Higher Risk Of Type 2 Diabetes Research Articles

Abstract P138: Smoking Timing, Genetic Susceptibility, and Risk of Incident Type 2 Diabetes: A Cohort Study in UK Biobank

Objective: Smoking is an establish risk factor for type 2 diabetes (T2D); however, little is known about how the timing patterns of smoking is related to T2D risk. We prospectively assessed the association of smoking timing with risk of T2D and examined whether smoking amount or genetic susceptibility might modify the relationship. Methods: This study analyzed 294815 participants from the UK Biobank who were free of diabetes at baseline and with complete data on the time from waking to the first cigarette. Cox proportional hazards models were used to evaluate the association between smoking timing and risk of incident T2D. Results: During a median follow-up time of 12 years, a total of 9937 incident cases of T2D were documented among the 294815 participants. Compared to non-smokers, shorter time from waking to the first cigarette was significantly associated with a higher risk of incident T2D ( P for trend <0.0001). The adjusted hazard ratio (aHR) associated with smoking timing was 1.46 (95% confidence interval [CI] 1.17-1.81) for >2 hours, 1.51 (1.21-1.87) for 1-2 hours, 1.58 (1.34-1.85) for 30-60 minutes, 1.86 (1.57-2.21) for 5-15 minutes, and 2.01 (1.60-2.54) for <5 minutes, respectively. We found that even among light smokers, those with the shortest time from waking to the first cigarette had a 105% higher risk of T2D (HR 2.05, 95% CI 1.52-2.76), which was comparable to heavy smokers. The genetic risk score for T2D did not modify this association ( P -interaction =0.51). Conclusions: Our findings indicate that shorter time from waking to the first cigarette is significantly associated with a higher risk of incident T2D, independent of the amount of cigarettes smoked.

Abstract P496: Proteomic BMI Signature and Incidence of Type 2 Diabetes

Background: Obesity exhibits heterogeneity in physiological states and metabolic phenotypes cross individuals. A recent study identified a proteomics signature of obesity which better captures such heterogeneity than classical Body Mass Index (BMI). Aims: We aimed to create a proteomics-inferred BMI signature and investigate whether the proteomic-inferred BMI was associated with incident Type 2 Diabetes (T2D). Methods: This study included 34,317 adults in the UK Biobank who were free of T2D and had complete data on proteomics metrics assessed by antibody-based Olink assay. Sixty-seven pre-identified proteins were available and used to create the proteomic BMI signature. Cox proportional models were used to estimate the association between proteomic BMI signature and incident T2D. Results: The proteomic BMI signature was strongly correlated with actual BMI (Pearson correlation coefficients=0.75, P <0.001). During a mean follow-up of 13.2 years, a total of 1,651 T2D events were documented. We observed that per standard deviation increase in proteomic BMI signature was significantly associated with a 250% higher risk of T2D (HR, 2.50, 95%CI, 2.37, 2.64). Compared with the lowest Quartile, the HR (95%CI) for the Quartile 2 to the Quartile 4 of proteomic BMI signature were 2.06 (1.61, 2.64), 3.56 (2.83, 4.48), and 10.0 (8.08, 12.4), respectively ( P -trend<0.001). Also, we observed that the associations between proteomic BMI signature and incident T2D were stronger in women than men ( P for interaction < 0.001). Compared with the lowest Quartile, the HR (95%CI) for highest Quartile of proteomic BMI signature were 16.63 (9.75, 28.36) for women and 8.06 (6.37, 10.19) for men. Conclusions: Higher proteomics-inferred BMI was significantly associated with higher risk of incident T2D, and such association was stronger in women than men. Our results suggest that proteomic BMI signature may serve as a surrogate metabolic indicator to better understand the mechanism underlying the association of BMI and T2D.

