The authors have declared that no competing interests exist.
Conceived and designed the experiments: CH ZY. Performed the experiments: CH NK KS MS DA TT. Analyzed the data: CH NK HY. Contributed reagents/materials/analysis tools: CH KS MS. Wrote the paper: CH NK ZY.
The aims of this study were to 1) determine the distinct patterns of body mass index (BMI) trajectories in Japanese children, and 2) elucidate the maternal factors during pregnancy, which contribute to the determination of those patterns.
All of the children (1,644 individuals) born in Koshu City, Japan, between 1991 and 1998 were followed in a longitudinal study exploring the subjects’ BMI. The BMI was calculated 11 times for each child between birth and 12 years of age. Exploratory latent class growth analyses were conducted to identify trajectory patterns of the BMI z-scores. The distribution of BMI trajectories were best characterized by a five-group model for boys and a six-group model for girls. The groups were named “stable thin,” “stable average,” “stable high average,” “progressive overweight,” and “progressive obesity” in both sexes; girls were allocated to an additional group called “progressive average.” Multinomial logistic regression found that maternal weight, smoking, and skipping breakfast during pregnancy were associated with children included in the progressive obesity pattern rather than the stable average pattern. These associations were stronger for boys than for girls.
Multiple developmental patterns in Japanese boys and girls were identified, some of which have not been identified in Western countries. Maternal BMI and some unfavorable behaviors during early pregnancy may impact a child’s pattern of body mass development. Further studies to explain the gender and regional differences that were identified are warranted, as these may be important for early life prevention of weight-associated health problems.
Childhood obesity is associated with cardiovascular
There have also been a few studies that have explored BMI trajectories in early childhood. A study in the United States monitored children aged 9–16 years and found 4 developmental patterns: “constant obesity,” “gradual obesity,” “obesity followed by recovery of normal weight,” and “never obese.” Another study in the United States identified 3 patterns among children up to 12 years old
Although the determinants of these differential growth patterns are largely unknown, environmental exposures
Maternal ages ranged from 16 to 42 years (mean, 28.9 years) for boys and from 18 to 44 years (mean, 28.9) for girls; paternal ages ranged from 17 to 48 years (mean, 32.0) for boys and from 18 to 56 years (mean, 31.9) for girls (
Boys | Girls | |||||||||||||||||||||
No. of Participants | Goup 1 Stable Thin | Group 2 & 3 Stable Average | Group 4 Progressive Overweight | Group 5 Progressive Obesity | No. of Participants | Group 1 Stable Thin | Group 2 & 4 Stable Average | Group 3 Progressive Average | Group 5 Progressive Overweight | Group 6 Progressive Obesity | ||||||||||||
