Clinical trial NCT04841668: Gut-Brain-axis: Targets for Improvement of Cognition in the Elderly
Official title: Gut-Brain-axis: Targets for Improvement of Cognition in the Elderly
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Type 2 Diabetes Mellitus
Keywords: Type 2 Diabetes Mellitus
Continous Glucose Monitoring
Cognition
Metformin
Gut microbiome
Magnetic resonance imaging
Physical activity monitor
Lead sponsor: Institut d'Investigació Biomèdica de Girona Dr. Josep Trueta
Sponsor class: OTHER
First posted: 2021-04-12
Enrollment: 136
Sex: ALL
Minimum age (years): 65
Maximum age (years): 80
Standard ages: OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Group A Study population Adult patients (≥ 65 years of age) recently diagnosed with T2DM according to the WHO and who have not been treated with metformin.

Group B Study population Adult patients (≥ 65 years of age) diagnosed with long-term T2DM according to the WHO classification, regardless of whether they take metformin or other treatment.

Brief summary: Cognitive disorders increase with age and in the presence of metabolic diseases such as Type 2 Diabetes Mellitus (T2DM). In addition, digestive disorders, changes in dietary pattern and decreased activity negatively influence the microbiome.

The hypothesis is that pharmacological intervention with metformin will modify the composition of the gut microbiota and cognition.

The study has a pilot longitudinal design, where each patient with T2DM will be followed for one year. Two groups will be recruited:

1. Group A: The aim will be to evaluate the associations between glucose (measured by continuous glucose monitoring (CGM)), cognitive function (by means of cognitive tests and magnetic resonance imaging (MRI)), physical activity (recorded by activity and sleep tracker devicer), metformin, diet (evaluated by nutritional survey) and composition of the microbiota (evaluated by metagenomics), during 12 months (6 months without metformin and 6 months with metformin treatment).
2. Group B: The aim will be to evaluate the associations between glucose, diet (evaluated by nutritional survey), cognitive function (by means of cognitive tests), physical activity (measured by activity and sleep tracker device), the treatment and composition of the microbiota (evaluated by metagenomics), during 12 months.

Detailed description: Subjects and methods:

Longitudinal study:

Patients with T2DM previously scheduled at the Service of Endocrinology, Diabetes and Nutrition (UDEN) of the Hospital "Dr. Josep Trueta" of Girona (Spain) will be recruited and studied.

GROUP A

This study consists of an initial phase, where the patient will be submitted as the only treatment to a balanced diet with an energy intake, calculated individually according to whether he/she is normal weight (25 Kcal x Kg) or overweight (20 Kcal x Kg of weight).

After this initial phase, in addition to continuing with the balanced diet treatment, patients will start treatment with metformin administered orally at an initial dose of 425 mg/d every 12 hours during the first 15 days and then continue with doses of 850 mg/d until the end of the study.

A glycemia sensor will be inserted for ten days, as well as an activity and sleep tracker device (Fitbit) to record physical activity during this period of time. Interstitial subcutaneous glucose concentrations will be monitored on an outpatient basis for a period of time of 10 consecutive days using a glucose sensor validated by the FDA (Dexcom G6 ®). The sensor will be inserted on day 0 and it will retire on day 10 midmorning.

This process will be repeated 10 days prior to the start of the of treatment with Metformin and 10 days before the end of the 6 month study phase with metformin. During the study, 6 visits will be made and each patient will be inserted with a total of 3 glycemia sensors and 3 physical activity monitors. In summary, the glycemia sensor and physical activity monitoring will be started at visits 1, 3, 5 and will be removed at visits 2,4,6.

Visit 1(day 1): Physical examination, Nutritional survey, Bioimpedance, Densitometry, CGM and Activity and sleep tracker device. Consent form

Visit 2 (day 10): Sample: blood, urine and feces. Diet, Neuropsychological test, CGM withdrawal, Activity and sleep tracker device withdrawal, MRI.

Visit 3 (day 170): Physical examination, Nutritional survey, Bioimpedance, CGM and Activity and sleep tracker device

Visit 4 (day 180): Sample: blood, urine and feces. Dietary follow-up, Neuropsychological test, CGM withdrawal and Activity and sleep tracker device withdrawal. Start of metformin treatment.

Visit 5 (day 350): Physical examination, Nutritional survey, Bioimpedance, CGM and Activity and sleep tracker device.

Visit 6 (day 360): Sample: blood, urine and feces. Dietary follow-up, Neuropsychological test, CGM withdrawal and Activity and sleep tracker device withdrawal. Metformin withdrawal.

GROUP B:

During the study, 5 visits will be made for this group:

Visit 1(day 1): Physical examination, Nutritional survey, Bioimpedance, Densitometry and Activity and sleep tracker device. Consent form.

