Abstract
Aim:
Women with diabetes are at higher risk of cardiovascular diseases (CVD) than men with diabetes; however, the sex difference in the association between the degree of risk-factor control and the risk of CVD in patients with diabetes is unclear.
Methods:
A total of 17,260 participants diagnosed with diabetes from the UK Biobank were included and matched with 86,300 non-diabetes controls based on age, sex, and assessment center. The main exposure was the number of risk factors within the target range, including glycated hemoglobin (HbA1c) level <53 mol/mol (7%), blood pressure <140/90 mm/Hg, low-density lipoprotein cholesterol <100 mg/dl, noncurrent smoking, and absence of microalbuminuria.
Results:
During a median follow-up of 13.3 years, a total of 3,338 incident CVD cases, including 2,807 ischemic heart disease (IHD) and 793 strokes, were documented. A more stringent control of risk factors was significantly associated with a lower risk of incident CVD, and such an association was significantly stronger in women than men. Compared with non-diabetes participants, the diabetes-related risk of CVD appeared to be eliminated if more than three risk factors were well controlled among women and men with diabetes. Moreover, clinical biomarkers (e.g., HbA1c and blood pressure) showed greater relative importance than other factors in women, whereas socioeconomic and psychological factors (e.g., education and depression) exhibited similar relative importance to clinical biomarkers in men with diabetes.
Conclusion:
Our findings highlighted the importance of raising awareness of sex differences in the management of CVD risk factors among patients with diabetes.
Keywords: diabetes, risk factor control, cardiovascular disease
INTRODUCTION
Diabetes mellitus, a significant chronic condition, imposes a substantial global burden, affecting over 537 million adults globally in 2021[1, 2]. Cardiovascular diseases (CVD), including ischemic heart disease (IHD) and stroke, are the most common complications of diabetes. Considerable evidence has shown that individuals with diabetes have a 2-3-fold higher risk of CVD than those without diabetes[3, 4]. Results from previous studies indicate that individually controlling cardiovascular-related risk factors such as glycemia, blood pressure, lipids, smoking, and microalbuminuria may significantly lower the subsequent risk of CVD[5-9], laying the foundation for clinical care of patients with diabetes[10-12]. However, findings from two recent studies conducted in the U.S. population indicated that, except for a minor improvement in lipid control and a reduction in smoking rate, the percentage of patients with diabetes meeting the glycated hemoglobin (HbA1c) or blood pressure target decreased from 2007 to 2018[13, 14]. In the UK, only 38.7% of individuals with diabetes met all treatment goal for HbA1c, blood pressure, and cholesterol in 2016[15]. These findings highlight the need to increase awareness of risk-factor control among patients with diabetes.
There is a scarcity of studies that have comprehensively assessed the relationship between management of multiple risk factors, including high glycemia, blood pressure, lipids, smoking, and microalbuminuria, with incident CVD among patients with diabetes. The existing literature also generated inconsistent results in this regard[16, 17]. It remains unclear whether the diabetes-related excess risk of CVD can be eliminated if multiple risk factors are well controlled. A Swedish study showed that patients with diabetes who had optimal risk factor control (control five risk factors: high glycemia, high blood pressure, hyperlipidemia, smoking, and albuminuria) had little or no excess risk of CVD when compared to the non-diabetes population[16]; whereas a study from the UK showed that such diabetes-related excess risk of CVD could not be fully diminished by optimal control of risk factors (control four risk factors: high glycemia, high blood pressure, hyperlipidemia, and smoking)[17]. Notably, compelling evidence has shown that the excess risk of CVD associated with diabetes is higher in women than men[18-22]. A more adverse cardiovascular risk profile has been observed in women with diabetes than men, including worse atherogenic dyslipidemia, more metabolic syndrome with or without diabetes, and impaired endothelium-dependent vasodilation[23]. However, no prior study has investigated the sex difference in the association between the degree of risk-factor control and the risk of CVD in patients with diabetes.
