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Share this. To schedule an appointment, submit the form below now or call our office by dialing Body Metrics Testing. Home Services Body Metrics Testing. What is Body Metrics Testing? For example: Higher muscle mass can protect against developing diabetes.

High water percentage may indicate kidney disease. Low bone density can indicate a trend toward osteoporosis. High body fat can lead to cardiovascular disease , hypertension , and other serious health issues.

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Perspective Back to Top Mone Zaidi, MD, PhD, MBA, MACP, FRCP Over the past two decades, we have worked on the idea that pituitary hormones have diverse functions beyond the unitary actions that appear traditionally in endocrine textbooks. Mone Zaidi, MD, PhD, MBA, MACP, FRCP.

Disclosures: Zaidi reports being the inventor or co-inventor on patents owned by the Icahn School of Medicine at Mount Sinai on the effects of follicle-stimulation hormone blockade on bone, fat and brain, being the potential recipient of any royalties arising from commercialization of the patents, and consulting for several finance platforms, including GLG, Guidepoint and Coleman.

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Having more muscle has some great health perks. It boosts your metabolism, helps prevent falls, and reduces the risk of chronic illnesses. Plus, a study in the Journal of Clinical Endocrinology and Metabolism even suggests that higher muscle mass can delay, if not outright prevent, diabetes.

The amount of fluid in your body, known as Total Body Water, is crucial for your health. Our bodies are mostly water, and it plays a vital role in regulating our temperature and getting rid of waste through sweating and urinating.

This is essential for our overall well-being. You want to aim for a healthy Visceral Fat level between 1 and In the world of healthcare, we know that having too much of this fat can cause problems. So, keeping that belly fat in check is a smart move for your health!

Bone Mass is the computation or estimated weight of bone mineral in the body. Studies suggest that a healthy bone mass for women weighing less than 50 kg is 1. For women weighing between 50 to 75 kg, the healthy bone mass is 2.

On the other hand, for men weighing less than 65 kg, the healthy bone mass is 2. For those weighing between 65 to 95 kg, the healthy bone mass is 3.

These steps will help you maintain a healthy bone mass and support your overall bone health. Protein Mass is all about the amount of protein in your body. Having low protein mass can be connected to having too much body fat. As we get older, our bodies need more protein because of something called anabolic resistance.

This basically means our bodies become less efficient at breaking down and making new protein. A study in the American Journal of Clinical Nutrition found that if you eat a bit more high-quality protein spread throughout the day, it can help you build and maintain muscle, especially as you get older.

Subcutaneous fat is the fat stored just under your skin. Now, having the right amount of subcutaneous fat is like having an energy reserve for your body. So, keeping an eye on your subcutaneous fat is a smart way to stay healthy and active. Standard weight is like a recommended healthy weight for someone, and it takes into account things like your height, gender, age, and body type.

Body weight is simply a way to measure how heavy a person is, either in pounds or kilograms. Your heart rate is like a measure of how fast your heart beats in a minute. Keeping your heart rate in these healthy ranges is a good sign that your heart is doing its job well.

DrKumo Digital Scale is a smart tool designed to help you manage your weight and health more effectively. This scale has special sensors that come into contact with the soles of your feet when you step on it.

These sensors send tiny electrical signals through your body. These signals encounter different levels of resistance from your muscles and other substances in your body. This resistance data is then sent to the DrKumo Mobile App, which does some clever calculations to figure out your body composition.

So, in simple terms, DrKumo Digital Scale is like having a smart health companion that helps you keep an eye on your body composition. Keeping track of your weight is crucial for maintaining good health. Digital scales that measure body composition use a method called electrical impedance.

When you step on these scales, they send a small electrical signal through your body. This signal encounters resistance from your muscles and other substances in your body.

The scale then calculates various health metrics based on this resistance. Monitoring your body composition is vital for your health. It provides insights into essential health metrics and can help identify potential risks like high blood pressure, diabetes, and heart disease. By keeping track of your body composition, you can manage your weight and collaborate with your healthcare provider to create a personalized treatment plan.

Digital scales, like those offered by DrKumo, provide valuable data that healthcare providers use to create personalized health plans tailored to your unique needs and goals.

The data collected by digital scales is typically sent to a secure cloud database, ensuring its safety and protection. Healthcare providers have access to this database, enabling them to better understand your health and create personalized treatment plans with confidence.

