The Effectiveness of Herbal Remedies on the Hot Flashes in Postmenopausal Women: A Systematic Review and Network Meta- analysis Study

doi:10.21203/rs.3.rs-2868153/v1

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The Effectiveness of Herbal Remedies on the Hot Flashes in Postmenopausal Women: A Systematic Review and Network Meta- analysis Study

https://doi.org/10.21203/rs.3.rs-2868153/v1

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Purpose:This study was done to conduct a network meta-analysis (NMA) which offers a comprehensive evaluation of the treatment effects of herbal medicine for flashes of post-menopausal women and identifies the most commonly used herbs.

Methods: According to PRISMA-NMA, a systematic search of Persian and English databases, including SID, Magiran, the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, EMBASE, CINAHL, and AMED, was carried out without a time limit until March 10, 2022. The careful examination of this study's questions was selected according to PICO criteria to find the appropriate keywords. A meta-analysis of the Bayesian network was carried out using “BUGSnet” (Bayesian inference Using Gibbs Sampling to conduct a Network meta-analysis) package in R software.

Result and conclusion: Our review includes 45 RCTs (n = 3615 patients). The effect estimates and credible intervals produced by the preceding model for severity outcome showed that the difference between placebo and Silybum marianum, Soy, and Valerian treatments are statistically significant at the 95% level. The Silybum marianum and soy medicine were high ranks than the other treatments suggesting that it is the most beneficial treatment for the outcome among the treatments included in the severity hot-flash evidence network. Ranking the value of all these herbal medicines makes opportunities that might help design treatment algorithms to be validated in prospective clinical trials.

Network meta-analysis

postmenopausal women

hot flashes

herbal medicine

Menopause is a natural time in a woman's life when menstruation stops and is usually confirmed when a woman has not had a period for 12 consecutive months. The estrogen level reaches < 25 pg / mL due to ovarian aging. During this period, abnormal production of estradiol (E2) occurs, and this condition continues until its permanent level decreases in postmenopausal women. During menopause, these changes and decreased levels of the hormone estrogen cause a range of distressing symptoms and vasomotor, the most common symptom of which is hot flashes, which significantly impact women's quality of life[1]. Hot flashes in post-menopausal women refer to transient spontaneous warmth or feeling hot on the face and neck that has become an important reason to ask for medical help[2]. Hot flashes generally affect more than 75% of postmenopausal women, and more than 30% experience moderate to severe hot flashes for at least ten years[1]. However, it has been observed that the prevalence of vasomotor symptoms has been reported differently between different cultures. For example, the prevalence of hot flashes is 10 to 20 percent in Indonesian postmenopausal women, 10 to 25 percent in Chinese postmenopausal women, and 58 to 93 percent in Western women. The most important reason for this difference is the lifestyle and consumption of a diet rich in soy phytoestrogens[3]. To date, hormone therapy, including estrogen therapy and combination therapies, is the first line of treatment for hot flashes in postmenopausal women, leading to a rapid reduction in symptoms. But such treatments can have long-term side effects such as thromboembolism, breast cancer, and stroke, which have caused concern in postmenopausal women, many of whom are seeking alternative therapies. In Switzerland, for example, the use of complementary medicine has increased dramatically, and traditional Chinese medicine is one of the most important treatments in this country[4]. The U.S. Food and Drug Administration Act of 1994 classified herbs as dietary supplements, requiring no pre-sale testing for safety or efficacy. Most of these products are not prescription and readily available. Since then, in many countries, herbal medicine has traditionally been popular among postmenopausal women for hot flashes, and some studies have been conducted in this field[3]. Using network meta-analysis, it is possible to estimate the effectiveness of different treatments and compare them. In this method, using an analysis, several treatments are compared by considering a combination of direct and indirect evidence extracted from clinical trial studies[5]. The purpose of this study was to conduct a network meta-analysis (NMA) which offers a comprehensive evaluation of the treatment effects of herbal medicine for flashes of post-menopausal women and identify the most commonly used herbs.

This study was conducted following the criteria of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-NMA)[6]. Accordingly, a systematic search of databases, organization of documents for review, selection of studies following the criteria defined by the authors, information extraction, analysis, and finally, the presentation of the final report was performed.