In this study, we compared the risk of CVD in well-controlled people with diabetes and matched controls without diabetes in women and men, respectively. Moreover, emerging evidence has shown that socioeconomic and psychological factors may also play an important role in predicting CVD risk in patients with diabetes [24]. We also particularly compared the relative importance of clinical biomarkers to socioeconomic and psychosocial risk factors, as well as lifestyle factors, in predicting CVD risk in women and men.
METHODS
Study Design and Population
The UK Biobank is a large population-based cohort study comprising more than 0.5 million participants aged 40 to 69 years recruited from across the U.K. between 2006 and 2010. The details of the study design have been described elsewhere[25]. The study was approved by the North West Multi-Centre Research Ethics Committee and the Tulane University (New Orleans, LA) Biomedical Committee Institutional Review Board, and written informed consent was obtained from all participants.
The UK Biobank linked participants to the hospital inpatient records containing data on admissions and diagnoses obtained from the Hospital Episode Statistics for England, Scottish Morbidity Record data for Scotland, and the Patient Episode Database for Wales. ICD-9 and ICD-10 (International Classification of Diseases, 9th and 10th revisions) codes were used to define participants with diabetes and CVDs. Participants with diabetes at baseline (n = 26,857) were ascertained based on whether they had a diabetes incident prior to or equal to the date of attending the assessment center or had a self-reported history of diabetes diagnosed by a doctor. Details and ICD codes used to define baseline diabetes are presented in Table S1. In this study, a total of 17,260 participants with diabetes were included in the main analysis, after excluding 6,081 participants with prevalent CVD and 3,516 participants with missing values on five risk factors at baseline, including HbA1c, blood pressure, low-density lipoprotein (LDL) cholesterol, microalbuminuria, or smoking. Furthermore, each participant with diabetes was matched at random with five controls who did not have diabetes or CVD based on age (±2 years), sex, and assessment center. A total of 86,300 participants were selected as controls at baseline.
Definition of Risk-Factor Control
According to the previous studies and guidelines[11, 14, 16, 17], five risk factors were used to define patients with diabetes with varying numbers of risk factors that met the recommended guidelines (ranging from 0-5), including HbA1c, blood pressure, LDL cholesterol, smoking, and microalbuminuria. We defined glycemic control as HbA1c level less than 53 mmol/mol (7.0%), lipid control as LDL cholesterol level less than 100 mg/dL, blood pressure control as mean SBP <140 mmHg or SBP <130 mmHg in the presence of renal impairment, retinopathy, or cerebrovascular disease[17], absence of microalbuminuria as an albumin-to-creatinine ratio (ACR) less than 30 mg/g, and noncurrent smoking as a combination of never and past smoking[14]. Biochemistry measures (HbA1c, LDL cholesterol, urine albumin, and creatinine) were performed at central laboratory. HbA1c was measured using high-performance liquid chromatography analysis on a Bio-Rad VARIANT II Turbo. LDL cholesterol was measured using enzymatic protective selection analysis on a Beckman Coulter AU5800. The albumin-to-creatinine ratio was calculated using urine albumin and creatinine concentration[26]. Urine albumin was measured using immunoturbidimetric analysis, and urine creatinine was measured using enzymatic analysis on a Beckman Coulter AU5400. Blood pressure was measured twice by a trained nurse at the assessment center using electronic a blood pressure monitor (Omron 705 IT, OMRON Healthcare Europe B.V., Hoofddorp, Netherlands). Average levels of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were used in this study, with automated measurements preferred. If the automated one was missing or unavailable, manual measurement was selected instead[27]. Smoking status was categorized as never, past, and current smoking.
