Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2021 Jun 1.
Published in final edited form as: Drug Alcohol Depend. 2020 Mar 20;211:107965. doi: 10.1016/j.drugalcdep.2020.107965

Prospective associations between parental substance use and youth highly processed food intake in the Fragile Families and Child Wellbeing Study1

Jenna R Cummings a,b, Ashley N Gearhardt a
PMCID: PMC7233374  NIHMSID: NIHMS1578525  PMID: 32361368

Abstract

Background:

Given growing evidence of overlap in characteristics of addictive substances and highly processed foods (e.g., ice cream), transdiagnostic approaches may be appropriate. Prior work indicates youth with parents who use addictive substances are at risk for greater substance use. The current study tested hypotheses that parental substance use behaviors would prospectively predict greater youth highly processed food intake [but not minimally processed food intake (e.g., fruit)].

Methods:

The Fragile Families and Child Wellbeing Study was a longitudinal birth cohort study of youth (N = 4,898) born in large U.S. cities in 1998–2000. The current study was an archival data analysis using parental substance use data collected at youth birth and when youth were age 3, and youth diet data collected when youth were ages 5, 9, and 15.

Results:

Over and above the influence of covariates including family income, prenatal maternal illicit drug use significantly predicted 0.34, 0.23, and 1.32 servings increase in youth sugary food/beverage intake at ages 5, 9, 15, respectively, and 0.25 servings increase in youth snack foods or chips intake at age 9. Prenatal maternal illicit drug use did not significantly predict youth fruit and vegetable intake at any age. Less consistent and weaker significant prospective associations emerged between postnatal maternal substance use and youth diet, and between paternal substance use and youth diet.

Conclusions:

Prenatal exposure to illicit drugs may present transdiagnostic risk for greater youth highly processed food intake and substance use. Future causal and mechanistic research is warranted.

Keywords: highly processed food, parental substance use, transdiagnostic

1. Introduction

Evidence is growing that highly processed foods (HPF) with refined carbohydrates and/or fat (e.g., ice cream) cause biopsychobehavioral effects similar to those of addictive substances. HPF unlike minimally processed foods (MPF; e.g., fruit) may hyper activate neural reward circuitry (e.g., ventral striatum; DiFeliceantonio et al., 2018), induce addictive-like responses (e.g., intense cravings; Schulte et al., 2015), and cause psychological withdrawal symptoms (e.g., increased negative emotions) after restriction (Falbe et al., 2018; Schulte et al., 2018). Transdiagnostic approaches to investigating diet and addictive behaviors may be appropriate (see Edge and Gold, 2011 for a review).

One transdiagnostic approach is exploring if parental substance use predicts youth diet. Youth with parents who use addictive substances are at risk for greater substance use (Biederman et al., 2000; Mays et al., 2014) and preliminary evidence suggests they may also be at risk for greater HPF intake (Cummings, Gearhardt, et al., 2019; Cummings, Lumeng, et al., 2019; Haghighi et al., 2013; Mennella et al., 2010; Riedel et al., 2014). Youth with a family history of alcoholism were more likely to prefer sweet tastes (Mennella et al., 2010). Youth prenatally exposed to cigarette use ate more fat (Haghighi et al., 2013) and, in multiple studies, had greater odds of having obesity (Riedel et al., 2014). Greater parental annual drinks consumed and nicotine dependence symptoms were associated with greater youth reward-driven eating (e.g., food responsiveness; Cummings, Lumeng, et al., 2019), and greater maternal nicotine dependence symptoms predicted steeper increases in youth reward-driven eating over 6 years (Cummings, Gearhardt, et al., 2019). Nonetheless, evidence that parental substance use selectively predicts youth HPF (but not MPF) intake would provide stronger support that HPF are implicated in addictive processes, and prior work has yet to test this directly.