Visit 2 (day 10): Sample: blood, urine and feces. Diet, Neuropsychological test and Activity and sleep tracker device withdrawal.

Visit 3 (day 180): Diet follow-up.

Visit 4 (day 350): Physical examination, Nutritional survey, Bioimpedance and Activity and sleep tracker device.

Visit 5 (day 360): Sample: blood, urine and feces. Diet follow-up, Neuropsychological test and Activity and sleep tracker device withdrawal.

DATA COLLECTION OF SUBJECTS LONGITUDINAL STUDIES:

1. Subsidiary data: Age, sex and birth date.
2. Clinical variables:

   * Weight
   * height,
   * body mass index
   * waist and hip perimeters
   * waist-to-hip ratio
   * blood pressure (systolic and diastolic)
   * fat mass and fat free-mass (bioelectric impedance and DEXA)
   * smoking status
   * alcohol intake
   * registry of usual medicines
   * personal history of blood transfusion and/or donation
   * record of family history of obesity, cardiovascular events and diabetes
   * psychiatric and eating disorder history.
3. Laboratory variables: 15cc of blood will be extracted from fasted subjects to determine the following variables using the usual routine techniques of the clinical laboratory:

   * hemogram
   * glucose
   * bilirubin
   * aspartate aminotransferase (AST/GOT)
   * alanine aminotransferase (ALT/GPT)
   * gamma-glutamyl transpeptidase (GGT)
   * urea
   * creatinine
   * uric acid
   * total proteins,
   * albumin
   * total cholesterol \| HDL cholesterol \| LDL cholesterol
   * triglycerides,
   * glycated haemoglobin (HbA1c)
   * ferritin \| soluble transferrin receptor
   * ultrasensitive C reactive protein
   * erythrocyte sedimentation rate
   * lipopolysaccharide binding protein
   * free thyroxine (free T4) \| thyroid stimulating hormone (TSH) \| baseline cortisol -plasma insulin
   * inflammation markers \| interleukin 6 (IL-6). An additional 15cc of blood (plasma-EDTA) will be extracted for further analyses.
4. Stool samples collection: A stool sample will be provided from each patient. The sample should be collected at home or in the hospital, sent to the laboratory within 4 hours from the collection, fragmented and stored at -80ºC.

   -Analysis of intestinal microbiota in stool:
   * Determination of bacterial DNA and mRNA and study of the LBP binding protein in blood for the detection of bacterial translocation. LBP binding protein in blood for the detection of bacterial translocation. Hiseq and Nextseq technology (qPCR and protein analysis (WB, ELISA), OMICS (RNAseq, 16S, Metabolomics, Metagenomics).
   * Inflammatory and immunological markers will be determined using ELISA (enzyme-linked immunosorbent assay) and immunohistochemistry (IHC) equipment and quantitative real-time PCR validation. For qPCR, total RNA will be isolated from different tissues and will transcribe into cDNA.
   * Determination of metabolic profile and metabolite analysis.
5. Intestinal barrier function:Exposure to a lactulose:mannitol test before/after surgery. Plasma samples will be used to measure intestinal permeability markers: bacterial endotoxin, sCD14, LBP, ZO-1, and I-FABP.
6. Urine sample collection: Necessary to determine alterations in the metabolic pathways involved in tryptophan metabolism, and to determine the role of the intestinal microbiota in these metabolic changes.
7. MRI: The necessary sequences will be acquired for the calculation of the BrainAGE biomarker and the characterization of the networks involved in cognitive functions. For the acquisition a 1.5 T scanner (Ingenia; Philips Medical Systems) will be used 1,5 T scanner (Ingenia; Philips Medical Systems) will be used for the acquisition. First, recovery-inversion sequence (T2-FLAIR) will be used to exclude subjects with pre-existing brain lesions. Subsequently, structural sequences will be acquired sequences will then be acquired to measure the integrity of cerebral gray matter (T1-weighted), tracts of weighted), of the white matter tracts (DTI), iron accumulation (R2\*), and (R2\*), and functional sequences in resting-state (T2\*-weighted echo-planar imaging, EPI).
8. Neuropsychological examination: Different domains of cognition will be explored: memory (Test aprendizaje verbal-TAVEC, Rey-Osterrieth Complex Figure) attention and executive function(WAIS-IV, Trail making test (Part A y B), Stroop test). In addition, cognitive impairment will be evaluated with Lobo's Mini-Cognitive Exam. These tests will be useful to define the changes in the cognitive profile associated with the pharmacological intervention with metformin.

The information will remain registered in a notebook and will be computerized in the database of the study.

STATICAL METHODS:

Sample size: Since this is intended as a pilot study, no formal sample size calculation is required. A general rule is to recruit 30 or more patients to estimate a parameter and 15-20 participants per group to obtain reasonable estimates for medium to large effect sizes.

