Abstract Objective To explore the effect of temperature variability (TV) on admissions and deaths for cardiovascular diseases (CVDs). Method The admissions data of CVDs were collected in 4 general hospitals in Jinchang City, Gansu Province from 2013 to 2016. The monitoring data of death for CVDs from 2013 to 2017 were collected through the Jinchang City Center for Disease Control and Prevention. Distributed lag nonlinear model (DLNM) was combined to analyze the effects of TV (daily temperature variability (DTV) and hourly temperature variability (HTV)) on the admissions and deaths for CVDs after adjusting confounding effects. Stratified analysis was conducted by age and gender. Then the attribution risk of TV was evaluated. Results There was a broadly linear correlation between TV and the admissions and deaths for CVDs, but only the association between TV and outpatient and emergency room (O&ER) visits for CVDs have statistically significant. DTV and HTV have similar lag effect. Every 1 ℃ increase in DTV and HTV was associated with a 3.61% (95% CI: 1.19% ~ 6.08%), 3.03% (95% CI: 0.27% ~ 5.86%) increase in O&ER visits for CVDs, respectively. There were 22.75% and 14.15% O&ER visits for CVDs can attribute to DTV and HTV exposure during 2013–2016. Males and the elderly may be more sensitive to the changes of TV. Greater effect of TV was observed in non-heating season than in heating season. Conclusion TV was an independent risk factor for the increase of O&ER visits for CVDs, suggesting effective guidance such as strengthening the timely prevention for vulnerable groups before or after exposure, which has important implications for risk management of CVDs.

This study explores the effects of short-term exposure to PM10 on related biomarkers of diabetes. Based on the platform of "Jinchang Cohort", a total of 420 patients with type 2 diabetes, pre-diabetes and normal blood glucose are randomly selected. The nearest neighbor model is used to estimate individual exposure levels. IL-6, VCAM-1, 8-iso-PGF2α and INS are detected by ELISA. A multiple linear regression model is conducted to evaluate the effects of PM10 on the biomarkers. For every 10 μg/m3 increase in PM10 concentration, it is found that IL-6 increases by 0.45% (95%CI: 0.19%—0.88%) at lag 6 d, and PM10 is most significantly associated with VCAM-1 at lag 0 d (increase: 1.16%, 95%CI: (0.43%—2.28%)) in the prediabetic group. PM10 is most significantly associated with IL-6 (increase: 1.52%, (95%CI: 0.51%—2.53%)) at lag 6 d, while 8-iso-PGF2α increases by 2.01% (95%CI: 0.29%—3.73%) at lag 3 d, and the relationship between PM10 and HOMA-β is most significant at lag 0—7 d (decrease: 4.63%, (95%CI: −8.00%—−1.13%)) for type 2 diabetes patients. Short-term exposure to PM10 can lead to inflammation, oxidative damage and islet β cell dysfunction.

Blood pressure has been shown to change by outdoor temperature, but whether intra- and inter-day temperature variability (TV) will bring higher effect on BP is not clear. Based on a prospective cohort study, the mixed effect model was selected to estimate the relationship between TV (daily temperature variability (DTV) and hourly temperature variability (HTV)) and BP (systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), and mean arterial pressure (MAP)) after adjusting for confounding variables. We found that there was a positive linear correlation between TV and BP. The results of DTV and HTV were basically consistent, but the effect estimates of HTV seemed to be larger. Gender, age, BMI, education level and BP status may modify the relationship between TV and BP. The effect of TV on BP was greater in non-heating season than in heating season. Our work contributes to a further macro mechanism evidence for the TV-CVDs association.

Background: Previous studies have found that exposed to low and high outdoor temperature was associated with cardiovascular diseases morbidity and mortality. The risk factors for cardiovascular disease include high blood lipid, high uric acid (UA) and high fasting plasma glucose (FPG). However, few studies have explored the effects of low and high temperature on these metabolic indicators. Objective: To explore the effect of low and high temperature on metabolic indicators in adults from northwest of China. Results: A nonlinear relationship between outdoor temperature and metabolic indicators was found. For the cold effects, each 5 degrees C decrease of mean temperature was associated with an increase of 5.07% (95%CI: 352%, 6.63%) in TG and 2.85% (95% CI: 2.18%, 3.53%) in UA, While a decrease of 338% (95% CI: 2.67%, 4.09%) in HDL-C and 1.26% (95% CI: 0.48%, 2.04%) in LDL-C. For the heat effects, each 5 degrees C increase in mean temperature was associated with 1.82% (95% CI: 0.89%, 2.76%), 0.56% (95% CI: 0.11%, 1.00%), 5.82% (95% CI: 4.58%, 7.06%), 9.02% (95% CI: 7.17%, 10.87%), 0.20% (95% CI: 0.01%, 0.40%), and 1.22% (95% CI: 0.19%, 2.24%) decrease in TC, TG, HDL-C, LDL-C, UA and FPG. Age, smoking, drinking, high-oil diet and hyperlipidemia might modify the association between mean temperature and metabolic indicators. Conclusion: There was a significant effect of cold and hot temperature on metabolic indicators in a high altitude area of northwestern China. These results provide a basis for understanding the underlying mechanism of the influence of temperature on metabolic diseases. (C) 2021 Elsevier B.V. All rights reserved.

