• Users Online: 538
  • Print this page
  • Email this page


 
 
Table of Contents
REVIEW ARTICLE
Year : 2022  |  Volume : 5  |  Issue : 1  |  Page : 7-13

Nutrition assessment tools in children with chronic kidney disease


Department of Pediatric Nephrology, St John's Medical College Hospital, Bengaluru, Karnataka, India

Date of Submission15-Apr-2022
Date of Decision04-Jun-2022
Date of Acceptance08-Jun-2022
Date of Web Publication28-Jun-2022

Correspondence Address:
Arpana Iyengar
Department of Pediatric Nephrology, St John's Medical College Hospital, Bengaluru - 560 034, Karnataka
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ajpn.ajpn_8_22

Rights and Permissions
  Abstract 


Children with chronic kidney disease (CKD) are vulnerable to undernutrition and its accompanying consequences. Assessment of nutrition in these children is confounded by the presence of edema and overhydration. There is no single measure that can comprehensively reflect the underlying nutrition status. Hence, there is a need to explore nutrition assessment tools that reflect body composition without being affected by hydration status. Many tools of assessment that are widely studied in adults with CKD get extrapolated to children. Studies on nutritional assessment focusing on pediatric patients with CKD are needed for early recognition and long-term monitoring of nutrition status. This review attempts to provide an understanding of the utility and limitations of tools available for assessment of nutrition and body composition in the context of undernutrition in pediatric CKD.

Keywords: Anthropometry, body composition, dialysis, protein-energy wasting, subjective global nutritional assessment, under nutrition


How to cite this article:
Iyengar A. Nutrition assessment tools in children with chronic kidney disease. Asian J Pediatr Nephrol 2022;5:7-13

How to cite this URL:
Iyengar A. Nutrition assessment tools in children with chronic kidney disease. Asian J Pediatr Nephrol [serial online] 2022 [cited 2022 Aug 18];5:7-13. Available from: https://www.ajpn-online.org/text.asp?2022/5/1/7/348537




  Introduction Top


Nutritional disorders in children with chronic kidney disease (CKD) encompass 20%–45% of undernutrition and 15%–30% of overnutrition, and contribute to morbidity and mortality.[1],[2] Undernutrition due to pediatric CKD is more common in under-resourced nations compared to resource-rich countries, where overweight or obesity also prevails. Limited data from developing countries reveal the burden of undernutrition to be 31%–60% in those with CKD 2–5 and 38% in those on dialysis.[3],[4] Undernutrition in children with CKD and kidney failure is governed by complex interactions between appetite, inflammation, dietary nutrient intake, comorbidities, increased energy expenditure, and metabolic rate. There is no single measure that can comprehensively reflect the underlying nutritional status, thereby providing an impetus for ongoing research. The International Society of Nephrology-Global Kidney Health  Atlas More Details, through a multinational survey, recently highlighted the gaps in undertaking formal nutritional assessment in adult patients with CKD across the globe, reinforcing the need for nephrologists to have a good understanding of the utility and contextual interpretation of various nutrition assessment tools.[5] This review explores the role of available assessment tools for undernutrition with a special focus on tools that reflect body composition in pediatric CKD. Attention to details on growth retardation and obesity is beyond the scope of this review.


