The Seven Methods for the Evaluation of Nutritional Status—ABCDEFG: Narrative Review

Featured Application

The proposed ABCDEFG framework for nutritional assessment provides a comprehensive tool for both clinical and community settings. By integrating traditional methods with ecological, functional, and genomic evaluations, it supports the shift toward personalized and precision nutrition. Its application is especially valuable for the early detection and management of malnutrition, sarcopenia, and chronic diseases, and it also provides a robust foundation for advancing research in nutrigenomics and the gut microbiota.

Abstract

Background: Nutritional status assessment is the cornerstone of the Nutrition Care Process, guiding diagnosis, intervention, and monitoring. The classical ABCD model (Anthropometry, Biochemical, Clinical, Dietary) has been widely applied; however, it presents limitations in addressing current nutritional and epidemiological challenges. Objective: This narrative review aims to synthesize and update the scientific evidence on the expanded nutritional assessment model, known as ABCDEFG, which incorporates the Ecological–microbiota (E), Functional (F), and Genomic–nutrigenomic (G) approaches. Methods: A narrative review of the literature was conducted through PubMed, Scopus, and Web of Science, covering publications from 2013 to 2025. Articles were selected based on relevance to at least one of the seven assessment domains. Findings were synthesized descriptively and critically, highlighting applications, strengths, and limitations. Results: The ABCDEFG framework offers a multidimensional perspective of nutritional assessment. While anthropometric, biochemical, clinical, and dietary methods remain essential, the inclusion of ecological dimensions (gut microbiota, environmental influences), functional measures (e.g., muscle strength, physical performance), and genomics enables a more sensitive and personalized evaluation. This integrative approach supports better clinical decision-making and research innovation in nutrition and health sciences. Conclusions: The seven-method model broadens the scope of nutritional assessment, bridging traditional and emerging tools. Its application enhances the capacity to identify nutritional risks, design targeted interventions, and advance precision nutrition.

1. Introduction

Nutritional status assessment is the first stage of the Nutrition Care Process (NCP), a framework standardized by the Academy of Nutrition and Dietetics to ensure an integrated and reproducible approach in both clinical and community settings. The NCP consists of four interrelated phases: assessment, diagnosis, intervention, and monitoring, with assessment serving as the foundation for evidence-based decision-making in health and nutrition [1].

Traditionally, assessment methods were grouped under the ABCD acronym (Anthropometric, Biochemical, Clinical, and Dietary), which established core parameters for identifying nutritional status across diverse populations [2,3]. However, recent epidemiological and nutritional shifts characterized by the coexistence of undernutrition and overnutrition, dietary transitions, and population aging have exposed important limitations of this classical model [4,5].

In response, new analytical dimensions have been introduced to enrich professional practice. This has led to the development of the expanded ABCDEFG model, which incorporates three additional components alongside the traditional four: E (Ecological assessment and gut microbiota), F (Functional assessment), and G (Genomic and nutrigenomic assessment) (Figure 1).

Figure 1. The seven methods in the nutritional assessment process and the recommended format for presenting case studies in Nutrition and Dietetics are summarized in the diagram. It depicts the continuum of the Nutrition Care Process (NCP), which consists of four stages: (1) nutrition assessment and reassessment, (2) nutritional diagnosis, (3) nutritional intervention, and (4) nutrition monitoring and evaluation. On the right, seven complementary nutritional assessment methods are outlined: anthropometry and body composition, biochemical assessment, clinical signs related to nutrition, dietary assessment, ecological factors (including gut microbiota), functional assessment, and genetic evaluation. Together, these methods provide comprehensive data for identifying nutritional problems and determining their etiologies using the PES format (Problem, Etiology, Signs and Symptoms). This information supports structured case analysis and documentation through the ADIME format (Assessment, Diagnosis, Intervention, Monitoring, and Evaluation).

This expanded framework reflects the evolution of nutritional science toward a more integrative perspective that goes beyond morphological and biochemical indicators to include functional, molecular, and environmental factors affecting health [6].

However, despite the broad use of the classical ABCD model, the literature lacks an integrated framework that combines ecological–microbiota, functional, and genomic/nutrigenomic dimensions with traditional assessment domains. Existing evidence addresses these components separately, without clarifying how they collectively strengthen diagnosis, intervention, and monitoring within the NCP. This gap underscores the need for an updated and integrative perspective, which the present narrative review aims to address by examining the scientific basis, applications, strengths, and limitations of the proposed ABCDEFG framework. The objective of this narrative review is to synthesize and update scientific evidence on nutritional assessment methods, highlighting their applications, strengths, and limitations, as well as proposing an updated conceptual framework to guide professionals in nutrition and health sciences.

2. Materials and Methods

This narrative review considered a comprehensive body of literature relevant to the seven components of the ABCDEFG nutritional assessment framework. Literature searches were conducted in PubMed, Scopus, and Web of Science using combinations of keywords including nutritional assessment, nutrition status evaluation, anthropometry, muscle function, gut microbiota, genetics and nutrition, and biochemical markers.

Eligible sources included original research articles, systematic reviews, clinical guidelines, and consensus statements addressing at least one ABCDEFG domain. Animal studies, editorials, letters to the editor, and publications without full-text availability were excluded.

Titles and abstracts were independently screened by two reviewers (R.Z.-V. and L.D.D.B.) to assess relevance. Articles were advanced to full-text review only when both reviewers agreed on inclusion. Reference lists of selected publications were also manually screened to identify additional relevant sources.

Following selection, studies were categorized according to the nutritional assessment domain addressed. Full-text evaluation was performed to extract evidence supporting anthropometric, biochemical, clinical, dietary, ecological, functional, and genomic–nutrigenomic components. Where relevant, observations related to the potential role of multispecies probiotic formulations were noted in the context of microbiota-related findings.

Although this review follows a narrative rather than systematic methodology, efforts were made to ensure transparency and methodological rigor. The literature search covered publications from 2013 to 2025, reflecting the period during which functional, microbiota-related, and nutrigenomic concepts became increasingly integrated into nutritional assessment.

Database searches yielded approximately 98 records. After title and abstract screening and exclusion of studies lacking relevance to nutritional assessment or the conceptual development of the ABCDEFG framework, a final set of 65 publications was included. No formal flow diagram was applied, as sources were selected based on conceptual relevance and contribution to emerging assessment domains beyond traditional models.

This approach was designed to balance methodological transparency with the exploratory and integrative aims of a narrative review, prioritizing conceptual coherence over exhaustive systematic coverage.

3. Results

3.1. Anthropometry and Body Composition

3.1.1. Anthropometry and Kinanthropometry

The wide range of methods and contexts in which anthropometry is applied has created a growing need for written protocols, equipment calibration, and standardized training to ensure data accuracy and comparability [7]. Kinanthropometry, derived from the Greek kínisi (movement), ánthropos (human), and métron (measure), refers to the quantitative evaluation of human body structure in relation to movement and performance. Its contributions extend beyond sports science, providing valuable insights into body composition, proportionality, and somatotype, which can inform clinical decision-making [8].

To promote methodological consistency, the International Society for the Advancement of Kinanthropometry (ISAK) established globally recognized protocols that detail measurement techniques, anatomical landmarks, calibration requirements, and reporting standards [9].

ISAK currently offers two levels of standardized protocols: the restricted protocol (21 measurements) for Level 1 certification and the full protocol (43 measurements) for Level 2. Using these measurements, body composition can be estimated through fractionation models ranging from two to five components, including adipose tissue, muscle, bone, residual mass, and skin. Additional outputs include sums of skinfolds, corrected girths, somatotype, proportionality indices, and Z-scores. These indicators have broad applications in evaluating health, growth, physical development, and athletic performance optimization [10].

Further details related to anthropometric indicators are available in the Supplementary Material.

3.1.2. Body Composition

• Five-Component Fractionation Model

Kinanthropometry by the complete method can determine body composition in 5 fractions. Table 1 shows the distribution of total body mass into five anatomical components—adipose, muscular, bone, skin, and residual mass—expressed in kilograms. The estimated total mass is compared with the structured-brute mass, showing both the absolute (kg) and relative (%) differences. It should be considered that the adipose mass is not the same as the percentage of body fat, because the adipose mass contemplates all the components of the adipocyte, that is to say, in addition to the fat mass, water and minerals [11].

