Audited —·Last updated 27 Jul 2026·5 citations·Tier 1·0 uses

BMI Calculator for Children

Calculate BMI-for-age percentiles for kids and teens using CDC growth charts. Get z-scores and weight status categories based on age and sex.

BMI Calculator for Children

Weight unit
Height unit
Biological sex
BMI
17.5583
BMI-for-age percentile
73.35
BMI-for-age z-score
0.6235

Background.

Body mass index in children does not function as a fixed diagnostic threshold the way it does in adults. A BMI of 22 kg/m² indicates obesity in a seven-year-old boy but represents a healthy weight in a thirty-year-old woman. Because children grow in both height and adiposity as they age, the Centers for Disease Control and Prevention publishes sex-specific BMI-for-age reference curves that replace the adult cut-points of 18.5, 25, and 30 with percentile-based categories that evolve month by month from age two through nineteen. Pediatricians, school nurses, and parents use these percentiles to screen for underweight, healthy weight, overweight, and obesity during well-child visits, and the values are recorded longitudinally to detect concerning trajectories even when a single measurement falls within the normal range.

The CDC 2000 Growth Charts constitute the clinical standard in the United States and are widely adopted in pediatric practice globally. They were constructed from nationally representative survey data collected between 1963 and 1994, with supplemental data to ensure adequate representation of infants and toddlers. Rather than plotting raw BMI against age on a linear scale, the reference uses the LMS method developed by Tim Cole and Pamela Green in 1992. This technique applies a Box-Cox power transformation to normalize the skewed distribution of BMI at each month of age and sex, producing a z-score that can be converted to a percentile using the standard normal cumulative distribution function. The resulting percentiles are analogous to height-for-age or weight-for-age percentiles: they describe where a particular child's BMI falls relative to the reference population of the same age and sex.

Clinical interpretation follows four categories defined by the CDC. A child below the 5th percentile is classified as underweight, a range that may prompt evaluation for malnutrition, endocrine disorders, or chronic disease. The 5th to 84th percentile inclusive is considered healthy weight. The 85th to 94th percentile marks overweight, a screening category that warrants dietary counseling and activity assessment. At or above the 95th percentile, the classification is obesity, which is associated with elevated risk of type 2 diabetes, nonalcoholic fatty liver disease, obstructive sleep apnea, and adverse psychosocial outcomes in pediatric populations. These thresholds were chosen to align with adult health risk while accounting for the natural variability of body composition during growth and puberty.

The calculator presented here accepts weight and height in either metric or US customary units, the child's age in years and months, and biological sex. It computes raw BMI using the standard Quetelet formula, then maps the result to the appropriate CDC LMS parameters for that exact age and sex, calculates the z-score, and reports the corresponding percentile and weight status category. The tool is designed for screening, not diagnosis. A single elevated percentile should be confirmed with additional measures such as waist circumference, assessment of family history, and review of growth velocity over time. Nevertheless, the percentile output provides an objective, normed benchmark that moves the conversation beyond subjective judgments of a child's body size. School-based screening programs in multiple states use these same percentiles to track population-level trends in pediatric adiposity over time.

What is bmi calculator for children?

BMI-for-age is the preferred metric for assessing weight status in children and adolescents aged two to twenty years. It is calculated as body mass in kilograms divided by the square of stature in meters, identical to adult BMI, but its interpretation depends on comparison to age- and sex-specific reference data rather than fixed universal thresholds. The metric accounts for the fact that healthy adiposity changes dramatically during childhood: a typical two-year-old has a higher BMI than a typical six-year-old, and pubertal growth spurts alter the relationship between mass and height in sex-specific ways.

The output of a BMI-for-age assessment is expressed as a percentile, which indicates the percentage of the reference population of the same age and sex that has a lower BMI. For example, a child at the 75th percentile has a higher BMI than three-quarters of the reference cohort. The z-score quantifies the same position in standard-deviation units relative to the transformed reference distribution. Both measures are derived from the LMS method, which normalizes the right-skewed distribution of adiposity at each age. BMI-for-age is recommended by the CDC, the American Academy of Pediatrics, and the World Health Organization for routine growth monitoring, though it is acknowledged that the metric does not distinguish between fat mass and lean mass and may misclassify very muscular children.

How to use this calculator.

  1. Enter the child's weight in kilograms or pounds, selecting the correct unit.
  2. Enter the child's height in centimeters or inches, selecting the correct unit.
  3. Enter the child's age in years and months, ensuring the total falls between 2 years 0 months and 19 years 11 months.
  4. Select the child's biological sex, male or female.
  5. Click calculate to receive the BMI, BMI-for-age percentile, z-score, and weight status category.
  6. Review the percentile in the context of prior measurements to assess growth trajectory rather than a single data point.
  7. Consult a pediatric healthcare provider if the category is underweight, overweight, or obese, or if the percentile has crossed two major lines upward or downward over time.

