Evaluation of the relationship between cardiac autonomic function and glucose variability and HOMA-IR in prediabetes

Reframing prediabetes: A call for better risk stratification and intervention

Prediabetes is an intermediate state of glucose homeostasis whereby plasma glucose concentrations are above normal but below the threshold of diagnosis for diabetes. Over the last several decades, criteria for prediabetes have changed as the cut points for normal glucose concentration and diagnosis of diabetes have shifted. Global consensus does not exist for prediabetes criteria; as a result, the clinical course and risk for type 2 diabetes vary. At present, we can identify individuals with prediabetes based on three glycemic tests (hemoglobin A1c, fasting plasma glucose, and 2-h plasma glucose during an oral glucose tolerance test). The majority of individuals diagnosed with prediabetes meet only one of these criteria. Meeting one, two, or all glycemic criteria changes risk for type 2 diabetes, but this information is not widely known and does not currently guide intervention strategies for individuals with prediabetes. This review summarizes current epidemiology, prognosis, and intervention strategies for individuals diagnosed with prediabetes and suggests a call for more precise risk stratification of individuals with prediabetes as elevated (one prediabetes criterion), high risk (two prediabetes criteria), and very high risk (three prediabetes criteria). In addition, the roles of oral glucose tolerance testing and continuous glucose monitoring in the diagnostic criteria for prediabetes need reassessment. Finally, we must reframe our goals for prediabetes and prioritize intensive interventions for those at high and very high risk for type 2 diabetes.

Glycemic variability assessed using continuous glucose monitoring in individuals without diabetes and associations with cardiometabolic risk markers: A systematic review and meta-analysis

BACKGROUND & AIMS

Continuous glucose monitoring (CGM) provides data on short-term glycemic variability (GV). GV is associated with adverse outcomes in individuals with diabetes. Whether GV is associated with cardiometabolic risk in individuals without diabetes is unclear. We systematically reviewed the literature to assess whether GV is associated with cardiometabolic risk markers or outcomes in individuals without diabetes.

METHODS

Searches were performed in PubMed/Medline, Embase and Cochrane from inception through April 2022. Two researchers were involved in study selection, data extraction and quality assessment. Studies evaluating GV using CGM for ≥24 h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta-analyzes were performed, depending on outcome.

RESULTS

Seventy-one studies were included; the majority were cross-sectional. Multiple measures of GV are higher in individuals with compared to without prediabetes and GV appears to be inversely associated with beta cell function. In contrast, GV is not clearly associated with insulin sensitivity, fatty liver disease, adiposity, blood lipids, blood pressure or oxidative stress. However, GV may be positively associated with the degree of atherosclerosis and cardiovascular events in individuals with coronary disease.

CONCLUSION

GV is elevated in prediabetes, potentially related to beta cell dysfunction, but less clearly associated with obesity or traditional risk factors. GV is associated with coronary atherosclerosis development and may predict cardiovascular events and type 2 diabetes. Prospective studies are warranted, investigating the predictive power of GV in relation to incident disease. GV may be an important risk measure also in individuals without diabetes.

Difference on Glucose Profile From Continuous Glucose Monitoring in People With Prediabetes vs. Normoglycemic Individuals: A Matched-Pair Analysis

INTRODUCTION

Comprehensive characteristics of the glycemic profile for prediabetes derived by continuous glucose monitoring (CGM) are unknown. We evaluate the difference of CGM profiles between individuals with prediabetes and normoglycemic individuals, including the response to oral glucose tolerance test (OGTT).

METHODS

Individuals with prediabetes matched for age, sex, and BMI with normoglycemic individuals were instructed to use professional CGM for 1 week. OGTT was performed on the second day. The primary outcomes were percentages of glucose readings time below range (TBR): <54 or <70 mg/dL, time in range (TIR): 70 to 180 mg/dL, and time above range (TAR): >180 or >250 mg/dL. Area under the curve (AUC) was calculated following the OGTT. Glucose variability was depicted by coefficient of variation (CV), SD, and mean amplitude of glucose excursion (MAGE). Wilcoxon sign-ranked test, McNemar mid P-test and linear regression models were employed.

RESULTS

In all, 36 participants (median age 51 years; median body mass index [BMI] = 26.4 kg/m2) formed 18 matched pairs. Statistically significant differences were observed for 24-hour time in range (TIR; median 98.5% vs. 99.9%, P = .013), time above range (TAR) >180 mg/dl (0.4% vs. 0%, P = .0062), and 24-hour mean interstitial glucose (113.8 vs. 108.8 mg/dL, P = .0038) between people with prediabetes compared to normoglycemic participants. Statistically significant differences favoring the normoglycemic group were found for glycemic variability indexes (median CV 15.2% vs. 11.9%, P = .0156; median MAGE 44.3 vs. 33.3 mg/dL, P = 0.0043). Following OGTT, the AUC was significantly lower in normoglycemic compared to the prediabetes group (median 18615.3 vs. 16370.0, P = .0347 for total and 4666.5 vs. 2792.7, P = .0429 for incremental 2-hour post OGTT).