Covariates
A touch-screen questionnaire was used to collect self-reported information, including age, sex, race, Townsend Deprivation Index, education levels, physical activity, healthy diet, depression, family history of heart disease and stroke (among first-degree relatives including father, mother, and siblings), diabetes medication (insulin use, oral antidiabetic drugs only, and neither), antihypertensive medication, and statin or other lipid lowering medication. Regular physical activity was defined as >150 minutes of moderate intensity activity per week, >75 minutes of vigorous activity per week, or an equivalent combination per week[28]. Moderate intensity physical activity is defined as activities that make you get warmer, breathe harder, and your heartbeat faster, but you can still carry on a conversation. Vigorous activity normally results in being out of breath or sweating. The healthy diet score was calculated by red meat intake (<median), vegetable intake (≥median), fruit intake (≥median), and fish intake (≥median); One point was given for each favorable diet factor, and the total diet score ranged from 0 to 5; A healthy diet was defined as a diet score ≥3[29]. Depressive symptoms were assessed using the two questions from the Patient Health Questionnaire-2, including the frequency of depressed mood and disinterest or absence of enthusiasm in the previous 2 weeks. Participants were coded as 0, 1, 2, or 3 based on the degree of their responses. Specifically, the response categories were: “not at all” coded as 0, “several days” coded as 1, “more than half the days” coded as 2, and “nearly every day” coded as 3. The total depression score is the sum of the 2 items, ranging from 0 to 6; depression was defined as a depression score ≥3[30]. Information on antidiabetic medication was extracted from both self-reported data and the nurse’s verbal interview[31].
Height and weight were measured during the assessment visit, and body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m2). The estimated glomerular filtration rate (GFR) was calculated using serum creatinine and age based on race and sex[32]. Information on diabetes diagnosis duration (days from the date of diabetes onset to the date of baseline), diabetes complications (diabetes-related amputation, neuropathy, cataract, retinopathy, and arthropathy), and diabetes types was defined through the inpatient health record[29]. Further details of these measurements can be found on the UK Biobank website (https://biobank.ctsu.ox.ac.uk/showcase).
Outcomes
The primary outcome of the present study was incident CVD and its two major component endpoints: IHD and stroke. We defined outcomes according to the ICD-10 codes: I21–I25 for IHD, and I60, I61, I63, and I64 for stroke. Information on the timing of outcomes was collected through cumulative medical records of hospital diagnoses. The follow-up time was calculated from the date of baseline to the date of outcome diagnosis, death, or the censoring date (Dec. 31, 2022), whichever occurred first. Detailed information on the ascertainment of outcomes is available online at https://biobank.ctsu.ox.ac.uk/showcase/label.cgi?id=2000.
Statistical Analysis
Analyses of covariance (generalized linear models) and Chi-square were used to compare continuous and categorical variables between women and men. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated by multivariable Cox proportional hazard models to assess the association of the number of risk factor controls with the risk of CVDs, with follow-up years as the underlying time metric. The proportional hazards assumption was tested based on Schoenfeld residuals, and all analyses were satisfied. Models were adjusted for age, race/ethnic, Townsend Deprivation Index, education levels, diabetes diagnosis duration, diabetes complications, types of diabetes, regular physical activity, a healthy diet, depression, family history of heart disease or stroke, obesity, estimated GFR, medication usage of antidiabetic, antihypertensive, and statin or other lipid-lowering, and/or sex. To investigate the sex difference, an interaction between sex and the number of risk factor control was tested by adding a cross-product term to the models.
To investigate the extent to which the excess CVD risk related to diabetes might be attenuated or potentially eliminated by controlling for risk factors, HRs and 95% CIs were calculated to estimate CVD risk based on the number of risk factor controls among women and men with diabetes as compared with their matched non-diabetes counterparts.
To provide estimates of how important the clinical biomarkers, socioeconomic and psychological factors, and lifestyle risk factors are in predicting CVD, we analyzed the relative importance of these risk factors by calculating the R2 values of the Cox models[16, 29, 33, 34]. The explainable log-likelihood attributed to each risk factor was also calculated to test the consistency of our results[16].
Statistical analyses were performed with SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and R version 3.6.1 (R Foundation for Statistical Computing, Vienna, Austria). All P values were two-sided, and P < 0.05 was considered statistically significant.
