Alpha-linolenic acid (ALA) is inversely related to development of adiposity in school-age children

Abstract

Background/Objectives

Studies in adults indicate that dietary polyunsaturated fatty acid (PUFA) composition may play a role in development of adiposity. Because adipocyte quantity is established between late childhood and early adolescence, understanding the impact of PUFAs on weight gain during the school-age years is crucial to developing effective interventions.

Subjects/Methods

We quantified N-3 and N-6 PUFAs in serum samples of 668 Colombian schoolchildren aged 5–12 years at the time of recruitment into a cohort study, using gas-liquid chromatography. Serum concentrations of N-3 (ALA, EPA, DHA) and N-6 PUFAs (LA, GLA, DGLA, AA) were determined as % total fatty acids. Children’s anthropometry was measured annually for a median of 30 months. We used mixed-effects models with restricted cubic splines to construct population body mass index-for-age z-score (BAZ) growth curves for age-and sex-specific quartiles of each PUFA.

Results

N-3 ALA was inversely related to BAZ gain after adjustment for sex, baseline age and weight status, and household socioeconomic level. Estimated BAZ change between 6 and 14 years among children in the highest quartile of ALA compared to those in the lowest quartile was 0.45 (95% CI: 0.07, 0.83) lower (P-trend=0.006).

Conclusions

N-3 ALA may be protective against weight gain in school-age children. Whether improvement in PUFA status reduces adiposity in pediatric populations deserves evaluation in randomized trials.

INTRODUCTION

Childhood obesity poses one of the most serious public health challenges. Many countries, including those in Latin America (1), have experienced a marked rise in pediatric overweight and obesity rates in the past three decades, and the relative increase in prevalence was sharper in developing as compared to developed countries (2). Although the nutrition transition was identified as a major contributor to overweight and obesity in less affluent settings, obesity rates continue to rise and weight gain prevention remains elusive. Disentangling the effects of specific dietary components is a critically urgent area of investigation.

Dietary fatty acid composition may play a role in the development of adiposity. Special attention has been given to the omega-3 (N-3) and omega-6 (N-6) essential polyunsaturated fatty acids (PUFA). Dietary precursors of N-3 and N-6 PUFAs include 18:3 (N-3) alpha-linolenic acid (ALA) and 18:2 (N-6) linoleic acid (LA), respectively. As the primary 18-carbon member of the N-3 series, ALA can be desaturated and elongated into eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Through a shared enzymatic pathway, LA (the main 18-carbon N-6 PUFA) is converted to gamma-linolenic acid (GLA), dihomo-gamma-linolenic acid (DGLA), and arachidonic acid (AA). These long-chain PUFAs can be oxidized to produce eicosanoids, hormone-like signaling molecules that are involved in important biological processes including inflammation and cell differentiation. Because the N-3 and N-6 PUFA families compete for the same enzymes in their biosynthesis, they have interactive and opposing actions. Specifically, PUFAs in the N-6 pathway may stimulate adipogenesis (3), while the N-3 series can reduce fat mass through amelioration of inflammation (4) and adipocyte hypotrophia (5).

Although there is no clear link between N-6 PUFA and human obesity, studies in adults suggest that N-3 PUFA intake is inversely related to body mass index (BMI) (6, 7). Pre-birth cohorts provide some evidence that maternal PUFA intake during pregnancy (8) and lactation (9) influences offspring body composition in early childhood. However, findings from randomized controlled trials evaluating effects of maternal peripartum N-3 PUFA supplementation on offspring adiposity have been inconsistent (10) and current evidence in children is restricted to a handful of mixed findings from small cross-sectional studies (11–13). Considering that adipocyte quantity is established between late childhood and early adolescence (14), understanding the potential impact of PUFAs on weight gain during the school-age years is critical to developing effective interventions. Furthermore, there is need to evaluate these associations in developing countries where essential N-3 PUFA deficiency is widely prevalent and recent shifts toward increased consumption of N-6 PUFAs coincide with the increase in obesity rates.

