BODY BENCHMARKS

Name: Body Benchmarks Human Metrics Database
Creator: Body Benchmarks

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BUN, Adult, All, All

BUN - Health metric data from CDC NHANES 2017-2020

Body Benchmarks Research TeamMPH, MS

Health Metrics Specialists

Dedicated team of health scientists and data analysts specializing in body metrics research and population health data.

About Body Publications

Reviewed by: Health Metrics Team

Comprehensive Guide to BUN, Adult, All, All

Clinical interpretation of this metric requires nuanced understanding of population distributions and demographic variation. This reference guide addresses All in the Adult age bracket, specifically within All populations, providing healthcare-relevant benchmarks. With a central tendency of 14 and well-characterized percentile distributions, these data support informed clinical decision-making. From measurement methodology to health implications, this analysis covers the essential aspects that practitioners and patients alike need to understand.

What is BUN?

A measurement of this metric The measurement of this metric in standard units provides objective health data that supports clinical decisions and personal health monitoring. Population reference values contextualize individual measurements within expected ranges.

How is BUN Measured?

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.

Distribution & Percentiles

The chart below shows how BUN is distributed across the population. The percentile values help you understand where you fall relative to others in your demographic group.

Insufficient data for visualization

This metric does not have enough statistical parameters for generating a visualization.

Understanding Percentile Distribution

Interpreting your this metric measurement requires understanding what percentiles mean in practical terms. If your value matches the 50th percentile (14), you're exactly at the population median—neither high nor low relative to others. Values between 11.3 and 16.7 (25th to 75th percentile) are considered typical, representing the central half of the distribution. The extremes—below 7.4 or above 20.6—represent the outer 10% where values are less common but not necessarily abnormal. Percentile position helps contextualize your measurement within the broader population.

Percentile Values Breakdown

5th Percentile (P5)

7.42

5% of the population falls below this value. This represents the lower range of typical variation.

25th Percentile (P25)

11.3

25% of the population falls below this value. This represents the lower-middle range.

50th Percentile (Median)

This is the middle value. 50% of the population falls below and 50% falls above this value.

75th Percentile (P75)

16.7

75% of the population falls below this value. This represents the upper-middle range.

95th Percentile (P95)

20.58

95% of the population falls below this value. This represents the upper range of typical variation.

Mean (Average)

The arithmetic average of all values. This may differ from the median if the distribution is skewed.

Statistical Summary

Standard Deviation4

Distribution TypeNormal

PopulationAdult, All

Demographic Variations in BUN

Biological sex creates distinct patterns in this metric through hormonal, anatomical, and physiological differences. All individuals demonstrate characteristic this metric distributions shaped by sex-specific hormonal profiles, body composition, and metabolic patterns. These differences emerge during puberty and persist throughout adulthood, making sex-specific reference data essential. Within All populations aged Adult, sex-based differences remain consistent with broader patterns while showing population-specific nuances. Using sex-matched benchmarks enables meaningful comparison that accounts for fundamental biological variation.

Factors Affecting BUN

this metric emerges from the interplay of nature and nurture across the lifespan. Genetic factors establish physiological frameworks, while lifestyle choices, environmental conditions, and health status shape specific values. Age-related changes add another layer of influence. Recognizing this complexity helps interpret measurements accurately and identify realistic opportunities for health optimization.

Health Implications of BUN

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 Kidney Function, 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.

Clinical Significance

From clinical perspective, this metric provides actionable health information when properly contextualized. In kidney function 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 Insights

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.

Practical Applications

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.

🇯🇵 地域の健康データ: 日本

✓公式情報源で確認済みのデータ

日本のデータは厚生労働省が毎年実施する「国民健康・栄養調査」に基づいています。2019年調査では約5,000世帯が参加しました。

日本の国民皆保険制度は全国民をカバーし、定期健康診断と予防医療を重視しています。

公式データ厚生労働省 ↗

注：主要データはCDC NHANES（米国）からのものです。地域統計は公式の国民健康調査に基づいています。 (2024-01)

📊Data Transparency & Sources

Sources & References

Source Citation

Source:CDC NHANES 2017-2020

Year:2020-2024

Population:Adult All (All)

Evidence Level:Level 1 (nationally representative survey)

View Original Source →

Frequently Asked Questions

Is my this metric within normal limits?

Normal this metric encompasses a range of values that varies by demographic group. For individuals aged Adult, All, All population, the median value is 14. Values between the 5th and 95th percentiles (7.4 to 20.6) represent normal variation. Using demographic-matched benchmarks ensures appropriate comparison.

Where does my this metric rank compared to others?

Percentiles show where your this metric falls relative to others in your demographic group. At the 50th percentile (14), half the population is above and half below. Between the 25th (11.3) and 75th (16.7) 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.

What influences changes in this metric?

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 Adult 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.

What this metric values require medical attention?

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.

Why do this metric values differ across ethnic groups?

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.

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