炎性因子白细胞介素6及肿瘤坏死因子a与2型糖尿病发病相关性的Meta分析

目的评价炎性因子白细胞介素6（IL-6）及肿瘤坏死因子a（TNF-a）与2型糖尿病发病的相关性。方法按照国际Cochrane协作网的系统评价方法，全面收集在基线水平检测过IL-6或TNF—a水平，并且终点事件为糖尿病的前瞻性队列研究。结果有关IL-6共有6个研究符合纳人标准。Meta分析结果显示：在校正混杂因素之后，基线时IL-6水平位于最高四分位数的人群发生2型糖尿病的风险是位于最低四分位数的人群的2．33倍[95％置信区间：（1．89～2．87），P〈0．00001]；发生糖尿病事件的人群在基线时的IL-6水平明显高于非糖尿病事件人群（P〈0．001）。有关TNF—a共有5个研究符合纳入标准。基线时TNF-a水平位于最高四分位数的人群发生2型糖尿病的风险是位于最低四分位数的人群的1．53倍[95％置信区间：（1．23—1．90），P=0．0002]，其中4个研究提示发生糖尿病的人群在基线时TNF-a即明显升高（P〈0．01）。结论炎性因子IL-6、TNF-a参与了2型糖尿病的发生，可能是2型糖尿病发病的危险因素，有必要在人群中对其进行早期筛查和早期干预。     

饮酒量与糖尿病前期3 年转归关系的前瞻性研究

目的探讨饮酒量与糖尿病前期3 年转归的关系。方法研究对象来自泸州地区2 型糖尿病患者肿瘤发生风险的流行病学研究,从基线调查中筛选出糖尿病前期人群1 125 例。按饮酒量分为不饮、少量、中量和大量饮酒4 组,按体质指数（BMI）是否大于24 kg/m2 分2 组。对所调查对象进行3 年随访。比较不同饮酒量和BMI 水平糖尿病发生情况。结果完成随访1 116 例,失访9 例。不饮、少量、中量和大量饮酒组糖尿病发病率分别为18.81%、 8.80%、16.51%、26.53%。Cox 比例风险回归分析显示,调整性别、年龄、BMI、热量、基线血糖因素后,少、中、大量饮酒组与不饮酒组糖尿病发病相对风险比RR 及95%CI 分别为0.49（0.31～0.79）、1.15（0.69～1.92）、1.95（1.25～3.04）。按BMI 分组比较：BMI＜24 kg/m2 人群少、中、大量饮酒组与不饮酒组糖尿病发病风险比RR 及95%CI 分别为：0.40 （0.20～0.80）、0.77（0.30～1.97）、1.10（0.46～2.64）;BMI≥24 kg/m2 人群少、中、大量饮酒组与不饮酒组糖尿病发病风险比RR 及95%CI 分别为：0.62（0.33～1.66）、1.49（0.80～2.80）、2.64（1.55～4.47）。结论少量饮酒可以降低BMI＜ 24 kg/m2糖尿病前期人群的糖尿病发病风险,对于BMI≥24 kg/m2 人群大量饮酒则增加糖尿病发病风险。     

血清胱抑素C与子痫前期子代出生体重的相关性分析

目的 探讨血清胱抑素C与子痫前期子代出生体重的相关性。方法 回顾性纳入2019年1月至2021年12月在福州市第一医院住院的规范产检并诊断子痫前期的孕妇为子痫前期组,同期就诊的正常妊娠孕妇作为对照组。采用Pearson相关分析和线性回归分析确定不同孕周血清胱抑素C与子痫前期子代出生体重的相关性。结果 共纳入24例子痫前期孕妇和190例正常妊娠孕妇。子痫前期组孕妇在孕12、26和38周三个不同时期的血清胱抑素C显著高于对照组,且子痫前期组孕妇的新生儿出生体重明显低于对照组,差异有统计学意义（P <0.05）。Pearson相关分析显示,孕26和38周孕妇血清胱抑素C与出生体重呈负相关（r=-0.605、-0.426,P <0.05）。线性回归分析显示,孕26周（β=-0.17,95%CI:-850.61～-97.47,P=0.01）和孕38周（β=-0.18,95%CI:-667.05～-55.07,P=0.02）血清胱抑素C与出生体重呈独立负相关。结论 血清胱抑素C可能影响子痫前期子代出生体重。     

