黃慧靜,曹永紅,洪瓊,鄒玲玲,戴武
AGE預測2型糖尿病患者肌少癥發(fā)生風險
黃慧靜1,2,曹永紅1,洪瓊1,鄒玲玲1,戴武1,2
1.安徽醫(yī)科大學附屬合肥醫(yī)院(合肥市第二人民醫(yī)院)內分泌科,安徽合肥 230011;2.安徽醫(yī)科大學第五臨床醫(yī)學院,安徽合肥 230032
分析2型糖尿病(type 2 diabetes mellitus,T2DM)患者發(fā)生肌少癥的危險因素,建立列線圖預測模型探討糖基化終末產物(advanced glycation end product,AGE)預測T2DM患者肌少癥的患病風險。選取2021年10月至2022年10月于合肥市第二人民醫(yī)院住院的T2DM患者180例為研究對象,根據是否合并肌少癥將其分為對照組(=146)和肌少癥組(=34)。比較兩組患者的一般資料,采用Logistic回歸分析探討T2DM患者發(fā)生肌少癥的危險因素,并建立列線圖模型。兩組患者的年齡、病程、AGE、肌力、起立試驗、四肢骨骼肌質量指數(appendicular skeletal muscle mass index,ASMI)、體質量指數(body mass index,BMI)、糖化血紅蛋白、尿白蛋白/肌酐比值比較差異均有統(tǒng)計學意義(<0.05),多因素回歸分析結果顯示BMI、肌力、AGE均是T2DM患者發(fā)生肌少癥的獨立危險因素(<0.05);以BMI、肌力、AGE建立預測模型,經驗證該模型校準度良好,具有良好的區(qū)分度。繪制受試者操作特征曲線發(fā)現(xiàn)其預測T2DM患者發(fā)生肌少癥的曲線下面積為0.933,有良好的預測價值;校正曲線及決策曲線分析評估結果顯示該模型具有更高的凈收益和更好的臨床應用價值。AGE是T2DM患者發(fā)生肌少癥的獨立危險因素,對T2DM患者的肌少癥診斷具有一定的預測作用。
2型糖尿??;糖基化終末產物;肌少癥;列線圖
肌少癥與老年人的多種不良健康結局相關[1-2]。2型糖尿?。╰ype 2 diabetes mellitus,T2DM)患者肌少癥的發(fā)病率較普通人群明顯增加,甚至認為肌少癥是糖尿病新出現(xiàn)的并發(fā)癥[3-4]。糖基化終末產物(advanced glycation end product,AGE)是指在非酶促條件下,蛋白質、氨基酸、脂類或核酸等大分子物質的游離氨基與還原糖的醛基經過縮合、重排、裂解、氧化修飾后產生的一組穩(wěn)定的終末產物。AGE的蓄積可消耗機體抗氧化機制,導致β細胞損傷、胰島素抵抗及慢性炎癥,從而與糖尿病及其并發(fā)癥的發(fā)生、發(fā)展密切相關。AGE的積累與T2DM患者肌少癥的相關性研究還在初步階段。因此,本研究擬探究AGE與T2DM患者肌少癥有無相關性,并建立列線圖模型評估AGE對肌少癥的預測價值。
選取2021年10月至2022年10月于合肥市第二人民醫(yī)院住院的T2DM患者180例為研究對象。納入標準:①診斷明確的2型糖尿病患者;②入院前3個月有穩(wěn)定的降糖方案;③年齡50~80歲。排除標準:①妊娠期和哺乳期女性;②合并心、腦、腎、肝等臟器嚴重疾??;③合并精神疾病或軀體殘疾無法配合檢查者;④懷疑或確有酒精、藥物濫用史者;⑤糖尿病急性并發(fā)癥者;⑥合并腫瘤、甲狀腺疾病、感染、自身免疫性疾病、影響體內激素水平的相關疾??;⑦有家族遺傳性疾病;⑧有減肥藥、甲狀腺激素、生長激素、糖皮質激素、性激素等藥物應用史;⑨3個月內服用維生素D、雙膦酸鹽等影響骨代謝的藥物;⑩合并慢性呼吸道疾??;?被檢查者前臂內側皮膚有瘢痕、苔蘚樣硬化斑、傳染性皮膚疾病者;?合并帕金森病、運動神經元病、腦卒中后遺癥、嚴重認知功能障礙、重度骨關節(jié)炎、類風濕關節(jié)炎、重度骨質疏松、營養(yǎng)不良及長期臥床等影響軀體功能者。根據是否合并肌少癥將納入患者分為對照組(=146)和肌少癥組(=34)。本研究經合肥市第二人民醫(yī)院倫理委員會批準(倫理審批號:2022yzd001),所有患者均簽署知情同意書。
1.2.1 基本信息 身高、體質量指數(body mass index,BMI)、收縮壓(systolic blood pressure,SBP)、舒張壓(diastolic blood pressure,DBP)、糖尿病病程。
1.2.2 實驗室指標 禁食8h后,次日凌晨空腹采集患者的靜脈血,采用全自動生化分析儀測定相關指標:空腹血糖(fasting plasma glucose,F(xiàn)BG)、血鈣、磷、三酰甘油(triglyceride,TG)、總膽固醇(total cholesterol,TC)、高密度脂蛋白(high density lipoprotein,HDL)、低密度脂蛋白(low density lipoprotein,LDL)、糖化血紅蛋白(glycated hemoglobin,HbA1c)、空腹C肽、尿白蛋白/肌酐比值(albumin/ creatinine ratio,ACR)等生化免疫指標。
1.2.3 骨骼肌及軀體功能 通過GE lunar雙能X線骨密度測量儀測量四肢骨骼肌質量后計算四肢骨骼肌質量指數(appendicular skeletal muscle mass index,ASMI)。使用彈簧握力器測定肌力:站立位伸肘測量握力,如果老年人不能獨自站立,則選用坐位測量,用優(yōu)勢手或兩只手分別最大力量等距收縮,至少測量2次,選取最大讀數。軀體功能:受試者在床邊用身體能承受的最快速度連續(xù)5次起立,記錄時間,5次起立試驗時間≥12s則反映軀體功能下降。
