Impairment of cutaneous microcirculation and vasoreactivity in primary hypothyroidism

Abstract

The aim

of this study was to identify potential diagnostic markers of microcirculation disorders in patients with primary hypothyroidism.

Materials and methods

This cross-sectional study included patients with overt (OH, n = 14) and subclinical (SH, n = 37) hypothyroidism, and healthy volunteers (HV, n = 50). Using laser Doppler flowmetry (LDF), mean cutaneous blood flow in the dorsal forearm was assessed at rest (M), during cooling to 10 °C (M_10°C), heating to 35 °C (M_35°C) and to 42 °C (M_42°C). The standard deviation (σ) and the coefficient of variation of cutaneous blood flow (Kv=σ/M) at rest were calculated. The amplitudes of microvascular blood flow oscillations in frequency ranges corresponding to the neurogenic, myogenic, respiratory, and pulse mechanisms of microcirculation modulation were also calculated (A_n, A_m, A_resp, A_card). Laboratory parameters were evaluated.

Results

The OH group had reduced blood flow variability (σ) compared to the HV (p=0.025) and SH (p=0.037) groups. Kv was lower in OH compared to SH (p=0.041). Acard was decreased in the OH group compared to the HV (p=0.001) and SH (p=0.012) groups. M_42°C was reduced in OH compared to HV (p=0.014). Hypercholesterolemia and increased IL-6 were found in the OH and SH groups compared to HV. In OH, the red blood cell distribution width (RDW) was increased compared to HV (p=0.026).

Conclusion

Overt hypothyroidism is associated with decreased total and pulse variability of cutaneous microvascular blood flow, decreased cutaneous hyperemia during local heating to 42 °C, and increased RDW. These microcirculatory disorders are accompanied by hypercholesterolemia and increased IL-6 levels.

Keywords

primary hypothyroidism microcirculation cutaneous blood flow laser doppler flowmetry thermal hyperemia red blood cell distribution width IL-6

Introduction

Deficiency of thyroid hormones causes systemic changes in metabolism and disruption of the functioning of many organs and systems of the body.¹ In particular, hypothyroidism is associated with an increased risk of cardiovascular events.² Traditionally, the main pathophysiological significance in the development of cardiovascular complications of hypothyroidism is attributed to myocardial pathology and macroangiopathy (heart rhythm and conduction disturbances, heart failure, hypertension, atherosclerosis of the coronary and other arteries).^3–5

Nevertheless, the importance of microvasculature in ensuring adequate perfusion and oxygenation of tissues is well known, and microcirculatory alterations are involved in the pathogenesis of many cardiovascular diseases, as well as complications of rheumatic and endocrine pathologies.^6–8 Decreased thyroid hormone levels may lead to changes in the microcirculation through increased peripheral vascular resistance and endothelial dysfunction.⁵ Independent pathogenetic factors of impaired microcirculation and tissue hypoperfusion may include bradycardia, decreased cardiac output and circulating blood volume, which are characteristic of severe hypothyroidism.⁹

There are very few controlled studies assessing microcirculation and microvascular reactivity in hypothyroidism.

Laser Doppler flowmetry (LDF), based on the recording of the Doppler frequency shift of light reflected from moving red blood cells, is a simple and accessible method for non-invasive assessment of microvascular blood flow in the skin and mucous membranes.⁷ LDF is used to assess microcirculation both under physiological conditions, for example, physical exercise,¹⁰ and in disease states, including hypertension,¹¹ diabetes mellitus,¹² etc.

Cutaneous microcirculation can serve as a potentially representative model of microcirculation in other human organs, as indicated by a number of studies.^13,14 The use of functional tests with physical (local heating and cooling, occlusion of the brachial artery) or pharmacological (application of acetylcholine and nitroprusside) stimuli allows to obtain additional diagnostic information about the regulatory mechanisms of microcirculation and vascular reactivity, identify pathological processes compensated at rest, and increases the reproducibility of measurement results.^15–18

Hypothyroidism has previously been shown to be associated with longer duration of post-occlusion reactive hyperemia (PORH) in the skin, assessed by LDF.¹⁷ Similar results were obtained when assessing PORH using videocapillaroscopy. However, at rest, microvascular perfusion was reduced in overt hypothyroidism and increased after disease compensation.¹⁹

The above-mentioned studies did not evaluate microcirculation in patients with subclinical hypothyroidism, although this is a clinically relevant issue due to conflicting results of macrovascular studies and controversial treatment strategies in this group of patients.^20–22 In addition, it is necessary to take into account other metabolic and functional disorders that occur in the body with a deficiency of thyroid hormones (hypercholesterolemia, anemia with changes in the size and shape of red blood cells, systemic inflammation), which can be independent pathogenetic factors in microcirculation disorders and cardiovascular complications.^23–28

It is known that microvascular blood flow is not constant and is subject to oscillations (flowmotion), caused by both rhythmic changes in the diameter of microvessels (vasomotions) and “passive” oscillations in blood flow introduced into the microvascular bed by the pulse wave or breathing rhythm.²⁹ The spectral characteristics of vasomotion, in turn, are determined by the physiological mechanisms of regulation of vascular tone, primarily by the activity of the endothelium, perivascular nerves and the myocytes of the vascular wall.³⁰

Therefore, spectral analysis (particularly wavelet analysis) of microvascular blood flow oscillations recorded using LDF has diagnostic potential for identifying microcirculatory disorders in hypothyroid patients.

Thus, non-invasive assessment of microcirculation in patients with primary hypothyroidism and the study of the dependance of the severity of microcirculatory disorders on the level of thyroid hormones and other laboratory manifestations of hypothyroidism are of scientific and clinical interest for the early diagnosis of cardiovascular complications.³¹

The aim of this study was to identify potential diagnostic markers of microcirculation impairment in patients with primary hypothyroidism and to assess the relationship between these impairments and the severity of hypothyroidism.

Materials and methods

Study design

This cross-sectional clinical study was conducted from February 2023 to December 2024.

The study protocol was approved by the local ethics committee (protocol no. 02-23, January 26, 2023). All participants provided written informed consent.

Inclusion criteria for patients with primary hypothyroidism

Primary hypothyroidism (decreased thyroid hormone levels due to autoimmune thyroid disease or radical thyroidectomy).

Men and women aged 18 and older.

Body mass index (BMI) of 18–34.9 kg/m².

Inclusion criteria for conditionally healthy volunteers

Males and females aged 18 years and older

BMI of 18–34.9 kg/m².

Exclusion criteria for both groups

Taking levothyroxine for more than three days at the time of inclusion for the overt hypothyroidism subgroup

A history of cardiovascular disease (arrhythmia, coronary heart disease, heart valve disease, heart failure, or peripheral arterial disease), stroke, or acute coronary syndrome within one month prior to inclusion in the study

A history of prediabetes or diabetes mellitus (type 1, type 2, or other specific types).

A history of endocrine diseases, such as adrenal gland, pituitary gland, or parathyroid disorders

Skin diseases (e.g., scleroderma, systemic lupus erythematosus, trauma, and scarred skin) in the area of cutaneous blood flow assessment.

