Acta Scientific Neurology (ASNE) (ISSN: 2582-1121)

Research Article Volume 8 Issue 10

DXA And Walk Analysis - As A Probability Tool To T2dm In Near Future

Seema Tewari*, Dilip Verma, Manish Bajpai, Ganesh Yadav, Anit Parihar and KK Sawlani

Department of Physiology, King Geroge Medical College, India

*Corresponding Author: Seema Tewari, Department of Physiology, King Geroge Medical College, India.

Received: August 22, 2025; Published: September 25, 2025

Abstract

Introduction: India is relatively young country as compared to western or far eastern countries. After 40 years of age nearly all are prone to prediabetes and then diabetes due to rapid epidemiological transition and positive familial history as shown by CURES STUDY from south India. Prediabetes and Diabetes are positively co-related to central abdominal fat. It is already proved that android pattern of fat distribution is co-related with prediabetes and diabetes not gynoid fat in the subject. Visceral Adipose Tissue (VAT) is the fat accumulated in viscera and muscles. Subcutaneous Adipose Tissue (SAT) is the fat accumulation in subcutaneous region of whole body. This adiposity feature is the main risk factor for prediabetes which further leads to diabetes and then sarcopenia and frailty. Dual X-ray Absorptiometry (DXA) calculates fat in the form of VAT, SAT very readily and effectively by an affordable, less harmful and non-invasive tool as compared to CT scan and MRI scan. So, DXA can diagnose prediabetes in time, then this endemic can be preventable. This study is a part of my ethically approved large study of sarcopenia and walk test in TYPE 2 DIABETES MILLETUS (T2 DM).

Method: This is a cross-sectional-study of 46-patients (23 normal-subjects and 23 T2DM patients), taken from diabetic-clinic of department-of-medicine KGMU-UP. T2DM patients (n = 23) with history more than 5 years with mean HBA1C of 7.5 and compared with normal persons (n = 23). DXA is used to calculate BMI, VAT, SAT, FMI (FAT MASS INDEX), LMI (LEAN MASS INDEX), FMR-A/G- FAT MASS RATIO (ANDROID/GYNOID), FMR-T/L - FAT MASS RATIO (TRUCK/LIMB RATIO) in T2DM patients (n = 23) with history more than 5 years with mean HBA1C of 7.5 and compared with normal persons (n = 23).

Result: Across both sexes demonstrated an increase in adiposity measures (BMI, FM, FMI, VAT) peaking in the 50 -< 60 yrs age category, with lean mass indices remaining comparatively uniform across age. This pattern suggests that, within the case group, middle age was associated with greater fat accumulation-especially visceral fat-without substantial loss of lean tissue BMI mean at 50 years of age. BMI of case (T2DM) in male is calculated as 27.4 ± 1.9 and in female as 28.9 ± 3.4, BMI of normal subject in male is calculated as 26 ± 4.4 and in female as 27.5 ± 4.4. FMI of case (T2DM) in male is calculated as 8.19 ± 2.54 and in female as 10.34 ± 2.74, FMI of normal subject in male is calculated as 5.3 ± 3.1 and in female as 8.3 ± 3.1. LMI of case (T2DM) in male is calculated as 18.06 ± 2.64 and in female as 16.13 ± 1.56, LMI of normal subject in male is calculated as 20.8 ± 2.4 and in female as 19.6 ± 1.9. FMR-A/G of case (T2DM) in male is calculated as 1 and in female as 1, FMR-A/G of normal subject in male is calculated as 1.02 and in female as 0.76. FMR-T/L of case (T2DM) in male is calculated as 3 and in female as 1.14, FMR-T/L of normal subject in male is calculated as 2.6 and in female as 1.35.