When selecting a digital scale for body composition monitoring, consider a few key factors. Also, ensure it measures a wide range of body composition parameters to meet your specific health monitoring needs.

Digital scales, like the ones from DrKumo, are changing the game when it comes to keeping tabs on your health. They go way beyond just telling you your weight; they give you a full picture of your body. No need to wait — start your journey to a healthier you right now.

Reach out to DrKumo and check out our cool range of digital scales. Contact DrKumo today! Revolutionize your healthcare with real-time. Early signs of type 2 diabetes may indicate the presence of this condition.

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October 19, How Digital Scales Work in Remote Patient Monitoring. Share this post:. Discover how digital scales function in remote patient monitoring. Learn why continuous body composition monitoring matters.

Table of Contents. BMI for Adults Underweight Below Body Fat Percentage Body fat percentage is all about how much fat you have in relation to your total body weight.

Basal Metabolic Rate BMR BMR, or Basal Metabolic Rate, is the energy your body needs for essential functions at rest, like keeping your heart and organs running. Muscle Mass Muscle mass is the estimated weight of the muscle in your body, including skeletal, cardiac, and digestive muscles, along with the water they hold.

Body Water The amount of fluid in your body, known as Total Body Water, is crucial for your health. Healthy Visceral Fat Level In the world of healthcare, we know that having too much of this fat can cause problems.

Ethical approval was obtained from the institutional review board of the QBB EXQF-QBB-RES-ACC and was carried out according to the Declaration of Helsinki. All participants in this study provided their written consent to share all their data in research studies, using an identification number but without revealing their identity.

A trained healthcare team at the QBB used standard methods to record the anthropometric measurements. The participants were asked to wear light clothes, without shoes, when measurements were being taking for height and weight.

The measurements included those for weight, height, waist circumference WC and hip circumference HC. Body weight kg and height cm were measured by using a calibrated scale and a wall-mounted stadiometer Seca, Hamburg, Germany.

Using a non-stretchable tape, WC cm was determined at the abdominal region, at the level of the umbilicus at the midpoint between the last rib of the body and the top of the iliac crest. Waist to hip ratio WHR was also calculated.

Overall adiposity, total body fat TBF , visceral fat, and regional fat distribution trunk, AF and GF and BMD were measured using Lunar iDXA SN , GE Healthcare, USA.

For BMD, three regions of interest were measured on the GE Lunar iDXA machine—the anteroposterior AP lumbar spine, the left femur and the whole-body composition.

The Lunar iDXA performed a daily six-point calibration which provided highly sensitive measurements with normal, osteopenic and osteoporotic BMD values, as well as lean, normal and obese values BMI was calculated using weight kg over the square height in meters m Measurements were obtained by Lunar iDXA SN , GE Healthcare, USA BMD indicators in the lumbar spine, femoral neck FN , femur and total BMD, and the total body T-score Difference in the BMD between participant and a healthy young adult.

Self-administered health and lifestyle questionnaires were used to obtain data such as age, level of education, smoking status and physical activity.

The level of education was divided into three categories—lower education, up to secondary school; medium, technical or professional school; and higher education, University and above.

The physical activity levels were expressed as metabolic equivalents METs in hours per week were calculated based on the frequency and duration of the different types of physical activity Face-to-face interviews were conducted at the QBB clinic by professional nurses, to gather information about health status, related family medical history and usage of medications of participants.

The data was analyzed using SPSS for Windows version 23 and STATA version 17 , and the results were presented as mean and standard deviations SD for continuous variables and as percentages for categorical variables. A comparison between the groups was made using a t -test for continuous variables and a chi-square test χ 2 for categorical variables.

The non-linear association between different types of FM indicators and the T-score of total BMD was assessed by the restricted cubic spline method, with models adjusted for age, smoking, physical activity and the use of supplements. Three knots were put at the 10, 50, and 90th percentile.

A p -value for non-linearity was obtained by testing the regression coefficient of the second spline equal to zero, while the overall p -value for the association was obtained by testing the regression coefficient of the two splines simultaneously equal to zero.

Characteristics of the participants categorized according to their z-scores of the total BMD are presented in Table 1. The mean age of the participants was Most of the participants had obtained higher education More than half of the participants were using vitamin supplements Table 1.

