The simultaneous occurrence of gestational diabetes and hypertensive disorders of pregnancy affects fetal growth and neonatal morbidity.

BACKGROUND: Gestational diabetes is associated with increased risk of hypertensive disorders of pregnancy, but there are limited data on fetal growth and neonatal outcomes when both conditions are present. OBJECTIVE: We evaluated the risk of abnormal fetal growth and neonatal morbidity in pregnancies with co-occurrence of gestational diabetes and hypertensive disorders of pregnancy. STUDY DESIGN: In a retrospective study of 47,093 singleton pregnancies, we compared the incidence of appropriate for gestational age birthweight in pregnancies affected by gestational diabetes alone, hypertensive disorders of pregnancy alone, or both gestational diabetes and hypertensive disorders of pregnancy with that in pregnancies affected by neither disorder using generalized estimating equations (covariates: maternal age, nulliparity, body mass index, insurance type, race, marital status, and prenatal care site). Secondary outcomes were large for gestational age birthweight, small for gestational age birthweight, and a neonatal morbidity composite outcome (stillbirth, hypoglycemia, hyperbilirubinemia, respiratory distress, encephalopathy, preterm delivery, neonatal death, and neonatal intensive care unit admission). RESULTS: The median (interquartile range) birthweight percentile in pregnancies with both gestational diabetes and hypertensive disorders of pregnancy (50 [24.0-78.0]; N=179) was similar to that of unaffected pregnancies (50 [27.0-73.0]; N=35,833). However, the absolute rate of appropriate for gestational age birthweight was lower for gestational diabetes/hypertensive disorders of pregnancy co-occurrence (78.2% vs 84.9% for unaffected pregnancies). Adjusted analyses showed decreased odds of appropriate for gestational age birthweight in pregnancies with both gestational diabetes and hypertensive disorders of pregnancy compared with unaffected pregnancies (adjusted odds ratio, 0.72 [95% confidence interval, 0.52-1.00]; P=.049), and in pregnancies complicated by gestational diabetes alone (adjusted odds ratio, 0.78 [0.68-0.89]; P<.001) or hypertensive disorders of pregnancy alone (adjusted odds ratio, 0.73 [0.66-0.81]; P<.001). The absolute risk of large for gestational age birthweight was greater in pregnancies with both gestational diabetes and hypertensive disorders of pregnancy (14.5%) than in unaffected pregnancies (8.2%), without apparent difference in the risk of small for gestational age birthweight (7.3% vs 6.9%). However, in adjusted models comparing pregnancies with gestational diabetes/hypertensive disorders of pregnancy co-occurrence with unaffected pregnancies, neither an association with large for gestational age birthweight (adjusted odds ratio, 1.33 [0.88-2.00]; P=.171) nor small for gestational age birthweight (adjusted odds ratio, 1.32 [0.80-2.19]; P=.293) reached statistical significance. Gestational diabetes/hypertensive disorders of pregnancy co-occurrence carried an increased risk of neonatal morbidity that was greater than that observed with either condition alone (gestational diabetes/hypertensive disorders of pregnancy: adjusted odds ratio, 3.13 [2.35-4.17]; P<.001; gestational diabetes alone: adjusted odds ratio, 2.01 [1.78-2.27]; P<.001; hypertensive disorders of pregnancy alone: adjusted odds ratio, 1.38 [1.26-1.50]; P<.001). CONCLUSION: Although pregnancies with both gestational diabetes and hypertensive disorders of pregnancy have a similar median birthweight percentile to those affected by neither condition, pregnancies concurrently affected by both conditions have a higher risk of abnormal fetal growth and neonatal morbidity.

Regulation strategies for two-output biomolecular networks.

