Effects of a telemedically supported home-based lifestyle intervention in patients with chronic ischaemic heart disease and type 2 diabetes mellitus

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Federal Joint Committee Germany

Background

Ischaemic heart disease is the leading cause of global mortality and when accompanied by type 2 diabetes mellitus (T2DM), prognosis is even worse. Lifestyle (LS) interventions are recommended, however, evidence in patients with both chronic ischaemic heart disease (CIHD) and T2DM is scarce. Furthermore, LS interventions were generally performed in a resource intensive supervised setting. Therefore, this study examined whether an individualised telemedically supported home-based LS intervention has a different effect on changes in glycosylated haemoglobin (HbA1c) compared to usual care (UC) in patients with CIHD and T2DM.

Methods

Eligible patients (ICD-10: I20-25; HbA1c≥6.5% or anti-diabetic drugs) were randomly assigned (1:1) to LS or UC at 11 sites. LS intervention consisted of home-based exercise training (ET) and nutritional recommendations following 7-day nutrition diaries. ET was based on the results of a cardiopulmonary exercise test (CPET), i.e. ventilatory thresholds and peak oxygen consumption, and was provided and recorded by a smartphone application. Intensities varied and volume was gradually increased to reach at least 150 min/week. Every 2-4 weeks, patients were contacted by telephone to receive feedback and adjust the training (if necessary). CPETs and nutrition diaries were analysed by a core laboratory, which also provided the feedback to the patients. Primary endpoint was the change in HbA1c after 6 months. Secondary endpoints included change in weight, waist circumference, HDL, LDL, triglycerides and quality of life (QoL). The main analysis included all patients with baseline and follow-up (FU) measures (full analysis). In a per-protocol analysis (PPA), patients randomised to LS who performed less than 2/3 of the scheduled exercise time or filled less than 2 out of 3 nutrition diaries were excluded.

Results

Among 499 enrolled patients (mean age, 68 years; 16% female; mean HbA1c, 6.9%), 402 (81%) completed the 6-month FU. From 201 patients who completed FU in the LS group, 76 (38%) were included in the PPA [81 (40%) adhered to ET, 144 (72%) filled at least 2 nutrition diaries]. In the full analysis, mean change in HbA1c was significantly different between LS and UC (-0.1% [95% CI, -0.2 to 0.0], p=0.04). Furthermore, significant differences were found for changes in weight (p<0.001) and the QoL mental component score (MCS) of the Short-Form-36 questionnaire (p=0.006, Table 1). In the PPA, mean changes in HbA1c (p=0.002), weight (p<0.001), waist circumference (p<0.001), triglycerides (p=0.03) and the QoL MCS (p=0.004) were significantly different between groups (Table 1).

Conclusion

In patients with CIHD and T2DM, a telemedically supported home-based LS intervention significantly reduced HbA1c as compared to UC. However, only in the per-protocol analysis, change in HbA1c met a clinically meaningful difference of 0.3% between groups. Therefore, future studies should aim at increasing adherence to such interventions.

Inter-individual differences in the response to an exercise training intervention in patients with metabolic syndrome

Background

Regular physical exercise and standardized exercise training (ET) programmes positively affect a number of metabolic and cardiovascular risk factors. Yet, response to ET varies between patients and between individual risk parameters. We investigated inter-individual responses regarding blood pressure, heart rate, lipids and glucose metabolism, as well as physical fitness to a 16-weeks supervised exercise training protocol in patients with metabolic syndrome (MetS).

Methods

29 MetS patients (20M/9F, age 52–70 y, BMI 24.9–38.2 kg/m2) exercised for 16 weeks in a supervised setting with varying volume and intensity. At baseline and at completion of the programme, blood pressure, heart rate, plasma lipids, glucose and insulin levels, as well as physical fitness were assessed, as were quantitative and morphological leukocyte parameters, cytokines and 86 metabolically relevant plasma proteins. Associations between individual risk parameters, leukocyte profile and plasma proteins were explored by recursive partitioning and network analysis. T-distributed stochastic neighbor embedding and hierarchical clustering were used for clustering based on cardiometabolic target parameters at 16 weeks.

Results

In the whole cohort, VO2peak increased and plasma counts of total leukocytes, neutrophils, monocytes (classical and intermediate), NK cells and effector and regulatory CD4+ lymphocytes, as well as circulating endothelial microvesicles were reduced at the end of the ET programme versus baseline. Plasma levels of carboxylesterase 1 decreased and soluble interleukin-7 receptor increased. Based on patient's cardio-metabolic parameters, three clusters were identified: cluster 1 had lower values of triglycerides, total and LDL cholesterol, cluster 2 had higher levels of triglycerides and cluster 3 had a greater heart rate recovery and lower insulin levels, but higher total and LDL cholesterol levels at follow-up. In cluster 1, absolute and relative NK cell counts were decreased and no morphological activation was observed. In cluster 2, reduced relative counts of NK-T cells were decreased, but their size was increased. In cluster 3, size of most effector leukocyte subtypes (CD8+, CD4+ Teff, NK-T, neutrophils) was increased at follow-up versus baseline. Network analysis suggested four distinct networks of (1) triglycerides, (2) VO2peak, (3) diastolic BP and (4) a cluster containing total and LDL cholesterol, insulin and HR recovery, each associated with partly distinct sets of plasma proteins and leukocyte parameters.

Conclusion

Despite improved physical fitness in all patients participating in the 16 weeks ET programme, inter-individual differences regarding cardio-metabolic target parameters and leukocyte activation was evident. Identification of specific leukocyte responses and of plasma protein “fingerprints” might help to optimize patient-specific prevention programmes, integrating medication and lifestyle optimization.

Funding Acknowledgement

Type of funding source: Public grant(s) – National budget only. Main funding source(s): DZHK, DSHF