Hypoproteic Diet in Acromegaly

Acromegaly Clinical Trial

— IpoProAcro  

Official title:

Deciphering the Role of a Low Protein Diet in Disease Control in Acromegalic Patients

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05298891
• Other study ID #: CE 008/22
• Status: Not yet recruiting
• Phase: N/A
• Start date: September 1, 2024
• Est. completion date: March 1, 2026

Study information

• Verified date: September 2023
• Source: Azienda Ospedaliero Universitaria Maggiore della Carita
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

Since protein and AAs are master regulator of GH and IGF-I secretion, we hypothesized that a low protein diet could reduce GH and IGF-I levels in acromegalic patients in addition to conventional therapy. Furthermore, we aim to explore metabolomic, microbiota, and micro-vesicle fingerprints of GH hypersecretion during conventional therapy and after a low protein diet

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 12
• Est. completion date: March 1, 2026
• Est. primary completion date: December 1, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 65 Years

Eligibility

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

Related Conditions & MeSH terms

• Acromegaly

Intervention

Other:
• Usual clinical practice + hypoproteic diet
Diet will be composed by: energy equal to daily energy expenditure (estimated by indirect calorimetry * physical activity factor) fats 28-35% carbohydrates 50-60% proteins 0,7-0,8g/kg of body weight 10-13% Diet will be given to the patient after the first visit and the study will start once the patient begins the diet.

Locations

Country	Name	City	State
Italy	: Italy Pediatric Endocrine Service of AOU Maggiore della Carità of Novara; SCDU of Pediatrics, Department of Health Sciences, University of Eastern Piedmont	Novara

Sponsors (1)

Lead Sponsor	Collaborator
Azienda Ospedaliero Universitaria Maggiore della Carita

Country where clinical trial is conducted

Italy, 

References & Publications (11)

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

* Note: There are 11 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Change in disease related hormones	Variation of GH, IGF-1, IGFBP1, IGFBP3 hormones	Change from Baseline GH, IGF-1, IGFBP1, IGFBP3 blood levels at 15 days, 30 days, 45 days, 60 days
Secondary	Change in weight	Variation of body weight assessed through body mass index change (BMI)(kg/m2)	Change from Baseline BMI at 15 days, 30 days, 45 days, 60 days
Secondary	Change in body circumferences	Variation of body circumferences (waist, hips)	Change from Baseline circumferences at 15 days, 30 days, 45 dyas, 60 days
Secondary	Change in metabolic control	Change of cardio-metabolic risk factors: lipid profile	Change from Baseline lipid profile at 15 days, 30 days, 45 days, 60 days
Secondary	Change in metabolic control	Change of cardio-metabolic risk factors: insulin resistance (HOMA-IR)	Change from Baseline lipid profile at 60 days
Secondary	Change in kidney profile	Variation of serum creatinin	Change from Baseline Serum Creatinin at 15 days, 30 days, 45 days, 60 days
Secondary	Change in liver profile	Variation of liver markers(AST, ALT, GGT)	Change from Baseline Serum Creatinin at 15 days, 30 days, 45 days, 60 days
Secondary	Change in uric acid	Variation of uric acid in blood through enzymatic determination	Change from Baseline uric acid in blood at 15 days, 30 days, 45 days, 60 days
Secondary	Change in body composition	Change of body composition (fat mass %) (BIVA)	Change from Baseline fat mass% at 60 days
Secondary	Change in body composition	Change of body composition (fat mass %) (DXA)	Change from Baseline fat mass% at 60 days
Secondary	Change in blood count	Variation of blood count	Change from Baseline blood count at 15 days, 30 days, 45 days, 60 days
Secondary	Change in microbiota	Variation of prevalence of microbiota phyla through DNA sequencing of stools	Change from Baseline of prevalence of microbiota phyla at 15, 30 days, 45 days, 60 days
Secondary	Change in omics profile	Variation of lipidomic profile of stools through liquid and gas chromatography	Change from Baseline omic profile of stools at 15, 30 days, 45 days, 60 days
Secondary	Change in omics profile	Variation of proteomic profile of stools through liquid and gas chromatography	Change from Baseline omic profile of stools at 15, 30 days, 45 days, 60 days
Secondary	Change in microvesicles	Variation of urinary microvesicles levels	Change from Baseline microvesicles levels at 15, 30 days, 45 days, 60 days
Secondary	Change in microvesicles	Variation of serum microvesicles levels	Change from Baseline microvesicles levels s at 15, 30 days, 45 days, 60 days
Secondary	Change in basal metabolic rate	Variation of basal metabolic rate (kcal)	Change from Baseline basal metabolic rate at 60 days