Associations of appetitive behaviors in 7 year old children with their cardiometabolic health at 10 years of age

Journal Pre-proof Associations of appetitive behaviors in 7 year old children with their cardiometabolic health at 10 years of age Sarah Warkentin, Ana C. Santos, Andreia Oliveira PII:

S0939-4753(20)30023-5

DOI:

https://doi.org/10.1016/j.numecd.2020.01.007

Reference:

NUMECD 2212

To appear in:

Nutrition, Metabolism and Cardiovascular Diseases

Received Date: 30 September 2019 Revised Date:

6 January 2020

Accepted Date: 7 January 2020

Please cite this article as: Warkentin S, Santos AC, Oliveira A, Associations of appetitive behaviors in 7 year old children with their cardiometabolic health at 10 years of age, Nutrition, Metabolism and Cardiovascular Diseases, https://doi.org/10.1016/j.numecd.2020.01.007. 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. © 2020 Published by Elsevier B.V. on behalf of The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University.

Associations of appetitive behaviors in 7 year old children with their cardiometabolic health at 10 years of age Sarah Warkentina; Ana C. Santos a,b; Andreia Oliveiraa,b

a

EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal; Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto, Porto, Portugal.

b

Corresponding Author Sarah Warkentin, EPIUnit – Instituto de Saúde Pública, Universidade do Porto [Institute of Public Health of the University of Porto] Address: Rua das Taipas, 135-139, 4050-600 - Porto, Portugal. Tel.: +351 222 061 820. Email: [email protected]

Declarations of interest None

Word counts: Abstract: 245 Text: 3893 Number of references: 52 Number of Figures: 2 Number of Tables: 4

List of abbreviations CVD: Cardiovascular diseases BMI: Body Mass Index CEBQ: Children`s Eating Behavior Questionnaire CEBQ-EF: Enjoyment of food CEBQ-FR: Food responsiveness CEBQ-EOE: Emotional overeating CEBQ-DD: Desire to drink CEBQ-SR: Satiety responsiveness CEBQ-SE: Slowness in eating CEBQ-FF: Food fussiness CEBQ-EUE: Emotional undereating TG: Triglycerides HDL-c: High-density lipoprotein-cholesterol WC: Waist circumference SBP: Systolic blood pressure HOMA-IR: Homeostatic model assessment-insulin resistance M: Means SD: Standard deviation Md: Median IQR: Interquartile range

1 2

Introduction Cardiovascular diseases (CVDs) are end-points of gradual progression of atherosclerosis and this

3

process begins early in life, with clinical manifestations occurring decades later [1]. It is nowadays

4

believed that, by promoting optimal cardiovascular health in adolescents, long-term beneficial effects can

5

be seen in adulthood [2]. Cardiovascular health, as proposed by the American Heart Association (AHA),

6

includes health behaviors (such as being physically active, eating a healthy diet, being in the normal Body

7

Mass Index (BMI) range and not smoking) and risk factors (i.e. having normal blood pressure, glucose

8

and cholesterol levels) [3]. In light of this, weight alone does not seem to be a good measure of CVD risk;

9

children with normal weight but already showing metabolical alterations may not be correctly targeted

10

and still have a higher risk of future CVDs [4]. In addition, cohort studies worldwide have described

11

strong and significant tracking, between childhood and adulthood, of different cardiovascular risk factors,

12

such as having high serum lipids, BMI, and blood pressure [5,6], besides the tracking of health behaviors,

13

such as being physically active [7] and eating a healthy diet [8]. Thus, identifying those children at greater

14

risk, by using a cluster of preclinical CVD markers besides actual weight, may help in the development of

15

targeted interventions that could decrease morbidity and mortality and also be cost-effective [9,10].

16

The AHA and the World Health Organization (WHO) link chronic diseases, which are the leading

17

cause of death and disabilities worldwide, to social determinants and behavioral risk factors [3,10], such

18

as eating habits. Aiming to measure eating behaviors, a number of psychometric tools have been

19

developed in the past three decades. The Children`s Eating Behavior Questionnaire (CEBQ), designed to

20

assess a range of appetitive traits [11], previously described as important predictors of child eating and

21

weight status [12–17]. Positive responses to food, or ´food approach` behaviors, such as showing

22

enjoyment in food intake or eating in response to external food cues, are hypothesized to contribute to

23

energy intake [12] and weight gain [13–17] among children. ‘Food avoidant’ behaviors, such as child`s

24

fussy eating, child`s sensitivity to feelings of fullness and slowness in eating, are likely to reduce food

25

intake [18]. Since nowadays most children dwell within obesogenic environments with great availability

26

of various energy-dense, palatable, cheap, and nutrient-poor foods [19,20], these appetitive traits are

27

likely to enhance the risk of future CVDs.

28

Studies of obesity-related behaviors have been published in the past years, linking these appetitive

29

behaviors to weight status and body composition[14–17,21–25], but there is a lack of studies that

30

associate these appetitive traits to important cardiometabolic risk factors. The aim of this study was to

31

investigate whether appetitive behaviors among 7-year-olds are associated with their cardiometabolic

32

health three years later. We predicted that CEBQ subdomains measuring ‘food approach` behaviors, i.e.

33

Enjoyment of food, Food responsiveness, Emotional overeating and Desire to drink would be positively

34

associated with cardiometabolic risk, while the ´food avoidant` subdomains Satiety responsiveness,

35

Slowness in eating, Food fussiness and Emotional undereating would be higher scored in those children

36

with lower risk.

37 38

Methods

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Study population

1

40

This study included singleton children from Generation XXI, an ongoing prospective population-

41

based birth cohort from northern Portugal, described elsewhere [26,27]. Children were recruited at birth

42

in 2005/2006 at public Maternity hospitals of Porto. Of all eligible mothers, 91% agreed to participate (8

43

495 mothers and 8 647 children at baseline).

44

All families were invited for the follow-ups at ages 4 years (y) (2009-2011), 7y (2012-2014) and 10y

45

(2015-2017). The present study included data from the baseline, and the 7y and 10y follow-ups. From the

46

6 115 children that attended at both 7y and 10y follow-ups, and after exclusion of children without data of

47

interest the present investigation included 2 951 children (please see the inclusion and exclusion

48

flowchart in Figure 1).

