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. Author manuscript; available in PMC: 2022 Jan 19.
Published in final edited form as: Brain Imaging Behav. 2021 Mar 18;15(6):2746–2755. doi: 10.1007/s11682-021-00466-z

The role of maternal BMI on brain food cue reactivity in children: a preliminary study

Shan Luo 1,2,3,4, Brendan Angelo 1,2, Ting Chow 5, John R Monterosso 3,6, Anny H Xiang 5, Paul M Thompson 4,6, Kathleen A Page 1,2,6
PMCID: PMC8448787  NIHMSID: NIHMS1688667  PMID: 33738669

Abstract

Children of overweight and obese parents have an increased risk of obesity. Little is known the neural mechanisms underlying this relationship, specifically the brain systems implicated in self-regulation of food intake. The primary goal here is to examine relationships between maternal body mass index (BMI) and brain responses to food cues in children. Seventy-six children (8.62 ± 1.02 years; 28 M,48F) were included in this study. Height and weight were assessed for children and their biological parents. Maternal height and weight before pregnancy were extracted from the Electronic Medical Records (EMR). BMI (kg/m2) or BMIz (age- and sex-specific BMI) were calculated. Children underwent a magnetic resonance imaging session where they viewed food and non-food images before and after glucose ingestion. The dorsolateral prefrontal cortex (dlPFC) and anterior cingulate cortex (ACC) food cue reactivity was the measurement of interest for region-of-interest (ROI) analyses. Whole-brain exploratory analysis was performed as well. Non-parametric methods were used for data analysis. ROI and whole brain analyses showed that maternal current BMI was inversely associated with child’s ACC and dlPFC food cue reactivity after glucose ingestion, adjusting for age and sex. No significant relationships were found between paternal BMI and child’s food cue reactivity. Child BMIz was negatively associated with the ACC food cue reactivity after glucose ingestion. Our results supported the role of maternal adiposity on child’s responses to appetitive food cues in brain self-regulation circuitry, which may influence eating behavior and obesity risk in children.

Keywords: Maternal obesity, Childhood obesity, Brain, Food cue reactivity, Self-regulation

Introduction

Parental obesity is one of the strongest predictors of childhood obesity. Epidemiological studies have shown that children with overweight/obese parents are at an increased risk for obesity themselves (Danielzik et al. 2002; Whitaker et al. 2010). Evidence suggests that these inter-generational effects may be stronger for mothers than fathers (Danielzik et al. 2002; Lawlor et al. 2008; Whitaker et al. 2010).

Studies have begun to examine potential mechanisms underlying inter-generational transmission of obesity. Many obesity associated genes are preferentially expressed in the central nervous system (Locke et al. 2015), suggesting that the brain may play an important role in the etiology of obesity. Food cue paradigms have been widely used in the obesity field to measure how the brain responds to appetitive food cues commonly seen in everyday life. An array of brain regions has been shown to be recruited during food cue tasks (van Meer et al. 2015), including regions involved in metabolic signaling (e.g., hypothalamus), reward and motivation (e.g., ventral and dorsal striatum, insula, amygdala, orbital frontal cortex) and self-regulation (e.g., dorsolateral prefrontal cortex [dlPFC] and anterior cingulate cortex [ACC]). Very few studies have examined how neural responses to food cues differ between children with overweight/obese vs. normal weight parents. Stice et al. (2011) used parental obesity status to categorize lean adolescents into high- vs. low-risk for obesity (high-risk: lean adolescents with two overweight/obese parents; low-risk: lean adolescents with lean parents) and failed to detect group differences during the presentation of food cues (Stice et al. 2011). A recent neuroimaging study used biological mothers’ obesity status to operationalize risk for obesity in lean adolescents (high-risk: lean adolescents with overweight/obese mothers; low-risk: lean adolescents with lean mothers), and observed weaker responses to food-denoting words in the self-regulation circuitry (e.g., dlPFC, ACC) among adolescents at high- vs. low-risk for obesity (Carnell et al. 2017). Surprisingly, in opposition to their original hypothesis, both groups of investigators failed to observe heightened brain responses to food cues in reward and motivation systems in adolescents at high- vs. low-risk for obesity.

