Influence of Different Reference Databases on Categorization of Bone Mineral Density: A Study on Rural Postmenopausal Women from Southern India. | Department of Endocrinology, Diabetes & Metabolism
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Influence of Different Reference Databases on Categorization of Bone Mineral Density: A Study on Rural Postmenopausal Women from Southern India.

  1. Department of Endocrinology, Christian Medical College, Vellore, Tamil Nadu, India.

Indian journal of endocrinology and metabolism Vol. 22 · Issue 5 · pp. 579-583

PMID 30294563 DOI 10.4103/ijem.ijem_77_18

Cite This Article

Kripa Elizabeth Cherian, Nitin Kapoor, Hesarghatta Shyamasunder Asha, Nihal Thomas, Thomas Vizhalil Paul. Influence of Different Reference Databases on Categorization of Bone Mineral Density: A Study on Rural Postmenopausal Women from Southern India. Indian journal of endocrinology and metabolism. 2018;22(5):579-583. doi:10.4103/ijem.ijem_77_18

Abstract

BACKGROUND AND OBJECTIVES: Currently available DXA (Dual energy X-ray Absorptiometry) scanners utilise bone mineral density (BMD) of Caucasian population to calculate T scores and categorise BMD. We studied the influence of various databases on classification of BMD in south-Indian postmenopausal women aged above 50 years.

METHODOLOGY: This was a cross-sectional study. Hologic DXA scanner was used to estimate BMD at lumbar spine (LS) and femoral neck (FN). T scores of ≤-2.5, -2.4 to -1, -0.9 to +1 were diagnostic of osteoporosis, osteopenia and normal respectively. Three reference databases(Italian, Korean and north Indian) were used to recalculate T scores. The agreement (K=kappa) between manufacturer provided database and the other databases was studied. The impact of different databases in diagnosing osteoporosis in subjects with FN fracture was assessed.

RESULTS: A total of 1956 postmenopausal women with mean (SD) age of 62 (4.3) years and 211 femoral neck(FN) fracture subjects with mean(SD) age of 68 (7.2) years were recruited. In subjects with fracture, osteoporosis at FN was found in 72% with Caucasian, 88% with North Indian, 56% with Italian, and 45% with Korean database. On comparing manufacturer provided database with the other population-specific reference, there was perfect agreement with north Indian (κ = 0.81 [FN], κ = 0.82 [LS]) and good agreement with the Italian database (κ = 0.78 [FN], κ = 0.74 [LS]).

CONCLUSION: North-Indian database identified most of the participants with FN fracture as having osteoporosis and had perfect agreement with the manufacturer's database. Follow up studies will further validate the impact of utilizing this database in clinical practice.

Keywords

  • Osteoporosis
  • fracture
  • population-specific database
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