What are the long-term effects of severe vs moderate energy restriction on lean mass and other aspects of body composition?This randomized clinical trial included 101 postmenopausal women with obesity. At 12 months, participants who had undergone severe energy restriction experienced approximately 2-fold greater weight and fat loss, approximately 1.5 times as much loss of whole-body lean mass , and approximately 2.5 times as much loss of total hip bone mineral density compared with participants who had undergone moderate energy restriction.Although severe energy restriction is an effective obesity treatment, caution is necessary when implementing it in postmenopausal women, especially those with osteopenia or osteoporosis. This randomized clinical trial compares the long-term effects of weight loss via severe vs moderate energy restriction on lean mass and other aspects of body composition among postmenopausal women with obesity. Severely energy-restricted diets are the most effective dietary obesity treatment. However, there are concerns regarding potential adverse effects on body composition.To compare the long-term effects of weight loss via severe vs moderate energy restriction on lean mass and other aspects of body composition.The Type of Energy Manipulation for Promoting Optimum Metabolic Health and Body Composition in Obesity (TEMPO) Diet Trial was a 12-month, single-center, randomized clinical trial. A total of 101 postmenopausal women, aged 45 to 65 years with body mass index  from 30 to 40, who were at least 5 years after menopause, had fewer than 3 hours of structured physical activity per week, and lived in the Sydney metropolitan area of New South Wales, Australia, were recruited between March 2013 and July 2016. Data analysis was conducted between October 2018 and August 2019.Participants were randomized to either 12 months of moderate (25%-35%) energy restriction with a food-based diet (moderate intervention) or 4 months of severe (65%-75%) energy restriction with a total meal replacement diet followed by moderate energy restriction for an additional 8 months (severe intervention). Both interventions had a prescribed protein intake of 1.0 g/kg of actual body weight per day, and physical activity was encouraged but not supervised.The primary outcome was whole-body lean mass at 12 months after commencement of intervention. Secondary outcomes were body weight, thigh muscle area and muscle function (strength), bone mineral density, and fat mass and distribution, measured at 0, 4, 6, and 12 months.2; 95% CI, −6.5 to −1.9 cm2). However, decreases in whole-body lean mass and thigh muscle area were proportional to total weight loss, and there was no difference in muscle (handgrip) strength between groups. Total hip bone mineral density , whole-body fat mass , abdominal subcutaneous adipose tissue , and visceral adipose tissue  loss were also greater for the severe group than for the moderate group at 12 months.A total of 101 postmenopausal women were recruited . Compared with the moderate group at 12 months, the severe group lost more weight , lost more whole-body lean mass , and lost more thigh muscle area (effect size, −4.2 cmSevere energy restriction had no greater adverse effect on relative whole-body lean mass or handgrip strength compared with moderate energy restriction and was associated with 2-fold greater weight and fat loss over 12 months. However, there was significantly greater loss of total hip bone mineral density with severe vs moderate energy restriction. Therefore, caution is necessary when implementing severe energy restriction in postmenopausal women, particularly those with osteopenia or osteoporosis.12612000651886anzctr.org.au Identifier:  Severely energy-restricted diets result in significantly greater short- and long-term8 weight loss compared with food-based diets that involve moderately restricting dietary energy intake by approximately 500 kcal/d (2100 kJ/d). In addition, total meal replacement diets are cheaper than the average per capita food expenditure in Australia and cost 3 times less to administer than food-based diets in terms of dietetics support.9Effective obesity treatments are needed to reduce obesity-related morbidities and costs.11 and these diets are not routinely used by health care professionals.12 This may be because of reported adverse effects ,6 the lack of training and resources available for pretreatment evaluation and monitoring during these diets, and possibly also concerns that severe energy restriction may adversely affect body composition  compared with moderate energy restriction. For example, some studies have reported that larger energy deficits induce greater loss of fat-free mass in adults with overweight or obesity.16 In contrast, other studies have shown no difference in lean body mass following severe or moderate energy restriction in participants with obesity.18 As another example, some studies in women with overweight or obesity suggested that weight loss of more than 14% of initial weight during 3 to 4 months resulted in significant bone loss,20 whereas moderate weight loss of approximately 5% to 7% of initial body weight over 6 months resulted in little21 or no22 bone loss. In contrast, the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) trial showed that men and women with overweight on a severely energy-restricted diet  showed no greater loss of whole-body or hip BMD than individuals on a moderately energy-restricted diet.23 However, the studies cited here were not randomized clinical trials, were pilot studies, were of limited duration , or were not specifically designed to assess muscle and bone health.Despite being an effective and affordable dietary obesity treatment, a number of prominent clinical obesity treatment guidelines from around the world show limited support for the use of total meal replacement diets,25 in our review of the literature, no study directly compared the long-term effects of severe vs moderate energy restriction  on lean mass and other aspects of body composition in people with obesity. We aimed to address this in the Type of Energy Manipulation for Promoting Optimum Metabolic Health and Body Composition in Obesity (TEMPO) Diet Trial.Although randomized clinical trials have evaluated the effects of severe vs moderate energy restriction on long-term weight maintenance in adults with overweight and obesity,CONSORT) reporting guideline, and the full trial protocol can be found in The TEMPO Diet Trial was a single-center, randomized clinical trial. Ethical approval was obtained from the Sydney Local Health District, Royal Prince Alfred Hospital Human Research Ethics Committee. It was conducted at the Charles Perkins Centre Royal Prince Alfred Clinic on the University of Sydney campus in Camperdown, New South Wales, Australia, with magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) scans performed at I-Med Radiology (Camperdown). Reporting in this article is aligned with the Consolidated Standards of Reporting Trials  from 30 to 40, at least 5 years after menopause, with less than 3 hours of structured physical activity per week , and living in the Sydney metropolitan area of New South Wales, Australia. We included women with this menopausal status for 2 reasons: first, to circumvent known effects of female hormone cycles and the menopausal transition on parameters under investigation in the trial and, second, because women older than 50 years have a 4-fold higher rate of osteoporosis and a 2-fold higher rate of osteopenia than men.30 Specifically, they were stratified by age  and body mass index . Individuals in each of the 4 stratified groups were then randomized in blocks of 2 and with a 1:1 ratio into 1 of 2 interventions.31 To avoid bias, randomization was undertaken by 1 of us (A.S.) who had no contact with participants before randomization and was not involved in implementing the interventions. None of us who undertook screening or clinical testing  were aware of the method used for randomization, and we were not able to predict which intervention a particular participant would be randomized to.Participants were randomized (and enrolled) to either the severe or moderate intervention  using st32 The guide provides recommendations on the average number of standard servings of the 5 core food groups  that an individual should consume to meet nutritional requirements based on age and sex. To simplify adherence to the moderate intervention, we defined 6 food groups. The meat and meat alternative and reduced fat dairy food groups were collapsed into a proteins group, and starchy vegetables were incorporated into the grains and cereals group to form a carbohydrates group, while participants also had groups for vegetables, fruits, fats, and discretionary foods. The severe intervention involved a severe energy restriction of 65% to 75% relative to estimated energy expenditure for 4 months (16 weeks) or until a body mass index of no lower than 20 was reached, whichever came first. This was achieved using a total meal replacement diet  supplemented with a whey protein isolate  to achieve the prescribed protein target (described later). This was followed by moderate energy restriction  for the remaining period to 12 months (52 weeks). Both diets were individualized for each participant and were nutritionally sound. That is, the diet used in the moderate intervention was designed to meet nutrient requirements with minimum energy intake,31 while the severe intervention used a commercial total meal replacement product and supplemental protein that rendered it close to the recommended nutrient requirements.33 For both interventions, a protein intake of 1.0 g/kg of actual body weight per day was prescribed. Participants were encouraged to gradually increase step counts to a total of 8000 to 12 000 steps/d, including 30 to 60 min/d of moderate to vigorous physical activity.34 Although physical activity was encouraged, it was not supervised. Since the use of food diaries to measure adherence to the prescribed diet is difficult to assess because of missing dietary records and underreporting among participants with overweight and obesity,35 weight loss was used to monitor adherence to the diets.36 We expected approximately 1.5 to 2.5 kg/wk weight loss for participants in the severe intervention6 and approximately 0.5 to 1.0 kg/wk weight loss for participants in the moderate intervention.37 To increase adherence to the diet, participants attended individual dietary appointments with the trial dietitian approximately every 2 weeks for the first 26 weeks of the intervention  and then approximately every month until 52 weeks . The development process and rationale behind the dietary interventions for the TEMPO Diet Trial as well as full details of the dietary interventions have been published previously.31The moderate intervention involved a moderate energy restriction of 25% to 35% relative to estimated energy expenditure for a total of 12 months (52 weeks). This was achieved using a food-based diet, with recommendations based on the Australian Guide to Healthy Eating.27 All body composition outcomes were measured before the start of the intervention (0 months) and at 4, 6, and 12 months  after commencing the interventions. Height was measured only at 0 months. All data were collected with participants lightly clothed , without shoes, and with all metal jewelry, accessories, and electronic devices removed.This article reports the primary outcome for the TEMPO Diet Trial (lean mass at 12 months after commencement of the intervention) as well as the other outcomes related to body composition, all of which are secondary outcomes. The body composition outcomes reported in this article are listed in 38 A hydraulic hand dynamometer  was used to assess handgrip strength of the dominant and nondominant hands. For 2 participants who were ambidextrous, the right and left hand were designated the dominant and nondominant hand, respectively. Waist and hip circumferences were measured to the nearest 0.1 cm using a narrow, flexible, and inelastic steel tape . Waist circumference was measured at the midaxillary line , while the hip circumference measurement was taken at the point of greatest protuberance of the participant’s buttocks when viewed from the side. The ratio of waist to hip circumference was calculated by dividing waist circumference by hip circumference. A 3-T MRI scanner  was used to assess abdominal fat volume  as well as thigh muscle area and thigh fat area . Intrahepatic lipid was measured by volume localized proton MRS (1H-MRS) and calculated as methylene peak area + methyl peak area × 100 / water peak area, corrected for T2 effects. Accelerometers  