There are weaknesses in these studies. Prior studies were limited in power due to small sample sizes and the inclusion of high- vs. low-risk for obesity groups. This may have led to failures to detect group differences in brain food cue reactivity. To our knowledge, no study has examined neurobiological underpinnings of familial risk for childhood obesity systematically across varying levels of maternal BMI (as a continuous variable). Prior work only examined associations between maternal current BMI and brain food cue reactivity, but it remains unknown if prenatal exposure to maternal obesity may also affect the brain self-regulation circuitry. Furthermore, the role of paternal BMI on brain food cue reactivity in children has not been examined.

In this study, we investigated relationships between maternal BMI (both pre-pregnancy BMI and current BMI) and brain food cue reactivity in healthy children aged 7 to 11 years old. We hypothesized that maternal BMI would be inversely related to the dlPFC and ACC responses to food cues. We additionally explored relationships between maternal BMI and food cue reactivity throughout the whole brain. We also tested relationships between paternal BMI and brain food cue reactivity to better understand whether altered activity in the self-regulation circuitry during childhood is specific to the maternal influence.

Methods

Participants

We included 106 mother-child pairs from an ongoing BrainChild study (https://www.drkatiepage.com/brainchildstudy/), which aims to examine central and peripheral glucose metabolisms in children with varying levels of exposure to maternal obesity and gestational diabetes. 30 participants were excluded from the final data analysis for the following reasons: 29 due to motion (≥2 mm of movement in any direction), and one due to abnormal brain findings.

Children between the ages of 7 to11 years old were recruited from Kaiser Permanente Southern California (KPSC). Child participants were healthy, free from any psychological and neurological disorders, all right-handed, and had normal or corrected to normal vision. Parents gave written informed consent and children provided informed assent, and all experimental protocols were approved by the Institutional Review Board of the University of Southern California and KPSC.

In-person visits: anthropometric measures

Participants came in for two in-person visits - an initial visit to gather anthropometric and other physical measures, and a magnetic resonance imaging (MRI) visit. The initial visit occurred at the Clinical Research Unit of the USC Diabetes and Obesity Research Institute. Height and weight measures were obtained on mother and child pairs. Fathers were given the option of having height and weight measured during in-person visit and/or providing self-reported measures.

Height was measured using a stadiometer to the nearest 0.1 cm, and weight was measured using a calibrated digital scale to the nearest 0.1 kg by trained staff. BMI (kg/m2) was calculated for both mothers and children. Specific to children, we also calculated BMI percentiles (age- and sex-specific), and BMIz (age- and sex-specific standard deviation scores or BMI z-scores) based on the Center for Disease Control (CDC) normative data (2000 CDC Growth Charts for the United States 2002). Child participants were given the option of having Tanner stage assed by physical examination (Marshall and Tanner 1969, 1970) and/or by a validated sex-specific assessment questionnaire for children and parents, which contained both illustrations and explanatory text where necessary (Rasmussen et al. 2015). Thirty-six participants opted for both physical examination and questionnaire, while 39 participants opted for self-reported puberty status only. One participant opted for physical examination only and did not provide a self-report. Tanner staging assessed by questionnaire was the same as physical examination for 36 participants with both measurements. Seventeen fathers had both measured and self-reported BMI data, 48 fathers had self-reported BMI only, and 11 fathers had missing BMI data. Correlation between self-reported and measured paternal BMI was 0.975.

We extracted maternal height and weight measures closest to last menstrual period from the KPSC Electronic Medical Record (EMR) and calculated maternal pre-pregnancy BMI (kg/m2).

In-person visits: MRI

The second visit occurred at the USC Dana and David Dornsife Neuroimaging Center, where child participants were first trained using a mock scanner system, and then imaged using a Siemens MAGNETOM Prismafit 3-Tesla MRI scanner (Siemens Medical Systems) with a 20-channel phased array coil. All scans were collected in the morning between 8 and 10 am after a 12-h overnight fast, which helped standardize the time since last meal intake across participants. Prior to scanning, children were instructed to rate their hunger from 1 to 5, where 1 meant that they were very hungry, 3 meant they felt just right, and 5 meant they were not hungry at all, using a picture questionnaire validated in primary school children (Bennett and Blissett 2014). Afterwards, participants were placed in the MRI scanner and were shown a food cue task (for details, see Luo et al. 2019) at baseline and again at ~10-min after consumption of a premade Glucola drink (1.75 g/kg of weight). Given the primary interest of the parent study, glucose ingestion (vs. meal) was used to match the study design of the oral glucose tolerance testing done in a separate visit. At the end of MRI session (~20–30 min after drink), participants were instructed to rate their hunger using the same picture scale. The initial five participants of this study did not have hunger ratings. Other imaging acquisitions were also performed but not included in this study.

