Kilocalories, metabolism and daily approximation

The broader picture

Every day nutritionists face the need to balance the food energy intake with the metabolic needs of the patient.
In the still predominant practice of low-calorie diets, for example, the aim is to reduce more or less significantly the daily energy intake provided by food (typically measured in Kcal) compared to the estimated expenditure based on parameters such as age, sex, weight and activity of the patient.
Although it is impossible for nutritionists to precisely assess the body energy balance, there is unfortunately a general oversimplification of the subject, almost as if to mean that, since accurate estimates are not available, it is worth neglecting the issue and acting in a rough way.

The mistake in the hypothesis

Various equations and theories try to approximate the daily energy requirements based on anthropometric parameters, age, sex or type of physical activity. However, these tools have clear intrinsic limitations that mostly lead to an overestimation of the patients’ metabolism, even in terms of basal metabolic rate.
It should be noted here that basal metabolic rate (or BMR, BIA-ACC) means the amount of energy (usually expressed in daily Kcal) consumed by an individual in conditions of maximum physical and mental rest, in an environment with a comfortable temperature and fasting for about twelve hours. This being said, it is clear that an energy supply equal to at least the BMR is the minimum requirement to avoid an energy deficit in the body.
Clearly, too, the daily energy demand is necessarily higher than the BMR, which is the minimum energy that is required for the survival of the body’s cellular mass, provided that rest is absolutely ideal.

Figure 1: balance between energy intake and demand


In summary, the picture resulting from the above is shown in Figure 1: considering the sum of BMR and active energy expenditure, the balance of daily energy intake/expenditure should be simple enough. Not that this is wrong in absolute terms, on the contrary: the issue is defined correctly, the problem rather lies in the practice – a practice that, sometimes out of necessity, sometimes due to misplaced confidence, relies too often on standard equations, overlooking the inaccuracy of these tools.
Let’s put aside for a moment the issue of active energy expenditure, although not simple, and let’s focus only on the assessment of the basal metabolic rate. The criticism that we want to bring forward is, in the end, the following: are two patients of the same age, of the same sex, and anthropometrically similar, certainly characterized by an equal or similar BMR? The short answer is: “no, not at all”.

Metabolic changes

Normally, the patient is not “healthy as a horse”. Although it may sound a bit ironic, this concept should emphasize how unusual it would be for a perfectly healthy person to come to a nutritionist’s practice to get advice on how to eat.
Obesity, diabetes, functional gastroenteric disorders are only some of the most common examples of conditions for which people request some kind of help in this area, and they all are characterized by more or less significant changes in the physiological balance of the endocrine system and of body composition, as well as by the onset of various symptoms or by the chronicity of inflammatory processes.
Events that are attributable to this sphere occur very easily even in the absence of symptoms: the scientific literature has long since clarified, for example, that the increase and persistence of stressors over time (be they physical, psychosocial, exogenous or endogenous) lead, in the long run, to changes in the circadian rhythmicity of stress hormones (glucocorticoids, cortisol - HPA axis index - BIA-ACC); at the onset of this imbalance, an apparently healthy subject would therefore experience, to some extent, a lower ability to metabolize carbohydrates due to cortisol-insulin antagonism, for example. Pushing the hypothesis forward, this would lead to a change in the body composition to the detriment of the lean mass (FFM, Fat Free Mass, roughly made up of muscular, bone tissue and internal organs excluding fat - BIA-ACC), which is one of the main factors that define the BMR.
A different issue, but leading to parallel outcomes, pertains to the alteration of extracellular pH, such as the tendency to acidosis that is related to the presence of chronic inflammatory processes: as is well known, acidosis involves the progressive decay of the enzymatic activity and hence of the organic absorption of nutrients (for example caused by food with a positive PRAL). In these conditions, what is the point of taking nutrients that are not balanced with the real metabolic capacities? It would rather be advisable to stimulate the metabolism, so as to avoid a decrease in nutrient absorption leading to the loss of FFM.
There are a very large number of interactions, in addition to those briefly mentioned, that can cause changes in metabolism, only a few of which are shown in Figure 2.

