Cystatin C - Health metric data from Clinical Laboratory Standards
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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 0.8 to 1 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. 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 Cystatin C 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.
Interpreting your this metric measurement requires understanding what percentiles mean in practical terms. If your value matches the 50th percentile (0.9), you're exactly at the population median—neither high nor low relative to others. Values between 0.8 and 1 (25th to 75th percentile) are considered typical, representing the central half of the distribution. The extremes—below 0.7 or above 1.1—represent the outer 10% where values are less common but not necessarily abnormal. Percentile position helps contextualize your measurement within the broader population.
0.65
5% of the population falls below this value. This represents the lower range of typical variation.
0.8
25% of the population falls below this value. This represents the lower-middle range.
0.9
This is the middle value. 50% of the population falls below and 50% falls above this value.
1
75% of the population falls below this value. This represents the upper-middle range.
1.15
95% of the population falls below this value. This represents the upper range of typical variation.
0.9
The arithmetic average of all values. This may differ from the median if the distribution is skewed.
Population-specific patterns in this metric reflect the complex interplay of genetics, environment, and health behaviors. All populations exhibit characteristic this metric distributions that differ from other groups due to genetic predisposition, dietary patterns, physical activity norms, and environmental factors. These differences are well-documented in health research and inform demographic-specific reference ranges. Within All All aged Adult, these patterns become more specific, reflecting the intersection of multiple demographic factors. Acknowledging these differences doesn't imply better or worse—just different normal ranges that enable more accurate assessment.
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.
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.
From clinical perspective, this metric provides actionable health information when properly contextualized. In kidney function 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.
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.
Os dados antropométricos brasileiros vêm da Pesquisa de Orçamentos Familiares (POF) 2008-2009 do IBGE, com medições diretas de mais de 180.000 pessoas.
O Sistema Único de Saúde (SUS) oferece cobertura universal gratuita com programas de atenção básica e monitoramento de saúde populacional.
Dados oficiais de IBGE / Ministério da Saúde ↗
Nota: Os dados principais são do CDC NHANES (EUA). As estatísticas locais são de pesquisas nacionais oficiais. (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 0.9. Values between the 5th and 95th percentiles (0.7 to 1.1) 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 (0.9), half the population is above and half below. Between the 25th (0.8) and 75th (1) 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.
