Association of Metformin Use With Risk of Venous Thromboembolism in Adults With Type 2 Diabetes

A General-Population-Based Cohort Study

Tingting Sha; Yuqing Zhang; Changjun Li; Guanghua Lei; Jing Wu; Xiaoxiao Li; Zidan Yang; Chao Zeng; Jie Wei

Am J Epidemiol. 2022;191(5):856-866.

Methods

Data Sources

We used data from The Health Improvement Network (THIN), an electronic medical record database derived from the records of general practitioners in the United Kingdom. It draws approximately 17 million participants from 790 general practices, which is representative of the UK population in terms of demographic structure and prevalence of key comorbid conditions.^[4] Health-care information, including data on sociodemographic characteristics, anthropometric factors, lifestyle factors, diagnoses, prescriptions, hospital admissions, and referrals, as well as results of laboratory tests, is prospectively gathered and anonymized. The Read classification system^[22] is used to code specific diagnoses, whereas a dictionary based on the Multilex classification system (FDB Multilex; First Databank UK Ltd., Exeter, United Kingdom)^[23] is used to code medications.^[24] The validity of THIN for use in clinical and epidemiologic research studies has been demonstrated previously.^[25] The scientific review committee for the THIN database (The Health Improvement Network Ltd., London, United Kingdom) and the institutional review board at Xiangya Hospital (Changsha, China) approved this study, with a waiver of informed consent.

Study Design and Cohort Definition

The risks of DVT and PE increase rapidly with age; both diseases are rare before age 40 years.^[26,27] Additionally, since the number of individuals aged 90 years or older in THIN is small, the THIN institutional review board requires that persons aged ≥90 years should not be included in analyses to avoid the potential risk of unintentional (deductive) identification of a specific individual. Thus, we included participants who were 40–90 years of age, had a history of type 2 diabetes based on the Read codes,^[28,29] and had at least 1 year of continuous enrollment in a general practice participating in THIN from January 2000 to December 2018. The code list for type 2 diabetes is provided in Web Table 1 (available at https://doi.org/10.1093/aje/kwab291). We conducted a cohort study to compare the risks of VTE between metformin initiators and sulfonylurea initiators. Initiation of either metformin use or sulfonylurea use was defined as not having a history of use of either medication before entering the study and refilling at least 1 prescription within 60 days after the end of the supply of the first prescription.^[30] The date of the first refill was denoted as the index date. We did not exclude the patients with type 2 diabetes who used other types of antidiabetic medication before the first prescription of either metformin or a sulfonylurea. Because metformin is contraindicated for patients with severe chronic kidney disease,^[31] we excluded persons with severe chronic kidney disease,^[30] defined as an estimated glomerular filtration rate less than 30 mL/minute/1.73 m² on at least 2 occasions more than 90 days apart or at least 1 Read code for advanced chronic kidney disease.^[32] In addition, we also excluded persons who had a history of VTE before the index date or who had no general practitioner visit or specialist referral within 1 year before the index date.

To account for potential secular trends in metformin and sulfonylurea prescriptions in relation to various covariates,^[33] we divided calendar time into 19 1-year time blocks ranging from January 2000 to December 2018. Follow-up ended on December 31, 2019. Within each time block, inverse probability of treatment weighting was conducted using a logistic regression model in which the propensity of initiation of metformin versus sulfonylureas was regressed on the following covariates: sociodemographic factors, study entry time, lifestyle factors, type 2 diabetes duration, comorbid conditions, prescriptions, and health-care utilization prior to the index date. Inverse probability of treatment weighting applies weights corresponding to 1/propensity score for patients in the metformin group and [1/(1 − propensity score)] for those in the sulfonylurea group. To exclude patients with extreme weights, we truncated weights at the 95th percentile with this threshold (i.e., replaced any weight greater than the 95th percentile with this threshold).^[34,35]

Assessment of Outcomes

The primary outcome of interest was a diagnosis of incident VTE (the combined endpoint of PE and DVT), with secondary outcomes of PE and DVT separately. An individual was considered to have developed the outcome of interest if he or she had a recorded Read code of PE or DVT and received anticoagulant treatment.^[36] Since VTE is potentially fatal and some individuals might have died before receiving an anticoagulant, we also considered participants to have developed VTE if they died within 1 month after a recorded code of PE or DVT but without a prescription for an anticoagulant or autopsy results.^[37] A code list for PE, DVT, and anticoagulants is available in Web Table 2. This VTE definition has been validated and adopted in previous studies, with a sensitivity rate of 94% in the UK Clinical Practice Research Datalink, which is similar to the THIN database.^[4,36,38,39]

