Total Cholesterol - 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 145 to 199 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 This health metric, measured in appropriate units, provides valuable information for health assessment and monitoring. Understanding what this metric represents and how it varies across populations helps individuals and healthcare providers make informed decisions.
Quality assurance in this metric measurement involves multiple layers of verification and standardization. The NHANES program implements comprehensive quality control including equipment maintenance schedules, technician certification requirements, duplicate measurement protocols, and statistical monitoring of measurement distributions. These measures ensure data integrity across the entire survey period. Whether in clinical settings or research contexts, quality this metric measurement requires: validated equipment, trained personnel, standardized protocols, and ongoing quality monitoring. These elements combine to produce trustworthy health data.
The chart below shows how Total Cholesterol 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 106 to 238, the 5th-to-95th percentile range of 132 represents typical population variation. The narrower interquartile range of 54 (from 145 to 199) captures where most values concentrate. This natural variation reflects the diversity in healthy populations.
106
5% of the population falls below this value. This represents the lower range of typical variation.
145
25% of the population falls below this value. This represents the lower-middle range.
172
This is the middle value. 50% of the population falls below and 50% falls above this value.
199
75% of the population falls below this value. This represents the upper-middle range.
238
95% of the population falls below this value. This represents the upper range of typical variation.
172
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 18-29 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 Male 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.
What can your this metric measurement tell you about potential health actions? Values within normal ranges generally require continued monitoring rather than intervention. Values at extremes may suggest opportunities for lifestyle modification or the need for further evaluation. Key questions to consider: Has your this metric changed significantly over time? Do you have symptoms related to this metric? Do other health indicators suggest concern? Are lifestyle modifications possible? Many factors influencing this metric respond to lifestyle modifications, making proactive health management potentially impactful.
Clinical utility of this metric extends beyond simple comparison to population norms. Healthcare providers consider: how values compare to demographic-matched benchmarks, whether significant changes have occurred, presence of associated symptoms, and relationship to other clinical findings. individual clinical significance depends on broader context. this metric contributes specific information to blood lipids evaluation. This nuanced approach enables meaningful clinical decision-making.
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.
한국 데이터는 질병관리청이 매년 실시하는 국민건강영양조사(KNHANES)에 기반합니다. 이 조사는 전국적으로 약 10,000명을 대상으로 직접 측정을 실시합니다.
한국의 국민건강보험제도는 전 국민을 대상으로 하며, 국가건강검진 프로그램을 통해 정기적인 건강 모니터링을 제공합니다.
공식 데이터 질병관리청 ↗
참고: 주요 데이터는 CDC NHANES(미국)에서 가져온 것입니다. 지역 통계는 공식 국가 건강 조사에서 가져온 것입니다. (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 172. Values between the 5th and 95th percentiles (106 to 238) 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 (172), half the population is above and half below. Between the 25th (145) and 75th (199) 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.
