Sex differences in brain metabolite concentrations in healthy children - proton magnetic resonance spectroscopy study (1HMRS)

Maternal exposure to polystyrene nanoplastics alters fetal brain metabolism in mice

INTRODUCTION

Plastics used in everyday materials accumulate as waste in the environment and degrade over time. The impacts of the resulting particulate micro- and nanoplastics on human health remain largely unknown. In pregnant mice, we recently demonstrated that exposure to nanoplastics throughout gestation and during lactation resulted in changes in brain structure detected on MRI. One possible explanation for this abnormal postnatal brain development is altered fetal brain metabolism.

OBJECTIVES

To determine the effect of maternal exposure to nanoplastics on fetal brain metabolism.

METHODS

Healthy pregnant CD-1 mice were exposed to 50 nm polystyrene nanoplastics at a concentration of 106 ng/L through drinking water during gestation. Fetal brain samples were collected at embryonic day 17.5 (n = 18-21 per group per sex) and snap-frozen in liquid nitrogen. Magic angle spinning nuclear magnetic resonance was used to determine metabolite profiles and their relative concentrations in the fetal brain.

RESULTS

The relative concentrations of gamma-aminobutyric acid (GABA), creatine and glucose were found to decrease by 40%, 21% and 30% respectively following maternal nanoplastic exposure when compared to the controls (p < 0.05). The change in relative concentration of asparagine with nanoplastic exposure was dependent on fetal sex (p < 0.005).

CONCLUSION

Maternal exposure to polystyrene nanoplastics caused abnormal fetal brain metabolism in mice. The present study demonstrates the potential impacts of nanoplastic exposure during fetal development and motivates further studies to evaluate the risk to human pregnancies.

Meta-analysis and open-source database for in vivo brain Magnetic Resonance spectroscopy in health and disease

Proton (1H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease

Proton ( 1 H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo . Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T 2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impact, however, has been limited due to small sample sizes and methodological variability in addition to intrinsic limitations of the method itself such as its sensitivity to motion. The lack of standardization from a data acquisition and data processing perspective makes it difficult to pool multiple studies and/or conduct multisite studies that are necessary for supporting clinically relevant findings. Based on the experience of the ENIGMA MRS work group and a review of the literature, this manuscript provides an overview of the current state of MRS data harmonization. Key factors that need to be taken into consideration when conducting both retrospective and prospective studies are described. These include (1) MRS acquisition issues such as pulse sequence, RF and B0 calibrations, echo time, and SNR; (2) data processing issues such as pre-processing steps, modeling, and quantitation; and (3) biological factors such as voxel location, age, sex, and pathology. Various approaches to MRS data harmonization are then described including meta-analysis, mega-analysis, linear modeling, ComBat and artificial intelligence approaches. The goal is to provide both novice and experienced readers with the necessary knowledge for conducting MRS data harmonization studies.

Neurometabolic changes in neonates with congenital heart defects and their relation to neurodevelopmental outcome

BACKGROUND

Altered neurometabolite ratios in neonates undergoing cardiac surgery for congenital heart defects (CHD) may serve as a biomarker for altered brain development and neurodevelopment (ND).

METHODS

We analyzed single voxel 3T PRESS H¹-MRS data, acquired unilaterally in the left basal ganglia and white matter of 88 CHD neonates before and/or after neonatal cardiac surgery and 30 healthy controls. Metabolite ratios to Creatine (Cr) included glutamate (Glu/Cr), myo-Inositol (mI/Cr), glutamate and glutamine (Glx/Cr), and lactate (Lac/Cr). In addition, the developmental marker N-acetylaspartate to choline (NAA/Cho) was evaluated. All children underwent ND outcome testing using the Bayley Scales of Infant and Toddler Development Third Edition (BSID-III) at 1 year of age.

RESULTS

White matter NAA/Cho ratios were lower in CHD neonates compared to healthy controls (group beta estimate: -0.26, std. error 0.07, 95% CI: -0.40 - 0.13, p value <0.001, FDR corrected p value = 0.010). We found no correlation between pre- or postoperative white matter NAA/Cho with ND outcome while controlling for socioeconomic status and CHD diagnosis.

CONCLUSION

Reduced white matter NAA/Cho in CHD neonates undergoing cardiac surgery may reflect a delay in brain maturation. Further long-term MRS studies are needed to improve our understanding of the clinical impact of altered metabolites on brain development and outcome.

IMPACT

NAA/Cho was reduced in the white matter, but not the gray matter of CHD neonates compared to healthy controls. No correlation to the 1-year neurodevelopmental outcome (Bayley-III) was found. While the rapid change of NAA/Cho with age might make it a sensitive marker for a delay in brain maturation, the relationship to neurodevelopmental outcome requires further investigation.

