Prevalence of Type 2 Diabetes Risk in a Higher Education Community of North-Eastern Portugal: a Prospective Cross-sectional Observational Analysis

Menstrual cycle characteristics and risk of coronary heart disease and type 2 diabetes

BackgroundThe 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).MethodsThis 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.ResultsThe 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%).ConclusionsFINDRISC 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.

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.

Glycemic index, glycemic load, and risk of type 2 diabetes: results from 3 large US cohorts and an updated meta-analysis

PurposeGenetic and lifestyle/environmental factors as well as their interplay contribute to the pathogenesis of type 2 diabetes (T2D). Several trials have shown that lifestyle intervention is effective in the prevention of T2D, but there are no trials that have taken into account the genetic risk of the participants. The aim of our T2D-GENE trial (ClinicalTrials.gov ID: NCT02709057) is to investigate the effects of lifestyle intervention on the prevention of T2D in participants with a high genetic risk of T2D compared with participants with a low genetic risk of T2D.MethodsBoth intervention and control groups include 300 participants with low and 300 participants with high genetic risk for T2D. Genetic risk was evaluated by genetic risk score, and these two groups were matched additionally for fasting plasma glucose concentration, age, and body mass index. Corresponding control groups (300 participants each) do not have lifestyle intervention. The inclusion criteria are impaired fasting glucose at entry with or without impaired glucose tolerance, age 50–75 years, and body mass index ≥25 kg/m2. The primary outcome is incident T2D and the intervention lasts for 3 years.ConclusionIf the effects of the lifestyle intervention are independent from the genetic risk of the participants, our study will be of great importance for the entire T2D research community, health care providers, and individuals at high risk for T2D. In this case, lifestyle intervention is beneficial for all individuals at risk for developing T2D, independently of genetic risk.ClinicalTrials.gov IDNCT02709057 March 15, 2016

Introduction: Despite the largely reported inverse association between coffee intake and type 2 diabetes (T2D) risk by observational studies, the causality of such association is not well established. Hypothesis: We assessed the hypothesis that the observationally inverse association between coffee intake and T2D risk is causal. Methods: In a nested case-control study within the Singapore Chinese Health Study cohort, we included 2436 T2D cases and 2436 matched controls. At biospecimen collections (1999-2004), participants were free of diagnosed T2D, cardiovascular disease and cancer. Cases were participants who reported to have physician-diagnosed T2D at follow-up visits during 2006-2010. Controls were randomly selected among those who remained free of T2D and were matched to the index cases by age, sex, dialect group and date of biospecimen collection. We 1) tested the association between coffee intake and T2D risk using conditional logistic regression analysis; 2) tested the association of the nine single-nucleotide polymorphisms (SNPs) with coffee intake or risk of T2D; 3) used the instrumental variable (IV) estimators to quantify the strength of the causal association of coffee intake and risk of T2D. The IV estimator (β IV ) which is identical to that derived by the widely used two-stage least squares method, was calculated through dividing the β of the regression coefficients for SNP-T2D by SNP-coffee associations. Results: Median intake (inter quartile range) for coffee intake was 1.00 (0.36-2.51) cups/day in this population. Coffee intake was observationally associated with lower risk of T2D and OR (95% CI) was 0.93 (0.88-0.98) in multivariate adjusted model. We found three SNPs that were significantly associated with higher coffee intake with β coefficients (95% CI) as follows: 0.73 (0.23, 1.24) for rs4410790 (P=0.004), 0.72 (0.22, 1.22) for rs6968554 (P=0.005), and 1.60 (0.28, 2.91) for rs7800944 (P=0.017). Among these tree SNPs, two SNPs were marginally associated with higher T2D risk with OR (95% CI) identically 1.08 (0.99, 1.18) for 7:17284577 and 7:17287106, and the other one was insignificantly associated with lower risk (OR 0.94; 95% CI 0.75, 1.18). The estimated β IV coefficients (95% CI) was not statistically significant for any of the three SNPs: 0.10 (-0.04, 0.25) for rs4410790 (P=0.16), 0.11 (-0.04, 0.25) for rs6968554 (P=0.15), and -0.04 (-0.19, 0.11) for rs7800944 (P=0.60). Conclusions: High coffee intake was observationally associated with lower T2D risk, but our finding does not support a causal association.

