Associations Between Screen Exposure, Multidimensional Sleep Indicators, and Type 2 Diabetes: A Cross-sectional Study Using US National Survey Data.

This study aimed to evaluate the association of sleep quality and its long-term change with the risk of type 2 diabetes mellitus (T2DM) and to assess the relationship between sleep duration and the risk of T2DM according to categories of sleep quality. 5728 participants free of T2DM at wave 4 from the English Longitudinal Study of Ageing were included and received a follow-up with a median time of 8years. We created a sleep quality score to evaluate sleep quality, which was based on three Jenkins Sleep Problems Scale questions (the frequency of feeling hard to fall asleep, waking up at night, and feeling tired in the morning) and one question for rating overall sleep quality. Participants were allocated into three groups according to their baseline sleep quality scores (groups of good [4-8], intermediate [8-12], and poor quality [12-16]). Sleep duration was assessed by a self-reporting sleep hours from each participant. 411 (7.2%) T2DM cases were documented during the follow-up. Compared with the good quality group, subjects with poor sleep quality showed a significantly higher risk of T2DM (hazard ratio (HR) 1.45, confidence interval (CI) 1.09, 1.92). In participants with good baseline sleep quality, those who experienced worsened sleep quality showed a significantly increased T2DM risk (HR 1.77, 95% CI 1.26, 2.49). Type 2 diabetes mellitus risk was not changed regardless of sleep duration in subjects with good quality. Short sleep duration (≤4h) was associated with an elevated T2DM risk in participants with intermediate sleep quality, and both short (≤4h) and prolonged sleep time (≥9h) were associated with an increased T2DM risk in the poor sleep quality group. Poor sleep quality is correlated with an increase in T2DM risk, and regulating sleep quality to a good range could potentially be an effective approach for preventing T2DM.

(1) Limited evidence has shown the mediating effects of work characteristics and sleep duration on obesity and type 2 diabetes mellitus (T2DM) among adults. The objective of this study is to assess the interaction effects between sleep duration and effort–reward imbalance (ERI) on the risk of obesity and T2DM among Taiwanese public servants aged 40–60. (2) A national survey for Taiwanese public servants was conducted by multistage stratified random cluster sampling based on proportional probabilistic sampling. A total of 11,875 participants aged 40–60 years old were collected; (3) 3.6% of participants had self-reporting T2DM diagnosed by a physician and the prevalence of overweight and obesity were 44.0% and 15.8%, respectively. There was a significant correlation between sleep hours for the workday and risk of T2DM in non-obese and obese groups (odds ratio, OR = 1.48 and 1.39, respectively), but this did not exist for the weekend/vacation group. Similar trends in the two groups by sleep hours on a workday, obesity and overweight were significantly associated with the risks of T2DM. Clearly, sleep duration and ERI were moderating factors on the association between BMI and on the prevalence of T2DM. (4) A short sleep duration and heavy job stress contributes to the risk of weight gain and T2DM development.

Objective This study aimed to assess the associations between multidimensional sleep features and type 2 diabetes mellitus (T2DM). Methods We conducted a systematic search across the PubMed, Embase, Web of Science, and Scopus databases for observational studies examining the association between nighttime sleep duration, nighttime sleep quality, sleep chronotype, and daytime napping with type 2 diabetes mellitus (T2DM), up to October 1, 2024. If I 2 < 50%, a combined analysis was performed based on a fixed-effects model, and vice versa, using a random-effects model. Results Our analysis revealed that a nighttime sleep duration of less than 7 h (odds ratio [OR] = 1.18; 95% CI = 1.13, 1.23) or more than 8 h (OR = 1.13; 95% CI = 1.09, 1.18) significantly increased the risk of T2DM. Additionally, poor sleep quality (OR = 1.50; 95% CI = 1.30, 1.72) and evening chronotype (OR = 1.59; 95% CI = 1.18, 2.13) were associated with a notably greater risk of developing T2DM. Daytime napping lasting more than 30 min augments the risk of T2DM by 7-20%. Interactively, the incidence of T2DM was most significantly elevated among individuals with poor sleep quality and nighttime sleep duration of more than 8 h (OR = 2.15; 95% CI = 1.19, 3.91). Conclusions A U-shaped relationship was observed between sleep duration and type 2 diabetes mellitus (T2DM), with the lowest risk occurring at a sleep duration of 7 to 8 h. Additionally, poor sleep quality, evening chronotypes, and daytime napping exceeding 30 min emerged as potential risk factors for T2DM. These high-risk sleep characteristics interacted with one another, amplifying the overall risk of developing the disease.

