Le basi scientifiche delle metodica Morphogram®

Morphogram® è un metodo ideato dal Dott. Paolo De Cristofaro. Il software, sviluppato dal nostro Team di ricerca e sviluppo dopo oltre 30 anni di esperienza in nutrizione clinica, studi e ricerche, utilizza algoritmi proprietari per la valutazione della Composizione Corporea, dello Stato Nutrizionale e della stadiazione dei fattori di rischio cardio-metabolico-infiammatori.

Questi algoritmi hanno come fondamenta scientifiche le seguenti pubblicazioni:

1) Bedogni G, Borghi A, Battistini N. Manuale di valutazione antropometrica dello stato nutrizionale. Milano: Edra;2001.

2) Frisancho AR. Anthropometric standards fot the assessment of growt and nutritional status. Ann Arbor: University of Michigan Press;1990.

3) Bedogni G, Cecchetto G. Manuale ANDID di valutazione dello stato nutrizionale. SEU Editrice. Roma 2009.

4) Han TS, Sattar N, Lean M: ABC of obesity. Assessment of obesity and its clinical implications.BMJ 2006;333:695-698.

5) B. E. Ainsworth, W.L. Haskell, A.S. Leon et al.: Compendium of physical activities: classification of energy costs of human physical activities. Med.Sci. Sports Exerc.,Vol.25,No.1,pp.71-80, 1993.

6) Vaz M, Karaolis N, Draper A and Shetty P: A compilation of energy costs of physical actibities. Public Health Nutrition:8(7a), 1153-1183.

7) Tudor-Locke C, Bassett Jr DR: How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med 2004;34(1):1-8.

8) Tudor-Locke C, Williams JE, Reis JP, Pluto D: Utility of pedometers for assessing physical activity. Sprts Med 2004;34(5):281-291

9) Hills, Andrew P, Najat Mokhtar and Nuala M. Byrne: Assessment of physical activity and energy expenditure: an overview of objective measures. Front Nutr, 2014;1:5.

10) Global strategy on diet, physical activity and health. WHO 2004.

11) Darren ER, Warburton PhD and Shannon SD Bredin PhD: Reflections on physical activity and health: what should we recommend? Canadian Journal of Cardiology 32(2016) 495-504.

12) R. Toni, A. Porro: Il costituzionalismo italiano: preludio alla genetica dei disordini endocrino-metabolici. L’Endocrinologo Vol. 13,n.1, pp35-38. Editrice Kurtis 2012.

13) Carter J: The somatotypes of athletes: a review. Hum Biol, 1970, 42:535-69.

14) Carter J, Hearth B: Somatotyping:development and applications. Cambridge: Cambridge University Press, 1990.

15) Giampietro M: L’alimentazione per l’esercizio fisico e lo sport. Il Pensiero Scientifico Editore, 1990.

16) Grant JP: Handbook of total parenteral nutrition. Philadelphia 1980: WB Saunders.

17) Grant A: Nutitional assessment guidelines. Cutter Medical, 1979.

18) Bornoroni C: Biotipologia, la scienza dell’individualità umana. Anno 2000. Ed. CEA.

19) Al-Gindan YY, Hankey CR, Govan L et al.: Derivation and validation of simple anthropometric equations to predict adipose tissue mass and total fat mass with MRI as the reference method. Br J Nutr.2015 Dec14:114(11);1852-1867.

20) Cui Z, Truesdale KP, Cai J and Stevens J: Evaluation of Anthropometric Equations to assess body fat in adults: NHANES 1999-2004. Med Sci Sports Exerc, 2014; 46, 1147-1158.

21) Fuchs RJ, Theis CF, Lancaster MC. A nomogram to predict lean body mass in men. Am J Clin Nutr. 1978;31:673-678.

22) Brennan EH. Development of a binomial involving abthropometric measurement for predicting lean mass in young women. Master thesis. Incarnate World College, San Antonio, Texas. 1974.

23) Weltman A, Seip RL, Tran ZV: Practical assessment of body composition in adult obese males. Hum Biol 1987. June:59(3):523-55.

24) Weltman A, Levine S, Seip RL, Tran ZV: Accurate assessment of body composition in obese females. Am J Clin Nutr. 1988 Nov:48 (5):1179-83.

25) Lee DH, Keum N, Hu FB et al.: Development of anthropometric prediction equations for lean body mass, fat mass and percent fat in adults using the National Health and Nutrition Examination Survey (NHANES) 1999-2006. British Journal of Nutrition, 2017, 118,858-866.