In this study, we prospectively examined the association of N-3 and N-6 PUFA biomarkers at the time of recruitment into a cohort study with changes in adiposity in low- and middle-income schoolchildren from Bogotá, Colombia, a country at the early stages of the nutrition transition.

SUBJECTS AND METHODS

Study population

This study included a subsample of the Bogotá School Children Cohort (BSCC), an ongoing investigation of health and nutrition conducted among children from public schools in Bogotá, Colombia. Details on recruitment and study design have been published (15). Briefly, 3,202 school children 5–12 years of age were recruited from Bogotá’s public school system in February 2006. The study population is representative of families from low- and middle-income socioeconomic backgrounds in the city since we used a random sampling strategy and the public school system enrolls the majority of children from these groups. The parents or primary caregivers of all children gave written informed consent prior to enrollment into the study. The Ethics Committee of the National University of Colombia Medical School approved the study protocol. The Institutional Review Board at the University of Michigan approved use of data and samples from the study.

At enrollment, we sent the children’s families self-administered questionnaires that inquired on maternal sociodemographic (age, marital status, education, socioeconomic status) and anthropometric characteristics (height, weight), and the child’s physical activity and sedentary habits (82% response). Trained research assistants visited the schools to obtain anthropometric measurements and a fasting blood sample from the children. Height was measured without shoes to the nearest 1 mm using a wall-mounted portable stadiometer (Seca, Hanover, MD), and weight was measured in light clothing to the nearest 0.1 kg on solar-powered electronic scales (Tanita, Arlington Heights, IL). We obtained follow-up anthropometric measurements in June and November 2006, and once yearly thereafter by visiting the schools or the children’s homes if they were absent from school on the day of assessment.

Laboratory methods

We obtained an 8-hour fasting blood specimen by venipuncture in 88% of children at baseline and placed one aliquot in a tube without anti-coagulant for separation of serum. On the same day, the samples were transported on dry ice and protected from sunlight to the National Institute of Health in Bogotá. We carried out a complete blood count and cryopreserved serum at −80°C until transportation to the U.S. for analyses. We extracted lipids from serum samples and prepared fatty acid methyl esters of total lipids with BF₃-methanol. Methyl esters were extracted from a thin layer chromatography plate, and the solvents were dried and re-suspended in hexane. Approximately 2 mL of sample was injected via an auto sampler and analyzed on a gas-liquid chromatography machine using a 100 m SP-2560 column with optimum conditions for separation (Model 6890N, Agilent, Santa Clara, CA). Eluted peaks were analyzed with Chemstation software (Agilent). The concentration of each fatty acid was determined using a calibration curve with C17:0 methyl ester as the standard. Serum concentrations of N-3 ALA, EPA, and DHA, and N-6 LA, GLA, DGLA, and AA were each expressed as a percentage of total fatty acids. Inter-assay coefficients of variation (CV) ranged from 1.1 to 2.3% for all PUFAs.

Statistical analysis

We selected a random sample of 687 children for fatty acids quantification among those with a stored serum specimen. Of them, 668 children who had valid anthropometric measurements at baseline and at least one additional follow-up measurement comprised the final study population. These children did not differ from the rest of the BSCC in terms of age or baseline anthropometry; or maternal demographic characteristics or BMI status. Nevertheless, compared to children excluded from analyses, the study sample included more girls (54.6% vs. 50.1%) and a higher proportion of children from families in the upper two socioeconomic strata (66.2% vs. 59.5%).