Abstract MP26: Dietary Patterns, Metabolome Profile and Risk of Type 2 Diabetes

Background: Plasma metabolites have been associated with type 2 diabetes (T2D) risk and may reflect metabolic homeostasis as a result of the interplay among diet, genetics, and the gut microbiome. Hypothesis: We hypothesized that specific multi-metabolite signatures can characterize the adherence and metabolic response to various dietary patterns and are associated with incident T2D. Methods: We analyzed 20578 participants in the Nurses’ Health studies and Health Professional Follow-up Study (NHS/HPFS), Hispanic Community Health Study/Study of Latinos, and Women’s Health Initiative, whose blood metabolome were profiled by liquid chromatography-mass spectrometry. We applied elastic net regression to develop (n=1206 in a NHS/HPFS lifestyle sub-study) and validate (in the remaining samples) metabolic signatures for 3 dietary recommendation-based diets (a Mediterranean diet - AMED, a healthy eating index - AHEI, and an anti-hypertensive diet - DASH), 3 plant-based diets (PDIs), and 2 mechanism-based diets (proinflammatory [EDIP] and insulinemic [EDIH] diets). We tested associations between the dietary metabolic signatures and incident T2D in 14060 initially T2D-free participants (1832 cases in up to 22 years of follow-up). In sub-samples, we further examined genetic and microbial factors (shotgun sequencing) associated with metabolic signatures. Results: We identified 8 metabolic signatures, each consisted of 37-66 metabolites and was robustly correlated with the corresponding dietary pattern index in all validation cohorts ( r =0.11-0.38; P < 8.06 х10 -9 ). We noted shared and distinct metabolites cross metabolic signatures of various dietary patterns. In multivariable-analyses, metabolic signatures of healthful diets (i.e., AMED, AHEI, DASH, healthful PDI) were associated with a lower T2D risk (hazard ratio [HR]: 0.82-0.90; P < 3х10 -6 ), whereas metabolic signatures of unhealthful diets (e.g., EDIP and EDIH) were associated with a higher T2D risk (HR: 1.23-1.26; P < 2х10 -15 ). The metabolic signatures mediated 26%-56% of the associations between their corresponding dietary patterns and T2D risk ( P < 0.01). Further, a proportion of variation in the dietary metabolic signatures was explained by genetic variants (9.9% for EDIP to 34.5% for PDI) and gut microbial diversity (0.2% for PDI to 14.9% for EDIP). Dietary metabolic signatures were associated with 7 genetic loci including those involved in fatty acid and energy metabolism (e.g., FADS1/2 and CERS4 , P < 5х10 -8 ), and the abundance of 39 gut bacterial species (FDR <0.05). Conclusions: We identified metabolic signatures that characterized the adherence and metabolic responses (related to genetics and gut microbiome) to various dietary patterns, and were associated with T2D risk, in ethnically diverse populations. Metabolomic profiling may facilitate personalized nutritional interventions for T2D prevention.

Fasting plasma metabolites reflecting meat consumption and their associations with incident type 2 diabetes in two Swedish cohorts

BackgroundConsumption of processed red meat has been associated with increased risk of developing type 2 diabetes (T2D), but challenges in dietary assessment call for objective intake biomarkers. ObjectivesThis study aimed to investigate metabolite biomarkers of meat intake and their associations with T2D risk. MethodsFasting plasma samples were collected from a case–control study nested within Västerbotten Intervention Program (VIP) (214 females and 189 males) who developed T2D after a median follow-up of 7 years. Panels of biomarker candidates reflecting the consumption of total, processed, and unprocessed red meat and poultry were selected from the untargeted metabolomics data collected on the controls. Observed associations were then replicated in Swedish Mammography clinical subcohort in Uppsala (SMCC) (n = 4457 females). Replicated metabolites were assessed for potential association with T2D risk using multivariable conditional logistic regression in the discovery and Cox regression in the replication cohorts. ResultsIn total, 15 metabolites were associated with ≥1 meat group in both cohorts. Acylcarnitines 8:1, 8:2, 10:3, reflecting higher processed meat intake [r > 0.22, false discovery rate (FDR) < 0.001 for VIP and r > 0.05; FDR < 0.001 for SMCC) were consistently associated with higher T2D risk in both data sets. Conversely, lysophosphatidylcholine 17:1 and phosphatidylcholine (PC) 15:0/18:2 were associated with lower processed meat intake (r < −0.12; FDR < 0.023, for VIP and r < −0.05; FDR < 0.001, for SMCC) and with lower T2D risk in both data sets, except for PC 15:0/18:2, which was significant only in the VIP cohort. All associations were attenuated after adjustment for BMI (kg/m2). ConclusionsConsistent associations of biomarker candidates involved in lipid metabolism between higher processed red meat intake with higher T2D risk and between those reflecting lower intake with the lower risk may suggest a relationship between processed meat intake and higher T2D risk. However, attenuated associations after adjusting for BMI indicates that such a relationship may at least partly be mediated or confounded by BMI.