大港油田地区不同性别及BMI的工作人群糖尿病风险因素分析

摘要： 目的 探讨大港油田地区不同性别工作人群及不同体质量指数 （BMI） 亚组的糖尿病风险因素。方法 采用横断面研究方法, 通过芬兰糖尿病风险评分问卷确定高风险人群, 将其分为糖尿病组和非糖尿病组。采用多因素 logistic回归分析不同性别及不同BMI亚组人群的糖尿病风险因素。结果 研究纳入1 995例, 男性985例 （49.4%）。男性人群糖尿病风险因素中血糖升高史 （HHBG） 风险比最高 （OR=3.183, 95%CI： 1.921~5.272）, 其次为年龄、 天冬氨酸转氨酶 （AST）、 高血压及治疗史 （HHT）、 吸烟, 三酰甘油 （TG） 风险最低 （OR=1.494, 95%CI： 1.033~2.162）; 女性人群中丙氨酸转氨酶 （ALT） 风险比最高 （OR=2.383, 95%CI： 1.153~4.927）, 其次为腰围、 TG和年龄, HHBG风险比最低（OR=2.081, 95%CI： 0.947~4.571）; BMI＜23.9 kg/m2 亚组男性风险因素为HHBG、 TG, 女性为年龄、 TG; 24.0 kg/m2 ≤ BMI＜27.9 kg/m2 亚组男性风险因素为年龄、 HHBG、 HHT, 女性为年龄、 ALT、 TG; BMI≥28.0 kg/m2 亚组男性风险因素为 HHBG、 HHT、 AST、 ALT, 女性为年龄。结论 油田地区男性工作年龄人群糖尿病主要风险因素依次为HHBG、 年龄、 AST、 HHT、 吸烟、 TG, 在不同BMI亚组中HHBG始终为糖尿病风险因素; 女性依次为ALT、 腰围、 TG、 年龄、 HHBG, 年龄成为不同BMI亚组共同风险因素。     

成年人群中糖化血红蛋白和非酒精性脂肪肝的关系

目的 评估成年人群中糖化血红蛋白（HbA1c）与非酒精性脂肪肝（NAFLD）之间的关系。方法 本研究以天津市第一中心医院健康体检科招募2017年健康体检者11 943例为研究对象，包括体格检查和血液生化指标检测。采用2010年全国人口年龄构成作为标准人口进行年龄患病率标化，计算不同年龄、性别间NAFLD患病率的差异。应用Logistic回归模型评估HbA1c与NAFLD之间的关系。结果 NAFLD患病率为49.44%，标化患病率为40.46%。HbA1c≥6.5%组NAFLD 患病率（75.60%）高于6.0%≤HbA1c＜6.5%组（69.35%）和HbA1c＜6.0%组（44.60%），差异有 统计学意义（P＜0.05）。70岁以下人群中，NAFLD患病率随着HbA1c的升高而增加（P＜0.05）。在校正年龄、性别、体质量指数等混杂因素后，与 Quartile1 组（HbA1c＜5.2%）相比，Quartile3 组（5.5%≤HbA1c＜5.8%），Quartile4 组（HbA1c≥5.8%）发生NAFLD的风险值OR（95%CI）分别为1.41（1.220~1.631）和2.003（1.714~2.342）。在HbA1c＜6.0%人群中，与Q1组（HbA1c＜5.2%）相比，Q3组（5.4%≤HbA1c＜5.6%）、Q4组（5.6%≤HbA1c＜6.0%）发生NAFLD的风险值 OR（95%CI）分别为 1.267（1.087~1.477）和 1.557（1.339~1.811）。结论 在成年人群中 HbA1c 水平升高是发生NAFLD的独立危险因素。     

