Association of Maternal Prepregnancy Body Mass Index With Fetal Growth and Neonatal Thalamic Brain Connectivity Among Adolescent and Young Women

Spann, Marisa; Scheinost, Dustin; Feng, Tianshu; Barbato, Kristiana; Lee, Seonjoo; Monk, Catherine; Peterson, Bradley S.

doi:10.1001/jamanetworkopen.2020.24661

Cited by 21 publications

(38 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This indicated that pre-pregnancy weight might influence longevity in the offspring. Furthermore, recent findings suggest that pre-pregnancy BMI is associated with the development of regulation of body weight and thalamic functional brain connectivity in the offspring [ 20 ]. Pre-pregnancy BMI may be one of the most important modifiable environmental factors that may dictate the life expectancy of and brain development in newborns, and their later health status and risk of chronic diseases [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

High pre-pregnancy BMI with a history of gestational diabetes mellitus is associated with an increased risk of type 2 diabetes in Korean women

Shin

Lee

2021

PLoS ONE

View full text Add to dashboard Cite

Despite the importance of pre-pregnancy body mass index (BMI) and a history of gestational diabetes mellitus (GDM) in the progression of GDM to type 2 diabetes, few studies have evaluated the combined effect of high pre-pregnancy BMI and GDM status on the future development of type 2 diabetes in Korean women. This study aimed to examine the relationship of pre-pregnancy BMI and GDM history with the risk of type 2 diabetes among Korean women. In addition, the effects of pre-pregnancy BMI and current BMI on the risk of type 2 diabetes were evaluated. Women who gave birth in the Health Examinees Study of the Korean Genome and Epidemiology Study from 2004 to 2013 (n = 59,258) were included in this study. Multivariable logistic regression was used to examine the association of pre-pregnancy BMI categories (underweight: <18.5 kg/m2; normal: 18.5–22.9 kg/m2; overweight: 23.0–24.9 kg/m2; obese: ≥25.0 kg/m2) and GDM history with the risk of type 2 diabetes after controlling for the following covariates: age, education, income, smoking status before the first pregnancy, alcohol consumption, regular exercise, menarche age, first pregnancy age, and first pregnancy outcome. Compared to women with normal pre-pregnancy BMIs, women with overweight and obese pre-pregnancy BMIs had higher odds of developing type 2 diabetes (adjusted odds ratio [AOR]: 1.13, 95% confidence interval [CI]: 1.02–1.25 and AOR: 1.29, 95% CI: 1.10–1.50, respectively) after controlling for covariates. Women with pre-pregnancy BMIs <23 kg/m2 and current BMIs ≥23 kg/m2 had increased odds of developing type 2 diabetes (AOR: 1.64, 95% CI: 1.51–1.78) compared to those with pre-pregnancy BMIs <23 kg/m2 and current BMIs <23 kg/m2. Among women without a history of GDM, those with overweight and obese pre-pregnancy BMIs had increased odds of developing type 2 diabetes compared to those with normal pre-pregnancy BMIs (AOR: 1.12, 95% CI: 1.01–1.24 and AOR: 1.23, 95% CI: 1.05–1.44, respectively). Among women with GDM, those with obese pre-pregnancy BMIs had increased odds of developing type 2 diabetes (AOR: 3.84, 95% CI: 1.52–9.87). This study showed that there was a higher likelihood of developing type 2 diabetes in women who were overweight or obese before pregnancy with a history of GDM compared to their counterparts without a history of GDM. Furthermore, high pre-pregnancy BMI or high current BMI increased the risk of type 2 diabetes in Korean women, regardless of GDM history. This emphasizes the importance of maintaining a healthy weight status before and after pregnancy to prevent the future risk of type 2 diabetes.

show abstract

Section: Introductionmentioning

confidence: 99%

High pre-pregnancy BMI with a history of gestational diabetes mellitus is associated with an increased risk of type 2 diabetes in Korean women