Association of plasma angiogenin with risk of major cardiovascular events in type 2 diabetes

BackgroundAngiogenin, an enzyme belonging to the ribonucleases A superfamily, plays an important role in vascular biology. Here, we sought to study the association of plasma angiogenin and major adverse cardiovascular events (MACEs) in patients with type 2 diabetes (T2D).MethodsThis prospective study included 1083 T2D individuals recruited from a secondary hospital and a primary care facility. The primary outcome was a composite of four-point MACE (nonfatal myocardial infarction, stroke, unstable angina pectoris leading to hospitalization and cardiovascular death). Circulating angiogenin was measured by a proximity extension assay. Cox regression models were used to evaluate the association of baseline plasma angiogenin with the risk of MACE.ResultsDuring a median follow-up of 9.3 years, 109 (10%) MACE were identified. Plasma angiogenin was significantly higher in participants with MACE than in those without MACE (P < 0.001). Doubling of plasma angiogenin concentration was associated with a 3.10-fold (95% CI 1.84–5.22) increased risk for MACE. The association was only moderately attenuated after adjustment for demographic and cardiometabolic risk factors (adjusted HR 2.38, 95% CI 1.34–4.23) and remained statistically significant after additional adjustment for estimated glomerular filtration rate (eGFR) and urinary albumin to creatinine ratio (uACR) (adjusted HR 1.90, 95% CI 1.02–3.53). A consistent outcome was obtained when plasma angiogenin was analysed as a categorical variable in tertiles.ConclusionsPlasma angiogenin was associated with the risk of future cardiovascular events in patients with T2D and may be a promising novel biomarker for identifying high-risk T2D patients for early management.

A diet-wide Mendelian randomization analysis: causal effects of dietary habits on type 2 diabetes.

Traditional clinical studies have indicated a link between certain food intakes and type 2 diabetes (T2D), but the causal relationships between different dietary habits and T2D remain unknown. Using Mendelian randomization (MR) approaches, we investigated the potential causal association between dietary habits and T2D risk. We collected publicly available genome-wide association studies' summary statistics for 18 dietary habits from the UK Biobank and T2D data from the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium. We applied the inverse variance weighted (IVW) method, supplemented with the MR-Egger method, weighted median method (WMM), simple method, weighted mode, MR-Egger regression, and the MR pleiotropy residual sum and outlier test to determine whether a particular diet was causal for T2D. Reliable and robust MR estimates demonstrated that poultry intake has a causal effect on a higher risk of T2D (IVW: OR 6.30, 95% CI 3.573-11.11, p = 2.02e - 10; WMM: OR 5.479, 95% CI 0.2758-10.88, p = 1.19e - 06). Conversely, dried fruit intake (IVW: OR 0.380, 95% CI 0.237-0.608, p = 5.57e - 05; WMM: OR 0.450, 95% CI 0.321-0.630, p = 3.33e - 06) and cereal intake (IVW: OR 0.455, 95% CI 0.317-0.653, p = 1.924e - 05; WMM: OR 0.513, 95% CI 0.379-0.694, p = 1.514e - 05) were causally associated with T2D as protective factors. Sensitivity analyses confirmed the reliability and robustness of these findings. Our study established the causal effects of poultry intake, dried fruit intake, and cereal intake on T2D, identifying poultry intake as a risk factor and the other two as protective factors. Further research into potential mechanisms is required to validate these novel findings.

Newly Diagnosed Type 2 Diabetes in Children and Adolescents in Bangladesh- A Cross Sectional Study

Background: Type 2 diabetes (T2D) has become a serious health problem in children and adolescents as the prevalence of obesity is increasing. Objective: To observe the baseline clinical and laboratory findings and treatment modalities of children and adolescents with newly diagnosed T2D. Materials &amp; Methods: This cross-sectional study was done from Jan- Dec.2017 in Changing Diabetes in Children (CDiC), Paediatric diabetes OPD clinic in BIRDEM among newly diagnosed T2D children and adolescents. Results: Among 68 patients majority were female (64.7%). Mean age was 13.1 ± 2.5 years. Ten patients were below 10 years. Most of the patients were from urban &amp; semi urban area and high to middle socioeconomic status. Family history was present in 91.2% and 22.1 % mother had H/O GDM. Forty two patients (61.8%) had typical and 22 (32.3%) had atypical symptoms. Thirty seven children had acanthosis nigricans (54.4%). Majority were found overweight (53/68), and (12/68) were obese. Mean HbA1c was 10.2 ± 2.8. Life style modification was advised to all. Insulin was started along with metformin in patients with high HbA1c and 33.8% patients were given metformin only. While comparing, BMI was found high in basal and metformin group. Lower fasting blood glucose and HbA1c were observed in patients with metformin only. Conclusion: T2D is emerging in children and adolescents. The screening of children at high risk of T2D is essential for preventive and timely remedial measures. BANGLADESH J CHILD HEALTH 2022; VOL 46 (1) : 9-13

Age and sex differences in the association between neighborhood socioeconomic environment and incident diabetes: Results from the diabetes location, environmental attributes and disparities (LEAD) network

