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ORIGINAL RESEARCH ARTICLE

Use of Statins and the Risk of Incident Diabetes: A Retrospective Cohort Study

Busuyi S. Olotu1,2,3 • Marvin D. Shepherd2 • Suzanne Novak2,3 • Kenneth A. Lawson2 •

James P. Wilson2 • Kristin M. Richards2 • Rafia S. Rasu1

� Springer International Publishing Switzerland 2016

Abstract

Introduction Even though several landmark statin trials

have demonstrated the beneficial effects of statin therapy

in both primary and secondary prevention of cardiovas-

cular disease, several studies have suggested that statins

are associated with a moderate increase in risk of new-

onset diabetes. These observations prompted the US

FDA to revise statin labels to include a warning of an

increased risk of incident diabetes mellitus as a result of

increases in glycosylated hemoglobin (HbA1c) and fast-

ing plasma glucose. However, few studies have used US-

based data to investigate this statin-associated increased

risk of diabetes.

Objective The primary objective of our study was to

examine whether the use of statins increases the risk of

incident diabetes mellitus using data from the Thomson

Reuters MarketScan � Commercial Claims and Encounters

Database.

Method This study was a retrospective cohort analysis

utilizing data for the period 2003–2004. The study popu-

lation included new statin users aged 20–63 years at index

who did not have a history of diabetes.

Results The proportion (3.4 %) of statin users

(N = 53,212) who had incident diabetes was higher

than the proportion (1.2 %) of non-statin users

(N = 53,212) who had incident diabetes. Compared

with no statin use and controlling for demographic and

clinical covariates, statin use was significantly associ-

ated with increased risk of incident diabetes (hazard

ratio 2.01; 99 % confidence interval 1.74–2.33;

p \ 0.0001). In addition, risk of diabetes was highest among users of lovastatin, atorvastatin, simvastatin, and

fluvastatin. Diabetes risk was lowest among pravastatin

and rosuvastatin users.

Discussion Because the potential for diabetogenicity dif-

fers among different statin types, healthcare professionals

should individualize statin therapy by identifying patients

who would benefit more from less diabetogenic statin

types.

& Busuyi S. Olotu [email protected]

Marvin D. Shepherd

[email protected]

Suzanne Novak

[email protected]

Kenneth A. Lawson

[email protected]

James P. Wilson

[email protected]

Kristin M. Richards

[email protected]

Rafia S. Rasu

[email protected]

1 Department of Pharmacy Practice, University of Kansas

School of Pharmacy, 2010 Becker Dr., Lawrence, KS 66047,

USA

2 Division of Health Outcomes and Pharmacy Practice, College

of Pharmacy, The University of Texas at Austin, 2409

University Avenue A1930, Austin, TX 78712-1120, USA

3 Austin Outcomes Research, 1600 Flintridge Rd,

West Lake Hills, TX 78746, USA

Am J Cardiovasc Drugs

DOI 10.1007/s40256-016-0176-1

Key Points

Statin therapy was significantly associated with

increased risk of new-onset diabetes mellitus.

Risk of diabetes was highest among lovastatin and

atorvastatin users and lowest among pravastatin and

rosuvastatin users.

Statin therapy needs to be individualized by

identifying patients who would benefit more from

less diabetogenic statin types.

1 Introduction

In February 2012, the US FDA approved important safety

labeling changes for statins, including an increased risk of

incident diabetes mellitus as a result of increases in gly-

cosylated hemoglobin (HbA1c) and fasting plasma glucose

(FPG) found to be associated with statin therapy [1]. The

FDA based its decision on a combination of results from

randomized controlled trials (RCTs) [2–5], meta-analyses

of RCTs [6–8], a systematic review [9], and a few obser-

vational studies [10, 11], which indicated an increased risk

of incident diabetes due to statin therapy.

The potential increased risk in diabetes associated with

statin use is significant because patients with dyslipidemia

being treated with statins already have a baseline increased

risk of diabetes due to abnormal lipid levels, combined

with comorbidities such as high blood pressure, increased

weight and body mass index (BMI), metabolic syndrome,

and cardiovascular diseases. Statin-induced diabetes risk is

not a desirable outcome because the already increasing

incidence and prevalence of prediabetes and diagnosed

diabetes contributes significantly to increasing rates of

morbidity and mortality among Americans [12]. Diabetes

is the seventh leading cause of death in the USA, with

attendant complications such as kidney failure, heart

attack, stroke, and amputation [13]. Diabetes also imposes

a substantial economic burden on the US population in the

form of increased direct and indirect medical costs [14].

Several RCTs, meta-analyses of RCTs, and observa-

tional studies have examined the association between statin

use and incidence of diabetes. The JUPITER (Justification

for the Use of Statins in Prevention: an Intervention Trial

Evaluating Rosuvastatin) trial was one of the RCTs the

FDA used in reaching a decision to change the labeling for

all statins [5]. A secondary outcome of the JUPITER study

results showed that rosuvastatin was significantly associ-

ated with a 26 % increased risk of diabetes [5].

Similar to the JUPITER study, the PROSPER (Pravas-

tatin in Elderly Individuals at Risk of Vascular Disease)

study found a 32 % increased risk of diabetes associated

with pravastatin [15]. The results from the JUPITER and

PROSPER studies were consistent with results from other

RCTs [2–4] that showed statins to be associated with

increased risk of diabetes. However, results from other

RCTs showed that statins may be associated with a reduced

risk (or protective effect) of diabetes rather than an

increased risk [16, 17].

