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Nutrients 2014, 6(8), 3169-3186; https://doi.org/10.3390/nu6083169

Article
Nutrient Intake in Italian Infants and Toddlers from North and South Italy: The Nutrintake 636 Study
1
Department of Pediatrics, Luigi Sacco Hospital, University of Milano, Via G.B. Grassi 74, 20157 Milano, Italy
2
Azienda Sanitaria Locale, Corso Italia 19, 20122 Milano, Italy
3
Ospedale San Carlo, Via Ospedale 21, 20037 Paderno Dugnano, Italy
4
Italian Association of Dietitians, Vicolo S. Silvestro 6, 37122 Verona, Italy
5
Human Nutrition Unit, Department of Experimental Medicine, La Sapienza University, Viale Regina Elena 324, 00161 Roma, Italy
6
Nutrition and Consumers Information Office, Directorate of Hygiene, Food Safety and Nutrition, Ministry of Health, Viale G. Ribotta 5, 00144 Roma, Italy
7
Department of Clinical Sciences and Community Health (DISCCO) and Fondazione IRCCS Cà Granda-Ospedale Maggiore Policlinico, University of Milano, Via Commenda 9, 20122 Milano, Italy
8
Liver Research Center, AREA Science Park, Strada Statale 14 - km 163.5, 34012 Basovizza, Italy
9
International Center for the Assessment of Nutritional Status (ICANS), University of Milano, Via Botticelli 21, 20133 Milano, Italy
*
Author to whom correspondence should be addressed.
Received: 5 May 2014; in revised form: 28 July 2014 / Accepted: 31 July 2014 / Published: 8 August 2014

Abstract

:
We performed a cross-sectional study to compare the intake of energy, macronutrients, fiber, sodium and iron and the anthropometric status of infants and toddlers living in North (Milano) and South Italy (Catania). Nutrient intake was evaluated using a 7-day weighed food record. Out of 400 planned children aged 6 to 36 months, 390 (98%) were recruited, 189 in Milano and 201 in Catania. The mean (standard deviation) age was 17 (9) months in Milano and 17 (10) months in Catania. Anthropometry, energy intake and macronutrient intake were similar in Milano and Catania. However, iron intake was 27% lower and fiber intake 16% higher in Milano than in Catania. Despite normal anthropometry and energy intake, in the pooled sample there was a high intake of proteins, simple carbohydrates, saturated fats and sodium, and a low intake of iron and fiber compared to Italian reference values. This is the first study to report the macro- and micro-nutrient intake of children aged <12 months using the 7-day weighed food record and one of the very few studies that have employed such reference method in children from the general population.
Keywords:
epidemiology; cross-sectional study; anthropometry; food intake; weighed food dietary record; infants; toddlers

1. Introduction

There is a general agreement that the promotion of healthy lifestyles during the first years of life is central to the prevention of obesity and its complications [1]. However, evidence linking infant nutrition with adult obesity is presently lacking [2], a situation that is partly attributable to the unavailability of good-quality data on the nutrient intake of infants and toddlers [3,4,5]. Besides requiring accurate instruments and experienced operators in all ages of life [6,7], a detailed assessment of nutrient intake is more challenging in preschoolers than in schoolchildren because the former cannot be enrolled and studied at school [3,4]. Under the Italian National Health System, children are cared for by family pediatricians [8]. This offers the possibility to control the logistic barrier associated with the enrollment of infants and toddlers but does not attenuate the need of using the best available instruments to assess food intake [5,9].
The 7-day weighed food-record (7DWFR) is the accepted reference method for the assessment of nutrient intake [6,7] and offers accurate estimates of macronutrient intake compared to direct chemical analysis [10,11]. A recent systematic review concluded that weighed records are needed to obtain more accurate estimates of nutrient intake in children, especially when micronutrient intake is of interest [5]. Iron and sodium are especially important micronutrients for infants and toddlers, the first because of the frequency of iron-deficiency anemia [12,13] and the second because of its potential role in later cardiovascular disease [13,14]. To our knowledge, no study so far has evaluated the macro- and micro-nutrient intake of children aged <12 months using the 7DWFR even if the macronutrient intake of children participating to a randomized controlled trial (RCT) has been recently evaluated using the 3-day weighed food-record (3DWFR) [15,16]. An increasing north to south gradient of obesity is known to exist within Europe [17] and a similar gradient has been reported among Italian children aged ≥2 years [18,19]. It is however unknown whether such a gradient is present in the first years of life.
The aim of the present study, known as Nutrintake 636 (NI636), was to compare the intake of energy, macronutrients, fiber, sodium and iron and the anthropometric status of infants and toddlers living in North (Milano) and South Italy (Catania).

