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Completed

NCT Number: NCT02907684

The Impact of Almond Nut Consumption on Markers of CVD & Metabolic Health

The purpose of this study is to investigate the cardio-metabolic health effects of consuming almond nuts in place of habitual (usual) snack products in adults at moderate risk of developing cardiovascular disease

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Key information

Age range

30 year–70 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

King's College London, Diabetes and Nutritional Sciences Division

London, SE1 9NH, United Kingdom

About this study

Tree nuts are recommended in the prevention and management of cardiovascular disease (CVD) largely based on their LDL (low density lipoprotein) lowering effects, but the CVD risk reduction observed with tree nut consumption is greater than that predicted by their hypocholesterolemic effects alone. Other health benefits have also been noted by our group, such as moderation of postprandial lipemia , as well as by others such as modified postprandial glycemia , decreased blood pressure (BP) , improvement in oxidant status and weight loss. Robust evidence for the protective cardio-metabolic effects of nuts from the PREDIMED study has highlighted the association between nut consumption and decreased risk of cardiovascular events, obesity, metabolic syndrome and type 2 diabetes (T2DM). However, there is a paucity of evidence on the effects of almonds on vascular function in humans (BP and endothelium-dependent vasodilation (EDV)), although there is evidence that almonds promote nitric oxide (NO) release in animals consuming high-fat diets. Fundamental to vascular health is a well-functioning liver and there is increasing evidence to demonstrate that the accumulation of liver fat is a causative factor in the development of cardio-metabolic disorders. Non-alcoholic fatty liver disease (NAFLD) is now considered the hepatic manifestation of the metabolic syndrome (MetS); recent data has shown that it is linked to increased CVD risk via direct effects on vascular function (and EDV) independently of obesity and MetS . NAFLD is thought to affect 30% of the population in developed countries, and up to two-thirds of people with obesity and 50% of people with hyperlipidemia. Development of fatty liver, mainly attributable to obesity and elevated postprandial lipemia, is associated with increased inflammation, oxidative stress, insulin resistance, dyslipidemia and impaired EDV, and predicts risk of CVD and T2DM .

Therefore, the long-term goal of this research is to understand the mechanisms underpinning how dietary change can drive favourable modification of CVD disease risk and to identify patterns in population food choices, specifically almond consumption, that tend to correlate with reduced CVD disease risk. The primary aim of this proposal is to investigate, in a randomised controlled, parallel arm, 6-wk dietary intervention (n=100) whether replacing snacks based on refined carbohydrates and poor in micronutrients/non-nutrient bioactives (NNB) with nutrient/NNB-dense, whole almond snacks can influence liver fat content (a key metabolic driver of insulin resistance and vascular dysfunction, and a hallmark of metabolic syndrome) and EDV (brachial FMD being an independent predictor of CVD events, in addition to related biomarkers of cardio-metabolic disease risk. The snacks products provide participants with 20% of their energy requirements via either whole almonds or as muffins/crackers that have been designed to mimic the average UK snack.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Subjects will be male or female, aged between 30-70 years who regularly consume ≥2 snack products a day. A principal aim is to identify and recruit subjects with increased risk of CVD, in order to increase the sensitivity of the study subjects to dietary change. Subjects who are at above average risk for developing CVD (relative risk >1.5) will be selected using a metabolic scoring system (scoring ≥2 points), adapted from the Framingham risk score system, as used previously by Chong et al. 2012. Subjects will give their own written informed consent.

Exclusion criteria

  • Non-snack consumers (assessed as subjects consuming <2 snack products per day by a specific FFQ (food frequency questionnaire) at screening, adapted from the short Health Survey for England (2007) Eating Habits Questionnaire).
  • A reported history of myocardial infarction or cancer.
  • Being fitted with a heart pacemaker.
  • Presence of metal inside the body (implants, devices, shrapnel, metal particles in eyes from welding etc.). History of black-outs/epilepsy.
  • Diabetes mellitus (fasting plasma glucose >7 mmol/L).
  • Chronic coronary, renal or bowel disease or history of cholestatic liver disease or pancreatitis.
  • Presence of gastrointestinal disorder or use of a drug, which is likely to alter gastrointestinal motility or nutrient absorption.
  • History of substance abuse or alcoholism (past history of alcohol intake >60 units/men or 50 units/women).
  • Currently pregnant, planning pregnancy, breastfeeding or having had a baby in the last 12 months.
  • Allergy or intolerance to nuts.
  • Unwilling to follow the protocol and/or give informed consent.
  • Weight change of > 3 kg in preceding 2 months. BMI <18 kg/m2 (underweight) or >40 kg/m2 (morbidly obese due to potential technical difficulties making FMD and ambulatory blood pressure (ABP) measurements).
  • Current smokers or individuals who quit smoking within the last 6 months.
  • Participation in other research trials involving dietary or drug intervention and/ or blood collection in the past 3 months.
  • Unable or unwilling to comply with study protocol.
  • The above criteria will be measured using the screening questionnaires and from physical (blood pressure, weight, height) and biochemical measurements (full lipid count, liver function test, full blood count, glucose and insulin) made during the screening visit. Participant eligibility will be assessed against the inclusion/exclusion criteria and 'fitness' to participate will be assessed and signed off by a clinician.

