導(dǎo)語：如何才能寫好一篇遺傳學(xué)重疊效應(yīng)，這就需要搜集整理更多的資料和文獻(xiàn)，歡迎閱讀由公文云整理的十篇范文，供你借鑒。

篇1

高中生物教材中細(xì)胞質(zhì)遺傳是以紫茉莉?yàn)槔Ｙ|(zhì)基因在正交和反交時(shí)出現(xiàn)明顯的不同,因?yàn)槭芫珪r(shí)中只帶有很少的細(xì)胞質(zhì),使得受精卵中的細(xì)胞質(zhì)幾乎全部來自于卵細(xì)胞,這樣受細(xì)胞質(zhì)內(nèi)的遺傳物質(zhì)控制的性狀實(shí)際上是由卵細(xì)胞傳給后代的,因此會(huì)表現(xiàn)為母系遺傳現(xiàn)象。分析花斑紫茉莉遺傳的原因:當(dāng)花斑紫茉莉?yàn)槟副緯r(shí),紫茉莉的卵原細(xì)胞在減數(shù)分裂時(shí),細(xì)胞質(zhì)中的基因并不像核基因那樣有規(guī)律地分離,而是隨機(jī)地、不均等地分配到子細(xì)胞中去,因此會(huì)產(chǎn)生三種卵細(xì)胞,從而會(huì)產(chǎn)生三種不同的植株。這種隨機(jī)性和不均等性就導(dǎo)致了細(xì)胞質(zhì)遺傳的第二個(gè)特點(diǎn):后代不出現(xiàn)一定的分離比。所以花斑植株可以產(chǎn)生3種子代。而對(duì)與動(dòng)物或人的線粒體DNA的遺傳后代性狀如何分析?比如一母親患有某線粒體遺傳病,她的后代性狀應(yīng)該是怎樣呢?首先,我們知道他的子代不可能出現(xiàn)3種性狀,而只有患病或不患病。在我們常見的一些題目里面一般是認(rèn)為:母親患病則所有子代都患病,認(rèn)為這是母系遺傳的最好表現(xiàn)。其實(shí)不對(duì)。我們先來了解下線粒體DNA(mtDNA)的遺傳學(xué)特征。

與核DNA相比,mtDNA有獨(dú)特的遺傳學(xué)特征:

1.mtDNA存在于細(xì)胞質(zhì)中,所以其遺傳方式為母系遺傳,父系不將其mtDNA傳遞給子代,因而,發(fā)生在生殖細(xì)胞中的mtDNA突變能引起母系家族性疾病;而發(fā)生在發(fā)育過程中或體細(xì)胞中的mtDNA突變,則會(huì)引起散發(fā)性疾病和與年齡有關(guān)的氧化磷酸化活性降低。

2.一個(gè)細(xì)胞中往往有成百上千個(gè)線粒體。如果一個(gè)細(xì)胞內(nèi)所有mtDNA都一致,稱為同質(zhì)性,當(dāng)mtDNA發(fā)生突變時(shí)就會(huì)導(dǎo)致一個(gè)細(xì)胞內(nèi)同時(shí)存在野生型和突變型兩種mtDNA,稱其為異質(zhì)性。當(dāng)異質(zhì)性細(xì)胞分裂時(shí),突變的mtDNA的比例在子代細(xì)胞中會(huì)發(fā)生漂變,分裂旺盛的細(xì)胞(如血細(xì)胞)往往有排斥突變mtDNA的趨勢(shì),朝著具有全部正常型mtDNA的方向發(fā)展;也就是說野生型mtDNA對(duì)突變型mtDNA有保護(hù)和補(bǔ)償作用,因此,mtDNA突變時(shí)并不立即產(chǎn)生嚴(yán)重后果而分裂不旺盛的細(xì)胞(如肌細(xì)胞、神經(jīng)細(xì)胞),則會(huì)逐漸積累突變型mtDNA漂變的結(jié)果,使其表型也隨之發(fā)生變化。

3.mtDNA突變的表型就應(yīng)主要由某種組織中野生型mtDNA與突變型mtDNA的相對(duì)比例及該種組織對(duì)線粒體ATP供應(yīng)的依賴程度決定。中樞神經(jīng)系統(tǒng)、心臟、骨骼肌、腎臟、內(nèi)分泌腺和肝臟對(duì)能量的需求較高,因而mtDNA的突變表型也往往容易表現(xiàn)出來。同時(shí)細(xì)胞中突變mtDNA的比例必須達(dá)到一定程度才足以引起某器官或組織的功能異常,即具有閾值效應(yīng)。突變型mtDNA的表達(dá)受細(xì)胞中線粒體的異質(zhì)性水平以及組織器官維持正常功能所需的最低能量影響,可產(chǎn)生不同的外顯率和表現(xiàn)度。且細(xì)胞分裂時(shí),突變型和野生型mtDNA發(fā)生分離,隨機(jī)地分配到子細(xì)胞中,使子細(xì)胞擁有不同比例的突變型mtDNA分子。

篇2

關(guān)鍵詞：人類基因組 基因克隆 基因組學(xué) 結(jié)構(gòu)基因組 功能基因組

人類基因組計(jì)劃（human genome project,HGP）是由美國科學(xué)家、諾貝爾獎(jiǎng)獲得者Renato dulbecco于1986年在雜志《Science》上發(fā)表的文章中率先提出的，旨在闡明人類基因組脫氧核糖核酸（DNA）3×109核苷酸的序列，闡明所有人類基因并確定其在染色體的位置，從而破譯人類全部遺傳信息。美國于1990年正式啟動(dòng)人類基因組計(jì)劃，估計(jì)到2003年完成人類基因組全部序列測(cè)定。歐共體、日本、加拿大、巴西、印度、中國也相繼提出了各自的基因組研究計(jì)劃[1]。由于各國政府和科學(xué)家的共同努力，HGP目前已在為全球范圍的合作項(xiàng)目；隨著數(shù)理化、信息、材料等學(xué)科的滲透和工業(yè)化管理模式的引進(jìn)，HGP已真正成為生命科學(xué)領(lǐng)域的科學(xué)工程，基因組（genomics）作為一門新興學(xué)科也應(yīng)運(yùn)而生。

與此同時(shí)，科學(xué)界也在思索人類基因組計(jì)劃完成后的下一步工作，因此就有了“后基因組計(jì)劃”（post-genome project）的提法。大多數(shù)科學(xué)家認(rèn)為原定于2003年所完成的人類基因組計(jì)劃只是一個(gè)以測(cè)序?yàn)橹鞯慕Y(jié)構(gòu)基因組學(xué)（structural genomics）研究，而所謂的“后基因組計(jì)劃”應(yīng)該是對(duì)基因功能的研究，即所謂的功能基因組學(xué)（functional genomics）。此外，一些新的概念如：“蛋白質(zhì)組（proteome）”、“環(huán)境基因組學(xué)（environmental genomics）”和“腫瘤基因組解剖學(xué)計(jì)劃（cancer genome anatomy project,CGAP）”等等也在不斷向外延伸。

一、結(jié)構(gòu)基因組學(xué)

（一）人類基因組作圖

人類基因組作圖根據(jù)使用的標(biāo)記和手段不同，初期的作圖有二種：一是通過計(jì)算連鎖的遺傳標(biāo)記之間重組頻率而確定它們相對(duì)距離的遺傳連鎖圖，一般用厘摩（cM）來表示；二是確定各遺傳標(biāo)記之間物理距離的物理圖，一般用堿基（bp）或千堿基（kb）或兆堿基（Mb）來表示。1cM的遺傳距離大致上相當(dāng)于1Mb的物理距離。隨著研究工作的進(jìn)展，遺傳圖和物理圖逐漸發(fā)生整合，在此基礎(chǔ)上大量引入基因標(biāo)記，從而形成了新一代的轉(zhuǎn)錄圖[1]。

1.遺傳連鎖圖 遺傳連鎖圖（genetic map）繪制需要遺傳標(biāo)記，早期的遺傳標(biāo)記主要為生化標(biāo)記，20世紀(jì)80年代中期以限制性片段長度多態(tài)性（RFLP）、串聯(lián)重復(fù)序列拷貝多態(tài)性和小衛(wèi)星重復(fù)順序等遺傳標(biāo)記為主，這類標(biāo)記的數(shù)量較少，信息也較低；20世紀(jì)80年代后期發(fā)展的短串聯(lián)重復(fù)序列（short tandem repeat,STR）也稱微衛(wèi)星（microsatellite,MS）標(biāo)記，主要為二核苷酸重復(fù)序列，如：（CA）n，它們?cè)谌旧w上分布較均勻，信息含量明顯高于RFLP，因而成為遺傳連鎖分析極為有用的標(biāo)記；近年來，單個(gè)堿基的多態(tài)性（single nucleotide polymorphism,SNP）標(biāo)記又被大量使用，其意義已超出了遺傳作圖的范圍，而成為研究基因組多樣性和識(shí)別、定位疾病相關(guān)基因的一種新標(biāo)記。

2.物理圖 物理圖（physical map）包含了兩層意義，一是獲得分布于整個(gè)基因組的30000個(gè)序列標(biāo)簽位點(diǎn)（sequence tagged site,STS），這可使基因組每隔100kb距離就有一個(gè)標(biāo)記；二是在此基礎(chǔ)上構(gòu)建覆蓋每條染色體的大片段DNA克隆，如：酵母人工染色體（yeast ar tificial chromosome,YAC）或細(xì)菌人工染色體（bacterial artificial chromosome,BAC）、人工附加染色體（human artificial episomal chromosome,HAEC）和人工噬菌體染色體（P1 bacteriophage artificial chromosome,PAC）等連續(xù)克隆。這些圖譜的制作進(jìn)一步定位其它基因座提供了詳細(xì)的框架[2]。

3.轉(zhuǎn)錄圖 構(gòu)建轉(zhuǎn)錄圖的前提條件是獲得大量基因轉(zhuǎn)錄本即信使核糖核酸（mRNA）的序列，人類基因組中的基因數(shù)目約在10萬左右，構(gòu)建轉(zhuǎn)錄圖首先需要獲得人類基因的表達(dá)序列標(biāo)簽（expressed sequence tag,EST），以此建立一張人類的轉(zhuǎn)錄圖，并與遺傳圖的交叉參照。

4.DNA序列的生物信息學(xué) HGP一開始就與信息高速公路和數(shù)據(jù)庫技術(shù)形成了同步發(fā)展。迄今，國際上四個(gè)大的生物信息中心即美國的國家生物技術(shù)信息中心（NCBI）、基因組序列數(shù)據(jù)庫（GSDB）、歐洲分子生物實(shí)驗(yàn)室（EMBL）和日本DNA數(shù)據(jù)庫（DDBJ）已經(jīng)建立和維持了源自數(shù)百種生物的互補(bǔ)DNA（cDNA）和基因組DNA序列的大型數(shù)據(jù)庫。這些中心和全球的基因組研究實(shí)驗(yàn)室通過網(wǎng)點(diǎn)、電子郵件或者直接與服務(wù)器和數(shù)據(jù)庫聯(lián)系而獲得的搜尋系統(tǒng)，使得研究者可以在多種不同的分析系統(tǒng)中對(duì)序列數(shù)據(jù)庫提出質(zhì)詢，這些分析包括基因的發(fā)現(xiàn)、蛋白質(zhì)模體的鑒別、調(diào)控元件的分析、重復(fù)序列的鑒別、相似性的分析、核苷酸組成的分析以及物種間的比較等。

（二）基因組的基本結(jié)構(gòu)和進(jìn)化

人類基因組研究的目的，不僅為了單純地積累數(shù)據(jù)，而且要提示數(shù)據(jù)中所蘊(yùn)藏的內(nèi)在規(guī)律[3]，從而更好地認(rèn)識(shí)生命體。近年來，隨著模式生物體測(cè)序的相繼完成和人類基因組測(cè)序速度的加快（到1999年12月已宣布完成人類第22號(hào)染色體的完全測(cè)序），特別是生物信息所提供的強(qiáng)有力的分析和綜合手段，使人人能夠逐漸透過浩瀚的基因組序列信息，去探索一些更為本質(zhì)的問題，如：基因組的復(fù)雜度與生物進(jìn)化、基因組編碼序列的結(jié)構(gòu)、基因和蛋白家族、基因家族的大小及其進(jìn)化。

（三）疾病的基因組學(xué)

HGP的直接始動(dòng)因素是要解決包括腫瘤在內(nèi)的人類疾病的分子遺傳學(xué)問題[4]，因此與人類健康密切相關(guān)。另一方面，8000多種單基因遺傳病和多種大面積危害人群健康的多基因疾?。ㄈ纾耗[瘤、心血管病、代謝性疾病、神經(jīng)疾病、精神疾病、免疫性疾病）的致病基因和疾病相關(guān)基因占人類基因組中相當(dāng)大的一部分。因此，疾病基因的定位、克隆和鑒定是HGP的核心部分。