2. Experimental Section

2.1. Study Design

NI636 is a cross-sectional study aimed at comparing: (1) the intake of energy (E) (main outcome); (2) the intake of total carbohydrates (CHO), simple carbohydrates (CHOS), total fats (FAT), saturated fats (SFA), proteins (PRO), fiber, sodium and iron (secondary outcomes) and; (3) the weight, length/height and body mass index (BMI) (secondary outcomes) of Italian infants and toddlers living in North (Milano, Lombardy) and South (Catania, Sicily) Italy. NI636 children were enrolled among the infants and toddlers cared for by a convenience sample of Italian family pediatricians. NI636 pediatricians were recruited by the principal investigator (GVZ) by word of mouth at scientific meetings on infant nutrition. Inclusion criteria were: (1) children aged 6.0 (±0.5), 9.0 (±0.5), 12.0 (±1.0), 18.0 (±1.0), 24.0 (±1.0) or 36.0 (±1.0) months and, (2) parents with a good command of the Italian language. Exclusion criteria were any acute illnesses (e.g., influenza) or chronic diseases (e.g., food allergy) known to interfere with normal nutrition and growth. The study protocol was approved by the Ethical Committee of the Luigi Sacco Hospital (Milano, Italy) and the parents of the children gave their written consent to participate.

2.2. Sample Size

We aimed at detecting a median difference of 100 kcal·day−1 of E between Milano and Catania with a power ≥80% and a type I error rate of 5% taking into account the effects of sex and age. 100 kcal·day−1 of E (10%) was considered the minimum between-place biologically relevant difference on the basis of an expected median intake of 1000 kcal·day−1 in the pooled sample. The same difference of 10% was considered the minimum between-place biologically relevant difference for the nutrients (CHO, CHOS, FAT, SFA, PRO, fiber, sodium and iron). Our experience in validating the 7DWFR against chemical analysis suggests that a threshold of 10% is a reasonable choice to guard against instrumental error [10,11]. To calculate sample size, we applied Monte Carlo simulation to a multivariable median regression model having E (kcal·day−1) as response variable and the following predictors: (1) a between-place difference of 100 kcal·day−1 of E, (2) a between-sex effect ≥20% of median E and, (3) a one-degree fractional polynomial (FP) of age modelling the continuous energy-age relationship [20,21,22]. From the multiple scenarios generated by Monte Carlo simulation, we obtained that we had to enroll 400 children, 200 in Milano and 200 in Catania. On the basis of previous experience with similar epidemiological studies [23,24], we estimated that we had to recruit 600 subjects to obtain a number of 400 subjects.

2.3. Recruitment Procedure

Each NI636 pediatrician sent an anonymized list of potentially eligible children to the methodologist (GB). Such list included a unique pediatrician code, a unique patient number and the sex and birthdate of each child. The children from Milano were pooled together and the same was done for the children from Catania. From these pools, 300 children were randomly selected per place. Within-place pooling was done in order to control for the within-place between-pediatrician variability. We tried to obtain a similar between-pediatrician distribution of age and sex but this was not possible for children aged 6 and 9 months, for whom some pediatricians contributed more subjects both in Milano and Catania. The anonymized list with the randomly selected children was returned to the pediatricians who decoded it and shared it with the dietitians (CC, AM, MCC and GC).

2.4. Family Data

The dietitians collected the following sociodemographic and anthropometric data for both the parents of NI636 children: school degree, profession, physical activity level and self-reported weight and height. The dietitians also collected the pre-pregnancy weight and the weight increase during pregnancy for the mothers of NI636 children. BMI was calculated and classified according to the World Health Organization (WHO) [25].

2.5. Anthropometry

Anthropometric measurements were performed by the dietitians following international guidelines [26]. Weight was measured using a medical-certified SECA 384/385 baby scale (SECA, Hamburg, Deutschland). Supine length was measured using a medical-certified SECA 417 measuring board in children aged <24 months (SECA, Hamburg, Deutschland). Height was measured using a medical-certified SECA 213 portable stadiometer in children aged ≥24 months (SECA, Hamburg, Deutschland). Standard deviation scores (SDS) of weight, length, height and BMI were calculated using WHO reference data for children [27].

2.6. Food Intake

Food intake was evaluated by the dietitians using a 7DWFR, i.e., the accepted reference method for the assessment of food intake [6,7]. The 7DWFR was administered to the parents of the children during a first encounter lasting 30 to 45 min. When the 7DWFR was returned one week later, the dietitians discussed its contents with the parents, asked for clarifications when needed, and wrote their comments in an apposite section of the 7DWFR. The second encounter also lasted from 30 to 45 min. Detailed recipes were always obtained for foods eaten at home. Menus with detailed recipes were obtained from the canteen staff for children eating at the kindergarten. Single ingredients were always recorded on the 7DWFR. We did not evaluate breast milk intake for two reasons. The first reason is that a random sample of 10 mothers interviewed during a pilot study uniformly expressed concern about the need of regularly weighing their children before and after breastfeeding. The second reason is the lack of good-quality data to model breast milk composition of Italian mothers.

