- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05298891
Hypoproteic Diet in Acromegaly (IpoProAcro)
Deciphering the Role of a Low Protein Diet in Disease Control in Acromegalic Patients
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
Nutrients are crucial modifiers of the GH/IGF-I axis. In particular, a close cross-talk between proteins and amino acids (AAs) and GH/IGF-I secretion exists.
Both AAs and proteins affect GH secretion. AAs stimulate GH secretion upon oral administration, with different potency among studies, being the combination of arginine and lysine the most powerful. Soy proteins also stimulate GH secretion when ingested either as hydrolysed proteins or free AAs. Furthermore, the acute GH response to AAs ingestion may be influenced by the daily amount of dietary protein/AAs consumption: diets high in proteins apparently increase basal GH levels.
AAs and proteins have a positive effect on IGF-I secretion as well. In general, high levels of proteins, especially animal and dairy proteins, and consumption of branched chain amino acids (BCAAs) increase serum IGF-I levels.
Considering pathological GH conditions, metabolomic analysis of acromegalic patients suggests that the main metabolic fingerprint of GH hypersecretion is a reduction in BCAAs, related to the disease activity. Moreover, there is evidence that GH, rather than IGF-I, is the main mediator of such metabolic fingerprint, which may be related to increased uptake of BCAAs by the muscles, increased gluconeogenesis, and raised consumption of BCAAs.
Thus, in acromegaly, a tailored diet is a further strategy that may contribute to blunt GH/IGF-I secretion. Indeed, some authors recently suggested that "personalized" or "precision" nutrition in some conditions and diseases could have an impact on their phenotype, combining dietary recommendations with individual's genetic makeup, metabolic and microbiome characteristics, and environment. However, studies on precision nutrition in acromegaly are still in a neonatal era.
Study Type
Enrollment (Estimated)
Phase
- Not Applicable
Contacts and Locations
Study Locations
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Novara, Italy, 28100
- : Italy Pediatric Endocrine Service of AOU Maggiore della Carità of Novara; SCDU of Pediatrics, Department of Health Sciences, University of Eastern Piedmont
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- Age 18/65
- Diagnosis of Acromegaly
- In therapy with somatostatin analogues
Exclusion Criteria:
- pregnancy or lactation
- alchool or drugs abuse
- cancer
- Hematological diseases
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Treatment
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
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Experimental: Acromegalic adult in therapy with somatostatin analogues
Patients will continue the usual medical outpatient visits cadency and will keep the same pharmacological therapy throughout the whole duration of the study.
Drugs have to include somatostatin analogues.
At the same time, patients will be trained by an expert dietician in the habit of an isocaloric and hypoproteic diet and will come back at 2,4,6 and 8 weeks after T0 for all the necessary study assessments and compliance checking.
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Diet will be composed by:
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Change in disease related hormones
Time Frame: Change from Baseline GH, IGF-1, IGFBP1, IGFBP3 blood levels at 15 days, 30 days, 45 days, 60 days
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Variation of GH, IGF-1, IGFBP1, IGFBP3 hormones
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Change from Baseline GH, IGF-1, IGFBP1, IGFBP3 blood levels at 15 days, 30 days, 45 days, 60 days
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Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Change in weight
Time Frame: Change from Baseline BMI at 15 days, 30 days, 45 days, 60 days
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Variation of body weight assessed through body mass index change (BMI)(kg/m2)
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Change from Baseline BMI at 15 days, 30 days, 45 days, 60 days
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Change in body circumferences
Time Frame: Change from Baseline circumferences at 15 days, 30 days, 45 dyas, 60 days
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Variation of body circumferences (waist, hips)
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Change from Baseline circumferences at 15 days, 30 days, 45 dyas, 60 days
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Change in metabolic control
Time Frame: Change from Baseline lipid profile at 15 days, 30 days, 45 days, 60 days
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Change of cardio-metabolic risk factors: lipid profile
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Change from Baseline lipid profile at 15 days, 30 days, 45 days, 60 days
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Change in metabolic control
Time Frame: Change from Baseline lipid profile at 60 days
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Change of cardio-metabolic risk factors: insulin resistance (HOMA-IR)
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Change from Baseline lipid profile at 60 days
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Change in kidney profile
Time Frame: Change from Baseline Serum Creatinin at 15 days, 30 days, 45 days, 60 days
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Variation of serum creatinin
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Change from Baseline Serum Creatinin at 15 days, 30 days, 45 days, 60 days
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Change in liver profile
Time Frame: Change from Baseline Serum Creatinin at 15 days, 30 days, 45 days, 60 days
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Variation of liver markers(AST, ALT, GGT)
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Change from Baseline Serum Creatinin at 15 days, 30 days, 45 days, 60 days
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Change in uric acid
Time Frame: Change from Baseline uric acid in blood at 15 days, 30 days, 45 days, 60 days
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Variation of uric acid in blood through enzymatic determination
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Change from Baseline uric acid in blood at 15 days, 30 days, 45 days, 60 days
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Change in body composition
Time Frame: Change from Baseline fat mass% at 60 days
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Change of body composition (fat mass %) (BIVA)