RESULTS
Baseline characteristics
The baseline characteristics of patients with diabetes according to sex are presented in Table 1. Among 17,260 patients with diabetes, 39.7% were women. Compared with men, women at baseline were slightly younger, less likely to be White, had higher levels of deprivation, lower education, shorter diagnosed duration, lower levels of SBP and DBP, and higher levels of LDL cholesterol and BMI. Women were more likely than men of being noncurrent smokers, adopting healthier diet habits, experiencing depression, and having a family history of heart disease and stroke. Conversely, women were less likely than men to engage in regular physical activity, take antihypertensive medication, or take cholesterol-lowering medication. The baseline characteristics of matched controls without diabetes are presented in Table S2. Women without diabetes were slightly younger, had lower education, higher levels of LDL cholesterol, and lower levels of SBP and DBP than men without diabetes.
Table 1.
Baseline Characteristics of 17,260 Women and Men with Diabetes
| Variable | Women | Men |
|---|---|---|
| No. of participants | 6857 | 10403 |
| Age, years | 59.01±7.27 | 59.31±7.21 |
| White ethnicity, n (%) | 5927 (86.44) | 9219 (88.62) |
| Townsend Deprivation Index | −0.48±3.33 | −0.67±3.36 |
| Education, n (%) | ||
| ≤10 years | 3145 (45.87) | 3931 (37.79) |
| 11-19 years | 2058 (30.01) | 3476 (33.41) |
| ≥20 years | 1544 (22.52) | 2802 (26.93) |
| Diabetes diagnoses duration, years | 8.59±10.11 | 9.02±10.74 |
| Diabetes complications, n (%) | 170 (2.48) | 331 (3.18) |
| Type 1 diabetes, n (%) | 685 (9.99) | 829 (7.97) |
| Noncurrent smoker, n (%) | 6278 (91.56) | 9172 (88.17) |
| Regular physical activity, n (%) | 2956 (43.11) | 4795 (46.09) |
| Healthy diet, n (%) | 4827 (70.42) | 5572 (53.56) |
| Depression, n (%) | 689 (10.05) | 787 (7.57) |
| Family history, n (%) | ||
| Heart disease | 3403 (49.63) | 4282 (41.16) |
| Stroke | 2112 (30.80) | 2836 (27.26) |
| Body mass index, kg/m2 | 31.93±6.58 | 30.63±5.26 |
| Estimated GFR, mL/min/1.73 m2 | 89.56±16.31 | 90.12±15.70 |
| Glycated hemoglobin, mmol/mol | ||
| Millimoles per mole | 52.77±13.82 | 52.34±13.78 |
| Percent | 6.98±1.26 | 6.94±1.26 |
| LDL-cholesterol, mg/dl | ||
| Millimoles per liter | 2.85±0.80 | 2.66±0.75 |
| Milligrams per deciliter | 109.86±31.04 | 102.74±28.80 |
| Blood pressure, mm Hg | ||
| Systolic | 139.83±17.56 | 142.67±16.46 |
| Diastolic | 80.92±9.55 | 82.81±9.41 |
| Microalbuminuria, n (%) | 1177 (17.16) | 1862 (17.90) |
| Antihypertensive medication, n (%) | 3953 (57.65) | 6239 (59.97) |
| Cholesterol-lowering medication, n (%) | 5015 (73.14) | 7896 (75.90) |
| Antidiabetic medication, n (%) | ||
| Oral antidiabetic drugs only | 3295 (48.05) | 5327 (18.57) |
| Insulin | 1443 (21.04) | 1932 (18.57) |
| Neither | 2119 (30.90) | 3144 (30.22) |
| Number of risk factor control | 3.18±1.03 | 3.18±1.04 |
Data are present as mean±SD or n (%).
The body mass index is the weight in kilograms divided by the square of the height in meters. The GFR was estimated with the use of the Modification of Diet in Renal Disease equation. To convert values for cholesterol to milligrams per deciliter, divide by 0.02586. To convert values for HbA1c to percent, divide by 10.929 and plus 2.15.
LDL, low-density lipoprotein; GFR, glomerular filtration rate.
Association Between the Degree of Risk Factor Control and the Risk of Cardiovascular Outcomes Among Patients with Diabetes.