  Human Body Composition Top


Understanding the intricacies of nutrition assessment calls for a sound knowledge of body composition. Human body is broadly divided into compartments (C) of fat mass (FM) and fat-free mass (FFM). FFM constitutes cellular tissue, total body water (TBW), and bone mineral content, as shown in [Figure 1]a. FM and FFM are included in the two-compartment (2C) model; FM, lean tissue mass (cellular tissue and TBW), and bone mineral content in the 3C model; and FM, TBW, cellular tissue, and bone mineral compartments in the 4C model [Figure 1]a. Multicompartment models provide measures of specific body elements such as protein, nitrogen, minerals, and glycogen.[6] The FFM compartment can be further categorized into body cell mass (BCM) and extracellular mass (fluid and solids). As depicted in [Figure 1]b, specific tools to identify body compartments have evolved with time. Body mass index (BMI) and mid-arm circumference (MAC) are surrogate anthropometry measures for combined FM and FFM, while skin fold thickness is a surrogate measure for FM. Dual X-ray absorptiometry (DXA) is the reference tool for FM and bone mineral content assessment and is also useful in measuring lean tissue mass or muscle. Bioimpedance analysis (BIA) can be used to measure TBW, FFM, and FM. A multifrequency BIA can detect both extracellular and intracellular compartments of TBW and BCM.
Figure 1: Illustration of body composition (a) as body compartments, and (b) in relation to assessment tools reflecting the compartments tested. (a) FM and FFM are two broad compartments. FFM constitutes cellular tissue, TBW that includes ECW and ICW, and bone mineral content. (b) BMI and MAC reflect both FM and FFM. Dual X-ray absorptiometry is a reference tool for the bone compartment. FFM, FM, and TBW can be estimated using BIA. FM is assessed by DXA, BIA, and SFT. BCM and ECM can be estimated by BIVA and WBPC. FM: Fat mass, FFM: Fat-free mass, ECW: Extracellular water, ICW: Intracellular water, BMI: Body mass index, MAC: Mid-arm circumference, DXA: Dual X-ray absorptiometry, BIA: Bioimpedance analysis, SFT: Skin fold thickness, BCM: Body cell mass, ECM: Extracellular mass, WBPC: Whole-body potassium counter

Click here to view


In children with CKD, assessment of body composition is confounded by the presence of edema and overhydration. Hence, there is a need to explore methods to detect body composition that are unaffected by hydration status. BCM is composed of cellular tissue and intracellular components and is the body's actively metabolizing, oxygen-consuming compartment: an ideal reflection of optimal nutrition. Measurement of BCM by a whole-body potassium counter (WBPC) is a safe and noninvasive method that is unaffected by changes in hydration status but is not a feasible tool for use in routine clinical practice.

Body composition changes with age, and is altered in chronic disease states. In children with CKD, it is important to note that all measures of body composition ideally need to be interpreted after adjusting to height. There is evidence to suggest increased FM, reduced FFM, and increased TBW in patients with CKD.


  Tools for Nutritional Assessment Top


The Pediatric Renal Nutrition Taskforce[7] recommends nutritional assessment using a dietary history or recall, appetite assessment, and measures of height for age, height velocity, weight for height, euvolemic weight for age, and BMI. The guideline also highlights the need to further explore the scope of other tools that reflect body composition.

Dietary assessment

Dietary assessment includes a diet history, diet diary, and a food frequency questionnaire (FFQ).[5] A 24-h multiple diet recall for at least a 3-day period (including weekdays and weekends) is easy to implement and is the preferred method for children aged 4–10 years. It provides details on the frequency of intake, portion size, and cooking methods. However, underreporting is a limitation. A diet diary prospectively records food intake over a week and is useful, especially in younger children. Diet diary is the reference method with no burden of recall, and captures variation in food by weight or portion size. The FFQ approach is ideal for use at a community level or for special food/population groups and reflects food consumption patterns over a given time period from a list of foods. Interpretation of dietary intake can be done using the Pediatric Renal Nutrition Taskforce recommendations.[8]

Over the years and as an aftermath of the COVID-19 pandemic, technology-based dietary assessments have emerged as a promising and low-cost tool for use at both individual and population levels.[9] The image-based and web-based assessment methods, though not specifically studied in children with CKD, are observed to be useful among children and adolescents in general.[10],[11] Nevertheless, these smartphone apps and technology-based tools come with limitations of acceptability, responsiveness, accuracy, and consistency.

