High Maternal Age and Low Pre-Pregnancy Body Mass Index Correlate with Lower Birth Weight of Male Infants

Sayuri Fukuda1, Yurika Tanaka, Kiyomi Harada

  • 1Department of Food and Nutrition, Kyoto Bunkyo Junior College.

In Japan, the percentage of leanness has been increasing in young women, and the percentage of low birth weight infants (< 2,500 g) has increased. Moreover, the average age of primiparas rose 3.5 years during the last 30 years. The purpose of this study was to clarify the relationship between maternal age and the influence of maternal pre-pregnancy physique on the neonatal physique of infants. Questionnaires were issued to the participants and collected when they submitted their gestational notifications at their local ward office in Kyoto Prefecture. After delivery, we obtained information on the course of the pregnancy and the neonatal physique of the infants from the participant's maternal passbooks. A total of 454 mothers (age 20 ≥) were analyzed: 161 young mothers (aged 20 to 29 years), 185 mothers (aged 30 to 34 years), and 108 older mothers (age ≥ 35). Overall, the mean rate of leanness (pre-pregnancy BMI < 18.5) was 23.8%. We found that birth weight was significantly lower in female infants, born to lean young mothers, compared to non-lean young mothers, whereas no significant difference was detected in other mothers (age ≥ 30), irrespective of pre-pregnancy BMI. By contrast, male infants, born to older lean mothers (age ≥ 35), showed significantly lower birth weight. Thus, maternal pre-pregnancy BMI exerts differential effects on the fetal growth (neonatal physique), depending on the maternal age and the sex of infants. We need to improve BMI in pre-pregnancy women, especially those in the twenties and 35 years old or over.

Related Concept Videos

Two-Way ANOVA01:17

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
3.5K
Coefficient of Correlation01:12

Coefficient of Correlation

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
8.9K
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.3K
Obesity01:24

Obesity

The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
1.5K
Correlations02:20

Correlations

Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
36.8K
Factors Affecting Body Temperature01:28

Factors Affecting Body Temperature

As a nurse, it is vital to understand the factors affecting body temperature to monitor variations and effectively evaluate deviations from regular.
Factors may  include:
9.8K