Data resources and search strategies

To find related articles, a systematic search of articles in Persian and English databases, including SID, Magiran, the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, EMBASE, CINAHL, and AMED was carried out without a time limit until March 10, 2022. Also, hand searches were performed in other resources in the reference list of the identified manuscript and Google scholar. To find the appropriate keywords, the preliminary published studies, Medical Subject Headings (MESH Terms) in the PubMed database, as well as the careful examination of the questions of this study, were selected according to PICO criteria [7]. PICO criteria included:

Participants: All women with menopause without age restrictions were examined.

Intervention: Since this study aimed to select the best and most effective herbal medicine to improve hot flashing, the articles selected herbal medicines.

Control: Evaluating the effects of each treatment group compared to the placebo.

Outcome: outcome measures included hot flush frequency and severity.

Eligible primary studies were identified in English electronic databases using the search strings containing four English keywords, and their Persian equivalents were used in Persian databases. These keywords included Menopause, vasomotor symptoms, Herbal medicine, and hot flashes. The Boolean search method is used to combine keywords. Sample search strategies are shown in Table1.

Table1 search strategy keywords

population	outcome	Intervention and control	study
("menopause"[TIAB] NOT Medline[SB])	vasomotor disorder[All Fields]	herbal remed	randomized controlled trial
"menopause"[MeSH Terms]	vasomotor disorders[All Fields]	herb$ medic	controlled clinical trial
menopausal[Text Word]	"climacteric"[MeSH Terms]	placebo	randomized
menopause[Text Word]	climacteric[Text Word]	control	clinical trials as topic
"postmenopause"[MeSH Terms]	("hot flashes"[MeSH Terms]		randomly
postmenopause[Text Word]	hot flashes[Text Word		trial
"premature ovarian failure"[Text Word]	("flushing"[MeSH Terms] OR flushing[Text Word])
"ovarian failure, premature"[MeSH Terms]	"night sweats"[All Fields]
ovarian failure[Text Word]	flushing"[MeSH Terms] OR flushing[Text Word]

Inclusion and Exclusion Criteria

Inclusion criteria: 1.RCT studies, 2. studies that examined the effect of Herbal remedies on hot flashes

Exclusion criteria: 1. Case-control studies 2. Case reports 3. Letter to editor 5.Cross-over studies 4. Studies whose full text is not available 5. Unrelated studies 6. Studies with insufficient data 7. Duplication studies 8. A systematic review and meta-analysis studies.

Data Extraction

For each chosen study, socio-demographic and clinical variables were extracted. The risk of bias was assessed through a checklist resulting from the integration of quality assessment tools for quantitative studies[8], and two authors (Shahsavari S and Keshavarzi F) independently evaluated the risk of bias by the Cochrane Collaboration Tool [9]. Since the weight of the conclusion from the meta-analysis depends mainly on the validity of the findings of individual studies, it is essential to evaluate the quality of the study (attachment table1). The tool used in the present study assesses the potential areas for the following bias that:

1. Bias in selecting to evaluate recruitment and randomization methods.

2. Blinding Outcome Assessment examining the knowledge of outcome assessors about participants' intervention or exposure situation

3. Incomplete data as a result of random cancellation and deletion assessment. This item assesses whether the risk of accidental removal is related to treatment.

4. Assessing significant differences between groups before intervening in confounding variables

5. Data collection methods evaluate the validity and reliability of instruments

Search results were uploaded to Excel2013, and duplicates were removed. Two independent researchers evaluated the suitability of the studies. The first screening related to titles and abstracts, and the final screening related to the full text. The first and second authors independently screened and rated each study, and disagreements were resolved through consensus discussion. The final score determined whether the study was at low, moderate, or high risk of bias. The longest follow-up period was considered for an article with several follow-up periods. The mean frequency and severity of hot flashes in each study before and after the intervention was extracted for both experimental and control groups.

Measures of Treatment Effect

All responses (frequency and severity of hot flash) were continuous. We calculated mean and STD deviance for responses in each group and presented 95% confidence intervals (CI) for the mean difference between treatment groups.