49 50 51

Measures Since our sample is composed of children, we examined variables that are associated with known

52

cardiometabolic risk [10] rather than hard end-points of cardiovascular events. These variables included

53

triglycerides (TG), high-density lipoprotein-cholesterol (HDL-c), waist circumference (WC), systolic

54

blood pressure (SBP) and homeostatic model assessment-insulin resistance (HOMA-IR).

55

Venous blood samples were collected in a fasting condition. TG and HDL-c were measured using an

56

enzymatic colorimetric assay and HOMA-IR was computed as follows: glucose (mg/dL) x insulin

57

(µU/mL)/405 [28]. Blood pressure was measured from the brachial artery of the right arm, with a random

58

zero sphygmomanometer (Medel® ELITE, S. Polo de Torrile, Italy), being recorded as the mean of two

59

measurements, with a 5-minute interval. Height, weight and WC were measured according to standard

60

procedures [29]. BMI was computed for each child, and age- and sex- specific BMI reference z-scores

61

[30] were calculated. The measurement of cardiometabolic parameters and anthropometric data were

62

collected in the 7y follow-up.

63

Cardiometabolic risk z-scores, adjusted for child´s sex and age, were created. Z-scores of TG, HDL-

64

c, WC and HOMA-IR were calculated as follows: Z= ([value of continuous variable - mean]/SD). For

65

blood pressure, in order to avoid misclassification of children who are very tall or short, z-scores were

66

adjusted for age, sex and height, according to the American Academy of Pediatrics [31]. Next, we

67

categorized cardiometabolic risk, in which the higher risk group was composed by individuals in the

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upper quartile of TG, HOMA-IR, WC and SBP z-scores and in the first quartile of HDL-c z-score [32]

69

and the remaining children were categorized as having “lower risk”. We chose to estimate adverse

70

glucose homeostasis using the HOMA-IR score rather that fasting glucose, as defined by the International

71

Diabetes Federation (IDF) definition [33], since HOMA-IR is a frequently used parameter in clinical and

72

epidemiological research and provides a reliable estimate of insulin resistence among chidren and

73

adolescents [34]. The inclusion of risk factors other than the traditional IDF factors in the calculation of

74

metabolic risk may strengthen the ability of tracking children in higher risk of cardiovascular diseases, by

75

improving sensitivity and specificity [32].

76

Eating behaviors were measured at 7y using a translated and previously validated version of the

77

original CEBQ to Portuguese school-aged children [13]. Parents or main caregivers were asked to

78

respond to the questionnaire, which is divided into eight subdomains: Enjoyment of food (CEBQ-EF, e.g.

79

My child enjoys eating), Food responsiveness (CEBQ-FR, e.g. My child's always asking for food), Desire

2

80

to drink (CEBQ-DD, e.g. If given the chance, my child would drink continuously throughout the day),

81

Emotional overeating (CEBQ-EOE, e.g. My child eats more when worried), Food fussiness (CEBQ-FF,

82

e.g. My child refuses new foods at first), Satiety responsiveness (CEBQ-SR, e.g. My child gets full up

83

easily), Slowness in eating (CEBQ-SE, e.g. My child takes more than 30 minutes to finish a meal) and

84

Emotional undereating (CEBQ-EUE, e.g. My child eats less when s/he is angry). The response format is a

85

5-point Likert scale, ranging from ´Never` to ´Always`. In accordance with the original scale, five of the

86

items were reverse-scored due to opposite phrasing. In questionnaires that had <50% of missing data

87

items (~3%), data were recovered by replacement for the average of the remaining questions within each

88

subdomain of the participant. Albuquerque and colleagues (2017) showed a good internal consistency of

89

the validated CEBQ (Cronbach´s α ranged from 0.74 to 0.85 [13]).

90

Child`s dietary intake was evaluated through a food frequency questionnaire that determined

91

frequency of intake at 7y. It was previously validated in a sub-sample from Generation XXI, by

92

comparing data with 3-day food records and nutrient biomarkers [35]. Parents or the main caregiver were

93

asked how many times, on average, the child had consumed that specific food during the previous six

94

months. Response options ranged from ´4 times or more per day` to ´Never`, and answers were converted

95

into daily frequency. The mean daily intake of fruit and vegetables was calculated by including data from

96

four food groups: fruits, vegetable soups, cooked and raw vegetables. Vegetable soups were asked

97

separately to vegetables, as it is a common preparation in Portugal. This variable was then dichotomized

98

into greater or equal to 5 a day vs. lower consumption of fruit and vegetables per day, according to the

99

WHO recommendations.

100

Physical activity at 7y was accessed by the questions: ´How long does the child spend, on average

101

per day, in active leisure-time activities (e.g. running, playing ball, cycling)? during the week (Monday to

102

Friday)` and ´How long does the child spend, on average per day, in active leisure-time activities (e.g.

103

running, playing ball, cycling)? during the weekend (Saturday to Sunday)`, and answers were given in

104

average time, per week/weekend, in hours. Time of physical activity was summed and a continuous

105

variable with the daily average time spend in physical activities during the whole week, in hours, was

106

created.

107

Maternal characteristics at baseline were assessed by interviews with trained professionals, using

108

standardized questionnaires. The current study used maternal educational level, recorded by completed

109

years of schooling, and BMI before pregnancy, calculated using weight and height (Kg/m2), as control

110

variables in the analyses.

111 112 113

Statistical Analysis Continuous variables were described as means (M) and standard deviations (SD) or as median (Md)

114

and interquartile range (IQR) and binary variables as counts and percentages. For continuous variables,

115

independent sample t-tests or Mann-Whitney U-tests were performed to analyze differences on boys and

116

girls; for categorical variables, chi-square tests were performed.