Imaging data acquisition parameters

Blood-oxygen-level-dependent (BOLD) functional scans were acquired using a single-shot gradient echo planar imaging sequence with the following parameters: Repetition time (TR) = 2000 milliseconds (ms), echo time (TE) = 25 ms, bandwidth = 2520 Hz/pixel, flip angle = 85°, field of view (FOV) = 220 × 220 mm, matrix size = 64 × 64, and a slice thickness of 4 mm, for a total of 32 slices covering the whole brain. In addition, a 3D Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequence was collected as a high-resolution structural template for multi-subject registration, with the following parameters: TR = 1950 ms, TE = 2.26 ms, flip angle = 9°, inversion time = 900 ms, matrix size = 256 × 256 × 224, and a voxel resolution = 1 × 1 × 1 mm3. The individual scan times were 3 min and 16 s for the functional scan, and 4 min and 14 s for the structural scan.

fMRI analysis

To preprocess and analyze the collected fMRI data, we used the fMRI Expert Analysis Tool (FEAT) version 6.0 provided within the FMRIB Software Library (FSL) toolkit, from the team at the Oxford University Centre for Functional MRI (https://fsl.fmrib.ox.ac.uk/fsl/). The preprocessing on the fMRI data was performed in 2 distinct parts; first, the raw functional data were run through FEAT’s preprocessing pipeline, which included motion correction by MCFLIRT, BET brain extraction, and spatial smoothing with a full-width at half-maximum Gaussian kernel of 5 mm. These functional data were mapped to a high-resolution structural scan from each participant, and then registered into standard space using affine transformation to the Montreal Neurological Institute T1-weighted 2-mm brain template with 12 degrees of freedom, provided through FSL’s FLIRT. Second, to further correct for motion artifacts, these data were processed through ICA-AROMA (Pruim et al. 2015), which uses Independent Component Analysis to separate out components of the data that are related to head motion and remove them using linear regressions. Each individual’s denoised data were used as inputs for the first level FEAT analysis. Temporal filtering was applied (100 s), and the food and non-food events were added to the General Linear Model (GLM) after convolution with a canonical hemodynamic response function. Motion confounds were generated by fsl_motion_outliers to be used as regressors of no-interest. For each participant, food versus non-food cue contrast maps were created through the first-level analysis, and then fed into a random-effects group-level analysis using FMRIB’s Local Analysis of Mixed Effects (FLAME1).

ROI analysis

Anatomical, bilateral ROIs of the ACC and dlPFC were defined using the Harvard-Oxford Cortical Structural Atlas, with probability threshold equal to or over 50%. Percent signal change in each ROI was extracted from food vs. non-food contrasts at baseline and after glucose drink ingestion using Featquery. Due to skewed distributions of data, we used Spearman correlations to examine relationships among maternal current BMI, maternal pre-pregnancy BMI, child BMIz and food cue reactivity in each ROI. In models where additional covariates such as age and sex were included, we first ranked ROI data, then regressed against age and sex, while standardized residuals from the regression model were used for Spearman correlation analysis. Exploratory analysis was conducted examining paternal current BMI in relation to brain food cue reactivity in the ROIs because paternal BMI measures were mostly self-reported.

Whole-brain analysis

We also performed an exploratory whole brain analysis to examine relationships between maternal BMI (maternal pre-pregnancy BMI and maternal current BMI separately) and food cue reactivity. This was done by including maternal BMI as a covariate in the group level analysis for baseline pre- and post-glucose data separately. We then included additional covariates of age and sex in the model to account for age and sex related effects in food cue reactivity. Lastly, we ran a whole brain analysis to examine relationships between child BMIz and food cue reactivity. Similar approach was used for examining relationships between paternal current BMI and food cue reactivity throughout the whole brain. All whole-brain analyses were corrected for multiple comparisons using Z > 3.1, p < .05.

Results

Mother-child pairs characteristics (Table 1)

Table 1.