Assessment of Network Assumed

To obtain valid NMA results and easier interpretation, it is assumed that network transitivity (potential modifiers of treatment effects are distributed similarly across trials), network consistency (estimates of indirect effects are consistent with direct effects), and homogeneity (interpretation of treatment effects should be homogeneous throughout the trial) should be established.

Statistical Analysis

A Bayesian network meta-analysis was carried out using “BUGSnet” (Bayesian inference Using Gibbs Sampling to conduct a Network meta-analysis) package in R software. BUGSnet requires the user to have installed Just Another Gibbs Sampler (JAGS) on their computer[10]. In this package, for meta-analysis network analysis such as paired meta-analysis analysis, the first step is to define and process the data in such a way that the names of studies (ID) and treatment arms in them are defined as variable names and match each other using the function data.prep(). Net.plot() and net.tab() functions can be used to draw meta-analysis networks in graphical and tabular format[11]. Once identified, one can use the data.plot() function within BUGSnet to assess the heterogeneity of these modifiers within an evidence network. To examine inconsistency at the global level, the fit of the inconsistency model can be compared against a model in which consistency is assumed using the nma.fit() function and comparing the DICs. Local inconsistency can be explored on the leverage plots produced by nma.fit() and using the nma.compare() function, which produces a plot comparing the posterior mean deviance for the inconsistency and the consistency model. We chose to implement the inconsistency of the model in BUGSnet because it easily handles different network structures and multi arm trials, which is not the case with other methods such as the Bucher method [12]. Because the outcomes were continuous and multi-arm trials were included in the analysis, a normal probability model for multi-arm trials was used, and random effects network meta-analyses were performed. We conducted an NMA on the dataset by fitting a generalized linear model with an identity link function and normal likelihood function to account for the continuous outcome specified through the nma.model(). To be consistent with the NICE-DSU technical support document, we specified a burn-in of 50,000 iterations followed by 100,000 iterations with 10,000 adaptations in the nma.run() function. We can also graphically display the probability of the ranking of each treatment within a surface under the cumulative ranking curve (SUCRA) plot.

A summary of the studies involved in the network meta-analysis is reported in Table 2. Herbal medicines as the intervention treatment in the studies, including Black cohosh, Danggui Buxue Tang (DBT), EstroG-100, evening primrose, fenugreek seed, Flaxseed, herbal, hop, Hypericum and Vitex, labisia pumila and eurycoma longifolia, Vitex, Passion Flower, lavender, hop, Red clover, Sage, Soy, Valerian and Licorice.

The interventions and outcome measures included hot flash frequency and severity, and respectively, the network was based on 253, 120 pairwise comparisons. The net graph for hot flash frequency and severity of hot flash is shown in Fig. 2. The size of the nodes corresponds to the number of participants per treatment, and the thickness of the lines indicates the number of randomized trials that included a direct comparison of treatments.St John’s wort was used as herbal medicine in the intervention group in 3 studies [13–15]. In 4 studies, the treatment group was Licorice [13, 16–18].