117

In order to investigate the associations between appetitive behaviors and cardiometabolic parameters,

118

linear regressions were conducted with each of the five parameters that composed the cardiometabolic

119

risk, stratified by child´s sex. A set of variables were entered in the model (maternal BMI and educational

3

120

level, mean daily time of physical activity, and mean daily consumption of fruit and vegetables) to

121

investigate if associations remained unchanged. A second adjustment was performed, with the inclusion

122

of the first set of confounders plus the remaining cardiometabolic parameters (e.g. HDL-c model adjusted

123

for Model 1 plus TG, SBP, HOMA-IR and WC z-scores). To estimate the association of each CEBQ

124

subdomain at 7y with the cardiometabolic risk group at 10y, logistic regressions were performed. The

125

same covariates included in the linear regressions were used for adjustment in the logistic regression

126

models and these were chosen based on previous literature and tested in the current sample. In addition to

127

the first adjustment, we further adjusted the models for child’s BMI at 7y of age.

128

Statistical analyses were carried out using SPSS (Statistical Package for Social Sciences) v. 25.0

129

(SPSS Inc., Chicago, IL). Statistical significance and 95% Confidence intervals were described using

130

Bonferroni`s correction.

131 132 133 134

Ethics Generation XXI was approved by the University of Porto Medical School/ S. João Hospital Centre Ethics Committee and by the Portuguese Data Protection Authority.

135

All the phases of the study complied with the Ethical Principles for Medical Research Involving

136

Human Subjects expressed in the Declaration of Helsinki. Accordingly, a written informed consent from

137

the parents (or legal substitute) and an oral consent from the children were obtained in each evaluation.

138 139 140

Results Mothers had, on average, 12 years of schooling, with a median age at baseline of 30 years. Girls

141

scored, on average, higher in CEBQ-SE compared to boys. At the age of 10, one in every four children

142

was classified as overweight (BMIz +1 to +2 SD) and 18% of boys and nearly 16% of girls were obese

143

(BMIz >+2 SD). Regarding cardiometabolic parameters at 10 years of age, girls showed significantly

144

lower values of glucose (Girls M±SD: 86.30 mg/dL±6.10 vs. Boys: 87.82 mg/dL ±9.71), HDL-c (Girls

145

M±SD: 53.84 mg/dL±10.52 vs. Boys: 56.60 mg/dL±10.34) and higher values of TG (Girls Md±IQR:

146

64.00 mg/dL±34.00 vs. Boys: 56.00 mg/dL±31.00), HOMA-IR (Girls Md±IQR: 2.02±1.66 vs. Boys:

147

1.52±1.15) and WC (Girls Md±IQR: 66.50 cm±14.42 vs. Boys 64.90 cm±13.00).

148

For both sexes, five subdomains had significant differences, when comparing the group with a

149

higher cardiometabolic risk to the reference group (i.e. lower risk). The ´food avoidant` measures, CEBQ-

150

SR, CEBQ-SE and CEBQ-FF were lower among children in the risk group. On the other hand, children

151

with more ´food approach` behaviors, such as CEBQ-FR and CEBQ-EF had significantly higher values in

152

the higher risk group. Additionally, girls with higher scores in Emotional overeating were also more

153

frequently classified in the higher cardiometabolic risk group (Figure 2).

154

Linear regressions were performed in order to investigate the associations of the CEBQ appetitive

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behaviors at 7y and the later individual cardiometabolic parameters z-scores in both sexes (Table 2a and

156

Table 2b). Regarding ´food avoidant` behaviors, greater responsiveness to satiety cues (CEBQ-SR)

157

showed a protective effect in cardiometabolic health, especially among girls (HDL-c: adj. model 1

158

β=0.21, 95%CI 0.10; 0.32; HOMA-IR: adj. model 1 β=-0.19, 95%CI -0.30; -0.09; WC: adj. model 1 β=-

159

0.46, 95%CI -0.55; -0.36; SBP: adj. β=-0.12, 95%CI -0.20; -0.04). The association of food avoidant

4

160

subdomains and WC was the only one that remained significantly associated in boys and girls after

161

further adjusting for the remaining cardiometabolic parameters, although it was greatly weakened.

162

Children with slower eating rate (CEBQ-SE) showed a positive association with HDL-c and a negative

163

association (i.e. a protective effect) with TG, HOMA-IR, WC and SBP z-scores, but almost all of these

164

associations (with exception to WCz) were no longer significant after further adjustment for the

165

remaining cardiometabolic parameters. Food fussiness showed a negative association with WCz, with

166

significant effects only among girls (adj. model 2 β=-0.11, 95%CI -0.19;-0.03), independently of the other

167

cardiometabolic parameters. Eating less in response to emotional stimuli was positively associated with

168

HDL-c levels only among girls, and negatively associated with WC among boys, after adjustment for the

169

remaining cardiometabolic parameters.

170

´Food approach` behaviors, such as Enjoyment of food, Food responsiveness and Emotional

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overeating, showed strong positive effects, in both sexes, in TG, HOMA-IR, WC and SBP z-scores. The

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positive associations of the ´food approach` behaviors CEBQ-EF, CEBQ-FR and CEBQ-EOE and WC

173

remained after further adjustments (i.e. adjusting for Model 1 plus the remaining cardiometabolic

174

parameters – TG, HDL-c, SBP and HOMA-IR z-scores), in both boys and girls. Desire to drink only

175

showed a significant positive association, in the first adjusted model (Model 1), with SBP among girls.

176

However, this effect was not maintained after further adjustments (Model 2).

177

The associations between appetitive behaviors at 7y and the cardiometabolic risk score at 10y are

178

described in Table 3. Overall, children with higher scores in ‘food avoidant` subdomains, namely CEBQ-

179

SR and CEBQ-SE showed lower cardiometabolic risk, whereas children with higher scores in CEBQ-EF,

180

CEBQ-FR and CEBQ- EOE showed higher odds of being in the higher cardiometabolic risk group. In

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multivariate analyses, less appetitive children (with higher scores in CEBQ-SR) and who eat slowly had

182

lower odds of high cardiometabolic risk; the associations were consistent in both sexes, with similar odds

183

in CEBQ-SR and CEBQ-SE. After initial adjustment for potential confounders, children with higher

184

Enjoyment of food, Food responsiveness, and Emotional overeating, showed the greatest cardiometabolic

185

risks; boys had between 2.50 and 3.66 times greater cardiometabolic risk, and girls had a risk greater than

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2 in all appetitive behaviors. Eating more in response to emotional stimuli was significantly associated

187

with greater cardiometabolic risk only among girls (CEBQ-EOE: OR=2.18, 95CI% 1.23; 3.87). In the

188

additional adjustment for child’s BMIz at 7 years of age, the associations did not remain statistically

189

significant (adjusted Model 2).