Сhronic kidney disease (glomerular filtration rate <60 mL/min/1.73 m²) or liver failure

Cancer

A hemoglobin level of less than 100 g/L

Fever and symptoms of acute respiratory infection at the time of study enrollment

Intense physical activity on the day of the study

Intake of alcohol, vasoactive drugs, or chemotherapy

Pregnancy or lactation

A short questionnaire was filled out by each participant, which included questions about personal information (medical and pharmacological history, smoking, caffein and alcohol consumption, concomitant medical therapies).

Population and conditions of examination

A total of 50 healthy volunteers (HV) and 51 patients with hypothyroidism were enrolled in the study. The patients with hypothyroidism were divided into two groups: overt hypothyroidism (OH, n = 30) defined by increased serum thyroid stimulating hormone (TSH) levels (>4.2 μIU/mL) and low free thyroxine (fT4) levels (<10.8 pmol/L); subclinical hypothyroidism (SH, n = 27) defined by increased serum TSH levels and fT4 within the normal range. All examinations and instrumental studies were performed at an ambient temperature of 22–26°C after a 15-min acclimatization period between 9:00 a.m. and 12:00 p.m. on an empty stomach.

Anthropometric and physiological measurements

The following basic anthropometric and physiological parameters were measured in each participant: BMI (kg/m²); systolic, diastolic and mean arterial pressure using an automated sphygmomanometer «OMRON M2 Classic» (OMRON HEALTHCARE Co., Ltd, Japan); heart rate and arterial blood oxygen saturation (SpO₂) by a ChoiceMMed finger pulse oximeter (Beijing Choice Electronic Technology Co., Ltd, China); axillary body temperature by an electronic thermometer «OMRON Eco Temp Basic» (OMRON HEALTHCARE Co., Ltd, Japan).

Laser Doppler flowmetry (LDF) with wavelet analysis of skin blood flow oscillations, local cooling and heating tests

The LAZMA ST system (SPE LAZMA Ltd, Russia) with the corresponding software (version 3.2.0.475) were used to measure cutaneous microvascular blood flow and perform temperature functional tests. The diagnostic protocol described in the «LAZMA ST» system user manual was used in this study. The 3-mm optical fiber probe combined with the temperature probe of the «LASMA ST» system was placed on the dorsal forearm of the dominant hand (in the wristwatch area) and fixed to the skin with a tape, avoiding excessive pressure on the investigated area (Figure 1). This site was selected because it provides a relatively flat skin surface that facilitates stable probe positioning and minimizes motion artefacts during LDF recording. In addition, the forearm is widely used as a standardized site for evaluating cutaneous microvascular function in clinical and experimental studies involving patients with cardiovascular and endocrine diseases.^11,18 Compared with acral sites such as the fingertip, the forearm region is less influenced by sympathetic activation (stress) and ambient temperature fluctuations. A light source with an emission wavelength of 1064 nm is used, and the volume of tissue being probed is approximately 1.5 mm.³

Figure 1.

Positioning of the 3-mm fiber-optic probe of the LASMA ST laser Doppler flowmetry system (a) inside the temperature probe of the LASMA-TEST module (b). The latter is used to measure skin temperature and to perform local cooling and heating on the dorsal side of the forearm.

The assessment of skin perfusion parameters was performed in a sitting position and included temperature registration of the investigated area.

The LDF recording at rest was carried out for 4 min. The following baseline parameters of cutaneous blood circulation were calculated: M, perfusion units (PU) – the mean cutaneous microvascular blood flow during the measurement time; σ, PU – the standard deviation of cutaneous microvascular blood flow oscillations (a measure of individual variability in skin perfusion); Kv, % – skin perfusion coefficient of variation (σ/M). The skin LDF signal was also subjected to an amplitude-frequency wavelet transformation using a complex Morlet wavelet. The time-averaged flowmotion amplitudes (A, PU) were estimated in the frequency ranges that correspond to neurogenic (0.02–0.06 Hz, A_n), myogenic (0.06–0.2 Hz, A_m); respiratory (0.2–0.6 Hz, A_resp) and cardiac (0.6–1.6 Hz, A_card) blood flow regulation mechanisms.³⁰ The relative (normalized) amplitudes were also calculated as A/σ ratio (Figure 2).

Figure 2.

Representative laser Doppler flowmetry (LDF) recording and wavelet-based spectral decomposition. Panel A shows a representative LDF recording obtained from the forearm skin of a healthy female participant (24 years old). Microvascular blood flow is not constant but exhibits physiological fluctuations (flowmotion). Primary analysis of the LDF signal includes calculation (using dedicated software) of mean cutaneous microvascular blood flow (M), its standard deviation (σ), and the coefficient of variation of skin perfusion (Kv = σ /M). Panel B illustrates the spectral decomposition of the same signal using wavelet analysis, which enables quantification of time-averaged flowmotion amplitudes within distinct frequency bands, including the amplitude of cardiac (pulse) oscillations (A_card). PU, perfusion units; A_n, A_m, and A_resprepresent the amplitudes of neurogenic, myogenic, and respiratory oscillations, respectively; Hz, hertz.

After recording the LDF at rest, local cooling to 10 °C, heating to 35 °C and 42 °C were successively performed in the same area of the skin, with simultaneous recording of LDF for 1, 4 and 4 min, respectively (Figure 3). The following LDF parameters were evaluated during local cooling and heating: M_10°C, M_35°C, and M_42°C (PU) (average cutaneous microvascular blood flow values during cooling to 10 °C, heating to 35 °C, and heating to 42 °C, respectively); and ΔM_10°C, ΔM_35°C, and ΔM_42°C (%) (relative changes in cutaneous microvascular blood flow during cooling to 10 °C, heating to 35 °C, and heating to 42 °C, respectively, calculated as (M_t°C - M) * 100 / M). The assessment of microvascular reactivity during temperature tests is based on the fact that local cooling causes vasoconstriction (replaced by transient vasodilation during prolonged cooling), and heating causes progressive vasodilation and hyperemia (with maximum values at 42°C).³²

Figure 3.

Flow diagram of temperature functional tests on the dorsal side of the dominant forearm.

Thyroid hormones, hematological and biochemical blood parameters

Fasting venous blood samples were taken after the LDF parameters were assessed. Serum concentrations of TSH, free triiodothyronine (fT3), fT4, thyroperoxidase antibodies (anti-TPO), thyroglobulin antibodies (anti-TG), total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides, and interleukin-6 (IL-6) were measured using a Cobas 8000 platform immunoassay analyzer (Roche Diagnostics Ltd, Germany), by electrochemiluminescence detection. A complete blood count was performed using a Mindray BC-6200 M Hematology Analyzer (Mindray, China) and included measurement of the red blood cell distribution width (RDW, %). The units of measurement and reference intervals for the tested blood parameters are provided in Table S1 of the Supplementary Appendix.

Other examination

On the day of the study, all participants were assessed for quality of life using the Medical Outcomes Study Short Form Health Survey (SF-36) after their first meal.