Prospective observational studies uniformly link vitamin D deficiency with the incidence of type 2 diabetes mellitus (T2DM), yet trials supplementing participants at risk of T2DM with vitamin D to reduce progression to T2DM have yielded inconsistent results. Inconsistencies between supplementation trials may be due to insufficient dosing or small sample sizes. Observational studies may also have reported spurious associations due to uncontrolled confounding by lifestyle or genetic factors. Alternatively, observational and intervention studies may not be entirely comparable. Observational studies show an association between higher vitamin D status, which is predominantly derived from sun exposure, and decreased incidence of T2DM. Trials intervene with vitamin D supplementation, and therefore may be missing alternate causes of the effect of sun exposure, as seen in observational studies. We propose that sun exposure may be the driving force behind the associations seen in observational studies; sun exposure may have additional benefits beyond increasing serum 25-hydroxyvitamin D (25OHD) levels. We performed an electronic literature search to identify articles that examined associations between sun exposure and T2DM and/or glucose metabolism. A best evidence synthesis was then conducted using outcomes from analyses deemed to have high methodological quality. Ten eligible full-text articles were identified, yielding 19 T2DM-related outcomes. The best evidence analysis considered 11 outcomes which were grouped into six outcome types: T2DM, fasting glucose, glucose tolerance, fasting insulin, insulin secretion and insulin sensitivity. There was moderate evidence to support a role of recreational sun exposure in reducing odds of T2DM incidence. High-level evidence was lacking; evidence presented for other outcomes was of low or insufficient level. This review highlights significant gaps in research pertaining to sun exposure and T2DM-related outcomes. Further research is encouraged as we aim to identify novel preventative strategies for T2DM.

Background: Prolonged use of electronic devices has become a routine part of daily life and is increasingly recognized as a contributor to poor sleep health. Sleep disturbances linked to screen exposure are an emerging public health concern. Objective: To assess the association between screen time and sleep disorders among adults. Methods: A cross-sectional study was conducted from January 2024 to January 2025 on 82 adults at Services Hospital, Lahore. Data were collected using a structured questionnaire that included demographic characteristics, screen time patterns, and sleep-related variables. Standardized instruments such as the Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) were used. Statistical analysis was performed using chi-square tests and t-tests, with p &lt; 0.05 considered significant. Results: The majority of participants (62.2%) reported poor sleep quality, while 35.4% slept less than six hours daily. Excessive screen exposure was strongly associated with disturbed sleep. Participants with more than six hours of daily screen use showed a 94.7% prevalence of poor sleep quality compared with 21.4% among those with less than two hours (p = 0.001). Smartphone use and pre-bedtime screen exposure were significantly correlated with delayed sleep onset and daytime sleepiness. Conclusion: Excessive screen time, particularly in the evening, is significantly associated with poor sleep quality and reduced sleep duration in adults. Reducing screen exposure before bedtime may help improve sleep hygiene and overall health.

Objective To explore the association between sleep quality and the increasing risk of type 2 diabetes mellitus (T2DM). Methods A total of 771 patients aged 25-70 years living in Xuzhou City of Jiangsu Province for at least 5 years were enrolled for the survey of risk factor related noninfectious chronic disease in 2013. In this investigation, those who suffered from other types of diabetes, neuropathy, other endocrine disease, cardiovascular, renal and hepatic dysfunction, dyspnea or cancer were excluded. To reduce the influence of confounding factors, another 771 participants were enrolled as controls. Each case was arranged to have a control who was matched in age (difference not more than 3 years), gender, residence and family history. All the participants were interviewed with self-designed questionnaire, and sleep quality was measured by Pittsburgh Sleep Quality Index (PSQI) questionnaire. Student's t test, Chi-square and multivariate logistic regression were used for data analysis. Results The PSQI score in the T2DM patients vs. the controls were 5.15±2.40 vs. 2.71±1.93 (t=21.96, P<0.01). The scores of sleep-related factors, including subjective poor sleep quality, bedtime resistance, short sleep duration, sleep efficiency, sleep disturbance, use of sleep medication and daytime dysfunction, of the T2DM patients were higher than those of the controls. The proportion of sleep related behaviors of the T2DM patients was higher, except for early awakening, cold feeling and nightmare. Poor sleep quality was associated with the increasing risk of T2DM (odds ratio 2.06, 95% CI 1.69-2.52). In multivariate logistic regression, when adjusted for confounding factors, the risk of T2DM was still increased (odds ratio 1.72, 95% CI 1.62-1.83). Sleep-related factors (e.g. subjective poor sleep quality, bedtime resistance, short sleep duration, sleep efficiency and sleep disturbance) were correlated with the risk of T2DM (odds ratio was 3.34, 1.63, 1.10, 1.87 and 3.89, respectively). Conclusion Low quality of sleep may be strongly associated with an increased risk of T2DM. Key words: Diabetes mellitus, type 2; Sleep initiation and maintenance disorders; Risk factors; Case-control studies