20世紀(jì)90年代之前，絕大多數(shù)人類遺傳性疾病的原發(fā)生化基礎(chǔ)尚不清楚，無法用表型-蛋白質(zhì)-基因的傳統(tǒng)途徑進(jìn)行研究。在HGP的遺傳和物理作圖帶動(dòng)下，出現(xiàn)了最初被稱為“反求遺傳”、90年代初又改稱為“定位克隆法”的全新思路。該思路的關(guān)鍵內(nèi)容是：應(yīng)用細(xì)胞遺傳學(xué)定位和家第連鎖分析方法，首先將疾病基因定位于染色體的特定位置，然后通過進(jìn)一步的遺傳和物理作圖，使相關(guān)區(qū)域壓縮至1Mb之內(nèi)，此時(shí)即可構(gòu)建YAC、BAC、PAC、HAEC或粘粒（comid）等克隆重疊樣，從中分離基因，并在正常人和患者的DNA中進(jìn)行結(jié)構(gòu)比較，最終識(shí)別出疾病基因。包括囊性纖維化、Huntington舞蹈病、遺傳性結(jié)腸癌、乳腺癌等一大批重要疾病的基因是通過“定位克隆”發(fā)現(xiàn)的，從而為這些疾病的基因診斷和未來的基因治療奠定了基礎(chǔ)。隨著人類基因圖的日臻完善，一旦某個(gè)疾病位點(diǎn)被定位，即可從局部的基因圖中遴選出結(jié)構(gòu)、功能相關(guān)的基因進(jìn)行分析，將大大提高疾病基因發(fā)現(xiàn)的效率。

目前，人類疾病的基因組學(xué)研究，已深入到多基因疾病這一難點(diǎn)。多基因疾病難以用一般的家系遺傳連鎖分析取得突破，需要在人群和遺傳標(biāo)記的選擇、數(shù)學(xué)模型的建立、統(tǒng)計(jì)方法的改進(jìn)等方面進(jìn)行不斷的探索。

二、功能基因組學(xué)

HGP當(dāng)前的整體發(fā)展使功能基因組學(xué)提到了議事日程[5]，出現(xiàn)了結(jié)構(gòu)和功能基因組學(xué)向功能基因組學(xué)過渡、轉(zhuǎn)化的過程。一般認(rèn)為，在功能基因的組研究中可能的核心科學(xué)問題有基因組的多樣性和進(jìn)化規(guī)律；基因組的 表達(dá)及其調(diào)控；模式生物體基因組研究等。

（一）基因組多樣性

人類是一個(gè)具有多樣性的群體，不不同群體和個(gè)體在生物學(xué)性狀以及在對(duì)疾病的易感性/抗性上的差別，反映了進(jìn)化過程中基因組與內(nèi)、外環(huán)境相互作用的結(jié)果。開展人類基因組多樣性的系統(tǒng)研究，無論是對(duì)于了解人類的起源和進(jìn)化，還是對(duì)于醫(yī)學(xué)均會(huì)產(chǎn)生重大的影響。各種常見多因素疾?。ㄈ纾焊哐獕?、糖尿病和精神分裂癥等）相關(guān)基因的研究將成為功能基因組時(shí)代的研究熱點(diǎn)。除了利用多態(tài)性遺傳標(biāo)記進(jìn)行精細(xì)定位這一傳統(tǒng)途徑，也將采用基因組水平再測(cè)序的方法直接識(shí)別變異序列，即選取一定數(shù)量的受累和未受累個(gè)體，對(duì)所有疾病相關(guān)或候選基因的全序列（或其編碼區(qū)）進(jìn)行再測(cè)序，準(zhǔn)確定位其變異相關(guān)標(biāo)記位點(diǎn)。同樣，腫瘤研究也需要對(duì)腫瘤相關(guān)基因進(jìn)行大規(guī)模的再測(cè)序。

（二）識(shí)別人類基因的共同變異

已知大多數(shù)人類基因的等位基因數(shù)量是有限的，常僅有2～3種。形成這種遺傳多樣性局限性的原因，很有可能是因?yàn)楝F(xiàn)代人類來源于一個(gè)相當(dāng)小的群體，這有助于揭開許多疾病敏感性的奧秘。如：載脂蛋白E基因有三種主要變型（E2、E2和E4），可以解釋老年癡呆癥和心血管疾病的風(fēng)險(xiǎn)性；血管緊張素原轉(zhuǎn)換酶（ACE）與心血管疾病一定相關(guān)性；化學(xué)趨化因子受體CKR-5在一定程度上影響對(duì)人類免疫缺陷病毒（HIV）的敏感性等。非編碼區(qū)對(duì)評(píng)價(jià)疾病風(fēng)險(xiǎn)也是重要的，精確定位非編碼區(qū)變異的方法可以是對(duì)調(diào)控區(qū)域變異的系統(tǒng)性篩查，也可利用精密遺傳圖在人類群體中識(shí)別祖先染色體節(jié)段。

三、藥物基因組學(xué)

基因組多樣性也在一定程度上決定了人體對(duì)藥物的反應(yīng)，通過對(duì)影響藥物代謝或效應(yīng)通路有關(guān)基因的編碼序列的再測(cè)序，有可能提示個(gè)體對(duì)藥物反應(yīng)差異的遺傳學(xué)基礎(chǔ)，這就是“藥物基因組學(xué)”（pharmacogenomics）的主要內(nèi)容[6]；以此作為延伸，提示個(gè)體對(duì)環(huán)境反應(yīng)差異的遺傳學(xué)基礎(chǔ)的環(huán)境基因組學(xué)也已露端倪。

四、蛋白質(zhì)組學(xué)

蛋白質(zhì)組學(xué)是要從整體上研究蛋白質(zhì)及其修飾狀態(tài)。目前正在發(fā)展標(biāo)準(zhǔn)化和自動(dòng)化的二維蛋白質(zhì)凝膠電泳的工作體系，包括用一個(gè)自動(dòng)系統(tǒng)來提取人類細(xì)胞的蛋白質(zhì)，繼而用色譜儀進(jìn)行部分分離，再用質(zhì)譜儀檢測(cè)二維修飾，如：磷酸化和糖基化。此外，也有人在設(shè)計(jì)和制作各種蛋白質(zhì)生物芯片；蛋白質(zhì)的另一個(gè)重要工作內(nèi)容是建立蛋白質(zhì)相互作用的系統(tǒng)目錄。生物大小即蛋白-蛋白和蛋白-核酸之間的互作構(gòu)成了生命活動(dòng)的基礎(chǔ)，這些互作有可能以通用的或特殊的“陷井”（如：酵母雙雜交系統(tǒng)）加以識(shí)別[7]。

總之，基因組學(xué)正方興未艾，其現(xiàn)實(shí)意義和深遠(yuǎn)意義已得到全體人類的共識(shí)，預(yù)期在不遠(yuǎn)的將來，人類基因組學(xué)將對(duì)人類的健康、計(jì)劃生育、優(yōu)生優(yōu)育產(chǎn)生重大影響。

參考文獻(xiàn)

1 Rowen L. Mahairas G, hood.L.Science,1997;278:605-607

2 Goffeau A,Barrell h,Bussey H et al. Sceince,1996;274:546-567

3 Kleyn PW,Vesell eS.Develop Sci,1998;18:1820

4 Housman D,Ledley fD.Nature Biotech,1998;16:492

5 Hitert P,Boguski m.Science,1997;278:568

篇3

【關(guān)鍵詞】 糖尿?。患谞钕偌膊?；并發(fā)癥

doi：10.3969/j.issn.1004-7484（s）.2013.08.216 文章編號(hào)：1004-7484（2013）-08-4294-01

糖尿病在臨床是一種常見的慢性疾病，對(duì)患者的危害較大。糖尿病合并甲狀腺疾病包括臨床甲亢、亞臨床甲亢、臨床甲減及亞臨床甲減，其中前三種各占1-2%左右，臨床甲減占10%左右，且隨年齡增長，臨床甲減患病率升高，女性居多。甲狀腺疾患可加速糖尿病加重、惡化，促進(jìn)某些慢性并發(fā)癥的發(fā)生，尤其當(dāng)糖尿病患者血糖升高、病情不穩(wěn)定時(shí)，這時(shí)甲狀腺疾病患者病情有可能加重，應(yīng)及時(shí)采取對(duì)癥措施，降低患者的病死率，實(shí)現(xiàn)對(duì)患者病情的良好控制[1].現(xiàn)在就我院2011年10月――2012年10月收治的40例糖尿病合并甲狀腺疾病患者，進(jìn)行回顧分析。

1 資料與方法

1.1 一般資料 我院2011年10月――2012年10月收治的40例2型糖尿病合并甲狀腺疾病患者，男11例，女29例，年齡在19-75歲，平均年齡48.25歲。以上均符合1997年美國糖尿病協(xié)會(huì)（ADA）制定的糖尿病的診斷標(biāo)準(zhǔn)，其中19例符合甲狀腺功能減退（甲減）；13例符合亞臨床甲減；8例符合甲狀腺功能亢進(jìn)（甲亢）。

1.2 臨床表現(xiàn) 19例糖尿病合并甲減患者，12例有口渴多飲，尿頻量多，雙下肢浮腫、麻涼痛，乏力，畏寒，偶有胸悶氣短，便秘，夜寐差，病人無惡心、嘔吐、發(fā)熱等癥；7例尿頻量多，乏力，雙下肢麻涼，雙目視物不清，偶有腹脹，大便正常，夜眠尚可。既往無高血壓、冠心病等病史。

1.3 實(shí)驗(yàn)室檢查 將40例患者空腹采血，給予血糖、糖化血紅蛋白、血常規(guī)、游離T3（FT3）、游離T4（FT4）及TSH檢查；口服100克饅頭試驗(yàn)：分別于0、30、60、120、180min抽取靜脈血進(jìn)行血糖、C肽水平及胰島素檢測(cè)。測(cè)得血糖值為8.5-21.7mmol/L，胰島素釋放試驗(yàn)呈高峰延遲出現(xiàn)或低平曲線，葡萄糖耐量試驗(yàn)呈糖尿病型血糖曲線，19例甲減患者FT3、FT4低于正常，TSH升高，13例亞甲減患者FT3、FT4正常，TSH升高，8例糖尿病合并甲亢患者TSH降低，F(xiàn)T3、FT4高于正常。

1.4 治療及愈后 40例糖尿病患者初步治療的目的是降低血糖、解除因血糖增高所致癥狀，強(qiáng)調(diào)飲食治療是一項(xiàng)基礎(chǔ)治療措施，不論病情輕重或有無并發(fā)癥都應(yīng)嚴(yán)格執(zhí)行和長期堅(jiān)持?？偀崃亢蜖I養(yǎng)成分須適應(yīng)生理需要，進(jìn)餐時(shí)要定量，力爭(zhēng)胰島功能恢復(fù)。糖尿病合并甲亢時(shí)，應(yīng)根據(jù)患者病情用藥，糖尿病治療以胰島素、二甲雙胍、格列美脲、瑞格列奈、阿卡波糖等藥物為主；對(duì)糖尿病酮癥酸中毒患者宜給予胰島素持續(xù)靜脈滴注或胰島素泵持續(xù)皮下注射，迅速糾正代謝紊亂，爭(zhēng)取搶救時(shí)機(jī)。除上述措施，糖尿病合并甲亢時(shí)，常用藥物以甲巰咪唑、丙級(jí)硫氧嘧啶為主。部分甲減患者根據(jù)病情，必要時(shí)需口服左甲狀腺素治療；亞臨床甲減患者應(yīng)定期隨訪，根據(jù)病情調(diào)整用藥，若血脂偏高可給予服藥。當(dāng)甲亢癥狀控制后，胰島素應(yīng)減量10%-30%，口服降糖藥也應(yīng)減少20%-45%。