Additionally, prior work has not systematically investigated whether the link between parental substance use and youth diet is specific to maternal versus paternal use, to prenatal versus postnatal use, and to use of certain addictive substances. Specifying under which conditions parental substance use predicts youth diet is practically important. If prenatal exposure to addictive substances predicts greater youth HPF intake, for example, this highlights an early and critical risk period for screening and prevention efforts.

Lastly, with the exception of one study (Cummings, Gearhardt, et al., 2019), prior work has been cross-sectional, which limits support for a causal influence of parental substance use on youth diet. To fill literature gaps, the current study tested hypotheses that greater prenatal and postnatal maternal substance use and paternal substance use would prospectively predict greater youth HPF (but not MPF) intake.

2. Methods

2.1. Design

The current study was an archival data analysis of the Fragile Families and Child Wellbeing Study (FFCWS), a longitudinal birth cohort study of youth born in large U.S. cities in 1998–2000. The primary aim of the FFCWS was to investigate conditions of unmarried parents and the youth born into these families (for details see: https://fragilefamilies.princeton.edu/). Data were collected on mothers, fathers, and youth at birth and when youth were ages 1, 3, 5, 9, and 15. The current study aims, hypotheses, and analysis plans were preregistered on the Open Science Framework (https://osf.io/zg6am).

2.2. Participants

The FFCWS (N = 4,898) recruited participants through a multi-staged, clustered sampling procedure and oversampled births to unmarried parents (Reichman et al., 2001). The current study used all available data for each analysis performed (see Tables 1–2 for ns in each analysis) with sample sizes similar to prior work using these data (Osborne and Berger, 2009). See Supplementary Material (Table S1) for demographic information on participants2.

Table 1.

Prospective Associations Between Maternal Substance Use (Prenatal and Postnatal) and Youth Diet.