Aim. To study the risk of type 2 diabetes (T2D) using the Finnish Diabetes Risk Score (FINDRISC) and its contribution to all-cause mortality and cardiovascular events in the Russian population aged ­25-64 years.Material and methods. Data from cross-sectional studies ESSE-RF and ESSE-RF2 are included. The random sample was formed using the Kish method. Response was ~80%. The modular questionnaire in­cluded socio-demographic variables, medical history, and main risk factors for noncommunicable diseases. Blood was collected from the antecubital vein on an empty stomach. Biochemical parameters were determined in the clinical diagnostic laboratory of the National Medical Research Center for Therapy and Preventive Medicine. The presence of T2D was determined by questionnaire and/or fasting plasma glucose level ≥7,0 mmol/L. Obesity was defined as a body mass index of ≥30,0 kg/m2; abdominal obesity was defined as a waist circumference of ≥102 cm in men and ≥88 cm in women. FINDRISC risk was graduated as follow: low (<7), slight (7-11), moderate (12-14), high (15-20), very high (>20). Persons with diabetes and pregnant women were excluded. The final sample included 26418 people (10268 men and 16150 women). From 14 regions, ESSE-RF and ESSE-RF2 formed a prospective follow-up cohort (n=22812), median follow-up — 7,5 years). Statistical processing was performed using the open-source statistical programming language and environ­ment R (version 4.1).Results. A fifth of people aged 25-64 years are at ≥ moderate risk of T2D. The rate of FINDRISC ≥15 was 10,1% (women 12,4% vs men 6,4%, p<0,001); ≥12 points — 23,7%. A close relationship was found between impaired fasting glucose and the risk of T2D with FINDRISC ≥15 and ≥12 (p<0,001). Survival worsens for FINDRISC ≥12 and ≥15, with the worst survival rates in individuals with T2D (p<0,001). The likelihood of cardiovascular events consistently increases with FINDRISC ≥12, ≥15, and T2D. In the Cox model, only T2D is significant for all-cause mortality; FINDRISC ≥15 and T2D are significant for the cardiovascular and combined endpoints.Conclusion. An important task of the medical community is to identify individuals at risk of T2D at the population level. Early prevention of T2D risk factors can delay or prevent both T2D and cardiovascular events.

ObjectivesPresent study examines the relationship between the estimated risk of developing type 2 diabetes (T2D) and health-related quality of life (HRQoL). We quantify the association between Finnish Diabetes Risk Score (FINDRISC) and HRQoL, and examine the potential use of FINDRISC as tool to evaluate HRQoL indirectly.MethodsWe conducted a cross-sectional study comprising 707 Finnish people without a diagnosis of T2D between the ages of 51 and 75 years. The risk of developing T2D was assessed using the validated and widely used FINDRISC (range 0–26 points), and quality of life was measured using two preference-based HRQoL instruments (15D and SF-6D) and one health profile instrument (SF-36). Effects of the individual FINDRISC items and demographic and clinical characteristics, such as co-morbidities, on HRQoL were studied using multivariable Tobit regression models.ResultsLow HRQoL was significantly and directly associated with the estimated risk of developing T2D. An approximate 4–5 point change in FINDRISC score was observed to be associated with clinically noticeable changes in the preference-based instrument HRQoL index scores. The association between HRQoL and the risk of developing T2D was also observed for most dimensions of HRQoL in all applied HRQoL instruments. Overall, old age, lack of physical activity, obesity, and history of high blood glucose were the FINDRISC factors most prominently associated with lower HRQoL.ConclusionsThe findings may help the health care professionals to substantiate the possible improvement in glucose metabolism and HRQoL potentially achieved by lifestyle changes, and better convince people at high risk of T2D to take action towards healthier lifestyle habits. FINDRISC may also provide an accurate proxy for HRQoL, and thus by estimating the risk of T2D with the FINDRISC, information about patients’ HRQoL may also be obtained indirectly, when it is not feasible to use HRQoL instruments.