Our exposures of interest were serum concentrations of N-3 (ALA, EPA, and DHA) and N-6 (LA, GLA, DGLA, and AA) PUFAs. We also examined the ratio of AA to EPA + DHA (AA:EPA+DHA), an indicator of the relative proportion of N-6 to N-3 intake. First, we compared the distribution of each PUFA by child and maternal characteristics to identify variables that may confound the association between PUFAs and development of adiposity. Children’s body mass index (BMI)-for-age (BAZ) and height-for-age z-scores (HAZ) were calculated using the World Health Organization (WHO) sex-specific growth reference for children 5–19 years (16). Child weight status was categorized as thin (BAZ<−2), adequate (BAZ≥−2 and ≤1), overweight (BAZ>1 and ≤2), and obese (BAZ>2). Maternal BMI was calculated from measured height and weight in 49% of the mothers and from self-reported data otherwise. We classified maternal weight status according to the standard adult BMI categories. Household socioeconomic status corresponded to the government’s classification (1-lowest through 6-highest; maximum of 4 in study population) assigned to each household for tax and planning purposes. We assessed the significance of these associations with the Wald test. For ordinal characteristics, we obtained a test for linear trend.

Next, we examined associations of the PUFAs quantified at baseline with change in BMI-for-age z-score (BAZ) during follow-up. We estimated average BAZ growth curves for sex- and age-specific quartiles of each PUFA marker using mixed-effects models for repeated measurements with restricted cubic splines (17) as previously described (18). Cubic splines represent non-linear terms for the distribution of age-at-assessment that allow smoothing of nonlinear BAZ changes over time. The cublic spline function consists of piecewise cubic polynomials that are smoothly joined at joint points, or ‘knots.’ It is ‘restricted’ because the polynomials at the tails are constrained to be linear. We fixed the knots at 5.7, 9.2, 10.8, and 13.8 years to reflect curvilinear portions of the WHO BMI-for-age child growth reference (16). In the spline models, BAZ was the outcome and the predictors included sex- and age-specific quartiles of each PUFA marker, linear and spline terms for child age-at-assessment in decimal years, and interaction terms between quartiles of the PUFA marker and the child age terms. Random effects for the intercept and the linear term for age were included to account for within-child correlations between repeated BAZ measurements in the estimation of the variance. These methods do not require an equal number of measurements, or that the measurements were obtained at the same time for participants; thus, all available measurements were included in the models. Using the growth curves constructed for children in each quartile of the PUFA markers, we estimated attained BAZ at 6 and 14 years. Our primary outcome of interest was the change in BAZ between these two age points. We also estimated the difference in BAZ change between children in the lowest and highest quartiles of each PUFA at 6 and 14 years.

In multivariable analysis, we estimated differences and 95% confidence intervals (CI) in BAZ change from 6 to 14 years after accounting for known predictors of childhood weight gain. The final model included the child’s sex, baseline age and weight status, and household socioeconomic status. We evaluated for effect modification by sex and also conducted stratified analyses. There was no indication that relations of the PUFA markers with BAZ were different for boys and girls; thus, the results presented are for all children.

All analyses were carried out with use of SAS 9.3 (SAS Institute Inc, Cary, NC).

RESULTS

Mean ± SD age of children was 8.8 ± 1.7 at the time of recruitment; 54.6% were girls. Mean BAZ at baseline was 0.09 ± 0.98, and 18% (n=122) of the children were overweight or obese (BAZ >1). During the median of 30.1 months of follow-up (interquartile range: 29.8–30.8 months), each child contributed a median of 4 BMI measurements, for a total of 2891 measurements.

At baseline, boys had 0.04 (95% CI: 0.02, 0.06) % higher GLA and 0.09 (95% CI: 0.04, 0.15) % higher DGLA than girls. Older children had higher ALA, DHA and DGLA concentrations, but lower AA:EPA+DHA than younger children (Table 1). Baseline height-forage z-score (HAZ) was positively related to AA (P-trend=0.09) and AA:EPA+DHA (P-trend=0.05). Higher BAZ at baseline corresponded with lower LA (P-trend=0.02), and higher GLA (P-trend=0.008) and DGLA (P-trend<0.0001). There was a weak inverse association between maternal BMI and LA. Compared to poorer children, those of higher socioeconomic status had lower ALA, EPA, GLA, and DGLA, and higher DHA, LA, and AA.

TABLE 1.