ObjectiveWorse neighborhood socioeconomic environment (NSEE) may contribute to an increased risk of type 2 diabetes (T2D). We examined whether the relationship between NSEE and T2D differs by sex and age in three study populations. Research design and methodsWe conducted a harmonized analysis using data from three independent longitudinal study samples in the US: 1) the Veteran Administration Diabetes Risk (VADR) cohort, 2) the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort, and 3) a case-control study of Geisinger electronic health records in Pennsylvania. We measured NSEE with a z-score sum of six census tract indicators within strata of community type (higher density urban, lower density urban, suburban/small town, and rural). Community type-stratified models evaluated the likelihood of new diagnoses of T2D in each study sample using restricted cubic splines and quartiles of NSEE. ResultsAcross study samples, worse NSEE was associated with higher risk of T2D. We observed significant effect modification by sex and age, though evidence of effect modification varied by site and community type. Largely, stronger associations between worse NSEE and diabetes risk were found among women relative to men and among those less than age 45 in the VADR cohort. Similar modification by age group results were observed in the Geisinger sample in small town/suburban communities only and similar modification by sex was observed in REGARDS in lower density urban communities. ConclusionsThe impact of NSEE on T2D risk may differ for males and females and by age group within different community types.

Dietary Sodium Intake and Risk of Incident Type 2 Diabetes

ObjectiveTo fill the knowledge gap of the relation between long-term dietary sodium intake and type 2 diabetes (T2D), we evaluate the association between the frequency of adding salt to foods, a surrogate marker for evaluating the long-term sodium intake, and incident T2D risk. MethodsA total of 402,982 participants from UK Biobank (March 13, 2006 – October 10, 2010) who were free of diabetes, chronic kidney disease, cancer, or cardiovascular disease at baseline, and had completed information on adding salt were analyzed in this study. ResultsDuring a median of 11.9 years of follow-up, 13,120 incident cases of T2D were documented. Compared with participants who “never/rarely” added salt to foods, the adjusted HRs were 1.11 (95% CI, 1.06 to 1.15), 1.18 (95% CI, 1.12 to 1.24), and 1.28 (95% CI, 1.20 to 1.37) across the groups of “sometimes,” “usually,” and “always,” respectively (P-trend<.001). We did not find significant interactions between the frequency of adding salt to foods and baseline hypertension status and other covariates on the risk of incident T2D. The observed positive association was partly mediated by body mass index, waist to hip ratio, and C-reactive protein, with a significant mediation effect of 33.8%, 39.9%, and 8.6%, respectively. The significant mediation effect of body mass index was largely driven by the body fat mass rather than the body fat-free mass. ConclusionOur findings for the first time indicate that higher frequency of adding salt to foods, a surrogate marker for a person’s long-term salt taste preference and intake, is associated with a higher T2D risk.

Health inequalities in type 2 diabetes: contributions of exposures in the residential environment

Abstract Background People with lower socioeconomic position (SEP) have higher risk of type 2 diabetes (T2D). Besides genetic predisposition, the exposome shapes this risk. This includes exposures in the residential environment. We explored the relationships between SEP, the residential food environment, walkability, greenspace, and T2D. Methods Cross-sectional and longitudinal data from 9188 participants were analyzed. Prevalent T2D was measured by an oral glucose tolerance test. Incident T2D was self-reported in annual follow-up questionnaires. Education, income, and occupation were measured as indicators of SEP. The residential environment was operationalized by address level Food Environment Healthiness Index (FEHI), number of fast-food outlets (FF), walkability index (WI), and proportion of greenspace (GS). Logistic regression and survival analyses were used to model the associations between SEP, the environment and T2D. The proportion of mediation of the association between SEP and T2D was estimated in a causal mediation analysis using a simulation approach. Results Over a median follow-up of 6.2 years, 207 incident cases were identified. Lower SEP was associated with higher risk of T2D. Hazard ratios (HR) were 2.0 (95%CI 1.6-2.6), 1.8 (1.4-2.3) and 1.8 (1.2-2.6) for an interquartile range decrease (IQR) of education, income, and occupation, respectively. HRs for IQR changes of the environmental factors were: FEHI 1.2 (1.0-1.4), FF 0.9 (0.8-1.0), WI 1.2 (1.0-1.6) and GS 1.2 (1.0-1.4). Regression on prevalent T2D yielded similar results. Lower SEP was associated with a less healthy environment (e.g., FEHI -0.08 (-0.10--0.06) for education). Environmental exposures mediated between 0.3% (-8.6-8.6) and 8.5% (2.3-27.4) of the longitudinal associations. Conclusions Lower SEP was associated with prevalent and incident T2D. People with lower SEP lived in a slightly less healthy residential environment. Some of the SEP-T2D association could be mediated by the environment. Key messages • A less healthy residential food environment, lower walkability and less green space might mediate some of the association between SEP and T2D. • More research is needed to clarify the extent to which environmental pathways contribute to health inequalities.