Table 2

Summary information on studies included in network meta-analysis.
Row	First author	Publication year	Setting	outcome	Trt_num	treatment	Sample size	Mean	SD	Treatment Duration(day)	Mean age	age_SD
1	Barnard, N. D.[19]	2021	America	Frequency hotflash	1	soy	17	1.3	0.38	84	53.3	4.4
				Frequency hotflash	2	placebo	17	2.5	0.58	84	55.5	4.1
				Severity hotflash	1	soy	17	0.8	0.33	84	53.3	4.4
				Severity hotflash	2	placebo	17	2.2	0.56	84	55.5	4.1
2	DASTENAEI,B. [20]	2020	Iran	Severity hotflash	1	Evening Primrose	44	2.59	0.58	28	54.61	3.75
					2	placebo	46	1	0.78	28	54.71	4.56
					3	Vitex	51	0.92	0.79	28	54.61	3.75
3	Chinnappan, S. M.[21]	2020	Canada	Frequency hotflash	1	labisia pumila and eurycoma longifolia	38	5.06	4.51	168	51.53	2.53
3	Chinnappan, S. M.[21]	2020	Canada	Frequency hotflash	2	placebo	37	5.79	5.63	168	51.41	2.74
4	Saberi, Z. [22]	2020	Iran	Frequency hotflash	1	Silybum marianum	38	1.34	0.17	84	52	2.4
				Frequency hotflash	2	placebo	35	3.34	0.17	84	51	2.1
				Severity hotflash	1	Silybum marianum	38	1.62	0.08	84	52	2.4
				Severity hotflash	2	placebo	35	5.6	0.09	84	51	2.1
5	Dadfar,F.[23]	2019	Iran	Frequency hotflash	1	sage	30	1.8	0.155	28	51.4	0.706
5	Dadfar,F.[23]	2019	Iran	Frequency hotflash	2	placebo	30	2.6	0.177	28	52.6	0.643
6	Eatemadnia, A. [24]	2019	Iran	Frequency hotflash	1	St John’s wort	35	0.6	0.84	56	50.49	2.74
				Frequency hotflash	2	placebo	35	3.97	0.96	56	50.63	2.87
				Severity hotflash	1	St John’s wort	35	0.37	0.49	56	50.49	2.74
				Severity hotflash	2	placebo	35	2.14	0.73	56	50.63	2.87
7	Masoumi,sz[25]	2019	Iran	Frequency hotflash	1	sage	40	1.98	0.733	56	52.25	4.79
7	Masoumi,sz[25]	2019	Iran	Frequency hotflash	2	Black cohosh	40	1.94	0.674	56	54.25	5.66
8	Imhof, M.[26]	2018	Austria and Rumania and Germany	Frequency hotflash	1	soy	54	2.65	2.19	168	45to < 70
8	Imhof, M.[26]	2018	Austria and Rumania and Germany	Frequency hotflash	2	placebo	80	2.7	2.11	168	45to < 70
9	Mehrpooya, M.[27]	2018	Iran	Frequency hotflash	1	Black cohosh	40	1.05	0.49	56	53.72	4.8
				Frequency hotflash	2	Evening Primrose	40	1.22	0.7	56	49.64	10
				Severity hotflash	1	Black cohosh	40	1.5	0.55	56	53.72	4.8
				Severity hotflash	2	evening primrose	40	1.92	0.61	56	49.64	10
10	Dastenaei,M.B. [28]	2018	Iran	Frequency hotflash	1	evening primrose	46	7.89	0.37	28	54.6	3.75
				Frequency hotflash	2	placebo	44	11.16	0.56	28	54.7	4.56
				Severity hotflash	1	evening primrose	46	1	0.78	28	54.6	3.75
				Severity hotflash	2	placebo	44	2.59	0.58	28	54.7	4.56
11	Motaghi,B. [29]	2018	Iran	Frequency hotflash	1	Vitex	50	0.8	0.65	28	54.17	3.74
11	Motaghi,B. [29]	2018	Iran	Frequency hotflash	2	placebo	50	2.6	1.14	28	54.7	4.56
12	Jenabi, E.[30]	2018	Iran	Frequency hotflash	1	Valerian	30	3.24	2.29	60	50.8	2.93
				Frequency hotflash	2	placebo	30	6.81	4.32	60	51.2	3.25
				Severity hotflash	1	Valerian	30	0.6	0.67	60	50.8	2.93
				Severity hotflash	2	placebo	30	1.9	0.71	60	51.2	3.25
13	Motaghi,B. [31]	2017	Iran	Frequency hotflash	1	evening primrose	50	7.89	0.37	28	54.6	3.75
13	Motaghi,B. [31]	2017	Iran	Frequency hotflash	2	placebo	50	11.16	0.56	28	54.7	4.56
14	Aghamiri, V.[32]	2016	Iran	Frequency hotflash	1	hop	60	49.8	4.17	56	49.8	4.17