Variables | n | (%) | n | (%) | n | (%) | n | (%) | n | (%) | n | (%) | n | (%) | n | (%) | n | (%) | n | (%) | n | (%) |
Year of birth | ||||||||||||||||||||||
1991 | 107 | (100%) | 14 | (13.1%) | 80 | (74.8%) | 10 | (9.3%) | 3 | (2.8%) | 111 | (100%) | 19 | (17.1%) | 65 | (58.6%) | 6 | (5.4%) | 19 | (17.1%) | 2 | (1.8%) |
1992 | 111 | (100%) | 10 | (9.0%) | 87 | (78.4%) | 11 | (9.9%) | 3 | (2.7%) | 105 | (100%) | 8 | (7.6%) | 69 | (65.7%) | 16 | (15.2%) | 9 | (8.6%) | 3 | (2.9%) |
1993 | 107 | (100%) | 5 | (4.7%) | 89 | (83.2%) | 6 | (5.6%) | 7 | (6.5%) | 93 | (100%) | 17 | (18.3%) | 52 | (55.9%) | 11 | (11.8%) | 8 | (8.6%) | 5 | (5.4%) |
1994 | 95 | (100%) | 10 | (10.5%) | 77 | (81.1%) | 6 | (6.3%) | 2 | (2.1%) | 136 | (100%) | 17 | (12.5%) | 84 | (61.8%) | 15 | (11.0%) | 14 | (10.3%) | 6 | (4.4%) |
1995 | 110 | (100%) | 11 | (10.0%) | 81 | (73.6%) | 13 | (11.8%) | 5 | (4.5%) | 112 | (100%) | 16 | (14.3%) | 70 | (62.5%) | 9 | (8.0%) | 12 | (10.7%) | 5 | (4.5%) |
1996 | 90 | (100%) | 11 | (12.2%) | 66 | (73.3%) | 10 | (11.1%) | 3 | (3.3%) | 99 | (100%) | 11 | (11.1%) | 66 | (66.7%) | 9 | (9.1%) | 10 | (10.1%) | 3 | (3.0%) |
1997 | 113 | (100%) | 19 | (16.8%) | 74 | (65.5%) | 14 | (12.4%) | 6 | (5.3%) | 91 | (100%) | 12 | (13.2%) | 58 | (63.7%) | 5 | (5.5%) | 15 | (16.5%) | 1 | (1.1%) |
1998 | 92 | (100%) | 14 | (15.2%) | 70 | (76.1%) | 6 | (6.5%) | 2 | (2.2%) | 72 | (100%) | 13 | (18.1%) | 46 | (63.9%) | 7 | (9.7%) | 5 | (6.9%) | 1 | (1.4%) |
Total | 825 | (100%) | 94 | (11.4%) | 624 | (75.6%) | 76 | (9.2%) | 31 | (3.8%) | 819 | (100%) | 113 | (13.8%) | 510 | (62.3%) | 78 | (9.5%) | 92 | (11.2%) | 26 | (3.2%) |
Maternal age (years): means (SD) | 824 | 28.5 | (3.84) | 28.7 | (4.29) | 29.5 | (4.13) | 28.5 | (4.20) | 810 | 29.2 | (4.58) | 28.6 | (4.28) | 29.5 | (4.42) | 30.0 | (4.18) | 30.3 | (5.24) | ||
Maternal Body Mass Index (kg/m2): means (SD) | 720 | 19.9 | (2.60) | 20.7 | (2.61) | 22.2 | (3.47) | 22.7 | (3.30) | 694 | 19.5 | (2.38) | 20.6 | (2.62) | 21.3 | (3.21) | 21.5 | (2.87) | 23.7 | (4.55) | ||
Maternal educational attainment (more than high school) | 297 | (52.1%) | 36 | (56.3%) | 228 | (52.9%) | 23 | (43.4%) | 10 | (45.5%) | 301 | (52.3%) | 38 | (46.3%) | 196 | (54.6%) | 29 | (55.8%) | 30 | (47.6%) | 8 | (40.0%) |
Maternal parity (first birth) | 322 | (39.1%) | 38 | (40.4%) | 243 | (39.0%) | 25 | (34.2%) | 16 | (48.5%) | 357 | (43.6%) | 47 | (41.2%) | 234 | (46.3%) | 35 | (42.7%) | 32 | (35.2%) | 9 | (34.6%) |
Child’s Body Mass Index (kg/m2) at birth; means (SD) | 812 | 12.6 | (1.15) | 12.8 | (1.20) | 12.7 | (1.09) | 12.8 | (0.87) | 809 | 12.4 | (1.26) | 12.9 | (1.27) | 12.4 | (1.26) | 12.9 | (1.19) | 13.7 | (1.67) | ||
Maternal lifestyle at pregnancy registration | ||||||||||||||||||||||