were used to assess physical activity.39 Physical activity data will not be presented in this article; however, it was used as a covariate in our analysis. Physical activity data were only included as a covariate in the analysis if participants wore the accelerometer for a minimum of 5 of 7 designated days for at least 85% of each 24-hour day. Mean physical activity across the days the accelerometer was worn was then reported as metabolic equivalents of task .Dual-energy x-ray absorptiometry (DXA), using a Discovery W bone densitometer (Hologic), was used to assess whole-body lean mass, whole-body fat mass, and BMD of the total left hip and anterior posterior lumbar spine (L1-L4).42 Notably, attrition at 12 months in our trial was 16%, which fell within the attrition prediction used in our sample size calculation.For our primary outcome of whole-body lean mass at 12 months after intervention commencement, we calculated that a target sample size of 100 participants would provide a power of 90% at a 2-sided α level of 5%, allowing for up to 20% attrition as seen in previously published weight loss interventions.P < .05, and tests were 2-tailed. Fisher exact tests were used to compare categoric demographic characteristics  and attrition between groups . To compare continuous variables between groups at baseline (0 months), Mann-Whitney tests were used. All continuous variables were assessed for normality before analysis using histograms and P-P plots. Where data were not normally distributed , a natural log transformation was applied to obtain a normal distribution.Statistical analyses were performed using SPSS statistical software version 24 for Windows (IBM Corp). Statistical significance was accepted as 43 Therefore, this model allowed for participants to have partial missing data and still be included without imputation. Intervention group and time  were included as fixed effects, and participant was included as a random effect. For all outcomes, baseline values of the relevant variable were added as a covariate in the analysis. As the degree of physical activity is known to influence measures of body composition, physical activity  was added as a covariate, as measured at each point. When the overall P value for the interaction between group and time was less than .05, comparisons between groups at each point were made, using Bonferroni adjustments to correct for multiple comparisons. As a secondary analysis, the model was further adjusted for weight at each point, because lighter individuals are known to have lower whole-body lean mass and BMD than heavier individuals.45 This was to determine if there was an effect of the interventions beyond that which would be expected due to weight loss.To compare longitudinal changes between groups, intention-to-treat analysis was performed using data from all participants originally randomized, using random-effects linear mixed models. Mixed-model analyses were used instead of standard repeated measures analysis of variance because of the likelihood that there would be dropouts and missed visits that precluded the use of the classic approach.P value for the interaction between group and time was less than .05, comparisons between time points and baseline (0 months) within each group were made, using Bonferroni adjustments to correct for multiple comparisons.Within-group changes were analyzed with repeated measures linear-mixed models. Intervention group and time were included in the model as fixed effects and time as a repeated measure, with physical activity  added as a covariate. Maximum-likelihood estimation was used, and an unstructured covariance matrix was specified. When the overall P = .05)  .P < .001) (P < .001) (P < .001). This represents a 2.5-fold to 3.0-fold greater likelihood of losing a clinically significant amount of weight  with the severe vs moderate intervention.The severe group had a significantly lower weight than the moderate group at all points after baseline  . Both gr < .001) B. AssumiP = .005) (P = .005).The severe group had significantly lower values of whole-body lean mass compared with the moderate group at all points after baseline  . Similar2; 95% CI, −6.5 to −1.9 cm2; estimated marginal mean at 12 months: −8.2  cm2 vs −3.9  cm2; P < .001)  . After aP = .02), but no significant changes from baseline in the dominant hand  . The sevant hand .3; 95% CI, −0.029 to −0.005 g/cm2; estimated marginal mean: −0.032  g/cm2 vs −0.015  g/cm2; P = .002), but there were no significant differences between groups in lumbar spine BMD  or whole-body BMD   . After aP = .38) . SpecifiT-score of –1 to –2.5)46 at the femoral neck in the severe group but not the moderate group . In contrast, in the total hip there was no difference between the severe and moderate groups in the number of participants with osteopenia at 0 or 12 months . There were no participants with osteoporosis (defined as a T-score of –2.5 or less)46 at 0 or 12 months in the severe or moderate groups.There was a significant increase in the number of participants with osteopenia  (P < .001) (P < .001) , intrahepatic lipid , thigh subcutaneous adipose tissue area  as well as thigh subfascial fat area  and intermuscular fat area  at all points after baseline  , and the < .001) . Both gr < .001) , abdominbaseline . Both grThere were 8 adverse events . These adverse events were reported by participants to the research team or occurred in the clinic during the clinical testing day. No adverse event was considered serious. In the severe intervention, the adverse events were hemorrhoids (2 participants [4.0%]), gallstones (2 participants [4.0%]), and hair loss (2 participants [4.0%]). In the moderate intervention, there were 2 episodes of migraine (1 participant [2.0%]), probably precipitated by fasting prior to clinical testing. This participant had a history of migraines and continued with the trial but only underwent outcome measurements that did not require fasting.47 These striking findings were offset by an approximately 2.5-fold greater loss of total hip BMD with severe energy restriction compared with moderate energy restriction, a difference not accounted for by the greater weight loss.This randomized clinical trial demonstrated that, compared with moderate energy restriction over a 12-month period, severe energy restriction resulted in the following: (1) approximately 1.5 times as much loss of whole-body lean mass and thigh muscle area, although these losses were proportional to the amount of weight lost; (2) no difference in handgrip strength; (3) approximately twice as much weight loss ; (4) approximately twice as much total fat loss; and (5) a healthier fat distribution, as indicated by approximately twice the loss of waist circumference, waist-to-hip circumference ratio, and abdominal adipose tissue volume . Participants in the severely energy-restricted intervention were also 3 times less likely to discontinue the trial compared with those in the moderately energy-restricted intervention. This is possibly because the large and rapid weight loss associated with severe energy restriction has been shown to be encouraging and because the total meal replacement diet used to achieve it is simple and convenient.48 However, after the 12-month severe intervention, total hip BMD loss (approximately 3.3%) was 2.4 to 3.3 times higher than these annual BMD losses. Interestingly, the decrease in BMD continued over the whole 12 months of the severe intervention, even though weight loss had plateaued by 6 months. This occurred despite a dietary protein prescription of 1 g/kg of actual body weight per day in both groups49 and despite the fact that the total meal replacement products used in this trial contained more than the Australian recommended dietary intake for vitamin D and calcium for women aged 51 to 70 years.33 This loss of BMD may have been exacerbated because of the population selected for this trial . For example, in a study where men and women with obesity were prescribed a total meal replacement diet until they reached 15% weight reduction in 3 to 6 months, all participant groups exhibited significant decreases in body weight at 2 years, and both groups showed loss of BMD over 2 years, but this loss was statistically significant only among women.50 This suggests that women may have a greater propensity to lose BMD following weight loss. Research also suggests that BMD loss may be exacerbated in postmenopausal women  compared with women who are still in the perimenopausal transition.48In this trial, the participants in the moderate group experienced approximately 1.3% reduction in total hip BMD after 12 months, similar to the annual rate of BMD loss at the hip in the early postmenopausal years (approximately 1.0%-1.4%).52 However, this bone loss must be considered in light of the beneficial effects of substantial weight loss on other health outcomes and health care costs. For example, although a 3% to 5% loss of initial weight has generally been accepted as being clinically significant,53 recent research shows that greater weight losses, ie, of 7.7%, 10%, 15%, or 20%, dose-dependently improve health outcomes.57 In addition, if treated effectively, the costs of obesity-related health complications would be significantly reduced.2 Thus, implementing effective obesity treatments is essential to reducing obesity-related comorbidities and the associated costs. Thus, while the current trial should not discourage the use of total meal replacement diets as a treatment for obesity in postmenopausal women, further investigation is needed to determine the long-term consequences of the associated BMD loss on health outcomes such as osteoporotic fractures and to determine how BMD losses could be prevented in this population during and after these diets.The consequences of accelerated BMD loss with a severely energy-restricted dietary obesity treatment are clinically concerning, especially if BMD loss continues beyond the 12-month intervention, because it has been linked to an increased risk of osteoporosis and fragility fracture.58 In addition, DXA assesses bone quantity and not bone quality  or osteoporotic fracture incidence, and it is possible that, despite BMD loss, bone quality and strength may have been preserved in our participants. Despite these technical limitations, DXA remains the criterion standard and the only available test for measuring BMD in clinical practice as well as being an important predictor of osteoporotic fracture.60 A further limitation of our trial is that participants were predominantly white, which limits the generalizability of the findings to populations of other races.Strengths of this study include the 12-month randomized clinical trial design and the criterion-standard techniques used for the assessment of body composition, notably our primary outcome of lean mass. Another strength is that data variability was reduced by analysis of all data by a single researcher . There are, however, some limitations that must be noted, 1 of which is the technical limitation of DXA, which can only measure 2 tissue types at any time . Thus, being a 2-compartment model for the determination of body composition, a possible confounder is that the DXA analysis assumes a constant hydration of lean soft tissue, which is not always true, as hydration varies with age, sex, and disease. To help control for this, all participants were measured after an overnight fast (≥8 hours) and after voiding their bladder once at home before attending our clinical research facility and again when they arrived at the clinic. Another technical limitation of DXA is that changes in BMD observed with large weight losses might be exaggerated because of the varying amounts of soft tissue over time, which can result in unpredictable errors in DXA bone measurements, to up to 20%.In this randomized clinical trial, severe energy restriction with a total meal replacement diet in postmenopausal women with obesity induced greater weight loss and approximately 1.5-fold as much loss of whole-body lean mass and thigh muscle area compared with moderate energy restriction over 12 months. While these losses of lean tissues were proportional to the amount of weight lost and while muscle strength  was unaffected by severe vs moderate energy restriction, there was an approximately 2.5-fold greater loss of total hip BMD with severe compared with moderate energy restriction, a difference not accounted for by the greater weight loss. Therefore, caution is necessary when implementing severe energy restriction in postmenopausal women with obesity, especially in those with osteopenia or osteoporosis, for whom concurrent bone-strengthening treatments  are recommended.