Participant Characteristics

Mean (SD) Range
Child Characteristics
Age at visit (years) 8.62 (1.02) 7.33–11.34
BMI (kg/m2) 18.98 (4.15) 13.79–34.01
BMI percentile 69.78 (26.19) 5.28–99.58
BMI z-scores 0.75 (1.02) −1.64 −2.64
N (%)
Sex Male: 28 (36.8%)
Female: 48 (63.2%)
Tanner Stage of Pubertal Development Tanner Stage 1:68 (89.5%)
Tanner Stage 2: 5 (6.6%)
Tanner Stage 3: 2 (2.6%)
Tanner Stage 4: 1 (1.3%)
Weight Status Category Normal Weight (BMI percentile ≥5th and<85th): 48 (63.2%)
Overweight (BMI percentile ≥85th and<95th): 9 (11.8%)
Obese (BMI percentile ≥95th): 19 (25%)
Maternal Characteristics
Age at visit (years) 40.47 (5.34) 27–52
Pre-pregnancy BMI (kg/m2) 29.91 (6.77) 19.55–49.27
Current BMI (kg/m2) 31.36 (7.66) 19.63–58.85
N (%)
Race/Ethnicity Hispanic: 39 (51.3%)
Non-Hispanic Black: 12 (15.8%)
Non-Hispanic White: 16 (21.1%)
Other: 9 (11.8%)
Education High School: 14 (18.4)
Some College: 24 ((31.6%)
College and Post: 38 (50%)
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Seventy-six children (age: 8.62 ± 1.02 years; sex: 28 males, 48 females) were included in the final neuroimaging data analysis, of whom 89.5% were prepubertal (Tanner Stage <2). Of these 76 participants, 63.2% were of normal weight, 11.8% were overweight, and 25% were obese. Child participant BMI z-scores ranged from −1.64 to 2.64, and percentiles ranged from 5.28 to 99.58, respectively.

Maternal pre-pregnancy BMI ranged from 19.55 kg/m2 to 49.27 kg/m2, maternal current BMI varied from 19.63 to 58.85 kg/m2, and these two measures were significantly correlated with each other (rho = 0.77, P < 0.0001). Maternal current BMI was significantly associated with child’s BMIz (rho = 0.28, P = 0.02), and there was a positive trend of relationship between maternal pre-pregnancy BMI and child’s BMIz (rho = 0.19, P = 0.098).

Paternal current BMI varied from 22.60 to 46.99 kg/m2. Paternal current BMI was significantly related to both maternal BMI (maternal pre-pregnancy BMI: rho = 0.37, P = 0.003; maternal current BMI: rho = 0.34, P = 0.005) and child’s BMI z-scores (rho = 0.34, P = 0.006).

Relationship of parental BMI vs. brain food cue reactivity

Maternal current BMI vs. brain food cue reactivity

ROI analysis revealed that maternal current BMI was inversely related to the dlPFC (rho = −0.27, P = 0.02) and ACC (rho = −0.28, P = 0.02) responses to food cues after glucose ingestion (Fig. 1). Results remained significant after adjusting for child’s age and sex (dlPFC: rho = −0.26, P = 0.02; ACC: rho = −0.29, P = 0.01). Similarly, whole-brain analysis showed that greater levels of maternal current BMI were associated with lower food cue reactivity in the ACC, posterior cingulate cortex, right superior frontal gyrus, right frontal pole, right angular gyrus, left precuneous, bilateral lateral occipital cortex following glucose ingestion (Fig. 2, Table 2). Adjusting for age and sex had minimal effects on whole brain analysis results.

Fig. 1.

Fig. 1

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Negative relationships between maternal current BMI and food cue reactivity in the anterior cingulate cortex (ACC, left panel) and dorsolateral prefrontal cortex (dlPFC, right panel) after glucose ingestion

Fig. 2.

Fig. 2

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Whole brain analysis results showed negative correlations between maternal current BMI and food cue reactivity after glucose ingestion (Z > 3.1, p < 0.05, cluster correction for multiple comparisons)

Table 2.