2 結(jié) 果

在患者的長期密切配合及良好的治療，臨床癥狀緩解，血糖得到了滿意的控制，F(xiàn)T3、FT4、TSH檢查均恢復(fù)正常。

3 討 論

一般來說，糖尿病合并甲減的發(fā)病時(shí)間可以以糖尿病癥狀為先，也可以以甲減癥狀為先，不過就近年來的臨床資料顯示，甲減先發(fā)生的概率更高。糖尿病是胰島素分泌減少或/和機(jī)體對(duì)胰島素產(chǎn)生抵抗，而導(dǎo)致的一種以慢性高血糖為特征的代謝性疾病。甲狀腺功能減退癥是由多種原因引起的甲狀腺激素合成、分泌或生物效應(yīng)不足引起的一組內(nèi)分泌疾病。[1]目前認(rèn)為，糖尿病與甲狀腺疾病有內(nèi)在聯(lián)系，其中1型糖尿病與甲狀腺患者有共同的遺傳學(xué)基礎(chǔ)和免疫學(xué)基礎(chǔ)。2型糖尿病由于遺傳上的缺陷和易感性，以及免疫平衡的破壞，加上病毒、飲食、環(huán)境、情緒等誘發(fā)因素，而發(fā)生免疫疾病之間的重疊現(xiàn)象[2]研究發(fā)現(xiàn)年齡越大患這兩種疾病的機(jī)率就高，糖尿病合并甲亢的發(fā)病率在23%-38%女性高于男性，均有多食、消瘦，雖然兩種疾病也各有其相應(yīng)癥狀，對(duì)于糖尿病患者，病情突然惡化，出現(xiàn)用糖尿病無法解釋的驚慌、煩躁、怕熱、多汗、心慌、手顫等癥狀，或三多一少加重，消瘦明顯，或者引起酮癥酸中毒和心力衰竭，都應(yīng)懷疑是否合并了甲亢，應(yīng)及時(shí)檢查甲狀腺功能。[3]糖尿病合并甲減后肝糖原的合成分解，葡萄糖的吸收與利用均發(fā)生障礙，一方面由于甲狀腺激素缺乏，可使組織代謝所必需的酶產(chǎn)生不足或活性降低，導(dǎo)致機(jī)體對(duì)碳水化合物的代謝緩慢；另一方面由于甲狀腺激素缺乏，機(jī)體對(duì)降糖藥物降解速度減慢，因此糖尿病合并甲減患者更易出現(xiàn)低血糖，應(yīng)注意監(jiān)測(cè)血糖，避免低血糖的發(fā)生。

總之，糖尿病合并甲狀腺疾病，一旦明確診斷，治療時(shí)應(yīng)兩者兼顧，需注意胰島素與甲狀腺素之間的互相作用和影響，調(diào)節(jié)二者的平衡，更好控制血糖改善甲狀腺功能。

參考文獻(xiàn)

[1] 徐正才，鄭曉燕，陳芳建，等.糖尿病患者甲狀腺激素的變化與血糖水平的相關(guān)性分析[J].放射免疫學(xué)雜志，2009，45（6）：87-88.

篇4

（江西省林業(yè)科技實(shí)驗(yàn)中心，江西 信豐 341600）

【摘要】隨著《中國生物多樣性保護(hù)戰(zhàn)略和行動(dòng)指南（2010-2030）》的貫徹實(shí)施，生物多樣性監(jiān)測(cè)與評(píng)價(jià)工作將在全國范圍陸續(xù)開展。進(jìn)化生態(tài)學(xué)作為闡述生物多樣性演化規(guī)律和機(jī)理的基礎(chǔ)性學(xué)科，其數(shù)量研究方法在20世紀(jì)70年代后得到了迅速的發(fā)展。本文從三個(gè)層面系統(tǒng)性總結(jié)、篩選了進(jìn)化生態(tài)學(xué)在植物生態(tài)領(lǐng)域的主流研究方法，其中在生態(tài)系統(tǒng)層面，群落演替的主成分分析和聚類分析方法、群落的可恢復(fù)性、可持續(xù)性、變異性、抗干擾性、邊緣效應(yīng)等主題被篩選為主要分析方法；在種群層面，種間關(guān)聯(lián)指數(shù)、相關(guān)系數(shù)、分離指數(shù)、生態(tài)位寬度指數(shù)、生態(tài)位重疊指數(shù)等概念可以全面闡釋植物種群的演替規(guī)律；在遺傳層面，哈迪-溫伯格平衡度的檢測(cè)、等位基因頻率、多態(tài)位點(diǎn)百分?jǐn)?shù)、平均位點(diǎn)的等位基因數(shù)、平均位點(diǎn)的預(yù)期雜合度、Nei氏遺傳分化系數(shù)、Nei氏遺傳一致度、遺傳距離、聚類分析、遺傳貢獻(xiàn)率等方法在分子進(jìn)化分析中的應(yīng)用相對(duì)廣泛。

關(guān)鍵詞 生物多樣性評(píng)價(jià)；生物多樣性監(jiān)測(cè)；進(jìn)化生態(tài)學(xué)

Review of Evolutionary Ecology Study and Its Application on Biodiversity Monitoring and Assessment

LIU Huan　OUYANG Tianlin　TIAN Cheng-qing

（Jiangxi Provincial Forest science and Technology Experiment Cente， Xinfeng Jiangxi 341600，China）

【Abstract】After Chinese Biodiversity Conservation Strategy and Action Planning (2010-2030) is implemented in China, biodiversity monitoring and assessment projects are increasing steadily in national wide. The statistical methods of evolutionary ecology study have been developed quickly since 1970s, which provides the theory underlying the interpretation of biodiversity evolution in ecosystem. This article summarizes the evolutionary ecology methods which have been relatively broadly applied on botanical species from three layers: for ecosystem diversity, the principle component index (PCI) and cluster analysis for community succession analysis, ecosystem resilience, sustainability, variance, resistance capacity and edge effects are identified as the main analysis methods; for species diversity, the conceptions of inter-specific association, rank correlation coefficient, segregation index, coefficient of niche breadth and coefficient of niche overlap can fully interpret the succession of plant populations in ecosystem; for genetic diversity, the methods including Hardy-Weinberg equilibrium, allele frequency, percentage of polymorphic loci, mean number of alleles per locus, mean expected heterozygosity per locus, Nei’ coefficient of gene differentiation, Nei’ genetic identity, genetic distance and cluster analysis, genetic contribution rate have been identified as main methods for analysis of molecular evolution.

【Key words】Biodiversity assessment；Biodiversity monitoring；Evolutionary ecology

0　Introduction

According to the Chinese Biodiversity Conservation Strategy and Action Planning (2010-2030), there are three thorny issues threatening biodiversity conservation in national wide: degradation of ecosystem function in some area; deterioration of endangered species; continuous loss of genetic resources. The methods of evolutionary ecology study from three layers (ecosystem, species, genetics) provides substantial theory explaining these threats so that conservation strategies can be worked out properly.

After Environmental Standard for the Assessment of Regional Biodiversity (HJ623-2011) is implemented in China, multivariate methods of evolutionary ecology study become essential to classify the basic units for biodiversity assessment at both ecosystem layer (classification of communities) and genetic layer (classification of sub-populations).

After Environmental Standard on Classifying the Categories of Genetic Resources (HJ 626-2011) comes into force in China, the methods of evolutionary ecology provide the theoretical basis not only for understanding the evolutionary process of endangered species, but also becomes compulsory for ranking genetic resources (or endangered species) between CategoryⅠand categoryⅡ.

This review article systematically summarizes the main themes of evolutionary ecology study of plant species from three layers, with discussion of selecting suitable methods for biodiversity monitoring and assessment work.

1　Ecosystem Diversity

1.1　Cluster Analysis and Principal Component Analysis (PCA)

According to the Technical Guideline for Ecological Assessment, the significance of dominant plant species is calculated by a combination of density, frequency and dominance, which becomes the basis of cluster analysis or PCA for community classification[1], which becomes the essential units for biodiversity assessment at ecosystem layer. Bu et al.,(2005) adopted both fuzzy cluster analysis and principal component analysis (PCA) methods to classify 13 sampling plots into 5 communities, which included 15 botanical species located in loess hilly region. Both methods led to similar conclusions in terms of community classification. According to the restoration duration required by each community, the temporal succession of 5 plant communities was identified as: Artemisia scoparia community-Leymus scalinus community-Stipa bungeana community-Artemisia gmelinii community-Hippophae rhamnoides community [2].

Anwar et al.,(2009) selected multivariate methods of cluster analysis and principal component analysis to understand corticolous lichen species composition and community structure characteristics in the forest ecosystem of Southern Mounffiins of Urumqi, China. There were thirty nine corticolous lichen species found, which were classified into 5 orders, 13 families and 26 genera. According to the multivariate analysis, three types of communities were classified, including community Lecanora hageni(Ach.)Ach. + Physcia stellaris(L.)Nyl. + L.saligna(Schrad.)Zahlbr; community Physcia aipolia(Humb.)Furm. + Ph.dimidiata(Arn.)Nyl + Cladonia pyxidata(L.)Hoffm; and community Xanthoria fallax (Hepp) Arnold + X.elegans(Link.)Th.Fr, whose structures were significantly influenced by altitude and tree type [3].

The composition and community structure of dominant species were analyzed by Cai et al., (2007) on the basis of multivariate methods of both principal component analysis and cluster analysis with the survey data of phytoplankton in spring, summer, autumn and winter from 1998 to 1999 in the West Guangdong Waters. According to the cluster analysis, phytoplankton species were classified into 2 communities in each season of spring, summer and autumn, with one inshore group and one offshore group, whereas the differentiation of species community was not significant in winter time. The seasonal succession of dominant species was Skeletonema costatum, Navicula subminuscula, Thalassionema nitzschioides, and Thalassiosira subtilis in spring, summer, autumn and winter respectively. However, the freshwater species, Oscillatoria sp. became the dominant species in summer as well [4].

Wang & Peng adopted both species similarity analysis (including coefficient of community, percentage of similarity and coefficient of similarity) and cluster analysis methods to classify plant communities and examine the environmental gradient effects on community succession in Dinghu Mountain, which indicated that Cryptocarya chinensis communities varied with different altitude gradient. Ten plant communities were compared and contrasted, revealing the mutual effects and evolutionary patterns among these communities [5].

1.2　Ecosystem Resilience

Ecological resilience is the capacity of disturbed ecosystem restored into its primitive conditions[6]. Zhang et al., (2013) assessed the ecosystem resilience quantitatively by using social-ecological system (SES) model in Northern Highlands of Yuzhong County, and resulted in the conclusion that the resilience of ecosystem was determined by both drought stress and ecosystem sensitivity to drought condition [7].

To order to assess community resilience and restoration success, Renaud et al., (2013) developed two indices including Community Structure Integrity Index measuring the proportion of species diversity for the reference community in comparison to the restored or degraded community, as well as the Higher Abundance Index assessing the proportion of the species abundance which was higher than the reference community. Three examples were illustrated for the application of two indices, including fictitious communities; A recently restored (2 years) Mediterranean temporary wetland (Camargue in France) for the assessment of restoration efficiency; and a recently disturbed pseudo-steppe plant community (La Crau area in France) assessing the natural community resilience, which demonstrated that these two indices were not only able to assess the static value of ecosystem function, but also to analyze the temporal and spatial dynamics of ecosystem evolution [8]. Nevertheless, compared with Zhang et al., (2013) model, social disturbance was not integrated into Renaud et al., (2013) model.

Additionally, 5 succession phases of the restoration of degraded ecosystem in Jinyun Mountain were investigated by Li et al.,(2007), including Shrubby grass land, Masson Pine early stage, Masson Pine late stage, Coniferous broad-leaved mixed forest and Evergreen broad --- leaved forest stage. Under the same climate conditions, criteria of species diversity, light absorption, community temperature, cumulate cover of arbor and community pole temperature became the main indicators for the succession of ecosystem restoration. However, among these indicators, both cumulate cover of arbor and community pole temperature were identified to be the best two indicators, and the other indicators were advised as the minor ones for consideration [9].

1.3　Ecosystem Sustainability

Ecosystem sustainability is the potential or manifested ability for ecosystem to perpetually sustain its interior composition, structure and function so that ecosystem is able to develop and evolve healthily [6]. Hu Dan (1997) presented methodology for assessment of ecosystem sustainability on the basis of identifying and evaluating ecosystem components, structure and function, which was consisted of 12 items and more than 30 variables, indicating the dynamics of sustainable ecosystem[10]. However, social factors were not considered in this methodology. In comparison, Yu et al., (2007) developed a quantitative index system for the assessment of eco-tourism sustainability in TianMuShan Natural Reserve, which included 25 criteria selected from three aspects: Environment, Society-Culture and Economics. On the basis of this method, a case study in Tianmushan Nature Reserve was introduced to demonstrate sustainability assessment in ecosystem [11].

1.4　Ecosystem Resistance

Ecosystem resistance is the ability of ecosystem to boycott the external disturbance and sustain its primitive conditions[6]. Hou et al., (2012) pointed out that the criteria of assessing eco-resistance were consisted of decomposition rate of ground combustibles, increase of ground combustibles, spontaneous combustion caused by lightning, indigenous pest, invasive pests and occurrence of pest[12]. However, quantitative method (such as the weight of each criterion) was not presented in this research. In comparison, Guo et al., (2012) presented the criteria for the assessment of eco-resistance which were consisted of the degree of pest invasion (or disease infection) and the fire incidence, with a weight of 0.6891 and 0.3109 respectively [13].