Current Smoking (+) | 53 | (6.5%) | 4 | (4.3%) | 33 | (5.4%) | 9 | (11.8%) | 7 | (22.6%) | 44 | (5.5%) | 10 | (9.0%) | 24 | (4.8%) | 2 | (2.6%) | 6 | (6.7%) | 2 | (8.0%) |
Alcohol consumption (+) | 65 | (8.0%) | 7 | (7.6%) | 50 | (8.2%) | 5 | (6.7%) | 3 | (9.7%) | 87 | (10.9%) | 9 | (8.1%) | 56 | (11.3%) | 6 | (8.1%) | 11 | (12.2%) | 5 | (20.8%) |
Eating habits: Skipping breakfast (+) | 169 | (20.8%) | 15 | (16.3%) | 117 | (19.1%) | 25 | (32.9%) | 12 | (38.7%) | 168 | (20.7%) | 20 | (17.9%) | 101 | (20.0%) | 17 | (21.8%) | 23 | (25.3%) | 7 | (26.9%) |
Eating habits: Having afternoon snack (one or more times/day) | 612 | (76.0%) | 71 | (78.0%) | 470 | (77.3%) | 50 | (66.7%) | 21 | (67.7%) | 603 | (75.3%) | 86 | (78.9%) | 371 | (74.2%) | 63 | (81.8%) | 66 | (74.2%) | 17 | (65.4%) |
Eating habits: Having midnight snack every day (+) | 28 | (3.7%) | 4 | (4.5%) | 21 | (3.6%) | 2 | (3.0%) | 1 | (3.3%) | 20 | (2.6%) | 4 | (3.8%) | 10 | (2.1%) | 1 | (1.3%) | 3 | (3.4%) | 2 | (8.3%) |
Sleep status (Average, in a weekday) | ||||||||||||||||||||||
Sleep duration (hours): means (SD) | 805 | 7.4 | (0.78) | 7.4 | (0.90) | 7.3 | (0.80) | 6.9 | (1.03) | 805 | 7.36 | (0.86) | 7.34 | (0.92) | 7.5 | (0.94) | 7.25 | (0.82) | 7.0 | (0.93) | ||
Working (+) | 362 | (44.2%) | 36 | (38.7%) | 272 | (43.8%) | 34 | (45.9%) | 20 | (64.5%) | 375 | (46.3%) | 42 | (37.5%) | 244 | (48.4%) | 39 | (50.0%) | 40 | (44.4%) | 10 | (38.5%) |
Paternal age (years): means (SD) | 811 | 32.3 | (5.72) | 31.7 | (5.29) | 32.4 | (4.72) | 32.2 | (5.37) | 805 | 31.9 | (5.96) | 31.4 | (5.22) | 33.1 | (4.84) | 32.8 | (5.21) | 33.8 | (5.53) | ||
Paternal lifestyle at pregnancy registration | ||||||||||||||||||||||
Current Smoking (+) | 556 | (68.1%) | 57 | (62.0%) | 422 | (68.3%) | 48 | (63.2%) | 29 | (93.5%) | 545 | (67.5%) | 75 | (67.0%) | 339 | (67.4%) | 53 | (67.9%) | 58 | (65.2%) | 20 | (76.9%) |
Other family member’s lifestyle at pregnancy registration | ||||||||||||||||||||||
Current Smoking (+) | 615 | (78.4%) | 70 | (76.9%) | 468 | (78.8%) | 54 | (77.1%) | 23 | (79.3%) | 618 | (79.1%) | 84 | (79.2%) | 398 | (81.6%) | 55 | (74.3%) | 63 | (71.6%) | 18 | (72.0%) |
Abbreviations: SD, Standard deviation.
When modeling the BMI trajectory, the Bayesian Information Criterion (BIC) score increased as more groups were added. Therefore, based on clinical knowledge and the objectives of the analyses, a five-group model was selected for the boys and a six-group model for the girls (
Error bars indicate the standard error of the mean for each observed group. Group 1, “stable thin”; Group 2, “stable average”; Group 3, “stable high average”; Group 4, “progressive overweight”; Group 5, “progressive obesity.”
Error bars indicate the standard error of the mean for each observed group. Group 1, “stable thin”; Group 2, “stable average”; Group 3, “progressive average”; Group 4, “stable high average”; Group 5, “progressive overweight”; Group 6, “progressive obesity.”