医疗自付费用对T2DM血糖和抑郁的影响

探讨不同自付费用对2型糖尿病（T2DM） 患者血糖和抑郁的影响。选取2011年4—12月门诊初次诊治的T2DM患者293例,包括纳入医保145例（A组）和未纳入医保148例（B组）,比较2组治疗1年的医疗费用、血糖和抑郁情况。根据四分位法将2组患者的自付费用分为q1、q2、q3和q4四个区间,比较四个区间的血糖和抑郁情况,采用Logistic回归分析不同区间发生抑郁的风险。A组1年内的医疗费用、自付费用、HbA1c和FPG水平以及抑郁发生率明显优于B组,差异均具有统计学意义（P＜0.05）。A组自付费用四分位区间上的HbA1c和FPG水平以及抑郁发生率,差异均无统计学意义（P＞0.05）。B组自付费用四分位区间上的HbA1c和FPG水平以及抑郁发生率,差异具有统计学意义（P＜0.05或P＜0.01）。以A组q1区间作为对照,Logistic回归分析显示B组q1（OR=0.945,95%CI 0.898～0.967,P＜0.01）,q2（OR=0.469,95%CI 0.336～0.655, P＜0.05）,q3（OR=0.586, 95%CI0.423～0.812,P＜0.05）,q4（OR=0.926,95%CI 0.716～1.198,P＜0.01）,发生抑郁的风险都有所升高。医保能够有效减轻糖尿病患者经济负担,且能够改善糖尿病控制水平以及降低抑郁发生率,有助于疾病恢复。     

身体质量指数联合三酰甘油与高密度脂蛋白胆固醇比值预测 2型糖尿病风险

目的 利用体检指标中身体质量指数(BMI)和三酰甘油与高密度脂蛋白胆固醇比值(triglyceride to high-density lipoprotein cholesterol,TG/HDL-C)联合预测2型糖尿病(type 2 diabetes mellitus,T2DM)发病风险。方法 基于瑞慈医疗集团2010—2016年体检人群的数据库,通过Cox比例风险模型观测不同BMI的人群基线TG/HDL-C对随访期间T2DM风险的影响,并分析TG/HDL-C与BMI是否存在交互作用。结果 在随访期间,13 685例研究对象中共有315例最终诊断为T2DM。以BMI=24 kg/m²为界,将所有研究对象分成两个亚组。BMI<24 kg/m²的亚组共有8 238例研究对象,96例在随访结束时患T2DM,对潜在的混杂因素进行调整后,升高的TG/HDL-C导致T2DM事件的风险较高[HR 95%CI=1.47(1.23,1.74),P<0.001]。与最低五分位数(Q1)相比,升高的TG/HDL-C五分位数(Q2～Q5)T2DM发病率...     

糖尿病“看图对话”工具应用体会

根据杨文英等对2007～2008年进行的全国,14个省市的糖尿病流行病学调查研究发现,20岁以上人群的糖尿病总患病率为9．7％,空腹血糖受损（IFG）和/或糖耐量损害（IGT）的患病率高达15．5％,估算糖尿病的总患病人数达9240万,糖尿病前期人数达1．48亿。面对糖尿病患病率的快速增长和大量的糖尿病前期人群,对糖尿病知识的宣传教育尤为重要。     

初诊2型糖尿病患者微血管并发症患病率及相关因素分析（附286例报告）

糖尿病（Diabetes mellitus,DM）是一组以长期高血糖为主要特征的代谢综合征,可并发眼、肾、神经、心血管等多脏器损害.2型糖尿病（Type 2 diabetes mellitus,T2DM）占全部糖尿病的90%以上,起病隐匿,早期获诊不易,往往在其并发症出现后才被诊断,给我们临床医师对糖尿病的防治带来一定困难.近年来研究发现,2型糖尿病微血管并发症不仅仅在临床糖尿病时期出现,在糖尿病前期人群中其患病率已升高.葡萄糖耐量受损（IGT）的人群中,视网膜病变及微量白蛋白尿的患病率分别达10%和16%,其发生率是正常糖耐量人群的3～4倍.吴红艳等研究显示2型糖尿病在初诊时视网膜病变及肾病患病率分别高达13%及20.6%,因此及时检出糖尿病前期及糖尿病患者微血管并发症极为重要.     

