Cardiometabolic Health
Conditions
Keywords
Sleep efficiency, Glycemic control, Flow mediated dilation
Brief summary
An investigation into habitual potato consumption (mashed, boiled, baked) on sleep quality and chronic glycaemic control, established risk factors for cardiometabolic diseases, versus habitual consumptions of non-nutrient-dense starchy staples (white rice, pasta, and couscous).
Detailed description
In the last few years, researchers have undergone efforts to conduct well-controlled trials to investigate the cardiometabolic health effect of consuming potatoes as part of a healthy diet. None to our knowledge, however, have investigated the harmful dyad of poor sleep and adverse glycaemic control, 2 interrelated factors which can exacerbate cardiometabolic (CM) health outcomes. The nutrient density of potatoes, namely being the single richest source of potassium per serving, provides a plausible mechanism in which the potatoes may act to improve health markers. The primary objective of this study is to investigate whether consuming a portion of nutrient-dense potatoes in the evening meal, in place of other refined carbohydrates, can improve sleep quality, and improve nocturnal, and 24-hour, glycaemic control, both risk factors for CM diseases. Secondly, this study aims to investigate the effects of potato consumption on endothelium-dependent vasodilation, which can further interrelate to this web of interactions, and other measures of CM health. Study design: A randomised, two-parallel arm, in-clinic and remote, 12-week dietary intervention study. Study population: Healthy male and female 40-80-year-olds, who consume ≤4 fruits and vegetables per day and have sub-optimal sleep quality. The study aims to recruit a total of 80 participants, allowing for an estimated 15% dropout rate, to reach 80% power at a significance level of 0.025 (based on two outcomes). The allocation ratio is 1:1 intervention to control. Statistical analysis: Differences between groups (minimized for age, gender, BMI) will be analysed using Linear mixed models for outcomes with 3 timepoints (urinary outcomes, dietary intake data), with change being the dependent factor, subject ID as a random effect, treatment and season as fixed effects, and baseline outcome and BMI as covariates. Variables with 2 timepoints will be assessed with an ANCOVA regression model. Locations: Metabolic Research Unit, 4th floor, Franklin Wilkins Building, Waterloo campus, Kings College London, SE1 9NH. Screening assessment: Participants will be initially assessed for suitability against the inclusion-exclusion criteria via an online questionnaire. The outputs of the questionnaire will be assessed by the study team. Some exclusion criteria will be assessed at the baseline clinic visit before any baseline measures are provided. Study duration: There will be a 2-week run-in period, followed by a 12-week dietary intervention. Dietary intervention: The intervention (potato group) will consume at least 230 g of white potatoes (including fresh and frozen baked, boiled, and mashed potato products) in their evening meal, providing \ 1000 mg potassium, enough to increase national median intakes up to recommended intakes. The control group will consume isoenergetic amounts of non-nutrient-dense starchy staples (white pasta, white rice, or couscous). Although participants cannot be blinded to what they're consuming, they will be blinded to whether they are in the control or the intervention group, to reduce the risk of bias. Participants will be required to source the potatoes and make these meals themselves, however, they will be provided with rotating 4-weekly recipe cards, with instructions on how to prepare meals. Participants will be required to attend several virtual one-to-ones with the study team, including an introductory call, a virtual run-in induction where they will be run through the study equipment, and 2 in-person clinic visits. Participants will be responsible for applying some study equipment from home, to reduce clinic visits and improve study retention. Compliance: Compliance will be monitored via several methods. Firstly, 24 h urinary potassium excretion, which is shown to recover 75% of potassium intake, secondly self-reported compliance will be measured through evening meal checklists. We also plan to use dietary recalls at weeks 2, 4, and 8 to reinforce dietary advice, and to make adaptations to rotating menus based on individual needs, if participants are struggling with adherence. Detailed dietary intake will be assessed through 4-day food diaries at weeks 0, 6, and 12- this data will be used for analysis. Flow-mediated dilation: A Doppler ultrasound will be utilised to capture continuous ultrasound videos to measure flow-mediated dilation. These will be analysed with automated software provided by Maastricht University. Anthropometry: Weight, height, waist and hip circumference, blood pressure, and body fat will be taken using standard procedures, in duplicates by a trained researcher at baseline and endline clinic visits. Blood samples: Fasting blood samples will be collected from a superficial antecubital vein via venepuncture before and after the dietary intervention, by a trained researcher. Participants will be asked to record and monitor the following information: Self-reported compliance with dietary intervention, weekly weight (data diaries), and habitual dietary intake (4-day diet diaries).