An identical 5 groups were described for girls. Groups 1, 2, 4, 5, and 6 were named as “stable thin,” “stable average,” “stable high average,” “progressive overweight,” and “progressive obesity,” respectively (
A sensitivity analysis using the alternative dataset that included the BMI scores calculated at birth did not alter the numbers or shapes of the observed trajectory patterns.
Among the factors evaluated at the time of pregnancy, the mother’s BMI, smoking habits, skipping of breakfast, and sleep duration, as well as paternal smoking were associated with differences in the BMI trajectory patterns among boys. The child’s year of birth, mother’s age, alcohol consumption, snacking habits, psychosocial and socioeconomic status (e.g., educational attainment), and paternal age were not associated with the observed trajectory patterns. Amongst the girls, only the mother’s age and BMI, as well as the father’s age were associated with the BMI trajectory patterns (
Variables | Girls | Boys | ||||||
Crude | Adjusted |
Crude | Adjusted |
|||||
OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI | |
Maternal age | ||||||||
Stable thin | 1.03 | 0.98–1.08 | 0.99 | 0.94–1.04 | ||||
Stable average | 1.00 | 1.00 | ||||||
Progressive average (girls only) | 1.05 | 0.99–1.11 | ||||||
Progressive overweight | 1.07 | 1.01–1.12 | 1.05 | 0.99–1.11 | ||||
Progressive obesity | 1.06 | 0.97–1.16 | 1.01 | 0.93–1.10 | ||||
Maternal body mass index | ||||||||
Stable thin | 0.82 | 0.74–0.91 | 0.87 | 0.78–0.96 | ||||
Stable average | 1.00 | 1.00 | ||||||
Progressive average (girls only) | 1.09 | 1.00–1.20 | ||||||
Progressive overweight | 1.12 | 1.04–1.21 | 1.17 | 1.08–1.27 | ||||
Progressive obesity | 1.27 | 1.13–1.42 | 1.22 | 1.09–1.36 | ||||
Maternal educational attainment (more than high school) | ||||||||
Stable thin | 0.68 | 0.34–1.38 | 1.14 | 0.67–1.94 | ||||
Stable average | 1.00 | 1.00 | ||||||
Progressive average (girls only) | 0.95 | 0.53–1.71 | ||||||
Progressive overweight | 0.72 | 0.34–1.51 | 0.68 | 0.38–1.21 | ||||
Progressive obesity | 0.53 | 0.19–1.51 | 0.74 | 0.31–1.75 | ||||
Maternal parity (first childbirth) | ||||||||
Stable thin | 0.81 | 0.54–1.23 | 1.06 | 0.68–1.65 | ||||
Stable average | 1.00 | 1.00 | ||||||
Progressive average (girls only) | 0.86 | 0.54–1.38 | ||||||
Progressive overweight | 0.63 | 0.40–1.00 | 0.81 | 0.49–1.36 | ||||
Progressive obesity | 0.61 | 0.27–1.40 | 1.47 | 0.73–2.97 | ||||