2.7. Data Entry

The dietitians recorded all data on anonymized case report forms and entered them into a specifically developed web-based application. Such application allowed the entry of new foods and was accessible using the HyperText Transfer Protocol over Secure Socket Layer (HTTPS).

2.8. Food Composition

Food records entered into the database were resolved into a working foodlist of unique items on the basis of food name and brand. Each item of the working foodlist was manually checked by the food database manager (ET) and by the methodologist and linked with food composition data to produce a definitive foodlist. The definitive foodlist was generated and linked with food composition data and 7DWFRs by specifically developed Stata 13.1 programs (Stata Corp., College Station, TX, USA). Food composition data was predominantly obtained from the Food Composition Database for Epidemiological Studies in Italy [28] and from a specifically developed infant food database. When a nutrient was not available in such databases, we obtained it: (1) from another Italian food composition database [29] or, (2) from the ingredients specified on the food label.

2.9. Statistical Analysis

Descriptive statistics are reported as 50th (P50), 25th (P25) and 75th (P75) percentiles for non-Gaussian distributed variables and as mean and standard deviation (SD) for Gaussian-distributed variables. Discrete variables are reported as counts and percentages. Between-place comparisons of age were performed using Student’s unpaired t-test and between-place comparisons of sex using Fisher’s exact test. Exact logistic regression was used to test whether the odds of breastfeeding (discrete: 0 = not breastfed; 1 = breastfed) differed between place (discrete: 0 = Catania; 1 = Milano) with and without correction for age (continuous: 6, 9, 12, 18, 24 and 36 months) [30]. The contribution of place to the outcomes of interest (E, CHO, CHOS, FAT, SFA, PRO, fiber, iron, sodium, weight, length/height and BMI) was evaluated using multivariable quantile regression models employing P25, P50 and P75 as response variable and place (discrete: 0 = Catania; 1 = Milano), sex (discrete: 0 = female; 1 = male) and age (continuous: 6, 9, 12, 18, 24 and 36 months) as predictors [22]. One-degree fractional polynomials (FP) were used to account for non-linear relationships of the outcomes with age [21]. Because most of the between-place differences were negligible on biological grounds (≤10%, see Sample Size), we pooled together the children in Milano and Catania and refitted the regression models using only sex and age as predictors. Such models were then used to estimate nutrient intake in the pooled sample. Statistical analysis was performed using Stata 13.1 (Stata Corp., College Station, TX, US).

3. Results

3.1. Characteristics of NI636 Children

NI636 was performed between 15 September 2012 and 24 February 2013 and recruited 390 (98%) out of 400 planned children, 189 in Milano and 201 in Catania. NI636 children were enrolled by 11 out of 20 family pediatricians (4 in Milano and 7 in Catania) contacted by the principal investigator (see Recruitment Procedure). All NI636 children were Caucasians and their distribution by age is given in Table 1.
Table 1. Within-place distribution of age and family characteristics of NI636 children.
Table 1. Within-place distribution of age and family characteristics of NI636 children.
VariableMilano (%)Catania (%)
Age class
6 months2013
9 months1318
12 months1622
18 months1618
24 months1716
36 months1813
School degree—fathers a
Elementary03
Middle1339
High4343
University4415
School degree—mothers b
Elementary01
Middle735
High3839
University5525
Profession—fathers b
Unemployed17
Employed6460
Freelance3532
Retired01
Profession—mothers
Unemployed1061
Employed7430
FreelanceRetired16090
BMI class (WHO)—fathers
Underweight01
Normal5738
Overweight3544
Obesity class 1715
Obesity class 211
Obesity class 301
BMI class (WHO)—mothers
Underweight96
Normal weight7565
Overweight1321
Obesity class 127
Obesity class 200
Obesity class 311
Physical activity—fathers
None5372
Light1612
Moderate2812
Heavy34
Physical activity—mothers c
None7387
Light139
Moderate134
Heavy10
Cigarette smoking—fathers
No6957
Yes3143
Cigarette smoking—mothers
No8277
Yes1823
a–c: Not available for 2, 1 and 3 parents. The entries in each column sum to 100%. WHO = World Health Organization.
The within-age between-pediatrician distribution of NI636 children is given in Table A1 of the Appendix. The mean (SD) age was 17 (9) months in Milano and 17 (10) months in Catania (p = 0.3, unpaired Student’s t-test). Males made up 50% of the sample in Milano and 49% in Catania (p = 1.0, Fisher’s exact test).
A total of 81 children (21%) were being breastfed, 42 in Milano and 39 in Catania. The frequency of breastfeeding was 43%, 30%, 16%, 9%, 1% and 1% at 6, 9, 12, 18, 24 and 36 months, respectively. The odds of being breastfed were similar in Milano and Catania with (odds ratio (OR) = 1.2, exact 95% confidence interval (CI) 0.7 to 2.0, exact p = 0.6) and without correction for age (OR = 1.2, exact 95% CI 0.7 to 2.2, exact p = 0.5). No child was exclusively breastfed, i.e., breastfed without any additional food, drink or water [31].
Ten percent of children in Catania and 51% in Milano were eating their lunch at the kindergarten from Monday to Friday. One percent of children in Catania and 27% in Milano were practicing structured exercise, mostly in water (100% in Catania and 80% in Milano). All children in Milano and 92% in Catania practiced such structured exercise once a week.