Assessment of Covariates

Information on sociodemographic and anthropometric characteristics, lifestyle factors, comorbid conditions, and medication use was selected on the basis of clinical significance before the index date (Table 1). Data on body mass index (weight (kg)/height (m)²) measurements, cigarette smoking status (never, former, or current smoker), alcohol consumption (never, former, or current drinker), Townsend deprivation index, comorbidity (myocardial infarction, stroke, hypertension, liver diseases, chronic kidney diseases, polycystic ovary syndrome, etc.), and prescriptions (statins, nonsteroidal antiinflammatory drugs, opioids, hormone replacement therapy, etc.) were collected before the index date. Data on indicators of health-care utilization (numbers of hospitalizations, general practitioner visits, and specialist referrals) were derived from the past 1 year before the index date.

Statistical Analysis

Person-years of follow-up were calculated as the amount of time from the index date to the date of any of the following events: an incident VTE, death, drug discontinuation (i.e., continuous use of a drug allowing for a 60-day grace period), a switch to or addition of a comparator antidiabetic drug, disenrollment from a general practice participating in THIN, or the end of the study period (i.e., December 31, 2019), whichever came first. We plotted the cumulative incidence curves for VTE, PE, and DVT accounting for the competing risk of death. We performed Cox proportional hazards regression to estimate hazard ratios (HRs) with 95% CIs and conducted the Kolmogorov-Smirnov supremum test to assess the proportional hazards assumption. If the proportional hazards assumption was violated, we then estimated the HR at 2 years, 4 years, 6 years, and 10 years of follow-up.^[4] We also estimated the rate differences in the risk of VTE between the metformin group and the sulfonylurea group.

We conducted several sensitivity analyses to assess the robustness of our results. First, we performed an intention-to-treat analysis to assess the relationship between initiation of metformin use and the risk of VTE (or PE/DVT). Specifically, person-years of follow-up were calculated from the index date to the date of an outcome of interest, death, disenrollment from a general practice participating in a THIN general practice, or the end of the study period (i.e., December 31, 2019), irrespective of any treatment switching, augmentation, or discontinuation. Second, since hemoglobin A1c (HbA1c) is a sensitive proxy for blood glucose control in type 2 diabetes and is a potential risk factor for VTE, we further adjusted for HbA1c level prior to the index date to control for potential confounding. Third, to minimize potential selection bias, we repeated the primary analyses among the incident cases of type 2 diabetes instead of the prevalent cases. Fourth, since cancer is a strong risk factor for VTE, we excluded persons with a history of cancer before the index date from the analyses. Fifth, to evaluate the robustness of the assumptions about the prescription refill period, we varied the prescription refill period from 60 days (a 60-day supply) to 90 days (a 90-day supply).^[40] Sixth, because hematocrit might be associated with metformin use and might affect the results, we conducted a sensitivity analysis by adjusting the level of hematocrit prior to the index date to control its potentially confounding effect. Finally, we also added potential covariates to the Cox proportional hazards model as a sensitivity analysis to evaluate the robustness of our results.

We also evaluated the associations of antidiabetic therapy with 2 positive control outcomes—namely, all-cause mortality and incident cardiovascular disease (CVD)—to further assess the robustness of our study findings.^[41,42] Death was confirmed by the patient's death date as recorded in THIN, linked to the UK National Health Service.^[24] Incident CVD was defined as newly developed myocardial infarction, stroke, or heart failure if the participant had a recorded corresponding Read code during the following year.^[43]

All P values were 2-sided, and P < 0.05 was considered statistically significant for all tests. Statistical analyses were conducted using SAS, version 9.4 (SAS Institute, Inc., Cary, North Carolina) and R, version 3.5.1 (R Foundation for Statistical Computing, Vienna, Austria).

