Free T3 - Health metric data from American Thyroid Association
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How does your this metric compare to others in your demographic group? For All population of All in the Adult age range, understanding normal variation is crucial for meaningful health assessment. Values typically range from 2.2 to 3.8, encompassing 90% of the population. This guide provides the data-driven insights you need to interpret your measurements accurately and make informed decisions about your health.
A measurement of this metric As a quantifiable health indicator measured in appropriate units, this metric enables systematic health assessment. Understanding normal ranges and what influences this metric empowers better health management.
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 Free T3 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 range of this metric values in the population spans considerable variation, all within normal bounds. From 2.2 to 3.8, the 5th-to-95th percentile range of 1.6 represents typical population variation. The narrower interquartile range of 0.7 (from 2.7 to 3.3) captures where most values concentrate. This natural variation reflects the diversity in healthy populations.
2.18
5% of the population falls below this value. This represents the lower range of typical variation.
2.66
25% of the population falls below this value. This represents the lower-middle range.
3
This is the middle value. 50% of the population falls below and 50% falls above this value.
3.34
75% of the population falls below this value. This represents the upper-middle range.
3.82
95% of the population falls below this value. This represents the upper range of typical variation.
3
The arithmetic average of all values. This may differ from the median if the distribution is skewed.
Demographic factors shape this metric values in meaningful ways that must be considered for accurate interpretation. Ethnicity influences this metric through genetic, environmental, and cultural factors unique to All populations. Research consistently shows demographic-specific patterns that make matched reference data essential. Age-related changes in the Adult group reflect developmental, hormonal, and lifestyle factors characteristic of this life stage. Biological sex differences affect this metric through hormonal influences, body composition variations, and physiological distinctions between All individuals and others. Using demographic-matched benchmarks ensures your comparison reflects meaningful variation rather than expected population differences.
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.
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 Endocrine, 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.
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.
Scientific understanding of this metric continues to evolve through ongoing research. Current research explores how this metric relates to health outcomes, what factors influence it, and how benchmarks should be updated as populations change. This evolving science ensures that reference values remain relevant and useful.
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.
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 Adult, All, All population, the median value is 3. Values between the 5th and 95th percentiles (2.2 to 3.8) 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 (3), half the population is above and half below. Between the 25th (2.7) and 75th (3.3) 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.
