Abstract
Background
Cardiovascular risk factors represent an important health issue in older adults. Previous findings suggest that meditation training could have a positive impact on these risk factors. The objective of this study was to investigate the effects of an 18-month meditation-based intervention on cardiovascular health.
Methods
Age-Well was a randomized, controlled superiority trial with blinded end point assessment, including community-dwelling cognitively unimpaired adults 65 years and older enrolled between November 24, 2016, and March 5, 2018, in France. One hundred and thirty-four participants were included in this secondary analysis. Participants were randomly affected to an intervention group that received an 18-month meditation-based program or to comparison groups (active control group i.e. non-native language training or passive control group i.e. no intervention). The main outcome was change in the Framingham Risk Score (FRS); other outcomes were changes in cardiovascular and metabolic risk factors.
Results
There was no difference in FRS change after 18 months between trial arms (p = .38). When assessing individual cardiovascular or metabolic risk factors, meditation training was associated with a greater reduction in diastolic blood pressure than the comparison group in participants with intermediate to high cardiovascular risk (FRS > 10%) at baseline (p = .03).
Conclusion
An 18-month meditation training was not effective to increase overall cardiovascular health in older adults, but improved diastolic blood pressure in a subgroup analysis including at-risk participants. These results suggest a potential benefit of a long-term meditation intervention in older adults at-risk of cardiovascular diseases, and highlights the need for future research in more targeted populations.
Trial registration
ClinicalTrials.gov Identifier: NCT02977819.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-024-05550-9.
Keywords: Cardiovascular risk factor, Metabolic risk factor, Meditation, Mindfulness, Randomized controlled trial
Background
Cardiovascular diseases (CVD) are the number one cause of death globally, taking an estimated 17.9 million lives each year [1, 2]. Thus, preventing CVD in the general population is a major public health issue. Among older adults, preventive cardiovascular medication can be difficult to manage, with a risk of inappropriate prescribing and adverse effects (e.g., falls, bleeds, electrolyte disturbances, cognitive deficits, and mortality) [3, 4]. There is emerging evidence from observational studies and randomized controlled trials (RCT) in favour of non-pharmacological programs for the prevention of CVD, including meditation and mindfulness-based stress reduction programs [5–11]. An estimation from a dynamic population-based microsimulation model suggested that a meditation intervention may avert nearly 200,000 stroke cases and 50,000 stroke-related deaths over the course of 15 years in the United States, with higher benefits in older adults [12]. Mindfulness meditation, loving-kindness and compassion meditation allowed participants to engage more effectively in self-regulation skills and behaviours lowering CVD risk [13], and enhanced attention control, metacognitive monitoring, emotion regulation and prosocial skills [14]. Moreover, the relaxation response is a multifaceted practice that can involve awareness and tracking of breaths or repetition of a word or short phase leading to beneficial cardiovascular effects [8]. Given the low risks of meditation, and based on positive results of short-term RCT [9–11, 15–19], the American Heart Association recommends that this intervention may be considered as an adjunct to guideline-directed CVD risk reduction [8]. However, we currently lack data from RCT with long-term interventions in older adults. The duration of the intervention should exceed that of the commonly used 8-week mindfulness-based stress reduction program to demonstrate an effect on protracted age-related biological processes [14, 20].
The Age-Well study is a single center RCT aiming to assess the effect of an 18-month preventive meditation-based intervention directly targeting the attentional and emotional dimensions of ageing to promote mental health and well-being in older adults [21]. Results of this study confirm the feasibility of 18-month meditation interventions and non-native language training in older adults, with high adherence and very low attrition [22]. This RCT is a long-term intervention and a rare opportunity to better understand the effect of meditation interventions on CVD and metabolic risk factors in older adults.
The main objective of this study was to evaluate the effect of a long-term meditation intervention on the overall CVD risk using the Framingham Risk Score (FRS) [23]. The secondary objectives were to assess the effect of the intervention on individual CVD and metabolic risk factors, in a population of healthy older adults.
Methods
Participants and study design
The Age-Well RCT (Medit-Ageing European project) design has been fully described elsewhere [21, 22]. The main inclusion criteria were age ≥ 65 years, being cognitively unimpaired and functionally independent, living at home, having an educational level ≥ 7 years, being motivated to effectively participate in the project and signing the informed consent form, not having present or past regular or intensive practice of meditation or comparable practices. The main exclusion criteria were the presence of a major neurological or psychiatric disorder, presence of a chronic disease or acute unstable illness, current or recent medication that may interfere with cognitive functioning, physical or behavioural inability to perform the follow-up visits as planned in the study protocol.
Out of 157 participants who attended a screening visit, 137 were randomized (ratio 1:1:1) to meditation training (n = 45), non-native language training (n = 46), and no intervention (n = 46). Participants were enrolled between November 24, 2016, and March 5, 2018. Two participants were excluded from all secondary analyses by the Trial Steering Committee for not meeting eligibility criteria (i.e., history of head trauma and amyotrophic lateral sclerosis diagnosis during the study [with a likely subclinical state at inclusion]). A total of 135 participants were finally included (Fig. 1). One death not related to the study (myocardial infarction) was reported at follow-up, and one participant revealed not having followed his/her allocated arm (randomized to no intervention but attended non-native language training and was analyzed within the non-native language training arm). Thus, for the analysis, the meditation group had 45 participants and the comparison group had 89 participants (Fig. 1).
Fig. 1.