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Table 1. Baseline Characteristics of Adults With Type 2 Diabetes Using Either Metformin or a Sulfonylurea in the Health Improvement Network Database, United Kingdom, 2000–2019
Variable	Before IPTW					After IPTW
	Metformin Use		Sulfonylurea Use		Standardized Difference	Metformin Use		Sulfonylurea Use		Standardized Difference
	Mean (SD)	%	Mean (SD)	%	Standardized Difference	Mean (SD)	%	Mean (SD)	%	Standardized Difference
Demographic characteristics
Age, years	62.3 (11.1)		67.1 (11.5)		0.427	62.8 (11.2)		63.9 (11.2)		0.091
Socioeconomic deprivation index score^a	2.7 (1.4)		2.7 (1.4)		0.002	2.7 (1.4)		2.8 (1.4)		0.022
Female sex		42.8		41.4	0.029		42.8		44.5	0.035
Body mass index^b	32.5 (6.3)		27.5 (5.1)		0.868	32.0 (6.4)		31.6 (6.3)		0.056
Lifestyle factors
Alcohol consumption					0.048					0.025
None		21.9		23.7			22.1		23.2
Past drinker		4.1		3.7			4.1		4.1
Current drinker		74.0		72.7			73.8		72.7
Cigarette smoking					0.077					0.012
None		46.5		47.5			46.7		47.2
Past smoker		36.6		33.4			36.3		35.9
Current smoker		16.9		19.1			17.1		16.9
Duration of diabetes, years	2.1 (3.9)		2.3 (3.9)		0.034	2.1 (3.9)		2.2 (3.7)		0.017
Comorbidity
Hypertension		59.0		52.8	0.125		58.4		58.1	0.006
Liver disease		4.2		4.6	0.016		4.3		4.2	0.002
Transient ischemic attack		2.7		4.2	0.080		2.9		3.2	0.017
Ischemic heart disease		16.6		22.1	0.141		17.3		17.6	0.007
Congestive heart failure		3.1		7.2	0.183		3.7		4.1	0.018
Myocardial infarction		7.1		10.0	0.105		7.5		7.4	0.004
Angina		9.8		13.7	0.121		10.3		10.4	0.005
Pneumonia or infection		7.0		7.6	0.022		7.1		6.9	0.007
Hyperlipidemia		20.2		16.5	0.095		19.8		19.6	0.005
Dementia		0.8		1.4	0.061		0.8		0.9	0.010
Varicose veins		6.5		6.6	0.007		6.5		6.9	0.018
Depression		13.7		10.5	0.096		13.3		13.4	0.001
Chronic obstructive pulmonary disease		5.3		6.5	0.052		5.5		5.8	0.015
Atrial fibrillation		5.3		8.8	0.138		5.8		6.4	0.027
Fall		8.4		8.1	0.013		8.4		8.9	0.018
Gastrointestinal bleeding		2.0		2.8	0.052		2.1		2.2	0.001
Cancer		8.5		12.3	0.125		9.0		9.8	0.029
Peripheral vascular disease		1.7		2.9	0.082		1.9		2.0	0.013
Rheumatoid arthritis		1.3		2.0	0.055		1.4		1.4	0.005
Stroke		3.8		6.1	0.105		4.1		4.6	0.024
Polycystic ovary syndrome		0.5		0.1	0.074		0.4		0.2	0.035
Fracture^c		0.4		0.9	0.056		0.5		0.5	<0.001
Trauma^c		0.0		0.0	0.002		0.0		0.0	0.009
Surgery^c		3.4		5.1	0.084		3.5		3.8	0.016
Medication use
Nonsteroidal antiinflammatory drugs		77.0		71.2	0.131		76.5		76.7	0.005
Opioids		33.2		28.4	0.106		32.9		33.5	0.014
Angiotensin-converting enzyme inhibitors		50.1		42.4	0.155		49.4		48.9	0.009
β-receptor inhibitors		38.9		39.5	0.012		39.1		39.7	0.013
Previous antidiabetic medications^d		99.9		100.0	0.039		99.9		100.0	0.039
Calcium channel blockers		36.9		34.4	0.051		36.7		36.5	0.005
Corticosteroids		59.0		54.1	0.099		58.6		58.8	0.004
Nitrates		14.6		17.1	0.070		14.9		14.7	0.008
Statins		68.0		49.0	0.393		66.0		65.1	0.020
Aspirin		41.6		42.5	0.020		41.8		41.6	0.004
Hormone replacement therapy		16.5		11.6	0.142		16.0		16.4	0.010
Health-care utilization^c
No. of hospitalizations	0.3 (1.0)		0.4 (1.3)		0.090	0.4 (1.8)		0.4 (1.0)		0.004
No. of general practice visits	7.1 (6.2)		7.7 (6.8)		0.091	7.3 (6.7)		7.5 (6.2)		0.031
No. of specialist referrals	0.6 (1.0)		0.5 (1.0)		0.124	0.6 (1.0)		0.6 (1.0)		0.009

Abbreviations: IPTW, inverse probability of treatment weighting; SD, standard deviation.
^aSocioeconomic deprivation was measured via the Townsend deprivation index; scores were grouped into quintiles from 1 (least deprived) to 5 (most deprived).
^bWeight (kg)/height (m)².
^cDuring the year prior to the index date.
^dThe first prescription of either metformin or a sulfonylurea was identified as the previous antidiabetic medication. The proportions of use of other antidiabetic drugs before the first prescription of either metformin or sulfonylurea were well balanced between the 2 comparison groups (3.33% vs. 3.47%).

