The search for articles was conducted in PubMed, Scopus, Web of Science, and SciELO, with no restrictions on language or year of publication. The search terms included combinations of keywords and controlled descriptors (MeSH terms and DeCS) related to the indicators being investigated, for example, WHtR, C index, ABSI, NC, APMT, among others.

The anthropometric indicators addressed in this review are summarized in Table 1, which displays the designation of parameters, authors responsible for introducing them, year of publication, and main goals. The Central Illustration illustrates the classification of indicators as "traditional" and "unconventional," highlighting their respective purposes in health assessment.

Short stature in adults, although associated with malnutrition in early life in contexts of social vulnerability,^12-16 is not a highly accurate indicator, as not all individuals with short stature experienced chronic nutritional deprivation during growth.^17-19

Since the 2000s, there has been an increase in the number of publications involving head circumference^20-23 as an indicator of brain development under a wide range of aspects, including drug exposure,²³ the impact of nutritional status on brain function and development,²⁴ the effects of growth hormone treatment,²⁵ and the beneficial effects of breastfeeding on normal head growth, even in situations of social vulnerability.²⁶

Malnutrition in early life is a condition associated with higher occurrence of chronic diseases in adulthood.^27-30 Some studies on this relationship have used short stature as an indicator of malnutrition in early life. However, other non-nutritional factors can also cause stunting. Depending on the severity of malnutrition in early life, the human body reacts by compromising mainly weight and length gain, but prioritizing, as far as possible, normal brain growth, resulting in individuals with heads that are disproportionate in relation to their respective height. Based on this premise, Ferreira et al.⁴ created the HHI, a novel anthropometric indicator for malnutrition in early life, mainly in cross-sectional studies involving adults, in which information on birth weight or nutritional status during the perinatal period is unavailable. The HHI is obtained by means of the following equation:

To validate this indicator, the authors used height and head circumference data from 3109 women. HHI > 1.028 (75th percentile) was the best cutoff point to predict obesity, and it was used to define disproportion between head size and respective height. The strength of associations with various outcomes was tested for both disproportionality and short stature. After adjustment for confounding factors, the strongest associations were observed for disproportionality. The prevalence ratios for were as follows, respectively: obesity (2.61 versus 1.09), abdominal obesity (2.11 versus 1.42), hypertension (1.24 versus 0.90), hypercholesterolemia (2.98 versus 1.65), and hypertriglyceridemia (1.47 versus 0.91), all with p < 0.05.

These results led to the conclusion that disproportion between head size and respective height is a more accurate indicator of malnutrition in early life than short stature, given that the latter can be caused by factors other than those of nutritional origin, whereas high HHI is likely due to metabolic adaptations brought about by malnutrition in early life.

The WHtR is a parameter that indicates the pattern of body fat distribution. Although the application of WHtR is not as common as other more traditional indexes, it is not a novel concept, having been proposed as a measurement for assessing health risks since the 1990s.³¹ It is fairly simple to calculate, by means of the following equation:

The WHtR has been consolidated as a method for assessing health risk due to its simplicity, low cost, and accuracy in identifying central adiposity. Unlike other markers, WHtR uses a single cutoff point. WHtR > 0.5 has been the most widely adopted value to indicate high risk of cardiovascular diseases, regardless of sex, age, or ethnicity.³²

Based on this cutoff point, the National Institute for Health and Care Excellence in the United Kingdom issued a guideline encouraging individuals to maintain their WC below half their height, as a preventive strategy to avoid severe health complications.³³

However, the adoption of a standard cutoff point for anthropometric indicators, such as WHtR, is questionable due to variations in body proportions between different ethnicities, sexes and ages.³⁴ A study including women in the Brazilian state of Alagoas demonstrated that a cutoff point of 0.54 for WHtR was more accurate in identifying hypertension, reinforcing the need to adapt this tool according to the population context.³⁵

The validity of WHtR as a reliable tool for identifying health risks has been widely corroborated. A meta-analysis³⁶ revealed that increased WHtR is associated with a significantly greater risk of mortality due to cardiovascular disease (23%) and cancer (21%). Often considered the most efficient anthropometric indicator, WHtR has demonstrated superiority over BMI in detecting cardiometabolic risks in a meta-analysis including more than 300,000 adults.³⁷ Studies conducted with adults in Sri Lanka also confirmed the greater predictive capacity of WHtR when compared to WHR.³⁸ The better performance of WHtR can be explained by the fact that the formula incorporates height, a relatively constant variable in adults, making it capable of offering a more precise notion of body proportionality.⁵

Nonetheless, the superiority of WHtR is not an absolute consensus. A meta-analysis³⁹ found no evidence of superiority in the majority of cardiometabolic risk factors in children and adolescents. Moreover, a Brazilian study with women of African descent⁴⁰ showed that WC had equal or better performance than WHtR in identifying the majority of risk factors.