Study flow diagram
Intervention
The intervention was previously described in details [21]. There were 3 trial arms (meditation training, non-native language training, and no intervention). As the objective herein was to specifically assess the effect of meditation on cardiovascular health, the population was divided in 2 groups in this specific study: the meditation group and the comparison group bringing together the active control (non-native language training) and the passive control (no intervention) groups. The analyses were also done by differentiating the 3 trial arms.
The meditation intervention consisted of an original secular program of meditation training labelled “The Silver Santé Study Meditation Programme” especially designed for the study based on pre-existing interventions [21], with the objective of personal development and healthy ageing, and was provided by expert meditator instructors. The objective of this 18-month intervention program was to develop mindfulness, kindness, and compassion abilities as additional psychological resources to cope with challenges related to the physical, cognitive, and psychological aspects of aging. In the active control group, the non-native language training program was a cognitively stimulating intervention (English exercises designed to reinforce each participant’s abilities in understanding, writing, and speaking). In the passive control group (no intervention), participants were requested not to change their habits and continue living as they used to before engaging in the study and until the end of the study. They were specifically asked not to engage in any meditation or non-native language training.
The meditation and the non-native language training interventions were structurally equivalent in overall course length, class time, and home activities, and matched in administration, dosage, and duration. All intervention groups consisted of 14 participants. For both interventions, participants followed 2-h weekly group sessions, daily home practice (at least 20 min per day), and one day of more intense practice during the intervention (5 h during the day). The intervention in the meditation group was delivered by meditation expert teachers and took place at the Pôle de Formations et de Recherche en Santé (PFRS), in Caen, France. There were five meditation teachers, 2 females and 3 males. Two of them were certified Mindfulness-Based Stress Reduction teachers, one was a former Jogye Buddhist nun, and two were psychiatrists and psychotherapists, certified in Compassion Focused Therapy and Mindfulness Therapy. All had several years of teaching experience. The first 9 months of the intervention are dedicated to the teaching of mindfulness meditation whereas the 9 following months are dedicated to the teaching of the meditation on loving kindness and compassion. In each weekly group session, there was a time for presentation, a time for sharing, and a time for practice; the first two sessions of each month included an equal amount of these three aspects (3 × 40 min), session 3 included more sharing (30/60/30 min), and session 4 more practice (30/30/60). For each intervention, participants were provided with supports (manual and audio) for their practice. A questionnaire of daily questions was given to participants, about the well-being of the person, the formal practice time for the day, the duration of their informal practice and the motivation to practice during the day.
FRS measure
The main outcome measure for this study was the FRS, assessing individuals’ overall CVD risk [23]. FRS was computed using age, sex, systolic blood pressure (BP) and hypertension treatment, high-density lipoprotein (HDL), total cholesterol, current smoking status, diabetes mellitus and antidiabetic treatments, as previously described [23]. There was one missing data for FRS at baseline (due to missing data for total cholesterol and HDL in one participant), and two missing data at follow-up. All the variables were acquired at baseline and follow-up (18 months). BP was measured at the time of the medical interview and neuroimaging exams and was averaged over three consecutive assessments, in a seated position at rest, and as stress-free as possible. The same procedure was done for the baseline and the follow-up visit.
Individual CVD and metabolic risk factors
Other individual CVD and metabolic risk factors were studied as secondary and exploratory outcomes and included: systolic BP, diastolic BP, pulse pressure, body mass index (BMI), waist/hip ratio, glycaemia, insulin, homeostatic model assessment of insulin resistance (HOMA-IR), hypopnea-apnoea index, creatinine, urea, triglyceride, total cholesterol, HDL, low-density lipoprotein (LDL), total cholesterol, C-reactive protein (CRP), and high-sensitivity CRP (hs-CRP). Hypertension was defined by systolic BP ≥ 140 mmHg and/or diastolic BP ≥ 90 mmHg, with BP measures averaged over three consecutive assessments, in a seated position at rest, and as stress-free as possible on the same day.
Pulse pressure was the difference between the systolic BP and diastolic BP. BMI corresponded to weight in kilograms divided by height in meters squared, waist/hip ratio corresponded to hip circumference divided by weight circumference. HOMA-IR was computed using the equation (insulin x glycaemia) / 22.5, and corresponded to a metabolic risk index [24]. The apnoea-hypopnea index (AHI) was used to estimate sleep apnoea, and was measured using objective measures of sleep from polysomnography, as previously described [21, 25]. Laboratory measures were obtained from blood sampling (details in supplementary materials). All these variables were acquired at baseline and follow-up (18 months). At baseline, there were one missing data for HDL and hs-CRP, 8 missing data for hypopnea-apnoea index, and 42 missing data for glycaemia and HOMA-IR.
Ethics, safety, and study monitoring
The Age-Well RCT was approved by the ethics committee (Comité de Protection des Personnes CPP Nord-Ouest III, Caen; trial registration numbers: EudraCT: 2016–002441-36; IDRCB: 2016-A01767-44; ClinicalTrials.gov Identifier: NCT02977819) and adheres to the Standard Protocol Items: Recommendations for Interventional Trials guidelines for clinical trial protocols [26]. This study followed the Consolidated Standards of Reporting Trials (CONSORT) reporting guideline. In line with Good Clinical Practice guidelines, a trial steering committee was established and an external Data and Safety Monitoring Board, independent of the sponsor, was appointed [21].
