by differences in attentional engagement as may be the case in cognitive or sensory tasks. The present data supports the feasibility of data collection and meaningful analysis using a resting state paradigm in this population.Our study had a number of strengths, notably including the uniqueness of the study sample and the successful collection of electrophysiologic measures in a remote, rural setting. The mobility of our EEG equipment facilitated access to a geographically isolated sample which allowed the study team to bring the lab to the participants. We found that the homozygous carriers of the IMPA1 mutation employing human electrophysiology, the sample size was limited. This was to large extent unavoidable due to a number of factors including the geographically isolated nature of the population, the inability of a number of homozygous IMPA1 mutation candidates to participate in the study because of the severity of their impairment, and the lack of on-site lab facilities and expertise to conduct longer-term data collection. We conducted a high-powered multivariate statistical analysis of the data to capitalize on the familial pedigree structure of our sample, but were still limited by the sample size which was underpowered relative to the conservative corrections for our post-hoc multiple comparisons. However, given the rarity of the sample, we elected to present the findings of the current study with the understanding that they require replication in additional cohorts. On a related note, despite the much higher proportion of psychosis exhibited in individuals who were homozygous or heterozygous for the mutant allele, the sample size limited a more definitive examination of the phenotypic relationship between intellectual disability and psychosis in the context of the IMPA1 mutation. Such associations between psychiatric symptoms and cognitive impairments are common in neuropsychiatric disorders such as schizophrenia, but the precise nature of the relationship will require much larger samples, perhaps primarily comparing heterozygous to wildtype groups given the severe cognitive impairment manifesting in individuals homozygous for the mutant allele. Finally, future investigations could also attempt higher-density EEG recordings to facilitate anatomic source analyses of the resting state networks.There are some limitations that should be considered when interpreting the results. While it is the first study of its kind to investigate the IMPA1 mutation in a geographically isolated, consanguineous cohort. Our findings of disturbances in frontal theta and more global alpha band disturbances raises the possibility of perturbations in certain cellular subtypes and aspects of the inositol metabolic pathways. More mechanistically definitive studies will require preclinical models with controlled manipulation of the IMPA1 expression and electrophysiological recordings. Based on these important preliminary findings, now we are expanding the studies to patient-derived neuronal cell lines in order to clearly elucidate the mechanisms by which impairment of IMPA1 can alter important signaling pathways that may lead to development of intellectual disability and to correlate with in vivo measures of brain activity and cognitive capacity to better understand the pathway from genetic variants to behavior. To our knowledge, however, this current study is the first human in vivo phenotypic assessment of brain function disturbances associated with the IMPA1 mutation, thus representing an important first step towards understanding the pathophysiology of intellectual disability associated with the mutation that affects this critical metabolic pathway.We conducted the first electrophysiologic phenotyping human study of the


Genetic resource banks (GRB) are a valuable tool for maintaining genetic variability and preserving breeds from pathogens or catastrophe, enabling us to assess and correct breeding schemes, minimizing the impact of genetic drift and facilitating diffusion. This study tests their efficiency in re-establishing two extinct populations of a synthetic rabbit line selected for daily weight gain, using vitrified embryos from two generations (18th and 36th) separated by 15 years of genetic selection. The effect of long-term storage of vitrified embryos in liquid nitrogen was also evaluated. A total of 516 vitrified embryos using the same protocol were transferred into 54 recipients. The embryos had been maintained in liquid nitrogen during 2 different periods, (i) 1 year  and (ii) 15 years . A total of 80.0% (8/10 to 18th) and 60.0% (9/15 to 36th) of the foundational sire families were eventually re-established. Over approximately one year, animals within each population were crossed to produce the next generation and re-establish the original population size. Our study demonstrated that our GRB of embryos vitrified 15 years ago is a successful strategy to re-establish rabbit populations to continue the breeding programme. The Animal Science Department set up a genetic resource bank (GRB) in 1993 to assist the Universidad Politécnica de Valencia (UPV) in the development and management of a genetic improvement programme for meat rabbits to meet the need for animals required by rabbit meat producers. Intensive meat production in rabbits is based on a three-way crossbreeding scheme involving maternal and paternal synthetic lines to produce the animals for consumption . Work onThe broadest definition of a GRB refers to a storage facility for gametes and embryos from threatened populations, with the specific and deliberate aim of using them in a breeding programme on some future occasion. Among the criteria for setting up a cryobank are: (i) offspring will be rederived from the cryobank; (ii) rederived offspring will have exhibited the desired genotype; and (iii) the rederived offspring should produce offspring . GRBs haAgainst this background, the aim of the present study was to test the efficiency of using our rabbit embryo cryobank to re-establish populations and the effect of long-term storage in liquid nitrogen of embryos vitrified 15 years ago.All chemicals, unless otherwise stated, were reagent-grade and purchased from Sigma-Aldrich Química S.A. . All the experimental procedures used in this study were performed in accordance with Directive 2010/63/EU EEC for animal experiments and reviewed and approved by the Ethical Committee for Experimentation with Animals of the Universidad Politécnica de Valencia, Spain (research code: 2015/VSC/PEA/00061).The experiment was carried out with animals from two Spanish commercial rabbit lines , with free access to water and commercial pelleted diets (minimum of 15 g of crude protein per kg of dry matter (DM), 15 g of crude fibre per kg of DM, and 10.2 MJ of digestible energy (DE) per kg of DM). The photoperiod was set to provide 16 h of light and 8 h of dark, and the room temperature was regulated to keep temperatures between 14°C and 28°C.All embryos were from line R, while females from line A were used solely as recipients. Embryos were obtained from females after the third birth. To this end, females were inseminated with semen from an unrelated male to avoid an increase in consanguinity. In addition, males were selected within sire families in order to reduce inbreeding. Another practice to reduce inbreeding was the avoidance of mating between animals having common grandparents. At the time of artificial insemination, females were administered 1 μg of buserelin acetate  to induce ovulation and euthanized 72 hours later with an intravenous injection of 200 mg/Kg of pentobarbital sodium . Then, embryos were recovered by perfusion of each oviduct and uterine horn with 10 mL pre-warmed Dulbecco Phosphate Buffered Saline (DPBS) supplemented with 0.2% of Bovine Serum Albumin (BSA). After recovery  of pseudopregnant recipient females from line A following the procedure described by Besenfelder and Brem . OvulatiBriefly, the equipment used was a Karl Storz laparoscope, which is a 0°-mm straight-viewing laparoscope, 30-cm in length, with a 5-mm working channel . Recipients were sedated by intramuscular injection of 5 mg/Kg of xylazine . As surgical preparation for laparoscopy, anaesthesia was performed by intravenous injection into the marginal ear vein of 6 mg/Kg of ketamine hydrochloride . During laparoscopy, 12 mg of morphine hydrochloride  was administered intramuscularly. First, the abdominal region was shaved, and the animals were then placed on an operating table in a vertical position (head down at 45-degree angle). This vertical positioning ensures that the stomach and intestines are cranially located so that the Fallopian tubes form a downwardly pointing loop between the ovaries and uterus. Only an endoscope trocar was inserted into the abdominal cavity. When the trocar was removed, the abdomen was insufflated with CO2 and the endoscope was then inserted. For embryo transfer, embryos were aspirated in a 17-gaugue epidural catheter , introduced into the inguinal region with an epidural needle and then inserted in the oviduct through the infundibulum. After transfer, does were treated with antibiotics  and analgesics .Implantation rates were assessed by laparoscopy following the previous procedure, noting the number of embryos implanted at day 12 from total embryos transferred and birth rate . Embryonic losses were calculated as the difference between transferred embryos and implanted embryos expressed as percentage. Foetal losses were calculated as the difference between total offspring at birth and implanted embryos expressed as percentage.The numbers of individuals for both generations were determined at birth, at weaning (28-days-old) and at adult age . Finally, for one year approximately, founders within each generation were crossed to produce offspring and then re-establish the original population size. The total numbers of individuals for both populations were determined at the end of the fattening at 63 days (selection age).The experimental design of this study is shown in The effects of storage on the percentage of embryos retrieved from the ministraw after warming and the percentage of transferable embryos were compared using a general linear model including the storage time in liquid nitrogen with two levels (15 and 1 year) as a fixed factor. The error was designated as having a binomial distribution using probit link function. Binomial data for implantation rate, offspring rates at birth  and embryonic and foetal loss rates were assigned as 1 if positive development had been achieved, or a 0 if it had not. To compare litter size, a general linear model was also performed, including the storage time in liquid nitrogen with two levels (15 and 1 year) as a fixed factor as previously.A P value of less than 0.05 was considered to indicate a statistically significant difference. The data are presented as least square mean ± standard error mean. All statistical analyses were carried out using a commercially available software program .After transfer, there were no significant differences in implantation rate  for embryos stored for up 15 and 1 years, thus generating the founders within population for the 18th and 36th generation.Founders within each population, 12 females and 5 males for the 18th generation and 18 females and 10 males for the 36th generation were crossed. A total of 166 litters were produced over a year (81 and 85 for the 18th and 36th generation, respectively, Little is known about the efficiency of using GRBs to re-establish mammal populations, particularly with livestock embryos, as cryopreservation has generally been applied as a tool for the storage and exchange of valuable animals. Embryo cryopreservation is widely used to re-establish breeding colonies in laboratory animals . Our curAt present, millions of offspring have been born from cryopreserved embryos of more than 40 mammalian species . NeverthMoreover, in this study, GRB efficiency based on the total number of offspring regenerated by the number of thawed embryos was 23.0%. This has been posited as the only measure that accurately reveals real-world production of offspring from cryopreserved embryos . These dCollectively, the data indicate that a "minimal bank" of 259 embryos was sufficient to re-establish the foundation population for this genotype. However, genetic diversity has been identified as an important factor influencing a population's long-term potential for survival . In rabbIn this case, we applied the vitrification technique with the commonly used ministraw device, developed 23 years ago in our laboratory . AlthougOur results here clearly demonstrate that vitrified-warmed embryos stored in liquid nitrogen for 15 years maintained the same capacity to regenerate a disbanded population. Indeed, the theoretical discussion on the duration of storage is one of the most recurring issues in cryobiology . In thisIn conclusion, our study showed that a GRB of embryos using vitrification is a successful strategy to re-establish populations in rabbit for at least 15 years. Moreover, our result has important practical implications for the establishment of GRBs in rabbit to ensure that sufficient embryos are available. Our results showed that for this genotype the efficiency was less than 10% in terms of animals that generate offspring. This outcome suggests that increasing the size of the embryo bank could be a strategy to avoid any risk in the future.


One of the most common symptoms observed among most of the Parkinson’s disease patients that affects movement pattern and is also related to the risk of fall, is usually termed as “freezing of gait (FoG)”. To allow systematic assessment of FoG, objective quantification of gait parameters and automatic detection of FoG are needed. This will help in personalizing the treatment. In this paper, the objectives of the study are (1) quantification of gait parameters in an objective manner by using the data collected from wearable accelerometers; (2) comparison of five estimated gait parameters from the proposed algorithm with their counterparts obtained from the 3D motion capture system in terms of mean error rate and Pearson’s correlation coefficient (PCC); (3) automatic discrimination of FoG patients from no FoG patients using machine learning techniques. It was found that the five gait parameters have a high level of agreement with PCC ranging from 0.961 to 0.984. The mean error rate between the estimated gait parameters from accelerometer-based approach and 3D motion capture system was found to be less than 10%. The performances of the classifiers are compared on the basis of accuracy. The best result was accomplished with the SVM classifier with an accuracy of approximately 88%. The proposed approach shows enough evidence that makes it applicable in a real-life scenario where the wearable accelerometer-based system would be recommended to assess and monitor the FoG. FoG is one of the symptoms that completely immobilize the gait normally seen in the advanced stage of Parkinson’s disease (PD) ,2. The bThe paper is organized as follows: This section reports the previous studies that describe the digital mediums used for measuring the PD. Lipsmeier et al. have proposed a method of using a smartphone to collect the accelerometer and gyroscope data for detecting abnormalities in PD patients during a clinical treatment monitoring. In this study, six activities are monitored on a daily basis, in which the participants have to carry the smartphone in their trouser pocket. They found that the smartphone-based, digital-biomarkers-based clinical treatment is feasible and reliable, and can be recommended for long-term monitoring of the PD patients and for other biomedical research activities . San LucThis section reports the previous validation studies based on the wearable devices. Coste et al.  have proThe above-mentioned literature confirms the success of various digital mediums for the effective diagnosis of PD. The findings from the previous work helped in designing the proposed approach. Especially the feasibility of wearable device based study for the objective