Introduction: The relationship between artificially sweetened beverage (ASB) intake and type 2 diabetes (T2D) risk remains inconclusive. Few studies have evaluated whether circulating metabolites that reflect ASB consumption may unveil potential mechanisms underlying the association between ASB consumption and T2D risk. Hypothesis: We hypothesized that a novel metabolomic profile reflecting habitual ASB consumption would positively associate with incident T2D among US adults. Methods: We quantified 120 plasma metabolites with liquid chromatography-mass spectroscopy in N=3,424 Nurses’ Health Study (NHS), NHSII, and Health Professionals Follow-up Study (HPFS) discovery cohort participants, and N=1,870 Framingham Heart Study (FHS) replication cohort participants. Habitual ASB consumption (servings/day; low-calorie cola or other carbonated beverages) was estimated from food frequency questionnaires at the time of blood draw. We used elastic net regression with 10-fold-cross-validation to age- and body mass index- adjusted metabolite levels to identify a metabolite profile associated with higher ASB consumption (ln-transformed). We then derived the continuous ASB metabolomic score as the weighted sum of these metabolites and replicated the analysis in an internal testing set and FHS. Finally, we evaluated the association of quartiles (Q) of the ASB-derived metabolomic score in NHS/NHSII/HPFS and FHS cohorts with incident T2D risk after blood draw (baseline for analysis) using Cox regression models to estimate hazard ratios (HR) and 95% confidence interval (CI), adjusted for demographics, lifestyle, diet, and body mass index at blood draw. Results: The ASB-derived metabolomic profile included 61 metabolites, primarily lipids and amino acids (Pearson correlation coefficients [ r ] between ASB self-reported intake and metabolomic score: r=0.08 [95% CI: 0.05, 0.11; p <0.0001] and r=0.09 [95% CI: 0.04, 0.14; p =0.001], for discovery and replication, respectively). Mean follow-up was 20 and 16 years from blood draw, including 359 and 241 confirmed incident T2D cases for NHS/NHSII/HPFS and FHS, respectively. Compared with participants in the lowest quartile of the ASB-derived metabolomic profile, higher quartiles were incrementally and significantly at higher T2D risk in NHS/NHSII/HPFS (HR [CI] for Q2, Q3, Q4 vs. Q1: 1.45 [1.04, 2.02], 1.83 [1.33, 2.52], 1.62 [1.18, 2.22], respectively; p-trend =0.003) and FHS (1.76 [1.24, 2.48], 1.82 [1.28, 2.58], 2.45 [1.77, 3.40], respectively; p-trend <0.0001). Conclusions: A robust metabolomic profile of ASB intake was not identified, yet the objective and replicable ASB-derived metabolomic profile was associated with higher incident T2D risk. The identified metabolites may reflect ASB intake, western dietary patterns, underlying metabolic health, and/or T2D-related pathophysiology.

Lifestyle intervention prevents or delays type 2 diabetes (T2D) in subjects at a high risk of T2D. However, it is not known whether genetic variants modify the effect on incident T2D during lifestyle intervention. To investigate whether a low or high genetic risk has effects on incident T2D in a group-based lifestyle intervention study. The T2D-GENE trial involved 973 men from the Metabolic Syndrome in Men (METSIM) cohort, aged 50-75 years, body mass index ≥25 kg/m2, fasting plasma glucose 5.6-6.9 mmol/L, hemoglobin A1c < 48 mmol/mol, and either a low or high genetic risk score for T2D. There were 2 intervention groups, a low (n = 315) and high genetic risk for T2D (n = 313). They were provided with a 3-year group-based intervention with access to a web portal focused on healthy diet and physical activity. There were also corresponding population-based control groups at low (n = 196) and high (n = 149) genetic risk for T2D who had two laboratory visits (0 and 3 years) and general health advice as a part of their METSIM cohort protocol. The primary outcome was incident T2D, and a secondary outcome was glycemia. The intervention significantly lowered the risk of T2D among the participants with a high genetic risk for T2D [hazards ratio (HR) 0.30, 95% confidence interval (CI) 0.16-0.56, P < .001) whereas in the low genetic risk group the effect was not significant (HR 0.69, 95% CI 0.36-1.32, P = .262). The intervention effect was not significantly different between the high and low genetic risk groups (P = .135). The intervention significantly ameliorated the worsening of glycemia and decreased weight both in the low and high genetic risk groups. Our results showed that individuals with a high genetic risk for T2D benefitted from a low-cost group-based intervention focusing on healthy diet and physical activity. Therefore, all individuals at risk of T2D should be encouraged to make lifestyle changes regardless of genetic risk.