Treatment and study plan

Almonds

Dietary Supplement

Participants to consume almonds as snacks to contribute to 20% of their energy requirements daily for 4 weeks

Muffins/Crackers

Dietary Supplement

Participants to consume muffins/crackers as snacks to contribute to 20% of their energy requirements daily for 4 weeks

NB all participants will have a run in period for 2 weeks whereby muffins are consumed, this is prior to randomisation.

Primary outcomes

  1. Endothelium-dependent vasodilation

    Time frame: Baseline (week 2)

    Measured via flow mediated dilation (FMD)

  2. Endothelium-dependent vasodilation

    Time frame: Week 8 (after 2 week run in)

    Measured via flow mediated dilation (FMD)

  3. Liver fat %

    Time frame: Baseline (week 2)

    Via MRI and magnetic resonance spectroscopy (MRS) analysis. Only a subset of 48 participants with aim of 20 per each arm to complete

  4. Liver fat %

    Time frame: Week 8 (after 2 week run in)

    Via MRI and MRS analysis. Only a subset of 48 participants with aim of 20 per each arm to complete

Secondary outcomes

  1. Pancreatic fat

    Time frame: Baseline (week 2)

    Via body MRI. Only a subset of 48 participants with aim of 20 per each arm to complete.

  2. Abdominal fat

    Time frame: Baseline (week 2)

    Via body MRI. Only a subset of 48 participants with aim of 20 per each arm to complete.

  3. Muscle fat

    Time frame: Baseline (week 2)

    Single measurement via body MRI. Only a subset of 48 participants with aim of 20 per each arm to complete.Muscle fat will be measured in the soleus muscle in the lower calf.

  4. Pancreatic fat

    Time frame: Week 8 (after 2 week run in)

    Single measurement via body MRI. Only a subset of 48 participants with aim of 20 per each arm to complete.

  5. Abdominal fat

    Time frame: Week 8 (after 2 week run in)

    Single measurement via body MRI. Only a subset of 48 participants with aim of 20 per each arm to complete.

  6. Muscle fat

    Time frame: Week 8 (after 2 week run in)

    Single measurement via body MRI. Only a subset of 48 participants with aim of 20 per each arm to complete. Muscle fat will be measured in the soleus muscle in the lower calf.

  7. Body composition: body weight

    Time frame: Week 0, prior to 2 week run in

    Using Tanita scales

  8. Body composition: body weight

    Time frame: Week 2 'Baseline'

    Using Tanita scales

  9. Body composition: body weight

    Time frame: Week 4

    Using Tanita scales

  10. Body composition: body weight

    Time frame: Week 6

    Using Tanita scales

  11. Body composition: body weight

    Time frame: Week 8

    Using Tanita scales

  12. Body composition: body mass index

    Time frame: Week 0, prior to 2 week run in

  13. Body composition: body mass index

    Time frame: Week 2 'baseline'

  14. Body composition: body mass index

    Time frame: Week 4

  15. Body composition: body mass index

    Time frame: Week 6

  16. Body composition: body mass index

    Time frame: Week 8

  17. Body composition: Waist circumference

    Time frame: Week 0, prior to 2 week run in

  18. Body composition: Waist circumference

    Time frame: Week 2 'baseline'

  19. Body composition: Waist circumference

    Time frame: Week 4

  20. Body composition: Waist circumference

    Time frame: Week 6

  21. Body composition: Waist circumference

    Time frame: Week 8

  22. Body composition: Hip circumference

    Time frame: Week 0 (prior to 2 week run in)

  23. Body composition: Hip circumference

    Time frame: Week 2 'baseline'

