Waist Circumference - Health metric data from CDC NHANES 2017-2020
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Population health research has established robust benchmarks for this metric across diverse demographic groups. This analysis focuses specifically on Male aged 18-29, with data representing All populations. The interquartile range of 84.7 to 103.5 represents the central 50% of values where most healthy individuals fall. Understanding these benchmarks enables more accurate health monitoring and supports evidence-based decision-making.
A measurement of this metric Within the field of anthropometry—the science of human body measurement—this metric stands as a key indicator of physical status. Measured in appropriate units, it provides quantifiable data for health assessment, research studies, and clinical practice. Understanding this metric helps contextualize an individual's physical characteristics within broader population norms.
Measurement context significantly influences this metric values, making standardization essential. NHANES protocols carefully control measurement conditions to minimize contextual variability. Survey centers maintain consistent environments, and measurement timing follows prescribed schedules. These controls enable meaningful comparison of values across the entire survey population. Factors such as time of day, recent physical activity, food intake, and environmental conditions can affect this metric. Standardized measurement protocols account for these variables, ensuring that observed differences reflect true variation rather than measurement artifacts.
The chart below shows how Waist Circumference is distributed across the population. The percentile values help you understand where you fall relative to others in your demographic group.
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This metric does not have enough statistical parameters for generating a visualization.
The distribution of this metric values across the population follows a characteristic pattern that reveals important health insights. The central 90% of values fall between 71.1 (5th percentile) and 117.1 (95th percentile), defining the typical range for healthy individuals. At the center, the median value of 94.1 indicates that half the population falls above and half below this point. The interquartile range—84.7 to 103.5—encompasses the middle 50% of values, representing the most common range. Understanding where your measurement falls within this distribution provides meaningful context for health assessment.
71.1
5% of the population falls below this value. This represents the lower range of typical variation.
84.7
25% of the population falls below this value. This represents the lower-middle range.
94.1
This is the middle value. 50% of the population falls below and 50% falls above this value.
103.5
75% of the population falls below this value. This represents the upper-middle range.
117.1
95% of the population falls below this value. This represents the upper range of typical variation.
94.1
The arithmetic average of all values. This may differ from the median if the distribution is skewed.
Age significantly influences this metric through biological processes that vary across the lifespan. Young adults aged 18-29 typically demonstrate values reflecting peak physical development and physiological efficiency. For Male All individuals, age-specific benchmarks account for these developmental patterns. Age-appropriate reference data ensures accurate interpretation regardless of life stage.
Multiple layers of influence shape this metric at both population and individual levels. Genetic variation explains why different demographic groups show different typical ranges. Within those ranges, lifestyle factors including nutrition, activity, and health habits determine individual positioning. Environmental factors such as socioeconomic status, access to healthy foods, and physical activity opportunities further contribute to observed patterns. This multifactorial nature makes this metric a reflection of overall life circumstances.
this metric values contribute to overall health risk assessment when interpreted alongside other factors. Extreme values—particularly those below the 5th or above the 95th percentile—may indicate increased health risks depending on the specific metric and clinical context. However, being at an extreme doesn't automatically mean poor health; some individuals naturally fall at distribution tails. Risk assessment considers: absolute values, trends over time, family history, lifestyle factors, and co-existing health conditions. Within Anthropometry, this metric contributes specific risk information that clinicians integrate with broader health assessment. Understanding your this metric as one piece of a larger health puzzle supports informed decision-making.
From clinical perspective, this metric provides actionable health information when properly contextualized. In anthropometry assessment, this metric helps clinicians evaluate current status, track changes, and guide interventions. but individual assessment considers the complete clinical picture. Discussion with healthcare providers enables personalized interpretation relevant to your specific health situation.
Research on this metric has established robust population benchmarks that inform clinical practice and public health policy. The NHANES program, conducted continuously since the 1960s, provides among the most comprehensive this metric data available. This nationally representative survey combines standardized physical measurements with health interviews, enabling researchers to understand how this metric relates to health outcomes across diverse populations. NHANES data has informed countless research studies, clinical guidelines, and health policies.
Applying this metric knowledge to real-world health decisions involves several practical considerations. First, obtain accurate measurements under appropriate conditions. Second, compare your values to demographic-matched benchmarks. Third, consider trends over time rather than isolated values. Fourth, discuss findings with healthcare providers who can integrate this metric with your complete health picture. Fifth, if warranted, take evidence-based actions to optimize your this metric through lifestyle modifications or medical interventions.
Los datos españoles provienen de la Encuesta Nacional de Salud (ENSE) 2017, realizada por el Ministerio de Sanidad y el INE con más de 23.000 participantes.
El Sistema Nacional de Salud español ofrece cobertura universal gratuita con énfasis en atención primaria y medicina preventiva.
Datos oficiales de Ministerio de Sanidad ↗
Nota: Los datos principales son de CDC NHANES (EE.UU.). Las estadísticas locales son de encuestas nacionales oficiales. (2024-01)
Normal this metric encompasses a range of values that varies by demographic group. For individuals aged 18-29, Male, All population, the median value is 94.1. Values between the 5th and 95th percentiles (71.1 to 117.1) represent normal variation. Using demographic-matched benchmarks ensures appropriate comparison.
Percentiles show where your this metric falls relative to others in your demographic group. At the 50th percentile (94.1), half the population is above and half below. Between the 25th (84.7) and 75th (103.5) percentiles represents the middle half of the distribution—where most healthy values fall. Percentiles at extreme ends (below 5th or above 95th) are less common but not necessarily abnormal. Context matters for interpretation.
this metric can change over time due to age-related processes, lifestyle modifications, health conditions, and interventions. Some factors are relatively fixed (like genetics), while others respond to deliberate changes (like exercise or diet). In the 18-29 age range, age-related changes may be occurring. Tracking your this metric over time reveals personal trends that provide valuable health information. Consistent measurement conditions enable meaningful comparison of values over time.
Consider discussing your this metric with a healthcare provider if: values fall significantly outside normal range (below 5th or above 95th percentile), you've noticed substantial changes over time, values are associated with symptoms, or you have questions about health implications. Being at a percentile extreme doesn't automatically indicate problems—many healthy individuals naturally fall at distribution tails. Clinical significance depends on context, symptoms, and other health factors. Healthcare providers can offer personalized interpretation.
this metric values differ across ethnic groups due to genetic, environmental, and lifestyle factors. All populations show characteristic patterns that reflect population-specific genetics, dietary traditions, activity patterns, and environmental influences. These differences are normal and expected—not indicators of better or worse health. Using All-specific reference data ensures your comparison reflects meaningful variation rather than expected population differences. This demographic specificity improves the accuracy and relevance of health assessment.