190 191 192

Discussion Appetitive behaviors of 7-years-old children were associated with cardiometabolic risk factors three

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years later, however these associations were influenced by child`s weight status. First, a higher metabolic

194

risk among children with greater scores in ´food approach` behaviors, such as CEBQ-EF, CEBQ-FR and

195

CEBQ-EOE was observed. Studies related these behaviors to child`s excessive body weight in developed

196

[12,13] and developing countries [23,36]. The majority used BMI as the only indicator of child`s

197

nutritional status, however this does not provide sufficient information on body composition and does not

198

solely reflect the complexity of obesity [37–39]. Visceral fat accumulation is a major contributor to

199

cardiometabolic risk [37] and the inclusion of direct measures of adiposity, such as WC and other

5

200

cardiometabolic markers, is warranted. A small number of cross-sectional studies have linked subjective

201

eating behaviors with adiposity markers. Studies among Finish [24], English [40] and Malaysian [25]

202

school-aged children showed a positive association between ´food approach` behaviors and the same

203

body fat indicator used in this study (i.e. WC) and an inverse relation with ´food avoidant` measures

204

(CEBQ-SE and CEBQ-SR). To our knowledge, this is the first study, with a longitudinal design, that

205

investigated the relationship between eating behaviors and well-known cardiometabolic risk factors years

206

later.

207

The current study shows associations of appetitive traits and cardiometabolic parameters, however,

208

most of these effects disappeared when adjusting for other cardiometabolic parameters. Notwithstanding,

209

it is important to highlight that the greatest effects of appetitive traits were related to WC, showing that

210

metabolic traits in childhood are mainly driven by adiposity. Besides using the traditional indicator of

211

BMI, other health markers, such as WC and HOMA-IR, may be used in the identification of children with

212

higher cardiometabolic risk. In the current study, WC remained as the outcome more consistently and

213

strongly related with appetite behaviors. Furthermore, a recent study that explored the accuracy of body

214

fat patterns and single measures, using the same Portuguese birth cohort study as shown here, evidenced

215

that, in children, waist-to-height ratio is a proxy of DXA-fat mass, rather than DXA-central fat [41].

216

Nonetheless, in the current study, BMI was shown to be a key parameter in the relationship between

217

appetitive traits and the cardiometabolic risk.

218

Food fussiness showed a strong protective effect in WC among girls and HOMA-IR among boys.

219

Studies among school-aged children and adolescents reported an inverse relation between fussy/picky

220

eating and BMI [42,43]. Therefore, even though Food fussiness was associated with decreased

221

cardiometabolic parameters, this does not mean that a fussier child is a healthier child, since this behavior

222

is associated with other risks, such as the consumption of a low variety of foods [44,45], disliking of

223

fruits and vegetables [18] and micronutrient deficiencies [45].

224

Greater Satiety responsiveness, Slowness in eating and Emotional undereating have been negatively

225

associated with child adiposity [21,23,25,46], which are reflected in the current results. Children who eat

226

faster are less sensitive to internal satiety cues and children who overeat depending on their emotional

227

state, tend to have higher adiposity and, consequently, a higher cardiometabolic risk. Furthermore, a

228

lower consumption of food groups, such as fruit and vegetables, bread, protein foods, snacks and total

229

energy intake was seen among preschoolers with higher scores in CEBQ-SR [12]. In adults, eating

230

quickly has been associated with an adverse lipid profile, higher blood pressure and glucose levels and

231

obesity, suggesting that a slower eating rate has an effect in satiety control [47]. Finally, hyperphagic

232

response to stress and distress increases the risk of weight gain, since stress conditions tend to favour the

233

consumption of sweet foods in the absence of hunger [48]. The greater effect of eating in response to

234

emotions in the cardiometabolic risk score was observed among girls in the current sample, showing that

235

this group is probably the most affected by this behavior.

236

A child`s interest in food consumption (CEBQ-EF), and eating in response to external food cues

237

(CEBQ-FR) [11], showed the greatest odds in the cardiometabolic risk group. This effect remained

238

significant when investigating the cardiometabolic risk score without the inclusion of the direct measure

239

of adiposity (WC), showing its effect in the increase of cardiometabolic risk, independently of child´s fat

6

240

quantity and distribution (data not shown). However, when further adjusting the logistic regression

241

models for child’s BMI at 7 years, the effects were no longer observed, suggesting that child’s BMI at 7

242

years may underpin the relationship between food approach and food avoidant behaviors at 7 years and

243

cardiometabolic risk 3 years later. We hypothesized that appetitive traits would be associated with

244

cardiometabolic health in school-age children and our findings show that the associations were in the

245

predicted direction of associations – however largely dependent on child’s weight status. As described,

246

today`s obesogenic food environment may lead to a food consumption beyond energy requirements,

247

driven by pleasure and not by physical hunger. CEBQ-EF and CEBQ-FR have been linked to a reward-

248

related approach motivation, leading to overconsumption among preschoolers [49]. Children with higher

249

scores in appetitive behaviors such as CEBQ-FR showed a weaker satiety regulation and a higher risk of

250

having an unhealthy diet, with preference for noncore foods [18]. Moreover, it is important to highlight

251

that only 40% of the children met WHO`s recommendation in the consumption of fruit and vegetables.

252

Previous analyses in the Generation XXI cohort have shown that adherence at 7y to a dietary pattern rich

253

in energy-dense foods, processed meat and low in vegetables increased several cardiometabolic

254

parameters at 10y [50]. So, to maintain an adequate weight and prevent the occurrence of CVD, a healthy

255

dietary pattern, with increased amounts of fruits, vegetables, whole grains, legumes and seafoods, with

256

age-appropriate portion sizes and foods, is encouraged [20].