During a median follow-up of 13.3 years, a total of 3,338 incident CVD cases (2,807 IHD and 793 strokes) were documented. Significant inverse associations were observed between the number of risk-factor controls and the risks of CVD outcomes among patients with diabetes (Table 2). In multivariable-adjusted models, per 1 additional control of risk factor was significantly associated with a 17% lower risk of CVD (HR, 95%CI, 0.83, 0.81-0.86), a 16% lower risk of IHD (HR, 95%CI, 0.84, 0.81-0.88), and a 24% lower risk of stroke (HR, 95%CI, 0.76, 0.71-0.82).
Table 2.
Multivariable-Adjusted HRs (95% CIs) for Cardiovascular Diseases Among 17,260 Patients with Diabetes
| No. of risk factor control |
Cardiovascular Disease |
Ischemic Heart Disease |
Stroke |
|||
|---|---|---|---|---|---|---|
| No. of cases/total |
HR (95%CI) | No. of cases/total |
HR (95%CI) | No. of cases/total |
HR (95%CI) | |
| 0-1 | 288/981 | 1 (Reference) | 245/981 | 1 (Reference) | 75/981 | 1 (Reference) |
| 2 | 755/3243 | 0.79 (0.69, 0.91) | 627/3243 | 0.79 (0.68, 0.92) | 209/3243 | 0.82 (0.63, 1.07) |
| 3 | 1241/6260 | 0.70 (0.61, 0.79) | 1041/6260 | 0.71 (0.61, 0.81) | 293/6260 | 0.61 (0.47, 0.78) |
| 4 | 840/5175 | 0.56 (0.49, 0.64) | 707/5175 | 0.57 (0.49, 0.66) | 175/5175 | 0.45 (0.34, 0.59) |
| 5 | 214/1601 | 0.48 (0.40, 0.57) | 187/1601 | 0.51 (0.42, 0.61) | 41/1601 | 0.37 (0.25, 0.54) |
| Per 1 control | 3338/17260 | 0.83 (0.81, 0.86) | 2807/17260 | 0.84 (0.81, 0.88) | 793/17260 | 0.76 (0.71, 0.82) |
Models are adjusted for age, sex, race/ethnic, Townsend Deprivation Index, education levels, diabetes diagnosis duration, diabetes complication, types of diabetes, physical activity, diet, drinking, depression, obesity, estimated glomerular filtration rate, medication of antidiabetic, antihypertensive, and statin or other lipid lowering, and family history of heart disease or stroke (in the corresponding model).
HR, hazard ratio; CI, confidential interval.
Sex Difference in the Association Between the Degree of Risk Factor Control and the Risk of Cardiovascular Outcomes.
We found that the inverse association between the number of risk-factor control and the risks of CVD events was significantly stronger in women than men, with P for interaction equal to 0.017 (Table 3). Controlling each additional risk factor was associated with a 20% lower risk of CVD in women (HR, 95% CI, 0.80, 0.75–0.84) and a 15% lower risk of CVD in men (HR, 95% CI, 0.85, 0.82–0.89). For participants who controlled all five risk factors, the HR (95%CI) was 0.41 (0.30–0.55) and 0.52 (0.42–0.65) in women and men, respectively, compared to their counterparts who controlled zero or only one risk factor. Similarly, significant sex differences were also observed for IHD (P-interaction = 0.009) but not for stroke (P-interaction = 0.445).
Table 3.