1.5　Ecosystem Variance

Ecosystem variance is divided into spatial heterogeneity and functional heterogeneity, which reflects the complex or variance of species distribution pattern and community structure influenced by available resources and environmental conditions [6]. Liu et al., (2010) adopted β Sorenson index to investigate the variability of plant communities of grass land in Ordos, Inner Mongolia of China, which was restored from grazing land. The relations between restoration duration and variability of plant communities was deduced in this research: compared with stabilized sand (25～30 a), higher variability existed in semi- mobile sand (restoration duration:5～10 a) and semi- stabilized sand (restoration duration:15～20 a). β Sorenson index for plant communities with dominant species Artemisia ordosica or Hedysarum laeve (restoration duration:5～20 a) was approximately 1.2, while the variability index of Artemisia ordosica (restoration duration: 30a) sand was twice than that of Hedysarum laeve (restoration duration: 30a), and faster growth rate was reported in Artemisia ordosica (restoration duration: 30a) sand [14].

Zhang et al.,(1988) analyzed the succession of pioneer meadow communities in abandoned farmland located in the high land of Gansu Province South. Heterogeneity index of H1 was deduced in this study, with value ranging from 0 to 1. Two meadow communities were investigated, with H1 heterogeneity indices of 0.11 and 0.15 respectively, which revealed relatively low heterogeneity between them[15].

1.6　Edge Effect

Edge effect typically exists in the ecotone between different plant communities, which is caused by the mutual interactions between different plant species from various communities, leading to characteristics in terms of species composition, configuration and function differed from the original communities [6]. Wang & Peng (1986) quantified the edge effects of plant communities in DingHuShan Nature Reserve by a model, with discussion of both positive and negative effects of community edges [16].

Eugenie et al., (2001) quantified the edge effects on plant communities caused by 6 recent clearcut edges adjacent to Pinus banksiana and Pinus resinosa plantations in the Great Lakes region. 10 sampling plots were randomly placed at 19 distances along a 240 transect which spanned from clearcut, across the edge, into the forest interior, with an estimation of percentage cover of each understory plant species. Species richness was significantly higher in Pinus banksiana lines than Pinus resinosa lines, with 18 and 2 unique species respectively. Species with clear preference for the clearcut, edge habitats or interior were respectively reflected by depth-of-edge influence, with composition gradient examined by the Detrended correspondence analysis (DCA) of distance sampled on the basis of species richness. Finally a synthesis model was designed to calculate the plant species distributions across forest/clearcut edges [17].

2　Species Diversity

2.1　Inter-specific Association, Rank Correlation Coefficient, Segregation Index

Inter-specific association is the mutual association between different species in terms of spatial distribution patterns in various habitats, which is divided into the competition relationship defined by segregation index (negative correlation), as well as interdependence relation calculated by rank correlation coefficient (positive correlation) [6].

On the basis of 25 sampling plots, 375 quadrats and 150 transect lines, Zhang et al., (2013) adopted eight indices of Diffusion Coefficient (C), Negative Binomial Parameters (K), Average Crowed Degree (m＊), Index of Clumping (I), Index of Patchiness (PI), Green index (GI), Cassie index (CA), Moristia index (Iδ ) and Variance of Percentages (VP) to analyze the spatial distribution patterns and overall correlation between dominant plant species in Gansu Donghuang xihu Desert Wetland ecosystems. The results revealed that significant positive correlation existed between dominant species populations in shrub layer and tree layer, whereas significant negative correlation was reported between dominant species in tree-shrub-grass layer and grass layer. Further more, the 2×2Contigency Table of Chi-square statistics, Association Coefficient (AC), Percentage of Co-occurence (PC) and other methods were conducted additionally to analyze the correlation significance and intensity between dominant species, leading to the results that correlation between dominant species was not significant in most cases and logarithm with significantly negative correlation was more than positive one, which indicated various requirements of habitat and resources for different species [18].

Yan et al., (2009) adopted Contingency Table and Spearman Rank coefficient to analyze the inter-specific association and inter-specific covariance between Artemisia annua and its associated plant species in the natural fostering base from 2006 to 2007. The results showed that flooding disturbance led to insignificant effects on inter-specific association, but significant effects on inter-specific covariance. However, flooding effect on inter-specific covariance varied between different species pairs, indicating that inter-specific covariance of paired species was depended on both environmental conditions and ecological characters, which became more sensitive to environmental disturbance than inter-specific association [19].

Wang et al., (2014) applied statistical methods of 2×2 contingency table V ratio, X2 (Yate’ s correction), Ochiai Index (OI), Dice Index (DI), Point Correlation Coefficient (PCC), Jaccard index (JI), Association Coefficient (AC) and Spearman correlation coefficient to analyze the inter-specific association between epiphytic plant species in ancient cultivated tea plantation. For the 127 tea trees measured at individual scale, significant inter-specific association was reported, whereas insignificant association was found among 31 plots measured at plot scale. Indices of both Association Coefficient (AC) and Spearman correlation coefficient well indicated the inter-specific association between epiphytic species in consistence with X2 test, which revealed positive association between Bulbophyllum sp. and Drynaria propinqua, Davallia cylindrica and Liparis elliptica, Dendrobium capillipes and Lysionotus petelotii，as well as negative association between Bulbophyllum ambrosia and Dendrobium capillipes, Bulbophyllum ambrosia and Lysionotus petelotii, Bulbophyllum nigrescens and Dendrobium chrysanthum, Ascocentrum ampullaceum and Peperomia tetraphylla [20].

2.2　Coefficient of Niche Breadth and Coefficient of Niche Overlap

Niche breadth is the total available resources which can be utilized by a species (or other biological unit), and niche overlap is the competition phenomenon that two or more species with similar niche breadth compete for the limited resources in the common space for survival [6].

Field study were conducted by Chen et al.,(2014) to analyze the niche breadth and overlap of 12 plant species on 70 forest plots in Bawangling National Nature Reserve, presenting the descending order of niche breadth for 12 species: Aquilaria sinensis, Nephelium topengii, Camellia sinensis var. assamica, Alseodaphne hainanensis, Keteleeria hainanensis, Podocarpus imbricatus, Firmiana hainanensis, Parakmeria lotungensis, Cephalotaxus mannii, Michelia hedyosperma, Ixonanthes reticulata, Dacrydium pierrei. The results revealed that the niche breadth of a species was determined by its range of spatial distribution; in most cases, higher niche overlap value was usually found between species with broader niche breadth, except Michelia hedyosperma and Firmiana hainanensis species of narrow niche breadth; the low niche breadth of Michelia hedyosperma and Ixonanthes reticulate species partially led to smaller populations, which was advised to give the priority for conservation [21].

Both niche breadth and niche overlap of 10 shrub species and 11 herb species were examined by Gao et al., (2014) under a mixed forest consisted of Picea crassifolia and Betula platyphylla in high hill regions in Datong County, Qinghai Province. The results indicated broader niche breadth for species Potentilla fruticosa and Salix cupularis in shrub layer, as well as species Polygonum viviparum and Fragaria orientalis in herb layer. Higher niche overlap was found usually between populations with broader niche breadth. Nevertheless, some populations with narrow niche breadth also showed high niche overlap. The niche overlap between different species of a genera tended to be smaller, which would be attributed to their evolution and succession [22].

Statistical methods of Variance ratio, χ2-test based on a 2×2 contingency table and the test of association indices (Jaccard, Dice and Ochiai) were selected by Yu et al.,(2012) to examine the inter-specific association of 22 Pyrola decorata communities in Taibai Mountain. Results reported that only 5 paired species showed significant positive association (P<0.05), with 2 paired species showing highly significant positive association (P<0.01), whereas insignificant association was reported between the rest species pairs. For Jaccard index analysis, 84.42% of total species pairs were under 0.25 value of Jaccard index, and 12.31 % of total species pairs ranged from 0.25 to 0.50, while only 3.26% of total species pairs were over 0.50. These results revealed weak inter-specific association between investigated communities which tended to be independent [23].

3　Genetic Diversity

3.1　Hardy-Weinberg Equilibrium

Hardy-Weinberg equilibrium is the principle for the parental generation and their offspring to assess the degree of equilibrium between observed genotypic frequencies and allele frequencies in sexual reproduction process[6]. Both Hardy-Weinberg equilibrium and population structure of 283 Hevea brasiliensis Wickham germplasm were examined and analyzed by Fang et al., (2013), with 25 EST-SSRs loci detected. According to the results, 13 of total 25 EST-SSRs loci deviated Hardy-Weinberg equilibrium. The 283 Hevea brasiliensis Wickham germplasm were divided into 4 groups, and the amount of each group was 155, 110, 61 and 22 respectively. 20 locus combinations (6.67%) were significant linkage disequilibrium (P<0.05), and 5 of them were significant linkage disequilibrium at P<0.01 level [24].

3.2　Genetic Diversity

There are a number of conceptions to quantify genetic diversity, mainly including allele frequency, percentage of polymorphic loci, mean number of alleles per locus, mean expected heterozygosity per locus, Nei’ coefficient of gene differentiation, Nei’ genetic identity.

90 accessions were chosen by Xu et al., (1999) from total 22637 accessions in the National Genebank of soybean species, with selection criteria of nine agronomic traits, including disease resistance to SCN race No.3 and SCN race No.4, rust, SMV, and tolerance to cold, drought, salt, 100 seed weight and protein content. Five maximum and five minimum accessions in the Genebank were selected for comparison for each trait. The genetic diversity of 90 (G. max) soybean and one wild soybean (G. soja) accession were assessed by both agronomic trait analysis and microsatellite DNA or SSR markers. In total twelve pairs of SSR primers were applied and 83 alleles were detected with an average of 6.9 alleles per locus. Simple matching similarity coefficients between each pairs of genotypes were analyzed and clustered by Unweighted Paired Group Method Using Arithmetic Averages (UPGMA), revealing that soybean germplasms could be identified by SSR technique. However, the cluster analysis based on agronomic traits was not identical to SSR markers [25].

The genetic diversity of 38 Paulownia fortunei provenances, with 15 individuals per provenance, was deduced by Li et al., (2011) with technique of inter-simple sequence repeats (ISSR). In total 95 amplified DNA fragments were detected by 9 primers leading to clear and unique polymorphic bands, which were screened from 100 ISSR primers. There were 88 polymorphic loci among 95 amplified DNA fragments, resulting in the percentage of polymorphic loci (PPL) of 92.63%. The PPL at species level ranged from 32.63% (Fuzhou, Jiangxi) to 56.84% (WuZhou Guangxi and Jiu Jiang, Jiangxi) with the mean percentage of 47.16%. The mean values of effective number of alleles (Ne), Nei&acute;s gene diversity index (H) and Shannon&acute;s Information index (I) between different provenances were calculated as 1.3910, 0.2424 and 0.3765 respectively, indicating abundant genetic diversity between them. The Coefficient of Gene Differentiation (GGst) of provenances was 0.3539, and the genetic variation between provenances accounted for 35.39% of total genetic variation, revealing that genetic variation between different individuals of each provenance was higher. Genetic Identity of provenances varied from 0.39 to 0.82, showing the relatively broad genetic basis and abundant genetic variation among provenances. According to Genetic Identity, the provenances of Kaili, Guizhou, and Liuzhou, Guangxi showed closest relationship with Genetic Identity of 0.82, whereas longer genetic distance was reported between Hengyang (Hunan) and Zhuji (Zhejiang) populations, and between Hengyang (Hunan) and Zhenning County (Guizhou) populations, with Genetic Identity of 0.39. In total 38 provenances were classified into 3 groups by UPGMA cluster analysis, with little correlation between genetic distance and geographic distance among those provenances [26].

Genetic diversity of wild soybean population in the region of Beijing China was evaluated by Yan et al., (2008) with 40 primer pairs. In total ten populations were sampled with 28-30 individuals per population. 526 alleles were detected with a mean value of 13.15 per locus. The average value of Expected Heterozygosity per locus (He) and Observed Heterozygosity per locus (Ho) were 0.369 and 1.29% respectively for the wild soybean populations, and the mean Shannon index (I) was 0.658. The mean value of between-population genetic diversity (Hs) and within-population genetic diversity (DST) were 0.446 and 0.362 respectively. The average Coefficient of Gene Differentiation for loci (GGst) between populations was estimated as 0.544. Center-Western ecotype showed more abundant genetic diversity than the Northern and Eastern ecotypes, geographic heterozygosity was found in the genetic divergence patterns of natural populations between the Taihang and the Yanshan mountains. The genetic diversity of drought-tolerant population was poor, indicating the potential value of tolerance gene (s) for breeding [27].