Clonal Hematopoiesis of Indeterminate Potential (CHIP) and Incident Type 2 Diabetes Risk.

Clonal hematopoiesis of indeterminate potential (CHIP) is an aging-related accumulation of somatic mutations in hematopoietic stem cells, leading to clonal expansion. CHIP presence has been implicated in atherosclerotic coronary heart disease (CHD) and all-cause mortality, but its association with incident type 2 diabetes (T2D) is unknown. We hypothesized that CHIP is associated with elevated risk of T2D. CHIP was derived from whole-genome sequencing of blood DNA in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) prospective cohorts. We performed analysis for 17,637 participants from six cohorts, without prior T2D, cardiovascular disease, or cancer. We evaluated baseline CHIP versus no CHIP prevalence with incident T2D, including associations with DNMT3A, TET2, ASXL1, JAK2, and TP53 variants. We estimated multivariable-adjusted hazard ratios (HRs) and 95% CIs with adjustment for age, sex, BMI, smoking, alcohol, education, self-reported race/ethnicity, and combined cohorts' estimates via fixed-effects meta-analysis. Mean (SD) age was 63.4 (11.5) years, 76% were female, and CHIP prevalence was 6.0% (n = 1,055) at baseline. T2D was diagnosed in n = 2,467 over mean follow-up of 9.8 years. Participants with CHIP had 23% (CI 1.04, 1.45) higher risk of T2D than those with no CHIP. Specifically, higher risk was for TET2 (HR 1.48; CI 1.05, 2.08) and ASXL1 (HR 1.76; CI 1.03, 2.99) mutations; DNMT3A was nonsignificant (HR 1.15; CI 0.93, 1.43). Statistical power was limited for JAK2 and TP53 analyses. CHIP was associated with higher incidence of T2D. CHIP mutations located on genes implicated in CHD and mortality were also related to T2D, suggesting shared aging-related pathology.

Association of tyrosine hydroxylase 01 (TH01) microsatellite and insulin gene (INS) variable number of tandem repeat (VNTR) with type 2 diabetes and fasting insulin secretion in Mexican population

PurposeA variable number of tandem repeats (VNTR) in the insulin gene (INS) control region may be involved in type 2 diabetes (T2D). The TH01 microsatellite is near INS and may regulate it. We investigated whether the TH01 microsatellite and INS VNTR, assessed via the surrogate marker single nucleotide polymorphism rs689, are associated with T2D and serum insulin levels in a Mexican population.MethodsWe analyzed a main case–control study (n = 1986) that used univariate and multivariate logistic regression models to calculate the risk conferred by TH01 and rs689 loci for T2D development; rs689 results were replicated in other case–control (n = 1188) and cross-sectional (n = 1914) studies.ResultsTH01 alleles 6, 8, 9, and 9.3 and allele A of rs689 were independently associated with T2D, with differences between sex and age at diagnosis. TH01 alleles with ≥ 8 repeats conferred an increased risk for T2D in males compared with ≤ 7 repeats (odds ratio, ≥ 1.46; 95% confidence interval, 1.1–1.95). In females, larger alleles conferred a 1.5-fold higher risk for T2D when diagnosed ≥ 46 years but conferred protection when diagnosed ≤ 45 years. Similarly, rs689 allele A was associated with T2D in these groups. In males, larger TH01 alleles and the rs689 A allele were associated with a significant decrease in median fasting plasma insulin concentration with age in T2D cases; the reverse occurred in controls.ConclusionLarger TH01 alleles and rs689 A allele may potentiate insulin synthesis in males without T2D, a process disabled in those with T2D.

Gene-diet interaction analysis using novel weighted food scores discovers the adipocytokine signaling pathway associated with the development of type 2 diabetes.

