Blood flow is no longer measured at a single site but between an area and the LDI due to being non-contact cannot interfere with the final results. LDI is a 1mm laser beam that uses a mirror to scan in two dimensions. A small amount of light penetrates the skin; the depth depends on wavelength and absorption, of area scanned and interacts with cells and tissues. Speed and density of moving cells determine the signal sent to detector. Discovery Technology International defines the amount of tissue measured as: we have estimated that for well-perfused tissue such as muscle, the mean sampling depth for our probes is in the region 0.5-1.0 mm with a concomitant sampling volume in the region 0.3-0.5 mm3. For cutaneous measurements, the sampling depth is likely to be in the range 1.0 -- 1.5 mm. These estimates have been obtained heuristically through many years of experience and are based on both in vitro observations and mathematical modeling of photon diffusion through 'imaginary tissues' using Monte-Carlo techniques.

Acetylcholine (Ach) and Sodium Nitroprusside (SNP)

Acetylcholine and also Sodium Nitroprusside injected in to the site to be scanned by the LDI caused vasodililation and allowed for scanning of the areas more accurately and studies prove the two injects are reproducible. One group of researchers used the following technique:

Drugs used: 2.5 ml of 1% acetylcholine chloride (Sigma Chemical Co., St. Louis, MO, U.S.A.) was introduced into the anodal chamber. 2.5 ml of 1% sodium nitroprusside (Sigma) was introduced into the cathodal chamber. The vehicle for both drugs was 0.5% sodium chloride solution. (Balmain et al., 2007)

Results

All statistical analyses were performed using SPSS 15.0 for Microsoft Windows. The data was checked for normality using the Shapiro-Wilk test, as the number of subjects was less than fifty. Those data that were not normally distributed were transformed and reassessed. A repeated measure ANOVA was used to determine significant differences between time points for repeated LDI, PWV and PWA measurements. A value of P (0.050 was used to define significance and a 95% confidence interval. The data presented in tables and graphs is displayed as mean ± SD (standard deviation), unless otherwise stated.

Outliers have been checked but not eliminated, although some of them are not within the range of 2 SD, they are not eliminated due to the consistency between all 4 visits as per subject.

Statistical results from studies

From Patti LDI ACH 2003 we can see the dramatic difference between using Area under Curve vs. Incremental Area under Curve. The values for AUC are almost always half the values gotten from the IAUC.

ACH AUC

SEM

Std Dev

0

6

12

18

ACH IAUC

SEM

Std Dev

0

6

12

79.54888

18

52.00063

ANOVA / Bland and Altman

The Bland & Altman plot (Bland & Altman, 1986 and 1999) is a statistical method to compare two measurements techniques. In this graphical method the differences (or alternatively the ratios) between the two techniques are plotted against the averages of the two techniques. Horizontal lines are drawn at the mean difference, and at the limits of agreement, which are defined as the mean difference plus and minus 1.96 times the standard deviation of the differences. ( as quoted from Medcalc.be, 2010) ANOVA, or analysis of variance, represents several statistical models and methods and it correlates all the different variables in to components. ANOVA determines whether or not the means of several groups are related and converts t-test two sample results into generalized groups. ANOVA can compare and analysis the data from data containing more than three means. An example from the Handbook of Biological Statistics in regards to using ANOVA; you could measure the amount of transcript of a particular gene for multiple samples taken from arm muscle, heart muscle, brain, liver, and lung. The transcript amount would be the measurement variable, and the tissue type would be the nominal variable.

Microbiologybytes.com (2010) breaks down the ANOVA process and terminology as:

ANOVA jargon:

Way = an independent variable, so a one-way ANOVA has one independent variable, two-way ANOVA has two independent variables, etc. Simple ANOVA tests the hypothesis that means from two or more samples are equal (drawn from populations with the same mean). Student's t-test is actually a particular application of one-way ANOVA (two groups compared).

Factor = a test or measurement. Single-factor ANOVA tests whether the means of the groups being compared are equal and returns a yes/no answer, two-factor ANOVA simultaneously tests two or more factors, e.g. tumour size after treatment with different drugs and/or radiotherapy (drug treatment is one factor and radiotherapy is another). So, "factor" and "way" are alternative terms for the same thing (inpependent variables).