  24. Body composition: Hip circumference

    Time frame: Week 4

  25. Body composition: Hip circumference

    Time frame: Week 6

  26. Body composition: Hip circumference

    Time frame: Week 8

  27. Blood pressure

    Time frame: Week 0 (prior to 2 week run in)

  28. Blood pressure

    Time frame: Week 2 'baseline'

  29. Blood pressure

    Time frame: Week 4

  30. Blood pressure

    Time frame: Week 6

  31. Blood pressure

    Time frame: Week 8

  32. 24 hour ambulatory blood pressure

    Time frame: Week 2 'Baseline

  33. 24 hour ambulatory blood pressure

    Time frame: Week 8

  34. 24 hour heart rate variability

    Time frame: Week 2 'baseline'

  35. 24 hour heart rate variability

    Time frame: Week 8

  36. Fecal short chain fatty acids

    Time frame: Week 2 'baseline

    Subset of participants, n=30

  37. Fecal short chain fatty acids

    Time frame: Week 8

    Subset of participants, n=30

  38. Gut microbiota

    Time frame: Week 2 'baseline'

    Subset of participants, n=30

  39. Gut microbiota

    Time frame: Week 8

    Subset of participants, n=30

  40. Fasting insulin

    Time frame: week 2 'baseline'

  41. Fasting insulin

    Time frame: week 8

  42. Fasting glucose

    Time frame: Week 2 'baseline'

  43. Fasting glucose

    Time frame: Week 8

  44. Fasting non esterified fatty acids (NEFA)

    Time frame: Week 2 'baseline'

  45. Fasting non esterified fatty acids (NEFA)

    Time frame: Week 8

  46. Plasma Total cholesterol

    Time frame: Week 2 'baseline

    Fasting

  47. Plasma Total cholesterol

    Time frame: Week 8

    Fasting

  48. Plasma LDL cholesterol

    Time frame: Week 2 'Baseline'

    Fasting

  49. Plasma LDL cholesterol

    Time frame: Week 8

    Fasting

  50. Plasma HDL cholesterol

    Time frame: Week 2 'Baseline'

    Fasting

  51. Plasma HDL cholesterol

    Time frame: Week 8

    Fasting

  52. Plasma HDL:LDL ratio

    Time frame: Week 2 'Baseline'

    Fasting

  53. Plasma HDL:LDL ratio

    Time frame: Week 8

    Fasting

  54. Plasma triglyceride concentration

    Time frame: Week 2 'baseline'

    Fasting

  55. Plasma triglyceride concentration

    Time frame: Week 8

    Fasting

  56. Homeostasis model assessment estimated insulin resistance (HOMA-IR)

    Time frame: Week 2 'Baseline'

    Fasting (calculated from insulin and glucose)

  57. Homeostasis model assessment estimated insulin resistance (HOMA-IR)

    Time frame: Week 8

    Fasting (calculated from insulin and glucose)

  58. Plasma adiponectin

    Time frame: Week 2 'Baseline'

  59. Plasma adiponectin

    Time frame: Week 8

  60. Plasma resistin

    Time frame: Week 2 'baseline'

  61. Plasma resistin

    Time frame: Week 8

  62. Plasma leptin

    Time frame: Week 2 'baseline'

  63. Plasma leptin

    Time frame: Week 8

Other outcomes

  1. Adverse events

    Time frame: Through study completion, average of 1.5 years.

  2. Snack product acceptability

    Time frame: Week 6

    Questionnaire for participants to rate acceptability including self-rated enjoyment, sensory aspects, gastrointestinal effects, palatability, and appetite sensations, and likelihood that they will continue to consume the almonds/muffins as a snack after the study has ended

  3. 4 day food diaries

    Time frame: 4 days at screening

  4. 4 day food diaries

    Time frame: 4 days at week 0 'Baseline'

  5. 4 day food diaries

    Time frame: 4 days at week 6

Sponsors and collaborators

Lead sponsor

King's College London

Other

Collaborators

  • Almond Board of California

Registry information

Official study title

A Randomised, Controlled Parallel Dietary Intervention to Investigate the Effect of Almond Snack Consumption on Cardio-metabolic Disease Risk Markers Compared With Isocaloric Snacks, in Adults at Moderate Risk of Cardiovascular Disease

Acronym: Almonds

Important dates

Study start
2017
Primary completion
2019
Study completion
2019
First posted
Sep 20, 2016
Registry last updated
Dec 2, 2019

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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