To clarify the expression of N6-methyladenosine (m6 A) modulators involved in the pathogenesis of type 2 diabetes mellitus (T2DM). We further explored the association of serum insulin-like growth factor 2 mRNA-binding proteins 3 (IGF2BP3) levels and odds of T2DM in a high-risk population. The gene expression data set GSE25724 was obtained from the Gene Expression Omnibus, and a cluster heatmap was generated by using the R package ComplexHeatmap. Differential expression analysis for 13 m6 A RNA methylation regulators between nondiabetic controls and T2DM subjects was performed using an unpaired t test. A cross-sectional design, including 393 subjects (131 patients with newly diagnosed T2DM, 131 age- and sex-matched subjects with prediabetes, and 131 healthy controls), was carried out. The associations between serum IGF2BP3 concentrations and T2DM were modeled by restricted cubic spline and logistic regression models. Two upregulated (IGF2BP2 and IGF2BP3) and 5 downregulated (methyltransferase-like 3 [METTL3], alkylation repair homolog protein 1 [ALKBH1], YTH domain family 2 [YTHDF2], YTHDF3, and heterogeneous nuclear ribonucleoprotein [HNRNPC]) m6 A-related genes were found in islet samples of T2DM patients. A U-shaped association existed between serum IGF2BP3 levels and odds of T2DM according to cubic natural spline analysis models, after adjustment for body mass index, waist circumference, diastolic blood pressure, total cholesterol, and triglyeride. Multivariate logistic regression showed that progressively higher odds of T2DM were observed when serum IGF2BP3 levels were below 0.62 ng/mL (odds ratio 3.03 [95% confidence interval 1.23-7.47]) in model 4. Seven significantly altered m6 A RNA methylation genes were identified in T2DM. There was a U-shaped association between serum IGF2BP3 levels and odds of T2DM in the general Chinese adult population. This study provides important evidence for further examination of the role of m6 A RNA methylation, especially serum IGF2BP3 in T2DM risk assessment.

BackgroundRisk of type 2 diabetes mellitus (T2DM) is increased in metabolically obese but normal-weight people. However, we have limited knowledge of how to prevent T2DM in normal-weight people. We aimed to evaluate the association between triglyceride glucose (TyG) index and incident T2DM among normal-weight people in rural China.MethodsWe included data from 5706 people with normal body mass index (BMI) (18.5–23.9 kg/m2) without baseline T2DM in a rural Chinese cohort followed for a median of 6.0 years. A Cox proportional-hazard model was used to assess the risk of incident T2DM by quartiles of TyG index and difference in TyG index between follow-up and baseline (TyG-D), estimating hazard ratios (HRs) and 95% confidence intervals (CIs). A generalized additive plot was used to show the nonparametric smoothed exposure–response association between risk of T2DM and TyG index as a continuous variable. TyG was calculated as ln [fasting triglyceride level (mg/dl) × fasting plasma glucose level (mg/dl)/2].ResultsRisk of incident T2DM was increased with quartiles 2, 3 and 4 versus quartile 1 of TyG index (adjusted HR [aHR] 2.48 [95% CI 1.20–5.11], 3.77 [1.83–7.79], and 5.30 [2.21–12.71], Ptrend < 0.001 across quartiles of TyG index). Risk of incident T2DM was increased with quartile 4 versus quartile 1 of TyG-D (aHR 3.91 [2.22–6.87]). The results were consistent when analyses were restricted to participants without baseline metabolic syndrome and impaired fasting glucose level. The generalized additive plot showed cumulative increased risk of T2DM with increasing TyG index.ConclusionsRisk of incident T2DM is increased with increasing TyG index among rural Chinese people, so the index might be an important indicator for identifying people at high risk of T2DM.

