A risk scoring system to predict the risk of new‐onset hypertension among patients with type 2 diabetes

Abstract Hypertension (HTN), which frequently co‐exists with diabetes mellitus, is the leading major cause of cardiovascular disease and death globally. This study aimed to develop and validate a risk scoring system considering the effects of glycemic and blood pressure (BP) variabilities to predict HTN incidence in patients with type 2 diabetes. This research is a retrospective cohort study that included 3416 patients with type 2 diabetes without HTN and who were enrolled in a managed care program in 2001–2015. The patients were followed up until April 2016, new‐onset HTN event, or death. HTN was defined as diastolic BP (DBP) ≥ 90 mm Hg, systolic BP (SBP) ≥ 140 mm Hg, or the initiation of antihypertensive medication. Cox proportional hazard regression model was used to develop the risk scoring system for HTN. Of the patients, 1738 experienced new‐onset HTN during an average follow‐up period of 3.40 years. Age, sex, physical activity, body mass index, type of DM treatment, family history of HTN, baseline SBP and DBP, variabilities of fasting plasma glucose, SBP, and DBP and macroalbuminuria were significant variables for the prediction of new‐onset HTN. Using these predictors, the prediction models for 1‐, 3‐, and 5‐year periods demonstrated good discrimination, with AUC values of 0.70–0.76. Our HTN scoring system for patients with type 2 DM, which involves innovative predictors of glycemic and BP variabilities, has good classification accuracy and identifies risk factors available in clinical settings for prevention of the progression to new‐onset HTN.


INTRODUCTION
The global prevalence of diabetes mellitus (DM) and hypertension (HTN) continually rises, and both have emerged as major medical and public health concerns. According to the 9th edition of the IDF Diabetes Atlas, the projected number of adults living with diabetes will increase from 463 million (9.3%) to 700 million by 2045 (10.9%). 1 About 35%-70% of diabetes-associated vascular complications in diabetic population, including cardiovascular diseases, stroke, lower extremity amputations, chronic renal disease, diabetic retinopathy, and blindness, have been attributed to HTN. 2 In addition, DM and HTN share similar risk factors, such as obesity, dyslipidemia, insulin resistance, and gene, 3 with HTN showing a significantly higher prevalence in diabetic patients. Compared with the nondiabetic population, the prevalence of HTN is 1.5-2.0 times more common in the diabetic population. 4 The coexistence of DM and HTN must be avoided to reduce the microvascular and macrovascular complications of diabetes. Risk prediction models for HTN must be developed to lower its incidence and to improve its prevention in population with diabetes.
The benefits of predictive models are the quantification of the strength of associations for measurable and modifiable risk factors and generation of risk estimates. These point systems can be utilized by nurse practitioners, physicians, and health professionals without the need for understanding complex statistical models because the point systems require simple calculation. In addition, clinicians can be guided by these point systems for their decision making regarding treatments and assistance in motivating patients to modify their behaviors. Another strength of this point system is that patients can easily estimate and monitor their disease risks over time. Published prediction models have been developed to predict HTN, primarily in general and patient populations, using electronic health records (EHRs). [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] Although diabetes is a significant predictor of new-onset HTN, 11,15,25 no prediction model has been created for patients with type 2 diabetes. The prediction models for HTN risks in general and patient populations cannot consider diabetes-specific predictors, such as diabetes duration, poor glycemic control, and diabetes medication. 26 In addition, glycemic and blood pressure (BP) variabilities are novel factors that are associated with diabetic microvascular and macrovascular complications, arousing the interest of researchers in the field. The potential biological mechanisms of these factors arise from oxidative stress, 27 which induces endothelial injury and thus increases cardiovascular risk. 28  were randomly assigned to the derivation and validation sets in a 2:1 ratio. Figure S1 shows the flowchart for study patient selection. Ethical approval was obtained from the Ethical Review Board of CMUH (CMUH109-REC2-166).