257

Since cardiometabolic risk factors tend to occur simultaneously and not alone, we clustered these

258

cardiometabolic risks in childhood by creating a risk score. A study of pooled birth cohorts evidenced that

259

the IDF diagnostic criteria included less children at risk, when compared to the clustering of different

260

factors, such as HOMA-IR, cardiorespiratory fitness and leptin [32]. The use of continuous, age-adjusted,

261

standardized variables have been described and seems to be a more accurate method to track

262

cardiometabolic risk in children and adolescents [51,52], as was done in the current study. However, we

263

should be aware that higher levels of cardiometabolic parameters are not indicative of existing disease, as

264

most children in our sample are apparently healthy. Nonetheless, our study suggests that at early ages,

265

such as 7y, having certain appetitive behaviors could have an association with metabolic parameters,

266

leading in the long-term to diseases.

267

Regarding study`s limitations, since the sample is composed of school-aged children, we currently

268

were not able to study associations between eating behaviors and clinical outcomes of cardiometabolic

269

events. Instead, we used well-known cardiometabolic markers that could predict future CVDs. We also

270

did not adjust for puberty, but stratified analysis by sex were conducted and, at 10y, pubertal development

271

is just beginning (only 3% of girls had already had menarche). The strengths of the current study include

272

the large sample size and the prospective design of the study. Moreover, the current study had available

273

standardized measures of weight, height and WC instead of reported values, and a standardized method in

274

the assessment of cardiometabolic parameters and eating behaviors was followed.

275

This is the first study to investigate appetitive behaviors in school-aged children and their relation to

276

cardiometabolic risk factors three years later. The majority of studies in child’s eating behaviors link

277

appetitive behaviors to adiposity (through BMI or weight), but lack in associating them to a wider range

278

of metabolic markers. Clustering additional important CVD risk factors along with the child’s adiposity in

7

279

the tracking of altered eating behavior might strengthen the ability of tracking children at higher risk of

280

CVDs.

281

Our study adds important findings in the investigation of eating behaviors and cardiometabolic health

282

in children. Appetitive behaviors of 7-year-old children were associated with cardiometabolic risk factors

283

three years later, but this association is largely dependent of child´s body fat (WC) and weight (BMI),

284

strong predictors of cardiometabolic health. When investigating cardiometabolic parameters in isolation,

285

appetitive traits such as Food responsiveness, Enjoyment of food and Emotional overeating increased

286

cardiometabolic risk, but again this is highly dependent on a child`s level of body fat. In general, ´food

287

avoidant’ behaviors protect against cardiometabolic risk and ‘food approach’ behaviors increase

288

cardiometabolic risk.

289 290

Acknowledgements

291

Generation XXI was funded by Programa Operacional de Saúde – Saúde XXI, Quadro Comunitário de

292

Apoio III and Administração Regional de Saúde Norte (Regional Department of Ministry of Health). It

293

has support from the Portuguese Foundation for Science and Technology and from the Calouste

294

Gulbenkian Foundation. This study was supported through FEDER from the Operational Programme

295

Factors of Competitiveness – COMPETE and through national funding from the Foundation for Science

296

and Technology – FCT (Portuguese Ministry of Education and Science) under the projects “Appetite

297

regulation and obesity in childhood: a comprehensive approach towards understanding genetic and

298

behavioural influences” (PTDC/SAU-EPI/30334/2017; POCI-01-0145-FEDER-030334);

299

“Appetite and adiposity - evidence for gene-environment interplay in children” (IF/01350/2015); “HIneC:

300

When do health inequalities start? Understanding the impact of childhood social adversity on health

301

trajectories from birth to early adolescence” (POCI-01-0145-FEDER-029567; Reference: PTDC/SAU-

302

PUB/29567/2017), and through Investigator Contracts (IF/01350/2015 – AO ; IF/01060/2015 - ACS).

303

The authors gratefully acknowledge the families enrolled in Generation XXI for their kindness, all

304

members of the research team for their enthusiasm and perseverance and the participating hospitals and

305

their staff for their help and support.

306 307 308 309 310 311 312 313 314 315 316 317 318

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Figure legends Figure 2: SE: Standard error; TG: Triglycerides; HOMA-IR: Homeostatic model assessment-insulin resistance; WC: Waist circumference; SBP: Systolic blood pressure; HDL-c: High-density lipoprotein cholesterol. CEBQ: Children’s Eating Behavior Questionnaire; CEBQ-SR: Satiety responsiveness; CEBQ-SE: Slowness in eating; CEBQ-FF: Food fussiness; CEBQ-EUE: Emotional undereating; CEBQDD: Desire to drink; CEBQ-FR: Food responsiveness; CEBQ-EF: Enjoyment of food; CEBQ-EOE: Emotional overeating. *p<0.05; **p≤0.001 according to Mann-Whitney U-test.

13

Boys

Girls Mean±SD

p-value

Mother`s characteristics Age at baseline (y)* Education at baseline (y)* BMI before pregnancy (Kg/m2)*

30.00±6.00 12.00±7.00 23.08±4.82

30.00±6.00 12.00±8.00 23.13±4.76

0.519 0.957 0.854

912(59.60) 618(40.40) 1.29±1.14

829(58.30) 593(41.70) 1.14±1.00²

0.0012

2.68±0.68 2.85±0.88 2.95±0.78 2.46±0.76 2.25±0.82 2.04±0.74 3.04±0.78 1.82±0.61

2.71±0.68 3.01±0.86¹ 2.95±0.75 2.44±0.75 2.18±0.79¹ 2.05±0.81 3.03±0.82 1.83±0.67

0.279 <0.0011 0.985 0.454 0.010 0.561 0.641 0.632

140.93±6.27 37.19±8.27 18.58±3.17

141.41±6.70 38.30±9.24 18.98±3.58

0.044 0.0011 0.0011

880 (57.50) 375 (24.50) 275 (18.00)

826 (58.10) 370 (26.00) 226 (15.90)

0.274

87.82±9.71 56.60±10.34 56.00±31.00 1.52±1.15 64.90±13.00 108.50±12.00 68.87±6.96

86.30±6.10 53.84±10.52 64.00±34.00 2.02±1.66 66.50±14.42 108.00±13.00 68.36±7.02¹

<0.0011 <0.0011 <0.0012 <0.0012 <0.0012 0.495 0.023

1501 (98.20) 28 (1.80)