CONCLUSION

Individuals with prediabetes have different glucose profiles compared to normoglycemic individuals. CGM might be helpful in individuals with borderline glucose values for a more accurate reclassification.

The effect of insulin resistance on the heart rate recovery in healthy individuals with a low-to-moderate cardiovascular disease risk profile

The association between insulin resistance (IR) and heart rate recovery index (HRRI) has been reported previously, but the cardiovascular disease (CVD) risk profile of the subjects was unclear in these studies. Therefore, we evaluated the association between IR and HRRI in apparently healthy individuals without metabolic syndrome who had a low-to-moderate CVD risk profile. A total of 182 eligible subjects were retrospectively included in the study. The subjects were divided into two groups according to the homeostasis model assessment of IR (HOMA-IR) value. HOMA-IR ≥2.5 was defined as IR (+) group (92 subjects), and <2.5 as IR (-) group (88 subjects). HRRI-2 was found by subtracting the heart rate at the second minute in the post-exercise recovery period from the maximum heart rate. Abnormal HRRI was defined as HRRI-2 that is ≤42 beats. The mean age of the patients was 41.91 ± 8.64 and 49.4% of them were female. Abnormal HRRI rates were significantly higher in the IR (+) group (37.2% vs 18.2%; p = 0.004). A negative correlation was detected between HRRI-2 and HOMA-IR (r = -0.416; p < 0.001). HOMA-IR (Odds Ratio (OR) = 1.57; confidence interval (CI) = 1.10-2.23; p = 0.013) and maximum heart rate during exercise (OR = 0.95; CI = 0.91-0.99; p = 0.013) as independent variables of abnormal HRRI. The HOMA-IR value of 2.82 was identified as an effective cutoff point for the prediction of abnormal HRRI (area under the curve: 0.658; CI: 0.570-0.746; p = 0.001). In this study, it was shown that IR without metabolic syndrome reduces HRRI in healthy individuals with a low-to-moderate CVD risk profile.

Cardiovascular Risk Among Saudi Adults with Prediabetes: A Sub-Cohort Analysis from the Heart Health Promotion (HHP) Study

Background

This study aimed to estimate the prevalence of prediabetes among Saudi adults and to evaluate their risk of developing cardiovascular diseases.

Methods

This is a cohort of 2470 Saudi adults attending employee clinics in the university hospital. WHO-STEPs approach was used to collect sociodemographic (age, gender, and education), clinical (body mass index and blood pressure) and laboratory data (HbA1c, lipid profile and vitamin D concentration). Prediabetes was defined according to HbA1c level of 5.7–6.4%. Cardiovascular risk (CVR) scores were evaluated using the Framingham Risk Score. SPSS was used for data analysis to investigate the relation between different CVR and prediabetes.

Results

Prediabetes affected 25.1% of the study population. Males had higher rates of prediabetes than females (27.5% versus 23.5%). The prevalence of prediabetes increased from 11.6% among young individuals (18–29 years) to 56.0% among participants 60 years and older. Prediabetes patients exhibited considerably higher levels of all cardiovascular risk factors and nearly half of them (49.3%) had at least two risk factors. The prevalence of intermediate CVR among prediabetics was 13.2% compared to just 2.9% among the normal group, and high CVR was defined in 3.7% among prediabetics compared to only 1.7% in the normal group. Having prediabetes increased the odds to develop higher CVR of 2.64 times compared to those without prediabetes (OR = 2.64, 95% CI = 1.51–4.64) and the level of vitamin D did not affect the odds of CVR.

Conclusion

Prediabetes is quite prevalent among Saudi adults, and they are at a higher risk of cardiovascular diseases. Patients with prediabetes have higher cardiac risk scores when compared to normal participants across the whole spectrum of (25(OH)D) concentrations. Additionally, no significant correlation was observed between HbA1c and (25(OH)D) levels in prediabetics or normoglycemic subjects.

Very Low-Carbohydrate High-Fat Diet Improves Risk Markers for Cardiometabolic Health More Than Exercise in Men and Women With Overfat Constitution: Secondary Analysis of a Randomized Controlled Clinical Trial

Purpose

This randomized controlled parallel-group study examined the effects of a very low-carbohydrate high-fat (VLCHF) diet and high-intensity interval training (HIIT) program over 12-weeks on cardiometabolic risk factors in individuals with overfat constitution.