Anthropometry measures

BMI reflects both FFM and FM. BMI is more often used to diagnose overweight and obesity compared to undernutrition or wasting in pediatric CKD. In the presence of short stature in CKD, it is important to interpret BMI that is indexed to height age. Waist circumference and waist-to-hip ratio are height-independent tools for use in detecting central obesity in these patients. As BMI does not differentiate between muscle and fat compartments, obesity could be underestimated in children with reduced muscle mass. Increased BMI has been associated with mortality and cardiovascular risks in children with CKD.[2],[12] However, similar literature on low BMI or waist circumference and clinical outcomes in these children is scarce. Low BMI, defined as < 5th centile for height age and gender, is observed to be significantly associated with the presence and severity of protein-energy wasting (PEW) in children with CKD and those on dialysis,[4] and is one of the parameters used to define frailty in these children.[13]

MAC, defined as the circumference of the arm at the midpoint between the olecranon and acromion, reflects both the muscle and fat compartments of the upper arm. MAC has been a useful tool to diagnose undernutrition in general pediatric population. A large study of about 10,000 children, including those with underlying kidney diseases, revealed MAC to have high sensitivity (92%) to identify children with no malnutrition and a high specificity (99%) and low sensitivity (30%) to detect severe malnutrition.[14] Reduced MAC was noted to be common in children on dialysis and was strongly associated with the presence and severity of PEW in children with CKD and those on dialysis.[12],[15] In those on dialysis, deficits in MAC were associated with insufficient dietary intake of energy, iron, and vitamins.[16] MAC is an integral component in the criteria to diagnose frailty in children with CKD along with BMI, C-reactive protein, and fatigue.[15]

Appetite, taste, and smell assessment

Assessment of appetite has gained focus in the evaluation of children with CKD. Reduced appetite is associated with PEW and low quality of life in these children.[2],[16] Alteration in appetite, taste, and smell contribute to specific eating behaviors in these children. A 5-point rating scale (very good, good, fair, poor, and very poor) has been used to assess appetite in children with CKD.[17],[18] Psychometric appetite assessment tools specific to children have been used in developed countries. Validated tools like the Children's Eating Behavior Questionnaire consists of 35 items evaluated on a 5-point scale. Food/satiety responsiveness, slowness in eating, food fussiness/enjoyment, emotional undereating/overeating, and desire for drinks are domains evaluated.[19] The Early Childhood Appetite and Satiety Tool has been developed for under-5 age children belonging to low-income countries.[20] Other recently studied tools for appetite and taste include picture-based assessment for children aged 4–10 years (using pictures of individualized activities over desire to eat) and a Visual Analog Scale (the patient marks the point of current state perceived on a line with two extreme states of appetite) for children >8 years old.[21] These tools have been in vogue for research and are yet to be implemented in routine clinical practice.

Subjective Global Nutritional Assessment tool and Nutrition-Focused Physical Examination

Subjective Global Nutritional Assessment (SGNA) is a recommended and valid tool for nutrition assessment in adults with CKD and dialysis, but similar studies on its utility in children are limited.[22] This tool consists of both nutrition-focused medical history and physical examination to identify undernutrition. SGNA includes 10 parameters (7 items in medical history and 3 in physical examination) for detection of undernutrition.[23] Medical history has domains of anthropometry, dietary intake, gastrointestinal symptoms, functional capacity, and metabolic stress of disease. Nutrition-Focused Physical Examination consists of assessment for muscle wasting, subcutaneous fat loss, and presence of edema. Physical examination findings specific to subcutaneous fat loss are identified on cheeks, ribs, and buttocks and muscle wasting is looked for over the clavicle, shoulder, scapula, thigh, knee, and calf. In a study on SGNA in children on dialysis, only 5 out of the 10 parameters (anthropometry, diet intake, functional capacity, subcutaneous fat loss, and muscle wasting) were strongly associated with the presence and severity of undernutrition.[24] Moreover, SGNA demonstrated poor agreement with objective measures of nutrition (MAC and serum albumin) and was not useful in picking up a change in nutritional status on a median follow-up of 8 months in these children.