1390 (97.70) 32 (2.30)

0.436

Child`s eating and physical activity habits at 7y Fruit and vegetables intake (portions/day) (n(%)) <5 ≥5 Average time spent in physical activity (hours/day)*

0.477

Child`s eating behaviors at 7y (CEBQ) CEBQ-SR CEBQ-SE CEBQ-FF CEBQ-EUE CEBQ-DD CEBQ-FR CEBQ-EF CEBQ-EOE Child`s characteristics at 10y Height (cm) Weight (Kg) BMI (Kg/m2) Weight status (n(%)) Normal weight (-2 to +1SD) Overweight (+1 to +2SD) Obesity (>+2SD) Child`s cardiometabolic characteristics at 10y Glucose (mg/dL) HDL-c (mg/dL) TG (mg/dL)* HOMA – IR* WC (cm)* SBP (mmHg)* DBP (mmHg) Cardiometabolic risk score (n(%)) Lower risk Higher risk

Table 1. Mothers` and child`s characteristics at baseline and follow-ups at 7y and 10y of age (n=2 951). ¹statistical significance using Bonferroni correction, in independent sample t-test; ²statistical significance using Bonferroni correction, in Mann-Whitney U-test; SD: Standard deviation; *Median and interquartile range (Md(IQR)); CEBQ: Children’s Eating Behavior Questionnaire; CEBQ-SR: Satiety responsiveness; CEBQ-SE: Slowness in eating; CEBQ-EF: Enjoyment of food; CEBQFR: Food responsiveness; CEBQ-DD: Desire to drink; CEBQ-FF: Food fussiness; CEBQ-EUE: Emotional undereating; CEBQEOE: Emotional overeating. Child’s eating behaviors could range from 1 - ´Never` to 5 - ´Always`. BMI: Body Mass index; HDL-c: High-density lipoprotein cholesterol; TG: Triglycerides; HOMA-IR: Homeostatic model assessment-insulin resistance; WC: Waist circumference; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; Higher risk: children in the 4th quartile of TG, HOMAIR, WC and SBP z-scores and in the 1st quartile of HDL-c z-score.

Table 2a. Linear regression models showing associations between appetitive behaviors at 7y and lipids profile and insulin resistance at 10y, stratified by child´s sex. Appetitive behaviors at 7y CEBQ-SR CEBQ-SE CEBQ-FF CEBQ-EUE CEBQ-DD CEBQ-FR CEBQ-EF CEBQ-EOE

CEBQ-SR CEBQ-SE CEBQ-FF CEBQ-EUE CEBQ-DD CEBQ-FR CEBQ-EF CEBQ-EOE

Crude model

HDL-c z-score Adj. model 1

Adj. model 2

Crude model

0.13 (0.03; 0.23) 0.11 (0.03; 0.19) 0.06 (-0.03; 0.15) 0.03 (-0.06; 0.12) -0.06 (-0.14; 0.03) -0.15 (-0.25; -0.06) -0.15 (-0.24; -0.06) -0.05 (-0.16; 0.07)

0.12 (0.02; 0.23) 0.11 (0.03; 0.19) 0.06 (-0.03; 0.16) 0.02 (-0.07; 0.11) -0.05 (-0.14; 0.03) -0.15 (-0.24; -0.05) -0.14 (-0.23; -0.05) -0.04 (-0.15; 0.08)

0.05 (-0.06; 0.15) 0.04 (-0.04; 0.12) 0.03 (-0.06; 0.12) 0.02 (-0.07; 0.10) -0.03 (-0.11; 0.06) -0.02 (-0.12; 0.08) -0.05 (-0.14; 0.05) 0.03 (-0.08; 0.15)

-0.07 (-0.17; 0.04) -0.10 (-0.18; -0.02) -0.06 (-0.15; 0.04) 0.02 (-0.08; 0.11) 0.07 (-0.01; 0.16) 0.21 (0.12; 0.31) 0.13 (0.04; 0.22) 0.09 (-0.03; 0.21)

0.22 (0.11; 0.33) 0.16 (0.08; 0.24) 0.10 (0.00; 0.19) 0.11 (0.01; 0.20) -0.02 (-0.11; 0.07) -0.17 (-0.26; -0.08) -0.18 (-0.27; -0.09) -0.09 (-0.20; 0.02)

0.21 (0.10; 0.32) 0.15 (0.07; 0.24) 0.10 (0.00; 0.20) 0.10 (0.00; 0.20) -0.01 (-0.10; 0.09) -0.16 (-0.25; -0.07) -0.17 (-0.26; -0.08) -0.08 (-0.19; 0.03)

0.08 (-0.03; 0.18) 0.04 (-0.04; 0.12) 0.04 (-0.05; 0.13) 0.07 (-0.02; 0.16) 0.02 (-0.07; 0.10) 0.00 (-0.09; 0.09) -0.02 (-0.11; 0.07) 0.03 (-0.07; 0.13)

-0.10 (-0.21; 0.00) -0.10 (-0.18; -0.01) -0.03 (-0.13; 0.06) -0.03 (-0.13; 0.07) 0.04 (-0.06; 0.13) 0.18 (0.09; 0.27) 0.15 (0.06; 0.24) 0.13 (0.02; 0.24)

TG z-score Adj. model 1 Adj. model 2 Boys β (95% CI)* -0.07 0.06 (-0.17; 0.04) (-0.04; 0.16) -0.01 -0.10 (-0.08; 0.07) (-0.18; -0.02) -0.06 0.00 (-0.15; 0.03) (-0.09; 0.08) 0.02 0.03 (-0.07; 0.11) (-0.05; 0.11) 0.07 0.04 (-0.01; 0.16) (-0.04; 0.12) 0.08 0.21 (-0.01; 0.18) (0.12; 0.30) -0.01 0.13 (-0.10; 0.08) (0.03; 0.22) 0.09 0.01 (-0.03; 0.20) (-0.10; 0.12) Girls β (95% CI)* -0.10 0.07 (-0.21; 0.01) (-0.04; 0.17) 0.05 -0.10 (-0.04; 0.13) (-0.18; -0.01) -0.05 0.02 (-0.15; 0.05) (-0.07; 0.11) -0.03 0.02 (-0.13; 0.07) (-0.07; 0.11) 0.04 0.01 (-0.06; 0.13) (-0.07; 0.10) 0.02 0.17 (-0.07; 0.11) (0.08; 0.26) -0.02 0.14 (-0.10; 0.07) (0.05; 0.23) 0.02 0.12 (-0.08; 0.13) (0.01; 0.23)

