Circulating microRNA as a Potential Biomarker for Skeletal Disease in Primary Hyperparathyroidism: A Case-control Study.

OBJECTIVE: The goal of this study was to characterize the microRNA (miRNA) expression signatures in patients with PHPT and identify miRNA biomarkers of bone homeostasis. SUMMARY BACKGROUND DATA: Primary hyperparathyroidism (PHPT) is associated with increased bone turnover and decreased bone mass. miRNA are markers of bone remodeling. METHODS: We performed a prospective case-control study of post-menopausal females with PHPT and control subjects matched for race, age, and BMD. We collected clinical and biochemical data, assessed BMD by dual-energy X-ray absorptiometry, and measured 27 serum miRNAs related to bone remodeling. We used linear regression to assess the correlation between miRNA levels, conventional biochemical markers and BMD. RESULTS: A total of 135 subjects were evaluated, including 49 with PHPT (discovery group), 47 control patients without PHPT, and an independent validation cohort of 39 PHPT patients. Of 27 miRNAs evaluated, nine (miR-335-5p, miR-130b-3p, miR-125b-5p, miR-23a-3p, miR-152-3p, miR-582-5p, miR-144-5p, miR-320a and miR-19b-3p) were differentially expressed in PHPT compared to matched control subjects. All nine differentially expressed miRNAs significantly correlated with levels of serum parathyroid hormone (PTH), and eight of the nine correlated with calcium levels. No differentially expressed miRNAs were consistently correlated with markers of BMD. Subjects with PHPT segregate from controls based on the signature of these nine miRNAs on principle component analysis. CONCLUSIONS: These data suggest that PHPT is characterized by a unique miRNA signature that is distinct from postmenopausal and idiopathic osteoporosis. Levels of specific miRNAs significantly correlate with PTH, suggesting that bone remodeling in PHPT may be mediated in part by PTH-induced changes in miRNA.

Identification of Sarcopenia Components That Discriminate Slow Walking Speed: A Pooled Data Analysis.

BACKGROUND: The Sarcopenia Definitions and Outcomes Consortium (SDOC) sought to identify cut points for muscle strength and body composition measures derived from dual-energy x-ray absorptiometry (DXA) that discriminate older adults with slow walking speed. This article presents the core analyses used to guide the SDOC position statements. DESIGN: Cross-sectional data analyses of pooled data. SETTING: University-based research assessment centers. PARTICIPANTS: Community-dwelling men (n = 13,652) and women: (n = 5,115) with information on lean mass by DXA, grip strength (GR), and walking speed. MEASUREMENTS: Thirty-five candidate sarcopenia variables were entered into sex-stratified classification and regression tree (CART) models to agnostically choose variables and cut points that discriminate slow walkers (<0.80 m/s). Models with alternative walking speed outcomes were also evaluated (<0.60 and <1.0 m/s and walking speed treated continuously). RESULTS: CART models identified GR/body mass index (GRBMI) and GR/total body fat (GRTBF) as the primary discriminating variables for slowness in men and women, respectively. Men with GRBMI of 1.05 kg/kg/m2 or less were approximately four times more likely to be slow walkers than those with GRBMI of greater than 1.05 kg/kg/m2 . Women with GRTBF of less than 0.65 kg/kg were twice as likely to be slow walkers than women with GRTBF of 0.65 kg/kg or greater. Models with alternative walking speed outcomes selected only functions of GR as primary discriminators of slowness in both men and women. DXA-derived lean mass measures did not consistently discriminate slow walkers. CONCLUSION: GR with and without adjustments for body size and composition consistently discriminated older adults with slowness. CART models did not select DXA-based lean mass as a primary discriminator of slowness. These results were presented to an SDOC Consensus Panel, who used them and other information to develop the SDOC Position Statements. J Am Geriatr Soc 68:1419-1428, 2020.

Validation of rapid 4-component body composition assessment with the use of dual-energy X-ray absorptiometry and bioelectrical impedance analysis.

Background: The 4-component (4C) model is a criterion method for human body composition that separates the body into fat, water, mineral, and protein, but requires 4 measurements with significant cost and time requirements that preclude wide clinical use. A simplified model integrating only 2 measurements-dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA)-and 10 min of patient time has been proposed. Objective: We aimed to validate a rapid, simplified 4C DXA + BIA body composition model in a clinical population. Design: This was a cross-sectional observational study of 31 healthy adults. Participants underwent whole-body DXA, segmental BIA, air displacement plethysmography (ADP), and total body water (TBW) measurement by deuterium (D2O) dilution. 4C composition was calculated through the use of the Lohman model [DXA mineral mass, D2O TBW, ADP body volume (BV), scale weight] and the simplified model (DXA mineral mass and BV, BIA TBW, scale weight). Accuracy of percentage of fat (%Fat) and protein measurements was assessed via linear regression. Test-retest precision was calculated with the use of duplicate DXA and BIA measurements. Results: Of 31 participants, 23 were included in the analysis. TBWBIA showed good test-retest precision (%CV = 5.2 raw; 1.1 after outlier removal) and high accuracy to TBWD2O [TBWD2O = 0.956*TBWBIA, R2= 0.92, root mean squared error (RMSE) = 2.2 kg]. %Fat estimates from DXA, ADP, D2O, and BIA all showed high correlation with the Lohman model. However, only the 4C simplified model provides high accuracy for both %Fat (R2 = 0.96, RMSE = 2.33) and protein mass (R2= 0.76, RMSE = 1.8 kg). %Fat precision from 4C DXA + BIA was comparable with DXA (root mean square-SD = 0.8 and 0.6 percentage units, respectively). Conclusions: This work validates a simplified 4C method that measures fat, water, mineral, and protein in a 10-min clinic visit. This model has broad clinical application to monitor many conditions including over/dehydration, malnutrition, obesity, sarcopenia, and cachexia.