26) Babcock CJ, Kirby TE, McCarroll ML et al.: A comparison of military circumference equations to skinfold-based equations to estimate body composition. Military Medicine,171,1:60,2006.

27) Body composition and military performance-many things to many people. The Journal of strength and conditioning research, May 2012.

28) Rush EC, Plank LD, Laulu MS and Robinson SM: Prediction of percentage body fat from anthropometric measurements; comparison of New Zealand European and Polynesian young women. Am J Clin Nutr 1997;66:2-7.

29) Gomez-Ambrosi J, Silva C, Catalan V et al.: Clinical usefulness of a new equation for estimating body fat. Diabetes Care 35:383-388, 2012.

30) Janmahasatian Sarayut, Duffull SB, Ash S et al.: Quantification of lean bodyweight. Clin Pharmacokinet 2005:44(10):1051-1065.

31) M EJ Lean, Thang S Han, and Paul Deurenberg. Predicting body composition by densitometry from simple anthropometric measurements. Am J Clin Nutr 1996; 63:4-14. American Society for Clinical Nutrition.

32) Woolcott OO, Bergman RN: Relative fat mass (RFM) as a new estimator of whole-body fat percentage. A cross-sectional study in American adult individuals. Sci Rep. 2018;8:10980.

33) Sun G, Cahill F et al.: Concordance of BAI and BMI with DXA in the Newfoundland population. Obesity (Silver Spring), 2013-Mar,21(3):499-503.

34) Bergman RN et al. A better index of body adiposity. Obesity (Silver Spring),2011 May;19(5):1083-9.

35) Geliebter A, Atalayer D, Flancbaum L and Gibson CD: Comparison of body adiposity index (BAI) and Body Mass Index (BMI) with estimations of percentage body fat in clinically severe obese women. Obesity, 2013 Mar; 21(3):493-498.

36) Hodgdon JA, Friedl K: Development of the dod body composition estimation equations. Technical Document No.99-2B. Naval Health Research Center 1999.

37) Carly L. Steed, MFN, RD; Benjamin R. Krull, MFN, RD; et al.: Relationship between body fat and physical fitness in army rotc cadets. Military Medicine, 181,9:1007,2016.

38) Friedl KE, Westphal KA, Marchitelli L J et al.: Evaluation of anthropometric equations to assess body-composition changes in young women. Am J Clin Nutr 2001;73:268-75

39) Body Composition and Physical Performance: Applications for the Military Services. Bernadette M. Marriott and Judith Grumstrup-Scott, Editors; Committee on Military Nutrition Research. Washington(DC):National Academies Press (US);1990.

40) Ashwell M: Charts based on body mass index and waist-to-height ratio to assess the health risk of obesity: a review. The Open Obesity Journal, 2011,3,78-84.

41) A. Coin, G. Sergi, N. Minicuci et al.: Fat-free mass and fat mass reference values by dual-energy X-ray absorptiometry (DEXA) in 20-80 year-old italian population. Clinical Nutrition 2008, 27, 87-94.

42) A. Coin, S. Giannini, N. Minicuci et al.: Limb fat-free mass and fat mass reference values by dual energy X-ray absorptiometry (DEXA) in a 20-80 year-old italian population. Clinical Nutrition (2012),doi:10.1016/J.clnu.2012.01.012.

43) Schutz Y, Kyle UG, Pichart C: Fat-free mass index and fat mass index percentiles in Caucasians aged 18-98 Y. International Journal of obesity (2002)26,953-960.

44) Ramirez-Vélez R, Correa-Bautista JE, Sanders-Tordecilla A et al.: Percentage of Body Fat and Fat Mass Index as a screening tool for metabolic syndrome prediction in Colombian University Students. Nutrients 2017,9,1009.

45) Bigaard J, Frederiksen K, Tjonneland A et al.:  Body fat and fat-free mass and all-cause mortality. Obes Res.2004;12(7):1042-49.

46) I. Janssen, S. B. Heymsfield and R. Ross: Low relative skeletal muscle mass (sarcopenia) in older persons is associated with functional impairment and physical disability. J Am Geriatr Soc 50:889-896,2002.

47) Bahat G, Tufan A, Tufan F et al.: Cut-off point to identify sarcopenia according to European Working Group on Sarcopenia in Older People (EWGSOP) definition. Clinical Nutrition 2016 in press (http://dx.doi.org/10.1016/i.clnu.2016.02.002).

48) Guerrero-Romero F, Rodriguez-Moran M: Abdominal Volume Index. An Anthropometry.based index for estimation of obesity is strongly related to impaired glucose tolerance and type 2 diabetes mellitus. Archives of Medical Research 34(2003) 428-432.