Conclusions: In a nutshell, we found in this study there is a mirror (inverse ✂) relation between normal subjects and T2DM patients after 55 years of age as for as the graph is concerned in body parameters (BMI, FMI, LMI, FMR-A/G AND FMR-T/L). North Indians (from 40 to 70 years in 46 patients-23 each) of urban and rural background has been found to have deranged body parameters (BMI, FMI, LMI, FMR-A/G AND FMR-T/L) that causes T2DM in later life. In addition, this study reports LMI reference values with regard to fat mass quantities, showing a positive association with increasing FMI percentiles and BMI categories. Public health awareness after proper screening by DXA, government sponsored Diabetes campaign in the form of screening of vulnerable population in specific age group from 40 years (positive family history and epidemiological history) via DXA diagnosing T2DM in the form of prediabetes and later diabetes, interventions should target modifiable risk factors to slow down the diabetes epidemic in this population.

Keywords: Dexa; T2DM; Walk Tests; Left Cerebral Dominance

References

  1. Borga M., et al. “Advanced body composition assessment: from body mass index to body composition profiling”. Journal of Investigative Medicine 66 (2018): 1-9.
  2. Pietrobelli A., et al. “Dual-energy X-ray absorptiometry body composition model: review of physical concepts”. American Journal of Physiology 271 (1996): E941-951.
  3. Prior BM., et al.In vivo validation of whole body composition estimates from dual-energy X-ray absorptiometry”. Journal of Applied Physiology 83 (1997): 623-630.
  4. Borga M., et al. “Advanced body composition assessment: from body mass index to body composition profiling”. Journal of Investigative Medicine 66 (2018): 1-9.
  5. Toombs RJ., et al. “The impact of recent technological advances on the trueness and precision of DXA to assess body composition”. Obesity 20 (2012): 30-39.
  6. Laskey MA. “Dual-energy X-ray absorptiometry and body composition”. Nutrition 12 (1997): 45-51.
  7. International Diabetes Federation. IDF Diabetes Atlas. 6th Brussels, Belgium, International Diabetes Federation (2013).
  8. McKeigue PM., et al. “Relation of central obesity and insulin resistance with high diabetes prevalence and cardiovascular risk in South Asians”. Lancet 337 (1991): 382-386.
  9. Dowse GK. “Incidence of NIDDM and the natural history of IGT in Pacific and Indian Ocean populations”. Diabetes Research and Clinical Practice 34 (1996): S45-S50.
  10. Hippisley-Cox J., et al. “Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore”. BMJ 338 (2009): b880.
  11. Mohan V., et al. “Incidence of diabetes and pre-diabetes in a selected urban south Indian population (CUPS-19)”. Journal of the Association of Physicians of India 56 (2008): 152-157.
  12. Deepa M., et al. “The Chennai Urban Rural Epidemiology Study (CURES) study design and methodology (urban component) (CURES-I)”. Journal of the Association of Physicians of India 51 (2003): 863-870.
  13. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. “Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III)”. JAMA 285 (2001): 2486-2497.
  14. Matthews DR., et al. “Homeostasis model assessment: insulin resistance and beta cell function from fasting plasma glucose and insulin concentrations in man”. Diabetologia 28 (1985): 412-419.
  15. Deepa M., et al. “Prevalence of metabolic syndrome using WHO, ATPIII and IDF definitions in Asian Indians: the Chennai Urban Rural Epidemiology Study (CURES-34)”. Diabetes/Metabolism Research and Reviews 23 (2007): 127-134.
  16. Alvin C Powers., et al. “Diabetes Mellitus: Diagnosis, Classification and Pathophysiology”. HARRISON 3100 PAGE NUMBER 21ST Edition (2012).
  17. American Diabetes Association. “Diagnosis and classification of diabetes mellitus”. Diabetes Care1 (2010): S62-S69.
  18. Vald´ es S., et al. “Population-based incidence of type 2 diabetes in northern Spain: the Asturias Study”. Diabetes Care 30 (2007): 2258-2263.
  19. Ramachandran A., et al. “Significance of impaired glucose tolerance in an Asian Indian population: a follow-up study”. Diabetes Research and Clinical Practice 2 (1986): 173-178.