The influence of dietary patterns measured using Recommended Food Score (RFS) with foods with high amounts of antioxidant nutrients for Type 2 diabetes (T2D) was analyzed. Our analysis aims to find associations between dietary patterns and T2D and conduct a gene-diet interaction analysis related to T2D. Data analyzed in the current study were obtained from the Korean Genome and Epidemiology Study Cohort. The dietary patterns of 46 food items were assessed using a validated food frequency questionnaire. To maximize the predictive power of the RFS, we propose two weighted food scores, namely HisCoM-RFS calculated using the novel Hierarchical Structural Component model (HisCoM) and PLSDA-RFS calculated using Partial Least Squares-Discriminant Analysis (PLS-DA) method. Both RFS (OR: 1.11; 95% CI: 1.03- 1.20; P = 0.009) and PLSDA-RFS (OR: 1.10; 95% CI: 1.02-1.19, P = 0.011) were positively associated with T2D. Mapping of SNPs (P < 0.05) from the interaction analysis between SNPs and the food scores to genes and pathways yielded some 12 genes (CACNA2D3, RELN, DOCK2, SLIT3, CTNNA2, etc.) and pathways associated with T2D. The strongest association was observed with the adipocytokine signalling pathway, highlighting 32 genes (STAT3, MAPK10, MAPK8, IRS1, AKT1-3, ADIPOR2, etc.) most likely associated with T2D. Finally, the group of the subjects in low, intermediate and high using both the food scores and a polygenic risk score found an association between diet quality groups with issues at high genetic risk of T2D. A dietary pattern of poor amounts of antioxidant nutrients is associated with the risk of T2D, and diet affects pathway mechanisms involved in developing T2D.

Type 2 Diabetes and Its Association With Psychiatric Disorders in Young Adults in South Korea

Because type 2 diabetes (T2D) has become increasingly prevalent among young adults, the study of the association of T2D with psychiatric disorders in young adults is important for early detection and timely intervention. To determine whether a diagnosis of a psychiatric disorder is associated with increased risk of developing T2D in young adults. This large-scale prospective cohort study used data collected by the South Korean National Health Insurance Service between 2009 and 2012, representing 97% of the South Korean population. Young adults aged 20 to 39 years with and without diagnoses of psychiatric disorders were included in the study. Young adults with missing data and those with a history of T2D were excluded from the study. The cohort was followed up to monitor development of T2D until December 2018. Data were analyzed from March 2021 to February 2022. Diagnosis of 1 of 5 psychiatric disorders, including schizophrenia, bipolar disorder, depressive disorder, anxiety disorder, and sleep disorder. The primary outcome was newly diagnosed T2D during a follow-up period of 7.59 years. The incidence rate of T2D was calculated as the number of new cases per 1000 person-years during the follow-up period. The Cox proportional hazards regression model was used to estimate the hazard ratios (HRs) and 95% CIs for T2D incidence. Exploratory analyses were performed for subgroups stratified by age and sex. In total, 6 457 991 young adults (mean [SD] age, 30.74 [4.98] years; 3 821 858 men [59.18%]) were followed up, including 658 430 individuals with psychiatric disorders. The cumulative incidence of T2D differed significantly between individuals with and without psychiatric disorders (log-rank test, P < .001). Incidence rates of T2D for individuals with and without psychiatric disorders were 2.89 and 2.56 per 1000 person-years, respectively. Individuals with a diagnosis of any psychiatric disorder showed a higher risk of developing T2D than those without a diagnosis (adjusted HR, 1.20; 95% CI, 1.17-1.22). The adjusted HRs for T2D were 2.04 (95% CI, 1.83-2.28) for individuals with schizophrenia, 1.91 (95% CI, 1.73-2.12) for individuals with bipolar disorder, 1.24 (95% CI, 1.20-1.28) for individuals with depressive disorder, 1.13 (95% CI, 1.11-1.16) for individuals with anxiety disorder, and 1.31 (95% CI, 1.27-1.35) for individuals with sleep disorder. In this large-scale prospective cohort study of young adults, 5 psychiatric disorders were significantly associated with an increased risk of developing T2D. Young adults with schizophrenia and bipolar disorder in particular were at a higher risk of T2D. These results have important implications for early detection of and timely intervention in T2D for young adults with psychiatric disorders.

Large scale application of the Finnish diabetes risk score in Latin American and Caribbean populations: a descriptive study.

The prevalence of type 2 diabetes (T2D) continues to increase in the Americas. Identifying people at risk for T2D is critical to the prevention of T2D complications, especially cardiovascular disease. This study gauges the ability to implement large population-based organized screening campaigns in 19 Latin American and Caribbean countries to detect people at risk for T2D using the Finnish Diabetes Risk Score (FINDRISC). This cross-sectional descriptive analysis uses data collected in a sample of men and women 18 years of age or older who completed FINDRISC via eHealth during a Guinness World Record attempt campaign between October 25 and November 1, 2021. FINDRISC is a non-invasive screening tool based on age, body mass index, waist circumference, physical activity, daily intake of fruits and vegetables, history of hyperglycemia, history of antihypertensive drug treatment, and family history of T2D, assigning a score ranging from 0 to 26 points. A cut-off point of ≥ 12 points was considered as high risk for T2D. The final sample size consisted of 29,662 women (63%) and 17,605 men (27%). In total, 35% of subjects were at risk of T2D. The highest frequency rates (FINDRISC ≥ 12) were observed in Chile (39%), Central America (36.4%), and Peru (36.1%). Chile also had the highest proportion of people having a FINDRISC ≥15 points (25%), whereas the lowest was observed in Colombia (11.3%). FINDRISC can be easily implemented via eHealth technology over social networks in Latin American and Caribbean populations to detect people with high risk for T2D. Primary healthcare strategies are needed to perform T2D organized screening to deliver early, accessible, culturally sensitive, and sustainable interventions to prevent sequelae of T2D, and reduce the clinical and economic burden of cardiometabolic-based chronic disease.

