Triglycerides - 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 80 to 140 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 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. NHANES measurements adhere to detailed procedural manuals that specify every aspect of the measurement process. From subject preparation to data recording, each step follows standardized procedures that minimize measurement error. 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 Triglycerides 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 38 to 182, the 5th-to-95th percentile range of 144 represents typical population variation. The narrower interquartile range of 60 (from 80 to 140) captures where most values concentrate. This natural variation reflects the diversity in healthy populations.
38
5% of the population falls below this value. This represents the lower range of typical variation.
80
25% of the population falls below this value. This represents the lower-middle range.
110
This is the middle value. 50% of the population falls below and 50% falls above this value.
140
75% of the population falls below this value. This represents the upper-middle range.
182
95% of the population falls below this value. This represents the upper range of typical variation.
110
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.
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.
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 110. Values between the 5th and 95th percentiles (38 to 182) 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 (110), half the population is above and half below. Between the 25th (80) and 75th (140) 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.