We describe a pediatric case with a sudden decrease in rSO2 during MICS.Minimally invasive cardiac surgery (MICS) for simple congenital heart defects has become popular, and monitoring of regional cerebral oxygen saturation  without changes in other parameters following pericardium traction. The rSO2 and RRBP immediately recovered after removal of pericardium fixation. Obstruction of the right innominate artery secondary to the pericardium traction would have been responsible for it.An 8-month-old male underwent minimally invasive ventricular septal defect closure. He developed a sudden decrease in rSO2 depression alerting us to the risk of cerebral ischemia. We should be aware that pericardium traction during MICS can lead to cerebral ischemia, which is preventable by cautious observation of the patient.Pericardium traction, one of the common procedures during MICS, triggered rSO Upon arrival to the operating room, general anesthesia was induced with 5% sevoflurane in a mixture of 50% nitrous oxide and 50% oxygen. Following placement of an intravenous catheter, endotracheal intubation was facilitated with 70 μg fentanyl and 10 mg rocuronium. Anesthesia was maintained with 1–5% sevoflurane in the air, with concomitant fentanyl and rocuronium as required. During CPB, fentanyl, midazolam, and rocuronium were intravenously administered. Monitoring included electrocardiography and measurement of invasive right radial artery blood pressure (RRBP) and right femoral arterial blood pressure (RFBP), oxygen saturation of peripheral artery , ETCO2 36 mmHg, CVP 10 mmHg, and Tnaso 36.3 °C. We confirmed that [Hb] was 10.2 g/dL without any signs of sudden hemorrhage in the surgical area. On the basis of these parameters, the decreases of RRBP and rSO2 might have been caused by an obstruction of the right innominate artery, secondary to the pericardium and aortic root traction. We promptly discussed this issue with the surgeons and reached a consensus to remove the pericardium fixation. Following removal, RRBP increased to 59/33 mmHg and rSO2 to > 50%. Transient reduction in RRBP and rSO2 continued for approximately 30 s until the pericardial fixation was removed. Subsequently, the traction of pericardium was modified to maintain rSO2 stability. After modification, the operation proceeded uneventfully. Postoperatively, the patient was transferred to the intensive care unit (ICU) under sedation and intubated. The patient gained consciousness and was extubated 3 h later, without any neurological abnormalities. The patient was discharged from the ICU on postoperative day 3.Following lower partial sternotomy, the pericardium was towed cephalad and fixed to expose the aorta. Suddenly, RRBP decreased from 82/44 to 26/22 mmHg, followed by a subsequent decrease in rSO2 that alerted us to the risk of cerebral ischemia. The change in rSO2 was sufficient to cause us to speculate that cerebral ischemia might have occurred in the patient.In this case, pericardium traction, one of the common procedures during MICS, triggered a decrease in rSO2 was related to the surgical procedure in cardiac surgery [2 values as we experienced.Recently, a smaller incision during median sternotomy has become an alternative technique during cardiac surgeries for simple congenital heart deformities, such as closure of VSD and atrial septum defects , 5. The  surgery , 9, wher2 values measured by NIRS are trustworthy physiological indicators during pediatric cardiovascular surgeries [2 values dropped simultaneously, (2) SpO2 on the left hand remained unchanged, (3) the RFBP was maintained, (4) there was no apparent hemorrhage, (5) the CVP did not change, and (6) the patient’s heart rate and body temperature remained stable. Our decision to insert two artery catheters at the right radial artery and right femoral artery and monitor SpO2 at the left hand and foot may have been excessive. However, we aimed to monitor the blood flow at all the extremities during pediatric MICS in order to be alert to any signs indicating obstruction of blood flow. It ultimately gave us more clues to understand what happened in this emergent situation and for future reference.Accumulating studies show that the rSOurgeries , 7. An iurgeries , 11. Fol2 value. Although standard values of rSO2 vary depending on the type of sensor used [2 on the left hand provided us enough information. A carotid artery ultrasound and/or Transcranial Doppler ultrasound may be preferable for detecting cerebral malperfusions.There are limitations to the use of NIRS devices for monitoring of cerebral saturation. In the present case, we chose the INVOS sensor that reflects a mixture of Hb saturation in venous, capillary, and arterial blood . Alternasor used , 14, thesor used , 7. In t2, but we were able to prevent cerebral ischemia during the surgery with useful monitoring including rSO2, arterial blood pressure, and SpO2 at extremities. To achieve better quality of life after congenital heart surgery, we should be aware that pericardium traction during MICS can lead to cerebral ischemia that is preventable by cautious observation of the patient.In conclusion, we experienced a pediatric case of MICS in which pericardium traction, one of the common procedures in MICS, caused a decrease in rSO


However, to our knowledge, no studies have performed extensive characterization of the microbiome in early life in a large, multi-centre population. Here we analyse longitudinal stool samples from 903 children between 3 and 46 months of age by 16S rRNA gene sequencing  and metagenomic sequencing , as part of the The Environmental Determinants of Diabetes in the Young (TEDDY) study. We show that the developing gut microbiome undergoes three distinct phases of microbiome progression: a developmental phase (months 3–14), a transitional phase (months 15–30), and a stable phase (months 31–46). Receipt of breast milk, either exclusive or partial, was the most significant factor associated with the microbiome structure. Breastfeeding was associated with higher levels of Bifidobacterium species (B. breve and B. bifidum), and the cessation of breast milk resulted in faster maturation of the gut microbiome, as marked by the phylum Firmicutes. Birth mode was also significantly associated with the microbiome during the developmental phase, driven by higher levels of Bacteroides species (particularly B. fragilis) in infants delivered vaginally. Bacteroides was also associated with increased gut diversity and faster maturation, regardless of the birth mode. Environmental factors including geographical location and household exposures (such as siblings and furry pets) also represented important covariates. A nested case–control analysis revealed subtle associations between microbial taxonomy and the development of islet autoimmunity or type 1 diabetes. These data determine the structural and functional assembly of the microbiome in early life and provide a foundation for targeted mechanistic investigation into the consequences of microbial–immune crosstalk for long-term health.The development of the microbiome from infancy to childhood is dependent on a range of factors, with microbial–immune crosstalk during this time thought to be involved in the pathobiology of later life diseases Metagenomic sequencing analysis of stool samples from 903 children as part of the TEDDY study shows that breastfeeding was the most important factor associated with microbiome structure, and the cessation of breast milk resulted in faster maturation of the gut microbiome. After rarefaction and limiting samples to 3–46 months of age, we analysed the microbiome  and functional metagenome (metagenomic sequencing only) from longitudinal stool samples  and three US states  were analysed. The children represent those who seroconverted to islet cell autoantibody positivity or developed type 1 diabetes (T1D) and matched controls. Stool samples were collected, on average, monthly from around 3 months of age as part of the The Environmental Determinants of Type 1 Diabetes in the Young (TEDDY) studyIn this cohort of children that are at-risk for developing islet autoimmunity (IA) or T1D, we aimed to (1) characterize definitively the longitudinal gut microbiome development from 3 to 46 months of age; (2) determine selected maternal and postnatal influences on the developing bacterial community during this same time period of early development; and (3) use a nested case–control analysis to investigate the potential of the microbiome as a predictor for the development of IA or T1D.Proteobacteria and Bacteroidetes) and the Shannon diversity index changed significantly during the transitional phase, and all phyla and the Shannon diversity index were unchanged during the stable phase . As infants aged, the microbiomes of their stools diversified into clusters 4–8 during months 15–30 . Microbiome stabilization, in which infants’ samples remained in the same cluster at consecutive time points, was observed from month 31 of life. Clusters 8–10 were the most dominant during the stable phase, with these clusters characterized by high alpha diversity and dominance of genera within the Firmicutes phyla. The three microbiome phases and changes in taxa are consistent with other cohorts18 and were supported by the metagenomic sequencing data , metagenomic sequencing taxa (species level), and functional metabolic capacity (Kyoto encyclopedia of genes and genomes (KEGG) modules)  gene cluster, whereas after the cessation of breast milk, most B. longum strains no longer carried these genes. This potentially reflects the ability of B. longum subsp. infantis and subsp. longum to use mammalian- and plant-derived oligosaccharides, respectively24. B. bifidum also persisted after the cessation of breastfeeding, and this species is able to switch HMO to mucin degradation24. Vatanen et al.14 show experimentally that B. breve, B. longum and B. bifidum, which make up DMM clusters 1–3 , owing to the dominance of Bifidobacterium in infants receiving breast milk. To explore microbiome maturation further, we used microbiota age and microbiota-by-age Z-scores (MAZ) as previously described25, with a model of 20 operational taxonomic units (OTUs) that explained 72% of the variance  , but converged in the stable phase (microbiota age P = 0.331 and MAZ score P = 0.196) , hallmarked by increased levels of Firmicutes26.As the infant ages, the proportion of solid foods in the diet increases (and the amount of breast milk