Dual X-ray absorptiometry

DXA is a reference tool for body composition assessment (FM, appendicular muscle mass, and bone density) in patients with CKD. However, as the FFM compartment is affected by hydration status, the interpretation of lean tissue mass is a challenge in patients with CKD. Besides, these measures need to be interpreted in the context of differences in height and pubertal status between healthy children and those with CKD. DXA was found to be useful in identifying regional lean mass deficits indicating skeletal muscle wasting in children with moderate-to-severe CKD.[25] DXA has been used to detect body fat and FM in children with CKD but cannot be used repeatedly in clinic due to high cost and exposure to low radiation.

Bioimpedance analysis and bioimpedance vector analysis

A single-frequency BIA is a tool based on the 2C model. It estimates body composition based on the resistance of the body to the passage of an alternating electrical current, and is not influenced by body weight. The bioelectric index is the phase angle (PA) that is derived from a proportion between resistance and reactance. Mathematical and regression models based on measurements obtained help to determine the hydration status. With regard to body composition, BIA has an advantage of detecting TBW; a multifrequency BIA can detect both extracellular and intracellular water) and FFM. Body fat and FM can be measured by BIA in children and reference charts for Indian children are available.[26] However, in a study on children with CKD that compared BIA with the reference tool (i.e., DXA), there was poor agreement between body fat and FM.[27] This raises concerns on the utility of BIA for interpretation of body fat in children with CKD. In addition, estimating lean mass by BIA has been noted to be inaccurate in the presence of altered hydration in children on dialysis.[28] PA has been used as a parameter to detect undernutrition in children and adults. However, the role of PA as a measure of nutritional status in children with CKD is not clear. A recent study showed an association of PA with muscle strength in children, adolescents, and adults.[29]

Based on BIA measures of resistance (R), which corresponds to flow of current in fluids, and reactance (Xc), which reflects flow through cell membranes, a vector graph can be created [Figure 2] that provides interpretation of both hydration status and nutrition profile (cell mass). Bioimpedance vector analysis (BIVA) is a dynamic tool used to assess body composition in children.[30] As shown in [Figure 2], the resistance (R) and reactance (Xc), each adjusted for height (H), are plotted on a “R/H-Xc/H” graph, with the four quadrants representing overhydration, dehydration, increased cell mass, and decreased cell mass. By plotting these measures in healthy children, reference ellipses can be created and patient's measures can be plotted for interpretation. Although BIVA is used in adults with CKD, its utility in children with CKD is limited. In children on hemodialysis, BIVA has been shown to be useful in targeting dry weight.[31] Similarly, in children on chronic peritoneal dialysis, BIVA has the potential to track shifts across quadrants of cell mass and hydration when monitored over time.[32]
Figure 2: Reference graph of BIVA derived from bioimpedance analysis for euvolemic weight assessment and BCM estimation. The child's parameters of reactance and resistance indexed to height are plotted on the graph to determine the hydration or nutrition status. X-axis depicts resistance/height and y-axis depicts reactance/height. The four quadrants represent states of overhydration, dehydration, and decreased and increased BCM. The ellipses represent the 50th, 75th, and 95th reference centiles. BIVA: Bioimpedance vector analysis, BCM: Body cell mass

Click here to view


Whole-body potassium counter

As 98% of total body potassium is contained within the BCM, the measurable natural radioactive isotope (40K) can be used to derive BCM. This novel method of measuring BCM is with a WBPC is unaffected by hydration but has not been well studied in childhood CKD.[33] In addition, total body protein and skeletal muscle mass can also be measured by the WBPC. Although this method of measuring BCM was discovered five decades ago, with evolving technical advances, the past decade has witnessed a new system of WBPC that is safe and is independent of tissue hydration.[34] Concerns regarding the interpretation of BCM in the presence of hyperkalemia need to be addressed. Importantly, there is a need to explore surrogate clinical tools that best reflect BCM in these children.