Statistical Analyses
Sociodemographic, clinical, and laboratory variables were described using mean and standard deviation (SD), or number and percentage (%).
For the main objective, linear mixed-effects models were fitted with FRS as the dependent variable, and trial arm (i.e. meditation vs comparison group), visit (two time points: baseline and 18 months), and a trial arm by visit interaction term as independent variables. FRS was log-transformed because of a skewed distribution, as previously done [27]. For the secondary objectives, linear mixed models were run for continuous variables with each CVD and metabolic risk factor as dependent variables, and trial arm (i.e., meditation training vs comparison group), visit, and a trial arm by visit interaction term as independent variables. Glycaemia, triglyceride, and CRP were log-transformed because of skewed distribution. All models were adjusted for age and sex (except for the main objective, because FRS was already computed using age and sex), and models studying BP, glycaemia, or blood lipid levels (cholesterol, triglyceride, LDL, HDL) were also adjusted for antihypertensive medication, antidiabetic medication, or lipid-lowering medications, respectively. For each model, the distribution of residuals was inspected, and collinearity was tested using a variation inflation factor (VIF, which was low, i.e. < 5 for all models except when analysing BMI in which VIF was moderate i.e. < 7.5).
Analyses were rerun in a subgroup of at-risk participants with an intermediate to high baseline CVD risk score (FRS > 10%), as well as by separating the comparison group into non-native language training and no intervention, i.e., with the 3 trial arms as predictors (meditation training, non-native language training, and no intervention).
Finally, a multinomial logistic model was run, with change in hypertension status (as previously defined) as the dependent variable with 3 levels: i) unchanged indicating participants without change in hypertension status between baseline and follow-up (with or without hypertension), ii) better indicating participants with hypertension at baseline and without hypertension at follow-up, and iii) worse indicating participants without hypertension at baseline and with hypertension at follow-up. The trial arm was the independent variable, and the model was firstly not adjusted to respect the validity of the logistic model (adequate number of events per independent variable), and then adjusted for confounders (i.e., age, sex, and antihypertensive medication) due to clinical relevance of these covariates.
Statistical analyses were performed using the R software version 4.0.5 (R Core Team, www.R-project.org) and RStudio Version 1.4.1106. For all analyses, per protocol principles were used for comparisons between groups; no correction for multiple comparisons was applied and the significance level was set to p < 0.05.
Results
Participant characteristics
Characteristics of participants are described in Table 1. As expected, there were no major differences in any demographic characteristics of CVD risk factors between groups (Table 1).
Table 1.
Baseline characteristics of participants
| Meditation Training Group | Comparison Group | Overall | |
|---|---|---|---|
| N | 45 | 89 | 134 |
| Age,years | 69.00 (3.69) | 68.72 (3.77) | 68.81 (3.73) |
| Sex, F | 31 (68.89%) | 51 (57.30%) | 82 (61.19%) |
| Education, years | 13.11 (3.07) | 13.21 (3.11) | 13.18 (3.08) |
| Cardiovascular disease history, Yes | 22 (48.89%) | 30 (33.71%) | 52 (38.81%) |
| Smoking, Yes | 1 (2.22%) | 5 (5.62%) | 6 (4.48%) |
| Cardiovascular medication, count | 0.62 (0.78) | 0.47 (0.76) | 0.52 (0.76) |
| Antihypertensive medication, Yes | 20 (44.44%) | 27 (30.34%) | 47 (35.07%) |
| Anticoagulant medication, Yes | 12 (26.67%) | 17 (19.10%) | 29 (21.64%) |
| Antidiabetic medication, Yes | 3 (6.67%) | 3 (3.37%) | 6 (4.48%) |
| Lipid-lowering medication,Yes | 10 (22.22%) | 15 (16.85%) | 25 (18.66%) |
| Framingham Risk Score,(% | 19.66 (12.35) | 19.23 (13.03) | 19.37 (12.76) |
| Framingham Risk Score < 10% (N) | 10 (22.22%) | 25 (28.09%) | 35 (26.12%) |
| Framingham Risk Score 10–20% (N) | 17 (37.78%) | 34 (38.20%) | 51 (38.06%) |
| Framingham Risk Score > 20% (N) | 18 (40.00%) | 29 (32.58%) | 47 (35.07%) |
| Framingham Risk Score, log | 1.21 (0.27) | 1.19 (0.28) | 1.20 (0.28) |
| Systolic BP (mmHg) | 138.02 (21.93) | 133.17 (19.02) | 134.80 (20.09) |
| Diastolic BP (mmHg) | 80.69 (10.41) | 79.11 (9.82) | 79.64 (10.01) |
| Pulse pressure (mmHg) | 57.38 (17.03) | 54.08 (14.37) | 55.19 (15.33) |
| BMI (kg/m2) | 26.17 (4.64) | 26.20 (4.15) | 26.19 (4.30) |
| Waist/Hip Ratio | 0.90 (0.08) | 0.93 (0.08) | 0.92 (0.08) |
| HOMA-IR | 2.25 (1.33) | 2.15 (1.12) | 2.18 (1.18) |
| AHI | 25.47 (15.46) | 25.22 (14.59) | 25.30 (14.82) |
| Creatinine (μmol/L) | 69.73 (9.48) | 74.88 (12.69) | 73.15 (11.92) |
| Urea (mmol/L) | 5.86 (1.65) | 5.80 (1.22) | 5.82 (1.37) |
| Glycaemia (g/L) | 0.95 (0.13) | 0.93 (0.11) | 0.94 (0.11) |
| Insulin (pmol/L) | 64.05 (32.50) | 69.55 (32.45) | 65.04 (32.35) |