Table 1. Five-component anatomical fractionation.

Component	Total
Adipose mass (kg)	12.61
Muscular mass (kg)	29.34
Bone mass (kg)	7.46
Skin mass (kg)	3.65
Residual mass (kg)	7.84
Estimated total mass (kg)	59.56
Kg dif estructured-brute mass	−1.34
% dif estructured-brute mass	−2.22

Obtained from ISAK Metry software version v2.0.5 [12].

• Segmental Muscular and Adipose Distribution

The ISAK Metry software [12] also provides segmented estimates of adipose and muscle tissue. Adipose mass is regionally distributed into upper (biceps, triceps, subscapular), central (suprailiac, supraspinal, abdominal), and lower segments (mid-thigh and calf skinfolds). The muscle mass is segmented into upper limbs (arms), thorax and lower limbs (thighs and legs) (Figure 2).

Figure 2. Muscular–adipose distribution. The figure illustrates segmental estimates of muscle and adipose mass derived from anthropometric assessment. The bar graph presents the relaxed and corrected circumferences of the arm, mid-thigh, and calf, reflecting regional muscle–fat composition. On the right, the schematic human figure depicts the relative contribution of adipose and muscular tissue across body segments. Data were obtained using the ISAK Metry software [12].

• Adiposity and Muscularity Indices

Two key proportional indices can be derived from anthropometric fractionation: the adipose–muscle index (adipose mass in kg divided by muscle mass in kg) and the muscle–bone index (muscle mass in kg divided by bone mass in kg). Lower adipose–muscle index values denote a more favorable balance between muscle and adipose tissue. The muscle–bone index, in contrast, provides an estimate of the muscular potential permitted by the underlying skeletal frame, providing insight into constitutional morphology, structural robustness, and functional capacity (Figure 3).

Figure 3. Adiposity and muscularity. The figure summarizes anthropometric indicators of adiposity and muscularity derived from skinfolds and corrected limb circumferences, including regional fat distribution, muscular Z-scores, and the adipose–muscle and muscle–bone indices, both classified as normal. Data were obtained using the ISAK Metry software [12].

3.1.3. Skeletal Proportions and Bone Distribution

Bone distribution allows the derivation of several proportionality indices with broad applications in assessing physical development and athletic aptitude (Figure 4).

Figure 4. The figure presents anthropometric indices used to assess skeletal proportions and body segment relationships. Variables include relative arm span, cormic index, forearm–arm index, leg–thigh index, acromio–iliac index, and intermembral index, each accompanied by its classification and interpretation. The diagrams on both sides illustrate the measurement points for upper- and lower-limb segments and trunk proportions. Together, these indices describe the geometric and functional balance among limb lengths, trunk height, and the shoulder–hip ratio, contributing to the evaluation of somatotype, biomechanics, and constitutional morphology. Data were obtained using the ISAK Metry software [12].

3.1.4. Somatotype Analysis

Kinanthropometry also enables the calculation of the three somatotype components, which are widely used in disciplines such as soccer [11]. Using the ISAK Metry tool [12], the corresponding somatotype chart can be generated. These values allow the determination of a somatotype profile—of which 13 variants are described—and its application in evaluating and optimizing athletic performance [13] (Figure 5).

Figure 5. The figure displays the individual’s somatotype based on endomorphic, mesomorphic, and ectomorphic components, placing the subject in the meso-ectomorphic region, indicative of moderate muscularity, low adiposity, and linear body proportions. Data were generated using the ISAK Metry software [12].

3.1.5. Health-Related Anthropometric Indices

Figure 6 shows that anthropometry generates information on some health indices, such as waist circumference, to determine cardiovascular risk, among others.

Figure 6. Health index derived from anthropometric and adiposity indicators. The figure synthesizes key metrics commonly used to assess cardiometabolic and nutritional status. These parameters include waist circumference, waist–hip ratio, conicity index, abdominal and triceps skinfolds, and body mass index (BMI), each contrasted with established reference values for healthy populations. A color-coded scale (green = optimal, yellow = moderate risk, red = high risk) enhances interpretation by categorizing the degree of associated risk. The evaluated subject presents a normal BMI (20.3 kg/m²), optimal skinfold levels (<12 mm), and low cardiovascular risk according to waist circumference, while the waist–hip ratio of 0.84 indicates moderate risk of central adiposity. As a whole, this composite profile provides an integrated overview of body composition and cardiovascular risk. Obtained from ISAK Metry software [12].

3.2. Technologies for Body Composition Assessment

3.2.1. Bioelectrical Impedance Analysis (BIA)

Bioelectrical impedance analysis (BIA) is a non-invasive, widely used technique for estimating body composition parameters such as fat-free mass, total body water, and skeletal muscle index. The method is based on measuring the body’s resistance and reactance when exposed to a low-amplitude electrical current. BIA has demonstrated solid clinical utility in diverse populations, including individuals with chronic kidney disease undergoing hemodialysis, where it supports nutritional monitoring and fluid status evaluation [14]. Its rapid application, portability, and relatively low cost make it suitable for both clinical and field settings.

3.2.2. Muscle Ultrasound (MU)

Muscle ultrasound (MU) enables the real-time visualization and quantification of muscle architecture, including thickness, pennation angle, and cross-sectional area. It has proven to be an effective tool for diagnosing and monitoring sarcopenia, particularly in older adults and hospitalized patients [15]. For example, measurements of rectus femoris muscle thickness show strong correlations with functional indicators such as handgrip strength (HGS) and the fat-free mass index, supporting its value as a surrogate marker of muscular function and overall physical performance [16].

The integration of BIA and MU provides a more comprehensive assessment of muscular health by combining quantitative (mass, volume, hydration) and qualitative (structure, tissue integrity) information. This multimodal approach improves diagnostic accuracy for conditions such as sarcopenia and cachexia and enhances the capacity to track intervention-related changes over time [16]. Considering the rising prevalence of muscle-wasting disorders in aging populations, incorporating both tools into routine clinical practice supports earlier detection, targeted nutritional strategies, and optimized rehabilitation interventions.

Table 2 summarizes the use of BIA and MU in hospital, community and sports settings.

Table 2. Use of bioelectrical impedance analysis (BIA) and muscle ultrasound (MU) in Hospital, Community and Sports settings.

Settings	Hospital	Community	Sports	References
Bioelectrical impedance analysis (BIA)	In clinical environments, BIA is employed to assess body composition in patients with chronic conditions such as kidney disease, heart failure, and cancer. It aids in evaluating nutritional status, fluid balance, and muscle mass, which are critical for patient management and prognosis.	In community health programs, BIA serves as a non-invasive, cost-effective tool for assessing body composition and detecting obesity, malnutrition, and sarcopenia, particularly among older adults. Its application in large-scale screenings supports early identification of nutritional risks and metabolic imbalances.	Athletic programs use BIA to monitor athletes’ body composition, hydration status, and muscle mass. This information guides training regimens and nutritional plans to optimize performance and recovery.	[14,17,18,19]
Muscle Ultrasound (MU)	Muscle ultrasound is utilized to detect muscle wasting and monitor sarcopenia in hospitalized patients, particularly the elderly. It provides real-time imaging to assess muscle thickness and quality, facilitating early intervention strategies.	While less common in community settings due to equipment and training requirements, muscle ultrasound can be employed in mobile clinics or health fairs to assess muscle health, especially in populations at risk for muscle degeneration.	In sports medicine, muscle ultrasound assists in evaluating muscle injuries, monitoring rehabilitation progress, and assessing muscle adaptations to training. It offers detailed insights into muscle architecture and function.	[15,19]

3.3. Nutritional Biochemistry

Biochemical assessment is a fundamental component of nutritional evaluation. It relies on the quantification of biomarkers in blood, urine, and other biological fluids to identify nutrient deficiencies, excesses, metabolic dysfunctions, or inflammatory states that may compromise health. These biomarkers provide objective and sensitive information that complements anthropometric and clinical findings, allowing early detection of nutritional risk and guiding targeted interventions. Table 3 summarizes the most frequently used biochemical indicators for assessing nutritional status, including proteins, micronutrients, inflammatory markers, and metabolic risk indicators.