Measurement of muscle mass and strength

Reduced muscle mass is an important component of undernutrition in CKD, which leads to muscle wasting. Tools to assess muscle mass have not been widely explored in children with CKD. Muscle mass can be measured by anthropometry, BIA, DXA, SGNA, computed tomography (CT), magnetic resonance imaging (MRI), and ultrasonography. Mid-arm muscle area, derived from MAC as follows, provides an estimation of the area of muscle portion of the arm excluding the bone.

Mid-arm muscle area (mm2) = (Mid-upper arm circumference [mm] – [π × TSF])2/4π, where TSF stands for triceps skin fold thickness.

Mid-arm muscle area has been found to be a valid surrogate of muscle mass in adults and could be explored as an independent assessment tool[35] for muscle wasting or as a risk predictor of PEW in children with CKD. However, this estimate reflects regional muscle mass and may not be representative of wasting in other parts of the body.

A multifrequency BIA can measure muscle mass in adults which correlates with measurements from isotopic methods and MRI.[36] CT can assess quadriceps muscle cross-sectional area and muscle density without the influence of the hydration state.[37] MRI has advantages over CT in not exposing patients to radiation and in being more efficient in separating water from fat compartments, thus facilitating measurement of lean mass. MRI assessments, using the psoas and quadriceps muscles, have been used to predict sarcopenia in adults. Muscle ultrasonography is a low-cost, portable tool that is not influenced by fluid overload or rapid fluid shifts across body compartments. A study on ultrasonography measurement of muscle thickness of the quadriceps in adults on hemodialysis has shown promising results in detecting risk for PEW.[38]

Besides muscle mass, assessment of muscle function is gaining importance as a measure of frailty in these patients. Hand grip strength is a simple, objective, and noninvasive bedside tool used to assess muscle function. Measurement is undertaken with a hydraulic dynamometer, and the interpretation of values is based on z-scores derived from normative reference charts. Impaired muscle strength was observed in children with CKD, particularly in, those with underlying nonglomerular disease, and impaired muscle strength noted was independent of growth retardation or BMI.[39]


  Protein-Energy Wasting Top


This entity refers to a severe form of undernutrition that is driven by interplay of nutritional and nonnutritional factors in the uremic milieu. In children, the diagnostic criteria for PEW are adapted from adult criteria, and include five parameters: BMI, MAC, biochemical measures (serum albumin, cholesterol, transferring, and C-reactive protein), reduced appetite, and short stature. PEW was classified as mild, standard, and modified based on the number of parameters fulfilled.[40] Few studies have observed anthropometry measures (both BMI and MAC) to be more useful than biochemical parameters in diagnosing PEW in children with CKD.[4],[40] Among the biochemical parameters, serum cholesterol and serum transferrin were not useful in defining PEW in these children. Low serum albumin (<3.8 g/dL), though prevalent, was not associated with PEW. Inflammation that was highly prevalent in children across stages of CKD was associated with low anthropometry parameters only in CKD 2–4 and not in those on dialysis.[4] These findings call for a need to revisit the diagnostic parameters for PEW assessment in children with CKD.


  Conclusion and Areas of Future Research Top


In summary, assessment of nutrition status is closely linked to measuring components of body composition. Many tools of assessment have been widely studied in adults with CKD but cannot be extrapolated to children. The utility and limitations of various assessment tools that need to be explored in children are listed in [Table 1]. Method-specific standardized protocols need to be developed across age groups. Age- and gender-based body composition equations and prediction formulae need to be created for disease-specific populations. Method-specific errors need to be quantified. Importantly, assessment tools and measures need to be linked to hard clinical outcomes.
Table 1: Strengths and gaps of tools used for nutritional assessment in pediatric chronic kidney disease