Genetic diversity of totally 13 Cannabis populations from different origins was deduced by Hu et al., (2012) using POPGENE 3.2 Software. AFLP results indicated that the most abundant genetic diversity was found in Yunnan population, with Percentage of Polymorphic Loci (PPL) of 88.82%, Nei&acute;s total genetic diversity (He) of 0.3011, and Shannon Index (I) of 0.4571; and followed by the Heilongjiang population with Percentage of Polymorphic Loci (PPL) of 75.66%, Nei&acute;s total genetic diversity (He) of 0.2572, and Shannon Index (I) of 0.3897. The PPL, Ht and Hs of 13 Cannabis populations was 92.11%, 0.3837 and 0.1640 respectively. Coefficient of genetic differentiation between populations (GGst) was 0.5725, revealing that genetic variation between populations accounted for 57.25% of the total genetic variation, and the other 42.75% of total genetic variation was attributed to the genetic variation between individuals within population. Both genetic distance and genetic identity of Cannabis were calculated on the basis of Nei&acute;s (1978) method, for further analysis of genetic differentiation among populations. Genetic identity among populations ranged from 0.6556 to 0.9258, with the highest value of 0.9258 between Guangxi population and Sichuan population. The genetic identity between Yunnan population and Guizhou population, Yunnan population and Sichuan population were 0.9196 and 0.9173 respectively, while the lowest genetic identity was found between Gansu and Shanxi populations. These findings became the scientific evidence for identification of Cannabis seed and provided the indicators for breeding and evolutionary analysis [28].

The genetic diversity of 120 individuals from six natural populations of Abies chensiensis was analyzed by Li et al., (2012) on the basis of 10 simple sequence repeat markers. The genetic diversity, genetic structure and changes in gene flow between different populations were analyzed, revealing 149 alleles in 10 microsatellite loci with a value of 14.9 as the average number of alleles per locus (A). The effective number of alleles per locus (Ne), the mean expected heterozygosity (He), the mean observed heterozygosities per locus (Ho), the Shannon diversity index (I), the proportion of genetic differentiation among populations (FST), and gene flow between the populations were 7.7, 0.841, 0.243, 2.13, 6.7% and 3.45, respectively. Insignificant correlation was found between genetic distance and geographic distance (r=0.4906, P>0.05). The relatively low genetic diversity was reported in the 6 natural populations of A.chensiensis, and inner-population genetic variation accounted for the majority of total genetic variation [29].

However, it is worthwhile mentioning that the analysis of genetic diversity is significantly influenced by sampling size. For example, the genetic integrity of Sorghum bicolor L. Moench. was studied by Xu et al.,(2012) adopting SSRs technique, as one of the most commonly used markers for the assessment of genetic diversity, population structure studies and marker-assisted selection. In total ten groups of sorghum with different sample sizes (including 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 individuals per group) were selected randomly and 25 polymorphic microsatellite primers were conducted for the assessment of genetic diversity indices (the average number of alleles, effective number of alleles, Shannon Index, Observed Heterozygosity, Expected Heterozygosity, Percentage of Polymorphic loci and the Frequency of Rare Alleles). According to the correlation between genetic diversity indices and sample sizes, the number of alleles, effective number of alleles, Shannon index increased correspondingly to the increase of sample sizes, with the peak increase rate at a sample size of 40 individuals. Consequently, the sample size of 40 individuals accounted for 98.5% of total numbers of alleles, 99.1 % of total effective numbers of alleles and 98.5% of total Shannon indexes among 100 individuals, indicating the 40 individuals as optimal sample size for the SSRs technique in gorghum integrity assessment [30].

3.3　Genetic Variation

Genetic variation assessment mainly adopts the conception of genetic distance and evolutionarily significant unit (ESU), usually deduced by cluster analysis, PCA, or evolutionary tree analysis. However, both cytological and DNA molecular markers are able to achieve this.

The karyotype of characteristics and evolutionary relationships among the traditional Chinese medicine Sophora flavescens from four different origins was investigated by Duan et al., (2014). The karyotypes and chromosome numbers of Sophora flavescens were calculated by using root-tip squashing method and clustered by the karyotype resemblance-near coefficient, which linked all the genetic materials.

The chromosome numbers of Sophora flavescens from Chifeng Inner Mongolia， Changzhi Shaanxi, Meixian Shaanxi and Chengdu Sichuan all were 18 and belonged to 1 A type, with karyotype formulas of 2n = 2x = 18 = 18m(2SAT), 2n = 2x = 18 = 14m(1SAT) + 4sm(1SAT), 2n = 2x = 18 = 16m(2SAT) + 2sm and 2n = 2x = 18 = 18m(2SAT) respectively. The karyotype asymmetry index of Sophora flavescens from Chifeng Inner Mongolia, Changzhi Shaanxi, Meixian Shaanxi and Chengdu Sichuan were 56.32%, 57.88%, 59.41 % and 54. 32%, respectively. According to Karyotype clustering analysis, the closest genetic relationship was reported between S. flavescens from Chengdu and Chifeng, with the highest karyotype resemblance-near coefficient of 0.9929, and their evolution distance was 0.0072. In comparison, the farthest genetic relationship was found between S. flavescens from Chengdu and Meixian, with the lowest karyotype resemblance-near coefficient of 0.9533, and their evolution distance was 0.0478. Karyotype of Sophora flavescents from Chengdu was the most primitive among them, followed by those from Chifeng, Changzhi and Meixian. The conclusion of this study provided cytological information for germplasms identification, and became the basis of genetic variation and genetic relationship analysis of Sophora flavescens[31].

To explore the genetic distance in evolutionary process among 6 Bupleurum medical plants, including B.longeradiatum, B.smityii, B. longicaule var. amplexicaule, B. scorzonerifolium, B. chinense, B. falcatum, karyotype parameters identification was adopted by Song et al.,(2012), which used the cluster analysis of karyotype resemblance-near coefficient and evolutionary distance, based on the calculation of the relative length, arm ratio, centromere index. The highest karyotype resemblance-near coefficient (0.9920) and smallest evolutionary distance (De = 0.0080) existed between B. scorzonerifolium and B. chinense, revealing the closest relationship between them. In comparison, the minimum karyotype resemblance-near coefficient (0.4794) and the maximum evolutionary distance (De = 0.7352) was reported between B. smityii and B. falcatum [32].

In Luo et al., (2006) study, 200 two-line combinations were matched by mating 5 photo/ thermal-sensitive genic male-sterile lines and 40 varieties. The genetic distance (GD) between 5 sterile lines and 40 varieties was examined by SSR markers, with the discussion between genetic distance and heterosis. The correlation of genetic distance varied with yield per F1 plant, heterobeltiosis of F1 yield, effective panicles, panicle length spikelets per panicle, density of spikelet setting, seed setting rate, and 1000 grain weight, due to various gene materials or different range of genetic distance. When the genetic distance between Tianfeng S and its paternal varieties ranged from 0.6286 to 2.5257, the correlation of genetic distance with yield per F1 plant or its heterobeltiosis appeared to be significant at P<0.05 level; As the genetic distance between Peiai 64S and paternal varieties ranged from 0.8247 to 1.5315, their correlation between genetic distance and yield per F1 plant was significant at P<0.05 level; furthermore, for all parents of two-line combinations with genetic distance ranged from 0. 5333 to 1.5, the correlation between heterobeltiosis of yield per F1 plant and genetic distance appeared to be significant at P < 0. 05 level; the correlation of yield per F1 plant with genetic distance was significant at P < 0. 05 level, as the genetic distance ranged from 0.5333 to 1.0; the significance of correlation between yield per F1 plant and genetic distance was at P < 0. 01 level, when genetic distance ranged at three layers: between 1.0 and 1.5; 0.5333 and 1.5; 0.5333 and 2.5257. This genetic distance analysis indicated the appropriate range for mating combinations of hybrid rice [33].

An endemic species of Sinomanglietia glauca, which is unique in Yichun in Jiangxi Province and Yongshun of Hunan Province in Central China, has been listed in Category I of the National Key Protected Wild Plants in 1999 (as asynonym of Manglietia decidua). Xiong et al.,(2014) study covered all of four populations of S. glauca, which had been identified so far, and the genetic diversity and genetic variation was investigated by nuclear microsatellite markers. According to the results, S. glauca showed relatively low genetic diversity with the average number of alleles (A) of 2.604 and the mean expected heterozygosity (HE) of 0.423, but presented significant genetic variation with high genetic differentiation FST of 0.425. Cluster analysis by STRUCTURE and Principal Coordinated Analysis indicated that Jiangxi and Hunan populations were classified into two independent groups. Only one natural breeding population was identified in Jiangxi, while two were found in Hunan, with significant genetic variation. The heterozygosity was found to be excessive significantly, which might be caused by allelic frequencies differed between male and female parents occasionally in a small population. The results indicated that S. glauca would experience bottleneck(s) in recent evolution history, which led to reduction of population size, loss of genetic diversity and strong population differentiation. The genetic diversity study resulted in the advices that S.glauca should be classified as three conservation units according to their evolutionary units: Jiangxi unit and Hunan unit, and the Hunan populations could be further divided into two sub-management units (YPC and LJC) [34].

3.4　Genetic Contribution Rate

Genetic contribution rate was firstly proposed by Petit et al., (1998). For the standardization of the allelic richness results across populations, the technique of rarefaction is established to facilitate assessment of the expected number of different alleles among equal-sized samples derived from different populations, which is divided into two components: the first is relevant to the degree of population diversity and the second is related to its divergence from the other populations [35].

4　Conclusion

As discussed above, the multivariate methods of evolutionary ecology study become essential to classify the communities and sub-populations at ecosystem layer and at genetic layer respectively for biodiversity assessment work. Due to three thorny issues threatening biodiversity defined by Chinese Biodiversity Conservation Strategy and Action Planning (2010-2030), biodiversity monitoring projects need to be implemented at three layers, and application of 3S technology on biodiversity monitoring with high-resolution remote sensing imagines is advised by Liu et al., (2014) [36], e.g. investigation of the distribution change of dominant plant species over ten years in a national park by using object-oriented classification of Quickbird remote sensing imagines, and then the temporal and spatial dynamics of biodiversity evolution at both ecosystem layers and species layers should be discussed on the basis of evolutionary ecology study. Additionally, biodiversity monitoring projects should be conducted according to the Technical Guidelines for Biodiversity Monitoring --- Terrestrial Vascular Plant (HJ 710.1-2014).

For genetic layer, a combination of cytological markers and DNA molecular markers is advised by Liu et al., (2014) for classification of sub-populations [37], mainly due to the consideration of saving the cost and reliability of differentiation methods. Nevertheless, it is worthwhile mentioning that the conclusion drawn by multivariate cluster analysis between cytological markers and DNA molecular markers would not be consistent, possibly due to gene recombination and gene mutation. Consequently, the multivariate cluster analysis for sub-population classification would be more reliable on the basis of DNA molecular markers. The software of computing both polygenetic (gene by gene analysis) and phylogenomic (the whole genome comparison) methods is suggested by Ahmed (2009) [38].

According to the Environmental Standard on Classifying the Categories of Genetic Resources (HJ 626-2011) in China, there are three kinds of DNA molecular methods pointed out for ranking genetic resources (or endangered species) between categoryⅠand categoryⅡ, including assessment of genetic diversity, evolutionarily significant unit (ESU), or genetic contribution rate, which have been substantially discussed above. However, it is worthwhile noting that any one of these three methods is acceptable for environmental engineers to conduct this environmental standard, although there is debate between these methods in terms of selection priority, such as Chen et al., (2002) [39].

According to the Chinese Biodiversity Conservation Strategy and Action Planning (2010-2030), prediction of climate change effects on biodiversity conservation is significant, and the application of CTMs model on prediction of climate change effects on biodiversity is advised by Liu Huan (2014) [40]. However, the knowledge of evolutionary ecology study derived from the biodiversity monitoring projects in the past may be required for this prediction work.

【References】

［1］環(huán)境保護(hù)部環(huán)境工程評(píng)估中心．全國環(huán)境影響評(píng)價(jià)工程師職業(yè)資格考試考點(diǎn)要點(diǎn)分析[M]．中國環(huán)境出版社，2008．

［2］卜耀軍，等．模糊聚類和排序在植被演替研究中的綜合應(yīng)用[J]．2005.

［3］艾尼瓦爾·吐米爾,熱衣木·馬木提,阿不都拉·阿巴斯．烏魯木齊南部山區(qū)森林生態(tài)系統(tǒng)樹生地衣群落結(jié)構(gòu)[J]．2009.

［4］蔡文貴,等.粵西海域浮游植物群落結(jié)構(gòu)特征的多元分析與評(píng)價(jià)[J].2007.

［5］王伯蓀,彭少麟.鼎湖山森林群落分析[J].中山大學(xué)學(xué)報(bào),1985.

［6］陶玲,任珺.進(jìn)化生態(tài)學(xué)的數(shù)量研究方法[M].北京:中國林業(yè)出版社,2004.