N-3 and N-6 polyunsaturated fatty acids by child sociodemographic and maternal characteristics in 668 school-age children from Bogotá, Colombia¹

		N-3			N-6				N-6:N-3
	N2	ALA 18:3(n-3)	EPA 20:5(n-3)	DHA 22:6(n-3)	LA 18:2(n-6)	GLA 18:3(n-6)	DGLA 20:3(n-6)	AA 20:4(n-6)	AA:EPA+DHA
Overall		0.49 (0.15)	0.22 (0.15)	2.30 (0.90)	30.28 (3.10)	0.30 (0.15)	1.61 (0.38)	5.96 (1.16)	2.62 (0.93)
Child Sex
F	365	0.49 (0.15)	0.21 (0.13)	2.28 (0.86)	30.30 (3.07)	0.28 (0.14)	1.57 (0.35)	5.96 (1.11)	2.61 (0.87)
M	303	0.48 (0.15)	0.22 (0.16)	2.32 (0.93)	30.25 (3.13)	0.32 (0.17)	1.66 (0.39)	5.96 (1.20)	2.62 (1.00)
P3		0.76	0.68	0.62	0.86	0.002	0.002	0.98	0.94
Child’s Age
5–6 Y	115	0.47 (0.15)	0.22 (0.13)	2.18 (0.77)	29.61 (3.21)	0.30 (0.16)	1.56 (0.30)	6.03 (1.08)	2.72 (0.84)
7–8 Y	215	0.48 (0.14)	0.22 (0.13)	2.20 (0.87)	30.83 (2.89)	0.30 (0.16)	1.55 (0.36)	5.95 (1.13)	2.71 (1.02)
9–10 Y	286	0.49 (0.15)	0.22 (0.16)	2.40 (0.90)	30.28 (3.00)	0.30 (0.15)	1.66 (0.40)	6.00 (1.19)	2.54 (0.89)
11–12 Y	52	0.53 (0.19)	0.20 (0.13)	2.42 (1.17)	29.45 (3.75)	0.30 (0.16)	1.67 (0.38)	5.64 (1.24)	2.44 (0.91)
P trend4		0.02	0.33	0.007	0.91	0.68	0.0003	0.23	0.008
Height-for-age Z score5
< −2.0	62	0.50 (0.16)	0.23 (0.13)	2.24 (0.81)	30.46 (3.35)	0.33 (0.16)	1.64 (0.44)	5.70 (1.20)	2.50 (0.82)
−2.0 to < −1.0	212	0.48 (0.14)	0.21 (0.13)	2.34 (0.81)	30.61 (2.80)	0.28 (0.15)	1.56 (0.38)	5.94 (1.21)	2.53 (0.92)
−1.0 to < 1.0	370	0.48 (0.15)	0.22 (0.16)	2.29 (0.97)	30.06 (3.18)	0.31 (0.15)	1.63 (0.36)	6.02 (1.13)	2.68 (0.94)
≥1.0	24	0.56 (0.17)	0.19 (0.11)	2.27 (0.76)	30.22 (3.54)	0.32 (0.19)	1.62 (0.26)	5.92 (0.92)	2.66 (1.00)
P trend4		0.70	0.83	0.93	0.11	0.62	0.40	0.09	0.05
BMI-for-age Z-score5
< −2.0	12	0.52 (0.14)	0.25 (0.17)	2.21 (0.57)	30.32 (3.35)	0.27 (0.14)	1.74 (0.48)	5.91 (1.48)	2.42 (0.34)
≥ −2.0 to ≤ 1.0	534	0.49 (0.15)	0.21 (0.13)	2.27 (0.87)	30.41 (3.04)	0.29 (0.15)	1.57 (0.36)	5.96 (1.14)	2.63 (0.93)
> 1.0 to ≤ 2.0	106	0.49 (0.14)	0.23 (0.21)	2.43 (1.05)	29.81 (3.22)	0.34 (0.17)	1.74 (0.36)	5.98 (1.22)	2.56 (0.95)
> 2.0	16	0.49 (0.14)	0.23 (0.14)	2.29 (0.78)	28.81 (3.47)	0.33 (0.14)	1.89 (0.46)	5.69 (1.09)	2.52 (1.09)
P trend4		0.92	0.52	0.18	0.02	0.008	<0.0001	0.72	0.61
Maternal BMI
<18.5 kg/m2	16	0.43 (0.12)	0.15 (0.11)	2.38 (0.72)	29.00 (2.19)	0.31 (0.14)	1.65 (0.26)	5.58 (0.89)	2.39 (0.82)
18.5–24.9 kg/m2	341	0.49 (0.15)	0.22 (0.16)	2.30 (0.89)	30.57 (3.12)	0.30 (0.15)	1.58 (0.35)	6.02 (1.18)	2.64 (1.00)
25.0–29.9 kg/m2	166	0.49 (0.15)	0.20 (0.13)	2.31 (0.96)	29.67 (3.27)	0.31 (0.16)	1.61 (0.36)	5.92 (1.16)	2.60 (0.83)
≥30 kg/m2	43	0.49 (0.13)	0.24 (0.14)	2.31 (0.90)	30.19 (2.62)	0.28 (0.17)	1.68 (0.49)	5.88 (1.29)	2.55 (0.93)
P trend4		0.74	0.69	0.98	0.09	0.80	0.26	0.63	0.73
Household SES 6
1 (lowest)	49	0.49 (0.13)	0.28 (0.12)	1.88 (0.68)	30.37 (2.43)	0.35 (0.14)	1.75 (0.35)	5.92 (1.13)	3.03 (1.39)
2	177	0.52 (0.16)	0.22 (0.18)	2.26 (0.86)	29.82 (3.15)	0.31 (0.15)	1.64 (0.36)	5.80 (1.18)	2.56 (0.86)
3	353	0.47 (0.14)	0.21 (0.13)	2.37 (0.92)	30.32 (3.14)	0.29 (0.16)	1.58 (0.38)	6.01 (1.14)	2.57 (0.89)
4 (highest)	89	0.46 (0.13)	0.20 (0.13)	2.30 (0.92)	30.97 (3.06)	0.31 (0.16)	1.60 (0.40)	6.11 (1.18)	2.69 (0.86)
P trend4		0.0004	0.006	0.006	0.02	0.07	0.006	0.05	0.27