Click here to view


Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Apostolou A, Printza N, Karagiozoglou-Lampoudi T, Dotis J, Papachristou F. Nutrition assessment of children with advanced stages of chronic kidney disease – A single center study. Hippokratia 2014;18:212-6.  Back to cited text no. 1
    
2.
Brady TM, Roem J, Cox C, Schneider MF, Wilson AC, Furth SL, et al. Adiposity, sex, and cardiovascular disease risk in children with CKD: A longitudinal study of youth enrolled in the chronic kidney disease in children (CKiD) study. Am J Kidney Dis 2020;76:166-73.  Back to cited text no. 2
    
3.
Gupta A, Mantan M, Sethi M. Nutritional assessment in children with chronic kidney disease. Saudi J Kidney Dis Transpl 2016;27:733-9.  Back to cited text no. 3
[PUBMED]  [Full text]  
4.
Iyengar A, Raj JM, Vasudevan A. Protein energy wasting in children with chronic kidney disease and end-stage kidney disease: An observational study. J Ren Nutr 2021;31:270-7.  Back to cited text no. 4
    
5.
Wang AY, Okpechi IG, Ye F, Kovesdy CP, Brunori G, Burrowes JD, et al. Assessing global kidney nutrition care. Clin J Am Soc Nephrol 2022;17:38-52.  Back to cited text no. 5
    
6.
Kuriyan R. Body composition techniques. Indian J Med Res 2018;148:648-58.  Back to cited text no. 6
[PUBMED]  [Full text]  
7.
Nelms CL, Shaw V, Greenbaum LA, Anderson C, Desloovere A, Haffner D, et al. Assessment of nutritional status in children with kidney diseases – Clinical practice recommendations from the Pediatric Renal Nutrition Taskforce. Pediatr Nephrol 2021;36:995-1010.  Back to cited text no. 7
    
8.
Shaw V, Polderman N, Renken-Terhaerdt J, Paglialonga F, Oosterveld M, Tuokkola J, et al. Energy and protein requirements for children with CKD stages 2-5 and on dialysis-clinical practice recommendations from the Pediatric Renal Nutrition Taskforce. Pediatr Nephrol 2020;35:519-31.  Back to cited text no. 8
    
9.
Cade JE. Measuring diet in the 21st century: Use of new technologies. Proc Nutr Soc 2017;76:276-82.  Back to cited text no. 9
    
10.
Kouvari M, Mamalaki E, Bathrellou E, Poulimeneas D, Yannakoulia M, Panagiotakos DB. The validity of technology-based dietary assessment methods in childhood and adolescence: A systematic review. Crit Rev Food Sci Nutr 2021;61:1065-80.  Back to cited text no. 10
    
11.
Lai JS, Loh J, Toh JY, Sugianto R, Colega MT, Tan KH, et al. Evaluation of paper-based and web-based food frequency questionnaires for 7-year-old children in Singapore. Br J Nutr 2021:1-35. doi: 10.1017/S0007114521004517. PMID: 34776027.  Back to cited text no. 11
    
12.
Roberts MJ, Mitsnefes MM, McCulloch CE, Greenbaum LA, Grimes BA, Ku E. Association between BMI changes and mortality risk in children with end-stage renal disease. Pediatr Nephrol 2019;34:1557-63.  Back to cited text no. 12
    
13.
Sgambat K, Matheson MB, Hooper SR, Warady B, Furth S, Moudgil A. Prevalence and outcomes of fragility: A frailty-inflammation phenotype in children with chronic kidney disease. Pediatr Nephrol 2019;34:2563-9.  Back to cited text no. 13
    
14.
Stephens K, Orlick M, Beattie S, Snell A, Munsterman K, Oladitan L, et al. Examining mid-upper arm circumference malnutrition z-score thresholds. Nutr Clin Pract 2020;35:344-52.  Back to cited text no. 14
    