Significant Clusters from Whole-brain Analysis of Maternal Current BMI and Food Cue Reactivity

Region Peak Voxel Coordinates (mm) Max Z
Anterior Cingulate Cortex 4, 42, 14 4.29
Posterior Cingulate Cortex 0, −46, 16 5.09
Right Superior Frontal Cortex 6, 50, 26 4.35
Right Medial Frontal Pole 6, 58, 0 4.97
Right Angular Gyrus 62, −54, 22 4.57
Right Lateral Occipital Cortex, superior division 48, −62, 36 4.36
Right Middle Temporal Gyrus/Lateral Occipital Cortex, inferior division 62, −60, 10 3.79
Left Medial Precuneus Cortex −6, −58, 36 4.36
Left Lateral Occipital Cortex, superior division −34, −72, 44 4.91
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Maternal pre-pregnancy BMI vs. brain food cue reactivity

There was a negative trend of relationships between maternal pre-pregnancy BMI and food cue reactivity in the ACC (rho = −0.18, P = 0.12) and dlPFC (rho = −0.11, P = 0.33) following glucose ingestion in the ROI analysis. We did not observe significant clusters associated with maternal pre-pregnancy BMI from the whole brain analysis.

As expected, maternal current BMI and maternal pre-pregnancy BMI were highly correlated. When both variables were included as predictors in a linear regression model with child’s ACC and dlPFC food cue reactivity after glucose as dependent variables respectively, we found a significant negative association between maternal current BMI and child’s post-glucose ACC food cue reactivity (β = −0.39, P = 0.047) and marginally significant relationship between maternal current BMI and dlPFC food cue reactivity after glucose (β = −0.34, P = 0.08). These results suggested that maternal current BMI plays an independent role from maternal pre-pregnancy BMI on post-glucose ACC food cue reactivity.

Paternal current BMI vs. brain food cue reactivity

Paternal BMI was not significantly related to child’s brain food cue reactivity in the ACC and dlPFC before or after glucose ingestion.

Relationships between child BMIz and food cue reactivity

Child BMIz was negatively associated with the ACC food cue reactivity after glucose ingestion (rho = −0.25, P = 0.03), and similar negative relationship was also observed in the dlPFC but not significant (P = 0.12). There were no significant clusters observed from the whole brain analysis using BMIz as a covariate for food cue reactivity after glucose.

Given three-way significant relationships among maternal current BMI, post-glucose ACC food cue reactivity, and child’s BMIz, we further explored if there is any evidence of the brain being a mediator of the maternal-child relationship in BMI. Due to moderate sample size and cross-sectional design of our study, a formal mediation analysis was not performed. However, we examined changes in relationship between maternal current BMI and child’s BMIz after adjustment of post-glucose ACC food cue reactivity. The maternal-child relationship in BMI was attenuated and became marginally significant after adjustment of ACC food cue reactivity after glucose consumption (rho = 0.28, P = 0.02 before adjustment; rho = 0.21, P = 0.07 after adjustment). We additionally investigated changes in relationship between maternal current BMI and post-glucose ACC food cue reactivity after adjustment of child’s BMI, given a possible mediating role of child’s BMI. The association of maternal current BMI vs. ACC food cue reactivity after glucose consumption was attenuated but remained significant after adjusting for child’s BMIz (rho = −0.28, P = 0.01 before adjustment; rho = −0.23, P = 0.04 after adjustment). Taken together, these results suggested that post-glucose ACC food cue reactivity may in part mediate the relationship between maternal current BMI and child’s BMI.

Maternal current BMI or child’s BMIz were not significantly related to child’s dlPFC or ACC food cue reactivity before glucose ingestion. Further controlling for baseline food cue reactivity had minimal effects on relationships between mother’s current BMI and child’s food cue reactivity in the ACC and dlPFC following glucose.

Children reported similar levels of hunger at the pre- and post-drink time points, with an average rating of 2.24 ± 1.04 and 2.13 ± 0.94, respectively. There were no significant differences between the pre- and post-drink hunger ratings (t(1,70) = 0.82, P = 0.41). There were no significant correlations between hunger ratings and food cue reactivity in the ACC and dlPFC at both time points. Hunger scores at each time point were not significantly correlated with either paternal BMI or child’s BMIz.

Discussion

In this study, we examined neurobiological underpinnings of familial risk for childhood obesity systematically across varying levels of maternal BMI. ROI and whole brain analyses both showed that higher maternal current BMI were associated with lower child’s food cue reactivity in the ACC and dlPFC following glucose consumption. As the ACC and dlPFC have key roles in dietary self-regulation, these results indicated the role of maternal weight status on child’s responses to appetitive food cues in brain self-regulation circuitry, which may influence eating behavior and obesity risk in children.