  20. Mohan V., et al. “Secular trends in the prevalence of diabetes and impaired glucose tolerance in urban South India the Chennai Urban Rural Epidemiology Study (CURES-17)”. Diabetologia 49 (2006): 1175-1178.
  21. Anjana RM., et al. “Incidence of Diabetes and Prediabetes and Predictors of Progression Among Asian Indians: 10-Year Follow-up of the Chennai Urban Rural Epidemiology Study (CURES)”. Diabetes Care8 (2015): 1441-1448.
  22. Bosy-Westphal A and Muller MJ. “Identification of skeletal muscle mass depletion across age and BMI groups in health and disease-there is need for a unified definition”. International Journal of Obesity 39 (2015): 379-386.
  23. Morrison SA., et al. “Comparison of the lunar prodigy and iDXA dual-energy X-ray absorptiometers for assessing total and regional body composition”. Journal of Clinical Densitometry 19 (2016): 290-297.
  24. Kelly TL., et al. “Dual energy X-Ray absorptiometry body composition reference values from NHANES”. PLOS ONE 4 (2009): e7038.
  25. Fan B., et al. “National Health and Nutrition Examination Survey whole-body dual-energy X-ray absorptiometry reference data for GE Lunar systems”. Journal of Clinical Densitometry 17 (2014): 344-377.
  26. Imboden MT., et al. “Reference standards for lean mass measures using GE dual energy x-ray absorptiometry in Caucasian adults”. PLOS ONE 12 (2017):
  27. Imboden MT., et al. “Reference standards for body fat measures using GE dual energy x-ray absorptiometry in Caucasian adults”. PLOS ONE 12 (2017): e0175110.
  28. Hong S., et al. “Characteristics of body fat, body fat percentage and other body composition for Koreans from KNHANES IV”. Journal of Korean Medical Science 26 (2011): 1599-605.
  29. Swainson MG., et al. “Ageand sex-specific reference intervals for visceral fat mass in adults”. International Journal of Obesity 44 (2020): 289-296.
  30. Coin A., et al. “Fat-free mass and fat mass reference values by dual-energy X-ray absorptiometry (DEXA) in a 20-80 year-old Italian popu lation”. Clinical Nutrition 27 (2008): 87-94.
  31. Shuhart CR., et al. “Executive summary of the 2019 ISCD position development conference on monitoring treatment, DXA cross-calibration and Least significant change, spinal cord injury, peri-prosthetic and orthopedic bone health, transgender medicine, and pediatrics”. Journal of Clinical Densitometry 22 (2019): 453-471.
  32. Valde´s S., et al. “Population-based incidence of type 2 diabetes in northern Spain: the Asturias Study”. Diabetes Care 30 (2007): 2258-2263.
  33. Edelstein SL., et al. “Predictors of progression from impaired glucose tolerance to NIDDM: an analysis of six prospective studies”. Diabetes 46 (1997): 701-710.
  34. Wang H., et al. “Incidence rates and predictors of diabetes in those with prediabetes: the Strong Heart Study”. Diabetes/Metabolism Research and Reviews 26 (2010): 378-385.
  35. Engberg S., et al. “Progression to impaired glucose regulation and diabetes in the population-based Inter99 study”. Diabetes Care 32 (2009): 606-611.
  36. Ramachandran A., et al. “Significance of impaired glucose tolerance in an Asian Indian population: a follow-up study”. Diabetes Research and Clinical Practice 2 (1986): 173-178.
  37. Mohan V., et al. “Secular trends in the prevalence of diabetes and impaired glucose tolerance in urban South India the Chennai Urban Rural Epidemiology Study (CURES-17)”. Diabetologia 49 (2006): 1175-1178.
  38. David G Carey., et al. “Abdominal Fat and Insulin Resistance in Normal and Overweight Women: Direct Measurements Reveal a Strong Relationship in Subjects at Both Low and High Risk of NIDDM”. Diabetes5 (1996): 633-638.

Citation

Citation: Seema Tewari., et al. “Walk And Dexa Test Predicts T2dm By Appendicular Right Lateralisation Loss".Acta Scientific Neurology 8.10 (2025): 61-79.

Copyright

Copyright: © 2025 Seema Tewari., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




Metrics

Acceptance rate32%
Acceptance to publication20-30 days

Indexed In




News and Events


Contact US