Data source
The data source was the computerized database of Taiwanese

Measurements
Upon enrolment in the DCMP program, the study patients had a series of medical tests for urine, blood, lifestyle behaviors, body measurements, and medical history gathered at baseline and annually through standardized computerized questionnaire administered by a case manager. in the morning urine sample was determined by urinary creatinine (Jaffe's kinetic method) and albumin (colorimetyl bromcresol purple), which were measured by an autoanalyzer. Urinary ACR ranging from 30 to 300 mg/g creatinine was defined as microalbuminuria and above 300 mg/g creatinine as macroalbuminuria.
The estimated glomerular filtration rate (eGFR) was estimated based on serum creatinine levels, in accordance with the Chronic Kidney Disease Epidemiology Collaboration equation. 29 The measurements for calculating glycemic variability were FPG or HbA1c measurements within 1 year of entry to DCMP for those who had at least two measurements.

Medication-related variables
The variables for pharmacologic agent use were derived from the dataset of DCMP program. The types of anti-diabetes treatment containing various oral hypoglycemic agents, such as, metformin, sulfonylurea, thiazolidinedione, meglitinide, and biguanide, and insulin therapy were extracted. Other medication-related variables included kidney disease medications, HTN medications, cardiovascular medications, and lipid-lowering medications. All these medications were each divided into two categories: yes versus no.

Comorbidities
Baseline comorbidities consisted of hyperlipidemia, coronary artery disease, severe hypoglycemia, postural hypotension, peripheral neuropathy, nephropathy, diabetic ketoacidosis, and hyperglycemic hyperosmolar nonketotic coma. All the comorbidities were each divided into two classes: yes versus no.

Outcome measures
The main outcome measure was HTN event, which was determined by at least two systolic blood pressure (SBP) and diastolic blood pressure (DBP) measurements. The onset of HTN is defined as one of the following two criteria of 2020 International Society of Hypertension

Statistical analysis
Means with standard deviations (SDs) of continuous variables and proportions of categorical variables were used to describe baseline characteristics of all study patients. The glycemic and BP variabilities were adjusted for the numbers of visit to reduce measurement bias. The CVs of FPG, HbA1c, SBP, and DBP were divided by the square root of the ratio of total visits to total visits minus one. 31 The standardized effect size was used to compare the differences in baseline characteristics between the derivation and validation sets. Crude and multivariateadjusted hazard ratios with 95% confidence intervals (CIs) for risk or protective predictors of HTN were evaluated by Cox proportional hazard models.
The derivation set was used to generate a prediction model, and the validation set was used for assessment of the predictive accuracy.
Then, the steps from the Framingham heart study was used as guides to construct the risk score function. 32 The steps are shown in Supplement A.
The area under curve (AUC) of receiver operating characteristic (AUROC) curve for 1-, 3-, and 5-year of HTN incidence from probabilities of logistic regressions model was applied to assess the predictive accuracy of the HTN risk prediction model. The correct Harrell's C-statistic of the AUC was also applied to time-to-event analysis. The AUC can be used as the index for assessing the capability of the model to correctly discriminate study patients into HTN or non-HTN cases.
The values of AUC ranged from 0 to 1, where a value higher than 0.7 indicates good discriminatory capability of the model. For the assessment of the discriminatory capability of the risk model, we compared three subgroups with low, medium, and high sum risk scores determined by tertiles of the total score in the validation set. Calibration of the HTN risk prediction model for the validation set was tested by Hosmer-Lemeshow x 2 method. Internal validation was performed to correct the potential for overfitting or "optimism" by using 1000 times of bootstrap resampling. 33 Model calibration was carried out to assess the agreement between model-predicted and observed probabilities. Calibration-in-large approach was used to calculate the intercept for evaluation of the extent to which predictions are systematically extremely low or extremely high. The value of intercept zero suggests the lack of systematic estimation of predicted probabilities. Furthermore, calibration slope was estimated for the extremeness of predicted probabilities. If the value of slope was close to one, then model overfitting was not observed. The mean absolute error in the calibration for slope and intercept was revealed during calibration assessment; the error indicates the discrepancy between the observed and bias-corrected calibrated values. The net reclassification improvement (NRI) and integrated discrimination improvement (IDI) values were used to assess the added value of our scoring system compared with USA's HTN risk score. 24 All statistical analyses were conducted by SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Significance level was set at two-tailed p < .05.

RESULTS
The derivation and validation sets comprised 2278 and 1138 patients,  The Kaplan-Meier estimates for the cumulative HTN incidence curves of the three groups are shown in Figure 3 (log-rank p < .001).