| Triglycerides (mmol/L) | 1.23 (0.43) | 1.33 (0.69) | 1.30 (0.61) |
| Total Cholesterol (mmol/L) | 6.27 (1.14) | 6.21 (1.16) | 6.23 (1.15) |
| HDL Cholesterol (mmol/L) | 1.64 (0.35) | 1.68 (0.41) | 1.67 (0.39) |
| LDL Cholesterol (mmol/L) | 4.07 (1.03) | 3.94 (0.99) | 3.98 (1.00) |
| CRP (mg/L) | 2.15 (3.98) | 2.06 (2.96) | 2.09 (3.32) |
| hs-CRP (μg/mL) | 2.78 (4.79) | 2.60 (3.92) | 2.66 (4.21) |
Mean (SD) or N (%) are indicated
Abbreviations: AHI Apnoea-Hypopnoea Index (number of respiratory events per hour of sleep), BMI body mass index, BP blood pressure, CRP C-reactive protein, HDL high-density lipoprotein, HOMA-IR homeostatic model assessment of insulin resistance, LDL low-density lipoprotein
Effects of meditation on FRS
The mean FRS changed in the comparison group (from 19.23% to 20.79%), and in the meditation group (from 19.66% to 19.25%; Fig. 2). In a linear mixed-effects model using log-transformed FRS, with one missing data in each group, these between-group differences over time were not significant (estimate -0.03, SE 0.03; 95% confidence interval [-0.08;0.03], t value -0.87, p = 0.38). Results were unchanged when separating the comparison group into non-native language training and no intervention, as well as in the subgroup analysis of at-risk participants with intermediate to high baseline CVD risk score.
Fig. 2.
Changes in Framingham Risk Score before and after the intervention. Individual changes (thin lines) and mean changes (heavy lines) on the left, and mean changes with adjusted y-axis on the right, in Framingham Risk Score (log-transformed) before and after the intervention. The interaction between time and group (mixed model) was not significant
Effects of meditation on other CVD and metabolic risk factors
Systolic BP decreased in the meditation training and comparison groups, without group difference in a linear mixed-effects model. The effect of meditation training was also not significant on other CVD and metabolic risk factors (see details in Table 2). Results were unchanged when separating the comparison group into non-native language training and no intervention.
Table 2.
Effects of meditation on cardiovascular and metabolic risk factors in the whole group
| Pre-intervention | Post-intervention | Interaction Time * Group | |||
|---|---|---|---|---|---|
| Comparison Group | Meditation Training Group | Comparison Group | Meditation Training Group | ||
| Systolic BP (mmHg) | 133.17 (19.02) | 138.02 (21.93) | 130.42 (19.04) | 131.14 (19.01) | p = 0.23 |
| Diastolic BP (mmHg) | 79.11 (9.82) | 80.69 (10.41) | 77.93 (9.65) | 77.39 (8.93) | p = 0.23 |
| Pulse pressure (mmHg) | 54.08 (14.37) | 57.38 (17.03) | 52.48 (15.06) | 53.71 (14.73) | p = 0.51 |
| BMI (kg/m2) | 26.20 (4.15) | 26.17 (4.64) | 26.36 (4.51) | 26.35 (4.96) | p = 0.96 |
| Waist/Hip Ratio | 0.93 (0.08) | 0.90 (0.08) | 0.94 (0.07) | 0.94 (0.11) | p = 0.08 |
| HOMA-IR | 2.15 (1.12) | 2.25 (1.33) | 2.36 (1.32) | 2.42 (1.42) | p = 0.96 |
| AHI | 25.22 (14.59) | 25.47 (15.46) | 21.25 (13.05) | 23.65 (12.80) | p = 0.46 |
| Creatinine (μmol/L) | 74.88 (12.69) | 69.73 (9.48) | 75.21 (14.23) | 69.36 (8.12) | p = 0.52 |
| Urea (mmol/L) | 5.80 (1.22) | 5.86 (1.65) | 6.09 (1.36) | 6.07 (1.38) | p = 0.68 |
| Glycemia (g/L) | 0.93 (0.11) | 0.95 (0.13) | 0.96 (0.14) | 1.00 (0.23) | p = 0.60 |
| Insulin (pmol/L) | 69.55 (32.45) | 64.05 (32.50) | 67.75 (31.27) | 66.84 (33.54) | p = 0.93 |
| Triglyceride (mmol/L) | 1.33 (0.69) | 1.23 (0.43) | 1.23 (0.61) | 1.30 (0.63) | p = 0.10 |
| Total Cholesterol (mmol/L) | 6.21 (1.16) | 6.27 (1.14) | 6.17 (1.06) | 6.21 (1.23) | p = 0.97 |
| HDL Cholesterol (mmol/L) | 1.68 (0.41) | 1.64 (0.35) | 1.62 (0.40) | 1.55 (0.28) | p = 0.40 |
| LDL Cholesterol (mmol/L) | 3.94 (0.99) | 4.07 (1.03) | 4.01 (0.90) | 4.09 (1.07) | p = 0.74 |
| CRP (mg/L) | 2.06 (2.96) | 2.15 (3.98) | 2.29 (4.42) | 1.45 (1.28) | p = 0.63 |
| hs-CRP (μg/mL) | 2.60 (3.92) | 2.78 (4.79) | 3.44 (8.17) | 1.95 (1.71) | p = 0.45 |
Mean (SD) are indicated, at baseline (pre-intervention) and follow-up (post-intervention) in the whole group. P values were obtained from linear mixed models
Abbreviations: AHI Apnoea-Hypopnoea Index (number of respiratory events per hour of sleep), BMI body mass index, BP blood pressure, CRP C-reactive protein, HDL high-density lipoprotein, HOMA-IR homeostatic model assessment of insulin resistance, LDL low-density lipoprotein
In at-risk participants with intermediate to high baseline CVD risk score (FRS > 10%), mean diastolic BP changed in the comparison group (from 80.25mmHg to 79.66mmHg), and more importantly in the meditation training group (from 82.66mmHg to 77.69mmHg), with an interaction between trial arm and visit (estimate -4.19, SE 1.90; 95% confidence interval [-7.90;-0.48], t value -2.21, p = 0.03). In this subsample, systolic BP changes (from 138.94mmHg to 135.08mmHg in the comparison group and from 142.71mmHg to 135.69mmHg in the meditation group) and other cardiovascular and metabolic risk factors changes were not significant.