Table 3. Key Biochemical Parameters in Nutritional Assessment.

Nutritional Assessment Category	Parameter (Normal Range)	Clinical Relevance	References
Evaluation of Protein Status	Albumin (3.5–5.0 g/dL)	Indicator of long-term protein status. Low levels may suggest malnutrition, inflammation or liver disease.	[20]
	Prealbumin (15–36 mg/dL)	More sensitive indicator of nutritional status, useful to evaluate recent changes in protein intake.	[20]
	C-Reactive Protein (<0.3 mg/dL)	Used in combination with albumin and prealbumin to assess chronic inflammation and its impact on nutritional status.	[21]
Evaluation of Energy Status and Macronutrient Metabolism	Fasting Glucose (70–100 mg/dL)	Assesses carbohydrate metabolism and may indicate hypoglycemia, insulin resistance or diabetes.	[22]
	Glycosylated hemoglobin (<5.7% normal, >6.5% diabetes)	Indicates glycemic control over the past 3 months.	[22]
	Lipid Profile: Total cholesterol (<200 mg/dL). LDL (<100 mg/dL) HDL (>40 mg/dL in men and >50 mg/dL in women) Triglycerides (<150 mg/dL)	Assesses cardiovascular risk and fat metabolism.	[23]
Evaluation of Renal Function	Blood Urea Nitrogen (BUN, 7–20 mg/dL) Creatinine (0.6–1.3 mg/dL)	BUN reflects the concentration of nitrogen in the blood derived from urea, a product of protein catabolism formed in the liver and excreted by the kidneys. Elevated serum creatinine levels are indicative of decreased glomerular filtration rate (GFR) and may suggest acute or chronic kidney disease.	[23]
Serum Osmolarity (275–295 mOsm/kg)	Electrolytes: Sodium (135–145 mEq/L). Potassium (3.5–5.0 mEq/L) Chlorine (96–106 mEq/L) Calcium (8.5–10.5 mg/dL)	Evaluates hydration status.	[24]
Micronutrient Status Evaluation	Serum iron (50–170 mcg/dL) and Ferritin (12–300 ng/mL in men, 12–150 ng/mL in women)	They assess iron stores and risk of anemia.	[25]
	Vitamin B12 (>200 pg/mL)	Deficiency associated with megaloblastic anemia and neuropathies.	[25]
	Folate (>3 ng/mL)	Indispensable in DNA synthesis and red blood cell formation.	[25]
	Vitamin D (30–50 ng/mL)	Important in bone health and immune function.	[25]
	Zinc (70–120 mcg/dL)	Related to the immune system and healing.	[25]

LDL, Low-Density Lipoprotein; HDL, High-Density Lipoprotein.

In clinical practice, these procedures are used to diagnose nutritional deficiencies, evaluate the response to dietary interventions, and monitor health status in individuals with chronic or acute conditions. Collectively, they form a comprehensive framework for assessing nutritional care outcomes, enabling clinicians to detect specific nutrient imbalances, quantify the effectiveness of nutritional modifications, and support the long-term maintenance of metabolic stability and disease control [26].

Accordingly, biochemical evaluation constitutes a core pillar of nutritional assessment. Its proper interpretation—integrated with clinical, anthropometric, and dietary information—facilitates the development of effective intervention strategies aimed at improving patient health [27].

3.4. Clinical Signs Related to Nutrition

In addition to dietary and anthropometric assessments, the systematic identification of clinical signs related to nutrition provides essential information about an individual’s nutritional status and may reveal deficiencies or excesses of specific nutrients [28].

These signs can be subtle or pronounced and may affect diverse systems, including the skin, hair, nails, eyes, oral cavity, cardiovascular system, gastrointestinal tract, and nervous system [29]. It is important to highlight that clinical signs alone are not diagnostic; however, when interpreted alongside biochemical, dietary, and anthropometric data, they contribute to a comprehensive and accurate evaluation of the patient’s nutritional status [30].

3.4.1. Clinical Signs Associated with Nutritional Deficiencies

Several nutritional deficiencies produce characteristic clinical signs that may guide the clinician toward early detection of underlying metabolic or dietary imbalances. Below are some of the most relevant manifestations associated with common nutrient deficiencies:

Protein Deficiency: Protein deficiency may manifest through peripheral edema, loss of muscle mass, thinning and fragility of hair, brittle nails, and delayed wound healing [31]. In severe forms, protein-energy malnutrition can progress to kwashiorkor, a syndrome characterized by generalized edema, abdominal ascites, dermatoses, and changes in hair pigmentation and texture [32].

Iron deficiency is the most prevalent nutritional deficiency globally [33]. Clinically, it may present with pallor, fatigue, weakness, reduced attention span, koilonychia (spoon-shaped nails), and glossitis [34].

Vitamin A deficiency commonly results in ocular alterations such as xerophthalmia (dryness of the conjunctiva), night blindness, and the presence of Bitot’s spots, which are keratinized plaques on the conjunctival surface [35]. Advanced deficiency can lead to keratomalacia (corneal softening) and increased susceptibility to infectious diseases. Among children, this deficiency significantly elevates morbidity and mortality risk [36].

Vitamin C Deficiency: Vitamin C deficiency produces symptoms including bleeding gums, petechiae, ecchymoses, impaired wound healing, and arthralgia [37]. In advanced cases, scurvy may occur, presenting with severe fatigue, musculoskeletal pain, gingival hypertrophy, and widespread hemorrhagic manifestations [38].

Calcium Deficiency: Calcium deficiency is associated with neuromuscular irritability, presenting as tetany, paresthesia, and generalized muscle weakness [39]. Chronic deficiency contributes to decreased bone mineral density and osteoporosis, while in children, it may impair growth and bone development due to inadequate mineralization during key developmental stages [40].

3.4.2. Interpretation of Clinical Signs

It is essential to highlight that clinical signs may be nonspecific and can be associated with multiple causes, not only nutritional ones. Therefore, a complete medical history, including dietary, medical, and pharmacological backgrounds, as well as complementary examinations (laboratory tests and imaging studies), is crucial to confirm the nutritional deficiency and rule out other potential causes of clinical signs [41,42]. Thus, clinical evaluation of nutritional status should be an integral part of patient assessment.

A general inspection of the patient is recommended, looking for signs such as pallor, edema, muscle mass loss, and alterations in the skin and hair. Subsequently, a more detailed physical examination should be performed, assessing different body systems for specific signs of nutritional deficiencies. It is important to carefully document clinical findings and correlate them with dietary, anthropometric, and biochemical information to obtain a complete assessment of the patient’s nutritional status. The identification of clinical signs related to nutrition is a valuable tool in nutritional status assessment. These signs, in conjunction with dietary, anthropometric, and biochemical information, allow for a comprehensive patient evaluation and can reveal specific nutrient deficiencies or excesses [43]. Healthcare professionals must be familiar with the most relevant clinical signs associated with common nutritional deficiencies and use them as part of their clinical practice to improve patient care and monitoring of those at risk of malnutrition. The principal clinical signs related to nutritional deficiencies are summarized in Table 4, based on information from previous studies [31,32,33,34,35,36,37,38,39,40].

Table 4. Clinical Signs Related to Nutrient Stores.

Clinical Sign	Associated Nutritional Deficiency
Skin
Pale skin	Iron deficiency
Xerosis (dry skin)	Vitamin A, essential fatty acid deficiency
Seborrheic dermatitis	Zinc, vitamin B3 (niacin) deficiency
Delayed wound healing	Vitamin C, zinc, protein deficiency
Petechiae	Vitamin C deficiency
Hair and Nails
Alopecia	Zinc, biotin, protein deficiency
Trichorrhexis	Protein, iron deficiency
Koilonychia	Iron deficiency
Onychorrhexis	Protein deficiency
Eyes
Xerophthalmia	Vitamin A deficiency
Night blindness	Vitamin A deficiency
Bitot’s spots	Vitamin A deficiency
Oral Cavity
Gingivitis	Vitamin C deficiency
Glossitis	Iron, vitamin B2 (riboflavin), vitamin B3 (niacin), vitamin B6 (pyridoxine) deficiency
Angular cheilitis	Iron, vitamin B2 (riboflavin) deficiency
Gastrointestinal Tract
Diarrhea	Zinc, vitamin B3 (niacin), folic acid, vitamin B12 (cobalamin) deficiency
Nervous System
Asthenia and adynamia	Iron, vitamin B12 (cobalamin) deficiency
Irritability	Vitamin B1 (thiamine) deficiency
Attention deficit	Iron deficiency
Musculoskeletal System
Muscle weakness	Protein, vitamin D deficiency
Growth delay	Protein, calcium, vitamin D deficiency
Other
Edema	Protein deficiency

In addition to the clinical signs described above, it is important to highlight that additional nutrition-related physical findings have been systematically documented in specialized resources. The Nutrition-Focused Physical Exam for Adults: An Illustrated Handbook provides a comprehensive and clinically structured physical examination approach and includes pictorial representations of various stages of muscle and fat depletion, vitamin and mineral deficiencies, and fluid imbalance [44].