Crude model

HOMA-IR z-score Adj. model 1

Adj. Model 2

-0.21 (-0.31; -0.10) -0.15 (-0.23; -0.07) -0.10 (-0.19; -0.01) -0.02 (-0.11; 0.08) 0.05 (-0.04; 0.13) 0.24 (0.15; 0.34) 0.22 (0.13; 0.31) 0.17 (0.05; 0.28)

-0.20 (-0.30; -0.10) -0.15 (-0.23; -0.07) -0.12 (-0.21; -0.03) -0.01 (-0.10; 0.08) 0.05 (-0.04; 0.13) 0.23 (0.14; 0.32) 0.21 (0.12; 0.30) 0.15 (0.04; 0.27)

0.00 (-0.09; 0.09) 0.03 (-0.04; 0.10) -0.07 (-0.15; 0.01) 0.02 (-0.06; 0.10) -0.01 (-0.08; 0.06) -0.06 (-0.15; 0.03) -0.03 (-0.11; 0.05) 0.00 (-0.10; 0.09)

-0.20 (-0.31; -0.09) -0.22 (-0.30; -0.13) -0.05 (-0.14; 0.05) -0.08 (-0.17; 0.02) 0.08 (-0.01; 0.17) 0.26 (0.17; 0.35) 0.24 (0.15; 0.32) 0.19 (0.08; 0.30)

-0.19 (-0.30; -0.09) -0.21 (-0.29; -0.13) -0.08 (-0.18; 0.02) -0.06 (-0.16; 0.03) 0.06 (-0.04; 0.15) 0.23 (0.15; 0.32) 0.22 (0.14; 0.31) 0.15 (0.05; 0.26)

0.04 (-0.06; 0.13) -0.03 (-0.10; 0.05) 0.01 (-0.08; 0.09) -0.03 (-0.11; 0.05) 0.02 (-0.06; 0.10) -0.02 (-0.10; 0.07) -0.01 (-0.10; 0.07) -0.01 (-0.11; 0.08)

HDL-c: High-density lipoprotein cholesterol; TG: Triglycerides; HOMA-IR: Homeostatic model assessment-insulin resistance. CEBQ-SR: Satiety responsiveness; CEBQ-SE: Slowness in eating; CEBQ-FF: Food fussiness; CEBQ-EUE: Emotional undereating; CEBQ-DD: Desire to drink; CEBQ-FR: Food responsiveness; CEBQ-EF: Enjoyment of food; CEBQ-EOE: Emotional overeating.*95% Confidence interval with Bonferroni correction. Model 1 adjusted for maternal BMI and education, child mean daily time of physical activity, and mean daily consumption of fruits and vegetables at 7y. Model 2 adjusted for Model 1 plus remaining cardiometabolic parameters. Bold values are significant.

Table 2b. Linear regression models showing associations between appetitive behaviors at 7y and waist circumference and systolic blood pressure at 10y, stratified by child´s sex. Appetitive behaviors at 7y CEBQ-SR CEBQ-SE CEBQ-FF CEBQ-EUE CEBQ-DD CEBQ-FR CEBQ-EF CEBQ-EOE

CEBQ-SR CEBQ-SE CEBQ-FF CEBQ-EUE CEBQ-DD CEBQ-FR CEBQ-EF CEBQ-EOE

Crude model

WC z-score Adj. model 1

-0.47 (-0.57; -0.37) -0.37 (-0.44; -0.29) -0.08 (-0.17; 0.01) -0.11 (-0.20; -0.02) 0.08 (-0.01; 0.16) 0.56 (0.47; 0.65) 0.51 (0.42; 0.59) 0.36 (0.24; 0.47)

-0.45 (-0.54; -0.36) -0.35 (-0.43; -0.28) -0.09 (-0.17; 0.00) -0.08 (-0.17; 0.01) 0.07 (-0.01; 0.16) 0.53 (0.45; 0.61) 0.48 (0.40; 0.56) 0.32 (0.21; 0.43)

-0.48 (-0.58; -0.38) -0.40 (-0.48; -0.32) -0.14 (-0.24; -0.04 -0.09 (-0.19; 0.01) 0.10 (0.01; 0.19) 0.52 (0.43; 0.60) 0.49 (0.40; 0.57) 0.38 (0.27; 0.48)

-0.46 (-0.55; -0.36) -0.38 (-0.45; -0.30) -0.17 (-0.26; -0.08) -0.07 (-0.16; 0.02) 0.06 (-0.03; 0.15) 0.48 (0.40; 0.56) 0.46 (0.38; 0.54) 0.31 (0.21; 0.42)

Adj. model 2 Crude model Boys β (95% CI)* -0.05 -0.36 (-0.12; 0.03) (-0.44; -0.27) -0.28 -0.08 (-0.34; -0.21) (-0.14; -0.02) -0.03 0.00 (-0.11; 0.05) (-0.06; 0.07) -0.02 -0.08 (-0.09; 0.05) (-0.15; 0.00) 0.04 0.05 (-0.03; 0.11) (-0.02; 0.11) 0.42 0.12 (0.34; 0.49) (0.05; 0.19) 0.38 0.11 (0.31; 0.45) (0.04; 0.17) 0.06 0.25 (-0.03; 0.14) (0.15; 0.34) Girls β (95% CI)* -0.33 -0.13 (-0.41; -0.24) (-0.21; -0.05) -0.26 -0.11 (-0.32; -0.19) (-0.17; -0.05) -0.04 -0.11 (-0.12; 0.03) (-0.19; -0.03) -0.03 -0.01 (-0.10; 0.05) (-0.08; 0.07) 0.03 0.10 (-0.05; 0.10) (0.03; 0.17) 0.34 0.17 (0.27; 0.40) (0.11; 0.24) 0.32 0.18 (0.25; 0.39) (0.11; 0.24) 0.22 0.16 (0.13; 0.30) (0.07; 0.24)

