Cardiovascular Risk Factors: It's Time to Focus on Variability!

Visit-to-visit variability in multiple biological measurements and cognitive performance and risk of cardiovascular disease: A cohort study

Background

Visit-to-visit variability in single biological measurements has been associated with cognitive decline and an elevated risk of cardiovascular diseases (CVD). However, the effect of visit-to-visit variability in multiple biological measures is underexplored. We investigated the effect of visit-to-visit variability in blood pressure (BP), heart rate (HR), weight, fasting plasma glucose, cholesterol, and triglycerides on cognitive performance and CVD.

Methods

Data on BP, HR, weight, glucose, cholesterol, and triglycerides from study visits in the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial were used to estimate the association between visit-to-visit variability, cognitive performance (Mini Mental State Examination (MMSE) score) and CVD (non-fatal stroke, non-fatal myocardial infarction, or cardiovascular death). Visit-to-visit variation for each measurement was estimated by calculating each individuals visit-to-visit standard deviation for that measurement. Participants whose standard deviation was in the highest quarter were classified as having high variation. Participants were grouped into those having 0, 1, 2, 3, or ≥ 4 high variation measurements. Regression and survival models were used to estimate the association between biological measures with MMSE and CVD with adjustment for confounders and mean measurement value.

Results

After adjustment for covariates, higher visit-to-visit variability in BP, HR, weight, and FPG were associated with poorer MMSE and a higher risk of CVD. Effect sizes did not vary greatly by measurement. The effects of high visit-to-visit variability were additive; compared to participants who had no measurements with high visit-to-visit variability, those who had high visit-to-visit variability in ≥4 measurements had poorer MMSE scores (-0.63 (95 % CI -0.96 to -0·31). Participants with ≥4 measurements with high visit-to-visit variability compared to participants with none had higher risk of CVD (hazard ratio 2.46 (95 % CI 1.63 to 3.70).

Conclusion

Visit-to-visit variability in several measurements were associated with cumulatively poorer cognitive performance and a greater risk of CVD.

Interpretable prediction models for disability in older adults with hypertension: the Chinese Longitudinal Healthy Longevity and Happy Family Study

BACKGROUND

Older adults with hypertension have a high risk of disability, while an accurate risk prediction model is still lacking. This study aimed to construct interpretable disability prediction models for older Chinese with hypertension based on multiple time intervals.

METHODS

Data were collected from the Chinese Longitudinal Healthy Longevity and Happy Family Study for 2008-2018. A total of 1602, 1108, and 537 older adults were included for the periods of 2008-2012, 2008-2014, and 2008-2018, respectively. Disability was measured by basic activities of daily living. Least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Five machine learning algorithms combined with LASSO set and full-variable set were used to predict 4-, 6-, and 10-year disability risk, respectively. Area under the receiver operating characteristic curve was used as the main metric for selection of the optimal model. SHapley Additive exPlanations (SHAP) was used to explore important predictors of the optimal model.

RESULTS

Random forest in full-variable set and XGBoost in LASSO set were the optimal models for 4-year prediction. Support vector machine was the optimal model for 6-year prediction on both sets. For 10-year prediction, deep neural network in full variable set and logistic regression in LASSO set were optimal models. Age ranked the most important predictor. Marital status, body mass index, score of Mini-Mental State Examination, and psychological well-being score were also important predictors.

CONCLUSIONS

Machine learning shows promise in screening out older adults at high risk of disability. Disability prevention strategies should specifically focus on older patients with unfortunate marriage, high BMI, and poor cognitive and psychological conditions.

Comparison of the effects of triglyceride variability and exposure estimate on clinical prognosis in diabetic patients

BACKGROUND

Hypertriglyceridemia is an important feature of dyslipidemia in type 1 and type 2 diabetic patients and associated with the development of atherosclerotic cardiovascular disease. Recently, variability of lipid profile has been suggested as a residual risk factor for cardiovascular disease. This study compared the clinical impact of serum triglyceride variability, and their cumulative exposure estimates on cardiovascular prognosis in diabetic patients.

METHODS

A total of 25,933 diabetic patients who had serum triglyceride levels measured at least 3 times and did not have underlying malignancy, myocardial infarction (MI), and stroke during the initial 3 years (modeling phase) were selected from three tertiary hospitals. They were divided into a high/low group depending on their coefficient of variation (CV) and cumulative exposure estimate (CEE). Incidence of major adverse event (MAE), a composite of all-cause death, MI, and stroke during the following 5 years were compared between groups by multivariable analysis after propensity score matching.