Interventions
DIETARY_SUPPLEMENTHabitual potato consumption
Potatoes are to be consumed in the evening meal, every evening for 12-weeks.
DIETARY_SUPPLEMENTHabitual non-nutrient-dense staple consumption
White rice, white pasta or white couscous are to be consumed in the evening meal, every evening for 12-weeks.
Sponsors
Alliance for Potato Research and Education
Maastricht University
King's College London
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
BASIC_SCIENCE
Masking
SINGLE (Outcomes Assessor)
Masking description
Participants will be blinded to whether they are receiving the intervention or control, but it is not possible to blind them to the food itself. The researcher conducting the statistical analysis will be blinded prior to analysis, using a second set of unique study ID codes.
Intervention model description
12-week Randomised controlled trial
Eligibility
Sex/Gender
ALL
Age
40 Years to 80 Years
Healthy volunteers
Yes
Inclusion criteria
* Self-reported healthy adults * Aged 40-80 years old * Intake of ≤4 portions of fruits and vegetables (excluding potatoes) per day * Low sleep quality index of \>5 (Pittsburgh sleep quality index).
Exclusion criteria
* Shift workers and those with multiple jobs or carers who are required to wake through the night * Unwilling to refrain from dietary supplements * Unwilling to follow protocol and/or give informed consent * Diagnosed with Cardiovascular Disease (CVD), Type 2 Diabetes, Celiac disease, Insomnia, Sleep apnoea * Presence of gastrointestinal disorder * Users of drugs that are likely to alter gastrointestinal motility or nutrient absorption * History of substance abuse or alcoholism * Currently pregnant, planning pregnancy, breastfeeding, or having had a baby 12 months prior * Weight change of \>3kg in the preceding 2 months * BMI \<18.5kg/m2 or \> 35kg/m2, * Current smokers, or individuals who quit smoking in the last 6-months * Frequently consume wholemeal products (1-2 times per day, in the short screening FFQ) * Never consumed refined starchy staples, such as white pasta or rice (rarely or never, in the short screening FFQ) * High potato consumption (4-6 times per week, in the short screening FFQ) * High risk of obstructive sleep apnoea (answer yes to \>3 questions, in STOP-Bang questionnaire) * Vegetarian, Vegan, or pescatarian dietary preferences. * Diagnosed with mental health disorders, and/or on medicine for their mental health (antipsychotics, antidepressants, beta-blockers) * Chronic user of antihistamines * Fasting glucose \>7mmol/l (finger prick test at baseline clinic) * Is taking weight loss, or glucose regulating medications. * Has been unstable on blood pressure lowering medications for at least 3-months prior to enrollment.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Change in 7-day sleep efficiency | Pre- and post-intervention: 12 weeks | Differences in % sleep efficiency at endline vs baseline. Sleep efficiency % is described as the total time spent asleep/ total time spent in bed x 100. |
| Change in 12-day nocturnal mean glucose | Pre- and post-intervention: 12 weeks | Differences in mean nocturnal glucose, measured using a Freestyle Libre 3 continuous glucose monitor. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Change in the nocturnal CV% | Pre- and post-intervention: 12 weeks | Difference in 12-day nocturnal coefficient of variation (CV%) |
| Change in nocturnal MAGE | Pre- and post-intervention: 12 weeks | Difference in 12-day nocturnal Mean Amplitude of Glycemic Excursions (MAGE). |
| Change in nocturnal MODD | Pre- and post-intervention: 12 weeks | Difference in 12-day nocturnal Mean of Daily Differences (MODD). |
| Change in mean daytime glycaemic control | Pre- and post-intervention: 12 weeks | Differences in 12-day mean daytime glucose. |
| Difference in daytime TIR | Pre- and post-intervention: 12 weeks | Difference in 12-day daytime Time In Range for non-diabetic populations (TIR nd). |
| Change in the daytime CV% | Pre- and post-intervention: 12 weeks | Difference in 12-day daytime coefficient of variation (CV%) |