Feedback control theory facilitates the development of self-regulating systems with desired performance which are predictable and insensitive to disturbances. Feedback regulatory topologies are found in many natural systems and have been of key importance in the design of reliable synthetic bio-devices operating in complex biological environments. Here, we study control schemes for biomolecular processes with two outputs of interest, expanding previously described concepts based on single-output systems. Regulation of such processes may unlock new design possibilities but can be challenging due to coupling interactions; also potential disturbances applied on one of the outputs may affect both. We therefore propose architectures for robustly manipulating the ratio/product and linear combinations of the outputs as well as each of the outputs independently. To demonstrate their characteristics, we apply these architectures to a simple process of two mutually activated biomolecular species. We also highlight the potential for experimental implementation by exploring synthetic realizations both in vivo and in vitro. This work presents an important step forward in building bio-devices capable of sophisticated functions.

Gestational Glucose Intolerance and Birth Weight-Related Complications.

OBJECTIVE: To evaluate the risks of large-for-gestational-age birth weight (LGA) and birth weight-related complications in pregnant individuals with gestational glucose intolerance, an abnormal screening glucose loading test result without meeting gestational diabetes mellitus (GDM) criteria. METHODS: In a retrospective cohort study of 46,989 individuals with singleton pregnancies who delivered after 28 weeks of gestation, those with glucose loading test results less than 140 mg/dL were classified as having normal glucose tolerance. Those with glucose loading test results of 140 mg/dL or higher and fewer than two abnormal values on a 3-hour 100-g oral glucose tolerance test (OGTT) were classified as having gestational glucose intolerance. Those with two or more abnormal OGTT values were classified as having GDM. We hypothesized that gestational glucose intolerance would be associated with higher odds of LGA (birth weight greater than the 90th percentile for gestational age and sex). We used generalized estimating equations to examine the odds of LGA in pregnant individuals with gestational glucose intolerance compared with those with normal glucose tolerance, after adjustment for age, body mass index, parity, health insurance, race and ethnicity, and marital status. In addition, we investigated differences in birth weight-related adverse pregnancy outcomes. RESULTS: Large for gestational age was present in 7.8% of 39,685 pregnant individuals with normal glucose tolerance, 9.5% of 4,155 pregnant individuals with gestational glucose intolerance and normal OGTT, 14.5% of 1,438 pregnant individuals with gestational glucose intolerance and one abnormal OGTT value, and 16.0% of 1,711 pregnant individuals with GDM. The adjusted odds of LGA were higher in pregnant individuals with gestational glucose intolerance than in those with normal glucose tolerance overall (adjusted odds ratio [aOR] 1.35, 95% CI 1.23-1.49, P <.001). When compared separately with pregnant individuals with normal glucose tolerance, those with either gestational glucose intolerance subtype had higher adjusted LGA odds (gestational glucose intolerance with normal OGTT aOR 1.21, 95% CI 1.08-1.35, P <.001; gestational glucose intolerance with one abnormal OGTT value aOR 1.77, 95% CI 1.52-2.08, P <.001). The odds of birth weight-related adverse outcomes (including cesarean delivery, severe perineal lacerations, and shoulder dystocia or clavicular fracture) were higher in pregnant individuals with gestational glucose intolerance with one abnormal OGTT value than in those with normal glucose tolerance. CONCLUSION: Gestational glucose intolerance in pregnancy is associated with birth weight-related adverse pregnancy outcomes. Glucose lowering should be investigated as a strategy for lowering the risk of these outcomes in this group.

Development of a Definition of Postacute Sequelae of SARS-CoV-2 Infection.