14	Aghamiri, V.[32]	2016	Iran	Frequency hotflash	2	placebo	60	50.5	5	56	50.5	5
15	Rad, S.[33]	2016	Iran	Frequency hotflash	1	sage	46	2.31	0.84	56	NA	NA
15	Rad, S.[33]	2016	Iran	Frequency hotflash	2	placebo	47	5.32	2.14	56	NA	NA
16	Kazemzadeh, R.[34]	2016	Iran	Frequency hotflash	1	lavender	50	10.58	7.34	84	51.5	0.1
16	Kazemzadeh, R.[34]	2016	Iran	Frequency hotflash	2	placebo	50	19.7	13.4	84	52.24	0.1
17	Torkestani,A. [35]	2015	Iran	Frequency hotflash	1	soy	30	0.5	0.63	56	50.2	3.6
				Frequency hotflash	2	fenugreek seed	30	0.7	0.76	56	51.4	3.8
				Severity hotflash	1	soy	30	1	0.64	56	50.2	3.6
				Severity hotflash	2	fenugreek seed	30	1.3	0.64	56	51.4	3.8
18	ASALI,Z. [15]	2013	Iran	Severity hotflash	1	St John’s wort	30	1.1	0.84	42	51.7	3.3
18	ASALI,Z. [15]	2013	Iran	Severity hotflash	2	Passion Flower	29	1	0.7	42	51.8	2.6
19	Farzaneh, F.[36]	2013	Iran	Frequency hotflash	1	evening primrose	28	3.2	1.8	42	51.9	3.3
19	Farzaneh, F.[36]	2013	Iran	Frequency hotflash	2	placebo	28	3.7	2	42	51.9	3.3
20	Sadeghi A.[37]	2013	Iran	Frequency hotflash	1	sage	40	2.28	2.82	56	51.6	3.2
20	Sadeghi A.[37]	2013	Iran	Frequency hotflash	2	placebo	40	3.52	2.98	56	51.4	3.2
21	Sadeghi,A.H. [38]	2013	Iran	Frequency hotflash	1	sage	42	2.28	2.82	56	51.6	3.2
21	Sadeghi,A.H. [38]	2013	Iran	Frequency hotflash	2	placebo	42	3.52	2.98	56	51.4	3.2
22	Mirabi, P.[39]	2013	Iran	Frequency hotflash	1	Valerian	35	4.83	0.52	56	51.2	2.6
				Frequency hotflash	2	placebo	33	7.75	0.32	56	51.7	2.9
				Severity hotflash	1	Valerian	35	5.23	1.52	56	51.2	2.6
				Severity hotflash	2	placebo	33	9.86	1.95	56	51.7	2.9
23	Colli, M. C. [40]	2012	Brazil	Severity hotflash	1	Flaxseed E	28	6.08	2.78	180	53.57	4.6
					2	placebo	25	6.96	2.67	180	56.57	6.1
					3	Flaxseed M	22	4.54	3.14	180	54.16	4.5
24	Chang, A.[41]	2012	Korea	Frequency hotflash	1	EstroG-100	29	0.79	0.73	84	53.2	5.7
24	Chang, A.[41]	2012	Korea	Frequency hotflash	2	placebo	33	2.06	0.76	84	54.1	5.9
25	Nahidi, F. [18]	2012	Iran	Frequency hotflash	1	Licorice	45	6.43	0.94	56	53	3.19
				Frequency hotflash	2	placebo	45	7.75	0.55	56	52.69	2.8
				Severity hotflash	1	Licorice	45	2	0.7	56	53	3.19
				Severity hotflash	2	placebo	45	1.7	0.7	56	52.69	2.8
26	Sekhavat,L. [42]	2012	Iran	Frequency hotflash	1	soy	45	6.7	2.5	84	51.8	2.2
				Frequency hotflash	2	placebo	45	12.1	2.4	84	52.6	1.4
				Severity hotflash	1	soy	45	4.3	0.6	84	51.8	2.2
				Severity hotflash	2	placebo	45	7.1	2.4	84	52.6	1.4
27	Nahidi, F. [17]	2011	Iran	Frequency hotflash	1	Licorice	34	5.1	0.52	28	53.29	2.99
				Frequency hotflash	2	placebo	34	7.75	0.34	28	52.97	2.59
				Severity hotflash	1	Licorice	34	1.41	0.49	28	53.29	2.99
				Severity hotflash	2	placebo	34	2.14	0.55	28	52.97	2.59
28	Enjezab.B[43]	2010	Iran	Frequency hotflash	1	soy	31	2.16	1.48	90	50.34	4.78
28	Enjezab.B[43]	2010	Iran	Frequency hotflash	2	placebo	31	5.88	2.61	90	50.34	4.78
29	Abdali, K.[13]	2010	Iran	Frequency hotflash	1	St John’s wort	45	1.8	1.03	56	50.52	3.72
				Frequency hotflash	2	placebo	43	2.65	0.81	56	50.29	3.52
				Severity hotflash	1	St John’s wort	45	0.93	0.44	56	50.52	3.72
				Severity hotflash	2	placebo	43	1.7	0.62	56	50.29	3.52