The Clinical Assessment of Nutritional Status (CANS) Score is another useful tool that enhances the identification of nutritional status in newborns. This scoring system integrates clinical signs of fat and muscle stores, allowing for an accessible and reliable evaluation of neonatal nutritional condition, particularly in settings where biochemical or anthropometric data may be limited [45].

3.5. Dietary Assessment

Assessing dietary intake involves collecting information on all foods and beverages (and in some cases supplements) consumed during a specific period of time. This information can be measured at different levels (national, collective or individual). Measuring the dietary intake of subjects, however, is particularly difficult and subject to both random and systematic measurement errors, so it is of great importance for the practitioner to know the strengths and weaknesses of each of the methods. Therefore, the level of assessment and the method must be carefully selected in relation to the objective of the research or intervention to be achieved, in the case that the assessment is at the individual level. Thus, nutritionists and researchers face a great challenge when selecting the method to be applied; for more details, Bates and Dao describe how to achieve an adequate selection of the method [46,47].

3.5.1. Methods for Assessing Dietary Intake

Table 5 shows the most commonly used methods for assessing dietary intake, both retrospective and prospective, and their strength, rationale, advantages and disadvantages, while Table 6 shows the recommended area of application of the method. Among the most commonly used methods are the frequency of consumption (FC), single or repeated 24 h recall (R24hrs), and consumption diaries (DC) [48], which can be administered for a variable number of days. However, the estimation of the food portion continues to be a great challenge for professionals in the area. For this, tools have been designed, such as the determination of standardized portion sizes, the use of standardized home measurements, and images and food models, which must be specific for the population under study.

Table 5. Most commonly used methods for assessing dietary intake, their strength, rationale, advantages and disadvantages.

	Method	Strength	Rationale	Advantages	Disadvantages	References
Prospective	Duplicate meal method	Considered the gold standard for evaluating micronutrient or contaminant intake.	Used in controlled institutional studies where portion sizes are predetermined. Participants set aside duplicate samples of all foods and beverages consumed over a defined period, which are weighed and analyzed in a laboratory. Useful for the direct measurement of dietary exposure, nutrient content, and the influence of environmental factors (e.g., soil composition affecting selenium, iodine, zinc).	Highly accurate nutrient intake estimation. Independent of food composition databases. Minimizes omission of consumed foods.	Very costly due to sample preparation and laboratory analyses. Not feasible for large-scale studies. May alter usual eating behavior, leading to intake underestimation. Data handling requires trained personnel and is time-consuming.	[49]
	Consumption diaries	Considered the reference method in validation studies.	Participants record foods consumed through estimated or weighed entries. High validity and accuracy when properly performed.	Detailed intake data. No interviewer required. Weighed entries improve estimation precision. Reduces recall bias.	High participant burden and requires training. Underreporting is common. Multiple days required to estimate usual intake. Data quality decreases as recording period increases. Costly in time and resources.	[48]
Retrospective	Frequency of consumption	Useful for large-scale studies examining dietary patterns and associations with disease.	Collects frequency of consumption of specific foods or food groups over predefined time periods (daily to yearly). Can be interviewer-administered or self-administered. Semi-quantitative versions include portion sizes to estimate nutrient intake.	Captures intake of frequently and infrequently consumed foods. Can assess portion sizes and preparation methods. Does not alter eating behavior. Low respondent burden and relatively low cost. Efficient for epidemiological research.	Requires careful design, population adaptation, and validation. Limited food lists may miss relevant items → underestimation. Less suitable for populations with diverse food patterns. Recall-dependent → memory bias. Challenging for children and older adults.	[49,50]
	24 h Recall	Widely used for estimating mean intake in large populations.	Records all foods and beverages consumed in the prior 24 h using a standardized, open-ended interview format. Quantitative information is converted to energy/nutrient intake using national food composition tables.	Captures context: meal patterns, preparation, location. Does not influence food choice. Low cognitive burden → improved accuracy. Suitable for low-literacy populations.	Multiple recalls required to estimate usual intake. Recall-dependent → memory bias. Requires trained interviewers familiar with local food culture. Data entry and analysis are resource-intensive.	[49,51]
	Dietary History	Developed for clinical evaluation of typical long-term intake.	Combines 3-day food diary, 24 h recall, and a checklist of common foods. Conducted through an in-depth interview on usual eating behavior. Portion size recorded using standardized measures.	Provides comprehensive information on habitual intake, eating patterns, and seldom-consumed foods. Useful for assessing prevalence of inadequate diets.	Requires highly trained personnel and lengthy interviews (~90 min). Recall-dependent → memory bias. High participant burden and unsuitable for children/older adults. Costly and demanding in terms of time and staff.	[49,52]

Table 6. Recommended area of application of the method.

Method	Research	Community Nutrition	Outpatient Clinical Nutrition	Hospital Clinical Nutrition
Consumption Diaries	X	-	X	-
Duplicate meal method	X	-	-	-
Frequency of consumption	X	X	X	-
24 h Recall	X	X	X	X
Dietary history	X	-	-	X

Sign “X“ indicates the method is applicable and sign “-“ indicates not applicable or no data.

Among the visual materials studied, a food atlas has proved to be the most effective, clear and easily accessible tool for consultation and transport, which allows the quantification of food and nutrient intake and decreases the bias of people’s self-assessment. Therefore, the purpose of this tool is to complement traditional self-reporting methods (reminders or 24 h records) or to serve as a stand-alone method to provide the primary record of dietary intake [49]. Thus, when photographs of foods are used in the identification of portion sizes, the respondent’s ability to relate the amount consumed to that presented through an image is enhanced [53,54]. Moreover, digital photography has proven to be unobtrusive and accurate and has become the method of choice for assessing food consumption, both individually and collectively.

Ecuador published its first food atlas in 2019 [55] and a second edition in 2022 [56] as a support tool for professionals. This second edition presents 142 foods in their usual form of consumption and 49 representative home measurements for the Ecuadorian population. Once the portion consumed or the amount of food ingested has been estimated, the energy and nutrient intake should be multiplied by the values of the nutritional content of the foods, which are shown in the tables of chemical composition of foods. Given the great variability in nutritional composition among the different regions of the world, it is recommended to use nutritional composition tables specific to the country or region. Ecuador has two tools available: the food composition table of the city of Cuenca, which presents information on foods prepared in this city [57], and the table of chemical composition of foods based on nutrients of interest to the Ecuadorian population, which provides the nutritional content of more than a thousand locally used foods, including information on the nutritional labeling of more than 300 processed and ultra-processed foods [58].

3.5.2. Nutrient–Drug Interactions

Given the diversity of nutritional assessment tools, picking the proper method depends greatly on the setting, the objective of the study, and the characteristics of the population [59,60]. Assessing nutrient–drug interactions (NDIs) is a key complementary component of nutritional evaluation, particularly in clinical and aging populations where polypharmacy is widespread [61,62]. Despite not being a traditional dietary intake technique, NDIs have a direct impact on nutrient needs, metabolism, and absorption, which shapes the dietary (D) dimension’s accuracy and interpretation within the ABCDEFG framework. Integrating NDIs into dietary assessment helps practitioners to identify hazards that may impair nutritional status, interfere with treatment efficacy, or need personalized dietary interventions.