Statistical analysis

All statistical analyses were performed using IBM SPSS Statistics software (version 27.0.1, IBM Corp., NY), Statistica software (version 12.0, StatSoft, USA), and the Python programming language (version 3.11). The type of data distribution was assessed using the Shapiro–Wilk test. Continuous variables are presented as the median and range. Categorical variables are presented as absolute and relative frequencies. Three-group comparisons were performed with the Kruskal-Wallis test for independent variables. The Kruskal-Wallis test was followed by the Dunn-Bonferroni adjusted post hoc test for multiple comparisons. Receiver operating characteristic (ROC) curves were calculated to evaluate the diagnostic efficacy of σ and A_card parameters in differentiating skin microcirculation impairment in OH versus SH and in OH versus HV. The area under the ROC curve (AUROC) and its 95% confidence interval were calculated. Youden index was used to select the optimal cutoff point for σ and A_card values. Spearman's partial rank correlation analysis was performed to assess the relationship between the two variables, while accounting for the effects of age and BMI. The chi-square test (χ²) or Fisher's exact test with Bonferroni correction were performed to compare for categorical variables in independent samples. P values less than 0.05 were considered statistically significant (two-sided significance level adjusted for multiple comparisons).

Results

Table 1 shows the main characteristics of the participants. The three study groups were comparable in terms of body weight and sex. However, patients in the OH group were older than those in the HV group (median age 44 vs 29 years, respectively). The potential confounding effect of the age difference on the results of the study was eliminated by using partial rank correlations of the main study variables with laboratory markers of hypothyroidism in hypothyroid patients (see below). All groups had a higher percentage of females than males: 92% in the HV group, 78.6% in the OH group, and 91.9% in the SH group (Table 1). Hypothyroidism was mainly due to chronic autoimmune thyroiditis, as confirmed by high levels of TPOAb and TgAb (Table 2). The study groups were comparable in terms of blood pressure, heart rate, blood oxygen saturation, forearm skin temperature, and room temperature. Patients in the OH group had lower axillary body temperature (p=0.021 vs SH). Patients in both hypothyroidism groups (OH and SH) had lower SF-36 scores than healthy participants (p=0.004 and p=0.002 vs HV group, respectively).

Table 1.

Clinical characteristics of the study participants.

Variables	N	Overt hypothyroidism	N	Subclinical hypothyroidism	N	Healthy volunteers	p*
Age, years	14	44	37	36	50	29	0.016
		[37;53]		[29;43]		[25;42]
Gender (male), n (%)		3 (21.4)		3 (8.1)		4 (8.0)	0.298
Body mass index, kg/m2	14	27	37	27.3	50	23.1	0.104
		[21.9; 31.6]		[22.5;30.5]		[20.9;28.4]
Cause of hypothyroidism, n (%)
Hashimoto's thyroiditis		12 (85.7)		29 (82.9)		NA	-
Thyroidectomy		0 (0)		2 (5.7)
Thyroidectomy and Radioactive Iodine Therapy		1 (7.1)		0 (0)
Radioactive Iodine Therapy		1,7 (1)		4 (11.4)
Systolic blood pressure, mm Hg	14	121	37	120	50	118	0.578
		[110;132]		[109;127]		[113; 124]
Diastolic blood pressure, mm Hg	14	82	37	76	50	74	0.119
		[71;86]		[69;83]		[66;81]
Mean arterial pressure, mm Hg	14	96	37	90	50	89	0,1755
		[86;101]		[82;98]		[83;95]
Heart rate, beats per minute	14	70	37	75	50	74.5	0.115
		[66;75]		[71;86]		[68;80]
SpO₂, %	14	97	37	97	50	97	0.413
		[96;98]		[97;98]		[97;98]
Body temperature, °С	14	36	37	36.3	50	36.2	0.026
		[35.8;36.1]		[36;36.5]		[35.9;36.4]
Room temperature, °С	14	25.3	35	25.6	45	25.1	0.776
		[24.1;26.3]		[24;26.3]		[23.9;26.1]
Skin temperature, °С	14	28	37	28	50	28	0.668
		[27;28]		[26;28]		[26;30]
SF-36	11	55.5	37	61.3	47	76.6	<0.001
		[36.9;71.7]		[49;72.8]		[64;83.5]

Note: Continuous variables are presented as the median and interquartile range (Me [Q1; Q3]). Categorical variables are presented as absolute and relative frequencies.

*P-value for testing differences between the groups of overt and subclinical hypothyroidism and healthy volunteers (Kruskal–Wallis test for quantitative variables and chi-square test for categorical variables; bolded values indicate p < 0.05).

SpO₂, arterial blood oxygen saturation; SF-36, 36-Item Short Form Health Survey.

Table 2.

Laboratory parameters of thyroid function, lipid profile, complete blood count, and interleukin-6 levels.

Variables	N	Overt hypothyroidism	N	Subclinicalhypothyroidism	N	Healthy volunteers	p*	р, post-hoc
TSH, μIU/ml	14	30.6	37	7.3	50	1.5	<0.001	p_o−h<0.001
		[19.4; 65.9]		[5.2; 15.4]		[0.9; 2.3]		p_s−h<0.001p_o−s =0.119
fТ4, pmol/L	14	8.5	37	13.9	49	15.7	<0.001	p_o−h<0.001
		[6.3; 10.7]		[12.1; 16.3]		[14.4; 17]		p_s−h=0.054p_o−s <0.001
fТ3, pmol/L	14	3.5	37	4.2	49	4.8	<0.001	p_o−h<0.001
		[2.9; 4.1]		[3.5; 4.6]		[4.4; 5.1]		p_s−h<0.001p_o−s=0.157
TPOAb, IU/L	14	239.5	36	137.5	49	8.9	<0.001	p_o−h<0.001
		[45.5; 600.1]		[19.4; 375]		[8.9; 14.9]		p_s−h<0.001p_o−s >0.9
TgAb, IU/L	13	97	36	47.7	47	15.8	0.001	p _o−h = 0.071
		[17.2; 567]		[14.5; 22.7]		[18.8; 214.5]		p _s−h = 0.002p_o−s >0.9
Interleukin-6, pg/ml	10	3.1	34	2	47	1.4	0.001	p _o−h = 0.002
		[2; 4]		[1.4; 2.8]		[1.4; 1.9]		p _s−h = 0.023p_o−s = 0.312
Total cholesterol, mmol/l	14	6.7	37	5.3	50	4.8	<0.001	p_o−h<0.001
		[5.1; 8.1]		[4.6; 5.9]		[4.4;5.2]		p_s−h=0.044 p _o−s = 0.048
LDL cholesterol, mmol/l	14	4	37	3.4	50	2.8	<0.001	p_o−h<0.001
		[3.2; 5.4]		[2.9; 3.9]		[2.3; 3.2]		p _s−h = 0.011p_o−s = 0.201
HDL cholesterol, mmol/l	14	1.8	37	1.4	50	1.5	0.058	−
		[1.3; 2.1]		[1.1; 1.7]		[1.3; 1.8]
Triglycerides, mmol/l	14	1.7	37	1.1	50	0.9	<0.001	p_o−h=0.001
		[1.3; 2.1]		[0.9; 1.5]		[0.7; 1]		p_s−h=0.050p_o−s =0.171
WBC, 10*9/L	13	6.2	36	6.5	47	6.1	0.241	−
		[5.3; 8.1]		[5.3; 8.5]		[4.5; 7.1]
RBC, 10*12/L	14	4.5	37	4.4	48	4.5	0.039	p_o−h=0.99
		[4.2; 4.7]		[4.1; 4.5]		[4.3; 4.8]		p_s−h=0.034p_o−s =0.641
Hb, g/L	13	131	37	130	47	135	0.151	−
		[121; 147]		[122; 134]		[125: 143]
Ht, %	13	39	37	38.2	47	40	0.315	−
		[36.3; 42.3]		[36; 40.4]		[37; 42]
MCV, fl	13	90.4	37	88	47	87.6	0.261	−
		[87; 93]		[86.3; 92]		[84.5; 91]
MCH, pg	13	31	37	30	47	30	0.827	−
		[28.2;31]		[29; 31]		[29; 31]
MCHC, g/l	13	33	37	33.9	47	34.1	0.043	p_o−h = 0.039
		[32.8; 33.5]		[32.8; 34.8]		[33.1; 35]		p_s−h = 0.808p_o−s = 0.288
RDW, %	13	14	36	12.9	46	12	0.014	p_o−h = 0.026
		[12.8; 14.4]		[12; 14]		[12; 13]		p_s−h = 0.128p_o−s = 0.745
PLT, 10*9/L	14	239	37	287	48	290.5	0.146	−
		[206; 288]		[230; 340]		[233.5; 339.5]
ESR, mm/hour	11	10	23	11	39	7	0.073	−
		[6; 20]		[5; 19]		[5; 13]