1269-P: Trajectories of MicroRNAs Are Associated with Trajectories of Weight but Not Fasting Blood Glucose in the Diabetes Prevention Program Trial

Introduction: Circulating microRNAs (miRs) are emerging as promising biomarkers of risk for type 2 diabetes (T2D) and responses to risk-reduction interventions, but no prior study has examined longitudinal associations between miRs and fasting blood glucose (FBG) or weight in adults at high risk for T2D. Prior studies have been cross-sectional or only measured longitudinal outcomes. We determined the association between longitudinal trajectories of miRs, FBG, and weight in a subset of participants from the Diabetes Prevention Program (DPP). Methods: This study was a secondary analysis of a subset of participants (N=150) from DPP. A custom Fireplex assay was used to quantify 58 miRs from banked plasma samples collected at baseline, 1-year, and 2-years. Generalized linear mixed models were used to analyze associations between the miRs, FBG (mg/dL), and weight (kg) over time, adjusting for covariates (i.e., age, body mass index, race or ethnicity, gender, trial arm, and high density lipoprotein cholesterol). The Benjamini-Hochberg false discovery rate method was used to adjust for multiple comparisons. Results: The sample was 73% female, 19% Black, 14% Hispanic, and mean age was 51 ± 11 years. At baseline, the participants were normotensive, obese, and had dyslipidemia and prediabetes. After adjusting for multiple comparisons, 39 miRs were significantly associated with weight, of which 12 miRs (let-7c-5p, miR-1066-5p, miR-1287-5p, miR-133b, miR-15a-5p, miR-15b-5p, miR-181c-3p, miR-197-5p, miR-23b-5p, miR-296-5p, miR-877-5p, miR-98-5p) remained significant after adjusting for covariates. In the FBG model, none of the miRs were significant. Conclusions: Twelve miRs were associated with longitudinal weight but no miRs were associated with FBG trajectories in a subset of participants from the DPP trial. These miRs may provide mechanistic, diagnostic, or therapeutic insights about T2D prevention. Disclosure K.A.Lewis: None. J.C.Florez: Consultant; AstraZeneca, Novo Nordisk, Other Relationship; AstraZeneca, Merck &amp; Co., Inc. B.Aouizerat: None. A.M.Kanaya: None. E.Flowers: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK124228, X01DK115999)

1376-P: Dietary Sodium Intake and Risk of Incident Type 2 Diabetes

Background: Type 2 Diabetes (T2D) and hypertension often coexist and share many common risk factors. High dietary sodium intake is a well-established risk factor for hypertension, whereas few studies have assessed the relation between dietary sodium intake and type 2 diabetes (T2D). In this study, we aimed to evaluate the association between the frequency of adding salt to foods, a surrogate marker for evaluating the long-term sodium intake, and incident T2D risk. Methods: A total of 402,982 participants from UK biobank, who were free of diabetes, chronic kidney disease, cancer, or cardiovascular disease at baseline, and had completed information on adding salt were analyzed in this study. Cox proportional hazards models were used to estimate the association between the frequency of adding salt to foods and incident T2D. Results: During a median of 11.8 years of follow-up, 13,120 incident T2D were documented. Compared with participants who Never/Rarely added salt to foods, the adjusted Hazard Ratios (95% confidence interval) were 1.13 (1.08, 1.17), 1.20 (1.14, 1.26), and 1.39 (1.30, 1.49) across the groups of Sometimes, Usually, and Always, respectively (P-trend&amp;lt;0.001). We did not find significant interactions between the frequency of adding salt to foods and baseline hypertension status and other covariates on the risk of incident T2D. The observed positive association was partly mediated by body mass index (BMI), waist to hip ratio, and C-reactive protein, with a significant mediation effect of 32.9%, 36.6% and 7.0%, respectively. The significant mediation effect of BMI was largely driven by the body fat mass rather than the body fat-free mass. Conclusions: Our findings for the first time indicate that higher frequency of adding salt to foods, a surrogate marker for a person’s long-term salt taste preference and intake, is associated with a higher T2D risk. Disclosure X.Wang: None. H.Ma: None. R.Tang: None. Q.Xue: None. X.Li: None. Y.Heianza: None. L.Qi: None.