15.
Pontón-Vázquez C, Vásquez-Garibay EM, Hurtado-López EF, de la Torre Serrano A, García GP, Romero-Velarde E. Dietary intake, nutritional status, and body composition in children with end-stage kidney disease on hemodialysis or peritoneal dialysis. J Ren Nutr 2017;27:207-15.  Back to cited text no. 15
    
16.
García De Alba Verduzco J, Hurtado López EF, Pontón Vázquez C, de la Torre Serrano A, Romero Velarde E, Vásquez Garibay EM. Factors associated with anthropometric indicators of nutritional status in children with chronic kidney disease undergoing peritoneal dialysis, hemodialysis, and after kidney transplant. J Ren Nutr 2018;28:352-8.  Back to cited text no. 16
    
17.
Ayestaran FW, Schneider MF, Kaskel FJ, Srivaths PR, Seo-Mayer PW, Moxey-Mims M, et al. Perceived appetite and clinical outcomes in children with chronic kidney disease. Pediatr Nephrol 2016;31:1121-7.  Back to cited text no. 17
    
18.
Harmer M, Wootton S, Gilbert R, Anderson C. Association of nutritional status and health-related quality of life in children with chronic kidney disease. Qual Life Res 2019;28:1565-73.  Back to cited text no. 18
    
19.
Wardle J, Guthrie CA, Sanderson S, Rapoport L. Development of the children's eating behaviour questionnaire. J Child Psychol Psychiatry 2001;42:963-70.  Back to cited text no. 19
    
20.
Nahar B, Hossain M, Ickes SB, Naila NN, Mahfuz M, Hossain D, et al. Development and validation of a tool to assess appetite of children in low income settings. Appetite 2019;134:182-92.  Back to cited text no. 20
    
21.
Triador L, Colin-Ramirez E, Mackenzie ML, Tomaszewski E, Shah K, Gulayets H, et al. A two-component pictured-based appetite assessment tool is capable of detecting appetite sensations in younger children: A pilot study. Nutr Res 2021;89:45-55.  Back to cited text no. 21
    
22.
Ikizler TA, Burrowes JD, Byham-Gray LD, Campbell KL, Carrero JJ, Chan W, et al. KDOQI clinical practice guideline for nutrition in CKD: 2020 update. Am J Kidney Dis 2020;76:S1-107.  Back to cited text no. 22
    
23.
Secker DJ, Jeejeebhoy KN. How to perform Subjective Global Nutritional assessment in children. J Acad Nutr Diet 2012;112:424-31.e6.  Back to cited text no. 23
    
24.
Iyengar A, Ashok JM, Vasudevan A. Subjective global nutritional assessment (SGNA) in children on chronic dialysis – A prospective observational study. Indian J Nephrol 2021. [Epub ahead of print]. Available from: https://www.indianjnephrol.org/preprintarticle.asp?id=344857. [Last accessed on 2022 Jun 20].  Back to cited text no. 24
    
25.
Foster BJ, Kalkwarf HJ, Shults J, Zemel BS, Wetzsteon RJ, Thayu M, et al. Association of chronic kidney disease with muscle deficits in children. J Am Soc Nephrol 2011;22:377-86.  Back to cited text no. 25
    
26.
Chiplonkar S, Kajale N, Ekbote V, Mandlik R, Parthasarathy L, Borade A, et al. Reference centile curves for body fat percentage, fat-free mass, muscle mass and bone mass measured by bioelectrical impedance in Asian Indian children and adolescents. Indian Pediatr 2017;54:1005-11.  Back to cited text no. 26
    
27.
Iyengar A, Kuriyan R, Kurpad AV, Vasudevan A. Body fat in children with chronic kidney disease – A comparative study of bio-impedance analysis with dual energy x-ray absorptiometry. Indian J Nephrol 2021;31:39-42.  Back to cited text no. 27
  [Full text]  
28.
Milani GP, Groothoff JW, Vianello FA, Fossali EF, Paglialonga F, Consolo S, et al. Bioimpedance spectroscopy imprecisely assesses lean body mass in pediatric dialysis patients. J Pediatr Gastroenterol Nutr 2018;67:533-7.  Back to cited text no. 28
    