糖尿病和抑郁“亲如兄弟”

最新调查显示。我国20岁以上人群总体糖尿病患病率已达9．7％（9200万）．糖尿病前期的患病率更高达15．5％（1．482亿）。有学者报道。在糖尿病患者中有临床意义的抑郁症状的发生率约为21．8％-60％,而在一般人群中的发生率是4％。即糖尿病抑郁症状的发生率至少是一般人群的5倍。     

高三酰甘油腰围表型与糖尿病前期及2型糖尿病发病风险的关系

摘要：目的探讨高三酰甘油（TG）腰围表型与糖尿病前期及2型糖尿病（T2DM）发病风险的关系。方法从参与2011年4—11月四川省泸州地区2型糖尿病患者肿瘤发生风险流行病学调查研究的基线调查者中筛选出1 722例糖耐量正常者,以血TG≥1.7 mmol/L、男性腰围≥90 cm或女性腰围≥80 cm为切点,分为TG和腰围正常组（856例）、单纯高三酰甘油血症组（133例）、单纯腹型肥胖组（518例）、高三酰甘油血症-腰围表型（HTWC）组（215例）,行5年随访观察,比较各组间糖尿病前期及T2DM发病情况。结果随访5年后,1 722例糖耐量正常者中共445例发展为糖尿病前期,67例发展为T2DM。TG和腰围正常组、单纯高三酰甘油血症组、单纯腹型肥胖组、HTWC组糖尿病前期发病率分别为20.7%（177/856）、21.1%（28/133）、30.5%（158/518）、38.1%（82/215）,T2DM发病率分别为2.9%（25/856）、3.8%（5/133）、4.4%（23/518）、6.5%（14/215）。在调整了年龄、性别、空腹血糖、餐后2 h血糖、收缩压、糖化血红蛋白后,多分类无序Logistic回归分析结果显示,HTGW表型人群的糖尿病前期及T2DM发病风险较正常组升高,其OR（95%CI）分别为1.961（1.387～2.773）与2.638（1.279～5.441）。结论HTWC表型是糖尿病前期及T2DM患病的危险因素,可作为筛选两者高风险人群的简便指标。     

A pharmacokinetic study of the combined administration of amiodarone and ximelagatran, an oral direct thrombin inhibitor

The oral direct thrombin inhibitor ximelagatran is being developed for the prevention and treatment of thromboembolism. This single-blind, randomized, placebo-controlled, parallel-group study investigated the potential for the interaction of ximelagatran (36 mg every 12 hours for 8 days, measured as its active form melagatran in blood) and amiodarone (single 600-mg oral dose on day 4) in healthy male subjects (n = 26). For amiodarone + ximelagatran versus amiodarone + placebo, geometric mean ratios (90% confidence intervals for amiodarone AUC(0-120) and C(max) were 0.87 (0.69-1.08) and 0.86 (0.66-1.11), respectively. For desethylamiodarone, the principal metabolite of amiodarone, the corresponding ratios were 1.00 (0.89-1.12) for AUC(0-120) and 0.92 (0.77-1.09) for C(max).The geometric mean ratios (90% confidence intervals) for ximelagatran + amiodarone versus ximelagatran were 1.21 (1.17-1.25) for melagatran AUC(0-12) and 1.23 (1.18-1.28) for melagatran C(max). These confidence intervals were within or only slightly outside the interval, suggesting no interaction (0.8-1.25 for the effect of amiodarone on melagatran and 0.7-1.43 for the effect of melagatran on amiodarone or desethylamiodarone). Amiodarone did not affect the concentration-effect relationship of melagatran on activated partial thromboplastin time. Ximelagatran was well tolerated when coadministered with a single dose of amiodarone. Evaluation of the safety of the combination is needed to confirm that the relatively small pharmacokinetic changes in this study are of no clinical significance.     