| Change in daytime MAGE | Pre- and post-intervention: 12 weeks | Difference in 12-day daytime Mean Amplitude of Glycemic Excursions (MAGE). |
| Change in daytime MODD | Pre- and post-intervention: 12 weeks | Difference in 12-day daytime Mean of Daily Differences (MODD). |
| Change in mean 24-hour glycaemic control | Pre- and post-intervention: 12 weeks | Differences in 12-day mean 24-hour glucose. |
| Difference in 24-hour TIR | Pre- and post-intervention: 12 weeks | Difference in 12-day 24-hour Time In Range for non-diabetic populations (TIR nd). |
| Change in the 24-hour CV% | Pre- and post-intervention: 12 weeks | Difference in 12-day 24-hour coefficient of variation (CV%) |
| Change in 24-hour MAGE | Pre- and post-intervention: 12 weeks | Difference in 12-day 24-hour Mean Amplitude of Glycemic Excursions (MAGE). |
| Change in 24-hour MODD | Pre- and post-intervention: 12 weeks | Difference in 12-day 24-hour Mean of Daily Differences (MODD). |
| Change in heart rate variability during mental stress | Pre- and post-intervention: 12 weeks | A combination of time domain, frequency domain and non-linear methods (NN intervals, heart rate, rMSSD, pNN50, SDNN, TINN, SDANN, SD1/SD2, High Frequency, Low Frequency, Very Low Frequency, Ultra Low Frequency HF:LF, total power), will be compared from pre- to post-mental stress. |
| Change in 24-hour heart rate variability | Pre- and post-intervention: 12 weeks | A combination of time domain, frequency domain and non-linear methods (NN intervals, heart rate, rMSSD, pNN50, SDNN, TINN, SDANN, SD1/SD2, High Frequency, Low Frequency, Very Low Frequency, Ultra Low Frequency HF:LF, total power). |
| Change in mean nocturnal AUC | Pre- and post-intervention: 12 weeks | Difference in 12-day mean nocturnal Area Under the Curve (AUC). |
| Nocturnal continuous blood pressure | Whole 12 week period. | Measured with an Aktiia Photoplethysmography watch. |
| Daytime continuous blood pressure | Whole 12 week period. | Measured with an Aktiia Photoplethysmography watch. |
| Change in blood pressure | Pre- and post-intervention: 12 weeks | Diastolic and systolic blood pressure, measured by oscillometry. |
| Change in fasting plasma glucose | Pre- and post-intervention: 12 weeks | Fasting plasma glucose concentration |
| Change in fasting insulin | Pre- and post-intervention: 12 weeks | Fasting insulin concentration |
| Change in fasting HbA1C | Pre- and post-intervention: 12 weeks | Fasting HbA1C concentration |
| Change in plasma lipids | Pre- and post-intervention: 12 weeks | Including: total lipids, HDL and LDL-cholesterol, and Triacylglycerols. |
| Change in serum potassium | Pre- and post-intervention: 12 weeks | Serum potassium mmol/L |
| Change in sleep duration, in minutes | Pre- and post-intervention: 12 weeks | Sleep duration (minutes) |
| Change in sleep duration, in % | Pre- and post-intervention: 12 weeks | Sleep duration (%) |
| Change in sleep latency | Pre- and post-intervention: 12 weeks | Sleep latency is defined as the time it takes to fall asleep from the time intended to fall asleep. |
| Change in body fat % | Pre- and post-intervention: 12 weeks | Body fat % measured using TANITA bioelectrical impedance scales |
| Change in waist circumference (cm) | Pre- and post-intervention: 12 weeks | Waist circumference (cm) |
| Change in BMI kg/m2 | Pre- and post-intervention: 12 weeks | BMI kg/m2, measured using a stadiometer and TANITA scales |
| Calystegine potato biomarker | Pre- and post-intervention: 12 weeks | Calystegine will be analysed as an exploratory biomarker of potato consumption (a polyhydroxylated nortropane alkaloid), by Triple Quadrupole mass spectrometry (MS/MS). |
| Change in Endothelial function | Pre- and post-intervention: 12 weeks | Mean differences in flow-mediated dilation (%) |
| Difference in nocturnal TIR | Pre- and post-intervention: 12 weeks | Difference in 12-day nocturnal Time In Range for non-diabetic populations (TIR nd). |
Countries
United Kingdom
Outcome results
None listed