Evidence from meta-analyses of RCTs also indicates

that statins may be associated with a moderate increase in

risk of diabetes of about 9 % as found in the 2010 meta-

analysis of 19 RCTs by Sattar et al. [8], the 2011 meta-

analysis of 17 RCTs by Mills et al. [6], and the 2013 meta-

analysis of 55 RCTs by Naci et al. [18].

Evidence from observational studies appears to follow

that observed from RCTs and meta-analyses of RCTs with

respect to the direction and strength of association between

statin therapy and incidence of diabetes. The 2012

prospective cohort study by Culver et al. [11] showed that

statin use was associated with a 47 % increased risk of

new-onset diabetes mellitus among postmenopausal

women participating in the longitudinal WHI (Women’s

Health Initiative) study. The retrospective cohort studies by

Danaei et al. [19], Wang et al. [20], and Zaharan et al. [21]

reported a 14, 15, and 20 % increased risk in diabetes,

respectively. In a more recent retrospective cohort study by

Mansi et al. [22], an 87 % higher odds of incident diabetes

was recorded among statin users compared with non-statin

users.

The main purpose of the present study was to examine

whether the use of statins increases the risk of incident

diabetes mellitus using data from the Thomson Reuters

MarketScan �

Commercial Claims and Encounters Data-

base. This is warranted because the findings linking statin

therapy to the development of incident diabetes are

inconsistent. While some studies indicate a moderate, sta-

tistically significant increase in risk of diabetes with statin

therapy [2–4, 6–8, 10, 11, 18–21, 23, 24], others indicate

that statins are protective or reduce the risk of diabetes

[16, 17], while still others suggest the increase in risk is not

statistically significant [25–27].

In addition, a majority of the observational studies that

examined the association between statin use and the

development of diabetes were conducted using non-US

population data. Thus, further examination of this topic

among the US population was required. The US population

may differ from other populations in terms of the preva-

lence of risk factors such as overweight, obesity, and car-

diovascular diseases that may put people at an increased

risk of diabetes, independent of the effects of statin therapy

[28]. For example, the USA has the highest rate of obesity

(defined as BMI of C30, an independent risk factor for

diabetes and cardiovascular disease) among all high-

B. S. Olotu et al.

income populations such as those found in North America,

Canada, and Europe [29]. This is in contrast to people from

East Asia (e.g., Taiwan, China, and Japan), the BMIs for

whom are known to be among the lowest in the world [29].

2 Methods

The study protocol submission was granted an institution

review board waiver by the Office of Research Support of

The University of Texas at Austin’s Institution Review

Board because a secondary analysis of de-identified data

does not meet the criteria for human subjects research.

2.1 Study Design

The study utilized a retrospective cohort design using an

administrative claims database containing patients’ phar-

macy and medical claims (i.e., the MarketScan �

Com-

mercial Claims and Encounters Database) for the period of

1 January 2003 to 31 December 2004. The study popula-

tion consisted of patients in the MarketScan database who

(1) were aged 20–63 years at the index date (defined as

date of the first prescription claim for a statin or a non-

statin drug during the index period 1 July 2003 to 1 January

2004); (2) did not have a diagnosis of diabetes (Interna-

tional Classification of Diseases, Ninth revision, Clinical

Modification [ICD-9-CM] codes 250.xx) in the pre-index

period (defined as a period of at least 6 months before the

first index medication was received) to ensure measure-

ment of incident rather than prevalent diabetes cases; and

(3) were continuously enrolled in their health plan during

the pre-index period and for at least 1 year after the index

date. To further strengthen the association between expo-

sure and outcome, subjects were required to have a mini-

mum drug exposure period of at least 6 months, after

which the outcome (i.e., diabetes occurrence) can be con-

sidered valid. Thus, we excluded diabetes cases that

occurred within 6 months of initial drug exposure.

2.2 Cohort Formation

In the retrospective cohort design, we formed two cohorts of

patients from the retrospective data. The first cohort, called

statin users (or the exposed group) were (1) patients who

received at least one statin prescription for atorvastatin,

fluvastatin, lovastatin, pravastatin, rosuvastatin, or simvas-

tatin during the index period ranging from 1 July 2003 to 1

January 2004; (2) new statin users (i.e., new statin use was

established if a subject had no prescription for a statin

medication in the 6 months before their first statin use; to

establish a history of drug use of at least 6 months, the ear-

liest first use of any statin drug was set at 1 July 2003); and (3)

those who did not fill a statin drug that was in a combined

dosage form with any other drug or agent.

The second cohort, called non-statin users (or the

unexposed group) were patients who did not receive statin

medication. These two cohorts were then ‘‘followed-up’’

until (1) manifestation of diabetes; or (2) end of the follow-

up period (31 December 2004) without manifestation of

diabetes. We based drug exposure on the intention-to-treat

principle (i.e., subjects were assumed to be treated with the

medication to which they were exposed at the index date).

2.3 Outcome Measures and Statistical Analysis

We analyzed the retrospective cohort design using two dif-

ferent statistical methods. This was to assess whether the

magnitude and significance of the risk ratios associating

statin use with diabetes would differ depending on the nature

of the dependent variable. We used the Cox proportional

hazards regression when survival time (defined as the time

from receipt of index medication to manifestation of diabetes

or end of the study period if no diabetes occurred) was the

dependent variable of interest. In contrast, we used the binary

logistic regression analysis when the dependent variable of

interest was presence or no presence of incident diabetes.