Characteristics of the study population according to BMD category a. The anthropometric measurements according to BMD category are shown in Table 2. Participants with a normal BMD were heavier and had a high BMI, WC and HC.

There was a significant difference between those with normal and low BMD in body composition indicators visceral FM, android FM, gynoid FM, trunk FM, total FM and total LM , with high values among the normal BMD categories.

Those who were OW accounted for More than half of the participants were obese There was a significant difference in the prevalence of obesity 2 and obesity 3 among the participants, with a higher proportion among the normal BMD category, However, there was no significant difference in the prevalence of obesity 1 among those with a normal BMD Table 2.

Anthropometric measurements according to BMD category a. The partial correlation between indicators for bone mineral and body composition is shown in Table 3. Visceral FM 0. All FM variables did not show a significant association with femoral neck FN BMD.

In contrast, visceral FM 0. Compared with other body composition variables, total LM had a significantly higher positive association with all bone mineral indicators. Table 3. Partial correlation between bone mineral indicators and body composition variables a. The association between the T-score of the total BMD and components of fat distribution total FM, android FM, gynoid FM, and trunk FM are illustrated in Figure 1.

The model was adjusted for age, physical activity, supplement use and smoking status. In the graph of total FM, the line was not as steep after the total FM reached 40 kg.

For android FM, the approximate threshold value was 4 kg and the increase in the T-score was not as steady for values above the threshold. The approximate threshold value for gynoid FM was 6 kg, and when the gynoid FM was below 6 kg, the T-score showed a small linear increase.

In the trunk FM graph, the approximate threshold value was 20 kg, and the line was not as steep when the trunk FM was above 20 kg. Figure 1. Association between whole body Tscore and body composition indicators.

Graphical representation of non-linear association between total fat mass, android fat mass, gynoid fat mass, and trunk fat mass with T-score of total BMD derived using the restricted cubic spline method.

The models were adjusted for are age, physical activity, supplement use, and smoking status. To the best of this research group's knowledge, this was the first study to demonstrate the relationship between fat distribution and BMD among Qatari women. The prevalence of obesity in this study population was higher This rate of increase is consistent with the fact that the prevalence of obesity in Qatar is increasing Compared with FM, a strong positive association was found between LM and BMD at different sites lumbar spine, femur, and FN , as well as with total BMD and the T-score of total BMD.

The association between fat distribution variables and the T-score of total BMD resulted in a significant non-linear curve. According to this study, those with obesity had a higher BMD, which was consistent with the study by Salamat et al.

Similarly, other studies have reported that obesity was positively related to an increased bone mass 13 , 14 , Qiao et al.

In contrast, Gameil et al. Additionally, Beck et al. Therefore, a critical analysis of region-specific BMD should be undertaken. Age and BMI-adjusted partial correlation indicated that total LM had a significant positive correlation with BMD at different regions, and this agreed with the findings of several other studies 29 , A study by Xiao et al.

They concluded that the effect of LM on BMD could be ascribed to the mechanical influence of muscle, which generates a positive effect on osteogenesis. The findings of this study also indicated that total LM was more positively associated with BMD indicators than the components of fat distribution.

Similar findings were observed by Ilesanmi-Oyelere et al. However, Wang et al. This difference in findings might be explained by the association of body composition with bone mass being significantly determined by the parameter of the bone analyzed Additionally, both FM and LM have crucial effects on bone mass, based on the bone parameters used, the site of skeleton where measurements were taken and menopausal status This study also found that gynoid FM and total FM had a significant negative correlation with BMD at different regions.

Similar findings were reported by Casale et al. Conversely, the results were inconsistent with those of Kapuš et al. This contradiction could be due to the differential effect of FM on bone among postmenopausal and premenopausal women.

While this study did not categorize the participants according to their menopausal status, total FM might be a stronger indicator of BMD in postmenopausal rather than premenopausal subjects However, the results of this research agree with those of Salamat et al.

This negative effect with increased TFM was attributed to the increased levels of cytokines that are proinflammatory. Similarly, Kim et al. Furthermore, this study observed a non-linear relationship between the components of fat distribution and the total BMD indicator T-score , where the slope was not as steep as the TFM increased.