Repeated measures: Used when members of a sample are measured under different conditions. As the sample is exposed to each condition, the measurement of the dependent variable is repeated. Using standard ANOVA is not appropriate because it fails to take into account correlation between the repeated measures, violating the assumption of independence. This approach...

where research requires repeated measures, such as longitudinal research which measures each sample member at each of several ages - age is a repeated factor. This is comparable to a paired t-test.
So the variance is the mean of the squared deviations about the mean (MS) or the sum of the squared deviations about the mean (SS) divided by the degrees of freedom.

The Bland and Altman Plot can be used to compare the repeated measurements on a variety of subjects and determine the repeatability of methods being used.

Many of the techniques listed in this paper are used in conjunction with one or more of the other techniques. The correlation between the techniques seem to be based from the same principles and are graphed from the same mold.

Ach AUC and Ach IAUC

Discussion

The difference of the arterial stiffness between males and females is evident more at earlier ages and tend to decrease with age. Women have a lower arterial stiffness due to hormones and hormone therapies. (Nagia, Early, Kemper, Bacal, & Metter, 1997) Still this study (Patty LDI ACH 2003) only tested a group of seven men and tested seventeen women. The sample population is low in the number of men and accuracy would need to be tested.

The extremely large means, standard deviations, and standard error of mean show large inconsistencies in the numbers and bring the topic of repeatability in to question.

In Patty LDI PWA 2003, these numbers correlate to proven studies where women had an augmentation index of approximately 22 and the men's range was approximately 8. (Yasmin & Brown, 1999)

Patty LDI PWA 2003

Patty LDI PWA 2003

The repeatability of Patty LDI PWA 2003 was exception well for the 18-week period and the gender differences were consistant with studies conducted and mentioned earlier in the paragraph.

All of the data, charts, and calculations in this study show the same repeatibility and results found in other studies.

Conclusion

Arterial stiffness and cardiovascular disease are directly or indirectly related to a vast number of disease and conditions. Arterial stiffening is caused by a vast number of conditions which vary with gender, age, and demographics. Cardiovascular disease could become the next major cause of death around the world if treatments are not produced they more accurately combat the stiffening of the arteries.

The techniques such as Laser Doppler Imaging and Pulse Wave Analysis indicate the future of research in regards to the stiffening of arteries is very bright. ANOVA combined with the Bland and Altman plots guarantee the results will be more accurate and repeatable saving wasted time and money as well as resources.

Treatment in the form of non-invasive procedures allows the test to be performed on the patient in a less stressful method and give a more relaxed result. This results in accurate results not bias by the subject being uncomfortable or in distress. With all the new techniques on the market today early detection of arterial stiffening can lead to decrease in the number of stroke and heart attack victims from cardiovascular disease and the hundreds of diseases like diabetes, spinal cord injuries, etc. that have links and are correlated with cardiovascular disease or the stiffening of arteries.

Bibliography

Arnett, D. (n.d.). Arterial Stiffness and Hypertension. Retrieved on April 12, 2010 from http://www.fac.org.ar/scvc/llave/hbp/arnett/arnetti.htm

Bailey, B.; Jacobsen, D.; LeCheminant, J.; Kirk, E.; & Donnelly, J. (2003). The Effect of Analysis Method in Determining Change in Post Exercise Oxygen Consumption.

Retrieved on April 12, 2010 from http://journals.lww.com/acsm-msse/Fulltext/2003/05001/The_Effect_of_Analysis_Method_in_Determining.1004.aspx

Balmain, S., Padmanabhan, N., Ferrel, W., Morton, J. & McMurray, J. (2007). Differences in arterial compliance, microvascular function and venous capacitance between patients with heart failure and either preserved or reduced left ventricular systolic function. Retrieved on April 12, 2010 from http://eurjhf.oxfordjournals.org/content/9/9/865.full

Berry, S. (2008). Manipulation of lipid bioaccessibility of almond seeds influences postprandial lipemia in healthy human subjects. Retrieved on April 12, 2010 from http://www.ajcn.org/cgi/content/full/88/4/922

Blacher, J. & Safar, M. (2005). Large-artery stiffness, hypertension and cardiovascular risk in older patients. Retrieved on April 12, 2010 from http://www.nature.com/nrcardio/journal/v2/n9/full/ncpcardio0307.html

Brown, Y. & Brown, M.; (1999). Similarities and differences between augmentation index and pulse wave velocity in the assessment of arterial stiffness. Retrieved on April 12, 2010

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