［7］張向龍,等.基于恢復(fù)力定量測(cè)度的社會(huì) 生態(tài)系統(tǒng)適應(yīng)性循環(huán)研究:以榆中縣北部山區(qū)為例[J].2013.

［8］Jaunatre, R., et al., New synthetic indicators to assess community resilience and restoration success[J].2013.

［9］李艷霞,等.退化生態(tài)系統(tǒng)恢復(fù)指示性指標(biāo)的研究[J].2007.

［10］胡聃.生態(tài)系統(tǒng)可持續(xù)性的一個(gè)測(cè)度框架[J].1997.

［11］于玲,王祖良,李俊清.自然保護(hù)區(qū)生態(tài)旅游可持續(xù)性評(píng)價(jià)：以浙江天目山 自然保護(hù)區(qū)為例[J].2007.

［12］侯海潮,等.森林健康經(jīng)營理論應(yīng)用研究:以塞罕壩機(jī)械林場(chǎng)為例[J].河北林果研究,2012,27(1).

［13］郭峰，等．華北土石山區(qū)典型天然次生林生態(tài)系統(tǒng)健康評(píng)價(jià)研究[J].水土保持研究,2012,19(4).

［14］劉碩,賀康寧,王曉江.鄂爾多斯沙地不同退牧年限植物群落多樣性及變異性研究[J].西北植物學(xué)報(bào),2010,30(3).

［15］張大勇,王剛,趙松嶺.甘南亞高山草甸棄耕地植物群落演替的數(shù)量研究Ⅰ.演替先鋒群落的特征分析[J].中國草地,1988(06):14-19.

［16］王伯蓀,彭少麟.鼎湖山森林群落分析:邊緣效應(yīng)[J].中山大學(xué)學(xué)報(bào),1986(4).

［17］Euskirchen, E.S., J. Chen and R. Bi, Effects of edges on plant communities in a managed landscape in northern Wisconsin[J]. Forest Ecology And Management, 2001(148).

［18］張瑾,等.甘肅敦煌西湖荒漠濕地生態(tài)系統(tǒng)優(yōu)勢(shì)植物種群分布格局及種間關(guān)聯(lián)性[J].中國沙漠,2013,33(2).

［19］閆志剛,等.黃花蒿野生群落的種間關(guān)系及其對(duì)水淹干擾響應(yīng)[J].廣西植物, 2009,29(6).

［20］王青,等.景邁-芒景古茶園茶樹群落寄附生植物種間聯(lián)結(jié)研究[J].西部林業(yè)科 學(xué),2014,43(3).

［21］陳玉凱,等.海南島霸王嶺國家重點(diǎn)保護(hù)植物的生態(tài)位研究[J].植物生態(tài)學(xué)報(bào),2014,38(6).

［22］高二鵬,等.青海大通腦山區(qū)青海云杉+白樺混交林主要種群的生態(tài)位特征[J].中國水土保持科學(xué),2014,12(3).

［23］余貝貝,等.太白山雅美鹿蹄草群落植物種間關(guān)聯(lián)性[J].東北林業(yè)大學(xué)學(xué)報(bào), 2012,40(11).

［24］方家林,等.橡膠樹魏克漢種質(zhì)群體結(jié)構(gòu)分析[J].基因組學(xué)與應(yīng)用生物學(xué), 2013,32(5).

［25］許占友,等.利用 SSR 標(biāo)記鑒定大豆種質(zhì)[J].中國農(nóng)業(yè)科學(xué),1999,32.

［26］李芳東,等.白花泡桐種源遺傳多樣性的ISSR分析[J].中南林業(yè)科技大學(xué)學(xué)報(bào),2011(07):1-7.

［27］嚴(yán)茂粉,李向華,王克晶.北京地區(qū)野生大豆種群SSR標(biāo)記的遺傳多樣性評(píng)價(jià)[J].植物生態(tài)學(xué)報(bào),2008,32(4).

［28］胡尊紅,等.大麻品種遺傳多樣性的 AFLP 分析[J].植物遺傳資源學(xué)報(bào),2012, 13(4).

［29］李為民李思鋒,黎斌.利用SSR分子標(biāo)記分析秦嶺冷杉自然居群的遺傳多樣性[J].植物學(xué)報(bào),2012,47(4).

［30］許玉鳳,等.高粱微衛(wèi)星分析中遺傳完整性樣本量的確定[J].華北農(nóng)學(xué)報(bào), 2012,27(3).

［31］段永紅,等.不同產(chǎn)地苦參核型及似近系數(shù)聚類分析[J].中國藥學(xué)雜志,2014(14):1194-1199.

［32］宋蕓,喬永剛,吳玉香.6種柴胡屬植物核型似近系數(shù)聚類分析[J].中國中藥雜志,2012(08):1157-1160.

［33］羅小金,等.利用 SSR 標(biāo)記分析水稻親本間遺傳距離與雜種優(yōu)勢(shì)的關(guān)系[J].植物遺傳資源學(xué)報(bào), 2006,7(2).

［34］熊敏,等.華木蓮居群遺傳結(jié)構(gòu)與保護(hù)單元[J].生物多樣性,2014.22(4).

［35］PETIT, R., ABDELHAMIDELMOUSADIK and O. AND, Identifying Populations for Consevation on the Basis of Genetic Markers[J]. Conservation Biology, 1998,12(4).

［36］Liu Huan, et al., A Brief Review of 3S Technology Application on Biodiversity Monitoring and Assessment[J]. Science & Technology Information,2014(3).

［37］劉煥,張洪初,唐秋盛.保護(hù)遺傳學(xué)方法在生物多樣性監(jiān)測(cè)和評(píng)價(jià)領(lǐng)域的應(yīng)用研究[J].科技視界,2014(8).

［38］Mansour, A., Phylip and Phylogenetics. Gene Genomes and Genomics[J].2009.

［39］陳小勇,等.重要物種優(yōu)先保護(hù)種群的確定[J].生物多樣性,2002,10(3).

篇5

作者單位：330006 南昌大學(xué)研究生院醫(yī)學(xué)部,江西贛州市立醫(yī)院神經(jīng)內(nèi)科（陳錦瓊）；南昌大學(xué)附屬贛州醫(yī)院神經(jīng)內(nèi)科（李廣生）

缺血性腦卒中是全世界最主要的致死和致殘性疾病，主要病理基礎(chǔ)是顱內(nèi)外動(dòng)脈粥樣硬化斑塊形成和動(dòng)脈狹窄，越來越多的證據(jù)表明頸動(dòng)脈斑塊破裂和繼發(fā)的血栓形成是比動(dòng)脈狹窄更重要的卒中危險(xiǎn)因素，頸動(dòng)脈斑塊的研究也越來越受到重視，本文就近年來頸動(dòng)脈斑塊的相關(guān)研究進(jìn)行綜述報(bào)告。

1 頸動(dòng)脈斑塊的形成和分類

頸動(dòng)脈斑塊的形成是外界環(huán)境因素和內(nèi)在多基因調(diào)控異常共同作用的結(jié)果，頸動(dòng)脈斑塊的發(fā)展是一個(gè)動(dòng)態(tài)平衡過程，即平滑肌細(xì)胞產(chǎn)生的膠原纖維組成斑塊帽與通過金屬蛋白酶等介導(dǎo)的基質(zhì)降解之間的平衡，打破了平衡，斑塊的穩(wěn)定性下降，則將成為不穩(wěn)定性斑塊或易損性斑塊［1］。在臨床實(shí)踐中，一般籠統(tǒng)的將動(dòng)脈粥樣硬化斑塊劃分為穩(wěn)定斑塊（硬斑塊）和易損斑塊（軟斑塊/不穩(wěn)定斑塊）兩類，易損斑塊是臨床干預(yù)的對(duì)象。

所謂易損斑塊，是指易于形成血栓或可能迅速進(jìn)展為罪犯病變的斑塊［2］。按照2003年Naghavi M等以尸檢研究資料為依據(jù)提出的診斷標(biāo)準(zhǔn)，易損斑塊包括五個(gè)主要特征及五個(gè)次要特征［3-4］：五個(gè)主要特征包括：①斑塊內(nèi)活動(dòng)性炎癥――斑塊內(nèi)單核細(xì)胞、巨噬細(xì)胞浸潤，有時(shí)會(huì)有T淋巴細(xì)胞浸潤。②薄纖維帽及大脂質(zhì)核心：一般認(rèn)為纖維帽厚度小于100 μm、脂核占斑塊體積40%以上時(shí)，粥樣斑塊易于發(fā)生破裂。③血管內(nèi)皮侵蝕伴有表面血小板凝集。④裂隙樣斑塊。⑤管腔狹窄大于90%。而五個(gè)次要特征包括：①斑塊表面結(jié)節(jié)樣鈣化。②僅在血管內(nèi)鏡下可見的黃亮斑塊。③斑塊內(nèi)出血。④血管內(nèi)皮功能障礙。⑤血管重塑形。

但是既往研究對(duì)易損斑塊的定義是針對(duì)冠狀動(dòng)脈而言，對(duì)于大血管的頸動(dòng)脈顯然不合適。最近Mauriello A等［5］通過組織病理學(xué)研究，將頸動(dòng)脈易損斑塊定義為纖維帽厚度＜165 μm并且巨噬細(xì)胞浸潤＞25個(gè)/高倍視野，這是否合適需要后續(xù)的研究進(jìn)行證實(shí)。

2 頸動(dòng)脈斑塊的影像學(xué)

目前在體評(píng)估頸動(dòng)脈斑塊性質(zhì)的方法主要包括無創(chuàng)性（如超聲、CT和MRI）和有創(chuàng)性（如DSA、血管內(nèi)超聲、血管內(nèi)MRI）檢查，每種檢查方法各有優(yōu)缺點(diǎn)。

2.1 超聲 在各種無創(chuàng)檢查中，血管超聲是最早,也是應(yīng)用最廣泛的檢查手段之一；超聲檢查可以觀察血管管壁及管腔的形態(tài), 測(cè)量血管的內(nèi)徑、外徑、截面積、管壁厚度，根據(jù)血管壁回聲強(qiáng)弱分析血管內(nèi)膜有無斑塊形成，并可測(cè)量斑塊大小、長度；一般低回聲和等回聲斑塊內(nèi)多含有富脂成分、壞死物質(zhì)和出血，常與易損斑塊有關(guān)，而高回聲斑塊多富含纖維和鈣化，提示穩(wěn)定斑塊，斑塊表面不規(guī)則提示潰瘍形成；其不足之處在于受操作者技術(shù)熟練程度、圖像的空間分辨率和組織對(duì)比分辨率的限制，對(duì)斑塊內(nèi)部的組織學(xué)特性評(píng)價(jià)有一定局限性。

三維超聲能夠重建血管的三維圖像，顯示血管在空間上的變化，有助于更好地區(qū)分斑塊表面和血管壁的解剖結(jié)構(gòu)。Heliopoulos J等［7］證實(shí)三維超聲可以顯著提高頸動(dòng)脈潰瘍斑塊的檢出率。

血管內(nèi)超聲虛擬組織成像利用不同組織不同頻率信號(hào)回聲強(qiáng)度，連同采集血管內(nèi)超聲成像資料的振幅，可以將不同組織成分呈現(xiàn)不同顏色區(qū)分纖維斑塊、混合斑塊、鈣化斑塊和壞死核心。Diethrich EB等［8］經(jīng)組織病理學(xué)對(duì)照，發(fā)現(xiàn)血管內(nèi)超聲虛擬組織成像對(duì)薄帽纖維粥樣斑塊診斷的準(zhǔn)確性為99.4%，鈣化薄帽纖維粥樣斑塊為96.1%，纖維粥樣斑塊為85.9%，纖維鈣化斑塊為85.5%，病理性內(nèi)膜增厚為83.4%，認(rèn)為血管內(nèi)超聲虛擬組織成像對(duì)斑塊的鑒別與組織病理學(xué)的結(jié)果有很強(qiáng)的一致性。Tamakawa N等［9］的研究也證實(shí)血管內(nèi)超聲虛擬組織成像能有效客觀評(píng)價(jià)頸動(dòng)脈斑塊組成，而且重復(fù)性好。但是目前臨床應(yīng)用的血管內(nèi)超聲的組織分辨率為100～150 μm，對(duì)于厚度小于100 μm的纖維帽尚無法準(zhǔn)確識(shí)別。