Child’s BMI at birth | ||||||||
Stable thin | 0.72 | 0.06–0.85 | 0.86 | 0.72–1.04 | ||||
Stable average | 1.00 | 1.00 | ||||||
Progressive average (girls only) | 0.75 | 0.62–0.91 | ||||||
Progressive overweight | 1.01 | 0.85–1.21 | 0.93 | 0.75–1.14 | ||||
Progressive obesity | 1.46 | 1.10–1.95 | 0.99 | 0.14–1.34 | ||||
Maternal lifestyle at pregnancy registration | ||||||||
Current Smoking (+) | ||||||||
Stable thin | 1.98 | 0.92–4.26 | 1.87 | 0.71–4.95 | 0.80 | 0.28–2.32 | 0.57 | 0.16–1.97 |
Stable average | 1.00 | 1.00 | ||||||
Progressive average (girls only) | 0.54 | 0.12–2.33 | 0.67 | 0.14–3.28 | ||||
Progressive overweight | 1.43 | 0.57–3.59 | 1.89 | 0.63–5.67 | 2.37 | 1.09–5.16 | 1.80 | 0.72–4.53 |
Progressive obesity | 1.74 | 0.39–7.79 | 1.75 | 0.19–15.99 | 5.14 | 2.07–12.81 | 5.42 | 1.89–15.5 |
Alcohol consumption (+) | ||||||||
Stable thin | 0.69 | 0.33–1.45 | 0.58 | 0.25–1.36 | 0.92 | 0.41–2.10 | 0.89 | 0.38–2.09 |
Stable average | 1.00 | 1.00 | ||||||
Progressive average (girls only) | 0.69 | 0.29–1.67 | 0.82 | 0.33–2.06 | ||||
Progressive overweight | 1.09 | 0.55–2.18 | 1.17 | 0.55–2.48 | 0.80 | 0.31–2.08 | 0.74 | 0.25–2.18 |
Progressive obesity | 2.07 | 0.74–5.75 | 0.96 | 0.20–4.66 | 1.20 | 0.35–4.09 | 1.59 | 0.44–5.80 |
Eating habits: Skipping breakfast (+) | ||||||||
Stable thin | 0.87 | 0.51–1.48 | 0.99 | 0.53–1.82 | 0.83 | 0.46–1.49 | 0.70 | 0.37–1.34 |
Stable average | 1.00 | 1.00 | 1.00 | 1.00 | ||||
Progressive average (girls only) | 1.11 | 0.62–1.99 | 1.10 | 0.53–2.25 | ||||
Progressive overweight | 1.35 | 0.80–2.28 | 1.44 | 0.79–2.65 | 2.08 | 1.24–3.50 | 2.02 | 1.08–3.78 |
Progressive obesity | 1.47 | 0.60–3.60 | 2.09 | 0.66–6.67 | 2.68 | 1.27–5.68 | 3.50 | 1.52–8.08 |
Eating habits: Having afternoon snack (one or more times/day) | ||||||||
Stable thin | 1.30 | 0.79–2.15 | 1.23 | 0.70–2.19 | 1.04 | 0.61–1.77 | 1.25 | 0.70–2.24 |
Stable average | 1.00 | 1.00 | 1.00 | 1.00 | ||||
Progressive average (girls only) | 1.56 | 0.85–2.89 | 1.48 | 0.74–2.93 | ||||
Progressive overweight | 1.00 | 0.60–1.67 | 0.97 | 0.55–1.71 | 0.59 | 0.35–0.98 | 0.54 | 0.30–0.97 |
Progressive obesity | 0.66 | 0.29–1.51 | 0.65 | 0.23–1.85 | 0.62 | 0.28–1.34 | 0.52 | 0.23–1.22 |
Eating habits: Having midnight snack every day (+) | ||||||||
Stable thin | 1.85 | 0.57–6.00 | 1.75 | 0.40–7.71 | 1.25 | 0.42–3.74 | 0.71 | 0.15–3.29 |