Note. The data are presented as the median and interquartile range (Me [Q1; Q3]).

*P-value for testing differences between the groups of overt and subclinical hypothyroidism and the group of healthy volunteers (Kruskal–Wallis test). The Dunn-Bonferroni post hoc test was used for pairwise comparisons: p_o−h, overt hypothyroidism versus healthy volunteers; p_s−h, subclinical hypothyroidism versus healthy volunteers; and p_o−s, overt hypothyroidism versus subclinical hypothyroidism.

P-values for pairwise comparisons are reported only if the overall comparison test is significant.

Statistically significant differences between groups are highlighted in bold (p < 0.05).

TSH, thyroid-stimulating hormone; fT4, free thyroxine; fT3, free triiodothyronine; TPOAb, thyroid peroxidase antibodies; TgAb, thyroglobulin antibodies; LDL, low-density lipoprotein; HDL, high-density lipoprotein; WBC, white blood cell; RBC, red blood cell; Hb, hemoglobin; Ht, hematocrit; MCV, mean cell volume; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; RDW, red blood cell distribution width; PLT, platelet count; MPV, mean platelet volume; ESR, erythrocyte sedimentation rate.

Table 3 shows the results of the skin perfusion assessment at rest. There was no difference in mean skin perfusion (M) on the dorsal forearm between the studied groups. At the same time, individual variability of skin perfusion (σ) was lower in the OH and SH groups compared to the HV group. The coefficient of variation of skin perfusion (Kv) was also reduced in the OH group compared with the SH group. Wavelet analysis of cutaneous microvascular blood flow oscillations revealed a statistically significant decrease in the absolute amplitude of pulse oscillations (A_card) only in the OH group (Table 3).

Table 3.

Cutaneous microvascular blood flow and parameters of its variability at rest (baseline).

Variables	N	Overt hypothyroidism	N	Subclinical hypothyroidism	N	Healthy volunteers	p*	р, post-hoc
M, PU	14	5.95	37	6.00	50	6.65	0.242	−
		[5.00; 7.00]		[4.40; 7.49]		[4.99; 8.00]
s, PU	14	0.85	37	1.20	49	1.27	0.022	p_o−h=0.025
		[0.68; 1.08]		[0.68; 1.08]		[0.95; 1.52]		p_s−h>0.9p_o−s=0.037
Кv, %	14	14.86	37	21.63	50	19.01	0.041	p_o−h=0.389
		[11.91; 18.84]		[16.50; 27.46]		[15.47; 22.19]		p_s−h=0.432p_o−s=0.041
A_n, PU	14	0.384	37	0.445	49	0.458	0.259	−
		[0.260; 0.469]		[0.384; 0623]		[0.327; 0.597]
A_m, PU	14	0.281	37	0.369	49	0.428	0.090	−
		[0.198; 0.412]		[0.292; 0.471]		[0.308; 0.559]
A_resp, PU	14	0.166	37	0.214	49	0.243	0.242	−
		[0.140; 0.234]		[0.159; 0.251]		[0.155; 0.363]
A_card, PU	14	0.493	37	0.586	49	0.599	0.001	p_o−h=0.001
		[0.418; 0.526]		[0.518; 0.698]		[0.548; 0.672]		p_s−h>0.9p_o−s =0.012
A_n / σ	14	0.42	37	0.41	49	0.39	0.547	−
		[0.34; 0.49]		[0.32; 0.48]		[0.3; 0.48]
A_m / σ	14	0.33	37	0.33	49	0.34	0.861	−
		[0.29; 0.37]		[0.27; 0.36]		[0.26; 0.4]
A_resp / σ	14	0.23	37	0.19	49	0.20	0.405	−
		[0.18; 0.27]		[0.13; 0.26]		[0.14; 0.30]
A_card / σ	14	0.62	37	0.5	49	0.52	0.612	−
		[0.46; 0.66]		[0.42; 0.62]		[0.43; 0.66]

Note: Data are presented as the median and interquartile range (Me [Q1; Q3]).

*P-value for testing differences between groups with overt or subclinical hypothyroidism and the group of healthy volunteers (Kruskal–Wallis test). The Dunn-Bonferroni post hoc test was used for pairwise comparisons: p_o−h, overt hypothyroidism versus healthy volunteers; p_s−h, subclinical hypothyroidism versus healthy volunteers; and p_o−s, overt hypothyroidism versus subclinical hypothyroidism.

Statistically significant differences between groups are highlighted in bold (p < 0.05).

P values for pairwise comparisons are reported only if the overall comparison test is significant.

M, the average value of cutaneous microvascular blood flow during the measurement time; σ, the standard deviation of cutaneous microvascular blood flow oscillations (a measure of individual variability in skin perfusion); Kv, the skin perfusion coefficient of variation (σ/M); A_n, A_m, A_resp, and A_card, the absolute amplitudes of cutaneous microvascular blood flow oscillations in the neurogenic, myogenic, respiratory, and cardiac (pulse) frequency ranges, respectively;

A_n / σ, A_m / σ, A_resp / σ, A_card / σ, he relative (normalized) amplitudes in the same frequency ranges;

PU, perfusion units.

A partial rank correlation analysis that accounted for the potential effects of age and BMI found moderate positive relationships between σ and fT4 (r = 0.35, p = 0.03) and between A_card and fT4 (r = 0.32, p = 0.05).

During thermal challenges, most participants exhibited the expected decrease in skin perfusion with cooling and the expected increase with heating (Figure 4). However, in rare cases, a minimal response or an increase in skin perfusion with cooling was observed in both healthy subjects and patients with hypothyroidism.

Figure 4.

The line graphs illustrate the changes in cutaneous microvascular blood flow in healthy volunteers (a), patients with overt (b) and subclinical hypothyroidism (c) during local cooling to 10°C and local heating to 35°C.

In tests with local cooling and heating of the skin to 10°C and 35°C, respectively, the study groups did not differ (p>0.05 in Kruskal–Wallis test) either in skin perfusion (M_10°С and M_35°С) or in its percentage change relative to Baseline (ΔM_10°С, and ΔM_35°С) during thermal challenges (Figure 5(a) to (d)). However, a subsequent test with local heating to 42 °C revealed a decrease in peak thermal hyperemia (M_42°С) in the OH group compared with the HV group (Figure 5(e) and (f)).