49) Perona JS, Schmidt Rio-Valle J, Ramirez-Vélez R et al. : Waist circumference and abdominal volume index are the strongest anthropometric discriminators of metabolic syndrome in Spanisc adolescents. European Journal of Clinical Investigation, 2018, Volume 49, Issue 3.

50) Vikram G, Kripa MP:Abdominal volume index and conicity index in predicting metabolic abnormalities in young women of different socioeconomic class. International Journal of Medical Science and Public Health, 2016, Vol.5, Issue 4.

51) Kwon H, Kim D, Sung kim J: Body fat distribution and the risk of incident metabolic syndrome: a longitudinal cohort study. Scientific Reports/7:10955/2017.

52) Ross R, Rissanen J, Hudson R: Sensitivity associated whth the identification of viscera adipose tissue levels using waist circumference inmen and women: effects of weight loss. Int J Obes Relat Metab Disord 1996, 20 (6), 533-8.

53) Ramirez-Vélez R, Correa-Bautista J E, Anders-Tordecilla A et al.: Percentage of body fat and fat mass index as a screening tool for metabolic syndrome prediction in Colombian university students. Nutrients 2017, 9, 1009.

54) Freedland E S: role of critical visceral adipose tissue threshold (CVATT) in metabolic syndrome: implication for controlling dietary carbohydrates: a review. Nutrition & Metabolism December 2004.

55) World Health Organization. Physical status: the use and interpretation of anthropometry. Geneva: World Health Organization; 1995.

56) Gadelha AB, Myers J, Moreira S et al.: Comparison of adiposity indices and cut-off values in the prediction of metabolic syndrome in postmenopausal women.Diabetes Metab Syndr 2016;10(3):143-8.

57) Pinnick KE, Nicholson G, Manolopoulos KN et al.: Distinct developmental profile of lower-body adipose tissue defines resistance against obesity-associated metabolic complication. Diabetes.2014,63(11):3785-97.

58) Snijder MB, Dekker GM, Visser M et al.: Associations of hip and thigh circumferences independent of waist circumference with the incidence of type 2 diabetes: the Hoorn Study. Am J Clin Nutr 2003;77:1192-7.

59) Piers LS, Soares Mj Frandsen SL, O’Dea K: Indirect of body composition are useful for groups but unrealiable in individuals. Int J Obes Relat Metab Disord. 2000;24:1145-1152.

60) Han TS, Sattar N, Lean M: ABC of obesity. Assessment of obesity and its clinical implications. BMJ 2006;333:695-698.

61) Iannella P: Evaluation of adiposity in obese people: the attendibility of predictive equations using simple anthropometric measures. Trends Med 2012;12(1):13-20.

62) LL-Y Lim, Sam-ang Seubsman, A Sleigh, C Bain: Validity of self-reported abdominal obesity in Thai adults: A comparison of waist circumference, waist-to-hip ratio and waist-to-stature ratio. Nutr Metab Cardiovasc Dis, 2012; 22(1):42-9.

63) Shankuan Zhu et al.: Combination of BMI and waist circumference for identifying cardiovascular risk factors in whites. Res Obes, 2004;12(4):633-45.

64) De Koning et al.: Waist circumference and waist-to hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. Eur Heart J, 2007;28 (7): 850-6.

65) Manolopoulos KN, Karpe F, Frayn KN. Gluteofemoral body fat as a determinant of metabolic health. Int J of Obesity, 2010;34:949-959.

66) Hitze B, Bosy-Westphal A, Bielfeldt F et al.: Measurement of waist circumference at four different sites in children, adolescents and young adults: concordance and correlation with nutritional status as well as cardiometabolic risk factors. Obes Facts 2008; 1 (5): 243-249.

67) Bays HE, Gonzalez- Campoy JM, Bray GA et al.: Pathogenic potential of adipose tissue and metabolic consequences of adipocyte hypertrophy and increased visceral adiposity. Expert Rev. Cardiovasc. Ther 2008;6:343-368.

68) Huxley R, Mends S, Zhelezn-yakov E, et al.: Body mass index, waist circumference and waist-hip ratio as predictors of cardiovascular risk- a review of the literature. Eur J Clin Nutr 2010; 64 : 16-22.

69) Kuk JL, Janiszewski PM, Ross R: Body mass index and hip and thigh circumferences are negatively associated with visceral adipose tissue after control for waist circumference.Am J Clin Nutr 2007;85:1540-4.

70) Ross R, Berentzen T, Bradishaw AJ et al.: Does the relationship between waist circumference, morbidity and mortality depend on measurement protocol for waist circumference? Obes Rev 2008;9:312-325.