SBP z-score Adj. model 1

Adj. Model 2

-0.04 (-0.11; 0.03) -0.07 (-0.13; -0.02) 0.00 (-0.06; 0.07) -0.01 (-0.08; 0.05) 0.03 (-0.03; 0.10) 0.11 (0.04; 0.18) 0.10 (0.03; 0.16) 0.04 (-0.04; 0.13)

0.03 (-0.06; 0.11) -0.02 (-0.09; 0.04) 0.03 (-0.05; 0.10) 0.01 (-0.07; 0.08) 0.03 (-0.04; 0.10) 0.05 (-0.04; 0.13) 0.04 (-0.04; 0.12) 0.01 (-0.08; 0.11)

-0.12 (-0.20; -0.04) -0.10 (-0.17; -0.04) -0.04 (-0.12; 0.03) 0.00 (-0.07; 0.07) 0.08 (0.01; 0.15) 0.16 (0.09; 0.23) 0.17 (0.10; 0.23) 0.14 (0.05; 0.22)

-0.04 (-0.13; 0.06) 0.00 (-0.08; 0.07) -0.02 (-0.10; 0.06) 0.02 (-0.06; 0.11) 0.03 (-0.05; 0.11) 0.05 (-0.04; 0.13) 0.06 (-0.03; 0.14) 0.04 (-0.06; 0.13)

WC: Waist circumference; SBP: Systolic blood pressure. CEBQ-SR: Satiety responsiveness; CEBQ-SE: Slowness in eating; CEBQFF: Food fussiness; CEBQ-EUE: Emotional undereating; CEBQ-DD: Desire to drink; CEBQ-FR: Food responsiveness; CEBQ-EF: Enjoyment of food; CEBQ-EOE: Emotional overeating. *95% Confidence interval with Bonferroni correction. Model 1 adjusted for maternal BMI and education, child mean daily time of physical activity and mean daily consumption of fruit and vegetables at 7y. Model 2 adjusted for Model 1 plus remaining cardiometabolic parameters. Bold values are significant.

Table 3. Logistic regression models showing associations between appetitive behaviors at 7y and the cardiometabolic risk score at 10y, stratified by child´s sex (n=2 951). Girls

Boys Appetitive behaviors at 7y CEBQ-SR CEBQ-SE CEBQ-FF CEBQ-EUE CEBQ-DD CEBQ-FR CEBQ-EF CEBQ-EOE

Participants no.

Lower risk: 1501 Higher risk: 28

Crude model 0.37 (0.16; 0.89) 0.47 (0.24; 0.92) 0.57 (0.29; 1.12) 0.68 (0.34; 1.38) 1.16 (0.63; 2.14) 2.53 (1.48; 4.32) 3.72 (1.87; 7.40) 1.87 (0.86; 4.07)

Adjusted model 1 OR (95% CI)* 0.39 (0.16; 0.93) 0.49 (0.25; 0.95) 0.54 (0.27; 1.09) 0.71 (0.35; 1.44) 1.14 (0.61; 2.12) 2.50 (1.45; 4.32) 3.66 (1.81; 7.40) 1.75 (0.80; 3.82)

Adjusted model 2 0.95 (0.37; 2.46) 1.00 (0.48; 2.07) 0.64 (0.31; 1.32) 0.79 (0.37; 1.69) 0.96 (0.50; 1.83) 1.03 (0.53; 2.00) 1.50 (0.68; 3.31) 0.93 (0.43; 2.05)

Participants no.

Crude model

Lower risk: 1390 Higher risk: 32

0.35 (0.16; 0.79) 0.48 (0.26; 0.88) 0.65 (0.34; 1.25) 0.78 (0.40; 1.51) 1.12 (0.61; 2.04) 2.47 (1.56; 3.91) 2.36 (1.29; 4.30) 2.39 (1.38; 4.15)

Adjusted model 1 OR (95% CI)* 0.37 (0.17; 0.82) 0.49 (0.27; 0.91) 0.60 (0.31; 1.17) 0.80 (0.41; 1.56) 1.02 (0.55; 1.89) 2.33 (1.46; 3.71) 2.22 (1.22; 4.04) 2.18 (1.23; 3.87)

Adjusted model 2 0.78 (0.30; 2.01) 1.03 (0.54; 2.00) 0.83 (0.41; 1.68) 0.83 (0.40; 1.72) 0.82 (0.42; 1.63) 1.19 (0.69; 2.06) 0.98 (0.49; 1.97) 1.31 (0.71; 2.41)

Cardiometabolic risk: higher risk category was classified as being in the 4th quartile of waist circumference, systolic blood pressure, triglycerides and homeostatic model assessment-insulin resistance zscores and 1st quartile of high-density lipoprotein-cholesterol z-score. CEBQ-SR: Satiety responsiveness; CEBQ-SE: Slowness in eating; CEBQ-FF: Food fussiness; CEBQ-EUE: Emotional undereating; CEBQ-DD: Desire to drink; CEBQ-FR: Food responsiveness; CEBQ-EF: Enjoyment of food; CEBQ-EOE: Emotional overeating. OR: Odds ratio; *95% Confidence interval with Bonferroni correction. Bold values are significant. Model 1 adjusted for maternal BMI and education, child mean daily time of physical activity and mean daily consumption of fruit and vegetables at 7y. Model 2 adjusted for Model 1 plus child’s BMIz at 7y.

Figure 1. Study flowchart of participants.

Figure 2. Mean (+SE) of child’s appetitive behaviors at 7y of age, according to higher risk (4th quartile of TG, HOMA-IR, WC and SBP z-scores and 1st quartile of HDL-c z-score) and lower risk group of the cardiometabolic risk at 10y of age, stratified by sex.

Highlights of the manuscript “Associations of appetitive behaviors at 7 years old with cardiometabolic health in 10- year-old children” • • • •

Child appetitive traits are associated with cardiometabolic risk 3 years later; Food approach behaviors are associated with increased cardiometabolic risk; Food avoidant behaviors are associated with lower cardiometabolic risk; Associations with cardiometabolic health are highly dependent on child`s adiposity.