OBJECTIVE:The relationship between outdoor temperature and blood pressure (BP) has been inconclusive. We analyzed data from a prospective cohort study in northwestern China to investigate the effect of outdoor temperature on BP and effect modification by season.METHODS:A total of 32,710 individuals who participated in both the baseline survey and the first follow-up in 2011-2015 were included in the study. A linear mixed-effect model and generalized additive mixed model (GAMM) were applied to estimate the association between outdoor temperature and BP after adjusting for confounding variables.RESULTS:The mean differences in systolic blood pressure (SBP) and diastolic blood pressure (DBP) between summer and winter were 3.5 mmHg and 2.75 mmHg, respectively. After adjusting for individual characteristics, meteorological factors and air pollutants, a significant increase in SBP and DBP was observed for lag 06 day and lag 04 day, a 0.28 mmHg (95% CI: 0.27-0.30) per 1 °C decrease in average temperature for SBP and a 0.16 mmHg (95% CI: 0.15-0.17) per 1 °C decrease in average temperature for DBP, respectively. The effects of the average temperature on both SBP and DBP were stronger in summer than in other seasons. The effects of the average temperature on BP were also greater if individuals were older, male, overweight or obese, a smoker or drinker, or had cardiovascular diseases (CVDs), hypertension, and diabetes.CONCLUSIONS:This study demonstrated a significant negative association between outdoor temperature and BP in a high-altitude environment of northwest China. Moreover, BP showed a significant seasonal variation. The association between BP and temperature differed by season and individuals' demographic characteristics (age, gender, BMI), unhealthy behaviors (smoking and alcohol consumption), and chronic disease status (CVDs, hypertension, and diabetes).

目的:探究疾控中心微生物实验室检验质量影响因素及解决措施.方法:选取2017年4月-2020年4月疾控中心微生物实验室期间采集的微生物标本332份,按时间顺序分为两组.对照组未实施全面质量控制管理模式;研究组实施全面质量控制管理模式.比较两组质量问题,并对其影响因素进行分析.结果:研究组质量问题发生率低于对照组,差异有统计学意义(P<0.05);研究组所致微生物实验室检验质量问题的样本及试剂因素占比低于对照组,差异有统计学意义(P<0.05).结论:疾控中心微生物实验室质量问题与多种因素有关,建议工作人员予以高度重视,积极采取相应管理措施,以保证微生物实验室质量控制效果,为微生物标本检验准确性提供有力保障.