Drug pharmacokinetics and pharmacodynamics can be dramatically altered by food and dietary factors, which can affect absorption, metabolism, distribution, and excretion. On the other hand, some drugs may have a harmful effect on nutrient bioavailability, resulting in insufficiencies or higher needs [61]. Including validated tools to assess NDIs in clinical practice enhances patient safety, optimizes therapeutic efficacy, and supports personalized nutrition strategies. In both hospital and outpatient settings, screening tools like structured questionnaires offer a useful method for early detection of possible NDIs. To date, no fully standardized methods exist for the assessment of NDIs. Table 7 presents a plausible set of questions that may be considered for evaluation of NDIs [63].

Table 7. Example of a screening questionnaire for Nutrient–Drug interactions (NDIs).

Item	Question	Response	Clinical Relevance
1	Do you regularly consume grapefruit or grapefruit juice?	Yes/No	Inhibitor of CYP3A4: increasing drug plasma levels (e.g., statins, calcium channel blockers).
2	Do you use supplements or foods high in calcium along with your prescription drugs?	Yes/No	Interference with antibiotics like tetracyclines, bisphosphonates, thyroid hormones.
3	Do you consume alcohol while taking your medications?	Yes/No	Alters drug metabolism, risk of hepatotoxicity with certain drugs.
4	Do you drink tea or coffee within 1 h of taking your medication?	Yes/No	Reduction in absorption of iron, levothyroxine, bisphosphonates.
5	Do you follow a high-fiber diet or consume fiber supplements?	Yes/No	Decrease absorption of some oral drugs (e.g., digoxin, metformin).
6	Do you use herbal supplements (e.g., St. John’s Wort, ginseng, ginkgo)?	Yes/No	Potential for strong pharmacokinetic interactions.
7	Have you noticed any changes in appetite, digestion, or nutrient tolerance since starting your medication?	Yes/No	Helps detect drug-induced nutrient malabsorption or intolerance.

Score: ≥2 “Yes” answers → High risk of potential food–drug interactions; requires further dietary counseling and pharmacological review. 0–1 “Yes” answer → Low risk; routine monitoring recommended.

Such an assessment is crucial to monitor the risks associated with NDIs. Recently, for example, a study was conducted to assess the knowledge, attitudes, and awareness of Registered Nurses regarding food–drug interactions. The study involved administering a structured 40-question survey to 278 nurses, revealing a significant deficit in both knowledge and attitudes among nurses toward food–drug interactions [63]. Therefore, screening for NDIs not only complements dietary intake measurement but also enhances the clinical applicability of the ABCDEFG model by incorporating pharmaceutical influences into nutritional decision-making.

3.6. Ecological Assessment: Microbiome and Gut Microbiota

Ecological assessment includes the evaluation of environmental, dietary, and microbial factors influencing gastrointestinal and systemic health. The gut microbiota is a central component of this dimension due to its dynamic interaction with the host’s metabolic, immune, and neuroendocrine functions. Patterning of microbiota through molecular, sequencing, and bioinformatic approaches enables a more sensitive and personalized evaluation of nutritional status, complementing traditional anthropometric, biochemical, and clinical methods [64]. This ecological perspective also integrates lifestyle determinants, medication exposure, and dietary patterns, all of which modulate microbial diversity, composition, and function.

3.6.1. Tools for Screening the State of Gut Microbiota

Advances in sequencing technologies and bioinformatics have significantly enhanced our ability to profile these microbial communities, providing insights into their contributions to health and disease [65].

The process begins with microbial DNA extraction, followed by sequencing techniques such as 16S rRNA gene amplification for bacteria or ITS for fungi. Alternatively, shotgun metagenomic approaches are used to encompass the full genetic composition of the microbial community [65]. The sample quality, protocols used for extraction, and sequencing platforms (e.g., Illumina, Nanopore, PacBio) significantly impact data reliability.

For the bioinformatic workflow, raw sequencing data first undergo quality control using tools such as FastQC [66] and Trimmomatic [67], followed—when applicable—by assembly steps for metagenomic datasets. Taxonomic classification is typically performed with curated reference databases, including SILVA [68], Greengenes [69], or GTDB [70]. For 16S rRNA analyses, established pipelines such as QIIME2 or DADA2 are commonly employed [71], whereas shotgun metagenomic profiling may utilize tools such as METAnnotatorX2, MetaPhlAn, Kraken2, or HUMAnN [72]. Functional inference can be conducted using prediction tools like PICRUSt [73] or directly through gene annotation derived from shotgun metagenomic sequencing.

For common microbiota analysis indicators, Alpha diversity (e.g., Shannon, Simpson) and Beta diversity (e.g., UniFrac, Bray–Curtis) metrics are calculated alongside relative abundance profiles to understand the complexity and variation within microbial communities [74]. Whereas, associations between specific taxa and nutritional states or dietary patterns are explored using statistical analyses and multivariate models [75]. This approach helps us to understand how different factors influence gut microbiota composition and, subsequently, human health.

Data interpretation must be cautious due to high inter-individual variability, lack of standardization in pipelines, and the absence of a universally defined healthy microbiome [76,77]. Moreover, predicted functional analyses should not replace transcriptomic, proteomic, or metabolomic validation.

Within the ecological–microbiota dimension (E), key indicators include dysbiosis risk scores such as the Dys-R questionnaire, stool consistency and gastrointestinal function scales, molecular profiling through 16S rRNA sequencing or shotgun metagenomics, recent antibiotic exposure, and dietary patterns known to modulate gut microbial composition. In practice, gut microbiota profiling through 16S rRNA sequencing has become the gold standard for assessing ecological nutritional status [78].

However, when sequencing is not available, complementary tools can be integrated into the ecological assessment. These include the Dys-R questionnaire (Table 8) [79], the Bristol scale together with medication history (antibiotics and non-antibiotics) [65,80,81], evaluation of diet based on the intestinal microbiota [82] (Table 9), and, in the case of oncological patients, the gastrointestinal function evaluation scale (Table 10) [83].

Table 8. Dys-R Questionnaire.

Questions		Score
A	Were you born by cesarean section? 0 = No 1 = Yes
B	Were you breastfed for less than 6 months? 0 = No 2 = Yes
C	Do you consume ultra-processed foods more than 3 times per week? 0 = No 1 = Yes
E	Have you taken antibiotics in the last 3 months? 0 = No 3 = Yes
F	Have you taken non-steroidal anti-inflammatory drugs in the last 3 months? 0 = No 2 = Yes
G	Are you currently undergoing treatment or follow-up for any health problem? 0 = No 3 = Yes
Total score (Maximum 14 points) Score > 8: Risk of dysbiosis, characterized by low richness of gut microbiota. Score ≤ 8: No risk of dysbiosis, probably adequate gut microbiota richness.		Total

Note: Table based on [79].

Table 9. Evaluation of diet based on gut microbiota.

Effect of Diet on Gut Microbiota		Modifications of the Gut Microbiota
		Beneficial Bacteria		Opportunistic Pathogens
Diet components	Diet/food	Bifidobacteria	Lactobacilli	Bacteroides	Clostridia
Protein	Animal protein (lean)	Increases	-	Decreases	-
	Animal protein (high fat)	Decreases	-	-	Increases
	Whey protein extract (Dairy or Supplements)	Increases	Increases	Decreases	Decreases
	Pea Protein Extract (Peas)	Increase	Increases	-	-
Lipids	Rich in fat (Saturated—LCT)		Decreases	Increases	Increases
	Low fat (low fat—Low LCT)	Increases	-	-	-
	Saturated fat (LCT): lard		-	Increase	-
	Unsaturated fat (polyunsaturated): fish oil	Increases	Increases	-	-
Carbohydrates	Glucose, Sucrose, Fructose (fruits with peel)	Increases	-	Decreases	-
	Lactose (dairy products and derivatives)	Increases	Increases	Decreases	Decreases
	Artificial sweeteners (saccharin)	Decreases	Decreases	Increases	Decreases
	Gluten-free diets	Decreases	Decreases	-
	Soluble fiber (prebiotics): fruits and raw salads	Increases/SCFA	Increases/SCFA	-	Decreases
	Dietary fiber: whole grain cereals and wheat bran	Increases	Increases	-
	Dietary fiber: FOS, polydextrose and AOS	-	-	-	Decreases
	Dietary fiber: resistant starches	Increases	Increases	-	-
Polyphenols	catechins, flavonols, flavones, anthocyanins, proanthocyanidins and phenolic acids	Increases	Increases	Decreases	Decreases
	Red wine	-	-	Increases	-
Supplementation	Probiotic Capsule	Increases/SCFA	Increases/SCFA	-	-

SCFA: short-chain fatty acids, LCT: long-chain triglycerides, FOS: fructooligosaccharides, AOS: arabinooligosaccharides. Note: Table based on [82].