Figure 2: Examples of interactions involved in metabolic changes

In many cases, the onset of these events triggers so-called “vague and non-specific” symptoms (or MUS, Medically Unexplained Symptoms, see Table 1 - MUS [46]), which are often the first warning signal for the patient.
It should be noted, however, that the identification of symptoms relies on the patient’s subjective perception. Hence, even in the absence of disorders it should not be taken for granted that statistical equations are applicable to any patient.

  • Persistent fatigue or tiredness not relieved by sleep
  • Mood disturbances
  • Persistent cold hands and feet
  • Persistent insomnia or sleepiness
  • Anxiety, apathy, panic attacks
  • Appetite changes
  • Acidity and stomach pain, feeling full, bloating after meals, nausea
  • Persistent constipation, alternating bowel pattern
  • Irritable bowel syndrome
  • Poor sweating during exercise

Table 1: Vague and Non-specific Symptoms (MUS) – simplified, from Vague and Non-specific Symptoms Self-Assessment Form – MUS® [46].


Towards an improvement

The availability of detailed and exhaustive information on the patient’s status is by no means a given, and it would be in any case an uneconomical and lengthy option requiring a long series of laboratory tests. This is not, in most cases, a situation that you typically come across in a clinical nutrition practice.
However, it is possible to obtain objective parameters that go beyond a mere anthropometric survey and allow to make decisions more effectively. A rapid non-invasive analysis of body composition, if performed with an adequate degree of precision, can say a lot about a patient’s health.
The parameters that can be identified through the clinical analysis of body composition (using the BIA-ACC tools in combination with the PPG Stress Flow device for the analysis of the autonomic nervous system) provide important measures of the patient’s hydration level, his adipose tissue content, his metabolic capacity, his degree of chronic systemic inflammation.

The BIA-ACC test, as now demonstrated by scientific literature, offers a good degree of accuracy in estimating body composition and BMR, and is a feasible solution in almost any situation, thanks to its cost-effectiveness and ease of management. Having a more accurate and correct measurement is undoubtedly the first step towards improving the results obtained in terms of nutritional balance, reducing the risk of unbalancing the calorie intake compared to the real needs and the real metabolic capacities of the patient.
Currently, the technology applied to clinical nutrition has reached more advanced stages; an example in this sense is the integrated platform BioTekna Plus (“Clinical Nutrition” application), which has been available for some time and is accessible to a wide community of users. This platform allows to perform nutritional analyses in real time starting from BIA-ACC, PPG Stress Flow parameters as well as from interviews on the patient’s vague and non-specific symptoms (MUS) and eating habits (Eating Habits).
Among the data collected by the BioTekna Plus integrated platform, the analysis of MUS offers a lot of information about the patient, because it allows to further characterize the data obtained from the BIA-ACC and PPG Stress Flow examination, up to the possibility of outlining a profile of circadian hormonal rhythmicity (see HPA axis index - BIA-ACC and SNS, ANS - PPG Stress Flow).
In practical terms: how useful can it be to know when a certain type of food is well tolerated by the patient throughout the day? How useful can it be to promote, through precise nutritional choices, phases of pancreatic rest, or thyroid activity? The potential improvements obtained under nutritional therapy by such capacities are of inestimable value. They can make a difference between a general weight loss by an obese patient and a selective loss of fat mass, or between the need to use hypoglycemic drugs at every meal and the possibility of minimizing hyperglycemic peaks.
The Biotekna Plus integrated platform (Figure 3) fills the gap between instrumental data and the nutritionist’s strategies. These can be adapted to the characteristics of the individual patient, thanks to the ability to calculate the trend of the metabolic response in 24 hours (glycemic load, Food Insulin Index, AGEs, PRAL) both in the investigation phase – i.e. checking the patient’s typical metabolic behavior and in relationship with his habits – and in the therapeutic hypothesis phase, thus allowing the nutritionist to verify the appropriateness of his choices.