Although still uncommon in clinical practice, WHtR has been gaining popularity over the years, and it is being consolidated as an additional public health tool for identifying health risks. Due to its simplicity, low cost, and good accuracy, it can be considered a valuable instrument in combination with other measurements to assess individual health conditions.

Obesity is a factor that is considered to be associated with different health problems. However, the topographic distribution of adipose tissue appears to be more relevant than the total amount of body fat. This is due to the fact that central obesity is an important risk factor for the development of various health problems.⁴¹

From an anatomical point of view, fat accumulation can occur in the form of subcutaneous fat and visceral fat.⁴² Fat deposits around vital organs are the most harmful, and they have been associated with the accumulation of abdominal fat and metabolic disorders.⁴³

In the 1990s, the epidemiologist Rodolfo Valdez proposed the C index as an indicator of abdominal obesity, arguing that individuals with less fat in the central region have a cylindrical appearance, whereas those with more fat around the waist exhibit a modified profile with a double cone shape,⁶ as illustrated in Figure 1.

The index is obtained by means of the following equation, using waist circumference, body weight, and height measurements:

Since its introduction, some studies have been designed to determine the possible relationship between the C index and health problems.^45-47

Motamed et al.⁴⁸ investigated the capacity of different indicators of central obesity (WC, WHR, WHtR, abdominal volume index, and C index) to predict cardiovascular events. The study included 3199 participants between the ages of 40 and 79 years. The authors concluded that the C index and WHR had greater discriminatory accuracy compared to the other parameters. Other studies, however, have not found similar results.^11,35,40,49

In a sample composed of 10,432 individuals of both sexes between 40 and 69 years of age, Feng et al.¹¹ analyzed the following 17 obesity-related indicators regarding their ability to predict cardiovascular diseases and multimorbidity: BMI, body fat percentage, C index, Clínica Universidad de Navarra-Body Adiposity Estimator, ABSI, body adiposity index, WC, WHR, WHtR, body roundness index, abdominal volume index, triglyceride glucose index, lipid accumulation product, visceral adiposity index, Chinese visceral adiposity index, waist triglyceride index, and cardiometabolic index. The best predictors for the analyzed outcomes, considering the differences between sexes, were as follows: body fat percentage to discriminate hypertension among men and Chinese visceral adiposity index to discriminate hypertension among women; for dyslipidemia, the best predictors were BMI among men and Clínica Universidad de Navarra-Body Adiposity Estimator among women. The cardiometabolic index and Chinese visceral adiposity index were the most accurate predictors of diabetes in men and women, respectively. Regarding multimorbidity, the triglyceride glucose index was the best predictor, regardless of sex.

These findings highlight the variability of results according to the indicators and outcomes analyzed, suggesting the need for a personalized approach that considers differences between sexes when choosing the most appropriate anthropometric parameter to assess health risks.

Regarding the C index, studies have reported that, although it showed an association with all outcomes investigated, its predictive capacity was lower than that of all other indicators analyzed, corroborating the findings of studies conducted in our laboratory.^35,40 One study was conducted with a probabilistic sample of 3143 women from the Brazilian state of Alagoas (20 to 49 years old). The predictors analyzed were BMI, WC, WHR, WHtR, body fat percentage, and C index. Among all indicators, WHtR stood out as the best predictor of hypertension.³⁵ Another study, conducted with a random sample of 1661 women from Quilombola communities in Alagoas, tested the following indicators: BMI, WC, WHtR, C index, body fat percentage, and ABSI. The outcomes to be identified were arterial hypertension, diabetes, hypertriglyceridemia, hypercholesterolemia, and low High-Density Lipoprotein (HDL) levels. WC was the most accurate predictor for identifying higher risk of cardiovascular disease in women of African descent, demonstrating an area under the receiver operating characteristic curve superior or similar to the other predictors in most of the analyses performed.⁴⁰

The WHR is a parameter for predicting cardiovascular disease risk.⁵⁰ It is obtained by dividing WC by hip circumference. According to the World Health Organization (WHO), the cutoff points for defining risk are 0.85 and 0.90 for women and men, respectively.³⁴ In the late 1940s, research developed by Vague provided a basis for the hypothesis that fat accumulation in the abdominal region is related to clinical outcomes resulting from obesity. The outcomes evaluated were arterial hypertension, diabetes mellitus, and dyslipidemia (hypertriglyceridemia, hypercholesterolemia, and low HDL).⁵¹

In the same manner as the C index, elevated WHR values are associated with adverse cardiovascular and metabolic outcomes. However, when compared to other anthropometric measurements, such as WC and WHtR, WHR generally has a lower predictive capacity for these outcomes.