assessment of various characteristics of PD as well as the effectiveness of machine learning techniques for automatic classification of PD characteristics inspired us to design the FoG detection system. In this study, a FoG detection system has been designed, investigated, and validated that can be used in the clinical environment as well as the home environment. Fifty-one patients were enrolled in this study. Out of 51 patients, 36 patients have shown symptoms of FoG and rest have not shown any symptom of FoG, although they are diagnosed with PD. All participants wore accelerometers on both knees. Two wearable triaxial accelerometers with sampling frequency of 32 Hz  were used. The triaxial accelerometer measures body movements in all directions: anterior–posterior, medio-lateral, and vertical. It is small and lightweight (35 mm × 35 mm × 13 mm and 13.7 gm). It is sensitive to acceleration from −8 g to 8 g, allowing for monitoring of almost all human physical activities. The wearable accelerometers are placed at the left as well as right knee, and tightly fastened using straps. The position of accelerometer at the left knee, as well as the right knee, is 34 cm above the ground as shown in (1)Patients who were diagnosed with PD clinically ,(2)Age between 40 and 80,(3)Hoehn and Yahr stages 2–4,(4)Patients who showed gait disturbance.All participants gave their consent to participate in this study. This study was approved by the institutional review board at Haeundae Paik Hospital, Busan, South Korea (IRB No. 2017-01-028). The criteria for inclusion are as follows:Demographic data of the patients are as follows: Mean age is 71.60 ± 9.01. Mean disease duration is 34.89 ± 27.77 and mean UPDRS motor scale is 21.02 ± 11.21. Mean H&Y stage is a relatively early stage (2.08 ± 0.76). All participants walked in a room where the motion was captured using a three-dimensional (3D) motion capture system  environment. The walking test in this study was carried out in a rectangular area measuring 8 m × 6 m. The motion was captured using 12 infrared cameras available in the 3D motion capture system (VICON). All participants were asked to walk along the 6 m track for three times. The quantified spatiotemporal parameters include step time, stride time, step length, stride length, and walking speed.This study is a prospective, cross-sectional study. In this study, the collected data has been processed using a computer with following specification: window 10, 3.60 GHz 64-Bit Intel Core i7-7700 processor and 8 GB RAM. Rawassizadeh et al.  have proThe gait parameters including step time, stride time, step length, stride length, and walking speed were calculated using the data obtained from a triaxial wearable accelerometer. Previous researchers have reported the importance of the five parameters for the detection of PD. Hollman et al.  have proThe percentage rate of error between gait parameters obtained from accelerometer data and those obtained from 3D motion capture system is calculated as follows:The primary endpoint is to estimate the mean error rate for gait parameters obtained from wearable accelerometer compared to those obtained from 3D motion capture system, and to evaluate the correlation between step time, stride time, step length, stride length, and walking speed estimated from both the methods. The secondary endpoint is to compare the performance of the classifiers in predicting FoG and no FoG for the same dataset.The Minitab version 18 is used for statistical analysis. Pearson’s correlation coefficients between gait parameters obtained from wearable accelerometer data, various clinical scales, and 3D motion capture system were used for concurrent validity. MATLAB (version R2018a) was used for development of validation and classification algorithm.The gait cycle is detected using the method proposed by Del et al. . The firIC events within the gait cycle are used to estimate the five spatiotemporal parameters. The equations used to estimate the step time and stride time are as follows:i represents the index of the sequence of IC events in the signal. The inverted pendulum model [The flowchart for proposed accelerometer-based validation of gait parameters in FoG patients is shown in um model  is extenD represents the change in the height of the wearable accelerometer that can be computed using double integration of the vertical acceleration between two consecutive IC events. In order to avoid iterative time-consuming training time required for finding individual multiplying factor for each patient, a generic multiplying factor The equations used to estimate the step length and stride length are as follows:In addition to the aforementioned five spatiotemporal gait parameters, nine conventional statistical features are also used in this study for FOG classification. The 14 features along with their description are listed in In recent years with the challenge of making a real-time system, machine learning techniques have a huge role to play in terms of detection, monitoring, and prediction from large number of data. Rawassizadeh et al.  have user = 0.961, p < 0.01), , , , , respectively. The performances of four machine-learning techniques are compared in terms of accuracy, sensitivity, and specificity. The results obtained using the four classifiers are shown in the The mean error rate was found below 10%. The SVM classifier with radial basis function provides the highest accuracy of 91.42%. It indicates that out of 15 patients that includes both the classes we could predict 46 patient correctly. It also gives the highest sensitivity as well as specificity of 90.89% and 91.21% respectively. This result indicates that out of 36 FoG patients, the system correctly identified 32 as FoG and out of 15 no FoG patients, the system correctly identified 14 as no FoG patients. The FoG classification confusion matrix is shown in This study proposed another novel approach for quantifying the gait characteristics for FoG detection and automatic FoG detection system in PD using machine learning techniques. We have incorporated previous researcher’s findings to improve the result. Coste et al.  have meaThe novelties of the proposed study are as follows: (1) This validation study involves 39 patients which is well above the minimum 30 number of sample size suggested in , which iThe wearable accelerometer showed acceptable accuracy in estimating gait parameters in FoG patients. The results show that the mean error rate for gait parameters obtained from accelerometer data is approximately 8% compared to those obtained from 3D motion capture system. Compared to the existing approaches for FoG detection using machine-learning techniques, the classification results obtained using the proposed approach outweighs their performance. Further, the number patients considered in this study is more compared to the previously reported studies. The performance comparison of different FoG detection methods using machine-learning techniques is shown in the In this paper, low-cost wearable sensors were used to assess the feasibility of a measurement system before implementing it in the practical environment. An excellent level of agreement was found between the gait parameters obtained from wearable accelerometer-based method, 3D motion capture system and clinical scales, with less than 10% error. The wearable triaxial accelerometer-based method could be considered as a low-cost alternative method for detection of FoG in PD patients. Good classification accuracy was found for the discrimination of FoG and no FoG patients. The good classification accuracy helps in monitoring the FoG and no FoG Patients. The wearable accelerometer can enable long-term monitoring and assessment of gait in a home-based free-living environment. In the future, the validation methods will be tested with bigger sample sizes as well as in real-life scenarios. Further, advanced machine learning techniques will be employed for better classification performance. This kind of study will help clinicians in assessing and monitoring the FoG patients.