Youth Age 5 (n = 1699) Youth Age 9 (n = 2302) Youth Age 15 (n = 816)
R2 B(SE) p 95% CI R2 B(SE) p 95% CI R2 B(SE) p 95% CI
Youth Sugary Food/Beverage Intake .04 .06 .05
 Covariates
  Youth Age 0.004(0.01) .661 [−0.01, 0.02] −0.01(0.004) .008 [−0.02, −0.003] −0.01(0.01) .393 [−0.04, 0.01]
  Youth Biological Sex 0.07(0.05) .178 [−0.03, 0.16] 0.02(0.04) .674 [−0.05, 0.08] −0.16(0.14) .244 [−0.43, 0.11]
  Youth Race/Ethnicity 0.22(0.05) <.001 [0.12, 0.32] 0.25(0.04) <.001 [0.17, 0.32] 0.33(0.15) .026 [0.04, 0.62]
  Family Income 0.20(0.05) <.001 [0.10, 0.31] 0.22(0.04) <.001 [0.14, 0.29] 0.37(0.15) .012 [0.08, 0.66]
  Maternal Body Mass Index 0.001(0.003) .744 [−0.01, 0.01] −0.003(0.002) .119 [−0.01, 0.001] 0.02(0.01) .058 [−0.001, 0.04]
 Maternal Substance Use
  Prenatal Alcohol 0.04(0.09) .671 [−0.13, 0.21] 0.05(0.06) .455 [−0.08, 0.17] 0.03(0.24) .914 [−0.44, 0.49]
  Prenatal Cigarette −0.01(0.08) .871 [−0.17, 0.14] 0.003(0.06) .962 [−0.11, 0.12] 0.10(0.24) .679 [−0.37, 0.56]
  Prenatal Illicit Drug 0.34(0.13) .009 [0.08, 0.59] 0.23(0.10) .017 [0.04, 0.42] 1.32(0.43) .002 [0.47, 2.16]
  Postnatal Alcohol 0.03(0.04) .414 [−0.04, 0.10] −0.02(0.03) .464 [−0.07, 0.03] −0.05(0.11) .649 [−0.25, 0.16]
  Postnatal Cigarette 0.14(0.06) .033 [0.01, 0.26] 0.07(0.05) .144 [−0.02, 0.16] 0.10(0.19) .592 [−0.27, 0.48]
  Postnatal Illicit Drug −0.06(0.10) .574 [−0.26, 0.14] 0.01(0.08) .866 [−0.14, 0.16] −0.21(0.29) .470 [−0.78, 0.36]
Youth Snack Foods or Chips Intake .05 .07
 Covariates
  Youth Age −0.01(0.01) .233 [−0.03, 0.01] −0.01(0.01) .094 [−0.02, 0.001]
  Youth Biological Sex −0.02(0.06) .770 [−0.13, 0.09] 0.01(0.04) .845 [−0.08, 0.09]
  Youth Race/Ethnicity 0.2(0.06) <.001 [0.21, 0.44] 0.37(0.04) <.001 [0.29, 0.46]
  Family Income 0.24(0.06) <.001 [0.12, 0.35] 0.28(0.05) <.001 [0.19, 0.37]
  Maternal Body Mass Index −0.002(0.004) .649 [−0.01, 0.01] −0.01(0.003) .026 [−0.01, −0.001]
 Maternal Substance Use
  Prenatal Alcohol −0.05(0.10) .639 [−0.24, 0.15] 0.02(0.08) .825 [−0.13, 0.17]
  Prenatal Cigarette −0.09(0.09) .337 [−0.27, 0.09] 0.02(0.06) .691 [−0.09, 0.14]
  Prenatal Illicit Drug 0.12(0.15) .437 [−0.18, 0.41] 0.25(0.12) .035 [0.02, 0.49]
  Postnatal Alcohol 0.02(0.04) .658 [−0.06, 0.10] 0.03(0.03) .392 [−0.03, 0.09]
  Postnatal Cigarette 0.24(0.07) .001 [0.10, 0.39] 0.12(0.06) .037 [0.01, 0.23]
  Postnatal Illicit Drug −0.12(0.12) .326 [−0.34, 0.11] −0.002(0.09) .979 [−0.19, 0.18]
Youth Fruits or Vegetables Intake .04 .04
 Covariates
  Youth Age −0.01(0.01) .501 [−0.02, 0.01] −0.01(0.004) .266 [−0.01, 0.004]
  Youth Biological Sex 0.11(0.05) .028 [0.01, 0.22] 0.12(0.04) .002 [0.04, 0.20]
  Youth Race/Ethnicity 0.34(0.05) <.001 [0.23, 0.44] 0.24(0.04) <.001 [0.16, 0.32]
  Family Income 0.12(0.06) .031 [0.01, 0.23] 0.15(0.04) <.001 [0.06, 0.23]
  Maternal Body Mass Index 0.002(0.003) .593 [−0.01, 0.01] 0.01(0.002) .064 [0.001, 0.01]
 Maternal Substance Use
  Prenatal Alcohol −0.09(0.09) .322 [−0.27, 0.09] 0.03(0.07) .718 [−0.11, 0.16]
  Prenatal Cigarette −0.10(0.08) .249 [−0.26, 0.07] −0.04(0.07) .567 [−0.17, 0.09]
  Prenatal Illicit Drug −0.03(0.14) .822 [−0.30, 0.24] −0.09(0.11) .419 [−0.31, 0.13]
  Postnatal Alcohol 0.03(0.04) .412 [−0.04, 0.10] 0.02(0.03) .553 [−0.04, 0.07]
  Postnatal Cigarette 0.11(0.07) .088 [−0.02, 0.25] 0.08(0.05) .137 [−0.02, 0.18]
  Postnatal Illicit Drug −0.24(0.11) .027 [−0.45, −0.03] −0.002(0.09) .985 [−0.17, 0.17]
Open in a new tab

Youth Biological Sex (0 = Male, 1 = Female), Youth Race/Ethnicity (0 = Non-Black, 1 = Black), and Family Income (0 = No Food Stamps, 1 = Food Stamps) were dummy coded for analyses.

Table 2.

Prospective Associations Between Paternal Substance Use and Youth Diet.