Figure 3: Clinical nutrition application integrated in the BioTekna Plus platform.

The use of the integrated platform BioTekna Plus facilitates the nutritionist’s task because, in addition to simplifying the balance of the daily energy intake based on a correct BMR estimate, it helps evaluate which are the most appropriate moments (nutritional sequencing) for food intake, in order to stimulate the metabolism in the most favorable phases and support the recovery of the physiological hormonal rhythmicity – this aspect being fundamental for the recovery of the well-being of patients characterized by a very low basal metabolic rate, for whom a too strong calorie reduction could even be harmful.

Authors: Dario Boschiero - Date: 04/12/2020

Attention: these contents can be freely used for personal learning purposes only. The use is regulated by Law No. 633/1941 and subsequent amendments, as well as by the copyright and patent legislation in force. Any use for commercial and profit-making purposes is forbidden.


  1. Epstein RM, Shields CG, Meldrum SC, Fiscella K, Carroll J, Carney PA, Duberstein PR, Physicians' responses to patients' medically unexplained symptoms, Psychosom Med, 2006 Mar-Apr, 68(2):269-76;
  2. Keller J, Flores B, Gomez RG, Solvason HB, Kenna H, Williams GH, Schatzberg AF, Cortisol Circadian Rhythm Alterations in Psychotic Major Depression, Biol Psychiatry, 2006 Feb 1;
  3. Ringsberg KC, Krantz G, Coping with patients with medically unexplained symptoms: work-related strategies of physicians in primary health care, J Health Psychol, 2006 Jan, 11(1):107-16;
  4. Cutolo M, Villaggio B, Otsa K, Aakre O, Sulli A, Seriolo B, Altered circadian rhythms in rheumatoid arthritis patients play a role in the disease's symptoms, Autoimmun Rev. 2005 Nov, 4(8):497-502;
  5. Takahashi T, Ikeda K, Ishikawa M, Kitamura N, Tsukasaki T, Nakama D, Kameda T, Anxiety, reactivity, and social stress-induced cortisol elevation in humans, Neuro Endocrinol Lett, 2005 Aug, 26(4):351-4;
  6. Buckley TM, Schatzberg AF, Aging and the role of the HPA axis and rhythm in sleep and memory-consolidation, Am J Geriatr Psychiatry, 2005 May, 13(5):344-52;
  7. Buckley TM, Schatzberg AF, On the interactions of the hypothalamic-pituitary-adrenal (HPA) axis and sleep: normal HPA axis activity and circadian rhythm, exemplary sleep disorders, J Clin Endocrinol Metab, 2005 May, 90(5):3106-14;
  8. Gluck ME, Geliebter A, Hung J, Yahav E, Cortisol, hunger, and desire to binge eat following a cold stress test in obese women with binge eating disorder, Psychosom Med, 2004 Nov-Dec;66(6):876-81;
  9. Backhaus J, Junghanns K, Hohagen F, Sleep disturbances are correlated with decreased morning awakening salivary cortisol, Psychoneuroendocrinology, 2004 Oct, 29(9):1184-91;
  10. Crofford LJ, Young EA, Engleberg NC, Korszun A, Brucksch CB, McClure LA, Brown MB, Demitrack MA, Basal circadian and pulsatile ACTH and cortisol secretion in patients with fibromyalgia and/or chronic fatigue syndrome, Brain Behav Immun, 2004 Jul, 18(4):314-25;
  11. Woivalin T, Krantz G, Mantyranta T, Ringsberg KC, Medically unexplained symptoms: perceptions of physicians in primary health care, Fam Pract, 2004 Apr, 21(2):199-203;