Stable average | 1.00 | 1.00 | 1.00 | 1.00 | ||||
Progressive average (girls only) | 0.62 | 0.08–4.92 | N/A |
|||||
Progressive overweight | 1.64 | 0.44–6.10 | 2.42 | 0.56–10.39 | 0.82 | 0.19–3.58 | 1.12 | 0.23–5.44 |
Progressive obesity | 4.24 | 0.87–20.51 | 8.28 | 0.99–69.48 | 0.92 | 0.12–7.08 | 0.97 | 0.12–8.02 |
Sleeping duration (per 1 hour longer) | ||||||||
Stable thin | 1.03 | 0.82–1.29 | 1.11 | 0.84–1.45 | 1.08 | 0.84–1.38 | 1.16 | 0.88–1.54 |
Stable average | 1.00 | 1.00 | 1.00 | 1.00 | ||||
Progressive average (girls only) | 1.17 | 0.90–1.53 | 1.30 | 0.95–1.80 | ||||
Progressive overweight | 0.89 | 0.69–1.15 | 0.97 | 0.73–1.30 | 0.87 | 0.66–1.15 | 0.87 | 0.63–1.20 |
Progressive obesity | 0.69 | 0.44–1.08 | 0.85 | 0.29–2.48 | 0.55 | 0.37–0.83 | 0.56 | 0.35–0.89 |
Working (+) | ||||||||
Stable thin | 0.64 | 0.42–0.97 | 0.58 | 0.35–0.94 | 0.81 | 0.52–1.27 | 0.64 | 0.38–1.07 |
Stable average | 1.00 | 1.00 | 1.00 | 1.00 | ||||
Progressive average (girls only) | 1.07 | 0.66–1.72 | 1.09 | 0.63–1.90 | ||||
Progressive overweight | 0.85 | 0.54–1.34 | 0.83 | 0.50–1.38 | 1.09 | 0.67–1.77 | 1.35 | 0.78–2.36 |
Progressive obesity | 0.67 | 0.30–1.50 | 0.54 | 0.19–1.50 | 2.33 | 1.10–4.95 | 2.81 | 1.21–6.52 |
Paternal smoking (+) | ||||||||
Stable thin | 0.98 | 0.63–1.52 | 1.00 | 0.61–1.65 | 0.76 | 0.48–1.19 | 0.68 | 0.41–1.12 |
Stable average | 1.00 | 1.00 | 1.00 | 1.00 | ||||
Progressive average (girls only) | 1.03 | 0.62–1.71 | 1.05 | 0.59–1.86 | ||||
Progressive overweight | 0.91 | 0.56–1.45 | 1.03 | 0.61–1.75 | 0.80 | 0.48–1.31 | 0.70 | 0.40–1.22 |
Progressive obesity | 1.61 | 0.64–4.09 | 1.91 | 0.63–5.83 | 6.73 | 1.59–28.51 | 14.23 | 1.89–107.1 |
Other family member’s smoking (+) | ||||||||
Stable thin | 0.86 | 0.51–1.46 | 1.03 | 0.57–1.88 | 0.90 | 0.53–1.52 | 0.84 | 0.47–1.48 |
Stable average | 1.00 | 1.00 | 1.00 | 1.00 | ||||
Progressive average (girls only) | 0.65 | 0.37–1.16 | 0.63 | 0.33–1.19 | ||||
Progressive overweight | 0.57 | 0.34–0.96 | 0.65 | 0.37–1.16 | 0.91 | 0.50–1.64 | 0.75 | 0.39–1.41 |
Progressive obesity | 0.58 | 0.24–1.43 | 0.82 | 0.25–2.67 | 1.03 | 0.41–2.59 | 0.99 | 0.38–2.58 |
Abbreviations: BMI, body mass index; CI, confidence interval; OR, odds ratio.
Adjusted for children’s birth year and BMI, and maternal age, BMI at the time of pregnancy registry, parity, and educational attainment.
Because of small number, estimates for “eating midnight snack” are not presented.