Youth Age 5 (n = 1324) Youth Age 9 (n = 1702) Youth Age 15 (n = 614)
R2 B(SE) p 95% CI R2 B(SE) p 95% CI R2 B(SE) p 95% CI
Youth Sugary Food/Beverage Intake .04 .05 .05
 Covariates
  Youth Age 0.001(0.01) .957 [−0.02, 0.02] −0.004(0.01) .421 [−0.01, 0.01] 0.01(0.02) .497 [−0.02, 0.05]
  Youth Biological Sex 0.08(0.05) .131 [−0.02, 0.19] 0.02(0.04) .571 [−0.06, 0.10] −0.11(0.16) .510 [−0.42, 0.21]
  Youth Race/Ethnicity 0.23(0.06) <.001 [0.12, 0.34] 0.24(0.04) <.001 [0.15, 0.32] 0.57(0.18) .001 [0.23, 0.91]
  Family Income 0.21(0.06) <.001 [0.10, 0.32] 0.22(0.04) <.001 [0.14, 0.30] 0.43(0.17) .011 [0.10, 0.75]
  Maternal Body Mass Index 0.01(0.004) .220 [−0.003, 0.01] −0.001(0.002) .668 [−0.01, 0.004] 0.01(0.01) .285 [−0.01, 0.03]
 Paternal Substance Use
  Alcohol 0.003(0.03) .920 [−0.06, 0.06] −0.02(0.02) .472 [−0.06, 0.03] 0.15(0.09) .095 [−0.03, 0.33]
  Cigarette −0.14(0.14) .337 [−0.42, 0.14] 0.16(0.11) .140 [−0.05, 0.37] −0.38(0.47) .420 [−1.31, 0.55]
  Illicit Drug 0.02(0.08) .835 [−0.15, 0.18] 0.03(0.06) .646 [−0.09, 0.15] 0.19(0.25) .433 [−0.29, 0.68]
Youth Snack Foods or Chips Intake .04 .06
 Covariates
  Youth Age −0.01(0.01) .208 [−0.04, 0.01] −0.004(0.01) .536 [−0.02, 0.01]
  Youth Biological Sex −0.02(0.06) .718 [−0.15, 0.10] −0.01(0.05) .821 [−0.11, 0.09]
  Youth Race/Ethnicity 0.32(0.07) <.001 [0.19, 0.45] 0.39(0.05) <.001 [0.29, 0.49]
  Family Income 0.30(0.07) <.001 [0.17, 0.43] 0.26(0.05) <.001 [0.16, 0.36]
  Maternal Body Mass Index −0.001(0.004) .847 [−0.01, 0.01] −0.004(0.003) .168 [−0.01, 0.002]
 Paternal Substance Use
  Alcohol 0.001(0.04) .972 [−0.07, 0.07] 0.05(0.03) .049 [0.001, 0.11]
  Cigarette 0.04(0.17) .825 [−0.29, 0.36] 0.15(0.13) .270 [−0.12, 0.41]
  Illicit Drug −0.06(0.10) .553 [−0.25, 0.13] −0.07(0.08) .354 [−0.22, 0.08]
Youth Fruits or Vegetables Intake
 Covariates
  Youth Age −0.01(0.01) .267 [−0.03, 0.01] −0.01(0.01) .170 [−0.02, 0.003]
  Youth Biological Sex 0.14(0.06) .016 [0.03, 0.25] 0.11(0.05) .020 [0.02, 0.20]
  Youth Race/Ethnicity 0.32(0.06) <.001 [0.20, 0.44] 0.22(0.05) <.001 [0.13, 0.32]
  Family Income 0.16(0.06) .009 [0.04, 0.28] 0.12(0.05) .016 [0.02, 0.21]
  Maternal Body Mass Index 0.003(0.004) .499 [−0.01, 0.01] 0.01(0.003) .068 [0.001, 0.01]
 Paternal Substance Use
  Alcohol 0.02(0.03) .618 [−0.05, 0.08] 0.05(0.03) .072 [−0.004, 0.10]
  Cigarette −0.35(0.15) .020 [−0.65, −0.05] 0.24(0.13) .053 [−0.003, 0.49]
  Illicit Drug −0.07(0.09) .441 [−0.24, 0.11] −0.09(0.07) .207 [−0.23, 0.05]
Open in a new tab

Youth Biological Sex (0 = Male, 1 = Female), Youth Race/Ethnicity (0 = Non-Black, 1 = Black), and Family Income (0 = No Food Stamps, 1 = Food Stamps) were dummy coded for analyses.