Assessment of abdominal fat compartments using DXA in premenopausal women from anorexia nervosa to morbid obesity.

OBJECTIVE: To test a newly developed dual energy X-ray absorptiometry (DXA) method for abdominal fat depot quantification in subjects with anorexia nervosa (AN), normal weight, and obesity using CT as a gold standard. DESIGN AND METHODS: 135 premenopausal women (overweight/obese: n = 89, normal-weight: n = 27, AN: n = 19); abdominal visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and total adipose tissue (TAT) areas determined on CT and DXA. RESULTS: There were strong correlations between DXA and CT measurements of abdominal fat compartments in all groups with the strongest correlation coefficients in the normal-weight and overweight/obese groups. Correlations of DXA and CT VAT measurements were strongest in the obese group and weakest in the AN group. DXA abdominal fat depots were higher in all groups compared to CT, with the largest % mean difference in the AN group and smallest in the obese group. CONCLUSION: A new DXA technique is able to assess abdominal fat compartments including VAT in premenopausal women across a large weight spectrum. However, DXA measurements of abdominal fat were higher than CT, and this percent bias was most pronounced in the AN subjects and decreased with increasing weight, suggesting that this technique may be more useful in obese individuals.

Dual-energy X-ray performs as well as clinical computed tomography for the measurement of visceral fat.

Visceral adipose tissue (VAT) is associated with adverse health effects including cardiovascular disease and type 2 diabetes. We developed a dual-energy X-ray absorptiometry (DXA) measurement of visceral adipose tissue (DXA-VAT) as a low cost and low radiation alternative to computed tomography (CT). DXA-VAT was compared to VAT assessed using CT by an expert reader (E-VAT). In addition, the same CT slice was also read by a clinical radiographer (C-VAT) and a best-fit anthropomorphic and demographic VAT model (A-VAT) was developed. Whole body DXA, CT at L4-L5, and anthropometry were measured on 272 black and white South African women (age 29 ± 8 years, BMI 28 ± 7 kg/m(2), waist circumference (WC) 89 ± 16 cm). Approximately one-half of the dataset (n = 141) was randomly selected and used as a training set for the development of DXA-VAT and A-VAT, which were then used to estimate VAT on the remaining 131 women in a blinded fashion. DXA-VAT (r = 0.93, standard error of the estimate (SEE) = 16 cm(2)) and C-VAT (r = 0.93, SEE = 16 cm(2)) were strongly correlated to E-VAT. These correlations with E-VAT were significantly stronger (P < 0.001) than the correlations of individual anthropometry measurements and the A-VAT model (WC + age, r = 0.79, SEE = 27 cm(2)). The inclusion of anthropometric and demographic measurements did not substantially improve the correlation between DXA-VAT and E-VAT. DXA-VAT performed as well as a clinical read of VAT from a CT scan and better than anthropomorphic and demographic models.

Dual energy X-Ray absorptiometry body composition reference values from NHANES.

In 2008 the National Center for Health Statistics released a dual energy x-ray absorptiometry (DXA) whole body dataset from the NHANES population-based sample acquired with modern fan beam scanners in 15 counties across the United States from 1999 through 2004. The NHANES dataset was partitioned by gender and ethnicity and DXA whole body measures of %fat, fat mass/height(2), lean mass/height(2), appendicular lean mass/height(2), %fat trunk/%fat legs ratio, trunk/limb fat mass ratio of fat, bone mineral content (BMC) and bone mineral density (BMD) were analyzed to provide reference values for subjects 8 to 85 years old. DXA reference values for adults were normalized to age; reference values for children included total and sub-total whole body results and were normalized to age, height, or lean mass. We developed an obesity classification scheme by using estabbody mass index (BMI) classification thresholds and prevalences in young adults to generate matching classification thresholds for Fat Mass Index (FMI; fat mass/height(2)). These reference values should be helpful in the evaluation of a variety of adult and childhood abnormalities involving fat, lean, and bone, for establishing entry criteria into clinical trials, and for other medical, research, and epidemiological uses.