Headache attributed to traumatic injury to the head (HAIH) is claimed to be the most common sequela following mild traumatic brain injury (MTBI), but epidemiological evidence is scarce. We explored whether patients with MTBI had an increase in headache suffering following injury compared with controls. We also studied predictors of headache.The Trondheim MTBI follow-up study is a population-based, controlled, longitudinal study. We recruited patients exposed to MTBI and controls with minor orthopedic injuries from a trauma center and a municipal outpatient clinic, and community controls from the surrounding population. Information on headache was collected through questionnaires at baseline, and 3 and 12 months post-injury. We used a generalized linear mixed model to investigate the development of headache over time in the three groups, and logistic regression to identify predictors of headache.We included 378 patients exposed to MTBI, 82 trauma controls, and 83 community controls. The MTBI-group had a larger increase in odds of headache from baseline to the first 3 months post-injury than the controls, but not from baseline to 3–12 months post-injury. Predictors for acute HAIH were female sex and pathological imaging findings on computed tomography (CT) or magnetic resonance imaging (MRI). Predictors for persistent HAIH were prior MTBI, being injured under the influence of alcohol, and acute HAIH. Patients who experience HAIH during the first 3 months post-injury have a good chance to improve before 12 months post-injury. Female sex, imaging findings on CT or MRI, prior MTBI, and being injured under the influence of alcohol may predict exacerbation of headache. The head injury population are not headache-free pre-injury, but rather a part of the general population, of which in Norway 37% report having suffered from headache during the last year.8 To identify patients in need of intervention it is important to recognize those at risk for developing HAIH. Previous studies have identified several predictors, such as sex, age, multiple head injuries, and a number of acute symptoms following the head injury.9–13 However, there is large variation in follow-up time and poor consistency between study results.14The incidence of MTBI is high, and a close follow-up of all patients would require a large effort by the health care service.The main aim of this prospective, controlled study, including both hospitalized and non-hospitalized patients with MTBI, was to explore if patients with MTBI had an increase in headache suffering after the injury compared with a control group with minor orthopedic injuries and a community control group. The second aim was to study predictors of acute and persistent HAIH in the MTBI group.This population-based, controlled, prospective cohort study included three study groups: one group exposed to MTBI, one group exposed to minor orthopedic injuries but no head injury (trauma controls), and one group of community controls not exposed to any injury. The MTBI group and the trauma controls were recruited from two emergency departments (EDs) in Trondheim, Norway: St. Olav's Hospital , a regional Level 1 trauma center, and Trondheim Municipal Emergency Clinic, a general practitioner-run, outpatient clinic, with a catchment area of 229,000 inhabitants. The community control group was recruited from the same catchment area and matched to the MTBI group with regard to age, sex, and education. The trauma control group was matched with regard to age and sex.15 The inclusion period was April 2014 to December 2015 for the MTBI group and June 2014 to December 2017 for the controls. Inclusion criteria for the MTBI group were having experienced MTBI and age 16–59 years. TBI was categorized as mild according to World Health Organization (WHO) criteria: Glasgow Coma Scale (GCS) score 13–15 at presentation, and either witnessed loss of consciousness (LOC) <30 min, confusion, or post-traumatic amnesia (PTA) <24 h.16 Inclusion criteria for trauma controls were fractures or symptoms from soft-tissue injuries lasting ≥48 h. Exclusion criteria were the same as for patients with MTBI; also excluded were head or neck injury (including whiplash injury), multi-trauma, and trauma to dominant upper extremity.Details of how the MTBI group was recruited, diagnostic criteria, and exclusion criteria have been described earlier.Information regarding the injury resulting in an MTBI and clinical symptoms at the ED, including information about alcohol influence at the time of injury, was collected through medical records and by interviewing the participants. The GCS score was observed by the study personnel or recorded from the medical record. If lacking, the history and clinical descriptions in the medical records were used to estimate a score. This method was used on 29 (7.7%) of the MTBI participants. Duration of PTA was recorded as the time after injury for which the patient had no continuous memory . Previous MTBI was defined as having experienced one or more head injuries fulfilling diagnostic criteria for MTBI. The clinical radiology report was used to classify CT findings into intracranial findings and cranial fractures. An MRI examination was performed on a 3.0 Tesla Siemens Skyra System  if the patient lived within a 1-h drive, there was a time slot for MRI available within 72 h, and the patient consented to MRI  was defined as headache occurring >14 days/month. The participants were also asked to report use of acute medication for headache during the previous month, enabling the diagnosis of medication overuse headache (MOH) defined as CDH with acute medication use >14 days/month. The validity of these questionnaire-based diagnoses has previously been evaluated and found to be acceptable.To be able to examine predictors for new headache or exacerbation of previously reported headache from baseline to the first 3 months post-injury (acute HAIH) and from baseline to 3–12 months post-injury (persistent HAIH) respectively, headache sufferers were classified with regard to headache frequency. Each participant was then categorized into one of the following four groups: no headache suffering, headache suffering <7 days/month, headache suffering 7–14 days/month, and headache suffering >14 days/month. We defined exacerbation of headache as new onset of headache or increased frequency of previously reported headache.18 Lower secondary school grades were chosen as measure of socioeconomic status because of the young age of many of the participants, making it difficult to use the traditional measures such as income or duration of education. In Norway, lower secondary school grades have been found to correlate strongly with the socioeconomic status in the family.19Demographic variables and other health-related variables were collected through an interview with the study participants within 2 weeks after the injury. These were the known confounders: age, sex, socioeconomic status, and AUDIT score , categorized from a continuous variable into two categories with score boundaries <8 and ≥8.Continuous data are presented as means and standard deviations (SDs), whereas categorical data are presented as frequencies and percentages. We used a generalized linear mixed model (GLMM) to compare the development of headache status regarding headache suffering or no headache suffering between the three groups  over time . To control for known confounders, the model included age, sex, socioeconomic status, and alcohol use as covariates, in addition to group, time, and a group-time interaction term. A random, subject-specific intercept on the logit scale was included to account for within-subject dependencies. Missing outcome variables for subjects with at least one observation on outcome were handled by the model, whereas missing explanatory variables were handled by listwise deletion.We used logistic regression to identify predictors for exacerbation of headache within the first 3 months post-injury (acute HAIH) among the participants answering both the baseline and 3-month questionnaires, and exacerbation of headache 3–12 months post-injury (persistent HAIH) among the participants answering both the baseline and 12-month questionnaires, for the MTBI participants. We examined sex , age (continuous variable), socioeconomic status (continuous variable), GCS score (13–14/15), PTA (1–24 h/<1 h), previous MTBI (yes/no), pathological imaging findings  on CT or MRI (yes/no), and being influenced by alcohol at time