2.3. Measures

2.3.1. Outcomes.

2.3.1.1. Youth diet.

When youth were ages 5 and 9, primary caregivers answered the question, “On a typical day, about how many servings of the following foods does your child eat?” for the following: “soda (e.g., Coke, Pepsi),” “candy or sweets,” “fresh fruit or vegetables,” “frozen or canned vegetables,” and “snack foods or chips.” Response options ranged from 0 (“None”) to 5 (“5 or more servings”). For the current study, responses for soda and candy or sweets intake (rs = 0.34–0.43, ps < .001) were averaged to estimate sugary food/beverage intake and responses for fresh fruit or vegetables and frozen or canned vegetables (rs = 0.35–0.41, ps < .001) were averaged to estimate fruit and vegetables intake. When youth were age 15, youth answered the question, “In a typical day, how many regular, non-diet sweetened drinks do you have? Include regular soda, juice drinks, sweetened tea or coffee, energy drinks, flavored water, or other sweetened drinks.” Responses were freely generated and ranged from 0 to 20 drinks. For the current study, responses were used to estimate sugary/food beverage intake at age 15 (questions regarding soda, candy or sweets, fresh fruit or vegetables, frozen or canned vegetables, and snack foods or chips were not asked at age 15).

2.3.2. Predictors.

2.3.2.1. Parental substance use.

Prenatal maternal substance use was assessed at childbirth. Alcohol use was assessed with the question, “During your pregnancy, about how often did you drink alcoholic beverages?” Response options ranged from 1 (“Nearly every day”) to 5 (“Never”) and were reverse scored. Cigarette use was assessed with the question, “During your pregnancy, how many cigarettes did you smoke?” Response options ranged from 1 (“2 or more packs a day”) to 4 (“None”) and were reverse scored. Illicit drug use was assessed with the question “During your pregnancy, about how often did you use drugs such as marijuana, crack cocaine, or heroin?” Response options ranged from 1 (“Nearly every day”) to 5 (“Never”) and were reverse scored.

Postnatal maternal and paternal substance use were assessed when youth were age 3, with the exception of postnatal maternal cigarette use being assessed when youth were age 5. Alcohol use was assessed with the question, “The next questions are about how frequently you drink alcoholic beverages. By a ‘drink’ we mean either a bottle of beer, a wine cooler, a glass of wine, a shot of liquor, or a mixed drink. With these definitions in mind, what is the largest number of drinks you had in any single day during the past 12 months—none, between one and three, four to ten, eleven to twenty, or more than twenty drinks in a single day?” Response options ranged from 0 (“None”) to 4 (“More than 20”). Cigarette use was assessed with the question, “How many packs per day did you usually smoke?” Response options ranged from 1 (“Half a pack or less”) to 6 (“None”). Illicit drug use was assessed with the question, “The next questions are about your use of drugs on your own. By ‘on your own,’ we mean either without a doctor’s prescription, in larger amounts than prescribed, or for a longer period than prescribed. In the past 12 months, how often did you use sedatives, tranquilizers, amphetamines, analgesics, inhalant, marijuana, cocaine, LSD or other hallucinogens, and/or heroin on your own?” Response options ranged from 1 (“Every day or almost every day”) to 6 (“Never”) and were reverse scored.