Several demographic and clinical covariates were con-

trolled for in each of the regression models, including age,

sex, hyperlipidemia (ICD-9-CM codes 272.0, 272.1, 272.2,

and 272.4), obesity (278.00 and 278.01), hypertension

(401.0, 401.1, and 401.9), number of prescriptions for all

diabetogenic medications used (i.e., thiazide diuretics,

beta-blockers, antipsychotics, antidepressants, immuno-

suppressants, and glucocorticoids), and the Charlson

Comorbidity Index (CCI) score. We estimated this score

using the Dartmouth–Manitoba method [30]. We used

SAS � for Windows

� version 9.3 (SAS Institute, Cary, NC,

USA) and IBM SPSS Statistics version 21 (SPSS Inc.,

Chicago, IL, USA) to perform data manipulation and

analyses and evaluated statistical significance at p \ 0.01.

2.4 Sensitivity Analysis

Sensitivity analyses were conducted to examine how the risk

ratios were influenced by (1) using two types of statistical

analysis (i.e., Cox and logistic regression models) to estimate

the measures of risk; (2) controlling for significant time-

dependent covariates (i.e., independent variables that vio-

lated the proportionality of hazards assumption) in the Cox

models; and (3) using a regression-based propensity score

covariate adjustment rather than traditionally controlling for

all covariates in both the Cox and the logistic regression

models. Though susceptible to the effects of unmeasured

covariates, propensity score analysis helps reduce the effects

of confounding when using observational data [31].

Statins and Risk of Incident Diabetes

3 Results

3.1 Patient Selection Criteria and Sample Size

Figures 1 and 2 describe the inclusion/exclusion criteria

used in selecting both the statin user and the non-statin

user cohort, respectively. The study sample consisted of

106,424 subjects aged 20–63 years at index date and who

had no evidence of diabetes both in the pre-index period

and 6 months after the index date. The mean length of the

pre-index period used to exclude prevalent diabetes cases

did not significantly differ between statin users

(8.86 months, standard deviation [SD] 1.74) and non-

statin users (8.74 months, SD 1.76). Both the statin and

non-statin user cohorts were followed from the earliest

index date of 1 July 2003 until they had a diagnosis of

diabetes or reached the end of the study period (31

December 2004) without a diabetes diagnosis. The study

sample (N = 106,424) comprised equal proportions of

statin users (N = 53,212) and non-statin users

(N = 53,212).

3.2 Demographic and Clinical Characteristics

Table 1 shows the differences in demographic and clinical

characteristics between statin users and non-statin users.

An independent samples t test showed that mean age was

Unique number of patients using lipid-lowering agents (N=1,223,169): aged 20-63 at index

Excluded (N=220,032): users of non-statin drugs

Statin users (N=1,003,137)

Excluded (N=902,962): index statin drug cases did not fall within the index period of July 1, 2003 and January 1, 2004

New statin users (N=100,175)

Excluded (N=3,638): had diabetes during six months of post- index period

Excluded (N=30,664): had diabetes during the pre-index period

New statin users without pre-index diabetes (N=69,511)

New statin users without pre-index diabetes and six-months post-index diabetes (N=65,873)

Statin user cohort (N=53,212)

Excluded (N=12,661): not continuously enrolled during the pre-index period and for at least a year after the index date

Fig. 1 Inclusion/exclusion criteria for the statin cohort

B. S. Olotu et al.

significantly higher (p \ 0.0001) among statin users (52.1 years) than among non-statin users (40.5 years).

Similarly, the mean CCI score was significantly higher

among statin users (0.24) than among non-statin users

(0.04). In addition, the results showed that the proportion

of statin users with a diagnosis of hyperlipidemia (50.8

vs. 6.1 %), obesity (0.9 vs. 0.3 %), and hypertension (30.6

vs. 4.1 %) was significantly higher than that of non-statin

users. Furthermore, the proportion of statin users who

received at least one diabetogenic medication prescription

(45.6 %) or who had a CCI score of C1 (12.4 %) was

higher than the proportion of non-statin users who

received at least one diabetogenic medication prescription

(12.2 %) or who had a CCI score of C1 (2.3 %). Given

these significant differences, we controlled for all demo-

graphic and clinical variables in the Cox and logistic

regression models.

3.3 Incidence of Diabetes

Table 2 shows a breakdown of the weighted and

unweighted incidence density and cumulative incidence of

diabetes by statin use. A total of 106,424 subjects were

followed for 1,608,088.77 months, and 2478 new cases of

diabetes were found, resulting in an unweighted diabetes

incidence density rate of 1.54 per 1000 person-months or

*A simple random sampling (without replacement) of exactly N=53,112 from N=395,339. Baseline characteristics of enrolled (N=53,212) and unenrolled controls (N=342,127) were not statistically different.

Unique, non-statin index drug cases (N=716,049)

Excluded (N=14,607): aged <20 or >63 at index

Non-statin users aged 20 – 63 years at index (N=701,442)

Excluded (N=1,197): had diabetes during six months of post-index period

Excluded (N=16,085): had pre-index diabetes

Non-statin users without pre-index diabetes (N=685,357)

Non-statin users without pre-index diabetes and six-months post-index diabetes (N=684,160)

Final non-statin user cohort (N=53,212)*

Excluded (N=288,821): not continuously enrolled during the pre-index period and for at least a year after the index date

Non-statin user cohort (N=395,339)

Fig. 2 Inclusion/exclusion criteria for the non-statin cohort

Statins and Risk of Incident Diabetes

an unweighted diabetes cumulative incidence of 23.2 per

1000 population.