Table 10. Gastrointestinal function assessment scale.

Symptoms/Severity Assessment
Category	Symptom	Category 1	Category 2	Category 3	Category 4
1	Do you have intestinal gas?	Absent/Nearly none (0)	Mild (1)	Moderate (2)	Severe (3)
2	Do you feel abdominal bloating during the day?	No bloating (0)	Mild (1)	Moderate (2)	Severe (3)
3	Do you have the presence of bowel sounds?	Absent (0)	Mild (1)	Moderate (2)	Severe (3)
4	Do you feel abdominal pain?	No discomfort (0)	Mild to moderate discomfort (1)	Somewhat severe discomfort (1)	Very severe discomfort (1)
5	Do you feel any discomfort in your rectum during bowel movements?	No discomfort (0)	Mild (1)	Moderate (2)	Severe (3)
6	How many bowel movements per day?	0–2 bowel movements per day (0)	3–4 bowel movements per day (1)	5–6 bowel movements per day (2)	7 or more bowel movements per day (3)
7	What is the consistency of the bowel movements?	All bowel movements are solid (0)	Bowel movements solid and soft (pasty) (1)	Soft stools (2)	Liquid stools (3)
8	Do you feel the urge to have a bowel movement?	Not urgent (0)	Somewhat urgent (1)	Urgent (2)	Very urgent (3)
9	Do you feel pain during bowel movements?	No pain (0)	Mild (1)	Moderate (2)	Severe (3)
Score		Total:
Interpretation		A: Normal function	B: Mild dysfunction	C: Moderate dysfunction	D: Severe dysfunction
		0 to 5 points	6 to 11 points	12 to 20 points	21 to more points

Note: Table based on [83].

The Dys-R questionnaire was developed in Brazil in 2023 using a sample of 219 individuals with chronic diseases and a control group of 52 healthy participants from a tertiary hospital. All participants completed the Dysbiosis Frequently Asked Questions Management (DYS/FQM) semi-structured questionnaire to collect sociodemographic and clinical information, and fecal samples were obtained for microbiota analysis. The results showed that there were notable differences between the groups, evidencing a lower richness of intestinal microbiota as the main marker of intestinal dysbiosis and a positive predictive value of 79%, concluding that the new Dys-R questionnaire showed a good performance to rule out dysbiosis [79].

Dysbiosis is also frequent among oncologic patients, where medical treatments can yield substantial benefits when nutritional status is adequate but may also generate secondary effects or complications depending on the stage of disease [84].

One of the most common complications of this type of treatment is the increased prevalence of malnutrition [83]. This may be due to high predisposition in oncological treatment (chemotherapy and radiotherapy) related to gastrointestinal toxicity such as oral mucositis, constipation, esophagitis, intestinal perforations, hypertransaminasemia and the presence of diarrhea [85]. Among the most frequent gastrointestinal toxicities is chemotherapy- or radiotherapy-induced diarrhea. The NCI classifies diarrhea-related adverse events as follows: grade 1 (up to four stools per day), grade 2 (four to six stools per day), grade 3 (seven or more stools per day), and grade 4 (life-threatening consequences). This treatment-induced toxicity disrupts the intestinal microbiota, which in turn indirectly compromises nutritional status [86].

This alteration of the intestinal microbiota, induced by chemotherapeutic agents and exposure to radiotherapy [85], is referred to as intestinal dysbiosis. It is typically manifested through gastrointestinal symptoms such as diarrhea, abdominal pain, and abdominal distension. Therefore, a nutritional strategy would be to supplement with probiotics (live microorganisms) that provide immunological and non-immunological benefits with respect to the lymphoid tissue associated with the intestinal mucosa (GALT) in the oncologic patient submitted to medical therapies [85,86].

3.6.2. Physiological Axes and Systemic Implications of the Gut Microbiota

In addition to taxonomic and functional profiling, ecological evaluations should consider the physiological networks that connect the gut microbiota with multiple organ systems [86]. This broader viewpoint emphasizes the gut–muscle, gut–brain, and gut–liver axes, which together elucidate how microbial metabolites and community composition affect metabolic homeostasis, immune responses, and host physiology, essential elements of nutritional status.

The gut–muscle axis has gained particular relevance in the context of sarcopenia, frailty, and age-related functional decline. A recent systematic review concluded that modifying the gut microbiota through bacterial depletion, fecal transplantation, probiotics, prebiotics, or SCFAs can directly alter muscle phenotype [87]. In fact, in older adults, correlations have been observed between microbial diversity and composition and parameters of muscle mass, strength, and physical performance, with some intervention studies showing functional improvements after microbiota modulation [88]. This opens up a range of possibilities that could hold therapeutic potential, although further mechanistic and interventional research is needed.

The gut–brain axis is also part of the spectrum of systemic interactions. Accumulating evidence suggests that the microbiota influences the regulation of appetite, eating behavior, common mental illnesses, immunity, and even emotional states [89,90]. This relationship is mediated by the production of microbial metabolites, neurotransmitters, and neuromodulators and through neuroendocrine or neural signaling pathways. Although still emerging, this field suggests that dysbiosis caused by dietary changes, antibiotic exposure, or chronic illness may disrupt the production of compounds such as serotonin or GABA, potentially impacting appetite, food intake, mood, anxiety, or depression [91,92].

The gut–liver axis reflects the bidirectional relationship between the gut microbiota, intestinal permeability, microbial metabolites, and hepatic functionality [93]. In states of dysbiosis, increased intestinal permeability can facilitate the translocation of endotoxins into the portal circulation, thereby exacerbating liver inflammation, insulin resistance, and metabolic dysfunction and contributing to the development or progression of diseases such as fatty liver disease, dyslipidemia, or metabolic syndrome [94,95,96,97]. Although direct studies in humans are still limited, this mechanism is consistent with recent theories linking the gut microbiota, lipid metabolism, chronic inflammation, and nutritional impairment.

Dietary patterns, meal timing, sleep habits, and circadian rhythm can alter the composition and functional diversity of the gut microbiota [98]. This, in turn, affects metabolic regulation, microbial metabolite production, and inflammation, potentially predisposing individuals to metabolic, liver, and inflammatory disorders [99]. Therefore, for a comprehensive ecological assessment, it is essential to consider not only “what is eaten” but also “when and how” it is consumed. Incorporating chrononutritional variables, meal timing, sleep patterns, and lifestyle behaviors provides a more accurate understanding of microbiota-mediated influences on nutritional health [100,101,102,103].

In clinical and community settings, the ecological dimension supports multiple applications, including screening for dysbiosis, guiding the use of prebiotic and probiotic strategies, tailoring fiber intake to optimize gut microbial balance, and managing treatment-related gastrointestinal toxicity, particularly in oncology and other high-risk populations. Additionally, this field informs community-level dietary interventions aimed at promoting microbial diversity and improving long-term digestive and metabolic health.

Within the ABCDEFG framework, alterations in gut microbiota composition modify the clinical interpretation of dietary intake, biochemical markers, and anthropometric indicators. Similar nutritional profiles may therefore reflect distinct metabolic or inflammatory states when dysbiosis, recent antibiotic exposure, or impaired gastrointestinal function are present, leading to different diagnostic priorities and targeted interventions focused on microbiota modulation rather than solely on nutrient quantity or caloric intake.

3.7. Functional Assessment

Functional assessment refers to the evaluation of muscle strength, physical performance, mobility, and functional reserve as indicators of nutritional status.

Functional assessment is a key component in the evaluation of nutritional status and in the follow-up of nutritional interventions, since loss of muscle function is an essential feature in malnutrition, and its recovery is an indicator of nutritional improvement [104]. It has been reported that functional status on admission to intensive care units (ICU) is an independent predictor of mortality, especially in cancer patients [105,106].