71) Ashwell M, Gunn P, Gibson S: Waist- to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obesity Reviews, 2012, 13, 275-286.

72) ACSM’s: Guidelines for exercice testing and prescription. American College of Sports Medicine. Ninth Edition 2014.

73) Shivanjali Kumar et al.: Waist-tigh ratio: a surrogate marker for type 2 diabetes mellitus in Asian North Indian  patients. Indian journal of endocrinology and metabolism, 2018;22,47-9.

74) Zillikens MC, Yazdanpanah M, Pardo LM et al.: Sex-specific genetics effects influence variation in body composition. Diabetologia. 2008 Dec;51(12):2233-41.

75) Ben-Noon L, Laor A.: Relationship of neck circumference to cardiovascular risk factors. Obes Res, 2003;11:226-31.

76) Ben-Noon L,  Sohar E, Laor A: Neck circumference as a simple screening measure for identifying overweight and obese patients. Obes Res, 2001; 9:470-7.

77) Yang GR, Yuan SY, Fu HJ et al.: Neck circumference positively related with central obesity, overweight and metabolic syndrome in Chinese sibjects with type 2 diabetes: Beijing community diabetes study 4. Diabetes Care, 2010;33:2465-7.

78) Bentivoglio M, Bergamini E, Fabbri M et al.: Sindrome dell’apnea ostruttiva notturna e malattie cardiovascolari. G Ital Cardiol 2008; 9 (7):472-481.

79) Fantin F, Comellato G, et al.: Relationship between neck circumference, insulin resistance and arterial stiffness in overweight and obese subjects. European Journal of preventive cardiology, 0(00)1-9,2017.

80) Alice W. Ho, Douglas E. Moul, Jyoti Krishna: Neck circumference-Height Ratio as a predictor of sleep related Breathing disorder in children and adults. Journal of Clinical Sleep Medicine, Vol 12, No.3, 2016.

81) Sutherland K, Keenan BT, Bittencourt L et al.: A global comparison of anatomic risk factors and their relationship to obstructive sleep apnea severity in clinical samples. Journal of Clinical Sleep Medicine, Vol 15, No.4, 2019.

82)Pieterse S, Manandhar M, Ismail S: The association between nutritional status and handgrip strenght in older Rwandan refugees. Eur J Clin Nutr; 56:933-9, 2002.

83) Dodds RM, Syddall HE, Cooper R et al.: Grip Strength across the Life Course: Normative Data from Twelve British Studies 2014.

84) Guralnik GM, Simonsick EM, Ferrucci L et al.: A short phisycal performance battery assessing lower extremity function: association with self-reported disability and prediction  of mortality and nursing home admission. J Gerontol 1994; 49:M85-M94.

85) Graham J, Ostir G, Fisher S, Ottembacher K: Assessing walking speed in clinical research: a systematic review. J Eval Clin Pract 2008;14:552-562.

86) Mifflin MD et al.: A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr;51:241-7,1990.

87) LARN, livelli di assunzione di riferimento di Nutrienti ed Energia. SINU IV revisione 2014.

88) P. De Cristofaro: Basi Metodologiche dell’Approccio Psiconutrizionale. SEE Editrice. Firenze 2002.

89) Battistini NC, Bedogni G: Impedenza bioelettrica e composizione corporea. Ed. Edra, Milano 1998.

90) Mazariegos M, Kral JG, Wang J et al.: Body composition and surgical treatment of obesity. Effect of weight loss on fluid distribution. Ann.Surg. 1992, Jul216(1):69-73.

91) Segal KR, Burastero S, Chun A et al.: Estimation of extracellular and total body water by multiple-frequency bioelectrical-impedance measurement. Am J Clin Nutr 1991 Jul 54(1):26-9.

92) Waki M, Kral JG, Mazariegos M et al.: Relative expansion of extracellular fluid in obese vs. nonobese womwn. Am J Physiol 1991Ang261(2Pt1):E199-203.

93) Lukaski, C.H.; Kyke, U.G.; Kondrup, J. Assessment of adult malnutrition and prognosis with bioelectrical impedance analysis: Phase angle and impedance ratio. Curr. Opin. Clin. Nutr. Metab. Care 2017, 20, 330–339.

94) Marra M, Sammarco R, De Filippo E et al.: Resting energy expenditure, body composition and phase angle in anorectic, ballet dances and constitutionally lean males. Nutrients 2019, 11, 502.

Rimani sempre aggiornato

Iscriviti alla nostra newsletter

Ricevi aggiornamenti dal nostro Blog sulla nutrizione.