Finally, the risk of becoming hypertensive between baseline and follow-up tended to be lower in participants in the meditation training group: 2.2% of participants in the meditation training group and 14.6% of participants in the comparison group had worse hypertension status at follow-up (in comparison to participants with unchanged hypertension status, odds ratio 0.13, 95% confidence interval [0.02–1.07], p = 0.058 in the unadjusted model, and odds ratio 0.11, 95% confidence interval [0.01–0.092], p = 0.042 in the model adjusted for age, sex, and antihypertensive medication; Fig. 3).
Fig. 3.
Changes in hypertension status from baseline to follow-up according to intervention group. Changes in hypertension status: Unchanged indicates participants without change in hypertension status between baseline and follow-up (with or without hypertension), Better indicates participants with hypertension at baseline and without hypertension at follow-up, and Worse indicates participants without hypertension at baseline and with hypertension at follow-up
Discussion
The present study conducted in healthy older adults indicated that an 18-month meditation-based intervention was not associated with a significant change in overall CVD risk, as assessed by the FRS. The meditation intervention tended to decrease the risk of becoming hypertensive during follow-up, and had a significant benefit on diastolic BP, which decreased by approximately 5 mmHg at 18 months in subgroups analysis (in at-risk participants with intermediate to high baseline CVD risk score).
The lack of significance on the main objective in the present study may be explained by the lack of statistical power. The sample size calculation of the Age-Well RCT was made for the main outcome of the Age-Well RCT [21] and was not powered for the present secondary analysis. Many RCT and meta-analyses were performed to evaluate the effects of meditation-based interventions on cardiovascular outcomes [9–11, 15–19], particularly in populations with high CVD risk at baseline, e.g. in patients with hypertension [10, 19] or CVD [11, 16, 17]. Overall, they showed a decrease in systolic BP from 2.5 to 5.5 mmHg, and a decrease in diastolic BP from 2 to 4.5 mmHg with meditation-based interventions, with differences on the effect on systolic or diastolic BP according to studies [8]. Here, while the mean systolic BP decreased by 6.88 mmHg in the meditation training group (vs 2.75 mmHg in the comparison group), and the mean diastolic BP decreased by 3.3 mmHg in the meditation training group (vs 1.18 mmHg in the comparison group), the trial arm by visit interaction was not significant. Thus, the present results are comparable to those from previous meta-analyses, and these differences, even if low, appear to us clinically relevant [28]. While FRS is a robust, clinically relevant, and well-known outcome, this score has rarely been used as the main outcome in previous meditation-based studies [29].
An original aspect of the present study was the inclusion of a large group of healthy older adults, without pre-specified cardiovascular conditions, and with relatively low CVD risk. Indeed, participants with a chronic disease or acute unstable illness were excluded from this study, and only 35% had antihypertensive medication (lower than in previous studies, e.g. 60% had antihypertensive medication in a recent study including 1170 community dwelling older adults of median age 74) [30]. Another interest of the present study was to evaluate this intervention in a group of older adults, aged 65 years and over. This seems relevant for two main reasons. Firstly, while older adults are the most affected by CVD risk, they are also the most at risk of adverse drug events [31], including cardiovascular medications [31, 32]. In this context, non-pharmacological interventions appear particularly relevant (usually in addition to pharmacological interventions) to reduce the overall CVD risk in this at-risk population. In the present study, there was no serious adverse events related to the intervention (see details in the primary outcome paper [22]). One death not related to the study (myocardial infarction) was reported, and the participant was not included in the analysis (due to lack of data at follow-up). One could argue that this constitutes a bias in the present results, but as the results on the primary objective of this study were negative, we assume that there is no over-estimation of a possible effect of the intervention. Secondly, previous evidence suggests that the impact of meditation interventions on blood pressure may be higher in older adults [9, 15]. To explain this higher effect of meditation in older adults, Shi and collaborators hypothesised that older adults show higher motivation to reduce medications with non-pharmacological interventions, and may have more time to practice meditation, compared to younger adults [9]. However, in a qualitative study of mindfulness practice in older adults, Parra and collaborators showed that time management remains one of the main barrier for meditation (older adults were very busy despite being retired) [33]. Despite the long-duration of the Age-Well RCT (18 months), the latter showed a high adherence rate and compliance of patients [22]. However, no significant relationship between practice time and effect of intervention on FRS was found (data not shown).