of injury  (yes/no) as potential predictors. In addition, we performed a separate analysis examining acute HAIH (yes/no) as a predictor for persistent HAIH, including the previously mentioned predictors as covariates. Missing data were handled by listwise deletion.20 Age was found to be linearly related to the logit of the dependent variable and could thus be included as a continuous variable. The results for both analyses are presented as odds ratios (ORs) with 95% confidence intervals (CIs). P-values <0.05 were considered statistically significant. We used IBM SPSS Statistics software version 25 for the logistic regression and Stata/MP version 15.1 for the GLMM.Linearity of age as a continuous variable with respect to the logit of the probability of headache (yes/no) was assessed with the Box-Tidwell procedure.The Regional Committee for Research Ethics approved the study (REK 2013/754). Participants, or parents of participants <18 years of age, gave informed consent.The MTBI group consisted of 378 participants. The control groups consisted of 82 trauma controls and 83 community controls. Questionnaire response rates are reported in The mean age in the MTBI group was 31.2 years (SD: 13.0 years) and 65.3% were male. Only 31.2% of the MTBI participants were admitted to a hospital; 51.7% of these were observed <24 h.Traumatic pathology was revealed in 37 (9.8%) of the MTBI participants. Intracranial pathology was revealed in 32 (8.5%), 16 (4.2%) had cranial fractures, and 11 (2.9%) had both cranial fracture and intracranial pathology.The proportion reporting baseline headache suffering was 30.5% of the MTBI group, 26.9% of the trauma controls, and 38.2% of the community controls. The odds of baseline headache suffering were not significantly different between the three groups . The MTBp = 0.035 for the interaction between time and group). The development in odds for headache over time differed significantly between the groups  reported CDH, and 8.6% (n = 9) reported MOH. During the 3–12-month post-injury period, 35.7% of the subjects reported exacerbation of headache compared with baseline. Of these, 57.9% reported migraine headache, 19.7% (n = 15) reported CDH, and 9.2% (n = 7) reported MOH.Female sex  and pathological imaging findings on CT or MRI  were significant positive predictors for acute HAIH, but this was no longer the case for persistent HAIH . SignifiIn this large, prospective, controlled study, MTBI participants had a larger increase in odds of headache from baseline to the first 3 months post-injury than both control groups, but the change in odds of headache from baseline to 3–12 months post-injury did not differ between the groups. Predictors for acute HAIH were female sex and pathological imaging findings on CT or MRI. For persistent HAIH the predictors were prior MTBI, being injured under the influence of alcohol, and acute HAIH.The patients with MTBI had a significant increase in odds of headache between baseline and the first 3 months post-injury and between baseline and 3–12 months post-injury. From the first 3 months post-injury to 3–12 months post-injury they had a decrease in odds of headache. The increase in odds of headache from baseline to the first 3 months post-injury was significantly larger for the MTBI group than for trauma controls and the community controls. However, the ORs of headache from baseline to 3–12 months post-injury did not differ between the groups. We also saw, somewhat surprisingly, that the control groups had an increase in headache suffering from baseline to 3–12 months post-injury, but this was only statistically significant for community controls.21 But this could also be a demonstration of the dynamic course of headache. In a study of a random sample of 5,000 adults selected from five general practices in the United Kingdom, the authors showed that 24% of respondents without recent headache (last 3 months) at baseline reported headache in at least one follow-up the following year.22 Hence, increase in headache suffering could be observed also in persons not exposed to a head injury.This increase in odds of headache suffering in the control groups, seen together with the decrease in odds of headache suffering from the first 3 months post-injury to 3–12 months post-injury in the MTBI group could explain why the MTBI group did not differ from the control groups between baseline and 3–12 months post-injury. The increase in odds of headache suffering from baseline to 3–12 months post-injury in the control groups could have been caused by participation bias (those who suffer from headache are more likely to continue answering the questionnaires throughout the study); another explanation could be that we had a young study population and many of our study participants were in the age where headache disorders start.Taken together, our results showed that the MTBI participants developed acute HAIH. We cannot, however, conclude that the MTBI group developed persistent HAIH, because the control groups without MTBI also had an increase in odds of headache suffering.24 In the Lithuanian study, migraine occurred significantly more often in patients with MTBI than in patients with a minor orthopedic trauma 3 months post-injury, but headache diagnoses and headache frequency occurred in similar proportions as in the controls 12 months post-injury.23 The authors of the Norwegian study did not find any association between previous head injury and headache 22 years after hospitalization for head injury.24 However, the large difference in follow-up time (12 months vs. 22 years) warrants caution when comparing these results with our findings.Our findings of acute HAIH but not persistent HAIH are in line with a controlled study from Lithuania and a population-based study from Norway.12 However, only 18% of the participants in the American study reported having a problem with headache pre-injury. This is considerably lower than in the general population in the United States, which gives reason to suspect recall bias.25 Another explanation could be that the study included only hospitalized patients, which could mean their population had more severe MTBI than ours.12In our study, 47.9% of the participants in the MTBI group reported exacerbation of headache from baseline to the first 3 months post-injury and 35.7% from baseline to 3–12 months post-injury. This is somewhat lower than the findings in a prospective study without controls on hospitalized patients with MTBI from the United States, where 62% of the subjects reported exacerbation of headache at 3 months and 58% at 12 months.26 In our study, female sex was a significant positive predictor of acute HAIH. This is in line with several other studies that have shown female sex to be a risk factor of HAIH.28Headache is more prevalent among women than men in the general population.29 Research on imaging findings as a predictor for headache is scarce, but some studies have investigated imaging findings as a predictor for post-concussive symptoms (PCS), a cluster of post-traumatic symptoms, including headache. A prospective study from Finland found no significant differences in the rates of PCS 1 month and 1 year following MTBI, in participants with or without MRI abnormalities.30 A prospective study from the United States found no statistically significant differences in the rates of PCS at 3 or 6 months following MTBI between participants with or without imaging findings on CT or MRI.31Imaging findings on CT or MRI  were a significant positive predictor of acute HAIH but not persistent HAIH. However, in a recent study from Korea, the authors found that HAIH occurred more frequently 12 months post-injury in patients with minimal traumatic intracranial hemorrhage after MTBI than in those without.32 In a previous population-based historical cohort study, we found that subjects having more than one head injury showed an even greater odds of headache compared with controls than all individuals exposed to head injury without specification of iteration.11 Finally, in a study aiming to predict which patients were at risk for developing PCS (with a median duration of 19 weeks), multiple head injuries was one of the predictors33Prior MTBI was a significant positive predictor of persistent HAIH in our study. This is consistent with several previous studies. A retrospective study that examined the risk of CDH found that the odds of CDH increased with the number of lifetime head or neck injuries.Being injured under