OGTT - Health metric data from American Diabetes Association Standards of Care 2024
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Population health research has established robust benchmarks for this metric across diverse demographic groups. This analysis focuses specifically on All aged Adult, with data representing All populations. The interquartile range of 93.1 to 126.9 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.
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 OGTT 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 68.9 to 151.1, the 5th-to-95th percentile range of 82.3 represents typical population variation. The narrower interquartile range of 33.8 (from 93.1 to 126.9) captures where most values concentrate. This natural variation reflects the diversity in healthy populations.
68.88
5% of the population falls below this value. This represents the lower range of typical variation.
93.13
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.
126.88
75% of the population falls below this value. This represents the upper-middle range.
151.13
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.
Biological sex creates distinct patterns in this metric through hormonal, anatomical, and physiological differences. All individuals demonstrate characteristic this metric distributions shaped by sex-specific hormonal profiles, body composition, and metabolic patterns. These differences emerge during puberty and persist throughout adulthood, making sex-specific reference data essential. Within All populations aged Adult, sex-based differences remain consistent with broader patterns while showing population-specific nuances. Using sex-matched benchmarks enables meaningful comparison that accounts for fundamental biological variation.
Multiple determinants shape this metric at individual and population levels. Inherited factors, developmental conditions, lifestyle choices, and current health status all contribute to observed values. Environmental and socioeconomic factors further influence patterns. Understanding these diverse influences supports nuanced interpretation of this metric measurements and realistic health goal setting.
this metric represents one component within the complex picture of overall health. No single metric defines health status; this metric values gain meaning when interpreted alongside other measurements, symptoms, lifestyle factors, and health history. A value that appears concerning in isolation may be less significant when viewed within broader context—and vice versa. Within Metabolic assessment, this metric contributes specific information that clinicians synthesize with related indicators. Healthcare providers integrate this metric with other data to form comprehensive assessments and recommendations. Maintaining perspective on this metric as part of a larger health picture supports balanced interpretation and appropriate response to individual values.
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.
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.
I dati italiani provengono dall'indagine multiscopo ISTAT "Aspetti della vita quotidiana" che raccoglie informazioni sulla salute di circa 20.000 famiglie ogni anno.
Il Servizio Sanitario Nazionale (SSN) italiano garantisce copertura universale con servizi di prevenzione e monitoraggio della salute.
Dati ufficiali da ISTAT / Ministero della Salute ↗
Nota: I dati principali provengono da CDC NHANES (USA). Le statistiche locali sono da indagini sanitarie nazionali ufficiali. (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 110. Values between the 5th and 95th percentiles (68.9 to 151.1) 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 (93.1) and 75th (126.9) 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.