Beyond blood pressure, we investigated the relationships between meditation and other CVD and metabolic risk factors. We failed to show protective effects of a meditation training on these factors. While a very large non-interventional observational study showed that participants who engaged in meditation have significantly lower prevalence of hypercholesterolemia and diabetes [5], a review summarised conflicting results on the effects of mindfulness-based interventions on these metabolic factors [6]. These contrasting findings highlight the need to further explore the impact of meditation interventions on metabolic factors.
There are limitations in the present study, as previously discussed: this secondary analysis was possibly underpowered for the cardiovascular outcome, due notably to the a priori defined sample size, and powered for the primary outcome of the Age-Well RCT; the numerous confounding factors involved in cardiovascular risk assessment and the multiple-test corrections not performed in the statistical analysis. Nevertheless, we assume that since the study's results were not globally significant, this does not overestimate our findings. Moreover, the results on DBP may be interpreted cautiously, as they come from a secondary analysis, in a subsample, and the relevance and impact of decreasing DBP in older adults is questionable [34]. Finally, mechanistic aspects and mindfulness assessments were not included in this study as the main outcome was negative, and due to the limited sample size.
The present study reinforces the fact that meditation interventions could be part of preventive strategies to limit the burden of CVD and may be beneficial for older adults in promoting other relevant and major aspects during ageing that are affective health and quality of life [22, 33, 35, 36]. Future intervention studies are needed in older adults, with larger samples and more targeted populations. Since there is an important variability in participant’s response to meditation interventions, that is dependent on their characteristics and contextual factors [37], the aim should also be to better personalise interventions. In future analyses, it will be relevant to monitor lifestyle and medication changes during interventions to disentangle direct effect of meditation on cardiovascular health versus effect of lifestyle changes associated with meditation [38]. However, medication changes were rare during the study (for example there were only 6 changes for hypertension treatment during follow-up), and another study of the Medit-Ageing research group did not highlight differences in lifestyle factors after the interventions (article in preparation).
Conclusion
To conclude, this 18-month meditation-based intervention failed to significantly improve the overall CVD risk, as assessed by the FRS in a population of healthy older adults. However, the intervention tended to improve diastolic BP in participants with intermediate to high CVD risk at baseline. Beyond cardiovascular prevention, meditation may be beneficial for overall health and quality of life during ageing, and future studies, using multi-domain and personalised approaches are needed to achieve this.
Supplementary Information
Acknowledgements
The authors thank all the Medit-Ageing Research Group members, the Cyceron MRI-PET staff members, Delphine Smagghe (Inserm Tansfert), Marine Faure, Jeanne Lepetit and Aurélia Cognet for their help with recruitment, data acquisition, or administrative support; the EUCLID team, the sponsor (INSERM, Hélène Espérou), and the participants of the study for their contribution. The Medit-Ageing Research Group is listed in the Appendix.
The authors thank Elizabeth Kuhn (Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", NeuroPresage Team, Institut Blood and Brain @ Caen-Normandie, Cyceron, 14000 Caen, France), as well as Virginie Dauphinot and Pauline Desnavailles (Clinical and Research Memory Center of Lyon, Hospices Civils de Lyon, France) for their advice on statistical analysis. The authors also thank Véréna Landel (Direction de la Recherche en Santé, Hospices Civils de Lyon, France) for help in manuscript preparation and language editing.
Authors’ contributions
All authors meet the ICMJE criteria for authorship and contributed meaningfully in the study conception and design, and in the drafting and revising of the manuscript for critically important intellectual content. AGC, JG, GC and GP contributed to the study concept and design. JG, FF, CP, EFD, AC, GR, VDLS, GC, GP took part in the acquisition and processing of the data, and quality check control. AC, EFG, and VDLS took part in participants’ recruitment and selection process, clinical evaluation, and monitoring of participants. AGC, JG and GP contributed to the statistical analysis. AGC and GP drafted the manuscript. All authors took part in revising the manuscript for content, read and approved the final manuscript.
Funding
This work (Age-Well randomized clinical trial, a part of the Medit-Ageing project) was supported by the European Union’s Horizon 2020 Research and Innovation Program (grant 667696), Région Normandie (Label d’Excellence), and Fondation d’Entreprise MMA des Entrepreneurs du Futur. Institut National de la Santé et de la Recherche Médicale (Inserm) is the sponsor. The funders and sponsor had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Data availability
The data underlying this report are made available on request following a formal data sharing agreement and approval by the consortium and executive committee (https://silversantestudy.eu/2020/09/25/data-sharing). The data that support the findings of this study are available from Dr G. Poisnel but restrictions apply to the availability of these data. Data are however available from Dr. G. Poisnel upon reasonable request and with permission of the executive committee.
Declarations
Ethics approval and consent to participate
The Age-Well RCT was approved by the ethics committee (Comité de Protection des Personnes CPP Nord-Ouest III, Caen; trial registration numbers: EudraCT: 2016–002441-36; IDRCB: 2016-A01767-44; ClinicalTrials.gov Identifier: NCT02977819). All participants gave their written informed consent to the study prior to enrollment.
Consent for publication
Not applicable.