¹Values are means (SD).

²Totals may be <668 because of missing values.

³Wald test from bivariate linear regression models.

⁴From bivariate regression models in which a variable representing the ordinal predictor was introduced as continuous.

⁵According to the World Health Organization (WHO) 2007 Child Growth Reference (16).

⁶According to the government’s classification for tax and planning purposes.

In the longitudinal analysis, we estimated average BAZ growth curves for quartiles of each N-3 (Table 2) and N-6 PUFA (Table 3). We found an inverse relation between ALA and BAZ change during follow-up (P-trend=0.008), which persisted after accounting for sex, baseline age and weight status, and household socioeconomic status (P-trend=0.006). Estimated adjusted BAZ change from age 6 to 14 years was 0.45 (95% CI: 0.07, 0.83) z lower for children in the 4^th quartile of N-3 ALA than for those in the 1^st quartile. Among the N-6 PUFAs, higher GLA levels at baseline was also related to lower BAZ gain, although the trend only approached significance (P-trend=0.05). In comparison to children in the lowest GLA quartile, estimated adjusted change in BAZ for those in the highest quartile was 0.42 (95% CI: 0.00, 0.84) z lower (Table 3). BAZ change was not associated with the other PUFA markers, or with AA:EPA+DHA.

TABLE 2.

