Aging, Protein Metabolism
Conditions
Keywords
Older adults, Metabolomics, Protein intake, Protein metabolism, Acute, Postprandial, Cognitive function, Mood, Appetite
Brief summary
The study aims to investigate the acute effects of different protein sources (i.e., casein, whey, soy) on metabolomic profiles (e.g., amino acids in the blood), along with other outcomes such as cognitive function, mood, and appetite. While the study does not directly aim to prevent, prognosticate, diagnose, or alleviate any disease, it may provide insights that could inform dietary recommendations and support disease prevention efforts in older adults.
Detailed description
This study will be a double-blind, randomised, placebo-controlled trial using a crossover study design. 25 older adults (aged 60 to 80) will be recruited and assigned to consume 4 different test drinks in a random order on 4 different visits. This study aims to investigate whether higher protein consumption of different sources will influence postprandial plasma and urinary metabolomic profiles, cognition, mood and appetite. The study will comprise 1 screening visit and 4 test visits separated separated by a minimum washout period of \>7 days
Interventions
DIETARY_SUPPLEMENTCasein Protein Isolate Drink
30 g protein equivalent of casein protein isolate dissolved in \~250 mL of water
DIETARY_SUPPLEMENTWhey Protein Isolate Drink
30 g protein equivalent of whey protein isolate dissolved in \~250 mL of water
DIETARY_SUPPLEMENTSoy Protein Isolate Drink
30 g protein equivalent of soy protein isolate dissolved in \~250 mL of water
DIETARY_SUPPLEMENTMaltodextrin Drink
30 g carbohydrate equivalent of maltodextrin powder dissolved in \~250 mL of water
Sponsors
National University of Singapore
Bezos Earth Fund
Study design
Allocation
RANDOMIZED
Intervention model
CROSSOVER
Primary purpose
PREVENTION
Masking
TRIPLE (Subject, Investigator, Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
60 Years to 80 Years
Healthy volunteers
Yes
Inclusion criteria
* English-literate and able to give informed consent * Adults 60 to 80 years old (inclusive) * (If applicable) Postmenopausal * BMI of 18·5 to 30 kg/m2 (inclusive)
Exclusion criteria
* Currently participating in another clinical study that involves altering the regular diet and/or involves consuming a dietary supplement or investigated food product as an intervention * Weight change \>3 kg in the past 3 months * Exercising vigorously over the past 3 months (i.e., engaging in high-intensity exercises for more than 3 hours a week, where high-intensity exercises leave one out of breath and unable to talk or sing during the workout) * Current smokers or have quit smoking for less than 5 years * Drinking \>2 alcoholic drinks per day (i.e., one drink is defined as either 150 ml of wine, 340 ml of beer/cider or 45 ml of distilled spirit) * If drinking ≤2 alcoholic drinks per day, not willing to stop consumption of alcoholic beverages two days before and on the day of the test visits (total 12 days) * Not willing to stop consumption of caffeinated beverages 10 to 12 hours before and on the day of the test visits (total 4 days) * Taking supplements containing omega-3, magnesium, gamma-aminobutyric acid (GABA), L-theanine, melatonin and/or 5-hydroxytryptophan for the past 3 months, and not willing to discontinue usage 3 months before and during the study duration * Currently on a specialised diet (e.g., vegetarian, vegan, weight loss diet) * Had serious food allergies in the past, or having intolerance, sensitivities or allergies to any of the following: milk/dairy products (e.g., casein, whey), starch/starch-derived products (e.g., rice, corn, wheat, potato), soy/soy-derived products, gluten, eggs, nuts, peanuts, fish and crustaceans. * Disease or impairment of the kidney and/or liver * Currently diagnosed with diabetes, sleep disorders, gastrointestinal issues (e.g., lactose intolerance, irritable bowel syndrome), gout and/or G6PD deficiency * History of diseases known to affect the central nervous system (e.g., heart disease, stroke, migraine headaches, multiple sclerosis, Parkinson's disease, mental illnesses) * Taking antibiotics 2 weeks prior to study participation * Taking medication known to interfere with cognitive function and mental wellbeing (e.g., benzodiazepines, antidepressants, other central nervous agents) * Taking anti-hypertensive or cholesterol-lowering drugs for less than 3 years unless medically declared by an official doctor to be in stable condition * Poor peripheral venous access based on past experiences with blood drawing
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Changes in metabolites found in plasma (plasma metabolomics) | Baseline, 15, 30, 60, 90, 120, 180, 240, 300 minutes | Untargeted metabolomics profiling of plasma will be conducted using ultra performance liquid chromatography-quadrupole time of flight-mass spectrometry (UPLC-QTOF-MS) |
| Changes in metabolites found in urine (urinary metabolomics) | Baseline, 60, 120, 180, 240, 300 minutes | Untargeted metabolomics profiling of urine will be conducted using ultra performance liquid chromatography-quadrupole time of flight-mass spectrometry (UPLC-QTOF-MS) |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Cognitive Function | Baseline, 15, 60, 120, 180, 240, 300 minutes | The Computerised Mental Performance Assessment System (COMPASS) will be administered via a laptop. The COMPASS tests consist of 5 consecutive tests, including Digit Vigilance, Stroop, Rapid Visual Information Processing, Computerised Corsi Blocks Lite, Alphabetic Working Memory and Serial Subtraction, where the correctness and reaction time will be recorded. The test is expected to take around 15 minutes. |
| Mood | Baseline, 15, 60, 120, 180, 240, 300 minutes | The Profile of Mood States (POMS) questionnaire will be administered, which contains various words that describe feelings. Participants will indicate, for each word, how they currently feel on scale of 0 (Not at all) to 4 (Extremely). |
Countries
Singapore
Contacts
PRINCIPAL_INVESTIGATORJung Eun Kim, Ph.D., R.D.
National University of Singapore, Department of Food Science and Technology
Outcome results
None listed