Statistical analyses: It will be based on a descriptive analysis (mean, standard deviation, sample size, median, minimum and maximum) of the quantitative parameters and the indication of the frequency of the remaining categorical parameters. Comparisons between groups will be based on a paired samples t-test or a chi-square test. The results of these analyses may be useful to assess whether further analyses are needed to adjust for possible imbalance in the baseline characteristics of the patients.

The changes in the composition of the gut microbiota after the intervention with metformin will be analyzed using Heatmaps, Principal Component Analysis (PCA) and PLSDA. For the multivariate statistical analysis (PLSDA and hierarchical clustering). The variables that comprise the characteristics of the intestinal microbiota and cognitive tests will be logarithmically transformed, filtered with interquartile range estimation and staggered by autoscale calculation (mean and divided by the standard deviation of each variable) by using the Metaboanalyst platform.

The changes determined in the gut microbiota and cognition variables will be explored in relation to the changes in the secondary variables (metabolic, metabolome, inflammation parameters) by linear regression analysis in SPSS. Brain image variables will be analyzed with specialized programs (MATLAB, SPM12).

Eligibility criteria: Group A

Inclusion Criteria:

1. Age between 55 and 80 years.
2. Patients with recently diagnosed T2DM (last 6 months), according to the WHO classification.
3. Patients in whom written informed consent has been obtained for participation in the study.

Exclusion Criteria:

1. HbA1c ≥ 9%
2. Metformin treatment in the past 6 months
3. Creatinine greater than 1.2 and glomerular filtration rate less than 40
4. Serious systemic disease not related to obesity, including any type of cancer, severe kidney disease or liver disease, and known type 1 diabetes.
5. Systemic diseases with intrinsic inflammatory activity such as rheumatoid arthritis, Crohn's disease, asthma, or chronic infection (e.g., HIV, active tuberculosis) or any type of infectious disease.
6. Current treatment for malignant neoplasia, other than basal cell or squamous cell skin cancer.
7. Class III or IV heart disease, known ischemic cardiovascular disease
8. Kidney failure, history of kidney transplant, or current dialysis treatment
9. Serum liver enzymes (GOT, GPT) above twice the upper limit of normal. Obvious signs or symptoms of liver disease, acute or chronic hepatitis.
10. Chronic constipation (stool habit ≥ 7 days)
11. Pregnancy or breastfeeding
12. Treatments that affect glucose metabolism or the intestinal microbiota with biguanides, sulfonylurea secretagogues or non-sulfonylurea secretagogues, insulin sensitizers, insulin, thiazolidinediones, alpha glucosidase inhibitors, incretin mimetics, Dipeptidyl peptidase IV inhibitors, use of cathartics.
13. Chronic anti-inflammatory treatment with steroidal drugs (during the previous 3 months).
14. Symptoms and / or clinical signs of infection in the previous month.
15. Antibiotic, antifungal or antiviral treatment active in the previous 3 months.
16. Treatment with glucocorticoids chronic or during the 2 months prior to inclusion in the study.
17. Treatment with a weight loss product during the previous two months
18. Immunosuppressant treatment.
19. Excessive alcohol consumption (alcohol intake greater than 40 g per day (women) or 80 g / day (men)) either acute or chronic, or drug use. History of drug or alcohol abuse.
20. Patients with severe eating disorders
21. History of alterations in iron balance (known chronic hemoglobinopathies or anemia, genetic hemochromatosis, hemosiderosis from any cause, atransferrinemia, paroxysmal nocturnal hemoglobinuria).
22. Important psychiatric history.
23. Participation in any other study.
24. People whose freedom is under legal or administrative requirement.

Group B

Inclusion Criteria:

1. Age between 65 and 80 years.
2. Patients with long-term T2DM according to the WHO classification
3. Patients in whom written informed consent has been obtained for participation in the study.

Exclusion Criteria:

