Estradiol - Health metric data from AACE
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Population health research has established robust benchmarks for this metric across diverse demographic groups. This analysis focuses specifically on Female aged Adult, with data representing All populations. The interquartile range of 100 to 250 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 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.
The procedure for measuring this metric follows evidence-based protocols designed to maximize accuracy and reproducibility. Key procedural elements include: appropriate subject positioning, correct equipment use, consistent timing, and accurate recording. When these elements are standardized, this metric measurements provide reliable data for health assessment and comparison.
The chart below shows how Estradiol 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.
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 30 (5th percentile) and 450 (95th percentile), defining the typical range for healthy individuals. At the center, the median value of 150 indicates that half the population falls above and half below this point. The interquartile range—100 to 250—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.
30
5% of the population falls below this value. This represents the lower range of typical variation.
100
25% of the population falls below this value. This represents the lower-middle range.
150
This is the middle value. 50% of the population falls below and 50% falls above this value.
250
75% of the population falls below this value. This represents the upper-middle range.
450
95% of the population falls below this value. This represents the upper range of typical variation.
150
The arithmetic average of all values. This may differ from the median if the distribution is skewed.
The intersection of demographic factors creates unique patterns in this metric that require matched reference data for accurate assessment. For Female All individuals aged Adult, the combination of ethnicity, age, and sex produces a specific profile that differs from other demographic combinations. Using precisely matched reference data provides the most relevant comparison for your individual measurement. This demographic specificity enhances the clinical utility and personal relevance of benchmark comparisons.
The factors influencing this metric span genetic inheritance, lifestyle behaviors, environmental conditions, and overall health status. This complexity means that individual values reflect numerous influences working together. While genetic factors set certain parameters, lifestyle modifications may still influence where values fall within those limits. Understanding these determinants supports meaningful interpretation of individual measurements.
Tracking this metric over time provides more valuable health information than single measurements. While individual values inform current status, trends reveal important patterns: stable values suggest consistent health status, while changing values may indicate developing conditions or successful interventions. Establishing your personal baseline—through repeated measurements under similar conditions—enables meaningful comparison over time. Regular monitoring, whether through clinical visits or personal tracking, supports early detection of meaningful changes and evaluation of health interventions.
Healthcare providers interpret this metric within comprehensive clinical assessment. but clinical interpretation weighs individual values against patient history, symptoms, other measurements, and treatment goals. Within Endocrine assessment, this metric contributes specific diagnostic and monitoring value. Clinicians use this metric data for screening, diagnosis, treatment monitoring, and outcome assessment—always interpreted within individual clinical context.
Research on this metric has established robust population benchmarks that inform clinical practice and public health policy. Population research on this metric combines rigorous measurement protocols with representative sampling to establish reliable benchmarks. These data support clinical practice, public health surveillance, and ongoing research.
this metric data serves practical purposes across multiple contexts. For individuals: understanding your values relative to benchmarks, tracking changes over time, and informing health discussions with providers. For healthcare: screening, diagnosis, treatment monitoring, and outcome assessment. For researchers: studying population health trends, evaluating interventions, and identifying health disparities. For public health: surveillance, policy development, and health promotion. This multi-level utility makes this metric benchmarks valuable across the health ecosystem.
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)
Normal this metric encompasses a range of values that varies by demographic group. For individuals aged Adult, Female, All population, the median value is 150. Values between the 5th and 95th percentiles (30 to 450) 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 (150), half the population is above and half below. Between the 25th (100) and 75th (250) 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 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.
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.
