| Literature DB >> 30934969 |
Ana Diez-Fernández1,2, Celia Álvarez-Bueno3,4, Vicente Martínez-Vizcaíno5,6, Mercedes Sotos-Prieto7,8,9, José I Recio-Rodríguez10,11, Iván Cavero-Redondo12,13.
Abstract
The aim of this review was to determine the relationship between dairy product consumption and arterial stiffness, measured by pulse wave velocity (PWV). We systematically searched the Medline, Embase and Web of Science databases until 30th January 2019 for cross-sectional data from studies addressing the association between dairy product consumption and PWV. This study was registered with PROSPERO (CRD42018110528). Both the inverse-variance fixed effects method and the DerSimonian and Laird method were used to compute pooled estimates of effect size (ES) and the respective 95% confidence intervals (CIs). Seven studies were included in the meta-analysis, with a total of 16,443 patients. Total dairy product (ES = -0.03; 95% CI [-0.04, -0.01]) and cheese (ES = -0.04; 95% CI [-0.07, -0.01]) consumption were weak, but significantly associated with lower PWV levels. Conversely, milk intake showed no significant association with PWV (ES = 0.02; 95% CI [-0.01, 0.05]). Heterogeneity in the ES was not important for the three groups of dairy products assessed. This systematic review and meta-analysis of seven studies found no detrimental effects of dairy product consumption on arterial stiffness measured by PWV. Due to the scarcity of studies, further investigations are warranted to clarify the role of dairy products on arterial stiffness.Entities:
Keywords: arterial stiffness; dairy product; meta-analysis; milk; pulse wave velocity; systematic review
Mesh:
Year: 2019 PMID: 30934969 PMCID: PMC6520823 DOI: 10.3390/nu11040741
Source DB: PubMed Journal: Nutrients ISSN: 2072-6643 Impact factor: 5.717
Figure 1Literature search PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) consort diagram.
Search strategy for Medline.
| Dairy Related Terms | Arterial Stiffness Related Terms | |
|---|---|---|
| “milk” | AND | “pulse wave velocity” |
Characteristics of the studies included.
| Study | Country/Type of Design | Mean BMI (kg/m2) | Mean SBP/DBP (mmHg) | Mean PWV (m/sec) | Dietary Record | Type of Dairy Product | Mean Dairy Products Consumption | Main Results | Covariables | |
|---|---|---|---|---|---|---|---|---|---|---|
| Arnberg K et al, 2012 [ | Denmark/Cross-sectional | 193/13.2 ± 0.7 | 25.2 ± 2.3 | 111 ± 7/65.3 ± 6.3 | 4.81 ± 0.71 | Precoded food record of 4 days [ | Milk | 224 g/d | Negative association between milk intake and PWV. | Age, gender, MAP, Tanner stage, heart rate, HOMA, serum TG, serum HDL cholesterol and BMI. |
| Campbell M et al, 2018 [ | USA/Cross-sectional | 22/26.3 ± 4.2 | 33.2 ± 3.5 | 124.3 ± 8.5/79.5 ± 7.6 | 6.34 ± 0.88 | DHQ-II of one month [ | Dairy products | 194.25 g/d | Negative correlation between dairy products and PWV. | Age, BMI, MAP, SBP, PP and waist circumference. |
| Crichton G et al, 2012 [ | USA/Cross-sectional | 587/63.8±12.4 | 29.5 ± 6.4 | 128.9 ± 19.6/77 ± 9.3 | 10.25 ± 2.8 | Nutrition and Health questionnaire [ | Milk and dairy product | NA | According to categories of dairy products frequency consumption, higher intake of dairy food was related with lower PWV, PP, and SBP values. | Age, education, sex, race, weight, heart rate, antihypertensive drug treatment, MAP, waist circumference, total cholesterol, HDL and LDL cholesterol, Center for Epidemiologic Studies Depression Scale raw score + grains per day, vegetables per day, sweets per day, protein per day, and total food servings per day. |
| Gomes Ribeiro A et al, 2018 [ | Brazil/Cross-sectional | 12892/51.6±8.9 | 26.8 ± 4.6 | 121.1 ± 17.1/76.3 ± 10.7 | 9.31 ± 1.81 | Semi-quantitative FFQ [ | Milk, cheese, yogurt, butter | 316.60 g/d | Inverse association between the intake of dairy products with SBP, PP and PWV. Higher frequency of dairy product consumption was significantly associated with lower PWV values. | Age, sex, race, income, weight, height, waist circumference, smoking status, alcohol intake, physical activity, fasting glucose, total cholesterol, MAP, antidiabetic drugs (yes/no), lipid-lowering drugs (yes/no), antihypertensive drugs (yes/no), calorie intake and non-dairy food groups. |
| Livingstone K et al, 2013 [ | UK/Longitudinal and cross-sectional | 2373/56.9 ± 4.5 | 26.4 ± 3.5 | 144.3 ± 18.5/84.9 ± 10.1 | 11.47 ± 2.67 | FFQ [ | Milk, cheese, butter and cream | 346 g/d | Higher intake of dairy products significantly decreased PP and SBP values, but there were not differences in PWV results across quartiles of dairy product consumption. | Age, alcohol consumption, smoking habits, social class, physical activity, total energy intake, and fat intake, heart rate, MAP, and drug use. |
| Petersen K et al, 2015 [ | Australia/Cross-sectional | 95/58.0±12.0 | 34.0 ± 6.9 | 129 ± 14/73 ± 10 | 9.6 ± 1.8 | DQES v2 FFQ of 3 days [ | Milk, yogurt and cheese | 387 g/d | Significant inverse association between total dairy intake and yogurt with PWV. | Age, central MAP, BMI, heart rate and antihypertensive medication prescription. |
| Recio-Rodriguez JI et al 2014 [ | Spain/Cross-sectional | 265/55.9±12.2 | 27.3 ± 4.3 | 122.3 ± 17.9/77.6 ± 10.8 | 7.60 ± 2.00 | Semi-quantitative 137-item FFQ [ | High-fat dairy and low-fat dairy | 133.1 g/d | Low-fat dairy products consumption was associated with lower PWV values, but whole-fat products increased PWV values. | Age, sex, BMI, smoking, SBP, total cholesterol, energy intake and the presence of diabetes, antihypertensive, antidiabetic and lipid-lowering drugs. |
BMI: Body mass Index; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; PWV: Pulse Wave Velocity; m/sec: meters/second; FFQ: Food-frequency questionnaire. NA: Not available.