1. HbA1c ≥ 9%
2. Creatinine greater than 1.2 and glomerular filtration rate less than 40
3. Serious systemic disease not related to obesity, including any type of cancer, severe kidney disease or liver disease, and known type 1 diabetes.
4. Systemic diseases with intrinsic inflammatory activity such as rheumatoid arthritis, Crohn's disease, asthma, or chronic infection (e.g., HIV, active tuberculosis) or any type of infectious disease.
5. Current treatment for malignant neoplasia, other than basal cell or squamous cell skin cancer.
6. Class III or IV heart disease, known ischemic cardiovascular disease.
7. Kidney failure, history of kidney transplant, or current dialysis treatment
8. Serum liver enzymes (GOT, GPT) above twice the upper limit of normal. Obvious signs or symptoms of liver disease, acute or chronic hepatitis.
9. Chronic constipation (stool habit ≥ 7 days)
10. Pregnancy or breastfeeding
11. Chronic anti-inflammatory treatment with steroidal drugs (during the previous 3 months).
12. Symptoms and / or clinical signs of infection in the previous month.
13. Antibiotic, antifungal or antiviral treatment active in the previous 3 months.
14. Treatment with glucocorticoids chronic or during the 2 months prior to inclusion in the study.
15. Treatment with a weight loss product during the previous two months.
16. Immunosuppressant treatment.
17. Excessive alcohol consumption (alcohol intake greater than 40 g per day (women) or 80 g / day (men)) either acute or chronic, or drug use. History of drug or alcohol abuse.
18. Patients with severe eating disorders
19. History of alterations in iron balance (known chronic hemoglobinopathies or anemia, genetic hemochromatosis, hemosiderosis from any cause, atransferrinemia, paroxysmal nocturnal hemoglobinuria).
20. Important psychiatric history.
21. Participation in any other study.
22. People whose freedom is under legal or administrative requirement.

