Since 2008, the American Diabetes Association has recommended metformin as an adjunct to intensive lifestyle intervention for people with prediabetes, especially those with BMI ≥35 kg/m2, with history of gestational diabetes mellitus (hxGDM), or aged <60 years (1). Prior studies assessing trends of metformin use among patients with prediabetes yielded mixed results and were limited by small sample size, use of self-reported information, insufficient control for important patient characteristics, and lack of recent data (2,3). In this study, we used a claims-based data set to examine metformin use among >900,000 people with prediabetes from 2008 to 2020.

Optum Clinformatics is a large national database containing commercial health insurance medical/pharmacy claims data and available outpatient laboratory results. We selected patients with index prediabetes diagnosis between 2008 and 2020 according to hemoglobin A1c (HbA1c) 5.7–6.4%, fasting plasma glucose 100–125 mg/dL, 2-h post–stimulated glucose (oral glucose tolerance test) 140–199 mg/dL, or ICD-9 and ICD-10 diagnosis codes (4). We excluded patients <20 and ≥60 years old, those on Medicare, and those who within 1 year of index prediabetes diagnosis did not have continuous insurance enrollment or had type 1 or 2 diabetes (HbA1c >6.4%, fasting plasma glucose >126 mg/dL, oral glucose tolerance test >199 mg/dL, or qualifying ICD-9 or ICD-10 code), prescription of nonmetformin antidiabetes medications, metformin contraindication kidney disease stage 3 or above, or alternative metformin indication polycystic ovarian syndrome (4). ICD-9 and ICD-10 were used to identify BMI ≥35 kg/m2 (ICD-9 V85.35–V85.39/V85.4; ICD-10 Z68.35–Z68.39/Z68.4) and hxGDM (ICD-9 648.8/ICD-10 O24).

We estimated incidence of metformin prescription fill within 12 months after index prediabetes diagnosis, with subgroup analysis on those with BMI ≥35 kg/m2 or hxGDM, via age- and sex-adjusted logistic regression with year dummy variables. Multivariate logistic regression was performed to assess the association of metformin use with age, sex, race (White (reference)/Black/Hispanic/Asian), BMI ≥35 kg/m2, and hxGDM, with two-sided P < 0.05 considered significant. This study was approved by the University of Southern California Institutional Review Board.

There were 945,414 patients with index prediabetes diagnosis between 2008 (n = 63,251) and 2020 (n = 84,883), among whom 47,257 (5.0%) had BMI ≥35 kg/m2 or hxGDM (610 [0.96%] in 2008 and 8,774 [10.3%] in 2020). Age- and sex-adjusted metformin use in the overall cohort increased from 1.14% in 2008 to 2.01% in 2020 (P < 0.001 for the difference) (Fig. 1A). In subgroup analysis, use increased between 2008 and 2020 from 0.87% to 4.99% (P < 0.001), with substantial increases between 2008–2009 and 2012–2017 but no significant change from 2017 (4.90%, 95% CI 4.37–5.43) to 2020 (4.99%, 95% CI 4.53–5.45; P = 0.798).

Multivariate logistic regression revealed independent, positive associations of metformin use with BMI ≥35 kg/m2 (odds ratio [OR] 2.74, 95% CI 2.62–2.86), hxGDM (OR 2.52, 95% CI 2.27–2.79), and female sex (OR 1.92, 95% CI 1.86–1.99), while Asian relative to White race was associated with lower use (OR 0.83, 95% CI 0.77–0.89) (Fig. 1B). These findings are generally consistent with those of previous studies (2,3).

Despite the American Diabetes Association’s long-standing recommendation of metformin for diabetes prevention, <1 in 20 adults with prediabetes and BMI ≥35 kg/m2 or hxGDM used metformin in 2020. In a recent study, investigators found the strongest predictor of whether patients receive any treatment for prediabetes (including metformin) to be their primary care physicians’ treatment rates, which varied substantially (5). Efforts to improve diabetes prevention may benefit from lessons learned with other guidelines and from implementation science more generally. There was improvement between 2008 and 2020, and some of it may have stemmed from systemic changes in U.S. health care. The Affordable Care Act has expanded health insurance coverage and promoted payment and delivery reforms, potentially catalyzing improvements in care throughout the health care sector.

It must be noted that some patients with prediabetes not taking metformin may still have received appropriate care via intensive lifestyle intervention or other diabetes medications (e.g., sodium–glucose cotransporter 2 inhibitor for comorbid heart failure or kidney disease, although patients taking such medications are excluded in our study) (1).

Our research design improves on the limitations of the existing literature including small sample size (N < 8,000), use of self-reported data, older data (before 2014), and lack of control for metformin contraindications or alternate indications (2,3). Compared with prior studies where investigators did not control for time since diagnosis, our focus on patients with newly diagnosed prediabetes and incident metformin use with a fixed follow-up period also better accounts for patient heterogeneity in disease severity and allows for clearer characterization of changes in prescription fill patterns over time. Nevertheless, study limitations include a commercially insured sample that may not generalize, lack of sensitivity with using ICD-9 and ICD-10 codes to identify patients with BMI ≥35 kg/m2 or hxGDM, and inability to examine intensive lifestyle intervention participation, which may account for some patients’ lack of metformin use in our study. Future research should investigate intensive lifestyle intervention alongside metformin use and also examine patterns and determinants of care over time and across areas.

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. T.T.C. contributed to the conceptualization and design of the study, conducted analyses and interpretation of data, and led the drafting and revisions of the article. Y.G. contributed to the design of the study, performed data collection and cleaning to compile the final analytic data set, and contributed to revisions of the article for important intellectual content. J.A.R. led the conceptualization and design of the study and contributed to the interpretation of the data analyses and revisions of the article for important intellectual content. T.T.C. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

1.
American Diabetes Association Professional Practice Committee
.
3. Prevention or delay of type 2 diabetes and associated comorbidities: Standards of Medical Care in Diabetes—2022
.
Diabetes Care
2022
;
45
(
Suppl. 1
):
S39
S45
2.
Tseng
E
,
Yeh
HC
,
Maruthur
NM
.
Metformin use in prediabetes among U.S. adults, 2005–2012
.
Diabetes Care
2017
;
40
:
887
893
3.
Liu
C
,
Foti
K
,
Grams
ME
,
Shin
JI
,
Selvin
E
.
Trends in self-reported prediabetes and metformin use in the USA: NHANES 2005-2014
.
J Gen Intern Med
2020
;
35
:
95
101
4.
Wu
J
,
Ward
E
,
Threatt
T
,
Lu
ZK
.
Metformin prescribing in low-income and insured patients with prediabetes
.
J Am Pharm Assoc (2003)
2017
;
57
:
483
487
5.
Speaker
SL
,
Rastogi
R
,
Sussman
TA
,
Hu
B
,
Misra-Hebert
AD
,
Rothberg
MB
.
Treatment of patients with prediabetes in a primary care setting 2011–2018: an observational study
.
J Gen Intern Med
2021
;
36
:
923
929
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license.