the influence of alcohol was another significant positive predictor of persistent HAIH. We cannot conclude whether being injured under the influence of alcohol is a proxy for a generally high alcohol consumption or if being injured under the influence of alcohol is a true risk factor of developing HAIH. The association between alcohol influence at the time of injury and the damage that occurs in the brain must be examined specifically to answer this question.10Finally, we found that acute HAIH was a significant positive predictor for persistent HAIH. This is in line with the results from a Norwegian study that examined predictors of headache 22 years following head injury.17 We did a baseline assessment of pre-injury headache suffering, wherein the proportions reporting headache suffering during the last year prior to the MTBI were similar in all groups. The proportions were also very similar to a population-based study on headache prevalence in Trøndelag county in Norway in 2006–08, in which the same validated headache questionnaire as in the present study was used.3 This means that our study is at low risk for recall bias. The 12-month follow-up time allowed us to examine persistent HAIH, which according to ICHD-3, is of greater than 3 months' duration.1 We included both hospitalized MTBI participants and non-hospitalized MTBI participants from a general practitioner-run, out-patient clinic. We therefore believe that there was low risk of selection bias, which is a problem when the study population is recruited from trauma hospitals, headache clinics, or special clinics with a focus on health problems following TBI. In the statistical analysis, we controlled for known confounders for MTBI and headache, such as age, sex, socioeconomic status, and alcohol use.The major strengths of this study are its population-based and prospective study design and appropriate comparison groups. This study emphasizes the importance of including control groups, as we saw a significant increase in headache suffering also in the community control group. We used a validated headache questionnaire and a renowned MTBI classification that makes the study accessible to comparison with other studies, also future ones.34 This may, like recall bias, lead to an exaggerated increase in headache suffering throughout the study. This could be the reason for the increase in headache suffering in the community control group. One could speculate that because so many of the trauma controls got their injury during sport activities and fewer were influenced by alcohol at the time of injury, they represent a healthier group than the MTBI group and community controls. We had a young study population and many of our study participants were in the age where headache disorders start.21 Also, we did not ask the participants about the time of onset for their exacerbation of headache. Therefore, we cannot specify if the onset of headache was within 7 days after head injury, which is a criterion for classifying a headache as HAIH, according to ICHD-3.1 However, the authors of ICHD-3 state that this criterion is somewhat arbitrary and conclude that further research is needed to determine which interval might be more appropriate.1Further, the community control group had a low response rate at 3 months (49.4%). Finally, our results may not be valid for all age groups, because we only studied participants from 16 to 59 years of age.In studies where the participants are included after their MTBI, assessment of pre-injury headache suffering may always be influenced by recall problems. Also, participants with more headache suffering could be more likely to continue to answer all questionnaires throughout a study because they have more personal interest in it.3 Also, previous research has found MOH to be more prevalent among persons exposed to head injury.35A considerable proportion of patients exposed to an MTBI will experience new headache or exacerbation of previously reported headache. However, a large number of these will improve during the first year following the MTBI and this should be the main message to the patients. However, prior MTBI, being injured under the influence of alcohol, and having acute HAIH are positive predictors for persistent HAIH, and these patients should receive greater attention by the health care service. Of the patients exposed to MTBI who developed persistent HAIH, 9.2% fulfilled criteria for MOH. The corresponding proportion in the general population is 1.0%.Exacerbation of headache during the first 3 months after MTBI is likely related to the head injury, and predictors for acute HAIH were female sex and pathological imaging findings on CT or MR. However, many patients will experience improvement of their headache before reaching 12 months post-injury. Patients exposed to MTBI should be advised against overuse of analgesics, and management of patients with persistent HAIH should include treatment against MOH when medication overuse is suspected.


Research using data-driven cluster analysis has proposed five subgroups of diabetes with differences in diabetes progression and risk of complications. We aimed to compare the clinical utility of this subgroup-based approach for predicting patient outcomes with an alternative strategy of developing models for each outcome using simple patient characteristics.1c) in an independent trial cohort (RECORD [n=4447]).We identified five clusters in the ADOPT trial (n=4351) using the same data-driven cluster analysis as reported by Ahlqvist and colleagues. Differences between clusters in glycaemic and renal progression were investigated and contrasted with stratification using simple continuous clinical features . We compared the effectiveness of a strategy of selecting glucose-lowering therapy using clusters with one combining simple clinical features (sex, BMI, age at diagnosis, baseline HbAClusters identified in trial data were similar to those described in the original study by Ahlqvist and colleagues. Clusters showed differences in glycaemic progression, but a model using age at diagnosis alone explained a similar amount of variation in progression. We found differences in incidence of chronic kidney disease between clusters; however, estimated glomerular filtration rate at baseline was a better predictor of time to chronic kidney disease. Clusters differed in glycaemic response, with a particular benefit for thiazolidinediones in patients in the severe insulin-resistant diabetes cluster and for sulfonylureas in patients in the mild age-related diabetes cluster. However, simple clinical features outperformed clusters to select therapy for individual patients.The proposed data-driven clusters differ in diabetes progression and treatment response, but models that are based on simple continuous clinical features are more useful to stratify patients. This finding suggests that precision medicine in type 2 diabetes is likely to have most clinical utility if it is based on an approach of using specific phenotypic measures to predict specific outcomes, rather than assigning patients to subgroups.UK Medical Research Council. Type 2 diabetes is a heterogeneous, multifactorial condition, comprising 90–95% of all cases of diabetes and affecting over 400 million people worldwide. There is great interest in better characterising the heterogeneity in type 2 diabetes and in exploiting this heterogeneity to improve care and outcomes for individuals with type 2 diabetes.31c, and homoeostatic model assessment (HOMA) 2 measured insulin resistance and β-cell function. The four type 2 diabetes clusters were cluster 2, severe insulin-deficient diabetes (SIDD); cluster 3, severe insulin-resistant diabetes (SIRD); cluster 4, mild obesity-related diabetes (MOD); and cluster 5, mild age-related diabetes (MARD). Ahlqvist and colleagues showed potentially clinically important differences in disease progression and risk of complications between the clusters in observational follow-up, most notably a striking increase in the risk of diabetic kidney disease in cluster 3 (SIRD).Ahlqvist and colleaguesThe key question for any subgroup analysis is the clinical utility of the subgroups, and in particular whether the proposed subgroups differ in response to therapy, which could help to inform treatment strategies.Evidence before this studyA study by Ahlqvist and colleagues proposed a novel stratification method for patients with diabetes, using a data-driven cluster analysis in Scandinavian