Quality assessment tool for observational cohort and cross-sectional studies.
| Arnberg K et al, 2012 | Campbell M et al, 2018 | Crichton G et al, 2012 | Gomes Ribeiro A et al, 2018 | Livingstone K et al, 2013 | Petersen K et al, 2015 | Recio-Rodriguez JI et al, 2014 | |
|---|---|---|---|---|---|---|---|
| 1. Was the research question or objective in this paper clearly stated? | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| 2. Was the study population clearly specified and defined? | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| 3. Was the participation rate of eligible persons at least 50%? | Yes | NA | Yes | Yes | Yes | Yes | NA |
| 4. Were all the subjects selected or recruited from the same or similar populations (including the same time period)? Were inclusion and exclusion criteria for being in the study prespecified and applied uniformly to all participants? | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| 5. Was a sample size justification, power description, or variance and effect estimates provided? | No | No | No | Yes | No | No | Yes |
| 6. For the analyses in this paper, were the exposure(s) of interest measured prior to the outcome(s) being measured? | No | No | No | No | No | No | No |
| 7. Was the timeframe sufficient so that one could reasonably expect to see an association between exposure and outcome if it existed? | No | No | No | No | No | No | No |
| 8. For exposures that can vary in amount or level, did the study examine different levels of the exposure as related to the outcome (e.g., categories of exposure, or exposure measured as continuous variable)? | No | No | Yes | Yes | Yes | No | No |
| 9. Were the exposure measures (independent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| 10. Was the exposure(s) assessed more than once over time? | NA | NA | NA | NA | NA | NA | NA |
| 11. Were the outcome measures (dependent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| 12. Were the outcome assessors blinded to the exposure status of participants? | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| 13. Was loss to follow-up after baseline 20% or less? | NA | NA | NA | NA | NA | NA | NA |
| 14. Were key potential confounding variables measured and adjusted statistically for their impact on the relationship between exposure(s) and outcome(s)? | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
NA: Not applicable.
Figure 2Forest plot of the estimated rate ratio of dairy products consumption and pulse wave velocity (PWV), by type of dairy product and overall.
Sensitivity analysis by removing one by one the included studies.
| References | ES (95% CI) |
|
|
|---|---|---|---|
|
| |||
| Campbell M et al, 2018 [ | −0.03 (−0.04, −0.01) | 0.0 | 0.725 |
| Crichton G et al, 2012 [ | −0.03 (−0.04, −0.01) | 0.0 | 0.888 |
| Gomes Ribeiro A et al, 2018 [ | −0.01 (−0.08, 0.05) | 0.0 | 0.785 |
| Livingstone K et al, 2013 [ | −0.03 (−0.05, −0.02) | 0.0 | 0.889 |
| Petersen K et al, 2015 [ | −0.03 (−0.04, −0.01) | 0.0 | 0.722 |
| Recio-Rodríguez JI et al, 2014 [ | −0.03 (−0.04, −0.01) | 0.0 | 0.759 |
|
| |||
| Arnberg K et al, 2012 [ | 0.02 (−0.01, 0.05) | 0.0 | 0.954 |
| Gomes Ribeiro A et al, 2018 [ | 0.00 (−0.07, 0.08) | 0.0 | 0.866 |
| Livingstone K et al, 2013 [ | 0.02 (−0.01, 0.05) | 0.0 | 0.815 |
| Petersen K et al, 2015 [ | 0.02 (−0.01, 0.05) | 0.0 | 0.815 |
|
| |||
| Gomes Ribeiro A et al, 2018 [ | −0.08 (−0.31, 0.15) | 47.5 | 0.168 |
| Livingstone K et al, 2013 [ | −0.04 (−0.07, −0.01) | 36.0 | 0.211 |
| Petersen K et al, 2015 [ | −0.04 (−0.06, −0.01) | 0.0 | 0.815 |
Meta-regressions of the PWV and total dairy products consumption by age, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP) of included studies.
| Covariates | ß (95% CI) |
|
|---|---|---|
| Age (years) | −0.00 (−0.01, 0.01) | 0.983 |
| BMI (kg/m2) | −0.01 (−0.07, 0.04) | 0.518 |
| SBP (mmHg) | 0.00 (−0.00, 0.01) | 0.461 |
| DBP (mmHg) | 0.00 (−0.01, 0.02) | 0.395 |
| PP (mmHg) | 0.00 (−0.01, 0.01) | 0.512 |
SBP: Systolic blood pressure; DBP: Diastolic blood pressure; PP: Pulse pressure.
Figure 3Assessment of potential publication bias by Harbord test.