Table 2. Association Between Metformin Use and Risk of Venous Thromboembolism Among Adults With Type 2 Diabetes in the Health Improvement Network Database, United Kingdom, 2000–2019
Medication	No. of Events	Mean Duration of Follow-up, years	Event Rate per 1,000 Person-Years	RD per 1,000 Person-Years	95% CI	HR	95% CI
Venous Thromboembolism
Metformin	555	3.60	1.3	−0.8	−1.3, −0.3	0.65	0.51, 0.84
Sulfonylurea	75	2.63	2.1	0	Referent	1.00	Referent
Pulmonary Embolism ^a
Metformin	266	3.60	0.6	−0.4	−0.7, −0.1	0.71	0.50, 1.01
Sulfonylurea	35	2.63	1.0	0	Referent	1.00	Referent
Deep Vein Thrombosis ^b
Metformin	340	3.60	0.8	−0.7	−1.1, −0.3	0.64	0.48, 0.87
Sulfonylurea	49	2.63	1.3	0	Referent	1.00	Referent

Abbreviations: CI, confidence interval; HR, hazard ratio; RD, rate difference.
^aIn this analysis, we excluded persons who had a history of pulmonary embolism before the index date, finally including 119,116 metformin users and 14,054 sulfonylurea users.
^bIn this analysis, we excluded persons who had a history of deep vein thrombosis before the index date, finally including 118,317 metformin users and 13,981 sulfonylurea users.

Table 3. Sensitivity Analyses of the Association Between Metformin Use and Risk of Venous Thromboembolism Among Adults With Type 2 Diabetes in the Health Improvement Network Database, United Kingdom, 2000–2019
Medication	No. of Events	Mean Duration of Follow-up, years	Event Rate per 1,000 Person-Years	RD per 1,000 Person-Years	95% CI	HR	95% CI
Intention-to-Treat Analysis ^a
Metformin	785	3.68	1.7	−0.5	−0.9, −0.1	0.80	0.72, 0.90
Sulfonylurea	129	3.82	2.3	0	Referent	1.00	Referent
Adjustment for HbA1c Level ^b
Metformin	475	3.53	1.3	−0.7	−1.3, −0.1	0.68	0.50, 0.91
Sulfonylurea	51	2.65	2.0	0	Referent	1.00	Referent
Incident Type 2 Diabetes Patients ^c
Metformin	463	3.53	1.3	−1.0	−1.6, −0.5	0.60	0.45, 0.80
Sulfonylurea	56	2.48	2.2	0	Referent	1.00	Referent
Exclusion of Patients With Cancer ^d
Metformin	486	3.64	1.3	−0.5	−1.0, −0.1	0.75	0.56, 1.00
Sulfonylurea	57	2.69	1.8	0	Referent	1.00	Referent
Varying the Length of the Prescription Refill Period ^e
Metformin	480	2.90	1.4	−1.10	−1.5, −0.4	0.58	0.45, 0.75
Sulfonylurea	71	2.15	2.4	0	Referent	1.00	Referent
Adjustment for Hematocrit Level ^f
Metformin	375	3.37	1.4	−1.6	−2.4, −0.7	0.57	0.41, 0.78
Sulfonylurea	46	2.33	2.9	0	Referent	1.00	Referent
Adjustment for Potentially Confounding Factors ^g
Metformin	555	3.60	1.3	−0.6	−1.0, −0.1	0.73	0.55, 0.96
Sulfonylurea	75	2.63	2.1	0	Referent	1.00	Referent

Abbreviations: CI, confidence interval; HbA1c; hemoglobin A1c; HR, hazard ratio; RD, rate difference.
^aThis analysis followed up the patients from the index date and continued until the date of an outcome of interest, irrespective of treatment discontinuation.
^bThis analysis adjusted the level of HbA1c before the index date among the individuals who had information on HbA1c level.
^cThis analysis excluded the patients diagnosed with type 2 diabetes before the study period.
^dThis analysis excluded patients diagnosed with cancer before the index date.
^eThis analysis varied the length of the prescription refill period from 60 days (a 60-day supply) to 90 days (a 90-day supply).
^fThis analysis adjusted the level of hematocrit before the index date among the individuals who had information on hematocrit level.
^gThis analysis adjusted for all of the baseline covariates listed in Table 1 in an inverse probability of treatment–weighted Cox model.

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Association of Metformin Use With Risk of Venous Thromboembolism in Adults With Type 2 Diabetes

A General-Population-Based Cohort Study

Methods

Data Sources

Study Design and Cohort Definition

Assessment of Outcomes

Assessment of Covariates

Statistical Analysis

Tables

Tables

Tables

References

Authors and Disclosures

Authors and Disclosures

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