Multivariable-Adjusted HRs (95% CIs) for Cardiovascular Diseases Among 17,260 Women and Men with Diabetes
| Sex | No. of risk factor control |
P-interaction | |||||
|---|---|---|---|---|---|---|---|
| 0-1 | 2 | 3 | 4 | 5 | Per 1 control | ||
| Cardiovascular Disease | 0.017 | ||||||
| Women | 1 (Reference) | 0.68 (0.54, 0.86) | 0.55 (0.44, 0.69) | 0.44 (0.35, 0.55) | 0.41 (0.30, 0.55) | 0.80 (0.75, 0.84) | |
| Men | 1 (Reference) | 0.86 (0.73, 1.02) | 0.78 (0.66, 0.92) | 0.63 (0.53, 0.75) | 0.52 (0.42, 0.65) | 0.85 (0.82, 0.89) | |
| Ischemic Heart Disease | 0.009 | ||||||
| Women | 1 (Reference) | 0.67 (0.52, 0.86) | 0.57 (0.45, 0.73) | 0.44 (0.34, 0.57) | 0.39 (0.28, 0.56) | 0.80 (0.75, 0.85) | |
| Men | 1 (Reference) | 0.86 (0.72, 1.04) | 0.78 (0.66, 0.93) | 0.64 (0.53, 0.77) | 0.57 (0.45, 0.72) | 0.87 (0.83, 0.90) | |
| Stroke | 0.445 | ||||||
| Women | 1 (Reference) | 0.82 (0.52, 1.30) | 0.51 (0.33, 0.81) | 0.46 (0.28, 0.74) | 0.55 (0.30, 1.00) | 0.80 (0.71, 0.90) | |
| Men | 1 (Reference) | 0.83 (0.59, 1.15) | 0.66 (0.48, 0.90) | 0.45 (0.32, 0.63) | 0.28 (0.17, 0.47) | 0.74 (0.68, 0.81) | |
Models are adjusted for age, sex, race/ethnic, Townsend Deprivation Index, education levels, diabetes diagnosis duration, diabetes complication, types of diabetes, physical activity, diet, drinking, depression, obesity, estimated glomerular filtration rate, medication of antidiabetic, antihypertensive, and statin or other lipid lowering, and family history of heart disease or stroke (in the corresponding model).
HR, hazard ratio; CI, confidential interval.
Risk of Cardiovascular Outcomes in Patients with Diabetes and Different Degrees of Risk Factor Control Compared with Non-Diabetes Controls
To estimate the extent to which the excess risk of CVD related to diabetes might be attenuated or potentially eliminated by jointly controlling for the risk factors, we assessed the risk of CVD according to the number of risk-factor controls among men and women with diabetes as compared to their matched non-diabetes controls (Figure 1). Our findings showed a stepwise decrease in CVD risks for each additional increase in the number of risk factors controlled in women and men (all P-trends < 0.001). The highest risks of CVD were observed for women and men with zero or one risk factor under control (HR, 95%CI, 2.38, 1.69–3.35 for women; HR, 95%CI, 1.65, 1.32–2.06 for men). Notably, the excess risk of CVD related to diabetes became non-significant if more than three risk factors were well controlled within the target range in women (HR, 95%CI, 1.02, 0.83–1.26 for 4 risk factor control) and men (HR, 95%CI, 1.01, 0.89–1.15 for 4 risk factor control), as compared with their matched non-diabetes controls. Similar results were observed for IHD. For stroke, the excess risk related to diabetes became non-significant if more than two risk factors were well controlled among women and men.
Figure 1.
Adjusted Hazard Ratios for Outcomes, According to Number of Risk-Factor Variables Within the Target Ranges, Among Patients with Diabetes, as Compared with Matched Controls.
HR, hazard ratio; CI, confidential interval; Error bars indicate 95%CIs.
Strength of Risk Factors in Predicting Cardiovascular Outcome in Patients with Diabetes
Figure 2 shows the relative importance of clinical biomarkers, socioeconomic and psychological factors, and lifestyle factors in predicting CVD in men and women with diabetes, respectively. We observed that the relative importance of risk factors varied between women and men. Among women, the clinical biomarkers appeared to be stronger than others. The five strongest risk factors associated with CVD risk in women were HbA1c, LDL cholesterol, smoking, estimated GFR, and diabetes diagnosis duration. Among men, we observed that socioeconomic risk factors were ranked high in addition to clinical biomarkers. The top five strongest risk factors were ACR, estimated GFR, LDL cholesterol, diabetes diagnosis duration, and education. Furthermore, the Townsend Deprivation Index and depression are also ranked relatively high in men with diabetes, and their importance is higher than lifestyle factors. Similar results were observed for IHD (Figure S1). To validate our results, we examined the relative strengths of these risk factors for CVDs by using explained log-likelihood (Figure S2) and found that the results were similar to those obtained from the explained relative risk (R2) models.