The formula.

The Quetelet index, or body mass index, was introduced in 1832 by the Belgian polymath Adolphe Quetelet as a measure of the average build in populations. It is defined as weight divided by the square of height. In adults, this ratio correlates with adiposity and metabolic risk, which permitted the World Health Organization to establish fixed thresholds for underweight, normal weight, overweight, and obesity. In children, however, the correlation between BMI and adiposity varies with developmental stage, and the distribution of BMI at any given age is right-skewed rather than normal. This means that adult thresholds are clinically meaningless below age twenty, and simple standard-deviation bands would misclassify children because the skewness differs by age and sex.

To solve this problem, Tim Cole and Pamela Green proposed the LMS method in 1992, which has since become the international standard for constructing growth reference curves. At each month of age and for each sex, three parameters are estimated from the reference population data: L (lambda), the power in a Box-Cox transformation; M (mu), the median; and S (sigma), the coefficient of variation. The Box-Cox transformation maps the skewed BMI distribution to a normal distribution. For a given child's BMI, the z-score is computed by first applying the power transformation (BMI/M)^L, then centering and scaling by the parameters L and S. If L is exactly zero, the transformation collapses to the natural logarithm. The resulting z-score follows a standard normal distribution, so the percentile is obtained from the cumulative distribution function.

The CDC 2000 Growth Charts provide LMS parameters for ages twenty-four through two hundred forty months. The calculator looks up the L, M, and S values corresponding to the child's exact age in months and sex, interpolating between tabulated months if necessary. The z-score is then capped at ±3.0 for extreme values to avoid outliers driven by measurement error or rare pathological conditions. The percentile output is the primary clinical communication tool, while the z-score is used in research and in electronic health record algorithms that flag children whose trajectories deviate from expected channels.

A worked example.

Example

A nine-year-and-six-month-old boy weighs 32 kilograms and stands 135 centimeters tall. His mother wants to know how his BMI compares to other boys his age. First, convert his height to meters: 135 divided by 100 equals 1.35 meters. His BMI is 32 divided by 1.35 squared, which is 32 divided by 1.8225, yielding 17.56 kilograms per meter squared. Using the CDC 2000 reference for a 114-month-old male, the LMS parameters are L equals negative 1.557, M equals 16.14, and S equals 0.1266. Raising the BMI-to-M ratio to the power L gives 0.8765. Subtracting 1 and dividing by L times S produces a z-score of 0.626. The standard normal cumulative distribution function converts this to the 73.4th percentile. Because this falls between the 5th and 85th percentiles, the calculator classifies him as healthy weight. The result suggests his adiposity is consistent with approximately three-quarters of boys his exact age. His pediatrician will recheck his percentile at his next annual well-child visit to monitor growth velocity.

age Months6
age Years9
weight32
height135

Frequently asked questions.