Estimated change in BMI-for-age Z among 668 school-age children from Bogotá, Colombia according to quartiles of N-3 polyunsaturated fatty acids and AA:EPA+DHA ¹

	N	BMI-for-age z-score 2		Change2	Difference in Change3	P4
		6 y	14 y	14 y – 6 y	β (95% CI)
ALA 18:3(n-3)						0.006
Q1	168	−0.14 ± 0.12	0.22 ± 0.10	0.36 ± 0.15	Reference
Q2	166	−0.23 ± 0.14	0.32 ± 0.14	0.55 ± 0.20	0.17 (−0.29, 0.62)
Q3	169	−0.01 ± 0.12	0.07 ± 0.11	0.08 ± 0.15	−0.28 (−0.69, 0.12)
Q4	165	0.06 ± 0.11	−0.01 ± 0.12	−0.08 ± 0.14	−0.45 (−0.83, −0.07)
Difference Q4-Q1		0.20 (−0.12, 0.53)	−0.24 (−0.54, 0.07)
EPA 20:5(n-3)						0.24
Q1	170	−0.04 ± 0.13	0.15 ± 0.13	0.19 ± 0.18	Reference
Q2	164	−0.24 ± 0.13	0.25 ± 0.13	0.50 ± 0.17	0.32 (−0.15, 0.79)
Q3	166	−0.18 ± 0.12	0.09 ± 0.11	0.27 ± 0.15	0.08 (−0.36, 0.52)
Q4	168	0.14 ± 0.11	0.10 ± 0.11	−0.04 ± 0.14	−0.19 (−0.62, 0.23)
Difference Q4-Q1		0.17 (−0.17, 0.51)	−0.06 (−0.39, 0.28)
DHA 22:6(n-3)						0.09
Q1	166	0.01 ± 0.12	0.05 ± 0.11	0.04 ± 0.15	Reference
Q2	168	−0.03 ± 0.11	0.14 ± 0.11	0.17 ± 0.14	0.16 (−0.22, 0.55)
Q3	169	−0.27 ± 0.14	0.08 ± 0.12	0.35 ± 0.19	0.34 (−0.11, 0.79)
Q4	165	−0.04 ± 0.12	0.31 ± 0.14	0.35 ± 0.17	0.35 (−0.09, 0.78)
Difference Q4-Q1		−0.05 (−0.38, 0.29)	0.26 (−0.10, 0.62)
AA:EPA+DHA						0.50
Q1	167	0.09 ± 0.12	0.36 ± 0.13	0.27 ± 0.16	Reference
Q2	165	−0.13 ± 0.13	0.15 ± 0.13	0.29 ± 0.17	0.03 (−0.42, 0.47)
Q3	170	−0.08 ± 0.13	0.02 ± 0.11	0.10 ± 0.16	−0.18 (−0.62, 0.25)
Q4	166	−0.20 ± 0.12	0.04 ± 0.11	0.23 ± 0.15	−0.09 (−0.51, 0.34)
Difference Q4-Q1		−0.29 (−0.62, 0.05)	−0.32 (−0.67, 0.02)

¹Quartiles for indicators are sex- and age-specific according to their distributions in the study population.

²Values are mean ± SE. Estimates are from growth curves built using mixed effects models with restricted cubic splines that accounted for within-child repeated BMI measurements.

³Differences in change are adjusted for sex, baseline age, baseline weight status, and household socioeconomic stratum.

⁴Test for linear trend from a linear regression model where an ordinal variable that represented quartiles of each fatty acid indicator was entered as a continuous predictor.

TABLE 3.

Estimated change in BMI-for-age Z among 668 school-age children from Bogotá, Colombia according to quartiles of N-6 polyunsaturated fatty acids ¹