3.2. Characteristics of the Parents of NI636 Children

The characteristics of the parents of NI636 children are given in Table 1. In line with the available epidemiological studies in Italy, parents in Milano had higher school degrees than those in Catania. The number of freelance fathers was similar in Milano and Catania (35% vs. 32%) but there were more freelance mothers in Milano than in Catania (16% vs. 9%). A similar number of freelance mothers were caring for 6-month infants in Milano and Catania (19% vs. 16%). The higher frequency of overweight and obesity and the correspondingly lower physical activity in Catania was expected on the basis of the available epidemiological studies [17]. There was a higher number of smoking parents in Catania than in Milano but the difference was lower among mothers. The median pre-pregnancy weight (59 vs. 58 kg) and the median weight increase during pregnancy (12 vs. 12 kg) were similar in Milano and Catania.

3.3. Effect of Place on the Anthropometry of NI636 Children

There was no statistically significant between-place difference in any anthropometric dimension except for the P75 of length (quantile regression models not shown). However, this difference (−1.0 cm for Milano vs. Catania, p < 0.05) is clearly negligible on biological grounds as it amounts to 1% of the corresponding percentile.

3.4. Effect of Place on the Energy and Nutrient Intake of NI636 Children

The between-place differences in the P25, P50 and P75 of energy and nutrient intake are given in Table 2.
Table 2. Difference in daily energy and nutrient intake between Milano and Catania.
Table 2. Difference in daily energy and nutrient intake between Milano and Catania.
NutrientΔP50ΔP25ΔP75
E (kcal)−17 (−2%)−20 (−3%)−2 (0%)
E (kcal·kg·weight−1)−3 (−4%)2 (3%)−7 (−8%)
CHO (% E)−4 * (−7%)−3 * (−6%)−3 * (−5%)
CHOS (% CHO)−3 * (−8%)−1 (−3%)−1 (−2%)
FAT (% E)2 * (6%)3 * (10%)2 * (6%)
SFA (% FAT)1 (3%)1 (3%)−1 (−3%)
PRO (% E)1 * (7%)1 * (8%)1 * (6%)
PRO (g·kg·weight−1)0.2 (7%)0.2 (10%)0.1 (3%)
Sodium (mg)−17 (−2%)17 (6%)6 (1%)
Iron (mg)−1.4 * (−27%)−0.5 * (−15%)−2.0 * (−29%)
Fiber (g)1.1 * (16%)0.8 * (15%)1.6 * (18%)
* p < 0.05 for Milano vs. Catania. Values are point estimates from multivariable quantile regression using age, sex and place as predictors (see Statistical analysis). The value between parentheses is obtained by dividing the point estimate by the median value of the corresponding intake in the pooled sample and is considered biologically relevant when greater than 10% (see Sample Size). Abbreviations: CHO = carbohydrates; CHOS = simple carbohydrates; ΔP25= difference in the 25th percentile between Milano and Catania; ΔP50 = difference in the 50th percentile between Milano and Catania; ΔP75 = difference in the 75th percentile between Milano and Catania; E = energy; FAT = fats; PRO = proteins; SFA = saturated fatty acids.
The median daily energy intake, i.e., the main outcome, was −17 (95% CI −67 to 32) kcal lower in Milano than in Catania, well below the value that we considered biologically relevant, i.e., 100 kcal (see Sample Size). This finding is coherent with the similar anthropometric status (see Effect of Place on the Anthropometry of NI636 Children), as anthropometry is a long-term indicator of energy balance [27]. According to our pre-specified threshold of 10% (see Sample Size), only the differences in iron intake (−27% at P50 in Milano) and fiber intake (+16% at P50 in Milano) could be considered biologically relevant. Interestingly, the differences detected at P25 and P75 were consistent with those detected at P50.