2.3.3. Covariates.

Youth birthdate and biological sex were recorded at birth. Youth reported race/ethnicity at age 15. Primary caregivers answered the dichotomous (Yes/no) question, “Have you received income from food stamps?” when youth were ages 5 and 9. This was chosen to index family income because of its relevance to youth diet. Study staff measured maternal height and weight when youth were ages 5 and 9. Maternal body mass index (BMI) was constructed using the standard formula (kg/m2).

2.4. Data Analysis

Data are available at https://fragilefamilies.princeton.edu/documentation. All variables were assessed for normality. Due to positive skew, all prenatal maternal substance use variables as well as postnatal maternal and paternal illicit drug use variables were dichotomized: 0 (“No use”) or 1 (“Use”). Also, postnatal maternal and paternal cigarette use variables were recoded to have one of three response categories: 0 (“None”), 1 (“1 pack or less per day”), or 2 (“Greater than 1 pack per day”). Multiple regressions were used to predict youth sugary food/beverage intake at ages 5, 9, and 15, and snack foods or chips as well as fruits and vegetables intake at ages 5 and 9, from parental substance use variables over and above covariates. Maternal and paternal substance use variables were tested separately. Analyses were conducted in SPSS Version 26 (IBM Corp.) using the pairwise deletion approach for missing data. Significance was set at p < .05.

3. Results

See Supplementary Material (Table S2) for descriptives of outcome and predictor variables3.

3.1. Youth HPF Intake

Over and above the influence of covariates, prenatal maternal illicit drug use significantly predicted 0.34, 0.23, and 1.32 servings increase in youth sugary food/beverage intake at ages 5, 9, and 15, respectively, and 0.25 servings increase in youth snack foods or chips intake at age 9. A one-unit increase in postnatal maternal cigarette use significantly predicted 0.14 servings increase in youth sugary food/beverage intake at age 5, and 0.24 and 0.12 servings increase in youth snack foods or chips intake at ages 5 and 9, respectively. A one-unit increase in paternal alcohol use significantly predicted 0.05 servings increase in snack foods or chips at age 9. No other significant associations emerged.

3.2. Youth MPF Intake

Over and above the influence of covariates, postnatal maternal illicit drug use significantly predicted 0.24 servings decrease in fruit or vegetable intake at age 5. A one-unit increase in paternal cigarette use significantly predicted 0.35 servings decrease in fruit or vegetable intake at age 5. No other significant associations emerged.

4. Discussion

The current study found some evidence supporting hypotheses that parental substance use behaviors prospectively predict greater youth HPF (but not MPF) intake. Specifically, prenatal maternal illicit drug use significantly and selectively predicted greater youth HPF intake at ages 5, 9, and 15. Prior work similarly found that youth prenatally exposed to cigarette use ate more fat (Haghighi et al., 2013) but did not investigate prenatal exposure to other addictive substances. Adults who use illicit drugs often drink alcohol and smoke cigarettes too (Kao et al., 2000; Richter et al., 2002), so statistically accounting for use of multiple addictive substances may explain the discrepancy across studies. Indeed, when independently tested in post-hoc analysis, prenatal maternal alcohol and cigarette use significantly predicted greater youth HPF intake at age 5 (see Supplementary Material)4.

The current study findings are consistent with rodent models indicating prenatal exposure to addictive substances including alcohol, cocaine, morphine, marijuana, and nicotine alters offspring development of neural reward circuitry and behaviors mediated by this circuitry (e.g., drug-seeking behavior; Malanga and Kosofsky, 2003). Effects in these models have been non-specific (e.g., prenatal exposure to cocaine increased offspring alcohol-seeking behavior; Malanga and Kosofsky, 2003). Thus, prenatal exposure to addictive substances may increase intake of HPF through similar reward-based mechanisms (Gagin et al., 1996) and/or neural adaptations (Haghighi et al., 2013).