Background: Five of the most important Type 2 Diabetes (T2D) prediction scores are: Cambridge, Finnish Diabetes Risk Score (FINDRISC), Rotterdam, Kuwaiti and Omani. These scores are based on the premise that Insulin Resistance (IR) is the primary cause of T2D. Yet, data in Africans suggest β-cell-failure without obesity is the most common cause of T2D. It is unknown whether these scores can identify undiagnosed T2D when the cause is β-cell-failure. Aims: Our aim was to compare in 514 African-born Blacks (age 38±10 (mean±SD); 68% male; BMI 28±5 kg/m 2 ) the ability of Cambridge, FINDRISC, Rotterdam, Kuwait and Omani scores to identify previously undiagnosed T2D due to either IR or β-cell-failure. Methods: Diabetes was diagnosed by OGTT. Insulin resistance was defined by the lowest quartile of the Matsuda index (≤2.97). Beta-cell failure was defined as T2D diagnosis in the absence of IR. Body mass index (BMI), waist circumference (WC) and visceral adipose tissue (VAT) was measured. Area under the Receiver Operator Characteristic curve (AROC) estimated sensitivity, specificity, and criteria for AROC were: 0.70-0.79=acceptable; 0.80-0.89=excellent; &gt;0.90=outstanding. Logistic regression determined odds of T2D at the optimal cut-point for each score. Results: The prevalence of T2D was 7% (38/514). Of the T2D cases, IR occurred in 61% (23/38) and β-cell failure in 39% (15/38). Age did not differ by group, but the IR group had higher BMI (31±5 vs 27±4 kg/m 2 P &lt;0.001) and WC (101±10 vs 91±10 cm P &lt;0.001). This is supported by the higher VAT in the IR group (161±73 vs 106±62 cm 2 P &lt;0.001), an important physiological correlate of insulin resistance. All scores showed acceptable to outstanding predictability for T2D due to IR, ranging from Cambridge at AROC=0.75, to the FINDRISC with AROC=0.92. Only weak to moderate predictability for all scores were shown in β-cell-failure (Cambridge at AROC=0.48 to FINDRISC with AROC=0.63). All models predicted significant odds of T2D due to IR, with FINDRISC≥10 OR=6.8 (95%CI, 2.6, 17.8) P&lt;0 .001; Rotterdam≥7 OR=8.5 (95%CI, 3.1, 23.2) P&lt;0 .001; Cambridge≥23 OR=11.4 (95%CI, 2.6, 50.0) P =0.001) proving most effective. Only the Cambridge score significantly predicted odds of T2D in β-cell-failure (OR=5.7 (95%CI 1.58, 20.54) P =0.001). Conclusions: All 5 diabetes risk scores effectively predicted undiagnosed T2D in Africans when the etiology was IR, but only the Cambridge score predicted T2D when the cause was β-cell failure. The Cambridge risk score rates overweight status, not just obesity status, which might explain why it was successful in predicting T2D in β-cell failure and the others were not. Existing scores which predict T2D might be modified, to effectively assess T2D risk when β-cell failure is the primary cause.

Background: Recently, a dietary index reflecting adherence to the 2017 French food-based dietary guidelines, the Programme National Nutrition Santé - guidelines score 2 (PNNS-GS2), has been developed, but evidence on its level of adherence and incident type 2 diabetes (T2D) risk is limited. Objective: We aimed to investigate the association between adherence to the simplified PNNS-GS2 (sPNNS-GS2) and the risk of incident T2D in a French cohort of women. Methods: Between 1993 and 2014, 71,450 women aged over 18 years were examined, and thereafter followed up for the occurrence of T2D within the E3N-EPIC prospective cohort. Dietary data were collected at baseline via a food frequency questionnaire, and the sPNNS-GS2 was derived for each participant. Multivariable Cox regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) of T2D for continuous and quintile groups of the sPNNS-GS2. Effect modification by body mass index (BMI) and mediation by BMI and waist-hip ratio (WHR) were explored. Results: The mean age was 52.9 years (SD 6.7). During a mean follow-up of 19 years, 3679 incident T2D cases were identified and validated. There was a linear association between adherence to sPNNS-GS2 and T2D risk (P non-linear = 0.85). In the fully adjusted model, each 1-SD increase in the sPNNS-GS2 was associated with a 8% decrease risk of T2D [HR (95% CI), 0.92 (0.89, 0.95)]. Compared to those with the lowest adherence (first quintile), the third [HR (95% CI), 0.91 (0.82, 1.00)] up to the fifth quintile group [HR (95% CI), 0.79 (0.71, 0.88)] had a lower T2D risk. There was a sPNNS-GS2 x BMI interaction on T2D risk (P interaction &lt; 0.01) but not with WHR. The overall association was partly mediated by BMI (18%) and WHR (59%). Conclusion: Higher adherence to the 2017 French food-based dietary guidelines was associated with a lower risk of T2D in women. Furthermore, our mediation analysis suggests that the lower risk of T2D in women associated with adherence to the sPNNS-GS2 is partly mediated by obesity measured by BMI or WHR.