30	Simbalista, R. L. [44]	2010	Brazil	Frequency hotflash	1	Flaxseed	20	5.2	3.9	84	52	2.9
30	Simbalista, R. L. [44]	2010	Brazil	Frequency hotflash	2	placebo	18	4	2.4	84	52.7	4.2
31	van der Sluijs, C. P.[45]	2009	England	Frequency hotflash	1	herbal	47	6.89	2.3	56	55.68	4.78
31	van der Sluijs, C. P.[45]	2009	England	Frequency hotflash	2	placebo	46	6.57	3.3	56	55.66	3.59
32	Al-Akoum, M.[14]	2009	canada	Frequency hotflash	1	St John’s wort	20	4.6	4.1	90	53.4	4.8
32	Al-Akoum, M.[14]	2009	canada	Frequency hotflash	2	placebo	20	6.7	4	90	54	5.8
33	Van Die, M. D. [46]	2009	Australia	Frequency hotflash	1	Hypericum and Vitex	50	10.86	1.34	112	52.5	3.8
33	Van Die, M. D. [46]	2009	Australia	Frequency hotflash	2	placebo	50	9.32	1.36	112	51.9	4.3
34	Haines, C. J. [47]	2008	China	Frequency hotflash	1	DBT	45	0.7	2.7	180	52.8	4.9
34	Haines, C. J. [47]	2008	China	Frequency hotflash	2	placebo	39	0.1	0.8	180	51.3	4.6
35	Ebrahimi, M. [48]	2008	Iran	Severity hotflash	1	soy	40	2.8	0.1	84	51.67	4.29
35	Ebrahimi, M. [48]	2008	Iran	Severity hotflash	2	placebo	40	7.6	0.1	84	49.37	6.3
36	Abdolahi,F[49]	2007	Iran	Frequency hotflash	1	Licorice	29	2.66	1.68	56	50.1	3.7
36	Abdolahi,F[49]	2007	Iran	Frequency hotflash	2	placebo	24	3.5	2.62	56	51.1	3.8
37	Nahas, E. A. P. [50]	2007	Brazil	Frequency hotflash	1	soy	38	3.1	2.3	300	55.1	6
				Frequency hotflash	2	placebo	38	5.9	4.3	300	56.2	7.7
				Severity hotflash	1	soy	38	2.8	2.4	300	55.1	6
				Severity hotflash	2	placebo	38	5.3	3	300	56.2	7.7
38	Boroomandfar,KH. [51]	2007	Iran	Severity hotflash	1	Vitex	27	1.77	0.84	30	49.02	0.1
38	Boroomandfar,KH. [51]	2007	Iran	Severity hotflash	2	placebo	27	2.7	0.46	30	49.02	0.1
39	Lewis, J. E.[52]	2006	Canada	Frequency hotflash	1	soy	33	3.37	2.55	112	53.3	3.1
					2	placebo	33	3.79	3	112	52.9	3.6
					3	Flaxseed	33	2.6	2.42	112	53.2	2.9
				Severity hotflash	1	soy	33	2.88	1.17	112	53.3	3.1
					2	placebo	33	2.9	1.15	112	52.9	3.6
					3	Flaxseed	33	2.34	1.09	112	53.2	2.9
40	Kazemian,A.[53]	2006	Iran	Frequency hotflash	1	Valerian	29	2.75	3.68	60	52.76	4.7
				Frequency hotflash	2	placebo	20	5.88	5.02	60	52.76	4.7
				Severity hotflash	1	Valerian	29	1.1	0.72	60	52.76	4.7
				Severity hotflash	2	placebo	20	1.75	0.71	60	52.76	4.7
41	kazamian,A. [54]	2006	Iran	Severity hotflash	1	Passion Flower	23	1.78	0.95	30	50.48	0.1
41	kazamian,A. [54]	2006	Iran	Severity hotflash	2	placebo	27	2.29	0.82	30	49.52	0.3
42	KAZEMIAN,A. [55]	2005	Iran	Severity hotflash	1	Vitex	27	1.77	0.84	30	NA	NA
					2	placebo	27	2.29	0.82	30	NA	NA
					3	Passion flower	23	1.78	0.95	30	NA	NA
43	Atkinson, C.[56]	2004	England	Frequency hotflash	1	Red clover	86	1.2	2.3	360	55.1	4.7
43	Atkinson, C.[56]	2004	England	Frequency hotflash	2	placebo	91	1.5	2	360	55.2	4.9
44	Abbaspoor, Z. [57]	2003	Iran	Frequency hotflash	1	soy	30	5.45	1.74	28	49.9	0.1
44	Abbaspoor, Z. [57]	2003	Iran	Frequency hotflash	2	placebo	31	9	2.54	28	50.3	0.1
45	Burke, G. L.[58]	2003	America	Frequency hotflash	1	soyMI	70	1.7	0.27	365	51	0.3
					2	placebo	76	1.4	0.23	365	50.9	0.3
					3	soyHI	65	1.6	0.27	365	50.5	0.3
				Severity hotflash	1	soyMI	70	1.7	1.13	365	51	0.3
					2	placebo	76	1.4	1.003	365	50.9	0.3
					3	soyHI	65	1.6	1.04	365	50.5	0.3