Comparison of 3 risk factor–based screening tools for the identification of prediabetes

ObjectiveTo compare risk factor–based screening tools for identifying prediabetes.MethodsParticipants in an employer-based wellness program were tested for glycosylated hemoglobin (A1C) at a regularly scheduled appointment, and prediabetes risk factor information was collected. The likelihood of having prediabetes and the need for laboratory testing were determined based on 3 risk factor–based screening tools: the Prediabetes Screening Test (PST), Prediabetes Risk Test (PRT), and 2016 American Diabetes Association guidelines (ADA2016). The results from the screening tools were compared with those of the A1C test. The predictive ability of the PST, PRT, and ADA2016 were compared using logistic regression. Results were validated with data from a secondary population.ResultsOf the 3 risk factor–based tools examined, the PRT demonstrated the best combination of sensitivity and specificity for identifying prediabetes. From July 2016 to March 2017, 740 beneficiaries of an employer-sponsored wellness program had their A1C tested and provided risk factor information. The population prevalence of prediabetes was 9.3%. Analysis of a second independent population with a prediabetes prevalence of more than 50% of confirmed PRT’s superiority despite differences in the calculated sensitivity and specificity for each population.ConclusionBecause PRT predicts prediabetes better than PST or ADA2016, it should be used preferentially.     

Alcohol consumption predicts hypertension but not diabetes

OBJECTIVE: This study examines the associations between alcohol consumption, Type 2 diabetes and hypertension in a native American population. METHOD: Data were collected in a population-based cross-sectional and prospective study conducted on 3,789 individuals aged > or = 20 years. Reported alcohol consumption was classified as never, occasional or < 1 a day, 1-2 drinks a day, > or = 3 drinks a day, and occasional heavy drinking. The prevalence and incidence of diabetes and hypertension by categories of alcohol intake were determined. RESULTS: About 68% of men and 39% of women reported some degree of alcohol consumption. There was no association between alcohol consumption and prevalence or incidence of diabetes, but a positive, statistically significant association between blood pressure and alcohol consumption was found in both genders. After adjustment for age, body mass index (BMI) and diabetes in a proportional hazards model in men, moderate drinkers (occasional or < 1 drink a day and 1-2 drinks a day combined) had 1.24 (95% confidence interval: 0.98-1.57) and occasional heavy drinkers had 1.49 (1.02-2.17) times the incidence of hypertension as nondrinkers. The corresponding estimates of hypertension incidence for women were 1.53 (1.29-1.83) for moderate drinking and 1.38 (0.81-2.36) for occasional heavy drinking. As only 1% of participants reported > or = 3 drinks a day, this group was excluded from these analyses. CONCLUSIONS: Alcohol consumption did not affect the development of Type 2 diabetes, but it was associated with increased risk of hypertension, and this effect was independent of diabetes or BMI in both genders.     

Poly drug use,chemsex drug use,and associations with sexual risk behaviour in HIV-negative men who have sex with men attending sexual health clinics

BackgroundRecreational drug use and associated harms continue to be of significant concern in men who have sex with men (MSM) particularly in the context of HIV and STI transmission.MethodsData from 1484 HIV-negative or undiagnosed MSM included in the AURAH study, a cross-sectional, self-completed questionnaire study of 2630 individuals from 20 sexual health clinics in the United Kingdom in 2013–2014, was analysed. Two measures of recreational drug use in the previous three months were defined; (i) polydrug use (use of 3 or more recreational drugs) and (ii) chemsex drug use (use of mephedrone, crystal methamphetamine or GHB/GBL). Associations of socio-demographic, health and lifestyle factors with drug use, and associations of drug use with sexual behaviour, were investigated.ResultsOf the 1484 MSM, 350 (23.6%) reported polydrug use and 324 (21.8%) reported chemsex drug use in the past three months. Overall 852 (57.5%) men reported condomless sex in the past three months; 430 (29.0%) had CLS with ≥2 partners, 474 (31.9%) had CLS with unknown/HIV+ partner(s); 187 (12.6%) had receptive CLS with an unknown status partner. For polydrug use, prevalence ratios (95% confidence interval) for association with CLS measures, adjusted for socio-demographic factors were: 1.38 (1.26, 1.51) for CLS; 2.11 (1.80, 2.47) for CLS with ≥2 partners; 1.89 (1.63, 2.19) for CLS with unknown/HIV+ partner(s); 1.36 (1.00, 1.83) for receptive CLS with an unknown status partner. Corresponding adjusted prevalence ratios for chemsex drug use were: 1.38 (1.26, 1.52); 2.07 (1.76, 2.43); 1.88 (1.62, 2.19); 1.49 (1.10, 2.02). Polydrug and chemsex drug use were also strongly associated with previous STI, PEP use, group sex and high number of new sexual partners. Associations remained with little attenuation after further adjustment for depressive symptoms and alcohol intake.ConclusionThere was a high prevalence of polydrug use and chemsex drug use among HIV negative MSM attending UK sexual health clinics. Drug use was strongly associated with sexual behaviours linked to risk of acquisition of STIs and HIV.     