Figure 2.
Relative Importance of Risk Factors for Predicting Cardiovascular disease Among 17,260 Patients with Diabetes.
LDL, low-density lipoprotein; GFR, glomerular filtration rate; BMI, body mass index; ACR, albumin-to-creatinine ratio.
DISCUSSION
In this prospective study, we observed that a higher degree of risk factor control was significantly associated with a lower risk of incident CVD, and that this association was significantly stronger in women than in men. Compared with participants without diabetes, the diabetes-related CVD risk appeared to be eliminated in women and men with diabetes if more than three risk factors were well controlled. Furthermore, in women with diabetes, we found clinical biomarkers showed a greater relative importance than most lifestyle factors, socioeconomic and psychological factors in predicting CVD; however, in men with diabetes, socioeconomic and psychological factors also played an important role in predicting CVD in addition to clinical biomarkers.
For the first time, we reported a sex-specific association between the degree of risk factor control and incident CVD events. CVD is the most common complication of diabetes, and a large body of evidence has shown that diabetes poses a greater risk of CVD in women than in men[18-22]. Several randomized clinical trials supported that interventions targeting individual risk factors, including HbA1c, blood pressure, cholesterol, microalbuminuria, and smoking, could prevent the CVD risk in patients with diabetes[5-9]. However, it remains unclear whether the protective effects of risk-factor control differ by sex. We found that women with diabetes could benefit more than men with diabetes when they achieved a similar degree of recommended targets for risk factor controls. Moreover, we evaluated the sex-specific associations between the degree of risk factor control and subtypes of CVD events; for the IHD, we found similar sex differences as for CVD but not for stroke. These results should be interpreted with caution due to the relatively limited statistical power for the stroke-related analysis as compared with IHD, particularly for the stratified analyses by sex. Future studies with larger sample sizes are needed to verify our results.
Two potential reasons may explain the observed sex difference in the association between risk-factor control and the risk of CVD in patients with diabetes. The first possible reason is that the average duration of prediabetes in women was longer (10.3 years) than in men (8.5 years), suggesting that women may be exposed to CVD risk factors for longer periods of time and carry a greater CVD risk burden than men[35]. Another possible reason could be that we found other competing risk factors (socioeconomic and psychological factors) may dilute the importance of clinical biomarkers in predicting CVD in men with diabetes. Future studies are needed to explore the mechanisms underlying the sex difference in the association between risk-factor control and CVD in patients with diabetes.
Intriguingly, we observed that socioeconomic factors (educational attainment and the Townsend Deprivation Index) played an important role in CVD prediction in men with diabetes, with relative importance closely following clinical biomarkers. These findings were supported by the results of several previous studies in the general population, in which social inequalities in health were weaker in women than men[36], but not all[37]. These results may be explained in part by the different roles that men and women played at work and at home. For example, job insecurity due to low educational attainment appeared to be more likely to contribute more to health inequality among men, whereas family structure mattered more for women [38]. Moreover, we found that depression also played an important role in predicting CVD among men with diabetes. A recent study from the Prospective Urban Rural Epidemiological (PURE) study showed that depression status was more strongly associated with CVD risk in men than in women[39]. These results suggest that, in addition to controlling for clinical biomarkers, additional attention should be paid to socioeconomic and psychosocial risk factors for men with diabetes.