As observed in Figs. 3 and 4, the mean age of participants within each treatment arm (the individual points) is similar to the overall mean age of participants within the evidence base (the red dotted line). According to the standard deviation (the +/− error bars), the variability of ages within each treatment arm also appears to be similar (where available). Based on this analysis, one would conclude that age distribution has no meaningful heterogeneity.

We compared the fit of both a fixed- and random-effects model. According to a visual examination of the leverage plots and comparison of the DIC values produced by the nma.fit(), the random effects model would be preferred over the fixed effects model for this particular dataset because the DIC value is lower and because there are fewer outliers in the leverage plot (Fig. 5,6).

As visualized in the SUCRA plot (Fig. 7), the hop and lavender curve is consistently above the other treatments’ curves, suggesting that it is the most beneficial treatment concerning the outcome among the treatments included in the frequency hot flash evidence network.

The Silybum marianum and soy curves are consistently above the other treatments’ curves, suggesting that it is the most beneficial treatment concerning the treatment outcome included in the severity hot flash evidence network.

The effect estimates and credible intervals produced by the preceding model are displayed in a league heat plot (Fig. 9). In Fig. 9, one can see that the difference between hop and other treatments is statistically significant at the 95% level. The effect estimates and credible intervals produced by the foregoing model are displayed in a league heat plot (Fig. 10) for severity outcome. In Fig. 10, one can see that the difference between placebo and Silybum marianum, Soy, and Valerian treatments are statistically significant at the 95% level.

Fig. 11 demonstrates the enrolled studies' Inconsistency plot. We conclude that there is a lack of evidence to suggest inconsistency within the network.

Discussion

We conducted a systematic review and network meta-analysis to synthesize the comparative effectiveness evidence for herbal medicines used to prevent frequency and severity of hot flash in postmenopausal women. Indeed, although most of these treatments have undergone randomized clinical trials, a clear understanding of their relative efficacy is still lacking, and in the absence of predictive biomarkers, treatment choice is exclusively based on patient characteristics. Thus, ranking the value of all these opportunities might be of help in designing treatment algorithms to be validated in prospective clinical trials.