decreases)ium Fig. . Infants96) Fig. . After t28. Conversely, infants not receiving breast milk showed rapid turnover of the metabolic capacity, and the ‘dicarboxylate-hydroxybutyrate cycle’ (M00374) and ‘reductive acetyl-CoA (Wood–Ljungdahl)’ (M00377) pathways were increased. Modules relating to vitamins B7  were also increased in all time points up to 14 months in non-breastfed infants, a function that is associated with the adult microbiome28.Breastfeeding was the only covariate that was significantly associated with metabolic potential Fig. . PlottinB. breve accounted for the highest number of significant modules in early life, and was replaced by B. bifidum after 6 months of life , we were able to directly determine from which taxa each gene orthology (and thus module) was derived see . Each paife Fig. . In non-ife Fig. . This pr29. The analytical cohort consisted of a subset with an equal number of samples for each case–control pair. The IA cohort consisted of 632 children and 6,194 stool samples and the T1D cohort consisted of 196 children and 1,540 stool samples, as of 31 May 2012  in cases of IA (Supplementary Table Parabacteroides the most significant (P < 0.001). Eleven bacterial genera were lower in T1D cases, including four unclassified Ruminococcaceae, Lactococcus (P = 0.020), Streptococcus (P = 0.032), and Akkermansia (P = 0.045) , using risk set samplingP = 0.032) , and thus the statistically significant result should be interpreted with caution. Overall, the conditional logistic regression models of community dynamics suggest that microbiome stability was not strongly related to the onset of IA or T1D.Conditional logistic regression models showed no significant associations between either the numbers of unique states exhibited or the number of transitions between different states per subject for IA , extensive metadata, and the use of both amplicon and metagenomic sequencing. We showed that the first year of life is a key phase for the development of the microbiome, with the receipt of breast milk being the main factor that influences microbiome development over this period. Birth mode, geographical location, household siblings and furry pets were also associated with the microbiome over this period. We considered the first year of life as developmental, the second year of life as transitional, and from year three of life the microbiome stabilized. These precise ages may shift when investigators include samples before month 3 or beyond month 46 of life.12. We found subtle changes in the relative abundance of bacterial genera between cases (IA and/or T1D) and matched controls. T1D cases showed higher levels of Streptococcus sp. and Lactococcus sp., which is consistent with the findings of Vatanen et al.14 in the companion paper. In accordance with previous work, the abundance of Akkermansia was also higher in controls in the current study, which may be indicative of enhanced gut integrity10.The current cohort is largely white, non-Hispanic and is drawn from a population of infants at high genetic risk for T1D, some of whom developed autoimmunity or diabetes. Temporal alpha diversity and community dynamics were comparable between cases and controls, which is in contrast to findings reported in other cohorts and may reflect the increased number of subjects and samples in the TEDDY cohort8. The current study provides several testable hypotheses of microbiome development in infancy, and it remains important to determine the potential mechanism of altered early life microbiome and the subsequent effect on immune development and functioning. With a more comprehensive understanding of the crucial early life phases and their effect on health and disease, lifestyles and therapeutics can be tailored to support optimal microbial–immune homeostasis.The overall microbiome development and significant covariates are in concordance with previous reports in westernized populations, although caution should be exercised when extrapolating the findings from the TEDDY cohort of children with risk factors of developing T1D to the wider population. Nevertheless, the significant covariates reported in the current study have been independently linked to the risk of later life diseases such as obesity, asthma and allergy30. Matching factors for case and control children were geographical location, sex and family history of T1D.The TEDDY Study is composed of six clinical research centres: three in the United States , and three in Europe . Children enrolled are followed prospectively from three months to 15 years with study visits every three months until age 4 years and every three or six months thereafter depending on autoantibody positivity. Stool samples and associated metadata were collected as of 31 May 2012. Stool samples were collected monthly from 3 to 48 months of life, then every three months until the age of 10 years, and then biannually thereafter, into the three plastic stool containers provided by the clinical centre. Children who were antibody negative after 4 years of age were encouraged to submit four times a year even though after 4 years their visits schedule switched to biannual. Parents sent the stool containers at either ambient or +4 °C temperature with guaranteed delivery within 24 h in the appropriate shipping box to the NIDDK repository if living in the United States or their affiliated clinical centre if living in Europe. The European clinical centres stored the stool samples and sent monthly bulk shipments of frozen stool to the NIDDK repository. The population (both cases and controls) is based on children at high risk for T1D based on their HLA genotype with 10% based on family history in addition to HLA. Detailed study design and methods have been previously published31. Given the prospective nature of the TEDDY design, information and recall bias are greatly minimized. Because the children do not have event outcome at time of enrolment and are followed, there is no reason for any systematic differences between groups of the study participants in the accuracy of the information collected.Metadata were collected using validated questionnaires that have been either published or extensively scrutinized by experts. Information about mothers, pregnancy and birth was collected during the three month clinic visit by questionnaire and included the mode of birth , the infant’s 5-min Apgar score, pregnancy complications, information about maternal diabetes  or gestational diabetes), gestational age, and maternal medication use  during pregnancy. TEDDY provides many tools, such as ‘The TEDDY book’, to the parents to assist in real-time collection of all events in their child’s life to ensure bias and error are minimized. At each visit the study personnel will go over the TEDDY book with the primary caretaker and extract pertinent information using standardized study forms. Data are extracted by trained staff members during scheduled visits every three months starting at 3 months of age and entered directly via stand forms (web forms or teleforms), which are transmitted electronically. Front-end constraints are used in the web application to prevent the entry of invalid data and The TEDDY Error Reporting and Verification System (ERVS) consists of a set of programs that conduct automated quality control on the data, report and resolve errors, an integrated database for storing error data, and a set of programs that generate reports for monitoring data cleaning efforts. The details of the system have been publishedThe TEDDY study was approved by local US Institutional Review Boards and European Ethics Committee Boards in Colorado’s Colorado Multiple Institutional Review Board, Georgia’s Medical College of Georgia Human Assurance Committee (2004–2010), Georgia Health Sciences University Human Assurance Committee (2011–2012), Georgia Regents University Institutional Review Board (2013–2015), Augusta University Institutional Review Board (2015–present), Florida’s University of Florida Health Center Institutional Review Board, Washington state’s Washington State Institutional Review Board (2004–2012) and Western Institutional Review Board (2013–present), Finland’s Ethics Committee of the Hospital District of Southwest Finland, Germany’s Bayerischen Landesärztekammer  Ethics Committee, Sweden’s Regional Ethics Board in Lund, Section 2 (2004–2012) and Lund University Committee for Continuing Ethical Review (2013–present). All parents or guardians provided written informed consent before participation in genetic screening and enrolment. The study was performed in compliance with all relevant ethical regulations.32 for the matched IA case–control study estimated 80% power, α = 0.01, two-sided test to detect an odds ratio > 3 for an exposure with 5% prevalence to an odds ratio > 1.8 for an exposure with 20% prevalence. The experiments were not randomized, and investigators were not blinded to allocation during experiments and outcome assessment.A priori power calculations using discrete Cox’s proportional hazards regression35. Bacterial DNA was extracted using the PowerMag Microbiome DNA isolation kit following the manufacturer’s instructions. The V4 region of the 16S rRNA gene was amplified by PCR and sequenced on the MiSeq platform (Illumina) using the 2 × 250 bp paired-end read protocol. The read pairs were demultiplexed and reads were merged using USEARCH v7.0.109036. Merging allowed zero mismatches and a minimum overlap of 50 bases, and merged reads were trimmed at the first base with a q ≤ 5. A quality filter was applied to the resulting merged reads and those containing above 0.5% expected errors were discarded. Sequences were stepwise clustered into OTUs at a similarity cut-off value of 97% using the UPARSE algorithm37. Chimeras were removed using USEARCH v7.0.1090 and UCHIME v4.2. To determine taxonomies, OTUs were mapped to a version of the SILVA Database38 containing only the 16S V4 region using USEARCH v7.0.1090. Abundances were recovered by mapping the merged reads to the UPARSE OTUs. A custom script constructed a rarefied OTU table from the output files generated in the previous two steps for downstream analyses of taxonomic relative abundance, alpha diversity, and beta diversity (including UniFrac)39. A total of 114,313,601 reads  were obtained from 16S rRNA gene sequencing and each sample was rarefied to 3,000 reads. Stringent merging parameters account for the relatively low number of OTUs, with the number of