However, postnatal maternal cigarette use and paternal alcohol use significantly predicted greater youth HPF intake and less youth MPF intake (albeit weakly). This suggests these parental substance use behaviors may also play a small role in elevating risk for youth HPF intake and in elevating risk for youth poor diet more generally. Independent of prenatal exposure, youth may genetically inherit vulnerability towards poor diet (Kohnke, 2008). Also, family environmental factors (e.g., stressful, less parental monitoring) associated with parental substance use may contribute to youth poor diet (Chassin et al., 1993).

Results should be interpreted in light of study strengths and limitations. The sample was large and diverse, but parental substance use rates were low. Future work should be replicated in more clinically severe samples and identify whether there exists a dose-response association between parental substance use and youth diet. Moreover, youth diet was measured through parent- and youth-report, and certain items (e.g., “snack foods or chips”) may have been too vaguely described. Parent-reports of youth diet have advantage because youth diet can be difficult to observe in the laboratory and because parents inform on youth diet to clinicians (Wardle et al., 2001). Consistency across findings for parent- and youth-reports also bolsters confidence in results, but future work with objective youth diet measures would strengthen conclusions. Also, although the longitudinal design strengthens the case for causality, readers should recognize the potential for confounding and collider biases to affect findings. Even with the covariate-control approach, these results should not be interpreted as causal findings.

In sum, the current study provides evidence that prenatal exposure to illicit drugs predicts greater youth HPF intake up to 15 years later. Future research should use causal designs and investigate potential reward-based and neural mechanisms. Transdiagnostic approaches to investigating addictive behaviors and HPF intake may yield novel theoretical and practical benefits. For instance, identifying youth at risk for both greater HPF intake and substance use early in development could lead to more efficient prevention efforts (Collins and Varmus, 2015; Prochaska and Prochaska, 2011).

Supplementary Material

1

Highlights.

  • Prenatal exposure to illicit drugs predicted greater youth sugary food/drink intake

  • Prenatal exposure to illicit drugs did not predict youth fruit and vegetable intake

  • Consistent findings were observed when youth were ages 5, 9, and 15

5. Acknowledgments

The current study was supported by a Small Research Grant Award from the Center for Human Growth and Development at the University of Michigan, Ann Arbor. Jenna R. Cummings was supported by Award Number T32HD079350 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of funding agencies including the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Footnotes

Publisher's Disclaimer: This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflict of interest

No conflict declared

1

Supplementary material can be found by accessing the online version of this paper.

2

Supplementary material can be found by accessing the online version of this paper.

3

Supplementary material can be found by accessing the online version of this paper.

4

Supplementary material can be found by accessing the online version of this paper.

