Test Preparation

The methodology for measuring endothelial function and vascular reactivity using DTM has been previously described [21 (link)–25 (link)]. All DTM tests were performed using a VENDYS® 6000 Portable System (Endothelix, Houston, TX), a PC-based system that fully automates the cuff reactive hyperemia protocol. The general test setup and a sample VENDYS test report are shown in Figure 1. During subject preparation, blood pressure cuffs were placed on both of the subject's upper arms, and VENDYS skin temperature sensors were affixed to both of the subject's index fingers. The software-driven DTM test began with an automated measurement of blood pressure and heart rate obtained from the left arm cuff. Following a 5-minute period of patient and temperature stabilization, a 5-minute cuff occlusion (cuff inflated to 30 mmHg above systolic BP) of the right arm was performed. During the cuff occlusion period, fingertip temperature in the right hand decreased because of the absence of warm circulating blood. When the cuff was released after the 5-minute occlusion, hyperemic blood flow to the forearm and hand was restored, and this resulted in a “temperature rebound” in the fingertip that is directly related to the subject's hyperemic blood flow response, endothelial function, and vascular reactivity [21 (link), 22 (link)]. Using the recorded fingertip temperatures, the ambient temperature of the testing room, the observed slope of temperature decline, and a multivariate bioheat formula, the VENDYS software calculated and plotted a zero reactivity curve (ZRC). The ZRC served as an internal control and showed the expected temperature rebound curve, if zero vascular reactivity was present and the other variables remained the same. In other words, the ZRC is the expected temperature curve, if no vasodilatation and subsequent reactive hyperemia had occurred [21 (link)]. Vascular reactivity index (VRI) was determined by taking the maximum difference between the observed temperature rebound curve and the ZRC during the reactive hyperemia period. VRI ranged from 0.0 to 3.5 and was classified as being indicative of poor (0.0 to <1.0), intermediate (1.0 to <2.0), or good (≥2.0) vascular reactivity.
The VENDYS DTM Test Registry includes age, sex, blood pressure, heart rate, VRI, and fingertip temperature measurements recorded during DTM tests. The Registry does not include other health related information. All DTM tests were performed in ambulatory care clinical settings. This study includes a total of 6,084 patients from 18 clinics that volunteered to submit their data to the Registry. The number of each type of medical practice is as follows: cardiology = 9, general/family practice = 4, antiaging = 3, and internal medicine = 2.
Statistical analyses were performed using MATLAB (The MathWorks, Inc., Natick, MA). Variable data were expressed as mean ± SD. VRI scores in men and women were compared using unpaired Student's t-test. Comparisons of categorical data (e.g., proportion of subjects with good VRI in men versus women) were performed using Fisher's exact test. Pairwise correlations were examined using Pearson's correlation coefficient, and correlations between VRI and multiple patient characteristics (i.e., age, sex, blood pressure, and heart rate) were evaluated using multiple linear regression analysis. p value < 0.05 was considered significant. When performing statistical comparisons, tests with missing data were excluded from the comparison. “Cold Finger Flag” was defined as the condition in which the right finger temperature at start of cuff occlusion (time 300 s) is ≤27°C. Previous DTM testing had shown that right finger t300 temperatures < 27°C often resulted in technically poor results. “Sympathetic Response Flag” was defined as the condition in which left finger temperature continuously declines (>0.5°C temperature drop over a 5-minute time period) after right arm-cuff occlusion. When evaluating VRI, tests that exhibited “Cold Finger Flag” (n = 353) or “Sympathetic Response Flag” (n = 294) were excluded from the analyses. In addition to monitoring temperature at the index finger of the right arm, we studied temperature changes at the index finger of the left (nonoccluded) arm and observed interesting signals that are currently under further investigations and not included in the results below.

To test the efficiency of AliGROOVE we designed two sets of nucleotide and amino acid sequence data using 4-taxon and 6-taxon trees (Figure 1). The topology of the 4-taxon setup (setup A, Figure 1a) contained two long branches of unrelated taxa (with branch lengths BL 2 = 0.1,0.3,0.5,0.7,0.9,1.1,1.3,1.5) under three different branch length conditions for the other two short terminal branches (BL 3 = 0.1,0.12,0.14 and RB = 0.1) and two different lengths of the short internal branch (BL 1 = 0.01,0.02). The 6-taxon setup (setup B, Figure 1b) contained two long internal branches (BL 2 = 0.1,0.3,0.5,0.7,0.9,1.1,1.3,1.5), separated by a short internal branch (BL 1 = 0.01) while the lengths of terminal branches are kept constant (BL 3 = 0.01 and RB = 0.1). For both test setups, 100 alignments were generated for each step of BL 2 branch elongation. Sequence length of each alignment of setup A was set to 250,000 character state positions and for setup B to 50,000 character state positions to reduce the calculation time. All alignments were generated with INDELible v.1.03 [24 (link)]. In order to simulate nucleotide sequence data we used the Jukes-Cantor model (JC) of sequence evolution and for amino acid sequence data the BLOSUM62 substitution model. All data were simulated with among site rate variation (ASRV), using a mixed-distribution model with a shape parameter α = 1.0, and a proportion of invariant sites ρ_inv= 0.3. ASRV was modelled using a continuous Γ-rate distribution while indel events were not simulated.
Trees of simulated data were inferred with PhyML_3.0_linux64 [25 (link), 26 (link)]. We analyzed the data with a mixed-distribution model (JC+ Γ + I) and correct parameter values (α = 1.0, ρ_inv= 0.3), except for the categorization of the gamma distribution. The number of relative substitution rate categories was set to four (c = 4) and tree topologies and branch lengths were optimized. Maximum Likelihood analyses were performed and evaluated with a Perl pipeline. For each branch length-combination, we generated 100 data replicates and recorded the frequencies of correct and incorrect tree reconstructions using correct alignments and nearly correct substitution models (Figures 2, 3, Additional files 1, 2, 3).