1.2.4 AGE檢測 采用AGE Pro型糖基化終末產物無創(chuàng)檢測儀檢測受試者皮膚AGE水平,在前臂腹側連續(xù)測量3次取平均值。
1.2.5 肌少癥診斷標準 肌少癥診斷符合2019亞洲肌少癥診斷共識[5]。肌少癥診斷:①肌肉量減少:男性ASMI<7.0kg/m2、女性ASMI<5.4kg/m2;②肌肉力量降低:男性握力<28kg、女性握力<18kg;③起立實驗陽性。以上①②項為診斷必備條件,③為診斷輔助條件。
兩組患者的年齡、病程、AGE、肌力、起立試驗、ASMI、BMI、HbA1c、ACR比較差異均有統(tǒng)計學意義(<0.05),見表1。
多因素回歸分析結果顯示BMI、肌力、AGE均是T2DM患者發(fā)生肌少癥的獨立危險因素(<0.05),見表2。
表1 兩組患者的一般資料比較
注:1mmHg=0.133kPa
表2 影響T2DM患者發(fā)生肌少癥的多因素回歸分析
圖1 預測模型的列線圖
根據上述篩選出的獨立危險因素構建T2DM患者肌少癥發(fā)病風險的預測模型,見圖1;以收集數據的第一個樣本AGE=86.8、BMI=19.10kg/m2、肌力=28.8kg為例,預測其對應肌少癥的發(fā)生概率為22.8%。使用Bootstrap內部驗證法對列線圖模型進行驗證,校準曲線和Y=X直線相近模型校準度良好,列線圖模型的C指數為0.933,校正后的C指數為0.927,說明模型擬合較好,見圖2。繪制ROC曲線發(fā)現(xiàn)列線圖模型預測T2DM患者發(fā)生肌少癥的AUC為0.933(95%:0.897~0.970),有良好預測價值,見圖3。臨床獲益DCA評估結果顯示多因素預測模型列線圖具有更高的凈收益和更好的臨床應用價值,見圖4。
圖2 預測T2DM患者發(fā)生肌少癥列線圖的校準曲線
圖3 預測T2DM患者發(fā)生肌少癥列線圖的ROC曲線
圖4 預測T2DM患者發(fā)生肌少癥列線圖的決策曲線
肌少癥近年來逐漸受到關注,糖尿病與其發(fā)生發(fā)展也有一定相關性。本研究結果顯示肌少癥患病率為18.9%,男性較女性略高,但差異無統(tǒng)計學意義。目前關于性別對肌少癥的影響尚無定論,有研究發(fā)現(xiàn)男性或女性的肌少癥患病率明顯升高[6-7];但也有研究認為T2DM患者肌少癥的發(fā)病率無性別差異[8]。針對性別對其的影響還需要更多的研究去探索。本研究中肌少癥組患者年齡更大,肌力和ASMI更低。關于起立試驗兩組患者的平均時間均在正常范圍內,但多因素回歸分析顯示沒有顯著相關性,關于肌少癥患者軀體功能是否受損及受損程度如何,未來需要擴大樣本量進一步探究。
既往研究發(fā)現(xiàn)HbA1c水平與肌肉質量受損[9]、肌肉力量[10]和身體表現(xiàn)[11]無關,本研究結果與上述研究一致。另外,本研究發(fā)現(xiàn)BMI與T2DM患者肌少癥的發(fā)生相關,肌少癥患者的BMI值更低,有研究表明隨著BMI的增加,肌少癥的患病率顯著降低,提示低體質量的T2DM患者更易患肌少癥,與Fukuoka等[12]研究一致。
AGE在糖尿病患者的各種組織中沉積,與慢性高血糖狀態(tài)[13]、糖尿病慢性并發(fā)癥[14]、糖尿病患者骨代謝[15]的發(fā)生發(fā)展有相關性。本研究發(fā)現(xiàn)肌少癥組患者較對照組有更高的AGE積累,多因素回歸分析顯示AGE是T2DM患者肌少癥發(fā)生的獨立危險因素。既往研究發(fā)現(xiàn)肌肉量減少與隨著年齡增長而積累的AGE相關,AGE可增加氧化應激和炎性細胞因子[16]。此外,AGE在老年人和嚙齒動物衰老模型中誘導肌肉蛋白的交聯(lián)和分解,通過多條信號通路誘導糖尿病小鼠骨骼肌萎縮和功能障礙,AGE在小鼠后肢肌肉中不斷積累與其肌肉質量、肌肉耐力和再生能力的下降有關[17-18]。有報道稱快縮肌纖維中的AGE積累與肌肉膠原蛋白交聯(lián),增加肌肉僵硬并降低肌肉收縮的強直力[19]。說明累積的AGE與T2DM患者的肌肉質量減少有關。
本研究構建一個可量化且簡單的列線圖來預測T2DM患者肌少癥的發(fā)生風險。在內部驗證之后,發(fā)現(xiàn)其具有高度的預測準確性,DCA也證明該模型具有較好的臨床價值。此外,列線圖因其容易獲得的具體參數而在臨床具有一定的實用價值,對臨床評估患者肌少癥患病風險具有簡單直接的幫助,未來需要收集更多的數據來完善此列線圖。
本研究具有一定局限性,首先是橫斷面設計,不能推斷因果關系;第二,沒有關于患者長期飲食運動的信息,不能忽視其對肌肉質量和力量的影響;第三,缺乏炎癥相關指標的采集,不能評估炎癥相關因素對肌少癥的影響;最后,樣本量不夠大也是推廣該結果的重要限制因素,需要后續(xù)擴大樣本進行前瞻性研究探索AGE與肌少癥之間的關系。
綜上所述,AGE積累是T2DM患者發(fā)生肌少癥的獨立危險因素,結合AGE、BMI和肌力的多因素預測模型對T2DM患者肌少癥的發(fā)生有很好的預測價值。
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AGE predict the risk of sarcopenia in type 2 diabetes mellitus patients