species by metagenomics around fourfold higher than the number of OTUs by 16S rRNA gene sequencing.16S rRNA gene sequencing methods were adapted from the methods developed by the NIH-Human Microbiome Project and the Earth Microbiome Projecthttp://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) and cutadapt v1.9dev2 for adaptor and quality trimming, and PRINSEQ v0.20.540 for sample dereplication and low complexity filtering. In addition, Bowtie2 v2.2.341 was used to map reads to a database containing complete genomes and assemblies for bacteria, viruses, human, and vectors in the NCBI whole-genome sequencing (WGS) archive (as of March 2015). Reads in which the highest identity matches were not bacterial were removed from subsequent analysis. The edit distance (Levenshtein distance) was used to determine the score of the alignments to the reference genomes42. For bacterial reads, the highest scoring match (greater than 90%) was chosen per read considering only the top 25 highest scoring alignments. In the event of multiple identical top scoring hits, the lowest common ancestor was determined.Individual libraries constructed from each sample were pooled and loaded onto the HiSeq 2000 platform (Illumina) and sequenced using the 2 × 100 bp paired-end read protocol. The process of quality filtering, trimming, and demultiplexing was carried out by in-house pipeline developed by assembling publicly available tools such as Casava v1.8.2 (Illumina) for the generation of fastqs, Trim Galore v0.2.8  were obtained from metagenomic sequencing and for subsequent analysis each sample was rarefied to 100,000 reads.Reads in which the genomic coordinates overlap with known KEGG orthologueshttp://www.R-project.org) using the ggplot package46.The analysis was conducted in two parts: (1) characterize the longitudinal maturation of the microbiome and (2) determine the significant covariates that influence microbiome development. For both parts of analysis, alpha diversity (richness and Shannon diversity) was calculated at the OTU-level for 16S rRNA gene sequencing and species-level for metagenomics data. Alpha diversity and taxonomic abundance were modelled using LOESS regression, and implemented and plotted with 95% confidence intervals in R  and some covariates will alter the microbiome for an unknown time frame , covariates were classified as ‘before’, ‘during’, or ‘after’. In the case a covariate was negative for an infant, all samples would be classified as ‘never’. In instances in which several onsets of a covariate were possible , after the first onset the covariate was classified as ‘after’ for the remaining samples, unless another event occurred, in which case ‘during’ would be applied where appropriate according to the start and stop dates. Analysis was performed at specific time windows, including samples collected between months 3–6, 7–10, 11–14, 15–18, 19–22, 23–26, 27–30 and 31–40. Only the first sample collected from a given child was included in each time window to account for repeated measures.https://cran.r-project.org/web/packages/vegan/index.html) comparing the difference in the centroids of each group relative to the total variation. Ordination was performed using NMDS based on Bray–Curtis dissimilarity. The significance value was determined based on 10,000 permutations. All P values derived from envfit were adjusted for multiple comparisons using FDR adjustment (Benjamini–Hochberg procedure)48. In total, 22 covariates with known associations to gut microbiome development in neonates, infants, and children were included in the envfit analysis and the grouping used for within each variable is presented in Extended Data Table 49, diabetes medication 50, BMI52, gestational weight gain category (excess or non-excess)53, preeclampsia52, maternal probiotic consumption54, as well as offspring factors such as prematurity55, birth mode56, gender56, receipt of breast milk and/or formula59, introduction of solid foods61, geographical location57, probiotics62, vitamin D supplementation63, antibiotics18, household siblings64, household furry pets65, living on a farm with animals67, day-care exposure68, coeliac disease69, acute disease, and chronic disease69.The effect size and significance of each covariate were determined using the ‘envfit’ function in ‘vegan’  covariates to associate with microbial taxon or pathway abundances. Mixed-effects linear models using a variance-stabilizing arcsin square root transform on relative abundances are then used to determine the significance of putative associations from among this reduced set. Nominal P values across all associations are then adjusted using the Benjamini–Hochberg FDR method. Here, microbial features with corrected q < 0.25 were reported. All 22 covariates tested in the envfit were included in the adjustment regardless of significance by envfit. Subject age was also included to adjust for potential age driven changes in taxa within each three-month time window and IA and T1D outcome were included to adjust for the nested case control nature of the cohort. The default MaAsLin parameters were applied . All P values were adjusted for multiple comparisons using FDR48.MaAsLin was used for adjustment of covariates when determining the significance of taxa (genus level for 16S rRNA gene sequencing and species level for metagenomic sequencing) contributing to a specific variable, while accounting for potentially confounding covariates71 was performed as previously described25, using the ‘randomForest’ R package72. In brief, the model was trained on 150 randomly selected full term (>37 weeks gestation), vaginally delivered, breastfed infants who had a minimum of 10 samples included in the final dataset. The model was built using the default parameters: growing 10,000 trees and n/3 OTUs randomly sampled at each split, in which n represents the number of OTUs. The model was further refined by applying ‘rfcv’ with tenfold cross-validation resulting in the inclusion of 20 OTUs to train the final model based on percentage increase in mean-squared error. These 20 OTUs explained 72% of the total variance of the model . The age of the subject predicted by this model was termed microbiota age and was further used to determine MAZ scores using the formulae described preiously25. Significant differences in alpha diversity, microbiota age, and MAZ scores were calculated using linear mixed-effects models in R, with the ‘lmer’ command within the ‘lme4’ package73. We included random slopes and intercept for individual children, and evaluated delivery mode, age, Bacteroides positive or negative, predominant diet, geographical location, presence of siblings, and presence of household pets as fixed effects. To perform these piecewise longitudinal models, we divided samples into the three developmental phases . Owing to the relatively low number of samples in the exclusive and never breastfed groups, the analysis of breast milk status was conducted based on ‘some breast milk’ or ‘after breast milk’, with these groups found to cluster with exclusive and never breastfed, respectively.The random forest regression model74. A dataset with equal numbers of cases and control samples was created to preform conditional logistic regression of summary metric variables . On average, cases tended to have more samples than controls, and therefore had more transitions and observed states, which resulted in spurious associations between our metrics and disease outcome. For this purpose, we created a dataset in which case and control samples were matched to the paired case based on the nearest sample by day of life . This resulted in an analytical cohort of 316 IA cases and 316 paired controls  and 98 T1D cases and 98 paired controls . For consistency, we used these datasets for all matched case–control analyses. The IA and T1D analysis was based on 16S rRNA gene sequencing data only and analysis of the metagenomic sequencing data  are presented in the companion paper14.The development of persistent confirmed IA was assessed every three months. Persistent autoimmunity was defined by the presence of confirmed islet autoantibody on two or more consecutive visits. The date of persistent autoimmunity was defined as the draw date of the first sample of the two consecutive samples that deemed the child persistent confirmed positive for a specific autoantibody (or any autoantibody). T1D was defined according to American Diabetes Association criteria for diagnosis73. To perform these piecewise longitudinal models, we divided samples into the three developmental phases . Conditional logistic regression of matched case–control pairs was performed on the top 50 most dominant bacterial genera from samples prior to disease diagnosis. Odds ratios were calculated with 95% confidence intervals, adjusted for potential confounding variables, including age at sample collection, HLA genotype, mode of delivery, and duration of breastfeeding. Abundance information for genera was entered into the model as log2-transformed read counts. A value of 0.01 was added to avoid 0 s. The Benjamini–Hochberg procedure was applied to correct for multiple comparisons48 and corrected P < 0.05 was considered significant.16S rRNA gene sequencing data was used to determine differences between alpha diversity (number of OTUs (richness) and Shannon’s diversity index), microbiota age, and MAZ scores. Significant differences in alpha diversity, microbiota age, and MAZ scores were calculated using linear mixed-effects models in R, with the ‘lmer’ command within the ‘lme4’ packageFor each subject, the total number of clusters exhibited throughout sampling per infant and the number of transitions between different clusters from one sample to the next were calculated to provide summary measures of microbiome stability over time. These summary metrics were then used in conditional logistic regression to assess the relationship of microbiome stability with IA and T1D. Odds ratios were calculated with 95% confidence intervals, adjusted for potential confounding variables, including HLA genotype, mode of delivery, duration of breastfeeding, number of antibiotic courses, and number of infectious episodes.Further information on research design is available in the Nhttps://github.com/StewartLab/Stewart_TEDDY_Microbiome_Analysis. Other analysis software including quality control, taxonomic, and functional profilers is publicly available and referenced as appropriate.Code for the