Why can't I use adult BMI cut-points for my child?
Adult thresholds of 18.5, 25, and 30 kg/m² were derived from epidemiological correlations between BMI and mortality risk in fully grown populations. Children undergo rapid changes in the ratio of lean mass to fat mass, limb length to trunk length, and overall body proportions as they progress through infancy, childhood, and puberty. A BMI of 20 kg/m² is near the median for a twelve-year-old girl but would classify a five-year-old girl as obese. The CDC BMI-for-age percentiles account for these developmental shifts by comparing each child only to reference data from the same age and sex, ensuring that the classification reflects relative adiposity within a homogeneous developmental cohort rather than an arbitrary adult standard.
What is the LMS method and why is it necessary?
LMS stands for Lambda-Mu-Sigma. It is a statistical technique that applies a Box-Cox power transformation to normalize skewed growth data at each age point. Without LMS, percentiles computed from raw BMI would be inaccurate because BMI distributions in children are right-skewed, meaning a small number of children with high adiposity stretch the upper tail. The L parameter controls the skewness correction, M is the median, and S is the coefficient of variation. Together they allow any individual BMI to be converted to a z-score that follows a standard normal distribution. This method was introduced by Cole and Green in 1992 and is now used by the CDC, WHO, and every major pediatric growth reference.
Can BMI-for-age misclassify athletic or muscular children?
Yes. Like adult BMI, BMI-for-age measures mass relative to height without distinguishing adipose tissue from muscle, bone, or viscera. A young athlete with substantial lean muscle mass may have a BMI above the 85th percentile and be classified as overweight despite having low body fat. Conversely, a sedentary child with low muscle mass but normal adiposity may fall in the healthy weight range despite having excess fat. For this reason, the American Academy of Pediatrics recommends that BMI-for-age be used as a screening tool, not a diagnostic test. When a percentile is elevated, clinicians may follow up with skinfold thickness measurements, bioelectrical impedance analysis, or dual-energy x-ray absorptiometry to assess body composition directly.
What does it mean if my child's percentile crosses two major lines on the growth chart?
Crossing two major percentile lines—defined as the 5th, 10th, 25th, 50th, 75th, 85th, 90th, and 95th percentiles—in either direction over a six- to twelve-month interval is a red flag that warrants clinical evaluation. A rapid upward crossing may indicate excessive weight gain, precocious puberty, or endocrine dysfunction such as Cushing syndrome. A rapid downward crossing may signal malnutrition, chronic illness, eating disorders, or malabsorption. The CDC and the American Academy of Pediatrics emphasize that growth velocity and trajectory are more informative than any single measurement. Parents should keep a record of percentile trends and bring them to well-child visits for interpretation by the pediatrician.
How does the WHO child growth standard differ from the CDC reference?
The WHO Multicentre Growth Reference Study, published in 2006, describes how children should grow under optimal conditions—breastfed, healthy, and living in environments that support unconstrained growth. The CDC 2000 charts, by contrast, describe how children in the United States did grow during the 1960s through 1990s, including both breastfed and formula-fed infants and children from diverse socioeconomic backgrounds. The WHO standard is recommended for children under two years globally, while the CDC reference remains common in US clinical practice for ages two to twenty. The two systems yield slightly different percentiles because their reference populations and inclusion criteria differ.
At what age does a child transition to adult BMI categories?
The CDC BMI-for-age reference covers ages two through nineteen years and eleven months. At age twenty, individuals transition to adult BMI thresholds because growth in stature is largely complete and the correlation between BMI and metabolic risk stabilizes. The 85th percentile at age nineteen corresponds closely to a BMI of 30 kg/m² for males and females, providing continuity between the pediatric and adult classification systems. Healthcare providers typically begin using adult cut-points at the patient's twentieth birthday, though some electronic health record systems automatically switch at the first visit after age nineteen.
What is a z-score and how is it different from a percentile?
A z-score measures how many standard deviations an individual's transformed BMI lies above or below the median of the reference population. It is a continuous, symmetric scale centered at zero. A z-score of 0 corresponds to the 50th percentile, +1 to roughly the 84th percentile, and +2 to roughly the 97.7th percentile. Percentiles are easier for parents to interpret because they describe relative rank, but z-scores are preferred in research and clinical algorithms because they preserve the full continuous distribution and can be averaged across groups. The calculator outputs both so that users can communicate with clinicians using whichever metric their practice prefers.
Should I be concerned if my child is at the 90th percentile but looks healthy?
The 90th percentile falls within the overweight screening category (85th to 94th percentile), which indicates a higher-than-average adiposity relative to same-age peers. It does not mean the child is necessarily unhealthy, but it does signal an increased probability of persistent overweight into adulthood and a higher risk of insulin resistance, dyslipidemia, and hypertension compared to children below the 85th percentile. The American Academy of Pediatrics recommends dietary counseling, increased physical activity, and reduction of sedentary screen time for children in this range. A single measurement should be confirmed with repeat assessments and review of the growth trajectory before any intervention is prescribed.
Can I use this calculator for children under two years old?
No. For children under twenty-four months, weight-for-length percentiles are used instead of BMI-for-age because the relationship between mass and length changes rapidly during infancy and BMI is not a valid indicator of adiposity in this age group. The WHO and CDC both provide weight-for-length charts for infants from birth to twenty-four months. Parents and clinicians should use those references for infants and toddlers under two, then switch to BMI-for-age at the second birthday. Using BMI-for-age below age two can produce misleading classifications because the metric was not validated in that developmental window.

References& sources.

  1. [1]Kuczmarski, R.J., Ogden, C.L., Guo, S.S., et al. (2002). "2000 CDC Growth Charts for the United States: Methods and Development." Vital and Health Statistics 11(246).
  2. [2]Cole, T.J. and Green, P.J. (1992). "Smoothing reference centile curves: the LMS method and penalized likelihood." Statistics in Medicine 11(10):1305-1319. doi:10.1002/sim.4780111007
  3. [3]CDC (2022). "About Child & Teen BMI."
  4. [4]Himes, J.H. and Dietz, W.H. (1994). "Guidelines for overweight in adolescent preventive services: recommendations from an expert committee." Am J Clin Nutr 59(2):307-316. doi:10.1093/ajcn/59.2.307
  5. [5]Barlow, S.E. and the Expert Committee. (2007). "Expert Committee Recommendations Regarding the Prevention, Assessment, and Treatment of Child and Adolescent Overweight and Obesity: Summary Report." Pediatrics 120(Suppl 4):S164-S192. doi:10.1542/peds.2007-2329C
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