Objective: To explore the effect of long-term exposure to ambient particulate matter (PM10) on the prevalence of diabetes and fasting plasma glucose (FPG). Methods: The subjects of the study were from the baseline population of "Jinchang Cohort", and 24 285 subjects were finally included after excluding incomplete home address information and diabetic diagnosis information. The demographic characteristics, lifestyle and health status of the survey subjects were collected through questionnaire, physical examination and laboratory tests. ArcGIS software was used to match the nearest environmental monitoring stations for each subject according to residential address. Two-year average concentrations of PM10 were calculated to estimate exposure level. The logistic regression and the multiple linear regression were conducted to assess the effects of ambient PM10 on the prevalence of diabetes and FPG. The restricted cubic spline was used to quantify the dose-response relationship. Stratified analysis and effect modification analysis were also performed. Results: The age of 24 285 participants was (49.32±8.60) years, and the BMI was (24.22±6.09) kg/m2. There were 13 950 (57.44%) males and 2 066 (8.51%) diabetic patients. After adjusting for confounders, for every 10 μg/m3 increase in the average PM10 concentration in the first two years of the survey, the prevalence of diabetes increased [OR (95%CI) =1.05 (1.01-1.09)]and the FPG level elevated [β (95%CI) = 0.061 (0.047-0.076) mmol/L]. The results of the restricted cubic spline analysis showed a nonlinear relationship between PM10 concentration and FPG level (P<0.001). Further subgroup analysis showed that female [OR (95%CI) =1.10 (1.03-1.18)], people over 50 years old [OR (95%CI) =1.06 (1.02-1.11) ], subjects with family history of diabetes [OR (95%CI) = 1.13 (1.04-1.23) ], and with hypertension [OR (95%CI) = 1.07 (1.02-1.12) ] had a stronger association between the prevalence of diabetes and PM10 exposure (all P interaction values were<0.05). The effects of PM10 on FPG were more significant in people older than 50 years[β (95%CI) = 0.080 (0.050-0.109) mmol/L], with family history of diabetes [β (95%CI) = 0.087 (0.036-0.137) mmol/L], and hypertension [β (95%CI) = 0.077 (0.046-0.108) mmol/L] (all P interaction values were<0.05). Conclusions: Long-term exposure to ambient PM10 increases the diabetes prevalence and FPG. People older than 50 years old, with family history of diabetes and hypertension could be more sensitive to the effects of PM10 exposure.

目的 探讨金昌市热浪对高血压疾病住院人数的影响及潜在混杂因素.方法 收集2011-2016年金昌市3所综合医院每日高血压住院信息及同期气象资料和大气污染物资料.描述分析纳入研究的气象因素与高血压住院人数,分别采用单向回顾性1∶6病例交叉研究、双向对称1∶6病例交叉研究和按时间分层的病例交叉研究方法,在控制星期几效应、相关气象因素和环境污染物等混杂因素后,建立热浪与高血压住院人数的COX回归模型.根据效应值选择滞后效应最大的滞后天数,进行年龄、性别的分层分析,根据热浪持续类型分析热浪对高血压住院人数的影响.结果 研究期间共纳入5次热浪事件.热浪期间,高血压住院人数随温度变化而变化.3种病例交叉研究方法结果显示,热浪对高血压住院人数的变化具有影响,且存在一定的滞后效应.其中,单向回顾性1∶6病例交叉研究结果中效应值最大,热浪在单滞后2d和累积滞后3d对高血压住院人数影响的OR分别为2.707、4.796.分层分析发现,热浪对男性、女性高血压住院人数均具有影响,且对男性的影响更大(OR=5.900).65岁以下年龄组人群对热浪较为敏感(OR=6.266).热浪持续时间越长,高血压住院人数增加风险越大.结论 热浪对金昌市居民高血压疾病住院人数的增加具有影响.

Diurnal temperature range (DTR) is a meteorological indicator closely associated with global climate change. Thus, we aim to explore the effects of DTR on the outpatient and emergency room (O&ER) admissions for cardiovascular diseases (CVDs), and related predictive research. The O&ER admissions data for CVDs from three general hospitals in Jinchang of Gansu Province were collected from 2013 to 2016. A generalized additive model (GAM) with Poisson regression was employed to analyze the effect of DTR on the O&ER admissions for all cardiovascular diseases, hypertension, ischemic heart disease (IHD) and stoke. GAM was also used to preform predictive research of the effect of DTR on the O&ER admissions for CVDs. There were similar positive linear relationships between DTR and the O&ER visits with the four cardiovascular diseases. And the cumulative lag effects were higher than the single lag effects. A 1 °C increase in DTR corresponded to a 1.30% (0.99–1.62%) increase in O&ER admissions for all cardiovascular diseases. Males and elderly were more sensitivity to DTR. The estimates in non-heating season were higher than in heating season. The trial prediction accuracy rate of CVDs based on DTR was between 59.32 and 74.40%. DTR has significantly positive association with O&ER admissions for CVDs, which can be used as a prediction index of the admissions of O&ER with CVDs.

