Fasting Plasma Glucose

Risk Exposure Overview

Glucose is the primary energy source of the cells of the human body. Homeostasis of glucose metabolism is assessed by measuring plasma glucose levels either by measuring fasting plasma glucose (FPG), performing an oral glucose tolerance test (OGTT), or analyzing glycated hemoglobin (HbA1c). These measurements can be used to classify subjects as having normal glucose metabolism (FPG <100 mg/dL, OGTT <140 mg/dL, HbA1c <5.7%), impaired glucose metabolism (FPG 100 to <126 mg/dL, OGTT 140 to 199 mg/dL, HbA1c 5.7 to 6.4%), or diabetes mellitus (FPG >=126 mg/dL, OGTT >=200 mg/dL, HbA1c >=6.5%). HbA1c is specific for diabetes but not very sensitive and has greater utility to monitor diabetes control over 2 to 3 months.

[Normal-FPG-Levels]

GBD 2019 Modeling Strategy

In GBD, FPG is modeled as a continuous variable using ST-GPR based on estimates of mean FPG in a representative population, individual-level data of fasting plasma glucose measured from surveys, or estimates of diabetes prevalence in a representative population.

Fasting plasma glucose is frequently tested or reported in surveys aiming at assessing the prevalence of diabetes mellitus. In these surveys, the case definition of diabetes may include both a glucose test and questions about treatment for diabetes. People with positive history of diabetes treatment may be excluded from the FPG test. Thus, the mean FPG in these surveys would not represent the mean FPG in the entire population. In this event, we estimated the prevalence of diabetes assuming a definition of FPG>126 mg/dL (7mmol/L), then crosswalked it to our reference case definition, and then predicted mean FPG.

The theoretical minimum-risk exposure level (TMREL) for FPG is 4.8-5.4 mmol/L for those risk-outcome pairs where risk is assessed on a continuous basis. This was calculated by taking the person-year weighted average of the levels of FPG that were associated with the lowest risk of mortality in the pooled analyses of prospective cohort studies. The TMREL is no diabetes for those outcomes where risk is assessed on a categorical basis. The risk-outcome pairs are listed below, along with whether they are continuous or categorical.

[Prospective-cohort-studies]

FPG Risk-Outcomes Pairs

Type

Outcome

Continuous

Ischemic heart disease

Continuous

Ischemic stroke

Continuous

Subarachnoid hemorrhage

Continuous

Intracerebral hemorrhage

Continuous

Peripheral vascular disease

Continuous

Type 1 diabetes

Continuous

Type 2 diabetes

Continuous

Chronic kidney disease due to Type 1 diabetes

Continuous

Chronic kidney disease due to Type 2 diabetes

Categorical

Drug-resistant tuberculosis

Categorical

Drug-susceptible tuberculosis

Categorical

Multidrug-resistant tuberculosis without extensive drug resistance

Categorical

Extensively drug-resistant tuberculosis

Categorical

Liver cancer due to NASH

Categorical

Liver cancer due to other causes

Categorical

Pancreatic cancer

Categorical

Ovarian cancer

Categorical

Colorectal cancer

Categorical

Bladder cancer

Categorical

Lung cancer

Categorical

Breast cancer

Categorical

Glaucoma

Categorical

Cataracts

Categorical

Dementia

Vivarium Modeling Strategy

Scope

Mean FPG is a continuous exposure modelled in GBD using an ensemble distribution. FPG will be a target of lifestyle interventions in the simulation; the outcomes affected are described in the overall concept model document.

Restrictions

GBD 2019 Risk Exposure Restrictions

Restriction Type

Value

Notes

Male only

False

Female only

False

YLD only

False

YLL only

False

Age group start

10

[25, 29 years)

Age group end

235

[95, 125 years)

Assumptions and Limitations

The quantity of interest is exposure to the mean fasting plasma glucose level regardless of whether that level is naturally occurring or occurs via use of medication; we assume full reversibility of risk and do not account for duration of exposure to elevated FPG.

The values for FPG generated include exposures outside of a reasonably expected range. In addition, we do not think relative risks continue in a log linear pattern indefinitely, as is implemented in this model. A natural ceiling of risk associated with a single risk factor probably exists.

To account for this, we implemented maximum and minimum exposures based on NHANES. The maximum was set to include 99.5% of NHANES data, meaning that 0.5% or fewer participants had values more extreme than the maximum.

The minimum FPG is 1 and the maximum is 16 mmol/L.

Another noticed issue with GBD data is striations in the standard deviation values. The graph below visually shows this issue. After speaking with GBD modelers, it seems that these are known issues and will not be addressed in GBD 2021. These striations are causing higher than expected FPG exposures. To account for this, we are instead using standard deviations derived from NHANES data.

../../../_images/FPG_SD.png

To create these new standard deviation draws, we bootstrapped the NHANES data to a random resampling of the original dataset, and then calculated the standard deviations by age/sex. The full workbook can be found here

Data Description Tables

The rei_id for FPG is 105.

Components

Components

ME_ID

Notes

Mean exposure

8909

Standard deviation

/ihme/costeffectiveness/artifacts/vivarium_nih_us_cvd/raw_data/fpg_std_nhanes_draw_level.csv

Due to limited sample size in older age groups, please use the 80-85 age group for all simulants 80+

Relative risk, continuous

9056

Must be accessed with get_draws

Relative risk, categorical

9057

Must be accessed with get_draws

The exposure values should be used to represent the distribution of mean fasting plasma glucose levels that the simulants will be assigned in the model.

Validation Criteria

  1. Does the mean in the model match the mean in GBD?

  2. Does the standard deviation in the model match the std in the artifact?

References

Normal-FPG-Levels

Gurung, Purnima. Plasma Glucose. StatPearls [Internet]., U.S. National Library of Medicine, 2 Sept. 2020, www.ncbi.nlm.nih.gov/books/NBK541081/.

Prospective-cohort-studies

Singh GM, Danaei G, Farzadfar F, Stevens GA, Woodward M, Wormser D, et al. (2013) The Age-Specific Quantitative Effects of Metabolic Risk Factors on Cardiovascular Diseases and Diabetes: A Pooled Analysis. PLoS ONE 8(7): e65174. https://doi.org/10.1371/journal.pone.0065174