registry data to identify five reproducible subgroups of adult-onset diabetes. The authors showed differences between the clusters in disease progression and risk of complications in observational follow-up. The authors suggested the clusters might help with therapy selection in the future but did not test whether the clusters could inform therapy choice. We searched Scopus, Web of Science, and Google Scholar for citations of the original study, searching for follow-up studies assessing the reproducibility, clinical utility, and role in treatment selection of the proposed data-driven clusters up to Jan 1, 2019. We identified a study that identified similar clusters in a Chinese population and a small mixed American population but that did not examine any aspect of clinical utility because clinical follow-up was not available. A second study of Danish patients applied a similar cluster analysis and, with duration of diabetes as an additional input variable, identified five subgroups of type 2 diabetes that differed to those in the original study, and differed in the prevalence of diabetes complications. No studies were found that tested the clinical utility and the role in treatment of the proposed cluster-based approach.Added value of this studyThis study advances the concept of heterogeneity in type 2 diabetes by testing the clinical utility of the data-driven cluster approach proposed by Ahlqvist and colleagues. The cluster analysis was repeated, and differences by cluster in disease progression and treatment response were assessed in newly diagnosed participants in the ADOPT trial with randomised, protocol-driven, follow-up data available. We found that the clusters were reproducible and differed in progression and treatment response. However, simple clinical measures were as or more useful than were the clusters for stratifying each outcome assessed.Implications of all the available evidencePatients with type 2 diabetes differ in treatment response and risk of disease progression, raising the possibility of a practical, stratified approach that is clinically orientated. Our study suggests a prediction model approach, combining phenotypic measures to predict specific outcomes for individual patients, is likely to have greater clinical utility than approaches that use clinical features to assign individuals to subgroups.We aimed to establish the clinical utility of the clusters by analysing two large existing trial datasets of individuals randomised to metformin, sulfonylurea, and thiazolidinedione therapy, ADOPT and RECORD.2, HbA1c 7·0–9·0% (53–75 mmol/mol), and no evidence of renal impairment (serum creatinine >130 μmol/L) for men and women.The primary study population comprised newly diagnosed, drug-naive, individuals with type 2 diabetes, recruited between April, 2000, and June, 2002, followed up until June, 2006, who had participated in the ADOPT trial of glycaemic durability, randomly assigned to metformin, sulfonylurea (glibenclamide), or thiazolidinedione (rosiglitazone) monotherapy for up to 5 years (n=4351).We followed up individuals from ADOPT and RECORD from randomisation until the earliest of: the primary outcome of the original trial, censor date, 5 years, or the occurrence of an outcome of interest. Full individual-level trial data were accessed through Clinical Trial Data Transparency Portal .1c comprised the other measures required for cluster analysis.In ADOPT we calculated HOMA2 measures of insulin resistance and β-cell function with fasting C-peptide and fasting-glucose measures using the HOMA2 calculator.1c from 1 year up to 5 years, thus allowing for an initial period of treatment response up to 1 year.Glycaemic progression was defined as the change in HbA2) to confirmed chronic kidney disease stage 3 (two consecutive measures of eGFR <60 mL/min per 1·73 m2). eGFR was calculated using the chronic kidney disease epidemiology collaboration (CKD-EPI) equation;2 were excluded from the analysis of chronic kidney disease and those with UACR 30 mg/g or more at their baseline visit were excluded from analysis of albuminuria outcomes.Chronic kidney disease was defined as progression from normal glomerular filtration rate . AUC HbA1c is equivalent to the time-updated HbA1c measure used in the UK Prospective Diabetes Study outcomes model.1c was approximately equal between the three drugs.Glycaemic response was defined as achieved HbAIn ADOPT, we repeated the clustering approach of Ahlqvist and colleagues.1c trajectories from randomisation up to 5 years for each cluster were first estimated using a repeated-measures mixed-effects model, including fixed effects for study visit, assigned cluster, and a study visit by cluster interaction. Patient-level random effects and an unstructured covariance matrix were specified for this and subsequent mixed-effects models. All individuals within a trial were pooled to assess progression, regardless of randomised therapy. To estimate glycaemic progression by cluster, the same model was then fitted but with HbA1c change from 1 year as the outcome. We estimated the mean annual rate of glycaemic progression for each cluster by updating the cluster model to replace study visit with time as a linear covariate. Mean HbA1c by age was estimated using the same model but with a linear term for continuous age at diagnosis replacing the clusters. For each model we estimated the proportion of variance explained (R2) by the fixed effects, Akaike information criterion, and the adequacy index.17In both cohorts, mean HbA2 and the discrimination ability (Harrell's C-index) of the unadjusted cluster Cox model compared with a Cox model with continuous baseline eGFR as a linear term.We compared the cumulative incidence of chronic kidney disease by cluster using Kaplan-Meier plots and both unadjusted and baseline eGFR (continuous linear term) adjusted Cox proportional hazard models with cluster as a categorical variable. We estimated R1c response to the three drugs differed across the clusters in ADOPT. Average HbA1c trajectories by drug were estimated up to 3 years for each cluster separately, using repeated-measures mixed-effects models with fixed effects for study visit, drug, visit by drug interaction, and visit by baseline HbA1c interaction. 3-year AUC HbA1c was estimated for each drug in each cluster as the integral of the area under the mean HbA1c trajectory, using the trapezoidal rule.We tested whether HbA1c. Models to predict HbA1c were developed in ADOPT using two strategies: (1) using the clusters and (2) using clinical features. For the clusters strategy, we estimated HbA1c response for each drug at the cluster level and applied this to all individuals within the cluster. This strategy treats individuals within a cluster as homogenous for treatment response to a particular drug. For the clinical features strategy, we combined sex and linear terms for age at diagnosis, baseline BMI, and baseline HbA1c  in a multivariable model to estimate HbA1c response specific to each individual for each drug. The benefit of using each strategy developed in the ADOPT trial to select treatment for individuals was then tested in an external trial population, RECORD.We investigated whether clusters were more useful than simple clinical features to select a drug for individual patients based on predicted 3-year AUC HbA1c for each drug was estimated at cluster level. For the clinical features strategy, 3-year AUC HbA1c was estimated for each individual on the basis of their precise clinical characteristics, using multivariable repeated-measures mixed-effects models for each drug. Each model had HbA1c up to 3 years as the outcome, with age at diagnosis, BMI, baseline HbA1c, and study visit by baseline HbA1c interaction as continuous linear terms, and study visit and sex as fixed effects. Model performance for each strategy was assessed using R2.For the clusters strategy, 3-year AUC HbAThe purpose of a treatment selection model is to select the most effective therapies for individual patients, and therefore improve outcome at a population level, rather than to predict drug response accurately. Therefore, the true test of a treatment selection model is whether it can robustly identify individuals who are likely to benefit from particular therapies.191c on each drug. In the clusters strategy, these predictions were according to the individual's assigned cluster . In the clinical features strategy, predictions were made at the individual level, estimated from precise clinical features. For each strategy, we applied a simple decision rule to assign individuals into two groups, one concordant and one discordant. Discordant individuals were those randomly assigned to a drug with a predicted 3-year AUC HbA1c that is 3 mmol/mol higher  than that of the drug predicted to be their best drug; all other individuals were defined as concordant.1c between the concordant and discordant groups. 