In addition, statin users (N = 53,212) were followed-up

for an average of 15.01 months (SD 1.86, median 15.04)

while non-statin users (N = 53,212) were followed-up for

an average of 15.21 months (SD 1.81, median 15.31). The

unweighted diabetes incidence density rate for statin users

(2.29 per 1000 person-months) was higher than that for

non-statin users (0.8 per 1000 person-months). This means

that, if 1000 statin users were followed-up for 1 month,

2.29 new cases of diabetes would be recorded. This rate is

higher than the 0.8 new cases of diabetes that would be

recorded if 1000 non-statin users were followed-up for

1 month.

Among the statin types, the unweighted diabetes inci-

dence density rate was highest for lovastatin (2.97 per 1000

person-months) and decreased, consecutively, for fluvas-

tatin (2.61 per 1000 person-months), rosuvastatin (2.38 per

1000 person-months), simvastatin (2.29 per 1000 person-

months), and atorvastatin (2.24 per 1000 person-months).

The diabetes incidence density rate was lowest for

pravastatin (2.01 per 1000 person-months).

Table 1 Differences in demographic and clinical characteristics between statin users and non-statin users

Covariates Statin users (N = 53,212) Non-statin users (N = 53,212) Significance

Age 52.1 ± 7.8 40.5 ± 11.9 p \ 0.0001 Categorized p \ 0.0001 20–34 1633 (3.1) 17,327 (32.6)

35–44 7224 (13.6) 14,572 (27.4)

45–54 20,186 (37.9) 13,820 (26.0)

55–63 24,169 (45.4) 7493 (14.1)

Sex p \ 0.0001 Male 26,704 (50.2) 27,694 (52.0)

Female 26,508 (49.8) 25,518 (48.0)

Hyperlipidemia a

27,007 (50.8) 3245 (6.1) p \ 0.0001 Obesity

a 498 (0.9) 165 (0.3) p \ 0.0001

Hypertension a

16,278 (30.6) 2175 (4.1) p \ 0.0001 Diabetogenic medications 5.9 ± 9.1 0.8 ± 3.1 p \ 0.0001 Categorized

b p \ 0.0001

0 28,917 (54.3) 46,696 (87.8)

1–5 5400 (10.1) 3579 (6.7)

6–10 5413 (10.2) 1515 (2.8)

[10 13,482 (25.3) 1422 (2.7) Type

c

Thiazides 4797 (9.0) 1006 (1.9) p \ 0.0001 Beta-blockers 11,928 (22.4) 1698 (3.2) p \ 0.0001 Antipsychotics 740 (1.4) 181 (0.3) p \ 0.0001 Antidepressants 13,617 (25.6) 4497 (8.5) p \ 0.0001 Immunosuppressants 203 (0.4) 33 (0.1) p \ 0.0001 Glucocorticoids 52 (0.1) 32 (0.1) p = 0.029

Charlson comorbidity index score 0.24 ± 0.92 0.04 ± 0.39 p \ 0.0001 Categorized p \ 0.0001 0 46,599 (87.6) 52,021 (97.8)

1–5 6009 (11.3) 1093 (2.1)

6–10 596 (1.1) 97 (0.2)

[10 8 (0.02) 1 (0.002)

Data are presented as N (%) or mean ± standard deviation a Proportion of statin users and non-statin users who had a diagnosis of the indicated condition

b Categorized number of prescriptions for all diabetogenic medications

c Proportion of statin users and non-statin users who used each type of diabetogenic medication

B. S. Olotu et al.

3.4 Kaplan–Meier Survival Analysis by Exposure

Type

Study results showed that the estimated mean survival time

(i.e., time to a diabetes diagnosis in months) for statin users

(17.8, standard error [SE] 0.006) was shorter than that for

non-statin users (18.0, SE 0.003).

In addition, Fig. 3 shows two Kaplan–Meier survival

curves comparing the survival probabilities against time

for statin users and non-statin users. The log-rank test

comparing the distribution of the two survival curves

showed that statin users had a significantly shorter sur-

vival probability over time than non-statin users

(v2 = 604; df = 1; p \ 0.0001). From Fig. 3, the 12-month survival probability for statin users (98.6 %)

was lower than that for non-statin users (99.3 %). At

18 months, the survival probability for statin users fell to

0.963 (i.e., 96.3 % of statin users survived past 18 months

without having diabetes) compared with 98.2 % of non-

statin users who survived past 18 months without having

diabetes. Thus, statin users had a shorter survival time

(and shorter survival probabilities over time) than non-

statin users.

3.5 Kaplan–Meier Survival Analysis by Statin Type

Study results showed that the estimated mean survival time

(in months) was shortest among rosuvastatin users (16.15,

SE 0.03) and increased, consecutively, among lovastatin

(17.74, SE 0.02), fluvastatin (17.77, SE 0.03), simvastatin

(17.80, SE 0.01), and atorvastatin (17.81, SE 0.01) users.

Pravastatin (17.83, SE 0.02) users had the longest esti-

mated mean survival time.

In addition, Fig. 4 shows six Kaplan–Meier survival

curves comparing the survival probabilities against time

among users of different statin types. The log-rank test

comparing the distributions of the six survival curves

showed that at least one of the survival curves significantly

differed from another (v2 = 20.5; df = 5; p = 0.001).