	N	BMI-for-age z-score 2		Change2	Difference in Change3	P4
		6 y	14 y	14 y – 6 y	β (95% CI)
LA 18:2(n-6)						0.46
Q1	168	0.01 ± 0.14	0.34 ± 0.12	0.33 ± 0.17	Reference
Q2	166	−0.08 ± 0.12	0.14 ± 0.13	0.22 ± 0.17	−0.09 (−0.53, 0.36)
Q3	169	−0.10 ± 0.11	0.06 ± 0.13	0.16 ± 0.16	−0.17 (−0.61, 0.27)
Q4	165	−0.15 ± 0.12	0.03 ± 0.12	0.18 ± 0.16	−0.14 (−0.58, 0.29)
Difference Q4-Q1		−0.17 (−0.53, 0.20)	−0.32 (−0.64, 0.01)
GLA 18:3(n-6)						0.05
Q1	167	−0.46 ± 0.13	0.11 ± 0.12	0.56 ± 0.16	Reference
Q2	167	−0.09 ± 0.12	0.05 ± 0.12	0.14 ± 0.16	−0.38 (−0.81, 0.05)
Q3	165	0.09 ± 0.12	0.17 ± 0.12	0.08 ± 0.16	−0.48 (−0.91, −0.05)
Q4	169	0.13 ± 0.12	0.26 ± 0.12	0.13 ± 0.16	−0.42 (−0.84, 0.00)
Difference Q4-Q1		0.58 (0.24, 0.92)	0.15 (−0.18, 0.49)
DGLA 20:3(n-6)						0.38
Q1	168	−0.39 ± 0.12	−0.03 ± 0.11	0.36 ± 0.15	Reference
Q2	165	−0.13 ± 0.12	0.16 ± 0.13	0.28 ± 0.17	−0.10 (−0.52, 0.32)
Q3	164	0.02 ± 0.12	0.04 ± 0.13	0.02 ± 0.17	−0.34 (−0.77, 0.09)
Q4	171	0.17 ± 0.13	0.41 ± 0.11	0.24 ± 0.16	−0.12 (−0.54, 0.29)
Difference Q4-Q1		0.56 (0.22, 0.91)	0.44 (0.14, 0.75)
AA 20:4(n-6)						0.27
Q1	166	0.05 ± 0.12	0.16 ± 0.12	0.11 ± 0.15	Reference
Q2	167	0.01 ± 0.13	0.20 ± 0.12	0.19 ± 0.16	0.09 (−0.34, 0.52)
Q3	167	−0.13 ± 0.13	0.07 ± 0.10	0.21 ± 0.16	0.10 (−0.31, 0.52)
Q4	168	−0.23 ± 0.12	0.15 ± 0.15	0.38 ± 0.18	0.25 (−0.20, 0.70)
Difference Q4-Q1		−0.28 (−0.60, 0.05)	−0.01 (−0.38, 0.36)

¹Quartiles for indicators are sex- and age-specific according to their distributions in the study population.

²Values are mean ± SE. Estimates are from growth curves built using mixed effects models with restricted cubic splines that accounted for within-child repeated BMI measurements.

³Differences in change are adjusted for sex, baseline age, baseline weight status, and household socioeconomic stratum.

⁴Test for linear trend from a linear regression model where an ordinal variable that represented quartiles of each fatty acid indicator was entered as a continuous predictor.

DISCUSSION

In this longitudinal investigation of school-age children in Bogotá, Colombia – a setting where obesity is becoming a serious public health problem, we explored the relation of PUFA markers with BMI trajectories. We found that lower serum concentrations of N-3 alpha-linolenic acid (ALA) were related to greater gains in adiposity during the follow-up period.

To date, only a few small cross-sectional investigations examined the relation of specific PUFAs with weight status in children, and the findings regarding ALA have been inconsistent. In a study of 60 overweight and 60 normal weight French adolescents, overweight youth had lower PUFA-to-saturated fatty acids ratio than normal-weight participants, although ALA concentrations were similar between the two groups (12). In another study of 10 obese and 15 lean adolescents, lower PUFA concentrations were observed in obese participants, mostly driven by DHA, with no difference in ALA (13). In recent investigations of Australian schoolchildren (11) and pre-pubertal Spanish children (19), erythrocyte and plasma ALA levels were positively related with obesity status. The challenge of reconciling these discrepancies is further compounded by the cross-sectional nature of the designs, since the associations could be explained by an effect of adiposity on fatty acid profiles (20).