  12. Smith RC, Korban E, Kanj M, Haddad R, Lyles JS, Lein C, Gardiner JC, Hodges A, Dwamena FC, Coffey J, Collins C, A method for rating charts to identify and classify patients with medically unexplained symptoms, Psychother Psychosom, 2004 Jan-Feb;73(1):36-42;
  13. Mello Ade A, Mello MF, Carpenter LL, Price LH, Update on stress and depression: the role of the hypothalamic-pituitary-adrenal (HPA) axis, Rev Bras Psiquiatr, 2003 Oct, 25(4):231-8;
  14. Smith RC, Lein C, Collins C, Lyles JS, Given B, Dwamena FC, Coffey J, Hodges A, Gardiner JC, Goddeeris J, Given CW, Treating patients with medically unexplained symptoms in primary care, J Gen Intern Med, 2003 Jun, 18(6):478-89;
  15. Chan O, Inouye K, Riddell MC, Vranic M, Matthews SG, Diabetes and the hypothalamo-pituitary-adrenal (HPA) axis, Minerva Endocrinol, 2003 Jun;28(2):87-102;
  16. Gaillard RC, [Interactions between the immune and neuroendocrine systems: clinical implications], J Soc Biol, 2003, 197(2):89-95;
  17. Albrecht S, Naugle AE, Psychological assessment and treatment of somatization: adolescents with medically unexplained neurologic symptoms, Adolesc Med, 2002 Oct, 13(3):625-41;
  18. Vicennati V, Ceroni L, Gagliardi L, Gambineri A, Pasquali R, Comment: response of the hypothalamic-pituitary-adrenocortical axis to high-protein/fat and high-carbohydrate meals in women with different obesity phenotypes, J Clin Endocrinol Metab, 2002 Aug, 87(8):3984-8;
  19. Rodenbeck A, Huether G, Ruther E, Hajak G, Interactions between evening and nocturnal cortisol secretion and sleep parameters in patients with severe chronic primary insomnia, Neurosci Lett, 2002 May 17;324(2):159-63;
  20. Crofford LJ, The hypothalamic-pituitary-adrenal axis in the pathogenesis of rheumatic diseases, Endocrinol Metab Clin North Am, 2002 Mar, 31(1):1-13;
  21. Reid S, Whooley D, Crayford T, Hotopf M, Medically unexplained symptoms--GPs' attitudes towards their cause and management, Fam Pract, 2001 Oct, 18(5):519-23;
  22. Gaillard RC, Interaction between the hypothalamo-pituitary-adrenal axis and the immunological system, Ann Endocrinol (Paris), 2001 Apr, 62(2):155-63;
  23. Elsenbruch S, Orr WC, Diarrhea and constipation-predominant IBS patients differ in postprandial autonomic and cortisol responses, Am J Gastroenterol, 2001 Feb, 96(2):460-6;
  24. Racciatti D, Guagnano MT, Vecchiet J, De Remigis PL, Pizzigallo E, Della Vecchia R, Di Sciascio T, Merlitti D, Sensi S, Chronic fatigue syndrome: circadian rhythm and hypothalamic-pituitary-adrenal (HPA) axis impairment, Int J Immunopathol Pharmacol, 2001 Jan, 14(1):11-15;
  25. Epel E, Lapidus R, McEwen B, Brownell K, Stress may add bite to appetite in women: a laboratory study of stress-induced cortisol and eating behavior, Psychoneuroendocrinology, 2001 Jan, 26(1):37-49;
  26. Vicennati V, Pasquali R, Abnormalities of the hypothalamic-pituitary-adrenal axis in nondepressed women with abdominal obesity and relations with insulin resistance: evidence for a central and a peripheral alteration, J Clin Endocrinol Metab, 2000 Nov, 85(11):4093-8;
  27. Blazejova K, Nevsimalova S, Illnerova H, Hajek I, Sonka K, [Sleep disorders and the 24-hour profile of melatonin and cortisol], Sb Lek, 2000, 101(4):347-51;