The Distribution of Major Brain Metabolites in Normal Adults: Short Echo Time Whole-Brain MR Spectroscopic Imaging Findings

This prospective study aimed to evaluate the variation in magnetic resonance spectroscopic imaging (MRSI)-observed brain metabolite concentrations according to anatomical location, sex, and age, and the relationships among regional metabolite distributions, using short echo time (TE) whole-brain MRSI (WB-MRSI). Thirty-eight healthy participants underwent short TE WB-MRSI. The major metabolite ratios, i.e., N-acetyl aspartate (NAA)/creatine (Cr), choline (Cho)/Cr, glutamate + glutamine (Glx)/Cr, and myoinositol (mI)/Cr, were calculated voxel-by-voxel. Their variations according to anatomical regions, sex, and age, and their relationship to each other were evaluated by using repeated-measures analysis of variance, t-tests, and Pearson’s product-moment correlation analyses. All four metabolite ratios exhibited widespread regional variation across the cerebral hemispheres (corrected p < 0.05). Laterality between the two sides and sex-related variation were also shown (p < 0.05). In several regions, NAA/Cr and Glx/Cr decreased and mI/Cr increased with age (corrected p < 0.05). There was a moderate positive correlation between NAA/Cr and mI/Cr in the insular lobe and thalamus and between Glx/Cr and mI/Cr in the parietal lobe (r ≥ 0.348, corrected p ≤ 0.025). These observations demand age- and sex- specific regional reference values in interpreting these metabolites, and they may facilitate the understanding of glial-neuronal interactions in maintaining homeostasis.

Placental metabolite profiles in late gestation for healthy mice

INTRODUCTION

During pregnancy, appropriate placental metabolism is essential for fetuses to reach their growth potential. However, metabolic mechanisms during pregnancy remain poorly understood. Determination of the levels of placental metabolites in healthy pregnancy and how they change throughout gestation is critical for understanding placental function.

OBJECTIVE

To determine the effects of gestational age on placental metabolites using healthy pregnant mice.

METHODS

In the present study, we collected placental tissue samples from healthy pregnant mice at three timepoints in late gestation (n = 16 placentas per gestational age). Metabolite profiles were determined using ¹H high-resolution magic angle spinning magnetic resonance spectroscopy (HRMAS MRS).

RESULTS

Using HRMAS MRS, we identified 14 metabolites in murine placental tissue samples. The relative concentration of 12 of the 14 metabolites remains unchanged throughout late gestation. Lysine was found to decrease significantly (p = 0.04) and glucose showed an inverted U-shape relationship (p = 0.03) with gestational age.

CONCLUSION

This study demonstrated the feasibility of HRMAS MRS to determine relative metabolite concentrations in murine placental tissue. These findings establish baseline levels of placental tissue metabolite profiles and will serve as reference ranges for future studies using mouse models of fetal distress.

Impact of glucose metabolism on the developing brain

Glucose is the most important substrate for proper brain functioning and development, with an increased glucose consumption in relation to the need of creating new brain structures and connections. Therefore, alterations in glucose homeostasis will inevitably be associated with changes in the development of the Nervous System. Several studies demonstrated how the alteration of glucose homeostasis - both hyper and hypoglycemia- may interfere with the development of brain structures and cognitivity, including deficits in intelligence quotient, anomalies in learning and memory, as well as differences in the executive functions. Importantly, differences in brain structure and functionality were found after a single episode of diabetic ketoacidosis suggesting the importance of glycemic control and stressing the need of screening programs for type 1 diabetes to protect children from this dramatic condition. The exciting progresses of the neuroimaging techniques such as diffusion tensor imaging, has helped to improve the understanding of the effects, outcomes and mechanisms underlying brain changes following dysglycemia, and will lead to more insights on the physio-pathological mechanisms and related neurological consequences about hyper and hypoglycemia.

Network Modeling Sex Differences in Brain Integrity and Metabolic Health

Hypothesis-driven studies have demonstrated that sex moderates many of the relationships between brain health and cardiometabolic disease, which impacts risk for later-life cognitive decline. In the present study, we sought to further our understanding of the associations between multiple markers of brain integrity and cardiovascular risk in a midlife sample of 266 individuals by using network analysis, a technique specifically designed to examine complex associations among multiple systems at once. Separate network models were constructed for male and female participants to investigate sex differences in the biomarkers of interest, selected based on evidence linking them with risk for late-life cognitive decline: all components of metabolic syndrome (obesity, hypertension, dyslipidemia, and hyperglycemia); neuroimaging-derived brain-predicted age minus chronological age; ratio of white matter hyperintensities to whole brain volume; seed-based resting state functional connectivity in the Default Mode Network, and ratios of N-acetyl aspartate, glutamate and myo-inositol to creatine, measured through proton magnetic resonance spectroscopy. Males had a sparse network (87.2% edges = 0) relative to females (69.2% edges = 0), indicating fewer relationships between measures of cardiometabolic risk and brain integrity. The edges in the female network provide meaningful information about potential mechanisms between brain integrity and cardiometabolic health. Additionally, Apolipoprotein ϵ4 (ApoE ϵ4) status and waist circumference emerged as central nodes in the female model. Our study demonstrates that network analysis is a promising technique for examining relationships between risk factors for cognitive decline in a midlife population and that investigating sex differences may help optimize risk prediction and tailor individualized treatments in the future.