Importance: SARS-CoV-2 infection is associated with persistent, relapsing, or new symptoms or other health effects occurring after acute infection, termed postacute sequelae of SARS-CoV-2 infection (PASC), also known as long COVID. Characterizing PASC requires analysis of prospectively and uniformly collected data from diverse uninfected and infected individuals. Objective: To develop a definition of PASC using self-reported symptoms and describe PASC frequencies across cohorts, vaccination status, and number of infections. Design, Setting, and Participants: Prospective observational cohort study of adults with and without SARS-CoV-2 infection at 85 enrolling sites (hospitals, health centers, community organizations) located in 33 states plus Washington, DC, and Puerto Rico. Participants who were enrolled in the RECOVER adult cohort before April 10, 2023, completed a symptom survey 6 months or more after acute symptom onset or test date. Selection included population-based, volunteer, and convenience sampling. Exposure: SARS-CoV-2 infection. Main Outcomes and Measures: PASC and 44 participant-reported symptoms (with severity thresholds). Results: A total of 9764 participants (89% SARS-CoV-2 infected; 71% female; 16% Hispanic/Latino; 15% non-Hispanic Black; median age, 47 years [IQR, 35-60]) met selection criteria. Adjusted odds ratios were 1.5 or greater (infected vs uninfected participants) for 37 symptoms. Symptoms contributing to PASC score included postexertional malaise, fatigue, brain fog, dizziness, gastrointestinal symptoms, palpitations, changes in sexual desire or capacity, loss of or change in smell or taste, thirst, chronic cough, chest pain, and abnormal movements. Among 2231 participants first infected on or after December 1, 2021, and enrolled within 30 days of infection, 224 (10% [95% CI, 8.8%-11%]) were PASC positive at 6 months. Conclusions and Relevance: A definition of PASC was developed based on symptoms in a prospective cohort study. As a first step to providing a framework for other investigations, iterative refinement that further incorporates other clinical features is needed to support actionable definitions of PASC.

Gestational Glucose Intolerance and Risk of Future Diabetes.

OBJECTIVE: Pregnant individuals are universally screened for gestational diabetes mellitus (GDM). Gestational glucose intolerance (GGI) (an abnormal initial GDM screening test without a GDM diagnosis) is not a recognized diabetes risk factor. We tested for an association between GGI and diabetes after pregnancy. RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort study of individuals followed for prenatal and primary care. We defined GGI as an abnormal screening glucose-loading test result at ≥24 weeks' gestation with an oral glucose tolerance test (OGTT) that did not meet GDM criteria. The primary outcome was incident diabetes. We used Cox proportional hazards models with time-varying exposures and covariates to compare incident diabetes risk in individuals with GGI and normal glucose tolerance. RESULTS: Among 16,836 individuals, there were 20,359 pregnancies with normal glucose tolerance, 2,943 with GGI, and 909 with GDM. Over a median of 8.4 years of follow-up, 428 individuals developed diabetes. Individuals with GGI had increased diabetes risk compared to those with normal glucose tolerance in pregnancy (adjusted hazard ratio [aHR] 2.01 [95% CI 1.54-2.62], P < 0.001). Diabetes risk increased with the number of abnormal OGTT values (zero, aHR 1.54 [1.09-2.16], P = 0.01; one, aHR 2.97 [2.07-4.27], P < 0.001; GDM, aHR 8.26 [6.49-10.51], P < 0.001 for each compared with normal glucose tolerance). The fraction of cases of diabetes 10 years after delivery attributable to GGI and GDM was 8.5% and 28.1%, respectively. CONCLUSIONS: GGI confers an increased risk of future diabetes. Routinely available clinical data identify an unrecognized group who may benefit from enhanced diabetes screening and prevention.

Genome-Scale Metabolic Modelling of Lifestyle Changes in Rhizobium leguminosarum.