Competing interests
Independent of this work, AGC is an unpaid sub-investigator or principal investigator in Alzheimer’s disease clinical trials: NCT04867616 (UCB Pharma), NCT04241068 (Biogen), NCT05310071 (Envision), NCT03446001 (TauRx Therapeutics), NCT03444870 (Roche), NCT04374253 (Roche), NCT04777396 (Novo Nordisk), NCT04777409 (Novo Nordisk), NCT04770220 (Alzheon), NCT05423522 (Medesis Pharma).
GC reported grants, personal fees and non-financial support from Institut National de la Santé et de la Recherche Médicale (Inserm), grants from European Union’s Horizon 2020 Research and Innovation program under grant agreement No 667696 (PI), grants from Fondation d’entreprise MMA des Entrepreneurs du Futur, during the conduct of the study; personal fees from Fondation Entrepreneurs MMA, grants and personal fees from Fondation Alzheimer, grants from Région Normandie, grants from Fondation Recherche Alzheimer, grants from Association France Alzheimer, outside the submitted work.
GP has received research support from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 667696, from the Institut National de la Santé et de la Recherche Médicale (Inserm) and from Région Normandie.
GP, GC participated to the DSMB of the Age-Well trial and to the ExCom of Medit-Ageing.
No other disclosures were reported.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.WHO. Global health estimates: life expectancy and leading causes of death and disability. 2019. https://www.who.int/data/maternal-newborn-child-adolescent/gama/activities-of-gama. Accessed 11 Feb 2021.
- 2.WHO. Cardiovascular diseases (CVDs). https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). Accessed 9 Mar 2021.
- 3.Malek Makan A, van Hout H, Onder G, Finne-Soveri H, van der Roest H, van Marum R. Prevalence of preventive cardiovascular medication use in nursing home residents. Room for deprescribing? The SHELTER study. J Am Med Dir Assoc. 2017;18:1037–42. [DOI] [PubMed] [Google Scholar]
- 4.Scott IA, Hilmer SN, Reeve E, Potter K, Le Couteur D, Rigby D, et al. Reducing inappropriate polypharmacy: the process of deprescribing. JAMA Intern Med. 2015;175:827. [DOI] [PubMed] [Google Scholar]
- 5.Krittanawong C, Kumar A, Wang Z, Narasimhan B, Jneid H, Virani SS, et al. Meditation and cardiovascular health in the US. Am J Cardiol. 2020;131:23–6. [DOI] [PubMed] [Google Scholar]
- 6.Loucks EB, Schuman-Olivier Z, Britton WB, Fresco DM, Desbordes G, Brewer JA, et al. Mindfulness and cardiovascular disease risk: state of the evidence, plausible mechanisms, and theoretical framework. Curr Cardiol Rep. 2015;17:112. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Levine GN, Cohen BE, Commodore-Mensah Y, Fleury J, Huffman JC, Khalid U, et al. Psychological health, well-being, and the mind-heart-body connection: a scientific statement from the American Heart Association. Circulation. 2021;143:e763–83. [DOI] [PubMed] [Google Scholar]
- 8.Levine GN, Lange RA, Bairey-Merz CN, Davidson RJ, Jamerson K, Mehta PK, et al. Meditation and cardiovascular risk reduction. J Am Heart Assoc. 2017;6:e002218. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Shi L, Zhang D, Wang L, Zhuang J, Cook R, Chen L. Meditation and blood pressure: a meta-analysis of randomized clinical trials. J Hypertens. 2017;35:696–706. [DOI] [PubMed] [Google Scholar]
- 10.Conversano C, Orrù G, Pozza A, Miccoli M, Ciacchini R, Marchi L, et al. Is mindfulness-based stress reduction effective for people with hypertension? A Systematic Review and Meta-Analysis of 30 Years of Evidence. Int J Environ Res Public Health. 2021;18: 2882. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Scott-Sheldon LAJ, Gathright EC, Donahue ML, Balletto B, Feulner MM, DeCosta J, et al. Mindfulness-based interventions for adults with cardiovascular disease: a systematic review and meta-analysis. Ann Behav Med. 2020;54:67–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Ambavane RA, Khademi A, Zhang D, Shi L. Modeling the impact of transcendental meditation on stroke incidence and mortality. J Stroke Cerebrovasc Dis. 2019;28:577–86. [DOI] [PubMed] [Google Scholar]
- 13.Nardi WR, Harrison A, Saadeh FB, Webb J, Wentz AE, Loucks EB. Mindfulness and cardiovascular health: qualitative findings on mechanisms from the mindfulness-based blood pressure reduction (MB-BP) study. PLoS One. 2020;15: e0239533. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Lutz A, Chételat G, Collette F, Klimecki OM, Marchant NL, Gonneaud J. The protective effect of mindfulness and compassion meditation practices on ageing: hypotheses, models and experimental implementation. Ageing Res Rev. 2021;72: 101495. [DOI] [PubMed] [Google Scholar]
- 15.Bai Z, Chang J, Chen C, Li P, Yang K, Chi I. Investigating the effect of transcendental meditation on blood pressure: a systematic review and meta-analysis. J Hum Hypertens. 2015;29:653–62. [DOI] [PubMed] [Google Scholar]
- 16.Gathright EC, Salmoirago-Blotcher E, DeCosta J, Balletto BL, Donahue ML, Feulner MM, et al. The impact of transcendental meditation on depressive symptoms and blood pressure in adults with cardiovascular disease: a systematic review and meta-analysis. Complement Ther Med. 2019;46:172–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Abbott RA, Whear R, Rodgers LR, Bethel A, Thompson Coon J, Kuyken W, et al. Effectiveness of mindfulness-based stress reduction and mindfulness based cognitive therapy in vascular disease: a systematic review and meta-analysis of randomised controlled trials. J Psychosom Res. 2014;76:341–51. [DOI] [PubMed] [Google Scholar]
- 18.Anderson JW, Liu C, Kryscio RJ. Blood pressure response to transcendental meditation: a meta-analysis. Am J Hypertens. 2008;21:310–6. [DOI] [PubMed] [Google Scholar]
- 19.Rainforth MV, Schneider RH, Nidich SI, Gaylord-King C, Salerno JW, Anderson JW. Stress reduction programs in patients with elevated blood pressure: a systematic review and meta-analysis. Curr Scien Inc. 2008;9:520. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.American Psychological Association. Mindfulness meditation: a research-proven way to reduce stress. 2020. https://www.apa.org. https://www.apa.org/topics/mindfulness/meditation. Accessed 22 Mar 2023.