Figure 5.

Basic parameters of skin thermal tests (local cooling and heating) in healthy volunteers (HV), patients with overt hypothyroidism (OH), and patients with subclinical hypothyroidism (SH). The average values and relative changes of cutaneous microvascular blood flow during local cooling to 10 °C (M_10°C and ΔM_10°C) (a and b), local heating to 35 °C (M_35°C and ΔM_35°C) (c and d), and local heating to 42 °C (M_42°C and ΔM_42°C) (e and f). Kruskal–Wallis test p-value: * For an overall comparison of hypothyroid patients and healthy volunteers (HV). Dunn-Bonferroni post hoc test p-value: ^ For pairwise comparisons between OH and HV. The p-values for pairwise comparisons are reported only if the overall comparison test was significant. In each box, the central line is the group's median, while the edges are the 25th and 75th percentiles. Note: A thermal test with local heating up to 42 °C was performed on only some of the participants (HV: n = 22; OH: n = 6; SH: n = 8).

The results of the laboratory tests are presented in Table 2. Total cholesterol, LDL, and triglycerides levels were higher in the OH and SH groups than in the HV group. Healthy participants had the lowest (normal) levels of these parameters. Only total cholesterol levels differed significantly between the OH and SH groups.

A complete blood count revealed a moderate decrease in mean corpuscular hemoglobin concentration (MCHC) and a concurrent increase in RDW in the OH group compared to the HV group. There were no significant differences in these parameters between the SH group and the HV and OH groups. Controlling for age and BMI, a partial correlation analysis revealed moderate positive and inverse relationships between RDW and TSH (r = 0.44, p = 0.01) and RDW and fT4 (r = −0.32, p = 0.05), respectively.

IL-6 levels were higher in the OH and SH groups than in the HV group. Participants with overt hypothyroidism had the highest parameter value.

The cutoff points of the LDF-derived blood flow variability parameters were determined to identify microcirculatory alterations in hypothyroidism. Values of σ and A_card of less than 1.26 and 0.529 PU, respectively, allow for the identification of impaired skin microcirculation in OH compared to HV. The average discriminatory power is high for A_card and moderate for σ (Table 4, Figure 6(a)). Values of σ and A_card of less than 0.985 and 0.527 PU, respectively, allow for the identification of impaired skin microcirculation in OH compared to SH. The average discriminatory power is similar for both σ and A_card (Table 4, Figure 6(b)).

Figure 6.

ROC curve analysis of cutaneous microvascular blood flow variability parameters (A_card, σ) for overt hypothyroidism versus healthy volunteers (a) and overt hypothyroidism versus subclinical hypothyroidism (b).

Table 4.

Diagnostic accuracy of cutaneous microvascular blood flow variability parameters in identifying microcirculation impairment in overt hypothyroidism.

Parameter	Cut-off point	Sensitivity, %	Specificity, %	AUC ROC	95% CI	p
Overt hypothyroidism vs healthy volunteers
σ, PU	<1.26	85.7	54.2	0.727	0.573–0.881	0.004
A_card, PU	<0.529	78.6	85.4	0.827	0.684–0.969	<0.001
Overt hypothyroidism vs subclinical hypothyroidism
σ, PU	<0.985	71.4	67.6	0.734	0.569–0.898	0.005
A_card, PU	<0.527	78.6	60.3	0.753	0.597–0.908	0.001

Note: σ is the standard deviation of cutaneous microvascular blood flow oscillations, and Acard is the amplitude of these oscillations in the cardiac (pulse) range (0.6–1.6 Hz).

AUC-ROC, area under the ROC curve; CI, confidence interval.

Discussion

This study demonstrated that patients with overt hypothyroidism had reduced overall variability of cutaneous microvascular blood flow due to decreased amplitude of pulse oscillations, and reduced peak hyperemia with local heating to 42 °C, but not with moderate heating to 35 °C. These microcirculatory disturbances were accompanied by hypercholesterolemia, decreased MCHC, increased anisocytosis (RDW), and IL-6 levels compared with the subclinical hypothyroidism group and healthy participants.

The mean forearm skin perfusion at rest did not differ between the study groups, which reproduces the previously obtained result in the study by A. Mihor et al.¹⁷ On the other hand, Pazos-Moura CC et al. showed that in overt hypothyroidism, capillary blood flow velocity, assessed by videocapillaroscopy at rest, was decreased, but increased upon reaching the euthyroid state.¹⁹ This discrepancy in results may be due to the use of different methods for assessing skin perfusion. In particular, unlike capillaroscopy, the LDF signal results from the multiplication of the red blood cell velocity (detected) with the red blood cell concentration (estimated). Regional features of skin circulation in the forearm and nail fold area also need to be taken into account.

The decrease in the overall variability of cutaneous microvascular blood flow (σ and Kv) in our study was due to a decrease in the amplitude of its pulse oscillations (A_card), revealed using spectral wavelet analysis. We found only one study assessing cutaneous blood flow variations in hypothyroidism, which demonstrated a trend towards decreased mean capillary blood flow velocity, capillary pulse wave amplitude and average vasomotion amplitude compared to healthy participants, but these differences did not reach statistical significance.³³ This study was conducted on a relatively small sample of subjects in whom perfusion was assessed in a different anatomical site (the foot) and during a local heating test at 32 °C and 44 °C.

А_card depends on pulse pressure, heart rate, vessel wall stiffness and blood rheology.^30,34 The decrease in A_card in the OH group could be due to an increase in peripheral vascular resistance. The causes of the latter in hypothyroidism may be endothelial dysfunction,³⁵ and impaired relaxation of vascular smooth muscles.³⁶ Unfortunately, we did not evaluate the amplitude of slow endothelial oscillations due to the limited recording time of LDF (4 min).

In our study, microcirculation disorders in patients with overt hypothyroidism were accompanied by hypercholesterolemia and hypertriglyceridemia, which are characteristic of this pathology.²³ Our data are consistent with the results of M. Rossi et al., who demonstrated a trend towards decreased A_card in participants with hypercholesterolemia and no other comorbidities.¹⁸ According to large epidemiological and randomized clinical trials, high LDL cholesterol is associated with an increased risk of atherosclerosis and cardiovascular events.^37,38

Elevated IL-6 levels in patients with overt and subclinical hypothyroidism may be a marker of chronic systemic inflammation.³⁹ In our study, the predominant cause of hypothyroidism was chronic autoimmune thyroiditis. In this disease, the thyroid tissue is infiltrated by macrophages and monocytes, resulting in the release of proinflammatory cytokines, including IL-6. This maintains inflammation and damage to thyrocytes.³⁹ In addition to maintaining low-grade systemic inflammation, elevated IL-6 levels are associated with cardiovascular risks through effects on large and small vessels.⁴⁰ Excessive IL-6 production has been shown to cause impaired endothelium-dependent aortic relaxation, increased reactive oxygen species, and resistance vessel dysfunction, accompanied by vascular fibrosis and altered vascular smooth muscle cell phenotype.⁴¹ In a meta-analysis of markers of endothelial dysfunction in hypothyroidism, IL-6 was one of 5 elevated parameters among 25 molecules assessed.²⁷

