Cardiovascular Journal of Africa: Vol 26 No 6 (November/December 2015)

CARDIOVASCULAR JOURNAL OF AFRICA • Volume 26, No 6, November/December 2015

216

AFRICA

To define the role of the autonomic nervous system in the

pathogenesis of certain diseases, a number of studies have used

HRV analysis. However, the results may not agree, presumably

due to methodological differences. HRV analysis can be carried

out by two different methods, time-domain and frequency-

domain methods. The frequency-domain method separates

heart rate signals by frequency and density. Although its basic

principle is simple, it is technically difficult and complex.

The time-domain method is based on analysis of the

interval between normal pulses in 24-hour ECG recording.

Low-frequency (LF) HRV is an index of sympathetic activity,

whereas high-frequency (HF) HRV reflects parasympathetic

activity. Increased LF:HF ratio is characterised by an autonomic

nervous dysfunction.

Among time-domain HRV indices, SDNN, SDANN and

SDNN index reflect the heart rate, and their decrease is related

to diminished vagal and increased sympathetic modulation of

the sinus node.

In this study, we used the time-domain method

and found that time-domain HRV variables differed significantly,

showing increased sympathetic activity in patients with primary RP.

Among these variables, intervals during the 24-hour period

(SDNN index) and the proportion of adjacent normal R–R

intervals

50 ms (pNN50) were found to be independent

variables in multivariate logistic regression analysis. However, it

is obvious that the results of the study will be elucidated more

after including frequency-domain HRV indices. Despite this

limitation, our study gives inspiration for further larger sample

sized prospective studies.

Assessment of the autonomic nervous system may play an

important role in understanding the underlying mechanism of

primaryRP. PrimaryRPpatientsmayhavehigher resting sympathetic

tone or decreased parasympathetic tone. Autonomic parameters of

the cardiovascular system can be non-invasively assessed with HRV.

The clinical course and prognosis of various cardiac and systemic

disorders could be obtained with this assessment.

Many articles in the literature have speculated that HRV

could be used in various cardiac and non-cardiac diseases for

autonomic regulation, but a limited number of studies have

used HRV on patients with primary RF. Koszewicz

et al

. found

that patients with primary RP did not have the autonomic

stability found in healthy individuals.

In another study, Ferri

et al.

studied HRV changes in patients with systemic sclerosis.

They found significantly higher HRV and lower circadian and

spectral indices of HRV in systemic sclerosis patients, compared

to control subjects.

Similarly in our study, we demonstrated an autonomic

imbalance suggesting increased sympathetic or reduced

parasympathetic activity demonstrated by time-domain HRV

indices in primary RP patients compared to controls. Among

time-domain variables of HRV indices, the mean of all five-

minute standard deviations of N–N (normal R–R) intervals

during the 24-hour period (SDNN index) and the proportion of

adjacent normal R–R intervals

50 ms (pNN50) were found to

be most associated with primary RP.

Conclusion

The current study demonstrated significant differences in time-

domain parameters of HRVanalysis during asymptomatic 24-hour

intervals and indicated the presence of an autonomic imbalance

(increased sympathetic and decreased parasympathetic activity)

in patients with primary RP compared to healthy controls. The

exact mechanism of the relationship between primary RP and

autonomic imbalance remains unclear and needs further studies.

Future prospective studies may be helpful to demonstrate the

role of HRV analysis in evaluating the progression and treatment

effectiveness of patients with primary RP.

References

1. Prete M, Fatone MC, Favoino E, Perosa F. Raynaud’s phenomenon:from

molecular pathogenesis to therapy.

Autoimmun Rev

2014;

(6): 665–667.

2. Mikulska D. Raynaud’s phenomenon: pathogenesis and prevalance.

Ann

Acad Med Stetin

2010;

(1): 11–14.

3. Koszewicz M, Gosk-Bierska I, Bili

ska M, Podemski R, Budrewicz S,

Adamiec R,

et al

. Autonomic dysfunction in primary Raynaud’s phenom-

enon.

Int Angiol

2009;

(2): 127–131.

4. Gayraud M. Raynaud’s phenomenon.

Joint Bone Spine

2007;

(1): e1–8.

5. Bakst R, Merola JF, Franks AG, Sanchez M. Raynaud’s phenomenon:

pathogenesis and management.

J Am Acad Dermatol

2008;

(4): 633–653.

6. Cooke JP, Marshall JM. Mechanisms of Raynaud’s disease.

Vasc Med

2005;

(4): 293–307.

7. Turton EP, Kent PJ, Kester RC. The aetiology of Raynaud’s phenomenon.

Cardiovasc Surg

1998;

(5): 431–440.

8. Herrick AL. Pathogenesis of Raynaud’s phenomenon.

Rheumatology

(Oxford) 2005;

(5): 587–596.

9. Stauss HM. Heart rate variability.

Am J Physiol Regul Integr Comp Physiol

2003;

285

(5): R927–931.

10. Malik M. Heart rate variability.

Curr Opin Cardiol

1998;

(1): 36–44.

11. Pieper SJ, Hammill SC. Heart rate variability: technique and investiga-

tional applications in cardiovascular medicine.

Mayo Clin Proc

1995;

(10): 955–964.

12. Ozdemir O, Soylu M, Alyan O, Geyik B, Demir AD, Aras D,

et al.

Association between mean platelet volume and autonomic nervous system

functions: Increased mean platelet volume reflects sympathetic overactivity.

Exp Clin Cardiol

2004;

(4): 243–247.

13. Zaza A, Lombardi F. Autonomic indexes based on the analysis of heart

rate variability: a view from the sinus node.

Cardiovasc Res

2001;

(3):

434–442.

14. Ferri C, Emdin M, Giuggioli D, Carpeggiani C, Maielli M, Varga A,

et al

Autonomic dysfunction in systemic sclerosis time and frequency domain

24 hour heart rate variability analysis.

Br J Rheumatol

1997;

: 669–676.

Table 3. Univariate analysis and multivariate logistic

regression analysis based on independent variables

likely to affect the presence of primary RP

Variable

Univariate analysis

Multivariate analysis*

OR 95% CI

SDNN (ms)

0.287 0.025 0.979 0.955–1.004 0.107

RMSDD (ms)

0.297 0.020 1.012 0.957–1.069 0.684

SDNN index

0.409 0.001 1.138 1.049–1.235 0.002

NN50 count (%) 0.340 0.007 1.000 1.000–1.000 0.711

pNN50

0.281 0.028 0.881 0.785–0.989 0.032

SDNN: standard deviation of all R–R intervals, RMSSD: the mean

square root of the difference of successive R–R intervals, SDNN

index: the mean of all five-minute standard deviations of N–N

(normal R–R) intervals during the 24-hour period, NN50 count:

successive N–N intervals differing more than 50 ms, pNN50: the

proportion of adjacent normal R–R intervals

50 ms.

-value at the last step, where the independent variables remained in

the backward LR multivariate regression model.