BackgroundObesity is a well-known risk factor for type 2 diabetes mellitus (T2DM). Studies have shown that the Chinese visceral adiposity index (CVAI), a novel visceral adiposity indicator, is positive associated with the risk of T2DM in the Chinese population. This study aimed to investigate the correlation between CVAI and incident T2DM in a Japanese population.MethodsWe performed a secondary analysis of open-access data from a retrospective cohort study. This study included 15,464 participants who received regular medical examinations at Murakami Memorial Hospital. All participants underwent a questionnaire survey, physical examination, and blood biochemical testing at baseline. The main outcome was new-onset T2DM during follow-up. Cox regression analysis and Kaplan–Meier analysis were used to analyze the risk of CVAI on T2DM, and we conducted smooth curve fitting. Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive value of CVAI, body mass index (BMI), and waist circumference (WC) for incident T2DM.ResultsDuring a median follow-up time of 5.39 years, 373 new-onset T2DM events were observed. Kaplan–Meier curves showed that the incidence of T2DM increased as the CVAI increased (log-rank χ2 = 187.1076 and 129.6067 in males and females, respectively, both P <0.001). After adjustment for covariates, per 1 increase of CVAI was associated with a 1.0133-fold and 1.0246-fold higher risk of incident T2DM in males and females, respectively (both P <0.001). Those individuals in the top CVAI quartile group had the highest risk of new-onset T2DM (HR = 3.1568 and 5.8415 in males and females, respectively, both P <0.05). A nonlinear relationship was identified by the smooth fitting curve between CVAI and T2DM events in both genders. ROC analysis indicated that CVAI had better predictive power than BMI and WC in both genders.ConclusionOur results demonstrate that CVAI was significantly associated with an increased risk of new-onset T2DM in Japanese adults.

ObjectiveExtensive literature put forward the link between sleep and type 2 diabetes mellitus (T2DM), however, little is known about the underlying causality of the associations. Here we aim to assess the causal relationships between five major sleep-related traits and T2DM.Design, Setting, and ParticipantsTwo-sample Mendelian randomization (MR) was utilized to investigate the potential causal relations. Independent genetic variants associated with five sleep-related phenotypes—insomnia, sleep duration, short sleep duration, long sleep duration, and morningness—were chosen as instrumental variables to estimate the causal associations with T2DM. Summary statistics were acquired from the genome-wide association studies of UK Biobank and 23andMe (for sleep-related measures), the DIAbetes Genetics Replication And Meta-analysis and the FinnGen (for T2DM).Main MethodsIndividual Cochran’s Q statistic was applied to remove the pleiotropic instruments, global Q statistics and MR-Egger regression were adopted to test for the global heterogeneity and horizontal pleiotropy of the screened instruments, respectively. Two T2DM cohorts were selected to analyze their associations with sleep traits. A modified inverse variance weighted (IVW) estimate was performed to combine the ratio estimators from each instrument and acquire the causal estimate, alternative methods including IVW with first-order weights, simple and weighted median estimations, and MR-Egger regression were conducted as sensitivity analyses, to ensure the robustness and solidity of the findings.ResultsTwo-sample MR supported findings for an adverse effect of genetically predicted insomnia on T2DM risk (odds ratio [OR] = 1.14, 95% confidence interval [CI]: 1.09–1.19, p = 1.29E–08) at the Bonferroni-adjusted level of significance (p < 0.005). We further investigated the causal role of T2DM on insomnia but obtained a non-significant estimation. There was also little evidence for the causal effect of other sleep-related measures on T2DM. Results were largely consistent when leveraging two different T2DM cohorts, and were robust among various sensitivity analyses.ConclusionFindings provide significant evidence for an adverse effect of insomnia on T2DM risk. The study extends fundamental knowledge to further understanding of the pathophysiological mechanisms of T2DM, and points out the non-negligible role of insomnia on epidemiologic intervention and clinical therapeutics of T2DM.