Diabetes mellitus (DM) is a major global health issue, with type 2 diabetes (T2D) accounting for over 90% of cases. Community pharmacies, given their accessibility, are well positioned to assist in early detection and management of T2D. This study evaluated post-pandemic T2D risk in a Portuguese population using the Finnish Diabetes Risk Score (FINDRISC) across five community pharmacies. A total of 494 participants aged 40 or older without a prior diagnosis of diabetes were assessed. The mean FINDRISC score was 12.3, and 29.8% were identified as high or very high-risk, with 8.7% referred to general practitioners for follow-up based on elevated glycated hemoglobin (HbA1c). Key risk factors include age, body mass index, waist circumference, lack of physical activity, and family history of diabetes. Lower educational levels were also associated with higher diabetes risk. Community pharmacies are shown to play an essential role in screening and educating at-risk populations, emphasizing the importance of physical activity, healthy diets, and regular monitoring. These findings reinforce the value of community pharmacists in mitigating T2D risk and enhancing public health outcomes through cost-effective, validated screening tools like FINDRISC. Finally, pre-pandemic FINDRISC studies discussed show similar results suggesting that the COVID-19 pandemic did not significantly impact the overall risk profile for T2D.

To prospectively assess the association of smoking timing with the risk of type 2 diabetes (T2D) and examine whether smoking amount or genetic susceptibility might modify the relationship. A total of 294 815 participants without diabetes from the UK Biobank, including non-smokers and smokers with data on the time from waking to first cigarette, were included. Cox proportional hazards models were used to evaluate the association between smoking timing and the risk of incident T2D. During a median follow-up time of 12 years, a total of 9937 incident cases of T2D were documented. Compared with non-smokers, a shorter time from waking to first cigarette was significantly associated with a higher risk of incident T2D (P for trend < .001). In the fully adjusted model, the hazard ratios (HRs) (95% confidence interval) associated with smoking timing were 1.46 (1.17-1.81) for more than 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 less than 5 minutes. We found that even among those who reported being light smokers, those with the shortest time from waking to first cigarette had a 105% higher risk of T2D with an HR of 2.05 (1.52-2.76), which was comparable with heavy smokers. The genetic risk score for T2D did not modify this association (P-interaction = .51). Our findings indicate that shorter time from waking to first cigarette is significantly associated with a higher risk of incident T2D.

Background: Type 2 diabetes (T2D) is a complex disease driven by lifestyle and genetic factors. The extent to which dietary fat quality may modify T2D genetic burden on the incidence of T2D is unknown. Methods: We used Cox proportional-hazards models to calculate adjusted hazard ratios (HRs) for T2D among 103,2participants of European descent from 15 prospective cohort studies. T2D genetic risk profile was characterized by a 68-variant genetic risk score (GRS) weighted by published effect sizes. Diet was recorded using validated cohort-specific dietary assessment tools. Findings: During a median follow-up of 12 years, 20,451 participants developed T2D. The relative risk of T2D per increment of 10 risk alleles in the GRS was 1.68 (95% confidence interval [CI] 1.62-1.74). Increasing monounsaturated fat intake in place of carbohydrates was associated with a higher risk of T2D (HR per 5% of energy 1.08, 95% CI 1.02-1.15), while increasing polyunsaturated or total ω-3 fat intake in place of carbohydrates was associated with a lower risk of T2D (HR per 5% of energy 0.92, 95% CI 0.85-1.00; and HR per increment of 1g/d 0.95, 95% CI, 0.92-0.99, respectively). We did not observe evidence of significant interactions between dietary fat subtypes and GRS on the risk of T2D. Interpretation: In the present long-term prospective study including 103,2participants, our results support that genetic risk profile and monounsaturated fat intake were each associated with a higher risk of T2D, whereas polyunsaturated fat intake was associated with a lower risk of T2D. Findings from this study suggest that dietary fat recommendations do not need to be tailored to individual T2D genetic risk profile for the primary prevention of T2D, and that dietary fat subtypes associate with the risk of T2D across the spectrum of T2D genetic risk. Disclosure J. Merino: None. H.S. Dashti: None. M. Guasch: None. C. Ellervik: None. C. Smith: None. T.O. Kilpelainen: None. D. Chasman: None. J.C. Florez: Consultant; Self; Intarcia Therapeutics, Inc.. Consultant; Spouse/Partner; Santen.