Although the study groups did not differ in hemoglobin or hematocrit levels, we found an increase in RDW in the OH group. RDW is a component of a complete blood count that represents the degree to which red blood cell (RBC) sizes deviate from normal values (anisocytosis). It is used as a laboratory marker in the differential diagnosis of anemias.⁴² In the few previous studies where the parameter was investigated as a potential marker of thyroid dysfunction, similar results were obtained: RDW had a negative relationship with the level of fT3 and a positive relationship with the level of TSH.⁴³ In another study, no relationship was found between RDW and the level of thyroid hormones.⁴⁴ The reasons for the increase in RDW in hypothyroidism may be impaired erythropoiesis due to decreased erythropoietin synthesis,⁴⁵ increased RBC clearance in inflammation,⁴⁶ and an increase in the cholesterol content in RBC membranes in dyslipidemia.^47,48

Elevated RDW is an independent risk factor for cardiovascular death in the general population,⁴⁹ which may result from alterations in blood rheology (decreased RBC deformability).⁵⁰ Rheological properties of blood are intravascular regulators of microcirculation and affect the functioning of the hemostasis system in microvessels.^28,51 It was shown that in patients with ischemic stroke, blood viscosity was negatively correlated with the amplitude of flowmotion, including in the range of pulse oscillations.⁵² Although direct measurements of blood viscosity and RBC microrheology were not performed in this study, we hypothesize that both dyslipidemia²⁶ and increased RDW contributed to the reduced overall cutaneous blood flow variability in the OH group.

An important strength of this study is that we assessed cutaneous microcirculation not only in overt, but also in subclinical hypothyroidism, and directly compared both patient groups with healthy volunteers. Although patients with subclinical hypothyroidism demonstrated several laboratory abnormalities (the increase in total cholesterol, LDL, triglycerides and IL-6) and a reduced quality of life, no significant microcirculatory disturbances were found in this group. These findings suggest that relevant systemic alterations may already be present in subclinical hypothyroidism, whereas overt hypothyroidism is associated with a more advanced stage in which microcirculatory dysfunction becomes apparent, potentially contributing to an increased cardiovascular risk.

Local cooling and moderate heating tests in this study did not reveal significant changes in cutaneous vasoreactivity in hypothyroidism. Thermal hyperemia after 4-min local heating is a manifestation of the first phase of vasodilation, which is caused mainly by activation of thermosensitive vanilloid receptors type 1 of sensory peptidergic fibers with subsequent release of neuropeptides (substance P, calcitonin gene-related peptide, neurokinin A) Adrenergic fibers releasing norepinephrine and neuropeptide Y exert a sensitizing effect by potentiating the axon reflex and vasodilation through activation of β2 adrenergic receptors.^15,32 In another study, during local heating of the skin of the foot to 33 °C, thermal hyperemia only tended to be lower in patients with overt hypothyroidism compared to healthy participants (without statistical significance of the difference).³³ These data indicate a low diagnostic value of the moderate local heating test in hypothyroidism.

The cutaneous blood flow response to local cooling is characterized by an initial decrease in perfusion, which may be followed by transient vasodilation and secondary progressive vasoconstriction. Vasoconstriction is mediated primarily by activation of sympathetic adrenergic fibers and increased sensitivity of postsynaptic α_2c-adrenergic receptors.³² As in our study, the study by Mihor A et al. did not reveal significant differences in the relative decrease in cutaneous blood flow with local cooling to 8 °C in patients with overt hypothyroidism compared to healthy controls.¹⁷ Another study assessing skin perfusion in patients with overt hypothyroidism found no difference in the degree of reduction in skin blood flow with hypothermia compared with subclinical thyrotoxicosis.⁵³ Thus, the results of these studies indicate the absence of disturbances in the sympathoadrenal regulation of skin perfusion (with different types of cooling) in patients with overt and subclinical hypothyroidism. A possible explanation is that, unlike β₁- and β₂-adrenoreceptors, the expression and activity of α₂-adrenoreceptors does not change significantly in patients with hypothyroidism.⁵⁴

In our study, we used the diagnostic protocol from the official manual for the LAZMA ST device to record LDF and conduct thermal tests, which prescribes local heating to 35 °С for 4 min. Vasodilation in the forearm skin, mediated by the axon reflex, can develop in the temperature range of 30–35 °С.⁵⁵ However, a more pronounced response during the first phase of thermal hyperemia is observed at temperatures above 37°C, reaching a maximum at 42°C, due to the sensitizing effect of the adrenergic nervous.³² In this regard, we conducted an additional series of measurements of cutaneous microvascular blood flow during local heating to 42 °C in some participants. In patients with overt hypothyroidism, a decrease in peak thermal hyperemia (M_42°C) was detected. A similar trend (which did not reach statistical significance) was noted in the study of M. Weiss et al. during local heating to 44 °C, however, the authors did not specify the duration of heating.³³ Despite the small sample size, our study's results suggest that cutaneous vascular reactivity in response to local heating (42 °C) is impaired in overt primary hypothyroidism. This finding can be explained by a decrease in the potentiating effect of adrenergic fibers on the axon reflex during heating to 42 °C, due to a decrease in the expression of β₂-adrenoreceptors on the smooth muscle membrane in hypothyroidism.^32,56

Our study used ROC analysis to determine the cutoff points of σ and A_card values to differentiate skin microcirculation disorders in overt and subclinical hypothyroidism. According to the area under the ROC curve, the A_card parameter had the highest discriminatory ability in differentiating microcirculation disorders in patients with overt hypothyroidism and healthy participants. The parameters had an average discriminatory ability in differentiating microcirculation disorders in overt and subclinical hypothyroidism.

Limitations of the study

As in the cross-sectional studies by M. Weiss et al. and A. Mihor et al., patients with overt hypothyroidism in our study were older than participants in the other two groups, though they were still within the middle-aged range. Selection bias is a common limitation of cross-sectional studies.⁵⁷ It is known that aging is associated with functional and structural changes in microcirculation.⁵⁸ In our study, age differences between the OH and HV groups were overcome through partial correlation analysis. The analysis revealed that the differences observed in microcirculation parameters (σ, Kv, A_card, and RDW) between the groups were associated with TSH and fT4 levels rather than age differences.

Some patients in the SH group were taking levothyroxine sodium. These patients were included in the analysis because their blood thyroid hormone profile corresponded to the status of “subclinical hypothyroidism”. We assumed that the state of microcirculation would depend on thyroid hormone levels, regardless of replacement therapy drug use. Although our study had a larger sample size than previous studies assessing microcirculation in hypothyroidism, the relatively small number of subjects in the overt hypothyroidism group, especially in the local heating test at 42 °C, could reduce the statistical power of our results.

We used the shortened, 15-min LDF recording protocol at rest and during temperature tests. This did not allow us to evaluate the amplitude of microvascular blood flow oscillations of endothelial origin and the second phase of thermal hyperemia, for which 20–30 min of heating is necessary. However, this examination protocol is faster and more comfortable for patients, making it more promising for use in routine clinical functional diagnostics.

