Fasting Glucose - 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 78 to 112 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 Healthcare providers and researchers utilize this metric (in appropriate units) as part of thorough health evaluation. This metric contributes to understanding overall health status and identifying potential areas of concern.
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 Fasting Glucose 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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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 54 (5th percentile) and 136 (95th percentile), defining the typical range for healthy individuals. At the center, the median value of 95 indicates that half the population falls above and half below this point. The interquartile range—78 to 112—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.
54
5% of the population falls below this value. This represents the lower range of typical variation.
78
25% of the population falls below this value. This represents the lower-middle range.
95
This is the middle value. 50% of the population falls below and 50% falls above this value.
112
75% of the population falls below this value. This represents the upper-middle range.
136
95% of the population falls below this value. This represents the upper range of typical variation.
95
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 18-29, 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.
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.
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 Metabolic, 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.
From clinical perspective, this metric provides actionable health information when properly contextualized. In metabolic assessment, this metric helps clinicians evaluate current status, track changes, and guide interventions. but individual assessment considers the complete clinical picture. Discussion with healthcare providers enables personalized interpretation relevant to your specific health situation.
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.
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.
Les données françaises proviennent de l'étude ESTEBAN (Étude de SanTé sur l'Environnement, la Biosurveillance, l'Activité physique et la Nutrition), menée par Santé Publique France.
Le système de santé français offre une couverture universelle avec un accent sur la médecine préventive et le suivi régulier des indicateurs de santé.
Données officielles de Santé Publique France ↗
Note : Les données principales proviennent de CDC NHANES (USA). Les statistiques locales sont issues d'enquêtes nationales officielles. (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 95. Values between the 5th and 95th percentiles (54 to 136) 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 (95), half the population is above and half below. Between the 25th (78) and 75th (112) 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.