Methods

Ninety-one participants out of 109 completed the study. The participants were randomly allocated to the HIIT (N = 22), VLCHF (N = 25), VLCHF+HIIT (N = 25), or control (N = 19) groups for 12 weeks. Fasting plasma samples were collected before the intervention and after 4 and 12 weeks. The analyzed outcomes included complete blood count, glucose, insulin, glycated hemoglobin, triglycerides (TG), cholesterol, high- and low-density lipoprotein (HDL-C and LDL-C), lipoprotein(a), adiponectin (Adpn), leptin (Lep), tumor necrosis factor α (TNF-α), other interleukins (hs-IL-6, IL-1β, and IL-10), and IL-1RA. The homeostasis model assessment of insulin resistance (HOMA-IR), Adpn/Lep ratio, TG/HDL-C ratio, and TyG index were calculated and analyzed. Blood pressure was measured before the intervention, after 4, 8, and 12 weeks (ClinicalTrials.gov: NCT03934476).

Results

Absolute changes in HOMA-IR, Adpn/Lep ratio, LDL-C, and diastolic blood pressure after 12 weeks differed by study groups (p < 0.05). The most pronounced changes were revealed in the VLCHF (ΔM [95% CI]; HOMA-IR: −0.75 [−1.13; −0.55]; Adpn/Lep: 9.34 [6.33; 37.39]; LDL-C: 0.06 [−0.12; 0.50] mmol/l) and VLCHF+HIIT (HOMA-IR: −0.44 [−1.14; 0.12]; Adpn/Lep: 4.26 [2.24; 13.16]; LDL-C: 0.25 [−0.04; 0.50] mmol/l) groups.

Conclusions

A 12-week VLCHF diet intervention in individuals with overfat constitution is effective for favorable changes in HOMA-IR (compared to HIIT), Adpn/Lep ratio, and diastolic blood pressure. HIIT, or HIIT combined with the VLCHF diet, had no additional benefits for the analyzed variables. No adverse side effects were observed.

A High Protein Diet Is More Effective in Improving Insulin Resistance and Glycemic Variability Compared to a Mediterranean Diet-A Cross-Over Controlled Inpatient Dietary Study

The optimal dietary pattern to improve metabolic function remains elusive. In a 21-day randomized controlled inpatient crossover feeding trial of 20 insulin-resistant obese women, we assessed the extent to which two isocaloric dietary interventions—Mediterranean (M) and high protein (HP)—improved metabolic parameters. Obese women were assigned to one of the following dietary sequences: M–HP or HP–M. Cardiometabolic parameters, body weight, glucose monitoring and gut microbiome composition were assessed. Sixteen women completed the study. Compared to the M diet, the HP diet was more effective in (i) reducing insulin resistance (insulin: Beta (95% CI) = −6.98 (−12.30, −1.65) µIU/mL, p = 0.01; HOMA-IR: −1.78 (95% CI: −3.03, −0.52), p = 9 × 10⁻³); and (ii) improving glycemic variability (−3.13 (−4.60, −1.67) mg/dL, p = 4 × 10⁻⁴), a risk factor for T2D development. We then identified a panel of 10 microbial genera predictive of the difference in glycemic variability between the two diets. These include the genera Coprococcus and Lachnoclostridium, previously associated with glucose homeostasis and insulin resistance. Our results suggest that morbidly obese women with insulin resistance can achieve better control of insulin resistance and glycemic variability on a high HP diet compared to an M diet.

Cardiovascular autonomic neuropathy in people with type 2 diabetes mellitus; investigation of its association with classical cardiovascular risk factors using cardiovascular autonomic reflex tests: a cross-sectional study

Background

People with type 2 diabetes mellitus (DM) are at increased risk of cardiovascular disease (CVD). Cardiovascular autonomic neuropathy (CAN) is an underdiagnosed risk factor for CVD, which is prevalent among people with 2DM and can lead to CVD and CVD-related mortality. Little is known about the risk factors associated with CAN in type 2DM. Thus, the study was aimed to assess CAN using five cardiovascular autonomic reflex tests (represented by Ewing’s score) and explore the factors associated with CAN in people with type 2DM. The studied factors include traditional and serological CVD risk factors obtained from a fasting blood sample and cardiorespiratory fitness (CRF) obtained via attainment of the highest peak of volumes of O₂ (VO_2Peak).

Results

Univariate analysis revealed a significant positive correlation between resting systolic blood pressure (SBP) and Ewing’s score (r=0.47, p=.02) and an inverse correlation between VO_2Peak and Ewing’s score (r=−0.64, p=.001). Multivariate linear regression revealed that a significant model that included resting SBP and VO_2Peak explained 93.8% of Ewing’s score variance.

Conclusion

CAN was associated with two CVD parameters, including resting SBP and CRF, which may indicate the importance of controlling these two factors to prevent or reduce CAN in people with type 2DM.