6. References

  1. Biederman J, Faraone SC, Monuteaux MC, and Feighner JA 2000. Patterns of alcohol and drug use in adolescents can be predicted by parental substance use disorders. Pediatrics. 106, 792–797. [DOI] [PubMed] [Google Scholar]
  2. Chassin L, Pillow DR, Curran PJ, Molina BSG, and Barrera M 1993. Relation of parental alcoholism to early adolescent substance use: A test of three mediating mechanisms. J. Abnorm. Psychol 102, 3–19. [DOI] [PubMed] [Google Scholar]
  3. Collins FS, and Varmus H 2015. A new initiative on precision medicine. N. Engl. J. Med 372, 794–795. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Cummings JR, Gearhardt AN, Miller AL, Hyde LW, and Lumeng JC 2019. Maternal nicotine dependence is associated with longitudinal increases in child obesogenic eating behaviors. Pediatric Obesity. 14, e12541. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Cummings JR, Lumeng JC, Miller AL, Hyde LW, Siada R, and Gearhardt AN 2019. Parental substance use and child reward-driven eating behaviors. Appetite. 144, 104486. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. DiFeliceantonio AG, Coppin G, Rigoux L, Thanarajah SE, Dagher A, Tittgemeyer M, and Small DM 2018. Supra-additive effects of combining fat and carbohydrate on food reward. Cell Metabolism. 28, 33–44. [DOI] [PubMed] [Google Scholar]
  7. Edge PJ, and Gold MS 2011. Drug withdrawal and hyperphagia: lessons from tobacco and other drugs. Current Pharmaceutical Design. 17, 1173–1179. [DOI] [PubMed] [Google Scholar]
  8. Falbe J, Thompson HR, Patel A, and Madsen KA 2018. Potentially addictive properties of sugar-sweetened beverages among adolescents. Appetite. 133, 130–137. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Gagin R, Cohen E, and Shavit Y 1996. Prenatal exposure to morphine alters analgesic responses and preference for sweet solutions in adult rats. Pharmacol. Biochem. Behav 55, 629–634. [DOI] [PubMed] [Google Scholar]
  10. Haghighi A, Schwartz DH, Abrahamowicz M, Leonard G, Perron M, Richer L, Veillette S, Gaudet D, Paus T, and Pausova Z 2013. Prenatal exposure to maternal cigarette smoking, amygdala volume, and fat intake in adolescence. JAMA Psychiatry. 70, 98–105. [DOI] [PubMed] [Google Scholar]
  11. Kao TC, Schneider SJ, and Hoffman KJ 2000. Co-occurrences of alcohol, smokeless tobacco, cigarette, and drug use by lower ranking military personnel. Addict. Behav 25, 253–263. [DOI] [PubMed] [Google Scholar]
  12. Kohnke MD 2008. Approach to the genetics of alcoholism: a review based on pathophysiology. Biochem. Pharmacol 75, 160–177. [DOI] [PubMed] [Google Scholar]
  13. Malanga CJ, and Kosofsky BE 2003. Does drug abuse beget drug abuse? Behavioral analysis of addiction liability in animal models of prenatal drug exposure. Developmental Brain Research. 147, 47–57. [DOI] [PubMed] [Google Scholar]
  14. Mays D, Gilman SE, Rende R, Luta G, Tercyak KP, and Niaura R 2014. Parental smoking exposure and adolescent smoking trajectories. Pediatrics. 133, 983–991. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Mennella JA, Pepino MY, Lehmann-Castor SM, and Yourshaw LM 2010. Sweet preferences and analgesia during childhood: effects of family history of alcoholism and depression. Addiction. 105, 666–675. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Osborne C, and Berger LM 2009. Parental substance abuse and child well-being. Journal of Family Issues. 30 341–370. [Google Scholar]
  17. Prochaska JJ, and Prochaska JO 2011. A review of Multiple Health Behavior Change interventions for primary prevention. Am. J. Lifestyle Med 5, 208–221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Reichman NE, Teitler JO, Garfinkel I, and McLanahan SS 2001. Fragile Families: sample and design. Children and Youth Services Review. 23, 303–326. [Google Scholar]
  19. Richter KP, Ahluwalia HK, Mosier MC, Nazir N, and Ahluwalia JS 2002. A population-based study of cigarette smoking among illicit drug users in the United States. Addiction. 97, 861–869. [DOI] [PubMed] [Google Scholar]
  20. Riedel C, Schonberger K, Yang S, Koshy G, Chen Y, Gopinath B, Ziebarth S, and von Kries R 2014. Parental smoking and childhood obesity: higher effect estimates for maternal smoking in pregnancy compared with paternal smoking—a meta-analysis. Int. J. Epidemiol 43, 1593–1606. [DOI] [PubMed] [Google Scholar]
  21. Schulte EM, Avena NM, and Gearhardt AN 2015. Which foods may be addictive? The roles of processing, fat content, and glycemic load. PLoS One. 10, e0117959. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Schulte EM, Smeal JK, Lewis J, and Gearhardt AN 2018. Development of the Highly Processed Food Withdrawal Scale. Appetite. 131, 148–154. [DOI] [PubMed] [Google Scholar]
  23. Wardle J, Guthrie CA, Sanderson S, and Rapoport L 2001. Development of the Children’s Eating Behaviour Questionnaire. J. Child Psychol. Psychiatry 42, 963–970. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1578525-supplement-1.docx (92.9KB, docx)

RESOURCES