29.
Custódio Martins P, de Lima TR, Silva AM, Santos Silva DA. Association of phase angle with muscle strength and aerobic fitness in different populations: A systematic review. Nutrition 2022;93:111489.  Back to cited text no. 29
    
30.
Wells JC, Williams JE, Ward LC, Fewtrell MS. Utility of specific bioelectrical impedance vector analysis for the assessment of body composition in children. Clin Nutr 2021;40:1147-54.  Back to cited text no. 30
    
31.
Iyengar AA, Vasudevan A. Targeting dry weight of children on hemodialysis by bio-impedance analysis. Asian J Pediatr Nephrol 2019;2:54-5.  Back to cited text no. 31
  [Full text]  
32.
Reddy S, Iyengar A. Assessment of overhydration in children on continuous ambulatory peritoneal dialysis by bio-impedance vector analysis – A longitudinal observational study. Selected Abstracts of the 33rd Annual Conference of the Indian Society of Pediatric Nephrology, 10-12 December, 2021. Asian J Pediatr Nephrol 2022;5:1-15.  Back to cited text no. 32
    
33.
Murphy AJ, Ellis KJ, Kurpad AV, Preston T, Slater C. Total body potassium revisited. Eur J Clin Nutr 2014;68:153-4.  Back to cited text no. 33
    
34.
Naqvi S, Bhat KG, Preston T, Devi S, Joseph J, Sachdev HS, et al. The development of a whole-body potassium counter for the measurement of body cell mass in adult humans. Asia Pac J Clin Nutr 2018;27:1190-7.  Back to cited text no. 34
    
35.
Frisancho AR. New norms of upper limb fat and muscle areas for assessment of nutritional status. Am J Clin Nutr 1981;34:2540-5.  Back to cited text no. 35
    
36.
Kaysen GA, Zhu F, Sarkar S, Heymsfield SB, Wong J, Kaitwatcharachai C, et al. Estimation of total-body and limb muscle mass in hemodialysis patients by using multifrequency bioimpedance spectroscopy. Am J Clin Nutr 2005;82:988-95.  Back to cited text no. 36
    
37.
Sabatino A, D'Alessandro C, Regolisti G, di Mario F, Guglielmi G, Bazzocchi A, et al. Muscle mass assessment in renal disease: The role of imaging techniques. Quant Imaging Med Surg 2020;10:1672-86.  Back to cited text no. 37
    
38.
Narayanan SS, Tallman D, Chinna K, Goh BL, Gafor AH, Ahmad G, et al. Association of ultrasound-derived metrics of the quadriceps muscle with protein energy wasting in hemodialysis patients: A multicenter cross-sectional study. Nutrients 2020;12:3597.  Back to cited text no. 38
    
39.
Hogan J, Schneider MF, Pai R, Denburg MR, Kogon A, Brooks ER, et al. Grip strength in children with chronic kidney disease. Pediatr Nephrol 2020;35:891-9.  Back to cited text no. 39
    
40.
Abraham AG, Mak RH, Mitsnefes M, White C, Moxey-Mims M, Warady B, et al. Protein energy wasting in children with chronic kidney disease. Pediatr Nephrol 2014;29:1231-8.  Back to cited text no. 40
    


    Figures

  [Figure 1], [Figure 2]
 
 
    Tables

  [Table 1]



 

Top
 
  Search
 
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

 
  In this article
Abstract
Introduction
Human Body Compo...
Tools for Nutrit...
Protein-Energy W...
Conclusion and A...
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed545    
    Printed4    
    Emailed0    
    PDF Downloaded76    
    Comments [Add]    

Recommend this journal