transition model showing the progression of samples through each DMM cluster, which are presented in Fig. Any methods, additional references, Nature Research reporting summaries, source data, statements of data availability and associated accession codes are available at 10.1038/s41586-018-0617-x.Supplementary InformationThis file contains Supplementary Notes 1-3, Supplementary References and a full list of members of the TEDDY Study GroupReporting SummarySupplementary Table 1EnvFit P values for significant covariates determined by NMDS based on Bray-Curtis dissimilarity distance for each dataset.Supplementary Table 2MaAsLin P values for significant taxa for 16S rRNA gene sequencing (genus level taxa) and metagenomic sequencing (species level taxa and metabolic modules). Mixed effects linear models using a variance-stabilizing arcsin square root transform on relative abundances were used to determine the significance. P values were adjusted for multiple comparisons using the Benjamini-Hochberg false discovery rate (FDR) method.Supplementary Table 3Analytical population overview and results from each statistical analyses related to islet autoantibody and Type 1 Diabetes onset. P values based on conditional logistic regression of the nested case control cohort, with adjustment for age, human leukocyte antigen type, delivery mode, and breast milk feeding. P values were adjusted for multiple comparisons using the Benjamini-Hochberg false discovery rate (FDR) method.Supplementary Table 4Bacterial genera determined to be core microbiome  in the three microbiome phases between each of the geographical locations.


Data on early miscarriage incidence is limited due to various social and methodological barriers. We report on 24-month pregnancy outcomes of 299 female Ugandan HIV clients in committed relationships with an intention to conceive. Miscarriage data are reported as auxiliary findings to a larger study (5R01HD072633).127 (42%) participants reported a pregnancy during the study; among the remaining 172, 82 indicated they stopped trying to conceive, and 16 dropped out prior to month 24. Of the 127 pregnancies, 55 (43%) resulted in live births, 67 (53%) in spontaneous miscarriage, 1 (< 1%) in stillbirth, 1 (< 1%) in abortion, and 3 (2%) in unknown outcomes. Three-quarters (75%) of miscarriages for which time until miscarriage was available were reported to occur in the first trimester (mean = 11.3 weeks gestation). The 67 participants who reported a miscarriage tended to be older (mean 33 vs. 30 years), but the significance of age did not persist after adjusting for multiple tests. We observed relatively low rates of pregnancy and high rates of miscarriage among this cohort of HIV-positive women wanting to conceive. Rigorously designed studies are needed to better understand the observed high rate of early miscarriage among HIV-infected women. In Uganda, one-third of women report at least one miscarriage (spontaneous or induced) or stillbirth during their lifetime . Data isThis brief communication reports on 24-month pregnancy outcomes of a longitudinal cohort of HIV-positive women with an intention to conceive who were followed for 24 months. Miscarriage was not a targeted outcome of this study, thus we report these data in light of several limitations to be addressed in future studies. We report the incidence and timing of pregnancy and miscarriage among female participants, and examine predictors of miscarriage.Data were collected as part of a prospective cohort study, which assessed use of safer conception methods (SCM) , and barriers and facilitators to SCM among people living with HIV who want to conceive. We followed 400 patients receiving HIV care at The AIDS Service Organization clinics in Kampala and Jinja, Uganda. Data for this manuscript, however; are limited to the n = 299 female participants. Eligible participants were at least 18 years old, married or in committed heterosexual relationships, and reported an intention to conceive over the following 24 months. Clients were eligible regardless of their partner’s HIV status, and provided written informed consent before participating in the baseline survey. Follow-up surveys were conducted at months 6, 12, 18 and 24. Analyses were limited to participants’ first reported pregnancy between baseline and the 24-month survey. If the client became pregnant and delivered a child, an additional interview was conducted within 1 month of the delivery. A more detailed description of the study methods and measures has been previously published .During follow-up surveys, participants were asked if they became pregnant since the prior survey. Pregnancy outcomes were assessed at each follow-up: still pregnant, miscarriage , stillbirth , or live birth. We examined the timing of pregnancy initiation by calculating the number of weeks between study enrollment and the date the respondent learned of her pregnancy; date of conception could not be estimated for most pregnancies due to missing data. In 10 cases for which the date the respondent learned of pregnancy was missing, we estimated this date using the midpoint of the 6-month interval preceding the survey in which the pregnancy was reported to have begun . Pregnancies and pregnancy outcomes were not clinically confirmed. Women reporting a miscarriage were asked to estimate the week of gestation at which the miscarriage became known.Sample characteristics were summarized, and a Kaplan–Meier curve was used to display time to pregnancy, with respondents who stopped trying to become pregnant censored at the time of the survey in which they reported they were no longer trying Fig. . Figure Using logistic regression, we explored demographics and health characteristics  and psychosocial measures  as potential predictors of miscarriage of stillbirth .3 and 59% were on ART at baseline. All were in committed relationships [38.1% married and 35.1% reported that either they (n = 3) or their male partners (n = 102) had another spouse/partner]. Three-quarters had disclosed their HIV status to their male partner. 35% reported having an HIV-positive partner, 28% had an HIV-negative partner and 37% did not know their partner’s status. At enrollment, 92% desired a child within 12 months.Among the n = 299 female participants, mean age was 32.0 years , 59% had any secondary level education, and all had at least one child . Average CD4 cell count was 453 cells/mmBy month 24, a total of 127 women had become pregnant at least once. Only first pregnancies during the course of the study were used in these analyses. Of the 172 (58%) female participants who did not report a pregnancy, 82 stopped seeking conception at some point during the study for various reasons , and 16 discontinued study participation prior to month 24. Two of the women who discontinued participation and 7 who stopped trying to conceive were determined by the study obstetrician gynecologist to be infertile at some point during the study.Of the 127 first pregnancies, 55 (43%) resulted in live birth, 67 (53%) in miscarriage, 2% Other . Pregnancy outcome was unknown for the remaining 3 (2%) pregnancies. Rates of miscarriage were similar for pregnancies where the HIV status of the male partner was positive (36%), negative (28%), and missing or unknown (38%). Rates of miscarriage were higher among women on ART at baseline (59%) verses those who were not (43%), but the difference was not statistically significant.p value = 0.015). The date of first antenatal visit was unavailable for 69% of all pregnancies and for 85% of miscarriages. But among the 40 women for whom this information was available, only 18 (45%) reported antenatal care during the first trimester . Thus, most miscarriages occurred before antenatal or prevention of mother-to-child transmission (PMTCT) care typically began; limiting the ability to evaluate length of ART exposure prior to miscarriage.The timing of miscarriage  was available for 52 of the 67 miscarriages. As shown in the histogram in Fig. The 67 female participants who reported a miscarriage were older on average at baseline (33 vs. 30 years) compared to female participants who did not miscarry. Education, partner HIV status, number of past deliveries, previous miscarriage, CD4 count, ART, social support, perceived community stigma regarding childbearing, and depression were also assessed in unadjusted and adjusted logistic regression analyses, but only maternal age was significantly associated with miscarriage ). The statistical significance of age did not persist after adjusting for multiple testing.This cohort of women living with HIV who wanted to conceive achieved relatively low rates of pregnancy, and high rates of miscarriage among those who became pregnancy. After 2 years of prospective follow-up, less than half (42%) of women living with HIV reported a pregnancy, and 54% of pregnancies resulted in pregnancy loss .This reported miscarriage rate of 53% among reproductive aged women living with HIV is exceptionally high. Miscarriage was significantly higher in the absence of antenatal care (65% vs. 25%), but the early timing of the miscarriages (75% by 13 weeks gestation) likely pre-empted formal enrollment in antenatal care for many. We explored several potential predictors of miscarriage among HIV-infected female participants, but only age emerged as significant, and only without adjustment for multiple testing.In the general population, an estimated 15–25% of clinically recognized pregnancies end in miscarriage , and thiThere are several limitations to be considered when contextualizing these findings. As a secondary outcome, the study was not designed to rigorously measure pregnancy outcomes and relied on self-reported rather than clinically confirmed pregnancies. Data regarding the signs and symptoms that lead a woman to conclude she had miscarried were also limited. ART regimen details were not available to explore associations with miscarriage. The underreporting of induced miscarriages is possible due to stigma and legal status , 17; howEven in the context of these methodologic limitations, the high rate of miscarriage observed in this study among women living with HIV in Uganda can generate hypotheses for investigation in future studies. Recognizing the limitations of this study, we hope these preliminary analyses will lead to more rigorous evaluations of miscarriage, particularly in early pregnancy, to inform strategies to optimize pregnancy outcomes.