In this study, we performed NMA for 22 treatments and placebo. Currently, Bayesian NMA is the most appropriate method for evaluating the strength of evidence for treatments that are not directly compared. This systematic review of herbal medicine for postmenopausal hot flash was based on 46 RCTs. There were 3210 postmenopausal women. The minimum follow-up period for patients with herbal medicine was 28 days. Although the quality of studies design was relatively properly, there were still serious issues in them. Allocation concealment was not reported by a large proportion of the included studies. A large percentage of studies were not designed as double-blind trials, which can partially affect the primary outcome. Intention-to-treat (ITT) was not properly implemented in most of the studies, and over than half of studies hadn’t a protocol for missing and incomplete data. Some studies did not report the complete formulation and composition of the investigated drugs. However, since most studies reported the expected results, reporting bias was minimal. According to the conducted investigations, no other possible bias was identified in the reviewed studies. As we known, Any treatment used to reduce and control menopausal symptoms must be proven effective in clinical studies, which require to implement a placebo-controlled trial or a head-to-head comparison with a known effective treatment, or both. Comparison with placebo is of great importance, because the placebo effect is often present in most treatments related to the control of menopausal symptoms. For this reason, in this study, placebo was placed as the basic treatment of the studies, and all the studies in which there was a comparison with the placebo were included in the study, and in this way, it was possible to compare the direct and indirect effects of the treatments. The final results of this study are shown that Silybum marianum and soy most beneficial treatment than others with respect to the outcome included the frequency of hot flashes. Also, basis on effect size of frequency of hot flashes for between herbal medicines, difference between hop and other treatments are statistically significant at the 95% level. For severity of hot flash, difference between placebo and Silybum marianum ,Soy, and Valerian treatments are statistically significant at the 95% level but other treatment aren’t significant effect size with each other. Silybum marianum is a plant that is used medicinally all over the world, but few studies and researches have focused on its benefits and properties. Considering that based on clinical evidence and studies, this plant is effective in reducing menopause symptoms, it seems that future studies need to be designed and focused on its metabolism, pharmacokinetics and bioavailability[59]. Soy consumption varies significantly from one country to another and by race, and the average daily consumption of soy in Asian countries is 4 times higher than in European and North American countries. It has also been shown that the reduction of hot flashes from soy consumption in different countries has provided different results. Soy foods are a rich source of three isoflavones, of which equol is the main isoflavonoid and is produced by intestinal bacteria, and it is assumed that equol reduces menopausal symptoms, which should be further investigated[60]. Valerian is a phytoestrogenic plant that various studies have shown that the phytoestrogen in valerian reduces the frequency and severity of hot flashes, and a simple and non-invasive herbal treatment can be used to reduce menopause symptoms[61].

In this study, we found evidence to determine which herbs are the best treatments for controlling the frequency and severity of menopausal hot flashes. Also, there was evidence of a significant difference for most of the herbal medicines compared to the placebo. Ranking the value of all these herbal medicines makes opportunities that might help design treatment algorithms to be validated in prospective clinical trials.

Of course, it seems that in order to ensure the effectiveness of herbal medicines, it is better to compare medicinal herbs and hormone therapy that are used as routine treatment.

network meta-analysis: NMA

Preferred Reporting Items for Systematic Reviews and Meta Analyses: PRISMA-NMA

Danggui Buxue Tang: DBT

Cochrane Central Register of Controlled Trials: CENTRAL

confidence interval: CI

Just Another Gibbs Sampler: JAGS

Bayesian inference Using Gibbs Sampling to conduct a Network meta-analysis: BUGSnet

Deviance Information Criterion: DIC

Data Availability

All data generated or analyzed during this study are included in this published article.

Conflicts of Interest

The authors declare that they have no competing interests.

Funding Statement

No any sources of funding for the research work and their role in the design of the study and collection, analysis, interpretation of data, and in writing the manuscript.

Authors’ contributions

Shahsavari.S, Keshavarzi.F and Tabatabaei. SM performed the analysis and Project development, Data Collection, Manuscript writing.

Shahsavari.S , Salari.N and Jambarsang.S interpreted the results.

Shahsavari.S , Keshavarzi.F, Zhale.M and Salari.N drafted the paper.

All authors read and approved the final manuscript.

Acknowledgments

The authors would like to express their appreciation towards the indirect assistance support of the Research and Technology Department of Kermanshah University of Medical Science paper.

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No competing interests reported.

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The Effectiveness of Herbal Remedies on the Hot Flashes in Postmenopausal Women: A Systematic Review and Network Meta- analysis Study

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