Some studies suggested that 24-h temperature change (TC24) was one of the potential risk factors for human health. However, evidence of the short-term effect of TC24 on outpatient and emergency department (O&ED) visits and hospitalizations for cause-specific cardiovascular diseases (CVDs) is still limited. The aim of this study is to explore the short-term effects of TC24 on O&ED visits and hospitalizations for CVDs in northwest China which is an area with large temperature variation. The O&ED visits records for CVDs of 3 general hospitals and the inpatient records for CVDs of 4 general hospitals were collected from January 1, 2013, to December 31, 2016, in Jinchang City, northwest China. Meteorological and air pollution data were also obtained during the same study period from local meteorological monitoring station and environmental monitoring station, respectively. A generalized additive model (GAM) with Poisson regression was employed to analyze the effects of TC24 on O&ED visits and hospitalizations for CVDs. V-shaped relationship were found between TC24 and O&ED visits and hospitalizations for CVDs, including total CVD, hypertension, coronary heart disease (CHD) and stroke. Stratified analysis showed that men and patients over 65 years old were more susceptible to temperature changes. The estimates in non-heating months were higher than in full year. TC24 can affect the O&ED visits and hospitalizations for CVDs in this study. This study provides useful data for policy makers to better prepare local responses to the impact of changes in temperature on population health.

目的:分析金昌市病毒性腹泻的流行特征及病原学构成.方法:对金昌市2012-2018年4家医院采集的病毒性腹泻病例样本进行病毒核酸检测,统计分析实验室检测结果(描述流行病学方法).结果:共采集腹泻病例标本2116份,病毒核酸检测阳性率依次为轮状病毒19.90％、杯状病毒12.00％、星状病毒3.97％、腺病毒3.73％;轮状病毒具有明显的季节高峰,其基因型主要为G3、G9、P8,诺如病毒感染率12.00％,星状病毒、腺病毒维持在较低水平.结论:儿童腹泻存在较高的病毒感染率,以A组轮状病毒和诺如Ⅱ型病毒为主要病毒,同时存在混合感染模式.

[背景]寒潮对高血压疾病的发生可能具有潜在风险.在高海拔寒冷地区开展极端气温对高血压住院人数影响的关联研究对高血压疾病的预防及极端天气的预警具有参考意义.[目的]探讨甘肃省金昌市寒潮对高血压疾病住院人数的影响及潜在混杂因素.[方法]收集2011-2016年金昌市3所综合医院每日高血压住院信息及同期气象资料和大气污染物资料,分析寒潮期间高血压住院人数的变化.对气象因素、大气污染物与高血压住院人数进行相关性分析,采用3种匹配比例(1∶2、1∶4、1∶6)的双向对称病例交叉研究设计,在控制星期几效应、节假日效应、气象因素(平均相对湿度)和环境污染物(SO2、NO2、PM10)等混杂因素后,建立寒潮与高血压住院人数的Cox回归模型.选择滞后效应最大的滞后天数,进行敏感度分析及对年龄、性别进行分层分析,并分析不同寒潮持续时间(24、48、72h)对高血压住院人数的影响,其中24h寒潮和48h寒潮合并后简称为24、48h寒潮.[结果]在2011-2016年间共纳入9次寒潮事件.寒潮期间,高血压住院人数随温度变化而出现不同变化趋势,且存在一定滞后效应.其中,双向对称1∶2病例交叉研究在单滞后5d时对高血压住院人数影响的效应值最大,OR值为1.142 (95％CI:1.053～1.237).在单滞后5d时,24、48h寒潮和72h寒潮均对高血压住院人数具有影响,且24、48h寒潮对高血压住院人数影响更大[OR (95％ CI) =1.218 (1.072～1.385)].分层分析发现,在单滞后5d时,寒潮对男性、女性高血压住院人数均具有影响,且对男性的影响更大[OR (95％ CI) =1.191(1.041～1.364)];相比65岁以上人群,65岁以下人群更易受寒潮影响[OR (95％ CI) =1.201(1.043～1.383)].敏感度分析结果显示,模型基本稳健,OR波动范围小.[结论]寒潮对金昌市居民高血压疾病住院人数的增加具有一定的影响.不同年龄、性别及寒潮持续时间对寒潮的影响均存在修饰作用.