72-OR: Higher Genetic Risk for Type 2 Diabetes Is Associated with Faster Decline of Beta-Cell Function

Objective: Polygenic risk score (PRS) for type 2 diabetes (T2D) has been reported to be associated with beta cell function, but whether it is also associated with the longitudinal change of beta cell function is largely unknown. We aimed to evaluate the association of PRS and the trajectory of beta cell function in a community based prospective cohort. Method: We analyzed 6,323 participants without T2D at baseline from Ansan-Ansung cohort with 75-g 2-hour oral glucose tolerance tests taken biennially for 14 years. Disposition index (DI), derived from insulinogenic index at 60 minutes and Matsuda index, was used as a marker for beta cell function. PRS was calculated using 1.2 million variants from trans-ancestry T2D genome-wide association study using Bayesian regression (PRS-CSx). Participants were stratified into low (1st quintile), intermediate (2nd to 4th quintiles) and high genetic risk (5th quintile). Lifestyle was categorized as unfavorable, intermediate and favorable lifestyle according to Life’s Essential 8. Linear mixed model was applied. Result: During a median follow-up of 13 years, 379 (30.6%), 875 (23.0%) and 171 (13.4%) participants developed T2D in high, intermediate and low genetic risk group, respectively. Participants in high genetic risk group, compared to low genetic risk group, had a 33% lower DI at baseline (53 [95% CI 50, 56] vs 79 [74, 84]; P = 7.9×10-15), and a 1.7-fold faster rate of decline in log2(DI) per year (-0.035 [-0.028, -0.042] vs -0.020 [-0.013, -0.027]; P = 0.0026). Notably, we found significant interaction between the rate of decline in DI and T2D PRS (P = 8.5×10-5). Healthy lifestyle was associated with decreased rate of decline in DI across all genetic risk group. Conclusion: Having a high genetic risk for T2D was associated with not only a lower DI at baseline, but also a faster rate of decline in DI. Genetic information could be used to identify those at risk for rapid decline of beta cell function and emphasize lifestyle modification. Disclosure H.Lee: None. J.Choi: None. H.Son: None. K.Park: None. N.H.Cho: None. S.Kwak: None. Funding Ministry of Health &amp; Welfare, Republic of Korea

Early-life tobacco smoke exposure, genetic susceptibility and the risk of type 2 diabetes in adulthood: A large prospective cohort study

ObjectivesWe aimed to assess the relationships between early-life tobacco smoke exposures and the incident risk of type 2 diabetes (T2D) in later life as well as the joint effects and interactions between genetic susceptibility and early-life tobacco exposures. MethodsWe used data on in utero tobacco exposure and the age of smoking initiation to estimate the status of early-life tobacco exposure in the UK Biobank. Cox proportion hazard models were applied to estimate the associations between early-life tobacco exposure and T2D risk and investigate joint effects and interactions of early-life tobacco smoke exposure with genetic susceptibility. ResultsAmong 407,943 subjects from the UK Biobank, 17,115 incident cases were documented during a median follow-up of 12.80 years. Compared with subjects without prenatal tobacco exposure, those with in utero tobacco exposure had a higher risk of T2D with a hazard ratio (HR) (95 % confidence interval [CI]) of 1.11 (1.08, 1.15). Besides, the HRs (95 % CIs) of incident T2D for smoking initiation in adulthood, adolescence, and childhood (vs. never smokers) were 1.36 (1.31, 1.42), 1.44 (1.38, 1.50), and 1.78 (1.69, 1.88), respectively (P trend <0.001). No interaction between early-life tobacco exposure and genetic susceptibility was observed. In addition, participants with prenatal (HR 4.67 [95 % CI 4.31, 5.06]) or childhood (6.91 [6.18, 7.72]) tobacco exposure combined with high genetic risk showed the highest risk of T2D, compared to low genetic risk subjects without early-life smoke exposure. ConclusionEarly-life tobacco exposure was associated with an increased risk of T2D later in life regardless of genetic background. This highlights the significance of education campaigns aimed at reducing smoking among children, adolescents, and pregnant women as an effective measure to combat the T2D epidemic.

Accelerometer-measured intensity-specific physical activity, genetic risk and incident type 2 diabetes: a prospective cohort study

ObjectiveAlthough 30 min/day of moderate-intensity physical activity is suggested for preventing type 2 diabetes (T2D), the current recommendations exclusively rely on self-reports and rarely consider the genetic risk. We examined...