Within the functional dimension, relevant indicators include HGS measured by dynamometry, habitual gait speed, the Short Physical Performance Battery (SPPB), the Timed Up and Go (TUG) test, knee extension strength assessments, and tongue strength as a marker of oral and swallowing function [107].

3.7.1. Techniques to Measure Muscle Strength and Function

• Muscle Strength

Muscle strength is defined as the ability of a muscle or group of muscles to exert tension against a resistance in a given movement and at a given execution speed [108,109].

The most commonly used test is HGS dynamometry. HGS is a useful indicator of general health status and, specifically, of early mortality from any cardiovascular cause, as well as disability [110,111]. The Academy of Nutrition and Dietetics and the American Society for Parenteral and Enteral Nutrition recommend the use of HGS to document impaired physical function, depending on the patient’s circumstances [112].

Other muscle strength indicators include knee flexion/extension and tongue strength, which are described in Table 11.

Table 11. Indicators of muscle strength and their clinical use.

Indicator	Clinical Use	Cut of Points	Protocols
Handgrip strength	- Indicator of dynapenia - Criteria for sarcopenia [113]	EWGSOP2 [113]: <27 kg for men <16 kg for women	- General population [113,114] - Hospitalized and ICU [115,116]
	Criteria for malnutrition [117]	Measurably reduced according to normative standards.
Knee flexion/extension	- Diagnosis of sarcopenia [118]. - Frailty [119].	Šarabon N. et al. [120]. Males: - 1.34–2.23 Nm/kg for extended knee angle - 2.92–3.45 Nm/kg for mid-range knee angle - 2.50–3.06 Nm/kg for flexed knee angle Females: - 1.01–1.50 extended knee angle - 2.08–2.74 for mid-range knee angle - 2.04–2.71 Nm/kg for flexed knee angle	- General population [121] - Older adults [122]
Tongue strength	- Indicator of oral frailty [123] - Clinical indicator of swallowing function [124]	Marín-Bernard E et al. [125]: Males: - Young adults: 50.15–59.91 kPa - Middle-aged adults: 45.02–57.76 kPa - Older adults: 40.71–49.89 kPa Females: - Young adults: 51.52–55.88 kPa - Middle-aged adults: 47.57–52.17 kPa - Older adults: 37.51–45.66 kPa	- General population [126] - Hospitalized older adults [127]

EWGSOP2: European Working Group on Sarcopenia in Older People; ICU: intensive care unit.

• Muscle Function

Muscle function tests are sensitive indicators of nutritional status and effective tools for monitoring nutritional interventions [126]. Muscle functional capacity in the context of nutritional assessment can be assessed by physical tests and structured questionnaires.

Gait velocity (GV) is a valid, reliable, and sensitive measure of functional capacity, commonly used to assess general health in various populations. Research has shown that older adults with overweight or obesity and slower gait speed are more likely to have nutritional risks, such as inadequate dietary intake [128]. A common variant of this measure is the four-meter gait speed test, which has established protocols and normative values for adults [129].

3.7.2. Physical Performance

Physical performance is a key component in the assessment of nutritional status due to its ability to capture the integrated effects of nutrition, metabolism, and overall health on an individual’s capacity to perform daily activities. Evaluating physical performance therefore provides a dynamic understanding of how nutrition influences functional independence, recovery potential, and quality of life, particularly in aging or clinically compromised populations [130,131].

• Short Physical Performance Battery

The SPPB (Figure 7) is an objective tool to assess three domains (balance, repeated rising from a chair, and gait speed) of lower-extremity physical function [132]. Scores on this tool are associated with several adverse health outcomes, including mortality [133].

Figure 7. Short Physical Performance Battery (SPPB). The figure depicts the standardized assessment of physical performance through three components: (1) balance tests (side-by-side, semi-tandem, and tandem stands), (2) gait speed over 4 m, and (3) the chair stand test. Each component is scored from 0 to 4 points, yielding a total score of 0–12 points that reflects lower-extremity function, frailty, and mobility status in older adults [132].

3.7.3. Timed up and Go Test

The Timed Up and Go (TUG) test evaluates mobility in older adults [134]. Also, TUG has been used to assess functional mobility [135] and as a predictor of falls and mortality. The test requires the participant to stand up from a chair, walk three meters around an obstacle, return, and sit down again. A completion time greater than 13.5 s has been proposed as a threshold for predicting fall risk in community-dwelling older adults, though this predictive value depends on the individual’s physical condition and is more relevant for those with lower functional capacity [136].

In clinical and community contexts, the functional dimension (F) plays a central role in frailty screening, the diagnosis and longitudinal monitoring of sarcopenia, and the planning of targeted rehabilitation strategies aimed at restoring or preserving functional capacity. Moreover, functional indicators are essential for community-based fall-risk surveillance, supporting early identification of mobility decline and guiding preventive interventions to maintain independence and quality of life.

Within the ABCDEFG framework, functional impairment modifies the clinical relevance of anthropometric or biochemical findings, prioritizing interventions aimed at restoring physical performance, muscle strength, and functional reserve rather than focusing exclusively on body composition correction or isolated nutrient deficits.

3.8. Genetics

• Genetic Tests and Their Influence on Nutritional Status

Genetics component assesses the genetic variations influencing nutrient metabolism, dietary response, disease susceptibility, and personalized nutrition strategies.

Nutrigenetics influences health and can condition the nutritional status of an individual [137], which is why it is essential to adhere to this parameter in nutritional assessment; knowledge of genetic variants allows the personalization of a food plan [138]. In the genetic dimension, key indicators include SNP panels involving genes such as MTHFR, APOE, FTO, and VDR; variants associated with food intolerances, such as those determining lactose persistence; and metabolic gene variants that influence nutrient utilization and dietary response. Table 12 details the genetic tests and their influence on nutritional status.

Table 12. Genetic Tests and Their Influence on Nutritional Status.

Genetic Test	Description	Impact on Nutritional Status	References
SNP genotyping	Identification of single-nucleotide polymorphisms related to nutrient metabolism.	Allows for personalized dietary recommendations based on genetic predisposition to certain deficiencies or diseases.	[139]
Exome sequencing	Analysis of all coding regions of the genome.	Detects mutations that can affect the absorption and metabolism of essential nutrients.	[140]
Macronutrient Metabolism Testing	Evaluation of genes related to carbohydrate, fat and protein metabolism.	Helps design personalized diets to optimize energy metabolism.	[138]
Analysis of Vitamin-Related Genes	Study of genetic variants that affect the absorption and utilization of vitamins.	Identifies risks of vitamin deficiencies, allowing specific nutritional interventions.	[139]
Genetic Food Intolerance Testing	Detection of genetic predisposition to intolerances such as lactose or gluten.	Facilitates dietary adaptation to avoid adverse symptoms and improve nutrient absorption.	[140]

The incorporation of genetic testing into nutritional practice has shown mixed results [139]. Some studies indicate that genetic information can motivate dietary changes, such as reducing sodium and alcohol consumption in individuals with high-risk genetic variants. However, overall evidence on the effectiveness of integrating genotyping into nutritional counseling remains limited and of variable quality [141].

A recent systematic review of randomized controlled trials evaluated the impact of personalized nutrition (including genetic information) on diet, physical activity, and health outcomes [142]. Despite the good methodological quality of the trials, consistent benefits were scarce. Positive effects were observed in reducing salt intake and, in some cases, improvements in fat consumption patterns. However, changes in parameters such as weight, lipid markers, or quality of life were generally inconsistent or not significant. In addition, the trials were mainly conducted in North American and European populations, which limits their global applicability.

On the other hand, nutrigenetics and nutrigenomics offer promising conceptual frameworks for understanding how genetic variants affect nutrient response and how diet can modulate gene expression [143,144]. Some studies identify variants related to lipid, caffeine, and sodium metabolism [145,146].

Within clinical and community settings, the genetic assessment supports personalized dietary recommendations by identifying genetic variants that influence nutrient metabolism, food tolerances, and individual dietary responses. This information helps uncover metabolic susceptibilities, guide specific nutrient requirements, and tailor interventions to optimize health outcomes. Additionally, genomic insights contribute to long-term risk reduction strategies by informing preventive nutrition approaches that account for inherited predispositions to chronic diseases.