Primary outcomes:
measure: Gut microbiota composition.
timeFrame: 12 months
measure: Cognitive impairment
timeFrame: 12 months
measure: Audioverbal memory
timeFrame: 12 months
measure: Visual memory
timeFrame: 12 months
measure: Depressive symptomatology
timeFrame: 12 months
measure: Impulsivity
timeFrame: 12 months
measure: Food Addiction
timeFrame: 12 months
measure: Behavioral inhibition
timeFrame: 12 months
measure: Behavioral activation
timeFrame: 12 months
measure: Visoconstructive function
timeFrame: 12 months
measure: Visuospatial perception
timeFrame: 12 months
measure: Naming
timeFrame: 12 months
measure: Selective and alternating attention
timeFrame: 12 months
measure: Attention and working memory
timeFrame: 12 months
measure: Inhibition
timeFrame: 12 months
measure: Phonemic verbal fluency
timeFrame: 12 months
measure: Semantic verbal fluency
timeFrame: 12 months
Secondary outcomes:
measure: The percentage of time in glucose target range (glucose level 70mg/dl-180mg/dl)
timeFrame: 12 months
measure: Effect on gut microbiota
timeFrame: 12 months
measure: The percentage of time in glucose range (glucose level below 100 mg/dl)
timeFrame: 12 months
measure: The percentage of time in glucose range (glucose level between 100-125 mg/dl)
timeFrame: 12 months
measure: The percentage of time in glucose range (glucose level between 126-139 mg/dl)
timeFrame: 12 months
measure: The percentage of time in glucose range (glucose level between 140-199 mg/dl)
timeFrame: 12 months
Other outcomes:
measure: Integrity of the brain gray matter
timeFrame: 12 months
measure: Integrity of the white matter tracts
timeFrame: 12 months
measure: Brain iron accumulation
timeFrame: 12 months
measure: Resting-state functional brain sequences
timeFrame: 12 months
measure: Insulin resistance
timeFrame: 12 months
measure: Markers of chronic inflammation: C-reactive protein, IL-6, adiponectin and soluble, tumor necrosis factor-α receptor fractions.
timeFrame: 12 months
measure: Glycosylated hemoglobin (HbA1c) value
timeFrame: 12 months
measure: The percentage of time in hyperglycaemia (glucose level above 180 mg/dl)
timeFrame: 12 months
measure: The percentage of time in hypoglycaemia (glucose level below 70 mg/dl)
timeFrame: 12 months
measure: The glycaemic risk measured with low blood glucose index (LBGI)
timeFrame: 12 months
measure: The glycaemic risk measured with high blood glucose index (HBGI)
timeFrame: 12 months
measure: The glycaemic variability measured with mean amplitude of glycaemic excursions (MAGE)
timeFrame: 12 months
measure: Burned calories
timeFrame: 12 months
measure: Steps
timeFrame: 12 months
measure: Distance
timeFrame: 12 months
measure: Plants
timeFrame: 12 months
measure: Minutes null activity
timeFrame: 12 months
measure: Minutes slight activity
timeFrame: 12 months
measure: Minutes mean activity
timeFrame: 12 months
measure: Minutes high activity
timeFrame: 12 months
measure: Calories consumption
timeFrame: 12 months
measure: Minutes asleep
timeFrame: 12 months
measure: Minutes awake
timeFrame: 12 months
measure: Bed time
timeFrame: 12 months
measure: Minutes light sleep
timeFrame: 12 months
measure: Minutes deep sleep
timeFrame: 12 months
measure: Minutes rapid eye movement (REM)
timeFrame: 12 months
measure: Number time awake
timeFrame: 12 months
Locations:
city: Girona
country: Spain
facility: Institut d'Investigació Biomèdica de Girona (IDIBGI)
Study officials:
affiliation: Institut d'Investigació Biomèdica de Girona (IDIBGI)
name: José M Fernández-Real, Ph.D.
role: PRINCIPAL_INVESTIGATOR
References:
citation: Fama R, Sullivan EV. Thalamic structures and associated cognitive functions: Relations with age and aging. Neurosci Biobehav Rev. 2015 Jul;54:29-37. doi: 10.1016/j.neubiorev.2015.03.008. Epub 2015 Apr 9.
citation: Williams KN, Kemper S. Interventions to reduce cognitive decline in aging. J Psychosoc Nurs Ment Health Serv. 2010 May;48(5):42-51. doi: 10.3928/02793695-20100331-03.
citation: Aigbogun MS, Stellhorn R, Krasa H, Kostic D. Severity of memory impairment in the elderly: Association with health care resource use and functional limitations in the United States. Alzheimers Dement (Amst). 2017 Apr 20;8:51-59. doi: 10.1016/j.dadm.2017.04.001. eCollection 2017.
citation: Plassman BL, Langa KM, Fisher GG, Heeringa SG, Weir DR, Ofstedal MB, Burke JR, Hurd MD, Potter GG, Rodgers WL, Steffens DC, McArdle JJ, Willis RJ, Wallace RB. Prevalence of cognitive impairment without dementia in the United States. Ann Intern Med. 2008 Mar 18;148(6):427-34. doi: 10.7326/0003-4819-148-6-200803180-00005.