A negative correlation between maternal current BMI and food cue reactivity in the dlPFC and ACC was observed in a prior report (Carnell et al. 2017). The ACC and dlPFC play an important role in dietary self-control, described as the capacity to inhibit appetitive responses from food rewards. fMRI studies in adults showed that activity in the dlPFC increased when participants were asked to suppress food cravings (Kober et al. 2010; Siep et al. 2012). The dlPFC was activated when self-controlled choices were made (e.g., choosing a healthier vs. tastier food option); and it modulates mesolimbic activity during self-controlled choices (Hare et al. 2009). Even in task paradigms where participants were passively viewing images of appetizing food, activity in the dlPFC was associated with reduced consumptive behaviors (Lopez et al. 2014, 2016) and with the success of weight loss treatment, up to three months (Neseliler et al. 2019). Furthermore, a causal relationship between the dlPFC’s inhibition control and eating behavior has been established by noninvasive brain stimulation studies. When the dlPFC was functionally disrupted by continuous theta burst stimulation, healthy lean participants reported increased cravings and consumption of snack foods (Lowe et al. 2014, 2018). In contrast, when transcranial direct current stimulation was applied to augment the function of the dlPFC, food craving and consumption were reduced (Fregni et al. 2008). Taken together, these results suggest that recruitment of the ACC and dlPFC is necessary for inhibiting food cravings and consumptive behaviors; and activity in these regions during dietary self-control tasks or food cue paradigms may be reflective of the functionality of self-regulation (either voluntary or involuntary).

There is converging evidence suggesting a link between impairments in brain circuitry implicated in self-regulation and obesity. For example, a recent meta-analysis in healthy adults reported that activity in the dlPFC and ACC during self-control tasks were negatively related to BMI (Han et al. 2018). The same negative relationships between activities in these self-regulatory regions and BMI have been observed in children and adolescents (Batterink et al. 2010; Bruce et al. 2013; Carnell et al. 2017; Jensen et al. 2017; Luo et al. 2019). Although a formal mediation analysis was not performed, our exploratory analysis showed that 1) relationship between maternal current BMI and child’s BMI was attenuated and became marginally significant after adjusting for post-glucose ACC food cue reactivity and 2) relationship between maternal current BMI and ACC food cue reactivity after glucose ingestion was attenuated but remained significant after controlling for child’s BMI. These results provided suggestive evidence that deficits in self-control brain circuitry could be one of potential mechanisms explaining the intergenerational effect of obesity. We acknowledge an alternative interpretation of the results, that is, child’s BMI may in part mediate the relationship between maternal BMI and child’s food cue reactivity. Future study is warranted to robustly examine the relationships among maternal BMI, post-glucose ACC food cue reactivity and child’s BMI.

Genetics play a strong role in familial transmission of obesity. Additionally, maternal obesity may influence child’s weight outcomes in two other ways. Mothers play a dominant role in offspring’s eating behavior and weight outcomes. Prior behavioral studies showed similarity between mothers and children in food intake (Elfhag et al. 2008; Longbottom et al. 2002; Munsch et al. 2011; Oliveria et al. 1992; Prichard et al. 2012; Vauthier et al. 1996; Wang et al. 2009), eating behavior (Campbell et al. 2007; Cutting et al. 1999; Elfhag et al. 2010; Hasenboehler et al. 2009; Jahnke and Warschburger 2008; Munsch et al. 2007; Zarychta et al. 2019; Zocca et al. 2011), and weight outcomes (Danielzik et al. 2002; Lawlor et al. 2008; Whitaker et al. 2010). Furthermore, an fMRI study (Lim et al. 2016) reported that children can project their mother’s food preferences (prioritizing health over taste attributes of food) into their own food choices. This is implemented through the dlPFC and the connection between the dlPFC and ventromedial prefrontal cortex, suggesting that these neural pathways play a critical role in the transmission of dietary self-control from mothers to children. Mothers also contribute to child’s weight outcomes through fetal effects. According to the developmental origin of disease theory, the intrauterine environment that the fetus was exposed to is linked to disease outcomes, including obesity (Gillman 2005). We used maternal pre-pregnancy BMI as a marker of exposure to maternal obesity during pregnancy and found non-significant negative relationships between maternal pre-pregnancy BMI and child’s ACC and dlPFC food cue reactivity after glucose ingestion. However, even after controlling for maternal pre-pregnancy BMI, the relationship between mother’s current BMI and child’s ACC response remained significant, and the relationship largely remained for child’s dlPFC food cue reactivity. Together with prior studies, our results suggested that the effect of mother’s current weight status on the child’s weight outcomes may be via the mother’s dominant role in the child’s food choices implemented at least in part by the brain self-regulation circuitry.