The results of this study suggest that there are at least 5 distinct BMI trajectory patterns in Japanese boys and 6 among girls. Further, the BMI at the early stages of life (age = 1.5 years) was indicative, to some extent, of the subjects’ BMI at 12 years of age. This finding is similar to those of recent studies that show that adiposity in childhood is positively associated with that in adulthood
A study in the United States by Mustillo et al. followed 991 white children aged 9–16 years and identified 4 groups with different developmental trajectories, including a group developing obesity and then returning to normal BMI after the age of 12 and a group developing obesity after the age of 12
Our study and the Canadian study
Typically, an adiposity rebound (the first increase in BMI after a nadir) happens around 5–6 years of age
Potential determinants of physical developmental patterns can be categorized into genetic predisposition, the prenatal environment, and the postnatal environment
One potential limitation of the present study is that the number of groups and the shape of each group’s trajectory are not fully validated. However, our preliminary analysis using categories based on BMI trajectory (e.g., the “stable, thin” pattern includes those who have BMI z score of −1 or less at baseline and at the last survey) showed similar trends in the association between these patterns and their potential determinants including maternal BMI and smoking during pregnancy. This supports the validity of our analytical approach. Another potential limitation is the lack of certainty regarding its generalization to other regions of Asia, as the samples were only collected from a single region within Japan. Another potential limitation is the lack of detailed data on the physical development in utero (gestational weight gain) that could also affect the growth trajectories after birth. Moreover, the estimates based on our multivariate models may not be sufficiently adjusted for their potential measured and unmeasured confounders. We selected the covariate to be adjusted based on the theoretical consideration of confounding and the validity of statistical modeling (e.g., avoiding multicollinearity between variables). Although a 12-year longitudinal study period was an advantage of this study, further studies may require an even longer observation period with repeated measurements. Such a study would be particularly important in order to understand the independent and interactive impact of heredity and pre- and postnatal environments on BMI trajectories
In conclusion, we found multiple trajectories of body mass development, which start to diverge early in life. Some modifiable factors were also identified, which could determine unfavorable trajectories. Based on data from this and other studies, BMI trajectories appear to vary across demographics, with gender and region being the main contributing elements. Data from this study support the concept that preventive interventions focused on the early development period, which target modifiable individual and environmental determinants, would likely be effective. A better understanding of the underlying mechanisms and determinants of BMI trajectory patterns are expected to make those interventions more effective.
The analyses were based on data obtained through Project Koshu, a register-based prospective cohort study in Japan. The study population comprised all 1,644 children (825 boys and 819 girls) born between April 1991 and March 1998 in Koshu City, Japan, and their mothers. The expectant mothers were recruited at the beginning of their pregnancy, throughout Koshu City, where the local law requires registration of all new pregnancies. During pregnancy registration, a questionnaire on the lifestyles and the habits of the mothers and their children and families was administered to the mothers. During infant medical examinations, data were obtained regarding the infant’s growth and physical characteristics. As the children entered school, anthropometric data continued to be collected during annual measurements in each grade, as required by the School Health Law. Data of 1518 children (768 boys and 750 girls; 92.3%) who had been followed for 12 years, with at least 1 usable data point in their follow-up period, were analyzed. Three pairs of twins as well as participants who lacked baseline information on weight and height were excluded from the data analyses. Overall participation rates fell during the course of the study from 84.6% at 18 months of age to 74.9% by age 12.
Data on the birth height and weight of the children in the study were obtained from the Maternal and Child Health Handbook. This record serves as an aid in monitoring child health and growth and is required to be provided to expectant mothers at the time of pregnancy registration
Although direct evidence regarding the determinants of trajectory patterns of childhood BMIs is lacking, some empirical studies have suggested that maternal health behaviors during pregnancy (smoking, alcohol consumption, eating habits, and sleep status), socioeconomic status, and maternal BMI scores impact a child’s weight
BMI trajectories were determined by fitting a semiparametric mixture model, using the PROC TRAJ macro in SAS version 9.2 (SAS Institute, Cary, NC)
Following Nagin’s suggestions
To explore the factors determining the BMI trajectory patterns in the children, the basic statistics were described, and their crude associations with BMI trajectory patterns were tested using univariate multinomial logistic regressions. Then, multivariate multinomial logistic regressions were fitted to identify the independent impact of each factor on the children’s BMI trajectory patterns. These analyses were performed separately for boys and girls because of the gender differences in physical development
This study was approved by the Ethical Review Board of the University of Yamanashi, School of Medicine. A full description of the setting, sample, and data collection methods can be found elsewhere
The authors thank the staff of the Administrative Office of Koshu City for their cooperation.