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  • br The lack of BIVA research in females limits


    The lack of BIVA research in females limits the ability to extrapolate results to women. Females differ physiologically to males (women generally have more body fat, less body water, shorter height and reduced muscle mass compared to men [41,42]). Of the three studies including women, two studied female specific cancers (breast [30,31], cervical [34]) and one studied with a mix of cancer types [22]. Therefore, no studies in the literature provide
    Study details R/H Xc/H Reference Fulvestrant dataa
    mean SD Mean SD
    a The Piccoli et al., 1995 [24] reference population data was used for all studies included in this analysis. BIVA software equations are included in the appendix.
    meaningful female-specific BIVA data for any cancers, other than those affecting the breast and cervix.
    Our findings are limited by a lack of information about the reasons why reference populations were chosen. This is problem-atic with the Cardoso et al. [34] study, which used an inappropriate reference population (European white adults) for their analysis of a Brazilian Pardo (mixed race) sample [34]. It is likely this population was chosen due to the lack of other suitable reference populations. Furthermore, seven populations used small control groups (n < 100) as their reference, which are inappropriate due to their small size. Although we used the Piccoli population as the reference for these studies, other reference populations may have been more suitable. This demonstrates the challenges of using the BIVA RXc z-score method appropriately when there is variability about how reference populations are chosen.
    Different bioimpedance analysers were used throughout the studies included in this paper. This may result in slight differences in reactance and resistance values, which may alter the BIVA RXc z-score interpretation. Finally, an inherent limitation of BIVA is that it is a qualitative assessment method which does not provide abso-lute body composition metrics [8]. Therefore, the method is unable to provide quantitative data on body composition variables (e.g. fat free mass, and fluid volume). This is why stratification of BIVA data according to clinical variables (e.g. disease stage, type and ethnicity) is needed to determine clinically meaningful outcomes.
    Implications to clinical practice and policy
    This analysis supports previous data that describes how body composition in cancer is related to a number of factors (e.g. cancer stage, type of disease) [36,43,44]. This study demonstrates the potential to use the BIVA RXc z-score method to undertake comparative, multi-group, body composition analysis, to compare differences in cancer according to disease stage and type. This has the potential to personalise therapeutic, nutrition and hydration based interventions according to an individual's physiology. Although the BIVA RXc z-score method has potential use in clinical practice, we are unable to recommend its routine clinical use (in cancer), due to the limited number of studies using the method and a lack of data to inform clinical interpretation.
    Future research possibilities
    Further research studies using bioimpedance are needed to evaluate differences in cancer, according to disease type, stage, ethnicity and gender. In order to improve the clinical usefulness of BIVA, future bioimpedance studies should report all the relevant data (and standard deviations) required to conduct BIVA [45] (i.e. age (years), Height (m), BMI (Height (H)2/m), weight (kg), R (Ohm), R/H (Ohm/m), Xc (Ohm), Xc/H (Ohm/m), PA (degrees)). Researchers should justify the reasons for the choice of reference populations, stating why the chosen population is best suited for their analysis. Inclusion of this information will enable researchers to conduct BIVA analyses without needing to contact investigators for further infor-mation. Researchers should aim to develop larger, appropriately powered, reference populations, to facilitate stratification (by age, gender, ethnicity and other clinical factors). As a priority, futures studies should generate data for non-white and female individuals.
    The BIVA RXc z-score method can be used to evaluate body composition in people with cancer. This method can be used to conduct analysis of body composition according to different vari-ables such as cancer type, stage, gender and ethnicity. Improved 
    assessment will lead to better understanding of the physiological and biological processes of advanced cancer. Consequently, BIVA may help healthcare professionals to personalise therapy in pa-tients with cancer, according to their physiology.
    Funding sources
    The authors received no specific funding for this work.
    Conflict of interest statement
    The authors declare that there is no conflict of interest.
    CRediT authorship contribution statement
    Amara Callistus Nwosu: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing