Show available content in
Abstract
Objective
To explore the difference in intestinal flora composition between individuals with sub-health status and healthy subjects.
Methods
From November, 2020 to May, 2021, a total of 150 nursing staff members in Nanfang Hospital were selected for this study, including 75 participants with sub-health status (SHS group) and 75 healthy participants (control group). Fecal samples were collected from all the participants for analysis of the diversity and species composition of the intestinal flora using high-throughput sequencing for V3-V4 region of 16S rRNA gene.
Results
The results of α diversity analysis showed no significant difference in Chao1 index between the two groups (P=0.619), but the Shannon index was significantly higher in SHS group than in the control group (P < 0.001). The results of β diversity analysis showed significant differences in the community structure between the SHS group and the control group (R=0.227, P=0.001). At the phylum level, the intestinal flora in the two groups were composed mainly of Bacteroidota, Firmicutes and Actinobacteriata, and of Prevotella, Bacteroides, Blautia and Faecalibacterium at the genus level. Species difference analysis identified significant differences in the relative abundance between the two groups in 4 phyla, 3 classes, 3 orders, 3 families and 3 genera (P < 0.05).
Conclusion
Compared with healthy subjects, the individuals with sub-health status have obviously unbalanced structure of the intestinal flora.
Keywords: sub-health status, intestinal flora, 16S rRNA, chronic fatigue syndrome
腸道菌群（GM）及其代謝產(chǎn)物在維持人體健康穩(wěn)態(tài)中起至關(guān)重要的作用[1, 2]，近年來，亞健康狀態(tài)與腸道菌群失衡的關(guān)系越來越受到關(guān)注。亞健康狀態(tài)（SHS）是人體處于健康和疾病之間的一種狀態(tài)[3, 4]，常伴有食欲不振、腹部不適、便溏便秘等胃腸道癥狀[5, 6]，慢性疲勞綜合征（CFS）為其中一種亞型[7]。Sheedy等[8]基于細(xì)胞培養(yǎng)的技術(shù)，觀察到CFS人群腸道中的革蘭氏陽性需氧菌明顯占優(yōu)勢(shì)，尤其是腸球菌和鏈球菌。Fremont等[9]研究來自比利時(shí)和挪威的CFS組和健康對(duì)照組腸道菌群的差異，發(fā)現(xiàn)CFS組的類桿菌屬（Bacteroides）減少。Shukla等[10]收集了CFS組和對(duì)照組的運(yùn)動(dòng)前、運(yùn)動(dòng)后15 min、48 h、72 h的癥狀報(bào)告、血液和糞便標(biāo)本，發(fā)現(xiàn)CFS人群主要菌群的相對(duì)豐度增加更顯著，血液中細(xì)菌清除時(shí)間延長(zhǎng)。
上述研究均提供了亞健康狀態(tài)人群腸道菌群失調(diào)的證據(jù)，但由于既往研究的納入標(biāo)準(zhǔn)、測(cè)序技術(shù)的不同，導(dǎo)致亞健康狀態(tài)人群具體何種腸道菌群改變及變化的特點(diǎn)尚無定論[11, 12]。近年來，16S rRNA測(cè)序技術(shù)的發(fā)展，彌補(bǔ)了傳統(tǒng)鑒定方法無法準(zhǔn)確鑒定細(xì)菌種屬的缺點(diǎn)，已廣泛用于腸道菌群的研究[13]，但未進(jìn)一步明確何種腸道菌群失調(diào)在亞健康狀態(tài)發(fā)生發(fā)展中起著重要作用。在本研究中，我們運(yùn)用16S rRNA測(cè)序技術(shù)，比較亞健康人群和健康人群的菌群構(gòu)成差異，明確其差異物種。
1. 資料和方法
1.1. 臨床資料
1.1.1. 一般資料 選取2020年11月~2021年5月南方醫(yī)科大學(xué)南方醫(yī)院工作的護(hù)理人員150例作為研究對(duì)象，其中亞健康人群75例為亞健康組（SHS組），男性6例，女性69例，年齡20~40歲；選擇75例同期健康人群作為健康對(duì)照組（H組），男性2例，女性73例；年齡21~40歲。組間年齡、性別、BMI、吸煙、飲酒差異無統(tǒng)計(jì)學(xué)意義（P均>0.05），SHS組亞健康總分明顯高于H組（P < 0.05）。由于抗生素和激素對(duì)腸道菌群影響較大[14]，要求所有志愿者在采樣前2月均未接受過任何抗生素和激素治療。所有參與志愿者均已簽署實(shí)驗(yàn)知情同意書。此項(xiàng)研究通過醫(yī)院倫理委員會(huì)批準(zhǔn)，所有納入的研究對(duì)象均簽署知情同意書（倫理編號(hào)：NFEC-2020- 278）。
1.1.2. 納入標(biāo)準(zhǔn) 亞健康組納入標(biāo)準(zhǔn)：（1）年齡在20~40歲，性別不限；（2）亞健康的判定標(biāo)準(zhǔn)參照《亞健康的中醫(yī)臨床研究指導(dǎo)原則（試行）》[3]，滿足以下條件：入組持續(xù)3個(gè)月以上反復(fù)出現(xiàn)的不適狀態(tài)或適應(yīng)能力顯著減退，但能維持正常工作；無重大器官器質(zhì)性疾病及精神心理疾病；盡管具有明確的非重大器官器質(zhì)性疾病或精神心理疾病診斷但無需用藥維持，且與目前不適狀態(tài)或適應(yīng)能力的減退無因果聯(lián)系；（3）亞健康評(píng)定量表診斷為亞健康狀態(tài)；（4）研究對(duì)象同意參加本次研究。健康組納入標(biāo)準(zhǔn)：（1）年齡20~40歲，性別不限；（2）無重大器官器質(zhì)性疾病及精神心理疾病；（3）盡管具有明確的非重大器官器質(zhì)性疾病或精神心理疾病診斷但無需用藥維持；（4）亞健康評(píng)定量表診斷為健康狀態(tài)；（5）研究對(duì)象同意參加本次研究。
1.1.3. 排除標(biāo)準(zhǔn) （1）入組前2月內(nèi)曾使用抗生素、微生態(tài)制劑者；（2）過敏體質(zhì)或?qū)λ幬镞^敏者；（3）妊娠或準(zhǔn)備妊娠、哺乳期婦女；（4）懷疑或確有酒精、藥物濫用史者；（5）有消化系統(tǒng)疾病病史及手術(shù)史者；（6）有高血壓、糖尿病、冠心病、乙肝、肺結(jié)核病史者；（7）認(rèn)知功能障礙不能給予充分知情同意者。
1.2. 調(diào)查工具
1.2.1. 一般情況調(diào)查問卷 調(diào)查內(nèi)容包括年齡、性別、BMI、是否吸煙、飲酒。
1.2.2. 亞健康評(píng)定量表[15] 該量表包括3個(gè)子量表（生理亞健康、心理亞健康和社會(huì)亞健康）、9個(gè)維度（精力、身體運(yùn)動(dòng)功能、社會(huì)適應(yīng)、正向情緒、社會(huì)資源與社會(huì)支持、認(rèn)知功能、心理癥狀、器官功能、身體癥狀）和39個(gè)條目，其中正向計(jì)分20條，反向計(jì)分19條。采用Likert 5級(jí)評(píng)分，總分為0~100分，各維度、子量表及總量表得分越高，表示健康狀況越好。計(jì)算公式：轉(zhuǎn)化分=（原始分－理論最低分）（/ 理論最高分－理論最低分）×100。排除疾病人群后，當(dāng)生理子量表低于閾值68，或心理子量表低于閾值67，或社會(huì)子量表低于閾值67時(shí)，被診斷為亞健康狀態(tài)。該量表Cronbach's α系數(shù)為0.917，重測(cè)信度為0.644。
1.3. 標(biāo)本收集
本研究采取病例對(duì)照研究的方法，根據(jù)納入、排除標(biāo)準(zhǔn)招募調(diào)查對(duì)象，首先由2名課題組成員（經(jīng)過量表評(píng)定一致性培訓(xùn)）對(duì)調(diào)查對(duì)象進(jìn)行亞健康評(píng)定量表評(píng)定，并收集調(diào)查對(duì)象的一般資料，當(dāng)場(chǎng)回收問卷，后期由雙人錄入電腦中。所有研究對(duì)象在清晨（6:00-9:00）用糞便采集管（IGE）收集中間段沒有接觸空氣和地面部分新鮮的糞便標(biāo)本2~3 g，按操作說明混勻后在15分鐘內(nèi)將裝有樣品的采集管置于-80 ℃冰箱保存。
1.4. DNA提取和PCR擴(kuò)增
基因組DNA通過E.Z.N.A.?DNA Kit（Omega Bio-tek, Norcross, GA, U.S.）試劑盒按照說明書進(jìn)行提取。使用紫外分光光度計(jì)（NanoDrop 2000, Thermo Scientific)檢測(cè)DNA的濃度和純度，用2%瓊脂糖凝膠電泳鑒定DNA完整性。使用引物515F（5'-GTGCCA GCMGCCGCGGTAA- 3）' 和806R6（5'- GGACTACVS GGGTATCT AAT-3）' 擴(kuò)增細(xì)菌16S rRNA基因的V3-V4高可變區(qū)。使用qiagen公司提供的膠回收試劑盒回收產(chǎn)物，Tris-HCl洗脫，2%瓊脂糖電泳檢測(cè)。
1.5. 文庫構(gòu)建和上機(jī)測(cè)序
使用TruSeq? DNA PCR-Free Sample Preparation Kit建庫試劑盒進(jìn)行文庫構(gòu)建，構(gòu)建好的文庫經(jīng)過Qubit和Q-PCR定量，文庫合格后，使用NovaSeq6000進(jìn)行上機(jī)測(cè)序。
1.6. 生物信息分析
樣本委托北京諾禾致源股技術(shù)有限公司進(jìn)行高通量測(cè)序，根據(jù)Barcode序列和PCR擴(kuò)增引物序列從下機(jī)數(shù)據(jù)中拆分出各樣本數(shù)據(jù)，截去Barcode和引物序列后使用FLASH軟件（V1.2.7）對(duì)每個(gè)樣本的reads進(jìn)行拼接，得到原始Tags數(shù)據(jù)（Raw Tags）。拼接得到的Raw Tags，需要經(jīng)過嚴(yán)格的過濾處理得到高質(zhì)量的Tags數(shù)據(jù)（Clean Tags）。參照Qiime（V1.9.1）的Tags質(zhì)量控制流程，進(jìn)行如下操作：a）Tags截?。簩aw Tags從連續(xù)低質(zhì)量值（默認(rèn)質(zhì)量閾值為≤19）堿基數(shù)達(dá)到設(shè)定長(zhǎng)度（默認(rèn)長(zhǎng)度值為3）的第一個(gè)低質(zhì)量堿基位點(diǎn)截?cái)?；b）Tags長(zhǎng)度過濾：Tags經(jīng)過截取后得到的Tags數(shù)據(jù)集，進(jìn)一步過濾掉其中連續(xù)高質(zhì)量堿基長(zhǎng)度小于Tags長(zhǎng)度75%的Tags。經(jīng)過以上處理后得到的Tags需要進(jìn)行去除嵌合體序列的處理，Tags序列通過與物種注釋數(shù)據(jù)庫進(jìn)行比對(duì)檢測(cè)嵌合體序列，并最終去除其中的嵌合體序列，得到最終的有效數(shù)據(jù)。通過USEARCH軟件對(duì)序列進(jìn)行聚類，將序列相似度≥97%的序列歸為同一OTU，然后通過OTU與數(shù)據(jù)庫比對(duì)，對(duì)OTU進(jìn)行物種注釋。α多樣性分析用Chao1指數(shù)和Shannon指數(shù)來評(píng)估。β多樣性用基于Weighted Unifrac距離的PCoA分析。基于LEfSe方法分析，LDA最小值設(shè)為4，進(jìn)行2組之間的比較，尋找在2組之間具有顯著差異的菌屬。
1.7. 統(tǒng)計(jì)學(xué)方法
采用SPSS 23.0統(tǒng)計(jì)學(xué)軟件進(jìn)行數(shù)據(jù)分析，計(jì)數(shù)資料采用頻數(shù)和構(gòu)成比描述，組間比較采用卡方檢驗(yàn)；正態(tài)分布的計(jì)量資料以均數(shù)±標(biāo)準(zhǔn)差表示，組間比較采用獨(dú)立樣本t檢驗(yàn)；偏態(tài)分布的計(jì)量資料，采用中位數(shù)和四分位數(shù)M（P25，P75）描述，組間比較采用MannWhitney U檢驗(yàn)。檢驗(yàn)水準(zhǔn)α=0.05。
2. 結(jié)果
2.1. 亞健康組與健康組一般情況比較
2組年齡、性別、BMI、吸煙、飲酒差異無統(tǒng)計(jì)學(xué)意義（P均>0.05），SHS組亞健康總分明顯高于H組（P < 0.05）（表 1）。
1. 亞健康組與健康組一般情況比較
Demographic and clinical characteristics of sub-health status group and control group
Variables SHS (n=75) H (n=75) Statistics P BMI: Body mass index; SHMS: Sub-Health Measurement Scale; apefers to the χ2 value. Age (year), median (IQR) 24(22,30) 25(23,30) -1.716 0.086 Gender, n(%) Female 69(46.0) 73(48.7) 2.114a 0.148 Male 6(4.0) 2(1.3) BMI(kg/m2)，Mean±SD 20.87±2.44 20.34±2.49 0.333 0.191 Smoking, n(%) Yes 4( 2.7) 1( 0.7) 1.860a 0.175 No 7(47.3) 74(49.3) Drinking, n(%) Yes 8(5.3) 10(0.7) 0.250a 0.618 No 67(44.7) 65(43.3) SHMS (scores), median (IQR) 47.14(44.29,52.14) 55.71(52.14,67.14) -6.368 <0.001 2.2. 亞健康組與健康組腸道菌群微生物多樣性分析
2.2.1. 測(cè)序數(shù)據(jù) 2組150個(gè)樣本共獲得平均每樣品測(cè)得96 391條tags。以97%的一致性將序列聚類成為OTUs（Operational Taxonomic Units），共得到3616個(gè)OTUs。SHS組獨(dú)有494個(gè)OTU，H組獨(dú)有1784個(gè)OTU，兩組共有1333個(gè)OTU（圖 1）。
1. 
SHS組與H組Venn圖
Venn diagram of OTU in the two groups. H: Healthy group; SHS: Sub-health status group.
2.2.2. α多樣性比較 等級(jí)聚類曲線（Rank Abundance曲線）結(jié)果表明2組腸道菌群的群落豐度較高，均勻度較好（圖 2）。物種累積箱形結(jié)果顯示箱形圖位置趨于平緩（圖 3），當(dāng)前樣本量足夠，現(xiàn)有的測(cè)序數(shù)據(jù)量可以反映樣本中菌群的多樣性信息。本研究中菌群豐度用Chao1指數(shù)來衡量；菌群多樣性用Shannon指數(shù)來衡量，Shannon指數(shù)越大，樣品中的物種多樣性越高。結(jié)果顯示，SHS組的Chao1指數(shù)與H組之間差異無統(tǒng)計(jì)學(xué)意義（P=0.619，圖 4），SHS組的Shannon指數(shù)升高且2組之間差異有統(tǒng)計(jì)學(xué)意義（P < 0.001，圖 5）。
2. 
等級(jí)聚類曲線
Rank abundance curves of the two groups.
3. 
物種累積箱型圖
Species accumulation boxplots.
4. 
Chao1指數(shù)組間差異箱形圖
Boxplot of Chao1 index in the two groups.
5. 
Shannon指數(shù)組間差異箱形圖
Boxplot of Shannon index in the two groups.
2.2.3. β多樣性比較 主坐標(biāo)分析PCoA圖顯示，除個(gè)別樣本存在偏差，亞健康組和健康組基本呈現(xiàn)在不同區(qū)域（圖 6）。經(jīng)過Anosim相似性分析發(fā)現(xiàn)，2組間群落結(jié)構(gòu)呈現(xiàn)顯著區(qū)別（R=0.227，P=0.001）。
6. 
基于Unweighted Unifrac距離的PCoA分析
PCoA analysis based on Unweighted Unifrac distance.
2.2.4. 腸道菌群結(jié)構(gòu)分析 在門水平，SHS組中豐度最高的4個(gè)門依次為：Firmicutes（59.5%）、Bacteroidota（31.9%）、Actinobacteriota（4.4%），變形菌門占2.5%。而HS組中，Bacteroidota（48.4%）、Firmicutes（41.1%）、變形菌門占5.3%、Actinobacteriota（2.5%）（圖 7）。
7. 
門水平上2組的相對(duì)豐度
Relative abundance of bacterial phyla in the two groups.
在屬水平上，H組中豐度最高的依次是：Prevotella（25.6%）、Bacteroides（19.5%）、Faecalibacterium（12.2%）、Blautia（3.3%）、Megamonas（2.1%）、Lachnospira（1.8%）、Bifidobacterium（1.8%）、Alloprevotella（1.3%）、Fusobacterium（1.3%）、Romboutsia（0.9%）。而在SHS組中豐度最高的依次是：Bacteroides（16.4%）、Prevotella（13.5%）、Blautia（11.8%）、Faecalibacterium（10.8%）、Bifidobacterium（3.6%）、Romboutsia（1.4%）、Lachnospira（1.3%）、Megamonas（0.6%）、Fusobacterium（0.5%）、Alloprevotella（0.3%）（圖 8）。
8. 
屬水平上2組的相對(duì)豐度
Relative abundance of bacterial genera in the two groups.
2.2.5. 腸道菌群差異分析 在LDA值分布柱狀圖中可以看出，SHS組的優(yōu)勢(shì)物種為厚壁菌門，H組的優(yōu)勢(shì)物種為擬桿菌門。通過LEfSe分析得出兩組在4個(gè)門3個(gè)綱3個(gè)目3個(gè)科3個(gè)屬的相對(duì)豐度差異有統(tǒng)計(jì)學(xué)意義（P < 0.05，圖 9）。
9. 
LDA分布柱狀圖
Histogram of LDA score distribution.
3. 討論
本研究運(yùn)用16S rRNA測(cè)序技術(shù)，對(duì)比亞健康組與健康組之間的腸道菌群，發(fā)現(xiàn)了亞健康組人群腸道菌群的變化特點(diǎn)，與健康組相比，亞健康組人群的腸道菌群組成結(jié)構(gòu)和多樣性上均存在顯著差異。結(jié)果顯示，在α多樣性分析的Shannon指數(shù)上，SHS組較H組明顯升高，且β多樣性分析也進(jìn)一步顯示2組間腸道菌群組成結(jié)構(gòu)上存在差異，表明亞健康人群腸道菌群多樣性增加，與健康人群相比結(jié)構(gòu)失衡。
本研究發(fā)現(xiàn)SHS組的Shannon指數(shù)較H組明顯升高，這一結(jié)果與Armstrong[16]的研究結(jié)果相反，后者通過CFS組和健康組的腸道菌群對(duì)比分析，發(fā)現(xiàn)CFS組的物種多樣性減少。研究結(jié)果差異的原因可能為：（1）與樣本來源的人種、地域等因素造成的差異有關(guān)[17]；（2）使用了不同的微生物鑒定技術(shù)[18]。
在門水平，2組的優(yōu)勢(shì)菌門均為擬桿菌門、厚壁菌門、放線菌門，在屬水平，2組的優(yōu)勢(shì)菌屬均為Prevotella、Bacteroides。這與人類腸道微生物群研究的先前報(bào)告一致[19]，人類腸道主要由厚壁菌門或擬桿菌門組成，厚壁菌門的優(yōu)勢(shì)屬為Ruminococc、Blautia、Eubacterium、Faecalibacterium，擬桿菌門的優(yōu)勢(shì)屬為Bacteroides、Prevotella。
為了進(jìn)一步確定關(guān)鍵菌群，本研究采用LEfSe方法，結(jié)果發(fā)現(xiàn)，亞健康組的厚壁菌門、放線菌門顯著高于健康組，變形菌門、擬桿菌門顯著低于健康組。這與Giloteaux等[20]的部分研究結(jié)果相似。Giloteaux等對(duì)一人患有CFS和另一人健康的單卵雙胞胎，進(jìn)行為期兩天的心肺運(yùn)動(dòng)測(cè)試，并對(duì)運(yùn)動(dòng)前和運(yùn)動(dòng)后的糞便標(biāo)本進(jìn)行了16S rRNA測(cè)序分析。在人類腸道微生物中，厚壁菌門與擬桿菌門（F/B）比率升高被廣泛認(rèn)為是腸道失調(diào)的標(biāo)志，并與肥胖[21]、糖尿病[22]和心血管[23]等疾病有關(guān)。我們?cè)诒敬窝芯恐幸驳玫絹喗】到M的F/B比率的比值高于健康組，這可能與亞健康狀態(tài)的發(fā)生發(fā)展有密切聯(lián)系。
本研究還發(fā)現(xiàn)，在屬水平，亞健康組的Blautia、Agathobacter顯著高于健康組，Prevotella顯著低于健康組，Giloteaux等[20]也發(fā)現(xiàn)CFS組的Prevotella顯著低于健康組。Prevotella分解蛋白質(zhì)和碳水化合物，發(fā)酵膳食纖維，產(chǎn)生短鏈脂肪酸，與葡萄糖穩(wěn)態(tài)和宿主代謝有關(guān)[24]，在自閉癥兒童中也發(fā)現(xiàn)Prevotella的減少[25]。值得注意的是在既往研究中并未發(fā)現(xiàn)Blautia的變化。Blautia屬于毛螺菌科，可導(dǎo)致人體血漿IL-1β水平升高，睡眠障礙已被證明與IL-1β升高有關(guān)[26]，Smith[27]發(fā)現(xiàn)Blautia的豐度與睡眠質(zhì)量呈負(fù)相關(guān)。李媛媛等[28]也提出，Blautia是區(qū)分慢性失眠患者與健康人的特征細(xì)菌。睡眠障礙是亞健康狀態(tài)發(fā)生發(fā)展的危險(xiǎn)因素，也是亞健康狀態(tài)的重要表現(xiàn)之一[29]，腸道菌群的改變與亞健康狀態(tài)人群的睡眠障礙及疲憊癥狀相關(guān)[30]，提示我們之后的研究可以從這一方面進(jìn)行深入研究。
綜上所述，我們發(fā)現(xiàn)亞健康狀態(tài)人群的腸道菌群具有整體結(jié)構(gòu)失衡的特征，厚壁菌門與擬桿菌門比率升高，并明確了差異物種，亞健康人群的Blautia、Agathobacter顯著增加，Prevotella顯著減少。目前，亞健康狀態(tài)的病因和發(fā)病機(jī)制至今尚未清楚，但菌群穩(wěn)態(tài)失調(diào)可能在亞健康狀態(tài)的發(fā)生發(fā)展中發(fā)揮重要的作用。本研究通過運(yùn)用高通量測(cè)序技術(shù)，將亞健康人群與健康人群進(jìn)行比較研究，不僅發(fā)現(xiàn)二者呈現(xiàn)明顯的不同，驗(yàn)證了亞健康狀態(tài)影響腸道菌群多樣性以及結(jié)構(gòu)的組成，而且還將差異物種進(jìn)一步鑒定至菌屬，有望為治療亞健康狀態(tài)提供思路。本研究的不足之處在于，僅初步分析亞健康人群腸道菌群的分布情況，隨著多組學(xué)技術(shù)的發(fā)展，下一步研究將結(jié)合代謝組學(xué)、蛋白質(zhì)組學(xué)等方法確定具有核心功能的亞健康人群腸道菌群種類。
Biography
張星星，在讀碩士研究生，E-mail: 1548580777@qq.com
Funding Statement
廣東省科技計(jì)劃項(xiàng)目（2019A141405048）; 廣州市科技計(jì)劃項(xiàng)目（202103000037）
Contributor Information
張 星星 (Xingxing ZHANG), Email: 1548580777@qq.com.
阮 偉清 (Weiqing RUAN), Email: jamela@sina.com.
References
1.Valdes AM, Walter J, Segal E, et al. Role of the gut microbiota in nutrition and health. http://www.nugo.org/wp-content/uploads/2016/09/NUGO-presentation-Bindels-sept-2016-FOR-WEBSITE.pdf. BMJ. 2018;361:k2179. doi: 10.1136/bmj.k2179. [Valdes AM, Walter J, Segal E, et al. Role of the gut microbiota in nutrition and health[J]. BMJ, 2018, 361: k2179.] [DOI] [PMC free article] [PubMed] [Google Scholar] 2.Fan Y, Pedersen O. Gut microbiota in human metabolic health and disease. Nat Rev Microbiol. 2021;19(1):55–71. doi: 10.1038/s41579-020-0433-9. [Fan Y, Pedersen O. Gut microbiota in human metabolic health and disease[J]. Nat Rev Microbiol, 2021, 19(1): 55-71.] [DOI] [PubMed] [Google Scholar] 3.朱 嶸. 《亞健康中醫(yī)臨床指南》解讀. 中國中醫(yī)藥現(xiàn)代遠(yuǎn)程教育. 2009;7(2):Ⅴ–Ⅵ. doi: 10.3969/j.issn.1672-2779.2009.02.061. [朱嶸. 《亞健康中醫(yī)臨床指南》解讀[J]. 中國中醫(yī)藥現(xiàn)代遠(yuǎn)程教育, 2009, 7(2): Ⅴ-Ⅵ.] [DOI] [Google Scholar] 4.Bi JL, Chen J, Sun XM, et al. The development and evaluation of a sub-health self-rating scale for university students in China. BMC Public Health. 2019;19(1):330. doi: 10.1186/s12889-019-6650-3. [Bi JL, Chen J, Sun XM, et al. The development and evaluation of a sub-health self-rating scale for university students in China[J]. BMC Public Health, 2019, 19(1): 330.] [DOI] [PMC free article] [PubMed] [Google Scholar] 5.Kim SE, Chang L. Overlap between functional GI disorders and other functional syndromes: what are the underlying mechanisms? Neurogastroenterol Motil. 2012;24(10):895–913. doi: 10.1111/j.1365-2982.2012.01993.x. [Kim SE, Chang L. Overlap between functional GI disorders and other functional syndromes: what are the underlying mechanisms [J]? Neurogastroenterol Motil, 2012, 24(10): 895-913.] [DOI] [PMC free article] [PubMed] [Google Scholar] 6.Maes M, Leunis JC, Geffard M, et al. Evidence for the existence of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) with and without abdominal discomfort (irritable bowel) syndrome. http://europepmc.org/abstract/med/25433843. Neuro Endocrinol Lett. 2014;35(6):445–53. [Maes M, Leunis JC, Geffard M, et al. Evidence for the existence of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) with and without abdominal discomfort (irritable bowel) syndrome [J]. Neuro Endocrinol Lett, 2014, 35(6): 445-53.] [PubMed] [Google Scholar] 7.張 金華, 許 軍, 黃 季萌, et al. 亞健康測(cè)量的定量化研究. 廣東醫(yī)學(xué). 2009;30(11):1746–8. doi: 10.3969/j.issn.1001-9448.2009.11.070. [張金華, 許軍, 黃季萌, 等. 亞健康測(cè)量的定量化研究[J]. 廣東醫(yī)學(xué), 2009, 30(11): 1746-8.] [DOI] [Google Scholar] 8.Sheedy JR, Wettenhall RE, Scanlon D, et al. Increased d-lactic Acid intestinal bacteria in patients with chronic fatigue syndrome. http://www.researchgate.net/profile/Neil_Mcgregor/publication/26331296_Increased_D-Lactic_Acid_Intestinal_Bacteria_in_Patients_with_Chronic_Fatigue_Syndrome/links/0912f507ddeec974a7000000/Increased-D-Lactic-Acid-Intestinal-Bacteria-in-Patients-with-Chronic-Fatigue-Syndrome.pdf. In Vivo. 2009;23(4):621–8. [Sheedy JR, Wettenhall RE, Scanlon D, et al. Increased d-lactic Acid intestinal bacteria in patients with chronic fatigue syndrome[J]. In Vivo, 2009, 23(4): 621-8.] [PubMed] [Google Scholar] 9.Frémont M, Coomans D, Massart S, et al. High-throughput 16S rRNA gene sequencing reveals alterations of intestinal microbiota in myalgic encephalomyelitis/chronic fatigue syndrome patients. Anaerobe. 2013;22:50–6. doi: 10.1016/j.anaerobe.2013.06.002. [Frémont M, Coomans D, Massart S, et al. High-throughput 16S rRNA gene sequencing reveals alterations of intestinal microbiota in myalgic encephalomyelitis/chronic fatigue syndrome patients[J]. Anaerobe, 2013, 22: 50-6.] [DOI] [PubMed] [Google Scholar] 10.Shukla SK, Cook D, Meyer J, et al. Changes in gut and plasma microbiome following exercise challenge in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) PLoS One. 2015;10(12):e0145453. doi: 10.1371/journal.pone.0145453. [Shukla SK, Cook D, Meyer J, et al. Changes in gut and plasma microbiome following exercise challenge in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)[J]. PLoS One, 2015, 10(12): e0145453.] [DOI] [PMC free article] [PubMed] [Google Scholar] 11.Newberry F, Hsieh SY, Wileman T, et al. Does the microbiome and virome contribute to myalgic encephalomyelitis/chronic fatigue syndrome? Clin Sci: Lond. 2018;132(5):523–42. doi: 10.1042/CS20171330. [Newberry F, Hsieh SY, Wileman T, et al. Does the microbiome and virome contribute to myalgic encephalomyelitis/chronic fatigue syndrome[J]? Clin Sci: Lond, 2018, 132(5): 523-42.] [DOI] [PMC free article] [PubMed] [Google Scholar] 12.Varesi A, Deumer US, Ananth S, et al. The Emerging Role of Gut Microbiota in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS): Current Evidence and Potential Therapeutic Applications. J Clin Med. 2021;10(21):5077. doi: 10.3390/jcm10215077. [Varesi A, Deumer US, Ananth S, et al. The Emerging Role of Gut Microbiota in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS): Current Evidence and Potential Therapeutic Applications[J]. J Clin Med, 2021, 10(21): 5077.] [DOI] [PMC free article] [PubMed] [Google Scholar] 13.Allali I, Arnold JW, Roach J, et al. A comparison of sequencing platforms and bioinformatics pipelines for compositional analysis of the gut microbiome. BMC Microbiol. 2017;17(1):194. doi: 10.1186/s12866-017-1101-8. [Allali I, Arnold JW, Roach J, et al. A comparison of sequencing platforms and bioinformatics pipelines for compositional analysis of the gut microbiome[J]. BMC Microbiol, 2017, 17(1): 194.] [DOI] [PMC free article] [PubMed] [Google Scholar] 14.Blaser MJ. Antibiotic use and its consequences for the normal microbiome. Science. 2016;352(6285):544–5. doi: 10.1126/science.aad9358. [Blaser MJ. Antibiotic use and its consequences for the normal microbiome[J]. Science, 2016, 352(6285): 544-5.] [DOI] [PMC free article] [PubMed] [Google Scholar] 15.許 軍, 馮 麗儀, 羅 仁, et al. 亞健康評(píng)定量表的信度效度研究. http://www.j-smu.com/CN/article/downloadArticleFile.do?attachType=PDF&id=2410. 南方醫(yī)科大學(xué)學(xué)報(bào). 2011;31(1):33–8. [許軍, 馮麗儀, 羅仁, 等. 亞健康評(píng)定量表的信度效度研究[J]. 南方醫(yī)科大學(xué)學(xué)報(bào), 2011, 31(1): 33-8.] [PubMed] [Google Scholar] 16.Armstrong CW, McGregor NR, Lewis DP, et al. The association of fecal microbiota and fecal, blood serum and urine metabolites in myalgic encephalomyelitis/chronic fatigue syndrome. http://link.springer.com/content/pdf/10.1007%2Fs11306-016-1145-z.pdf. Metabolomics. 2016;13(1):1–13. [Armstrong CW, McGregor NR, Lewis DP, et al. The association of fecal microbiota and fecal, blood serum and urine metabolites in myalgic encephalomyelitis/chronic fatigue syndrome[J]. Metabolomics, 2016, 13(1): 1-13.] [Google Scholar] 17.Gaulke CA, Sharpton TJ. The influence of ethnicity and geography on human gut microbiome composition. Nat Med. 2018;24(10):1495–6. doi: 10.1038/s41591-018-0210-8. [Gaulke CA, Sharpton TJ. The influence of ethnicity and geography on human gut microbiome composition[J]. Nat Med, 2018, 24(10): 1495-6.] [DOI] [PubMed] [Google Scholar] 18.Navaneetharaja N, Griffiths V, Wileman T, et al. A Role for the Intestinal Microbiota and Virome in Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS)? J Clin Med. 2016;5(6):55. doi: 10.3390/jcm5060055. [Navaneetharaja N, Griffiths V, Wileman T, et al. A Role for the Intestinal Microbiota and Virome in Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS)[J]? J Clin Med, 2016, 5(6): 55.] [DOI] [PMC free article] [PubMed] [Google Scholar] 19.Rajili?-Stojanovi? M, Smidt H, de Vos WM. Diversity of the human gastrointestinal tract microbiota revisited. Environ Microbiol. 2007;9(9):2125–36. doi: 10.1111/j.1462-2920.2007.01369.x. [Rajili?-Stojanovi? M, Smidt H, de Vos WM. Diversity of the human gastrointestinal tract microbiota revisited[J]. Environ Microbiol, 2007, 9(9): 2125-36.] [DOI] [PubMed] [Google Scholar] 20.Giloteaux L, Hanson MR, Keller BA. A pair of identical twins discordant for myalgic encephalomyelitis/chronic fatigue syndrome differ in physiological parameters and gut microbiome composition. Am J Case Rep. 2016;17:720–9. doi: 10.12659/AJCR.900314. [Giloteaux L, Hanson MR, Keller BA. A pair of identical twins discordant for myalgic encephalomyelitis/chronic fatigue syndrome differ in physiological parameters and gut microbiome composition [J]. Am J Case Rep, 2016, 17: 720-9.] [DOI] [PMC free article] [PubMed] [Google Scholar] 21.Magne F, Gotteland M, Gauthier L, et al. The Firmicutes/Bacteroidetes Ratio: A Relevant Marker of Gut Dysbiosis in Obese Patients? Nutrients. 2020;12(5):1474. doi: 10.3390/nu12051474. [Magne F, Gotteland M, Gauthier L, et al. The Firmicutes/Bacteroidetes Ratio: A Relevant Marker of Gut Dysbiosis in Obese Patients[J]? Nutrients, 2020, 12(5): 1474.] [DOI] [PMC free article] [PubMed] [Google Scholar] 22.Pascale A, Marchesi N, Govoni S, et al. The role of gut microbiota in obesity, diabetes mellitus, and effect of metformin: new insights into old diseases. Curr Opin Pharmacol. 2019;49:1–5. doi: 10.1016/j.coph.2019.03.011. [Pascale A, Marchesi N, Govoni S, et al. The role of gut microbiota in obesity, diabetes mellitus, and effect of metformin: new insights into old diseases[J]. Curr Opin Pharmacol, 2019, 49: 1-5.] [DOI] [PubMed] [Google Scholar] 23.Li J, Zhao F, Wang Y, et al. Gut microbiota dysbiosis contributes to the development of hypertension. Microbiome. 2017;5(1):14. doi: 10.1186/s40168-016-0222-x. [Li J, Zhao F, Wang Y, et al. Gut microbiota dysbiosis contributes to the development of hypertension[J]. Microbiome, 2017, 5(1): 14.] [DOI] [PMC free article] [PubMed] [Google Scholar] 24.Precup G, Vodnar DC. Gut Prevotella as a possible biomarker of diet and its eubiotic versus dysbiotic roles: a comprehensive literature review. Br J Nutr. 2019;122(2):131–40. doi: 10.1017/S0007114519000680. [Precup G, Vodnar DC. Gut Prevotella as a possible biomarker of diet and its eubiotic versus dysbiotic roles: a comprehensive literature review[J]. Br J Nutr, 2019, 122(2): 131-40.] [DOI] [PubMed] [Google Scholar] 25.Dan Z, Mao X, Liu Q, et al. Altered gut microbial profile is associated with abnormal metabolism activity of Autism Spectrum Disorder. Gut Microbes. 2020;11(5):1246–67. doi: 10.1080/19490976.2020.1747329. [Dan Z, Mao X, Liu Q, et al. Altered gut microbial profile is associated with abnormal metabolism activity of Autism Spectrum Disorder[J]. Gut Microbes, 2020, 11(5): 1246-67.] [DOI] [PMC free article] [PubMed] [Google Scholar] 26.Zielinski MR, Kim Y, Karpova SA, et al. Chronic sleep restriction elevates brain interleukin-1 beta and tumor necrosis factor-alpha and attenuates brain-derived neurotrophic factor expression. Neurosci Lett. 2014;580:27–31. doi: 10.1016/j.neulet.2014.07.043. [Zielinski MR, Kim Y, Karpova SA, et al. Chronic sleep restriction elevates brain interleukin-1 beta and tumor necrosis factor-alpha and attenuates brain-derived neurotrophic factor expression[J]. Neurosci Lett, 2014, 580: 27-31.] [DOI] [PMC free article] [PubMed] [Google Scholar] 27.Smith RP, Easson C, Lyle SM, et al. Gut microbiome diversity is associated with sleep physiology in humans. PLoS One. 2019;14(10):e0222394. doi: 10.1371/journal.pone.0222394. [Smith RP, Easson C, Lyle SM, et al. Gut microbiome diversity is associated with sleep physiology in humans[J]. PLoS One, 2019, 14 (10): e0222394.] [DOI] [PMC free article] [PubMed] [Google Scholar] 28.Li YY, Zhang B, Zhou Y, et al. Gut microbiota changes and their relationship with inflammation in patients with acute and chronic insomnia. Nat Sci Sleep. 2020;12:895–905. doi: 10.2147/NSS.S271927. [Li YY, Zhang B, Zhou Y, et al. Gut microbiota changes and their relationship with inflammation in patients with acute and chronic insomnia[J]. Nat Sci Sleep, 2020, 12: 895-905.] [DOI] [PMC free article] [PubMed] [Google Scholar] 29.Collin SM, Norris T, Gringras P, et al. Childhood sleep and adolescent chronic fatigue syndrome (CFS/ME): evidence of associations in a UK birth cohort. Sleep Med. 2018;46:26–36. doi: 10.1016/j.sleep.2018.01.005. [Collin SM, Norris T, Gringras P, et al. Childhood sleep and adolescent chronic fatigue syndrome (CFS/ME): evidence of associations in a UK birth cohort[J]. Sleep Med, 2018, 46: 26-36.] [DOI] [PMC free article] [PubMed] [Google Scholar] 30.Jackson ML, Butt H, Ball M, et al. Sleep quality and the treatment of intestinal microbiota imbalance in Chronic Fatigue Syndrome: a pilot study. Sleep Sci. 2015;8(3):124–33. doi: 10.1016/j.slsci.2015.10.001. [Jackson ML, Butt H, Ball M, et al. Sleep quality and the treatment of intestinal microbiota imbalance in Chronic Fatigue Syndrome: a pilot study[J]. Sleep Sci, 2015, 8(3): 124-33.] [DOI] [PMC free article] [PubMed] [Google Scholar] 