3.6 Cox Proportional Hazards Regression Models

Table 3 shows the results of the Cox proportional hazards

regression model comparing the hazard of diabetes

between statin users and non-statin users and between users

of each statin type and non-statin users while controlling

for demographic and clinical covariates. Sensitivity

Table 2 Weighted a and unweighted incidence density and cumulative incidence of diabetes by statin use

Subjects (N)

[a]

Sum of follow-up

months (person-months)

[b]

Number of new

cases of diabetes

[c]

Incidence density rate

(per 1000 person-months)

[c/b 9 1000]

Cumulative incidence

(per 1000 population)

[c/a 9 1000]

Study population 106,424 1,608,088.77 2478 1.54 23.2

4,554,802 68,836,394.87 115,720 1.68 25.4

Statin users 53,212 798,909.73 1833 2.29 34.4

2,454,285 36,851,101.44 88,208 2.39 35.9

Non-statin users 53,212 809,179.03 645 0.80 12.1

2,100,517 31,985,293.44 27,512 0.86 13.1

Atorvastatin users 27,155 408,534.83 917 2.24 33.8

1,245,296 18,730,198.34 44,408 2.37 35.7

Fluvastatin users 1638 24,883.32 65 2.61 39.7

76,533 1,163,607.06 3013 2.59 39.4

Lovastatin users 3570 53,448.25 159 2.97 44.5

178,617 2,673,568.39 8359 3.13 46.8

Pravastatin users 5870 89,767.29 180 2.01 30.7

275,824 4,215,723.83 8945 2.12 32.4

Rosuvastatin users 2766 37,341.83 89 2.38 32.2

124,383 1,678,244.14 4249 2.53 34.2

Simvastatin users 12,213 184,934.21 423 2.29 34.6

553,632 8,389,759.67 19,234 2.29 34.7

a Weighted values are in italics. MarketScan person-level national weights (available as a weight variable in the data) were constructed utilizing

weight estimates from the Household Component of the Medical Expenditure Panel Survey (MEPS). MEPS weights account for demographic

variables that include region (northeast, north central, south, west); age (0–17, 18–44, 45–64 years); and sex (male, female). The MarketScan

weight is the ratio of MEPS-based estimates in the different age/sex/region categories and the MarketScan number in the same category

Statins and Risk of Incident Diabetes

Fig. 3 Comparison of the survival probability curves of

statin users and non-statin users

Fig. 4 Graph comparing the survival probability curves of

different statin types

B. S. Olotu et al.

analysis of the hazard ratios (HRs) adjusting for significant

time-dependent covariates and utilizing propensity score

adjustment is also presented.

Compared with no statin use, and controlling for

covariates, statin use (as a class) was significantly associ-

ated with increased risk of incident diabetes mellitus (HR

2.01; 99 % confidence interval [CI] 1.74–2.33;

p \ 0.0001). In other words, the hazard of incident dia- betes for statin users was two times that for non-statin

users.

Except for pravastatin use, which was associated with a

non-significant increase in diabetes risk, the use of ator-

vastatin, fluvastatin, lovastatin, rosuvastatin, or simvastatin

was significantly associated with an increased risk of

incident diabetes. The risk of diabetes was highest among

lovastatin users (HR 2.07) and lowest among pravastatin

users (HR 1.29).

3.7 Logistic Regression Models

Table 4 shows the results of the logistic regression model

comparing the odds of incident diabetes between statin

users and non-statin users and between users of each statin

type and non-statin users while controlling for demo-

graphic and clinical covariates. Sensitivity analysis of the

odds ratios (ORs) utilizing propensity score adjustment is

also presented. Except for the non-significance of the OR

for the rosuvastatin group, the results of the logistic

regression did not differ significantly from the results

obtained for the Cox regression in terms of the magnitude

and significance of the HRs and ORs.

The results obtained for the logistic regression model

also suggest that statin use was significantly associated

with increased risk of diabetes. Except for pravastatin and

rosuvastatin users, who had non-significant increases in

risk, diabetes risk was significantly associated with the

use of atorvastatin, fluvastatin, lovastatin, or simvastatin.

The risk of diabetes was highest among lovastatin users

(OR 2.05) and lowest among rosuvastatin users (OR

1.21).

3.8 Sensitivity Analysis

As presented above, the results obtained for the Cox and

logistic regression analyses were similar in terms of the

magnitude and significance of the HRs and ORs. In addi-

tion, compared with the original Cox regression models,

where only demographic and clinical covariates were

adjusted for, further adjusting for significant time-depen-

dent covariates did not change the magnitude and signifi-

cance of the HRs (Table 3). However, the use of propensity

score adjustment in lieu of including all covariates in the

original Cox model was associated with (1) an increase in

the magnitude of the HRs for all statin users and users of

Table 3 Cox proportional hazards regression showing the association between statin use and incidence of diabetes

Total Incident

diabetes cases

Cox regression a

Cox regression adjusting for significant

time-dependent covariates a,b

Cox regression with

propensity score adjustment

Statin users 53,212 (100.0) 1833 (3.4) 2.01 (1.74–2.33)* 2.01 (1.74–2.33)* 2.07 (1.77–2.42)*

Atorvastatin 27,155 (51.0) 917 (3.4) 1.88 (1.59–2.24)* 1.89 (1.59–2.24)* 1.95 (1.62–2.35)*

Fluvastatin 1638 (3.1) 65 (4.0) 1.60 (1.08–2.38)

[p = 0.002]

