Long-term trajectories of BMI predict carotid stiffness and plaque volume in type 2 diabetes older adults: a cohort study

Abstract

Background High body mass index (BMI) is a risk factor for type 2 diabetes and cardiovascular disease. However, its relationships with indices of carotid stiffness and plaque volume are unclear. We investigated associations of long-term measurements of BMI with indices of carotid stiffness and atherosclerosis among non-demented diabetes patients from the Israel Diabetes and Cognitive Decline (IDCD) study. Methods Carotid ultrasound indices [carotid intima media thickness (cIMT), distensibility, elastography and plaque volume] were assessed in N = 471 participants. Mean BMI across all MHS diabetes registry measurements and trajectories of BMI were calculated. BMI was categorized into three trajectory groups representing: a relatively stable normal weight (n = 185, 44%), overweight trajectory (n = 188, 44.8%) and a trajectory of obesity (n = 47, 11.2%). Linear and logistic regressions estimated associations of carotid indices with mean BMI and BMI trajectories. Results Compared to the normal weight trajectory, an obesity trajectory was associated with carotid distensibility (β = − 3.078, p = 0.037), cIMT (β = 0.095, p = 0.004), and carotid elastography (β = 0.181, p = 0.004) but not with plaque volume (β = 0.066, p = 0.858). Compared with the normal weight trajectory, an obesity trajectory was associated with increased odds for impaired carotid distensibility (OR = 2.790, p = 0.033), impaired cIMT (OR = 5.277, p = 0.001) and large carotid plaque volume (OR = 8.456, p = 0.013) but not with carotid elastography (OR = 1.956, p = 0.140). Mean BMI was linearly associated with Distensibility (β = − 0.275, p = 0.005) and cIMT (β = 0.005, p = 0.026). Conclusions Long-term measurements of adiposity are associated with indices of carotid stiffness and plaque volume among older type 2 diabetes adults.

Great diversity in the utilization and reporting of latent growth modeling approaches in type 2 diabetes: A literature review

“… Clinical research Prevalent cases of T2D and incident cancer/depression or COPD exacerbation (n = 5,883) Months since diagnostic of cancer Tool: ICD-9, prescriptions, hospitalizations HbA1c trajectories Tool: Not reported Descriptive analyses for comparing trajectory groups (graphs) Time-point studied : 3 months Trajectory identification : 18 months Total follow-up : 84 months Latent class growth modeling [ 32 ] Bocquier 2019 [ 33 ] Cohort: Permanent Sample of Beneficiaries (2006–2015) Type of data source: Secondary data (Medico-administrative) 1-To identify temporal trajectories of seasonal influenza vaccination uptake 2- To describe their clinical characteristics. Healthcare utilization research Prevalent cases (n = 15,766) Predictors of trajectories Tools: Demographic, clinical, and healthcare utilization factors Trajectories of seasonal influenza vaccination Tool: binary variable for each influenza season Comparison between groups: ANOVA, X 2 Predictive model: multivariate logistic regression Time-point studied: 1 year Follow-up period: 10 years Group-based trajectory modeling [ 3 , 34 , 35 , 36 ] Botvin Moshe 2020 [ 37 ] Cohort: Israel Diabetes and Cognitive Decline study (from 1998, years not reported) Type of data source: Primary data (prospective cohort, registry) 1-To investigate the associations of long-term measurements of body mass index with indices of carotid stiffness and atherosclerosis among non-demented diabetes patients. Clinical research Prevalent cases (n = 471) Body mass index trajectories Tool: body mass index equation Carotid intime-media thickness, distensibility coefficient and elastography strain ratio, carotid plaque volume Tool: Carotid ultrasound Doppler Comparison between groups: t-tests, X 2 , ANOVA, Wilcoxon Explicative model: linear regression model or logistic regression model Time-point studied : 1 month Trajectory identification follow-up : 120 months prior baseline Outcome assessment: 36 months after baseline Multinomial modeling strategy [ 34 ...…”

“…Even though improving internal validity, restricting the selection of cases could affect generalizability of results. One study used propensity score matching [ 15 ], while other studies adjusted explanatory models with T2D duration or severity of T2D [ 37 , 58 , 58 , 59 , 63 , 67 ].…”

Prediction of carotid plaque by blood biochemical indices and related factors based on Fisher discriminant analysis

“…Therefore, the sensitivity was mainly improved, and the sensitivity of the models established was above 80 percent in this study. The higher the sensitivity of a model, the more patients can be screened, and the better the prevention effect at the population level that can be achieved [ 24 , 25 ]. Based on the population-based screening model, we specifically established the plaque location models to predict the specific location of plaque, to facilitate clinicians and imaging physicians to provide technical support for the prevention and early treatment of carotid plaque in the population.…”

The Predictive Value of Carotid Ultrasonography With Cardiovascular Risk Factors—A “SPIDER” Promoting Atherosclerosis

“…Furthermore, the frequency of carotid plaques was increased in patients with latent autoimmune diabetes in adults (LADA) compared to type 1 and type 2 diabetes, which was also increased with increasing diabetes duration in LADA (112). For diabetic complications, obesity, renal function decline, and diabetic retinopathy were investigated to be positively associated with the presence of carotid plaques (113)(114)(115). Therefore, carotid ultrasonography is necessary for the evaluation of vascular complications as well as the risk of cardiovascular events.…”