3.5. Effect of Place on the Energy and Nutrient Intake of NI636 Children

Due to the fact that it was only the amount of fiber and iron that differed in a biologically relevant way between Milano and Catania and there was no effect of place on anthropometry (see Effect of Place on the Anthropometry of NI636 Children), we removed place from the predictors of the quantile regression models and developed percentiles only on the basis of age and sex (quantile regression models not shown). Table 3, Table 4, Table 5, Table 6, Table 7 and Table 8 report the percentiles of anthropometry and nutrient intake estimated by applying such models.
Table 8 contrasts the nutrient intake of all the NI636 children with the Italian reference values [32].
Table 3. Percentiles of weight and length in the pooled sample of NI636 children.
Table 3. Percentiles of weight and length in the pooled sample of NI636 children.
AnthropometryWeight (kg) (n = 390)Weight (SDS) (n = 390)Length (cm) (n = 268)Length (SDS) (n = 268)
P50 *P25 *P75P50P25P75P50 *P25 *P75 *P50P25P75
F6 mo7.67.08.30.38−0.351.0567.564.968.90.34−0.701.37
M6 mo8.07.58.60.18−0.480.7868.567.070.00.16−0.531.05
F9 mo8.67.99.40.37−0.331.0471.068.573.20.31−0.691.25
M9 mo9.08.49.70.17−0.470.7872.070.674.30.13−0.520.93
F12 mo9.48.710.30.35−0.321.0474.572.176.90.28−0.681.13
M12 mo9.89.210.60.15−0.450.7875.574.278.00.10−0.510.81
F18 mo10.910.011.90.33−0.291.0481.579.283.00.22−0.670.89
M18 mo11.310.512.10.13−0.430.7882.581.384.10.04−0.500.57
F24 mo12.011.113.10.30−0.271.04------
M24 mo12.411.613.40.10−0.400.77------
F36 mo14.012.915.30.25−0.221.03------
M36 mo14.413.415.60.05−0.350.77------
* p < 0.05 for sex. Values are point estimates from multivariable quantile regression using age and sex as predictors (see Statistical Analysis). Abbreviations: F = females; M = males; mo = month; P25 = 25th percentile; P50 = 50th percentile; P75 = 75th percentile; SDS = standard deviation scores.
Table 4. Percentiles of height and body mass index in the pooled sample of NI636 children.
Table 4. Percentiles of height and body mass index in the pooled sample of NI636 children.
AnthropometryHeight (cm) (n = 122)Height (SDS) (n = 122)BMI (kg/m2) (n = 390)BMI (SDS) (n = 390)
P50P25P75P50P25P75P50P25P75P50P25P75
F6 mo------17.116.018.10.23−0.820.89
M6 mo------17.316.118.20.11−1.060.76
F9 mo------17.015.917.90.23−0.480.88
M9 mo------17.116.018.00.11−0.710.76
F12 mo------16.815.817.70.23−0.360.87
M12 mo------16.915.917.80.11−0.600.75
F18 mo------16.515.617.30.23−0.270.86
M18 mo------16.615.617.40.11−0.510.74
F24 mo87.284.990.00.41−0.401.1916.115.317.00.23−0.250.85
M24 mo88.086.491.00.22−0.271.1516.315.417.10.11−0.480.73
F36 mo95.091.898.00.05−0.810.8215.514.916.20.23−0.220.83
M36 mo95.893.399.0−0.14−0.680.7815.614.916.30.11−0.460.71
p > 0.05 for sex at all percentiles. Values are point estimates from multivariable quantile regression using age and sex as predictors (see Statistical Analysis). Abbreviations: BMI = body mass index; F = females; M = males; mo = month; P25 = 25th percentile; P50 = 50th percentile; P75 = 75th percentile; SDS = standard deviation scores.
Table 5. Percentiles of energy and carbohydrate intake in the pooled sample of NI636 children.
Table 5. Percentiles of energy and carbohydrate intake in the pooled sample of NI636 children.
NutrientsE (Kcal) (n = 390)E (Kcal·kg·weight−1) (n = 390)CHO (% E) (n = 390)CHOS (% CHO) (n = 390)
P50 *P25 *P75 *P50P25P75P50P25P75P50P25P75
F6 mo354213472483075575261392548
M6 mo419289545513774575262422749
F9 mo600460776695488565260393148
M9 mo665536849726087565261423348