Furthermore, WHR has limitations that make it difficult to interpret, making it disadvantageous in relation to other anthropometric indicators of central obesity.^52,53 WC measures visceral and subcutaneous fat, whereas hip circumference considers the amount of bone, muscle, and adipose tissue. Pouliot et al.⁵⁴ demonstrated that lean individuals can have the same WHR as obese individuals. Therefore, WHR is not recommended for monitoring changes in the amount of abdominal fat over time. The following scenario illustrates this limitation. Patient A has WC of 120 cm and hip circumference of 150 cm (120/150 = 0.80). Patient B has WC of 80 cm and hip circumference of 100 cm (80/100 = 0.80). Therefore, although patient A has much larger WC and hip circumference than patient B, both have the same WHR value.

Therefore, notwithstanding its simplicity and easy application, WHR may not offer significant advantages in relation to other indicators.^11,35,40,49

Considering the limitations of BMI and its high correlation with WC, the combined use of both measurements would not add much to the conclusions obtained in epidemiological studies, as they would result in similar interpretations. Therefore, Krakauer and Krakauer⁸ developed the ABSI as an alternative to assess mortality risk. This index uses measurements of weight, height, and WC, and it is calculated by means of allometric analysis, using to following the equation:

The term allometry designates changes in the relative dimensions of parts of an organism that are correlated with changes in its overall size.⁵⁵

The ABSI, created to assess the impact of central bulge in relation to body size, demonstrated, in a study with adults, that mortality rates increase exponentially with above-average values. Furthermore, 22% of the population mortality hazard was attributed to high ABSI, compared to 15% for BMI and WC, even after adjusting for other risk factors.⁸

The results showed that ABSI has a low correlation with BMI (r = 0.019), indicating that it can be used in conjunction with BMI for a more complete assessment of health risks. On the other hand, WC showed a high correlation with BMI (r = 0.881), suggesting that its use in combination with BMI would not provide additional benefits.⁸

An online calculator (https://nirkrakauer.net/sw/aABSI-calculator.html) makes it possible to obtain ABSI value and individual health risk classification by entering data on sex, age, weight, height, and WC. It is worth underscoring that there is no single cutoff point for ABSI, as the risk classification varies according to age and sex.

According to the authors of the ABSI, the online calculator offers the following three clinical benefits: (1) it improves the estimation of disease and mortality risk on the population level, when combined with BMI; (2) it makes possible to track the evolution of risk over time, helping assess the effectiveness of interventions; and (3) it may guide clinical decisions, such as the indication for bariatric surgery.⁵⁶

However, a study including Brazilian women of African descent⁴⁰ found unfavorable results for the ABSI. In that study, body fat percentage was the best predictor of arterial hypertension, whereas WHtR, C index, and WC were the best predictors of diabetes mellitus, hypertriglyceridemia, and low HDL, respectively. ABSI and BMI performed worse in predicting hypertension and hypertriglyceridemia. WC was considered the most appropriate predictor due to its good accuracy and methodological simplicity.

Since the he 1950s, neck skinfolds have been associated with a higher risk of obesity and its comorbidities, providing evidence of the extent to which subcutaneous fat in the upper body may interfere with health.^9,57

NC, a simple anthropometric measurement, indirectly indicates the accumulation of subcutaneous fat in this region. This is associated with greater cardiovascular risk, insulin resistance, and other health problems, due to its high lipolytic activity.^58-60

Measurements should be performed with an inelastic tape (mm), which should be positioned over the cricoid cartilage, perpendicular to the long axis of the neck, considering the midpoint of the cervical spine and anterior neck.⁶¹ In men with laryngeal prominence (Adam's apple), the measurement should be performed just below the prominence. The proposed cutoff point for defining risk has been shown to vary depending on population characteristics.

Recent studies have validated NC as an indicator of excess fat in this region and its relationship with health risks in different age groups and nationalities.^62-64

NC is correlated with WC and BMI in both sexes.⁶⁵ As an anthropometric predictor, NC is practical, fast, and effective in identifying obesity in children and adolescents, and it is not affected by physiological factors, such as abdominal distension or respiratory movements.^61,66

Opposable thumbs, a characteristic of human evolution, allow complex and precise movements. APMT was initially studied for purposes of neurological assessment, but it has recently been used as an anthropometric indicator of muscle depletion and functional capacity.⁶⁷ Only recently has this measurement been used as an anthropometric parameter for assessing nutritional status.⁶⁸

APMT is correlated with other predictors of malnutrition in hospitalized patients and healthy elderly individuals. Inactivity and muscle catabolism, which are common in diseases and in aging, can lead to atrophy of the adductor pollicis muscle, making it a predictor of nutritional risk. Adductor pollicis muscle mass tends to be maintained until age 65, but may decline after this age, even in healthy individuals.⁶⁷

Lameu et al.⁶⁷ identified that APMT was significantly correlated with BMI, NC, arm muscle circumference, arm muscle area, and calf circumference, but there was no correlation with fat-related parameters, such as triceps skinfold thickness and arm fat area.