  28. Harbuz MS, Chronic inflammatory stress, Baillieres Best Pract Res Clin Endocrinol Metab, 1999 Dec, 13(4):555-65;
  29. Shanks N, Harbuz MS, Jessop DS, Perks P, Moore PM, Lightman SL, Inflammatory disease as chronic stress, Ann N Y Acad Sci, 1998 May 1, 840:599-607;
  30. Leal AM, Moreira AC, Food and the circadian activity of the hypothalamic-pituitary-adrenal axis, Braz J Med Biol Res, 1997 Dec, 30(12):1391-405;
  31. Harbuz MS, Conde GL, Marti O, Lightman SL, Jessop DS, The hypothalamic-pituitary-adrenal axis in autoimmunity, Ann N Y Acad Sci, 1997 Aug 14, 823:214-24;
  32. Van Cauter EV, Polonsky KS, Blackman JD, Roland D, Sturis J, Byrne MM, Scheen AJ, Abnormal temporal patterns of glucose tolerance in obesity: relationship to sleep-related growth hormone secretion and circadian cortisol rhythmicity, J Clin Endocrinol Metab, 1994 Dec, 79(6):1797-805;
  33. Yehuda R, Boisoneau D, Mason JW, Giller EL, Glucocorticoid receptor number and cortisol excretion in mood, anxiety, and psychotic disorders, Biol Psychiatry, 1993 Jul 1-15, 34(1-2):18-25;
  34. Tsigos C, Young RJ, White A, Diabetic neuropathy is associated with increased activity of the hypothalamic-pituitary-adrenal axis, J Clin Endocrinol Metab, 1993 Mar, 76(3):554-8;
  35. Mantero F, Boscaro M, Glucocorticoid-dependent hypertension, J Steroid Biochem Mol Biol, 1992 Oct, 43(5):409-13;
  36. Angeli A, Glucocorticoid secretion: a circadian synchronizer of the human temporal structure, J Steroid Biochem, 1983 Jul, 19(1B):545-54;
  37. Zhurova MV, Lugovaia NA, [Carbohydrate tolerance and islet apparatus function in patients with different forms of hypothyroidism], Probl Endokrinol (Mosk), 1983 May-Jun, 29(3):36-40;
  38. Curtis GC, Nesse R, Buxton M, Lippman D, Anxiety and plasma cortisol at the crest of the circadian cycle: reappraisal of a classical hypothesis, Psychosom Med, 1978 Aug, 40(5):368-78;
  39. Pischon T, Girman CJ, Rifai N, Hotamisligil GS, Rimm EB, Association between dietary factors and plasma adiponectin concentrations in men, Am J Clin Nutr. 2005 Apr;81(4):780-6;
  40. Eisenlohr H, Metabolic syndrome: diagnosis and dietary intervention, Internist (Berl). 2005 Jan;46(1):57-67;
  41. Pereira MA, Swain J, Goldfine AB, Rifai N, Ludwig DS, Effects of a low-glycemic load diet on resting energy expenditure and heart disease risk factors during weight loss, JAMA. 2004 Nov 24;292(20):2482-90;
  42. Taylor E, Missik E, Hurley R, Hudak S, Logue E, Obesity treatment: broadening our perspective, Am J Health Behav. 2004 May-Jun;28(3):242-9;
  43. Lukezic M, Righini V, Di Natale B, De Angelis R, Norbiato G, Bevilacqua M, Chiumello G, Vasopressin and thirst in patients with posterior pituitary ectopia and hypopituitarism, Clin Endocrinol (Oxf). 2000 Jul;53(1):77-83;
  44. Kamoi K, Tamura T, Tanaka K, Ishibashi M, Yamaji T, Hyponatremia and osmoregulation of thirst and vasopressin secretion in patients with adrenal insufficiency, J Clin Endocrinol Metab. 1993 Dec;77(6):1584-8;
  45. Greenleaf JE, Problem: thirst, drinking behavior, and involuntary dehydration, Med Sci Sports Exerc. 1992 Jun;24(6):645-56.
  46. Boschiero D, BioTekna – Italy. MUS® - Medically Unexplained Symptoms Self-Evaluation n.2012001626, 2012.