1.60 (1.08–2.38) [p = 0.002] 1.95 (1.28–2.96)*

Lovastatin 3570 (6.7) 159 (4.5) 2.07 (1.57–2.74)* 2.09 (1.58–2.76)* 2.45 (1.82–3.25)*

Pravastatin 5870 (11.0) 180 (3.1) 1.29 (0.97–1.70)

[p = 0.019]

1.29 (0.98–1.70) [p = 0.019] 1.40 (1.04–1.87) [p = 0.003]

Rosuvastatin 2766 (5.2) 89 (3.2) 1.58 (1.10–2.27)

[p = 0.001]

1.59 (1.10–2.28) [p = 0.001] 1.75 (1.19–2.56)*

Simvastatin 12,213 (23.0) 423 (3.5) 1.71 (1.38–2.12)* 1.72 (1.39–2.13)* 1.79 (1.43–2.24)*

Non-statin

users c

53,212 (100.0) 645 (1.2) Reference Reference Reference

Data are presented as N (%) or hazard ratio (99 % confidence interval) [p value]

* Significant at p \ 0.0001 (the significance of each parameter estimate was evaluated at p \ 0.01) a Demographic and clinical covariates controlled for in each model include age, sex, hyperlipidemia, obesity, hypertension, use of diabetogenic

medications, and Charlson Comorbidity Index score b Significant time-dependent covariates controlled for in the models included statin model (sex, hyperlipidemia, hypertension, and diabetogenic

medication); atorvastatin model (sex, hyperlipidemia, and diabetogenic medication); fluvastatin model (age and diabetogenic medication);

lovastatin model (sex and diabetogenic medication); pravastatin model (sex, hyperlipidemia, and diabetogenic medication); rosuvastatin model

(sex and diabetogenic medication); simvastatin model (hyperlipidemia and diabetogenic medication) c Reference category for statin users and users of atorvastatin, fluvastatin, lovastatin, pravastatin, rosuvastatin, and simvastatin

Statins and Risk of Incident Diabetes

each statin type, and (2) the HR for pravastatin becoming

significant (p = 0.019 vs. p = 0.003).

Similar to above, using a propensity score adjustment in

lieu of including all covariates in the original logistic

regression model resulted in an increase in the magnitude

of the OR for all statin users and users of each statin type.

In addition, the HR for pravastatin became significant

(p = 0.01 vs. p = 0.001). However, the association of

rosuvastatin therapy and risk of diabetes remained non-

statistically significant even with propensity score adjust-

ment (p = 0.19 vs. p = 0.05) (see Table 4).

Furthermore, because of the potential problem of

immortal time bias, we analyzed the data without requiring

subjects to have a treatment duration of at least 6 months

before a diagnosis of diabetes could be considered valid.

Results of this sensitivity analyses revealed that the magni-

tude of the HRs increased for statin users and users of each

statin type, since more cases of incident diabetes would be

included. In this sensitivity analysis, the HR comparing the

risk of diabetes among statin users with that of non-statin

users are as follows: all statin users (HR 2.75), atorvastatin

(HR 2.43), fluvastatin (HR 2.06), lovastatin (HR 3.41),

pravastatin (HR 1.89), rosuvastatin (HR 1.62), and simvas-

tatin (HR 1.53). All associations were significant atp \ 0.01.

4 Discussion

4.1 Incidence of Diabetes

The unadjusted cumulative incidence of diabetes (over

2003–2004) among this study sample aged 20–63 years was

23.2 per 1000 population. This rate is higher than the unad-

justed incidence of diabetes of 7.8 per 1000 population of US

adults aged C20 years [12]. This discrepancy may be

because statin users constituted half the study population at

risk in this study, and statin users may be more likely than the

average population to have several risk factors for diabetes.

Thus, it might be reasonable that the rate of incident diabetes

for the current study population would be higher than that of

the US population that does not have half its population at

risk composed essentially of statin users.

However, the unadjusted diabetes incident density rate

obtained among this study population (1.54 per 1000 per-

son-months) was comparable to rates (unadjusted) obtained

in similar population-based observational studies of statin

use and incidence of diabetes as reported by Culver et al.

[11] (0.85 per 1000 person-months), Danaei et al. [19]

(0.96 per 1000 person-months), and Wang et al. [20] (1.75

per 1000 person-months).

Furthermore, only one observational study has com-

pared diabetes incident density rates between statin users

and non-statin users [19]. The unadjusted diabetes incident

density rates reported among statin users (2.29 per 1000

person-months) and non-statin users (0.8 per 1000 person-

months) in this study were comparable to those reported in

the Danaei et al. [19] study, which examined the risk of

type 2 diabetes mellitus among 285,864 men and women

aged 50–84 years. In that study, the unadjusted diabetes

incidence density rates for statin users (or statin ‘‘initia-

tors’’) and non-statin users (or statin ‘‘non-initiators’’) were

1.30 per 1000 person-months and 0.94 per 1000 person-

months, respectively [19].