An increased TFM might not have a protective effect on bone. This finding was comparable with the conclusions of Shi et al. They explained that TFM has a curved association with bone indicators, and a strong protective influence of TFM on bone occurs only in thin people.

For overweight and obese individuals, increased TFM could be damaging to bone health. Several potential mechanisms have been proposed to clarify the relationship between body composition parameters and bone mineral indicators.

The LM could positively affect the bone through muscle contractions, along with the loading effect due to the force of gravity 38 , and the advantageous hormonal effect of the skeletal musculature For example, muscle fibers release an insulin-like growth factor 1 IGF-1 which can trigger the growth of bone via receptors or the signaling mechanism of IGF The effect of TFM on bone is probably more complex, because both bone and adipose cells originate from a pluripotent mesenchymal stem cell 42 , and these cells can transform into both adipocytes and osteoblasts The TFM possibly affects the bone positively via the weight-loading mechanism 32 , and modulation of the endocrine pathway Adipocytes release a lot of hormones, including leptin, insulin, and adiponectin, that impact bone metabolism Additionally, the adipose tissue contains an aromatase enzyme that changes androgen to estrogen, leading to a high release of estrogen These hormones have a protective effect on bone and results in bone development by initiating osteoblast differentiation and the prevention of resorption by osteoclast.

In addition, estrogen enhances the synthesis of protein in muscles and the deposition of bone calcium, leading to an increase in BMD On the other hand, increased adiposity causes inflammation and increased inflammation promotes bone resorption Adipose tissue cells secrete a variety of proinflammatory cytokines, such as interleukin 6 IL-6 and TNF-α tumor necrosis factor-alpha.

These factors activate bone resorption and inhibit bone formation by the increased stimulation of the receptor activator of nuclear factor-kB ligand RANKL , which mediates osteoclast formation Furthermore, excessive adiposity also leads to a decrease in the release of the adiponectin hormone which is associated with differentiation of osteoblasts Therefore, excess adiposity causes a decline in BMD.

In diagnosing those with a low bone mass, this study highlighted the importance of considering the impact of fat distribution at different skeletal sites rather than relying on total BMD.

This finding has clinical implications as it would be helpful in improving the diagnostic criteria for conditions associated with low bone mass. Future studies could analyse the effect of fat distribution on BMD at different skeletal sites and the variation in the results among obesity categories.

This study had both strengths and limitations. One of the limitations was that a cross-sectional study design prevented the determination of causal association between body composition indicators and BMD.

Second, only the total BMD T-score was considered for determining the association of fat distribution with BMD; the BMD at different skeletal sites was not included.

Additionally, the study population could have affected the results as they mostly comprised overweight and obese participants. There could also be confounding bias due to residual confounders that were not included in the analysis, such as sex-hormone status, menopausal status, and the levels of bone biomarkers.

One of the strengths, however, was that the data obtained from the QBB included a large sample size with anthropometric measurements, body composition variables and bone mineral density indicators. This study illustrates that LM is a stronger predictor of BMD compared with FM.

Additionally, it showed that FM might not have a strong protective effect on bone health as the association of FM with T-score was non-linear and there was not much increase in the T-score when FM increased. Further investigations are necessary to confirm the association between FM and BMD by considering BMD at different regions of the body, as total body BMD might not be a better indicator of bone mass.

The datasets presented in this article are not readily available due to the confidential nature of the material. However, they can be available on request to Qatar Biobank study management team. The studies involving human participants were reviewed and ethical approval was obtained from the Institutional Review Board of the Qatar Biobank EXQF-QBB-RES-ACC and carried out according to the Declaration of Helsinki.

A written consent was obtained for all participants to share their data. AK and MR conceived and designed the study. SL, YK, TT, and GA contributed to the original draft preparation. AK wrote the manuscript, has full access to all data, and is responsible for the results accuracy.

MR and AA critically reviewed data analysis and contributed to the writing and editing of the manuscript. AK and ZS performed the statistical analysis.

All authors have read and agreed to the published version of the manuscript. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.

Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

We would like to express our sincere gratitude to Qatar Biobank for providing data that supported our research. Chen YY, Fang WH, Wang CC, Kao TW, Chang YW, Wu CJ, et al. Body fat has stronger associations with bone mass density than body mass index in metabolically healthy obesity.

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