citation: Muriach M, Flores-Bellver M, Romero FJ, Barcia JM. Diabetes and the brain: oxidative stress, inflammation, and autophagy. Oxid Med Cell Longev. 2014;2014:102158. doi: 10.1155/2014/102158. Epub 2014 Aug 24.
citation: Adelantado-Renau M, Esteban-Cornejo I, Rodriguez-Ayllon M, Cadenas-Sanchez C, Gil-Cosano JJ, Mora-Gonzalez J, Solis-Urra P, Verdejo-Roman J, Aguilera CM, Escolano-Margarit MV, Verdejo-Garcia A, Catena A, Moliner-Urdiales D, Ortega FB. Inflammatory biomarkers and brain health indicators in children with overweight and obesity: The ActiveBrains project. Brain Behav Immun. 2019 Oct;81:588-597. doi: 10.1016/j.bbi.2019.07.020. Epub 2019 Jul 19.
citation: Tan BL, Norhaizan ME. Effect of High-Fat Diets on Oxidative Stress, Cellular Inflammatory Response and Cognitive Function. Nutrients. 2019 Oct 25;11(11):2579. doi: 10.3390/nu11112579.
citation: Gareau MG. Cognitive Function and the Microbiome. Int Rev Neurobiol. 2016;131:227-246. doi: 10.1016/bs.irn.2016.08.001. Epub 2016 Sep 9.
citation: Rea K, Dinan TG, Cryan JF. The microbiome: A key regulator of stress and neuroinflammation. Neurobiol Stress. 2016 Mar 4;4:23-33. doi: 10.1016/j.ynstr.2016.03.001. eCollection 2016 Oct.
citation: Sherwin E, Dinan TG, Cryan JF. Recent developments in understanding the role of the gut microbiota in brain health and disease. Ann N Y Acad Sci. 2018 May;1420(1):5-25. doi: 10.1111/nyas.13416. Epub 2017 Aug 2.
citation: Ou Z, Kong X, Sun X, He X, Zhang L, Gong Z, Huang J, Xu B, Long D, Li J, Li Q, Xu L, Xuan A. Metformin treatment prevents amyloid plaque deposition and memory impairment in APP/PS1 mice. Brain Behav Immun. 2018 Mar;69:351-363. doi: 10.1016/j.bbi.2017.12.009. Epub 2017 Dec 15.
citation: Martin-Montalvo A, Mercken EM, Mitchell SJ, Palacios HH, Mote PL, Scheibye-Knudsen M, Gomes AP, Ward TM, Minor RK, Blouin MJ, Schwab M, Pollak M, Zhang Y, Yu Y, Becker KG, Bohr VA, Ingram DK, Sinclair DA, Wolf NS, Spindler SR, Bernier M, de Cabo R. Metformin improves healthspan and lifespan in mice. Nat Commun. 2013;4:2192. doi: 10.1038/ncomms3192.
citation: Metformin in Longevity Study (MILES). - Full Text View - ClinicalTrials.gov [Internet]. [cited 2020 May 19]. Availablefrom: https://clinicaltrials.gov/ct2/show/NCT02432287
citation: Sun L, Xie C, Wang G, Wu Y, Wu Q, Wang X, Liu J, Deng Y, Xia J, Chen B, Zhang S, Yun C, Lian G, Zhang X, Zhang H, Bisson WH, Shi J, Gao X, Ge P, Liu C, Krausz KW, Nichols RG, Cai J, Rimal B, Patterson AD, Wang X, Gonzalez FJ, Jiang C. Gut microbiota and intestinal FXR mediate the clinical benefits of metformin. Nat Med. 2018 Dec;24(12):1919-1929. doi: 10.1038/s41591-018-0222-4. Epub 2018 Nov 5.
citation: Kattenstroth JC, Kalisch T, Holt S, Tegenthoff M, Dinse HR. Six months of dance intervention enhances postural, sensorimotor, and cognitive performance in elderly without affecting cardio-respiratory functions. Front Aging Neurosci. 2013 Feb 26;5:5. doi: 10.3389/fnagi.2013.00005. eCollection 2013.
citation: Eyre HA, Siddarth P, Acevedo B, Van Dyk K, Paholpak P, Ercoli L, St Cyr N, Yang H, Khalsa DS, Lavretsky H. A randomized controlled trial of Kundalini yoga in mild cognitive impairment. Int Psychogeriatr. 2017 Apr;29(4):557-567. doi: 10.1017/S1041610216002155. Epub 2017 Jan 16.
citation: Gomes-Osman J, Cabral DF, Morris TP, McInerney K, Cahalin LP, Rundek T, Oliveira A, Pascual-Leone A. Exercise for cognitive brain health in aging: A systematic review for an evaluation of dose. Neurol Clin Pract. 2018 Jun;8(3):257-265. doi: 10.1212/CPJ.0000000000000460.
citation: Calero-Garcia MD, Navarro-Gonzalez E, Munoz-Manzano L. Influence of level of activity on cognitive performance and cognitive plasticity in elderly persons. Arch Gerontol Geriatr. 2007 Nov-Dec;45(3):307-18. doi: 10.1016/j.archger.2007.01.061. Epub 2007 Mar 12.
citation: Macpherson H, Teo WP, Schneider LA, Smith AE. A Life-Long Approach to Physical Activity for Brain Health. Front Aging Neurosci. 2017 May 23;9:147. doi: 10.3389/fnagi.2017.00147. eCollection 2017.
citation: Marseglia A, Xu W, Fratiglioni L, Fabbri C, Berendsen AAM, Bialecka-Debek A, Jennings A, Gillings R, Meunier N, Caumon E, Fairweather-Tait S, Pietruszka B, De Groot LCPGM, Santoro A, Franceschi C. Effect of the NU-AGE Diet on Cognitive Functioning in Older Adults: A Randomized Controlled Trial. Front Physiol. 2018 Apr 4;9:349. doi: 10.3389/fphys.2018.00349. eCollection 2018.