聲輻射力脈沖成像（acoustic radiation force impulse,ARFI）是一種新的超聲成像方法，成像時(shí)先確定需要進(jìn)行彈性檢測(cè)的感興趣區(qū), 探頭發(fā)射推力脈沖, 組織受力后產(chǎn)生縱向壓縮和橫向振動(dòng), 收集這些細(xì)微變化并演算出橫向剪切波速度值, 間接反映該區(qū)域組織的彈性程度。由于血管壁、軟組織、斑塊、鈣化的彈性度的差異，ARFI能夠很好的加于區(qū)分。在Allen JD等［10］的研究認(rèn)為ARFI能夠識(shí)別軟斑塊和硬斑塊，而且能夠鑒別易損或穩(wěn)定斑塊，這給頸動(dòng)脈斑塊的檢測(cè)提供了新方法。

2.2 多層螺旋CT 多層螺旋CT血管成像 （multi-section spial CT angiography，MSCTA）空間分辨率高，對(duì)頸動(dòng)脈斑塊的成分、形態(tài)、管腔的狹窄程度、斑塊位置以及斑塊周圍組織的評(píng)價(jià)均很有價(jià)值，尤其對(duì)斑塊的脂核和鈣化顯示較好，并且具有安全、方便、快速等特點(diǎn)。MSCTA容積重建（volume rendering technique，VRT）及最大密度投影（maximum intensity project，MIP）可從各個(gè)不同角度顯示觀察，VRT技術(shù)可對(duì)血管成像的透明度進(jìn)行調(diào)節(jié)，因而可以把管壁和斑塊與管腔分離觀察，有利于表面規(guī)則和不規(guī)則的斑塊發(fā)現(xiàn)。MSCTA通過對(duì)斑塊密度的CT值測(cè)量可以把斑塊進(jìn)行分類［11］，血栓CT值約20 HU，密度均勻，位于管腔內(nèi)側(cè)面；脂質(zhì)斑塊CT值40～50 HU，纖維斑塊50～120 HU，鈣化斑塊CT值>120 HU。由于富含脂質(zhì)的壞死核心、結(jié)締組織、出血的密度有明顯重疊，鈣化所致部分容積效應(yīng)也影響密度的測(cè)量，導(dǎo)致在評(píng)價(jià)斑塊表面形態(tài)和組織成分處于弱勢(shì)，而且放射線劑量和碘劑也限制了CTA的應(yīng)用。

然而，近年來隨著多層CT血管成像技術(shù)的進(jìn)步，它對(duì)頸動(dòng)脈斑塊的成分、形態(tài)、管腔的狹窄程度、斑塊位置以及斑塊周圍組織的評(píng)價(jià)的特異性和敏感性均有很大提高。在識(shí)別鈣化斑塊方面，多層CT的敏感性和特異性均達(dá)100%［12］；而在識(shí)別斑塊表面的潰瘍的敏感性、特異性、陽性預(yù)測(cè)值、陰性預(yù)測(cè)值分別可達(dá)93.75%、98.59%、96.74%、97.2%［13］；在斑塊成分識(shí)別上，MSCTA利用全自動(dòng)分析軟件可以明確標(biāo)記富脂的壞死核心、鈣化、出血產(chǎn)物以及剩余的結(jié)締組織［14］；在識(shí)別易損斑塊和穩(wěn)定斑塊方面也有一定的可行性，比如Haraguchi K等的研究發(fā)現(xiàn)易損頸動(dòng)脈斑塊其平均CT值是（27.7±7.5） HU，而穩(wěn)定頸動(dòng)脈斑塊其CT值為（60.4±20.8 HU）［15］。

2.3 核磁共振 MRI有較高的軟組織密度和空間分辨力，可以直接觀察血管管壁情況，對(duì)斑塊的大小、體積及斑塊組成提供較為準(zhǔn)確的信息，不但可以較準(zhǔn)確地顯示病變區(qū)域的整體解剖形態(tài)，而且可以根據(jù)斑塊的信號(hào)變化判斷其不同的結(jié)構(gòu)成分等，有利于斑塊易損性的評(píng)價(jià)。近幾年隨著MRI新序列的開展，對(duì)于斑塊檢查所采用的序列除了傳統(tǒng)的T1WI，T2WI，還包括了黑血技術(shù)和亮血技術(shù)。黑血技術(shù)的優(yōu)勢(shì)在于顯示斑塊的形態(tài)及其組成成分，如脂質(zhì)、出血及纖維組織，不足之處是采集時(shí)間相對(duì)較長。亮血技術(shù)即時(shí)間飛越成像（Time Of Flight，TOF）, 采集時(shí)間短，在顯示斑塊表面的纖維帽等低信號(hào)成分和鑒別斑塊內(nèi)出血方面出血等方面有優(yōu)勢(shì)。兩種技術(shù)相配合提高了對(duì)頸動(dòng)脈斑塊檢查的精確性。此外，各種靶向標(biāo)記的增強(qiáng)MRI技術(shù)可以更精確地幫助分析斑塊成分，甚至精確到細(xì)胞學(xué)水平。常用的釓造影劑和新型的超微順磁鐵氧化物（Ultrasmall Superparamagnetic Particles of Iron Oxide，USPIOs）均已經(jīng)用于粥樣斑塊成分的顯示。但MRI成像時(shí)間較長，呼吸運(yùn)動(dòng)、血管搏動(dòng)、吞咽及不自主運(yùn)動(dòng)均可引起運(yùn)動(dòng)偽影，是目前難以克服的缺點(diǎn)。

各種斑塊內(nèi)成分在高分辨MRI中的特點(diǎn)表現(xiàn)如下:①脂質(zhì)主要成分為膽固醇及膽固醇酯，T1WI和PDW為高信號(hào)，TOF像為等信號(hào)，T2WI可以顯示為低、等信號(hào)。②纖維組織主要為細(xì)胞外基質(zhì)，粥樣斑塊的纖維帽是由富含膠原的基質(zhì)和平滑肌細(xì)胞組成的。TOF像為接近高信號(hào)，T1WI為等信號(hào)，PDW高信號(hào)，T2WI信號(hào)變化較大。穩(wěn)定的纖維帽相對(duì)較厚而完整，而不規(guī)則、不連續(xù)的信號(hào)帶與組織病理上發(fā)現(xiàn)的破裂、薄弱及潰瘍的纖維帽一致。③鈣化在各序列上均呈現(xiàn)低信號(hào)，但是斑塊表面鈣化和伸展至管腔的鈣化結(jié)節(jié)由于易為黑血序列掩蓋而無法判別，而亮血序列易于檢測(cè)，另外較小的鈣化因與脂質(zhì)、壞死并存而出現(xiàn)混雜信號(hào)，單純MRI影像不易判斷。④出血隨時(shí)間變化信號(hào)改變較大。近期出血TOF像為高信號(hào)，T1WI為等信號(hào)，PDW和T2WI可有不同變化。⑤新生血管：多采用MRI增強(qiáng)檢查，斑塊內(nèi)新生血管表現(xiàn)為明顯強(qiáng)化區(qū)域。⑥血栓由于形成時(shí)間不同，信號(hào)變化不定。

T1加權(quán)三維磁化強(qiáng)度預(yù)備梯度回波序列（T1WI-3 d-MP RAGE）屬于快速容積掃描技術(shù),具有較高的空間分辨率和時(shí)間分辨率,對(duì)腦內(nèi)結(jié)構(gòu)（如白質(zhì)、灰質(zhì)和腦脊液）的對(duì)比度良好,能三維顯示人腦內(nèi)部精細(xì)解剖結(jié)構(gòu),有利于顯示小病灶及其細(xì)節(jié)。Hishikawa T等［16］運(yùn)用T1WI-3 d-MP RAGE與組織病理比較，對(duì)35個(gè)頸動(dòng)脈內(nèi)膜剝脫術(shù)的患者進(jìn)行研究，在T1WI-3 d-MP RAGE序列上呈高信號(hào)的頸動(dòng)脈斑塊與沒有高信號(hào)的頸動(dòng)脈斑塊比較，脂質(zhì)壞死核心區(qū)顯著更大，中位數(shù)分別為51.2%和49.0% （P 0.029），呈高信號(hào)的頸動(dòng)脈斑塊與低信號(hào)的斑塊比較有更嚴(yán)重的斑塊內(nèi)出血（P< 0.0001），而且斑塊內(nèi)出血的嚴(yán)重程度與脂質(zhì)壞死核心的大小顯著相關(guān)（P< 0.01）。

動(dòng)態(tài)對(duì)比度增強(qiáng)（dynamic contrast-enhanced, DCE）MRI是一種對(duì)斑塊進(jìn)行定量評(píng)估的MRI對(duì)比增強(qiáng)技術(shù)，可量化斑塊的新血管生成以及與之密切相關(guān)的斑塊炎癥。Kerwin WS等［17］利用DCE-MRI技術(shù)對(duì)動(dòng)脈粥樣硬化斑塊進(jìn)行定量分析，研究證實(shí)分?jǐn)?shù)血漿容量 （fractional plasma volume，Vp）與微血管的面積相關(guān)，而對(duì)比劑的轉(zhuǎn)移常數(shù)（transfer constant， Ktrans）與微血管的通透性相關(guān)，指明Ktrans值作為斑塊炎癥的定量非入侵標(biāo)記，可以區(qū)分不同的斑塊成分，并能對(duì)斑塊進(jìn)行可靠的分期，以預(yù)測(cè)斑塊的進(jìn)展。

3 頸動(dòng)脈斑塊與臨床

3.1 頸動(dòng)脈斑塊與卒中 頸動(dòng)脈斑塊引起缺血性卒中的原因包括： ①斑塊不穩(wěn)定破裂，破裂的斑塊栓塞遠(yuǎn)端的血管。②斑塊不斷增大，直接阻塞血管。③狹窄的頸動(dòng)脈使遠(yuǎn)端的灌注壓下降，導(dǎo)致分水嶺區(qū)供血不足，形成低灌注性梗死。④破裂或未破裂的斑塊表面粗糙，血小板和凝血因子被激活，形成血栓。

一般認(rèn)為頸動(dòng)脈斑塊如果具有如下特征則為易損斑塊：潰瘍、破損纖維帽、薄纖維帽、大的脂質(zhì)核心、斑塊內(nèi)大量新生血管形成等，而易損斑塊是腦卒中的危險(xiǎn)因素。Saba L等［18］研究證實(shí)了破損纖維帽和同側(cè)癥狀存在相關(guān)性，提示破損纖維帽可以作為潛在腦血管病的預(yù)測(cè)因子。而Takaya等［19］的研究進(jìn)一步指出，對(duì)于頸動(dòng)脈50-70%狹窄的患者，有薄或破損的纖維帽、斑塊內(nèi)出血、壞死脂質(zhì)核心的比例和血管壁的厚度與后續(xù)腦血管事件的發(fā)生密切相關(guān)。Homburg PJ等［20］ 發(fā)現(xiàn)頸動(dòng)脈潰瘍斑塊與斑塊體積、狹窄程度和富于脂質(zhì)的壞死核心成分顯著相關(guān)，即使在輕度狹窄的患者，相反鈣化與潰瘍斑塊沒有相關(guān)性。

但是Nandalur KR等［21］的研究則提示鈣化率可以作為預(yù)測(cè)卒中發(fā)生危險(xiǎn)的指標(biāo)，總斑塊體積、非鈣化斑塊體積或鈣化斑塊體積與癥狀之間均無顯著相關(guān)性，頸動(dòng)脈斑塊的鈣化率而不是頸動(dòng)脈斑塊的體積與狹窄患者的病情穩(wěn)定相關(guān)，特別是鈣化率>45%可能是無癥狀的一個(gè)界限。當(dāng)然，各個(gè)試驗(yàn)間的結(jié)論還有不一致性，比如Saam T等［22］發(fā)現(xiàn)無癥狀性斑塊和癥狀性斑塊在富含脂質(zhì)壞死核心區(qū)大小、鈣化、斑塊內(nèi)出血發(fā)生率方面，兩者間無顯著性差異；與無癥狀性斑塊比較，癥狀性斑塊的纖維帽破裂發(fā)生率更高 （P0.007），近管腔的出血或血栓發(fā)生率也更高（P0.039）；而且有更大的斑塊出血區(qū)（P0.003）和松散基質(zhì)區(qū)（P 0.014），以及更小的管腔面積（P0.008）。但是在富含脂質(zhì)壞死核心區(qū)、鈣化、斑塊內(nèi)出血發(fā)生率方面則兩者間無顯著性差異。

3.2 頸動(dòng)脈斑塊與冠心病 頸動(dòng)脈粥樣硬化與冠狀動(dòng)脈粥樣硬化具有共同的發(fā)病機(jī)制，頸動(dòng)脈斑塊的易損性可預(yù)示冠狀動(dòng)脈斑塊的易損性。