Shin

Lee

2021

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Sample sizes and ages at scan are shown in Supplementary Table 1 . Fifty-two studies were completely cross-sectional and performed only one scan per each subject ( Hazlett et al, 2012 ; Deniz Can et al, 2013 ; Deoni et al, 2013 , 2015 ; Gao et al, 2013 ; O’Muircheartaigh et al, 2013 , 2014 ; Qiu et al, 2013a , 2015b ; Broekman et al, 2014 ; Dean et al, 2014b , c , 2017 , 2018a , b ; Grewen et al, 2014 ; Travis et al, 2014 ; Zhang et al, 2014 ; Donald et al, 2015 ; Langer et al, 2015 ; Ou et al, 2015 ; Poh et al, 2015 ; Spann et al, 2015a , b , 2020a , b ; Ferradal et al, 2016 , 2019 ; Li et al, 2016 ; Sethna et al, 2016 ; Adibpour et al, 2018 , 2020 ; Lugo-Candelas et al, 2018 ; Monnelly et al, 2018 ; Chen et al, 2019 ; Hernandez-Castillo et al, 2019 ; Lebenberg et al, 2019 ; Lehtola et al, 2019 ; Ong et al, 2019 ; Tuulari et al, 2019 ; Acosta et al, 2020a , b , 2021 ; Alexander et al, 2020 ; Bruchhage et al, 2020 ; Camacho et al, 2020 ; Dowe et al, 2020 ; Fenchel et al, 2020 ; Gale-Grant et al, 2020 ; Graham et al, 2020 ; Merhar et al, 2020 ; Merz et al, 2020 ). The remaining studies ( n = 34) were longitudinal and conducted serial scans on the same individuals ( Geng et al, 2012 , 2016 ; ...…”

Section: Resultsmentioning

confidence: 99%

“…Serial scans included two to seven scans per subject, most typically three scans per subject. The majority of the studies ( n = 70) used 3 Tesla MRI scanners ( Geng et al, 2012 , 2016 ; Hazlett et al, 2012 ; Deoni et al, 2013 , 2015 ; Gao et al, 2013 , 2014a , b ; Li et al, 2013 , 2014a , b , c , 2015a , b ; Sadeghi et al, 2013 ; Alcauter et al, 2014 , 2015 ; Bompard et al, 2014 ; Chen et al, 2014 , 2019 ; Dean et al, 2014a , b , c , 2015a , b , 2017 , 2018a , b ; Grewen et al, 2014 ; Zhang et al, 2014 ; Donald et al, 2015 ; Langer et al, 2015 ; Spann et al, 2015a , b , 2020a , b ; Swanson et al, 2015 ; Chang et al, 2016 ; Croteau-Chonka et al, 2016 ; Ferradal et al, 2016 , 2019 ; Kim et al, 2016 ; Meng et al, 2017 ; Adibpour et al, 2018 , 2020 ; Lugo-Candelas et al, 2018 ; Monnelly et al, 2018 ; Ahn et al, 2019 ; Dai et al, 2019a , b ; Hernandez-Castillo et al, 2019 ; Hu et al, 2019 ; Lebenberg et al, 2019 ; Lehtola et al, 2019 ; Tuulari et al, 2019 ; Wang et al, 2019 , 2012 ; Acosta et al, 2020a , b , 2021 …”

