Transferrin Saturation (Adult, Male) - Health metric data from Clinical Laboratory Standards
About this page: This page combines data from authoritative sources with AI-assisted analysis to provide comprehensive metric benchmarks. The content has been written and reviewed by our team to ensure accuracy, relevance, and quality.
Our content creation process combines multiple approaches to ensure quality and accuracy:
Our commitment: We prioritize accuracy and transparency. If you find any errors or have concerns about content accuracy, please contact us.
How does your this metric compare to others in your demographic group? For All population of Male in the Adult age range, understanding normal variation is crucial for meaningful health assessment. Values typically range from 13.6 to 46.5, 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.
Reliable this metric measurement depends on properly calibrated equipment and trained personnel. For consistent results, equipment should be calibrated according to manufacturer specifications, measurement environment should be controlled, and standardized procedures should be followed precisely. These quality measures ensure that this metric values are meaningful and comparable.
The chart below shows how Transferrin Saturation (Adult, Male) 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.
Comparing your this metric to population percentiles reveals where you stand relative to others in your demographic group. A value at the 25th percentile (23.3) means approximately one-quarter of the population has lower values than yours. At the 75th percentile (36.8), three-quarters of the population falls below your value. These comparisons provide context for understanding your individual measurement.
13.55
5% of the population falls below this value. This represents the lower range of typical variation.
23.25
25% of the population falls below this value. This represents the lower-middle range.
30
This is the middle value. 50% of the population falls below and 50% falls above this value.
36.75
75% of the population falls below this value. This represents the upper-middle range.
46.45
95% of the population falls below this value. This represents the upper range of typical variation.
30
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 Male 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.
What determines your this metric? Multiple factors contribute, from inherited genetic traits to daily lifestyle choices. Environmental influences, health conditions, and life stage all play roles in shaping individual values. Recognizing this multifactorial nature supports realistic interpretation and informed health decisions. Some influences offer opportunities for modification while others must simply be understood and accepted.
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 Blood Count 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.
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.
Данные о здоровье населения России собираются Росстатом в рамках выборочных наблюдений состояния здоровья населения.
Система здравоохранения России обеспечивает бесплатную медицинскую помощь через систему обязательного медицинского страхования (ОМС) и включает регулярную диспансеризацию.
Официальные данные Росстат / Минздрав ↗
Примечание: Основные данные из CDC NHANES (США). Местная статистика из официальных национальных обследований здоровья. (2024-01)
Normal this metric encompasses a range of values that varies by demographic group. For individuals aged Adult, Male, All population, the median value is 30. Values between the 5th and 95th percentiles (13.6 to 46.5) 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 (30), half the population is above and half below. Between the 25th (23.3) and 75th (36.8) 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.