citation: Cerda B, Perez M, Perez-Santiago JD, Tornero-Aguilera JF, Gonzalez-Soltero R, Larrosa M. Gut Microbiota Modification: Another Piece in the Puzzle of the Benefits of Physical Exercise in Health? Front Physiol. 2016 Feb 18;7:51. doi: 10.3389/fphys.2016.00051. eCollection 2016.
citation: Fernandez-Real JM, Manco M. Effects of iron overload on chronic metabolic diseases. Lancet Diabetes Endocrinol. 2014 Jun;2(6):513-26. doi: 10.1016/S2213-8587(13)70174-8. Epub 2013 Dec 30.
citation: Geijselaers SLC, Sep SJS, Stehouwer CDA, Biessels GJ. Glucose regulation, cognition, and brain MRI in type 2 diabetes: a systematic review. Lancet Diabetes Endocrinol. 2015 Jan;3(1):75-89. doi: 10.1016/S2213-8587(14)70148-2. Epub 2014 Aug 24.
citation: Geijselaers SLC, Sep SJS, Claessens D, Schram MT, van Boxtel MPJ, Henry RMA, Verhey FRJ, Kroon AA, Dagnelie PC, Schalkwijk CG, van der Kallen CJH, Biessels GJ, Stehouwer CDA. The Role of Hyperglycemia, Insulin Resistance, and Blood Pressure in Diabetes-Associated Differences in Cognitive Performance-The Maastricht Study. Diabetes Care. 2017 Nov;40(11):1537-1547. doi: 10.2337/dc17-0330. Epub 2017 Aug 25.
citation: Luchsinger JA, Ma Y, Christophi CA, Florez H, Golden SH, Hazuda H, Crandall J, Venditti E, Watson K, Jeffries S, Manly JJ, Pi-Sunyer FX; Diabetes Prevention Program Research Group. Metformin, Lifestyle Intervention, and Cognition in the Diabetes Prevention Program Outcomes Study. Diabetes Care. 2017 Jul;40(7):958-965. doi: 10.2337/dc16-2376. Epub 2017 May 12.
citation: Kharabian Masouleh S, Beyer F, Lampe L, Loeffler M, Luck T, Riedel-Heller SG, Schroeter ML, Stumvoll M, Villringer A, Witte AV. Gray matter structural networks are associated with cardiovascular risk factors in healthy older adults. J Cereb Blood Flow Metab. 2018 Feb;38(2):360-372. doi: 10.1177/0271678X17729111. Epub 2017 Aug 31.
citation: Ryan CM, Freed MI, Rood JA, Cobitz AR, Waterhouse BR, Strachan MW. Improving metabolic control leads to better working memory in adults with type 2 diabetes. Diabetes Care. 2006 Feb;29(2):345-51. doi: 10.2337/diacare.29.02.06.dc05-1626.
citation: Weinstein G, Maillard P, Himali JJ, Beiser AS, Au R, Wolf PA, Seshadri S, DeCarli C. Glucose indices are associated with cognitive and structural brain measures in young adults. Neurology. 2015 Jun 9;84(23):2329-37. doi: 10.1212/WNL.0000000000001655. Epub 2015 May 6.
citation: Rolandsson O, Backestrom A, Eriksson S, Hallmans G, Nilsson LG. Increased glucose levels are associated with episodic memory in nondiabetic women. Diabetes. 2008 Feb;57(2):440-3. doi: 10.2337/db07-1215. Epub 2007 Oct 31.
citation: Marden JR, Mayeda ER, Tchetgen Tchetgen EJ, Kawachi I, Glymour MM. High Hemoglobin A1c and Diabetes Predict Memory Decline in the Health and Retirement Study. Alzheimer Dis Assoc Disord. 2017 Jan-Mar;31(1):48-54. doi: 10.1097/WAD.0000000000000182.
citation: Spauwen PJ, van Eupen MG, Kohler S, Stehouwer CD, Verhey FR, van der Kallen CJ, Sep SJ, Koster A, Schaper NC, Dagnelie PC, Schalkwijk CG, Schram MT, van Boxtel MP. Associations of advanced glycation end-products with cognitive functions in individuals with and without type 2 diabetes: the maastricht study. J Clin Endocrinol Metab. 2015 Mar;100(3):951-60. doi: 10.1210/jc.2014-2754. Epub 2014 Dec 2.
citation: Chavan SS, Huerta PT, Robbiati S, Valdes-Ferrer SI, Ochani M, Dancho M, Frankfurt M, Volpe BT, Tracey KJ, Diamond B. HMGB1 mediates cognitive impairment in sepsis survivors. Mol Med. 2012 Sep 7;18(1):930-7. doi: 10.2119/molmed.2012.00195.
citation: Kau AL, Ahern PP, Griffin NW, Goodman AL, Gordon JI. Human nutrition, the gut microbiome and the immune system. Nature. 2011 Jun 15;474(7351):327-36. doi: 10.1038/nature10213.
citation: Pedersen HK, Gudmundsdottir V, Nielsen HB, Hyotylainen T, Nielsen T, Jensen BA, Forslund K, Hildebrand F, Prifti E, Falony G, Le Chatelier E, Levenez F, Dore J, Mattila I, Plichta DR, Poho P, Hellgren LI, Arumugam M, Sunagawa S, Vieira-Silva S, Jorgensen T, Holm JB, Trost K; MetaHIT Consortium; Kristiansen K, Brix S, Raes J, Wang J, Hansen T, Bork P, Brunak S, Oresic M, Ehrlich SD, Pedersen O. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature. 2016 Jul 21;535(7612):376-81. doi: 10.1038/nature18646. Epub 2016 Jul 13.