Effect of propiverine on cytochrome P450 enzymes: a cocktail interaction study in healthy volunteers.

The present study was conducted to assess a possible in vivo effect of propiverine, an anticholinergic drug to treat urinary incontinence and related disorders, on the activity of intestinal CYP3A4 and of hepatic CYP3A4, CYP2C9, CYP2C19, and CYP1A2. The activity of the respective cytochromes P450 was measured using the following metrics of selective substrates given as a tailored low-dose phenotyping cocktail: intestinal availability of midazolam (2 mg orally), clearance of midazolam (1 mg i.v.), apparent clearance of tolbutamide (125 mg orally), urinary excretion of 4'-hydroxymephenytoin 0 to 8 h postdose (50 mg of mephenytoin orally), and the paraxanthine/caffeine plasma ratio 6 h postdose (150 mg of caffeine orally). These metrics were determined in 16 healthy young men at the end of 7 days of treatment with 15 mg of propiverine (test) or placebo (reference) twice daily. All phenotyping drugs were quantified by liquid chromatography-tandem mass spectrometry. Chronic propiverine treatment reduced hepatic and intestinal CYP3A4 activity slightly to 0.89-fold and 0.80-fold, respectively [90% confidence interval (CI) for test/reference ratios 0.85-0.93 and 0.72-0.89], with the combined effect resulting in a 1.46-fold increase in area under the curve of oral midazolam (90% CI 1.36-1.57). Propiverine had no relevant effect on CYP2C9, CYP2C19, and CYP1A2 (90% CI for test/reference ratios 0.93-1.00, 0.84-0.96, and 0.97-1.07, respectively). All study drugs were well tolerated. In conclusion, propiverine has a minor potential to cause drug-drug interactions.     

Lack of pharmacologic interaction between paroxetine and alprazolam at steady state in healthy volunteers

This investigation aimed to provide evidence on the lack of pharmacokinetic interaction of paroxetine (20 mg/d) and alprazolam (1 mg/d) in combined therapy. In addition, the central effects of both drugs when administered alone and in combination were assessed to rule out any relevant synergistic depressant central effect. Twenty-five healthy young adult volunteers participated in a double-blind, double-dummy, placebo-controlled, repeated dose (15 days), 4-period crossover study. Each subject received each of 4 treatment sequences (ie, paroxetine-alprazolam placebo, alprazolam-paroxetine placebo, paroxetine-alprazolam, and paroxetine placebo-alprazolam placebo) in randomized order. The ratios for area under the curve within a dosing interval and maximum plasma concentration of the paroxetine plus alprazolam sequence to single agent paroxetine were 1.07 (90% confidence interval = 0.99 to 1.16) and 1.05 (90% confidence interval = 0.97 to 1.13), respectively, with no statistically significant differences between the 2 treatments. Similarly, for alprazolam, ratios for the combined to the single treatment sequence were 0.99 (90% confidence interval = 0.93 to 1.05) and 1.00 (90% confidence interval = 0.94 to 1.07) for area under the curve within a dosing interval and maximum plasma concentration, respectively, showing no evidence for interaction. Comparative pharmacodynamics on the combination was assessed using 6 Psychomotor Performance Tests and 5 Visual Analogue Scales focused on mood variables. Alprazolam and paroxetine plus alprazolam induced similar and significant performance impairment and sedation after both single and repeated dose administration, being less evident on day 15. After dosing, paroxetine plus alprazolam showed a lower recovery pattern than alprazolam alone, especially on day 15. No treatment sequence showed cumulative effects after repeated dose administration. Psychomotor Performance Tests and Visual Analogue Scales data suggested lack of pharmacodynamic interactions. Accordingly, study results showed no evidence for pharmacologic interactions between paroxetine and alprazolam at steady state. The most commonly reported adverse event was drowsiness, with a higher incidence under both single and combined alprazolam treatments.