4.2 Statin Use and Risk of Diabetes

The results of the Cox and logistic regression analyses

showed that the risk of incident diabetes was higher among

Table 4 Binary logistic regression analysis showing the association between statin use and incidence of diabetes

Total Incident diabetes

cases

Logistic regression a

Logistic regression with propensity

score adjustment

Statin users 53,212 (100.0) 1833 (3.4) 2.01 (1.74–2.33)* 2.07 (1.76–2.43)*

Atorvastatin 27,155 (51.0) 917 (3.4) 1.89 (1.59–2.25)* 1.96 (1.63–2.36)*

Fluvastatin 1638 (3.1) 65 (4.0) 1.64 (1.10–2.46) [p = 0.001] 1.99 (1.30–3.05)*

Lovastatin 3570 (6.7) 159 (4.5) 2.05 (1.54–2.72)* 2.40 (1.79–3.21)*

Pravastatin 5870 (11.0) 180 (3.1) 1.33 (1.00–1.76) [p = 0.01] 1.44 (1.07–1.94) [p = 0.001]

Rosuvastatin 2766 (5.2) 89 (3.2) 1.21 (0.84–1.74) [p = 0.19] 1.33 (0.91–1.96) [p = 0.05]

Simvastatin 12,213 (23.0) 423 (3.5) 1.74 (1.40–2.15)* 1.81 (1.44–2.27)*

Non-statin users b

53,212 (100.0) 645 (1.2) Reference Reference

Data are presented as N (%) or odds ratio (99 % confidence interval) [p value]

* Significant at p \ 0.0001 (the significance of each parameter estimate was evaluated at p \ 0.01) a Demographic and clinical covariates controlled for in each model include age, sex, hyperlipidemia, obesity, hypertension, use of diabetogenic

medications, and Charlson Comorbidity Index score b Reference category for statin users and users of atorvastatin, fluvastatin, lovastatin, pravastatin, rosuvastatin, and simvastatin

B. S. Olotu et al.

statin users than among non-statin users. Even though the

strength of association (or magnitude of the risk ratios) of

statin use and incidence of diabetes was higher in this study

(HR 2.01 and OR 2.01), the increase in the risk of diabetes

that was associated with statin therapy, compared with no

statin therapy, is consistent with increases reported in

several observational studies. Statin therapy was signifi-

cantly associated with a 14, 15, 20, and 48 % increase in

risk of incident diabetes, respectively, in the retrospective

cohort studies by Danaei et al. [19] (HR 1.14, 95 % CI

1.10–1.19), Wang et al. [20] (HR 1.15, 95 % CI

1.08–1.22), and Zaharan et al. [21] (HR 1.20, 95 % CI

1.17–1.23), and the prospective cohort study by Culver

et al. [11] (HR 1.48, 95 % CI 1.38–1.59). Our study results

were more consistent with a 2015 retrospective cohort

study of statin use among healthy US adults by Mansi et al.

[22], which reported an 87 % increase in the odds of dia-

betes with statin use (OR 1.87, 95 % CI 1.67–2.01).

Meanwhile, the 2013 prospective cohort study by Izzo

et al. [27] (HR 1.03, 95 % CI 0.79–1.35) and the 2004

case–control study by Jick and Bradbury [26] (OR 1.1,

95 % CI 0.8–1.4) found that statin therapy was not sig-

nificantly associated with increased risk of diabetes

mellitus.

4.3 Comparison of the Risk of Diabetes Among

Users of Different Statin Types

The results of the Cox and logistic regression analyses

showed that lovastatin users (HR 2.07, OR 2.05) had the

highest risk of new-onset diabetes; this was followed,

consecutively, by users of atorvastatin (HR 1.88, OR 1.89),

simvastatin (HR 1.71, OR 1.74), and fluvastatin (HR 1.60,

OR 1.64). Rosuvastatin (HR 1.58, OR 1.21) and pravastatin

(HR 1.29, OR 1.33) users had the least risk of new-onset

diabetes among all statin users.

The potency hypothesis [32]—which correlates higher

statin potencies with more side effects—does not seem to

explain why the risks of diabetes were highest among users

of lovastatin, atorvastatin, simvastatin, or fluvastatin since

higher potency statins such as atorvastatin, rosuvastatin,

and simvastatin would be expected to have consistently

higher risk ratios than lower potency statins such as

lovastatin, pravastatin, and fluvastatin.

Evaluation of current observational studies of statin use

and incidence of diabetes [11, 19–21, 26, 27, 33–37]

appears to suggest that simvastatin and atorvastatin had the

greatest potential to be significantly associated with

increased risk of incident diabetes, while fluvastatin and

lovastatin had the least potential to be significantly asso-

ciated with an increase in risk. Pravastatin and rosuvastatin

appear to have moderate potential to be significantly

associated with increased risk of diabetes. This observation

is partly consistent with our results, which showed that

lovastatin, atorvastatin, simvastatin, and fluvastatin had the

strongest association with risk of diabetes, while pravas-

tatin and rosuvastatin had moderate associations with dia-

betes risk.

4.4 Study Strengths and Limitations

This study confirmed the hypothesis that statin therapy is

associated with an increase in risk of diabetes and helped

fill some gaps in the literature because there are few

observational studies examining this phenomenon using

US population data. This is significant because the US

population may differ from other populations in terms of

the risk factors for diabetes mellitus. The results of this

study further confirm the findings of other studies con-

ducted among US adult populations, where a higher dia-

betes risk was associated with statin therapy [10, 11, 22].

The current study has some limitations; however, it is

worthwhile to acknowledge some of its merits. First, the

study was adequately powered to detect significant asso-

ciations between statin use and incidence of diabetes if they

truly existed. Second, the study utilized two different sta-

tistical approaches, including the use of propensity score

covariate adjustment, to estimate the risk of diabetes

associated with statin use. To our knowledge, this is the

first observational study of statin use and incidence of

diabetes to conduct several sensitivity analyses utilizing a

combination of robust statistical methodologies.