Additionally, we used the wavelet transform for spectral analysis of microvascular blood flow oscillations at only the first four-minute stage of the protocol (at rest). Due to their limited duration and the non-stationary nature of the recorded signal, we did not subject the subsequent fragments of the LDF recording (cooling and heating phases) to spectral analysis. Nevertheless, this is a promising direction for further research.³⁰ Another avenue for future research is to complement the present findings with more advanced techniques for microcirculatory assessment (for example, long-term monitoring using wearable optical devices). Imaging approaches with higher spatial resolution, such as laser speckle contrast imaging, may allow a more detailed characterization of regional heterogeneity and functional alterations of the cutaneous microvasculature in both overt and subclinical hypothyroidism.

It should also be noted that when studying cutaneous blood flow, it is important to control the ambient temperature, since this factor can significantly affect cutaneous perfusion.

Conclusion

In primary hypothyroidism, impaired cutaneous microcirculation is manifested by a decrease in the overall variability of cutaneous microvascular blood flow (σ and Kv), which is caused by a decrease in the amplitude of pulse oscillations (A_card). Decreased peak hyperemia with local heating to 42 °C (but not 35 °C) indicates impairment of microvascular reactivity. These microvascular disturbances were only detected in overt hypothyroidism and were accompanied by hypercholesterolemia, hypertriglyceridemia, decreased MCHC, and increased RDW compared to healthy participants and those with subclinical hypothyroidism. ROC analysis showed the average discriminatory ability of σ and A_card values in identifying microcirculatory dysfunction in overt hypothyroidism. Further validation of these parameters is required as diagnostic and prognostic markers of cardiovascular complications of hypothyroidism.

Supplemental Material

sj-docx-1-chm-10.1177_13860291261443895 - Supplemental material for Impairment of cutaneous microcirculation and vasoreactivity in primary hypothyroidism

Supplemental material, sj-docx-1-chm-10.1177_13860291261443895 for Impairment of cutaneous microcirculation and vasoreactivity in primary hypothyroidism by Ekaterina G. Ryzhkova, Tatyana B. Morgunova, Ivan A. Ryzhkov, Ilya I. Amergoolov and Valentin V. Fadeyev in Clinical Hemorheology and Microcirculation

Footnotes

Ethical considerations and informed consent statements

This study was approved by the Ethics Committee of Sechenov First Moscow State Medical University on January 26, 2023 (Ethics Code: 02–23). All participants provided written informed consent prior to enrollment in the study. The research was conducted in accordance with the World Medical Association Declaration of Helsinki.

Author contributions

Ekaterina G. Ryzhkova: Conceptualization, data curation, formal analysis, investigation, methodology, project administration, visualization, and writing the original draft.

Tatyana B. Morgunova: Project administration, resources, supervision, and writing—review and editing.

Ivan A. Ryzhkov: Conceptualization, formal analysis, methodology, and writing—review and editing.

Ilya I. Amergoolov: Data curation, formal analysis, and investigation.

Valentin V. Fadeyev: Resources, supervision, and writing—review and editing.

All authors have read and agreed to the published version of the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Supplemental material

Supplemental material for this article is available online.

ORCID iDs

Ekaterina G. Ryzhkova

Tatyana B. Morgunova

Ivan A. Ryzhkov

Ilya I. Amergoolov

Valentin V. Fadeyev

References

Taylor

Medici

Hubalewska-Dydejczyk

, et al. Hypothyroidism. Lancet Lond Engl 2024; 404: 1347–1364.

Zúñiga

Balasubramanian

Mehmood

, et al. Hypothyroidism and cardiovascular disease: a review. Cureus 2024; 16: e52512.

Papadopoulou

Bakogiannis

Skrapari

, et al. Thyroid dysfunction and atherosclerosis: a systematic review. Vivo Athens Greece 2020; 34: 3127–3136.

Leite

Neves

Borges-Canha

, et al. Evaluation of thyroid function in patients hospitalized for acute heart failure. Int J Endocrinol 2021; 2021: 6616681.

Klein

Ojamaa

. Thyroid hormone and the cardiovascular system. Epstein FH, ed. N Engl J Med 2001; 344: 501–509.

Alexander

Osto

Schmidt-Trucksäss

, et al. Endothelial function in cardiovascular medicine: a consensus paper of the European Society of Cardiology working groups on atherosclerosis and vascular biology, aorta and peripheral vascular diseases, coronary pathophysiology and microcirculation, and thrombosis. Cardiovasc Res 2021; 117: 29–42.

Sellke

Abid

. Assessments of microvascular function in organ systems. Am J Physiol Heart Circ Physiol 2022; 322: H891–H905.

Rizzoni

Mengozzi

Masi

, et al. New noninvasive methods to evaluate microvascular structure and function. Hypertension 2022; 79: 874–886.

Udovcic

Pena

Patham

, et al. Hypothyroidism and the heart. Methodist DeBakey Cardiovasc J 2017; 13: 55.

10.

Kralj

Potočnik

Lenasi

. Evaluating transient phenomena by wavelet analysis: early recovery to exercise. Am J Physiol-Heart Circ Physiol 2024; 326: H96–H102.

11.

Fedorovich

Loktionova

Zharkikh

, et al. Skin microcirculation in middle-aged men with newly diagnosed arterial hypertension according to remote laser Doppler flowmetry data. Microvasc Res 2022; 144: 104419.

12.

Zherebtsova

Dremin

Makovik

, et al. Multimodal optical diagnostics of the microhaemodynamics in upper and lower limbs. Front Physiol 2019; 10: 416.

13.

IJzerman

de Jongh

Beijk

MAM

, et al. Individuals at increased coronary heart disease risk are characterized by an impaired microvascular function in skin. Eur J Clin Invest 2003; 33: 536–542.

14.

Holowatz

Thompson-Torgerson

Kenney

. The human cutaneous circulation as a model of generalized microvascular function. J Appl Physiol 2008; 105: 370–372.

15.

Roberts

van Gent

Hopkins

, et al. Reproducibility of four frequently used local heating protocols to assess cutaneous microvascular function. Microvasc Res 2017; 112: 65–71.

16.

Çekiç

Başaran

Filiz Başaran

, et al. Cutaneous microvascular reactivity and aortic elasticity in coronary artery disease: comparison of the laser Doppler flowmetry and echocardiography. Microvasc Res 2017; 109: 19–25.

17.

Mihor

Gergar

Gaberšček

, et al. Skin microvascular reactivity in patients with hypothyroidism. Clin Hemorheol Microcirc 2016; 64: 105–114.

18.

Rossi

Carpi

Di Maria

, et al. Skin blood flowmotion and microvascular reactivity investigation in hypercholesterolemic patients without clinically manifest arterial diseases. Physiol Res 2009; 58: 39–47.

19.

Pazos-Moura

Moura

Breitenbach

MMD

, et al. Nailfold capillaroscopy in hypothyroidism and hyperthyroidism: blood flow velocity during rest and postocclusive reactive hyperemia. Angiology 1998; 49: 471–476.

20.

Soto-García

Elizondo-Riojas

Rodriguez-Gutiérrez

, et al. Carotid intima-Media thickness in patients with subclinical hypothyroidism: a prospective controlled study. Clin Investig Med Med Clin Exp 2021; 44: E39–E45.

21.