Another important question is whether and to what extent the excess risk of CVD conferred by diabetes could be attenuated or eliminated by jointly controlling for the risk factors. Two recent nested case-control studies that sought to address this question generated inconsistent results[16, 17]. The first study, leveraging data from the Swedish national diabetes register, found that patients with diabetes who had no risk factor variables outside the target ranges (five risk factors including elevated levels of HbA1c, LDL cholesterol, blood pressure, albuminuria, and smoking) had little or no excess risk of CVD when compared to non-diabetes controls[16]. By contrast, another study from the UK found that patients with diabetes who had optimal risk factor control (nonsmokers, lower levels of total cholesterol, triglycerides, HbA1c, and systolic blood pressure) still had a 21% higher risk of CVD events compared with non-diabetes controls[17]. Differences in the examined risk factors, follow-up duration, and characteristics of the participants may partly explain such inconsistency. Of note, these earlier investigations did not account for the sex difference in diabetes-related risk of CVD. Although men have a higher risk of CVD than women in the general population, this sex difference was reversed in patients with diabetes, suggesting that diabetes may confer a greater excess CVD risk in women than in men[18, 40]. Indeed, in our study, women with diabetes had a 2.44-fold higher risk of CVD than women without diabetes (HR, 95%CI, 2.44, 2.25-2.64), whereas men with diabetes only had a 1.79-fold higher risk of CVD than men without diabetes (HR, 95%CI, 1.79, 1.70-1.89). Even though, we observed that the greater risk of CVD conferred by diabetes in women could also be eliminated if women with diabetes achieved a similar degree of risk factor control as men with diabetes. Our results showed that the diabetes-related risk of CVD was not significant in women and men if more than three risk factors were well controlled, with a comprehensive adjustment of related covariates in both patients with diabetes and matched controls.
Our study has several major strengths, including the large sample size of the diabetes cohort from the UK Biobank, the accurate definition of diabetes and outcomes that were obtained from the linked data of hospital records, as well as the comprehensive and detailed information on covariates in both patients with diabetes and matched controls. However, this study also has several limitations. First, the majority (87%) of our study participants are White European; whether our findings could be generalized to other racial/ethnic groups need further investigation. Second, the UK Biobank is not representative of the general population due to the low participation rate[48]. However, valid assessments of exposure-disease relationships may not require a representative population[49]. Third, information on the risk factors was available only at baseline; thus, we could not account for potential changes in the risk factors during the follow-up period. Further research is warranted to evaluate such an association. Fourth, due to the observational nature of this study, causality could not be derived. Further randomized clinical trials are needed to establish causality. Fifth, despite the large sample size of the study and long follow-up period, the incidence of stroke remained relatively low in terms of statistical power; thus, our results on stroke should be interpreted with caution. Sixth, we only included the two most common subtypes of CVD, IHD and Stoke, in this study. Further studies focusing on other CVD subtypes are needed. Future studies with multiple assessments of risk factor levels and extended, diverse populations are warranted to validate our results.
Our study indicates that the degree of risk-factor control is more strongly associated with a lower risk of CVD in women than men with diabetes, and a well-managed risk factor has the potential to eliminate the diabetes-related excess risk of CVD. Our findings also highlight the importance of heightening awareness of sex differences in the management of CVD risk factors among patients with diabetes.
Supplementary Material
Acknowledgments:
The authors’ responsibilities were as follows—XW, HM and LQ: designed the research; XW and HM: conducted the research; XW: analyzed the data or performed statistical analysis; XW: wrote the manuscript; LQ: had primary responsibility for the final content; and all authors: critically reviewed the manuscript and approved submission.
Funding/Support:
The study was supported by grants from the National Heart, Lung, and Blood Institute (HL071981, HL034594, HL126024), the National Institute of Diabetes and Digestive and Kidney Diseases (DK115679, DK091718, DK100383, DK078616), the Fogarty International Center (TW010790), Tulane Research Centers of Excellence Awards. Dr. Qi is also supported by P30DK072476 and NIGMS P20GM109036.
Role of the Funder/Sponsor:
The funding sources and sponsor had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Footnotes
Conflict of Interest Statement: The authors declare no conflicts of interest.
Transparency statement:
LQ affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.
Data Availability Statement:
The data that support the findings of this study are available from UK Biobank (https:// www. ukbio bank. ac. uk/), but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of UK Biobank.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from UK Biobank (https:// www. ukbio bank. ac. uk/), but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of UK Biobank.