- 21.Poisnel G, Arenaza-Urquijo E, Collette F, Klimecki OM, Marchant NL, Wirth M, et al. The age-well randomized controlled trial of the Medit-Ageing European project: effect of meditation or foreign language training on brain and mental health in older adults. Alzheimers Dement. 2018;4:714–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Chételat G, Lutz A, Klimecki O, Frison E, Asselineau J, Schlosser M, et al. Effect of an 18-month meditation training on regional brain volume and perfusion in older adults. JAMA Neurol. 2022;79:1165–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.D’Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care: the framingham heart study. Circulation. 2008;117:743–53. [DOI] [PubMed] [Google Scholar]
- 24.Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28:412–9. [DOI] [PubMed] [Google Scholar]
- 25.André C, Rehel S, Kuhn E, Landeau B, Moulinet I, Touron E, et al. Association of sleep-disordered breathing with Alzheimer Disease Biomarkers in Community-Dwelling Older Adults: A Secondary Analysis of a Randomized Clinical Trial. JAMA Neurol. 2020;77:716–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Chan A-W, Tetzlaff JM, Altman DG, Dickersin K, Moher D. SPIRIT 2013: new guidance for content of clinical trial protocols. Lancet. 2013;381:91–2. [DOI] [PubMed] [Google Scholar]
- 27.Edelman D, Oddone EZ, Liebowitz RS, Yancy WS, Olsen MK, Jeffreys AS, et al. A multidimensional integrative medicine intervention to improve cardiovascular risk. J Gen Intern Med. 2006;21:728–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Rahimi K, Bidel Z, Nazarzadeh M, Copland E, Canoy D, Ramakrishnan R, et al. Pharmacological blood pressure lowering for primary and secondary prevention of cardiovascular disease across different levels of blood pressure: an individual participant-level data meta-analysis. Lancet. 2021;397:1625–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Yadav R, Yadav RK, Sarvottam K, Netam R. Framingham risk score and estimated 10-year cardiovascular disease risk reduction by a short-term yoga-based LifeStyle Intervention. J Altern Complement Med. 2017;23:730–7. [DOI] [PubMed] [Google Scholar]
- 30.Kremer K-M, Braisch U, Rothenbacher D, Denkinger M, Dallmeier D. Systolic blood pressure and mortality in community-dwelling older adults: frailty as an effect modifier. Hypertension. 2022;79:24–32. [DOI] [PubMed] [Google Scholar]
- 31.Budnitz DS, Shehab N, Lovegrove MC, Geller AI, Lind JN, Pollock DA. US Emergency Department visits attributed to medication harms, 2017–2019. JAMA. 2021;326:1299–309. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Vu M, Sileanu FE, Aspinall SL, Niznik JD, Springer SP, Mor MK, et al. Antihypertensive deprescribing in older adult veterans at end of life admitted to veteran affairs nursing homes. J Am Med Dir Assoc. 2021;22:132-140.e5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Parra DC, Wetherell JL, Van Zandt A, Brownson RC, Abhishek J, Lenze EJ. A qualitative study of older adults’ perspectives on initiating exercise and mindfulness practice. BMC Geriatr. 2019;19:354. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Gaffney B, Jacobsen AP, Pallippattu AW, Leahy N, McEvoy JW. The diastolic blood pressure J-Curve in Hypertension Management: Links and Risk for Cardiovascular Disease. Integr Blood Press Control. 2021;14:179–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Geiger PJ, Boggero IA, Brake CA, Caldera CA, Combs HL, Peters JR, et al. Mindfulness-based interventions for older adults: a review of the effects on physical and emotional well-being. Mindfulness (N Y). 2016;7:296–307. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Jalali D, Abdolazimi M, Alaei Z, Solati K. Effectiveness of mindfulness-based stress reduction program on quality of life in cardiovascular disease patients. IJC Heart Vasc. 2019;23: 100356. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Buric I, Farias M, Driessen JMA, Brazil IA. Individual differences in meditation interventions: a meta-analytic study. Br J Health Psychol. 2022;27(3):1043–76. 10.1111/bjhp.12589. [DOI] [PMC free article] [PubMed]
- 38.Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;140:e596-646. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data underlying this report are made available on request following a formal data sharing agreement and approval by the consortium and executive committee (https://silversantestudy.eu/2020/09/25/data-sharing). The data that support the findings of this study are available from Dr G. Poisnel but restrictions apply to the availability of these data. Data are however available from Dr. G. Poisnel upon reasonable request and with permission of the executive committee.