Background: A large number of studies have found a positive association between diurnal temperature range (DTR) and cardiovascular diseases (CVDs) incidence and mortality. Few studies regarding the effects of DTR on blood pressure (BP) are available. Objective: To investigate the effects of DTR on BP in Jinchang, northwestern China. Methods: Based on a prospective cohort research, a total of 46,609 baseline survey data were collected from 2011 to 2015. The meteorological observation data and environmental monitoring data were collected in the same period. The generalized additive model (GAM) was used to estimate the relationship between DTR and BP after adjusting for confounding variables. Results: Our study found that there was a positive linear correlation between DTR and systolic blood pressure (SBP) and plus pressure (PP), and a negative linear correlation between DTR and diastolic blood pressure (DBP). With a 1 degrees C increase of DTR, SBP and PP increased 0.058 mmHg (95%CI: 0.018-0.097) and 0.114 mmHg (95%CI: 0.059-0.168) respectively, and DBP decreased 0.039 mmHg (95%CI:-0.065 similar to -0.014). There was a significant interaction between season and DTR on SBP and PP. DTR had the greatest impact on SBP and PP in hot season. The association between DTR and BP varied significantly by education level. Conclusion: There was a significant association between DTR and BP in Jinchang, an area with large temperature change at high altitudes in northwestern China. These results provide new evidence that DTR is an independent risk factor for BP changes among general population. Therefore, effective control and management of BP in the face of temperature changes can help prevent CVDs. (C) 2020 Elsevier B.V. All rights reserved.

Objectives: We aimed to assess the association between long-term exposure to ambient PM10 and risk of diabetes incidence, based on the "Jinchang Cohort" platform in the Northwest of China. Methods: We selected 19884 subjects who had not yet developed diabetes in the baseline and had completed survey information from "Jinchang Cohort". The residential address was used to match the nearest pollution monitoring station for each subject, and the average concentration of PM10 from baseline to follow-up were used as an estimate of individual exposure level. Cox regression model and restricted cubic splines functions were used to evaluate the effects of PM10 on the incidence of diabetes and the dose-response relationship after adjusting for confounding covariates. Results: We observed 791 new-onset diabetics with a total follow-up of 45254.16 person-years (incidence rate of 17.48 per 1000 person-years). The risk of diabetes incidence increased by 17% (HR = 1.17, 95%CI: 1.08-1.26) per 10 mu g/m(3) increase in environmental PM10, and the risk rises gradually with the rise of PM10 concentration. Comparing with the first quartile of PM10, the fully adjusted HRs (95%CI) for incident diabetes from the second to the fourth quartile of PM10 were 1.15 (95%CI: 0.93-1.43), 1.50 (95%CI: 1.22-1.84) and 1.44 (95%CI: 1.15-1.79), respectively (P for trend< 0.001). Stratified analyses suggested that the risk of diabetes incidence associated with ambient PM10 was higher in female, young to middle-aged people, overweight and obese subjects, and subjects with FPG level at baseline lower than 5.6 mmol/L. Conclusions: Long-term exposure to ambient PM10 significantly associated with a higher risk of diabetes development. Some urgent strategies may be advocated to reduce air pollution that can aid in preventing the prevalence of diabetes in the population.

为了探讨气态污染物NO2对高血压患者血压水平和脉压的短期影响,本文基于前瞻性队列研究,收集甘肃省金昌市2011年1月1日～2015年11月30日逐日NO2监测数据及同期气象观测数据,运用混合效应模型,在控制随机效应及其他混杂因素的基础上,分析NO2与高血压患者血压水平和脉压的关联性.结果表明:(1)NO2在滞后1d(lag1)时平均浓度每升高1个IQR,收缩压升高0.457mmHg(0.131～0.784),滞后7d(lag7)和4d(lag4)时NO2的平均浓度每升高1个IQR,舒张压上升0.276mmHg(0.025～0.527),脉压上升0.402mmHg(0.047～0.758),且结果均具有统计学意义.(2)性别、年龄、BMI、吸烟、饮酒、季节在NO2对高血压患者血压水平和脉压的效应中可能具有修饰作用.多污染物模型及调整沙尘影响后,NO2对高血压患者血压及脉压的影响保持一致.