DISCUSSION
This study developed and validated a scoring system for the risk of HTN in patients with type 2 diabetes based on 13 predictors. To our knowledge, this research is the first attempt to establish a simple scoring system for the risk of HTN, with focus on patients with type 2 diabetes. The system had good discriminatory capability with a Harrell's C-statistic of 0.70 (95% CI: 0.68-0.72) in the validation set. This scoring system considered risk factors that are generally accepted and available in clinical practice and are precisely measured to ensure its acceptability in clinical practice.
Diabetes is linked to impaired glucose tolerance or impaired fasting glucose through insulin resistance; concomitant islet beta-cell injury may lead to insulin deficiency, which affects the utilization of glucose by skeletal muscles, adipose tissues, and hepatic cells. 36 Insulin resistance increases tissue inflammation and reactive oxygen species production, thus resulting in endothelial dysfunction, inappropriate activation of the renin angiotensin aldosterone system, increased sympathetic nervous system activity, and abnormal sodium handling by the kidney. 37 These responses have been implicated in the complex pathophysiology of HTN. 37 Our study showed the significant association of FPG-CV with HTN risk in the final predictive model. Similar results were also observed in Chien's study, which showed fasting glucose as a significant factor in the HTN prediction model. 23 A review paper pointed out that early intervention by lifestyle modifications (weight loss, health dietary plan, reduction of dietary sodium intake, promotion of physical activity, and moderation of alcohol drinking) in persons with pre-hypertensive condition can reduce BP or prevent HTN. 38 The development of HTN prediction model for patients with type 2 diabetes will provide a rationale for the identification of high-risk individuals and improve the efficiency of prevention and treatment strategies for HTN prevention in such individuals.
The assigned scores for predictors in our scoring system provide information on HTN prevention for health professionals in clinical practice.
General obesity, which can be prevented by lifestyle modification, and physical activity contributed to 10 points in our scoring system. Baseline SBP and DBP and variations in FPG, SBP, and DBP, accounting for 18 points, can also be modified by lifestyle or treatment intervention.
Most existing HTN risk prediction models achieve acceptable good discrimination with an AUC over 0.70, 5,6,8-25 and four of them TA B L E 2 Cox models estimated hazard ratio and 95% confidence intervals of new-onset hypertension in derivation set  consider diabetes status 11,15,24 or fasting glucose. 23

Strengths and limitations
The strength of our study is the well-defined patient group and pioneer the potential selection bias arising from missing data, we analyzed our data by using the MI approach for handling missing data. Using complete case analysis as sensitivity analysis and similar findings were obtained. Second, given the lack of external validation, we did not validate our system in an external or independent sample. External validation can provide evidence on the system's generalizability to various population. However, we performed internal validation with a bootstrapping method, and results showed that our system can be generalized to other populations with similar characteristics.
Future research will be needed to examine external validation in independent datasets. Last, we didn't consider type of hypoglycemic drug because some categories such as insulin alone had very small sample size, resulting in the imprecision in estimating their effects.
Because the effects of oral hypoglycemic drug use and insulin plus oral hypoglycemic use were similar, we collapsed all hypoglycemic drugs into a category.

CONCLUSIONS
We developed and validated a simple point-based scoring system for HTN risk assessment using a hospital-based EHR dataset of a managed care program. The scoring system showed good prediction capability, discriminatory power, and calibration. Our scoring system provides a valid and inexpensive tool to estimate medium-term risks of new-onset HTN in patients with type 2 diabetes and can help in preventing the progression of new-onset HTN.

ACKNOWLEDGMENTS
This study was supported primarily by the Ministry of Science and

CONFLICT OF INTEREST
There are no conflicts of interest.

AUTHOR CONTRIBUTIONS
Tsai-Chung Li, Cheng-Chieh Lin, and Chia-Ing Li were responsible for the conception and design of the work and writing manuscript. Chiu-Shong Liu, Chih-Hsueh Lin, and Mu-Cyun Wang were responsible for data collection and data interpretation. Chia-Ing Li and Shing-Yu Yang were responsible for analysis. All authors read and approved the final manuscript.