HUANG Huijing, CAO Yonghong, HONG Qiong, ZOU Lingling, DAI Wu
1.Department of Endocrinology, Hospital Affiliated to Anhui Medical University (The Second People’s Hospital of Hefei), Hefei 230011, Anhui, China; 2.The Fifth School of Clinical Medicine, Anhui Medical University, Hefei 230032, Anhui, China
To analyze the risk factors of sarcopenia in patients with type 2 diabetes mellitus (T2DM), and establish a nomogram prediction model to investigate advanced glycation end product (AGE) predicts the risk of sarcopenia in T2DM patients.A total of 180 T2DM patients hospitalized in the Second People’s Hospital of Hefei from October 2021 to October 2022 were selected as study objects, and divided into control group (=146) and sarcopenia group (=34) according to whether they were complicated with sarcopenia. The general data of the two groups were compared, and the risk factors of sarcopenia in T2DM patients were discussed by Logistic regression analysis, and a nomogram model was established.There were significant differences in age, course of disease, AGE, muscle strength, standing test, appendicular skeletal muscle mass index (ASMI), body mass index (BMI), glycated hemoglobin and urinary albumin/creatinine ratio between the two groups (<0.05). Multivariate regression analysis showed that BMI, muscle strength and AGE were independent risk factors for sarcopenia in T2DM patients (<0.05). The prediction model was established based on BMI, muscle strength and AGE, and it was proved that the model had good calibration and differentiation. Receiver operating characteristic curve was drawn, and area under the curve was 0.933 in predicting the occurrence of sarcopenia in T2DM patients, which had good predictive value. Calibration curve and decision curve-analysis (DCA) evaluation results showed that the model had higher net benefit and better clinical application value.AGE is an independent risk factor for sarcopenia in T2DM patients, and it can predict the diagnosis of sarcopenia in T2DM patients.
Type 2 diabetes mellitus; Advanced glycation end product; Sarcopenia; Nomogram
R587.1
A
10.3969/j.issn.1673-9701.2023.25.011
合肥市衛(wèi)生健康應用醫(yī)學研究項目(合衛(wèi)科教〔2019〕172號);安徽醫(yī)科大學??蒲谢痦椖浚?020xkj247);合肥市第二人民醫(yī)院院級科研重點項目(2022yzd001)
戴武,電子信箱:daiwuhf@126.com
(2022–12–07)
(2023–08–08)