F12 mo747608928766293565260393348
M12 mo8126841001796992565260423548
F18 mo9217831080816896555158393447
M18 mo9868591153847495555158423647
F24 mo10258871156837097545056393546
M24 mo10909631229867796545057423746
F36 mo114810111232847198524854393544
M36 mo121310871305877897524854423744
* p < 0.05 for sex. Values are point estimates from multivariable quantile regression using age and sex as predictors (see Statistical analysis). Abbreviations: CHO = carbohydrates; CHOS = simple carbohydrates; E = energy; F = females; M = males; mo = month; P25 = 25th percentile; P50 = 50th percentile; P75 = 75th percentile.
Table 6. Percentiles of fat and protein intake in the pooled sample of NI636 children.
Table 6. Percentiles of fat and protein intake in the pooled sample of NI636 children.
NutrientsFAT (% E) (n = 390)SFA (% FAT) (n = 390)PRO (% E) (n = 390)PRO (g·kg·Weight−1) (n = 390)
P50P25P75P50P25P75P50P25P75 *P50P25P75
F6 mo302736302634119121.20.71.9
M6 mo3027363126351110131.40.92.1
F9 mo3027353430371311142.31.82.9
M9 mo3027353530391312152.52.03.1
F12 mo3027343533391412152.72.23.3
M12 mo3027343732401412162.92.43.4
F18 mo3128343735411513163.02.53.5
M18 mo3128343934421514173.22.73.7
F24 mo3229343836421614163.12.63.6
M24 mo3229344036431614183.32.83.7
F36 mo3330343937421614173.12.73.7
M36 mo3330344037441614183.32.93.8
* p < 0.05 for sex. Values are point estimates from multivariable quantile regression using age and sex as predictors (see Statistical analysis). Abbreviations: E = energy; F = females; FAT = fats; M = males; mo = month; P25 = 25th percentile; P50 = 50th percentile; P75 = 75th percentile; PRO = proteins; SFA = saturated fatty acids.
Table 7. Percentiles of iron, sodium and fiber intake in the pooled sample of NI636 children.
Table 7. Percentiles of iron, sodium and fiber intake in the pooled sample of NI636 children.
NutrientsIron (mg) (n = 390)Sodium (mg) (n = 390)Fiber (g) (n = 390)
P50P25 *P75P50P25 *P75P50P25P75
F6 mo1.81.24.375161573.51.95.6
M6 mo2.11.64.5145792334.02.36.2
F9 mo4.02.66.13602474936.04.57.4
M9 mo4.336.34313105696.54.98.0
F12 mo4.83.36.75634117326.95.48.3
M12 mo5.13.86.96334738087.45.88.9
F18 mo5.44.07.284864110687.56.19.2
M18 mo5.74.57.491870411448.16.59.8
F24 mo5.64.47.4105080513077.86.39.6
M24 mo5.84.87.6112186813838.36.710.2
F36 mo5.74.87.51336103616437.96.510.1
M36 mo6.05.27.71406109817198.56.910.7
* p < 0.05 for sex. Values are point estimates from multivariable quantile regression using age and sex as predictors (see Statistical analysis). Abbreviations: F = females; M = males; mo = month; P25 = 25th percentile; P50 = 50th percentile; P75 = 75th percentile.
Table 8. Nutrient intake of the NI636 children compared with Italian reference values (n = 390).
Table 8. Nutrient intake of the NI636 children compared with Italian reference values (n = 390).
Reference valueFrequency within Age Class (%)
6 mo9 mo12 mo18 mo24 mo36 mo
CHO < 45% E (RI-LL)3542510
CHO > 60% E (RI-UL)3224174110
CHOS < 15% E (SDT)32137939
FAT < 40% E (AI)8794----
FAT < 35% (RI-LL)--79917860
FAT < 40% (RI-UL)--931009790
SFA < 10% E (SDT)49533316229
PRO ≥ 1.32 g·kg·weight−1 (PRI)5484----
PRO ≥ 1.00 g·kg·weight−1 (PRI)--100100100100
Iron ≥ 11 mg (PRI)00----
Iron ≥ 8 mg (PRI)--28211914
Sodium > 1000 mg (UL)---436679
Fiber > 8.4 g·kcal·E−1--52384233
Abbreviations: AI = average intake; CHO = carbohydrates; CHOS = simple carbohydrates; E = energy; FAT = fats; LL = lower limit; mo = month; PRI = population reference intake; PRO = proteins; RI = reference intake; SDT = suggested dietary target; SFA = saturated fatty acids; UL = upper limit.