Morito N等［23］的研究提示高斑塊積分、低HDL-C、高空腹血糖依次是預(yù)測(cè)冠狀動(dòng)脈狹窄和/或狹窄嚴(yán)重程度的前3大因素；根據(jù)ROC曲線，頸動(dòng)脈斑塊積分預(yù)測(cè)冠狀動(dòng)脈狹窄的臨界值為1.9。Sugioka K等［24］的研究同樣證實(shí)頸動(dòng)脈斑塊積分與冠心病的病變程度相關(guān)，并發(fā)現(xiàn)斑塊積分（P0.001）和面積狹窄率（P 0.004）與冠心病多支病變有獨(dú)立的相關(guān)性。Kwon TG等［25］報(bào)道在韓國冠狀動(dòng)脈粥樣硬化患者中頸動(dòng)脈斑塊發(fā)生率為30.3% （516/1705），多因素分析提示老年（≥65歲）、高血壓、頸動(dòng)脈內(nèi)中膜增厚（≥1.0 mm）是頸動(dòng)脈斑塊發(fā)生的的獨(dú)立預(yù)測(cè)因素，同樣發(fā)現(xiàn)頸動(dòng)脈斑塊是冠狀動(dòng)脈多支病變的獨(dú)立預(yù)測(cè)因素。但是Takase B等［26］的研究則認(rèn)為頸動(dòng)脈斑塊負(fù)荷不能有效預(yù)測(cè)冠脈事件，而肱動(dòng)脈血流介導(dǎo)的內(nèi)皮依賴血管舒張功能和運(yùn)動(dòng)負(fù)荷試驗(yàn)則是冠脈事件的強(qiáng)烈預(yù)測(cè)因子。

在頸動(dòng)脈粥樣斑塊的成分與心血管事件相關(guān)性的一項(xiàng)前瞻性研究中［27］，發(fā)現(xiàn)局部斑塊組成是未來心血管事件發(fā)生的獨(dú)立預(yù)測(cè)因子。若斑塊有出血或顯著斑塊內(nèi)血管形成更易發(fā)生心血管事件；而巨噬細(xì)胞浸潤、巨大脂質(zhì)核心、鈣化、膠原以及平滑肌細(xì)胞浸潤與臨件沒有相關(guān)性。提示局部斑塊出血及斑塊內(nèi)大量血管形成與臨件獨(dú)立相關(guān)，且可作為臨床風(fēng)險(xiǎn)及藥物治療的獨(dú)立因子。

3.3 其他 在對(duì)一些特殊人群的研究中，證實(shí)頸動(dòng)脈斑塊的發(fā)生與多因素相關(guān)。

Roepke SK等［28］的研究提示阿爾茨海默病照看者比非照看者有更高的頸動(dòng)脈斑塊發(fā)生率，認(rèn)為在長期壓力下的阿爾茨海默病照看者對(duì)急性應(yīng)急的持續(xù)交感反應(yīng)可能促增加了動(dòng)脈粥樣硬化的發(fā)生。另外的一項(xiàng)研究發(fā)現(xiàn)TSH下降增加了頸動(dòng)脈斑塊的發(fā)生，同時(shí)也增加了卒中的發(fā)生率，但是TSH正常與頸動(dòng)脈斑塊或者卒中沒有相關(guān)性［29］。Kim JY等［30］在40例男性垂體功能減退癥和對(duì)照組比較研究中，發(fā)現(xiàn)頸動(dòng)脈斑塊發(fā)生率顯著升高（59.5% ：2.5%,P

個(gè)體基因型差異與不穩(wěn)定斑塊的發(fā)生也有相關(guān)性，在一項(xiàng)早發(fā)型冠心病的非裔家族后代的遺傳學(xué)研究中［36］，發(fā)現(xiàn)MCP-1 rs2857656 CC基因型與頸動(dòng)脈粥樣斑塊的發(fā)生存在獨(dú)立相關(guān)性；同時(shí)攜帶MCP-1 CC純合基因型和CCR2 V64I雜合或純合基因型的個(gè)體，與頸動(dòng)脈硬化斑塊的高發(fā)生率存在密切關(guān)系。另外的研究提示攜帶血小板膜糖蛋白Ⅲa基因Leu33Pro多態(tài)性的個(gè)體，可能發(fā)生動(dòng)脈粥樣硬化斑塊破裂的風(fēng)險(xiǎn)增加［37］；而國內(nèi)報(bào)告ALOX5AP基因SG13S114 A/T多態(tài)性可能與動(dòng)脈粥樣斑塊的穩(wěn)定性有關(guān)［38］。

4 小結(jié)

頸動(dòng)脈作為動(dòng)脈粥樣硬化的好發(fā)部位，位置表淺、容易檢測(cè)，同時(shí)可與頸動(dòng)脈內(nèi)膜剝脫術(shù)中獲取的斑塊標(biāo)本進(jìn)行對(duì)比研究，目前已經(jīng)成為研究動(dòng)脈硬化斑塊的首選部位。如何準(zhǔn)確的判斷頸動(dòng)脈斑塊的穩(wěn)定性，理解斑塊的發(fā)生發(fā)展過程，以及干預(yù)斑塊的形成，需要更多的研究來進(jìn)一步解答。

參考文獻(xiàn)

［1］ Fayad, Z A,Fuster, V, et a. Characterization of atherosclerotic plaques by magnetic resonance imaging. Ann N Y Acad Sci, 2000,902: 173-186.

［2］ Waxman S, Ishibashi F, Muller JE. Detection and Treatment of Vulnerable Plaques and Vulnerable Patients.Circulation， 2006，114:2390-2411.

［3］ Naghavi M, Libby P, Falk E, et al. From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies: Part I.Circulation, 2003, 108（14）: 1664-1672.

［4］ Naghavi M, Libby P, Falk E, et al. From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies: Part II.Circulation, 2003, 108（15）: 1772-1778.

［5］ Mauriello A, Sangiorgi GM, Virmani R,et al.A pathobiologic link between risk factors profile and morphological markers of carotid instability.Atherosclerosis， 2010，208（2）:572-580.

［6］ Andersson J, Sundstrm J, Kurland L, et al.The carotid artery plaque size and echogenicity are related to different cardiovascular risk factors in the elderly: the Prospective Investigation of the Vasculature in Uppsala Seniors （PIVUS）. study.Lipids， 2009，44（5）:397-403.

［7］ Heliopoulos J, Vadikolias K, Piperidou C, et al.Detection of carotid artery plaque ulceration using 3-dimensional ultrasound.J Neuroimaging, 2011,21（2）:126-131.

［8］ Diethrich EB, Pauliina Margolis M, Reid DB, et al. Virtual histology intravascular ultrasound assessment of carotid artery disease: the Carotid Artery Plaque Virtual Histology Evaluation （CAPITAL） study.J Endovasc Ther， 2007，14（5）:676-686.

［9］ Tamakawa N, Sakai H, Nishimura Y， et al.Evaluation of Carotid Artery Plaque Using IVUS Virtual Histology.Interv Neuroradiol， 2007，13 Suppl 1:100-5.

［10］ Allen JD, Ham KL, Dumont DM, et al.The Development and Potential of Acoustic Radiation Force Impulse （ARFI） Imaging for Carotid Artery Plaque Characterization.Vasc Med， 2011 Mar 29.

［11］ 戚躍勇,鄒利光,陳林,等. 多層螺旋CT血管成像在頸內(nèi)動(dòng)脈起始部狹窄介入治療中的應(yīng)用價(jià)值. 介入放射學(xué)雜志, 2007, 16 （10）-5.

［12］ Wintermark M, Jawadi SS, Rapp JH, et al. High-resolution CT imaging of carotid artery atherosclerotic plaques.AJNR Am J Neuroradiol， 2008，29:875-882.

［13］ Nendalur KR， Hardie AD， Raghavan P， et al. Composition of the stable carotid plaque insights from a muhidetector computed tomography study of plaque volume. Stroke,2007，38（3）：935-940.

［14］ Wintermark M, Arora S, Tong E， et al. Carotid plaque computed tomography imaging in stroke and nonstroke patients. Ann Neurol.2008,64（2）:149-157.

［15］ Haraguchi K, Houkin K, Koyanagi I, et al.Evaluation of carotid plaque composition by computed tomographic angiography and black blood magnetic resonance images.Minim Invasive Neurosurg， 2008，51（2）:91-94.

［16］ Hishikawa T, Iihara K, Yamada N, et al.Assessment of necrotic core with intraplaque hemorrhage in atherosclerotic carotid artery plaque by MR imaging with 3 d gradient-echo sequence in patients with high-grade stenosis. Clinical article. J Neurosurg， 2010，113（4）:890-896.

［17］ Kerwin WS, O'Brien KD, Ferguson MS, et al.Inflammation in carotid atherosclerotic plaque: a dynamic contrast-enhanced MR imaging study. Radiology，2006，241（2）:459-468.

［18］ Saba L, Mallarini G. Fissured fibrous cap of vulnerable carotid plaques and symptomaticity: are they correlated？ Preliminary results by using multi-detector-row CT angiography. Cerebrovasc Dis, 2009，27（4）:322-327.

［19］ Takaya N, Yuan C, Chu B, et al. Association between carotid plaque characteristics and subsequent ischemic cerebrovascular events: a prospective assessment with MRI-initial results.Stroke,2006，37（3）: 818-823.

［20］ Homburg PJ, Rozie S, van Gils MJ, et al. Association between carotid artery plaque ulceration and plaque composition evaluated with multidetector CT angiography. Stroke， 2011，42（2）:367-372.

［21］ Nandalur KR, Hardie AD, Raghavan P, et position of the stable carotid plaque: insights from a multidetector computed tomography study of plaque volume. Stroke， 2007，38（3）:935-940.

［22］ Saam T, Cai J, Ma L, et al. Comparison of symptomatic and asymptomatic atherosclerotic carotid plaque features with in vivo MR imaging. Radiology， 2006，240（2）:464-472.

［23］ Morito N, Inoue Y, Urata M, et al.Increased carotid artery plaque score is an independent predictor of the presence and severity of coronary artery disease. J Cardiol， 2008，51（1）:25-32.

［24］ Sugioka K, Hozumi T, Iwata S, et al.Morphological but not functional changes of the carotid artery are associated with the extent of coronary artery disease in patients with preserved left ventricular function. Stroke， 2008，39（5）:1597-1599.

［25］ Kwon TG, Kim KW, Park HW, et al.Prevalence and significance of carotid plaques in patients with coronary atherosclerosis. Korean Circ J，2009，39（8）:317-321.

［26］ Takase B, Matsushima Y, Uehata A, et al.Endothelial dysfunction, carotid artery plaque burden, and conventional exercise-induced myocardial ischemia as predictors of coronary artery disease prognosis. Cardiovasc Ultrasound， 2008，16,6:61.

［27］ Hellings WE, Peeters W, Moll FL, et position of carotid atherosclerotic plaque is associated with cardiovascular outcome: a prognostic study.Circulation， 2010，121（17）:1941-1950.

［28］ Roepke SK, Chattillion EA, von Knel R, et al.Carotid plaque in Alzheimer caregivers and the role of sympathoadrenal arousal. Psychosom Med， 2011，73（2）:206-213.

［29］ Drr M, Empen K, Robinson DM, et al.The association of thyroid function with carotid artery plaque burden and strokes in a population-based sample from a previously iodine-deficient area.Eur J Endocrinol,2008，159（2）:145-152.

［30］ Kim JY, Hong JW, Rhee SY, et al.Carotid atheromatic plaque is commonly associated with hypopituitary men. Pituitary,2011，14（2）:105-111.

［31］ Kronborg J, Johnsen SH, Njlstad I, et al.Proinsulin:insulin and insulin:glucose ratios as predictors of carotid plaque growth: a population-based 7 year follow-up of the Troms Study. Diabetologia,2007，50（8）:1607-1614.

［32］ Sumida Y, Nakayama M, Nagata M, et al.Carotid artery calcification and atherosclerosis at the initiation of hemodialysis in patients with end-stage renal disease. Clin Nephrol.2010，73（5）:360-369.

［33］ Herrmann J, Mannheim D, Wohlert C, et al.Expression of lipoprotein-associated phospholipase A（2） in carotid artery plaques predicts long-term cardiac outcome.Eur Heart J， 2009，30（23）:2930-8.

［34］ Rubin MR, Rundek T, McMahon DJ, et al.Carotid artery plaque thickness is associated with increased serum calcium levels: the Northern Manhattan study.Atherosclerosis,2007，194（2）:426-432.

［35］ Vik A, Mathiesen EB, Johnsen SH, et al.Serum osteoprotegerin, sRANKL and carotid plaque formation and growth in a general population-the Troms study.J Thromb Haemost， 2010，8（5）:898-905.

［36］ Nyquist PA, Winkler CA, McKenzie LM, et al.Single nucleotide polymorphisms in monocyte chemoattractant protein-1 and its receptor act synergistically to increase the risk of carotid atherosclerosis. Cerebrovasc Dis， 2009，28（2）:124-130.