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Change from Baseline fat mass% at 60 days
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Change in body composition
Time Frame: Change from Baseline fat mass% at 60 days
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Change of body composition (fat mass %) (DXA)
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Change from Baseline fat mass% at 60 days
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Change in blood count
Time Frame: Change from Baseline blood count at 15 days, 30 days, 45 days, 60 days
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Variation of blood count
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Change from Baseline blood count at 15 days, 30 days, 45 days, 60 days
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Change in microbiota
Time Frame: Change from Baseline of prevalence of microbiota phyla at 15, 30 days, 45 days, 60 days
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Variation of prevalence of microbiota phyla through DNA sequencing of stools
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Change from Baseline of prevalence of microbiota phyla at 15, 30 days, 45 days, 60 days
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Change in omics profile
Time Frame: Change from Baseline omic profile of stools at 15, 30 days, 45 days, 60 days
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Variation of lipidomic profile of stools through liquid and gas chromatography
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Change from Baseline omic profile of stools at 15, 30 days, 45 days, 60 days
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Change in omics profile
Time Frame: Change from Baseline omic profile of stools at 15, 30 days, 45 days, 60 days
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Variation of proteomic profile of stools through liquid and gas chromatography
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Change from Baseline omic profile of stools at 15, 30 days, 45 days, 60 days
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Change in microvesicles
Time Frame: Change from Baseline microvesicles levels at 15, 30 days, 45 days, 60 days
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Variation of urinary microvesicles levels
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Change from Baseline microvesicles levels at 15, 30 days, 45 days, 60 days
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Change in microvesicles
Time Frame: Change from Baseline microvesicles levels s at 15, 30 days, 45 days, 60 days
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Variation of serum microvesicles levels
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Change from Baseline microvesicles levels s at 15, 30 days, 45 days, 60 days
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Change in basal metabolic rate
Time Frame: Change from Baseline basal metabolic rate at 60 days
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Variation of basal metabolic rate (kcal)
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Change from Baseline basal metabolic rate at 60 days
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Collaborators and Investigators
Publications and helpful links
General Publications
- Caputo M, Pigni S, Agosti E, Daffara T, Ferrero A, Filigheddu N, Prodam F. Regulation of GH and GH Signaling by Nutrients. Cells. 2021 Jun 2;10(6):1376. doi: 10.3390/cells10061376.
- Suminski RR, Robertson RJ, Goss FL, Arslanian S, Kang J, DaSilva S, Utter AC, Metz KF. Acute effect of amino acid ingestion and resistance exercise on plasma growth hormone concentration in young men. Int J Sport Nutr. 1997 Mar;7(1):48-60. doi: 10.1123/ijsn.7.1.48.
- van Vught AJ, Nieuwenhuizen AG, Brummer RJ, Westerterp-Plantenga MS. Effects of oral ingestion of amino acids and proteins on the somatotropic axis. J Clin Endocrinol Metab. 2008 Feb;93(2):584-90. doi: 10.1210/jc.2007-1784. Epub 2007 Nov 20.
- Sellini M, Fierro A, Marchesi L, Manzo G, Giovannini C. [Behavior of basal values and circadian rhythm of ACTH, cortisol, PRL and GH in a high-protein diet]. Boll Soc Ital Biol Sper. 1981 May 15;57(9):963-9. Italian.
- Levine ME, Suarez JA, Brandhorst S, Balasubramanian P, Cheng CW, Madia F, Fontana L, Mirisola MG, Guevara-Aguirre J, Wan J, Passarino G, Kennedy BK, Wei M, Cohen P, Crimmins EM, Longo VD. Low protein intake is associated with a major reduction in IGF-1, cancer, and overall mortality in the 65 and younger but not older population. Cell Metab. 2014 Mar 4;19(3):407-17. doi: 10.1016/j.cmet.2014.02.006.
- Allen NE, Appleby PN, Davey GK, Kaaks R, Rinaldi S, Key TJ. The associations of diet with serum insulin-like growth factor I and its main binding proteins in 292 women meat-eaters, vegetarians, and vegans. Cancer Epidemiol Biomarkers Prev. 2002 Nov;11(11):1441-8.
- Hoppe C, Udam TR, Lauritzen L, Molgaard C, Juul A, Michaelsen KF. Animal protein intake, serum insulin-like growth factor I, and growth in healthy 2.5-y-old Danish children. Am J Clin Nutr. 2004 Aug;80(2):447-52. doi: 10.1093/ajcn/80.2.447.
- Romo Ventura E, Konigorski S, Rohrmann S, Schneider H, Stalla GK, Pischon T, Linseisen J, Nimptsch K. Association of dietary intake of milk and dairy products with blood concentrations of insulin-like growth factor 1 (IGF-1) in Bavarian adults. Eur J Nutr. 2020 Jun;59(4):1413-1420. doi: 10.1007/s00394-019-01994-7. Epub 2019 May 14.
- Beasley JM, Gunter MJ, LaCroix AZ, Prentice RL, Neuhouser ML, Tinker LF, Vitolins MZ, Strickler HD. Associations of serum insulin-like growth factor-I and insulin-like growth factor-binding protein 3 levels with biomarker-calibrated protein, dairy product and milk intake in the Women's Health Initiative. Br J Nutr. 2014 Mar 14;111(5):847-53. doi: 10.1017/S000711451300319X. Epub 2013 Oct 7.
- Li R, Ferreira MP, Cooke MB, La Bounty P, Campbell B, Greenwood M, Willoughby DS, Kreider RB. Co-ingestion of carbohydrate with branched-chain amino acids or L-leucine does not preferentially increase serum IGF-1 and expression of myogenic-related genes in response to a single bout of resistance exercise. Amino Acids. 2015 Jun;47(6):1203-13. doi: 10.1007/s00726-015-1947-8. Epub 2015 Mar 5.
- Coopmans EC, Berk KAC, El-Sayed N, Neggers SJCMM, van der Lely AJ. Eucaloric Very-Low-Carbohydrate Ketogenic Diet in Acromegaly Treatment. N Engl J Med. 2020 May 28;382(22):2161-2162. doi: 10.1056/NEJMc1915808. No abstract available.
Study record dates
Study Major Dates
Study Start (Estimated)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- CE 008/22
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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