3.6. Effect of between-Place Breastfeeding Frequency and between-Place Age Distribution on the Nutrient Intake and Anthropometry of NI636 Children

We fitted two additional multivariable quantile regression models to control for the potentially confounding effect of between-place breastfeeding frequency and between-place age distribution. Firstly, although the age-adjusted odds of breastfeeding was similar in Milano and Catania (see Characteristics of NI636 Children), we nonetheless tested whether there was an association between being breastfed in a given place and the outcomes of interest. To this aim, we added two covariates to the multivariable quantile regression models described under Statistical Analysis and reported above. Such covariates were breastfeeding (discrete: 0 = no; 1 = yes) and a breastfeedingXplace interaction (discreteXdiscrete). The main effect place (discrete: 0 = Catania; 1 = Milano) was already inside the models. The breastfeedingXplace interaction was not significant in all cases (p > 0.05). Secondly, although age had a similar distribution in Milano and Catania (see Characteristics of NI636 Children), we nonetheless tested whether there was an association between having a given age in a given place and the outcomes of interest. To this aim, we added a covariate to the multivariable quantile regression models described under Statistical Analysis and reported above. Such covariate was an ageXplace (continuousXdiscrete) interaction. The main effects age (continuous: 6, 9, 12, 18, 24 and 36 months) and place (discrete: 0 = Catania; 1 = Milano) were already inside the models. Age inside the ageXplace interaction was modeled using the same FP selected for age outside the interaction. The ageXplace interaction was not significant in all cases (p > 0.05).

4. Discussion

4.1. Main Findings

NI636 was aimed at comparing the intake of energy, macronutrients, fiber, sodium and iron and the anthropometric status of infants and toddlers living in Milano and Catania. Only the intake of iron and fiber differed in a biologically relevant way between Milano and Catania. Thus, contrarily to studies performed in convenience samples of Italian children ≥2 years [18,19], we did not detect an increasing north-to-south gradient of anthropometry and energy intake in the first 36 months of life. Despite the fact that anthropometry was within normal limits in most cases, NI636 children had a high intake of proteins, simple carbohydrates, saturated fatty acids and sodium, and a low intake of iron and fiber.

4.2. Strengths and Limitations

NI636 is the first study to report the macro- and micro-nutrient intake of children aged <12 months using the reference 7DWFR method and one of the very few studies that have employed a weighed food record in children from the general population [5]. A recent systematic review pointed out the need for using weighed food records to obtain more accurate estimates of nutrient intake in children [5]. Although a 3DWFR has recently been used to evaluate the changes of nutrient intake in infants taking part to a RCT [15,16], the 3DWFR is not as accurate as the 7DWR in quantifying nutrient intake and there is a great need of data to be obtained from the general pediatric population [6,7].
The main limitation of NI636 is that it was performed in a convenience sample. Judging from the higher school degrees and employment rates of NI636 parents compared to the general Italian population, it appears that we have selected an economically privileged sector of the population; therefore our findings are unlikely to generalize to the whole Italian population. We wish to point out, however, that we had no intention to select a representative sample of the general Italian population and we believe that our data represents an improvement over the current available data obtained mostly through surrogate methods in samples not drawn from the general population [5]. Another limitation of NI636 is that we did not estimate nutrient intake from breast milk. This is a choice that we did after having interviewed a random sample of mothers who uniformly expressed their concern for the need to regularly weigh their children before and after breastfeeding and because of the lack of reliable reference data for breast milk composition. However, we found no evidence that being breastfed in Milano vs. Catania had any effect on the outcomes of the study. This implies that the degree of underestimation of the nutrient intake of breastfed children will be comparable in Milano and Catania. A further limitation of NI636 is that no dietary assessment method can accurately estimate added salt in free living conditions. Thus, the sodium intake estimated by NI636 may be an underestimation of the true intake. Lastly, even if the 7DWFR is widely considered the dietary reference method [6,7], it is not without limitations [6,7,33]. For instance, more than 7 days of recording may be needed to accurately assess micronutrients [6,7]. However, the results from our data on micronutrient intake represent an improvement over existing data obtained at best with 3DWFR [5].

4.3. Anthropometry

Most of the NI636 children had normal values of weight for age, length/height for age and BMI for age (Table 3 and Table 4) [27]. For instance, the highest P75 of BMI was 0.89 SDS (6-month girls) (Table 4). Even if the energy and nutrient intake of breastfed NI636 children is underestimated because breast milk was not taken into account, the fact that median weight, length/height and BMI were within acceptable limits at all ages signals a good average nutritional status [27].

4.4. Proteins

The median intake of PRO increased from 1.2 to 3.3 g·kg·weight−1 from 6 to 36 months of age (Table 6). Even if the PRO intake of breastfed NI636 children is underestimated because the contribution of breast milk was not taken into account, there was a clear excess of PRO at all ages (Table 8). Observational studies have suggested that a high PRO intake may be a risk factor for later obesity and this hypothesis is currently being tested by RCTs [34]. We hope to be able to perform a follow-up study of NI636 children at about 5 to 7 years of age to test whether there is an association between PRO intake and more generally infant nutrition and later obesity [2,35].