Our longitudinal study is less susceptible to reverse-causation bias because we examined prospective changes in BAZ, the optimal measure of adiposity development in children that reflects fat growth rather than accrual of lean mass (21). Our results indicate that higher ALA is related to lower gains in adiposity. There are a several mechanisms that could explain this association. First, ALA reduces fat deposition by promoting expression of genes involved in hepatic fat oxidation (22) and thermogenesis (23). Second, ALA can displace LA from the shared delta6-desaturase enzyme, and prevent its conversion to AA, which is a trigger for adipogenesis through prostacyclin synthesis (an effector of adipocyte differentiation), and activation of cAMP pathways that favor pre-adipocyte maturation (3). Third, greater intake of fatty acids prone to oxidation is less likely to result in weight gain than intake of fatty acids prone to storage. ALA is the most highly oxidized fatty acid in humans (24), with >20% of ingested ALA catabolized for energy (25). Furthermore, greater ALA intake appears to increase oxidation rates rather than accumulating in tissues (26).

We also noted a marginally significant inverse relation between N-6 GLA and BMIZ change, which was unexpected since GLA is an intermediate in the biosynthesis of AA from LA – a pathway that ultimately leads to production of inflammatory and adipogenic AA-derived eicosanoids (3). However, a study in Zucker rats found that GLA administration reduced weight only among obese animals, implicating impaired LA to GLA desaturation in the etiology of obesity (27). Similarly, in a randomized controlled trial of 24 formerly obese adults, supplementation with 890 mg/day of GLA markedly reduced weight regain after 1 year (2.2 kg vs. 8.8 kg), suggesting that GLA is involved in suppression of fat accretion (28). This finding warrants additional investigation in other pediatric populations.

Our findings point towards potential nutritional interventions that could be implemented in populations with fatty acids status comparable to that of our study population. A previous study that investigated the composition and usage of cooking fats in Bogotá, Colombia found that mixed vegetable oils (composed of soybean, corn, and palm oil) and sunflower oil were most commonly used in this population (29). Biochemical analyses revealed that the mixed vegetable oils used by those of lower socioeconomic status had the highest ALA content. On the other hand, sunflower oil, which was preferred by the more affluent, contained negligible amounts of PUFAs and higher trans fat content (29). Because Colombia has one of the lowest intakes of marine fish worldwide (30), home-cooking oils represent the main source of dietary fatty acids in this setting. Thus, a feasible way to increase healthy PUFA intake could be to raise the proportion of soybean oil, which is rich in ALA, in the widely-used mixed vegetable oils or to promote replacement of cooking fats with soybean oil. Nutrition education campaigns may also be helpful for consumers to understand the importance of choosing healthy fats, and could be an effective way to foster selection of healthier cooking oils.

Our study has some limitations. First, we relied on one measurement of the PUFA markers at the time of enrollment as the primary exposure. However, serum fatty acids concentrations can be adequate biomarkers of long-term intake (31). Second, because PUFA concentrations are expressed as a relative percentage of total fatty acids, higher levels of one PUFA may correspond to a lower relative amount for others; caution is appropriate when interpreting markers of fatty acid intake. Third, generalizability of these findings to populations with different proportions of PUFA intake maybe limited.

In conclusion, higher N-3 ALA and, possibly, higher N-6 GLA are inversely associated with weight gain in school-age children. Despite the strengths of our longitudinal investigation, it is not possible to conclude that fatty acids status is causally related to the development of adiposity in children. This question deserves evaluation in intervention trials – perhaps, for example, through a randomized intervention of cooking oils in this particular population.

Abbreviations

PUFA

polyunsaturated fatty acids

ALA

alpha-linolenic acid

EPA

eicosapentaenoic acid

DHA

docosahexaenoic acid

LA

linoleic acid

GLA

gamma-linolenic acid

DGLA

dihomo-gamma-linolenic acid

AA

arachidonic acid

BMI

body mass index

BAZ

body mass index for age z-score

HAZ

height-for-age z-score