[背景]高血压已经成为威胁我国居民健康的重要疾病,但其潜在危险因素还未完全明确.[目的]通过研究甘肃省金昌市温度变化与高血压门急诊人数的关系,探讨潜在的影响高血压门急诊人数的气象因素.[方法]收集2012年1月1日-2015年12月31日甘肃省金昌市3所综合医院每日门急诊高血压就诊信息(ICD-10:|10-|15),以及同期气象资料(包括日平均气温、相对湿度、气压、风速)和大气污染资料(PM10、NO2、SO2).采用准Poisson回归广义相加模型,在控制时间的长期趋势、星期几效应、节假日效应、气象因素、环境污染物等混杂因素后,分别建立24h及48h温度变化与高血压门急诊人数的暴露反应关系模型,估计效应值,拟合暴露反应关系图,并进行年龄、性别的分层分析.[结果]研究期间共纳入高血压门急诊患者61438例.同期金昌市24h平均负变温、正变温及48 h平均负变温、正变温分别为(-2.18±1.91)℃、(1.80±1.26)℃和(-2.98±2.56)℃、(2.66±1.98)℃.广义相加模型分析结果显示:24h温度变化、48 h温度变化与每日高血压门急诊人数大致呈“U”型曲线.24h温度变化、48h温度变化对高血压门急诊人数均有影响,并存在滞后效应:24h负变温在滞后1d (ER=-1.25％,95％CI:-1.93％～-0.57％)、正变温在滞后5d (ER=2.77％,95％CI:1.86％～3.69％)效应值最大;48h负变温在滞后3d(ER=-0.67％,95％CI:-1.14％～-0.21％)、正变温在滞后1d (ER=1.53％,95％CI:0.94％～2.13％)效应值最大.24h及48 h正变温对高血压门急诊人数的影响明显大于负变温.分层分析发现变温对女性和65～74岁人群的高血压门急诊人数影响更大.[结论]金昌市24h及48h温度变化对高血压门急诊人数影响的暴露反应关系大致呈“U”型,且正负变温均会影响高血压门急诊人数.相对负变温,正变温对高血压门急诊人数影响更大.女性及老年高血压患者门急诊人数更易受温度变化影响.

为探讨颗粒物对金昌市高血压门急诊就诊人数影响的暴露反应关系,本文收集甘肃省金昌市2012年1月1日～2015年12月31日大气PM10、SO2、NO2数据及2014年1月1日～2015年12月31日大气PM2.5污染物监测数据及同期气象观测数据,同时收集近年金昌市三家综合医院的高血压门急诊日就诊病例.采用广义相加模型,分析不同大气污染物与高血压门急诊日就诊人数的关联性.结果表明,在单污染物模型中,滞后L07d时PM10平均浓度每升高一个IQR,高血压日门急诊人数增加2.30％(95％CI:1.30％～3.32％),L6d时PM2.5平均浓度每升高一个IQR,高血压日门急诊人数增加2.53％(95％CI:1.45％～3.62％).PM10和PM2.5对男性、65岁以上高血压患者门急诊影响更高.SO2和NO2与颗粒物之间存在协同效应,沙尘天气下PM10对高血压门急诊人数的影响由2.30％增加到2.36％,PM2.5的影响由2.53％减少到2.39％.研究得出颗粒物污染对金昌市高血压门急诊就诊人数具有不同程度的影响,其中细颗粒物(PM2.5)的效应更强.

Air pollution exposure may play an adverse role in diabetes. However, little data are available directly evaluating the effects of air pollution exposure in blood lipids of which dysfunction has been linked to diabetes or its complications. We aimed to evaluate the association between air pollution and lipids level among type 2 diabetic patients in Northwest China. We performed a population-based study of 3912 type 2 diabetes patients in an ongoing cohort study in China. Both spline and multiple linear regressions analysis were used to examine the association between short-term exposure to PM10, SO2, NO2 and total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). By spline analyses, we observed that the relationship between SO2 and HDL-C and LDL-C was shown to be non-linear (p_non-lin-association = 0.0162 and 0.000). An inverted U-shaped non-linear relationship between NO2 and LDL-C was found (p_non-lin-association < 0.0001). A J-shaped non-linear relationship between PM10 and TC, HDL-C (p_non-lin-association = 0.0173, 0.0367) was also revealed. In linear regression analyses, a 10 μg/m3 increment in SO2 was associated with 1.31% (95% CI: 0.40–2.12%), 3.52% (95% CI: 1.07–6.03%) and 7.53% (95% CI: 5.98–9.09%) increase in TC, TG and LDL-C, respectively. A 10 μg/m3 increment in PM10 was associated with 0.45% (95% CI: 0.08–0.82%), 0.29% (95% CI: 0.10–0.49%) and 0.83% (95% CI: 0.21–1.45%) increase in TC, HDL-C and LDL-C, respectively. For NO2, an increment of 10 μg/m3 was statistically associated with −3.55% (95% CI: −6.40–0.61%) and 39.01% (95% CI: 31.43–47.03%) increase in HDL-C and LDL-C. The adverse effects of air pollutants on lipid levels were greater in female and elder people. Further, we found SO2 and NO2 played a more evident role in lipid levels in warm season, while PM10 appeared stronger in cold season. The findings suggest that exposure to air pollution has adverse effects on lipid levels among type 2 diabetes patients, and vulnerable people may pay more attention on severe air pollution days.