Isailă

Stoian

Fulga

, et al. The relationship between subclinical hypothyroidism and carotid intima-Media thickness as a potential marker of cardiovascular risk: a systematic review and a meta-analysis. J Cardiovasc Dev Dis 2024; 11: 98.

22.

Stojković

Nedeljković-Beleslin

Tešić

, et al. Specific impact of cardiovascular risk factors on coronary microcirculation in patients with subclinical hypothyroidism. J Med Biochem 2022; 41: 299–305.

23.

Tarboush

Alsultan

Alourfi

. The correlation of lipid profile with subclinical and overt hypothyroidism: a cross-sectional study from Syria. Medicine (Baltimore) 2023; 102: e34959.

24.

Girkantaite

Laucyte-Cibulskiene

Ryliskyte

, et al. Laser Doppler flowmetry evaluation of skin microvascular endothelial function in patients with metabolic syndrome. Microvasc Res 2022; 142: 104373.

25.

Ahmed

Mohammed

. Effects of thyroid dysfunction on hematological parameters: case controlled study. Ann Med Surg 2020; 57: 52–55.

26.

Galos

Rabai

Szabo

, et al.

The influence of triglyceride and low-density-lipoprotein target levels on microcirculation: is there a difference?

Heliyon 2024; 10: e27954.

27.

Torres

Tellechea

. Biomarkers of endothelial dysfunction and cytokine levels in hypothyroidism: a series of meta-analyses. Expert Rev Endocrinol Metab 2025; 20: 119–128.

28.

Ebrahimi

Bagchi

. A computational study of red blood cell deformability effect on hemodynamic alteration in capillary vessel networks. Sci Rep 2022; 12: 4304.

29.

Cracowski

Roustit

. Human skin microcirculation. Compr Physiol 2020; 10: 1105–1154.

30.

Kralj

Lenasi

. Wavelet analysis of laser Doppler microcirculatory signals: current applications and limitations. Front Physiol 2022; 13: 1076445.

31.

Cappola

Desai

Medici

, et al. Thyroid and cardiovascular disease: research agenda for enhancing knowledge, prevention, and treatment. Thyroid Off J Am Thyroid Assoc 2019; 29: 760–777.

32.

Johnson

Minson

Kellogg

. Cutaneous vasodilator and vasoconstrictor mechanisms in temperature regulation. Compr Physiol 2014; 4: 33–89.

33.

Weiss

Milman

Rosen

, et al. Quantitation of thyroid hormone effect on skin perfusion by laser Doppler flowmetry. J Clin Endocrinol Metab 1993; 76: 680–682.

34.

Krupatkin

. Oscillatory processes in the diagnosis of the state of microvascular-tissue systems. Hum Physiol 2018; 44: 581–591.

35.

Hou

Zhang

, et al. Transiently impaired endothelial function during thyroid hormone withdrawal in differentiated thyroid cancer patients. Front Endocrinol 2023; 14: 1164789.

36.

Klein

Ojamaa

. Thyroid hormone: targeting the vascular smooth muscle cell. Circ Res 2001; 88: 260–261.

37.

Ross

. Atherosclerosis — an inflammatory disease. Epstein FH, ed. N Engl J Med 1999; 340: 115–126.

38.

Ference

Ginsberg

Graham

, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European atherosclerosis society consensus panel. Eur Heart J 2017; 38: 2459–2472.

39.

Xing

, et al. The immune system in hashimoto’s thyroiditis: updating the current state of knowledge on potential therapies and animal model construction. Autoimmun Rev 2025; 24: 103783.

40.

Rizzoni

De Ciuceis

Szczepaniak

, et al. Immune system and microvascular remodeling in humans. Hypertens Dallas Tex 1979 2022; 79: 691–705.

41.

Knopp

Jung

Wild

, et al. Myeloid cell-derived interleukin-6 induces vascular dysfunction and vascular and systemic inflammation. Eur Heart J Open 2024; 4: oeae046.

42.

Salvagno

Sanchis-Gomar

Picanza

, et al. Red blood cell distribution width: a simple parameter with multiple clinical applications. Crit Rev Clin Lab Sci 2015; 52: 86–105.

43.

Zhou

Guo

, et al. Association between red blood cell distribution width and thyroid function. Front Endocrinol 2021; 12: 807482.

44.

Wang

Huang

Meng

, et al. No obvious association exists between red blood cell distribution width and thyroid function. Biomark Med 2019; 13: 1363–1372.

45.

Berta

Gelaw

Shiferaw

, et al. Hematological abnormalities and associated factors among patients with hypothyroidism at the university of gondar comprehensive specialized hospital. J Blood Med 2024; 15: 157–169.

46.

Arkew

Gemechu

Haile

, et al. Red blood cell distribution width as novel biomarker in cardiovascular diseases: a literature review. J Blood Med 2022; 13: 413–424.

47.

Tziakas

Chalikias

Grapsa

, et al. Red blood cell distribution width: a strong prognostic marker in cardiovascular disease: is associated with cholesterol content of erythrocyte membrane. Clin Hemorheol Microcirc 2012; 51: 243–254.

48.

Uydu

Bostan

Atak

, et al. Cholesterol forms and traditional lipid profile for projection of atherogenic dyslipidemia: lipoprotein subfractions and erythrocyte membrane cholesterol. J Membr Biol 2014; 247: 127–134.

49.

Lichtman

. Red cell distribution width as a bellwether of prognosis. Blood Cells Mol Dis 2024; 109: 102884.

50.

Patel

Mohanty

Kanapuru

, et al. Association of the red cell distribution width with red blood cell deformability. Adv Exp Med Biol 2013; 765: 211–216.

51.

Jung

Rampling

. Role of blood viscosity in the microcirculation. Jung F, Gori T, eds. Clin Hemorheol Microcirc 2017; 64: 251–254.

52.

Tikhomirova

Mikhailova

Lychenko

, et al. Estimation of the microcirculation state in cerebrovascular disorders using laser Doppler flowmetry data and hemorheological parameters. Hum Physiol 2012; 38: 56–62.

53.

Broeders

EPM

Vijgen

GHEJ

Havekes

, et al. Thyroid hormone activates brown adipose tissue and increases non-shivering thermogenesis - A cohort study in a group of thyroid carcinoma patients. Ye J, ed. PLOS ONE 2016; 11: e0145049.

54.

Viticchi

Basso

Zaia

, et al. Thyroid hormone-induced up-regulation of β1- and β2-adrenergic receptors during aging. Arch Gerontol Geriatr 1992; 15: 367–374.

55.

Stephens

Charkoudian

Benevento

, et al. The influence of topical capsaicin on the local thermal control of skin blood flow in humans. Am J Physiol Regul Integr Comp Physiol 2001; 281: R894–R901.

56.

Arioglu

Guner

Ozakca

, et al. The changes in β-adrenoceptor-mediated cardiac function in experimental hypothyroidism: the possible contribution of cardiac β3-adrenoceptors. Mol Cell Biochem 2010; 335: 59–66.

57.

Kelly

Benkhedda

Brooks

SPJ

, et al. Risk of bias in cross-sectional studies: protocol for a scoping review of concepts and tools. MethodsX 2024; 12: 102610.

58.

Ungvari

Tarantini

Donato

, et al. Mechanisms of vascular aging. Circ Res 2018; 123: 849–867.

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