PurposeCreatinine to body weight (Cre/BW) ratio is considered the independent risk factor for incident type 2 diabetes mellitus (T2DM), but research on this relationship is limited. The relationship between the Cre/BW ratio and T2DM among Chinse individuals is still ambiguous. This study aimed to evaluate the correlation between the Cre/BW ratio and the risk of T2DM in the Chinese population.MethodsThis is a retrospective cohort study from a prospectively collected database. We included a total of 200,658 adults free of T2DM at baseline. The risk of incident T2DM according to Cre/BW ratio was estimated using multivariable Cox proportional hazards models, and a two-piece wise linear regression model was developed to find out the threshold effect.ResultsWith a median follow-up of 3.13 ± 0.94 years, a total of 4001 (1.99%) participants developed T2DM. Overall, there was an L-shaped relation of Cre/BW ratio with the risk of incident T2DM (P for non-linearity < 0.001). When the Cre/BW ratio (× 100) was less than 0.86, the risk of T2DM decreased significantly as the Cre/BW ratio increased [0.01 (0.00, 0.10), P < 0.001]. When the Cre/BW ratio (× 100) was between 0.86 and 1.36, the reduction in the risk of developing T2DM was not as significant as before [0.22 (0.12, 0.38), P < 0.001]. In contrast, when the Cre/BW ratio (× 100) was greater than 1.36, the reduction in T2DM incidence became significantly flatter than before [0.73 (0.29,1.8), P = 0.49].ConclusionThere was an L-shaped relation of Cre/BW ratio with incidence of T2DM in general Chinese adults. A negative curvilinear association between Cre/BW ratio and incident T2DM was present, with a saturation effect predicted at 0.86 and 1.36 of Cre/BW ratio (× 100).

To investigate the impact of dynamic change of waist circumference or body mass index (BMI) on type 2 diabetes mellitus (T2DM) populations in a cohort study. We not only obtained the baseline survey data from program 'Prevention of Multiple Metabolic Disorders and metabolic syndrome (MS) in Jiangsu Province'(PMMJS) which started in 1994, and we conducted twice follow-ups from January 2002 to August 2003, and March 2006 to November 2007. After excluding subjects who were found to have T2DM at baseline, cardiovascular disease(CVD), and BMI<18.5 kg/m(2) , and loss to follow up because of relocation, death or other reasons, a total of 3 461 subjects were included in this analysis. They received investigation including questionnaires investigation, measurement and laboratory examination. The differences of gender, smoking, alcohol drinking and T2DM family history in different groups were examined using χ(2)-test, median and inter-quartile range were calculated for TG, and they were examined by rank test. Four equal parts of the differences of waist circumference and BMI were carried out in the COX regression model, to investigate the association between 2 years change of waist circumference or BMI and incidence of T2DM. We also examined the association between BMI and waist circumference modification and incident risk of T2DM in subjects with normal baseline BMI, baseline obese subjects, subjects with normal baseline waist circumference and baseline abdominal obese subjects. A total of 3 461 participants (1 406 males, 2 055 females) were investigated, including 160 new T2DM cases (60 males, 100 females) who were from between baseline and the second following up. The accumulative incidence was 4.6% (60/3 461). Multivariate COX regression model analysis results showed that the T2DM risk was relatively high in the highest quartile of waist circumference D-value group(HR=2.06, 95% CI: 1.27-3.16), the T2DM risk was also high in the highest quartile of BMI D-value group (HR=1.30, 95% CI: 0.86-1.95). In subjects with abdominal obesity and normal waist circumference at baseline, the incidence rate of T2DM in non-control group was 7.1% (40/565) , 6.3% (45/645), higher than that in control group (3.4%(71/2 096), 4.5%(4/155)) (χ(2) values were 3.98 and 15.18, P values were 0.043 and <0.001). In subjects with normal waist circumference, T2DM risk was higher in non-control group than that in control group (HR=2.12, 95% CI: 1.40-3.22). In abdominal obese subjects, T2DM risk was also higher in non-control group than that in control group (HR=1.14, 95% CI: 1.04-1.92). If waist circumference was not controlled, T2DM risk was high, no matter BMI controlled or not (HR(95% CI) were 1.73(1.17-2.54), 2.45(1.63-3.69) respectively). Controlling the waistline could reduce the risk of diabetes, and once waist circumference was not controlled, T2DM risk would be increased no matter BMI was controlled or not.