Aphanizomenon and Dolichospermum. cHABs in Lake Winnipeg are primarily a result of eutrophication but may be exacerbated by the recent introduction of dreissenid mussels. Through multiple methods to monitor the potential for toxin production in Lake Winnipeg in conjunction with environmental measures, this study defined the baseline composition of a Lake Winnipeg cHAB to measure potential changes because of dreissenid colonization. Surface water samples were collected in 2013 from 23 sites during summer and from 18 sites in fall. Genetic data and mass spectrometry cyanotoxin profiles identified microcystins (MC) as the most abundant cyanotoxin across all stations, with MC concentrations highest in the north basin. In the fall, mcyA genes were sequenced to determine which species had the potential to produce MCs, and 12 of the 18 sites were a mix of both Planktothrix and Microcystis. Current blooms in Lake Winnipeg produce low levels of MCs, but the capacity to produce cyanotoxins is widespread across both basins. If dreissenid mussels continue to colonize Lake Winnipeg, a shift in physicochemical properties of the lake because of faster water column clearance rates may yield more toxic blooms potentially dominated by microcystin producers.Lake Winnipeg , the world’s 12th largest lake by area, is host to yearly cyanobacterial harmful algal blooms (cHABs) dominated by  The lake is separated into three distinct regions: a shallow south basin , with high turbidity from runoff from the Red River and sediment resuspension [2 and encompasses four provinces and two U.S. states [Lake Winnipeg  is the world’s 10th largest freshwater and the 12th largest lake overall by surface area  since the late 1950s [Over the past several decades, this major freshwater resource has shown an alarming decline in water quality as highlighted by historical data and core samples that have shown a distinct increase in phosphorus, carbon, and chlorophyll-te 1950s ,8. The gte 1950s  and was te 1950s .Microcystis, Dolichospermum, Planktothrix, Nostoc, Hapalosiphon, Pseudanabaena, Tolypothrix, Oscillatoria, Phormidium, Rivularia, and Anabaenopsis [Pseudanabaena rutilis-viridis, although the bloom assemblages are largely dominated by diazotrophic species  [Satellite imagery and in-lake biomass measures of Lake Winnipeg indicate that the frequency and magnitude of severe summer–fall cyanobacterial harmful algal blooms (cHABs) has increased ,12; howeaenopsis ,25,26,27rmannii) ,13. Samprmannii) ), these rmannii) ,22,23. Lrmannii) , which mrmannii) . Algal brmannii) ,31, but mcy, cyr, and sxt responsible for the production of MCs, cylindrospermopsins (CYNs), and saxitoxins (STXs), respectively. This approach allows a more detailed assessment of the genetic potential for toxin production than more traditional single-gene assays, and coupled with other measures, provides insight into the presence and expression of toxin-producing genes within a planktonic community.Toxic and non-toxic  strains of the same species often co-occur in the same bloom but are indistinguishable using traditional identification and enumeration techniques . Therefomcy, cyr, and sxt genes in summer and fall water samples collected from sites across the SB and NB during research cruises in 2013 , SRP, and TP concentrations were measured at 17 of the 23 summer sites. The highest concentrations of all N fractions and TDP were detected in the SB during the summer sampling , while the maxima for PP and TP were measured at site W3 on the western shore of the NB  were highest in the south of the lake and declined northward. This gradient corresponds to positive correlations across the lake with 0.71 < r < 0.80; p < 0.002).Nitrogen (N) was measured at all sites as NH2− - NO3−, TDN, TKN-F, TDP, and TP occurred at the south end of the lake  , 0.94 (p < 0.0000001), and 0.81 (p < 0.00005) respectively. During the fall, maximum concentrations of NO40 μg/L) . The maxFluoroprobe data was collected during the summer cruise only . These dAphanizomenon flos aquae complex and Dolichospermum spp. Also of note was that Planktothrix agardhii was a dominant non-diazotroph species at fall site 31 and 28. Diatoms were the next most abundant organisms in Lake Winnipeg, including dominance at summer site 67NS. The least abundant phylum were the Dinoflagellates, which were often absent from the summer samples entirely. Samples analyzed for community composition include eight summer sites and six fall sites . With thcyrA, sxtA, and mcyE genes were detected at 6, 2, and 17 sites, respectively  analysis  of the summer samples were also positive for CYN, but se sites . In contdetected . No STX  methods .cyrA, no sxtA, and 18 mcyE positive locations  and showed different spatial patterns than in the summer. Of the 18 sites tested using MS-MRM, only five and two sites tested positive for MC  and eight were dominated by Microcystis   and two were Planktothrix only (NB site 31 and the narrows site W8). There were no observed trends in the location of these sites and the presence of one MC producing species vs. the other. All the fall sites and two summer sites were chosen for the tic tree . A majormcyE gene abundances and both MS-MRM and ELISA MC data, with Pearson’s correlation coefficients of 1.0 (p < 0.001), 0.99 (p < 0.001), and 0.99 (p < 0.001) for qPCR vs. MS-MRM, qPCR vs. ELISA, and MS-MRM vs. ELISA respectively , 0.90 (p < 0.001), and 0.96 (p < 0.001) for PP vs. qPCR, MS-MRM, and ELISA respectively. A strong positive correlation between fluoroprobe measures of cyanobacterial abundance and both MC and mcyE data, , 0.86 (p < 0.001) and 0.90 (p < 0.01) for cyanobacteria vs. qPCR, MS-MRM, and ELISA respectively).For the summer data, strong positive correlations were observed among ectively . No signmcyE and MS-MRM , and mcyE and ELISA , although the relationship between MS-MRM and ELISA was not statistically significant   .While we understand that these data are from 2013, we feel it is important to report these values as a historical data set in which future studies can refer back to. The current study aimed to set baseline measurements for seasonal bloom toxin concentration as well as potential cyanotoxin producers in Lake Winnipeg, which marks an important first step which could be used to compare to the conditions in the lake after dreissenid colonization. Additionally, the data presents information regarding the key players in Lake Winnipeg blooms, which can be used for further research behind understanding the drivers of bloom dominance. This work is one of only a few studies to combine multiplex qPCR and toxin analysis to evaluate the bloom assemblages. Additionally, our work both demonstrated the potential value of this multiplex approach and provided important new insight into the capacity and expression of a range of cyanotoxins by Lake Winnipeg cHABs. Consistent with earlier studies on this lake, our data showed that the current bloom assemblages produce generally low levels of MC toxins, largely during the summer in the NE regions, likely reflecting both the higher transparency of the NB compared to the more turbid southern basin (a factor that has been linked to increased microcystin production) ,38,39, acyrA demonstrates that potential CYN-producers are expressing these toxins [sxtA genes at two sites highlights the potential future threat of this third group of cyanotoxins known to cause severe health problems in many locations worldwide [N-methylamino-L-alanine (BMAA), a neurotoxin produced by some cyanobacterial species. BMAA has previously been detected in Lake Winnipeg [In addition, our multiplex qPCR data show that the production capacity for MC and other cyanotoxins is widespread across both basins, demonstrating a potential for more toxic blooms in this lake that future changes in food web or climate regimes could facilitate. Lake Winnipeg summer blooms in 2013 were largely dominated by cyanobacteria  as repore toxins , while torldwide . While wWinnipeg ,44, ensuMicrocystis aeruginosa and Planktothrix agardhii  indicated the presence of co-existing agardhii . These t regions , while Mrbulence ,47. It ithe fall , but becmcy shown previously [As expected, the lake showed a general nutrient gradient from the south to the north, consistent with the majority of the nutrient load to Lake Winnipeg entering from the major tributaries in the south and the general south-north flow ,3. One neviously . While tmcy quantification suggests all three methods provided consistent results. While the ELISA and MS-MRM methods were correlated, there were minor differences in the detection of MC which can likely be attributed to the fact that the ELISA method has lower sensitivity than HPLC methods. At the low concentrations detected here . Following the restoration of this and the other Laurentian Great Lakes under the US-Canada Great Lakes Water Quality Agreement , these blooms dissipated. As water quality improved, binational monitoring efforts were decreased; however, during this time, invasive dreissenid mussels were introduced into the Great Lakes and became established in Lake Erie in the 1990s. In parallel, non-point source nutrient inputs into Lake Erie also increased