4.5. Carbohydrates and Fiber

Most NI636 children had an intake of CHO within 45% and 60% of E, i.e., within the limits suggested by the Italian guidelines [32] (Table 8). Although the fraction of E contributed by CHO decreased with age (Table 5), very few children at all ages had an intake of CHOS within the recommended value of 15% [32] (Table 8). Even if the present recommendations for CHO intake in children are extrapolated from adult data [32], the STRIP RCT has shown that reducing CHOS helps lower triglycerides in children [36]. STRIP has also shown the added benefits of a fiber-rich diet on triglycerides [36]. Using the threshold of 8.4 g·1000 kcal·E−1 suggested by the Italian guidelines, from 33% to 52% of NI636 children aged ≥12 months had an insufficient intake of fiber [32].

4.6. Fats

Despite the fact that the fraction of E contributed by FAT was nearly stable from 6 to 36 months (Table 6), there was a clear age-associated increase in SFA as percentage of FAT (Table 6) and most of the children were well above the limit of 10% of E suggested by the Italian guidelines (Table 8) [32].

4.7. Sodium

Although it is agreed that sodium intake should be limited during infancy because of its possible association with later cardiovascular disease [13,14], from 43% to 79% of NI636 children aged ≥12 months were consuming more sodium than the daily upper limit of 1000 mg defined by the Italian guidelines (Table 8) [32]. This is even more important in view of the fact that added salt may have escaped accurate quantification by the 7DWFR.

4.8. Iron

Iron-deficiency is the most common cause of anemia in infants and toddlers [12] and an evaluation of iron intake performed with reference methods is especially important in the first three years of life [5]. Using the population reference intake (PRI) as cut-point, as suggested by the Italian guidelines [32], very few children had a satisfactory iron intake. PRI has the obvious limitation that a child with a physiological requirement under the 97.5th percentile of the reference distribution will be classified as nutrient-deficient. However, PRI is the metric presently suggested for population studies and, according to this metric, most of our children had un unsatisfactory intake of iron [32].

5. Conclusions

NI636 is the first study to report the macro- and micro-nutrient intake of children aged <12 months using the reference 7-day weighed food record and one of the very few studies that have employed such reference method in children from the general population. NI636 showed that infants and toddlers living in Milano and Catania have a similar anthropometric status and nutrient intake with the exception of iron and fiber intake. They have a high intake of proteins, simple carbohydrates, saturated fats and sodium and a low intake of iron and fiber.

Acknowledgments

Nutrintake 636 was supported by an educational grant from Mellin SPA (Milan, Italy). The sponsor had no role in the design and conduct of the study; in the collection, analysis, and interpretation of the data; or in the preparation and review of the manuscript. We are grateful to the family pediatricians who enrolled the children (S. De Carlis, A. Pedone, G. Patanè, G. Miano, R. Santomauro, A. Santangelo, G. Bottaro, L. Caltabiano, M.V. Cunietti, M.D. Centamore and E. Piacentini). We thank the Italian Association of Dietitians (ANDID) for supporting the study and helping with the selection of study dietitians. We are grateful to the informatics engineers P. Natale and E. Pulimeno for developing and maintaining the web-based application. We thank the dietitian P. Gnagnarella (European Institute of Oncology, Milan, Italy) for sharing with us her experience with food composition analysis. We thank Ben Newton for revising the English text. Lastly, we are very grateful to the families of the children for their enthusiastic participation to the study.

Author Contributions

G.V.Z. co-designed the study and was responsible for it; C.C., A.M., M.C.C. and G.C. performed dietary and anthropometric measurements; V.D.B., L.G. and C.A. co-designed the study; C.M. helped with data management; E.T. co-designed the study and was responsible for the food composition database; G.B. co-designed the study, performed statistical analysis and drafted the manuscript.

Appendix

Table A1. Within-age between-pediatrician distribution of NI636 children.
Table A1. Within-age between-pediatrician distribution of NI636 children.
Pediatrician6 mo9 mo12 mo18 mo24 mo36 mo
CT116%8%9%12%5%7%
CT211%10%11%4%5%5%
CT33%11%8%3%3%3%
CT45%16%12%13%19%9%
CT52%3%4%3%3%5%
CT63%8%8%12%9%12%
CT72%3%7%7%6%2%
MI10%0%0%0%5%16%
MI219%21%24%27%33%16%
MI332%16%13%16%11%24%
MI48%3%4%3%2%2%
Total100%100%100%100%100%100%
Values are percentages within age classes. Percentages may not total 100 because of rounding. Abbreviations: MI = Milano; CT = Catania.

Conflicts of Interest

The authors declare no conflict of interest.

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