Background: Plasma linoleic acid (C18:2n-6, LA) has been associated with a lower risk of type 2 diabetes mellitus (T2DM) in prospective cohort studies, but associations of dietary LA with T2DM risk are inconsistent. Objective: To study both plasma and dietary LA in relation to incident T2DM in post-myocardial infarction (MI) patients who received state-of-the-art drug treatment. Methods: We included 3,377 patients (80% male) from the Alpha Omega Cohort aged 60-80 y who had an MI &lt;10 y before study enrollment and who were initially free of T2DM. At baseline, LA was measured in plasma cholesterol esters as a proportion of total fatty acids. Dietary LA intake was estimated from a validated 203-item food-frequency questionnaire that had been specifically designed for measuring fatty acid intake. Incident T2DM was ascertained through self-reported physician diagnosis and medication use. HRs for incident T2DM in quintiles of plasma LA and dietary LA were obtained from multivariable Cox models that included age, sex, lifestyle and dietary factors. For dietary LA, we used a substitution model in which LA was iso-calorically replaced with the sum of saturated plus trans fatty acids. Results: From the lowest to the highest quintile, dietary LA ranged from 3.5 to 8.8 percent of energy and the proportion of plasma LA ranged from 43.7 to 56.3%. Plasma and dietary LA were weakly correlated (Spearman r = 0.14, P &lt;.0001). During a median follow-up of 3.4 years, 171 cases of T2DM occurred. Plasma LA was associated with a significantly lower risk of T2DM (HR Q5vsQ1 : 0.45; 95% CI: 0.27 - 0.76; P for trend = 0.002). Dietary LA was not associated with T2DM risk (HR Q5vsQ1: 0.97; 95% CI: 0.53 - 1.76; P for trend = 0.85). Conclusion: Plasma LA but not dietary LA was associated with lower T2DM risk in post-MI patients. The utility of plasma LA as a biomarker of dietary LA warrants further investigation.

Objective To explore the relationship between advancing age and the risk of developing coronary artery disease(CAD), type 2 diabetes mellitus(T2DM), and both coronary artery disease and type 2 diabetes mellitus(CAD+ T2DM). Methods A case-control study was conducted to investigate the relationship between advancing age and the risk of CAD, T2DM, and CAD+ T2DM in middle-aged and elderly patients. Results Aging was independently associated with increased risk of T2DM and CAD + T2DM(P<0.05). Compared with patients aged below 60, the risk of T2DM was higher in patients aged 70 or over(OR=3.80, 95% CI: 2.39-6.04, P=0.000); The risk of CAD+ T2DM was lower in patients aged below 60 than in patients aged 60 to 69(OR=4.14, 95%CI: 2.60-6.60, P=0.000)and in patients aged 70 or over(OR=11.50, 95%CI: 7.18-18.42, P=0.000). Patients of older ages had a 2.78 times higher risk of developing CAD+ T2DM. Conclusions The onset of T2DM, and CAD+ T2DM is associated with age. Key words: Aging; Coronary disease; Comorbidity

Prior cohort studies reported paradoxical results between proton pump inhibitor (PPI) usage and the risk of type 2 diabetes mellitus (T2DM). We investigated the correlation between the use of PPIs and T2DM risk, constructed predictive models, and identified the key genes involved. In the correlation analysis, we extracted and analyzed the data from the National Health and Nutrition Examination Survey (NHANES) database and the FDA Adverse Event Reporting System (FAERS) database to examine the relationship between the use of PPIs and T2DM risk. Then, a nomogram was constructed to estimate the T2DM risk probability in patients treated with PPIs by using the optimal predictors identified by the least absolute shrinkage and selection operator and logistic regression methods. Finally, we investigated the key genes modulated by PPI usage in patients with T2DM by combining various bioinformatics techniques such as network pharmacology, difference analysis, and weighted gene co-expression network analysis. In the NHANES database, regardless of whether PPI usage was merely included or used to adjust for covariates, the binomial regression models indicated a positive correlation between PPI usage and T2DM risk (all p < 0.001). In the FAERS database, the T2DM signal for patients using PPIs was significant (lower limit of the reporting odds ratio was greater than 1). Sex, race, age, educational level, obesity, hypertension, and high cholesterol were included in the nomogram to predict the probability of PPI usage-induced T2DM risk (all p < 0.05). By intersecting the key cluster and the intersection of PPI usage-related genes and T2DM-related genes, we finally identified two crucial genes, AGT and JAK2, that may be involved in PPI usage-induced T2DM risk. Our findings revealed that PPI treatment can increase the risk of T2DM. Additionally, we were successful in constructing a new nomogram to identify individuals at high risk of developing T2DM among patients using PPIs and completed a preliminary exploration of possible gene targets and mechanisms. Our study will be useful in alerting clinicians to the T2DM risk involved in PPI treatment and allowing them to take early prevention and intervention measures.