3-year AUC HbA1c by concordant or discordant group was estimated from a mixed-effects model with study visit, concordant or discordant group, baseline HbA1c, study visit by concordant or discordant group interaction, and visit by baseline HbA1c interaction as fixed effects. We tested the sensitivity of results to the HbA1c threshold used to define concordance by repeating the analysis at HbA1c thresholds of 0, 1, 2, and 4 mmol/mol.We therefore applied the following steps to test the effectiveness of each treatment selection strategy. For each individual in RECORD, we applied the models developed in ADOPT to obtain estimates of 3-year AUC HbAIn RECORD, we compared the time to the trial primary outcome, cardiovascular hospitalisation or cardiovascular death, by cluster using unadjusted and baseline age-adjusted Cox proportional hazard models.1c response for each ANDIS-derived cluster and compared model performance of the ADOPT defined clusters and ANDIS clusters. All analyses were done using R (version 3.4.1).To examine the utility of the original ANDIS clusters, we assigned individuals in ADOPT to their ANDIS cluster on the basis of their Euclidean distance from the cluster centres defined by Ahlqvist and colleagues.The funders had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all data and had final responsibility for the decision to submit for publication.We found that the clusters identified by Ahlqvist and colleagues were reproducible in trial populations. 4003 individuals in ADOPT had valid baseline measures for cluster assignment. Of these, 3802 were in the intention-to-treat population and so were eligible for analysis of patient outcomes. We found a clear pattern of differences between clusters in clinical characteristics , and wer1c trajectories by cluster from randomisation to 5 years are shown in the 1c change per year increase in age at diagnosis: . Other measures of model performance were also similar  and discrimination ability (C-statistic 0·90) than did the clusters ; these results were similar to those in RECORD . Relative change from baseline in eGFR and time to 30% decline in eGFR did not differ by cluster ; baseline UACR was a more useful measure .There was no clear pattern of difference between clusters in baseline UACR , in inci1c response to the different drugs differed across clusters in ADOPT , and for sulfonylurea therapy in cluster 5 .Patterns of HbAin ADOPT . As defi 5 MARD; . HoweverIn the independent validation cohort (RECORD) we found clinical features outperformed the clusters for treatment selection. In RECORD, we tested the performance for treatment selection of the two strategies developed in ADOPT . To our knowledge, for the first time we have shown differences by cluster in treatment response. However, clusters were markedly outperformed by models that used simple clinical features for the prediction of glucose-lowering response and for treatment selection. Overall, the results suggest that there will be greater clinical utility from modelling clinical features directly, rather than from using clinical features to place patients into subgroups .Although there were restricted eligibility criteria for the ADOPT and RECORD trials, subgroups defined by cluster analysis were similar to those seen in non-selective Scandinavian cohorts, and subsequently Chinese and US cohorts.A key strength of trial data over previous observational data is the availability of protocol-driven follow-up, meaning that we were able to do a systematic assessment and show that the clusters differ in disease progression. This possibility is a considerable advantage over routine follow-up, in which therapy introduction is not protocol driven.We established that the clusters differ in response to different glucose-lowering therapies. This finding was possible because of the randomised, systematic therapy given. We found a particular benefit for cluster 3 (SIRD) with thiazolidinediones, and for cluster 5 (MARD) with sulfonylureas.The fact that clusters are reproducible and can help to predict progression and response to therapy is important. However, a key question raised in response to the original article is whether it is more clinically useful to use clinical features to assign a patient to a subgroup and then treat in a way that is best for that subgroup, or to use clinical features to predict patient outcomes directly using outcome-specific models.1c, and BMI) explained more variation in response than did the clusters. However, this finding gives little insight into whether a strategy based on the clusters model or on a continuous features model is more useful to select between treatment options for an individual patient.1c between the two groups provided a measure of the population-level effect of each treatment selection strategy. In RECORD, we found a small benefit (1·8 mmol/mol over 3 years) of selecting therapy by cluster; by contrast, there was a greater benefit (3·9 mmol/mol) selecting treatment using the clinical features model (difference in 3-year HbA1c; For treatment response we found that models combining four simple clinical measures (age, sex, baseline HbAStrengths of this study include the use of data from two large, long-term, randomised trials, in which we were able to not only reproduce the clustering approach of Ahlqvist and colleagues, but also to describe diabetes progression and treatment response in protocol-driven follow-up. Furthermore, we were able to test treatment selection based on clusters compared with clinical features in an independent validation dataset. The treatment selection rule we applied was designed to test clinical utility in this study, rather than to maximise outcomes for the population or individuals. Approaches to assess treatment selection strategies are not well developed and are the subject of ongoing methodological research.An important difference between this study and the study by Ahlqvist and colleagues is in the analysis of renal progression. Although we excluded individuals with pre-existing kidney disease, in the Scandinavian population-based cohorts people with pre-existing kidney disease when diagnosed with diabetes were not excluded and the onset of renal dysfunction was set to the first time that an abnormal value was found in clinical testing after diagnosis.Precision medicine is successfully established in monogenic and neonatal diabetes, in which defining discrete aetiological subtypes with differing genetic causes that have different optimal treatment requirements has been possible.The known heterogeneity in type 2 diabetes, together with the differences we have observed in clinical outcomes, raises the possibility of a practical clinical application of precision medicine in type 2 diabetes in the near future. Our study supports the suggestion that the optimal approach to tailor management on the basis of risk of progression and therapeutic response will be to use phenotypic measures to predict specific outcomes for individuals using multivariable models, rather than define subgroups and assume all individuals are homogeneous within each subgroup.The methodology we have applied in this study, harnessing existing trial data at an individual level to test a precision medicine strategy developed in other data, offers an exciting, low-cost framework to assess novel precision medicine approaches without a prospective trial. Such trial datasets are increasingly available to researchers to answer secondary research questions.In conclusion, we have shown that cluster-defined subgroups are reproducible and can help to define individuals that vary in the risk of diabetes progression and in glycaemic response to common therapeutic options. Our study shows that a prediction model approach combining phenotypic measures to predict specific outcomes for individual patients is likely to have greater clinical utility than is subgroup assignment. Existing trial data offer an exciting opportunity to evaluate the potential of precision medicine approaches to improve patient outcomes in type 2 diabetes.