Biological nitrogen fixation in rhizobium-legume symbioses is of major importance for sustainable agricultural practices. To establish a mutualistic relationship with their plant host, rhizobia transition from free-living bacteria in soil to growth down infection threads inside plant roots and finally differentiate into nitrogen-fixing bacteroids. We reconstructed a genome-scale metabolic model for Rhizobium leguminosarum and integrated the model with transcriptome, proteome, metabolome, and gene essentiality data to investigate nutrient uptake and metabolic fluxes characteristic of these different lifestyles. Synthesis of leucine, polyphosphate, and AICAR is predicted to be important in the rhizosphere, while myo-inositol catabolism is active in undifferentiated nodule bacteria in agreement with experimental evidence. The model indicates that bacteroids utilize xylose and glycolate in addition to dicarboxylates, which could explain previously described gene expression patterns. Histidine is predicted to be actively synthesized in bacteroids, consistent with transcriptome and proteome data for several rhizobial species. These results provide the basis for targeted experimental investigation of metabolic processes specific to the different stages of the rhizobium-legume symbioses. IMPORTANCE Rhizobia are soil bacteria that induce nodule formation on plant roots and differentiate into nitrogen-fixing bacteroids. A detailed understanding of this complex symbiosis is essential for advancing ongoing efforts to engineer novel symbioses with cereal crops for sustainable agriculture. Here, we reconstruct and validate a genome-scale metabolic model for Rhizobium leguminosarum bv. viciae 3841. By integrating the model with various experimental data sets specific to different stages of symbiosis formation, we elucidate the metabolic characteristics of rhizosphere bacteria, undifferentiated bacteria inside root nodules, and nitrogen-fixing bacteroids. Our model predicts metabolic flux patterns for these three distinct lifestyles, thus providing a framework for the interpretation of genome-scale experimental data sets and identifying targets for future experimental studies.

Biomolecular mechanisms for signal differentiation.

Cells can sense temporal changes of molecular signals, allowing them to predict environmental variations and modulate their behavior. This paper elucidates biomolecular mechanisms of time derivative computation, facilitating the design of reliable synthetic differentiator devices for a variety of applications, ultimately expanding our understanding of cell behavior. In particular, we describe and analyze three alternative biomolecular topologies that are able to work as signal differentiators to input signals around their nominal operation. We propose strategies to preserve their performance even in the presence of high-frequency input signal components which are detrimental to the performance of most differentiators. We find that the core of the proposed topologies appears in natural regulatory networks and we further discuss their biological relevance. The simple structure of our designs makes them promising tools for realizing derivative control action in synthetic biology.

Metabolic control of nitrogen fixation in rhizobium-legume symbioses.

Rhizobia induce nodule formation on legume roots and differentiate into bacteroids, which catabolize plant-derived dicarboxylates to reduce atmospheric N2 into ammonia. Despite the agricultural importance of this symbiosis, the mechanisms that govern carbon and nitrogen allocation in bacteroids and promote ammonia secretion to the plant are largely unknown. Using a metabolic model derived from genome-scale datasets, we show that carbon polymer synthesis and alanine secretion by bacteroids facilitate redox balance in microaerobic nodules. Catabolism of dicarboxylates induces not only a higher oxygen demand but also a higher NADH/NAD+ ratio than sugars. Modeling and 13C metabolic flux analysis indicate that oxygen limitation restricts the decarboxylating arm of the tricarboxylic acid cycle, which limits ammonia assimilation into glutamate. By tightly controlling oxygen supply and providing dicarboxylates as the energy and electron source donors for N2 fixation, legumes promote ammonia secretion by bacteroids. This is a defining feature of rhizobium-legume symbioses.

How Rhizobia Adapt to the Nodule Environment.

Rhizobia are a phylogenetically diverse group of soil bacteria that engage in mutualistic interactions with legume plants. Although specifics of the symbioses differ between strains and plants, all symbioses ultimately result in the formation of specialized root nodule organs that host the nitrogen-fixing microsymbionts called bacteroids. Inside nodules, bacteroids encounter unique conditions that necessitate the global reprogramming of physiological processes and the rerouting of their metabolism. Decades of research have addressed these questions using genetics, omics approaches, and, more recently, computational modeling. Here, we discuss the common adaptations of rhizobia to the nodule environment that define the core principles of bacteroid functioning. All bacteroids are growth arrested and perform energy-intensive nitrogen fixation fueled by plant-provided C4-dicarboxylates at nanomolar oxygen levels. At the same time, bacteroids are subject to host control and sanctioning that ultimately determine their fitness and have fundamental importance for the evolution of a stable mutualistic relationship.