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. 2025 Aug 14;25(1):2755.
doi: 10.1186/s12889-025-23635-x.

Independent and combined relationships between nighttime light exposure, air pollution, PM2.5 constituents, greenness and diabetes or high blood sugar: a national prospective cohort study

Siyu Qing  1 , Ziyue Liang  1 , Yifang Liang  1 , Renfang Zhang  1 , Xiaojie Chen  1 , Wenqing Wang  1 , Chunejie Xu  2 , Fei Lin  3 , Yongbin Wang  4
Affiliations

Independent and combined relationships between nighttime light exposure, air pollution, PM2.5 constituents, greenness and diabetes or high blood sugar: a national prospective cohort study

Siyu Qing et al. BMC Public Health. .

Abstract

Background: Diabetes or high blood sugar (D/HBS) is a major global public health challenge, with a particularly high burden among middle-aged and older adults in China. While traditional risk factors (e.g., obesity, sedentary behavior) are well-studied, the impacts of environmental factors such as nighttime light (NL), air pollution, and the normalized difference vegetation index (NDVI) on D/HBS remain underexplored.

Methods: Using data from the China Health and Retirement Longitudinal Study (CHARLS, 2011-2015), we included 11,865 participants aged ≥ 45 years without baseline D/HBS. Cox proportional hazards models were employed to evaluate the independent effects of NL, air pollutants (PM1, PM2.5, NO2, etc.), and NDVI on D/HBS risk, adjusting for confounders. Interaction effects, joint effects, and mediation effects were further analyzed.

Results: The median follow-up duration was 4.0 years. Among the 11,865 baseline participants, 371 developed incident D/HBS. NL was significantly associated with increased D/HBS risk (per IQR increase: HR = 1.15, 95% CI: 1.06-1.26). High exposure to PM2.5 (Q4: HR = 1.46, 95% CI: 1.06-2.01) and NO2 (Q4: HR = 1.44, 95% CI: 1.07-1.92) elevated risk, while higher NDVI (Q3: HR = 0.52, 95% CI: 0.37-0.72) showed a protective effect. Synergistic effects were observed between NL and PM2.5 (RERI = 0.61, 95% CI: 0.13-1.09) or NO3- (RERI = 0.59, 95% CI: 0.11-1.08). Mediation analyses indicated that air pollutants, specifically NH4+ (proportion = - 68.3%) and NO3- (proportion = - 64.1%), partially attenuated the protective role of NDVI.

Conclusions: NL and air pollution are independent risk factors for D/HBS, with synergistic effects exacerbating disease risk. NDVI confers protection by reducing pollutant exposure. Comprehensive strategies targeting light pollution control, air quality improvement, and optimized green space planning are imperative. Future studies should integrate individualized exposure assessments and elucidate causal environmental-metabolic pathways.

Keywords: Air pollution; Diabetes/high blood sugar; Greenness; Nighttime light exposure; Prospective cohort study.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The CHARLS cohort received primary ethical approval from the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). Participants provided informed consent, with data anonymized for confidentiality. This study used de-identified secondary data from CHARLS and adhered to the ethical principles outlined in the Declaration of Helsinki. For this specific secondary data analysis, the research protocol was reviewed and approved by the institutional review board of Xinxiang Medical University (No: XYLL-2019072). No separate ethical approval was required for the analysis of this existing, anonymized dataset beyond the approvals obtained, as this study constituted secondary analysis of previously collected and approved data under conditions that met relevant institutional and national guidelines for exemption. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Exposure-response curves of NL, air pollutants, and NDVI with risk of D/HBS. A Exposure-response curves of NL with risk of D/HBS; B Exposure-response curves of PM1 with risk of D/HBS; C Exposure-response curves of PM2.5 with risk of D/HBS; D Exposure-response curves of PM10 with risk of D/HBS; E Exposure-response curves of NO2 with risk of D/HBS; F Exposure-response curves of O3 with risk of D/HBS; G Exposure-response curves of BC with risk of D/HBS; H Exposure-response curves of NH4+ with risk of D/HBS; I Exposure-response curves of OM with risk of D/HBS; J Exposure-response curves of SO42- with risk of D/HBS; K Exposure-response curves of NO3- with risk of D/HBS; L Exposure-response curves of NDVI with risk of D/HBS. Abbreviations: D/HBS = Diabetes or high blood sugar; NL = nighttime light; PM1 = particulate matter with an aerodynamic diameter ≤1 μm; PM2.5 = particulate matter with an aerodynamic diameter ≤2.5 μm; PM10= particulate matter with an aerodynamic diameter ≤10 μm; NO2 = nitrogen dioxide; O3 = ozone; BC = black carbon; NH4+= ammonium; OM = organic matter; SO42− = sulfate; NO3−= nitrate; NDVI = normalized difference vegetation index. Adjusted for age, gender, marital status, education level, smoking status, alcohol consumption, sleep state and residential area. Solid line represents point estimates and dashed lines represent 95% confidence intervals
Fig. 2
Fig. 2
Associations of NL, air pollutants, and NDVI with risk of D/HBS. Abbreviations: D/HBS = Diabetes or high blood sugar; NL = nighttime light; PM1 = particulate matter with an aerodynamic diameter ≤1 μm; PM2.5 = particulate matter with an aerodynamic diameter ≤2.5 μm; PM10 = particulate matter with an aerodynamic diameter ≤10 μm; NO2 = nitrogen dioxide; O3 = ozone; BC = black carbon; NH4+ = ammonium; OM = organic matter; SO42− = sulfate; NO3− = nitrate; NDVI = normalized difference vegetation index. Model 1 was adjusted for age and gender. Model 2 included additional adjustments for marital status and education level. Model 3 further controlled for smoking status, alcohol consumption, sleep state and residential area. Solid points represent point estimates, and the line segments represent 95% confidence intervals
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