RESULTS

Although there was a slight difference, both the high CV group and the high CEE group had a higher cardiovascular risk profile including male-dominance, smoking, alcohol, dyslipidemia, and chronic kidney disease compared to the low groups. After the propensity score matching, the high CV group showed higher MAE incidence compared to the low CV group (9.1% vs 7.7%, p = 0.01). In contrast, there was no significant difference of MAE incidence between the high CEE group and the low CEE group (8.6% vs 9.1%, p = 0.44). After the multivariable analysis with further adjustment for potential residual confounding factors, the high CV was suggested as an independent risk predictor for MAE (HR 1.19 [95% CI 1.03-1.37]).

CONCLUSION

Visit-to-visit variability of triglyceride rather than their cumulative exposure is more strongly related to the incidence of MAE in diabetic patients.

Type 2 Diabetes Incidence and Mortality: Associations with Physical Activity, Fitness, Weight Loss, and Weight Cycling

Cardiometabolic diseases, including cardiovascular disease (CVD) and type 2 diabetes (T2D), are the leading cause of death globally. Because T2D and obesity are strongly associated, weight loss is the cornerstone of treatment. However, weight loss is rarely sustained, which may lead to weight cycling, which is associated with increased mortality risk in patients with T2D. Meta-analyses show that weight loss is not generally associated with reduced mortality risk in T2D, whereas weight cycling is associated with increased all-cause and CVD mortality. This may be attributable in part to increased variability in CVD risk factors that often accompany weight cycling, which studies show is consistently associated with adverse CVD outcomes in patients with T2D. The inconsistent associations between weight loss and mortality risk in T2D, and consistent findings of elevated mortality risk associated with weight cycling, present a conundrum for a weight-loss focused T2D prevention and treatment strategy. This is further complicated by the findings that among patients with T2D, mortality risk is lowest in the body mass index (BMI) range of ~25-35 kg/ m 2 . Because this "obesity paradox" has been consistently demonstrated in 7 meta-analyses, the lower mortality risk for individuals with T2D in this BMI range may not be all that paradoxical. Physical activity (PA), cardiorespiratory fitness (CRF), and muscular fitness (MF) are all associated with reduced risk of T2D, and lower risk of CVD and all-cause mortality in individuals with T2D. Reducing sedentary behavior, independent of PA status, also is strongly associated with reduced risk of T2D. Improvements in cardiometabolic risk factors with exercise training are comparable to those observed in weight loss interventions, and are largely independent of weight loss. To minimize risks associated with weight cycling, it may be prudent to adopt a weight-neutral approach for prevention and treatment of individuals with obesity and T2D by focusing on increasing PA and improving CRF and MF without a specific weight loss goal.

Long-Term Blood Pressure Variability and Major Adverse Cardiovascular and Cerebrovascular Events After Intracerebral Hemorrhage

Background Survivors of intracranial hemorrhage (ICH) are at increased risk for major adverse cardiovascular and cerebrovascular events (MACCE), in the form of recurrent stroke and myocardial Infarction. We investigated whether long-term blood pressure (BP) variability represents a risk factor for MACCE after ICH, independent of average BP. Methods and Results We analyzed data from prospective ICH cohort studies at Massachusetts General Hospital and the University of Hong Kong. We captured long-term (ie, visit-to-visit) BP variability, quantified as individual participants' variation coefficient. We explored determinants of systolic and diastolic BP variability and generated survival analyses models to explore their association with MACCE. Among 1828 survivors of ICH followed for a median of 46.2 months we identified 166 with recurrent ICH, 68 with ischemic strokes, and 69 with myocardial infarction. Black (coefficient +3.8, SE 1.3) and Asian (coefficient +2.2, SE 0.4) participants displayed higher BP variability. Long-term systolic BP variability was independently associated with recurrent ICH (subhazard ratio [SHR], 1.82; 95% CI, 1.19-2.79), ischemic stroke (SHR, 1.62; 95% CI, 1.06-2.47), and myocardial infarction (SHR, 1.54; 95% CI, 1.05-2.24). Average BP during follow-up did not modify the association between long-term systolic BP variability and MACCE. Conclusions Long-term BP variability is a potent risk factor for recurrent hemorrhage, ischemic stroke, and myocardial infarction after ICH, even among survivors with well-controlled hypertension. Our findings support the hypothesis that combined control of average BP and its variability after ICH is required to minimize incidence of MACCE.

Association between Variability of Metabolic Risk Factors and Cardiometabolic Outcomes

Despite strenuous efforts to reduce cardiovascular disease (CVD) risk by improving cardiometabolic risk factors, such as glucose and cholesterol levels, and blood pressure, there is still residual risk even in patients reaching treatment targets. Recently, researchers have begun to focus on the variability of metabolic variables to remove residual risks. Several clinical trials and cohort studies have reported a relationship between the variability of metabolic parameters and CVDs. Herein, we review the literature regarding the effect of metabolic factor variability and CVD risk, and describe possible mechanisms and potential treatment perspectives for reducing cardiometabolic risk factor variability.