leading to the annual blooms dominated by toxic Microcystis [Microcystis over Aphanizomenon or Dolichospermum [Microcystis, potentially leading to more toxic blooms and shifts in lower trophic level dynamics, significantly impacting the ecology of the lake and socioeconomics of the region. The potential for an analogous scenario to Lake Erie in Lake Winnipeg is sobering reality, given the similarities of these two lakes.The current blooms in Lake Winnipeg are similar to the Lake Erie blooms of the 1950s–1970s. Excessive inputs of nutrients and suspended material during the first half of the 1900s led to significant cHABs across Lake Erie which were primarily dominated by diazotrophic, turbidity tolerant species of rocystis . It is hospermum . Howeverospermum ,54 may acyrA and sxtA data highlight the advantage of using a multiplex qPCR assay over more traditional qPCR single target assays for a more comprehensive measure of bloom toxin potential. The qPCR data also suggest that some Lake Winnipeg bloom assemblages contained strains capable of producing multiple toxins or a mixed population of species capable of producing different toxins. The co-occurrence of three different toxin genes at very low concentrations in the lake at individual locations raises the concern that lake users (human and non-human) may, in the future, be subjected to synergistic toxic effects that are potentially stronger than exposure to any individual toxin.This work has set a baseline to evaluate the effects of a potential regime shift in the phytoplankton communities in Lake Winnipeg. The taxa and genes for toxin production exist at low levels in Lake Winnipeg and given the response of Lake Erie to a similar invasion of dressinids, may result in a shift toward more toxic blooms. These results also demonstrate the use of multiplex q-PCR pre-bloom to access and monitor water bodies for toxin-forming potential. The ability of this approach to detect toxin genes and the associated toxins produced was validated using multiple analytical method. This research also confirmed the widespread presence of both the genetic potential and expression of two important classes of cyanotoxins, cylindrospermopsin and saxotoxins, in Lake Winnipeg. Currently, these are present at low levels; however, future changes in climate and foodweb regimes has the potential to sign alter this production, indicating a need for research on the drivers of Lake Winnipeg cHABs post dressinid colonization. The Namao. Triplicate samples were filtered  via vacuum filtration (<15 torr) and the volume of water was recorded. The volume of water ranged from 100 to 750 mL per filter, depending on the site. Filters were placed into cryo vials and stored at −80 °C until processed. Two filters were used for molecular analysis and one was used for instrumental toxin analysis as described below. Additional samples were collected and fixed at a 5% final concentration Lugol’s solution for microscopy analysis of biomass and species composition.Water samples were collected from 23 sites in June 2013 (summer) and 18 sites in September 2013   as part Phytoplankton and planktonic protozoans were considered to have the same density as water. A maximum of 2 mL subsamples of the Lugol’s preserved whole water sample are settled in a Utermöhl settling chamber. Samples were allowed to settle for 4 or more hours to allow even the tiniest cells to settle. An inverted microscope was used for sample identification and enumeration using magnifications between 125× and 625×. The small (<20 μm) numerous cells are enumerated on a 250 μm transect at the higher magnification and the larger (>20 μm cells) less numerous cells are enumerated at the lower magnification on ½ of the chamber. At least 200 organisms are enumerated and classified to the lowest taxonomic entity possible in a reasonable time frame. Using standard geometric shapes for the cells and a specific gravity of one biomass conversions are calculated.Rational for number of organisms counted is based on QAQC studies which have evaluated counting and sampling errors with the Utermöhl inverted microscope method ,57,58,59Taxonomic identifications are based on several morphological taxonomic guides and keys listed in Freshwater Algae of North America first and second editions. Taxonomy is still evolving and with the recent multiphasic approach using natural morphology, culturing, cytology, ecology and sequencing and the current microscopic analysis documents the taxa-based features visible at the time of capture. 2−, NO3− at the University of Alberta  using standard methods [Single water samples from each site were filtered through a membrane filter . Aliquots were stored at 4 °C until processed for total dissolved N, NO methods . Soluble methods . Total p methods . At each sampling site during the summer cruise a fluoroprobe  was lowered to approximately 1 m below the water surface. The probe estimates the abundances of four major groups of phytoplankton, “blue-green” algae (Cyanobacteria), “green” algae (chlorophytes and euglenophytes), “brown” algae , and “red” algae (cryptophytes) in the surrounding water based on spectral fluorescence patterns and manufacturer calibrations. No correction was made for background chromophoric dissolved organic material. Data were visualized using Ocean Sneaker’s Tool software .® Pro protocol  with 0.1 mm glass beads for 3 min at 2800 rpm using a BeadBug™ microtube homogenizer . Samples were eluted into a total volume of 100 µL. Absorbance readings of DNA samples were taken at 260 nm and 280 nm using a Take3™ micro volume plate on a Synergy plate reader  to determine the quality and quantity of DNA in each sample. Extracted DNA samples were stored at −20 °C until analysis.Duplicate frozen filters were thawed and extracted using the PowerPlant2O (80 µL) was added to each tube of a CyanoDTec™ cyanotoxin detection kit  and processed following kit directions. The kit comes with fully validated primers and probes for the detection of total cyanobacteria (16S) as well as an internal amplification control to determine if any PCR inhibitors existed in the individual extracts. The kit also included with fully validated primers and probes for the quantification of the cyrA, sxtA, and mcyE genes, which are involved in the gene operons linked to the production of cylindrospermopsins, saxitoxins, and microcystins, respectively. A synthetic standard of known toxin gene copy  was assayed in serial dilutions spanning five orders of magnitude (1 × 106–1 × 102) to generate a standard curve for each aforementioned target toxin gene. Thermal cycling and fluorescence detection was performed using a Bio-Rad iQ™5 real-time PCR detection system . Threshold cycle values for each sample were determined using Bio-Rad iQ™5 Optical System Software . Gene copy/mL for each water sample was calculated after normalization for test procedures.Molecular grade HmcyA primers that detect potential microcystin-producing genotypes in Microcystis, Planktothrix, and Dolichospermum [mcyA clone library was generated from the amplified PCR products by insertion into pCR4-TOPO TA vector  and transformed into chemically competent One Shot TOP10 Escherichia coli cells. DNA sequencing was performed  and the resulting sequences were trimmed and dereplicated. Sequence alignment and phylogeny was completed using Mega 7.0 [mcyA sequences generated in this study were clustered at 99% identity using UCLUST [mcyA sequences from known toxin producing species, a Maximum-likelihood tree was generated.PCR amplifications were then performed on 18 fall and two summer field samples using ospermum . These pospermum ,62,63,64Mega 7.0 . For a sg UCLUST ; the mosELISA was used to analyze total microcystins when chl-a exceeded 5 µg/L. This assay  quantifies the ADDA moiety present in all microcystins and is expressed in terms of MC-LR equivalents. Filter-retained particulate microcystins  were extracted in 10 mL of Milli-Q water using ultrasound (three 60 s bursts with a 60 s pause between bursts) and centrifuged to pellet the cellular and filter debris. Extracts were then filtered through a 25 mm GF/F filter for the removal of suspended debris and MC concentrations were measured following the method described by Fischer et al. . MC concm/z 100–1200). Data were analyzed using Agilent MassHunter Qualitative Analysis software . A standard curve (1/y2 weighting) was established for each toxin  by integrating the peak area of the quantifier ion from duplicate standards (6 concentrations ranging from 0–10 ng/µL), with a limit of detection of 0.5 ng on the Phenomenex column. Standards were prepared in methanol and analyzed in the same manner as the samples. To measure the amount of each toxin in the samples, the peak area of the quantifier ion was compared to the appropriate standard curve (Mass spectrometry multiple reaction monitoring (MS-MRM) was used to analyze samples from all collection sites for multiple MC congeners as well as STX and CYN . Sample rd curve . The limStatistical relationships between nutrient concentrations, toxin concentrations, and genetic analysis was determined by calculating Pearson’s correlation coefficient using R version 3.5.3.