4. Discussion

The findings of this narrative review demonstrate that the ABCDEFG framework represents a significant conceptual evolution in nutritional assessment, providing a multidimensional perspective that bridges classical and emerging methodologies. This expanded model addresses several limitations of the traditional ABCD approach, which, despite its extensive use in clinical and public health contexts, often fails to capture the complexity of nutritional alterations influenced by environmental, functional, and molecular factors. Similar perspectives have been highlighted in the recent literature, emphasizing the transition from descriptive to integrative nutritional evaluation models [147,148].

Anthropometry remains the cornerstone of nutritional assessment due to its accessibility, cost-effectiveness, and validated clinical value [149]. However, new approaches such as kinanthropometry have refined its analytical capacity, enabling the estimation of five-component body composition models (adipose, muscular, skeletal, skin, and residual mass) with standardized ISAK protocols [150]. Recent evidence also supports the complementary use of BIA and MU as precise, non-invasive methods to assess muscle quality and detect sarcopenia [17]. Şengül Ayçiçek et al. (2025) have demonstrated strong correlations between rectus femoris thickness and functional parameters such as HGS and gait velocity [151]. Together, these findings underscore the need to jointly assess structural and physiological parameters when predicting clinical outcomes and monitoring recovery.

Biochemical assessment provides objective insights into metabolic and nutritional states. Current research supports the integration of inflammatory and protein markers—such as CRP, albumin, and prealbumin—to improve sensitivity in detecting protein-energy malnutrition and systemic inflammation [21,152]. The inclusion of renal function and micronutrient biomarkers aligns with current European Society of Clinical Nutrition and Metabolism (ESPEN) [153] and American Society for Parenteral and Enteral Nutrition (ASPEN) [154] recommendations that advocate for multidimensional biochemical panels in clinical nutrition practice.

Similarly, the identification of clinical signs associated with nutrient deficiencies remains essential. This synthesis converges with the evidence presented by Milbar et al. [155], who noted that physical signs, though nonspecific, gain diagnostic value when correlated with biochemical and dietary data [30].

The integration of NDIs within the dietary assessment dimension substantially expands the interpretive capacity of the ABCDEFG framework. NDIs represent a clinically relevant interface in which medications may alter nutrient absorption, metabolism, distribution, and excretion, while nutrients may influence drug pharmacokinetics and therapeutic efficacy [60]. These bidirectional effects are particularly relevant in individuals with chronic diseases, multimorbidity, or polypharmacy, where NDIs can distort the apparent dietary adequacy, mask nutrient deficiencies, precipitate metabolic imbalances, or modify the expected response to nutritional interventions. Consequently, dietary data cannot be fully interpreted without accounting for concurrent pharmacological exposures. By incorporating NDIs into routine assessment, the ABCDEFG model strengthens the link between dietary behavior, biochemical and functional markers, therapeutic effectiveness, and patient safety, thereby enhancing its clinical relevance and rigor.

According to the ecological component, it represents one of the most innovative contributions of the ABCDEFG model. Gut microbiota composition is now recognized as a determinant of energy homeostasis, immune modulation, and nutrient bioavailability [156]. This review highlights the utility of combining sequencing techniques (16S rRNA and shotgun metagenomics) with screening tools such as the Dys-R questionnaire for the identification of dysbiosis risk. Nevertheless, as other authors note [157], the lack of standardized bioinformatic pipelines and reference values continues to hinder the translation of microbiome analysis into routine nutritional practice.

Functional assessment through HGS, gait speed, and the SPPB provides a dynamic evaluation of nutritional and physiological integrity. Consistent with previous studies, our synthesis supports that muscle strength and physical performance are independent predictors of frailty, morbidity, and mortality [158]. The integration of these measures within the nutritional assessment process enhances the capacity to identify early functional impairment and evaluate the efficacy of interventions.

Finally, the genomic and nutrigenomic component introduces a precision-oriented perspective, acknowledging genetic variability as a determinant of nutrient metabolism and dietary response. Genetic polymorphisms in FTO, MTHFR, APOE, and VDR genes have been associated with differential responses to lipid intake, folate metabolism, and vitamin D utilization. However, despite the theoretical appeal, systematic reviews report inconsistent findings regarding the clinical utility of nutrigenetic testing [159]. The present review agrees with those authors in recommending further multicenter research to validate the predictive power and ethical applicability of genetic tests in routine nutritional care [140]. Additionally, ethical considerations must be considered to guarantee the right of access and user protection in direct-to-consumer genetic testing (DTC-GT) to avoid ethical issues such as professional training, consumers/recipients’ dilemmas, and DTC-GT positions [160]. Altogether, the genomic–nutrigenomic dimension adds explanatory power to nutritional variability but still requires methodological standardization and robust clinical validation before routine incorporation into nutritional care.

Although the individual components of the ABCDEFG model are well established in clinical nutrition practice, the expanded framework itself would require staged evaluation prior to implementation as an integrated assessment tool. Future validation should include: (1) feasibility studies assessing workflow, time requirements, cost, and acceptability in clinical and community settings; (2) internal validation examining reproducibility, coherence between components, and agreement with established clinical outcomes; and (3) external validation across diverse populations, age groups, and epidemiological contexts [161,162,163,164]. These refinements provide a clear foundation for potential empirical testing while maintaining the purpose of this review as a conceptual synthesis rather than a predictive or statistical instrument.

In contrast to traditional ABCD or ABCD-plus approaches, the ABCDEFG framework is conceived as an integrative decision-making model rather than a checklist of parallel assessment components. Instead of treating ecological–microbiota (E), functional (F), and genomic/nutrigenomic (G) domains as additive extensions, this framework positions them as interacting dimensions that actively modify the interpretation, prioritization, and clinical relevance of anthropometric, biochemical, clinical, and dietary findings [165]. As a result, similar anthropometric or biochemical profiles may correspond to distinct metabolic, functional, or biological phenotypes when contextualized by functional impairment, microbiota-related alterations, or genetic susceptibility.

This systems-oriented perspective enables more nuanced diagnostic reasoning and supports personalized nutritional decision-making, particularly in complex scenarios such as aging, chronic disease, and metabolically heterogeneous populations. By explicitly integrating body composition, functional capacity, microbial ecology, and biological individuality within a unified assessment logic [166]. The ABCDEFG framework represents a conceptual evolution beyond traditional assessment models and aligns with contemporary advances in precision nutrition and integrative health.

5. Limitations

This narrative review has several limitations that should be acknowledged. First, although a comprehensive literature search was conducted across major databases, the study design did not follow a systematic review protocol; therefore, the selection of articles may have been influenced by publication bias and the authors’ judgment. Second, the heterogeneity among studies in terms of population characteristics, assessment tools, and outcome indicators limited the possibility of quantitative synthesis and direct comparison between findings.

Additionally, the proposed ABCDEFG model has not yet undergone full validation in diverse clinical and community contexts. Some of its components—particularly the ecological (microbiota) and genomic–nutrigenomic dimensions—still require standardized methodologies, reference values, and cost-effective tools to facilitate their routine use in professional practice. Future research should focus on multicenter validation studies, integration of digital technologies, and development of standardized protocols to strengthen the reliability and applicability of this comprehensive nutritional assessment framework.

6. Conclusions

Nutritional assessment remains the foundation of the Nutrition Care Process, directing diagnosis, intervention, and monitoring across clinical and community settings. Although the classical ABCD model has long been applied, it presents limitations when addressing contemporary challenges such as the double burden of malnutrition, aging populations, and the rise in chronic diseases. The expanded ABCDEFG model incorporates additional dimensions that allow for a more comprehensive and integrative evaluation, linking traditional indicators with environmental, physiological, and molecular domains. This broader framework has the potential to support more individualized and context-specific approaches to care.

Nevertheless, the evidence supporting improvements in prevention, early diagnosis, and management remains preliminary and should be interpreted with caution. Current findings highlight promising opportunities but are not yet sufficient to draw firm conclusions about its clinical effectiveness. Further validation, standardization, and testing across diverse populations and health systems are essential to strengthen its applicability and determine its true impact on clinical practice.