Despite these study strengths, it is important that the

study results be interpreted in light of its limitations. One

of the main study limitations was the possibility of disease

misclassification. Because the data were limited to 2 years

in length, a short pre-index period (ranging between

6 months and 1 year) was used to identify and exclude

prevalent diabetes cases. This has the potential to increase

diabetes incidence among the study population, as some

prevalent diabetes cases may be misclassified as incident

diabetes cases. However, this limitation might be attenu-

ated by both statin users and non-statin users being equally

exposed to the same sets of study inclusion and exclusion

criteria and by further excluding diabetes cases that

occurred within 6 months of subjects starting their medi-

cations. In addition, the length of the pre-index period used

to exclude prevalent diabetes cases did not significantly

differ between statin users and non-statin users. However,

utilizing a longer pre-index period to exclude prevalent

diabetes cases would have strengthened the argument for

the associations observed.

Second, we were not able to control for some variables

that may increase diabetes risk in one group compared with

another, independent of statin use. These variables include

race/ethnicity, family history of diabetes, cholesterol level,

Statins and Risk of Incident Diabetes

body mass index, and presence or absence of prediabetes.

We attempted to attenuate these limitations by (1) adjusting

for disease comorbidities (using the CCI score, which

accounted for baseline diseases such as myocardial

infarction, congestive heart failure, peripheral vascular

disease, cerebrovascular disease, dementia, chronic pul-

monary disease, rheumatologic disease, peptic ulcer dis-

ease, mild or severe liver disease, hemiplegia, renal

disease, any malignancy, and AIDS) and accounting for the

use of diabetogenic medications, (2) controlling for obesity

(though under-reported in the data), hypertension, and

hyperlipidemia diagnoses, and (3) utilizing a regression-

based propensity score covariate adjustment. In particular,

obesity was under-reported in our data and may not be

adequately controlled for. Since obesity and metabolic

syndrome are important risk factors for diabetes and may

be more prevalent among statin users, it is possible that the

increased risk of diabetes observed among statin users is

partly explained by these inadequately controlled-for

variables. Future observational studies should fully adjust

for these important variables that could account for the

increased risk of diabetes, irrespective of statin therapy.

Finally, the MarketScan Database that was used for this

study used data collected in 2003–2004 and did not include

people aged C65 years. Changes in statin prescribing and

utilization patterns means there is a possibility that study

results might be different if current data were applied. In

addition, bias could be introduced because older people

may have more comorbidities and risk factors for diabetes.

Furthermore, the dataset was sourced mainly from large

employers with private insurance and, therefore, may not

generalize well to the entire US population or to other US

populations such as Medicaid, Medicare, and uninsured

populations.

4.5 Study Implications

Although the benefit of statins in primary and secondary

prevention of cardiovascular disease is well-documented,

this study and several other studies have suggested that

statins are associated with a moderate increase in risk of

new-onset diabetes. These previous observations

prompted the FDA to revise statin labels to include a

warning of an increased risk of incident diabetes mellitus

[1]. Even though the precise pathway by which statins

induce incident diabetes is still unclear, statins are

thought to worsen glycemic control and increase FPG

and insulin resistance, thereby possibly leading to dia-

betes mellitus [10].

Because types and doses of statin differ in their ability

to reduce low-density lipoprotein cholesterol (i.e., the

‘‘bad’’ cholesterol) as well as in their diabetogenic

potential, Navarese et al. [32] suggested there might be a

need for physicians to individualize statin therapy, espe-

cially among people with low cardiovascular risk. This

tailored therapy should be based on sound clinical judg-

ment, the patient’s overall cardiovascular risk and meta-

bolic profile, and the type and dose of statin used [32].

According to Navarese et al. [32], pravastatin seems to be

the least diabetogenic statin currently available on the

market, and it could be ideal for patients with hyperlipi-

demia who have a low risk of cardiovascular disease but

who have a high predisposition for diabetes. The results

of this study also support this argument, as the risk of

diabetes was lowest among pravastatin and rosuvastatin

users. However, it should be noted that even though study

results indicate that statin therapy may be associated with

increased diabetes risk, there is evidence that the car-

diovascular benefits offered by statin therapy may out-

weigh the potential for increased risk in diabetes. The

meta-analysis of 13 statin trials with 91,140 participants

by Sattar et al. [8] indicated that statin therapy was

associated with 5.4 fewer deaths from coronary heart

disease and non-fatal myocardial infarction per 255

patients treated over 4 years. This is in contrast to only

one additional case of new-onset diabetes recorded per

255 patients treated with statins over 4 years [8].

5 Conclusion

The results of the study lend support to the hypothesis that

statin therapy is significantly associated with increased risk

of new-onset diabetes. This increased risk was found not

only in the use of statins as a class, but each statin type was

also significantly associated with an increased risk of

incident diabetes mellitus. Nevertheless, healthcare pro-

fessionals can use a targeted approach to optimize the

management of their patients by identifying those who

would benefit more from less diabetogenic statin types.

Acknowledgments The authors acknowledge Thomson Reuters for providing us with the 2003–2004 MarketScan data as part of the

Thomson Reuter’s MarketScan Dissertation Support Program.

Compliance with Ethical Standards

This study complied with standards required for observational study

of de-identified data. The study did not involve human subjects.

Funding No external funding was used in the preparation of this manuscript.

Conflict of interest All authors, Busuyi Olotu, Marvin Shepherd, Suzanne Novak, Kenneth Lawson, James Wilson, Kristin Richards,

and Rafia Rasu, declare they have no conflicts of interest that might

be relevant to the contents of this manuscript.

B. S. Olotu et al.

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