Similarly, we did not observe neural correlates of maternal weight status on child’s food cue reactivity in reward and motivation circuitry such as the striatum and OFC from the whole brain analysis. Although our sample is larger than those in previous studies, the null results should be interpreted with caution. Unlike prior studies that only examined brain food cue reactivity in a fasted state, we investigated relationships between parental BMI and food cue reactivity in a fasted and fed state. Significant relationships between maternal current BMI and food cue reactivity were observed in a fed state but not in a fasted state, suggesting a specific effect of maternal current BMI on postprandial food cue reactivity in the ACC and dlPFC. Future studies with a larger sample size and more complex study design (e.g., with a control condition such as a water drink on a separate day) are needed to extend these findings.

Our results may have important clinical implications. Intervention programs targeting inhibition control may be fruitful for childhood obesity prevention and intervention (Jiang et al. 2016; Porter et al. 2018). As mothers play an important role in offspring’s food choices and weight outcomes, it might be instrumental to consider mother as an agent of change for child obesity treatment. Maternal dietary counseling in the first year of child’s life was associated with healthier eating in childhood (Vitolo et al. 2010), supporting the notion that helping mothers make healthy food choices might be an effective strategy for reducing childhood obesity, while also providing health benefits for the mother.

We acknowledge some limitations of our study. First, due to the cross-sectional design, we were not able to establish the directionality of the relationship between mothers’ current BMI and food cue reactivity in children. Experimental manipulation on mothers’ BMI (e.g., weight loss treatment) and a longitudinal design may help in answering this question. Second, although we explored relationships between paternal BMI and children’s brain food cue reactivity, our results need to be replicated given that most of the paternal BMI measurements were self-reported. Third, future studies may consider a task that directly probes inhibition control to further establish the role of the ACC and dlPFC in self-regulation of eating behavior in the presence of appetitive food cues.

Conclusions

In conclusion, we observed that increases in maternal current BMI were associated with reductions in child’s responses to food cues in the ACC and dlPFC after glucose ingestion. Together with other studies, our results supported the role of maternal adiposity on brain responses to appetitive food cues in the self-regulation circuitry, which may influence eating behavior and obesity risk in the offspring.

Acknowledgements

We thank the volunteers who participated in these studies. We also thank Ana Romero for assistance with study coordination, Mayra Martinez and Janet Mora-Marquez for recruitment; the Dana and David Dornsife Cognitive Neuroimaging Center at USC, especially Bosco Tjan and J.C. Zhuang for assistance with developing MRI protocols.

Funding

This work was supported by an American Diabetes Association Pathway Accelerator Award (#1-14-ACE-36) (PI: K.A.P) and in part by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH) R03DK103083 (PI: K.A.P), R01DK116858 (PIs: K.A.P, A.H.X), and K01DK115638 (PI: SL). A Research Electronic Data Capture, REDCap, database was used for this study, which is supported by the Southern California Clinical and Translational Science Institute (SC CTSI) through NIH UL1TR001855. P.M.T. is funded in part by NIH grant U54 EB020403.

Footnotes

Conflicts of interest P.M.T. discloses that he received grant support from Biogen, Inc. (Boston, USA), for research unrelated to this manuscript. The other authors do not have any conflict of interest. The submitted work has not been published previously and is not being considered for publication elsewhere. Material has not been reproduced from prior publications, whether by the same or different authors. Any previously published material is explicitly quoted and referenced. Each author has read and completed all sections of the Author Disclosure Form.

Declarations

Parents gave written informed consent and children provided informed assent, and all experimental protocols were approved by the Institutional Review Board of the University of Southern California and Kaiser Permanente Southern California.

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