Section: Resultsmentioning

confidence: 99%

Infant and Child MRI: A Review of Scanning Procedures

et al. 2021

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI) is a safe method to examine human brain. However, a typical MR scan is very sensitive to motion, and it requires the subject to lie still during the acquisition, which is a major challenge for pediatric scans. Consequently, in a clinical setting, sedation or general anesthesia is often used. In the research setting including healthy subjects anesthetics are not recommended for ethical reasons and potential longer-term harm. Here we review the methods used to prepare a child for an MRI scan, but also on the techniques and tools used during the scanning to enable a successful scan. Additionally, we critically evaluate how studies have reported the scanning procedure and success of scanning. We searched articles based on special subject headings from PubMed and identified 86 studies using brain MRI in healthy subjects between 0 and 6 years of age. Scan preparations expectedly depended on subject’s age; infants and young children were scanned asleep after feeding and swaddling and older children were scanned awake. Comparing the efficiency of different procedures was difficult because of the heterogeneous reporting of the used methods and the success rates. Based on this review, we recommend more detailed reporting of scanning procedure to help find out which are the factors affecting the success of scanning. In the long term, this could help the research field to get high quality data, but also the clinical field to reduce the use of anesthetics. Finally, we introduce the protocol used in scanning 2 to 5-week-old infants in the FinnBrain Birth Cohort Study, and tips for calming neonates during the scans.

show abstract

“…Prior investigations into maternal obesity and overweight during pregnancy related infant neurodevelopment have revealed widespread functional connectivity and white matter tract alterations in the neonate brain [22][23][24][25] . Similarly, a recent study found that higher pre-pregnancy maternal BMI during gestation associated with variations in functional connectivity in fetal prefrontal, frontal and insular brain regions 43 .…”

Section: Discussionmentioning

confidence: 99%

“…The delicate developmental and recon guration processes in brain functional networks during gestation and the rst years, respectively, present a time window in brain development, that has been shown to be particularly vulnerable for disruption by endogenous and exogenous factors 13 . Prior human MRI neonate studies focusing on obese and overweight pregnancies have revealed that maternal adiposity is associated with widespread alterations in the anterior brain white matter tract integrity 22 and in functional networks 23,24 with emphasis on sensory cue and reward processing, cognitive and motor control in the neonate brain 25 .…”

Section: Introductionmentioning

confidence: 99%

Maternal Pre-pregnancy BMI Associates With Neonate Brain Local Synchrony in the Left Superior Frontal Gyrus: a Pilot Study

Rajasilta

Häkkinen

Björnsdotter

et al. 2021

Preprint

View full text Add to dashboard Cite

Introduction: Maternal obesity/overweight during pregnancy has reached epidemic proportions and has been linked with adverse outcomes for the offspring, including cognitive impairment and increased risk for neuropsychiatric disorders. Prior neuroimaging investigations have reported widespread aberrant functional connectivity and white matter tract abnormalities in neonates born to obese mothers. Here we explored whether maternal pre-pregnancy adiposity is associated with alterations in local neuronal synchrony in the neonate brain. Methods: 21 healthy mother-neonate dyads from uncomplicated pregnancies were included in this study (age at scanning 26.14 ± 6.28 days, 12 male). The neonates were scanned with a 6-minute resting-state functional magnetic resonance imaging (rs-fMRI) during natural sleep. Regional homogeneity (ReHo) maps were computed from obtained rs-fMRI data. Multiple regression analysis was performed to assess the association of pre-pregnancy maternal body-mass-index (BMI) and ReHo. Results: Maternal adiposity measured by pre-pregnancy BMI was positively associated with neonate ReHo values within the left superior frontal gyrus (FDR/FWE –corrected p < 0.005). Conclusions: Our results imply that maternal pre-pregnancy BMI associates with local functional synchrony within the neonate left superior frontal gyrus. In line with previous studies, our findings indicate that maternal pre-pregnancy BMI has a programming influence on the developing neonate brain functional networks.

show abstract

Association of Maternal Prepregnancy Body Mass Index With Fetal Growth and Neonatal Thalamic Brain Connectivity Among Adolescent and Young Women

Cited by 21 publications

References 53 publications

High pre-pregnancy BMI with a history of gestational diabetes mellitus is associated with an increased risk of type 2 diabetes in Korean women

High pre-pregnancy BMI with a history of gestational diabetes mellitus is associated with an increased risk of type 2 diabetes in Korean women

Infant and Child MRI: A Review of Scanning Procedures

Maternal Pre-pregnancy BMI Associates With Neonate Brain Local Synchrony in the Left Superior Frontal Gyrus: a Pilot Study

Contact Info

Product

Resources

About