citation: Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalova L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E. Personalized Nutrition by Prediction of Glycemic Responses. Cell. 2015 Nov 19;163(5):1079-1094. doi: 10.1016/j.cell.2015.11.001.
citation: Ferri CP, Prince M, Brayne C, Brodaty H, Fratiglioni L, Ganguli M, Hall K, Hasegawa K, Hendrie H, Huang Y, Jorm A, Mathers C, Menezes PR, Rimmer E, Scazufca M; Alzheimer's Disease International. Global prevalence of dementia: a Delphi consensus study. Lancet. 2005 Dec 17;366(9503):2112-7. doi: 10.1016/S0140-6736(05)67889-0.
citation: Rotella CM, Monami M, Mannucci E. Metformin beyond diabetes: new life for an old drug. Curr Diabetes Rev. 2006 Aug;2(3):307-15. doi: 10.2174/157339906777950651.
citation: Hundal RS, Inzucchi SE. Metformin: new understandings, new uses. Drugs. 2003;63(18):1879-94. doi: 10.2165/00003495-200363180-00001.
citation: Hundal RS, Krssak M, Dufour S, Laurent D, Lebon V, Chandramouli V, Inzucchi SE, Schumann WC, Petersen KF, Landau BR, Shulman GI. Mechanism by which metformin reduces glucose production in type 2 diabetes. Diabetes. 2000 Dec;49(12):2063-9. doi: 10.2337/diabetes.49.12.2063.
citation: Perriello G, Misericordia P, Volpi E, Santucci A, Santucci C, Ferrannini E, Ventura MM, Santeusanio F, Brunetti P, Bolli GB. Acute antihyperglycemic mechanisms of metformin in NIDDM. Evidence for suppression of lipid oxidation and hepatic glucose production. Diabetes. 1994 Jul;43(7):920-8. doi: 10.2337/diab.43.7.920.
citation: Zhou G, Myers R, Li Y, Chen Y, Shen X, Fenyk-Melody J, Wu M, Ventre J, Doebber T, Fujii N, Musi N, Hirshman MF, Goodyear LJ, Moller DE. Role of AMP-activated protein kinase in mechanism of metformin action. J Clin Invest. 2001 Oct;108(8):1167-74. doi: 10.1172/JCI13505.
citation: Musi N, Hirshman MF, Nygren J, Svanfeldt M, Bavenholm P, Rooyackers O, Zhou G, Williamson JM, Ljunqvist O, Efendic S, Moller DE, Thorell A, Goodyear LJ. Metformin increases AMP-activated protein kinase activity in skeletal muscle of subjects with type 2 diabetes. Diabetes. 2002 Jul;51(7):2074-81. doi: 10.2337/diabetes.51.7.2074.
citation: Goodarzi MO, Bryer-Ash M. Metformin revisited: re-evaluation of its properties and role in the pharmacopoeia of modern antidiabetic agents. Diabetes Obes Metab. 2005 Nov;7(6):654-65. doi: 10.1111/j.1463-1326.2004.00448.x.
citation: Ikeda T, Iwata K, Murakami H. Inhibitory effect of metformin on intestinal glucose absorption in the perfused rat intestine. Biochem Pharmacol. 2000 Apr 1;59(7):887-90. doi: 10.1016/s0006-2952(99)00396-2.
citation: Bajzer M, Seeley RJ. Physiology: obesity and gut flora. Nature. 2006 Dec 21;444(7122):1009-10. doi: 10.1038/4441009a. No abstract available.
citation: Cani PD, Delzenne NM. Gut microflora as a target for energy and metabolic homeostasis. Curr Opin Clin Nutr Metab Care. 2007 Nov;10(6):729-34. doi: 10.1097/MCO.0b013e3282efdebb.
citation: Prentice AM, Gershwin ME, Schaible UE, Keusch GT, Victora CG, Gordon JI. New challenges in studying nutrition-disease interactions in the developing world. J Clin Invest. 2008 Apr;118(4):1322-9. doi: 10.1172/JCI34034.
citation: Lozupone CA, Hamady M, Cantarel BL, Coutinho PM, Henrissat B, Gordon JI, Knight R. The convergence of carbohydrate active gene repertoires in human gut microbes. Proc Natl Acad Sci U S A. 2008 Sep 30;105(39):15076-81. doi: 10.1073/pnas.0807339105. Epub 2008 Sep 19.
citation: Wen L, Ley RE, Volchkov PY, Stranges PB, Avanesyan L, Stonebraker AC, Hu C, Wong FS, Szot GL, Bluestone JA, Gordon JI, Chervonsky AV. Innate immunity and intestinal microbiota in the development of Type 1 diabetes. Nature. 2008 Oct 23;455(7216):1109-13. doi: 10.1038/nature07336. Epub 2008 Sep 21.
citation: Eldridge SM, Chan CL, Campbell MJ, Bond CM, Hopewell S, Thabane L, Lancaster GA; PAFS consensus group. CONSORT 2010 statement: extension to randomised pilot and feasibility trials. Pilot Feasibility Stud. 2016 Oct 21;2:64. doi: 10.1186/s40814-016-0105-8. eCollection 2016.
citation: Browne RH. On the use of a pilot sample for sample size determination. Stat Med. 1995 Sep 15;14(17):1933-40. doi: 10.1002/sim.4780141709.
citation: Cohen, J. (1977). Statistical power analysis for the behavioral sciences. New York:Academic Press)
citation: Sim J, Lewis M. The size of a pilot study for a clinical trial should be calculated in relation to considerations of precision and efficiency. J Clin Epidemiol. 2012 Mar;65(3):301-8. doi: 10.1016/j.jclinepi.2011.07.011. Epub 2011 Dec 9.