Bragagnolo et al.⁶⁹ found that 62.8% of surgical patients in the preoperative period had APMT below 13.4 mm, associated with BMI of 21.5 kg/m². This characterization indicated the need for clinical observation and periodic assessment of these individuals’ nutritional status. The findings of this study demonstrate that APMT is a reliable method for nutritional assessment in surgical patients. In addition to the correlation with other anthropometric parameters, it has good sensitivity and specificity. In summary, this method, which is easy to perform and low-cost, offers safety in the assessment of nutritional status, and it may be applied in clinical practice in surgical patients.

Bezerra⁷⁰ assessing the risk of malnutrition in patients with chronic heart failure, correlated APMT with subjective global assessment. They observed that, with increased values of APMT measurement, the risk of malnutrition according to the subjective global assessment fell by 20.3%, and the measurement showed good sensitivity and specificity. However, the study highlights the importance of using this tool in conjunction with other anthropometric parameters to assess the risk of malnutrition.

It is worth underscoring that APMT has a strong correlation with the functional capacity of the person being assessed, especially in elderly individuals. Accordingly, in individuals with professional activities that do not demand much work from the adductor pollicis muscle or even individuals who have been bedridden for long periods, the measurements may be reduced. On the other hand, exercises or repetitive activities of a muscle group during a certain time period preserve muscle size and function.⁷¹

These data suggest that APMT assessment is a useful resource in clinical practice. Although the measurement is not yet fully validated, its relationship with other anthropometric predictors and its high sensitivity and specificity mean that its use in association with other indicators may be of great value.

Bragagnolo et al.⁶⁹ proposed 13.4 mm as the cutoff point for malnutrition in surgical patients, whereas Aguiar et al.⁷² suggested 12.8 mm, with divergent results in onco-hematological patients. Gonzalez et al.⁶⁸ found higher mean APMT in healthy individuals, with a reduction in those over 60 years of age, corroborating Phillips et al.⁷³ Lameu et al.⁶⁷ found lower values than those identified by Gonzalez et al.⁶⁸ The lack of a single cutoff point for APMT, due to the influence of the type of target population and study design, highlights the need for further research to establish standards according to sex, age, and ethnicity, in addition to standardized measurement protocols.

The advantages of APMT include the fact that the measurement is simple, non-invasive, fast, easy to perform, and low-cost, in addition to its well-defined anatomical reference. However, it has also shown some limitations. For instance, edema in the hands can overestimate the results. Furthermore, APMT still lacks established and validated cutoff points, and an individual's work activity can influence the measurement.

It is necessary to recognize some limitations inherent to this review. First, the heterogeneity among the included studies, in terms of design, population, and analysis methods, may complicate direct comparison of the results and generalization of the conclusions. Moreover, the lack of standardization in the assessment of some anthropometric indicators may introduce variability in the measurements and inconsistencies between different studies.

Another important point to consider is the applicability of the indicators in different populations. The anthropometric characteristics and the prevalence of the outcomes analyzed vary between different ethnic groups and populations. Therefore, the external validity of the indicators may be limited, especially for populations with anthropometric profiles that are different from the Brazilian population, as exemplified in the study by Feng et al.¹¹ with a Chinese population.

It is also essential to highlight the influence of socioeconomic and environmental factors on the prevalence of NCDs and on associations with anthropometric indicators. Social inequality, access to health services, and diet quality are factors that can modulate the relationship between anthropometric indicators and NCD risk.^74,75 Therefore, interpretation of the results should take into consideration the socioeconomic and environmental context of the population studied.

Finally, this study focused on unconventional anthropometric indicators, which are less widely used in clinical and epidemiological practice. The scarcity of research on some of these indicators may limit a full understanding of their properties and applicability. Further studies with representative samples and standardized methods are crucial to deepen knowledge about these indicators and determine their true utility in assessing NCD risks, as well as other applications.

In spite of these limitations, this review significantly contributes to discussions on the use of unconventional anthropometric indicators in clinical and epidemiological practice. By highlighting the characteristics, advantages, and disadvantages of each indicator, this study assists health professionals and managers in selecting tools that are appropriate for their objectives.