# Hemoglobin A1C Variability as a Risk Factor for Diabetes Complications

> **NIH NIH R01** · HARVARD MEDICAL SCHOOL · 2021 · $520,406

## Abstract

Persistent hyperglycemia predicts the development of microvascular complications (e.g. retinopathy and
nephropathy) in patients with diabetes. However, the relationship between glucose control and organ damage
is complex. Reducing hemoglobin A1c (A1c) prevents or delays microvascular complications but
cardiovascular disease (CVD) and mortality are inconsistently affected. Thus, there is a need to develop new
quality measures beyond A1c alone to better identify and treat patients at risk for complications and mortality.
A1c variability, as measured by fluctuations in A1c over time, is a strong candidate. Several studies show a
significant relationship between increased A1c variability and microvascular disease and CVD. While A1c
variability carries important risk information, variance measures such as standard deviation, may not be
clinically intuitive. Thus, our goal is to develop a new quality measure of A1c variability – A1c time in range
(TIR) – that helps clinicians and patients control A1c in a way that balances long-term benefits and risks. We
will define A1c TIR as the percentage of days a patient's A1c levels are in a specific target range, based on
their clinical characteristics and clinical practice guidelines. We will study A1c TIR in a generalizable
nationwide sample of over 365,000 patients from the Department of Veterans Affairs and Kaiser Permanente.
We will apply advanced statistical methods that stringently control for selection bias by using an instrumental
variable design, including process quality controls. These methods allow us to draw causal inferences between
TIR and risk of new diabetes complications. We will study the predictors of A1c TIR, which we hypothesize will
be affected by provider practice patterns, individual patient-level characteristics and medications. We will test
the hypothesis that higher A1c TIR confers lower risk of diabetes complications. We will also study the
converse – A1c time out-of-range (TOR), with interest in deviations both above (TOR [high]) and below the
range (TOR [low]) to determine if either is uniquely associated with micro- or macrovascular complications.
Then we will investigate clinical factors, hypoglycemic events and rapid declines in A1c, as mediators of the
relationship between TIR and adverse outcomes. Each is linked to mortality and early worsening of diabetes
complications, respectively. This study will advance diabetes care by developing a novel quality measure that
identifies patients in a risk-stratified way and helps clinicians tailor diabetes treatment based on a patient's
unique goals of care. Such a new measure will be used by clinicians at the point-of-care and by healthcare
systems for population health management.

## Key facts

- **NIH application ID:** 10114130
- **Project number:** 5R01DK114098-03
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** PAUL R CONLIN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $520,406
- **Award type:** 5
- **Project period:** 2019-03-01 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10114130

## Citation

> US National Institutes of Health, RePORTER application 10114130, Hemoglobin A1C Variability as a Risk Factor for Diabetes Complications (5R01DK114098-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10114130. Licensed CC0.

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